Psychological Distance and Algorithm Aversion: Congruency and Advisor Confidence

References

• Alter S (2022) Understanding artificial intelligence in the context of usage: Contributions and smartness of algorithmic capabilities in work systems. Internat. J. Inform. Management 67:102392.Crossref, Google Scholar
• Bar-Anan Y, Liberman N, Trope Y (2006) The association between psychological distance and construal level: Evidence from an implicit association test. J. Experiment. Psych. General 135(4):609–622.Crossref, Google Scholar
• Bar-Anan Y, Liberman N, Trope Y, Algom D (2007) Automatic processing of psychological distance: Evidence from a Stroop task. J. Experiment. Psych. General 136(4):610.Crossref, Google Scholar
• Berger B, Adam M, Rühr A, Benlian A (2021) Watch me improve—Algorithm aversion and demonstrating the ability to learn. Bus. Inform. Systems Engrg. 63(1):55–68.Crossref, Google Scholar
• Berson Y, Halevy N, Shamir B, Erez M (2015) Leading from different psychological distances: A construal-level perspective on vision communication, goal setting, and follower motivation. Leadership Quart. 26(2):143–155.Crossref, Google Scholar
• Bryksina O, Xiao N (2020) Can side-by-side comparisons compromise decision outcomes? A construal-level view of evaluation-mode effects. ACR North Amer. Adv. 48:234–235.Google Scholar
• Burgoon EM, Henderson MD, Markman AB (2013) There are many ways to see the forest for the trees: A tour guide for abstraction. Perspective Psych. Sci. 8(5):501–520.Crossref, Google Scholar
• Burton JW, Stein MK, Jensen TB (2020) A systematic review of algorithm aversion in augmented decision making. J. Behav. Decision Making 33(2):220–239.Crossref, Google Scholar
• Castelo N, Bos MW, Lehmann DR (2019) Task-dependent algorithm aversion. J. Marketing Res. 56(5):809–825.Crossref, Google Scholar
• Chen R, Xu X, Shen H (2017) Go beyond just paying: Effects of payment method on level of construal. J. Consumer Psych. 27(2):207–217.Crossref, Google Scholar
• de Véricourt F, Perakis G (2020) Frontiers in Service Science: The management of data analytics services: New challenges and future directions. Service Sci. 12(4):121–129.Link, Google Scholar
• Dhar R, Kim EY (2007) Seeing the forest or the trees: Implications of construal level theory for consumer choice. J. Consumer Psych. 17(2):96–100.Crossref, Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. J. Experiment. Psych. General 144(1):114–126.Crossref, Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2018) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64(3):1155–1170.Link, Google Scholar
• Dwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T, et al. (2021) Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Internat. J. Inform. Management 57:101994.Crossref, Google Scholar
• Eckerd S, DuHadway S, Bendoly E, Carter CR, Kaufmann L (2021) On making experimental design choices: Discussions on the use and challenges of demand effects, incentives, deception, samples, and vignettes. J. Oper. Management 67(2):261–275.Crossref, Google Scholar
• Efendić E, Van de Calseyde PP, Evans AM (2020) Slow response times undermine trust in algorithmic (but not human) predictions. Organ. Behav. Human Decision Processes 157:103–114.Crossref, Google Scholar
• Fennell E (2023) Action identification characteristics and priming effects in ChatGPT. https://osf.io/preprints/psyarxiv/aqbvk/download.Google Scholar
• Förster J (2009) Cognitive consequences of novelty and familiarity: How mere exposure influences level of construal. J. Experiment. Soc. Psych. 45(2):444–447.Crossref, Google Scholar
• Grinfeld G, Wakslak C, Trope Y, Liberman N (2024) Construing hypotheticals: How hypotheticality affects level of abstraction. J. Experiment. Soc. Psych. 110:104543.Crossref, Google Scholar
• Gruber J, Hargittai E, Karaoglu G, Brombach L (2021) Algorithm awareness as an important Internet skill: The case of voice assistants. Internat. J. Comm. 15:1770–1788.Google Scholar
• Gurzki H, Schlatter N, Woisetschläger DM (2019) Crafting extraordinary stories: Decoding luxury brand communications. J. Advertising 48(4):401–414.Crossref, Google Scholar
• Hayes AF (2017) Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (Guilford Publications, New York).Google Scholar
• Ho CK, Ke W, Liu H, Chau PY (2020) Separate vs. joint evaluation: The roles of evaluation mode and construal level in technology adoption. Management Inform. Systems Quart. 44(2):725–746.Crossref, Google Scholar
• Hou YTY, Jung MF (2021) Who is the expert? Reconciling algorithm aversion and algorithm appreciation in AI-supported decision making. Nichols J, ed. Proc. ACM Human-Comput. Interaction (Association for Computing Machinery, New York), 1–25.Google Scholar
• Hsee CK, Zhang J (2010) General evaluability theory. Perspect. Psych. Sci. 5(4):343–355.Crossref, Google Scholar
• Huang MH, Rust RT (2018) Artificial intelligence in service. J. Service Res. 21(2):155–172.Crossref, Google Scholar
• Jain H, Padmanabhan B, Pavlou PA, Raghu TS (2021) Editorial for the special section on humans, algorithms, and augmented intelligence: The future of work, organizations, and society. Inform. Systems Res. 32(3):675–687.Link, Google Scholar
• Jung D, Dorner V, Glaser F, Morana S (2018) Robo-advisory: Digitalization and automation of financial advisory. Bus. Inform. Systems Engrg. 60:81–86.Crossref, Google Scholar
• Kim TW, Duhachek A (2020) Artificial intelligence and persuasion: A construal-level account. Psych. Sci. 31(4):363–380.Crossref, Google Scholar
• Kim K, Zhang M, Li X (2008) Effects of temporal and social distance on consumer evaluations. J. Consumer Res. 35(4):706–713.Crossref, Google Scholar
• Kirshner SN (2024) GPT and CLT: The impact of ChatGPT’s level of abstraction on consumer recommendations. J. Retailing Consumer Services 76:103580.Crossref, Google Scholar
• Kirshner SN, Moritz BB (2023) For the future and from afar: Psychological distance and inventory decision‐making. Production Oper. Management 32(1):170–188.Crossref, Google Scholar
• Kramer MF, Schaich Borg J, Conitzer V, Sinnott-Armstrong W (2018) When do people want AI to make decisions? Proc. AAAI/ACM Conf. AI Ethics Society (ACM, New York), 204–209.Google Scholar
• Lee D, Ham CD (2023) AI vs. human: Rethinking the role of agent knowledge in consumers’ coping mechanism related to influencer marketing. J. Interactive Advertising 23(3):241–258.Crossref, Google Scholar
• Lehmann CA, Haubitz C, Fügener A, Thonemann U (2020) Keep it mystic?: The effects of algorithm transparency on the use of advice. Proc. Internat. Conf. Inform. Systems (ACM, New York).Google Scholar
• Liberman N, Förster J (2009) The effect of psychological distance on perceptual level of construal. Cognition Sci. 33(7):1330–1341.Google Scholar
• Liberman N, Trope Y (2008) The psychology of transcending the here and now. Science 322(5905):1201–1205.Crossref, Google Scholar
• Logg JM, Minson JA, Moore DA (2019) Algorithm appreciation: People prefer algorithmic to human judgment. Organ. Behav. Human Decision Processes 151:90–103.Crossref, Google Scholar
• Longoni C, Bonezzi A, Morewedge CK (2019) Resistance to medical artificial intelligence. J. Consumer Res. 46(4):629–650.Crossref, Google Scholar
• Lu B, Hao S, Pinedo M, Xu Y (2021) Frontiers in Service Science: Fintech operations—An overview of recent developments and future research directions. Service Sci. 13(1):19–35.Link, Google Scholar
• Maglio SJ, Trope Y, Liberman N (2013) Distance from a distance: Psychological distance reduces sensitivity to any further psychological distance. J. Experiment. Psych. General 142(3):644.Crossref, Google Scholar
• Makridis CA, Mishra S (2022) Artificial intelligence as a service, economic growth, and well-being. J. Service Res. 25(4):505–520.Crossref, Google Scholar
• Maslowska E, Malthouse EC, Hollebeek LD (2022) The role of recommender systems in fostering consumers’ long-term platform engagement. J. Service Management 33(4/5):721–732.Crossref, Google Scholar
• Matzner T (2024) Algorithms as complementary abstractions. New Media Soc. 26(4):1799–1815.Crossref, Google Scholar
• Montoya AK, Hayes AF (2017) Two-condition within-participant statistical mediation analysis: A path-analytic framework. Psych. Methods 22(1):6.Google Scholar
• Peng CH, Yin D, Zhang H (2020) More than words in medical question-and-answer sites: A content-context congruence perspective. Inform. Systems Res. 31(3):913–928.Link, Google Scholar
• Prahl A, Van Swol L (2017) Understanding algorithm aversion: When is advice from automation discounted? J. Forecasting 36(6):691–702.Crossref, Google Scholar
• Simpson D, Segrave M, Quarshie A, Kach A, Handfield R, Panas G, Moore H (2021) The role of psychological distance in organizational responses to modern slavery risk in supply chains. J. Oper. Management 67(8):989–1016.Crossref, Google Scholar
• Sniezek JA, Van Swol LM (2001) Trust, confidence, and expertise in a judge-advisor system. Organ. Behav. Human Decision Processes 84(2):288–307.Crossref, Google Scholar
• Snyder C, Keppler S, Leider S (2022) Algorithm reliance under pressure: The effect of customer load on service workers. Preprint, submitted April 6, https://dx.doi.org/10.2139/ssrn.4066823.Google Scholar
• Steidle A, Werth L, Hanke EV (2011) You can’t see much in the dark. Soc. Psych. 42(3):174–184.Crossref, Google Scholar
• Swart J (2021) Experiencing algorithms: How young people understand, feel about, and engage with algorithmic news selection on social media. Soc. Media Soc. 7(2):20563051211008828.Google Scholar
• Trope Y, Liberman N (2010) Construal-level theory of psychological distance. Psych. Rev. 117(2):440–463.Crossref, Google Scholar
• Trope Y, Liberman N, Wakslak C (2007) Construal levels and psychological distance: Effects on representation, prediction, evaluation, and behavior. J. Consumer Psych. 17(2):83–95.Crossref, Google Scholar
• Wakslak CJ (2012) The where and when of likely and unlikely events. Organ. Behav. Human Decision Processes 117(1):150–157.Crossref, Google Scholar
• Wakslak CJ, Trope Y, Liberman N, Alony R (2006) Seeing the forest when entry is unlikely: Probability and the mental representation of events. J. Experiment. Psych. General 135(4):641.Crossref, Google Scholar
• Yeomans M, Shah A, Mullainathan S, Kleinberg J (2019) Making sense of recommendations. J. Behav. Decision Making 32(4):403–414.Crossref, Google Scholar
• Yin M, Wortman Vaughan J, Wallach H (2019) Understanding the effect of accuracy on trust in machine learning models. Proc. CHI Conf. Human Factors Comput. Systems (ACM, New York), 1–12.Google Scholar
• Zaki J, Bolger N, Ochsner K (2008) It takes two: The interpersonal nature of empathic accuracy. Psych. Sci. 19(4):399–404.Crossref, Google Scholar
• Zhao M, Xie J (2011) Effects of social and temporal distance on consumers’ responses to peer recommendations. J. Marketing Res. 48(3):486–496.Crossref, Google Scholar
• Zhong B, Makhija MV, Morris S (2022) Abstract vs. concrete: How managers’ construal influences organizational control systems and problem solving. Organ. Sci. 33(6):2322–2345.Link, Google Scholar
• Zizzo DJ (2010) Experimenter demand effects in economic experiments. Experiment. Econom. 13:75–98.Crossref, Google Scholar