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This study aims to investigate how women social entrepreneurs in Indonesia use various behaviors to address challenges to their leadership authority created by socioreligious patriarchal norms in this Muslim society.

An exploratory study of six Muslim women social entrepreneurs was conducted using multiround, semistructured interviews in a contrast sample of three women who work with their husbands and three women who work without their husband’s involvement.

The study identifies a variety of leadership behaviors that women entrepreneurs use to mitigate the constraining impact of strong patriarchal religious gender norms. Observations revealed surprisingly effective micro adjustments often based on relationship-specific private negotiations between the entrepreneurs and their husbands.

Future research focused on the husbands’ perspectives and behaviors, as well as extensions to other patriarchal religions and societies, are encouraged.

Recognition of the crucial role of spousal relationships suggests the need for more holistic approaches to support women social entrepreneurship, e.g. by integrating husbands into related outreach programs.

Religious gender stereotypes such as the stronger altruistic orientation of women can help counteract, to a degree, Muslim patriarchal norms when women lead social enterprises. Leadership of social enterprises by women promises to promote more gender equality over time, even if associated private and relationship-specific accommodations are not intended to challenge religious gender norms.

This study contributes to emerging research on the crucial role of spousal relationships for women’s entrepreneurship and the impact of private micro arrangements between spouses to mitigate the constraining impact of Muslim gender norms. Muslim women entrepreneurs approved of the religious gender norms that constrained them, in contrast to the more “feminist” perspectives common in women entrepreneurs in more secular and Christianity-dominated western societies.

Project ventures are an increasingly prevalent organizational form in many industries. The management literature has stressed their flexibility and adaptability advantages. This chapter focuses on the learning implications of the source of flexibility most essential to project ventures: the ability to switch partners during project formation and execution. This partnering flexibility creates opportunities to respond to new knowledge about characteristics of project tasks and project partners. Partnering flexibility, however, also creates learning challenges. The short-term nature of relationships between project partners and the disintegration of the project team after project completion challenges the accumulation and transfer of knowledge to future projects. Beyond the introduction of related learning opportunities and challenges, we identify potential contingency factors in the project context that shape when partner flexibility will have beneficial versus harmful effects. On the organizational level, we propose that project-governing permanent organizations can support project-venture learning. On the industry level, we highlight potential learning benefits of standardized partner roles and coordination practices. Thus, our chapter introduces a multilevel contingency framework for the evaluation of both learning opportunities and challenges of partnering flexibility in project-venture settings. We formulate testable propositions focused on partner-project fit and project performance.

The use of artificial intelligence (AI) in management research is still nascent and has primarily focused on content analyses of text data. Some method scholars have begun to discuss the potential benefits of far broader applications; however, these discussions have not led yet to a wave of corresponding AI applications by management researchers. This chapter explores the feasibility and the potential value of using AI for a very specific methodological task: the reliable and efficient capturing of higher-level psychological constructs in management research. It introduces the capturing of basic emotions and emotional authenticity of entrepreneurs based on their macro- and microfacial expressions during pitch presentations as an illustrative example of related AI opportunities and challenges. Thus, this chapter provides both motivation and guidance to management scholars for future applications of AI to advance management research.

Andreas Al-Laham has been holding the chair for strategic and international management at the University of Mannheim since September 2009. After his studies of economics and business administration at the Technical University of Dortmund he received his PhD (1996) and Habilitation (2000) degree at the same University, Faculty of Business Administration, Chair of Strategic and International Management. From 2000 to 2002 he worked as a visiting research scholar and visiting professor for strategic management and organizational theory at the J.L. Rotman School of Management, University of Toronto, Canada. Afterward he became professor of international management and business policy at the University of Stuttgart. In 2004 he took a professorship of strategic management at the CASS Business School, City University of London, UK. Up till today, he is visiting professor for General Management and International Strategy. Between 2006 and 2009 he held the chair for management and international strategy at the University of Kaiserslautern. He has written several books, for example! Strategisches Management. Theoretische Grundlagen-Prozesse-Implementierung (together with M. K. Welge), Organisationales Wissensmanagement. Vahlens Handbücher der Wirtschafts- und Sozialwissenschaft, Praxis des strategischen Managements (together with M. K. Welge and P. Kajüter) and Strategieprozesse in deutschen Unternehmungen. His current research focuses on evolutionary dynamics in the German biotech-industry, alliances and network dynamics as well as the internationalization of SME.

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 modelling 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.The chapter draws on methodological literature in economics, applied psychology, and the philosophy of science to point out fundamental features of baseline modelling. Examples come from research in international business and management, emerging market economies and developing countries.Baseline modelling 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. Baseline modelling takes a more inductive and iterative approach to modelling and theory development. Because baseline modelling holds substantial potential, international-management scholars should explore its opportunities for advancing scientific progress.

Purpose – This chapter reports on a rapidly growing trend in the analysis of data about emerging market (EM) 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 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 EM 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.

Null-hypothesis significance tests (NHST) are a very troublesome methodology that dominates the quantitative empirical research in strategy and management. Inherent limitations and inappropriate applications of NHST impede the accumulation of knowledge and fill academic journals with meaningless “findings,” and they corrode researchers' motivation and ethics. Inherent limitations of NHST include the use of point null hypotheses, meaningless null hypotheses, and dichotomous truth criteria. Misunderstanding of NHST has often led to applications to inappropriate data and misinterpretation of results.

Researchers should move beyond the ritualistic and often inappropriate use of NHST. The chapter does not advocate a best way to do research, but suggests that researchers need to adapt their methods to reflect specific contexts and to use evaluation criteria that are meaningful for those contexts. Researchers need to explain the rationales that guided the selection of evaluation measures and they should avoid excessively complex models with many variables. The chapter also offers four more focused recommendations: (1) Compare proposed hypotheses with naïve hypotheses or the outcomes of alternative treatments. (2) Acknowledge the uncertainty that attends research findings by stating confidence limits for parameter estimates. (3) Show the substantive relevance of findings by reporting effect sizes – preferably with confidence limits. (4) Use statistical methods that are robust against deviations from assumptions about population distributions and the representativeness of samples.

Welcome to the fifth volume of Research Methodology in Strategy and Management (RMSM). We are delighted to provide you with this latest installment of the RMSM series and hope that you will find it to be as informative and educational as we do. This volume represents the work of a diverse set of scholars, some of whom are former presidents and fellows of the Academy of Management, some are ascending and up-and-comers, while others are highly accomplished methodologists who come from outside the strategy field. All of the authors have drawn upon deep and rich methodological experiences to put together excellent chapters. Their contributions not only address important and timely methodological topics but provide plain and straightforward insights as to how we can improve the application of research methods in strategy and management.

If we look to the uniqueness of IB/IM scholarship and ask where it stands separate from standard and traditional management and business research we really have only two differentiating, but exceedingly important, factors that justify discussing IB/IM as a separate research paradigm (See, e.g., Devinney, Pedersen, & Tihanyi, 2010).