First this paper introduces the concepts of virtual manufacturing system (VMS). The host enterprise and the multiple numbers of supply and distribution enterprises that…
First this paper introduces the concepts of virtual manufacturing system (VMS). The host enterprise and the multiple numbers of supply and distribution enterprises that make up a VMS, and the hierarchical and horizontal relationship that exists between these enterprises are explained. The steps involved in formation and operation of a VMS are then analyzed in detail. Second, we present a three view based methodological approach to make a multi‐agent model of VMS. Finally, with the help of a testing prototype, we show how to develop an autonomous Internet based data collection system for operation of VMS in accordance with the proposed methodological approach.
When an enterprise wants to design its distribution chain, it needs first to assess all possible distributors, then select the eligible ones to form the design model. This assessing process can be finished by distributor benchmarking. In this paper, a new approach is developed to benchmark distributors. The benchmarking process is done by the following three steps. First, all factors needed for benchmarking a distributor are identified by a systematic analysis. Second, an internet‐based information acquisition module is developed to get all needed information from possible distributors. Third, an inference module based on the combination of fuzzy logic and array‐based logic is developed to benchmark a distributor. As the information acquisition module is implemented via Internet, and the inference process for benchmarking a distributor is executed by computer applications, it is possible to realize online distributor benchmarking by the approach provided in this paper.
This study aims to investigate how prior reviews posted by other consumers affect subsequent consumers’ evaluations and to what extent the review temporal distance can…
This study aims to investigate how prior reviews posted by other consumers affect subsequent consumers’ evaluations and to what extent the review temporal distance can increase or reduce the social influence of prior reviews. In this study’s restaurant context, review temporal distance refers to the duration between dining time and review time of a dining experience.
The data of paired online restaurant reservations and reviews are analyzed using Ordered Logit Model. Two robustness checks are conducted to test the stability of the main estimation results.
The empirical results demonstrate that consumers’ restaurant evaluation is socially influenced by both the prior average review rating and number of prior reviews; review temporal distance has a direct negative effect on consumers’ restaurant evaluation; and review temporal distance increases the social influence of prior reviews.
This study suggests that online review matters. Both restaurants and the online review platforms should encourage consumers to share their experiences and post online reviews immediately after their consumption.
The study contributes to the literature on electronic word-of-mouth, social influence and psychological distance. First, the bi-directional nature of social influence on electronic word-of-mouth for experience-oriented product is documented. Second, for the first time, this study examines how review temporal distance could affect the social influence on consumers’ restaurant evaluation.