INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV November 2025 | Special Issue on Management

Chatbot Acceptance in Marketing: Literature Review

Nadia Sfar

Lecturer at the Faculty of Economics and Management of Mahdia University of Monastir, Tunisia

DOI: https://doi.org/10.47772/IJRISS.2025.914MG00245

Received: 12 December 2025; Accepted: 20 December 2025; Published: 25 December 2025

ABSTRACT

The digital transformation of consumer behavior and service delivery has accelerated the adoption of AI-

powered chatbots in marketing. This paper synthesizes existing literature to examine the diverse applications

of chatbots across key marketing domains, including advertising, mobile commerce, e-services, and branding,

where they enhance engagement, service quality, and personalized customer experiences. A central focus is the

pervasive use of the Technology Acceptance Model (TAM) as the primary theoretical framework for studying

chatbot adoption. While acknowledging TAM's robust analytical utility, the review concurrently identifies and

discusses its critical limitations, such as technological narrowness, structural oversimplification, and

methodological constraints.

Keywords: Chatbot Adoption, Technology Acceptance, AI, TAM model, Marketing

INTRODUCTION

The digital transformation of consumer behavior has deeply reshaped the way with which services are

delivered. As internet users now average 2.5 hours per day on social networks (Kemp, 2022), brands are

increasingly adopting digital platforms to address changing customer needs. This transition is supported by

sophisticated virtual agents, which provide real-time, personalized service and help merge traditional in-person

interactions with digital expectations (Hagberg et al., 2016). Today’s consumers show a growing preference for

digital channels, valuing their greater accessibility, cost savings, and efficiency over offline options (Escobar,

2016).

Marketing has been one of the first domains affected by AI's transformative impact, leading to numerous

developments (Chintalapati, 2022; Kumar et al., 2024). Brands are increasingly leveraging AI-powered

chatbots equipped with Natural Language Understanding (NLU) and Machine Learning (ML), to strengthen

consumer engagement and streamline interactions, interpret his feedback and anticipate his needs, foster more

natural communication, offer real-time assistance, and deliver tailored promotional messages (Lim et al., 2022;

Ford et al., 2023).

The present paper examines several applications of chatbots identified in marketing literature. Particular

attention is given to the Technology Acceptance Model (TAM), as it serves as the primary theoretical lens

through which chatbot adoption is studied in this field.

1.METHODOLOGY

This article employs a narrative (semi-systematic) review methodology (Snyder, 2019), appropriate for

synthesizing literature across interdisciplinary fields and capturing the evolving landscape of chatbot

marketing. Our analysis focused on peer-reviewed articles published between 2015 and 2024, sourced from

major databases including Scopus, Web of Science, and EBSCO Business Source Complete. Search terms

included combinations of 'chatbot,' 'AI,' 'marketing,' 'adoption,' 'TAM,' and 'Generative AI.' Inclusion criteria

prioritized empirical studies and conceptual papers in leading marketing, business, and information systems

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV November 2025 | Special Issue on Management

journals. While not a fully systematic review with formal meta-analysis, this approach allows for thematic

synthesis and identification of dominant theoretical trajectories and emerging gaps

II. LITERATURE REVIEW

This review synthesizes literature on marketing chatbots through three interconnected thematic lenses: (1)

Functional Applications (advertising, m-commerce, e-service, branding), (2) Theoretical Foundations

(dominant vs. alternative models), and (3) Technological Evolution (from rule-based to GenAI-powered

chatbots). This structure allows us to not only catalog applications but also analyze comparative drivers,

theoretical adequacy, and evolutionary trajectories.

According to Alsharhan et al. (2024)’ systematic review, studies in the marketing domain (around 20 studies)

have explored chatbot applications across various areas, including advertising, mobile marketing, mobile

advertising, and branding, targeting different consumer segments.

In recent years, academic research on AI advertising has grown significantly, providing valuable insights into

chatbots potential applications (Neumann et al., 2019), core functions (Campbell et al., 2021; Wu et al., 2021),

defining features (Lee & Cho, 2019; Van den Broeck et al., 2019; Smith, 2020; Kietzmann et al., 2021), and

associated challenges (Palos-Sanchez et al., 2019; Watts & Adriano, 2021). Among these, Van den Broeck et al.

(2019) specifically investigated the effectiveness of chatbot-delivered advertising. Their study focused on how

consumers perceive the relevance and intrusiveness of chatbot ads, revealing that these factors significantly

shape evaluations through perceived usefulness and helpfulness. Notably, their findings emphasize that

message acceptance plays a mediating role between users’ perception of intrusiveness and their assessment of

chatbot advertising's utility and potential influence on patronage behavior (Ford et al., 2023).

The proliferation of mobile technologies in managerial applications has increased scholarly attention to mobile

marketing (Shankar & Balasubramanian, 2009; Venkatesh et al., 2012), mobile advertising (Andrews et al.,

2015; Bart et al., 2014), and mobile commerce (Shankar et al., 2016) – terms often used interchangeably in the

literature despite their conceptual nuances (Leppäniemi & Karjaluoto, 2005). The rise of mobile commerce (m-

commerce) has accelerated messaging-enabled platforms, fostering conversational commerce – AI-driven

chatbots that redefine B2C interactions (de Cosmo et al., 2021). Empirical studies demonstrate the impact of

these AI-driven entities in enhancing customer experience through real-time engagement and personalized

decision support (Sestino et al., 2020), with context-aware messaging critically shaping consumer attitudes and

behaviors (Go & Sundar, 2019). Recent work further specifies adoption drivers, including compliance-boosting

chatbot features (Adam et al., 2021) and smartphone-specific utility perceptions (Kasilingam, 2020), reflecting

growing academic interest in this domain (Sharma et al., 2024).

Chatbots are increasingly developing in e-service as well, representing a promising opportunity to improve

customer service quality and performance (Misischia et al., 2022). The literature identifies five customer-

related functions of chatbot, presented as five chatbots’ marketing efforts. These are interaction, entertainment,

trendiness, customization and problem-solving (Chung et al., 2020). Misischia et al. (2022) divided these five

functions in two major categories: “improvement of service performance” which includes interaction,

entertainment and problem-solving, and “fulfillment of customer’s expectations” which encompasses

trendiness, customization. These categories represent the core objectives of chatbot implementation in

marketing.

Branding has been also identified as a critical application area for chatbots in marketing (Alsharhan et al.,

2024).

In fact, the rapid adoption of brand chatbots on social networking platforms has transformed direct consumer

brand communication (Appel et al., 2020), enhancing research attention to chatbots branding implications

(Chung et al., 2020; Zarouali et al., 2018). Research reveals that the more a consumer perception of chatbot as

helpfulness and utility, the less is his feeling of intrusiveness toward chatbot-initiated advertising messages.

Moreover, Kull et al. (2021) establish that initial chatbot messages employing a warm (versus competent) tone

effectively diminish self-brand distance and subsequently increase behavioral brand engagement. Further,

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV November 2025 | Special Issue on Management

Cheng and Jiang (2021) provide empirical evidence that chatbot marketing enhances consumer-brand

relationships through three dimensions of communication quality - credibility, accuracy, and competence -

which collectively generate favorable brand responses. Additionally, scholars have examined the impact of

chatbots on customer relationship management (CRM). Lee and Li (2023) analyze how AI chatbot affordances

influence customer perceptions and brand loyalty, whereas Youn and Jin (2021) explore how different types of

customer – AI chatbot relationships affect consumer relationship quality.

The significance of using chatbots is particularly evident in luxury fashion branding, where chatbots' ability to

deliver personalized engagement aligns with the sector's emphasis on exclusivity and customer-centric

experiences (Zeng et al., 2023). Leading luxury brands like Burberry, Gucci, and Louis Vuitton exemplify

effective chatbot implementations that enhance customer interactions (Chung et al., 2020; Lee & Choi, 2017;

Zeng et al., 2023), while early adopters like Tommy Hilfiger demonstrate advanced applications for query

resolution (Rossi, 2020) and Sephora/Estée Lauder showcase personalized recommendation systems (Landim

et al., 2022).

Empirical studies in luxury branding highlight chatbots' capacity to provide tailored information (Sangar,

2012), enhance customer personalized shopping experience (Chung et al., 2020; Zeng et al., 2023), and

improve satisfaction through time-efficient content delivery and preference memory (Landim et al., 2022).

This shift positions AI-driven chatbots as transformative tools that bridge luxury brands' traditional service

ethos with digital commerce demands. Nevertheless, while studies recognize chatbots value in luxury brand

engagement, critical gaps persist between technological potential and practical implementation: chatbots

remain underutilized relative to their capabilities in bridging luxury brands' traditional service ethos with

digital commerce demands (Pantano & Pizzi, 2020; Zeng et al., 2023).

Furthermore, there is a significant impact of Generative AI (GenAI) on business processes, including the

marketing sector where (GenAI) offers diverse applications (Chan and Choi, 2025). The integration of

Generative AI (GenAI) into marketing strategies and decision-making holds great potential for both businesses

and consumers. It can assist in content creation (such as blog posts and emails, generate visual assets for

advertisements and virtual try-ons), respond to consumer inquiries, support sentiment analysis, identify

consumer behavior patterns, and enhance personalization through product recommendations (Gill, 2023).

However, despite its growing relevance, research on the impact of GenAI-powered chatbots in marketing

strategies and consumer behavior is still scarce in leading business and marketing journals. Within the

marketing field, GenAI is expected to redefine customer experiences, shape attitudes and behaviors, and

influence customer relationship management. Given this shift, it is essential for marketers to gain a deep

understanding of how consumers interact with a technology and how these interactions drive decision-making

before fully adopting GenAI solutions (Chan & Choi, 2025). Peres et al. (2023) explore the implications of

Generative AI (GenAI) across various domains, including marketing. They emphasize the importance of

understanding how GenAI can enhance marketing activities while also identifying its potential challenges.

One of the most significant ways GenAI is transforming digital marketing is through personalized content

creation. By leveraging advanced AI models, businesses can tailor marketing messages to individual

consumers, improving engagement and effectiveness (Chan & Choi, 2025). A prime example of this is

ChatGPT, which has gained widespread recognition due to its generative pre-trained transformer (GPT)

architecture and impressive capabilities. Built on Large Language Models (LLMs), ChatGPT utilizes deep

learning techniques and extensive training on vast amounts of internet data to generate human-like responses

(Hermann & Puntoni, 2024). In practice, ChatGPT is being increasingly integrated into marketing strategies.

Tafesse and Wien (2024) examine real-world applications of ChatGPT in marketing by analyzing user

engagement on social media. Using webscraping techniques to collect tweets related to ChatGPT and

marketing, they identify key themes, including its role in content marketing and digital marketing.

Beyond its applications in marketing field, researchers have also explored factors influencing consumer

adoption of GenAI technology. For instance, Gude (2023) investigates the critical determinants shaping

consumers' willingness to use ChatGPT, while Abdelkader (2023) finds that a positive customer experience

with ChatGPT enhances consumer satisfaction with digital marketing. Moreover, Hoffmann et al. (2024)

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV November 2025 | Special Issue on Management

highlight ChatGPT’s potential in facilitating scale development in consumer behavior research. These studies

underscore the evolving role of GenAI in reshaping consumer interactions and brand engagement in the digital

era.

As synthesized in Table 1, chatbot applications span key marketing domains with distinct functions, drivers,

and outcomes.

Table 1: Comparative Analysis of Chatbot Applications Across Marketing Domains

Marketing

Domain

Primary Functions Key Adoption Drivers

& Objectives

Measured

Outcomes

Illustrative

Studies

Deliver targeted ads, Perceived

usefulness, Ad

evaluation, Van den

Advertising

assess message

relevance/intrusi

veness, mediate intrusiveness,

patronage

behavior

perceived

helpfulness, low

message

utility

Broeck

et

al.

perception,

patronage

intention

(2019); Ford et

al. (2023); Lee

& Cho (2019)

acceptance

Enable

Context-aware

messaging,

Customer

Sestino et al.

(2020); Go

Sundar (2019);

Mobile

Commerce

(mCommerce)

conversational

commerce,

provide

experience,

consumer

attitudes/beh

aviors,

&

complianceboosting

features,

smartphone-

real-

Adam

(2021);

et

al.

time

engagement,

offer

specific utility

adoption

rates

Kasilingam

(2020)

personalized

decision support

Improve

service Service

performance Service quality, Misischia

et

al.

E-Services

quality/performa

nce via

interaction,

improvement

(interaction,

customer

(2022); Chung

et al. (2020)

satisfaction,

operational

performance

entertainment,

problem-solving),

fulfillment

entertainment,

trendiness,

of

customization,

problem-solving

customer

expectations

(trendiness,

customization)

Transform

direct Perceived

Reduced

brand

self- Appel et al. (2020);

Branding

consumer-brand

communication,

enhance brand

helpfulness/utility,

warm vs. competent

tone,

communication

quality (credibility,

Kull

et

al.

distance,

behavioral

brand

(2021);

Cheng & Jiang

(2021); Lee &

Li (2023)

engagement,

improve

CRM

engagement,

brand

loyalty,

relationship

quality

Marketing

Domain

Primary Functions Key Adoption Drivers

& Objectives

Measured

Outcomes

Illustrative

Studies

accuracy, competence)

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Deliver personalized Alignment with

Customer

Zeng et al.

Luxury

engagement,

provide tailored centricity, personalized

exclusivity, customer-

satisfaction,

(2023); Chung et

Fashion

Branding

personalized

al.

(2020);

information,

enhance

exclusive

shopping

experience

quality,

brand-

customer

relationship

strength

Landim et al.

(2022); Sangar

(2012)

experience,

timeefficient content

delivery

experiences

Content

creation, Advanced

Consumer

Chan & Choi

(2025); Tafesse

Generative

AI (GenAI)

Applications

sentiment

analysis,

behavior pattern (GPT/LLM

identification,

hyperpersonaliza

personalization, human-

like interaction

engagement,

satisfaction

with digital

marketing,

scale

&

Wien

Gude

(2024);

(2023);

capabilities),

user

positive

experience,

Hoffmann et al.

(2024)

tion,

facilitation

research

research utility

development

in research

III. TECHNOLOGY ACCEPTANCE MODEL (TAM)

Existing marketing literature on chatbot adoption has largely centered on applying and extending the

Technology Acceptance Model (TAM) framework (Naude, 2019; Linh & Wu, 2023; Alsharhan et al., 2024).

Introduced by Fred D. Davis in 1986 and built on the Theory of Reasoned Action (Fishbein, 1979), (TAM) has

emerged as a robust analytical framework for predicting and explaining technology adoption decisions (Savari

et al., 2021). This influential model has been widely applied in research to examine user acceptance of

emerging digital technologies and new e-services across diverse contexts (Davis, 1989; Davis and Venkatesh,

1996). It provided a psychological framework to understand human behavior; a perspective which was notably

absent from the Information Systems (IS) literature at the time (Davis, 1989; Davis, 1993). While foundational

theories of technology acceptance originated in the 1970s (Fishbein & Ajzen, 1975), (TAM) subsequently

formalized these concepts, establishing itself as a seminal theoretical foundation for information systems

research.

The (TAM) model was developed with several objectives: to predict user behavior through identifiable

psychological mechanisms, and to guide implementation strategies by identifying pre-adoption facilitators

(Rondan-Cataluña et al., 2015; Marikyan & Papagiannidis, 2023). Based on evidence in previous studies (e.g.

Johnson and Payne, 1985; Payne, 1982; Robey, 1979), TAM postulates two core cognitive determinants of

acceptance: perceived usefulness (PU) and perceived ease of use (PEOU) (Marikyan & Papagiannidis, 2023).

These constructs represent fundamental belief dimensions that shape behavioral intentions toward information

technologies (Davis, 1989). Formally, TAM posits that technology adoption decisions derive from a cost-

benefit evaluation where PU reflects anticipated system benefits and PEOU captures expected usage effort

(Davis, 1989).

PU reflects an individual's belief that a technology enhances task performance. This concept draws from

Bandura's (1982) notion of outcome judgment, which emphasizing the expectation of a positive result as a

driver of behavior, and has been operationalized based on empirical findings that link performance expectancy

to system usage (Robey, 1979). On the other hand, PEOU captures the belief that system usage requires

minimal effort (Davis, 1989), grounded in self-efficacy theory which emphasizes capability judgments

(Bandura, 1982; Davis, 1989). Both constructs function as behavioral antecedents, where adoption decisions

involve: (1) assessing performance enhancement potential (PU), and (2) evaluating required usage effort

(PEOU) (Hill et al., 1987).

The Technology Acceptance Model (TAM) posits that technology adoption follows three hierarchically

organized stages: (1) external factors (e.g., system design characteristics) creating user perceptions, (2)

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cognitive evaluations (perceived usefulness [PU] and perceived ease of use [PEOU]), which subsequently

shape (3) affective responses (attitudes and behavioral intentions), ultimately leading to technology usage

(Davis, 1989, 1993). While perceived usefulness directly affects usage, ease of use only matters if it makes the

technology seem more useful. Essentially, people adopt technologies they find both helpful and easy to use

(Davis, 1989, 1993). Thus, TAM conceptualizes technology acceptance as an affectively-mediated cognitive

evaluation process, where ease-of-use perceptions determine whether users ever recognize the technology's

usefulness.

Figure 1. The Technology Acceptance Model

Source: Davis (1989)

Despite its enduring relevance, the Technology Acceptance Model (TAM) has faced increasing criticism for its

conceptual limitations across three key dimensions. First, its narrow technological focus overlooks critical

nontechnological factors, including individual differences (Marikyan & Papagiannidis, 2024) and

organizational contexts like workflow integration (Holden & Karsh, 2010). Second, its oversimplified structure

reduces adoption to three static constructs (PU, PEOU, intention), ignoring both actual usage patterns (Shachak

et al, 2015) and emerging technology complexities like AI transparency (Shin, 2021) or human-AI reciprocity

(Wirtz et al., 2023). Third, its methodological rigidity manifests in basic usage metrics such as frequency or

duration, failing to distinguish between requisite use and value-adding use (Mclean et al., 2011) and an

assumption of linear intention-behavior linkages – which traces back to Fishbein and Ajzen’s 1975 Theory of

Reasoned Action - failing to capture adoption as a dynamic process (Rogers, 2003). While TAM’s parsimony

enabled rapid assessments (Venkatesh & Bala, 2008), these limitations constrain its relevance for

contemporary digital ecosystems despite its over 12,000 citations (Shachak et al., 2019).

Theoretical

Model

Core

Constructs

Application

Chatbot Studies

in Key Insights for Limitations for Chatbot

Chatbots

Context

Technology

Acceptance

Model

Perceived

Usefulness

(PU),

Dominant

framework

Establishes

baseline

Technological Narrowness:

utility- Ignores non-technological factors

for

predicting effort evaluation; (individual

differences,

(TAM)

Perceived

Ease of Use

(PEOU),

Attitude,

initial

adoption

chatbot parsimonious for organizational context)

early-stage

Structural

Reduces

Oversimplification:

adoption to static

decisions; used adoption studies

across

constructs; ignores actual usage

patterns and AI-specific

Theoretical

Model

Core

Constructs

Application

Chatbot Studies

in Key Insights for Limitations for Chatbot

Chatbots

Context

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Behavioral

Intention

marketing

domains

complexities

reciprocity)

3.

(transparency,

Methodological Constraints:

Relies on basic usage metrics;

assumes linear intention-behavior

linkage

To address TAM's limitations, scholars have begun to apply or call for more nuanced frameworks. The Unified

Theory of Acceptance and Use of Technology (UTAUT2) introduces hedonic motivation and habit, crucial for

engaging consumer technologies (Venkatesh et al., 2012). The Service-Scape or SERVQUAL models help

evaluate chatbots as digital service agents, emphasizing reliability and empathy (Parasuraman et al., 1988).

Furthermore, theories of anthropomorphism (Blut et al., 2021) and human-computer interaction (HCI) are

essential for understanding how chatbot design (e.g., warmth, competence) influences trust and relationship

quality, moving beyond mere utility.

CONCLUSION

The marketing literature demonstrates extensive chatbot applications across key domains including advertising,

mobile marketing, and branding (Alsharhan et al., 2024). Research in AI advertising provides critical insights

into chatbot functionalities and implementation challenges (Ford et al., 2023), while mobile commerce studies

highlight their positive impact on customer perceptions (Sharma et al., 2024). Particularly in e-service

contexts, chatbots have emerged as valuable tools for improving service quality and operational performance

(Misischia et al., 2022). The proliferation of brand chatbots on social media platforms has further stimulated

research into their branding implications (Chung et al., 2020; Zarouali et al., 2018), with notable success in

luxury sectors where they facilitate personalized shopping experiences (Chung et al., 2020; Zeng et al., 2023),

deliver tailored information (Sangar, 2012), and boost satisfaction (Landim et al., 2022). These developments

coincide with the broader emergence of Generative AI (GenAI) and its diverse marketing applications (Chan &

Choi, 2025).

The Technology Acceptance Model (TAM) remains the predominant theoretical framework for studying

chatbot adoption in marketing (Alsharhan et al., 2024), valued for its robust analytical approach to technology

adoption decisions (Savari et al., 2021). However, contemporary scholarship has identified important

limitations, including its narrow technological focus (Marikyan & Papagiannidis, 2024), structural

oversimplification (Shachak et al., 2015), and methodological constraints (McLean et al., 2011).

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