INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

How Anthropomorphism And Initial Trust Shape Chatbot Customer

Experience? The Moderating Role Of Technology Anxiety

Ons Baati

Faculty of Economics and Management of Mahdia, University of Monastir

DOI : https://doi.org/10.51583/IJLTEMAS.2025.1411000109

Received: 08 December 2025; Accepted: 15 December 2025; Published: 23 December 2025

ABSTRACT:

Based on the stimulus-organism-response (SOR) model, this study aims to investigate the effect of

anthropomorphism and initial trust on customer experience. It also assesses the moderating effect of technology

anxiety on the relationship between anthropomorphism and customer experience, which remains underexplored.

Data were collected from 385 Tunisian students and analyzed using partial least squares structural equation

modeling. Results indicate that anthropomorphism has a positive influence on both initial trust and customer

experience, and that initial trust has a positive effect on customer experience. Findings also reveal that

technology anxiety negatively moderates the relationship between anthropomorphism and customer experience.

This study provides valuable insights for managers and system developers.

Keywords: Anthropomorphism, Initial trust, Technology anxiety, Chatbot customer experience, SOR Model

INTRODUCTION

The rise of artificial intelligence (AI) has revolutionized the way companies interact with customers (Yatawara

et al., 2025). Particularly, AI chatbots are performing the majority of consumer interactions over internet (Lu &

Zhang, 2025), and redefining the customer experience (Chen et al., 2021). Chatbots, also known as virtual

assistants, respond to customer inquiries, deliver 24/7 customer support, and enable human-like interactions (Li

et al., 2023).

However, despite the significant progress in chatbot deployment, many consumers still express doubts and

skepticism toward its usage (Pavone & Desveaud, 2024). They distrust chatbots (Maduku et al., 2025) and prefer

human interaction (Gouveia & Santos, 2025). Therefore, the first interaction is prominent. It should feel helpful,

clear and resembling human interaction to mitigate frustration. The implementation of anthropomorphic chatbots

can be a potential solution to alleviate customer uncertainty and to make a better experience with the system

(Sayed & Abutaleb, 2025). Anthropomorphic features of chatbot make people more reassured when interacting

whith them, as they consider chatbots as human partners. However, the beneficial influence of

anthropomorphism on customer experience may not hold for everyone. Technology anxiety might act as a key

boundary condition that negatively moderate this effect. In addition, chatbots that appear excessively human

might provoke the uncanny valley effects such as eeriness feelings, which in turn negatively affect trust (Song

& Shin, 2024). Given this dual consequences of anthropomorphism, its ability to enhance engagement or trigger

discomfort, we propose that initial trust should take precedence in chatbot development to mitigate Uncanny

Valley effects and preserve the benefits of moderate anthropomorphism.

Despite the extensive prior literature on chatbot adoption, customer experience, which is an important outcome,

should be thoroughly examined (Gouveia & Santos, 2025). Based on the stimuli-organism-response (SOR)

theory, this paper aims to investigate an integrated framework combining anthropomorphism, initial trust,

technology anxiety, and chatbot customer experience.

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LITERATURE REVIEW

Stimulus-Organism-Response (SOR) theory

The SOR framework, proposed by Mehrabian and Russell (1974), proposes that external stimuli (S) can

influence individuals’ internal cognitions, feelings and emotions (O), leading to behavioral responses (R). The

SOR is a relevant framework that explain individuals’ experiences or behaviors in human–chatbot interactions

(Truong & Chen, 2025). In this study, the SOR framework is applyed to illustrate how chatbot

anthropomorphism (stimulus) can affect customer experience (response) through initial trust (organism).

Anthropomorphism can be defined as “the attribution of human characteristics or traits to nonhuman agents”

(Epley et al., 2007). The anthropomorphic attributes of chatbots, such as language and design, are considered as

stimuli since these external factors influence the perception and the interaction with chatbot. known as the “initial

phase of customer trust relationship with a new technology” (Mostafa & Kasamani, 2022), initial trust is

considered as the internal affective factor triggred by anthropomorphic features of the chatbot. Customer

experience, conceptualized as “customers' subjective responses resulting from any contact with companies”

(Martínez Puertas et al., 2024), constitutes the response component shaped during the interaction.

Hypotheses development

2.1. Anthropomorphism and Chatbot initial trust

Anthropomorphism plays a pivotal role in facilitating the interaction with the chatbot and in enhancing trust

(Yanxia et al., 2024). Individuals interacting with human-like conversational agent may exhibit a strengthened

sense of social presence and trustworthiness (Shi et al., 2025). Thereby, anthropomorphic design elements elicit

emotional responses such as comfort and psychological proximity, which can affect initial trust (Sfar et al.,

2025).

H1. Anthropomorphism positively influences chatbot initial trust

2.2. Chatbot initial trust and experience with chatbot

Trust begins with initial trust, which is crucial for consumers lacking prior experience with the emerging

technology (Lin & Lee, 2025). Initial trust is is instrumental in mitigating uncertainties and fostering consumer

acceptance and adoption of chatbot (Sboui et al., 2024). When individuals manifest initial trust they are more

likely to interpret ambiguous responses more charitably and to positively evaluate the interaction.

H2. Initial trust positively influences customer experience with chatbots

2.3. Anthropomorphism and experience with chatbot

Anthropomorphism is viewed as one of the key elements in establishing humanized interactions between

conversational agents and customers (Sheehan et al., 2020). Human-like characteristics (e.g., use of dialectal

language) enhance customers’satisfaction, help consumers establish an emotional relationship with the chatbot,

and improve their overall experience (Rizomyliotis et al., 2022).

H3. Anthropomorphism positively influences customer experience with chatbots

2.4. Moderator role of technology anxiety

Technology anxiety refers to “the degree to which a person has difficulty or doubts in understanding and using

new technology” (Meuter et al., 2003). It is considered as a stressor that lead to negative outcomes such as

creepiness and distrust (Maduku et al., 2025). When confronted with a new technology (chatbots in this case),

individuals are frustrated and skeptical since they do not fully understand and doubt the technology behind

chatbots (Habib et al., 2025). The effect of anthropomorphism on customer experience can depend on customer’s

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anxiety levels. Individuals with low level of anxiety may benefit from the positive effects of chatbot

anthropomorphism, be more cool, and evaluate positively the interaction. Conversly, users having a high level

of anxiety perceive the same anthropomorphic characteristics, which should be reassuring, as a source of

disconfort and creepiness. Despite its relevance, the moderator effect of technology anxiety remains

underexplored.

H4. Technology anxiety negatively moderates the relationship between anthropomorphism and customer

eperience

The conceptual model is presented in Figure 1

Technology Anxiety

Anthropomorphism

Experience

with chatbot

Chatbot initial trust

Organism

Response

Stimulus

Figure 1. Conceptual model

METHODOLOGY AND RESULTS

Methodology

An online survey was administered to a sample of 385 Tunisian students aged between 20 and 23 years. The

sample was composed of 77.9% females and 22.1% males. Participants were selected using convenience

sampling method. All the measures included were a seven-point likert scales (1=strongly disagree; 7=strongly

agree). They were adapted from the literature. The list of items is reported in the Table1.

Results

To assess the hypotheses, a partial least squares structural modelling (PLS-SEM) approach throught Smart PLS

4 was applied. The results of the initial measurement model show that the item TA3 has a low factor loading

(0.131), that is below the threshold of 0.7, so it was removed (Hair et al., 2019). All the composite reliability

coefficients are above 0.7. Similarly the Cronbach's alpha coefficients exceed 0.7, indicating a good internal

consistency. AVE coefficients surpass 0.5, indicating that the scales have good convergent validity (Henseler et

al., 2016) (See Table 1). Finally, to examine discriminant validity, we referred to the HTMT matrix. It reveals

the good discriminant validity of the constructs since all coefficients are below 0.9 (See Table 2). Before the

assessment of structural model, multicollinearity was inspected. The examination of the variance inflation factors

(VIF) showed that the VIF value of CIT4 is very high (27.856), inducing its suppression as recommended by

Hair et al. (2021). As shown in the Figure 2, the values of R²are acceptable since they are higher than 0,2 (Hair

et al., 2021). The hypotheses test results, illustrated in Table 3, indicate that all the hypotheses are significant

and supported.

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Table 1.Measurement model assessment

Constructs

Items

Loadings Cronbach's CR

alpha

AVE

Anthropomorphism

(Mohammed &

Ferraris, 2025)

0.705

0.860

0.867

ANTH1

ANTH2

ANTH3

ANTH4

Chatbots communicate in a manner that

feels authentic and not artificial

0.847

0.880

0.847

0.780

Chatbots exhibit human-like characteristics

in their interactions

The interactions with chatbots feel realistic

rather than mechanical

Chatbots engage users in a polished and

natural manner

Chatbot Initial

Trust (Oliveira et

al., 2014)

0.898

0.900 0.777

CIT1

CIT2

CIT3

CIT4

Chatbots seem dependable

Chatbots seem secure

0.939

0.904

0.699

0.959

Chatbots were created to help the client

Chatbots seems trustworthy

Experience with

chatbot (Trivedi,

2019)

0.892

0.941

0.940

EWC1

EWC2

I enjoyed using (brand) chatbot

0.944

The experience of using (brand) chatbot was 0.959

interesting

EWC3

I am happy with the experience of using

(brand) chatbots

0.931

Technology

Anxiety (Meuter et

al., 2005)

0.659

0.807

0.758

TA1

I feel apprehensive about using

0.736

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Table 3. Hypotheses test results

Hypotheses

ANTH -> CIT

t-value

9.967

8.378

7.564

2.045

p-value

0.000

Conclusion

Supported

0.455

0.390

0.364

-0.092

0.000

0.041

CIT -> EWC

ANTH -> EWC

TA x ANTH -> EWC

DISCUSSION AND MANAGERIAL IMPLICATIONS.

As mentioned above, all the assumptions are confirmed. First, anthropomorphism significantly and positively

affect both initial trust and customer experience, corroborating the findings of Rizomyliotis et al. (2022) and

Sfar et al. (2025). Chatbots having human-like traits enhance trust, engender enjoyable interactions leasing to

favorable evaluations and pleasant experiences (Fotheringham & Wiles, 2023). Given that customers tend to

trust technology more when it feels like talking to a person, not a machine, companies should add human-like

features to their chatbots such as linguistic cues, and program them to respond with warmth and humor (Shams

et al., 2024). Companies should also implement chatbots that provide customized interactions and proactive

problem solving functionalities. Second, initial trust positively influences customer experience, confirming the

findings of Sboui et al. (2024). This reveal the priority of the first touchpoint with customers. Establishing

credibility early through testimonials can be a powerful solution to develop chatbot initial trust. Finally, findings

indicate that technology anxiety negatively moderates the relationship between anthropomorphism and customer

experience. Managers should incorporate technology anxiety as a key segmentation criterion in his strategies.

For segments expressing high technology anxiety, managers should optimize chatbot design to mitigate their

apprehension, customize interactions, and improve overall customer experience.

CONCLUSIONS

This study used the S-O-R model to investigate the influence of chatbot anthropomorphism on customers’ initial

trust, and how this variable subsequently drive customer experience. Additionally, the results highlight the

negative moderating effect of technology anxiety on the relationship between anthropomorphism and customer

experience. This study address calls from Baabdullah et al. (2022) to test the technology anxiety as a key inhibitor

in the experience with virtual agents.

This study encounter several limitatations. First, this study is made in Tunisian context, and more particular with

a young generation. To improve its generalizability, future research should use larger and more diverse samples

across cultures, age cohorts, and adoption contexts. Furthermore, a longitudinal study is recommended to

investigate the evolving customer experience through repeated interactions with chatbots over time.

Additionally, subsequent studies incorporating additional variables such as privacy concerns (Gouveia &

Santos, 2025), perceived risk, and chatbots' communication quality Baabdullah et al. (2022) could strengthen

the model’s explanatory capacity. Lastly, more extensive empirical investigation of the uncanny valley

phenomenon could help refine design guidelines that enhance user comfort and trust through appropriate human-

like features.

REFERENCES

1. Baabdullah, A. M., Alalwan, A. A., Algharabat, R. S., Metri, B., & Rana, N. P. (2022). Virtual agents

and flow experienceꢀ: An empirical examination of AI-powered chatbots. Technological Forecasting and

Social Change, 181, 121772. https://doi.org/10.1016/j.techfore.2022.121772

www.ijltemas.in

Page 1157

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

2. Chen, J.-S., Le, T.-T.-Y., & Florence, D. (2021). Usability and responsiveness of artificial intelligence

chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution

Management, 49(11), 1512-1531. https://doi.org/10.1108/IJRDM-08-2020-0312

3. Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing humanꢀ: A three-factor theory of

anthropomorphism.

295X.114.4.864

Psychological

Review,

114(4),

864-886.

https://doi.org/10.1037/0033-

4. Fotheringham, D., & Wiles, M. A. (2023). The effect of implementing chatbot customer service on stock

returnsꢀ: An event study analysis. Journal of the Academy of Marketing Science, 51(4), 802-822.

https://doi.org/10.1007/s11747-022-00841-2

5. Gouveia, J., & Santos, S. (2025). Rethinking the Customer Journeyꢀ: Impact of AI for Consumers and

Businesses—A Systematic Literature Review and Research Agenda. International Journal of Consumer

Studies, 49(6), e70136. https://doi.org/10.1111/ijcs.70136

6. Habib, M. D., Attri, R., Salam, M. A., & Yaqub, M. Z. (2025). Retail consumers’ conundrumꢀ: An in-

depth qualitative study navigating the motivations and aversion of chatbots. Journal of Retailing and

Consumer Services, 82, 104147. https://doi.org/10.1016/j.jretconser.2024.104147

7. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least

Squares Structural Equation Modeling (PLS-SEM) Using Rꢀ: A Workbook. Springer International

Publishing. https://doi.org/10.1007/978-3-030-80519-7

8. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results

of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203

9. Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology researchꢀ:

Updated

guidelines.

Industrial

Management

Data

Systems,

116(1),

2-20.

https://doi.org/10.1108/IMDS-09-2015-0382

10. Li, Y., Gan, Z., & Zheng, B. (2023). How do Artificial Intelligence Chatbots Affect Customer Purchase?

Uncovering the Dual Pathways of Anthropomorphism on Service Evaluation. Information Systems

Frontiers. https://doi.org/10.1007/s10796-023-10438-x

11. Lin, B., & Lee, W. (2025). Service robots and initial trust dynamicsꢀ: Consumers’ ethical challenges in

tourism

and

hospitality.

Journal

Travel

Tourism

Marketing,

42(3),

282-306.

https://doi.org/10.1080/10548408.2025.2468460

12. Lu, Y., & Zhang, J. (2025). Balancing identity diversity and product contextsꢀ: Understanding consumer

trust in AI-enhanced chatbot services. Journal of Retailing and Consumer Services, 84, 104205.

https://doi.org/10.1016/j.jretconser.2024.104205

13. Maduku, D. K., Rana, N. P., Mpinganjira, M., & Thusi, P. (2025). Exploring the ‘Dark Side’ of AI ‐

Powered Digital Assistantsꢀ: A Moderated Mediation Model of Antecedents and Outcomes of Perceived

Creepiness. Journal of Consumer Behaviour, 24(3), 1194-1221. https://doi.org/10.1002/cb.2462

14. Martínez Puertas, S., Illescas Manzano, M. D., Segovia López, C., & Ribeiro-Cardoso, P. (2024).

Purchase intentions in a chatbot environmentꢀ: An examination of the effects of customer experience.

Oeconomia Copernicana, 15(1), 145-194. https://doi.org/10.24136/oc.2914

15. Mehrabian, A. and Russell, J.A. (1974), "An Approach to Environmental Psychology, The MIT Press,

Cambridge, Mass

16. Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing among Alternative Service

Delivery Modesꢀ: An Investigation of Customer Trial of Self-Service Technologies. Journal of

Marketing, 69(2), 61-83. https://doi.org/10.1509/jmkg.69.2.61.60759

17. Meuter, M. L., Ostrom, A. L., Bitner, M. J., & Roundtree, R. (2003). The influence of technology anxiety

on consumer use and experiences with self-service technologies. Journal of Business Research, 56(11),

899-906.

18. Mohammed, A., & Ferraris, A. (2025). Exploring motivational drivers of AI chatbot adoption in

emerging marketsꢀ: Insights from the stimulus-organism-response model for service automation. The

Bottom Line. https://doi.org/10.1108/BL-11-2024-0189

19. Mostafa, R. B., & Kasamani, T. (2022). Antecedents and consequences of chatbot initial trust. European

journal of marketing, 56(6), 1748-1771.

20. Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile

banking adoptionꢀ: When UTAUT meets TTF and ITM. International journal of information

management, 34(5), 689-703.

www.ijltemas.in

Page 1158

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

21. Pavone, G., & Desveaud, K. (2024). Strategic Implications of Chatbots in Marketingꢀ: Exploring

Applications and Factors of Customer Acceptance. In L. Matosas-López (Éd.), The Impact of

Digitalization on Current

Marketing Strategies

(p. 1-18). Emerald Publishing Limited.

https://doi.org/10.1108/978-1-83753-686-320241001

22. Rizomyliotis, I., Kastanakis, M. N., Giovanis, A., Konstantoulaki, K., & Kostopoulos, I. (2022). “How

mAy I help you today?” The use of AI chatbots in small family businesses and the moderating role of

customer

affective

commitment.

Journal

Business

Research,

153,

329-340.

https://doi.org/10.1016/j.jbusres.2022.08.035

23. Sayed, D., & Abutaleb, S. (2025). Anthropomorphic chatbots as a catalyst for online customer experience

(CX)ꢀ: The case of Egyptian consumers. Journal of Marketing Communications, 1-15.

https://doi.org/10.1080/13527266.2025.2453229

24. Sboui, M., Baati, O., & Sfar, N. (2024). Influencing factors and consequences of chatbot initial trust in

telecommunication

servicesꢀ:

study

Generation

The

TQM

Journal.

https://doi.org/10.1108/TQM-02-2024-0085

25. Sfar, N., Sboui, M., & Baati, O. (2025). The impact of chatbot anthropomorphism on customer

experience and chatbot usage intentionꢀ: A technology acceptance approach. International Journal of

Quality and Service Sciences, 17(2), 168-194. https://doi.org/10.1108/IJQSS-06-2024-0079

26. Shams, G., Kim, K. K., & Kim, K. (2024). Enhancing service recovery satisfaction with chatbotsꢀ: The

role of humor and informal language. International Journal of Hospitality Management, 120, 103782.

https://doi.org/10.1016/j.ijhm.2024.103782

27. Sheehan, B., Jin, H. S., & Gottlieb, U. (2020). Customer service chatbotsꢀ: Anthropomorphism and

adoption. Journal of Business Research, 115, 14-24. https://doi.org/10.1016/j.jbusres.2020.04.030

28. Shi, X., Niu, G., Jin, S., Yang, W., & Sun, X. (2025). The Influence of anthropomorphism on trust in

artificial intelligenceꢀ: Take virtual agent as an example. International Journal of Human-Computer

Studies, 205, 103644. https://doi.org/10.1016/j.ijhcs.2025.103644

29. Song, S. W., & Shin, M. (2024). Uncanny Valley Effects on Chatbot Trust, Purchase Intention, and

Adoption Intention in the Context of E-Commerceꢀ: The Moderating Role of Avatar Familiarity.

International

Journal

Human–Computer

Interaction,

40(2),

441-456.

https://doi.org/10.1080/10447318.2022.2121038

30. Trivedi, J. (2019). Examining the Customer Experience of Using Banking Chatbots and Its Impact on

Brand Loveꢀ: The Moderating Role of Perceived Risk. Journal of Internet Commerce, 18(1), 91-111.

https://doi.org/10.1080/15332861.2019.1567188

31. Truong, T. T. H., & Chen, J. S. (2025). When empathy is enhanced by human–AI interactionꢀ: An

investigation of anthropomorphism and responsiveness on customer experience with AI chatbots. Asia

Pacific Journal of Marketing and Logistics, 37(12), 3908-3925. https://doi.org/10.1108/APJML-10-

2024-1464

32. Yanxia, C., Shijia, Z., & Yuyang, X. (2024). A meta-analysis of the effect of chatbot anthropomorphism

on the customer journey. Marketing Intelligence & Planning, 42(1), 1-22. https://doi.org/10.1108/MIP-

03-2023-0103

33. Yatawara, K., Sampath, T., Kalupahana, P. L., Rathnayake, S., Jayasuriya, N., & Rathnayake, N. (2025).

A Systematic Review on Consumer Adoption of AI-driven Chatbots. Vision: The Journal of Business

Perspective, 09722629251332349. https://doi.org/10.1177/09722629251332349

www.ijltemas.in

Page 1159