There is increasing interest in the use of artificial intelligence (AI) to improve organizational decision-making. However, research indicates that people’s trust in and choice to rely on “AI decision aids” can be tenuous. In the present paper, we connect research on trust in AI with Mayer, Davis, and Schoorman’s (1995) model of organizational trust to elaborate a conceptual model of trust, perceived risk, and reliance on AI decision aids at work. Drawing from the trust in technology, trust in automation, and decision support systems literatures, we redefine central concepts in Mayer et al.’s (1995) model, expand the model to include new, relevant constructs (like perceived control over an AI decision aid), and refine propositions about the relationships expected in this context. The conceptual model put forward presents a framework that can help researchers studying trust in and reliance on AI decision aids develop their research models, build systematically on each other’s research, and contribute to a more cohesive understanding of the phenomenon. Our paper concludes with five next steps to take research on the topic forward.
AlanA.CostanzaE.FischerJ.RamchurnS.RoddenT.JenningsN. R. (2014, May 5–9). A field study of human-agent interaction for electricity tariff switching. 13th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Paris, France.
2.
AokiN. (2021). The importance of the assurance that “humans are still in the decision loop” for public trust in artificial intelligence: Evidence from an online experiment. Computers in Human Behavior, 114(4), Article 106572. https://doi.org/10.1016/j.chb.2020.106572.
3.
BahnerJ. E.ElepfandtM. F.ManzeyD. (2008, September 22–26). Misuse of diagnostic aids in process control: The effects of automation misses on complacency and automation bias. Human Factors and Ergonomics Society 52nd Annual Meeting, Los Angeles, CA. https://doi.org/10.1177/154193120805201906.
4.
BeldadA. D.HegnerS. M. (2018). Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: A structural equation modeling approach. International Journal of Human–Computer Interaction, 34(9), 882–893. https://doi.org/10.1080/10447318.2017.1403220.
5.
BirosD. P.DalyM.GunschG. (2004). The influence of task load and automation trust on deception detection. Group Decision and Negotiation, 13(2), 173–189. https://doi.org/10.1023/b:grup.0000021840.85686.57.
6.
BlauP. M. (1968). Social exchange. International Encyclopedia of the Social Sciences, 7(4), 452–457.
7.
BurrC.CristianiniN.LadymanJ. (2018). An analysis of the interaction between intelligent software agents and human users. Minds and Machines, 28(4), 735–774. https://doi.org/10.1007/s11023-018-9479-0.
8.
ChanceyE. T.BlissJ. P.YamaniY.HandleyH. A. (2017). Trust and the compliance–reliance paradigm: The effects of risk, error bias, and reliability on trust and dependence. Human Factors, 59(3), 333–345. https://doi.org/10.1177/0018720816682648.
9.
ColquittJ. A.ScottB. A.LePineJ. A. (2007). Trust, trustworthiness, and trust propensity: A meta-analytic test of their unique relationships with risk taking and job performance. Journal of Applied Psychology, 92(4), 909–927. https://doi.org/10.1037/0021-9010.92.4.909.
10.
CostaA. C.FulmerC. A.AndersonN. R. (2018). Trust in work teams: An integrative review, multilevel model, and future directions. Journal of Organizational Behavior, 39(2), 169–184. https://doi.org/10.1002/job.2213.
11.
CropanzanoR.MitchellM. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management, 31(6), 874–900. https://doi.org/10.1177/0149206305279602.
12.
DasT. K.TengB.-S. (2001). Trust, control, and risk in strategic alliances: An integrated framework. Organization Studies, 22(2), 251–283. https://doi.org/10.1177/0170840601222004.
13.
DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008.
DietvorstB. J.SimmonsJ. P.MasseyC. (2018). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Science, 64(3), 1155–1170. https://doi.org/10.1287/mnsc.2016.2643.
16.
EndsleyM. R.KirisE. O. (1995). The out-of-the-loop performance problem and level of control in automation. Human Factors, 37(2), 381–394. https://doi.org/10.1518/001872095779064555.
17.
FeathermanM. (2001). Extending the technology acceptance model by inclusion of perceived risk. AMCIS 2001 Proceedings, 148, 758–760.
18.
FreyC. B.OsborneM. A. (2017). The future of employment: How susceptible are jobs to computerisation?Technological Forecasting and Social Change, 114(2017), 254–280. https://doi.org/10.1016/j.techfore.2016.08.019.
19.
GhazizadehM.LeeJ. D.BoyleL. N. (2012). Extending the technology acceptance model to assess automation. Cognition, Technology & Work, 14(1), 39–49. https://doi.org/10.1007/s10111-011-0194-3.
20.
GillathO.AiT.BranickyM. S.KeshmiriS.DavisonR. B.SpauldingR. (2021). Attachment and trust in artificial intelligence. Computers in Human Behavior, 115(52), Article 106607. https://doi.org/10.1016/j.chb.2020.106607.
21.
GliksonE.WoolleyA. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057.
22.
HaenleinM.KaplanA. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925.
23.
HeggestadE. D.ScheafD. J.BanksG. C.Monroe HausfeldM.TonidandelS.WilliamsE. B. (2019). Scale adaptation in organizational science research: A review and best-practice recommendations. Journal of Management, 45(6), 2596–2627. https://doi.org/10.1177/0149206319850280.
24.
HittM. A.BeamishP. W.JacksonS. E.MathieuJ. E. (2007). Building theoretical and empirical bridges across levels: Multilevel research in management. Academy of Management Journal, 50(6), 1385–1399. https://doi.org/10.5465/amj.2007.28166219.
25.
HoG.PavlovicN.MyersV.ArrabitoR. (2013, September). Reducing false alarms in automated target recognition by lowering the level of automation. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 57, No. 1, pp. 374-378). Sage CA: Los Angeles, CA: SAGE Publications.
26.
HöddinghausM.SondernD.HertelG. (2021). The automation of leadership functions: Would people trust decision algorithms?Computers in Human Behavior, 117, Article 106635. https://doi.org/10.1016/j.chb.2020.106635.
27.
HoffK. A.BashirM. (2015). Trust in automation: Integrating empircal evidence on factors that influence trust. Human Factors, 57(3), 407–434. https://doi.org/10.1177/0018720814547570.
28.
HöstereyS.OnnaschL. (2021, September 01). Manipulating situational risk in human-automation research–Validation of a new experimental paradigm in virtual reality. Human Factors and Ergonomics Society Annual Meeting, Los Angeles, CA.
29.
ImI.KimY.HanH.-J. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information & Management, 45(1), 1–9. https://doi.org/10.1016/j.im.2007.03.005.
30.
JacobyJ.KaplanL. B. (1972). The components of perceived risk. Chicago, IL: Third Annual Conference of the Association for Consumer Research.
31.
JarvenpaaS. L.KnollK.LeidnerD. E. (1998). Is anybody out there? Antecedents of trust in global virtual teams. Journal of Management Information Systems, 14(4), 29–64. https://doi.org/10.1080/07421222.1998.11518185.
32.
JonesG. R.GeorgeJ. M. (1998). The experience and evolution of trust: Implications for cooperation and teamwork. Academy of Management Review, 23(3), 531–546. https://doi.org/10.5465/amr.1998.926625.
33.
KahnemanD. (1973). Attention and effort (Vol. 1063). State College, PA: Citeseer.
34.
KaplanA. D.KesslerT. T.BrillJ. C.HancockP. (2021). Trust in artificial intelligence: meta-analytic findings. Human Factors. Online Firsthttps://doi.org/10.1177%2F00187208211013988.
35.
KohnS. C.De VisserE. J.WieseE.LeeY.-C.ShawT. H. (2021). Measurement of trust in automation: A narrative review and reference guide. Frontiers in Psychology, 12, Article 604977. https://doi.org/10.3389/fpsyg.2021.604977.
36.
KramerR. M. (1999). Trust and distrust in organizations: Emerging perspectives, enduring questions. Annual Review of Psychology, 50(1), 569–598. https://doi.org/10.1146/annurev.psych.50.1.569.
37.
LadegardG.GjerdeS. (2014). Leadership coaching, leader role-efficacy, and trust in subordinates. A mixed methods study assessing leadership coaching as a leadership development tool. The Leadership Quarterly, 25(4), 631–646. https://doi.org/10.1016/j.leaqua.2014.02.002.
38.
LangerM.KönigC. J.BuschV. (2021). Changing the means of managerial work: Effects of automated decision support systems on personnel selection tasks. Journal of Business and Psychology, 36(5), 751–769. https://doi.org/10.1007/s10869-020-09711-6.
39.
LeeM. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society, 5(1), 2053951718756684. https://doi.org/10.1177/2053951718756684.
40.
LeeJ.MorayN. (1992). Trust, control strategies, and allocation of function in human machine systems. Ergonomics, 35(10), 1243–1270. https://doi.org/10.1080/00140139208967392.
LongC. P.SitkinS. B. (2018). Control–trust dynamics in organizations: identifying shared perspectives and charting conceptual fault lines. Academy of Management Annals, 12(2), 725–751. https://doi.org/10.5465/annals.2016.0055.
43.
MadsenM.GregorS. (2000). Measuring human-computer trust. 11th Australasian Conference on Information Systems, Brisbane, Australia.
44.
ManzeyD.ReichenbachJ.OnnaschL. (2012). Human performance consequences of automated decision aids: The impact of degree of automation and system experience. Journal of Cognitive Engineering and Decision Making, 6(1), 57–87. https://doi.org/10.1177/1555343411433844.
45.
MayerR. C.DavisJ. H. (1999). The effect of the performance appraisal system on trust for management: A field quasi-experiment. Journal of Applied Psychology, 84(1), 123–136. https://doi.org/10.1037/0021-9010.84.1.123.
46.
MayerR. C.DavisJ. H.SchoormanF. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.5465/amr.1995.9508080335.e
47.
McAllisterD. J. (1995). Affect-and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of Management Journal, 38(1), 24–59. https://doi.org/10.5465/256727.
McKnightD. H.CarterM.ThatcherJ. B.ClayP. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems (TMIS), 2(2), 1–25. https://doi.org/10.1145/1985347.1985353.
50.
MetcalfL.AskayD. A.RosenbergL. B. (2019). Keeping humans in the loop: pooling knowledge through artificial swarm intelligence to improve business decision making. California Management Review, 61(4), 84–109. https://doi.org/10.1177/0008125619862256.
MöhlmannM.ZalmansonL. (2017, December 10–13). Hands on the wheel: Navigating algorithmic management and Uber drivers’ autonomy. 38th International Conference on Information Systems (ICIS 2017), Seoul, South Korea.
53.
MuirB. M. (1994). Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics, 37(11), 1905–1922. https://doi.org/10.1080/00140139408964957.
54.
OksanenA.SavelaN.LatikkaR.KoivulaA. (2020). Trust toward robots and artificial intelligence: An experimental approach to human–technology interactions online. Frontiers in Psychology, 12, Article 568256. https://doi.org/10.3389%2Ffpsyg.2020.568256.
55.
O’NeillT.McNeeseN.BarronA.SchelbleB. (2020). Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature. Human Factors. Online Firsthttps://doi.org/0018720820960865.
56.
PakR.FinkN.PriceM.BassB.SturreL. (2012). Decision support aids with anthropomorphic characteristics influence trust and performance in younger and older adults. Ergonomics, 55(9), 1059–1072. https://doi.org/10.1080/00140139.2012.691554.
ParasuramanR.SheridanT. B.WickensC. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 30(3), 286–297. https://doi.org/10.1109/3468.844354.
59.
ParryK.CohenM.BhattacharyaS. (2016). Rise of the machines: A critical consideration of automated leadership decision making in organizations. Group & Organization Management, 41(5), 571–594. https://doi.org/10.1177/1059601116643442.
60.
PavlouP. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275.
RobinsonS. L. (1996). Trust and breach of the psychological contract. Administrative Science Quarterly, 41(4), 574–599. https://doi.org/10.2307/2393868.
63.
RotterJB (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80(1), 1–28. https://doi.org/10.1037/h0092976.
RousseauD. M.SitkinS. B.BurtR. S.CamererC. (1998). Not so different after all: A cross-discipline view of trust. Academy of Management Review, 23(3), 393–404. https://doi.org/10.5465/amr.1998.926617.
66.
SchnallR.HigginsT.BrownW.Carballo-DieguezA.BakkenS. (2015). Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Studies in Health Technology and Informatics, 216, 467–471. https://www.ncbi.nlm.nih.gov/pubmed/26262094.
67.
SchoormanF. D.BallingerG. (2006). Leadership, trust and client service in veterinary hospitals. Unpublished Working paper. New York, NY: Purdue University.
68.
SchoormanF. D.MayerR. C.DavisJ. H. (1996). Empowerment in veterinary clinics: The role of trust in delegation. Journal of Trust Research, 6(1), 76–90.
69.
SchoormanF. D.MayerR. C.DavisJ. H. (2007). An integrative model of organizational trust: Past, present, and future. Academy of Management Review, 32(2), 344–354. https://doi.org/10.5465/amr.2007.24348410
70.
SharanN. N.RomanoD. M. (2020). The effects of personality and locus of control on trust in humans versus artificial intelligence. Heliyon, 6(8), Article e04572. https://doi.org/10.1016/j.heliyon.2020.e04572.
71.
ShinD. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, Article 102551. https://doi.org/10.1016/j.ijhcs.2020.102551.
72.
ShresthaY. R.Ben-MenahemS. M.Von KroghG. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257.
73.
ShresthaY. R.KrishnaV.von KroghG. (2021). Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges. Journal of Business Research, 123, 588–603. https://doi.org/https://doi.org/10.1016/j.jbusres.2020.09.068.
74.
StantonN. A.YoungM. S. (2000). A proposed psychological model of driving automation. Theoretical Issues in Ergonomics Science, 1(4), 315–331. https://doi.org/10.1080/14639220052399131.
75.
StuckR. E.HolthausenB. E.WalkerB. N. (2020). The role of risk in human-robot trust. In NamC. S.LyonsJ. B. (Eds.), Trust in human-robot interaction (pp. 179–194). Amsterdam, Netherlands: Elsevier.
76.
TaddeoM. (2009). Defining trust and E− trust: Old theories and new problems. International Journal of Technology and Human Interaction, 5(2), 23–35. https://doi.org/10.4018/jthi.2009040102.
77.
TambeP.CappelliP.YakubovichV. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910.
78.
Von KroghG. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409. https://doi.org/10.5465/amd.2018.0084.
79.
WangH.SunL.BertinoE. (2014). Building access control policy model for privacy preserving and testing policy conflicting problems. Journal of Computer and System Sciences, 80(8), 1493–1503. https://doi.org/10.1016/j.jcss.2014.04.017.
80.
WeiJ.BoltonM. L.HumphreyL. (2020). The level of measurement of trust in automation. Theoretical Issues in Ergonomics Science, 22(3), 274–295. https://doi.org/10.1080/1463922x.2020.1766596.
81.
Yuviler-GavishNGopherD (2011). Effect of descriptive information and experience on automation reliance. Human Factors, 53(3), 230–244. https://doi.org/10.1177/0018720811406725.
82.
ZuboffS. (1988). In the age of the smart machine: The future of work and power. New York, NY: Basic Books.
