A case study on attitude towards online auction use applying quantile regression analysis

Yen Chun Chen, Hsun Chi Chu, Jyun Yi Wu, Nomin Tsembel, Yung Cheng Shen

Research output: Contribution to journalArticlepeer-review

Abstract

This study uses the technology acceptance model (TAM) as a basis, extended by conducting quantile regression to examine the factors influencing the use of an online auction Website and then comparing the results to the conventional linear regression-type approaches. Specifically, the TAM has been widely assessed in various research contexts by using ordinary least squares regression model and structural equation modelling. However, these two methods focus on describing the ‘average’ behaviour of a conditional distribution, thus overlooking extreme value and outlier effect. Following this, this study applied quantile regression model to examine the TAM for online auctions, thereby complementing previous empirical findings about the TAM. Using survey data from 476 online auction users with experience selling items on Yahoo!Auction, the results indicate that perceived ease of use is most important for customers with a more favourable attitude towards the site, whereas perceived enjoyment plays a more crucial role in improving attitudes towards using the site among customers with less favourable attitudes towards the site. Findings also suggest that perceived usefulness is more effective in improving attitude towards using the site among customers with moderately favourable attitudes than among customers with unfavourable and very favourable attitudes.

Original languageEnglish
Pages (from-to)872-892
Number of pages21
JournalTotal Quality Management and Business Excellence
Volume30
Issue number7-8
DOIs
Publication statusPublished - 2019 May 19

Keywords

  • perceived ease of use
  • perceived enjoyment
  • perceived usefulness
  • quantile regression
  • technology acceptance model

ASJC Scopus subject areas

  • Business, Management and Accounting(all)

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