Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review

被引:71
作者
Al Naqbi, Humaid [1 ]
Bahroun, Zied [1 ]
Ahmed, Vian [1 ]
机构
[1] Amer Univ Sharjah, Coll Engn, Dept Ind Engn, POB 26666, Sharjah, U Arab Emirates
关键词
generative artificial intelligence; work productivity enhancement; management; chatbots; ChatGPT; ethics; knowledge management; review; PERFORMANCE; UNIVERSITY; CHATBOT; CHATGPT;
D O I
10.3390/su16031166
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this review, utilizing the PRISMA methodology, a comprehensive analysis of the use of Generative Artificial Intelligence (GAI) across diverse professional sectors is presented, drawing from 159 selected research publications. This study provides an insightful overview of the impact of GAI on enhancing institutional performance and work productivity, with a specific focus on sectors including academia, research, technology, communications, agriculture, government, and business. It highlights the critical role of GAI in navigating AI challenges, ethical considerations, and the importance of analytical thinking in these domains. The research conducts a detailed content analysis, uncovering significant trends and gaps in current GAI applications and projecting future prospects. A key aspect of this study is the bibliometric analysis, which identifies dominant tools like Chatbots and Conversational Agents, notably ChatGPT, as central to GAI's evolution. The findings indicate a robust and accelerating trend in GAI research, expected to continue through 2024 and beyond. Additionally, this study points to potential future research directions, emphasizing the need for improved GAI design and strategic long-term planning, particularly in assessing its impact on user experience across various professional fields.
引用
收藏
页数:37
相关论文
共 144 条
[11]   Assessing the capabilities of ChatGPT to improve additive manufacturing troubleshooting [J].
Badini, Silvia ;
Regondi, Stefano ;
Frontoni, Emanuele ;
Pugliese, Raffaele .
ADVANCED INDUSTRIAL AND ENGINEERING POLYMER RESEARCH, 2023, 6 (03) :278-287
[12]   Is ChatGPT scary good? How user motivations affect creepiness and trust in generative artificial intelligence [J].
Baek, Tae Hyun ;
Kim, Minseong .
TELEMATICS AND INFORMATICS, 2023, 83
[13]  
Banerjee S., 2018, NATURE INSPIRED COMP, V2015, P203, DOI DOI 10.1007/978-981-10-6747-1_23
[14]   Generative artificial intelligence [J].
Banh, Leonardo ;
Strobel, Gero .
ELECTRONIC MARKETS, 2023, 33 (01)
[15]   A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice [J].
Bankins, Sarah ;
Ocampo, Anna Carmella ;
Marrone, Mauricio ;
Restubog, Simon Lloyd D. ;
Woo, Sang Eun .
JOURNAL OF ORGANIZATIONAL BEHAVIOR, 2024, 45 (02) :159-182
[17]  
Borji A., 2023, Battle of the Wordsmiths: Comparing ChatGPT, GPT-4, Claude, and Bard
[18]   The Chatbot Usability Scale: the Design and Pilot of a Usability Scale for Interaction with AI-Based Conversational Agents [J].
Borsci S. ;
Malizia A. ;
Schmettow M. ;
van der Velde F. ;
Tariverdiyeva G. ;
Balaji D. ;
Chamberlain A. .
Personal and Ubiquitous Computing, 2022, 26 (1) :95-119
[19]   Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT [J].
Budhwar, Pawan ;
Chowdhury, Soumyadeb ;
Wood, Geoffrey ;
Aguinis, Herman ;
Bamber, Greg J. ;
Beltran, Jose R. ;
Boselie, Paul ;
Lee Cooke, Fang ;
Decker, Stephanie ;
DeNisi, Angelo ;
Dey, Prasanta Kumar ;
Guest, David ;
Knoblich, Andrew J. ;
Malik, Ashish ;
Paauwe, Jaap ;
Papagiannidis, Savvas ;
Patel, Charmi ;
Pereira, Vijay ;
Ren, Shuang ;
Rogelberg, Steven ;
Saunders, Mark N. K. ;
Tung, Rosalie L. ;
Varma, Arup .
HUMAN RESOURCE MANAGEMENT JOURNAL, 2023, 33 (03) :606-659
[20]   A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study [J].
Bussola, Nicole ;
Xu, Joshua ;
Wu, Leihong ;
Gorini, Lorenzo ;
Zhang, Yifan ;
Furlanello, Cesare ;
Tong, Weida .
CHEMICAL RESEARCH IN TOXICOLOGY, 2023, 36 (08) :1321-1331