Smartphone Dependency and Its Impact on Purchase Behavior
Bibliographic record
Abstract
People nowadays seem to become dependent towards smartphone due to its convenience, great camera features, easy applications’ installations, and more importantly, it can do most of the computer functions on the go. Significantly, smartphone usage in Malaysia is growing enormously and has become a significant and lucrative industry. This study aims to understand the antecedents and the outcome of the smartphone dependency among smartphone consumers. Two theories - the theory of uses and gratification and the media dependency theory- were used as the theoretical basis for this study, in order to determine the motivations to use a smartphone and to define dependency and its outcome. The antecedent variables were convenience, social need and social influence; and all of these dimensions are conceptualized as one-dimensional. The outcome of dependency on smartphones was expected to be the purchase behavior. Data analyses were based on 226 valid questionnaires that were collected among smartphone users. The result from the Confirmatory Factor Analysis of Partial Least Square (PLS) shows that only social needs and social influence significantly influenced the smartphone dependency among consumers, therefore indicating that these two factors are important to influence dependency on the smartphone. In addition, the analysis also verifies that the purchase behavior is the outcome of the dependency on the smartphone. Based on these results, marketers could focus on creating dependency among consumers on smartphone usage based on the consumers’ social need, which eventually will promote future purchase behavior in the long run. More importantly, understanding social influences as antecedents.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".