Uncertain Environment and Organizational Performance: The Mediating Role of Organizational Innovation
Bibliographic record
Abstract
<p><strong><em>Purpose: </em></strong>How to survive<strong><em> </em></strong>in dynamic and uncertain business environment?, is one of the biggest<strong><em> </em></strong>challenge for corporations today. To answer this question, current study<strong> </strong>examines the role of organizational innovation for improving performance in today’s competitive, dynamic and uncertain business environment.<strong><em></em></strong></p><p><strong><em>Design:</em></strong> The study used structured closed-ended survey questionnaire and data is collected through self-administered technique to increase the response rate. The unit of analysis is the employees working in cellular industry in Pakistan. The analysis techniques includes, validity analysis through confirmatory factor analysis (CFA) reliability analysis through Cronbach alpha, correlation analysis, hypotheses testing utilizing structure equation modeling (SEM) in AMOS software whereas, mediation through method of Baron and Kenny (1986).</p><p><strong><em>Findings:</em></strong> Results show that organizational innovation plays the mediating role between uncertain environment and organizational performance. <strong></strong></p><p><strong><em>Practical implications: </em></strong>The study proposes<strong> </strong>that organizational innovation is inevitable<strong> </strong>for maintaining organizational performance in uncertain business environment particularly in dynamic industries.</p><p><strong><em>Originality:</em></strong> The current study proposed and tested an important conceptual model that explains the mediating role of organizational innovation to enhance the organizational performance in uncertain business environment.</p>
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".