In the following chapter, this qualitative data base is analyzed in order to draw con-clusions first of all on the developed propositions, but also going beyond those. Before describing the procedure used in the context of this work, it seems appropriate to loose a few clarifying sentences on the underlying epistemological approach taken. Indeed the dispute between positivist and interpretative stances seems to be especially prevailing for case study research16. The basic idea of pure positivism is that all human thinking and knowledge can be derived from sensation, i.e. the use of the human senses (Fried-man, 1999, p. 89). Thus, one can argue that all ideas can be logically composed from experience. Taking up the example of Hanfling (1981, p. 10), a ’golden mountain’, al-though not a realistic concept, can be imagined by logically joining the ideas of gold and mountains, two formerly known concepts. Thus, taking the concept to another stage, it is obvious that, by further and further breaking down ideas into their basic components, one ultimately has to arrive at basic statements that are impossible to be further ana-lyzed. These basic components, which are immutable, objectively given structures and objects, can be, through simple observation, verified to be true or false17. Therefore, turning the concept upside down, every complex theory or concept can be proven to be true or false by breaking it down into its basic components, observing if these basic components are found to be true, and analyzing if the basic components are composed in a logical and realistic way.
Pure Interpretative research in contrast assumes that such ultimately verifiable ba-sic components do not exist. Rather the research derives knowledge from interaction with the research objects. Orlikowski and Baroudi (1991, p. 13) argue that “reality, and our knowledge thereof, are social products and hence incapable of being understood independent of the social actors (including the researchers) that construct and make sense of that reality.” Furthermore, they state that “the researcher can never assume a value-neutral stance, and is always implicated in the phenomena being studied. Re-searchers’ prior assumptions, believes, values, and interests always intervene to shape their investigations.” Along the same lines, Soeffner and Hitzler (1994) argue that the human environment, which influences the understanding and interpretation of reality, is by no means restricted in any way. It is rather a constantly evolving concept that
16 Similar to the discussion on different research strategies above, also no clear definition exists for epistemological approaches. Denzin and Lincoln (2005) for example consider positivism as one out of four distinct interpretative paradigms in qualitative research. The subsequent discussion is thus not intended to give a holistic overview over epistemological issues in general. It therefore contrasts the two approaches as for example in Lee (1991, and the there quoted literature).
17 See also the discussion between normative and positive statements in Section 2.3.1.3.
moves with the individual, that changes, when the individual changes, and that is closely interdependent with the individual. Therefore, they argue that interpretative research is at the same time universal and relative. Universal in a meaning that principles that are used are of general validity. Relative in a way that the application of theses principles, might yield - depending on the situation of the individual - different results.
In this context, IS research commonly uses a more positivist approach18. This has been explained for example by Gadenne (1997), who argues that the IS research community is, as it is largely influenced by reference disciplines, split with regard to the epistemological approach used. One part of IS research is often is grounded in physical, technological, or mathematical foundations, which make the use of positivistic research appropriate.
However, the other part of IS is, because it deals with the triad of humans, tasks, and technology, considered to be rather a part of social science, than one of natural science.
Accordingly, positivistic research is not as appropriate as interpretative research19. This is well reflected in the fact that a vast majority of literature supports the argument that a more interpretative approach would improve the overall quality of research in IS Lee (1999); Klein and Myers (1999); Serafeimidis and Smithson (2000); Butler (1998);
Orlikowski and Baroudi (1991); Gadenne (1997). The present study thus tends to be more interpretative in nature.
Thus, the advice of Dube and Pare (2003, p. 597) that “one of the keys [for rigorous case study research] is to include better documentation particularly regarding issues related to the data collection and analysis process,” has to be considered carefully in the light of their focus on positivist case study research. However, as the usage of the word pure in first two passages of this section, as well as the precautious statement that the present study tends to be more interpretative already indicate, no empirical research approach should be considered to be purely either the one or the other. Rather, “in the actual practice of empirical research, we believe that all of us (...) are closer to the center, with multiple overlaps” (Miles and Huberman, 1994, p. 4 ff).
This interpretative background has to be considered in the subsequent description of the data analysis process. Following Patton (2002, p. 463), the first step in such a qualitative data analysis process “involves identifying, coding, categorizing, classifying, and labeling the primary patterns in the data. This essentially means analyzing the core content of interviews (...) to determine what’s significant.” The content analysis in the
18 Vessey et al. (2002) found in their study on diversity in the IS field (that addressed a total of 488 articles published in the five year period 1995 - 1999) that only 4.7 percent of all the examined articles had an interpretative background. However, this is even an increase in comparison with an earlier study, that found only 3.2 percent of the articles having an interpretative background Orlikowski and Baroudi (1991).
19 This does not mean that a positivistic approach is not used in this context, even if not entirely appropriate. Western culture in general, and specifically the scientific culture, is firmly rooted in a long, historical, positivistic tradition. This results in a climate in which, “knowledge claims that are not grounded in positivist thought are simply dismissed as ascientific and therefore invalid”
(Hirschheim, 1985, p. 3).
present study is based on the theoretical framework20developed in the preceding chapter, a common approach to avoid data overload (Miles and Huberman, 1994). In order to achieve this goal, codes have been developed for the theoretically developed propositions.
“Codes are tags or labels for assigning units of meaning to the descriptive or inferential information compiled during a study. Codes usually are attached to ”chunks” of varying size - words, phrases, sentences, or whole paragraphs” (Miles and Huberman, 1994, p. 56). Thus, each of the propositions has been assigned a brief label: Innovation, Technology, and Market for the benefits explaining the emergence of the new industry structure; Standardization, Monitoring, and Personal Relations for the mechanisms used to manage it. With the use of this scheme, the transcripts of the interviews have then been coded. Obviously, this is not an easy task, as it “involve[s] both technical and creative dimensions. (...) No abstract processes of analysis, no matter how eloquently named and finely described can substitute for the skill, knowledge, experience, creativity, diligence, and work of the qualitative analyst” (Patton, 2002, p. 466). Furthermore, this process of content analysis can be expected to be a reoccurring one, in which passages are constantly re-coded until finally a set of interview passages emerges for each code.
Indeed, all the interviews had to be read several times until the passages have been assigned the appropriate code. As coding has in part changed throughout the reading, this process has been supported by a software that has been used to dynamically re-label the passages.
Once the coding has been stable for consecutive readings, the coded interviews have been used in a twofold way, as is has also been described by Crabtree and Miller:
Once a codebook has been prepared, different approaches may be taken for using the codebook, in particular: (a) using codes as data management tool in which segments of similar text are printed for subsequent reading and analysis, and (b) coding text and then counting the frequency of different code occurrences as a means of identifying key areas for further investigation (Crabtree and Miller, 1992, p. 95).
This twofold analysis also refers to the above made distinction between a positivistic and an interpretative epistemological approach. The first round of counting frequencies is more positivistic in that it attempts to measure the importance of each of the given category of benefits or management mechanisms and draws conclusions about the re-search questions from this understanding of the constituent parts. This approach is not an uncommon part of case study research in the field of IS (Dibbern et al., 2007; Sherif et al., 2006).
20 Since the study is based on an existing framework, it is deductive in nature. This has already been mentioned in Section 3.2.2. As the discussion in this section shows, the unfolding research process is deductive only in a first step. At a later stage a inductive reasoning is conducted. Such an two-staged approach is not uncommon in exploratory case studies (Miller and Crabtree, 1992; Patton, 2002).
However, as it has been argued above, this study is intended to be more interpretative.
Thus, the counting of frequencies is only considered to be the first of two analyses. This for two reasons. First, especially for the hub cases, the resulting data base of coded interview fragments is still extensive. Second, and probably even more important, the sheer number of passages in an interview that are relevant for a certain proposition is a very limited indicator for determining the significance for this proposition. This has been very well described by Stake (1995, p. 32) in his discussion of different degrees of complexity in coding schemes: “The simpler the datum [that is coded], the easier it is to develop distributions and statistical analyses. The more complex the item, the more individual interpretation it will need when analyzed.” Thus, the here conducted analysis of interview fragments can only be in part based upon quantitative analysis as those mentioned above. This difficulty of quantitative measures on qualitative data has also been recognized by Miles and Huberman (1994, p. 56): “Converting words into numbers and then tossing away the words gets a researcher into all kinds of mischief.” Thus, as it has been stated above, the more decisive aspect is the interpretative analysis of the content of these passages, which does not readily lend itself to numerical descriptions21. Therefore, a second round of analysis is conducted in which the underlying background of each fragment is carefully considered in the light of each proposition. This two-legged process is also common for both organizational (Lee, 1991) and IS research (Dibbern et al., 2007; Sherif et al., 2006)22. It is thus reflected in the subsequent case analysis.
Here, first the pure frequency of fragments that are relevant for each proposition is discussed without regarding the context of these fragments. Then, in the following detailed, rich case narratives, the most descriptive interview fragments are quoted in order to illustrate exactly this context found in each specific case.
However, that fact that this data analysis process is to a considerable extent based on individual interpretation also very well reflects the key challenge in qualitative case studies. Obviously, the determination of which passage is supporting or not supporting which proposition to which degree has to be cautiously considered as not being entirely objective. This is probably even more the case for the determination of the specific role that each fragment plays in the context of a given proposition. From a positivist stand-point, it could be argued that this reduces the reliability of the here conducted study.
Reliability in the context of case studies has been defined by Yin (2003) by the fact “that if a later investigator followed the same procedures as described by an earlier investigator and conducted the same case study all over again, the later investigator should arrive at the same findings and conclusions.” However, here again the interpretative nature of the present study and the advice to be aware of the epistemological orientation of
21 In this context, the above mentioned warnings to force qualitative data into quantitative measures that has been raised for example by Mintzberg (1979a) or Pettigrew (1992) have to be re-emphasized.
22 The ambiguity and fuzziness of this approach, and thus also its difficulty, is well illustrated by the fact that Miles and Huberman (1994, p. 254), who have argued for a careful usage of numerical measurements above, state that “doing qualitative analysis of all data with the aid of numbers is a good way of testing for possible bias, and seeing how robust our insights are.”
cited references has to be emphasized. The second edition of the book written by Yin has been labeled ”an excellent guide for a more quantitative approach” to case studies by Stake (1995)! Thus, what is considered as weakness from a positivist perspective is considered to be an integral part of case study research by the more interpretatively oriented community. Stake (1995) for example takes this difficulty as reason to consider case study research as an art rather than a craft. Patton is arguing along the same line, when he states that
there are no formulas for determining significance. No ways exist of perfectly replicating the researcher’s analytical thought processes. No straightforward tests can be applied for reliability and validity. In short, no absolute rules exist except perhaps this: Do your very best with your full intellect to fairly represent the data and communicate what the data reveal given the purpose of the study (Patton, 2002, p. 433).
This advice has to be heeded even more, as the coding of the interviews has revealed that there are no clear cut boundaries between the benefit categories as well as between the management mechanisms23. Rather, often an interview fragment that belongs to one category also belongs to another24, denoting a link between the two. Thus, after this first deductive inquiry, a second round of inductive inquiry is conducted in order to shed more light on the relationships between the proposed benefits and management mechanisms.
Here the proceeding is similar to that of the first inquiry. Again, codes have been assigned to either the relationships between the benefits or the management mechanisms. Then, in a second evaluation round those fragments have been selected that very illustratively show these relationships. These are then quoted in the end of the corresponding case discussions. Again, it can be argued that both parts of this selection process are not entirely objective. However, in order to counter this partial subjectivity, another advice of Patton (2002) is followed. He recognizes that an important part of qualitative research is the development of findings that go beyond what the interviewees are conscious of.
One way to test these findings is indeed to present them to the interviewees:
The best and most stringent test of observer constructions is their recog-nizability to the participants themselves. When participants themselves say,
“Yes, that is there, I had simply never noticed it before,” the observer can be reasonably confident that he has tapped into extant patterns of participation (Lofland, 1971, p. 34).
23 In the terms of Patton (2002, p. 457) this might indicate that both categories are typologies rather than taxonomies. The latter “completely classify a phenomenon through mutually exclusive and exhaustive categories (...). Typologies, in contrast, are built on ideal-types or illustrative endpoints rather than a complete and discrete set of categories.”
24 The facilitation of this multiple coding of one interview fragment has been described by Miles and Huberman (1994) as a key advantage of using a computer-based coding software, such as it has been done here.
Thus, as a last test, the final version of this work has been submitted to all the in-terviewees for additional remarks and adjustments. The inin-terviewees have approved this work, and thus also given their implicit consent to the developed typologies. Af-ter this description of different research strategies, the selection and description of the case study approach as the most appropriate one, and the discussion on the data collec-tion and analysis procedures, the following chapter now addresses the actual empirical analysis of the data collected in the different case companies.
After having discussed the methodological approach chosen for this study in the preced-ing chapter, in the present chapter the actual data analysis is conducted. In this context it has been emphasized in the literature on multiple case study research that each case should be first of all analyzed as if it were the only one. Stake (2006, p. 1) comments on this phase of multi-case research with the word that “during work on the single case, the collection of cases remains mostly at the back of the mind.” Only after the single cases have been analyzed in due depth, a cross-case analysis addresses similarities and differences between them. However, as the present study addresses two groups of cases (hubs and spokes), a slight deviation from this approach is considered fruitful. Rather than first analyzing all cases and then drawing conclusions across them, this chapter is subdivided into three large sections.
The first is the analysis of the hub cases. In the first two parts of this section, each of the hub cases is analyzed with regard to the research objectives as if it would be the only case. Then, in the last part of this section, the cross-case analysis of the hub cases is conducted. In order to truly understand the role of hubs in the IS development network, both similarities as well as differences between the cases have to be discussed here. The second section then follows along the same lines for the analysis of the spoke cases. Again first a brief narrative is developed for each of the eight individual cases with regard to the research objectives. In a second step these cases are integrated into a cross-case analysis of the spoke organizations. Here again both similarities and differences between the spoke cases are discussed in due depth. Finally, both perspectives are integrated in the last section of this chapter. This develops a holistic analysis with a special focus on the interactions between hubs and spokes in the entire IS development network.