Antecedents and consequences of using information from customers involved in new service development
Bibliographic record
Abstract
Purpose – The current research aims to analyze antecedents and consequences of using the information provided by customers involved in new service development (NSD). It also seeks to examine the moderating effect of technological turbulence on the antecedents and consequences of information use. Design/methodology/approach – Data were collected from service firms in a variety of industrial sectors. Unit of analysis was a NSD project in which current or potential customers were involved during its development process. A self-administered mail survey was used to collect the data. A total of 102 complete questionnaires were returned. The proposed model was tested using partial least squares. Findings – The results indicate that the instrumental use of the information collected from customers involved in NSD can lead to higher service advantage and service newness and in turn to higher market performance. Findings reveal that higher recording and reviewing of information collected from customers involved in NSD result in greater use of the information during the NSD process. An important result is that under technologically turbulent environments, recording and reviewing information from customers involved in NSD is less likely to result in a higher instrumental use of such information. Finally, the authors' results show that when technological turbulence is high the instrumental use of information from customers involved in NSD has lower value for both service advantage and service newness. Originality/value – Findings confirm the importance of customer involvement for NSD in a business context. Using the information from customers involved in NSD to resolve specific problems or make decisions regarding NSD projects can result in enhanced service performance. Moreover, information processing capabilities are key antecedents to instrumental information use. Finally, managers should be aware of the potential negative effect of technological turbulence.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".