Mapping the foundations of organizational values: a hybrid review
Bibliographic record
Abstract
Purpose Organizational values (OVs) are essential in determining a company’s culture, identity and level of success. Therefore, a thorough review of the field is crucial for evidence-based practice, to decide on the best course of action, determine the most pressing research questions and improve knowledge. The purpose of this study is to conduct a hybrid review of the field of OVs to provide a panoramic view of the field. Design/methodology/approach The authors used a hybrid review that combined bibliometric analysis with a systematic literature review. The authors retrieved bibliometric information of 1,421 articles from the Scopus database and processed it through VOSViwer and Biblioshiny (using the bibliometrics R package) to conduct network and thematic analysis, respectively. Furthermore, 52 articles were selected for systematic literature review, and content analysis was conducted using Voyant tools. Findings Through bibliometric analysis, this study identified the clusters of intellectual structure and four categories (established, emerging, basic and niche) of key themes. OV research has been largely focused its effects and alignment or congruence. This review showed researcher has studied consequences of OVs at an organizational level, individual level effects and society level. Review has underscored the significance of value alignment within organizations. But there is a limited understanding of the procedures and tactics for successfully integrating values through all levels of the organization. Research limitations/implications Future research in OVs formation or development would be insightful. In near future studies explaining role of strategic managers and leaders in terms of developing, internalizing OVs would be insightful. The role is to guide its employees and offer an ethical framework to them, but future studies focusing on top management are the need of the hour. Longitudinal studies focusing on evolution of OVs across the organizational life cycle would offer new dimension.For practitioners, there is a need to create formal procedures and tactics pertaining to OV integration. There is imminent need for HR professionals and strategists to adopt or develop contextually relevant, evidence-based instruments to evaluate OVs congruence and integration within the organization. Originality/value This is the first attempt to the best of the authors’ knowledge to review OV literature critically and comprehensively. The hybrid approach uses staged approaches to uncover current knowledge and provide directions for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".