How Philip Kotler has helped to shape the field of marketing
Bibliographic record
Abstract
Abstract Purpose – Philip Kotler is one of the pioneers who has contributed to the broadening of academic inquiry in the field of marketing. He has had a significant role in shaping how marketing is taught to and practised by students and managers of marketing. By examining the personal and macroenvironmental influences that have come to shape his work, this paper seeks to explore how Philip Kotler has achieved such influence in the field of marketing. Design/methodology/approach – The research was driven by a desire to understand the context in which Kotler developed his work, including the personal influences on his life as well as the macroenvironmental forces within which his work has emerged. To this end, the reseaerch employed qualitative techniques to analyze a number of data sources including depth interviews with Philip Kotler and nine of his colleagues, participant observation at Kotler's 75th birthday celebration hosted by the Kellogg School, a review of marketing textbooks, and a review of relevant literature. Findings – The research reveals the keys to Philip Kotler's success are his ability to learn from the people around him and the events of the times, and his ability to integrate this knowledge into succinct, well‐communicated, timely lessons for others to follow. Kotler's work emerged within a period of time that has witnessed a thrust towards marketing as a science and the rise of the managerial school of thought. Given this context, the significance of Kotler's work is that it has contributed to the legitimacy of the field of marketing as both a rigorous academic discipline and a managerial domain of strategic importance within organizations. Practical implications – Gaining an understanding of Philip Kotler and his work contributes to our understanding of how the marketing field has been shaped, including the kinds of academic inquiry marketers deem legitimate and the nature of how we teach students to practice marketing management. Originality/value – Little attention has been paid to the factors that have influenced the work of Philip Kotler and how he has, in turn, come to shape the field of marketing. This research allows the reader to see the man behind the work and the influences on his thinking.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".