Examining the mediating role of customer empowerment: the impact of chatbot usability on customer satisfaction in Jordanian commercial banks

Alshibly, Haitham H.; Alwreikat, Asma; Morgos, Rafaa; Abuaddous, Murad Yousef Article Examining the mediating role of customer empowerment: the impact of chatbot usability on customer satisfaction in Jordanian commercial banks Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Alshibly, Haitham H.; Alwreikat, Asma; Morgos, Rafaa; Abuaddous, Murad Yousef (2024) : Examining the mediating role of customer empowerment: the impact of chatbot usability on customer satisfaction in Jordanian commercial banks, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-24, https://doi.org/10.1080/23311975.2024.2387196 This Version is available at: https://hdl.handle.net/10419/326472 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Examining the mediating role of customer empowerment: the impact of chatbot usability on customer satisfaction in Jordanian commercial banks Haitham H. Alshibly, Asma Alwreikat, Rafaa Morgos & Murad Yousef Abuaddous To cite this article: Haitham H. Alshibly, Asma Alwreikat, Rafaa Morgos & Murad Yousef Abuaddous (2024) Examining the mediating role of customer empowerment: the impact of chatbot usability on customer satisfaction in Jordanian commercial banks, Cogent Business & Management, 11:1, 2387196, DOI: 10.1080/23311975.2024.2387196 To link to this article: https://doi.org/10.1080/23311975.2024.2387196 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 08 Aug 2024. Submit your article to this journal Article views: 4659 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 InformatIon & technology management | research artIcle Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2387196 Examining the mediating role of customer empowerment: the impact of chatbot usability on customer satisfaction in Jordanian commercial banks haitham h. alshiblya , asma alwreikatb , rafaa morgosc and murad yousef abuaddousd aManagement information systems Department, Faculty of Business, al-Balqa applied university, as-salt, Jordan; bLibrary and information Management Department, al-Balqa applied university, as-salt, Jordan; cnational Center for Research, Ministry of science and technology, Khartoum, sudan; dFaculty of Business, al-Balqa applied university, as-salt, Jordan ABSTRACT this study examines the impact of chatbot usability on customer satisfaction in Jordanian commercial banks, focusing on the mediating role of customer empowerment. Using a quantitative approach, data was collected from bank customers in Jordan through surveys. Usability is evaluated through ease of use, responsiveness, reliability, and personalization. customer empowerment is measured by perceived control, information accessibility, and decision-making autonomy. the findings offer insights for banks to enhance chatbot systems, improve customer satisfaction, and empower interactions. results reveal that reliability and Personalization significantly predict customer satisfaction directly and indirectly through customer empowerment. ease of Use indirectly affects satisfaction via empowerment. responsiveness may not significantly impact satisfaction. these findings guide practitioners to enhance customer experiences, loyalty, and business success. 1. Introduction the banking sector has witnessed profound changes in customer interactions driven by technological advancements and novel customer service approaches. an innovation of note is the advent of chatbots, automated conversational agents capable of human-like interactions, offering swift support and responses to customer queries (Şenyapar, 2024). recognising their potential, banks integrate chatbots to bolster customer service, streamline operations, and curtail costs (cîmpeanu et al., 2023), these bots have become indispensable tools, enabling uninterrupted support, task automation, and handling high customer inquiries concurrently (raj etal., 2023). through efficient, personalized assistance, chatbots enhance customer satisfaction, cut costs, and improve operational efficiency (Dale, 2016). In banking, chatbots undertake various functions: answering inquiries, checking balances, processing transactions, and aiding product applications (li & Zhang, 2023; naqvi et al., 2024; Zhang et al., 2021). these intelligent systems harness real-time banking data to offer accurate information, even extending to personalized recommendations based on customer data and transactions (li et al., 2019; riikkinen et al., 2018). their adoption transforms customer service, revolutionizing interactions and raising service quality (maseke, 2024). chatbots, empowered by artificial intelligence, engage in natural language conversations emulating human interactions (Dale, 2016). chatbots as virtual personal assistants perform a variety of duties: addressing queries, providing account information, facilitating financial transactions (maseke, 2024), and advising personalized recommendations and solutions (adamopoulou & moussiades, 2020). Beyond enriching customer experiences, chatbots sustain cost savings by automating routine tasks and enhancing security through cautious fraud detection (rani & suresh, 2023). yet, a comprehensive © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group CONTACT Rafaa Morgos [email protected] national Center for Research, Ministry of science and technology,Khartoum11111, sudan. https://doi.org/10.1080/23311975.2024.2387196 this is 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 reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY received 23 october 2023 revised 7 may 2024 accepted 7 July 2024 KEYWORDS chatbots; usability; customer satisfaction; customer empowerment; Jordanian commercial banks SUBJECTS Information technology; Business, management and accounting; social Work 2 h. h. alshIBly etal. understanding of the factors affecting customer satisfaction in chatbot interactions is needed. chatbot usability characteristics such as ease of use, responsiveness, reliability, and personalization are key in influencing satisfaction (chen et al., 2021; Davis, 1989; nagadeepa et al., 2024). moreover, customer empowerment – granting control and decision-making autonomy – positively impacts satisfaction and loyalty (alshibly & chiong, 2015; liu et al.,2022; naqvi et al., 2024). Despite extensive research on chatbot usability and empowerment, limited work is dedicated to Jordanian commercial banks. this study fills this gap by investigating the relationship between chatbot usability, customer empowerment, and satisfaction within Jordanian commercial banks. more specifically, the objectives of this research are: to assess how usability facets impact satisfaction and to investigate the role that customer empowerment plays as a mediator between chatbot usability and satisfaction. this study bears theoretical and practical significance. It contributes empirically to the literature on chatbot usability, empowerment, and their impact on satisfaction, enhancing understanding of these dynamics in the Jordanian banking context. Valuable insights will aid Jordanian banks in optimizing chatbots for superior customer experiences, aligning with expectations. the next sections will address the implications for theory, practice, and future research directions after delving into the literature on chatbot usability and customer empowerment, outlining the research hypotheses, outlining the methodology, and presenting the findings. 2.Theoretical background and conceptual framework 2.1. Chatbots usability in the banking industry chatbots are conversational agents that constitute computer software designed to simulate human-like conversations and interact with humans through the installed voice or textual interfaces (Dignum & Dignum, 2020; li & Zhang, 2023; luger & sellen, 2016). these systems are based on natural language processing (nlP) and artificial intelligence (aI) technologies to interact with customers, provide them with information, and perform other tasks related to customer support (Zhang et al., 2021). as chatbot capabilities advance, they are revolutionizing human-computer interaction by shaping the future of digital engagement (følstad & Kvale, 2018). nowadays, chatbots have applications in customer service, e-commerce, healthcare, and education (Jiang et al., 2022; shaalan et al., 2022). they might be considered the new trend of human-computer interaction, with researchers saying it will shape the future of this interaction (shaalan et al., 2022). chatbots in banking enhance customer service by implementing the provision of round-the-clock support, reducing waiting time, and providing immediate customer service support (shaalan et al., 2022). these multifaced aI agents adequately handle queries, give account-specific information, facilitate transactions, and smartly lead customers through different banking procedures (Bahoo et al., 2024). Powered by nlP and machine learning capabilities, chatbots are transforming customer interactions by offering personalized, timely responses that reshape the contours of the banking experience (aleedy etal., 2019; Zhu etal., 2024). By adeptly managing diverse banking functions, chatbots efficiently address high customer volumes and customize service delivery. this revolutionizes how customers engage with financial institutions through the power of conversational technologies (maseke, 2024). the effectiveness of chatbots within the banking industry is related to their usability, a multifaceted concept that includes aspects such as reliability, personalization, and responsiveness as defined by Iso9241-11 (2018). high levels of usability drive positive beliefs in chatbot usefulness and satisfaction. focusing on chatbot usability enhances clients’ experiences, adoption rates, and chatbot integration success (Kim et al., 2024). the tam serves as a foundational framework for understanding user acceptance and adoption of technology (Qteisha et al., 2013). Perceived ease of use and usefulness are pivotal tam factors (Davis, 1989). these factors are seamlessly extended to chatbots, where dimensions of chatbot usability, including chatbot ease of use, reliability and chatbot responsiveness, significantly influence customers’ perceptions of chatbot functionality (chen etal., 2021). this aligns with the social Presence theory, which emphasizes the importance of social cues in shaping users’ perceptions (short et al., 1976). In the case of chatbots, increased personalization enhances social presence, resulting in favourable user perceptions and increased cogent BUsIness & management 3 engagement (shumanov & Johnson, 2021). When chatbots offer a human-like interface, reliable functionality, and personalized and relevant communication, users are more likely to perceive them as helpful tools (le & nguyen, 2024). By understanding and leveraging these factors, banks can enhance the usability of their chatbot systems, leading to improved user acceptance, customer satisfaction, and overall effectiveness in the banking sector. one of the primary antecedents of information system usage behaviour is perceived ease of use (Qteisha et al., 2013). Perceived ease of use was described by Davis (1989) as the extent to which a person feels that using a technology requires no mental effort. Within the chatbot context, it describes how easy users think the chatbot is to use and navigate. It encapsulates how users view interacting with a chatbot interface to be easy and intuitive (Brooke, 1996), which has a big impact on user satisfaction and adoption (nielsen, 1993; Venkatesh & Davis, 2000). chatbot responsiveness refers to the perception of customers regarding providing prompt and accurate answers to the customer’s inquiries by the chatbot as well as helping when needed (o’Brien & toms, 2008), which greatly affects customer engagement. higher perceived responsiveness leads to greater customer satisfaction and continued use of the chatbot service (Kim et al., 2024). chatbot reliability refers to the degree to which customers perceive the chatbot as consistently available and accessible whenever they need assistance (Delone & mclean, 2003; li etal., 2021; yun & Park, 2022). as defined by Kim and chang (2020), it captures the perceived consistent and accurate performance of a chatbot over multiple interactions, handles errors effectively and provides appropriate solutions. In the context of chatbot, personalization refers to the chatbot’s ability to adjust its interactions to fulfil the unique wants and preferences of each customer (lin & lu, 2011). It may include offering personalized recommendations or suggestions based on customer preferences, remembering previous interactions, and tailoring responses accordingly (De groot, 2022; silva & canedo, 2024). Previous research highlights the central role of chatbot usability in enhancing and maintaining high levels of customer satisfaction across different industries (cao et al., 2024; magno & Dossena, 2023; Peruchini et al., 2024). It has also demonstrated that key usability factors including ease of use, responsiveness, reliability, and personalization significantly impact customers’ perceptions and use of chatbot services (li et al., 2021; magno & Dossena, 2023; yun & Park, 2022). however, more empirical research is still needed to translate generalized findings into practical strategies customized for banking chatbots (cao et al., 2024; Peruchini et al., 2024). 2.2. Customer empowerment in the banking sector customer empowerment is a complex concept and is often referred to as consumers’ having informed choices, controlling the type of interaction, and being able to influence decisions (alshibly & chiong, 2015). It includes the provision of customers with an environment where these customers can frequently share information, be involved in service design in various ways and interact with service employees in service delivery (Xie et al., 2020). according to han etal. (2019), empowering customers refers to customers’ perception of control over their service experience, which is shaped by the strategies employed by companies (alshibly & chiong, 2015). hence, perceived control plays a crucial role in building up active engagement and creating customer empowerment. empowered customers who trust and feel actively involved are more likely to engage in interactions (füller et al., 2009). self-efficacy, as explained by Venkatesh and Davis (2000) and Bandura (1977), is one of the determinants that significantly influences engagement, empowerment, and overall experiences. high self-efficacy leads to increased engagement, empowerment, and positive interactions, while negative experiences can result in low self-efficacy. customer empowerment in the banking sector involves providing customers with the resources needed to make informed decisions, regulate their interactions, and exert influence over their experienced financial service outcomes (singh, 2020; Zimmerman, 2000). this empowerment paradigm encompasses three core components: perceived control (anderson & agarwal, 2010), knowledge empowerment (spreitzer, 1995), and influence empowerment (conger & Kanungo, 1988). 4 h. h. alshIBly etal. Perceived control refers to empowered customers taking an active role in their interactions and exerting control over the service experience (gupta & sharma, 2019). this sense of control improves self-efficacy and empowerment, both of which are crucial for providing positive customer experiences (Bandura, 1977). empirical studies have shown that giving customers control over chatbot interactions leads to positive outcomes such as increased satisfaction and empowerment (Xu et al., 2022). Knowledge empowerment involves providing customers with financial information that allows them to make more informed decisions (saarijärvi et al., 2013). transparent communication channels and the dissemination of relevant information build customer trust (chen & Xie, 2008), increasing engagement levels and satisfaction (Dexe et al., 2021). Influence empowerment occurs when customers believe that their input enhances the service (füller etal., 2009). Interactions driven by feedback from customers who believe that their ideas are being taken seriously and used to improve service quality can increase customer retention rates. In banking, customer empowerment is fostered using tools such as chatbots (Buehler & maas, 2018; Dabholkar & Bagozzi, 2002). chatbots enable customers to access and obtain information, resolve queries, and complete transactions easily and efficiently, thereby fostering customer empowerment by enabling customers sense of control over the service experience (Buehler & maas, 2018; Dabholkar & Bagozzi, 2002). empowered customers who have a perception of control are more likely to experience an increased sense of satisfaction (Brodie et al., 2011). Positive experiences contribute to strengthening customers’ self-efficacy and their belief in having more power (Bandura, 1977). In addition to enabling active customer engagement and perceiving a sense of control, chatbots also empower customers by equipping the necessary tools for informed decision-making (Buehler & maas, 2018). chatbots play a significant role in this regard by providing customers with real-time access to account details, financial services information, and personalized suggestions and recommendations (Buehler & maas, 2018). according to shin et al. (2020), customer feedback has an additional impact on customer empowerment, as well. customers can shape chatbot conversations and functions by giving feedback actively. research by hsieh et al. (2022) stresses that the integration of feedback loops enables customers to propose improvements, complain about problems and vote for new features to be added to the chatbots. chatbots with these feedback mechanisms make customers engaged and constantly contribute to their improved experiences. chatbots can offer client-specific advice and recommendations by using a vast set of client information collected and processed with aI and machine learning techniques. customers can then make appropriate choices that are relevant to their needs and financial objectives (Xie et al., 2020). customers are empowered to make informed decisions as a result (alshibly & chiong, 2015). 2.3. Chatbot usability and customer empowerment the usability of chatbots has a significant impact on customer empowerment (chien & yao, 2023). It refers to how effectively and efficiently customers can interact with a chatbot to achieve their goals and perform desired tasks (nielsen, 1993). chatbot-enhanced usability has the potential to empower customers by facilitating effective interactions and giving them more control, access to information, and decision-making impact (ameur & rached, 2021; Wang et al., 2023). Usability dimensions are linked. User-centric chatbot design promotes empowerment through interaction control, knowledge, and influence (silva & canedo, 2024). When a chatbot is designed with high usability features, which is measured by its easiness of use, reliability, responsiveness, and personalization, it means that it is intuitive, easy to navigate, and provides clear and concise responses to user queries (silva & canedo, 2024). When clients perceive the chatbot as a user-friendly interface and responsive to their enquiries, they feel empowered because they can get the data and support they need easily. such usability features empower customers by providing them with more sense of control, more knowledge, and more decision-making influence (chien & yao, 2023; Wang et al., 2023). Knowledge plays a pivotal role in empowering customers within the banking sector, as emphasized by Dwivedi etal. (2011). chatbots by providing customers with real-time access to valuable information, cogent BUsIness & management 5 empower customers to make well-informed decisions and smarter financial choices (fan et al., 2022). moreover, by facilitating transparent decision-making processes, chatbot usability enables customers to gain a comprehensive understanding of their financial options and the underlying rationale behind suggested decisions (Jiang etal., 2022). this transparency, coupled with the availability of pertinent information, empowers customers to make informed choices and smarter financial choices that align with their unique financial goals and preferences (fan et al., 2022). chatbot usability features also are a source of more customers’ perceived control and confidence (chien & yao, 2023). customers gain more confidence when they can easily comprehend how the chatbot operates, when they can use chatbot tools without complications and when they can do the things they want without unreasonable effort or confusion (Wang etal., 2024). a chatbot that responds clearly and promptly further bolsters this sense of control and has greater influence over decision-making (Brodie etal., 2011; Wang etal., 2024). the evidence indicates chatbots optimized for ease of use can help maximize customer empowerment. consequently, we propose the following hypothesis: h1: ease of Use positively affects customer empowerment. chatbots that respond quickly and efficiently contribute to customers’ perception of responsiveness. customers who believe their questions are promptly answered are more likely to feel empowered during their interactions with the chatbot (chen et al., 2023). thus, we propose the following hypothesis: h2: responsiveness positively affects customer empowerment. Previous studies (li et al., 2021; Wang et al., 2024) suggest that consistently available and accessible chatbots influence customer empowerment. When customers perceive the consistent and accurate performance of a chatbot over multiple interactions, including effective error handling and providing appropriate solutions, they feel empowered as they can rely on the chatbot to provide reliable assistance. therefore, we put forth the following hypothesis: h3: reliability positively affects customer empowerment. Personalized interactions tailored to individual customer preferences increase their sense of control and influence during the interaction process (gao & liu, 2022; Jiang et al., 2022). customers who perceive chatbots to be more personalized are more likely to feel empowered during interactions thus, we propose the following hypotheses: h4: Personalization positively affects customer empowerment. 2.4. Chatbot usability and customer satisfaction customer satisfaction within chatbot interactions epitomizes contentment and fulfilment, capturing effectiveness, efficiency, and quality of engagement (oliver, 1980). grounded in the expectancy-Disconfirmation theory (eDt), meeting or surpassing expectations augments satisfaction, while misalignment diminishes it (Bhattacherjee, 2001). the technology acceptance model (tam) posits that usability dimensions—ease of use, responsiveness, reliability, and personalisation—influence perceived technology usefulness and interaction ease (Davis, 1989). recent studies (Dwivedi et al., 2023) underscore the pivotal role of chatbot usability in engagement and satisfaction. ease of use, reliability, and responsiveness enhance satisfaction, while personalization enriches social presence. chatbot customer satisfaction is dependent on chatbot ease of use, chatbot responsiveness, and personalized interactions. smooth navigation, quick responses, and personalized support are essential. highly usable chatbots in banking provide convenience, accuracy, and ease of access (hari etal., 2022). Usability influences customer satisfaction by improving the overall experience, engagement, and convenience (chen et al., 2021). ease of use is an important factor that influences satisfaction levels significantly 6 h. h. alshIBly etal. (ashfaq et al., 2020; ma et al., 2024). although responsiveness is important, personalization is more significant and leads to higher levels of satisfaction (naqvi et al., 2024). however, research yields mixed results, demonstrating context-specific complexity (Brodie etal., 2011; chung et al., 2020). contextual specifics and user preferences have important roles. li and Zhang (2023) highlight the varying impact of usability across industries and the importance of contextual approaches. the relationship between chatbot usability and customer satisfaction is complex and depends on several factors. comprehensive research needs to be conducted for a better comprehension of such complexity (le & nguyen, 2024). Drawing on established principles of human-computer interaction, we argue that user-friendly interfaces and intuitive interaction processes contribute significantly to overall satisfaction. Previous research by liang et al. (2018) emphasizes the importance of smooth usability in creating positive user experiences. When chatbots are simple to use, customers are more likely to navigate them with ease, leading to higher levels of satisfaction. this study therefore suggests the following: h5: ease of use positively affects customer satisfaction. a chatbot capable of responding to all user queries creates confidence and improves user satisfaction levels. this argument is consistent with the research of Kim et al. (2024) and chen et al. (2023), which underline the immense importance of consumers’ perceived responsiveness in enhancing their satisfaction level. the chatbot’s performance of prompt and effective responses intertwines with a positive user experience that is the foundation of increased satisfaction (Kim et al., 2024). this research proposes the following further hypothesis: h6: responsiveness positively affects customer satisfaction. reliable chatbot performance, defined as accurate and consistent responses, is essential for establishing confidence among users (mostafa & Kasamani, 2022). customers who perceive chatbots to be reliable are more likely to rely on them for assistance, resulting in higher satisfaction (ashfaq et al., 2020). this assertion aligns with prior research emphasizing the importance of reliability in user interactions. as a result, we formulated the following hypothesis: h7: reliability positively affects customer satisfaction. finally, we propose that the chatbot’s ability to adjust its interactions to fulfil the unique wants and preferences of each customer will increase customer satisfaction. Personalized interactions foster a sense that the chatbot understands their individual needs and preferences thereby improving the overall customer banking experience (Kim et al., 2024). When chatbots provide personalized responses, customers feel understood and valued (De groot, 2022)., which increases their satisfaction with the service. We therefore propose the following hypotheses: h8: Personalization positively affects customer satisfaction. 2.5. Customer empowerment and customer satisfaction alshibly and chiong (2015) suggest that empowered customers, who feel valued and in control, experience higher levels of satisfaction. empirical evidence suggests there is a positive association between customer empowerment and customer satisfaction (Wen et al., 2022). empowered customers actively engage with banking services, make informed decisions, and are more likely to advocate for positive recommendations (Kataria et al., 2023). Bharti etal. (2023) propose by implementing empowered strategies, such as user-friendly and responsive chatbots that enhance information access and perceived control, banks can improve customer satisfaction. empowered customers experience higher satisfaction through a sense of control over the banking relationship and services (Xie et al., 2020). for example, when a chatbot empowers customers to independently check balances, pay bills, or transfer funds on their own schedule, it reduces frustration from dependency on bank agents during limited hours. this autonomy and flexibility improve satisfaction. cogent BUsIness & management 7 additionally, empowered customers derive satisfaction from making informed choices (rane et al., 2023). for example, a personalized chatbot that recommends suitable products based on customers’ financial goals and constraints helps them select options that truly meet their needs. this empowered decision-making leads to post-purchase satisfaction rather than doubt over optimal choices. furthermore, empowered customers actively engage in problem-solving rather than passively waiting for issues to be addressed (Dai et al., 2023). for example, a reliable and transparent chatbot that customers trust to resolve requests and complaints independently improves satisfaction by reducing hassle versus traditional channels. In all these examples, empowerment improves satisfaction by granting customers a greater sense of control, capability and confidence over their banking needs through accessible, responsive and personalized digital services. transparency and decision-making autonomy further enhance empowerment, leading to increased satisfaction. When customers feel empowered, they perceive chatbots as useful tools, which in turn boosts their satisfaction (Kataria et al., 2023). Based on this rationale, we present the following hypothesis: h9: customer empowerment positively affects customer satisfaction. 2.6. Mediating role of customer empowerment in chatbot usability and customer satisfaction this study predicts that customer empowerment plays a key mediating role between chatbot usability and customer satisfaction. this aligns with self-Determination theory (sDt), which emphasizes the importance of autonomy and control in driving satisfaction (Deci & ryan, 2000). empowerment reflects the level of independence and influence people feel within chatbot interactions. Prior research confirms it mediates the relationship between usability and satisfaction (Jiang et al., 2022). according to sDt, perceptions of autonomy directly impact fulfilment (Deci & ryan, 2000). this framework supports the idea that optimizing a chatbot’s usability can strengthen customers’ sense of empowerment. and customer empowerment, in turn, positively colours their satisfaction. Beyond usability alone, engaging customers in co-creation may further feed into empowerment and satisfaction. allowing input helps customers personalize assistance to better suit their needs and preferences. It also fosters perceptions of empowerment influence. Prior research establishes customer empowerment as a mediator between chatbot usability and satisfaction (Jiang et al., 2022). as sDt indicates, perceptions of autonomy and control are integral to fostering satisfaction (Deci & ryan, 2000). this framework supports the notion that optimization of chatbot usability can strengthen empowerment, positively impacting satisfaction. engaging customers in co-creation aligns with empowerment and is proposed to further enhance satisfaction (füller et al., 2009). several studies provide support for customer empowerment as a mediator between chatbot usability and customer satisfaction. yun and Park (2022) found that empowerment mediated the relationship between chatbot usability and customer satisfaction. their findings showed that usable and autonomous chatbots enhanced empowerment, thereby increasing satisfaction levels. notably, they also demonstrated the partial mediating role of empowerment in mitigating the negative impact of chatbot errors on satisfaction. additional research reinforces the mediating role of customer empowerment. nguyen et al. (2020) highlighted its importance for self-service technology satisfaction and linked empowerment to chatbot conversational abilities. similarly, Jiang et al. (2022) emphasized empowerment’s significance for chatbot adoption and customer satisfaction. Previous research clearly shows that improving a chatbot’s usability increases customer empowerment, leading to better satisfaction results. however, the exact psychological reasons behind this relationship are still emerging. rooted in the theory of empowerment, this study aims to provide deeper insights. It proposes that empowerment – conceptualized as perceptions of control and influence over interactions – is heightened when chatbots are deemed simple to use. and ultimately, this strengthened sense of empowerment fosters higher satisfaction levels. thus, it is hypothesized that: h10: customer empowerment mediates the relationship between ease of Use and customer satisfaction. 14 h. h. alshIBly etal. In contrast, the relationship between responsiveness and customer empowerment was not found to be statistically significant (β = −0.010, t = 0.263, p = 0.793). the negligible effect size (ƒ2 = 0.000) and non-significant p-value provide no evidentiary basis to accept h2. this null finding suggests responsiveness may not directly impact levels of perceived customer empowerment in the given research context. ease of Use exhibited a positive and significant relationship with customer empowerment (β = 0.284, t = 6.627, p < 0.001), as shown in table 4. this effect was further supported by a medium effect size (ƒ2 = 0.138). the r2 value of 0.440 for customer empowerment indicates a moderate level of explanatory power for the model. these findings provide support for h1. In contrast, responsiveness did not have a significant relationship with customer empowerment (β = −0.010, t = 0.263, p = 0.793). therefore, h2 is not supported. the results further indicate that reliability had a significant positive relationship with customer empowerment (β = 0.353, t = 8.017, p < 0.001), lending support to h3. the medium effect size (ƒ2 = 0.173) substantiates the predictive impact of this relationship. similarly, Personalization was found to influence customer empowerment positively and significantly (β = 0.324, t = 8.070, p < 0.001), corroborating h4. the effect size (ƒ2 = 0.169) also signified a medium level of predictive relevance as per cohen’s thresholds. In summary, reliability, personalization, and ease of use enhance empowerment. however, responsiveness did not emerge as a significant predictor of empowerment. these mixed findings provide initial insights into how chatbot qualities determine customer empowerment. the direct effects of ease of Use (h5) and responsiveness (h6) on customer satisfaction were not substantiated, as their path coefficients were non-significant (p > 0.05). this suggests these attributes may not directly impact satisfaction in this context. In contrast, reliability demonstrated a strong, positive, and significant relationship with satisfaction (β = 0.295, p < 0.001), lending support to h7. the large effect Table 5. Hypothesis testing summary. Hypothesis β t-value p-value ƒ² R2R2 adjusted support H1: ease of use -> Customer empowerment 0.284 6.627 0.000 0.138 0.440 0.434 Yes H2: Responsiveness -> Customer empowerment −0.010 0.263 0.793 0.000 no H3: Reliability -> Customer empowerment 0.353 8.017 0.000 0.173 Yes H4: Personalization -> Customer empowerment 0.324 8.070 0.000 0.169 Yes H5: ease of use -> Customer satisfaction 0.025 0.918 0.358 0.002 0.794 0.791 no H6: Responsiveness -> Customer satisfaction 0.003 0.102 0.919 0.000 no H7: Reliability -> Customer satisfaction 0.295 9.578 0.000 0.283 Yes H8: Personalization -> Customer satisfaction 0.267 9.524 0.000 0.269 Yes H9: Customer empowerment -> Customer satisfaction 0.532 15.915 0.000 0.769 Yes H10: ease of use -> Customer empowerment -> Customer satisfaction 0.153 5.727 0.000 Yes H11: Responsiveness -> Customer empowerment -> Customer satisfaction −0.005 0.260 0.795 no H12: Reliability -> Customer empowerment -> Customer satisfaction 0.186 7.730 0.000 Yes H13: Personalization -> Customer empowerment -> Customer satisfaction 0.171 7.687 0.000 Yes cogent BUsIness & management 15 size (ƒ2 = 0.283) highlights reliability’s prominent direct influence. similarly, Personalization was found to impact satisfaction positively and significantly (β = 0.267, p < 0.001), corroborating h8. the large effect size (ƒ2 = 0.269) strengthens this linkage. most notably, customer empowerment exerted an even stronger effect on satisfaction (β = 0.532, p < 0.001), supported by a very large effect (ƒ2 = 0.769). this underscores the primary importance of empowerment in driving satisfaction, this supports h9, confirming the importance of customer empowerment in driving satisfaction. taken together, these contrasting outcomes provide new theoretical insight. specifically, while reliability, Personalization and empowerment directly enhance satisfaction as expected, ease of Use and responsiveness may not have direct effects. thus, dependability, reliability, Personalization, and empowerment emerge as especially salient predictors of post-interaction perceptions. this study also explored the hypothesized indirect effects of chatbot attributes on customer satisfaction, with customer empowerment serving as the mediator. the analysis employed Pls-sem with bootstrap resampling to evaluate the significance of the indirect relationships. the analysis revealed a significant positive indirect effect of ease of Use on satisfaction (β = 0.153, t = 5.727, p < 0.001), supporting h10 that ease of Use positively impacts satisfaction by fostering empowerment. for h11, the indirect path from responsiveness to satisfaction through empowerment was not significant (β = −0.005, t = 0.260, p = 0.795), indicating responsiveness may not influence satisfaction indirectly via empowerment. h12 posited reliability would positively influence satisfaction indirectly through empowerment. results showed a significant standardized indirect path coefficient (β = 0.186, t = 7.730, p < 0.001), supporting h12. this indicates reliability enhances satisfaction by strengthening empowerment. h13 hypothesized a positive indirect effect of Personalization on satisfaction through empowerment. analysis revealed a significant standardized indirect path coefficient (β = 0.171, t = 7.687, p < 0.001), providing support for h13. this suggests Personalization boosts satisfaction by positively impacting empowerment. In summary, ease of Use, reliability and Personalization significantly strengthened satisfaction indirectly by positively shaping empowerment. however, the mediated effect for responsiveness was not supported. these findings provide novel insights into how specific chatbot qualities may or may not mould key outcomes via the mechanism of empowerment. 4.4. Customer empowerment mediation analysis mediation analyses were conducted to examine the relationships between variables and their mediators using both Preacher and hayes’ technique and Pls-sem. the usage of various approaches such as subgroup analysis and confidence interval did not only analyze the statistical but also the practical significance of the study. Initially, we implemented the method proposed by Preacher and hayes (2008) to run both the direct and the indirect models. finally, to evaluate the statistical significance of the indirect effect, we used a bootstrapping method which was suggested by Preacher and hayes (2008). next, we employed Pls-sem (hair et al., 2021) to proportionately measure total, direct, and indirect effects. Pls-sem provided a detailed investigation into the case of mediating effect by measuring Vaf from the mediator the hair etal. (2021) guidelines were used in assigning the effect sizes, which showed the individual research findings between such significance and practical importance, as well as the strength of the third variable which mediates. the finding is, for example, that Vaf below 20% indicated a low mediation impact. also, Vaf between 20% and 80% pointed out that it was a moderate or effective mediation. the values that exceeded 80% pointed towards a strong mediation effect. the use of a multi-dimensional strategy served the purpose because the results of mediation analysis vary in statistical significance and practical importance. therefore, mediation analyses were performed to check possible moderate associations between various mediators within the design. In the initial analysis, we investigated the relationship between ease of use and customer satisfaction, exploring the potential mediating role of customer empowerment. the results revealed a significant total effect of ease of Use on customer satisfaction (β = 0.178, t = 5.167, p < 0.001). moreover, the indirect effect of ease of Use on customer satisfaction through customer empowerment was significant (β = 0.153, t = 5.727, p < 0.001), while the direct effect was not significant (β = 0.025, t = 0.918, p > 0.05). the Vaf 16 h. h. alshIBly etal. analysis indicated that the indirect effect through customer empowerment accounted for 85.96% of the total effect, signifying a substantial mediation effect. conversely, responsiveness did not demonstrate significant total, indirect, or direct effects on customer satisfaction (β= −0.002, p > 0.05), direct (β = 0.003, p > 0.05), or indirect (β= −0.005, p > 0.05) effects, suggesting that responsiveness does not significantly influence customer satisfaction, either directly or indirectly through customer empowerment. reliability exhibited significant total (β = 0.481, p < 0.001) and indirect (β = 0.186, p < 0.001) effects. the Vaf value of 38.67% indicated partial mediation, with the direct effect also being significant (β = 0.295, p < 0.001). similarly, Personalization showed significant total (β = 0.438, p < 0.001) and indirect (β = 0.171, p < 0.001) effects, with a Vaf of 39.04%, reflecting partial mediation. additionally, the direct effect was significant (β = 0.267, p < 0.001). Please refer to (table 6). In addition to looking at significance levels, bootstrapped confidence intervals we also examined for all indirect effects. confidence intervals indicate a range of plausible values that are likely to include the true population parameter (hair etal., 2021). By not containing zero within the lower and upper bounds, confidence intervals provide statistical evidence complementing significance testing that an indirect effect exists (hair et al., 2021). specifically, the 95% confidence interval for the indirect effect of ease of Use on satisfaction through empowerment was 0.109 to 0.200. this interval does not contain zero, supporting the presence of an indirect effect. for reliability, the 95% confidence interval for the indirect effect on satisfaction through empowerment was 0.133 to 0.245, also excluding zero. the 95% confidence interval for the indirect effect of Personalization on satisfaction through empowerment was 0.123 to 0.223, further corroborating an indirect relationship. see figure 1 for the structural model with path coefficients. 5. Discussion and implications 5.1. Interpretation of results this study advances our theoretical understanding of the interrelationships between chatbot usability, customer empowerment, and customer satisfaction in the context of Jordanian commercial banks. our findings shed light on how chatbot usability impacts customer satisfaction through the lens of customer empowerment playing a mediating role. Building upon and validating preceding research inspecting the effect of chatbot usability characteristics, such as ease of use, reliability, and personalization, on customer empowerment (Dabholkar & Bagozzi, 2002; li & Bernoff, 2008), this study’s results provide further compelling evidence that ease of use, reliability, and personalization each make a positive, distinct contribution to enhancing customer empowerment in the context of banking through chatbot interactions. specifically, ease of use facilitates navigational control and empowerment by enabling intuitive interaction through user-friendly interfaces. When chatbots are easy to use, customers feel empowered to obtain the information and complete the tasks they need via simple, logical interactions (shumanov & Johnson, 2021). reliability is shown to build customer empowerment through a consistently available, accessible, and dependable chatbot performance that creates a sense of informed choices, interaction control, and decision influence. specifically, customers valued a chatbot that was reliably present whenever needed to assist (Dabholkar & Bagozzi, 2002). not having to worry about a chatbot being offline or inaccessible empowered customers to engage at their convenience. furthermore, customers felt empowered by a chatbot that consistently responded helpfully and knowledgeably to their queries. Knowing they could depend on the chatbot to perform reliably and resolve issues built confidence in its capabilities over time. this reliable chatbot performance supported a sense of autonomy by allowing customers to bank independently without restrictions. It also facilitated learning as customers explored the chatbot’s functions without fear of errors or unpredictable behaviour. Perhaps most importantly, reliable chatbot interactions conveyed that a customer’s efforts to engage with the technology could genuinely make an cogent BUsIness & management 17 impact. resolving issues and completing tasks through the dependable chatbot empowered customers and affirmed their autonomy. additionally, chatbot personalization boosts customer empowerment by tailoring its responses and suggestions to individual customer needs and preferences, making customers feel valued and capable of influencing conversations. tailored, personalized recommendations or suggestions empower customers to get information and assistance targeted to their unique needs and preferences (li & Bernoff, 2008). the results of the current study show that strengthening customers’ perceptions of the three key dimensions of empowerment caused through chatbot interactions – perceived control, knowledge empowerment, and influence empowerment – subsequently increases satisfaction levels. Particularly, the results reveal that higher perceptions of empowerment across these dimensions, cultivated via chatbot use, directly impact greater reported customer satisfaction. the dimensions captured critical elements shown to comprise psychological empowerment in prior research (alshibly, 2014). perceived control encompassed sovereignty and choice in banking interactions. Knowledge empowerment reflected knowledge development and understanding of banking services obtained from the chatbot. Influence empowerment represents the influence of the chatbot’s responses, and the significance felt from providing feedback. When customers felt more in control of their banking tasks, learned from the chatbot, and believed their input mattered, their overall satisfaction significantly increased. this indicates chatbot design features and engagement opportunities that strengthen these empowerment domains can boost Table 6. Relationships between variables and their mediators. type of effect effect Path coefficient T-test p-value Remark total effect ease of use -> Customer satisfaction 0.178 5.167** 0.000 sig total effect (p < 0.05) indirect effect ease of use -> Customer empowerment -> Customer satisfaction 0.153 5.727** 0.000 sig indirect effect (p < 0.05) Direct effect ease of use -> Customer satisfaction 0.025 0.918 0.358 not sig Direct effect (p > 0.05) VaF ie/te 85.96% Conclusion the indirect effect accounts for 85.96% of the total effect, indicating a substantial mediation effect of Customer empowerment in the relationship between ease of use and Customer satisfaction. total effect Responsiveness -> Customer satisfaction −0.002 0.085 0.933 not sig total effect (p > 0.05) indirect effect Responsiveness -> Customer empowerment -> Customer satisfaction −0.005 0.260 0.795 not sig indirect effect (p > 0.05) Direct effect Responsiveness -> Customer satisfaction 0.003 0.102 0.919 not sig Direct effect (p > 0.05) Conclusion the results indicate that there is no significant total effect, indirect effect, or direct effect of Responsiveness on Customer satisfaction. this suggests that Responsiveness does not have a significant impact on Customer satisfaction either directly or indirectly through the mediating variable of Customer empowerment total effect Reliability -> Customer satisfaction 0.481 13.602 0.000 sig total effect (p < 0.05) indirect effect Reliability -> Customer empowerment -> Customer satisfaction 0.186 7.730 0.000 sig indirect effect (p < 0.05) Direct effect Reliability -> Customer satisfaction 0.295 9.578 0.000 sig Direct effect (p < 0.05) VaF 38.67% Conclusion the VaF indicates that the mediator variable, Customer empowerment, explains approximately 38.67% of the variance in the relationship between Reliability and Customer satisfaction. this suggests a moderate level of mediation effect, where the mediator partially explains the relationship between the independent variable (Reliability) and the dependent variable (Customer satisfaction) (Hair et al., 2021). total effect Personalisation -> overall Customer satisfaction 0.438 14.312 0.000 sig total effect (p < 0.05) indirect effect Personalisation -> Customer empowerment -> Customer satisfaction 0.171 7.687 0.000 sig indirect effect (p < 0.05) Direct effect Personalisation -> overall Customer satisfaction 0.267 9.524 0.000 sig Direct effect (p < 0.05) VaF 39.04% Conclusion the VaF indicates that the mediator variable, Customer empowerment, explains approximately 39.04% of the variance in the relationship between Personalization and overall Customer satisfaction. this suggests a moderate level of mediation effect, where the mediator partially explains the relationship between the independent variable (Personalization) and the dependent variable (overall Customer satisfaction). 18 h. h. alshIBly etal. satisfaction. effectively empowering customers psychologically through chatbot relationships, in turn, facilitates positive perceptions and satisfaction ions over time. In addition, the present study provides a more comprehensive understanding of how specific chatbot capabilities cultivate empowerment and satisfaction, with implications for research and practice. although responsiveness is often associated with satisfaction, our study did not find a significant link between responsiveness and customer empowerment. this implies factors beyond the timeliness and speed of responses provided by the chatbot may be more critical to empowering customers. existing literature supports this result. studies across industries show that chatbot responsiveness alone may not satisfy customers’ need to tap into a chatbot’s domain knowledge or complete tasks successfully for example, in hospitality (chiu & lin, 2018) and sharing economies (hu & hsu, 2018), responsiveness did not meaningfully influence empowerment. similarly, in mobile (Kuo etal., 2009), self-service (lin, 2015), e-commerce (nguyen et al., 2020), and social media contexts (Jiang et al., 2022). though responsiveness supports experiences, its direct impact on empowerment appears limited across diverse contexts (Kim etal., 2024). the study mediation analysis results demonstrated customer empowerment’s mediating role in the relationship between chatbot usability and customer satisfaction. higher empowerment strengthens the positive impact of usability on satisfaction, the findings support the theoretical frameworks that emphasize the positive impact of empowerment on customer satisfaction (alshibly, 2014). By demonstrating that customer empowerment mediates the relationship between usability and satisfaction, this study reinforces the understanding that when chatbots are designed to be user-friendly, reliable, and personalized, they can empower customers in their interactions. empowered customers feel more in control, engaged, and satisfied with their overall chatbot experience. consequently, the outcomes of this study establish the belief that commercial banks should focus on creating chatbots that are not only usable but also empower customers to create chatbot experiences that lead to higher levels of customer satisfaction. additionally, the results provide important insights into how ease of use, responsiveness, reliability, and personalization impact customer satisfaction with a chatbot. specifically, customer empowerment plays a key mediating role. the findings offer new knowledge on how chatbot design qualities impact the important outcome of customer satisfaction. empowerment plays an essential mediating role, particularly for ease of use. managerial implications suggest focusing on chatbot usability, reliability, and personalization to boost customer empowerment and satisfaction. Figure 1. structural model with path coefficients. cogent BUsIness & management 19 5.2. Theoretical and practical implications theoretically, the study results align with the tam model and emphasize the importance of usability in technology acceptance and user satisfaction. the study also reveals a positive association between themes favouring several human facets of chatbot usability, namely ease of use, reliability, and personalization, and links them to higher customer empowerment. the study findings also are in line with previous research of Zouari and abdelhedi (2021) and ghani et al. (2022), which emphasize the significance of factors like ease of use, reliability, and personalization that enhance customer empowerment and satisfaction in the banking industry. moreover, the study demonstrated that the effect on customer empowerment and satisfaction differ from each case, and responsiveness that works in Jordanian commercial banks is of limited. that develops subtle understanding demonstrates the complex nature of customer satisfaction and empowerment inside the chatbot world and allows insights for future research and practical improvements. Practically, the study findings highlight the need to have a client-friendly and personalized chatbot to boost customer satisfaction. the fact that customer empowerment acts as a mediating role clearly describes the importance of all the activities aimed at empowering customers during the interaction with dedicated virtual assistants. these new prospects are results for academics in this area and improve the planning of other research and professional uses of the chatbot technology. the most important thing here is to make sure that banks build chatbots with great usability, reliability, and personalization. having natural language processing algorithms, machine learning models and design foundations, centred around the users is what will make the chatbots more intuitive and improve engagement (saranya et al., 2020). 5.3. Limitations and future research avenues though providing valuable insights, this research has some limitations that future works can address. first, the focus of this study only on Jordanian commercial banks limits the generalization of it is findings to other contexts (chauhan etal., 2022). future research may conduct cross-cultural studies across industries/domains to strengthen the understanding of the relationships between the study variables. future research collecting data via different methods would offer a richer, more holistic view of how constructs interact in practice. combining survey, experimental, interview and observational data in mixed methods designs would allow for triangulation and help disentangle the impacts of extraneous variables. thirdly, this study examined a narrowed set of chatbot factors known to impact customer outcomes. further research is needed to highlight additional factors such as trust, security, and privacy (Wang et al., 2024). finally, this study was a cross-sectional study, it is also recommended to reexamine the same model on a longitudinal basis, as attitudes and behaviours may change over long-term chatbot use. Authors’ contribution Dr. haitham alshibly was chief in shaping this article’s concept, structuring the research framework, and analyzing the collected data. Dr. asma alwreikat and Dr. rafaa morgos were instrumental in the writing of the main body of the paper, ensuring its clarity and credibility. With his carefully watchful eye, Dr. murad yousef abuaddous not only did a meticulous review and revision of the manuscript but also literally elevated its scholarly value. Declaration of AI use We acknowledge the use of chatgPt. 4 in helping us to review our writing in the final stage of preparing the assessment. the prompt used is: ‘clean up any typos, grammatical mistakes, and misplaced punctuation in the text. Don’t change the sentence structure’. Disclosure statement no potential competing interest was reported by the authors. 20 h. h. alshIBly etal. Funding no funding was received. About the authors Haitham H. Alshibly is Professor of management Information systems at al Balqa applied University, Jordan. he received his Bsc and mBa from the University of Jordan and his PhD in information systems from the University of newcastle, australia. he is the vice president of quality assurance and center affairs at the moment. Professor alshibly has been actively pursuing research related to customer empowerment, e-commerce, s-commerce, e-government, e-marketing, and decision support systems, as well as eDms evaluation and adoption. he has published over 100 papers in the form of books, book chapters, journal articles, and conference papers, among others. Asma Alwreikat is associate Professor of information and library science at al-Balqa applied University. alwreikat received her master’s degree and her PhD. from aberystwyth University. UK. she is now the Director of learning and teaching technology center at al-Balqa applied University. Rafaa Morgos, works for the national center of research at the Documentation and scientific Information center. ministry of science and technology. Khartoum, sudan. Dr. Murad Yousef Abuaddous is an associate professor of accounting at al-Balqa applied University. he has extensive experience in quality assurance, industry link, and grants management. he has also conducted several training sessions on various topics such as leadership, IP, and strategic planning. Dr. abuaddous has published several journal articles and conference papers, and he has been awarded a recent grant for a sustainability challenge program. he is also a reviewer for several journals. ORCID haitham h. alshibly http://orcid.org/0000-0002-9442-6672 asma alwreikat http://orcid.org/0000-0003-4844-6127 Data availability statement Data and material supporting this research can be available upon a reasonable request to the first author: Dr. haitham h. alshibly [email protected].jo. References adamopoulou, e., & moussiades, l. (2020). chatbots: history, technology, and applications. Machine Learning with Applications, 2, 100006. https://doi.org/10.1016/j.mlwa.2020.100006 aleedy, m., shaiba, h., & Bezbradica, m. (2019). generating and analyzing chatbot responses using natural language processing. International Journal of Advanced Computer Science and Applications, 10(9), 100006. https://doi. org/10.14569/IJacsa.2019.0100910 alshibly, h. h. (2014). a free simulation experiment to examine the effects of social commerce website quality and customer psychological empowerment on customers’ satisfaction. Journal of Business Studies Quarterly, 5(4), 21–40. alshibly, h., & chiong, r. (2015). customer empowerment: Does it influence electronic government success? a citizen-centric perspective. Electronic Commerce Research and Applications, 14(6), 393–404. https://doi.org/10.1016/j. elerap.2015.05.003 ameur, h. B., & rached, K. s. B. (2021). effect of empowerment on consumer satisfaction in an online brand community: moderating role of responsiveness. International Journal of Business and Psychology, 3(1), 15–30. anderson, c. J., & agarwal, r. (2010). Practicing safe computing: a multimethod empirical examination of home computer user security behavioral intentions. MIS Quarterly, 34(3), 613–643. https://doi.org/10.2307/25750694 ashfaq, m., yun, J., yu, s., & loureiro, s. m. c. (2020). I, chatbot: modeling the determinants of users’ satisfaction and continuance intention of aI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j. tele.2020.101473 Bahoo, s., cucculelli, m., goga, X., & mondolo, J. (2024). artificial intelligence in finance: a comprehensive review through bibliometric and content analysis. SN Business & Economics, 4(2), 23. https://doi.org/10.1007/ s43546-023-00618-x Bandura, a. (1977). self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191 cogent BUsIness & management 21 Bharti, s. s., Prasad, K., sudha, s., & Kumari, V. (2023). customer acceptability towards aI-enabled digital banking: a Pls-sem approach. Journal of Financial Services Marketing, 28(4), 779–793. https://doi.org/10.1057/s41264-023-00241-9 Bhattacherjee, a. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921 Brodie, r. J., hollebeek, l. D., Jurić, B., & Ilić, a. (2011). customer engagement: conceptual domain, fundamental propositions & implications for research in service marketing (lead article; with commentaries). Journal of Service Research, 14(3), 252–271. https://doi.org/10.1177/1094670511411703 Brooke, J. (1996). sUs: a "Quick and Dirty" usability scale. In P. W. Jordan, B. thomas, B. a. Weerdmeester, & a. l. mcclelland (eds.), Usability evaluation in industry (pp. 189–194). routledge. Buehler, P., & maas, P. (2018). consumer empowerment in insurance: effects on performance risk perceptions in decision making. International Journal of Bank Marketing, 36(6), 1073–1097. https://doi.org/10.1108/ IJBm-12-2016-0182 cao, l., sarkar, s., ramesh, B., mohan, K., & Park, e. h. (2024). shift of ambidexterity modes: an empirical investigation of the impact of artificial intelligence in customer service. International Journal of Information Management, 76, 102773. https://doi.org/10.1016/j.ijinfomgt.2024.102773 central Bank of Jordan. (2022). annual report. chauhan, s., akhtar, a., & gupta, a. (2022). customer experience in digital banking: a review and future research directions. International Journal of Quality and Service Sciences, 14(2), 311–348. https://doi.org/10.1108/IJQss-02-2021-0027 chen, J. s., le, t. t. y., & florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, 49(11), 1512–1531. https://doi.org/10.1108/IJrDm-08-2020-0312 chen, Q., lu, y., gong, y., & Xiong, J. (2023). can aI chatbots help retain customers? Impact of aI service quality on customer loyalty. Internet Research, 33(6), 2205–2243. https://doi.org/10.1108/Intr-09-2021-0686 chen, y., & Xie, J. (2008). online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Science, 54(3), 477–491. https://doi.org/10.1287/mnsc.1070.0810 chien, y. h., & yao, c. K. (2023). enhanced engineering design behaviour using chatbots with user experience. Behaviour & Information Technology, 42(12), 2030–2041. https://doi.org/10.1080/0144929X.2022.2106308 chin, W. W. (2010). how to write up and report Pls analyses. In Handbook of partial least squares. (pp. 655–690). springer. chiu, c. m., & lin, c. P. (2018). Determining the technology acceptance of mobile tour guide service: an empirical study. International Journal of Marketing Studies, 10(3), 1–13. chung, m., Ko, e., Joung, h., & Kim, s. (2020). chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587–595. https://doi.org/10.1016/j.jbusres.2018.10.004 cîmpeanu, I. a., Dragomir, D. a., & Zota, r. D. (2023). Banking chatbots: how artificial intelligence helps the banks. Proceedings of the International Conference on Business Excellence, 17(1), 1716–1727. (https://doi.org/10.2478/ picbe-2023-0153 cohen, J. (2013). Statistical power analysis for the behavioral sciences. routledge. conger, J. a., & Kanungo, r. n. (1988). the empowerment process: Integrating theory and practice. The Academy of Management Review, 13(3), 471–482. https://doi.org/10.2307/258093 Dabholkar, P. a., & Bagozzi, r. P. (2002). an attitudinal model of technology-based self-service: moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184–201. doi: https:// doi.org/10.1177/0092070302303001 Dai, y., liu, a., & lim, c. P. (2023). reconceptualizing chatgPt and generative aI as a student-driven innovation in higher education. Procedia CIRP, 119, 84–90. https://doi.org/10.1016/j.procir.2023.05.002 Dale, r. (2016). the return of the chatbots. Natural Language Engineering, 22(5), 811–817. https://doi.org/10.1017/ s1351324916000243 Davis, f. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 De groot, J. I. m. (2022). the personalization paradox in facebook advertising: the mediating effect of relevance on the personalization–brand attitude relationship and the moderating effect of intrusiveness. Journal of Interactive Advertising, 22(1), 57–74. https://doi.org/10.1080/15252019.2022.2032492 Deci, e. l., & ryan, r. m. (2000). the what and why of goal pursuits: human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/s15327965PlI1104_01 Delone, W. h., & mclean, e. r. (2003). the Delone and mclean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748 Dexe, J., franke, U., & rad, a. (2021). transparency and insurance professionals: a study of swedish insurance practice attitudes and future development. The Geneva Papers on Risk and Insurance-Issues and Practice, 46, 1–26. Dignum, V., & Dignum, f. (2020). agents are dead. In Long live agents! (pp. 1701–1705). Blue sky Idea Paper. Dwivedi, y. K., Balakrishnan, J., Baabdullah, a. m., & Das, r. (2023). Do chatbots establish “humanness” in the customer purchase journey? an investigation through explanatory sequential design. Psychology & Marketing, 40(11), 2244–2271. https://doi.org/10.1002/mar.21888 22 h. h. alshIBly etal. Dwivedi, y. K., rana, n. P., chen, h., & Williams, m. D. (2011 A meta-analysis of the unified theory of acceptance and use of technology (UTAUT) [Paper presentation]. Paper Presented at the IfIP International Working conference on governance and sustainability in Information systems-managing the transfer and Diffusion of I. fan, h., han, B., & gao, W. (2022). (Im) Balanced customer-oriented behaviors and aI chatbots’ efficiency–flexibility performance: the moderating role of customers’ rational choices. Journal of Retailing and Consumer Services, 66, 102937. https://doi.org/10.1016/j.jretconser.2022.102937 følstad, a., & Kvale, K. (2018). chatbots in customer service: a comparative study. Computers in Human Behavior, 89, 245–251. fornell, c., & larcker, D. f. (1981). evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104 füller, J., mühlbacher, h., matzler, K., & Jawecki, g. (2009). consumer empowerment through internet-based co-creation. Journal of Management Information Systems, 26(3), 71–102. https://doi.org/10.2753/mIs0742-1222260303 gao, y., & liu, h. (2022). artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective. Journal of Research in Interactive Marketing, 17(5), 663–680. (ahead-of-print), https://doi.org/10.1108/ JrIm-01-2022-0023 ghani, e. K., ali, m. m., musa, m. n. r., & omonov, a. a. (2022). the effect of perceived usefulness, reliability, and coVID-19 pandemic on digital banking effectiveness: analysis using technology acceptance model. Sustainability, 14(18), 11248. https://doi.org/10.3390/su141811248 guest, g., Bunce, a., & Johnson, l. (2006). how many interviews are enough? an experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903 gupta, a., & sharma, D. (2019). customers’ attitude towards chatbots in banking industry of India. International Journal of Innovative Technology and Exploring Engineering, 8(11), 1222–1225. https://doi.org/10.35940/ijitee. J9366.0981119 hair, J. f., hult, g. t. m., ringle, c. m., & sarstedt, m. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). (3rd ed.). sage. han, X., fang, s., Xie, l., & yang, J. (2019). service fairness and customer satisfaction: mediating role of customer psychological empowerment. Journal of Contemporary Marketing Science, 2(1), 50–62. https://doi.org/10.1108/ Jcmars-01-2019-0003 hari, h., Iyer, r., & sampat, B. (2022). customer brand engagement through chatbots on bank websites–examining the antecedents and consequences. International Journal of Human–Computer Interaction, 38(13), 1212–1227. https://doi.org/10.1080/10447318.2021.1988487 henseler, J., ringle, c. m., & sarstedt, m. (2015). a new criterion for assessing discriminant validity in variance based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/ s11747-014-0403-8 hsieh, s. h., lee, c. t., & tseng, t. h. (2022). Psychological empowerment and user satisfaction: Investigating the influences of online brand community participation. Information & Management, 59(1), 103570. https://doi. org/10.1016/j.im.2021.103570 hu, y., & hsu, c. (2018). examination of social presence-empowerment relationship in 3D virtual environments: case studies in digital shared spaces. Computers in Human Behavior, 85, 304–313. Iso9241-11. (2018). ergonomics of human-system interaction. Part 11: Usability: Definitions and concepts. doi:https:// www.iso.org/obp/ui/#iso:std:iso:9241:-11:ed-2:v1:en Jiang, h., cheng, y., yang, J., & gao, s. (2022). aI-powered chatbot communication with customers: Dialogic interactions, satisfaction, engagement, and customer behavior. Computers in Human Behavior, 134, 107329. https://doi. org/10.1016/j.chb.2022.107329 Jiang, K., Qin, m., & li, s. (2022). chatbots in retail: how do they affect the continued use and purchase intentions of chinese consumers? Journal of Consumer Behaviour, 21(4), 756–772. https://doi.org/10.1002/cb.2034 Kataria, P., Kumar, s., & gupta, V. P. (2023). customer empowerment, customer retention and firm’s performance: the mediating role of innovation and customer satisfaction. South Asian Journal of Management, 30(1), 164–186. Kim, m., & chang, B. (2020). the effect of service quality on the reuse intention of a chatbot: focusing on user satisfaction, reliability, and Immersion. International Journal of Contents, 16(4), 1–15. Kim, J. s., Kim, m., & Baek, t. h. (2024). enhancing user experience with a generative aI chatbot. International Journal of Human–Computer Interaction, 1–13. ahead of print. https://doi.org/10.1080/10447318.2024.2311971 Kuo, y. f., Wu, c. m., & Deng, W. J. (2009). the relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in Human Behavior, 25(4), 887–896. https://doi.org/10.1016/j.chb.2009.03.003 le, X. c., & nguyen, t. h. (2024). the effects of chatbot characteristics and customer experience on satisfaction and continuance intention toward banking chatbots: Data from Vietnam. Data in Brief, 52, 110025. https://doi. org/10.1016/j.dib.2023.110025 liang, l. J., choi, h. c., & Joppe, m. (2018). exploring the relationship between satisfaction, trust and switching intention, repurchase intention in the context of airbnb. International Journal of Hospitality Management, 69, 41–48. https://doi.org/10.1016/j.ijhm.2017.10.015 cogent BUsIness & management 23 li, c., & Bernoff, J. (2008). harnessing the power of the oh-so-social web. MIT Sloan Management Review, 49(3), 36– 42. li, l., lee, K. y., emokpae, e., & yang, s. B. (2021). What makes you continuously use chatbot services? evidence from chinese online travel agencies. Electronic Markets, 31(3), 575–599. https://doi.org/10.1007/ s12525-020-00454-z lin, B. (2015). self-service technologies: empowering or depressing? Journal of Service Management, 26(5), 648–665. lin, h. f., & lu, h. P. (2011). Predicting consumer intention to use mobile commerce: an empirical test of competing theories. British Journal of Management, 24(11), 658–678. li, g., shen, y., Zhao, P., lu, X., liu, J., liu, y., & hoi, s. c. (2019). Detecting cyberattacks in industrial control systems using online learning algorithms. Neurocomputing, 364, 338–348. doi: . https://doi.org/10.1016/j.neucom.2019.07.031 liu, f., lai, K.-h., Wu, J., & Duan, W. (2021). listening to online reviews: a mixed-methods investigation of customer experience in the sharing economy. Decision Support Systems, 149, 113609. https://doi.org/10.1016/j.dss.2021.113609 li, c.-y., & Zhang, J.-t. (2023). chatbots or me? consumers’ switching between human agents and conversational agents. Journal of Retailing and Consumer Services, 72, 103264. https://doi.org/10.1016/j.jretconser.2023.103264 liu, y., li, X., & Xiang, Z. (2022 The effect of chatbot-customer interaction on consumer brand advocacy: Exploring the role of chatbots [Paper presentation]. 2022 Ieee 12th International conference on electronics Information and emergency communication (IceIec) (pp. 185–190). Ieee. https://doi.org/10.1109/IceIec54567.2022.9835050 luger, e., & sellen, a. (2016). like having a really bad Pa the gulf between user expectation and experience of conversational agents. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, (pp. 5286–- 5297.). ma, J., Wang, P., li, B., Wang, t., Pang, X. s., & Wang, D. (2024). exploring user adoption of chatgPt: a technology acceptance model Perspective. International Journal of Human–Computer Interaction, 1–15. ahead of print. https:// doi.org/10.1080/10447318.2024.2314358 magno, f., & Dossena, g. (2023). the effects of chatbots’ attributes on customer relationships with brands: Pls-sem and importance–performance map analysis. The TQM Journal, 35(5), 1156–1169. https://doi.org/10.1108/ tQm-02-2022-0080 maseke, B. f. (2024). the transformative Power of artificial Intelligence in Banking client service. South Asian Journal of Social Studies and Economics, 21(3), 93–105. https://doi.org/10.9734/sajsse/2024/v21i3787 mostafa, r. B., & Kasamani, t. (2022). antecedents and consequences of chatbot initial trust. European Journal of Marketing, 56(6), 1748–1771. https://doi.org/10.1108/eJm-02-2020-0084 nagadeepa, c., Pushpa, a., mukthar, K. P. J., rurush-asencio, r., sifuentes-stratti, J., & rodriguez-Kong, J. (2024). User’s continuance intention towards banker’s chatbot service – a technology acceptance using sUs and ttf model. hamdan, a., alareeni, B. and Khamis, r. (ed.) Digital technology and changing roles in managerial and financial accounting: Theoretical knowledge and practical application (studies in managerial and financial accounting (Vol. 36, pp. 65–77), emerald Publishing limited. https://doi.org/10.1108/s1479-351220240000036006 naqvi, m. h. a., hongyu, Z., naqvi, m. h., & Kun, l. (2024). Impact of service agents on customer satisfaction and loyalty: mediating role of chatbots. Journal of Modelling in Management, 19(2), 470–491. https://doi.org/10.1108/ Jm2-01-2023-0004 nguyen, t., Van, t., Pham, m., Dung, t., Dung, t., & Bich, t., P. (2020). Impact of service quality, customer satisfaction and switching costs on customer loyalty. The Journal of Asian Finance, Economics and Business, 7(8), 395–405. https://doi.org/10.13106/jafeb.2020.vol7.no8.395 nielsen, J. (1993). Usability engineering. aP Professional. o’Brien, h. l., & toms, e. g. (2008). What is user engagement? a conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology, 59(6), 938–955. https://doi. org/10.1002/asi.20801 oliver, r. l. (1980). a cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405 Parasuraman, a., Zeithaml, V. a., & malhotra, a. (2005). e-s-QUal: a multiple item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213–233. https://doi.org/10.1177/1094670504271156 Park, J.-h. (2014). the effects of personalization on user continuance in social networking sites. Information Processing & Management, 50(3), 462–475. https://doi.org/10.1016/j.ipm.2014.02.002 Peruchini, m., da silva, g. m., & teixeira, J. m. (2024). Between artificial intelligence and customer experience: a literature review on the intersection. Discover Artificial Intelligence, 4(1), 4. https://doi.org/10.1007/s44163-024-00105-8 Preacher, K. J., & hayes, a. f. (2008). asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/brm.40.3.879 Qteisha, m. t., alshibly, h., & alqatawna, J. (2013). factors influencing the adoption of e-learning in Jordan: an extended tam model. European Journal of Business and Management, 5(18), 84–101. raj, r., singh, a., Kumar, V., & Verma, P. (2023). analyzing the potential benefits and use cases of chatgPt as a tool for improving the efficiency and effectiveness of business operations. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(3), 100140. https://doi.org/10.1016/j.tbench.2023.100140 rane, n. l., achari, a., & choudhary, s. P. (2023). enhancing customer loyalty through quality of service: effective strategies to improve customer satisfaction, experience, relationship, and engagement. International Research Journal of Modernization in Engineering Technology and Science, 5(5), 427–452.