Addressing the Visual Turn in Management Research: New Insights for Optimal Distinctiveness and Identity Research

• Applicant: Professorin Dr. Cornelia Storz
• Subject Area: Operations Management and Computer Science for Business Administration ; Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics ; Sociological Theory ; Statistics and Econometrics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 535589750

Organizations increasingly rely on images, logos, videos, design and other visual texts to compete, communicate or to shape identities (Boxenbaum et al., 2018). Visual texts represent a distinct semiotic mode of communication, offering direct sensory, aesthetic, and affective experiences (Weiss et al., 2022) that surpass the cognitive processing of verbal texts (Eisenman, 2013; Meyer et al., 2018). However, the dominance of verbal texts as “the sole, or, at least, dominant semiotic mode” of communication persists (Meyer et al., 2018: 392). Notably, machine learning approaches have revolutionized the analysis of verbal texts, yielding valuable insights in various areas of management research, including optimal distinctiveness (Haans, 2019), identity (Fisch and Block, 2021), entrepreneurship (Vossen and Ihl, 2020), knowledge spillover (Choi et al., 2021), and innovation research (Arts et al., 2021; Kaplan and Vakili, 2015). In stark contrast, the analysis of visual texts remains underdeveloped (Boxenbaum et al., 2018). When management research does examine visuals, it tends to rely on qualitative methods or limited sample sizes (Capetta et al., 2006). This gap hinders the emergence of new management insights that are robust, externally valid, and more attuned to the complex dynamics of multi-modal communication (Filitz et al., 2015; Meyer et al., 2018; Talke et al., 2009). Our research proposal addresses this gap. We select two important and interrelated research fields within management research – optimal distinctiveness and identity research – to highlight how computer-assisted approaches for visual analysis (called Computer Vision or CV) could expand or even change how we think about visuals and organisations. The distinguishing features of our research proposal – the attention to the role of temporal and cultural contexts in optimal distinctiveness and to identity in location decisions – can be applied to many management contexts. Our specific focus is on the highly dynamic and innovative film industry, where Korean producers have shaken up the incumbent Hollywood industry with their distinct and novel Hallyuwood style; the Korean film Parasite, an “originality catapult” (Blum, 2019), has won an Oscar as the first-ever non-English film. Cultural products are also an economically important industry; films and television alone, according to the US Motion Picture Association, support 2.4 million jobs, pay out $186 billion in total wages, and comprise over 122,000 businesses. In conclusion: While machine learning has benefited the analysis of verbal texts, visual texts have been largely neglected. CV has the potential to significantly enhance our understanding of the role of visuals for organizations. Thus, we believe that our research proposal can contribute to the next wave of the "visual turn" by building upon the previous "linguistic turn" (Boxenbaum et al., 2018).

• DFG Programme: Research Grants
• International Connection: Canada, Netherlands, South Korea, USA
• Cooperation Partners: Professor Dr. Tian Heong Chan; Professor Dr. Keun Lee; Professor Dr. Michael Lounsbury; Professor Dr. Alexander Vossen