Computer systems cannot improve organizational performance if they aren't used. Unfortunately, resistance to end-user systems by managers and professionals is a widespread problem. To better predict, explain, and increase user acceptance, we need to better understand why people accept or reject computers. This research addresses the ability to predict peoples' computer acceptance from a measure of their intentions, and the ability to explain their intentions in terms of their attitudes, subjective norms, perceived usefulness, perceived ease of use, and related variables. In a longitudinal study of 107 users, intentions to use a specific system, measured after a one-hour introduction to the system, were correlated 0.35 with system use 14 weeks later. The intention-usage correlation was 0.63 at the end of this time period. Perceived usefulness strongly influenced peoples' intentions, explaining more than half of the variance in intentions at the end of 14 weeks. Perceived ease of use had a small but significant effect on intentions as well, although this effect subsided over time. Attitudes only partially mediated the effects of these beliefs on intentions. Subjective norms had no effect on intentions. These results suggest the possibility of simple but powerful models of the determinants of user acceptance, with practical value for evaluating systems and guiding managerial interventions aimed at reducing the problem of underutilized computer technology.information technology, user acceptance, intention models

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Previous research indicates that perceived usefulness is a major determinant and predictor of intentions to use computers in the workplace. In contrast, the impact of enjoyment on usage intentions has not been examined. Two studies are reported concerning the relative effects of usefulness and enjoyment on intentions to use, and usage of, computers in the workplace. Usefulness had a strong effect on usage intentions in both Study 1, regarding word processing software (β=.68), and Study 2, regarding business graphics programs (β=.79). As hypothesized, enjoyment also had a significant effect on intentions in both studies, controlling for perceived usefulness (β=.16 and 0.15 for Studies 1 and 2, respectively). Study 1 found that intentions correlated 0.63 with system usage and that usefulness and enjoyment influenced usage behavior entirely indirectly through their effects on intentions. In both studies, a positive interaction between usefulness and enjoyment was observed. Together, usefulness and enjoyment explained 62% (Study 1) and 75% (Study 2) of the variance in usage intentions. Moreover, usefulness and enjoyment were found to mediate fully the effects on usage intentions of perceived output quality and perceived ease of use. As hypothesized, a measure of task importance moderated the effects of ease of use and output quality on usefulness but not on enjoyment. Several implications are drawn for how to design computer programs to be both more useful and more enjoyable in order to increase their acceptability among potential users.

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We provide a comprehensive and user-friendly compendium of standards for the use and interpretation of structural equation models (SEMs). To both read about and do research that employs SEMs, it is necessary to master the art and science of the statistical procedures underpinning SEMs in an integrative way with the substantive concepts, theories, and hypotheses that researchers desire to examine. Our aim is to remove some of the mystery and uncertainty of the use of SEMs, while conveying the spirit of their possibilities.

Emotions are mental states of readiness that arise from appraisals of events or one’s own thoughts. In this article, the authors discuss the differentiation of emotions from affect, moods, and attitudes, and outline an appraisal theory of emotions. Next, various measurement issues are considered. This is followed by an analysis of the role of arousal in emotions. Emotions as markers, mediators, and moderators of consumer responses are then analyzed. The authors turn next to the influence of emotions on cognitive processes, which is followed by a study of the implications of emotions for volitions, goal-directed behavior, and decisions to help. Emotions and customer satisfaction are briefly explored, too. The article closes with a number of questions for future research.

Using a grounded theory approach, the authors investigate the nature and consequences of brand love. Arguing that research on brand love needs to be built on an understanding of how consumers actually experience this phenomenon, they conduct two qualitative studies to uncover the different elements ("features") of the consumer prototype of brand love. Then, they use structural equations modeling on survey data to explore how these elements can be modeled as both first-order and higher-order structural models. A higher-order model yields seven core elements: self-brand integration, passion-driven behaviors, positive emotional connection, long-term relationship, positive overall attitude valence, attitude certainty and confidence (strength), and anticipated separation distress. In addition to these seven core elements of brand love itself, the prototype includes quality beliefs as an antecedent of brand love and brand loyalty, word of mouth, and resistance to negative information as outcomes. Both the firstorder and higher-order brand love models predict loyalty, word of mouth, and resistance better, and provide a greater understanding, than an overall summary measure of brand love. The authors conclude by presenting theoretical and managerial implications.