Passenger behavioral intention, Service quality, Satisfaction, Perceived value, Switching barrier,

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Cross-efficiency evaluation, an extension of data envelopment analysis (DEA), can eliminate unrealistic weighing schemes and provide a ranking for decision making units (DMUs). In the literature, the determination of input and output weights uniquely receives more attentions. However, the problem of choosing the aggressive (minimal) or benevolent (maximal) formulation for decision-making might still remain. In this paper, we develop a procedure to perform cross-efficiency evaluation without the need to make any specific choice of DEA weights. The proposed procedure takes into account the aggressive and benevolent formulations at the same time, and the choice of DEA weights can then be avoided. Consequently, a number of cross-efficiency intervals is obtained for each DMU. The entropy, which is based on information theory, is an effective tool to measure the uncertainty. We then utilize the entropy to construct a numerical index for DMUs with cross-efficiency intervals. A mathematical program is proposed to find the optimal entropy values of DMUs for comparison. With the derived entropy value, we can rank DMUs accordingly. Two examples are illustrated to show the effectiveness of the idea proposed in this paper.

Purpose – The paper aims to establish a causal relationship model that helps to realize how consumer involvement with the cause moderates the effect of company-cause fit on consumers’ corporate associations, and how their corporate associations regarding a company’s social responsibility programs influence their satisfaction with the company and the company’s corporate image, in the backdrop that the use of corporate social responsibility initiatives to affect consumers’ preference has become a common strategy. Design/methodology/approach – In the main study, the authors conducted a between-subjects factorial design to test the research model. A total of 400 questionnaires were distributed, and a valid sample of 389 participants was obtained. Findings – The results show that high-fit programs have a positive influence on the perceived corporate ability (CA) and corporate social responsibility (CSR) associations. CA associations directly influence corporate image and consumer satisfaction, while CSR associations indirectly impact consumer satisfaction through corporate image. Furthermore, consumers’ involvement with the cause increases the relationship between company-cause fit and CA associations. Originality/value – These conclusions have important implications for a better understanding of consumer evaluation of CSR initiatives. Theoretically, this research increases understanding of the interaction effects of perceived company-cause fit and consumer involvement with the cause on consumer evaluation of a company engaged in CSR, and a richer insight into the role of CA and CSR associations in consumer evaluations of companies engaged in CSR campaigns. Managerially, this research shows how managers can choose CSR programs causes that are most likely to promote favorable customer CA and CSR associations, thereby improving the company’s corporate image and customer satisfaction.

The selection of advanced manufacturing technologies (AMTs) is an essential yet complex decision that requires careful consideration of various performance criteria. In real-world applications, there are cases that observations are difficult to measure precisely, observations are represented as linguistic terms, or the data need to be estimated. Since the growth of engineering sciences has been the key reason for the increased utilization of AMTs, this paper develops a fuzzy network data envelopment analysis (DEA) to the selection of AMT alternatives considering multiple decision-makers (DMs) and weight restrictions when the input and output data are represented as fuzzy numbers. By viewing the multiple DMs as a network one, the data provided by each DM can then be taken into account in evaluating the overall performances of AMT alternatives. In the solution process, we obtain the overall and DMs efficiency scores of each AMT alternative at the same time, and a relationship in which the former is a weighted average of the latter is also derived. Since the final evaluation results of AMTs are fuzzy numbers, a ranking procedure is employed to determine the most preferred one. An example is used to illustrate the applicability of the proposed methodology.

Cross-efficiency evaluation effectively distinguishes a set of decision-making units (DMUs) via self- and peer-evaluations. In constant returns to scale, this evaluation technique is usually applied for data envelopment analysis (DEA) models because negative efficiencies will not occur in this case. For situations of variable returns to scale, the negative cross-efficiencies may occur in this evaluation method. In the real world, the observations could be uncertain and difficult to measure precisely. The existing fuzzy cross-evaluation methods are restricted to production technologies with constant returns to scale. Generally, symmetry is a fundamental characteristic of binary relations used when modeling optimization problems. Additionally, the notion of symmetry appeared in many studies about uncertain theories employed in DEA problems, and this approach can be considered an engineering tool for supporting decision-making. This paper proposes a fuzzy cross-efficiency evaluation model with fuzzy observations under variable returns to scale. Since all possible weights of all DMUs are considered, a choice of weights is not required. Most importantly, negative cross-efficiencies are not produced. An example shows that this paper’s fuzzy cross-efficiency evaluation method has discriminative power in ranking the DMUs when observations are fuzzy numbers.