Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formulate a unified model that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted R 2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar

The growing interest in Structured Equation Modeling (SEM) techniques and recognition of their importance in IS research suggests the need to compare and contrast different types of SEM techniques so that research designs can be appropriately selected. After assessing the extent to which these techniques are currently being used in IS research, the article presents a running example which analyzes the same dataset via three very different statistical techniques. It then compares two classes of SEM: covariance-based SEM and partial-least-squares-based SEM. Finally, the article discusses linear regression models and offers guidelines as to when SEM techniques and when regression techniques should be used. The article concludes with heuristics and rule of thumb thresholds to guide practice, and a discussion of the extent to which practice is in accord with these guidelines.

Starting in September of 1997, David Gefen will be an assistant professor at the Drexel

Our trust conceptualizations have benefited from discussions with Ellen Berscheid and Larry Cummings of the University of Minnesota. The authors also thank three anonymous reviewers from the Organizational Behavior division of the 1996 meeting of the Academy of Management for their comments on an earlier version of this paper. THE MEANINGS OF TRUST What does the word ‘trust ’ mean? Scholars continue to express concern regarding their collective lack of consensus about trust’s meaning. Conceptual confusion on trust makes comparing one trust study to another problematic. To facilitate cumulative trust research, the authors propose two kinds of trust typologies: (a) a classification system for types of trust, and (b) definitions of six related trust types that form a model. Some of the model’s implications for management are also outlined. 2 THE MEANINGS OF TRUST “...trust is a term with many meanings. ” (Williamson, 1993: 453) “Trust is itself a term for a clustering of perceptions. ” (White, 1992: 174) Scholars and practitioners widely acknowledge trust's importance. Trust makes cooperative endeavors happen (e.g., Arrow, 1974; Deutsch, 1973; Gambetta, 1988). Trust is a key to positive interpersonal relationships in

ABSTRACT: This paper aims to predict consumer acceptance of e-commerce by proposing a set of key drivers for engaging consumers in on-line transactions. The primary constructs for capturing consumer acceptance of e-commerce are intention to transact and on-line transaction behavior. Following the theory of reasoned action (TRA) as applied to a technology-driven environment, technology acceptance model (TAM) variables (perceived usefulness and ease of use) are posited as key drivers of e-commerce acceptance. The practical utility of TAM stems from the fact that e-commerce is technology-driven. The proposed model integrates trust and perceived risk, which are incorporated given the implicit uncertainty of the e-commerce environment. The proposed integration of the hypothesized independent variables is justified by placing all the variables under the nomological TRA structure and proposing their interrelationships. The resulting research model is tested using data from two empirical studies. The first, exploratory study comprises three experiential scenarios with 103 students. The second, confirmatory study uses a sample of 155 on-line consumers. Both studies strongly support the e-commerce acceptance model by validating the proposed hypotheses. The paper discusses the implications for e-commerce theory, research, and practice, and makes several suggestions for future research.

Innovation diffusion theory provides a useful perspective on one of the most persistently challenging topics in the IT field, namely, how to improve technology assessment, adoption and implementation. For this reason, diffusion is growing in popularity as a reference theory for empirical studies of information technology adoption and diffusion, although no comprehensive review of this body of work has been published to date. This paper presents the results of a critical review of eighteen empirical studies published during the period 1981-1991. Conclusive results were most likely when the adoption context closely matched the contexts in which classical diffusion theory was developed (for example, individual adoption of personal-use technologies) or when researchers extended diffusion theory to account for new factors specific to the IT adoption context under study. Based on classical diffusion theory and other recent conceptual work, a framework is developed to guide future research in IT diffusion. The framework maps two classes of technology (ones that conform closely to classical diffusion assumptions versus ones that do no0 against locus of adoption (individual versus organizational), resulting in four IT adoption contexts. For each adoption context, variables impacting adoption and diffusion are identified. Additionally, directions for future research are discussed. 1.

The Technology Acceptance Model (TAM) represents an important theoretical contribution toward understanding IS usage and IS acceptance behaviors [6, 19]. However, as noted by several IS researchers [cf: 4, 5, 6, 9, 14], TAM is incomplete in one important respect: it doesn't account for social influence in the adoption and utilization of new information systems. Davis [4] and Davis et al. [6] noted that it is important to account for subjective norm (SN), the construct denoting social influence. However, they observed that the conceptualization of SN based on TRA (Theory of Reasoned Action) has theoretical and psychometric problems. Specifically, they observed that it is difficult to distinguish if usage behavior is caused by the influence of referents on one's intent or by one's own attitude. They suggested that this problem may be circumvented by using an alternative theoretical basis for conceptualizing SN, specifically in terms of Kelman's [10, 11] processes of social influence (compliance, identification and internalization). Within the context of organizational enterprisewide implementation and adoption of collaboration and communication technologies, this study establishes theoretical and empirical bases for the above conceptualization originally suggested by Davis and his colleagues. The construct of social influence is operationalized in terms of Kelman's processes of internalization, identification and compliance. Analyses of field study data provide evidence of the reliability and validity of the proposed constructs, factor structures and measures. The findings enable future researchers to account for social influence in further investigating TAM.

Introduction The task of deciding when and how to innovate is not an easy one. Consider the following managerial quandaries: . A CIO has joined a firm that lags in the adoption of emerging information technologies. He wonders: just how innovative should this firm be going forward, and what can be done to position it to be more willing and able to assume the challenge of early adoption? . A VP of marketing resides in a firm that generally leads in IT innovation, and must decide whether to endorse the immediate adoption of a particular innovation with major implications for marketing strategy. She wonders: are her firm's needs in this area and "readiness" to adopt sufficient to justify taking the lead with this specific innovation? If so, how should the assimilation process be managed? . A product manager must design a deployment strategy for an innovative software development tool. He wonders: how fast can this technology diffu

Information systems success is one of the most widely used dependent variables in information systems research. In this paper, we identify a range of measures that can be used to assess the success of open source software (OSS) projects. We identify measures based on a review of the literature, a consideration of the OSS development process and an analysis of the opinions of OSS developers. For each measure, we provide examples of how they might be used in a study of OSS development.

The authors would like to thank the participants of the 1991 Queen's-HEC workshop and two anonymous reviewers and the Editor in chief of JMIS for their helpful comments on an earlier version of this paper. This study was supported financially by École des Hautes Études Commerciales de Montréal and the University of California at Irvine. Survey research is believed to be well understood and applied by MIS scholars. It has been applied for several years, it is well defined, and it has precise procedures which, when followed closely, yield valid and easily interpretable data. Our assessment of the use of survey research in the MIS field between 1980 and 1990 indicates that this perception is at odds with reality. Our analysis indicates that survey methodology is often misapplied and is plagued by five important weaknesses: (1) single method designs where multiple methods are needed, (2) unsystematic and often inadequate sampling procedures, (3) low response rates, (4) weak linkages between units of analysis and respondents, and (5) over reliance on cross-sectional surveys where longitudinal surveys are really needed. Our assessment also shows that the quality of survey research varies considerably among studies of different purposes: explanatory studies are of good quality overall, exploratory and descriptive studies are of moderate to poor quality.