Evaluating User Adoption of Citizen Development Platform: A Case Study Using the Technology Acceptance Model
Bibliographic record
Abstract
This study investigates user acceptance of a Citizen Development tool within a German energy company using the Technology Acceptance Model (TAM). As digital transformation accelerates across industries, empowering non-technical employees to create digital solutions through no-code platforms becomes increasingly relevant. The research applies TAM to analyze how perceived usefulness, perceived ease of use, and usage intention influence actual user acceptance of the tool. A quantitative survey was conducted with 177 employees, and data analysis was performed using SPSS and R Studio. The findings reveal that usage intention is the strongest predictor of user acceptance, explaining over 55% of the variance. Furthermore, both perceived usefulness and perceived ease of use significantly influence intention to use, with perceived usefulness having a greater impact. Perceived ease of use also has a moderate influence on perceived usefulness. These results confirm TAM’s applicability in the energy sector and underline the importance of user-centered design and training. The study concludes with practical recommendations for enhancing digital adoption and highlights the potential for expanding the TAM framework to include additional variables. Limitations include a cross-sectional design, and a single-company focus, suggesting avenues for future longitudinal and cross-industry research.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".