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Game Research » Review of “User acceptance of information technology: Toward a unified view”

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Review of “User acceptance of information technology: Toward a unified view”

  • Full reference
    Venkatesh, V.; Morris, M. G.; Davis, G. B. ; Davis, F. D. (2003): User acceptance of information technology: Toward a unified view, MIS Quarterly, Vol 27, No. 3, pp. 425-478.

  • Recommendation
    Recommended for scientific researchers with focus on technology acceptance in general, especially interesting with regard to age as a moderator.

  • Summary

The main focus of the article is a review and empirical comparison of technology acceptance models resulting in a new unifying model, which the authors also empirically evaluate at the end of the article. The emphasis of the comparison is on those models and theories employing intention and/or usage as the key dependant variable; the basic framework is depicted in Figure 1.

Figure 1: Basic concept underlying the chosen user acceptance models (p. 427)Based on their literature review of technology acceptance models, the authors identify eight key competing models with 32 constructs and four significant moderating variables, namely experience, voluntariness, gender, and age. The authors furthermore review prior model comparison studies, summarizing the major drawbacks and how they will avoid these weaknesses as follows:

  • Technologies studied
    Drawback in earlier research: often simple, individual SW as opposed to complex business systems
    –> this research puts the focus on more complex business applications
  • Participants
    Drawback in earlier research: Most models tested with students
    –> in this study the models are tested with employees
  • Timing of measurement
    Drawback in earlier research: Most studies were conducted long after the technology had been adapted
    –> therefore in this research, three tests from time of introduction to stages of greater experience
  • Nature of measurement
    Drawback in earlier research: typically cross-sectional and/or between-subject comparison
    –> in this study all models on all participants are compared
  • Voluntary vs. mandatory settings
    Drawback in earlier research: Most models were tested in voluntary settings
    –> in this research both settings are tested

Consequently, the authors chose heterogenic participants and settings with regard to technologies, organizations, industries, business functions, and nature of use.

The research was conducted as a longitudinal field study: Several surveys with a (pretested) questionnaire containing all eight models were conducted at three different points in time, capturing the growing system experience as each survey started with the introduction of a new software system into a company. The actual usage behaviour was measured over a six months period. All scales were adapted from literature. The authors only included those moderators previously tested in the literature.

Results and major findings of the model comparison tests:

The predictive validity of all models increased when the moderating variables were introduced, except for Motivational Model (MM) and Social Cognitive Theory (SCT).

For every model there was at least one construct, which stayed significant though all three tests and this construct also had the strongest influence. Several constructs were initially significant, but became insignificant over the three tests.

The authors thus identified seven constructs, which seemed significant direct determinants of intention or usage in at least one model. The authors then analysed these constructs further and determined that the following four constructs would be direct significant determinants of user acceptance and usage behaviour: performance expectancy, effort expectancy, social influence, and facilitating conditions. In the following, I will sum up the author’s reasoning for inclusion of constructs. (The authors also explain in their paper why they expected the other construct to be nonsignificant as determinants of intention. A discussion I chose to omit within this summary.)

Performance Expectancy

“Performance expectancy is defined as the degree to which an individual believes that using the system will help him or her to attain gains in job performance.” (p.447) From the different models, the authors identified 5 constructs as measuring some kind of performance expectancy, namely perceived usefulness (TAM/TAM2 and C-TAM-TPB), extrinsic motivation (MM), job-fit (MPCU), relative advantage (IDT), and outcome expectations (SCT). (p.447)

In all models performance expectancy was the strongest predictor of intention and significant in all three tests. (p.447)

Prior research shows that effort expectancy is of higher importance for women and suggests that extrinsic rewards may be of higher importance to younger persons. As has been pointed out in earlier studies, the analysis of gender effects can be misleading without reference to age. (p.450)

Effort Expectancy

“Effort expectancy is defined as the degree of ease associated with the use of the system.” (p.450) In the compared models, three constructs reflect the notion of effort expectancy, namely perceived ease of use (TAM/TAM2), complexity (MPCU), and ease of use (IDT). (p.450) This construct was significant in the first test in mandatory as well as voluntary settings, but became insignificant in the following two tests. (p.450)

As prior research also suggests, effort expectancy constructs are stronger determinants for women and for older workers. As already pointed out above, gender and age interact with each other and thus the authors expect gender, age and experience to be interrelated. (p.450)

Social Influence

“Social influence is defined as the degree to which an individual perceives that important others believe he or she should use the new system.” (p.451) Concepts capturing social influence as direct determinants of behavioural intention are part of TRA, TAM2, TPB/DTPB, C-TAM-TPB, MPCU, and IDT. (p.451)

Constructs of social influence were only significant in mandatory settings and there only in the early stages of the experience with a system. (p.451 f.)

Literature shows that older workers are more likely to place importance on social influence, decreasing however with increasing experience. (p.453)

Facilitating Conditions

“Facilitating conditions are defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system.” (p.453) With regard to this definition, the following constructs of the eight compared models capture the notion of facilitating conditions: perceived behavioral control (TPB/DTPB, C-TAM-TPB), facilitating conditions (MPCU), and compatibility (IDT). (p.453)

In their model comparison, the authors found out that when both performance expectancy constructs and effort expectancy constructs were present, facilitating conditions became nonsignificant in predicting intention. However, the results also showed that facilitating conditions had significant impact on usage behaviour. (p.454)

As has been shown in prior research, for older workers receiving assistance is more important than for younger workers. (p.454) Thus, and as also has been confirmed in earlier studies, age and experience are expected to moderate facilitating conditions, which have a significant influence on usage behaviour. (p.454)

In conclusion of the findings combined with the findings of earlier studies, the authors thus derive the model UTAUT as depicted in Figure 2.


Figure 2: UTAUT (p.447)

In the following, the author’s tested their model first with a preliminary test with pooled data from the described comparison study and then conducted two further external surveys with two new companies in the same way as the initial surveys.

These tests provide strong empirical support for UTAUT as depicted above. Most importantly, experience, voluntariness, gender, and age were confirmed as significant moderating influences. Almost every relationship in the model is moderated. Age, for example, moderates all key relationships of the model. (p.469)

The authors thus emphasise the importance of moderators and strongly recommend further exploration of these variables and underlying reasons. (p.469)

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