Modelo de deformaciones

According to Taylor-Powell and Renner (2003) there is no single or best way for data analysis; to adopt a particular approach depends on three aspects: (i) the questions the researcher wants to answer, (ii) the needs of those who will use the information, and (iii) data resources. Although both qualitative and quantitative data analysis approaches are used in this research, the highly qualitative nature of this research calls for interpretation and qualitative techniques which constitute the bulk of the data analysis and evaluation of this research.

115 3.10.1 Qualitative Data Analysis (Phase II/ Semi-Structured Interview)

The data analysis process is concerned with adopting an analysis strategy which appropriates the achievement of research objectives. Meanwhile, this research relies particularly on the qualitative semi-structured interview technique for gathering data, while other used instruments were employed either to assist in achieving the objectives of the main instrument, or to support research findings as a recommended implementation of triangulation.

Qualitative data consist of word of mouth and observation, not numbers. To achieve an in-depth understanding for the phenomenon being evaluated that may include dealing with a large volume of data requires creativity, discipline, and systematic approach (Taylor-Powell & Renner, 2003).

Generally, relevant literature has focused on three main steps in qualitative data analysis as shown in figure 3.5: noticing and coding, collecting and sorting instances of things, and thinking about interesting things (examining the pieces of a puzzle) as Seidel (1998) suggested.

Figure 3.5: The Data Analysis Process Source: Seidel (1998)

116 In this research, Miles and Huberman’s (1994) systematic qualitative analysis approach is used as a general guide for analysing collected data. The process in which “raw form” collected data were converted into a form that allows exploring, presenting and describing the content of primary qualitative data undergoes the following three phases: (1) preparing and checking data, (2) classifying and coding data interpreting, and (3) exploring and presenting data.

1- Preparing and Checking Data

In addition to checking secondary data, each audio-taped interview was evaluated ―as an initial step― in order to determine in general the high related sections that represent a source of important date.

The Interview Data Transcription process was initiated at the same day upon completion of the interview. Transcribing audio-taped interviews consumed a great deal of effort and time; the process required an average of six times the time each recorded interview took to do. By listening frequently to the recorded audio-tape interview, the researcher ensured he was familiar with the content. Transcribed audio interviews and recorded notes were checked, re-read and revised by the researcher many times in order to verify the authenticity of the interview transcripts’ translation. The researcher’s spouse who is currently a researcher at the University of Leicester, and who speaks both Arabic and English fluently, assisted in checking the translation for more confirmation.

Qualitative analysis of audio interviews required the researcher to focus on all participants’ answers, expressions and tones (Saunders et al., 2007) not only what is said or recorded. Hence, the researcher focused his attention on ensuring that the audio- taped interview transcripts represent the interview content as accurately as possible.

To prepare for the interpretation process, each transcribed interview was uploaded to a computer and identified based on the following information:

 Interview date and place

 The firm

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2. Classifying and Coding Data

Coding data refers to the systematic way in which data are condensed into smaller analysable units or names that emerge from gathered data. According to Strauss (1987) the excellence of qualitative research rests to a large extent on the quality of coding, the process which involves the discovery and naming of categories. Classifying and coding data facilitates the organisation, retrieval and interpretation of data. It is accomplished through the creation of categories and concepts derived from the collected data themselves, inductively preparing data for further in-depth analysis.

Taylor-Powell and Renner (2003) suggested two steps for categorising and coding data: first, to identify themes or patterns (e.g. ideas, concepts, behaviours, interactions, incidents or phrases), and second, to organise patterns into coherent categories that summarise and bring meaning to the text. This process could be accomplished within different steps according to Strauss and Carbin (1990) who indicated three levels of coding; open, axial, and selective.

Open coding is the initial stage in data acquisition which refers to breaking down the

data into separate units of meaning. It is unrestricted, unfocused and ‘open’ coding (Goulding, 1999) which is done by scrutinising the ‘fieldnote’, interview, or other document very closely; line by line or even word by word (Strauss 1987, p.28). According to Strauss and Corbin (1998, p.60) the first step in the coding process is (what does the word seems to mean or what could it mean?). Hence, the researcher can start with some questions that need to be continually addressed, which helps in open coding;

 What is this? What does it represent? (Goulding, 1999).

 What is happening in these data?

 What is the basic socio-psychological problem?

 What accounts for it?

 What patterns are occurring here?” (O’Callaghan, 1996 cited in Goulding, 1999, p.12).

Thus, the result of open coding is a developed list of meaningful categories (e.g. conditions, actions, interactions or consequences) including all emerging codes.

Axial coding: is the next step after open coding which focuses on making connections

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Selective coding: refers to “the process of selecting the central or core category,

systematically relating it to other categories, validating those relationships, and filling in categories that need further refinement and development” (Strauss & Corbin, 1990, p.116). Compared to axial coding, selective coding is a higher level of abstraction that includes selecting the core category, relating it to other categories and confirming and explaining those relationships. Thus, it is viewed as an advanced stage of research analysis in which categories are related to the core category ultimately becoming the basis for the grounded theory (Babchuk, 1997).

Different factors are used for choosing core coding (Strauss & Corbin, 1998):

 It must be central and appear frequently in the data.

 The explanation that evolves by relating the categories is logical and consistent.

 The name or phrase used to describe the central category should be sufficiently abstract.

 The concept is able to explain variation as well as the main point made by the data.

The role of the researcher in determining the core category is crucial as an “author of a theoretical reconstruction” (Mills et al., 2006) who develops the matrix that stimulates analysts’ thinking about the relationships between emerged categories (Strauss & Corbin, 1998). A process helps to frame a ‘story’ which is a key aspect in formulating the grounded theory (Moghaddam, 2006). “The story line is the final conceptualization of the core category, and as such, this “conceptual label” must fit the stories/data it represents (Strauss & Corbin, 1990, p. 121).

To conclude, selective coding is of critical importance to the research analysis process by which the specific core category is determined and positioned at the centre of the evaluation being explored (Moghaddam, 2006).

3. Interpreting, Exploring and Presenting Data

At this step of the data analysis, the researcher draws his attention towards making sense of the collected data. The key areas of focus in this stage are explaining patterns and examining relationships within a collection, and also across collections, and making general discoveries about the phenomenon that is being evaluated (Seidel, 1998).

119 Hence, in practice, the researcher scrutinises the potential connections between the categories and also will develop a descriptive framework of the participants’ attitudes regarding research phenomenon. Conclusion-drawing and verification of findings form the final phase of the analysis process.

3.10.2 Quantitative Data Analysis (Phase I, Short Questionnaires)

Quantitative data analysis aims at making sense of the collected quantitative data (numbers) to permit meaningful interpretation. The quantitative data analysis process is concerned with adopting appropriate analysis techniques with regard to the nature of collected data, instruments used and intended research objectives.

As a preparatory step, all collected questionnaires were reviewed for completeness and accuracy and incomplete questionnaires were identified for exclusion from analysis. Also, valid questionnaires were numbered (identified) for computer statistical processing purposes.

Determining the appropriate kinds of statistical measurements or comparisons to be used is based on ‘what’ aspects (variables) should be measured, and ‘how’ to measure these aspects (variables)? In this research, mean (M) and standard deviation (SD), One- Way Anova and Scheffe Post Hoc as applications of the Statistical Package for Social Science (SPSS) version 16.0, were conducted in order to determine telecommunications operators’ adopted level of marketing orientation.