The literature shows some recent calls for an end to 'unaccountable' marketing (Rust et al., 2001; Sheth and Sharma, 2001) and the use of customer lifetime value as an appropriate marketing metric (Rust et al., 2001). Some commentators recommend the application of shareholder value measures to the valuation of customer relationships (Uyemara, 1997; Mariotti, 1996). The thesis evaluates the application of shareholder value measures to the valuation of customers. Shareholder value involves both risk and return; therefore, the thesis argues, the risk of the customer or segment has to be identified before that customer's role in creating shareholder value for an organisation can be assessed. In addition, Relationship Marketing suggests that customer relationships have value in ways that are not easy to capture using traditional customer profitability analysis. The thesis explores different methods of valuing relationship benefits. As a result of the literature review, a model of the total value of the customer is developed which defines the Total Value of the Customer as the lifetime economic value of the customer (customer lifetime value adjusted for risk), plus the value of relationship benefits (Referral and Reference Effects, and Learning and Innovation). The model is operationalized using shareholder value measures and then tested in two collaborative research projects. The research finds that managers in the participating companies do not have good information about the lifetime value of their customer relationships. Evidence of changes in strategy as a result of a better understanding of the value of the customer is found. The research contributes to theory and knowledge through defining and calculating the Total Value of the Customer; demonstrating the application of shareholder value measures to the valuation of customers; finding and measuring relationship benefits measuring customer risk; and finding a link between the value of the customer and targeted marketing strategies.

M.B.A. / Applications of Emotional Intelligence in the workplace are almost infinite. Emotional Intelligence is instrumental in resolving a sticky problem with a coworker, closing a deal with a difficult customer, criticising your boss, staying on top of a task until it is completed, and in many other challenges affecting your success. Emotional Intelligence is used both interpersonally (helping yourself) and interpersonally (helping others) (Weisinger, 1998:xvi). One of the most difficult and rewarding practices of emotional intelligence is to help others help themselves (Weisinger, 1998:181). A work organisation is an integrated system that depends upon the interrelationship of the individuals who are part of it. How each person performs affects the company as a whole. That's why it is important to the success of the company not only that all employees perform to the best of their abilities but that they also help others do the same (Weisinger, 1998:183). A general attitude toward one's job; the difference between the amount of rewards workers receive and the amount they believe they should receive. A person's job is more than just the obvious activities — it requires interaction with co-workers and bosses, following organisational rules and policies, meeting performance standards, living with working conditions that are often less than ideal. Therefore job satisfaction is not straight forward (Robbins 1996: 190). Service variability refers to the unwanted or random levels of service quality customers receive when they patronise a service. Variability is primarily caused by the human element, although machines may malfunction causing a variation in the service. Various service employees will perform the same service differently and even the same service employees will provide varying levels of service from one time to another. Unfortunately, because of the variability characteristic of services, standardisation and quality control are more difficult (Kurtz & Clow 1998: 14). To ensure quality at the source refers to the philosophy of making each worker responsible for the quality of his work. This incorporates the notions of do it right. Workers are expected to provide goods or services that meet specifications and to find and correct mistakes that occur. Each worker becomes a quality inspector for his own work (Stevenson 1996: 103). This dissertation is therefore looking at the different viewpoints of experts on emotional intelligence and to identify characteristics important to render quality client service.

M.Comm. / Extensive studies have been done in the past on measuring service quality where the service is delivered on a face-to-face encounter. This study assesses and measures online service quality where there is no face-to-face encounter. The service quality measures are particularly on Internet Banking service. The research problem has been stated as the lack of insight into customer perceptions on Internet Banking service quality by management in South African banks. The purpose of this study was to explore customers’ perceptions on key electronic service dimensions or factors of Internet Banking service quality. The primary objective of the study was to have an insight into how Internet Banking customers in South Africa perceive their respective banks’ performance on pre-defined electronic service quality dimensions. The secondary objective was to determine if there was any difference in Internet Banking service quality perception based on age, gender, or primary bank offering the service (service provider). Even though online shopping and Internet Banking are online services there are subtle differences between the two services. With online shopping there is a physical item that gets traded and in Internet Banking only services are traded. It is for this reason that the original E-S-Q instrument was slightly adjusted. Some of the dimensions that were excluded from the original E-S-Q instrument include flexibility, price knowledge and customization Given the purpose and objectives of the study a quantitative approach was taken as the major research approach for the study. The sampling design was a nonprobability sampling one because the convenience method of sampling was used. The survey population was all online banking users, utilizing services from South African banks. A slightly revised electronic service quality (E-S-Q), a service quality measurement instrument, was used in this study. Data was collected via a web based self administered survey. The original E-S-Q instrument measured customer service quality from an online shopping experience point of view. This study aimed at gleaning respondents’ perceptions on key Internet Banking service dimensions.

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Mathematical statistics. Johannesburg, February 2014. / In this study we examine the question of which statistical mod- els work well in predicting customer defection in the retail mobile telecommunication industry. For each of the two data sets that were used (mobile call pattern and billing, and time taken to churn data), four statistical models were tted and compared namely; arti cial neural networks, decision trees, logistic regression and support vector machines. The arti cial neural network model proved to be supe- rior than the other three models when tted on both data sets. This model gave the best area under the receiver operating characteristic curve (0.93 for call pattern data and 0.88 for billing and time taken to churn data), highest lift at 10 per cent of the population (7.01 for call pattern data and 2.12 for billing and time taken to churn data) and lowest misclassi cation rate (0.04 for call pattern data and 0.19 for billing and time taken to churn data). The logistic regression model under performed the other models when tted to call pattern data and came out as third when tted to billing and time taken to churn data whereby they outperformed the decision tree model. Support vector machine came out as the second best model for billing and time taken to churn data and third when tted to call pattern data. Decision tree model performed well when tted to call pattern data and worst when tted to billing and time taken to churn data The study showed that in the retail mobile telecommunication industry, companies can increase revenue streams and competitive advantage by using data mining techniques to predict customers that are likely to churn. The next step for the business is to embark on retention programs to use these methods to reduce churners.

University of Macau / Faculty of Business Administration. / Department of Accounting and Information Management

Frederick Hong-kit Yim. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 90-108). / Abstracts in English and Chinese. / ABSTRACT (ENGLISH) --- p.i / ABSTRACT (CHINESE) --- p.iii / ACKNOWLEDGEMENTS --- p.iv / TABLE OF CONTENTS --- p.v / LIST OF TABLES --- p.vii / LIST OF FIGURES --- p.viii / Chapter CHAPTER ONE --- INTRODUCTION --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Research Objectives --- p.4 / Chapter 1.3 --- Outline of This Study --- p.4 / Chapter CHPATER TWO --- BACKGROUND AND PREVIOUS RESEARCH --- p.5 / Chapter 2.1 --- Reasons for the Prominence of Relationship Marketing --- p.5 / Chapter 2.1.1 --- The Growth of the Service Economy --- p.5 / Chapter 2.1.2 --- The Heightening of Competitive Intensity --- p.6 / Chapter 2.1.3 --- The Building of Customer Relationships to Gain a Competitive Advantage --- p.7 / Chapter 2.2 --- "Relationship Marketing, CRM and CRM Orientation" --- p.8 / Chapter 2.3 --- Two Major Confusions concerning CRM identified in the Literature --- p.11 / Chapter 2.3.1 --- Obsessive Emphasis on the Technology Component --- p.11 / Chapter 2.3.2 --- The Distinction between CRM Orientation and Market Orientation --- p.11 / Chapter CHAPTER THREE --- CONCEPTUALIZATION: CRM ORIENTATION --- p.13 / Chapter 3.1 --- Support for our Conceptualization --- p.14 / Chapter 3.2 --- The Components of the CRM Orientation --- p.16 / Chapter 3.2.1 --- Focusing on Key Customers --- p.17 / Chapter 3.2.1.1 --- Customer-centric Marketing --- p.18 / Chapter 3.2.1.2 --- Key Customer Lifetime Value Identification --- p.20 / Chapter 3.2.1.3 --- Personalization --- p.22 / Chapter 3.2.1.4 --- Interactive Cocreation Marketing --- p.24 / Chapter 3.2.2 --- Organizing around CRM --- p.26 / Chapter 3.2.2.1 --- Organizational Structure --- p.26 / Chapter 3.2.2.2 --- Organization-wide Commitment of Resources --- p.28 / Chapter 3.2.2.3 --- Human Resources Management --- p.28 / Chapter 3.2.2.3.1 --- Market Training and Education --- p.29 / Chapter 3.2.2.3.2 --- Internal Communication --- p.30 / Chapter 3.2.2.3.3 --- Reward Systems --- p.31 / Chapter 3.2.2.3.4 --- Employee Involvement --- p.31 / Chapter 3.2.3 --- Knowledge Management --- p.32 / Chapter 3.2.3.1 --- Knowledge Learning and Generation --- p.34 / Chapter 3.2.3.2 --- Knowledge Dissemination and Sharing --- p.36 / Chapter 3.2.3.3 --- Knowledge Responsiveness --- p.37 / Chapter 3.2.4 --- Technology-based CRM --- p.38 / Chapter CHAPTER FOUR --- RESEARCH METHODOLOGY --- p.41 / Chapter 4.1 --- Overview --- p.41 / Chapter 4.2 --- Item Generation and Content Validity --- p.41 / Chapter 4.3 --- Instrument Pretest --- p.44 / Chapter 4.4 --- Sample and Data Collection --- p.47 / Chapter 4.5 --- Identification of the Underlying Factor Structure --- p.56 / Chapter 4.6 --- Item Analysis and Reliability Assessment --- p.58 / Chapter 4.7 --- Validity Assessment --- p.60 / Chapter 4.7.1 --- Convergent Validity --- p.60 / Chapter 4.7.2 --- Discriminant Validty --- p.63 / Chapter 4.7.3 --- Nomological Validity --- p.66 / Chapter 4.8 --- The Relative Impacts of CRM Orientation and Market Orientation on Business Performance --- p.72 / Chapter CHAPTER FIVE --- DISCUSSION --- p.77 / Chapter 5.1 --- Academic and Managerial Contributions --- p.77 / Chapter 5.2 --- Implications --- p.78 / Chapter 5.3 --- Limitations --- p.81 / Chapter 5.4 --- Directions for Future Research --- p.81 / APPENDICES --- p.85 / APPENDIX I. QUESTIONNAIRE --- p.85 / APPENDIX II. CUSTOMER RELATIONSHIP MANAGEMENT ORIENTATION SCALE ITEMS (AFTER PURIFICATION) --- p.88 / REFERENCES --- p.90

by Chan King-Yan, Chu Kin-Yan Jeannie, Hsu Mei-Ying. / Thesis (M.B.A.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaf 64). / ABSTRACT --- p.ii / TABLE OF CONTENTS --- p.iv / LIST OF TABLES --- p.vi / Chapter / Chapter I. --- Project Overview --- p.1 / Chapter II. --- Methodologies Applied --- p.3 / Step 1 Exploratory Research --- p.3 / Step 2 Interviews --- p.3 / Chapter III. --- The importance of CRM in China banking industry --- p.5 / Economic Globalization and China's Accession into WTO --- p.5 / The Influence of Information Technology (IT) --- p.5 / Changes in the Dynamics of Supply and Demand in the Financial Market --- p.5 / Rapid Response to Market Demands and High Level of Customization --- p.6 / Chapter IV. --- Current situation of CRM --- p.7 / The Reasons for the Failure of CRM --- p.7 / Treat Technology as the Primary Driver of Customer Strategy --- p.7 / Lack of Executive Support --- p.7 / Data Is Ignored --- p.8 / Information system (IS) organization and business users cannot work together --- p.8 / No Attention Is Paid to Skill Sets --- p.9 / CRM in China --- p.9 / CRM Industry Is in Chaos --- p.9 / Resistance of CRM Implementation --- p.10 / Willingness to Invest in CRM Training --- p.10 / Chapter V. --- Business environment of China --- p.11 / Politics --- p.12 / Economics --- p.15 / Demographic --- p.16 / Technology --- p.20 / Chapter VI. --- CRM implementation in Guangdong Development Bank in China --- p.22 / Introduction of Guangdong Development Bank --- p.22 / Strengths and Weaknesses --- p.22 / Feasibility and Benefits of Executing CRM --- p.26 / The Effect of External and Internal Factors on Strategy Formulation --- p.30 / Strategy Translation and Implementation --- p.34 / Stage 1 - Develop Prerequisite Arenas before Implementing CRM --- p.34 / Stage 2: Establish CRM system --- p.41 / Stage 3: Performance Indicators --- p.43 / Chapter VII --- RECOMMENDATIONS --- p.45 / Actions to Guarantee Long-term Success --- p.45 / APPENDIX --- p.49 / BIBLIOGRAPHY --- p.64

A gestão de clientes é um processo que envolve a tomada de decisões estratégicas, que influenciam a composição do portfólio de clientes da companhia, e operacionais, que afetam o relacionamento dos clientes com a empresa no dia a dia. O framework sugerido nesta tese propicia a integração dessas duas perspectivas, permitindo aos gestores alocarem melhor os recursos de marketing, por possibilitarem (a) o incremento da eficiência da carteira de clientes, a partir da sua otimização, e (b) a identificação dos clientes mais propensos a gerarem lucros futuros, com base na modelagem de customer lifetime value (CLV) desenvolvida. A abordagem de otimização do portfólio de clientes foi elaborada para auxiliar os gestores a definirem os segmentos que devem ser alvo dos investimentos de marketing e tem como objetivo indicar a composição da carteira de clientes que proporcionará a rentabilidade, a diversificação do risco e a lucratividade desejadas pelos acionistas. A abordagem sugerida é uma adaptação para o marketing da teoria financeira do portfólio. Foram incluídas restrições específicas para a área de gestão de clientes que asseguram a exequibilidade dos portfólios recomendados, tanto em relação à necessidade de aquisição de clientes ou de redução da participação dos segmentos na carteira, quanto em relação à manutenção da lucratividade da empresa. Ademais, foram incorporadas opções de estimação do retorno, tais como a inclusão da tendência à série com base na modelagem SUR, além de serem avaliadas a utilização de duas proxies para o risco, a variância e o Conditional Value at Risk. De acordo com o framework de gestão de clientes proposto, a implementação das decisões estratégicas é viabilizada a partir da integração da análise dos resultados obtidos pela otimização com a avaliação proporcionada pelo modelo de CLV sugerido. Este, além de englobar a evolução do comportamento do cliente ao longo do relacionamento da empresa, considera o retorno e a matriz de probabilidade de troca de segmento de maneira individualizada. A heterogeneidade da matriz de Markov foi alcançada a partir da combinação convexa da matriz de transição geral com a matriz personalizada de cada cliente, possibilitando, assim, a priorização de clientes pertencentes a um mesmo segmento. O framework sugerido foi aplicado na base de clientes de uma grande empresa que atua nacionalmente na indústria de serviços financeiros. Após a constatação de que os segmentos podem gerar diferentes retornos e representar distintos níveis de risco para a companhia, foi feita a comparação dos resultados dos portfólios recomendados com o realizado. Os portfólios sugeridos desempenharam melhor de maneira consistente em termos de lucratividade e de eficiência, medida a partir do sharpe ratio. Em relação ao modelo de CLV, os resultados foram comparados com os obtidos a partir do modelo de Pfeifer & Carraway (2000), utilizado como ponto de partida para o seu desenvolvimento. As modificações incorporadas, além de possibilitarem a individualização por cliente, aumentaram a precisão da previsão dos valores individuais e a qualidade do ordenamento, mantendo a capacidade de avaliação do valor da base. Para resumir, foi proposto um framework de gestão de clientes que inclui a avaliação do risco, possibilitando aos gestores uma visão holística do negócio e particular de cada cliente. / Customer management is a process that involves strategic decision-making, which influence the composition of the customer portfolio, and operational decision making, which affect the relationship of each customer with the company. The proposed framework provides the integration of the strategic and operational perspectives, empowering managers to better allocate marketing resources as it enables (a) the increase of the efficiency of the customer portfolio, through its optimization, and (b) the identification of the customers that are more likely to bring profit in the future, through the customer lifetime value (CLV) model developed. The customer portfolio optimization method was built to help managers to define the customer segments that should be the target of their marketing investments. Its purpose is to indicate the customer portfolio composition that will provide the return, profitability and risk diversification desired by shareholders. The suggested approach is an adaptation to marketing of financial portfolio theory. In this way, customer management specific constrains were included to ensure the applicability of the recommended portfolios in terms of either the necessity of acquiring new customers or reducing the importance of a given segment in the portfolio as well as in terms of maintaining the company’s profitability. Furthermore, options of estimating return were incorporated such as the inclusion of the trend in the time series based SUR modeling as well as the optimizations were evaluated considering two proxies for risk, variance and Conditional Value at Risk. According to the proposed framework, the implementation of the strategic decisions concerning the changes needed in the customer portfolio become possible through the integration of the results of the optimization with the estimation of the value of each customer provided by the CLV model developed. In this model, besides accounting for the evolution of the customer behavior throughout the duration of his relationship with the company, we also consider, for each customer, his individual return and his individual transition matrix. The heterogeneity of the Markov matrix was reached with a convex combination of the general transition matrix and the personalized matrix of each customer. It, therefore, enables managers to priorize customers of the same segment. The suggested framework was applied to the customer database of a large national company from the financial services industry. Once evidenced that the customer segments can generate different returns and can have different levels of risk for the company, we compared the results of the recommended with the current. The portfolios suggested by the optimization performed consistently better in terms of profitability and efficiency, measured through sharpe ratio. Concerning the CLV model developed, we compared the results with Pfeifer & Carraway (2000) model, which was used as the start point for our model. The improvements implemented not only allowed the estimation of CLV at the individual level, but also increased the precision of the predictions for the customer lifetime values and for the customer ranking, maintaining the quality of the customer equity forecast. To sum up, our proposed framework which includes risk assessment enables marketing managers to have a holistic vision of their customer portfolio and to drilldown into a particular vision of each customer.

There is now a trend to move closer to the customer in the form of long-term and lastingrelationships. The market today is characterized by growing competition with new playersconstantly arising. In order to gain competitive advantage with the increasingly challengingmarket, companies require to place the customer in the centre. Being close to the customerand engage in successful efforts to create customer loyalty has become a critical successfactor in many businesses. Especially when the customers in today's market is becomingincreasingly unfaithful and continuously looking for new companies with new productofferings.Many companies have now also realized the importance of trying to retain existing customersas it is more profitable than constantly trying to acquire new ones. In times of highcompetition a loyalty programme can be used to create customer loyalty. A type of loyaltyprogramme is a customer club. By making use of customer clubs as loyalty programs, thecompany can achieve customer loyalty and differentiate their product offering. The motivebehind most customer clubs is to create purchase fidelity.Many fashion companies face the challenge of working with customer care in a way thatstrengthens the company's relationship with their customers. If a fashion company's customerclub is losing loyalty it may cause a serious problem for the company. The following problemis something that the clothing company MQ face today. We had therefore the task ofundertaking to examine the underlying causes of MQ's lack of customer loyalty amongmembers in the company’s customer club.The purpose of the paper was to examine and describe the important factors affecting thedeclining customer loyalty of MQ. By mapping and analyzing possible links between thevarious factors affecting the decrease in customer loyalty, we wanted to seek the underlyingcauses of the MQ's customer loss.In order to seek the underlying causes of the MQ's decreased customer loyalty a study on asample of the company downgraded gold- and silvermembers in the customer club wascarried out. The survey was carried out by telephone interviews in which a prestructuredquestionnaire served as input. We have also done interviews with key people in MQ.In our theoretic frame of references, we have used theories of Ralf Blomqvist et al. MagnusSöderlund, Philip Kotlas et al. and Stephan A. Butscher. We have also made use of twoscientific articles on the impact of loyalty programs.On collecting the empirical data we quickly realized that the customer club average age of 38years did not correspond well with MQ's target group of 20-40 years. The main reason forrespondents decreasing purchases of MQ in 2008 was that the range is not appealing to them.We also found that MQ had updated concept and audience in recent years and that it mayhave contributed to a vague picture of the company among its customers.In conclusion, we have found that it is primarily the older customers who are no longerattracted by MQ's range. Decreased interest in the company as a fashion supplier has led tocustomer members not visiting MQ's stores often. It has led to reduced sales and downgradedcustomers in the customer club. Most of the respondents in the survey felt that they are notinfluenced by its membership of MQ's customer club. They do not choose MQ over otherstores simply because of their membership in the company's customer club. Customer clubmembers value mostly the economic benefits of membership. / Program: Textilekonomutbildningen

Wan, Chun Ying Lisa. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 84-92). / Abstracts in English and Chinese ; appendix also in Chinese. / ABSTRACT (ENGLISH) --- p.i / ABSTRACT (CHINESE) --- p.iii / TABLE OF CONTENTS --- p.iv / LIST OF TABLES --- p.vi / LIST OF FIGURES --- p.viii / Chapter CHAPTER ONE --- INTRODUCTION --- p.1 / Chapter 1.0 --- Overview --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Research Objectives --- p.4 / Chapter 1.3 --- Significance of This Thesis --- p.5 / Chapter 1.4 --- Outline of This Thesis --- p.7 / Chapter CHPATER TWO --- LITERATURE REVIEW AND MODEL DEVELOPMENT --- p.8 / Chapter 2.0 --- Overview --- p.8 / Chapter 2.1 --- Conceptual Definitions --- p.8 / Chapter 2.1.1 --- Two Types of Service Failure --- p.8 / Chapter 2.1.1.1 --- Service Quality --- p.8 / Chapter 2.1.1.2 --- Service Failure --- p.10 / Chapter 2.1.1.3 --- Exchange Resources --- p.11 / Chapter 2.1.1.4 --- Intended Contributions --- p.12 / Chapter 2.1.2 --- Personal Values --- p.13 / Chapter 2.1.2.1 --- Fate Belief --- p.14 / Chapter 2.1.2.2 --- Face Concern --- p.17 / Chapter 2.1.3 --- Dissatisfaction --- p.20 / Chapter 2.1.4 --- Dissatisfaction Responses --- p.22 / Chapter 2.1.4.1 --- Complaining and Negative Word of Mouth --- p.22 / Chapter 2.1.4.2 --- Tipping Behavior --- p.23 / Chapter 2.2 --- Hypotheses --- p.26 / Chapter 2.2.1 --- Impacts of Fate Belief and Face Concern on Dissatisfaction --- p.27 / Chapter 2.2.2 --- Impacts of Fate Belief and Face Concern on Dissatisfaction Responses --- p.31 / Chapter CHAPTER THREE --- RESEARCH METHODOLOGY --- p.34 / Chapter 3.0 --- Overview --- p.34 / Chapter 3.1 --- Research Design --- p.34 / Chapter 3.2 --- Pretest --- p.36 / Chapter 3.2.1 --- Participants --- p.36 / Chapter 3.2.2 --- Design --- p.36 / Chapter 3.2.3 --- Materials --- p.37 / Chapter 3.2.3.1 --- Scenarios Development --- p.37 / Chapter 3.2.3.2 --- Fate belief and Face Concern Scales --- p.38 / Chapter 3.2.3.3 --- Manipulation Check Items --- p.38 / Chapter 3.2.3.4 --- Dependent Measures --- p.39 / Chapter 3.2.4 --- Procedures --- p.39 / Chapter 3.2.5 --- Results --- p.40 / Chapter 3.3 --- The Main Study --- p.42 / Chapter 3.3.1 --- Participants --- p.42 / Chapter 3.3.2 --- Design --- p.43 / Chapter 3.3.3 --- Materials --- p.43 / Chapter 3.3.3.1 --- Scenarios --- p.43 / Chapter 3.3.3.2 --- Fate Belief and Face Concern Scales --- p.45 / Chapter 3.3.3.3 --- Manipulation Check Items --- p.45 / Chapter 3.3.3.4 --- Dependent Measures --- p.45 / Chapter 3.3.4 --- Procedures --- p.46 / Chapter CHAPTER FOUR --- RESULTS AND DISCUSSION --- p.49 / Chapter 4.0 --- Overview --- p.49 / Chapter 4.1 --- Manipulation Checks --- p.49 / Chapter 4.2 --- Reliability and Validity of Scales --- p.50 / Chapter 4.2.1 --- Reliability Analysis --- p.50 / Chapter 4.2.2 --- Factor Analysis --- p.51 / Chapter 4.3 --- Classification of Fate Belief and Face Concern --- p.52 / Chapter 4.4 --- Hypotheses Testing --- p.56 / Chapter 4.4.1 --- The Impacts of Fate Belief and Face Concern on Customer Dissatisfaction --- p.57 / Chapter 4.4.2 --- The Impacts of Fate Belief and Face Concern on Dissatisfaction Responses --- p.65 / Chapter 4.5 --- Other Findings --- p.69 / Chapter 4.6 --- Discussion --- p.71 / Chapter CHAPTER FIVE --- CONCLUSION --- p.73 / Chapter 5.0 --- Overview --- p.73 / Chapter 5.1 --- Contributions --- p.73 / Chapter 5.1.1 --- Theoretical Contributions --- p.73 / Chapter 5.1.2 --- Managerial Contributions --- p.74 / Chapter 5.2 --- Limitations --- p.76 / Chapter 5.3 --- Future Research Directions --- p.77 / Chapter 5.4 --- Conclusion --- p.80 / APPENDIX I PRETEST SCENARIOS --- p.81 / APPENDIX II BOOKLETS --- p.83 / REFERENCE --- p.84