A persistent challenge in social science research is understanding whether and when empirical results generalize beyond a specific study’s sample or context. In 2016, Rich Bettis, Connie Helfat and Myles Shaver produced a special issue of Strategic Management Journal containing several “quasi-replications” which examined whether and when results derived from particular industry, temporal, or geographic categories apply in adjacent research settings. In this issue, Lori’s paper with Ram Ranganathan and Anindya Ghosh explored how taken-for-granted determinants of alliance formations might vary across industries and timeframes. Some typical predictors – such as product-market similarity between two potential alliance partners – generated robust positive results across the industries and timeframes analyzed. Yet others – such as previous alliances between a pair of firms – yielded dramatically different results in the chemicals industry: Previous alliances were positively associated with future alliances in the 1980s, but negatively associated with them in the 1990s.
As Bettis et al (2016) note, any inability to quasi-replicate results need not invalidate the original study, but rather, offers an opportunity to theorize about conditions (read underlying variables) that may characterize the two distinct contexts. Might it be increasing technological maturity, or increasing industry consolidation, over the chemicals industry from the 1980s to the 1990s that shaped the opportunities and motivations to form new alliances with prior partners? This is an empirical question that is becoming increasingly easy to study across wider ranges of industries and timeframes as our ability to create and manipulate expansive datasets continues to grow.
Variables that connote concepts like industry maturity or industry concentration can help us to see connections across various industry-timeframe combinations, such that we might find alliance formation dynamics in the semiconductor industry in the 2000s to resemble those of the chemicals industry in the 1990s. Borrowing a concept from linear algebra, we call these underlying variables “basis variables”, as they allow us to transform typical categorical orderings along industries (such as SIC codes) or time (such as decades), allowing us to abstract from specific industry-timeframe contexts to more general constructs. In other words, by transforming our usual categorical representations of research settings, basis variables can promote commensurability, where seemingly distinct settings become comparable, enabling middle-range theorizing as theoretical contingencies are revealed. While there are many measures that researchers might identify as candidates for basis variables, we posit that these variables may group into three key constructs: uncertainty of the context, interdependence in the context, and distribution of attributes within the context.
We’ve developed these ideas in the working paper “Commensurability and collective impact in strategic management research: When non-replicability is a feature, not a bug.” How might our field make more progress if we coalesced around some basis variables that illuminated when and where research findings were more likely to generalize?