Introduction
When considering philosophical and scientific foundations of research, the major focus is made on analyzing a social impact of information technology on society and economy. In this regard, theoretical and conceptual frameworks provide valid social and philosophical guidelines for an interpretive assessment of the social impact of ICT (O’Donnell and Henriksen, 2002, p. 92). Noting that information technologies correlate with social and business sciences, human beings are also involved in this sphere (Nwokah, Kiabel, and Briggs, 2009, p. 430). In this respect, the context of IT research is based on close interaction between information users and the information systems they are engaged with.
Approach to Inquiry: Induction versus Deduction
Ambiguity and confusion arises between induction and deduction when the argument is based on stressing probability, even if it is quite high, or on probability that is equated to certainty (Srinagesh, 2009, p. 185). Hence, deduction is more based on the analysis of consequences derived from a particular assumption whereas induction is based on the summary of explicit or probable facts that are just admitted, but not proved. In other words, deduction is based on existing certainties whereas induction postulates what may happen or what is possible.
Type of Data: Numeric versus Narrative Data
Taking into consideration qualitative and quantitative underpinning of research, the primary emphasis should be put on character and origins on the data itself, but not on the methods used to process information (Marczyk, DeMatteo, Festinger, 2010). In this respect, qualitative data presents a construct that can be received from a mere description of different variations and changes and evaluation of particular objects. Quantitative or numerical data analysis lies in measuring and rating attitudes based on summary statements. In other words, all observations and facts are quantified rather than described.
Testing Hypotheses and Theories versus Generating Hypotheses and Building Theory
Testing hypotheses implies the analysis of a null hypothesis and correlating it with an alternative hypothesis to denounce the null one (Marczyk, DeMatteo, Festinger, 2010). In contrast, the process of generating hypotheses often involves the identification of qualitative research methods and target groups as the basis for data analysis. Building appropriate research questions contributes to generating predictions and analyzing results ((Marczyk, DeMatteo, Festinger, 2010). It leads to the formulation of good and provoking hypotheses that are sufficient for creating concepts and building theories. Hence, hypothesis generation is the initial stage for building a research that logically ends with testing and evaluating plans and theories.
Two Rationales for Using a Mixed Methods Approach
The usage of a mixed method approach to research implies that qualitative and quantitative aspects will be engaged to study a particular question from both perspectives. Hence, the first rationale for using mixed methods approaches lies in the necessity of addressing several dimensions for a particular questions to better understand the process and find out relevant studies (Clark and Creswell, 2010, p. 9). The second rationale of using this approach is premised on the complexity and diversity of the investigated problem and on the presence of a great number of variables of different characters.
Reference List
Clark, V. L., and Creswell, J. W. (2010). Designing and Conducting Mixed Methods Research. US: SAGE.
Marczyk, G. R., DeMatteo D., Festinger, D. (2010). Essentials of Research Design and Methodology. US: John Willey and Sons.
Nwokah, N. G., Kiabel, B. D., and Briggs, A. E. (2009). Philosophical Foundations and Research Relevance: Issues for Marketing Information Research. European Journal of Scientific Research. 33(3), pp. 429-437.
O’Donnell, D., and Henriksen, L. B. (2002). Philosophical foundations for a critical evaluation of the social impact of ICT. Journal of Information Technology. 17, pp. 89-99.
Srinagesh, K. (2006). The Principles of Experimental Research. US: Butterworth-Heinemann.