The impact of comparative affective states on online brand perceptions: a five-country study
Bibliographic record
Abstract
Purpose \n– The extant literature highlights the significant role of brand perceptions in buying behavior and brand equity. Despite the importance of brand perceptions and the proliferation of online brands, research in an online context is still scarce. The purpose of this paper is to address this gap by investigating the effect of positive and negative comparative affective states (online vs offline) on online brand perceptions. Consistent with existing evidence, highlighting the role of culture on brand perceptions and affective states, this research is conducted in a cross-national setting to identify the stability of the hypothesized relationships among countries. \nDesign/methodology/approach \n– The study uses consumer survey data from five countries (UK, USA, Australia, Canada and China). After imposing metric and factor variance invariance, we used multi-group CFA to test the hypotheses regarding the impact of positive and negative comparative affective states on online brand perceptions across the five countries in the sample. \nFindings \n– The results show that positive comparative affective states have a significant and positive impact on online brand perceptions across the countries studied, although the impact size varies by country. The findings also show that negative comparative affective states, which are context-specific and not induced by any particular brand, have no effect on online brand perceptions across the country samples. \nPractical implications \n– Managers can use the findings reported in this research to inform their branding strategies. For instance, managers may focus on triggering feelings of comfort online as these lead to more favorable online brand perceptions rather than on supressing feelings of caution, as the latter do not directly impact online brand perceptions. \nOriginality/value \n– The study builds on and extends the recent work of Christodoulides et al. (2013) by focussing on online brand perceptions and looking into the role of affective states in a cross-national setting
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".