1 Arashigis

Utaut Dissertation Format

Technology acceptance models or theories are commonly used in studies aiming at predicting and explaining the individual behaviours towards the acceptance and usage of new technologies. This paper reports part of the findings from a doctoral research project which focused at analysing the acceptance and usage of open access within public universities in Tanzania. The study was guided by the Unified Theory of Technology Acceptance and Usage (UTAUT) model). The survey questionnaire targeted 544 respondents selected through stratified random sampling from a population of 1088 university researchers at six public universities in Tanzania. A response rate of 73 percent was achieved and the binary logistic regression statistics of the Statistical Package for Social Sciences (SPSS) was used for data analysis. The study findings suggest support for the application of the UTAUT model in studying the adoption of open access in a research environment. Among the findings, attitude, awareness, effort expectancy and performance expectancy were established as the key determinants for the researchers’ behavioural intentions of open access usage. Similarly, age, awareness, behavioural intention, facilitating conditions and social influence were found to significantly affect researchers’ actual usage of open access. These factors should therefore be taken into account in the planning and implementation of open access projects. A further validation of the open access research model in similar research institutions in Tanzania and elsewhere is recommended.

3.1. Generational Differences in UTAUT Predictors

First, we conducted a series of independent samples t-tests to determine the relationship between UTAUT determinants (performance expectancy, effort expectancy, social influence and facilitating conditions) and actual use behavior. We asked participants “have you ever used a tablet” which they answered yes or no. In brief, people who reported that they use tablets had significantly higher means for all determinants than people who report that they do not use tablets, see Table 2.

Table 2

T-test Results for Comparing Tablet Users and Non-Users

We conducted one-way ANOVAs and a MANCOVA to address hypotheses about whether generational differences existed in individuals’ intentions regarding tablet use and adoption. There is some discrepancy among scholars concerning the temporal order of behavior (e.g., actual/current tablet use) and attitudes (e.g., UTAUT variables and intention to use tablets), that is the question of if use creates attitudes or if attitudes are predictive of use. Though our strategy to try to tease apart this concern statistically, we examined the results of both a series of ANOVAs that do not control for use and a MANCOVA with actual use as a covariate. Our concern with conducting only a MANCOVA was grounded in the knowledge that because of the temporal order assumption of the test, the analysis model would assume that the behavior (tablet use) changes or precedes the attitude (intention to use the tablet), which we feel may contradict the theoretical framework.

For performance expectancy, ANOVA results indicated a significant mean difference, F(3, 824)=12.41, p>.001, across the four generations. GenX reported the highest level of performance expectancy (M=3.75, SD=1.05), followed by GenY (M=3.67, SD=1.01), Boomers (M=3.46, SD=1.06), and Builders (M=2.96, SD=1.23). GenX reported a higher level of performance expectancy than GenY. Only Builders were significantly different from all other generational groups. Thus, H1 was supported (see Table 3 for details).

Table 3

Generational Differences in UTAUT Predictors and Behavioral Intention to Use Tablets ANOVAs

For effort expectancy, ANOVA results also indicated a significant mean difference, F(3, 821)=55.75, p>.001, across the four generations. GenY reported the highest level of effort expectancy (M=4.11, SD=.82), followed by GenX (M=3.97, SD=.96), Boomers (M=3.60, SD=1.03), and Builders (M=2.61, SD=1.17), recalling that effort expectancy is coded such that a higher number indicates perceptions that less effort will be required to use a tablet. There were significant differences between all generations except between GenX and GenY. Thus, H2 was supported (see Table 3 for details).

For social influence, ANOVA results indicated a significant mean difference, F(3, 822)=5.52, p=.000, across the four generations. GenY reported the highest level of social influence (M=3.41, SD=.85), followed by GenX (M=3.40, SD=.92), Boomers (M=3.30, SD=.83), and Builders (M=3.00, SD=1.05). Builders are different from all the other groups, however, Boomers are different from Builders only. Thus, H3 was supported (see Table 3 for details).

For facilitating conditions, ANOVA results also indicated a significant mean difference, F(3, 818)=23.58, p=.000, across the four generations. GenX reported the highest level of facilitating conditions (M=4.00, SD=.80), followed by GenY (M=3.95, SD=.77), Boomers (M=3.70, SD=.83), and Builders (M=3.10, SD=1.12). Generation X and Boomers perceptions were not significantly different, however, GenY was different from older generations including Builders and Boomers. Thus, H4 was supported (see Table 3 for details).

For behavioral intention, ANOVA results indicated a significant difference, F(3, 823)=39.68, p=.000, across the four generations. GenX reported the highest level of behavioral intention (M=4.37, SD=.74), followed by GenY (M=4.30, SD=.77), Boomers (M=4.14, SD=.88), and Builders (M=3.18, SD=1.32). Only Builders were significantly different from all other generational groups (see Table 3 for details).

We also conducted a MANCOVA controlling for participants weekly hours of tablet use with generational group (Builder, Boomer, Generation X, Generation Y) as the independent variable and performance expectancy, effort expectancy, social influence, facilitating conditions, and tablet use intention as the dependent variables. There was a main effect for generational differences (F(15,2361) = 12.63, p < .001; Pillai’s Trace). Between-subjects effects revealed significant differences between generational groups for all but one determinant: Performance Expectancy ((F(3,789) = 9.60, p < .001), Effort Expectancy ((F(3,789) = 48.37, p < .001), Facilitating Conditions ((F(3,789) = 19.93, p < .001), and Intention ((F(3,789) = 37.93, p < .001). Social Influence was not significant ((F(3,789) = 2.26, p = .08), however, the observed power for this determinant was .57, compared to 1.00 for all other determinants. The generational mean differences within determinants were similar in strength to those found in the ANOVAs (see Table 4), with two exceptions. First, in effort expectancy, the difference between Boomers and Generation X changed from p < . 01 to p = .012. Second, the ANOVA reveal significant differences between Builders and all other generational groups for social influence, but there were no significant mean differences between generational groups for social influence in the MANCOVA, which was underpowered (see Table 4 for details).

Table 4

Generational Differences in UTAUT Determinants and Behavioral Intention to Use Tablets based on estimated marginal means through MANCOVA with hours of tablet use as a covariate

4.2. Prediction of Behavioral Intention to Use Tablets

Another goal of this study was to explore how UTAUT determinants predict tablet intentions. The research question seeks to understand how the formation of anticipated behavioral intention is affected by performance expectancy, effort expectancy, social influence, and facilitating conditions.

We used a stepwise regression analysis with moderators age, gender, experience of tablet use (“Have you ever used a tablet” y/n), and hours of tablet use in the first block, and the UTAUT subscales (performance expectancy, effort expectancy, and social influence) traditionally noted as the three predictors of use intention in the second block. The results of this regressions are presented in Table 5.

Table 5

Prediction of Behavioral Intention to Use Tablets

In the first block where control variables entered (Adj. R2 = .13, F(4,750) = 27.98, p < .001), age negatively (β= −.18, t = −4.99, p < .001) and experience of tablet use positively (β = .26, t = 6.79, p < .001) predicted anticipated behavioral intention. Gender (β = .07, t = 1.90, p = .06) and hours of tablet use (β = −.05, t = −1.27, p = .20) were included in the first block as controls, but were not significant. The addition of the second block resulted with a significant change, R2 change = .11, F(5,749) = 48.35, p < .001, where only effort expectancy entered the model and positively (β = .42, t = 10.64, p < .001) predicted intention to use a tablet in the next three months. In the final model, age negatively, gender positively, experience of tablet use positively, hours of table use negatively, and effort expectancy positively predicted 24% of the variance in tablet use intention. Performance expectancy and social influence were not significant in the final model (see Table 5 for details).

Facilitating conditions do not directly predict intention in Venkatesh et al.’s (2003) model, but instead predict use behavior. Nevertheless, because some existing research tests this association, we executed a stepwise regression identical to the first only with the addition of facilitating conditions in the second block to explore how facilitating conditions may contribute to tablet use intentions. The results of this regressions are presented in Table 6.

Table 6

Prediction of Behavioral Intention to Use Tablets with Facilitating Conditions

In the first block where control variables entered (Adj. R2 = .13, F(4,747) = 27.82, p < .001), age negatively (β= −.18, t = −4.99, p < .001) and experience of tablet use positively (β = .26, t = 6.76, p < .001) predicted anticipated behavioral intention. Gender (β = .07, t = 1.94, p = .05) and hours of tablet use (β = −.05, t = −1.27, p = .21) were included in the first block as controls, but were not significant. The addition of the second block resulted with a significant change, R2 change = .11, F(5,746) = 48.11, p < .001, where effort expectancy entered the model and positively (β = .42, t = 10.61, p < .001) predicted intention. Facilitating conditions entered on the third block (R2 change = .01, F(6,745) = 41.56, p < .001; β = .13, t = 2.63, p < .05). In the final model, age negatively, gender positively, experience of tablet use positively, hours of tablet use negatively, effort expectancy positively, and facilitating conditions positively predicted 25% of the variance in tablet use intention. Performance expectancy and social influence were not significant in the final model (see Table 6 for details).

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