Quantitative and qualitative methods in organizational research

Blending quantitative and qualitative methods is an approach to studying organizational phenomena that seeks to leverage the strengths and compensate for the weaknesses of each method that is used separately. Notably, quantitative data offer greater objectivity and precision but lack richness, while qualitative data provide insight about mechanisms but bring subjectivity. Selecting the appropriate method for a given research question is an essential skill for organizational researchers. High-quality research involves a good fit between the methods used and the nature of the contribution to the literature. This article describes a contingency framework that relates the state of prior theory and research to the design of a current research project, paying particular attention to when to mix qualitative and quantitative data. Whereas contributions to mature literatures are best made with quantitative methods, and contribute to nascent literature’s call for qualitative data, intermediate research is best served by a mix of both approaches

The advocacy trap: when legitimacy building inhibits organizational learning

This paper describes a relationship between legitimacy building and learning for a new firm in a nascent industry. Through a longitudinal study of a new firm in the nascent smart city industry, we found that the firm failed to make progress on important internal initiatives, despite notable external successes, including prestigious employees, well-known partner companies, and extensive positive media attention. Exploring these concurrent developments, we discovered a surprising relationship between the firm's external successes and its internal failures. We propose that the legitimacy building that helped the new firm establish external success gave rise to cognitive and emotional internal dynamics that inhibited organizational learning. We call this dynamic the advocacy trap. By suggesting a downside to legitimacy building and identifying a novel barrier to organizational learning - rooted in cognition and emotion, and especially salient in new firms and nascent industries - our discovery opens several new avenues for research in entrepreneurship and organizational learning

Teaming routines in complex innovation projects

Innovation projects thrive when they bring together individuals with diverse expertise. But, to succeed, such projects must support effective teaming: coordination and collaboration between experts working together in shifting, fluid ways across physical, status, or knowledge boundaries. In this chapter, the authors develop the idea of teaming routines: routines that enable coordination and collaboration between experts across multiple boundaries. The chapter draws on longitudinal data from Lake Nona, a novel mega-project in the built environment, to explore how leaders enabled the development of teaming routines that supported innovation. By theorizing the processes through which leaders can build teaming routines into complex, large-scale innovation projects, the authors add to the existing literature, which has largely focused on routines in the context of a single organization or an alliance between two organizations

Leading change for sustainability: the role of megaprojects and multiplier firms

This chapter highlights the roles of multiplier firms and megaprojects in leading change for sustainability in the built environment. Multiplier firms are organizations that offer on-going sustainability solutions to a range of client organizations. Megaprojects are finite-duration initiatives involving multiple organizations in the delivery of a large-scale development, such as a new ecologically sustainable city. Drawing on illustrative case studies of multiplier firms and megaprojects engaged in sustainability-related initiatives, the chapter also explores the value of learning logic—in contrast to blueprint logic—for leading change. Blueprint logic describes a top-down approach to change that relies on well-developed plans, while learning logic involves experimentation, iteration, and sourcing ideas widely to make progress on complex goals. A learning logic is appropriate for leading change for sustainability, because effective techniques for success are not yet know