Customer participation risk management: conceptual model and managerial assessment tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 it