Challenges in Collecting Qualitative Data for Information Systems Studies

Challenges in Collecting Qualitative Data for Information Systems Studies

Tiko Iyamu (Cape Peninsula University of Technology, South Africa) and Irja Naambo Shaanika (Namibia University of Science and Technology, Namibia)
Copyright: © 2018 |Pages: 10
DOI: 10.4018/978-1-5225-2255-3.ch389
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Abstract

In recent years, there has been shift towards understanding why things happen in the way that they do, from the qualitative perspectives. However, the qualitative methods are sometimes considered either too trivial or difficult by many postgraduate students. This is attributed to fact that there is lack of formula or specific procedure in the application of the methods, which manifest from its subjective nature. The subjectivism makes it even more difficult during data collection and analysis, mainly because it requires special skills and knowledge to interrogate the subject within context. Many postgraduate students falls short in their attempts to exhume quality and rich data from the participants in their natural settings as they develop, implement, use and interact with systems. This is the main reason why two empirical studies of the same objectives could possibly produce different results.
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Background

IS/IT are used to support and enable organisations’ operations including strategic intents. IS/IT does not operate in vacuum, but in socially constructed environments. According to Iyamu, Sekgweleo and Mkhmazi (2014), IS is not only made up of technology by its self, it also includes human and non-human actors, making it more complex than often seen from afar. The multifaceted nature of information systems does not make studies in the field easier. Also, there is a great diversity in the research methods and approaches that are employed in IS studies (Myers & Avison, 2002). However, it is believed that qualitative research methods are being used increasingly in evaluation of IS/IT studies (Kaplan & Maxwell, 2005). Qualitative methods are often employed to study the socio-technical aspects of IS, and to help researchers including postgraduate students to draw conclusions on why things happen in the way that they do (Iyamu, 2010).

Theoretically, many postgraduate students in the field of IS are knowledgeable about data collection methods, techniques and approaches. However, in practice, there are numerous challenges in how their knowledge is applied (Hennink, Hutter & Ajay, 2011). This has led to many students not able to complete their studies, or take longer to do so. This is the primary motivation of this study. This chapter discusses hands-on experience, reveals pitfalls and challenges in collecting qualitative data, using semi-structured technique, towards achieving research objectives. The remainder of this article is divided into six main parts. The first and second covers literature review. The third discusses the processes that are involved in data collection. The fourth presents the major challenges that are encountered when the semi-structured method is employed in data collection. Future research is stated in the sixth part. Finally, a conclusion is drawn.

Key Terms in this Chapter

Continuation: Sequential order in an interview process.

Saturation: A point where nothing new is forthcoming.

Probing: Examination and further question based on response to previous question.

Repositioning: Readjustment to original structure of a guideline.

Culmination: A sudden dead-end.

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