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Purpose
While customer experience (CE) is recognized as a critical determinant of business success, both academics and managers are yet to find a means to gain a comprehensive understanding of CE cost-effectively. The authors argue that the application of relevant AI technology could help address this challenge. Employing interactively prompted nar...
Contexts in source publication
Context 1
... on a comprehensive review of CE literature, the experiential element of feelings has been referred to as affect (e.g., Holbrook and Hirschman, 1982), feelings (e.g., Schmitt, 1999), emotions (e.g., Shaw and Ivens, 2002), hedonic value (e.g., Klaus and Maklan, 2011), or mood ( Bagdare and Jain, 2013). Table 1 summarizes the semantic interpretations associated with the CE feeling element. Accommodating the interpretations from Table 1 into a semantically precise model requires understanding how these terms differ in meaning and how they are represented within the fields of psychology concerned with studying subjective experience. ...Context 2
... 1 summarizes the semantic interpretations associated with the CE feeling element. Accommodating the interpretations from Table 1 into a semantically precise model requires understanding how these terms differ in meaning and how they are represented within the fields of psychology concerned with studying subjective experience. Dividing these terms into two hierarchical levels (as shown in Table 1) allows for a natural grouping and increased semantic clarity. ...Context 3
... the interpretations from Table 1 into a semantically precise model requires understanding how these terms differ in meaning and how they are represented within the fields of psychology concerned with studying subjective experience. Dividing these terms into two hierarchical levels (as shown in Table 1) allows for a natural grouping and increased semantic clarity. Provided that the hierarchical levels can be distinguished based on their temporal characteristics (Fox, 2018), it is argued here that feelings and affect act as higher-level umbrella terms (level 1) to the feeling element. ...Similar publications
New chat AI applications like ChatGPT offer an advanced understanding of question context and memory across multi-step tasks, such that experiments can test its deductive reasoning. This paper proposes a multi-role and multi-step challenge, where ChatGPT plays the classic twenty-questions game but innovatively switches roles from the questioner to...
Citations
... It is essential to engage the consumer at the appropriate moment and address them individually (Carvajal, 2011). A viable method for doing this is via engaging with the firm via a chatbot, hence enhancing the customer experience (Mende et al., 2019;Sidaoui et al., 2020). Figure 2 illustrates the correlation between chatbot quality and customer experience. ...
... It is essential to engage the consumer at the appropriate moment and address them individually (Carvajal, 2011). A viable method for doing this is via engaging with the firm via a chatbot, hence enhancing the customer experience (Mende et al., 2019;Sidaoui et al., 2020). Figure 2 illustrates the correlation between chatbot quality and customer experience. ...
AI-powered chatbots are becoming more widespread in the tourist industry for handling customer service difficulties; nevertheless, little is known about how they recover from these situations. Customer loyalty is anticipated to be positively impacted by the widespread use of chatbots. Using chatbots' performance and interaction customization as a lens, this research aims to examine how symbolic recovery affects tourists' loyalty. Additionally, it explores how chatbots' symbolic recovery affects tourists' ability to reconcile and remain loyal. The research used structural equation modeling to analyze data collected from 500 tourists through an online questionnaire distributed From November till January 2025. Based on the results, it appears that chatbots may help tourists to settle and continue to be loyal by increasing customer loyalty via symbolic recuperation. Another way in which chatbots' symbolic recovery affects tourists' loyalty is via tourist satisfaction. The results give promising information that tourist organizations may use to improve chatbots' symbolic recovery. Businesses catering to tourists will be motivated to use chatbots for service recovery if they are efficient in helping with symbolic rehabilitation and ensuring customer loyalty.
... In addition, guest experience, as one of the core competencies in the luxury guest accommodation industry, directly affects guest satisfaction and loyalty. And AI chatbots, as a new type of interaction, have great potential for enhancing the guest experience (Sidaoui et al., 2020). Through in-depth research on the application of AI in luxury guest accommodation and its impact on the guest experience, it can provide more specific and effective guidance and suggestions for luxury guest accommodation enterprises to help them optimise their service processes, improve their service level, and thus increase guest satisfaction and loyalty. ...
Luxury hotels prioritise exceptional, personalised service. Recently, the industry has seen a rise in the use of robots to enhance customer experiences, particularly through efficient check-ins. Consumers have responded positively, expecting more AI-driven services in luxury hotels. AI chatbots are becoming central to operations, helping guests with inquiries, room service, and travel guidance. However, research on AI chatbot features, such as adaptability, responsiveness, and reliability, remains limited, especially in the luxury hotel sector. Additionally, the influence of information quality (e.g. relevance, security, and privacy) on customer satisfaction and intention to continue using these services is underexplored. While AI chatbots have been studied in education, banking, and healthcare, their role in hospitality requires further investigation. This chapter develops a conceptual framework through a systematic review of relevant academic theories, focusing on the factors shaping guest experiences with AI in luxury hotels. By critically evaluating these theories, the chapter aims to enhance understanding of the relationship between AI technologies and guest satisfaction within luxury accommodations.
... This study builds upon recent studies that have offered insights into factors influencing chatbot adoption, including user experience (Jiménez-Barreto et al., 2021;Sidaoui et al., 2020), perceived competence (Kim & Hur, 2024;Song et al., 2023), and anthropomorphism (Cheng et al., 2022;Han, 2021). However, research on e-lifestyles in the context of chatbot usage remains limited. ...
The growing implementation of chatbots in the service industry reflects continued innovation in this area. However, concerns persist regarding chatbot competence in addressing consumer inquiries, potentially hindering both the interaction experience and consumer performance. This study investigates the factors driving consumer performance in chatbot interactions. Data were collected via an online survey of 336 Indonesian consumers. Partial least squares-structural equation modeling (PLS-SEM) was employed to test the structural model. The findings reveal that e-lifestyle significantly influences both chatbot experience and competence. Anthropomor-phism significantly impacts chatbot experience, competence, and the resulting consumer performance. Moreover , both chatbot experience and competence significantly affect consumer performance. Furthermore, chatbot competence significantly shapes the user experience. This study contributes to both theoretical and practical understanding of chatbots within the service industry.
... Among these, chatbot-related elements are often subdivided into functional capabilities (e.g., the ability to execute tasks accurately), system performance (e.g., interface responsiveness and reliability), and anthropomorphic features that enhance perceived human likeness and relational cues (Chaves & Gerosa, 2021). These diverse factors interact to shape customer perceptions and emotional responses, which may range from satisfaction and trust to frustration or scepticism, depending on the quality of the interaction (Sidaoui et al., 2020). ...
In contemporary Customer Relationship Management (CRM), empathy plays a pivotal role in cultivating trust and long-term engagement. This chapter, Building Empathy with AI: Enhancing Customer Relationships, explores how Emotion AI, Robotics AI, and Sentiment Analysis are reshaping CRM by enabling context-aware, emotionally responsive interactions. Through tools such as facial recognition, vocal tone analysis, and sentiment detection, firms can interpret emotional cues in real time and tailor responses that resonate with customer needs. The chapter critically examines the integration of these technologies, emphasizing how they enhance service personalization while preserving a human-centric ethos. Drawing on case studies across sectors, it evaluates the ethical boundaries of emotional automation and proposes a framework to responsibly align AI innovation with empathy, privacy, and trust—offering practical insights for scholars, practitioners, and policymakers alike.
... Additionally, when chatbots are perceived as useful, enable customers to accomplish tasks more quickly, and ensure that customers can enhance the effectiveness to complete tasks, then customers' overall self-service experiences are positively impacted (Silva et al., 2023a). Multiple studies have explored and validated the importance of securing a positive chatbot service experience in a business-to-consumer (B2C) setting Haugeland et al., 2022;Sidaoui et al., 2020). Nevertheless, the current results expand on customer chatbot usage experience by emphasising the role of perceived ease of use, perceived playfulness, and perceived usefulness as critical drivers of customers' chatbot experiences (e.g., chatbots). ...
Purpose
The study aims to explore the influence of selected precursors to self-service technology customer experience (when using chatbots) and the role of trustworthiness, control and self-service technology customer experience on self-service technology satisfaction.
Design/methodology/approach
The study applied an explanatory research design and data collection was secured through self-administered questionnaires from millennials who engaged with a chatbot over six months. A total of 359 responses were used for data analysis. The measurement and structural models were assessed using structural equation modelling.
Findings
Perceived usefulness, perceived playfulness and perceived ease of use significantly and positively influence chatbot self-service technology experience. Moreover, trustworthiness, chatbot self-service technology experience and control significantly and positively influence self-service technology satisfaction.
Research limitations/implications
The tested model validates the hypothesised relationships between perceived usefulness, perceived playfulness, perceived ease of use, self-service technology experience, trustworthiness, control and chatbot self-service technology satisfaction. As such, chatbot users’ self-service technology experiences are directly linked to their three precursors and postcedent, self-service technology satisfaction. Conclusively, self-service technology satisfaction is directly guided by users’ trustworthiness and control when engaging with chatbots.
Practical implications
The study’s results can assist businesses to better understand the drivers of millennials’ self-service technology experiences with chatbots. Furthermore, it can guide these businesses on the critical importance of trustworthiness, self-service technology experience and control as drivers of self-service technology satisfaction.
Originality/value
Limited studies have examined how millennials’ self-service technology experiences relate to selected precursors and postcedents in emerging African markets.
... Synchronous feedback communication channels enjoy the support of chatbots or conversational agents. Chatbots have been used in various contexts to collect and address customers' feedback swiftly and cost-effectively (Adam et al., 2021;Omisakin et al., 2020;Sidaoui et al., 2020). These often AI-based systems communicate with human customers interactively via natural language. ...
Online customer feedback management (CFM) is becoming increasingly important for businesses. Providing timely and effective responses to guest reviews can be challenging, especially as the volume of reviews grows. This paper explores the response process and the potential for artificial intelligence (AI) augmentation in response formulation. We propose an orchestration concept for human–AI collaboration in co-writing within the hospitality industry, supported by a novel NLP-based solution that combines the strengths of both human and AI. Although complete automation of the response process remains out of reach, our findings offer practical implications for improving response speed and quality through human–AI collaboration. Additionally, we formulate policy recommendations for businesses and regulators in CFM. Our study provides transferable design knowledge for developing future CFM products.
... From research on online retail experience, factors affecting consumers' experience during the online shopping process that have been identified include brand image, visual page layout (Beig & Nika, 2022). Furthermore, some scholars have proposed that personalized services contribute to the improvement of consumer satisfaction and loyalty (Rane et al., 2023), an argument which is further developed by Sidaoui et al. (2020), stating that the content of related services should consider the subjective feelings and emotions of consumers during the shopping experience process. In addition to these perspectives, Mbete and Tanamal's (2020) study emphasized that the key to creating a positive online shopping experience lies in informativeness, which pertains to the capacity to enlighten consumers regarding the characteristics of products. ...
Digital technologies represented by AR (Augmented Reality), VR (Virtual Reality), and digital twins, along with the expansion of metaverse platforms and digital marketing concepts, have attracted the attention of numerous sports fashion product consumers and brands, particularly in the category of sports shoes. Therefore, in the context of digital technologies, understanding the factors that affect consumer experience and the preferences in the online purchasing process of sports shoes is very important. This study employs Latent Dirichlet Allocation topic analysis to analyze 44,110 online user posts and comments from social platforms, extracting thematic elements of consumer experience needs for purchasing sports shoes online. The information obtained is further encoded and designed into a questionnaire, which is then utilized alongside the Kano model to analyze the overall preferences of consumer experience needs. The results indicate that webpage design and basic product information are considered as Must-be attributes for user experience needs; providing information on after-sales service policies and product comment, products’ special feature information, and online size testing are recognized as Performance attributes. Additionally, high-tech interaction methods, visual presentation, personalized customization, virtual try-on, apparel matching recommendations, and dressing scenario recommendations are identified as Attractive attributes. The study reveals that in the context of new digital technology development, the online shopping experience for sports shoes is enhanced across four dimensions: platform experience augmentation, product experience augmentation, user demand augmentation, and interactive experience augmentation. These four dimensions collectively constitute the holistic experience design for the online retail platform. Therefore, this research provides case references and theoretical insights for researchers and developers in the fields of brand marketing, experience design, and product service innovation.
... Formulating interview questions is fundamental to interview design [49]. The interview guide was developed after extensively examining the relevant scholarly literature [50][51][52] in the context of chatbot service. ...
With the rapid advancement of artificial intelligence (AI), chatbots represent a transformative tool in digital customer engagement, reshaping customer–brand relationships. This paper explores AI chatbots on customer–brand interactions by analyzing key features, such as interaction, perceived enjoyment, customization, and problem-solving. Based on the Technology Acceptance Model (TAM), the research investigates how these attributes influence perceived ease of use, perceived usefulness, customer attitudes, and ultimately, customer–brand relationships. Adopting a mixed-methods approach, this study begins with qualitative interviews to identify key engagement factors, which then inform the design of a structured quantitative survey. The findings reveal that AI chatbot features significantly enhance customer perceptions, with ease of use and usefulness in shaping positive attitudes and strengthening brand connections. The research further underscores the role of AI-driven personalization in delivering sustainable customer engagement by optimizing digital interactions, reducing resource-intensive human support, and promoting long-term brand loyalty. By integrating TAM with customer–brand relationship theories, this study contributes to AI and sustainability research by highlighting how intelligent chatbots can facilitate responsible business practices, enhance operational efficiency, and promote digital sustainability through automation and resource optimization. The findings provide strategic insights for businesses seeking to design AI-driven chatbot systems that improve customer experience and align with sustainable digital transformation efforts.
... Additionally, a study by Nielsen reported that 53% of consumers are more likely to purchase products from businesses with which they can communicate via realtime messaging (Nielsen, 2018). Research by Manchester Business School has shown that participants perceive this technology as highly intriguing, exciting, and futuristic (Sidaoui et al., 2020). Moreover, these AI-driven assistants help companies reduce customer support costs by 30% while saving time (Popescu, 2020;Juniper Research, 2021). ...
With the rise of AI in digital consumer experiences, one of the key challenges for businesses is predicting and influencing consumers’ behavioral and purchase intentions in AI-driven interactions. In this regard, there appears to be a knowledge gap in the literature that cannot be determined regarding the effect of technology readiness on behavioral and purchasing intentions and, accordingly, the mediating role of attitude toward AI technology and the AI-driven consumer chatbot experience. Therefore, this research aims to determine the relationships among technology readiness, attitude toward AI technology, AI-driven consumer chatbot experience, behavioral intentions, and purchase intentions. The primary focus of the study is to evaluate the impact of Turkish consumers' technology readiness on behavioral and purchase intentions through the serial multiple mediating roles of attitude toward AI technology and the AI-driven customer chatbot experience. A questionnaire designed in line with the purpose of the research was applied to 423 respondents using AI-driven chatbots during the digital shopping experience. Although technology readiness does not directly affect behavioral and purchase intentions, attitude toward AI technology and the AI-driven consumer chatbot experience play a serial multiple mediating role in the relationship between motivations for technology readiness and purchase intention.
