Customer participation risk management: conceptual model and managerial assessment tool
Bibliographic record
Abstract
Purpose Customer participation (CP) has received considerable interest in the service literature as a way to improve the customer experience and reduce service providers' costs. While its benefits are not in question, there is a paucity of research on potential pitfalls. This paper provides a conceptual foundation to address this gap and develops a comprehensive model of the risks of customer participation in service delivery, integrating research from the marketing, operations and supply chain management, strategy, and information technology fields. Design/methodology/approach The model is derived deductively by integrating insights from research in marketing, operations and supply chain management, strategy, and information technology. Findings This paper identifies three categories of potential risks of CP (i.e. market, operational, and service network) and discusses ways that firms can mitigate these risks. Building on the model, it develops a CP risk assessment tool that managers can use when evaluating increases in CP. Research limitations/implications The conceptual model proposed in this paper can serve as a robust basis for future research in customer participation, particularly in such areas as sharing economy services, service delivery networks, and experiential services. The risk assessment tool offers clear guidelines for managers who are considering an increase in customer participation in their service. Originality/value This is the first attempt to conceptually define customer participation risk and develop a comprehensive model of its drivers and strategies to mitigate it. This paper develops a straightforward method for managers to evaluate CP risk.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".