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The purpose of this paper is to explore the relationships between self‐construals and decision‐making styles of Korean college students.

A total of 872 Korean college students from three different Korean university campuses completed three‐part questionnaires (self‐construal, decision making styles and demographic information).

It was found that Korean college students' decision‐making styles can vary according to their self‐construals.

Although data were collected from three different universities located in different geographic locations, the study was conducted using a convenience sample rather than a random sample. Therefore, a randomly selected, diverse and geographical sampling is recommended to be able to generalize results from such a study to the total Korean college student population.

Findings suggest that high‐end luxury imported goods might appeal to Korean college students with Independent self‐construals. Even though there is an emerging new group of students with an independent self‐construal, the majority of Korean college students still hold a higher interdependent self‐construal than independent self‐construal. Therefore, different marketing strategies should be developed according to the different market segments among the Korean college students based on the different clothing product categories and quality.

Despite the growing global market and importance of studying cultural differences, little information about the influence of culture on consumer behaviour is available. The findings of the dual‐self concept within the Korean culture and their close relationship with decision‐making styles contribute to the theoretical knowledge base of contemporary consumer behaviour.

Incentives are effective at enhancing productivity, but research also suggests that performance incentives can have “unintended negative consequences” including increases in hazard/injuries, increases in errors, and reduction in cooperation, prosocial behaviors, and creativity. Relatively overlooked is whether, when, and how incentives can be designed to prevent such negative consequences. The authors review literature in several disciplines (construction, healthcare delivery, economics, psychology, and [some] management) on this issue. This chapter, in toto, sheds a generally positive light and suggests that, beyond productivity, incentives can be used to improve other outcomes such as safety, quality, prosocial behaviors, and creativity, particularly when the incentives are thoughtfully designed. The review concludes with several potential fruitful areas for future research such as investigations of incentive-effect duration.

This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming, powder metallurgy and composite material processing are briefly discussed. The range of applications of finite elements on these subjects is extremely wide and cannot be presented in a single paper; therefore the aim of the paper is to give FE researchers/users only an encyclopaedic view of the different possibilities that exist today in the various fields mentioned above. An appendix included at the end of the paper presents a bibliography on finite element applications in material processing for 1994‐1996, where 1,370 references are listed. This bibliography is an updating of the paper written by Brannberg and Mackerle which has been published in Engineering Computations, Vol. 11 No. 5, 1994, pp. 413‐55.

The purpose of this paper is to examine antecedents of consumer online spending.

A sample of 766 college students in the USA completed surveys using intercept interviews on a college campus. The research examines the consumer's level of technological savvy, experience with online shopping, level of confidence that online activities are not monitored, worry about other parties obtaining credit card information, comfort providing personal information online, and concern for online privacy when predicting the amount a consumer will spend online.

The findings reveal that consumer experience with online shopping and level of comfort with providing personal information online were significant predictors of the amount spent online. Surprisingly, privacy concerns were not a significant factor in online spending.

The results provide useful information to online marketers and privacy advocates by revealing factors that influence the amount spent by consumers via the internet.

This paper fills an identified gap in the literature on online shopping in that most research to date has either focused on regulatory issues or consumer demographics related to online privacy concerns.

This article draws on a reasoned action perspective and the two fundamental dimensions (i.e. warmth and competence) of the Stereotype Content Model (SCM) to analyze customers' chatbot-related attitudes and usage intentions in service retailing. The authors investigate how chatbot, customer, and contextual characteristics, along with perceptions of chatbot warmth and competence, shape customers' chatbot-related attitudes. Furthermore, the authors analyze whether the customer generation or the service context moderates the relationship between chatbot-related attitudes and usage intentions.

The results are based on two studies (N = 807). Study 1 relies on a 2 (chatbot communication style: high vs low social orientation) × 2 (customer generation: generation X [GenX] vs generation Z [GenZ]) × 2 (service context: restaurant vs medical) between-subjects design. Study 2 relies on a similar number of respondents from GenX and GenZ who answered questions on scheduling a service with either the dentist or the favorite restaurant of the respondents.

GenZ shows more positive attitudes toward chatbots than GenX, due to higher perceptions of warmth and competence. While GenZ has similar attitudes toward chatbots with a communication style that is high or low in social orientation, GenX perceives chatbots with a high social orientation as warmer and has more favorable attitudes toward chatbots. Furthermore, the positive effect of chatbot-related attitudes on usage intentions is stronger for GenX than GenZ. These effects do not significantly differ between the considered contexts.

This research formulates future directions to stimulate debate on factors that service retailers should consider when employing chatbots.

The proliferation of (big) data provides numerous opportunities for service advances in practice, yet research on using data to advance service is at a nascent stage in the literature. Many studies have discussed phenomenological benefits of data to service. However, limited research describes managerial issues behind such benefits, although a holistic understanding of the issues is essential in using data to advance service in practice and provides a basis for future research. The purpose of this paper is to address this research gap.

“Using data to advance service” is about change in organizations. Thus, this study uses action research methods of creating real change in organizations together with practitioners, thereby adding to scientific knowledge about practice. The authors participated in five service design projects with industry and government that used different data sets to design new services.

Drawing on lessons learned from the five projects, this study empirically identifies 11 managerial issues that should be considered in data-use for advancing service. In addition, by integrating the issues and relevant literature, this study offers theoretical implications for future research.

“Using data to advance service” is a research topic that emerged originally from practice. Action research or case studies on this topic are valuable in understanding practice and in identifying research priorities by discovering the gap between theory and practice. This study used action research over many years to observe real-world challenges and to make academic research relevant to the challenges. The authors believe that the empirical findings will help improve service practices of data-use and stimulate future research.

The purpose of this paper is to study the role of business intelligence (BI) in achieving agility in supply chain context by examining the relationship between BI competence, agile capabilities, and agile performance of the supply chain.

A theoretical framework is developed drawing on the resource-based view, the dynamic capabilities perspective, and the competence-capability relationship paradigm, as well as an extensive review of the literature. Structural equation modeling is employed to analyze the data collected from Iranian manufacturers in the automotive industry.

The empirical results support the conceptualization of supply chain BI competence as a multi-dimensional construct comprising managerial, technical, and cultural competence, and confirm that it is a key enabler of supply chain agility in terms of both agile capabilities and agile performance. The results also provide support for partial mediation of agile capabilities on the relationship between BI competence and agile performance of the supply chain.

This paper provides a response to the identified need for empirical evidence on the benefits derived from BI, especially in the supply chain context. It also contributes to the existing supply chain agility literature by providing insight into the value and role of BI in enhancing agile capabilities and performance in the inter-organizational supply chain.

This paper aims to present a nonlinear finite element model (FEM) of the Brushless DC (BLDC) motor and the application of the optimal linear–quadratic control-based method to determine the excitation voltage and current waveform considering the minimization of the energy injected to the input circuit and energy lost. The control problem is designed and analyzed using the feedback gain strategy for the infinite time horizon problem.

The method exploits the distributed parameters, nonlinear FEM of the device. First, dynamic equations of the BLDC motor are transformed into a suitable form that makes an ARE (algebraic Riccati equation)-based control technique applicable. Moreover, in the controller design, a Bryson scaling method is used to obtain desirable properties of the closed-loop system. The numerical techniques for solving ARE with the gradient damping factor are proposed and described. Results for applied control strategy are obtained by simulations and compared with measurement.

The proposed control technique can ensure optimal dynamic response, small steady-state error and energy saving. The effectiveness of the proposed control strategy is verified via numerical simulation and experiment.

The authors introduced an innovative approach to the well-known control methodology and settled their research in the newest literature coverage for this issue.

The purpose of this paper is to examine the status of green supply chain management (GSCM) research in terms of how the field is represented along a number of dimensions including journal, year, country, university, publishing house, authors, research design, research methods, data analysis techniques, multi criteria decision-making methods, research topics/issues and major industries actively involved.

A range of online databases from 1998 to August 2013 were searched containing the word “green supply chain” in their title and in the phrases to provide a comprehensive listing of journal articles on GSCM. Based on this a total of 177 articles were found and the information on a series of variables was gathered. Each of these articles was further reviewed and classified. The review and classification process was independently verified. All papers were allocated to the main and sub-categories based on the major focus.

The major findings shows that survey research holds greater credibility and the trend in survey research is moving from exploratory to model building and testing. GSCM research related to organizational practices, environmental issues, process, performance and sustainability were found to be most widely published topics within the GSCM domain.

This paper is limited in reviewing those articles which contains the word “green supply chain” in the title and the phrases of the articles.

The present review will provide increased understanding of the current state of research and what still needs to be investigated in the GSCM discipline.

The purpose of this paper was the development of a model enabling precise determination of phosphorus concentration profile in the emitter layer of a silicon solar cell on the basis of diffusion doping process duration and temperature. Fick’s second law, which is fundamental for describing the diffusion process, was assumed as the basis for the model.

To establish a theoretical model of the process of phosphorus diffusion in silicon, real concentration profiles measured using the secondary ion mass spectrometry (SIMS) method were used. Samples with the phosphorus dopant source applied onto monocrystalline silicon surface were placed in the heat zone of the open quartz tube furnace, where the diffusion process took place in the temperature of 880°C-940°C. The measured real concentration profiles of these samples became template profiles for the model in development.

The model was developed based on phenomena described in the literature, such as the influence of the electric field of dopant ionized atoms and the influence of dopant atom concentration nearing the maximum concentration on the value of diffusion coefficient. It was proposed to divide the diffusion area into low and high dopant concentration region.

A model has been established which enabled obtaining a high level of consistency between the phosphorus concentration profile developed theoretically and the real profile measured using the SIMS method. A coefficient of diffusion of phosphorus in silicon dependent on dopant concentration was calculated. Additionally, a function describing the boundary between the low and high dopant concentration regions was determined.