Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior

Abstract

Using the Technology Acceptance Model (TAM), this research investigated gender differences in the overlooked context of individual adoption and sustained usage of technology in the workplace. User reactions and technology usage behavior were studied over a five-month period among 342 workers being introduced to a new software system. At all three points of measurement, compared to women, men's technology usage deci-1 Ron Weber was the accepting senior editor for this paper. sions were more strongly influenced by their perceptions of usefulness. In contrast, women were more strongly influenced by perceptions of ease of use and subjective norm, although the effect of subjective norm diminished over time. These findings were robust even after statistically controlling for key confounding variables identified in prior organizational behavior research (i.e., income, occupation, and education levels), and another possible confound from technology research, prior experience with computers in general. Thus, in addition to identifying key boundary conditions in the role of the original TAM constructs (perceived usefulness and perceived ease of use), this research provides the basis for the integration of subjective norm into the model. In light of these findings, implications for theory and practice are discussed.

Factors Affecting Re-usage Intentions of Virtual Communities Supporting Cosmetic Products

“…The findings indicate that females' VC re-usage intention is more affected by performance expectancy than that of male. According to Venkatesh and Morris (2000), compared with that of women, men's technology usage decision is strongly influenced by the perceived usefulness of a new software system. However, in technology re-usage, our finding is reversed.…”

“…According to the findings of previous studies (e.g., Venkatesh & Morris, 2000;Lynott & McCandless, 2000;Venkatesh et al, 2003;Miller, 2012), age and gender have been theorized to play moderating roles in the effects of performance expectancy, effort expectancy, and social influence on behavioral intention. Previously, researchers claimed that demographic variables are usually the significant moderators of technology usage.…”

Electronic Health Record Impact on Work Burden in Small, Unaffiliated, Community-Based Primary Care Practices

“…This analysis is part of a multi-method study, conceptually informed by The Primary Care Change Model 27 and the Technology Acceptance Model, [28][29][30][31][32][33][34] to understand how EHRs are used in small, independent primary care practices.…”

Physicians’ and Users’ Perceptions towards Wearable Health Devices

“…Attitude is the users" insight developed about the technology, whether or not to use them. In initial stages of adoption, men weigh PU as an important factor than EOU, whereas women emphasized on EOU to a higher extent [14]. For implementation of mobile healthcare systems in healthcare organizations, attitude of medical practitioners does not play a vital role since they are just asked to adopt the technologies after the decision has been made by organizations [11].…”

“…PU is the improvement in performance, achieved as a result of adoption of a technology. EOU is the degree of easiness with which, a consumer can use the technology [14,15]. Attitude is the users" insight developed about the technology, whether or not to use them.…”