Understanding small and medium enterprises’ behavioral intention to adopt social commerce: a perceived value perspective
Bibliographic record
Abstract
Purpose This study aims to examine the factors (Stimuli) enhancing perceived utilitarian, social and conditional values (Organisms) of social commerce (s-commerce) platforms and their impact on small and medium enterprises’ (SMEs’) behavioral intention (Response) to adopt s-commerce. Design/methodology/approach Survey data were gathered from 304 Indian SMEs using s-commerce platforms. Data were analyzed using SmartPLS 3 software. Findings The results indicated that perceived values significantly impact SMEs’ behavioral intention to adopt s-commerce. Among conditional, utilitarian and social values, the conditional value of s-commerce sites was found to be the strongest motivator for SMEs to adopt s-commerce. Research limitations/implications This research contributes to the growing literature on s-commerce, explaining how perceived value influences the decision of SMEs to adopt s-commerce platforms. Practical implications Among the significant influencers, perceived usefulness and perceived reputation were found to be the most effective triggers that stimulate perceived values of s-commerce sites. The findings draw due attention from policymakers toward environmental cues such as the legal and regulatory environment, which are instrumental in creating the most important perceived value for SMEs, i.e. conditional value. Originality/value By employing the inputs from the theory of consumption values and the Stimulus-Organism-Response framework, this original study looked beyond the technology factors and examined the role of perceived values of s-commerce platforms in shaping SMEs’ behavioral intention to adopt.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".