Determinants Of Retail Electronic Purchasing: A Multi-Period Investigation1
Why this work is in the frame
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Bibliographic record
Abstract
AbstractAcademic research as well as business literature recognizes the importance of electronic commerce and retail electronic purchasing (REP), i.e., the use of the Internet by individuals to purchase goods and services. This paper draws upon established theoretical areas of marketing and innovation adoption to identify the factors expected to influence REP. It empirically examines the effects of these factors, as well as the changes in these factors and in their effects over time, using publicly available multi-period data. The results indicate that REP is facilitated by the individual’s household income, educational level, Internet use and Internet search, as well as by perceptions of Internet security and the perceived quality of web vendors’ sales processes. We did find some surprising results: most notably, there was no association between REP and the perceived quality of web vendors’ post-sales activities. Moreover, there was no significant change over time in the perceived quality of web vendors’ sales and post-sales activities. However, Internet use, Internet search, and perceptions of Internet security, show an increasing trend.RésuméTant le milieu de la recherche universitaire que les publications d’affaires reconnaissent l’importance du commerce électronique et du commerce de détail en ligne, c’est-à-dire l’utilisation d’Internet par les particuliers pour acheter des biens et services. Cet article s’inspire de concepts théoriques établis dans les domaines du marketing et de l’adoption de l’innovation pour dégager les facteurs susceptibles d’avoir une incidence sur le commerce de détail en ligne. De manière empirique, il examine les effets de ces facteurs, de même que l’évolution de ces facteurs et de leurseffets au fil du temps, en s’appuyant sur des données multi-période accessibles au public. Les résultats démontrent que l’accessibilité au commerce de détail en ligne est liée au revenu du ménage, au niveau d’instruction, à l’utilisation d’Internet et à la recherche dans Internet ainsi qu’aux perceptions de la sécurité sur le Web et de la qualité des procédés de vente des cyberdétaillants. Nous avons obtenu certains résultats surprenants : étonnamment, il ne semble pas y avoir de lien entre les achats en ligne et la perception de qualité des activités après-vente des détaillants. De plus, on n’a noté aucun changement significatif dans la perception à long terme de la qualité des activités de vente et des activités après-vente du cyberdétaillant. Cependant, l’utilisation d’Internet, la recherche dans Internet et les perceptions à l’égard de la sécurité sont en progression.Key Words: Retail Electronic PurchasingElectronic CommerceInternetInnovation AdoptionConsumer BehaviorMots-clés: commerce de détail en lignecommerce électroniqueInternetadoption de l’innovationcomportement du consommateur Additional informationNotes on contributorsRichard E. BurroughsDr. Richard Burroughs is Assistant Professor of Management Information Systems at Barry University in Miami Shores, Florida. Dr. Burroughs has over 20 years of experience in the private sector managing large system implementations. Between 1995 and 1999 he managed a large public e-government web site. He has published research on teaching M.I.S., improving I.T. personnel productivity, the adoption of electronic commerce, and case studies on international information systems. His current research interests include electronic commerce design and implementation issues.Rajiv SabherwalDr. Rajiv Sabherwal is the Emery C. Turner Professor of Information Systems at University of Missouri, St. Louis. Dr. Sabherwal’s research focuses on knowledge management, strategic management of the information systems function, and social aspects of systems development. He has published extensively on these topics, in journals including Information Systems Research, MIS Quarterly, Organization Science, California Management Review, Decision Sciences, Communications of the ACM, European Journal of Information Systems, Journal of MIS, and Accounting, Management, and Information Technology. He is an Associate Editor for MIS Quarterly and the IEEE Transactions on Engineering Management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it