Toward an Integrative Understanding of Information Technology Training Research across Information Systems and Human-Computer Interaction: A Comprehensive Review
Bibliographic record
Abstract
Researchers investigating issues in the domain of training and human-computer interaction share a common interest in ensuring that users are skilled in the use of Information Technologies (IT). When users have the necessary skills, they can utilize IT productively and also have a pleasant human-to-computer interaction. Over the past three decades, Information System (IS) researchers have made considerable efforts in identifying the most effective ways to develop users’ IT skills. However, at this point in time, there are many changes taking place in the IT environment and organizations find it challenging to keep their employees trained and updated on IT skills. Hence, it is important for the IS community to respond by taking the lead in identifying and conducting research that can help organizations effectively address these challenges. We take the first step in conducting a comprehensive review of training research published in major IS and HCI journals over the past three decades so as to synthesize IT training research, provide an integrative understanding of findings, and propose directions for future research. Our study indicates that while IS research on training has made steady progress in advancing our understanding of alternative IT training methods and cognitive learning processes, it also has several shortcomings. Past research has: a) focused primarily on the training program without sufficient attention to activities prior to and after the program, b) used a small set of theoretical foundations, and c) focused on a few topics and on single-user systems rather than integrated enterprise systems. Critical issues such as improving user motivations prior to training, transfer of training skills to the workplace, assessment of training, and supporting user learning that occurs after training have not been given adequate attention. We identify several research opportunities by tapping into relatively unexplored theories and urge researchers to continue research to address the gaps identified in this comprehensive review as well as to develop innovative methods to help employees learn through newer channels, such as e-learning and social media.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".