Social media and consumer buying behavior decision: what entrepreneurs should know?
Bibliographic record
Abstract
Purpose This paper aims to investigate the impacts of social media on the Pakistani consumers' buying behavior, which could be reflected in either complex buying, variety seeking, dissonance reducing or habitual buying. Entrepreneurs need to know how their loyal and prospective customers feel, think and how do they decide on purchasing certain products and services. Design/methodology/approach The self-administered online questionnaire is used to collect feedback from consumers in order to analyze the data and come up with the findings. A sample size of 396 respondents was used to analyze and find a relationship between social media and consumer buying behavior. Findings Social media is found to have a partially significant impact on Pakistani consumers' buying behavior; word of mouth and content credibility are the two factors that influence Pakistani consumers' buying behavior. Pakistani consumers, below the age of 40, possess more complex buying behavior, which alerts entrepreneurs to consider it for their future marketing strategies. Practical implications Entrepreneurs should make an effort to be differentiated from others while keeping customers aware of the products they provide. In addition, customers should not spend too much time when comparing brands; rather, businesses should make it more captive. Originality/value This paper provides different results in comparison to the previous studies, in terms of the factors influencing consumers' buying behavior.
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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 | 0.004 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".