Little research has examined the impacts of enterprise resource planning (ERP) systems implementation on job satisfaction. Based on a 12-month study of 2,794 employees in a telecommunications firm, we found that ERP system implementation moderated the relationships between three job characteristics (skill variety, autonomy, and feedback) and job satisfaction. Our findings highlight the key role that ERP system implementation can have in altering well-established relationships in the context of technology-enabled organizational change situations. This work also extends research on technology diffusion by moving beyond a focus on technology-centric outcomes, such as system use, to understanding broader job outcomes.

Managers in modern organizations are confronted with ever-increasing volumes of information that they must evaluate when making a decision. Data warehousing and data mining technologies have given managers a number of valuable tools that can help them store, retrieve, and analyze information contained in large databases; however, maximizing user performance with these tools remains a challenge for information systems professionals. One important and under-explored aspect of the effectiveness of these tools is the design of the query interface. In this study, we compared the use of visual and text-based interfaces on both low and high complexity tasks. Results demonstrated that decision maker performance was more accurate using the text-based interface when task complexity was low; however, decision makers using the visual interface performed better when task complexity was high. In addition, decision makers’ subjective mental workload was significantly lower when using the visual interface, regardless of task complexity. In contrast to expectations, less time was needed to make a decision on low complexity tasks when using the visual interface, but those results were reversed under conditions of high task complexity. These results have important implications for the design of managerial decision-making systems, particularly in complex decision-making environments.

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 R2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar results (adjusted R2 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.