Managing seller‐buyer new product development relationships for customized products: a contingency model based on transaction cost analysis and empirical test
Bibliographic record
Abstract
Sellers often customize their product offerings in order to increase the value offered to individual buyers and gain a competitive advantage over the seller's competitors. However, such customization has a downside—it usually requires considerable seller‐buyer interactions aimed at matching the seller's technological capabilities with the buyer's needs, which can pose exchange risks such as the safeguarding and adaptation problems noted in the transaction cost analysis literature. In the present study, we develop a contingency model to investigate the impact of product customization on sellers' perceived relationship satisfaction and subsequent expectations of relationship continuity. We draw on the logic of transaction cost analysis to hypothesize that product customization's effect on satisfaction and continuity may be moderated by three activities that sellers may engage in during the new product development (NPD) process: education, product knowledge generation, and joint new product development. Our substantive hypotheses were tested with data from a national survey of 296 small to medium size firms in several high‐tech industries using a series of hierarchical OLS regression models. Overall, we found mixed support for our hypotheses. The results indicated that joint new product development reduced the negative effect of product customization on seller satisfaction and enhanced customization's positive effect on continuity, as expected. Contrary to our expectations, product knowledge generation activities increased the negative effect of customization on satisfaction; it also had no significant moderating impact on continuity. Buyer education activities were found to reduce the negative impact of customization on satisfaction, but showed no moderating effect on continuity. This study offers important theoretical and managerial implications. It is one of the first to rely on transaction cost analysis as a basis for examining how various relationship activities conducted during the new product development process moderate product customization's effect on qualitative outcomes. Whereas traditional NPD processes have emphasized unilateral approaches to product development, our study provides evidence of how bilateral approaches to NPD can benefit sellers of innovations. We provide new insights for managers to consider when deciding whether to engage buyers early on and then continue interacting with them throughout the product development process when developing customized products.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".