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The purpose of this paper is to advance our understanding of the roles that service employees’ responses to high job demands play in service innovation, by examining the effects that service employees’ motivational orientation in self-regulation (regulatory focus) and their emotional labour strategy have on their creativity.

By integrating regulatory focus theory and emotion regulation theory, the authors developed a theoretical model to propose the links between promotion and prevention regulatory foci, different emotional labour strategies and frontline employee creativity. The research hypotheses were tested using hierarchical linear model based on data collected from 304 frontline employees and 72 supervisors in 51 restaurants.

The results showed that promotion focus was positively related to frontline employee creativity while prevention focus was negatively related to it. In addition, both emotional labour strategies (deep acting and surface acting) mediated the effect of promotion focus on frontline employee creativity. Surface acting mediated the effect of prevention focus on frontline employee creativity.

This is the first research conducted to explain, from a self-regulatory perspective, the influence that is exerted on service employees’ service innovation by their responses to high job demands. The findings identify the effects that service employees’ promotion focus or prevention focus in self-regulation have on their creativity, and the data unravel the role of emotional labour strategy as the mediating mechanism that explains the influence of regulatory focus on service employee creativity. On the basis of the findings, managerial directions are offered with regard to managing service employees’ regulatory focus and emotional labour, with a view to enhancing the creativity and innovation within a service organisation.

The purpose of this paper is to explore new methods to improve supply chain management in uncertain environment, more specifically, to tackle the uncertain demand problem and the inventory optimization problem faced by most supply chain systems.

The paper develops a multi‐objective inventory optimization model, which combines the classic grey prediction GM(1,1) model with the metaheuristic method. The former is applied to achieve the forecasting mechanism in supply chain operations, and the latter is applied to optimize the model solution.

Results show that the grey‐based forecasting mechanism performs better than other prediction methods, such as the double exponential smoothing method used in this paper. The solution of the multi‐objective inventory optimization model is also improved with the integration of grey prediction method. These indicate the importance of a forecasting mechanism in supply chain management.

The paper succeeds in constructing a novel inventory optimization model and in providing a novel supply chain management framework. It shows for the first time that grey prediction method combined with metaheuristic method may be a valid approach to supply chain management under uncertain environment.

This article reviews and discusses issues in the translation of international brand names to Chinese, and provides a framework for international brand managers who want to expand into China. Linguistic differences between Chinese and English are wide and deep, making translation of brand names difficult. Cultural context, pronunciation, written vs. oral language, and the meaning of characters are just a few examples of such difficulties. We discuss four global product‐naming strategic alternatives available to country/brand managers, along with their usage. The four approaches include (1) dual extension, (2) brand meaning extension, (3) brand feeling extension, and (4) dual adaptation. We also provide examples of brands utilizing the different approaches.

With respect to the multiple-attribute decision-making problem with subjective preference for a certain attribute whose weight-value range have been given over other attributes whose weight values are unknown, a method based on the mean value of the grey number is proposed to analyse the decision-making problem. This method is used to choose a supply-chain partner under the condition that the decision makers have a preference for a certain attribute of various alternatives. The paper aims to discuss these issues.

First, the middle value of the preferred attribute’s weight-value range is supposed to be its weight value according to the content of the mean value of the grey number. Second, to reflect the decision maker’s subjective preference information, an improved optimisation model that requests the minimum deviation between the actual and expected numerical value of each attribute is constructed to assess the attributes’ weights. Third, the correlated degree and the correlation matrix, which are determined by the weight values of all attributes, are used to rank all the alternatives.

This paper provides a method for making a decision when decision makers have a preference for a certain attribute from an array of various alternatives, and the range of the certain attribute’s weight value is given but the weight value of the other attributes is unknown. When applied to supply-chain partner selection, this method proves feasible and effective.

This method is feasible and effective when applied to supply-chain partner selection, and can be applied to other kinds of decision-making problems. This means it has significant theoretical importance and extensive practical value.

Based on the mean value of the grey number, an optimisation model is built to determine the importance degree of each attribute, then the correlated degree of each alternative is combined to rank all the alternatives. This method can suit the decision makers’ subjective preference for a certain attribute well.