A Theory of Marketing’s Contribution to Customers’ Perceived Value
Bibliographic record
Abstract
The way customers perceive value is fundamental to business survival and success and is central to marketing theory and practice. Perceived value as an overarching concept describes the contribution of marketing as a key driver in a firm’s success. Though perceived value has gained the attention of academics and practitioners in recent years, the conceptual roots have diverged in different directions. This article synthesizes the concept of customers’ perceive value and presents a theory of marketing’s role in it. This study develops a new theoretical framework of how marketing drives perceived value of customers by reducing and eliminating marketplace imperfections that are always present in oligopolistic markets. This study develops a new theoretical framework of how marketing drives perceived value of customers. We first consider the constituents of customer value—the types of costs and benefits that determine customer’s utility from a purchase, and how marketing can influence these costs and benefits. We then draw upon economics and marketing theories to argue that irrespective of the conceptual lens one adopts, marketing’s primary function is to contribute to perceived value for firms’ customers, which in turn allows marketing to capture value. This article presents four distinct propositions, which articulate how marketing creates value for customers by reducing different types of marketplace imperfections. This article contributes to theory buildings in marketing literature. Laying out theoretical arguments to establish the existence of a marketing-value relationship is critical to validate the relevance and importance of marketing within the firm. The propositions presented in this article can be further used to investigate marketing challenges and questions. From a managerial perspective, it is critical to understand the role of the marketing function in influencing customer value as well. Improved empirical understanding of marketing’s role will enable CEOs to better leverage their firms’ marketing to achieve strategic priorities. Furthermore, by identifying specific marketing activities that create value for customer in different circumstances, managers can more effectively build and leverage their marketing and allocate resources more judiciously. This article synthesizes key developments in the area of customers’ perceived value and presents four novel propositions on marketing’s role in creating customer value.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".