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Published: 2021-10-31

Applying ethology to design human-oriented technology. Experimental study on the signalling role of the labelling effect in technology’s empowerment

University of Łódź
Center for Artificial Intelligence and Cybercommunication Research, Poland; Georgetown University, USA
techno-empowerment autonomous system technology acceptance ethology country of origin certification new technology management


We transpose ethological and sociological theory on autonomous technology management using two signals: country of origin and security certificate status. Our research shows that to understand the degree of attractiveness of human-oriented technology that has been techno-empowered, we should analyze the natural interspecies interaction taking place in the ecological niche. A 2×4 between-subject experiment on a fictitious brand was designed to test three hypotheses regarding autonomous office assistant empowerment. Two hundred ninety-five people (54% females) participated in the study. We found that people have a higher intention to use autonomous office assistants if their country of origin is unknown but a security certificate is provided. Gender moderates the ‘label effect’ so that females have a higher intention to allow autonomous office assistant to make independent decisions if they do not know the country of origin but a safety certificate is provided, whereas for males, neither of these labels influences such intention significantly.


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  1. Acheampong, R.A., & Cugurullo, F. (2019). Capturing the behavioural determinants behind the adoption of autonomous vehicles: Conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars, Transportation Research Part F: Traffic Psychology and Behaviour, 62, 349-375,
  2. Ahmed, S.A., & d’Astous, A. (2001). Canadian consumers’ perceptions of products made in newly industrializing East Asian countries. International Journal of Commerce and Management, 11(1), 54-81.
  3. Akerkar, R. (2019). Artificial intelligence for business. Springer: Briefs in Business.
  4. Bedué, P., & Fritzsche, A. (2021). Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption. Journal of Enterprise Information Management.
  5. Blais, A., Massicotte, L., & Yoshinaka, A. (2001). Deciding who has the right to vote: a comparative analysis of election laws. Electoral studies, 20(1), 41-62.
  6. Borau, S., Otterbring, T., Laporte, S., & Fosso Wamba, S. (2021). The most human bot: Female gendering increases humanness perceptions of bots and acceptance of AI. Psychology & Marketing, 38(7), 1052-1068,
  7. Bourke, A.F.G. (2011). Principles of social evolution. Oxford University Press, USA.
  8. Brooks, B. (2021). Get ready for self-driving banks, Financial Times, retrieved from:
  9. Charness, N., Yoon, J. S., Souders, D., Stothart, C., & Yehnert, C. (2018). Predictors of attitudes toward autonomous vehicles: The roles of age, gender, prior knowledge, and personality. Frontiers in psychology, 9, 2589,
  10. Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009). Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing, 5(4), 277-298.
  11. Daugherty, P. R., & Wilson, H. J. (2018). Human+ machine: Reimagining work in the age of AI. Harvard Business Press.
  12. David, P. (2017). Cobots – a helping hand to the healthcare industry, Universal robots, retrieved from:
  13. De Visser, E. J., Monfort, S. S., Goodyear, K., Lu, L., O’Hara, M., Lee, M. R., ... & Krueger, F. (2017). A little anthropomorphism goes a long way: Effects of oxytocin on trust, compliance, and team performance with automated agents. Human factors, 59(1), 116-133.
  14. Deane, J. K., Goldberg, D. M., Rakes, T. R., & Rees, L. P. (2019). The effect of information security certification announcements on the market value of the firm. Information Technology and Management, 20(3), 107-121.
  15. Diamond, M. (2002). Sex and gender are different: Sexual identity and gender identity are different. Clinical child psychology and psychiatry, 7(3), 320-334.
  16. Elton, Ch. S. (2001). Animal Ecology. University of Chicago Press.
  17. Endsley, M. R. (2017). From here to autonomy: lessons learned from human–automation research. Human factors, 59(1), 5-27.
  18. Fogg, B. J. (2002). Persuasive technology: Using computers to change what we think and do. Ubiquity, 2002(December), 2.
  19. Gabrys, B. (Ed.). (2006). Knowledge-Based Intelligent Information and Engineering Systems: 10th International Conference, KES 2006, Bournemouth, UK, October 9-11 2006, Proceedings, Part II (Vol. 4252). Springer.
  20. Gaedeke, R. (1973). Consumer attitudes toward products made in developing countries. Journal of Retailing, 49(2), 13-24.
  21. Galina, S., & Zarina, S. (2019). Development of smart technology for complex objects prediction and control on the basis of a distributed control system and an artificial immune systems approach. Advances in Science, Technology and Engineering Systems, 4(3), 75-87.
  22. Geary, D. C. (2010). Male, female: The evolution of human sex differences. American Psychological Association.
  23. Gilbert, J., & Oladi, R. (2021). Labor‐eliminating technical change in a developing economy. International Journal of Economic Theory, 17(1), 88-100.
  24. Grinnell, J. (1917). The niche-relationships of the California Thrasher. The Auk, 34(4), 427-433.
  25. Hamilton, W. D. (1971). Geometry for the selfish herd. Journal of theoretical Biology, 31(2), 295-311.
  26. Hamilton, W. D., & Zuk, M. (1982). Heritable true fitness and bright birds: a role for parasites?. Science, 218(4570), 384-387.
  27. Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y., De Visser, E. J., & Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human factors, 53(5), 517-527.
  28. Higgins, S. H., & Shanklin, W. L. (1992). Seeking Mass Market Acceptance for High-Technology Consumer. The Journal of Consumer Marketing, 9(1), 5-14.
  29. Hrdy, S. B. (1980). The langurs of Abu: female and male strategies of reproduction. Harvard University Press.
  30. Hudson, J., Orviska, M., & Hunady, J. (2019). People’s attitudes to autonomous vehicles. Transportation research part A: policy and practice, 121, 164-176,
  31. Jianakoplos, N. A., & Bernasek, A. (1998). Are women more risk averse?. Economic inquiry, 36(4), 620-630.
  32. Jones, S. E. (2013). Against technology: From the Luddites to neo-Luddism. Routledge.
  33. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25,
  34. Kim, K., Choe, C., & Lee, D. (2020). Overlapping certification and technical efficiency of ICT convergence companies in South Korea. Science and Public Policy, 47(4), 514-524,
  35. Kim, N., & Kwon, K. M. (2015). Certification benefits in high-tech industry: Evidence from supply contracts in the Korean market. Emerging Markets Finance and Trade, 51(5), 1001-1020.
  36. Kirkpatrick, M. (1982). Sexual selection and the evolution of female choice. Evolution, 36(1), 1-12. doi:10.2307/2407961
  37. Koschate-Fischer, N., Diamantopoulos, A., & Oldenkotte, K. (2012). Are consumers really willing to pay more for a favorable country image? A study of country-of-origin effects on willingness to pay. Journal of International Marketing, 20(1), 19-41.
  38. Krebs, C. J. (2008). Ecology: The experimental analysis of distribution and abundance. Person Editorial House.
  39. Kuntsevich, R. (2018). AI and sex toys. Retrieved from
  40. Laroche, M., Yang, Z., McDougall, G. H., & Bergeron, J. (2005). Internet versus bricks-and-mortar retailers: An investigation into intangibility and its consequences. Journal of retailing, 81(4), 251-267.
  41. Lusk, J. L., Brown, J., Mark, T., Proseku, I., Thompson, R., & Welsh, J. (2006). Consumer behavior, public policy, and country‐of‐origin labeling. Applied Economic Perspectives and Policy, 28(2), 284-292.
  42. Lyons, J. B., Vo, T., Wynne, K. T., Mahoney, S., Nam, C. S., & Gallimore, D. (2021). Trusting autonomous security robots: the role of reliability and stated social intent. Human factors, 63(4), 603-618.
  43. Magnuson, J. J., & Gooding, R. M. (1971). Color patterns of pilotfish (Naucrates ductor) and their possible significance. Copeia, 1971(2), 314-316.
  44. Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.
  45. Maurtua, I., Ibarguren, A., Kildal, J., Susperregi, L., & Sierra, B. (2017). Human–robot collaboration in industrial applications: Safety, interaction and trust. International Journal of Advanced Robotic Systems, 14(4), 1729881417716010.
  46. McAfee, A., & Brynjolfsson, E. (2017). Machine, platform, crowd: Harnessing our digital future. WW Norton & Company.
  47. Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. 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.
  48. Michel, L., Nicolas, P., Louise, A. H., & Mehdi, M. (2005). The influence of country image structure on consumer evaluations of foreign products. International Marketing Review, 22(1), 96-115.
  49. Modliński, A. (2021a). The coming of autonomous machines. Multi-method research on the limits of techno-empowerment.
  50. Modlinski, A., & Pinto, L. M. (2020). Managing substitutive and complementary technologies in cultural institutions: Market/mission perspectives. Management: Journal of Contemporary Management Issues, 25(Special issue), 1-10.
  51. Modliński, A., Gwiaździński, E., & Karpińska-Krakowiak, M. (2021). To target the (pr)opponents of autonomous vehicles. The effects of gender and religiosity on attitudes towards autonomous vehicles.
  52. Modlinski, A., Skworonski, D. (2021). Robopowers? The phenomenon of technoempowerment in the socio-organizational context.
  53. Mori, M., MacDorman, K. F., & Kageki, N. (2012). The uncanny valley [from the field]. IEEE Robotics & Automation Magazine, 19(2), 98-100.
  54. Mostafa, R. H. (2015). The impact of country of origin and country of manufacture of a brand on overall brand equity. International Journal of Marketing Studies, 7(2), 70.
  55. Nappi, I., & de Campos Ribeiro, G. (2019). Internet of Things technology applications in the workplace environment: a critical review. Journal of Corporate Real Estate, 22(1), 71-90.
  56. Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of social issues, 56(1), 81-103.
  57. O’brien, M. (2018). Boston Dynamics' scary robot videos: Are they for real?,, retrieved from:
  58. Parker, D., Reason, J. T., Manstead, A. S., & Stradling, S. G. (1995). Driving errors, driving violations and accident involvement. Ergonomics, 38(5), 1036-1048.
  59. Pelau, C., Dabija, D. C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122, 106855.
  60. Pettigrew, S., Fritschi, L., & Norman, R. (2018). The potential implications of autonomous vehicles in and around the workplace. International journal of environmental research and public health, 15(9), 1876.
  61. Puri, M. (1996). Commercial banks in investment banking conflict of interest or certification role?. Journal of Financial Economics, 40(3), 373-401.
  62. Rodríguez, O. F., Fernández, F., & Torres, R. S. (2011). Impact of information technology certifications in Puerto Rico. Management Research, 9(2), 137–153.
  63. Roth, M. S., & Romeo, J. B. (1992). Matching product catgeory and country image perceptions: A framework for managing country-of-origin effects. Journal of international business studies, 23(3), 477-497.
  64. Santos, J. C., Coloma, L. A., & Cannatella, D. C. (2003). Multiple, recurring origins of aposematism and diet specialization in poison frogs. Proceedings of the National Academy of Sciences, 100(22), 12792-12797.
  65. Schaefer, K. E., Chen, J. Y., Szalma, J. L., & Hancock, P. A. (2016). A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems. Human factors, 58(3), 377-400.
  66. Sevanandee, B., & Damar-Ladkoo, A. (2018). Country-of-Origin effects on consumer buying behaviours. A Case of Mobile Phones. Studies in Business and Economics, 13(2), 179–201.
  67. Sörqvist, P., Haga, A., Langeborg, L., Holmgren, M., Wallinder, M., Nöstl, A., ... & Marsh, J. E. (2015). The green halo: Mechanisms and limits of the eco-label effect. Food quality and preference, 43, 1-9.
  68. Suzuki, K., & Avellaneda, C. N. (2018). Women and risk-taking behaviour in local public finance. Public Management Review, 20(12), 1741-1767. doi:10.1080/14719037.2017.1412118
  69. Tay, B. T., Low, S. C., Ko, K. H., & Park, T. (2016). Types of humor that robots can play. Computers in Human Behavior, 60, 19-28.
  70. Tennant, C., Stares, S., & Howard, S. (2019). Public discomfort at the prospect of autonomous vehicles: Building on previous surveys to measure attitudes in 11 countries. Transportation research part F: traffic psychology and behaviour, 64, 98-118,
  71. Toh, S. M., Morgeson, F. P., & Campion, M. A. (2008). Human resource configurations: investigating fit with the organizational context. Journal of Applied Psychology, 93(4), 864.
  72. Trivers, R. L. (2017). Parental investment and sexual selection (pp. 136-179). Routledge.
  73. Turner, C., & McClure, R. (2003). Age and gender differences in risk-taking behaviour as an explanation for high incidence of motor vehicle crashes as a driver in young males. Injury control and safety promotion, 10(3), 123-130.
  74. Waytz, A., Heafner, J., & Epley, N. (2014). The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology, 52, 113-117.
  75. Winn, Z. (2020). A human-machine collaboration to defend against cyberattacks. Massachusetts Institute of Technology, retrieved from 2020/patternex-machine-learning-cybersecurity-0221
  76. Wood, S., & Hoeffler, S. (2013). Looking Innovative: Exploring the Role of Impression Management in High‐Tech Product Adoption and Use. Journal of Product Innovation Management, 30(6), 1254-1270.
  77. Xu, X., & Fan, C. K. (2019). Autonomous vehicles, risk perceptions and insurance demand: An individual survey in China. Transportation research part A: Policy and practice, 124, 549-556.

How to Cite

Modliński, A., & Gladden, M. (2021). Applying ethology to design human-oriented technology. Experimental study on the signalling role of the labelling effect in technology’s empowerment. Human Technology, 17(2), 164–189.