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Technology, Humanness, and Trust: Rethinking Trust in Technology
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Information systems (IS) research has demonstrated that humans can and do trust technology. The current trust in technology literature employs two different types of trust in technology constructs. Some researchers use human-like trust constructs (e.g., benevolence, integrity, and ability), while other researchers use system-like trust constructs (e.g., helpfulness, reliability, and functionality). Interestingly, past research shows that both sets of measures influence important dependent variables, but the literature does not explain when one type should be used instead of the other type. In this paper, we use trust, social presence, and affordance theories to shed light on this research problem. We report on two studies. In study 1, we argue first that technologies vary in their perceived “humanness”. Second, we argue that, because users perceive two technologies to differ in humanness, they will develop trust in each technology differently (i.e., along more human-like criteria or more system-like criteria). We study two technologies that vary in humanness to explore these differences theoretically and empirically. We demonstrate that, when the trust construct used aligns well with how human the technology is, it produces stronger effects on selected outcome variables than does a misaligned trust construct. In study 2, we assess whether these technologies differ in humanness based on social presence, social affordances, and affordances for sociality. We find that these factors do distinguish whether technology is more human-like or system-like. We provide implications for trust-in-technology research.
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“In other words, people can hold trust and distrust at the same time, influenced by different sets of factors and considerations.”
From Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education (2025) · cites this paper as [69]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Although much of the existing research prioritizes trust, and some recent studies have started to explore distrust [69, 125], the role of distrust in the adoption of GenAI remains underexplored and insufficiently acknowledged.”
From Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education (2025) · cites this paper as [69]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While previous research often views trust and distrust as two extremes of a single dimension or uses these two terms interchangeably-implying high trust means low distrust-some scholars have argued that trust and distrust can be two independent constructs in certain contexts [69, 125].”
From Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education (2025) · cites this paper as [69]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Nevertheless, trust is a complex concept and is built upon human users’ interaction and consequent assessment of the system from cognitive, emotional, and social dimensions [91-93].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [92]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
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“To understand trust relationships in such scenarios, two research streams emerged that either adapted the concept of interpersonal trust to conceptualize trust in human-like IT artifacts (Lankton et al. 2015), or revised the concept of trust in technology to account for automation technology and autonomous systems (J.D. Lee and See 2004).”
From Trustworthy artificial intelligence (2021) · cites this paper as Lankton et al. 2015
The cited source says what the claim attributes to it.
In the citing paper
“While previous research often views trust and distrust as two extremes of a single dimension or uses these two terms interchangeably-implying high trust means low distrust-some scholars have argued that trust and distrust can be two independent constructs in certain contexts [69, 125].”
From Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education (2025) · cites this paper as [69]
The cited source says what the claim attributes to it.
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“Specifically, the literature on trust in technology has distinguished between human-like trust constructs and systemlike trust constructs (Lankton et al., 2015).”
From Why do users trust algorithms? A review and conceptualization of initial trust and trust over time (2022) · cites this paper as Lankton et al., 2015
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In the citing paper
“Within this stream of research, the social presence theory (Short et al., 1976) claims that a technology's attributes affect individuals' perception of technology as more human-like than systemlike (Lankton et al., 2015).”
From Why do users trust algorithms? A review and conceptualization of initial trust and trust over time (2022) · cites this paper as Lankton et al., 2015
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In the citing paper
“Generally, when users are dealing with algorithms that share human-like characteristics, their trust beliefs are triggered by features of interpersonal trust: benevolence, competence and ability , and integrity (Mayer et al., 1995; Lankton et al. 2015; Gefen et al., 2003).”
From Why do users trust algorithms? A review and conceptualization of initial trust and trust over time (2022) · cites this paper as Lankton et al. 2015
In the citing paper
“Most IS research nowadays employs a dualistic perspective on trust (see Table 1).First, trust in a specific person or organization (a moral and volitional agent) (Lankton et al. 2015;McKnight et al. 2011), such as trust in an e-vendor (Gefen et al. 2003) or virtual team members (Robert et al. 2009).”
From Trustworthy artificial intelligence (2021) · cites this paper as Lankton et al. 2015
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Bartosiak, M., & Modliński, A. (2022) · Career Development International · doi:10.1108/cdi-06-2022-0170 · Cited by 41
Bobko, P., Hirshfield, L., Eloy, L., Spencer, C., Doherty, E., Driscoll, J., & Obolsky, H. (2023) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2022.2086644 · Cited by 33
Metaverse in tourism: tourist involvement as a moderator
Choubey, V. M., Chakraborty, D., Sharma, A. J., Khorana, S., & Buhalis, D. (2024) · Asia Pacific Journal of Tourism Research · doi:10.1080/10941665.2024.2414881 · Cited by 38
Jiang, C., Liu, S., Lin, Z., Zhao, G., Duan, R. X., & Liang, K. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.08.019 · Cited by 34
The influence of religiosity on consumers' evaluations of brands using artificial intelligence
Minton, E. A., Kaplan, B., & Cabano, F. G. (2022) · Psychology and Marketing · doi:10.1002/mar.21727 · Cited by 44
Data spaces as meta-organisations
Guggenberger, T. M., Langdon, C. S., & Otto, B. (2025) · European Journal of Information Systems · doi:10.1080/0960085x.2025.2451250 · Cited by 38
Hybrid intelligence in hospitals: towards a research agenda for collaboration
Mirbabaie, M., Stieglitz, S., & Frick, N. (2021) · Electronic Markets · doi:10.1007/s12525-021-00457-4 · Cited by 37
Li, Y., Hou, R., & Tan, R. (2024) · European Journal of Marketing · doi:10.1108/ejm-11-2022-0827 · Cited by 39
Ghasemaghaei, M., & Kordzadeh, N. (2024) · Information Systems Journal · doi:10.1111/isj.12572 · Cited by 39
Social touch to build trust: A systematic review of technology-mediated and unmediated interactions
Valori, I., Jung, M. M., & Fairhurst, M. T. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108121 · Cited by 31
Users’ responses to humanoid social robots: A social response view
Premathilake, G. W., & Li, H. (2024) · Telematics and Informatics · doi:10.1016/j.tele.2024.102146 · Cited by 40
An investigation of technology trust and habit in IT use continuance: a study of a social network
Ambalov, I. A. (2021) · Journal of Systems and Information Technology · doi:10.1108/jsit-05-2019-0096 · Cited by 38
Appelganc, K., Rieger, T., Roesler, E., & Manzey, D. (2022) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434221104615 · Cited by 32
Effects of Humanlikeness and Conversational Breakdown on Trust in Chatbots for Customer Service
Law, E. L., Følstad, A., & van As, N. (2022) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3546155.3546665 · Cited by 37
Building consumer trust in the cloud: an experimental analysis of the cloud trust label approach
van der Werff, L., Fox, G., Masevic, I., Emeakaroha, V. C., Morrison, J. P., & Lynn, T. (2019) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-019-0129-8 · Cited by 29
In blockchain we trust? Demystifying the “trust” mechanism in blockchain ecosystems
Toufaily, É., & Zalan, T. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123574 · Cited by 37
Consumers acceptance of service robots in hotels: A meta-analytic review
Begum, N., Faisal, M. N., Sobh, R., Nunkoo, R., & Rana, N. P. (2024) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2024.104052 · Cited by 38
Trust in open data applications through transparency
Wiencierz, C., & Lünich, M. (2020) · New Media & Society · doi:10.1177/1461444820979708 · Cited by 33
Strazzullo, S. (2024) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2024.100621 · Cited by 34
Decomposition of perceived usefulness: A theoretical perspective and empirical test
Ambalov, I. A. (2021) · Technology in Society · doi:10.1016/j.techsoc.2020.101520 · Cited by 34
Internet of Things (IoT) enabled assistive care services: Designing for value and trust
Bhattacharya, S. D., Wainwright, D. W., & Whalley, J. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.333 · Cited by 29
Should Users Trust Advanced AI Assistants? Justified Trust As a Function of Competence and Alignment
Manzini, A., Keeling, G., Marchal, N., McKee, K. R., Rieser, V., & Gabriel, I. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658964 · Cited by 28
“Moreover, on many occasions humans are aware they are not interacting with a full moral agent (that has motives and intention-ality or can make commitments) but nonetheless experience their relationship with the AI as a trust relationship [20, 63].”
BackgroundRobot-Assisted Decision-Making: Unveiling the Role of Uncertainty Visualisation and Embodiment
Schömbs, S., Pareek, S., Gonçalves, J., & Johal, W. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642911 · Cited by 29
The NFT Purchasing Process and the Challenges to Trust at Each Stage
Zarifis, A., & Castro, L. A. (2022) · Sustainability · doi:10.3390/su142416482 · Cited by 27
The Power of Identity Cues in Text-Based Customer Service: Evidence from Twitter
Gao, Y. A., Rui, H., & Sun, S. (2023) · MIS Quarterly · doi:10.25300/misq/2022/17366 · Cited by 27
Sarker, S. (2016) · Journal of Information Technology · doi:10.1057/s41265-016-0003-9 · Cited by 27
Harborth, D., & Pape, S. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.585 · Cited by 27
Müller, L. S., Nohe, C., Reiners, S., Becker, J., & Hertel, G. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2196598 · Cited by 39
Hertel, G., Meeßen, S. M., Riehle, D. M., Thielsch, M. T., Nohe, C., & Becker, J. (2019) · Ergonomics · doi:10.1080/00140139.2019.1574361 · Cited by 26
Meyer‐Waarden, L., Cloarec, J., Adams, C., Aliman, D. N., & Wirth, V. (2022) · Systèmes d information & management · doi:10.3917/sim.214.0055 · Cited by 26
Role of users’ status quo on continuance intentions
Goyal, S., Venkatesh, V., & Shi, X. (2022) · Information & Management · doi:10.1016/j.im.2022.103686 · Cited by 30
Are You Hooked on Paid Music Streaming?
Chen, C. C., Leon, S., & Nakayama, M. (2017) · International Journal of E-Business Research · doi:10.4018/ijebr.2018010101 · Cited by 33
Torro, O., Jalo, H., & Pirkkalainen, H. (2021) · Communications of the ACM · doi:10.1145/3440868 · Cited by 24
Brunswicker, S., Zhang, Y., Rashidian, C., & Linna, D. W. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108516 · Cited by 32
Virtual influencers in marketing: addressing authenticity challenges through anthropomorphism
Yin, G., Pei, Y.-L., Farivar, S., Wang, F., & Wang, S. (2025) · Asia Pacific Journal of Marketing and Logistics · doi:10.1108/apjml-01-2025-0025 · Cited by 22
Ngo, T. T. A., Vo, T. T. A., & Phan, M. T. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105789 · Cited by 32
Zarifis, A., & Fu, S. (2023) · Digital · doi:10.3390/digital3040018 · Cited by 25
Technological trust from the perspective of digital payment
Szumski, O. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.09.032 · Cited by 24
Inhabiting Interconnected Spaces: How Users Shape and Appropriate Their Smart Home Ecosystems
Woźniak, M. P., Vöge, S., Krüger, R., Müller, H., Koelle, M., & Boll, S. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581497 · Cited by 26
Neves, B. B., Omori, M., Petersen, A. R., Vered, M., & Carter, A. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.117187 · Cited by 25
The effects of generative AI’s human-like competencies on clinical decision-making
Spatscheck, N., Schaschek, M., & Winkelmann, A. (2024) · Journal of Decision System · doi:10.1080/12460125.2024.2430731 · Cited by 28
Premathilake, G. W., Li, H., Li, C., Yong, L., & Han, S. (2024) · Industrial Management & Data Systems · doi:10.1108/imds-10-2023-0781 · Cited by 32
Goel, P., & Garg, A. (2025) · Journal of Consumer Marketing · doi:10.1108/jcm-05-2024-6915 · Cited by 34
Understanding human-AI trust in education
Pitts, G., & Motamedi, S. (2025) · Telematics and Informatics Reports · doi:10.1016/j.teler.2025.100270 · Cited by 58
Understanding digital contact tracing app continuance: Insights from India
Prakash, A. V., Das, S., & Rajasekharan Pillai, K. (2021) · Health Policy and Technology · doi:10.1016/j.hlpt.2021.100573 · Cited by 22
An integrative model of trust toward crypto-tokens applications: A customer perspective approach
Toufaily, É. (2022) · Digital Business · doi:10.1016/j.digbus.2022.100041 · Cited by 34
Not so trustless after all: Trust in Web3 technology and opportunities for brands
Ferraro, C., Wheeler, M. A., Pallant, J. I., Wilson, S. G., & Oldmeadow, J. A. (2023) · Business Horizons · doi:10.1016/j.bushor.2023.01.007 · Cited by 22
Human-Centered Trustworthy Framework: A Human–Computer Interaction Perspective
Sousa, S., Lamas, D., Cravino, J., & Martins, P. (2024) · Computer · doi:10.1109/mc.2023.3287563 · Cited by 33
Ebrahimi, S., Abdelhalim, E., Hassanein, K., & Head, M. M. (2024) · European Journal of Information Systems · doi:10.1080/0960085x.2024.2395531 · Cited by 32
Zhou, A. Y., Tsai, W. S., & Men, L. R. (2024) · International Journal of Business Communication · doi:10.1177/23294884241229223 · Cited by 29
Nissen, A., Conrad, C., & Newman, A. J. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580943 · Cited by 25
Shaping crowdlending investors’ trust: Technological, social, and economic exchange perspectives
Perdana, A., Jutasompakorn, P., & Chung, S. (2023) · Electronic Markets · doi:10.1007/s12525-023-00650-7 · Cited by 24
Trust-Based Determinants of Future Intention to Use Technology
Ejdys, J. (2020) · Foresight-Russia · doi:10.17323/2500-2597.2020.1.60.68 · Cited by 23
Explaining the Wait: How Justifying Chatbot Response Delays Impact User Trust
Zhang, Z., Tsiakas, K., & Schneegass, C. (2024) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3640794.3665550 · Cited by 28
Acceptance of Anthropomorphic Technology: A Literature Review
Bhatti, S. C., & Leidner, D. E. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.774 · Cited by 21
Nwankpa, J. K., & Datta, P. M. (2021) · European Journal of Information Systems · doi:10.1080/0960085x.2021.1978339 · Cited by 21
Orden-Mejía, M. A., Carvache‐Franco, M., Huertas, A., Carvache‐Franco, O., & Carvache‐Franco, W. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2425882 · Cited by 24
Dalvi‐Esfahani, M., ahmadabadi, H. B., Ramayah, T., Turner, J. J., Iahad, N. A., & Azar, N. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124291 · Cited by 23
Probing Determinants Affecting Intention to Adopt Cloud Technology in E-Government Systems
Abied, O., Ibrahim, O., Kamal, S. N.-I. M., Alfadli, I., Binjumah, W. M., Ithnin, N., & Nasser, M. (2022) · Sustainability · doi:10.3390/su142315590 · Cited by 30
Gregory, R. W., Beck, R., Henfridsson, O., & Yaraghi, N. (2024) · Academy of Management Review · doi:10.5465/amr.2023.0023 · Cited by 29
Whang, C. H., & Im, H. (2018) · International Journal of Retail & Distribution Management · doi:10.1108/ijrdm-06-2017-0122 · Cited by 19
A dual-factor theory of WTs adoption in aged care service operations – a cross-country analysis
Khaksar, S. M. S., Jahanshahi, A. A., Slade, B., & Asian, S. (2020) · Information Technology and People · doi:10.1108/itp-10-2018-0449 · Cited by 19
A comparison of features in a crowdsourced phishing warning system
Nguyen, C., Jensen, M. L., Durcikova, A., & Wright, R. T. (2021) · Information Systems Journal · doi:10.1111/isj.12318 · Cited by 24
Knote, R., Janson, A., Söllner, M., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3923706 · Cited by 19
Understanding the user perception of digital nudging in platform interface design
Hettler, F. M., Schumacher, J.-P., Anton, E., Eybey, B., & Teuteberg, F. (2024) · Electronic Commerce Research · doi:10.1007/s10660-024-09825-6 · Cited by 19
How to build trust in answers given by Generative AI for specific and vague financial questions
Zarifis, A., & Cheng, X. (2024) · Journal of Electronic Business & Digital Economics · doi:10.1108/jebde-11-2023-0028 · Cited by 21
Zhang, Y., & Yu, Z. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13721-3 · Cited by 19
Wan, C., Lee, D., Ng, P. M. L., & Leung, T. C. H. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2561771 · Cited by 20
Busch, P. A. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101872 · Cited by 21
Identifying top persuaders in mixed trust networks for electronic marketing based on word-of-mouth
Hu, X., Liu, S., Zhang, Y., Zhao, G., & Jiang, C. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.06.011 · Cited by 19
Følstad, A., Law, E. L.-C., & van As, N. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3690383 · Cited by 17
Human, Hybrid, or Machine? Exploring the Trustworthiness of Voice-Based Assistants
Weidmüller, L. (2022) · Human-Machine Communication · doi:10.30658/hmc.4.5 · Cited by 43
Liu, W., Xu, K., & Yao, M. Z. (2023) · Communication Research Reports · doi:10.1080/08824096.2023.2212899 · Cited by 18
Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires
Alsaid, A., Li, M., Chiou, E. K., & Lee, J. D. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1192020 · Cited by 23
DIGITAL INCLUSION AND ACCESSIBILITY: SOCIAL DIGITALISATION
Давиденко, Г., & Ukraine" (2023) · doi:10.58521/978-617-552-348-3-2023-236 · Cited by 17
Investigating re-use intentions for shared bicycles: A loyalty phase perspective
Liang, J., Eccarius, T., & Lu, C. (2021) · Research in Transportation Business & Management · doi:10.1016/j.rtbm.2021.100696 · Cited by 18
Schuetz, S. W., Steelman, Z. R., & Syler, R. A. (2022) · Decision Support Systems · doi:10.1016/j.dss.2022.113846 · Cited by 21
(Re)Conceptualizing trustworthy AI: A foundation for change
Wirz, C. D., Demuth, J. L., Bostrom, A., Cains, M. G., Ebert‐Uphoff, I., Gagne, D. J., Schumacher, A., McGovern, A., & Madlambayan, D. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2025.104309 · Cited by 23
Metadata
- Authors
- Marshall University, Lankton, N., McKnight, D. H., Michigan State University, Tripp, J., & Baylor University
- Affiliations
- Marshall University (US)
- Michigan State University (US)
- Baylor University (US)
- Venue
- Journal of the Association for Information Systems
- Publisher
- Association for Information Systems
- Published
- 2015-10
- Volume
- 16
- Issue
- 10
- Pages
- 880-918
- ISSN
- 1536-9323 (ISSN-L)
- OpenAlex
- W1854663754
- Cited by
- 553
- References
- 0
- Citations by year
- 2026: 102 · 2025: 99 · 2024: 93 · 2023: 69 · 2022: 59 · 2021: 52 · 2020: 41 · 2019: 13 · 2018: 11 · 2017: 11 · 2016: 3
- FWCI
- 13.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Association for Information Systems (publishedVersion)
- Journal of the Association for Information Systems (submittedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- trust in technology, benevolence, integrity, helpfulness, reliability, social presence, social affordances
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