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Organizational Adoption and Use of B2B Electronic Marketplaces: Efficiency- and Legitimacy-oriented Perspectives
Jai-Yeol Son Sauder School of Business
University of British Columbia Vancouver, BC V6T1Z2 CANADA
604-822-8392 (Phone); 604-822-0045 (Fax) [email protected]
Izak Benbasat Sauder School of Business
University of British Columbia Vancouver, BC V6T1Z2 CANADA
604-822-8396 (Phone); 604-822-0045 (Fax) [email protected]
October 2004
Copyright All Rights Reserved
Do not cite or quote without a written permission from the authors.
Acknowledgements: This research was supported by a grant from the Social Sciences and Humanities Research Council of Canada. We are grateful to the Purchasing Management of Association of Canada for its support in data collection. We are also thankful to Vivek Choudhury and Hock Hai Teo for their helpful comments on earlier versions of this paper.
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Organizational Adoption and Use of B2B Electronic Marketplaces: Efficiency- and Legitimacy-oriented Perspectives
ABSTRACT
Despite the large potential to transform the way that organizations conduct trading
activities, there is a paucity of empirical work that investigates the impetus for
organizational participation in Business-to-Business electronic marketplaces (B2B e-
marketplaces). With insights obtained from transaction cost theory and institutional
theory, this paper identifies two groups of factors, efficiency- and legitimacy-oriented,
respectively, that may influence organizational participation in B2B e-marketplaces.
The effects of these factors on adoption intent and on usage of B2B e-marketplaces
are empirically tested with data collected from the purchasing side of 98 potential
adopter and 85 current adopter organizations, respectively. The results of a Partial
Least Square analysis of the data show that all three efficiency-oriented factors—
product characteristics, demand uncertainty, and market volatility—and their
subconstructs have a significant influence on usage. However, only demand
uncertainty is found to have significant influence on adoption intent. The results also
show that two legitimacy-oriented factors—mimetic pressures, and normative
pressures—and their subconstructs have a significant influence only on adoption
intent, but not on usage. Finally, unlike efficiency-oriented factors, legitimacy-
oriented factors are found to contribute to a significant increase in variance
explained for only adoption intent among potential adopters.
Keywords: Interorganizational information systems, B2B electronic marketplaces, organizational adoption and use, transaction cost theory, institutional theory
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INTRODUCTION
The use of information technologies (IT) to facilitate the way in which organizations conduct
trading activities with their business partners has increased over the past several decades.
Often referred to as interorganizational information systems (IOS) or supply chain management
(SCM) technologies, these types of IT enable organizations to seamlessly integrate their
operations with external business partners and, in turn, streamline their cross-company
processes (Hammer 2001). The recent popularity of the Internet has opened up a new era of
B2B electronic commerce. As an example, much effort has been centered on the development
of B2B e-marketplaces, where buyers and sellers are brought together to conduct their trading
activities on-line (Wise and Morrison 2000). However, the level of organizational adoption of
B2B e-marketplaces has been somewhat disappointing. Many of the electronic marketplaces
that were established in the dot-com era have closed down, or are in danger of closing their
operations without ever turning a profit (Computerworld 2004; Premkumar 2003; Vaidyanathan
and Devaraz 2003).
The slower-than-projected level of adoption is due in large part to the fact that many B2B e-
marketplace makers in their rush to get on-line established their e-marketplaces without
carefully considering the underlying dynamics of organizational participation 1 in such
marketplaces. In fact, many B2B e-marketplaces driven by technological design considerations
have been struggling; on the other hand, others built with careful considerations of the nature of
organizational trading activities have been successful in spite of the current economic downturn
(Computerworld 2004). For instance, B2B e-marketplaces are gradually established as a viable
option for organizational trading activities in industries such as automobiles, metals, and
chemicals (The Economist 2004). With the impressive success of many B2B e-marketplaces,
1 Throughout this paper, organizational participation in B2B e-marketplaces encompasses both initial adoption and continued use of B2B e-marketplaces.
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market analyst firms, including Gartner Research and Meta Group, recently predicted the
renaissance of B2B e-marketplaces over the next several years (Computerworld 2004). It
appears that, notwithstanding the recent turmoil, the future of B2B e-marketplaces is optimistic
(Businessweek 2002).
Despite its large potential to transform organizational trading activities, limited attention has
been devoted to developing and empirically validating a research framework that investigates
the factors influencing organizational participation in B2B e-marketplaces. Prior research has
focused primarily on developing propositions based on a conceptual analysis (e.g., Malone et al.
1997), or reporting the findings of case studies (e.g., Choudhury 1997; Choudhury et al. 1998;
Hess and Kemerer 1994). No large-scale empirical study has yet been undertaken to test the
research propositions developed earlier, and/or others rooted in well-grounded theoretical
justification. This study aims to fill this gap.
Based on the research propositions developed earlier and the two theoretical foundations of
transaction cost theory and institutional theory, this study systematically investigates the factors
underlying organizational participation in B2B e-marketplaces. In particular, from the
perspective of organizations on the purchasing side, the effects of potential factors are
examined separately on adoption intent among potential adopters and on usage among
current adopters of B2B e-marketplaces. By doing so, we intend to identify and compare the
antecedents of organizational adoption and of use of B2B e-marketplaces. Of particular interest
in this study are vertical (i.e., industry specific) e-marketplaces that are not biased toward a
specific buyer or seller.
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CONCEPTUAL BACKGROUND
Efficiency Versus Legitimacy Perspectives
The key reasons for a new strategic action taken by an organization are based usually on two
primary perspectives, efficiency and/or legitimacy (Grewal et al. 2001; Roberts and Greenwood
1997). According to the efficiency-oriented perspective, organizations take strategic actions
based on the rationalistic expectation of enhancing the economic efficiency of their transactional
processes, whereas, the legitimacy-oriented perspective places greater emphasis on social
norms and institutional expectations existing in the external environment of an organization as a
means of justifying the strategic actions taken by the organization. Transaction cost theory
(Williamson 1979, 1985) and institutional theory (DiMaggio and Powell 1983) have widely been
used as the theoretical foundations for the efficiency and legitimacy perspectives, respectively
(Roberts and Greenwood 1997). However, it has often been noted that these two theories offer
seemingly contradictory interpretations of new organizational phenomena (Roberts and
Greenwood 1997). Whereas the latter offers an “over-socialised” interpretation that focuses on
organizations’ tendency for taking legitimation processes, the former provides an “under-
socialised” interpretation focusing on organizations’ tendency for achieving economic efficiency
(Granovetter 1985).
Transaction Cost Theory
Transaction cost theory (Williamson 1979, 1985) was originally developed to understand an
organization’s choice between two polar modes of governance structures, namely markets
versus hierarchies, in coordinating the flow of materials. Market is used to refer to outsourcing
activities in which the flow of materials is coordinated by supply and demand forces via external
transactions among different firms. Hierarchy, on the other hand, describes in-house production
in which the flow of materials is coordinated by means of the managerial decisions made within
the firm (Malone et al. 1987). More recently, a new governance structure termed "hybrid" by
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Williamson (1991) and "relational" by Macneil (1980), has emerged as an intermediate
mechanism between market and hierarchy. Transaction cost theory suggests that, all else
being equal, organizations strive to minimize their total costs, which includes both transaction
and production costs, by choosing the best organizational governance structure for their
particular situation. The decisions related to organizational governance structures can best be
explained by consideration of the interplay between three key dimensions of transactions (asset
specificity, uncertainty, and frequency) and two main assumptions of human behavior (bounded
rationality and opportunism).
Since its inception, transaction cost theory has been applied to interpret a variety of economic
and social phenomena, ranging from marriage between individuals, to vertical integration,
corporate finance, financial markets, marketing channel relationships, franchising, regulation,
and international trade (Rindfleisch and Heide 1997; Shelanski and Klein 1995). In IS studies,
transaction cost theory has been used predominantly as a guiding theory to systematically
analyze the effects of the rise of computer and network technologies on an organization’s
economic activities (e.g., Bakos and Kemerer 1992; Clemons and Row 1992; Clemons et al.
1993; Malone et al. 1987).
Malone et al.’s seminal paper (1987) proposed that organizations would choose the best
alternative among IT-enabled governance structures, based largely on their economic efficiency
rationale. They identified two distinct types of IT-enabled governance structures: electronic
market and electronic hierarchy. They described (i) the electronic market as outsourcing
activities where the flow of materials is coordinated by supply and demand forces and external
transactions among different firms through electronic networks; and, (ii) the electronic hierarchy
as in-house production, or outsourcing from another firm in a close, electronically mediated,
supplier relationship.
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Choudhury (1997) extended the prior typology of IT-enabled governance structures by
classifying the organization’s online trading activities through electronic networks into three
distinct types: electronic monopoly, electronic dyads, and multilateral IOS. This typology
captures the different kinds of online trading mechanisms available to organizations relatively
well. The typology has a direct correspondence to the underlying organizational behaviour in
off-line trading activities. According to Choudhury (p.4), an organization may “(i) routinely make
all its purchases from an external source (sole sourcing, supported by an electronic monopoly),
(ii) make each purchase from one of a smallest set of preferred suppliers (relational market
exchanges, supported by electronic dyads), or (iii) shop the entire market for each purchase,
potentially buying from a different seller each time (spot market exchange, supported by an
electronic market, or a multilateral IOS).”
The literature on IT-enabled governance structures identifies certain factors related to
transaction costs suggesting circumstances under which organizations should benefit most from
a particular type of IT-enabled governance structures for their trading activities. Conceptual
analysis by Malone et al. (1987) proposed asset specificity and complexity of products as two
important product characteristics variables that could influence organizations’ strategic choice of
an IT-enabled governance structure. Based on a case study of the aircraft parts industry,
Choudhury and his colleagues (1997, 1998) proposed additional variables that could potentially
influence organizational decisions on the choice of the IT-enabled governance structure, namely,
uncertainty attributable to the nature of the demand as well as to the market environment of the
products that organizations need to acquire.
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Institutional Theory
While transaction cost theory focuses on economic efficiency, institutional theory places greater
emphasis on the taken-for-granted nature of the decisions made by organizations (Roberts and
Greenwood 1997). This view was originally taken to explain why there exists a great deal of
homogeneity, rather than diversity, among organizations in their forms and operations
(DiMaggio and Powell 1983).
Over the past several decades, institutional theory has become an integral reference framework
for the study of organizational adoption of innovations. It has successfully been applied to
investigate organizational adoption of various innovations ranging from organizational
governance structures and forms (e.g., Burns and Wholey 1993; Lee and Pennings 2002;
Palmer et al. 1993; Yiu and Makino 2002), to management strategies and practices (e.g.,
Haveman 1993; Mezias 1990; Sherer and Lee 2002; Westphal et al. 1997; Young et al. 2001).
Institutional theory has often been integrated with other theoretical frameworks to overcome its
over-socialized perspective (e.g., Mezias 1990; Roberts and Greenwood 1997; Sherer and Lee
2002). In the IS field, the framework has received relatively less attention, but several
researchers have employed the theoretical framework through which to understand the adoption
of IT innovations, such as adoption of electronic data interchange technology (Teo et al. 2003),
and IS outsourcing decisions (Ang and Cummings 1997).
The taken-for-granted nature of organizations’ decision making is best captured as the concept
of organizational legitimacy. Organizational legitimacy, which refers to as the acceptance of an
organization by its external environment (Deephouse 1996; DiMaggio and Powell 1983), is the
most central element in institutional theory. Legitimacy of an organization is conferred by
external constituents when the values and actions of the organization are congruent with those
of its constituents, such as key suppliers, resource and product consumers, regulatory agencies,
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and other organizations that produce similar services or products (DiMaggio an Powell 1983).
Organizational isomorphism, which describes that organizations are likely to adopt processes,
structures, and strategies that others have already taken, is considered the most fundamental
mechanisms through which organizations achieve their organizational legitimacy (Deephouse
1996; DiMaggio and Powell 1983). Isomorphism is often used as an uncertainty reduction
mechanism by organizations considering the adoption of new innovations. With respect to the
isomorphic processes, the literature identifies three specific types of external institutional
pressures facing an organization: mimetic, coercive, and normative pressures (DiMaggio and
Powell 1983).
Need for Comparison between Pre- Versus Post-Adoption Period
It has been argued that factors found to be strongly influential in explaining the adoption of an
innovation are not necessarily the same as those that explain its continued usage (Cooper and
Zmud 1990; Karahanna et al. 1999; Klonglan and Coward 1970; Laudon 1985). According to
Klonglan and Coward (1970), sociological variables play a more important role than economic
variables in explaining adoption, but economic variables are more important in explaining usage.
Likewise, Triandis (1971) suggested that, when a behavior is relatively new, the impacts of
social norms and affect on behaviors are stronger than economic variables. The findings of a
study by Karahanna et al. (2001) were generally supportive of these assertions. They found
that the effect of subjective norm (i.e., the view of important others about performing the
behavior in question) is larger on the behavioral intention of the initial adoption of a new
operating system than on behavioral intention of continued use.
With the exception of Cooper and Zmud (1990) and Laudon (1985), relatively little attention has
been paid to identifying and comparing the antecedents of organizational adoption and usage.
Corroborating the findings of Laudon (1985), Cooper and Zmud (1990) found in their study on
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the implementation of material requirement planning (MRP) system in organizations that factors
rooted in rational task-technology fit have strong impacts on MRP adoption, but not on MRP
infusion. They postulated that sociopolitical and learning-related variables have strong impacts
only in explaining MRP infusion behaviors in organizations. While fragmented, empirical
evidence is also found in previous studies that have investigated the antecedents of either
adoption or usage of IT innovations in organizations. For instance, studies of EDI adoption
(Chwelos et al. 2001), and of EDI usage (Hart and Saunders 1998; Son et al. forthcoming)
reveal that trading partner pressures have a high positive impact on EDI adoption, but not on
EDI usage, in organizations.
Given the significance of the distinction between pre- and post-adoption periods as noted above,
this study examines the antecedents (to be described in the next section) of adoption intent
among potential adopters and of usage among adopters of B2B e-marketplaces, separately.
Following Chwelos et al (2001) and Teo et al. (2003), adoption intent was chosen as the
dependent variable for potential adopters. For adopters, drawing upon the literature on the
organizational assimilation process of innovations (Cooper and Zmud 1990; Fichman and
Kemerer 1997; Meyer and Goes 1988), a multi-stage model was chosen to capture the level of
participation in B2B e-marketplaces. After examining the multi-stage models proposed earlier,
including a three-state model consisting of exploration, expert, and passive states developed in
an earlier study of B2B e-marketplace participation (Grewal et al. 2001) and a six-stage model
of organizational IT assimilation that includes awareness, interest, evaluation/trial, commitment,
limited deployment, and general deployment (Fichman and Kemerer 1997), a three-stage model
consisting of trial, exploration, and commitment was developed to be used as the dependent
variable for adopters in the post-adoption period. The participation level of an organization is
classified as: 1) the exploration stage when the organization has been registered in a B2B
marketplace, but has not yet started to conduct trading activities via the marketplace, 2) the trial
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stage when it has made several transactions via an B2B e-marketplace, but is still evaluating
pros and cons of doing business via the B2B e-marketplace, and 3) the commitment stage
when the organization has made full commitment to using a B2B e-marketplace because trading
via a B2B e-marketplace is an important part of its operations. In accordance with prior studies
(e.g., Fichman and Kemerer 1997), we assume a linear progression through the three
assimilation stages in the post-adoption period.
RESEARCH MODEL AND HYPOTHESES
Based on the literature review described earlier, we identify two groups of factors, efficiency-
oriented and legitimacy-oriented, that may affect organizational adoption and use of B2B e-
marketplaces. Figure 1 represents our research model. The remainder of this section will
describe each of the research hypotheses to be tested in this study.
--------------------------------------------------------- Insert Figure 1 about here
---------------------------------------------------------
Efficiency-Oriented Perspective
The nature of the products traded should be taken into consideration in understanding
organizations’ strategic move toward B2B e-marketplaces. Drawing upon the conceptual
analysis by Malone et al. (1987), two characteristics of the products—asset specificity and
complexity—are likely to influence the move to B2B e-marketplaces. Asset specificity of a
product refers to the extent to which a product used by a firm cannot be easily utilized by other
firms. Product complexity refers to the amount of information necessary to describe attributes of
a product. Unlike asset specificity, product complexity had not received much attention in the
theoretical work of transaction cost analysis (Williamson 1979, 1985) before Malone et al. (1987)
analyzed its effects on an organization’s IT-enabled governance choice. Malone et al. indicated
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that asset specificity and complexity are not always correlated each other and, therefore, should
be considered logically independent.
Due mainly to the need for the closer coordination with trading partners and higher
communication costs, products that are high in both asset specificity and product complexity are
expected to lead to the use of electronic hierarchies, rather than electronic markets as the
preferred coordination mechanisms (Malone et al. 1997). Little research has been conducted to
empirically validate these predictions, with the notable exception of Choudhury et al. (1998).
However, their findings, based on a case study of the aircraft parts industry, were not strongly
supportive of these arguments. Aircraft parts, which were low in both asset specificity and
product complexity, were transacted in electronic hierarchies as well as electronic markets.
Notwithstanding, based on the theoretical reasoning of Malone et al. (1987), we propose the
following hypotheses regarding the effects of product characteristics on adoption and use of
B2B e-marketplaces in organizations on the purchasing side:
H1: The characteristics of products will have a significant impact on adoption intent and use of a
B2B e-marketplace among organizations on the purchasing side.
H1(a) and H1(b): Asset specificity of products as a whole that are transacted via a B2B e-
marketplace will have a negative impact on (a) adoption intent and (b) use of a B2B e-
marketplace among organizations on the purchasing side.
H1(c) and H1(d): Complexity of products as a whole that are transacted via a B2B e-
marketplace will have a negative impact on (c) adoption intent and (d) use of a B2B e-
marketplace among organizations on the purchasing side.
Uncertainty, along with asset specificity, is a key antecedent of an organization’s decision on
governance structures (Rindfleish and Heide 1997). Transaction cost theory suggests that the
ability of organizational decision makers to act rationally is limited due to the unpredictable
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circumstances surrounding an organization (i.e., environmental uncertainty) as well as the
difficulty of monitoring the performance of exchange partners (i.e., partnership uncertainty).
Prior studies do not provide a consensus on the nature of the relationship between uncertainty
and organization governance structures. According to Sutcliffe and Zaheer (1998), the problem
could be due in large part to the broad conceptualization of the construct in prior studies, which
often did not confine it to specific research purposes and contexts. Following Choudhury (1997),
we identify the two specific types of uncertainty that may influence organizational adoption and
use of B2B e-marketplaces. They include uncertainty attributable to the nature of the demand
(“demand uncertainty of products”), and to the market environment of the products (“market
volatility”) that organizations need to acquire. Demand uncertainty of a product is
conceptualized with two attributes about the extent to which the frequency of purchases of a
product is predictable (frequency uncertainty of demand), and the extent to which the volume
of purchases of a product is predictable (volume uncertainty of demand), respectively.2
In general, organizations faced with an increased level of uncertainty could offer two responses
(Williamson 1979, 1985; Zaheer and Venkatraman 1995). One is to cooperate more with
current business partners so as to cope with the increased level of uncertainty. The other
alternative is to sacrifice customized design features in favor of more standardized goods or
services that are more suitable for market governance. As such, two different arguments are
plausible about the effects of the demand uncertainty of products on organizations’ adoption
and use of B2B e-marketplaces.
We could expect a negative impact of demand uncertainty on an organization’s adoption and
use of a B2B e-marketplace, either because electronic hierarchies may be more suitable, or
because an organization can not justify the investment in participating in the B2B e-marketplace.
2 The two dimensions were adapted from technological uncertainty and volume uncertainty in Choudhury (1997).
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First, faced with a higher level of uncertainty of demand, buyers and suppliers may opt to cope
with the uncertainty together by enabling seamless integration of cross-organizational business
processes through electronic hierarchies. For this reason, B2B e-marketplaces could be a less
viable option.
Second, when high levels of uncertainty exist about the timing of the next purchase of products
(frequency uncertainty of demand) and the amount of the purchase of products (volume
uncertainty of demand), organizations can not easily quantify the benefits of engaging in trading
activities via the e-marketplace. However, organizations need to make considerable financial
commitments toward establishing and maintaining electronic linkages with others via a B2B e-
marketplace as well as paying a periodic subscription fee for participation in these marketplaces.
The return on those investments will be higher when organizations regularly purchase (sell)
large volume of products frequently via the e-marketplace. However, as the levels of
uncertainty about the volume and frequency of product demand are greater, organizations can
not easily develop the justification for such investments. 3 We also conjecture that the
establishment of a strategic investment rationale is even more critical in the post dot com era
where IT investments are closely scrutinized for a higher, less risky return on investment (Carr
2003). Based on this line of reasoning, we propose the following hypotheses regarding the
effects on organizations’ adoption and use of B2B e-marketplaces:
H2: The demand uncertainty of products will have a negative impact on adoption intent and use
of a B2B e-marketplace among organizations on the purchasing side.
H2(a) and H2(b): Frequency uncertainty of demand of products as a whole that are transacted
via a B2B e-marketplace will have a negative impact on (a) adoption intent and (b) use of a B2B
e-marketplace among organizations on the purchasing side.
3 The strategic investment rationale, which refers to an organization’s identification of promising new strategic opportunities and justification of resource commitment toward its implementation, strongly influences the implementation of new technological innovations (Chatterjee et al. 2002).
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H2(c) and H2(d): Volume uncertainty of demand of products as a whole that are transacted via
a B2B e-marketplace will have a negative impact on (c) adoption intent and (d) use of a B2B e-
marketplace among organizations on the purchasing side.
Alternatively, we could postulate favorable impacts of demand uncertainty on organizations’
participation in B2B e-marketplaces. Since products with a higher level of demand uncertainty
may lead suppliers to experience unexpected production costs or excess capacity, and buyers
to experience stock-outs or excess inventory (Walker and Weber 1984), close long-term buyer-
supplier relationships supported by electronic hierarchies may be of limited value. Given the
undesirable consequences of high demand uncertainty, organizations may opt to trade in
electronic marketplaces to take the advantage of electronic brokerage effects. Put differently,
when the demand of products are characterized as being more uncertain in terms of the volume
and frequency, the benefits of electronic brokerage effects in electronic markets could outweigh
the opportunity cost of forgoing the advantage of electronic integration effect in electronic
hierarchies. We accordingly develop the following alternative hypotheses:
H2a: The demand uncertainty of products will have a positive impact on adoption intent and use
of a B2B e-marketplace among organizations on the purchasing side.
H2a(a) and H2a(b): Frequency uncertainty of demand of products as a whole that are
transacted via a B2B e-marketplace will have a positive impact on (a) adoption intent and (b)
use of a B2B e-marketplace among organizations on the purchasing side.
H2a(c) and H2a(d): Volume uncertainty of demand of products as a whole that are transacted
via a B2B e-marketplace will have a positive impact on (c) adoption intent and (d) use of a B2B
e-marketplace among organizations on the purchasing side.
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The uncertainty attributable to market environment of products is labelled as market volatility.
The market of a product is characterized as volatile when the environment changes rapidly in
terms of the prices and players, such as buyers and sellers (Bello and Gilliland 1997;
Choudhury 1997). As in the case of demand uncertainty, it is plausible to make two alternative
arguments about how organizations could cope with high levels of market volatility: i.e., tightly
coupling the relationship with current trading partners for future transaction needs, or actively
pursuing a new trading partnership when a new transaction need arises. However, prior
empirical work is generally supportive of the latter, namely that this type of uncertainty has
unfavorable impacts on forging close long-term relationships between buyers and sellers (e.g.,
Bensaou 1997; Heide and John 1990; Son et al. forthcoming). Previous empirical findings lead
us to expect that, when organizations are faced with high levels of market volatility, B2B e-
marketplaces may be an attractive option. Sine it reduces the search costs associated with
obtaining information on sellers and their product offerings in the market (Bakos 1991). Based
on this line of reasoning, we posit the following regarding the effects on organizations’ adoption
and use of B2B e-marketplaces:
H3(a) and H3(b): The market volatility of products as a whole that are transacted via a B2B e-
marketplace will have a positive impact on (a) adoption intent and (b) use of a B2B e-
marketplace among organizations on the purchasing side.
Legitimacy-Oriented Perspective
The shortcoming of a purely efficiency-oriented approach to understand an action taken by an
organization, based on transaction cost theory, is that it ignores sociological aspects in the
process of organizational decision making. We, therefore, incorporate a set of factors from
institutional theory into our research model of organizational adoption and use of B2B e-
marketplaces to make it more comprehensive. Based on the literature of institutional theory
(DiMaggio and Powell 1983), three types of institutional pressures—coercive, mimetic, and
17
normative—from the external environment surrounding an organization are identified as the
determinants of organizational adoption and use of B2B e-marketplaces.
One of the isomorphic processes offered as an explanation for organizational homogeneity is
conformity to mimetic pressures, which implies that organizations change over time to be more
similar to other organizations in their environment (DiMaggio and Powell 1983, Haveman 1993).
Often referred to as bandwagon effects (Abrahamson and Rosenkopf 1993; Pangarkar and
Klein 1997), mimetic isomorphism causes organizations to imitate an action taken by others
without much consideration once enough organizations in their environment have already taken
the same action (March 1981). Organizations often closely monitor actions taken by similar
others within their industry and their successful practices which in turn serve as the two bases of
imitation (Haverman 1993). Faced with high levels of uncertainty about the outcome of a certain
course of actions, organizations may be able to achieve legitimacy by following the collective
action of early adopters (Tolbert 1985) and the “best practice” adopted by other similar
organizations (DiMaggio and Powell 1983; Sherer and Lee 2002). This mimicking behavior
enables organizations to minimize the costs associated with searching for alternatives and the
risks borne by first movers (Cyert and March 1963; DiMaggio and Powell 1983).
Although there is a cumulative body of the literature on mimetic isomorphism in other disciplines,
relatively little attention has been paid to assess the role of mimetic pressures within the IS
discipline, with the exception of empirical studies by Teo et al. (2003), and Soon and Cummings
(1997). Teo et al. (2003) found a significant impact of mimetic pressures on organizations’
adoption of financial EDI technology. Similarly, Soon and Cummings (1997) found a significant
influence of peer banks’ practices in making IS outsourcing decision in the banking industry.
However, although not explicitly built on institutional theory, several IS studies have recognized
the important role of mimetic pressures. As an example, the Kodak effect, which refers to the
influence of Kodak’s pioneering IS outsourcing deal with IBM on organizations’ IS outsourcing
18
decisions, has been empirically examined in the IS literature (e.g., Hu et al. 1997; Loh and
Venkatraman 1992). Like Haveman (1993) and Teo et al. (2003), we focus on two specific
types of mimetic pressures, the extent of adoption of competitors in their industry and the
perceived success of competitor adopters, and propose their posit influence on
organizations’ adoption and use of B2B e-marketplaces as follows:
H4: Mimetic pressures will have a positive impact on adoption intent and use of a B2B e-
marketplace among organizations on the purchasing side.
H4(a) and H4(b): The extent of adoption of competitors will have a positive impact on (a)
adoption intent and (b) use of a B2B e-marketplace among organizations on the purchasing side.
H4(c) and H4(d): Perceived success of competitor adopters will have a positive impact on (c)
adoption intent and (d) use of a B2B e-marketplace among organizations on the purchasing side.
Another major impetus toward homogeneity among organizations is conformity to coercive
pressures, which implies that organizations are subject to “both formal and informal pressures
exerted by other organizations upon which they are dependent and cultural expectations in the
society within which organizations function” (DiMaggio and Powell 1983). An organization’s
stakeholders who may exert formal and informal pressures to solicit compliance include trading
partners, such as customers and suppliers in exchange relationships, investors, government
regulatory agencies, and parent corporations (Srinivasan et al. 2002). These pressures may
take several forms such as force, threats, persuasion, and invitations to join in collusion
(DiMaggio and Powell 1983). For instance, government regulatory agencies may exert direct
pressures on organizations in certain industries by mandating a particular organizational
practice (Mezias 1990; Soon and Cummings 1997). Although no attempt is directly made to
influence, certain strategic actions taken by dominant organizations in an industry may be
exerted as indirect pressures on other organization in the industry. According to the resource-
19
dependency theory (Pfeffer and Salancik 1978), the source of those pressures generally arises
from the stakeholders’ ability to control scarce resources critical to the survival of the
organizations being influenced.
The effects of coercive pressures have often been examined in prior studies in the context of
the adoption of IT innovations among organizations. Coercive pressures from stakeholders
strongly influence the adoption of electronic business (e-business) technology (Srinivasan et al.
2002). Many IOS adoption studies have observed that pressures from trading partners are
among the most important factors in explaining EDI adoption (Chwelos et al. 2001; Iskandar et
al. 2001; Premkumar and Ramamurthy 1995; Teo et al. 2003). As an example, Teo et al. (2003)
found a significant and positive association between organizations’ intent to adopt EDI and the
perceived dominance of their customers and suppliers that have already adopted EDI. Though
not empirically investigated yet, anecdotal evidence suggests the importance of coercive
pressures manifested by the participation of an industry’s dominant players in the successful
development of B2B e-marketplaces. Consortium-based B2B e-marketplaces (e.g., Covisint in
the automobile industry, Exostar in the aerospace and defense industry, etc.), where several
dominant players in an industry form a consortium and develop a B2B marketplace for their
industry, have been most successful to date despite the recent economic downturn
(Computerworld 2004). Based on this observation as well as the theoretical reasoning
described above, we propose that the decision to adopt and use a B2B e-marketplace in
organizations on their purchasing side is influenced by the perceived dominance of supplier
adopters who are participating in the B2B e-marketplace.
H5(a) and H5(b): Perceived dominance of supplier adopters will have a positive impact on (a)
adoption intent and (b) use of a B2B e-marketplace among organizations on the purchasing side.
20
Finally, homogeneity among organizations over time is attributed in part to organizations’
conformity to normative pressures, which implies that strategic processes taken by
organizations are subject to values and norms shared among the members of their social
network (Scott 2001; Zukin and DiMaggio 1990). Organizations will likely behave based on their
belief about what is expected of, and what is viewed as appropriate among members in their
social networks (Deephouse 1996; Scott 2001). Normative pressures can be exerted from a
variety of sources, including trade associations, professional associations, accreditation
agencies, channel members, or professions themselves (Grewal and Dharwadkar 2002).
Through direct and indirect interactions with them, organizational decision makers can learn
about the desirable and undesirable consequences of taking certain organizational actions.
This is analogous to the notion of “informational social influence” proposed in the interpersonal
relationship context (Deutsch and Gerard 1955), which suggests that the value of a new
innovation is communicated through the interactions with other actors in an individual’s social
network.
Given that industry-specific B2B e-marketplaces are mainly designed for supporting trading
activities between buyers and sellers in an industry, (potential) trading partners’ actions with
regard to a B2B e-marketplace are expected to play a crucial role in an organization’s decision
to move toward the e-marketplace. Buyers will not be motivated to use a B2B e-marketplace
without enough suppliers; on the other hand, sellers will not be motivated to use a B2B e-
marketplace without buyers—what is often addressed as the “chicken and egg” problem
(Kollman 2001). The theoretical reasoning rooted in the critical mass perspective (Markus
1987) and the network externality perspective (Farrell and Saloner 1985) also suggests that the
value of an organization’s participation in a B2B e-marketplace would vary largely based on the
number of trading partners who have already adopted the e-marketplace. While no empirical
work has been carried out to examine the association in the context of B2B e-marketplaces,
21
previous studies in other types of SCM technologies, such as EDI, have generally supportive of
this assertion (Bouchard 1993; Teo et al. 2003). Hence, we posit that, from the perspective of
purchasing organizations, the extent of adoption of suppliers of B2B e-marketplaces would
be an important determinant in their decision to adopt and use B2B e-marketplaces.
Another type of normative pressure originates from the participation in trade and
professional associations (Teo et al. 2003) Normative rules about organizational behavior
are defined and promulgated through the active participation in a wide array of events like
conferences, workshops, and educational programs organized by trade and professional
associations (DiMaggio and Powell 1983). Individuals participating in those events, or
subscribing to the professional publications of the associations, would learn the acceptable
norms of practices and affect the behavior of their organization accordingly. As an example, the
Automotive Industry Action Group (AIAG) has played an active role in the diffusion of several IT-
related initiatives such as EDI technology (Emmelheinz 1993). Empirical evidence exists about
the strong influence of normative pressures from trade and professional associations on
financial EDI adoption (Teo et al. 2003). Likewise, normative pressures from trade and
professional associations promoting transactions via a B2B e-marketplace in an industry will
likely foster the adoption and use of the e-marketplace in organizations. We therefore propose
that trade and professional associations as well as suppliers would be the important sources of
normative pressures. Accordingly, we posit the following regarding the effects of these two
sources of normative pressures:
H6: Normative pressures will have a positive impact on adoption intent and use of a B2B e-
marketplace among organizations on the purchasing side.
H6(a) and H6(b): The extent of adoption of suppliers will have a positive impact on (a) adoption
intent and (b) use of a B2B e-marketplace among organizations on the purchasing side.
22
H6(c) and H6(d): Participation in professional and trade associations will have a positive impact
on (c) adoption intent and (d) use of a B2B e-marketplace among organizations on the
purchasing side.
While their findings are not equivocal, previous studies (described in the previous section)
suggest that it is worth investigating the differential effects of the two sets of factors (efficiency-
and legitimacy-oriented) underlying the adoption and use of B2B e-marketplaces. In particular,
we conjecture that legitimacy-oriented factors have stronger impacts on the adoption intent in
the pre-adoption period than on the usage in the post-adoption period. Isomorphic processes
are considered to play a particularly important role in circumstances where higher levels of
uncertainty are associated with the outcome of certain organizational actions (DiMaggio and
Powell 1983). Compared to the post-adoption period in which organizations have actually
experienced to some extent the services provided by a B2B e-marketplace, organizations in the
pre-adoption period will face higher levels of uncertainty concerning the pros and cons of trading
via a B2B e-marketplace. Hence, we expect that legitimacy-oriented factors would have
stronger impacts on the adoption intent in the pre-adoption period, than on the usage in the
post-adoption period. However, previous findings on IOS implementation suggest that factors
related to organizations’ rationalistic orientation, such as perceived benefits, are influential in
explaining both initial adoption in the pre-adoption period (Chau and Hui 2001; Chwelos et al.
2001; O’Callaghan et al. 2002) and continued usage in the post-adoption period (Hart and
Saunders 1998; Premkumar et al. 1994; Son et al. forthcoming). Hence, we do not explicitly
propose the differential effects of efficiency-oriented factors between the two periods. Based on
the postulation on differential effects of legitimacy-oriented factors over the two periods, we
hypothesize the following:
23
H7: Legitimacy-oriented factors will have stronger impacts on the level of adoption intent in the
pre-adoption period than on the usage in the post-adoption period.
H8: Efficiency-oriented factors will have stronger impacts on the usage in the post-adoption
period than legitimacy-oriented factors.
Effects of two other variables were controlled, based on findings in previous empirical studies on
IS adoption and implementation in organizations. The control variables include IT capabilities of
an organization (Chwelos et al. 2001; Grewal et al. 2001; Grover 1993), and organization size
(Grover et al. 1997; Rai and Bajwa 1997; Rogers 1995). Both of these two control variables are
expected to positively influence the adoption intent as well as usage in the post-adoption.
However, we do not explicitly propose and test hypotheses related to the effects of the control
variables, since our focus in this study lies on the theoretical variables described earlier.
RESEARCH METHOD
Development of Measures
To test the hypotheses, data were collected from a survey questionnaire administered to
members of the Purchasing Management Association of Canada (PMAC) during the three-
month period from November 2003 to January 2004. Two versions of the questionnaire were
prepared: 1) for adopters in the post-adoption period, and 2) for non-adopters in the pre-
adoption period. Respondents were asked to choose an appropriate version of the
questionnaire depending on whether or not their firms had been participating in a B2B e-
marketplace established for the industry in which they operate. Further, respondents who
chose the adopter version of the questionnaire were asked to indicate the name of the e-
marketplace adopted, and the level of participation in the e-marketplace, using the descriptions
of the three stages of the participation level: exploration, trial, and commitment stages (see
Appendix A). The instrument development process began with a search for prior studies that
24
contained scales for the constructs used in our study. Wherever possible, existing measures
that were proven to be reliable and valid were adapted from prior studies; otherwise, new
measures were developed for this study. All latent constructs were measured with multiple
items on 7-point Likert scales, ranging from 1 (strongly disagree) to 7 (strongly agree), except
for market variability which was measured using a semantic scale.
Consistent with our conceptualization of the constructs described earlier, four of the main
constructs (i.e., product characteristics, demand uncertainty, mimetic pressures, and normative
pressures) were operationalized as a formative, emergent construct formed from several
subconstructs, similar to the approach taken by Chwelos et al. (2001) and Teo et al. (2003).
Asset specificity and complexity of products were assessed with three-item measures
developed by closely following the descriptions of the constructs found in Malone et al. (1997).4
The two types of demand uncertainty, frequency uncertainty and volume uncertainty, were
operationalized mainly based on the extant conceptual definitions of the constructs found in
Walker and Weber (1984) and Choudhury (1997). Market volatility was directly adapted from a
multi-item scale used in Bello and Gilliland (1997). When answering the questions designed to
measure the efficiency-oriented constructs, respondents were asked to consider products as a
whole that their organizations could purchase from a B2B e-marketplace since the unit of
analysis of this study is the individual organization rather than an individual product. The
measures for the legitimacy-oriented constructs were directly adapted from measures used in
Teo et al. (2003) with some minor modifications so that they are suitable for the context of this
study. IT capabilities of an organization were assessed with four-item measures adapted from
King and Teo (1996), and Grewal et al (2001). Organization size was measured by asking
respondents last year’s total revenue and the number of employees in their organization. Since
4 Since this study focuses on products transacted in a B2B e-marketplace that an organization has already adopted, or may adopt, rather than a portfolio of products that an organization purchases, respondents were asked to consider products as a whole (i.e., on average) when answering questions measuring product characteristics and demand uncertainty.
25
these two items were intended to measure different aspects of organization size that are not
necessarily correlate highly each other, they were viewed as a formative, rather than reflective,
scale for organization size.
Several academics and members of the staff at PMAC reviewed the first draft of the
questionnaire for the clarity of instructions and content validity. Using a modified version of the
instrument based on their feedback, pilot tests were conducted with a pilot sample (n = 148)
randomly selected from the membership of the PMAC. Respondents in the pilot test were
asked to provide feedback and suggestions for improvement where instructions or questions
were not clear, while answering all the questions by following instructions given. A total of 37
respondents (32 non-adopters and 5 adopters) returned the completed questionnaires; 12
survey packages were returned as undeliverable. Based on the feedback of the pilot test, the
survey instrument was deemed acceptable to be used in the subsequent main survey. Details
of the scales for all constructs, including the references of existing scales from which they were
adapted, are in Appendix A.
Sample and Data Collection Procedure
From the PMAC national membership list of 6,663 members, we removed 387 members since
their organizations (e.g., government agencies, consulting firms, financial institutions, etc.) are
not considered to be the potential adopters of B2B e-marketplaces. A total of 3,138 potential
respondents were randomly chosen from the remaining list of 6,276 members. Of the 3,138
potential respondents, a sample frame of 148 members randomly chosen was utilized for the
pilot study and the remaining 2,990 members constituted a sample frame for the main study.
We mailed them a survey questionnaire package including a cover letter endorsed by the
president of the PMAC to encourage the participation in the survey and the two versions (one
for potential adopters and the other for current adopters) of the questionnaire. An online version
26
of the questionnaire was also available so that respondents had an option to participate in the
study either by mailing a completed survey questionnaire or by completing and submitting the
online version of the questionnaire. In an effort to increase the response rate, a follow-up
postcard and a replacement survey package were mailed separately about one week and two
weeks after the initial survey package was sent. Although the most up-to-date membership list
was used in selecting the sample frame, 137 survey packages were returned as undeliverable.
A total of 625 respondents completed and returned the questionnaires either by mail (487; 78%)
or online (138; 22%), yielding a response rate of 22%. Of the 625 responses returned, there
were 540 non-adopter versions (86%) and 85 current adopter versions (14%). Following
Karahanna et al (1999), we took into consideration only the potential adopter organizations
among the organizations that had not yet adopted a B2B e-marketplace. Respondents in the
potential adopter organizations were asked whether they were aware of a B2B e-marketplace
available for the industry their organization operated in. 92 of the non-adopter organizations
responded ‘yes’ to the question. This allowed us to ascertain that a B2B e-marketplace to be
potentially adopted existed in the respondent organization’s industry. When more than one
person in an organization returned the completed questionnaires, we included the responses of
the most senior member of the organization. One response in the adopter organizations
indicating that their organization would discontinue using a B2B e-marketplace was discarded.
After further discarding unusable questionnaires with missing data, we obtained 92 responses
from organizations who had not yet adopted and 80 responses from the organizations that had
already adopted B2B e-marketplaces. Since same steps had been undertaken to screen out
unusable responses in the pilot study sample, the remaining responses (6 from potential
adopters and 5 from current adopters) were added to the main study sample since no major
revisions had been made after the pilot phase. Hence, 98 and 85 completed responses in total
27
were used for the potential adopters and current adopters, respectively, in the subsequent data
analysis for testing of hypotheses.
As shown in Table 1, there were a wide array of industries and a fair distribution of responding
organizations in terms of their size in the potential adopter and adopter samples. Both potential
and current adopters ranged from relatively small firms with total revenue below C$50 million
(23.4% and 25.9%, respectively) to large firms with total revenue over C$1 billion (25.5% and
34.1%, respectively). The responding organizations also widely varied in their number of
employees. 43.9% and 31.8% of the organizations in the potential adopter and current adopter
samples, respectively, had less than 500 employees, while 12.3% and 20.0% of the
organizations in the two samples reported more than 500 employees. The average number of
years that the adopter firms had been using a B2B e-marketplace was 3.84 years. Most of the
respondents reported their job titles related to the purchasing function such as purchasing
manager, buyer, and procurement manager.
--------------------------------------------------------- Insert Table 1 about here
---------------------------------------------------------
Nonresponse bias was assessed for the non-adopter and adopter samples separately using the
procedure recommended by Armstrong and Overton (1977). No significant differences between
the first one third and last one third of all respondents were found on the key research variables
under study, as well as other variables such as the size of firms and the number of years since
participating in a B2B e-marketplace. We also found no significant differences on those
variables by comparing responses received by mail with those via the Web for the non-adopter
and adopter samples separately.
28
DATA ANALYSIS
PLS (Partial Least Squares) approach to structural equation modeling using PLS-Graph v. 3.0
was used to validate the measures and test our research model. Structural equation modeling
was chosen for its ability to account for measurement errors for unobserved constructs and to
examine the significance of structural paths simultaneously. Furthermore, given the ability to
model both formative and reflective latent constructs under small- and medium-size samples
(Chin 1998), the PLS approach to structural equation modeling is considered to be more
appropriate than covariance-based modeling approaches such as LISREL and AMOS in our
study.
Measure Validation
We first evaluated the instrument for several psychometric properties. As the control variable of
organization size was measured with formative items that are not necessarily correlate highly
each other (Chin 1998), the formative scale was excluded in the validation process. Individual
item reliabilities were examined based on item loadings between an indicator and its posited
underlying factor. An indicator intended to measure perceived dominance of suppliers construct
in the adopter sample did not load well on the construct. Its loading was lower than the
suggested acceptable value of .5 or .6 (Chin 1998) and, therefore, the item was dropped from
the construct in the adopter sample. The item was also dropped in the non-adopter sample for
the sake of consistency of the measures between the two samples, which could provide direct
comparisons of the results in the subsequent structural model analysis. All retained item
loadings were greater than the suggested acceptable value (see Table 2) and all constructs had
an average variance extracted (AVE) of at least .50, adequately demonstrating convergent
validity. The square root of AVE for each multi-item construct was compared with the
correlations between the construct and other constructs (Barclay et al. 1995; Chin 1998). As
shown in Tables 3 and 4, the square root of the AVE (diagonal elements) was found to be larger
29
than the correlations (off-diagonal elements) between the constructs. Further, no cross-loading
issues were found at the individual item level (Appendices B and C), adequately demonstrated
discriminant validity of the scales. Internal consistency of constructs was assessed by
estimating a composite reliability measure (Fornell and Larcker 1981). As shown in Tables 3
and 4, all of the composite reliability measures were greater than the suggested minimum level
of .70 (Nunnally 1978), indicating an acceptable level of internal consistency.
The means and standard deviations of the individual items along with the significance of mean
differences between potential and current adopter samples are reported in Table 2. PLS-
computed correlations among the constructs are reported in Tables 3 and 4.
--------------------------------------------------------- Insert Tables 2, 3, and 4 about here
---------------------------------------------------------
Results of Analyses
Second-order factor structures were constructed to model subconstructs and their association
with underlying constructs (see Figure 2 and 3). Hypotheses related to the effects of
subconstructs were assessed by examining both the weights of the formative subconstructs and
path coefficients from the underlying constructs to the main dependent constructs of interest
(i.e., adoption intent, and adoption level) in each sample. The results of the structural model
estimation including standardized path coefficients, their t-statistics and significance based on
one-tailed t tests, and the amount of variances explained (R2) are shown in Figures 2 and 3 for
the non-adopter and adopter samples, respectively. The bootstrap procedure with 200
resamples was used to compute standard errors.
--------------------------------------------------------- Insert Figures 2 and 3 about here
---------------------------------------------------------
30
Based on the significance of the path coefficients in the full research models (Model 3 in Table
5), hypotheses 2, 4, and 6 were supported in the potential adopter sample, and hypotheses 1, 2,
and 3 were supported in the current adopter sample. When the main hypotheses were
supported in each sample, their corollaries with respect to the effects of associated
subconstructs were assessed by examining the weights of the formative subconstructs. In the
potential adopter sample, hypotheses 2(a), 2(c), 4(a), 4(c), 6(a), and 6(c) were supported.
In the current adopter sample, hypotheses 1(b), 1(d), 2(b), and 2(d) were supported.
Following the procedure described in Gefen et al. (2000) and Mathieson et al. (2001), pseudo F
test was employed to assess the impacts of adding a set of the theoretical factors on the
change in R2 of adoption intent and usage in the two samples when the other set of the
theoretical factors are already present in the model.5 The results indicated that adding the
efficiency-oriented factors to the legitimacy-oriented factors contributed to a significant increase
in variance explained for both adoption intent among potential adopters and usage among
current adopters. Adding the legitimacy-oriented factors to the efficiency-oriented factors
contributed a significant increase in variance explained for only adoption intent among potential
adopters. Hypothesis 7 was assessed by comparing the path coefficients from legitimacy-
oriented factors to adoption intent among the potential adopters (Figure 2) and to usage among
the current adopters (Figure 3) and comparing the contribution of legitimacy-oriented factors to
the explained variances between the potential and current adopters. Hypothesis 8 was
assessed by comparing the contribution of efficiency-oriented factors and legitimacy-oriented
factors to the explained variances in current adopters (Figure 3). While two of the path
coefficients from mimetic pressures and normative pressures were significant for the potential
adopter, none of them were strong for the current adopters. While the contribution of the
efficiency-oriented factors was significant for both potential and current adopters, the 5 The effect size of f2 was estimated as (R2
full - R2exculded) / (1 - R2
full) and the pseudo F statistic was computed by multiplying f2 by (n - k - 1) where n is the sample and k is the number of independent constructs.
31
contribution of the legitimacy-oriented factors was significant in explaining the variance of only
adoption intent for potential adopters, but not of usage for current adopters (see Tables 5 and 6).
Taken together, these results were generally supportive of hypotheses 7 and 8.
--------------------------------------------------------- Insert Tables 5 and 6 about here
---------------------------------------------------------
DISCUSSION AND CONCLUSION
The objective of this study is to examine the effects of efficiency- and legitimacy-oriented factors
on organizational participation in B2B e-marketplaces, and compare the effects of the
antecedent variables in the pre- and post-adoption periods. Efficiency- and legitimacy-oriented
factors were drawn primarily from transaction cost theory and institutional theory, respectively.
To the best of our knowledge, this study is the first to incorporate the two sets of factors into a
single framework and empirically test it in the context of organizational participation of B2B e-
marketplaces. The effects of the potential factors were examined separately on the adoption
intent among potential adopters in the pre-adoption period and on the usage among adopters in
the post adoption period. Interestingly, the effects of several factors, including product
characteristics, market volatility, mimetic pressures, and normative pressures, were not the
same in the two different periods. Each of the factors was found to be strongly influential in
explaining either the adoption intent or the usage, but not both. A detailed discussion of the
findings follows.
Discussion of the Findings
Among the three efficiency-oriented factors in our research model, only demand uncertainty was
found to have a significant influence on both adoption intent and usage of B2B emarketplaces.
As expected, our results show that demand uncertainty and its subconstructs of frequency
32
uncertainty and volume uncertainty have a negative impact on potential adopters’ adoption
intent, and adopters’ use of B2B e-marketplaces. When organizations are faced with high levels
of uncertainty about the frequency and the volume of their purchases, they were found to be
less likely to participate in B2B e-marketplaces for their trading activities. When the demand of
products transacted in a B2B e-marketplace is characterized high in both frequency uncertainty
and volume uncertainty, the e-marketplace was found to be less attractive to both potential and
current adopters. However, these results were contradictory to our alternative argument that
organizations with high levels of demand uncertainty would be inclined to take advantage of the
electronic brokerage benefits of B2B e-marketplaces, since they do not want to develop close
long-term relationships with their trading partners.
With respect to the effects of product characteristics, both asset specificity and complexity of
products were found to have significant and negative impacts on the use of e-marketplaces, but
not on the adoption intent. Organizations transacted via an e-marketplace more extensively
when products transacted in the e-marketplace are characterized as low in both asset specificity
and product complexity. Our results on the usage of B2B e-marketplaces are supportive of
Malone et al.’s (1987) arguments that have not previously received empirical support based on
large scale field studies. Like product characteristics, market volatility was found to have a
significant influence only on the usage, but not on the adoption intent. Unlike in the case of
demand uncertainty, electronic brokerage benefits of e-marketplaces appeared to play a key
role in increasing the usage of a B2B e-marketplace among current adopters. In sum, the e-
marketplace usage among organizations in the post-adoption period increases when facing two
types of uncertainty: uncertainty attributable to the nature of the demand (demand uncertainty of
products), and to the market environment of the products (market volatility).
33
With respect to the effects of legitimacy-oriented factors, we found that two isomorphic
processes, mimetic and normative pressures, have significant impacts only on the adoption
intent, but not on the usage. All of their subconstructs (i.e., adoption among competitors,
perceived success of competitor adopters, adoption among suppliers, and participation in trade
and professional associations) were also found to be strongly influential on organizations’
intention to initially adopt a B2B e-marketplace. Interestingly, these legitimacy-oriented factors
did not play the same important role in explaining the usage of B2B e-marketplaces for current
adopters. We also found that adding legitimacy-oriented factors to efficiency-oriented factors
contributed to a significant increase in variance explained for only adoption intent for potential
adopters, but not usage for current adopters. Based on these results, we conjecture that
organizations endeavor to legitimize their decision to initially adopt (or, not to adopt) B2B e-
marketplaces through isomorphic processes due to a higher level of uncertainty about the
outcome, and the interactive nature of the new innovation supporting trading activities among
organizations. However, once they have adopted a B2B e-marketplace, isomorphic processes
were not strongly influential in increasing their usage of the e-marketplace; rather, efficiency-
oriented factors appeared to predominate.
Coercive pressures did not significantly explain both the adoption intent and the usage. This
was inconsistent with Teo et al. (2003), which reported their positive impacts on the adoption of
EDI among potential organizations. The differences in the nature of the two types of IOS
technologies can be offered as a plausible explanation for these conflicting findings. Since EDI
technology is mainly used to support trading activities between existing buyers and sellers,
coercive pressures from their trading partners were strongly influential on an organization’s
decision to adopt the EDI technology. However, the effect of coercive pressures, such as the
perceived dominance of supplier adopters, was not a significant predictor of the initial adoption
34
of B2B e-marketplaces, where finding a new trading partner is among the primary motivations
for participation.
Managerial and Theoretical Implications
The findings of this study have important implications for both B2B e-marketplace developers
and organizations who consider adopting B2B e-marketplaces. B2B e-marketplace developers
are advised to consider both efficiency- and legitimacy-oriented motives to influence
organizational participation in their e-marketplaces. It is worth remarking that legitimacy-
oriented motives do not have the same important role in organizational decisions between initial
adoption and continued use of B2B e-marketplaces. B2B e-marketplace developers are
advised to emphasize legitimacy-oriented benefits through exerting mimetic and normative
pressures in promoting their e-marketplaces to potential adopters, rather than to current
participants. However, the ability of B2B e-marketplace developers to bring more participants to
their e-marketplaces is not sufficient to build vibrant e-marketplaces where large volumes of
transactions are made between buyers and sellers. B2B e-marketplace participants who joined
largely due to legitimacy-oriented motives may not actively transact with others in the e-
marketplace without realizing efficiency-oriented benefits. To create the liquidity of their B2B e-
marketplaces, the e-marketplace developers should take into consideration what types of
products should mainly be transacted, in terms of their characteristics, the nature of demand,
and their market volatility. For instance, it is recommended that, for organizations to routinize
purchasing online through e-marketplaces, B2B e-marketplace developers focus more on
products that are low in both asset specificity and product complexity.
Potential adopting organizations are also advised to consider both legitimacy- and efficiency-
oriented reasons in their decision to adopt a B2B e-marketplace. Organizations may not
eventually transact much via a B2B e-marketplace when their decision to adopt the e-
35
marketplace was primarily driven by legitimacy-oriented motives that have been developed
through external pressures in their environment. In such cases, their investment necessary for
initial adoption of the B2B e-marketplace may be wasted. In this sense, it is recommended that
organizations should seek for efficiency-oriented reasons for participating in a B2B e-
marketplace in the initial adoption decision stage.
Limitation and Future Research
This study advances our understanding of the effects of efficiency- and legitimacy-oriented
factors in organizational adoption and use of emerging B2B e-marketplaces. Empirical support
was provided based on a large scale field survey for propositions developed by Malone et al.’s
(1987) based on conceptual analysis and Choudhury’s (1997) based on case research findings.
In addition, this study extended Teo et al.’s (2003) study of EDI adoption in organizations by
providing additional evidence for applicability of institutional theory as a guiding theory in
understanding organizational adoption of IOS technologies. In fact, this study took a step
further by showing that factors rooted in institutional theory have strong impacts only on initial
adoption, but not on continued use, of IOS technologies.
However, several limitations of this study deserve consideration. First, this study was
conducted only from the perspective of organizations on the purchasing side. The research
model of this study was developed and tested within the context of organizations on the
purchasing organizations. For instance, efficiency-oriented factors, such as demand uncertainty
of products in the research model, may not be applicable to organizations on the selling side.
Another limitation is common method bias prevalent in survey-based empirical studies that
measure all variables in a single questionnaire (Premkumar et al. 1994). Using Harman’s one-
factor statistical test (Podsakoff and Organ 1986), we assessed whether common method bias,
is a serious issue in this study. We performed an exploratory factor analysis with all the items
36
used to measure the constructs in our research model. Neither a single factor nor a general
factor was found in the analysis. Further, the first factors emerged accounted for only 17% and
19% of the variance in the potential adopter and the current adopter samples, respectively.
Taken together, common method bias does not appear to be a serious threat in this study.
Another limitation lies in the use of adoption intent rather than actual behavior of initial adoption
of a B2B e-marketplace. This approach was chosen over the dichotomous yes/no decision
approach (e.g., Grover 1993; Lai 1994; Iskandar et al. 2001; Saunders and Clark 1991), since it
could mitigate the backward-looking problem that is prevalent in IT adoption studies with cross-
sectional study designs (Premkumar et al. 1994). The dichotomous approach is considered to
be problematic because the reconstruction of pre-adoption perception among adopters may not
be reliable (Fischhoff 1975; O’Callaghan et al. 1993). However, it should also be noted that our
approach of using adoption intent is considered disadvantageous in that it relies on the
intention-behavior link.
We recommend that future research in this area should refine our research model to be suitable
for organizations on the selling side and empirically validate it. Given that participation from both
buyers and sellers is a key to building a successful B2B e-marketplace, the future study’s
findings, combined with those of this study, would contribute to more comprehensive
understanding of organizational participation in B2B e-marketplaces. Another potential avenue
for future research is to replicate our approach of comparing the effects of efficiency- and
legitimacy-oriented factors between the pre- and post-adoption periods within other
technological contexts. While this study compared the effects of the two groups of factors within
the context of B2B e-marketplaces, it would be worth examining their differential effects in
organizational adoption and use of other technologies, such as dyadic IOS.
37
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Table 1: Profile of Organizations in the Samples Potential Adopters (N=98) Current Adopters (N=85)
Frequency Percentage Frequency Percentage
Industry Groups Aerospace/aviation 6 6.1 5 5.9 Automotive 11 11.2 9 10.6 Chemicals 2 2.0 5 5.9 Consumer Products 2 2.0 6 7.1 Forest Product 5 5.1 2 2.4 Information Tech./ Electronics 10 10.2 4 4.7 Industrial Products 8 8.2 8 9.4 Minerals/Metals 5 5.1 7 8.2 Energy (Oil, Gas, & Utility) 19 19.4 9 10.6 Wholesale/Retail 2 2.0 6 7.1 Transportation 5 5.1 6 7.1 Others 21 21.4 16 18.8 Missing 2 2.0 2 2.4
Total 98 100.0 85 100.0 Annual Sales Revenue (in C$)
Less than $1 million 1 1.0 2 2.4 $1 million~$5 million 4 4.1 5 5.9 $5 million~$10 million 6 6.1 3 3.5 $10 million~$50 million 12 12.2 12 14.1 $50 million~$200 million 25 25.5 10 11.8 $200 million~$500 million 15 15.3 13 15.3 $500 million~$1 billion 9 9.2 8 9.4 $1 billion~$5 billion 12 12.2 18 21.2 More than $5 billion 4 4.1 11 12.9 Missing 10 10.2 3 3.5
Total 98 100.0 85 100.0 Number of Employees
Less than 500 43 43.9 27 31.8 500~999 17 17.3 8 9.4 1,000~4,999 24 24.5 32 37.6 5,000~10,000 4 4.1 5 5.9 More than 10,000 8 8.2 12 14.1 Missing 2 2.0 1 1.2
Total 98 100.0 85 100.0 Years with B2B e-marketplace
Less than 3 years 26 30.6 3~4 years 36 42.4 5~6 years 11 12.9 Above 6 years 5 5.9 Missing 7 8.2
Total 85 100.0
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Table 2: Descriptive Statistics and Item Loadings
Potential Adopters Current Adopters Construct
Items Mean Std. Dev.
Loading (Weight) t-Value Mean
Std. Dev.
Loading (Weight) t-Value
Mean Difference
IN1 4.45 1.57 0.92 62.19 n/a IN2 4.81 1.64 0.87 30.05 n/a IN3 3.43 1.45 0.85 20.62 n/a ST1 2.39 0.67 1.00 n/a n/a PS1 4.59 1.80 0.92 44.12 4.08 2.02 .88 26.40 * PS2 4.40 1.83 0.93 60.01 3.59 1.92 .93 69.28 *** PS3 3.92 1.72 0.80 13.55 3.54 1.86 .74 9.53 n.s. PC1 4.89 1.50 0.95 134.01 4.35 1.66 .94 62.75 ** PC2 4.91 1.49 0.95 101.90 4.55 1.64 .96 93.99 n.s. PC3 4.34 1.46 0.85 17.81 3.91 1.71 .90 45.92 * TU1 2.32 1.48 0.87 11.38 2.45 1.44 .65 3.30 n.s. TU2 2.91 1.49 0.85 8.58 3.48 1.62 .52 2.11 *** TU3 3.43 1.66 0.79 10.76 3.49 1.62 .88 5.99 n.s. VU1 4.18 1.73 0.71 4.58 3.78 1.63 .74 8.51 *** VU2 4.63 1.56 0.76 5.22 4.66 1.58 .87 12.51 n.s. VU3 4.17 1.57 0.88 11.99 4.36 1.65 .86 12.25 n.s. VL1 3.58 1.47 0.90 3.51 3.48 1.46 .92 7.68 n.s. VL2 3.60 1.38 0.73 2.94 3.42 1.30 .94 8.41 n.s. VL3 3.82 1.44 0.88 3.58 3.73 1.47 .76 5.23 n.s. CA1 2.98 1.55 0.93 59.34 4.75 1.54 .92 13.41 **** CA2 3.61 1.56 0.87 21.04 5.00 1.57 .88 13.30 **** CA3 3.06 1.65 0.91 37.43 4.95 1.79 .88 12.80 **** CS1 2.43 1.38 0.88 27.70 4.38 1.54 .75 9.59 **** CS2 2.86 1.50 0.91 33.65 4.36 1.47 .78 8.96 **** CS3 2.79 1.63 0.88 36.35 4.56 1.71 .92 13.60 **** SD1 4.60 1.78 0.81 4.93 4.80 1.63 .60 2.04 n.s. SD2 4.17 1.72 0.78 3.94 4.58 1.73 .93 3.15 n.s. SD4 4.87 1.65 0.88 5.48 5.05 1.56 .86 3.70 n.s. SA1 3.56 1.60 0.94 73.51 4.49 1.47 .92 12.81 **** SA2 3.90 1.71 0.94 72.16 5.05 1.57 .92 13.56 **** PR1 2.68 1.48 0.84 15.58 3.86 1.57 .86 11.84 **** PR2 3.67 1.67 0.87 30.51 4.46 1.54 .87 12.65 **** IC1 5.35 1.57 0.80 5.42 5.45 1.60 .95 11.62 n.s. IC2 5.55 1.37 0.95 6.14 5.71 1.57 .97 9.22 n.s. IC3 5.61 1.31 0.98 6.50 5.75 1.55 .96 9.17 n.s. IC4 5.53 1.39 0.90 7.32 5.62 1.60 .95 8.65 n.s. SZ1 5.56 1.73 (0.56) 0.76 5.99 2.123 (-.61) -.57 * SZ2 2.78 0.81 (0.52) 0.84 3.00 0.99 (1.46) 1.44 n.s.
1. See Appendix A for item name abbreviations 2. Weights are reported for SZ1 and SZ2, formative indicators for organization size. 3. Log transformation was made for the number of employees for the subsequent analysis.. 4. The significance of difference is determined based on t-tests for comparing means of two independent samples. n.s.: not significant; * p < .10; ** p < .05; *** p < .01; **** p < .001
45
Table 3: Composite Reliability, AVE, Construct Correlation for Potential Adopters C.R. IN AS PC FU VU VL CA CS SD SA PR IC SZ
IN .912 0.881 PS .914 0.076 0.884 PC .943 0.065 0.527 0.920 FU .874 -0.205 -0.012 0.048 0.835 VU .828 -0.023 0.019 0.111 0.296 0.786 VL .877 0.058 0.142 0.138 0.004 0.351 0.840 CA .929 0.257 0.126 0.090 0.083 -0.139 -0.129 0.903 CS .921 0.246 0.015 -0.048 0.045 -0.186 0.099 0.585 0.891 SD .866 0.229 0.237 0.250 0.051 -0.142 -0.114 0.203 0.174 0.826 SA .937 0.259 -0.001 0.173 0.045 -0.135 -0.144 0.341 0.466 0.320 0.939 PR .843 0.276 0.061 0.040 0.111 -0.085 -0.137 0.312 0.418 0.386 0.490 0.884 IC .951 -0.075 -0.008 -0.103 -0.046 0.029 -0.087 -0.021 0.004 -0.030 -0.064 0.134 0.910 SZ n/a -0.207 0.074 0.020 -0.038 -0.118 -0.007 0.073 -0.002 -0.010 -0.195 0.026 0.295 0.928
- Diagonal elements display the square root of average variance extracted (AVE). - C.R.: Composite Reliability; IN: adoption intent; AS: asset specificity; PC: product complexity; FU: technological uncertainty; VU: volume uncertainty; VL: volatility; CA: the extent of competitor adoption; CS: perceived success of competitor adopters; SD: perceived dominance of supplier adopters; SA: the extent of supplier adoption; PR: participation in promoting organizations; IC: IT capabilities; SZ: size
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Table 4: Composite Reliability, AVE, Construct Correlation for Current Adopters C.R. US AS PC FU VU VL CA CS SD SA PR IC SZ
US n/a 1 AS 0.886 -0.023 0.850 PC 0.951 -0.158 0.413 0.931 FU 0.731 -0.121 0.094 0.149 0.698 VU 0.866 -0.153 0.108 0.249 0.433 0.827 VL 0.907 0.223 0.204 0.167 0.157 0.212 0.875 CA 0.924 0.085 0.053 -0.101 -0.462 -0.173 0.064 0.895 CS 0.857 0.158 0.089 0.163 -0.318 0.165 0.371 0.592 0.817 SD 0.846 0.073 0.156 0.014 -0.260 -0.033 0.201 0.310 0.269 0.809 SA 0.916 0.092 -0.039 0.080 -0.271 -0.072 0.197 0.687 0.544 0.390 0.919 PR 0.852 0.278 0.165 0.054 -0.131 0.044 0.184 0.182 0.331 0.247 0.375 0.861 IC 0.978 0.171 0.150 0.127 -0.086 -0.055 0.187 0.054 0.098 0.079 0.021 -0.148 0.957 SZ n/a 0.153 0.162 0.029 0.209 0.062 -0.141 -0.195 -0.105 -0.098 0.169 -0.086 0.227 0.792
US: usage
47
Table 5: Structural Model Comparisons Potential Adopters Current Adopters
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Product Characteristics
.09 (1.04) .02
(.21) -.17** (-1.95) -.18**
(-1.76)
Demand Uncertainty
-.20** (-1.87) -.19**
(-1.77) -.22** (-2.06) -.20**
(-1.70)
Market Volatility .08 (.55) .13
(.95) .33*** (3.21) .30***
(2.78)
Mimetic Pressures .20** (1.94)
.18** (1.85) .03
(0.19) -.03
(-.19)
Coercive Pressures .13 (1.06)
.13 (1.12) -.01
(-.09) -.04
(-.30)
Normative Pressures .14*
(1.30) .16*
(1.40) .21* (1.29)
.18 (1.13)
IT Capabilities .00 (-.04)
-.02 (-.14)
.00 (-.02)
.07 (.54)
.12 (1.05)
.07 (.63)
Size -.23** (-1.97)
-.20** (-1.76)
-.21** (-1.96)
.23* (1.53)
.14 (.88)
.23* (1.57)
R2 .09 .17 .21 .18 .09 .20 - Model 1: efficiency-oriented factors + control variables only, Model 2: legitimacy-oriented factors + control variables only, Model 3: Full model - t-values are in parentheses; * p < .10; ** p < .05; *** p < .01 Table 6: Results of Pseudo F-Test
Comparison R2:
Model 1 R2:
Model 2 R2:
Model 3 f2 ValuePseudo F Statistic
Degrees of Freedom
1 .09 .21 .15 14.28**** (1, 95) Potential Adopters 2 .17 .21 .05 4.76** (1, 95)
3 .18 .20 .03 2.08 (1, 82) Current Adopters 4 .09 .20 .14 11.42*** (1, 82) ** p < .05; *** p < .01; **** p < .001
48
Figure 1: Research Framework
Legitimacy-Oriented Factors
Control Variables: IT Capabilities, Organization Size
Efficiency-Oriented Factors
Organizational Participation - Adoption Intent
(Potential Adopters) - Usage
(Current Adopters)
Product Characteristics - Asset Specificity - Complexity
Demand Uncertainty - Frequency Uncertainty - Volume Uncertainty
Market Volatility
Mimetic Pressures - Adoption Among Competitors - Perceived Success of
Competitor Adopters
Coercive Pressures - Perceived Dominance of
Supplier Adopters
Normative Pressures - Adoption Among Suppliers - Participation in Organizations
Promoting B2B e-Marketplaces
49
Figure 2: Results of the Model Testing for Potential Adopters
.16*
.18**
-.19** .13
-.21** .00
.66***
.54*** .61*** .71*** .52***
.50***
.55***
.57*** Adoption among Competitors
Perceived Success of Competitor
Adopters
Adoption among Suppliers
Participation in Promoting
Organizations
Mimetic Pressures
Coercive Pressure: Perceived Dominance of Supplier Adopters
Normative Pressures
Adoption Intent (R2 = .21)
Asset Specificity
Complexity
Product Characteristics
Frequency Uncertainty
Volume Uncertainty
Market Volatility
Demand Uncertainty
IT Capabilities
.02
* p < .1, ** p < .05, *** p < .01 (one-tailed tests)
Organization Size
.13
50
Figure 3: Results of the Model Testing for Current Adopters
.18
-.03
-.20** .30***
.23* .07
.65***
.49*** 69*** 39*** .77***
.56***
.50***
.62*** Adoption among Competitors
Perceived Success of Competitor
Adopters
Adoption among Suppliers
Participation in Promoting
Organizations
Mimetic Pressures
Coercive Pressure: Perceived Dominance of Supplier Adopters
Normative Pressures
Usage (R2 = .20)
Asset Specificity
Complexity Frequency Uncertainty
Volume Uncertainty
Market Volatility
Demand Uncertainty
IT Capabilities
-.18**
* p < .1, ** p < .05, *** p < .01 (one-tailed tests)
Organization Size
-.04
Product Characteristics
51
Appendix A: Measurement Items Adoption Intent (only for potential adopters) IN1. We intend to participate in a B2B e-marketplace. IN2. It is likely that our firm will take some steps to participate in a B2B e-marketplace in the future. IN3. How soon do you think that your firm will participate in a B2B e-marketplace? [ ] Less than 6 months; [ ] 6 to 12 months; [ ] 12 to 18 months; [ ] 18 to 24 months; [ ] More than 24 months; [ ] No plan to participate in a B2B e-marketplace Note: 1. Measures below are from the current adopter version. Slight modifications in the wordings of measures made for
the non-adopter version are shown in brackets. 2. The B2B e-marketplace below refers to an e-marketplace in which a respondent’s organization is currently
participating (or may participate in the future). Usage (only for adopters)
Exploration Stage
• We are registered in the B2B e-marketplace, but carry virtually no business via the e-marketplace.
• We are evaluating pros and cons of doing business via the B2B e-marketplace.
Trial Stage
• We have made several transactions via the B2B e-marketplace. • We are still evaluating pros and cons of doing business via the B2B e-marketplace. • Doing business via the e-marketplace has still NOT become an important part of our
business operations.
Commitment Stage • We are making transactions via the B2B e-marketplace, whenever necessary. • Doing business via the B2B e-marketplace is an important part of our business
operations.
Measures for Efficiency-Oriented Constructs: Please note that, when answering the questions in this section, please consider “the products” as referring to the products as a whole that your firm may be able to purchase via the B2B e-marketplace [may consider purchasing via a B2B e-marketplace]. Asset Specificity of Products AS1. The products need to be designed specifically to our needs. AS2. The products need to be customized (or, tailored) specifically to our needs before we buy. AS3. The products are of value to only a small number of buyers. Complexity of Products PC1. A large amount of information is required to describe the products. PC2. Many attributes are required to describe the products. PC3. The specifications of the products are relatively longer than other products we buy. Frequency Uncertainty of Demand *FU1. We purchase the products on a regular basis. *FU2. We purchase the products more frequently than other products we buy. *FU3. We can accurately predict when we next need to purchase the products next time. Volume Uncertainty of Demand VU1. It is difficult to accurately estimate the volume of the products in our next purchase. VU2. Our demand volume of the products fluctuates a lot over time. *VU3. Our demand volume of the products is very stable over time. Market Volatility
52
How do you rate the overall market environment of the products? VL1. Stable/Volatile VL2. Certain/Uncertain VL3. Changes Slowly/Change Rapidly Measures for Legitimacy-Oriented Constructs: Adoption among Competitors CA1. Many of our competitors are currently participating in the B2B e-marketplace [in a B2B e-marketplace]. CA2. Many of our competitors will be participating in the B2B e-marketplace [in a B2B e-marketplace] in the near future. CA3. Our key competitors are currently participating in the B2B e-marketplace [in a B2B e-marketplace]. Perceived Success of Adopted Competitors CS1. Our competitors that participate in the B2B e-marketplace [in a B2B e-marketplace] are benefiting greatly. CS2. Our competitors that participate in the B2B e-marketplace [in a B2B e-marketplace] are perceived favorably by others in our industry. CS3. Our competitors that participate in the B2B e-marketplace [in a B2B e-marketplace] are perceived favorably by their suppliers. Perceived Dominance of Supplier Adopters With regard to suppliers currently participating in the B2B e-marketplace [in a B2B e-marketplace], … SD1. our firm’s well-being depends on the suppliers’ resources. SD2. our firm cannot easily switch away from the suppliers. SD3. our firm must maintain good relationships with the suppliers. SD4. the suppliers are the core suppliers in a concentrated industry. Adoption among Suppliers SA1. Many of our suppliers are currently participating in the B2B e-marketplace [in a B2B e-marketplace]. SA2. Many of our suppliers will be participating in the B2B e-marketplace [in a B2B e-marketplace] in the near future. Participation in Professional and Trade Associations PR1. Large pressure is placed on our firm to participate in B2B e-marketplaces by industry sources (e.g., industry or trade associations). PR2. We actively participate in industry, trade, or professional associations that promote participation in e-B2B marketplaces. Control Variables Firm Size SZ1. What was the approximate annual sales or revenue in the last financial year (in C$)? [ ] Less than $1 million; [ ] $1-5 million; [ ] $5-10 million; [ ] $10-50 million; [ ] $50-200 million; [ ] $200-500 million; [ ] $500-1billion; [ ] $1-5 billion; [ ] More than $5 billion SZ2.a How many people does your firm currently employ? approximately ______ IT Capabilities IC1. Our firm has strong IT planning capabilities. IC2. Our firm has skilled IT staff. IC3. Our firm has the knowledge necessary for deploying IT applications. IC4. Our firm is experienced in deploying IT applications. *: reverse-coded a Log transformation was made for subsequent analysis purposes, due to its departure from normality based on results from skewness and kurtosis analysis.
53
Appendix B: Construct-Item Correlations for Potential Adopters
IN PS PC TU VU VL CA CS SD SA PR IC IN1 0.92 0.09 0.00 -0.23 -0.08 0.03 0.20 0.22 -0.22 0.14 0.21 0.03 IN2 0.87 -0.02 0.06 -0.18 0.00 0.01 0.17 0.15 -0.22 0.25 0.28 -0.01 IN3 0.85 0.09 0.13 -0.14 0.01 0.10 0.30 0.27 -0.17 0.28 0.24 0.16 AS1 0.10 0.92 0.55 -0.08 0.05 0.14 0.13 0.03 -0.18 0.09 0.11 0.04 AS2 0.07 0.93 0.48 0.02 0.02 0.18 0.13 0.08 -0.17 -0.06 0.03 0.01 AS3 -0.01 0.80 0.34 0.03 -0.02 0.04 0.08 -0.09 -0.30 -0.05 0.01 -0.03 PC1 0.11 0.50 0.95 -0.02 0.11 0.14 0.09 -0.05 -0.23 0.17 0.03 0.11 PC2 0.05 0.49 0.95 0.04 0.14 0.17 0.00 -0.08 -0.20 0.13 0.05 0.10 PC3 0.05 0.46 0.85 0.12 0.05 0.07 0.16 0.00 -0.27 0.17 0.03 0.07 FU1 -0.23 0.03 0.02 0.87 0.14 -0.03 0.07 0.02 -0.07 0.05 0.10 0.06 FU2 -0.20 0.10 0.04 0.85 0.06 -0.03 0.18 0.14 -0.02 0.01 0.02 -0.02 FU3 -0.09 -0.13 0.06 0.79 0.48 0.06 -0.02 -0.03 -0.04 0.05 0.14 0.07 VU1 0.00 -0.13 0.09 0.19 0.71 0.16 -0.06 -0.20 0.15 -0.05 -0.04 -0.06 VU2 0.03 0.16 0.15 0.23 0.88 0.40 -0.06 -0.09 0.07 -0.13 -0.01 0.00 VU3 -0.09 -0.02 0.02 0.27 0.76 0.25 -0.21 -0.16 0.12 -0.14 -0.15 -0.02 VL1 0.04 0.18 0.16 -0.02 0.32 0.90 -0.18 0.04 0.11 -0.13 -0.19 0.15 VL2 -0.02 0.14 0.18 0.01 0.33 0.73 -0.17 -0.05 0.05 -0.11 -0.21 0.08 VL3 0.03 0.07 0.12 0.04 0.35 0.88 -0.07 0.08 0.07 -0.13 -0.10 -0.01 CA1 0.21 0.22 0.09 0.07 -0.06 -0.07 0.93 0.51 -0.17 0.25 0.25 0.04 CA2 0.29 -0.04 0.06 0.06 -0.18 -0.18 0.87 0.55 -0.24 0.42 0.29 0.03 CA3 0.19 0.17 0.09 0.09 -0.13 -0.09 0.91 0.52 -0.14 0.25 0.30 -0.01 CS1 0.19 0.06 -0.01 0.04 -0.25 0.05 0.52 0.88 -0.16 0.40 0.32 0.00 CS2 0.28 0.06 -0.03 -0.05 -0.21 0.13 0.59 0.91 -0.20 0.42 0.45 -0.02 CS3 0.19 -0.08 -0.09 0.15 -0.03 0.08 0.44 0.88 -0.10 0.43 0.34 0.01 SD1 0.18 0.23 0.29 0.06 -0.08 -0.03 0.26 0.24 -0.81 0.26 0.31 0.03 SD2 0.12 0.28 0.17 0.15 -0.02 -0.16 0.18 0.02 -0.78 0.15 0.25 0.11 SD4 0.24 0.14 0.17 -0.03 -0.20 -0.11 0.10 0.14 -0.88 0.34 0.37 -0.02 SA1 0.22 -0.01 0.10 0.10 -0.02 -0.06 0.29 0.46 -0.29 0.94 0.46 0.03 SA2 0.27 0.00 0.22 -0.02 -0.23 -0.21 0.35 0.42 -0.31 0.94 0.46 0.09 PR1 0.17 0.11 0.01 0.20 0.02 0.03 0.20 0.39 -0.39 0.38 0.84 0.00 PR2 0.30 0.00 0.06 0.00 -0.16 -0.25 0.33 0.33 -0.27 0.45 0.87 -0.22 IC1 0.02 -0.03 -0.06 -0.05 -0.03 -0.13 -0.07 -0.16 0.05 -0.05 0.07 -0.80IC2 -0.05 0.00 -0.04 0.04 0.03 -0.15 0.00 -0.01 -0.03 0.03 0.20 -0.95 IC3 -0.07 -0.02 -0.14 -0.10 0.01 -0.06 -0.05 -0.03 0.08 -0.12 0.07 -0.98 IC4 0.00 -0.02 -0.11 -0.11 -0.05 -0.13 -0.02 -0.04 0.04 -0.01 0.15 -0.90
54
Appendix C: Construct-Item Correlations for Current Adopters
US PS PC TU VU VL CA CS SD SA PR IC Usage 1.00 -0.02 -0.16 -0.12 -0.15 0.22 0.09 0.16 0.07 -0.09 -0.28 0.17 AS1 0.02 0.88 0.37 0.01 0.12 0.14 0.04 0.11 0.27 -0.03 -0.27 0.15 AS2 0.03 0.93 0.41 0.08 0.06 0.17 -0.04 0.03 0.10 0.10 -0.19 0.06 AS3 -0.14 0.73 0.25 0.17 0.10 0.22 0.16 0.10 0.00 0.03 0.09 0.19 PC1 -0.17 0.30 0.94 0.15 0.21 0.16 -0.17 0.13 -0.03 -0.05 -0.01 0.08 PC2 -0.12 0.36 0.96 0.13 0.22 0.16 -0.11 0.12 -0.01 -0.08 -0.04 0.13 PC3 -0.15 0.49 0.90 0.13 0.26 0.14 0.00 0.20 0.07 -0.09 -0.10 0.14 FU1 0.02 0.17 0.19 0.65 0.12 0.18 -0.40 -0.29 -0.14 0.23 0.18 0.08 FU2 0.01 0.04 -0.14 0.52 0.02 -0.02 -0.20 -0.38 -0.31 0.20 0.02 -0.04 FU3 -0.17 0.04 0.15 0.88 0.51 0.13 -0.37 -0.18 -0.19 0.20 0.09 -0.14 VU1 -0.22 0.25 0.29 0.43 0.74 0.10 -0.27 0.09 -0.06 0.12 -0.10 -0.05 VU2 -0.09 0.05 0.25 0.26 0.87 0.23 -0.06 0.18 0.05 0.01 -0.08 -0.04 VU3 -0.08 -0.02 0.08 0.38 0.86 0.19 -0.10 0.14 -0.06 0.05 0.06 -0.05 VL1 0.17 0.26 0.10 0.12 0.15 0.92 0.01 0.27 0.21 -0.12 -0.28 0.23 VL2 0.24 0.20 0.24 0.20 0.20 0.94 -0.01 0.32 0.10 -0.16 -0.17 0.19 VL3 0.16 0.06 0.06 0.08 0.21 0.76 0.20 0.39 0.26 -0.25 -0.02 0.06 CA1 0.04 0.15 -0.08 -0.43 -0.24 0.08 0.92 0.57 0.27 -0.56 -0.16 0.05 CA2 0.12 0.01 -0.04 -0.41 -0.08 0.04 0.88 0.62 0.31 -0.77 -0.29 0.10 CA3 0.08 -0.02 -0.17 -0.39 -0.14 0.04 0.88 0.37 0.24 -0.50 -0.02 -0.01 CS1 0.09 0.33 0.17 -0.33 0.17 0.42 0.45 0.75 0.31 -0.43 -0.30 0.07 CS2 0.09 -0.03 0.32 -0.12 0.06 0.24 0.36 0.78 0.10 -0.37 -0.29 0.16 CS3 0.20 -0.05 -0.03 -0.32 0.17 0.27 0.61 0.92 0.24 -0.52 -0.24 0.03 SD1 0.03 0.04 -0.02 -0.44 -0.08 0.17 0.28 0.22 0.60 -0.34 -0.32 0.08 SD2 0.08 0.13 -0.06 -0.12 -0.04 0.17 0.26 0.23 0.93 -0.36 -0.18 -0.02 SD4 0.04 0.21 0.20 -0.27 0.02 0.18 0.25 0.24 0.86 -0.28 -0.20 0.24 SA1 0.05 0.03 0.01 -0.25 -0.09 0.18 0.72 0.44 0.32 -0.92 -0.32 -0.04 SA2 0.12 -0.10 0.14 -0.25 -0.04 0.18 0.55 0.56 0.40 -0.92 -0.37 0.00 PR1 0.13 0.17 -0.09 -0.12 0.09 0.12 0.24 0.42 0.28 -0.31 -0.86 0.09 PR2 0.34 0.11 0.17 -0.11 -0.01 0.19 0.08 0.16 0.15 -0.33 -0.87 0.16 IC1 0.21 0.17 0.15 -0.08 -0.06 0.24 0.02 0.13 0.07 0.02 -0.21 0.95 IC2 0.12 0.11 0.10 -0.09 -0.05 0.21 0.08 0.15 0.08 -0.01 -0.10 0.97 IC3 0.17 0.12 0.10 -0.07 -0.07 0.14 0.06 0.03 0.07 0.02 -0.09 0.96 IC4 0.13 0.16 0.13 -0.09 -0.02 0.11 0.07 0.07 0.10 0.05 -0.15 0.95
