Tandem 1

Prof. Dr. Oliver Mauroner + Univ.-Prof. Dr. Andranik Tumasjan

Sebastian Woelke and Christian Zern

The emergence of new technologies is posing new challenges to companies’ value creation processes in ever-shorter time cycles (Teece, 2012). Today, new—mostly digital—technologies are transforming companies’ products, services, processes, and business models, in some cases in disruptive ways, to the extent that even the long-established logic of entire industries—which has prevailed for decades—is being called into question (e.g., the automotive industry; Teece & Linden, 2017). Companies must engage more frequently and intensively with the relevance of new technologies to their business model and industry in order to decide whether to invest resources in adopting a specific new technology (e.g., blockchain technology) to capitalize on new business opportunities or to establish new business models and processes (Dutra, Tumasjan & Welpe, 2018; Friedlmaier, Tumasjan & Welpe, 2018). Two fundamental entrepreneurial capabilities or processes can be considered central to this decision:

· Identifying relevant new technologies and trends

· Testing the technologies and trends identified as relevant

It seems evident that there are strong interdependencies between these two areas. Given the multitude of available digital technologies (e.g., artificial intelligence, machine learning, big data, blockchain/DLT technology), organizations must decide where their relevant competitive advantages may lie and how these can be integrated into existing processes to fully leverage their potential. Research can also contribute to this effort; however, it must currently be described as highly fragmented and spread across various subdisciplines of management research when it comes to identifying relevant new trends and technologies. Heterogeneous theoretical frameworks and empirical applications coexist—primarily in the fields of strategic management, entrepreneurship, and innovation management—which are grouped under concepts such as “environmental scanning” (Pryor, Holmes Jr., Webb & Liguori, 2017), “proactive strategic scanning” (Parker & Collins, 2010), “entrepreneurial alertness” (Tang, Kacmar & Busenitz, 2012), “sensing” (Teece, 2007), or “corporate foresight” (Rohrbeck & Kum, 2018)—examine different aspects of these entrepreneurial capabilities. This heterogeneity of concepts and constructs for identifying relevant new technologies and trends hinders theoretical and empirical progress toward a better understanding of the antecedents, moderators, and consequences. Therefore, based on our literature review, the following significant research gaps and corresponding needs exist:

· There is no systematic overview of existing theoretical and empirical findings. This indicates the need to develop an integrative theoretical model that incorporates a multilevel and longitudinal perspective based on existing findings (systematic review, construct clean-up, and integrative theoretical model).

· While previous literature has generally focused on identifying relevant technologies, there is little insight into how the context of digital transformation and the associated changes influence this complex of issues. Recent research, however, demonstrates that digital technologies are fundamentally changing the nature of business opportunities and innovation management, and therefore existing theoretical concepts and assumptions must, in some cases, be fundamentally reexamined (Nambisan, 2017; Nambisan, Lyytinen, Majchrzak, & Song, 2017). This points to the need to conceptualize, theorize, and empirically test a model for identifying relevant trends in the context of new digital technologies and business models. For example, the growing number of technologies, shorter cycles, and the increasing density of information and media channels require a greater ability to distinguish relevant technologies and trends from irrelevant ones—that is, not only to identify and recognize new trends, but also to systematically evaluate and select them based on their relevance (“signal vs. noise”). To date, however, there is little existing research in the context of these new conditions.

The corresponding first research question is therefore:

How can the identification of relevant new technologies and trends be organized and institutionalized within companies?

Research on the subsequent task—testing the technologies and trends classified as relevant—is strongly influenced by the academic discourse on dynamic capabilities (e.g., Teece, Pisano & Shuen, 1997; Zollo & Winter, 2002). This refers to the ability of organizations to dynamically reconfigure their strategic and operational resources, including through the integration and recombination of internal and external resources (Teece, Peteral & Leih, 2016). Companies require strong dynamic capabilities to quickly create and implement new business models in order to thrive in the emerging digital economy (Achtenhagen, Melin & Naldi, 2013; Karimi & Walter, 2015; Velu, 2017). The focus is currently mostly on business model innovation—that is, issues concerning the impact of new technologies on existing (and new) business models of established companies and startups (e.g., Hölzle, Schoder, Spiri & Götz, 2017; Mauroner, 2016; Teece, 2018; Teece & Linden, 2017). Various studies examine, for example, the combination of business model innovation and big data (De Mauro, Greco & Grimaldi, 2015) or, more generally, process-based business model innovations in the context of technologies (Cavalcante, Kesting & Ulhøi, 2011). Companies must position themselves to establish and expand access to the necessary resources in order to test technologies and ultimately develop innovations from them (Tang, Tang & Katz, 2014). There is a significant need for research into how organizations experiment with new technologies—in the sense of dynamic capabilities—and make them compatible with their own resource base (i.e., integrate them). Shortcomings in the context of digital transformation exist, for example, in the understanding of learning and experimentation processes, which must proceed at an ever-faster pace. With regard to the further breakdown of dynamic capabilities (Teece, 2007) into “sensing” (opportunities and threats), “seizing,” and “transforming” (the organization’s business model), the focus of this research is on “seizing.” This is the ability to seize opportunities and actually integrate them into the organization—in the sense of a fit between experimentation and the organization’s own resource base. An initial approach in the form of an exploratory study has been presented by Warner and Wäger (2018). Experimentation and reflection on it—in the sense of double-loop learning (see Argyris & Schön, 1978)—must also take place in shorter cycles. In this context, a connection can be drawn to potential external sources of resources and technologies, which have become increasingly diverse in the recent past: collaborations, networks, acquisitions, open innovation, co-creation, engagement with user communities, and access to digital resources via development environments (Möslein, 2009; Drescher, Mauroner & Pabst, 2017).

The second research question is therefore:

How can the learning and testing of new technologies and trends take place within the framework of open innovation and value creation processes?