Reflexivity in Interpretive Research

“What we call our data are really our own constructions of other people’s constructions of what they and their compatriots are up to” Geertz, 1977, p. 9) .

As this quote suggests, it is critically important that researchers be reflexive of their role in the research process, particularly when using interpretive data collection and analysis methods. In this section I will review the empirical guidelines that I followed in conducting and applying interpretive case research in empirical settings, and how these guidelines influenced my empirical approach. In the tradition of theory and method going hand in hand, I pay careful attention to choose guidelines that have also been employed by exemplar institutional logics and rhetorical analysis studies (Suddaby and Greenwood, 2005; C. Jones and Livne Tarandach, 2008; Treem et al., 2015; Hsu, Huang and Galliers, 2014). Similar to these studies my research is also concerned with how competing actors mobilise justificatory accounts like rhetoric and other symbolic mechanisms to negotiate the process of implementing new organisational objects like technology according to particular ‘logics of appropriateness’ or beliefs, ways of working and values. Using prescriptions from these studies and the work of (Walsham, 1995; 2006) on “doing interpretive research” in information systems, I will outline four guidelines in the next section that should be used to judge this work (Klein and Myers, 1999), and explain how I use them in my study.

First, concerning the object of study, interpretive sociology suggests that researchers should reflect upon how their philosophical beliefs influence their choice of the object to study (Pan and Tan, 2011; Meyer, 2001). Similar to Walsham (2006) I accept interpretive sociology’s epistemological position that (1) our knowledge of reality and human action is a social construction by human actors, but, in critical realist spirit (2) I strongly believe that there is an objective reality of structures that have the power to affect human action. For example in institutional analysis, as “speaking presupposes a language as well as material resources such as vocal chords”, social action (observable actions in empirical domain) possesses causal powers to reproduce and transform institutions, but is itself always embedded in and enabled by higher order institutions and institutional logics in the domain of the actual and real respectively (Leca and Naccache, 2006; Ekström, 1992). While actors do not always perceive constructs in the domain of the actual and real, researchers training and knowledge of related literature allows them to reveal and describe these higher order constructs in order to generate theory. For these reasons my journey of constructing a phenomenon to study took many years of iterating between empirical field data and pre-constructed explanations of my empirical observations in the literature. The latter part of this journey is illustrated in my introduction. First, I worked on a pre-constructed problem of “technology resistance” arising from low cultural fit (Lapointe and Rivard, 2005) between ESN’s open, unstructured practices and traditional organisational values of efficiency and privacy. It was during my data collection, after being exposed to translation as an analytical lens and uncovering the conflicting interests and ideologies of practitioners within different functional departments that I started to focus on “intra-organisational diffusion”. With this my analytic focus was on the competing discourse and legitimating strategies influencing the spread of technology between departments, and the interrelationship between the sequence and type of rhetorical claims underpinning actors legitimating strategies and institutional logics underlying their conflicting ideologies.

For the second guideline, Eisenhardt (1989) notes that ‘[data] triangulation made possible by multiple data collection methods provides stronger substantiation of constructs and hypotheses’ (p. 538). In other words, she does not hold either qualitative and quantitative methods as “best practice” for data collection, but advocates using as many methods as possible that would allow for effective triangulation. Because my study concentrates on institutional views on technology diffusion and how actors’ competing logics are mobilized to diffuse ESN practices through discourse and rhetoric, I thought it necessary to privilege intensive qualitative methods over wide scale quantitative analysis. I studied one organisation that was in the process of adopting enterprise social networking, using interview and documentary analysis data collection techniques to get a full understanding of the discursive strategies used, the rationale for employing these strategies, and actors’ views and interpretations with regards to ESN’s role in the organisation (Walsham, 2006, p.1995). To get a better understanding of the organisation and the logics prevalent among functional organisational departments I supplemented my interview and documentary data with observations of team meetings and everyday working practices, and archival analysis of company web pages, popular press reports, industry analysis and financial statements.

The third empirical guideline for interpretive case research is to follow the “the principle of suspicion”. Rich empirical fields are filled with symbolic stimuli that can cause researchers and social actors to express bias or misinterpret the social order for what it really is. In my research, I consciously and necessarily drew on the Klein and Myers (1999) principle of dialogic reasoning to practice suspicion. According to this principle, researchers should embrace the possibility of contradictory stories emerging from the data under the light of different theoretical preconstructions or lenses, and continue to refine their findings under different lenses through multiple stages of data collection and analysis. For example, the early data collection and analysis suggested that early ESN implementation was a failure until the implementation team took action to improve its low cultural fit and lack of pragmatic

all the ESN failures and subsequent successes as an interrelated process of diffusion marked by instances of limited diffusion rather than failure, and eventual success or wider diffusion. With the shift towards conceptualizing the phenomena as a process of diffusion I embraced the literature on technology diffusion as a social movement and a discursive accomplishment and did further analysis on the nature of the discourse around the ESN implementations.

Finally, the fourth guideline regards generalizing from inductive research I try as far as possible to build a ‘transferrable’ model from the empirical case. (Lincoln and Guba, 1985) illustrates the concept of transferability in case research in the following excerpt:

“How can one tell whether a working hypothesis developed in Context A might be applicable in Context B? We suggest that the answer to that question must be empirical: the degree of transferability is a direct function of the similarity between the two contexts, what we shall call “fittingness”. Fittingness is defined as the degree of congruence between the sending and receiving context. If Context A and Con- text B are sufficiently congruent, then working hypotheses from the sending originating context may be applicable in the receiving context” (p. 124).

Using a preliminary study to build my model was also a good way of setting a base to later test the transferability of my methodology and findings (working hypotheses), and refining and extending these findings. A good model should combine the data in such a systematic way that it can be further tested through further data collection and can reveal invariants that describe the phenomenon beyond the particular case from which it was constructed. In line with this, my aim was to build a model from my case that would allow naturalistic generalizability, so that readers have- (1) an appropriate base of descriptive information to make judgments on whether my insights are applicable to other contexts, and (2) sufficient information to understand the findings (Lincoln and Guba, 1985; Hellström, 2006). To develop such a systematic model, I followed the eight steps of (Pan and Tan, 2011) structured- pragmatic situation approach to conducting case research, and the “retroductive” coding strategy that Walsham (1995) recommends to draw specific implications from empirical data.

Both of these approaches are built on a systematic iterative approach to comparing empirical data. Broadly, after the research phenomena is identified, alternative explanations for the phenomena are

constructed through a forensic review of literature and empirical data, then the mechanisms driving the phenomena are identified and described. In describing these mechanisms, the researcher should be sensitive to the role of the conceptions and actions of human actors, and the location of empirical events within a particular space and time. I applied these four guidelines to the conduct of my empirical research at a financial firm and a consulting firm. In the next sections, I will briefly discuss the empirical site and how my selection criteria for this site, and follow this with more detail of my data collection and analysis.