Diversity in Business-to-Business Information Exchange: An

TOBIN E. PORTERFIELD
Diversity in Business-to-Business
Information Exchange: An Empirical
Analysis of Manufacturers and their
Trading Partners
Abstract
This article examines the performance implications of information exchange in industrial supply
chains. While e.xisting literature has addressed the critical role of information exchange in supply
chain integration, existing studies fail to address the specific characteristics of information
e.xchange that ajfect performance. Through a transaction cost economics theoretical lens, hypotheses are developed and tested to explore the effects of information volume and information
diversity on firm performance. The hypotheses are tested using an original dataset of twenty-three
manufacturing firms that e.xchange information with their trading partners using an electronic
intermedian. Results indicate a positive relationship for information volume and a negative
relationship for information diversity as related to firm performance.
The exchange of information between firms
within supply chains is of great interest to both
researchers and practitioners. Supply chain literature recognizes the value of exchanging information to improve the supply chain performance in key functional areas such as
logistics (Daugherty et al. 2002: Lieb and
Butner 2œ7). The exchange of information is
noted for its role in interfirm integration, sharing of performance data, and transparency
(Cruijssen et al. 2007). Information exchange
is also recognized for its positive performance
effects when leveraged to dampen the buliwhip
effect (Lee et al. 1997; Cachón and Fisher
2000; Machuca and Barajas 2004; Steckel et
al. 2004). Strategically, the leveraging of information exchanged between firms is noted for
its effects on competition (Sanders and Premus
2002) and specific areas of firm performance
(Zsidisin et al. 2007). Complimenting the research on information exchange from a supply
Mr. Porlerfïeid is assistant professor. Department of
eBusiness and Technology Management, College of
Business and Economics, Towson University, Towson,
Maryland 21252; e-mail tporterfîelà@towson.edu.
The author would like to thank Brian Lowell from the
University of Maryland • University College for his
assistance in dala collection and validation as well as
Joseph Bailey and Phil Evers from the University of
Maryland - College Park for their guidance in the
development of this research stream.
chain and logistics perspective is research on
the use of information technology (IT) to span
organizational boundaries. IT research recognizes thai using IT to exchange information
within the context of supply chains provides
additional benefits to the participants (Bakos
and Brynjoifsson 1993; Mukhopadhyay et al.
1995; Mukhopadhyay and Kekre 2002). The
benefits to firms include decreased inventory
investment (Mukhopadhyay et al. 1995). improved customer service (Allen et al. 1992),
and reduced shipment errors (Srinivasan et al.
1994).
While firms may choose to exchange information with their trading partners in order to
improve supply chain performance, they must
balance the risks associated with providing information that can be used against them. Sharing forecast information with a supplier may
allow the supplier to efficiently schedule production or it may alert the supplier to an opportunity to re-negotiate pricing. A supplier that
uses information for opportunistic gain may
impair the performance of its trading partner.
Information exchange then becomes a doubleedged sword where it is a source of efficiency
in coordinating firm resources across the supply chain but can allow firms to act selfishly.
Prior research on information exchange has
been limited to studies using perceived mea-
2008
DIVERSITY IN B2B INFORMATION EXCHANGE
sures of information exchange collected
throujzh surveys (Whipple et al. 2(H)2l and
modeling studies that have simulated information exchange (Cachón and Fisher 2000; Cachon and Lariviere 2001; Ángulo et al. 2004:
Gauret al. 2005b). Even studies using objective
measures of information exchange have been
limited to the study of a single buyer to its
multiple suppliers (Mukhopadhyay et al.
1995). This study takes a unique approach by
using archival data of actual electronic information exchanges from multiple firms within
an electronic exchange network.
By observing information exchange occurring though an !T-enabled channel, this
study utilizes unique measures of information
exchange characteristics. This study uses specific IT-based measures to capture information
exchange volumes and information exchange
diversity.
HYPOTHESES
To be effective in dynamic markets, firms
integrate externally witb their trading partners
(Rozen/weig et al. 2003; Vickery et al. 2003).
Foundational to this integration is the exchange
of information that will support the coordination of supply chain participants (Porter and
Millar 1985; Cooper et al. 1997; Mobcrg et al.
2002). Existing literature supports the positive
effects of integration on firm competitive performance (Daugherty et al. 2002; Whipple et
al. 2002). As firms interact with their trading
partners, they have the opportunity to minimize
the cost of exchanging information by leveraging technology. Electronic data interchange (EDI) is a specific technology that uses
standardized formats to electronically exchange business documents within and between organizations. In an EDl-enabled environment, firms may exchange large volumes
of information with their trading partners at a
minimal cost. Once the initial cost of formatting the information and establishing the communication link is made, the incremental cost
of each additional document is minimal. Although the upfront cost of creating an EDI
relationship has been noted as a deterrent to
EDI implementation (Iacovou et al. 1995;
Crum et al. 1998). these costs become sunk
costs once the firm implements the technology.
Once the electronic channel is established.
37
firms can lower overall transaction costs by
increasing the volume of information exchanged through the channel or by including
more trading partners in the network (Cruni et
al. 1998). Erom an ordering cost perspective,
inventory turnover can be improved by placing
multiple orders for smaller quantities. Additional information including forecasts, production schedules, point-of-sale demand data, and
inventory positions can all be exchanged electronically to improve the coordination of interfirm processes. Thus,
HI: Informalion exchange volume is positively associated with firm performance.
Information diversity refers to the number
of unique types of information exchanged by a
firm. Eirms can choose how much information
they exchange with their trading partners and
how they exchange that information. EDI is
used by firms to efficiently exchange businessto-business (B2B) information on a timely and
cost-effective basis. A base level of information exchange must occur in order to do business in the supply chain. At a minimum, the
buyer identifies what they want to purchase
and when they require delivery. The supplier
then confirms the pricing and availability back
to the buyer. When the transfer of the physical
product is complete, invoice and payment documents are exchanged. Each of these foundational information exchanges can be made electronically using the standard EDI requisition,
purchase order, invoice, and remittance documents. But beyond basic transactions, firms
can create unique competitive advantages by
exchanging additional information (Dyer and
Singh 1998). Empirical research has found that
in an EDI-enabled relationship there is a positive relationship between production schedule
sharing and supplier performance (Walton and
Marucheck 1997). In a case-based research example, a retailer that provides point-of-sale
data to its manufacturer can create processes
whereby the manufacturer monitors demand
and automatically replenishes the retailer stock
(Lee et al. 1999). The retailer can improve
performance in the form of lower stockouts
and higher inventory turnover. The manufacturer can better plan production and balance
safety stock across the supply chain. The exchange of additional information can support
38
TRANSPORTATION JOURNAL™
ihe development of processes that increase the
efficiency of the supply chain. Thus,
H2: Information exchange diversity- is positively associated with firm performance.
DATA COLLECTION AND RESEARCH
METHODOLOGY
This study tests the effects of information
exchange characteristics on firm performance.
The data used in this study were gathered as
part of a larger study of how information exchange affects supply chain relationships. Data
for the measurement of B2B information exchange is gathered from an electronically mediated industrial exchange network. This proprietary data has been made available by one of the
world's largest providers of B2B integration
services. Additional data on firm characteristics and performance are gathered from Standard and Poor's Compustat database.
Since these data are collected through an
electronic exchange network that uses EDI
standards for the coding of the transactions,
distinct information exchange characteristics
can be captured at the firm level. There are
two EDI standards used to format data for exchange. The EDI integrator supplying data for
this study supports both ANSI and EDIEACT
formatted EDI messages. Each of these two
standards is accepted in practice; however,
firms that use EDI can choose which format
to implement or may implement a combination
of both standards. EDI documents can be exchanged through proprietary telecommunications networks or through existing Internet connects. The provider of these data supports both
a proprietary network and AS2-enabled Internet transactions.
By using EDI transactions as a measure of
firm information exchange, this study captures
specific measures of infonnation exchange
characteristics. Eirst. information exchange
volume (INEO_VOLUME) is measured based
on the number of electronic business documents sent and received by the firm. The EDI
integrator that manages the electronic exchange tracks the number of transactions processed on a monthly, quarterly, and yearly basis. The quarterly measures are used to match
with the quarterly firm data gathered from
Compustat.
Summer
Firms must choose not only how much information is exchanged with their trading partners
but also what information is exchanged. At a
base level, firms may simply send electronic
purchase orders to their suppliers and receive
back an electronic invoice. While this rudimentary application of EDI improves the cost efficiency of the purchasing cycle, far more information can be exchanged through EDI and used
to support additional supply chain initiatives.
A study of Campbell Soup Company identified
that additional information was exchanged that
allows Campbell to monitor end customer demand such that the company centrally manages
the replenishment of its retailers (Lee et al.
1999). Information diversity in an EDI environment is a measure of how many different
types of infonnation are exchanged by the firm
(Massetti and Zmud 1996). Hundreds of business documents are included in the EDI document formats within the ANSI and EDIEACT
standards and each is identified by a unique
transaction code. Some examples of the transaction codes and their descriptions are provided
in Table 1. The information diversity measure
(INEO_DIVERSITY) is operationalized by
counting the unique transaction codes sent or
received by a firm during the quarter. The quarterly firm diversity measures represent average
values for the firm across the two-year study
period. The averaging technique alleviates
variances created by seasonality in the transaction characteristics.
Additional variables are collected from Standard and Poor's Compustat database. The dependent variable in this study is inventory
turnover (INVENTORY_TURNOVER). Inventory turnover is often used in supply chain
management empirical research due to its proximity to the actual physical goods that move in
the supply chain (Kalwani and Naiayandas
1995; Droge and Germain 2000; Gaur et al.
2(X)5a). High-level firm performance measures
such as ROI, ROA, and stock price are often too
far removed from the operations of the firm to
measure the effectiveness of supply chain strategies without the noise of other firm activities.
Studies have recognized that larger firms
experience economies of scale in their inventory turnover such that there is a positive correlation between inventory turnover and firm size
{Gaur et al. 2005a). Controlling for firm size
2008
DIVERSITY IN B2B INFORMATION EXCHANGE
39
Table 1. Examples of EDI Transaction Types on the Network
Transaction
Code
EDI Standard
ANSI
ANSI
ANSI
ANSI
ANSI
EDIFACT
EDIFACT
EDIFACT
BDIFACT
EDIFACT
511
810
850
856
888
INVOIC
REMADV
ORDCHG
SLSFCT
PRODAT
Description
Requisition
Invoice
Purchase Order
Shipping Notice
Item Maintenance
Invoice
Remittance Advice
Purchase Order Change
Sales Forecast
Product Data Message
Table 2. Measures and Data
Variable
Dependent Variable
INVENTORY_TURNOVERi
Detinition
The ratio of a firm's cost of goods sold to the firm's inventory value. The
average is calculated across two years of data.
Independent Variables
INFO^DIVERSITY,
The average number of information exchanges with trading partners through
an electronic intermediary. The average is based on quarterly observations
averaged across two years of data.
The average number of unique information types exchanged through an
electronic intermediary. The value is based on quarterly observations
averaged across two years of data.
Control Variables
FlRM_S¡ZEi
Firm average quarterly sales. The average is calculated across two years of
data.
Where i is the ßrm level observation
can tninimize these confounditig effects. Similar studies have measured firm size using total
assets, sales, and the number of employees
(Zhu and Kraemer 2002). For the purposes of
the study, the measure of firm sales reported
quarterly in the Compustat database is used as
the measure of firm size (FIRM_SIZE). The
variables used in the model are summarized in
Table 2.
Research Methodology
The goal of this study is to expand the understanding of the relationship between interfirm
inventory exchange characteristics and firm
performance. This is achieved by focusing on
the tvi'o hypothesized aspects of information
exchange and testing the hypotheses using a
large sample of manufacturing firms. An ordinary least squares (OLS) regression is used
to estimate the relationship between the ftrm
information exchange characteristics (volume
and diversity) and firm performance (inventory
turnover). The model is expressed in Equation I.
INVENTORY_TÜRNOVERi = ßy
(1)
where i is the firm level observation
r
40
TRANSPORTATION JOURNAL^"
Sample
The exchange network used to suppoii this
research provide.s EDI services for over 100
technology champion firms, A technology
champion firm in this context is a business that
has formed a contractual relationship with the
network service provider to electronically route
its EDI transactions. As part of the agreement.
the network service provider establishes relationship.s with each of the technology champion firm's trading partners to format their
business documents using EDI. Trading partners of the technology champion firm include
both customers and suppliers of the focal firm.
Finns were identified for inclusion in the study
based on their level of participation in the exchange network. Since there is competition in
the network service provider market, some
technology champion firms split their electronic traffic among multiple providers. Eirms
that were known to not have 100 percent of
their electronic information exchange through
the network were eliminated from the study.
The study is also restricted to publicly traded
firms so that reliable performance measures
and firm characteristics could be collected from
Compustat. Additionally, a manufacturing focus was adopted to minimize the variance created by including multiple echelons of the supply chain. The resulting twenty-three ilrms are
included in the sample.
Within the manufacturing segment there is
variation in the information exchange attributes
and firm performance. Table 3 provides a stratification of the key measures by their two-digit
SIC codes. The number of firms representing
each segment is noted in column two. Some
manufacturing segments are represented by
data from multiple individual firms. The
twenty-three firms in this sample represent nine
distinct manufacturing segments. The average
inventory turnover is approximately twelve but
ranges from four for the Chemicals and Allied
Products finns to forty-three for firms in Industrial and Commercial Machinery.
Descriptive Statistics of the Data
Descriptive statistics for each variable are
included in Table 4. The firm performance variable (INVENTORY_TURNOVER) has a relatively large standard deviation given the mean
of the sample. Even with a focus on manufacturing firms, there is large variance among
Summer
firms. Firms vary in the volume of information
exchanged, as shown by the large variance in
the average quarterly volume of information
exchanged (INFO_VOLUME). The mean of
information exchange volume indicates that,
on average, firms in the satiiple exchange 0.38
million documents per quarter. Quarterly information exchange volume ranges from twentysix thousand business documents to over one
million. Similarly, the firms in this study vary
in the diversity of information exchanged
(INFO_DIVERSITY). The average number of
document types exchanged by firms in the sample is twenty-two but ranges from a low of
nine to high of thirty-seven. These values represent an average for the firm across two years
of data so that variations based on seasonality
would not affect the analysis.
Due to a violation of the assumption of normality in the raw data, the inventory turnover
variable (INVENTORY_TURNOVER) was
modified using a natural log transfonnation.
The results of the OLS regression ate reported
using the logged dependent variable. Additionally, a pair-wise correlation table is provided
to evaluate the relation.sbips between the explanatory variables. Table 5 provides the specific correlations and their statistical significance. The only statistically significant
correlation is the positive relationship (0.3983)
between the size of tbe firm (FIRM_SIZE) and
the volume of information exchanged
(1NFO_VOLUME). Researchers suggest that
correlations between explanatory variables will
not bias the coefficient estimates if all pairwise
correlations are below 0.50 (Dielman 2005). It
is not surprising that larger firms would exchange more information than smaller firms.
The measure of firm size is included in the
regression model to control for the effects of
firm size on firm performance. By including
firm size as a control variable, the remaining
effects of information exchange characteristics
on firm perfonnance can be specifically addressed by the model results. While the relationship between firm size and information exchange volume is statistically significant, it is
not so large that it will cause a bias in the
regression results.
RESUI,TS
The OLS model results show a good statistical fit for the data based on the statistically
2(X)H
DIVERSITY IN B2B INFORMATION
EXCHANGE
41
Table 3. Summary of Participating Firms by 2-Digit SIC Code
Average
Quarterly
Inventory
Turnover
Firm
SI c-2 Counts
20
25
26
28
34
35
36
37
38
i
1
1
7
1
4
2
4
2
Description
FOÍHJ and Kindred Products
Furniture and Fixtures
Paper and Allied Producís
Chemicals and Allied Producís
Fabricated Metal Producís
Industrial and Commeicial Machinery
Electronics and Electrical Equipmenc
Transportalion Equipment
Measuring. Atialyzing and Controlling Equipment
Average
Average
Quarterly
Quarterly
Information Inrormation
Volume*
Types
4.10
i 3.29
4.66
3-79
5.52
43.38
5.64
9.37
4.63
0.27
0.13
0.38
0.33
0.12
0.84
0.26
0.32
O.ll
30.12
11.25
28.63
37.33
16.88
19.54
25.06
21.40
18.36
Average
Net
Sales**
4.732.13
619.93
4,780.38
6,451.26
1.676.17
5.925.85
2.742.54
5,769.93
2.242.04
23
uvcragL- iiirormaiitii! volume is siaia) in millions of transai-lioni
average net sales are staled In millions of düüars
Table 4. Descriptive Statistics
INVENTORY TURNOVER
INFO VOLUME*
INFO DIVERSITY
Control Variable
FIRM.SIZE**
Mean
S.D.
Min
Max
12.42
0.38
22.16
231.89
0.37
8.79
1.28
0.03
9.25
85.09
1.46
37.33
4.944.34
3.696.31
619.93
12,839.13
2.1 I'lrms arc intluded in ihis tlaiasel ln=23i
"' iiiftirmütion volume is expresseii us millions of iransaciions per 4Uaner
' timi size is based on millions of dollars of nel sales
Table 5. Summary of Participating Firms by 2-Digit SIC Code
INFO VOLUME
INFO.VOLUME
INFO_DIVERSITY
FIRM SIZE
INFO DIVERSITY
FIRM SIZE
1.000
0.2077
0.3416
0.3983
0.0598
LOOO
0.3339
0.1195
1.000
>l:ilisiÍLally signific:ini pairwise uorre kit ions are highlighted in bolJ and italics
lhe llrsl value in each cell is ihc correlation and the second value is the p-value measure of staiistieal signitlciince
significant results of the E-test. Additionally,
the explanatory power of the model is high
based on an R-square of 0.5459. This suggests
that over 50 percent of the variance in average
inventory turnover is explained by the model.
Complete model fit statistics are provided in
Table 6. Additional details on the development
and interpretation of the OLS regression are
provided in Appendix A.
The coefficient for the measure of firm quarterly
information
exchange
volume
(INFO.VOLUME) is statistically significant
and positive. This result provides support for
Hypothesis 1. which states that the volume of
TRANSPORTATION JOURNAL™
42
Table 6. OLS Regression Results
Model 1
(log)IN VENTOR Y.TURNOVER
Explanatory Variables
INFO.VOLUME
INFO.DIVERSITY
constant
Coef.
(Std Err)
P<|t|
2.06
(0.4530)
-0.0326
(0.0185)
1.985!
(0.4236)
0.000 ***
Control Variables
FIRM SIZE -0.00004
(0.00005)
F-Siatistic
7.62
Probabiiily>F 0.0015
R-square 0.5459
Observations
23
ti» ^oi
0.094
sig
**
0.000 *"*
0.424
ns
-*< 05 * < ] signiftcatice level
information exchange is positively associated
with firm performance. The coefficient for the
information diversity measure {INFO_DIVERSITY) is negative and statistically significant.
This resultdoes not support Hypothesis 2. which
states a positive relationship between information diversity and firm performance. The surprising result for Hypothesis 2 will be addressed further in the discussion and
conclusion section. Full results from the OLS
regression are provided in Table 6.
DISCUSSION AND CONCLUSION
This study uses unique quantitative measures of information exchange characteristics
and tlnds an important result showing a strong
relationship between the volume of information exchange and firm performance. Prior literature has attempted to address this issue using
modeling, perceived measures, or single firm
analyses. This is the first study to address the
issue using quantitative archival data from a
large number of firms in one sector of the
economy.
The positive result for Hypothesis 1 supports
the resultsof prior literature on the relationship
between information exchange volume and
firm performance. Although not surprising, it
is useful to confirm the positive effects of information exchange within the context of an electronically-mediated network using archival information exchange data. Managerially, this
Summer
result is interesting since it supports current
practices of expanding the electronic gathering
and exchanging of information across the supply chain. At an extreme level, Wal-Mart integrates all suppliers by requiring a mix of RFID,
EDI, and Web-based technologies (Retail
Link ®) to mandate electronic information exchange.
The surprising result for Hypothesis 2 requires further analysis. While the hypothesized
positive relationship between information diversity and firm performance was not supported, the negative and statistically significant
result is important. Firms in this sample that
exchanged more types of information with
trading partners experienced lower inventory
turnover. Since this sample includes only firms
that are identified as manufacturers, these results may indicate that increased information
diversity in manufacturing supply chains
allows trading partners to act opportunistically.
In a study of grocery retailers, it was shown
that suppliers were more likely to act opportunistically when supply chain dependence or
power imbalance exist within a relationship
(Morgan et al. 2007). As more information is
available to trading partners, they may choose
to take actions that are not in the best interest
of the manufacturer. This situation may be affected by the level of asset specificity of products transacted with manufacturers. If a manufacturer requires a very specific input and a
supplier has additional information, the supplier may be able to raise prices or hold up
supply in order to extract additional concessions.
An alternative explanation of this surprising
result has been identified in related research.
It has been suggested that as firms take advantage of the technology that allows them to exchange more types of business documents,
there is no guarantee that the firms are able to
integrate that information into their internal or
external processes (Clarke 1992; Massetti and
Zmud 1996). This situation may be explained
by the relationship between integration and
communication (Stank et ai. 2005). This research suggests that in order for information
to add value, it must be exchanged in an environment of integrated processes such that the
information can be leveraged for the benefit
of the firm. If the data are exchanged but not
2008
DIVERSITY IN B2B INFORMATION EXCHANGE
Figure 1. Non-linear Data Relationships
High
High
Information Volume
actually used in the decision making process,
the data do not provide value for the firm.
Closely related to research on information
integration is the study of information overload. Researchers using a simulation methodology have noted that information overload can
adversely affect firm performance (Steckel et
al. 2004). This suggests that the additional information types exchanged are not only failing
to contribute to performance but may actually
be detrimental to performance by causing noise
in the channel.
Managerially, the negative relationship between information diversity and inventory
turnover is interesting. The results and existing
research would suggest that managers use caution pertaining to decisions of what information
10 exchange, with whom to exchange information, and how the information is integrated into
business processes.
Alternate Non-linear Model Specification
The potential exists for a non-linear relationship between the explanatory variables and
finn performance. A non-linear relationship
would occur if, for example, in the case of
information exchange volume, firm performance was high for both low levels of information exchange and high levels of information
exchange but low for intermediate volumes of
information exchange (see Figure 1 ). This situation is appropriately described as a u-shaped
relationship. Similarly, an inverted u-shaped
relationship would occur when firm performance was low for both low and high volumes of
information exchange but high for intermediate
volumes of information exchange.
43
The U-shaped relationship is easily accommodated in regression analysis by including
the square of the variable as part of the model.
Alternate Model A includes the squared variable for information exchange volume
(INFO_VOLUME_SQ). Similarly, Alternate
Model B includes the squared variable for information diversity iINFO_DIVERSITY_SQ).
The results for the alternative non-linear models are provided in Table 7.
The statistical fit of the alternate models to
the data is acceptable based on the F-statistic.
The R-square measures indicate that both models explain a large portion of the variation in
firm performance. However, the coefficient estimates are not statistically significant for the
squared variables. Additionally, the Adjusted
R-Square indicates that including the squared
variables in the two alternate models does not
improve the explanatoi-y power of the alternate
models over the original model. These results
suggest that the original linear model is better
than the non-linear specification for understanding the relationship between information
exchange characteristics and firm performance.
LIMITATIONS AND FUTURE RESKARCH
This article fills a gap in the literature by
specifically using archival data and moving
away from the often-used perceived measures
of information exchange; however, the dataset
is limited in its available measures. As noted
in the discussion, the data provide visibility
into the specific information exchange characteristics but do not capture how the information
is being integrated into the business processes.
Additional research is needed to simultaneously consider the information exchange characteristics and the level of interfirm process
integration.
Further investigation is needed to evaluate
the unexpected negative relationship of information diversity and inventory turnover. First,
this model may be tested in other sectors of
the supply chain to clarify whether the results
are unique to the manufacturing sector. Second,
additional qualitative data could be collected
through a survey instrument to capture performance, integration, and information exchange characteristics variables.
The performance variable in this article, inventory turnover, captures one dimension of
TRANSPORTATION JOURNAL™
44
Summer
Table 7. Alternate Model Specification Results
Altérnale Model B
Alternate Model A
{log)tN VENTOR Y_TURNOVER
Explanatory Variables
INFO_VOLUME
INFO_DIVERSITY
INFO_VOLUMH_SQ
Coef.
(Std Err}
-0.6208
(1.7641)
-0.0071
(0.0241)
1.9583
(1.2465)
Coef.
P<|t|
sig
(Std ErT)
P<|t|
sig
0.729
ns
0.000
***
0.772
ns
2.08
(0.4665)
-0.0754
(0.1286)
0.565
ns
0.134
ns
0.0009
(0.0027)
2.4327
(1.3997)
0.740
ns
0.099
*
-0-00004
(0.00005)
0.410
ns
INFO_D[VERSITY_SQ
constant
Control Variables
RRM.SIZE
F-Statistic
Prohahility>F
R-square
Adjusted R-square
Observations
1.9t33
fn.4I07)
0.000
***
-0.00004
(0.00004)
0.377
ns
6.77
().fM}l7
0.6007
0.5120
23
5.47
0.0046
0.5488
0.4485
23
*** <,()! **<,05 " <.l sjgnirn.-uni.-e level
supply cbain performance. Additional supply
chain performance measures such as responsiveness and innovation may extend the understanding of how information exchange characteristics affect performance.
This article makes a significant contribution
to the research on information exchange in
supply chain relationships. Using a unique archival dataset, the positive effects of information exchange volume are validated. The article
then opens additional discussions by providing
new insights into the existence of negative outcomes related to information diversity.
APPENDIX
Regression Analysis Overview
This study uses regression analysis to test
the relationship between firm performance and
specific measures of information exchange.
Regression analysis is a statistical technique
used to model the relationship between a dependent variable (firm performance) and a series of independent variables (information exchange and firm characteristics). The
independent variables in a regression analysis
are often called the "explanatory" variables
because they are used to explain tbe variation
in the dependent variable. In the case of this
study, the regression analysis is evaluating the
variation in firm performance by considering
the variation In information exchange characteristics. In other words, the researcher is statistically testing whether there is a relationship
between the variation in firm performance and
the variation in how finns exchange information with their trading partners.
Ordinary Least Squares Regression
The ordinary least squares (OLS) regression
is a basic form of regression analysis. When
there are two or more explanatory variables,
tbe relationship cannot be simply drawn on a
two-dimensional graph but it can still be modeled as a linear equation that best "fits'" tbe
data. The equation that best describes the relationship between the dependent variable
(INVENTORY_TURNOVER) and the explanatory variables (INFO^VOLUME, INFO_DIVERSITY, and FIRM_SIZE) is written as
shown in Equation I. The statistical software
then evaluates values for the coefficients (ßo,
ßi- ßi- ß.i) so that the squared differences between the estimated equation and the actual
200S
DIVERSITY IN B2B INFORMATION EXCHANGE
45
data are minimized (Dielman 2005). The regression routines available in software packages such as EXCEL, SPSS. STATA. and SAS
estimate specific values for each of the coefficients in the equation based on the dataset provided. The sign and magnitude of the estimated
coefficients can then be interpreted by the researcher to understand the relationship between
the dependent variable and each of the explanatory variables. For this study, the coefficient
estimates are provided in column 2 of Table 6.
Along with the individual coefficient estimates,
the statistical software provides additional measures to test how well the estimated model fits
the data. Two of these measures are the F-statistic and the R-square value, which are provided
in Table 6. The F-statÍstÍc is a comparative measure of model fit. By comparing Ihe calculated
F-statistic with the critical value on an F table
(provided in most statistics textbooks), the researcher can verify that the estimated model is
useful in explaining the variation in the dependent variable at a 95 percent level of confidence.
The R-square value provides a measure of how
much of the variation in the dependent variable
is explained by the model. In this study, the proposed model explains 55 percent (R"=Ü.5459)
of the variation in the dependent variable
(INVENTORY.TURNOVERJ.
and is included as a control variable in the
model.
When using an OLS regression for the statistical analysis of data, there are conditions and
assumptions that must be met in order for the
output to be valid for hypothesis testing. The
following discussion provides details of the specific validation procedures used in this study.
For regression models with multiple explanatory variables, it is expected that the explanatory variables are related to the dependent variable. In statistics, this relationship between
variables is referred to as correlation and can
vary in direction (positive or negative) and
magnitude (between -I and 1), If. however, the
explanatory variables are strongly related to
each other, the regression model will fail to
provide accurate coefficient estimates. This
correlation within the explanatory variables is
referred to as multicoUinearity. Table 5 provides measures of the correlations between
pairs of explanatory variables. For the data
u.sed in this .study, the pairwisc correlations are
relatively small (0.3983 correlation between
F1RM_SIZE and INFO_VOLUME). A rule of
thumb used by researchers is that multicoUinearity is not a serious problem when all pairwise correlations are below 50 percent (Dielman 2005).
Dataset
The dataset for this study is well suited for
regression analysis using OLS. Each of the
twenty-three individual observations represents information about one firm in the study.
The observation includes the inventory turnover for the firm and its information exchange
characteristics for the same time period. Inventory turnover is the measure of firm pcribrmance that serves as the dependent variable in
this study. The information exchange characteristics for the firm during that same period are
included in the observation as the explanatory
variables. It is expected that there are factors
beyond the scope of this study that affect the
differences between firms' inventory turnovers. If the researcher can identify measures
of those factors, the measures can be included
as control variables in the model. Including
additional control variables can decrease the
unexplained variation in the dependent variable. In this study, firm size was recognized
as a factor that influences inventory turnover
As noted earlier, the variation in the dependent variable not explained by the model is
referred to as error. The error can be specifically identified for each observation (firm) by
calculating the difference between the actual
firm performance and the firm performance
estimated by the regression coefficients. It is
expected that across all twenty-three observations, the error is normally distributed and the
variance in error is constant across all firms.
The distribution of error is easily tested by
plotting the standardized error versus the standardized predicted values. For data that are
normally distributed. 95 percent of the plotted
values should be between - 2 and +2 (that is,
within two standard deviations of the mean).
The data in this study did not produce normally
distributed values because the dependent variable (1NVENTORY_TURNOVER) was not
normally distributed. This situation was corrected by taking the natural log of the dependent variable and rerunning the regression
(Dielman 2005).
46
TRANSPORTATION JOURNAL™
The other key assumption is that the error
variance is equal across all observations. In
statistics, this condition is referred to us heterogeneity. Heterogeneity can be verified by using
the Breusch-Pagan/Cook-Weisburg test. This
test compares the error variance for each observation and its predicted value based on a chisquare test statistic. Again, this test statistic
can be compared with the critical value from
a chi-square table to verify that the range of
variance is within a 95 percenl tolerance. If
the error variance was not equal across the
observations, then the estimated coefficients
would not be useful for explaining the relationship between the dependent and explanatory
variables at all levels of the dependent variable.
Additional infonnation about creating and
interpreting the output from an OLS regression
can be found in most introductory business
statistics texts, such as Applied Regression
Analysis (DiQlman 2005), which has been referenced in this article.
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