The Role of Strategic Technology Alliances (STA) Towards Organizational Performance in Manufacturing Industry: The Perspective of Developing Countries
Bibliographic record
Abstract
Purpose: This research investigates strategic technology alliances (STA) in Malaysian manufacturing firms and the impact on organizational performance. The outcomes of this paper shed light on the underpinning theories under Resource Based View (RBV) and Organizational Learning (OL) as an antecedent for the successful implementation of strategic technology alliances in the manufacturing industry. At the end of the paper provides a model that describing the success factor of strategic technology alliances en route to improve organizational performance and remain competitive.Design/methodology/Approach: Research conducted through survey design by collecting questionnaires from 335 Malaysian manufacturers. The empirical analysis performed by using structural equation modeling (SEM) to represent the findings as this statistical method is more robust compared to others.Findings: The empirical analysis show that absorptive capacity, type of alliance and strategic technology alliance has positive relationship towards improving organizational performance in terms of market share, profit, sales level and manufacturing capabilities. However, in the other coin, resource base availability is insignificant towards organizational performance. Furthermore, technology transfer also only partially mediates STA and organizational performance. Despite the onset of successful alliance formation and resultant technology transfer, firms still need to invest in developing their resources, employee skills, production methods and industrial processes in order to sustain their competitiveness in the global economy.Originality/value: Many organization embrace technology transfer as part of the strategic weapons to maintain business sustainability. However, there is lack of empirical evidence profiling the antecedent on how organization could success in becoming the developers of their own technology. Therefore, this research attempts to provide a model as guidance to managers in developing country as a key to success in forming technology alliances with foreign countries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".