Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review
Abstract
:1. Introduction
2. Methodology
- Literature retrieval—The first and most important step in the data collecting process, this step comprises selecting acceptable search keywords and key phrases to thoroughly gather relevant articles relating to the targeted topic. Pre-existing papers and publications in several areas of employee competency development were collected from the Scopus database. The above process was carried out using a list of keywords, such as “productivity”, “Conversational AI”, “employees”, “Chatbot”, “Generative Artificial Intelligence”, “efficiency”, “workers”, “ChatGPT”, and “GAI”. Thus, authors were able to conduct more targeted research across titles, keywords, and abstracts as a result of these phrase combinations. Over the years 1989 to September 2023, the main data-gathering method resulted in the inclusion of 683 research publications.
- Literature screening—The PRISMA statement, a well-known and stringent technique for performing systematic reviews and meta-analyses, affected the literature screening procedure in this investigation as indicated in Figure 1. This technique provides an organized structure for ensuring the systematic identification, selection, and evaluation of the relevant literature, hence improving the review process’s accuracy and repeatability [19]. Initially, 683 papers were collected for this study. After removing duplicates, 646 papers remained. Further rigorous review to align with the research scope reduced this to 159 relevant reviews, articles, and publications from 2014 to September 2023.
- Content analysis—The content analysis process involved a thorough examination and organization of a large body of material, particularly research journals, to identify recurring themes and patterns. This study’s focus was on exploring how GAI enhances staff efficiency, categorizing articles into distinct categories and sub-fields. This systematic classification provided a comprehensive understanding of GAI’s multifaceted impact within organizations, facilitating the extraction of significant findings and insights from the data.
- Bibliometric analysis—The bibliometric analysis in this study systematically examined the academic literature, focusing on citations and references within articles. Its goal was to enhance understanding of the impact, trends, and connections in academia. By analyzing citation patterns, co-authorship, and keywords, it identified key authors, pivotal publications, emerging research areas, and collaborative networks.
3. Content Analysis
3.1. Application of GAI in Academia and Research
3.2. Application of GAI in Engineering and Technology
3.3. Application of GAI in Communication and Cultural Studies
3.4. Application of GAI in the Medical and Healthcare Discipline
3.5. Application of GAI in Agriculture, Agricultural Sciences, Government, and Public Administration
3.6. Application of GAI in Business and Organizational Management
3.7. Application of GAI in Miscellaneous Professional Fields
3.8. Application of GAI in Computer Science and Artificial Intelligence
4. Bibliometric Analysis
4.1. Co-Occurrence Map Based on Text Data
4.2. Co-Occurrence Map Based on Keywords
4.3. Co-Occurrence Map Based on Country of Co-Authorship
4.4. Co-Occurrence Map Based on Authorship
4.5. Data Analysis Based on Document Field
4.6. Data Analysis on Document Type, GAI Tools Used, and Research Types
5. Conclusions and Future Research
Author Contributions
Funding
Conflicts of Interest
References
- Melehy, A.K. The Impact of AI on the Economy: A Comprehensive Analysis. 2023. Available online: https://www.researchgate.net/publication/370400293_The_Impact_of_AI_on_the_Economy_A_Comprehensive_Analysis (accessed on 12 December 2023).
- Jutel, M.; Zemelka-Wiacek, M.; Ordak, M.; Pfaar, O.; Eiwegger, T.; Rechenmacher, M.; Akdis, C. The artificial intelligence (AI) revolution: How important for scientific work and its reliable sharing. Allergy 2023, 78, 2085–2088. [Google Scholar] [CrossRef] [PubMed]
- Hadi, M.U.; Al-Tashi, Q.; Qureshi, R.; Shah, A.; Muneer, A.; Irfan, M.; Zafar, A.; Shaikh, M.; Akhtar, N.; Wu, J.; et al. Large Language Models: A Comprehensive Survey of Its Applications, Challenges, Limitations, and Future Prospects. 2023. Available online: https://www.techrxiv.org/users/618307/articles/682263-large-language-models-a-comprehensive-survey-of-its-applications-challenges-limitations-and-future-prospects (accessed on 12 December 2023).
- Mhlanga, D. The Value of Open AI and Chat GPT for the Current Learning Environments and The Potential Future Uses. SSRN Electron. J. 2023. [Google Scholar] [CrossRef]
- Hadi, M.; Najm, M. Introduction to ChatGPT: A new revolution of artificial intelligence with machine learning algorithms and cybersecurity. Sci. Arch. 2023, 4, 276–285. [Google Scholar] [CrossRef]
- Borji, A.; Mohammadian, M. Battle of the Wordsmiths: Comparing ChatGPT, GPT-4, Claude, and Bard. 2023. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4476855 (accessed on 12 December 2023).
- Feuerriegel, S.; Hartmann, J.; Janiesch, C.; Zschech, P. Generative AI. Bus. Inf. Syst. Eng. 2023, 66, 111–126. [Google Scholar] [CrossRef]
- Banh, L.; Strobel, G. Generative artificial intelligence. Electron. Mark. 2023, 33, 63. [Google Scholar] [CrossRef]
- Lund, B.; Wang, T. Chatting about ChatGPT: How may AI and GPT impact academia and libraries? Libr. Hi Tech News 2023, 40, 26–29. [Google Scholar] [CrossRef]
- Nah, F.; Zheng, R.; Cai, J.; Siau, K.; Chen, L. Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. J. Inf. Technol. Case Appl. Res. 2023, 25, 277–304. [Google Scholar] [CrossRef]
- Morris, M. Scientists’ Perspectives on the Potential for Generative AI in their Fields. 2023. Available online: https://montrealethics.ai/scientists-perspectives-on-the-potential-for-generative-ai-in-their-fields/ (accessed on 12 December 2023).
- Mao, J.; Chen, B.; Liu, J. Generative Artificial Intelligence in Education and Its Implications for Assessment. TechTrends 2023, 68, 58–66. [Google Scholar] [CrossRef]
- Zhang, P.; Kamel Boulos, M. Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges. Future Internet 2023, 15, 286. [Google Scholar] [CrossRef]
- Epstein, Z.; Hertzmann, A.; Herman, L.; Mahari, R.; Frank, M.; Groh, M.; Schroeder, H.; Smith, A.; Akten, M.; Fjeld, J.; et al. Art and the Science of Generative AI: A Deeper Dive. arXiv 2023, arXiv:2306.04141. [Google Scholar]
- Cardon, P.; Getchell, K.; Carradini, S.; Fleischmann, A.C.; Stapp, J. Generative AI in the Workplace: Employee Perspectives of ChatGPT Benefits and Organizational Policies. 2023. Available online: https://osf.io/preprints/socarxiv/b3ezy (accessed on 12 December 2023).
- Govori, A.; Sejdija, Q. Future prospects and challenges of integrating artificial intelligence within the business practices of small and medium enterprises. J. Gov. Regul. 2023, 12, 176–183. [Google Scholar] [CrossRef]
- Gonçalves, R.; Dias, Á.; Costa, R.; Pereira, L.; Bento, T.; Rosa, Á. Gaining competitive advantage through artificial intelligence adoption. Int. J. Electron. Bus. 2022, 1, 386–406. [Google Scholar] [CrossRef]
- Wamba-Taguimdje, S.; Fosso Wamba, S.; Jean Robert, K.K.; Tchatchouang, C.E. Influence of Artificial Intelligence (AI) on Firm Performance: The Business Value of AI-based Transformation Projects. Bus. Process Manag. J. 2020, 26. [Google Scholar] [CrossRef]
- Patole Sanjay Principles and Practice of Systematic Reviews and Meta-Analysis; Springer: Cham, Switzerland, 2021.
- Budhwar, P.; Chowdhury, S.; Wood, G.; Aguinis, H.; Bamber, G.J.; Beltran, J.R.; Boselie, P.; Lee Cooke, F.; Decker, S.; DeNisi, A.; et al. Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Hum. Resour. Manag. J. 2023, 33, 606–659. [Google Scholar] [CrossRef]
- Varnavsky, A.N. Chatbot to Increase the Effectiveness of the «flipped Classroom» Technology. In Proceedings of the 2022 2nd International Conference on Technology Enhanced Learning in Higher Education, TELE 2022, Lipetsk, Russian, 26–27 May 2022; pp. 289–293. [Google Scholar] [CrossRef]
- Košecka, D.; Balco, P.; Murgor, S.C. Chatbot at University, a Communication Tool to Increase Work Productivity. Lect. Notes Netw. Syst. 2022, 527, 74–84. [Google Scholar] [CrossRef]
- Sebastian, D.; Nugraha, K.A. Academic Customer Service Chatbot Development using TelegramBot API. In Proceedings of the 2021 2nd International Conference on Innovative and Creative Information Technology, ICITech 2021, Salatiga, Indonesia, 23–25 September 2021; pp. 221–225. [Google Scholar] [CrossRef]
- Suresh, N.; Mukabe, N.; Hashiyana, V.; Limbo, A.; Hauwanga, A. Career Counseling Chatbot on Facebook Messenger using AI. In Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence, Windhoek, Namibia, 9–12 August 2021; pp. 65–73. [Google Scholar] [CrossRef]
- Colace, F.; De Santo, M.; Lombardi, M.; Pascale, F.; Pietrosanto, A.; Lemma, S. Chatbot for e-learning: A case of study. Int. J. Mech. Eng. Robot. Res. 2018, 7, 528–533. [Google Scholar] [CrossRef]
- Dergaa, I.; Chamari, K.; Zmijewski, P.; Saad, H.B. From human writing to artificial intelligence generated text: Examining the prospects and potential threats of ChatGPT in academic writing. Biol. Sport 2023, 40, 615–622. [Google Scholar] [CrossRef]
- Leiker, D.; Gyllen, A.R.; Eldesouky, I.; Cukurova, M. Generative AI for Learning: Investigating the Potential of Learning Videos with Synthetic Virtual Instructors. Commun. Comput. Inf. Sci. 2023, 1831, 523–529. [Google Scholar] [CrossRef]
- Muhyidin, A.; Setiawan, M.A.F. Developing UNYSA Chatbot as Information Services about Yogyakarta State University. J. Phys. Conf. Ser. 2021, 1737, 012038. [Google Scholar] [CrossRef]
- Irwan, D.; Ali, M.; Ahmed, A.N.; Jacky, G.; Nurhakim, A.; Ping Han, M.C.; AlDahoul, N.; El-Shafie, A. Predicting Water Quality with Artificial Intelligence: A Review of Methods and Applications. Arch. Comput. Methods Eng. 2023, 30, 4633–4652. [Google Scholar] [CrossRef]
- Ren, Y.; Lin, J.; Tang, S.; Zhou, J.; Yang, S.; Qi, Y.; Ren, X. Generating natural language adversarial examples on a large scale with generative models. Front. Artif. Intell. Appl. 2020, 325, 2156–2163. [Google Scholar] [CrossRef]
- Wang, Y.; Vinogradov, A. Improving the Performance of Convolutional GAN Using History-State Ensemble for Unsupervised Early Fault Detection with Acoustic Emission Signals. Appl. Sci. 2023, 13, 3136. [Google Scholar] [CrossRef]
- Ratajczak, J.; Siegele, D.; Niederwieser, E. Maximizing Energy Efficiency and Daylight Performance in Office Buildings in BIM through RBFOpt Model-Based Optimization: The GENIUS Project. Buildings 2023, 13, 1790. [Google Scholar] [CrossRef]
- Zhou, Q.; Xue, F. Pushing the boundaries of modular-integrated construction: A symmetric skeleton grammar-based multi-objective optimization of passive design for energy savings and daylight autonomy. Energy Build. 2023, 296, 113417. [Google Scholar] [CrossRef]
- Zhang, J.; Liu, N.; Wang, S. Generative design and performance optimization of residential buildings based on parametric algorithm. Energy Build. 2021, 244, 111033. [Google Scholar] [CrossRef]
- Manuel, F.; Philipp, E.; Boris, K.; Stefan, G.; Antonio, D.; Valentyn, B. Numerical performance predictions of artificial intelligence-driven centrifugal compressor designs. Am. Soc. Mech. Eng. Fluids Eng. Div. Publ. FEDSM 2020, 1. [Google Scholar] [CrossRef]
- Venkatesh, K.; Pratibha, K.; Annadurai, S.; Kuppusamy, L. Reconfigurable architecture to speed-up modular exponentiation. In Proceedings of the 2019 International Carnahan Conference on Security Technology, Chennai, India, 1–3 October 2019. [Google Scholar] [CrossRef]
- Ahmad, A.; Waseem, M.; Liang, P.; Fahmideh, M.; Aktar, M.S.; Mikkonen, T. Towards Human-Bot Collaborative Software Architecting with ChatGPT. In Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, Oulu, Finland, 14–16 June 2023; pp. 279–285. [Google Scholar] [CrossRef]
- Liu, X.; Ma, H.; Liu, Y. A Novel Transfer Learning Method Based on Conditional Variational Generative Adversarial Networks for Fault Diagnosis of Wind Turbine Gearboxes under Variable Working Conditions. Sustainability 2022, 14, 5441. [Google Scholar] [CrossRef]
- Zhu, J.-F.; Hao, Z.-K.; Liu, Q.; Yin, Y.; Lu, C.-Q.; Huang, Z.-Y.; Chen, E.-H. Towards Exploring Large Molecular Space: An Efficient Chemical Genetic Algorithm. J. Comput. Sci. Technol. 2022, 37, 1464–1477. [Google Scholar] [CrossRef]
- Nawrocki, P.; Radziszowski, D.; Sniezynski, B. Heterogeneous Information Access System with a Natural Language Interface in the Context of Organization of Events. Commun. Comput. Inf. Sci. 2021, 1371, 188–200. [Google Scholar] [CrossRef]
- Badini, S.; Regondi, S.; Frontoni, E.; Pugliese, R. Assessing the capabilities of ChatGPT to improve additive manufacturing troubleshooting. Adv. Ind. Eng. Polym. Res. 2023, 6, 278–287. [Google Scholar] [CrossRef]
- Tsai, M.-H.; Chan, H.-Y.; Chan, Y.-L.; Shen, H.-K.; Lin, P.-Y.; Hsu, C.-W. A chatbot system to support mine safety procedures during natural disasters. Sustainability 2021, 13, 654. [Google Scholar] [CrossRef]
- Shi, L. Application Model Construction of Traditional Cultural Elements in Illustration Design under Artificial Intelligence Background. Mob. Inf. Syst. 2022, 2022, 1–9. [Google Scholar] [CrossRef]
- Meng, L.; Schaffer, S. A Reporting Assistant for Railway Security Staff. In Proceedings of the 2nd Conference on Conversational User Interfaces, Bilbao, Spain, 22–24 July 2020; pp. 1–3. [Google Scholar] [CrossRef]
- Zhong, B.; He, W.; Huang, Z.; Love, P.E.D.; Tang, J.; Luo, H. A building regulation question answering system: A deep learning methodology. Adv. Eng. Inform. 2020, 46, 101195. [Google Scholar] [CrossRef]
- Lin, Y.-Z.; Chuang, J.-Y.; Sheng, I.C.; Cheng, Y.T.; Chang, C.-C.; Yang, Y.-C.; Hsueh, H.-P.; Huang, C.-H. Development of a task-oriented chatbot application for monitoring Taiwan photon source front-end system. In Proceedings of the 12th International Workshop on Emerging Technologies and Scientific Facilities Controls, PCaPAC 2018, Hsinchu, Taiwan, 16–19 October 2018; pp. 228–229. [Google Scholar] [CrossRef]
- Angeline, R.; Gaurav, T.; Rampuriya, P.; Dey, S. Supermarket Automation with Chatbot and Face Recognition Using IoT and AI. In Proceedings of the 3rd International Conference on Communication and Electronics Systems, ICCES 2018, Coimbatore, India, 15–16 October 2018; pp. 1183–1186. [Google Scholar] [CrossRef]
- Saka, A.B.; Oyedele, L.O.; Akanbi, L.A.; Ganiyu, S.A.; Chan, D.W.M.; Bello, S.A. Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities. Adv. Eng. Inform. 2023, 55, 101869. [Google Scholar] [CrossRef]
- Yazici, S. A machine-learning model driven by geometry, material and structural performance data in architectural design process. Proc. Int. Conf. Educ. Res. Comput. Aided Archit. Des. Eur. 2020, 1, 411–418. [Google Scholar]
- Zhou, Q.; Li, B.; Han, L.; Jou, M. Talking to a bot or a wall? How chatbots vs. human agents affect anticipated communication quality. Comput. Hum. Behav. 2023, 143, 107674. [Google Scholar] [CrossRef]
- Wagner, N.; Kraus, M.; Tonn, T.; Minker, W. Comparing Moderation Strategies in Group Chats with Multi-User Chatbots. In Proceedings of the 4th Conference on Conversational User Interfaces, Glasgow, UK, 26–28 July 2022. [Google Scholar] [CrossRef]
- Casillo, M.; De Santo, M.; Mosca, R.; Santaniello, D. An Ontology-Based Chatbot to Enhance Experiential Learning in a Cultural Heritage Scenario. Front. Artif. Intell. 2022, 5, 808281. [Google Scholar] [CrossRef]
- Carvalho, I.; Ivanov, S. ChatGPT for tourism: Applications, benefits and risks. Tour. Rev. 2023. [Google Scholar] [CrossRef]
- Lopezosa, C.; Codina, L.; Pont-Sorribes, C.; Vállez, M. Use of generative artificial intelligence in the training of journalists: Challenges, uses and training proposal. Prof. De La Inf. 2023, 32. [Google Scholar] [CrossRef]
- Płaza, M.; Trusz, S.; Kęczkowska, J.; Boksa, E.; Sadowski, S.; Koruba, Z. Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications. Sensors 2022, 22, 5311. [Google Scholar] [CrossRef]
- Comulada, W.S.; Rezai, R.; Sumstine, S.; Flores, D.D.; Kerin, T.; Ocasio, M.A.; Swendeman, D.; Fernández, M.I. A necessary conversation to develop chatbots for HIV studies: Qualitative findings from research staff, community advisory board members, and study participants. In AIDS Care—Psychological and Socio-Medical Aspects of AIDS/HIV; Taylor Francis Group: Abingdon, UK, 2023. [Google Scholar] [CrossRef]
- Gala, D.; Makaryus, A.N. The Utility of Language Models in Cardiology: A Narrative Review of the Benefits and Concerns of ChatGPT-4. Int. J. Environ. Res. Public Health 2023, 20, 6438. [Google Scholar] [CrossRef] [PubMed]
- Santandreu-Calonge, D.; Medina-Aguerrebere, P.; Hultberg, P.; Shah, M.-A. Can ChatGPT improve communication in hospitals? Prof. Inf. 2023, 32. [Google Scholar] [CrossRef]
- Nandini Prasad, K.S.; Sudhanva, S.; Tarun, T.N.; Yuvraaj, Y.; Vishal, D.A. Conversational Chatbot Builder—Smarter Virtual Assistance with Domain Specific AI. In Proceedings of the 2023 4th International Conference for Emerging Technology, INCET 2023, Belgaum, India, 26–28 May 2023. [Google Scholar] [CrossRef]
- Lecler, A.; Duron, L.; Soyer, P. Revolutionizing radiology with GPT-based models: Current applications, future possibilities and limitations of ChatGPT. Diagn. Interv. Imaging 2023, 104, 269–274. [Google Scholar] [CrossRef] [PubMed]
- Ong, H.; Ong, J.; Cheng, R.; Wang, C.; Lin, M.; Ong, D. GPT Technology to Help Address Longstanding Barriers to Care in Free Medical Clinics. Ann. Biomed. Eng. 2023, 51, 1906–1909. [Google Scholar] [CrossRef] [PubMed]
- Yang, Z.; Liu, Y.; Ouyang, C.; Ren, L.; Wen, W. Counterfactual can be strong in medical question and answering. Inf. Process. Manag. 2023, 60, 103408. [Google Scholar] [CrossRef]
- Bussola, N.; Xu, J.; Wu, L.; Gorini, L.; Zhang, Y.; Furlanello, C.; Tong, W. A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study. Chem. Res. Toxicol. 2023, 36, 1321–1331. [Google Scholar] [CrossRef]
- Grupac, M.; Zauskova, A.; Nica, E. Generative Artificial Intelligence-based Treatment Planning in Clinical Decision-Making, in Precision Medicine, and in Personalized Healthcare. Contemp. Read. Law Soc. Justice 2023, 15, 45–62. [Google Scholar] [CrossRef]
- Panthier, C.; Gatinel, D. Success of ChatGPT, an AI language model, in taking the French language version of the European Board of Ophthalmology examination: A novel approach to medical knowledge assessment. J. Fr. Ophtalmol. 2023, 46, 706–711. [Google Scholar] [CrossRef] [PubMed]
- Tustumi, F.; Andreollo, N.A.; de Aguilar-Nascimento, J.E. Future of the Language Models in Healthcare: The Role of chatGPT. Arq. Bras. Cir. Dig. 2023, 36, e1727. [Google Scholar] [CrossRef] [PubMed]
- Escorcia-Gutierrez, J.; Mansour, R.F.; Leal, E.; Villanueva, J.; Jimenez-Cabas, J.; Soto, C.; Soto-Díaz, R. Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems. Mob. Netw. Appl. 2023. [Google Scholar] [CrossRef]
- Strunga, M.; Urban, R.; Surovková, J.; Thurzo, A. Artificial Intelligence Systems Assisting in the Assessment of the Course and Retention of Orthodontic Treatment. Healthcare 2023, 11, 683. [Google Scholar] [CrossRef] [PubMed]
- Wang, E.T.G.; Chen, A.P.S.; Liu, C.W. A Hybrid Evaluation of AI Chatbots in Taiwan Agriculture Services. In Proceedings of the 2021 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2021, Taichung, Taiwan, 18–20 November 2021; pp. 112–118. [Google Scholar] [CrossRef]
- Feitosa, W.R.; Do Patrocinio, F.O.; Santos, S.R.; Silva, S.C.E. Proposal for a Chatbot Prototype in the Plant Health Department of Brazilian Ministry of Agriculture. In Proceedings of the 2020 IEEE/ITU International Conference on Artificial Intelligence for Good, AI4G 2020, Geneva, Switzerland, 21–25 September 2020; pp. 17–21. [Google Scholar] [CrossRef]
- Ramadoss, P.; Ananth, V.; Navaneetha, M.; Oviya, U. E -Xpert Bot -Guidance and Pest Detection for Smart Agriculture using AI. In Proceedings of the 2023 12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023, Bhopal, India, 8–9 April 2023; pp. 797–802. [Google Scholar] [CrossRef]
- Usip, P.U.; Udo, E.N.; Asuquo, D.E.; James, O.R. A Machine Learning-Based Mobile Chatbot for Crop Farmers. Commun. Comput. Inf. Sci. 2022, 1666, 192–211. [Google Scholar] [CrossRef]
- Tsai, M.-H.; Yang, C.-H.; Chen, J.Y.; Kang, S.-C. Four-Stage Framework for Implementing a Chatbot System in Disaster Emergency Operation Data Management: A Flood Disaster Management Case Study. KSCE J. Civ. Eng. 2021, 25, 503–515. [Google Scholar] [CrossRef]
- Walkowiak, E. Task-interdependencies between Generative AI and Workers. Econ. Lett. 2023, 231, 111315. [Google Scholar] [CrossRef]
- Bankins, S.; Ocampo, A.C.; Marrone, M.; Restubog, S.L.D.; Woo, S.E. A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. J. Organ. Behav. 2023. early review. [Google Scholar] [CrossRef]
- Araujo, T.; Van Zoonen, W.; Ter Hoeven, C. “A Large Playground”: Examining the Current State and Implications of Conversational Agent Adoption in Organizations. Int. J. Innov. Technol. Manag. 2022, 19. [Google Scholar] [CrossRef]
- Makhija, P.; Chacko, E. Efficiency and Advancement of Artificial Intelligence in Service Sector with Special Reference to Banking Industry. In Fourth Industrial Revolution and Business Dynamics: Issues and Implications Efficiency and Advancement of Artificial Intelligence in Service Sector with Special Reference to Banking Industry; Springer Science + Business Media: Berlin/Heidelberg, Germany, 2021; pp. 21–35. [Google Scholar]
- Illescas, C.; Ortega, T.; Jadán-Guerrero, J. Gender Bias in Chatbots and Its Programming. Smart Innov. Syst. Technol. 2023, 337, 481–489. [Google Scholar] [CrossRef]
- Anagnoste, S.; Biclesanu, I.; D’Ascenzo, F.; Savastano, M. The Role of Chatbots in End-To-End Intelligent Automation and Future Employment Dynamics. In Springer Proceedings in Business and Economics; Springer: Berlin/Heidelberg, Germany, 2021; pp. 287–302. [Google Scholar] [CrossRef]
- Straßer, T.; Axmann, B. Analysis and evaluation of ai applications in logistics. Logist. J. 2021, 2021. [Google Scholar] [CrossRef]
- Chen, B.; Wu, Z.; Zhao, R. From fiction to fact: The growing role of generative AI in business and finance. J. Chin. Econ. Bus. Stud. 2023, 21, 471–496. [Google Scholar] [CrossRef]
- Leo John, R.J.; Potti, N.; Patel, J.M. Ava: From data to insights through conversation. In Proceedings of the CIDR 2017—8th Biennial Conference on Innovative Data Systems Research, Chaminade, CA, USA, 8–11 January 2017. [Google Scholar]
- Al-Ababneh, H.; Borisova, V.; Zakharzhevska, A.; Tkachenko, P.; Andrusiak, N. Performance of Artificial Intelligence Technologies in Banking Institutions. WSEAS Trans. Bus. Econ. 2023, 20, 307–317. [Google Scholar] [CrossRef]
- Fan, H.; Gao, W.; Han, B. Are AI chatbots a cure-all? The relative effectiveness of chatbot ambidexterity in crafting hedonic and cognitive smart experiences. J. Bus. Res. 2023, 156, 113526. [Google Scholar] [CrossRef]
- Saengrith, W.; Viriyavejakul, C.; Pimdee, P. Problem-Based Blended Training via Chatbot to Enhance the Problem-Solving Skill in the Workplace. Emerg. Sci. J. 2022, 6, 1–12. [Google Scholar] [CrossRef]
- Chithra Apoorva, D.A.; Brahmananda, S.H. A future research scope: Survey on an artificial interactive agent. Int. J. Adv. Sci. Technol. 2020, 29, 6158–6166. [Google Scholar]
- Virkar, M.; Honmane, V.; Rao, S.U. Humanizing the chatbot with semantics based natural language generation. In Proceedings of the 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019, Madurai, India, 15–17 May 2019; pp. 891–894. [Google Scholar] [CrossRef]
- Chandar, P.; Khazaeni, Y.; Davis, M.; Muller, M.; Crasso, M.; Liao, Q.V.; Shami, N.S.; Geyer, W. Leveraging Conversational Systems to Assists New Hires During Onboarding. Lect. Notes Comput. Sci. Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform. 2017, 10514, 381–391. [Google Scholar] [CrossRef]
- Steinbauer, F.; Kern, R.; Kröll, M. Chatbots assisting German business management applications. Lect. Notes Comput. Sci. Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform. 2019, 11606, 717–729. [Google Scholar] [CrossRef]
- Piyatumrong, A.; Sangkeettrakarn, C.; Witdumrong, S.; Cherdgone, J. Chatbot technology adaptation to reduce the information gap in RD center: A case study of an IT research organization. In Proceedings of the PICMET 2018—Portland International Conference on Management of Engineering and Technology: Managing Technological Entrepreneurship: The Engine for Economic Growth, Proceedings, Honolulu, HI, USA, 19–23 August 2018. [Google Scholar] [CrossRef]
- Hsu, C.-L.; Lin, J.C.-C. Understanding the user satisfaction and loyalty of customer service chatbots. J. Retail. Consum. Serv. 2023, 71, 103211. [Google Scholar] [CrossRef]
- Hung, P.D.; Trang, D.T.; Khai, T. Integrating Chatbot and RPA into Enterprise Applications Based on Open, Flexible and Extensible Platforms. Lect. Notes Comput. Sci. Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform. 2021, 12983, 183–194. [Google Scholar] [CrossRef]
- Silva, S.C.; De Cicco, R.; Vlačić, B.; Elmashhara, M.G. Using chatbots in e-retailing—How to mitigate perceived risk and enhance the flow experience. Int. J. Retail Distrib. Manag. 2023, 51, 285–305. [Google Scholar] [CrossRef]
- Bialkova, S. How to Optimise Interaction with Chatbots? Key Parameters Emerging from Actual Application. Int. J. Hum.-Comput. Interact. 2023. [Google Scholar] [CrossRef]
- Mehrolia, S.; Alagarsamy, S.; Moorthy, V. Will Users Continue Using Banking Chatbots? The Moderating Role of Perceived Risk. FIIB Bus. Rev. 2023. [Google Scholar] [CrossRef]
- Lappeman, J.; Marlie, S.; Johnson, T.; Poggenpoel, S. Trust and digital privacy: Willingness to disclose personal information to banking chatbot services. J. Financ. Serv. Mark. 2023, 28, 337–357. [Google Scholar] [CrossRef]
- Kar, A.K.; Kushwaha, A.K. Facilitators and Barriers of Artificial Intelligence Adoption in Business—Insights from Opinions Using Big Data Analytics. Inf. Syst. Front. 2023, 25, 1351–1374. [Google Scholar] [CrossRef]
- Xu, Q.; Yan, J.; Cao, C. Emotional Communication Between Chatbots and Users: An Empirical Study on Online Customer Service System. Lect. Notes Comput. Sci. Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform. 2022, 13336, 513–530. [Google Scholar] [CrossRef]
- Colace, F.; De Santo, M.; Pascale, F.; Lemma, S.; Lombardi, M. BotWheels: A petri net based Chatbot for recommending tires. In Proceedings of the 6th International Conference on Data Science, Technology and Applications, DATA 2017, Madrid, Spain, 24–26 July 2017; pp. 350–358. [Google Scholar] [CrossRef]
- Dwivedi, Y.K.; Kshetri, N.; Hughes, L.; Slade, E.L.; Jeyaraj, A.; Kar, A.K.; Baabdullah, A.M.; Koohang, A.; Raghavan, V.; Ahuja, M.; et al. “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int. J. Inf. Manag. 2023, 71, 102642. [Google Scholar] [CrossRef]
- Iparraguirre-Villanueva, O.; Obregon-Palomino, L.; Pujay-Iglesias, W.; Sierra-Liñan, F.; Cabanillas-Carbonell, M. Productivity of incident management with conversational bots—A review. IAES Int. J. Artif. Intell. 2023, 12, 1543–1556. [Google Scholar] [CrossRef]
- Cao, Y.; Carmona, V.I.S.; Liu, X.; Hu, C.; Iskender, N.; Beyer, A.; Möller, S.; Polzehl, T. On the Impact of Self-efficacy on Assessment of User Experience in Customer Service Chatbot Conversations. Lect. Notes Electr. Eng. 2022, 943, 253–262. [Google Scholar] [CrossRef]
- Temple, J.G.; Burkhart, B.J.; McFadden, E.T.; Elie, C.J.; Portnoy, F. Cognitive Solutions in the Enterprise: A Case Study of UX Benefits and Challenges. Adv. Intell. Syst. Comput. 2020, 965, 267–274. [Google Scholar] [CrossRef]
- Banerjee, S.; Singh, P.K.; Bajpai, J. A comparative study on decision-making capability between human and artificial intelligence. Adv. Intell. Syst. Comput. 2018, 652, 203–210. [Google Scholar] [CrossRef]
- Deksne, D.; Vasiljevs, A. Collection of resources and evaluation of customer support chatbot. Front. Artif. Intell. Appl. 2018, 307, 30–37. [Google Scholar] [CrossRef]
- Noy, S.; Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 2023, 381, 187–192. [Google Scholar] [CrossRef]
- Gilardi, F.; Alizadeh, M.; Kubli, M. ChatGPT outperforms crowd workers for text-annotation tasks. Proc. Natl. Acad. Sci. USA 2023, 120, e2305016120. [Google Scholar] [CrossRef]
- Hassani, H.; Silva, E.S. The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionizing the Field. Big Data Cogn. Comput. 2023, 7, 62. [Google Scholar] [CrossRef]
- Yue, P.; Yuan, T. Artificial Intelligence-Assisted Interior Layout Design of CAD Painting. Comput.-Aided Des. Appl. 2023, 20, 64–74. [Google Scholar] [CrossRef]
- Deng, Z.G.; Lv, J.; Liu, X.; Hou, Y.K. Bionic Design Model for Co-creative Product Innovation Based on Deep Generative and BID. Int. J. Comput. Intell. Syst. 2023, 16, 8. [Google Scholar] [CrossRef]
- Wang, K. On the Application of Artificial Intelligence in Local Legislation. Appl. Math. Nonlinear Sci. 2023, 9. [Google Scholar] [CrossRef]
- Weekes, T.R.; Eskridge, T.C. Responsible Human-Centered Artificial Intelligence for the Cognitive Enhancement of Knowledge Workers. Lect. Notes Comput. Sci. Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform. 2022, 13518, 568–582. [Google Scholar] [CrossRef]
- Jo, Y.-W.; Kim, H.-W. Generative Adversarial Network based Cost-efficiency data Augmentation for AI Object Detection on De-palletizing Robots. J. Inst. Control Robot. Syst. 2022, 28, 888–896. [Google Scholar] [CrossRef]
- Hardi, R.; Pee, A.N.C.; Abdullah, M.H.L.B.; Pitogo, V.A.; Pribadi, A.S.; Rusdi, J.F. Academic Smart Chatbot to Support Emerging Artificial Intelligence Conversation. In Proceedings of the 2022 International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2022, Denpasar, Indonesia, 10–12 November 2022; pp. 194–199. [Google Scholar] [CrossRef]
- Xu, D.; Zhu, F.; Liu, Q.; Zhao, P. Improving exploration efficiency of deep reinforcement learning through samples produced by generative model. Expert Syst. Appl. 2021, 185, 115680. [Google Scholar] [CrossRef]
- Hyun Baek, T.; Kim, M. Is ChatGPT scary good? How user motivations affect creepiness and trust in generative artificial intelligence. Telemat. Inf. 2023, 83, 102030. [Google Scholar] [CrossRef]
- Alamleh, H.; Alqahtani, A.A.S.; Elsaid, A. Distinguishing Human-Written and ChatGPT-Generated Text Using Machine Learning. In Proceedings of the 2023 Systems and Information Engineering Design Symposium, SIEDS 2023, Charlottesville, VA, USA, 27–28 April 2023; pp. 154–158. [Google Scholar] [CrossRef]
- Kuang, E.; Jahangirzadeh Soure, E.; Fan, M.; Zhao, J.; Shinohara, K. Collaboration with Conversational AI Assistants for UX Evaluation: Questions and How to Ask them (Voice vs. Text). In Proceedings of the Conference on Human Factors in Computing Systems, Hamburg, Germany, 23–28 April 2023. [Google Scholar] [CrossRef]
- Manshad, M.S.; Brannon, D.C. Gender-based conversational interface preferences in live chat systems for financial services. J. Financ. Serv. Mark. 2022, 28, 822–834. [Google Scholar] [CrossRef]
- Casadei, A.; Schlogl, S.; Bergmann, M. Chatbots for Robotic Process Automation: Investigating Perceived Trust and User Satisfaction. In Proceedings of the 2022 IEEE International Conference on Human-Machine Systems, ICHMS 2022, Orlando, FL, USA, 17–19 November 2022. [Google Scholar] [CrossRef]
- Gao, Z.; Jiang, J. Evaluating Human-AI Hybrid Conversational Systems with Chatbot Message Suggestions. In Proceedings of the International Conference on Information and Knowledge Management, Proceedings, Gold Coast, Australia, 1–5 November 2021; pp. 534–544. [Google Scholar] [CrossRef]
- Tai, W.; Zhou, F.; Trajcevski, G.; Zhong, T. Revisiting Denoising Diffusion Probabilistic Models for Speech Enhancement: Condition Collapse, Efficiency and Refinement. In Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Washington, DC, USA, 7–14 February 2023; Volume 37, pp. 13627–13635. [Google Scholar]
- Subramani, M.; Jaleel, I.; Mohan, S.K. Evaluating the performance of ChatGPT in medical physiology university examination of phase I MBBS. Adv. Physiol. Educ. 2023, 47, 270–271. [Google Scholar] [CrossRef] [PubMed]
- Roberts, I.G.; Watumull, J.; Chomsky, N. Universal Grammar. In Xenolinguistics: Towards a Science of Extraterrestrial Language Universal Grammar; Taylor and Francis: Abingdon, UK, 2023; pp. 165–181. [Google Scholar]
- Xu, M.; Niyato, D.; Chen, J.; Zhang, H.; Kang, J.; Xiong, Z.; Mao, S.; Han, Z. Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses. IEEE J. Sel. Top. Signal Process. 2023, 17, 1064–1079. [Google Scholar] [CrossRef]
- Radoi, T.-C. Artificial Intelligence in Data Analysis for Open-Source Investigations. In Proceedings of the 15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2023, Bucharest, Romania, 29–30 June 2023. [Google Scholar] [CrossRef]
- Jiang, F.; Ma, J.; Webster, C.J.; Chiaradia, A.J.F.; Zhou, Y.; Zhao, Z.; Zhang, X. Generative urban design: A systematic review on problem formulation, design generation, and decision-making. Prog. Plan. 2023, 180, 100795. [Google Scholar] [CrossRef]
- Aydin, N.; Ayhan Erdem, O. A Research On The New Generation Artificial Intelligence Technology Generative Pretraining Transformer 3. In Proceedings of the 3rd International Informatics and Software Engineering Conference, IISEC 2022, Ankara, Turkey, 15–16 December 2022. [Google Scholar] [CrossRef]
- Perez-Castro, A.; Martínez-Torres, M.R.; Toral, S.L. Efficiency of automatic text generators for online review content generation. Technol. Forecast. Soc. Chang. 2023, 189, 122380. [Google Scholar] [CrossRef]
- Ionuț-Alexandru, C. Experimental Results Regarding the Efficiency of Business Activities through the Use of Chatbots. Smart Innov. Syst. Technol. 2022, 276, 323–332. [Google Scholar] [CrossRef]
- Rawat, B.; Bist, A.S.; Rahardja, U.; Aini, Q.; Sanjaya, Y.P.A. Recent Deep Learning Based NLP Techniques for Chatbot Development: An Exhaustive Survey. In Proceedings of the 2022 10th International Conference on Cyber and IT Service Management, CITSM 2022, Yogyakarta, Indonesia, 20–21 September 2022. [Google Scholar] [CrossRef]
- Rzepka, C.; Berger, B.; Hess, T. Voice Assistant vs. Chatbot—Examining the Fit between Conversational Agents’ Interaction Modalities and Information Search Tasks. Inf. Syst. Front. 2022, 24, 839–856. [Google Scholar] [CrossRef]
- Borsci, S.; Malizia, A.; Schmettow, M.; van der Velde, F.; Tariverdiyeva, G.; Balaji, D.; Chamberlain, A. The Chatbot Usability Scale: The Design and Pilot of a Usability Scale for Interaction with AI-Based Conversational Agents. Pers. Ubiquitous Comput. 2022, 26, 95–119. [Google Scholar] [CrossRef]
- Camargo, J.; Nunes, J.; Antunes, M.J.; Mealha, O.; Abrantes, C.; Nobrega, L. Building datasets for automated conversational systems designed for use-cases. In Proceedings of the 2022 International Conference on Interactive Media, Smart Systems and Emerging Technologies, IMET 2022, Limassol, Cyprus, 4–7 October 2022. [Google Scholar] [CrossRef]
- Lee, Y.K. How complex systems get engaged in fashion design creation: Using artificial intelligence. Think. Ski. Creat. 2022, 46, 101137. [Google Scholar] [CrossRef]
- Mohana, P.P. A Survey of Modern Deep Learning based Generative Adversarial Networks (GANs). In Proceedings of the 6th International Conference on Computing Methodologies and Communication, ICCMC 2022, Erode, India, 29–31 March 2022; pp. 1146–1152. [Google Scholar] [CrossRef]
- Kathirvelu, M.; Janaranjani, A.; Navin Pranav, A.T.; Pradeep, R. Voice Recognition Chat bot for Consumer Product Applications. In Proceedings of the IEEE International Conference on Data Science and Information System, ICDSIS 2022, Hassan, India, 29–30 July 2022. [Google Scholar] [CrossRef]
- Nadiyah, K.; Dewi, G.S. Quality Control Analysis Using Flowchart, Check Sheet, P-Chart, Pareto Diagram and Fishbone Diagram. Ind. Syst. Optim. J. 2022, 15, 183–188. [Google Scholar] [CrossRef]
- Waltman, L. A unified approach to mapping and clustering of bibliometric networks. J. Informetr. 2010, 4, 629–635. [Google Scholar] [CrossRef]
- George, D.; Lehrach, W.; Kansky, K.; Lázaro-Gredilla, M.; Laan, C.; Marthi, B.; Lou, X.; Meng, Z.; Liu, Y.; Wang, H.; et al. A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs. Science 2017, 358, eaag2612. [Google Scholar] [CrossRef] [PubMed]
- Grover, T.; Rowan, K.; Suh, J.; McDuff, D.; Czerwinski, M. Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. ACM Int. Conf. Proceeding Ser. 2020, 390–400. [Google Scholar] [CrossRef]
- Sperlì, G. A Cultural heritage framework using a Deep Learning based Chatbot for supporting tourist journey. Expert Syst. Appl. 2021, 183, 115277. [Google Scholar] [CrossRef]
- Dowling, M.; Lucey, B. ChatGPT for (Finance) research: The Bananarama Conjecture. Financ. Res. Lett. 2023, 53, 103662. [Google Scholar] [CrossRef]
- Ali, H.; Aysan, A.F. What will ChatGPT revolutionize in the financial industry? Mod. Financ. 2023, 1, 116–129. [Google Scholar] [CrossRef]
Themes | Authors | Focus |
---|---|---|
Using GAI to improve academic and research workflows | Budhwar et al. (2023) [20] | Investigates the complex relationship between GAI and Human Resource Management (HRM), analyzing the deep repercussions of new technologies on employment dynamics and giving useful insights for HRM practitioners and scholars. |
Varnavsky (2022) [21] | Demonstrates a transformational approach to the “Flipped Classroom” teaching style, employing a Telegram chatbot to improve student engagement with course materials and evaluations, appealing to educators and researchers looking for increased efficiency. | |
Košecka et al. (2022) [22] | Indicates the benefits of integrating chatbot services within universities to improve operational effectiveness and efficiency, with a particular emphasis on their adoption in university contexts, improving educational institutions through the use of Generative AI Models. | |
Sebastian and Nugraha (2021) [23] | Highlights the cost-effective use of chatbots for streamlined customer service management across academic institutions’ numerous online accounts, emphasizing optimization of resources in response to the expanding social media environment, and providing insights into the use of Telegram API and webhook methods. | |
Suresh et al. (2021) [24] | Addresses the issue of insufficient career assistance for university students and graduates by developing an effective chatbot that provides job advice. This project assists individuals in making educated professional decisions that are in line with their interests and beliefs. | |
Colace et al. (2018) [25] | Prioritizes university student support by creating a chatbot prototype committed to supporting academic institutions and their students. | |
Ethical issues in AI-enhanced academia and research | Dergaa et al. (2023) [26] | Examines the influence of ChatGPT and other Natural Language Processing (NLP) technologies on academic efficiency, addressing possible ethical and credibility issues in research and writing, and engaging academics, researchers, and individuals in a critical analysis. |
AI-powered tools are transforming education and research | Leiker et al. (2023) [27] | Analyzes the incorporation of generative AI into educational video content; this evaluation examines the potential of AI-generated materials as alternatives for traditionally created instructional videos, with the goal of improving accessibility in online education. |
Muhyidin et al. (2021) [28] | Investigates the area of chatbot-based communication media, delving into development and quality assessment to improve information distribution and communication efficiency within the field of chatbot-based public relations communication, using advanced Chatbots and Conversational Agents for academics and education. | |
Irwan et al. (2023) [29] | Examines strategies for forecasting water quality, with a special emphasis on AI-based models. The goal of this investigation into water quality prediction using AI algorithms and approaches is to aid researchers, professionals, and specialists in the area. | |
Ren et al. (2020) [30] | Highlights efficient adversarial text creation for attacking text classification models and goes into the subject of NLP {XE “Natural language processing: (NLP)”} adversarial machine learning. This specialist information is intended for academics, researchers, and experts, and provides insights into the interconnections between AI methods and security concerns. |
Themes | Authors | Focus |
---|---|---|
Design automation and engineering efficiency | Wang and Vinogradov (2023) [31] | Emphasizes increasing Early Failure Detection (EFD) {XE “Early Failure Detection: (EFD)”} in industrial machine components by using an intelligent data analysis technique called Acoustic Emission (AE) {XE “Acoustic Emission: (AE)”} signal processing, with a main goal of improving efficiency and accuracy for industrial machinery maintenance experts. |
Ratajczak et al. (2023) [32] | Provides algorithm-aided design workflows in architecture, offering a methodology and toolkit to enhance design processes by incorporating modern methods and methodologies, optimizing building shape and window-to-wall ratio while taking energy and daylight performance into account; this increases architect and designer productivity. | |
Zhao et al. (2022) [33] | Investigates the combination of generative architectural design and technology integration, addressing obstacles to selecting aesthetic solutions through performance-based generative design, and integrating a technology-driven solution based on Sketch-Based Image Retrieval (SBIR) {XE “Sketch-Based Image Retrieval: (SBIR)”} algorithms to improve architectural design productivity. | |
Zhang et al. (2021) [34] | Demonstrates a technical approach that combines architecture, energy optimization, and Artificial Intelligence to automatically generate and evaluate design strategies for energy-efficient residential buildings, as well as discuss the use of a text-based chatbot for staff participation in completing workplace mental health assessments. | |
Manuel et al. (2020) [35] | Presents an innovative approach to optimize and construct turbine machinery aimed at achieving targeted performance goals, offering valuable insights and methodologies for industry professionals and technical experts. | |
Venkatesh et al. (2019) [36] | Outlines the creation of adaptable hardware designs to enhance cryptographic processes, catering to the requirements of cryptographers, hardware engineers, and information security researchers through the application of traditional exponential techniques in Diffie–Hellman protocols. | |
Ahmad et al. (2023) [37] | Examines Architecture-Centric Software Engineering (ACSE) {XE “Architecture-Centric Software Engineering: (ACSE)”} and focuses on the critical role of AI-powered DevBots like ChatGPT in optimizing software architecture design for increased effectiveness and productivity, with a key audience of software architects, engineers, and researchers in mind. | |
Liu et al. (2022) [38] | Identifies a ground-breaking strategy for mechanical fault detection in wind turbines, demonstrating a transfer learning technique based on Conditional Generative Adversarial Networks (CVAE-GANs), resulting in improved mechanical fault diagnostic efficiency and efficacy. | |
Zhu et al. (2022) [39] | Discovers the Chemical Genetic Algorithm for Large Molecular Space (CALM), {XE “Chemical Genetic Algorithm for Large Molecular Space: (CALM)”} a unique method developed for efficient creation and optimization of molecules with particular features, with an emphasis on enhancing the performance and efficiency of molecular generation and optimization procedures. | |
GAI improves Human–Machine Interaction | Nawrocki et al. (2021) [40] | Explores the development and integration of chatbots to improve communication and provide personalized event-related information, using Natural Language Processing (NLP) and Human–Computer Interaction (HCI) {XE “Human-Computer Interaction: (HCI)”}, with a focus on providing specific information to event and conference participants via chatbot technology. |
Badini et al. (2023) [41] | Investigates the use of ChatGPT for optimizing the Additive Manufacturing (AM) process, with a primary emphasis on improving the speed and precision of G-code generation for 3D printing. | |
Tsai et al. (2021) [42] | Examines the potential of chatbot technology in increasing the productivity of government disaster response and safety processes, with a focus on disaster response and safety measures. | |
Shi (2022) [43] | Focuses on using AI techniques to improve creative and artistic processes in representation art, particularly on improving image generation quality, investigating the development of a new activation function (SReLU), and employing Convolutional Neural Networks (CNNs) {XE “Convolutional Neural Networks: (CNNs)”} and Generative Adversarial Networks (GANs). {XE “Generative Adversarial Networks: (GANs)”} | |
Meng and Schaffer (2020) [44] | Reveals the development of tools aimed at improving security reporting in railway settings, with a particular emphasis on improving the performance of railway security staff and personnel through the integration of conversational interfaces, which falls under the domain of Railway Security and Mobile Multimodal Interaction. | |
Zhong et al. (2020) [45] | Considers the combination of NLP and Deep Learning techniques in the construction sector to deliver rapid replies via the chatbot-based question answering system (QAS4CQAR), with the goal of optimizing energy usage and efficiency. | |
Lin et al. (2018) [46] | Studies the creation of a task-oriented chatbot application aimed at monitoring and accessing information associated with the front-end system of a Taiwanese photon source, with the aim of improving the efficiency of defect detection and information retrieval. | |
Angeline et al. (2018) [47] | Points out the integration of Artificial Intelligence and the Internet of Things (IoT) into supermarket automation to improve customer satisfaction by empowering customers easily and efficiently, while providing support to supermarket chains and companies interested in deploying automated shopping systems. | |
Saka et al. (2023) [48] | Offers a systematic review of Conversational AI in the Architecture, Engineering, and Construction (AEC) {XE “architecture, engineering, and construction: (AEC)”} industry, with the goal of understanding its present-day development and exploring possible applications, obstacles, and opportunities, emphasizing the importance of improving productivity and efficiency in the AEC industry through the use of Conversational AI. | |
AI-boosted creativity and safety in selected fields | Yazici (2020) [49] | Indicates the improvement in architectural decisions by combining data on geometry, materials, and structural performance, with an emphasis on improving time savings and design outcomes through the use of Machine Learning (ML) techniques such as Artificial Neural Networks (ANN), Non-Linear Regression (NLR), and Gaussian Mixture (GM). |
Themes | Authors | Focus |
---|---|---|
Enhancing user experience and perception with chatbots | Zhou et al. (2023) [50] | Evaluates the impact of chatbots on users’ perceptions of communication effectiveness, offering findings with implications for chatbot design, and using chatbots broadly in multiple situations. |
Wagner et al. (2022) [51] | Investigates the dynamics of group chatbots in multi-party dialogues, focusing on coordinating and negotiating collective appointments. This study aids in enhancing user interface and interaction design for multi-user chatbot systems, with the goal of refining chatbot behaviors in group settings to boost user engagement, usability, and overall efficiency. | |
AI in cultural contexts and tourism | Casillo et al. (2022) [52] | Emphasizes the use of ontological approaches and chatbot technology to personalize and enrich the cultural heritage tourism experience, particularly in the framework of technology applications for cultural heritage and experiential tourism, with the goal of enhancing cultural heritage discovery and learning through digital technologies. |
Carvalho and Ivanov (2023) [53] | Analyzes the applications, benefits, and risks of ChatGPT and large languages in the tourism context with the goal of developing a research agenda to investigate their implications in the industry, with a focus on the use of AI, specifically ChatGPT, in tourism and hospitality to increase efficiency and productivity. | |
AI in industry-specific communication | Lopezosa et al. (2023) [54] | Provides insights into the integration of AI tools such as ChatGPT in journalism education, offering a training program for this integration based on interviews with college professors and academics to assess the potential uses and implications of AI in this field, with a focus on improving productivity in content production tasks. |
Płaza et al. (2022) [55] | Includes the creation of an emotion classification system for detecting emotions in conversational material in the contact center business, with a specific emphasis on NLP and emotion recognition applications to improve customer experience in contact centers. |
Themes | Authors | Focus |
---|---|---|
Enhancing healthcare communication and support with GAI | Comulada et al. (2023) [56] | Explores the use of chatbots in the treatment of Human Immunodeficiency Virus (HIV) {XE “Human Immunodeficiency Virus: (HIV)”} and healthcare support, with a special emphasis on improving the efficiency and convenience of healthcare interventions, particularly in the context of HIV. |
Gala and Makaryus (2023) [57] | Highlights the importance of healthcare personnel having excellent training to maximally utilize AI and model languages while addressing possible dangers and limitations, particularly helping to improve cardiology practice by combining these algorithms. | |
Santandreu et al. (2023) [58] | Examines issues associated with interaction in healthcare, focusing on the potential advantages of technology such as ChatGPT and NLP tools while noting that such tools should enhance human engagement in healthcare environments rather than replace it entirely. | |
Nandini et al. (2023) [59] | Illustrates the prospective benefits of using chatbots with Artificial Intelligence for medical consultations, with the primary goal of increasing medical support and patient care in the medical sector. | |
AI in medical decision making and diagnosis | Lecler et al. (2023) [60] | Provides perspectives on the applications of ChatGPT in radiology practice, including report generation, clinical decision support, and patient communication, with the aim of increasing radiology practices through AI and GAI-based models. |
Ong et al. (2023) [61] | Focuses on improving the delivery of healthcare by investigating the possibilities of AI technologies, such as ChatGPT and LLMs, to improve the treatment of patients, promote health equality, and battle healthcare inequities in marginalized regions. | |
Yang et al. (2023) [62] | Intends to improve the efficiency of clinical diagnosis and treatment results inside medical question-and-answer systems by addressing data disparities and pseudo-correlation concerns, with a focus on a counterfactual-based method for enhancing medical question-answering. | |
Bussola et al. (2023) [63] | Introduces the “PathologyAI” system, with an emphasis on automating the inspection of pathology slides using AI technology, and focuses on a technological method for automating pathology analysis, notably in the context of animal toxicity studies. | |
Grupac et al. (2023) [64] | Discusses how ChatGPT may aid medical professionals and patients in evaluating complicated healthcare data and delivering individualized treatment suggestions, with a special focus on medical decisions and personalized healthcare. | |
Panthier and Gatinel (2023) [65] | Assesses ChatGPT’s success in passing the European Board of Ophthalmology (EBO) {XE “European Board of Ophthalmology: (EBO)”} exam in French, as well as its prospective role in enhancing productivity in medical education and knowledge evaluation. | |
AI advancements in healthcare technology and research | Tustumi et al. (2023) [66] | Investigates the role of Machine Learning (ML), specifically ChatGPT, in the healthcare sector, particularly in assisting medical teams in identifying, treating, and preventing diseases using evidence-based protocols and data-driven decisions, with the overarching goal of improving healthcare efficiency through the use of AI. |
Escorcia et al. (2023) [67] | Centers on the use of Artificial Intelligence and blockchain technology in Internet of Things (IoT) {XE “Internet of Things: (IOT)”} healthcare systems, with a focus on the development of an innovative system (AIBS-IoTH) intended to improve energy efficiency and security in medical information management. | |
Strunga et al. (2023) [68] | Emphasizes the use of sophisticated Artificial Intelligence (AI) software in orthodontic treatment, with the goal of improving the accuracy, efficiency, and patient experience in this field through the use of AI-based evaluation and tracking software. |
Themes | Authors | Focus |
---|---|---|
Transforming agricultural sustainability and public governance | Wang et al. (2021) [69] | Points out the application of AI-powered chatbots to improve consumer experience and productivity in the agricultural sector, with results demonstrating the suitability of a hybrid recall generation approach for agricultural chatbots, evaluating their efficiency and construction costs, and exploring customer satisfaction factors in Taiwan’s agricultural sector. |
Feitosa et al. (2020) [70] | Focuses on the development of chatbot prototypes for the Brazilian Ministry of Agriculture with the primary purpose of enhancing the execution of services and providing plant health information. | |
Precision agriculture and efficient public service delivery | Ramadoss et al. (2023) [71] | Indicates the incorporation of Machine Learning and Artificial Intelligence with the purpose of increasing agricultural output, being under the umbrella of Chatbots and Conversational Agents, catering to farmers and agricultural researchers in this sector. |
AI collaboration for agricultural advancements and informed governance | Usip et al. (2022) [72] | Utilizes agricultural technology and knowledge propagation, naming the government as an ideal client for obtaining input on governance through agricultural services, to enhance agricultural knowledge availability and production with a mobile chatbot created exclusively for crop producers. |
Tsai et al. (2021) [73] | Explores government agencies, particularly those involved in disaster response operations, and their experiences with deploying chatbot systems for managing data, with the goal of improving data management effectiveness within the government sector. |
Themes | Authors | Focus |
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Strategic AI integration in business operations | Walkowiak (2023) [74] | Discusses advances in technology and economics, with an emphasis on microeconomics and productivity in the economy, all within the framework of Generative AI Models. |
Bankins et al. (2023) [75] | Explores the impact of AI technology, notably generative AI tools, on individual, social, and organizational aspects, and examines numerous issues associated with human–AI interaction in work settings. | |
Araujo et al. (2022) [76] | Focuses on the area of technology adoption and its consequences for organizational processes, with particular emphasis on the Adoption of Conversational Agents and their related expectations at the organizational front lines. | |
Makhija and Chacko (2021) [77] | Examines the implementation of Artificial Intelligence (AI) within the financial services context, focusing mainly on its application for reducing expenses, efficiency improvement, and customer engagement, addressing the growth, advantages, obstacles, and customers of AI in this sector, shedding light on both its potential and vulnerabilities. | |
Illescas et al. (2023) [78] | Concentrates on the deployment of chatbots in the realms of technological transformation and change management in enterprise, particularly in the context of technology adoption, with the goal of increasing efficiency through automation and chatbot integration to facilitate the digital evolution process. | |
Anagnoste et al. (2021) [79] | Assists companies in making well-informed digital transformation judgments by focusing on the topics of digital transformation and automation in business, highlighting the reshaping of business models, decreasing work, and generating occupations more suitable for humans. | |
Straßer and Axmann (2021) [80] | Compares individual AI use cases for their applicability to actual logistics applications, with an emphasis on efficiency-enhancing AI solutions in logistics and a larger goal of industrial effectiveness and logistics optimization. | |
Chen et al. (2023) [81] | Highlights recent advancements and practical applications of (AI) in business and finance, citing potential benefits, restrictions, and challenges associated with deploying generative AI, resulting in greater accessibility, efficiency, and cost savings. | |
Leo et al. (2017) [82] | Explores enterprise-level productivity by improving the efficiency of data science components, with the goal of bridging the gap between the natural language expression of a business workflow and the manual translation of that workflow into algorithmic software. | |
Empowering employee efficiency and skills development | Al-Ababneh et al. (2023) [83] | Illustrates the real-world advantages of incorporating AI into large companies, such as increased labor productivity and significant cost savings, through an examination of the impact of Artificial Intelligence technology on the performance of large companies, with a particular emphasis on the banking sector. |
Fan et al. (2023) [84] | Investigates the effects of various forms of chatbot flexibility on consumer satisfaction and provides significant information for service providers on the optimal deployment of AI chatbots in customer interactions. | |
Saengrith et al. (2023) [85] | Comes under the category of education and human resource development, involving the use of chatbots to improve the ability to solve problems in the workplace, with an emphasis on developing skills for problem solving among employees through integrated training with a chatbot. | |
Chithra and Brahmananda (2020) [86] | Provides useful knowledge into the architecture, platforms, development frameworks, and also the benefits and drawbacks of interactive agents, with the goal of improving customer experience and optimizing efficiency across various industries, with a particular emphasis on the use of NLP and NLU technologies. | |
Virkar et al. (2019) [87] | Focuses on strategies intended at boosting chatbot conversational skills and improving chatbot interactions in many industries, particularly in the commercial and financial domains to enhance staff productivity. | |
Chandar et al. (2017) [88] | Plans the practical implementation of a conversational system aimed to assist new recruits throughout their onboarding process inside a business, with the goal of improving training efficiency through the use of AI-based Conversational Agents. | |
Steinbauer et al. (2019) [89] | Emphasizes optimizing business software interaction with chatbots, notably inside Customer Relationship Management (CRM) software, assessing the influence on staff activities and user experiences, and eventually increasing the productivity of customer relationship management and business processes. | |
AI-driven business transformation and digital evolution | Piyatumrong et al. (2018) [90] | Focuses on enhancing performance in internal information distribution using chatbot technology to improve information exchange within Research and Development (R&D) companies. |
Hsu and Lin (2023) [91] | Falls into the evaluation of the quality of service provided by AI chatbots in customer service with the objective of increasing client satisfaction and trust, and is especially concerned with AI-powered chatbots and their function in customer service. | |
Hung et al. (2021) [92] | Provides helpful information into the use of technologies such as Robotic Process Automation (RPA) {XE “Robotic Process Automation: (RPA)”} and chatbots to improve business processes and overall efficiency, focusing on RPA and chatbots in the context of business productivity. | |
Silva et al. (2023) [93] | Discusses data from an online survey and uses modeling with structural equations to study the elements that impact consumer preferences to use chatbots for online shopping, with the overriding objective of improving the online shopping experience with chatbots. | |
Bialkova (2023) [94] | Delivers perspectives on the aspects influencing customer satisfaction in chatbot interactions, with a particular emphasis on chatbot accessibility, with the goal of improving chatbot functionality and user enjoyment in Human–Computer Interaction (HCI). | |
Quality and user perception enhancement | Mehrolia et al. (2023) [95] | Investigates the influence of several aspects of chatbot service quality on user satisfaction and retention, while also considering the reducing impact of perceived risk, with the goal of understanding user expectations and improving services delivered by AI-powered chatbots. |
Lappeman et al. (2023) [96] | Analyzes the interaction between privacy issues, confidence, and user self-disclosure, with a particular focus on the convergence of banking marketing and technology, covering digital privacy concerns and their influence on user honesty. | |
Kar and Kushwaha (2023) [97] | Offers knowledge about the elements that might determine the success of AI projects in business, notably in the field of business decision making, centered on the use of Artificial Intelligence (AI) to improve decision-making processes and experimentation in the business environment. | |
Xu et al. (2022) [98] | Discovers the quality assessment mechanism in user–chatbot interaction examining the relationship between chatbot performance and user perception, and eventually building a model for quality assessment in communication, concentrating on the influence of chatbot performance on user opinion. | |
Colace et al. (2017) [99] | Includes the creation of a conversational workflow prototype for a chatbot specialized in wheels, utilizing a Petri-net-based model to offer suitable tires to users, with the goal of improving user experience and efficiency in the tires domain through chatbot interactions. |
Themes | Authors | Focus |
---|---|---|
Assessing the impact and potential of GAI in organizational transformation | Dwivedi et al. (2023) [100] | Gives unique visions into the potential benefits and risks of Artificial Intelligence technologies in a variety of sectors, including computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing; it not only seeks to improve productivity across a variety of businesses and areas but also investigates the ethical and legal issues associated with the use of AI. |
Iparraguirre et al. (2023) [101] | Discovers the various advantages and typologies of Artificial Intelligence (AI) in the realm of information systems, delving into the broad surroundings of Conversational Agents (bots) within different subdivisions, encompassing customer service, healthcare, and presentation, all contributing to the advancement of business processes. | |
Cao et al. (2022) [102] | Presents insights into prospective applications for customization and user adaption strategies in the context of technical client service chatbots, helping to measure user experience factors in customer support areas. | |
Enhancing user experiences and productivity with GAI | Temple et al. (2020) [103] | Focuses on increasing employee efficiency and educated decision making through the strategic implementation of cognitive solutions, including AI Algorithms and Techniques, across many organizational fields. |
Banerjee et al. (2018) [104] | Shows the variables driving human decision making and their relevance to chatbots, with a comparative examination of human and AI decision making, offering perspectives to the broader field of AI. | |
Deksne and Vasiljevs (2018) [105] | Promotes productivity and client satisfaction through chatbot installation, with application in customer service center development and assessment. |
Themes | Authors | Focus |
---|---|---|
Enhancing professional productivity | Noy and Zhang (2023) [106], Gilardi et al. (2023) [107], Hassani and Silva (2023) [108], Yue and Yuan (2023) [109], Deng et al. (2023) [110], Wang (2023) [111], Weekes and Eskridge (2022) [112], Jo and Kim (2022) [113], Hardi et al. (2022) [114], Xu et al. (2021) [115]. |
|
Understanding user interactions and motivations | Hyun and Kim (2023) [116], Alamleh et al. (2023) [117], Kuang et al. (2023) [118], Manshad and Brannon (2022) [119], Casadei et al. (2022) [120], Gao and Jiang (2021) [121]. |
|
AI in specific domains and use cases | Tai et al. (2023) [122], Subramani et al. (2023) [123], Roberts et al. (2023) [124], Xu et al. (2023) [125], Radoi (2023) [126], Jiang et al. (2023) [127], Aydin and Ayhan (2022) [128]. |
|
Ethical and philosophical implications | Perez et al. (2023) [129], Ionuț (2022) [130], Rawat et al. (2022) [131], Rzepka et al. (2022) [132], Borsci et al. (2022) [133], Camargo et al. (2022) [134], Lee (2022) [135], Mohana (2022) [136], Kathirvelu et al. (2022) [137]. |
|
Rank | Term | Occurrences | Relevance Score |
---|---|---|---|
1 | Machine Learning | 18 | 1.1329 |
2 | Deep Learning | 16 | 1.2878 |
3 | Efficiency | 16 | 0.6708 |
4 | User interface | 11 | 0.8669 |
5 | Human | 11 | 1.3399 |
6 | Performance | 6 | 0.8336 |
7 | Language | 6 | 1.334 |
8 | Language model | 5 | 5.2478 |
Rank | Term | Occurrences | Total Link Strength |
---|---|---|---|
1 | Artificial Intelligence | 60 | 144 |
2 | Chatbots | 45 | 101 |
3 | Chatbot | 42 | 83 |
4 | Machine Learning | 18 | 67 |
5 | Natural Language Processing | 15 | 63 |
6 | Natural Language Processing Systems | 16 | 58 |
7 | ChatGPT | 17 | 53 |
8 | Deep Learning | 16 | 48 |
9 | Efficiency | 16 | 45 |
10 | Human | 11 | 44 |
Rank | Term | Documents | Citations | Total Link Strength |
---|---|---|---|---|
1 | United States | 32 | 400 | 29 |
2 | China | 28 | 83 | 14 |
3 | India | 19 | 179 | 13 |
4 | United Kingdom | 14 | 223 | 29 |
5 | Italy | 12 | 249 | 19 |
6 | Germany | 9 | 135 | 17 |
7 | Australia | 8 | 154 | 23 |
8 | Netherlands | 7 | 135 | 22 |
9 | Poland | 7 | 133 | 13 |
10 | Taiwan | 7 | 141 | 13 |
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Al Naqbi, H.; Bahroun, Z.; Ahmed, V. Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review. Sustainability 2024, 16, 1166. https://doi.org/10.3390/su16031166
Al Naqbi H, Bahroun Z, Ahmed V. Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review. Sustainability. 2024; 16(3):1166. https://doi.org/10.3390/su16031166
Chicago/Turabian StyleAl Naqbi, Humaid, Zied Bahroun, and Vian Ahmed. 2024. "Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review" Sustainability 16, no. 3: 1166. https://doi.org/10.3390/su16031166
APA StyleAl Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review. Sustainability, 16(3), 1166. https://doi.org/10.3390/su16031166