Details of Research Outputs

This paper examines the relationships among productivity, consumer satisfaction and profitability using the conventional statistical regression and the new fuzzy regression approaches. For service firms in the context of Hong Kong, we verify the profit hypothesis that individually both productivity and consumer satisfaction are positively linked to profitability as well as the tradeoff hypothesis that aggregately there are negative interactions between productivity and consumer satisfaction for enhancing profitability. Hence service firms should balance their efforts in productivity and consumer satisfaction, possibly by employing appropriate information technologies to improve productivity while without hurting consumer satisfaction, to optimize their profitability. The study takes advantage of the Hong Kong Consumer Satisfaction Index and deliberately focuses on total rather than partial productivity. Several models are first estimated using the ordinary least squares (OLS) method and the results generally support the two hypotheses, but the OLS approach also leaves two puzzles that estimates of the regression coefficients are: (1) not significant before considering the interactions between consumer satisfaction and productivity but significant after introducing the interaction term, and (2) significant although sample data for productivity and the interaction term are highly correlated. These puzzles, together with the observed imprecision in productivity and profitability measurements and especially the subjectivity in measuring consumer satisfaction, lead us to adopt the fuzzy linear regression (FLR) techniques to further examine the two hypotheses. The popular FLR model continues to favor our research hypotheses but fail to offer any additional insights into the examined relationships over the OLS models. We then propose a revised FLR model which, in addition to reconfirming the hypotheses, does help to explain the encountered puzzles and fuzziness, and hence suggests an encouraging methodology for marketing. © 2005 Elsevier B.V. All rights reserved.