Analyzing selection criteria weight of exhibition contractors using analytic network process
Department of Tourism and MICE, Chung Hua University, Hsinchu city, Taiwan.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 011–019
Publication history:
Received on 22 May 2024; revised on 28 June 2024; accepted on 01 July 2024
Abstract:
Selecting exhibition contractors based solely on the lowest price often results in low-quality services and products due to unreasonably low bids. To address this issue, the Most Advantageous Bid (MAB) method is proposed, emphasizing comprehensive evaluation criteria such as technical capabilities, management systems, cost efficiency, collaborative abilities, past performance, delivery guarantees, and quality control. Implementing the MAB method involves allocating weights to evaluation criteria and sub-criteria, which is a significant challenge. This study utilized the Analytic Network Process (ANP) and expert interviews to identify and analyze the weights of suitable evaluation criteria and sub-criteria, establishing a systematic evaluation framework. The study formulated five main evaluation criteria and sixteen sub-criteria. The top criteria are professional competence and service quality (32.16%), technological capability and innovative solutions (23.65%), customer service and support (17.67%), cost efficiency and price competitiveness (14.93%), and risk management and compliance (11.60%). This shift indicates a greater emphasis on quality and technological capabilities over mere price competitiveness. The results provide a comprehensive MAB evaluation model and offer guidelines for enhancing the accuracy and fairness of supplier evaluations. These findings can help businesses and government agencies develop tailored MAB evaluation models, improving the efficiency and effectiveness of supplier selection.
Keywords:
Lowest Price Bidding; Most Advantageous Bid; Analytic Network Process
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0