Why do consumers consume masstige products? A cross-cultural investigation through the lens of self-determination theory
Bibliographic record
Abstract
Shaping consumers' attitudes and influencing their consumption of masstige brands via intrinsic and extrinsic motivation is an unexplored research area. By applying self-determination theory, this study attempts to divulge how extrinsic and intrinsic motivations in a cross-cultural context affect masstige consumption behavior. Thus, a research framework and hypotheses are developed and validated using cross-sectional research. The data were collected from 437 Canadian and 412 Indian respondents using Amazon Mechanical Turk. Path analysis was performed using covariance-based structural equation modeling (CB-SEM) to test the hypothesized relationship. The path analysis established that attitudes and masstige consumption are influenced by both extrinsic and intrinsic motivations and further studies established that attitude partially mediates the effect of extrinsic and intrinsic motivations on masstige consumption. However, the moderation analysis failed to establish the role of Hofstede's cultural variable (i.e., power distance) in modifying the effect of extrinsic and intrinsic motivation on masstige consumption for Indian consumers, while it did in the Canadian context, albeit on select relationships. The findings of this study may be useful to masstige marketers in understanding cultural differences when designing promotional campaigns that resonate with consumers.
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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 | 0.003 | 0.006 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".