Perceived Risk in Apparel Online Shopping: A Multi Dimensional Perspective LE RISQUE PERÇU DANS DES ACHATS EN LIGNE D'HABILLEMENT : UNE PERSPECTIVE DE DIMENSIONNELLE MULTIPLE
Bibliographic record
Abstract
Abstract: The purpose of this study, drawing on marketing and psychometric paradigms, is to investigate the effect of risk perception dimensions on apparel internet purchase intention among Saudi consumers. A web-based survey was conducted to measure consumers' perception of the six types of risk associated with apparel online shopping and their influence on purchase intention. Three hundred responses were collected. Results showed that not all the considered risk constructs have the same influences on apparel internet purchasing intention. Specifically, time and performance risks have the most significant influence followed by privacy and social risks. Key words: Consumer behavior; Apparel; Internet shopping; Saudi Arabia Resume: Le but de cette etude, dessinant sur le marketing et les paradigmes psychometriques,est d'etudier l'effet des dimensions de perception de risque sur l'intention d'achat d'habillement sur l'Internet parmi les consommateurs saouthens. Une enquete basee sur le WEB a ete menee pour mesurer consommateurs des six types du risque lies aux achats en ligne d'habillement et de leur influence sur l'intention d'achat. Trois cents reponses ont ete rassemblees. Les resultats ont prouve que non toutes les constructions considerees de risque ont la meme influence sur l'Internet d'habillement achetant l'intention. Specifiquement, le temps et les risque de representation ont l'influence la plus significative suivie de l'intimite et des risques sociaux. Mots cles: Comportement du consommateur; Habillement; Achats d'Internet; Arabie Saoudite 1. INTRODUCTION Despite the economic downturn during 2008, Electronic commerce (e-commerce) has continued to experience exponential growth. According to Forrester report, the forecasts online retail sales in the U.S. will be nearly $250 billion, up from $155 billion in 2009. During 2009, online retail sales were up 11 percent, compared to 2.5 percent for all retail sales. With the rapid development of the World Wide Web and an increasing percentage of the worldwide population gaining internet access, e-commerce will play an important economic role (Chiang & Nunez 2007). The popularity of internet shopping has stimulated widespread research aimed at attracting and retaining consumers from either a consumer- or a technology-oriented view (Jarvenpaa and Todd 1997). However, many scholars have argued that perceived risk in internet shopping negatively influence consumer behavior during online shopping (Park, Lee, and Ahn, 2004) and intention to shop online (Salisbury, Pearson, Pearson, and Miller, 2001; Pavlou, 2003). Similar to other developed and developing nations, there has been a tremendous increase in internet users in Saudi Arabia; where there were one million users during 2001 and around 9.6 million users in the beginning of 2009 with 35% annual growth and 38% usage among the population (Alriyadh, Sep.2009). According to the latest World Internet User Statistics report, Saudi Arabia ranked second after Iran among Middle Eastern countries in internet usage. The Saudi Arabian market is considered the largest retail market in the Middle East. According to Business Monitor International report (2010), the forecast average annual private consumption growth in Saudi Arabia is 7.9% between 2011 and 2014. In the e-commerce dimension, most of the Saudi web sites are very weak in buying and selling facilities and do not apply transaction processing, trust, e-payment, and rewards and loyalty programs. Saudi Arabia's consumers spent online more than $3.28 billion in B2C e-commerce. The Arab Advisors Group (the major survey of Internet users in Saudi Arabia), revealed that 48.36% of internet users in Saudi Arabia reported purchasing products and services online and through their mobile handsets during 2007. According to master card report for the first quarter of 2009, Saudi consumers have the highest consumption and spending rates in The Middle East and Africa in spite of the economic downturn. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".