The negative effect of team’s prior experience and technological turbulence on new service development projects with customer involvement
Bibliographic record
Abstract
Purpose – The current study examines the negative moderating effects of team’s prior experience and technological turbulence on the antecedents and consequences of using information provided by customers involved in new service development (NSD). It also examines one way to mitigate the proposed negative effects. Design/methodology/approach – The unit of analysis was NSD projects in which customers had been involved during the development process. A self-administered mail survey was used to collect the data. The proposed model was tested using hierarchical path analysis. Findings – Results show that team’s prior experience reduces the extent to which recorded and shared information from customers involved in NSD is used for project-related decisions during the development process. Findings also reveal that technological turbulence can reduce the positive effect of using information provided from customers involved in NSD on new service advantage and service newness. Finally, results show that involving lead users in NSD can help reduce the negative moderating effects of team’s prior experience and technological turbulence. Originality/value – The literature on information use suggests that availability of information does not guarantee its use. In keeping with this argument, the current study reveals that for NSD projects with customer involvement, team’s prior experience and technological turbulence are part of the challenge of making effective use of the new knowledge that customers bring to the development project. Firms are advised to collaborate with lead users as a way to attenuate this problem.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".