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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
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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]
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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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Wang, Y. L., & Tong, X. (2021) · IEEE Access · doi:10.1109/access.2021.3075952 · Cited by 1
Zitan, H., & Chafik, K. (2021) · International Conference on Data Science, E-Learning and Information Systems (DATA) · doi:10.1145/3460620.3460763 · Cited by 1
Understanding the Formation of Trust in Blockchain
Ali, V., Norman, A. A., & Azzuhri, S. R. (2021) · IEEE International Conference on Computing (ICOCO) · doi:10.1109/icoco53166.2021.9673567 · Cited by 1
Acceptance of Biometric Authentication Security Technology on Mobile Devices
Malatji, W. R., Zuva, T., & Eck, R. V. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-33-4909-4_11 · Cited by 1
Tenzin, S. (2021) · Murdoch University Research Portal · Cited by 1
Citizens’ Trust in Ai Applications: Does One Ai Fit All?Preprint
Alcarazo, L. O. D. Z. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4101157 · Cited by 1
Human-centered trust framework: An HCI perspectivePreprint
Sousa, S., Cravino, J., Martins, P., & Lamas, D. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.03306 · Cited by 1
Exploring Women’s Perspectives on Learning and Trust in Automated Vehicles: A Socio-Ecological Lens
Abusafia, A. H. A., Schroeter, R., & Soro, A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791737
Yacoubian, L. J. (2025) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2025.26.2.2140 · Cited by 1
Odusanya, K. A. (2018) · Loughborough University Institutional Repository (Loughborough University)
Research Progress of Trust Evaluation
Wang, Y., & Tong, X. (2020) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei51763.2020.9263498
Researching Human-AI Collaboration through the Design of Language-Based Query Assistance
Pafla, M. (2020) · UWSpace (University of Waterloo)
Blomqvist, K., Möhlmann, M., & Järvenpää, S. L. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.663 · Cited by 1
Trust Transfer in a Complex Technology Configuration (Ctc)Preprint
Agogo, D., Wright, R. T., Miltgen, C. L., & Milne, G. R. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3778739
Gambaran Proses Trust-Building pada Interaksi Pengguna Situs Perkencanan Daring
Octaviolan, T. C., & Ardi, R. (2021) · Buletin Riset Psikologi dan Kesehatan Mental (BRPKM) · doi:10.20473/brpkm.v1i1.26763
Measuring the Adoption of Biometric Verification Technique Implementations on Mobile Devices
Malatji, W. R., VanEck, R., & Zuva, T. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-90318-3_13
Navigating Through Changes of a Digital World
Hauk, N., & Hauswirth, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-86144-5_37
Krüeger, F. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108770880.002
E-learning for student support, inclusion and equity in diverse post-pandemic teaching contexts
Omidire, M. F., & Maroga, M. J. (2022) · doi:10.35293/tetfle.v3i1.4109
Gonzalez, J. C. (2022) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.20579568 · Cited by 1
Can Social Media Make Us More Trusting?
Bohler, J. A., Drake, J. R., Paul, R. C., & Kisling, E. L. (2022) · Journal of Electronic Commerce in Organizations · doi:10.4018/jeco.305738
Measuring trust: A text analysis approach to compare, contrast, and select trust questionnairesPreprint
Alsaid, A., Li, M., Chiou, E. K., et al. (2022) · doi:10.31234/osf.io/5eyur
Fuzzy-Trace Theory and Risky Decisions in The Setting of The Digital Economy
Amril, Bambang Tjahjadi, & I Made Narsa (2022) · Quality - Access to Success · doi:10.47750/qas/23.190.06
Study on the Discursive Strategies of Wired to Repair Trust in Blockchain
Su, Q. (2023) · Scientific and Social Research · doi:10.26689/ssr.v5i2.4727 · Cited by 1
Rigsbee, S., Basantis, A., Camacho, B., et al. (2023) · doi:10.1145/3597512.3600203
Evaluating Flexibility and Trust in Complex Decision Scenarios Using Decision Support SystemsPreprint
Reiners, S., Müller, L. S., Riehle, D. M., Becker, J., & Hertel, G. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4528603
Implications of the Instruments View of Computer Simulation
Alvarado, R. (2023) · Synthese Library/Synthese library · doi:10.1007/978-3-031-38647-3_7
Propensity to trust: comforting touch between trustworthy human and robot partners.Preprint
Valori, I., Fan, Y., Jung, M. M., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3738758/v1
Zhang, W., Zhang, Y., & Wu, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4755624
“DATAİZM”E KARŞI VERİ GÜVENLİĞİ VE ADALETİ
Ümütlü, A. Y., & Afşar, Ö. A. (2024) · Adalet Dergisi · doi:10.57083/adaletdergisi.1483346
Trustworthy Artificial Intelligence for Social Governance
Leizhen, Z., Song, X., & Changwu, Y. (2024) · 中国社会科学：英文版 · doi:10.1080/02529203.2024.2367319 · Cited by 1
Measuring Impact on Confidence in Institutions by their Use of Software Components
Paulin, A. (2024) · Central and Eastern European eDem and eGov Days · doi:10.1145/3670243.3670249
A Vision to Enhance Trust Requirements for Peer Support Systems by Revisiting Trust Theories
Gheidar, Y., Lessard, L., & Yao, Y. (2024) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re59067.2024.00052 · Cited by 1
Trustful Trading in Shared Augmented Reality
Ritter, M., Liew, K., Wickramasinghe, Y. S., & Lukosch, S. (2024) · Symposium on Spatial User Interaction · doi:10.1145/3677386.3682086 · Cited by 1
Masciotra, V., & Boudrias, J. (2024) · Ad machina l avenir de l humain au travail · doi:10.1522/radm.no8.1840
Wewetzer, L., Goetz, K., Steinhäuser, J., & Freischmidt, S. (2024) · doi:10.2196/preprints.69777
Three Essays on Internet of Things Adoption and Use
Aldossari, M. Q. (2019) · University of North Texas Digital Library (University of North Texas) · doi:10.12794/metadc1505259
Usability Impact of Conversational Chatbot
Rasheed, M., Iqbal, M. W., Fatima, A., Saleem, M., Bhatti, S. M., & Ahsan, M. J. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-77620-5_13
Trust in conflict: Notes on a research programPreprint
Deitelhoff, N., Forst, R., Hediger, V., et al. (2025) · doi:10.31235/osf.io/x24zq_v1
Virtual Gamification in a PBS-based SETA Program
Stacey, K. J., & Landry, J. P. (2025) · Journal of The Colloquium for Information Systems Security Education · doi:10.53735/cisse.v12i1.214
Grundlagen, Entwicklungslinien und Perspektiven Digitaler Identitäten und Nachweise
Anke, J., Richter, D., & Seiffert, M. (2025) · Springer eBooks · doi:10.1007/978-3-658-47708-0_1
Communicating about sexual violence on TikTok: A content analysis
Nicolla, S., Lazard, A. J., Freelon, D., et al. (2025) · New Media & Society · doi:10.1177/14614448251341484
Jize, L., & Jamaludin, D. F. A. (2025) · International Journal of Academic Research in Business and Social Sciences · doi:10.6007/ijarbss/v15-i6/25676
Trust in Voice Assistants: A Multidimensional Conceptualization Based on In-Home User InterviewsPreprint
Frehmann, K., Ziegele, M., Cruciger, R., Grobe, A.-K., & Bavaj, F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5340213
Tyagi, S. K., Mairaru, S., & Das, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5348600
The Psychology of Smart Consumer Products
Jomon Jose, M., & Mathews, J. R. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-3010-5.ch004
Tyagi, S. K., Mairaru, S., & Das, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5356405
Novikov, Z., Magon, H., Li, X., Olsen, E., Bohn, R. E., & Singer, S. J. (2025) · Health Care Management Review · doi:10.1097/hmr.0000000000000450
Trust Design Framework in Blockchain-Enabled Supply Chains
Ostern, N., Simonet, J., & Obwegeser, N. (2025) · Springer eBooks · doi:10.1007/978-3-031-90210-9_16
The Influences of AI Locus of Causality and Trust in AI on the Adoption of AI Advice
Lee, K., Cho, W., & Woo, H. (2025) · Journal of Organizational Computing and Electronic Commerce · doi:10.1080/10919392.2025.2574195
Fang, C., Guo, F., & Belpaeme, T. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217721
Sarran, D., & Datt, V. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5824947
AI Dilemma for Business: Fear of Missing Out and the Strategic Adoption of Artificial IntelligencePreprint
Joshi, C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5860824
Kang, J., & Huang, Y. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3639299
Beyond first impressions: how chatbot familiarity reshapes Gen Z's evolving customer experience?
Baati, O., Sfar, N., & Sboui, M. (2025) · Marketing Intelligence & Planning · doi:10.1108/mip-05-2025-0387
Mesleki Gelişimde Çevrim içi Öğrenme Ortamlarına Yönelik Öğretmen Güveni Ölçeği Geliştirme Çalışması
Arslan, S., & Özkök, G. A. (2025) · Ondokuz Mayis Univ Egitim Fakultesi · doi:10.7822/omuefd.1664833
Navigating frictions and drivers in international research collaboration – the case of BRICS nations
Pigola, A., Fischer, B., de Moraes, G. H. S. M., Meissner, D., Zhou, Y. U. A. N., & Miao, Z. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103241 · Cited by 1
Sarran, D., & Datt, V. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6121934
Kim, Y., Acaf, Y., Du, T., & Tao, J. J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6124130
Wang, Z., & Chun, W. (2026) · Journal of Interactive Advertising · doi:10.1080/15252019.2025.2609546
Oh, O., Harrison, S., Sefcik, J. S., Richmond, T. S., Hodgson, N. A., & Demiris, G. (2026) · The Journals of Gerontology Series A · doi:10.1093/gerona/glag018 · Cited by 1
From Digital to Disturbing: Exploring the Role of Metaverse Affordances in Sexual HarassmentPreprint
So, B. T. K., Wong, R. Y. M., Cheung, C. M. K., & Thatcher, J. B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6237976
Mühl, C., Kührt, C., Gayraud, K., Piechowski, S., Valori, I., Hulin, T., Nguyen, G. T., Pötter, M. A., Krzywinski, J., Singh, H., et al. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-330044-8.00017-8
Ethical, legal and societal implications of Tactile Internet with Human-in-the-Loop
Kaesling, K., Engesser, S., Fairhurst, M. T., Jugov, T., Klös, V., Krzywinski, J., Lauber-Rönsberg, A., Neumann, N., Pannasch, S., Strufe, T., et al. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-330044-8.00037-3
CONFIGURING TRUST IN GENERATIVE AI: INTEGRATING TRUST THEORY AND USER SEGMENTATIONPreprint
Akhmedova, A., Marimón, F., Mas‐Machuca, M., & Amat‐Lefort, N. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6278331
Reconfiguring Intimacy With AI Companions
Wenxi, W. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6623-4.ch003
The Supervisory Paradox: How AI Delegation Transforms Productive Labor into Cognitive VigilancePreprint
Tripp, J. F. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6503239 · Cited by 1
Self-citationLa confiance en contexte numérique : étude de ses déterminants et lien avec les émotions
Cho, J. A. S. (2022) · theses.fr (ABES) · doi:10.70675/bd8159c5zfaf1z4358z9c77zec68bcf24150
L’impact de l’information sur la confiance en crowdfunding : Cas Libanais
helou, W. E. (2024) · theses.fr (ABES) · doi:10.70675/13b103afze406z49fbza220z0c6865a66405
Anggraini, C. N., Rachmadania, N., Nugroho, C., Wulandari, A., & Rina, N. (2026) · Frontiers in Political Science · doi:10.3389/fpos.2026.1784691 · Cited by 3
Dey, S., & Kumar, S. (2026) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2026.103071 · Cited by 2
Ling, T.-W., Peng, W., Meng, J., & Liu, J.-H. (2026) · CHI Extended Abstracts · doi:10.1145/3772363.3798608
Ramli, M. B., Hashim, S., Rozali, M. Z., & Abdullah, A. N. (2026) · International Journal of Education Psychology and Counseling · doi:10.35631/ijepc.1162041
How Trust Evolves in Human–AI Interaction: A Dynamic Pathway from Calibration to Failure and RepairPreprint
Zhu, Y., Tan, B., He, Y., Wang, C., & Zhang, T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6612500 · Cited by 1
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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