Have a personal or library account? Click to login
An Exploration of the Applications, Challenges, and Success Factors in AI-Driven Product Development and Management Cover

An Exploration of the Applications, Challenges, and Success Factors in AI-Driven Product Development and Management

Open Access
|Aug 2024

References

  1. Aguwa, C., Olya, M.H. and Monplaisir, L., 2017. Modeling of fuzzy-based voice of customer for business decision analytics, Knowledge-Based Systems, 125, pp.136–145. https://doi.org/10.1016/j.knosys.2017.03.019.
  2. Atsalakis, G., 2014. New technology product demand forecasting using a fuzzy inference system, Operational Research, 14(2), pp.225–236. https://doi.org/10.1007/s12351-014-0160-y.
  3. Ballestar, M.T., Grau-Carles, P. and Sainz, J., 2019. Predicting customer quality in e-commerce social networks: a machine learning approach, Review of Managerial Science, 13(3), pp. 589–603. https://doi.org/10.1007/s11846-018-0316-x.
  4. Bosch, J., 2019. From efficiency to effectiveness: Delivering business value through software, Lecture Notes in Business Information Processing, 370 LNBIP, pp.3–10. https://doi.org/10.1007/978-3-030-33742-1_1.
  5. Bosch, J., Olsson, H.H. and Crnkovic, I., 2018. It takes three to tango: Requirement, outcome/data, and AI driven development, in. CEUR Workshop Proceedings, pp. 177–192.
  6. Burgess, A., 2018. The Executive Guide to Artificial Intelligence. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-63820-1.
  7. Byrd, M. and Darrow, R., 2021. A note on the advantage of context in Thompson sampling, Journal of Revenue and Pricing Management, 20(3), pp.316–321. https://doi.org/10.1057/s41272-021-00314-1.
  8. Chen, J.-S., Le, T.-T.-Y. and Florence, D., 2021. Usability and responsiveness of artificial intelligence chatbot on online customer experience in eretailing, International Journal of Retail and Distribution Management, 49(11), pp.1512–1531. https://doi.org/10.1108/IJRDM-08-2020-0312.
  9. Chen, T. and Wang, Y.-C., 2018. A fuzzy collaborative intelligence approach for estimating future yield with DRAM as an example, Operational Research, 18(3), pp.671–688. https://doi.org/10.1007/s12351-017-0312-y.
  10. Cricelli, L., Grimaldi, M. and Vermicelli, S., 2022. Crowdsourcing and open innovation: a systematic literature review, an integrated framework and a research agenda, Review of Managerial Science, 16(5), pp.1269–1310. https://doi.org/10.1007/s11846-021-00482-9.
  11. Cubric, M., 2020. Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study, Technology in Society, 62. https://doi.org/10.1016/j.techsoc.2020.101257.
  12. Desouza, K.C., Dawson, G.S. and Chenok, D., 2020. Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector, Business Horizons, 63(2), pp.205–213. https://doi.org/10.1016/j.bushor.2019.11.004.
  13. Duan, Y., Edwards, J.S. and Dwivedi, Y.K., 2019. Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda, International Journal of Information Management, 48, pp.63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021.
  14. Eren, B.A., 2021. Determinants of customer satisfaction in chatbot use: evidence from a banking application in Turkey, International Journal of Bank Marketing, 39(2), pp.294–311. https://doi.org/10.1108/IJBM-02-2020-0056.
  15. Figalist, I., Elsner, C., Bosch, J. and Olsson, H.H., 2020. Breaking the Vicious Circle: Why AI for software analytics and business intelligence does not take off in practice, in. Proceedings - 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020, pp.5–12. https://doi.org/10.1109/SEAA51224.2020.00013.
  16. Flick, U., 2023. An Introduction to Qualitative Research, SAGE Publications Ltd.
  17. Garg, P., Patil, A., Soni, G., Keprate, A. and Arora, S., 2021. Machine learning-based abnormality detection approach for vacuum pump assembly line, Reliability: Theory and Applications, 16, pp.176–187. https://doi.org/10.24412/1932-2321-2021-264-176-187.
  18. Giannakis, M., Dubey, R., Yan, S., Spanaki, K. and Papadopoulos, T., 2022. Social media and sensemaking patterns in new product development: demystifying the customer sentiment, Annals of Operations Research, 308(1–2), pp.145–175. https://doi.org/10.1007/s10479-020-03775-6.
  19. Gurkan, H. and de Véricourt, F., 2022. Contracting, Pricing, and Data Collection Under the AI Flywheel Effect, Management Science, 68(12), pp.8791–8808. https://doi.org/10.1287/mnsc.2022.4333.
  20. Huang, T., Bergman, D. and Gopal, R., 2019. Predictive and Prescriptive Analytics for Location Selection of Add-on Retail Products, Production and Operations Management, 28(7), pp.1858–1877. https://doi.org/10.1111/poms.13018.
  21. Hughes, G.D. and Chafin, D.C., 1996. Turning New Product Development into a Continuous Learning Process, Journal of Product Innovation Management, 13(2), pp.89–104. https://doi.org/10.1111/1540-5885.1320089.
  22. Jain, T., Meenu and Sardana, H.K., 2020. Quality edge extraction of mechanical CAD parts for intelligent manufacturing, International Journal of Process Management and Benchmarking, 10(1), pp.22–47. https://doi.org/10.1504/IJPMB.2020.104230.
  23. Jin, B.E. and Shin, D.C., 2020. Changing the game to compete: Innovations in the fashion retail industry from the disruptive business model, Business Horizons, 63(3), pp.301–311. https://doi.org/10.1016/j.bushor.2020.01.004.
  24. Johnson, P.C., Laurell, C., Ots, M. and Sandström, C., 2022. Digital innovation and the effects of artificial intelligence on firms’ research and development – Automation or augmentation, exploration or exploitation?, Technological Forecasting and Social Change, 179, p.121636. https://doi.org/10.1016/j.techfore.2022.121636.
  25. Koulouriotis, D.E. and Mantas, G., 2012. Health products sales forecasting using computational intelligence and adaptive neuro fuzzy inference systems, Operational Research, 12(1), pp.29–43. https://doi.org/10.1007/s12351-010-0094-y.
  26. Langone, R., Cuzzocrea, A. and Skantzos, N., 2020. Interpretable Anomaly Prediction: Predicting anomalous behavior in industry 4.0 settings via regularized logistic regression tools, Data and Knowledge Engineering, 130. https://doi.org/10.1016/j.datak.2020.101850.
  27. Lee, I. and Shin, Y.J., 2020. Machine learning for enterprises: Applications, algorithm selection, and challenges, Business Horizons, 63(2), pp.157–170. https://doi.org/10.1016/j.bushor.2019.10.005.
  28. Liao, Y., Ragai, I., Huang, Z. and Kerner, S., 2021. Manufacturing process monitoring using time-frequency representation and transfer learning of deep neural networks, Journal of Manufacturing Processes, 68, pp.231–248. https://doi.org/10.1016/j.jmapro.2021.05.046.
  29. Liebe, U. and Meyerhoff, J., 2021. Mapping potentials and challenges of choice modelling for social science research, Journal of Choice Modelling, 38. https://doi.org/10.1016/j.jocm.2021.100270.
  30. Luo, Z., Huang, S. and Zhu, K.Q., 2019. Knowledge empowered prominent aspect extraction from product reviews, Information Processing and Management, 56(3), pp.408423. https://doi.org/10.1016/j.ipm.2018.11.006.
  31. Luoma, J., Ruutu, S., King, A.W. and Tikkanen, H., 2017. Time delays, competitive interdependence, and firm performance, Strategic Management Journal, 38(3), pp.506–525. https://doi.org/10.1002/smj.2512.
  32. Magistretti, S., Dell’Era, C. and Messeni Petruzzelli, A., 2019. How intelligent is Watson? Enabling digital transformation through artificial intelligence, Business Horizons, 62(6), pp.819–829. https://doi.org/10.1016/j.bushor.2019.08.004.
  33. McKinsey, 2021. Global survey: The state of AI in 2021 McKinsey. [online] Available at: <https://www.mckinsey.com/capabilities/quan-tumblack/our-insights/global-survey-the-state-of-ai-in-2021> [Accessed: 29 January 2023].
  34. Mishra, A.N. and Pani, A.K., 2020. Business value appropriation roadmap for artificial intelligence, VINE Journal of Information and Knowledge Management Systems, 51(3), pp.353–368. https://doi.org/10.1108/VJIKMS-07-2019-0107.
  35. Paschen, U., Pitt, C. and Kietzmann, J., 2020. Artificial intelligence: Building blocks and an innovation typology, Business Horizons, 63(2), pp.147–155. https://doi.org/10.1016/j.bushor.2019.10.004.
  36. Patil, D.J. and Mason, H., 2015. Data Driven. O’Reilly Media, Inc.
  37. Prem, E., 2019. Artificial intelligence for innovation in Austria, Technology Innovation Management Review, 9(12), pp.5–15. https://doi.org/10.22215/timreview/1287.
  38. Puntoni, S., Reczek, R.W., Giesler, M. and Botti, S., 2021. Consumers and Artificial Intelligence: An Experiential Perspective, Journal of Marketing, 85(1), pp.131–151. https://doi.org/10.1177/0022242920953847.
  39. Pustokhina, I.V., Pustokhin, D.A., Aswathy, R.H., Jayasankar, T., Jeyalakshmi, C., Díaz, V.G. and Shankar, K., 2021. Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms, Information Processing and Management, 58(6). https://doi.org/10.1016/j.ipm.2021.102706.
  40. Santana, M. and Díaz-Fernández, M., 2022. Competencies for the artificial intelligence age: visualisation of the state of the art and future perspectives, Review of Managerial Science [Preprint]. https://doi.org/10.1007/s11846-022-00613-w.
  41. Soltani-Fesaghandis, G. and Pooya, A., 2018. Design of an artificial intelligence system for predicting success of new product development and selecting proper market-product strategy in the food industry, International Food and Agribusiness Management Review, 21(7), pp.847–864. https://doi.org/10.22434/IFAMR2017.0033.
  42. Symeonidis, S., Peikos, G. and Arampatzis, A., 2022. Unsupervised consumer intention and sentiment mining from microblogging data as a business intelligence tool, Operational Research, 22(5), pp.6007–6036. https://doi.org/10.1007/s12351-022-00714-0.
  43. Tubadji, A., Huang, H. and Webber, D.J., 2021. Cultural proximity bias in AI-acceptability: The importance of being human, Technological Forecasting and Social Change, 173, p.121100. https://doi.org/10.1016/j.techfore.2021.121100.
  44. Viswanandhne, S., Kumar, A.S., Elwin, G.R., Priya, R., Praveen, V. and Priyanka, S., 2019. Improved decision making and enhanced recommendation systems in applications made possible through prescriptive analytics, International Journal of Scientific and Technology Research, 8(10), pp.2231–2233.
  45. Zhang, M., Fan, B., Zhang, N., Wang, W. and Fan, W., 2021. Mining product innovation ideas from online reviews, Information Processing and Management, 58(1). https://doi.org/10.1016/j.ipm.2020.102389.
  46. Zhao, D., Xue, D., Wang, X. and Du, F., 2022. Adaptive vision inspection for multi-type electronic products based on prior knowledge, Journal of Industrial Information Integration [Preprint]. https://doi.org/10.1016/j.jii.2021.100283.
  47. Zirar, A., 2023. Can artificial intelligence’s limitations drive innovative work behaviour?, Review of Managerial Science [Preprint]. https://doi.org/10.1007/s11846-023-00621-4.
DOI: https://doi.org/10.2478/fman-2024-0009 | Journal eISSN: 2300-5661 | Journal ISSN: 2080-7279
Language: English
Page range: 139 - 156
Published on: Aug 25, 2024
Published by: Warsaw University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2024 Aron Witkowski, Andrzej Wodecki, published by Warsaw University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.