Content uploaded by Peter Green
Author content
All content in this area was uploaded by Peter Green on Oct 17, 2023
Content may be subject to copyright.
Communications of the Association for Information Systems
!/,5-& 24*$,&
On Information Technology Competencies for
Collaborative Organizational Structures
Acklesh Prasad
Queensland University of Technology"$+,&3)02"3"%154&%5"5
Peter Green
Queensland University of Technology
/,,/74)*3".%"%%*4*/.",7/2+3"4 );0"*3&,"*3.&4/2($"*3
:*3-"4&2*",*3#2/5()44/8/5#84)&/52.",3"4,&$42/.*$*#2"28&4)"3#&&."$$&04&%'/2*.$,53*/.*./--5.*$"4*/.3/'4)&
33/$*"4*/.'/2.'/2-"4*/.834&-3#8"."54)/2*9&%"%-*.*342"4/2/',&$42/.*$*#2"28&/2-/2&*.'/2-"4*/.0,&"3&$/.4"$4
&,*#2"28"*3.&4/2(
&$/--&.%&%*4"4*/.
2"3"%$+,&3)".%2&&.&4&2..'/2-"4*/. &$)./,/(8/-0&4&.$*&3'/2/,,"#/2"4*6&2(".*9"4*/.",425$452&3
Communications of the Association for Information Systems!/,24*$,&
6"*,"#,&"4 );0"*3&,"*3.&4/2($"*36/,*33
C
ommunications of the
A
I
S
ssociation for nformation
ystems
Research Paper ISSN: 1529-3181
Volume 38 Paper 22 pp. 375 – 399 March 2016
On Information Technology Competencies for
Collaborative Organizational Structures
Acklesh Prasad
School of Accountancy,
Queensland University of Technology
acklesh.prasad@qut.edu.au
Peter Green
School of Accountancy,
Queensland University of Technology
p.green@qut.edu.au
Abstract:
There is an uptake of organizations’ involvement in collaborative organizational structures (COS). Consistent with the
resource-centric views of the firm, we suggest that the COS members need to contribute their managed IT
competencies to their COS, whose synergies would create COS IT competencies. We suggest three key IT
competencies for COS: proactive top management decision synergy, collaborative and agile IT infrastructure, and
cross-functional tactical management synergy. Using survey data, we found evidence of a positive association
between these COS IT competencies and the collaborative rent-generating potential of the COS. We also found a
positive association between the collaborative rent-generating potential of the COS and the business value of the
COS members. The results suggest that developing COS IT competencies add value to a COS and its members. This
study provides guidance for organizations looking to leverage their involvement in a COS.
Keywords: Collaborative Organizational Structures, Collaborative IT, Relational View of Firm, Dynamic Capabilities,
Collaborative Rent Generating Potential, IT Competencies, Business Process Performance, Firm Performance.
This manuscript underwent peer review. It was received 01/04/2015 and was with the authors for 5 months for 2 revisions. Shuk Ying
Ho served as Associate Editor.
376 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
1 Introduction
In this study, we suggest information technology (IT) competencies for collaborative organizational
structures (COS). IT competencies are IT-related knowledge that enables one to uniquely leverage IT
(Teece, Pisano, & Shuen, 1997). For example, business managers’ IT competency refers to the set of IT-
related explicit and tacit knowledge that they possess that enables them to exhibit IT leadership in their
area of business (Bassellier, Reich, & Benbasat, 2001). A COS is a group of related organizations, which
are normally part of product or service value chains, engaged in a recursive process of working with each
other to achieve shared goals while remaining independent organizations (Weber & Chathoth, 2008). A
value chain is a chain of activities that organizations perform to deliver a product or service to the market
(Porter, 1985). Structures such as COS facilitate co-creation, co-sharing of skills, co-management, co-
development, and co-innovation among its members (Ceccagnoli, Forman, Huang, & Wu, 2012; de Rond,
2003; Grover & Kohli, 2012; Rai, Pavlou, Im, & Du, 2012). The IT competencies help a COS in facilitating
these activities, which results in business value for its members.
Organizational collaboration is not a recent phenomenon: organizations have been part of alliances for a
long time (Barringer & Harrison, 2000; Hamel, 1991; Oliver, 1990). However, the backbone of today’s
collaboration is the heavy investment in IT to establish a shared IT infrastructure: the inter-organizational
IT infrastructure (Langfield-Smith, 2008; Mayer & Teece, 2008; Weber & Chathoth, 2008). An (inter-
organizational) IT infrastructure is the composite hardware, software, network resources, and services
required to operate and manage an IT environment. This inter-organizational IT infrastructure allows COS
members to innovate processes that contribute to producing goods and delivering services. The COS
members are able to access wider aspects of product and service value chains in an inter-organizational
IT infrastructure (Ziggers & Tjemkes, 2010). For example, an inter-organizational IT infrastructure permits
vendor-managed inventory (Cetinkaya & Lee, 2000), where the supplier is authorized to manage
inventories of agreed-on stock-keeping units at retail locations. Most organizations’ final products or
services are outcomes of activities of wider inter-organization value chains (Iyer, Aubeterre, & Singh,
2008; Zeng, Sun, Duan, Liu, & Wang, 2013). Thus, by being part of a COS, organizations anticipate better
leverage of IT and better gain incremental value from investing in IT compared to a similar in-firm
investment in IT (Dyer & Singh, 1998), in helping then to achieve a better, or sustain an existing
competitive advantage (Ceccagnoli et al., 2012; Grover & Kohli, 2012; Pavlou & El Sawy, 2004; Rai et al.,
2012). .
The strategic necessity hypothesis 1 (Powell & Dent-Micallef, 1997), however, suggests that, in a
competitive environment, the nature of IT investments in COS are similar. In this situation, organizational
competencies, in complementary with these IT investments, provide business value to a COS and its
members (Barney, 1991; Mata, Fuerst, & Barney, 1995). Significant research on the effect of IT on inter-
organizational relationships exists. For example, studies have focused on IT’s effects in reducing
transaction and coordination costs in inter-organizational relationships (Brynjolfsson, Malone, Gurbaxani,
& Kambil, 1994; Mithas, Tafti, Bardhan, & Mein Goh, 2012), IT-enabled flexibility on alliance performance
(Tafti, Mithas, & Krishnan, 2013), profiles and communication and value (Rai et al., 2012), and alliance in
a platform ecosystem (Ceccagnoli et al., 2012). The strategy literature has also considered alliance
capability (Gulati, 1999; Gulati, Nohria, & Zaheer, 2000; Zollo & Winter, 2002). Research has also focused
on complementarity between client and vendor IT capabilities (Han, Lee, Chun, & Seo, 2013), and the
impact of general IT capabilities on firm performance in various contexts (e.g., supply chains) (Liu, Ke,
Wei, & Hua, 2013). However, the nature of IT competencies required to leverage the IT investments in a
COS setting has not received any resolute attention. Thus, we address the following question in this
research.
RQ1: What IT competencies leverage IT in a COS setting?
Figure 1 shows our conceptual model. With it, we suggest IT competencies for COS and their association
with COS members’ business value. We discuss this conceptual model in the following sections.
1 The strategic necessity hypothesis comprises two propositions: 1) IT provides value to a firm by increasing internal and external
coordinating efficiencies, and firms that do not adopt them have higher cost structures and, therefore, competitive disadvantage; and
2) notwithstanding the first point, firms cannot expect IT to produce sustainable advantages because most IT is readily available to
all firms—competitors, buyers, suppliers, and potential new entrants—in competitive factor markets (Powell & Dent-Micallef, 1997).
Communications of the Association for Information Systems 377
Volume 38 Paper 22
Figure 1. Conceptual Model
We concur with the arguments of the resource-based view of the firm that suggests that an organization
comprises a bundle of resources (Barney, 1991). This bundle of resources contains the homogenous
(common) and the heterogeneous (competencies) resources. Organizations’ competencies leverage the
common resources to help them achieve and maintain a competitive position (Barney, 1991; Mata et al.,
1995). Thus, in Figure 1, we suggest that a COS requires COS-specific IT competencies to leverage the
homogenous IT investment of the COS’s members. Because a COS is not independent of its members,
we suggest in Figure 1 that the path of developing COS-specific IT competencies starts with the member
organizations. First, the members need to identify and manage their IT competencies. Then, they need to
understand the synergies between their managed IT competencies to develop COS-specific IT
competencies. Because the extant literature suggests various ways in which organizations identify and
manage their IT competencies (see, e.g., Mata et al., 1995; Melville, Kraemer, & Gurbaxani, 2004; Wade
& Hulland, 2004), we do not repeat these arguments in this study. Rather, we suggest in Figure 1 that the
extent to which COS members identify and manage their IT competencies determines the extent of their
contribution in combining their managed IT competencies to develop COS-specific IT competencies. We
suggest that a proactive top management decision strategy, collaborative and agile IT infrastructure, and
cross-functional tactical management synergy as IT competencies for COS.
We also suggest in Figure 1 that these IT competencies leverage the IT investment in a COS, which
contribute to the COS’s collaborative rent-generating potential (Borgatti & Cross, 2003; Dyer & Singh,
1998). Collaborative rent, which results from combining COS members’ idiosyncratic resources, is the
incremental value generated from a collaborative relationship not generated alone by the collaborative
members (Dyer & Singh, 1998). The final elements of Figure 1 suggest that the members leverage the
subsequent collaborative rent of their COS to improve aspects of their business processes, which
contribute to the firm’s overall performance.
We used smart PLS, a component-based SEM technique, to analyze field survey data from 188
respondents to validate our proposed research model. The analysis showed a positive association
between COS members’ ability to identify and manage their IT competencies and the IT competencies of
their COS. There was also positive association between the COS IT competencies and the COS’s
collaborative rent-generating, which was positively associated with members’ business process
performance. The analysis also showed a positive association between business process performance
and overall firm performance of COS members. These outcomes imply that the suggested COS IT
competencies can contribute to developing capacity of a COS from which its members could source
business value to achieve or maintain their competitive advantage.
This paper proceeds as follows. In Section 2, we present the study’s theoretical framework and develop
our hypotheses. In Section 3, we present the study’s research design. In Sections 4 and 5, we present
and discuss the study’s results. In Section 6, we discuss the study’s contributions and directions for future
research, and, in Section 7, discuss the study’s limitations and conclude the paper.
2 Theory and Hypotheses
2.1 Microfoundations and IT Competencies for COS
We propose the study’s research model as depicted in Figure 2 below. We discuss this model in the
following sections.
378 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
Figure 2. Research Model
2.2 IT and Organizational Collaboration: An Overview
Organizations have collaborated for many years. Oliver (1990) notes early forms of inter-organizational
collaborative relationships that include trade associations, voluntary agency federations, joint ventures,
joint programs, cooperative financial interlocks, and agency sponsor linkages. Barringer and Harrison
(2000) discuss newer forms of inter-organizational collaborative relationships such as networks, consortia,
alliances, trade associations, and interlock directorates. Recent inter-organizational collaborative
relationships take the form of virtual organizations (Camarinha-Matos, Afsarmanesh, Galeano, & Molina,
2009; Markus & Agres, 2000) and virtual corporations (Staples, Hulland, & Higgins, 1999). Many prior
inter-organizational collaborative structures are still present today. However, a subtle difference in today’s
inter-organizational collaboration is that IT is a major component in establishing these collaborative
structures in the form of a shared IT infrastructure. The result of this level of IT participation in COS has
created digital business ecosystems (Camarinha-Matos & Afsarmanesh, 2005; Ceccagnoli et al., 2012).
These IT-intensive collaborations have broadened the expectations of the invested IT. The IT investments
in these settings are no longer about costs: they are about growth and innovation (Majchrzak & Malhotra,
2013; Patel, Fernhaber, McDougall-Covin, & van der Have, 2014). Thus, while IT continues to support
business processes, its core role has shifted to proactively influencing business strategy (Burton, 2005).
This shifting focus of IT’s role has seen the need for collaboration change from a tool to perform standard
processes at less cost to that of a source for innovation. This level of expectation from IT in a COS setting
means developing better competencies to maximize IT’s value.
2.3 The Need for IT Competencies
The resource-based view of the firm (RBV) advocates that an organization comprises common and
unique resources (Barney, 1991; Peteraf, 1993; Wernerfelt, 1984). A resource is common if it is easily
available to other organizations in an industry sector. A resource is unique if it helps differentiate an
organization from other organizations in an industry sector or a similar group. Thus, unique resources are
organizations’ competencies. These organizational competencies leverage the common resources in
unique ways to provide competitive advantage to organizations. The competencies that are rare, valuable,
and appropriable help organizations attain competitive advantage (Barney, 1991). If these competencies
are inimitable, non-substitutable, and immobile, they help organizations manage the attained competitive
advantage (Barney, 1991).
IT investment in a COS is considered a common resource because other organizations can make the
same investments in their COS settings. To obtain superior outcome from their COS, the members need
to identify their IT competencies that they could take to their COS. A concern with the RBV is that it
proposes the competencies at a point in time (see, e.g., Amit & Schoemaker, 1993; Barney, 1991; Barney,
1996; Mata et al., 1995; Melville et al., 2004; Wade & Hulland, 2004).
Communications of the Association for Information Systems 379
Volume 38 Paper 22
Organizations need to continuously improve their business processes to remain competitive. This situation
requires continuous investment in resources, including IT. Consequently, members need to manage their
IT competencies to continue to obtain distinctive value from their existing and new IT (Teece, 2007).
Managing competencies relates to a continuous process of learning to leverage the new and changing
common resources. Thus, managed competencies are dynamic competencies. A dynamic competency
results from an organization’s ability to integrate, build, and reconfigure internal and external
competencies to address rapidly changing environments (Teece et al., 1997). Thus, a COS’s members
would need to bring their managed competencies, which are dynamic, to their COS.
2.4 Microfoundations for COS IT Competencies
A COS’s IT competencies also need to be dynamic because sustainable collaborative rent requires
continuously improving ways to leverage the COS’s resources. A key to building dynamic competencies is
understanding its microfoundations (Teece, 2007). Microfoundations are things whose synergies create
dynamic competencies. For a COS setting, these microfoundations relate to members’ managed IT
competencies. Furthermore, Helfat et al. (2007) suggest that technical fitness and evolutionary fitness
determine the extent of dynamic competencies. Technical fitness relates to how effectively a competency
performs its function, whereas evolutionary fitness refers to how well the competency enables a firm to
make a living (Helfat et al., 2007). According to Teece (2007), dynamic competencies assist organizations
in achieving evolutionary fitness by managing the environment, which would promote innovation in an
organization. Importantly, managing dynamic competencies has to be part of a recursive process in which
the microfoundations need to adjust to new knowledge and changes in the business environment. Thus, in
a COS setting, the interaction between the COS members would need to be continuous to maintain the
synergy of their IT competencies.
The relational view of the firm (Borgatti & Cross, 2003; Dyer & Singh, 1998) also relates to Teece’s (2007)
dynamic capabilities argument and is a basis to maintain the synergy of COS members’ IT competencies.
It suggests that members’ critical resources—their IT competencies—need to extend beyond their
organizational boundaries (Dyer & Singh, 1998). The collaborative rent from a COS is possible when
members are willing to make COS-specific IT investments and recognize the synergy between their IT
competencies to leverage the investments (Dyer, 1996). That is, the microfoundations in the form of the
members’ managed IT competencies would be the source of unique collaborative rent for the COS.
Knowledge sharing promotes synergies between the microfoundations of the COS’s members.
Organizations often learn by collaborating with others (Levinson & Asahi, 1995). Thus, the COS-specific
IT competencies are the critical source of new ideas and innovation for a COS.
A large number of IT competencies leverage organizations’ IT resources (Barney, 1991; Mata et al.,
1995). Of these, the human resource (management) competencies are critical for effectively leveraging IT
resources (Bassellier, Benbasat, & Reich, 2003; Bassellier et al., 2001; Bharadwaj, Bharadwaj, &
Konsynski, 2000; Ngai, Chau, & Chan, 2011). Top managers determine the critical issues of timing and
intensity of adopting IT resources (Powell & Dent-Micallef, 1997; Ray, Barney, & Muhanna, 2004; Ray,
Muhamma, & Barney, 2005). Thus, we suggest that the members’ top management’s commitment to IT is
an important microfoundation for the COS IT competency of proactive top management decision synergy.
The tactical managers (the process managers) play a critical role in ensuring strategic alignment (Chan,
Huff, Barclay, & Copland, 1997; Henderson & Venkatraman, 1993). The common understanding on the
role of IT between the IT and business managers of the member organizations needs to expand in COS.
We suggest that these microfoundations contribute to an array of IT-business manager relationships, a
COS IT competency of a cross-functional tactical management synergy. The degree of fit of an IS to a
business process is contingent on the IT infrastructure on which the information system is developed
(Bhatt, Emdad, Roberts, & Grover, 2010; Broadbent, Weill, & Neo, 1999; Weill, Subramani, & Broadbent,
2002). We suggest that COS would require an IT competency of a collaborative and agile IT infrastructure
to innovate in a value chain. The top managers’ timing and intensity decisions and the IT and business
managers’ common understanding on IT’s strategic value provide the appropriate IT to a COS, which,
along with COS members’ ability to organize these resources, are important microfoundations for a COS
IT competency of a collaborative and agile IT infrastructure.
We suggest that these COS IT competencies contribute to its collaborative rent-generating potential. The
members can then leverage this potential and improve aspects of their business processes. Improved
business processes contribute to the member organization’s overall performance. In Sections 2.5 to 2.9,
380 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
we discuss the nature of the relationship between IT competencies, collaborative rent-generating
potential, and the business value of the COS members.
2.5 Understanding and Managing the Microfoundations of COS-specific IT
Competencies
Developing IT competencies for a COS starts with its members. Thus, COS members need to identify
their competencies (Bhatt & Grover, 2005; Doherty & Terry, 2009; Liu et al., 2013). An organization’s
efforts in understanding its key competencies contribute to directing resources to manage these
competencies (Banerji, Leinwand, & Mainardi, 2009). This situation is especially pertinent in relation to a
COS and its members. Consistent with the RBV argument, organizations’ knowledge of their
competencies indicate competencies’ presence at a point in time (Teece, 2007). To sustain a competitive
position, organizations need to maintain a continuous balance between common resources and
leveraging competencies (Powell & Dent-Micallef, 1997; Wade & Hulland, 2004).
Members expect a long-term commitment to a COS. Thus, there will continuous IT-related investments to
manage the COS and contemporary IT will be regularly available to a COS. In this case, the members will
manage their IT competencies. Managing IT competencies concerns maintaining or improving one’s
ability to leverage new but common IT resources by understanding ways to integrate the IT into existing
business processes in unique ways (Peppard & Ward, 2004). Thus, member organizations’ ability to
contribute relevant IT competencies to their COS is contingent on their ability to manage their IT
competencies. They also need to recognize the synergy between their managed IT competencies to
contribute to the effectiveness of the wider value chain that the COS manages. Thus, we propose:
H1a: COS members’ ability to identify and manage their IT competencies is positively associated
with their COS IT competency of proactive top management decision synergy.
H1b: COS member’s ability to identify and manage their IT competencies is positively associated
with their COS IT competency of a collaborative and agile IT infrastructure.
H1c: COS members’ ability to identify and manage their IT competencies is positively associated
with their COS IT competency a cross-functional tactical management synergy.
2.6 Proactive Top Management Decision Synergy and Collaborative Rent-
generating Potential
Top management plays an important role in managing and directing organizational resources (Hambrick,
1987). Top management comprises individuals at the highest level of organizational management who
have the day-to-day responsibility of managing an organization. In relation to IT, top management’s
commitment to IT ensures that organizations effectively deploy IT (Doll, 1985; Preston & Karahanna,
2009; Ray et al., 2005). For instance, top managers make critical adoption and diffusion decisions about
IT (Ray et al., 2005), and when an organization chooses to adopt and diffuse IT affects its value to the
organization (Karahanna & Straub, 1999; Musa, Meso, & Mbarika, 2005). Managers’ knowledge of the IT
affects the timing of the above decisions. Top management’s commitment to IT also indicates their
understanding of IT’s function in the context of their organizations’ strategy, structure, and systems
(Henderson & Venkatraman, 1993). This understanding places IT as a central component of business
thinking, and how IT influences organizations’ strategic decisions (Powell & Dent-Micallef, 1997). Such
commitment affects the continuity of IT investments; ensures the organization’s information systems (IS)
strategy, which supports its business strategy; and helps align IS planning with business planning (Powell
& Dent-Micallef, 1997; Wade & Hulland, 2004). Some evidence suggests that top management’s
commitment to IT-related initiatives has contributed to business value in organizations (see e.g., Powell &
Dent-Micallef, 1997; Ray et al., 2005). Thus, top management’s commitment to make decisions on IT is
an important IT competency for an organization.
In a COS setting, member organizations’ top management need to be proactive when making decisions
on IT. An effective and proactive approach to making decisions on IT requires organizations to anticipate
and understand other members’ IT decisions. In this way, a particular decision will contribute to the
collective resource of the COS. An important antecedent to achieving this collective resource is
organizations’ top management’s in-firm IT decision making competency (Ziggers & Tjemkes, 2010). With
sustained within-firm top management competency, top managers can extend their knowledge to their
COS. Top managers will have a better appreciation of the vision of their COS, which will promote
synergistic top management decisions to promote these vision. Synergistic decisions are possible when
Communications of the Association for Information Systems 381
Volume 38 Paper 22
members understand each other’s decision values and make IT-related and other decisions that are
consistent with the COS’s overall vision. The resulting decisions are proactive and result in organizations’
swiftly identifying and subsequently leveraging opportunities for their COS. Thus, organizations’ top
managers’ ability to cooperatively make decisions, which we term “proactive top management decision
synergy” in this paper, is an important IT competency for a COS. Coordinated IT-related decisions should
provide better IT for a COS. Furthermore, they should improve the COS’s structure to leverage the
invested IT. Members will be able to better leverage the collective IT that improves various aspects of their
value chain, which means that organizations in a COS will better leverage IT compared to those acting
individually. Thus, proactive top management decision synergy is an important IT competency for a COS,
and it will contribute to its collaborative rent-generating potential. Thus, we propose:
H2: Proactive top management decision synergy is positively associated with a COS’s
collaborative rent-generating potential.
2.7 Collaborative and Agile IT Infrastructure and Collaborative Rent-generating
Potential
Information systems are developed on an organization’s IT infrastructure (Duncan, 1995). Effective IS
must depict the real world that it manages (Wand, Monarchi, Parsons, & Woo, 1995; Wand & Weber,
2002). Thus, organizations continually manage their IT infrastructure with contemporary IT to facilitate
changes to the IS (Bhatt et al., 2010; Lu & Ramamurthy, 2011). For example, organizations regularly
update their network technologies, communication platforms, and data-sharing facilities to manage their
communication infrastructure.
Moreover, IT infrastructure supports a COS’s integrated IS. However, we can expect a COS’s
infrastructure expectations to differ from a single organization’s because the COS would manage an
expanded number of IS. These IS would control a diverse range of business processes that form part of a
product or service value chain. Importantly, a COS’s IT infrastructure should promote collaboration and
provide an agile platform to leverage opportunities (Sambamurthy, Bharadwaj, & Grover, 2003).
Collaboration refers to co-developing products/services or recombining products and services, jointly
designing systems, and sharing managerial or technical expertise (Tafti et al., 2013). Agility relates to an
organization’s ability to rapidly adapt to market and environmental changes in productive and cost-
effective ways (Chakravarty, Grewal, & Sambamurthy, 2013). Thus, the elements of collaboration and
agility are important to a COS’s IT infrastructure. While top management’s proactive decisions ensure
contemporary IT is available, appropriately configuring IT should result in a better IT infrastructure for a
COS to develop and manage its integrative IS.
In a competitive environment, the window for new opportunities is often small. Organizations need to
quickly reorganize their IS that manage the related business processes to leverage these opportunities. A
collaborative and agile IT infrastructure platform assists organizations in managing this IS management
process. A COS can achieve distinctive value-generating outcomes with these information systems. Thus,
a collaborative and agile IT infrastructure is an important IT competency for a COS. A flexible IT
infrastructure, which has an element of agility, has contributed to business value in other organizational
settings (see, e.g., Ray et al., 2005; Sambamurthy et al., 2003).
A collaborative and agile IT infrastructure allows members to make swift adjustments to their IS that
manage the business processes to take advantage of the available opportunities. The members will have
a better set of coordinated IT to enable innovation and collaboration across the value chain, which will
enable members to achieve better performance through innovatively adjusting their business processes
because this form of IT infrastructure enables an improved fit of IT to a COS’s wider value-chain
processes. A collaborative and agile IT infrastructure also allows a COS to quickly adapt its IS to the real
world. A collaborative and agile IT infrastructure improves a COS’s collaborative rent-generating potential.
Thus, we propose:
H3: Collaborative and agile IT infrastructure is positively associated with a COS’s collaborative
rent-generating potential.
382 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
2.8 Cross-functional Tactical Management Synergy and Collaborative Rent-
generating Potential
IT and the business unit managers have different roles in organizations. IT managers introduce and
implement IT to develop IT infrastructure, whereas business unit managers use the resultant IS to
manage the business processes. Thus, aligning IT managers’ and business managers’ decisions is
important in managing IT in organizations (Henderson & Venkatraman, 1993; Nelson & Cooprider, 1996;
Rockart, 1988). Termed the IT-business alignment, it is an organization’s ability to use IT effectively to
achieve the set business objectives (Agarwal, Sambamurthy, & Brown, 2009; Henderson & Venkatraman,
1993).
An important consideration in achieving IT-business alignment is the common understanding between
these managers of IT’s requirements to achieve the organization’s objectives (Reich & Benbasat, 2000).
Business settings such as those in COS have a complex IT-business alignment. A COS’s integrative IS
environment means that an IT manager needs to have an understanding of IT’s wider implications.
Similarly, the business unit manager will need to recognize the value of IT for the extended value chain.
Thus, a COS requires an array of IT-business alignment decisions. These IT-related decisions need to be
aligned with the member organizations’ objectives and also with the COS’s objectives. The IT and
business unit managers need to recognize the synergies between the objectives of member organizations
and the ways in which these synergized objectives relate to forming a COS. The result is an IT
competency of cross-functional tactical management synergy, which is an important IT competency for a
COS.
The notion of working toward a common goal is embedded in the concept of appreciation (Nelson &
Cooprider, 1996). Appreciation needs to be of the resource (IT in this case) itself rather than the providers
(Swanson, 1974). Appreciation refers to various beliefs regarding the appreciated object (Swanson,
1974). In a COS, once the IT and business managers appreciate and have respect for the invested IT,
they establish a synergy comprising mutual respect and understanding (Bostrom, 1989; Nelson &
Cooprider, 1996). This respect and understanding is the cornerstone of a successful knowledge-sharing
environment (Nelson & Cooprider, 1996). Furthermore, appreciation is the cornerstone of recognizing
synergies between the objectives of IT and business unit managers in a COS. Successfully understanding
the common objectives of IT and business unit managers’ decisions has led to significant benefits to
organizations (see, e.g., Armstrong & Sambamurthy, 1999; Jeffers, Muhamma, & Nault, 2008; Nelson &
Cooprider, 1996; Ray et al., 2005; Reich & Benbasat, 2000).
We propose the COS IT competency of a cross-functional tactical management synergy. This COS IT
competency relates to an improved understanding of the degree and magnitude of process conversions
required in a COS to take advantage of the value of a COS setting. With this competency, tactical
managers can better envision how to establish a COS and have the capacity to contribute to refining
aspects of the COS’s value chains. The outcome of this foresight can influence the effectiveness of the
COS’s value chain’s wider functions and enhance the COS’s collaborative capacity. Hence, a cross-
functional tactical management synergy improves a COS’s collaborative rent-generating potential. Thus,
we propose:
H4: The COS IT competency of a cross-functional tactical management synergy is positively
associated with the collaborative rent-generating potential of a COS.
2.9 Collaborative Rent-generating Potential and Business Value of COS Members
Organizations participate in COS to identify opportunities beyond their organization to achieve or sustain
their competitive position (Weber & Chathoth, 2008). However, to obtain such benefits, organizations must
first contribute to the COS’s collaborative rent-generating potential. Organizations then leverage this
capacity to improve the value-chain sections that they manage. These improvements create better or
more effective products and services and better mechanisms for delivering these products and services to
key stakeholders. These outcomes contribute to an organization’s business value in terms of delivering
superior products, managing threats from new entrants, managing and understanding key stakeholders,
and maintaining a competitive edge in their industry (Porter, 2001; Turban & Volonino, 2011).
IT’s collaborative capacity is a powerful tool for improving business value (Smith & McKeen, 2008, 2011).
Organizations invest in COS-based IT to improve their collaborative capacity (Tafti et al., 2013). For
example, organizations invest and develop social network platforms to engage in partner-level
Communications of the Association for Information Systems 383
Volume 38 Paper 22
collaborations. Organizations also invest in beyond-organization enterprise resource planning (ERP)
systems to obtain a unified view of various resources in structures such as COS.
The collaborative rent-generating potential and the resulting collaborative rent of these efforts is scalable,
compatible, modular, and can handle multiple business applications and business models (Bhatt et al.,
2010; Byrd & Turner, 2001). Collaborative rent permits members to improve firm specific value through
collaboration in managing business processes (Dyer, 1997). That is, the COS members can segregate
and realize specific value from the collaborative rent of the COS relative to organizations that do not
engage in COS (Dyer, 1996; Dyer & Singh, 1998). The collaborative rent provides members with a much
wider platform to refine existing and develop new information systems for their business processes. The
capacities of these new information systems represent a better fit of IT to their business processes. The
result of this fit is intelligent ways to create business value in the member organizations. This situation
means that a COS’s collaborative rent is an important source of business value for that COS’s members.
Thus, we propose:
H5: The collaborative rent-generating potential of a COS is positively associated with the COS’s
members’ business process performance.
There is a path for creating business value from investment in IT (Kohli & Grover, 2008). In a COS setting,
members can leverage the collaborative rent to improve their part of the combined value chains’ business
processes, which means that any leverage of collaborative rent will first affect the business processes of
the COS’s members. This situation is consistent with the notion that the first focal point of measuring IT’s
business value should be the IT-managed business processes (Alter, 2003; Dehning & Richardson, 2002;
Lim, Dehning, Richardson, & Smith, 2011). Members’ improved business processes assists in their
leveraging their assets and investments and in lowering their cost of sales and service operations. These
outcomes contribute to their firm-level performance and are important because organizations need to
evaluate their overall return on investments in COS-related resources against their firm-level return
matrices. However, firm-level returns are only possible through the effectively leveraging the collaborative
rent at the business process level. Thus, we propose:
H6: A COS member’s business processes performance is positively associated with its firm-level
performance.
2.10 Control Variables
Several other factors could also affect a COS’s collaborative rent-generating potential. An organization’s
maturity can affect its performance (Benbasat, Dexter, & Mantha, 1980; Choe, 1996; Mahmood & Becker,
1985). For example, Choe (1996) found that maturity factors such as user involvement and the capability
of IS personnel influenced the performance of an accounting IS. Mature COS often have a more aligned
and integrated IT infrastructure through more targeted IT investments. Thus, we control for the length of
engagement in COS as a proxy for COS maturity. An organization’s size determines its level of resources,
which can affect business value (Harris & Katz, 1991; Tallon & Kraemer, 2006). The level of a COS’s
members’ commitment and the intensity of their resource contribution, could have an impact on a COS’s
collaborative rent-generating potential. Thus, we use number of employees as a proxy to control for the
size of a COS’s members. IT maturity can also influence business value (Herbsleb, Zubrow, Goldenson,
Hayes, & Paulk, 1997; Luftman, 2000). For example, Devaraj and Kohli (2000) suggest that IT maturity
affects IT’s payoff in the healthcare industry. The maturity of a COS’s members’ IT infrastructure could
affect IT integration, which could contribute to the COS’s collaborative rent-generating potential. We
control for the maturity of COS members’ IT use by considering the number of years they have invested in
IT as a proxy. We discuss the research design relating to validation of the study’s proposed model in
Section 3.
3 Research Design
We employed a survey design to collect data to validate our research model.
384 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
3.1 Measurement Items, Instrument Development, and Test
We used perceptive measures for the three suggested COS IT competencies, collaborative rent-
generating potential, and measures of business process performance2. We used reported financial data
on return on assets (ROS), return on equity (ROE) and return on sales (ROS) for COS members firm-level
performance. Table 1 lists the key studies that we referred to develop a pool of measurement items3. We
also held discussions with organizations that collaborate in COS or COS-related structures. The table also
defines the study’s constructs.
Table 1. Key Reference Studies for the Construct Measurement Items
Construct Definition Key Source(s) of Reference
Identifying and managing
within-firm IT
competencies
The COS members’ ability to identify and
manage their IT competencies
Barney (1991), Wade & Hulland (2004),
Mata et al. (1995), Melville et al. (2004)
Proactive top
management decision
synergy
The decision synergy between COS
members’ top managers
Ray et al. (2005), Powell & Dent-Micallef
(1997)
Collaborative and agile IT
infrastructure
The COS IT infrastructure that facilitates
collaboration and is responsive to
opportunities
Armstrong & Sambamurthy (1999),
Broadbent et al. (1999), Chung, Rainer, &
Lewis (2003), Liu et al. (2013), Lu &
Ramamurthy (2011), Mitchell & Zmud (1999)
Cross-functional tactical
management synergy
The IT-business alignment between a COS
members’ IT and unit managers
Agarwal et al. (2009), Brown & Magill
(1994), Caron (1994), Oh & Pinsonneault
(2007), Reich (2000), Tallon (2007)
Collaborative rent-
generating potential
A COS’s capacity to generate incremental
value that the COS members would not
generate alone
Baker, Gibbons, & Murphy (2002), Bensaou
(1997), Fang, Wu, Fang, Chang, & Chao
(2008), Goo & Huang (2008), Grover & Kohli
(2012), Wasko, Faraj, & Teigland (2004)
Business processes
performance
The performance of a COS members’
business processes as relating to reductions
in expenditure and improvement in
productivity
Mitra & Chaya (1996)
We used the approach that Davis (1989) and Moore and Benbasat’s (1991) suggest to develop and
validate the measurement items for the study’s constructs. To validate our approach, we generated the
items, sorted and refined them, and conducted a pilot test. Twelve fellow faculty colleagues with interest
and expertise relating to this research participated in the item sorting and refinement processes. The
Cohen’s Kappa (κ) for each pair of judges estimates their inter-rater reliability (Cohen, 1988).
Table 2. Measurement Items for the Constructs
Identifying and managing within-firm IT competencies (IMCP)
Our organization is able to identify factors that make better use of our IT
Our organization knows that we are better users of IT than our competitors
Our organization has valued some of human resources for being competent users of IT
Our organization constantly considers ways to improve our ability to use our IT in unique ways
Our organization trains and rewards individuals and groups that know how our IT works in our organization
Our organization invests in IT that provide stronger infrastructure on which we could build our information systems
Proactive top management decision synergy (PTES)
Our top management work closely with other members of our collaborative alliances
Our top management considers the impact of proposals and decisions on our organization and also on our
collaborative members
Our top management discusses decisions of top management of COS members when deliberating on proposals
Collaborative and agile IT infrastructure (CAIP)
2 We could not use objective measures of business process performance because the ORBIS database contained too many missing
values on operational expenses.
3 These studies do not use these measures but refer to the themes and concepts that related to the constructs.
Communications of the Association for Information Systems 385
Volume 38 Paper 22
Table 2. Measurement Items for the Constructs
Our collaborative structure has a flexible and smart IT base on which we consider various process improvements
Our collaborative structure has an IT base that allows for quick reorganization of our value chain processes
Our collaborative structure has an IT base that allows for quick development or refinement of information systems
Our collaborative structure has an IT base that has a sense of shared ownership and control
Cross-functional tactical management synergy (CTMS)
Our unit managers regularly interact with unit managers of our collaborative structure
Our IT and unit managers work closely with each other and with the managers of our alliance members
Our tactical level IT-related decision making involves interaction with IT and unit managers of our collaborative
structure
Our IT and unit managers have a good understanding of the business processes of our wider value chains of our
collaborative structure
Collaborative rent-generating potential (CRGP)
Our IT investment in the collaborative structure has improved the visibility of our business processes
Our IT investment in the collaborative structure has improved our ability to generate new business models
Our IT investment in the collaborative structure has improved our new product development efforts
Our IT investment in the collaborative structure has improved our partner collaboration efforts
Business process performance (PROP)
Our selling cost per employee has reduced significantly compared to our competitors.
Our labor cost has reduced significantly compared to our competitors.
Our operating expenditure has reduced significantly compared to our competitors.
Our sales revenue per employee has been outstanding compared to our leading competitors.
Firm performance of the COS’s members
—
actual reported financial data
Return of assets, return on equity, return on sales (Dehning & Richardson, 2002; Dehning, Richardson, & Zmud,
2007; Mitra, 2005; Mitra & Chaya, 1996)
Note:
We measured all items are on an eight-point Likert scale (no basis for answering (0), strongly disagree (1), disagree
(2), slightly disagree (3), neutral (4), slightly agree (5), agree (6), strongly agree (7)).
Cohen’s kappa measures the agreement between two raters who each classify N items (60 in this study)
into C (6 in this study) mutually exclusive categories. Kappa values below 0.60 indicate low or weak level
of agreement, values between 0.60 and 0.80 indicate full agreement, and values between 0.80 and 1.00
indicate almost perfect agreement (Cohen, 1988). The Cohen’s Kappa (κ), of the refined pool of measures
indicated that inter-rater reliability for the participants was in the full agreement (κ = 0.60 – 0.80) or almost
perfect agreement (κ = 0.81 – 1.00) range. The outcome of this sorting and subsequent refinement
process was a set of near-final measurement items for each construct.
Thirty graduate students from an MIS MBA course representing organizations that engage in some form
of IT-based collaboration participated in the pilot test. Using a component-based statistical package to
perform a preliminary factor analysis of the pilot test data, we found that the data exhibited normal
measurement qualities. Table 2 presents the final measurement items for the research model’s
constructs.
3.2 Sampling Frame Construction and Survey Administration
We obtained the contact details of organizations that could be part of a COS from the ORBIS database.
ORBIS is a publication of Bureau van Dijk Electronic Publishing (BvDEP). We also obtained actual firm
performance data from this database. ORBIS integrates information from numerous sources such as
company overviews and stock data and earnings estimates and complements this data with their own
research to create a dynamic global research tool. ORBIS provides information on public and private
companies across the globe. The ORBIS database does not explicate details of organizations’
collaborative efforts. Therefore, our sample selection had to be non-random and purposeful. Furthermore,
while some organizations collaborate internationally, a large number of organizations collaborate at a
national or regional level. For this reason, we selected organizations from one country, Australia, in our
sampling frame. The Australian economy has significant international connections and it is at the forefront
of technological innovation. Australia ranks in the top 10 in critical indicators such as secure Internet
servers, business-to-consumer Internet use, and e-participation index. Australia has also become a
source of various distinctive technologies—especially in the areas of e-health, e-government, and financial
386 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
services (Takabi, Joshi, & Gail-Joon, 2010). Most of these technologies are applied in collaborative
settings. Finally, considering the phenomena under investigation in this study, we have no reason to
believe that organizations in Australia would not provide results readily generalizable to organizational
settings in other developed countries around the world.
We analyzed organizations’ corporate reports and other media releases, visited their websites, and
contacted them by phone and email to obtain information on their collaborative engagements. We
considered cues such as discussions on membership initiatives, IT investment announcements, and
creation or expansion of e-commerce-based revenue models as indications of IT-based collaborative
efforts. At the end of this exercise, 976 target respondents (companies) from this database constituted the
sampling frame of this study.
We approached the contacts with the initial instrument package via email followed by two email
reminders. The initial email contained the link to the survey. We sent reminders, which contained the link
to the survey, after three weeks. At the conclusion of this instrument administration process, we received
188 valid responses, a response rate of 19.26 percent. We matched this perceptive data with the contacts
reported firm performance data. Table 3 presents the industry and the occupation demographics of the
survey’s respondents. Furthermore, 28 percent of responding organizations had less than 200 employees,
35 percent had between 200 and 600 employees, 17 percent between 600 and 1200 employees, and 20
percent had more than 1200 employees. Thirty percent of responding organizations had been investing in
IT for less than 10 years, 44 percent between 10 and 20 years, 18 percent between 20 and 30 years, and
8 percent for more than 30 years. The industry sector, position, and size demographic information indicate
that the dataset contained a fair distribution of organizations and respondents.
Table 3. Industry and Respondent Demographics (n = 188)
Industry secto
r
Frequency Position Frequency
Retail/wholesale/distribution 37 Chief financial officer 80
Hospitality/tourism/travel 31 Chief information officer 44
Banking/finance 29 Director of MIS 17
Healthcare 25 Team leader 10
Transportation/logistics 18 General manager 9
Others 13 Chief executive officer 7
Education 10 Branch//division manager 6
Construction 5 Chief operating officer 5
Media/entertainment/publishing 5 Business analyst 5
Professional service 5 Managing director 3
Agriculture 3 Others 2
Mining 3
Telecommunications 3
Real Estate 1
Our survey data is self-reported and, thus, subject to bias. As such, we performed various validity tests on
the survey data. We used a t-test (p<0.05) to test for non-response bias with the first and the last thirty
responses for all measures including control variables. We used the contacts that responded after the
second reminder as the proxy for non-responders (Armbrust et al., 2010). The results showed no
significant differences on any of the variables of the study. T-values of the measures ranged from 0.18 to
1.66. We also examined common methods bias and found no issues4. The ORBIS database contained
information about the number of employees for 96 of the organizations that responded to the survey. We
4 First, we performed an exploratory factor analysis (EFA) with unrotated principal components analysis (PCA). Six components
emerged with Eigenvalues greater than 1and a cumulative variance of 82.2 percent. The first component explained 28.7 percent
variance. Second, we conducted PCA with varimax rotation. Again, six components emerged with Eigenvalues greater than 1. The
rotated component matrix showed better clustering of the measures compared to the unrotated matrix. Third, we conducted principal
axis factoring with varimax rotation. Six factors emerged and the explained variance was similar to PCA analysis. Finally, we loaded
all variables on one factor. The first factor explained 28.7 percent variance, and there were five more factors with eigenvalues
greater than 1.
Communications of the Association for Information Systems 387
Volume 38 Paper 22
performed a t-test (p<0.05) to determine the extent to which the self-reported data matched the published
data. We assigned a number (1-7) to each employee scale and compared this data with the published
data. We did not find any significant difference between the self-reported and the published data (t =
0.55). A small number of responses contained missing data, and Little's MCAR test found the data to be
missing completely at random (p = 0.354). Maximum likelihood estimation (MLE), implemented by the EM
(expectation maximization) algorithm in the SPSS missing values option, imputed the missing data. These
outcomes indicate that the data was valid for our assessing its measurement and structural properties.
4 Results
4.1 Assessing the Measurement Model
We used SmartPLS, a components-based structural equation modeling (SEM) tool, to test the theoretical
relationships amongst latent variables (structural path) and the relationship between the latent variables
and their indicators (measurement paths). Rather than assuming equal weights for all indicators of a
scale, the PLS algorithm allows for each indicator to vary in how much it contributes to the composite
score of the latent variable (Chin, Marcolin, & Newsted, 2003). This situation means that the weaker
relationships between the indicators and latent constructs have a lower weighting. This varied weighting
also carries through to estimating the structural model. Thus, PLS is a preferable technique when
compared with single-item regression, which assumes error-free measurement, and with summated
regression, which assumes equal-weighted measurement. Table 4 shows basic descriptive information on
the survey data and the outer loading when evaluated using SmartPLS. Most measurement items had a
mean response value between 4 and 5. The mean of the outer loadings of all manifest variables was
above 0.70, which suggests a strong association between measurement items and their associated
constructs. The outer model standard deviation values indicate that the outer model loading were close to
the means values.
Table 4. Survey Data and Outer Model Mean and Standard Deviation
Survey data
min. Survey data
max. Survey data
mean Survey data
STDEV Outer model
mean Outer model
STDEV
CAIP1 0 7 4.210 1.560 0.961 0.024
CAIP2 1 7 4.710 1.580 0.940 0.028
CAIP3 1 7 4.960 1.680 0.903 0.023
CAIP4 1 7 4.720 1.820 0.955 0.017
CRGP1 0 7 5.390 1.330 0.746 0.026
CRGP2 2 7 4.640 1.810 0.801 0.009
CRGP3 1 7 5.340 1.360 0.920 0.049
CRGP4 0 7 5.540 1.270 0.921 0.062
CTMS1 0 7 2.780 1.010 0.917 0.008
CTMS2 2 7 2.660 1.090 0.862 0.023
CTMS3 2 7 2.710 1.120 0.919 0.017
CTMS4 2 7 5.170 1.330 0.742 0.033
IMCP1 1 7 4.910 1.540 0.866 0.052
IMCP2 1 7 4.960 1.450 0.886 0.033
IMCP3 1 7 5.350 1.280 0.729 0.004
IMCP4 0 7 5.390 1.170 0.803 0.009
IMCP5 1 7 5.210 1.240 0.812 0.014
IMCP6 1 7 4.950 1.490 0.870 0.026
PTES1 1 7 4.500 1.700 0.836 0.014
PTES2 2 7 4.810 1.610 0.875 0.019
PTES3 1 7 5.100 1.390 0.813 0.009
388 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
Table 4. Survey Data and Outer Model Mean and Standard Deviation
PROP1 1 7 4.920 1.440 0.814 0.023
PROP2 1 7 4.570 1.860 0.842 0.017
PROP3 1 7 3.890 1.360 0.863 0.033
PROP4 0 7 4.670 1.250 0.825 0.052
Key: IMCP: identifying and managing IT competencies, PTES: proactive top executive synergy, CAIP: collaborative
and agile IT infrastructure platform, CTMS: cross-functional tactical management synergy, CRGP: collaborative rent-
generating potential, PROP: business process performance
Table 5 presents the factor loading, standard error, and t-statistics and the cross loadings. All items
factored under their expected headings (constructs). Confirmatory factor analysis (CFA) via SmartPLS
showed the factor loadings for constructs loaded highly on their designated constructs. The measurement
items had a factor loading above the rule of thumb of a loading of 0.70, which indicates that the construct
accounted for at least 50 percent of the variance in the manifest variable (Hair, Anderson, Tatham, &
Black, 2008). Cross loadings analysis revealed the manifest variables loaded highly only on the desired
latent variable. Table 6 presents the results of the measurement model assessment, which includes
Cronbach’s alphas, average variance extracted, composite readability, and inter-construct correlations.
The alpha coefficients of all constructs was higher than 0.70 (Nunnally, 1978). The more accurate
composite reliabilities, which avoid the assumption of equal weightings, were above 0.80. The average
variance extracted were all above the acceptable 0.50 level (Chin, 1988). The square root of average
variance extracted values (bold), which represent the average association of each construct to its
measures, were higher than the correlations with the other constructs and indicates that the construct
closely related to its own measures rather than to those of other constructs.
Table 5. Factor Loading, Cross Loading, Standard Error and T-statistics
Factor
loading Std. error t-stat CAIP CRGP CTMS IMCP PTES PROP
CAIP1 0.963 0.024 32.48 0.963 0.326 0.307 0.388 0.337 0.057
CAIP2 0.948 0.028 28.86 0.948 0.329 0.341 0.385 0.341 0.053
CAIP3 0.907 0.023 36.24 0.907 0.210 0.321 0.375 0.273 0.131
CAIP4 0.958 0.017 51.26 0.958 0.287 0.356 0.385 0.320 0.256
CRGP1 0.747 0.026 29.22 0.430 0.747 0.407 0.495 0.426 0.263
CRGP2 0.804 0.090 9.29 0.429 0.804 0.434 0.447 0.410 0.307
CRGP3 0.924 0.049 18.80 0.460 0.924 0.340 0.415 0.448 0.287
CRGP4 0.924 0.062 14.75 0.460 0.924 0.340 0.415 0.448 0.250
CTMS1 0.919 0.008 85.41 0.231 0.362 0.919 0.428 0.290 0.305
CTMS2 0.863 0.023 37.65 0.327 0.419 0.863 0.428 0.338 0.262
CTMS3 0.919 0.017 53.98 0.233 0.362 0.919 0.327 0.288 0.332
CTMS4 0.743 0.033 23.67 0.355 0.467 0.743 0.369 0.267 0.032
IMCP1 0.867 0.052 12.55 0.361 0.397 0.381 0.867 0.466 0.325
IMCP2 0.885 0.033 24.02 0.319 0.395 0.410 0.885 0.472 0.316
IMCP3 0.730 0.004 89.18 0.326 0.307 0.450 0.730 0.322 0.289
IMCP4 0.805 0.009 79.54 0.261 0.326 0.346 0.805 0.241 0.278
IMCP5 0.814 0.014 65.37 0.324 0.336 0.340 0.814 0.231 0.230
IMCP6 0.870 0.026 28.69 0.373 0.359 0.414 0.870 0.245 0.330
PTES1 0.838 0.014 63.00 0.186 0.306 0.249 0.423 0.838 0.385
PTES2 0.876 0.019 46.29 0.351 0.367 0.288 0.473 0.876 0.306
PTES3 0.813 0.009 97.46 0.301 0.358 0.368 0.449 0.813 0.268
PROP1 0.815 0.015 79.64 0.068 0.055 0.270 0.275 0.374 0.815
PROP2 0.844 0.014 80.12 0.111 0.093 0.231 0.422 0.301 0.844
Communications of the Association for Information Systems 389
Volume 38 Paper 22
Table 5. Factor Loading, Cross Loading, Standard Error and T-statistics
PROP3 0.863 0.025 66.58 0.142 0.115 0.287 0.363 0.339 0.863
PROP4 0.827 0.033 58.96 0.151 0.091 0.314 0.293 0.250 0.827
Key: IMCP: identifying and managing IT competencies, PTES: proactive top executive synergy, CAIP: collaborative
and agile IT infrastructure platform, CTMS: cross-functional tactical management synergy, CRGP: collaborative rent-
generating potential , PROP: business process performance
Note: the values in bold represent the measurement item loadings on the assigned constructs. Other values are cross
loadings.
Table 6. Measurement Properties of Data
AVE COR CO
A
CAIP CRGP CTMS IMCP PTES PROP
CAIP 0.892 0.971 0.959 0.944
CRGP 0.729 0.914 0.874 0.650 0.854
CTMS 0.682 0.892 0.829 0.350 0.489 0.826
IMCP 0.689 0.869 0.770 0.406 0.563 0.677 0.830
PTES 0.710 0.880 0.797 0.337 0.649 0.365 0.624 0.843
PROP 0.784 0.912 0.895 0.443 0.566 0.353 0.457 0.387 0.885
Key: AVE: average variance extracted, COR: composite reliability, COA: Cronbach’s alpha, IMCP: identifying and
managing IT competencies, PTES: proactive top executive synergy, CAIP: collaborative and agile IT infrastructure
platform, CTMS: cross-functional tactical management synergy, CRGP: collaborative rent-generating potential,
PROP: business process performance
4.2 Assessing the Structural Model
Figure 3 (next page) presents our assessment of the data’s structural properties. The results show a
favorable and significant association between COS members’ ability to identify and manage their IT
competencies and their ability to contribute to the COS’s IT competencies. This outcome supports H1a-c.
The COS IT competencies of proactive top management decision synergy, collaborative and agile IT
infrastructure platform, and cross-functional tactical management synergy contributed positively to the
COSs’ collaborative rent-generating potential. They explained 65.5 percent variance in collaborative rent-
generation potential. These outcomes support H2-4. Data also shows a positive relationship between
COSs’ collaborative rent-generating potential and the business process performance of their members.
This outcome supports H5. Finally, we found a favorable and significant association between the business
process performance of COS members and their firm-level performance measured with return on assets,
return on equity, and return on sales. This outcome supports H6. Overall, there was a good fit of data to
the proposed model IT competencies for COS. The control variables of business maturity, firm size, and
IT maturity did not have a favorable association with collaborative rent-generating potential5. A possible
explanation for this outcome could relate to the fact that these factors influenced the nature of the COS
members’ IT competencies, and their impact may have been captured in the measurement of these IT
competencies. For example, mature businesses would have a better understanding of their competencies
and, thus, would contribute more towards the IT competencies of their COS.
We conducted several further analyses to evaluate the quality of the structural model. First, we conducted
the effect size, which is a measure of a specific predictor construct’s impact on an endogenous construct
(Hair, Hult, Thomas, Ringle, & Sarstedt, 2014). The (ƒ²) effect size measures the change in the R² value
5 We conducted several additional analyses on the survey data. We evaluated the impact of industry type. We grouped the industry
type into IT-intensive and non-IT intensive industries. We considered banking and finance, education, healthcare, hospitality, media,
professional services, telecommunications, and retail as IT-intensive industries. We divided the dataset equally from the others
group. The t-test indicated some differences in responses (t-values: 0.53 to 1.16). We observed similar differences in path model
analysis with the two groups of data. However, these differences were not statistically significant. We evaluated the impact of the
respondent type on the response to survey questions. We grouped the business analyst, IT manager, director MIS, and the CIO as
“technical respondents” and others as “business” respondents. A t-test (p<0.05) indicated that the responses of the groups were not
significantly different (t-values: 0.66 to 1.33). We also evaluated the structural model with these groups of data and did not observe
significant differences in the model path coefficients and the t-values.
390 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
when a specified exogenous construct is omitted from the model. We evaluated the effect size of the three
COS IT competencies on collaborative rent-generating potential. We found an effect size of 0.258 for
proactive top management decision synergy, 0.313 for collaborative and agile it infrastructure, and 0.159
for cross-functional tactical management synergy. Cohen (1988) suggests values less than 0.02 to be
small effect size, values above 0.15 to be medium effect size, and values over 0.35 to be large effect.
Thus, the effect sizes of our three COS IT competencies were medium. We also evaluated the values
predictive relevance (Q²). We used the cross-validated communality approach that Hair et al. (2014)
suggests: this approach uses only the construct scores estimated for the target endogenous construct to
predict the omitted data points. We performed this analysis on the collaborative rent-generating potential
found a Q² value of 0.458. Hair et al. (2014) suggest values of 0.02, 0.15, and 0.35 indicate that an
exogenous construct has a small, medium, or large predictive relevance for a selected endogenous
construct. Finally, we calculated the goodness of fit (GoF) as Tenenhaus, Vinzi, Chatelin, and Lauro
(2005) suggest, which is the geometric mean of average communality and average R2 of all endogenous
constructs. We found a GoF of 0.433, which indicates an overall good prediction power of the study’s
model. We discuss the results in the next section.
Figure 3. Structural Model Fit
5 Discussion
Organizations are engaging in new relationships and new business structures to survive in today’s
turbulent and competitive environment (Verdecho, Alfaro-Saiz, & Rodriguez-Rodriguez, 2012).
Furthermore, organizations are increasingly using IT in a shared environment to collaborate by being part
of collaborative organizational structures (COS) (Hung, Chang, Yen, Kang, & Kuo, 2011). In a COS,
organizations expect to better use IT than they would on their own. They expect that better understanding
the expanded value chain will contribute to their better managing their internal business processes. This
outcome is possible because organizations anticipate better collaborative rent, which they can leverage to
improve their business processes. However, the strategic necessity of investing in IT (Powell & Dent-
Micallef, 1997) means that COS have a similar form and level of IT investment. In this situation, a COS
would require IT competencies to leverage IT in unique ways to attain and maintain a level of competitive
advantage (Han et al., 2013; Kühnhardt, 2010; Liu et al., 2013; Melville et al., 2004; Powell & Dent-
Micallef, 1997).
Because a COS is not an independent organization, its IT competencies result from the synergies of its
members’ IT competencies. Achieving competitive advantage from invested IT requires members to act
proactively to identify and manage their IT competencies. Thus, we suggest that a COS’s members that
are able to identify and manage their IT competencies will be in a better position to contribute to their
COS’s IT competencies because the members’ appreciating their IT competencies leads to their
protecting themselves and ensuring their longevity (Johnston & Vitale, 1988). By its nature, a COS is a
Communications of the Association for Information Systems 391
Volume 38 Paper 22
dynamic structure and experiences rapid changes in its organization and expectations. Thus, a COS’s
members need dynamic IT competencies to leverage the COS’s new and changing resources.
With this study, we suggest key IT competencies for COS. However, organizations in their organizational
settings ascertain the value of these IT competencies. Organizations anticipate better outcomes by being
part of a collaborative setting such as a COS. Otherwise, organizations would have no incentive to incur
an opportunity cost in the absence of this benefit. However, ensuring collaborative value at the outset is
important in achieving this outcome for a COS’s members. For these reasons, we suggest that three COS
IT competencies (top management decision synergy, collaborative and agile IT infrastructure, and cross-
functional tactical management synergy) contribute to COSs’ collaborative rent-generating potential. We
found support for these relationships by analyzing our survey data, which is important because COS
members anticipate benefits from this aggregate collaborative rent. That is, COS members anticipate that
they are able to secure better value from this collaborative rent compared to a similar effort in their
organization.
Ultimately, we can expect that a COS’s members evaluate their opportunity cost of engaging in one. They
anticipate incremental value from their engagement in a COS compared to a similar within-firm efforts. As
such, members need to anticipate firm-specific business value from their COS’s collaborative rent, which
they must do because they are ultimately responsible to their own stakeholders. These stakeholders
require their organization to explain what resources they sacrifice against the value they gain from
participating in a COS. Thus, we suggest that a COS’s collaborative rent-generating potential contributes
to the COS’s members’ business process performance. Furthermore, we suggest that the business
process performance of a COS’s members contributes to their firm-level return on assets, sales, and
equity. Our results suggest that the members are able to leverage the collaborative rent of their COS with
improved business processes. This benefit also flows to their overall firm-level performance, evidenced
with better returns on their assets, equity, and sales. Overall, this study provides interesting insights on
antecedents of IT competencies for a COS, the IT competencies for a COS, and a trajectory of flow of
benefits from the leverage of IT with these competencies.
6 Contributions and Directions for Future Research
With this research, we make several contributions to theory and practice. First, we suggest IT
competencies for COS to leverage their invested IT. We include ways to identify and manage a COS’s
members’ IT competencies and recognize the synergies between these IT competencies to develop IT
competencies for the COS. Second, we suggest ways to measure the effectiveness of a COS’s IT
competencies. In this suggestion, we include a trajectory for assessing value from the COS’s collaborative
rent-generating potential to the COS’s members’ business process performance and overall performance.
These suggestions present future research with opportunities to apply these concepts in different
collaborative settings to suggest and determine the effectiveness of the IT competencies we present.
Researchers can consider collaborative rent-generating potential in various related settings to evaluate
the initial value of collaborative structures. Even though the study’s trajectory for developing and
assessing competence is generic, it sets important steps in linking the IT competencies of COS members
to a COS’s IT competencies. Future research could consider ways to manage specific IT competencies
and how these specific IT competencies relate to a COS’s specific IT competencies. Researchers also
have opportunities to adopt an interpretive design to obtain a deeper understanding of leveraging IT in a
specific COS setting. Furthermore, future research could also consider how the collaborative rent-
generating potential relates to specific business process performance and then to overall firm-level
performance.
Third, in this study, we blend arguments of various theoretical perspectives and present a set of
arguments that provides a guide for developing competencies from in a within-organization setting to an
across-organization setting. We blend various resource-related theoretical views to consider
organizational resources in a holistic environment. This effort presents future research with opportunities
to better understand various organizational resources and the ways in which the understanding and
combinations of these resources could benefit various organizational settings.
For practice, the findings imply that organizations need to be proactive and resourceful when collaborating
with other organizations. Organizations should contribute to the capacity of a collaborative structure to
later source firm-specific values from these structures. A member’s capacity to contribute to a
collaborative structure depends on how well it understands the eventual successful mechanics of the
392 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
collaborative structure. As such, members need to carefully evaluate their decisions to engage in
collaboration to ensure their anticipated outcomes. Organizations should often make such evaluations to
ensure they reap continuous benefits from their COS.
7 Limitations
This research has several limitations. As we indicate in Section 2, we suggest a generic trajectory of
developing competence and assessing value for a COS. Thus, we do not suggest specific managed IT
competencies that contribute to a COS’s specific IT competencies. This may limit the study’s rigor to some
extent. However, the generic arguments are consistent with the theory and do appropriately inform the
path of competence development in a COS setting. We employ generic perceptive measures for
collaborative rent-seeking behavior because it suggests a model that one can apply to various
collaborative settings. While well validated, inherent bias may exist in these measures of collaborative
rent-seeking behavior. Tangible and published measures of collaborative rent-seeking behavior would be
appropriate. Focus on a specific type of COS that may relate to a specific industry or a group of products
could address this issue, but such a study may limit the findings’ external validity. We tested for various
biases and did not find any issues. However, while we made every effort to address biases associated
with the survey research design, some bias inherently exist, which means readers should exercise some
caution when interpreting our findings. The study’s survey response rate was 19.26 percent, which is
acceptable for mid- and top management as potential respondents (Jeffers et al., 2008; Ray et al., 2005)
but may limit the validity of the findings. Moreover, we combined the concepts of collaborative IT
infrastructure and agile IT infrastructure into a single discrete construct, which is a potential limitation of
our proposed model. While these concepts have significant synergy, future research could evaluate these
concepts individually to provide better insights on their contribution towards the collaborative rent-
generating potential. Finally, while we received responses from organizations representing several
industries, technology firms were not strongly represented. Technology firms engage in significant
upstream and downstream collaboration and play a key role in facilitating collaboration between
organizations. The absence of responses of these organizations in the dataset may have some influence
on the extent of association between COS competencies and members’ business value.
8 Conclusion
Research on ways to leverage and value IT need to be proactive. Organizations will continue to use IT in
new and dynamic ways. The recent shift into cloud computing service is one example of this radical use of
IT in a different setting. Of course, stakeholders will continue to question the value of IT in organizations.
Most of these questions arise because much of IT investments’ business value is intangible. However,
researchers and others have adequately established IT’s importance to organizations. Our efforts must
continue to address the “how” and “where” questions relating to IT. With this study, we progress our
understanding in that direction. We present a trajectory for developing IT competence to effectively use IT
in a COS setting. It also suggests ways to value the benefits of using IT in the COS and ways to value its
members. The study’s outcomes provides organizations with confidence in how they use IT. Importantly,
we add an important body of knowledge on how IT could be effectively leveraged and on its subsequent
value evaluated in the changing business structures.
Communications of the Association for Information Systems 393
Volume 38 Paper 22
References
Agarwal, R., Sambamurthy, V., & Brown, C. V. (2009). Editors' comments: Special issue on IT-business
alignment. MIS Quarterly Executive, 8(1), ii-v.
Alter, S. (2003). 18 reasons why IT-reliant work systems should replace “the IT artifact” as the core
subject matter of the IS field. Communications of the AIS, 12, 365-394.
Amit, R., & Schoemaker, P. H. J. (1993). Strategic assets and organizational rent. Strategic Management
Journal, 14(1), 33-46.
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin,
A., Stoica, I., & Zaharia, M. (2010). A View of Cloud Computing. Communications of the ACM,
53(4), 50-58.
Armstrong, C. P., & Sambamurthy, V. (1999). Information technology assimilation in firms: The influence
of senior leadership and IT infrastructures. Information Systems Research, 10(4), 304-331.
Baker, G., Gibbons, R., & Murphy, K. J. (2002). Relational contracts and the theory of the firm. Quarterly
Journal of Economics, 117, 39-85.
Banerji, S., Leinwand, P., & Mainardi, S. (2009). Cut costs and grow stronger: A strategic approach to
what to cut and what to keep. Boston, MA: Harvard Business Press Books.
Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management,
17(1), 99-120.
Barney, J. B. (1996). The resource-based theory of the firm. Organization Science, 7(5), 469.
Barringer, B. R., & Harrison, J. S. (2000). Walking a tightrope: Creating value through inter-organizational
relationships. Journal of Management, 26(3), 367-403.
Bassellier, G., Benbasat, I., & Reich, B. H. (2003). The influence of business managers’ IT competence on
championing IT. Information Systems Research, 14(4), 317-336.
Bassellier, G., Reich, B., & Benbasat, I. (2001). Information technology competence of business
managers: A definition and research model. Journal of Management Information Systems, 17(4),
159-182.
Benbasat, I., Dexter, A. S., & Mantha, R. W. (1980). Impact of organizational maturity on information skill
needs. MIS Quarterly, 4(1), 21-34.
Bensaou, M. (1997). Inter-organizational cooperation: The role of information technology an empirical
comparison of U.S. and Japanese supplier relations. Information Systems Research, 8(2), 107-124.
Bharadwaj, A. S., Bharadwaj, S. G., & Konsynski, B. R. (2000). A resource based perspective of IT
capability and firm performance: An empirical investigation. MIS Quarterly, 24(1), 169-196.
Bhatt, G., Emdad, A., Roberts, N., & Grover, V. (2010). Building and leveraging information in dynamic
environments: The role of IT infrastructure flexibility as enabler of organizational responsiveness
and competitive advantage. Information and Management, 47(7-8), 341-349.
Bhatt, G. D., & Grover, V. (2005). Types of information technology capabilities and their role in competitive
advantage: An empirical study. Journal of Management Information Systems, 22(2), 253-278.
Borgatti, S. P., & Cross, R. (2003). A relational view of information seeking and learning in social
networks. Management Science, 49(4), 432,414.
Bostrom, R. (1989). Successful application of communication techniques to improve the system
development process. Information & Management, 16(5), 279-295.
Broadbent, M., Weill, P., & Neo, B. S. (1999). Strategic context and patterns of IT infrastructure capability.
Journal of Strategic Information Systems, 8(2), 157-187.
Brown, C. V., & Magill, S. L. (1994). Alignment of the IS functions with the enterprise: Toward a model of
antecedents. MIS Quarterly, 18(4), 371-403.
Brynjolfsson, E., Malone, T., Gurbaxani, V., & Kambil, A. (1994). Does information technology lead to
smaller firms? Management Science, 40(12), 1628-1644.
394 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
Burton, B. (2005). How to define a collaboration strategy that drives business value. Gartner. Retrieved
from https://www.gartner.com/doc/483308/define-collaboration-strategy-drives-business
Byrd, T. A., & Turner, D. E. (2001). An exploratory analysis of the value of the skills of IT personnel: Their
relationship to IS infrastructure and competitive advantage. Decision Sciences, 32(1), 21-54.
Camarinha-Matos, L., & Afsarmanesh, H. (2005). Collaborative networks: A new scientific discipline. In L.
M. Camarinha-Matos, H. Afsarmanesh, & M. Ollus (Eds.), Virtual organizations (pp. 73-80). New
York: Springer.
Camarinha-Matos, L. M., Afsarmanesh, H., Galeano, N., & Molina, A. (2009). Collaborative networked
organizations—concepts and practice in manufacturing enterprises. Computers & Industrial
Engineering, 57(1), 46-60.
Caron, J. R. (1994). Business reengineering at Cigna Corporation: Experiences and lessons learned from
the first five years. MIS Quarterly, 18(3), 233-251.
Ceccagnoli, M., Forman, C., Huang, P., & Wu, D. J. (2012). Co-creation of value in a platform ecosystem:
The case of enterprise software. MIS Quarterly, 36(1), 263-290.
Cetinkaya, S., & Lee, C. (2000). Stock replenishment and shipment scheduling for vendor-managed
inventory systems. Management Science, 46(2), 217-232.
Chakravarty, A., Grewal, R., & Sambamurthy, V. (2013). Information technology competencies,
organizational agility, and firm performance: Enabling and facilitating roles. Information Systems
Research, 24(4), 976-997.
Chan, S., Huff, D., Barclay, W., & Copland, D. G. (1997). Business strategic orientation, information
systems strategic orientation, and strategic alignment. Information Systems Research, 8(2), 125-
150.
Chin, W. (1988). The partial least squares approach for structural equation modeling. In G. A. Marcoulides
(Ed.), Modern methods for business research (pp. 295-336). Mahwah, NJ: Lawrence Erlbaum
Associates.
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling
approach for measuring interaction effects: Results from a Monte Carlo simulation study and an
electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217.
Choe, J.-M. (1996). The relationships among performance of accounting information systems, influence
factors, and evolution level of information systems. Journal of Management Information Systems,
12(4), 215-244.
Chung, S. H., Rainer, R. K. J., & Lewis, B. R. (2003). The impact of information technology infrastructure
flexibility on strategic alignment and application implementations. Communications of the
Association for Information Systems, 11, 191-206.
Cohen, J. (1988). Statistical power analysis for the behavior sciences. Hillsdale, NJ: Erlbaum.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information
technology. MIS Quarterly, 13(3), 319-340.
De Rond, M. (2003). Strategic alliances as social facts. Cambridge: Cambridge University Press.
Dehning, B., & Richardson, V. J. (2002). Returns of investment technology: A research synthesis. Journal
of Information Systems, 16(1), 7-30.
Dehning, B., Richardson, V. J., & Zmud, R. W. (2007). The financial performance effects of IT-based
supply chain management systems in manufacturing firms. Journal of Operations Management,
25(4), 806-824.
Devaraj, S., & Kohli, R. (2000). Information technology payoff in the health-care industry: A longitudinal
study. Journal of Management Information Systems, 16(4), 41-67.
Doherty, N. F., & Terry, M. (2009). The role of IS capabilities in delivering sustainable improvements to
competitive positioning. The Journal of Strategic Information Systems, 18(2), 100-116.
Communications of the Association for Information Systems 395
Volume 38 Paper 22
Doll, W. J. (1985). Avenues for top management involvement in successful MIS development. MIS
Quarterly, 9(1), 17-34.
Duncan, N. B. (1995). Capturing flexibility of information technology infrastructure: A study of resource
characteristics and their measure. Journal of Management Information Systems, 12(2), 37-57.
Dyer, J. (1996). Specialized supplier networks as a source of competitive advantage: Evidence from the
auto industry. Strategic Management Journal, 17(4), 271-291.
Dyer, J., & Singh, H. (1998). The relational view: Cooperative strategy and sources of inter-organizational
competitive strategy. Academy of Management Review, 23(4), 660-679.
Dyer, J. H. (1997). Effective inter-firm collaboration: How firms minimize transaction costs and maximize
transaction value. Strategic Management Journal, 18(7), 535-556.
Fang, S.-R., Wu, J.-J., Fang, S.-C., Chang, Y.-S., & Chao, P.-W. (2008). Generating effective inter-
organizational change: A relational approach. Industrial Marketing Management, 37(8), 977-991.
Goo, J., & Huang, C. D. (2008). Facilitating relational governance through service level agreements in IT
outsourcing: An application of the commitment–trust theory. Decision Support Systems, 46(1), 216-
232.
Grover, V., & Kohli, R. (2012). Co-creating IT value: New capabilities and metrics for multi-firm
environments. MIS Quarterly, 36(1), pp. 225-232.
Gulati, R. (1999). Network location and learning: The influence of network resources and firm capabilities
on alliance formation. Strategic Management Journal, 20(5), 397-420.
Gulati, R., Nohria, N., & Zaheer, A. (2000). Strategic networks. Strategic Management Journal, 21(3), 203-
215.
Hair, J., Anderson, R., Tatham, R., & Black, W. (2008). Multivariate data analysis (vol. 6). New York:
Prentice Hall.
Hair, J. F., Hult, G., Thomas, M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares
structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage.
Hambrick, D. C. (1987). The top management team: Key to strategic success. California Management
Review, 30(1), 88-108.
Hamel, G. (1991). Competition for competence and inter-partner learning within international strategic
alliances. Strategic Management Journal, 12, 83-103.
Han, H.-S., Lee, J.-N., Chun, J. U., & Seo, Y.-W. (2013). Complementarity between client and vendor IT
capabilities: An empirical investigation in IT outsourcing projects. Decision Support Systems, 55(3),
777-791.
Harris, S. E., & Katz, J. L. (1991). Firm size and the information technology investment intensity of life
insurers. MIS Quarterly, 15(3), 333-352.
Helfat, C., Finkelstein, S., Mitchell, W., Peteraf, M., Singh, H., Teece, D., & Winter, S. (2007). Dynamic
capabilities: Understanding strategic change in organizations. Oxford, UK: Blackwell.
Henderson, J., & Venkatraman, N. (1993). Strategic alignment: Leveraging information technology for
transforming organizations. IBM Systems Journal, 32(1), 472-484.
Herbsleb, J., & Zubrow, D. Goldenson, D., Hayes, W., & Paulk, M. (1997). Software quality and the
capability maturity model. Communications of the ACM, 40(6), 30-40.
Hung, S.-Y., Chang, S.-I., Yen, D. C., Kang, T.-C., & Kuo, C.-P. (2011). Successful implementation of
collaborative product commerce: An organizational fit perspective. Decision Support Systems,
50(2), 501-510.
Iyer, L., Aubeterre, F. D., & Singh, R. (2008). A semantic approach to secure collaborative inter-
organizational e-business processes (SSCIOBP). Journal of the Association for Information
Systems, 9(3/4), 233-269.
396 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
Jeffers, P. I., Muhamma, W. A., & Nault, B. R. (2008). Information technology and process performance:
An empirical investigation of the interaction between IT and non-IT resources. Decision Sciences,
39(4), 703-735.
Johnston, H. R., & Vitale, M. R. (1988). Creating competitive advantage with inter-organizational
information systems. MIS Quarterly, 12(2), 152-165.
Karahanna, E., & Straub, D. W. (1999). Information technology adoption across time: A cross-sectional
comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183-213.
Kohli, R., & Grover, V. (2008). Business value of IT: An essay on expanding research directions to keep
up with the times. Journal of the Association for Information Systems, 9(1), 23-39.
Kühnhardt, L. (2010). Region-building: Regional integration in the world (Vol. 25). New York: Berghahn.
Langfield-Smith, K. (2008). The relations between transactional characteristics, trust and risk in the start-
up phase of a collaborative alliance. Management Accounting Research, 19(4), 344-364.
Levinson, N., & Asahi, M. (1995). Cross-national alliances and inter-organizational learning.
Organizational Dynamics, 24, 50-64.
Lim, J.-H., Dehning, B., Richardson, V. J., & Smith, R. E. (2011). A meta-analysis of the effects of IT
investment on firm financial performance. Journal of Information Systems, 25(2), 145-169.
Liu, H., Ke, W., Wei, K. K., & Hua, Z. (2013). The impact of IT capabilities on firm performance: The
mediating roles of absorptive capacity and supply chain agility. Decision Support Systems, 54(3),
1452-1462.
Lu, Y., & Ramamurthy, K. (2011). Understanding the link between information technology capability and
organizational agility: An empirical examination. MIS Quarterly, 35(4), 931-954.
Luftman, J. (2000). Assessing business-IT alignment maturity. Communications of the Association for
Information Systems, 4, 1-50.
Mahmood, M. A., & Becker, J. D. (1985). Effect of organizational maturity on end-users' satisfaction with
information systems. Journal of Management Information Systems, 2(3), 37-64.
Majchrzak, A., & Malhotra, A. (2013). Towards an information systems perspective and research agenda
on crowdsourcing for innovation. The Journal of Strategic Information Systems, 22(4), 257-268.
Markus, M. L., & Agres, B. M. C. E. (2000). What makes a virtual organization work? MIT Sloan
Management Review, 42(1), 13-26.
Mata, F. J., Fuerst, W. L., & Barney, J. B. (1995). Information technology and sustained competitive
advantage: A resource-based analysis. MIS Quarterly, 19(4), 487-505.
Mayer, K. J., & Teece, D. J. (2008). Unpacking strategic alliances: The structure and purpose of alliance
versus supplier relationships. Journal of Economic Behavior & Organization, 66(1), 106-127.
Melville, N., Kraemer, K., & Gurbaxani, V. (2004). Information technology and organizational performance:
an integrative model of IT business value. MIS Quarterly, 28(2), 283-321.
Mitchell, V. L., & Zmud, R. W. (1999). The effects of coupling IT and work process strategies in redesign
projects. Organization Science, 10(4), 424-438.
Mithas, S., Tafti, A., Bardhan, I., & Mein Goh, J. (2012). Information technology and firm profitability:
Mechanisms and empirical evidence. MIS Quarterly, 36(1), 205-224.
Mitra, S. (2005). Information technology as an enabler of growth in firms: An empirical assessment.
Journal of Management Information Systems, 22(2), 279-300.
Mitra, S., & Chaya, A. K. (1996). Analyzing cost effectiveness of organizations: The impact of information
technology spending. Journal of Management Information Systems, 13(2), 29-57.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of
adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
Communications of the Association for Information Systems 397
Volume 38 Paper 22
Musa, P. F., Meso, P., & Mbarika, V. W. A. (2005). Toward sustainable adoption of technologies for
human development in Sub-Saharan Africa: Precursors, diagnostics, and prescriptions.
Communications of the Association for Information Systems, 15(1), 592-608.
Nelson, K., & Cooprider, J. (1996). The contribution of shared knowledge to IS group performance. MIS
Quarterly, 20(4), 409-432.
Ngai, E. W. T., Chau, D. C. K., & Chan, T. L. A. (2011). Information technology, operational, and
management competencies for supply chain agility: Findings from case studies. The Journal of
Strategic Information Systems, 20(3), 232-249.
Nunnally, J. C. (1978). Psychometric theory (Vol. 2). New York, NY: McGraw-Hill.
Oh, W., & Pinsonneault, A. (2007). On the assessment of the strategic value of information technologies:
Conceptual and analytical approaches. MIS Quarterly, 31(2), 239-265.
Oliver, C. (1990). Determinants of inter-organizational relationships: Integration and future directions. The
Academy of Management Review, 15(2), 241-265.
Patel, P. C., Fernhaber, S. A., McDougall-Covin, P. P., & van der Have, R. P. (2014). Beating competitors
to international markets: The value of geographically balanced networks for innovation. Strategic
Management Journal, 35(5), 691-711.
Pavlou, P. A., & El Sawy, O. A. (2004). Understanding the “black box” of dynamic capabilities: A missing
link to the strategic role of IT in turbulent environments (working paper). University of California.
Peppard, J., & Ward, J. (2004). Beyond strategic information systems: Towards an IS capability. The
Journal of Strategic Information Systems, 13(2), 167-194.
Peteraf, M. A. (1993). The cornerstones of competitive advantage: A resource-based view. Strategic
Management Journal, 14(3), 179-191.
Porter, M. E. (1985). Comparative advantage: Creating and sustaining superior performance. New York:
The Free Press.
Porter, M. E. (2001). Strategy and the Internet. Harvard Business Review, 79(2), 63-78.
Powell, T. C., & Dent-Micallef, A. (1997). Information technology as competitive advantage: The role of
human, business, and technology resources. Strategic Management Journal, 18(5), 375-405.
Preston, D., & Karahanna, E. (2009). Antecedents of IS strategic alignment: A nomological network.
Information Systems Research, 20(2), 159-179.
Rai, A., Pavlou, P. A., Im, G., & Du, S. (2012). Interfirm IT capability profiles and communications for co-
creating relational value: Evidence from the logistics industry. MIS Quarterly, 36(1), 233-A235.
Ray, G., Barney, J. B., & Muhanna, W. A. (2004). Capabilities, business processes, and competitive
advantage: Choosing the dependent variable in empirical tests of the resource-based view.
Strategic Management Journal, 25(1), 23-37.
Ray, G., Muhamma, W. A., & Barney, J. B. (2005). Information technology and the performance of
customer service process: A resource-based analysis. MIS Quarterly, 29(4), 625-653.
Reich, B. H., & Benbasat, I. (2000). Factors that influence the social dimension of alignment between
business and information technology objectives. MIS Quarterly, 24(1), 81-113.
Rockart, J. F. (1988). The line takes the leadership—IS management in the wired society. Sloan
Management Review, 29(4), 57-64.
Sambamurthy, V., Bharadwaj, A., & Grover, V. (2003). Shaping agility through digital options: Re-
conceptualizing the role of information technology in contemporary firms. MIS Quarterly, 27(2), 237-
262.
Smith, H. A., & McKeen, J. D. (2008). Developments in practice XXXI: Social computing: How should it be
managed? Communications of the Association for Information Systems, 23, 409-418.
Smith, H. A., & McKeen, J. D. (2011). Enabling collaboration with IT. Communications of the Association
for Information Systems, 28(1), 243-254.
398 On Information Technology Competencies for Collaborative Organizational Structures
Volume 38 Paper 22
Staples, D. S., Hulland, J. S., & Higgins, C. A. (1999). A self-efficacy theory explanation for the
management of remote workers in virtual organizations. Organization Science, 10(6), 758-776.
Swanson, E. B. (1974). Management information systems: Appreciation and involvement. Management
Science, 21(2), 178-188.
Tafti, A., Mithas, S., & Krishnan, M. S. (2013). The effect of information technology–enabled flexibility on
formation and market value of alliances. Management Science, 59(1), 207-225.
Takabi, H., Joshi, J. B. D., & Gail-Joon, A. (2010). Security and privacy challenges in cloud computing
environments. IEEE Security & Privacy, 8(6), 24-31.
Tallon, P. P. (2007). A process-oriented perspective on the alignment of information technology and
business strategy. Journal of Management Information Systems, 24(3), 227-268.
Tallon, P. P., & Kraemer, K. L. (2006). The development and application of a process-oriented
“thermometer” of IT business value. Communications of AIS, 17, 2-51.
Teece, D. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable)
enterprise performance. Strategic Management Journal, 28(13), 1319-1350.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic
Management Journal, 18, 509-533.
Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational
Statistics and Data Analysis, 48(1), 159-205.
Turban, E., & Volonino, L. (2011). Information technology for management (8th ed.). New York: Wiley.
Verdecho, M.-J., Alfaro-Saiz, J.-J., & Rodriguez-Rodriguez, R. (2012). Prioritization and management of
inter-enterprise collaborative performance. Decision Support Systems, 53(1), 142-153.
Wade, M., & Hulland, J. (2004). Review: The resource-based view and information systems research:
Review, extension, and suggestions for future research. MIS Quarterly, 28(1), 107-142.
Wand, Y., Monarchi, D. E., Parsons, J., & Woo, C. C. (1995). Theoretical foundations for conceptual
modelling in information systems development. Decision Support Systems, 15(4), 285-304.
Wand, Y., & Weber, R. (2002). Research commentary: Information systems and conceptual modeling—a
research agenda. Information Systems Research, 13(4), 363-376.
Wasko, M. M., Faraj, S., & Teigland, R. (2004). Collective action and knowledge contribution in electronic
networks of practice. Journal of the Association for Information Systems, 5(11/12), 493-513.
Weber, K., & Chathoth, P. K. (2008). Strategic alliances handbook of hospitality marketing management.
Oxford: Butterworth-Heinemann.
Weill, P., Subramani, M., & Broadbent, M. (2002). Building IT infrastructure for strategic agility. MIT Sloan
Management Review, 44(1), 57-65.
Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171-180.
Zeng, Q., Sun, S. X., Duan, H., Liu, C., & Wang, H. (2013). Cross-organizational collaborative workflow
mining from a multi-source log. Decision Support Systems, 54(3), 1280-1301.
Ziggers, G. W., & Tjemkes, B. (2010). Dynamics in inter-firm collaboration: The impact of alliance
capabilities on performance. International Journal of Food System Dynamics, 1(2), 151-166.
Zollo, M., & Winter, S. G. (2002). Deliberate learning and the evolution of dynamic capabilities.
Organization Science, 13(3), 339-351.
Communications of the Association for Information Systems 399
Volume 38 Paper 22
About the Authors
Acklesh Prasad is a Senior Lecturer in Business Information Systems in the School of Accountancy at
the Queensland University of Technology. He received his PhD in Business Information Systems from
The University of Queensland. He has published his research in the Journal of Information Systems,
International Journal of Accounting Information Systems, Australasian Journal of Information Systems,
Asia Pacific Journal of Association of Information Systems, and the Accounting Research Journal.
Peter Green is the Head of School of Accountancy and Professor of Accounting at the Queensland
University of Technology. He has more than 20 years’ experience as an academic and academic
manager. He has also undertaken several commercial research projects with companies such as SAP,
KPMG, QCA as well as the Auditor-General offices throughout Australia. He has published his work in top
journals such as MIS Quarterly, Accounting and Finance, European Journal of Information Systems,
Journal of Information Systems, and International Journal of Accounting Information Systems.
Copyright © 2016 by the Association for Information Systems. Permission to make digital or hard copies of
all or part of this work for personal or classroom use is granted without fee provided that copies are not
made or distributed for profit or commercial advantage and that copies bear this notice and full citation on
the first page. Copyright for components of this work owned by others than the Association for Information
Systems must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on
servers, or to redistribute to lists requires prior specific permission and/or fee. Request permission to
publish from: AIS Administrative Office, P.O. Box 2712 Atlanta, GA, 30301-2712 Attn: Reprints or via e-
mail from publications@aisnet.org.