AI-based chatbots in conversational commerce and their effects on product and price perceptions.

Justina Sidlauskiene, Yannick Joye, Vilte Auruskeviciene
Author Information
  1. Justina Sidlauskiene: ISM University of Management and Economics, Gedimino Ave. 7, LT-01103 Vilnius, Lithuania.
  2. Yannick Joye: Center for Economic Expertise, Faculty of Economics and Business Administration, Vilnius University, Saulėtekio Av. 9, 2Nd Building, 10222 Vilnius, Lithuania.
  3. Vilte Auruskeviciene: ISM University of Management and Economics, Gedimino Ave. 7, LT-01103 Vilnius, Lithuania.

Abstract

The rise of AI-based chatbots has gradually changed the way consumers shop. Natural language processing (NLP) technology and artificial intelligence (AI) are likely to accelerate this trend further. However, consumers still prefer to engage with humans and resist chatbots, which are often perceived as impersonal and lacking the human touch. While the predominant tendency is to make chatbots appear more humanlike, little is known about how anthropomorphic verbal design cues in chatbots influence perceived product personalization and willingness to pay a higher product price in conversational commerce contexts. In the current work, we set out to test this through one pre-test ( = 135) and two online experiments ( = 180 and 237). We find that anthropomorphism significantly and positively affects perceived product personalization, and that this effect is moderated by situational loneliness. Moreover, the results show that the interaction between anthropomorphism and situational loneliness has an impact on the willingness to pay a higher product price. The research findings can be used for future applications of AI-driven chatbots where there is a need to provide personalized and data-driven product recommendations.

Keywords

References

  1. Psychiatry Res. 2020 Aug;290:113117 [PMID: 32480121]
  2. Int J Environ Res Public Health. 2021 Jan 06;18(2): [PMID: 33419194]
  3. Perspect Psychol Sci. 2015 Mar;10(2):238-49 [PMID: 25866548]
  4. ACM Trans Interact Intell Syst. 2016 May;6(1): [PMID: 28966875]
  5. JMIR Ment Health. 2017 Jun 06;4(2):e19 [PMID: 28588005]
  6. J Pers Soc Psychol. 2006 Nov;91(5):975-993 [PMID: 17059314]
  7. Psychol Sci. 2008 Feb;19(2):114-20 [PMID: 18271858]
  8. Pers Soc Psychol Bull. 2019 May;45(5):780-793 [PMID: 30264659]
  9. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2012 Nov;37(11):1124-8 [PMID: 23202622]
  10. Child Dev. 1969 Dec;40(4):969-1025 [PMID: 5360395]
  11. J Pers Soc Psychol. 1980 Sep;39(3):472-80 [PMID: 7431205]
  12. Schizophr Res. 2000 Nov 30;46(1):25-30 [PMID: 11099882]
  13. J Pers Soc Psychol. 2003 Dec;85(6):1193-202 [PMID: 14674824]
  14. Psychol Sci. 2008 Oct;19(10):1023-9 [PMID: 19000213]
  15. Child Dev. 2007 Nov-Dec;78(6):1675-88 [PMID: 17988314]
  16. Psychol Rev. 2007 Oct;114(4):864-86 [PMID: 17907867]
  17. J Appl Psychol. 2002 Feb;87(1):33-42 [PMID: 11916214]

Word Cloud

Created with Highcharts 10.0.0chatbotsproductpriceperceivedpersonalizationcommercelonelinessAI-basedconsumersAIwillingnesspayhigherconversationalanthropomorphismsituationalPerceivedrisegraduallychangedwayshopNaturallanguageprocessingNLPtechnologyartificialintelligencelikelyacceleratetrendHoweverstillpreferengagehumansresistoftenimpersonallackinghumantouchpredominanttendencymakeappearhumanlikelittleknownanthropomorphicverbaldesigncuesinfluencecontextscurrentworksettestonepre-test = 135twoonlineexperiments = 180237findsignificantlypositivelyaffectseffectmoderatedMoreoverresultsshowinteractionimpactresearchfindingscanusedfutureapplicationsAI-drivenneedprovidepersonalizeddata-drivenrecommendationseffectsperceptionsAnthropomorphismChatbotsConversationalSituational

Similar Articles

Cited By