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Information systems capabilities and indirect relational value: Sustaining networked organizations

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Information systems capabilities and indirect relational value: Sustaining networked organizations

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Content INFORMATION SYSTEMS CAPABILITIES AND INDIRECT RELATIONAL
VALUE: SUSTAINING NETWORKED ORGANIZATIONS
Copyright 2003
by
Nilesh Saraf
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BUSINESS ADMINISTRATION)
May 2003
Nilesh Saraf
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UMI Number: 3103965
Copyright 2003 by
Saraf, Nilesh
All rights reserved.
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U N IVER S ITY OF SOUTHERN C A LIFO RN IA
THE GRADUATE SCHOOL
UNIVERSITY PARK
LOS ANGELES, CALIFORNIA 90089-1695
This dissertation, written by
under the direction o f h IS dissertation committee, and
approved by a ll its members, has been presented to and
accepted by the Director o f Graduate and Professional
Programs, in partial fulfillm ent o f the requirements fo r the
degree o f
DOCTOR OF PHILOSOPHY
D irector
Date May 1 6 . 2003
Dissertation C
Chair
/
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Acknowledgements
I am very thankful to my committee members, Prof. Omar El Sawy, Prof.
Sanjay Gosain, Prof. Gareth James, Prof. William Maxwell and Prof.
Christoph Schlueter-Langdon, for guiding this dissertation.
I am thankful to Dr. Langdon and Dr. El Sawy for their constant prodding to
make my research relevant and to help me keep in-focus the basic research
objectives of my dissertation - without these inputs my research would not
have steered to completion. Their guidance made this process all the more
worthwhile. The rigor inculcated in my research activity by my advisor,
Dr.Langdon, will manifest itself in my future research also. I am thankful for
the extensive amounts of time he spent on helping me refine key points of
this dissertation. I appreciate Dr. El Sawy’s guidance in helping me extract
the learnings from the study that actually would make this process
meaningful. I also thank him for his continuous attention to my research
progress. I am thankful to Dr. Gosain for his willingness to help me solve the
research issues that I faced at every step. In certain aspects this study also
builds on two prior USC dissertations that include Dr. Gosain’s and Dr.
Malhotra’s dissertations. This made Dr. Gosain’s inputs to my project all the
more valuable.
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I highly appreciate the access provided by the Institute for Study of Business
Markets (ISBM), Penn State University, to their member database and Dr.
Bernie Jaworski for his involvement in this research. I thank the numerous
executives at the Cellular Telecommunications & Internet Association (CTIA)
conference at Las Vegas, NV and the Collaborate West conference,
Anaheim, CA, for their time and interest in my research.
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Table of Contents
List of Tables...................................................................................................... vii
List of Figures.....................................................................................................viii
Abbreviations........................................................................................................ix
ABSTRACT......................................................................................................... xi
1. Introduction and Research Questions..........................................................1
1.1. Value in Inter-organizational Networks.................................................2
1.2. Research Questions: Role of IT in Enhancing Value from Business
Network Linkages............................................................................................ 4
1.3. Contribution...........................................................................................12
2. Literature Research...................................................................................18
2.1. IT and Business Performance............................................................. 19
2.1.1. Understanding IS Capabilities.....................................................22
2.1.2. IT and Business Performance at the Dyadic Level...................23
2.2. Evolution of Business Network Relationships................................... 29
2.3. IS Research and Evolution of Business Relationships......................33
2.3.1. Level of Analysis in IS Research................................................ 33
2.3.2. Limitations of IS literature: Linking IS Capabilities with Business
Value ......................................................................................................36
3. Research Model and Hypotheses............................................................. 39
iv
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3.1. Knowledge Sharing as an Intermediating Variable......................... 40
3.2. Knowledge Sharing and Indirect Relational Value.......................... 41
3.3. IS Capabilities......................................................................................45
3.4. Hypotheses: IS range, IS integration, IS reach.................................49
3.4.1. IS integration and knowledge sharing....................................... 50
3.4.2. IS Range and knowledge sharing..............................................52
3.4.3. IS reach and knowledge sharing................................................57
3.4.4. Comparing with Reach and Range as conceptualized in
literature......................................................................................................59
3.5. Organizational Variables and Knowledge Sharing........................... 61
3.5.1. Control Variables: Workflow Interdependence and Social Tie
Strength......................................................................................................62
3.5.2. Moderating effects of Workflow Interdependence and Social
Ties between IS Integration and Knowledge Sharing............................. 65
4. Research Design......................................................................................... 73
4.1. Survey Design, Instrument Validation and Data Collection..............73
4.2. Sample Selection and Survey Administration................................... 78
4.3. Tests of Validity....................................................................................85
4.4. Limitations of Research Design and Data Collection Method 89
5. Data Analysis...............................................................................................92
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5.1. Partial Least Squares and Ordinary Least Squares.........................93
5.2. OLS regression results........................................................................96
5.3. PLS analysis........................................................................................99
5.3.1. Structural model results with and without interaction terms ..100
5.3.2. Testing alternate structural paths.............................................. 102
6. Discussion of Results................................................................................105
7. Implications................................................................................................117
7.1. Contributions to Theory and Conceptual Understanding.................117
7.2. Implications for Future Research.....................................................125
7.3. Implications for Practice....................................................................129
References...................................................................................................... 132
vi
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List of Tables
Table 1: Types of IOS according to the nature of inter-firm business activity
.....................................................................................................................25
Table 2: Information technologies, IS capabilities and business performance
28
Table 3: Main model relationships and theory bases.....................................68
Table 4: Initial pretest survey instrument: Construct name, # of items and
type of scale............................................................................................... 75
Table 5: List of key interviewees and findings.................................................77
Table 6: Final Survey: List of items measuring the constructs and their
Cronbach’s alpha.......................................................................................84
Table 7: Descriptive statistics........................................................................... 92
Table 8: OLS results with interaction terms.....................................................97
Table 9: OLS results excluding interaction effects.......................................... 97
Table 10: Assessing discriminant validity: Inter-construct correlations (N=69)
...................................................................................................................104
Table 11: Main effects of organizational variables on knowledge sharing.. 105
vii
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List of Figures
Figure 1: Interdependencies between relationships (Holm et al. 1999a, p.
469)............................................................................................................... 4
Figure 2: Conceptualization of indirect relational value.................................... 9
Figure 3: Scope of information systems research and practice.....................35
Figure 4: A conceptual framework for investigating the value of IT in business
networks......................................................................................................41
Figure 5: Research Model................................................................................ 49
Figure 6: Reach and Range as conceptualized in IS literature (Keen 1991) 60
Figure 7: Our conceptualization of IS Reach, IS Range and IS Integration..61
Figure 8: OLS results for model with interaction effects.................................98
Figure 9: Research model results................................................................... 100
Figure 10: Research model results excluding interaction effects................102
Figure 11: Testing alternate structural paths: IS Integration and IS Reach
with Indirect Relational Value..................................................................103
viii
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Abbreviations
API Application Programming Interface
ANOVA Analysis of Variance
B2B Business-to-Business
B2C Business-to-Consumer
BU Business Unit
CRM Customer Relationship Management
DSS Decision Support Systems
EDI Electronic Data Interchange
EIS Executive Information Systems
ERP Enterprise Planning System
ETL Extract Transfer and Load
GDSS Group Decision Support Systems
IOIS Inter-Organizational Information Systems
IOS Inter-Organizational Systems
IS Information Systems
ISBM Institute for Study of Business Markets
IT Information Technology
KMS Knowledge Management Systems
PLS Partial Least Squares
PRM Partner Relationship Management
SCM Supply Chain Management
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SIC Standard Industry Classification
TCE Transaction Cost Economics
TDP Tokyo Digital Phone
TPM Transaction Processing Monitors
XML extensible Markup Language
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ABSTRACT
Information systems (IS) capabilities are understood to help enterprises
derive synergies across their business activities. Yet, the theory explaining
the linkages between information technology (IT) and value in business
networks is still under development. Our research model addresses this
deficiency by first grounding the notion of value, our eventual dependent
variable, in terms of indirect relational value. Indirect relational value refers to
the value that accrues to networked enterprises by virtue of their linkages
with other enterprises. Examples include benefits such as cross-selling and
increased customer satisfaction due to collaboration with a business partner.
Second, as recommended by the literature (Barua et al. 1995), we
conceptualize knowledge sharing across BUs as our intermediating output
variable. Results from our empirical study validate the intermediating
structure of the model. Finally, three IS capabilities and two attributes of
inter-enterprise relationships, constitute the set of independent variables.
We test our model in the boundary-spanning context of business-to-business
(B2B) sales, where the phenomenon we are investigating is particularly
important. Using a survey questionnaire, we analyze responses from 69
managers/business unit heads with responsibilities in B2B sales. Since in the
current business environment, multi-unit companies are often constituted by
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merged/acquired entities, inter-unit relationships are a proxy for inter
enterprise relationships.
Our findings have important theoretical and practical significance. First, since
IS integration significantly affects knowledge sharing, this suggests that the
investments in IS integration can be the locus of absorptive capacity of
enterprises, by which they are able to share knowledge with their partners.
Second, since knowledge sharing significantly affects indirect relational
value, it suggests that specific IS capabilities can significantly enhance
relational value at a network level. Third, since workflow interdependencies
and social ties’ strength across enterprises, are significant, this indicates that
these two relationship attributes can compensate for an insufficient level of IS
capabilities. Finally, the presence of individual level electronic communication
media and richer application functionality do not increase knowledge sharing
in the B2B sales process. Thus, this study also offers specific guidance about
which IS capabilities maybe important for successful B2B sales performance.
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1. Introduction and Research Questions
According to both the practitioner and academic literature, value generated
from business relationships can have two dimensions. The first dimension is
the value generated within a relationship and the second dimension is the
value generated when knowledge and solutions from a focal relationship
between two partners are transferred to a relationship with another partner.
An example of the first is a product developed by two partners, where the
expertise from this relationship cannot be reapplied significantly in any other
relationship. An example of the second dimension is when organizations
derive benefits primarily because of their ability to leverage one relationship
with their other relationships. An information broker is an ideal example of
such a case. It is this logic of leveraging one relationship with other
relationships that forms the basis of the emergence of networked
organizations.
The linkage between the emergence of business networks and information
technology is suggested by much of literature. Supported by richer forms of
communication, business activities and products have become more
information intensive (Apte and Mason 1995). Information intensity has also
increased with an economy-wide dis-aggregation of firms (Zenger and
Hesterly 1997) since greater number of firms than ever before participate in
delivering a single product or service to end users (lansiti and West 1997).
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This has resulted in modular enterprises (Hagel 1999) having collaborative
and multi-dimensional relationships. Organizations are thus seen as
operating in a web of relationships where those organizations which strive to
connect business processes of different partners and derive synergies
emerge more successful (Sawhney and Parikh 2001). Information technology
is a key to sustaining these networks of relationships by enabling synergies
across these (El Sawy et al. 1999a).
1.1. Value in Inter-organizational Networks
Increasing attention is focused on the study of business networks as
organizational forms distinct from markets or firms. This is because value
addition is often done jointly by business partners rather than by individual
firms transacting at arms-length through market mechanisms. Accordingly, a
significant amount of literature offers understanding of value creation at a
dyadic level. For example, Zajac and Olsen (1993) analyze how the
relationship-specific investments by business partners contribute to higher
value (Zajac and Olsen 1993). Here, value is seen as accruing to those
business entities that are directly involved in a relationship.
However, the network paradigm also suggests that for organizations
embedded in multiple business relationships, value can accrue because of
indirect connections with other organizations through a common business
entity. Thus, individual BUs of organizations are able to offer complementary
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products and services or cross-sell their products. That is, there are
interdependencies even between those actors who are not transacting
directly but may have common partners, suppliers or customers. There has
been growing recognition of such interdependence effects in the literature. In
a network consisting of collaborative relationships for product design and
development, Eriksson et al., note that “the understanding of how technology
adaptations in one relationship are contingent on adaptations in another is a
key to understanding the dynamics of knowledge transfer between business
relationships in a network” (Eriksson and Hohenthal 2001, p. 92, italics
mine).
An example of interdependencies in a business network is when a focal
organization’s relationship with one supplier contributes to better serving the
focal organization’s customers (Holm et al. 1999b, p.468). For example in the
case of Ericsson and Tokyo Digital Phone (TDP), Ericsson had to establish
ties with a local Japanese firm Toshiba in order to gain necessary country-
specific expertise and linguistic competence (Figure 1). The success of the
project between Ericsson and Tokyo Digital Phone (TDP) depended
considerably on the success of the collaboration between Ericsson and
Toshiba.
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Figure 1: Interdependencies between relationships (Holm et al. 1999a, p. 469)
Toshiba of Japan
Ericsson radio systems
of Sweden
Ericsson Toshiba
Telecom Systems of
Japan
Tokyo Digital Phone
(TDP) of Japan
The importance of information technology to manage business activities in a
network context is increasingly important for good performance (Jap and Mohr
2002). We propose that as such interdependencies between firms increase, it is
fruitful to explore how IS capabilities can contribute to value in business
networks.
1.2. Research Questions: Role of IT in Enhancing Value from
Business Network Linkages
The potential to advance the theory on the value of IS in business networks
can be realized by a deeper examination of the literature on value creation in
business relationships. As a step towards this, we note that the recent
literature presents two different arguments of how value is derived by firms.
First, the resource based view (Barney 1991) contends that competitive
advantage results when a single firm delivers value using sustainable
inimitable resources possessed and controlled by it. On the other hand, the
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relational view suggests that the “(dis)advantages of an individual firm are
often linked to the (dis)advantages of the network of relationships within
which a firm is embedded” (Dyer and Singh 1998b). This view refers to
relational value as value that a firm derives by virtue of a relationship with a
specific partner as opposed to relationship with alternative potential partners.
Our first research question examines the role of IS in yielding value in
relationships and uses the above two opposing viewpoints. In the following
discussion we term inter-organizational information systems (IOIS) as a
component of the IT portfolio of enterprises that is deployed to support
business activities with external entities. Thus, IOIS is conceptually distinct
from IT investments deployed to support primarily internal operations.
Research question #1: Does inter-organizational information systems (IOIS)
help to derive value from relationships? Does IOIS help to “unlock” sources
of hidden value in relationships (and thus increase relational value)? Or is
value derived by higher IS investments that primarily enhance internal
operations only?
The above set of questions is interesting for the following reasons. First, it
investigates whether, for a focal business entity, its IOIS investments
enhance value from its relationships. Second, we also ask whether IOIS
complements other attributes of business relationships (e.g. formal and
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informal characteristics of organization such as inter-enterprise workflow
interdependence and social ties). The literature does offer good support for
the notion that a relationship can, in itself, be a value-bearing asset (Madhok
and Tallman 1998b). However, we ask whether IOIS can augment this
dimension of value. Third, we also ask whether, in addition to IOIS, the ISs
deployed internally also help enterprises to derive value from their external
business relationships.
The above set of research questions also motivates us to explore whether IS
capabilities may have multiple dimensions that may impact relational value
differently. We elaborate on this in our research question 3.
Next, there are various sources of value creation in business networks. For
example, four interdependent dimensions that have been identified by Amit
and Zott (2000) are efficiency, complementarity, lock-in and novelty. The
transaction cost argument has primarily stressed the efficiency value of IS
investments in terms of transactional cost savings (Amit and Zott 2001).
However, it has been argued that TCE considerations render the link
between organization and competitive advantage rather tenuous (Leibeskind
1996). Therefore, we investigate whether IS can augment value beyond
higher efficiency, namely by increasing knowledge sharing. The role of
knowledge sharing in yielding value is supported by proponents of the
relational view - they suggest that antecedents to relational value are
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broadly, relation-specific assets, knowledge -sharing routines,
complementary resources/capabilities and effective governance (Dyer and
Singh 1998b).
Research question #2: How should relational value be conceptualized? Can
inter-organizational information systems augment relational value by
increasing knowledge sharing?
The above research question is interesting because few empirical studies
have attempted to establish a link between IS variables and relational value
from a transactional value viewpoint (Zajac and Olsen 1993). It suggests that
organizations may invest excessively in transaction-specific and relation-
specific assets to not only increase efficiency but also to enhance value by
enhancing inter-organizational learning. We believe that the knowledge-
based perspective on organizational relationships has not yet been exploited
fully to conceptualize how IS capabilities yield value in business networks
(Amit and Zott 2001). Though practitioner models of business value of IT
incorporate ROI figures, it is suggested that the exact ROI figures on IT
investments are less relevant, i.e., IT investment decisions should be based
on overall business value rather than revenue generation measures (OR/MS
April 2002, p. 20).
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We propose to conceptualize value as indirect relational value. This is
variously labeled as network related benefit or second-order benefits.
Literature on the business network perspective has viewed the business
dyad as a transmitter of benefits rather than a value-adding entity by itself
(Halinen et al. 1999). This connectedness perspective is also extensively
discussed in marketing literature from a social exchange perspective
(Anderson et al. 1994). It proposes that a firm invests in a focal relationship
to the extent that the relationship can augment the focal firm’s benefits from
other relationships (and vice versa). This network dimension of relational
value differs from the concept of relational value proposed by Dyer and Singh
(1998). As discussed earlier, Dyer and Singh conceptualize relational value
purely at a dyadic level without considering the “network-related” benefits. By
indirect relational value we mean the extent to which a focal business entity’s
relationships with other entities, benefits the focal entity’s overall business
network. However, though social network literature offers a formalized
approach to treat dyadic relationships as transmitters of resources and
influence, this study, in its current form, does not use such formalization.
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Figure 2: Conceptualization of indirect relational value
Sellers Buyers
©
Focal BU
* B
Other BU
A-A’ Sales relationship
A-B-A’ Customer satisfaction
A-B-B’ Cross-selling
Literature has also emphasized the potential of information systems as an
enabling tool for the design of network forms of organizations (Fulk and
DeSanctis 1995; Grandori and Soda 1995). Theory building based on case
reports suggests how inter-organizational information systems impact
structure of market networks. These studies conclude that lOSs allow richer
and more complex “mixed-mode structures” to be managed (Holland and
Lockett 1997, p. 476) than was possible before the proliferation of lOSs.
Similar case-based discussion of a retail supply chain context has
distinguished between first and second-order effects. The first order effect is
mainly in terms of efficiency gains. The second order effects are in terms of
“informating” the participants for performing other value-added activities such
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as forecasting, negotiation, planning etc. (Cunnigham and Tynan 1993).
Currently the Internet is not only facilitating electronic market exchanges but
also enabling value-adding “orchestration” of business activity in retail
networks (El Sawy et al. 1999a; Venkatraman and Henderson 1998).
The next research question explores in more detail the role of IS in
enhancing value. Specifically, it is important to identify those facets of IT
investments that enhance value in relationship networks. Apart from the
traditional concepts of reach and range of IT infrastructure, there is recent
interest and recognition in IS literature of the importance of achieving
integration across different enterprise systems. Here integration is
conceptually distinct from the functionality offered by these enterprise system
modules (Markus 2000; McKeen and Smith 2002). One of the significant
activities in implementation and maintenance of ERP systems is the need to
integrate the ERP package with legacy systems as also to integrate the
business processes across various software modules (Sabine and Swanson
1999). With the increasing IT intensity of most enterprises the importance of
IS integration also has increased considerably (Weber and Pliskin 1996, p.
83). For conducting e-business it is critical to achieve integration of IT
systems across enterprises (Hasselbring 2000).
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Research question #3: What is the role of different IS capabilities in
enhancing value in networked relationships through knowledge sharing
across relationships?
There are three IS research streams that support our endeavor of linking IS
capabilities with business value. First, IT infrastructure is viewed as a
resource (Bharadwaj 2000) that can yield sustainable competitive advantage
(Byrd and Turner 2001; Mata et al. 1995). The primary informing theory for
this body of literature is the resource-based view of the firm (Wernerfelt
1984). It leads researchers to suggest that IT infrastructure of a firm can help
to exploit synergies across organizational divisions (Bharadwaj 2000, p. 176;
Ciborra and Hanseth 1998). Thus, one business partner of a focal firm can
also derive advantages due to the focal firm’s relationship with other
business partners. Studies also attest to the effectiveness of EDI in
combining resources across multiple organizations (Hart and Estrin 1991).
Second, the information processing theory of coordination (Galbraith 1973)
also offers a basis for theorizing about synergistic effects of IT. This literature
explores how certain attributes of IT infrastructure are related to, or are
driven by, different organizational variables such as interdependence (Wybo
and Goodhue 1995), IT governance modes (Sambamurthy and Zmud 1999),
decentralized authority (Hitt and Brynjolfsson 1997), etc. The third research
stream is relatively recent and somewhat related to the first stream in that it
examines value from IT infrastructure in supply chain networks. The value is
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in terms of collaborative knowledge creation (Malhotra 1999) or in terms of
richer information exchange (Gosain 1999). These dissertations have
focused on the positive impacts of IT infrastructure and relational capabilities
at a dyadic level and from a transaction value perspective (Zajac and Olsen
1993).
1.3. Contribution
This dissertation study attempts to develop a deeper understanding about
the effect of different enterprise IS capabilities on inter-organizational
knowledge sharing and thus indirect relational value. Our primary motivation
arises from two directions. First, literature suggests reach and range as two
dimensions of IT infrastructure that may benefit organizations by deriving
synergies across sub-units or with external partners (Keen 1991). However,
literature also suggests that the theoretical explanations of how ITs may yield
synergies differ depending on the type of IT. Communication oriented
technologies such as email, video conferencing and virtual collaborative tools
may have a different impact compared to process technologies such as
supply chain management software (SCM), customer relationship
management (CRM), and other such software packages. Whereas the
former enhance communication among individuals and groups, the latter are
integrated with the IT applications of external parties through connecting their
software interfaces (e.g., APIs). Therefore, empirical studies need to sharpen
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the conceptualization of these IT infrastructure dimensions and theoretically
delineate their effects on business value more precisely.
Our second source of motivation is the limited notion of business value as
empirically studied in IS literature. To our knowledge, in existing IS-related
empirical studies, value has been conceptualized as an outcome primarily at
the dyadic level. We shall attempt to extend this conceptualization to a
network level, i.e., how IS capabilities can yield indirect relational value. This
is particularly relevant in the business network era as described earlier.
This dissertation also builds on two previous dissertations (Gosain 1999;
Malhotra 1999). Broadly, there are two important similarities. Similar to this
study, both works have contributed to literature on inter-organizational
networks and IT infrastructure from a relational value perspective. Further
both works have recognized that a direct linkage between IT infrastructure
capabilities and business value is difficult to establish without considering the
intermediate variables. Therefore, they have included intermediate variables
such as knowledge creation (Malhotra 1999) and business process
integration and flexibility (Gosain 1999) as their dependent variables. An
important distinction is that whereas the Malhotra (1999) has theorized richly
on the nature of knowledge and its creation, this study has focused on
establishing a link between IS capabilities and business value in relationships
with comparatively less stress on a richer conceptualization of knowledge
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sharing. The second distinction lies in the level of analysis. Both studies have
assumed a business dyad as their level of analysis. Their conceptualization
of all constructs also is at the dyadic level. This limits the generalizability of
their empirical findings to a network context where the interdependencies
across relationships are a key feature. As against this, in this study the level
of analysis is a BU-to-BU relationship but our construct definitions capture
the “network” effect. This enables us to extend the implications to the inter-
organizational network context.
The findings provide a basis for theorizing why certain IS capabilities may
increase indirect relational value whereas some do not. Specifically, we
found that none of the IS capabilities dimensions that we conceptualized are
positively associated with indirect relational value. This contradicts the
intuition from existing IS literature. Instead, the knowledge sharing construct
mediates this link between IS capabilities and indirect relational value.
Whereas one dimension of IS capabilities is significantly associated with
knowledge sharing, the other two dimensions are not. In addition, we also
find that the non-IT related attributes of business relationships also
complement the IS capabilities in yielding business value. These findings
guide us to reassess our understandings of the role of IS capabilities.
Implications of the findings are interesting from different theoretical
viewpoints. First, absorptive capacity is the capability of individuals, groups
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or organizations to recognize the value of new, external information, to
assimilate it, and to apply it to commercial ends and to the enhancement of
the innovative capabilities. The finding that IS capabilities are associated with
increased knowledge sharing is an evidence that those IS capabilities may
increase absorptive capacity. We found that one of the dimensions of IS
capabilities, IS integration is associated with increased knowledge sharing.
Second, the social capital perspective suggests that higher intellectual capital
is created when an enterprise harnesses three dimensions of social capital
that includes the cognitive dimension. We suggest that by enabling certain
elements of this cognitive dimension, information technologies maybe
enabling higher knowledge sharing. Cognitive dimension of social capital is
higher when organizational members share cognitive schemas (or mental
models) of business activities. This enhances the combination capability of
individuals as they have a prior knowledge base that is used to process
newer knowledge. An IT application portfolio with rich functionality
instantiates these elements of the cognitive dimension of social capital and
thus may lead to higher knowledge sharing. However, interestingly, we did
not find evidence that IT applications that are rich in functionality and widely
accessible to employees, lead to higher knowledge sharing.
Based on the media richness theory we hypothesized that richer
communication capability may enhance knowledge sharing. This was not
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evident from our findings. We conclude that better communication capability
maybe primarily a necessity rather than a key driver of knowledge sharing.
Our findings indicate that IS capabilities may be playing a selective role in
enhancing knowledge exchange with external entities. IT cannot always
substitute for other organizational factors in enabling knowledge sharing. For
example, rather than the richer electronic communication media, strong
social ties maybe the actual antecedent of higher knowledge sharing.
Accordingly, we did find that social ties significantly enhance knowledge
sharing. Similarly, we found that another attribute of business relationships,
interdependence in tasks, also leads to higher knowledge sharing.
This empirical study faces certain limitations. For more robust theoretical
generalizations,-more work needs to be done. First, we suggest that future
studies need to focus on another aspect of this model, i.e., the knowledge
sharing construct. Our focus was on conceptualizing IS capabilities and
indirect relational value. Therefore, in this study alone it was not feasible for
us to distinguish operationally between knowledge and information or
between strategic, tactical and operational information sharing. A more
comprehensive model would also attempt to capture other dimensions of
relational value using more objective data sources as against a survey.
Second, the study was conducted in the context of inter-unit relationships in
multi-divisional companies. We believe that in order to investigate our
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research questions, the BU-to-BU relationships in the sales context is an
appropriate proxy for inter-organizational relationships. However,
generalizations at the inter-organizational level have to be made cautiously
keeping this assumption in mind.
Overall, our findings can guide organizations towards achieving IS
capabilities to enhance performance in their relationship networks. First, the
results suggest that IT investments can be made to exploit synergies across
their relationships and not just within their dyadic relations. Second,
conceptually, the IS capabilities that we had included in our model, pertain to
the ITs that are deployed at the interfaces between enterprises and the ITs
that are deployed to enable the internal operations of enterprises. Therefore,
the findings guide the practitioner as to how to target IT investments better to
yield higher value from their business relationships. For example, in our field
context, investments in IT integration with external entities maybe more
beneficial compared to IT investments that augment internal operations.
Third, our findings may reorient the managers’ perspective about the utility of
various IT initiatives that may include developing a best-of-breed IT
application portfolio, enabling virtual collaboration, e-business integration and
implementing workflow systems.
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2. Literature Research
This section proceeds as per the following steps:
First, we review how IS research has explored the role of IT in enhancing
business performance. Early IS research was focused at a lower level of
analysis than at the firm or a business-unit level. Therefore, a direct link
between IT portfolio and firm performance could be established only recently.
Therefore, to understand how IT yields value at the business network level,
we conceptualize IS capabilities at the firm level. In our empirical study, the
business unit in a large organization is our unit of analysis since each BU is
considered as a separate entity in terms of its business performance. This
assumption is valid for exploring our research questions and can be justified
from a practical viewpoint using industry examples. An extreme example
where the subunits within large companies can be treated as separate
entities is the case of a multinational corporation (Ghoshal and Bartlett 1990).
Another common example is the case of high technology companies that are
rapidly acquiring and merging with other enterprises, e.g. CISCO (Holloway
et al. 1998). The various business units of multi-unit companies thus face the
dilemma of integrating with the parent company but at the same time
maintaining disparate information systems and processes to operate in their
product and customer segments.
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Second, we discuss how business performance is increasingly dependent on
the portfolio of relationships developed and maintained by enterprises. This
discussion finds support in literature on business networks. Based on this,
we argue that to advance the IS literature we need to understand how IT
delivers business value at the network level.
Finally, we note that despite the need for advancing IS research to the
network level, researchers have not addressed the network level of analysis
in their investigation of the strategic value of IS. We then elaborate on this
point by discussing how research on the business value of IT has advanced
to the inter-organizational level along a continuum characterized by three
stages: intra-organizational, dyadic and, only recently, the network level.
2.1. IT and Business Performance
We suggest that in order to understand the value of information technology at
the firm level or network level, it is useful to conceptualize IS capabilities not
in terms of the individual information technologies (as was customary in early
IS research on EIS, DSS and TPM); but at the level of the IT portfolio
implemented by individual business units. Though a significant amount of
research has been attempted on how IT influences business performance,
the level of analysis was largely at a lower level than the firm level, i.e. at the
level of the artifact or at the group level. In these studies the usage of a
particular type of IT under examination was treated as the independent
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variable. Such research was restricted to understanding the benefits of
information technologies such as DSS, EIS and transaction-processing
monitors that facilitate individual-computer interactions. Here IS success was
measured in terms of completeness, accuracy, relevance, currency and
timeliness of information (Miller and Doyle 1987). Similarly, information value
was operationalized in terms of sufficiency, understandability and decision
relevance (King and Epstein 1983). Although one study measured the
benefits of executive information systems (EIS) at the firm level (Belcher
1993) (that is, in terms of improved productivity, improved decision making,
information distribution cost savings, services replacement cost savings, and
software replacement cost savings), there are few large-scale empirical
studies to ascertain such benefits. At the next level, the value of GDSS/email
was studied at the group level of analysis. Since the core functionality offered
by GDSS was facilitating group level activity, theories of task/technology fit
(Zigurs and Buckland 1998) were used to study the linkage between GDSS
and group performance.
Over the last two decades research on IT value has evolved in terms of an
increasing level of analysis, i.e., to the firm as the unit of analysis. This
research, conducted primarily in the context of EDI, measured benefits of
EDI in terms of inventory turn over, purchasing cost, timeliness, etc.
(Chatfield and Yetton 2000; Clark and Stoddard 1996). In order to examine
IT value at the firm level, later research based on production economics,
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organizational theory and consumer theories aggregated the IT investments
in a firm (Hitt and Brynjolfsson 1996) to study its effect on firm performance
or used psychometric scales to measure IT in terms of IT capability
(Bharadwaj 2000). With these measures of IT as independent variables,
findings suggest a direct impact of IT spending on firm productivity and
customer value (Hitt and Brynjolfsson 1996; Nault 1995).
We note from the above discussion that there is a trade-off between the
varying levels of analyses described above. In order to explore how IT yields
value at the firm level, treating IT at the artifact level of analysis is limiting in
that, its impact on the overall firm performance cannot be understood
completely. This is because any particular IT (e.g. EDI, CRM or SCM)
automates only a selected portion of the business activity that may or may
not directly impact firm performance. In contrast, by examining the effect of
IT at the portfolio level using aggregate measures such as IT investments as
a percentage of revenue, there remains a risk of ignoring the role of firm-level
strategic variables that intermediate the relationship between IT and
profitability. We believe that using aggregate IT investment measures, such
as dollar volume, precludes development of a more precise theory of how IT
investments impact firm performance.
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2.1.1. Understanding IS Capabilities
To understand the nature of IS capabilities at the firm level, we utilize the
notion of capability as developed by Choudhury and Xia (1999). Capability is
a capacity for a team of resources to perform some task or activity.
Capabilities involve complex patterns of coordination between people and
between people and other resources (p. 62). Alternately, capability has also
been referred to as a firm’s capacity to deploy resources, usually in
combination, using organizational processes, to effect a desired end.
We define IS capabilities of a firm as “the aggregate information processing
capabilities of the firm’s investment in information technology artifacts”. This
excludes investment in managerial expertise, skills and incentives to
augment the value of the IT artifacts. Bharadwaj et al. (2001) measure the
different first-order facets of IT capability construct such as IT business
partnerships, external IT linkages, business IT strategic thinking, IT business
process integration, IT management and IT infrastructure (Bharadwaj et al.
2001). Therefore, they define IT capability as a second order construct and it
refers to the fundamental competencies, skills, and tacit know-how that an
organization develops overtime to effectively acquire, deploy and leverage
its IT investments in pursuit of its business strategies and value chain
activities.
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While defining and measuring IS capabilities, our focus is not on the
particular information technologies. Rather we view various information
technologies as contributing in aggregate to certain higher-level IS
capabilities. Such a shift in emphasis, from analyzing value of individual
technologies such as GDSS and DSS to identifying higher-level IS
capabilities, e.g., structured connectivity and data integration, can be seen
from a review of literature (Table 2). We believe that, rather than specific
information technologies, the aggregate IOIS capabilities maybe more
relevant to understanding organizational performance.
2.1.2. IT and Business Performance at the Dyadic Level
In this section we examine the literature on inter-organizational information
systems (IOS) to better understand how enterprises can derive value from IS
capabilities. By IOS, we mean those components of IT artifacts deployed by
organizations and used primarily for interactions with other business entities.
Thus, it is possible that a particular IT artifact maybe deployed partly to
manage internal operations and partly to transact with outside entities. For
example, certain aspects of a complex ERP system may permit inter-
organizational transactions, whereas other features of the ERP may purely
be used for internal operations.
Though, our empirical study is conducted in the context of inter-unit
relationships in multidivisional firms, we argue that the understanding gained
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from IOS literature is significantly applicable at the inter-unit level. This is
because the profile of IT application portfolio within different divisions in a
multi-unit firm often resembles an IT portfolio of enterprises in an inter-
organizational network. Large firms often have evolved to their current form
by a process of mergers and acquisitions. Often, in these firms, different sub
units may be just as integrated with external entities as with other divisions
within the firm. Therefore, findings from IT infrastructure related studies at the
inter-unit level are also applicable to those B2B relationships where there are
strong bonds between partnering firms. Further literature on IT infrastructure
issues in multi-unit firms is scarce let alone organizational literature on multi
unit firms (Ghoshal and Bartlett 1990).
To identify the scope and context under which we shall investigate our
research questions, first we note the type of lOSs that have been researched
earlier. This focus may help to be more discerning with regard to the different
value implications of each type of IOS. In Table 1 we segregate the different
IOS depending on 1) the nature of business activity that a particular IOS is
primarily deployed for (i.e., functional dimension) and 2) the structural
arrangements between the different firms (i.e., the structural dimension). The
functional dimension reflects the main type of interactions between the
organizations that are conducted through the IOS (the type of interactions
can be either exchange or production). The structural dimension focuses on
the nature of firm participation. If the IOS supports multiple parties to interact
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on a common activity (e.g. designing a building in a construction project) then
the IOS is said to support a multi-lateral production activity.
Table 1: Types of IOS according to the nature of inter-firm business activity
FUNCTIONAL
DIMENSION
[Content of
business
interactions]
Production (Hart and Estrin 1991)
- Collaborative product
design, manufacturing
and integration
(www.agileweb.com.
Greis 1997)
- CAD/CAM - “Stealth
bomber” (Argyres 1999b)
- Procurement systems
Exchange (Ageshin 2001)
___________- EDI (Emmelhainz 1990)
Electronic markets
(Bakos 1991a;
Choudhury 1997)
Bilateral Multilateral
STRUCTURAL DIMENSION
[Nature of participation]
The above characterization of lOISs along the structural and functional
dimension has been based on prior literature (Bakos 1991b; Johnston and
Vitale 1988). Along the functional dimension, IOS can support only exchange
activity such as pure electronic marketplaces (e.g. eBay) or can support joint
production activity. Johnston and Vitale (1988) describe lOISs as handling
only boundary transactions and more complex lOISs as permitting back-
office chores as well as retrieving and analyzing data. Along the structural
dimension, lOISs are essentially information links or electronic markets
(Bakos 1991b); or an IOIS can be an electronic dyad, electronic monopoly or
a multilateral IOIS (Choudhury 1997).
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However, the description of the above lOISs as supporting distinctly different
types of inter-firm activity (Table 1) is not clear cut. EDI technology was used
bilaterally, i.e., to facilitate exchange between two fixed parties as in the case
of the retailing industry (Vijayasarathy and Robey 1997), whereas multiple
participants in electronic markets can submit bids for the same stock of
goods and have those delivered/shipped to the destination. But in addition to
payment transactions EDI also supported auxiliary activities such as
shipment, inventory management, etc., between partners. While both EDI
and electronic market technologies supported exchange between parties, the
proprietary EDI technology normally restricted exchange transactions
between two parties, whereas electronic markets (either Internet or Intranet
based) supported multiple parties due to its standardized technology.
Technologies that supported product design and development were designed
to either support such activity bilaterally or multilaterally. In the case of
semiconductor industry, an advanced form of EDI supported supply chain
activities (Hart and Estrin 1991) but only across separate buyer-seller dyads.
Whereas, in the aircraft industry, multiple producers collaborated using an
integrated database and developed different parts of an aircraft that were
later integrated (Argyres 1999b).
Thompson’s categorization of interdependences of activities (Thompson
1967) has also been used to describe lOISs (Kumar and van Dissel 1990).
This categorization refers to networked lOISs supporting reciprocal
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interdependence, value/supply-chain lOISs supporting sequential
interdependence, and lOISs with shared databases /applications as
supporting pooled interdependencies. Similarly, another paper (Emmelhainz
1990) describes three levels of EDI use where the user at the highest level
uses EDI as a catalyst to reengineer the internal business processes and
take full advantage of the technology. Four aspects of EDI functionality -
volume, diversity, breadth and depth of transactions have also been
elaborated upon (Massetti and Zmud 1996). Nambisan and Wang (2000)
describe the IOIS functionality as a hierarchy proceeding from simple
information system linkages permitting information access to more complex
ones permitting collaborative work and core-business transactions
(Nambisan and Wang 2000).
Table 2 demonstrates how different information system processing
capabilities in the context of lOSs are conceptualized and their outcomes
measured in literature. Benefits of IOS were measured depending on the
type of IOS under study. For example, during the last decade, EDI formed an
important context for examining how organizations benefit from IT
implemented at the inter-organizational level. EDI-related benefits were
measured in terms of efficiency parameters such as inventory turn over or
purchasing costs (Clark and Stoddard 1996). However, empirical and
theoretical work linking the various IS processing capabilities and outcomes
are scarce. Therefore, the first column of Table 2 below presents IS
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processing capabilities that were either independent or dependent variables
in various empirical studies. The second column presents outcome variables
in literature that are measured as dependent variables in related studies.
Table 2: Information technologies, IS capabilities and business performance
IS processing capabilities associated
with inter-organizational systems
Outcomes associated with
implementing inter-
organizational systems
■ Structured connectivity (Gosain
1999)
■ Standardization (Wybo and Goodhue
1995)
■ Depth" of EDI - Electronic Data
Interchange (Massetti and Zmud 1996)
■ Internal integration (Truman 2000)
■ Data Integration (Goodhue et al.
1992)
■ Electronic integration (Zaheer and
Venkatraman 1994)
Unitarv level: Inventory turns
(Clark and Stoddard 1996),
purchase-order costs & cycle
time (Chatfield and Yetton
2000)
Dvadic level: Partner linked
flexibility (Gosain 1999), shared
knowledge creation (Malhotra
1999), partnership satisfaction
(Walton 1996)
The above discussion indicates that the type of transactions conducted
through inter-organizational systems vary from rudimentary to rich and
complex. Therefore, in order to assess the business value from these lOSs
requires a detailed examination of the business context. We observe that the
business context that we are seeking to address our research questions in,
has not been examined in earlier IOS related research. To better understand
the value of IT to companies we suggest that it maybe important to first
understand the nature of business relationships and how these are evolving.
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2.2. Evolution of Business Network Relationships
The context for this dissertation study is B2B sales activity in multi-divisional
firms where enterprises are constituted by merged acquired entities, or
different parts of the enterprise serve similar customer segments or product
markets. However, literature on information systems in multi-divisional firms
is not yet developed but just emerging (Tanriverdi 2000). Therefore, we
argue that extant literature on IOS be applied to formulate our key
propositions. Therefore, this also requires a discussion on how different
business units derive value from their relationships with other BUs. An
important aim of this discussion would be to justify that findings from our
empirical study conducted at the business unit level can be then transported
to the inter-organizational level.
As also described in the introduction (Section 1) there is a considerable
amount of literature that examines the increasing dis-aggregation of
economic activity marked by the break-up of monolithic, vertically integrated
firms into smaller organizations. The resulting patterns of relationships in
different contexts and industries have been richly analyzed and labeled
variously as supply chains, value constellations, value webs, business
ecosystems, etc. For example, the supply chain of electronic components
industry members, is also described as a value constellation (lansiti and
West 1997). Here the interactions between different enterprises are
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interdependent and rich in knowledge about products and services (El Sawy
et al. 1999a). Similarly, the term business ecosystem was labeled in order to
capture the rapid flux of strategic business activity crossing traditional
boundaries of individual supply chains and industries (Moore 1996). In a
business ecosystem competition is intense because firms no longer compete
in a particular industry but very easily cross industry boundaries. As another
example, the notion of a value chain has been replaced by the concept of a
value web and is described as a “web of virtual enterprises that behave like
living, amoeba-like organisms” (Andrews and Hahn 1998).
Since there are multiple terms used to depict the current networked business
environment, we use the generic term, business networks, to represent any
scenario where relationships between different business groups or entities
are interdependent, i.e., they have impact on other relationships. The above
metaphors such as value constellations, value webs, supply chain network,
etc., are used in particular industry or strategic contexts - we term all of
these as business networks.
We extend the conceptualization of relational value in the context of business
networks where linkages across enterprises are for collaborative purposes.
As against arm-length transactions, in the concept of relational value has its
basis in several bodies of literature. From the traditional supply chain
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perspective, as increasingly more tiers of supply chain participants are
involved in information sharing, it creates deeper visibility within the supply
chain. This is termed as collaborative integration where all partners
exchange information and share knowledge around business processes (El
Sawy 2002). The benefits are not only in efficiency terms such as lower time
to market, lower inventory turns, etc., but also in value terms such as better
market fit of products and customer satisfaction. In this scenario we
segregate the benefits that can be obtained by a) transcending tiers of supply
chain partners in the current supply chain to create better visibility and b) by
supply chain partners delivering benefits to the business activity by virtue of
their participation in other parallel supply chains. Thus, broadly, one can view
business networks as yielding two types of indirect relational value.
1) Within-supply chain benefits: A hypothetical example in the context of
a high tech telecommunications industry would be a direct connection
between manufacturers (e.g. CISCO) and customers (e.g. AT&T) that results
in better information exchange. This in turn leads to enhanced product
design and other requirements. Thus, instead of the resellers only interacting
with the buyers, additional value and efficiency benefits can be generated by
involving the manufacturers in product delivery.
2) Cross-supply chain benefits: As supply chain partners are also
involved in other parallel supply chains, they filter in important information
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about the market needs and competitive environment. For example, if EDS
also resells products complementary to CISCO then it is able to enhance
CISCO’S product offering by stocking complementary components for
buyers. As another example, if EDS also resells competitors products, it can
transfer important market information to CISCO and therefore benefits the
focal supply chain.
In literature, relational value is synonymous with collaboration specific quasi
rents (Madhok and Tallman 1998a) and is tied specifically to the alliance.
The combination of firm-specific resources and transaction specific resources
formed during the alliance enables a level of accomplishment, which the
partners are unable to attain in the absence of the collaboration (Madhok and
Tallman 1998a, p.329). At a network level it was demonstrated that a higher
level of indirect connection of a focal actor with other entities led to a higher
creation of value. (Holm et al. 1999a).
The above description presents a significant opportunity for IS research.
Qualitative studies have distinctly identified various benefits of lOSs that
include higher market responsiveness and improved ability to predict
reactions of customers, channels and competitors to competitive initiatives.
Despite the overall belief in the trade and academic literature of the business
value of IOS, there is limited amount of research that investigates which IS
capabilities are important for higher relational value in business networks.
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Towards this end, different research streams can be applied to understand
how IS capabilities are useful to organizations. For example, it maybe fruitful
to hypothesize how IT affect absorptive capacity (Zahra and George 2002) or
help to create and maintain social capital (Nahapiet and Ghoshal 1998).
2.3. IS Research and Evolution of Business Relationships
This section summarizes our discussion before we attempt to develop the
research model. First, we note how the purpose behind IT deployment in
organizations has changed as the relative focus of IT investments shifted
from an intra-organizational level to the business network level (Error!
Reference source not found.Figure 3) (Table 1). That is, to the extent that IT
has been deployed to manage relationships, its strategic role has
strengthened (Nolan 2001). Then, literature in light of our research questions,
we identify what opportunities exist for contributing to IS.
2.3.1. Level of Analysis in IS Research
From a review of IS literature we identified three broad stages along which
the business purpose of IT has evolved. During the first stage, in the past, IT
was deployed largely to improve intra-organizational efficiency and
effectiveness. The IS functionality was not only transaction oriented but also
enabled communication between groups, decision makers and facilitated
executives’ information search. Thus, other than transaction processing
systems, research on other ITs such as decision support systems (DSS),
group decision support systems (GDSS) (Poole and DeSanctis 1989) and
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executive information systems (EIS) (Watson et al. 1991), etc., was also
conducted. Significant amount of research in the past two decades was
focused at the intra-organizational level.
In the second stage, deployment of IT investments to support business
activity at the (dyadic) inter-organizational level was concurrent with the
development of sophisticated IT products (Figure 3). This category of IT is
termed as inter-organizational systems (IOS). IS literature later focused on a
form of IOS, i.e., electronic data interchange (EDI). EDI is considered to be
an inflexible and transaction oriented proprietary technology where investors
face large switching costs ((Bakos and Brynjolfsson 1993). With
standardization of ITs, especially with the Internet technology, more flexible
ITs constitute lOSs (Straub and Watson 2001). These ITs offer functionality
ranging from automated, repetitive transactions to more collaborative and
customized workplace technologies . Thus, currently investments in lOSs are
aimed to not only save transaction costs (or to achieve higher market
access), etc., but also to enable better knowledge sharing and learning
between two or more organizations.
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Figure 3: Scope of information systems research and practice
Level of Deployment
►
Firm Business Dyads Business Networks
Intra- ----- N Inter-organizational ----- N, Inter-organizational
organizational
----- 1/
IS
1 ----- ✓
IS
- Transaction processing - EDI (Emmelhainz 1990) - Extranets/Portals (Press 1999)
systems, DSS, ESS (Ein- - CAD/CAM (Argyres - CAD/CAM (Argyres 1999b)
Dor and Segev 1993) 1999b; Press 1999) - Partner relationship management software
(Marcus 2001)
In the third stage, ITs are being deployed for a higher order function - that of
managing interactions between different business relationships as against
interactions at the dyadic level (stage 3:Figure 3). These interactions using
ISs are not restricted to automated transactions only but richer interactions
also (Pedersen and Larsen 2000). For example, inter-organizational systems
such as e-markets, extranets and portals facilitate more frequent and
complex interactions between groups across different firms and those
engaged in product design (Argyres 1999a), customer support and
collaborative planning (Sherman 1998), etc. Similarly, knowledge
management systems yield externalities in creation and dissemination of
knowledge (Ofek and Sarvary 2001). By enabling interactions between
entities spread across two or more firms, these systems give rise to scope or
scale economies. As a result, organizations are able to offer better products
to customers or reuse existing knowledge. Therefore, emerging literature
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suggests that in the third stage, lOSs are playing an important role in
sustaining newer forms of organizational networks.
Underlying the functionality of web-portals, extranets and other IT artifacts
are a variety of core IT components that encompass technologies such as
web services, XML wrappers, data warehousing/mining modules, off-the-
shelf packages such as SAP, BaaN, Siebel, etc. In view of the profusion of
these technologies it is necessary for managers to identify how these ISs
contribute to their performance in their business networks. This can guide
investment in emerging technologies to not only improve benefits from
relationships at a dyadic level but also at the business network level.
2.3.2. Limitations of IS literature: Linking IS Capabilities with Business
Value
Literature does indicate that IS capabilities help in exploiting synergies within
organizations in business networks (Ciborra and Hanseth 1998; Keen 1991).
Keen (1991) identified two dimensions of the business scope of IT
infrastructure, i.e., reach and range, as enabling organizations to maximize
value. However, it is not clear how and why IS capabilities can create indirect
relational value. For example, Weill and Broadbent (1998) also find that firms
who exploit synergies across business units have higher levels of IT
investment (Broadbent et al. 1999a). However, different ITs impact business
activities in different ways, which makes it necessary to delineate a sharper
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theoretical explanation than in existing literature. Similarly, Kayworth and
Sambamurthy (2000) describe how the extent of standardization of PC/LAN
architecture can be used to balance infrastructure responsiveness for
localized exploitation as opposed to enterprise-wide integration (Kayworth
and Sambamurthy 2000). However, they have not analyzed the relationship
between IT integration and its performance effects. Similarly, using a
contingency argument Wybo and Goodhue (1995) suggest that semantic
data standards are used to manage interdependence between organizational
subunits but find weak support for this thesis (Wybo and Goodhue 1995). As
discussed in section 1, we shall attempt to further sharpen the constructs,
reach and range (Keen 1991), and advance understanding as to how these
can impact indirect relational value.
On reviewing the nature of lOSs under study and the related IS capabilities
we find that IS research on business value has two main limitations that we
attempt to address in this dissertation. First, the theoretical bases for defining
business value have predominantly a transaction cost economics viewpoint.
It suggests that a major benefit of lOSs is saving transaction costs and thus
economizing on exchange activity between firms. We argue that 1) a broader
role of IS in enhancing production and not just exchange (Table 1) needs to
be recognized. This shift to a more balanced view of both the production and
exchange functions of IT enables consideration of knowledge and resource-
based logics to explore business value of IT in organizational networks.
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While organizational theorists have already recognized the utility of this
approach (Amit and Zott 2001, p.499), IS research in this direction is only
recently emerging. Assimilation of this argument in IT research can stimulate
development of interesting IS variables and their effects on value. Second,
business value-related IS research has restricted the conception of business
value at the dyadic level and has not advanced it to the network level. We
note that though the logic of complementarity effect is consistent with theory
in IS literature, few studies have explored how i) IT can enhance synergistic
effects across multiple dyads (Gosain et al. 2001) or how ii) IT can yield
benefits through indirect network connections (Ritter 2000).
In the next section, we develop the research model with IS capabilities as the
independent variables affecting indirect relational value. Using the theoretical
foundation offered by knowledge-based theories, we argue that knowledge
sharing intermediates the effect of IS capabilities on indirect relational value.
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3. Research Model and Hypotheses
For understanding how IT yields business value, a suggested approach is to
conceptualize key intermediating variables that in turn impact business value
(Barua et al. 1995). For example, for tracing and measuring IT contributions,
Barua et al. (1995) propose a two-stage analysis that first examines the IT
impact on “intermediate and higher level output variables”. In the next stage,
various organizational theories can be used to investigate how these
intermediating variables impact business value.
Following the above approach, Figure 4 explains that we need to identify
important intermediating variables that mediate the impact of IS capabilities
on indirect relational value. It is to be noted that much of existing IS literature
has not adopted this approach but has hypothesized a direct effect of IT-
related constructs on business value (Table 2, section 2). However, we
proceed by first identifying a suitable intermediating variable and then by
using suitable theories to understand the linkage between certain IS
capabilities and the intermediating variable. Literature offers various theories
to explain this linkage, since IT is viewed as a facilitating technology.
Figure 4 also indicates that an increase in the selected intermediating
variable need not be the outcome of IT investments only. Other
organizational factors, including organizational restructuring, alliances and
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business process reengineering (BPR), may also complement impact of IT
investments on the intermediating variable.
3.1. Knowledge Sharing as an Intermediating Variable
As mentioned above, literature presents several alternative intermediating
constructs (or value antecedents) that may mediate the impact of information
technology on business value (Figure 4). Inter-organizational relationship
literature proposes value antecedents such as knowledge sharing routines,
relation-specific assets, trust, etc., that may lead to higher relational value
(Dyer and Singh 1998b). Significant qualitative and empirical studies strongly
point to ability of IT to enhance information exchange and organizational
learning. Therefore, we extend IS literature to examine how knowledge
sharing may mediate the impact of IT on business value. Our overall
proposition is that knowledge sharing moderates the impact of IS capabilities
on indirect relational value.
Figure 5 is the research model. Table 3, at the end of this section,
summarizes the literature based on which our hypotheses are proposed.
40
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Figure 4: A conceptual framework for investigating the value of IT in business networks
Examples of
Organizational Initiatives
Organizational
Restructuring, M&A, ,
Alliances, etc. ,
IT Investments
Examples:
-Relation specific assets
-Knowledge sharing
-Bargaining power
-Cost savings
3.2. Knowledge Sharing and Indirect Relational Value
Prior literature richly describes how knowledge sharing is a potentially
significant antecedent of relational value. For example, it is argued that a
production network with superior knowledge-transfer mechanism among
users, suppliers and manufacturers will be able to “out innovate” production
networks with less effective knowledge-sharing mechanisms (Hippel 1988).
Similarly, knowledge sharing routines refer to a pattern of inter-firm
interactions that permits the transfer, recombination or creation of specialized
knowledge (Dyer and Singh 1998b). Based on benefits such as improved
innovation, knowledge sharing routines has been proposed as one of the
antecedents of relational rents. Empirical literature also strongly supports the
41
Business Process
Reengineering
Intermediating
Variables
Value in Business
Networks
_________________
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linkage between knowledge sharing and business unit performance. For
example, within the intra-organizational network of large multi-unit
companies, it was found that business units receiving knowledge from other
business units performed better in terms of innovation rate and financial
performance (Tsai 2001).
We define indirect relational value as the extent to which a focal business
entity’s relationships with other entities, benefits the focal entity’s overall
business network. A related construct called business network connection
refers to the influence that relationships with third parties have upon the focal
relationship (Holm et al. 1999b). A higher business network connection was
hypothesized to eventually lead to higher value creation in a focal
relationship.
Our definition of indirect relational value differs from the relational value as
conceptualized at a dyadic level (Dyer and Singh 1998b). Unlike this dyadic
level notion of relational value, our definition of indirect relational value views
a relationship as a transmitter of benefits (Halinen et al. 1999).
Indirect relational value is a predominant outcome of business network
relationships according to atleast two streams of literature on organizational
theory, i.e., social exchange theory and social networks. Social exchange
theory (Kelley and Thibaut 1978; Thibaut and Kelley 1959) suggests that in a
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context where organizations conduct and maintain a configuration of ongoing
relationships (Dwyer et al. 1987; Hallen et al. 1991), organizations may
benefit from one relationship to the extent that it enhances the other
relationships (and vice versa). This can happen because knowledge and
solutions from a focal relationship can be re-applied to other relationships
and vice versa (Anderson et al. 1994).
The different ways in which indirect relational value may accrue to
organizations, through other connected firms, is also investigated in the
social network literature (Nohria and Eccles 1992). Social network analysis
treats business entities as linked in a network. Benefits accrue to these
entities depending on their structural position in the network. Since there is
more than one way in which a structural position yields benefits (and
therefore contributes to its performance), it is critical to understand the
particular network mechanism that yields the benefits to a network entity
(Burt 2000). These different types of mechanisms are closure, brokerage,
prominence, contagion, etc. (Burt 2000). For example, if a network entity
brokers information between multiple network entities and thus gains
arbitrage, the member is said to accrue network benefits through a brokerage
mechanism. Similarly if a central member in a supply chain maintains rich
and efficient relationships with other members, the central member has
access to the knowledge and expertise of its partners, i.e., a closure
mechanism yields benefits to the network entity. In our research context we
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are not interested in the brokerage benefits accruing to any particular
network member but in the benefits resulting from intense, rich interactions
with the network partners. These benefits could be access to new customers,
acquisition of knowledge and expertise and improving coordination to
improve efficiency of business operations (Dyer and Nobeoka 2000;
Hagedoorn and Duysters 1999).
The notion of indirect relational value is also consistent with the economies of
scope argument in a production context (Armour and Teece 1980). Here an
organization can save on costs by reapplying its existing resources to the
activities performed in other parts of the organization. Such resources are
broadly labor and capital. However, in the B2B context, economies of scope
can result as the BUs within the organizations learn to reapply the knowledge
of the other BUs in order to derive cross-selling benefits or higher customer
satisfaction. This results in faster customer acquisition or higher customer
loyalty. The overall result is higher indirect relational value derived by an
organization from its portfolio of relationships. Recent IS literature also
indicates the benefits of transporting knowledge acquired during a particular
transaction to other business activities by using knowledge management
systems (Ofek and Sarvary 2001).
We operationalized knowledge sharing as the extent to which an enterprise
shares knowledge about its business environment with other business units,
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its partners, and its customers. Here, rather than the type of knowledge, we
focus only on the manner in which knowledge about the business
environment is shared with other entities.
Hypothesis 1: Knowledge sharing across BUs is positively related to indirect
relational value.
As mentioned before, empirical studies testing the existence of indirect
relational value are recent (Holm et al. 1999b). The above hypothesis is
supported strongly by theoretical literature on alliances. However, though
empirical literature on social networks, described above, confirms the
existence of indirect relational value, these studies have a different
methodological approach. In the social network method, all firms in the
sample are connected to each other through network linkages. This permits
structural analysis (Wasserman and Faust 1994). Unlike this, in our context,
the units of analysis are not necessarily linked to each other within a single
network.
3.3. IS Capabilities
The importance of information and communications technologies in
enhancing knowledge management in organizations is increasingly being
realized (Teece 1998). IT has been found to aid inter-organizational learning
(Scott 2000). Similarly, advanced ITs are understood to enhance
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organizational intelligence (Huber 1990). Thus, IT effects are hypothesized at
the intra-organizational as well as inter-organizational level to result not just
in improved data or information transfer, but also in learning and intelligent
decision-making.
Media richness theory (Daft and Lengel 1986) and information-processing
theory (Galbraith 1973), both are consistent with our overall argument that
higher knowledge sharing can result from IS capabilities. According to
Galbraith (1973), organizations are information processing entities and they
either structure themselves to reduce information processing costs or adopt
IT to do so. IT helps to reach information at low cost to the key decision
making points in the organizational structure. Media richness theorists view
various communication media long a richness continuum and suggests that
more equivocal messages can be communicated by using richer media (Daft
and Lengel 1986). Thus, if advanced ITs can be considered as enabling the
transfer of richer and uncodifiable knowledge.
Empirical studies also have revealed that organizations benefit from higher
internal IS integration (Truman 2000), process innovation (Clark and
Stoddard 1996) and IS embeddedness (Chatfield and Yetton 2000). Email,
intranets, and extranets were actually found to be significant in knowledge
sharing (OR/MS April 2002, p. 23). Thus, the existing IS literature provides a
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significant level of intuition for our proposition that IS capabilities can be used
to improve knowledge sharing across business entities.
Proposition 1: The IS capabilities of an enterprise can enable greater
knowledge sharing across its relationships with other enterprises.
In the context of B2B sales channels, there is likely to be a need on the part
of a focal business entity for a high volume of information to facilitate
innovation and to allow scouting for significant market developments, new
customers, and access to other useful resources—such as financing and
regulatory procedures—that can reduce time and resource requirements
(Walter et al. 2001). In large companies with multiple BUs serving
overlapping customer segments or product markets, the B2B sales activity
also involves maintaining linkages with the other BUs for enhancing their
product/service offerings or for cross-selling.
In the above scenario, IS capabilities provide such BUs, the enterprises and
their business partners, with a common platform for sharing knowledge.
Literature on IT infrastructure identifies two dimensions, reach and range as
leading to increased synergies across business units (Broadbent et al.
1999a). However, there are few empirical studies that attempt to establish a
theoretical link between IS capabilities and knowledge sharing.
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Therefore, we shall now conceptualize IS capabilities in terms of its three
distinct facets, IS integration, IS range and IS reach. First, for understanding
how IS integration may lead to higher knowledge sharing, we argue using the
absorptive capacity viewpoint (Zahra and George 2002). We propose that IS
integration of a focal BU with other BUs represents the absorptive capacity of
the focal BUs towards these other BUs. Second, higher IS range implies a
higher extent to which the IT portfolio represents codified rules for performing
the business activities and a higher extent to which employees exposed to
the IT portfolio. Thus, higher IS range can be viewed as enhancing the
cognitive dimension of social capital within the enterprises (Nahapiet and
Ghoshal 1998). The cognitive dimension of social capital refers to resources
that provide shared representation, interpretations, and systems of meaning
among parties (p.244). Thus IS range can also indirectly contribute to
knowledge sharing. Finally, higher IS reach implies a richer media for
employee-to-employee connectivity and thus may enhance transfer of
knowledge.
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Figure 5: Research Model
ANTECEDENTS
Information Systems ™ ' ^ C G CONSEQUENCES
Capabilities V A K iA bL b
Workflow
Interdependence
IS Reach
IS Range (Access)
Relational
Value
Knowledge
Sharing
IS Range
(Functionality)
Social Tie Strength
IS Integration
/ •
- Social Tie Strength X IS integration
- Workflow Interdependence X IS
integration
3.4. Hypotheses: IS range, IS integration, IS reach
Our choice of the three different IS capabilities dimensions is based on a
review of prior IS literature. Reach and range of IT infrastructure are
traditionally discussed as key dimensions in literature (Keen 1993). However,
more recently, the need for higher IS integration is significant in view of the
emerging trend where enterprises invest in multiple enterprise application
products for their various functional needs. The excessive number of differing
standards and vendors supplying these enterprise software products has led
to an acute need for integrating these systems (Magretta 1998; Markus
2000).
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3.4.1. IS integration and knowledge sharing
We apply the understanding offered by the theory of absorptive capacity to
understand the role of IS integration in enabling knowledge sharing (Cohen
and Levinthal 1990). By IS integration we mean the extent to which the
software application components across business entities are blended into a
functional whole. Various technologies are used to integrate applications
across enterprises. These are composed of or referred to as XML, web
services, gateways, middleware technologies, integration brokers, etc.
(Altman et al. 1999). Application programming interfaces (APIs) are the
standardized interfaces supplied by application software vendors. Once
implemented, these software applications are integrated using these APIs
with those of other sub-units or business partners. IS integration enables a
seamless exchange of data and invocation of business processes across
enterprise boundaries (Hasselbring 2000). Ideally, in addition to achieving
interoperability in terms of data and application, IS integration also involves
achieving interoperability at the semantic level (Yang and Papazoglou 2000).
The theory on absorptive capacity views internal knowledge structures in
firms as critical to the absorption, assimilation, enhancement, and application
of useful external knowledge (Cohen and Levinthal 1990). Though,
absorptive capacity argument refers to the internal knowledge structures, we
present our argument based on a competing viewpoint that absorptive
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capacity can also be embedded in the business network linkages (Kogut
2000). Dyer and Singh (1998) term this as partner-specific absorptive
capacity (Dyer and Singh 1998a). This means that combinative capabilities
(i.e., coordination rules and capabilities to combine resources possessed by
different firms) are nurtured within network links between organizations. This
enables business units to acquire and process external knowledge.
Therefore, we argue that IS integration with external entities represents an
enhanced absorptive capacity of the organization. The locus of this
absorptive capacity is in the integrated information systems. Having
integrated ISs with well-defined data and application standards (e.g.,
application programming interfaces, or APIs) enables organizations to share
richer knowledge across their business relationships (Markus 2000). Greater
IS integration implies that the complex rules of coordination have been
streamlined. Railing and Housel (1990) have described how a company
authorized a new integrated data network as an opportunity to integrate
islands of information spread throughout the company (Railing and Housel
1990). The emerging literature on collaborative integration identifies the
significant benefits that can be gained by organizations trying to successfully
automate and “informate” (Zuboff 1988) their business activities beyond their
boundaries (El Sawy et al. 1999b).
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Currently, as enterprises are implementing increasingly sophisticated IT
applications to conduct business with other enterprises, higher IS integration
represents a significant amount of encoding of inter-firm business rules. For
example, CISCO makes less than half of the products it sells. This is
because, its order fulfillment process is well integrated with its suppliers such
that CISCO’S customers’ orders are passed on to the suppliers without any
intervention by CISCO (Markus 2000).
Thus, though empirical studies investigating the positive impact of integration
on knowledge sharing are limited, there is significant qualitative evidence.
Our next hypotheses contributes to literature by empirically testing the effect
of IS integration on knowledge sharing.
Hypothesis 2: IS integration is positively related to knowledge shanng
across BUs.
3.4.2. IS Range and knowledge sharing
In literature, IS range refers to the extent to which an IT application portfolio
offers complex and sophisticated features for conducting a business unit’s
internal operations (Weill and Broadbent 1998). This is different from the
construct of IS integration discussed above. For example, an IT portfolio that
consists of several best-of-breed applications, each with highly sophisticated
features, can be considered as having a greater range than a portfolio with a
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monolithic, integrated ERP application but no in-depth functionality. The
trade-offs faced by IT managers in deciding for a best-of-breed versus an
integrated IT infrastructure have been analyzed by Dewan et al. (Dewan et
al. 1995). It demonstrates that IS range and IS integration may be considered
as distinct (we shall discuss this distinction in more detail later).
We refer to IS range of an enterprise as the multiplicity of features offered in
aggregate by the inventory of software applications within the business unit.
IS range has two dimensions. On the one hand it is important to recognize
the inventory of software applications, i.e., the richness of application
functionality. On the other hand, it is important to recognize the extent to
which this inventory of IT applications is accessible to users. From the
perspective of an IT manager it is often not only important to decide to
purchase licenses but also to decide the number of seats. Each seat typically
increases the total cost. In other words, if access to an IT portfolio is
restricted to a select group of employees (i.e., lower number of seats), it is
considered to have a smaller range than one that makes all IT applications
available to all employees for performing their business activities.
There is not much empirical support in literature on whether IS range can
lead to higher knowledge sharing. We theorize this link based on the
absorptive capacity (Zahra and George 2002) and social capital viewpoints
(Nahapiet and Ghoshal 1998). A higher IS range signifies a higher level of
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absorptive capacity that helps the employees to assimilate internal
information better and generate newer insights into its business activities. For
example, organizations using sophisticated forecasting and workflow tools
are better able to combine information from different internal sources (Basu
and Kumar 2002). Thus, having a greater IS range (in terms of functionality)
implies that the employees have access to rich and in-depth knowledge
about their business activities. Nahapiet and Ghoshal (1998) also refer to the
combination capability that results when prior related knowledge is made
available to an organization to combine and process newer knowledge (p.
249). Thus, higher number of features in a software application implies a
higher extent to which a system of shared representation of the business
operations is encoded in the IT applications. It also implies that an implicit,
rich knowledge structure is imposed on the organization, which can be used
to process and share knowledge and to thus to create higher value.
Further, when all employees have wide access to the software applications
to perform their work, they develop a common cognitive frame of reference
and a shared representation of the environment and business activity. This is
similar to the “cognitive dimension” of social capital (Nahapiet and Ghoshal
1998) that facilitates creation of intellectual capital. Therefore, higher IS
range in terms of access to employees should also lead to higher knowledge-
sharing.
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Since empirical evidence of the link between IS range and knowledge
sharing is not present, we treat this hypotheses as exploratory in nature.
Hypothesis 3a: IS range (functionality) is positively related to knowledge
sharing across BUs.
Hypothesis 3b: IS range (access) is positively related to knowledge sharing
across BUs.
Distinction between IS integration and IS range
In the above hypotheses, integration is conceptually distinct from IS range,
i.e., the functionality ottered by these enterprise system modules (Markus
2000; McKeen and Smith 2002). One of the significant activities in the
implementation and maintenance of ERP systems is the need to integrate
the ERP package with legacy systems as well as to integrate the business
rules across various software modules (Sabine and Swanson 1999). While IT
investments in these software modules are made to equip the organizations
to perform specialized business functions, IT investment in integration
projects are aimed to integrate these diverse software modules. Such
integration-related investments include IT components such as gateways,
brokers, and other types of middleware (Altman et al. 1999; Chellappa and
Saraf 2000). With the increasing IT intensity of most business enterprises,
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the importance of IS integration has also increased considerably (Weber and
Pliskin 1996, p. 83).
IS range and IS integration need not be considered as exclusive to an IT
component also. Each IT application offers both dimensions of IS
capabilities, IS range and IS integration. The first dimension, IS range, is that
which automates business activities, i.e., internal operations. Therefore, the
set of IT applications and databases such as supply chain management
software, CRM, logistics packages, etc., all are primarily designed to perform
well-defined business functions. These components may measure high on IS
range dimension. The second dimension is the integration capability, i.e.,
extent of integration of applications and databases across various
organizational units. This may be achieved through integration products such
as middleware (Altman et al. 1999) or web sen/ices (Schlueter-Langdon and
Hars 2002). Thus, integration products may be viewed as measuring high on
IS integration dimension but low on IS range dimension. Similarly, apart from
performing internal operations, software applications such as CRM, SCM,
etc., may also offer varying levels of integration capability.
The trade-off between high IS integration versus high IS range is clearly
articulated by Dewan et al.’s discussion on corporate standards (Dewan et al.
1995). They analyze the contingencies under which organizations will adopt
best-of-breed products (high IS range but low integration) or a monolithic
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enterprise application (possibly low IS range but high integration). Thus,
though an organization may implement only a small number of applications
related to manufacturing (e.g. planning, inventory, scheduling and
outsourcing) but these can be highly integrated across subunits.
The distinction between IS range and IS integration also appears in
economics literature (Chellappa and Saraf 2001). Economic models of
compatibility and standards in component industries consider the strategic
nature of interoperability as distinct from the component functionality
(Economides 1989; Farrell and Saloner 1992; Matutes and Regibeau 1988).
A high-end monolithic ERP system can measure low on the breadth of
features it offers, the type of internal business operations it supports and the
restricted access it may offer the employees; but the ERP system can
measure high on integration capability. As another example, large investment
in XML technology is often made not to provide front-end for web access to
customers and partners but to perform back-office functions such as Extract,
Transfer and Load (ETL) data across different applications of different
subunits. This investment can be said to augment IS integration rather than
IS range.
3.4.3. IS reach and knowledge sharing
Reach refers to the extent to which employees within the ecosystem of
multiple organizations are electronically connected with one another. Greater
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reach is characterized by increased connectivity among individuals and
groups. Whereas IS range represents the sophistication of IT applications in
terms of automating or facilitating internal operations in a business entity, IS
reach captures the extent to which the employee-to-employee
communications is supported across business unit boundaries. Dedicated,
real-time, high-bandwidth information infrastructure provides a richer medium
through which more complex and useful information can be transmitted.
Such infrastructure also enables the extension of information to the point of
consumption or usage (Galbraith 1973).
There is significant empirical support for this hypothesis in theoretical
literature. However, empirical evidence of knowledge sharing effects at the
network level is limited. In other words, few studies have investigated
whether higher IS reach can enable business units to share knowledge about
customer relationships and product/market requirements in a B2B sales
context. Media richness theory supports the view that the ability of IT
infrastructure to facilitate richer communication across business entities is
likely to lead to greater knowledge sharing (Daft and Lengel 1986).
Accordingly to this theory various communication media lie along a richness
continuum and more equivocal messages can be communicated by using
richer media facilitated by ITs. Thus, having higher IS reach implies
availability of richer media.
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The hypotheses below can be viewed as having moderate empirical support
in literature. Empirical studies found that connectivity offered by information
technology is highly related to richer and more effective information sharing
(Carlson and Zmud 1999; Jarvenpaa and Staples 2000; Staples and
Jarvenpaa 2001). For example, e-mail, intranets, and extranets were found
to be more significant in knowledge sharing than talking to people in person
(OR/MS April 2002, p. 23). We hypothesize that business units having
greater reach across other business units will have higher levels of
knowledge sharing.
Hypothesis 4: IS reach is positively related to knowledge sharing across
BUs.
3.4.4. Comparing with Reach and Range as conceptualized in literature
The diagrams below describe how our conceptualization of IS integration, IS
reach and IS range differs from that proposed by Keen (1991). Figure 6
depicts how the two dimensions, reach and range, are conceptualized in
traditional IT infrastructure literature (Keen 1991).
However, we argue that IS literature can benefit by segregating the different
ITs depending on the theoretical explanations of how these ITs can impact
knowledge sharing. For example, in Figure 6, the technologies that facilitate
individual-to-individual communication such as email and videoconferencing
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have different impacts than technologies such as application programming
interfaces (APIs), middleware or web services that integrate software
applications. Thus, our attempt is to segregate the different IS capabilities
depending on their theoretical link with the knowledge sharing construct.
Figure 7 depicts how our conceptualization differs from that in Figure 6.
Figure 6: Reach and Range as conceptualized in IS literature (Keen 1991)
Reach
Anyone, anywhere
Customers or suppliers
regardless of IT base
Customers or suppliers with
similar IT base as ours
Across different Business
Units (BUs) abroad
Across different BUs
domestically
Across geographical
spread single BU locations
Within a single business
unit location
Send Access to
messages stored
Perform Complex
simple transactions
information transactions on multiple *
mtranet applications
Range
Though both IS integration and reach are facets of the IS capabilities of
enterprises interacting with other enterprises, the two elements are distinct
from a conceptual standpoint (Figure 7). While IS reach signifies the
availability of richer media for individual or group-level communication across
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BUs, it does not involve the encoding of business rules of coordination.
Therefore, IS reach can be viewed as facilitating unstructured coordination,
while IS integration facilitates encoded, structured coordination across BUs
(e.g. application programming interfaces (APIs)).
Figure 7: Our conceptualization of IS Reach, IS Range and IS Integration
Across different BUs
Aero ss g e og rap hi cal...........
spread single BU locations
Within a single business
unit locatio
Relate<| ^s:
Videoconferencing, email,
groupwjare, virtual
collaboration tools, eitc.
Reach
Related ITs:
Application
programming
interfaces (APIs),
middleware, web
services, etc.
Integration
-Rar-ge
Send
messages
Access
to stored
info or
intranet
Perform
simple
transactions
Complex
transactions
on multiple
applications
3.5. Organizational Variables and Knowledge Sharing
IT is considered to be primarily an enabling technology. In addition to IT, it
typically takes other factors in order to yield business value. Among those
other factors, organizational factors, and those specifically related to inter-
organizational relationships, are deemed important (Dyer and Singh 1998b)
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In the context of knowledge sharing, the literature points to two variables of
workflow interdependence and social tie strength that can enhance
knowledge sharing.
3.5.1. Control Variables: Workflow Interdependence and Social Tie
Strength
Workflows are the materials, objects, or clients that are sent or transported
between people within organizational units. Workflow interdependence refers
to the work connectedness of unit personnel, or the extent to which people in
a unit are interdependent upon one another to perform their individual jobs
(Van de ven and Ferry 1980, p. 166).
In connection with workflow within and across organizations, there is
significant literature about business routines. According to our interpretation
of literature on workflow management and on organizational routines,
routines refer to a sequence of formalized business tasks that in turn
constitute a workflow (Basu and Kumar 2002; Feldman 2000). Routines play
an important role in organizational learning (Feldman 2000; Nelson and
Winter 1982; Pentland and Rueter 1994). A key thesis in literature is that
organizational learning occurs as routines are continuously altered by
agents. Baum and Singh, for instance, categorize routines as genealogical
entities that “pass on their information largely intact in successive
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replications” and that are “concerned with the conservation and transfer of
production and organizing skills and knowledge” ((Baum and Singh 1994).
Though a workflow can be argued as associated with knowledge sharing,
what is not yet empirically examined is what role higher workflow
interdependence plays in enabling knowledge sharing. Empirical studies
suggest that all routines are continuously subject to change as a result of
which organizational learning occurs. The potential for change in internal
dynamics of the routine itself lies in the “thoughts and reactions of the people
who participate in the routines” (Feldman 2000). As these people
increasingly modify the routines, they enact their understanding of the
business activity and the environment. Therefore, we argue that in a highly
interdependent workflow where business tasks are highly reciprocal, such
routines become conduits for learning by the participants across BUs. We
can operationalize workflow at the inter-unit level because our unit of
analysis - a business unit is often an independent business-unit in large
firms.
In the B2B sales environment highly interdependent tasks can be associated
with more intense knowledge exchange across BUs. For example, business
processes such as cross-selling, research and product development are
characterized by highly reciprocal tasks between business units, especially
those serving overlapping customer and product segments. In this context,
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high interdependence would result in the sharing of knowledge such as
insights about customer preferences, new product attributes and market
needs. Though we believe that theoretical support for this hypothesis is
strong, there is not much empirical support for workflow interdependence
being associated with higher knowledge sharing across BUs.
Hypothesis 5: Workflow interdependence is positively related to knowledge
sharing across BUs.
Social capital theory describes the relational dimension of social capital as
characterized by strong social ties between employees (Nahapiet and
Ghoshal 1998). Social ties embody trust between parties. Shared norms
engendered by social ties positively influence knowledge exchange
processes. Similarly, obligations and expectations arising from social ties
represent a commitment to future activity between the parties having social
ties (Nahapiet and Ghoshal 1998).
In empirical literature strong social ties have been found to enable transfer of
tacit knowledge (Hansen 1999). Inter-unit relations were also found to be
significant in enhancing knowledge transfer in intra-organizational knowledge
networks (Hansen 2002). In a recent extension of social capital theory, it is
found that social capital yields three distinctly different kinds of information
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benefits in the form of information volume, information diversity and
information richness (Koka and Prescott 2002).
In the B2B sales scenario there is considerable information available on
market needs, new technologies, product attributes and business
opportunities. Sales executives are continuously scouting for ways to satisfy
customers and acquire new customers. Here strong social ties can help to
filter out relevant information to improve sales effort such as customer
satisfaction, cross-selling and customer acquisition. We hypothesize that
social ties among individuals across business units significantly influence
knowledge sharing and thus yield indirect relational value.
Hypothesis 6: Social tie strength is positively related to knowledge sharing
across BUs.
3.5.2. Moderating effects of Workflow Interdependence and Social Ties
between IS Integration and Knowledge Sharing
We expect that highly interdependent workflow is likely to accentuate the
positive effect of IS integration on knowledge sharing. Information technology
is most often used in automating workflows (Basu and Kumar 2002). The
literature on business process reengineering identifies the added benefits of
streamlining business processes with the help of information technology
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(Broadbent et al. 1999b; Hall et al. 1993). BPR initiatives are characterized
by formalized workflow established across participating business units.
The integration of workflows across different business entities is
accomplished by connecting related IT components using standardized
technologies such as application programming interfaces (APIs) (Yang and
Papazoglou 2000). This is because by using a well-integrated information
systems portfolio, the information generated during the workflow can be more
effectively captured and disseminated across the participating BUs. This can
result in higher knowledge sharing about environmental conditions, business
opportunities and customer requirements across BUs. Recent empirical
findings in IS literature indicate that standardized process interfaces across
supply chain partners can lead to richer information exchange (Gosain 1999).
Similarly, inter-organizational process interconnectedness was found to lead
to higher knowledge creation (Malhotra 1999).
Though, to our knowledge, empirical studies that confirm the moderating
impact of workflow interdependence on knowledge sharing do not exist,
extant literature strongly offers this intuition.
Hypothesis 7: Workflow interdependence positively moderates the effect of
IS integration and knowledge sharing across BUs.
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Having a highly integrated IS may not necessarily lead to higher knowledge
sharing. However, one can expect that if social ties are present users are
more likely to take advantage of a well integrated IT infrastructure. Thus IS
integration may actually enhance knowledge sharing. That is, social ties may
be complementary to IS integration since individual ties may act as “human
patches” to smoothen exceptions and coordination problems across BUs. For
example, the lack of seamless data/application integration can be overcome
on an ad hoc basis when individuals across BUs have strong social ties.
Similarly, in a qualitative study relations between users of ERP systems have
been found to play a significant role in maintenance of these systems
(Sabine and Swanson 1999).
Hypothesis 8: Social tie strength positively moderates the effect of IS
integration on knowledge sharing across BUs.
In this section we presented a set of hypotheses (Table 3) to capture the
effect of three IS capabilities, IS reach, range and IS integration, on
knowledge sharing across BUs. In addition, we also included two control
variables, workflow interdependence and social tie strength. Because two
these variables have a significantly strong support in the literature, we also
explored their moderating effect on the linkage between IS integration and
knowledge.
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Table 3: Main model relationships and theory bases
Main relationships in
the research model
Theoretical support
Knowledge sharing and
indirect relational value
- Strong support at the dyadic level
Literature: Business value of network connections
(Holm et al. 1999a), knowledge transfer and
performance in intra-organizational networks (Tsai
2001), learning in inter-firm networks (Hagedoorn
and Duysters 1999), social exchange theory
(Anderson et al. 1994; Thibaut and Kelley 1959)
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Table 3 (cont.)
IS capabilities and
knowledge sharing
- Strong support for IS integration and knowledge
sharing at dyadic level. E-business integration
(Markus 2000; Zahra and George 2002) absorptive
capacity (Zahra and George 2002), corporate
compatibility standards (Dewan et al. 1995)
- Weak support for IS range with knowledge
sharing. Cognitive dimension of social capital
(Nahapiet and Ghoshal 1998)
- Moderate support for IS reach with knowledge
sharing. Media-richness theory (Daft and Lengel
1986), Information processing theory (Galbraith
1973), channel expansion theory (Carlson and
Zmud 1999), information sharing (Staples and
Jarvenpaa 2001)
Workflow
interdependence and
knowledge sharing
- Weak support at dyadic level
Literature: oraanizational routines (Feldman 2000:
Pentland and Rueter 1994)
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Table 3 (cont.)
Social ties and
knowledge sharing
- Strong support at network level
Literature: knowledae/information transfer throuah
social ties (Granovetter 1973; Hansen 1999; Koka
and Prescott 2002; Tsai 2001)
Interaction between
workflow
interdependence and IS
integration
- Strong support
Literature: suddIv chains (Gosain 1999: Malhotra
1999), business process reengineering (Hall et al.
1993)
Interaction between
social tie strength and IS
integration
- Weak support
Literature: Relational foundations of ERP
maintenance (Sabine and Swanson 1999)
Whereas some hypotheses that we presented are strongly supported in
literature, others are not. Therefore, an attempt is made by developing theory
to support these hypotheses.
Hypothesis 1 proposes that knowledge sharing across business units is
associated with higher indirect relational value. Here, knowledge sharing is
the independent variable. There is significant literature support for knowledge
sharing related to relational value defined at a dyadic level but not in terms of
indirect relational value.
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Literature support for hypotheses 2 to 6 is lacking in two respects. First, the
transaction value framework described earlier is only recently being used to
support the association between IS related constructs and knowledge
creation and information exchange (Gosain 1999; Malhotra 1999). Second,
though knowledge sharing has been extensively conceptualized in prior
studies there is no empirical extension of this construct to capture the
interdependence across relationships - i.e., how knowledge about an
enterprises business network can be shared with business partners. Social
exchange theory does suggest that enterprises form relationships so that
knowledge and solutions from one relationship can be transported to other
relationships - however, we find that there is no empirical study supporting
this effect (Anderson et al. 1994).
Hypotheses 2 to 6 propose that different dimensions of IS capabilities and
two distinct organizational factors lead to higher knowledge sharing across
BUs. Here knowledge sharing is the dependent variable. Specifically
hypotheses 2 to 4 propose that the three IS capabilities (IS integration, range
and reach) lead to higher knowledge sharing. In literature, whereas the
linkage between IS integration and knowledge sharing is strongly supported
at the dyadic level, there is scarce literature examining the linkage between
IS range and knowledge sharing. Therefore, this hypothesis is treated as an
exploratory one.
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Hypothesis 5 finds moderate amount of support in emerging literature on how
business process interfaces may lead to richer information exchange (Gosain
1999). However, the literature on business process reengineering offers
considerable support at the intra-firm level. But as mentioned previously, the
conceptualization of knowledge sharing in this study differs from that in prior
literature.
Hypothesis 6 can be said to be strongly supported in literature on social ties.
The social network literature supports the idea that social ties are a rich
source of information about actors’ network environment, opportunities and
solutions to the business problems.
Hypotheses 7 and 8 investigate the interaction of social ties and workflow
interdependence with IS integration. Both hypotheses can be considered to
be supported weakly in literature. Specifically, hypothesis 7 proposes how in
the presence of workflow interdependence, IS integration has an enhanced
impact on knowledge sharing. This hypothesis finds support on research on
the enabling effect of IT on BPR initiatives. Hypothesis 8 investigates
whether social ties positively moderate the effect of IS integration on
knowledge sharing.
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4. Research Design
4.1. Survey Design, Instrument Validation and Data Collection
The primary method used to validate the research hypotheses was a
questionnaire survey. The survey was designed as a one-shot, cross-
sectional study. Pretests of the research model and questionnaire instrument
were done in three phases. The pretests helped to confirm that the survey
items would demonstrate discriminant and convergent validity in the final
data set. Following pretests, the actual data collection was done in three
rounds.
We developed an initial pretest questionnaire (Table 4) by including 6 items
for IS integration, 5 items each for IS range (access) and IS range
(functionality). For IS reach, we included 6 items and 5 items for knowledge
sharing. For other constructs, we used 3-4 items each with the exception of
indirect relational value, for which we used 2 items. For measuring workflow
interdependence we used the 4-item based Guttman scale (Van de ven and
Ferry 1980). Psychometric scales were used for measuring all constructs.
Most construct definitions and scales were new or represented significant
refinements of existing constructs. We used Likert scaling with 1-5 strongly
disagree - strongly agree response scales for all constructs (except the
workflow interdependence construct).
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We used our own scale for measuring indirect relational value in the following
manner. In the initial phases of the data collection we used two main items to
capture indirect relational value, with each item also including 3-4 sub items.
For example, for each of the two items measuring the indirect relational value
construct (Table 6), we had included Likert scale sub-items such as i)
benefits from lower time to market, ii) customer satisfaction and iii) cross-
selling benefits, all resulting from the network linkage with other BUs.
However, at the end of phase one of data collection, we concluded that the
response time for the questionnaire is increased significantly because of
these sub items. Most of the responses to these sub-items were highly
correlated with the response to the main item. Therefore, for our remaining
data collection, we measured indirect relational value using only two main
items. We note that an earlier attempt to measure the value of a relationship
also used only two items each with factor loadings of 0.7 and 0.72 (Holm et
al. 1999b). Survey method literature also notes the merits of single item
measures as compared to multiple item measures (Gardner et al. 1998).
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Table 4: Initial pretest survey instrument: Construct name, # of items and type of
scale
Construct name # of items in
the initial
phases
Type of
scale
Literature
source
IS integration 6 Psychometric New scale
IS range (access) 5 Psychometric New scale
IS range
(functionality)
5 Psychometric New scale
IS reach 6 Psychometric New scale
Social ties 4 Psychometric New scale
Workflow
interdependence
4 Guttman
(Van de ven
and Ferry
1980)
Knowledge sharing 5 Psychometric New scale
Indirect relational
value
2 Psychometric New scale
During our first pretest we aggregated and randomized the scale items for all
three independent IS variables in our research model (IS reach, IS range
(functionality), IS range (access) and IS integration). Then, we asked a class
of eight graduate students in a Ph.D. seminar class to allocate these scale
items to four categories which we renamed as connectivity, functionality,
access and integration. The percentage of wrongly allocated questions was
approximately 3 percent. We corrected the wording of the items based on
this feedback. This pretest helped to confirm the face validity of the survey
items for the IS-related constructs.
In the second pretest we administered the instrument to a class of 20 MBA
students. This pretest included survey items for not just the IS variables but
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also for the entire research model. The students were requested to assume
that they were business unit heads and to comment on ambiguously worded
items. A few items had to be altered so as to avoid confusion about the unit
of analysis. Although we did not test the discriminant and convergent validity
using conventional analysis, the responses from the MBA students indicated
that the survey items may likely demonstrate convergent and discriminant in
future data sets that we shall collect. While preparing the questionnaire for
the next phase (i.e., interviews), we reverse-coded selected items. Then, in
order to avoid halo effects, items for IS variables were interspersed among
themselves but were maintained in a separate section in the survey. A similar
procedure was used when including items measuring organizational
variables. Thus, the questionnaire had three separate sections titled value
measurements, organizational characteristics and information systems
characteristics.
The third pretest was conducted through interviews with six industry
professionals in the high-technology sector. The interviews (lasting 45
minutes to 1 hour) were used to ascertain the face validity of the research
model and the research instrument (Table 5)(Peter 1981). An unstructured
interview technique was employed in which the interviewees were first asked
about the nature of the information technology they employed in their
interactions with their customers. For interviewees who were consultants, we
posed questions about their understanding of what type of sell-side IT
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applications were currently in demand. Care was taken so as not to reveal
the research model to the interviewees, and especially our interest in the
network benefits of their business relationships. For performing B2B sales
activities, we also asked about the importance of IT applications in reducing
costs, i.e., for performing current activities more efficiently. We found that for
B2B sales activity, rather than saving costs, increasing knowledge exchange
about the customer and market requirements is considered to be the key to
better sales performance.
Table 5: List of key interviewees and findings
# Description of interviewees
durina survev development
Kev findinas and discussion points
1
IT professional/SAP
implementer
Integration issues significant even after
implementing monolithic ERP systems. The
trade-off between best-of-breed applications
and monolithic ERP systems is very pertinent.
2
Past customer account
manager in a large
telecommunications service
provider company
Cross-selling and customer satisfaction is one
of the major objectives of mergers and
acquisitions. Ability to mix and match service
components in collaboration with partners and
other BUs is a key capability.
3
Strategy consultant to
several multi-divisional
organizations
The second-order effects of business
relationships are extremely important, even in
areas that are not considered high
technology.
4
IT professional in a
telecom/networking
equipment multi-divisional
firm
IT applications are extremely important in
recording and retrieving customer-specific
knowledge captured by other field-service
teams. These applications are also connected
at the back end in order to provide feedback
to designers and R&D.
5
Business development
manager of a supply chain
software vendor
Information integration in a supply chain is
increasingly managed through information
“cockpits” in SCM packages. This feature is
common now with increasing prevalence of
business process networks.
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Ta ble 5 (cont.)
6
Research director of an IT
consulting firm
Partner relationship management is an
emerging technology that already has
significant relevance in terms of managing
sales leads, recruiting channel partners, and
managing channel relationships.
4.2. Sample Selection and Survey Administration
The following criteria were established for identifying target respondents.
First, the respondents had to be managers involved in B2B sales activities.
Second, the target companies had to be in IT-intensive industry sectors. We
left the definition of a “division” to the interpretation of the respondent. In the
questionnaire, each respondent was asked to define the primary criterion
used to identify boundaries between divisions (e.g., products made,
customer segments addressed, manufacturing processes used, etc.).
The actual data collection was done from August 15 to mid-November 2002,
in three consecutive rounds. The two criteria for identifying the pool of
respondent company business units were as follows. First, the context had to
be business-to-business sales activity, and it was to be IT-intensive. Second,
the organizations had to be medium- to large-size multi-unit companies. The
questionnaires were administered to those managers who were involved in
B2B sales activity in these companies. During our exploratory interviews in
the third phase of the pretest described above, we concluded that the most
suitable respondents would be sales/marketing managers, product/business
development managers, or business unit heads within target companies.
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In the first round of data collection, a suitable pool of target companies and
their managers was accessed through a significant source of survey
research data, the Institute for Study of Business Markets (ISBM) at
Pennsylvania State University. Detailed discussions with affiliated ISBM
researchers regarding our research model and the questionnaire
requirements confirmed the suitability of the respondent profile that we had
identified. The quality of responses was expected to be good because the
various companies had registered as ISBM members for participation in their
research and educational programs. From the much larger list maintained at
ISBM, contact details of 120 respondents were forwarded to us because of
their significant interaction history with ISBM. However, as we proceeded to
collect data using two rounds of e-mails and one round of phone calls, our
sampling frame was reduced to 82, which we describe next.
An introductory e-mail was forwarded by ISBM to this list of potential
respondents. During the same week, phone calls were placed to all
respondents to confirm their receipt of the original e-mail, their suitability as
key informants and to invite their participation in the study. During this phase,
we found that the list was not current and that several individuals had either
left their companies or had transferred to other sections within their
companies. These people were therefore no longer appropriate as
respondents. Other individuals did not respond to our e-mail and were found
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to be inaccessible by phone. Therefore, from the initial sampling pool of 113
respondents, actual contact was made with only 54 respondents or with their
assistants. These respondents (or their assistants) either asked us to resend
the questionnaire or offered to take a look and have it answered if possible.
Of these 54 respondents, 16 either had been transferred to another unit and
were no longer suitable or declined to participate. On a pro rata basis, we
calculated the number of respondents out of the initial 113, who would have
likely responded to our questionnaire. We approximated this number to 82,
which can be considered to be our sampling frame.
In the two weeks following the launch, we received only 6 responses—i.e.,
6.6%. Two weeks later, another round of e-mails was sent, and phone calls
were made, resulting in the receipt of three more responses. Thus, our first
attempt to collect data from a pool of 82 respondents from the ISBM list
resulted in only nine responses (a response rate of 11%). Several companies
on the list were global companies with operations in both the U.S. and
Europe. Since the size of data set after this first attempt was small, it was not
possible to compute preliminary results.
In the second round of data collection, we narrowed our sample to include
only high-technology companies in SIC categories with four-digit codes
starting with 35, 36, 48, and 73). The choice was made because high-tech
companies tend to be more IT intensive in their B2B sales activities. After
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selecting top companies in the above categories, data was collected using
personal contacts so as to ascertain the suitability of respondents for
answering both, the IS-related survey items and the items measuring
organizational variables. At the end of this round, we had 23 responses, out
of which 4 were discarded because significant number of items were not
responded to. This was sufficient for us to test the validity of the survey
measures.
Since the construct measures are new, we performed factor analysis only on
the IS-related scale items. It revealed four dominant factors with eigen-values
higher than 1. However, not all items loaded cleanly on their original scale
items. On eliminating 2 selected items for each of the IS constructs, yielded
high Cronbach’s alpha (>0.70) for the remaining scale items. Cronbach’s
alpha for other multi-item measures was high and above 0.7. This enabled us
to reduce the questionnaire length. Thus, for our final round of data
collection, we included between three and four items for each construct in the
model except for indirect relational value. To measure indirect relational
value we had included only two items (Table 6).
For the final round of data collection, we attended conferences of high-tech
companies. This method of access was chosen specifically for the purposes
of ascertaining the suitability of the respondents, achieving a higher quality of
data and a higher response rate. Brief interviews with sales managers at
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their conference booths established their suitability to participate. The
executives either chose to answer the questionnaire at the conference or
agreed to respond within two weeks. Forty-four responses were received
from a total of 104 companies approached at two conferences (a response
rate of 42.3%). Out of these 44 responses, 9 were discarded due to
incomplete responses (the questionnaires had more than three non-response
items). In addition, we also collected data from 18 global companies
headquartered outside the United States (Japan, India, and Germany) and
engaged in financial services, telecommunications, and semiconductors.
Our final data set includes 69 usable responses. All of the companies had
more than $19 million USD in current revenues, 54 of the companies had
revenues of more than $100 million, and 39 companies had revenues of
more than $500 million. Ranking our sample in terms of revenue revealed
that all of the top-ten companies had revenues of more than $15.4 billion
USD. Revenue rankings for SIC industry categories were extracted from
Mergent Online Company database. Particularly, the sample includes top
ranked companies in terms of revenues in the following industries: 1) 2 out
of the top 10 companies in the 4812 SIC code (Radiotelephone
communications), 2) 4 out of the top 20 companies in the 4813 SIC code
(Telephone communications except radiotelephone), 3) 3 out of the top 10
companies in the 3663 SIC code category (Radio & T V broadcasting &
communications equipment), 4) 2 out of the top 10 companies in the 7372
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SIC industry category (Prepackaged software), 5) 1 company out of the top
10 in the 3825 SIC code (Instruments to measure electricity) and 6) 2 of the
top 20 companies offering enterprise software systems (Source:
http://www.manufacturinqsvstems.com).
Because some of the companies were based in India and Japan, the
revenue profile may not be an accurate indicator of the size of these
companies (companies in these countries could have lower revenues in
terms of U.S. dollars but still are significantly large and IT intensive). Most
companies were publicly traded either on U.S. or international stock
exchanges.
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Table 6: Final Survey: List of items measuring the constructs and their Cronbach’s
alpha
Construct name
Indicators
[VARIABLE NAME] (Item loadings in PLS output)
Cronbach's
alpha **
IS integration
Data is entered only once in order for it to be retrieved by
most applications of our other BUs. [INT1] (0.7474*)
We can easily share data with our other BUs. [INT2]
(0.7473*)
We have successfully integrated most of our databases
with those of our other BUs. [INT3] (0.8087*)
We have successfully integrated most of our software
applications with those of our other BUs. [INT4] (0.7437
*)
0.7708
IS range
Access
All employees within our business unit can access most of
our software applications. [RANGE1] (0.8855 *)
The same portfolio of software applications is available on
every computer terminal within our business unit.
[RANGE2] (0.8620*)
Within our business unit we make available most of our
software applications to all employees in all departments.
[RANGE3] (0.8451*)
Functionality
In comparison with standard implementations, our software
applications have a large number of features. [RANGE4]
(0.8619*)
We have software applications to support all of our main
business activities. [RANGES] (0.7691*)
Every activity at the operational level is supported by our
software applications. [RANGE6] (0.8183*)
0.8217
0.7567
IS reach
Every individual in our business unit can communicate via
the computer with every individual in our BUs.
[REACH 1] (0.6485*)
We have high bandwidth connections to support
audio/video applications, online engineering product
design, etc., with our BUs. [REACH2] (0.7139*)
Employees in our business unit are linked in real-time with
the information systems of our BUs. [REACH3]
(0.8259*)
Employees within our business unit are electronically
connected through dedicated lines with all our BUs.
[REACH4] (0.8757*)
0.7659
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Table 6 (cont.)
Workflow
interdependence
Independent workflow case: work and activities are
performed by all business units separately and do not
flow between them.
Sequential workflow case: work and activities flow to or
from other BUs but mostly in only one direction.
Reciprocal workflow case: work and activities flow between
your BU and other BUs in a back-and-forth manner over
a period of time.
Team workflow case: work and activities come into your
BU and you and other BUs diagnose problems, solve
problems, and collaborate as a group in meetings to
deal with the work.
(1-5 Scale: Almost none of the work, About 10-40% of all
the work, About half of all the work, About 60-90% of all
the work, Almost all of the work)
Guttman
scale (one
dimensional)
Social tie
strength
Personnel in our business unit often have friendships with
personnel in our other BUs. [TIES]
Single-item
measure
Knowledge
sharing
We frequently share knowledge about our business
environment (e.g., other business relationships) with our
BUS. [KNOW1 ](0.7446 *)
Knowledge about all our channel partners, competitors,
etc., is shared with our other BUs. [KNOW2] (0.7578*)
Business insights are exchanged between us and our other
BUs. [KNOW3] (0.8377*)
We keep most of our business units informed about events
and ramifications in any one of our relationships.
[KNOW4] (0.8702*)
0.8114
Indirect
relational value
We benefits because our other BUs have a relationship
with our customers. [VALUE1] (0.9536*)
We benefits because our other BUs have a relationship
with our potential customers. [VALUE2] (0.9392*)
0.8830
Parentheses include the name of the measure and the loadings on the latent construct as
retrieved from the PLS output.
* p<0.05
** N=69
4.3. Tests of Validity
We tested for non-response bias, common method variance and variance
arising from different methods of accessing data.
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1. We also made a significant change in one of the construct items for
our next phase of data analysis, i.e., for ordinary least squares (OLS)
regression and partial least squares (PLS). We found that the reliability
estimate using Cronbach’s alpha for the construct social tie strength, had
deteriorated to 0.46. Further, during the model testing (using OLS and PLS),
the 4-item measure was so highly correlated with knowledge sharing, that it
subdued the effects of all other constructs. On reexamination of the items for
this construct, we realized that 2 of the scale items were significantly
overlapping with the knowledge sharing construct. Another item was likely
misinterpreted by the respondents since it asked about the “informal favors
traded with other BUs”. This might have been construed as politically
sensitive question and therefore not correlated with our main item which is
“Personnel in our business unit often have friendships with personnel in our
other BUs”. Therefore, in our data analysis, we used only this one item
representing social tie strength.
We had chosen to develop our own scale for measuring social tie strength
but guided by past literature. A key consideration we did not use scales from
prior research was that the wording of the scale item should not also
encompass certain aspects of the dependent variable, knowledge sharing.
Thus, for example, we did not find that the scale used by Ibarra (1992) was
suitable for our purpose since it measured advice, support, communication
and also friendship (Ibarra 1992). Using this scale, a resulting strong
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correlation between social tie strength and knowledge sharing would have
been an artifact of our scale items.
2. Non-response items were less than 1% across all the respondents
aggregated. We replaced the gaps in responses with the means for the
particular construct for that particular respondent.
3. Since the data collection was done in more than two stages, we
assessed the difference between the firms in our sampling frame who had
responded and those who had not. There was no significant difference in
terms of revenue (of the last full year of operation) between the respondent
and non-respondent companies.
4. Common method variance occurs when a single respondent answers
both the predictors and the dependent variables. This may result in
significant effects even when they are not actually present. Common method
variance was checked using Harmon’s one-factor test (Harmon 1967) This
involves a principal components analysis of the questionnaire instrument.
The higher the number of factors with eigen-values greater than one and the
more these account for a substantial variance in the instrument, the lesser is
the common method variance (Podsakoff and Organ 1986). A principal
components analysis of all constructs in our model yielded 10 factors with
eigenvalues greater than 1.0 and accounted for 76% of the total variance.
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The first factor had an eigenvalue of 6.27 and accounted for 21.6% of the
total variance. The second and third factors (with eigenvalues 3.8 & 2.0)
accounted for 13% and 7% additional variance. We conclude from this that a
substantial amount of common method variance does not appear to be
present.
5. Although the source of data collection varied from the initial approach
at ISBM to conferences of high-tech companies, we found no significant
response bias in the data set by virtue of the method of access. This was
ascertained by conducting ANOVA tests of selected key variables. Our
questionnaire also included an indicator item to capture the basis on which
the BUs within the enterprises were organized. The various basis of
structuring listed were product-based, customer segment based, and
manufacturing process-based. ANOVA was used to test for biases arising
from differences in the bases of organizational structuring across
respondents. This factor was revealed as not significant.
We assessed the psychometric properties of the scales in terms of i) internal
consistency measured using Cronbach’s alpha ii) item loadings on latent
constructs and iii) discriminant validity. The item loadings on individual
constructs and discriminant validity is ascertained during the PLS analysis.
All these are presented in the next section.
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4.4. Limitations of Research Design and Data Collection Method
A main concern is that the scales for some independent constructs are newly
developed to capture the proposed definitions. Our objective in doing so was
to develop more refined measures to achieve discriminant validity. However,
to confirm that our scales indeed are reliable our instrument may need to be
tested in the context of future IT studies and in the context of various
emerging IT contexts.
Due to the overall difficulty in accessing respondents we designed the
instrument so as to be applicable to only the marketing/sell-side
professionals. However, to operationalize the IT variables more rigorously,
future studies may be significantly enhanced by targeting the “MIS executive
- business unit manager” pair during questionnaire administration. For
example, with our instrument, we were not able to successfully measure
internal and external integration separately. Similarly the data and application
level integration are two dimensions of integration that could not be
measured separately. Our instrument could not achieve this distinction
between different dimensions due to the limited knowledge of B2B managers
regarding their IT infrastructure. By targeting an “MIS executive - business
unit manager” respondent pair researchers can collect data on the
dimensionalities of individual constructs of IS integration.
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There are two limitations with the sample profile - the sample size and the
heterogeneous industry context. A lower sample size precludes eliminating
all multi-collinearity effects, if any. The other limitation of the sample is the
lack of homogeneity in terms of the industry context. The reliability of the
findings would have improved if the entire sample belonged to a single
industry. Though tests to verify the industry-specific effects proved that
industry effects are not significant, the model results would have been more
robust had such heterogeneity across the sample been controlled in the
sample itself.
A longitudinal data set would have helped to further support a causality
argument. For this purpose, an ideal longitudinal data collection field context
would be one where enterprises are in the process of upgrading their
information systems. More specifically, our independent variables would
capture the extent to which these enterprises enhance their reach, range and
integration differentially. The dependent variable of knowledge sharing and
indirect relational value would be measured with a certain time lag post IT
implementations.
While we decided to develop positively worded scales for all items in our final
questionnaire survey, this was because in the initial pretests, we detected
that responses would be significantly distorted with negatively-worded items.
Compounded with the limitations in the sample size and the unavailability of
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a B2B sales manager-IT manager respondent pair (for accuracy), we opted
to used positively worded items scales only. However as described in
previous sub-section, typical tests for common method variance detected
insignificant halo effects.
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5. Data Analysis[N2]
contains the actual questions for each construct. Means, ranges, and
standard deviations of each construct are reported in Table 7. The data was
subjected to reliability tests (Table 6). For multi-item reflective constructs, the
reliability estimates were calculated using Cronbach’s alpha, which was
sufficiently above 0.7, indicating good internal consistency (Fornell and
Larcker 1981). Cronbach’s alpha is a more conservative reliability measure
than composite reliability that is reported in Table 10.
Table 7: Descriptive statistics
Construct
Sample average
Scale 1— to 5
Standard deviations
IS integration (INT) 3.70 0.87
IS range (RANGE-A) 3.57 1.06
IS range (RANGE-F) 3.67 0.85
IS reach (REACH) 4.23 0.90
Workflow
interdependence
(WFLOW)
(varies from 1.9 to 9.6 on
the Guttman Scale)
5.32 1.87
Social tie strength (TIES) 4.13 0.84
Knowledge sharing
(KNOW)
4.09 0.70
Relational value (VALUE) 3.79 0.95
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5.1. Partial Least Squares and Ordinary Least Squares
We tested the research model by operationalizing it as a multiple-indicator
structural equation model. The hypotheses were examined using partial least
squares (PLS) (Chin 2003). In addition, we also employed ordinary least
squares (OLS) regression to test the hypotheses.
PLS is a commonly used technique in IS research in which theory
development and testing are the primary objectives and the measurement
scales are new. A detailed bibliography and discussion of PLS is available at
http://disc-nt.cba.uh.edu/chin/indx.html. The sample size was also sufficient for
our data analysis method. For PLS (consistent with the heuristic for multiple
regression), the sample size should be 10 times the maximum number of
paths leading into any one construct in the structural equation model
(Tabachnick and Fidell 1989).
PLS is also more appropriate than LISREL(Joreskog 1973) in this situation
because of the nature of the contribution attempted in this study. LISREL is
suitable when the constructs are well established so that correlations across
error terms of different constructs are considerably close to zero (Chin
1998b). Further, in LISREL the existence of causality (not magnitude) is
believed to be well established in prior literature. Thus, LISREL may be used
for confirmatory research—i.e., when a very strong theory base already
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exists. As against this theoretical context, there is a lack of prior empirical
studies to support many of our hypotheses robustly. The items for our
constructs are new which means that the error terms of the construct items
have not been earlier ascertained to co-vary solely through their latent
constructs.
PLS algorithm is highly sophisticated since it includes multiple techniques. By
running a series of iterative regression analyses, PLS tries to maximize the
explained variance without constraining the error terms of the construct
measures to covary solely through one construct. This flexibility arises
because the PLS algorithm also includes canonical correlation analysis.
These techniques make PLS suitable for testing hypotheses when the theory
is under development and the precise relationship between the variables is
not known. Another advantage of PLS is that the significance estimates of
structural paths, used to evaluate the model, are non-parametrically
computed. In LISREL, the goodness of fit measure assumes strict
multivariate normality, which is too restrictive for our underlying theory and
data models.
Though, we have used OLS to gauge the effect sizes and to ascertain the
nature of association among dependent and independent variables (i.e.,
positive or negative), PLS is a more comprehensive data analytic method.
First, it tests both, the measurement as well as the structural model
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simultaneously. Using OLS, the measurement model cannot be tested
simultaneously with the structural model. Second, whereas OLS may be
useful when there is only one dependent variable, it is not appropriate when
there are more than one dependent variable. Third, PLS is also useful to
simultaneously test for alternate structural models. For example, in addition
to our hypothesized relationships, we also tested for a direct association
between i) IS integration and indirect relational value and ii) IS reach with
indirect relational value. Insignificant path estimates for these two
relationships would help to confirm the importance of incorporating
knowledge sharing as the intermediating variable in our conceptual
framework.
Whereas OLS results (Table 8 and Table 9) were mostly consistent with PLS
results, it may not always be so. This is because in PLS analysis, the
canonical correlation component of the PLS algorithm computes item
loadings on latent constructs such that maximum variation in the dependent
variable is explained. As against this method, in OLS analysis, we computed
the mean of the items thus assuming that all items load equally well on the
latent construct. Therefore, where OLS results (using the mean of survey
items as latent construct scores) might yield a certain construct as not
significant, but PLS results might indicate otherwise also, and vice versa.
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5.2. OLS regression results
To run OLS regression, whereas the means of the multiple item constructs
were used in order to compute the construct scores, the construct, workflow
interdependence, was measured using a Guttman scale (Van de ven and
Ferry 1980).
Two sets of OLS regressions were run, one excluding the interaction effects
hypothesized in the research model in Figure 5. These results are as in
Table 9. The other set included the interaction effects, i.e., the full research
model (Table 8). Since our research model has an intermediating structure
with knowledge sharing as the intermediating variable, the first table below
(Table 8a) tests those hypotheses where knowledge sharing across BUs is
the dependent variable. The next table (Table 8b) presents OLS results for
the hypothesis that proposes the linkage between knowledge sharing and
indirect relational value.
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Table 8: OLS results with interaction terms
Table 8a: Dependent variable: Knowledge Sharing (KNOW)
Parameter
estimate
Std
Error
T-ratio Prob>|t|
Intercept 1.777 0.606 2.934 0.005
REACH -0.132 0.135 -0.979 0.331
RANGE-A 0.123 0.088 1.405 0.165
RANGE-F 0.072 0.125 0.577 0.566
I NT 0.273 0.114 2.390 ** 0.020
WFLOW 0.096 0.045 2.157 ** 0.035
TIES 0.163 0.096 1.703* 0.094
TIES X INT 0.000 0.130 0.001 0.999
WFLOW X I NT -0.082 0.059 -1.393 0.169
Table 8b: Dependent variable: Indirect Relational Value (VALUE)
Parameter
estimate
Std
Error
T-ratio Prob>|t|
Intercept 1.877 0.645 2.910 0.005
KNOW 0.467 0.156 3.005 ** 0.004
p < 0.05
p < 0.1
Table 9: OLS results excluding interaction effects
Dependent variable: Knowledge Sharing (KNOW)
Parameter
estimate
Std
Error
T-ratio Prob>|t|
Intercept 1.872 0.600 3.121 0.003
INT 0.272 0.111 2.453 ** 0.017
RANGE-A 0.130 0.087 1.484 0.143
RANGE-F 0.021 0.120 0.171 0.865
REACH -0.149 0.130 -1.149 0.255
WFLOW 0.089 0.042 2.096 ** 0.040
TIES 0.205 0.091 2.243 ** 0.028
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** p < 0.05
* p<0.1
The OLS results (Table 8a, Table 8b, Table 9 and Figure 8) indicate that IS
integration and workflow interdependence is significantly related to
knowledge sharing across BUs (p<0.05). However, social tie strength is also
positively associated with knowledge sharing when the research model
excludes the interaction effects. In the full model, however, this linkage is not
significant. Knowledge sharing is also significantly associated with indirect
relational value (Table 8b).
Figure 8: OLS results for model with interaction effects
Social Ties
IS Integration
i.162
KNOWLEDGE
SHARING
R2 =0.333
RELATIONAL
VALUE
R2 =0.120
IS Range (Access)
0.272*
0.123
IS Range (Functionality)
0.467*
0.072
i.131
IS Reach .096*
0.000 '-0.08
Workflow WIX Social ties X
Interdependence IS integration IS integration
(Wl)
* = p<0.05
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5.3. PLS analysis
The results support the convergent and discriminant validity of our
constructs. Discriminant validity is confirmed when the average variance
extracted (AVE) by each construct is larger than the inter-construct
correlations(Chin 1998a). Accordingly, Table 10 indicates that our constructs
have discriminant validity. We also confirmed discriminant validity in another
way. We ascertained that the correlations of each construct with their own
items are significantly higher than the correlations between the constructs
and items measuring other constructs
The significance of the path coefficients in the structural model was assessed
by a bootstrapping technique. Two hundred samples were generated
randomly, and the path coefficients were re-estimated for each of these
samples. The significance of a path coefficient was computed non-
parametrically, based on the vector of parameters generated from each of
the 200 samples. This approach is typically adopted in most PLS analyses
(Chin 1998a). The loadings for the item measures and their significance
results are shown in Table 6.
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Figure 9: Research model results
Social Ties
IS Integration
, 0.122
RELATIONAL
VALUE
R2 =0.132
KNOWLEDGE
SHARING
R2 =0.410
IS Range (Access)
0.28’
0.128
IS Range (Functionality)
0.364’
0.154
|.042
.271 IS Reach
0.128 -0.261
Workflow Social ties X WIX
Interdependence IS integration IS integration
(Wl)
* = p<0.05
5.3.1. Structural model results with and without interaction terms
The structural model results obtained for the overall model (including
hypotheses 1 through 8) are shown in Figure 9. In PLS, the structural model
is assessed by R-squared values of the endogenous constructs and should
be interpreted as in multiple regression analysis. The model displays an R-
square of 41% for the knowledge sharing construct, which can be considered
to be moderate to substantial. R-square for the indirect relational value
construct is 13.2% which can be considered to be weak. However,
considering that we included only one of the many antecedents of business
value this can be considered to be significant.
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The direct relationship between integration and knowledge sharing is readily
supported, whereas the direct relationship of reach and range with
knowledge sharing is not supported. Workflow interdependence significantly
explains knowledge sharing, as was expected. But the moderating effect of
workflow interdependence on the linkage between integration and knowledge
sharing is not supported. Social tie strength is not significant in the full model
that includes interaction effects but is significant when the interaction terms
are excluded. However, the moderating effect of social tie strenth on IS
integration and knowledge sharing is not supported. Therefore, it is
reasonable to conclude that there are significant multi-collinearity effects
arising from the interaction terms so as to render social tie strength as not
significant in the full model Figure 9.Finally, the relationship between
knowledge sharing and indirect relational value is strong.
According to the sample size heuristics for PLS a sample size of 80 (=8
incoming paths in (Figure 9) X 10) is ideal. Considering our sample size of
69, we tested the structural model by excluding the interaction effects.
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Figure 10: Research model results excluding interaction effects
Social Ties
IS Integration
,216 *
RELATIONAL
VALUE
KNOWLEDGE
SHARING
R*=IL332
IS Range (Access) 0.34
0.168
IS Range (Functionality)
0.370*
0.066
.251 * IS Reach
Workflow
Interdependence
* = p <0.05
5.3.2. Testing alternate structural paths
We also tested the structural model by including a direct path between IS
integration and IS reach with indirect relational value. As seen in
Figure 11, the path estimate between IS integration and indirect relational
value is -0.067 and between IS reach and indirect relational value it is -0.062.
On bootstrapping these paths are not found to be significant. This finding
attests to the relevance of including an intermediate variable, i.e., knowledge
sharing.
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Figure 11: Testing alternate structural paths: IS Integration and IS Reach with Indirect
Relational Value
Social Tie Strength
Integration
Indirect
Relational Value w l eqge Sharing
Range-Fundionality
0.149 !32
Reach
Workflow Interdependence
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Table 10: Assessing discriminant validity: AVE and Inter-construct correlations (N=69)
Diagonal elements are the Average Variance Extracted (AVE) estimated for each construct. Off-diagonal elements are the correlations among the
constructs. For discriminant validity, the AVE (i.e., the diagonal elements) should be larger than the square of the off-diagonal elements (Chin
1998).These results pertain to the research model excluding interaction terms (Figure 9).
Constructs
Construct (# of items)
Construct
Label
Construct
Composite
Reliability
Integration
Range
(Access)
Range
(Functionality)
Reach Workflow
Social
ties
Knowledge
sharing
Indirect
relational
value
Integration across BUs (4) INT 0.847 0.581
Breadth of access to IT
portfolio (3)
RANGE-A 0.901 0.314 0.752
Depth of functionality of IT
portfolio (3)
RANGE-F 0.858 0.482 0.560 0.668
Reach of IT portfolio across
BUs(4)
REACH 0.853 0.521 0.378 0.409 0.595
Workflow ** (4) WFLOW
-
0.151 0.123 -0.111 -0.093 1.0
Social tie strength (1) TIES - 0.045 0.193 0.109 0.110 0.108 1.0
Knowledge sharing across
BUs (4)
KNOW 0.879 0.408 0.335 0.268 0.187 0.324 0.281 0.647
Indirect relational value (2) VALUE 0.945 -0.212 -0.247 0.001 -0.095 -0.302 -0.194 -0.364 0.896
**Guttman scale used
o
6. Discussion of Results
The following hypotheses developed in section 3 were tested and results are
summarized as below (Figure 9 and Figure 10).
Table 11: Main effects of organizational variables on knowledge sharing
Hypotheses Support level
Hypothesis 1: Knowledge sharing across BUs is
positively related to indirect relational value.
Supported (p<0.05)
Hypothesis 2: Integration is positively related to
knowledge sharing across BUs.
Supported (p<0.05)
Hypothesis 3a: Range (functionality) is positively
related to knowledge sharing across BUs.
Hypothesis 3b: Range (access) is positively
related to knowledge sharing across BUs.
Not supported
Hypothesis 4: Reach is positively related to
knowledge sharing across BUs.
Not supported
(p<0.05)
Hypothesis 5: Workflow interdependence is
positively related to knowledge sharing across
BUs.
Supported (p<0.05)
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Table 11 (cont.)
Hypothesis 6: Social tie strength is positively
related to knowledge sharing across BUs.
Not supported *
Hypothesis 7: Workflow interdependence
positively moderates the effect of IS integration
on knowledge sharing across BUs.
Not supported
Hypothesis 8: Social tie strength positively
moderates the effect of IS integration on
knowledge sharing across BUs.
Not supported
Alternate structural paths tested (Figure 11):
- IS integration and indirect relational value - Not supported
- IS reach and indirect relational value - Not supported
* Supported in the reduced model (Figure 10).
The measurement model was validated from the results of the PLS analysis.
The PLS tests indicate that the construct measures demonstrate discriminant
and convergent validity. Thus, the results support our construct
conceptualizations.
In addition, several hypotheses in our structural model are also well
supported. Broadly, there are four sets of results: i) knowledge sharing
across BUs is positively related to higher relational value (Hypothesis 1: Path
coefficient = 0.364, R-squared = 0.132) ii) Of the three dimensions of IS
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capabilities, IS integration is positively correlated with knowledge sharing
across BUs (Hypothesis 2: Path coefficient = 0.28, R-squared = 0.41) but IS
range and IS reach are not positively related to knowledge sharing across
BUs (Hypotheses 3 and 4) iii) Workflow interdependence across BUs is
positively related to knowledge sharing (Hypothesis 5: Path coefficient =
0.271) and social tie strength across BUs is not positively related to
knowledge sharing (Hypothesis 6: Path coefficient = 0.122). However social
tie strength is significantly related to knowledge sharing when the interaction
terms are excluded from the PLS analysis (Path coefficient = 0.216). This
suggests that multi-collinearity is present iv) Finally, the moderating effects of
both, workflow interdependence and social tie strength on the relationship
between integration and knowledge sharing, are not significant (Hypotheses
7 and 8).
Validating the intermediating structure of the research model
A key aspect of our model is its intermediating structure. We included
knowledge sharing across BUs as the intermediating variable based on prior
recommendations in literature, that the impact of IT on lower level outcome
variables should be first investigated (Barua et al. 1995). Then, the impact of
these lower level outcome variables on business value should be confirmed.
Overall, the results support our basic argument that IS capabilities may
significantly increase indirect relational value by enabling higher knowledge
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sharing across business entities. We confirmed that this intermediating
structure is indeed useful by also testing the alternate structural paths, where
the IS capabilities are directly linked to indirect relational value. These paths
were found to be not significant.
Knowledge sharing and indirect relational value (Hypothesis 1)
Whereas extant literature offers strong support that knowledge sharing leads
to higher value in dyadic relationships, we grounded the notion of value at a
higher level, that is, in terms of indirect relational value. In dyadic
relationships, knowledge sharing routines bridging organizations are
understood to lead to higher relational rents in terms of product innovation
(Dyer and Singh 1998a). Though there is extensive theoretical literature
related to how knowledge sharing yields relational value in dyadic
relationships, empirical studies of how knowledge sharing, at a network level,
may enhance indirect relational value, is scarce.
As per our conceptualization, indirect relational value is consistent in certain
ways with the concepts of second-order benefits, network benefits or
economies of scope. Indirect relational value can accrue as cross-selling
benefits or higher customer satisfaction resulting from collaboration with a
complementary supplier. Thus, an important reason why enterprises form
relationships is also because of their potential to enhance other business
relationships of the focal enterprise (Anderson et al. 1994).
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The overall finding that knowledge sharing across BUs mediates the impact
of IS capabilities on indirect relational value, is interesting for two additional
reasons. First, this finding is one of the few empirical evidences that IS
capabilities may not only confer significant bargaining power or save costs,
but may also enhance knowledge sharing (and thus indirect relational value)
at the inter-enterprise level. Second, the construct of knowledge sharing
across BUs, as defined and operationalized in this study, measures the
network effect. That is, we had hypothesized that IS capabilities enable
enterprises to share knowledge pertaining to their business relationships with
their collaborators. The findings support the view that business value is
derived from such transportability of knowledge across enterprise
boundaries. Typically, knowledge sharing across BUs is important in multi
divisional firms, which serve related product markets or customer segments.
Further, these firms may have grown through mergers and acquisition and
thus face a considerable gap in communicating information across BUs. Due
to a lower extent of inter-unit knowledge sharing, multi-divisional firms may
often suffer from a lack of synergy among their BUs (Tsai 2002).
IS integration and knowledge sharing (Hypothesis 2)
A key finding was that integration of IT applications across BUs is strongly
associated with knowledge sharing across BUs. This corroborates our
findings during exploratory phase also, where the interviewees referred to the
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use of IT in structuring and coordinating the complex activities of supply
chain management and B2B sales efforts. We noted that to enhance B2B
activity, software products, e.g., customer relationship management (CRM),
partner relationship management (PRM) and supply chain software (SCM)
products, such as those offered by Microsoft, SAP and Siebel, etc., enable
collaboration among business divisions and with sell-side/buy-side partners.
These trends are evident in boundary spanning functions such as sourcing or
supply chain execution in multi-divisional enterprises. Similarly, enhanced IT
platforms, such as collaborative trading exchanges, enable a group of
enterprises to trade complex products and services.
To understand why this finding is interesting, it maybe necessary to
understand IS integration conceptually. Superior IS integration across IT
portfolios of participating entities is a key aspect of recent enterprise ITs.
Implementing these technologies involves standardizing IT portfolio at the
data and application level. Once the data formats and application are
standardized across participating enterprises, it signifies not only that data
can be exchanged smoothly across enterprises, but that the enterprises have
developed common mental models of the business activity (El Sawy 2002).
Therefore, these integrated information systems support richer information
exchange. In addition, once these mental models are synchronized,
knowledge sharing need not occur only through electronic channels, but can
occur through other media just as in the case where individuals exchange
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richer information through a process of iterative re-interpretation (Boland et
al. 1994). Thus, in addition to creating an electronic channel, IT integration
may also provide an opportunity and a platform for knowledge sharing.
However, we make the above conclusion cautiously in view of a limitation of
our study. That is, we did not measure knowledge sharing in terms of its tacit
and explicit components; rather only in terms of how and who in a business
network, knowledge is shared with.
IS range and knowledge sharing (Hypothesis 3a and 3b)
The lack of support for hypotheses 3a and 3b suggests that neither does
sophisticated IT functionality (IS range - functionality) lead to better
knowledge sharing across BUs, nor does wider access for employees to the
IT applications (IS range- access). This was unexpected because much of
literature suggests that the more sophisticated are those components of an
IT portfolio that support internal operations, the better and richer is the
employees’ understanding of the business activity. Since enterprise software
systems have an in-built business model and schema, it increases the
absorptive capacity of individuals towards assimilating and sharing intra-
organizational information. Based on this argument, though we hypothesized
that higher IS range (functionality) should result in higher extent of
knowledge sharing, but this was not supported.
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Organizations often constrain access to ERP functionality in order to save on
the ERP vendor licensing costs. We hypothesized that such restricted access
to employees within a BU, i.e., low IS range (access), maybe dysfunctional.
Conversely, if all employees in a particular BU have access to the common
ERP interface, it should enhance their ability to access and interpret
information about the business activity in a similar way. Considering the B2B
sales scenario, we expected higher IS range (access) to impact knowledge
sharing across enterprise boundaries.
Lack of support for both the above hypotheses suggests that rather than
contributing to higher relational value, higher IS range may be beneficial in
terms of cost savings. It is also possible that instead of IS range, other facets
of an organization, e.g., its internal structure or the formal and informal
network of employees within a BU, may be more effective in enhancing
knowledge sharing.
IS reach and knowledge sharing (Hypothesis 4)
The hypothesis that higher IS reach may lead to higher knowledge sharing
was based on the exploratory phase findings and also on the literature
review (Carlson and Zmud 1999; Jarvenpaa and Staples 2000; Staples and
Jarvenpaa 2001). It is commonly understood that e-mail, intranets, and
extranets are important media for exchanging information (Miranda and
Saunders 2003). Greater reach indicates higher connectivity among
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individuals and groups. Therefore, it was hypothesized that higher
connectivity may enable richer communication across these individuals and
thus aid knowledge sharing. Lack of support for IS reach (Hypothesis 4) as
well as for IS range (Hypothesis 3a and 3b) in enhancing knowledge sharing
suggests that these two dimensions of IT infrastructure capabilities maybe
competitive necessities that are necessary only to eliminate inefficiencies but
do not necessarily yield higher relational value. Alternately, IS reach and IS
range possibly maybe playing an important role in creation of knowledge
rather than knowledge sharing. However, we also did not examine the impact
of IS capabilities on knowledge creation.
Workflow interdependence and knowledge sharing (Hypothesis 5 & 7)
Another interesting finding was that workflow interdependence is positively
related to knowledge sharing across business units. Workflow consists of
business routines which are understood as conduits through which
knowledge, learning and skills are transferred across participants in these
routines. Higher workflow interdependence means that the tasks are more
reciprocal. That is, the tasks are more tightly connected compared to those in
a sequential workflow. A significant amount of literature describes how inter-
organizational asset connectedness or tighter linkages across enterprises,
yield benefits in terms of better coordination and therefore higher relational
rents (Dyer and Singh 1998b). We hypothesized that knowledge sharing
benefits may accrue from higher workflow interdependence because it
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results in more intensive information exchange as the business routines are
performed across BUs. The results supported this hypothesis.
In a B2B context, reciprocal interdependence of tasks and activities across
BUs is characterized by an intensive information exchange. This may not
only reduce inefficiencies but may also lead to sharing knowledge about the
market and the environment. Thus, rather than base our argument on the
cost-saving effects of streamlining workflow we based our rationale on the
transactional value framework (Zajac and Olsen 1993). An integrated and
reciprocal workflow provides a platform for exchange of richer information. It
provides an opportunity for more intense socio-technical interactions. An
earlier study at the dyadic level supports the importance of integrating
workflow in creating higher breadth of information exchange (Gosain 1999).
The significance of the interaction term (workflow X IS integration) means
that for more reciprocal tasks across BUs, IS integration plays a stronger role
in enhancing knowledge sharing. An example of a non-reciprocal task would
be overnight batch transfer of banking transactions where higher IS
integration may help to increase efficiency. However, for reciprocal
workflows, IS integration may actually improve knowledge sharing by
enabling information capture, processing and dissemination, for value-added
decision-making in a B2B sales scenario. For example, partner relationship
management (PRM) software helps to capture sales leads and enables
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reciprocal information exchange (regarding the sales leads) between
different marketing groups. Thus, this feature may enhance cross-selling
benefits. Therefore, having an integrated IS across various sub-units enables
better knowledge sharing about these sales leads, and for processing other
market needs and opportunities. The use of lOSs for enhancing enterprise
responsiveness to customers and suppliers and for responding quickly to
competitive behavior, is also suggested in early IS qualitative research
(Johnston and Vitale 1988).
Social ties and knowledge sharing (Hypothesis 6 & 8)
We had included social tie strength as a control variable since there is
considerable amount of evidence of the utility of social ties for information
access (Nohria and Eccles 1992). However, strength of social tie was found
significant only in the model in Figure 10, which excludes the interaction
terms.
We do not rule out the importance of social ties in sharing knowledge across
BUs because of several reasons. Our survey instrument did not explicitly
capture the type of knowledge that is shared across BUs. There is strong
evidence that strong social ties are important for transferring tacit knowledge
across enterprise sub-units but not in transferring explicit knowledge (Hansen
1999). Further, social tie strength was measured using only a one-item scale
and therefore may not have captured additional dimensions of this construct.
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Since social tie strength was revealed as significant after excluding the
interaction terms from the research model, it also suggests that multi-
collinearity effects maybe present. However, a limited sample size precludes
eliminating multi-collinearity effects.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
7. Implications
The study attempts to enhance the understanding of the role of IT in the
emerging networked business environment. Whereas, the insights gained
from extant research literature focus significantly on the cost reduction
offered by IT, the theory development in this study contributes to an
understanding of the role of IT infrastructure, from a knowledge-based and
relational value perspective.
7.1. Contributions to Theory and Conceptual Understanding
A. Transactional value from lOISs
The findings from this study provided theory testing for the transactional
value argument. Transactional value framework (Zajac and Olsen 1993)
proposes that organizations may gain more value from asset-specific
investments in alliances, than the transaction costs they incur in sustaining
those alliances. This value, in excess of the transaction costs, arises from
innovation and knowledge transfer benefits. In other words, favorable
alliance outcomes from implementing IOIS result not only because of the
reduction of inefficiencies, but also accrue by enabling higher knowledge
sharing among enterprise partners.
Transactional value framework rightly indicates that the competitive
advantage of EDI is not sustainable. For example, the cost savings from EDI
between enterprises maybe considered as a competitive necessity rather
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than yielding any sustainable business value. Advantage of IOIS that are
competitive necessities (e.g. EDI, SABRE airline reservation system) may be
diminishing because of standardization of information technologies (e.g.
Internet protocols).
Our findings point to the role of the intermediating variable, knowledge
sharing across business units. That is, we find that IOIS can enable higher
knowledge sharing across enterprises, which in turn enhances relational
value. This finding provides an important link between IT and business value
from a transactional value viewpoint.
B. Introducing knowledge sharing as an intermediating variable
The findings also confirmed the approach suggested by much of literature
that studies on the impact of IT on business value can be enhanced by
including lower level outcome measures. To our knowledge, this is one of the
few studies that attempt to test the significance of the intermediating variable,
knowledge sharing, and to also find that a direct linkage between IS
capabilities and value is not significant.
For example, it is traditionally understood that an integrated IT portfolio can
yield higher value to enterprises due to synergies. However, a direct
association between IS integration and relational value was not significant
whereas the hypothesized path with knowledge sharing as the intermediating
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variables is. These findings provide a basis for future models on IT and
business value to sharpen their conceptualization by including other
intermediating outcome variables.
C. Economies of scale effects of information systems
The findings test the emerging theory about effects of knowledge
management systems on competitive advantage (Ofek and Sarvary 2001).
Recent literature on knowledge management systems (KMS) identifies two
distinct effects of KMSs (Hansen et al. 1999; Ofek and Sarvary 2001). The
first effect is akin an to economies of scale effect, where subunits can reuse
the knowledge already generated in other parts of an organization, to serve
their customers or market segments. Economies of scale also arise as the
customer base increases, because of which incremental cost per customer
served decreases. Second, KMSs also offer the ability to generate
fundamentally new and richer knowledge that enhances the quality of
services to customers. Both these basic types of KMSs are prevalent in
consulting organizations (Hansen et al. 1999).
Thus, this finding bridges the gap between practitioner reports on KMSs
(Ring and Van de Ven 1992) and formal models of KMSs (Ofek and Sarvary
2001). The study provides evidence of the first of the above two effects. That
is, knowledge-related benefits from information systems can be in terms of
economies of scale and scope. Future research efforts can aim towards a
complete empirical study of the types of KMSs and their effects on
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enterprises. That is, the research models can include knowledge creation
and assimilation also as intermediating variables that may lead to higher
relational value.
D. Understanding lOSs as the loci of absorptive capacity
This study suggests that inter-organizational information systems (IOIS)
deployed by enterprises may be the locus of partner-specific absorptive
capacity of enterprises. Absorptive capacity of individuals or organizations is
their ability to identify, assimilate and exploit external information for the
purpose of creating higher value (Zahra and George 2002). To achieve
integration at the inter-organizational level, enterprises customize their IT
interfaces to varying extents and achieve tighter coupling with their business
partners. Thus, when an IOS is implemented it results in application and data
level integration with the information systems of partnering enterprises. In
this way enterprises develop a common language platform based on which
they can exchange richer information. This is similar to the role that
standards play in enabling more abstract information exchange (Antonelli
1994). Following from this understanding of the role of IS integration, an
interesting implication for theory development, is that IS integration can
contribute to a key strategic capability of enterprises, i.e., the ability to share
knowledge with their business network partners.
We apply a similar reasoning to understand why higher workflow
interdependence at a dyadic level should lead to higher knowledge sharing
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across BUs. At a dyadic level earlier studies provide evidence that having
standardized processes and content interfaces is associated with a higher
breadth of information exchange (Gosain 1999). In the B2B sales scenario,
an interdependent workflow across enterprises not only enhances
information exchange between the two partners, but the nature of knowledge
shared is such that it benefits other relationships of the focal enterprises also.
By conceptualizing knowledge sharing as related to a business relationship
network, our findings indicate that maintaining a formalized workflow has
ramifications at the network level. That is, benefits of higher workflow
interdependence create spillover effects in terms of higher relational value.
Thus, both the above findings have implications for more focused theory
development and testing.
E. IS integration as a competitive advantage, but IS range is not
The findings provide support in other ways also for advancing theory about
the effects of IS capabilities on business value. Since IS range (functionality)
is not significant in our results it may be interpreted that certain IT capabilities
may only be competitive necessities (but not competitive advantage).
Consider a comparison between IS range (functionality) and IS integration,
and how these may relate to the competitiveness of an enterprise. Since an
IT portfolio typically consists of large number of application components, an
integrated IS portfolio becomes so specific to a company and its business
units that it may be a form of competitive advantage (e.g., Dell Computer is
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integrated with its business partners). An IT portfolio integrated over time is
path dependent and is not easily imitable by competitors and rivals (Ciborra
and Hanseth 1998). In IS literature sustainable competitive advantage has
been understood to be derived primarily from the managerial expertise with
regard to IT management in companies (Mata et al. 1995). We suggest that
IS integration may also be a form of sustainable competitive advantage.
In contrast to IS integration, a business unit would measure high on IS range-
functionality when it implements software application packages internally. Not
only is implementation easier but also the functionality is also easily imitable,
as numerous vendors offer these packages.
F. IOIS enhances competitive advantage of business networks
This study also contributes to the debate on the source of competitive
advantage of firms. The resource-based view (RBV) proposes that firm
competitive advantage is based on the stock of resources internal to a firm
(Barney 1991). The knowledge-based view (KBV) proposes that these
resources are knowledge-based (Conner and Prahalad 1996). This study
finds that knowledge sharing across enterprise BUs is associated with
indirect relational value. Therefore, this suggests that the knowledge-based
argument may be valid.
While the findings do not negate the RBV, there is support for an emerging
view that competitive advantage lies in a firm’s network of external
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relationships. Our study supports this latter view by indicating that IT
integration and workflow interdependence across BUs can be a source of
value. Under restricted assumptions that such value is sustainable and
inimitable for a sufficient amount of time, it needs to be investigated whether
such value may yield competitive advantage (Barney 2001). However, since
this study was conducted at the inter-unit level, generalizations to competitive
advantage at the inter-enterprise level may need to be substantiated with
more complete empirical studies.
G. Role of IOIS in social capital research
The finding that a sophisticated intra-organizational IT portfolio (i.e., IS
range) does not lead to higher knowledge sharing, maybe interpreted from a
social capital perspective also. The social capital viewpoint describes the
organizational advantage as arising from the three social capital dimensions
- relational (e.g. informal work ties), structural (e.g. formal organization) and
cognitive (e.g. common organizational goals) (Nahapiet and Ghoshal 1998).
It is possible that IT deployed for internal operations may not contribute
significantly to knowledge sharing with external entities. However such a
conclusion may be an artifact of the field context and therefore warrants finer
examination. This finding falls short of contributing to theory but suggests the
need for a more in-depth investigation of how IS range (functionality)
interplays with these social capital dimensions to yield higher value.
H. Extending social network analysis to IOIS research
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Our research model also tested for the existence of a key element of social
network theory, i.e., the evidence of a network effect. This finding provides
impetus towards employing social network analysis in research on IOIS.
It is observed that the social network perspective as a theoretical lens has
been underexploited in IOS research. An important requirement for such a
research effort to be launched is the evidence and measurement of a
network effect through lOSs. Though a network externality effect has already
been identified in research on Automated Teller Machines (ATMs) empirical
evidence of similar network effects from IOS is scarce. As will be described
in the next subsection, the findings that IS integration can lead to benefits in
terms of knowledge sharing at the network level, provides an empirical
foundation to formal analysis of IOS networks based on social network
theory. For example, formal literature describes a parameter that represents
extent to which a focal actor derives benefits depending on the strength of
linkages with another actor (Braun 1997). Equilibrium analyses are then
made depending on rational choice arguments of benefits and costs from
such linkages (Chellappa and Saraf 2001). To illustrate, one can view the R-
squared of the indirect relational value (approximately 0.14) as representing
roughly the network benefit of a linkage.
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7.2. Implications for Future Research
A. A promising research direction that can be pursued is to understand in
more depth what role IS integration plays in enhancing absorptive capacity
between enterprises. Rather than considering absorptive capacity as a single
construct, a process view of how absorptive capacity is developed and
sustained using IT, can inform IS strategy. Further research can build upon
this study to understand how the four dimensions of absorptive capacity
(Zahra and George 2002), acquisition, assimilation, transformation and
exploitation, can be enabled using IT integration.
B. A key aspect of the absorptive capacity developed by enterprises in
the context of a specific business network, is that such absorptive capacity is
not generic, but is partner-specific (Dyer and Singh 1998b). Based on the
notion that IT linkages embody the partner-specific absorptive capacity of
organizations, social network theory and analytical methods can be utilized to
understand the performance implication for enterprises embedded in
collaborative integration networks (El Sawy 2002). Collaborative integration
networks are stable networks of buyers and sellers, who need to maintain
strong collaborative linkages to rapidly exchange rich information. Example
outcomes are successful product development and order fulfillment. Such
networks are more robust and sustain under dynamic environments.
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C. In a similar context as above, after sufficient understanding of the role
and outcomes of electronic linkages across two enterprises is gained, social
network method can be utilized to achieve a higher level of analysis. This
would help to enhance understanding of the impact of industry-wide
electronic networks. For example, in the context of collaborative trading
exchanges, a “socio-electronic” network of such enterprises can be
visualized where the electronic linkages may represent the ties between
actors. These are not purely plug-and-play electronic linkages but are “sticky”
because of the customization in IT and path dependence in the IT
architectures of partnering enterprises (Ciborra and Hanseth 1998). For each
enterprise in this network, social network concepts such as centrality and
brokerage can be measured to understand the performance impact of IT on
each of the participating enterprises. It can then be investigated whether an
enterprise having high degree centrality or high brokerage, has high market
influence in these settings. Also, high centrality in this socio-electronic
network may have implications on the enterprises’ IS flexibility and strategic
flexibility. Thus, applying social network perspective can also enable
research on how electronic networks are altering the industry dynamics.
Based on similar studies as this dissertation, researchers can contribute
towards building the necessary theory elements for a fuller utilization of the
social network method.
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D. Since IS reach and IS range are not found to be significant in
enhancing knowledge sharing, it should make researchers aware that not all
IS capabilities should be assumed to impact business value in a similar
fashion. Therefore, finer dimensions of IS capabilities need to be
conceptualized in order to disentangle the confounding effects of IT
infrastructure as a composite variable. For example, the effect of
collaborative software application or groupware on knowledge sharing may
be different than how email infrastructure enables knowledge sharing.
Similarly, the data level and application level integration may have different
effects on how knowledge is shared across enterprises. Research in the
direction outlined above may guide more sophisticated IT architecture design
practices.
E. On the same note, more focused research on how organizational
characteristics interplay with IT infrastructure dimensions, can yield
interesting insights for managing IT infrastructure. Our empirical study
focused on a boundary-spanning function such as B2B sales activity. In
contrast to B2C marketing activity, B2B sales relationships are much more
dynamic, require higher customization of products and services, are more
information intensive, and involve considerable unstructured information
processing. Therefore, an integrated IT portfolio may positively enable B2B
sales activity whereas IS integration may need to be achieved differently for
successful B2C sales activity.
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F. The results of this study should be a guide towards more precision in
identifying IT impact on specific dimensions of business value in different
contexts. For example, implications for the effect of different IS capabilities
would be different depending upon the business network type under
consideration. This is because literature on buyer-seller relationships (retail
supply chains) highlights different research issues than does the literature on
manufacturing supply chains. In the traditional buyer-seller relationships
understanding customer needs and adaptation of products/services is
considered a critical activity (Hallen et al. 1991). In supply chain literature the
willingness of supply chain members to share demand, production and
inventory information up and down the supply chain (Lee et al. 1997) is
important. Whereas, for network of subunits within a multi-national
corporation, a prime concern is successful knowledge sharing (Tsai 2002).
Depending on the above distinctions between the different network contexts
(buyer-seller relationship or supply chains) even the type of network effect
may differ. In some networks, arbitrage may be the primary way in which
relational rents are derived by companies. An example is an information
broker who depends on two information sources, buyers and sellers, for
gaining arbitrage. Another type of network advantage is achieved by close
collaboration among a cluster of complementary suppliers (closure effect -
(Uzzi 1996)). Thus, future research can be more focused to conceptualize
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finer distinctions between IS capabilities depending on different type of
network context (supply chain/buyer-seller) and different type of network
effects (e.g., arbitrage or closure).
G. Are firms superior to markets? A significant amount of research in
organizational theory explores in what ways and under what circumstances
firms exist and therefore, are superior to market mechanisms (Almeida et al.
2002). Findings of this study should motivate future research on how IT is
enabling multiple partnering enterprises to derive the same synergies that
are a characteristic of firms. An interesting direction is to explore whether, by
employing integrated ISs to support an integrated, reciprocal workflow with
other enterprises, firms are able to derive the same synergies from their
alliances that are the mainstay of vertically and horizontally integrated firms.
This would stimulate an interesting debate about whether firms are likely to
be superseded by business networks. Some literature already notes the
increasing role of information technology in dis-aggregation of the economy
(Apte and Mason 1995; Zenger and Hesterly 1997).
7.3. Implications for Practice
A. This study provides evidence that as a deliberate and well-defined
information systems strategy, IT can be specifically deployed to yield higher
indirect relational value. The need to derive synergies across multiple
relationships may prove critical in dynamic B2B situations characterized by
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mergers and acquisitions and where product markets and customer
segments are continuously evolving.
B. Traditionally, managers have viewed information systems as primarily
a cost-reduction or efficiency enhancing tool. This study provides further
support that, at the inter-organizational level, IT can be consciously deployed
to enable knowledge sharing.
C. However, the findings also provide more detailed guidance. They
indicate that, where knowledge sharing across enterprises or business
entities is a key requirement, it may be enhanced by establishing an
integrated workflow across these entities or by implementing extensive IT
integration tools and applications. Such investment in IT integration need not
be under-estimated as providing purely an electronic medium for exchanging
data and information. But it acts as a context platform based on which
knowledge sharing begins to occur across enterprises.
D. Support for some of the IT infrastructure dimensions and lack of
support for others reorients the perspectives of managers with respect to the
business value of various IT initiatives in a B2B sales context (e.g., business
process reengineering, developing a best-of-breed portfolio, enabling virtual
collaboration, e-business integration, workflow systems, etc.) For example:
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o Since IS reach was not significantly related to knowledge sharing, it
indicates that achieving connectivity across enterprises by deploying
virtual collaborative systems may not be useful in deriving higher
relational value unless it also establishes a concrete workflow across
collaborators.
o A groupware system that supports unstructured interaction across
participants may not yield indirect relational value but may either be more
suitable for knowledge creation or may merely be a competitive necessity
(Cash and Konsynski 1985).
o Similarly, the findings suggest that benefits from business process
reengineering (BPR) may be under-estimated. Since BPR across
enterprise boundaries establishes a concrete workflow across these
enterprises, the benefits from BPR may not just be in efficiency terms,
i.e., cycle-time reduction or an increase in inventory turn-over. Depending
on how reciprocal the workflow is designed as, knowledge sharing may
be one of the more significant side benefits also.
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Asset Metadata

Creator Saraf, Nilesh (author)

Core Title Information systems capabilities and indirect relational value: Sustaining networked organizations

School Graduate School

Degree Doctor of Philosophy

Degree Program Business Administration

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag business administration, management,OAI-PMH Harvest

Language English

Contributor Digitized by ProQuest (provenance)

Advisor Schlueter-Langdon, Christoph (committee chair), El Sawy, Omar A. (committee member), Gosain, Sanjay (committee member), James, Gareth (committee member), Maxwell, William (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-378575

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Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the au...

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