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Soziotechnische Gestaltung von Chatbots

  • Ulrich GnewuchEmail author
  • Jasper Feine
  • Stefan Morana
  • Alexander Maedche
Chapter
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Part of the Edition Informatik Spektrum book series (EIS)

Zusammenfassung

Chatbots sind softwarebasierte Systeme, die mittels natürlicher Sprache mit Menschen interagieren. Viele Unternehmen setzen zunehmend auf Chatbots, um Kunden bei der Suche nach Informationen über Produkte oder Dienstleistungen und bei der Durchführung einfacher Prozesse zu unterstützen. Nichtsdestotrotz ist die Akzeptanz von Chatbots bei vielen Nutzern derzeit noch gering. Ein Grund dafür ist, dass sich die Interaktion mit Chatbots nur selten natürlich und menschlich anfühlt. Es wächst deshalb die Erkenntnis, dass neben einer guten technischen Plattform auch weitere Faktoren bei der Gestaltung von Chatbots beachtet werden sollten. Die soziotechnische Gestaltung von Chatbots fokussiert sich daher auf die sozialen Signale eines Chatbots. Diese sozialen Signale (z. B. Lächeln, Sprachstil oder Antwortgeschwindigkeit) spielen nicht nur in der zwischenmenschlichen Kommunikation, sondern auch in der Interaktion mit Chatbots eine grosse Rolle. Dieser Artikel erläutert die Grundlagen der soziotechnischen Gestaltung von Chatbots, verdeutlicht die Wirkung sozialer Signale anhand eines Forschungsbeispiels und diskutiert kritisch die Vermenschlichung von Chatbots.

Notes

Danksagung

Die Autoren dieses Kapitels danken allen Teilnehmern der hier vorgestellten Studien und allen Kolleginnen und Kollegen am Institute of Information Systems and Marketing für ihre Unterstützung. Ein besonderer Dank gilt Marc T. P. Adam für seine Mitarbeit in diesem Forschungsprojekt.

Literatur

  1. Appel J, von der Pütten A, Krämer NC, Gratch J (2012) Does Humanity Matter? Analyzing the Importance of Social Cues and Perceived Agency of a Computer System for the Emergence of Social Reactions during Human-Computer Interaction. Advances in Human-Computer Interaction, 2012:1–10.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. Benyon D, Turner P, Turner S (2005) Designing interactive systems: People, activities, contexts, technologies. Pearson Education.Google Scholar
  4. Bickmore TW, Picard RW (2005) Establishing and maintaining long-term human-computer relationships. ACM Transactions on Computer-Human Interaction, 12(2): 293–327.CrossRefGoogle Scholar
  5. Bitkom (2018) Jeder Vierte will Chatbots nutzen. Bitkom Research. https://www.bitkom.org/Presse/Presseinformation/Jeder-Vierte-will-Chatbots-nutzen.html. Erstellt: 18.01.2017. Letzter Zugriff: 08. August 2018.
  6. Brandtzaeg PB, Følstad A (2018) Chatbots: Changing User Needs and Motivations. Interactions, 25(5): 38–43.CrossRefGoogle Scholar
  7. Braslavski P, Blinov V, Bolotova V, Pertsova K (2018) How to Evaluate Humorous Response Generation, Seriously? In: Proceedings of the 2018 Conference on Human Information Interaction&Retrieval – CHIIR ’18. New York, New York, USA.Google Scholar
  8. Burgoon J, Guerrero L, Floyd K (2010) Nonverbal communication. Routledge, New York, NY, USA.Google Scholar
  9. Cafaro A, Vilhjálmsson HH, Bickmore T (2016) First Impressions in Human--Agent Virtual Encounters. ACM Transactions on Computer-Human Interaction (TOCHI), 23(4): 24.CrossRefGoogle Scholar
  10. Catrambone R, Stasko J, Xiao J (2004) ECA as user interface paradigm. In: From brows to trust. Springer, 239–267.Google Scholar
  11. Chae SW, Lee KC, Seo YW (2016) Exploring the Effect of Avatar Trust on Learners’ Perceived Participation Intentions in an e-Learning Environment. International Journal of Human–Computer Interaction, 32(5): 373–393.CrossRefGoogle Scholar
  12. Clavel C, Callejas Z (2016) Sentiment analysis: from opinion mining to human-agent interaction. IEEE Transactions on Affective Computing, 7(1): 74–93.CrossRefGoogle Scholar
  13. Colby KM, Weber S, Hilf FD (1971) Artificial paranoia. Artificial Intelligence, 2(1): 1–25.CrossRefGoogle Scholar
  14. Collier G (2014) Emotional expression. Psychology Press, New York, NY, USA.CrossRefGoogle Scholar
  15. Crozier R (2017) Lufthansa delays chatbot’s responses to make it more ‚human‘. iTnews Australia. https://www.itnews.com.au/news/lufthansa-delays-chatbots-responses-to-make-it-more-human-462643. Erstellt: 24. Mai 2017. Letzter Zugriff: 30. August 2018.
  16. D’Onofrio S, Portmann E, Franzelli M, Bürki C (2018) Cognitive Computing: Theoretische Grundlagen und Praxisbeispiele der Schweizerischen Post. Informatik-Spektrum, 41(2): 113–122.CrossRefGoogle Scholar
  17. Dale R (2016) The return of the chatbots. Natural Language Engineering, 22(05): 811–817.CrossRefGoogle Scholar
  18. de Visser EJ, Monfort SS, McKendrick R, Smith MABB, McKnight PE, Krueger F, Parasuraman R (2016) Almost human: Anthropomorphism increases trust resilience in cognitive agents. Journal of Experimental Psychology: Applied, 22(3): 331–349.Google Scholar
  19. Derrick DC, Meservy TO, Jenkins JL, Burgoon JK, Nunamaker JF (2013) Detecting Deceptive Chat-Based Communication Using Typing Behavior and Message Cues. ACM Transactions on Management Information Systems, 4(2): 1–21.CrossRefGoogle Scholar
  20. Dybala P, Ptaszynski M, Rzepka R, Araki K (2009) Activating Humans with Humor--A Dialogue System That Users Want to Interact with. IEICE TRANSACTIONS on Information and Systems, 92(12): 2394–2401.CrossRefGoogle Scholar
  21. Endrass B, Rehm M, André E (2011) Planning Small Talk behavior with cultural influences for multiagent systems. Computer Speech, Language, 25(2): 158–174.CrossRefGoogle Scholar
  22. Fogg BJ (2002) Computers as Persuasive Social Actors. In: Persuasive Technology: Using Computers to Change What We Think and Do. Morgan Kaufmann Publishers, San Francisco, CA, USA, 89–120.Google Scholar
  23. Følstad A, Brandtzæg PB (2017) Chatbots and the new world of HCI. Interactions, 24(4): 38–42.CrossRefGoogle Scholar
  24. Gartner (2018) Gartner Says 25 Percent of Customer Service Operations Will Use Virtual Customer Assistants by 2020. https://www.gartner.com/newsroom/id/3858564. Erstellt: 19. Februar 2018. Letzter Zugriff: 11. September 2018.
  25. Gefen D, Straub DW (1997) Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model. MIS Quarterly, 21(4): 389–400.CrossRefGoogle Scholar
  26. Gnewuch U, Morana S, Adam MTP, Maedche A (2018a) Faster is Not Always Better: Understanding the Effect of Dynamic Response Delays in Human-Chatbot Interaction. In: Proceedings of the European Conference on Information Systems (ECIS). Portsmouth, UK.Google Scholar
  27. Gnewuch U, Morana S, Adam MTP, Maedche A (2018b) ‚The Chatbot is typing …‘ – The Role of Typing Indicators in Human-Chatbot Interaction. In: Proceedings of the 17th Annual Pre-ICIS Workshop on HCI Research in MIS. San Francisco, CA, USA.Google Scholar
  28. Gnewuch U, Morana S, Maedche A (2017) Towards Designing Cooperative and Social Conversational Agents for Customer Service. In: Proceedings of the 38th International Conference on Information Systems (ICIS). Seoul, South Korea.Google Scholar
  29. Hevner AR, March ST, Park J, Ram S (2004) Design Science in Information Systems Research. MIS Quarterly, 28(1): 75–105.CrossRefGoogle Scholar
  30. Holtgraves T, Han T-L (2007) A procedure for studying online conversational processing using a chat bot. Behavior Research Methods, 39(1): 156–163.CrossRefGoogle Scholar
  31. Holtgraves T, Ross SJ, Weywadt CR, Han T-L (2007) Perceiving artificial social agents. Computers in Human Behavior, 23(5): 2163–2174.CrossRefGoogle Scholar
  32. Hoxmeier JA, DiCesare C (2000) System Response Time and User Satisfaction : An Experimental Study of Browser-based Applications. In: Proceedings of the 6th Americas Conference on Information Systems (AMCIS). Long Beach, CA, USA.Google Scholar
  33. Isbister K, Nass C (2000) Consistency of personality in interactive characters: verbal cues, non-verbal cues, and user characteristics. International Journal of Human-Computer Studies, 53: 251–267.CrossRefGoogle Scholar
  34. Jentsch E (1997) On the psychology of the uncanny (1906). Angelaki: Journal of the Theoretical Humanities, 2(1): 7–16.MathSciNetCrossRefGoogle Scholar
  35. Johnson K (2017) Facebook Messenger hits 100,000 bots. https://venturebeat.com/2017/04/18/facebook-messenger-hits-100000-bots/. Erstellt: 18. April 2017. Letzter Zugriff: 08. August 2018.
  36. Khawaja MA, Chen F, Marcus N (2010) Using language complexity to measure cognitive load for adaptive interaction design. In: Proceedings of the 15th International Conference on Intelligent User Interfaces (IUI ’10).Google Scholar
  37. Kincaid JP, Fishburne RP, Rogers RL, Chissom BS (1975) Derivation Of New Readability Formulas (Automated Readability Index, Fog Count And Flesch Reading Ease Formula) For Navy Enlisted Personnel. Branch Report, 8–75.Google Scholar
  38. Knote R, Janson A, Eigenbrod L, Söllner M (2018) The What and How of Smart Personal Assistants: Principles and Application Domains for IS Research. In: Multikonferenz Wirtschaftsinformatik. Lüneburg, Germany.Google Scholar
  39. Krämer NC (2005) Social Communicative Effects of a Virtual Program Guide. In: R. T. Panayiotopoulos T., Gratch J., Aylett R., Ballin D., Olivier P. (ed.), Intelligent Virtual Agents. IVA 2005. Springer, Berlin, Heidelberg.Google Scholar
  40. Krämer NC, Kopp S, Becker-Asano C, Sommer N (2013) Smile and the world will smile with you – The effects of a virtual agent’s smile on users’ evaluation and behavior. International Journal of Human-Computer Studies, 71(3): 335–349.CrossRefGoogle Scholar
  41. Kremp M (2018) Künstliche Intelligenz: Google Duplex ist gruselig gut. Spiegel. http://www.spiegel.de/netzwelt/web/google-duplex-auf-der-i-o-gruselig-gute-kuenstliche-intelligenz-a-1206938.html. Erstellt: 09. Mai 2018. Letzter Zugriff: 30. August 2018.
  42. Leathers DG (1976) Nonverbal communication systems. Allyn and. Bacon, Inc., Boston, MA, USA.Google Scholar
  43. Li J, Zhou MX, Yang H, Mark G (2017) Confiding in and Listening to Virtual Agents: The Effect of Personality. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces – IUI ’17.Google Scholar
  44. Liao QV, Hussain MM, Chandar P, Davis M, Crasso M, Wang D, Muller M, Shami NS, Geyer W (2018) All Work and no Play ? Conversations with a Question-and-Answer Chatbot in the Wild. In: CHI 18.Google Scholar
  45. Markoff J, Mozur P (2015) For Sympathetic Ear, More Chinese Turn to Smartphone Program. New York Times. https://www.nytimes.com/2015/08/04/science/for-sympathetic-ear-more-chinese-turn-to-smartphone-program.html. Erstellt: 31. Juli 2015. Letzter Zugriff: 05. Dezember 2018.
  46. Mayer RE, Johnson WL, Shaw E, Sandhu S (2006) Constructing computer-based tutors that are socially sensitive: Politeness in educational software. International Journal of Human-Computer Studies, 64(1): 36–42.CrossRefGoogle Scholar
  47. McBreen H (2002) Embodied conversational agents in e-commerce applications. In: Socially Intelligent Agents. Springer, 267–274.Google Scholar
  48. McTear MF (2017) The Rise of the Conversational Interface: A New Kid on the Block? In: FETLT 2016. Springer, Cham, 38–49.Google Scholar
  49. Mooallem J (2017) Mr. Know-It-All: Is it OK For Me to Ask Customer Service Reps if They’re Robots? https://www.wired.com/story/mr-know-it-all-are-customer-service-reps-robots/. Erstellt: 10. März 2017. Letzter Zugriff: 30. August 2018.
  50. Moon Y (1999) The effects of physical distance and response latency on persuasion in computer-mediated communication and human-computer communication. Journal of Experimental Psychology: Applied, 5(4): 379–392.Google Scholar
  51. Moore RJ, Arar R, Ren G-J, Szymanski MH (2017) Conversational UX Design. In: Proceedings of the SIGCHI Conference: Extended Abstracts on Human Factors in Computing Systems (CHI ’17). Denver, CO, USA.Google Scholar
  52. Morana S, Friemel C, Gnewuch U, Maedche A, Pfeiffer J (2017) Interaktion mit smarten Systemen – Aktueller Stand und zukünftige Entwicklungen im Bereich der Nutzerassistenz. Wirtschaftsinformatik, Management, 9(5): 42–51.CrossRefGoogle Scholar
  53. Mori M (1970) The uncanny valley. Energy, 7(4): 33–35.Google Scholar
  54. Mumford E (2006) The story of socio-technical design: reflections on its successes, failures and potential. Information Systems Journal, 16(4): 317–342.CrossRefGoogle Scholar
  55. Nass C, Fogg BJ, Moon Y (1996) Can computers be teammates? International Journal of Human Computer Studies, 45(6): 669–678.CrossRefGoogle Scholar
  56. Nass C, Moon Y (2000) Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, 56(1): 81–103.CrossRefGoogle Scholar
  57. Nass C, Steuer J, Tauber ER (1994) Computers are social actors. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Boston, MA, USA.Google Scholar
  58. Puzakova M, Rocereto JF, Kwak H (2013) Ads are watching me: A view from the interplay between anthropomorphism and customisation. International Journal of Advertising, 32(4): 513–538.CrossRefGoogle Scholar
  59. Sah YJ, Peng W (2015) Effects of visual and linguistic anthropomorphic cues on social perception, self-awareness, and information disclosure in a health website. Computers in Human Behavior, 45: 392–401.CrossRefGoogle Scholar
  60. Stucki T, D’Onofrio S, Portmann E (2018) Chatbot – Der digitale Helfer im Unternehmen: Praxisbeispiele der Schweizerischen Post. HMD Praxis Der Wirtschaftsinformatik, 55(4): 725–747.CrossRefGoogle Scholar
  61. Stucki T, D’Onofrio S, Portmann E (2020) Chatbots gestalten mit Praxisbeispielen der Schweizerischen Post. HMD Best Paper Award 2018. Essentials. Springer Vieweg, Wiesbaden.CrossRefGoogle Scholar
  62. Trager GL (1958) Paralanguage: A first approximation. Studies in Linguistics, 13: 1–11.Google Scholar
  63. Verhagen T, van Nes J, Feldberg F, van Dolen W (2014) Virtual Customer Service Agents: Using Social Presence and Personalization to Shape Online Service Encounters. Journal of Computer-Mediated Communication, 19(3): 529–545.CrossRefGoogle Scholar
  64. Wallace RS (2009) The Anatomy of A.L.I.C.E. In: R. Epstein, G. Roberts,, G. Beber (eds.), Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Springer Netherlands, Dordrecht, 181–210.CrossRefGoogle Scholar
  65. Walther JB (1992) Interpersonal Effects in Computer-Mediated Interaction. Communication Research, 21(4): 460–487.CrossRefGoogle Scholar
  66. Walther JB, Tidwell LC (1995) Nonverbal cues in computer-mediated communication, and the effect of chronemics on relational communication. Journal of Organizational Computing, 5(4): 355–378.CrossRefGoogle Scholar
  67. 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.CrossRefGoogle Scholar
  68. Weizenbaum J (1976) Computer power and human reason: From judgment to calculation. W. H. Freeman, Co, New York, NY, USA.Google Scholar
  69. Wirtschaftswoche (2016) Menschenähnliche Computerwesen: Wie die Grenzen zwischen Menschen und Robotern verschwimmen. https://www.wiwo.de/technologie/forschung/menschenaehnliche-computerwesen-wie-die-grenzen-zwischen-menschen-und-robotern-verschwimmen/14684090.html. (Erstellt: 16. Oktober 2016. Letzter Zugriff: 08. August 2018).
  70. Wrede-Grischkat R (2007) Manieren und Karriere: internationale Verhaltensregeln für Führungskräfte. Springer.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Ulrich Gnewuch
    • 1
    Email author
  • Jasper Feine
    • 1
  • Stefan Morana
    • 1
  • Alexander Maedche
    • 1
  1. 1.Karlsruher Institut für Technologie (KIT)Institute of Information Systems and Marketing (IISM)KarlsruheDeutschland

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