Untangling Utilitarian and Hedonic Consumption Behaviors in Online Shopping
Bibliographic record
Abstract
Increasingly, researchers have come to acknowledge that consumption activities comprise both utilitarian and hedonic elements. Whereas utilitarian consumption accentuates the achievement of predetermined outcomes typical of cognitive customer behavior, its hedonic counterpart relates to affective customer behavior in dealing with the emotive and multi-sensory aspects of the consumption experience. While utilitarian consumption activities appeal to the rationality of customers in inducing their intellectual buy-in of the consumption experience, customers’ emotional buy-in can only be attained through hedonic consumption activities. The same can be said for online shopping. Because the online shopping environment is characterized by the existence of an IT-enabled web interface that acts as the focal point of contact between customers and vendors, its design should embed utilitarian and hedonic elements in order to create a holistic consumption experience. Drawing on the Expectation Disconfirmation Theory (EDT), this study advances a model that not only delineates between utilitarian and hedonic customer expectations for online shopping but also highlights how these expectations can be best served through design elements of e-commerce websites catering to functional and aesthetic performance respectively. The model is then empirically verified via an online survey administered on a sample of 303 student respondents. Theoretical contributions and pragmatic implications to be gleaned from our empirical findings are discussed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".