The analysis of Qualitative Data

Analysing qualitative data always yields unique results. That is, dealing with data which is rich, complex and fairly extensive. The advantages of qualitative data include richness of descr

explanation of processes in clear local context; moreover, qualitative data facilitates the preservation of chronological flow, seeing accurately the relation between events and consequences and extract fruitful explanations (Miles and Huberman,

data differ from quantitative data in different aspects as illustrated in figure 5.2.

Figure 5.2: Comparisons between qualitative and quantitative data

Despite the attractiveness and richness of qualitative data, the process of analysing qualitative data is not an easy task. The difficulty of analysing qualitative data arises from the nature of the data itself.

Dey (1993) argued that the nature of qualitative re

the subtleties and complexities associated with qualitative analysis. It is also mentioned by Yin (2009) that analysing a case study is one of the least developed and most difficult part of case study research. The difficulty of in the use of qualitative data is due to the fact that the methods of analysis are often not well formulated (Miles and Huberman, 1994). However, in order to overcome these established by developing the interview agenda (appendix B), field work procedure was planned (section 4.5), observation of work place was authorized, and list of required documentations was developed. Multiple data collection sources were applied simultaneously in the two case companies case companies’ background (motivation for implementing, CRM projects, and CRM projects implementation (implementation phases, CRM components, and CRM success factors) as explained in the proposed conceptual framework.

Following sections illustrates data analysis strategy and technique and discusses results of data and analysis of these data.

5.3 The analysis of Qualitative Data

Analysing qualitative data always yields unique results. That is, dealing with data which is rich, complex and fairly extensive. The advantages of qualitative data include richness of descr

explanation of processes in clear local context; moreover, qualitative data facilitates the preservation of chronological flow, seeing accurately the relation between events and consequences and extract fruitful explanations (Miles and Huberman, 1994). According to Saunders et al (2007), qualitative data differ from quantitative data in different aspects as illustrated in figure 5.2.

Figure 5.2: Comparisons between qualitative and quantitative data, Source: Saunders et al.

the attractiveness and richness of qualitative data, the process of analysing qualitative data is not an easy task. The difficulty of analysing qualitative data arises from the nature of the data itself.

Dey (1993) argued that the nature of qualitative research and striving to be rigorous have emphasised the subtleties and complexities associated with qualitative analysis. It is also mentioned by Yin (2009) that analysing a case study is one of the least developed and most difficult part of case study arch. The difficulty of in the use of qualitative data is due to the fact that the methods of analysis are often not well formulated (Miles and Huberman, 1994). However, in order to overcome these established by developing the interview agenda (appendix B), field work procedure was planned st of required documentations was developed. Multiple data collection sources were applied simultaneously in the two case companies case companies’ background (motivation for implementing, CRM projects, and CRM projects implementation (implementation phases, CRM components, and CRM success factors) as explained in the proposed conceptual framework.

Following sections illustrates data analysis strategy and technique and discusses results of data

Analysing qualitative data always yields unique results. That is, dealing with data which is rich, complex and fairly extensive. The advantages of qualitative data include richness of description and explanation of processes in clear local context; moreover, qualitative data facilitates the preservation of chronological flow, seeing accurately the relation between events and consequences and extract 1994). According to Saunders et al (2007), qualitative data differ from quantitative data in different aspects as illustrated in figure 5.2.

Source: Saunders et al.( 2007)

the attractiveness and richness of qualitative data, the process of analysing qualitative data is not an easy task. The difficulty of analysing qualitative data arises from the nature of the data itself.

search and striving to be rigorous have emphasised the subtleties and complexities associated with qualitative analysis. It is also mentioned by Yin (2009) that analysing a case study is one of the least developed and most difficult part of case study arch. The difficulty of in the use of qualitative data is due to the fact that the methods of analysis are often not well formulated (Miles and Huberman, 1994). However, in order to overcome these ollow a clear strategy and an appropriate

M. Almotairi Page 79 technique. The starting point of analysing qualitative data could be considering some analytic practices as stated by Miles and Huberman (1994) as follows:

• Affixing codes to a set of field notes drawn from interviews or observations

• Noting reflections or other remarks in the margin

• Sorting and sifting through these materials to identify similar phrases, relationships between variables, patterns, themes, distinct differences between subgroups, and common sequences

• Isolating these patterns and processes, commonalities and differences, and taking them out to the field in the next wave of data collection

• Gradually elaborating a small set of generalizations that cover the consistencies discerned in the database

• Confronting those generalizations with a formalized body of knowledge in the form of constructs or theories.

The purpose of adopting such an approach is to organise qualitative data and hence to analyse the data systematically and rigorously.

5.3.1 Case Study Analysis Strategy

After manipulating or organising qualitative data there is still a need for broader strategy to analyse collected qualitative data. The absence of such a strategy could result in false starts, wasting huge amounts of time, and jeopardizing the entire case study analysis (Yin, 2009). Following a clear strategy in analysing qualitative data helps treating data fairly and accurately.

Among different types of analysis of case study evidence mentioned by Yin (2009), relying on theoretical propositions is chosen to be the general strategy for analysing data for this research. This type of strategy is most preferred when there are propositions that lead the research or alternatively when there is a proposition of a conceptual framework. Such a strategy is consistent with deductive analytical procedure. For this research, the proposed conceptual framework for CRM successful implementation was the basis for designing the case study and it was reflected in creating the research questions. Applying this strategy will help the researcher focus on data of certain type and ignore other data. Based on this strategy, the data collection was designed to evaluate the implementation of the proposed conceptual framework in light of practical implementation of CRM

Evaluation of the Implementation of CRM in Developing Countries Chapter 5

M. Almotairi Page 80 projects by case companies. For example, the questions of the interviews were designed based on the components of the proposed conceptual framework. Moreover, other evidence of data such as observation, documentation, and archival records were organised and reviewed based on the design of the conceptual framework and its evaluation. In addition, the use of data analysis strategy that relies on the theoretical proposed framework helps to develop alternative explanations to be examined through organising the entire case study (Yin, 2009).

5.3.2 Case Study Analysis Techniques

After deciding which strategy will be applied in analysing case study, a technique or a procedure by which the data is to be analysed should be identified. Yin (2009) and Saunders et al (2007) have provided an extensive illustration for different types of analytical techniques that are applicable for qualitative analysis. Among these analytical techniques pattern matching is most suitable for data analysis for this research. As an analysis technique, the logic of pattern matching analysis involves the comparison between empirically based pattern and a predicted one or several alternative predictions. Such a technique helps predicting a pattern of outcomes based on theoretical propositions to explain expected findings (Saunders et al, 2007). To use the pattern matching techniques, a conceptual framework has to be developed and then the adequacy of the framework is tested as a means to explain the findings (Saunders et al, 2007). For this research, the conceptual framework was developed in chapter Three; hence the proposed conceptual framework was the based for the strategy for data analysis (relying on theoretical proposition). Consequently, the conceptual framework will be based of matching the predicted theoretical pattern (feasibility of the conceptual framework) and the empirical pattern (actual implementation). In other words, the analysis technique will be used to evaluate the feasibility of the proposed conceptual framework by conducting a comparison between two patterns; empirical implementation and theoretical framework to explain the findings of the research. If the pattern of data matches that which has been predicted through the conceptual framework then explanation is found. Therefore, the conceptual framework will be broken down to its main constructs: CRM implementation phases, CRM Components, and CRM success factors. Subsequently, each component will be divided into its sub-components. Thus, the conceptual framework’s main and sub- components will be used as the predicted pattern forming the conceptual framework for CRM successful implementation to be compared with the actual (pattern) implementation of CRM projects of both companies (Company1 and Company2). Actual (empirical) implementation of CRM projects will be divided into similar components of the conceptual framework, where it is appropriate, to conduct the comparison between the predicted and empirical patterns.

M. Almotairi Page 81 5.3.3 Case Study Analysis

The proposed conceptual framework was developed in chapter 3. As described earlier, the conceptual framework consists of three constructs: CRM implementation phases, CRM components, and CRM success factors. Therefore, to evaluate the feasibility of the framework, the research methodology was designed. Consequently, the data collection was designed based on evaluating and assessing the conceptual framework as described in chapter 4. Hence, the evaluation process is based on pattern matching technique by which the comparison between the empirical (practical) implementation by case companies and the predicted implementation (the conceptual proposed framework). If the pattern of implementing CRM by the case study companies matches the conceptual framework, the framework would yield feasibility. On the other hand if differences between actual and predicted patterns were found, these differences will be discussed and therefore any required modifications to the framework will be addressed. The case study analysis will be conducted based on the selected analysis strategy and technique that were justified for this research.

Hence, in order to evaluate the conceptual framework, the collected data on CRM implementation from case companies will be analysed based on the constructs of the conceptual framework as illustrated in Table 5.1

Evaluation of the Implementation of CRM in Developing Countries Chapter 5

M. Almotairi Page 82 Table 5.1: Analysis Aspects