Grounded theory method in information systems research: its nature, diversity and opportunities

^†The alphabetical order of authorship indicates the authors' equal participation on this paper.

This article introduces this special issue by exploring the nature and diversity of the Grounded Theory Method (GTM) and the opportunities associated with its use in Information Systems (IS) research. As guest editors for this special issue and as practicing grounded theorists, we feel that the nature of GTM is often misunderstood and its label abused in different ways. Therefore, in this editorial article, we attempt to clarify what we see as the essential characteristics of GTM. We also present some issues that are commonly discussed among grounded theorists, but which are less well known publicly. We argue that GTM can be a powerful tool for IS scholars interested in theory development, allowing researchers to conduct pioneering research with both flexibility and rigour. This argument is illustrated by the seven papers that we have selected for this special issue. These papers represent the wide variety of ways in which GTM can be used in our field. Further, they show how, through innovative uses of GTM, IS researchers can address some long-standing research challenges.

Introduction

While the nature of the Grounded Theory Method (GTM) has been addressed many times in the Information Systems (IS) literature, we find it important to discuss this once again. Revisiting this issue now may seem peculiar, especially given that GTM first crashed through the glass ceiling of IS publishing almost two decades ago (with Orlikowski, 1993), since which time many excellent GTM articles have appeared in top IS journals. In view of such a body of writing over this period, it may be assumed that the nature of GTM is clear to most scholars. However, after having read a great number of GTM papers while working on this special issue, and drawing on our prior editorial experience, we believe that there is still a need to explain what GTM entails in terms of its fundamental characteristics and in terms of its potential for IS research.

There are two key reasons for questioning the nature of GTM. First, we find that expert reviewers and editors continue to struggle with the nature and boundaries of GTM while examining papers claiming to be GTM studies. At the same time, some articles using GTM continue to focus on explaining and justifying the method beyond what is necessary or reasonable. This defensive practice may indicate a perception in our field that our 40-year-old method lacks maturity. However, we believe that this practice is unwarranted given IS researchers’ willingness to embrace diverse methodological perspectives and the credibility that the method has gained in enabling new theory development in the IS field (Taylor et al, 2010).

The second reason for revisiting the nature of GTM is the prevalence of false methodological claims (mislabelling) associated with GTM (Suddaby, 2006; Urquhart & Fernandez, 2006; Jones & Noble, 2007). On the basis of our practice, reviews of the literature and our experiences in editing this special issue, we have identified three types of mislabelling. The first form of mislabelling involves an author's claim to have conducted GTM to attain legitimacy in cases in which GTM has not actually been applied (Suddaby, 2006). Fortunately, with the growing pool of qualified reviewers, fewer such papers are surviving the review process.

The second form of mislabelling occurs by omission; that is, when GTM is not reported as such. A worrying case of this occurs when reviewers ask authors, without valid justification, to ‘drop the GTM claim’ – perhaps because members of the review team do not understand the method or its flexibility. However, putting pressure on authors to be implicit about their method leads them to misrepresent their work, and is thus a violation of truth in reporting research. Such violations could create a perception of institutional antagonism against GTM, which could foster future omissions caused by auto-censorship; for example, when authors have followed GTM, but are afraid that a more stringent reviewer may set unattainable standards of rigour, or that they will encounter a reviewer biased against the method. In such cases, the authors may quote secondary methodological sources (e.g., Eisenhardt's (1989) paper instead of Glaser and Strauss's cornerstone books) hoping that the reader will understand their enterprise as building theory from data, but will not hold them to the standards associated with building such theory in a rigorous fashion.

The third, and perhaps the most problematic, form of mislabelling occurs when researchers claim to have used GTM when they have only done so partially. The most common example of this is GTM techniques used to code data without due regard to fundamental methodological concepts, such as theoretical sampling, constant comparison or theoretical saturation. In these cases, the researcher can only legitimately claim to have used certain procedures developed within GTM, but not the method, which implies more than just coding techniques.

The issues related to what constitutes good GTM are germane to what constitutes good research in general; and they are just as complex. We consider GTM a flexible way of conducting research that prioritises exploration of a given phenomenon in a predominantly inductive theory development paradigm. In other words, GTM provides the infrastructure, the skeleton and a process by which data can be gathered and analysed; as such, the method offers a number of techniques to operationalise exploration to reach an internally valid conceptualisation. However, GTM does not require a specific type of data, nor does it demand the adoption of a particular ontology or epistemology. Therefore, flexibility is one of the great assets of GTM. This does mean that to conduct a GTM study in an adaptable manner, without breaking fundamental tenets or making false claims, one needs a solid understanding of the method's core elements and their application.

Fundamental characteristics of a GTM study

The articles in this special issue illustrate our views on what constitutes excellent GTM research in IS. They also deliver insights into how GTM can be used within other research traditions and techniques, demonstrating the flexibility and power behind this method. We maintain that when the canons of the method are respected GTM offers a wealth of options for the conducting of research. It can accommodate different philosophical perspectives, use a great breadth of data types and be mixed with other methods by researchers who understand the risk and benefits of doing so.

Flexibility is a powerful attribute of this method. However, while it can be a virtue when exercised by a skilful researcher, it can be a danger for the inexperienced. Consequently, understanding the basic tenets of GTM is of paramount importance to researchers and gatekeepers at our journals. In our learned view, a researcher can claim to have conducted GTM if they have met the following criteria:

1. Theory Development. The objective of the study was to develop theory rather than to test theory (Glaser & Strauss, 1967; Glaser, 1978; Strauss, 1987) or to provide a rich description of a phenomenon based on a systematic exploration of the accounts of the phenomenon (through interviews, observations, archival materials or quantitative data sources) (Strauss & Corbin, 1990, 1998). Proponents of classic GTM (Glaser & Strauss, 1967; Glaser, 1978, 1998) focus on theory development, while followers of the Straussian approach can also use GTM for rich and rigorous descriptions of a phenomenon (Strauss & Corbin, 1998).

2. Constant comparison. Constant comparison was used to analyse data from different standpoints. It should be noted that analytical and theoretical memos act as the pivotal point for comparison, emergence, sampling and theoretical densification (Glaser & Strauss, 1967; Strauss, 1987; Glaser, 1998; Charmaz, 2006). In GTM, memos are critical, internal sense-making techniques through which constant comparison is achieved. They help researchers understand their data, the relations in their data and the gaps in their data. Memos are therefore transitional analysis steps that do not require disclosure, but without which grounded theory is not possible (Glaser, 1978). Memos can take the form of diagrams, text narratives, propositions, mind maps and other techniques that are suitable to both the idea being documented and to the cognitive preferences of the researcher as sense maker (Charmaz, 2006).

3. Iterative coding. Constant comparison led to theory developed through several iterations of data coding. In this process, concepts are defined, their dimensions developed and abstracted out. The concepts are then interrelated to each other and, potentially, to the extant literature. While the word ‘coding’ evokes an image of some mechanical exercise of assigning short labels to all data points; in practice, it is a way of building theory from data in a systematic fashion, by reducing the data and abstracting out the context. This coding is not required at the word or even at the paragraph level. Instead, the granularity of the code is defined by research interest, the nature of the data and the researcher's philosophical stance. This means that coding can be done on any level, including at the episodic, project, individual and organisational level.

4. Theoretical sampling. The data were collected based on theoretical sampling, with collection ceasing when the data reach theoretical saturation. Theoretical sampling does not aim to identify representative populations, but rather to enrich the emerging concept. In other words, the driver for sampling is the need to understand the nature and dimensions of emerging conceptualisations further, usually by sampling data in a way that varies a particular set of dimensions that emerge from prior data analysis. Theoretical sampling also helps reduce sampling bias and insufficient variation in data, while maintaining focus on the researcher's goals (Jones & Noble, 2007). Similarly, the notion of theoretical saturation guides the researcher to stay in the field and continue collecting data until new theoretical constructs cease arising and it becomes possible for the researcher to predict what the analysis of the next data point is likely to say.

5. Management of preconceptions. The study was not driven by existing theories. This is not to say that the researcher's own convictions, based on their research paradigm (i.e., as described in Burrell & Morgan, 1979), cannot guide their judgements – this is impossible to avoid. Instead, GTM requires that we avoid using specific theories pertaining to the phenomenon under study as the starting point for data collection and analysis. An exception is where researchers follow previous GTM studies to move from substantive theory to formal theory (Glaser, 2007). However, a priori theories and other preconceptions regarding the research domain should be dealt with in accordance with the method. Specifically, a priori theory of the phenomenon should be treated as a kind of data to be compared against evidence from the substantive field of enquiry, and not as a way of interpreting the data. In validating emergent theory, extant theoretical lenses may (and perhaps should) be used to explain how the emergent theory is related to the greater body of literature (Glaser, 1978, 1998; Charmaz, 2006).

6. Inextricable link between data collection and analysis. The data collection and analysis activities were intrinsically related; done almost at the same time, in a recursive process, in which data analysis alternated with data collection until saturation was reached. (New data yields no new insights for the researcher/s involved).

In using these criteria, questions may emerge as to what course of action to follow when GTM is only partially used. While these criteria are important as a set, we believe that research goals can and should guide how and to what extend each criterion is applied to the research in question. However, by definition, a GTM study is undertaken to build theory from data, the theory must be developed through the core processes of constant comparison and theoretical sampling and the data must be coded in some consistent fashion.

Methods are appropriated and modified by agents, who often challenge methodological definitions and limitations. We have observed two common cases of appropriation and deviation in GTM. First, when a researcher is engaged in theory building, if their high-level theorising is guided by extant literature, while their micro-level theorising is done in a grounded, inductive fashion (following GTM principles), it is still valid to call this study a GTM study. However, it becomes necessary to explain how the research goals for the study led researchers to modify classical GTM for their purposes.

Second, occasionally, GTM researchers develop new theories based on data that they have collected for another purpose in the past (i.e., Levina & Su, 2008). This approach has the potential to violate both theoretical sampling and the linking of data collection and analysis criteria. However, if the data that were previously collected are rich enough (e.g., a multi-year ethnographic study), it may allow for strong theory building, following constant comparison and coding approaches. Similarly, by using the online archival data sets that are now pervasive in IS research (see Vaast and Walsham, and Waters and Gasson in this issue), researchers can collect the complete data set in one go, without the need to perform additional iterations. In these cases, the clear articulation of the research context plays an important role in determining whether the method used can be labelled as GTM. Again, the burden is placed on the authors to justify their methodological claim.

Having described these deviations as potentially correct uses of the method, we must also point out that a significant level of knowledge is required to perform a deviation without breaking key methodological tenets. Thus, regardless of GTMs inherent flexibility, if a researcher has not developed theory from data through constant comparison, followed a theoretical sampling strategy or used some form of data coding, they cannot be said to have performed a GTM study. Instead, such researchers can only go so far as to acknowledge that they have borrowed specific techniques from a GTM toolbox for their research purposes.

Our proposed criteria are a slightly more granular elaboration of the criteria proposed by others (e.g., Allan, 2007, Levina & Su, 2008; Urquhart et al, 2010). The added granularity should help researchers elaborate on and justify their implementation of the method. Researchers who have followed GTM in a manner we have described above should feel comfortable stating this in their papers, and should demonstrate with details how the criteria have been applied in their work. At the same time, as with any set of criteria, some researchers will be constrained from making claims that fall strictly outside GTM. However, we hope that, given the growth of GTM in IS research (Urquhart et al, 2010), this list of criteria will help researchers that use the method to be more open and assertive in publishing their work.

Overcoming the Glaser–Strauss schism and focusing on the common ground

Beyond employing what we or other authors see as the fundamental characteristics of GTM, GTM researchers often face the recurrent question of what kind of GTM they are using, as over the years variants of the methods have developed. The most notable of these has been the split between Glaser and Strauss, the founders of GTM. Our view is that the fundamental characteristics of GTM outlined above encapsulate the essence of GTM research across the spectrum of traditions and overshadow the differences. We find that excessive focus on the differences between the traditions is not helpful to GTM researchers. However, here we will briefly summarise the key disagreement between the traditions, as this disagreement is addressed, directly or by inference, in the papers we have selected.

The famous disagreement on the manner in which GTM should be applied between Glaser and Strauss stems from the publication of Strauss & Corbin's (1990)Basics of Qualitative Research. This book was intended as a guide on how to build theory from data, to clarify GTM for researchers. In particular, a coding paradigm was presented: a set of general conceptual categories to help researchers uncover the relationships among the concepts they had generated through coding. The final step in this paradigm, involving uncovering concepts, was axial coding. Specifically, axial coding recommended that in developing a grounded theory emergent categories should be grouped into intervening and causal conditions, context, actions/interactions, strategies and consequences.

Some researchers found this guidance on axial coding helpful for theory development. However, others, including Glaser, rejected it as a way of imposing a priori theory on the researcher and the phenomenon. The debate was complicated by the fact that Glaser and Strauss’ original 1967 text, The Discovery of Grounded Theory, suggested a need for relating conceptual categories to build theory, yet did not give a name for this coding step (which would come to be termed axial coding in Strauss & Corbin's (1990) text).

Researchers, including those in the field of IS, often refer to Strauss and Corbin's work when stating that they have engaged in axial coding for the purpose of building their theories by relating categories of concepts (e.g., Orlikowski, 1993; Levina & Ross, 2003). However, many of them do not use the actual coding paradigm suggested by Strauss & Corbin (1990). IS researchers tend to use other coding paradigms when combining grounded theory with their favourite grand theories, such as structuration theory (Orlikowski, 1993) or the theory of complementarities (Levina & Ross, 2003).

Researchers new to grounded theory are often troubled by this debate or ‘schism’. We consider that focusing on these issues is a distraction; in particular, because the elements in common between the ‘types’ of GTM are far more important that the dissimilarities. It is also a distraction because (other than the use of the Strauss and Corbin coding paradigm in generating theory) the other aspects of the approaches are quite close. The advocates of there being only one true way to conduct GTM tend to restrict its application, in our view, unnecessarily. Far more important to a novice GTM researcher is to learn the basic philosophical concepts articulated in the method. This can be done by starting with the classic text (Glaser & Strauss, 1967) and then adding extra methodological advice, depending on their study's needs (e.g., Glaser, 1978; Strauss, 1987; Strauss & Corbin, 1990; Glaser, 1998; Charmaz, 2006). Our experience shows that if a researcher stays true to the data they may discover that their a priori chosen coding paradigm does not work and abandon it for another paradigm that better fits their data and helps them explain or predict the phenomena they are observing (see Urquhart, 2001).

We believe that, as authors, reviewers and editors, we should not be overly concerned with the type of grounded theory adopted for a given piece of research. Instead, we should focus on whether the key standards of GTM were implemented and whether the paradigm used for coding the data was consistent with the researchers’ proclaimed philosophical stance. Further, we should evaluate how informative the employed paradigm is in the case of their research phenomenon and, most importantly, ask whether it has helped generate novel insights for IS scholarship.

Emerging opportunities in conducting GTM

The number and quality of the almost 50 articles submitted in response to this special edition bears testament to the body of committed and innovative IS researchers using GTM. We have noticed the growth in the use of GTM in IS research over the years, and we believe that the method will continue to gain in popularity through recognition of its value, due in large part to its flexibility. Much of this flexibility lies in the opportunities to utilise and integrate GTM with other research methods, and to use GTM on qualitative as well as quantitative data. If one follows the Glaserian axiom that ‘all is data’, great creative opportunities exist for IS researchers. Digital and technological developments are affecting the way traditional and emerging quantitative and qualitative research methods are being used, and the nature and quantity of data that can be generated. In this new era of wide access to data, especially quantitative and multimedia data, researchers can effectively integrate GTM into their work while working within the research paradigm with which they are most comfortable.

The article by Mattarelli, Bertolotti and Macri (in this issue) is an excellent example of the flexible application of GTM. The authors detail how GTM shaped their use of ethnographic techniques and focus group discussions. Taking the focus groups as an example, researchers using this technique have data opportunities way beyond the generation and analysis of interview transcripts. Participants in discussions may access and use visual stills (photographs, drawings and diagrams) or moving images or audio to express feelings and emotional relationships. Participants may also have the time, space and support to draw, paint, create models, act out scenarios and tell stories. These forms of expression can occur in physical face-to-face contexts or in virtual contexts in e-groups. In the context of e-groups, participants may be asked to reflect upon certain research tasks and collect materials (e.g., text, documents and images) to share for further discussion. The capturing, display and sharing of these engagements reveals data. This process would be a powerful means of giving added focus and depth to conventional interview data. Focus group discussions encompass a range of different research philosophies and techniques to engage with and elicit responses from participants. As a result, many types of data can be generated – it is not a single method.

This is also true for the use of ethnographic techniques. Ethnography may be viewed not as a single method, but as a research discipline based on culture as an organising concept (Stewart, 1998). Again, using the article by Mattarelli, Bertolotti and Macri as an example, ethnography can be applied to generate many creative data opportunities. In applying ethnographic methods, a range of interviewing and observational techniques can be used. Where observational techniques are employed, ethnographers do not always observe directly. Much observation may be of cultural artefacts, physical traces, objects or still and moving images associated with participants (Silverman, 2001).

Visual and observational developments in ethnography are also being used to create what has been described as ‘netnography’(Kozinets, 2006). Netnography can refer to the passive process of following conversations and interactions on the Internet at the individual level, or may indicate a more active engagement with participants via direct questioning, or becoming a participant observer in online communities. Netnography uses trace data, but this kind of data use is not a development solely related to netnography (May et al, 2008). Trace analysis has been used in many other contexts to encapsulate patterns of behaviour and social interactions. With the growth of interactions conducted over the Internet and via social media, many new forms of data using passive observations and trace analyses can be generated. The paper by Waters and Gasson (in this issue) provides an excellent example of the use of trace data.

Considering the potentially huge quantitative data sets generated from physical and/or electronic trace sources, it may be necessary to employ quantitative data analysis techniques. In the context of GTM, the description of phenomena through quantitative measurement and analyses can be directed, as can the exploration of quantitative data sets and the search for patterns and meaning. There should be no singular route to capture and analyse data in GTM. As already discussed, GTM builds theory on data, and the GTM researcher must be prepared to embrace rich and diverse sources of data to build that theory.

To emphasise this point, our selection of papers for this special issue was guided by two aims. First, we wanted to showcase exemplary uses of GTM in its traditional manifestation – for use in qualitative data collection and theory development. However, we also wanted to show how GTM could serve new and innovative purposes, such as in analysing quantitative data, performing a literature review and embedding GTM within ethnography. In the next section, we briefly introduce each paper in the issue and identify how they demonstrate excellence and/or innovative use of GTM.

The selected papers

After the initial call for papers in 2009, we received 48 full papers. Of these, seven were selected. We are pleased to present the following papers:

1. Vaast and Walsham, ‘Grounded theorising for electronically mediated social contexts’: This paper reviews a focused body of IS research concerned with inductive, theory-building scholarship on electronically mediated social contexts (EMSCs). The first contribution of this paper is in meticulously explaining and justifying, through references to prior methodological sources, the validity of using GTM in EMSCs. Its second key contribution is in showing how the researchers have handled the characteristics of EMSCs in their work and, in particular, how they have addressed the characteristics and qualities of data collection, data analysis and theory building. The article draws implications and recommendations for GTM researchers interested in investigating these original and fascinating environments, which are becoming increasing significant.

2. Mattarelli, Bertolotti and Macri. ‘The use of ethnography and grounded theory in the development of a management information system’: This article discussed the potential for merging ethnography and GTM within a participatory IS development process. The grounded model was the starting point for conducting a series of focus groups, during which the organisational actors were allowed to face process conflict, while defining the requirements of a new management IS. This paper breaks new grounds in combining GTM with other methods and approaches.

3. Wolfsvinkel, Furtmueller and Wilderom. ‘Using Grounded Theory as a method for rigorously reviewing literature’: This paper demonstrates how GTM can be utilised to great effect in conducting a rigorous and critical review of the literature on a topic. The authors use GTM to build on Webster & Watson's (2002) five-stage literature review process(i.e., the emergence of new themes, issues and opportunities; interrelationships and dependencies in or beyond a particular area; and inconsistencies). This process has been a guideline for IS researchers in conducting literature review for almost 10 years. The use of GTM in addition to these categories allows the researcher to draw on prior literature to build new theory that can guide either theory testing through deductive studies or further theory development through inductive studies.

4. Boudreau, Abraham, Junglas and Watson. ‘Enriching our theoretical repertoire: the role of evolutionary psychology in technology acceptance’: This paper, in the style of exemplary traditional GTM scholarship, illustrates how a body of literature, which was not envisioned at the onset of the study, was used to facilitate the development of formal theory. The researchers reveal how the lens of evolutionary psychology can be helpful in understanding technology acceptance across three organisational sites. They illustrate how many variations of the technology acceptance model share these same theoretical foundations. They argue that focusing exclusively on the socio-cognitive lens can lead to overlooking enhanced explanations of technology acceptance, indicating that new theoretical perspectives are necessary.

5. Hekkala and Urquhart. ‘Everyday Power Struggles: Living in an IOIS Project’: This paper represents an exemplar use of GTM in both drawing on and adding to an existing theory (in this case, the theory of power). The author have followed an inter-organizational IS project for 3 years using a variety of qualitative data collection techniques. While they went into the setting without a theoretical lens, through interrelated stages of data collection and analysis, they found theories of power to contribute the most to their understanding of the inductive themes, concepts and relationships. In the paper, they showcase how to clearly and succinctly integrate existing theories and then to extend them through inductive theorizing. Not only does the paper develop a novel concept of project power, but it also shows how this concept leads to unique empirical and theoretical insights on IS issues.

6. Waters and Gasson. ‘Using a Grounded Theory approach to study online collaboration behaviours’: This paper represents an excellent empirical example of applying GTM in IS research, using data generated though trace analysis. It discusses how an interpretive theory of action was explored and developed through iterative cycles of grounded theory generation. A strong rational for employing GTM in area's rich with theories of learning is established. The incorporation of mixed-data sources into an interpretive grounded theory process is described as is a theoretical sampling strategy using ‘complementary comparison’ to feed back into a new cycle of constant comparison.

7. Matavire and Brown. ‘Profiling Grounded Theory approaches in Information Systems research’: With the aim of clearly articulating how GTM has been used in IS research, this paper presents a critical review of a comprehensive body of IS articles. The selected literature focuses on studies that adopted GTM and that were published in the top eight IS-centric journals (1985–2008). The review reveals four main GTMs in use, namely: (1) classic GTM, (2) an evolved GTM, (3) the use of GTM as part of a mixed methodology and (4) the application of grounded theory techniques, typically for data analysis purposes. The review also reveals conflicts in the understanding of GTM and explains how understanding these conflicts can advance our scholarships.

Conclusion

We were delighted with the number and the quality of submissions received for this special issue. While the quantity of submissions made it challenging to find a sufficient number of qualified and timely reviewers, the outcome of this collective work is highly positive and invigorating. With this issue, we are able to showcase the uses of GTM within IS research by presenting different applications and interpretations that, while fitting GTM, are innovative in conception and execution. We clearly saw that going beyond conference-length papers (e.g., Allan, 2007) enabled GTM researchers to showcase the true strength of the method. We also highlight the flexibility of the methodology, as it is used for different research goals and by different types of researchers. We hope that those researchers not yet familiar with GTM will become more open to it, as this powerful method not only allows us to build theory (rather than borrow it from other fields), but also supports us in our exploration of a wide range of research paradigms and types of data. We also hope that, through this short introduction, we have been able to clarify the nature of this method, and demystify some of the issues associated with GTM. It is our sincere wish to make GTM more approachable to new practitioners.

The flexibility of GTM makes it particularly suitable for application in the socio-technical milieu of IS, in which the nature of data changes with technological advancements. GTM is a rigorous approach that, in our view, can and should be adapted and developed in accordance with the particular needs of IS research. While the method has more than 40 years of tradition, and its procedural development appears generally stable, we believe that future innovations in GTM will arise from its use with new types of data and within new social constructs.

As we have argued, GTM not only allows and encourages the use of multiple types of data, but it also blends well with other methodological approaches. The challenge facing IS researchers relying on this method is how to blend GTM appropriately and effectively. We see this special issue as one of the first critical steps in answering this challenge, and expect that our efforts will promote further work in this area. Indeed, those researchers with the inclination and skill to advance GTM so that it may more appropriately address the data and questions of the twenty-first century will be able to capitalise on what we see as exciting research opportunities.

With this in mind, the papers we present in this special issue have been selected to provide readers with an indication of the wide-ranging possibilities (and challenges) encapsulated within this method.

Additional information

Notes on contributors

David F Birks

About the authors

David F. Birks, Ph. D., is a Professor of Marketing at the University of Winchester, U.K. His primary teaching and research interests lie in how digital and technological developments affect traditional and emerging research methods. He is the co-author of Europe’s leading Marketing research methods textbook, ‘Marketing Research: An Applied Approach’ (MALHOTRA, NK, BIRKS DF and WILLS P (2012) Marketing Research: An Applied Approach (4th edn.). London: FT/Pearson). He is a Committee member of the Association of Survey Computing (ASC). The ASC is a world-leading society for the advancement of knowledge in software and technology for research surveys and statistics. He is currently supporting developments to expand ASC's agenda to qualitative methodologies and data analysis.

Walter Fernandez

Walter Fernandez, Ph. D., is an Associate Professor and Co-Director of the National Centre for Information Systems Research at the Australian National University. His research focuses on the management of major projects, ICT-enabled organisational change and modernisation, and qualitative research methods. He serves on several international journals as a Reviewer, Associate Editor and Senior Editor. He is the Inaugural Chair of the Association of Information Systems’ Special Interest Group on Grounded Theory Methodology. He has delivered GTM seminars and workshops in more than 10 countries.

Natalia Levina

Natalia Levina, Ph. D., is an Associate Professor in the Information, Operations and Management Sciences Department at the Stern School of Business, New York University. She uses organisational theories to understand the social-dynamics involved in managing multi-party collaborative relationships in off-line and online contexts. She serves as a Senior Editor at Information Systems Research journal and is an Editorial Board Member at Organization Science and Information and Organizations. She is the Founding Vice Chair of the Association of Information Systems’ Special Interest Groupon Grounded Theory Methodology and has taught GTM in doctoral seminars in Europe and the United States. She received her Ph.D. in Information Technologies from the Massachusetts Institute of Technology, Sloan School of Management.

Syed Nasirin

Syed Nasirin, Ph.D., is an Associate Professor in IS at Universiti Malaysia Sabah. His research focuses on wireless systems implementation and he has been involved with many IS implementation projects in both developing countries and in the U.K. He graduated from the University of Bath with a Ph.D. in Information Systems and was a faculty member at Thames Valley University and Brunel University prior to taking up his current position.