Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors
Bibliographic record
Abstract
BACKGROUND: Compulsive and addictive forms of consumption and buying behaviour have been researched in both business and medical literature. Shopping enabled via the Internet now introduces new features to the shopping experience that translate to positive benefits for the shopper. Evidence now suggests that this new shopping experience may lead to problematic online shopping behaviour. This paper provides a theoretical review of the literature relevant to online shopping addiction (OSA). Based on this selective review, a conceptual model of OSA is presented. METHOD: The selective review of the literature draws on searches within databases relevant to both clinical and consumer behaviour literature including EBSCO, ABI Pro-Quest, Web of Science - Social Citations Index, Medline, PsycINFO and Pubmed. The article reviews current thinking on problematic, and specifically addictive, behaviour in relation to online shopping. RESULTS: The review of the literature enables the extension of existing knowledge into the Internet-context. A conceptual model of OSA is developed with theoretical support provided for the inclusion of 7 predictor variables: low self-esteem, low self-regulation; negative emotional state; enjoyment; female gender; social anonymity and cognitive overload. The construct of OSA is defined and six component criteria of OSA are proposed based on established technological addiction criteria. CONCLUSIONS: Current Internet-based shopping experiences may trigger problematic behaviours which can be classified on a spectrum which at the extreme end incorporates OSA. The development of a conceptual model provides a basis for the future measurement and testing of proposed predictor variables and the outcome variable OSA.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".