Behavioral Intention of technology integration in theological educator's teaching practices at ATS-accredited institutions
Description
Hypotheses Grounded in UTAUT (Venkatesh et al., 2003), this study tested whether four constructs — Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions — significantly predict theological educators' behavioral intention to integrate technology into their teaching practices. The general hypothesis was that each construct would positively and significantly predict behavioral intention, consistent with prior UTAUT research in broader higher education contexts. A corresponding null hypothesis was tested for each predictor (i.e., that the construct would show no significant relationship with behavioral intention). What the Data Shows A multiple linear regression analysis (N = 101) tested the combined and individual predictive strength of the four constructs. The overall model was statistically significant and explained 62.5% of the variance in behavioral intention (R² = .625) — a substantial effect for behavioral research of this kind. Three of the four predictors were statistically significant: Performance Expectancy (β = .48), Effort Expectancy (β = .30), Social Influence (β = .23) Facilitating Conditions (β = .00, p = .965) did not significantly predict behavioral intention. This is the study's most notable finding, since Facilitating Conditions — access to resources, infrastructure, and support — is often assumed to be a primary driver of technology adoption in the broader literature. Notable Finding and Interpretation The non-significance of Facilitating Conditions suggests that in theological education specifically, having the necessary resources or infrastructure available does not, by itself, translate into intention to adopt technology. Instead, adoption appears driven more by attitudinal and social factors — whether faculty personally believe technology helps their teaching (Performance Expectancy), find it manageable to use (Effort Expectancy), and perceive it as valued within their institutional community (Social Influence). This points to a distinctive feature of specialized, mission-driven, and communally-oriented institutions like ATS-accredited seminaries: structural support alone is insufficient without attitudinal buy-in and communal endorsement.
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Steps to reproduce
Data Collection and Reproducibility This study employed a correlational, predictive quantitative design grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003) to examine behavioral intention toward technology integration among theological educators. Population and Sampling The target population consisted of faculty at Association of Theological Schools (ATS)-accredited institutions. Participants were recruited via [recruitment channel — e.g., email invitation through ATS/professional listservs; confirm exact method]. A total of 101 usable responses (N = 101) were retained for analysis after data cleaning, which included the removal of 2 outlier cases identified. Instrument Data were collected using a survey instrument adapted from the original UTAUT scales (Venkatesh et al., 2003), used with documented permission. The instrument measured four constructs — Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions — along with Behavioral Intention as the outcome variable, using Likert-type items. Internal consistency reliability was strong across constructs, with Cronbach's alpha coefficients ranging from .79 to .92. Data Collection Procedure The survey was administered electronically via [survey platform — e.g., Qualtrics/Google Forms; confirm] during [data collection window; confirm dates]. Participation was voluntary and anonymous, with informed consent obtained prior to survey access, consistent with [IRB/institutional approval details; confirm]. Data Analysis Data were analyzed using [statistical software — e.g., SPSS; confirm version] to compute: Descriptive statistics and reliability coefficients (Cronbach's alpha) per construct Pearson correlation coefficients among all study variables Multiple linear regression, with Behavioral Intention regressed on the four UTAUT predictors Assumptions of multiple regression (normality, linearity, homoscedasticity, multicollinearity) were tested prior to interpreting results. The final model was statistically significant and explained 62.5% of the variance in behavioral intention (R² = .625). Performance Expectancy (β = .48), Effort Expectancy (β = .30), and Social Influence (β = .23) emerged as significant predictors; Facilitating Conditions did not significantly predict behavioral intention (β = .00, p = .965). Reproducibility Notes Researchers seeking to replicate this study would need: (1) access to the adapted UTAUT instrument (available via the original authors' permission protocol or by request from this study), (2) a comparable sample of theological education faculty at ATS-accredited institutions, (3) standard statistical software capable of multiple linear regression and reliability analysis (e.g., SPSS, R, or jamovi), and (4) adherence to the same data cleaning protocol (outlier screening, assumption testing) prior to running the regression model.
Institutions
- The University of Arizona Global CampusCalifornia, San Diego