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Preference analysis was conducted among supporter club members of the German national soccer team. Survey results based on 493 completed questionnaires underline the market potential of official fan loyalty programmes due to a high average willingness to pay and a general preference for cheap and easy to implement 'right of first refusal' benefits for tickets as the main supporters club feature. Adequately designed supporters clubs may present soccer clubs with a new source of income while creating opportunities to improve stadium atmosphere and security.

Although the health information seeking behavior of consumers through the internet has received great attention, limited attempt has been made to integrate both the health information seeking behavior and the usage behavior in a mobile online context. The purpose of this paper is to explore the factors that influence consumer mobile health information seeking (MHIS) and usage behavior based on information quality, perceived value, personal health value, and trust.

A survey was conducted to collect data. A two-step approach of structure equation modeling based was used to test the measurement model and hypothesis model.

Information quality, perceived value, and trust were found to have positive effects on both the intention to seek and to use health information, and that the intention to seek affects the intention to use. Among the three components of perceived value, the utilitarian and epistemic values were found to have significant effects on intention to seek. In addition, the current health status of health consumers moderates the relationships between MHIS and usage intention and their determinants.

Studies have primarily focussed on online health information seeking behavior, whereas a few of these studies have examined the seeking behavior intention and the usage behavior intention in a general model. The results indicate that health information usage behavior intention is closely related to the seeking behavior intention in the mobile context, which enriches the research on the relationship between information seeking and its outcomes. Furthermore, this study highlights the impact of information quality, perceived value, and trust on the intention to seek, and the impacts of information quality and trust on the intention to use, which have been overlooked in previous studies on MHIS.

The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system.

By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop.

The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision.

Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

The purpose of this paper is to make the mobile e-commerce shopping more convenient and avoid information overload by a mobile e-commerce recommendation system using an improved Apriori algorithm.

Combined with the characteristics of the mobile e-commerce, an improved Apriori algorithm was proposed and applied to the recommendation system. This paper makes products that are recommended to consumers valuable by improving the data mining efficiency. Finally, a Taobao online dress shop is used as an example to prove the effectiveness of an improved Apriori algorithm in the mobile e-commerce recommendation system.

The results of the experimental study clearly show that the mobile e-commerce recommendation system based on an improved Apriori algorithm increases the efficiency of data mining to achieve the unity of real time and recommendation accuracy.

The improved Apriori algorithm is applied in the mobile e-commerce recommendation system solving the limitation of the visual interface in a mobile terminal and the mass data that are continuously generated. The proposed recommendation system provides greater prediction accuracy than conventional systems in data mining.

This chapter explores some of the complexities involved when undertaking research at an international level in the area of “inclusive” education and “special needs” education. The complexities encountered by researchers working in these fields, mirror many of the challenges that comparativists in education studies find themselves addressing. Drawing from earlier investigations and from reports by international organizations, this chapter highlights some of the dilemmas and challenges that researchers face when considering inclusion and special needs education in different countries. Differing interpretations of “inclusion” are discussed and then contrasted with thinking around “special needs” practices. The chapter moves forward to analyze how the adoption of differing theoretical frameworks can influence the way that “disability” is conceptualized and therefore how inclusive and special needs education are interpreted and then put into practice. The chapter argues that cross-cultural work opens up opportunities for further development and learning in this field. We further argue that such cross-cultural work can become a mechanism to instigate fundamental change in education.

The purpose of this paper is to analyse and investigate how intensified regulatory requirements related to outsourcing have influenced and changed the outsourcing activities of German financial institutions.

The study involved interviewing 11 outsourcing experts in the German financial sector, including four of the five largest banks in Germany. In coding and analysing the collected data, this study adopted the approach of a qualitative content analysis framework.

The study found that the revised legal requirements have had a significant and potentially negative impact on the efficiency of outsourcing, leading to a necessity for German financial institutions to internally realign their outsourcing managements. The study further revealed practical realigned methods German financial institutions executed to meet the legal requirements.

The impact, meaning and relevance of legal requirements in the outsourcing environment of German financial institutions has been relatively under-researched from a qualitative perspective and focused on other primary fields of investigation like outsourcing decisions and outcomes. This study has, by adopting a qualitative approach, addressed the identified gap by providing first-hand insights and new knowledge.

Colossal information is available in cyberspace from a variety of sources such as blogs, reviews, posts and feedback. The mentioned sources have helped in improving various business processes from product development to stock market development. This paper aims to transform this wealth of information in the online medium to economic wealth. Earlier approaches to investment decision-making are dominated by the analyst's recommendations. However, their credibility has been questioned for herding behavior, conflict of interest and favoring underwriter's firms. This study assumes that members of the online crowd who have been reliable, profitable and knowledgeable in the recent past will continue to be so soon.

The authors identify credible members as experts using multi-criteria decision-making tools. In this work, an alternative actionable investment strategy is proposed and demonstrated through a mock-up. The experimental prototype is divided into two phases: expert selection and investment.

The created portfolio is comparable and even profitable than several major global stock indices.

This work aims to benefit individual investors, investment managers and market onlookers.

This paper takes into account factors: the accuracy and trustworthiness of the sources of stock market recommendations. Earlier work in the area has focused solely intelligence of the analyst for the stock recommendation. To the best of the authors’ knowledge, this is the first time that the combined intelligence of the virtual investment communities has been considered to make stock market recommendations.

While the understanding of customer satisfaction is a key success factor for service enterprises, existing elicitation approaches suffer from several drawbacks such as high manual effort or delayed availability. However, the rise of analytical methods allows for the automatic and instant analysis of encounter data captured during service delivery in order to identify unsatisfied customers.

Based on encounter data of 1,584 IT incidents in a real-world service use case, supervised machine learning models to predict unsatisfied customers are trained and evaluated.

We show that the identification of unsatisfied customers from encounter data is well feasible: via a logistic regression approach, we predict dissatisfied customers already with decent accuracy—a substantial improvement to the current situation of “flying blind”. In addition, we are able to quantify the impacts of key service elements on customer satisfaction.

The possibility to understand the relationship between encounter data and customer satisfaction will offer ample opportunities to evaluate and expand existing service management theories.

Identifying dissatisfied customers from encounter data adds a valuable methodology to customer service management. Detecting unsatisfied customers already during the service encounter enables service providers to immediately address service failures, start recovery actions early and, thus, reduce customer attrition. In addition, providers will gain a deeper understanding of the relevant drivers of customer satisfaction informing future new service development.

This article proposes an extendable data-based approach to predict customer satisfaction in an automated, timely and cost-effective way. With increasing data availability, such AI-based approaches will spread quickly and unlock potential to gain important insights for service management.

Increasing the temperature of gas flows passing through hot tubes is one of the industrial interests. Operations in the gas phase with high temperature variations involve engineers with the compressible fluids problems. The paper aims to discuss this issue.

In this study, a mathematical three-dimensional turbulent model is applied for investigating the heat transfer and laminar gas flow inside the thermal developing zone of a hot tube. The Favre Averaged Navier–Stokes and energy equations and also the Reynolds Stress Model are numerically solved to obtain the fluid velocity and temperature profiles inside this the tube. This model is validated using the experimental data and also well-known formulas in this science.

Finally, effects of inlet volumetric flow rate, heating conditions of the tube wall and tube angle on the temperature and velocity distributions of the gaseous phase inside this zone are investigated.

The compressible laminar gas flow and also heat transfer in the thermal developing zone of a hot tube is studied using a three-dimensional turbulent model.