Transferring knowledge for organisational customers by knowledge intensive business service marketing firms
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the knowledge transfer processes of knowledge intensive business service firms by focusing on the knowledge for customer, which is the knowledge about the service provider's products and services, specifically “before‐sale” knowledge, and the transfer of this knowledge in order to develop customers. Design/methodology/approach The authors conducted an in‐depth qualitative study of the knowledge transfer process undertaken by a sample of six global knowledge intensive service firms, to use knowledge transfer as a means of customer development. Findings The results of this study suggest that customer absorptive capacity influences the role that knowledge for customers has in ultimately determining whether customer development will occur. Where tacit knowledge transfer occurs, it is restricted to loyal, high share customers. With respect to methods of transfer, the findings reveal that knowledge‐intensive business service firms transferring explicit knowledge utilise both formal and informal methods. Research limitations/implications Data collection was cross‐sectional and longitudinal research would have the benefit of examining how customer knowledge transfer changes over time during the customer development process (pre‐sale, during sale and post‐sale customer development). Future research studying other types of knowledge transfer, such as during‐sale and after‐sale knowledge transfer, are also encouraged. Practical implications Managers should be open to employing numerous types of media in transferring both explicit and tacit knowledge rather than restricting themselves to the normative “explicit‐formal‐media lean” versus “tacit‐informal‐media rich” categorisations in the literature. Originality/value Understanding the role of customer knowledge transfer in the development of existing organisational customers is particularly important in the context of knowledge intensive business service firms. The extant literature recognises that customer development efforts are critically important in increasing service adoption and firm performance but there exists a dearth of research on customer knowledge transfer in the context of professional service organisations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".