Naomi Haefner

Naomi Haefner

Innovation and Technology Management Scholar

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publications

Coordination mechanisms for international innovation in SMEs: Effects on time-to-market and R&D task complexity as a moderator

Published in Small Business Economics, 2015

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.

Recommended citation: Palmié, M., Zeschky, M., Winterhalter, S., Sauter, P. W., Haefner, N., & Gassmann, O. (2016). "Coordination mechanisms for international innovation in SMEs: Effects on time-to-market and R&D task complexity as a moderator." Small Business Economics. 46(2). https://doi.org/10.1007/s11187-015-9683-8

Artificial intelligence and innovation management: A review, framework, and research agenda

Published in Technological Forecasting and Social Change, 2020

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.

Recommended citation: Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). "Artificial intelligence and innovation management: A review, framework, and research agenda." Technological Forecasting and Social Change, 162, 120392. https://doi.org/10.1016/j.techfore.2020.120392

The dominant logic of Big Tech in healthcare and pharma

Published in Drug Discovery Today, 2022

Digital health and digital pharma are considered supportive tools for patients and healthcare providers (HCPs), making the market highly attractive for industry players. Not surprisingly, Tech Giants have started to move into this area. We utilized established management models and publicly available information sources, such as annual company reports, and performed a thorough analysis to uncover the underlying business models of Alphabet, Amazon, Apple, IBM, and Microsoft in order to better understand their intention and process of entering the healthcare and pharma industries. Our results indicate that they do address the needs of patients and physicians, while having built clear value propositions, value chains, and revenue models to sustainably revolutionize the healthcare and pharma industries.

Recommended citation: Schuhmacher, A., Haefner, N., Honsberg, K., Goldhahn, J., & Gassmann, O. (2022). "The dominant logic of Big Tech in healthcare and pharma." Forthcoming in Drug Discovery Today, 28(2), 103457. https://doi.org/10.1016/j.drudis.2022.103457

With(out) a little help from my friends? Reconciling incongruous findings on stakeholder management, innovation, and firm performance

Published in Entrepreneurship Theory and Practice, 2023

Are stakeholder management and innovation substitutes or complements in affecting firm performance? Extant research provides support for both positions and thus leaves us with a puzzle. We conduct an exploratory fuzzy set qualitative comparative analysis (fsQCA) of 204 publicly listed European firms combining survey and archival data to formulate theory on how stakeholder management and innovation work (in)effectively together. Distinguishing between internal and external stakeholders and exploitative and exploratory innovation, we elaborate that managing for stakeholders and innovation can be both substitutes and complements depending on a set of contingencies. We discuss boundary conditions and implications for future research.

Recommended citation: Haefner, N., Palmié, M., & Leppänen, P. T. (2023). "With(out) a little help from my friends? Reconciling incongruous findings on stakeholder management, innovation, and firm performance." Entrepreneurship Theory and Practice, 47(1), 142-171. https://doi.org/10.1177/10422587211024497

Implementing and scaling artificial intelligence: A review, framework, and research agenda

Published in Technological Forecasting and Social Change, 2023

Artificial intelligence (AI) will have a substantial impact on firms in virtually all industries. Without guidance on how to implement and scale AI, companies will be outcompeted by the next generation of highly innovative and competitive companies that manage to incorporate AI into their operations. Research shows that competition is fierce and that there is a lack of frameworks to implement and scale AI successfully. This study begins to address this gap by providing a systematic review and analysis of different approaches by companies to using AI in their organizations. Based on these experiences, we identify key components of implementing and scaling AI in organizations and propose phases of implementing and scaling AI in firms.

Recommended citation: Haefner, N., Parida, V., Gassmann, O., & Wincent, J. (2023). "Implementing and scaling artificial intelligence: A review, framework, and research agenda." Technological Forecasting and Social Change, 197, 122878. https://doi.org/10.1016/j.techfore.2023.122878

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