Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2020 | OriginalPaper | Buchkapitel

Conversational Agents in Healthcare: Using QCA to Explain Patients’ Resistance to Chatbots for Medication

verfasst von : Lea Müller, Jens Mattke, Christian Maier, Tim Weitzel

Erschienen in: Chatbot Research and Design

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Complete information is very important to the accuracy of diagnosis in healthcare. Therefore, the idea to use conversational agents recording relevant information and providing it to healthcare facilities is of rising interest. A promising use case of the involvement of conversational agents is medication, as this data is often fragmented or incomplete. The paper at hand examines the hindrances in the way of patients sharing their medication list with a chatbot. Basing on established theories and using fuzzy-set qualitative comparative analysis (QCA), we identify bundles of factors that influence patients lacking willingness to interact with a chatbot. Those typologies of patients can be used to address these hindrances specifically, providing useful insights for theory and healthcare facilities.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Nächstes Kapitel An Approach for Ex-Post-Facto Analysis of Knowledge Graph-Driven Chatbots – The DBpedia Chatbot
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
2.
Tschanz, M., Dorner, T.L., Denecke, K.: eMedication Meets eHealth with the Electronic Medication Management Assistant (eMMA). eHealth (2017)
3.
Lampert, M.L., Kraehenbuehl, S., Hug, B.L.: Drug-related problems: evaluation of a classification system in the daily practice of a Swiss University Hospital. Pharm. World Sci.: PWS 30, 768–776 (2008)CrossRef
4.
Gall, W., Aly, A.-F., Sojer, R., Spahni, S., Ammenwerth, E.: The national e-medication approaches in Germany, Switzerland and Austria: a structured comparison. Int. J. Med. Inform. 93, 14–25 (2016)CrossRef
5.
Kassirer, J.P.: Imperatives, expediency, and the new diagnosis. Diagnosis 1, 11–12 (2014)CrossRef
6.
7.
Samuelson, W., Zeckhauser, R.: Status quo bias in decision making. J. Risk Uncertainty 1, 7–59 (1988)CrossRef
8.
Kim, H.-W., Kankanhalli, A.: Investigating user resistance to information systems implementation. A status quo bias perspective. MIS Q. 33, 567–582 (2009)CrossRef
9.
Lee, K., Joshi, K.: Examining the use of status quo bias perspective in IS research: need for re-conceptualizing and incorporating biases. Inf. Syst. J. 27, 733–752 (2017)CrossRef
10.
Fleming, S.M., Thomas, C.L., Dolan, R.J.: Overcoming status quo bias in the human brain. Proc. Natl. Acad. Sci. U.S.A. 107, 6005–6009 (2010)CrossRef
11.
Klaus, T., Wingreen, S.C., Blanton, J.E.: Resistant groups in enterprise system implementations: a Q-methodology examination. J. Inf. Technol. 25, 91–106 (2010)CrossRef
12.
Al-Ramahi, M., Noteboom, C.: A systematic analysis of patient portals adoption, acceptance and usage: the trajectory for triple aim? In: Hawaii International Conference on System Sciences 2018 (HICSS-51) (2018)
13.
Schuetzler, R., Grimes, M., Giboney, J., Buckman, J.: Facilitating natural conversational agent interactions: lessons from a deception experiment. In: ICIS 2014 Proceedings (2014)
14.
15.
Comendador, B.E.V., Francisco, B.M.B., Medenilla, J.S., Mae, S.: Pharmabot: a pediatric generic medicine consultant chatbot. J. Autom. Control Eng. 3, 137–140 (2015)CrossRef
16.
Lehto, T., Oinas-Kukkonen, H., Pätiälä, T., Saarelma, O.: Consumers’ perception of a digital health check: an empirical investigation. In: ECIS 2012 Proceedings (2012)
17.
Jena, R.: Sharing personal health information: personalization versus privacy. In: AMCIS 2015 Proceedings (2015)
18.
Sherer, S.A.: Patients are not simply health IT users or consumers: the case for “e Healthicant” applications. CAIS 34, 17 (2014)CrossRef
19.
Schuetzler, R.M., Grimes, G.M., Giboney, J.S.: An investigation of conversational agent relevance, presence, and engagement. In: AMCIS 2018 Proceedings (2018)
20.
Denecke, K., Lutz Hochreutener, S., Pöpel, A., May, R.: Talking to ana. In: Kostkova, P., Grasso, F., Castillo, C., Mejova, Y., Bosman, A., Edelstein, M. (eds.) DH 2018, Proceedings of the 2018 International Conference on Digital Health: Lyon, France, 23–26 April 2018, pp. 85–89. The Association for Computing Machinery, New York (2018)
21.
Oh, K.-J., Lee, D., Ko, B., Choi, H.-J.: A chatbot for psychiatric counseling in mental healthcare service based on emotional dialogue analysis and sentence generation. In: Proceedings of the 18th IEEE International Conference on Mobile Data Management, MDM 2017, Daejeon, South Korea, 29 May–1 June 2017, pp. 371–375. IEEE Computer Society, Los Alamitos (2017)
22.
Laumer, S., Maier, C., Eckhardt, A., Weitzel, T.: User personality and resistance to mandatory information systems in organizations: a theoretical model and empirical test of dispositional resistance to change. J. Inf. Technol. 31, 67–82 (2016)CrossRef
23.
Piderit, S.K.: Rethinking resistance and recognizing ambivalence: a multidimensional view of attitudes toward an organizational change. Acad. Manag. Rev. 25, 783–794 (2000)CrossRef
24.
Polites, G.L., Karahanna, E.: Shackled to the status quo: the inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Q. 36, 21–42 (2012)CrossRef
25.
Polites, G.L., Karahanna, E., Seligman, L.: Intention–behaviour misalignment at B2C websites: when the horse brings itself to water, will it drink? Eur. J. Inf. Syst. 27, 22–45 (2017)CrossRef
26.
Fernandez, R., Rodrik, D.: Resistance to reform: status quo bias in the presence of individual-specific uncertainty. Am. Econ. Rev. 81, 1146–1155 (1991)
27.
Kahneman, D., Knetsch, J.L., Thaler, R.H.: Anomalies: the endowment effect, loss aversion, and status quo bias. J. Econ. Perspect. 5, 193–206 (1991)CrossRef
28.
Mattke, J., Maier, C., Müller, L., Weitzel, T.: Bitcoin resistance behavior: a QCA study explaining why individuals resist bitcoin as a means of payment. In: ICIS 2018 Proceedings, San Francisco, CA (2018)
29.
30.
Lankton, N., Luft, J.: Uncertainty and industry structure effects on managerial intuition about information technology real options. J. Manag. Inf. Syst. 25, 203–240 (2008)CrossRef
31.
Woodside, A.G.: Embrace•perform•model: complexity theory, contrarian case analysis, and multiple realities. J. Bus. Res. 67, 2495–2503 (2014)CrossRef
32.
Klaus, T., Blanton, J.E.: User resistance determinants and the psychological contract in enterprise system implementations. Eur. J. Inf. Syst. 19, 625–636 (2010)CrossRef
33.
Fiss, P.C.: A set-theoretic approach to organizational configurations. Acad. Manag. Rev. 32, 1180–1198 (2007)CrossRef
34.
35.
Campbell, J.T., Sirmon, D.G., Schijven, M.: Fuzzy logic and the market: a configurational approach to investor perceptions of acquisition announcements. Acad. Manag. J. 59, 163–187 (2016)CrossRef
36.
Pelli, D.G., Tillman, K.A.: The uncrowded window of object recognition. Nat. Neurosci. 11, 1129 (2008)CrossRef
37.
Misangyi, V.F., Greckhamer, T., Furnari, S., Fiss, P.C., Crilly, D., Aguilera, R.: Embracing causal complexity: the emergence of a neo-configurational perspective. J. Manag. 43, 255–282 (2017)
38.
Spivack, A.J., Woodside, A.G.: Applying complexity theory for modeling human resource outcomes: antecedent configurations indicating perceived location autonomy and work environment choice. J. Bus. Res. 102, 109–119 (2019)CrossRef
39.
Dwivedi, P., Joshi, A., Misangyi, V.F.: Gender-inclusive gatekeeping: how (mostly male) predecessors influence the success of female CEOs. Acad. Manag. J. 61, 379–404 (2018)CrossRef
40.
Park, Y., Sawy, O.E., Fiss, P.: The role of business intelligence and communication technologies in organizational agility: a configurational approach. J. Assoc. Inf. Syst. 18, 1 (2017)
41.
42.
Müller, L., Mattke, J., Maier, C., Weitzel, T.: The curse of mobile marketing: a mixed methods study on individuals’ switch to mobile ad blockers. In: ICIS 2017 Proceedings, p. 13 (2017)
43.
Pflügner, K., Mattke, J., Maier, C.: Who is stressed by using ICTs? A qualitative comparison analysis with the big five personality traits to understand technostress. In: Proceedings of the 14th International Conference on Wirtschaftsinformatik, Siegen, Germany (2019)
44.
Lowry, P.B., D’Arcy, J., Hammer, B., Moody, G.D.: “Cargo Cult” science in traditional organization and information systems survey research: a case for using nontraditional methods of data collection, including Mechanical Turk and online panels. J. Strat. Inf. Syst. 25, 232–240 (2016)CrossRef
45.
Bennett, A.A., Gabriel, A.S., Calderwood, C., Dahling, J.J., Trougakos, J.P.: Better together? Examining profiles of employee recovery experiences. J. Appl. Psychol. 101, 1635–1654 (2016)CrossRef
46.
Marx, A.: Towards More Robust Model Specification in QCA Results from a Methodological Experiment. American Sociological Association, Philadelphia, PA (2006)
47.
Chin, W., Thatcher, J.B., Wright, R.T.: Assessing common method bias: problems with the ULMC technique. MIS Q. 36, 1003–1019 (2012)CrossRef
48.
Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y., Podsakoff, N.P.: Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879 (2003)CrossRef
49.
Ray, S., Kim, S.S., Morris, J.G.: Research note—online users’ switching costs: their nature and formation. Inf. Syst. Res. 23, 197–213 (2012)CrossRef
50.
Lankton, N., McKnight, H.: Examining two expectation disconfirmation theory models: assimilation and asymmetry effects. J. Assoc. Inf. Syst. 13, 88–115 (2012)
51.
Morgeson, F.P., Humphrey, S.E.: The work design questionnaire (WDQ): developing and validating a comprehensive measure for assessing job design and the nature of work. J. Appl. Psychol. 91, 1321 (2006)CrossRef
52.
Kim, D.J., Ferrin, D.L., Rao, H.R.: A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decis. Support Syst. 44, 544–564 (2008)CrossRef
53.
Carmines, E.G., Zeller, R.A.: Reliability and Validity Assessment. Sage Publ., Newbury Park (2008)
54.
Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18, 39–50 (1981)CrossRef
55.
56.
El Sawy, O.A., Malhotra, A., Park, Y., Pavlou, P.A.: Research commentary—seeking the configurations of digital ecodynamics: it takes three to tango. Inf. Syst. Res. 21, 835–848 (2010)CrossRef
57.
Fiss, P.C.: Building better causal theories: a fuzzy set approach to typologies in organization research. Acad. Manag. J. 54, 393–420 (2011)CrossRef
58.
Schneider, C.Q., Wagemann, C.: Standards of good practice in qualitative comparative analysis (QCA) and fuzzy-sets. Comp. Sociol. 9, 397–418 (2010)CrossRef
59.
Rihoux, B., Ragin, C.C.: Qualitative comparative analysis using fuzzy sets (fsQCA) (2009)
60.
Müller, L., Mattke, J., Maier, C., Weitzel, T., Graser, H.: Chatbot acceptance. In: Joseph, D., van Slyke, C., Allen, J.P., Quesenberry, J., Wiesche, M. (eds.) Proceedings of the 2019 on Computers and People Research Conference - SIGMIS-CPR 2019, pp. 35–42. ACM Press, New York (2019)
61.
Davenport, S., Goldberg, D., Millar, T.: How psychiatric disorders are missed during medical consultations. Lancet 330, 439–441 (1987)CrossRef
Metadaten
Titel
Conversational Agents in Healthcare: Using QCA to Explain Patients’ Resistance to Chatbots for Medication
verfasst von
Lea Müller
Jens Mattke
Christian Maier
Tim Weitzel
Copyright-Jahr
2020
Buch
Chatbot Research and Design

Print ISBN: 978-3-030-39539-1

Electronic ISBN: 978-3-030-39540-7

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-39540-7_1