How customer and product orientations shape political brands
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a theory of consumer and product orientation in the realm of political branding to illustrate how politicians can choose to position and present themselves to voters. It is evident that some politicians play an active role in shaping the beliefs and actions of their constituents, while others are more influenced by voter sentiment. The effectiveness of the political strategy is highly influenced by the market realities of the voting body in question. Design/methodology/approach A dichotomy is presented to shed light on how consumer and product orientation might influence the way in which politicians choose to address the public. Specifically, four modified strategic orientation archetypes are presented and analyzed with particular focus on political brands and strategy. Findings Product and consumer orientations have been shown to also be applicable to the strategic positioning of political brands. While it can be argued that no strategy is superior over another, careful consideration of the political environment in question and subsequent execution of an appropriate stance can be used to better manage the relationship between the electorate and politicians. Research limitations/implications This study provides academics in this area with a comprehensive examination of strategic orientation literature in political contexts, and lays out a strong groundwork for future studies. In this burgeoning area of research, there are several opportunities for marketing and political strategy academics to dive deeper into the intricacies that drive politicians to adopt specific strategic orientations, and how these strategies evolve over time and in differing political environments. Practical implications This analysis suggests that there are opportunities for political strategists to explore the relationship between the identified strategic orientations and political brands, and for political marketing scholars to investigate the modes of focus presented. Originality/value This analysis provides better understanding of how politicians can influence voters and voters can influence political brands, and how the strategic orientation archetypes can be used to influence decisions about political strategy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".