Methodology Research Data

Introduction

According to Walliman (2001), a methodology explains the theory behind the research methods or approaches. This chapter highlights the theories behind the methodology employed and examines the research methods that are most appropriate for this research which help to better understand the topic under investigation.

This research undertakes an analytical review of customer retention techniques of Indian banks, using Citibank as a case study. This chapter outlines how this analysis is undertaken and describes the rationale behind the choice of research design and the construction of the method.

Research Method Construction

Much of the research undertaken in social sciences is primary. This is based on the collection of primary data, that is, data originated by the researcher for the purpose of the investigation at hand (Stewart and Kamins, 1993). Primary analysis is the original analysis of data in a research study. It is what one typically imagines as the application of statistical methods.

However, not every study or research undertaking must begin with the collection of primary data. In some cases, the information required is already available from published sources. This is called secondary research – the summation, collation, and/or synthesis of existing research. Secondary information consists of sources of information collected by others and archived in some form. These sources include reports, industry studies, as well as books and journals.

The collection, generation, and dissemination of information is growing. This means that there exists a tremendous amount of secondary data that is relevant to today's decision-making problems. Knowledge accumulation increasingly relies on the integration of previous studies and findings. Glass (1976) argues that when the literature on a topic grows and knowledge lies untapped in completed research studies, "this endeavour (of research synthesis) deserves higher priority ... than adding a new experiment or survey to the pile" (Glass, 1976, p. 4).

One of the main reasons to value secondary data comes from the ease of collection for research use (Houston, 2004). This information can be of considerable importance for two reasons.

  • Time savings – typically, the time involved in searching secondary sources is much less than that needed to complete primary data collection.

  • Cost effectiveness – similarly, secondary data collection in general is less costly than primary data collection. For the same level of research budget a thorough examination of secondary sources can yield a great deal more information than can be had through a primary data collection exercise.

Another, and perhaps more important, benefit to researchers from employing secondary data is that alternative types of data can provide multi-method triangulation to other research findings (Houston, 2004). This is because the knowledge bases regarding many constructs, such as retention and loyalty, have been built heavily through survey research approaches. All things being equal, secondary data should be used if it helps researchers to solve the research problem (Saunders et al., 2006).

If there exists data that solves or lends insight into the research problem, then little primary research has to be conducted. Because resource constraints are always a problem for the researcher, it makes good sense to exhaust secondary data sources before proceeding to the active collection of primary data. In addition, secondary data may be available which is entirely appropriate and wholly adequate to draw conclusions and answer the question or solve the problem.

This secondary analysis may involve the combination of one data set with another, address new questions or use new analytical methods for evaluation. Secondary analysis is the re-analysis of data for the purpose of answering the original research question with different statistical techniques, or answering new questions with old data. Secondary analysis is an important feature of the research and evaluation landscape. Generally, secondary research is used in problem recognition and problem clarification.

However, in addition to being helpful in the definition and development of a problem, secondary data is often insufficient in generating a problem solution (Davis, 2000). Whilst the benefits of secondary sources are considerable, their shortcomings have to be acknowledged. There is a need to evaluate the quality of both the source of the data and the data itself. The first problem relates to definitions. The researcher has to be careful, when making use of secondary data, of the definitions used by those responsible for its preparation.

Another relates to source bias. Researchers have to be aware of vested interests when they consult secondary sources. Those responsible for their compilation may have reasons for wishing to present a more optimistic or pessimistic set of results for their organisation. Also, secondary data can be general and vague and therefore may not help with decision-making. In addition, data may be incomplete. Finally, the time period during which secondary data was first compiled may have a substantial effect upon the nature of the data.

Considering these shortcomings, primary data collection strategy was also adopted after analysis and collection of secondary data. This was purposely done by the author as the author wanted to analyze the previous similar researches before drafting a primary data collection questionnaire. In constructing the primary data collection method, data needs were first specified. Primary data was collected in the form of interviews with Citibank operational and branch managers, focus groups were also conducted with a sample of Citibank customers.

These methods were considered to be the most appropriate in terms of achieving the objectives of the study and worked out best within the time and cost constraints. Semi-structured probing interview with Citibank management staff revealed in depth information and insights on customer retention and relationship banking. Focus groups conducted with Citibank customers was the best way to get information out of them as ideas from person sparked off ideas from another and the group gelled together very well.

Also, facial expressions and bodily movements indicated quite a lot in a focus group. It wasn't feasible to conduct telephonic interview or video-conferencing due to the costs involved. Though, initially some thoughts were given to conducting telephonic interviews with Citibank employees, but later on the idea was shelved because of time and cost constraints.

Secondary data for this research concentrated on collecting data from books, journals, online publications, white papers, previous researches, newspapers (Economic Times) taped interviews, websites, research databases etc. Secondary data was collected and partially analyzed before embarking onto primary data collection methods so that the designing of focus group and interview questions can be framed properly.

Although, most of the secondary data was collected by the time primary data collection methods were embarked upon, but secondary data collection didn't stop altogether. In a way, the data collected from the secondary data and the data gathered from field research helped in triangulation. The field research also helped in testing the hypothesis that was developed after studying the concepts and theories (deductive approach).

Also, after gaining sufficient insight on the topic, it made it easier for the author to frame the questionnaire, because first the questions to test the hypothesis were framed and then specific questions were framed which would have helped in forming a hypothesis (inductive approach). Primary research tried to delve as deep as possible into areas which could not have been covered by secondary research and where first-hand information was absolutely necessary to come to a definitive conclusion.

Research Approach

Qualitative method is a kind of research that produces findings not arrived at by means of statistical procedures or other means of quantification. It is based on a meaning expressed through words (Saunders, 2006).

Qualitative research method often provides rich descriptive and exploratory data and is exploratory in nature. Quantitative methods on the other hand, uses numbers and statistical method, it tends to be based on meanings derived from numbers.

The research approach used for this research is primarily qualitative. Both the primary data collection methods concentrate on qualitative data collection. But, quantitative data is also collated in the form of company reports. Company reports were reviewed to analyse the effect of retention measures on management accounts.

So, both quantitative and qualitative methods of data collection technique is applied, although the major part of the research relies upon qualitative data and its analysis. Qualitative secondary information from a variety of sources are gathered like Citibank Case Studies, Web page , Reference books , Journals , Online journals, Newspaper and Magazine Articles , Taped interviews , Business news channel views , Research Agency) databases . Quantitative data from Citibank Company Reports and other supermarkets are collected and analyzed to compare and contrast the effect of various retention initiatives.

The Research Design

A research design is the framework or a plan for a study used as a guide to collect and analyse data, it is the blueprint that is followed (Churchill and Iacobucci 2005; pg 73). Kerlinger (1996; pg 102) defines 'a plan and structure of investigation to obtain answers to research questions.' The plan here means the overall scheme or program of the research and includes an outline of what the researcher seeks to do from hypotheses testing to the final analysis of the data.

A structure is framework organization or configuration of the relations among variables of a study (Robson, 2002; pg 73). The research design expresses both the structure of the research and the plan of investigation used to obtain empirical evidence on the relations of the problem. Some of the common approaches to research design include exploratory research, descriptive research and causal research.

For the purpose of this research, an exploratory research is conducted as little previous researches are available on customer retention in Indian banks. Hence little information is available on how to solve the research since there is little past references. The focus of this study is on gaining insights and familiarity with the subject area of customer retention for more rigorous investigation at a later stage.

The approach is very open and a wide range of data and information can be gathered and it will provide the conclusive answer to the problem defined. This research will study which existing theories or concepts with regards to customer retention can be applied to the problem defined. It will rely on extensive face to face interviews conducted with Bank Managers of Citibank to understand the concept of customer retention and how it is implemented. One of the reasons for carrying out an exploratory study for the purpose of this study is, because some facts about customer retention are known by the author but more information is required to build a theoretical framework.

Sample

Sample selection in this study, was driven by the need to allow maximum variation in conceptions. Individual managers were interviewed according to their expected level of insight regarding customer retention. In total, five interviews were conducted, all participants were employed by Citibank in India.

In addition, two interviewees had been directly involved in developing the retention strategy while the other three had gained experience in implementing retention strategies. Thus, the likelihood of uncovering a range of variations between conceptions of retention was increased.

Focus group participants banked with Citigroup in some form or the other (current accounts, credit cards, loans etc). These participants represented a mix of genders, age, banking experience, discipline and experience of banking with different banks.

Method of Data Collection

Data was collected using a semi-structured interview technique, which is characterized by (Booth, 1997 as being both open and deep.Open refers to the fact that the researcher is open to be guided by the responses made by the interviewee (Marton, 1994; Booth, 1997). Deep describes how, during the interview, individual interviewees are encouraged to discuss their conceptions in depth until both the researcher and the interviewee reach a mutual understanding about the phenomenon in question (Booth, 1997; Svensson, 1997).

In this study, this facilitated the prompting of interviewees to move beyond the concept of retention and into relationship building and loyalty. All face-to-face interviews were conducted with a single member separately in the participant's office, with the interviews lasting between 30 and 40 minutes. Initially a "community of interpretation" (Apel, 1972) between the researcher and participant was established, with the researcher explaining that the objective of the research was to understand what constitutes effective retention strategy and the importance of retention within the banking community.

The question encouraged the participants to reflect upon and articulate their own lived experience of retention. They also focused on the "structural-how" aspects of customer retention. In asking about the roles and activities related to retention, it tried to figure out the 'how' component of retention. The interviews progressed around these topics, with participants guiding the agenda based on the extent of their interest in the topic.

For example, the majority of interviewees drew upon comparisons between the American banking systems when expressing their views on the retention process. In addition to the primary questions, follow-up questions were asked as appropriate. Examples included "What do you mean by that?", "What happens?", and "Is that how you see your role?" These questions encouraged individual participants to elaborate the meaning of customer retention.

Data Analysis

All five interviews and focus group sessions were taped and then transcribed verbatim. The transcripts were then analysed by the research team using investigator triangulation (Janesick, 1994). In line with the suggestions of Francis (1996), a structural framework for organizing the transcripts was first developed.

This prevented the research team getting lost in the enormous amount of text contained in each transcript and ensured we focused on the underlying meaning of the statements in the text, rather than on the specific content of particular statements.

The components of the framework were dimensions of supervisors' conceptions, which were not predetermined by the researchers but were revealed in the texts. The phenomenographic approach seeks to identify and describe the qualitatively different ways of experiencing a specific aspect of reality (Marton, 1981, 1986, 1988, 1994, 1995; Van Rossum & Schenk, 1984; Johansson, Marton, et al., 1985; Sa¨ ljo¨ , 1988; Sandberg 1994, 1997, 2000, 2001; Svensson 1997).

These experiences and understandings, or ways of making sense of the world, are labelled as conceptions or understandings. The emphasis in phenomenography is on how things appear to people in their world and the way in which people explain to themselves and to others what goes on around them, including how these explanations change (Sandberg, 1994).

The framework we used to organize the data in each transcript comprised four dimensions of the explanations that supervisors used to make sense of their world, as expressed by them in the interview:

(a) What the interviewee's conception of supervision meant to the interviewee in terms of the goal of supervision (referential-what);

(b) How the conception was translated by the interviewee into roles and activities (structural-how);

(c) What the conception meant to the interviewee in terms of the desired outcomes of the PhD supervision (referential-what); and

(d) What factors influenced the interviewee's conception (external influences). The organizing framework was then used to reduce the text in each interview transcript to its essential meaning. Each researcher reread the first interview transcript. Discussion, debate, and negotiation then followed as we applied the components of our organizing framework to the first interviewee. Where differences of opinion occurred, a researcher attempted to convince the others of the veracity of their claims.

As a result of this ongoing and open exchange, we reached agreement about the components of the first interviewee's conceptions that we believed were most faithful to the interviewee's understanding of their lived experience of PhD supervision, as represented by their interview transcript. We then repeated this process for the next interviewee until all of the transcripts had been reduced into the organizing framework.

Conceptions began to emerge from our organizing framework as we alternated between what the interviewees considered PhD supervision to be, how they enacted supervision in their roles and activities, and why they had come to this understanding of supervision. Once these conceptions emerged, we tentatively grouped together interviewees who shared conceptions of supervision that were similar to each other and were different from those conceptions expressed by other super-visors. We then cross-examined our interpretations of each interviewee's understanding of supervision by proposing and debating alternative interpretations.

This cross-examination continued until we, as a group, reached agreement on two issues: First, we believed we had established the most authentic interpretation of each interviewee's understanding.

Second, we believed we had grouped interviewees expressing qualitatively similar understandings into the same category of description and had grouped interviewees expressing qualitatively different conceptions into different categories of description. Five categories of description, which we labelled as Conceptions 1 through to 5, emerged from this process.

Through the same iterative process, and through open dialogue and debate between the members of the research team, we were then able to map the five conceptions into an outcome space. The outcome space illustrates the relationships between the differing conceptions in two ways: First, the outcome space illustrates the outcome of higher priority sought by the supervisor (completion of the PhD or new insight).

Second, it distinguishes the fundamental approach to supervision as either pushing (the student is a self-directed learner) or pulling (the student is a managed learner) the student through the process. Table 2 summarizes the techniques we applied, as derived from the literature, to improve the validity and reliability of our interpretations of the interviewee's experiences as expressed in the transcripts.