Identifying supply chain effect in predicting industrial IT acceptance - A case of RFID in retail chain stores

碩士 === 中興大學 === 行銷學系所 === 95 === Over the past two decades, radio frequency identification (RFID) has received great attention of industries and researchers. However, research on industrial acceptance of RFID was rarely seen. We integrated Diffusion of innovation theory (DOI), and Technology accepta...

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Bibliographic Details
Main Authors: Hsin-Chieh Wu, 吳欣潔
Other Authors: 蔡明志
Format: Others
Language:en_US
Published: 2007
Online Access:http://ndltd.ncl.edu.tw/handle/34998065506319990951
Description
Summary:碩士 === 中興大學 === 行銷學系所 === 95 === Over the past two decades, radio frequency identification (RFID) has received great attention of industries and researchers. However, research on industrial acceptance of RFID was rarely seen. We integrated Diffusion of innovation theory (DOI), and Technology acceptance model (TAM) as a basis for investigating the acceptance and use of IT. However, “supply chain coordination” has become a critical success factor for firms to align supply chain members’ objectives and coordinate activities so as to optimize system performance. Literature review indicates that DOI did not clearly categorize the coordination between supply chain members, and was not sufficient to present the supply chain effect on the benefit and cost of firms. This study aims to identifying supply chain effect in predicting industrial RFID acceptance. Retail chain stores in Taiwan were selected as empirical studies because they maybe the forerunner of RFID and their sales power in the market was great. Among 322 retail chain stores in Taiwan, 115 chains have accepted our invitations for interviews. In the importance average points, Anticipated satisfaction (3.95) is the highest one; following is Organizational characteristics (3.94), Expected difficulty (3.90), and Supply chain coordination (3.83). Even is the lowest one (Environmental characteristics) is still get 3.78 in average. These mean that the five variables we construct are all between the degrees of important to very important. Further research is suggested to increase the sample size and calibrate model, and then this study will be more mature.