Brunel Business School

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1 Brunel Business School PhD SYMPOSIUM 23rd & 24th March 2009 How the Implementation of E-CRM Enhances E-Loyalty at Different Adoption Stages of Transactional Cycle? An Empirical Study of Mobile Commerce Website in the UK Student Name: Talhat Alhaiou Student ID:

2 Abstract E-CRM emerges from the Internet and web technology to facilitate the implementation of CRM. E-CRM focuses on internet- or web-based interaction between companies and their customers. Researchers have taken different approaches and focused on a variety of aspects in investigating E-loyalty with the implementation of E-CRM. This research seeks to fulfil literature void with regarding to the development and empirical validation of E-loyalty model in a mobile commerce web site at different adoption stages of transactional cycle (Pre- Purchase, At-Purchase and Post-Purchase). Based on IS and marketing literature, a comprehensive set of constructs and hypotheses were compiled with a methodology for testing them Keywords: CRM, E-CRM, Transactional Cycle, Mobile Commerce Website, E-satisfaction, E-trust, E- loyalty 2

3 1. Introduction Internet technologies provide companies with tools to adapt to changing consumers needs and could be used to secure economic, strategic, and competitive advantages. From a marketing perspective, the Internet is not merely another marketing tool, it can be a strategic tool to help companies increase consumer loyalty. With the rapid growth of electronic business and the rise of internet-based services, the internet has provided a platform to deliver CRM functions on the Web, thus giving rise to an innovative concept the E-CRM. In order to have a better understanding of the roles of the Internet in enhancing consumer relationships, the links between CRM attributes delivered on the Internet (E-CRM) and E- loyalty worth further investigation. Little research has empirically tested the critical factors that influence an individual E-loyalty when buying mobile products/services online. Based on the gaps found in the literature, this study was designed to investigate ECRM factors that affect on consumers E-loyalty when buying mobile products/services online at different adoption stages of transactional cycle (Pre-Purchase, At-Purchase and Post-Purchase). The study also examines the relative importance of such factors. Thus, the research problem investigates in this thesis is: How the Implementation of E-CRM Enhances E-loyalty at different adoption stages of transactional cycle 3

4 2. Research issues and objectives The purpose of this research is to identify E-CRM factors influencing E-loyalty at different adoption stages of transactional cycle (Pre-purchase, At-purchase, Post-purchase). The research problem addressed in this thesis is: How the Implementation of E-CRM Enhances E-loyalty at different adoption stages of transactional cycle Based on the research problem above, the specific objectives of this research are: Identify E-CRM factors influencing E-loyalty at different adoption stages of transactional cycle Explore the relative importance of E-CRM factors that affect Pre-Purchase/e-Satisfaction Explore the relative importance of e-crm factors that affect At-Purchase/e-Satisfaction Explore the relative importance of e-crm factors that affect Post-Purchase/e-Satisfaction Explore the relation between e-trust/e-satisfaction Explore the relation between e-trust/e-loyalty Explore the relation between e-satisfaction /e-loyalty 3. A Conceptual Model The Conceptual framework provides the foundation on which an entire research project is based (Sekaran, 2000). It describes the relationship between variables that contribute to the research problem. The Conceptual framework provides a clear understanding of the dynamics of the problem being investigated and thus facilitates the generation of testable hypotheses. Based on an exploratory research, this study identified ten variables that are considered relevant to the research problem. The independent variables (IV) for this study include Pre- Purchase/e-CRM, At-Purchase/e-CRM, and Post-Purchase/e-CRM, while the use e- 4

5 satisfaction and e-loyalty are listed as the dependent variables (DV). These variables build up a Conceptual framework that is inline with the objectives of this research. Figure 1 bellow illustrates a schematic diagram that represents the Conceptual framework of this study and Table 1 Summarise the Hypotheses of this study. FIGURE 1: A Conceptual Model for E-Loyalty of Mobile Commerce Website Pre-Purchase/E-CRM Website Design H1 Search Capabilities Loyalty Programme H3 H2 At-Purchase/E-CRM Security/Privacy Payment Methods H4 H5 E-Satisfaction (E-SQ) H11 Mobile Website Users E-Loyalty (E-LO) H6 H9 H10 Post-Purchase/E-CRM H7 Order Tracking H8 On- time Delivery E-Trust Customer Service 5

6 Table 1: Summary of the Hypotheses Hypotheses H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 Description Site Design will have a positive effect on E-Satisfaction Search Capabilities will have a positive effect on E-Satisfaction Loyalty Programme will have a positive effect on E-Satisfaction Security/Privacy will have a positive effect on E-Satisfaction Payment Methods will have a positive effect on E-Satisfaction Order Tracking will have a positive effect on E-Satisfaction On- time Delivery will have a positive effect on E-Satisfaction Customer Service will have a positive effect on E-Satisfaction E-Trust will have a positive effect on E-Satisfaction E-Trust will have a positive effect on E-Loyalty E-Satisfaction will have a positive effect on E-loyalty This study proposes that the effectiveness of an E-CRM program in enhancing E-loyalty is a major determinant of the extent to which specific variables (Web-Site Presentation, Search Capabilities, Payment Methods, Loyalty Programme, Privacy and security...) will be implemented. In turn, the use of the Internet in building consumer relationships (E-CRM) could increase Pre-Purchase/e-Satisfaction, which leads to acquire At-Purchase/e-Satisfaction and Post-Purchase/E-Satisfaction leading to E-loyalty. However, without a good understanding of the dimensions of Pre-Purchase/e-Satisfaction, At-Purchase/e-Satisfaction, and Post-Purchase/E-Satisfaction, firms may not be able to differentiate their offerings across consumer segments and may easily lose their consumers. Although not illustrated in the diagram above, this study also seeks to investigate the dimensions of Pre-Purchase/e-CRM, At-Purchase/e-CRM, and Post-Purchase/E-CRM. Drawn from the literature, this study 6

7 proposes that Pre-Purchase/e-CRM is constructed from five independent variables: Web-Site Design, Search Capabilities, and Loyalty Programme. On the other hand, Payment Methods, and Privacy/security are proposed as the independent variables of At-Purchase/e-CRM, while Post-Purchase/E-CRM is constructed from three independent variables: Order tracking, on time delivery and after sale service. 4. Data Collection Method Data collection is the process by which the opinions and useful information from target respondents about the topic are collected, classified, and categorized according to their demographic and socioeconomic characteristics (Churchill 1987). This section aims to justify the selection of a Survey Method as the most suitable data collection method for this study. 4.1 Selection of Survey Method There are different methods for data collection identified in the literature, including mail, face-to-face, telephone, electronic mail, and a combination of these methods (Cooper & Schindler 2001; Sekaran 2000; Zikmund 1997). The decision to choose a survey method may be based on a number of factors that include sampling, type of population, question form, question content, response rate, costs, and duration of data collection (Aaker et al. 2000). The most appropriate survey method for this research was a personally administered one. This study used a self-administered survey because it had the advantages of versatility, speed, and worked as a checkpoint to ensure that all respondents in this study could understand the concepts they are answering (Grossnickle & Raskin 2001; Churchill 1987). The key strengths of a self-administered survey are mainly cost and accuracy (Aaker, Kumar & Day 1998). The objectives of this study involved getting information from Internet users about their attitudes and motivations in buying Mobile Phone online. The target respondents are the people who are familiar With the Internet and use the Internet to buy mobile 7

8 phone/accessories. In general, a self-administered survey is easily designed and administered. In addition, Respondents can carefully consider and answer questions at their discretion. In a survey, respondents may be asked a variety of questions regarding their behaviour, attitudes, demographic and lifestyle characteristics (Malhotra 1999). Typically, the questions are standard and structured, which means a formal questionnaire is prepared and questions are asked in a prearranged order. 4.2 Sample Design Sample is defined as part of the target population, carefully selected to represent the total population (Cooper & Schindler 2001). The process of sampling involves selecting a sufficient number of cases from the target population to make conclusions about the whole population, including the process to determine population, sampling frame, sampling method, sample size, and sample selection (Sekaran, 2000). The sampling strategy adopted in this study is discussed next. Target Population A population is the totality of cases that conform to some designated specifications, which could be people, events, or things of interest to the researcher (Sekaran 2000; Churchill 1987). The target population in this study is defined as all consumers who are use the internet to buy mobile phones/services in the UK. However, it is too expensive and impractical in reality to use the total population in this study (Sproull 1995). An appropriate sampling frame is identified next. Sampling Frame and Sampling unit A sampling frame is a list of representative persons in a target population from which the sample may be drawn (McPhail 2000). It is a subset or list of Internet users, who are also 8

9 mobile phone users in the UK. The sample unit in this study is the individual, who use the internet to buy mobile phone/services in the UK. Sample Size The required sample size depends on factors such as the proposed data analysis techniques, financial and access to sampling frame (Malhotra 1999). The proposed data analysis technique for this research is Structural Equation Modelling (SEM), which is very sensitive to sample size and less stable when estimated from small samples (Tabachnick & Fidell 2001). A sample size of 200 persons is recommended to be sufficient for data analysis (Hair et al. 1998). The researcher expects to have 500 persons participating in this study. 5. Selecting a Data Analysis Strategy The final step is to select the appropriate statistical analysis technique. To do this, research elements, namely the research problem, objectives, characteristics of data and the underlying properties of the statistical techniques are considered (Malhotra 1999). To meet the purposes of this study, descriptive and inferential analyses will be applied. Descriptive Statistics Refers to the transformation of raw data into a form that would Provide information to describe a set of factors in a situation that will make them easy to understand and interpret (Kassim 2001; Zikmund 2000). This analysis gives a meaning to data through frequency distribution, mean, and standard deviation. Inferential Analysis Refers to the cause-effect relationships between variables. Inferential statistics used for this research were correlations, structural equation modelling (SEM). 9

10 Correlation Analysis. Correlation analysis will be used to test the existence of relationships between variables being studied. Factor Analysis. Prior to multivariate analyses, an exploratory factor analysis will be performed to identify the common items of an underlying dimension, or also called factor (Hair, et al. 1998). Through this extraction technique, it was obvious which factors should be considered: the higher/lower loading factors will obviously produce higher/lower values. SEM. Moving onto the second inferential analysis, SEM will be used to measure the relationships between the independent variables and dependent variables. Since this study required the hypothesized models to be tested for the best-fit, SEM seemed to be the appropriate analysis method as it produces more comprehensive overall goodness-of-fit than those found in other traditional methods (Ramanathan 1989). AMOS a software package (Arbuckle 1997; Byrne 2001; Tabachnick & Fidell 2001) will be used for SEM as it is userfriendly software that provides a graphical user interface, which is easy to understand. 9. Bibliography 1. Bodet, G., (2008). Customer satisfaction and loyalty in service: Two concepts, four constructs, several relationships, Journal of Retailing and Consumer Services 15 (2008) Cheung, C, M., Lee, M, K, (2006). Understanding Consumer Trust in Internet Shopping: A Multidisciplinary Approach, Journal of the American Society for Information Science and Technology, 57(4): , Chalmeta, R., (2006). Methodology for customer relationship management, the journal of systems and software, 79, (7)

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