AGE DIFFERENCES IN TECHNOLOGY ADOPTION DECISIONS: IMPLICATIONS FOR A CHANGING WORK FORCE

By Michael G. Morris, V. Venkatesh. 2000

1,799 citations recorded by the source corpus.

This research investigated age differences in individual adoption and sustained usage of technology in the workplace using the theory of planned behavior. User reactions and technology usage behavior were studied over a 5-month period among 118 workers being introduced to a new software system. At 2 points of measurement, compared to older workers, younger workers' technology usage decisions were more strongly influenced by attitude toward using the technology. In contrast, older workers were more strongly influenced by subjective norm and perceived behavioral control, although the effect of subjective norm diminished over time. These findings were robust, even after controlling for key confounding variables identified in prior organizational behavior research (i.e., income, occupation, and education levels). Theoretical and practical implications for understanding the effects of aging on technology adoption and usage in the workplace are discussed.

Subfields: Demography, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/0e4be1fb213baf4c8fb6420eece7eb175b6ae4be

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Citations in the corpus

What Drives Electronic Commerce across Cultures? Across-Cultural Empirical Investigation of the Theory of Planned Behavior (2002, methodology): “The two specific items related to the dimension of product purchase were adopted from Ohanian (1991), while the intent to receive information was based on Choudhury et al.”
Applying Ajzen's Theory of Planned Behavior to a Study of Online Course Adoption in Public Relations Education. (2012, methodology): “For example, Morris and Venkatesh (2000) used the Theory of Planned Behavior to study workers’ decisions about technology usage and their attitudes toward adoption of technology, and Fortin (2000) used the model to study the behavior of “clipping online coupons.””
Older adults: Are they ready to adopt health-related ICT? (2013, neutral): “In contrast, other studies showed that, relative to younger adults, older adults reported less comfort, lower efficacy, and less control over computing technologies [16,43], perceptions that are likely to decrease acceptance [8,12,44].”
The Role of Espoused National Cultural Values in Technology Acceptance (2006, neutral): “Inconsistent results concerning the relationship between social norms and behavioral intention have been attributed to moderating effects by experience, gender, voluntariness, and age (Karahanna et al. 1999; Morris and Venkatesh 2000; Venkatesh and Davis 2001*: Venkatesh et al. 2003).”
Smartphone use and work-home interference: The moderating role of social norms and employee work engagement (2015, background): “Morris and Venkatesh (2000) argue that workers are influenced by and respond to informational input from both peers and supervisors.”
Putting Entrepreneurship Education Where the Intention to Act Lies: An Investigation Into the Impact of Entrepreneurship Education on Entrepreneurial Behavior (2014, background): “Also, age has been related both to intentions (Morris & Venkatesh, 2000) and entrepreneurial behavior (Reynolds, 1987), which is why we also controlled for age, and because there is evidence that parents who are entrepreneurs serve as role models and increase entrepreneurship behavior (Bosma,…”
Trust in motives, trust in competence: Separate factors determining the effectiveness of risk communication (2008, background): “…that attitudes and values, particularly those relating to risk and technology, are more likely to be similar in people of the same sex and similar age (Deakin, Aitken, Robbins and Sahakian, 2004; Morris and Venkatesh, 2000; Morris, Venkatesh and Ackerman, 2005; Rosen, 2003; Siegrist et al., 2005).”
Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet (2006, background): “For instance, there have been attempts to incorporate social norm [75], perceived enjoyment [28], and gender/age moderators [52,76] into the TAM's framework to increase further its explanatory power.”
Investigating the determinants and age and gender differences in the acceptance of mobile learning (2009, methodology): “Based on the UTAUT and previous literature (Morris & Venkatesh, 2000; Venkatesh & Morris, 2000), gender and age are theorised to play a moderating role on the influence of performance expectancy on behavioural intention.”
User acceptance of wireless short messaging services: Deconstructing perceived value (2007, background): “For age, younger users may place higher importance on extrinsic rewards and lower importance on ease of use, and are less likely to conform to others’ opinions [35].”
A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research (2014, methodology): “Consistent with Zahra’s (2007) notion of contextualization, prior research on technology adoption has explored the contingencies (e.g., Morris and Venkatesh 2000, Venkatesh and Morris 2000) and challenged the assumptions of general adoption models (e.g., Burton-Jones and Hubona 2006).”
User Acceptance of Information Technology: Toward a Unified View (2003, neutral): “Gender and age differences have been shown to exist in technology adoption contexts also (Morris and Venkatesh 2000; Venkatesh and Morris 2000).”