Information Technology Training for a Globalized Workforce – Challenges, Tools and Research Directions
Bibliographic record
Abstract
IT training research is one of the dominant themes in IS research for the past two decades and has provided a rich knowledge base of tools and techniques to impart IT training to employees (Compeau et al. 1995; Sharma and Yetton 2007). IT training is a critical enabler of information system acceptance and use, because employees who undergo training have higher positive attitudes than those who do not (Cooper and Zmud 1990, Xia and Lee 2000). But conducting business in a global workspace has created additional challenges for IT training professionals and organizational consultants. Training service firms with names such as “Global Computer Education,” “International Training Services,” Training for a Global World,” are becoming quite commonplace. Business Information systems, instead of being simple one-user systems, have become complex, large, integrated systems used by many different employees and require more learning and coordination efforts on the part of employees (Gattiker and Goodhue 2005, Santhanam et al. 2007, Sharma and Yetton 2007). Hence, new training methods such as virtual training, situational learning, and behavior modeling are being researched to support employee learning and expand upon the traditional face-to-face lecture based training (Alavi and Leidner 2001, Yi and Davis 2003, Gallivan et al. 2005, Santhanam et al. 2008). IT support staff also have to play a critical role as trainers as they support employees’ learning process long after training programs are completed (Haggerty and Compeau 2002, Pawlowski and Robey 2004). IT staff/trainers can learn from these research findings that could help them better manage training on new information technologies and cope with training employees in a global workspace.
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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 | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.019 | 0.032 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".