Modeling Nonusers’ Behavioral Intention towards Mobile Chatbot Adoption: An Extension of the UTAUT2 Model with Mobile Service Quality Determinants

Paraskevi, Gatzioufa; Saprikis, Vaggelis; Avlogiaris, Giorgos

doi:https://doi.org/10.1155/2023/8859989

Human Behavior and Emerging Technologies

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Abstract Introduction Results Discussion Limitations Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 8859989 | https://doi.org/10.1155/2023/8859989

Modeling Nonusers’ Behavioral Intention towards Mobile Chatbot Adoption: An Extension of the UTAUT2 Model with Mobile Service Quality Determinants

Gatzioufa Paraskevi,¹Vaggelis Saprikis,¹and Giorgos Avlogiaris²

Academic Editor: Pinaki Chakraborty

Received06 Jul 2023

Revised05 Oct 2023

Accepted24 Oct 2023

Published13 Nov 2023

Abstract

Artificial intelligence agents (chatbots), which are programs to communicate with users primarily in customer service contexts, are an alternative interaction channel supporting businesses in the digital environment and vital components in customer service. The present empirical paper, which is aimed at identifying and discussing the factors motivating nonusers to adopt the specific technology in mobile contexts, proposes a comprehensive conceptual model, which combines the UTAUT 2 behavioral theory with variables of mobile service quality contexts, such as information quality, privacy concerns, interface, and equipment, as well as trust and mobility factors. Data analysis, based on the partial least squares structural equation modeling (PLS-SEM) statistical method, revealed that performance expectancy, facilitating factors, hedonic motivation, mobility, trust, and service quality positively affect nonusers’ behavioral intention to adopt chatbots. In addition, equipment, interface, and trust have a significant impact on users’ trust in the context of mobile chatbots. Personal data privacy issues also have a negative effect on trust, in contrast to effort expectancy, which positively affects performance expectancy. As mobile service quality factors have not been investigated before in the context of chatbots, the findings of the present research are expected to provide useful insights both to academia and the business industry.

1. Introduction

Artificial intelligence (AI) agents (voice or text), typical of the specific trend, offer individuals the opportunity of direct communication with businesses at any time and day. More specifically, AI agents integrated in marketing strategy have become an efficient tool for customer service, saving money, time, and human resources. They have also offered various benefits ranging from understanding and responding to customer requests [1, 2] to consistent, agile, and effortless friendly service delivery [3]. By simultaneously responding to various requests and a huge number of queries, chatbot use has increased service efficiency and quality [4]. Overall, conversational voice or text marketing implemented via AI agents is a critical communication and interaction channel, which activates consumers, provides necessary personalized information about products or services, and prompts purchases [5]. Business technology giants, such as Amazon, Google, IBM, and Apple, have already integrated AI agents to increase revenues. Amazon’s recommendations and Google’s predictive search are examples of AI adoption technologies not widely known to many, despite their frequent use [6]. Notably, the global chatbot market size is forecast to grow from 190.8 million USD in 2016 to 1.25 billion in 2025, with the highest level of acceptance in customer service and customer relationship management [7].

In addition, as the use of mobile phones (smartphones), which have become an integral part of daily life, especially for young people, has considerably increased, the investigation of AI adoption factors has become more imperative than before, prompting businesses to foster consumer attraction at any time and place [8]. It is worth noting that since 2010, communication carried out via smartphones and messenger apps has offered online businesses significant opportunities to increase revenues and promote interaction with customers [9].

In this framework, the investigation of the adoption factors and use of AI agents as an alternative communication channel has been variously researched in healthcare [10, 11], financial services [12, 13], social media [14], the tourism industry [15, 16], customer service [3, 17–21], mobile commerce [22], the business industry [23, 24], insurance [25, 26], and education [27, 28].

The present research is an exploratory approach to the factors motivating nonusers’ adoption of AI agents on smartphones. More specifically, it is aimed at contributing to understanding the factors affecting chatbot nonusers to adopt the specific leading innovation technology. It is worth noting that in popular online academic databases, such as Science Direct, Emerald, Taylor and Francis, Elsevier, Research Gate, Wiley, IEEE Explore, ACN Digital Libraries, and Google Scholar, the factors affecting the adoption and use of AI agents among nonusers were investigated only in 16 empirical surveys over the last 6 years. As mobile service quality variables have been examined in a mobile context but have not yet been tested in the context of chatbots, the present research provides valuable information on the specific topic and is the first to explore variables, such as information quality, privacy concerns, interface, and equipment, in terms of their effect on the preadopters’ intention to adopt and use AI agents.

In detail, the present empirical research is organized as follows: the first section discusses the increasing use of AI agents and investigates the relevant factors affecting nonusers’ behavioral intention to adopt chatbots, by employing the UTAUT 2 theory developed by Venkatesh et al. [29], in combination with variables of mobile service quality, namely, information quality, interface, privacy concerns, and equipment, accompanied with trust and mobility variables to explore nonusers’ behavioral intention to adopt chatbots. The second part includes a theoretical overview of the extant literature, and the third part is a discussion of the theoretical model followed by the formulation of the relevant research hypotheses. The fourth and fifth parts describe the research methodology and analysis, respectively, whereas the sixth part includes a detailed discussion of the outcomes. Finally, the last part includes a discussion of the research conclusions as well as the theoretical and practical implications, the potential limitations, and suggestions for future research.

2. Theoretical Background

In recent years, there has been growing interest in text and text-to-speech artificial intelligence (AI) agents to improve customer service and offer benefits to modern businesses. AI agents have been variously defined as follows: (a) “software agents that facilitate automated conversation through natural language processing” ([30], p.1); (b) “natural language processing systems acting as a virtual conversational agent mimicking human interactions” ([31], p.4).

They have been incorporated in business digital marketing strategies and perceived as an essential additional communication and interaction channel [32], answering to customer requests and questions on a 24-hour basis and, thus, saving human resources and money [18]. Hlee et al. [33] also hold that, in customer service, users’ behavior towards adopting AI agents is significantly affected by both emotional and functional interaction features.

Notably, in modern times, the significance of AI agents has been enhanced by the easy and convenient use of smartphones for all online interactions and communication with businesses [34] despite the privacy and security issues, which have been generally raised, as various mobile apps request disclosure of users’ personal data [35]. AI agents, used as an innovative and effective communication channel in online environments, have offered great benefits in various fields. Melian-Gonzalez et al. [36] emphasized the significance of AI agents in the tourism industry, whereas Fryer et al. [27] and Quah and Chua [37] in education settings and banking, respectively. In addition, the use of AI agents was mostly highlighted in the healthcare industry [10, 38, 39], the social media [14], customer service [3, 18], and online shopping using mobile phones [22].

The present research is aimed at contributing to the ongoing research investigating the factors affecting the behavioral intention to adopt and use AI agents in customer service, which have already been researched in previous studies focused on relevant AI agent areas and applications, with special emphasis on chatbot use in the business industry. In detail, customer service, a most common and popular research topic [17, 18, 20, 40], was examined in qualitative or quantitative surveys and, based on theoretical behavioral models, was explored in terms of the factors driving users and/or nonusers to interaction and customer service via AI agents. In addition, the contribution and innovative approach of the present research involves the integration and examination of factors related to mobile service quality. These factors, namely, quality of information, privacy issues, interface, and mobile network equipment, examined for the first time in the chatbot intention literature, promote a deeper understanding of mobile service quality variables, which, combined with UTAUT 2 factors, as well as mobility and trust, will offer scholars and practitioners a profound insight into further research and improvement of mobile customer service.

3. Conceptual Model and Research Hypotheses

The proposed conceptual model (Figure 1), based on the unified theory of acceptance and use of technology 2 (UTAUT 2) by Venkatesh et al. [29] and including additional variables, is aimed at investigating the factors affecting behavioral intention towards chatbot adoption. In detail, it comprises three groups of variables: (i) UTAUT 2 variables, (ii) mobile service quality variables, and (iii) trust and mobility variables.

The UTAUT 2 approach, which was preferred to other behavioral models, is the newest, well-known, and widely applied behavioral paradigm, based on theories, such as the technology acceptance model (TAM), the theory of reasoned action (TRA), the theory of planned behavior (TPB), and the diffusion of innovations (DOI) theory. According to Diekmann and Theuvsen [41], UTAUT 2 is a model suggested for technology nonusers. The specific model is suitable to investigate the factors motivating nonusers’ chatbot adoption in mobile contexts as it is considered a better explanatory intention model than other relative paradigms by providing a more comprehensive approach to new technology acceptance [42].

The proposed model was also based on relevant research carried out by Lu et al. [43] and Stiakakis et al. [44], which focus on the quality of mobile contexts and the underlying determinants, given that nowadays smartphones are used as major and fundamental tools for online transactions. Stiakakis et al. [44] discussed the impact of mobile-related dimensions by grouping them into 3 major categories comprising mobile service (m-service) quality. In addition, the determinants of the present research drew on Akter et al. [45] and Ozer et al. [46], who discussed the factors affecting service quality.

Finally, trust and mobility factors were also added to the proposed conceptual model to offer a much more comprehensive approach to the examined topic. Similarly, various other researchers, such as Oliveira’s et al. [47], have already utilized the same approach to their empirical studies.

Figure 1 provides a detailed overview of all variables examined in the proposed model.

3.1. UTAUT 2 Variables

3.1.1. Behavioral Intention

Intention to use has been found to have a very significant effect on individuals’ attitudes towards new technologies [29, 48, 49]. Behavioral intention is defined as the “degree to which a person has formulated conscious plans to perform or not perform some specified future behavior” ([50], p. 214). Human behavior is affected by individuals’ intention to develop a specific attitude [51]. Accordingly, the proposed conceptual model employed behavioral intention based on Venkatesh et al. [29], who describe it as “a person’s subjective probability that he/she will adopt and use chatbots through his/her smartphone.”

3.1.2. Performance Expectancy

Performance expectancy is “the degree to which using a technology will provide benefits to consumers in performing certain activities” ([29], p.159). In the literature of the behavioral intention to adopt and use chatbots, the specific variable has a significant effect in combination with other predictors of the UTAUT and UTAUT 2 theory. The positive effect of the variable on the intention to use was demonstrated by a number of researchers, such as Kuberkar and Singhal [52], Balakrishnan et al. [17], and Melian-Gonzalez et al. [36]. Based on the assumption that nonusers are expected to use chatbots when they are confident that they will have a positive effect, the following hypothesis is formulated:

H1: performance expectancy (PE) positively affects behavioral intention to adopt chatbots.

3.1.3. Effort Expectancy

Effort expectancy is “the degree of ease associated with consumers’ use of technology” ([29], p. 159). Thus, if consumers consider a specific technology easy to use, they perceive it as more useful and valuable [53]. The specific variable, commonly examined in a large number of studies investigating chatbots, was found to have a positive effect on the intention to use [17, 52, 54]. The effect of effort expectancy on performance expectancy was also found in the social (s)-commerce and m-commerce literature [55–57]. Thus, the following hypotheses are made:

H2a: effort expectancy positively (EE) affects behavioral intention to adopt chatbots.

H2b: effort expectancy positively (EE) affects Performance expectancy (PE).

3.1.4. Social Influence

Social influence is the “degree to which individuals perceive that important others believe they should use a new system” ([49], p. 451). Familiar groups, such as family and relatives, friends, colleagues, and fellow students, who influence others to adopt a technology, play a major role in the intention to adopt a new technology [58]. Overall, various studies have confirmed the impact of the specific variable on the behavioral intention to use chatbots [59, 60]. Thus, the following hypothesis is formulated:

H3: social influence positively (SI) affects behavioral intention to adopt chatbots.

3.1.5. Facilitating Conditions

Facilitating conditions are defined as the “degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system” ([49], p. 453). This implies that when individuals are competent users of smartphones and applications, they are capable of interacting with chatbots. Kuberkar and Singhal [52] adopted UTAUT 2 and suggested that facilitating conditions positively influence the intention of nonusers to use chatbots. Thus, the following hypothesis was made:

H4: facilitating conditions (FC) positively affects behavioral intention to adopt chatbots.

3.1.6. Hedonic Motivation

Hedonic motivation involves individuals’ willingness to engage in a new technology that is expected to induce pleasant experiences, as suggested by Venkatesh et al. [29]. In the case of mobile contexts, the specific variable was found to determine customers’ decision to use e-banking [61]. Research on the intention to use and adopt chatbots demonstrated the value of hedonic motivation [36, 60, 62]. Thus, the relevant hypothesis is formulated as follows:

H5: hedonic motivation (HM) positively affects behavioral intention to adopt chatbots.

3.2. Mobile Service Quality Variables

Mobile service quality has been variously investigated [43, 45, 63–65]. The present research, which explores mobile service quality variables, was based on Stiakakis and Georgiadis [64] and Lu et al. [43], who distinguish three categories of mobile service quality. Two of these categories, namely, interaction quality and environment quality, were congruent with the present research purpose, whereas the third category, outcome quality, was rejected as it addresses chatbot users. The variables of the specific two categories, which were assumed to best fit the purpose and type of the research carried out, generated meaningful and consistent results.

3.2.1. Information Quality

Interaction quality includes, among others, the quality of information offered to consumers by m-service providers [43]. E-commerce research demonstrated the significance of information during interaction with companies on service websites [66, 67]. According to Stiakakis and Georgiadis [64], information quality in mobile service environments, which is a sub-dimension of interaction quality, implies accurate and up-to-date information delivery to customers. In addition, the significance of mobile service information was emphasized in other surveys, such as Vlachos and Vrechopoulos [65], Tan and Chou [68], and Chae et al. [69], who suggested that accurate and up-to-date information provided by chatbots on smartphones positively affects intention to use. Therefore, it is hypothesized that

H6: information quality (IQ) positively affects behavioral intention to adopt chatbots.

3.2.2. Privacy Concerns

Privacy concerns have been defined as the users’ anxiety of managing personal information online [70]. In online contexts, in which users tend to disclose personal information and data, privacy concerns involve poor management or abuse of personal information [71]. In the context of e-commerce and online customer service, users share sensitive personal and financial data when making purchases or searches [72]. Research on the intention to use chatbots identified privacy concerns as negatively affecting intention [73, 74]. In addition, privacy concerns negatively affect user trust [26]. Thus, the following hypotheses were made:

H7a: privacy concerns (PC) negatively affect behavioral intention to adopt chatbots.

H7b: privacy concerns (PC) negatively affect trust (ΤR).

3.2.3. Interface

Context quality, a dimension which positively affects service quality, was widely researched in the literature for both overall service quality [67, 75–77] and mobile service quality [43, 69]. Tarasewich et al. [78] demonstrated that smartphone design was critical to task completion. Interface is a context variable related to navigation, aesthetics, design, music, and display of objects in mobile devices [43, 64, 78]. Zhao et al. [79] highlighted the significance of interface in understanding and evaluating overall mobile quality service. Research demonstrated that interface affects mobile user trust [80, 81], since well-designed interfaces allow for interactivity and easy navigation and promote trust [81, 82], thus resulting in mobile chatbot adoption and trust. Thus, the following hypotheses are formulated:

H8a: interface (INTF) positively affects behavioral intention to adopt chatbots.

H8b: interface (INTF) positively affects trust (TR).

3.2.4. Equipment

Equipment involves two dimensions, wireless telecommunication delivered by service providers and smartphones [43, 64], which contribute to the assessment of mobile service quality. As users must have access to stable mobile networks and stable connections, as well as to fast, uninterrupted responses to requests [69], mobile telecom service providers’ equipment and customers’ devices are critical to enabling users to adopt and trust chatbots. Thus, the following hypotheses are formulated:

H9a: equipment (EQP) positively affects behavioral intention to adopt chatbots.

H9b: equipment (EQP) positively affects trust (TR).

3.3. Additional Variables

3.3.1. Trust

Trust involves being willing to have confidence in others, in mediated or interactive processes [83], and implies integrity, reliance, and beneficial influence. Trust becomes more significant in settings of high uncertainty, such as digital environments [84]. Corritore et al. [85] suggested that the major determinants of trust in digital environments are perceptions of reliability, ease of use, and risk of interaction. There have also been several empirical studies investigating the positive effect of trust on the behavioral intention to use chatbots [26, 52]. The above considerations contribute to the following hypothesis:

H10: trust (TR) positively affects behavioral intention to adopt chatbots.

3.3.2. Mobility

A critical dimension of service quality on mobile phones is mobility, namely, access to information, communication, and services from any place using mobile devices and wireless networks beyond the time limit [86, 87]. In the relevant literature, mobility was demonstrated to affect intention to use. The impact of mobility has been studied in the context of mobile payment services [88, 89] and mobile shopping [88, 90]. More specifically, it was argued that mobility affects customers’ behavioral intention to use smartphones. Moreover, Almahri et al. [54] assumed that there is a positive and significant effect of mobility on the intention to use m-commerce. Thus, the following hypothesis is made:

H11: mobility (MOB) positively affects the intention to adopt chatbots.

4. Research Methodology

4.1. Variable Operationalization

Based on an extensive review of the relevant literature, the research variables were measured on a five-point Likert scale (Table 1). In detail, 4 items were used to measure interface and effort expectancy; 3 items for performance expectancy, social influence, facilitating conditions, behavioral intention, trust, mobility, information quality, and privacy concerns; and 2 items for equipment and hedonic motivation.

4.2. Data Collection and Sampling

Data collection was based on an e-questionnaire to be answered via Google Forms from September to October 2022. The e-questionnaire was first drafted in English, and afterwards, it was translated into Greek. It was also followed by a pilot study on a sample of 20 people to discover possible failures or inaccuracies and test item and variable validity and reliability.

Data collection was based on convenience sampling and focused on students (undergraduate, postgraduate, and doctoral candidates) of 3 Greek public universities, who were chatbot nonusers. University students, often used in primary research sampling, enable making valuable findings [103–106]. Notably, students have been widely used in research investigating the intention to use chatbots [28] as they are representative for their strong tendency to adopt new technologies [107]. Overall, young people tend to use mobile phone applications and other modern technologies more frequently to interact with businesses [108].

The research was organized by sending a hyperlink to 1,280 students on the university’s asynchronous e-class student platform from September to October 2022. The e-questionnaire was accompanied by a short text explaining the purpose of the research. During the first month of the research, 285 individuals answered the questionnaire, and after a follow-up notification, the total number of responses was 411. To test for nonresponse bias between the first and second-round response groups, the Kolmogorov-Smirnov test was applied. The sample distributions did not exhibit any statistical difference; thus, there was no nonresponse bias [57]. Therefore, the final sample of the present empirical study includes 411 answers, accounting for 32.1% of the response rate, which is acceptable [109].

As regards the participants’ demographic information, Table 2 demonstrates that the majority of the respondents are female (57.2%), whereas male respondents are 42.8%. In terms of age, 45% of them are 18-21 years old, and 57.9% are aged 22-25.

4.3. Data Analysis

To investigate the conceptual research model and its hypotheses (Figure 1), the structural equation modeling (SEM) using maximum likelihood estimation was carried out. SEM combines the attributes of a measurement model and a structural model; thus, it is considered a proper method for examining the underlying hypothesized structural relations between multiple independent and dependent variables, using estimated regression parameters. It is primarily applied when the scope of the study is the validation of an established theory [110]. Its statistical objective is to estimate model parameters that minimize the differences between the observed sample covariance matrix and the covariance matrix estimated after the revised theoretical model is confirmed [111]. IBM SPSS Amos 24 version software was used to apply SEM.

Regarding measurement, the model enables the evaluation of the latent variables reflecting constructs of interest. Remarkably, the dimensions for all twelve (12) constructs were first measured via confirmatory factor analysis (CFA). In addition, they were also measured for reliability and convergent validity purposes. Based on the extant literature, convergent validity uses two basic recommended standards, among others, for evaluating the measuring model: (1) all indicator factor loading values should be more than 0.4 [111]; (2) composite reliability (CR) should exceed 0.6 [112], when the specific measurement scale is applied. Finally, factor analysis was applied utilizing principal component analysis (PCA) and orthogonal rotation (VARIMAX). The factor analysis was carried out to identify the usual method bias and check whether data variance can be principally related to a single factor [113].

Subsequently, when the constructs reached the prerequisite measurement standards, the relationships between the constructs were calculated. This establishes the structural model, which is applied to test the strength and direction of the relationships among the theoretical constructs. To estimate the overall goodness-of-fit model, a combination of measures should be applied. In detail, the chi-square/df ratio should be lower than 5 [114]; the goodness-of-fit index (GFI), the comparative fit index (CFI), the normed fit index (NFI), the incremental fit index (IFI), and the Tucker-Lewis index (TLI) values should exceed 0.90 [115], and the root mean square error of approximation (RMSEA) should be lower than 0.08 [116].

5. Results

This section discusses the results of the SEM procedure used to investigate the hypothesized variable relationships, thus providing an examination of the measurement and structural models. The confirmatory factor analysis (CFA) model involves 36 items that represent the 12 underlying constructs (Table 3). More specifically, factor analysis that uses PCA and VARIMAX was employed to examine the validity of the variables to group measurement items into latent factors and calculate factor loadings. To investigate the suitability of the data for factor analysis, however, various measures were applied as well. In detail, Bartlett’s tests of sphericity (, ) proved that the correlation matrices had significant correlations among the variables. The Kaiser-Meyer-Olkin (KMO) result was -0.888, and the measurement of sampling adequacy (MSA) ranged from 0.723 to 0.949, indicating that both values were acceptable. The MSA values were all higher than 0.50 [117]. The results showed the suitability of factor analysis. The values of all factor loading indicators ranged from 0.469 to 0.903 (Table 3), exceeding the threshold [117, 118], whereas composite reliability (CR) also exceeded 0.6 [112], ranging from 0.672 to 0.930 (Table 3). Consequently, convergent validity was satisfied. Moreover, to investigate the reliability of the questionnaire items, Cronbach’s alpha test was used. The results (Table 3), ranging from 0.731 to 0.954, exceeded the threshold of 0.7 [117]. Finally, the twelve (12) latent factors can interpret 81.780% of the variance of the measurement items (Table 3).

As regards the structural model, the results (Table 4) demonstrate a good model fit. The model’s overall goodness-of-fit was assessed using a combination of measures. The data fit the model well, as suggested by these measures, the recommended values of which are shown in Table 4. Thus, an adequately fitted model should have a chi-square/df ratio less than 5 [114]; the comparative fit index (CFI) and incremental fit index (IFI) values should be greater than 0.90 [115], and a root mean square error of approximation (RMSEA) should be less than 0.08 [116]. The structural model, illustrated in Figure 2, was suitable (Table 4) as the basic model indicators are satisfactory according to the recommended values in the literature. Moreover, the strength and direction of the relationships between the theoretical constructs of the structural model were investigated. The full model results are demonstrated in Table 5 and Figure 2, which demonstrate the path diagram with the occurring standardized regression coefficients, presenting the direction and magnitude of relationships among variables. The structural model reveals that 10 out of the 15 hypotheses were confirmed (Table 5), and five, namely, effort expectancy, social influence, privacy concerns, interface, and equipment (H2a, H3, H7a, H8a, and H9a) were rejected. On the contrary, as anticipated, performance expectancy has a positive impact on behavioral intention (H1; , ), and effort expectancy has a positive impact on performance expectancy (H2b; , ). In addition, facilitating conditions and hedonic motivation have a positive impact on behavioral intention (H4; , and H5; , , respectively). Information quality has a positive impact on behavioral intention (H6; , ), and privacy concerns have a negative impact on trust (H7b; , ). Moreover, interface has a strong positive impact on trust (H8b; , ), and equipment has a positive impact on trust (H9b; , ). Finally, trust has a positive impact on behavioral intention (H10; , ), and mobility demonstrates to have a positive impact on behavioral intention (H11; , ). Overall, 49% of the variance in behavioral intention is explained by all involved factors (, Table 5).

6. Discussion

Data analysis demonstrated that most variables of the UTAUT 2 theory were established, except for effort expectancy and social influence, which proved to be nonstatistically significant for behavioral intention to adopt chatbots (H2a and H3). In the extant literature, effort expectancy was found not to affect the intention to use chatbots [14, 24, 119]. Students, as early adopters of new and AI technologies, find it easy to use chatbots, especially on smartphones. However, despite its significance as regards new technology acceptance, ease of use is not critical to developing attitudes [120]. Effort expectancy involves specific learning curve technologies, which do not include chatbots; their use is not considered difficult, as it implies simply typing questions on mobile applications [119]. However, effort expectancy is found to have a positive effect on performance expectancy, which, thus supports H2b hypothesis of the model.

Social influence did not affect the adoption of chatbots (H3) in customer service, which was also demonstrated by Balakrishnan et al. [17]. This finding is supported by the theory of Gupta et al. [121], who argue that a powerful information system (IS) is based on social influence rather than cognitive response. The use of online chats is a daily routine for most people who visit a website or social networking platforms using a mobile application. This implies familiarity and, thus, no impact on the intention to use chatbots.

Regarding the effect of performance expectancy on the behavioral intention to use chatbots (H1), it was demonstrated that it had a positive impact on the behavioral intention to adopt and use chatbots [17, 52, 54, 59, 60, 119]. AI agents enable users to increase productivity, enjoy better and more effective service delivery, and receive responses to requests saving time and effort.

Facilitating conditions (smartphone device and knowing how to use applications) positively affect the intention to use chatbots (H4). The present research endorses this positive relationship in line with similar research carried out on chatbots, such as Mogaji et al. [59], Kuberkar and Singhal [52], Trapero et al. [60], Balakrishnan et al. [17], and Sitthipon et al. [11]. Notably, Sitthipon et al. [11] highlighted that facilitating factors have the highest predictive power in the behavioral intention to use health chatbots.

In addition, hedonic motivation was found to have a positive impact on the intention to adopt chatbots (H5). When users “talk” to chatbots, they tend to ask questions they would not often ask a human personal assistant. Thus, for a pleasant user experience, chatbots frequently include entertaining elements or games in their programming environments [122]. The specific assumption is also corroborated by De Cicco et al. [123], Nyagadza et al. [13], Kasillingam [124], Selamat and Windasari [24], and Melian-Gonzalez et al. [36].

Trust positively affects the intention to use chatbots (H10). The specific hypothesis was substantiated in the proposed research model and is congruent with similar research carried out by Cardona et al. [26], Coopamooto et al. [73], Kassilingam [124], Kuberkar and Singhal [52], Murtarelli et al. [40], and Pillai and Sivathanu [125]. Trust is a key factor affecting the intention to use AI technologies, such as chatbots [126].

As regards mobility, it was found that mobility positively affects the behavioral intention to adopt chatbots, thus establishing hypothesis H11 of the proposed conceptual model. Notably, in the digital environment, mobility is particularly valued and is an integral component of chatbots. In several studies, mobility is a variable with a positive effect on the behavioral intention to adopt and use in m-commerce, on account of the easy access to information and communication via wireless networks, beyond time and place limits [98, 101, 127].

Finally, the impact of mobile-related variables on the intention to use chatbots was also established. To our knowledge, it is the first time the examined variables have been investigated in mobile service contexts in the framework of AI agents’ adoption intention.

In addition, concerns about the privacy of information and personal data in online transactions have been variously raised in the relevant literature [71]. Surprisingly, H7a hypothesis about the negative effect of privacy issues was not established in the survey, thus demonstrating that young people are not concerned with privacy violation caused by chatbot use. However, the research highlighted that the negative effect of privacy issues in terms of user trust was established (H7b). As demonstrated, when potential users do not trust this new technology, the intention to adopt is negatively affected. The specific considerations are emphasized in several surveys on chatbot use [20, 26, 73, 74, 93]. When users are uncertain of information privacy in their interaction with chatbots, there is no intention to adopt and use.

Notably, information quality integrated in mobile interaction quality involves offering accurate and correct information to users [68]. Interaction quality also includes information quality and implies the degree of effective communication of m-service providers with customers to enable problem-solving [63]. Information quality was found to significantly affect customer satisfaction and loyalty to a company [69], which was also established in the specific research (H6).

In terms of equipment, namely, the devices used for communication with AI agents and mobile network stability, as well as in terms of response speed, the relevant research demonstrated that these are significant components of mobile service perceived quality [43, 64, 69, 77], which affect trust [91]. Equipment was not found to positively affect the intention to use chatbots (H9a). As telecommunication network systems and bandwidth have considerably improved, users do not assume that this is a problem. As they can always have stable connections, fast speed, and network stability, chatbot adoption is not affected, unless it is examined in terms of trust (H9b). When users trust new technology, equipment has a positive effect on adoption.

With regard to interface and its effect on mobile communication quality, it was demonstrated that smartphone user interface (UI), good navigation, and unbroken display affect perceived service quality and task completion as well as user trust [43, 64, 78, 91]. In the present research, interface did not positively affect the intention to use, which rejects hypothesis H8a. However, it affects intention indirectly, when combined with trust, which, thus, confirms hypothesis H8b. Nowadays, as interface quality is enhanced and there are no significant differences among chatbot applications, users are more familiar with all interface types. Moreover, chatbot developers follow high-quality UI standards to offer the best user experience.

6.1. Theoretical Implications

The present research contributes to the extant literature of investigating the behavioral intention to adopt and use AI agents on smartphones. To our knowledge, no previous research has integrated mobile service quality variables in behavioral variables, as those suggested by UTAUT 2, and examined them in terms of their effect on the intention to adopt chatbots and trust. Despite the fact that several model variables (mobility, trust, and privacy concerns) were examined in the context of using chatbots, no research has investigated the combination of all the specific variables in mobile contexts. The positive effect of effort expectancy on performance expectancy in mobile chatbot contexts is a fundamental finding of the present research.

In addition, a small number of studies have been conducted only on nonusers of AI agents. The majority of studies involve AI users or comparison of users and nonusers. Notably, it is the first time that relationships between mobile phone service quality and intention to adopt have been investigated and established in a sample of nonusers of chatbots. Thus, the specific theoretical model can be employed to research other topics, such as m-commerce and s-commerce.

6.2. Managerial and Practical Implications

The findings of the present research corroborate the theoretical background and provide valuable managerial insights into the relevance of chatbots in the business industry. Businesses, realizing the importance of fast and immediate service delivery, have adopted chatbots as communication tools, which contribute to enhancing customer satisfaction and loyalty and create a competitive advantage to achieve a higher market share. By understanding the advantages deriving from the adoption and use of chatbots, enterprises are aimed at service quality. The growing use of smartphones, especially among young people, requires that marketing policies make efforts to improve mobile features and information, such as interface design and information quality. Information privacy and perceived trust in interaction with chatbots are also significant, as they promote intention to use, on which managers should focus. As customers provide information to enterprises via conversational commerce, chatbots are used as tools for gathering and using personal information to the benefit of enterprises. The less effort users make to interact with chatbots, the higher the impact of perceived performance. Thus, to motivate nonusers to communicate with companies and achieve good service delivery via chatbots, managers should seriously consider chatbot ease of use, possibly by improving algorithms and quality of communication.

The increasing use of chatbots can help marketers design and promote relevant messages and delivery methods on mobile phones to enable offering customers a pleasant user experience and satisfaction and stimulate the continuous use of this technology and the creation of added value for enterprises.

Overall, the results of the present research can contribute to stimulating managers, marketers, and businesses to formulate mobile-related policies and strategies and provide valuable guidelines about the strategy and policy design of modern organizations based on the great potential of chatbots.

7. Limitations and Future Research

Despite the significant findings of the present research, there are several limitations to be considered. First, as the study was carried out in only 3 Greek universities, future research could focus on comparing relevant findings from other Greek or international universities and explore potential similarities or differences. Further research could also be conducted on a sample of different age groups to investigate the impact of the examined factors. Second, the present research used only a sample of nonusers of AI agents. Thus, it would be interesting to carry out a comparative study on a sample of both users and nonusers of chatbots. Additionally, apart from mobility and trust, other variables, such as satisfaction with using chatbots and recommendation, could be considered in future research among chatbot users.

Finally, in the framework of the specific conceptual model, further considerations should be made, such as other facilitating factors for the intention to adopt and use chatbots in mobile contexts (anthropomorphic elements, perceived intelligence, attitude, degree of innovation, and problem-solving) as well as constraints to their behavioral intention (technology anxiety, low information reliability, complexity, and compatibility).

8. Conclusion

AI agents or chatbots are a promising leading innovation technology applied in customer service, which has reshaped communication and interaction in e-/m-commerce. Focusing on smartphones and chatbot use quality, the present research explored the motivating factors for nonusers to adopt chatbots on their mobile phones. By using the UTAUT 2 theory combined with variables of mobile service quality and additional variables, such as trust, mobility, and privacy concerns, the research attempted to investigate the impact of these variables on behavioral intention to adopt chatbots. The research outcomes demonstrate the impact of performance, facilitating factors, hedonic motivation, mobility, trust, equipment, interface, and information quality on the behavioral intention to adopt chatbots. Social influence and effort expectancy were not found to influence intention to use. Overall, the research contributes to providing valuable information to the relevant literature and generating significant advantages both for the academia and the business industry.

Data Availability

The data will be available on request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This article was funded by the University of Western Macedonia in Greece.

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Copyright © 2023 Gatzioufa Paraskevi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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