Strategic Integration of Artificial Intelligence for Sustainable Businesses: Implications for Data Management and Human User Engagement in the Digital Era
Abstract
:1. Introduction
2. Theoretical Review
2.1. Competitive Landscape for AI Integration
- Most executives say AI tools in companies have boosted productivity already, supported cost savings, and enabled innovation of products and services for the needs of the market.
- The pressure of competition is significant, since falling behind in AI adoption is one of the major concerns among customer experience leaders and mainstream companies in different industries.
- There is an increasing trend of large companies investing in AI, when compared to the period of the last 5 years, so there is evident disruption in today’s businesses, leading to AI becoming the mainstream technology.
- US-based companies, as the forerunners, mostly agree that adopting AI technology has helped them create better customer experiences.
2.2. Data Management Practices around Use of AI
- data ethics;
- data security;
- regulatory compliance.
2.3. AI Utilization in Interactions with Clients
2.4. Research Hypothesis Formulation
3. Methodological Framework
3.1. Sample Description
3.2. AI Adoption Index Framework
Instrument Design—AI Adoption Index Components
- Number of AI applications: Count the distinct AI applications or use cases implemented within the company. Of course, a necessary precondition is that the observed company “lies” on a lot of structured and unstructured data.
- Investment in AI Infrastructure: Quantify the financial resources allocated to AI-related hardware, software, and infrastructure. This can include continuous improvements in the form of capital expense, but also existing licensing and maintenance costs.
- Share of employees trained in AI: Determine the percentage of employees who have received AI-related training or certifications. A precondition for this factor is an established data literacy concept in the company, with a skilled workforce able to work in data science and machine learning operations.
4. Results of Quantitative Research
5. Key Findings and Discussion
- Theoretical findings determined several components of AI adoption index that are key for surveying data officers from sampled companies.
- Results of quantitative research uncovered distinct AI adoption profiles among medium and large companies.
5.1. Key Findings and Insights
5.2. Potential Implications
- Innovation: Theoretical integration of AI into sustainable business practices can drive innovation. AI-powered technologies can create new sustainable products, services, and business models [73].
- Environmental Impact: Theoretical frameworks help businesses evaluate and minimize their environmental footprint. AI facilitates real-time monitoring and data-driven decisions to reduce waste and emissions [77].
- Impact on Green marketing: On the one hand, AI applications and systems in marketing—in essence—pursue sales’ objectives and increase consumption, but on the other hand, AI in marketing can be a powerful force in promoting supply- and demand-side sustainability efforts [78].
6. Conclusions
- AI Adoption and Regional Dynamics: Investigate the factors influencing the adoption of AI for sustainability in the West Balkans, with a special focus on the blooming IT sector in Serbia. Analyze how political, economic, and cultural factors shape the strategic integration of AI in different sectors and regions within the West Balkans.
- Ethical and Regulatory Frameworks: Examine the ethical implications of AI integration and propose guidelines and regulatory frameworks tailored to the West Balkans. Explore the balance between innovation and ethical considerations to ensure responsible AI-driven sustainability practices.
- AI-Driven Impact Assessment: Develop methodologies to assess the tangible environmental, social, and economic impact of AI integration in West Balkan companies. Quantify the benefits and challenges to provide empirical evidence for informed decision making and policy development.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- IBM. Global AI Adoption Index 2022. 2023. Available online: https://www.ibm.com/watson/resources/ai-adoption (accessed on 4 September 2023).
- McKinsey. AI-powered Marketing and Sales Reach New Heights with Generative AI. 2023. Available online: https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/ai-powered-marketing-and-sales-reach-new-heights-with-generative-ai (accessed on 12 October 2023).
- Government of the Republic of Serbia. Strategy for the Development of AI in Serbia for the Period of 2020–2025. 2020. Available online: https://www.media.srbija.gov.rs/medsrp/dokumenti/strategy_artificial_intelligence.pdf (accessed on 12 October 2023).
- Vinuesa, R.; Azizpour, H.; Leite, I.; Balaam, M.; Dignum, V.; Domisch, S.; Felländer, A.; Langhans, S.D.; Tegmark, M.; Nerini, F.F. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 2020, 11, 233. [Google Scholar] [CrossRef]
- Jabbar, A.; Akhtar, P.; Dani, S. Real-time big data processing for instantaneous marketing decisions: A problematization approach. Ind. Mark. Manag. 2020, 90, 558–569. [Google Scholar] [CrossRef]
- Finances Online. Vital Artificial Intelligence Statistics. 2023. Available online: https://financesonline.com/artificial-intelligence-statistics/ (accessed on 11 October 2023).
- Abassi, A.; Sarker, S.; Chiang, R. Big data research in information systems: Toward an inclusive research agenda. J. Assoc. Inform. Syst. 2016, 17, i–xxxii. [Google Scholar] [CrossRef]
- Espina-Romero, L.; Noroño Sánchez, J.G.; Gutiérrez Hurtado, H.; Dworaczek Conde, H.; Solier Castro, Y.; Cervera Cajo, L.E.; Rio Corredoira, J. Which Industrial Sectors Are Affected by Artificial Intelligence? A Bibliometric Analysis of Trends and Perspectives. Sustainability 2023, 15, 12176. [Google Scholar] [CrossRef]
- Ali, L. A fractal-fractional-order modified Predator-Prey mathematical model with immigrations. Math. Comput. Simul. 2023, 207, 466–481. [Google Scholar] [CrossRef]
- Li, M. Novel extended mixed controller design for bifurcation control of fractional order Myc/E2F/miR-17-92 network model concerning delay. Math. Methods Appl. Sci. 2023, 1, 1–21. [Google Scholar] [CrossRef]
- Xua, H. Extended hybrid controller design of bifurcation in a delayed chemostat model. MATCH Commun. Math. Comput. Chem. 2023, 90, 609–648. [Google Scholar] [CrossRef]
- Huang, C. Bifurcations in a fractional-order BAM neural network with four different delays. Neural Netw. 2021, 141, 344–354. [Google Scholar] [CrossRef]
- Mikalef, P.; Framnes, V.A.; Danielsen, F.; Krogstie, J.; Olsen, D. Big Data Analytics Capability: Antecedents and Business Value. In Proceedings of the Pacific Asia Conference on Information Systems, Langkawi Island, Malaysia, 16–20 July 2017; p. 136. [Google Scholar]
- PwC. Global artificial intelligence study. 2021. Available online: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html (accessed on 8 October 2023).
- Adobe. Digital Trends. 2020. Available online: https://www.adobe.com/content/dam/dx/us/en/resources/reports/pdf/digital-trends-2020-in-financial-services.pdf (accessed on 8 October 2023).
- Schneider, S.; Leyer, M. Me or information technology? Adoption of artificial intelligence in the delegation of personal strategic decisions. Manag. Decis. Econ. 2019, 40, 223–231. [Google Scholar] [CrossRef]
- Wang, Y.; Kung, L.; Byrd, T.A. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Chang. 2018, 126, 3–13. [Google Scholar] [CrossRef]
- Nishant, R.; Kennedy, M.; Corbett, J. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. Int. J. Infor. Manag. 2020, 53, 102104. [Google Scholar] [CrossRef]
- Di Vaio, A.; Hassan, R.; Alavoine, C. Data intelligence and analytics: A bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness. Technol. Forecast. Soc. Chang. 2022, 174, 121201. [Google Scholar] [CrossRef]
- Dirican, C. The Effects of Technological Development and Artificial Intelligence Studies on Marketing. J. Manag. Market. Logist. 2015, 2, 170–178. [Google Scholar] [CrossRef]
- Toniolo, K.; Masiero, E.; Massaro, M.; Bagnoli, C. Sustainable Business Models and Artificial Intelligence: Opportunities and Challenges. In Knowledge, People, and Digital Transformation. Contributions to Management Science; Matos, F., Vairinhos, V., Salavisa, I., Edvinsson, L., Massaro, M., Eds.; Springer: Cham, Switzerland, 2020; pp. 103–117. [Google Scholar]
- Hansen, E.B.; Iftikhar, N.; Bogh, S. Concept of easy-to-use versatile artificial intelligence in industrial small & medium-sized enterprises. Procedia Manuf. 2020, 51, 1146–1152. [Google Scholar]
- Daradkeh, F.M.; Hassan, T.H.; Palei, T.; Helal, M.Y.; Mabrouk, S.; Saleh, M.I.; Salem, A.E.; Elshawarbi, N.N. Enhancing Digital Presence for Maximizing Customer Value in Fast-Food Restaurants. Sustainability 2023, 15, 5690. [Google Scholar] [CrossRef]
- Oesterreich, T.D.; Anton, E.; Teuteberg, F. What translates big data into business value? A meta-analysis of the impacts of business analytics on firm performance. Inf. Manag. 2022, 59, 103685. [Google Scholar] [CrossRef]
- Palomares, I.; Martínez-Cámara, E.; Montes, R.; García-Moral, P.; Chiachio, M.; Chiachio, J.; Alonso, S.; Melero, F.J.; Molina, D.; Fernández, B.; et al. A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: Progress and prospects. Appl. Intell. 2021, 51, 6497–6527. [Google Scholar] [CrossRef]
- Ranjan, J.; Foropon, C. Big Data Analytics in Building the Competitive Intelligence of Organizations. Int. J. Inf. Manag. 2021, 56, 102231. [Google Scholar] [CrossRef]
- Lee, Y.S.; Kim, T.; Choi, S.; Kim, W. When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy. Technovation 2022, 118, 102590. [Google Scholar]
- Kelly, S.; Kaye, S.; Trespalacios, O.O. What factors contribute to the acceptance of artificial intelligence? A systematic review. Telemat. Inform. 2023, 77, 101925. [Google Scholar] [CrossRef]
- Bolesnikov, M.; Popović Stijačić, M.; Radišić, M.; Takači, A.; Borocki, J.; Bolesnikov, D.; Bajdor, P.; Dzieńdziora, J. Development of a business model by introducing sustainable and tailor-made value propositions for SME clients. Sustainability 2019, 11, 1157. [Google Scholar] [CrossRef]
- Kotouza, M.T.; Tsarouchis, S.F.; Kyprianidis, A.C.; Chrysopoulos, A.C.; Mitkas, P.A. Towards Fashion Recommendation: An AI System for Clothing Data Retrieval and Analysis. In Artificial Intelligence Applications and Innovations, Proceedings of the 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, 5–7 June 2020; Springer: Cham, Switzerland, 2020. [Google Scholar]
- Dwivedi, Y.K.; Hughes, L.; Ismagilova, E.; Aarts, G.; Coombs, C.; Crick, T.; Duan, Y.; Dwivedi, R.; Edwards, J.; Eirug, A. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 2021, 57, 101994. [Google Scholar] [CrossRef]
- Meesters, M.; Heck, P.; Serebrenik, A. What Is an AI Engineer? An Empirical Analysis of Job Ads in the Netherlands. In Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, CAIN 2022, Pittsburgh, PA, USA, 16–24 May 2022; pp. 136–144. [Google Scholar]
- Lahsen, M. Should AI be designed to save us from ourselves? Artificial intelligence for sustainability. IEEE Technol. Soc. Mag. 2020, 39, 60–67. [Google Scholar] [CrossRef]
- Berawi, M.A.; Suwartha, N.; Asvial, M.; Harwahyu, R.; Suryanegara, M.; Setiawan, E.A.; Surjandari, I.; Zagloel, T.Y.M.; Maknun, I.J. Digital Innovation: Creating Competitive Advantages. Int. J. Technol. 2020, 11, 1076–1080. [Google Scholar] [CrossRef]
- Ntoutsi, E.; Fafalios, P.; Gadiraju, U.; Iosifidis, V.; Nejdl, W.; Vidal, M.E.; Ruggieri, S.; Turini, F.; Papadopoulos, S.; Krasanakis, E.; et al. Bias in data-driven artificial intelligence systems—An introductory survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2020, 10, e1356. [Google Scholar] [CrossRef]
- Boddington, P.; Rump, B. Ethics in computing, AI and data use: A case for a key stage 1-4 computer ethics curriculum. ACM Trans. Comput. Educ. 2020, 20, 1–20. [Google Scholar]
- Bessen, J. Artificial intelligence and jobs: The role of demand. In The Economics of Artificial Intelligence: An Agenda; University of Chicago Press: Chicago, IL, USA, 2018; pp. 291–307. [Google Scholar]
- Rhem, A.J. Ethical Use of Data in AI Applications. Intech Open 2023, 1–234. [Google Scholar] [CrossRef]
- Mandy, C. The Future of Data Security: Staying Ahead of AI, Forbes. 2023. Available online: https://www.forbes.com/sites/forbestechcouncil/2023/05/26/the-future-of-data-security-staying-ahead-of-ai/?sh=788e464c14e3, (accessed on 9 October 2023).
- Brasseur, K. ChatGPT back in Italy after user privacy updates. Compliance Week J. 2023. Available online: https://www.complianceweek.com/data-privacy/chatgpt-back-in-italy-after-user-privacy-updates/33019.article (accessed on 9 October 2023).
- Bessen, J.E.; Impink, S.M.; Reichensperger, L.; Seamans, R. GDPR and the Importance of Data to AI Startups; NYU Stern School of Business: New York, NY, USA, 2020. [Google Scholar]
- Timan, T.; Mann, Z. Data Protection in the Era of Artificial Intelligence: Trends, Existing Solutions and Recommendations for Privacy-Preserving Technologies; Springer: Cham, Switzerland, 2021. [Google Scholar] [CrossRef]
- Rutter, R.N.; Barnes, S.J.; Roper, S.; Nadeau, J.; Lettice, F. Social media influencers, product placement and network engagement: Using AI image analysis to empirically test relationships. Ind. Manag. Data Syst. 2021, 121, 2387–2410. [Google Scholar] [CrossRef]
- Burnaev, E.; Mironov, E.; Shpilman, A.; Mironenko, M.; Katalevsky, D. Practical AI Cases for Solving ESG Challenges. Sustainability 2023, 15, 12731. [Google Scholar] [CrossRef]
- Mohamed, M.; Weber, P. Trends of digitalization and adoption of big data & analytics among UK SMEs: Analysis and lessons drawn from a case study of 53 SMEs. In Proceedings of the 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Cardiff, UK, 15–17 June 2020; pp. 1–6. [Google Scholar] [CrossRef]
- Ardito, L.; Scuotto, V.; Del Giudice, M.; Petruzzelli, A.M. A bibliometric analysis of research on Big Data analytics for business and management. Manag. Decis. 2019, 57, 1993–2009. [Google Scholar] [CrossRef]
- Takyar. AI Use Cases & Applications Across Major Industries. Leeway Hertz. 2023. Available online: https://www.leewayhertz.com/ai-use-cases-and-applications/ (accessed on 10 October 2023).
- Haleem, A.; Javaid, M.; Qadri, M.A.; Singh, R.P.; Suman, R. Artificial intelligence (AI) applications for marketing: A literature-based study. Int. J. Intell. Netw. 2022, 3, 119–132. [Google Scholar] [CrossRef]
- Vyas, S.; Jain, S.S.; Choudhary, I.; Chaudhary, A. Study on Use of AI and Big Data for Commercial System. In Proceedings of the 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates, 4–6 February 2019; pp. 737–739. [Google Scholar] [CrossRef]
- Chen, T.; Keng, B.; Moreno, J. Multivariate arrival times with recurrent neural networks for personalized demand forecasting. In Proceedings of the IEEE International Conference on Data Mining Workshops, ICDMW, Singapore, 17–20 November 2018; pp. 810–819. [Google Scholar]
- Banners, Y.; Hunermund, P. How Midsize Companies Can Compete in AI. Harvard Business Review. 2021. Available online: https://hbr.org/2021/09/how-midsize-companies-can-compete-in-ai (accessed on 6 October 2023).
- Luan, H.; Tsai, C.C. A review of using machine learning approaches for precision education. Educ. Technol. Soc. 2021, 24, 250–266. [Google Scholar]
- Mach-Król, M.; Hadasik, B. On a Certain Research Gap in Big Data Mining for Customer Insights. Appl. Sci. 2021, 11, 6993. [Google Scholar] [CrossRef]
- Maslej, N.; Fattorini, L.; Brynjolfsson, E.; Etchemendy, J.; Ligett, K.; Lyons, T.; Manyika, J.; Ngo, H.; Niebles, J.C.; Parli, V.; et al. The AI Index 2023 Annual Report; AI Index Steering Committee, Institute for Human-Centered AI, Stanford University: Stanford, CA, USA, 2023. [Google Scholar]
- Bettoni, A.; Matteri, D.; Montini, E.; Gładysz, B.; Carpanzano, E. An AI adoption model for SMEs: A conceptual framework. IFAC-Pap. 2021, 54, 1. [Google Scholar] [CrossRef]
- Rhawashdeh, A.; Bakhit, M.; Abaalkhail, L. Determinants of artificial intelligence adoption in SMEs: The mediating role of accounting automation. Int. J. Data Netw. Sci. 2023, 7, 25–34. [Google Scholar] [CrossRef]
- Chatterjee, S.; Rana, N.P.; Dwivedi, Y.K.; Baabdullah, A.M. Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technol. Forecast. Soc. Chang. 2021, 170, 120880. [Google Scholar] [CrossRef]
- Gusak, J.; Cherniuk, D.; Shilova, A.; Katrutsa, A.; Bershatsky, D.; Zhao, X.; Eyraud-Dubois, L.; Shlyazhko, O.; Dimitrov, D.; Oseledets, I.; et al. Survey on Efficient Training of Large Neural Networks. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22), Vienna, Austria, 23–29 July 2022. [Google Scholar]
- Ghasemaghaei, M. Does data analytics use improve firm decision making quality? The role of knowledge sharing and data analytics competency. Decis. Support. Syst. 2019, 120, 14–24. [Google Scholar] [CrossRef]
- McKinsey. Driving Impact at Scale from Automation and AI. Digital McKinsey. February 2019. Available online: https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/driving%20impact%20at%20scale%20from%20automation%20and%20ai/driving-impact-at-scale-from-automation-and-ai.pdf (accessed on 21 April 2021).
- Lodge, J.M.; Thompson, K.; Corrin, L. Mapping out a research agenda for generative artificial intelligence in tertiary education. Australas. J. Educ. Technol. 2023, 39, 1–8. [Google Scholar] [CrossRef]
- Thayyib, P.V.; Mamilla, R.; Khan, M.; Fatima, H.; Asim, M.; Anwar, I.; Shamsudheen, M.K.; Khan, M.A. State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary. Sustainability 2023, 15, 4026. [Google Scholar] [CrossRef]
- Van Hai, V.; Nhung, H.L.T.K.; Prokopova, Z.; Silhavy, R.; Silhavy, P. Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis. IEEE Access 2022, 10, 83249–83264. [Google Scholar] [CrossRef]
- Bresciani, S.; Ferraris, A.; Romano, M.; Santoro, G. Building a Digital Transformation Strategy. In Digital Transformation Management for Agile Organizations: A Compass to Sail the Digital World; Emerald Publishing Limited: Bingley, UK, 2021; pp. 5–27. [Google Scholar]
- Herrmann, H.; Masawi, B. Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review. Strateg. Chang. 2022, 31, 549–569. [Google Scholar] [CrossRef]
- Dean, J.; Ghemawat, S. MapReduce: Simplified data processing on large clusters. Commun. ACM 2008, 51, 107–113. [Google Scholar] [CrossRef]
- O’Leary, D.E. Massive data language models and conversational artificial intelligence: Emerging issues. Intell. Syst. Account. Financ. Manag. 2022, 29, 182–198. [Google Scholar] [CrossRef]
- Verma, S.; Sharma, R.; Deb, S.; Maitra, D. Artificial intelligence in marketing: Systematic review and future research direction. Int. J. Inf. Manag. Data Insights 2021, 1, 100002. [Google Scholar] [CrossRef]
- Davenport, T.H.; Ronanki, R. Artificial intelligence for the real world. Harv. Bus. Rev. 2018, 96, 108–116. [Google Scholar]
- López-Robles, J.R.; Otegi-Olaso, J.R.; Gómez, I.P.; Cobo, M.J. 30 years of intelligence models in management and business: A bibliometric review. Int. J. Inf. Manag. 2019, 48, 22–38. [Google Scholar] [CrossRef]
- Batistič, S.; van der Laken, P. History, evolution and future of big data and analytics: A bibliometric analysis of its relationship to performance in organizations. Br. J. Manag. 2019, 30, 229–251. [Google Scholar] [CrossRef]
- Chen, H.-M.; Schütz, R.; Kazman, R.; Matthes, F. How Lufthansa Capitalized on Big Data for Business Model Renovation. MIS Q. Exec. 2017, 16, 19–34. [Google Scholar]
- Ahmed, E.; Yaqoob, I.; Hashem, I.A.T.; Khan, I.; Ahmed, A.I.A.; Imran, M.; Vasilakos, A.V. The role of big data analytics in Internet of Things. Comput. Netw. 2017, 129, 459–471. [Google Scholar] [CrossRef]
- Zhao, J.; Gómez Fariñas, B. Artificial Intelligence and Sustainable Decisions. Eur. Bus. Org. Law Rev. 2023, 24, 1–39. [Google Scholar] [CrossRef]
- Hermann, E. Artificial intelligence in marketing: Friend or foe of sustainable consumption? AI Soc. 2023, 38, 1975–1976. [Google Scholar] [CrossRef]
- Bak, J. Transforming data into business value through analytics and AI. Harvard Business Review. 2023. Available online: https://cloud.google.com/resources/hbr-data-and-ai-report?utm_campaign=601986593f6e3e0001a7de49&utm_content=640b471b7e90ac000140083d&utm_medium=smarpshare&utm_source=linkedin (accessed on 11 October 2023).
- Gandomi, A.; Haider, M. Beyond the hype: Big data concepts methods and analytics. Int. J. Inf. Manag. 2015, 35, 2–137. [Google Scholar] [CrossRef]
- Yahia, N.B.; Hlel, J.; Colomo-Palacios, R. From Big Data to Deep Data to Support People Analytics for Employee Attrition Prediction. IEEE Access 2021, 9, 60447–60458. [Google Scholar] [CrossRef]
- Patel, J. An Effective and Scalable Data Modeling for Enterprise Big Data Platform. In Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 9–12 December 2019; pp. 2691–2697. [Google Scholar] [CrossRef]
- Singh, S.K.; El-Kassar, A.-N. Role of big data analytics in developing sustainable capabilities. J. Clean. Prod. 2019, 213, 1264–1273. [Google Scholar] [CrossRef]
- Tripathi, A.; Bagga, T.; Sharma, S.; Vishnoi, S.K. Big Data-Driven Marketing enabled Business Performance: A Conceptual Framework of Information, Strategy and Customer Lifetime Value. In Proceedings of the 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 28–29 January 2021; pp. 315–320. [Google Scholar] [CrossRef]
- Kelić, A.; Almisreb, A.; Tahir, N.M.; Bakri, J. Big Data and Business Intelligence—A Data Driven Strategy for Business in Bosnia Herzegovina. In Proceedings of the 2022 IEEE 10th Conference on Systems, Process & Control (ICSPC), Malacca, Malaysia, 17 December 2022; pp. 226–230. [Google Scholar] [CrossRef]
- Chai, B.; Zhang, Q.; Chen, Q.; Zhao, T.; Gao, K. Research on Applications of Artificial Intelligence in Business Management of Power Grid Enterprises. In Proceedings of the 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chengdu, China, 20–22 December 2019; pp. 683–688. [Google Scholar] [CrossRef]
- Pathak, A.; Sharma, S.D. Applications of Artificial Intelligence (AI) in Marketing Management. In Proceedings of the 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), Uttar Pradesh, India, 14–16 December 2022; pp. 1738–1745. [Google Scholar] [CrossRef]
- Kitsios, F.; Kamariotou, M. Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda. Sustainability 2021, 13, 2025. [Google Scholar] [CrossRef]
- Hsieh, K.L. Applying an expert system into constructing customer’s value expansion and prediction model based on AI techniques in leisure industry. Exp. Syst. Appl. 2009, 36, 2864–2872. [Google Scholar] [CrossRef]
- Pappas, I.O.; Mikalef, P.; Giannakos, M.N.; Krogstie, J.; Lekakos, G. Big data and business analytics ecosystems: Paving the way towards digital transformation and sustainable societies. Inf. Syst. e-Bus. Manag. 2018, 16, 479–491. [Google Scholar] [CrossRef]
- Vidgen, R.; Shaw, S.; Grant, D.B. Management challenges in creating value from business analytics. Eur. J. Oper. Res. 2017, 261, 626–639. [Google Scholar] [CrossRef]
- Sujata, J.; Aniket, D.; Mahasingh, M. Artificial intelligence tools for enhancing customer experience. Int. J. Recent Technol. Eng. 2019, 2, 700–706. [Google Scholar]
- Akhtar, P.; Frynas, J.G.; Mellahi, K.; Ullah, S. Big Data-Savvy Teams’ Skills, Big Data-Driven Actions and Business Performance. Br. J. Manag. 2019, 30, 252–271. [Google Scholar] [CrossRef]
- Schmarzo, B. Big Data Business Model Maturity Index. In Big Data MBA: Driving Business Strategies with Data Science; John Wiley & Sons: Hoboken, NJ, USA, 2015; pp. 1–24. [Google Scholar] [CrossRef]
- Hradecky, D.; Kennell, J.; Cai, W.; Davidson, R. Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. Int. J. Inf. Manag. 2022, 65, 102497. [Google Scholar] [CrossRef]
- Almeida, F. Foresights for big data across industries. Foresight 2022, 25, 334–348. [Google Scholar] [CrossRef]
Freq. | % of Total | ||
---|---|---|---|
Company size | Medium | 178 | 74% |
Large | 62 | 26% | |
Country of origin | Serbia | 144 | 60% |
EU | 69 | 29% | |
Rest of world | 27 | 1% | |
No. of years doing business | 1–5 | 14 | 6% |
6–15 | 97 | 40% | |
16–25 | 109 | 45% | |
26+ | 20 | 9% | |
Industry | IT | 45 | 19% |
Banking | 7 | 3% | |
Agriculture | 14 | 3% | |
Telecom | 14 | 6% | |
Logistics | 35 | 15% | |
Manufacturing | 68 | 28% | |
Ecommerce | 57 | 24% | |
Annual revenue range | EUR 5–10 million | 44 | 18% |
EUR 10–50 million | 102 | 43% | |
EUR 50–100 million | 76 | 32% | |
EUR 100 million+ | 18 | 8% |
Research Factor | Assigned Weight |
---|---|
Number of years applying Big Data and AI | 0.1 |
Share of business processes that include Big Data and/or AI | 0.1 |
Total number of AI applications | 0.2 |
Annual investments in AI infrastructure | 0.2 |
Share of employees trained in Big Data and AI | 0.2 |
Number of collaborations with AI ecosystem | 0.1 |
Share of partnerships in total implementation of AI innovation projects | 0.1 |
Versus Overall Average | Within the Same Industry Sector | Between One and All Other Industry Sectors | ||||||
---|---|---|---|---|---|---|---|---|
Low Correlation * | Medium Correlation * | High Correlation * | Low Correlation | Medium Correlation | High Correlation | |||
Industry | IT | +21% | 4% | 12% | 84% | 47% | 20% | 23% |
Banking | −5% | 32% | 22% | 46% | 7% | 17% | 76% | |
Agriculture | −14% | 35% | 41% | 24% | 76% | 11% | 13% | |
Telecom | +11% | 21% | 13% | 66% | 38% | 22% | 40% | |
Logistics | +3% | 15% | 19% | 68% | 42% | 23% | 35% | |
Manufacturing | +11% | 14% | 10% | 76% | 49% | 37% | 14% | |
Ecommerce | +26% | 5% | 10% | 85% | 55% | 32% | 13% |
AI Adoption Index (0—Minimum Value; 1—Maximum Value) | Mean Value | Median Value | Standard Deviation | Minimum Value | Maximum Value |
---|---|---|---|---|---|
Medium-sized companies | 0.32 | 0.24 | 0.34 | 0.03 | 0.72 |
Large-sized companies | 0.23 | 0.26 | 0.12 | 0.04 | 0.83 |
AI Adoption Index | Number of Sampled Companies |
---|---|
Low (less than 0.3) | 65 (27%) |
Medium (between 0.31 and 0.70) | 163 (68%) |
High (higher than 0.71) | 12 (5%) |
Means for Obtaining Data about Factor | Variable | Coefficient of Regression | Standard Error | p-Value |
---|---|---|---|---|
Publicly available database | Company size | 0.13 | 0.12 | 0.04 |
Annual revenue | 0.23 | 0.14 | 0.01 | |
Industry type | 0.22 | 0.03 | 0.01 | |
Data delivered by data officer from sampled company | Number of years applying Big Data and AI | 0.45 | 0.31 | 0.01 |
Share of business processes that include Big Data and/or AI | 0.49 | 0.12 | 0.01 | |
Total number of AI applications | 0.67 | 0.11 | 0.01 | |
Annual investments in AI infrastructure | 0.59 | 0.05 | 0.01 | |
Share of employees trained in Big Data and AI | 0.38 | 0.45 | 0.03 | |
Number of collaborations with AI ecosystem | 0.19 | 0.11 | 0.01 | |
Share of partnerships in total implementation of AI innovation projects | 0.35 | 0.21 | 0.01 |
AI Adoption Index | Medium Companies | Large Companies | Total |
---|---|---|---|
Low | 57 | 18 | 65 |
Medium | 118 | 35 | 163 |
High | 3 | 9 | 12 |
Total | 178 | 62 | 240 |
AI Adoption Index | |||
---|---|---|---|
Key Factors | Low | Medium | High |
Number of years applying Big Data and AI | Less than 6 years | 6–9 years | 9+ years |
Number of years in business | More than 16 years | Between 6 and 25 years | Less than 10 years or more than 25 years |
Share of business processes that include Big Data and/or AI | 2–4% | 5–14% | 15–19.5% |
Total number of AI applications | Less than 10 | Between 11 and 15 | More than 15 |
Annual investments in AI infrastructure | Less than EUR 1 million | Between EUR 1 and 20 million | Above EUR 20 million |
Annual revenue range | Less than EUR 10 million | Between EUR 10 and 100 million | More than EUR 100 million |
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Jankovic, S.D.; Curovic, D.M. Strategic Integration of Artificial Intelligence for Sustainable Businesses: Implications for Data Management and Human User Engagement in the Digital Era. Sustainability 2023, 15, 15208. https://doi.org/10.3390/su152115208
Jankovic SD, Curovic DM. Strategic Integration of Artificial Intelligence for Sustainable Businesses: Implications for Data Management and Human User Engagement in the Digital Era. Sustainability. 2023; 15(21):15208. https://doi.org/10.3390/su152115208
Chicago/Turabian StyleJankovic, Svetozar D., and Dejan M. Curovic. 2023. "Strategic Integration of Artificial Intelligence for Sustainable Businesses: Implications for Data Management and Human User Engagement in the Digital Era" Sustainability 15, no. 21: 15208. https://doi.org/10.3390/su152115208