Artificial intelligence: a modern approach

Artificial Intelligence in Psychological Practice: Current and Future Applications and Implications

Luxton, D. D. (2014) · Professional Psychology Research and Practice · doi:10.1037/a0034559 · Cited by 297

State of the Art: Reproducibility in Artificial Intelligence

Gundersen, O. E., & Kjensmo, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11503 · Cited by 293

Ethical Decision Making during Automated Vehicle Crashes

Goodall, N. J. (2014) · Transportation Research Record: Journal of the Transportation Research Board · doi:10.3141/2424-07 · Cited by 292

AI-assisted peer review

Checco, A., Bracciale, L., Loreti, P., Pinfield, S., & Bianchi, G. (2021) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-00703-8 · Cited by 286

An integrated artificial intelligence framework for public management

Wirtz, B. W., & Müller, W. M. (2019) · Public Management Review · doi:10.1080/14719037.2018.1549268 · Cited by 285

Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey

Rigas, E. S., Ramchurn, S. D., & Bassiliades, N. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2376873 · Cited by 280

Application of artificial intelligence in gastroenterology

Yang, Y. J., & Bang, C. S. (2019) · World Journal of Gastroenterology · doi:10.3748/wjg.v25.i14.1666 · Cited by 263

Human-machine collaboration in managerial decision making

Haesevoets, T., De Cremer, D., Dierckx, K., & Van Hiel, A. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106730 · Cited by 280

Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education

Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021) · IEEE Access · doi:10.1109/access.2021.3097962 · Cited by 274

Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions

Mellit, A., & Kalogirou, S. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110889 · Cited by 280

Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

Sadeghi, A., Sheikholeslami, F., & Giannakis, G. B. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2787979 · Cited by 257

A Bayesian approach to discovering truth from conflicting sources for data integration

Zhao, B., Rubinstein, B. I. P., Gemmell, J., & Han, J. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2168651.2168656 · Cited by 264

Technology-driven service strategy

Huang, M.-H., & Rust, R. T. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0545-6 · Cited by 263

Finding approximate POMDP solutions through belief compression

Roy, N., Gordon, G., & Thrun, S. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1496 · Cited by 254

The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents

Bostrom, N. (2012) · Minds and Machines · doi:10.1007/s11023-012-9281-3 · Cited by 254

Artificial Discretion as a Tool of Governance: A Framework for Understanding the Impact of Artificial Intelligence on Public Administration

Young, M. M., Bullock, J. B., & Lecy, J. D. (2019) · Perspectives on Public Management and Governance · doi:10.1093/ppmgov/gvz014 · Cited by 255

Testing machine learning based systems: a systematic mapping

Riccio, V., Jahangirova, G., Stocco, A., Humbatova, N., Weiss, M., & Tonella, P. (2020) · Empirical Software Engineering · doi:10.1007/s10664-020-09881-0 · Cited by 251

A high-level overview of AI ethics

Kazim, E., & Koshiyama, A. S. (2021) · Patterns · doi:10.1016/j.patter.2021.100314 · Cited by 269

Decision tree classifiers for automated medical diagnosis

Azar, A. T., & El-Metwally, S. M. (2013) · Neural Computing and Applications · doi:10.1007/s00521-012-1196-7 · Cited by 242

IRobot: Teaching the Basics of Artificial Intelligence in High Schools

Burgsteiner, H., Kandlhofer, M., & Steinbauer, G. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9864 · Cited by 241

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Lo Piano, S. (2020) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-0501-9 · Cited by 229

Understanding the interplay of artificial intelligence and strategic management: four decades of research in review

Keding, C. (2021) · Management Review Quarterly · doi:10.1007/s11301-020-00181-x · Cited by 231

Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy

Asadi, H., Dowling, R., Yan, B., & Mitchell, P. (2014) · PLoS ONE · doi:10.1371/journal.pone.0088225 · Cited by 218

Decision Making in Multiagent Systems: A Survey

Rizk, Y., Awad, M., & Tunstel, E. W. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2840971 · Cited by 219

Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies

Renz, A., & Hilbig, R. (2020) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-020-00193-3 · Cited by 218

A survey on neural speech synthesis

Tan, X., Qin, T., Soong, F., & Liu, T.-Y. (2021) · arXiv · doi:10.48550/arxiv.2106.15561 · Cited by 216

Battery Safety: Machine Learning-Based Prognostics

Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216

Will algorithms blind people? The effect of explainable AI and decision-Makers’ experience on AI-supported decision-making in government

Janssen, M., Hartog, M., Matheus, R., Yi Ding, A., & Kuk, G. (2022) · Social Science Computer Review · doi:10.1177/0894439320980118 · Cited by 192

Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease—A review

Belić, M., Bobić, V., Badža, M., Šolaja, N., Đurić-Jovičić, M., & Kostić, V. S. (2019) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2019.105442 · Cited by 194

Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation

Eriksson, T., Bigi, A., & Bonera, M. (2020) · The TQM Journal · doi:10.1108/tqm-12-2019-0303 · Cited by 193

An artificial intelligence educational strategy for the digital transformation

Cantú-Ortiz, F. J., Galeano Sánchez, N., Garrido, L., Terashima-Marin, H., & Brena, R. F. (2020) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-020-00702-8 · Cited by 192

What the Near Future of Artificial Intelligence Could Be

Floridi, L. (2019) · Philosophy & Technology · doi:10.1007/s13347-019-00345-y · Cited by 191

A software engineering perspective on engineering machine learning systems: State of the art and challenges

Giray, G. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.111031 · Cited by 187

Application of data fusion techniques and technologies for wearable health monitoring

King, R. C., Villeneuve, E., White, R. J., Sherratt, R. S., Holderbaum, W., & Harwin, W. S. (2017) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2016.12.011 · Cited by 182

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

Muggleton, S. H., Lin, D., & Tamaddoni-Nezhad, A. (2015) · Machine Learning · doi:10.1007/s10994-014-5471-y · Cited by 184

The role of causality in judgment under uncertainty

Krynski, T. R., & Tenenbaum, J. B. (2007) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.136.3.430 · Cited by 183

A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction

Bahram, M., Hubmann, C., Lawitzky, A., Aeberhard, M., & Wollherr, D. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2506642 · Cited by 175

AI agents and agentic systems: a multi-expert analysis

Hughes, L., Dwivedi, Y. K., Malik, T., Shawosh, M., Albashrawi, M. A., Jeon, I., Dutot, V., Appanderanda, M., Crick, T., De’, R., et al. (2025) · Journal of Computer Information Systems · doi:10.1080/08874417.2025.2483832 · Cited by 168

Neural Mechanisms of Hierarchical Planning in a Virtual Subway Network

Balaguer, J., Spiers, H., Hassabis, D., & Summerfield, C. (2016) · Neuron · doi:10.1016/j.neuron.2016.03.037 · Cited by 163

Application of wrapper approach and composite classifier to the stock trend prediction

HUANG, C., YANG, D., & CHUANG, Y. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.05.035 · Cited by 167

Next-generation agentic AI for transforming healthcare

Karunanayake, N. (2025) · Informatics and Health · doi:10.1016/j.infoh.2025.03.001 · Cited by 165

Public and private value creation using artificial intelligence: An empirical study of AI voice robot users in Chinese public sector

Wang, C., Teo, T. S. H., & Janssen, M. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102401 · Cited by 161

Symbol emergence in robotics: a survey

Taniguchi, T., Nagai, T., Nakamura, T., Iwahashi, N., Ogata, T., & Asoh, H. (2016) · Advanced Robotics · doi:10.1080/01691864.2016.1164622 · Cited by 150

Battery Fault Diagnosis and Failure Prognosis for Electric Vehicles Using Spatio-Temporal Transformer Networks

Zhao, J., Feng, X., Wang, J., Lian, Y., Ouyang, M., & Burke, A. F. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121949 · Cited by 152

Habits as action sequences: hierarchical action control and changes in outcome value

Dezfouli, A., Lingawi, N. W., & Balleine, B. W. (2014) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2013.0482 · Cited by 139

Machine learning and natural language processing in psychotherapy research: alliance as example use case.

Goldberg, S. B., Flemotomos, N., Martinez, V. R., Tanana, M. J., Kuo, P. B., Pace, B. T., Villatte, J. L., Georgiou, P. G., Van Epps, J., Imel, Z. E., et al. (2020) · Journal of Counseling Psychology · doi:10.1037/cou0000382 · Cited by 141

Microalgae with Artificial Intelligence: A Digitalized Perspective on Genetics, Systems and Products

Teng, S. Y., Yew, G. Y., Sukačová, K., Show, P. L., Máša, V., & Chang, J.-S. (2020) · Biotechnology Advances · doi:10.1016/j.biotechadv.2020.107631 · Cited by 145

Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art – with Reflections on Present AIM Challenges

Kulikowski, C. A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677895 · Cited by 144

Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies

Tesauro, G. (2007) · IEEE Internet Computing · doi:10.1109/mic.2007.21 · Cited by 143

Thinking Inside the Box: Controlling and Using an Oracle AI

Armstrong, S., Sandberg, A., & Bostrom, N. (2012) · Minds and Machines · doi:10.1007/s11023-012-9282-2 · Cited by 144

The integration of AI in nursing: addressing current applications, challenges, and future directions

Wei, Q., Pan, S., Liu, X., Hong, M., Nong, C., & Zhang, W. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1545420 · Cited by 143

Towards Verified Artificial Intelligence

Seshia, S. A., Sadigh, D., & Sastry, S. S. (2016) · arXiv · doi:10.48550/arxiv.1606.08514 · Cited by 142

An investigation of barriers to Hong Kong K-12 schools incorporating Artificial Intelligence in education

Wang, T., & Cheng, E. C. K. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100031 · Cited by 141

Economic Opportunity and Evolution: Beyond Landscapes and Bounded Rationality

Felin, T., Kauffman, S., Koppl, R., & Longo, G. (2014) · Strategic Entrepreneurship Journal · doi:10.1002/sej.1184 · Cited by 140

Artificial intelligence, bureaucratic form, and discretion in public service

Bullock, J., Young, M. M., & Wang, Y.-F. (2020) · Information Polity · doi:10.3233/ip-200223 · Cited by 139

Motor Development

Adolph, K. E., & Robinson, S. R. (2015) · Handbook of Child Psychology and Developmental Science · doi:10.1002/9781118963418.childpsy204 · Cited by 135

Advertising benefits from ethical Artificial Intelligence algorithmic purchase decision pathways

Rodgers, W., & Nguyen, T. (2022) · Journal of Business Ethics · doi:10.1007/s10551-022-05048-7 · Cited by 137

Futures of artificial intelligence through technology readiness levels

Martínez-Plumed, F., Gómez, E., & Hernández-Orallo, J. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2020.101525 · Cited by 130

Basilar membrane-inspired self-powered acoustic sensor enabled by highly sensitive multi tunable frequency band

Han, J. H., Kwak, J.-H., Joe, D. J., Hong, S. K., Wang, H. S., Park, J. H., Hur, S., & Lee, K. J. (2018) · Nano Energy · doi:10.1016/j.nanoen.2018.08.053 · Cited by 128

Human Judgment in algorithmic loops: Individual justice and automated decision‐making

Binns, R. (2022) · Regulation & Governance · doi:10.1111/rego.12358 · Cited by 134

Digital interaction literacy model – conceptualizing competencies for literate interactions with voice-based AI systems

Carolus, A., Augustin, Y., Markus, A., & Wienrich, C. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100114 · Cited by 128

Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation

Gruson, D., Helleputte, T., Rousseau, P., & Gruson, D. (2019) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2019.04.013 · Cited by 125

Applications of AI in classical software engineering

Barenkamp, M., Rebstadt, J., & Thomas, O. (2020) · AI Perspectives · doi:10.1186/s42467-020-00005-4 · Cited by 123

Advances in intelligent and autonomous navigation systems for small UAS

Bijjahalli, S., Sabatini, R., & Gardi, A. (2020) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2020.100617 · Cited by 123

EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications

Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124

Graph-based root cause analysis for service-oriented and microservice architectures

Brandón, Á., Solé, M., Huélamo, A., Solans, D., Pérez, M. S., & Muntés-Mulero, V. (2020) · Journal of Systems and Software · doi:10.1016/j.jss.2019.110432 · Cited by 121

Leveraging educational data mining for real-time performance assessment of scientific inquiry skills within microworlds.

Gobert, J. D., Sao Pedro, M. A., Baker, R. S. J. D., Toto, E., & Montalvo, O. (2012) · Zenodo · doi:10.5281/zenodo.3554645 · Cited by 122

Artificial intelligence and knowledge management: Questioning the tacit dimension

Sanzogni, L., Guzman, G., & Busch, P. (2017) · Prometheus · doi:10.1080/08109028.2017.1364547 · Cited by 120

Information and communication technology solutions for outdoor navigation in dementia

Teipel, S., Babiloni, C., Hoey, J., Kaye, J., Kirste, T., & Burmeister, O. K. (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2015.11.003 · Cited by 120

The brain's connective core and its role in animal cognition

Shanahan, M. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2012.0128 · Cited by 115

Using artificial intelligence to analyse and teach communication in healthcare

Butow, P., & Hoque, E. (2020) · The Breast · doi:10.1016/j.breast.2020.01.008 · Cited by 118

On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda

Pandl, K. D., Thiebes, S., Schmidt-Kraepelin, M., & Sunyaev, A. (2020) · IEEE Access · doi:10.1109/access.2020.2981447 · Cited by 115

A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

Shahriar, S., Allana, S., Hazratifard, S. M., & Dara, R. (2023) · IEEE Access · doi:10.1109/access.2023.3287195 · Cited by 114

A real-time autonomous highway accident detection model based on big data processing and computational intelligence

Ozbayoglu, M., Kucukayan, G., & Dogdu, E. (2016) · 2016 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2016.7840798 · Cited by 114

Characterising user capabilities to support inclusive design evaluation

Persad, U., Langdon, P., & Clarkson, J. (2007) · Universal Access in the Information Society · doi:10.1007/s10209-007-0083-y · Cited by 113

Quantum inference on Bayesian networks

Low, G. H., Yoder, T. J., & Chuang, I. L. (2014) · Physical Review A · doi:10.1103/physreva.89.062315 · Cited by 109

The use of AI in public services: results from a preliminary mapping across the EU

Misuraca, G., van Noordt, C., & Boukli, A. (2020) · Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance · doi:10.1145/3428502.3428513 · Cited by 108

Virtual worlds vs books and videos in history education

Ijaz, K., Bogdanovych, A., & Trescak, T. (2017) · Interactive Learning Environments · doi:10.1080/10494820.2016.1225099 · Cited by 106

Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Mijumbi, R., Gorricho, J.-L., Serrat, J., Claeys, M., De Turck, F., & Latre, S. (2014) · 2014 IEEE Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2014.6838258 · Cited by 109

Online routing and battery reservations for electric vehicles with swappable batteries

Adler, J. D., & Mirchandani, P. B. (2014) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2014.09.005 · Cited by 103

The influence of generative artificial intelligence on creative cognition of design students: a chain mediation model of self-efficacy and anxiety

Hwang, Y., & Wu, Y. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1455015 · Cited by 104

Adaptive Opportunistic Routing for Wireless Ad Hoc Networks

Bhorkar, A. A., Naghshvar, M., Javidi, T., & Rao, B. D. (2012) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2011.2159844 · Cited by 102

Choking on the money: reward-based performance decrements are associated with midbrain activity

Mobbs, D., Hassabis, D., Seymour, B., Marchant, J. L., Weiskopf, N., Dolan, R. J., & Frith, C. D. (2009) · Psychological Science · doi:10.1111/j.1467-9280.2009.02399.x · Cited by 103

Technology functions for personalized learning in learner-centered schools

Lee, D., Huh, Y., Lin, C.-Y., & Reigeluth, C. M. (2018) · Educational Technology Research and Development · doi:10.1007/s11423-018-9615-9 · Cited by 101

Identification of a strategic brain network underlying processing speed deficits in vascular cognitive impairment

Duering, M., Gonik, M., Malik, R., Zieren, N., Reyes, S., Jouvent, E., Hervé, D., Gschwendtner, A., Opherk, C., Chabriat, H., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.084 · Cited by 89

Goal-directed control and its antipodes

Dayan, P. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.03.004 · Cited by 93

The promise and peril of using a large language model to obtain clinical information: ChatGPT performs strongly as a fertility counseling tool with limitations

Chervenak, J., Lieman, H., Blanco-Breindel, M., & Jindal, S. (2023) · Fertility and Sterility · doi:10.1016/j.fertnstert.2023.05.151 · Cited by 87

Robots: ethical by design

Dodig Crnkovic, G., & Çürüklü, B. (2012) · Ethics and Information Technology · doi:10.1007/s10676-011-9278-2 · Cited by 90

Counterfactual Explanations for Multivariate Time Series

Ates, E., Aksar, B., Leung, V. J., & Coskun, A. K. (2021) · 2021 International Conference on Applied Artificial Intelligence (ICAPAI) · doi:10.1109/icapai49758.2021.9462056 · Cited by 88

How Machine Learning is Changing e-Government

Alexopoulos, C., Lachana, Z., Androutsopoulou, A., Diamantopoulou, V., Charalabidis, Y., & Loutsaris, M. A. (2019) · Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance · doi:10.1145/3326365.3326412 · Cited by 88

Fixing the program my computer learned: barriers for end users, challenges for the machine

Kulesza, T., Wong, W.-K., Stumpf, S., Perona, S., White, R., Burnett, M. M., Oberst, I., & Ko, A. J. (2009) · Proceedings of the 14th international conference on Intelligent user interfaces · doi:10.1145/1502650.1502678 · Cited by 83

Discovering outlying aspects in large datasets

Vinh, N. X., Chan, J., Romano, S., Bailey, J., Leckie, C., Ramamohanarao, K., & Pei, J. (2016) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0453-2 · Cited by 83

Identification of acoustic emission sources for structural health monitoring applications based on convolutional neural networks and deep transfer learning

Hesser, D. F., Mostafavi, S., Kocur, G. K., & Markert, B. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.108 · Cited by 82

Ontology and epistemology for agent-based wayfinding simulation

Raubal, M. (2001) · International Journal of Geographical Information Science · doi:10.1080/13658810110061171 · Cited by 81

A systematic mapping review at the intersection of artificial intelligence and self-regulated learning

Banihashem, S. K., Bond, M., Bergdahl, N., Khosravi, H., & Noroozi, O. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00548-8 · Cited by 82

AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence

Clune, J. (2019) · arXiv · doi:10.48550/arxiv.1905.10985 · Cited by 82

Artificial intelligence in education and assessment methods

Braiki, B. A., Harous, S., Zaki, N., & Alnajjar, F. (2020) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v9i5.1984 · Cited by 79

A systematic literature review of empirical research on applying generative artificial intelligence in education

Zhang, X., Zhang, P., Shen, Y., Liu, M., Wang, Q., Gašević, D., & Fan, Y. (2024) · Frontiers of Digital Education · doi:10.1007/s44366-024-0028-5 · Cited by 83

Conjectural Equilibrium in Multiagent Learning

Wellman, M. P., & Hu, J. (1998) · Machine Learning · doi:10.1023/a:1007514623589 · Cited by 75

Product Categories as Judgment Devices: The Moral Awakening of the Investment Industry

Arjaliès, D.-L., & Durand, R. (2019) · Organization Science · doi:10.1287/orsc.2018.1267 · Cited by 75