From a distance and generalizable to up close and grounded: Reclaiming a place for qualitative methods in international business research

Abstract

journal of International Business Studies (2011) 42, 573-581. doi: 10.1057/jibs.2011.19 It is exhilarating to write the introduction to this Special Issue as it presents an important opportunity for qualitative research to reclaim its position as an integral part of international business (IB) research. The field of IB was founded on a rich qualitative research legacy (e.g., Bartlett, 1979; Crozier, 1965; Fayerweather, 1969; Johanson & Vahlne, 1977; Kindleberger, 1956; Prahalad, 1975; Wilkins, 1970, 1974). Over time, however, such methods have been progressively marginalized in our field as quantitative methods have become the norm. This shift in emphasis has mirrored the broader trend toward more positivistic empirical methods in the social sciences. The field of IB is multidisciplinary in scope, and interdisciplinary in content and methodology (statement of JIBS editorial policy). However, as our respective home disciplines came under growing pressure to solidify their legitimacy as scientific endeavors that merit scholarly attention, it is not surprising that we began to take on the norms propagated within our own academic communities that typically equated quantitative data with hard science. While there are clear merits associated with quantitative methods, the multi-cultural, multi-dimensional and dynamic nature of the field of IB lends itself to a broad range of research methodologies, qualitative methods being one of them. In order to understand the complexities of emergent and evolving phenomena scattered over distance, and the differentiated contexts typical to many topics under investigation in IB, it is often inappropriate to engage in large-scale, cross-sectional studies or reductionist methods in the absence of well-developed theory. Rather, thick description, exploratory research and comparative case analysis that focus on inductive theory building and hypotheses generation may be more suitable.