# Exploring Determinants That Influence the Usage Intention of AI-Based Customer Services in the UAE

Nasser Abdo Saif Almuraqab, Sajjad M Jasimuddin, Fateh Saci

► To cite this version:

Nasser Abdo Saif Almuraqab, Sajjad M Jasimuddin, Fateh Saci. Exploring Determinants That Influence the Usage Intention of AI-Based Customer Services in the UAE. Journal of Global Information Management, 2024, 32 (1), pp.1 - 16. 10.4018/jgim.343308. hal-04584633

HAL Id: hal-04584633

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Submitted on 23 May 2024

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Distributed under a Creative Commons Attribution 4.0 International License
# Exploring Determinants That Influence the Usage Intention of AI-Based Customer Services in the UAE

Nasser Abdo Saif Almuraqab, Sajjad M Jasimuddin, Fateh Saci

► To cite this version:

Nasser Abdo Saif Almuraqab, Sajjad M Jasimuddin, Fateh Saci. Exploring Determinants That Influence the Usage Intention of AI-Based Customer Services in the UAE. Journal of Global Information Management, 2024, 32 (1), pp.1 - 16. 10.4018/jgim.343308. hal-04584633

HAL Id: hal-04584633

https://hal.science/hal-04584633v1

Submitted on 23 May 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Exploring Determinants That Influence the Usage Intention of AI-Based Customer Services in the UAE

Nasser Abdo Saif Almuraqab, University of Dubai, UAE

Sajjad M. Jasimuddin, Kedge Business School, France*

ID https://orcid.org/0000-0003-2627-9241

Fateh Saci, University of Nimes, France

ID https://orcid.org/0000-0003-2519-3761

## ABSTRACT

Artificial intelligence (AI) is revolutionizing the way customers interact with organizations and companies. There is a lack of research into AI-enabled customer experiences. Hence, this study aims to use the relevant literature to propose a conceptual framework for how the integration of AI in customer service can lead to an improved AI-enabled customer experience. Five propositions drawn from the reviewed literature present the main factors needed to ensure end users’ acceptance of AI customer service in the United Arab Emirates (UAE). Our theoretical model extends the trust-commitment theory and service quality model, and incorporates perceived problem-solving ability, to address these factors and thereby guide the successful implementation of AI based customer service projects. The paper will help in understanding the key issues surrounding AI customer service applications that may support successful operations.

## KEYWORDS

AI, Convenience, Customer Service, Perceived Sacrifice, Service Quality, Trust Commitment Theory

Technology is rapidly changing the nature of service, as well as customers’ service experiences and interactions with service providers (Bitner, 2017; van Doorn et al., 2017; Islam et al., 2015; Islam et al., 2017; Jasimuddin et al., 2017; He et al., 2021; Rahman et al., 2022a; Rahman et al., 2022b). Customers and companies seeking operational efficiency are driving the current need for artificial intelligence (AI) in service (Huang & Rust, 2018; Wirtz et al., 2018). A chatbot, for example, may help a business minimize customer queues and wait times, as well as personnel expenditures (Ostrom et al., 2019; Turel & Connelly, 2013; Xu, 2016). However, AI service robots affect consumers’ relationships with service providers because of changes in human touch points along the customer journey and perceptions of robot social presence (van Doorn et al., 2017). The dilemma for service companies is not whether to adopt AI, but rather, how to leverage its ability to increase the efficiency

DOI: 10.4018/JGIM.343308

*Corresponding Author

This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.
# Exploring Determinants That Influence the Usage Intention of AI-Based Customer Services in the UAE

Nasser Abdo Saif Almuraqab, University of Dubai, UAE

Sajjad M. Jasimuddin, Kedge Business School, France*

ID https://orcid.org/0000-0003-2627-9241

Fateh Saci, University of Nimes, France

ID https://orcid.org/0000-0003-2519-3761

## ABSTRACT

Artificial intelligence (AI) is revolutionizing the way customers interact with organizations and companies. There is a lack of research into AI-enabled customer experiences. Hence, this study aims to use the relevant literature to propose a conceptual framework for how the integration of AI in customer service can lead to an improved AI-enabled customer experience. Five propositions drawn from the reviewed literature present the main factors needed to ensure end users’ acceptance of AI customer service in the United Arab Emirates (UAE). Our theoretical model extends the trust-commitment theory and service quality model, and incorporates perceived problem-solving ability, to address these factors and thereby guide the successful implementation of AI based customer service projects. The paper will help in understanding the key issues surrounding AI customer service applications that may support successful operations.

## KEYWORDS

AI, Convenience, Customer Service, Perceived Sacrifice, Service Quality, Trust Commitment Theory

Technology is rapidly changing the nature of service, as well as customers’ service experiences and interactions with service providers (Bitner, 2017; van Doorn et al., 2017; Islam et al., 2015; Islam et al., 2017; Jasimuddin et al., 2017; He et al., 2021; Rahman et al., 2022a; Rahman et al., 2022b). Customers and companies seeking operational efficiency are driving the current need for artificial intelligence (AI) in service (Huang & Rust, 2018; Wirtz et al., 2018). A chatbot, for example, may help a business minimize customer queues and wait times, as well as personnel expenditures (Ostrom et al., 2019; Turel & Connelly, 2013; Xu, 2016). However, AI service robots affect consumers’ relationships with service providers because of changes in human touch points along the customer journey and perceptions of robot social presence (van Doorn et al., 2017). The dilemma for service companies is not whether to adopt AI, but rather, how to leverage its ability to increase the efficiency

DOI: 10.4018/JGIM.343308

*Corresponding Author

This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.
and efficacy of the services that they deliver (Huang & Rust, 2018; Ostrom et al., 2019; Rust, 2019; Wirtz et al., 2018; Varsha et al., 2021; Chen et al., 2023).

AI is now being used in service areas, such as education, finance, health care, commerce, and transportation. More investigation of AI as an end-to-end service solution rather than just a component of a company's customer service system (Xiao & Kumar, 2019) is needed. Better knowledge of consumers' ideas, feelings, and behavior when they are exposed to AI-enabled frontline service interactions is also required (Ostrom et al., 2019). We analyzed the relevant literature for this study, proposed a framework, and attempted to fill this gap. In the following discussion, we first explore the theory on the use of AI in customer service and AI problem-solving capabilities in service contexts. We subsequently describe our theoretical framework for AI use in customer service. We then present three field investigations that support our theoretical framework. We conclude the study with a discussion of the broad theoretical and managerial implications of our findings, as well as future research objectives.

# LITERATURE REVIEW

## The Notion of AI

Xu et al. (2020) defined AI in customer service as the following: A technology-enabled system for analyzing real-time service situations using data obtained from digital and/or physical sources in order to deliver individualized recommendations, alternatives, and answers to customers' inquiries or issues, even those that are extremely complicated. We used client and service staff literature (Lu et al., 2020) to demonstrate the many sorts of AI-enabled service encounters and their interactions with the financial and banking sectors (Foroughi et al., 2019). Ostrom et al. (2019) classified AI-enabled service interactions into three types: AI supported, AI augmented, and AI performed. In AI-supported service interactions, frontline staff execute a service and directly interact with consumers while relying on AI for assistance behind the scenes with decision-making or modification of the service experience in real time, such as the use of AI by physicians to diagnose illnesses. In AI-augmented service interactions, AI interacts directly with consumers or is employed by frontline staff aiding them (rather than behind the scenes), enhancing the typical contact with enhanced information or novel services, such as real-time language translation. In AI-performed service interactions, AI replaces employees by interacting directly with customers to co-create and provide the full-service experience; examples include chatbots used in retail and banking, as well as virtual assistants such as Apple's Siri, Amazon's Alexa, and Google Assistant.

## Artificial Intelligence-Enabled Customer Experience

The total experience a customer has with a store is based on their encounters with and views about the brand (Oh et al., 2012; Verhoef et al., 2009; Tseng et al., 2023). According to Ameen et al. (2021), the application of AI technologies, such as machine learning, natural-language comprehension, and natural-language processing, may assist in analyzing consumer sentiment and feedback at scale, precision, and speed that humans cannot achieve. This statement implies that AI has the potential to become one of the most important tools for retailers to use to continually enhance the customer experience and remain competitive (Newman, 2019). AI technology is frequently employed in retail in tandem with other technologies, such as augmented reality, computer vision-driven picture identification, and predictive inventory (Saponaro et al., 2018). For these technologies to successfully improve customer experiences, a thorough understanding of the consumer, including their preferences and previous experiences, is required. Leveraging AI can assist in expediting this understanding because AI technologies learn how to engage with consumers based on data and customer profiles (Omale, 2019).

A significant problem for customer service providers is to strike a balance between service efficiency and service quality: Both researchers and practitioners highlight the potential benefits of
and efficacy of the services that they deliver (Huang & Rust, 2018; Ostrom et al., 2019; Rust, 2019; Wirtz et al., 2018; Varsha et al., 2021; Chen et al., 2023).

AI is now being used in service areas, such as education, finance, health care, commerce, and transportation. More investigation of AI as an end-to-end service solution rather than just a component of a company's customer service system (Xiao & Kumar, 2019) is needed. Better knowledge of consumers' ideas, feelings, and behavior when they are exposed to AI-enabled frontline service interactions is also required (Ostrom et al., 2019). We analyzed the relevant literature for this study, proposed a framework, and attempted to fill this gap. In the following discussion, we first explore the theory on the use of AI in customer service and AI problem-solving capabilities in service contexts. We subsequently describe our theoretical framework for AI use in customer service. We then present three field investigations that support our theoretical framework. We conclude the study with a discussion of the broad theoretical and managerial implications of our findings, as well as future research objectives.

# LITERATURE REVIEW

## The Notion of AI

Xu et al. (2020) defined AI in customer service as the following: A technology-enabled system for analyzing real-time service situations using data obtained from digital and/or physical sources in order to deliver individualized recommendations, alternatives, and answers to customers' inquiries or issues, even those that are extremely complicated. We used client and service staff literature (Lu et al., 2020) to demonstrate the many sorts of AI-enabled service encounters and their interactions with the financial and banking sectors (Foroughi et al., 2019). Ostrom et al. (2019) classified AI-enabled service interactions into three types: AI supported, AI augmented, and AI performed. In AI-supported service interactions, frontline staff execute a service and directly interact with consumers while relying on AI for assistance behind the scenes with decision-making or modification of the service experience in real time, such as the use of AI by physicians to diagnose illnesses. In AI-augmented service interactions, AI interacts directly with consumers or is employed by frontline staff aiding them (rather than behind the scenes), enhancing the typical contact with enhanced information or novel services, such as real-time language translation. In AI-performed service interactions, AI replaces employees by interacting directly with customers to co-create and provide the full-service experience; examples include chatbots used in retail and banking, as well as virtual assistants such as Apple's Siri, Amazon's Alexa, and Google Assistant.

## Artificial Intelligence-Enabled Customer Experience

The total experience a customer has with a store is based on their encounters with and views about the brand (Oh et al., 2012; Verhoef et al., 2009; Tseng et al., 2023). According to Ameen et al. (2021), the application of AI technologies, such as machine learning, natural-language comprehension, and natural-language processing, may assist in analyzing consumer sentiment and feedback at scale, precision, and speed that humans cannot achieve. This statement implies that AI has the potential to become one of the most important tools for retailers to use to continually enhance the customer experience and remain competitive (Newman, 2019). AI technology is frequently employed in retail in tandem with other technologies, such as augmented reality, computer vision-driven picture identification, and predictive inventory (Saponaro et al., 2018). For these technologies to successfully improve customer experiences, a thorough understanding of the consumer, including their preferences and previous experiences, is required. Leveraging AI can assist in expediting this understanding because AI technologies learn how to engage with consumers based on data and customer profiles (Omale, 2019).

A significant problem for customer service providers is to strike a balance between service efficiency and service quality: Both researchers and practitioners highlight the potential benefits of
client self-service, such as higher time efficiency, lower costs, and improved customer experience (Meuter et al., 2005; Scherer et al., 2015; Abdou & Jasimuddin, 2020; Kamdjoug et al., 2023). Computer-aided systems (CAs), as a self-service technology, promise not only to reduce costs (Gnewuch et al., 2017; Pavlikova et al., 2003) but also to improve service quality and provider-customer interactions. According to studies, CAs may save $1.3 trillion in current worldwide corporate expenditures associated to 265 billion customer service inquiries each year by 30% by shortening response times, freeing up employees for other tasks, and handling up to 80% of common issues (Reddy, 2017b; Maruti Techlabs, 2017). Chatbots alone are predicted to save businesses more than $8 billion per year in customer-supporting expenditures by 2022, a significant rise from the $20 million in estimated savings in 2017. (Reddy, 2017a). CAs thus guarantee corporations' ability to provide consumers with rapid, easy, and cost-effective solutions in the form of 24 x 7 electronic channels (Meuter et al., 2005). Customers often value not just conveniently available and flexible self-service channels, but also customized attention. Thus, enterprises should not completely transition to consumer self-service channels, especially at the start of a relationship with a client (Scherer et al., 2015), because the lack of a personal social actor in online transactions might result in sales loss (Raymond, 2001). CAs, on the other hand, have the ability to actively impact service interactions and to operate as surrogates for service professionals by fulfilling tasks that were previously performed by human service staff and by emulating social actors (Larivière et al., 2017; Verhagen et al., 2014).

Customers, for example, can resort to CAs that are accessible 24 x 7 instead of contacting a call center or writing an email to ask a question or register a complaint. According to Larivière et al. (2017), as the interface between companies and consumers gradually evolves “to become technology dominant (i.e., intelligent assistants acting as a service interface) rather than human-driven (i.e., service employee acting as service interface),” this self-service channel will become increasingly relevant (p 239). Furthermore, modern AI-based CAs have the ability to communicate human traits, such as friendliness, which are thought to be critical in service interactions (Verhagen et al., 2014). As a result, compared with previous online service interactions, CAs can mitigate the loss of human engagement by invoking notions of social presence and personalization. CAs, particularly chatbots, are now a reality in electronic marketplaces and customer service on numerous websites, social media platforms, and messaging applications. For example, between June 2016 and April 2019, the number of chatbots on Facebook Messenger increased from 11,000 to 300,000 (Adam et al., 2021). Although these technological artifacts are becoming more prevalent, past research has shown that chatbots continue to suffer from issues related to their immaturity, resulting in high failure rates and user distrust when it comes to the deployment of AI-based chatbots (Orlowski, 2017).

Furthermore, prior research has shown that human language abilities easily transfer to human-chatbot communication, but significant variations in the content and quality of such talks exist. For example, consumers converse with chatbots for longer periods of time, with a smaller vocabulary, and with more vulgarity (Hill et al., 2015). If consumers regard chatbots differently, their compliance with recommendations and requests made by the chatbot may suffer. As a result, the self-service technology's touted benefits may be called into question. It is therefore necessary to conceive the usage purpose and emphasize the key components in the design of chatbots/CA systems, as well as to comprehend consumers' usage concerns.

# THEORETICAL BACKGROUND AND CONCEPTUAL FRAMEWORK

## Trust Commitment Theory

The relevance of trust and commitment to a connection in the process of creating relationships between buyers and sellers is highlighted by the trust-commitment theory (Morgan & Hunt, 1994; Hasnain et al., 2016; Hasnain & Jasimuddin, 2012). Over the years, the theory has been studied in a wide range of contexts, including online retailing (Elbeltagi & Agag 2016), group buying websites
client self-service, such as higher time efficiency, lower costs, and improved customer experience (Meuter et al., 2005; Scherer et al., 2015; Abdou & Jasimuddin, 2020; Kamdjoug et al., 2023). Computer-aided systems (CAs), as a self-service technology, promise not only to reduce costs (Gnewuch et al., 2017; Pavlikova et al., 2003) but also to improve service quality and provider-customer interactions. According to studies, CAs may save $1.3 trillion in current worldwide corporate expenditures associated to 265 billion customer service inquiries each year by 30% by shortening response times, freeing up employees for other tasks, and handling up to 80% of common issues (Reddy, 2017b; Maruti Techlabs, 2017). Chatbots alone are predicted to save businesses more than $8 billion per year in customer-supporting expenditures by 2022, a significant rise from the $20 million in estimated savings in 2017. (Reddy, 2017a). CAs thus guarantee corporations' ability to provide consumers with rapid, easy, and cost-effective solutions in the form of 24 x 7 electronic channels (Meuter et al., 2005). Customers often value not just conveniently available and flexible self-service channels, but also customized attention. Thus, enterprises should not completely transition to consumer self-service channels, especially at the start of a relationship with a client (Scherer et al., 2015), because the lack of a personal social actor in online transactions might result in sales loss (Raymond, 2001). CAs, on the other hand, have the ability to actively impact service interactions and to operate as surrogates for service professionals by fulfilling tasks that were previously performed by human service staff and by emulating social actors (Larivière et al., 2017; Verhagen et al., 2014).

Customers, for example, can resort to CAs that are accessible 24 x 7 instead of contacting a call center or writing an email to ask a question or register a complaint. According to Larivière et al. (2017), as the interface between companies and consumers gradually evolves “to become technology dominant (i.e., intelligent assistants acting as a service interface) rather than human-driven (i.e., service employee acting as service interface),” this self-service channel will become increasingly relevant (p 239). Furthermore, modern AI-based CAs have the ability to communicate human traits, such as friendliness, which are thought to be critical in service interactions (Verhagen et al., 2014). As a result, compared with previous online service interactions, CAs can mitigate the loss of human engagement by invoking notions of social presence and personalization. CAs, particularly chatbots, are now a reality in electronic marketplaces and customer service on numerous websites, social media platforms, and messaging applications. For example, between June 2016 and April 2019, the number of chatbots on Facebook Messenger increased from 11,000 to 300,000 (Adam et al., 2021). Although these technological artifacts are becoming more prevalent, past research has shown that chatbots continue to suffer from issues related to their immaturity, resulting in high failure rates and user distrust when it comes to the deployment of AI-based chatbots (Orlowski, 2017).

Furthermore, prior research has shown that human language abilities easily transfer to human-chatbot communication, but significant variations in the content and quality of such talks exist. For example, consumers converse with chatbots for longer periods of time, with a smaller vocabulary, and with more vulgarity (Hill et al., 2015). If consumers regard chatbots differently, their compliance with recommendations and requests made by the chatbot may suffer. As a result, the self-service technology's touted benefits may be called into question. It is therefore necessary to conceive the usage purpose and emphasize the key components in the design of chatbots/CA systems, as well as to comprehend consumers' usage concerns.

# THEORETICAL BACKGROUND AND CONCEPTUAL FRAMEWORK

## Trust Commitment Theory

The relevance of trust and commitment to a connection in the process of creating relationships between buyers and sellers is highlighted by the trust-commitment theory (Morgan & Hunt, 1994; Hasnain et al., 2016; Hasnain & Jasimuddin, 2012). Over the years, the theory has been studied in a wide range of contexts, including online retailing (Elbeltagi & Agag 2016), group buying websites
(Wang et al., 2016), brand relationships in online communities (Zhang et al., 2018), social media fan pages (Akrout & Nagy, 2018), and online shipping behavior (Rehman et al., 2019; Hride et al., 2022; Wang et al., 2020). Each study has emphasized the critical importance of trust and relationship commitment in technology-mediated interactions between customers and businesses. Trust is one of the key components of the trust-commitment hypothesis (Morgan & Hunt, 1994). It is also a critical component for the success of automated services because it explains the interaction between humans and automation (Hengstler et al., 2016). According to Wang et al. (2020), privacy is an important component of trust because customers want to have some control over how businesses use their data. Furthermore, past research has demonstrated that trust may change the relationships between multiple elements in the context of AI use, such as service quality and convenience (Siau & Wang, 2018; Ferrario et al., 2020).

## Perceived Convenience

Morganosky (1986) defined service convenience as the capacity to complete a task in the shortest period of time with the least waste of human energy. Convenience leads to increased engagement (Roy et al., 2017; van Doorn et al., 2010). A convenient service saves time and effort while also permitting mobility—features that can be crucial in attracting clients' interest in a service (Chang et al., 2010). Following the coronavirus (COVID-19) health crisis, geographical convenience may be seen as even more important than before because people were forced to endure self-isolation and social separation (Fryer, 2020; Srivastava et al., 2022; AlShamsi et al., 2022; Rakshit et al., 2021). The location and time-saving aspects of convenience, particularly the influence perceived waiting times have on customer experience, have been extensively researched (Roy et al., 2017).

The ease of use of AI-enabled services may be divided into three categories. First, there is the availability of these services: AI-enabled self-service is available 24 hours a day, seven days a week, and customers have the flexibility to use the service from anywhere (Walch, 2019). Second, customers receive real-time information and assistance throughout their trip (Thiel, 2019). Third, AI-powered bots may initiate conversations with customers, deliver relevant information, and help at each touch point across the customer life cycle. This allows customers to get the answers they need when they need them, rather than having to wait online for an agent, which can enhance time to resolution and customer satisfaction (Walch, 2019). Customers' involvement with a brand experience is motivated by convenience (Roy et al., 2017; van Doorn et al., 2010; Almuraqab et al., 2021). Convenience promotes customer trust in the business and the technology used to offer a service by lowering or even eliminating obstacles for buyers (Reimers & Clulow, 2009; Ong et al., 2012). Furthermore, customers' total rating of a service's usefulness is influenced by their impression of convenience (Pham et al., 2018). Finally, businesses exploit ease to lessen the perceived sacrifices of their customers (Kim et al., 2014). As a result, an increase in convenience leads to a drop in perceived sacrifice, implying that convenience is adversely associated to perceived sacrifice. It is reasonable to expect that AI-enabled services will boost user convenience because they can be accessed at any time and from any location.

## Perceived Trust

A traditional definition of trust is the belief that one's vulnerabilities in a dangerous circumstance will not be exploited (Corritore et al., 2003). In the context of online shopping, this entails trusting the brand as well as the technology (Corritore et al., 2003). Recent research indicates that trust is critical in assuring the acceptance, continued advancement, and development of AI (Siau & Wang, 2018). Two streams of studies on trusting technology-mediated services have emerged: those on confidence in the technology (Ghazizadeh et al., 2012; Jasimuddin et al., 2019) and those on trust in innovative companies, including their communication and practices (Chiesa & Frattini, 2011; Nienaber & Schewe, 2014). In the context of AI-enabled customer service, trust encompasses not just the technology and brand but also the goal and process of using AI (Hengstler et al., 2016; Siau & Wang, 2018; Almuraqab & Jasimuddin, 2017). Although purpose displays confidence in intentions
(Wang et al., 2016), brand relationships in online communities (Zhang et al., 2018), social media fan pages (Akrout & Nagy, 2018), and online shipping behavior (Rehman et al., 2019; Hride et al., 2022; Wang et al., 2020). Each study has emphasized the critical importance of trust and relationship commitment in technology-mediated interactions between customers and businesses. Trust is one of the key components of the trust-commitment hypothesis (Morgan & Hunt, 1994). It is also a critical component for the success of automated services because it explains the interaction between humans and automation (Hengstler et al., 2016). According to Wang et al. (2020), privacy is an important component of trust because customers want to have some control over how businesses use their data. Furthermore, past research has demonstrated that trust may change the relationships between multiple elements in the context of AI use, such as service quality and convenience (Siau & Wang, 2018; Ferrario et al., 2020).

## Perceived Convenience

Morganosky (1986) defined service convenience as the capacity to complete a task in the shortest period of time with the least waste of human energy. Convenience leads to increased engagement (Roy et al., 2017; van Doorn et al., 2010). A convenient service saves time and effort while also permitting mobility—features that can be crucial in attracting clients' interest in a service (Chang et al., 2010). Following the coronavirus (COVID-19) health crisis, geographical convenience may be seen as even more important than before because people were forced to endure self-isolation and social separation (Fryer, 2020; Srivastava et al., 2022; AlShamsi et al., 2022; Rakshit et al., 2021). The location and time-saving aspects of convenience, particularly the influence perceived waiting times have on customer experience, have been extensively researched (Roy et al., 2017).

The ease of use of AI-enabled services may be divided into three categories. First, there is the availability of these services: AI-enabled self-service is available 24 hours a day, seven days a week, and customers have the flexibility to use the service from anywhere (Walch, 2019). Second, customers receive real-time information and assistance throughout their trip (Thiel, 2019). Third, AI-powered bots may initiate conversations with customers, deliver relevant information, and help at each touch point across the customer life cycle. This allows customers to get the answers they need when they need them, rather than having to wait online for an agent, which can enhance time to resolution and customer satisfaction (Walch, 2019). Customers' involvement with a brand experience is motivated by convenience (Roy et al., 2017; van Doorn et al., 2010; Almuraqab et al., 2021). Convenience promotes customer trust in the business and the technology used to offer a service by lowering or even eliminating obstacles for buyers (Reimers & Clulow, 2009; Ong et al., 2012). Furthermore, customers' total rating of a service's usefulness is influenced by their impression of convenience (Pham et al., 2018). Finally, businesses exploit ease to lessen the perceived sacrifices of their customers (Kim et al., 2014). As a result, an increase in convenience leads to a drop in perceived sacrifice, implying that convenience is adversely associated to perceived sacrifice. It is reasonable to expect that AI-enabled services will boost user convenience because they can be accessed at any time and from any location.

## Perceived Trust

A traditional definition of trust is the belief that one's vulnerabilities in a dangerous circumstance will not be exploited (Corritore et al., 2003). In the context of online shopping, this entails trusting the brand as well as the technology (Corritore et al., 2003). Recent research indicates that trust is critical in assuring the acceptance, continued advancement, and development of AI (Siau & Wang, 2018). Two streams of studies on trusting technology-mediated services have emerged: those on confidence in the technology (Ghazizadeh et al., 2012; Jasimuddin et al., 2019) and those on trust in innovative companies, including their communication and practices (Chiesa & Frattini, 2011; Nienaber & Schewe, 2014). In the context of AI-enabled customer service, trust encompasses not just the technology and brand but also the goal and process of using AI (Hengstler et al., 2016; Siau & Wang, 2018; Almuraqab & Jasimuddin, 2017). Although purpose displays confidence in intentions
(Hengstler et al., 2016), the process dimension alludes to the technology's understandability. Trust is likely to be reinforced when algorithms and functional logic are transparent (Jasimuddin et al., 2014; Lee & See, 2004; Jasimuddin et al., 2012). Building confidence in an innovative brand and communicating novel technology to clients are difficult tasks. Builders of brands frequently believe that the use of advanced technologies is sufficient to please customers; however, a wide range of studies has shown why some innovative technologies fail to go beyond technical issues (Heidenreich & Spieth, 2013), emphasizing the importance of trust in how providers of brands communicate the use of innovative technologies.

According to Hengstler et al. (2016), the incorporation of AI technology into the service process should be explained proactively, starting at the early phases of dissemination. Their reasoning is that when knowledge levels are low, brand advertising has a greater opportunity to influence societal adoption of new technology. Previous research has also shown that the more confident customers are in a brand from which they purchase, the more ready they are to engage in a long-term relationship with that company (Keiningham et al., 2017). When applied to retail marketing, this approach implies that a higher level of confidence in a company and its technology improves the consumer experience. Although prior research has shown a favorable association between customer experience and trust after the initial encounter, this positive relationship continues to future experiences (Njamfa, 2018). We believe that the link between trust and customer experience is especially apparent in the context of digital encounters owing to the sensitivity of handling consumer data.

## Perceived Service Quality

According to previous research on self-service technology, clients evaluate service quality in four main dimensions: (a) security, (b) reliability, (c) customer service, and (d) interface design (McKecnie et al., 2011; Wolfinbarger & Gilly, 2003). The amount and quality of personal information a company may acquire about customers has a big impact on the quality of AI-enabled services. Although much of this information is normally not sensitive, the aggregation of seemingly nonsensitive personal information (such as marketing preferences and choices) might result in a lengthy user profile that, if not well secured, would allow fraudsters to establish fraudulent identities from it (Cheatham et al., 2019). Saratchandran (2019) stated that AI improves customer service dependability by assuming “unbiased” client interactions. Although AI-enabled services are considerably more likely to trade old prejudices for new ones, they are much more scalable than traditional services and can serve a large number of clients at the same time. Chatbots and other AI-assisted customer care technologies are increasingly being used to automate and perhaps improve the customer journey (Treasure Data, 2019). Because many AI-enabled services are self-service, a well-designed user interface is frequently cited as a vital element in their success. In reality, AI has the ability to modify the user interface by controlling all aspects of the design, including visual components, typography, animations, and graphical information (Irfan, 2020).

Previous research has found that technical and functional service quality influences how people view brands (Chiou & Droge, 2006; Eisingerich & Bell, 2008; Almuraqab et al., 2017). In the absence of other information, the type of technology and how it is deployed by a service provider may serve as a proxy for its character from a consumer standpoint, assisting customers in establishing an initial degree of confidence. An AI-enabled service that consumers perceive as pleasant, compassionate, and responsive has the ability to instill trust in the company (Wang & Lin, 2017). Furthermore, from the standpoint of the consumers, the experience of high-quality service reduces their impressions of sacrifice (in terms of loss of control, loss of privacy, loss of money, effort, time consumption, or negative feeling, such as annoyance or irritation). Previous research has also recognized the influence of service quality on perceived value, which refers to the trade-off between advantages and sacrifices customers must make in exchange for getting a service (Gallarza et al., 2017; Li & Shang, 2020). According to several research studies, perceived sacrifice is separate from perceived service value (de Medeiros et al., 2016). Customers' impressions of a high-quality service are crucial for limiting
(Hengstler et al., 2016), the process dimension alludes to the technology's understandability. Trust is likely to be reinforced when algorithms and functional logic are transparent (Jasimuddin et al., 2014; Lee & See, 2004; Jasimuddin et al., 2012). Building confidence in an innovative brand and communicating novel technology to clients are difficult tasks. Builders of brands frequently believe that the use of advanced technologies is sufficient to please customers; however, a wide range of studies has shown why some innovative technologies fail to go beyond technical issues (Heidenreich & Spieth, 2013), emphasizing the importance of trust in how providers of brands communicate the use of innovative technologies.

According to Hengstler et al. (2016), the incorporation of AI technology into the service process should be explained proactively, starting at the early phases of dissemination. Their reasoning is that when knowledge levels are low, brand advertising has a greater opportunity to influence societal adoption of new technology. Previous research has also shown that the more confident customers are in a brand from which they purchase, the more ready they are to engage in a long-term relationship with that company (Keiningham et al., 2017). When applied to retail marketing, this approach implies that a higher level of confidence in a company and its technology improves the consumer experience. Although prior research has shown a favorable association between customer experience and trust after the initial encounter, this positive relationship continues to future experiences (Njamfa, 2018). We believe that the link between trust and customer experience is especially apparent in the context of digital encounters owing to the sensitivity of handling consumer data.

## Perceived Service Quality

According to previous research on self-service technology, clients evaluate service quality in four main dimensions: (a) security, (b) reliability, (c) customer service, and (d) interface design (McKecnie et al., 2011; Wolfinbarger & Gilly, 2003). The amount and quality of personal information a company may acquire about customers has a big impact on the quality of AI-enabled services. Although much of this information is normally not sensitive, the aggregation of seemingly nonsensitive personal information (such as marketing preferences and choices) might result in a lengthy user profile that, if not well secured, would allow fraudsters to establish fraudulent identities from it (Cheatham et al., 2019). Saratchandran (2019) stated that AI improves customer service dependability by assuming “unbiased” client interactions. Although AI-enabled services are considerably more likely to trade old prejudices for new ones, they are much more scalable than traditional services and can serve a large number of clients at the same time. Chatbots and other AI-assisted customer care technologies are increasingly being used to automate and perhaps improve the customer journey (Treasure Data, 2019). Because many AI-enabled services are self-service, a well-designed user interface is frequently cited as a vital element in their success. In reality, AI has the ability to modify the user interface by controlling all aspects of the design, including visual components, typography, animations, and graphical information (Irfan, 2020).

Previous research has found that technical and functional service quality influences how people view brands (Chiou & Droge, 2006; Eisingerich & Bell, 2008; Almuraqab et al., 2017). In the absence of other information, the type of technology and how it is deployed by a service provider may serve as a proxy for its character from a consumer standpoint, assisting customers in establishing an initial degree of confidence. An AI-enabled service that consumers perceive as pleasant, compassionate, and responsive has the ability to instill trust in the company (Wang & Lin, 2017). Furthermore, from the standpoint of the consumers, the experience of high-quality service reduces their impressions of sacrifice (in terms of loss of control, loss of privacy, loss of money, effort, time consumption, or negative feeling, such as annoyance or irritation). Previous research has also recognized the influence of service quality on perceived value, which refers to the trade-off between advantages and sacrifices customers must make in exchange for getting a service (Gallarza et al., 2017; Li & Shang, 2020). According to several research studies, perceived sacrifice is separate from perceived service value (de Medeiros et al., 2016). Customers' impressions of a high-quality service are crucial for limiting
the impact of perceived sacrifices, particularly those connected to a loss of human assistance and control, because AI-enabled services frequently do not require human engagement.

## Perceived Sacrifice

Perceived sacrifice pertains to “what is given up or sacrificed to obtain a product [or service]” (Zeithaml, 1988, p. 10) and encompasses monetary and nonmonetary costs, including time, effort, cognitive engagement, or feelings such as irritation and annoyance. Recent studies have emphasized the need to study the sacrifices customers make when using automated services, especially when there is a limited number of options available for them to choose from (André et al., 2018). Although monetary and nonmonetary sacrifices may be necessary to obtain a service, the potential implications of many nonmonetary sacrifices can be difficult to assess. Examples of such sacrifices are loss of control, loss of privacy, the potential loss of money, required time and effort, and negative emotions (de Kerviler et al., 2016; Merisavo et al., 2007; Shin & Lin, 2016). In the case of AI-enabled services, two additional nonmonetary compromises must be considered: a loss of human engagement and the possibility of social isolation (Davenport et al., 2020), both of which might harm the customer experience.

The existing service literature is replete with research highlighting the importance of human connection and offering nice customer service (Pham & Ahammad, 2017; Pinto et al., 2017). AI-enabled services, on the other hand, introduce a highly modern type of social interaction that necessitates high degrees of human collaboration and social coordination (Christakis, 2019). Customers, particularly first-time customers, may regard this as a sacrifice (Davenport et al., 2020). Furthermore, because of the organized nature of the customer experience and the necessity for personal data, AI-enabled services might be associated with a loss of human control (Murphy, 2017). AI-enabled services are typically highly organized, with the sequence of actions a customer must do often set by the technology's requirements rather than the user's demands. AI-enabled services also require personal data from customers to function properly, which may be regarded as an additional loss of control (Cheatham et al., 2019). Finally, the lack of human aid (human agency) in AI-enabled services may provide challenges for users, particularly those with no prior experience or those who may require more time to use these services comfortably. According to recent research, clients want a mix of technology and human operators (Gauvrit, 2019). Reduced human interaction may thus have a detrimental influence on the entire consumer experience.

## Perceived Problem-Solving Ability

We use problem-solving with AI literature (Fox, 1990; Kirsh, 1991; Steels, 2007; Jarrahi, 2018) to explore how organizations may deliver instant customer support by using types of AI such as chatbots to substitute human employees in a live system (Shankar, 2018). Chatbots use machine learning, deep learning, and natural language processing to address issues (Huang & Rust, 2018; Kaplan & Haenlein, 2019; Ostrom et al., 2019). Machine learning may replicate the human brain and accomplish complicated computing and decision-making tasks with little or no error (Kaplan & Haenlein, 2019). A chatbot can recognize keywords in a customer's inquiry and react to them with a logical response that is frequently constructed by relying on a sophisticated data store, resulting from natural language processing. Deep learning allows AI to change and enhance its replies each time it is used; deep learning also enables AI to update and expand the database from which it may choose future resources (Kaplan and Haenlein, 2019; McCarthy, 2007). These characteristics make a chatbot an effective problem-solving tool for use in customer care (Klie, 2013; Ostrom et al., 2019; Xiao & Kumar, 2019).

Figure 1 displays the conceptual framework that incorporates the key determinants that influence the usage intention of AI-based customer services in the United Arab Emirates (UAE). Based on the analysis above, we present the following propositions:
the impact of perceived sacrifices, particularly those connected to a loss of human assistance and control, because AI-enabled services frequently do not require human engagement.

## Perceived Sacrifice

Perceived sacrifice pertains to “what is given up or sacrificed to obtain a product [or service]” (Zeithaml, 1988, p. 10) and encompasses monetary and nonmonetary costs, including time, effort, cognitive engagement, or feelings such as irritation and annoyance. Recent studies have emphasized the need to study the sacrifices customers make when using automated services, especially when there is a limited number of options available for them to choose from (André et al., 2018). Although monetary and nonmonetary sacrifices may be necessary to obtain a service, the potential implications of many nonmonetary sacrifices can be difficult to assess. Examples of such sacrifices are loss of control, loss of privacy, the potential loss of money, required time and effort, and negative emotions (de Kerviler et al., 2016; Merisavo et al., 2007; Shin & Lin, 2016). In the case of AI-enabled services, two additional nonmonetary compromises must be considered: a loss of human engagement and the possibility of social isolation (Davenport et al., 2020), both of which might harm the customer experience.

The existing service literature is replete with research highlighting the importance of human connection and offering nice customer service (Pham & Ahammad, 2017; Pinto et al., 2017). AI-enabled services, on the other hand, introduce a highly modern type of social interaction that necessitates high degrees of human collaboration and social coordination (Christakis, 2019). Customers, particularly first-time customers, may regard this as a sacrifice (Davenport et al., 2020). Furthermore, because of the organized nature of the customer experience and the necessity for personal data, AI-enabled services might be associated with a loss of human control (Murphy, 2017). AI-enabled services are typically highly organized, with the sequence of actions a customer must do often set by the technology's requirements rather than the user's demands. AI-enabled services also require personal data from customers to function properly, which may be regarded as an additional loss of control (Cheatham et al., 2019). Finally, the lack of human aid (human agency) in AI-enabled services may provide challenges for users, particularly those with no prior experience or those who may require more time to use these services comfortably. According to recent research, clients want a mix of technology and human operators (Gauvrit, 2019). Reduced human interaction may thus have a detrimental influence on the entire consumer experience.

## Perceived Problem-Solving Ability

We use problem-solving with AI literature (Fox, 1990; Kirsh, 1991; Steels, 2007; Jarrahi, 2018) to explore how organizations may deliver instant customer support by using types of AI such as chatbots to substitute human employees in a live system (Shankar, 2018). Chatbots use machine learning, deep learning, and natural language processing to address issues (Huang & Rust, 2018; Kaplan & Haenlein, 2019; Ostrom et al., 2019). Machine learning may replicate the human brain and accomplish complicated computing and decision-making tasks with little or no error (Kaplan & Haenlein, 2019). A chatbot can recognize keywords in a customer's inquiry and react to them with a logical response that is frequently constructed by relying on a sophisticated data store, resulting from natural language processing. Deep learning allows AI to change and enhance its replies each time it is used; deep learning also enables AI to update and expand the database from which it may choose future resources (Kaplan and Haenlein, 2019; McCarthy, 2007). These characteristics make a chatbot an effective problem-solving tool for use in customer care (Klie, 2013; Ostrom et al., 2019; Xiao & Kumar, 2019).

Figure 1 displays the conceptual framework that incorporates the key determinants that influence the usage intention of AI-based customer services in the United Arab Emirates (UAE). Based on the analysis above, we present the following propositions:
<figure><img src="image_2.png" /><figcaption>Figure 1. Conceptual Framework</figcaption></figure>

* Perceived problem-solving capability is an important determinant that influences the usage intention of AI-based customer services in the UAE.

* Perceived trust is an important factor that influences the usage intention of AI-based customer services in the UAE.

* Perceived service quality is an important element in ensuring end users' acceptance of AI customer service in the UAE.

* Perceived convenience is a key factor that influences the successful implementation of AI customer service.

* Perceived sacrifice influences user acceptance of AI-enabled customer services, and thus it guides the successful implementation of AI-based customer service projects.

## DISCUSSION

This paper explored the major factors influencing end users' acceptance of AI-based customer service agent/bots in the UAE. After a review of the relevant literature, we explored five variables as critical to successful acceptance and to the enhancement of users' intention to use these services. We proposed a conceptual model that takes advantage of trust commitment theory (TCT) to start understanding the factors affecting adoption of these services. TCT is being successfully used in explaining the adoption of other related technologies and services, such as e-government, m-commerce, and m-banking. Note that technology is advancing very quickly, and organizations around the globe, particularly in the UAE, are managing to use the latest devices and technologies to enhance the services they offer to the public. We focused our research to be aligned with the UAE AI strategy, which sets a clear vision through its AI strategy, "to become the world leader in AI by 2031" (Artificial Intelligence, Digital Economy and Remote Work Applications Office, 2022). Against this background, we note that studying the factors in the adoption of the latest AI technologies to avoid failure in implementation is very important. Drawing on the previous literature, this paper identified the following factors:

* Perceived problem-solving

* Perceived trust

* Perceived service quality

* Perceived convenience

* Perceived sacrifice
<figure><img src="image_2.png" /><figcaption>Figure 1. Conceptual Framework</figcaption></figure>

* Perceived problem-solving capability is an important determinant that influences the usage intention of AI-based customer services in the UAE.

* Perceived trust is an important factor that influences the usage intention of AI-based customer services in the UAE.

* Perceived service quality is an important element in ensuring end users' acceptance of AI customer service in the UAE.

* Perceived convenience is a key factor that influences the successful implementation of AI customer service.

* Perceived sacrifice influences user acceptance of AI-enabled customer services, and thus it guides the successful implementation of AI-based customer service projects.

## DISCUSSION

This paper explored the major factors influencing end users' acceptance of AI-based customer service agent/bots in the UAE. After a review of the relevant literature, we explored five variables as critical to successful acceptance and to the enhancement of users' intention to use these services. We proposed a conceptual model that takes advantage of trust commitment theory (TCT) to start understanding the factors affecting adoption of these services. TCT is being successfully used in explaining the adoption of other related technologies and services, such as e-government, m-commerce, and m-banking. Note that technology is advancing very quickly, and organizations around the globe, particularly in the UAE, are managing to use the latest devices and technologies to enhance the services they offer to the public. We focused our research to be aligned with the UAE AI strategy, which sets a clear vision through its AI strategy, "to become the world leader in AI by 2031" (Artificial Intelligence, Digital Economy and Remote Work Applications Office, 2022). Against this background, we note that studying the factors in the adoption of the latest AI technologies to avoid failure in implementation is very important. Drawing on the previous literature, this paper identified the following factors:

* Perceived problem-solving

* Perceived trust

* Perceived service quality

* Perceived convenience

* Perceived sacrifice
In previous studies, perceived problem-solving was found to be critical. These studies noted that AI-based customer services' ability to understand customers' problems, respond to questions, and provide useful answers are very crucial and enhance users' intention to use the service. Xu et al. (2020) proved these factors are significant.

Some scholars also argued that consumer trust is an important factor for retailers to consider when introducing technologies, but it may be even more important when deploying AI. Findings from a study by Ameen et al. (2021) showed that trust plays a central role in AI-enabled experiences.

Perceived service quality is another important determinant of use of AI customer service. A study argued that interface design, security, reliability and customer service are important when using AI-based technologies (Ameen et al., 2021). Ameen et al.'s (2021) study also reflected the importance of service quality. Furthermore, Kim et al. (2014) discussed perceived convenience and determined that it is another important factor, especially in terms of time and location, that influences customer experience with AI-based assistants. Many other studies, such as those by Fryer (2020) and Ameen et al. (2021), have discussed the same subject.

Finally, perceived sacrifice can be treated as an element to increase the intention to use AI-based customer services. Perceived sacrifice (e.g., loss of privacy, loss of control, effort, time-consuming processes, irritation, and lack of human interaction) is very critical in influencing the usage intention (Gauvrit, 2019; Ameen et al., 2021).

Following a review of the relevant literature, we incorporated these five variables into the proposed conceptual framework to ensure the successful adoption of AI-based customer services. This paper presents an extension of TCT and service quality model with perceived problem-solving ability. This research helps to address the factors that influence user acceptance of AI-enabled customer services in the context of UAE. Using five propositions, we created a framework to identify the key factors that affect AI-customer service and guide the successful implementation of AI-based customer service projects.

## CONCLUSION

Drawing on the existing literature, we explored the relevant issues surrounding AI-based customer services acceptance by citizens and their relationship to people's intentions to accept these services. This paper will help in understanding the key issues surrounding such services that may assist in their successful operation and customer satisfaction with them. It identifies the determinants of the intention to use AI-based customer services to avoid failure in the implementation of these services. This paper also provides a conceptual framework relating to the successful implementation of AI-based services, which will enhance smart cities in the UAE. The successful adoption of these services requires the participation of all stakeholders (e.g., government employees, citizens, and businesses) during the planning and implementation phases. For this implementation to succeed, coordination of the activities of various government agencies and private sectors, as well as close cooperation of employees, managers, and IT specialists, is required.

Although this study presents strong evidence regarding the factors that affect end users' adoption of AI-based services, it should also be evaluated in light of its limitations. First, our research did not comprehensively cover all the factors that influence end-users' adoption of AI-based customer services. Other important variables should be taken into account in future work. Second, this paper is based on the existing literature. The paper warrants validation of the conceptual model and the possibility of generalizing the contribution's benefit to the region. An empirical study can be conducted in future to validate the conceptual model using the UAE environment and thereby help explore the determinants of successful AI customer services acceptance in the UAE and beyond.
In previous studies, perceived problem-solving was found to be critical. These studies noted that AI-based customer services' ability to understand customers' problems, respond to questions, and provide useful answers are very crucial and enhance users' intention to use the service. Xu et al. (2020) proved these factors are significant.

Some scholars also argued that consumer trust is an important factor for retailers to consider when introducing technologies, but it may be even more important when deploying AI. Findings from a study by Ameen et al. (2021) showed that trust plays a central role in AI-enabled experiences.

Perceived service quality is another important determinant of use of AI customer service. A study argued that interface design, security, reliability and customer service are important when using AI-based technologies (Ameen et al., 2021). Ameen et al.'s (2021) study also reflected the importance of service quality. Furthermore, Kim et al. (2014) discussed perceived convenience and determined that it is another important factor, especially in terms of time and location, that influences customer experience with AI-based assistants. Many other studies, such as those by Fryer (2020) and Ameen et al. (2021), have discussed the same subject.

Finally, perceived sacrifice can be treated as an element to increase the intention to use AI-based customer services. Perceived sacrifice (e.g., loss of privacy, loss of control, effort, time-consuming processes, irritation, and lack of human interaction) is very critical in influencing the usage intention (Gauvrit, 2019; Ameen et al., 2021).

Following a review of the relevant literature, we incorporated these five variables into the proposed conceptual framework to ensure the successful adoption of AI-based customer services. This paper presents an extension of TCT and service quality model with perceived problem-solving ability. This research helps to address the factors that influence user acceptance of AI-enabled customer services in the context of UAE. Using five propositions, we created a framework to identify the key factors that affect AI-customer service and guide the successful implementation of AI-based customer service projects.

## CONCLUSION

Drawing on the existing literature, we explored the relevant issues surrounding AI-based customer services acceptance by citizens and their relationship to people's intentions to accept these services. This paper will help in understanding the key issues surrounding such services that may assist in their successful operation and customer satisfaction with them. It identifies the determinants of the intention to use AI-based customer services to avoid failure in the implementation of these services. This paper also provides a conceptual framework relating to the successful implementation of AI-based services, which will enhance smart cities in the UAE. The successful adoption of these services requires the participation of all stakeholders (e.g., government employees, citizens, and businesses) during the planning and implementation phases. For this implementation to succeed, coordination of the activities of various government agencies and private sectors, as well as close cooperation of employees, managers, and IT specialists, is required.

Although this study presents strong evidence regarding the factors that affect end users' adoption of AI-based services, it should also be evaluated in light of its limitations. First, our research did not comprehensively cover all the factors that influence end-users' adoption of AI-based customer services. Other important variables should be taken into account in future work. Second, this paper is based on the existing literature. The paper warrants validation of the conceptual model and the possibility of generalizing the contribution's benefit to the region. An empirical study can be conducted in future to validate the conceptual model using the UAE environment and thereby help explore the determinants of successful AI customer services acceptance in the UAE and beyond.
CONFLICTS OF INTEREST

We wish to confirm that there are no known conflicts of interest associated with this publication and
there has been no significant financial support for this work that could have influenced its outcome.

FUNDING STATEMENT

No funding was received for this work.

PROCESS DATES

Received: December 26, 2022, Revision: February 9, 2024, Accepted: March 18, 2024

CORRESPONDING AUTHOR

Correspondence should be addressed to Sajjad Jasimuddin; sajjad.jasimuddin@kedgebs.com
## CONFLICTS OF INTEREST

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

## FUNDING STATEMENT

No funding was received for this work.

## PROCESS DATES

Received: December 26, 2022, Revision: February 9, 2024, Accepted: March 18, 2024

## CORRESPONDING AUTHOR

Correspondence should be addressed to Sajjad Jasimuddin; sajjad.jasimuddin@kedgebs.com
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Abdou, D., & Jasimuddin, S. M. (2020). The use of the UTAUT Model in the adoption of e-learning technologies — An empirical study in France based banks. [JGIM]. *Journal of Global Information Management*, 28(4), 38–51. doi:10.4018/JGIM.2020100103

Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. *Electronic Markets*, 31(2), 427–445. doi:10.1007/s12525-020-00414-7

Akrout, H., & Nagy, G. (2018). Trust and commitment within a virtual brand community: The mediating role of brand relationship quality. *Information & Management*, 55(8), 939–955. doi:10.1016/j.im.2018.04.009

Almuraqab, N. A. S., & Jasimuddin, S. M. (2017). Factors that influence end-users’ adoption of smart government services in the UAE: A conceptual framework. *The Electronic Journal of Information Systems Evaluation (EJISE)* 20(1), 11–23. https://academic-publishing.org/index.php/ejise/issue/view/18

Almuraqab, N. A. S., Jasimuddin, S. M., & Mansoor, W. (2017). Mobile government (mGovernment) adoption factors in the UAE: A conceptual framework based on UTAUT. *International Journal of Engineering Technology. Management and Applied Sciences*, 5(3), 14–19.

Almuraqab, N. A. S., Jasimuddin, S. M., & Mansoor, W. (2021). An empirical study of perception of the end-user on the acceptance of smart government service in the UAE. [JGIM]. *Journal of Global Information Management*, 29(6), 1–29. doi:10.4018/JGIM.20211101.oa11

AlShamsi, S. S., Ahmad, K. Z., & Jasimuddin, S. M. (2022). Curiosity, proactive personality, organizational culture and work engagement in the aviation industry in the UAE during Covid-19: A non-probabilistic moderated-mediation model. *Journal of General Management*, 030630702211412. Advance online publication. doi:10.1177/03063070221141201

Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. *Computers in Human Behavior*, 114, 106548. doi:10.1016/j.chb.2020.106548 PMID:32905175

André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Frank, D., Goldstein, W., Huber, J., van Boven, L., Weber, B., & Yang, H. (2018). Consumer choice and autonomy in the age of artificial intelligence and big data. *Customer Needs and Solutions*, 5(1), 28–37. doi:10.1007/s40547-017-0085-8

Bin Ahmad, K. Z., & Jasimuddin, S. M. (2021). The linkage between communication satisfaction, human resources management practices, person-organization fit, and commitment: Evidence from Malaysia. *IEEE Transactions on Professional Communication*, 64(1), 23–37. doi:10.1109/TPC.2020.3047428

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He, W., Zhang, J. Z., Wu, H., Li, W., & Shetty, S. (2021). A unified health information system framework for connecting data, people, devices, and systems. [JGIM]. *Journal of Global Information Management*, 30(11), 1–19. doi:10.4018/JGIM.305239
Corritore, C. L., Kracher, B., & Wiedenbeck, S. (2003). On-line trust: Concepts, evolving themes, a model. *International Journal of Human-Computer Studies*, 58(6), 737–758. doi:10.1016/S1071-5819(03)00041-7

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. *Journal of the Academy of Marketing Science*, 48(1), 24–42. doi:10.1007/s11747-019-00696-0

de Kerviler, G., Demoulin, N. T. M., & Zidda, P. (2016). Adoption of in-store mobile payment: Are perceived risk and convenience the only drivers? *Journal of Retailing and Consumer Services*, 31, 334–344. doi:10.1016/j.jretconser.2016.04.011

de Medeiros, J. F., Ribeiro, J. L. D., & Cortimiglia, M. N. (2016). Influence of perceived value on purchasing decisions of green products in Brazil. *Journal of Cleaner Production*, 110, 158–169. doi:10.1016/j.jclepro.2015.07.100

Eisingerich, A. B., & Bell, S. J. (2008). Perceived service quality and customer trust: Does enhancing customers' service knowledge matter? *Journal of Service Research*, 10(3), 256–268. doi:10.1177/1094670507310769

Elbeltagi, I., & Agag, G. (2016). E-retailing ethics and its impact on customer satisfaction and repurchase intention: A cultural and commitment-trust theory perspective. *Internet Research*, 26(1), 288–310. doi:10.1108/IntR-10-2014-0244

Ferrario, A., Loi, M., & Viganò, E. (2020). In AI we trust incrementally: A multi-layer model of trust to analyze human-artificial intelligence interactions. *Philosophy & Technology*, 33(3), 523–539. doi:10.1007/s13347-019-00378-3

Foroughi, B., Iranmanesh, M., & Hyun, S. S. (2019). Understanding the determinants of mobile banking continuance usage intention. *Journal of Enterprise Information Management*, 32(6), 1015–1033. doi:10.1108/JEIM-10-2018-0237

Fox, M. S. (1990). AI and expert system myths, legends, and facts. *IEEE Expert*, 5(1), 8–20. doi:10.1109/64.50853

Fryer, V. (2020). Understanding the COVID-19 effect on online shopping behavior. *The BigCommerce Blog*. https://www.bigcommerce.com/blog/covid-19-ecommerce/

Gallarza, M. G., Arteaga, F., Del Chiappa, G., Gil-Saura, I., & Holbrook, M. B. (2017). A multidimensional service-value scale based on Holbrook’s typology of customer value: Bridging the gap between the concept and its measurement. *Journal of Service Management*, 28(4), 724–762. doi:10.1108/JOSM-06-2016-0166

Ganguli, S., & Roy, S. K. (2011). Generic technology-based service quality dimensions in banking: Impact on customer satisfaction and loyalty. *International Journal of Bank Marketing*, 29(2), 168–189. doi:10.1108/02652321111107648

Gauvrit, P. (2019). *Why the future of customer service is AI and humans together*. Enghouse Interactive. https://www.eptica.com/blog/why-future-customer-service-ai-and-humans-together

Ghazizadeh, M., Lee, J. D., & Boyle, L. N. (2012). Extending the Technology Acceptance Model to assess automation. *Cognition Technology and Work*, 14(1), 39–49. doi:10.1007/s10111-011-0194-3

Gnewuch, U., Morana, S., & Maedche, A. (2017). Towards designing cooperative and social conversational agents for customer service. In *Proceedings of the Thirty Eighth Conference on Information Systems (ICIS 2017). Association for Information Systems*. https://aisel.aisnet.org/icis2017/HCI/Presentations/1?utm_source=aisel.aisnet.org%2Ficis2017%2FHCI%2FPresentations%2F1&utm_medium=PDF&utm_campaign=PDFCoverPages

Hasnain, S. S., & Jasimuddin, S. M. (2012). Barriers to knowledge transfer: Empirical evidence from the NGO (non-governmental organizations) sector in Bangladesh. *World Journal of Social Sciences*, 2(2), 135–150.

Hasnain, S. S., Jasimuddin, S. M., & Fuller-Love, N. (2016). Exploring causes, taxonomies, mechanisms and barriers influencing knowledge transfer: Empirical studies in NGOs. [IRMJ]. *Information Resources Management Journal*, 29(1), 39–56. doi:10.4018/IRMJ.2016010103

He, W., Zhang, J. Z., Wu, H., Li, W., & Shetty, S. (2021). A unified health information system framework for connecting data, people, devices, and systems. [JGIM]. *Journal of Global Information Management*, 30(11), 1–19. doi:10.4018/JGIM.305239
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Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. *Technological Forecasting and Social Change, 105*, 105–120. doi:10.1016/j.techfore.2015.12.014

Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human-human online conversations and human-chatbot conversations. *Computers in Human Behavior, 49*, 245–250. doi:10.1016/j.chb.2015.02.026

Hride, F. T., Ferdousi, F., & Jasimuddin, S. M. (2022). Linking perceived price fairness, customer satisfaction, trust, and loyalty: A structural equation modeling of Facebook-based e-commerce in Bangladesh. *Global Business and Organizational Excellence, 41*(3), 41–54. doi:10.1002/joe.22146

Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. *Journal of Service Research, 21*(2), 155–172. doi:10.1177/1094670517752459

Irfan, M. (2020). Artificial intelligence and the future of web design. Usability Geek. https://usabilitygeek.com/artificial-intelligence-and-the-future-of-web-design

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Islam, Z., Jasimuddin, S. M., & Hasan, I. (2017). The role of technology and socialization in linking organizational context and knowledge conversion: The case of Malaysian service organizations. *International Journal of Information Management, 37*(5), 497–503. doi:10.1016/j.ijinfomgt.2017.06.001

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Jasimuddin, S. M., Connell, N., & Klein, J. H. (2012). Knowledge transfer frameworks: An extension incorporating knowledge repositories and knowledge administration. *Information Systems Journal, 22*(3), 195–209. doi:10.1111/j.1365-2575.2011.00382.x

Jasimuddin, S. M., Connell, N. A. D., & Klein, J. H. (2006). What motivates organisational knowledge transfer? Some lessons from a UK-based multinational. *Journal of Information & Knowledge Management, 05*(02), 165–171. doi:10.1142/S0219649206001414

Jasimuddin, S. M., Li, J., & Perdikis, N. (2019). An empirical study of the role of knowledge characteristics and tools on knowledge transfer in China-based multinationals. [JGIM]. *Journal of Global Information Management, 27*(1), 165–195. doi:10.4018/JGIM.2019010109

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Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. *Technological Forecasting and Social Change, 105*, 105–120. doi:10.1016/j.techfore.2015.12.014

Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human-human online conversations and human-chatbot conversations. *Computers in Human Behavior, 49*, 245–250. doi:10.1016/j.chb.2015.02.026

Hride, F. T., Ferdousi, F., & Jasimuddin, S. M. (2022). Linking perceived price fairness, customer satisfaction, trust, and loyalty: A structural equation modeling of Facebook-based e-commerce in Bangladesh. *Global Business and Organizational Excellence, 41*(3), 41–54. doi:10.1002/joe.22146

Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. *Journal of Service Research, 21*(2), 155–172. doi:10.1177/1094670517752459

Irfan, M. (2020). Artificial intelligence and the future of web design. Usability Geek. https://usabilitygeek.com/artificial-intelligence-and-the-future-of-web-design

Islam, Z., Jasimuddin, S. M., & Hasan, I. (2015). Organizational culture, structure, technology infrastructure and knowledge sharing: Empirical evidence from MNCs based in Malaysia. *Vine, 45*(1), 67–88. doi:10.1108/VINE-05-2014-0037

Islam, Z., Jasimuddin, S. M., & Hasan, I. (2017). The role of technology and socialization in linking organizational context and knowledge conversion: The case of Malaysian service organizations. *International Journal of Information Management, 37*(5), 497–503. doi:10.1016/j.ijinfomgt.2017.06.001

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-Al symbiosis in organizational decision making. *Business Horizons, 61*(4), 577–586. doi:10.1016/j.bushor.2018.03.007

Jarvis, W., Ouschan, R., Burton, H. J., Soutar, G., & O'Brien, I. M. (2017). Customer engagement in CSR: A utility theory model with moderating variables. *Journal of Service Theory and Practice, 27*(4), 833–853. doi:10.1108/JSTP-04-2016-0081

Jasimuddin, S. M., Connell, N., & Klein, J. H. (2012). Knowledge transfer frameworks: An extension incorporating knowledge repositories and knowledge administration. *Information Systems Journal, 22*(3), 195–209. doi:10.1111/j.1365-2575.2011.00382.x

Jasimuddin, S. M., Connell, N. A. D., & Klein, J. H. (2006). What motivates organisational knowledge transfer? Some lessons from a UK-based multinational. *Journal of Information & Knowledge Management, 05*(02), 165–171. doi:10.1142/S0219649206001414

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Jasimuddin, S. M., Mishra, N., & Almuraqab, N. A. S. (2017). Modelling the factors that influence the acceptance of digital technologies in e-government services in the UAE: A PLS-SEM Approach. *Production Planning and Control, 28*(16), 1307–1317. doi:10.1080/09537287.2017.1375144

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. *Business Horizons, 62*(1), 15–25. doi:10.1016/j.bushor.2018.08.004

Keiningham, T., Ball, J., Benoit, S., Bruce, H. L., Buoye, A., Dzenkovska, J., Nasr, L., Ou, Y.-C., & Zaki, M. (2017). The interplay of customer experience and commitment. *Journal of Services Marketing, 31*(2), 148–160. doi:10.1108/JSM-09-2016-0337

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Li, Y., & Shang, H. (2020). Service quality, perceived value, and citizens’ continuous-use intention regarding e-government: Empirical evidence from China. *Information & Management*, 57(3), 103197. doi:10.1016/j.im.2019.103197

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Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies. *Journal of Marketing*, 69(2), 61–83. doi:10.1509/jmkg.69.2.61.60759

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Oh, L.-B., Teo, H.-H., & Sambamurthy, V. (2012). The effects of retail channel integration through the use of information technologies on firm performance. *Journal of Operations Management*, 30(5), 368–381. doi:10.1016/j.jom.2012.03.001

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Larivière, B., Bowen, D., Andreassen, T. W., Kunz, W., Sirianni, N. J., Voss, C., Wünderlich, N. V., & De Keyser, A. (2017). “Service Encounter 2.0”: An investigation into the roles of technology, employees and customers. *Journal of Business Research*, 79, 238–246. doi:10.1016/j.jbusres.2017.03.008

Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. *Human Factors*, 46(1), 50–80. doi:10.1518/hfes.46.1.50.30392 PMID:15151155

Li, Y., & Shang, H. (2020). Service quality, perceived value, and citizens’ continuous-use intention regarding e-government: Empirical evidence from China. *Information & Management*, 57(3), 103197. doi:10.1016/j.im.2019.103197

Maruti Techlabs. (2017). Can chatbots help reduce customer service costs by 30%. *Chatbots Magazine*. https://chatbotsmagazine.com/how-with-the-help-of-chatbots-customer-service-costs-could-be-reduced-up-to-30-b9266a369945

Merisavo, M., Kajalo, S., Karjaluoto, H., Virtanen, V., Salmenkivi, S., Raulas, M., & Leppäniemi, M. (2007). An empirical study of the drivers of consumer acceptance of mobile advertising. *Journal of Interactive Advertising*, 7(2), 41–50. doi:10.1080/15252019.2007.10722130

Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies. *Journal of Marketing*, 69(2), 61–83. doi:10.1509/jmkg.69.2.61.60759

Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. *Journal of Marketing*, 58(3), 20–38. doi:10.1177/002224299405800302

Morganosky, M. A. (1986). Cost-versus convenience-oriented consumers: Demographic, lifestyle, and value perspectives. *Psychology and Marketing*, 3(1), 35–46. doi:10.1002/mar.4220030104

Murphy, M. (2017). A mind of its own: Humanity is already losing control of artificial intelligence and it could spell disaster for our species, warn experts. *The Sun* (UK Ed.). https://www.thesun.co.uk/tech/3306890/humanity-is-already-losing-control-of-artificial-intelligence-and-it-could-spell-disaster-for-our-species/

Newman, D. (2019). 5 ways AI is transforming the customer experience. Forbes. https://www.forbes.com/sites/danielnewman/2019/04/16/5-ways-ai-is-transforming-the-customer-experience/?sh=2fa25fc7465a

Nienaber, A.-M., & Schewe, G. (2014). Enhancing trust or reducing perceived risk, what matters more when launching a new product? *International Journal of Innovation Management*, 18(1), 1450005. doi:10.1142/S1363919614500054

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Rahman, S., Islam, Z., Hossain, M., Abdullah, D. A., & Jasimuddin, S. M. (2022b). Organizational factors, ICT support and affective commitment: The case of Bangladesh-based service organizations. [JGIM]. *Journal of Global Information Management*, 30(1), 1–18. doi:10.4018/JGIM.302914

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Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. *Journal of Retailing*, 94(4), vi–xi. doi:10.1016/S0022-4359(18)30076-9

Shin, W., & Lin, T. T.-C. (2016). Who avoids location-based advertising and why? Investigating the relationship between user perceptions and advertising avoidance. *Computers in Human Behavior*, 63, 444–452. doi:10.1016/j.chb.2016.05.036

Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. *Cutter Business Technology Journal*, 31(2), 47–53. https://www.cutter.com/article/building-trust-artificial-intelligence-machine-learning-and-robotics-498981
Pham, Q. T., Tran, X. P., Misra, S., Maskeliūtnas, R., & Damaševičius, R. (2018). Relationship between convenience, perceived value, and repurchase intention in online shopping in Vietnam. *Sustainability (Basel)*, 10(1), 156. doi:10.3390/su10010156

Pham, T. S. H., & Ahammad, M. F. (2017). Antecedents and consequences of online customer satisfaction: A holistic process perspective. *Technological Forecasting and Social Change*, 124, 332–342. doi:10.1016/j.techfore.2017.04.003

Pinto, G. L., Dell'Era, C., Verganti, R., & Bellini, E. (2017). Innovation strategies in retail services: Solutions, experiences and meanings. *European Journal of Innovation Management*, 20(2), 190–209. doi:10.1108/EJIM-06-2015-0049

Rahman, S., Hossain, M., Islam, Z., & Jasimuddin, S. M. (2022a). Linkage between culture, leadership, and knowledge sharing in MNCs: The moderating role of training and development. [JGIM]. *Journal of Global Information Management*, 30(1), 1–21. doi:10.4018/JGIM.301200

Rahman, S., Islam, Z., Hossain, M., Abdullah, D. A., & Jasimuddin, S. M. (2022b). Organizational factors, ICT support and affective commitment: The case of Bangladesh-based service organizations. [JGIM]. *Journal of Global Information Management*, 30(1), 1–18. doi:10.4018/JGIM.302914

Rakshit, S., Mondal, S., Islam, N., Jasimuddin, S., & Zhang, Z. (2021). Social media and the new product development during COVID-19: An integrated model for SMEs. *Technological Forecasting and Social Change*, 107, 120869. doi:10.1016/j.techfore.2021.120869

Raymond, J. (2001). No more shoppus interruptus. *American Demographics*, 23(5), 39–40. https://adage.com/article/american-demographics/shoppus-interruptus/43597

Reddy, T. (2017a). Chatbots for customer service will help businesses save $8 billion per year. IBM. Retrieved April, 19, 2021.

Reddy, T. (2017b). *How chatbots can help reduce customer service costs by 30*. IBM.

Rehman, S. U., Bhatti, A., Mohamed, R., & Ayoup, H. (2019). The moderating role of trust and commitment between consumer purchase intention and online shopping behavior in the context of Pakistan. *Journal of Global Entrepreneurship Research*, 9(1), 43, 1–25. 10.1186/s40497-019-0166-2

Reimers, V., & Clulow, V. (2009). Retail centres: It's time to make them convenient. *International Journal of Retail & Distribution Management*, 37(7), 541–562. doi:10.1108/09590550910964594

Roy, S. K., Balaji, M. S., Sadeque, S., Nguyen, B., & Melewar, T. C. (2017). Constituents and consequences of smart customer experience in retailing. *Technological Forecasting and Social Change*, 124, 257–270. doi:10.1016/j.techfore.2016.09.022

Saponaro, M., Le Gal, D., Gao, M., Guisiano, M., & Maniere, I. C. (2018). Challenges and opportunities of artificial intelligence in the fashion world. In *Proceedings of the 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC)*, pp. 1–5. IEEE. doi:10.1109/ICONIC.2018.8601258

Saratchandran, V. (2019). Artificial intelligence (AI): Ways AI is redefining the future of customer service. *Becoming Human: Artificial Intelligence Magazine*. https://medium.com/becoming-human/artificial-intelligence-ai-ways-ai-is-redefining-the-future-of-customer-service-4dc667bfa59

Scherer, A., Wünderlich, N. V., & von Wangenheim, F. (2015). The value of self-service: Long-term effects of technology-based self-service usage on customer retention. *Management Information Systems Quarterly*, 39(1), 177–200. doi:10.25300/MISQ/2015/39.1.08

Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. *Journal of Retailing*, 94(4), vi–xi. doi:10.1016/S0022-4359(18)30076-9

Shin, W., & Lin, T. T.-C. (2016). Who avoids location-based advertising and why? Investigating the relationship between user perceptions and advertising avoidance. *Computers in Human Behavior*, 63, 444–452. doi:10.1016/j.chb.2016.05.036

Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. *Cutter Business Technology Journal*, 31(2), 47–53. https://www.cutter.com/article/building-trust-artificial-intelligence-machine-learning-and-robotics-498981
Srivastava, P. R., Zhang, Z. J., Eachempati, P., Trivedi, S. K., & Jasimuddin, S. M. (2022). The response of the scientific community to a global crisis: A systematic review of COVID-19 research in 2020. *British Journal of Health Care Management, 28*(2), 1-7. doi:10.12968/bjhc.2021.0068

Steels, L. (2007). Fifty years of AI: From symbols to embodiment—and back. In M. Lungarella, F. Iida, J. Bongard, & R. Pfeifer (Eds.), 50 years of artificial intelligence. Lecture notes in computer science, vol. 4850 (pp. 18–28). Springer, Berlin, Heidelberg. doi:10.1007/978-3-540-77296-5_3

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. *Industrial Marketing Management, 69*, 135–146. doi:10.1016/j.indmarman.2017.12.019

Thiel, W. (2019). The role of AI in customer experience.

Treasure Data. (2019). AI vs. human customer service: Survey data shows when consumers prefer a bot. Treasure Data. https://app.hushly.com/runtime/content/hweB8PE1UYgB6oQR

Tseng, H.-T., Lo, C.-L., & Chen, C.-C. (2023). The moderation role of AI-enabled service quality on the attitude toward fitness apps. [JGIM]. *Journal of Global Information Management, 31*(1), 1–20. doi:10.4018/JGIM.318694

Turel, O., & Connelly, C. E. (2013). Too busy to help: Antecedents and outcomes of interactional justice in web-based service encounters. *International Journal of Information Management, 33*(4), 674–683. doi:10.1016/j.ijinfomgt.2013.03.005

van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer engagement behavior: Theoretical foundations and research directions. *Journal of Service Research, 13*(3), 253–266. doi:10.1177/1094670510375599

van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato mr. roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. *Journal of Service Research, 20*(1), 43–58. https://psycnet.apa.org/doi/10.1177/1094670516679272. doi:10.1177/1094670516679272

Varsha, P. S., Akter, S., Kumar, A., Gochhait, S., & Patagundi, B. (2021). The impact of artificial intelligence on branding: A bibliometric analysis (1982–2019). [JGIM]. *Journal of Global Information Management, 29*(4), 221–246. doi:10.4018/JGIM.20210701.oa10

Verhagen, T., van Nes, J., Feldberg, F., & van Dolen, W. (2014). Virtual customer service agents: Using social presence and personalization to shape online service encounters. *Journal of Computer-Mediated Communication, 19*(3), 529–545. doi:10.1111/jcc4.12066

Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: Determinants, dynamics and management strategies. *Journal of Retailing, 85*(1), 31–41. doi:10.1016/j.jretai.2008.11.001

Walch, K. (2019). AI's increasing role in customer service. Forbes. https://www.forbes.com/sites/cognitiveworld/2019/07/02/ais-increasing-role-in-customer-service/?sh=75caf18673fc

Wang, E. S.-T., & Lin, R.-L. (2017). Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. *Behaviour & Information Technology, 36*(1), 2–10. doi:10.1080/0144929X.2016.1143033

Wang, W.-T., Wang, Y.-S., & Liu, E.-R. (2016). The stickiness intention of group-buying websites: The integration of the commitment–trust theory and e-commerce success model. *Information & Management, 53*(5), 625–642. doi:10.1016/j.im.2016.01.006

Wang, X., Tajvidi, M., Lin, X., & Hajli, N. (2020). Towards an ethical and trustworthy social commerce community for brand value co-creation: A trust-commitment perspective. *Journal of Business Ethics, 167*(1), 137–152. doi:10.1007/s10551-019-04182-z

Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. *Journal of Service Management, 29*(5), 907–931. doi:10.1108/JOSM-04-2018-0119
Srivastava, P. R., Zhang, Z. J., Eachempati, P., Trivedi, S. K., & Jasimuddin, S. M. (2022). The response of the scientific community to a global crisis: A systematic review of COVID-19 research in 2020. *British Journal of Health Care Management, 28*(2), 1-7. doi:10.12968/bjhc.2021.0068

Steels, L. (2007). Fifty years of AI: From symbols to embodiment—and back. In M. Lungarella, F. Iida, J. Bongard, & R. Pfeifer (Eds.), 50 years of artificial intelligence. Lecture notes in computer science, vol. 4850 (pp. 18–28). Springer, Berlin, Heidelberg. doi:10.1007/978-3-540-77296-5_3

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. *Industrial Marketing Management, 69*, 135–146. doi:10.1016/j.indmarman.2017.12.019

Thiel, W. (2019). The role of AI in customer experience.

Treasure Data. (2019). AI vs. human customer service: Survey data shows when consumers prefer a bot. Treasure Data. https://app.hushly.com/runtime/content/hweB8PE1UYgB6oQR

Tseng, H.-T., Lo, C.-L., & Chen, C.-C. (2023). The moderation role of AI-enabled service quality on the attitude toward fitness apps. [JGIM]. *Journal of Global Information Management, 31*(1), 1–20. doi:10.4018/JGIM.318694

Turel, O., & Connelly, C. E. (2013). Too busy to help: Antecedents and outcomes of interactional justice in web-based service encounters. *International Journal of Information Management, 33*(4), 674–683. doi:10.1016/j.ijinfomgt.2013.03.005

van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer engagement behavior: Theoretical foundations and research directions. *Journal of Service Research, 13*(3), 253–266. doi:10.1177/1094670510375599

van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato mr. roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. *Journal of Service Research, 20*(1), 43–58. https://psycnet.apa.org/doi/10.1177/1094670516679272. doi:10.1177/1094670516679272

Varsha, P. S., Akter, S., Kumar, A., Gochhait, S., & Patagundi, B. (2021). The impact of artificial intelligence on branding: A bibliometric analysis (1982–2019). [JGIM]. *Journal of Global Information Management, 29*(4), 221–246. doi:10.4018/JGIM.20210701.oa10

Verhagen, T., van Nes, J., Feldberg, F., & van Dolen, W. (2014). Virtual customer service agents: Using social presence and personalization to shape online service encounters. *Journal of Computer-Mediated Communication, 19*(3), 529–545. doi:10.1111/jcc4.12066

Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: Determinants, dynamics and management strategies. *Journal of Retailing, 85*(1), 31–41. doi:10.1016/j.jretai.2008.11.001

Walch, K. (2019). AI's increasing role in customer service. Forbes. https://www.forbes.com/sites/cognitiveworld/2019/07/02/ais-increasing-role-in-customer-service/?sh=75caf18673fc

Wang, E. S.-T., & Lin, R.-L. (2017). Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. *Behaviour & Information Technology, 36*(1), 2–10. doi:10.1080/0144929X.2016.1143033

Wang, W.-T., Wang, Y.-S., & Liu, E.-R. (2016). The stickiness intention of group-buying websites: The integration of the commitment–trust theory and e-commerce success model. *Information & Management, 53*(5), 625–642. doi:10.1016/j.im.2016.01.006

Wang, X., Tajvidi, M., Lin, X., & Hajli, N. (2020). Towards an ethical and trustworthy social commerce community for brand value co-creation: A trust-commitment perspective. *Journal of Business Ethics, 167*(1), 137–152. doi:10.1007/s10551-019-04182-z

Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. *Journal of Service Management, 29*(5), 907–931. doi:10.1108/JOSM-04-2018-0119
Wolfinbarger, M., & Gilly, M. C. (2003). eTailQ: Dimensionalizing, measuring and predicting etail quality. *Journal of Retailing*, 79(3), 183–198. doi:10.1016/S0022-4359(03)00034-4

Xu, J. D. (2016). Retaining customers by utilizing technology-facilitated chat: Mitigating website anxiety and task complexity. *Information & Management*, 53(5), 554–569. doi:10.1016/j.im.2015.12.007

Xu, Y., Shieh, C.-H., van Esch, P., & Ling, I.-L. (2020). AI customer service: Task complexity, problem-solving ability, and usage intention. [AMJ]. *Australasian Marketing Journal*, 28(4), 189–199. doi:10.1016/j.ausmj.2020.03.005

Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. *Journal of Marketing*, 52(3), 2–22. doi:10.1177/002224298805200302

Zhang, T., Bilgihan, A., Kandampully, J., & Lu, C. (2018). Building stronger hospitality brands through online communities. *Journal of Hospitality and Tourism Technology*, 9(2), 158–171. doi:10.1108/JHTT-04-2017-0032

Nasser A. Saif Almuraqab is a chief operating officer, assistant professor, and director of the IT Services Department at University of Dubai since October 2013. He earned a PhD in business administration (MIS) from Dubai Business School, University of Dubai. He also holds an MBA from Abu Dhabi University and a bachelor's degree in MIS from the UAE University. He has more than 17 years of experience in IT and project management and consultancy. He has published many articles in journals, including Production Planning & Control and Journal of Global Information. His research interests are in MIS and business and information fields, and areas such as m-government, m-services, m-learning, and technology acceptance.

Sajjad M. Jasimuddin is professor (professeur senior) at the Kedge Business School, France, since July 2012. He is visiting Renmin University of China and University of Dubai. Previously, he taught at Aberystwyth University (UK), Southampton University (UK), King Abdulaziz University (Saudi Arabia), and University of Dhaka (Bangladesh). Jasimuddin received a Master of Philosophy degree from Cambridge University, Cambridge, UK, and a Ph.D. from Southampton University, Southampton, UK, in 2005. He is associate editor of the Journal of Global Information Management and has authored a textbook and 15 chapters. He has had 120 articles published in journals such as European Journal of Operational Research, International Business Review, Information Systems Journal, International Journal of Production Research, Technological Forecasting & Social Change, IEEE Transactions on Engineering Management, Annals of Operational Research, Production Planning & Control, Electronic Markets, and Journal of Operational Research Society. He is the corresponding author and can be contacted at Sajjad.jasimuddin@kedgebs.com.

Fateh Saci is an associate professor at University of Nimes. He taught at the University Nice, University of Toulouse Capitole, University of Paris 2, and University of Perpignan. He earned a PhD from Nice University. His field of research is finance, accounting, corporate governance, and strategic alliances. He also was awarded two times in China as professor of excellence and awarded for the best paper at the Management and Business Academy International Business Conference, Brunel University, London, December 2015. He has authored six articles, have been published in Finance Research Letters, Australian Economic Papers, Research in International Business and Finance, Strategic Change, International Journal of Economics, Finance and Management Science, and Journal of Organisational Studies and Innovation.
Wolfinbarger, M., & Gilly, M. C. (2003). eTailQ: Dimensionalizing, measuring and predicting etail quality. *Journal of Retailing*, 79(3), 183–198. doi:10.1016/S0022-4359(03)00034-4

Xu, J. D. (2016). Retaining customers by utilizing technology-facilitated chat: Mitigating website anxiety and task complexity. *Information & Management*, 53(5), 554–569. doi:10.1016/j.im.2015.12.007

Xu, Y., Shieh, C.-H., van Esch, P., & Ling, I.-L. (2020). AI customer service: Task complexity, problem-solving ability, and usage intention. [AMJ]. *Australasian Marketing Journal*, 28(4), 189–199. doi:10.1016/j.ausmj.2020.03.005

Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. *Journal of Marketing*, 52(3), 2–22. doi:10.1177/002224298805200302

Zhang, T., Bilgihan, A., Kandampully, J., & Lu, C. (2018). Building stronger hospitality brands through online communities. *Journal of Hospitality and Tourism Technology*, 9(2), 158–171. doi:10.1108/JHTT-04-2017-0032

Nasser A. Saif Almuraqab is a chief operating officer, assistant professor, and director of the IT Services Department at University of Dubai since October 2013. He earned a PhD in business administration (MIS) from Dubai Business School, University of Dubai. He also holds an MBA from Abu Dhabi University and a bachelor's degree in MIS from the UAE University. He has more than 17 years of experience in IT and project management and consultancy. He has published many articles in journals, including Production Planning & Control and Journal of Global Information. His research interests are in MIS and business and information fields, and areas such as m-government, m-services, m-learning, and technology acceptance.

Sajjad M. Jasimuddin is professor (professeur senior) at the Kedge Business School, France, since July 2012. He is visiting Renmin University of China and University of Dubai. Previously, he taught at Aberystwyth University (UK), Southampton University (UK), King Abdulaziz University (Saudi Arabia), and University of Dhaka (Bangladesh). Jasimuddin received a Master of Philosophy degree from Cambridge University, Cambridge, UK, and a Ph.D. from Southampton University, Southampton, UK, in 2005. He is associate editor of the Journal of Global Information Management and has authored a textbook and 15 chapters. He has had 120 articles published in journals such as European Journal of Operational Research, International Business Review, Information Systems Journal, International Journal of Production Research, Technological Forecasting & Social Change, IEEE Transactions on Engineering Management, Annals of Operational Research, Production Planning & Control, Electronic Markets, and Journal of Operational Research Society. He is the corresponding author and can be contacted at Sajjad.jasimuddin@kedgebs.com.

Fateh Saci is an associate professor at University of Nimes. He taught at the University Nice, University of Toulouse Capitole, University of Paris 2, and University of Perpignan. He earned a PhD from Nice University. His field of research is finance, accounting, corporate governance, and strategic alliances. He also was awarded two times in China as professor of excellence and awarded for the best paper at the Management and Business Academy International Business Conference, Brunel University, London, December 2015. He has authored six articles, have been published in Finance Research Letters, Australian Economic Papers, Research in International Business and Finance, Strategic Change, International Journal of Economics, Finance and Management Science, and Journal of Organisational Studies and Innovation.