The effect of co-design and flow experience on

Issues in Business Management and Economics Vol.3 (4), pp. 59-66, April 2015
Available online at http://www.journalissues.org/IBME/
http://dx.doi.org/10.15739/IBME.2014.015
Copyright © 2015 Author(s) retain the copyright of this article
ISSN 2350-157X
Original Research Article
The effect of co-design and flow experience on customer
satisfaction and purchase intention online
Accepted 30 April, 2015
Wei-ping Pu*1,
Kuohsiang Chen2and
Meng-Dar Shieh 3
1Institute
of Creative Industries
Design, National Cheng Kung
University, No.1, University Road,
Tainan City 701, Taiwan (R.O.C.)
2Department of Creative Product
Design, I-Shou University, No.1, Sec. 1,
Syuecheng Rd., Dashu District,
Kaohsiung City 840, Taiwan (R. O. C.)
3Department of Industrial Design,
National Cheng Kung University, No.1,
University Road, Tainan City 701,
Taiwan (R.O.C.)
*Corresponding Author
E-mail : [email protected]
Tel.: +886-6-2757575 ext.54360
Fax:+886-4-25651078
In today’s intensely competitive business environments, offering customer
co-design of individual products online is a key enterprise strategy. Based on
the literature on mass customization and mature online shopping
environments, this study provides a framework for understanding how
customer satisfaction mediates the impacts of co-design and flow experience
on purchase intention. The research creates a scenario that allows
participants to complete a customer co-design task. An empirical study of 137
online participants is analyzed via structural equation modeling. The result
shows that both of customer co-design and flow experience are mediated by
customer satisfaction. However, a direct relationship between customer codesign and flow experience on purchase intention was not found. The finding
indicates that the creation of customer satisfaction during the co-design
process is a vital factor in purchase intention.
Key words: Mass customization, customer co-design, flow experience, customer
satisfaction, purchase intention
INTRODUCTION
The modern businesses are established on the basis of
marketing orientation. In today’s competitive business
environments, enterprises constantly look for new models
to meet customer demand. This forces back-end suppliers
to make changes to react to market trend (Birjandi et al.,
2013). Today, the already-made products can no longer
satisfy customers Instead, the focus is shifting to the level of
self-actualization. The desire to have individualized and
unique products is increasing as customers want
customized products to express their individuality. For the
Millennial Generation, the meaning and value of the
customized product is not the product itself but the
customer participation. Moreover, the concept of
customization that consumers seek involves more than
merely following customer requirements. Customers want
to be creative and do it by themselves (Flynn and Vencat,
2012). Generally, people have special feelings for products
in which they invest more time and energy. This value is
not provided by unilaterally by the supplier but stems from
the co-creation between customers and enterprises.
Inviting customers to join production processes is not a
new idea. Alvin Toffler’s 1980 book The Third Wave
predicted that would happen. It is now a commonplace.
Products from dolls to computers cars may now be
individualized and personalized in the production process.
For example, Audi introduced the Audi Exclusive customized
service, with more than 30,000 kinds of car color choices for
interior contents such as car plaques, seat belts, or floor
mats. In 2007, the total orders of Audi Exclusive per month
reached £100,000 in the UK market. Nike also constructed a
website, NikeID, to provide a service that allows customers
to personalize their own shoes, generating over 100 million
USD in 2010. According to the “Internet Retailer’s Top 500
Guide”, more than 50% of online retailers applied
customization as a marketing or sales skill to help their
business in 2012. The growing maturity of technology for
mass customization and the internet are key drivers of the
growing customization trend. In this study, Researchers
refer to the processes under which customers participate in
production and create their own unique product and
shopping experience as “customer co-design”.
Customer co-design not only provides enterprises with
Issues Bus. Manag. Econ.
60
more opportunities to understand customer demand, but
also increase people’s willingness to pay (Piller and Müller,
2004). When people put labor, time, and creativity into
customer co-design, it can increase their valuation of the
end product (Norton et al., 2012). However, firms must
overcome several issues. In addition to possessing mass
customization technology, the customer must be made to
feel comfortable and interested in customer co-design. Since
some customers worry about their ability to complete
design tasks, when enterprises provide too many choices, it
creates confusion (Piller, 2004; Piller et al., 2005). Piller
(2004) observed that products that are co-designed by
customers may also provide symbolic benefits which come
from the process of co-design (Reichwald et al., 2005).
An important precondition of co-design is that the
customer must be endowed with the capability of
performing the design-related tasks. This competency issue
involves a flow experience. Flow experience is often applied
in the research of computer users and environment related
issues (Ghani and Deshpande, 1994; Hoffman and Novak,
1996; Koufaris, 2002). Flow is a state of consciousness that
is sometimes experienced by individuals who are deeply
involved in an activity they are enjoying. Nantel et al. (2002)
found that flow construct positively influences the hedonic
value of customer online shopping experience. Chen (2006)
has argued that the most World-Wide Web users experience
positive moods while online. Scholars who study co-design
related issues also suggest the further research should focus
on the empirical examination of flow experience (Piller,
2004; Piller et al., 2005;). Yet such studies are scarce.
In the other hand, the co-design strategy be applied on
practices are very common in fashion industries. For
example, some companies such as Fashionplaytes, Zazzle,
Cafepress, and Spreadshirt provide the service online to
allow customer design clothes by them. All these companies
reported high double-digit sales growth in the recent year.
However, the studies focus on this field still rare.
With this in mind, this study argues that the relationship
between customer co-design, flow experience, and customer
behavior online needed to be explored. This research has
two primary objectives: (1) to investigate the relationship
between customer co-design, customer satisfaction and
purchase intention and (2) to test the relationship between
flow experience, customer satisfaction and purchase
intention. The remainder of this study is structured as
follows. First, the literature background is discussed. The
research setting and methodology are then described. Next,
the analyses and results are presented. Finally, conclusions
are drawn, together with limitations and suggestions for
future research.
Literature review and Hypotheses
Customer co-design,
purchase intention
customer
satisf action
and
Bateson (1985) asserted that customers might have the
propensity to choose the “do-it-themselves” approach across
many services, even when the service might be more
expensive or less convenient than traditional services. In
recent studies, customers play an active role in customer codesign based on mass customization. They should not be
viewed as just passive receptacles, but as a source of
productivity gain (Fitzsimmons, 1985; Lovelock & Young,
1979).
Scholars have advocated developing different interfaces
and web-based paths for co-design (Randall, Terwiesch and
Ulrich et al., 2005; Silveira et ., 2001). For enterprises, the
customer-firm interaction is conducive to creating product
value and customer relationship development (Miceli et al.,
2007), and knowledge-sharing between the customer and
the company will also lead a competitive advantage for the
company (Bhatti et al., 2014). It also helps to generate
creativity and innovation, as well as evaluate products and
obtaining customer feedback (Rowley et al., 2007; Piller et
al., 2005; Ulrich et., 2003).
Bagozzi and Dholakia (1999) argued that when
interacting with e-commerce sites, goal-oriented customers
focus on the transaction and tend to reduce elude social
contacts. By contrast, experiential users are committed to
the experience and fun and search for new stimuli
independently from specific aims. Ghose and Dou (1998)
also indicate that the higher interactivity level a webpage
offers, the more attractive it is.
Several studies have shown that customer co-design of
apparel allows customers to feel more comfortable with the
final product if they found it easier to design (Ulrich et al.,
2003). Scholars have found that matching customer’
requirements and co-creating products through web-based
interaction (Prahalad and Ramaswamy, 2004; Thomke and
von Hippel, 2002) broadens differentiation opportunities as
well as profitability for online users (Miceli et al., 2007).
When customers participate in co-design to create
something of their own, they may perceive that the product
has additional value (Franke and Piller, 2004). The value
might not only come from the product’s symbolic benefits,
but may also be generated in the process (Piller, 2004).
When people imbue products with their own time and
effort, the products have special meanings for them (Norton
et al, 2012). Anwar, Gulzar and Anwar (2011) argued that
serving customers on an individualized basis increases
customer satisfaction. Franke et al. (2010) showed that
when the customer felt “I designed it myself” in customer
co-design, the products generated a significantly higher
willingness to pay. Furthermore, Flynn and Vencat (2012)
found that when enterprises provided customer co-design,
repurchase, sales volume, price, and customer loyalty will all
increase. H1 and H2 inferred as follows:
H1: The greater the customer co-design, the greater the
customer satisfaction.
H2: The greater the customer co-design, the greater the
purchase intention.
Customer satisf action and purchase intention
Customers’ evaluation of products or services with regard to
Pu et al.
their needs and expectations can be evaluated by customer
satisfaction (Oliver, 1980). Oliver (1991) defined customer
satisfaction as how the customer evaluates a purchase
decision. Customer satisfaction influences customer’s
previous experience and purchase behavior (Bettman,
1979; Westbrook and Oliver, 1991).
As online shopping behavior has matured, the traditional
dimensions of customer satisfaction face scrutiny. Anderson
and Srinivasan (2003) surveyed customer satisfaction in the
virtual environment. They defined e-satisfaction as “the
contentment of the customer with respect to his or her
prior purchasing experience with a given electronic
commerce firm” (125), and find that e-satisfaction has an
impact on loyalty, but the relationship is moderated both by
customer-level factors (such as convenience motivation and
purchase size) and business-level factors (such as trust and
perceived value).
Customer’s behavior can usually be predicted by their
intentions. Thus, understanding customer’s purchase
intention is important. Intentional measures can be more
effective than behavioral measures in capturing the
consumers’ mindset because customers may make
purchases based on constraints instead of preferences (Day,
1969). Ajzen and Fishbein (1980) used purchase intention
to predict actual behavior. Purchase intention correlate with
actual behavior. Scholars have sought to forecast customer
behavior. Based on the above literature review, customer
satisfaction and purchase intention are the dependent
variables in this study. H3 inferred as follows:
H3: The greater the customer satisfaction, the greater the
purchase intention.
Flow experience, customer satisf action, and purchase
intention
In general, co-design activities are achieved by a toolkit
which must be preset. Although the toolkit provides
customers with the basic ability to complete the co-design
task, a number of factors must be considered during the
process of completing a co-design product with a customer.
These include the customer’s ability and experience with
computers and interfaces and customer creativity and
awareness of aesthetics. Moreover, the existence of a higher
risk of error between the virtual and actual product and the
longer manufacturing time may be factors that customers
feel create a more complex, risky, and uncertain shopping
situation (Piller et al., 2005).
When a customer perceives a higher benefit from the
customized product compared to its cost, they tend to be
more willing to adopt co-design (Franke and Piller, 2003). If
customers think that they could better identify appropriate
products than the company, their participation would
empower them to perceive more behavioral control, which
would result in higher evaluations of the resulting products
(Godek, 2002).
As mentioned above, psychological factors play a crucial
role in customer evaluations of the result of co-design. Piller
(2004) reviewed studies related to mass customization and
61
argued that customer co-design is a precondition to
customer satisfaction, but customers must also have the
ability to perform this task. This ability involves flow theory,
which refers to the user implementation of tasks in the
process, their skills, and challenges in achieving optimal
balance (Novak, Hoffman and Yung, 2000). It is also often
used to explain how customer satisfaction is increased by
the process (Csikszentmihalyi, 1990).
In recent years, the concept of flow has been increasingly
applied in studies of user online behaviors. Flow theory has
been applied to explore user’s intrinsic motivations, and to
accompany a dynamic process. Web users’ cognitive,
emotional and behavioral tendencies may be driven by flow
during Internet browsing (Bagozzi and Dholakia, 1999).
Flow may increase the loyalty of Internet users (Choi and
Kim, 2004), or may be seen as reinforcement that
strengthens user intention (Shin & Kim, 2008). Users can
feel a positive mood, even joy, or become highly focused
and creative when flow experiences occur (Chen, 2000;
Ghani, 1995; Novak and Hoffman, 1997). Chang & Wang
(2008) propose that when users’ beliefs about flow
experiences are salient for online communication, a greater
flow experience corresponds to a greater behavioral
intention to use online communication tools. Flow
experiences also influence customer unplanned purchases
and intention to return (Koufairs, 2002). Further, Novak et
al. (2000) argued that greater interaction with computers
will generate improved flow and provide a better shopping
experience. H4 and H5 inferred as follows:
H4: The greater the flow experience, the greater the
customer satisfaction.
H5: The greater the flow experience, the greater the
purchase intention.
METHODOLOGY
Research setting and data collection
Researchers collected the data for the empirical analysis
from an online questionnaire. In this study, in order to
enhance the customer motivation, the participants were
asked to imagine that they purchasing a t-shirt as a gift for
family member or friend.
Researchers created a website for the study that allows
participants to design and purchase t-shirts by themselves.
Participants were lead to the website, where researchers
provided ten patterns as design items for participants to
complete their work. The website also provided other
functions such as choosing patterns, combining different
patterns, enlarging, shrinking, and rotating patterns, as well
as choosing a location for the pattern. There was no time
limit for the design process, but all IP addresses were
recorded to eliminate the same participant repeating the
experiment.
According to the surveys on Internet usage in Taiwan, the
over 60% online shoppers are female and aged between 25
~ 34 years old (MIC, 2013). Moreover, market research has
Issues Bus. Manag. Econ.
62
Table 1. Descriptive statistics and correlation matrix
Construct
1. Customer satisfaction
2. Purchase intentions
3. Flow experience
4. Co-design experience
Mean
3.89
3.46
3.58
3.69
S.D.
.97
.99
.88
.93
also indicted the female heavy user of online shopping are
more than male. Most of these female online shoppers are
white-collar worker, aged between 25 ~ 39 years old in
Taiwan (E-ICP, 2013). After participants completed the tshirt design, they were asked to fill out the questionnaire
online. After data collection, researchers received 137 usable
responses. The demographic analyses of the sample showed
that the most of the sample was female (67.2%), and aged
were between 20-29 years old (78.8%).
Guadagnoli and Velicer (1988) review several studies that
conclude that absolute minimum sample sizes. They
suggested that a minimum subject to item ratio of at least
5:1. Rummel (1970) recommend the ratio should be 4:1. In
this research, measures of total items were 16, reasonable
number of samples should be more than 80, and researchers
received 137 usable responses.
Measures
All the constructs were measured using multi-item
perceptual scales and consisted of five-point Likert-type
scales ranging from 1 = “strongly disagree” to 5 = “strongly
agree”.
Customer co-design. Researchers modified the scales of
Fiore et al. (2001) and Ulrich et al (2003) to assess
customer co-design, which was measured by five items (“I
am willing to have co-design behaviors”, “I am willing to
spend more time on co-design”, “I find it is easy to make
design decisions”, “I am comfortable with the co-design
processes”, “I am interested in the design scnarios”).
Flow Experience. Following the scales of Hoffman and
Novak (1996) and Chang and Wang (2008), flow experience
was measured by five items (“I feel this is a challenging task
to design the t-shirt”, “I feel enjoyment when I design the tshirt”, “I perceive control when I design the t-shirt”, “I focus
when I am designing the t-shirt”, “I feel I can’t stop
designing the t-shirt”).
Customer satisfaction. For the measurement of
experienced satisfaction researchers modified three items
from Anderson and Srinivasan (2003) and Bai, Law and
Wen (2008). Customer satisfation was measured by three
items (“I am satisfied with my decision to design the t-shirt
by myself”, “if I had to design the t-shirt by myself again, I
still feel it is special”, “my choice to design the t-shirt by
myself was a wise one”).
Purchase Intention. Bai et al.(2008). Purchase intention
was measured via three items (“I would like to by the tshirt designed by myself”, “I would like to buy the t-shirt
1
1.00
.72***
.79***
.71***
2
3
4
1.00
.69***
.65***
1.00
.64***
1.00
designed by myself within the next six months” “I would
like to buy the t-shirt designed by myself within the next
two years”).
ANALYSIS AND RESULTS
The descriptive statistics and correlation matrix are shown
in Table 1. Researchers conducted a two-stage procedure as
recommended by Anderson and Gerbing (1988) in testing
the proposed framework. In the first stage, confirmatory
factor analysis (CFA) was conducted to evaluate the validity
of the measures using LISREL 8.51. Then, a structural
equation model (SEM) analysis was employed to
investigate the hypothesized structural relationships.
Measurement validation
Cronbach’s α coefficients can be calculated to test the
internal consistency reliabilities for each construct.
Cronbach’s α coefficients in this study ranged from .72 to
.96, indicating satisfactory levels of internal consistency.
Convergent validity was examined using CFA, as
recommended by Anderson and Gerbing (1988). The
standardized factor loadings ranged from .66 to .97 and
were statistically significant (p < .05) on their respective
constructs with fit statistics indicating an appropriate fit (χ2
= 126.45, d.f. = 96, comparative fit index (CFI) = .98,
goodness of fit index (GFI) = .90, parsimony goodness of fit
index (PGFI) = .63, nonnormed fit index (NNFI) = .98, root
mean square error of approximation (RMSEA) = .05,
standardized root mean square residual (SRMR) = .04). We
also used composite reliabilities (CR) and average variance
extracted (AVE) to identify convergent validity. In addition,
the CRs ranged from .87 to .92, indicating adequate levels of
reliability for the constructs as well as the AVEs ranged
from .66 to .79, exceeding the recommended level of .5.
Hence, the measurement model exhibited an adequate level
of convergent validity.
The discriminant validity was examined by three tests.
First, we calculated confidence intervals for the estimates of
the inter-construct correlations. The results showed that
none of the confidence intervals for the Phi values in the
measurement model included a value of 1.0. In addition,
discriminant validity was established by constraining the
correlation between a pair of constructs in the
measurement model and setting the correlation between
the two constructs equal to one. Researchers then
Pu et al.
63
Figure 1: Path model estimates for the full sample
--- ins
Note. *p<.05, **p<0.1, p<.001; Chi-square=126.45, RMSEA = .06, NFI = .94, NNFI = .98, GFI = .89, PGFI = .63, CFI = .98, SRMR =
.04
performed a pairwise chi-square difference test comparing
an unconstrained with a constrained structural equation
model. The Δχ2 statistics ranged from 7.59 to 16.23,
showing that all pairs of constructs were statistically
significant. Lastly, the AVEs of the constructs were greater
than the square of the correlation between any pair of
constructs (greatest square of correlation was .62). These
results indicate that discriminant validity was confirmed.
Therefore, discriminant validity was achieved.
satisfaction, and was supported (γ12 = .77, t = 7.34). H5
hypothesized a positive relationship between flow
experience and purchase intention, but was not supported
(γ22 = .19, t = .88). These results suggest a full mediation
model that showed a significant direct effect of flow
experience on purchase intention via customer satisfaction.
Hypotheses testing
Most early mass customization research discussed how it
helps enterprises maintain management strategy in line
with production or technology. Customer co-design is a
growing trend and recent studies have tackled the issue of
customization (Chang and Chen, 2009; Franke et al, 2010;
Norton et al., 2011). However, studies from the customer
point of view are lacking (Piller, 2004; Piller et al., 2005).
This research based on this viewpoint attempt to have a
better understanding of this phenomenon.
Drawing on the literature of mass customization,
customer co-design and flow experience, the study proposed
a conceptual framework and conducted an empirical study
of 137 online user responses to test the hypotheses. Our
results show that both customer co-design and flow
experience are strongly associated with greater customer
satisfaction, and customer satisfaction strongly influences
greater purchase intention online. The results are consistent
with the idea that co-design has a direct positive impact on
customer satisfaction (Chang et al., 2009). Scholars have
proposed customer co-design results greater purchase
behavior, more premium (Norton et al., 2012) and higher
The model in this study is estimated by SEM analysis. In
assessing overall model fit, the goodness-of-fit indices
indicated a good fit of the model with the data (χ2 =139.38,
d.f. = 96, NFI = .94, NNFI = .98, GFI = .89, PGFI = .63, CFI =
.98, RMSEA = .06, SRMR = .04). The structural results are
shown in Figure 1.
Inspection of the standardized parameter estimates
showed that H1 (Customer co-design→customer
satisfaction) (γ11 = .21, t = 2.94) was largely supported at
the 0.01 level. H2 hypothesized a positive relationship
between customer co-design and purchase intention, and
was not supported by (γ21 = .15, t = 1.50). As predicted by
H3, the findings showed customer satisfaction appeared to
positively influence purchase intention (β21 = .60, t = 2.60),
supporting H3. These results suggest a full mediation model,
which provided significant statistical relationships for the
direct effect of customer co-design on purchase intention
via customer satisfaction. H4 hypothesized a positive
relationship between flow experience and customer
CONCLUSION AND DISCUSSION
Issues Bus. Manag. Econ.
64
willingness to pay (Franke et al, 2010).
In addition, the results confirm that strong flow experience
have a positive influence on customer satisfaction. Flow
research indicates that when people in flow; they become
absorbed in their activity and get enjoyment (Ghani and
Deshpande (1994). Koufaris(2002) indicated that
enjoyment of the shopping experience is important for
customers’ intention to return. Thus, when customer enjoys
shopping online, it leads more satisfaction and makes them
return. Researchers can explain this using the concept by
experience economy, where customization is not a goal but
a way to create unique value for customers and greater
profit for the enterprise. Base on Franke et al (2010)
research, there is no doubt, people prefer the stuff which
created by themselves when they realized “I designed it
myself”.
Another important finding is our verification that strong
customer satisfaction mediates the effects of both customer
co-design and flow experience on purchase intention. Our
results show that the establishment of customer satisfaction
during customer co-design process is a key success factor. In
Norton et al (2012) research, they argued that labor leads to
increased valuation, however, this phenomenon only
happened when labor results in successful completion of
tasks. If participants fail to complete the task or built and
then destroyed their creation, the effect dissipated.
With regard to theoretical implications, the research
clarifies the relationship among customer co-design, flow
experience, customer satisfaction and purchase intention.
The study has found that direct relationship among
customer co-design, flow experience and customer
satisfaction, and the relationship between customer
satisfaction and purchase intention. We once again
highlighted the importance of psychological factors on
online shopping. If firms create a pleasant co-design
process, it leads positive results. And more important, this
research indicate that customer satisfaction plays a role as
mediator in this framework. For managers, the research
offer several insights for customer co-design firms. First,
this research shows that to create a flow experience
conduce the greater customer satisfaction and purchase
intention. To make online user enjoy the creating process is
necessary. Second, even though people prefer the stuff which
made by them, however, if customer do not satisfied with
the outcome, the effect does not exist anymore.
LIMITATIONS AND FUTURE RESEARCH
There are several limitations to this study. First, this study
used the process of designing a t-shirt as stimulus. Customer
involvement and knowledge may be lower than for other
products such as computers or cameras. Therefore, future
research is encouraged to replicate this study in other
product categories. Second, this study focuses on the
relation between customer co-design, flow experience,
customer satisfaction, and purchase intention. However,
people who like co-designing products enjoy not only the
product itself but also the process of design. Thus, the
cognitive value is exceeds the physical value (Franke et al,
2010; Hwang and Kim, 2007; Norton et al, 2012). This
indicates the necessity of discussing the customer co-design
issue from the cognitive view point. Furthermore, research
could also examine how self-serving bias effect on co-design
satisfaction and purchase intention. Self-serving bias refers
to people’s tendency to accept more credit for success and
less responsibility for failure in a jointly produced outcome
(Wolosin, Sherman and Till, 1973). The customer co-design
behavior might be highly related with self-serving bias.
Hence, these interesting areas for future research are the
issue of the co-design interface and its effects.
REFERENCES
Ajzen I, Fishbein M (1980). Understanding attitudes and
predicting social Behavior, Englewood cliffs,New Jersey:
Prentice Hall.
Anderson JC, Gerbing DW (1988). Structural equation
modeling in practice: A review and recommended two step approach. Psychol. bull. 103(3):411-423.Crossref
Anderson RE, Srinivasan SS (2003). E-satisfaction and Eloyalty: A contingency Framework. Psychol. Mark.
20(2):123-138.Crossref
Anwar A, Gulzar A, Anwar A(2011). Impact of self-designed
products on customer satisfaction. Interdis. J. Contemp.
Res. Bus. 3(5):546-552.
Bai B, Law R, Wen I (2008). The impact of website quality on
customer satisfaction and purchase intentions: evidence
from Chinese online visitors. Int. J Hosp. Manag.
27(3):391-402.Crossref
Bagozzi RP, Dholakia UM (1999). Goal setting and goal
striving in consumer behavior. J. mark. 63: 19-32.Crossref
Bateson JEG (1985). Self-service consumer: an wxploratory
study. J. retail. 61(3): 49-76.
Bettman JR (1979). Memory factors in consumer choice: a
review. J. mark. (43) 37-53.Crossref
Birjandi M, Hamidizadeh MR, Birjandi H (2013). Customer
segmentation based on ‘benefit sought approach’: case of
Sehat Shampoo in Iranian market. Issues Bus. Manag.
Econ. 1(2):13-21.
Bhatti KL, Latif S, Rao NI (2014). Knowledge sharing
intentions in doctors of private and government
hospitals. Issues Bus. Manag. Econ. 2(8):128-133.
Chang CC, Chen HY (2009). I want products my own way,
but which way? The effect of different product categories
and cues on customer responses to web-based
customizations. Cyb. Psychol. Beh. 12(1): 7-14. Crossref
Chang CC, Chen HY, Huang IC (2009). The interplay between
customer participantion and difficulty of design examples
in the online designing process and its effect on customer
satisfaction: mediational analyses. Cyb. Psychol. Beh.
12(2):147-154.Crossref
Chang HH, Wang IC (2008). An investigation of user
communication behavior in computer mediated
environments. Comput. hum. behav. 24(5): 2336-2356.
Pu et al.
Chen H (2006). Flow on the net –Detecting Web users’
positive affects and their flow states.Comput Hum Behav.
22(2): 221-233.Crossref
Choi D, Kim, J (2004). Why people continue to play online
games: in search of critical design factors to increase
customer loyalty to online contents. Cyb. Psychol. Beh.
7(1):11-24.Crossref
Csikszentmihaly M (1990). Flow: the psychology of optimal
experience. New York: Harper and Row.
Day GS (1969). A Two-dimensional concept of brand loyalty.
J. advert. res. 9(3):29-35.
E-ICP(2013). Taiwan Lifestyle Bible. Eastern Online Co.,
Lted., Retrieved from http://www.isurvey.com.tw.
Fiore AM, Lee SE, Kunz G, Campbell JR (2001). Relationships
between optimum stimulation level and willingness to use
mass customisation options. J. Fash. Mark. and Manag.
5(2):99-107.Crossref
Fitzsimmons JA (1985). Consumer participation and
productivity in service operations consumer participation
and productivity in service operations. Interfaces.
15(3):60-67.Crossref
Flynn A, Vencat E (2012). Custom Nation: Why
Customization Is the Future of Business and How to
Profit From It. Dallas: BenBella Books.
Franke N, Piller F (2004). Value creation by toolkits for user
Innovation and design: the case of the watch market. J.
Prod. Innov. Manage. 21(6): 401-415.Crossref
Franke N, Piller FT (2003). Key research issues in user
interaction with user yoolkits in a mass customisation
system. Int.J. Technol. Manag. 26(5-6):578-599.Crossref
Franke N, Schreier M, Kaiser U (2010). The “i designed it
myself” effect in mass customization. Manage. sci.
56(1):125-140.
Ghani JA (1995). Flow in human computer interactions: test
of a model," in Carey, J. (Ed.), Human Factors in
Information Systems: Emerging Theoretical Bases. New
Jersey: Ablex Publishing Corp.
Ghani JA, Deshpande SP (1994). Task characteristics and the
experience of optimal flow in human-computer
interaction. J. Psychol. 128(4): 381-391.Crossref
Gharbi JE, Tunis ESSEC (2002). The influence of flow on
hedonic
and
utilitarian
shopping
values.
Adv. Consum. Res. 29(1): 483-484.
Ghose S, Dou W (1998). Interactive functions and their
impacts on the appeal of internet presence sites. J.
Advert.Res. 38(2):29-43.
Godek J (2002). Personalization and customization:
implications for consumer decision making and behavior.
Adv. Consum. Res. 29(1): 155-157.
Guadagnoli E, Velicer, WF (1988). Relation of sample size to
the stability of component patterns. Pol Psychol bull, 103
(2): 265-275.LCrossref
Hoffman D, Novak PT (1996). Marketing in hypermedia
computer-mediated
environments
conceptual
foundations. J. Mark. 60:50-68.Crossref
Hwang Y, Kim DJ (2007). Customer self-service systems: the
effect of perceived web quality with service contents on
enjoyment, anxiety, and e-trust. Decis. Support Syst.
65
43 (7): 46-760.Crossref
Koufaris M (2002). Applying the technology acceptance
model and flow theory to online customer behavior. Inf.
Syst. Res. 13(2):205-223.Crossref
Lovelock CH, Young RF (1979). Look to consumers to
increase productivity. Harvard Bus. Rev. 57(3):168-178.
MIC(Marget Intelligence Center)(2013). Taiwan Ecommerce
YearBook.
Retrieved
from
http://ecommerce.org.tw/download_list.php?lno=2
Miceli GN, Ricotta F, Costabile, M (2007). Customizing
customization: a conceptual framework for interactive
personalization. J. Interact. Market. 21(2): 6-25.Crossref
Norton MI, Mochon D, Driel D (2012). The IKEA effect: when
labor leads to love. J. Consum. Psychol. 22: 453460.Crossref
Novak TP, Hoffman DL (1997). Measuring the flow
experience among web users. Interval Research
Corporation,
[On-line]
http://www2000.ogsm.vanderbilt.edu/.
Novak TP, Hoffman DL, Yung YF (2000). Measuring the
customer experience in online environments: a structural
modeling approach. Mark. Sci. 19(1): 22-42.Crossref
Oliver C (1991). Strategic responses to institutional
processes. Acad. Manage. Rev. 16 (1): 145–179.Crossref
Oliver RL(1980). A congitive model of the antecedents and
consequences of satisfaction decisions. J. Mark. Res.
17(4):460-469.Crossref
Piller F (2004). Mass customization: reflections on the state
of the concept. Int. J. Flex. Manuf. Syst. 16(4):313334.Crossref
Piller F, Müller M (2004). A new marketing approach to
mass customisation. Int. J Comput. Integr. Manuf.
17(7):583 – 593.Crossref
Piller F, Schubert P, Koch M, Möslein K (2005). Overcoming
mass confusion: collaborative customer co-design in
online communities. J. Comput. Mediat. Comm. 10(4):125. Crossref
Prahalad CK, Ramaswamy V (2004). Co-creation
experiences: the next practice in value creation. J. Interact.
Market. 18(3): 5-14.Crossref
Randall T, Terwiesch C, Ulrich KT (2005). Principles for user
design of customized products. Calif. Manage. Rev. 47(4):
68-85.Crossref
Reichwald R, Müller M, Piller F(2005). Satisfaction of
customer co-designers: process versus product
satisfaction. In Proceedings of the 34th Conference of the
European Marketing Academy (EMAC 2005), Milan: 2427.
Rowley J, Kupiec-Teahan B, Leeming E (2007). Customer
community and co-creation: a case study. Mark. Intell.
Plann. 25(2): 136 - 146.Crossref
Rummel RJ (1970), Applied Factor Analysis, Evanston,
Illinois: Northwestern Univ. Press.
Shin DH, Kim, WY (2008). Applying the technology
acceptance model and flow theory to cyworld user
behavior: implication of the web2.0 user acceptance. Cyb.
Psychol. Beh. 11(3):378-382.Crossref
Nantel J, Senecal S, Gharbi JE, (2002). The influence of flow
Issues Bus. Manag. Econ.
66
on hedonic and utilitarian shopping values. Consumer
Research, 29(1): 483-484.
Silveira GD, Borenstein D, Fogliatto FS (2001). Mass
customization: literature review and research directions.
Int. J. Prod. Econ. 72(1): 1-13.Crossref
Ulrich PV, Anderson-Connell LJ, Wu W (2003). Consumer
co-design of apparel for mass customization. J. Fash.
Mark. Manag. 74 (4):398-412.Crossref
Wolosin RJ, Sherman SJ, Till A (1973). Effects of Cooperation
and Competition on Responsibility Attribution after
Success and Failure. J Exp. Soc. Psychol. 9 (3): 220235.Crossref