@article{FuellerHutterWahletal.2022, author = {F{\"u}ller, Johann and Hutter, Katja and Wahl, Julian and Bilgram, Volker and Tekic, Zeljko}, title = {How AI revolutionizes innovation management - Perceptions and implementation preferences of AI-based innovators}, series = {Technological Forecasting and Social Change}, volume = {178}, journal = {Technological Forecasting and Social Change}, publisher = {Elsevier BV}, issn = {0040-1625}, doi = {10.1016/j.techfore.2022.121598}, pages = {22}, year = {2022}, abstract = {The application of AI is expected to enable new opportunities for innovation management and reshape innovation practice in organizations. Our exploratory study among 150 AI-savvy innovation managers reveals four different clusters in terms of how organizations may use and implement AI in their innovation management ranging from (1) AI-Frontrunners, (2) AI-Practitioners, and (3) AI-Occasional innovators to (4) Non-AI innovators. The different groups vary not only in their strategy, organizational structure, and skill-building but also in their perceived potential, understanding of the required changes, encountered challenges, and organizational contexts. Our study contributes to a better understanding of the current state of AI-based innovation management, its impact on future innovation practice, and differences in organizations' AI ambitions and chosen implementation approaches.}, language = {en} } @article{HutterFuellerHautzetal.2015, author = {Hutter, Katja and F{\"u}ller, Johann and Hautz, Julia and Bilgram, Volker and Matzler, Kurt}, title = {Machiavellianism or Morality: Which Behavior Pays Off In Online Innovation Contests?}, series = {Journal of Management Information Systems}, volume = {32}, journal = {Journal of Management Information Systems}, number = {3}, publisher = {Informa UK Limited}, issn = {0742-1222}, doi = {10.1080/07421222.2015.1099181}, pages = {197 -- 228}, year = {2015}, abstract = {Prior research on user behavior in online innovation contests has mainly focused on factors that positively impact prosocial, collaborative behavior, which should ultimately lead to innovative outcomes. However, little is known about the effects of more negative personal characteristics that might result in more competitive, antisocial, and even unethical behavior. This paper considers Machiavellianism as one of the traits that constitute the "dark triad of personality" and explores the relationship between Machiavellianism and participants' contribution behavior in online innovation contests. Specifically we investigate how Machiavellian characteristics influence individuals' contribution intensity, communication, and interaction behavior within the contest community as well as the quality and kind of their contributions. This study relies on multisource individual-level data from a large innovation contest in the field of public transportation. We find that the three dimensions of Machiavellianism—distrust of others, amorality, and desire for status—have very distinct behavioral consequences in the context of online innovation contests. Specifically, the oppositional consequences of amoral manipulation and striving for status on the one hand and showing distrust of others on the other hand concerning contribution quantity and contribution quality are found. This study contributes to a deeper understanding of negative personality traits such as Machiavellianism as powerful predictors of behavior and of success within competitive innovation environments and leads to important managerial implications regarding the design and management of innovation contests.}, language = {en} } @techreport{FuellerBilgramWahletal.2022, author = {F{\"u}ller, Johann and Bilgram, Volker and Wahl, Julian and Hutter, Katja}, title = {Autonomous Innovation - How AI is implemented in Innovation Management}, year = {2022}, abstract = {Success stories about the groundbreaking impact of artificial intelligence (AI) technology on business are omnipresent, and the benefits that might emerge for innovation management are the talk of the town. However, managers are still struggling to find the appropriate approach for applying AI in their innovation projects. At this early stage in the age of AI, companies differ in how they leverage the opportunities afforded by AI and seek guidance. Our study among 162 tech-savvy managers highlights the current state of AI usage in innovation management, reveals different implementation patterns, and provides guidance in finding the right approach to increase innovation performance using AI technology. We find that companies follow distinct implementation approaches that differ regarding strategy, organizational setup, capability building, and scaling, which results in three user groups: 39\% of participants consider themselves "AI-Leaders", the most progressive group when it comes to dealing with the technology and breaking ground in AI-driven innovation management. 30\% belong to the "AI-Pragmatists" user group that is ambitious in applying AI, while 31\% are „AI Aspirants" only rarely using AI for their innovation processes. A vast majority of the participants anticipates overall improvements in innovation performance by more than 50\%, as they expect the effectiveness and efficiency of new product development to increase significantly. However, companies still face severe implementation challenges, such as missing data access or a lack of technical expertise. Depending on their individual characteristics, companies should take deliberate strategies to advance in the autonomous innovation game.}, language = {en} }