Leveraging Artificial Intelligence in Business: Implications, Applications and Methods

References

• Agrawal, A., J. Gans, and A. Goldfarb. 2018. Prediction Machines: The Simple Economics of Artificial Intelligence. Brighton, MA: Harvard Business Press.
   Google Scholar
• Airoldi, E. M., D. M. Blei, E. A. Erosheva, and S. E. Fienberg. 2014. “Introduction to Mixed Membership Models and Methods.” Handbook of Mixed Membership Models and Their Applications 100: 3–14.
   Google Scholar
• Airoldi, E. M., D. M. Blei, S. E. Fienberg, and E. P. Xing. 2008. “Mixed Membership Stochastic Blockmodels.” Journal of Machine Learning Research 9: 1981–2014.
   PubMed Web of Science ®Google Scholar
• Arsénio, A., H. Serra, R. Francisco, F. Nabais, J. Andrade, and E. Serrano. 2014. “Internet of Intelligent Things: Bringing Artificial Intelligence Into Things and Communication Networks.” In Inter-cooperative Collective Intelligence: Techniques and Applications, edited by F. Xhafa and N. Bessis, 1–37. Berlin: Springer.
   Google Scholar
• Blei, D. M. 2012. “Probabilistic Topic Models.” Communications of the ACM 55 (4): 77–84.
   Web of Science ®Google Scholar
• Buhalis, D., T. Harwood, V. Bogicevic, G. Viglia, S. Beldona, and C. Hofacker. 2019. “Technological Disruptions in Services: Lessons from Tourism and Hospitality.” Journal of Service Management 30 (4): 484–506.
   Web of Science ®Google Scholar
• Campbell, C., S. Sands, C. Ferraro, H. Y. J. Tsao, and A. Mavrommatis. 2020. “From Data to Action: How Marketers Can Leverage AI.” Business Horizons 63 (2): 227–243.
   Web of Science ®Google Scholar
• Castillo, P. A., A. M. Mora, H. Faris, J. J. Merelo, P. García-Sánchez, A. J. Fernández-Ares, and M. I. García-Arenas. 2017. “Applying Computational Intelligence Methods for Predicting the Sales of Newly Published Books in a Real Editorial Business Management Environment.” Knowledge-Based Systems 115: 133–151.
   Web of Science ®Google Scholar
• Chen, Y. 2017. “Integrated and Intelligent Manufacturing: Perspectives and Enablers.” Engineering 3 (5): 588–595.
   Web of Science ®Google Scholar
• Chou, J. S., C. W. Lin, A. D. Pham, and J. Y. Shao. 2015. “Optimized Artificial Intelligence Models for Predicting Project Award Price.” Automation in Construction 54: 106–115.
   Web of Science ®Google Scholar
• Corea, F. 2016. “Key Data Challenges to Strategic Business Decisions.” In Big Data Analytics: A Management Perspective, edited by F. Corea, 19–24. Cham: Springer.
   Google Scholar
• Davenport, T. H. 2018. “From Analytics to Artificial Intelligence.” Journal of Business Analytics 1 (2): 73–80.
   Google Scholar
• Davenport, T., A. Guha, D. Grewal, and T. Bressgott. 2020. “How Artificial Intelligence Will Change the Future of Marketing.” Journal of the Academy of Marketing Science 48 (1): 24–42.
   Web of Science ®Google Scholar
• Davenport, T. H., and J. G. Harris. 2007. Competing on Analytics: The New Science of Winning. Boston, MA: Harvard Business School Press.
   Google Scholar
• Deka, G. C. 2014. “Big Data Predictive and Prescriptive Analytics.” In Handbook of Research on Cloud Infrastructures for Big Data Analytics, edited by P. Raji and G. C. Deka, 370–391. IGI Global.
   Google Scholar
• Delen, D., and M. D. Crossland. 2008. “Seeding the Survey and Analysis of Research Literature with Text Mining.” Expert Systems with Applications 34 (3): 1707–1720.
   Web of Science ®Google Scholar
• De Mauro, A., M. Greco, M. Grimaldi, and P. Ritala. 2018. “Human Resources for Big Data Professions: A Systematic Classification of Job Roles and Required Skill Sets.” Information Processing & Management 54 (5): 807–817.
   Web of Science ®Google Scholar
• Duan, Y., J. S. Edwards, and Y. K. Dwivedi. 2019. “Artificial Intelligence for Decision Making in the Era of Big Data–Evolution, Challenges and Research Agenda.” International Journal of Information Management 48: 63–71.
   Web of Science ®Google Scholar
• Dwivedi, Y. K., L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick, and V. Galanos. 2019. “Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy.” International Journal of Information Management, 101994.
   Web of Science ®Google Scholar
• Ebner, K., T. Bühnen, and N. Urbach. 2014. Think Big With Big Data: Identifying Suitable Big Data Strategies in Corporate Environments. In 2014 47th Hawaii International Conference on System Sciences (pp. 3748–3757). IEEE.
   Google Scholar
• Faccia, A., M. Y. K. Al Naqbi, and S. A. Lootah. 2019, August. Integrated Cloud Financial Accounting Cycle: How Artificial Intelligence, Blockchain, and XBRL Will Change the Accounting, Fiscal and Auditing Practices. In Proceedings of the 2019 3rd International Conference on Cloud and Big Data Computing (pp. 31–37).
   Google Scholar
• Galanos, V. 2019. “Exploring Expanding Expertise: Artificial Intelligence as an Existential Threat and the Role of Prestigious Commentators, 2014–2018.” Technology Analysis & Strategic Management 31 (4): 421–432.
   Web of Science ®Google Scholar
• Gatouillat, A., Y. Badr, B. Massot, and E. Sejdic. 2018. “Internet of Medical Things: A Review of Recent Contributions Dealing with Cyber–Physical Systems in Medicine.” IEEE Internet of Things Journal 5 (5): 3810–3822.
   Web of Science ®Google Scholar
• Giatsoglou, M., M. G. Vozalis, K. Diamantaras, A. Vakali, G. Sarigiannidis, and K. C. Chatzisavvas. 2017. “Sentiment Analysis Leveraging Emotions and Word Embeddings.” Expert Systems with Applications 69: 214–224.
   Web of Science ®Google Scholar
• Glynn, C., S. T. Tokdar, B. Howard, and D. L. Banks. 2019. “Bayesian Analysis of Dynamic Linear Topic Models.” Bayesian Analysis 14 (1): 53–80.
   Web of Science ®Google Scholar
• Grün, B. 2018. “Model-based Clustering.” Handbook of Mixture Analysis 2:163–198.
   Google Scholar
• Hair, J. F. 2007. “Knowledge Creation in Marketing: The Role of Predictive Analytics.” European Business Review 19 (4): 303–315.
   Google Scholar
• Hassabis, D., D. Kumaran, C. Summerfield, and M. Botvinick. 2017. “Neuroscience-inspired Artificial Intelligence.” Neuron 95 (2): 245–258.
   PubMed Web of Science ®Google Scholar
• Hazen, B. T., C. A. Boone, J. D. Ezell, and L. A. Jones-Farmer. 2014. “Data Quality for Data Science, Predictive Analytics, and Big Data in Supply Chain Management: An Introduction to the Problem and Suggestions for Research and Applications.” International Journal of Production Economics 154: 72–80.
   Web of Science ®Google Scholar
• Hessler, G., and K. H. Baringhaus. 2018. “Artificial Intelligence in Drug Design.” Molecules 23 (10): 2520.
   PubMed Web of Science ®Google Scholar
• Hosny, A., C. Parmar, J. Quackenbush, L. H. Schwartz, and H. J. Aerts. 2018. “Artificial Intelligence in Radiology.” Nature Reviews Cancer 18 (8): 500–510.
   PubMed Web of Science ®Google Scholar
• Hudson, V. M. 2019. Artificial Intelligence and International Politics. New York: Routledge.
   Google Scholar
• Jeschke, S., C. Brecher, T. Meisen, D. Özdemir, and T. Eschert. 2017. “Industrial Internet of Things and Cyber Manufacturing Systems.” In Industrial Internet of Things, edited by S. Jeschke, C. Brecher, H. Song, and D. Rawat, 3–19. Cham: Springer.
   Google Scholar
• Jiang, F., Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, and Y. Wang. 2017. “Artificial Intelligence in Healthcare: Past, Present and Future.” Stroke and Vascular Neurology 2 (4): 230–243.
   PubMedGoogle Scholar
• Johnson, K., F. Pasquale, and J. Chapman. 2019. “Artificial Intelligence, Machine Learning, and Bias in Finance: Toward Responsible Innovation.” Fordham Law Review 88: 499.
   Web of Science ®Google Scholar
• Johnson, K. W., J. T. Soto, B. S. Glicksberg, K. Shameer, R. Miotto, M. Ali, and J. T. Dudley. 2018. “Artificial Intelligence in Cardiology.” Journal of the American College of Cardiology 71 (23): 2668–2679.
   PubMed Web of Science ®Google Scholar
• Kaplan, A. 2020. “Retailing and the Ethical Challenges and Dilemmas Behind Artificial Intelligence.” In Retail Futures, edited by E. Pantano, 181–191. New York: Emerald Publishing Limited.
   Google Scholar
• Konar, A. 2018. Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain. CRC Press.
   Google Scholar
• Kumar, V., B. Rajan, R. Venkatesan, and J. Lecinski. 2019. “Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing.” California Management Review 61 (4): 135–155.
   Web of Science ®Google Scholar
• Kumar, K., and G. S. M. Thakur. 2012. “Advanced Applications of Neural Networks and Artificial Intelligence: A Review.” International Journal of Information Technology and Computer Science 4 (6): 57.
   Google Scholar
• Laínez, J. M., G. V. Reklaitis, and L. Puigjaner. 2010. “Linking Marketing and Supply Chain Models for Improved Business Strategic Decision Support.” Computers & Chemical Engineering 34 (12): 2107–2117.
   Web of Science ®Google Scholar
• Li, S. 2000. “The Development of a Hybrid Intelligent System for Developing Marketing Strategy.” Decision Support Systems 27 (4): 395–409.
   Web of Science ®Google Scholar
• Li, S. 2007. “AgentStra: An Internet-Based Multi-Agent Intelligent System for Strategic Decision-Making.” Expert Systems with Applications 33 (3): 565–571.
   Web of Science ®Google Scholar
• Lo, D., K. K. Tiba, S. Buciumas, and F. Ziller. 2019, July. An Emperical Study on Application of Big Data Analytics to Automate Service Desk Business Process. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) (Vol. 2, pp. 670–675). IEEE.
   Google Scholar
• Lycett, M. 2013. “‘Datafication’: Making Sense of (big) Data in a Complex World.” European Journal of Information Systems 22 (4): 381–386.
   Web of Science ®Google Scholar
• Malthouse, E. C., M. Haenlein, B. Skiera, E. Wege, and M. Zhang. 2013. “Managing Customer Relationships in the Social Media Era: Introducing the Social CRM House.” Journal of Interactive Marketing 27 (4): 270–280.
   Web of Science ®Google Scholar
• McAfee, A., E. Brynjolfsson, T. H. Davenport, D. J. Patil, and D. Barton. 2012. “Big Data: The Management Revolution.” Harvard Business Review 90 (10): 60–68.
   PubMed Web of Science ®Google Scholar
• McCall, B. 2020. “COVID-19 and Artificial Intelligence: Protecting Health-Care Workers and Curbing the Spread.” The Lancet Digital Health 2 (4): e166–e167.
   PubMedGoogle Scholar
• McCarthy, J. J., M. L. Minsky, and N. Rochester. 1959. Artificial Intelligence. Cambridge, MA: Research Laboratory of Electronics (RLE), Massachusetts Institute of Technology (MIT).
   Google Scholar
• Metaxiotis, K., and J. Psarras. 2003. “Expert Systems in Business: Applications and Future Directions for the Operations Researcher.” Industrial Management & Data Systems 103 (5): 361–368.
   Web of Science ®Google Scholar
• Mikalef, P., I. O. Pappas, J. Krogstie, and M. Giannakos. 2018. “Big Data Analytics Capabilities: A Systematic Literature Review and Research Agenda.” Information Systems and e-Business Management 16 (3): 547–578.
   Web of Science ®Google Scholar
• Milligan, G. W., and M. C. Cooper. 1985. “An Examination of Procedures for Determining the Number of Clusters in a Data set.” Psychometrika 50 (2): 159–179.
   Web of Science ®Google Scholar
• Moore, G. 1965. “Moore’s Law.” Electronics Magazine 38 (8): 114.
   Google Scholar
• Moro, S., P. Cortez, and P. Rita. 2016. “An Automated Literature Analysis on Data Mining Applications to Credit Risk Assessment.” In Artificial Intelligence in Financial Markets, edited by C. Dunis, P. Middleton, A. Karathanasopolous, and K. Theofilatos, 161–177. London: Palgrave Macmillan.
   Google Scholar
• Porter, M. F. 1980. “An Algorithm for Suffix Stripping.” Program 14 (3): 130–137.
   Google Scholar
• Prior, D. D., J. Keränen, and S. Koskela. 2019. “Customer Participation Antecedents, Profiles and Value-in-use Goals in Complex B2B Service Exchange.” Industrial Marketing Management 82: 131–147.
   Web of Science ®Google Scholar
• Ramaswamy, V., and K. Ozcan. 2018. “What is Co-Creation? An Interactional Creation Framework and its Implications for Value Creation.” Journal of Business Research 84: 196–205.
   Web of Science ®Google Scholar
• Rambocas, M., and B. G. Pacheco. 2018. “Online Sentiment Analysis in Marketing Research: A Review.” Journal of Research in Interactive Marketing 12 (2): 146–163.
   Web of Science ®Google Scholar
• Reutterer, T., A. Mild, M. Natter, and A. Taudes. 2006. “A Dynamic Segmentation Approach for Targeting and Customizing Direct Marketing Campaigns.” Journal of Interactive Marketing 20 (3–4): 43–57.
   Google Scholar
• Rogge-Solti, A., and G. Kasneci. 2014, September. Temporal Anomaly Detection in Business Processes. In International Conference on Business Process Management (pp. 234–249). Springer, Cham.
   Google Scholar
• Ryman-Tubb, N. F., P. Krause, and W. Garn. 2018. “How Artificial Intelligence and Machine Learning Research Impacts Payment Card Fraud Detection: A Survey and Industry Benchmark.” Engineering Applications of Artificial Intelligence 76: 130–157.
   Web of Science ®Google Scholar
• Schoech, D., H. Jennings, L. L. Schkade, and C. Hooper-Russell. 1985. “Expert Systems: Artificial Intelligence for Professional Decisions.” Computers in Human Services 1 (1): 81–115.
   Google Scholar
• Sestino, A., M. I. Prete, L. Piper, and G. Guido. 2020. “Internet of Things and Big Data as Enablers for Business Digitalization Strategies.” Technovation, 102173.
   Web of Science ®Google Scholar
• Shankar, P. R. 2012, July. The Cybernetics of Enabling Competence in People Competence Building to Ensure Quality and Productivity in People in Software Industries. In 2012 IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom) (pp. 83–87). IEEE.
   Google Scholar
• Steyvers, M., and T. Griffiths. 2007. “Probabilistic Topic Models.” Handbook of Latent Semantic Analysis 427 (7): 424–440.
   Google Scholar
• Sunikka, A., and J. Bragge. 2012. “Applying Text-Mining to Personalization and Customization Research Literature–Who, What and Where?” Expert Systems with Applications 39 (11): 10049–10058.
   Web of Science ®Google Scholar
• Szolovits, P. 2019. Artificial Intelligence in Medicine. London: Routledge.
   Google Scholar
• Tsai, C. F. 2014. “Combining Cluster Analysis with Classifier Ensembles to Predict Financial Distress.” Information Fusion 16: 46–58.
   Web of Science ®Google Scholar
• Turban, E. 1988. Decision Support and Expert Systems: Managerial Perspectives. New York: Macmillan.
   Google Scholar
• van Altena, A. J., P. D. Moerland, A. H. Zwinderman, and S. D. Olabarriaga. 2016. “Understanding Big Data Themes from Scientific Biomedical Literature Through Topic Modeling.” Journal of Big Data 3 (1): 23.
   Google Scholar
• Venkatasubramanian, V. 2019. “The Promise of Artificial Intelligence in Chemical Engineering: Is it Here, Finally?” AIChE Journal 65 (2): 466–478.
   Web of Science ®Google Scholar
• Vidgen, R., S. Shaw, and D. B. Grant. 2017. “Management Challenges in Creating Value from Business Analytics.” European Journal of Operational Research 261 (2): 626–639.
   Web of Science ®Google Scholar
• Wamba, S. F., A. Gunasekaran, S. Akter, S. J. F. Ren, R. Dubey, and S. J. Childe. 2017. “Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities.” Journal of Business Research 70: 356–365.
   Web of Science ®Google Scholar
• Yang, Y., and K. Siau. 2018. A Qualitative Research on Marketing and Sales in the Artificial Intelligence Age. Midwest United States Association for Information Systems (MWAIS) 2018 Proceedings.
   Google Scholar
• Zehir, C., T. Karaboğa, and D. Başar. 2020. “The Transformation of Human Resource Management and Its Impact on Overall Business Performance: Big Data Analytics and AI Technologies in Strategic HRM.” In Digital Business Strategies in Blockchain Ecosystems, edited by U. Hacioglu, 265–279. Cham: Springer.
   Google Scholar
• Zeithaml, V. A., R. T. Rust, and K. N. Lemon. 2001. “The Customer Pyramid: Creating and Serving Profitable Customers.” California Management Review 43 (4): 118–142.
   Web of Science ®Google Scholar
• Zhang, H., H. Nguyen, X. N. Bui, T. Nguyen-Thoi, T. T. Bui, N. Nguyen, and H. Moayedi. 2020. “Developing a Novel Artificial Intelligence Model to Estimate the Capital Cost of Mining Projects Using Deep Neural Network-Based Ant Colony Optimization Algorithm.” Resources Policy 66: 101604.
   Web of Science ®Google Scholar
• Zhang, D., X. Zhou, S. C. Leung, and J. Zheng. 2010. “Vertical Bagging Decision Trees Model for Credit Scoring.” Expert Systems with Applications 37 (12): 7838–7843.
   Web of Science ®Google Scholar
• Zhong, N., J. Liu, and Y. Yao. 2007. “Web Intelligence (WI).” In Wiley Encyclopedia of Computer Science and Engineering, edited by N. Zhong, 1–11.
   Google Scholar