Session 395

Beyond the Known Methods--Introduction to Emerging Techniques

Track R

Date: Monday, September 22, 2014


Time: 14:45 – 16:00

Common Ground

Room: Estocolmo


  • TBD

Title: Analyst-Based Similarity: A Dynamic Dyadic Network Measure of Firm Similarity


  • Adam Castor, University of Pennsylvania

Abstract: In this paper, I introduce a novel approach, called Analyst-Based Similarity, to measure the similarity of firms for particular application in Strategy research. This approach leverages the Wittgensteinian concept of family resemblances in concert with data on sell-side stock market analyst coverage. Imbedded in the coverage patterns are analyst assignment decisions made by investment banks that intentionally reduce information acquisitions costs by assigning analysts to similar sets of firms. Using social network methods, I leverage these coverage patterns to create a continuous and dynamic firm-dyadic measure of corporate similarity that has many advantages over existing measures. To demonstrate the value of this new measure, I use two different empirical settings: entry mode decisions and market reactions to M&A and alliance announcements.

Title: Business Ecosystems As Complex Adaptive Systems: An Agent-Based Modeling Approach


  • Kati Järvi, Hanken School of Economics
  • Samuli Kortelainen, Lappeenranta University of Technology
  • Jukka Huhtamäki, Tampere University of Technology

Abstract: This proposal examines business ecosystems as complex adaptive systems. We apply complexity theory to explain the inherent characteristics of business ecosystems and introduce business ecosystem as the level of analysis. Thus, we move beyond the current notions of business ecosystem as the mere context of individual firms, dyadic alliance interactions or firm-complementor relationships. Further, we introduce simulation and especially agent-based modeling as methodological approach to conduct research on the level of a business ecosystem. Whereas social network analysis can provide valuable insights to the temporal interconnection and interdependence between actors, agent-based modeling helps us to understand strategic actions and feedback in and dynamics and evolution of business ecosystems over time.

Title: Financial versus Consumer Market Responses to Emergent Phenomena: An Application to a Social Media-Inspired Boycott


  • Brian Richter, University of Texas at Austin
  • Timothy Werner, University of Texas at Austin

Abstract: We extend behavioral finance theories to explain how and when financial and consumer markets may respond differently to emergent phenomena. The nature of emergent phenomena may be incongruent with the assumptions underlying the commonly employed the financial-market event study, causing it to fail to provide reliable results. Given the problems in applying financial market event studies to emergent phenomena, we propose researchers adopt an additional methodology—synthetic control—that employs fewer and weaker assumptions in their analysis. Synthetic control is flexible enough to be applied to measures of consumer market responses—and therefore more able to provide reliable estimates of the causal effects. We test and find support for our methodological arguments in an application to “United Breaks Guitars,” the first major social media-inspired boycott.

Title: How to Use Multi Methods to Develop Causal Theories


  • Juan Pablo Vazquez Sampere, IE Business School
  • Angeles Montoro-Sánchez, University of Complutense-Madrid

Abstract: In this methodological paper we explain why ‘big data’ is not the answer to the call of how to build better theories in social sciences. We also explain the three main challenges that social sciences face when transitioning from studying problems to solving them (quantity and types of data, rugged landscapes and the limitations of mono-method designs). We introduce a novel qualitative- quantitative sequential multi- method research design that is not only understandable and replicable but that is also able to analyze large quantities of data and that has a clear and standardized methodology for shaping causal mechanisms. We argue that the application of this particular multi- method is instrumental for transitioning to causal based research.

Title: Using Item Response Theory to Improve Measurement in Management: An Application to Corporate Social Responsibility


  • Brian Richter, University of Texas at Austin
  • Robert Carroll, University of Rochester
  • David Primo, University of Rochester

Abstract: We introduce item response theory (IRT) to management and strategy research. IRT explicitly models firms’ and individuals’ observable actions in order to measure unobserved, latent characteristics. IRT models have helped researchers in other social science disciplines measure traits like political ideology, and they can help strategic management researchers improve their measures. To demonstrate this potential, we show how the method improves upon the de facto best measure of corporate social responsibility (CSR), the KLD Index, by creating IRT Responsibility scores from the underlying data along with estimates of how accurate these measures are. We show, for instance, that firms like Apple may not be as socially responsible as previously thought, while firms like Walmart may be more responsible than typically believed.

All Sessions in Track R...

Sun: 08:00 – 09:15
Session 253: Research Methods and Publishing, and Publishing Research Method Advances
Sun: 11:15 – 12:30
Session 310: Assessing the Broad Impact of Research
Mon: 08:00 – 09:15
Session 448: Looking to the Past--Going toward the Future
Mon: 14:45 – 16:00
Session 395: Beyond the Known Methods--Introduction to Emerging Techniques
Mon: 16:30 – 17:45
Session 369: The Future of Research Methods in Strategic Management Research
Tue: 11:00 – 12:15
Session 397: Beyond the Longitudinal--Panel Analysis
Tue: 17:15 – 18:30
Session 450: Looking for Truth--Curves in Research

Strategic Management Society