Papers

Research on individual-level technology adoption is one of the most mature streams of information systems (IS) research. In this paper, we compare the progress in the area of technology adoption with two widely-researched streams in psychology and organizational behavior: theory of planned behavior and job satisfaction. In addition to gauging the progress in technology adoption research, this allows us to identify some fruitful areas for future research. Based on our comparison, we conclude that there has been excellent progress in technology adoption research. However, as a next step, we call for research focused on interventions, contingencies, and alternative theoretical perspectives (to the largely social psychology-based technology adoption research. Also, we believe it would be important to use the comparisons discussed here as a basis to develop a framework-driven set of future research directions to guide further work in this area.

This article recognizes the maturity of individual-level technology-adoption research and suggests three broad future research directions. They are: (i) business process change and process standards, (ii) supply-chain technologies, and (iii) services. Each of these areas is identified based on the topics likely of interest to the readers of the “Decision Sciences” by closely examining “Decision Sciences'” editorial mission and the recent research published in it. Within each of these three different broad topic areas, a few different specific directions are identified. The directions outlined here are not meant to be exhaustive but rather potential directions that can result in a theoretical contribution to individual-level technology-adoption research and the specific topic area.

Recently, the model of adoption of technology in households (MATH) was developed and tested in the context of household personal computer (PC) adoption. In this study, we apply MATH to predict PC use. We conducted a nationwide survey including 370 households that owned at least one PC. Results indicate that attitudinal beliefs are extremely important in determining use of a PC in the household. In contrast to previous work examining adopters, normative and control beliefs were not significant in predicting use. Furthermore, several determinants of adoption that were important at different stages of the household lifecycle were found non-significant in predicting use for the same stages of the household lifecycle. Overall, the results demonstrate that the belief structure for household PC use is different from that of household PC adoption. Further, the results provide additional evidence regarding the importance of including household lifecycle in studies of household technology adoption and use.

This paper examines three specific manifestations of time—anticipation (proximal vs. distal), prior experience with the behavior, and frequency (episodic vs. repeat)—as key contingencies affecting the predictive validity of behavioral intention, perceived behavioral control, and behavioral expectation in predicting behavior. These three temporal contingencies are examined in two longitudinal field studies: (1) study 1—a 6-month study of PC purchase behavior among 861 households and (2) study 2—a 12-month study among 321 employees in the context of a new technology implementation in an organization. In study 1, where the episodic behavior of PC purchase was examined, we found that increasing anticipation (i.e., more distal) weakened the relationship between behavioral intention and behavior and strengthened the relationship between behavioral expectation and behavior. In contrast, increasing experience strengthened the relationship between behavioral intention and behavior and weakened the relationship between behavioral expectation and behavior. In study 2, where the repeat behavior of technology use was examine, we found two significant three-way interactions—(1) the relationship between behavioral intention and behavior is strongest when anticipation is low (i.e., proximal) and experience is high; and (2) the relationship between behavioral expectation and behavior is strongest when anticipation is high (i.e., distal) and experience is low.

Recent research has presented a conceptualization, metric, and instrument based on Microsoft Usability Guidelines (MUG; see Agarwal and Venkatesh 2002). In this paper, we use MUG to further our understanding of web and wireless site use. We conducted two empirical studies among over 1,000 participants. In study 1, conducted in both the United States and Finland, we establish the generalizability of the MUG conceptualization, metric, and associated instrument from the United States to Finland. In study 2, which involved longitudinal data collection in Finland, we delved into an examination of differences in factors important in determining web versus wireless site usability. Also, in study 2, based on a follow-up survey about site use conducted 3 months after the initial survey, we found support for a model of site use that employs the MUG categories and subcategories as predictors. The MUG-based model outperformed the widely employed technology acceptance model both in terms of richness and variance explained (about 70 percent compared to 50 percent).

We develop a theoretical model for predicting purchase behavior in electronic channels. The model suggests that website use (i.e., technology use), a key indicator of the degree to which a site is “sticky,” is a significant antecedent of purchase behavior. Furthermore, we relate the usability of a website to use behavior and purchase behavior. Specifically, individual characteristics and product type are argued to differentially influence the weights that customers place on five different categories of usability. The weighted ratings of the five categories together determine use behavior and purchase behavior, after controlling for purchase need, experience with similar sites, and previous purchase on the specific sites. The model was tested in a longitudinal field study among 757 customers who provided usability assessments for multiple websites from four different industries-i.e., airlines, online bookstores, automobile manufacturers, and car rental agencies. Six months later, 370 of these individuals provided responses to help understand the transition from visitor to customer, i.e., whether they actually transacted with a specific website. Results provided strong support for the model and yield important theoretical and practical implications.

Individual adoption of technology has been studied extensively in the workplace. Far less attention has been paid to adoption of technology in the household. In this paper, we performed the first quantitative test of the recently developed model of adoption of technology in the household (MATH). Further, we proposed and tested a theoretical extension of MATH by arguing that key demographic characteristics that vary across different life cycle stages would play moderating roles. Survey responses were collected from 746 U.S. households that had not yet adopted a PC. The results showed that the integrated model, including MATH constructs and life cycle characteristics, explained 74 percent of the variance in intention to adopt a PC for home use, a significant increase over baseline MATH that explained 50 percent of the variance. Finally, we compared the importance of various factors across household life cycle stages and gained a more refined understanding of the moderating role of household life cycle stage.

This research extends the theory of planned behavior by incorporating gender and age as moderators of user perceptions and individual adoption and sustained use of technology in the workplace. Individual reactions and technology use behavior were studied over a six-month period among 342 workers being introduced to a new software technology application. While previous studies in the literature have reported gender or age differences separately, the pattern of results from the study reported here indicated that gender effects in individual adoption and use of technology differed based on age. Specifically, gender differences in technology perceptions became more pronounced among older workers, but a unisex pattern of results emerged among younger workers. The theory and empirical results are also discussed in relation to the widely employed technology acceptance model. The results from this study suggest that old stereotypes that portray “technology” as a male-oriented domain may be disappearing; particularly among younger workers. In light of these findings, theoretical implications for researchers and practical suggestions for managers are discussed.

Recent research investigating gender differences has demonstrated that women and men make technology adoption decisions very differently. Specifically, using the theory of planned behavior, it has been shown that women make “balanced” decisions in that they are influenced by attitude, subjective norm, and perceived behavioral control; in contrast, men are influenced only by attitude. That research treated gender as a biological, dichotomous construct that is typical of much research in this area. This paper extends the line of inquiry by treating gender as a psychological construct by employing Bem’s Sex Role Inventory (BSRI). Individual reactions to the new technology and technology usage behavior were studied over a twelve-month period among 552 employees being introduced to a new computer-based system in the workplace. When considering gender as a psychological construct, important distinctions were revealed. Specifically, masculine sex-type individuals exhibited the same pattern as men in the previous research; feminine sex-typed individuals were different from women in that, they were influenced only by subjective norm and perceived behavioral control. The “balanced” decision-making process was observed only in the case of individuals categorized as androgynous. The high percentage of women who tested to be androgynous explains the divergence in results from the previous work, and provides evidence of changing sex roles for women in today’s organizations and society.

Errors in requirements specifications have been identified as a major contributor to costly software project failures. It would be highly beneficial if information systems developers could verify requirements by predicting workplace acceptance of a new system based on user evaluations of its specifications measured during the earliest stages of the development project, ideally before building a working prototype. However, conventional wisdom among system developers asserts that prospective users must have direct hands-on experience with at least a working prototype of a new system before they can provide assessments that accurately reflect future usage behavior after workplace implementation. The present research demonstrates that this assumption is only partially true. Specifically, it is true that stable and predictive assessments of a system’s perceived ease of use should be based on direct behavioral experience using the system. However, stable and behaviorally predictive measures of perceived usefulness can be captured from target users who have received information about a system’s functionality, but have not had direct hands-on usage experience. This distinction is key because, compared to ease of use, usefulness is generally much more strongly linked to future usage intentions and behaviors in the workplace. Two longitudinal field experiments show that pre-prototype usefulness measures can closely approximate hands-on based usefulness measures, and are significantly predictive of usage intentions and behavior up to six months after workplace implementation. The present findings open the door toward research on how user acceptance testing may be done much earlier in the system development process than has traditionally been the case. Such pre-prototype user acceptance tests have greater informational value than their post-prototype counterparts because they are captured when only a relatively small proportion of project costs have been incurred and there is greater flexibility to modify a new system’s design attributes. Implications are discussed for future research to confirm the robustness of the present findings and to better understand the practical potential and limitations of pre-prototype user acceptance testing.