This study investigated the impact of engaging with an anthropomorphic chatbot representing the environment on individuals’ perceived relevance and behavioral intention toward climate action. Over 3 weeks, 100 participants were involved in daily conversations with the chatbot. Using an autoregressive structural equation model for longitudinal mediation effects, the results revealed that frequent interaction with the chatbot fostered an intimate and personal relationship with the chatbot (PSI), despite its lack of authenticity, which, in turn, developed pro-environmental attitudes. These findings offer preliminary evidence that chatbots, through simulating conversations with non-human entities—the environment, have the potential to facilitate pro-environmental change.
Van LangePAHuckelbaAL. Psychological distance: how to make climate change less abstract and closer to the self. Curr Opin Psychol2021; 42: 49–53.
2.
SpenceAPoortingaWPidgeonN. The psychological distance of climate change. Risk Anal2012; 32: 957–972.
3.
WeberEU. Experience-based and description-based perceptions of long-term risk: why global warming does not scare us (yet). Clim Change2006; 77: 103–120.
4.
FoxJMcKnightJSunY, et al.Using a serious game to communicate risk and minimize psychological distance regarding environmental pollution. Telematics Inform2020; 46: 101320.
5.
AltmanITaylorDA. Social penetration: the development of interpersonal relationships. New York: Holt, Rinehart & Winston, 1973.
6.
UtzS. The function of self-disclosure on social network sites: not only intimate, but also positive and entertaining self-disclosures increase the feeling of connection. Comput Hum Behav2015; 45: 1–10.
7.
FølstadASkjuveM.Chatbots for customer service: user experience and motivation. In: Proceedings of the 1st International Conference on Conversational User Interfaces. ACM, 2019, pp.1–9.
8.
YounSJinSV. In AI we trust?” the effects of parasocial interaction and technopian versus luddite ideological views on chatbot-based customer relationship management in the emerging “feeling economy. Comput Hum Behav2021; 119: 106721.
9.
ChaixBBibaultJEPienkowskiA, et al.When chatbots meet patients: one-year prospective study of conversations between patients with breast cancer and a chatbot. JMIR Cancer2019; 5: e12856.
10.
GreerSRamoDChangYJ, et al.Use of the chatbot “vivibot” to deliver positive psychology skills and promote well-being among young people after cancer treatment: randomized controlled feasibility trial. JMIR Mhealth Uhealth2019; 7: e15018.
11.
GnewuchUMoranaSHeckmannC, et al.Designing conversational agents for energy feedback. In: ChatterjeeSDuttaKSundarrajRP (eds) International conference on design science research in information systems and technology. Chennai, India: Springer, 2018, pp.18–33.
12.
SuresanAMohanSSAryaMP, et al.A conversational AI chatbot in energy informatics. In: Pasumpon PandianAPalanisamyRNtalianisK (eds) Proceedings of international conference on intelligent computing, information and control systems: ICICCS 2020. Singapore: Springer, 2021, pp.543–554.
13.
TannenbaumMBHeplerJZimmermanRS, et al.Appealing to fear: a meta-analysis of fear appeal effectiveness and theories. Psychol Bull2015; 141: 1178–1204.
14.
WitteK. Putting the fear back into fear appeals: the extended parallel process model. Commun Monogr1992; 59: 329–349.
15.
RogersRW. A protection motivation theory of fear appeals and attitude change. J Psychol1975; 91: 93–114.
16.
BockarjovaMStegL. Can protection motivation theory predict pro-environmental behavior? Explaining the adoption of electric vehicles. Glob Environ Change2014; 28: 276–288.
17.
AjzenI. The theory of planned behavior. Organ Behav Hum Decis Process1991; 50: 179–211.
18.
De LeeuwAValoisPAjzenI, et al.Using the theory of planned behavior to identify key beliefs underlying pro-environmental behavior in high-school students: implications for educational interventions. J Environ Psychol2015; 42: 128–138.
19.
ReesJHKlugSBambergS. Guilty conscience: motivating pro-environmental behavior by inducing negative moral emotions. Clim Change2015; 130: 439–452.
20.
ChadwickAE. Toward a theory of persuasive hope: effects of cognitive appraisals, hope appeals, and hope on climate change attitudes. Health Commun2015; 30: 598–611.
21.
BallewMCarmanJVernerM, et al.The attitude-behavior gap on climate action: how can it be bridged? Yale University and George Mason University. New Haven, CT: Yale Program on Climate Change Communication, 2024.
22.
StoknesPE. Rethinking climate communications and the “psychological climate paradox”. Energy Res Soc Sci2014; 1: 161–170.
23.
PalmatierRWDantRPGrewalD, et al.Factors influencing the effectiveness of relationship marketing: a meta-analysis. J Mark2006; 70: 136–153.
24.
LiebersNVogelABrevesP, et al.The impact of familiarity with a communicator on the persuasive effectiveness of pandemic-related fear appeals explained through parasocial relationships. Mass Commun Soc2023; 27: 765–791.
25.
CollinsNLMillerLC. Self-disclosure and liking: a meta-analytic review. Psychol Bull1994; 116: 457–475.
26.
BergJH. Responsiveness and self-disclosure. In: DerlegaVJBergJH (eds) Self-disclosure: theory, research, and therapy. New York: Springer, 1987, pp.101–130.
27.
RafaeliS. From new media to communication. Sage Annu Rev Commun Res1988; 16: 110–134.
28.
HuangGWangS. Is artificial intelligence more persuasive than humans? A meta-analysis. J Commun2023; 73: 552–562.
29.
AltaySHacquinASChevallierC, et al.Information delivered by a chatbot has a positive impact on COVID-19 vaccines attitudes and intentions. J Exp Psychol Appl2021; 29: 52–62.
30.
AggarwalATamCCWuD, et al.Artificial intelligence–based chatbots for promoting health behavioral changes: systematic review. J Med Internet Res2023; 25: e40789.
31.
WangJPengL. Striking an emotional chord: effects of emotional appeals and chatbot anthropomorphism on persuasive science communication. Sci Commun2023; 45: 485–511.
32.
LiZLiangMLcR, et al.Stayfocused: examining the effects of reflective prompts and chatbot support on compulsive smartphone use. In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. May 2024: pp.1–19.
33.
AhnHKKimHJAggarwalP. Helping fellow beings: anthropomorphized social causes and the role of anticipatory guilt. Psychol Sci2014; 25: 224–229.
34.
AraujoT. Living up to the chatbot hype: the influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Comput Hum Behav2018; 85: 183–189.
35.
NassCMoonY. Machines and mindlessness: social responses to computers. J Soc Issues2000; 56: 81–103.
36.
LinJSEWuL. Examining the psychological process of developing consumer-brand relationships through strategic use of social media brand chatbots. Comput Hum Behav2023; 140: 107488.
37.
LiXSungY. Anthropomorphism brings us closer: the mediating role of psychological distance in user–AI assistant interactions. Comput Hum Behav2021; 118: 106680.
38.
TamKPLeeSLChaoMM. Saving Mr. Nature: anthropomorphism enhances connectedness to and protectiveness toward nature. J Exp Soc Psychol2013; 49: 514–521.
39.
ZhuHWongNHuangM. Does relationship matter? How social distance influences perceptions of responsibility on anthropomorphized environmental objects and conservation intentions. J Bus Res2019; 95: 62–70.
40.
KetronSNaletelichK. Victim or beggar? Anthropomorphic messengers and the savior effect in consumer sustainability behavior. J Bus Res2019; 96: 73–84.
41.
BergerCRCalabreseRJ. Some explorations in initial interaction and beyond: toward a developmental theory of interpersonal communication. Hum Commun Res1974; 1: 99–112.
42.
Emmers-SommerTM. The effect of communication quality and quantity indicators on intimacy and relational satisfaction. J Soc Personal Relationships2004; 21: 399–411.
43.
SchrodtPSolizJBraithwaiteDO. A social relations model of everyday talk and relational satisfaction in stepfamilies. Commun Monogr2008; 75: 190–217.
44.
KingstonPWNockSL. Time together among dual-earner couples. Am Sociol Rev1987; 52: 391–400.
45.
CummingsJNLeeJBKrautRE. Communication technology and friendship during the transition from high school to college. In: KrautRBryninMKieslerS (eds) Computers, phones and the internet: domesticating information technology. New York: Oxford University Press, 2006, pp.265–278.
46.
HobertSBerensF, et al.Small talk conversations and the long-term use of chatbots in educational settings – experiences from a field study. In: FølstadA (ed.) Chatbot research and design (CONVERSATIONS 2019), lecture notes in computer science. Switzerland: Springer, 2020, pp.260–272.
47.
LukTTLuiJHTWangMP. Efficacy, usability, and acceptability of a chatbot for promoting COVID-19 vaccination in unvaccinated or booster-hesitant young adults: pre-post pilot study. J Med Internet Res2022; 24: e39063.
48.
HoAHancockJMinerAS. Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot. J Commun2018; 68: 712–733.
49.
HortonDWohlR. Mass communication and para-social interaction: observations on intimacy at a distance. Psychiatry Interpers Biol Process1956; 19: 215–229.
50.
LeeEJJangJW. Not so imaginary interpersonal contact with public figures on social network sites: how affiliative tendency moderates its effects. Commun Res2013; 40: 27–51.
51.
TsaiWHSLiuYChuanCH. How chatbots’ social presence communication enhances consumer engagement: the mediating role of parasocial interaction and dialogue. J Res Interact Mark2021; 15: 460–482.
52.
PentinaIHancockTXieT. Exploring relationship development with social chatbots: a mixed-method study of replika. Comput Hum Behav2023; 140: 107600.
53.
BrevesPLiebersN. #Greenfluencing. The impact of parasocial relationships with social media influencers on advertising effectiveness and followers’ pro-environmental intentions. Environ Commun2022; 16: 773–787.
54.
LeeMParkJS. Do parasocial relationships and the quality of communication with AI shopping chatbots determine middle-aged women consumers’ continuance usage intentions?J Consum Behav2022; 21: 842–854.
55.
Korea Ministry of Employment and Labor. 2021 Survey Report on Labor Conditions by Employment Type. 2022. Available from: www.index.go.kr/unify/idx-info.do?idxCd=8085 (accessed 19 March 2025).
56.
LiMSuhA.Anthropomorphism in AI-enabled technology: a literature review. Electron Mark2022; 32: 2245–2275.
57.
ZavalLKeenanEAJohnsonEJ, et al.How warm days increase belief in global warming. Nat Clim Change2014; 4: 143–147.
58.
ManoHOliverRL. Assessing the dimensionality and structure of the consumption experience: evaluation, feeling, and satisfaction. J Consum Res1993; 20: 451–466.
59.
HeathYGiffordR. Free-market ideology and environmental degradation: the case of belief in global climate change. Environ Behav2006; 38: 48–71.
60.
Van der LindenS. The social-psychological determinants of climate change risk perceptions: towards a comprehensive model. J Environ Psychol2015; 41: 112–124.
61.
ZeleznyLCChuaPPAldrichC. New ways of thinking about environmentalism: elaborating on gender differences in environmentalism. J Soc Issues2000; 56: 443–457.
62.
GnewuchUMoranaSAdamMT, et al.Opposing effects of response time in human–chatbot interaction: the moderating role of prior experience. Bus Inf Syst Eng2022; 64: 773–791.
MuthénBO. Beyond SEM: general latent variable modeling. Behaviormetrika2002; 29: 81–117.
65.
HayesAF. Beyond baron and kenny: statistical mediation analysis in the new millennium. Commun Monogr2009; 76: 408–420.
66.
LiaoVQDavisMGeyerW, et al.What can you do?: studying social-agent orientation and agent proactive interactions with an agent for employees. In: Proceedings of the 2016 ACM conference on designing interactive systems. 2016: pp.264–275.
67.
Yildiz DurakH.Conversational agent-based guidance: examining the effect of chatbot usage frequency and satisfaction on visual design self-efficacy, engagement, satisfaction, and learner autonomy. Educ Inf Technol2022; 28: 471–488.
68.
ShengJTKairamSR. From virtual strangers to IRL friends: relationship development in livestreaming communities on twitch. In: Proceedings of the ACM on Human-Computer Interaction. 2020: pp.1–3.
69.
TukachinskyRWalterNSaucierCJ. Antecedents and effects of parasocial relationships: a meta-analysis. J Commun2020; 70: 868–894.
70.
BambergSMöserG. Twenty years after hines, hungerford, and tomera: a new meta-analysis of psycho-social determinants of pro-environmental behaviour. J Environ Psychol2007; 27: 14–25.
71.
ChawlaL. Childhood nature connection and constructive hope: a review of research on connecting with nature and coping with environmental loss. People Nat2020; 2: 619–642.
72.
SogaMGastonKJ. Extinction of experience: the loss of human–nature interactions. Front Ecol Environ2016; 14: 94–101.
73.
ChungLLKangJ. I'm hurt too": the effect of a chatbot's reciprocal self-disclosures on Users’ painful experiences. Arch Des Res2023; 36: 67–84.
74.
SkjuveMFølstadABrandtzægPB. A longitudinal study of self-disclosure in human–chatbot relationships. Interact Comput2023; 35: 24–39.
75.
WemyssDCellinaFLobsiger-KägiE, et al.Does it last? Long-term impacts of an app-based behavior change intervention on household electricity savings in Switzerland. Energy Res Soc Sci2019; 47: 16–27.
76.
DraxlerFBuschekDTavastM, et al.Gender, age, and technology education influence the adoption and appropriation of LLMs. arXiv preprint arXiv:2310.06556. https://arxiv.org/abs/2310.06556 (accessed 19 March 2025).
FolkDPWuCHeineS.Cultural variation in attitudes towards social chatbots. Cult Chatbots2025; 56: 219–239.
80.
BilfingerLBrummernhenrichBJucksR. Is it up to me or them? Insights from an experimental study on psychological reactance towards climate change mitigation appeals. Environ Commun2024; 18: 418–434.
81.
OdouPSchillM. How anticipated emotions shape behavioral intentions to fight climate change. J Bus Res2020; 121: 243–253.
82.
KesternichMOsberghausDBotzenWJW. The intention-behavior gap in climate change adaptation. ZEW-Centre for European Economic Research Discussion Paper2022: 22–055.
83.
McCambridgeJDe BruinMWittonJ.The effects of demand characteristics on research participant behaviours in non-laboratory settings: a systematic review. PLoS One2012; 7: e39116.
84.
DurmazADursunİKabadayıET.Are they actually sustainable? The social desirability bias in sustainable consumption surveys. In: BhattacharyyaJ (ed) Dealing with socially responsible consumers: studies in marketing. Singapore: Springer Nature Singapore, 2023, pp.533–560.
85.
FloridiLCowlsJBeltramettiM, et al.AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds Mach2018; 28: 689–707.
86.
HendersonPSinhaKAngelard-GontierN, et al.Ethical challenges in data-driven dialogue systems. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES). https://arxiv.org/abs/2304.03738
87.
Al AnaissyCVesicSNevejansN.Towards ethical argumentative persuasive chatbots. In: International Workshop on Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems, May 2023, pp.141–160. Springer Nature Switzerland.
88.
GuszczaJLeeMAmmanathB, et al.Human values in the loop: design principles for ethical AI. Deloitte Rev Technol Ethics2020; 26: 65–81.
89.
FerraraE.Should ChatGPT be biased? Challenges and risks of bias in large language models. arXiv preprint. https://arxiv.org/abs/2304.03738
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