This paper discusses the role of the researcher as a participant observer and reflective practitioner. The paper aims to assess the benefits and limitations of participant observation, and offer guidance as to how to manage the challenges inherent in this technique. The paper draws on the lead author's experience as a participant observer when working on her doctoral thesis: 'Factors Affecting the Viability of Electronic Marketplaces: an Empirical Investigation into International Steel Trading'. It discusses the issues and concerns resulting from participant observation and how these were dealt with in the case example. The empirical research was a longitudinal study tracking the evolution of steel electronic commerce between December 1998 and the present time. The events examined in this study were observed during the lead author's ten years at a large steel producertrading house. As a trader and a manager, the lead author was directly involved in the conduct of business. The study represents the contribution of an industry practitioner and, as such, provides a unique insight into a real‑world setting.

Abstract: This study developed a framework for qualitative inquiry and administration of social systems. It describes the mechanisms that decision‑makers, such as the police detectives, military commanders, and transformative managers use in their problem solving initiatives. First, the framework was reviewed and constructed for: (1) the theory of qualitative data analysis; (2) problem solving process; and (3) the correlation matrices. Then, based on 14 Points, an extracted framework and its methodology are presented. Finally, a manifest (praxis) is introduced for the framework developed for a project in development planning. This innovative framework can be used for policy‑making, qualitative data analysis, or problem solving and administration of social systems.

Activity Theory is used in this paper to demonstrate the process of critical analysis of qualitative data from two case studies. The paper explains the elements of an activity system (the subject, object, outcome, mediating tools, rules, community and division of labour). Thereafter, practical examples from the work of two recent PhD students are used to show the importance of identifying and analysing activities that are found either in the introduction or the current use of information systems in business organisations. These examples highlight the applicability of Activity Theory in analysing data from projects of interest to Business Management whose topics and contexts are very different. The first focusses on the introduction of an Accounting Information System to microbusinesses in a low‑income community in South Africa and the second focusses on Information Security Management in Mobile Network Organisations in Uganda. The examples illustrate the value of Activity Theory as a lens and as a way of stimulating critical analysis. Activity Theory is known for its ability to identify reasons for failure or disappointing performance in existing situations by highlighting contradictions either between different activities, between an earlier version of an activity and a later version as the activity evolves, or within an activity (between the elements of that activity). However, as shown in the first example, it can also be seen as a useful tool when proposing a new project as a predictor of success. Despite the fact that data is typically qualitative, the analytical process related to Activity Theory can be structured, which assists novice researchers or those unaccustomed to interpretivist analysis to uncover insights that are not immediately obvious. Activity Theory is said to act as a lens in data analysis and is particularly useful in organisational sciences for the theorization of technology‑mediated organizational change.

In data analysis the qualitative researcher seeks to produce a convincing explanation of the phenomena under investigation. Data analysis is an iterative process and requires reflection and interpretation on the researcher’s part on several levels. Interpretation suggests that there are no clear rules and that the researcher’s judgment, intuition and ability to highlight issues play an important part in the process. As a result, the issue as to how to analyse qualitative evidence is an area often poorly understood by researchers new to the interpretivist paradigm. The complexity of the data analysis process is increased due to the volume of evidence collected as part of a qualitative research study. The role of Computer Aided Qualitative Data Analysis Software (CAQDAS) in supporting this data analysis process is examined in this paper. It explores how CAQDAS can be used in facilitating the management of an extensive qualitative evidence base. CAQDAS enables researchers to manage qualitative data that would prove onerous through manual “pen and paper” methods. The paper examines the author’s use of the CAQDAS package N‑vivo in managing approximately 400 pages of single spaced interview transcripts resultant from a study on the evaluation of a new student ICT administrative system implementation in the Irish Institute of Technology (IoT) sector. This was an extensive empirical research study conducted across several case study sites and involved 49 informants and multiple sources of case study evidence. The objective was to develop a coherent cross‑case primary narrative of the system’s implementation from the evidence collected, reduce this to a set of key findings and ultimately develop a theoretical conjecture that provided fresh insights into the ICT investment evaluation process. The N‑vivo package served primarily as a support tool in managing the interview transcripts; in reflecting on the emerging themes; and in interpreting the body of evidence. It facilitated the identification of key points, the coding of key concepts that emerged from the body of evidence, and comparison between these concepts. It supported the later reclassification of concepts into a series of categories and sub categories; this helped to organise related concepts in relation to the overall research and facilitated greater understanding of the body of evidence. It supported the creation of memos to clarify emerging concepts and the categorisation of interview material to facilitate cross‑case analysis. Further, it facilitated analysis through for example relationship and model exploration. These features of N‑vivo played a vital role in producing a series of narrative accounts and ultimately the distillation of a new theoretical conjecture.