Multiple Conceptualizations of Small Business Web Use and Benefit

Decision Sciences Volume 34 Number 3 Summer 2003 Printed in the U.S.A.

Multiple Conceptualizations of Small Business Web Use and Benefit∗ Kurt A. Pflughoeft Market Probe Inc., 2655 North Mayfair Road, Milwaukee, WI 53226, e-mail: [email protected]

K. Ramamurthy School of Business Administration, University of Wisconsin-Milwaukee, P.O. Box 742, Milwaukee, WI 53201, e-mail: [email protected]

Ehsan S. Soofi School of Business Administration, University of Wisconsin-Milwaukee, P.O. Box 742, Milwaukee, WI 53201, e-mail: [email protected]

Masoud Yasai-Ardekani School of Business Administration, University of Wisconsin-Milwaukee, P.O. Box 742, Milwaukee, WI 53201, e-mail: [email protected]

Fatemeh (Mariam) Zahedi† School of Business Administration, University of Wisconsin-Milwaukee, P.O. Box 742, Milwaukee, WI 53201, e-mail: [email protected]

ABSTRACT Small businesses play an important role in the U.S. economy and there is anecdotal evidence that use of the Web is beneficial to such businesses. There is, however, little systematic analysis of the conditions that lead to successful use of and thereby benefits from the Web for small businesses. Based on the innovation adoption, organizations, and information systems (IS) implementation literature, we identify a set of variables that are related to adoption, use, and benefits of information technology (IT), with particular emphasis on small businesses. These variables are reflective of an organization’s contextual characteristics, its IT infrastructure, Web use, and Web benefits. Since the extant research does not suggest a single theoretical model for Web use and benefits in the context of small businesses, we adopt a modeling approach and explore the relationships between “context-IT-use-benefit” (CIUB) through three models—partial-mediator, reduced partial-mediator, and mediator. These models posit that the extent of Web use by small businesses and the associated benefits are driven by organizations’ contextual characteristics and their IT infrastructure. They differ in the endogeneity/exogeneity of the extent of IT sophistication, and in the direct/mediated effects of organizational context. We examine whether the relationships between variables identified in the literature ∗ The names are arranged in alphabetical order. All authors have contributed equally in the development of this manuscript. This research was partially supported by a grant from the Center for Study of Western Hemispheric Trade, University of Texas, El Paso. † Corresponding

author.

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Multiple Conceptualizations of Small Business Web Use and Benefit

hold within the context of these models using two samples of small businesses with national coverage, including various sizes, and representing several industry sectors. The results show that the evidence for patterns of relationships is similar across the two independent samples for two of these models. We highlight the relationships within the reduced partial-mediator and mediator models for which conclusive evidence are given by both samples. Implications for small business managers and providers of Web-based technologies are discussed.

Subject Areas: E-Commerce Infusion, Internet and Web, Multivariate Statistics, Parameter Estimation, and Structural Equation Models.

INTRODUCTION Small businesses represent an overwhelming majority of all businesses and account for almost one-half of the gross national product. They create two-thirds of new jobs and invent more than one-half of all technological innovations in the United States (U.S. Small Business Administration, 2001). The rapid growth of the Web, its enormous potential for small businesses, and its importance to the U.S. economy make it imperative to develop a greater understanding about the adoption and use of the Web by small businesses. The nonproprietary nature of the Web and its rapid evolution makes the Web perhaps the only technology that allows all participants, from the largest corporations to the smallest businesses, to operate on a level playing field. The widespread adoption of Web-based technologies by larger firms threatens the continued viability of small businesses. The Web allows larger firms to enter market niches of small businesses at little or no additional cost. On the other hand, the advent of the Web has leveled the playing field for small businesses, enabling them to mount an effective counterattack to larger firms’ encroachments. The use of the Web provides timely information on competitive conditions and increases the speed of transactions with trading partners. Thus, effective use of the Web seems to hold great promise for small businesses. Effective use of the Web could enable small businesses to proactively respond to competitive threats and to the demands of marketplace participants. Small businesses could also use the Web to dramatically increase the efficiency of their operations, thereby realizing tremendous benefits. Research on issues pertaining to small businesses in the information systems (IS) domain has focused on organizational characteristics associated with IS success, such as top management, vendor, and consultant support for adoption of information technologies, and the way small businesses deal with implementation issues (DeLone, 1988; Harrison, Mykytyn, & Riemenschneider, 1997; Iacovou, Benbasat, & Dexter, 1995; Raymond, 1985; Raymond & Bergeron, 1996; Thong, Yap, & Raman, 1996; Yap, Thong, & Raman, 1994). There is, however, a paucity of research on the use of the Web by small businesses and the conditions that affect its successful implementation. This study aims to shed light on this important issue by developing models that help to explain the underlying conditions that lead to the adoption and effective use of the Web by small businesses. The literature on adoption of information technology (IT) and IS implementation suggests that market conditions induce small businesses to use new information

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technologies. Market forces come from several sources such as the demands imposed by larger trading partners (Iacovou et al., 1995), the adoption of new information technologies by competitors (Thong, 1999), the small firms’ desires to achieve competitive parity with other businesses (Kliendl, 2000), and the effects of network externalities (Kauffman, McAndrews, & Wang, 2000). The literature on adoption of new information technologies further suggests that a firm’s ability to effectively deploy the next generation of information technologies depends upon firm-specific characteristics such as its level of IT knowledge base, managerial support, and the willingness to commit resources (Cragg & King, 1993; DeLone, 1988; and Thong, 1999). The organization and strategy literatures suggest that the scope of operations, and the nature of a firm’s product-market domain influence the level of information processing capacity that is required for effective communication, coordination, and control of activities (Daft, 1996), and for the requisite level of environmental scanning (Yasai-Ardekani & Nystrom, 1996). A broader market, product, or geographic scope of operations may necessitate adoption of Web-based technologies to deal with the greater attendant need for information processing capacity (Kettinger, Grover, Guha, and Segars, 1994). This paper explores the organizational and market factors associated with Web use and benefits for small firms, and the nature of these associations. This paper consists of eight sections and is structured as follows. The next section presents the theoretical foundation for selected variables drawing primarily from the innovation adoption, organizations, and IS implementation literatures. Previous research in these domains enables us to identify a set of variables that are related to adoption, use, and benefits of IT, with particular emphasis on small businesses. These variables are reflective of the firm’s business “context, IT infrastructure, Web use, and Web benefit” (CIUB). While the extant literature provides a foundation for selecting some of the important variables for modeling Web use and consequent benefits for small businesses, there is little theoretical evidence to support conceptualization of a single unique model that maps the relationships among these variables. The third section presents our conceptualizations of the relationships between organizational context, IT infrastructure, Web use, and Web benefits constructs drawn from the literature in terms of three different models: partial-mediator, reduced partial-mediator, and mediator. These models reflect that benefits from the Web vary depending on the extent to which the use of the Web is driven by the firm’s contextual characteristics and its IT sophistication. The models differ in the endogeneity/exogeneity of the extent of IT sophistication, and in the direct/mediated effects of organizational context. Although the benefit from using multiple perspectives is known to be critically important for investigators in organizations and strategic management literature and the need for multiple conceptualizations of complex relationships has already been recognized (Venkatraman, 1989), only a few studies in management information systems (MIS) and management have pursued multiple conceptualizations of relationships within a single study (Kraatz & Zajac, 2001; Nass, 1994; and Taylor & Todd, 1995). This paper develops alternative models and uses a modeling approach recommended by researchers in the methodological literature (for the latest developments see articles in the Journal

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of Mathematical Psychology, Special Issue on Model Selection 2000; Raykov & Marcoulides, 2001; and references therein). The fourth section discusses our use of a stratified sampling design for drawing two separate, independent samples of small businesses with national coverage, including various sizes, and representing several industry sectors. Use of two samples is the extra effort taken in this study to investigate the extent to which the pattern of evidence for the relationships found in one sample of small businesses holds in a second independent sample. This section reports on a comparison of the sample characteristics with the sampling frame, and a comparison of the characteristics of the two samples. The fifth section presents the empirical results. This section discusses the assessment of properties of the measurements that we have developed for small businesses based on the extant literature and reports the results for each of the three differently conceptualized structural equation models. The results from the two independent samples reveal that for two of the three models the patterns of evidence for the relationships is similar across the samples, thereby infusing more confidence in the validity of the results. The sixth section discusses the results of this study and their implications for researchers. The study implications for management practice are discussed in the seventh section. The final section offers concluding remarks. Appendix A provides discussion of the measurements, description of the data, and results of exploratory factor analysis. Appendix B provides additional empirical results.

LITERATURE REVIEW AND STUDY VARIABLES This section identifies the variables used in this study based on the innovation adoption, organizations, and IS implementation literatures.

Web Use Web use by businesses varies widely, from merely sending electronic mail messages to conducting e-commerce that includes financial transactions. Creating a Web site is often the starting point for a firm to use the Web for more relevant and useful business activities. The Web can be potentially used for a variety of purposes such as: (a) communicating internally and externally and sharing data; (b) searching for information on customers, suppliers, and competitors; (c) providing customer service and vendor support; (d) purchasing and selling products and services; and (e) collaborative work (Schneider & Perry 2001; Turban, King, Lee, Warkentin, & Chung, 2002). The Web provides a vast repository of information and facilitates extensive search, retrieval, and research of key competitive intelligence and other business information. This function is particularly important for small businesses, which normally lack resources to monitor their environments and regularly collect intelligence through other means. Although communication and search and retrieval of information are important business functions facilitated by the Web, a more advanced use can be electronic commerce, including purchasing, selling, and performing financial transactions. The e-commerce functionality may potentially be the most sought-after


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use of the Web, because the benefits from such use can be directly observed in terms of reduced costs, higher speed, and greater convenience to business partners. This aspect could be particularly significant for most small businesses that lack access to a wide distribution channel and are limited to their local markets and suppliers. Hence, in this study we examine Web use along two major dimensions: (a) information search and retrieval, and (b) extent of electronic commerce infusion.

Web Use and Realized Web Benefits Businesses primarily use the Web to enhance their competitive position and to increase the efficiency of their operations. Effective use of the Web may lead to greater market access in terms of the number of actual and potential customers served (Schneider & Perry, 2001). Access to a greater number of customers may in turn lead to higher sales, market share, and profitability levels. Availability of timely and accurate information and mutual access to databases can aid the firm immensely to manage its supplier and customer relations more effectively (Evans & Wurster, 1997). Moreover, disintermediation, the prospects for removal of organizational layers that perform the intermediary steps in the value chain, as well as the emergence of new cybermediaries, can be quite attractive for small businesses that want to be more efficient and cost-effective and establish closer ties with their trading partners (Turban et al., 2002). Direct communication between the firm and its consumers not only can increase the speed of transactions but also help reduce costs substantially (Mougayar, 1998). The use of the Web may provide opportunities for sales and customer support professionals to be closer to their end customers/consumers and to better understand and serve them (Brown & Fox, 1999). Interactive marketing, customized information and services, and customer self-service can further help generate loyalty and lock in customers, enhancing the firm’s competitive position (Hart & Saunders, 1998; Kalakota & Whinston, 1996; Turban et al., 2002). This is particularly important for small businesses, which normally do not have a large sales force to operate beyond their immediate local markets. Effective use of the Web may also lead to improved efficiencies in operations and in practically all other aspects of the value chain (Dobbs, 1999). Elimination of paperwork and free flow of information, both externally with suppliers and customers, and internally among employees, can lead to higher levels of productivity, speed, and other operational efficiencies (Henriott, 1999; Stein & Sweat, 1998). The availability of timely, accurate, and relevant information may help managers of small businesses, which normally lack the workforce to assemble the needed information, to make better decisions. Furthermore, use of the Web for conducting routine activities may free the limited number of employees in small businesses to enjoy a higher quality of relationships with their internal coworkers and counterparts in their customers/supplier organizations. Although conventional wisdom may view technology as a factor that depersonalizes the work environment (Nie & Erbing, 2000), effective use of the Web may lead to enhancement of interpersonal relationships. Thus, there are several benefits associated with Web use that allow small businesses to proactively relate to their environments and to achieve greater

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efficiencies. It may further help improve interpersonal relationships with the firm’s primary stakeholders. We therefore study Web benefits along the following dimensions: strategic benefits, operational efficiency, and enhanced intra-organizational and interorganizational relationships.

Market Pressure and Web Use The literature on adoption of information technology (Gatignon & Robertson, 1989) and research on interorganizational systems and electronic data interchange (Grover, 1993; Premkumar & Ramamurthy, 1995) characterize competitive necessity or market pressure as important environmental conditions that influence adoption of interorganizational systems that allow connectivity to customers and suppliers. Market pressures on the small businesses to use the Web come from several sources. They arise due to actions of competitors or mandates imposed by powerful buyers and suppliers. Large organizations often exercise their market power to exert control over their smaller buyers, suppliers, and competitors (Pfeffer & Salancik, 1978). Small businesses usually do not have strong bargaining power in relation to their buyers and suppliers (Porter, 1980) and are thus more vulnerable to persuasive and coercive influences of their marketplace participants (Pitts, 1991; Porter, 2001). Market pressures, typically exercised by the more powerful trading partners, have been observed to be one of the most critical factors for electronic data interchange (EDI) adoption by small firms because they are generally the weaker partners in the interorganizational trade relationships (Barber, 1991; Hwang, Pegels, Rao, & Sethi, 1992; Iacovou et al., 1995). When large firms use the Web, particularly for electronic commerce purposes, such firms may compel their small suppliers to adopt and use the Web (Bouchard, 1993). Similarly, large suppliers to small businesses may place demands on their smaller buyers to use new technologies. Competitors’ adoption and use of a new technology which may have a potential for enhancing their competitive positions encourages other firms to adopt the same or remain at a competitive disadvantage (Porter, 2001). The protection that small businesses enjoy because of their focus on niche markets may now be more easily eroded by actions of large competitors that have adopted the latest information technologies. For instance, Kleindl (2000) argues that the advent of the Internet/Web enables large competitors to enter such niche markets at little additional cost and that these actions may lead to erosion of small firms’ competitive positions. However, Kleindl further suggests that the same technologies which allow large businesses entry into markets served by small businesses will also enable the small businesses to mount an effective counterattack to large competitors’ encroachments. Moreover, use of the same new information technologies by small businesses may enable them to gain some of the same efficiencies attained by their large and more technologically capable competitors. Thus, the pressure exerted by these competitors generates a need for small businesses to adopt and use the same technologies to maintain and enhance their competitive position (Kleindl, 2000; Thong, 1999). Furthermore, the theory of network externalities suggests that an increase in the number of users of new technologies confers value to the technology and


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creates a bandwagon effect, thereby encouraging others to adopt the new technology (Katz & Shapiro, 1991; Kauffman et al., 2000). In a study of banking networks, Kauffman et al. argue that “banks value the shared network more highly as it grows; late entrants at some point in time may find that they must join the network to avoid competitive disadvantage” (2000, p. 65). As the largest shared network, the Web has network externalities that cannot be ignored by firms facing a large number of competitors, customers, and suppliers that use the Web. As the number of participants that use the Web in the firm’s market grows, pressure mounts on the firm to get on the bandwagon or to remain at a competitive disadvantage. Small businesses are particularly susceptible to pressures due to network externalities because they lack the economic clout to resist or offer alternative approaches.

Scope of Operations and Web Use Scope of operations, the nature of the firm’s product-market domain, influences the extent to which the firm may need systems and processes that help facilitate communication and coordination within the firm and with the external constituents (Daft, 1996). Firms with a broader scope of operations are more likely to have a complex network of relations with external customers and suppliers. Interaction with a larger network of customers and suppliers increases the difficulty of coordination and integration of activities and generates a need for processing a greater amount of information. Coordination is even more difficult when trading partners are geographically dispersed. Firms with broader scope of operations may also have a greater need for physical presence in dispersed locations. This greater dispersion also requires an increased level of communication and coordination of activities (Kettinger et al., 1994). Broader scope may further necessitate scanning and searching a broader array of external information (Yasai-Ardekani & Nystrom, 1996). Scanning and accurate interpretation help managers reduce the equivocality inherent in external information and formulate appropriate responses (Daft, Sormunen, & Parks, 1985; Yasai-Ardekani & Nystrom 1996). Thus, firms with broader scope of operations require IS designs that increase their information processing capacity so as to facilitate internal and external communication and coordination. Prior research has shown that use of advanced information technologies increases the availability of internal, external, and previously encountered information, allows quick retrieval of such information, and enhances information accessibility (Huber, 1990). The greater need for information processing may well be served by use of the Web as a vehicle for communication and coordination of activities. The use of the Web can collapse space and time constraints, making it an invaluable vehicle for retrieving, sharing, and communicating information, thereby facilitating coordination of activities (Turban et al., 2002). As the scope of a firm’s operations expands, it may become preferable, perhaps even necessary, to establish contacts and coordinate business transactions via electronic integration. Trading partners, particularly distant ones, may require their counterparts to link with them via a nonproprietary, public channel such as the Web. Electronic integration also reduces the costs of maintaining business relationships and facilitates faster and more accurate

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communications. Furthermore, since the Web is a large repository of external information, it serves as an excellent source of information on competitive conditions and may therefore be used as a vehicle for gathering environmental intelligence. Given the lack of resources to allocate specifically for environmental intelligence, the Web could be the single most important source for small firms to keep abreast of their market environments.

Investments in IT Infrastructure Small businesses have typically responded to pressures for use of new information technologies by adopting more sophisticated portfolios of application software and using them in broader areas of activities. Detailed case studies conducted by Cragg and King (1993) attribute the growth of IT applications in small businesses to the extent of competitive pressures encountered by firms. Raymond (1992) further reports that the level of IT sophistication as reflected by a broader applications portfolio is related to organizational development of small young firms. Billi and Raymond (1993) report that small retailers, facing competitive pressures from large retail chains, systematically invest in information technology to maintain their competitive position. In a survey of small businesses, Pollard and Hayne (1998) show that use of information systems for competitive advantage was ranked as the most important reason for adoption and use of information systems. Small businesses may therefore be compelled to commit to new technologies through sustained investments of resources. Cragg and King (1993) suggest that competitive pressures and CEOs’ support and enthusiasm for new technologies are among the key motivators of the adoption and extent of use of new applications software by small businesses. Raymond, Bergeron, and Rivard (1998) suggest that the level of organizational support for new technologies influences the realization of benefits by small businesses. Other studies of information systems usage also highlight the importance of top management support to the nature of IT use and its success in small businesses (DeLone, 1988; Raymond, 1984/1985; Raymond, 1985; Yap et al., 1994). Top management support is often reflected in the form of sustained commitment of resources toward new technologies. As noted, broader scope of operations increases the difficulties of internal and external communication as well as coordination and control of activities. It was further suggested that firms with a broader scope may experience a greater need to invest in information technologies that help increase their information processing capabilities, thereby facilitating communication, coordination, and integration of activities. Thus, small businesses with a broader scope of operations may be more likely to deploy a more sophisticated portfolio of applications and to invest a greater amount of resources on information technologies. Indeed, Julien (1995) notes that small businesses invest a greater amount of resources in new information technologies to enhance the information processing capacities of their organizations. IT Infrastructure and Web Use Studies of adoption and diffusion of IS (within small businesses) attribute the likelihood of adoption and extent of diffusion to lowering of knowledge barriers


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(Cragg & King, 1993; Thong, 1999). In an empirical study of adoption and extent of IS use by small businesses, Thong (1999) reports that greater IS knowledge base and greater resource commitment toward IS increase the likelihood of adoption of new information technologies and also increase the extent of use of such technologies. Detailed case studies of motivators and inhibitors of small business computing by Cragg and King (1993) further suggest that lack of IS knowledge is among the key inhibitors of the extent of development of IS applications in small businesses. Thong (1999) further suggests that due to small businesses’ lack of IS resources, they often rely on outside IS experts. However, regardless of the source of knowledge, it is learning by doing that reduces the IS knowledge barriers and facilitates adoption of other information technologies. Raymond’s (1985) study also suggests that greater investment of resources in terms of in-house computing and a more sophisticated applications portfolio increases the likelihood of adopting new information technology and stimulates a broader array of use. Thus, greater IT sophistication may provide the knowledge base for the organization to engage in integrating other information technologies with internal systems and applications. Greater investment of resources may not only help reduce IS knowledge barriers but, as mentioned, may also reflect the firm’s financial position and the level of top management support and enthusiasm for information technologies. Studies of adoption and use of IT have shown that top management support and enthusiasm is a key motivator of adoption and use of new information technologies (DeLone, 1988; Raymond 1984/1985; Raymond, 1985; Thong, 1999; Thong et al., 1996). A more sophisticated applications portfolio and investment of a greater amount of resources in information technologies reflect a higher level of IS knowledge and management commitment, and provide small businesses with an effective IT infrastructure. In recent years the availability of low-cost hardware, increased power and capacity of computers, and a variety of user-friendly software have made it possible for small businesses to enhance their IT infrastructure and take advantage of strategic possibilities of IT (Pollard & Hayne, 1998). The foregoing suggests that a firm’s ability to effectively deploy the next generation of information technologies may depend upon the existence of an appropriate IT infrastructure. In the context of small businesses such an infrastructure may be evidenced by the existence of a high level of IT sophistication and commitment of a greater amount of resources toward implementation of the new technologies.

MODELS AND METHODOLOGICAL APPROACH In this section, we present three conceptual frameworks followed by specifications of three structural equation models and the accompanying measurement equations. These models are used to explore whether the pairwise relationships identified in the literature hold within the context of these models. This section also gives a description of the data analysis methodologies used in the study.

Model Formulation The earlier discussion of the literature suggests that both market pressures and scope of the firms’ operations affect the extent of the use of Web-based technologies

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by small businesses. Small businesses respond to market pressures brought about by their larger trading partners to deploy technologies that these larger firms have adopted for conducting business activities. Also, it was mentioned that some small businesses may proactively adopt such technologies to counter the potential encroachments on their market niches by their larger counterparts, made possible by the advent of Web-based technologies. It was further mentioned that a broader scope of operations, with its attendant greater need for communication, coordination, and control of activities, generates a need for adoption of information technologies such as Web-based technologies that increase the information processing capacities of small businesses. Web use enables small businesses to integrate their activities with their trading partners to achieve seamless communication, and to effect greater coordination and control of activities. Effective use of the Web is also expected to lead to realization of both strategic and operational benefits. Therefore, in mapping the structural relationships between context, Web use, and Web benefits, context variables are clearly exogenous and Web use and benefits are endogenous. The small business literature further suggests that greater market pressures induce small businesses to commit resources to attain higher levels of IT sophistication. In the context of small businesses, greater IT sophistication is evidenced by deployment of a broader portfolio of software applications and reflects a higher level of IS knowledge base. The innovation adoption literature suggests that, in addition to a greater IS knowledge base, greater resource commitments toward new technologies facilitates adoption and implementation of the next generation of information technologies. In the context of small businesses, greater IT sophistication and commitment of resources toward the Web reflect the existence of an appropriate level of IT infrastructure required for effective use of the Web. While the literature identifies important variables for modeling Web use and benefit for small businesses, there is little theoretical evidence to support conceptualization of a unique model for the structural relationships among these variables. From the literature, we may conclude that context variables are exogenous, and that Web-related costs, Web use, and Web benefits are endogenous, but there is no clear conclusion regarding the endogeneity of IT sophistication. For example, a firm’s context may directly affect the use of newer Web-based technologies, or a greater IS knowledge base, evidenced by higher levels of IT sophistication, may be a mediator of the relationships between context and use of new Web-based technologies. Consideration of the endogeneity of some of the study variables leads to systems of structural equation models for Web use and benefits. Figure 1 shows three conceptual frameworks that capture the patterns of relationships discussed. In all three frameworks context is exogenous; Web-related costs, Web use, and Web benefits are endogenous. Information technology sophistication is also endogenous in the first and third frameworks, but it is exogenous in the second framework. Figure 1(a) shows the conceptual framework for what is typically referred to as a partial-mediator model (Venkatraman, 1989), which maps all the relationships discussed earlier, including the direct effect of the organizational context on Web use (shown by the dashed line), the indirect effect of context through IT sophistication (shown by the dotted line), and Web-related costs. In this conceptualization, IT sophistication, Web-related costs, Web use, and Web benefits are endogenous.


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Figure 1: Conceptual frameworks for three CIUB models. (a) Partial Mediator Model Context

IT Infrastructure

Web Use

Web Benefit

• IT Sophistication • Strategic Benefit • Market Pressure

• Information Search

• Scope of Operations

• E-commerce Infusion

• Operational Efficiency • Direct Contacts

• Web-related costs

(b) Reduced Partial Mediator Model • IT Sophistication • Strategic Benefit • Market Pressure






(c) Mediator Model

• IT Sophistication • Strategic Benefit • Market Pressure






·······

Indirect effect of “context” variables on Web use mediated by IT sophistication.

- - - - - - Direct effect of “context” variables on Web use. Model (b) is derived from (a) by excluding the indirect effect of context variables on Web use through IT sophistication; Model (c) is derived from (a) by excluding the direct effect of context variables on Web use and making their effect completely mediated/channeled through IT sophistication.

Figure 1(b) shows the conceptual framework for the reduced partial-mediator model in which context, Web-related costs, and IT sophistication jointly contribute to the use and the attendant benefits of the Web. It is based on the view that facing higher levels of market pressures, those with a broader scope of operations, and those that have developed a higher level of IT sophistication make greater use of the Web and thereby realize the attendant benefits. Furthermore, in this conceptualization, use of the Web requires significant and sustained commitment of resources toward the Web (Web-related costs). Firms that encounter higher levels of market pressures and those firms with broader scope of operations commit significant amounts of resources. Figure 1(c) shows the conceptual framework for the mediator model, in which context affects the level of IT infrastructure (IT sophistication and Web-related

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costs) and that IT infrastructure in turn affects Web use and the attendant benefits. In other words, the effects of context on Web use and benefits are channeled through IT sophistication and Web-related costs. This conceptualization is based on the view that firms that encounter higher levels of market pressures and those with broader scope of operations develop more extensive IT infrastructure. An appropriate IT infrastructure in turn enables these firms to make greater use of the Web and thereby realize the attendant benefits. Higher levels of IT sophistication are indicative of greater IS knowledge base, which facilitates adoption and use of the newer Web-based technologies. The use of the Web also requires significant and sustained commitment of resources toward the Web. Thus, in this conceptualization, the effects of context on Web use and the attendant benefits are channeled through both Web-related costs and IT sophistication; and this conceptualization is based on the argument that a firm’s ability to effectively deploy the next generation of information technologies may depend upon the existence of an appropriate IT infrastructure. In the context of small businesses, such an infrastructure may be evidenced by the existence of a high level of IT sophistication and a commitment of a greater amount of resources toward implementation of the new Web-based technologies.

Structural Equations The formulation of the three CIUB models are made more specific in the following system of linear equations: ηsoph = γ11 ξmktp + γ12 ξscp + ζsoph

(1)

ηcost = γ21 ξmktp + γ22 ξscp + ζcost

(2)

ηsrch = γ31 ξmkt + γ32 ξscp + β11 ηsoph + β12 ηcost + ζsrch

(3)

ηecom = γ41 ξmkt + γ42 ξscp + β21 ηsoph + β22 ηcost + ζecom

(4)

ηstr = β31 ηsrch + β32 ηecom + ζstr

(5)

ηopr = β41 ηsrch + β42 ηecom + ζopr

(6)

ηcntc = β51 ηsrch + β52 ηecom + ζcntc ,

(7)

where ξmktp , ξ scp , ηsoph , ηcost , ηsrch , ηecom , ηstr , ηopr , and ηcntc denote the latent constructs that represent market pressure, scope of operations, IT sophistication, Webrelated costs, information search, e-commerce, strategic benefits, operational efficiency, and direct contacts, respectively; γ i j , i = 1, 2, 3, 4 and j = 1, 2, represent the links between organizational contextual characteristics (market pressure and scope of operations) and IT sophistication, Web-related costs, information search, and e-commerce; β i j , i = 1, 2, 3, 4, 5 and j = 1, 2, are the links between the IT infrastructure variables (IT sophistication and Web-related costs), the Web use variables (information search and e-commerce), and the Web benefits variables (strategic benefits, operational efficiency, and direct contacts); and ζsoph , ζ cost , ζ srch , ζecom , ζ str , ζ opr , and ζ cntc are mean zero error terms. The set of structural equations (1)–(7) represents the partial-mediator model. The reduced partial-mediator model is represented by (2)–(7), where IT sophistication denoted as ξ soph , is exogenous, and its coefficients in equations (3) and (4) are


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replaced by γ 33 and γ 43 , respectively, to indicate the exogeneity. Finally, the mediator model is represented by (1)–(7) where γ 31 , γ 32 and γ 41 , γ 42 in equations (3) and (4) are set to zero.

Measurement Equations Measurements for organizational context, IT infrastructure, Web use, and Web benefits constructs specifically designed for small businesses are discussed in Appendix A, where the variables representing the contextual constructs are presented first, followed by the variables that represent IT infrastructure, Web use, and Web benefit. In each set, variables are grouped based on theoretical considerations and preliminary analyses of a larger set of questions. Tables A.1–A.8 of Appendix A contain descriptions of construct indicators, the labels for all response categories, the associated numerical scores as they were presented in the survey instrument, and the results of exploratory factor analysis (EFA). The measurement equations are specified as follows: X = x ξ + δ

(8)

Y = y η + ,

(9)

where the notations are defined as follows. X is the vector of all manifest variables for the constructs that are exogenous in the model denoted as ξ ; Y is the vector of manifest variables for the constructs that are endogenous in the model denoted as η; x and y are the matrices of respective factor loadings; and and δ are mean zero error terms. For example, for the case of the partial-mediator model, x is a 7 × 2 matrix and y is a 25 × 7 matrix constructed such that each factor is related solely to its indicators, as shown in Tables A.1, A.3, A.5, and A.7 in Appendix A. The first column of x has three nonzero elements for the three manifest variables of ξ mktp and the second column of x has four nonzero elements for the four manifest variables of ξ scp in Table A.1. Figure 2 shows the structural and measurement equations for the reduced partial-mediator and mediator models. (Due to space consideration error terms and variance estimates are not shown. Figure 2 will be discussed in the section that reports the empirical results.)

Data Analysis Methodology The set of structural equations (1)–(7) combined with the measurement equations (8) and (9) result in a system of linear structural equations (as shown in Figure 2). The most commonly used procedure for estimating structural equations is the normal theory maximum likelihood estimation (NTMLE), which requires that the vectors of manifest variables (X and Y) to be jointly multivariate normal. As noted in Appendix A, all variables in this study are ordinal, of which four are binary. Therefore, NTMLE is not applicable. For estimation of linear structural equations with nonnormal data, variants of weighted least squares (WLS) are developed that allow for asymptotically distribution-free (ADF) analysis. In the methodological literature, however, researchers have noted that “Despite this asymptotic optimality, the ADF method is not very popular among practitioners of covariance structure analysis due to its lack of robustness against small and moderate sample size” (Satorra & Bentler, 1994,

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Figure 2: Sample 1 estimates of two structural equation models. a. Reduced Partial-Mediator Model x31 x11 x12

x32 x33

1.00

x34

x35

.73 .64 .66

y21

.69

y22

y23

1.00 .90

ηstr

1.00 .89 .78

ξsoph

.70

ηsrch

.45

.44

x21 x22

1.00 .85

.17

.24

.13

.33

ξscp

.30

.01.

ηcost

.18

.45

ηecom

.19

ηopr .24

y42

.91

.23

.21.

y43 y44

.06.

.55

.69

x13

y41

.89 .90

.38

ξmktp

1.00

1.00

y51

.94

y52

.81 .74

y53 y54

.13

.47

x23

1.00 .93 .73

.53

x24

y11

y12

1.00

1.00 .92 .90

y13

y31

y32

ηcntc

y33

.82

y61 y62

.75

y63

b. Mediator Model y11 x11 x12

y12 y13

1.00

.72 .57

y14 .60

y15

y31

.74

1.00

y32 .90

y33

1.00 .92

ηstr

.78

ξmktp

ηsoph

.50 .40

x13

ηsrch

1.06

.32

ηopr

.48

x21 x22

1.00 .84

.23.

ξscp

.12.

ηcost

.16

ηecom

.19

x24

.53

y52

.91

y53 y54

1.00

y61

.94

y62

.81 .24 .75 .13

y63 y64

.21

.50

x23

.26

.89

.06

.56

. 42

y51

.90

.36

.71

1.00

1.00

y21

.92

y22

.73

1.00

y23

y41

.92

y42

.90

y43

ηcntc

1.00

y71

.81

y72

.75

y73 Legends: Conclusive (very strong, strong, or positive) evidence Lack of conclusive evidence

p. 404). A similar observation is made by Jöreskog and Sörbom (1996, p. 23). We use a version of WLS referred to as robust WLS, available in Mplus (Muthén & Muthén, 1998). This procedure does not require the stringent assumption of multivariate normality of the data (manifest variables X and Y). It accommodates moderate sample sizes and categorical and ordinal manifests. Based on the conditional normality of the distribution of the latent variables, the robust WLS provides limited information likelihood estimate (Muthén, 1984), robust standard errors, and T-ratios. For evaluating the model fit, the robust WLS of Mplus provides robust chi-square statistic (Satorra & Bentler, 1994), root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI). However, it is known that RMSEA is sensitive to departure from normality, and for the case of categorical and ordinal data, not much is known about this index.


481

Table 1: Thresholds for T-ratio based on the grade of evidence for Bayes factor. Threshold for T-ratio (Traditional P-value) Bayes Factor Odds in Favor

Grade of Evidence

First Sample n 1 = 251

Second Sample n 2 = 317

Between 1 and 3 to 1

Worth only a bare mention

2.35 ≤ T < 2.74 (P-value < .01)

2.40 ≤ T < 2.79 (P-value < .008)


Positive∗

2.74 ≤ T < 3.39 (P-value < .003)

2.79 ≤ T < 3.43 (P-value < .002)


Strong∗

3.39 ≤ T < 3.94 (P-value < .0004)

3.43 ≤ T < 3.99 (P-value < .0003)

150 or more to 1

Very strong∗

3.94 ≤ T (P-value < .00005)

3.99 < T (P-value < .00003)

∗

Considered a conclusive level of evidence in this study.

We assess the T-ratio using the thresholds derived based on an approximation of the Bayes factor (BF) by the Bayesian information criterion (BIC). In this context, BF is odds in favor of including a parameter in the model. This approach is “useful for guiding an evolutionary model building process” (Kass & Raftery, 1995) such as this study where we examine whether the relationships between the variables drawn from the literature hold within the context of the models. Table 1 shows the labels (“worth only a bare mention,” “positive,” ”strong,” and “very strong”) for the grades of evidence that correspond to the odds in favor of inclusion √ of a parameter in the model. The thresholds for the T-ratio are computed by |t| ≈ ln n + 2 ln BF, where n is the sample size (Kass & Raftery, 1995). Since the BF thresholds are determined by the sample size, they are not subject to the criticism leveled against the usual testing criteria where, for a sufficiently large sample, any arbitrarily small departure from the null can be shown as statistically significant. The last two columns of Table 1 show the thresholds for T-ratio for the two samples used in this study (n 1 = 251, n 2 = 317). We consider only the top three levels of evidence as “conclusive” (significant) support for the existence of a relationship in the model. For the purpose of comparing the strength of our results with the traditional significance testing, Table 1 also includes the P-values for the T-thresholds. Note that for each sample, our threshold for conclusive evidence is more stringent than the .005 levels of significance. In addition, as will be discussed in sequel, we rely on the two independent samples for assessing the existence of a relationship in a model. We will conclude that a relationship holds within a model only when the results of the two samples agree on the conclusiveness of the evidence, thereby providing models that are developed very cautiously.

SAMPLING DESIGN This study uses data collected by a mail survey of small businesses. The survey respondents were the CEOs/owners of small firms. The data reflect the perceptions of the CEOs/owners. The survey instrument was carefully pilot tested using a sample of 20 small businesses randomly drawn from a diverse set of local firms.

482


The participants in the pilot interviews were small business owners or managers in their firms. On average, each pilot interview lasted about two hours wherein the interviewees assessed the readability, clarity, and content validity of all measures. The instrument was also pilot tested using a number of academics with expertise in information systems and management. The final survey instrument incorporated the modifications suggested by both industry and academic experts. Our study employed two phases of data collection that used the same sampling design. We used a stratified sampling design to obtain data for small businesses within the three strata: region, size, and industry. The first phase occurred in October of 1998 and the second one occurred in April of 1999, an explosive growth period of Web adoption. Although the intent was to combine these samples, a subsequent comparison analysis (reported later) showed that it would be inappropriate to do so. Consequently, each sample was used independently to estimate the three structural equation models to assess the relationships between variables.

Sampling Frames The models are estimated using two independent samples drawn from the American Business Information (ABI) database that contains information on more than 10 million U.S. organizations (for-profit and nonprofit). For the first sampling frame, 52,000 small businesses (between 10 and 500 employees) in the ABI database were identified to have Web sites when matched to the ABI’s “Web Sites USA” database (American Business Information, 1998). This frame was partitioned into size-industry-region strata. From each stratum, a random sample of firms was selected. From a set of 6,000 mailed surveys, 297 responses were received by midDecember. A low response rate was anticipated due to the length of the survey and the intended audience. For the second sampling frame, all remaining small businesses (between 10 and 500 employees) were considered. This frame is far larger than the first one and includes more than two million small businesses in the ABI database. For this frame, no information regarding Web use was available and therefore both adopters and nonadopters of the Web are included in the mailing. The same stratified sampling design was used in this case also. From a set of 16,000 mailed surveys, 536 responses were received, including 461 adopters and 75 nonadopters. Due to the nature of the research in this paper, only the data from the adopters is considered from the second sample. Since the first sampling frame only included adopters, the stratified sampling design enables us to examine the “representativeness” of the first sample by comparing the characteristics of the respondents with all firms in the entire sampling frame. The first column of Table 2 shows distributions of the firm size, industry, and geographic area for the first sampling frame. For ease of presentation, the geographic area is grouped into five commonly referred to U.S. regions. Distributions of the firm size, industry, and region in sample 1 are shown in the second column of Table 2. (Due to listwise deletion, the effective sample size for the analysis reported in this paper is n 1 = 251.) The third column of Table 2 shows the chisquare, goodness-of-fit statistics for size, industry, and region distributions for the first sample. The chi-square statistic is not significant for firm size (2.33) or any of


483

Table 2: Distributions of size, industry, and region for the first and second samples. First Data Collection Wave Sampling Frame N = 52,048 Proportion in Each category

Second Data Collection Wave Sample 2a n 2 = 317

Sample 1 n 1 = 251

Proportion Chi-Square Proportion in Each (Goodness in Each category of Fit) Category

Chi-Square (Homogeneity of Two Samples)

Size 10–49 50–249 250–499 Total (DF = 2)

.64 .31 .05 1.00

.65 .28 .07 1.00

.01 .47 1.85 2.33

.55 .30 .15 1.00

1.98 .05 8.13 10.16

Industry Manufacturing Service Wholesale Retail Financial Total (DF = 4)

.23 .39 .11 .17 .09 1.00

.26 .48 .09 .10 .08 1.00

.43 4.98 .82 7.24 .86 14.33

.31 .29 .11 .18 .12 1.00

1.49 13.08 .23 5.90 2.43 23.12

Region East West South Midwest Mountain Total (DF = 4)

.29 .23 .19 .22 .07 1.00

.23 .22 .14 .34 .07 1.00

3.50 .06 3.00 15.76 .06 22.37

.23 .18 .16 .32 .10 1.00

.01 .79 .25 .13 1.31 2.50

a Adopters in the sample drawn from the second sampling frame that includes N = 2,111,602 adopters and nonadopters.

the levels within. However, the chi-square statistics for the industry (14.33) and the region (22.37) are significant. In the case of industry distribution, the proportion of the service sector for the sample is about 9% higher and of the retail sectors is 7% lower than the sampling frame. These two sectors contribute more than 85% to the goodness-of-fit chi-square, making it statistically significant. In the case of regional distributions, the proportion of firms from the Midwest is about 12% higher than the corresponding proportions for the sampling frame and contributes more than 70% to the goodness-of-fit chi-square. This difference is mainly due to higher response rates from Minnesota and Wisconsin. Since the second sampling frame includes both adopters and nonadopters, it is no longer possible to compute the chi-square goodness-of-fit statistics for the sample of adopters from this frame. However, it is possible to compare the homogeneity of the distributions in the first and second samples. The last two columns of Table 2 show the distributions of the firm size, industry, and

484


region in sample 2, and their chi-square comparison with the first sample. (Due to listwise deletion, the effective sample size of adopters is n 2 = 317.) The chisquare statistic is significant for firm size (10.16) and for the industry (23.12). However, the chi-square statistic is not significant for the region (2.50) or any of the levels within. For the size distributions, the difference is mainly due to the proportion of firms in the 250–499 category, which contributes more than 80% to the chi-square. In the case of the industry distributions, the proportions of firms in the service sector differ by about 19% (lower in the second sample) and in the retail sectors by 8% (higher in the second sample). These two sectors contribute about 80% to the chi-square for the homogeneity. Therefore, the two samples are different in size and industry distributions and similar in regional distributions. The results shown in Table 2 preclude our ability to combine the two samples. However, we use the two samples to investigate the extent to which the evidence for the patterns of relationships is similar in the two independent samples. Although an agreement between the results of two samples increases one’s confidence in the validity of the results, caution must be exercised in generalizing statistical results whenever a sample is incomplete due to nonresponse. In such situations, interpretation of the results as exploratory is prudent. In the final analysis, randomness is germane for formal statistical inference that must be assumed in studies like ours.

EMPIRICAL RESULTS Results for Measurement Because most of the research constructs used in this study are new, we decided to pursue exploratory factor analysis (EFA) before assessing the measurement model’s properties with confirmatory factor analysis (CFA). For each set of constructs, Appendix A gives the correlation matrix of indicators, the reliability coefficient (alpha), and the results of EFA. Since the variables are measured in ordinal scales, we use polychoric correlation for the analysis (Jöreskog & Sörbom, 1996; Muthén, 1984; Muthén & Satorra, 1995). The correlations between the indicators of each construct generally range from moderate to high, indicating convergent validity. The reliability measures, Cronbach’s alpha, range from .73 to .92. Although Cronbach’s alpha tends to underestimate reliability, in our case all are larger than the usual threshold of .70 (Nunnally, 1978; Yoon, Guimaraes, & O’Neal, 1995). In each set, the correlations within the indicators of a construct are considerably higher than those between the indicators of different constructs, indicating discriminant validity. Moreover, the results of EFA demonstrate clear structures of factor loadings, reflecting both convergent and discriminant validity for each set of constructs. The normalized variance explained by the factor models ranges from 61% to 74%. The goodnessof-fit indices indicate reasonable fit of the context and IT infrastructure factor models and excellent fit of Web use and Web benefit factor models. Table 3 reports the results for the measurement model (equation (8) and (9)) for sample 1. As seen from this table, the factor loadings of indicators of each


485

Table 3: Measurement equations results for the first sample. Coef.

T

Coef.

Context Web Use Market Pressure Information Search Customers’ expectation 1.000 – Competitors Competitors’ use .910 12.551 Customers Suppliers’ expectation .708 8.965 Suppliers Scope of Operations E-Commerce Infusion Market regional/national 1.000 – Servicing customers Market international .965 6.802 Receiving orders Supplier regional/national .540 3.434 Receiving payments Supplier international .603 5.626 IT Infrastructure IT Sophistication General office activities Data management Accounting Personnel management Sales and marketing Web-Related Costs Annual operating costs Initial set up costs Training costs

Fit Indices Chi-Square (DF) Normed Chi-Square RMSEA

1.000 .728 .629 .666 .700

– 7.784 6.517 7.013 7.738

1.000 – .923 18.938 .729 17.112

1018.7 (428) 2.38 .074

T

1.000 – .863 14.255 .773 12.561 1.000 – .921 10.552 .896 8.094

Web Benefit Strategic Actual customers 1.000 – Market expansion .895 23.585 Sales revenue .906 27.695 Potential customers .899 26.195 Operational Efficiency Speed 1.000 – Productivity .936 15.106 Work away from office .814 10.704 Customer needs .766 10.500 Direct Contacts With suppliers 1.000 – With customers .812 9.330 Among employees .754 8.055 SRMR CFI TLI

.082 .945 .948

construct are all high. All T-ratios provide conclusive evidence (T > 2.74) in favor of inclusion of each indicator in the relevant construct. Table 3 also shows several goodness-of-fit indices for the measurement model. The chi-square statistic for sample 1 is 1018.72 with 428 degrees of freedom. According to the chi-square statistic, the model fit may not be considered satisfactory. However, the normed chi-square (2.38) is in the acceptable range (2.0–4.0). Based on the values for RMSEA and SRMR (SRMR < .10), CFI, and TLI indices of fit (all ≥.90) we conclude that the fit of the measurement model is satisfactory (Marcoulides & Heck, 1993). Table 4 shows two reliability measures (alpha coefficient and construct variance). Both these measures indicate that the reliability is high. Overall, the results obtained for the measurement model confirm our selection of the indicators that were derived from theoretical considerations and exploratory analyses. Table 4 also shows the correlations among the constructs. The measurement model provides

.65 .84 .66 .91 .72 .68 .86 .63 .68

.73 .78

.74 .87

.79 .82

.92 .78 .74

Variance

.33 .55 .29

.64 .27

.30 .37

1.00 .22

1

.42 .03 .08

.45 .17

.09 .18

1.00

2

.17 .53 .37

.32 .23

1.00 .17

3

.21 .25 .21

.38 .34

1.00

4

.44 .52 .27

1.00 .33

5

.54 .46 .30

1.00

6

Construct Correlationa

1.00 .47 .55

7

1.00 .49

8

1.00

9

Correlations highlighted in gray or bold are included in the partial-mediator model. Correlations highlighted in gray are included in the reduced partial-mediator model and those highlighted in bold are included in the mediator model.

a

Context 1. Market Pressure 2. Scope of Operations IT Infrastructure 3. IT Sophistication 4. Web-Related Costs Web Use 5. Information Search 6. E-Commerce Web Benefit 7. Strategic 8. Operational Efficiency 9. Direct Contacts

Alpha

Reliability

Table 4: Correlation matrix of the latent constructs estimated by the measurement model.

486 Multiple Conceptualizations of Small Business Web Use and Benefit


487

T-ratios for the pairwise correlations among all constructs. For ease of presentation, these T-ratios are not shown. The T-ratios provided conclusive evidence for all correlations above .25. The results for the measurement equations (for sample 2) reproduced essentially the same as those for sample 1 and therefore are not reported here.

Results for Structural Equations The partial-mediator model includes the set of correlations that are highlighted in gray or bold in Table 4. The reduced partial-mediator model includes the set of correlations that are highlighted in gray only. The mediator model includes the set of correlations that are highlighted in bold only. In each model, the correlations between three Web benefits are retained. This may be justified in that each of the Web benefits may also be a means to achieving the remaining two other benefits— the ultimate ends for using the Web. In each model the correlation between the two context variables are set as zero in order to make their effects more distinguishable. All other correlations among the constructs are captured in the model indirectly. Figure 2 depicts estimates of the reduced partial-mediator and mediator models for sample 1. The links for which conclusive evidence is found are shown as solid lines. The links for which there is lack of conclusive evidence are shown as dashed lines. As shown in Figure 2(a) and Figure 2(b), for both models all the links from the constructs to their indicators are solid, confirming the measurement model shown in Table 3. Due to space consideration, only the sample 1 results for these two models are shown in the commonly used SEM form. The parameter estimates and their T-ratios for the structural equations of all three models for both samples are shown in Table B.1 and Table B.2 of Appendix B. The parameter estimates and their T-ratios for the measurement equations of these models are similar to those shown in Table 3 and are not reported in Figure 2. Table 5 shows the grades of evidence found in sample 1 and sample 2 for the links between the constructs in the three structural equations and the fit indices for the models. A conclusive level of evidence for a link is shown boldface. Lack of such evidence for a link is indicated by dashed line while “N/A” indicates that the relationship is not considered in the model. The overall goodness-of-fit indices for the models are shown in the lower panel of Table 5. All measures, except the chi-square, indicate reasonably good fit of the three models in the two samples. In Table 5, columns 1 and 2 show the results for the partial-mediator model. For this model, the two samples agree on the conclusiveness of evidence or lack of it for 15 of 21 links (10 conclusive evidence, 5 lack of conclusive evidence). In sample 1, there is conclusive evidence for only about 50% of links in the model (11 of the 21). In sample 2, the number of links with conclusive evidence increases to 15. A closer look of the results of sample 1 reveals that the partial-mediator model suffers from some well-known symptoms of collinearity (see Appendix B for more details). It is therefore not prudent to rely on the results of this model. The latter four columns of Table 5 summarize the evidence found in sample 1 and sample 2 for the links in the reduced partial-mediator and the mediator models. Unlike the case of partial-mediator model, the patterns of evidence are similar for each of these two models across the two samples. For the reduced partial-mediator

IT Sophistication Market pressure Scope of operations Web-related Costs Market pressure Scope of operations Information Search Market pressure Scope of operations IT sophistication Web-related costs E-Commerce Infusion Market pressure Scope of operations IT sophistication Web-related costs Strategic Information search E-commerce infusion Very Strong Strong Very Strong ----Very Strong Very Strong Positive ----Very Strong --------Very Strong Positive Very Strong

Very Strong -----

Very Strong Very Strong ---------

-----------------

Very Strong Very Strong

Sample 2

Very Strong -----

Sample 1

Partial Mediator


--------Positive Positive

Very Strong Very Strong Very Strong -----

Very Strong -----

N/A N/A

Sample 1

----Very Strong

Very Strong ----Positive Very Strong

Very Strong Very Strong Very Strong -----

Very Strong -----

N/A N/A

Sample 2

Reduced Partial Mediator

Table 5: Evidence for links in the three structural equations and indices of fit for the models.


N/A N/A Strong Positive

N/A N/A Very Strong -----

Very Strong -----

Very Strong Strong

Sample 1

Sample 2

Positive Very Strong

N/A N/A Strong Very Strong

N/A N/A Very Strong -----

Very Strong -----


Mediator


Very Strong Very Strong --------Positive Strong Strong 1107.8 443 2.501 .069 .080 .965 .961


Strong -----

----Very Strong Positive

1192.1 443 2.691 .082 .091 .943 .936

1275.1 445 2.865 .086 .096 .937 .929


Strong -----

Very Strong Strong

1326.0 445 2.980 .079 .088 .954 .949

----Strong Strong

---------


1416.3 447 3.168 .093 .099 .926 .093


Very Strong -----


1277.2 447 2.857 .077 .085 .956 .952

Positive Strong Strong

----Positive


Note: A conclusive level of evidence for a link is shown boldface. Dashed line indicates lack of conclusive evidence for a link. “N/A” indicates links not included in the model.

Operational Efficiency Information search E-Commerce infusion Direct Contacts Information search E-commerce infusion Links between Benefits Strategic with operational Direct contacts with strategic Direct contacts with operational Fit Indices Chi-Square Degrees of Freedom Normed Chi-Square RMSEA SRMR CFI TLI

Table 5: (continued) Evidence for links in the three structural equations and indices of fit for the models.

Pflughoeft, Ramamurthy, Soofi, Yasai-Ardekani, and Zahedi 489

490


model, the two samples agree on the conclusiveness of evidence or lack of it for 16 of 19 links (11 conclusive evidence, 5 lack of conclusive evidence). For the mediator model, the two samples agree on the conclusiveness of evidence or lack of it for 14 of 17 links (12 conclusive evidence, 2 lack of conclusive evidence). More specifically, Table 5 reveals the following patterns of evidence for the relationships between variables within or across the reduced partial-mediator and mediator models. In the mediator model, where IT sophistication is linked to market pressure and scope of operations, the two samples agree on the conclusiveness of evidence for each of the two links. For the Web-related costs’ links with market pressure and scope of operations, there is agreement across the two samples within each model and between the two models. The evidence for the link with market pressure is conclusive and there is a lack of conclusive evidence for the link with scope of operations. In the reduced partial-mediator model, where information search is linked to market pressure and scope of operations, there is agreement across the two samples on the conclusiveness of evidence for each of these links. In both models information search is linked to IT sophistication and Web-related costs. For these links, there is agreement across the two samples on the evidence within each model and between the models. In both models the evidence for the link with IT sophistication is conclusive and there is lack of evidence for the link with Webrelated costs. In the reduced partial-mediator model, where e-commerce is linked to market pressure and scope of operations, there is no agreement across the two samples about the evidence for the link with market pressure, but there is agreement across the two samples about lack of evidence for the link with scope of operations. In both models e-commerce is linked to IT sophistication and Web-related costs. For these links there is agreement across the two samples on the evidence within each model and between the models. In both models the evidence is conclusive for each of these links. The links between strategic benefits and two Web-use variables (information search and e-commerce) are present in both models. For the link with information search, there is no agreement across the two samples for the reduced partialmediator model; but for the mediator model the two samples agree and provide conclusive evidence. The links between operational efficiency and two Web-use variables (information search and e-commerce) are present in both models. For these links there is agreement across the two samples on the evidence within each model and between the models. In both models the evidence is conclusive for each of these links. The links between direct contact and the two Web-use variables (information search and e-commerce) are present in both models. There is no agreement about the evidence for the link with information search in either of two models. There is, however, agreement across the two samples about lack of evidence for the link with e-commerce in the reduced partial-mediator model; but there is no agreement across the two samples about the link with e-commerce in the mediator model. For the links between benefits, there is agreement across the two samples about lack of evidence for the link between strategic and operational benefits in the reduced partial-mediator model, but there is no agreement across the two samples


491

in the mediator model. There is, however, agreement across the two samples and between models on the conclusiveness of the evidence for the link between direct contacts and each of the strategic and operational benefits. As shown in Table 5, the patterns of evidence for the relationships are quite similar in two independent samples for the reduced partial-mediator and mediator models. Since sample 2 played no role in the original development of the CIUB models, the agreement across the two independent samples on the conclusiveness of evidence for the relationships between the constructs provides us more confidence to infer about the validity of these results. Such an insight could not have been obtained from a single-sample study.

DISCUSSION OF THE RESULTS Figure 3 summarizes the empirical results in terms of two models for small business Web use and benefits. Figure 3(a) shows the reduced partial-mediator model and Figure 3(b) shows the mediator model. The links shown in these figures are those for which conclusive evidence was found in both samples. As discussed above, the results for the partial-mediator model were discrepant in the two samples and statistically unreliable in sample 1. They are, therefore, not included in the discussion.

Context, IT Infrastructure, and Web Use Figure 3(a) depicts the results for the reduced partial-mediator model. In this model, both context variables (market pressure and the scope of operations) are directly related to Web use for information search. However, the effect of market pressure on Web use for e-commerce is channeled through Web-related costs. In this model, IT sophistication is exogenous and is related to Web use for information search and ecommerce. These results suggest that the effects of context and IT sophistication on information search are additive, and that each contributes to greater use of the Web for information search, and the resulting benefits. These results are consistent with the idea that (a) market pressures induce small businesses to make greater use of the Web for information search; (b) broader scope of operations may necessitate more extensive environmental surveillance; and (c) higher levels of IT sophistication facilitate the use of newer information technologies by small businesses. Thus, small businesses are expected to make greater use of the Web for information search on customers, suppliers, and competitors when they encounter higher market pressures, operate in a broader geographical domain, and possess a higher level of IT sophistication. Web-related costs measure the costs associated with Web use relative to a firm’s total operating cost and, therefore, reflect the extent of top management’s financial commitment to this technology. Although the initial cost of setting up a Web site may be negligible, small firms are often required to allocate a significant portion of their resources to develop the infrastructure technologies and employee training needed to conduct e-commerce. Figure 3(b) depicts the results for the mediator model. This model shows that market pressure and scope of operations are jointly related to IT sophistication, which in turn is related to Web use for information search and Web use for

492


Figure 3: Summary of the empirical results in terms of links with conclusive evidence found in both samples. (a) Reduced Partial-Mediator Model

IT Sophistication Market Pressure

Strategic

Information Search Operational Efficiency

Scope of Operations

E-Commerce

Direct Contacts

Web-Related Costs

(b) Mediator Model

IT Sophistication

Market Pressure

Strategic

Information Search Operational Efficiency

Scope of Operations

E-Commerce

Web-Related Costs

Direct Contacts

e-commerce. The patterns of relationships between context, Web-related costs, and Web use are the same as those shown in the reduced partial-mediator model (Figure 3(a)). The results of mediator model are consistent with the idea that (a) small businesses that encounter greater market pressures and those with a broader scope of operations develop higher levels of IT sophistication; (b) higher levels of IT sophistication in turn facilitate greater use of the Web for information search and e-commerce; (c) in addition to higher levels of IT sophistication, use of the more advanced functionality of the Web, e-commerce, requires commitment of a greater amount of resources; (d) greater resource commitments toward the Web are made in response to market pressure; and (e) the more complex use of the Web


493

technology (e-commerce) requires existence of a more responsive IT infrastructure as reflected by higher levels of IT sophistication and a greater commitment of resources toward the use of the newer Web-based technologies. The plausibility of both models suggests that IT sophistication may affect Web use for information search along with the direct effects of context (Figure 3(a)) or it may serve as a mediator of the effects of context on information search (Figure 3(b)). These results suggest that a small firm may have developed higher levels of IT sophistication via different paths—either as a result of alignment of IT sophistication to the demands of contextual environments or from being more proactive and visionary in building a foundation for (an uncertain) future. However, regardless of the development path, IT sophistication appears to prompt the small firms to embrace and exploit newer technologies and thereby realize the attendant benefits. Furthermore, both models suggest that although the use of the Web for information search may not require significant resource commitments, use of the Web for conducting business transactions requires significant resource commitments.

Web Use and Benefits The patterns of linkages between use and benefits of the Web are similar in the reduced partial-mediator and mediator models. In both models the use of the Web for e-commerce is related to strategic benefits. However, in the reduced partialmediator model, for the link between use of the Web for information search and strategic benefits the grade of evidence is not conclusive in both samples. In both models, use of the Web for information search and for e-commerce is related to operational efficiencies. Results of both models show no conclusive evidence for a direct link between Web use and direct contacts. But direct contact is related to both strategic benefits and operational efficiencies. Both models show that strategic and operational benefits are related to direct contact benefits. Greater strategic benefit due to Web use is associated with benefits in direct contacts with customers, suppliers, and among employees. As small businesses expand their market beyond their physical boundaries and increase their customer base, they rely increasingly on the Web to manage regular, standard contacts with their customers, suppliers, and within the company, which do not require face-to-face or phone communications. This allows small businesses to arrange direct contacts in exceptional cases, hence improving the quality and speed of communication without a proportional increase in resources for communication management. Furthermore, the association of benefits in operational efficiency and direct contact indicates that greater operational benefits due to Web use may result in greater direct contact benefits. Here, increase in the speed and quality of communication due to the use of the Web enhances operational efficiency, and thus enhances the nature of direct contacts with customers, suppliers, and employees. Although the effects of Web use on direct contact benefits are not direct and are mediated through strategic and operational benefits, these findings are quite interesting and seem to run counter to the traditional view that use of information technology depersonalizes the social context of the business environment. Our results suggest that electronic integration of the firm with its customers and suppliers and other stakeholders tends to generate strategic and operational

494


benefits that in turn enhance the quality of personal contact with trading partners as well as among the firm’s employees. The mediator model shows a relationship between Web use for information search and strategic benefits (Figure 3(b)), but there is a lack of evidence for this relationship in the reduced partial-mediator model. This result (of the mediator model) is consistent with the idea that greater use of the Web for information search is related to strategic benefits for small businesses. More extensive use of the Web, which is an excellent repository of external information, allows small businesses to retrieve information on customers, suppliers, and competitors, thereby enabling them to plan actions that could enhance their competitive position. Use of the Web for information search overcomes the extensive difficulties normally faced by small businesses in obtaining market intelligence. Greater environmental surveillance through the use of the Web enables small businesses to access a larger number of current and potential customers and helps them attain broader market coverage, increase their revenue, and improve their company image. Both models show that the use of the Web for information search is associated with operational efficiencies. Access to timely information may improve management of customer and supplier relations. The ability to learn quickly about and respond to customers’ needs and demands allows for building longer-term and more trusting relationships. Similarly, timely acquisition of knowledge about suppliers helps small businesses to plan their activities more effectively and coordinate with their suppliers and thereby realize greater operational efficiencies. The relationship between the extent of e-commerce infusion and strategic benefit, evident in both models, underscores the importance of e-commerce to small businesses in expanding their markets, increasing sales, and improving customer bases. Small businesses with greater e-commerce infusion are able to expand their customer base because spatial and temporal constraints are reduced. In addition, they are able to realize greater market penetration because e-commerce transactions are presumably more attractive to existing customers. Customers’ costs of transactions are reduced and they also garner added value from more timely and accurate transactions. Both models show a link between use of the Web for e-commerce and operational benefits. This result is reflective of a reduction in operating costs due to the speed with which transactions are conducted. Moreover, employees can devote their time to more important activities instead of routine operations. Finally, an ability to perform transactions electronically via the Web enables the employees of both trading partners to pursue telecommuting or teleworking. The ability to conduct business faster enables the firm to meet customer needs in a more timely fashion and to improve customer relations. Similarly, speed of communication and transactions with suppliers enables the firm to perform their procurement function in a more timely and efficient manner and further enhances coordination of procurement with suppliers.

MANAGERIAL IMPLICATIONS Our findings have important implications for managers of small businesses and providers of hardware, software, and Internet services. The most important


495

message for managers is that firms that align their Web use with the demands of their organizational context will stand to gain substantial benefits. Our findings should motivate managers of small businesses to evaluate their organizational contexts and adopt a proactive stance toward embracing the opportunities afforded by Web use. Small businesses that experience higher levels of market pressures and that have developed a responsive IT infrastructure would benefit greatly from use of the Web for environmental surveillance and electronic business transactions. Similarly, small businesses with a broader scope of operations would reap the benefits associated with the use of the Web for information search. Small business managers operating in competitive markets should consider proactively developing a responsive IT infrastructure by committing adequate resources for the Web and integrating a broad array of applications software in different areas of business activities. On the other hand, the mere existence of a sophisticated IT infrastructure could be a potent motivator for using and thus benefiting from the Web. If the current trends in Web use continue, firms that operate in relatively benign environments will be soon operating in market environments where pressure to use the Web will become pervasive. For these firms, the implications of our findings are that once they adopt the Web technology, its effective use will result in various benefits. Therefore, enhancing the level of IT sophistication and increasing commitment of resources to the Web technology will position firms to enjoy a multitude of benefits. Small businesses should, therefore, view Web-related costs as a long-term investment if they desire to exploit the potential inherent in this technology. For suppliers of Web-based technologies there are some important implications. Our findings help providers of Web-based technologies to focus on the most relevant market segments within the small business category. Web technology providers should channel their marketing efforts toward those small businesses that typically operate under high market pressures and normally use an extensive and sophisticated array of applications software products.

CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH This study explored two interrelated questions: What are the organizational and market factors associated with Web use and benefits for small firms? And, what is the nature of the associations between these factors and Web use and benefits? In addressing these research questions, we used the innovation adoption, organizations, and IS implementation literatures to identify a set of variables reflective of organizational contextual characteristics, IT infrastructure, Web use, and Web benefits. Since the existing theories did not lead us to a unique structural model, we pursued a multiple conceptualization approach. We considered three models and developed two plausible empirical models for exploring the links between organizational contextual variables, IT infrastructure, Web use, and Web benefits. These models posit that the extent of Web use by small businesses and the associated benefits are driven by organizations’ contextual characteristics and their IT infrastructure. An interesting aspect of this study is our attempt to demonstrate that complex organizational relationships could and often should be modeled in multiple

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ways. While researchers have urged the need for multiple conceptualizations of complex relationships, there are only very few studies in IS or organizational literature that have pursued multiple conceptualizations followed by empirical investigations in a single study. Our analysis may highlight the potential strength of a multiple-conceptualization approach in areas where the existing theories do not lend themselves to the formulation of a unique model. We reported on the estimation of three CIUB models using two national stratified random samples of small businesses. The analysis showed that the evidence for patterns of relationships captured by two of the three models may not be sensitive to the departure from some of the population characteristics. Our results shed some light on the dual role of IT sophistication. The reduced partial-mediator model indicated that the mere existence of sophisticated IT may lead small businesses to increase their use of the Web in the form of information search and e-commerce. On the other hand, the mediator model indicated that IT sophistication could be the result of market pressure and scope of operations, which in turn facilitates the use of the Web and realization of subsequent benefits. The plausibility of both models suggests the potential for pursuit of different paths to higher levels of IT sophistication. A more detailed analysis and clearer understanding of the development path of IT sophistication could suggest the nature of small businesses’ exploitation of technology investments and even the nature of their stance toward future technology developments. Future research should attempt to investigate this aspect. The reduced partial-mediator and the mediator models capture the role of Web-related costs in mediating the impact of market pressures on Web use for e-commerce. This clearly points out that mere introduction of newer technologies may not be adequate for making use of advanced functionalities of the technology. Significant and sustained investments may be necessary to reap the associated benefits. The two models confirm anecdotal evidence and common beliefs that Web use could be beneficial to small firms. The results show that Web use in the form of information search and e-commerce is associated with perceived strategic benefit and operational efficiency. Our study is not, however, without its own limitations and can be extended in several directions. We have used the robust WLS, which is a state-of-the-art methodology for analyzing linear structural equation models with categorical and ordinal data. We noted that the results for the partial-mediator model showed symptoms of collinearity in sample 1. We focused our analysis on the similarity of patterns of evidence across two samples and across the models within each sample. Recent methodological advances (Cheung & Rensvold, 2002; Raykov, 2001; Song, Lee, & Zhu, 2001) can be used for a broader implementation of a multipleconceptualization approach, such as more formal model comparison, model selection, and model invariance across multiple groups. The implementations of such advanced methodologies require extensive computational capabilities, particularly when the model is complex and the data is categorical. Another interesting plausible CIUB model is one that represents the IT infrastructure as a moderator of the relationships between the context and Web use. Such a model combines multitudes of complexities associated with nonlinearity of interaction terms in SEM


497

(Schumacker & Marcoulides, 1998) and estimation of large models with categorical data such as ours. An extension of this study to compare differences between firms in different industry or size categories, and to model the behavior of these different classes of small businesses would be of great importance. Additionally, our sampling frame did not include firms with fewer than 10 employees. Given the large proportion of very small firms, an extension of our work to study such businesses could shed additional light on the use and benefits of the Web for these very small businesses. Comparison of the patterns of relationships between context, IT infrastructure, use, and benefit for small and large organizations should be an interesting extension. In our study, we solicited and obtained responses from the owners/CEOs of these small- to medium-scale enterprises. On the one hand, it is possible that CEOs are probably not the best persons within the organization to provide information of an operational nature (e.g., IT sophistication). At the same time, however, given that the businesses we examined are predominantly small, the CEOs are generally expected to be quite familiar with all that occurs within their organizations and thus would be able to provide the most comprehensive and accurate responses. Given that the response rates were somewhat low, it is also possible that only those CEOs who are perhaps more committed to electronic commerce may have decided to respond to our survey, raising a pro-adoption bias. Caution must therefore be exercised in making generalizations from this study’s results. We measured IT sophistication in terms of “breadth” of the existing IT application portfolio. There is a need for refinement; while the breadth of application portfolio is important, it is perhaps more important to examine the extent to which applications are “integrated,” the absence of which can lead to “islands of automation.” Finally, in our analysis, we explored a limited number of organizational contextual variables in order to keep the complexity of the model and data requirements at a manageable level. We have represented the small firm’s scope of operations only by two of its components, namely geographical scope of markets and suppliers. A more comprehensive measure of scope of operations could include the nature of the small firm’s product-market diversity. A more complex set of models may uncover associations between additional organizational contextual variables, such as CEO/owner characteristics, industry information intensity, and Web use and benefits. Other types of Web use and Web characteristics such as the quality and content of Web site in terms of ease of use, content relevance, usefulness, reliability, responsiveness, and so on could be incorporated in a more complex set of models. [Received: December 2001. Accepted: March 2003.]

REFERENCES American Business Information (1998). Web sites USA [database]. Omaha, NE: American Business Information. Applegate, L. M., McFarlan, W. F., & McKenney, J. M. (1999). Corporate information systems management (5th ed.). New York: McGraw-Hill. Barber, N. F. (1991). Hubs and spokes: A paradigm shift. EDI World, 1(11), 6–9.

498


Billi, S., & Raymond, L. (1993). Information technology: Threats and opportunities for small and medium-sized enterprises. International Journal of Information Management, 13, 439–448. Bouchard, L. (1993). Decision criteria in the adoption of EDI. In J. L. DeGross, R. P. Bostrom, & D. Robey (Eds.), Proceedings of the Fourteenth Annual International Conference on Information Systems, Orlando, FL, 365–376. Brown, E., & Fox, J. (1999). Nine ways to win on the web. Fortune, 139(10), 112–121. Cheung, G. W., & Rensvold, R. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255. Clemons, E. (1991). Evaluations of strategic investments in information technology. Communications of the ACM, 34(1), 22–34. Cragg, P. B., & King, M. (1993). Small-firm computing: Motivators and inhibitors. MIS Quarterly, 17(1), 47–59. Daft, R. L. (1996). Management (4th ed.). Fort Worth, TX: Dryden. Daft, R. L., Sormunen, J., & Parks, D. (1985). Chief executive scanning, environmental characteristics, and company performance: An empirical study. Strategic Management Journal, 9, 123–139. DeLone, W. H. (1988). Determinants of success for computer usage in small business. MIS Quarterly, 12(1), 51–61. Dobbs, J. (1999). Competition’s new battleground: The integrated value chain. Cambridge, MA: Cambridge Technology Partners. Evans, P., & Wurster, T. (1997). Strategy and the new economics of information. Harvard Business Review, 75(5), 71–83. Gatignon, H., & Robertson, T. S. (1989). Technology diffusion: An empirical test of competitive effects. Journal of Marketing, 53(1), 35–49. Grover, V. (1993). An empirically derived model for the adoption of customerbased inter-organizational systems. Decision Sciences, 24(3), 603–640. Harrison, D. A., Mykytyn, P. P., & Riemenschneider, C. K. (1997). Executive decisions about adoption of information technology in small business: Theory and empirical tests. Information Systems Research, 8(2), 171–195. Hart, P., & Saunders, C. (1998). Emerging electronic partnerships: Antecedents and dimensions of EDI use from the supplier’s perspective. Journal of Management Information Systems, 14(4), 87–112. Henriott, L. (1999). Transforming supply chains into e-chains. Supply Chain Management Global Supplement, (Spring), 15–18. Huber, G. P. (1990). A theory of the effects of advanced information technologies on organizational design, intelligence, and decision-making. Academy of Management Review, 15, 47–71. Hwang, K. T., Pegels, C. C., Rao, H. R., & Sethi, V. (1992). Evaluating the implementation success and competitive impact of electronic data interchange systems. Working Paper, School of Management, SUNY, Buffalo.


499

Iacovou, C. L., Benbasat, I., & Dexter, A. S. (1995). Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly, 19(4), 465–485. Johnston, H. R., & Vitale, M. R. (1988). Creating competitive advantage with interorganizational information systems. MIS Quarterly, 12(2), 153–165. Jöreskog, K., & Sörbom, D. (1996). LISREL 8: User’s reference guide. Chicago: Scientific Software International. Julien, P.A. (1995). New technologies and technological information in small business. Journal of Business Venturing, 10, 459–477. Kalakota, R., & Whinston, A. B. (1996). Frontiers of electronic commerce. New York: Addison-Wesley. Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773–795. Katz, M. L., & Shapiro, C. (1991). Product introduction with network externalities. Journal of Industrial Economics, 40(1), 55–83. Kauffman, R. J., McAndrews, J., & Wang, Y. (2000). Opening the “black box” of network externalities in network adoption. Information Systems Research, 11(1), 61–82. Kettinger, W. J., Grover, V., Guha, S., & Segars, A. (1994). Strategic information systems revisited: A study in sustainability and performance. MIS Quarterly, 18(1), 31–58. Kleindl, B. (2000). Competitive dynamics and new business models for SMEs in the virtual marketplace. Journal of Developmental Entrepreneurship, 5(1), 73–85. Kraatz, M. S., & Zajac, E. J. (2001). How organizational resources affect strategic change and performance in turbulent environments: Theory and evidence. Organization Science, 12(5), 632–657. Marcoulides, G. A., & Heck, R. H. (1993). Organizational culture and performance: Proposing and testing a model. Organization Science, 4(2), 209–225. Mougayar, W. (1998). Opening digital markets (2nd ed.). New York: McGraw-Hill. Muthén, B. O. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49, 115–132. Muthén, B. O., & Muthén, L. K. (1998). Mplus: The comprehensive modeling program for applied researchers user guide. Los Angeles: Muthén and Muthén. Muthén, B. O., & Satorra, A. (1995). Technical aspects of Muthén’s LISCOMP approach to estimation of latent variable relations with comprehensive measurement model. Psychometrika, 60, 489–503. Nass, C. (1994). Knowledge or skills: Which do administrators learn from experience? Organization Science, 5(1), 38–50. Nie, N. H., & Erbing, L. (2000, February 17). Internet and society: A preliminary report. Stanford Institute for the Quantitative Study of Society. Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.

500


Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations. New York: Harper & Row. Pitts, J. M. (1991). Really working together. EDI World, 1(11), 18. Pollard, C. E., & Hayne, S. C. (1998). The changing faces of information system issues in small firms. International Small Business Journal, 16(3), 70–87. Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competitors. New York: Free Press. Porter, M. E. (2001). Strategy and the Internet. Harvard Business Review, 79(March), 63–78. Premkumar, G., & Ramamurthy, K. (1995). The role of interorganizational and organizational factors on the decision mode for adoption of interorganizational systems. Decision Sciences, 26(3), 303–336. Raykov, T. (2001). Approximate confidence interval for difference in fit of structural equation models. Structural Equation Modeling, 8, 458–469. Raykov, T., & Marcoulides, G. A. (2001). Can there be infinitely many models equivalent to a given covariance structure model? Structural Equation Modeling, 8, 142–149. Raymond, L. (1984/1985). Personal and attitudinal correlates of MIS success in small business. Journal of Business Administration, 15(1/2), 159–173. Raymond, L. (1985). Organizational characteristics and MIS success in the context of small businesses. MIS Quarterly, 9(1), 37–52. Raymond, L. (1992). Computerization as a factor in the development of young entrepreneurs. International Small Business Journal, 11(1), 23–34. Raymond, L., & Bergeron, F. (1996). EDI success in small and medium-sized enterprises: A field study. Journal of Organizational Computing and Electronic Commerce, 6(2), 161–172. Raymond, L., Bergeron, F., & Rivard, S. (1998). Determinants of business process reengineering success in small and large enterprises: An empirical study in the Canadian context. Journal of Small Business Management, 36(1), 72–85. Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. Von Eye and C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research. Thousand Oaks, CA: Sage. Schneider, G. P., & Perry, J. T. (2001). Electronic commerce. Cambridge, MA: Course Technology. Schumacker, R. E., & Marcoulides, G. A. (1998). Interaction and nonlinear effects in structural equation modeling. Mahwah, NJ: Erlbaum. Song, X., Lee, S., & Zhu, H. (2001). Model selection in structural equation models with continuous and polytomous data. Structural Equation Modeling, 8, 378– 396. Stein, T., & Sweat, J. (1998, November 9). Killer supply chains. InformationWeek, 708, 36–42.


501

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. Thong, J. Y. L. (1999). An integrated model of information systems adoption in small businesses. Journal of Management Information Systems, 15(4), 187– 214. Thong, J. Y. L., Yap, C. S., & Raman, K. S. (1996). Top management support, external expertise, and information systems implementation in small businesses. Information Systems Research, 7(2), 248–267. Turban, E., King, D., Lee, J., Warkentin, M., & Chung, M. H. (2002). Electronic commerce: A managerial perspective. Upper Saddle River, NJ: Prentice Hall. U.S. Small Business Administration. (2001). http://www.sbaonline.sba.gov/intro. html, accessed August 9, 2001. Venkatraman, N. (1989). The concept of fit in strategy research: Toward verbal and statistical correspondence. Academy of Management Review, 14(3), 423–444. Venkatraman, N., & Zaheer, A. (1990). Electronic integration and strategic advantage: A quasi-experimental study in the insurance industry. Information Systems Research, 1(4), 377–393. Yap, C. S., Thong, J. Y. L., & Raman, K. S. (1994). Effect of government incentives on computerization in small business. European Journal of Information Systems, 3(3), 191–206. Yasai-Ardekani, M., & Nystrom, P. C. (1996). Designs for environmental scanning systems: Tests of a contingency theory. Management Science, 42(2), 187– 204. Yoon, Y., Guimaraes, T., & O’Neal, Q. (1995). Exploring the factors associated with expert systems success. MIS Quarterly, 9(1), 83–96.

APPENDIX A Measurement Measures developed for organizational context, IT infrastructure, Web use, and Web benefits constructs specifically designed for small businesses are discussed below. Tables A.1–A.8 contain descriptions of construct indicators, the labels for all response categories, the associated numerical scores as they were presented in the survey instrument, distributions of the categories (percentages), and the results of exploratory factor analysis performed using sample 1. Organizational Context Market Pressure (α = .73) Drawing upon previous research (Bouchard, 1993; Grover, 1993; Pfeffer & Salancik, 1978; Porter, 1980; Premkumar & Ramamurthy, 1995), pressure to use the Web from key external stakeholders is measured using three six-point Likerttype scales—from 0 for none to 5 for very great. The measure covers the extent of Web use by the firm’s competitors, and the extent of customers’ expectations and suppliers’ expectations for the firm to use the Web for conducting business.

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Table A.1: Distributions of the indicators of the context constructs in sample 1. Percentage of Responses in Each Category

Market Pressure (to Use the Web)

None 0

Very Little 1

Little 2

Some 3

Great 4

Very Great 5

The extent of pressure faced by the company to use the Web from the following sources: Competitors’ use Customers’ expectation Suppliers’ expectation

1.99 1.59 7.97

7.97 9.96 18.33

18.73 19.12 28.69

47.41 40.64 30.68

18.33 22.31 11.55

5.58 6.37 2.79

No

Yes

14.74 46.61 13.15 58.57

85.26 53.39 86.85 41.43

Scope of Operations The company’s markets are regional or national The company’s markets are international The company’s supplier are regional or national The company’s supplier are international

Table A.2: Correlations and exploratory factor analysis for the indicators of the context constructs. Factor Analysis Loading

Polychoric Correlation of Indicators Market Pressure Construct

1

2

3

Business Scope 1

2

3

4

F1

Market Pressure Competitors’ use 1.00 .72 Customers’ expectation .64 1.00 .89 Suppliers’ expectation .33 .41 1.00 .46 Scope of Operations Markets regional or national .11 .19 .05 1.00 .12 Markets international .14 .26 .02 .76 1.00 .14 Suppliers regional or national .03 −.01 .21 .43 .21 1.00 .03 Suppliers international −.04 .04 .05 .39 .66 .40 1.00 −.04 Fit indices for factor model % of Variance Explained Chi-Square DF Normed Chi-Square SRMR

F2 .02 .11 .03 .76 .96 .41 .68

63.16 31.05 8 3.88 .08

Scope of Operations (α = .78) Drawing upon past research (Applegate, McFarlan, & McKenney, 1999; Daft, 1996; Kettinger et al., 1994; Yasai-Ardekani & Nystrom, 1996), geographical scope of the firm’s market and suppliers is used to represent scope of operation. Respondents were asked to identify markets they serve and their suppliers as local, statewide, regional, national, Canada, Mexico, and other international. Four composite binary


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Table A.3: Distributions of the indicators of the IT infrastructure constructs in sample 1. Percentage of Responses in Each Category

IT Sophistication The extent to which the company uses computer software/systems in each of the following areas: General office activities (e.g., word processing, appointment setting) Data management (e.g., Access, Paradox, other database systems) Accounting (e.g., receivable, payable, general ledger, budgeting) Personnel management (e.g., payroll, benefits, career planning) Sales and marketing

None 0

Very Little 1

Little 2

Some 3

Great 4

Very Great 5

.40

.00

.40

7.17

29.88

62.15

1.59

6.37

4.78

13.55

26.29

47.41

.40

.40

.80

4.78

22.71

70.92

7.17

5.58

12.75

24.70

24.70

25.10

1.59

3.19

9.56

29.48

33.86

22.31

.40 .40 10.76

25.10 30.28 33.07

35.46 40.64 40.24

30.68 23.51 11.95

6.77 3.98 1.99

1.59 1.20 1.99

Web-Related Costs Annual costs associated with the Internet and Web service relative to the company’s total operating costs: Initial setup costs Annual operating costs Training costs

indicators were created for measuring scope of operations. Two composite binary variables identify the firm’s market type—one to show if the firm serves regional or national markets, and the second to indicate if the firm operates in North American Free Trade Agreement (NAFTA) countries (Canada and Mexico) or other international markets. Firms with only local or statewide markets are identified by zeros for both indicators. Similarly, two composite binary variables represent whether the firm has regional-national suppliers (one indicator) or suppliers from Canada, Mexico, or other countries (another indicator). Composite variables were created to increase the market distinctions, to ensure the adequacy of observations in each cell, and to avoid indicator instability due to small number of observations.

IT Infrastructure IT Sophistication (α = .74) Based on the work of Raymond (1992) and similar to the ideas in Thong (1999), IT sophistication is represented by the extent of software use in various business functions. Five Likert-type scales—ranging from 0 (no use), and 1 through 5

504


Table A.4: Correlations and exploratory factor analysis of the indicators of the IT infrastructure constructs. Polychoric Correlation of Indicators Web-Related Costs

IT Sophistication Construct

1

2

3

4

5

1

2

3

IT Sophistication General office activities 1.00 Data management .52 1.00 Accounting .52 .41 1.00 Personnel management .40 .30 .50 1.00 Sales and marketing .34 .27 .28 .18 1.00 Web-Related Costs Initial setup costs .06 .13 −.08 −.01 .19 1.00 Annual operating costs .06 .15 −.07 .05 .19 .91 1.00 Training costs .08 .17 −.01 .10 .19 .71 .73 1.00 Fit indices for factor model % of Variance Explained Chi-square DF Normed Chi-square SRMR

Factor Analysis Loading F1

F2

.76 .66 .63 .51 .45

.09 .12 .00 .08 .06

.03 .89 .10 .97 .14 .67 61.26 34.68 13 2.67 .04

to represent very little use to very great use—measure the extent of software use for performing a variety of different business activities in areas of general office activities, data management, accounting, personnel management, and sales and marketing.

Web-Related Costs (α = .87) Web-related costs reflect the commitment of the firm to providing the necessary resources for Web-based services, and consist of the initial investment for setting up the system as well as the ongoing operating and training costs. Drawing upon ideas from Billi and Raymond (1993), DeLone (1988), Raymond (1985), and Schneider and Perry (2001), three Likert-type scales—ranging from (0) none to (5) very great—measure, relative to the company’s total operating costs, the initial cost of setting up Web services, the annual operating cost of the Web services, and the training cost associated with the use of the Web. Web Use Information Search (α = .79) Businesses use the Web to search for and capture information about their customers and suppliers as well as for surveillance of their competitors. Drawing upon ideas from Hart and Saunders (1998) and Yasai-Ardekani and Nystrom (1996), three Likert-type scales—ranging from (0) none to (5) very great—gauge the extent to


505

Table A.5: Distributions of the indicators for the Web-use constructs in sample 1. Percentage of Responses in Each Category

Information Search

None 0

Very Little 1

Little 2

Some 3

Great 4

Very Great 5

The extent to which the company uses the Internet to search and capture information about Customers Suppliers Competitors

11.55 10.76 3.59

19.52 17.13 15.54

15.14 18.33 15.54

31.87 30.68 33.07

12.75 13.55 17.93

9.16 9.56 14.34

27.09 11.95 15.54

9.96 3.19 15.94

16.73 1.59 28.69

1.99 .80 6.37

1.99 1.20 4.78

Extent of Electronic Commerce Infusion The extent of use of the (company’s) Web for the following business activities: Receiving orders Receiving payments Servicing customers

42.23 81.27 28.69

Table A.6: Correlations and exploratory factor analysis of the indicators of the Web-use constructs. Polychoric Correlation of Indicators Information Search Construct Information Search Customers Suppliers Competitors E-Commerce Infusion Receiving orders Receiving payments Servicing customers

E-Commerce Infusion

1

2

3

1.00 .51 .62

1.00 .48

1.00

.22 .29 .26

.08 .15 .16

.20 .25 .30

1

1.00 .72 .59

2

1.00 .53

3

1.00

Fit indices for factor model % of Variance Explained Chi-Square DF Normed Chi-Square SRMR

Factor Analysis Loading F1

F2

.75 .67 .84

−.04 .15 .25

.08 .08 .14

.92 .76 .65

73.57 1.47 4 .37 .01

Strategic Benefits Geographic expansion of firm’s markets Sales revenue Number of potential customers (prospects) Number of actual customers Operational Efficiency Productivity Speed of conducting business activities Ability of employees to work away from office Ability to meet customer needs Face-to-Face Contacts Direct contact (face-to-face or by phone) with customers Direct contact (face-to-face or by phone) with suppliers Direct contact (face-to-face or by phone) among employees

The extent to which the use of Internet and/or Web has affected the following aspects of the firm at this time: .00 .40 .40 .40 2.79 .40 2.39 .00 4.38 2.39 4.38

.40 .40 .00 .40 1.20 .40 1.20

Adversely −1

.40 .40 .40 .40

Very Adversely −2

62.15 82.07 74.50

47.41 33.07 31.08 54.18

50.60 48.21 25.90 42.63

None 0

29.08 13.15 12.35

42.23 52.99 54.18 34.26

37.45 43.43 52.59 47.81

Favorably +1

Percentage of Responses in Each Category

Table A.7: Distributions of the indicators for the Web benefit constructs in sample 1.

3.19 1.99 7.57

7.17 13.15 12.35 11.16

11.55 7.57 20.72 8.76

Very Favorably +2


Strategic Benefits Market expansion Sales revenue Potential customers Actual customers Operational Efficiency Productivity Speed Work away from office Customer needs Direct Contacts With customers With suppliers Among employees

Construct

1.00 .67 .78

.39 .36 .31 .24

.39 .27 .22

.31 .27 .28 .14

.32 .29 .20

2

1.00 .71 .72 .72

1

.45 .31 .22

.28 .23 .35 .09

1.00 .79

3

Strategic Benefits

.49 .45 .30

.27 .28 .39 .22

1.00

4

.11 .22 .16

1.00 .67 .42 .46

1

.19 .28 .19

1.00 .42 .47

2

.33 .36 .27

1.00 .33

3

4

1.00 .55 .43

1

1.00 .50

2

Direct Contacts

1.00

3

Fit indices for factor model % of Variance Explained Chi-Square DF Normed Chi-Square SRMR

.18 .43 .32

1.00

Operational Efficiency

Polychoric Correlation of Construct Indicators

Table A.8: Correlations and exploratory factor analysis of the indicators of the Web Benefit constructs.

.35 .15 .08

.19 .18 .24 .03

.80 .79 .82 .87

F1

71.12 28.79 25 1.15 .02

.03 .22 .17

.83 .77 .47 .56

.18 .27 .11 .14

F2

Loading

.64 .78 .61

−.00 .12 .27 .30

.11 .13 .19 .29

F3

Factor Analysis

Pflughoeft, Ramamurthy, Soofi, Yasai-Ardekani, and Zahedi 507

508


which the Web is used to search and capture information about customers, suppliers, and competitors.

E-Commerce Infusion (α = .82) Use of the firm’s Web site for conducting key business activities is used as a representation of the extent of use of the Web for e-commerce. Respondents were asked to indicate on three Likert-type scales—ranging from (0) none to (5) very great—the extent to which they use their Web site for receiving orders, receiving payments, and providing service to customers (Kalakota & Whinston, 1996; Mougayar, 1998; Schneider & Perry, 2001). Web Benefit Ideas from the literature on information technology for competitive advantage (Clemons, 1991; Evans & Wurster, 1997; Johnston & Vitale, 1988; Porter, 2001; Venkatraman & Zaheer, 1990) form the basis for developing the scales measuring Web benefits. Three constructs measure strategic, operational, and direct-contacts benefit of the Web. Benefits arising from the use of the Web are measured using eleven five-point Likert-type scales ranging from (−2) very adversely, (−1) adversely, (0) none, (1) favorably, to (2) very favorably. Negative and positive scores were used to measure adverse and favorable consequences of Web use respectively, and to avoid possible bias in eliciting answers regarding benefits. Strategic Benefit (α = .92) Four five-point Likert-type scales were used to measure the effect of Web use on geographic expansion of the firm’s markets, sales revenue, number of potential customers, and number of actual customers. Operational Efficiency (α = .78) Four five-point Likert-type scales were used to measure the effect of Web use on productivity, speed of conducting business activities, the ability of the firm’s employees to work away from the office (i.e., telecommute and telework), and the firm’s ability to meet customers’ needs. Direct Contacts (α = .74) Three five-point Likert-type scales were used to measure the effects of Web use on contact with customers, contact with suppliers, and contact among the firm’s employees on a face-to-face basis or by telephone.

APPENDIX B ESTIMATES OF STRUCTURAL PARAMETERS AND T-RATIOS Table B.1 shows the parameter estimates and their T-ratios for structural equations found in sample 1. Comparison of the results of the partial-mediator model with


509

Table B.1: Sample 1 parameter estimates and T-ratios for structural equations. Reduced Partial-Mediator Partial-Mediator

IT Sophistication Market Pressure Scope of Operations Web-Related Costs Market Pressure Scope of Operations Information Search Market Pressure Scope of Operations IT Sophistication Web-Related Costs E-Commerce Infusion Market Pressure Scope of Operations IT Sophistication Web-Related Costs Strategic Information Search E-commerce Infusion Operational Efficiency Information Search E-Commerce Infusion Direct Contacts Information Search E-commerce Infusion Links between Benefits Strategic with operational Direct contacts with strategic Direct contacts with operational

Mediator

Coef.

T

Coef.

T

Coef.

T

.329 .085

3.983 1.131

N/A N/A

N/A N/A

.500 .232

6.245 3.889

.461 .179

5.417 2.319

.441 .178

5.496 2.306

.403 .164

5.061 2.181

.662 .326 .193 .005

7.314 4.199 2.031 .068

.701 .331 .449 .012

8.909 4.311 5.523 .167

N/A N/A 1.057 .118

N/A N/A 6.697 2.097

.153 .120 .187 .170

1.608 1.669 2.059 2.520

.166 .125 .239 .192

2.107 1.774 3.192 2.854

N/A N/A .420 .188

N/A N/A 3.808 3.156

.369 .473

5.349 6.241

.380 .453

5.212 5.849

.356 .476

4.918 6.090

.517 .296

7.791 4.410

.553 .242

7.994 3.686

.558 .262

8.188 4.044

.279 .237

3.736 2.650

.300 .205

3.846 2.289

.315 .209

3.969 2.375

.057 .231 .132

1.414 5.006 3.288

.059 .234 .134

1.478 5.058 3.321

.060 .235 .126

1.518 5.051 3.121

Note: Links for which conclusive evidence is found are shown boldface. Links not included in the model are indicated by “N/A.”

the results of other two models (which are its submodels) show pronounced reduction of T-ratios for the coefficient of scope of operations in the IT sophistication equation and for the coefficients of IT sophistication in the information search and e-commerce equations. The presence of both the IT sophistication (equation [1] with two parameters) and the direct-effect parameters of the context variables on Web-use variables (four parameters γ 31 , γ 32 , γ 41 , and γ 42 in equations [3] and [4]) make the model too complex. Table B.2 shows the parameter estimates and their T-ratios for structural equations found in sample 2. The T-ratios that change from conclusive evidence to lack of conclusive evidence and vice versa are shaded in gray.

510


Table B.2: Sample 2 parameter estimates and T-ratios for structural equations. Reduced Partial-Mediator Partial-Mediator

IT Sophistication Market Pressure Scope of Operations Web-Related Costs Market Pressure Scope of Operations Information Search Market Pressure Scope of Operations IT Sophistication Web-Related Costs E-Commerce Infusion Market Pressure Scope of Operations IT Sophistication Web-Related Costs Strategic Information Search E-commerce Infusion Operational Efficiency Information Search E-Commerce Infusion Direct Contacts Information Search E-commerce Infusion Links between benefits Strategic with operational Direct contacts with strategic Direct contacts with operational

Mediator

Coef.

T

Coef.

T

Coef.

T

.250 .274

4.665 3.943

N/A N/A

N/A N/A

.314 .321

6.062 4.841

.402 .210

5.896 2.605

.381 .226

5.757 2.615

.422 .152

6.868 1.970

.387 .378 .259 .026

5.122 4.143 2.864 .451

.410 .464 .508 .014

6.140 5.202 7.191 .227

N/A N/A 1.014 .104

N/A N/A 6.808 1.986

.319 .129 .038 .342

4.287 1.446 .361 5.639

.283 .114 .232 .357

4.508 1.474 2.998 5.958

N/A N/A .433 .424

N/A N/A 3.452 7.819

.239 .584

2.934 8.166

.225 .592

2.600 7.849

.238 .582

2.905 8.036

.338 .492

4.414 6.485

.331 .493

4.100 6.179

.354 .481

4.597 6.291

.026 .138

.564 2.726

.024 .140

.486 2.614

.025 .138

.554 2.835

.131 .129 .102

2.849 3.915 3.632

.128 .128 .101

2.757 3.889 3.600

.132 .129 .102

2.850 3.837 3.601

Note: Links for which conclusive evidence is found are shown boldface. T-ratios that change from conclusive evidence to lack of conclusive evidence and vise versa are shaded in gray. Links not included in the model are indicated by “N/A”.

Kurt A. Pflughoeft is the director of information technology at Market Probe, an international market research company specializing in customer satisfaction and loyalty research. Dr. Pflughoeft is adjunct computer science professor at Wisconsin Lutheran College and previously he was assistant professor at the University of Wisconsin–Milwaukee and the University of Texas at El Paso. He has published in several academic journals including Structural Equation Modeling, Information and Management, Behavior and Information Technology, Omega, International Journal of Production Research, Journal of Manufacturing Systems, and


511

Statistics and Probability Letters. He received his bachelor’s degree in business administration, master’s degree in MIS, and PhD in management science from the University of Wisconsin–Milwaukee. K. Ramamurthy is a professor of MIS at the University of Wisconsin–Milwaukee. He has a bachelor’s degree in mechanical engineering and a graduate diploma in statistical quality control and operations research from India, an MBA from Canada, and a PhD degree in management information systems from the University of Pittsburgh. He has nearly 20 years of industry experience and has held several senior technical and executive positions. His current research interests include electronic commerce including interorganizational systems/electronic data exchange and the Internet; adoption, implementation, and diffusion of modern information technologies; total quality management including software quality; strategic IS planning; data resource management; decision and knowledge systems for individual and group support; self-directed teams; business process reengineering; and management of computer integrated manufacturing technologies. He has published over thirty articles in major journals including MIS Quarterly; Journal of Management Information Systems; IEEE Transactions on Software Engineering; Decision Sciences; IEEE Transactions on Systems, Man, and Cybernetics; International Journal of Production Research; Decision Support Systems; International Journal of Electronic Commerce; Journal of Organizational Computing and Electronic Commerce; International Journal of Human- Computer Studies; IEEE Transactions on Engineering Management; Journal of International Marketing; Journal of Technology and Engineering Management; International Journal of Man-Machine Studies; Omega; Transportation Journal; INFOR; and in a number of refereed conference proceedings. He is a charter member of Association for Information Systems, and has been elected to the Beta Gamma and Sigma honor society. Ehsan S. Soofi is a professor of management science and statistics at the School of Business Administration and a research associate at the Center for Research on International Economics, University of Wisconsin–Milwaukee. He received his bachelor’s degree in mathematics from UCLA, master’s degree in statistics from the University of California, Berkeley, and PhD in applied statistics from the University of California, Riverside. He has been an associate editor of Journal of the American Statistical Association since 1991 and an associate editor of Entropy: An International and Interdisciplinary Journal of Entropy and Information Studies, and served as a vice president of the International Association for Statistical Computing for 1999–2001. His research interests are in the areas of informationtheoretic and Bayesian statistics and their applications in management research and decision problems. He has published in statistics, econometrics, marketing, and management science fields. His papers have appeared in Journal of the American Statistical Association, Journal of the Royal Statistical Society, Biometrika, Journal of Econometrics, Marketing Science, Operations Research, European Journal of Operational Research, and Decision Sciences. In recognition of his contributions to statistical theory and applications in functional areas of business, he was named a Fellow of the American Statistical Association.

512


Masoud Yasai-Ardekani is a professor of strategic management at the School of Business Administration, University of Wisconsin–Milwaukee. He holds a bachelor’s degree in electrical engineering from the Imperial College of Science and Technology, University of London, a master’s degree in administrative sciences, and a PhD in management studies from the Graduate Business Center, City University, London. His research focuses on designs for strategic planning processes and their effectiveness; performance implications of strategy-environment alignments; strategic and structural responses to environments; and management of strategic change and turnaround. He has published his research in journals such as Academy of Management Journal, Academy of Management Review, Strategic Management Journal, IEEE Transactions on Engineering Management, Journal of Management Studies, Journal of Management, Decision Sciences, MIS Quarterly, and Management Science. Fatemeh (Mariam) Zahedi is Wisconsin Distinguished Professor, MIS Area at the School of Business, University of Wisconsin–Milwaukee. She received her doctoral degree from Indiana University. Her present areas of research include IS quality and satisfaction, e-commerce and Web interface design, intelligent interface, IS design (for components, health networks, and maintenance), decision support systems, and policy and decision analysis. She has published more than 40 papers in major refereed journals, including MIS Quarterly, Information Systems Research, Decision Sciences, IEEE Transactions on Software Engineering, IEEE Transactions on Professional Communications, Decision Support Systems, IIE Transactions, European Journal of Operations Research, Operations Research, Computers and Operations Research, Journal of Review of Economics and Statistics, Empirical Economics, Socio-Economic Planning Sciences, Interfaces, and others. She has numerous publications in conference proceedings, and is the author of two books: Quality Information Systems and Intelligent Systems for Business: Expert Systems with Neural Network. Dr. Zahedi serves on the editorial board of a number of journals and has a number of years of consulting and managerial experiences in developing information systems and performing policy analysis.

Multiple Conceptualizations of Small Business Web Use and Benefit

Multiple Conceptualizations of Small Business Web Use and Benefit

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