User satisfaction is a longstanding determinant of information systems (IS) post-adoption continuance intention. Traditionally, user satisfaction has been conceptualized as a bipolar continuum, stretching from high dissatisfaction to high satisfaction, with neutrality as a midpoint. This view may limit the effectiveness of user satisfaction measurements as it implies that users cannot experience mixed feelings of satisfaction and dissatisfaction. However, users often have simultaneous mixed feelings. By considering user satisfaction and dissatisfaction as separate unipolar dimensions, each ranging from neutral to high, we address the limitation of the bipolar approach. Data collected from an online survey supports this unipolar perspective, enabling the representation of mixed feelings and mitigating the limitations of the bipolar view. Furthermore, both user satisfaction and dissatisfaction uniquely contribute to continuance intention, broadening our understanding of user post-adoption behavior. This study paves the way for future research on the antecedents and consequences of user satisfaction and dissatisfaction.

The interplay between decision-making and decision-support tools has proven puzzling for many years. One of the most popular decision-support tools, what-if analysis, is no exception. Decades of empirical studies have found positive, negative, and null effects. In this paper, we contrast the marginal-analysis decision-making strategy enabled by what-if with the anchoring and adjustment decision-making strategies prevalent among unaided decision makers. By using an aggregate production planning decision task, we develop a Monte Carlo simulation to model 1000 independent what-if decision-making episodes across a myriad of conditions. Results mirror and explain seemingly contradictory findings across multiple prior experiments. Thus, this paper formalizes a simulation approach that expands the scope of previous findings regarding unaided versus what-if analysis aided decision making and suggests that relative performance is quite sensitive to task conditions. In this light, then, performance effect differences in past research are to be expected. While our analysis involves a single task context, the larger and more important point is that, even within a single task context, performance differences between unaided and aided decision making are emergent.

Many organizations attempt to deploy methodologies intended to improve software development processes. However, resistance by individual software developers against using such methodologies often obstructs their successful deployment. To better explain why individual developers accept or resist methodologies, five theoretical models of individual intentions to accept information technology tools were examined. In a field study of 128 developers in a large organization that implemented a methodology, each model explained significant variance in developers' intentions to use the methodology. Similar to findings from the tool adoption context, we found that, if a methodology is not regarded as useful by developers, its prospects for successful deployment may be severely undermined. In contrast to the typical pattern of findings in a tool context, however, we found that methodology adoption intentions are driven by: 1) the presence of an organizational mandate to use the methodology, 2) the compatibility of the methodology with how developers perform their work, and 3) the opinions of developers' coworkers and supervisors toward using the methodology. Collectively, these results provide surprising new insights into why software developers accept or resist methodologies and suggest what software engineering managers might do to overcome developer resistance.