The DOI matches its Crossref record.
The Feeling Economy: Managing in the Next Generation of Artificial Intelligence (AI)
Abstract
The capability of AI is currently expanding beyond mechanical and repetitive to analytical and thinking. A “Feeling Economy” is emerging, in which AI performs many of the analytical and thinking tasks, and human workers gravitate more toward interpersonal and empathetic tasks. Although these people-focused tasks have always been important to jobs, they are now becoming more important to an unprecedented degree. To manage more effectively in the Feeling Economy, managers must adapt the nature of jobs to compensate for the fact that many of the analytical and thinking tasks are increasingly being performed by AI, and, thus, human workers must place increased emphasis on the empathetic and emotional dimensions of their work.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Ordered by the difficulty with which AI can address them, there are mechanical, thinking, and feeling AI intelligences (Huang and Rust 2018; Huang et al. 2019).”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Huang et al. 2019
In the citing paper
“managers and management education underpreparing the next generation for feeling and emotional intelligence (Huang and Rust 2018; Huang et al. 2019)”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Huang et al. 2019
In the citing paper
“A consistent finding across domains was the necessity of a central entity to ensure transparency and alignment throughout the lifecycle of AI implementation39–41.”
From Advancing healthcare AI governance through a comprehensive maturity model based on systematic review (2026) · cites this paper as [39]
Citations
Cited by
Sorted by citations
Items 101–200 of 576
Past, present and future of AI in marketing and knowledge management
Marvi, R., Foroudi, P., & Cuomo, M. T. (2024) · Journal of Knowledge Management · doi:10.1108/jkm-07-2023-0634 · Cited by 89
Pedersen, C. L. (2021) · Psychology and Marketing · doi:10.1002/mar.21448 · Cited by 68
In pursuit of responsible innovation for precision agriculture technologies
Gardezi, M., Adereti, D. T., Stock, R., & Ogunyiola, A. (2022) · Journal of Responsible Innovation · doi:10.1080/23299460.2022.2071668 · Cited by 63
AI in sales: Laying the foundations for future research
McClure, C. E., Epler, R. T., Schmitt, L., & Rangarajan, D. (2024) · Journal of Personal Selling and Sales Management · doi:10.1080/08853134.2024.2329905 · Cited by 83
“For example, the “multiple type” view (see Huang, Rust, and Maksimovic 2019; Huang and Rust 2018) suggests that mechanical AI learns and adapts in a minimal way and is well-suited for tasks that are easily automated and fit for standardization.”
BackgroundUnderstanding Human-AI Augmentation in the Workplace: A Review and a Future Research Agenda
Trinh, N. T. T., & Elbanna, A. R. (2025) · Information Systems Frontiers · doi:10.1007/s10796-025-10591-5 · Cited by 109
Yang, C., & Hu, J. (2021) · Journal of Brand Management · doi:10.1057/s41262-021-00261-7 · Cited by 67
“…al. 2019; Yeomans et al. 2019), types of AI (e.g., mechanical AI, analytical AI, intuitive AI, and empathetical AI; Huang and Rust 2018, 2020, 2021; Huang et al. 2019), the degree of AI anthropomorphism (Kim et al. 2019; Mende et al. 2019; Moussawi et al. 2020; Waytz et al. 2014), consumer…”
BackgroundForecasting energy generation in large photovoltaic plants using radial belief neural network
Natarajan, Y., Kannan, S., Selvaraj, C., et al. (2021) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2021.100578 · Cited by 57
Vorobeva, D., Pinto, D. C., António, N., & Mattila, A. S. (2023) · Current Issues in Tourism · doi:10.1080/13683500.2023.2214353 · Cited by 68
“Third, merging an AI framing approach with the Feeling Economy, we reveal unique underlying processes – i.e. the combination of feeling (enjoyment) and thinking (ease of use) features and their impact on consumers ’ willingness to accept AI.”
BackgroundMethodInfluentialA Conceptual Framework on Defining Businesses Strategy for Artificial Intelligence
Caner, S., & Bhatti, F. (2020) · Contemporary Management Research · doi:10.7903/cmr.19970 · Cited by 73
“Furthermore, Huang, Rust, and Maksimovic (2019) asserted that there are three levels of AI with respect to intelligence: mechanical, thinking, and feeling intelligence.”
BackgroundSong, M., Chen, H., Wang, Y., & Duan, Y. (2024) · Journal of Destination Marketing & Management · doi:10.1016/j.jdmm.2024.100892 · Cited by 69
Conscious Empathic AI in Service
Esmaeilzadeh, H., & Vaezi, R. (2022) · Journal of Service Research · doi:10.1177/10946705221103531 · Cited by 58
“To this end, the proposed theory paves a path toward the most advanced and comprehensive form of arti fi cial intelligence, dubbed “ empathic AI ” by Huang and Rust (2018).”
BackgroundInfluentialArtificial intelligence, workers, and future of work skills
Bankins, S., Hu, X., & Yuan, Y. E. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101828 · Cited by 96
“The importance of soft skills such as those related to interpersonal skills, empathy, and social and emotional intelligence will also be critical as a new “feeling economy” emerges and workers focus more on tasks requiring these skills [54].”
BackgroundThe impact of different types of service robots usage in hotels on guests’ intention to stay
Çallı, B. A., Çallı, L., ÇALLI, D. S., & Çallı, F. (2022) · Journal of Hospitality and Tourism Technology · doi:10.1108/jhtt-09-2021-0266 · Cited by 56
Color effects on AI influencers’ product recommendations
Chan, K. W., Septianto, F., Kwon, J., & Kamal, R. S. (2023) · European Journal of Marketing · doi:10.1108/ejm-03-2022-0185 · Cited by 57
Singh, A., & Pandey, J. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1339782 · Cited by 87
Song, Y., Qiu, X., & Liu, J. (2025) · Systems · doi:10.3390/systems13080683 · Cited by 83
Fan, H., Gao, W., & Han, B. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107287 · Cited by 51
How AI tools can—and cannot—help organizations become more ethical
Cremer, D. D., & Narayanan, D. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1093712 · Cited by 64
Do, H., Lin, X., & Shipton, H. J. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115279 · Cited by 103
Huang, M., & Rust, R. T. (2022) · Journal of service management · doi:10.1108/josm-11-2021-0425 · Cited by 53
Self-citationThe GenAI Future of Consumer Research
Huang, M., & Rust, R. T. (2025) · Journal of Consumer Research · doi:10.1093/jcr/ucaf013 · Cited by 67
Self-citationSaviano, M., Prete, M. D., Mueller, J., & Caputo, F. (2023) · Journal of Knowledge Management · doi:10.1108/jkm-12-2022-0940 · Cited by 56
Gandía, J. A. G., Gavrila, S. G., Ancillo, A. D. L., & Núñez, M. T. D. V. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123908 · Cited by 61
Tuomi, A., & Ascenção, M. P. (2021) · Journal of Hospitality and Tourism Insights · doi:10.1108/jhti-07-2021-0175 · Cited by 53
Reskilling Workforce for the Artificial Intelligence Age: Challenges and the Way Forward
Pradhan, I. P., & Saxena, P. (2023) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80455-662-720230011 · Cited by 48
Exploring AI technology and consumer behavior in retail interactions
Petrescu, M., Krishen, A. S., Gironda, J. T., & Fergurson, J. R. (2024) · Journal of Consumer Behaviour · doi:10.1002/cb.2386 · Cited by 61
Yao, Q., Hu, C., & Zhou, W. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122948 · Cited by 43
Work Characteristics Needed by Middle Managers When Leading AI-Integrated Service Teams
Koponen, J. P., Julkunen, S., Laajalahti, A., Turunen, M., & Spitzberg, B. H. (2023) · Journal of Service Research · doi:10.1177/10946705231220462 · Cited by 71
“The idea of the feeling economy is that as AI becomes more capable of performing many analytical and thinking tasks, human workers are left to concentrate on em-pathetic and interpersonal tasks (Huang and Rust 2018; Huang, Rust, and Maksimovic 2019).”
BackgroundInfluentialOperations & supply chain management: principles and practice
Petropoulos, F., Akkermans, H. A., Akşin, O. Z., Ali, I., Babai, M. Z., Barbosa‐Póvoa, A. P., Battaïa, O., Besiou, M., Boysen, N., Brammer, S. J., et al. (2025) · International Journal of Production Research · doi:10.1080/00207543.2025.2555531 · Cited by 88
Advancing healthcare AI governance through a comprehensive maturity model based on systematic review
Hussein, R., Zink, A., Ramadan, B., Howard, F. M., Hightower, M., Shah, S., & Beaulieu-Jones, B. K. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02418-7 · Cited by 42
Lu, V. N., Wirtz, J., Kunz, W. H., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3806199 · Cited by 41
Armenia, S., Franco, E., Iandolo, F., Maielli, G., & Vito, P. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.123131 · Cited by 53
Assessing the Impact of AI Solutions’ Ethical Issues on Performance in Managerial Accounting
Vărzaru, A. A. (2022) · Electronics · doi:10.3390/electronics11142221 · Cited by 39
Jin, S. A., & Viswanathan, V. (2024) · AI & Society · doi:10.1007/s00146-023-01832-9 · Cited by 40
“Emerging themes in AI-enabled technologies entail “ feeling AI, emotional AI, empathetic AI, and affective computing ” (Huang et al. 2019).”
Quantifying human resource management: a literature review
Coron, C. (2022) · Personnel Review · doi:10.1108/pr-05-2020-0322 · Cited by 40
Lee, H., & Yi, Y. (2021) · Psychology and Marketing · doi:10.1002/mar.21628 · Cited by 41
Ramaul, L., Ritala, P., Kostis, A., & Aaltonen, P. (2026) · Journal of Management Studies · doi:10.1111/joms.13246 · Cited by 41
Gonçalves, A. R., Pinto, D. C., Shuqair, S., Dalmoro, M., & Mattila, A. S. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102748 · Cited by 50
Human trust in otherware – a systematic literature review bringing all antecedents together
Saßmannshausen, T. M., Burggräf, P., Hassenzahl, M., & Wagner, J. (2022) · Ergonomics · doi:10.1080/00140139.2022.2120634 · Cited by 42
“First, following Huang, Rust, and Maksimovic (2019) we are facing an economy change: from thinking to feeling.”
BackgroundHigh-Tech Defense Industries: Developing Autonomous Intelligent Systems
dos Reis, J. C. G., Cohen, Y., Melão, N. F., Costa, J., & Jorge, D. (2021) · Applied Sciences · doi:10.3390/app11114920 · Cited by 52
“[30] also proposed three artificial intelligences—mechanical, thinking, and feeling (Figure 1).”
BackgroundCustomer service with AI-powered human-robot collaboration (HRC): A literature review
Leocádio, D., Guedes, L., Oliveira, J., Reis, J., & Melão, N. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.01.120 · Cited by 37
Dianova, V. G., & Schultz, M. D. (2023) · Industry and Higher Education · doi:10.1177/09504222231199989 · Cited by 48
“With AI increasingly performing analytics (Huang et al., 2019), familiarity with humanities helps students develop fl exible thinking by drawing “ attention to alterity, difference, relationship, morality, and purpose … and [ful fi lling] the passion for connectedness and meaning ” (Bullough Jr,…”
BackgroundFioravante, R. (2024) · Sustainability · doi:10.3390/su16031232 · Cited by 46
Novak, T. P., & Hoffman, D. L. (2022) · Journal of Consumer Research · doi:10.1093/jcr/ucac014 · Cited by 36
Osburg, V., Yoganathan, V., Kunz, W. H., & Tarba, S. Y. (2022) · Journal of Service Research · doi:10.1177/10946705221118233 · Cited by 37
“Hence, as underlined by Huang et al. (2019), to manage the next generation of AI successfully, incumbent managers must adapt the nature of jobs to compensate for the fact that many of the analytical and thinking tasks are increasingly being executed by AI.”
BackgroundParticipatory action research in critical data studies: Interrogating AI from a South–North approach
Medrado, A. M. L., & Verdegem, P. (2024) · Big Data & Society · doi:10.1177/20539517241235869 · Cited by 48
“Huang, Rust and Maksimovic (2019), for instance, distinguish between AI systems that are mechanically intelligent and designed to perform repetitive tasks; systems that are thinking intelligent, and designed to learn and adapt from data autonomously; and AI systems that may become feeling…”
Kalyani, S., & Gupta, N. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00094-0 · Cited by 35
“In another study having research done on similar lines and having an exciting title talking about "The Feeling Economy," the authors state that going further, only jobs that require feelings will remain with human beings, and the thinking tasks will be taken over by machines [89].”
BackgroundGhosh, D., Ghosh, R., Chowdhury, S. R., & Ganguly, B. (2024) · Management Review Quarterly · doi:10.1007/s11301-023-00393-x · Cited by 41
“Therefore, the net effect will vary depending on the specific application and the industry (Agrawal et al. 2019; Huang et al. 2019).”
Ki, C.-W. C., Chong, S. M., Aw, E. C., Lam, M. M.-L., & Wong, C. W. Y. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.104094 · Cited by 42
Adopting AI in the Context of Knowledge Work: Empirical Insights from German Organizations
von Richthofen, G., Ogolla, S., & Send, H. (2022) · Information · doi:10.3390/info13040199 · Cited by 33
Salem, M. H., Selem, K. M., Khalid, R., Raza, M., & Valeri, M. (2023) · European Business Review · doi:10.1108/ebr-10-2022-0203 · Cited by 30
YANIT, M., Yanit, M., & Wan, F. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107870 · Cited by 31
Roy, B. K., & Pagaldiviti, S. R. (2023) · Smart Tourism · doi:10.54517/st.v4i1.2330 · Cited by 47
Digital brand equity: The concept, antecedents, measurement, and future development
France, S. L., Davcik, N. S., & Kazandjian, B. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115273 · Cited by 73
Unifying technology and people: revisiting service in a digitally transformed world
Kandampully, J. A., Bilgihan, A., & Li, D. K. (2021) · Service Industries Journal · doi:10.1080/02642069.2021.1965578 · Cited by 29
“According to Huang, Rust, et al. (2019), in the Feeling Economy employment and wages will be more valued and attributable to those tasks that relates to ‘ feelings ’ tasks.”
BackgroundInfluentialRoy, P., Ghose, B., Singh, P. K., Tyagi, P. K., & Vasudevan, A. (2025) · F1000Research · doi:10.12688/f1000research.160959.1 · Cited by 29
Li, H., & Tian, F. (2026) · Group Decision and Negotiation · doi:10.1007/s10726-026-09980-1 · Cited by 78
Enhancing corporate governance through AI: a systematic literature review
Ahdadou, M., Aajly, A., & Tahrouch, M. (2024) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2024.2326120 · Cited by 27
“AI can be brie fl y de fi ned as ‘ technologies that mimic human intelligence ’ (Huang, Rust, and Maksimovic 2019), ‘ machines that perform tasks that humans would perform ’ (Bolander 2019) or ‘ an algorithm that is capable of learning and thinking ’ (Eliasy and Przy-chodzen 2020).”
Leadership-competences in the era of artificial intelligence – a structured review
Bock, T., & von der Oelsnitz, D. (2025) · Strategy and Leadership · doi:10.1108/sl-09-2024-0100 · Cited by 41
dos Reis, J. C. G., Santo, P. E., & Melão, N. F. (2021) · Brazilian Journal of Operations & Production Management · doi:10.14488/bjopm.2021.010 · Cited by 33
“Later on, Huang et al. (2019) merged the analytical and intuitive intelligence into thinking intelligence.”
BackgroundMollah, M. A., Rana, M., Amin, M. B., Sony, M. M. A. A. M., Rahaman, M. A., & Fenyves, V. (2024) · Sustainability · doi:10.3390/su162410843 · Cited by 56
Alka’awneh, S. M. N., Halim, H. A., Wahed, M. K. Y. A., et al. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-73899-9_32 · Cited by 25
Role of Artificial Intelligence in Re-inventing Human Resource Management
Kalia, P., & Mishra, G. (2023) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80455-662-720230013 · Cited by 27
Xia, M. (2023) · SHS Web of Conferences · doi:10.1051/shsconf/202315503022 · Cited by 28
“Thus, field research in more industries and functions is needed to investigate the effects of human-AI collaboration in the feeling economy, especially in that have a high level of empathetic skills, such as business and financial operation, and first-line managers [1].”
BackgroundMethodInfluentialMazurova, E., & Standaert, W. (2024) · Information Technology and People · doi:10.1108/itp-11-2022-0915 · Cited by 28
Marrone, R. L., Fowler, S., Barthakur, A., Dawson, S., Siemens, G., & Singh, C. (2024) · School Leadership and Management · doi:10.1080/13632434.2024.2425019 · Cited by 26
“Huang et al. (2019) suggest that a different type of management will be necessary for organisations to handle the shift caused by AI.”
Khoa, D. T., Gip, H., Guchait, P., Wang, C., & Baaklini, E. S. (2022) · Journal of service management · doi:10.1108/josm-09-2021-0352 · Cited by 31
Developing and validating an AI-supported teaching applications' self-efficacy scale
Chou, C.-M., Shen, T.-C., Shen, T.-C., & Shen, C.-H. (2024) · Research and Practice in Technology Enhanced Learning · doi:10.58459/rptel.2024.19035 · Cited by 25
Xing, J., & Jiang, Q. (2024) · Kybernetes · doi:10.1108/k-10-2023-2237 · Cited by 28
Human-Artificial Intelligence in Management Functions: A Synergistic Symbiosis Relationship
Islami, X., & Mulolli, E. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2439615 · Cited by 46
“Research on the intersection of HR planning and AI has investigated various aspects of using AI to redesign employee tasks to be more efficient and effective (Huang, Rust, and Maksimovic 2019).”
Dynamics of Entrepreneurial Ecosystem and Entrepreneurship Development: Evidence from Africa
Abdulai, A., & Hussain, N. R. (2023) · Cogent Business & Management · doi:10.1080/23311975.2023.2292315 · Cited by 24
“An economy benefits from an enterprise-led policy when it deepens the number of enterprises borne out of the policy (Huang et al., 2018; World Bank, 2022), not only by early signals of entrepreneurial activities which are usually fragile and unsustainable.”
InfluentialÖzbey, F., & Yaşa, Y. (2025) · BMC Medical Education · doi:10.1186/s12909-024-06630-5 · Cited by 32
Chaturvedi, A., Yadav, N., & Dasgupta, M. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2456534 · Cited by 30
“The relationship between AI and SDM is complex and evolving, with AI acting as both a collaborator and a potential driver of change in the decision-making landscape (Huang et al., 2019).”
Shi, X., Niu, G., Jin, S., Yang, W., & Sun, X. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103644 · Cited by 28
Upskilling and Reskilling for a VUCA World
Achoki, P. M. (2023) · GiLE Journal of Skills Development · doi:10.52398/gjsd.2023.v3.i2.pp34-52 · Cited by 28
Benabou, A., & Touhami, F. (2025) · International journal of production management and engineering · doi:10.4995/ijpme.2025.21900 · Cited by 38
“…educational curricula to prioritise people skills alongside technological proficiency prepares future generations for deeper human-AI collaboration, focusing on emotional intelligence and complementarity between humans and AI as essential for the evolving workforce landscape (Huang et al., 2019).”
Modelling the factors in the adoption of artificial intelligence in Indian management institutes
Priya, S. S., Jain, V., Priya, M. S., Dixit, S. K., & Joshi, G. (2022) · foresight · doi:10.1108/fs-09-2021-0181 · Cited by 24
Delphi-based visual scenarios: An innovative use of generative adversarial networks
Zio, S. D., Calleo, Y., & Bolzan, M. (2023) · Futures · doi:10.1016/j.futures.2023.103280 · Cited by 19
Guest editorial: Challenges and prospects of AIoT application in hospitality and tourism marketing
Hsu, M.-J., Ting, H., Lui, T.-W., Chen, S., & Cheah, J. (2022) · Journal of Hospitality and Tourism Technology · doi:10.1108/jhtt-05-2022-326 · Cited by 23
Dong, X., Xie, J., & Gong, H. (2025) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2024.0468 · Cited by 22
AI Support Marketing: Understanding the Customer Journey towards the Business Development
Tanveer, M., Khan, N., & Ahmad, A.-R. (2021) · International Conference on Artificial Intelligence and Data Analytics (CAIDA) · doi:10.1109/caida51941.2021.9425079 · Cited by 23
“In marketing, the academic literature on AI can be classified into a few main kinds including: Psychological reactions to AI by consumers [14]; the impact of AI on jobs and the environment [15]; and, the management and policy problems relating to the AI algorithms in specific marketing issues [16].”
BackgroundDigital technologies as a new component of the business process
Nosova, S., & Norkina, A. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.06.076 · Cited by 19
B2B Buyers' emotions and regulatory focus in justice and switch back decisions
Chugh, R., Gould, A. N., Leach, M. P., & Liu, A. H. (2023) · Industrial Marketing Management · doi:10.1016/j.indmarman.2022.12.012 · Cited by 19
EI & AI In Leadership and How It Can Affect Future Leaders
Vivek, R., & Krupskyi, О. (2024) · European Journal of Management Issues · doi:10.15421/192415 · Cited by 38
Koponen, J. P., Julkunen, S., Laajalahti, A., Turunen, M., & Spitzberg, B. H. (2024) · Industrial Marketing Management · doi:10.1016/j.indmarman.2024.11.012 · Cited by 29
Bruneckienė, J., Jucevičius, R., Zykienė, I., Rapsikevičius, J., & Lukauskas, M. (2019) · Sustainability · doi:10.3390/su11246892 · Cited by 20
“Artificial intelligence has been widely used to analyse various economic concepts and problems, for example, in a city smartness analysis [16] prediction of knowledge-hiding behaviour [17] as well as modelling and forecasting [18,19].”
Lysyakov, M., & Viswanathan, S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3758338 · Cited by 18
Rehman, N., Huang, X., Mahmood, A., Abbasi, M. S., Qin, J., & Wu, W. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05584-3 · Cited by 18
“The next generation needs to be equipped with skills in arti fi cial intelligence, making the effective implementation of STEM education more important than ever (Huang et al. 2019).”
Mustopa, M., Nasikhin, N., Chamami, R., Nihayah, H., Habibullah, M. R., & Manshur, A. (2024) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.23.2.17 · Cited by 38
Saviano, M., Thomas, A., Prete, M. D., Verderese, D., & Sasso, P. (2025) · European Journal of Innovation Management · doi:10.1108/ejim-05-2024-0520 · Cited by 25
Yoganathan, V., Osburg, V., Kunz, W. H., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3806225 · Cited by 17
Zhao, Y., Tang, S., Zhang, H., & Lyu, L. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104400 · Cited by 28
The Impact of Intelligent Systems on Management Accounting
Marques, S., Gonçalves, R., Costa, R. L. D., Pereira, L., & Dias, Á. (2023) · International Journal of Intelligent Information Technologies · doi:10.4018/ijiit.324601 · Cited by 28
“Teaming up with AI is necessary and will have a deep impact on many aspects of our lives, jobs, and society as a whole, however it ́s not clear yet the magnitude of this change (Bolander, 2019; Huang et al., 2019).”
BackgroundInfluentialde Oliveira, E. R., & Rodrigues, P. (2021) · Electronic Journal of Knowledge Management · doi:10.34190/ejkm.19.2.2459 · Cited by 15
Impact of Artificial Intelligence on Marketing Research
Sáez‐Ortuño, L., Sánchez, J., Forgas‐Coll, S., Huertas-García, R., & Puertas, E. (2023) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-6684-9591-9.ch002 · Cited by 17
AI-driven HR transformation in Chinese automotive industry: strategies and implications
Khushk, A., Zhiying, L., Xu, Y., & Aman, N. (2025) · Business Process Management Journal · doi:10.1108/bpmj-11-2023-0915 · Cited by 25
The hard-working virtual agent in the service encounter boosts customer satisfaction
Söderlund, M., Oikarinen, E.-L., & Tan, T. M. (2022) · The International Review of Retail Distribution and Consumer Research · doi:10.1080/09593969.2022.2042715 · Cited by 16
“Many retailers and service firms use various forms of virtual agents (VAs) as the firm’s representative in interactions with customers (Henkel et al. 2020; Huang, Rust, and Maksimovic 2019).”
BackgroundCastaneda, A. R., Surachartkumtonkun, J., Maseeh, H. I., Thaichon, P., & Shao, W. (2025) · Journal of Service Theory and Practice · doi:10.1108/jstp-04-2024-0099 · Cited by 18
Artificial intelligence adoption in supply chain risk management: Scale development and validation
Kanti, P. S., Riaz, S., & Suchismita, D. (2022) · Ho Chi Minh City Open University Journal of Science- Economics and Business Administration · doi:10.46223/hcmcoujs.econ.en.12.2.2142.2022 · Cited by 23
“Past research has also shown that managers can succeed in their organizations by incorporating more AI in the thinking tasks and gravitating employees toward tasks that require emotion, empathy, and personal relationship skills (Huang, Rust, & Maksimovic, 2019).”
BackgroundLu, R., Peng, X., & Reve, T. (2024) · Journal of Management & Organization · doi:10.1017/jmo.2024.24 · Cited by 15
“Embracing digital technology and carrying out digital transformation helps firms such as TikTok to open up ‘new tracks’ in international markets and to establish new core competences (e.g., Huang, Rust, & Maksimovic, 2019).”
Metadata
- Authors
- Huang, M.-H., Rust, R., & Maksimovic, V.
- Affiliations
- National Taiwan University (TW)
- University of Maryland, College Park (US)
- Venue
- California Management Review
- Publisher
- SAGE Publications
- Published
- 2019-07-21
- Volume
- 61
- Issue
- 4
- Pages
- 43-65
- ISSN
- 0008-1256 (ISSN-L)
- 2162-8564
- OpenAlex
- W2964167195
- Cited by
- 591
- References
- 0
- Citations by year
- 2026: 90 · 2025: 131 · 2024: 116 · 2023: 86 · 2022: 73 · 2021: 59 · 2020: 32 · 2019: 4
- FWCI
- 58.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- California Management Review (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Corporate Management and Leadership
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- artificial intelligence, human-AI collaboration, job design, emotional labor, empathy, interpersonal work, future of work
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
