This article discusses the role of commonly used neurophysiological tools such as psychophysiological tools (e.g., EKG, eye tracking) and neuroimaging tools (e.g., fMRI, EEG) in Information Systems research. There is heated interest now in the social sciences in capturing presumably objective data directly from the human body, and this interest in neurophysiological tools has also been gaining momentum in IS research (termed NeuroIS). This article first reviews commonly used neurophysiological tools with regard to their major strengths and weaknesses. It then discusses several promising application areas and research questions where IS researchers can benefit from the use of neurophysiological data. The proposed research topics are presented within three thematic areas: (1) development and use of systems, (2) IS strategy and business outcomes, and (3) group work and decision support. The article concludes with recommendations on how to use neurophysiological tools in IS research along with a set of practical suggestions for developing a research agenda for NeuroIS and establishing NeuroIS as a viable subfield in the IS literature.
This paper introduces the idea of drawing upon the cognitive neuroscience literature to inform IS research (herein termed "NeuroIS"). Recent advances in cognitive neuroscience are uncovering the neural bases of cognitive, emotional, and social processes, and they offer new insights into the complex interplay between IT and information processing, decision making, and behavior among people, organizations, and markets. The paper reviews the emerging cognitive neuroscience literature to propose a set of seven opportunities that IS researchers can use to inform IS phenomena, namely (1) localizing the neural correlates of IS constructs, (2) capturing hidden mental processes, (3) complementing existing sources of IS data with brain data, (4) identifying antecedents of IS constructs, (5) testing consequences of IS constructs, (6) inferring the temporal ordering among IS constructs, and (7) challenging assumptions and enhancing IS theories. The paper proposes a framework for exploring the potential of cognitive neuroscience for IS research and offers examples of potentially fertile intersections of cognitive neuroscience and IS research in the domains of design science and human-computer interaction. This is followed by an example NeuroIS study in the context of e-commerce adoption using fMRI, which spawns interesting new insights. The challenges of using functional neuroimaging tools are also discussed. The paper concludes that there is considerable potential for using cognitive neuroscience theories and functional brain imaging tools in IS research to enhance IS theories.
Computer skills are key to organizational performance, and past research indicates that behavior modeling is a highly effective form of computer skill training. The present research develops and tests a new theoretical model of the underlying observational learning processes by which modeling-based training interventions influence computer task performance. Observational learning processes are represented as a second-order construct with four dimensions (attention, retention, production, and motivation). New measures for these dimensions were developed and shown to have strong psychometric properties. The proposed model controls for two pretraining individual differences (motivation to learn and self-efficacy) and specifies the relationships among three training outcomes (declarative knowledge, post-training self-efficacy, and task performance). The model was tested using PLS on data from an experiment (N=95) on computer spreadsheet training. As hypothesized, observational learning processes significantly influenced training outcomes. A representative modeling-based training intervention (retention enhancement) significantly improved task performance through its specific effects on the retention processes dimension of observational learning. The new model provides a more complete theoretical account of the mechanisms by which modeling-based interventions affect training outcomes, which should enable future research to systematically evaluate the effectiveness of a wide range of modeling-based training interventions. Further, the new instruments can be used by practitioners to refine ongoing training programs.
Seeking to improve software development, many organizations attempt to deploy formalized methodologies. This typically entails substantial behavioral change by software developers away from previous informal practices toward conformance with the methodology. Developers' resistance to such change often results in failure to fully deploy and realize the benefits of the methodology. The present research draws upon theories of intention formation and innovation diffusion to advance knowledge about why developers accept or resist following methodologies. Results from a field study within a large organization indicate that developers' intentions are directly influenced by their perceptions of usefulness, social pressure, compatibility, and organizational mandate. This pattern of intention determinants is quite different from that typically observed in studies of information technology tool adoption, revealing several key differences between the domains of tool versus methodology adoption. Specifically, although organizational mandate had a significant effect on intentions, the strength of its direct influence was the lowest among the four significant constructs, and usefulness, compatibility, and social pressure all influenced intentions directly, above and beyond the effects of organizational mandate. The findings suggest, contrary to popular belief, that an organizational mandate is not sufficient to guarantee use of the methodology in a sustained manner.
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[sup 2] of 69 percent). UTAUT was then confirmed with data from two new organizations with similar results (adjusted R[sup 2] of 70 percent). UTAUT thus provides a useful tool for managers needing to assess the likelihood of success for new technology introductions and helps them understand the drivers of acceptance in order to proactively design interventions (including training, marketing, etc.) targeted at populations of users that may be less inclined to adopt and use new systems. The paper also makes several recommendations for future research including developing a deeper understanding of the dynamic influences studied here, refining measurement of the core constructs used in UTAUT, and understanding the organizational outcomes associated with new technology use.
Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions for these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited high convergent, discriminant, and factorial validity. Perceived usefulness was significantly correlated with both self-reported current usage (r=.63, Study 1) and self-predicted future usage (r =.85, Study 2). Perceived ease of use was also significantly correlated with current usage (r=.45, Study 1) and future usage (r=.59, Study 2). In both studies, usefulness had a significantly greater correlation with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecedent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.