Management

Qualitative researchers utilize comparative and case-based methods to develop theory through elaboration or abduction. They pursue research in intermediate fields where some but not all relevant constructs are known (Edmonson & McManus, 2007). When cases and comparisons move beyond a few, it threatens researchers with information overload. Qualitative Comparative Analysis (QCA) is a novel method of analysis that is appropriate for larger case or comparative studies and provides a flexible tool for theory elaboration and abduction. Building on recently published exemplars from organizational research, we illuminate three key benefits of QCA: (1) allows researchers to examine cases as wholes, effectively addressing the complexity of action embedded in organizational phenomena; (2) provides indicators of whether results are reliable and valid so qualitative researchers, and others, can assess their findings within a study and across studies; and (3) explores potentially overlooked connections between qualitative and quantitative research.

Abstract Null-hypothesis significance tests (NHSTs) have received much criticism, especially during the last two decades. Yet many behavioral and social scientists are unaware that NHSTs have drawn increasing criticism, so this essay summarizes key criticisms. The essay also recommends alternative ways of assessing research findings. Although these recommendations are not complex, they do involve ways of thinking that many behavioral and social scientists find novel.

Le retour sur le devant de la scène des approches dites «dynamiques» de la stratégie n'est pas sans soulever quelques interrogations sur le management stratégique en tant que pratique et que discipline. La prolifération des spécialités, dans un champ qui fut longtemps dominé par des visions positivistes et déterministes de la performance (école du choix stratégique, analyse de l'adéquation entre ressources et opportunités, resource-based view,…), crée un sentiment de malaise partagé entre chercheurs et praticiens. La ...

Purpose
This chapter reports on a rapidly growing trend in the analysis of data about emerging market economies – the use of baseline models as comparisons for explanatory models. Baseline models estimate expected values for the dependent variable in the absence of a hypothesized causal effect, but set higher standards than do traditional null hypotheses tests that expect no effect.

Design/methodology/approach
Although the use of baseline models research originated in the 1960s, it has not been widely discussed, or even acknowledged in the emerging markets (EM) literature. We surveyed published EM studies to determine trends in the use of baseline models.

Findings
We categorize and describe the different types of baseline models that scholars have used in emerging markets studies, and draw inferences about the differences between more effective and less effective uses of baseline models.

Value
We believe that comparisons with baseline models offer distinct methodological advantages for the iterative development of better explanatory models and a deeper understanding of empirical phenomena.

• Purpose – This chapter reports on a rapidly growing trend in data analysis – analytic comparisons between baseline models and explanatory models. Baseline models estimate values for the dependent variable in the absence of hypothesized causal effects. Thus, the baseline models discussed in this chapter differ from the baseline models commonly used in sequential regression analyses.
Baseline modeling entails iteration: (1) Researchers develop baseline models to capture key patterns in the empirical data that are independent of the hypothesized effects. (2) They compare these patterns with the patterns implied by their explanatory models. (3) They use the derived insights to improve their explanatory models. (4) They iterate by comparing their improved explanatory models with modified baseline models.
• Methodology/approach – The chapter draws on methodological literature in economics, applied psychology, and the philosophy of science to point out fundamental features of baseline modeling. Examples come from research in international business and management, emerging market economies, and developing countries.
• Findings – Baseline modeling offers substantial advantages for theory development. Although analytic comparisons with baseline models originated in some research fields as early as the 1960s, they have not been widely discussed or applied in international management.
• Practical implications – Baseline modeling takes a more inductive and iterative approach to modeling and theory development.
• Originality/value of paper – Because baseline modeling holds substantial potential, international-management scholars should explore its opportunities for advancing scientific progress.

Research articles often give inaccurate information about how researchers developed hypotheses, analyzed data, and drew conclusions. Published articles sometimes report only some of the hypotheses that researchers tested, or some of the statistical analyses that researchers made. Articles often imply that researchers formulated all hypotheses before they examined their data when in fact they added or deleted hypotheses after they made some data analyses. Indeed, such covert practices are so common that new entrants into management research may think they are correct behavior. Yet, these practices create false impressions about the validity of research and they undermine the openness that ought to create trust among researchers.
Researchers have tried to halt these practices by labeling them “unethical” but their continued prevalence questions the effectiveness of wholly critical approaches. This article proposes a constructive path toward reform: advocating honesty about actual research processes by adding discussions of inferences drawn after data analyses. Post-hoc data analyses can stimulate important theoretical ideas; running alternative statistical models can deepen understanding of empirical patterns; lack of support for hypotheses can identify incorrect or incomplete theories. The management research culture should encourage these practices. The negative effects result from the lack of explicit reporting about them.

How can management and strategy scholars organize to generate more productive, more innovative, and more impactful research? With appropriate cultures and leaders, small and egalitarian discussion groups that we call “collegial nests” can become powerful generators of innovative ideas and creators of extraordinary scholars. Collegial nests need cultures that free participants to think critically, to cherish new viewpoints, and to speak freely without fear of ridicule. They also need leaders who model such cultures and facilitate frequent discussions. Two case examples illustrate how productive collegial nests can create better science and better scientists. To generate scientific innovation and progress on a large scale, many autonomous groups tackling related issues are desirable. Modern communication technology is making it feasible for groups to operate over large distances and to coordinate with each other at very low cost. Collegial nests offer greater potential for enhancing scholarly productivity and innovation than do attempts to regulate scholarship via hierarchical structures. Multiplicity can lower the probability of wasting resources on low-yield paths, egalitarian control can reduce the influence of vested interests, and a combination of shared goals and partial autonomy can integrate enthusiasm with sensible risk taking.