{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T05:32:37Z","timestamp":1767850357200,"version":"3.49.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Oper Res"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s10479-024-05929-2","type":"journal-article","created":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T09:01:46Z","timestamp":1711962106000},"page":"399-425","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Responsible artificial intelligence for measuring efficiency: a neural production specification"],"prefix":"10.1007","volume":"354","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6837-332X","authenticated-orcid":false,"given":"Konstantinos N.","family":"Konstantakis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1243-7176","authenticated-orcid":false,"given":"Panayotis G.","family":"Michaelides","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panos","family":"Xidonas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arsenios-Georgios N.","family":"Prelorentzos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aristeidis","family":"Samitas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,1]]},"reference":[{"key":"5929_CR1","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/s10479-021-04236-4","volume":"313","author":"I Abid","year":"2022","unstructured":"Abid, I., Ayadi, R., Guesmi, K., et al. (2022). A new approach to deal with variable selection in neural networks: An application to bankruptcy prediction. Annals of Operations Research, 313, 605\u2013623.","journal-title":"Annals of Operations Research"},{"issue":"2","key":"5929_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.afres.2022.100126","volume":"2","author":"M Addanki","year":"2022","unstructured":"Addanki, M., Patra, P., & Kandra, P. (2022). Recent advances and applications of artificial intelligence and related technologies in the food industry. Applied Food Research, 2(2), 100126.","journal-title":"Applied Food Research"},{"key":"5929_CR3","doi-asserted-by":"crossref","unstructured":"Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, p. 614342","DOI":"10.1155\/2014\/614342"},{"key":"5929_CR6","doi-asserted-by":"crossref","unstructured":"Akram, V., Al-Zyoud, H., Illiyan, A., Elloumi, F. (2023). Impact of technical efficiency and input-driven growth in the Indian food processing sector. Journal of Economic and Administrative Sciences, Vol. ahead-of-print No. ahead-of-print","DOI":"10.1108\/JEAS-05-2023-0108"},{"key":"5929_CR7","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s10479-020-03620-w","volume":"308","author":"S Akter","year":"2022","unstructured":"Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2022). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 308, 7\u201339.","journal-title":"Annals of Operations Research"},{"issue":"14","key":"5929_CR8","doi-asserted-by":"publisher","first-page":"4464","DOI":"10.1080\/00207543.2021.1966540","volume":"60","author":"A Al-Surmi","year":"2022","unstructured":"Al-Surmi, A., Bashiri, M., & Koliousis, I. (2022). AI based decision making: Combining strategies to improve operational performance. International Journal of Production Research, 60(14), 4464\u20134486.","journal-title":"International Journal of Production Research"},{"issue":"1","key":"5929_CR9","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1080\/13504850110048523","volume":"9","author":"PS Amaza","year":"2002","unstructured":"Amaza, P. S., & Olayemi, J. K. (2002). Analysis of technical inefficiency in food crop production in Gombe State. Nigeria. Applied Economics Letters, 9(1), 51\u201354.","journal-title":"Nigeria. Applied Economics Letters"},{"key":"5929_CR10","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s00146-019-00931-w","volume":"35","author":"T Araujo","year":"2020","unstructured":"Araujo, T., Helberger, N., Kruikemeier, S., & De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & so, c, 35, 611\u2013623.","journal-title":"AI & so, c"},{"issue":"1","key":"5929_CR13","doi-asserted-by":"publisher","first-page":"142","DOI":"10.2991\/ijcis.d.200127.002","volume":"13","author":"W Barsi","year":"2020","unstructured":"Barsi, W. (2020). Examining the impact of artificial intelligence (AI)-Assisted social media marketing on the performance of small and medium enterprises: toward effective business management in the saudi arabian context. International Journal of Computational Intelligence Systems, 13(1), 142\u2013152.","journal-title":"International Journal of Computational Intelligence Systems"},{"key":"5929_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-023-05624-8","author":"A Behl","year":"2023","unstructured":"Behl, A., Sampat, B., Pereira, V., & Jabbour, C. J. C. (2023). The role played by responsible artificial intelligence (RAI) in improving supply chain performance in the MSME sector: An empirical inquiry. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-023-05624-8","journal-title":"Annals of Operations Research"},{"key":"5929_CR15","unstructured":"Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company"},{"issue":"2020","key":"5929_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2139\/ssrn.3647625","volume":"00","author":"L Cao","year":"2020","unstructured":"Cao, L. (2020). AI in finance: A review. SSRN Electronic Journal, 00(2020), 1\u201347. https:\/\/doi.org\/10.2139\/ssrn.3647625","journal-title":"SSRN Electronic Journal"},{"key":"5929_CR17","doi-asserted-by":"publisher","unstructured":"Cao, J., & Wang, J. (2019). Stock price forecasting model based on modified convolution neural network and financial time series analysis. International Journal of Communication Systems, 32(12), e3987. https:\/\/doi.org\/10.1002\/dac.398","DOI":"10.1002\/dac.398"},{"key":"5929_CR18","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s10479-009-0618-0","volume":"185","author":"Q Cao","year":"2011","unstructured":"Cao, Q., Parry, M. E., & Leggio, K. B. (2011). The three-factor model and artificial neural networks: Predicting stock price movement in China. Annals of Operations Research, 185, 25\u201344.","journal-title":"Annals of Operations Research"},{"issue":"12","key":"5929_CR20","doi-asserted-by":"publisher","first-page":"4037","DOI":"10.1093\/rfs\/hhr096","volume":"24","author":"TJ Chemmanur","year":"2011","unstructured":"Chemmanur, T. J., Krishnan, K., & Nandy, D. K. (2011). How does venture capital financing improve efficiency in private firms? A look beneath the surface. The Review of Financial Studies, 24(12), 4037\u20134090.","journal-title":"The Review of Financial Studies"},{"key":"5929_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107760","volume":"112","author":"Y-C Chen","year":"2021","unstructured":"Chen, Y.-C., & Huang, W.-C. (2021). Constructing a stock-price forecast CNN model with gold and crude oil indicators. Applied Soft Computing, 112, 107760.","journal-title":"Applied Soft Computing"},{"key":"5929_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10796-022-10365-3","volume":"25","author":"D Dennehy","year":"2023","unstructured":"Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y. K., M\u00e4ntym\u00e4ki, M., & Pappas, I. O. (2023). Artificial intelligence (AI) and information systems: perspectives to responsible AI. Information System Frontiers, 25, 1\u20137.","journal-title":"Information System Frontiers"},{"issue":"2","key":"5929_CR412","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.foodpol.2007.08.003","volume":"33","author":"E Dimara","year":"2008","unstructured":"Dimara, E., Skuras, D., Tsekouras, K., & Tzelepis, D. (2008). Productive efficiency and firm exit in the food sector. Food Policy, 33(2), 185\u2013196.","journal-title":"Food Policy"},{"key":"5929_CR27","unstructured":"Dignum, V. (2017). Responsible artificial intelligence: designing AI for human values. ITU Journal, ICT Discoveries, Special Issue 1"},{"key":"5929_CR28","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3390\/jrfm13020023","volume":"13","author":"A Dutta","year":"2020","unstructured":"Dutta, A., Kumar, S., & Basu, M. (2020). A gated recurrent unit approach to bitcoin price prediction. Journal of Risk and Financial Management, 13, 23.","journal-title":"Journal of Risk and Financial Management"},{"key":"5929_CR29","unstructured":"Emerson, S., Kennedy, R., O\u2019Shea, L., & O\u2019Brien, J. (2019). Trends and applications of machine learning in quantative finance. In: 8th International Conference on Economics and Finance Research (ICEFR). Retrieved from https:\/\/ssrn.com\/abstract=3397005."},{"key":"5929_CR31","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.econmod.2014.03.024","volume":"40","author":"I Feng","year":"2014","unstructured":"Feng, I., & Zhang, J. (2014). Application of Artificial neural netowrks in tendency forecatingof economic growth. Economic Modelling, 40, 76\u201380.","journal-title":"Economic Modelling"},{"key":"5929_CR32","doi-asserted-by":"crossref","unstructured":"Ferasso, M., & Alnoor, A. (2022). Artificial neural network and structural equation modeling in the future. In Artificial Neural Networks and Structural Equation Modeling: Marketing and Consumer Research Applications (pp. 327\u2013341). Singapore: Springer Nature Singapore.","DOI":"10.1007\/978-981-19-6509-8_18"},{"key":"5929_CR33","doi-asserted-by":"crossref","unstructured":"Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., & Luetge, C. (2018). AI4People\u2014An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689\u2013707.","DOI":"10.1007\/s11023-018-9482-5"},{"key":"5929_CR34","doi-asserted-by":"publisher","first-page":"2123","DOI":"10.1007\/s10796-021-10142-8","volume":"25","author":"S Fosso Wamba","year":"2023","unstructured":"Fosso Wamba, S., & Queiroz, M. M. (2023). Responsible artificial intelligence as a secret ingredient for digital health: bibliometric analysis, insights, and research directions. Information System Frontiers, 25, 2123\u20132138.","journal-title":"Information System Frontiers"},{"key":"5929_CR35","doi-asserted-by":"crossref","unstructured":"Frick, F., Jantke, C., & Sauer, J. (2018). Innovation and productivity in the food vs. the high-tech manufacturing sector. Economics of Innovation and New Technology, 28(7), 674\u2013694.","DOI":"10.1080\/10438599.2018.1557405"},{"issue":"7","key":"5929_CR38","doi-asserted-by":"publisher","first-page":"2581","DOI":"10.3390\/app10072581","volume":"10","author":"N Ghatasheh","year":"2020","unstructured":"Ghatasheh, N., Faris, H., AlTaharwa, I., Harb, Y., & Harb, A. (2020). Business analytics in telemarketing: Cost-sensitive analysis of bank campaigns using artificial neural networks. Applied Sciences, 10(7), 2581.","journal-title":"Applied Sciences"},{"issue":"4","key":"5929_CR39","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1093\/qje\/qjx024","volume":"132","author":"G Gopinath","year":"2017","unstructured":"Gopinath, G., Kalemli-\u00d6zcan, \u015e, Karabarbounis, L., & Villegas-Sanchez, C. (2017). Capital Allocation and Productivity in South Europe. The Quarterly Journal of Economics, 132(4), 1915\u20131967.","journal-title":"The Quarterly Journal of Economics"},{"key":"5929_CR40","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s10479-020-03683-9","volume":"308","author":"P Grover","year":"2022","unstructured":"Grover, P., Kar, A. K., & Dwivedi, Y. K. (2022). Understanding artificial intelligence adoption in operations management: Insights from the review of academic literature and social media discussions. Annals of Operations Research, 308, 177\u2013213.","journal-title":"Annals of Operations Research"},{"issue":"10s","key":"5929_CR41","first-page":"893","volume":"11","author":"K Gupta","year":"2023","unstructured":"Gupta, K., Mane, P., Rajankar, O. S., Bhowmik, M., Jadhav, R., Yadav, S., Rawandale, S., & Chobe, S. V. (2023a). Harnessing AI for strategic decision-making and business performance optimization. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 893\u2013912.","journal-title":"International Journal of Intelligent Systems and Applications in Engineering"},{"key":"5929_CR42","doi-asserted-by":"publisher","first-page":"2257","DOI":"10.1007\/s10796-021-10174-0","volume":"25","author":"S Gupta","year":"2023","unstructured":"Gupta, S., Kamboj, S., & Bag, S. (2023b). Role of risks in the development of responsible artificial intelligence in the digital healthcare domain. Information System Frontiers, 25, 2257\u20132274.","journal-title":"Information System Frontiers"},{"key":"5929_CR43","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1016\/j.procs.2010.12.071","volume":"3","author":"E Guresen","year":"2011","unstructured":"Guresen, E., & Kayakutlu, G. (2011). Definition of artificial neural networks with comparison to other networks. Procedia Computer Science, 3, 426\u2013433.","journal-title":"Procedia Computer Science"},{"key":"5929_CR410","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.econmod.2020.06.008","volume":"91","author":"M Jahn","year":"2020","unstructured":"Jahn, M. (2020). Artificial neural network regression models in a panel setting: Predicting economic growth. Economic Modelling, 91, 148\u2013154.","journal-title":"Economic Modelling"},{"issue":"9","key":"5929_CR44","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1038\/s42256-019-0088-2","volume":"1","author":"A Jobin","year":"2019","unstructured":"Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389\u2013399.","journal-title":"Nature Machine Intelligence"},{"key":"5929_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.jafr.2020.100033","volume":"2","author":"V Kakani","year":"2020","unstructured":"Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033.","journal-title":"Journal of Agriculture and Food Research"},{"key":"5929_CR46","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s10479-017-2497-0","volume":"278","author":"M Kapelko","year":"2019","unstructured":"Kapelko, M. (2019). Measuring productivity change accounting for adjustment costs: Evidence from the food industry in the European Union. Annals of Operations Research, 278, 215\u2013234.","journal-title":"Annals of Operations Research"},{"key":"5929_CR414","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.foodpol.2018.03.017","volume":"84","author":"N Key","year":"2019","unstructured":"Key, N. (2019). Farm size and productivity growth in the United States Corn Belt. Food Policy, 84, 186\u2013195.","journal-title":"Food Policy"},{"key":"5929_CR413","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.econmod.2015.11.022","volume":"53","author":"KM Kiani","year":"2016","unstructured":"Kiani, K. M. (2016). On business cycle fluctuations in USA macroeconomic time series. Economic Modelling, 53, 179\u2013186.","journal-title":"Economic Modelling"},{"key":"5929_CR47","doi-asserted-by":"publisher","first-page":"4535567","DOI":"10.1155\/2021\/4535567","volume":"2021","author":"I Kumar","year":"2021","unstructured":"Kumar, I., Rawat, J., Mohd, N., & Husain, S. (2021). Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality, 2021, 4535567.","journal-title":"Journal of Food Quality"},{"key":"5929_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-022-05159-4","author":"SIC Lemos","year":"2022","unstructured":"Lemos, S. I. C., Ferreira, F. A. F., Zopounidis, C., Galariotis, E., & Ferreira, N. C. M. Q. F. (2022). Artificial intelligence and change management in small and medium-sized enterprises: An analysis of dynamics within adaptation initiatives. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-022-05159-4","journal-title":"Annals of Operations Research"},{"key":"5929_CR411","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.jbusres.2022.04.013","volume":"147","author":"PS Lo","year":"2022","unstructured":"Lo, P. S., Dwivedi, Y. K., Tan, G. W. H., Ooi, K. B., Aw, E. C. X., & Metri, B. (2022). Why do consumers buy impulsively during live streaming? A deep learningbased dual-stage SEM-ANN analysis. Journal of Business Research, 147, 325\u2013337.","journal-title":"Journal of Business Research"},{"key":"5929_CR51","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1007\/s10551-018-3921-3","volume":"160","author":"K Martin","year":"2019","unstructured":"Martin, K. (2019). Ethical implications and accountability of algorithms. Journal of Business Ethics, 160, 835\u2013850.","journal-title":"Journal of Business Ethics"},{"key":"5929_CR52","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1007\/s12393-021-09290-z","volume":"14","author":"NR Mavani","year":"2022","unstructured":"Mavani, N. R., Ali, J. M., Othman, S., Hussain, M. A., Hashim, H., & Rahman, N. A. (2022). Application of artificial intelligence in food industry\u2014a guideline. Food Engineering Reviews, 14, 134\u2013175.","journal-title":"Food Engineering Reviews"},{"key":"5929_CR53","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.ejor.2014.08.028","volume":"241","author":"P Michaelides","year":"2015","unstructured":"Michaelides, P., Tsionas, E. G., Vouldis, A., & Konstantakis, K. (2015). Global approximation to arbitrary cost functions: A Bayesian approach with application to US banking. European Journal of Operational Research, 241, 148\u2013160.","journal-title":"European Journal of Operational Research"},{"key":"5929_CR54","doi-asserted-by":"crossref","unstructured":"Michaelides, P. G., Tsionas, E. G., & Konstantakis, K. (2016), Non-linearities in financial bubbles: Theory and Bayesian evidence from S&P500. Journal of Financial Stability, Elsevier, vol. 24(C), pp. 61\u201370.","DOI":"10.1016\/j.jfs.2016.04.007"},{"issue":"2","key":"5929_CR56","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.ejor.2010.02.013","volume":"206","author":"PG Michaelides","year":"2010","unstructured":"Michaelides, P. G., Vouldis, A. T., & Tsionas, E. G. (2010). Globally flexible functional forms: The neural distance function. European Journal of Operational Research, Elsevier, 206(2), 456\u2013469.","journal-title":"European Journal of Operational Research, Elsevier"},{"key":"5929_CR59","doi-asserted-by":"crossref","unstructured":"Nazareth N, Y.V. R. Reddy, (2023). Financial applications of machine learning: A literature review. Expert Systems with Applications, 219,1. Article 19640","DOI":"10.1016\/j.eswa.2023.119640"},{"key":"5929_CR62","doi-asserted-by":"crossref","unstructured":"Ozbayoglu A.M., M.U. Gudelek, O.B. Sezer (2020). Deep learning for financial applications: A survey. Applied Soft Computing Journal, 93 (2020), Article 106384.","DOI":"10.1016\/j.asoc.2020.106384"},{"key":"5929_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2019.113191","volume":"129","author":"G Park","year":"2020","unstructured":"Park, G., & Song, M. (2020). Predicting performances in business processes using deep neural networks. Decision Support Systems, 129, 113191.","journal-title":"Decision Support Systems"},{"issue":"2","key":"5929_CR66","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1108\/JADEE-05-2020-0100","volume":"11","author":"BV Ruales Guzm\u00e1n","year":"2021","unstructured":"Ruales Guzm\u00e1n, B. V., Rodr\u00edguez Lozano, G. I., & Castellanos Dom\u00ednguez, O. F. (2021). Measuring productivity of dairy industry companies: An approach with data envelopment analysis. Journal of Agribusiness in Developing and Emerging Economies, 11(2), 160\u2013177.","journal-title":"Journal of Agribusiness in Developing and Emerging Economies"},{"issue":"24","key":"5929_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app9245574","volume":"9","author":"F Rundo","year":"2019","unstructured":"Rundo, F., Trenta, F., & A.L.di Stallo,. (2019). Machine Learning for quantitative finance applications: A Survey. Applied Sciences, 9(24), 1\u201320.","journal-title":"Applied Sciences"},{"key":"5929_CR69","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s10479-019-03144-y","volume":"297","author":"G Sermpinis","year":"2021","unstructured":"Sermpinis, G., Karathanasopoulos, A., Rosillo, R., et al. (2021). Neural networks in financial trading. Annals of Operations Research, 297, 293\u2013308.","journal-title":"Annals of Operations Research"},{"issue":"2","key":"5929_CR71","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1080\/13571516.2019.1592996","volume":"26","author":"M Setiawan","year":"2019","unstructured":"Setiawan, M. (2019). Persistence of price-cost margin and technical efficiency in the indonesian food and beverage industry. International Journal of the Economics of Business, 26(2), 315\u2013326.","journal-title":"International Journal of the Economics of Business"},{"key":"5929_CR72","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-022-04857-3","author":"K Sharma","year":"2022","unstructured":"Sharma, K., Dwivedi, Y. K., & Metri, B. (2022). Incorporating causality in energy consumption forecasting using deep neural networks. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-022-04857-3","journal-title":"Annals of Operations Research"},{"key":"5929_CR74","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.chieco.2012.12.004","volume":"24","author":"Y Sheng","year":"2013","unstructured":"Sheng, Y., & Song, L. (2013). Re-estimation of firms\u2019 total factor productivity in China\u2019s iron and steel industry. China Economic Review, 24, 177\u2013188.","journal-title":"China Economic Review"},{"key":"5929_CR76","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s10479-020-03762-x","volume":"297","author":"N Stege","year":"2021","unstructured":"Stege, N., Wegener, C., Basse, T., et al. (2021). Mapping swap rate projections on bond yields considering cointegration: An example for the use of neural networks in stress testing exercises. Annals of Operations Research, 297, 309\u2013321.","journal-title":"Annals of Operations Research"},{"issue":"4","key":"5929_CR77","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10551-022-05053-w","volume":"178","author":"YW Sullivan","year":"2022","unstructured":"Sullivan, Y. W., & Fosso Wamba, S. (2022). Moral judgments in the age of artificial intelligence. Journal of Business Ethics, 178(4), 917\u2013943.","journal-title":"Journal of Business Ethics"},{"key":"5929_CR79","doi-asserted-by":"publisher","first-page":"2139","DOI":"10.1007\/s10796-021-10146-4","volume":"25","author":"C Trocin","year":"2023","unstructured":"Trocin, C., Mikalef, P., Papamitsiou, Z., & Conboy, K. (2023). Responsible AI for digital health: A synthesis and a research agenda. Information System Frontiers, 25, 2139\u20132157.","journal-title":"Information System Frontiers"},{"key":"5929_CR81","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1016\/B978-0-12-815859-3.00030-5","volume":"2019","author":"MG Tsionas","year":"2019","unstructured":"Tsionas, M. G., Konstantakis, K. N., & Michaelides, P. G. (2019). The neural network production function: panel evidence for the United States, in Editor(s). Mike Tsionas, Panel Data Econometrics, Academic Press, 2019, 953\u2013978.","journal-title":"Mike Tsionas, Panel Data Econometrics, Academic Press"},{"issue":"3","key":"5929_CR82","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.jcorpfin.2010.12.004","volume":"17","author":"G Twite","year":"2011","unstructured":"Twite, G., & Tian, G. Y. (2011). Corporate governance, external market discipline and firm productivity. Journal of Corporate Finance, 17(3), 403\u2013417.","journal-title":"Journal of Corporate Finance"},{"key":"5929_CR83","unstructured":"Varian R. H. (1992), Microeconomic Analysis (3rd Edition), W.W Norton & Company Inc."},{"issue":"2","key":"5929_CR84","doi-asserted-by":"publisher","first-page":"37","DOI":"10.30525\/2256-0742\/2021-7-2-37-49","volume":"7","author":"Vasyl\u2019yeva, O.","year":"2021","unstructured":"Vasyl\u2019yeva, O. (2021). Assessment of factors of sustainable development of the agricultural sector using the Cobb-Douglas production function. Baltic Journal of Economic Studies, 7(2), 37\u201349.","journal-title":"Baltic Journal of Economic Studies"},{"issue":"7","key":"5929_CR85","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1108\/BPMJ-10-2019-0411","volume":"26","author":"S-L Wamba-Taguimdje","year":"2020","unstructured":"Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893\u20131924.","journal-title":"Business Process Management Journal"},{"issue":"2","key":"5929_CR86","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1016\/j.ejor.2009.01.009","volume":"200","author":"J Yang","year":"2010","unstructured":"Yang, J., Cabrera, J., & Wang, T. (2010). Nonlinearity, data-snooping, and stock index ETF return predictability. European Journal of Operational Research, 200(2), 498\u2013507.","journal-title":"European Journal of Operational Research"},{"issue":"6","key":"5929_CR87","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102728","volume":"58","author":"K Zhong","year":"2021","unstructured":"Zhong, K., Wang, Y., Pei, J., Tang, S., & Han, Z. (2021). Super efficiency SBM-DEA and neural network for performance evaluation. Information Processing and Management, 58(6), 102728.","journal-title":"Information Processing and Management"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-024-05929-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-024-05929-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-024-05929-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T13:03:30Z","timestamp":1762175010000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-024-05929-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,1]]},"references-count":65,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["5929"],"URL":"https:\/\/doi.org\/10.1007\/s10479-024-05929-2","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,1]]},"assertion":[{"value":"21 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The research presented here is original and has not been published or submitted for publication elsewhere. We affirm that we have followed all the ethical guidelines in this study. Furthermore, we attest that all the research findings presented here are based on sound scientific methods and rigorous analysis. We acknowledge the contributions of any co-authors or research collaborators who have assisted us in this work, and we affirm that all authors have approved the final manuscript for submission. We understand that this article is subject to the\n                      Journal'\n                      s peer review process. Finally, we state that we have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}