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Information Systems and Technology

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    The interpretation and application of triangulation in information systems research.
    (2020) Mutinta, Given Chigaya.; Govender, Irene.; McArthur, Brian Walter.
    Scholars argue that a single research method is inadequate to investigate a complex phenomenon. As a result, there is growing interest in academic communities in the practicability of mixing research techniques in a process of triangulation. The purpose of this study was to investigate the interpretation and application of triangulation within the disciplines of information systems (IS) at four universities in South Africa; the University of KwaZulu-Natal, the University of Cape Town, the University of the Witwatersrand, and Stellenbosch University. This study employed the exploratory and descriptive research designs, and mixed methods. The target population were academic staff in the IS disciplines. Census and purposive sampling were used to select participants for the quantitative and qualitative study respectively. A sample size of fifty (50) and eight (8) academics was drawn for the quantitative study and qualitative study respectively. Data was collected using document collection, questionnaires, and in-depth interviews. In-depth interviews and documents were analysed using thematic analysis technique. Questionnaires were analysed using the Statistical Package for the Social Sciences (SPSS) version 22.1. The findings show that all (100 per cent) respondents were aware of triangulation. Data source triangulation (100.0 per cent) and methodological (82.4 per cent) are the most known types of triangulation. Methodological (90.2 per cent), investigator (67.0 per cent), data source (65.6 per cent), space (60.8 per cent), theory (52.9 per cent), time (41.1 per cent) and analyst (14.0 per cent) triangulation are the most used in this order. In spite of high respondents’ high levels of knowledge of triangulation, the seven types of triangulation are mainly used to validate research findings and explain research problems. There is thus a gap between the knowledge of triangulation and application of triangulation. IS academics find it easy to use data source (65.6 per cent), time (45.3 per cent), methodological (37.0 per cent), investigator (35.0 per cent), time (40.0 per cent), time (29.0 per cent), and space triangulation (23.5 per cent) in this order. Intradisciplinary triangulation is the most used than interdisciplinary triangulation. The findings indicate that academics with doctorates find it easier to use different types of triangulation than those with master’s degrees. The findings show that the frequently used type of triangulation is data source (19.0 per cent) and methodological (14.0 per cent). Largely, the study suggests that triangulation should be interpreted as Data source, Investigator, Theoretical, Methodological, Analyst, Space, and Time (DITMAST) triangulation, and to be used to Validate findings, Explain research problem, Enrich research instruments, and Refute findings (VEER). There is need to empower IS academics with knowledge on the interpretation of the different types of triangulation (DITMAST) and their application (VEER) in research.