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Since imprecision, vagueness and ambiguity are often innate in human semantics, a flexible and tolerant method is needed to decode the voice of customer (VoC), so that the essential customer requirements can be identified and duly addressed. Quality function deployment (QFD) is a well‐known methodology for projecting the customer requirements onto the relevant design and production requirements and actions plan. This paper proposes an intelligent approach which extends the applications of QFD beyond its conventional boundary. The fuzzy inference technique is adopted to accommodate the possible imprecision and vagueness during VoC interpretation. The resulting model maps the customer requirements onto the relevant product attributes, taking into consideration their relationships and correlation during the inference process. The sub‐conclusions drawn from the fuzzy inference process are aggregated and defuzzified to yield the crisp design targets which can be used to guide the downstream manufacturing planning and control activities.

The purpose of this paper is to maximize the expected revenue of the outpatient department considering patient preferences and choices.

Patient preference refers to the preferred physician and time slot that patients hold before asking for appointments. Patient choice is the appointment decision the patient made after receiving a set of options from the scheduler. The relationship between patient choices and preferences is explored. A dynamic programming (DP) model is formulated to optimize appointment scheduling with patient preferences and choices. The DP model is transformed to an equivalent linear programming (LP) model. A decomposition method is proposed to eliminate the number of variables. A column generation algorithm is used to resolve computation problem of the resulting LP model.

Numerical studies show the benefit of multiple options provided, and that the proposed algorithm is efficient and accurate. The effects of the booking horizon and arrival rates are studies. A policy about how to make use of the information of patient preferences is compared to other naive polices. Experiments show that more revenue can be expected if patient preferences and choices are considered.

This paper proposes a framework for appointment scheduling problem in outpatient departments. It is concluded that more revenue can be achieved if more choices are provided for patients to choose from and patient preferences are considered. Additionally, an appointment decision can be made timely after receiving patient preference information. Therefore, the proposed model and policies are convenient tools applicable to an outpatient department.

This paper attempts to propose a virtual case‐based benchmarking system (VCBS) which incorporates computational intelligence technologies into partners' benchmarking process to support decision‐making.

The proposed system consists of three main modules: data repository module, OLAP module and case‐based reasoning (CBR) module. The VCBS is a web‐based application that enables users to access the system and submit information to the system in anywhere at anytime. The database repository, on the other hand, maintains and acquires the data that are generated in the transactions processes and other workflow processes. It also ensures the entire valuable data which are accessible for the management to make decisions. The OLAP and the CBR modules are considered as the brain of the VCBS. The CBR module is aimed for short‐listing candidate, while the OLAP module is utilized for benchmarking the short‐listed candidate.

The VCBS is particularly useful in situations where multiple supply chain partners are involved to achieve the common objective to produce the products to the best satisfaction of customer demands with the lowest possible cost.

Since data warehouse does not update in real time it only performs update periodically during non‐office hours to avoid network traffic. The solution provided to the company may not be the most updated information.

The proposed system improves the current practice of partner selection by adopting the computational intelligence technologies into the traditional partner selection process with the assimilation of data repository, CBR and OLAP to form the integrated system for evaluation of potential partners prior to the final decision.

We examine the citations from four international business (IB) journals over 2000‐2004 to show the areas, the journals, and the institutions that impact IB research. The leading works that influence IB research are primarily management journals, scholarly books, and IB journals. IB research is published in non‐IB journals, as well and this has influenced the recent research in IB journals. U.S. and non‐U.S. academic institutions and non‐academic organizations are among the top 100 institutions that impact IB research, indicating that this research is a truly global endeavor. Finally, recent IB research is influenced more by recent published research than by past research. Scholarly books have become less influential, while the economics, finance, and marketing journals show no change in the influence on IB research over time.

The concept and practice of e-services has become essential in business transactions. Yet there are still many organizations that have not developed e-services optimally. This is especially relevant in the context of Indonesian Airline companies. Therefore, many airline customers in Indonesia are still in doubt about it, or even do not use it. To fill this gap, this study attempts to develop a model for e-services adoption and empirically examines the factors influencing the airlines customers in Indonesia in using e-services offered by the Indonesian airline companies. Taking six Indonesian airline companies as a case example, the study investigated the antecedents of e-services usage of Indonesian airlines. This study further examined the impacts of motivation on customers in using e-services in the Indonesian context. Another important aim of this study was to investigate how ages, experiences and geographical areas moderate effects of e-services usage.

The study adopts a positivist research paradigm with a two-phase sequential mixed method design involving qualitative and quantitative approaches. An initial research model was first developed based on an extensive literature review, by combining acceptance and use of information technology theories, expectancy theory and the inter-organizational system motivation models. A qualitative field study via semi-structured interviews was then conducted to explore the present state among 15 respondents. The results of the interviews were analysed using content analysis yielding the final model of e-services usage. Eighteen antecedent factors hypotheses and three moderating factors hypotheses and 52-item questionnaire were developed. A focus group discussion of five respondents and a pilot study of 59 respondents resulted in final version of the questionnaire.

In the second phase, the main survey was conducted nationally to collect the research data among Indonesian airline customers who had already used Indonesian airline e-services. A total of 819 valid questionnaires were obtained. The data was then analysed using a partial least square (PLS) based structural equation modelling (SEM) technique to produce the contributions of links in the e-services model (22% of all the variances in e-services usage, 37.8% in intention to use, 46.6% in motivation, 39.2% in outcome expectancy, and 37.7% in effort expectancy). Meanwhile, path coefficients and t-values demonstrated various different influences of antecedent factors towards e-services usage. Additionally, a multi-group analysis based on PLS is employed with mixed results. In the final findings, 14 hypotheses were supported and 7 hypotheses were not supported.

The major findings of this study have confirmed that motivation has the strongest contribution in e-services usage. In addition, motivation affects e-services usage both directly and indirectly through intention-to-use. This study provides contributions to the existing knowledge of e-services models, and practical applications of IT usage. Most importantly, an understanding of antecedents of e-services adoption will provide guidelines for stakeholders in developing better e-services and strategies in order to promote and encourage more customers to use e-services. Finally, the accomplishment of this study can be expanded through possible adaptations in other industries and other geographical contexts.

This chapter reviews the methods available to hospitality and tourism researchers to perform moderation analysis with continuous variables in partial least squares structural equation modeling (PLS-SEM), with the objective of enhancing understanding and encouraging the use of these techniques in future papers. The product term method is presented first, followed by an empirical example/application in the context of hospitality and tourism. Two extensions, namely the two-stage approach that can help cope with formative and higher-order constructs, and the orthogonalizing approach that can help generate more accurate results and overcome multicollinearity among tourism variables in the presence of a continuous moderator variable, are then presented and discussed. The chapter concludes by presenting guidelines and recommendations for improving the use of interaction effects in analyses of tourism variables, as well as highlighting ongoing developments in both the product term method and PLS-SEM software.