Abstract

This article focuses on the triangulation of research methods and techniques in the discussion on the evaluation of marketing research. Research traditions in marketing can be referred to the division of research methodologies, according to which the methods of conceptual, theoretical, empirical and formal research is distinguished, based on the use of quantitative methods, mathematical and statistical models. In scientific research on marketing, conceptual methods (i.e. heuristic or creative methods) are used, with a primary focus on the analysis of qualitative aspects of the studied processes, thus making little reference to empirics. The methodology of empirical research in marketing is used to a great extent. It allows developing empirical models, thus making it possible to recognize, e.g. actually implemented models of marketing strategies that may be consistent with or deviate from the theoretical models. However, it should be noted that empirical models do not always have implementation properties, as they most often refer to the present or past reality. Nevertheless, these models may be subject to benchmarking. Moreover, they serve to make conceptual models more probable. For the further development of marketing, it becomes essential to increase the scope of application of formal research methodologies as well. The analysis of the literature indicates the need for an in-depth diagnosis of combined methods in the research process in the broader context of management sciences. The aim of the article is therefore expressed as a research problem in the form of the following question: Is methodological triangulation a prerequisite for research processes in the management sciences? The research results presented in this article are a fragment of complex study on research methodology. Moreover, the conducted empirical research confirms and theoretically proves that methodological triangulation is a necessary condition in research processes. In contrast, verification of developed methods, models or procedures is an essential condition allowing to combine science and management practice, while methodological triangulation supports research processes. Concerning the preceding, the author recommends in the present article to make a scientific effort aimed at elaborating a tool, method or procedure to select proper research methods and techniques for the research process within the scope of management science, with a particular emphasis on factors determining the process quality.

Keywords: methods, research techniques, marketing research, triangulation, research methodology.

JEL Classification: M31, O30, C44.

Cite as: Dzwigol, H. (2020). Innovation in Marketing Research: Quantitative and Qualitative Analysis. Marketing and Management of Innovations, 1, 128-135. https://doi.org/10.21272/mmi.2020.1-10

This work is licensed under a Creative Commons Attribution 4.0 International License

References

1. Acharjya, D.P., & Das, T.K. (2017). A framework for attribute selection in marketing using rough computing and formal concept analysis. IIMB management review, 29(2), 122-135. [Google Scholar] [CrossRef]
2. Avila-Robinson, A., & Wakabayashi, N. (2018). Changes in the structures and directions of destination management and marketing research: A bibliometric mapping study, 2005–2016. Journal of Destination Marketing & Management, 10, 101-111. [Google Scholar] [CrossRef]
3. Bezchasnyi, O. (2018). Reflexive coordination of communications in the construction of models for the development of an industrial enterprise. Virtual Economics, 1(1), 66-83. [Google Scholar][CrossRef]
4. Bryman, A. (2006). Integrating quantitative and qualitative research: How is it done? Qualitative research, 6(1), 97-113. [Google Scholar] [CrossRef]
5. Boiko, V., Misiuk, M., Kwilinski, A., & Boiko, L. (2019). Competitive advantages of wholesale markets of agricultural products as a type of entrepreneurial activity: the experience of Ukraine and Poland. Economic Annals-XXI,68. [Google Scholar] [CrossRef]
6. Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological bulletin, 56(2), 81–105. [Google Scholar] [CrossRef]
7. Chlipala, P. (2014). Triangulacja podejść metodologicznych w badaniach naukowych z dziedziny marketingu. Prace naukowe Uniwersytetu Ekonomicznego we Wroclawiu, 336, 39-48. [Google Scholar]
8. Cisek, S. (2010). «Metodologia mieszana» w badaniach nauki o informacji i bibliotekoznawstwie. In Biblioteki, informacja, ksiazka: interdyscyplinarne badania i praktyka w XXI wieku (ePublikacje Instytutu Informacji Naukowej i Bibliotekoznawstwa, nr 7) (pp. 88-94). Instytut Informacji Naukowej i Bibliotekoznawstwa, Uniwersytet Jagiellonski. [Google Scholar]
9. Cunningham, D.W., & Wallraven, C. (2011). Experimental design: From user studies to psychophysics. CRC Press. [Google Scholar]
10. Dalevska, N., Khobta, V., Kwilinski, A., & Kravchenko, S. (2019). A model for estimating social and economic indicators of sustainable development. Entrepreneurship and Sustainability Issues, 6(4), 1839-1860. [Google Scholar] [CrossRef]
11. Denizm, N. K. (1978). Sociological Methods: A Sourcebook (2nd ed.). New York: McGraw-Hill.
12. Dzwigol, H. (2018). Wspolczesne procesy badawcze w naukach o zarzadzaniu. Uwarunkowania metodyczne i metodologiczne. Warszawa: PWN [in Polish].
13. Dzwigol, H. (2019). Research methods and techniques in new management trends: research results. Virtual Economics, 2(1), 31-48. [Google Scholar]
14. Dzwigol, H., & Dzwigol-Barosz, M. (2018). Scientific Research Methodology in Management Sciences. Financial and Credit Activity: Problems of Theory and Practice, 2(25), 424-437. [CrossRef]
15. Dzwigol, H.; & Wolniak, R. (2018). Controlling w procesie zarzadzania chemicznym przedsiębiorstwem produkcyjnym. Przemysl Chemiczny, 97(7). [CrossRef]
16. Dzwigol, H., Dzwigol-Barosz, M., Zhyvko, Z., Miśkiewicz, R., & Pushak, H. (2019) Evaluation of the energy security as a component of national security of the country. Journal of Security and Sustainability Issues, 8(3). [Google Scholar] [CrossRef]
17. Dzwigol, H., Aleinikova, O., Umanska, Y., Shmygol, N., & Pushak, Y. (2019). An entrepreneurship model for assessing the investment attractiveness of regions. Journal of Entrepreneurship Education. [Google Scholar]
18. Gordon, R., Dibb S., Magee, C., Cooper, P., & Waitt, G. (2018). Empirically testing the concept of value-in-behavior and its relevance for social marketing. Journal of Business Research, 82, 56-67, [Google Scholar] [CrossRef]
19. Greene, J.C., Caracelli, V.J., & Graham, W.F., (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational evaluation and policy analysis, 11(3), 255-274. [Google Scholar] [CrossRef]
20. Jack, E. P., & Raturi, A. S. (2006). Lessons learned from methodological triangulation in management research. Management Research News. [Google Scholar] [CrossRef]
21. Jick, T. D. (1979). Mixing qualitative and quantitative methods: Triangulation in action. Administrative science quarterly, 24(4), 602-611. [Google Scholar] [CrossRef]
22. Johnson, B., & Gray, R., (2010). A history of philosophical and theoretical issues for mixed methods research. Sage handbook of mixed methods in social and behavioral research, 2, 69-94.[CrossRef]
23. Kaminska, B. (2018). Iterative signal processing in anticipatory management of industrial enterprise development. Virtual Economics, 1(1), 53-65. [Google Scholar] [CrossRef]
24. Konecki, K. (2000). Studia z metodologii badan jakosciowych. Teoria ugruntowania: Wydawnictwo Naukowe PWN, Warszawa. [Google Scholar]
25. Kostera, M. (2003). Antropologia organizacji: Metodologia badań terenowych, Wydawnictwo Naukowe PWN, Warszawa.
26. Kuzior, A., Kwilinski, A., & Tkachenko, V. (2019). Sustainable development of organizations based on the combinatorial model of artificial intelligence. Entrepreneurship and Sustainability Issues, 7(2), 1353-1376. [Google Scholar] [CrossRef]
27. Кwilinski, A. (2018a). Mechanism of modernization of industrial sphere of industrial enterprise in accordance with requirements of the information economy. Marketing and Management of Innovations, 4, 116-128. [Google Scholar] [CrossRef]
28. Kwilinski, A. (2018b). Mechanism of formation of industrial enterprise development strategy in the information economy. Virtual Economics, 1(1), 7-25. [Google Scholar] [CrossRef]
29. Kwilinski, A., Pajak, K., Halachenko, O., Vasylchak, S., Pushak, Y., & Kuzior, P. (2019). Marketing tools for improving enterprise performance in the context of social and economic security of the state: innovative approaches to assessment. Marketing and Management of Innovations, 4, 172-181. [Google Scholar] [CrossRef]
30. Labarca, C. (2017). Qualitative Research for beginners. Venezuela: Maracaibo.
31. Lim, W. M. (2018). Demystifying neuromarketing. Journal of Business Research, 91, 205-220. [Google Scholar] [CrossRef]
32. Niglas, K. (2004). The combined use of qualitative and quantitative methods in educational research. Tallinna Pedagoogikaülikooli Akadeemiline Raamatukogu.
33. O′Leary, Z. (2017). The essential guide to doing your research project. Sage. [Google Scholar]
34. Pajak, K. (2010). Rozwoj lokalny jako kategoria ekonomiczna [Local development as an economic category]. In A. Grzelak & K. Pajak (Eds.), Nowe trendy w metodologii nauk ekonomicznych[New  trends in the methodology of economic sciences] (pp. 185-215). Poznan: Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu [in Polish]. 
35. Pajak, K., Kaminska, B., & Kvilinskyi, O. (2016). Modern trends of financial sector development under the virtual regionalization conditions. Financial and credit activity: problems of theory and practice, 2(21), 204-217. [Google Scholar] [CrossRef]
36. Pajak, K., Kvilinskyi, O., Fasiecka, O., & Miskiewicz, R. (2017). Energy security in regional policy in Wielkopolska region of Poland. Economia i Srodowisko. [Google Scholar]
37. Patton, M.Q. (2002). Qualitative research and evaluation methods: Integrating theory and practice. Sage Publications. [Google Scholar]
38. Rodina, O., Savchenko, T., Basiurkina, N., & Kwilinski, A. (2019). Improvement of the assessment methods of product competitiveness of the specialized poultry enterprises. Management Theory and Studies for Rural Business and Infrastructure Development, 41(1), 43-61. [Google Scholar] [CrossRef]
39. Sebastian, V. (2014). Neuromarketing and evaluation of cognitive and emotional responses of consumers to marketing stimuli. Procedia-Social and Behavioral Sciences, 127, 753–757. [Google Scholar] [CrossRef]
40. Tkachenko, V., Kwilinski, A., Klymchuk, M., & Tkachenko, I. (2019). The Economic-Mathematical Development Of Buildings Construction Model Optimization On The Basis Of Digital Economy. Management Systems in Production Engineering, 27(2), 119-123. [Google Scholar] [CrossRef]
41. Quach, S., Thaichon, P., Lee, J. Y., Weaven, S., & Palmatier R.W. (2019). Toward a theory of outside-in marketing: Past, present, and future. Industrial Marketing Management. [Google Scholar] [CrossRef]
42. Quinton, S., & Reynolds, N. (2018). Understanding research in the digital age. Sage. [Google Scholar]
43. Stanczyk, S. (2016). Triangulacja – laczenie metod badawczych i urzetelnienie badan [Triangulation – combining research methods and research reliability]. In W. Czakon (Ed.), Podstawy metodologii badan w naukach o zarzadzaniu [Fundamentals of research methodology in management sciences] (pp. 78-90). Warszawa: Oficyna Wolters Kluwer Business [in Polish].
44. Thomas, G. (2017). How to do your research project: A guide for students. Sage [Google Scholar]
45. Vaivio, J., & Siren, A. (2010). Insights into method triangulation and «paradigms» in interpretive management accounting research. Management Accounting Research, 21(2), 130-141. [Google Scholar] [CrossRef]
46. Vasylieva, T., Lyeonov, S., Lyulyov, O., & Kyrychenko, K. (2018). Macroeconomic stability and its impact on the economic growth of the country. Montenegrin Journal of Economics, 14(1), 159-170. [Google Scholar] [CrossRef]
47. Venkatesh V., Brown S.A., & Bala H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS Ouarterly, 21-54. [Google Scholar]
48. Webb E.J., Campbell D.T., Schwartz R.D., & Sechrest, L. (1966). Unobtrusive measures: Nonreactive measures in the social sciences. [Google Scholar]
49. Zabinski, L. (2006). Paradygmaty nauk spolecznych a ich wykorzystanie w badaniach naukowych systemow zarzadzania marketingowego. Prace Naukowe Akademii Ekonomicznej we Wroclawiu (1107 Badania marketingowe w prestrzeni europejskiej), 17-26. [Google Scholar]