Transaction Cost Economics: An Analysis of Commitment in Asymmetrical Insurer-Broker Dyads An Exploratory Case Study of ING Canada and its Distribution Counterparties
Bibliographic record
Abstract
Since the early 1980's there has been a heightened academic interest in the field of commitment, particularly as it relates to business relationships. Knowledge of commitment continues to advance and has begun splintering and applied into specific and narrow fields. The particular field of interest in this study surrounds commitment levels in business relationships within property and casualty insurance distribution networks. \nThe intent of understanding and enhancing commitment levels is to allow stakeholders to explore new ways to improve profitability. This can be achieved by deepening the level of understanding and knowledge of relationship partners with a view to anticipating and fulfilling their needs better than the competition. However, commitment is intangible and involves many factors including human emotion. This increases the difficulty in comprehending the whole phenomenon of commitment. \nTo assist in furthering the knowledge in this area, transaction cost theory is examined and applied to insurance company and broker relationships. In seeking a greater understanding of the underlying drivers of commitment, this thesis investigates the theoretical contribution of transaction cost economics theory in assessing commitment levels. The purpose is to utilize the elements of transaction costs as a means to extend the awareness of how commitment is constructed, and to search for ways to improve and strengthen these relationships. \nThe primary research method consists of three major case studies within the Canadian property and casualty insurance distribution sector. The first case study explores the perspectives of insurance brokers in Ontario. The second study reveals the perceptions of relationship managers employed with ING Canada, the country's largest property and casualty insurance company. Lastly, the research incorporates a series of interviews with ING Canada senior executives to capture their perspectives and validate the research findings from the first two case studies. These investigations into the Canadian insurance industry have provided several outputs, chief among them is the development of a conceptual model referred to as the 'Commitment Wheel'. This model has the advantages of seating affective and calculative commitment at the centre of a moving environment of commitment enablers.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".