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篇名
船務代理業服務品質之影響與對策
並列篇名
DISCUSS THE SERVICE QUALITY INFLUENCE AND COUNTERMEASURES OF THE SHIPPING AGENCY
作者 周明道許筑婷王稟宏丁吉峯
中文摘要
高雄港為我國主要的國際商港,加上港務公司積極推動商港相關業務,期望未來會有越來越多的船舶選擇灣靠高雄港進行作業。其中船務代理業扮演一個相當重要的角色。船務代理業負責之業務以船舶進出港為核心業務,輔以碼頭裝卸業之安排、陸路運輸業者之洽談、船舶油水補給等相關業務。本研究的目的在於使用服務品質(SERVQUAL)的五項構面來了解影響高雄港船務代理業之因素,由於服務品質量表具備相當的信度與效度,如此可以更了解航商對於船務代理業之服務期望與認知感覺。研究結果顯示,經過模糊層級分析得到兩個構面是依序為「保證性」與「反應性」兩項構面,而關鍵成功因素的指標為1.「具良好的溝通及處理航運業務的能力」,2.「公司及時掌握市場資訊及風險管理」,3.「快速回應船東需求及反應事項」,4.「嚴格遵守契約」,5.「將委託物安全且正確的運至目的地」等五項。
英文摘要
Kaohsiung Port is the main international commercial port in Taiwan, and port companies are actively promoting commercial port-related businesses. It is hoped that more and more ships will choose to operate at Kaohsiung Port in the future. The shipping agency industry plays a very important role. The shipping agency industry is responsible for the core business of ships entering and leaving the port, supplemented by related businesses such as the arrangement of the terminal handling industry, the negotiation of land transport operators, and the supply of oil and water to ships. The purpose of this research is to use the five dimensions of SERVQUAL to understand the factors that affect the shipping agency industry in Kaohsiung Port. Since the service quality scale has considerable reliability and validity. In this way, we can better understand the service expectations and perceptions of the shipping agency industry. The results of the study show that the two dimensions obtained by FAHP analysis are in order of 'guarantee' and 'reactivity', and the key success factor indicator is 1. 'Good communication and ability to handle shipping business.' 2.'The company has a timely grasp of market information and risk management' 3. 'Quickly respond to shipowners' needs and responses' 4.'Strict compliance with the contract' 5.'Secure and correct delivery of the consignment to the destination' and other five items.
起訖頁 303-321
關鍵詞 高雄港船務代理業服務品質Kaohsiung Portshipping agencySERVQUAL
刊名 運輸計劃季刊  
期數 202212 (51:4期)
出版單位 交通部運輸研究所
該期刊-上一篇 數據不平衡下以機器學習方法預測交通事故嚴重性之分析
 

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