What makes up intentions to purchase the pioneer? A theory of reasoned action approach in India and the USA
Bibliographic record
Abstract
Purpose The purpose of this paper is to extend the research paradigm focusing on behaviorally-based first-mover advantages (FMA) by applying the widely-accepted Theory of Reasoned Action (TRA) and offers insights into differences between a mature market (USA) and an emerging market (EM) (India) regarding how intentions to purchase the pioneer are formed. Design/methodology/approach Utilizing samples of 208 USA and 194 Indian consumers, hypotheses examining the underlying beliefs, attitudes, social norms and purchasing intentions regarding pioneer brands are developed and tested using structural equation modeling. Findings Insights from the study suggest the TRA provides a means for assessing behaviorally-based FMAs across cultures, even as manifestations of purchase intentions differ significantly. According to the TRA and findings of this study, intentions are a function of overall attitudes and social norms. In the USA, individual attitudes were found to play a more significant role than social norms in formulating purchase intention. In India, social norms played a more dominant role in intention formation. Originality/value The study represents one of the first empirical attempts to shed light on the extent of behaviorally-based FMAs in an EM and how manifestations of intention to purchase the pioneer differ from mature markets. The study expands the behavioral paradigm of analysis to include one of the most sought-after EMs today (India) and provides one of the first empirical studies to utilize the TRA in addressing behaviorally-based FMAs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".