Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Journal on Multimodal User Interfaces
Published in: Journal on Multimodal User Interfaces 4/2020

29-05-2020 | Original Paper

Effects of personality traits on user trust in human–machine collaborations

Authors: Jianlong Zhou, Simon Luo, Fang Chen

Published in: Journal on Multimodal User Interfaces | Issue 4/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Data analytics-driven solutions are widely used in various intelligent systems, where humans and machines make decisions collaboratively based on predictions. Human factors such as personality and trust have significant effects on such human–machine collaborations. This paper investigates effects of personality traits on user trust in human–machine collaborations under uncertainty and cognitive load conditions. A user study of 42 subjects in a repeated factorial design experiment found that uncertainty presentation led to increased trust but only under low cognitive load conditions when users had sufficient cognitive resources to process the information. Presentation of uncertainty under high load conditions led to a decrease in trust. When further drilling down into personality trait groups of users, overall, users with low Openness showed the highest trust. Furthermore, under the low cognitive load condition, it was found that the trust was enhanced under ambiguity uncertainty with low Agreeableness, low Neuroticism, high Extraversion, high Conscientiousness, and high Openness. Under the high cognitive load condition, high Neuroticism and low Extraversion benefitted the trust without the uncertainty presentation. The results demonstrated that different personality traits affected trust differently under uncertainty and cognitive load conditions. A framework of user trust feedback loop was set up to incorporate the study results into human–machine collaborations for the meaningful participatory design.
previous article Exploring interaction techniques for 360 panoramas inside a 3D reconstructed scene for mixed reality remote collaboration

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
McElheran K, Brynjolfsson E (2016) The rise of data-driven decision making is real but uneven. In: Harvard Business Review. Accessed 2 Aug 2017
2.
Vaccaro M, Waldo J (2019) The effects of mixing machine learning and human judgment. Commun ACM 62:104–110
3.
Brown A, Chouldechova A, Putnam-Hornstein E, et al (2019) Toward algorithmic accountability in public services: a qualitative study of affected community perspectives on algorithmic decision-making in child welfare services. In: Proceedings of the 2019 CHI conference on human factors in computing systems. ACM, Glasgow, Scotland, UK, pp 41:1–41:12
4.
Chien S-Y, Lewis M, Sycara K et al (2018) The effect of culture on trust in automation: reliability and workload. ACM Trans Interact Intell Syst 8:29:1–29:31
5.
Zhou J, Chen F (2018) Human and machine learning: visible, explainable, trustworthy and transparent. Springer, Berlin
6.
Mitchell V, Ross T, May A et al (2016) Empirical investigation of the impact of using co-design methods when generating proposals for sustainable travel solutions. CoDesign 12:205–220
7.
Weinberg JB, Stephen ML (2002) Participatory design in a human-computer interaction course: teaching ethnography methods to computer scientists. In: Proceedings of the 33rd SIGCSE technical symposium on computer science education, pp 237–241
8.
Muller MJ (2003) Participatory design: the third space in HCI. In: Jacko JA, Sears A (eds) The human-computer interaction handbook. L. Erlbaum Associates Inc., Hillsdale, pp 1051–1068
9.
Wagstaff K (2012) Machine learning that matters. In: Proceedings of ICML2012, pp 529–536
10.
Zhou J, Khawaja MA, Li Z et al (2016) Making machine learning useable by revealing internal states update—a transparent approach. Int J Comput Sci Eng 13:378–389
11.
Fisher D, DeLine R, Czerwinski M, Drucker S (2012) Interactions with big data analytics. Interactions 19:50–59
12.
Donaldson T (2001) The ethical wealth of nations. J Bus Ethics 31:25–36
13.
Lee JD, See KA (2004) Trust in automation: designing for appropriate reliance. Hum Factors 46:50–80
14.
Adobor Henry (2006) Optimal trust? Uncertainty as a determinant and limit to trust in inter-firm alliancesnull. Leadership Org Dev J 27:537–553
15.
Parasuraman R, Sheridan TB, Wickens DC (2008) Situation awareness, mental workload, and trust in automation: viable, empirically supported cognitive engineering constructs. J Cognitive Eng Dec Making 2:140–160
16.
Chen F, Zhou J, Wang Y et al (2016) Robust multimodal cognitive load measurement. Springer, Berlin
17.
Khawaji A, Chen F, Zhou J, Marcus N (2014) Trust and cognitive load in the text-chat environment: the role of mouse movement. Proc OzCHI 2014:324–327
18.
Biros DP, Daly M, Gunsch G (2004) The influence of task load and automation trust on deception detection. Group Decis Negot 13:173–189
19.
Seungwon Kim, Lee GA, Sakata N (2013) Comparing pointing and drawing for remote collaboration. In: Proceedings of 2013 IEEE international symposium on mixed and augmented reality (ISMAR). IEEE, Adelaide, Australia, pp 1–6
20.
Cho J-H, Cam H, Oltramari A (2016) Effect of personality traits on trust and risk to phishing vulnerability: modeling and analysis. In: 2016 IEEE international multi-disciplinary conference on cognitive methods in situation awareness and decision support (CogSIMA), pp 7–13
21.
Hancock PA, Billings DR, Schaefer KE et al (2011) A meta-analysis of factors affecting trust in human-robot interaction. Hum Factors 53:517–527
22.
Hancock PA, Billings DR, Schaefer KE (2011) Can you trust your robot? Ergon Des 19:24–29
23.
Schaefer KE, Chen JYC, Szalma JL, Hancock PA (2016) A meta-analysis of factors influencing the development of trust in automation: implications for understanding autonomy in future systems. Hum Factors 58:377–400
24.
Candello H (2016) User methods and approaches to design cognitive systems. In: Marcus A (ed) Design, user experience, and usability: design thinking and methods. Springer, Berlin, pp 231–242
25.
Scantamburlo T (2015) Machine learning in decisional process: a philosophical perspective. ACM SIGCAS Comput Soc 45:218–224
26.
Watanabe S (1985) Pattern recognition: human and mechanical. Wiley, New York
27.
Fiebrink R, Trueman D (2012) End-user machine learning in music composition and performance. In: CHI 2012 workshop on end-user interactions with intelligent and autonomous systems. Austin, Texas
28.
Fails JA, Olsen, Jr. DR (2003) Interactive machine learning. In: Proceedings of IUI2003. pp 39–45
29.
Winkler RL (2015) The Importance of communicating uncertainties in forecasts: overestimating the risks from winter Storm Juno. Risk Anal 35:349–353
30.
Beller J, Heesen M, Vollrath M (2013) Improving the driver-automation interaction: an approach using automation uncertainty. Hum Factors 55:1130–1141
31.
Huettel SA, Stowe CJ, Gordon EM et al (2006) Neural signatures of economic preferences for risk and ambiguity. Neuron 49:765–775
32.
LeClerc J, Joslyn S (2015) The cry wolf effect and weather-related decision making. Risk Anal 35:385–395
33.
Kantowitz BH, Hanowski RJ, Kantowitz SC (1997) Driver acceptance of unreliable traffic information in familiar and unfamiliar settings. Hum Factors 39:164–176
34.
Uggirala A, Gramopadhye AK, Melloy BJ, Toler JE (2004) Measurement of trust in complex and dynamic systems using a quantitative approach. Int J Ind Ergon 34:175–186
35.
Helldin T, Falkman G, Riveiro M, Davidsson S (2013) Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving. In: Proceedings of the 5th international conference on automotive user interfaces and interactive vehicular applications. ACM, pp 210–217
36.
Kunze A, Summerskill SJ, Marshall R, Filtness AJ (2019) Automation transparency: implications of uncertainty communication for human-automation interaction and interfaces. Ergonomics 62:345–360
37.
Gosling SD, Gosling SD, Rentfrow PJ, Swann WB (2003) A very brief measure of the big-five personality domains. J Res Pers 37:504–528
38.
Gerber AS, Huber GA, Doherty D, Dowling CM (2012) Personality and the strength and direction of partisan identification. Polit Behav 34:653–688
39.
Gallego A, Oberski D (2012) Personality and political participation: the mediation hypothesis. Polit Behav 34:425–451
40.
Freitag M, PaulC Bauer (2016) Personality traits and the propensity to trust friends and strangers. Soc Sci J 53:467–476
41.
Fahr R, Irlenbusch B (2008) Identifying personality traits to enhance trust between organisations: an experimental approach. Manag Decis Econ 29:469–487
42.
Schneewind KA, Graf J (1998) Der 16-Persoenlichkeits-Faktoren-Test (16 PF-R)—Testmanual. Huber, Bern
43.
Chien S-Y, Sycara K, Liu J-S, Kumru A (2016) Relation between trust attitudes toward automation, Hofstede’s cultural dimensions, and big five personality traits. Proc Hum Factors Ergon Soc Annu Meeting 60:841–845
44.
Levine EE, Bitterly TB, Cohen TR, Schweitzer ME (2018) Who is trustworthy? Predicting trustworthy intentions and behavior. J Pers Soc Psychol 115:468–494
45.
Adalı S, Golbeck J (2014) Predicting personality with social behavior: a comparative study. Soc Netw Anal Min 4:159
46.
Tupes EC, Christal RE (1992) Recurrent personality factors based on trait ratings. J Pers 60:225–251
47.
48.
McCrae RR, Costa PT (2005) Personality in adulthood: a five-factor theory perspective, 2nd edn. The Guilford Press, New York
49.
Skinner J, Stewart B (2017) Organizational behaviour in sport. Taylor & Francis, London
50.
Deck C, Jahedi S (2015) The effect of cognitive load on economic decision making: a survey and new experiments. Eur Econ Rev 78:97–119
51.
Dinesen PT, Nørgaard AS, Klemmensen R (2013) The civic personality: personality and democratic citizenship. Polit Stud 62:134–152
52.
Kirschenbaum SS, Trafton JG, Schunn CD, Trickett SB (2014) Visualizing uncertainty: the impact on performance. Hum Factors 56:509–520
53.
Zhou J, Chen F (2015) Making machine learning useable. Int J Intell Syst Technol Appl 14:91
54.
Kim S, Billinghurst M, Lee G, et al (2019) Sharing emotion by displaying a partner near the gaze point in a Telepresence system, pp 86–91
55.
Kim S, Lee G, Huang W, et al (2019) Evaluating the combination of visual communication cues for HMD-based mixed reality remote collaboration, pp 1–13
Metadata
Title
Effects of personality traits on user trust in human–machine collaborations
Authors
Jianlong Zhou
Simon Luo
Fang Chen
Publication date
29-05-2020
Publisher
Springer International Publishing
Published in
Journal on Multimodal User Interfaces / Issue 4/2020
Print ISSN: 1783-7677
Electronic ISSN: 1783-8738
DOI
https://doi.org/10.1007/s12193-020-00329-9

Other articles of this Issue 4/2020

Journal on Multimodal User Interfaces 4/2020 Go to the issue

Editorial

Multimodal interfaces and communication cues for remote collaboration

Original Paper

Exploring interaction techniques for 360 panoramas inside a 3D reconstructed scene for mixed reality remote collaboration

Original Paper

The effects of spatial auditory and visual cues on mixed reality remote collaboration

Original Paper

Sharing gaze rays for visual target identification tasks in collaborative augmented reality

Original Paper

The combination of visual communication cues in mixed reality remote collaboration

Premium Partner

    Image Credits