The Influence of Social Network Behaviors on Energy and

The Influence of Social Network Behaviors on Energy
and Engagement
DISSERTATION
of the University of St. Gallen,
School of Management,
Economics, Law and Social Science and
International Affairs
to obtain the title of
Doctor of Philosophy in Management
submitted by
Ulrich Leicht-Deobald
from
Germany
Approved on the application of
Prof. Dr. Heike Bruch
and
Prof. Tomi Laamanen, PhD
Dissertation no. 4289
The University of St.Gallen, School of Management, Economics, Law, Social Sciences and International Affairs hereby consents to the printing of the present dissertation,
without hereby expressing any opinion on the views herein expressed.
St. Gallen, May 19, 2014
The President:
Prof. Dr. Thomas Bieger
Acknowledgements
The present dissertation draws on several sources for its inspiration. One is a line of
thought that Sumantra Ghoshal and others advanced at the end of the last century on
how social relationships between employees may improve organizational performance.
Another is open system theory, which continues to be held in high regard at the University of St. Gallen, as explicated in the framework of the “St. Gallen Management
Model”, and enjoys a lively and productive research tradition there.
Countless conversations have shaped the development of this work. First, I
must thank my first supervisor, Heike Bruch, for believing in this dissertation project
and providing me with a supportive academic environment that helped me to persist
even in the face of setbacks. Second, I am grateful to my second supervisor, Tomi
Laamanen, for challenging my ideas, which helped me to craft my arguments in a
stronger and more concise manner. Additionally, a number of international colleagues
helped me to sharpen my arguments and welcomed me into the scientific community. I
feel particularly indebted to Chak Fu Lam, Gretchen Spreitzer, and Ryan Quinn.
Furthermore, I owe thanks to AUDI AG in Ingolstadt for allowing us to collect
an exquisitely rich dataset within its organization and financially supporting work on
this dissertation. In particular, I am thankful to Heinz Hollerweger, our project partner
at Audi, and Nina Lins, my dearest fellow in this project who shared all the ups and
downs of this collaboration. Furthermore, I wish to thank Horst Glaser and Peter
Fromm for participating in this research project and Thomas Sigi for his support. Last
but not least, I am grateful to the numerous wonderful engineers at Audi, such as Stefan Kolpatzik and Moni Islam, to name just a few, who took part in our project and allowed me to learn more about their working world.
In German, there is rather appropriate term for a fellow student: Kommilitone,
stemming from the Latin commiles, literally means “brother in arms”. I am deeply
grateful that I have met so many wonderful people in my academic journey whom I
can call upon as brothers (and sisters) in arms. Some of the first fellows during my
studies at the University of Bremen include Jörg Bergmann, Nicola Deobald, Mario
Gruschinke, Anne-Lina Mörsberger, Jan Pries, Ben Rossner, and Christian Sell. I owe
my deepest appreciation to Helmut Reuter: It was our early conversations on Gestalt
psychology that sparked the flame of my scientific interests.
Furthermore, I wish to thank my current and former colleagues of the Institute
for Leadership and Human Resource Management at the University St. Gallen for their
valuable feedback and support. Among them are Miriam Baumgärtner, Stephan Böhm,
Kirill Bourovoi, Simon de Jong, Daniela Dolle, David Dwertmann, Andrea Fischer,
Josef Fischer, Hendrik Hüttermann, Silja Kennecke, Petra Kipfelsberger, Simon
Körner, Justus Kunz, Florian Kunze, David Maus, Geraldine Mildner, Ivonne
Preusser, Anneloes Raes, Regina Reinhardt, Markus Rittich, Andrea Schmid, Leonie
Spalckhaver, Nicole Stambach, and Slawomir Skwarek. I must give particular thanks
to Sandra Kowalevski, my office mate, for sharing my thoughts, sorrows, and hopes
throughout the course of this dissertation and Jay Binnewies for spending many hours
proofreading this dissertation.
Also, I would like to express my gratitude to the Swiss National Science Foundation for kindly providing financial support for my visiting scholarship to the ICPSR
Summer Program 2012 in Ann Arbor, Michigan. Further thanks to the library of the
University of St.Gallen for providing access to the constitutive academic literature of
this dissertation. Moreover, I owe thanks to the members of my academic peer mentoring group, namely Xena Welch Guerra, Marta Widz, Emmanuelle Reuter, and Michael
Boppel, for their support. Deepest thanks to Simon Albrecht for being a good-hearted
friend and companion for nearly three-quarters of my lifetime.
Last but not least, I am delighted to thank my family. I feel deeply grateful to
my parents, Johanna und Siegfried Leicht, for their unquestioning love, trust, and support throughout the years. My deepest gratitude goes to my precious wife, Nicola Deobald, and my beloved son, Jakob. They helped me to remember what is truly important in life when I was struggling with this dissertation. I thank them for their support and love, for letting me follow my good daimōn, and simply for being in my life.
Ann Arbor, Michigan., July 2014
Ulrich Leicht-Deobald
I
Table of Contents
List of Figures ......................................................................................................... IV
List of Tables ........................................................................................................... V
List of Abbreviations .............................................................................................. VI
Abstract ..................................................................................................................... 1
1
Introduction ............................................................................................................. 3
1.1
Abstract ............................................................................................................... 3
1.2 The Positive Organizational Scholarship Perspective ........................................ 4
1.2.1 Core Ideas of the Positive Organizational Scholarship Perspective ............. 4
1.2.2 Theoretical Relevance ................................................................................... 6
1.2.3 Practical Relevance ....................................................................................... 9
1.3 Motivation and Research Focus ...................................................................... 11
1.3.1 Can Organizations Be Economically Productive and Simultaneously Provide Space for Positive Social Interactions?................................................ 11
1.3.2 Analysis of Positive Social Interactions at Multiple Organizational
Levels ........................................................................................................... 14
1.3.3 Team Boundary Activities........................................................................... 16
1.3.4 Collective Human Energy in Organizations ................................................ 18
1.3.5 Intraorganizational Social Networks ........................................................... 21
1.4
2
Outline of the Dissertation................................................................................ 24
Linking Team Boundary-Buffering Activities and Innovative Performance: ...
A Moderated Mediation Model ........................................................................... 27
2.1
Abstract ............................................................................................................. 27
2.2
Introduction ...................................................................................................... 28
2.3 Theoretical and Hypotheses Development ....................................................... 31
2.3.1 Team Boundary-Buffering Activities and Team Productive Energy .......... 32
2.3.2 The Moderating Effect of Chronic Team Work Demand Overload ........... 34
2.3.3 Team Productive Energy and Team Innovative Performance .................... 35
2.3.4 The Mediating Effect of Team Productive Energy ..................................... 37
2.4 Description of Study Methods .......................................................................... 38
2.4.1 Data Collection ............................................................................................ 38
2.4.2 Sample ......................................................................................................... 39
II
Table of Contents
2.4.3 Measures ........................................................................................................ 9
2.5 Analyses and Results ........................................................................................ 41
2.5.1 Discriminant Validity of Measurement Model ........................................... 41
2.5.2 Analysis of Research Model........................................................................ 43
2.5.3 Test of Hypotheses ...................................................................................... 43
2.5.4 Robustness Check ........................................................................................ 48
2.6 Discussion ......................................................................................................... 49
2.6.1 Summary and Theoretical Contribution ...................................................... 49
2.6.2 Practical Contribution .................................................................................. 52
2.6.3 Limitations and Future Research ................................................................. 52
2.6.4 Conclusion ................................................................................................... 53
3
How Does Transformational Leadership Increase Team Productive Energy?
The Role of Team Boundary-Spanning Activities and Diversity ..................... 55
3.1
Abstract ............................................................................................................. 55
3.2
Introduction ...................................................................................................... 56
3.3 Theoretical Background and Hypotheses Development .................................. 57
3.3.1 The Motivational Potential of Resources .................................................... 57
3.3.2 Team Boundary-Spanning Activities and Team Productive Energy .......... 58
3.3.3 Transformational Leadership and Team Boundary-Spanning Activities.... 59
3.3.4 The Mediating Role of Team Boundary-Spanning Activities .................... 60
3.3.5 The Moderating Role of Demographic Diversity........................................ 61
3.4 Methods ............................................................................................................ 62
3.4.1 Data Collection ............................................................................................ 62
3.4.2 Sample ......................................................................................................... 63
3.4.3 Measures ...................................................................................................... 63
3.5 Analyses............................................................................................................ 66
3.5.1 Missing Data Analysis ................................................................................. 66
3.5.2 Assessment of Team Properties and Measurement Model ......................... 67
3.5.3 Multilevel SEM Mediation .......................................................................... 68
3.6 Results .............................................................................................................. 68
3.6.1 Descriptives ................................................................................................. 68
3.6.2 Multilevel Analysis ..................................................................................... 69
3.6.3 Test of Hypotheses ...................................................................................... 73
3.6.4 Additional Exploratory Analysis ................................................................. 78
3.7
Discussion ......................................................................................................... 78
Table of Contents
3.7.1
3.7.2
3.7.3
3.7.4
4
III
Summary and Theoretical Implications ...................................................... 78
Practical Implications .................................................................................. 80
Limitations and Future Research ................................................................. 80
Conclusion ................................................................................................... 81
Are High-Performance Work Systems Always Beneficial?
The Limiting Interaction with Employees’ Network Building ........................ 83
4.1
Abstract ............................................................................................................. 83
4.2
Introduction ...................................................................................................... 84
4.3 Theory and Hypotheses Development.............................................................. 86
4.3.1 Why Are High-Performance Work Systems Effective? ............................. 86
4.3.2 Employees’ Network Building Initiative and Organizational-Level
Absenteeism ................................................................................................ 88
4.3.3 The Interaction of High-Performance Work Systems and Employees’
Network Building Initiative......................................................................... 90
4.4 Method Section ................................................................................................. 92
4.4.1 Sample ......................................................................................................... 92
4.4.2 Measures ...................................................................................................... 93
4.5 Results .............................................................................................................. 96
4.5.1 Hypotheses Testing ..................................................................................... 96
4.5.2 Robustness Check ........................................................................................ 98
4.6 Discussion ......................................................................................................... 99
4.6.1 Theoretical Contribution ............................................................................. 99
4.6.2 Practical Contribution ................................................................................ 102
4.6.3 Limitations ................................................................................................. 103
4.6.4 Conclusion ................................................................................................. 103
5
Overall Discussion and Conclusion ................................................................... 105
5.1
Abstract ........................................................................................................... 105
5.2
5.3
Summary......................................................................................................... 106
Theoretical Integration of Most Important Research Findings ...................... 109
Overall Limitations and Directions for Future Research ............................... 112
Main Practical Implications ............................................................................ 114
Conclusion ...................................................................................................... 116
5.4
5.5
5.6
Appendix ..................................................................................................................... 117
References ................................................................................................................... 119
Curriculum Vitae ........................................................................................................ 151
IV
List of Figures
Figure 1-1 Overview of Chapter Structure ................................................................. 25
Figure 2-1 Moderated Mediation Structural Equation Model .................................... 45
Figure 2-2 Interaction between Team Productive Energy and Team Chronic Job
Demand Overload on Team Innovative Performance ............................... 47
Figure 3-1 Multilevel SEM Model with Decomposed Between and Within Effects . 74
Figure 3-2 Interaction between Transformational Leadership and Age Diversity on
Team Boundary-Spanning Activities ........................................................ 78
Figure 3-3 Interaction between Transformational Leadership and Educational
Diversity on Team Boundary-Spanning Activities ................................... 79
Figure 4-1 Interaction between High-Performance Work Systems and Employees’
Network Building Initiative .................................................................... 101
Figure 6-1 Multilevel Confirmatory Factor Analysis for Transformational ..................
Leadership ............................................................................................... 120
V
List of Tables
Table 1-1
Literatures and Constructs related to the Human Energy Concept ........... 19
Table 2-1
Means, Standard Deviations, and Zero Order Correlationsa ..................... 42
Table 2-2
Overall Structural Equation Model Fit Comparison ................................. 44
Table 2-3
Conditional Indirect Effects via Team Productive Energy predicting Team
Innovative Performance ............................................................................ 48
Table 3-1
Means, Standard Deviations, and Zero Order Correlations ...................... 72
Table 3-2
Overall Multi-Level SEM Model Fit Comparison .................................... 73
Table 3-3
OLS Regression Results for Simple Moderation ...................................... 76
Table 3-4
Conditional Indirect Effects via Team Boundary-Spanning Activities
predicting Team Productive Energy.......................................................... 79
Table 4-1
Means, Standard Deviations, and Correlations among Study Variables .. 98
Table 4-2
Results of Hierarchical Regression Analysis .......................................... 100
VI
List of Abbreviations
AIC
Akaike information criterion
AOM
Academy of Management
β
beta (standardized regression/path coefficient)
B
unstandardized regression coefficient
BIC
Bayesian information criterion
CEO
chief executive officer
CFA
confirmatory factor analysis
CFI
comparative fit index
CI
confidence interval
COR
conservation of resources
∆
delta (difference)
df
degrees of freedom
DGPs
Deutsche Gesellschaft für Psychologie (German Psychological Association)
d.h.
das heisst
EAWOP
European Association of Work and Organizational Psychology
Ed./Eds.
editor/editors
e.g.
example gratia/for example
et al.
et alii
F
f-test value
Γ
gamma (standardized path coefficient)
GNP
gross national product
GST
general systems theory
List of Abbreviations
VII
H
Hypothesis
HPWS
high-performance work systems
HR
human resources
HRM
human resource management
ICC
intraclass correlation coefficient
i.e.
id est/that is
IFI
incremental fit index
IPO
input-process-output
JD-R
job demands-resources
Λ
lambda (factor loading)
M
mean
MCFA
multilevel confirmatory factor analysis
MSEM
multilevel structural equation modeling
MAR
missing at random
MCAR
missing completely at random
MNAR
missing not at random
N/n
number of observations
Ns
not significant
OECD
Organisation for Economic Co-operation and
Development
P
level of significance
p.
Page
POS
positive organizational scholarship
R2
squared multiple correlation coefficient
R&D
research and development
RMSEA
root mean square error of approximation
VIII
List of Abbreviations
rwg
index of interrater agreement
SE
standard error
SD
standard deviation
SEM
structural equation modeling
SRMR
standardized root mean square residuals
TLI
Tucker-Lewis index
TFL
transformational leadership
u.a.
unter anderem
χ2
chi square value
1
Abstract
English. The positive organizational scholarship (POS) perspective focuses on capability-enhancing and life-giving dynamics in organizations. It complements the traditional problem-focused view of organizations by centering on enabling human conditions and examining how organizations may unlock the hidden potential of their employees. However, past POS research has primarily studied positive dynamics at the
individual level of analysis and frequently did not take into account the specific organizational context of these dynamics. Furthermore, most POS literature that has studied
positive phenomena within an organizational context has either been conceptual in
scope or relied on qualitative research methods. To complement this prior research,
this dissertation examines within three quantitative field studies how positive appetitive social interactions (i.e., interactions with desired consequences from the perspective of the involved individuals) at multiple organizational levels influence performance-relevant outcomes.
In Study 1, we examine whether and how team boundary-buffering activities
help teams increase their innovative performance using a sample of 89 research and
development (R&D) teams. Team boundary-buffering activities are a specific type of
social interaction directed toward disengaging from the external team environment and
managing external demands. In Study 2, we investigate, using a sample of 121 R&D
teams, whether and how transformational leadership increases teams’ sense of productive energy by enabling team boundary-spanning activities. These boundary-spanning
activities comprise team actions of engaging with the external team environment in
order to gain important resources and support. In Study 3, we explore, with a sample
of 161 organizations, whether the interplay between high-performance work systems
([HPWSs], a bundle of HR practices that amongst other include internal participatory
mechanisms, cross-functional and cross-trained teams, and high levels of training) and
employees’ network building initiative may negatively affect organizational-level absenteeism, although both aspects by themselves have positive effects.
This dissertation helps explaining how positive social interactions at multiple organizational levels (team boundary-buffering activities, team boundary-spanning activities, and employees’ network building initiative) facilitate human welfare in organizations and simultaneously contribute to organizations’ competitive advantage by reducing absenteeism, sustaining productive energy, and increasing innovative performance.
2
Abstract
Deutsch. Die Positive Organizational Scholarship (POS) Perspektive beschäftigt sich
mit ermöglichenden und lebensbejahenden Dynamiken in Organisationen. Diese Perspektive ergänzt die herkömmliche problemzentrierte Sichtweise auf Organisationen,
indem sie danach fragt, wie Organisationen das versteckte Potential ihrer Mitarbeiter
ausschöpfen können. Allerdings hat die bisherige POS Forschung vornehmlich positive Dynamiken auf der individuellen Ebene untersucht und dabei mitunter den organisationalen Rahmen ausser Acht gelassen. Der überwiegende Teil der bisherigen POS
Literatur, der sich mit genuin organisationalen Phänomenen beschäftigt, ist entweder
konzeptueller Natur oder gründet sich auf die Anwendung qualitativer Forschungsdesigns. Um diese Forschung zu ergänzen, untersucht diese Dissertation in drei quantitativen Feldstudien, ob und wie positive soziale Interaktionen (d.h. Interaktionen, deren
Konsequenzen von den beteiligten Individuen als positiv erlebt werden) auf verschiedenen organisationalen Ebenen leistungsrelevante Prozesse beeinflussen.
Studie 1 untersucht anhand einer Stichprobe von 89 Forschungs- und Entwicklungsteams, ob und wie Team Boundary-Buffering Aktivitäten helfen können, die innovative Leistung von Teams zu verbessern. Team Boundary-Buffering Aktivitäten
stellen einen bestimmten Typus von Team-Interaktionen dar, die darauf gerichtet sind,
störende Einflüsse aus der externen Teamumwelt abzupuffern. Studie 2 erforscht anhand einer Stichprobe von 121 Forschungs- und Entwicklungsteams, ob und wie
Transformationale Führung die produktive Energie in Teams dadurch erhöht, dass diese Art der Führung Team Boundary-Spanning Aktivitäten ermöglicht. Team Boundary-Spanning Aktivitäten sind darauf gerichtet, wichtige Ressourcen und soziale Unterstützung im Austausch mit der externen Teamumwelt zu generieren. Studie 3 untersucht anhand einer Stichprobe von 161 Organisationen, ob sich die grundsätzlich positiven Effekte von High-Performance Work Systems ([HPWSs], d. h. ein Bündel von
Human Ressource Praktiken, das u.a. interne Partizipationsmöglichkeiten, funktionsübergreifende Teams, und ein hohes Mass an Training beinhaltet) und die individuelle
Initiative einzelner Mitarbeitender zur Bildung von sozialen Netzwerken, gegenseitig
aufheben.
Die vorliegende Dissertation hilft zu verstehen, dass und wie verschiedene positive soziale Interaktionen auf unterschiedlichen organisationalen Ebenen (Team
Boundary-Buffering und Team Boundary-Spanning Aktivitäten und die Initiative einzelner Mitarbeitender, soziale Netzwerke zu bilden) zum menschlichen Wohlergehen
und gleichzeitig zu einem Wettbewerbsvorteil durch reduzierten Absentismus, erhöhte
produktive Energie, und gesteigerte Innovationsleistung beitragen.
3
1 Introduction
1.1 Abstract
This chapter familiarizes the reader with the positive organizational scholarship (POS)
perspective. Then, drawing from this POS perspective, this chapter develops the central motivation and research focus of this dissertation, namely how organizations can
be economically productive and simultaneously provide space for positive social interactions among their organizational members. Building on this research focus, this
chapter presents the focal concepts of this dissertation – i.e., team boundary activities,
productive energy, and employees’ network building initiative – and develops three
distinct research questions. These research questions will be refined, studied, and discussed in the following parts of this dissertation.
4
Introduction
“If we don’t make money, no amount of virtue will do our firm any good.
Wall Street will ignore us, and we will soon be out of business. We must
have bottom line performance for virtuousness in our firm to be taken seriously.”
— Jeffrey Schwartz, CEO, Timberland (2002)
1.2 The Positive Organizational Scholarship Perspective
1.2.1 Core Ideas of the Positive Organizational Scholarship Perspective
Positive organizational scholarship (POS) is an emergent field of organizational research that has received growing attention from researchers and practitioners in the last
decade (Dutton, Glynn, & Spreitzer, 2006). The POS perspective focuses on investigation of positive outcomes, processes, and attributes of organizations and affiliated individuals (Cameron, Dutton, & Quinn, 2003). In particular, POS researchers study lifegiving, capability-enhancing, and capacity-creating dynamics in organizations (Dutton
et al., 2006). These dynamics might be characterized by aspects such as flourishing,
thriving, being virtuous, or being highly energized (Cameron et al., 2003; Dutton et al.,
2006; Vogel & Bruch, 2012). Eventually, these dynamics are rooted in the ancient
Greek concept of excellence (Greek: ἀρετή [Cameron, Bright, & Caza, 2004]). To the
ancient Greeks, excellence meant that individuals possess the essential life skills to
achieve their highest human potential (Cawley III, Martin, & Johnson, 2000).
The POS lens does not provide a single theory or framework but draws from a
wide range of organizational theories (Dutton et al., 2006). However, the POS perspective builds on three conceptual pillars that are related to its title: First, POS is associated with the positive, because it focuses on life-giving, elevating, and generative states,
experiences, and dynamics (Dutton et al., 2006). Note that the focus on positivity does
not mean that POS denies negative aspects within organizations. However, mainstream
organizational science predominantly emphasizes problematic aspects of organizations
(Luthans & Youssef, 2007). Hence, POS aims at shifting the focus from disabling to
enabling conditions (Cameron et al., 2003). Second, POS is organizational, because it
focuses on organizational processes, methods, capabilities, and structures that facilitate
those positive dynamics (Cameron et al., 2003). Last but not least, POS draws upon
scholarship, because it affirms theoretically informed intentions that are supported by
Introduction
5
empirical data and analysis and offer organizational implications for theory, practice,
and teaching (Dutton et al., 2006).
However, POS does not come as a value-free perspective. It explicitly rests on
the assumption that enabling human conditions in organizations may unravel the hidden potential of people and increase their possibilities, which ultimately improves their
own welfare as well as the welfare of organizations (Dutton et al., 2006). An example
might explain how POS differs from traditional perspectives in the organizational sciences. The opening chapter of the first edited book on POS starts with a thought experiment (Cameron et al., 2003). First, Cameron and colleagues (2003) ask the reader to
think about a world in which most organizations are characterized by “greed, selfishness, manipulation, secrecy, and single-minded focus on winning” (p. 3). Drawing upon this example, the authors suggest that mainstream organizational sciences primarily
examine these kinds of organizations and are consequently concerned with theoretical
questions referring to “problem-solving, reciprocity, justice, managing uncertainty,
overcoming resistance, achieving profitability, and competing successfully against
each other” (p.3). In turn, Cameron and colleagues (2003) ask the reader to imagine a
world in which organizations are characterized by “appreciation, collaboration, virtuousness, vitality, and meaningfulness” (p. 3). They point to the idea that members of
such an organization might be characterized by trustworthiness and resilience and energized by relationships based on “compassion, loyalty, honesty, respect, and forgiveness (p.3)”. Members of such an organization might experience their social interactions as life-building rather than life-depleting. Cameron, Dutton, and Quinn (2003)
conclude that most prior organizational research has adopted assumptions and concepts regarding organizations that are in line with the first view. However, limited research has yet elaborated upon theories and concepts that are inspired by the second
view of organizations.
Following the previous POS research, this dissertation asserts that traditional organizational research tends to emphasize a problem-oriented view of organizations
(Roberts, 2006). This problem-oriented view leads to a tendency to predominantly focus on organizational deficits and aspects that are sub-optimal instead of examining
how and why positive dynamics evolve in organizations. Within a succinct literature
review, Caza and Caza (2008) found, for example, that five of the six most-cited articles in two of the most prestigious journals in the management field (Academy of
Management Journal and Administrative Science Quarterly) in 1979, 1989, and 1999
focused on organizational deficits rather than their potentiality. However, the deficit
model of organizations is not limited to organizational scholars. In a longitudinal study
6
Introduction
of language in the business press, Walsh (1999) showed that the use of words with
negative connotations increased almost fourfold within a period of 17 years. In contrast, the use of positive-biased language maintained its rarity within this period of
time. However, research from a POS perspective does not deny the helpfulness of the
problem-oriented view of organizations (Roberts, 2006). Thus, in line with other
scholars (Luthans & Youssef, 2007), this dissertation regards the POS perspective as a
complement to the dominant problem-oriented view of organizations rather than as a
substitute or replacement.
Although POS is a relatively new perspective in the organizational sciences, the
accentuation of positive dynamics is not unique to this point of view (Cameron et al.,
2003). Most notably, the core ideas of the POS perspective emerged within the positive psychology movement. The roots of the positive psychology movement advanced
partly in parallel but a few years earlier than the POS perspective (Dutton et al., 2006).
The positive psychology movement was initiated in 1998 by the experimental psychologist Martin Seligman, who was at that time president of the American Psychological Association. Seligman made the case that, since World War II, mainstream psychology had almost exclusively focused on dysfunctional behavior and human pathology (Cameron et al., 2003). Indeed, clinical psychologists had made considerable progress in designing interventions that help individuals to overcome their hardships;
however, Seligman criticized that, due to psychology’s focus on human pathology, this
field developed a negative bias and overlooked the positive side of human potentiality
(Cameron et al., 2003). In contrast, positive psychology strives to generate knowledge
in the following areas of study: positive experiences (e.g., happiness and joy), positive
individual traits (e.g., strengths and virtues), and positive institutions (e.g., communities and organizations). However, a review of the positive psychology literature revealed that this third research pillar, positive institutions, has been studied to a very
limited degree (Gable & Haidt, 2005; Hackman, 2009). Accordingly, POS and its micro-level “sister”, positive organizational behavior, broaden the perspective of positive
psychology by specifically focusing on the organizational context in which positive
dynamics can be unleashed (Dutton et al., 2006; Luthans & Youssef, 2007).
1.2.2 Theoretical Relevance
The following section provides an overview of how and why this dissertation is theoretically relevant for the literature of POS. Critical reviews have emphasized that research on POS and positive behavior in organizations has made great progress in stud-
Introduction
7
ying positive traits and state-like capacities (Hackman, 2009; Luthans & Youssef,
2007). However, Hackman (2009) observes that organizational scholars tend to follow
their colleagues from the positive psychology movement in primarily studying intrapersonal phenomena (such as hope, resilience, optimism, and self-efficacy) and not
genuine organizational phenomena.
Hence, the present dissertation focuses on relational practices that genuinely unfold within an organizational context (i.e., team boundary activities and intraorganizational social network initiative). Team boundary activities are defined as actions that
team members carry out within an organization to interact with stakeholders in their
external environment (Ancona & Caldwell, 1992b; Faraj & Yan, 2009). Employees’
network building initiative refers to employees’ use of their informal social networks
within an organization (Thompson, 2005). Both types of relational practices − team
boundary activities and employees’ network building initiative − are not feasible without an organizational environment. Hence, both concepts are by definition closely related to the organizational context (Johns, 2006).
Furthermore, reviews of the POS literature suggest that most organizational
scholars tend to analyze positive behaviors at the individual level (Cameron et al.,
2003; Hackman, 2009; Wright & Quick, 2009). For instance, prior research has studied the positive dynamics of individual employees’ flow (Quinn, 2005), high-quality
relationships (Carmeli & Gittell, 2009b), and thriving (Porath, Spreitzer, Gibson, &
Garnett, 2012). However, research on POS has only begun to study variables at the
level of groups and organizations (see Owens, Johnson, & Mitchell [2013]; West,
Patera, & Carsten [2009] for exceptions). To approach this gap, the present dissertation examines concepts at the levels of teams and organizations. In particular, Studies
1 and 2 observe positive team dynamics related to team boundary activities, whereas
Study 3 explores the positive effect of employees’ network building initiative at the
organizational level. Hackman (2009) suggests that, at its best, the field of organizational behavior explores cross-level interactions among individuals’ relational practices and their broader organizational context. Accordingly, in Study 3, we specifically
examine the cross-level interaction between individual employees’ network building
initiative (relational practice of individuals) and high-performance work systems (organizational context).
Moreover, Hackman’s (2009) reading of the POS literature raised questions regarding the empirical validity of several constructs of that field. Conversely, to date a
considerable number of individual-level POS constructs have been rigorously validat-
8
Introduction
ed, such as authenticity (Walumbwa, Avolio, Gardner, Wernsing, & Peterson, 2008),
thriving (Porath et al., 2012), and expressed humility (Owens et al., 2013). However,
Cameron and colleagues (2003) point to the fact that validated POS scales at higher
levels, such as the team and organizational level, are particularly rare. One of the few
exceptions that we are aware of is a unit-level measure of a concept called productive
energy (Cole, Bruch, & Vogel, 2012). Productive energy is defined as “shared experience and demonstration of positive affect, cognitive arousal, and agentic behavior
among unit members in their joint pursuit of organizationally salient objectives” (p.
447). Hence, we contribute to the POS literature by further examining the relationships
between the productive energy construct and its positive dynamics in Studies 1 and 2.
Also, the bulk of the POS research that genuinely focuses on organizational phenomena has either been conceptual (e.g., Dutton, Roberts, & Bednar, 2010; Heaphy &
Dutton, 2008; Quinn & Dutton, 2005; Roberts & Dutton, 2009; Spreitzer, Sutcliffe,
Dutton, Sonenshein, & Grant, 2005) or guided by qualitative research (Quinn &
Worline, 2008; Sonenshein, in press; Sonenshein, Dutton, Spreitzer, Sutcliffe, &
Grant, 2013). Given that established measurement instruments for the focal constructs
of this dissertation exist (i.e., team boundary activities [Faraj & Yan, 2009], employees’ network building initiative [Thompson, 2005], and productive energy [Cole et al.,
2012]) and given that the corresponding literature is relatively mature (Edmondson &
McManus, 2007), this dissertation uses quantitative research methods in the three empirical studies included in this dissertation. However, that is not to say that I advocate
any metaphysics of positivism, nor does it imply that I favor quantitative over qualitative research methods. In doing so, I simply aspire to extend the prior focus on qualitative research methods.
Last but not least, POS research has been criticized for being ahistorical
(Hackman, 2009). Hackman (2009) argues that POS researchers tend to cite relatively
recent literature from their own perspective and ignore literature from past decades. To
help integrate the POS literature with past organizational behavior research, this dissertation strives to link ideas from the POS perspective, for example on human energy
in organizations (Quinn, Spreitzer, & Lam, 2012), with established arguments from the
literature on team boundary activities (Ancona & Caldwell, 1992b; Faraj & Yan, 2009)
and intraorganizational social networks (Tsai & Ghoshal, 1998).
Introduction
9
1.2.3 Practical Relevance
Besides theoretically contributing to the literature of POS, this dissertation offers three
practical contributions to the field of management. First, it adds to the dialogue between management scholars and practitioners. In a widely cited article, Ghoshal
(2005) describes this dialogue as drawing upon the concept of double hermeneutics,
meaning a double-sided relationship (Giddens, 1987). In one direction, management
scholars interpret the reality in their field, which ultimately shapes the way they craft
theories. In the other direction, practitioners interpret these theories in such a way that
shapes their practice in the field (Giddens, 1987). For example, a theory that assumes
that managers act opportunistically and in turn draws its conclusions based on this belief will likely reinforce opportunistic behavior among managers (Ghoshal & Moran,
1996). Furthermore, a theory that draws implications for corporate governance on the
presumption that managers are not trustworthy may also influence managers to act less
credibly (Osterloh & Frey, 2003). Overall, Ghoshal (2005) argues that several of the
worst recent management excesses originated in ideas developed by management
scholars in recent decades.
Inspired by examples of managerial misconduct at the beginning of this century,
Ghoshal (2005) argues that this problem was aggravated by management scholars who
uncritically carried over their model of explanation from the natural sciences. The
classical model of the natural sciences draws upon a causal mode of explanation while
neglecting intentionality and human agency as valid sources of scientific reasoning
(Ghoshal, 2005). In order to adopt this scientific mode of explanation, many scholars
in the field of management have precluded intentionality and human agency from the
agenda of their theory building efforts (Ghoshal, 2005). Accordingly, for many scholars, moral reflections and ethical reasoning became an illegitimate aspect of scientific
inquiry (Ghoshal, 2005). Ghoshal (2005) concludes that the idea of “value-free” scholarship has been particularly harmful, because it actively discharges practitioners from
any sense of moral obligation in their everyday decision-making. In line with
Ghoshal’s (2005) argument, this dissertation incorporates its value assumptions as an
explicit part of the research agenda. Particularly, this dissertation offers a more positive view of the feasibility of intentionality and the human agency of managers’ behavior. Furthermore, this dissertation reinforces the idea that certain states of mind (e.g.,
appreciation, collaboration, virtuousness) are more desirable than others (e.g., greed,
selfishness, manipulation) and aims at facilitating the former while discouraging the
latter.
10
Introduction
Second, POS offers a fresh lens through which to view the organizational sciences by drawing attention to a broader domain of outcomes that have not been sufficiently studied (Cameron et al., 2003). A review of outcome measures of studies published
in the Academy of Management Journal between 1958 and 2000 revealed that, by far,
most papers reported solely on economic performance measures and tended to overlook measures of social welfare (Walsh, Weber, & Margolis, 2003). In their study,
economic performance incorporated measures of efficiency, productivity, and accounting- and market-related indices of value creation. Social welfare included measures of
health, satisfaction, justice, social responsibility, and environmental stewardship
(Walsh et al., 2003). Research from a POS perspective emphasizes that economic performance is not an end in itself (Roberts, 2006). On the contrary, Roberts (2006) argues that organizations should not strive for economic performance by any means.
One of the generative POS constructs that has been studied recently is the emergent
state of productive energy, which is also examined in this dissertation. Past research
shows that productive energy is a concept that enhances both economic performance
and social welfare (Cole et al., 2012; Raes, Bruch, & De Jong, 2013).
Third, this dissertation contributes to the conversation on organizational sustainability. Pfeffer (2010) suggested that the previous debate on sustainability has primarily
focused on economic and environmental arguments but disregarded the human aspect
of this topic. Nevertheless, organizational members have experienced increased levels
of psychological distress and disorders (e.g., burnout) within the last decades (OECD,
2012). For example, almost one-third of all employees in the European Union regularly experience psychological distress in their workplaces (Steinmann, 2005). As a consequence, the follow-up costs of stress-related diseases add up to 3-4% of the EU
countries’ overall gross national product (GNP, [Steinmann, 2005]). Similarly, in
Switzerland, the resulting costs of those disorders account for approximately 1.2% of
GNP (Ramaciotti & Perriard, 2003). This number corresponds to expenses of more
than 4 billion CHF (Ramaciotti & Perriard, 2003).
In general, until very recently the discussion on psychological strain focused on
lower-ranked employees, because they usually possess fewer psychological resources
(e.g., less decision-making autonomy [Stansfeld, Fuhrer, Head, Ferrie, & Shipley,
1997]). However, recently, the public eye has witnessed several incidents in which top
managers voluntarily quit their jobs for reasons related to psychological distress. For
instance, in 2011, Hartmut Ostrowski, the former CEO of the media group Bertelsmann, resigned because he doubted he could preside over the firm for another five
years while also retaining his full psychological integrity (von Terpitz & Siebenhaar,
Introduction
11
2011). Similarly, in 2011, António Horta-Osório, the former CEO of the banking
group Lloyds, after only eight months on the job, took a two-month absence after receiving a diagnosis of extreme job fatigue (Kwoh, 2013).
However, there might be reasons why, especially to date, it remains difficult for
organizations to sustain the human energy of their workforces. First, driven by global
market forces and accelerated innovation cycles, organizations face an augmented
need for organizational adaption (Bruch & Menges, 2010). As a result, organizational
members are confronted with various threats, including higher job demands (e.g.,
greater workload), greater competition, and increased risk of being laid off (Fritz,
Lam, & Spreitzer, 2011). There might be two reasons why providing positive social
interactions and human energy in this situation may play a decisive role for organizations. First, drawing on the logic of economic rationality (which will be discussed further in the next chapter), sustaining organizational members’ human energy may offer
organizations a competitive advantage (Fritz et al., 2011; Pfeffer, 1994, 2010). Second,
referring to the logic of the POS perspective, providing organizational members with a
generative organizational context could prove an important value in its own right.
1.3 Motivation and Research Focus
1.3.1 Can Organizations Be Economically Productive and Simultaneously Provide Space for Positive Social Interactions?
In the following section, this dissertation offers an overview of the motivation and focus of the research included in the dissertation. Drawing from this motivation and focus, this dissertation will advance three specific research questions at multiple organizational levels.
The POS perspective builds on different macro-level lenses to develop an understanding of life-giving, capability-enhancing, and capacity-creating dynamics in organizations. One of them is the resource-based view of the firm (Dutton, Glynn, &
Spreitzer, 2006). This view aims to explain why certain firms achieve an aboveaverage return on investment, whereas others do not. As a basic premise, this lens suggests that, in order to generate a competitive advantage, firms have to develop a bundle
of valuable resources (Wernerfelt, 1984). Resources are determined to be valuable
when they are rare and neither perfectly imitable nor substitutable without huge expense (Barney, 1991). Furthermore, these resources must allow an organization to
12
Introduction
generate a value-creating strategy that will not be adopted by any present or potential
competitor (Barney, 1991). Past research proposes that several intangible assets, such
as organizational culture or human resource systems, may function as a valuable resource for organizations (Barney, 1986; Lado & Wilson, 1994). Furthermore, research
from a POS perspective suggests that employees’ positive work-related identification
can create a competitive advantage (Dutton, Roberts, & Bednar, 2010).
The resource-based view of the firm complements the traditional neoclassical
theory, which builds on a market-based “outside-in” perspective by applying an “inside-out” view of organizations. The resource-based view emphasizes the role of organizations’ valuable resources as a foundation for above-average returns on investment, whereas the neoclassical market-based view focuses on achieving a superior
market position. In principle, the neoclassical theory and its later derivatives, such as
transaction cost theory and principal agent theory, draw from the assumption that organizations evolve as a consequence of market failure (Nahapiet & Ghoshal, 1998;
Williamson, 1975). By definition, these theories conceptualize social interactions in
organizations as motivated by economic interest and opportunism (Coase, 1937;
Jensen & Meckling, 1976).
On the contrary, the resource-based view of the firm is more flexible regarding
its behavioral assumptions (Amit & Schoemaker, 1993). One stream of literature
stemming from the resource-based view particularly focuses on knowledge-based value creation (Grant, 1996). This stream of literature stresses innovation capability as a
valuable resource for organizations (Argote, McEvily, & Reagans, 2003; Osterloh &
Frey, 2000). However, to successfully innovate, organizational members have to put
aside their self-interest in order to coordinate their joint efforts toward fulfillment of
the higher goals of the organization (Lindenberg & Foss, 2011). Whereas recent derivatives of the neoclassical theory refer to the nature of organizations as a nexus of contracts (e.g., Jensen & Meckling, 1976), literature stemming from this resource-based
view of the firm describe organizations as social communities (Kogut & Zander,
1992). For example, Kogut and Zander suggest that “organizations are social communities in which individual and social expertise is transformed into economically useful
products and services by the application of a set of higher-order organizing principles.
Firms exist because they provide a social community of voluntaristic action structured
by organizing principles that are not reducible to individuals” (1992, p. 384).
Accordingly, past social capital literature has proposed that social interactions
may provide a valuable resource for organizations (Leana & Van Buren III, 1999;
Introduction
13
Nahapiet & Ghoshal, 1998; Tsai, 2001). However, prior research has scarcely examined specifically positive social interactions (e.g., Baker, Cross, & Wooten, 2003).
Positive social interactions are characterized “by the pursuit of rewarding and desired
outcomes” of the involved individuals (Heaphy & Dutton, 2008, p. 139). This dissertation focuses on positive social interactions, because POS and positive psychology
scholars have argued that the mechanisms through which social interactions are experienced as beneficial are not simply the opposite of those through which social interactions are felt as distressing and harmful (Heaphy & Dutton, 2008; Reis & Gable,
2002). Past research defines positive social interactions as appetitive, which means
that their consequences are desired and welcome from the perspective of the involved
individuals (Heaphy & Dutton, 2008; Reis & Gable, 2002). On the contrary, negative
social interactions are regarded as aversive, which means that individuals experience
them as punishing and harmful (Heaphy & Dutton, 2008; Reis & Gable, 2002). For
example, positive social interactions may include experiences of growth, respect, and
mutuality (Miller & Stiver, 1997), whereas negative social interactions might include
feelings of distrust and exclusion (Cacioppo et al., 2002; Fleischmann, Spitzberg,
Andersen, & Roesch, 2005). Furthermore, prior conceptual work distinguishes between two fundamental types of social interactions: connections and relationships. A
connection supposes that two individuals have socially interacted with each other and
are mutually aware of it (Dutton & Heaphy, 2003). As Heaphy and Dutton (2008)
state, connections differ in length, varying from one moment to many, and may be recurring. When these connections recur, they are referred to as relationships (Heaphy &
Dutton, 2008). Hence, in a nutshell, connections are the micro-unit of relationships
(Heaphy & Dutton, 2008).
Past research has demonstrated that positive social interactions have many beneficial physiological consequences for individuals’ health, such as lowering one’s heart
rate and blood pressure, strengthening the immune response, and inducing a healthier
pattern of the stress hormone cortisol (Heaphy & Dutton, 2008). However, aside from
these beneficial individual-level consequences, it is not fully understood whether and
how positive social interactions may also prove a valuable resource for organizations.
Research motivation: Can organizations constitute social communities that encourage positive social interactions, with beneficial consequences for the involved individuals, while at the same time being economically productive?
14
Introduction
1.3.2 Analysis of Positive Social Interactions at Multiple Organizational Levels
In addition to evidence from the medical literature showing many favorable physiological consequences of positive social interactions at the individual level, a growing
body of POS literature examines a specific type of positive social interaction called
high-quality relationships. Among other aspects, these relationships are characterized
by positive regard, mutuality, and feelings of vitality (Stephens, Heaphy, & Dutton,
2012). Most studies in this stream of research show that high-quality relationships
have a positive effect on innovation. For example, Vinarski-Peretz, Binyamin, and
Carmeli (2011) found that high-quality relationships positively influence employees’
engagement in innovative behaviors. Accordingly, Carmeli and Spreitzer (2009)
showed that another aspect of high-quality relationships, connectivity, increased employees’ thriving, which ultimately supported their innovative performance. Finally,
Carmeli and Gittell (2009) found that high-quality relationships increase organizational members’ psychological safety, which in turn facilitates their ability to learn from
failure.
Furthermore, previous POS studies have demonstrated that positive social interactions do not emerge by themselves or simply by refraining from harmful practices.
Research particularly points to the important role of supervisors in establishing positive work relationships. For example, Atwater and Carmeli (2009) showed that, when
employees perceive the relationship with their supervisor as positive, they feel an increased sense of energy, which ultimately produces higher levels of creative work involvement. Carmeli, Atwater, and Levi (2011) demonstrated that employees’ relational
identification with their supervisors increased their knowledge exchange and their
identification with the organization. Furthermore, Carmeli and Spreitzer (2009) found
that employees’ trust among each other positively affected their connectivity.
However, the vast majority of studies on high-quality relationships have applied
to the individual level of analysis. Exceptions include two studies situated at the team
level. Brueller and Carmeli (2011) showed that team members’ high-quality relationships with their team leaders increase teams’ psychological safety climate, whereas
team members’ high-quality relationships with external stakeholders increase team
learning. Stephens, Heaphy, Carmeli, Spreitzer, and Dutton (2013) found that a particular facet of high-quality relationships called emotional carrying capacity (i.e., the capacity to express both positive and negative emotions in a constructive way) increased
the resilience of members of top management teams. Nevertheless, this line of POS
Introduction
15
research has been constrained by the fact that a validated scale of high-quality relationships has yet to be published.
In this dissertation, I aim at extending prior POS research by examining different
types of positive social interactions at multiple organizational levels. In doing so, I
draw upon a multilevel perspective of organizations. At its base, the multilevel perspective builds on ideas of the general systems theory (GST, [Boulding, 1956; von
Bertalanffy 1972]). The basic idea of GST is traced back to the ancient Aristotelian
principle that the whole is more than the sum of its parts (Kozlowski & Klein, 2000).
This principle stands in stark contrast to the reductionist, respective atomistic strategies of explanation, such as in economics and parts of the natural sciences (Kozlowski
& Klein, 2000). The central purpose of GST is to establish principles that generalize
across phenomena, disciplines, and levels of explanation.
In particular, this dissertation refers to the so-called meso paradigm of organizational behavior (House, Rousseau, & Thomashunt, 1995). This paradigm is characterized by careful consideration for the context of organizational behavior (House, et al.,
1995). Traditionally, organizational scholars have utilized either the macro perspective
(often referred to as organizational theory) or the micro perspective (often referred to
as organizational behavior) to explain organizational phenomena (Kozlowski & Klein,
2000). However, the former tends to devote little attention to the processes of human
agency, whereas the latter tends to forget the organizational context of behavior (Heath
& Sitkin, 2001; House, et al., 1995; Johns, 2006). The term context originates from
Latin and means “to weave together.” To take something out of its context is to remove it from its relationships to other parts, such as the larger whole or the setting in
which it operates (House, et al., 1995). However, given the lack of meso-level research
on positive social interactions, this dissertation aims at linking established concepts of
social interaction (i.e., team boundary activities and employees’ network building initiative) at multiple levels with the emergent state of productive energy and performance-relevant outcomes.
Overall research focus: How do different types of positive social interactions at
multiple organizational levels influence performance-related outcomes?
16
Introduction
1.3.3 Team Boundary Activities
In the following section, this dissertation will develop three specific research questions
at multiple organizational levels, drawing upon the literatures of team boundary activities, human energy in organizations, and intraorganizational social networks.
Before exploring boundary activities at the level of teams, two streams of macrolevel research are to be considered that sought to explain how organizations interact
across their organizational boundaries with their external environment. The first
stream of research explains boundary activities using an open systems perspective
(Katz & Kahn, 1966; Scott, 1992). However, this literature applies this open systems
view rather metaphorically, referring to structural and materialistic components of organizations rather than behaviors of humans (Klein, Tosi, & Cannella, 1999). For example, Scott (1992) proposes that organizations are technical systems that transform
inputs into outputs. Drawing upon this argument, he suggests that organizations seek
to buffer their core technologies from environmental influences through a variety of
strategies. For instance, they reduce the fluctuation of their inputs, stockpile raw materials, or forecast conditions that determine supply and demand in the market (Scott,
1992). Furthermore, Katz and Kahn (1966) state that organizations, viewed as open
systems, need to steadily import energy and information from their external environment in order to maintain their functioning. However, when referring to energy, Katz
and Kahn (1966) also use the term metaphorically in the sense of material inputs (such
as raw materials) and not specifically in the sense of human activities.
The second macro-level stream of research examining boundary activities at the
levels of organizations is the organizational design perspective (Galbraith, 1977). This
perspective emphasizes the importance of processing technical information between
different units of research and development (R&D) organizations (Galbraith, 1977;
Tushman & Nadler, 1978). Primarily, this stream of research has focused on how individuals span the boundaries of their R&D laboratory, respective departments, and organizations as a whole in order to improve innovative outcomes (Tushman, 1977). For
instance, this research described several organizational communication roles (e.g.,
communication stars, gatekeepers, and liaisons) that improve the organizational innovation process (Tushman, 1977; Tushman & Scanlan, 1981a). Overall, this research
shows that there is a positive link between cross-boundary communication and organizational innovation and performance (e.g., Allen, 1984).
By exploring boundary activities at the team level, Ancona (1987) was among
the first to particularly take into account the external context of teams embedded with-
Introduction
17
in organizations. Team boundary activities are defined as team processes directed toward establishing and managing external social linkages with stakeholders in their external environment (Marrone, 2010). In a series of pioneering papers, Ancona (formerly Gladstein) and Caldwell empirically explored the role of these team boundary activities by teams embedded within organizational contexts (Ancona, 1990; Ancona &
Caldwell, 1992a; Gladstein, 1984). Before this seminal work, scholars had studied
teams primarily in the laboratory setting (Ancona, 1987). In a sample of service teams,
Gladstein (1984) showed that not only internal team processes (maintenance and task
behaviors) influence team effectiveness but also team boundary activities. Building on
this insight, Ancona and Caldwell (1992a) mapped the different types of team boundary activities among research and development (R&D) teams. In general, they found
that R&D teams engage in vertical communication in order to meet the expectations of
upper management and horizontal communication in order to coordinate work, obtain
feedback, and scan the technical and market environment. Furthermore, Ancona
(1990) demonstrated that R&D teams were most effective when their supervisors supported team members’ management of social linkages across team boundaries.
This research on team boundary activities has traditionally focused on team actions that involve engagement with external environments. Usually, these actions are
directed toward importing important resources and support from the external environment. This process is defined as team boundary-spanning activities (Ancona &
Caldwell, 1992a). A huge body of research shows that these boundary-spanning activities help teams to increase their innovative performance (Hulsheger, Anderson, &
Salgado, 2009). However, past research has scarcely paid attention to the opposite
team actions of team boundary-buffering activities. These actions involve disengagement from the environment in order to manage external demands. For example, Faraj
and Yan (2009, p. 606) describe team boundary-buffering activities as “formal strategies and procedures and informal codes and norms for deflecting and managing external demands, on team members.” These activities involve monitoring the information
and resources that external stakeholders request from the team (Ancona & Caldwell,
1992a). However, prior research has not yet examined whether and how team boundary-buffering activities increase teams’ innovative performance.
Research question 1: How do team boundary-buffering activities influence team
innovative performance?
18
Introduction
1.3.4 Collective Human Energy in Organizations
Human energy is a concept that POS scholars became interested in because it is theorized to reflect life-giving dynamics in organizations (Dutton, 2003; Quinn & Dutton,
2005). Prior conceptual work distinguished between two distinct but related aspects of
human energy: physical energy and energetic activation. Physical energy is described
as the capacity to do work. Work in turn is defined as the product of the force that is
exercised on an object and the distance that it moves (Quinn, Spreitzer, & Lam, 2012).
At the level of humans, physical energy manifests in two ways: either as potential energy (which is available but unused and stored chemically within the body) or as kinetic energy, which animates human activities. These human activities are either intentional (such as conscious reflection or purposeful movement) or unintentional (such as
breathing or the beating of the heart [Quinn, et al., 2012]). Energetic activation, on the
other hand, refers to the subjective component of human energy. It explains the degree
to which people experience themselves as invigorated (Quinn, et al., 2012). Physical
energy, the somatic component of human energy, and energetic activation, the subjective component of human energy, are two corresponding but separate constructs
(Quinn, et al., 2012). In the following section, this dissertation will primarily focus on
energetic activation.
Several streams of research offer valuable insights to understand the subjective aspects of human energy at different organizational levels. These literatures include the
conservation of resources theory, attention restoration theory, ego-depletion theory,
broaden and build theory, self-determination theory, and ritual chain theory (Quinn, et
al., 2012). Table 1 provides an overview of these literatures and related constructs.
Most of these literatures are situated at the individual level of analysis. At the individual level, scholars have drawn from conservation of resources theory to explain how
the interplay between job demands and job resources influences individuals’ experience of being energized (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001). Furthermore, researchers have applied ego-depletion theory to illustrate how individuals’
energetic activation and physical energy are depleted through the exercise of selfcontrol (Baumeister, Bratslavsky, Muraven, & Tice, 1998). Additionally, POS scholars
have applied self-determination theory to investigate the influence of fulfilling individual psychological needs on the subjective component of individuals’ human energy
at work (Spreitzer, Sutcliffe, Dutton, Sonenshein, & Grant, 2005). In sum, this individual-level research teaches us that different kinds of resources, such as job resources
(e.g., autonomy, social support, supervisory coaching, performance feedback, and op-
Introduction
19
portunities for professional development [Xanthopoulou et al., 2009]) and personal
resources (e.g., recovery, self-efficacy, organizational-based self-esteem, and optimism [Sonnentag, Mojza, Demerouti, & Bakker, 2012; Xanthopoulou, Bakker,
Demerouti, & Schaufeli, 2009]) play an instrumental role in sustaining individuals’
subjective experience of feeling invigorated. Finally, researchers have established several individual-level constructs, such as work engagement (Schaufeli, Bakker, &
Salanova, 2006), thriving (Porath, Spreitzer, Gibson, & Garnett, 2012), vigor (Shraga
& Shirom, 2009), and subjective well-being (Diener, 2000) that measure different aspects of the subjective component of human energy.
Table 1-1 Literatures and Constructs Related to the Human Energy Concept
Levels of Analysis
Literatures
Constructs
Individual
- Conservation of resources
(Hobfoll, 2011)
- Attention restoration (Kaplan,
2001)
- Self-determination (Ryan & Deci,
2000)
- Energetic activation (Fredrickson,
2001)
- Ego depletion (Baumeister,
Bratslavsky, Muraven, & Tice,
1998)
- Work engagement (Schaufeli,
Bakker, & Salanova, 2006)
- Vigor (Shraga & Shirom, 2009)
- Thriving (Porath et al., 2012)
- Subjective well-being (Diener,
2000)
Dyadic
- Interaction ritual chains (R.
Collins, 2004)
Collective
- Organizational climate (Schneider
& Reichers, 1983)
- Productive energy (Cole et al.,
2012)
At the dyadic level, research on human energy has applied ritual chain theory to
explain energizing relationships (R. Collins, 2004). Ritual chain theory is a micro-level
sociological theory that suggests that human energy emerges within social interactions
of individual actors (R. Collins, 1981). Furthermore, this theory posits that macro-level
social structures (e.g., organizations, markets, and social trends) are built on, and ultimately created by, energizing social interactions (R. Collins, 1993). Applying social
network data, Casciaro and Lobo (2008) found that employees tend to avoid colleagues whom they perceive as de-energizing, even when these colleagues possess
20
Introduction
information that they urgently need. At the dyadic level, a validated scale on relational
energy has not yet been published.
At the collective level, past research has used the construct of productive energy
to encompass a sense of collective human energy. Productive energy is defined as “the
shared experience and demonstration of positive affect, cognitive arousal, and agentic
behavior among unit members in their joint pursuit of organizationally salient objectives” (Cole, Bruch, & Vogel, 2012, p. 447). Theoretically, prior research has embedded the construct of productive energy within the literature of psychological climate
(Raes, Bruch, & De Jong, 2013). The construct of productive energy shares several
characteristics with other constructs from the organizational behavior literature but is
also distinct from these constructs. For example, it shares an emotive aspect with the
individual-level concept of work engagement (Schaufeli, Bakker & Salanova, 2006).
However, productive energy is grounded in unit members’ collective striving toward
goals at a higher level (Cole et al., 2012). Furthermore, in line with the circumplex
model of affect (Russell, 1980), productive energy is characterized by high levels of
arousal and positive valence similar to affective states, such as enthusiasm, excitement,
happiness, or alertness. However, contrary to individual-level affective states, productive energy is defined as a collective-level construct. Furthermore, contrary to traditional work motivation concepts, productive energy is not framed primarily around
cognitive processes (e.g., Chen, Kanfer, DeShon, Mathieu, & Kozlowski, 2009) but is
instead closely related to motivation, because it encompasses the potentiality of devoting efforts to a joint course of action (Cole et al., 2012).
Past research shows that productive energy positively influences several positive outcomes and antecedents. For example, research has found that productive energy at the organizational level is associated with internal measures of organizational
effectiveness such as goal and organizational commitment and organizational performance (Cole, et al., 2012). Furthermore, prior studies demonstrated that productive
energy at the organizational level is related to negative turnover and increased employee job satisfaction (Raes, et al., 2013). In addition to these consequences, scholars
have begun to disentangle antecedents of productive energy. Prior research has found
that top management teams’ behavioral integration and organizations’ transformational
leadership climate were associated with productive energy at the organizational level
(Raes et al., 2013). At the level of teams, Kunze and Bruch (2010) show that transformational leadership buffers the negative effect of age-based faultlines on team productive energy.
Introduction
21
As mentioned, prior research has revealed that transformational leadership increases productive energy (Kunze & Bruch, 2010; Walter & Bruch, 2010). The second
research question of this dissertation examines how transformational leadership positively influences team productive energy. Specifically, this dissertation proposes that
transformational leadership increases productive energy at the team level through the
mediation of team boundary-spanning activities. The basic argument is that transformational leaders enable their team members to engage in resource-gaining social interaction across team boundaries which, in turn, increases team productive energy.
Research question 2: Do team boundary-spanning activities mediate the positive
link between transformational leadership and team productive energy?
1.3.5 Intraorganizational Social Networks
The literature on social networks distinguishes between intraorganizational and interorganizational social networks. This dissertation focuses on intraorganizational social
networks, because social interactions within an organization may function differently
from those within a market environment (Brass, Galaskiewicz, Greve, & Tsai, 2004).
In the following discussion, this dissertation will briefly review the literature on social
networks at the business-unit level, analyze the literature at the team level, and finally
review the literature at the dyadic level. The vast majority of this intraorganizational
research examines the consequences, rather than antecedents, of social networks.
At the level of business units, Tsai and Ghoshal (1998) showed that units that are
more central within an inter-unit resource exchange network tend to produce more
product innovations than less central units. Accordingly, Tsai (2001) found that the
positive association between a unit’s more central position in the network and its
product innovation increases when it has a superior ability to successfully replicate
new knowledge (i.e., absorptive capacity). Furthermore, Tsai (2002) demonstrated that
social interactions between units have a positive effect on inter-unit knowledge sharing, specifically among units that compete in the same market segments. Moreover,
Hansen (1999) found that particularly strong ties (e.g., friendship of specific persons)
between organizational units enable the transfer of complex knowledge, whereas weak
ties (e.g., mere acquaintance) speed up project execution when less complex
knowledge is involved. Additionally, Tortoriello, Reagans, and McEvily (2012) suggest that knowledge transfer between different organizational units is particularly effective when social interactions are frequent, pleasant and transmit non-redundant information. Accordingly, Tsai (2000) found that network centrality and trustworthiness
22
Introduction
explain the formation of new ties between newly formed units and already-existing
units when their strategic relatedness was high. In sum, research at the business unit
level emphasizes that informal social networks increase the transfer of knowledge and
product innovation.
At the level of teams, prior research has revealed the positive effects of inter- and
intrateam social networks. A meta-analysis by Balkundi and Harrison (2006) points to
evidence that teams that are more central in interteam networks show superior task
performance than those that are less central. Furthermore, this meta-analysis demonstrated that teams with dense intrateam networks show higher levels of task performance and team viability. However, recent studies challenge the evidence of these linear effects of inter- and intrateam networks. Gibson and Dibble (2013) found that engagement in external activities has a curvilinear effect on team effectiveness. Furthermore, Oh, Chung, and Labianca (2004) showed that moderate levels of intrateam networks have the strongest impact on group effectiveness because engagement in internal social networks may reach a point of diminishing returns. Accordingly, Chung and
Jackson (2013) demonstrated that the intrateam network of a specific form of instrumental ties, trust strength, has a curvilinear effect on team performance.
Furthermore, the research on team-level interaction examined the association between social networks and leadership. For example, the meta-analysis by Balkundi and
Harrison (2006) showed that teams in which the formal leaders are more central in intrateam networks achieved higher task performance. Additionally, Balkundi, Harrison,
and Kilduff (2011) demonstrated with time-lagged data that leaders who are more central in intrateam networks are perceived as transformational by their team members. In
sum, the team-level literature shows that internal and external social networks have a
positive effect on team performance, although there might points of diminishing returns for both kinds of networks.
At the dyadic level, prior research has shown that individuals with central roles
in social networks are associated with greater access to resources, stronger organizational attachment, higher job satisfaction, and greater job performance (Brass et al.,
2004; Sparrowe, Liden, Wayne, & Kraimer, 2001). Furthermore, a vast number of
studies demonstrate that social similarity increases the likelihood of having reciprocal
and trustful social relationships (Brass, et al., 2004). Generally, this positive effect has
been found for a large variety of different similarity dimensions, such as age, sex, education, prestige, social class, tenure, and occupation (Joshi, 2006; McPherson, SmithLovin, & Cook, 2001). Additionally, physical and temporal proximity determine
Introduction
23
whether and how individuals interact with each other socially (Brass et al., 2004).
Festinger, Schachter, and Back (1950) even suggest that this proximity is more important than the effect of social similarity. In the same vein, past research has emphasized that formal organizational structure can also constrain informal social networks
(Brass et al., 2004). For example, prior studies show that, in organic organizational
structures, individuals can interact in a more unconstrained and flexible manner than in
mechanistic ones (Tichy & Fombrun, 1979). Moreover, past research has shown that
personality has an effect on informal social networks (Brass et al., 2004). These studies propose that two personality traits, proactive personality and self-monitoring, positively influence individuals’ initiative to build informal social networks. A proactive
personality is a disposition directed toward taking action to influence one’s environment (Thompson, 2005). Self-monitoring is a personality characteristic indicating the
extent to which individuals monitor environmental cues and modify their behavior to
meet external expectations (Mehra, Kilduff, & Brass, 2001).
Past social network research suggested that there might be a conflict between social network practices at different organizational levels (Ibarra, Kilduff, & Tsai, 2005).
At the organizational level, a bundle of human resource practices referred to as highperformance work systems (HWPSs) has been proposed to facilitate a social structure
by bridging ties between different organizational sub-units (Evans & Davis, 2005).
HPWSs involve rigorous selection procedures, high levels of training, merit-based promotions, skill-based pay, group-based rewards, cross-functional and cross-trained
teams, grievance procedures, information sharing, and internal participatory mechanisms (Datta, Guthrie, & Wright, 2005). Past research has shown that HPWSs are associated with numerous positive organizational outcomes, such as lower turnover rates
(Sun, Aryee, & Law, 2007), higher worker productivity (Arthur, 1994; Datta et al.,
2005), improved manufacturing quality (Datta et al., 2005; MacDuffie, 1995), greater
firm innovation (Chang, Gong, Way, & Jia, 2013), enhanced firm growth (Patel,
Messersmith, & Lepak, 2013), and superior financial performance (C. J. Collins &
Clark, 2003; Huselid, 1995). However, this literature has seldom examined organizational-level absenteeism as an outcome of interest. The few prior studies found a positive effect of HPWSs on organizational-level absenteeism (Guthrie, Flood, Liu, &
MacCurtain, 2009; Zhou, Chew, & Spangler, 2005; Wood, Van Veldhoven, Croon, &
de Menezes, 2012). The third research question of this dissertation seeks to examine
whether HPWSs (an organizational-level practice to facilitate social networks) and individuals’ social network building (an individual-level practice) may impede each other and thus result in a detrimental effect on organizational-level absenteeism.
24
Introduction
Research question 3: Does the interplay between high-performance work systems and employees’ network building detrimentally affect organizational-level
absenteeism?
1.4 Outline of the Dissertation
As elaborated in the previous chapters, this dissertation examines positive social interactions at multiple organizational levels. Studies 1 and 2 examine boundary activities
and their outcomes at the level of teams. Hence, for these studies, data needed to be
collected at the level of teams. Study 3 explores cross-level interactions of high-performance work systems (HPWSs) and employees’ social network building initiative.
Thus, for this study, data needed to be gathered at the level of organizations. Figure 12 provides the chapter structure of this dissertation. The following paragraphs provide
a short summary of these five chapters.
Chapter 1: Introduction. This chapter familiarizes the reader with the POS perspective. It emphasizes the theoretical and practical relevance of the research topic and
explains the motivation and specific research focus of the dissertation. Next, based on
this research focus, this dissertation derives three research questions for the subsequent
empirical studies. Finally, this chapter provides an overview of the overall design and
structure of this dissertation. Chapters 2 through 4 present the three empirical studies.
The structure of these studies follows the standard format of quantitative deductive
theory-testing papers in the field of management.
Chapter 2: Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance. Chapter 2 presents the first empirical study of this dissertation. It examines whether and how team boundary-buffering activities affect team innovative performance. After introducing the theoretical rationale of this study, hypotheses are developed based on the conceptual framework of the job demands-resources
model. These hypotheses are tested and discussed against the background of the literature of team boundary activities, job demands and resources, and productive energy.
Introduction
Figure 1-1
25
Overview of Chapter Structure
Chapter 1: Introduction
The positive organizational scholarship perspective
Theoretical and practical relevance of the dissertation
Motivation and research focus
Research questions
Outline of the dissertation
Chapter 2: Study 1
Linking team boundary-buffering activities and innovative performance
Chapter 3: Study 2
How does transformational leadership increase team productive energy?
Chapter 4: Study 3
Are high-performance work systems always beneficial?
Chapter 5: Overall Discussion and Conclusion
Summary
Theoretical integration of most important research findings
Limitations and directions for future research
Practical implications
Conclusion
Chapter 3: Study 2 – How Does Transformational Leadership Increase
Team Productive Energy? Chapter 3 includes the second study of this dissertation.
This study explores whether the link between transformational leadership and team
productive energy is mediated by team boundary-spanning activities. This chapter develops and tests a theoretical model drawing from the conservation of resources theory
and social networks literature. The results of the study are discussed, referring to the
literatures of team productive energy, transformational leadership, and diversity.
26
Introduction
Chapter 4: Study 3 – Are High-Performance Work Systems Always Beneficial? Chapter 4 explores potential negative effects of the interactions between highperformance work systems and employees’ network building initiative on organizational-level absenteeism. This chapter in particular contributes to the literatures of
high-performance work systems, positive social interactions, and absenteeism.
Chapter 5: Overall Discussion and Conclusion. The final chapter revisits the
research motivation and research questions and discusses the most important results of
the three empirical studies against the background of the literatures on team boundary
activities, collective human energy, and intraorganizational social networks. Finally, it
summarizes the overall limitations and opportunities for future research and offers
practical implications.
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
27
2 Linking Team Boundary-Buffering Activities and Innovative Performance: A Moderated Mediation Model1
2.1 Abstract
Research on team boundary activities has traditionally focused on team actions that involve engagement with external environments for important resources and support
(i.e., boundary spanning) and paid significantly less attention to team actions that involve disengagement from the environment as a way to manage external demands (i.e.,
boundary buffering). As a result, little is known about how and when team boundarybuffering activities influence team innovative performance. To address this gap, this
chapter draws from research and theory of the job demands-resources model
(Demerouti, Bakker, Nachreiner, & Schaufeli, 2001) to argue that (1) boundary-buffering activities protect teams from distracting information, disruptive events, and negative emotions from the external environment, thereby enhancing team productive energy (Cole, Bruch, & Vogel, 2012), (2) team productive energy, in turn, is associated
with higher levels of team innovative performance, and (3) the effect of boundarybuffering activities on team innovative performance via team productive energy is
stronger among teams experiencing higher levels of chronic job demand overload. Using a multi-source field study of 89 operational automotive research and development
(R&D) teams comprising 813 employees and their team leaders, full support was
found for the hypothesized model.
Keywords: team innovative performance, team boundary-buffering activities,
team productive energy, chronic team job demand overload, moderated mediation
1
Earlier versions of this paper have been accepted and/or presented at several international,
peer-reviewed conferences, namely the 73rd AOM Annual Meeting 2013, the EAWOP Small
Group Meeting on Innovation 2013, and the 16th EAWOP Conference 2013.
28
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Perfor-
mance
2.2 Introduction
As organizations become increasingly de-bureaucratized and less hierarchically-structured (Cross, Yan, & Louis, 2000; Yan & Louis, 1999; Zammuto, Griffith, Majchrzak,
Dougherty, & Faraj, 2007), research and development (R&D) teams face increased
levels of pressure and demands from various stakeholders inside and outside of the
organization (Faraj & Yan, 2009). For example, upper management frequently expects
R&D teams to deliver innovative solutions to technical problems in order to meet
market demands, even if these have not been thoroughly explored (Keller, 2001). Similarly, customers habitually expect organizations to deliver low-cost, highly innovative
products quickly, placing additional demand on R&D teams (Edmondson &
Nembhard, 2009). Finally, in an increasingly competitive business landscape,
organizations often put employees on multiple projects simultaneously in order to
reduce costs and to exploit their functional expertise in different cross-functional
contexts. As a result, members of R&D teams are often asked to divide their time and
commitment among multiple teams and projects, causing conflicting priorities that can
work against team effectiveness (Marrone, 2010).
To manage the external pressures, demands, and interference placed on R&D
team members (Keller, 2001), these teams engage in what Faraj and Yan (2009, p.
606) described as boundary-buffering activities, defined as “formal strategies and procedures and informal codes and norms for deflecting and managing external demands
on team members.” These activities, which were previously labeled “guard” and “sentry” activities (Ancona & Caldwell, 1988), involve monitoring the information and
resources that external stakeholders request from the team (Ancona & Caldwell,
1992b). Furthermore, these activities include deciding how the team will react to these
external demands as well as controlling the information and resources that external
agents want to send into the team (Faraj & Yan, 2009). Boundary-buffering activities
are a strategy of disengagement in which teams close themselves off from exposure to
the external environment, thereby helping teams focus on group tasks and objectives to
achieve high team performance. Similarly, Yan and Louis (1999) suggest that boundary-buffering activities aim at sealing off the productive core of team activities, smoothing the variability of inputs and outputs, and forecasting variation and uncertainty. In
sum, teams utilize boundary-buffering activities to respond to environmental disturbances and disruptive environmental forces (Cross et al., 2000).
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
29
Although boundary-buffering activities are theorized to be an important activity
that helps protect an entity from environmental demands and pressures, a dearth of research has examined how and when these activities influence team innovative performance, defined as a team’s ability to generate innovative ideas, processes, productions,
or procedures (West & Farr, 1990). One reason for the lack of research on these activities that involve disengagement from the environment might be that past research has
primarily focused on how teams employ activities that include engagement with the
external environment, or what is known as boundary-spanning activities (Ancona &
Caldwell, 1992b). A rich body of literature suggests that boundary-spanning activities
help improve the innovation process within organizations (Ancona & Caldwell, 1992b;
Hulsheger, Anderson, & Salgado, 2009; Tushman, 1977; Tushman & Scanlan, 1981b).
However, past literature on team boundary activities does inform about whether and
how the complementary approach of team boundary buffering also increases teams’
innovative performance.
Given the dearth of research on team boundary-buffering activities, this chapter
examines whether, how, and when boundary-buffering activities influence team innovative performance. In this study, we draw from research and theory of the job
demands-resources model (JD-R) to explain why and how team boundary-buffering
activities increase team innovative performance by enhancing team productive energy.
Productive energy is defined as unit members’ “experience and demonstration of positive affect, cognitive arousal, and agentic behavior among unit members in their joint
pursuit of organizationally salient objectives” (Cole et al., 2012, p. 447). According to
the JD-R model, individuals who experience higher levels of job resources (e.g., autonomy and social support) through a motivational mechanism will be more likely to
sustain their energy and hence show higher levels of in-role and extra-role performance (Schaufeli & Taris, in press). The JD-R model defines job resources as “those
physical, psychological, social, or organizational aspects of the job that (a) reduce job
demands and the associated physiological and psychological costs, (b) are functional
in achieving work goals, or (c) stimulate personal growth, learning, and development”
(Demerouti, Bakker, Nachreiner, & Schaufeli, 2001, p. 501). Applying this insight to
the team level, we propose that R&D teams engaging in boundary-buffering activities
(a set of job resources that protects them from external demands) have more team productive energy as compared to those that do not engage in these activities, which ultimately increases team innovative performance.
30
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Perfor-
mance
However, a recent extension of the JD-R model from the stress literature (Seers,
McGee, Serey, & Graen, 1983), the so-called coping hypothesis, proposes that the existence of considerable job demands (e.g., time pressure and work overload) may actually increase the salience of job resources (Bakker, Hakanen, Demerouti, &
Xanthopoulou, 2007). Building on this idea, it is suggested that team boundary-buffering activities are more effective in teams that experience higher levels of chronic job
demand overload (defined as teams having too much work to do in the time available
[Beehr, Walsh, & Taber, 1976]), as compared to those that suffer low levels of chronic
job demand overload. Specifically, it is proposed that this is the case because team
boundary-buffering activities help teams with chronic job demand overload to focus
on their job and thus maintain a sense of team productive energy.
This study offers several important contributions. First, it is one of the first to explore the underlying theoretical processes that explain the consequences of team
boundary-buffering activities on team innovative performance. This is an important
contribution because past research on team boundary activities has predominantly focused on team boundary-spanning activities, assuming that team boundary-buffering
activities have little or even negative effects on team innovative performance (DrachZahavy & Somech, 2010). At the same time, the study improves the general conceptual understanding of team boundary activities. Prior work has called for a deeper consideration of how teams carry out critical team boundary activities (Marrone, 2010).
This dissertation responds to this call by introducing the idea of productive energy and
examining whether this construct may help to explain more about critical mechanisms
linking boundary-buffering activities and team innovative performance. Furthermore,
prior theoretical work has suggested that the effectiveness of different kinds of boundary activities is contingent upon unique boundary conditions (Choi, 2002; Marrone,
2010). Hence, the literature suggests that a more detailed understanding of the moderating conditions of specific boundary activities is needed. The scarce empirical work
that has studied moderating conditions of team boundary-buffering activities has applied an organizational design perspective (Galbraith, 1977; Tushman & Nadler, 1978)
and focused on structural contingency factors, namely, task uncertainty and resource
scarcity (Faraj & Yan, 2009). Complementing this perspective, this study contributes
by adding chronic team job demand overload as a psychological boundary condition.
Furthermore, we suggest that past research has insufficiently studied the role of
team boundary-buffering activities, in part because it primarily considered team
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
31
boundary activities as a strategy to overcome problems of information processing between different organizational units (Galbraith, 1977; Tushman & Nadler, 1978). To
complement this functional, cognitive perspective, we apply a person-centered, emotive perspective, drawing upon the construct of team productive energy as a mediator
that comprises cognitive, affective, and behavioral aspects (Cole et al., 2012). Productive energy is one of those constructs – along with humility (Owens et al., 2013) and
thriving (Spreitzer et al., 2005) – that emerged in response to the positive organizational scholarship movement (Cameron et al., 2003). This movement has called for
enlarging the realm of generative states and outcomes that can be legitimately studied
in their own right in the organizational sciences (Cameron et al., 2003). Past research
has demonstrated several beneficial effects of productive energy (Cole et al., 2012;
Raes et al., 2013; Walter & Bruch, 2010). We add to this literature by linking team
boundary-buffering activities as antecedent and team innovative performance as a consequence of team productive energy.
Last but not least, our study contributes to the conceptual and empirical work that
is directed toward extending the JD-R model. Specifically, it adds to recent JD-R literature that aims at incorporating the so-called coping hypothesis from the stress literature (Seers et al., 1983). These studies have found that high job demands can “boost”
the effect of job resources (Bakker et al., 2007; Bakker, van Veldhoven, &
Xanthopoulou, 2010). The current study investigates whether this coping effect also
holds at the team level and enhances the effectiveness of team boundary-buffering activities. Additionally, this dissertation is one of the first to apply the JD-R model at the
level of teams. It does so by applying unique unit-level theory and concepts (team
boundary-buffering activities, team productive energy) and not simply adopting the
reference of individual-level constructs to the team level and then aggregating them
(Bakker, Van Emmerik, & Van Riet, 2008a; Torrente, Salanova, Llorens, & Schaufeli,
2012b; Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2009). In using this unique
team-level construct, this dissertation responds to a desideratum stated in recent JD-R
literature (Schaufeli & Taris, in press).
2.3 Theoretical Background and Hypotheses Development
Early research on boundary activities built upon an organizational design perspective
(Galbraith, 1977), predominantly emphasizing the importance of processing technical
information between different organizational units (Tushman & Nadler, 1978). Overall, this initial research showed that there is a positive link between cross-boundary
32
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Perfor-
mance
communication and organizational performance (e.g., Allen, 1984). For instance, this
research described several organizational communication roles (e.g., communication
stars, gatekeepers and liaisons) that improve the organizational innovation process
(Tushman, 1977; Tushman & Scanlan, 1981a). Predominantly, this stream of research
has focused on how individuals span the boundaries of their R&D laboratories, respective departments, and organizations as a whole in order to improve innovative outcomes (Tushman, 1977). Although this research has taught us much about how R&D
laboratories can improve their innovation process through inter-unit communication, it
does not explain how the opposite activity of boundary buffering may influence the
innovation process.
Subsequent research has studied team boundary activities with R&D teams. This
work has either followed the initial research of boundary activities with its narrow focus on cross-boundary communication (Ancona & Caldwell, 1992a; Keller, 2001) or
conceptually confounded the inside-out process of boundary spanning with the outside-in process of boundary buffering (e.g., Ancona & Caldwell, 1990; Ancona &
Caldwell, 1992b). Although this line of inquiry has offered us a more in-depth understanding of how R&D teams engage in vertical communication in order to meet the
expectations of upper management and horizontal communication in order to coordinate work, obtain feedback, and scan the technical and market environment (Ancona &
Caldwell, 1992b), it has still mainly followed the conceptualization of boundary activities as a strategy to improve information processing between different organizational
units (Galbraith, 1977; Tushman & Nadler, 1978). Consequently, it has yet to elaborate upon the specific roles of team boundary-buffering activities.
2.3.1 Team Boundary-Buffering Activities and Team Productive Energy
Conceptually, this chapter draws upon the JD-R model to explain why team boundarybuffering activities maintain a collective sense of team productive energy (Schaufeli &
Taris, in press). Referring to the JD-R model, job resources help individuals, through a
motivational mechanism, to feel more ready to work and hence show higher levels of
in-role and extra-role performance (Schaufeli & Taris, in press). With few exceptions,
the JD-R model has mainly been applied at the level of individuals (Salanova, Agut, &
Peiro, 2005; Salanova, Llorens, Cifre, & Martinez, 2012; Salanova, Llorens, Cifre,
Martinez, & Schaufeli, 2003). However, this dissertation suggests that team boundarybuffering activities influence productive energy primarily at the team level because
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
33
these activities trigger processes of cognitive and affective contagion (Barsade, 2002;
Gibson, 2001) and behavioral entrainment (Ancona & Chong, 1996). Accordingly, the
following will conceptually link these team boundary-buffering activities with team
productive energy at a cognitive, affective and behavioral level.
On a cognitive level, team boundary-buffering activities positively influence
team productive energy by deflecting distracting external information. These activities
include several actions to maintain a team’s information processing capacity. For example, one specific activity implies filtering of external information (Ancona &
Caldwell, 1988). In the case that a certain piece of external information is given to
specific team members, and they decide that it is not relevant for the team’s mission,
they will not pass it on to the whole team. Another activity involves evaluating external requests (Ancona & Caldwell, 1988). In the case of external stakeholders requesting certain efforts from a team and team members deciding that these efforts are not
relevant to their mission, the team will not conform to this request. Additionally, team
members show helping behavior when high external demands are placed on individual
members (Faraj & Yan, 2009). This specific activity hinders cognitive overload of individual members and balances the cognitive load within a team. At the same time,
when team boundary-buffering activities are not present, external distractions hit a
team without being absorbed and lower its information-processing capacity and thus
its cognitive energy.
On an affective level, team boundary-buffering activities preserve team productive energy by buffering negative events in the external environment. For example,
when members of a boundary-buffering team are involved in a conflict with an external stakeholder, they try not to pass their negative affect on to their teammates. Furthermore, the members are careful with how they internally communicate external information that might cause insecurity and disturbances. In the case that they have to
communicate bad news, they do it in a way that preserves a team’s positive affective
state as much as possible. However, when teams do not exhibit boundary-buffering
activities and team members forward bad news and gossip without caution (e.g., about
lay-offs, failure of an important product, or a new firm strategy), this might immediately cause the affective state of a team – and ultimately its affective energy – to deteriorate.
Finally, on a behavioral level, team boundary-buffering activities increase behavioral readiness, and thus productive energy, by limiting external demands to a degree
that team members are capable of executing. For example, members of boundary-buff-
34
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Perfor-
mance
ering teams do so by clearly communicating to external stakeholders when they feel
overloaded with work. Furthermore, they decline external requests when they perceive
them as not legitimate. Additionally, in the case of external requests tending to overload the team members, they preserve their readiness to act by setting clear priorities
about what tasks to execute first. However, when teams fail to buffer their boundaries,
they might become burdened with external demands and lose their agility and behavioral energy.
H1: Team boundary-buffering activities are positively related to team productive energy.
2.3.2 The Moderating Effect of Chronic Team Job Demand Overload
The literature on the JD-R model has incorporated two alternative arguments from the
stress literature to explain a moderating effect between job demands and job resources
(Schaufeli & Taris, in press; Seers et al., 1983). The first argument is the so-called
buffering hypothesis, which suggests that the negative effect of job stress (respective
job demands) on job-related outcomes will be diminished for individuals who experience high levels of social support (respective job resources [Caplan, Cobb, French,
Van Harrison, & Pinneau, 1975]). However, this buffering hypothesis is not strongly
supported by empirical evidence (Beehr, 1976; Cohen & Wills, 1985). Hu, Schaufeli,
and Taris (2011) showed in a comprehensive study that job resources buffered the
negative effect of job demands on burnout only in one of two samples. Moreover, the
predictive power of this buffering effect dropped dramatically when controlling for the
additive main effect of a composite measure of job demands and job resources.
The second argument incorporated from the stress literature is the so-called coping hypothesis, which posits that the effect of social support (respective job resources)
on job-related outcomes is especially beneficial when individuals experience high levels of job strain stress (respective job demands [Seers et al., 1983]). For example, Jenkins (1979) suggested that, in the case of individuals suffering high levels of strain,
their requests for social support might be an adaptive coping strategy because it signals
the need to reduce stress on co-workers. However, in turn, asking for social support is
unnecessary if individuals experience low levels of strain. The argument of the coping
hypothesis is also in line with prior individual-level studies drawing upon the JD-R
model. For example, Bakker, van Veldhoven, and Xanthopoulou (2010) found that
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
35
employees’ job resources (i.e., skill utilization, learning opportunities, autonomy, colleague support, leader support, performance feedback, participation in decisionmaking, and career opportunities) increased their task enjoyment and organizational
commitment, especially when their job demands (i.e., workload and emotional demands) were high. Likewise, Bakker, Hakanen, Demerouti, and Xanthopoulou (2007)
revealed that teachers’ job resources (i.e., job control, supervisor support, information,
organizational climate, innovative teaching methods, and appreciation) increased their
work engagement, particularly when their pupils showed high levels of behavioral
misconduct. Finally, Xanthopoulou, Bakker, and Fischbach (2013) demonstrated that
employees’ personal resources (i.e., self-efficacy and optimism) specifically improved
their work engagement when job demands (i.e., emotional demands and dissonances)
were high.
Drawing on the argument of the coping hypothesis, this study proposes that team
boundary-buffering activities sustain and even increase productive energy, especially
when chronic team job demand overload is high, because boundary-buffering activities
then represent an active coping strategy to deflect additional external demands and
other types of outside pressures and inferences. However, when teams are not chronically overloaded with job demands, these team boundary-buffering activities are not
necessary and do not add any extra benefit.
H2: Chronic team job demand overload moderates the relationship between
team boundary-buffering activities and team productive energy: The positive effect is stronger when chronic team job demand overload is higher.
2.3.3 Team Productive Energy and Team Innovative Performance
Innovation has been described as a discontinuous process rather than separate, progressive stages (Schroeder, Van de Ven, Scudder, & Polley, 1989). However, Scott and
Bruce (1994) suggest that this process involves three distinct activities: idea generation, idea promotion, and idea realization. Drawing on those insights, we assume that
members of R&D teams carry out any combination of these innovation activities at
any given time. In the following, to explain why team productive energy increases
team innovative performance, we will again conceptually distinguish between the cognitive, emotional, and behavioral levels.
On a cognitive level, team productive energy increases team innovative performance because it broadens team members’ repertoire of thoughts (Fredrickson, 2003).
36
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Perfor-
mance
Broadening the repertoire of thoughts helps in developing new ideas, which is most
important for the activity of idea generation (Scott & Bruce, 1994). In particular, we
suggest that a broader repertoire of thought increases the fluency, originality, and flexibility of ideas. Idea fluency refers to the number of non-redundant ideas that are being
generated, originality to their uncommonness or infrequency, and frequency to the use
of a variety of different cognitive categories and perspectives (De Dreu, Baas, &
Nijstad, 2008). Teams with a broader repertoire of thoughts can choose from a greater
number of alternative, more original ideas from a wider range of cognitive categories.
This increases their chances of finding an innovative solution to a given, novel problem at hand (De Dreu & West, 2001). However, teams with a narrow repertoire of
thoughts focus on ideas that have already been developed and successfully applied in
the past.
On an affective level, team productive energy increases team innovative performance because it provides an affective climate that enables team members to connect
among themselves and with external stakeholders. Our argument why this is the case is
twofold: First, on an affective level, team productive energy supports team members’
sociability. Second, this sociability increases team members’ interconnectedness. Although there is not much empirical evidence demonstrating a positive link between
positive affect and sociability at the level of teams, there is a huge body of experimental evidence pointing to this link at the level of individuals (Eisenberg, Fabes, &
Murphy, 1995; Pavot, Diener, & Fujita, 1990; Watson, 1988; Watson, Clark,
McIntyre, & Hamaker, 1992). For example, Burger and Caldwell (2000) showed that
college seniors with high positive affect were more successful in subsequent social job
search activities and job interviews than those seniors with low positive affect. On the
contrary, Eisenberg and colleagues (1995) found that low emotional intensity was associated with individuals’ lack of sociability. However, most informative to support
our theoretical argument is a study that combines a laboratory and a field experiment:
In the first experiment, Berry and Hansen (1996) examined positive affect within dyadic social interactions. They found that individuals’ positive affect was positively
related to the quality of their social interactions (as rated by themselves, their interaction partners, and independent observers). In the second experiment, individuals kept
diaries of their social interactions in a field setting for one week. The results of this
study revealed that participants’ positive affect was positively related to both the quantity of their social interactions (rated as the absolute number and total time) and the
quality of these social interactions (in terms of recreation and enjoyment).
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
37
In turn, we suggest that team members’ increased level of sociability supports
their innovative performance because it helps them to connect among themselves as
well as with external stakeholders. As van de Ven (1986) states, most organizational
innovation challenges require solutions that can only be created and developed collectively. Consequently, connecting with others (inside and outside of the team) should
improve all three aforementioned innovation activities. Again, to the best of our
knowledge, there is no empirical evidence at the level of teams, but we may support
our argument with evidence at the level of individuals. For example, Estrada, Isen, and
Young (1997) demonstrated that individuals with induced positive affect better integrated divergent information and different perspectives than those in the control group
with no treatment. Accordingly, a meta-analysis found a positive link between positive
affect and creativity (Baas, De Dreu, & Nijstad, 2008). In particular, a recent study
showed that individuals’ highly activated positive affect predicted time lagged effects
of the previously mentioned innovation activities of idea generation, promotion, and
realization (Madrid, Patterson, Birdi, Leiva, & Kausel, 2014).
Finally, on a behavioral level, team productive energy increases team innovative
performance because it increases team members’ proactive behaviors (Grant &
Ashford, 2008) and broadens team members’ repertoire of actions (Fredrickson, 2003).
We suggest that both aspects contribute to the activities of idea promotion and realization. For example, to convince external stakeholders of the usefulness of new ideas,
team members have to proactively approach these stakeholders and appropriately adjust their behavior to the social context. Furthermore, realizing a new idea requires
team members to proactively tackle a new idea and change their usual behavioral patterns to explore new procedures, processes, or techniques. Again, on the individual
level of analysis, past research drawing on the JD-R model demonstrated that individuals’ personal initiative was positively related to innovation (Hakanen, Perhoniemi, &
Toppinen-Tanner, 2008).
H3: Team productive energy is positively related to team innovative performance.
2.3.4 The Mediating Effect of Team Productive Energy
We expect that team boundary-buffering activities and team innovative performance
are linked through team productive energy. Drawing upon the JD-R model, developing
Hypothesis 1, we explained how team boundary-buffering activities are conceptually
38
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Perfor-
mance
linked with team productive energy at cognitive, affective, and behavioral levels. In
turn, in Hypothesis 3, we explicated how productive team energy is associated with
team innovative performance at the same three levels. Furthermore, in Hypothesis 2,
drawing upon the coping hypothesis from the stress literature, we illustrated why we
suggest that team boundary-buffering activities are especially effective in creating
team productive energy, and ultimately team innovative performance, when chronic
team job demand overload is high. Binding together our complete conceptual research
model, we suggest the following:
H4a: Team productive energy positively mediates the link between team boundary-buffering behaviors and team innovative performance.
H4b: Chronic team job demand overload positively moderates the positive indirect link between team boundary-buffering activities and team innovative
performance (as mediated through team productive energy): The positive
indirect link will be stronger with high chronic team job demand overload.
2.4 Description of Study Methods
2.4.1 Data Collection
We collected data from operational R&D teams in the R&D division of a multinational
automotive company based in Germany. These teams were especially suitable to our
investigation of team boundary activities because they had to work within a highly interconnected, project-based organizational structure (Marrone, 2010). During the data
collection, several actions were taken to maximize the response rate. The head of the
division sent an e-mail to all employees, encouraging them to participate in our study.
This e-mail highlighted the importance of the study to the company and guaranteed the
confidentiality of their responses. At two and three weeks after the data collection had
started, we sent a reminder e-mail to non-respondents. Each R&D team received a
written report of the results shortly after the survey. Furthermore, we offered train-theleader workshops to provide team leaders the opportunity to discuss the results with
their teams.
To reduce common method effects, we collected data from three different
sources (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). First, we gathered answers
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
39
on team boundary-buffering activities and chronic team job demand overload from the
team members, because they were best suited to describe processes that take place
within their teams (Mathieu, Maynard, Rapp, & Gilson, 2008). Furthermore, we conceptualized team productive energy and team innovative performance as holistic properties of a dynamic system rather than properties of specific team members (Bell &
Fisher, 2012). Thus, second, we asked team leaders to rate the overall productive energy of their teams. This approach is in line with prior research (Kunze & Bruch, 2010).
Third, we gathered responses on team innovative performance from the supervisors of
the team leaders. In doing so, we aimed at preventing a self-serving bias, because
members and leaders of a team might rate their own team innovative performance
more positively than would a team’s external stakeholders (Ancona & Caldwell,
1988).
2.4.2 Sample
The original sample included 105 teams (786 team members, 95 team leaders, and 18
supervisors of team leaders). The overall response rate added up to 73%. Responses
represented 95 teams with matched data from all three different data sources. Following the team definition of McIntyre and Salas (1995), we excluded four teams from the
analysis that were comprised of fewer than three persons, because we explicitly aimed
at studying at least triads as opposed to smaller entities, such as dyads. Our final sample was comprised of 89 teams (724 team members, 89 team leaders, and 18 supervisors of team leaders), including six teams with only two responding team members, six
teams with three responding team members, nine teams with four responding team
members, and 68 teams with five or more responding team members. The vast majority of team members had a university degree (93%) and were male (91%). The average
team member was between 36 and 40 years old and had worked for the company for
12.7 years (SD = 10.0 years). Almost all participants’ first language was German.
2.4.3 Measures
We acquired responses for all items on a Likert-type scale ranging from (1) "strongly
disagree" to (5) "strongly agree", unless otherwise indicated.
Team boundary-buffering activities (α =.89). We measured team boundarybuffering activities using a four-item scale from Faraj and Yan (2009). These four
40
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Perfor-
mance
items had the stem “To what extent …” and continued as follows: (1) “are outside
pressures deflected or absorbed so that the team can work free of interference,” (2)
“are outsiders prevented from ‘overloading’ the team with either too much information
or too many requests,” (3) “does the team or team leader help team members manage
the demands placed on them by other organizational units,” and (4) “do you feel that
team members work in a well-buffered or protected environment.” Team members
rated the extent to which their team showed the activities described. The aggregation
procedure was justified by satisfactory aggregation statistics (ICC1 = .18; p < .001;
ICC2 = .64, median rwg = .85).
Team chronic job demand overload (α =.73). To measure team chronic job demand overload, we adapted a scale from Beehr and colleagues (1976). This measure
had the following three items: (1) “In my team, we frequently see ‘light at the end of
the tunnel’ after phases of intense work (reverse coded),” (2) “In my team, we regularly have the opportunity to rest and relax (reverse coded), (3) “Members of this team
have so much work to do that they are often overstrained.” A confirmatory factor
analysis (CFA) revealed that these three items had sufficient high factor loadings
(λ=.97, λ = .45, and λ = .75, respectively). However, we could not perform Chi2-based
fit indices because a CFA of a three-item measure does not provide any degrees of
freedom. Aggregating this scale to the team level was justified by satisfactory aggregation statistics (ICC1 = .20; p < .001; ICC2 = .66; median rwg = .83).
Team productive energy (α = .77). We measured team productive energy using
a 14-item scale from Cole and colleagues (2012) that builds upon a three-dimensional
reflexive measurement model (Jarvis, MacKenzie, & Podsakoff, 2003). Five items
capture how the emotional aspects of team productive energy are perceived (e.g.,
“People in my team feel enthusiastic in their job”); five items on how its cognitive aspects are perceived (e.g., “My work group is ready to act at any given time”); and four
items on how its behavioral aspects are perceived (e.g., “People in my work group go
out of their way to ensure that the company succeeds”). The overall model fit of a second-order CFA showed satisfactory results (χ2 [74] = 96.24, CFI = .90, IFI = .91,
SRMR = .07). In line with prior research (Kunze & Bruch, 2010), we averaged all
items to build an overall productive energy score.
Team innovative performance (α = .83). We quantified innovative team performance with a nine-item scale developed by Janssen (2001). This scale builds upon
Kanter’s (1988) model of different development stages in the innovation process.
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
41
Three items refer to idea generation ([1] “creating new ideas for improvement,” [2]
“searching out new working methods, techniques, or instruments,” and [3] “generating
original solutions for problems); three items refer to idea promotion ([4] “mobilizing
support for innovative ideas,” [5] “acquiring approval for innovative ideas,” and [6]
“making important organizational members enthusiastic about innovative ideas”); and
three items refer to idea realization ([7] “transforming innovative ideas into useful applications,” [8] “introducing useful ideas into the work environment in a systematic
way,” and [9] “evaluating the utility of innovative ideas”). Supervisors of the team
leaders rated the extent to which teams showed the described activities (Bell & Fisher,
2012). Consistent with Janssen (2001), we averaged all item responses to build an
overall score for team innovative performance.
Controls. Most of the members within the R&D teams of our sample under
study were members of numerous project teams simultaneously. We expected that this
multiple team membership might influence team productive energy and team innovative performance. Hence, we controlled for team members’ average number of project
team memberships. Additionally, prior research suggests that the degree to which
teams reach across their team boundaries in order to acquire important information,
resources, and support (i.e. team boundary spanning [Ancona & Caldwell, 1992b])
might positively affect team productive energy and team innovative performance
(Hulsheger et al., 2009) Thus, we controlled for team boundary spanning using a fouritem scale from Faraj and Yan (2009).
2.5 Analyses and Results
Table 2-1 shows the descriptive statistics and correlations of our study. As suggested
by our hypotheses, team boundary-buffering activities were positively related to team
productive energy (r = .33, p < .01). Furthermore, as predicted, team productive energy
was positively correlated with team innovative performance (r = .23, p < .01). However, team boundary-buffering activities did not directly relate to team innovative performance (r = .05, p = ns). And although chronic team job demand overload was highly
negatively associated with team-boundary buffering (r = -.67, p = .001), it did not directly relate to team productive energy (r = -.16, p = ns) and team innovative performance (r = .01, p = ns).
42
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Perfor-
mance
2.5.1 Discriminant Validity of Measurement Model
After aggregating the data to the level of teams, we performed CFAs to assess the convergent and discriminant validity of our constructs 2. Following Bentler’s (2007) and
Hu and Bentler’s (1999) advice for sample sizes smaller than 200, we reported two
incremental fit indices – the comparative fit index (CFI) and the incremental fit index
(IFI) – combined with the standardized root mean square residuals (SRMR). For an
acceptable fit, the SRMR should be below .08 (Browne & Cudeck, 1993) and the incremental fit indices should be above .90.
Table 2-1
Means, Standard Deviations, and Zero Order Correlationsa
Variable
Controls
Average number of project
1.
team memberships
Team boundary-spanning
2.
activities
Predictors
Team boundary-buffering
3.
activities
4. Team job demand overload
5. Team productive energy
Dependent
6. Team innovative performance
a
M
SD
1
2
3
4
5
4.74 2.41
3.12 0.30
-.15
2.81 0.47
-.16
3.50 0.47
.21
3.99 0.35
-.21
3.97 0.47
-.06
.51
***
*
-.36
***
-.67
***
*
.30
**
.33
**
.07
.05
-.16
.01
.23
**
N = 89 (teams); * p < .05; ** p < .01; *** p < .001 (two-tailed).
The CFA of the measurement model yielded a good fit (Table 2-2, model I: χ2
[59] = 74.44, CFI = .97, IFI = .93, SRMR = .07). Furthermore, the results indicated a
better fit was not provided by an alternative one-factor model (χ2 [65] =227.39,
CFI=.70, IFI = .67, SRMR = .14, Δχ2 = 152.95, Δdf = 6, p < .001), a two-factor model
in which team boundary-buffering activities with team chronic job demand overload as
well as team productive energy with innovative performance represented a single latent variable (χ2 [64] = 151.73, CFI = .84, IFI = .80, SRMR =.12, Δχ2 = 77.29, Δdf = 5,
p < .001), or a three-factor model in which team boundary-buffering activities with
The items within the subscales of team productive energy and team innovative performance
were parceled (a total aggregation model) prior to model estimation. No other constructs were
parceled.
2
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
43
team chronic job demand overload, team productive energy and team innovative performance represented single latent variables (χ2 [62] = 100.66, CFI = .93, IFI = .90,
SRMR = .07, Δχ2 = 26.22, Δdf = 3, p < .001). In sum, this analysis proposes that the
constructs of our measurement model are distinct from each other and thus that our
measurement model shows satisfactory discriminant validity.
2.5.2 Analysis of Research Model
Structural equations modeling (SEM) with the statistical software package Mplus 5.2
was used to test the hypothesized research model. SEM offers three advantages over
the traditional OLS regression framework. First, SEM offers the opportunity to take
measurement errors into account by modeling latent variables. Second, it provides a
simultaneous test of all hypothesized relationships at once. And third, it offers an
overall assessment of how well the research model fits the data (Bollen, 1989).
2.5.3 Test of Hypotheses
Figure 2-1 provides an overview of the tested relationships of our hypothesized research model.
Main effects. Hypothesis 1 suggested that team boundary-buffering activities increase team productive energy. The corresponding path in our model was significant,
supporting Hypothesis 1 (Figure 2-1: γ = .27, SE = .11, one-sided p < .05). Hypothesis
3 posited that team productive energy increases team innovative performance. Also,
this corresponding path weight was positive and significant and thus supported Hypothesis 3 (Figure 2-1: β = .39, SE = .21, p < .05). However, the direct effect of team
boundary-buffering activities on team innovative performance was not significant, neither in the presence of team productive energy (Figure 2-1: γ = -.06, SE = .13, p=ns)
nor in its absence (γ = .00, SE = .14, p = ns).
44
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
Table 2-2
Model
I Measurement model
II Mediation-only modela
III Moderated mediation modela
a
Overall Structural Equation Model Fit Comparison
χ2
df
CFI
IFI
SRMR
AIC
BIC
74.44
137.05
59
79
.97
.93
.07
1305.73
1417.72
.93
.93
.07
1728.55
1202.38
Including control variables (average number of project team memberships, team boundary spanning)
ΔAIC
ΔBIC
1855.47
422.82
437.75
1344.23
-428.30
-433.28
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
Figure 2-1
Moderated Mediation Structural Equation Model
N = 89 (Teams); standardized path coefficients are reported. The values in the parentheses are standard errors.
†
p < .10, * p < .05, ** p < .01, *** p < .001 (two-tailed).
45
46
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
Moderation effect. Hypothesis 2 proposed that the interaction of team boundarybuffering activities and team chronic job demand overload positively influences team
productive energy. To model this interaction effect, we used the latent moderated SEM
procedure suggested by Klein and Moosbrugger (2000) as implemented in Mplus
(Muthén & Muthén, 1998-2012). One advantage of this approach is that it takes into
account the non-normal distribution of an interaction between latent constructs; however, as a downside, it provides no Chi2-based overall fit statistics, such as the CFI or
IFI. Alternatively, we used two comparative fit indices to assess the fit of our moderated mediation model: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). The lower the values of these comparative fit statistics, the better the fit of the model (Burnham & Anderson, 2004). At first, we only tested the mediation model without including the interaction term. This step allowed us to assess the
mediation-only model using the widely known Chi2-based fit statistics, such as CFI
and IFI. In a second step, we added the latent interaction term (Klein & Moosbrugger,
2000), comparing the AIC and BIC values of the prior mediation-only model and this
final moderated mediation model. Table 2-1 summarizes the overall fit statistics of
those two models. The mediation model without an interaction term showed a good fit
of the data (Table 2-2, model II: χ2 [79] = 137.05, CFI = .93, IFI=.93, SRMR =.07,
AIC = 1728.55, BIC = 1855.47). However, entering the interaction term lowered the
AIC and BIC and thus further improved the fit of this model (Table 2-2, model III:
AIC = 1202.38, BIC = 1344.23, ΔAIC = -428.30, ΔBIC = -433.28). As expected, the
interaction between team boundary-buffering activities and team chronic job demand
overload was positively and significantly related to team productive energy (Figure 21: γ = .28, SE = 06, p < .01), supporting Hypothesis 2.
Furthermore, as illustrated in Figure 2-1, we visually inspected the shape of this
interaction following a procedure outlined by Aiken and West (1991). To provide
these graphs, the slopes of team boundary-buffering activities on team productive energy were plotted under the condition of high versus low team chronic job demand
overload (using one standard deviation below and above the mean as reference points).
A supplementing simple slope test proved that the slope of team boundary-buffering
activities on team productive energy was positive and significant when team chronic
job demand overload was high (γ = .40, SE = .12, p < .001) and non-significant when
team chronic job demand overload was low (γ = .13, SE = .11, p = ns), adding further
support for Hypothesis 2.
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
Figure 2-2
47
Interaction between Team Productive Energy and Team Chronic
Job Demand Overload on Team Innovative Performance
5.0
Team productive energy
4.5
4.0
3.5
Team chronic job demand
overload high
3.0
2.5
Team chronic job demand
overload low
2.0
1.5
1.0
0.5
0.0
Low
High
Team boundary-buffering activities
Mediation effect. Hypothesis 4 posited that team productive energy mediates the
relationship between team boundary-buffering activities and team innovative performance. Prior research proposed the use of non-parametric bootstrapping to analyze
such an indirect effect, because the distribution of an indirect effect does not meet the
assumption of normality (due to the fact that an indirect effect is the product of the
associated paths [Edwards & Lambert, 2007]). Technically, Mplus is not able to apply
non-parametric bootstrapping in conjunction with the latent interaction technique
(Klein & Moosbrugger, 2000). Thus, we utilized parametric bootstrapping by means of
the moderated mediation procedure outlined by Preacher and Hayes (2007). We applied 10,000 resamples to test the indirect effect between team boundary-buffering
activities and team innovative performance as mediated through team productive energy. We found a significant indirect effect (Table 2-3: effect size [a × b] = .08, SE =
.05, p < .05, 95% bias corrected confidence intervals: CILower limit = .01, CIUpper limit=
.22), supporting Hypothesis 4a.
48
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
Table 2-3
Conditional Indirect Effects via Team Productive Energy predicting
Team Innovative Performance
Moderator value
Low chronic job demand overload, - 1 SD (-.47)
Average chronic job demand, (.00)
High chronic job demand overload, + 1 SD (.47)
a
Bootstrapped conditional indirect effect
Indirect effect
SE
.05
.08
.11
.05
.05
.06
CI95%LL CI95%UL
-.01
.01
.02
.19
.22
.27
N = 89 (Teams). CI95%LL = lower limit 95% confidence interval; CI95%UL = upper limit 95%
confidence interval; bootstrap sample size = 10,000.
Conditional indirect effect. Furthermore, to test Hypothesis 4b, we examined
the conditional effect of chronic team job demand overload on the indirect effect of
team boundary-buffering activities on innovative team performance (through the mediation of team productive energy). To do so, we used the moderated mediation procedure outlined by Preacher and Hayes (2007). The conditional effect is the value of the
indirect effect conditioned on the values of the moderator, at one standard deviation
above and below the mean. Table 2-3 shows the indirect effect with 10,000 nonparametric bootstrapped resamples conditional on team chronic job demand overload.
The indirect effect was significant with high chronic team job demand overload (Table
2-3: effect size [a × b] = .11, SE = .06, p < .05, 95% bias corrected confidence intervals: CILower limit = .02, CIUpper limit = .27) but not significant with low team chronic job
demand overload (Table 2-3: effect size [a × b] = .05, SE = .05, p = ns, 95% bias corrected confidence intervals: CILower limit = -0.01, CIUpper limit = .19). These findings support Hypothesis 4b.
2.5.4 Robustness Checks
Following Becker’s (2005) advice, we reran all of the analyses excluding the control
variables. The path weight of the interaction between team boundary-buffering activities and chronic team job demand overload remained nearly the same (β = .28, SE=.07,
p < .01) while the effect of team productive energy on team innovative performance
was slightly smaller (β = .36, SE = .20, p < .05); finally, the influence of team boundary-buffering activities on team productive energy (γ = .38, SE = .11, p < .05) and the
indirect effect between team boundary-buffering activities and team innovative performance mediated through team productive energy were somewhat higher (effect size
[a × b] = .09, SE = .05, p = .05, 95% bias corrected confidence intervals: CILower limit =
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
49
0.02, CIUpper limit = .23). However, this reanalysis did not change the conclusions of any
hypothesized relationships.
2.6 Discussion
2.6.1 Summary and Theoretical Contribution
In this paper, we have examined whether we can explain the link between team-boundary buffering activities and team innovative performance by using team productive energy as a mediator. The results of our study show that the mediation of team productive energy indirectly explains the positive link between team boundary-buffering activities and team innovative performance. Furthermore, we found that this relationship
was increased under the condition of high chronic team job demand overload. Under
the condition of low chronic team job overload, team boundary-buffering activities did
not contribute to team productive energy and ultimately to team innovative performance.
Our paper offers several important theoretical contributions. First, this study
helps explain how and why team boundary-buffering activities and team innovative
performance are related. To the best of our knowledge, no prior study has yet examined this relationship. Only one prior study has explored the relationship between team
boundary-buffering activities and team performance (Faraj & Yan, 2009). Within a
sample of software development teams, this study did not find a direct effect, nor did it
find an indirect effect as mediated through psychological safety climate (Faraj & Yan,
2009).
We extend this prior research by showing that team boundary-buffering activities
positively influence team innovative performance through the mediation of team productive energy. Expanding the established functional, cognitive view on team boundary activities by a more person-centered, emotive approach, our study suggests that not
only cognitive mechanisms explain the effect of team boundary-buffering activities on
team innovative performance but also emotional and behavioral ones. Our study is the
first to demonstrate that team boundary-buffering activities also have a positive effect
on team innovative performance. Past research has only found a positive effect for
team boundary-spanning activities (Ancona & Caldwell, 1992b; Hulsheger et al.,
2009; Tushman, 1977; Tushman & Scanlan, 1981b).
50
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
Furthermore, past research has examined structural contingency factors of team
boundary-buffering activities drawing upon an organizational design perspective
(Faraj & Yan, 2009; Tushman & Nadler, 1978). This research found that task uncertainty positively moderates the effect of team boundary-buffering activities on psychological safety climate, but neither task uncertainty nor resource scarcity influenced the
effect of boundary-buffering activities on team performance (Faraj & Yan, 2009). We
extend this functional, cognitive approach of the organizational design perspective,
referring to the more person-centered, emotive view of the JD-R model (Schaufeli &
Taris, in press). Specifically, our study shows that a psychological factor (i.e., chronic
team job demand overload) supports the effect of team boundary-buffering activities
on team innovative performance. Applying the JD-R model in a broader sense, the results of our study suggest that the overall level of job demands might moderate the
positive link between team-level job resources and positive work-related team outcomes.
Second, our study expands literature on the JD-R model. Recently, work stemming from this field has incorporated the so-called coping hypothesis from the stress
literature (Schaufeli & Taris, in press; Seers et al., 1983). This recent JD-R research
found that a number of different job demands (such as workload, emotional demands,
students’ behavioral misconduct, and dissonances) boost the effect of job resources
(such as skill utilization, learning opportunities, autonomy, colleague support, leader
support, performance feedback, participation in decision-making, career opportunities,
job control, supervisor support, information, organizational climate, innovative teaching methods, appreciation, self-efficacy, and optimism) on task enjoyment, organizational commitment, and work engagement (Bakker et al., 2007; Bakker et al., 2010;
Xanthopoulou et al., 2013).
Theoretically, this literature suggests that job resources do not only buffer the
negative effect of specific job demands. Rather, job resources become more effective
under these circumstances because they help individuals to cope with high job demands. This argument is in line with the conservation of resources theory, (COR
[Hobfoll, 1989]), which posits that resources by themselves only modestly affect positive well-being. However, referring to this theory, job resources only become salient
when job demands are high. Our study is the first to empirically demonstrate this coping effect at the level of teams.
Additionally, our study is one of the first to conceptualize the JD-R model with
genuine team-level constructs. For example, two prior studies applied individual-level
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
51
constructs (e.g., emotional demands, autonomy, exhaustion, cynicism, and work engagement) with references to the individual level and then simply aggregated them to
the team level (Bakker, Van Emmerik, & Van Riet, 2008b; Xanthopoulou et al., 2009).
Furthermore, to account for the motivational mechanism of the JD-R model at the
team level, prior work has used an individual-level work engagement scale and
adapted its references to the level of teams (Torrente et al., 2012b). However, this
scale has not demonstrated its discriminant validity in comparison with established
constructs at the team level (Cronbach & Meehl, 1955; Torrente, Salanova, Llorens, &
Schaufeli, 2012a). Hence, we contribute by using the genuine collective-level construct of team productive energy, which has been already been shown to be distinct
from established team-level constructs, such as cohesion and collective efficacy (Cole
et al., 2012).
Third, our paper adds to the literature on productive energy. At the level of whole
organizations, past research found that top management teams’ behavioral integration
and transformational leadership climate increase productive energy (Raes et al., 2013;
Walter & Bruch, 2010). At the level of teams, a prior study showed that transformational leadership can buffer negative effects of age/gender-based team faultines on
team productive energy (Kunze & Bruch, 2010). Furthermore, past work demonstrated
that productive energy is associated with positive outcomes, such as higher goal commitment and organizational commitment of employees (Cole et al., 2012), greater employee satisfaction and reduced turnover intention (Raes et al., 2013), and enhanced
work performance (Cole et al., 2012). This paper contributes by adding team boundary-buffering activities as an antecedent and team innovative performance as a consequence of team productive energy.
Last but not least, prior conceptual work on team innovation has argued that past
research in this field has predominantly relied on laboratory studies (Nijstad & De
Dreu, 2002; West, 2002). These laboratory studies have focused primarily on the behavioral activity of idea generation, neglecting other important activities of the innovation process, such as idea implementation (Nijstad & De Dreu, 2002; West, 2002).
Consequently, Nijstad and De Dreu (2002) and West (2002) have called for the combination of these crucial activities of the innovation process and the study of them
within the context of “real” teams. We have responded to these calls with two courses
of action: First, we operationalized team innovative performance with a scale that
comprises three major activities of the innovation process (idea generation, idea promotion, and idea implementation [Kanter, 1988; Janssen, 2001]). And second, we test-
52
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
ed our theoretical model within a field study, relying on “real” automotive R&D
teams.
2.6.2 Practical Contribution
This paper shows that team boundary-buffering activities are effective in maintaining a
team’s productive energy and ultimately its innovative performance. Specific team
boundary-spanning activities include filtering and evaluating external requests and
showing helping behavior when external demands are placed on individual team members. Additionally, those activities comprise carefully communicating information that
might cause insecurity and disturbance and clearly communicating to external stakeholders when team members feel overloaded with work. Finally, team boundary-buffering activities consist of declining external requests when they are not legitimate and
setting clear priorities. Hence, organizations might want to systematically train their
teams to show these activities.
Furthermore, our study demonstrates that team boundary-buffering activities become even more effective when teams face high levels of chronic job demand overload. Nevertheless, in practice, it might be difficult to buffer the team boundaries in
the face of chronic team job demand overload. We think that might be the case because these two constructs are very highly negatively correlated (as Table 2-1 shows).
Thus, particularly when teams experience high levels of chronic job demand overload,
it might be difficult for them to direct their energies toward disengaging from the environment.
2.6.3 Limitations and Future Research
Our study has some limitations that offer opportunities for future research. First, in this
paper, we have argued for a specific chain of effects between team boundary-buffering
activities, team productive energy, and team innovative performance. However, our
cross-sectional dataset does not allow us to draw any causal inferences. Nevertheless,
the correlations in Table 2-1 point to the fact that, at least in our dataset, neither chronic team job demand overload can mediate the relationship between team boundarybuffering activities and team innovative performance nor can team boundary-buffering
activities mediate the relationship between team productive energy and team innovative performance.
Study 1 – Linking Team Boundary-Buffering Activities and Innovative Performance
53
Second, the activities of team boundary buffering might be more complex than
our research model assumes. Although we theoretically advanced a unidirectional
chain of relationships between team boundary-buffering activities and team productive
energy, we cannot rule out a reciprocal relationship between these two constructs. Literature from the conservation of resources theory suggests that people at first have to
invest resources in order to gain them again at later stages (Hobfoll, 1989). Hence, we
admit that teams might have to partly commit their productive energy to keep up
boundary-buffering activities in order to sustain their productive energy at later stages
of the process. Future research could explore the potential reciprocal nature of this relationship using a cross-lagged panel design (Kenny, 2005).
Third, the generalization of our findings is limited to the specific nature of the
teams under study. We suggest that our findings are particularly relevant for teams that
work in highly interconnected and project-oriented organizational settings, such as
R&D teams or project teams. Generally, we suggest that our results are more applicable to teams executing knowledge-intense tasks, as compared to teams that perform
manual tasks (Hollenbeck, Beersma, & Schouten, 2012). In this study, we focused on
internal effects of team boundary-buffering activities. Future research could extend
this view by simultaneously examining effects on external stakeholders.
2.6.4 Conclusion
This study is one of the first to explore the underlying theoretical process that explains
the consequences of team boundary-buffering activities on team innovative performance. We propose that past research has insufficiently studied the role of team
boundary-buffering activities because it primarily used a functional, cognitive view.
To complement this view, we apply a person-centered, emotional perspective. To do
so, we introduce the idea of productive energy and show that this construct can explain
the link between team boundary-buffering activities and team innovative performance.
At the same time, drawing upon a job demands-resources framework, our study is one
of the first to examine the so-called coping hypothesis at the level of teams. We
demonstrate that the effect of a set of specific job resources (i.e. team boundary buffering) is especially enhanced when teams face higher levels of chronic job demand overload.
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Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
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3 How Does Transformational Leadership Increase Team
Productive Energy? The Role of Team Boundary-Spanning
Activities and Diversity 3
3.1 Abstract
Prior human energy research has found that transformational leadership (TFL) increases productive team energy. Team productive energy is the demonstration of positive
affect, cognitive arousal, and agentic behavior among team members in their joint pursuit of organizationally salient objectives. In this paper, we examine whether team
boundary-spanning activities mediate the positive link between TFL and team
productive energy. Furthermore, we propose that demographic (age, gender, and educational level) diversity accentuates the relationship between transformational leadership and team boundary-spanning activities. We tested our moderated mediation model, based on multilevel structural equation modeling of data from 121 R&D teams,
comprising 896 employees and 98 team leaders. Our results supported a full mediation
of team boundary-spanning activities and partly supported the interaction between
TFL and demographic diversity. Our study contributes to the emerging field of human
energy in organizations by providing first insights into which mechanisms create collective energy in teams.
Keywords: Transformational leadership, team productive energy, team boundary-spanning
activities,
diversity,
demographic
diversity
3
Earlier versions of this paper have been presented at the conference of the DGPs 2012 and
the 73nd AOM Annual Meeting 2013.
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Study 2 – How Does Transformational Leadership Increase Team Productive Ener-
gy?
3.2 Introduction
The topic of productive energy in organizations has received growing interest from
scholars (Cole et al., 2012) as well as from practitioners (Bruch & Ghoshal, 2003).
Productive energy is defined as experience and demonstration of positive affect, cognitive arousal, and agentic behavior among unit members in their joint pursuit of organizationally salient objectives (Cole et al., 2012). Recent research shows that productive
energy is associated with positive outcomes such as higher goal commitment and organizational commitment of employees (Cole et al., 2012), more employee satisfaction
and reduced turnover intention (Raes et al., 2013), and enhanced work performance
(Cole et al., 2012).
Until now, scholars have mainly explored antecedents of productive energy at the
organizational level. This research has found that the behavioral integration of top
management teams and the overall climate of transformational leadership (TFL) in
organizations positively affect the productive energy climate in organizations (Raes et
al., 2013). However, only one previous study examined productive energy on the level
of teams (Kunze & Bruch, 2010). This study found that, besides buffering potential
negative effects of diversity, TFL increases team productive energy. We suggest that
TFL is an important antecedent of team productive energy because TFL is one of few
constructs in organizational behavior that is able to explain how followers transcend
their own self-interest in order to work toward the goals of a higher-level entity
(Shamir, 1990). This higher-level goal attainment is also a distinct feature of the productive energy construct (Cole et al., 2012). Although Kunze and Bruch (2010) found
that TFL increased team productive energy, they did not demonstrate by which mechanism this is achieved.
To explain how TFL positively affects team productive energy, we examine team
boundary-spanning activities as a mediator. Team boundary-spanning activities are defined as team actions through which teams reach out to their environment to obtain
critical resources, information, and support (Ancona & Caldwell, 1992b). Following
the team taxonomy of Marks, Mathieu, and Zaccaro (2001), team boundary-spanning
activities can be described as an action process of system monitoring in which a team
tracks its resources and environmental conditions as they are relevant to the team
tasks. Extending literature on the motivational potential of resources to the level of
teams (Schaufeli & Bakker, 2004), we build theory on how transformational leaders
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
57
increase team productive energy by acquiring additional external resources through
team boundary-spanning activities.
Furthermore, drawing from literature on homophily (McPherson, Smith-Lovin, &
Cook, 2001), we assess whether the interplay between demographic (age, gender, and
educational level) diversity and TFL influences team boundary-spanning activities and
ultimately team productive energy. Prior research has yet only considered the role of
job-related team diversity (functional background and tenure) on team boundary- spanning activities (Ancona & Caldwell, 1992a). We argue that, although demographic diversity has the potential problem of destabilizing social identification within the team,
it likewise may increase the range of potential external stakeholders outside of team
boundaries. Unchecked, the problems of demographic diversity prevail but, when
teams experience transformational leadership, the potential of demographic diversity
might increase TFL’s influence on team boundary-spanning activities and ultimately
on team productive energy.
By examining the mediating effect of team boundary-spanning activities and the
moderating effect of demographic diversity, we offer three contributions to the literature. First, we build theory on how additional external resources increase team productive energy. Second, we bridge the internal and external perspective on the effectiveness of TFL teams. Third, we extend the literature on team boundary-spanning activities by exploring demographic diversity as a moderator. We develop hypotheses and
test a multilevel structural equation model of data from 121 functional ongoing automotive R&D teams, comprising 896 employees and 98 team leaders.
3.3 Theoretical Background and Hypotheses Development
3.3.1 The Motivational Potential of Resources
We refer to resources as those aspects of a job that are functional in achieving work
goals, reducing job demands and their associated physiological and psychological
costs, and, finally, stimulating personal growth, learning, and development (Demerouti
et al., 2001). Several lines of research, for example the job characteristics theory
(Hackman & Oldham, 1980), have recognized that resources have a motivational potential. The conservation of resources theory (Hobfoll, 2001) posits that human motivation is directed toward the building, preservation, and proliferation of resources.
This theory states that individuals value resources in their own right or because they
allow them to acquire or protect other valued resources. According to the job de-
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gy?
mands-resources literature (Schaufeli & Bakker, 2004), resources are the antecedents
of a motivational process. Thus, the presence and ability of resources inspires personal
growth and increases motivation (Salanova et al., 2005).
In the present paper, we extend this argument from the individual level to the
level of teams. According to the team taxonomy of Marks, Mathieu, and Zaccaro
(2001), productive energy is an emergent state of motivation and confidence building.
As opposed to team processes, emergent states do not directly describe the social interaction among team members but the subsequent cognitive, motivational, and affective states of team interactions (Marks et al., 2001). Building on the input-processoutput (IPO) model of team research, Marks and colleagues (2001) suggest that emergent states may either act as an input or output of a process within the IPO model. In
line with the circumplex model of affect (Russell, 1980), productive energy is a state
that shares high levels of arousal and positive valence with affective states such as enthusiasm, excitement, happiness, or alertness, but it is directed toward the goals of an
organization. Productive energy is closely related to motivation because it taps the potentiality of devoting joint efforts to a certain course of action (Cole et al., 2012). Nevertheless, members of teams with high productive energy will not take these actions
when they assume that these efforts will not result in any consequences of importance
(Vroom, 1995).
3.3.2 Team Boundary-Spanning Activities and Team Productive Energy
We propose that team boundary-spanning activities increase team productive energy
by providing additional resources from the environment outside of the team. We expect that teams that are able to capitalize on external resources build more team productive energy as compared to those that only capitalize on their internal resources.
Productive energy manifests itself on a cognitive, emotional, and behavioral level
(Cole et al., 2012). Team boundary-spanning activities positively influence all three of
these sub-dimensions.
Related to the cognitive dimension, team members gain additional external resources (e.g., information or knowledge) that help them to solve cognitive challenges
at hand and execute their tasks successfully. In line with that argument, previous motivation literature on the individual level has found that the experience of competence
(Ryan & Deci, 2000) and mastery (Sonnentag, Binnewies, & Mojza, 2008) is positively associated with individuals’ motivation. Previous job demands-resources literature
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
59
on the individual level has found that information and knowledge can act as motivating resources (Schaufeli & Taris, in press). On the level of teams, prior literature from
a job demands-resources perspective has shown that feedback and successful coordination increase teams’ motivational state (Salanova et al., 2012).
Related to the emotional dimension, team members gain additional resources
(e.g., support from external stakeholders) that are important for their positive recognition within the organization. Supporting this argument, prior motivation literature has
found that individuals’ feelings of relatedness are associated with motivation
(Baumeister & Leary, 1995). On the individual level, a huge body of evidence from
the job demands-resources literature has found a motivational effect of social support
(e.g., Rich, Lepine, & Crawford, 2010; Sonnentag, Binnewies, & Mojza, 2010). In the
same vein, prior job demands-resources literature on the level of teams has shown that
a supportive climate is beneficial for teams’ motivational state (Salanova et al., 2012).
Related to the behavioral dimension, team members acquire additional external
resources (e.g. financial resources, time for projects) that enable teams to execute their
tasks free of inference (Faraj & Yan, 2009). In line with that argument, prior research
has found that individuals’ autonomy is related to their motivation (Hackman &
Oldham, 1980). The motivational role of autonomy is supported by literature from the
job demands-resources perspective on the level of individuals (Schaufeli, Bakker, &
Van Rhenen, 2009) and teams (Salanova et al., 2005; Salanova et al., 2012). On the
individual level, prior literature from the job demands-resources perspective has
shown the motivational potential of financial resources (Schaufeli & Taris, in press).
H1: Team boundary-spanning activities are positively related to team productive energy.
3.3.3 Transformational Leadership and Team Boundary-Spanning Activities
Transformational leaders increase team boundary-spanning activities by influencing
the team as a whole as well as the individual team members. First, transformational
leaders act as boundary-spanning role models themselves (Pastor, Mayo, & Shamir,
2007). TFL research has shown that leaders who are considered transformational by
their employees are more central within the informal organizational advice networks
than their non-transformational counterparts (Balkundi, Kilduff, & Harrison, 2011;
Bono & Anderson, 2005). Second, they align their team toward exceptional group
goals. To achieve these exceptional goals, teams have to acquire additional resources
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Study 2 – How Does Transformational Leadership Increase Team Productive Ener-
gy?
from external sources above and beyond their internal resources (Marrone, 2010). And
third, they establish an overarching vision for the team that incorporates the use of
team boundary-spanning activities.
Besides collectively influencing the team as a whole, transformational leaders individually support their team members in spanning team boundaries. First, they influence the interpersonal self-concept of their team members such that the team members
are able to build role-based relationships with stakeholders external to the team (Hogg,
van Knippenberg, & Rast, 2012; Howell & Shamir, 2005). Second and third, they intellectually challenge their team members and, corresponding to the exceptional group
goals, expect high performance from the individual team members in order to successfully span the team boundaries and acquire additional resources for the team.
Team leaders possess better access to important stakeholders than team members.
Consequentially, they use their access to acquire additional external resources for the
team. Having said this, team members have a more fine-grained knowledge of the concrete tasks of a team. Members of teams that experience TFL tap into their own as well
as their leaders’ networks to acquire the resources they need to contribute to exceptional team goals. With TFL, team leaders and members transcend their self-interest in
order to work toward common team goals (Shamir, House, & Arthur, 1993) and best
match their networks and task relevant knowledge to acquire the necessary external
resources. In the absence of TFL, team members might use their personal networks to
only promote their own goals (such as career improvement).
H2: Transformational leadership is positively related to team boundary-spanning activities.
3.3.4 The Mediating Role of Team Boundary-Spanning Activities
We expect that team boundary-spanning activities mediate the relationship between
TFL and team productive energy. Two prior studies, one at the team level and one at
the organizational level, showed that TFL raises productive energy (Kunze & Bruch,
2010; Walter & Bruch, 2010). However, the mechanism by which TFL increases productive energy is not yet clear.
We suggest that, especially at the team boundaries, team members have the opportunity to capitalize on new social interactions as compared to within the team.
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
61
However, prior team research suggests that the impact of internal social team interactions is limited (Chung & Jackson, 2013; Oh et al., 2004). It showed that the strength
of relationships among team members within a team has an inverted U-shaped influence on performance where, after a turning point of saturation, further strengthening
these internal relationships diminishes team performance (Chung & Jackson, 2013; Oh
et al., 2004). Strong internal relationships do not only lead to inner team satisfaction
and social identification but also potentially to in-group favoritism and out-group deevaluation (Brewer, 1979). We expect that especially teams with a transformational
leader utilize team boundary-spanning activities as a strategy to acquire additional resources, because these teams share exceptionally aligned group goals (Podsakoff,
MacKenzie, & Fetter, 1993).
H3: Team boundary-spanning activities mediate the positive relationship between
transformational leadership and team productive energy.
3.3.5 The Moderating Role of Demographic Diversity
We propose that teams’ demographic diversity accentuates TFL’s impact on team
boundary-spanning activities. We suggest that teams that are capable of managing high
demographic diversity within the team are able to establish relationships with a greater
range of external stakeholders, based on a mutual perception of social similarity and
social identity, as compared to teams with low demographic diversity. This point of
view is supported by works of literature on organizational demography (Joshi, 2006)
and homophily (see McPherson et al., 2001). In a seminal paper on organizational demography, Pfeffer (1985) proposed that common demographic characteristics (such as
age, gender, and educational level) partly shape the perception of social similarity and
social identification. For example, employees of the same age are more likely to have a
similar life history, share a set of common live experiences, and be at a similar point in
their life courses, thus jointly producing a feeling of shared social similarity and social
identification between them (Pfeffer, 1985). Homophily is the principle that contact
between similar people occurs at a higher rate than among dissimilar people
(McPherson et al., 2001). Literature on homophily has found ample evidence that people tend to build their informal social networks based on demographic characteristics
(McPherson et al., 2001).
Within a team, demographic diversity has potentially harmful effects because it
may decrease social identification with the team itself (Kearney & Gebert, 2009). Prior
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TFL research has shown that, by advancing team members’ collective self-concept
(Shamir et al., 1993; Howell & Shamir, 2005), transformational leaders buffer the negative effects of demographic diversity within the team that are related to the potential
threat of team social identification (Kearney & Gebert, 2009; Kunze & Bruch, 2010;
Shin & Zhou, 2003).
H4:
Demographic diversity moderates the relationship between transformational
leadership and team boundary-spanning activities; the relationship is stronger
with increased demographic diversity.
3.4 Methods
3.4.1 Data Collection
We gathered data from the R&D division of a multinational automotive company
through an online survey. The R&D division was situated at the company’s headquarters in Germany. Almost all participants’ first language was German. R&D teams are
especially appropriate to the study of team boundary-spanning activities because they
have to perform in a highly interconnected, project-based organizational context
(Ancona & Caldwell, 1992a). During the data collection process, several actions were
taken to ensure a high participation rate. The head of the R&D department sent a personalized e-mail to all employees working in the sub-units targeted by this study. This
e-mail emphasized the importance of the study and encouraged all employees to take
part. Furthermore, the head of the R&D department assured the confidentiality of all
employees. At two and three weeks after the data collection had started, we sent emails to remind non-respondents to take part. Each team received a written feedback
report with the team results after the data collection was finished. We also offered participation in a train-the-leader workshop for team leaders to enable them to discuss the
results with their team members.
To avoid a common source bias, we collected data on team productive energy
and TFL, the dependent and the independent variable, from different sources
(Podsakoff et al., 2003). Following Bell and Fisher (2012), we conceptualize team
productive energy as a holistic property of a dynamic system rather than a property of
specific team members. Hence, and in accordance with prior literature (Kunze &
Bruch, 2010), we collected responses on productive energy at the level of the team
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
63
leaders. Furthermore, we collected responses on TFL from team members because
they have the closest social proximity to their team leaders and are thus better suited to
rate them regarding TFL than, for example, leaders’ direct supervisors (Kark, Shamir,
& Chen, 2003). We also collected responses on team boundary-spanning activities at
the level of the individual team members because team members are best suited to describe processes that take place within their team (Marrone, 2010).
3.4.2 Sample
The original sample included 131 teams with responses from 108 team leaders and 906
team members. The overall response rate added up to 72.11% 4. Following the definition of a team from Salas, Dickinson, Converse, and Tannenbaum (1992), we excluded
10 teams from the analysis that comprised fewer than three persons, because we were
explicitly urged to study at least triads as compared to smaller entities, such as dyads.
To account for the missing data situation, we did not further reduce the dataset by
matching the variables under study using a listwise deletion procedure. We will further
explain our missing data approach in the analysis section. Our final sample comprised
121 teams, with responses from 102 team leaders and 896 team members.
Most of the team leaders and team members in our final sample were male
(team leaders: 96.08%, team members: 89.75%) and hold a university degree (team
leaders: 94.12%, team members: 77.27%). On average, team leaders have been working for the company for 14.87 years (SD = 7.63 years) and team members for 12.64
years (SD = 9.33 years). Because of the data security policy of the company, we were
merely able to collect categorical data for age. The average team leader was between
41 and 45 years old, and the average team member was between 36 and 40 years old.
3.4.3 Measures
All answers were collected on a Likert-type scale ranging from (1) "strongly disagree"
to (5) "strongly agree." Professional translators translated the items of the constructs to
German following a double-blind back-translation procedure to guarantee semantic
similarity with the English original (Schaffer & Riordan, 2003).
4
Team leaders: 83.97%, team members: 70.89%
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Transformational leadership. At present, different instruments are available to
measure transformational leadership (Bass & Avolio, 1995; Podsakoff, MacKenzie,
Moorman, & Fetter, 1990). We decided to use the Transformational Leadership Inventory (TLI) (Podsakoff et al., 1993; Podsakoff et al., 1990) because prior research has
faced difficulty replicating the factor structure of the most commonly used instrument,
the Multifactor Leadership Inventory (MLQ, Bass & Avolio, 1995; Heinitz & Rowold,
2007), in a German-speaking context. The TLI, however, showed satisfactory results
in a validation study of the German translation of the scale (Heinitz & Rowold, 2007).
Also, the TLI taps into a slightly different range of transformational leadership behaviors from those of the MLQ. One dimension explicitly represents how leaders foster
acceptance for group goals, which we regard as an important precondition of successfully acquiring resources outside the team.
To the best of our knowledge, prior validation studies of the TFL construct exclusively tested the factor structure of TFL scales on the individual employee level
(Bass & Avolio, 1995; Heinitz & Rowold, 2007; Podsakoff et al., 1990). However,
Dyer, Hanges, and Hall (2005) point out that the equivalence between the individual
and team level factor structure should be explicitly tested empirically to safely use the
same factor structure on the team as on an individual level. Following Dyer, Hanges,
and Hall (2005), we applied a multilevel confirmatory factor analysis (MCFA) to test
the factor structure of the TLI (see Appendix: Figure 7-1). Concerning the criteria of
Hu and Bentler (1999), the overall model attained a good fit (χ2 [409] = 964.72,
p<.001, CFI = .95, TLI = .95, RMSEA = .04, SRMRwithin= .05, SRMRbetween= .13).
At the level of teams, as on an individual level, the latent second-order TFL factor significantly explains all latent second-order dimensions except for the dimension
of high performance expectations, which received mixed results. Although at the level
of teams, the factor loading of high performance expectations exceeded the conventional cut-off value of .40, the second-order TFL factor did not significantly explain
leaders’ high performance expectations. Nevertheless, we retained the dimension of
high performance expectations in our team-level model because, as Podsakoff and colleagues (1990) have argued, based on an extensive literature review, high performance
expectations are an essential part of leaders’ TFL behaviors, and prior studies also incorporated this dimension (e.g., Podsakoff et al., 1993; Podsakoff et al., 1990). Nevertheless, through a robustness check, we will test whether our proposed research model
will also hold when this dimension is excluded from the team-level analysis. The TLI
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
65
showed very good reliability (Cronbach’s αindividual level = .94, Cronbach’s αteam level =
.95).
Team boundary-spanning activities. We measured team boundary-spanning
activities with a four-item scale developed by Faraj and Yan (2009). These four items
had the stem “To what extent does the team…” and continued (1) “encourage its
members to solicit information and resources from elsewhere in and/or beyond the
division,” (2) “encourage its members to try to influence important actors elsewhere in
and/or beyond the division on behalf of the team and its work,” (3) “value team members for making use of their relationships with others on behalf of the team,” and (4)
“depend upon information and resources actively solicited by team members, that is,
information and resources beyond what comes through official channels?”
We applied the same MCFA approach as described above (Dyer et al., 2005;
2
χ [5] = 25.38, p < .001, CFI = .96, TLI = .89, RMSEA = .07, SRMRwithin = .04,
SRMRbetween = .04). The overall model revealed an acceptable fit except for the TLI
value (Hu & Bentler, 1999). However, item (4), “To what extent does the team depend
upon information and resources actively solicited by team members, that is, information and resources beyond what comes through official channels?” did not load significantly on the common factor for team boundary-spanning activities (standardized
λinidividual-level = .01, SD = .09, p = ns standardized λteam-level = -.23, SD = .31, p = ns).
Hence, we excluded the last item from the analysis and gained an excellent overall
model fit (χ2[1] = .43, p = .51, CFI = 1.00, TLI = 1.01, RMSEA = .00, SRMRwithin =
.00, SRMRbetween= .01). The scale on team boundary-spanning activities showed satisfactory reliability (Cronbach’s αindividual level = .76, Cronbach’s αteam level = .72).
Team productive energy. We measured team productive energy with a scale developed by Cole and colleagues (2012). Team productive energy is a threedimensional reflective construct (Jarvis, MacKenzie, & Podsakoff, 2003) that
measures three dimensions of a global entity of human energy within teams. Five
items measure how the teams’ energy is perceived on a cognitive level (e.g., "My work
group is ready to act at any given time."), five items measure how it is perceived on an
emotional level (e.g., "People in my team feel enthusiastic about their jobs."), and four
items measure how it is perceived on a behavioral level (e.g., “People in my work
group go out of their way to ensure that the company succeeds.”). We applied a second-order confirmatory factor analysis to test the three-dimensional structure of the
team productive energy construct. The overall model received a good fit (χ2[62]=
83.52, p = .21, CFI=.94,TLI = .93, RMSEA = .04, with a 90% confidence interval be-
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gy?
tween .00 and .07). The reliability of the team productive energy scale was good
(Cronbach’s αteam level = .81).
Demographic diversity. We operationalized demographic diversity as age, gender, and educational level diversity and measured these variables separately with the
heterogeneity index suggested by Blau (1977), because we refer to diversity as variety
(Harrison & Klein, 2007). Team members had to classify themselves regarding age diversity into one of eight categories: 20 to 25 years (n = 14), 26 to 30 years (n = 127),
31 to 35 years (n = 178), 36 to 40 years (n = 150), 41 to 45 years (n = 168), 46 to 50
years (n = 136), 51 to 55 years (n = 69), and 56 to 65 years (n = 36). The 18 remaining
respondents did not indicate their age. Age diversity ranged from .00 to .88 (M = .64,
SD = .23), showing a good variation in the data. 788 team members were male, 80
were female, and 18 did not indicate their sex. Gender diversity ranged from .00 to .50
(M = .15, SD = .18), pointing to a rather low variation of gender. All respondents within our study shared a professional engineering background and had to classify themselves in terms of their educational level into one of seven categories: no formal degree (n = 2), only high school education (n = 20), vocational training (n = 17), technical college (n = 128), university of cooperative education (n = 16), university (of
applied science) (n = 596), and doctorate (n = 77). Forty respondents did not indicate
their level of education. Educational diversity ranged from .00 to .81 (M = .33, SD =
.23), showing a low to medium variation of levels of education.
Control variables. We added team size, team longevity, and team response rate
as control variables to our model. Stewart (2006) argues that large teams have higher
coordination costs and small teams fewer resources, conditions that could influence the
team productive energy. We measured team size as the sum of team members (Ancona
& Caldwell, 1992b). We included team longevity because Katz (1982) has shown that
external and internal team communication diminishes as team longevity increases. We
measured team longevity as the average time team members had been on the team
(Kearney & Gebert, 2009). We included team response rate as a control variable to
rule out differences in response rates as an alternative mechanism of explanation. We
measured the team response rate as the ratio of the number of all team members to the
number of members who participated in our study.
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3.5 Analysis
3.5.1 Missing Data Analysis
Researchers confronted with missing data cannot assume that that data is missing completely at random (MCAR, e.g., Maloney, Johnson, & Zellmer-Bruhn, 2010; Schafer
& Graham, 2002). To take a conservative point of view, we assumed for the variables
under study a mixed situation of missing at random (MAR) and not missing at random
(NMAR, Schafer & Graham, 2002). For our analysis, we thus applied full maximumlikelihood estimation (Schafer & Graham, 2002). To attenuate the potential bias of the
parameter estimations in a data situation of NMAR, we included gender, age, and educational level as auxiliary variables (Graham, 2003). Although we gathered these demographic characteristics as questionnaire items, they should be rather objective and
do not introduce any further measurement error to the model.
3.5.2
Assessment of Team Properties and Measurement Model
Prior to the analysis of our multilevel research model, we assessed whether the use of
TFL and team boundary-spanning activities as team properties was justified (Klein &
Kozlowski, 2000). We did not consider team productive energy for this analysis because we directly assessed it at the team level. First, we conducted F-tests to assess
whether TFL and team boundary-spanning activities significantly explain variance
between teams. That was the case for both TFL and team boundary-spanning activities
(TFL: F [120,766] = 3.72, p < .001, team boundary-spanning activities: F [120,764] =
2.14, p < .001). Next, we determined within-group agreement for TFL and team
boundary-spanning activities using rwg-values. For both constructs, median rwg-values
exceeded the conventional cutoff value of .70 (James, Demaree, & Wolf, 1984, 1993)
(TFL: median rwg = .97, team boundary-spanning activities: median rwg = .85). Finally,
we examined intra-class correlation coefficients for TFL and team boundary-spanning
activities (Bliese & Halverson, 1998) (TFL: ICC[1] = .26, ICC[2] = .73, team
boundary-spanning activities: ICC[1] = .15, ICC[2] =.53). The ICC[1]s of TFL and
team boundary-spanning activities exceeded the conventional cutoff value of .12, and
only the ICC[2] of team boundary-spanning activities fell below the conventional cutoff value of .70 (Glick, 1985). Jointly, these statistics justify analyzing the data for
TFL and team boundary-spanning activities at the team level.
68
Study 2 – How Does Transformational Leadership Increase Team Productive Ener-
gy?
After the evaluation of the team properties, we performed MCFAs to test the discriminant validity of the involved constructs. The fit of the measurement model was
satisfactory (χ2 [77] = 302.28, p < .001, CFI = .93, TLI = .91, RMSEA = .06, SRMRwithin= .04, SRMRbetween= .11). Alternative models reached a significantly worse model fit. A one-factor model, with TFL, boundary-spanning activities, and team productive energy loading on the same factor 5, achieved a significantly worse model fit
(χ2[81]= 585.12, p < .001, CFI = .85, TLI = .81, RMSEA = .08, SRMRwithin= .06,
SRMRbetween= .17, Δχ2 = 282.20,ΔF= 4, p < .001). In addition, a two-factor model,
with TFL and team boundary-spanning activities loading on the same factor2 (χ2[81]=
550.19, p < .001, CFI = .86, TLI = .82, RMSEA = .08, SRMRwithin= .06, SRMRbetween=
.13, Δχ2 = 247.27, Δdf= 3, p < .001) and a two-factor model with team boundary-spanning activities and team productive energy loading on the same factor (χ2[80]= 326.81,
p < .001, CFI = .93, TLI = .91, RMSEA = .06, SRMRwithin= .04, SRMRbetween= .17, Δχ2
= 23.88, Δdf= 2, p < .001) achieved significantly fit worse with the data. Jointly, these
analyses show a satisfactory discriminate validity of our measurement model.
3.5.3 Multilevel SEM Mediation
Multilevel structural equation modeling (SEM) can account for the contextual effect of
team processes. A contextual effect exists when the between-level effect of two constructs is higher than the corresponding within-level effect (Preacher, Zyphur, &
Zhang, 2010). In our study, we assessed whether a contextual effect between TFL and
team boundary-spanning activities exists. Multilevel SEM enables us to examine the
between- and within-level effect simultaneously (Preacher et al., 2010). Ordinary multilevel modeling analyzes the between-level effect by aggregating constructs to the
higher level (Preacher et al., 2010), which produces biased estimates when the between- and within-level effects differ (Muthén, 1994). Also, multilevel SEM handles
emergent team processes that predict outcomes from the bottom up on the higher level
(Klein & Kozlowski, 2000). In the case of contextual effects, multilevel SEM mediation is superior to ordinary multilevel analysis (Ludtke et al., 2008; Preacher et al.,
2011). We used full maximum-likelihood estimation with robust standard errors, as
implemented in the software package Mplus, to account for the unbalanced group sizes
of our data set (Muthén & Muthén, 1998-2007).
5
In this model, we forced the items of TFL and team boundary-spanning activities on an individual level to load on one factor.
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
69
3.6 Results
3.6.1 Descriptives
Table 3-1 shows the descriptive statistics of the variables under study. The means,
standard deviations, and zero correlations were simultaneously produced with full
maximum-likelihood estimation in MSEM. Obtaining correlations within the traditional OLS regression framework would have required pairwise deletion of variables
to account for the missing values. The OLS regression framework would also have
required aggregating the individual-level data to obtain team-level correlation.
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
71
TFL and team boundary-spanning activities were more highly correlated at the
team level (r = .84, p < .001) than at the individual level (r = .48, p < .001). This result
is in line with the definition of a contextual effect in which the effect at the higher level is stronger than the corresponding effect at the individual level (Preacher et al.,
2010).
3.6.2 Multilevel Analysis
We tested the research model building on a procedure proposed by Preacher et al.
(2010). First, as a necessary but not sufficient condition to fit the MSEM mediation
model, we solely tested the within-level structure. In this step, we allowed the between-level constructs to covary freely. In the second step, we added the between-level
structure of the model. After assuring an appropriate model fit for the MSEM mediation model, we entered the control variables in an additional step.
The overall model fit for the different steps of our model-building process is
shown in Table 3-2. The only-within model fit well with the data (model I: χ2[78] =
301.78, p < .001, CFI = .93, TLI = .91, RMSEA = .06, SRMRwithin = .04, SRMRbetween
= .11), as did the MSEM mediation model with the included between-level model
(model II: χ2[78] = 301.80, p < .001, CFI = .93, TLI = .91, RMSEA = .06, SRMRwithin
= .04, SRMRbetween= .11, Δχ2 = 0.02, Δdf = 0). The addition of the control variables
significantly decreased the model fit. However, it remained acceptable (model III:
χ2[111] = 373.97, p < .001, CFI = .93, TLI = .91, RMSEA = .05, SRMRwithin = .03,
SRMRbetween = .14, Δχ2 = 72.17, Δdf = 33, p < .001).
72
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
Table 3-1
Variable
Team level (N = 121)
Controls
1. Team size
2. Team longevity
3. Team participation rate
Predictors
4. Transformational leadership
5. Team boundary spanning
6. Age diversity
7. Gender diversity
8. Educational level diversity
Dependent
9. Productive team energy a
Variable
Individual level (N = 896)
1. Transformational leadership b
2. Team boundary spanning c
a
Means, Standard Deviations, and Zero Order Correlations
Mean
SD
1
10.47
6.45
0.72
5.69
3.21
0.21
.54
-.07
-.25
3.45
3.38
0.64
0.15
0.33
0.44
0.41
0.23
0.18
0.23
-.16
-.31
.61
-.07
.23
4.05
0.36
-.14
Mean
SD
1
3.41
3.35
0.68
0.75
(.94)
.48
2
*
***
**
3
*
-
-.31
-.61
.47
.00
.35
*
-.02
.14
.27
.15
.21
-.30
*
*
**
**
-.03
**
*
4
(.96)
.84
-.32
.11
-.17
.38
***
**
**
5
(.76)
.07
-.09
.53
.53
6
***
***
.16
.40
.27
7
*
***
**
.41
-.13
8
9
***
-.15
(.81)
2
***
(.72)
N = 102; b N = 887; c N = 889. Full maximum likelihood estimation with robust standard errors; coefficient alpha reliabilities in the main diagonal
in parentheses. *** p < .001; ** p < .01; * p < .05 two-tailed.
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
Table 3-2
Models
I
Only within model specified
Within and between mediation
model specified
Within and between mediation
III
model specified with controlsa
II
a
73
Overall Multi-Level SEM Model Fit Comparison
χ2
df
CFI TLI
RMSEA
Δχ2
Δdf
301.78***
78
0.93 0.91
.06
.04
.11
301.80***
78
0.93 0.91
.06
.04
.11
0.02
0
373.97***
111
0.93 0.91
.05
.03
.14
72.17***
33
SRMRwithin SRMRbetween
Including control variables team size, team longevity, team participation rate; all models include auxiliary variables gender, age, and
education. ***p < .001.
74
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
Figure 3-1
Multilevel SEM Model with Decomposed Between and Within Effects
Vision
I1
***
.88
(.06)
Common Goals
***
.98
(.08)
***
.97
(.02)
Role Modeling
***
.91
(.03)
Individualized
Support
Transformational
Leadership
***
.87
(.06)
Intellectual Stimulation
***
.83
(.08)
I3
I2
***
***
.91
(.10)
Cognitive
.99
(.01)
***
.76
(.08)
Team Boundary- Spanning
Activities
***
.78
(.12)
Team Productive Energy
**
.93
(.32)
Emotional
***
.76
(.08)
***
.56
(.09)
.37
(.25)
Behavioral
Indirect effect: .73*(.43)
High Performance
Expectations
Between level
Within level
Vision
***
.87
(.01)
Common Goals
Full maximum likelihood estimation
with robust standard errors; standardized
path coefficients are reported. The values
in the parentheses are standard errors.
Effects of control variables (team size,
team longevity, team participation rate,
and transformational leadership) are not
shown in the figure.
***
.85
(.01)
Role Modeling
***
.81
(.02)
Individual Support
***
.64
(.03)
Intellectual Stimulation
Individual Perceptions of
Transformational Leadership
***
.67
(.02)
High Performance Expectations
***
.33
(.05)
Individual Perceptions of Team
Boundary-Spanning
Activities
***
.63
(.05)
***
.58
(.04)
***
***
.69
(.04)
.63
(.05)
I2
I3
***
p < .001, **p < .01 two-tailed.
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
75
3.6.3 Test of Hypotheses
Main effects. Figure 3-1 depicts an overview of the tested relationships of our hypothesized research model controlling for the direct effect of TFL on team productive energy. Hypothesis 1 posited that team boundary-spanning activities increase team productive energy. The significant corresponding path weight in our mediation model
supported that hypothesis (β = .93, SE = .32, p < .01). Hypothesis 2 suggested that
TFL promotes team boundary-spanning activities. Also, the significant path weight
confirmed Hypothesis 2 (γ = .78, SE = .12, p < .001). The direct effect of TFL on team
productive energy becomes non-significant in the presence of team boundary-spanning
activities (γ = -.36, SE = .37, p = ns). 6
Mediation analysis. Preacher et al. (2010) argued that neither non-parametric
bootstrapping nor the stepwise approach by Baron and Kenny (1986) is applicable to
test the mediational analysis in MSEM. Thus, we utilized the delta method (Sobel,
1982). The effect size of the indirect effect between TFL and team productive energy
via team boundary-spanning activities was significant when controlling for TFL’s effect on team productive energy and thus confirmed Hypothesis 3 (effect size [a × b] =
.72, SE = .35, < .05). 7
To check for common method bias between TFL and team boundary-spanning activities, we
instrumented TFL with Ragins’s (1989) five-item leadership effectiveness scale as rated by
the supervisors of the team leaders (Antonakis, Bendahan, Jacquart, & Lalive, 2010). The
first-stage F statistic exceeded .10, indicating that the instrument is not weak (F = 11.42, df =
1, 111, p < .001, Stock & Yogo. 2005). The Wu-Hausman test (Hausman, 1978; Wu, 1973)
was non-significant, indicating that the instrument is exogenous (F = 1.78, df = 1, 110, p =ns).
The reanalysis with the included instrument did not change the conclusion of any hypothesized relationships. TFL’s influence on team boundary-spanning activities even increased
slightly (standardized γ = .82, SE = .12, p < .001).
7
Ludtke et al. (2011) showed that the MSEM approach is more reliable than the traditional
aggregation approach as long as the higher-level sample size exceeds 50 units. Nevertheless,
MSEM has a tendency to overestimate contextual effects (Ludtke et al., 2011). Thus, in a
post-hoc analysis, we recalculated the model with OLS regression based upon aggregated
team-level data. As with the MSEM approach, we confirmed the hypotheses, although standardized estimates were smaller (H1: β = .51, SE = .07, p < .001; H2: β = .30, SE .12, p<.05; H3:
effect size [a × b] = .15, SE .06, p < .05).
6
76
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
Table 3-3
Variable
OLS Regression Results for Simple Moderation
Team boundary-spanning
activitiesa
Model 1a
Model 1b
Controls
Team size
-.12
(.01)
Team longevity
-.13
(.01)
Team participation rate
.10
(.18)
Predictors
Transformational
leader.49 *** (.03)
ship (TFL)
Team boundary-spanning
activities
Age diversity
.15
(.21)
Educational level diversity -.07
(.21)
Gender diversity
.01
(.17)
TFL × age diversity
TFL × gender diversity
TFL × educational level diversity
2
.53
R
.22***
ΔR2
-.09
-.10
.10
.59
***
Team productive energyb
Model 2a
(.01)
(.01)
(.17)
.08
-.04
-.01
(.01)
(.01)
(.19)
.08
-.03
-.01
(.01)
(.01)
(.19)
(.03)
.10
(.05)
.11
(.05)
(.05)
.31
(.23)
(.22)
(.18)
-.24
.11
.02
.00
.05
(.24)
(.22)
(.18)
(.05)
(.04)
.05
(.04)
.31
.03
-.08
.02
.44
.03
-.22
***
*
.62
.10**
Model 2b
(.20)
(.20)
(.16)
(.03)
(.03)
-.24
.11
.02
*
(.04)
.46
.18***
*
(.05)
.46
.00
a
N = 121 teams. b N = 102 teams. Standardized regression coefficients are reported. The values in the parentheses are the standard errors. * p < .05; ** p < .01; *** p < .001 two-tailed.
The moderating role of demographic diversity. According to Bauer, Preacher, and Gil (2006), multilevel moderated mediation is not well suited for analysis with
structural equation modeling. Thus, we tested Hypothesis 4 within an OLS regression
framework using aggregated team-level data and a listwise deletion missing data procedure. The variance inflation factors of all regression analyses were below 2.5, indicating that multicollinearity was not a problem in our analyses with the aggregated
level data. After centering TFL and the demographic diversity characteristics (age,
gender, and educational level diversity), we tested the simple interactions of the demographic diversity characteristics with TFL on team boundary-spanning activities following the approach of Aiken and West (1991). The results of the moderation analysis
are shown in Table 3-3. In line with Hypotheses 4, the interaction between TFL and
age diversity was positively and significantly related to team boundary-spanning activities (model 1b: β = .44, SE = 03, p < .001). Not in line with Hypotheses 4, the interaction between TFL and gender diversity was not significantly related to team boundary-
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
77
spanning activities (model 1b: β = .03, SE = 03, p = ns). Furthermore, contrary to Hypothesis 4, the interaction of TFL and educational diversity was negatively and significantly related to team boundary-spanning activities (model 1b: β = -.22, SE= .04, p <
.05). To rule out a second-stage interaction as an explanation mechanism, after centering team boundary-spanning activities, we tested the interaction between age, gender,
and educational diversity on the relationship between team boundary-spanning activities and team productive energy. None of these interactions gained significance (model
2b: age diversity β = .00, SE = 05, p = ns, gender diversity β = .05, SE = .04, p= ns,
educational diversity β = .05, SE = .04, p = ns). In the case of age and educational diversity, these results indicate a first-stage moderated mediation (Edwards & Lambert,
2007).
Following Aiken and West (1991), Figures 3-2 and 3-3 illustrate the shape of
the first-stage interactions of age and educational diversity: To provide the graphs, the
regression lines of TFL on team boundary-spanning activities were plotted under the
condition of high versus low age-respective educational diversity (using one SD below
and above the mean as a reference point). We found that the slope of TFL’s effect on
team boundary-spanning activities becomes steeper with high age diversity (figure 2a:
β = .94, SE = .04, p < .001) than with low age diversity (β = .25, SE = .04, p < .05). In
contrast, we found that the slope of TFL’s effect on team boundary-spanning activities
becomes steeper with low educational diversity (figure 2b: β = .82, SE = .04, p < .001)
than with high educational diversity (β = .37, SE = .04, p < .05).
To further test our results, we used the moderated mediation procedure outlined by
Preacher and Hayes (2007). With this procedure, we examined the conditional effect,
which is the value of the indirect effect conditioned on the values of the moderator, of
TFL on teams’ productive energy (through the mediation of team boundary spanning)
at three values of age respective educational diversity: the mean, one standard deviation above the mean, and one standard deviation below the mean. Table 3-4 shows the
indirect effect, with 10,000 non-parametric bootstrapped resamples, conditional on age
and educational diversity. The indirect effect was greater with high age diversity (95%
bias corrected confidence intervals: CILower limit = .07, CIUpper limit = .37) than with low
age diversity (CILower
limit
= .03, CIUpper
limit
= .29). However, the indirect effect was
78
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
greater with low educational diversity (CILower limit = .05, CIUpper limit = .39) than with
high educational diversity (CILower limit = .06, CIUpper limit = .26). 8,9
Figure 3-2
Interaction between Transformational Leadership and Age Diversity
on Team Boundary-Spanning Activities
Team boundary spannning activities
5.0
4.5
4.0
3.5
3.0
Age diversity high
2.5
Age diversity low
2.0
1.5
1.0
0.5
0.0
Low
High
Transformational leadership
8
Following Becker’s (2005) advice, we reran all of analyses excluding the control variables.
This reanalysis did not change the conclusions of any hypothesized relationships.
9
Finally, we reran our all analyses, excluding the team level TFL dimension of high performance expectation because this did not significantly load on the TFL factor. Likewise, this
reanalysis did not change the conclusions of any hypothesized relationships.
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
Figure 3-3
79
Interaction between Transformational Leadership and Educational
Diversity on Team Boundary-Spanning Activities
Team boundary spannning activities
5.0
4.5
4.0
3.5
Educational level diversity
high
3.0
2.5
Educational level diversity
low
2.0
1.5
1.0
0.5
0.0
Low
High
Transformational leadership
Table 3-4
Conditional Indirect Effects via Team Boundary-Spanning Activities
predicting Team Productive Energy
Moderator value
Low age diversity, - 1 SD (-.21)
Average age diversity (.00)
High age diversity, + 1 SD (.21)
Low educational level diversity, - 1 SD (-.23)
Average educational level diversity (.00)
High educational level diversity, + 1 SD (.23)
a
Bootstrapped conditional indirect effect
CI 95% CI 95%
Indirect effect
SE
.12
.16
.21
.17
.15
.13
.06
.06
.07
.08
.06
.05
LL
.03
.06
.07
.05
.06
.06
UL
.29
.29
.37
.39
.29
.26
N = 102 teams. 95 % bias corrected confidence intervals; lower limit = LL, upper limit =
UL; bootstrap resamples: 10,000
80
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
3.6.4 Additional Exploratory Analysis
To further explain the non-significant interaction between TFL and gender diversity,
respective of the negative interaction between TFL and educational diversity, we posthoc tested a multilevel SEM model with added main effects of the ratio of female team
members and team members’ average educational level on team boundary-spanning
activities. We found that the ratio of female team members had no effect on team
boundary-spanning activities (standardized γ = .27, SE = .19, p = ns), whereas the average education level had a significant positive effect on team boundary-spanning activities (standardized γ = .61, SE = .16, p < .001). The overall model fit of the multilevel SEM model remained acceptable (χ2[160] = 454.47, p < .001, CFI = .92,
TLI=.91, RMSEA = .05, SRMRwithin= .04, SRMRbetween= .14).
3.7 Discussion
3.7.1 Summary and Theoretical Implications
In this study, we have sought to shed light on the question of how TFL positively affects team productive energy. We proposed that TFL enables team members to span
the team boundaries and gather external resources for their team. As hypothesized, the
results showed that team boundary-spanning activities fully mediate the positive relationship between TFL and team productive energy. The results of the facilitating role
of demographic diversity on the link between TFL and team productive energy were
mixed. TFL’s positive interaction with age diversity on team boundary-spanning activities was in line with the positive hypothesized effect, whereas the interaction with
gender diversity showed no effect, and the interaction with educational level diversity
even showed a negative effect on team boundary-spanning activities.
Our research extends existing human energy research by developing a more detailed understanding of how productive energy emerges at the level of teams. In a
broader sense, our findings bridge the knowledge of how human energy emerges on
the individual and collective levels. On the individual level, energy research has found
that personal resources (e.g., recovery, self-efficacy, and optimism [Sonnentag, Mojza,
Demerouti, & Bakker, 2012; Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2009])
as well as job-related resources (e.g., autonomy, performance feedback, and opportunities for professional development [Xanthopoulou et al., 2009]) contribute to team
members’ individual feelings of energy. On the level of social networks, prior research
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
81
has found that social interactions within companies can energize individuals (Cross,
Baker, & Parker, 2003). Bridging the individual and the collective level, the results of
our study show that social interaction across team boundaries can raise the collective
human energy within teams.
Our findings also expand our understanding of how TFL functions on the level of
teams and show the important role of team boundary-spanning activities in explaining
TFL’s effectiveness in teams. Prior TFL research at the team level solely investigated
internal team processes (e.g., Bass et al., 2003; Kark & Shamir, 2002; Kark et al.,
2003). The results of this study suggest that the internal perspective of the explanation
mechanism should be complemented by a perspective that focuses on how transformational leaders enable their team members to reach beyond their team boundaries.
Our findings also add to the boundary-spanning literature by incorporating a homophily perspective. Our findings only partly supported the view that demographic
diversity increases the range of reachable external stakeholders when facilitated by
TFL. We found this effect only in the situation of age diversity. This finding regarding
age diversity is in line with prior evidence from the homophily (McPherson et al.,
2001; Reagans, 2011) and diversity literatures (Kearney & Gebert, 2009; Kunze &
Bruch, 2010; Shin & Zhou, 2003). However, there might be different reasons for why
we did not find the expected effect for gender and educational level diversity. Prior
homophily research has found that, within organizations, men tend to build their informal networks more strongly according to the homophily principle than women
(Ibarra, 1992). However, within our post-hoc analyses, we did not find a negative relationship between the ratio of female team members and team boundary-spanning activities.
The most difficult finding to explain is the negative interaction between TFL and
educational level diversity. However, one reason why TFL does not increase the impact of educational level diversity might be that higher levels of education also cover
lower levels of education. For example, an employee with a doctoral degree also holds
Master’s and Bachelor’s degrees as well as a school-leaving certificate for university
entry. Thus, building on the homophily principle, employees with higher educational
levels can also connect with employees with less formal education but not vice versa
(McPherson et al., 2001). Given that the average educational level in our sample was
very high (77.27% of the team members in our sample held a university degree), this
high degree of educational level can be expected to already facilitate boundaryspanning activities by itself. This interpretation is supported by a positive relationship
82
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
between average educational level and team boundary-spanning activities in our posthoc analysis. However, diversity of educational level might weaken transformational
leaders’ ability to create an overarching vision that embraces the self-concepts of all
team members, regardless of their educational level. All in all, our findings show that
the impact of different dimensions of demographic diversity on team boundaryspanning activities is more complex than previously assumed.
Last but not least, we methodologically expand the management literature by applying multilevel structural equation modeling (MSEM, Preacher et al., 2010). We
show that the effect between TFL and boundary-spanning activities was higher at the
team level than at the individual level, indicating a contextual effect (Preacher et al.,
2010). This contextual effect supports the idea that the research question under study is
a collective phenomenon (Ludtke et al., 2008). Muthén (1994) showed that, in the
presence of a contextual effect, the aggregation approach to higher-level data conflated
the lower- and higher-level data. Thus, as shown by several methodologists (e.g.,
Ludtke et al., 2008; Preacher et al., 2011), in the present case, the MSEM approach is
less biased than the traditional aggregation approach.
3.7.2 Practical Implications
Companies might want to monitor and strengthen TFL and team productive energy.
Particularly in the context of project-based organizations (Faraj & Yan, 2009), individual managers could think of how they may enable their teams to acquire external
resources across team boundaries. Furthermore, managers could act as boundaryspanning role models themselves and utilize their own as well as the social network of
their teams to jointly capitalize on these network opportunities. Furthermore, to benefit
from positive effects of age diversity on team boundary-spanning activities, managers
might want to strengthen a collective sense of team identity (e.g., by reinforcing a
common narrative of the team’s past challenges and successes).
3.7.3 Limitations and Future Research
Besides the need to extend our knowledge on how human energy manifests on the level of teams, our study has several limitations that offer opportunities for future research. The generalizability of our findings is limited due to the specific nature of our
sample of R&D teams with highly educated, mostly male team members. Collaboration with other external stakeholders and gathering of resources (e.g. information,
Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
83
equipment, and support) are especially critical in this organizational context. Within a
less knowledge-driven and more standardized team context, team boundary spanning
might be less requisite. Furthermore, a more demographically diverse composition
within and among teams would facilitate a deeper examination of the impact of demographic diversity on team boundary spanning and its boundary conditions.
We gathered our dependent, productive team energy and the independent variable TFL from different sources to prevent a common method bias (Podsakoff et al.,
2003). However, we collected the data from TFL, team boundary spanning, and demographic diversity from the same source. To check for potential common method bias in
the relationship between TFL and boundary spanning, we instrumented TFL with a
variable from a different source following a procedure outlined by Antonakis et al.
(2010). This approach derived the same hypothesized conclusions. Regarding the interaction between TFL and demographic diversity, Siemsen, Roth, and Oliveira (2010)
showed that interaction effects as a matter of principle cannot be artifacts of common
method variance. Thus, we assume that common method variance is not a severe problem in our analyses.
Although we have proposed a certain sequence of the constructs within our model, we cannot draw any causal conclusions due to the cross-sectional nature of our data. Thus, future research should empirically assess the potential reversed causality between productive team energy and team boundary spanning. This might be realized
with a cross-lagged-panel design or a nonrecursive framework with instrumental variables (Antonakis et al., 2010). In a strictly methodological sense, only an experimental
design would enable us to draw any causal conclusions of the relationship between
team boundary spanning and productive team energy.
3.7.4 Conclusion
In this study, we hypothesized that transformational leadership (TFL) enables team
boundary-spanning activities which, in turn, increase team productive energy. Accordingly, we showed that team boundary-spanning activities largely explain the positive
influence of transformational leadership on team productive energy. Furthermore, we
expected that the effect of TFL on team boundary-spanning activities would be more
pronounced in teams with high demographic diversity because transformational leaders may buffer potentially harmful effects. At the same time, we expected that, based
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Study 2 – How Does Transformational Leadership Increase Team Productive Energy?
on a perception of social similarity, teams with high demographic diversity are better
able to reach a great variety of external stakeholders. We confirmed this facilitating
effect only for age diversity and not for educational and gender diversity. Our study
advances knowledge on antecedents of team productive energy.
85
4 Are High-Performance Work Systems Always Beneficial?
The Limiting Interaction with Employees’ Social Network
Building10
4.1 Abstract
Past research has found ample evidence that high-performance work systems (HPWSs)
positively affect organizational performance (Combs, Liu, Hall, & Ketchen, 2006).
However, this research has scarcely considered absenteeism as a focal outcome of interest. Drawing upon social exchange theory (Blau, 1964) and the literature on positive
social interactions (Heaphy & Dutton, 2008), we develop a theoretical model for why
HPWSs may have beneficial and detrimental effects on organizational-level absenteeism. Using a multi-source study with time-lagged field data, comprising 161 organizations with 15,401 employees, we found full support for our hypothesized model. Our
paper challenges the assumption that HPWSs are always beneficial. However, it suggests that their effect on absenteeism depends on employees’ network building.
Keywords: absenteeism, cross-level interaction, high-performance work systems, network building initiative, time-lagged data
10
An earlier version of this paper has been accepted for the 74nd AOM Annual Meeting 2014.
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Study 3 – Are High-Performance Work Systems Always Beneficial?
4.2 Introduction
A huge body of strategic HRM literature points to evidence that high-performance
work systems (HPWSs) are positively related to organizational performance (Combs et
al., 2006). Although neither conceptual (e.g., Lawler, 1992; Levine, 1995; Pfeffer,
1998) nor empirical efforts (e.g., Arthur, 1994; Huselid, 1995) have reached a strict
definition of what an HPWS is, there is general agreement that it includes rigorous
selection procedures, high levels of training, merit-based promotions, skill-based pay,
group-based rewards, cross-functional and cross-trained teams, grievance procedures,
information sharing, and internal participatory mechanisms (Datta, Guthrie, & Wright,
2005). Past research has shown that the use of HPWSs is associated with numerous
positive organizational-level outcomes, such as higher worker productivity (Arthur,
1994; Datta et al., 2005), improved manufacturing quality (Datta et al., 2005;
MacDuffie, 1995), greater firm innovation (Chang, Gong, Way, & Jia, 2013), enhanced firm growth (Patel, Messersmith, & Lepak, 2013), and superior financial performance (C. J. Collins & Clark, 2003; Huselid, 1995).
However, past HPWS research has relatively scarcely studied absenteeism as a
focal outcome of interest (Kehoe & Wright, 2013; Zatzick & Iverson, 2011). The majority of this research proposes that, overall, HPWSs reduce organizational-level absenteeism (Guthrie, Flood, Liu, & MacCurtain, 2009; Ramsay, Scholarios, & Harley,
2000; Way, Lepak, Fay, & Thacker, 2010; Zhou et al., 2005). However, prior research
suggests that there might be a “dark side” of HPWSs that limits the beneficial influence on organization absenteeism. For example, Wood, Van Veldhoven, Croon, and de
Menezes (2012) found that HPWSs indirectly encourage organizational-level absenteeism through increased levels of stressful emotions, whereas they did not find such a
harmful effect on financial performance and workers’ productivity. Accordingly, Jensen, Patel, and Messersmith (2013) showed that HPWSs can increase employee anxiety, role overload, and turnover intention, particularly when job control is low.
The lack of research explaining these inconsistent findings is remarkable, given
that absenteeism causes annual costs of millions of dollars for organizations and society as a whole (Dalton & Mesch, 1991; Mason & Griffin, 2003). A recent national survey of HR professionals in Great Britain revealed that the median yearly direct costs of
absences amounted to approximately $1,000 per employee (CIPD, 2013). However, in
addition to these direct costs, absenteeism causes significant indirect costs related to
Study 3 – Are High-Performance Work Systems Always Beneficial?
87
replacing absent employees and delaying schedules through the loss of work hours
(Dansereau, Alutto, & Markham, 1978).
In this paper, we will put forward a theoretical model for why HPWSs may have
both beneficial and detrimental effects on organizational-level absenteeism: Contrasting arguments from social exchange theory (Blau, 1964) with literature on positive
social interactions (Heaphy & Dutton, 2008), this paper puts forward the idea that an
HPWS may have the unintended effect of detrimentally interacting with employees’
network building initiative. Network building initiative is a behavior that aims at proactively influencing others within social interactions to realize self-determined objectives (Thompson, 2005). It emerges discretely at the individual employee level in a
voluntary way. Our central argument is that when employees tend to build few social
networks on their own initiative, HPWSs help to reduce organizational-level absenteeism by providing employees with a supportive social structure from the top down
(Evans & Davis, 2005). On the contrary, when employees proactively build strong social networks on their own, HPWSs rather impair their bottom-up initiatives and increase organizational-level absenteeism by demanding additional extra-role behavior
(Bolino, Klotz, Turnley, & Harvey, 2013; Van Dyne & Ellis, 2004).
Our paper offers three main theoretical contributions. First, it contributes to the
literature on HPWSs. A large body of research demonstrates that HPWSs positively
affect various dimensions of organizational performance (Combs et al., 2006). In this
paper, we challenge the implicit assumption that HPWSs are always beneficial by considering boundary conditions of the link between HPWSs and organizational-level absenteeism. Furthermore, Jiang, Takeuchi, and Lepak (2013) suggest that future crosslevel studies would do well to not only consider the top-down effect of HPWSs on
outcomes at lower levels but also the bottom-up effects of aggregated lower-level variables on outcomes at the unit level. We contribute to this literature by exploring the
cross-level interaction between HPWSs and the bottom-up effect of employees’ ability
to build social networks on organizational-level absenteeism.
Second, our paper contributes to the absenteeism literature. Rentsch and Steel
(2003) point out that – as compared to research at the individual level – absenteeism
research at the unit level is particularly rare and deserves additional research attention.
More specifically, past research focused on the level of groups and work units, but almost no research has considered absenteeism at the organizational level (see
[Markham, 1985] for an exception). For this reason, Rentsch and Steel (2003) call for
a more fine-grained examination of contextual characteristics of absenteeism at higher
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Study 3 – Are High-Performance Work Systems Always Beneficial?
levels. Accordingly, Harrison and Martocchio (1998) urge that more research is needed to better understand the organizational context of absenteeism. Responding to these
calls, we examine how HPWSs and employees’ network building initiative are related
to organizational-level absenteeism.
Third, this paper contributes to the literature of positive organizational scholarship. Heaphy and Dutton (2008, p. 156) propose that future research ought not to exclusively study positive social interactions at the individual level but also “address
questions about people’s larger relational landscape.” Accordingly, Cameron et al.
(2003) emphasize that proving effects at one level of analysis does not necessarily
mean that the effects exist at another. Based on this observation, the authors suggest
that future research should specifically explore whether positive dynamics at the individual level also reproduce themselves at higher levels, such as that of entire organizations. We contribute to this literature by exploring how organizational-level absenteeism is mitigated by a behavior that induces positive social interactions: employees’
network building initiative.
4.3 Theory and Hypotheses Development
4.3.1 Why Are High-Performance Work Systems Effective?
Drawing upon prior conceptual work, we consider an HPWS as an organization’s
strategy for intentionally managing the organization-employee relationship (Tsui,
Pearce, Porter, & Tripoli, 1997). Firms adopt an HPWS when their employees’ job
roles are difficult to define, tasks are complex, and short-term efforts are difficult to
evaluate (Sun, Aryee, & Law, 2007). By applying an HPWS, firms aim to encourage
their employees to engage in a long-term employment relationship, in which they flexibly adjust their work roles in response to varying tasks, invest in firm-specific
knowledge and skills, and voluntarily show extra-role behavior (Tsui et al., 1997). Extra-role behavior refers to activities that lie beyond the scope of a formal job description (Organ, 1988).
The so-called relational perspective on HPWSs uses social exchange theory
(Blau, 1964) to explain why HPWSs positively affect employees’ attitudes and behaviors (Sun et al., 2007). This perspective assumes that the organization-employee relationship is built upon the premises of interdependency, mutuality, and reciprocity (Sun
et al., 2007). Grounded on the norm of reciprocity (Gouldner, 1960), the relational per-
Study 3 – Are High-Performance Work Systems Always Beneficial?
89
spective posits that employees reciprocate in ways akin to how they are treated by their
organization. When organizations appreciate employees’ contributions and demonstrate concern about their positive well-being by investing in HR practices, employees
are expected to reciprocate by exerting positive work attitudes and behaviors toward
the organization (Jiang et al., 2013). Supporting this theoretical argument, prior empirical work found that the link between HPWSs and organizational performance was
partly mediated by the quality of social exchange (Takeuchi, Lepak, Wang, &
Takeuchi, 2007). Furthermore, numerous studies have affirmed that HPWSs increase
several positive attitudes (e.g., higher organizational commitment and job satisfaction)
and invoked behaviors (e.g., lower turnover and increased extra-role behaviors; [Jiang
et al., 2013; Messersmith, Patel, & Lepak, 2011; Sun et al., 2007)].
However, the above-mentioned logic of social exchange proposes that these positive work attitudes and behaviors might not be as discretionary as they appear at first
glance, because they are linked with certain inducements and expectations by the organization (Hom et al., 2009; Tsui et al., 1997). Exploring the idea that extra-role behavior may lose its relatively voluntary nature, Van Dyne and Ellis (2004, p. 181) developed a conceptual model of job creep, which occurs when “employees feel ongoing
pressure to do more than the requirements of their jobs”. The authors suggest that,
when extra-role behaviors are performed regularly, tasks that were formerly considered beyond the scope of their job requirements gradually become part of employees’
ordinary and expected obligations (Van Dyne & Ellis, 2004). They suggest that employees start feeling exhausted, cynical, frustrated, and angry when exposed to job
creep. Ultimately, job creep causes psychological resistance because it reduces employees’ personal control and their ability to voluntarily decide how they will engage
in extra-role behavior. Supporting this idea, past research has found that employees’
perceptions of the utilization of HPWSs predicted their anxiety, role overload, and
turnover intentions when employees were unable to exercise job control (Jensen et al.,
2013).
This prior conceptual and empirical work suggests that the influence of an
HPWS on employees’ positive attitudes and behaviors may be moderated by employees’ degree of job control and their ability to voluntarily decide on their extra-role behavior. However, this line of research has not considered the possible cross-level nature of an interaction between an HPWS’s unit-level effects and the effect of individual employee behaviors that detrimentally influence outcomes at the unit level. We suggest that such an effect is not unlikely, because the beneficial effects of an HWPS, in
terms of providing social structures and support for employees, may depend on the
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Study 3 – Are High-Performance Work Systems Always Beneficial?
extent to which employees have already provided such opportunities for themselves. In
the following, we will first theorize why employees’ network building initiatives reduce organizational-level absenteeism from the bottom up (Kozlowski & Klein, 2000).
Second, we will explain why an HPWS may lose its advantage and even harm organizational-level absenteeism when employees build rich social networks on their own
initiative.
4.3.2 Employees’ Network Building Initiative and Organizational-Level Absenteeism
Contrary to HPWSs, employees’ network building initiative emerges at the individual
level. We suggest that employees’ network building initiative may reduce organizational-level absenteeism through at least three mechanisms. First, it strengthens individual employees’ positive well-being (Heaphy & Dutton, 2008). Second, it promotes
individual employees’ ability to cope with work stress (Cohen & Wills, 1985). And
third, by initiating social interaction between employees, even across boundaries that
exist between distal organizational units, it contributes to employees’ social support
networks and the evolution of a shared absence culture (Rentsch & Steel, 2003). Past
work has characterized absence culture as “the beliefs and practices influencing the
totality of absences – their frequency and duration – as they currently occur within an
employee group or organization (that is, forming a characteristic pattern)” (ChadwickJones, Nicholson, & Brown, 1982, p. 7).
At the individual level of analysis, the absenteeism literature proposes two distinct theoretical mechanisms for why employees choose to be absent from work
(Johns, 1997). The first explanation posits that employees are voluntarily absent because they aim to avoid an aversive work situation (Harrison & Martocchio, 1998).
The second explanation suggests that employees are involuntarily absent because they
experience high levels of work stress (Darr & Johns, 2008). Both points of view are
supported by extensive empirical evidence (Darr & Johns, 2008; Hackett, 1989). We
propose that employees’ network building initiative enhances positive well-being and
reduces work stress because it initiates positive social interactions. Following Heaphy
and Dutton (2008, p. 139), positive social interactions are characterized “by the pursuit
of rewarding and desired outcomes”.
Past empirical research has shown that positive social interactions predict positive well-being and reduce stress, even when controlling for psychological diseases
and health-related behaviors such as diet, exercise, and smoking (Seeman & McEwen,
Study 3 – Are High-Performance Work Systems Always Beneficial?
91
1996; Uchino, Holt-Lunstad, Uno, & Flinders, 2001). Additionally, physiological research points to evidence that positive social interactions increase beneficial cardiovascular, immune, and neuroendocrine responses (Heaphy & Dutton, 2008). First, cardiovascular studies demonstrate that positive social interactions are significantly associated with lower heart rates (e.g., Evans & Steptoe, 2001; Undén, Orth-Gomér, &
Elofsson, 1991) and that – under stress – co-worker support lowers employees’ blood
pressure (Karlin, Brondolo, & Schwartz, 2003). Second, research on immune responses has accumulated a huge body of evidence that positive social interactions can
strengthen the immune system (Kiecolt-Glaser, McGuire, Robles, & Glaser, 2002).
Last but not least, neuroendocrine research has found that workers with high social
support have healthier patterns of the “stress” hormone cortisol compared to those with
low social support (Schnorpfeil et al., 2003). Furthermore, this research shows that
social interactions increase the level of the hormone oxytocin (Zak, Kurzban, &
Matzner, 2004). Oxytocin has been proposed to be related to mechanisms of trust and
reciprocity (Heaphy & Dutton, 2008). All in all, we suggest that this evidence supports
the argument that employees’ social network building initiative induces positive social
interactions, which in turn positively affect well-being and work stress.
At the unit level, conceptual research on absenteeism proposes that the opportunity for social interaction reinforces the evolution of a shared absence culture
(Rentsch & Steel, 2003). The concept of an absence culture refers to absence-related
behavioral patterns that are shared among organizational members who possess a
common organizational understanding of what is appropriate (Johns & Nicholson,
1982). When employees build strong social networks within their organization, they
have richer social support networks available to them (Uchino, 2004). Although network building initiative emerges bottom-up from the individual level of analysis, it
contributes to a shared absence culture by providing an opportunity for social interaction and an exchange of resources among employees (Rentsch & Steel, 2003).
Empirically, cross-level research on absenteeism supports the idea that employees adjust their absenteeism behavior to the underlying norms of their social context.
This cross-level work on absenteeism empirically demonstrates that the concept of
absence culture is capable of explaining a variability in individual-level employee absences that goes beyond what can be explained by individual-level constructs
(Gellatly, 1995; Markham & McKee, 1995; Mathieu & Kohler, 1990). These studies
operationalize an absence culture as individuals’ aggregated group-level absence rates
(Mathieu & Kohler, 1990). Furthermore, absenteeism research at the group and workunit level provides evidence that group positive affective tone and aggregated attitudes
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Study 3 – Are High-Performance Work Systems Always Beneficial?
(e.g., job satisfaction and organizational commitment) are negatively related to absenteeism at the group and work-unit level (Dineen, Noe, Shaw, Duffy, & Wiethoff, 2007;
George, 1990; Hausknecht, Hiller, & Vance, 2008; Mason & Griffin, 2003).
However, past research has not directly examined the relationship between employees’ network building initiative and absenteeism. In a qualitative review, Porter
and Steers (1973) stated that social interactions between employees are among the
most influential forces within organizations. They go on to say that these social interactions support and reinforce socialization within an organization and that their absence may conversely result in employees’ serious alienation from their workplace.
Accordingly, Waters and Roach (1971) demonstrated that satisfaction with co-workers
is negatively related to absenteeism, whereas Hogan and Hogan (1989) found that high
levels of social insensitivity and hostility are positively related to absence rates.
H1: Employees’ network building initiative is negatively related to organizationallevel absenteeism.
4.3.3 The Interaction of High-Performance Work Systems and Employees’ Network Building Initiative
We propose that, when employees tend to build few social networks on their own initiative, HPWSs help in reducing organizational-level absenteeism from the top down by
providing employees with a supportive social structure (Evans & Davis, 2005). On the
other hand, when employees proactively build strong social networks on their own,
HPWSs rather impair their bottom-up initiative and increase organizational-level absenteeism by demanding additional extra-role behavior (Bolino et al., 2013; Van Dyne
& Ellis, 2004).
Our argument builds on the idea that a social structure increases opportunity for
social interactions (Evans & Davis, 2005). In turn, social interactions enable positive
social interactions (Heaphy & Dutton, 2008). Furthermore, drawing upon the norm of
reciprocity (Gouldner, 1960) and previous empirical work (C. J. Collins & Smith,
2006; Nahapiet & Ghoshal, 1998), we propose that an HPWS facilitates a social climate of cooperation, trust, and a shared language among employees, which increases
the likelihood that employees will experience their social interactions as “positive”. In
addition, an HWPS encourages exchange of resources among employees at the unit
level and, therefore, the creation of stronger social ties and richer support networks,
Study 3 – Are High-Performance Work Systems Always Beneficial?
93
which reduce organizational-level absenteeism (Rentsch & Steel, 2003; Sun et al.,
2007).
We propose that, when employees build few social networks on their own initiative, they are aware of the fact that they greatly benefit from the social structure. Thus,
we expect them to feel explicitly obligated to reciprocate to the organization (Blau,
1964). Hence, they will demonstrate considerable amounts of extra-role behavior.
However, when employees show strong network building on their own initiative, an
HPWS will not positively influence – and might even impair – organizational-level
absenteeism. Hence, many aspects of this social structure do not add any value for
those employees; it hinders rather than helps, because it partly absorbs these employees’ time and energy. Accordingly, they might feel less obligated to reciprocate to the
organization because they know that those aspects of an HPWS’s social structure that
aim to facilitate social networks do not provide a benefit for them. Nevertheless, we do
not hold that employees who proactively build social networks on their own do not
need the opportunity for social interactions and a cooperative and trustful social climate. However, we do suggest that these employees are less dependent on an HPWS
and the corresponding social structure (Evans & Davis, 2005).
Furthermore, employees who proactively build strong social networks on their
own might already regard this activity as a voluntarily form of extra-role behavior.
When organizations expect additional extra-role behavior (Tsui et al., 1997), employees with high network building initiative might regard this demand as a pressure. Bolino, Turnley, Gilstrap, and Suazo (2010) introduced the construct of citizenship pressure to explain situations in which employees feel pressured to exhibit extra-role behaviors. The authors demonstrated that citizenship pressure is related to work-family
conflict, work-leisure conflict, job stress, and intentions to quit. In line with this argument, Vigoda-Gadot (2006) uses the concept of compulsory citizenship behaviors to
describe a situation in which extra-role behavior loses its voluntary nature and supervisors or other powerful individuals increase employees’ workloads beyond their job
descriptions. Empirically, Vigoda-Gadot (2006) showed that compulsory citizenship
behaviors are positively related to job stress, organizational politics, intentions to quit,
negligent behavior, and burnout. We propose that both citizenship pressure and compulsory citizenship behaviors contribute to both voluntary and involuntary absenteeism.
Additionally, when employees already show high levels of self-initiated network
building behavior, an HPWS may push employees to show additional initiative extra-
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Study 3 – Are High-Performance Work Systems Always Beneficial?
role behaviors, which might lead to role overload. In line with this argument, Bolino
and Turnley (2005) showed that high overall levels of individual initiative (which includes behaviors such as coming to work early or staying late, working at home, rearranging personal plans because of work, and taking on special projects) was positively
associated with role overload, work-family conflict, and job stress.
H2: High-performance work systems and employees’ network building initiative positively interact in the following way: High-performance work systems are negatively related to organizational-level absenteeism when employees’ network
building initiative is low. They are positively related to organizational-level absenteeism when employees’ network building initiative is high.
4.4 Method Section
4.4.1 Sample
For our study, we collected data in collaboration with a German-based benchmarking
agency. To participate, these organizations had to be located in Germany and not have
more than 5,000 employees. In exchange for their participation, they obtained a written benchmarking report. Overall, we contacted 179 companies, out of which 16 provided insufficient data, resulting in an organizational-level response rate of 90% (N=
161). The incorporated businesses were active in a range of industries, including production (27%), wholesale (6%) and retail (7%) trade, service (55%), and finance (5%).
On average, these companies employed 275 employees (SD = 607).
To avoid common source and method effects (Podsakoff et al., 2003), we gathered data from five different sources by means of three different methods. We surveyed three unique groups of employees with an IT-based questionnaire, interviewed
firms’ key HR representatives via telephone, and made use of firms’ archival HR data.
Akin to standard employee surveys and other prior studies (Kunze, Boehm, & Bruch,
2013; Kunze, de Jong, & Bruch, in press), participating firms sent a standardized email invitation to all of their employees briefly describing the study’s purpose. The email contained a link to a Web-based survey hosted by an independent IT company.
Overall, 15,401 employees voluntarily participated in the survey, resulting in a withinorganization response rate of 61% (SD = 23.9). Participants had a mean age of 37
years (SD = 10.5), included more males (59%) than females (41%), and had on average worked 9 years for their organization (SD = 8.5).
Study 3 – Are High-Performance Work Systems Always Beneficial?
95
A Web-based algorithm randomly assigned participants to one of four versions
of the employee survey. In our study, we used only three of the four versions, which
were each answered by a randomly selected 25% of employees from each firm. With
these three unique data sources, we collected responses on employees’ network building initiative (survey 1), job satisfaction and organizational commitment (survey 2),
and positive affective climate (survey 3). Furthermore, the key HR representative of
each firm responded to questions on HPWSs and provided information on firm size
and industry affiliation. The absenteeism data was directly imported from the companies’ archival HR records.
4.4.2 Measures
Unless otherwise noted, we collected the answers of our measures on a Likert-type
scale ranging from (1) “strongly disagree” to (5) “strongly agree”.
High-performance work systems (α = .72). We used a scale developed by
Datta and colleagues (2005) comprised of 18 high-performance human resource practices, based on the work of Guthrie (2001) and Huselid (1995). These practices address, for example, the extent to which organizations provide high levels of training
and information sharing, participatory mechanisms, grievance procedures, group-based
rewards, rigorous selection procedures, skill-based pay, and internal merit-based promotions. Key HR representatives were asked to provide estimates of the proportion (0100%) of employees covered by each of these HR practices. The mean of these individual practices represented a firm’s overall HPWS score. Because there was only a
single respondent per company, no aggregation statistics were necessary.
Network building initiative (α = .84). Ferris and colleagues (2005) developed
and validated a self-report network building measure (which they name “network ability”) as one dimension of political skill. Following Thompson (2005), we used only
the network building dimension and adopted a four-item scale to assess the extent to
which employees develop and use networks of people to exercise influence at work
(i.e., “I have developed a large network of colleagues and associates at work whom I
can call on for support when I really need to get things done”; “at work, I know a lot of
important people and am well connected”; “I am good at using my connections and
network to make things happen at work”; “I spend a lot of time and effort at work
networking with others”). The items were averaged and aggregated to the organizational level, which was justified by satisfactory aggregation statistics (ICC1 = .09; p <
.001; ICC2 = .75; median rwg= .71).
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Study 3 – Are High-Performance Work Systems Always Beneficial?
Absenteeism. In a time-based review of the absenteeism literature, Harrison
and Martocchio (1998) proposed that time frames between four months and one year
are particularly applicable to determine effects of job-related attitudes and social context on absenteeism. We thus measured absenteeism as the average days of absence
per employee within six months after the survey began using the firms’ archival HR
data.
Controls. Becker and Huselid (2006) argued that prior strategic HRM research
has frequently fell short in being able to rule out alternative explanations and thus correctly specify their research models in an unbiased manner. We aim to avoid this
drawback by rigorously controlling for those variables that have been, to the best of
our knowledge, studied in prior absenteeism research at the unit level. First, we included organization size as a control variable because past research has found that it is
related to absenteeism (Ingham, 1970) and larger firms may have more elaborate HR
practices in place than smaller ones (Jackson & Schuler, 1995). Size was the natural
logarithm of an organization’s number of employees (e.g., Datta et al., 2005; Huselid,
1995). Second, we controlled for organizational age, in years, because past research
suggests that HR practices advance over time (Guthrie, 2001). Third, we entered industry affiliation as a control because prior research has proposed that it systematically
explains variance in absenteeism (Harrison & Martocchio, 1998). The firms in our dataset were active in one of five industry sectors (i.e., manufacturing, wholesale and
retail trade, service, and finance). We controlled for these industries by entering corresponding dummy variables. Information on organizational size and industry affiliation
was provided by the firms’ key HR representatives. Third, prior unit-level and group
studies have found that aggregated attitudes (e.g., job satisfaction, organizational
commitment (Hausknecht et al., 2008]) and work group climate (e.g., positive affective tone [George, 1990]) are related to absenteeism. We controlled for aggregated job
satisfaction with a five-item scale following Judge, Parker, Colbert, Heller, and Ilies
(2001), for aggregated organizational commitment with a six-item scale by
Hausknecht and colleagues (2008), and for positive affective tone with a five-item
scale from Van Katwyk, Specter, Fox, and Kelloway’s (2000) Job-Related Affective
Well-Being Scale with adjusted reference to the organizational level. Job satisfaction
and organizational commitment were assessed with a Likert-type scale ranging from
(1) “strongly disagree” to (7) “strongly agree”. Finally, past research at the group and
individual levels has found that prior absenteeism was among the strongest predictors
of present absenteeism (Breaugh, 1981; Keller, 1983; Mathieu & Kohler, 1990). We
Study 3 – Are High-Performance Work Systems Always Beneficial?
97
controlled for past absenteeism using the archival data of the average days of absence
per employee within the 12 months prior to the study.
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Study 3 – Are High-Performance Work Systems Always Benefi-
cial?
Table 4-1 Means, Standard Deviations, and Correlations among Study Variables
1
2
3
4
5
6
Variable
Absenteeism
(subsequent 6 months)
Absenteeism (prior 12
months)
Organizational
commitment
Job satisfaction
Positive affective
climate
Employees' network
building iniative
7
High-performance
work systems
8
Organizational sizea
Organiztional age
9
10 Industry dummy
(production)
11 Industry dummy
(trade, wholesale)
12 Industry dummy
(service)
13 Industry dummy
(finance)
a
1
2
3
4
5
M
3.47
SD
2.45
5.51
3.52
4.98
.58
-.13
-.05
5.26
.50
-.20 **
-.27 ***
.77 ***
3.40
.37
-.23 **
-.25 **
.59 ***
.69 ***
3.44
.33
-.35 ***
-.21 **
.44 ***
.40 ***
.48 ***
55.08
14.92
-.32 ***
-.21 **
.18 *
.25 **
.32 ***
6
7
8
9
10
11
12
.67 ***
1.71
.23
.13
.06
39.80
35.19
.15
.16 *
.27
.44
.07
.07
.06
.23
.08
.55
.50
.05
.22
.11
.12
.11
.00
-.03
-.20 *
-.24 **
-.12
-.18 *
.01
-.14
-.20 *
-.04
-.06
.09
.01
-.09
-.03
-.07
-.10
-.14
-.12 *
-.04
.17 *
.14
.12
.07
.05
-.03
.04 *
-.01
.03
.02
-.03
-.07
-.02
N = 161 (organizations); Natural log of the number of employees
* p < .05
** p < .01
*** p < .001 (two-tailed)
.10
.28 ***
-.09
.01
-.22 **
.32 **
.03
-.27 **
.08
-.15
-.67 ***
-.27 **
-.14
-.06
.20 **
Study 3 – Are High-Performance Work Systems Always Beneficial
99
4.5 Results
Table 4-1 shows the means, standard deviations, and correlations for the study’s variables. The bivariate correlations show that absenteeism was negatively related to several study variables and controls: employees’ network building initiative (r = -.35, p <
.001), HPWSs (r = .32, p < .001), job satisfaction (r = -.20, p < .01), and positive affective tone (r = -.23, p < .01). The only variable under study that was positively associated with absenteeism was prior absenteeism (r = .67, p <.001).
4.5.1 Hypotheses Testing
We applied stepwise regression techniques for moderation analyses following a procedure outlined by Aiken and West (1991). In all of these models, variance inflation factors were below 4, indicating that multicollinearity was not a serious problem. All independent variables were z-standardized prior to analysis. The results are presented in
Table 4-2.
Hypothesis 1 posited that employees’ network building initiative is positively related to organizational-level absenteeism. First, in Model 1, we entered only the control variables as predictors of absenteeism. Second, in Model 2, we added employees’
network building initiative and HPWSs. HPWSs did not show a significant relationship (B = -.25, SE = .15, ns) in the presence of the further independent variables. Employees’ network building initiative was significantly related to absenteeism (B = -.50,
SE =.14, p < .01), providing support for Hypothesis 1.
Hypothesis 2 posited an interaction between HPWSs and employees’ network
building initiative on organizational-level absenteeism. In Model 3, we further entered
the interaction term of HPWSs and employees’ network building initiative. This interaction term was significantly related to absenteeism (B = .51, SE = .12, p < .001), in
line with Hypothesis 2.
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Table 4-2
Results of Hierarchical Regression Analysis
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101
Figure 4-1 illustrates a graphical plot that we performed to further inspect this interaction. As the graph shows, the slope of HPWS rises when employees’ network
building initiative was low (-1 SD) and falls when their network building initiative was
high (+1 SD), supporting Hypothesis 2. Furthermore, a one-sided simple slope test revealed that the slopes for both conditions were significant: When employees showed
low network building initiative, an HPWS was negatively associated with absenteeism
(B = -.75, SE =.17, p<.001); however, when they showed high networking network
building initiative, an HPWS was marginally positively related with absenteeism
(B=.28, SE =.18, p<.10).
Figure 4-1
Interaction between High-Performance Work Systems and Employees’ Network Building Initiative
Organizatonal-level absenteeism
5.0
4.5
4.0
3.5
Network building initiative
high
3.0
2.5
Network building initiative
low
2.0
1.5
1.0
0.5
0.0
Low
High
High-performance work systems
4.5.2 Robustness Check
To further inspect the robustness of our results, we ran an alternative model excluding
all insignificant control variables. The main effect of employees’ network building initiative was somewhat larger (B = -.79, SE = .18, p < .001), the interaction between
HPWS and employees’ network building initiative somewhat smaller (B = .35, SE =
.17, p < .05); however, differently from the research model including controls, HPWSs
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had a significant main effect on organizational-level absenteeism (B =-.56, SE =.18,
p<.01). The reanalysis did not change the conclusion of our two hypotheses, which indicates that our results were not biased by impotent controls (Becker, 2005).
4.6 Discussion
Strategic HRM literature has advanced a significant body of evidence that HPWSs
positively affect organizational performance (Combs et al., 2006). However, this research revealed inconsistent results regarding the effectiveness of HPWSs on organizational-level absenteeism (Ramsay et al., 2000; Wood et al., 2012). In this paper,
drawing upon social exchange theory (Blau, 1964) and literature on positive social
interactions (Heaphy & Dutton, 2008), we have sought to develop a theoretical model
on why an HPWS may have both beneficial and detrimental effects on organizationallevel absenteeism. Empirically, we found that, when employees build few social networks of their own initiative, an HPWS has a beneficial effect on organizational-level
absenteeism. At the same time, when employees proactively build strong social networks, an HPWS may have a detrimental effect.
4.6.1 Theoretical Contribution
This research offers a number of important theoretical contributions. First, it contributes to the strategic HRM literature. In this paper, we challenge the assumption that an
HPWS is always beneficial. There is an enormous amount of research showing positive consequences of HPWSs (Combs et al., 2006; Jiang, Lepak, Hu, & Baer, 2012). A
recent meta-analysis revealed that various bundles of high-performance work practices
have moderate positive effects on operational performance and financial performance
and moderate negative effects on voluntary turnover (Jiang et al., 2012). On the other
hand, smaller number of studies show negative effects of HPWSs. Jensen et al. (2013),
for example, demonstrated that employees’ perception of the utilization of HPWSs
predicted employees’ anxiety, role overload, and turnover intentions when employees
were unable to exercise job control. Accordingly, Kroon and colleagues (2009) found
that HPWSs are positively related to employees’ work demands, which in turn predict
employees’ emotional exhaustion. This evolving line of research challenges the dominant logic that an HPWS always has positive effects. Our paper contributes to this
burgeoning literature by demonstrating that an HPWS may indeed have a detrimental
effect on organizational-level absenteeism, conditional on whether employees already
build strong social networks on their own initiative.
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Furthermore, in recent years, strategic HRM research has moved from exploring
outcomes of HPWSs to investigating mediation mechanisms and cross-level effects
that might explain HPWSs’ effectiveness. This line of research proposes that the effectiveness of an HPWS is based on mechanisms of social exchange (C. J. Collins &
Clark, 2003; Sun et al., 2007; Takeuchi et al., 2007), relational coordination (Gittell,
Seidner, & Wimbush, 2010), positive social climates (C. J. Collins & Smith, 2006),
and intellectual capital (Jiang et al., 2012). Our study theoretically extends prior literature from a social exchange perspective (Blau, 1964) by confronting this social
exchange logic and its assumption of reciprocity (Gouldner, 1960) with the more wellbeing-focused literature of positive social interactions (Heaphy & Dutton, 2008). Our
study is among the first in the field of strategic HRM research to test the cross-level
effect of an individual-level property (i.e., network building initiative) that moderates
the effect of an organizational-level property (i.e., HPWSs) on an organizational-level
outcome (i.e., absenteeism). Prior cross-level studies have, for example, investigated
how the mediating role of organizational climate (concern for employee climate) mediates the cross-level relationship between establishment-level HPWSs and individuallevel job satisfaction and affective commitment (Takeuchi, Chen, & Lepak, 2009) or
the cross-level link between management-rated HPWSs at the branch level and employee-rated HPWSs at the individual level (Liao, Toya, Lepak, & Hong, 2009). To
the best of our knowledge, Aryee, Walumbwa, Seidu, and Otaye (2012) and Nishii,
Lepak, and Schneider (2008) are the only other studies that have explored a bottom-up
effect on higher-level outcomes. Aryee et al. (2012) found that individual aggregated
service performance predicted unit-level market performance after examining the
cross-level influence of HPWSs on individual service performance through individuals’ experiences of HR systems and shared service climate. At the individual level of
analysis, Nishii et al. (2008) showed that the specific attributions employees make
about the reasons why HR systems are in place can influence employees’ attitudes
(e.g., job satisfaction and affective commitment) toward these systems. The aggregated
employee attitudes were positively related to aggregate organizational citizenship behaviors and customer satisfaction at the unit level of analysis. Our study extends these
prior cross-level studies by showing that the bottom-up interaction of employees’ network building with HPWSs can seriously affect organizational-level absenteeism. Future strategic HRM research should explore whether other – in and of themselves –
beneficial proactive behaviors at the individual level, such as personal initiative (Frese,
Garst, & Fay, 2007), may detrimentally interact with the – in and of itself – beneficial
effect of firms’ HPWSs on further organizational performance outcomes.
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Second, this paper contributes to the absenteeism literature. Absenteeism is usually examined at the individual level of analysis because it is individuals who behave
and decide to be absent from work; hence, many theorized reasons for absenteeism
reflect characteristics of persons, such as attitudes and dispositions (Harrison &
Martocchio, 1998). However, scholars find growing evidence that conceptualizing absenteeism as a collective construct at higher levels, such as the group or work unit,
also provides unique insights concerning its antecedents and associations (Dineen et
al., 2007; Markham, 1985; Mason & Griffin, 2003; Rentsch & Steel, 2003). Theoretically, past absenteeism research has used the concept of absence culture to explain
why unit-levels factors may explain variability in absence rates (Rentsch & Steel,
2003). George (1990) suggests that unit-level absenteeism theory should not replace
individual-level theory. Accordingly, Rentsch & Steel (2003) point out that each level
of analysis demands distinctive conceptual work, although related predictions may
apply across different levels. Empirically, cross-level research on absenteeism supports the idea that employees adjust their absenteeism behavior to the underlying
norms of their social context (Dineen et al., 2007; George, 1990; Hausknecht et al.,
2008; Mason & Griffin, 2003).
This paper is one of the first to explore how an HPWS, as a bundle of various HR
practices, influences absenteeism at the organizational level of analysis. Prior research
has primarily examined specific absenteeism-related HR practices, typically at the individual level of analysis. This line of research examined, for example, how certain
work schedules, such as shift work and flexible working hours, and organizational
control policies influence individuals' decisions to be absent from work (Harrison &
Martocchio, 1998). This prior work demonstrated that shift work was related to higher
levels of individual absences (Farrell & Stamm, 1988) and that flexible working hours
were regularly associated with lower absenteeism (e.g., Dalton & Mesch, 1991). Furthermore, research on organizational control policies showed mixed results. HR practices based on transactional reward and punishment structures were found to reduce
some forms of absenteeism but induce others (Harrison & Martocchio, 1998). For instance, Schlotzhauer and Rosse (1985) showed that a positive reinforcement system diminished absenteeism. Other studies illustrate that punishment mechanisms for specific types of absences tend to stir up others (e.g., Miners, Moore, Champoux, &
Martocchio, 1994). However, a recent meta-analysis on HR practices revealed that, in
general, bundles of various HR practices are more effective than individual HR practices (Combs et al., 2006). Nevertheless, when controlling for prior organizational-
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level absenteeism, we did not find any additional main effect of HPWSs on organizational-level absenteeism in this study.
4.6.2 Practical Contribution
Overall, all variables in our research model explained 66% of the variability in organizational-level absenteeism. This is a relatively high percentage, when compared to alternative outcomes in previous strategic HRM studies. To the best of our knowledge,
prior strategic HRM research has not explained more than 50% of the variability in
distal organizational outcomes, such as voluntary turnover (e.g., Guthrie [2001]: 20%,
Huselid [1995]: 39%), productivity (e.g., Datta [2005]: 47%, Guthrie [2001]: 30%,
Huselid [1995]: 50%), or financial performance (e.g., C. J. Collins and Clark [2003]:
15-17%, C. J. Collins and Smith [2006]: 24-29%, Huselid [1995]: 12-17%). The explanatory power of our research model underlines the practical significance of our
findings.
From a managerial point of view, the results of our study are not merely statistically significant but also practically meaningful. Drawing upon previous research from
the strategic HRM field (e.g., Datta et al., 2005; Huselid, 1995), the regression results
in Table 4-1 can be interpreted as follows: With all other variables held constant, a
one-standard-deviation increase in employees’ network building initiative is related to
an average annual decrease in absenteeism of 4.38 days per employee (Model 2). The
subsequent simple slope test may be understood as: When employees’ network building initiative is low, a one-standard-deviation increase in HPWSs is associated with an
average annual decrease in absenteeism of 1.4 days per employee. On the other hand,
when employees’ network building initiative is high, a one-standard-deviation increase
in HPWSs is associated with an average annual upturn in absenteeism of 0.8 days per
employee.
Hence, managers are well advised to recognize this potential detrimental interaction between an HPWS and employees’ network building initiative. Nevertheless, we
found that (aside from prior organizational-level absenteeism) employees’ network
building initiative was the greatest predictor of organizational-level absenteeism. What
is more, past research has demonstrated that network building initiative is positively
related to proactive personality (Thompson, 2005). In light of this result, firms might
do well to assess their job candidates in terms of this personality trait in their selection
process. Furthermore, our research suggests that moderate levels of HPWSs and net-
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work building initiative might even partly substitute for each other by providing employees with a functioning social structure.
4.6.3 Limitations
This study is not without limitations, and further research is needed to refine and expand our work in important ways. First, we depended solely on one key informant
from HR, contacted via telephone, to respond to our HPWS measure. Past literature
has raised the concern that depending on a single respondent may undermine the reliability of the reported HR practice effects because it imposes serious measurement error
on the analysis (Gerhart, Wright, McMahan, & Snell, 2000). Huselid and Becker
(2000) replied that, when single respondents are key informants and HPWS measures
correspond to relatively objective attributes (e.g., percentage of the workforce covered
by regular employee surveys), the estimated effects of HR practices in large-scale multi-industry studies, such as ours, should not be severely biased.
Another point of concern might be that our independent variables partly depend
on cross-sectional data. However, we gathered the archival data of organizational-level
absenteeism, our dependent variable, over the subsequent six months; furthermore, the
records of prior absenteeism were collected from the 12 months before the data collection. Nevertheless, several independent variables, namely HPWSs, employees’ network building initiative, and other control variables were measured at the same time.
Prior theory and empirical work suggest that social networks might mediate the relationship between HPWSs and organizational outcomes (e.g., C. J. Collins, C. J. &
Clark, 2003; C. J. Collins & Smith, 2006; Evans & Davis, 2005; Takeuchi et al.,
2007). And indeed, in our argument for Hypothesis 2, we indirectly built on the idea
that the link between an HPWS and organizational-level absenteeism is partially mediated by social networks. However, we held back from explicitly adding this hypothesis
to our research model and exploring this issue in greater depth because, not least, social networks per se and employees’ network building initiative are conceptually not
exactly identical. Furthermore, our cross-sectional data did not allow us to draw any
clear causal relationship between an HPWS and employees’ network building initiative. Nevertheless, HPWSs and employees’ network building initiative are positively
correlated in our data (r = .27, p < .01). However, given the lack of conceptual equivalence and the cross-sectional restrictions of our data, we advocate for deeper empirical
examination of this question in future research with a research design better suited to
this aim.
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107
4.6.4 Conclusion
Past strategic HRM research has shown that high-performance work systems (HPWSs)
are associated with several favorable outcomes, such as higher productivity, firm
growth, and financial performance. In this paper, we challenge the assumption that
HPWSs are always positive and show that their effect on organizational-level absenteeism can be beneficial as well as detrimental depending on employees’ network
building initiative.
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5 Overall Discussion and Conclusion
5.1 Abstract
This is the closing chapter of this dissertation. First, it recaps the research motivation
of this dissertation (i.e., how organizations can be economically productive and simultaneously provide space for positive social interactions) and, based on this, links the
three derived research questions with the findings of the three empirical studies intended to respond to these questions. Then, the chapter highlights the most important
findings and integrates them into the literatures of team boundary activities, collective
human energy, and intraorganizational social networks. Finally, the chapter discusses
general limitations and directions for future research and offers the main practical implications.
Overall Discussion and Conclusion
109
5.2 Summary
This dissertation has drawn upon a positive organizational scholarship (POS) lens to
examine positive social interactions at multiple organizational levels. The POS lens focuses on the study of positive outcomes, processes, and attributes of organizations and
the associated organizational members (Cameron et al., 2003). A previous review of
the fields of POS and positive organizational behavior noted that scholars with this
kind of orientation tend to study intrapersonal states and trait-like capacities (such as
resilience, optimism, and hope) and thereby neglected the organizational context of
these phenomena (Hackman, 2009). To overcome this pitfall, this dissertation applies a
meso-level of analysis, which is characterized by careful consideration of the context
of organizational behavior (House et al., 1995). Furthermore, the discussion of the past
POS literature shows that several POS constructs had not been rigorously validated
(Hackman, 2009). Thus, this dissertation aimed to circumvent this drawback by using
validated scales (such as the one for productive energy [Cole et al., 2012]). Finally,
most prior POS studies that incorporated the organizational context were either conceptual or applied qualitative research methods (Cameron et al., 2003). Hence, this
dissertation, sought to complement prior POS research by applying quantitative research methods.
Furthermore, this work focused on the resource-based view of the firm to explore
whether organizations can be economically productive and simultaneously provide
space for positive social interactions. The resource-based view posits that, to gain a
competitive advantage, organizations have to create valuable resources (Barney,
1991). As mentioned before, resources are defined as valuable when they are rare, not
perfectly imitable or substitutable, and allow an organization to deploy a valuecreating strategy (Barney, 1991). Hence, this dissertation used the resource-based view
of the firm as an overarching framework to examine how positive social interactions at
different organizational levels may function as a valuable resource and thus contribute
to an organization’s competitive advantage.
However, prior research has explored consequences of positive social interactions mostly at the individual level of analysis (Cameron et al., 2003; Hackman, 2009;
Wright & Quick, 2009). For instance, a huge body of evidence from the medical sciences demonstrates that positive social interactions have beneficial physiological consequences for individuals’ well-being (Heaphy & Dutton, 2008). Furthermore, research on high-quality relationships shows that this kind of relationship (which is
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Overall Discussion and Conclusion
characterized by positive regard, mutuality, and feelings of vitality) tends to increase
individuals’ innovative performance (Carmeli & Gittell, 2009; Carmeli & Spreitzer,
2009; Vinarski-Peretz, Binyamin, & Carmeli, 2011). However, this line of research
has been constrained by the fact that a validated scale of high-quality relationships has
not yet been published. To complement this prior research, this dissertation has studied
positive social interactions at the level of teams and entire organizations.
First, Study 1 responded to Research question 1 (“How do team boundary-buffering activities influence team innovative performance?”). In this study, we examined
whether and how team boundary-buffering activities, a specific type of team social interaction directed toward a team’s external social environment, influence team innovative performance. Prior research on team boundary activities has predominantly focused on team actions that involve engagement with the external environment for important resources and support (i.e., boundary-spanning activities) and only to a lesser
degree considered team actions that involve disengagement from the environment as a
way to manage external demands (i.e., boundary-buffering activities). We suggest that
past research has insufficiently studied the role of team boundary-buffering activities,
in part because it primarily considered team boundary activities as a strategy to overcome problems of information processing between different organizational units
(Galbraith, 1977; Tushman & Nadler, 1978). To complement this view, we drew upon
research and the theory of the job demands-resources model (JD-R, [Demerouti, Bakker, Nachreiner, & Schaufeli, 2001]). Team boundary-buffering activities protect
teams from distracting information, disruptive events, and negative emotions in the
external environment. Our study shows that boundary-buffering activities sustain team
productive energy and ultimately team innovative performance. Furthermore, we
found that the effectiveness of team boundary-buffering activities is especially enhanced when teams face higher levels of chronic job demand overload.
Second, Study 2 was intended to answer Research question 2 (“Do team boundary-spanning activities mediate the positive link between transformational leadership
and team productive energy?”). In this study, we investigated whether transformational
leadership (TFL) increases team productive energy through the mediation of team
boundary-spanning activities. The basic idea of this study was that job resources have
a motivational potential for teams. As organizations become less hierarchically structured and increasingly de-bureaucratized (Cross et al., 2000; Yan & Louis, 1999;
Zammuto et al., 2007), teams benefit from engaging in boundary-spanning activities
toward their external environment in order to acquire additional resources. According-
Overall Discussion and Conclusion
111
ly, Study 2 shows that team boundary-spanning activities increase team productive
energy. Furthermore, this study finds that TFL enables team members to span their
team boundaries. In the context of our sample of functional R&D teams, the positive
effect of TFL on team productive energy could be fully explained by the effect of team
boundary-spanning activities. Whereas past research found that age diversity is harmful to team functioning (Kearney & Gebert, 2009; Kunze & Bruch, 2010), our study
suggests that, when carefully managed by a transformational leader, age diversity can
increase team boundary-spanning activities and ultimately bolster team productive energy.
Third, Study 3 was directed toward answering Research question 3 (“Does the
interplay between high-performance work systems and employees’ network building
initative detrimentally affect organizational-level absenteeism?”). In general, previous
research has found ample evidence that high-performance work systems (HPWSs)
positively affect organizational performance (Combs et al., 2006). However, very little
HPWS research has studied absenteeism as a focal outcome of interest (Kehoe &
Wright, 2013; Zatzick & Iverson, 2011). Nevertheless, the majority of this research
proposes that, overall, HPWSs reduce organizational-level absenteeism (Guthrie et al.,
2009; Ramsay et al., 2000; Way et al., 2010; Zhou et al., 2005). Contrasting arguments
from social exchange theory (Blau, 1964) with literature on positive social interactions
(Heaphy & Dutton, 2008), in this study we proposed a theoretical model for why
HPWSs may have both beneficial and detrimental effects on organizational-level absenteeism. Our central argument was that, when employees tend to build few social
networks on their own initiative, HPWSs help to reduce organizational-level absenteeism by providing employees with a supportive social structure from the top down
(Evans & Davis, 2005). On the contrary, when employees proactively build strong social networks on their own, an HPWS rather impairs their bottom-up initiatives and
provokes organizational-level absenteeism by demanding additional extra-role behavior (Bolino et al., 2013; Van Dyne & Ellis, 2004). Our empirical findings support the
idea that the beneficial effects of HPWSs and employees’ network building initiative
on organizational-level absenteeism may cancel each other out. In general, we found
that the reduction in bottom-up effect of network building initiative on organizationallevel absenteeism was more powerful than the top-down effect of HPWSs.
In sum, the results of these three studies extend our understanding of how different types of positive social interactions – which can all be characterized as beneficial
from the perspective of organizational groups or members (i.e., team boundarybuffering activities [Study 1], team boundary-spanning activities [Study 2], and social
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Overall Discussion and Conclusion
network building initiative [Study 3]) – positively influence performance-related outcomes at multiple levels. These positive outcomes are situated at the team level (e.g.,
increased team innovative performance [Study 1] and sustained team productive energy [Study 2]) and at the organizational level (e.g., reduced organizational-level absenteeism [Study 3]). Hence, the results of these three studies suggest that these different
types of positive social interactions at multiple levels may function as a valuable resource for organizations. However, as we have seen in Study 3, there might by tradeoffs between these bottom-up activities and top-down practices, such as highperformance work systems (HPWSs).
5.3 Theoretical Integrations of Most Important Research Findings
The following section highlights the most important findings of the three empirical
studies of this dissertation and integrates them with respect to the literatures of team
boundary activities, collective human energy, and intraorganizational social networks.
Previous literature on team boundary activities that stemmed from an organizational design perspective used an information-processing lens to study boundary activities (Ancona, 1987; Galbraith, 1977). Referring to this perspective, organizations
are defined as information-processing systems intended to deal with uncertainty
(Tushman & Nadler, 1978). Information processing is understood as gathering, interpreting, and synthesizing information in the course of organizational decision-making
(Tushman & Nadler, 1978). With the perspective pursued in this dissertation, the information-processing approach shares the assumption that organizations are open systems (Thompson, 1967). However, an information-processing lens reduces social interactions between subunits within an organization to a matter of cognitive problem
solving and coordination between interdependent actors (Lawrence & Lorsch, 1967).
Early literature on boundary activities referred to the processing of information between different organizational components metaphorically rather than to concrete social interactions between human beings (Tushman & Nadler, 1978). Consequently, the
organizational design perspective refers to organizations as a set of structural sub-units
linked by tasks rather than a social community of interacting humans (Tushman &
Nadler, 1978). The most important contributions of this dissertation for the literature
on team boundary activities is that we complement the structural, functional, and taskoriented approach of the organizational design perspective with a more personcentered, emotive perspective related to the job demands-resources (JD-R) literature.
Overall Discussion and Conclusion
113
A considerable difference between the top-down perspective of the organizational design perspective and the bottom-up perspective of the JD-R model is that the latter
offers a more detailed understanding of how humans use, consume, and replenish their
resources. Whereas the former refers to resources primarily from the perspective of organizations as material inputs, throughputs, and outputs, the latter refers to resources,
from the perspective of individuals, as those aspects of a job that are instrumental in
“achieving work goals, reducing job demands and their associated physiological and
psychological costs, and, finally, stimulating personal growth, learning, and development” (Demerouti et al., 2001, p.501). Whereas prior research did not find evidence
for the effectiveness of team boundary-buffering activities, our study suggests that
team boundary-buffering activities indeed increase team innovative performance as
mediated through the emotive state of team productive energy.
Another important contribution to the literature on team boundary activities is the
finding that age diversity can strengthen team boundary activities when carefully
managed by a transformational leader (TFL). The conceptual idea behind this research
was that social similarity of team members and external stakeholders may facilitate
team boundary-spanning activities (Joshi, Dencker, Franz, & Martocchio, 2010). If a
team brings together members with a variety of different social attributes (such as
age), they will be more likely to connect with various external stakeholders. Likewise,
they may have more problems in developing a shared team identity and resolving team
conflicts. On the one hand, transformational leadership helps in developing a shared
team social identity and preventing team conflict whereas, on the other hand, TFL
helps in orchestrating these team boundary-spanning activities. Prior research on
organizational demography suggests that there might be differences across contexts of
which social attributes (such as age, gender, race) allow people to build informal social
intergroup relations (Joshi, 2006). However, we suggest that in the context of the R&D
teams under study in this dissertation similarity of age is such a salient attribute. Our
results stands in contrast to prior findings that showed that, at best, transformational
leaders can buffer negative effects of age diversity (Kearney & Gebert, 2009; Kunze &
Bruch, 2010). Our study is the first to show that, when managed carefully by a
transformational leader, age diversity also may have positive effects.
Furthermore, another important contribution of this dissertation is that it examines the collective motivational potential of team productive energy. Past theoretical
work has pointed to the fact that traditional concepts of work motivation explain motivation primarily at the individual level of analysis (Gottschalg & Zollo, 2007; Shamir,
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Overall Discussion and Conclusion
1991) 11. To overcome this shortfall, strategy scholars introduced the collective-level
construct of joint production motivation, which is defined as any cooperative productive activity that includes people’s use of heterogeneous but complementary resources
and alignment toward common goals (Lindenberg & Foss, 2011). Past conceptual
work proposes that a joint production motivation can be a valuable resource for organizations and thus create a knowledge-based competitive advantage (Foss, 2011;
Lindenberg & Foss, 2011). However, the constructs of joint production motivation and
productive energy share some conceptual overlap. Both constructs extend work motivation theories at an individual level by emphasizing the alignment toward collective
organizational goals (Cole et al., 2012; Lindenberg & Foss, 2011). However, to date,
no empirical measure of joint production motivation exists (Lindenberg & Foss, 2011).
Thus, for future research, productive energy might be a viable option to measure the
collective motivation, respective joint production motivation, within an organization.
Furthermore, the research included in this dissertation proposes that there might
be a trade-off between generative dynamics emerging bottom-up at the individual level
of organizations (such as employees’ network building initiative) and organizational
practices implemented top-down (such as high-performance work systems [HPWSs]).
In this dissertation, we empirically found that, when employees build few social networks on their own initiative, an HPWS has beneficial effects on organizational-level
absenteeism. At the same time, when employees proactively build strong social networks on their own initiative, an HPWS may have limiting effects. In the same vein,
Jensen and colleagues (2013) found that employees’ perception of the utilization of an
HPWS is positively related to employees’ anxiety, role overload, and turnover intentions when they are unable to exercise job control. Furthermore, Wood, Van Veldhoven, Croon, and de Menezes (2012) showed that HPWSs indirectly encourage organizational-level absenteeism through increased levels of stressful emotions, whereas they
did not find such a harmful effect on financial performance and workers’ productivity.
Future research might do well to examine which kind of generative bottom-up dynamics may conflict and which may harmonize with different top-down organizational
practices and which kind of context may facilitate versus hinder this interplay.
11
Indeed, there is recent literature that extended work motivation to the level of teams (Chen
& Kanfer, 2006; Chen et al., 2009), but this work focuses merely on the interplay between
team and individual processes and does not cover the alignment of teams or individuals to the
goals of a higher level entity such as an organization.
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115
5.4 Overall Limitations and Directions for Future Research
The next chapter reviews general limitations of the empirical research included in this
dissertation and, based on these, develops suggestions for future research. First, one of
the major challenges in POS research is the potential existence of backward causality
between the focal concepts of interest (e.g., team boundary-spanning activities and
team productive energy) because positive phenomena tend to produce mutually reinforcing positive gain spirals (Cameron, Dutton, Quinn, & Wrzesniewski, 2003). For
example, Fredrickson and Joiner (2002) found within a longitudinal study that positive
affect at time 1 predicted college students’ coping with stress at time 2 which, in turn,
explained their positive affect at time 2. Burns and collaborators (2008) extended this
research with another longitudinal study showing that students’ positive affect and
coping at time 1 mutually increased their positive affect and coping at time 2. However, not expected was that they did not find a similar reciprocally reinforcing link between concepts of positive affect and social support. Furthermore, two experimental
longitudinal studies have shown that individuals’ positive affect is reciprocally related
to physical health (Kok et al., 2013; Kok & Fredrickson, 2010). Additionally, Kok and
colleagues (2013) found that this link was explained by individuals’ perceived quality
of social interactions. Drawing upon the logic and evidence from the literature of positive affect, it is plausible that some of the concepts examined in this dissertation might
also mutually reinforce each other through positive gain spirals. For example, in Studies 1 and 2, we suggest that both team boundary-buffering activities and team boundary-spanning activities unidirectionally increase team productive energy. However, in
turn, team productive energy may also strengthen these team boundary activities. In
order to empirically test these potentially reciprocal relationships, future research
should apply longitudinal research designs.
Overall, prior work has suggested that all different research methodologies have
their strengths and weaknesses (Chatman & Flynn, 2005). Chatman and Flynn (2005)
proposed a classification of two ideal types of research methodologies. At one end of a
continuum, one type of research offers insights by exploring, observing, and assessing
a phenomenon – for example, by using observational, survey, or archive data. At the
other end of this continuum, another type of research aims to control and manipulate a
phenomenon – for example, by applying laboratory and field experiments, scenario
techniques, or simulation studies. Chatman and Flynn (2005) propose a research program that includes both types of phenomenon examination, which they refer to as fullcycle organizational behavior research, can diminish the weaknesses of the different
116
Overall Discussion and Conclusion
methodologies. However, the research included in this dissertation primarily falls into
the first category of phenomenon investigation. Hence, future research on the relationship between boundary activities and team productive energy should ideally encompass studies tending to the second type of methodology.
First, future research may apply a cross-lagged panel design to test the relationship between boundary activities and team productive energy over time (Kenny,
2005). Within a cross-lagged panel design, two constructs are measured at two points
in time. To assess whether these two constructs influence each other over time, one
determines the effect of construct A (e.g., team boundary-spanning activities) at time 1
on construct B (e.g., team productive energy) at time 2 as well as the reciprocal effect
of construct B at time 1 on construct A at time 2 after controlling for the main effects
of both constructs. This kind of research allows researchers to answer questions like,
“Do team boundary-spanning activities increase team productive energy and vice versa, or do they relate to each other in a reciprocal manner?”
Second, field experiments would allow researchers to draw causal inferences
about the relationship between team boundary activities and team productive energy
by manipulating individual constructs (Campbell & Stanley, 1966). Future research
might draw on research on positive affect to gather ideas on how to manipulate team
productive energy. For example, to induce positive affect, this research has applied a
loving-kindness meditation (Fredrickson, Cohn, Coffey, Pek, & Finkel, 2008; Kok et
al., 2013). If it proves impossible to experimentally induce productive energy, one
could also apply a quasi-experimental design using propensity score matching.
Third, a quasi-experimental design using propensity score matching could likewise allow for the drawing of causal inferences regarding the constructs under study
(Rosenbaum & Rubin, 1983). The basic idea of propensity score matching is that,
based on a vector of control variables that also potentially influence the dependent variable, one can generate an experimental and a control group (Rosenbaum & Rubin,
1983). In principle, this procedure should derive two groups that have similar properties, except for the independent variable of interest. Hence, this propensity score method would allow for the assessment of whether a team showing higher levels of team
boundary-spanning activities at time 1 would also experience higher levels of team
productive energy at time 2, as compared with a team showing lower levels of team
boundary-spanning activities at time 1 (Rosenbaum & Rubin, 1983). Overall, as a next
step in research on the relationships between team boundary activities and team pro-
Overall Discussion and Conclusion
117
ductive energy, these techniques should allow for the drawing of inferences regarding
the causal relationship between team boundary activities and team productive energy.
A second limitation of the research presented in this dissertation is the restricted
generalizability of the findings. In principle, this dissertation proposes that various
types of positive social interaction at multiple organizational levels can add to a competitive advantage. For example, Studies 1 and 2 have shown that team boundarybuffering and -spanning activities positively influence team productive energy. Furthermore, Study 1 found that team boundary-spanning activities indirectly increase
team innovative performance while Study 3 demonstrated that employees’ network
building initiative reduces organizational-level absenteeism. However, these performance-related outcomes might not be equally important across different organizational
contexts. Specifically, these outcomes play an important role when employees are
faced with open-ended, knowledge-intense, and complex tasks (Osterloh & Frey,
2000; Spender, 1996) that require a high degree of firm-specific knowledge (Leana &
Van Buren III, 1999; Tsui et al., 1997). These characteristics hold for employees at the
level of general manager or member of a research and development unit. Usually, in
these contexts, labor costs are expensive, and organizations tend to strive for long-term
relationships with their employees. In these kinds of contexts, the described positive
social interactions at multiple organizational levels satisfy both the normative orientation of the POS perspective (to increase flourishing and thriving) and economic rationality in the sense that they contribute to organizations’ productivity. However, in contexts where organizations follow a low-price strategy and labor cost is cheap (e.g., in
textile production), it might be economically more effective, although contradictory to
the POS principles, to adhere to control strategies, as associated with the principal
agent theory (Jensen & Meckling, 1976).
5.5 Main Practical Implications
The following section summarizes the main practical implications of this dissertation.
First, as mentioned in the introduction, this dissertation aims at contributing to a dialogue between science and practice. The research included within this dissertation was
guided by the idea that organizations can be places that contribute to both human
flourishing and economic productivity. However, the different positive relational practices studied in this dissertation, namely team boundary-buffering activities, team
boundary spanning-activities, and employees’ social network building initiative, are all
118
Overall Discussion and Conclusion
characterized by facilitating human agency. All of these different types of positive social interaction are proactive employee behaviors. To a large extent, the individual
teams and employees have sole responsibility for how to shape their team boundary
activities and informal social network building.
Consequently, organizations cannot force these proactive relational behaviors
but they can enable and encourage them. For example, organizations could sensitize
their employees of how to manage different activities associated with their team
boundaries. Hence, as Study 1 suggests, organizations could train their employees for
instance regarding the different team boundary-buffering activities, such as filtering
and evaluating external requests, showing helping behavior when external demands
are placed on individual team members, setting clear priorities, carefully communicating information that might cause insecurity and disturbance, and clearly communicating to external stakeholders when team members feel overloaded with work. In a
final step, organizations could even empower teams to decline external requests when
they have reasons to believe they are not legitimate. Our findings show that these
boundary-buffering activities are particularly effective when teams feel overloaded
with work. Furthermore, as Study 2 shows, supervisors can play an instrumental role
in encouraging team members to engage in team boundary activities. For instance, supervisors might inspire their team members with an electrifying vision that comprises
encountering other external stakeholders across the team boundaries, acting as boundary-spanning role models themselves, and sharing their own social networks in order to
help their team members approach people who are pivotal in fulfilling their team tasks.
However, Study 3 of this dissertation gives cause for concern that also well intended organizational top-down practices, such as high-performance work systems,
might have unintended harmful side effects. Hence, although organizations are well
advised to offer their employees an enabling social structure that includes, for instance,
high levels of training, cross-functional and cross-trained teams, high levels of information sharing, and internal participatory mechanisms, Study 3 suggests that organizations also should leave space, energy, and time for their employees to voluntarily build
their social networks on their own initiative.
Second, by investigating the construct of productive energy, this dissertation
adds to the debate on organizational sustainability. As mentioned in the introduction,
Pfeffer (2010) pointed to the fact that organizations already tend to pay attention to the
Overall Discussion and Conclusion
119
environmental and economic aspects of this topic but consider the human aspect to a
lesser extent. However, the increased organizational rates of psychological distress and
diseases show that this human dimension of sustainability has also a significant impact
on organizations (OECD, 2012). Study 1 of this dissertation underscores that productive energy not only increases employees’ welfare, through outcomes such as higher
levels of job satisfaction and lower turnover intention (Raes et al., 2013), but also positively influences performance-related outcomes, such as team innovative performance.
Hence, particularly the construct of productive energy promises to be a linking pin that
weaves together human flourishing on the one hand with economic productivity on the
other.
5.6 Conclusion
Drawing from a positive organizational scholarship (POS) perspective, this dissertation focuses on the question of how organizations can be economically productive and
simultaneously provide space for positive social interactions. In sum, the results of the
three studies included in this dissertation propose that positive social interactions at
multiple organizational levels (i.e., team boundary-buffering activities, team boundaryspanning activities, and employees’ social network building initiative) positively influence performance and well-being-related outcomes at multiple organizational levels
(i.e., organizational-level absenteeism, team productive energy, and team innovative
performance). Overall, the findings of this dissertation suggest that different types of
positive social interactions can add to both human flourishing and organizations’ competitive advantage.
120
Appendix
6 Appendix
Figure 6-1
Multilevel Confirmatory Factor Analysis for Transformational Leadership
Between level
Transformational
Leadership
***
***
.94
(.26)
***
***
.93
(.24)
.95
(.07)
V1
V2
***
***
***
. 87 . 99 .93
(.06) (.04) (.05)
V3
V4
***
.94
(.10)
G1
***
.91
(.14)
G2
***
***
***
G
.73
(.04)
G4
R1
***
1.00
(. 00)
R2
.70
(1.02)
Intellectual
Stimulation
Role Model
.99 .98
(.03) (.04)
.70
(.96)
.86
(.18)
.87
(.41)
Group Goals
Vision
***
*
.95
(.23)
***
***
.96
(. 06)
.99
(. 01)
R3
N1
***
***
.97
(. 10)
***
.99
(. 08)
N2
High Performance
Expectations
Individualized Support
.97
(.23)
N3
I1
**
.99
(.32)
I2
***
***
***
.99 .98
(.23) (.24)
I3
.92
(.07)
I4
H1
***
1.00
(.00)
H2
Please turn over
***
.99
(.09)
H3
Appendix
121
Within level
Transformational
Leadership
***
***
.94
(.01)
***
***
***
.58
(.03)
.77
(.05)
. 77
(.06)
V1
V2
V3
***
.70
(.04)
V4
V5
***
.82
(.08)
G1
***
.72
(.07)
G2
***
***
***
G
.50
(.09)
G4
R1
***
***
.90
(.03)
***
.86
(. 05)
R2
**
.74
(.13)
.34
(.13)
Intellectual
Stimulation
Role Model
.80 .82
(.05) (.06)
***
.78
(.03)
.93
(.05)
Group Goals
Vision
***
***
.92
(.04)
.61
(. 06)
R3
N1
***
***
.71
(. 03)
***
.74
(. 03)
N2
High Performance
Expectations
Individualized Support
.81
(.06)
N3
I1
***
***
.81
.87
(.05) (.04)
I2
***
***
.80
(.07)
I3
.59
(.05)
I4
H1
***
***
.61
(.06)
.55
(.06)
H2
Full maximum likelihood estimation with robust standard errors. Standardized path coefficients are reported. The values in the parentheses are
standard errors. N = 121Teams with 887 team members.
***
**
*
p < .001, p < .01, p < .05.
H3
122
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Curriculum Vitae
Ulrich Leicht-Deobald was born on July 10, 1975, in Braunschweig, Germany.
EDUCATION
2011 – 2014
University of St. Gallen, Switzerland
Doctoral studies in Management (Dr. oec.)
2012
University of Michigan, United States of America
Program in Quantitative Research Methods
2005 – 2010
University of Bremen, Germany
M.A. in Psychology (Dipl. Psych.)
London School of Economics and Political Science, UK
Visiting student at the Department of Management
Ratsgymnasium Osnabrück, Germany
German Abitur (equivalent to high school diploma)
2008
1988 – 1995
WORK EXPERIENCE
Since 2014
University of St. Gallen, Switzerland
Senior Researcher, Institute for Leadership and HR Management
2014
University of Michigan, United States of America
Visiting Scholar at Institute for Social Research
2010 – 2014
University of St. Gallen, Switzerland
Research Associate, Institute for Leadership and HR Management
2008
Swiss Federal Institute of Technology Zurich, Switzerland
Intern, Center of Organizational and Occupational Science
University of Bremen, Germany
Teaching Assistant, Department of Human and Health Sciences
2006 – 2007
1997 – 2005
1996 – 1997
Deutsches Schauspielhaus Hamburg, Münchner Volkstheater,
Landestheater Detmold, Germany
Engagements as actor after attending drama school
Health Center Hamburg-Horn, Germany
Civil Service