How and why trust matters in post-adoptive usage: The mediating roles of internal and external self-efficacy
Bibliographic record
Abstract
• Focus on post-adoption , an important area that we need to better understand. • Explicating how and why trust in IT influences post-adoptive behavior (mediation). • Taking a theory-driven approach to studying post-adoption, which is rarely done. • Refraining from using adoption theories such as TAM in the post-adoption context. • Introduction of bootstrapping as an advanced test of mediation to IS research. Since the underutilization of technology often prevents organizations from reaping expected benefits from IT investments, an increasing body of literature studies how to elicit value-added, post-adoptive IT use behaviors. Such behaviors include extended and innovative feature use, both of which are exploratory in nature and can lead to improved work performance. Since these exploratory behaviors can be risky, research has directed attention to trust in technology as an antecedent to post-adoptive IT use. In parallel, research has examined how computer self-efficacy relates to post-adoptive IT use. While such research has found that both trust and efficacy can lead to value-added IT use and that they might do so interdependently, scant research has examined the interplay between these antecedents to post-adoptive IT use. Drawing on the Model of Proactive Work Behavior with a focus on its predictions about trust and efficacy, we develop a research model that integrates trust in technology and computer self-efficacy in the post-adoption context. Our model suggests that the two concepts are interdependent such that trust-related impacts on post-adoptive use behaviors unfold via computer-related self-efficacy beliefs. Contemporary tests of mediation on data from more than 350 respondents provided support for our model. Hence, our findings begin to open the black box by which trust-related impacts on post-adoptive behaviors unfold, revealing computer self-efficacy as an important mediating factor. In doing so, this study furthers understanding of how, and why, trust matters in post-adoptive usage, enabling strategic change management by elucidating the “fit” between technological characteristics and post-adoptive usage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".