Grounded Theory Approach

GT is a methodology where researchers become integrated within the studied environment (Charmaz, 2003; 2005; 2008) to systematically analyses data and new insights that lead to the development of a generalisable theory (Corbin & Strauss, 1990; Glaser, 1978). GT provides the best opportunity to understand informal filtering decisions (Shah &

Corley, 2006) as GT was designed “…to study emergent social or social psychological processes” (Charmaz, 2008, p. 159 citing Glaser, 1978 and Glaser & Strauss, 1967). GT can comprehend multiple variables and deviations surrounding complex environments (Corbin &

Strauss, 1990; Glaser, 1978; Strauss & Corbin, 1994) through information analysis, observations, and experiences to provide a strong, rich theory about informal decisions (Lazear, 1996; Strauss & Corbin, 1994). Charmaz (2005) notes that GT is an interactive methodology that can focus enquiry through constant re-evaluation to “...discover invisible relationships” concerning wonders being studied (p. 527). Therefore, GT is well equipped to develop a theory in an under-represented field that is relevant to practitioners (Fernández, 2004).

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Figure 3-2: Grounded Theory in Management Research

The figure above was constructed by Maital et al. (1998) using the processes as outlined by Kaplan (1998) to illustrate the progression of GT. GT starts with identifying a key management challenge (Maital et al., 2008). For this thesis, the research question represents those management challenges. The next stage of GT is observation, which is represented by the three case studies. Followed by written observations of a case study (i.e.

Chapters 4, 5, and 6), general innovations are identified. Like most GT, case studies provide critical insights into filtering (Gephart, 2004).

Under GT, innovations are applied, refined, and observed under constant comparison which continuously integrates and analysis new information to form a stronger theoretical basis (Charmaz, 2003; Corbin & Strauss, 1990; Glaser, 1978; Shah & Corley, 2006).

Constant comparison provides improved precision and consistency through rigorous, detailed, and clearly defined coding processes to consistently narrow, refine, and dissect this challenge to basic elements until a deep understanding of the phenomena is reached (Charmaz, 2003; 2005; Duchscher & Morgan, 2004; Glaser, 1978; 2009). This is applied to information until the potential of new knowledge is minimal (Corbin & Strauss, 1990;

Gephart, 2004); thus, category saturation (Suddaby, 2006) or theoretical saturation (Glaser, 1978; Glaser & Strauss, 1967) has occurred. Finally, GT returns to identification of a key management challenge (Maital et al., 2008).

Source: Kaplan (1998) as cited by Maital et al. (1998, p. 4).

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However, GT variations exist (see Charmaz, 2003; 2008; Duchscher & Morgan, 2004; Strauss & Corbin, 1994). A primary variation is when to conduct a literature review:

Before, during, or after data collection (Dunne, 2011; Neal, 2009). According to Glaser (1978; 2009), these variants (i.e. Charmaz, Corbin & Strauss) are actually qualitative data analysis and not proper applications of GT. Classic GT requires that literature reviews are conducted after data collection and prior to final analysis (Glaser, 1978; Glaser with Holton, 2004; Glaser & Strauss, 1967). Conversely, literature reviews can be undertaken early on (Charmaz, 2005; Luckerhoff & Guillemette, 2011; Neal, 2009) as researchers must determine when to conduct them (Dunne, 2011). Moreover, many doctoral students and inexperienced researchers using GT are advised to complete a comprehensive literature review prior to data collection as research committees are often not willing to allow projects to continue without initial literature consultations (Dunne, 2011; Luckerhoff & Guillemette, 2011). Whilst doctoral research sometimes fails to follow Classic GT (Glaser, 2009), Patton (2002) states that GT is well suited for doctoral dissertations with literature review completed prior to fieldwork (see Eva, 2007; Hulko, 2004).

Another variation is the use of technology (Fernández, 2004). Classic GT discourages technology (i.e. digital and video recordings) in capturing interviews and to analyse data as technology hinders critical analysis and thinking. Fernández (2004) contends digital recording technology assists researchers in interview re-immersion to allow for a deeper understanding by constantly comparing data and researcher interpretations.

Charmaz (2003) suggests that computer programmes like NUD·IST, NVIVO, Ethnograph, and HyperResearch can assist researchers with sorting, grouping, and analysing data.

These programmes help solve problems associated with large data sets; but Charmaz (2003) footnotes that some users have encountered difficulties when conducting theoretical coding.

Miller and Salkind (2002, p. 155) outline GT’s “…four central criteria: fit, work, relevance, and modifiability.” In essence, this research had to classify and fit (not force) data patterns into a working reality that was relevant to practitioners whilst continuously integrating information to allow for theory evolution (Charmaz, 2003; Glaser, 1978). For example: After decisions were analysed, data were detailed by occurrences, circumstances, and effects (Corbin & Strauss, 1990). This continuum allows theory to be modified but not destroyed (Glaser & Strauss, 1967).

Whilst variations in coding processes exists (Charmaz, 2003; 2008; Glaser, 2009), coding processes follow stringent criteria and remain central to GT (Charmaz, 2003; Glaser, 1978). Ng and Hase (2008) contend that the primary coding difference is that Strauss’

approach (Strauss & Corbin, 1994) reviews each word and maps possibilities whilst Glaser’s

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(1978; 2009) focuses on meanings and content to discover the story. Miller and Salkind (2002) distinguish between three stages of coding: Open, axial, and selective stages whilst Glaser with Holton (2004) propose that coding is open, selective, and theoretical. In essence, Strauss uses a microanalysis whilst Glaser a macroanalysis to discover theory (Allan, 2003).

Under GT, coding processes occur in stages beginning with open coding (Glaser, 1978; Glaser & Strauss, 1967). Open coding, according to Glaser with Holton (2004, p. 9), allows the researcher the freedom to “…become selective and focused on a particular problem.” To better understand the research concept, categories and properties of data begin to emerge in open coding (Charmaz, 2008; Glaser 1978) when data is fractured through microanalysis (Duchscher & Morgan, 2004). Open coding identifies basic meanings of data by a line-by-line analysis using memoing (Glaser with Holton, 2004) to describe the actions and explanations about observations or thoughts concerning occurrences in the form of short notes (Duchscher & Morgan, 2004; Glaser, 1978; 2009). Glaser (2009) suggests coding processes should not follow any preconceived thoughts or patterns as jargons can corrupt the emergence of theory. However, core categories known as a Basic Social Processes (BSP) can be discovered as these are often “…labeled by a ‘gerund’ (‘ing’)…”

(Glaser, 1978, p. 97). Charmaz (2008, p. 164) agrees with Glaser (1978) and offers this advice:

“Coding with gerunds, that is, noun forms of verbs, such as revealing, defining, feeling, or wanting, help to define what is happening in a fragment of data or a description of an incident. Gerunds enable grounded theorists to see implicit processes, to make connections between codes, and to keep their analyses active and emergent.”

Axial coding, also known as systematic GT (Miller & Salkind, 2002), is an intermediate step between open and selective coding processes whereby categories are delimitated (Charmaz, 2003; Duchscher & Morgan, 2004; Strauss & Corbin, 1994). Citing Corbin and Strauss (1988, p. 125), Charmaz (2008, pp. 159-160) states:

“Strauss and Corbin define axial coding as a way of specifying the dimensions of a category, relating categories to subcategories, delineating relationships between them, and bring the data back together into a coherent whole after being fractured them during initial coding.”

Under classic GT, the axial coding concept is not addressed (cf., Charmaz, 2003; 2008;

Glaser, 2009) as constant comparison allows concepts to emerge into theory (Glaser, 1978).

After the most significant codes and/or most frequent codes emerge, open coding yields to selective coding in Classic GT (Charmaz, 2008; Duchscher & Morgan, 2004).

Selective coding reduces open categories to core categories (Glaser, 1978; Ng & Hase, 2008). Research is more focused at this stage with questions directed to saturate categories.

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Selective coding helps produce ‘parsimonious theory’ or theory whose explanation is simple whilst being relatively generalisable (Glaser with Holton, 2004, p. 11). Selective coding generates substantive categories that become the fabric of conceptualisation for theoretical coding (Glaser with Holton, 2004).

Theoretical coding is the stage where “advanced coding” occurs under classic GT (Duchscher & Morgan, 2004, p. 609). Theoretical coding involves the researcher examining and conceptualising relationships associated with selectively coded data to form hypotheses that will be integrated into theory which is grounded in empirical data (Glaser, 1978; Ng &

Hase, 2008). The process of theoretical coding takes significant time and must not be forced into theory (Glaser with Holton, 2004; Nunes et al., 2010). In fact, theory must be reviewed from several perspectives through constant comparison to provide a rich theoretical base.

Charmaz (2008, p. 167) notes that “…a major strength of the grounded theory method is that these budding conceptualizations [theoretical coding] can lead researchers in the most useful, often emergent and unanticipated theoretical direction to understand their data.”

This thesis follows closely with GT discussed by Charmaz (2003; 2005; 2008) as consulting previous studies before field-work can concentrate efforts to understand informal decision environments. Furthermore, the use of recording technology has helped to capture the exact words, tones, and vocal cues of participants for a more comprehensive understanding of the meanings of actions. With reference to coding processes, open coding was used to identify general groups, selective coding to further define subgroups within a general group, and theoretical coding to define relationships amongst groups and subgroups (Glaser, 1978).

Figure 3-3: General Overview of Grounded Theory Research Process

INITIAL LITERATURE REVIEW

Literature Analysis Case Study Preparation

CASE STUDY CASE STUDY

CASE STUDY Case Study Integration, Review of

Literature, & Case Study Preparation

Case Study Integration & Literature Review

Case Study Integration, Review of Literature, & Case Study Preparation

FINAL ANALYSIS

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After each case, a follow-up literature search was conducted to identify relevant recent publications. This phase was repeated until three case studies were completed. The figure above illustrates this approach.

Figure 3-4: Pilot Studies' Context-Awareness Cycle Source: Nunes et al., 2010, p. 77

The first case study can be considered as a pilot study that guides subsequent case studies and data analysis and collection. Pilot studies are needed in GT research to mitigate risks stemming from unfocused research (Nunes et al., 2010). By focusing research, core concepts emerge allowing researchers to determine the next direction in data collection. In essence, researchers gather data, perform analysis, gather data, perform comparative analysis, and so forth as illustrated in the figure above. In this concept, three circles represent specific stages within this cycle that researcher adhere to: Recognise, capture and represent, and refine exploratory tools. The first cycle, recognise, identifies emerging core categories. The second cycle, capture and represent, describes core events. Finally, refine exploratory tools focuses upon relevant emerging categories to discover theoretical concepts.