How relational involvement moderates satisfaction paradox under competition? – Case of air express customer

碩士 === 國立中興大學 === 行銷學系所 === 101 === Customers stating to be satisfied may still be lack of loyalty, and the phenomenon was defined as satisfaction paradox. However, studies on the satisfaction–loyalty link are scarce in a business-to-business context, and lagging far behind consumer marketing. M...

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Bibliographic Details
Main Authors: Che-Yu Cheng, 鄭哲語
Other Authors: Ming-Chih Tsai
Format: Others
Language:en_US
Published: 2013
Online Access:http://ndltd.ncl.edu.tw/handle/72766907013481535261
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Summary:碩士 === 國立中興大學 === 行銷學系所 === 101 === Customers stating to be satisfied may still be lack of loyalty, and the phenomenon was defined as satisfaction paradox. However, studies on the satisfaction–loyalty link are scarce in a business-to-business context, and lagging far behind consumer marketing. Market-level competition and relational involvement are found to moderate the paradox. However, almost all of previous research has been conducted in single brand contexts. Effect of competition is grossly overlooked. Besides, relational involvement, including the number and types of relational activities, is in particular important to study for industrial satisfaction paradox. But the effect is still not systematically evaluated. As a result, this study aims to validate satisfaction measurements and evaluate the moderating effects of relational involvement under competition using an attribute threshold multinomial logit model. Samples of 124 valid high-tech customers consuming air express service are collected for data analysis. The analysis result validates the five satisfaction measurements for customer loyalty, including price, delay compensation, customer privacy, flexible delivery, and cargo tracing. Meanwhile, five relationship investments are classed into financial, social and structural bonds according to explanatory factor analysis. They are assessed to be all effective in mitigating satisfaction paradox through the analysis of a linear regression model. Finally, in terms of customer relational behaviors, relationship breadth is most effective to identify relational involvement, followed by relational depth. Relational age on the other hand accounts for no effect.