Andreas Schwab

Learning In Hybrid-Project Systems: The Effects of Project Performance on Repeated Collaboration

This study advances contingency theories of performance-outcome learning in hybrid-project systems, in which both project participants and superordinate organizations influence the formation of project ventures. We propose that performance-outcome learning depends on the perceived relevance of prior performance and on organizational control over project participants. We examine this framework using data on 239 U.S. movie projects from the years 1931-40. In keeping with our theory, higher project performance led to future collaborations with the same partners, contingent on prior collaborations, project similarity, and organizational control. Our findings imply distinct patterns of network evolution and unfolding adaptation of hybrid-project systems through slow-moving, local adjustments.

Incremental Organizational Learning from Multilevel Information Sources: Evidence for Cross-Level Interactions

The availability of both direct performance feedback at the organization level and vicarious information at the industry level raises the question of their relative impact, as well as potential multilevel interactions. Prior research suggests that an organizations own experience after adopting an innovative managerial practice tends to replace information collected by observing other organizations that implement the practice. The findings in this study show, however, that both organization-level performance feedback and population-level comparisons to other organizations affected incremental change of an innovative practice during its execution. The effects of these two information sources are not independent. Instead, results support a substitutional cross-level interaction. In addition, the study discovered that, when learning from their own experience, organizations engage in superstitious learning and do not let sufficient time pass before assessing the effects of prior changes. This study identifies principles that will promote a more integrated understanding of learning during the execution of innovative practices and contributes to the development of more fine-grained multilevel models of organizational learning.

Turnaround Strategies in Established Small Family Firms

This study employs a case-study approach to identify unique characteristics of established small family firms that affect their ability to initiate turnaround strategies when encountering an organizational crisis. In our case studies, we found evidence for family firms employing the standard strategies of top-management changes, infusion of external management expertise, and retrenchment that have been proposed in the general turnaround literature. The implementation of these strategies was, however, moderated by eight characteristics generally associated with family firms, including strong ties to the family firm, internal orientation, altruistic motives, and long-term goal orientation. The introduced framework contributes to a more fine-grained understanding of the turnaround challenges of established family firms and how they can be addressed. This is a topic of substantial practitioner interest considering the high failure rates of family firms.

PERSPECTIVE---Researchers Should Make Thoughtful Assessments Instead of Null-Hypothesis Significance Tests

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. Instead of making NHSTs, researchers should adapt their research assessments to specific contexts and specific research goals, and then explain their rationales for selecting assessment indicators. Researchers should show the substantive importance of findings by reporting effect sizes and should acknowledge uncertainty by stating confidence intervals. By comparing data with naive hypotheses rather than with null hypotheses, researchers can challenge themselves to develop better theories. Parsimonious models are easier to understand, and they generalize more reliably. Robust statistical methods tolerate deviations from assumptions about samples.

Temporary Organizing: Promises, Processes, Problems

Temporary organizing is introduced as process, form and perspective. Then key challenges and opportunities in the study of temporary organizing are discussed, including methodological issues, how to theorize time, and how to relate the temporary to the more permanent. This introductory article concludes with an overview of the special issue.

Managerial gender diversity and firm performance: : An integration of different theoretical perspectives

This study examines the relationship between managerial gender diversity (MGD) and firm performance. It outlines how extremely low and extremely high levels of MGD can trigger group processes that can impede the attainment of the performance benefits associated with moderate levels of MGD. Findings from longitudinal panel data from financial service firms in Portugal suggest that the effects of MGD on firm performance are best captured by a non-linear function with two breaking points. This study introduces a framework that combines different theoretical perspectives focused on tokenism, subgroup formation, divergent thinking, and other group processes linked to positive and negative gender-diversity consequences. Corresponding overall firm-performance outcomes are contingent upon the level of MGD.

Why All Researchers Should Report Effect Sizes and Their Confidence Intervals: Paving the Way for Meta-Analysis and Evidence-Based Management Practices

The growing body of empirical entrepreneurship studies and the advent of meta–analytic methodologies create new opportunities to develop evidence–based management practices. To support research on evidence–based practices, empirical studies should report meta–analysis relevant information, such as standardized effect–size measures and their confidence intervals. The corresponding changes in reporting practices are simple and straight forward—yet they promise strong contributions to the systematic accumulation of entrepreneurship knowledge over time.

Using Baseline Models to Improve Theories about Emerging Markets

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.

Organizational Learning Implications of Partnering Flexibility in Project-Venture Settings: A Multilevel Framework

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.

Why Baseline Modelling is Better than Null-Hypothesis Testing: Examples from International Business Research

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.