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The fields of market orientation and services marketing are still rather new domains of scientific research. It appears that they have quite a few challenging topics in common. This paper combines some of those topics. It focuses on the culture of market‐oriented services organisations and the leadership styles that may belong to such a culture. An open culture which is reflected in clarity in marketing goals and a strong drive to be the best (deliver superior value or quality) are the essential features of such a culture. This calls for achievement‐oriented leaders who care about people.

The purpose of this paper is to present an extended version of the Elaboration Likelihood Model (ELM‐model) to explain and predict which of the four cognitive processes that are distinguished in the literature, with respect to Country of Origin (CoO), can be expected to occur: the halo‐effect, the summary construct‐effect, the product attribute‐effect or the default heuristic‐effect.

Contrary to most of the previous theoretically‐oriented work on cognitive CoO‐effects, the epistemological background of the CoO‐ELM model proposed in this paper is of an inductive nature with theoretical propositions being derived from empirical data already gathered in the existing studies.

The outcome of this paper is a flow chart model leading to a set of theoretical propositions on which cognitive CoO‐effects can be expected to occur under different situational contexts.

This paper only focuses on the explanation of cognitive CoO‐effects, not on affective or conative/normative effects. Also, the CoO‐ELM model applies only to the processing of consumers' prior knowledge about a country's products and not about the country itself. Finally, the CoO‐ELM model still needs to be subjected to empirical verification. An important implication of this paper is that the CoO‐ELM framework makes the bulk of empirical data become more transparent given the four effects of cognitive CoO‐processes.

The CoO‐ELM model provides marketing practitioners with an easy and practical tool for the management of CoO‐cues.

This paper is the first attempt trying to catch all the cognitive CoO‐effects previously identified within a theoretically solid framework.

The purpose of this paper is to provide insight into how consumers cope with confusion caused by overload in information and/or choice. The paper investigates whether consumers who face different degrees of confusion use different coping strategies depending upon their decision‐making styles.

The Dutch mobile phone market is a typical example of a turbulent market, overloaded with information and/or choice, which creates consumer confusion. A survey was conducted among 203 mobile phone users, using valid and reliable multi‐item scales to measure consumer confusion, decision‐making styles and coping strategies. Cluster analysis and Mancova were used to provide insight into the results.

The paper finds that consumers of mobile phones can be characterized by combinations of decision‐making styles and find three clusters based on decision‐making styles: “price conscious and cautious” consumers, “brand‐loyal and quality‐driven” consumers, and “functionalist” consumers. Results show significant main effects of the degree of confusion and the decision‐making styles on the use of coping strategies as well as a significant interaction effect of these two. Higher levels of consumer confusion lead to an increased use of seven coping strategies: downsizing the consideration set; keeping status quo; reduced information search; search deferral; buying what others have bought; disengagement from decision; and decision delegation. “Price conscious and cautious” consumers engage less in downsizing the consideration set than the two other clusters, and are less inclined to keep the status quo as compared to “functionalist” consumers.

Because of the intangible and heterogeneous nature of services, knowledge about coping with confusion due to an overload in information and choice is especially important for service providers in their efforts to build and sustain strong relationships with consumers. Practical implications in terms of different approaches on how to cope with confused consumers are provided.

Generally it is assumed that quality improvements are important tools for companies to gain a sustainable competitive advantage. The result of product quality improvements depends, for example on the reaction of competitors. If competitors react intensively and quickly, the outcome of a product quality improvement might be different from what was expected. From an empirical study in The Netherlands, concludes that, in general, the typical reaction of competitors is quite a fast reaction with only one marketing mix instrument. The intensity of the competitive reaction appears to be lower in mature and declining markets than it is in growing markets, and improvements in reliability and service quality aspects appear to be important weapons in quality competition. Service quality improvements are difficult to imitate swiftly. Moreover, they lead to higher market shares for the initiating company. Quality competition based on other quality aspects, like conformance to standards and performance, does not result in a real competitive advantage. For these quality improvements, following a “traditional” competitive pattern of action and, more‐or‐less the same reaction, are quite easy to imitate. They are also needed just to keep up with the leading competitors in the industry. However, they do not lead to long‐term sustainable advantages.

Research has shown that more than half of attempted recovery efforts only reinforce dissatisfaction, producing a “double deviation” effect. Surprisingly, these double deviation effects have received little attention in service marketing literature. To fill this gap, this paper aims to develop and empirically test a model of how customers form satisfaction judgments in double deviation scenarios. The paper seeks to propose that emotions have a distinct and separate influence from perceived justice in explaining satisfaction with failed recovery.

The paper employs the critical incident technique to obtain data from banking customers and apply latent variable path analysis to test the proposed model.

The results of the study support the model and highlight the important role of specific recovery‐related emotions in double deviation contexts.

Future research should try to determine whether different specific negative emotions and/or the interactional and procedural components of justice affect post‐recovery judgments in double deviation scenarios.

The results show that specific emotions such as anger play an important role in explaining satisfaction with service recovery. The paper proposes that in future, customer satisfaction surveys could include items measuring specific emotions. This could increase their efficiency as managerial tools.

To the authors' knowledge, this has been the first attempt to model the effect of specific emotions triggered by the service recovery on satisfaction with service recovery and to empirically test a model of satisfaction with service recovery in double deviation scenarios. Furthermore, this study is based on the analysis of real service failures and recovery strategies.

Given the ways in which the research pressures on university staff are becoming seemingly ever greater, an issue of the European Journal of Marketing that is given over to a survey of the kinds of research initiatives which are currently being carried out is timely. The study which provides the basis for this was conducted between December 1994 and February 1995, with questionnaires being sent to staff in universities throughout Europe. At the time the final selection was made, a total of 150 responses had been received from 18 countries.

The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods.

First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data.

The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R² is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R² are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties.

This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.