An Analysis of Supply Chain Collaboration and Its Impact

on Firm Performance

An Integration of Social Capital, Justice, and Technology Use

Mai-Lun Chiu

, Chu-Ying Fu

and Ing-Long Wu

Department of Information Management, National Chung Cheng University, Chia-Yi, Taiwan

Department of Information Management, WuFeng University of Science and Technology, Chia-Yi, Taiwan

Keywords: Supply Chain Management, Collaboration, Social Capital Theory, Justice Theory, IS Success Model,

Firm Performance.

Abstract: Collaboration has become a critical component to fulfill the need for integration in the supply chain.

From a discussion of the literature, there are two major underlying concerns arising in the collaboration,

organization's sharing behaviors and technology use behaviors. In expectation confirmation theory, an

organization's sharing behaviors may initially mean a pre-expectation of common resources available in

the supply chain and further, a perceived fairness between participants for the willingness to participate

in the partnership. Social capital theory and justice theory, in essence, explain the two beliefs of supply

chain members as the IS success model defining the belief of technology use. This study integrates the

three key issues to examine collaboration in a comprehensive and unique way and its role in affecting

focal firm performance. Empirical findings have led to better understanding of the relative effects of the

three issues and a well-achieved organizational performance.

1 INTRODUCTION

Collaboration is recognized as a critical component

for the smooth flowing of an efficient supply chain

(Kwon and Suh, 2005; Richey et al., 2012). It is

characterized by the sharing of information,

knowledge, risk, and profits across the supply chain

(Mentzer et al., 2000; Smith et al., 2007). According

to previous research, two major concerns arise in

collaborative behaviors, organization's sharing

behaviors (Smith et al., 2007; Omar et al., 2012) and

technology use behaviors (Gunasekaran and Ngai,

2004; Subramani, 2004).

An organization’s sharing behaviors, in essence,

relates to a decision of two issues.

expectation-confirmation theory (ECT), this

behavior initiates a pre-expectation of common

resources available in the network and further

realizes a perceived fairness between partners for the

indication of their willingness to join the alliance

(Oliver, 1981; Bhattacherjee, 2001).

In the pre-expectation issue, social capital theory

(SCT) has been widely discussed in the supply chain

as social capital can be seen as a common resource

developed by supply chain partners for creating

unique value among competitors (Min et al., 2008;

Villena et al., 2011). Therefore, when individual

members own more resources in the supply chain,

they may lead to higher cooperative atmosphere and

behaviors. In the perceived fairness issue, justice

concept may explain the willingness of supply chain

partners to participate in inter-firm behaviors when it

is well perceived (Griffith et al., 2006; Sun et al.,

2009). Narasimhan et al. (2009) argued that

relational behaviors are motivated through the

perceived justice exercised by the more powerful

members in the exchange of resources. Further, if

reward is not forthcoming, the exchange behaviors

will cease to exist. Few studies have been performed

on both the pre-expectation and perceived fairness

issues with collaborative behaviors (Kankanhalli et

al,. 2005).

In the technology use issue, the Delone and

Mclean’s IS success model (D&M Model) has been

widely used in various IS contexts to explore user

acceptance (Venkatesh and Bala, 2008). However, it

has not been widely applied in supply chain

technologies (Zhang and Dhaliwal, 2009). When

supply chain members perceive satisfaction on the

Chiu M., Fu C. and Wu I..

An Analysis of Supply Chain Collaboration and Its Impact on Firm Performance - An Integration of Social Capital, Justice, and Technology Use.

DOI: 10.5220/0004400500050012

In Proceedings of the 15th International Conference on Enterprise Information Systems (ICEIS-2013), pages 5-12

ISBN: 978-989-8565-59-4

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

use of IOS, they may further stimulate their

willingness to participate in collaborative behaviors

(Sahin and Robinson, 2002). Finally, performance

impact is the ultimate concern for the success of

collaboration in a supply chain. In this study, we

examine performance impact in terms of a focal firm

performance in managing its partners.

Grounded on SCT, justice, and D&M Model, this

study proposes a novel research model to explore the

antecedents of collaboration and its impacts on firm

performance in a complete manner.

2 LITERATURE REVIEW

AND HYPOTHESES

DEVELOPMENT

Based on the above discussion, Figure 1 provides a

pictorial depiction of this research model. The

followings discuss the theoretical bases and

development of relevant hypotheses.

C ollab ration

User

satisfactio n

R elation al

capital

Structural

capital

C ogn itive

capital

D istribu tive

justice

Justice theory

Interactional

justice

Procedural

justice

Social capital theory

Inform ation

quality

System

quality

Service

quality

Technology use

Focal firm

perform ance

H10

H11

Figure 1: Research model.

2.1 Antecedents of Collaboration

The supply chain is not a chain of businesses with

one-to-one, business-to-business relationships, but a

network of businesses and relationships (Lambert et

al., 1998). Collaboration is an approach to managing

interdependencies requiring a sharing of knowledge,

information, and a much higher level of joint

decision-making and goal-setting aimed at

enhancing both common and individual advantages

(Zacharia et al., 2009). There are two main issues for

an organization's sharing behaviors. Based on ECT,

it may first consider a pre-expectation of common

resources available for network members and then

sense a perceived fairness between partners in a

decision for their willingness to join the partnership

(Bhattacherjee, 2001). A confirmation of an actual

behavior may require consideration of both the

beliefs of pre-expectation and perceived outcomes

(Chea and Luo, 2008). Finally, many studies on

supply chain have shown the effect of IOS on

participating in collaborative behaviors through

member satisfaction with system use (Sahin and

Robinson, 2002). The D&M model is an important

theory to define the relationship structure and

understand technology use behaviors in the supply

chain context (Delone and Mclean 2003).

2.2 Social Capital Theory

and Collaboration

Social capital refers to the resources embedded

within the network of human relationships (Nahapiet

and Ghoshal, 1998). Scholars of the supply chain

have highlighted that social capital reduces the

likelihood of conflicts and promotes cooperative

behavior in terms of its association with shared

vision, trusting belief, and social tie (Lawson et al.,

2008; Bernardes, 2010). Collaborative behaviors

would be stimulated for partners when social capital

is well built in the supply chain (Villena et al., 2011).

Social capital theory defines a three-capital structure,

structural, cognitive, and relational (Nahapiet and

Ghoshal, 1998; Wasko and Faraj, 2005).

Structural capital is related to impersonal

configuration of linkages among the network of

relations as a whole. It is like an entire network of

suitable relations between supply chain partners

(Villena et al., 2011). It implies that in a higher

structural capital, individual partners may more

easily obtain resources or help others when they

have more interactions that will raise the willingness

to participate in collaboration

(Patrashkova-Volzdoska et al., 2003). Cognitive

capital defines the resources providing shared

meaning and understanding between the network

members that help individuals share their

interactions, common visions, and language over

time (Wasko and Faraj, 2005). In the supply chain,

when partners want to interact over time with others

to share the same practice, and to learn the skills and

knowledge, it enhances the likelihood of partners

collaborating in the working environment to

complete their task (Krause et al., 2007). Relational

capital is a relation indicating the degree of

emotional intensity, commitment, and trust

connecting the individuals (Bernardes, 2010).

Researchers suggested that a good transaction

climate with mutual trust between partners may play

a critical role in facilitating their collaboration in the

supply chain (Patterson et al., 2003). Accordingly,

ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems

we can propose the three hypotheses.

H1. Structural capital positively affects

collaboration in the supply chain.

H2. Cognitive capital positively affects

collaboration in the supply chain

H3. Relational capital positively affects

collaboration in the supply chain.

2.3 Justice Theory and Collaboration

In business management, organizational members

view justice as a unified value providing principles

that can bind together conflicting parties and create

stable social structures (Luo, 2007). The justice

concept is widely applied to develop a theoretical

foundation to the understanding of relationships

between supply chain members (Griffith et al., 2006).

Specifically, justice theory provides a suitable

framework for understanding the creation of value in

collaborative relationships for interorganizational

members (Wagner et al., 2010). A higher level of

perceived justice for partners motivates the

willingness of exchange to strive for collaborative

behaviors and relationship continuity (Palmatier et

al., 2006). Colquitt et al. (2001) reviewed

justice-related studies comprehensively and

integrated three major components of justice:

distributive, procedural, and interactional.

Distributive justice refers to perceived fairness

where individuals assess the fairness of an exchange

by comparing their inputs to outcomes to form an

equity score (Son and Kim 2008).The distributive

justice in terms of input-outcome structure is

positively related to the willingness of partner firms

to participate in collaborative activities in the supply

chain (Griffith et al., 2006). In addition, supplier

chain partners intend to pursue their interests in an

exchange relationship to gain rewards (Higgins and

Ellis, 2009). This can be viewed as an economic

policy in the supply chain management. Procedural

justice refers to the process and the perceived

fairness of that process, associated with the

allocation of resources for members in limited

supply relative to demand (Konovsky, 2000).

Procedural justice focuses on the solution of a

controversy that must go through a series of formal

procedures before the decision is made, and

members that should have the right to express their

opinions in the procedures (Sun et al., 2009). It can

be viewed as a formal policy in the supply chain

management (Griffith et al., 2006). Interactional

justice refers to an individual’s perceptions of the

quality of interpersonal treatment received in the

decision making process (Cropanzano et al., 2002).

Interactional justice is most likely to be obtained

when the originators of justice treat the recipients

with truthfulness and respect in an exchange process

(Luo, 2007). It can be further viewed as a social

policy in the supply chain management. Interactional

justice may affect the level of commitments of

supply chain partners to their decisions regarding

relationship-building, and also influence interactions

at the firm level (Yang et al., 2008). Arguably, the

three relevant hypotheses are thus proposed.

H4. Distributive justice positively affects

collaboration in the supply chain.

H5. Procedural justice positively affects

collaboration in the supply chain.

H6. Interactional justice positively affects

collaboration in the supply chain.

2.4 Technology Use and Collaboration

As SCM importantly raises the issue of digitally

enabled features, this study further needs to examine

technology use behaviors by the D&M model. The

D&M model suggests that three technology

components, information, system, and service

quality, indicate a direct effect on user satisfaction

and system use and further creates net benefits

toward IS use (DeLone and McLean, 2003). In a

study by Wang (2008), when customers greatly

depend on information technology to communicate,

gain useful information and execute transactions in

an e-commerce environment, information, system,

and service quality are the major concerns for user

satisfaction toward IS adoption. Accordingly, we put

forward the three hypotheses.

H7. Information quality positively affects user

satisfaction in the supply chain.

H8. System quality positively affects user

satisfaction in the supply chain.

H9. Service quality positively affects user

satisfaction in the supply chain.

When supply chain technologies are mainly used

to support information sharing between partners in

terms of information, physical, and financial flow,

user satisfaction with the sharing thereby facilitates

collaboration behaviors across the supply chain

(Kahn et al., 2006; Smith et al., 2007). An

integration of various IS applications with a

complete and satisfied basis provides the capability

to generate cross-partner information in the supply

chain and further raises the need to collaborate

AnAnalysisofSupplyChainCollaborationandItsImpactonFirmPerformance-AnIntegrationofSocialCapital,Justice,

andTechnologyUse

between partners for performing inter-firm activities

smoothly (Rai et al., 2006).The arguments thus lead

to the hypothesis.

H10. User satisfaction positively affects

collaboration in the supply chain.

2.5 Firm Performance

Financial indicators are important in assessing

whether operational changes are improving the

financial health of a company, but are insufficient to

measure supply chain based firm performance.

These measures do not relate to important

organizational strategies and non-financial

performances, such as customer response and

product quality (Beamon, 1999; Kwon and Suh,

2005). In this study, financial and non-financial

indicators are defined to measure firm performance.

Collaboration aims at effectively integrating

various flow activities between partners for a

number of reasons (Kumar and van Dissel, 1996). It

thus indicates a potential link with the performance

impact of a focal firm. Firms able to collaborate at a

higher level of sharing knowledge and with access to

common resources are much more likely to improve

their performance and gain a source of long-term

competitive advantage. Specifically, collaboration

between partners affects not only operational

outcomes such as cost, quality, and cycle time, but

also non-operational outcomes such as customer

services, new product development, and reaction to

market changes (Zacharia et al., 2009). Accordingly,

one hypothesis is thus proposed.

H11. Collaboration positively affects focal firm

performance in the supply chain.

3 RESEARCH DESIGN

3.1 Instrument

3.1.1 Basic Information

This part collects basic information on

organizational characteristics including industry type,

annual revenue, number of employees, and number

of suppliers, as well as respondent characteristics,

including working experience, education level,

gender, and position.

3.1.2 Social Capital Theory

This part measures three social capital constructs.

The measuring items for structural capital are

adapted from the instruments developed by Robert et

al. (2008), including 3 items. The measuring items

for cognitive and relational capital are adapted from

the instrument developed by Villena et al. (2011),

including 4 items for each.

3.1.3 Justice Theory

This part measures the three justice constructs. The

measuring items for distributive and procedural

justice are adapted from the instrument developed by

Griffith et al. (2006), including 4 and 3 items

respectively. The measuring items for interactional

justice are adapted from the instrument developed by

Luo (2006), including 4 items.

3.1.4 Technology Use

This part measures four technology-based constructs.

The measuring items for information, system, and

service quality are adapted from the instrument

developed by Wang (2008), including 3 items for

each. User satisfaction is adapted from the

instrument developed by Wang (2008), including 3

items.

3.1.5 Collaboration

This part defines the extent to which focal firms

collaborate with their supply chain partners.

Collaboration defines all partners in the supply chain

actively working together toward common

objectives. The measuring items are adapted from

the instrument developed by Tan et al. (2002),

including 5 items.

3.1.6 Firm Performance

Financial measure is adapted from the instrument

developed by Vickery et al. (2003) and Li et al.

(2006), including four items, such as return on

investment, and cost structure. Non-financial

measure comprises are adapted from the instrument

defined by Beamon (1999), including five items,

such as market change, customer response, and

product quality.

3.2 Sample Design

The qualified firms for this study require an

emphasis on the investments of supply chain

technologies and have considerable experience in

SCM practice. It is assumed that larger firms would

be more likely to have these experiences. We

randomly selected 700 firms to be the study sample

ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems

from the population of 1500 firms. Furthermore, the

target respondents for the sample firms would be the

top managers, including the CEO, vice CEO, or

logistics/purchase executives. A total of 212

responses were received. After invalid responses

were deleted, this resulted in a sample size of 206

for a response rate of 29.4%.

3.3 Scale Validation

PLS is a structural equation modeling (SEM)

technique that employs a nonparametric and

component-based approach for estimation purposes.

This study uses PLS to analyze the measurement

model. PLS is the best analytical tool available to fit

the requirement of small sample size.

Reliability is evaluated by Cronbach’s α.

Convergent validity is assessed by three criteria,

factor loading, construct reliability, and average

variance extracted (Fornell and Larcker, 1981).

Discriminant validity is assessed by the measure that

AVE for a construct should be larger than the

squared correlation between the construct and other

constructs. The testing results indicate that reliability,

convergent and discriminant validity are all in a high

acceptable level.

4 STATISTICAL ANALYSIS

PLS was used to examine the structural model.

There are two steps in evaluating the structural

model. First, we needed to estimate standardized

path coefficients and their statistical significance for

testing the hypotheses. PLS does not provide a

significance test or confidence interval estimation.

We re-sampled 1000 times with Bootstrapping

analysis to obtain a stable result for these analyses.

Second, the coefficient of determination (

) for

endogenous variables was calculated to assess the

predictive power of this model. Figure 2 shows the

testing results of the structural model.

In the SCT, we found that structure capital is

reported as an important predictor of collaboration

(p<0.05, β=0.18). Hypothesis 1 is thus supported.

However, cognitive and relational capital are not

(β=0.08 and 0.05). Hypothesis 2 and 3 are thus not

supported. In the justice theory, distributive and

procedural justice are two notable precursors of

collaboration (p<0.05, β=0.21 and 0.19) while

interactional justice is not. Hypothesis 4 and 5 are

thus supported. In contrast, Hypothesis 6 is thus not

supported. In the technology use, we found that

information quality, system quality and service

quality are all reported as important predictors of

user satisfaction (p<0.01, β=0.27, 0.32, and 0.45).

Hypothesis 7, 8, and 9 are thus supported. They

jointly explain 63% of the variance in user

satisfaction (

=0.63). Subsequently, user

satisfaction plays a critical role in explaining

collaboration (p<0.01, β=0.42). Hypothesis 10 is

thus supported.

=.56

=.63

=.61

C o lla bration

User

sa tisfactio n

R elatio nal

capital

Structural

capital

Cognitive

ca pital

D istribu tive

justice

Justice theory

In teractio na l

justice

Procedural

justice

Social capital theory

Inform ation

quality

System

quality

Service

qu ality

Technology use

.27**

.32 **

.45 **

Focal firm

perform an ce

.18 *

.08

.05

.21 *

.19 *

.09

.42* *

.68 **

Figure 2: Result of the structure model Value on path:

Standardized coefficients (β),

: Coefficient of

determination, *: p<0.05, **: p<0.01.

The three sets of variables, SCT, justice theory,

and technology use, jointly explain 56% of the

variance in collaboration (

=0.56). In turn,

collaboration, as an important supply chain

mechanism, exercises its significant influence on

focal firm performance (p<0.01, β=0.68).

Hypothesis 11 is thus supported. It explains 61% of

the variance in focal firm performance (

=0.61).

5 FINDINGS AND DISCUSSIONS

Considered wholly for the three major issues, social

capital, justice, and technology use, they report

differentiated effects on collaborative behaviors. In

particular, the technology use with the three

components, information, system, and service

quality, is the most important precursor in

determining collaboration behaviors. That is, the

three components are all critical in influencing

collaboration. In contrast, both of the social

exchange issues, social capital and justice, with their

comprising elements, do not show as strong effects

as technology use. The former comprises structural,

cognitive, and relational capital and the latter are

distributive, procedural, and interactional justice.

Their elements are partially, not all, found in an

effect of significance. This is an interesting finding

AnAnalysisofSupplyChainCollaborationandItsImpactonFirmPerformance-AnIntegrationofSocialCapital,Justice,

andTechnologyUse

for this study.

Social capital, a pre-expectation belief of the

members in terms of the common goals, values, and

mutual trust in the supply chain before the decision

to participate in collaborative behaviors, may at

times be recognized with negative effect and

possibly produce social liability, although most

previous studies have thoroughly discussed its

positive impact on focal firm performance (Villena

et al. 2011).

Perceived justice, a post-expectation belief of the

members in terms of fair

outcomes, policies, and

interpersonal relations in the supply chain for the

decision to join collaborative behaviors, may often

be sensed to have imbalanced relations because there

is a powerful partner to control the decisions in the

relationships (Griffith et al., 2006). In fact, justice

between supply chain members may have been well

realized in most cases.

Further, technology use is identified as a physical

behavior of the members in the supply chain for the

decision to participate in collaborative behaviors.

The reasons for its importance may be twofold.

Advances in ICT have made integrating information

flows in the supply chain feasible, positioning ICT

as a key driver of collaborative effort. In fact, the

extent to which modern supply chains rely on ICT

has lead to the argument that it is impossible to

achieve an effective collaboration without ICT

(Sanders and Premus, 2005; Smith et al., 2007).

Next, technology use is a system-operational

behavior rather than a cognitive behavior, such as

social capital and perceived justice. That is, supply

chain members can be certainly to physically

perceive IT capability for its suitability. In the final

goal of firm performance, collaboration is the central

principle in creating flexible supply chains for the

target.

In this study, we have found that collaboration is

an important mediator in achieving final firm

performance from a combination of different sets of

drivers. As partners in the supply chain tend to be

more satisfied with their collaborative behaviors,

they will effectively eliminate waste (time and

material), both internally and externally, and can

particularly focus on their core competencies.

6 CONCLUSIONS

AND SUGGESTIONS

The findings have important implications for both

practitioners and researchers. For the practitioners,

the managers should recognize the value to assure

the goals for the developed collaborative effort that

are understood by all members. Many firms have

engaged in collaborative effort for their supply chain

members, yet not all collaborative efforts are

successful. Collaborative effort between members

may require significant investment in various

intangible and tangible resources. A better

understanding of the three important issues proposed

in this study can help managers improve the

possibility of success in collaborative effort. When

an investment occurs under the consideration of

specific social resources, it is important to identify

the psychological states or beliefs of partners in an

initial manner so they voluntarily initiate formal

social bonding. Therefore, managers need to fully

prepare for a cordial atmosphere among partners in

terms of their concerns about communication

channels, relational stability, mutual rewards, and

fair policies. Collaborative relationships are well

founded on the responses from the positive beliefs

and behaviors of these issues. Advances in ICT are a

further investment to make specific social resources

feasible in implementing collaborative behaviors.

Managers should be first in the preparation to reach

consensuses on these social resources and further

nurture IT capabilities in an interorganizational

boundary.

For the researchers, we approach supply chain

collaboration from an understanding of both the

pre-expectation and post-expectation beliefs as a

confirmation of willingness to participate in

collaborative effort and of the important enabling

role of supply chain technologies in the effort. Few

studies have proposed a similar structure in

examining collaborative effort. This approach is

both comprehensive and unique in understanding the

effect of collaboration on firm performance. In

particular, considering the D&M model for

technology use, it tries to pinpoint the importance of

one of the IT features, service quality. This is

because the IT-enabled supply chain is extremely

complex, involving numerous organizations and

users from different industries and hence, an

assurance of prompt and uninterrupted system

services is imperative.

Subsequent research could be based on this

foundation. First, this study is based on a survey

method and future research could conduct a case

study longitudinally to deeply understand the

physical collaboration between the focal firm and its

supply chain partners. Second, since the study

sample was selected from a combination of various

industries, the conclusions are more general and

ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems

comprehensive. Future research could be targeted

toward particular industries, for instance, the

high-tech electronics industry, to understand their

differences and similarities. This would provide

more insight into supply chain collaboration in the

particular industry. Besides, the role of cloud

computing in collaborative effort is an important

issue of technology use in the future.

Although this research has produced some useful

results, a number of limitations may be inherent in it.

First, the response rate was lower than desirable,

despite the various efforts to improve it. This may be

due to a lack of rich experience of most companies

on supply chain collaboration. However, the

response sample indicated no systematic

non-response bias and was well representative of the

study sample. Next, the respondents were originally

targeted to CEOs, Vice CEOs, and logistics/

purchasing executives. However, approximately

26.3% of the respondents are senior staff members.

Since senior managers in the larger firms are always

busy, some questionnaires may be completed by

their subordinates. In fact, staff members are those

people who are physically responsible for the daily

work. However, additional benefit would be gained

from creating a diversity of data sources with

multiple informants and therefore, an increase in the

variance of the variables of interest.

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ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems