Skip to main content

Advertisement

SpringerLink
Log in

How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents

  • Research Paper
  • Published:
Electronic Markets Aims and scope Submit manuscript

Abstract

A personal intelligent agent (PIA) is a system that acts intelligently to assist a human using natural language. Examples include Siri and Alexa. These agents are powerful computer programs that operate autonomously and proactively, learn and adapt to change, react to the environment, complete tasks within a favorable timeframe and communicate with the user using natural language to process commands and compose replies. PIAs are different from other systems previously explored in Information Systems (IS) due to their personalized, intelligent, and human-like behavior. Drawing on research in IS and Artificial Intelligence, we build and test a model of user adoption of PIAs leveraging their uique characteristics. Analysis of data collected from an interactive lab-based study for new PIA users confirms that both perceived intelligence and anthropomorphism are significant antecedents of PIA adoption. Our findings contribute to the understanding of a quickly-changing and fast-growing set of technologies that extend users’ capabilities and their sense of self​.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24, 665–694.

    Article  Google Scholar 

  • Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.

    Article  Google Scholar 

  • Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189.

    Article  Google Scholar 

  • Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics, 1(1), 71–81.

    Article  Google Scholar 

  • Bartneck, C., Verbunt, M., Mubin, O., & Al Mahmud, A. (2007). To kill a mockingbird robot. Paper presented at the human-robot interaction (HRI), 2007 2nd ACM/IEEE international conference.

  • Benbasat, I., & Barki, H. (2007). Quo vadis TAM? Journal of the Association for Information Systems, 8(4), 7.

    Article  Google Scholar 

  • Benbasat, I., & Zmud, R. W. (2003). The identity crisis within the IS discipline: Defining and communicating the discipline's core properties. MIS Quarterly, 183–194.

  • Bhattacherjee, A. (2002). Individual trust in online firms: Scale development and initial test. Journal of Management Information Systems, 19(1), 211–241.

    Article  Google Scholar 

  • Bos, N., Olson, J., Gergle, D., Olson, G., & Wright, Z. (2002). Effects of four computer-mediated communications channels on trust development. Paper presented at the Proceedings of the SIGCHI conference on human factors in computing systems.

  • Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 399–426.

  • Brown, S. A., Venkatesh, V., & Bala, H. (2006). Household technology use: Integrating household life cycle and the model of adoption of technology in households. The Information Society, 22(4), 205–218.

    Article  Google Scholar 

  • Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS quarterly, 689–707.

  • Chandler, J., & Schwarz, N. (2010). Use does not wear ragged the fabric of friendship: Thinking of objects as alive makes people less willing to replace them. Journal of Consumer Psychology, 20(2), 138–145.

    Article  Google Scholar 

  • Cheung, C. M., & Lee, M. K. (2012). What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems, 53(1), 218–225.

    Article  Google Scholar 

  • Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511–535.

    Article  Google Scholar 

  • Chin, W. W. (2010). Bootstrap cross-validation indices for PLS path model assessment handbook of partial least squares (pp. 83–97). Springer.

  • Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM): Thousand Oaks: SAGE Publications, Incorporated, US.

  • Chismar, W. G., and Wiley-Patton, S. 2003. “Does the Extended Technology Acceptance Model Apply to Physicians,” System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on: IEEE, p. 8. https://doi.org/10.1109/HICSS.2003.1174354.

  • Chiou, J. S., & Droge, C. (2006). Service quality, trust, specific asset investment, and expertise: Direct and indirect effects in a satisfaction-loyalty framework. Journal of the Academy of Marketing Science, 34(4), 613–627.

    Article  Google Scholar 

  • Ciechanowski, L., Przegalinska, A., Magnuski, M., & Gloor, P. (2019). In the shades of the uncanny valley: An experimental study of human–chatbot interaction. Future Generation Computer Systems, 92, 539–548.

    Article  Google Scholar 

  • Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309.

    Article  Google Scholar 

  • Cocosila, M., & Igonor, A. (2015). How important is the “social” in social networking? A perceived value empirical investigation. Information Technology & People, 28(2), 366–382.

    Article  Google Scholar 

  • Collins, K. (2018). Alexa vs. Siri vs. Google: Which can carry on a conversation best? Retrieved from https://www.nytimes.com/interactive/2018/08/17/technology/alexa-siri-conversation.html

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

    Article  Google Scholar 

  • Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 1–7.

  • Den Hartog, D. N., De Hoogh, A. H., & Keegan, A. E. (2007). The interactive effects of belongingness and charisma on helping and compliance. Journal of Applied Psychology, 92(4), 1131.

    Article  Google Scholar 

  • Dickinger, A., Arami, M., & Meyer, D. (2008). The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of Information Systems, 17(1), 4–11.

    Article  Google Scholar 

  • Druga, S., Williams, R., Breazeal, C., & Resnick, M. (2017). Hey Google is it OK if I eat you?: Initial explorations in child-agent interaction. Paper presented at the proceedings of the 2017 conference on interaction design and children.

  • Eisingerich, A. B., & Bell, S. J. (2007). Perceived service quality and customer trust: Does enhancing customers’ service knowledge matter? Journal of service research.

    Google Scholar 

  • Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864–886.

    Article  Google Scholar 

  • Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., et al. (2010). Building Watson: An overview of the DeepQA project. AI Magazine, 31(3), 59–79.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 382–388.

  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.

    Article  Google Scholar 

  • Gefen, D., & Straub, D. W. (2003). Managing user trust in B2C e-services. E-service Journal, 2(2), 7–24.

    Article  Google Scholar 

  • Gerow, J. E., Ayyagari, R., Thatcher, J. B., & Roth, P. L. (2013). Can we have fun@ work? The role of intrinsic motivation for utilitarian systems. European Journal of Information Systems, 22(3), 360–380.

    Article  Google Scholar 

  • Gnewuch, U., Morana, S., Adam, M. T., & Maedche, A. (2018). Faster is not always better: Understanding the effect of dynamic response delays in human-Chatbot interaction. Paper presented at the ECIS.

  • Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214.

    Article  Google Scholar 

  • Guthrie, S. (1993). Faces in the clouds: Oxford University press.

    Google Scholar 

  • Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458.

    Article  Google Scholar 

  • Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) (second ed.): Sage publications.

  • Hampton-Sosa, W., & Koufaris, M. (2005). The effect of web site perceptions on initial trust in the owner company. International Journal of Electronic Commerce, 10(1), 55–81.

    Article  Google Scholar 

  • Han, S., & Yang, H. (2018). Understanding adoption of intelligent personal assistants: A parasocial relationship perspective. Industrial Management & Data Systems, 118(3), 618–636.

    Article  Google Scholar 

  • Hart, M., & Porter, G. (2004). The impact of cognitive and other factors on the perceived usefulness of OLAP. The Journal of Computer Information Systems, 45(1), 47.

    Google Scholar 

  • Haslam, N., Loughnan, S., Kashima, Y., & Bain, P. (2008). Attributing and denying humanness to others. European Review of Social Psychology, 19(1), 55–85.

    Article  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

    Article  Google Scholar 

  • Hess, T. J., Fuller, M., & Campbell, D. E. (2009). Designing interfaces with social presence: Using vividness and extraversion to create social recommendation agents. Journal of the Association for Information Systems, 10(12), 1.

    Article  Google Scholar 

  • Hong, S.-J., & Tam, K. Y. (2006). Understanding the adoption of multipurpose information appliances: The case of mobile data services. Information Systems Research, 17(2), 162–179.

    Article  Google Scholar 

  • Hsu, C. L., & Lin, J. C. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65–74.

    Article  Google Scholar 

  • Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 127–143.

  • Janson, A., Söllner, M., & Leimeister, J. M. (2019). Ladders for learning: Is scaffolding the key to teaching problem solving in technology-mediated learning contexts? Academy of Management Learning & Education(ja).

  • Jarvenpaa, S. L., Tractinsky, N., & Saarinen, L. (1999). Consumer trust in an internet store: A cross-cultural validation. Journal of Computer-Mediated Communication, 5(2), 0–0.

  • Johnson, R. D., Marakas, G. M., & Palmer, J. W. (2008). Beliefs about the social roles and capabilities of computing technology: Development of the computing technology continuum of perspective. Behaviour & Information Technology, 27(2), 169–181.

    Article  Google Scholar 

  • Junglas, I., Goel, L., Abraham, C., & Ives, B. (2013). The social component of information systems-how sociability contributes to technology acceptance. Journal of the Association for Information Systems, 14(10), 585.

    Article  Google Scholar 

  • Jurafsky, D., & Martin, J. H. (2017). Dialog systems and chatbots. Speech and language processing, 3.

  • Kamis, A., Koufaris, M., & Stern, T. (2008). Using an attribute-based decision support system for user-customized products online: An experimental investigation. MIS quarterly, 159-177.

  • Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 183–213.

  • Khan, R. F., & Sutcliffe, A. (2014). Attractive agents are more persuasive. International journal of human-computer interaction, 30(2), 142–150.

    Article  Google Scholar 

  • Kiesler, S., Sproull, L., & Waters, K. (1996). A prisoner's dilemma experiment on cooperation with people and human-like computers. Journal of Personality and Social Psychology, 70(1), 47–65.

    Article  Google Scholar 

  • Kiesler, S., & Goetz, J. (2002a). Machine trait scales for evaluating mechanistic mental models of robots and computer-based machines. Unpublished manuscript, Carnegie Mellon University. Downloadable at https://www.cs.cmu.edu/~kiesler/anthropomorphismorg/ pdf/Machine_scale.Pdf.

  • Kiesler, S., & Goetz, J. (2002b). Mental models of robotic assistants. Paper presented at the CHI'02 extended abstracts on human factors in computing systems.

  • Kiesler, S., Powers, A., Fussell, S. R., & Torrey, C. (2008). Anthropomorphic interactions with a robot and robot-like agent. Social Cognition, 26(2), 169–181.

    Article  Google Scholar 

  • Kim, B., & Han, I. (2011). The role of utilitarian and hedonic values and their antecedents in a mobile data service environment. Expert Systems with Applications, 38(3), 2311–2318.

    Article  Google Scholar 

  • Kim, D. J. (2008). Self-perception-based versus transference-based trust determinants in computer-mediated transactions: A cross-cultural comparison study. Journal of Management Information Systems, 24(4), 13–45.

    Article  Google Scholar 

  • Kim, E., & Tadisina, S. (2007). A model of customers' trust in e-businesses: Micro-level inter-party trust formation. Journal of Computer Information Systems, 48(1), 88–104.

    Google Scholar 

  • Kim, H.-W., Chan, H. C., & Chan, Y. P. (2007). A balanced thinking–feelings model of information systems continuance. International Journal of Human-Computer Studies, 65(6), 511–525.

    Article  Google Scholar 

  • Kim, K. K., & Prabhakar, B. (2004). Initial trust and the adoption of B2C e-commerce: The case of internet banking. ACM sigmis database, 35(2), 50–64.

    Article  Google Scholar 

  • Kiseleva, J., Williams, K., Jiang, J., Hassan Awadallah, A., Crook, A. C., Zitouni, I., & Anastasakos, T. (2016). Understanding user satisfaction with intelligent assistants. Paper presented at the proceedings of the 2016 ACM on conference on human information interaction and retrieval.

  • Kline, R. B. (2011). Principles and practice of structural equation. Modeling.(3nd.

  • Knote, R., Janson, A., Söllner, M., & Leimeister, J. M. (2019). Classifying smart personal assistants: An empirical cluster analysis. Paper presented at the proceedings of the 52nd Hawaii international conference on system sciences.

  • Komiak, S. Y., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS quarterly, 941.

  • Köse, D. B., Morschheuser, B., & Hamari, J. (2019). Is it a tool or a toy? How user conceptions of a system’s purpose affect their experience and use. International Journal of Information Management, 49, 461–474.

    Article  Google Scholar 

  • Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205–223.

    Article  Google Scholar 

  • Lankton, N. K., McKnight, D. H., & Tripp, J. (2015). Technology, humanness, and trust: Rethinking Trust in Technology. Journal of the Association for Information Systems, 16(10), 880–918.

    Article  Google Scholar 

  • Laumer, S., Eckhardt, A., & Weitzel, T. (2012). Online gaming to find a new job–examining job seekers' intention to use serious games as a self-assessment tool. German Journal of Human Resource Management, 26(3), 218–240.

    Article  Google Scholar 

  • Laumer, S., Maier, C., & Gubler, F. T. (2019). CHATBOT acceptance in healthcare: Explaining user adoption of conversational agents for disease diagnosis.

  • Lee, H.-H., Fiore, A. M., & Kim, J. (2006). The role of the technology acceptance model in explaining effects of image interactivity technology on consumer responses. International Journal of Retail & Distribution Management, 34(8), 621–644.

    Article  Google Scholar 

  • Leswing, K. (2016). Here's why people don't use Siri regularly, even though 98% of iPhone users have tried it. Retrieved from https://www.businessinsider.com/98-of-iphone-users-have-tried-siri-but-most-dont-use-it-regularly-2016-6

  • Liao, Y., Vitak, J., Kumar, P., Zimmer, M., & Kritikos, K. (2019). Understanding the role of privacy and Trust in Intelligent Personal Assistant Adoption. Paper presented at the International Conference on Information.

  • Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114.

    Article  Google Scholar 

  • Link, K. E., Kreuz, R. J., Graesser, A. C., & Group, T. R. (2001). Factors that influence the perception of feedback delivered by a pedagogical agent. International Journal of Speech Technology, 4(2), 145–153.

    Article  Google Scholar 

  • Liu, D., Li, Y., & Thomas, M. A. (2017). A roadmap for natural language processing research in information systems. Paper presented at the proceedings of the 50th Hawaii international conference on system sciences.

  • Lopatovska, I., & Williams, H. (2018). Personification of the Amazon Alexa: BFF or a mindless companion. Paper presented at the proceedings of the 2018 conference on Human Information Interaction & Retrieval.

  • Lowry, P. B., Vance, A., Moody, G., Beckman, B., & Read, A. (2008). Explaining and predicting the impact of branding alliances and web site quality on initial consumer trust of e-commerce web sites. Journal of Management Information Systems, 24(4), 199–224.

    Article  Google Scholar 

  • Luger, E., & Sellen, A. (2016). Like having a really bad PA: The gulf between user expectation and experience of conversational agents. Paper presented at the proceedings of the 2016 CHI conference on human factors in computing systems.

  • Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., ... & Söllner, M. (2019). AI-Based Digital Assistants. Business & Information Systems Engineering, 1–10.

  • Maggio, E. (2018). Apple says that 500 million customers use Siri. Retrieved from https://www.businessinsider.com/apple-says-siri-has-500-million-users-2018-1

  • March, S., Hevner, A., & Ram, S. (2000). Research commentary: An agenda for information technology research in heterogeneous and distributed environments. Information Systems Research, 11(4), 327–341.

    Article  Google Scholar 

  • McBreen, H. M., & Jack, M. (2001). Evaluating humanoid synthetic agents in e-retail applications. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions On, 31(5), 394–405.

    Article  Google Scholar 

  • McCarthy, J., & Hayes, P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence. Readings in artificial intelligence, 431–450.

  • McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334–359.

    Article  Google Scholar 

  • McKnight, D. H., Cummings, L. L., & Chervany, N. L. (1998). Initial trust formation in new organizational relationships. Academy of Management Review, 23(3), 473–490.

    Article  Google Scholar 

  • McKnight, D. H., Kacmar, C. J., & Choudhury, V. (2004). Shifting factors and the ineffectiveness of third party assurance seals: A two-stage model of initial trust in a web business. Electronic Markets, 14(3), 252–266.

    Article  Google Scholar 

  • McTear, M. F. (2016). The rise of the conversational interface: A new kid on the block? In international workshop on future and emerging trends in language technology (pp. 38–49). Cham: Springer.

    Google Scholar 

  • Meyer von Wolff, R., Hobert, S., & Schumann, M. (2019). How May I Help You?–State of the Art and Open Research Questions for Chatbots at the Digital Workplace. Paper presented at the Proceedings of the 52nd Hawaii International Conference on System Sciences.

  • Mitchell, T. M., Caruana, R., Freitag, D., McDermott, J., & Zabowski, D. (1994). Experience with a learning personal assistant. Communications of the ACM, 37(7), 80–91.

    Article  Google Scholar 

  • Moon, Y. (2000). Intimate exchanges: Using computers to elicit self-disclosure from consumers. Journal of Consumer Research, 26(4), 323–339.

    Article  Google Scholar 

  • Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.

    Article  Google Scholar 

  • Moussawi, S., & Koufaris, M. (2019). Perceived intelligence and perceived anthropomorphism of personal intelligent agents: Scale development and validation. In Paper presented at the Hawaii international conference on system sciences (HICSS). Maui: HI http://hdl.handle.net/10125/59452.

    Google Scholar 

  • Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81–103.

    Article  Google Scholar 

  • Nass, C., & Steuer, J. (1993). Voices, boxes, and sources of messages. Human Communication Research, 19(4), 504–527.

    Article  Google Scholar 

  • Nass, C. I., & Brave, S. (2005). Wired for speech: How voice activates and advances the human-computer relationship: MIT press Cambridge.

    Google Scholar 

  • Nowak, K. L., & Rauh, C. (2005). The influence of the avatar on online perceptions of anthropomorphism, androgyny, credibility, homophily, and attraction. Journal of Computer-Mediated Communication, 11(1), 153–178.

    Article  Google Scholar 

  • Nunamaker, J. F., Derrick, D. C., Elkins, A. C., Burgoon, J. K., & Patton, M. W. (2011). Embodied conversational agent-based kiosk for automated interviewing. Journal of Management Information Systems, 28(1), 17–48.

    Article  Google Scholar 

  • Nwana, H. S. (1996). Software agents: An overview. The Knowledge Engineering Review, 11(03), 205–244.

    Article  Google Scholar 

  • Orlikowski, W. J., & Iacono, C. S. (2001). Research commentary: Desperately seeking the “IT” in IT research—A call to theorizing the IT artifact. Information Systems Research, 12(2), 121–134.

    Article  Google Scholar 

  • Parise, S., Kiesler, S., Sproull, L., & Waters, K. (1996). My partner is a real dog: Cooperation with social agents. Paper presented at the proceedings of the 1996 ACM conference on computer supported cooperative work.

  • Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.

    Article  Google Scholar 

  • Perez, S. (2017). Siri usage and engagement dropped since last year, as Alexa and Cortana grew. Retrieved from https://techcrunch.com/2017/07/11/siri-usage-and-engagement-dropped-since-last-year-as-alexa-and-cortana-grew/

  • Perez, S. (2018). 39 million Americans now own a smart speaker, report claims. Retrieved from https://techcrunch.com/2018/01/12/39-million-americans-now-own-a-smart-speaker-report-claims/

  • Pfeuffer, N., Benlian, A., Gimpel, H., & Hinz, O. (2019). Anthropomorphic information systems. Business & Information Systems Engineering, 1–11.

  • Pillai, A., & Mukherjee, J. (2011). User acceptance of hedonic versus utilitarian social networking web sites. Journal of Indian Business Research, 3(3), 180–191.

    Article  Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Article  Google Scholar 

  • Porcheron, M., Fischer, J. E., & Sharples, S. (2017). Do animals have accents?: Talking with agents in multi-party conversation. Paper presented at the proceedings of the 2017 ACM conference on computer supported cooperative work and social computing.

  • Powers, A., & Kiesler, S. (2006). The advisor robot: Tracing people's mental model from a robot's physical attributes. Paper presented at the proceedings of the 1st ACM SIGCHI/SIGART conference on human-robot interaction.

  • Pradhan, A., Mehta, K., & Findlater, L. (2018). Accessibility came by accident: Use of voice-controlled intelligent personal assistants by people with disabilities. Paper presented at the proceedings of the 2018 CHI conference on human factors in computing systems.

  • Purington, A., Taft, J. G., Sannon, S., Bazarova, N. N., & Taylor, S. H. (2017). Alexa is my new BFF: Social roles, user satisfaction, and personification of the amazon echo. Paper presented at the proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems.

  • Qiu, L., & Benbasat, I. (2005). Online consumer trust and live help interfaces: The effects of text-to-speech voice and three-dimensional avatars. International journal of human-computer interaction, 19(1), 75–94.

    Article  Google Scholar 

  • Qiu, L., & Benbasat, I. (2009). Evaluating anthropomorphic product recommendation agents: A social relationship perspective to designing information systems. Journal of Management Information Systems, 25(4), 145–182.

    Article  Google Scholar 

  • Qu, W. G., & Yang, Z. (2015). The effect of uncertainty avoidance and social trust on supply chain collaboration. Journal of Business Research, 68(5), 911–918.

    Article  Google Scholar 

  • Ramayah, T., & Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI Journal of Systems Management (IJSM), 3(3), 36–51.

    Google Scholar 

  • Reeves, B., & Nass, C. (1996). How people treat computers, television, and new media like real people and places: CSLI publications and Cambridge university press.

  • Ringle, C. M., Sarstedt, M., & Straub, D. (2012). A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly (MISQ), 36(1).

  • Rodrigues, L. F., Oliveira, A., & Costa, C. J. (2016). Playing seriously–how gamification and social cues influence bank customers to use gamified e-business applications. Computers in Human Behavior, 63, 392–407.

    Article  Google Scholar 

  • Russell, S., & Norvig, P. (2010). Artificial Intelligence: A modern approach (Vol. 25, Third ed.). Trenton: Pearson Education.

    Google Scholar 

  • Rzepka, C. (2019). Examining the use of voice assistants: A value-focused thinking approach.

  • Sannon, S., Stoll, B., DiFranzo, D., Jung, M., & Bazarova, N. N. (2018). How personification and interactivity influence stress-related disclosures to conversational agents. Paper presented at the companion of the 2018 ACM conference on computer supported cooperative work and social computing.

  • Sarikaya, R. (2017). The technology behind personal digital assistants. An overview of the system architecture and key components. In: IEEE Signal Processing Magazine, 34(1), 67–81. https://doi.org/10.1109/MSP.2016.2617341 .

    Article  Google Scholar 

  • Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G.-J., Elkins, A., et al. (2019). Machines as teammates: A research agenda on AI in team collaboration. Information & Management, 103174.

  • Seeger, A.-M., Pfeiffer, J., & Heinzl, A. (2017). When do we need a human? Anthropomorphic design and trustworthiness of conversational agents. Paper presented at the Proceedings of the Sixteenth Annual Pre-ICIS Workshop on HCI Research in MIS, AISeL, Seoul, Korea.

  • Sharma, N., & Patterson, P. G. (1999). The impact of communication effectiveness and service quality on relationship commitment in consumer, professional services. Journal of Services Marketing, 13(2), 151–170.

    Article  Google Scholar 

  • Shoham, Y. (1993). Agent-oriented programming. Artificial Intelligence, 60(1), 51–92.

    Article  Google Scholar 

  • Söllner, M., Hoffmann, A., & Leimeister, J. M. (2016). Why different trust relationships matter for information systems users. European Journal of Information Systems, 25(3), 274–287.

    Article  Google Scholar 

  • Steels, L., & Brooks, R. A. (1995). The artificial life route to artificial intelligence: Building embodied, situated agents: L. Trenton: Erlbaum Associates Hillsdale.

    Google Scholar 

  • Swanson, E. B. (1982). Measuring user attitudes in MIS research: A review. Omega, 10(2), 157–165.

    Article  Google Scholar 

  • Swanson, E. B. (1987). Information Channel disposition and use. Decision Sciences, 18(1), 131–145.

    Article  Google Scholar 

  • Teo, T. S., Srivastava, S. C., & Jiang, L. (2008). Trust and electronic government success: An empirical study. Journal of Management Information Systems, 25(3), 99–132.

    Article  Google Scholar 

  • Turing, A. M. (1950). Computing machinery and intelligence. Mind, 433–460.

  • Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 695–704.

  • Vance, A., Elie-Dit-Cosaque, C., & Straub, D. W. (2008). Examining trust in information technology artifacts: The effects of system quality and culture. Journal of Management Information Systems, 24(4), 73–100.

    Article  Google Scholar 

  • Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 71–102.

  • Wakefield, R. L., & Whitten, D. (2006). Mobile computing: A user study on hedonic/utilitarian mobile device usage. European Journal of Information Systems, 15(3), 292–300.

    Article  Google Scholar 

  • Wagner, K., Nimmermann, F., & Schramm-Klein, H. (2019). Is it human? The role of anthropomorphism as a driver for the successful acceptance of digital voice assistants. Paper presented at the proceedings of the 52nd Hawaii international conference on system sciences.

  • Wang, L. C., Baker, J., Wagner, J. A., & Wakefield, K. (2007). Can a retail web site be social? Journal of Marketing, 71(3), 143–157.

    Article  Google Scholar 

  • Wang, W., & Benbasat, I. (2005). Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems, 6(3), 4.

    Google Scholar 

  • Waytz, A., Cacioppo, J., & Epley, N. (2010). Who sees human? The stability and importance of individual differences in anthropomorphism. Perspectives on Psychological Science, 5(3), 219–232.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45.

    Article  Google Scholar 

  • Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102.

    Article  Google Scholar 

  • Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(02), 115–152.

    Article  Google Scholar 

  • Wu, J., & Lu, X. (2013). Effects of extrinsic and intrinsic motivators on using utilitarian, hedonic, and dual-purposed information systems: A meta-analysis. Journal of the Association for Information Systems, 14(3), 153.

    Article  Google Scholar 

  • Xu, J. D., Benbasat, I., & Cenfetelli, R. T. (2013). Integrating service quality with system and information quality: An empirical test in the e-service context. MIS Quarterly, 37(3).

  • Yang, X., Aurisicchio, M., & Baxter, W. (2019). Understanding affective experiences with conversational agents. Paper presented at the proceedings of the 2019 CHI conference on human factors in computing systems.

  • Yoo, Y. (2010). Computing in everyday life: A call for research on experiential computing. MIS Quarterly, 213–231.

  • Zeng, E., Mare, S., & Roesner, F. (2017). End user security and privacy concerns with smart homes. Paper presented at the thirteenth symposium on usable privacy and security ({SOUPS} 2017).

Download references

Author information

Authors and Affiliations

  1. Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, USA

    Sara Moussawi

  2. Baruch College, City University of New York, New York, NY, USA

    Marios Koufaris & Raquel Benbunan-Fich

Authors
  1. Sara Moussawi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marios Koufaris
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raquel Benbunan-Fich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sara Moussawi.

Additional information

Responsible Editor: Matthias Söllner

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Hybrid Intelligence in Business Networks

Appendices

Appendix 1

Following a comprehensive review of the literature, we identify six key characteristics of PIAs and present them in Table 2 below. Popular PIAs like Amazon’s Alexa, Google Assistant and Apple’s Siri, possess the characteristics of personalization, autonomy, communication, reactivity to the environment, learning and adaptation to change and task completion at varying levels (Moussawi and Koufaris, 2019; Collins, 2018), but mostly lack pro-activeness capabilities. In contrast, a geographical positioning system that assists the user with navigation is an example of an agent that is proactive and uses natural language to communicate with the user but current implementations lack natural language processing abilities.

Table 2 Literature review of PIA’s characteristics
Full size table
Table 3 Recent research on PIAs and conversational agents
Full size table

Appendix 2

Survey instrument items.

Perceived intelligence adapted from Moussawi and Koufaris (2019).

PInt1 - Siri can complete tasks quickly.

PInt2 - Siri can understand my commands.

PInt3 - Siri can communicate with me in an understandable manner.

PInt4 - Siri can find and process the necessary information for completing its tasks.

PInt5 - Siri is able to provide me with a useful answer.

Perceived anthropomorphism adapted from Moussawi and Koufaris (2019).

PAnt1 - Siri is able to speak like a human.

PAnt2 - Siri can be happy.

PAnt3 - Siri is friendly.

PAnt4 - Siri is respectful.

PAnt5 - Siri is funny.

PAnt6 - Siri is caring.

Perceived usefulness adapted from Moore and Benbasat (1991).

PU1 - If I were to start using Siri, it would enable me to accomplish my tasks more quickly.

PU2 - If I were to start using Siri, the quality of my life would improve.

PU3 - If I were to start using Siri, it would enhance my overall effectiveness.

PU4 - If I were to start using Siri, it would make my life easier.

PU5 - Using Siri would give me greater control over my daily life.

Perceived ease of use adapted from Moore and Benbasat (1991).

PEOU1 - Learning to use Siri is easy for me.

PEOU2 - Overall, I believe that Siri is easy to use.

PEOU3 - I believe that it is easy to get Siri to do what I want it to do.

PEOU4 - My interaction with Siri is clear and understandable.

Intention to adopt from Karahanna et al. (1999).

Int1 - I intend to start using Siri within the next month.

Int2 - During the next months, I plan to experiment with or regularly use Siri.

Initial trust adapted from McKnight et al. (2002).

Trust_Benevolence - If I required help, Siri would do its best to help me.

Trust_Integrity - I characterize Siri as honest.

Trust_Ability - Siri is competent and effective in helping me with my daily tasks.

Perceived enjoyment adapted from Kamis et al. (2008).

Enj1 - While using Siri, I find the interaction enjoyable.

Enj2 - While using Siri, I find the interaction interesting.

Enj3 - While using Siri, I find the interaction to be fun.

Personal innovativeness of IT adapted from Agarwal and Prasad (1998).

PIIT1 - If I heard about a new information technology, I would look for ways to experiment with it.

PIIT2 - Among my peers, I am usually the first to try out new information technologies.

PIIT3 - In general, I am not afraid to try out new information technologies.

PIIT4 - I like to experiment with new information technologies.

Propensity to trust adapted from Hampton-Sosa and Koufaris (2005).

Prop1 - It is easy for me to trust a person or an object.

Prop2 - My tendency to trust a person or an object is high.

Prop3 - I tend to trust a person or an object, even though I have little knowledge of it.

Data Analysis.

Table 4 Latent variable correlations andsquare root of the average variance extracted (bold in diagonal cells); HTMT values between parantheses
Full size table
Table 5 Evaluation of the formative measurement model
Full size table
Table 6 Cross-loadings, composite reliability and average variance extracted for the reflective measurement models
Full size table
Table 7 Correlations between the marker variable and other variables
Full size table
Fig. 4
figure 4

Experimental process

Full size image

Appendix 3

We ran multiple mediation tests (Table 8) where the effects of all mediators are considered simultaneously rather than independently. These tests are important when exogenous constructs exert their influence through more than one mediating variable (Hair Jr et al., 2016). Our mediating variables (perceived enjoyment, initial trust, perceived usefulness, perceived ease of use, perceived anthropomorphism) were slightly correlated (Table 4), so testing for multiple mediation was necessary to account for possible inflated effects (Hair Jr et al., 2016). Several studies have found support for relationships between perceived enjoyment and trust, and between perceived usefulness, perceived ease of use and trust (Hampton-Sosa and Koufaris, 2005; Qiu and Benbasat, 2009; Vance et al., 2008). We found full mediation effects of perceived enjoyment on the relationship between perceived anthropomorphism and intention to adopt. We found that perceived usefulness mediated the relationship between perceived intelligence and intention to adopt. We found that perceived anthropomorphism partially mediates the relationship between perceived intelligence and enjoyment, and between perceived intelligence and trust.

Table 8 Results of multiple mediation tests
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moussawi, S., Koufaris, M. & Benbunan-Fich, R. How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents. Electron Markets 31, 343–364 (2021). https://doi.org/10.1007/s12525-020-00411-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12525-020-00411-w

Keywords

JEL classification

Search

Navigation