A Study on the Relationship between the Typology of Knowledge-Intensive Businesses (KIBs) and Performance Measurement Systems (PMS): Evidence from Taiwan
Bibliographic record
Abstract
This study utilizes the source of knowledge (internal or external) to derive four types of knowledge-intensive businesses (KIBs): non-KIB, knowledge creator, knowledge introducer, and knowledge integrator. Moreover, this study investigates the differences among the four types of KIB in their organizational characteristics and performance measurement system (PMS). The proposed model is empirically evaluated using survey data collected from top or middle managers of 127 Taiwan companies i n which 109 companies are classified as KIBs. Based on the analysis of multiple regressions and MANOVA, the empirical results reveal that there are significant differences in the organizational characteristics and PMS among the four types of KIBs. There are three main contributions in this research: (1) we categorize the KIBs into four types by using the main source of internal or external knowledge in the companies. This typology of KIBs would contribute to our understanding of knowledge management. (2) This study uses environmental uncertainty, innovation strategy, size and absorptive capacity to construct the organizational configuration of different KIBs. (3) We find that there are significant differences among the four types of KIB in the financial or non-financial PMS. This study utilizes the source of knowledge (internal or external) to derive four types of knowledge-intensive businesses (KIBs): non-KIB, knowledge creator, knowledge introducer, and knowledge integrator. Moreover, this study investigates the differences among the four types of KIB in their organizational characteristics and performance measurement system (PMS). The proposed model is empirically evaluated using survey data collected from top or middle managers of 127 Taiwan companies i n which 109 companies are classified as KIBs. Based on the analysis of multiple regressions and MANOVA, the empirical results reveal that there are significant differences in the organizational characteristics and PMS among the four types of KIBs. There are three main contributions in this research: (1) we categorize the KIBs into four types by using the main source of internal or external knowledge in the companies. This typology of KIBs would contribute to our understanding of knowledge management. (2) This study uses environmental uncertainty, innovation strategy, size and absorptive capacity to construct the organizational configuration of different KIBs. (3) We find that there are significant differences among the four types of KIB in the financial or non-financial PMS.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".