@proceedings {9999,
	title = {The task matters: The effect of perceived similarity to AI on intention to use in different task types},
	year = {2025},
	month = {1/2025},
	address = {Waikaloa, HI},
	abstract = {<p>With the development of AI technologies, especially generative AI (GAI) like ChatGPT, GAI is increasingly assisting people in various tasks. However, people may have different requirements for GAI when using it for different kinds of tasks. For instance, when brainstorming new ideas, people may want GAI to propose different ideas that supplement theirs with different problem-solving perspectives, but for decision-making tasks, they may prefer GAI adopt a similar problem-solving process with people to make a similar or even the same decision as they would. We conducted an online experiment examining how perceived similarities between GAI and human task-solving influence people{\textquoteright}s intention to use GAI, mediated by trust, for four task types (creativity, planning, intellective, and decision-making tasks). We demonstrate that the effect of similarity on trust (and so intent to use AI) depends on the type of task. This paper contributes to understanding the impact of task types on the relationship between perceived similarity and GAI adoption, with implications for future use of GAI in various task contexts.</p>},
	doi = {10.24251/HICSS.2025.042},
	attachments = {https://waim.network/sites/crowston.syr.edu/files/AI_similarity.pdf},
	author = {Qingyu Liang and Kevin Crowston and Sangseok You}
}
@article {McLeay2020,
	title = {Replaced by a Robot: Service Implications in the Age of the Machine},
	journal = {Journal of Service Research},
	year = {2020},
	abstract = {Service organizations, emboldened by the imperative to innovate, are increasingly introducing robots to frontline service encounters. However, as they augment or substitute human employees with robots, they may struggle to convince a distrusting public of their brand{\textquoteright}s ethical credentials. Consequently, this article develops and tests a holistic framework to ascertain a deeper understanding of customer perceptions of frontline service robots (FLSRs) than has previously been attempted. Our experimental studies investigate the effects of the (1) role (augmentation or substitution of human employees or no involvement) and (2) type (humanoid FLSR vs. self-service machine) of FLSRs under the following service contexts: (a) value creation model (asset-builder, service provider) and (b) service type (experience, credence). By empirically establishing our framework, we highlight how customers{\textquoteright} personal characteristics (openness-to-change and preference for ethical/responsible service provider) and cognitive evaluations (perceived innovativeness, perceived ethical/societal reputation, and perceived innovativeness-responsibility fit) influence the impact that FLSRs have on service experience and brand usage intent. Our findings operationalize and empirically support seminal frameworks from extant literature, as well as elaborate on the positive and negative implications of using robots to complement or replace service employees. Further, we consider managerial and policy implications for service in the age of machines.},
	keywords = {brand usage intent, ethical/societal reputation, service experience, service innovativeness, service robots},
	issn = {15527379},
	doi = {10.1177/1094670520933354},
	author = {McLeay, Fraser and Osburg, Victoria Sophie and Yoganathan, Vignesh and Patterson, Anthony}
}
@article {Brundage2020,
	title = {Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims},
	year = {2020},
	pages = {1{\textendash}9},
	abstract = {With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose{\textendash}spanning institutions, software, and hardware{\textendash}and make recommendations aimed at implementing, exploring, or improving those mechanisms.},
	url = {http://arxiv.org/abs/2004.07213},
	author = {Brundage, Miles and Avin, Shahar and Wang, Jasmine and Belfield, Haydn and Krueger, Gretchen and Hadfield, Gillian and Khlaaf, Heidy and Yang, Jingying and Toner, Helen and Fong, Ruth and Maharaj, Tegan and Koh, Pang Wei and Hooker, Sara and Leung, Jade and Trask, Andrew and Bluemke, Emma and Lebensold, Jonathan and O{\textquoteright}Keefe, Cullen and Koren, Mark and Ryffel, Th{\'e}o and Rubinovitz, JB and Besiroglu, Tamay and Carugati, Federica and Clark, Jack and Eckersley, Peter and de Haas, Sarah and Johnson, Maritza and Laurie, Ben and Ingerman, Alex and Krawczuk, Igor and Askell, Amanda and Cammarota, Rosario and Lohn, Andrew and Krueger, David and Stix, Charlotte and Henderson, Peter and Graham, Logan and Prunkl, Carina and Martin, Bianca and Seger, Elizabeth and Zilberman, Noa and H{\'E}igeartaigh, Se{\'a}n {\'O} and Kroeger, Frens and Sastry, Girish and Kagan, Rebecca and Weller, Adrian and Tse, Brian and Barnes, Elizabeth and Dafoe, Allan and Scharre, Paul and Herbert-Voss, Ariel and Rasser, Martijn and Sodhani, Shagun and Flynn, Carrick and Gilbert, Thomas Krendl and Dyer, Lisa and Khan, Saif and Bengio, Yoshua and Anderljung, Markus}
}
@article {2019,
	title = {The future of women at work},
	volume = {McKinsey Global Institute},
	year = {2019},
	month = {06/2019},
	keywords = {consulting reports},
	url = {https://www.mckinsey.com/~/media/mckinsey/featured\%20insights/gender\%20equality/the\%20future\%20of\%20women\%20at\%20work\%20transitions\%20in\%20the\%20age\%20of\%20automation/mgi-the-future-of-women-at-work-full-report-june\%202019.ashx},
	author = {Anu Madgavkar and James Manyika and Mekala Krishnan and Kweilin Ellingrud and Lareina Yee and Jonathan Woetzel and Michael Chui and Vivian Hunt and Sruti Balakrishnan}
}
@article {2019,
	title = {The moderating roles of perceived organizational support and competitive psychological climate},
	journal = {Tourism Management},
	volume = {73},
	year = {2019},
	month = {Jan-08-2019},
	pages = {172 - 181},
	issn = {02615177},
	doi = {10.1016/j.tourman.2019.02.006},
	author = {Li, Jun (Justin) and Bonn, Mark A. and Ye, Ben Haobin}
}
@article {2019,
	title = {Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis},
	journal = {Trends in Cancer},
	volume = {5},
	year = {2019},
	month = {Jan-03-2019},
	pages = {157 - 169},
	issn = {24058033},
	doi = {10.1016/j.trecan.2019.02.002},
	author = {Levine, Adrian B. and Schlosser, Colin and Grewal, Jasleen and Coope, Robin and Jones, Steve J.M. and Yip, Stephen}
}
@article {2019,
	title = {Scaling up analogical innovation with crowds and AI},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {116},
	year = {2019},
	pages = {1870-1877},
	issn = {0027-8424},
	doi = {10.1073/pnas.1807185116},
	author = {Kittur, Aniket and Yu, Lixiu and Hope, Tom and Chan, Joel and Lifshitz-Assaf, Hila and Gilon, Karni and Ng, Felicia and Kraut, Robert E. and Shahaf, Dafna}
}
@article {2019,
	title = {A study of artificial intelligence and it{\textquoteright}s role on human resource management},
	journal = {International Journal of Research and Analytical Review},
	volume = {6},
	year = {2019},
	url = {https://www.researchgate.net/publication/331596981_A_Study_of_Artificial_Intelligence_and_its_role_in_Human_Resource_Management},
	author = {Vivek V. Yawalkar}
}
@article {Frank2019,
	title = {Toward understanding the impact of artificial intelligence on labor},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {116},
	year = {2019},
	month = {apr},
	pages = {6531{\textendash}6539},
	abstract = {<p>Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human{\textendash}machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.</p>},
	issn = {0027-8424},
	doi = {10.1073/pnas.1900949116},
	url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1900949116},
	author = {Frank, Morgan R and Autor, David and Bessen, James E and Brynjolfsson, Erik and Cebrian, Manuel and Deming, David J and Feldman, Maryann and Groh, Matthew and Lobo, Jos{\'e} and Moro, Esteban and Wang, Dashun and Youn, Hyejin and Rahwan, Iyad}
}
@article {2019,
	title = {What happens when the jobs disappear?},
	volume = {The Superintendent journal},
	year = {2019},
	url = {https://super.journalxr.com/journal/index.php/TSJ},
	author = {Andrew Yang}
}
@article {2018,
	title = {Artificial intelligence in the computer-age threatens human beings and working conditions at workplaces},
	journal = {Electronics Science Technology and Application},
	volume = {5},
	year = {2018},
	month = {Feb-08-2019},
	issn = {2251-2608},
	doi = {10.18686/esta.v5i3.76},
	author = {Saithibvongsa, Phothong and Yu, Jae Eon}
}
@article {2018,
	title = {Explanation to Avert Surprise},
	journal = {IUI Workshops},
	year = {2018},
	url = {https://www.semanticscholar.org/paper/Explanation-to-Avert-Surprise-Gervasio-Myers/8e3b3d9e9075095a87c96fa9690d2ccbf127234e},
	author = {Melinda Gervasio and Karen Myers and Eric Yeh and Boone Adkins}
}
@article {2018,
	title = {How cheap labor drives China{\textquoteright}s AI Ambitions},
	year = {2018},
	month = {11/2018},
	keywords = {human and machine intelligence},
	url = {https://www.nytimes.com/2018/11/25/business/china-artificial-intelligence-labeling.html},
	author = {Li Yuan}
}
@article {2018,
	title = {How to respond to the fourth industrial revolution, or the second information technology revolution? Dynamic new combinations between technology, market, and society through open innovation},
	journal = {Journal of Open Innovation: Technology, Market, and Complexity},
	volume = {4},
	year = {2018},
	month = {Jan-09-2018},
	pages = {21},
	doi = {10.3390/joitmc4030021},
	author = {Lee, MinHwa and Yun, JinHyo and Pyka, Andreas and Won, DongKyu and Kodama, Fumio and Schiuma, Giovanni and Park, HangSik and Jeon, Jeonghwan and Park, KyungBae and Jung, KwangHo and Yan, Min-Ren and Lee, SamYoul and Zhao, Xiaofei}
}
@book {2018,
	title = {Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and {\textquoteleft}Presence Only{\textquoteright} Data: An Overview of Applications and a Good Outlook},
	year = {2018},
	pages = {27 - 61},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-319-96976-3},
	doi = {10.1007/978-3-319-96978-7_2},
	author = {Huettmann, Falk and Craig, Erica H. and Herrick, Keiko A. and Baltensperger, Andrew P. and Grant Humphries and Lieske, David J. and Miller, Katharine and Mullet, Timothy C. and Oppel, Steffen and Resendiz, Cynthia and Rutzen, Imme and Schmid, Moritz S. and Suwal, Madan K. and Young, Brian D.},
	editor = {Grant Humphries and Magness, Dawn R. and Huettmann, Falk}
}
@conference {2018,
	title = {Theorizing human and bot co - production effects on information quality},
	booktitle = {Thirty - n inth International Conference on Information Systems,},
	year = {2018},
	url = {https://icis2018postergallery.weebly.com/416-social-media-and-digital-collaboration.html},
	author = {Amber G. Young and Amber G. Young and Gerald C. Kane}
}
@article {2017,
	title = {Artificial intelligence in research},
	journal = {Science},
	volume = {357},
	year = {2017},
	month = {Jul-07-2017},
	pages = {28 - 30},
	issn = {0036-8075},
	doi = {10.1126/science.357.6346.28},
	author = {Musib, Mrinal and Wang, Feng and Tarselli, Michael A. and Yoho, Rachel and Yu, Kun-Hsing and Andr{\'e}s, Rigoberto Medina and Greenwald, Noah F. and Pan, Xubin and Lee, Chien-Hsiu and Zhang, Jian and Dutton-Regester, Ken and Johnston, Jake Wyatt and Sharafeldin, Icell Mahmoud}
}
@article {2017,
	title = {{\textquoteleft}Hypernudge{\textquoteright}: Big Data as a mode of regulation by design},
	journal = {Information, Communication \& Society},
	volume = {20},
	year = {2017},
	month = {Feb-01-2017},
	pages = {118 - 136},
	issn = {1369-118X},
	doi = {10.1080/1369118X.2016.1186713},
	author = {Yeung, Karen}
}
@article {2017,
	title = {Understanding human-machine networks},
	journal = {ACM Computing Surveys},
	volume = {50},
	year = {2017},
	month = {Jan-04-2018},
	pages = {1 - 35},
	issn = {03600300},
	doi = {10.1145/3039868},
	author = {Tsvetkova, Milena and Yasseri, Taha and Meyer, Eric T. and Pickering, J. Brian and Engen, Vegard and Walland, Paul and L{\"u}ders, Marika and F{\o}lstad, Asbj{\o}rn and Bravos, George}
}
@article {2016,
	title = {The digital workforce and the workplace of the future},
	journal = {Academy of Management Journal},
	volume = {59},
	year = {2016},
	month = {Jan-06-2016},
	pages = {731 - 739},
	issn = {0001-4273},
	doi = {10.5465/amj.2016.4003},
	author = {Colbert, Amy and Yee, Nick and George, Gerard}
}
@article {2007,
	title = {Towards a theory for multiparty proactive communication in agent teams},
	journal = {International Journal of Cooperative Information Systems},
	volume = {16},
	year = {2007},
	month = {Jan-06-2009},
	pages = {271 - 298},
	issn = {0218-8430},
	doi = {10.1142/S0218843007001640},
	author = {Kaivan Kamali and Xiaocong Fan and John Yen, JOHN}
}
