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

[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. Aguwa C. Olya M.H. 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 . Search in Google Scholar

[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. 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 . Search in Google Scholar

[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. Ballestar M.T. Grau-Carles P. 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 . Search in Google Scholar

[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. 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 . Search in Google Scholar

[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. Bosch J. Olsson H.H. Crnkovic I. 2018 . It takes three to tango: Requirement, outcome/data, and AI driven development , in. CEUR Workshop Proceedings , pp. 177 – 192 . Search in Google Scholar

[6] Burgess, A., 2018. The Executive Guide to Artificial Intelligence. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-63820-1. Burgess A. 2018 . The Executive Guide to Artificial Intelligence . Cham : Springer International Publishing . https://doi.org/10.1007/978-3-319-63820-1 . Search in Google Scholar

[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. Byrd M. 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 . Search in Google Scholar

[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. Chen J.-S. Le T.-T.-Y. 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 . Search in Google Scholar

[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. Chen T. 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 . Search in Google Scholar

[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. Cricelli L. Grimaldi M. 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 . Search in Google Scholar

[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. 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 . Search in Google Scholar

[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. Desouza K.C. Dawson G.S. 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 . Search in Google Scholar

[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. Duan Y. Edwards J.S. 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 . Search in Google Scholar

[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. 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 . Search in Google Scholar

[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. Figalist I. Elsner C. Bosch J. 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 . Search in Google Scholar

[16] Flick, U., 2023. An Introduction to Qualitative Research, SAGE Publications Ltd. Flick U. 2023 . An Introduction to Qualitative Research , SAGE Publications Ltd . Search in Google Scholar

[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. Garg P. Patil A. Soni G. Keprate A. 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 . Search in Google Scholar

[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. Giannakis M. Dubey R. Yan S. Spanaki K. 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 . Search in Google Scholar

[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. Gurkan H. 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 . Search in Google Scholar

[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. Huang T. Bergman D. 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 . Search in Google Scholar

[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. Hughes G.D. 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 . Search in Google Scholar

[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. Jain T. Meenu 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 . Search in Google Scholar

[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. Jin B.E. 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 . Search in Google Scholar

[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. Johnson P.C. Laurell C. Ots M. 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 . Search in Google Scholar

[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. Koulouriotis D.E. 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 . Search in Google Scholar

[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. Langone R. Cuzzocrea A. 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 . Search in Google Scholar

[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. Lee I. 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 . Search in Google Scholar

[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. Liao Y. Ragai I. Huang Z. 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 . Search in Google Scholar

[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. Liebe U. 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 . Search in Google Scholar

[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. Luo Z. Huang S. 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 . Search in Google Scholar

[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. Luoma J. Ruutu S. King A.W. 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 . Search in Google Scholar

[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. Magistretti S. Dell’Era C. 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 . Search in Google Scholar

[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]. 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 ]. Search in Google Scholar

[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. Mishra A.N. 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 . Search in Google Scholar

[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. Paschen U. Pitt C. 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 . Search in Google Scholar

[36] Patil, D.J. and Mason, H., 2015. Data Driven. O’Reilly Media, Inc. Patil D.J. Mason H. 2015 . Data Driven . O’Reilly Media, Inc . Search in Google Scholar

[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. 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 . Search in Google Scholar

[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. Puntoni S. Reczek R.W. Giesler M. Botti S. 2021 . Consumers and Artificial Intelligence: An Experiential Perspective , Journal of Marketing , 85 ( 1 ), pp. 131 – 151 . https://doi.org/10.1177/0022242920953847 . Search in Google Scholar

[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. Pustokhina I.V. Pustokhin D.A. Aswathy R.H. Jayasankar T. Jeyalakshmi C. Díaz V.G. 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 . Search in Google Scholar

[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. Santana M. 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 . Search in Google Scholar

[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. Soltani-Fesaghandis G. 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 . Search in Google Scholar

[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. Symeonidis S. Peikos G. 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 . Search in Google Scholar

[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. Tubadji A. Huang H. 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 . Search in Google Scholar

[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. Viswanandhne S. Kumar A.S. Elwin G.R. Priya R. Praveen V. 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 . Search in Google Scholar

[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. Zhang M. Fan B. Zhang N. Wang W. 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 . Search in Google Scholar

[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. Zhao D. Xue D. Wang X. 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 . Search in Google Scholar

[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. 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 . Search in Google Scholar