Consumer pandemic animosity: scale development and validation
Bibliographic record
Abstract
Purpose This study introduces and investigates the concept of consumer pandemic animosity to (1) develop and validate a scale (i.e. CPAS) to measure consumer animosity in the context of a health pandemic; and (2) identify the effects of pandemic animosity on consumer purchase intentions in the field of general consumption and tourism. Design/methodology/approach The CPAS factor structure was initially tested on a sample of 201 American consumers based on participant interviews and expert evaluations. This exploratory phase identified two factors, namely CPAS emotions and beliefs, which were subsequently supported in the confirmatory factor analysis. Measurement and configural invariance of CPAS and discriminant and nomological validity were confirmed in an independent sample of 303 American consumers. A new sample of 203 Canadian consumers was used to test the external validity of CPAS by controlling for other types of consumer animosity dimensions. Structural equation modelling was used to test the effects of CPAS on consumer purchase intentions in general product consumption and tourism. Findings This study contributes to expanding on the conceptualization of the consumer animosity construct that has been dealt with in economics, politics, culture and religion but never of a pandemic health crisis to date. Results indicate the psychometric soundness of the CPAS and the multifaceted nature of this construct by clearly identifying two levels of animosity (i.e. beliefs and emotions). Moreover, the structural model shows a significant and unique impact of pandemic animosity on consumer purchase intentions and travel intentions. Originality/value This is the first empirical study proposing a new scale to measure the consumer disposition of animosity developed due to a pandemic affecting the world. It also offers a new dimension to the typology of animosity proposed by Jung et al . (2002): intentionality (intention-driven vs non-intention-driven). This paper presents a number of propositions that serve to identify testable hypotheses amenable both to validation and usefulness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".