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Innovation analytics: Leveraging artificial intelligence in the innovation process

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  • Kakatkar, Chinmay
  • Bilgram, Volker
  • Füller, Johann

Abstract

Artificial intelligence (AI) is about imbuing machines with a kind of intelligence that is mainly attributed to humans. Extant literature—coupled with our experiences as practitioners—suggests that while AI may not be ready to completely take over highly creative tasks within the innovation process, it shows promise as a significant support to innovation managers. In this article, we broadly refer to the derivation of computer-enabled, data-driven insights, models, and visualizations within the innovation process as innovation analytics. AI can play a key role in the innovation process by driving multiple aspects of innovation analytics. We present four different case studies of AI in action based on our previous work in the field. We highlight benefits and limitations of using AI in innovation and conclude with strategic implications and additional resources for innovation managers.

Suggested Citation

  • Kakatkar, Chinmay & Bilgram, Volker & Füller, Johann, 2020. "Innovation analytics: Leveraging artificial intelligence in the innovation process," Business Horizons, Elsevier, vol. 63(2), pages 171-181.
    • Handle: RePEc:eee:bushor:v:63:y:2020:i:2:p:171-181
    DOI: 10.1016/j.bushor.2019.10.006
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    References listed on IDEAS

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    1. Kakatkar, Chinmay & Spann, Martin, 2019. "Marketing analytics using anonymized and fragmented tracking data," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 117-136.
    2. Kakatkar, Chinmay & Spann, Martin, 2019. "Marketing analytics using anonymized and fragmented tracking data," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 117-136.
    3. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    4. Paschen, Ulrich & Pitt, Christine & Kietzmann, Jan, 2020. "Artificial intelligence: Building blocks and an innovation typology," Business Horizons, Elsevier, vol. 63(2), pages 147-155.
    5. Kaminski, Jermain & Hopp, Christian & Tykvová, Tereza, 2019. "New technology assessment in entrepreneurial financing – Does crowdfunding predict venture capital investments?," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 287-302.
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    Cited by:

    1. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    2. Murtaza Hussain & Shaohua Yang & Umer Sahil Maqsood & R. M. Ammar Zahid, 2024. "Tapping into the green potential: The power of artificial intelligence adoption in corporate green innovation drive," Business Strategy and the Environment, Wiley Blackwell, vol. 33(5), pages 4375-4396, July.
    3. Desouza, Kevin C. & Dawson, Gregory S. & Chenok, Daniel, 2020. "Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector," Business Horizons, Elsevier, vol. 63(2), pages 205-213.
    4. Mariani, Marcello M. & Nambisan, Satish, 2021. "Innovation Analytics and Digital Innovation Experimentation: The Rise of Research-driven Online Review Platforms," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    5. Just, Julian, 2024. "Natural language processing for innovation search – Reviewing an emerging non-human innovation intermediary," Technovation, Elsevier, vol. 129(C).
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    8. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    9. Muhammet Deveci & Dursun Delen & Weiping Ding, 2024. "AI-based decision support systems for sustainable business management under circular economy," Annals of Operations Research, Springer, vol. 342(1), pages 1-3, November.
    10. Pietronudo, Maria Cristina & Croidieu, Grégoire & Schiavone, Francesco, 2022. "A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    11. Paschen, Ulrich & Pitt, Christine & Kietzmann, Jan, 2020. "Artificial intelligence: Building blocks and an innovation typology," Business Horizons, Elsevier, vol. 63(2), pages 147-155.
    12. Busch, Malte & Duwe, Daniel, 2023. "Artificial intelligence in innovation processes. A study using the example of an innvation research institute," EconStor Research Reports 281981, ZBW - Leibniz Information Centre for Economics.
    13. Füller, Johann & Hutter, Katja & Wahl, Julian & Bilgram, Volker & Tekic, Zeljko, 2022. "How AI revolutionizes innovation management – Perceptions and implementation preferences of AI-based innovators," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    14. Black, J. Stewart & van Esch, Patrick, 2020. "AI-enabled recruiting: What is it and how should a manager use it?," Business Horizons, Elsevier, vol. 63(2), pages 215-226.
    15. Lupp, Daniel, 2023. "Effectuation, causation, and machine learning in co-creating entrepreneurial opportunities," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
    16. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    17. Yong Qin & Zeshui Xu & Xinxin Wang & Marinko Skare, 2024. "Artificial Intelligence and Economic Development: An Evolutionary Investigation and Systematic Review," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 1736-1770, March.
    18. Mariani, Marcello M. & Machado, Isa & Nambisan, Satish, 2023. "Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda," Journal of Business Research, Elsevier, vol. 155(PB).
    19. Tekic, Zeljko & Tekic, Anja, 2024. "Complex patterns of ICTs' effect on sustainable development at the national level: The triple bottom line perspective," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    20. Liu, Yang & Ying, Zhenzhou & Ying, Ying & Wang, Ding & Chen, Jin, 2024. "Artificial intelligence orientation and internationalization speed: A knowledge management perspective," Technological Forecasting and Social Change, Elsevier, vol. 205(C).

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