The Role of CEO Political Identity in Disrupting the Customer Journey: A Qualitative Case Study of Consumer Decision-Making in the EV Industry
Bibliographic record
Abstract
This research examines the role of CEO political identity in shaping consumer behavior throughout the customer journey. Despite the increase in political polarisation, literature on political branding has been limited. To address this research gap, this thesis integrates political psychology and marketing literature, drawing on Social Identity Theory and the Customer Journey Framework. Using Tesla and its CEO, Elon Musk, as a case study, a qualitative, interpretivist approach was adopted, encompassing 16 semi-structured interviews with consumers from Denmark and Canada, which offer nuanced contrasts in political climate and patterns of consumer engagement. The analysis identifies three key themes in each of the pre- and post-purchase stages, categorised by: brand perception, CEO perception, and political identity within the customer journey. Findings show that CEO political identity shapes consumer decisions in the pre- and post-purchase stages, acting as a signal of brand values. Specifically,political misalignment often leads to rejection, even when the product performs well. These responses are shaped by emotional salience and national context: Danish participants are moresensitive to value conflicts, while Canadian participants focus more on functionality. To influence consumer decision-making, managers should limit politically sensitive CEO visibility,separate brand identity from leadership, and maintain neutral brand advocacy. To the authors’knowledge, this is the first study to examine how CEO political identity shapes consumerdecision-making across the customer journey in multiple national contexts, offering practical insights for managing brand trust in politically divided markets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".