Beliefs, attitudes, and intentions are important factors in the adoption of computer technologies. While contemporary representations have focused on explaining the act of using computers, the role of learning to use the computer needs to be better understood within the overall adoption process. Inadequate learning can curtail the adoption and use of a potentially productive system. We introduce a new theoretical model, the theory of trying, in which computer learning is conceptualized as a goal determined by three attitude components: attitude toward success, attitude toward failure, and attitude toward the process of goal pursuit. Intentions to try and actual trying are the theoretical mechanisms linking these goal-directed attitudes to goal attainment. An empirical study is conducted to ascertain the construct validity and utility of the new theory within the context of the adoption of a word processing package. Specifically, we examine convergent validity, internal consistency reliability, stability, discriminant validity, criterion related validity, predictive validity, and nomological validity in a longitudinal field study of 107 users of the program. The new theory is compared to two models: the theory of reasoned action from the field of social psychology and the technology acceptance model, recently introduced in the management literature. Overall, the findings stress the importance of scrutinizing the goals of decision makers and their psychological reactions to these goals in the prediction of the adoption of computers.

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.

The nature of the attitude-behavior relation was investigated by use of structural equation models in a cross-lagged panel design. The theory of reasoned action and an augmented version of the theory, which included the frequency and recency of past behavior as covariates, were tested. Respondents were 254 undergraduates who provided behavioral and psychological reactions over two points in time to two kinds of goal-directed behaviors: event-planned goals (i.e., trying to lose weight) and event-triggered goals (i.e., initiating a conversation with an attractive stranger). The findings show that the theory of reasoned action as classically formulated explains trying to lose weight well but initiating a conversation poorly. Further, the introduction of frequency and recency effects brings into question predictions under the theory of reasoned action. Theoretical implications of the results are discussed. A weighted least squares method is applied to the asymptotic covariance matrix based on appropriate polychoric and tetrachoric correlations. Variates are assumed measured on only ordinal scales.

The present study presents evidence that standard 7-point behavioral intention (BI) scales, including those recommended by Fishbein and Ajzen (e.g., Ajzen & Fishbein, 1980), actually measure behavioral expectation (BE) much of the time. Three experiments, in which a total of 203 American students were used as subjects, spanned two distinct methodologies for assessing what subjects meant by their BI scale responses, two different 7-point intention scale formats, and 16 different target behaviors. For the behaviors studied, the subjects gave their BEs in response to BI questions 43.6% of the time overall, ranging from 12.8% (smoking cigarettes) to 72.7% (spill food or drink).

Before models are compared empirically, it must be ascertained that they are indeed comparable at a theoretical level. Unfortunately, the literature does not contain a standard procedure for making theoretical comparisons of models. The authors attempt to fill this lacuna by suggesting a scheme for checking the compatibility among models prior to empirical comparison.

Researchers have widely accepted the argument that the Fishbein model generally predicts better if the intention measure corresponds to the predicted behavior with respect to target, action, context, and time frame. Although the model is routinely applied in situations involving choice among multiple alternatives, researchers frequently overlook the need to measure intentions so that they correspond to the multiple target behaviors that constitute the subject's salient choice set. The present study was designed to demonstrate the effect of choice set on the measurement of behavioral intentions of American students. The data support the argument that Fishbein's intention measure is quite sensitive to the choice set involved, perhaps one reason why intention measures sometimes fail to predict behavior accurately.

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.

The role concept has been largely ignored in recent marketing channels literature. The authors address this issue from the wholesaling perspective, conceptualising the wholesaler′s role in terms of six marketing functions that they perform for manufacturers. Findings from a large sample mail survey of wholesaler and manufacturer members of the National Association of Wholesaler‐Distributors empirically support the authors′ expectations that wholesalers are perceived as important performers of each function by both wholesalers and manufacturers; that both wholesalers and manufacturers think the reliance on wholesalers to perform these functions has been increasing and will increase even further in the future; and that wholesalers perceive themselves to be more important performers of each marketing function than do manufacturers. The implications of these findings for channel roles, power, and conflict issues are then discussed.

Two meta-analyses were conducted to Investigate the effectiveness of the Fishbein and Ajzen model in research to date. Strong overall evidence for the predictive utility of the model was found. Although numerous instances were identified in which researchers overstepped the boundary conditions initially proposed for the model, the predictive utility remained strong across conditions. However, three variables were proposed and found to moderate the effectiveness of the model. Suggested extensions to the model are discussed and general directions for future research are given.

Consumer behavior has been extensively researched by economists and social psychologists under the labels of demand theory and attitude theory, respectively. These schools have historically ignored each other's contributions, largely because demand theory has focused on constrained choice while attitude theory has not. However, Warshaw, Sheppard and Hartwick have recently extended attitude theory so that constraints and choice are now endogenous to the model. The points of tangency and distinctiveness between this paradigm and that version of demand theory which is most pertinent to brand choice (i.e., Lancaster economics) are critically discussed, providing a frame of reference for future research at the boundary of economics and social psychology.

"A Field Application of the Fishbein and Ajzen Intention Model." The Journal of Social Psychology, 126(1), pp. 135–136

Social psychologists have extensively researched behavioral intention and its relation to future behavior, usually within the framework of M. Fishbein and I. Ajzen's (1975, Belief, attitude, intention and behavior: An introduction to theory and research, Reading, MA: Addison-Wesley) theory of reasoned action. However, the field has confounded two separate constructs while investigating intention: behavioral intention (BI) and what P. R. Warshaw, B. H. Sheppard, and J. Hartwick (in press, in R. Bagozzi (Ed.), Advances in marketing communication, Greenwich, CT: JAI Press) have coined behavioral expectation (BE), which is the individual's self-prediction of his or her future behavior. In this paper we define both constructs and explain how they differ in terms of the processes by which they are formed, their roles in determining behavior, and their utilities as behavioral predictors. We propose that behavioral expectation is the more accurate overall predictor since many common behaviors are unreasoned (i.e., mindless or habitual) behaviors, goal-type actions, or behaviors where the individual expects his or her intention to change in a foresseable manner. These are all cases where present intention (BI) is not the direct determinant of behavior but where the individual may be capable of appraising whatever additional determinants exist and of including them within his or her behavioral expectation. A study (N = 197) is reported in which student subjects received either a BE (n = 113) or a BI (n = 84) version of a questionnaire pertaining to their performance of 18 common behaviors. Overall, behavioral expectation was the better predictor of self-reported performance.

Researchers have largely overlooked the distinction between behavioral intention and behavioral expectation as predictors of one's own behavior. Moreover, the distinction between purely volitional behavior and behavioral goals, the latter of which may be impeded by such nonvolitional factors as lack of ability, lack of opportunity, habit, and environmental impediments, has also been blurred in the literature. Behavioral expectation is theorized to be based on a cognitive appraisal of one's behavioral intention and all other behavioral determinants of which one is aware. The present study argues and gives evidence that, although behavioral expectation and behavioral intention may have similar predictive accuracy for volitional behaviors, behavioral expectation is adequate, but behavioral intention may be inadequate for prediction of the accomplishment of behavioral goals.

While Nisbett and Wilson (1977) and other researchers (e.g., Ericsson & Simon, 1980) argue about the accuracy of self-reported data, the distinction between the ability and the willingness to accurately self-report has been somewhat overlooked. Namely, issues of self-understanding must be differentiated from those of self-presentation. Further, self-understanding that relates to specific behavioral domains must be differentiated from general self-understanding. The present article argues that general self-understanding is a potentially important individual difference variable that has been neglected in the literature. The relationship between general self-understanding and the accuracy of self-reported behavioral expectations is discussed and provocative preliminary findings are reported that suggest that further research on the topic is warranted.