Inter-Organizational Information Systems in the Internet Age
|
| < Day Day Up > |
|
Most researchers (Cavaye & Cragg, 1995; Reich & Benbasat, 1990; Rogers, 1995) agree that technology adoption occurs in stages. A number of stages have been proposed, with adoption and diffusion the most common (Rogers, 1995).
Technology adoption is characterized by actions related to learning about an innovation. These include collecting information, building knowledge, examining relevance and evaluating appropriateness. Technology adoption culminates in an innovation adoption decision. Rogers (1995) distinguished between technology adoption and diffusion stating that it is possible for an organization to adopt an innovation and not use it.
Technology diffusion involves an innovation’s implementation and use spreading over time. Technology diffusion studies focus on why and how an innovation’s use spreads and innovation characteristics lead to widespread acceptance. Many diffusion studies have use as the dependent variable. As such, in the review of these studies, we specifically cite “use” as the dependent variable rather than diffusion.
Table 2 categorizes IOIS research focusing on technology adoption and technology diffusion. We organize the table alphabetically by adoption/diffusion phenomenon. Studies focusing on B2B EC appear first, followed by EDI and then other IOISs. We include the power of the independent variables in parentheses in the significant independent variables column. Only Grover’s work in 1993 reported this information.
| Authors | Facilitators of Adoption and Diffusion (Significant Independent Variables) | Adoption/ Diffusion Phenomenon (Dependent Variable) | Research Method | Data Source | Theoretical Base |
|---|---|---|---|---|---|
| Deeter-Schmelz, Bizzari, Graham, & Howdyshell (2001) | Supplier support and communication convenience | B2B EC adoption | Survey | 222 members of the National Association of Purchasing Managers | Innovation diffusion theory |
| Ranganathan, Teo, Dhaliwal, Ang, & Hyde (2001) | Top management support, organizational change, strategy-related, project management, valuation, internal information technology, external information technology, collaboration, and external business environment | B2B EC deployment | Survey | 100 firms in Singapore | Innovation diffusion theory |
| Case study | Information technology executives | ||||
| Hope, Hermanek, Schlemmer, & Huff(2001) | Clear e-business vision, customer readiness and technological awareness, top management support, creative managerial thinking, information sharing and open communication, system marketing and promotion, staff skilled in technical and business issues, appropriate timing of project startup, clear and certain legislative and policy environment, current technology, and external expertise | B2B EC diffusion | Case study | 5 medium-sized companies in the transportation and logistics industry of New Zealand | None |
| Han & Noh (1999–2000) | System stability and data security | B2B EC use | Survey | 325 people with EC experience | None |
| Tabor (2001) | Customer-focused approach, easy-to-use technology, leadership, consistent goals and strategy, culture supporting innovation, relative advantage, product equity/trust, innovative characteristics, management commitment, team composition, core competence, project management, and technology performance | B2B EC use | Case study | A major U.S. airline | None |
| Premkumar, Ramamurthy, & Nilakanta (1994) | Relative advantage, technical compatibility, cost, and duration | EDI adaptation, internal diffusion, and external diffusion | Survey | Information systems and sales/ purchasing executives from 201 firms | Innovation diffusion theory |
| Bouchard (1993) | Key business partner implementation and key business partner mandating organization’s implementation | EDI adoption | Survey | 75 retail suppliers | Innovation diffusion theory and critical mass theory |
| Case study | 2 retail suppliers | ||||
| Computer- supported interviews | 10 retail suppliers | ||||
| Hart & Saunders (1997) | Power | EDI adoption | Theoretical framework and case study | 1 retail firm | Power |
| Premkumar et al. (1997) | Firm size, top management support, competitive pressure, and customer support | EDI adoption | Survey | 181 firms in the trucking industry | Innovation diffusion theory and resource dependency theory |
| Saunders & Clark (1992) | Cost | EDI adoption | Survey | 192 vendors | Power |
| Williams (1994) | Demand uncertainty, power, and relative advantage | EDI adoption | Interviews | Firms with and without channel power, consultants, EDI third-party providers | Organizational theory and power theory |
| Survey | 156 from customers, suppliers, shippers, and carriers, who are members of the Council of Logistics Management | ||||
| Premkumar & Ramamurthy (1995) | Internal need, top management support, competitive pressure, and exercised power | EDI adoption decision modes (proactive vs. reactive) | Survey | Information systems and sales/ purchasing executives from 201 firms | Power and social exchange theory |
| Teo, Tan, & Wei (1995) | Complexity, operational risk, strategic risk, and observability | EDI adoption intention | Survey | 112 senior managers of firms listed in the Singapore stock exchange | Innovation diffusion theory |
| Chwelos et al. (2001) | External pressure, perceived benefits, and readiness | EDI adoption intentions | Survey | 268 small to medium organizations in the Purchasing Managers Association of Canada | Critical mass theory, innovation diffusion theory, and power |
| Iacovou, Benbasat, & Dexter (1995) | Perceived benefits and external pressure | EDI adoption of small organizations | Case study | 7 managers of small organizations | Innovation diffusion theory and resource dependency theory |
| O'Callaghan, Kaufmann, & Konsynski (1992) | Perceived relative advantage | EDI computer-based interface offerings, adoption decision | Field interviews | 10 members of the Independent Insurance Agents of America | Innovation diffusion theory |
| Focus group | 1 member of the Independent Insurance Agents of America | ||||
| Surveys | 1242 members of the Independent Insurance Agents of America | ||||
| Damsgaard & Lyytinen (1998) | Inter-organizational collaboration, herd effect, environment favoring cooperation, trade organization support, and infrastructure | EDI diffusion | Field study | 9 organizations from 3 industries in Finland | Institutional theory and innovation diffusion theory |
| Cox & Ghoneim (1996) | Coherent strategy, top management support, meeting needs, review and continuous improvement, and integration into core business activities | EDI implementation and EDI integration benefits and barriers | Survey | 85 organizations from a variety of industries | None |
| Case study | 1 | ||||
| Premkumar & Ramamurthy (1995) | Proactive adoption | EDI implementation outcomes and effect of decision modes (proactive vs. reactive) | Survey | Information systems and sales/ purchasing executives from 201 firms | Power and social exchange theory |
| Crook & Kumar (1998) | Organizational context (organizational size, information technology capability, senior management commitment), environmental context (industry experience with EDI, nature of suppliers, nature of customers), external pressure, system benefits, and implementation support | EDI use | Case study using grounded theory | 4 organizations in four different industries | None |
| Hart & Saunders (1997) | Trust | EDI use | Theoretical framework and case study | 1 retail firm | Power |
| Grewal, Corner, & Mehta (2001) | Emphasizing efficiency motivations, deemphasizing legitimacy motivations, and information technology capabilities | Electronic market use | Survey | 306 participants in the Polygon marketplace | Institutional theory, transaction cost theory, and motivationability-framework |
| Cavaye & Cragg (1995) | Champion existence, extension of existing systems, experienced information systems staff, perceived customer need, user participation, low system cost, good marketing programs, and user technological awareness | Customer-oriented IOIS adoption | Case study | 9 profit-oriented firms selling a product/service | Innovation None |
| Reich & Benbasat (1990) | Product champion, top management support, proactive information systems function, external pressure, customer involvement, marketing the system, and perceived need | Customer-oriented strategic system adoption | Case study | 11 customer-oriented strategic systems, interviews with line and information systems management | None |
| Grover (1993) | Top management support (0.91), champion existence (0.69), compatibility (0.92), complexity (less)(-0.56), proactive role of information technology group (0.84), large size (0.67), existing information technology infrastructure (0.63), strategic information systems planning (0.47), and management risk-taking propensity (0.45) | Customer-based IOIS adoption | Survey | 226 senior executives | None |
| Runge (1985, 1988) | Product champion, customer involvement in development process, marketing efforts, extension of existing information systems, and ignoring or circumventing normal information system planning and approval processes | Telecommunicatio n- based information system adoption | Case study | 35 systems in Britain | None |
| Sabherwal & Vijayasarathy (1994) | Product information intensity, value chain information intensity, and environmental uncertainty | Telecommunicationuse between customers and suppliers | Survey | 86 senior executives from medium-sized companies | None |
Table 2 indicates that a number of theories underpin IOIS research, with innovation diffusion theory the most common. Rogers’ (1995) innovation diffusion theory applies to the study of adoption (decision to use) and diffusion (extent of implementation) of innovations within organizations by identifying innovation attributes influencing adoption. Innovation diffusion theory posits a user’s technology adoption decision as a rational choice based on perceived technological characteristics such as relative advantage, compatibility, trialability, observability and complexity.
Power theories also underpin IOIS studies. There are several notions on power. Emerson (1962) did some of the first work in power with social exchange theory. According to Emerson (1962), social exchange theory notes that “the dependence of actor X on actor Y is (a) directly proportional to X’s motivational investment in goals mediated by Y, and (b) inversely proportional to the availability of those goals to X outside the Y–X relationship” (p. 32). Thompson (1967) has a similar observation on power. Thompson (1967, p. 31) noted that an organization is dependent on some element of its task environment (a) in proportion to the organization’s need for resources or performances which that element can provide, and (b) in inverse proportion to the ability of other elements to provide the same resource or performance” (p. 31).
Another notion on power is resource dependency theory (Pfeffer, 1987; Pfeffer & Salancik, 1978). Resource dependency theory posits that an organization’s environment is unstable and organizations try to reduce vulnerabilities and increase power relative to their constituents in order to survive. The degree to which an organization is dependent upon external resources is determined by the resource’s importance, the organization’s discretion over it and whether alternatives exist. In applying this theory to technology adoption, resource dependency theory explains that inter-organizational relationships may not be based on efficiency. Rather, inter-organizational relationships may be formed to reduce environmental uncertainty and may be the result of having power and influence over dependent organizations.
Many IOIS studies cite the importance of achieving critical mass. Critical mass theory (Dybvig & Spatt, 1983; Granovetter, 1978, 1985; Markus, 1990; Oliver, Marwell, & Teixeira, 1985; Rohlfs, 1974) posits that some innovations require collaboration among potential adopters for any adopter to benefit. It further posits that if a network cannot obtain an installed base equal to the largest equilibrium network size, it will have to exit from the market if it cannot surpass critical mass and become self-sustaining. Critical mass theorists believe collective action participation is based on perceptions of what the group is doing. Participation decisions are influenced by who has participated, how many have participated and how much others have contributed.
A few IOIS studies mention institutional and organizational behavior theories. Institutional theory suggests that in efforts to survive, organizations strive to satisfy external stakeholders by adopting rules and practices that may not necessarily increase technical efficiency but increase legitimacy in external stakeholders’ eyes (DiMaggio & Powell, 1983; Meyer & Rowan, 1977). Organizational behavior theory (Thompson, 1967) suggests that organizational variables, such as size, influence technological innovation adoption. IOIS research studies find that a large firm size facilitates EDI adoption (Premkumar et al., 1997), EDI use (Crook & Kumar, 1998) and customer-based IOIS adoption (Grover, 1993).
|
| < Day Day Up > |
|