DATA ANALYSIS TECHNIQUE

According to Yin (2007), data analysis involves examination, categorisation, tabulation, or otherwise recombining the evidence to address the initial propositions of a study. Most researchers need to rely on experience and the literature to present the evidence in various ways, using various interpretations. This becomes necessary because statistical analysis is not used in all case studies.

For data collection, the theoretical framework that was proposed during the literature review was used to develop the case study protocol. The case study protocol is a set of semi-structured interview questions used throughout the case study and is very significant for multiple case studies. Besides increasing the reliability of the case study data, it guides the data collection through a pattern- matching method. Pattern matching links the data to propositions whereby several pieces of information from the same cases may be related. If the pattern matches, the internal reliability of the study is enhanced. It is important to understand that this work developed the concept and framework by making data induction from the case and does not begin with a theory to test or prove. The theoretical frameworks which consist of readiness category, besides guiding the research line of inquiry, has also helped in preparing an initial category for pattern matching used in the data analysis. The approach follows a recommendation by Eisenhardt (1989) which stated that the patterns within cross-case studies are constructed from the literature, and then look for within-group similarities and inter-group differences. He further argues that the cross-case analysis should preferably be used for searching patterns, and three tactics are suggested as following:

• Select categories and look for within-group similarities coupled with inter-group differences.

74 • Divide the data by data source to exploit unique insights possible from different

types of data collection.

Therefore in this research, the categories were based on the readiness categories that were identified during the literature review (the theoretical framework) and the case study data for readiness criteria was analysed to fit into the categories. In the case where no categories seem to fit, a new category would be introduced. Furthermore, Company A as the most complete case, was selected as a control case to determine similarities and differences derived from all other cases.

In addition to data analysis, most researchers need to rely on experience and interpretation of data from interviews and the literature to present the evidence in various ways. This becomes necessary in the situation where statistical analysis is not used in the analyses. Content analysis is a methodology in the social sciences for studying the content of communication. According to Manning & Cullum- Swan (1994), the technique can involve any kind of analysis where communication content is categorised and classified. Content analysis involves quantifying oriented techniques by which standardised measurements are applied to metrically defined units and these are used to characterise and compare the documents. The classification depends on identification of a group of words with the same meaning or connotation (Webber, 1990). The transformation of data into text involves reducing the data collected into a manageable, informative database. Strategies are taken to ensure that the quality of data is not lost during the process.

As for this research, the case study data was analysed by using the content analysis technique. The content analysis that was carried out for the interview was done to ascertain a pattern of responses amongst the participants according to the predefined category. The analysis of interviews began with the intra-case analysis of each case and was followed by cross-case analysis for all organisations involved. Intra-case analysis was concerned with individual analysis of cases based on multiple sources of evidence. The analysis aimed at gaining evidence as much as possible to identify the readiness criteria. The cross-case analysis was carried out to compare the findings from all case studies. It was undertaken simply by comparative analysis of data and information gathered during the data collection method. The comparative analysis analysed literal replication between cases and help the researcher to understand the differences and similarities of each case. Throughout the cases, the answers from different people to common questions or their analysis of different perspectives on central issues were grouped together as suggetsted by Patton (1990). The answers were classified in the context analysis on the issues, and were still based on the predefined categories which form the

75 literature. Subsequently, the emergent readiness criteria were theoretically validated by using the literature source, and a conceptual framework was proposed.

All interviews and workshops were audio recorded where allowed and suitable. Recordings were transcribed verbatim for analysis. Transcribing offers a point of transition between data collection and analysis as part of data management and preparation (Patton, 1990). All transcriptions were conducted by the interviewer rather than any outsourced transcribers, which provides an opportunity for the researcher to get immersed in the data and generate emergent insights. Coding allows the researcher to simplify and focus on specific characteristics of the data and assists the researcher in abstracting or thinking up from the data (Morse & Richards, 2002). It has been claimed that the excellence of the research rests in a large part on the excellence of the coding (Strauss, 1987). Morse & Richards (2002) distinguished three kinds of coding: the storage of information, termed descriptive coding in Miles & Huberman (1994), topic coding, and analytic coding for developing concepts. They suggest that the researcher needs to go beyond storing information and gathering materials by topics and move to creating and developing abstractions from the data. Coding, therefore, functions as a way to link data with information, topics, concepts, and themes. Content analysis is used to interpret data.

Furthermore, the data analysis of a case study research may occur simultaneously as data collection can be carried out as an interactive process. The research can move between the case studies and the literature review and back to the case studies to improve the design, analysis, and the researcher‟s understanding of the core issues. Patton (2002) stated that the term „pattern‟ usually refers to a descriptive finding while a „theme‟ takes a more categorical or topical form. Morse & Richards (2002) suggested that categorisation and conceptualisation are processes that enable the identification of a pattern. Therefore, the actual comparison between the predicted and actual pattern might not have any quantitative criteria. The discretion of the researcher is required for interpretations. Interpreting the results and the present findings is the last step of the process of qualitative data analysis. Interpretation goes beyond the description of data. It means attaching significance to what was found, making sense of findings, offering explanations, drawing conclusions, extrapolating lessons, making inferences, considering meanings, and otherwise imposing order on an unruly but surely patterned world (Patton, 2002). During qualitative interpretation, the researcher works back and forth between the data and their own perspective, as well as their understandings, to make sense of the evidence. The procedure of qualitative data analysis is as follows:

a) Data from the document-checking process is compiled and summarised.

76 c) Both the data from the document checking and interviews is compiled and categorised

using content analysis based on a predefined category (theoretical framework). d) Within each category, the data and information is further identified and classified into

nods (classification system). The nods represent the readiness criteria that were identified in each company.

e) Each readiness criteria from the research is identified and elaborated. The draft of the individual report is written.

f) The cross-case analysis is conducted to measure consistency in findings between cases by combining the findings in a matrix of categorisation and nods.

g) The data from the cross-case analysis is theoretically validated with the literature review and further discussed to make sense of data and conceptualise a readiness framework. h) The final report is drafted and conclusions are developed.

As for the workshop, a similar content analysis was conducted to make sense of the qualitative data of three different groups that were assigned in the workshop. In addition to that, in analysing the quantitative data for the questionnaire, a frequency of answers and an average index analysis was used for both. A capability and importance measurement of each individual readiness criteria and the presentation of the result were made in a radar form as can be seen in chapter 6, Framework Validation. Both of the analyses were used to depict the pattern of response made by the workshop participants. The average index (A.I) (Lim & Alum, 1995) is calculated by using the following formula:

Average Index = 5𝑛5+4𝑛4+3𝑛3+2𝑛2+1𝑛1 (𝑛5+𝑛4+𝑛3+𝑛2+𝑛1)

where n is the frequency of workshop participants who answered the following:

Table 3.4: The representation of n for the level of importance and capability

n Level of Importance Level of Capability

n5 Highly Important Highly Capable

n4 Important Capable

n3 Neutral Neutral

n2 Not Important Not Capable

77 It is important to note that the main use of the A.I was to determine the average response of the workshop participants for each readiness criterion. The A.I value reflected the importance of each readiness criterion where the closer the value to 5, the more importance the particular criterion imposed. The A.I value therefore assisted the researcher to justify whether the readiness criteria should stay within the framework or needed to be omitted from the conceptual model. The level of capability, on the other hand was just used to describe the average capability background of the workshop participants as measured accordingly to each readiness criteria. It served as additional information and did not contribute to the validation of the readiness criteria.