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Peng, G.C.; Nunes, M.B. (2007)
Publisher: Academics Conference International
Languages: English
Types: Other
Subjects:
It is common for inexperienced researchers and research students to aim at investigating very wide contexts such as countries (e.g. China, India, UK), regions (e.g. the Arab Countries) or even continents (e.g. Africa). Such studies in Information Systems (IS) are not only unrealistic and potentially unfeasible, but may result in findings that are neither significant nor meaningful. \ud \ud Research supervisors often face difficulties in explaining and resolving these common pitfalls in research proposals. This paper proposes the use of Political, Economic, Social and Technological (PEST, also often referred to as STEP) analysis as a tool to identify narrower contexts and focus research questions around feasible and meaningful regional contexts. It illustrates this process with the results of an analysis carried out as part of an ongoing PhD research project. The project aims to investigate the barriers and risks associated with the post-implementation of Enterprise Resource Planning (ERP) systems in Chinese companies. PEST analysis was used to define an appropriate region in China (i.e. Guangdong), as well as the type of company to be studied, namely State Owned Enterprises (SOE). This analysis was followed by a set of SWOT analyses in order to identify a suitable sector, namely the electronic and telecommunication manufacturing sector. The paper also shows how the researcher reviewed, compared and synthesised large amounts of literature and statistical data when constructing arguments and standpoints. \ud \ud This approach helped to develop a profound understanding of the Chinese context and has proved to be a valuable decision-making tool when selecting an appropriate Chinese region, a type of company and an industry sector in which to conduct the research. It resulted in the redefinition of the research question and in data collection and analysis that is more likely to produce useful, meaningful and generalisable findings.
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