Is diffusion of marketing competence necessary for a market orientation? A comparative investigation of marketing managers and their defining traits
Bibliographic record
Abstract
Purpose – Increasingly, scholars and analysts are urging firms to transition from a model in which marketing is a discrete function to a diffused approach in which marketing is everyone’s job. Prior research has examined differences in firm level performance. However, this firm level focus has overlooked what effects this transition might have on the managers who perform the marketing role. The purpose of this paper is to investigate manager level consequences of transitioning between these approaches by evaluating differences in person-environment (P-E) fit between marketers and non-marketers. Design/methodology/approach – The authors identify core marketing functions and relevant personality traits of marketing managers, based on the marketing literature. The authors then compare personality and career satisfaction data from 465 marketing managers against a larger, general employment sample of 3,100 employees. Finally, the authors examine the relationship of career satisfaction to each of these traits and investigate how these relationships differ across the two groups. Findings – The authors find important differences between marketers and non-marketers. Most importantly, the authors found that the relationships between personality and career satisfaction were significantly different for traits suggested by the research literature as important to the marketing function. In particular, customer orientation, visionary leadership, optimism, and assertiveness were all associated with higher career satisfaction for the marketing sample than for the general sample. Originality/value – This paper is among the first to examine manager level differences relevant to transitioning between firm level marketing approaches. For firms considering adopting the “everyone is a marketer” diffused approach, the findings reveal pitfalls that can lead to reduced career satisfaction, reduced manager performance, and increased turnover. As a result, the performance of firms that have already adopted a diffused approach may be misleading for those firms who have not. At a minimum, firms contemplating a transition to a diffused approach should conduct an assessment of P-E fit similar to that illustrated in this paper to assess the potential risks.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".