The harmonized information-technology and organizational performance model (HI-TOP)
Bibliographic record
Abstract
Purpose This study introduces the Harmonized Information-Technology and Organizational Performance Model (HI-TOP), which addresses the need for a holistic framework that integrates technology and human dynamics within organizational settings. This approach aims to enhance organizational productivity and employee well-being by aligning technological advancements with human factors in the context of digital transformation. Design/methodology/approach Employing a two-phased methodology, the HI-TOP model is developed through a literature review and text mining of industry reports. This approach identifies and integrates critical themes related to ICT integration challenges and opportunities within organizations. Findings This research indicates that successful ICT integration requires balancing technological advancements with human-centric considerations, including addressing technostress and promoting skills development. The HI-TOP model’s four components – Workforce Empowerment and Resource Strategy (WERS), Technology-Enhanced Information Architecture (TEIA), Organizational Information Processing Strategy (OIPS) and Knowledge Sharing Platform (KSP) – demonstrate operational and strategic synergy required to achieve enhanced organizational performance and adaptability. Originality/value The HI-TOP model contributes to the body of knowledge by providing a structured framework for understanding the interplay between technology and organizational dynamics, with an emphasis on employee well-being and overall organizational performance. Its originality lies in the integrative approach to model development, combining theory with empirical insights from industry data, thus offering actionable guidance for organizations navigating the complexities of digital transformation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".