argued in Chapter Two that service is a soft concept that cannot be separated or seen in isolation from people (service employees and customers), a qualitative methodology was seen to be more relevant. The debates on paradigm and methodologies begin with the reflection on positivism, interpretevism, and constructivismlconstructionism as the driving forces of research methodologies and methods. Positivism (developed by Auguste Comte) initially developed for natural sciences, was seen by Comte and others as the only way that would guarantee social progress through empirical evidence.
Durkheim also advocated this paradigm and introduced what is known as logical positivism (cause and effect).
One of the popular laws related to logical positivism was what Popper called falsification.
Positivists argue thatifthe research cannot be falsified, such research cannot be accepted as scientific. They then developed a quantitative methodology which they later imposed on social sciences. The positivist thinking and experience in natural objects influenced researchers to think that human beings too should be researched mechanistically with clinical objectivity, as advocated by positivists, and what works in laboratory or 'unnatural settings' as Patton calls such settings, should also work in other large contexts, or in the real world. These assumptions are clearly fallible, especially when it comes to human beings and or social sciences. A positivist also advocates that data must be reduced to statistics.
Interpretevists on the other hand reacted negatively to positivists' paradigms and inherent methodologies which were being imposed on social sciences. Some authors call interpretevism, anti-positivism. Interpretevists felt that logical pOSItIVIsm was not sufficient in addressing social complex problems which need phenomenological understanding of lived experiences of people in social sciences. A new breed of softer, qualitative methodologies thus emerged. Interpretevists, and later constructionists and constructivists argued that quantitative methodologies are mechanistic, non-systemic, linear and deductive and thus not relevant to social sciences. Subjectivity, inductivity, multi-perspective, and systemic methods were then developed (see table 3).
This dissertation thus used a qualitative methodology complemented by systems approach. A systems approach was used as a paradigm or thinking about research and the phenomenon under study, as well as applying the concepts of systems approach in a
theoretical literature discussion, and in developing research questions. The research conducted in this study adopted a triangulation method which included techniques such as unobtrusive observation, documents study, interviews and focus groups. The research was conducted over a period often months at the University of KwaZulu-Natal focusing on the student administrative service system as a high customer contact research context.
Participants included administrative staff (front line and management staff) and students.
The research provided enlightening results which offer a better understanding of the hallenges in service management. The next chapter provides a detail report or analysis and interpretation of these research findings.
CHAPTER FOUR
4. DATA ANALYSIS AND INTERPRETATION
4.1 INTRODUCTION
Chapter Three of this dissertation discussed in detail the research methodology, methods, and techniques used in this study. This chapter is now going to report (data display), interpret (find meaning), and make some conclusions and recommendations.
The data are displayed in tables with text rather than numbers. The first tables provide a synopsis of characteristics of units of analysis as discussed in Chapter Three, so as to demonstrate authenticity, reliability and richness of data and sources used in the study. In qualitative research, data analysis is a continuous and iterative process (see figure 11 below). It is a continuous process because analysis starts from the first set of data collected in the field. The data are analysed and further subsequent data collection is, to some extent guided by the first set of data. Itis iterative, because the researcher enters the context or field and acquires data, analyses the data, identifies emerging themes and patterns to be further pursued, goes back to the field and acquires more data to continue analysing.
Inqualitative research, data may be overwhelming because they do not get reduced into statistics, but remain large chunks of perpetually analysed data of various forms i.e. field notes, audio and video tapes, transcriptions, artefacts, etc. These data need to be managed properly. Data management is a framework for data analysis. Huberman &
Miles (Denzin& Lincoln, 1994: 428) define research data management as the operations needed for systematic, coherent process of data collection, storage and retrieval. In systems thinking language, data management would be the main system and data analysis the sub-system. In fact, data management fits well with systems thinking, because the process or steps of data collection, storage, and retrieval are interdependent, and, as emphasised by Huberman & Miles (Lincoln & Denzin 1994), a high quality of data is dependent on the management of data.
As discussed in Chapter Two, service conforms to open systems in that there are inputs which include customers, information from environment, a transformation process that is conducted by service employees with, and sometimes on the customer, in order (as an output value) to ameliorate, improve, develop, skill, facilitate, and change a customer or his/her property, physically, emotionally, and intellectually. This paradigm guided the analysis process. The underlying issues include communication, which is the key to service management, relationships which are the core aspects of human activity systems as advocated by Checkland (1981). Jackson (2000) sees relationships of parts of the system as important in the complex problems. The following diagram depicts the data management process as already discussed earlier in this chapter.
Figure 11
Components of data analysis - interactive model
Adapted from Hubennan& Miles (Denzin& Lincoln, 1994: 421)
Most authors seem to agree that qualitative data analysis means working with data, and involves looking for patterns and themes, and categories emerging in order to describe those patterns and themes. Huberman & Miles (Denzin & Lincoln, 1994), Silverman (2000), Bums (2000), Babbie& Mouton (2001), Morse& Richards (2002), Patton (2002) seem to agree that the primary purpose of analysing qualitative data is to find meaning.
The process of organising data is the core activity of analysis. Data are "organised so that comparisons, contrasts, and insights can be made and demonstrated" (Burns, 2000:
430). As discussed in Chapter Three, the research paradigm guides the whole research project, and this goes on to affect "the researcher's approach to analysis" (Crabtree &
Miller, in Denzin& Lincoln, 1994: 345).
The stage of analysing one's own data is also continuous - that means that every data collection session must be followed by analysis. Strauss (1987) sees this analysis process as inductive (collecting data), deductive (choosing important themes), and verification (assessing if such themes can be held as hypothesis and pursued). Until the research is complete, the continuous data analysis becomes an "interim analysis - in that it spreads collection of. and analysis throughout a study ... "(Huberman & Miles in Denzin &
Lincoln, 1994: 431).
This study made use of data analysis techniques for generating meaning developed by Miles& Huberman (1994: 12), also discussed by these two authors in Denzin& Lincoln (1994: 432). The techniques are discussed in the next section of this chapter. The diagram in figure 11, depicts the iterative process of continuous data analysis. The research for this study followed this interplay process between data collection and interim analysis.
As discussed in Chapter Three, the data collection techniques included unobtrusive observation, document study, individual interviews and focus groups, as Wolcott 1992 in Miles & Huberman (1994) puts it simply, "watching, asking or examining" (pg. 9).
Glaser 1978, quoted by Huberman & Miles in Denzin& Lincoln (1994: 438) advocating triangulation, explained that grounded theorists have long contended that theory generated from one data source works less well than slices of data from different sources.
Huberman &Miles go on to explain that triangulation converges different strengths, and that this "eliminates or mitigates biases of a single source. Each of these research techniques used in this study was linked by a process of data analysis. Each preceding technique served as a springboard for the inductive collection of data at the next level.
The SUDA model was a useful systems tool complementing the SSM, in systemically alternating between data collection and interim analysis. The SUDA cycle was entered at any stage depending on the research activity to be carried out. Soft systems methodology and SUDA work as a hybrid methodology. The SSM is a framework for the study and SUDA compliments the research techniques. The following section discusses in more detail the actual process followed in analysing data and sources of data.
4.2 DATA ANALYSIS PROCESS