Naomi Haefner

Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company’s entire innovation process. In response, we review and explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explain what is important to consider in making the transformation to the digital organization of innovation. We conclude our study by exploring directions for future research.

As SMEs increasingly internationalize their innovation activities, our study strives to improve our understanding of the coordination mechanisms that SMEs can adopt to orchestrate these activities. Building on the evolutionary theory of organizations, we link three established coordination mechanisms (centralization, formalization, and socialization) to the time-to-market of SMEs' product innovations. We also argue that the complexity of the internationalized R&D tasks moderates the relationship between the three coordination mechanisms and time-to-market. Survey data from 103 SMEs with international innovation activities broadly support our theoretical account. With respect to the main effects, our findings suggest that a high degree of centralization tends to prolong the time-to-market, whereas formalization tends to shorten it. The moderation results further indicate that centralization can become more beneficial when a firm internationalizes highly complex R&D tasks, while formalization tends to become less beneficial with increasing task complexity. Main and moderation effects with respect to socialization are inconclusive. We discuss the implications of these findings for the academic literature and management practice.

This paper explores how firms use resource orchestration (RO) to generate a technological regime advantage, particularly in the context of the advent of artificial intelligence (AI). While previous studies have primarily discussed firms' actions within their boundaries, comparatively little attention has been given to the role that RO activities play beyond the boundaries of the firm. To fill this gap, this paper examines the strategic actions of two large Swedish manufacturing multinationals in the mining and transportation industries in pioneering AI-based autonomous solutions. The qualitative analysis highlights three main strategic actions that incumbents initiate to ultimately generate a technological regime advantage. The findings and framework provide guidance for firms and serve as a foundation for further empirical academic research.

Prior research on the relationship between top management team (TMT) composition and firm performance has not been able to fully capture the combined influence of simultaneously present TMT characteristics in different contexts. We analyze the joint necessity and sufficiency of TMT heterogeneity and knowledge stock in 2,295 new ventures that went public on North American stock exchanges between 1990 and 2010. Our findings reveal equifinal configurations for high and low performance in Initial Public Offerings (IPOs), complements and substitutes, and the role of internal and external contingencies. Our study contributes to research on upper echelons, new venture teams, and IPOs.

This study examines how firms in the autonomous driving industry that pursue artificial intelligence-based innovations attempt to appropriate returns from these innovations. It intends to contribute to the literature on value appropriation from innovation by investigating the extent to which firms can and do keep the key components of AI systems (data set, training approach, and model) private versus publishing them. Using a qualitative research design, we establish that there are regulatory, technical, and enforcement aspects to the components that prompt firms to either protect or publish.

Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company’s entire innovation process. In response, we explore the implications for future innovation management. Using ideas from the Carnegie School and the Behavioral Theory of the Firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explaining what is important to consider in making the transformation to the digital organization of innovation. We also explore directions for future research.

Why do some firms benefit more from managing for stakeholders than others? Under which conditions can stakeholder management actually diminish firm performance? In order to improve our knowledge about these important questions, we consider a firm’s stakeholder management in conjunction with its innovation strategies, which also allows us to resolve the recent puzzle whether stakeholder management and innovation are complements or substitutes. Distinguishing between external and internal stakeholders and exploratory and exploitative innovation, we argue that firms will achieve high performance when they align their stakeholder management and innovation strategies and poor performance when they misalign these strategies. Combined primary survey and time-lagged secondary data from 222 European firms, analyzed by means of fuzzy set qualitative comparative analysis, strongly support our theoretical argument. Our findings also indicate that stakeholder management and innovation are complements in some configurations and substitutes in others. We discuss the implications of our findings for the academic literature and management practice.

While core self-evaluation (CSE) as a significant aspect of individual personality is said to positively affect job performance, our understanding of this relationship is still limited. So as to advance our knowledge of this issue, we need to consider (1) intervening constructs through which and (2) the situational context in which CSE affects job performance. Specifically, this study examines innovative work behavior and transformational leadership as key mediating and moderating constructs, respectively, of the CSE-supervisor rated job performance link. Building on the personality-trait based interactionist model of job performance, we argue that innovative work behavior (IWB) mediates the CSE-job performance link and consider transformational leadership (TFL) as an important situational context factor. We propose that TFL is a situation strengthener that can increase the IWB of employees low on CSE, but does not significantly impact high-CSE employees. Further, transformational leaders’ high performance expectations can lead them to systematically evaluate the job performance of their subordinates and especially the contribution of IWB to job performance less favorably than leaders that do not adopt a TFL approach. Testing our hypotheses with time-lagged, multi-informant data from 245 employee-supervisor dyads supports our expectations that IWB mediates the CSE-job performance link and that TFL dampens the positive effect of IWB on job performance. In contrast to our expectation, TFL does not significantly moderate the effect of CSE on IWB in our sample. We discuss the implications of these findings for the academic literature and management practice.