User Evaluation of Technology-Enabled Self-Service Kiosks: Service Assurance, Environmental Cues, and Technology Expertise

User Evaluation of Technology-Enabled Self-Service Kiosks: Service Assurance, Environmental Cues, and Technology Expertise | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article User Evaluation of Technology-Enabled Self-Service Kiosks: Service Assurance, Environmental Cues, and Technology Expertise Sangyung Lee, Yeong-Hyeon Choi, Young Ju Rhee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7993661/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 23 You are reading this latest preprint version Abstract This study investigates how service assurance and environmental cues influence users’ post-adoption evaluation of self-service technologies (SSTs) and self-service kiosks (SSKs), incorporating the moderating role of technology expertise within the technology continuance theory (TCT) framework. Although prior research has examined initial technology acceptance, post-adoption evaluations in automated service environments remain underexplored. Data were collected from Korean consumers with prior experience using SSTs/SSKs, and structural equation modeling (SEM) was applied to test the proposed model. The results revealed that service assurance significantly affects both environmental cues and post-adoption evaluations, while environmental cues also exert a significant mediating effect between service assurance and post-adoption evaluation. Moreover, technology expertise moderates the relationship between environmental cues and post-adoption evaluation, indicating that users with higher technological proficiency perceive greater experiential value from well-designed SST environments. This study extends the TCT by integrating service quality and servicescape perspectives, offering a comprehensive understanding of how users’ cognitive and experiential factors interact in technology-mediated services. The findings also suggest practical strategies for managers to enhance customer experience by improving service reliability, optimizing physical and digital atmospherics, and tailoring user support based on technology expertise. Overall, this research provides both theoretical advancement and managerial insights into sustaining positive user engagement in the evolving landscape of smart service environments. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Social science/Science technology and society self-service technology self-service kiosk service assurance environmental cues technology expertise Technology Continuance Theory (TCT) post-adoption evaluation Figures Figure 1 Figure 2 1. Introduction The rapid proliferation of self-service technologies (SSTs) and self-service kiosks (SSKs) has fundamentally transformed the nature of service encounters across industries such as retail, hospitality, and food service. These systems enable customers to perform transactions autonomously, offering convenience, efficiency, and operational scalability. Beyond simple automation, recent advances in artificial intelligence and smart interfaces have positioned SSTs and SSKs as essential components of digital service ecosystems (Singh & Yadav, 2025 ; Wong et al., 2022 ). As firms increasingly adopt these technologies to enhance customer experience and reduce labor dependency, understanding the determinants of users’ sustained engagement with such systems has become a pressing research priority. Despite extensive research on initial technology adoption, relatively fewer studies have examined users’ post-adoption evaluations of SSTs and SSKs—how consumers assess, confirm, and internalize their ongoing experiences after repeated use (Foroughi et al., 2023 ; Rahi et al., 2021 ). Traditional models such as the technology acceptance (Davis, 1989 ) and the expectation–confirmation theory (Bhattacherjee, 2001 ) offer useful foundations but tend to focus on functional efficiency rather than the broader experiential and environmental dimensions that shape customer value. The technology continuance theory (TCT) (Liao et al., 2009 ) extends these perspectives by explaining continued usage as a function of both cognitive confirmation and affective satisfaction. However, in the context of SSTs, users’ evaluations are often intertwined with their perceptions of service reliability and environmental quality, which influence confidence, comfort, and satisfaction during automated service encounters (Lee et al., 2009 ; Lin & Hsieh, 2011 ; Youssef et al., 2025 ). Recent studies reveal a paradigmatic shift in SST/SSK research—from focusing solely on speed and cost reduction to emphasizing emotional engagement, design aesthetics, and customer delight (Collier & Barnes, 2015 ; Lee, 2023b ; Stead et al., 2021 ). Moreover, technological competence and digital literacy have emerged as crucial individual differences moderating these perceptions (Guan et al., 2021 ; Nam et al., 2023 ). Users with greater technological expertise demonstrate stronger confidence and adaptability when interacting with automated systems, while less experienced users rely more heavily on service assurance and environmental cues for trust and satisfaction (Galdolage, 2021a ; Parasuraman, 2000 ). This study aims to develop and empirically test an extended TCT framework that incorporates service assurance and environmental cues as key experiential antecedents of users’ post-adoption evaluations in SST and SSK contexts. Furthermore, the study investigates the moderating role of technology expertise in shaping how users interpret and integrate these service experiences. By doing so, this research not only advances theoretical understanding of technology continuance behavior but also provides practical insights into how organizations can design more inclusive and engaging self-service environments. Finally, this study contributes to the broader literature by mapping recent research trends and theoretical developments surrounding SSTs and SSKs, offering a consolidated perspective on the evolving dynamics of human–technology interaction in automated service contexts. 2. Literature Review 2.1. Self-Service Kiosk The emergence of SST has transformed traditional service encounters by allowing customers to participate directly in service production and delivery without human employees. Among SST forms, SSKs have become especially prominent across retail, hospitality, transportation, and foodservice industries, offering greater efficiency, convenience, and autonomy (Lee et al., 2009 ; Vakulenko et al., 2018 ). SSKs enable users to perform transactions such as ordering, payment, and check-in with minimal staff interaction, thereby streamlining operations and reducing labor costs (Chau et al., 2025 ). Yet the increasing replacement of face-to-face encounters with automated systems raises new questions about how customers perceive, evaluate, and emotionally respond to technology-mediated services (Collier et al., 2017 ). Early research on SST primarily emphasized utilitarian benefits, such as speed, accuracy, and operational efficiency. Lee et al. ( 2009 ) demonstrated that the service quality delivered through SSKs has both direct and indirect effects on consumers’ retail patronage intentions via reliability, problem solving, and personal interaction. Similarly, Joshi and Joshi ( 2020 ) applied the SSTQUAL scale to measure quality perceptions in app-based transport services, showing that reliability and responsiveness significantly predict satisfaction and behavioral intention. These findings established that kiosk performance and service assurance remain critical even when human interaction is minimized. Over time, researchers began to explore the experiential and emotional dimensions of SSK use. Vakulenko et al. ( 2018 ) conducted a systematic review of SSK literature and identified multiple value categories—functional, emotional, and social—that collectively shape customer experience. Their follow-up work (Vakulenko et al., 2019 ) proposed an integrative framework of customer value in SSKs, distinguishing between instrumental (e.g., convenience, control) and affective (e.g., enjoyment, trust) value creation. This evolution reflects a broader shift in service research from technology acceptance toward value co-creation and experiential consumption. Lee ( 2023a ) likewise confirmed that consumers’ technology readiness, expressed through optimism and innovativeness, significantly affects adoption motives and satisfaction, while age moderates these relationships. Another study by Lee ( 2023b ) demonstrated that utilitarian and hedonic values derived from SST experiences positively influence perceived service quality and satisfaction, emphasizing that technology-driven encounters must deliver both functional efficiency and emotional gratification. The conceptual foundation of perceived value in SSKs aligns with Zeithaml ( 1988 ), who defined customer value as the trade-off between perceived benefits and costs. Kumar and Mittal ( 2015 ) extended this principle to technology-based banking, showing that consistent service performance and reliability underpin satisfaction in digital interfaces. Galdolage ( 2021a , 2021b ) further highlighted the dual importance of performance and convenience, identifying features such as usefulness, efficiency, and information richness as determinants of customer choice and satisfaction with SSTs. These insights underscore that effective SSK design must integrate technological capability with perceptual factors that reduce uncertainty and enhance confidence. The servicescape concept (Bitner, 1992 ) has increasingly been applied to SSK settings, illustrating how environmental cues shape customers’ sensory, cognitive, and emotional responses. Guan et al. ( 2021 ) found that servicescape attributes—cleanliness, layout, and modern aesthetics—positively influence customers’ attitudes toward SST usage, with employee and core service quality moderating the effect. Youssef et al. ( 2025 ) extended this notion in smart fast-food contexts, introducing the Smart Servicescape model in which aesthetics, functionality, symbolism, and security jointly enhance customer inspiration, satisfaction, and loyalty. Halstead and Richards ( 2014 ) integrated servicescape theory with SST research, arguing that well-designed interfaces, interactivity, and sensory stimulation enhance both cognitive evaluations and affective experiences. Roy ( 2014 ) likewise proposed that perceptions of the retailer’s servicescape moderate consumers’ interactive kiosk adoption behavior, underscoring the importance of the physical and ambient environment in shaping acceptance of automated services. Recent qualitative investigations have deepened understanding of how customers cognitively process new kiosk environments. Stead et al. ( 2021 ) employed an ethnographic schema-elicitation technique to reveal how consumers activate and modify schemas when navigating new SST-enabled servicescapes, highlighting the role of mental adaptation in technology experiences. Wong et al. ( 2022 ) advanced this work by conceptualizing smart service quality (SSQ) through five technology-infused dimensions—s-servicescape, s-assurance, s-responsiveness, s-reliability, and s-empathy—reflecting the fusion of physical and digital service elements in smart restaurants. Their findings suggest that as automation becomes integral to hospitality, service quality must be reframed through the lens of technological immersion and experiential restoration. Scholars have also examined how social and emotional factors influence responses to SSKs. Collier and Barnes ( 2015 ) emphasized the hedonic nature of self-service experiences, showing that enjoyment and fun—not only efficiency—predict customer delight. Nam et al. ( 2023 ) focused on elderly users and found that time pressure and perceived difficulty evoke negative emotions toward SST use, calling for inclusive and user-friendly designs. Ivkov et al. ( 2020 ) similarly explored hospitality students’ willingness to implement service robots, finding that expectations of business outcomes outweigh empathy or social influence, revealing generational and professional nuances in technology acceptance. Chau et al. ( 2025 ) added a cognitive dimension by examining how automation alters the service encounter script, demonstrating that explicit, system-driven communication in unmanned encounters can unintentionally reduce consumer satisfaction during service failures. Collier et al. ( 2017 ) complemented this by studying SST failure recovery and showing that customers prefer varying levels of employee intervention depending on social context and self-monitoring within the servicescape. Expanding beyond individual interactions, Singh and Yadav ( 2025 ) conducted a two-decade review of in-store technology research and proposed a conceptual framework emphasizing omnichannel integration, smart retail strategy, and evolving consumer expectations. Their synthesis reinforces that SSKs are part of a broader transformation in physical retail where digital interfaces merge with spatial design to shape customer journeys. Zine et al. ( 2016 ) contributed from a service-system design perspective, demonstrating how flexibility and customer participation in technology-based services improve assurance and experience quality. Similarly, Joshi and Joshi ( 2020 ) and Galdolage ( 2021a ) confirmed that customers value SSKs offering consistency, efficiency, and minimal physical exertion, supporting the role of automation in enhancing perceived convenience and control. Collectively, the literature positions SSKs as multidimensional service interfaces that combine functional performance, environmental design, and emotional engagement to co-create value for both customers and service providers. As Vakulenko et al. ( 2019 ) and Wong et al. ( 2022 ) suggest, the effectiveness of SSKs lies not merely in technological advancement but in how seamlessly they integrate into users’ experiential, affective, and spatial contexts. Contemporary research thus converges on the view that the long-term success of SSK implementation depends on balancing service assurance, environmental aesthetics, and user inclusion, ensuring that technology enhances rather than replaces the human essence of service encounters. Table 1 summarizes the key research trends identified in recent studies on SST and SSK. Table 1 Key Research Trends Theme Implications Studies Context Efficiency to Experience Focus has shifted from efficiency and cost reduction to enhancing hedonic value, emotion, and delight in SST experiences. The design of SSTs should emphasize engagement and enjoyment rather than mere operational speed. Collier & Barnes, 2015 ; Wong et al., 2022 Retail; smart dining; hospitality Servicescape Effects Physical and ambient elements, including cleanliness, layout, and aesthetics, shape user attitudes, satisfaction, and usage intentions. Investing in smart servicescape design that enhances aesthetics, functionality, and security is essential for positive user experiences. Guan et al., 2021 ; Halstead & Richards, 2014 ; Youssef et al., 2025 Banking halls; fast-food kiosks; multi-channel services Customer Value Frameworks Utilitarian, hedonic, and social values together determine satisfaction, loyalty, and continued use of SSKs. Effective kiosk design should balance convenience, control, enjoyment, and trust to maximize user value. Vakulenko et al. ( 2018 , 2019 ) Cross-industry (retail, transport, hospitality) Smart Service Quality Service quality has evolved into the concept of smart service quality, encompassing elements such as s-servicescape and s-assurance in technology-rich contexts. Integrating high-quality interfaces with responsive human support enhances the overall service experience. Joshi & Joshi, 2020 ; Lee et al., 2009 ; Wong et al., 2022 Smart restaurants; retail kiosks; mobility services Generational and Inclusion Factors Technology readiness, age, and digital literacy significantly influence users’ adoption motives and emotional responses toward SST. Developing inclusive designs that minimize time pressure and provide user assistance helps reduce digital barriers. Lee ( 2023a , 2023b ); Nam et al., 2023 Retail; fast-food; general consumers Failure and Recovery Effective service recovery depends on the social context, as unmanned encounters require distinct communication strategies. Calibrating staff involvement and kiosk message tone can improve user satisfaction following service failures. Chau et al., 2025 ; Collier et al., 2017 Ticketing kiosks; grocery self-checkout; hybrid services Performance and Information Richness Features such as usefulness, speed, clarity, and guidance reduce user friction and strengthen the willingness to use SSTs. Providing clear instructions, timely feedback, and efficient error recovery enhances overall usability. Galdolage ( 2021a , 2021b ); Kumar & Mittal, 2015 Online/onsite SST; banking; retail Strategic and Systemic Perspectives SSKs now operate as integral components of omnichannel ecosystems, highlighting the need for integrated theoretical approaches and longitudinal research. Aligning kiosk design with the broader customer journey and flexible service systems ensures consistent service experiences. Singh & Yadav, 2025 ; Zine et al., 2016 Omnichannel retail; manufacturing service systems 2.2. Technology Continuance Theory and Proposed Extended Model The theory of technology continuance, introduced by Liao et al. ( 2009 ), integrates the technology acceptance model (Davis, 1989 ; Davis et al., 1989 ) and the expectation–confirmation model (Bhattacherjee, 2001 ) to explain how users decide whether to continue using a technology after initial adoption. TCT posits that users’ confirmation of expectations and their perceived usefulness jointly determine satisfaction, which subsequently shapes continuance intention. Beyond simple acceptance, TCT extends the understanding of post-adoption behavior by emphasizing how users’ cumulative experiences and perceived benefits influence long-term engagement. Later research further refined the theory by incorporating constructs such as habit, technostress, and emotional attachment (e.g., Foroughi et al., 2023 ; Rahi et al., 2021 ), showing that sustained technological use is both a rational and affective process. Originally developed for information systems, TCT has been widely applied to various technology-mediated service environments such as mobile banking, e-commerce, and SSTs. These studies commonly demonstrate that users’ continuance intention depends not only on system performance but also on experiential quality and service reliability (Khayer & Bao, 2019 ; Wong et al., 2022 ). Previous literature particularly stresses that post-adoption behavior is shaped by users’ holistic evaluation of their service experience rather than by technical ease of use alone (Lee, 2023a ; Vakulenko et al., 2019 ). In SST and SSK contexts, users interpret technology through a service lens, where physical, emotional, and social dimensions jointly determine satisfaction and loyalty (Collier & Barnes, 2015 ; Youssef et al., 2025 ). Thus, integrating service-related factors into TCT offers a more realistic explanation of how technology-driven service experiences shape continued use. In the context of SSKs, research consistently finds that users’ post-adoption evaluations are grounded in their perceptions of both service assurance and environmental cues (Guan et al., 2021 ; Lee et al., 2009 ; Vakulenko et al., 2018 ). These antecedents jointly represent the functional and emotional value dimensions described by Zeithaml ( 1988 ). Service assurance captures users’ confidence in the reliability, responsiveness, and competence of service providers or systems (Brady & Cronin, 2001 ; Meuter et al., 2000 ; Parasuraman et al., 1988 ). In the digitalized retail context, Wong et al. ( 2022 ) reframed this construct as SSQ—comprising smart responsiveness, assurance, and reliability—demonstrating that even in automated environments, perceived human-like assurance remains crucial for satisfaction. Similarly, environmental cues, derived from Bitner’s ( 1992 ) servicescape framework, encompass perceptions of ambient conditions, spatial layout, and aesthetics that shape emotional reactions and behavioral intentions. Recent SST research (Nam et al., 2023 ; Youssef et al., 2025 ) reinforces that the smart servicescape—including visual clarity, functionality, and design symbolism—significantly affects users’ comfort, trust, and willingness to reuse kiosks. These findings suggest that experiential dimensions of SSTs are integral to sustaining user engagement, complementing the functional mechanisms emphasized in TCT. Building on this logic, the proposed model extends TCT by positioning service assurance and environmental cues as key antecedents of post-adoption evaluation. Unlike traditional TCT studies that focus on cognitive appraisal (confirmation → satisfaction → continuance), the extended framework incorporates experiential and contextual factors that shape emotional satisfaction and behavioral reinforcement. This perspective aligns with the shift in SST literature from purely utilitarian to hedonic and affective experiences (Collier et al., 2017 ; Vakulenko et al., 2019 ). It also reflects current theoretical trends emphasizing that the post-adoption stage is an experiential process where users’ functional reliability judgments are intertwined with aesthetic and sensory impressions (Stead et al., 2021 ; Youssef et al., 2025 ). The post-adoption evaluation construct in this study represents a global, integrative assessment of the SST experience. Rather than separating satisfaction and continuance intention, this unified factor captures users’ cognitive and affective appraisal of whether SSTs continue to deliver expected value and enjoyment (Davis et al., 1989 ; Liao et al., 2009 ). This approach is consistent with the evolution of TCT in recent studies (Foroughi et al., 2023 ; Rahi et al., 2021 ), which treat post-adoption as a higher-order evaluation encompassing perceived usefulness, satisfaction, and loyalty. In SSK contexts, where technology-mediated encounters blend service quality and design cues, such a unified construct offers stronger explanatory power for understanding user retention and brand attachment. Furthermore, this study introduces technology expertise as a moderating variable that conditions how users interpret service and environmental cues. Technology expertise refers to an individual’s perceived knowledge and capability to effectively use technological systems (Compeau & Higgins, 1995 ; Kim & Gupta, 2009 ; Thatcher & Perrewé, 2002 ). Users with higher expertise tend to evaluate SST environments more confidently, translating technical and aesthetic cues into positive post-adoption judgments. It is conceptually related to technology readiness (Parasuraman, 2000 ) and personal innovativeness (Agarwal & Prasad, 1998 ), yet it focuses more on the applied competence that shapes users’ real-world interactions with self-service interfaces. As found in Lee ( 2023b ) and Nam et al. ( 2023 ), individual differences in digital literacy, confidence, and readiness significantly moderate user satisfaction and continuance intention toward SSTs. Therefore, technology expertise provides a meaningful extension to TCT by integrating personal capability into the cognitive–experiential framework of post-adoption evaluation. In summary, the proposed extended TCT framework situates users’ continuance evaluation of SSTs within a broader experiential and individual-difference context. By linking functional reliability (service assurance), emotional comfort (environmental cues), and technological competence (expertise), this model advances understanding of how users form enduring post-adoption evaluations of SSKs. This integration bridges the traditional IS perspective of TCT with the evolving service-dominant logic of technology-mediated consumer experiences, offering a comprehensive theoretical foundation for explaining sustained SST use. 3. Hypotheses Development 3.1. Service Assurance and Post-Adoption Evaluation Service assurance refers to customers’ perceptions of a service provider’s reliability, responsiveness, and competence in delivering technology-based services (Brady & Cronin, 2001 ; Cronin & Taylor, 1992 ; Meuter et al., 2000 ; Parasuraman et al., 1988 ). Rooted in the SERVQUAL framework (Parasuraman et al., 1988 ), assurance reflects users’ confidence in the service provider’s knowledge and problem-solving ability, while responsiveness emphasizes dependable support. Later studies viewed service quality hierarchically, positioning interaction quality—employees’ behavior and expertise—as central to user evaluation (Brady & Cronin, 2001 ). In SST and SSK contexts, assurance remains vital because users still expect reliability and support even when interaction occurs via technology. Meuter et al. ( 2000 ) found that perceived support and problem-solving capacity strongly influence satisfaction, while Zeithaml et al. ( 1996 ) showed that assurance and responsiveness drive behavioral intentions. Recent findings reaffirm these effects in smart environments: smart assurance—the perceived reliability and security of automated systems—significantly enhances user comfort and satisfaction (Wong et al., 2022 ; Youssef et al., 2025 ). Lee ( 2023a ) also demonstrated that perceived reliability in SST use strengthens overall satisfaction and continuance intention. From the TCT perspective (Liao et al., 2009 ), perceived reliability functions as post-use confirmation, reinforcing users’ confidence that the technology continues to meet expectations. Such confirmation enhances satisfaction and perceived usefulness, fostering favorable post-adoption evaluations. Accordingly, service assurance is expected to strengthen both users’ perceptions of the service environment and their overall evaluation of SST experiences. Accordingly, the following hypotheses are proposed: H1: Service assurance positively influences environmental cues in self-service kiosk services. H2: Service assurance positively influences users’ post-adoption evaluation. 3.2. Environmental Cues and Post-Adoption Evaluation Environmental cues refer to the physical and atmospheric elements of a service setting that shape users’ sensory and emotional responses during service encounters (Baker et al., 1994 ; Bitner, 1992 ; Lin & Hsieh, 2011 ). Originating from Bitner’s ( 1992 ) servicescape framework, this concept explains how ambient conditions, spatial layout, and design symbols influence both cognitive and affective evaluations. A clean, modern, and aesthetically pleasing environment communicates quality and professionalism, thereby enhancing perceived value and satisfaction (Turley & Milliman, 2000 ). Baker et al. ( 1994 ) demonstrated that environmental design and cleanliness affect customers’ quality inferences and store image, while Wakefield and Blodgett ( 1996 ) confirmed that atmospheric appeal fosters satisfaction and behavioral intentions in service settings. In SST and SSK contexts, environmental cues function as critical signals of technological modernity and operational reliability. Lin and Hsieh ( 2011 ) validated this through the SSTQUAL scale, identifying the physical environment dimension as a strong predictor of satisfaction and behavioral intention. More recent studies reinforce this view in smart service environments. Youssef et al. ( 2025 ) revealed that smart servicescape elements—such as aesthetics, functionality, and financial security—significantly enhance customer inspiration, satisfaction, and loyalty. Similarly, Wong et al. ( 2022 ) emphasized that smart service quality depends on the synergy between physical design and digital interfaces, highlighting that a well-designed servicescape elevates users’ confidence and immersion. Nam et al. ( 2023 ) further observed that environmental appeal and convenience in SSKs enhance satisfaction, particularly among users with higher technology readiness and digital literacy. From the perspective of TCT (Liao et al., 2009 ), these environmental perceptions serve as experiential confirmations—users interpret positive physical and aesthetic cues as evidence that the technology consistently meets or exceeds their expectations. Favorable environments thereby reinforce perceived usefulness and satisfaction, strengthening post-adoption evaluations. Moreover, environmental cues may act as a mediating mechanism between service assurance and post-adoption evaluation: reliable service provision often manifests through well-managed physical settings that signal efficiency, care, and modernity (Stead et al., 2021 ). Thus, environmental cues are expected to both directly enhance users’ post-adoption evaluations and mediate the influence of service assurance within the extended TCT framework. Accordingly, the following hypotheses are proposed: H3: Environmental cues positively influence users’ post-adoption evaluation. H4: Environmental cues mediate the relationship between service assurance and post-adoption evaluation. 3.3. Moderating Role of Technology Expertise Technology expertise refers to an individual’s perceived knowledge and capability to effectively understand and use technological systems (Compeau & Higgins, 1995 ; Kim & Gupta, 2009 ; Thatcher & Perrewé, 2002 ). Prior studies conceptualize it as a personal capability encompassing users’ familiarity, knowledge, and experience with technology, which in turn shapes perceptions of usefulness and ease of use (Shih, 2006 ; Thatcher et al., 2006 ; Venkatesh et al., 2003 ). Conceptually, it is closely related to technology readiness and personal innovativeness, reflecting individuals’ confidence and enthusiasm toward engaging with new technologies (Agarwal & Prasad, 1998 ; Parasuraman, 2000 ; Vakulenko et al., 2019 ). In SST/SSK settings, higher expertise reduces perceived barriers and fosters favorable evaluations—especially when systems provide clear guidance and information richness (Galdolage, 2021a , 2021b ; Joshi & Joshi, 2020 )—whereas low-expertise users lean more on contextual cues such as service assurance and the servicescape. Recent work shows that technology-oriented or digitally literate users respond more positively to smart service quality and well-designed servicescapes, with stronger satisfaction and reuse intentions (Guan et al., 2021 ; Lee, 2023a , 2023b ; Nam et al., 2023 ; Stead et al., 2021 ; Wong et al., 2022 ; Youssef et al., 2025 ). Within the extended TCT framework (Liao et al., 2009 ), technology expertise is expected to moderate the strength of the relationships among key experiential factors in the SST context. First, users with higher technology expertise are more capable of recognizing how reliable and competent service delivery enhances the surrounding service environment. Thus, the effect of service assurance on environmental cues is likely to be stronger for users with higher technological orientation. Second, technologically confident users are also more likely to translate positive service assurance into favorable post-adoption evaluations, as they can better appreciate the value of a dependable and well-managed SST experience. Finally, users with high technology expertise are expected to derive greater satisfaction and perceived value from well-designed environmental cues, strengthening the relationship between environmental cues and post-adoption evaluation. Accordingly, the following hypotheses are proposed: H5: Technology expertise moderates the relationship between service assurance and environmental cues, such that the effect is stronger for users with higher technology expertise. H6: Technology expertise moderates the relationship between service assurance and post-adoption evaluation, such that the effect is stronger for users with higher technology expertise. H7: Technology expertise moderates the relationship between environmental cues and post-adoption evaluation, such that the effect is stronger for users with higher technology expertise. 3.4. Summary of the Proposed Model In summary, this study proposes an extended TCT framework in which users’ post-adoption evaluation of SSTs is influenced by both service-related and environmental factors. Service assurance and environmental cues serve as core experiential antecedents shaping users’ overall assessment of SST services, while technology expertise represents an individual-level moderator that explains variations in how users interpret and evaluate their experiences. The proposed conceptual framework is illustrated in Fig. 1 . 4. Methodology 4.1. Operational Definition The constructs employed in this study were operationalized by adapting and synthesizing conceptual definitions from prior literature on service quality, servicescape, and technology continuance behavior, and applying them to the context of SSTs and SSKs. Specifically, items measuring service assurance were derived from the SERVQUAL framework and subsequent studies emphasizing reliability, responsiveness, and competence in technology-enabled services (Brady & Cronin, 2001 ; Meuter et al., 2000 ; Parasuraman et al., 1988 ). Environmental cues were operationalized based on the servicescape theory, reflecting the physical and ambient attributes of service settings such as cleanliness, modernity, and employee appearance (Baker et al., 1994 ; Bitner, 1992 ; Lin & Hsieh, 2011 ). Finally, post-adoption evaluation was defined using the TCT framework (Liao et al., 2009 ), capturing users’ holistic satisfaction and comparative preferences toward SST-based services after continued use (Davis et al., 1989 ). Table 2 presents the operational definitions and corresponding reference studies used in this research. All measurement items were rephrased to fit the context of retail environments using self-service technologies, ensuring conceptual equivalence with prior validated scales. Before data collection, the questionnaire was reviewed by three academic experts in service management and information systems to ensure content validity and linguistic clarity. Minor wording adjustments were made to enhance readability. Table 2 Operational Definition Factor Measurement Variables Relevant studies Service Assurance Stores using self-service technology will actively resolve problems when they arise for customers. P1 Brady & Cronin, 2001 ; Meuter et al., 2000 ; Parasuraman et al., 1988 Stores using self-service technology will be capable of properly delivering services to customers. P2 Stores using self-service technology will have employees who are always willing to help customers. P3 Even when they are busy, employees in stores using self-service technology will respond to customers’ requests. P4 Stores using self-service technology will be able to handle issues related to refunds, mistakes, and safety. P5 Employees in stores using self-service technology will be polite and courteous to customers. P6 Employees in stores using self-service technology will have sufficient knowledge to answer customers’ inquiries. P7 Environmental Cue Stores using self-service technology will have clean buildings and modern facilities. P8 Baker et al., 1994 ; Bitner, 1992 ; Lin & Hsieh, 2011 Stores using self-service technology will have well-maintained ancillary facilities such as parking lots. P9 Employees in stores using self-service technology will be neatly dressed. P10 Post Adoption The services provided by stores using self-service technology are overall satisfactory. P11 Davis et al., 1989 ; Liao et al., 2009 The use of self-service technology has improved the overall quality of service. P12 I would be willing to revisit stores using self-service technology in the future. P13 Compared to other stores, I would prefer to use stores that adopt self-service technology in the future. P14 4.2. Data Collection Data for this study were collected through a paper-based survey administered in June 2023 targeting Korean consumers with prior experience using SSTs such as SSKs in restaurants, banks, and airports. Participants were recruited through convenience sampling, focusing on adults who had interacted with SST/SSK systems within the previous six months. The survey aimed to capture users’ perceptions of service assurance, environmental cues, post-adoption evaluations, and technology expertise, as well as basic demographic information. Data were collected anonymously, and participation was voluntary. In total, 218 valid responses were obtained and used for analysis after screening for missing or incomplete responses. This sample size satisfies the recommended minimum criteria for structural equation modeling (SEM) (Hair et al., 2018 ), ensuring adequate statistical power for the hypothesized model. Demographic characteristics of the respondents are summarized in Table 3 . The majority of participants were in their twenties (34.9%) and thirties (17.4%), reflecting the population segments most familiar with technology-based services in Korea. The gender distribution was relatively balanced, with 52.8% male and 47.2% female respondents. Most participants had completed college or university education (75.2%), while 16.5% held postgraduate degrees. In terms of technology expertise, measured by the item “People seek me out for explanations of the latest technologies,” responses were widely distributed, indicating variability in users’ self-assessed technological capability. This diversity allowed for a meaningful examination of moderating effects based on technology expertise within the proposed model. Table 3 Demographic Profile Variable Group Frequency Percentage Age 20–29 76 34.9 30–39 38 17.4 40–49 51 23.4 50–59 24 11.0 60- 29 13.3 Gender Male 115 52.8 Female 103 47.2 Tech Expertise -People seek me out for explanations of the latest technologies. Never 29 13.3 Rarely 71 32.6 Sometimes 80 36.7 Often 25 11.5 Always 13 6.0 Education High School 18 8.3 College/University 164 75.2 Graduate School (Master’s/PhD) 36 16.5 Table 4 presents the descriptive statistics of all measurement items used in the study, including their mean values, standard deviations, skewness, and kurtosis. The mean scores for all items ranged between 2.95 and 3.69, indicating generally positive perceptions of SST experiences among respondents. Standard deviations ranged from 0.74 to 0.98, suggesting moderate variability in responses. All skewness and kurtosis values fell within the acceptable threshold range of ± 2.0 (Hair et al., 2018 ), confirming that the data approximate a normal distribution suitable for SEM. Overall, these results indicate that the measurement items exhibit no serious deviations from normality and are appropriate for subsequent confirmatory factor and structural analyses. Table 4 Descriptive Statistics of Measurement Items Factor Variables Mean Std. Deviation Skewness Kurtosis Service Assurance P1 3.03 0.908 -0.026 -0.540 P2 3.24 0.809 -0.156 0.152 P3 3.01 0.938 0.209 -0.346 P4 2.95 0.980 0.131 -0.294 P5 3.28 0.870 -0.079 -0.122 P6 3.20 0.793 0.136 0.325 P7 3.32 0.863 -0.151 -0.473 Environmental Cue P8 3.69 0.788 -0.525 0.572 P9 3.31 0.943 -0.494 0.166 P10 3.27 0.881 -0.507 0.057 Post Adoption P11 3.50 0.781 0.014 -0.086 P12 3.33 0.880 -0.031 -0.258 P13 3.66 0.746 -0.094 0.441 P14 3.29 0.909 0.091 -0.357 5. Results 5.1. Reliability and Validity Analysis A confirmatory factor analysis (CFA) was conducted to assess the reliability and validity of the measurement model. As shown in Table 5 , all standardized factor loadings exceeded the recommended threshold of 0.70 (Hair et al., 2018 ), demonstrating adequate indicator reliability. The values of composite reliability (CR) ranged from 0.870 to 0.917, and Cronbach’s α values ranged from 0.866 to 0.918, both surpassing the acceptable criterion of 0.70 (Fornell & Larcker, 1981 ; Nunnally & Bernstein, 1994 ). In addition, average variance extracted (AVE) values ranged between 0.613 and 0.699, indicating satisfactory convergent validity. The overall model fit indices also met the recommended thresholds. These values indicate an acceptable model fit according to the criteria suggested by Hu and Bentler ( 1999 ) and Hair et al. ( 2018 ). Table 5 Reliability and Convergent Validity Analysis Factor Variables Factor Loading Average variance extracted (AVE) Composite reliability (CR) Cronbach α Service Assurance P1 0.834 0.613 0.917 0.918 P2 0.740 P3 0.787 P4 0.746 P5 0.785 P6 0.820 P7 0.766 Environmental Cue P8 0.805 0.691 0.870 0.866 P9 0.880 P10 0.807 Post Adoption P11 0.863 0.699 0.903 0.899 P12 0.886 P13 0.809 P14 0.783 Note(s): χ 2 (73) = 174.48 (p < 0.001), RMR = 0.029, SRMR = 0.0391, GFI = 0.906, AGFI = 0.864, NFI = 0.923, RFI = 0.904, IFI = 0.954, TLI = 0.942, CFI = 0.953, and RMSEA = 0.08 (90% CI [0.065, 0.095]) Discriminant validity was evaluated using the Fornell–Larcker criterion. As shown in Table 6 , the square root of each AVE (diagonal values) was greater than the corresponding inter-construct correlations (off-diagonal values), confirming discriminant validity (Fornell & Larcker, 1981 ). This indicates that each construct is empirically distinct and captures unique aspects of the SST experience. Table 6 Discriminant Validity Analysis Factor 1. 2. 3. 1. Service Assurance 0.783 2. Environmental Cue 0.668 0.831 3. Post Adoption 0.742 0.701 0.836 Note(s): The off-diagonal matrix shows the correlation between the factors. The numbers of the diagonal are the squared root of AVE. 5.2. Structural Equation Modeling Analysis A SEM analysis was conducted to test the hypothesized relationships among the constructs within the extended TCT framework. The overall model demonstrated a satisfactory fit to the data. All indices met or exceeded the recommended thresholds (Hu & Bentler, 1999 ; Hair et al., 2018 ), indicating a good model fit. As shown in Table 7 , the direct effects of service assurance on environmental cues (β = 0.668, p < 0.05) and post-adoption evaluation (β = 0.494, p < 0.05) were both significant, supporting the proposed relationships. Environmental cues also exerted a significant positive influence on post-adoption evaluation (β = 0.371, p < 0.01). The indirect effect of service assurance on post-adoption evaluation through environmental cues (β = 0.248, p < 0.01) was statistically significant, confirming the mediating role of environmental cues. These results collectively validate that users’ perceptions of reliable and competent service (service assurance) foster favorable environmental impressions, which subsequently enhance their overall evaluations of SSK experiences. Table 7 Direct, Indirect, and Total Effect Category Relationship Standardized Effect (β) Hypotheses Exo. (ξ) Med. (ηₘ) Endo. (η) Direct Effect Service Assurance Environmental Cue 0.668* H1 Supported Environmental Cue Post Adoption 0.371** H3 Supported Service Assurance Post Adoption 0.494* H2 Supported Indirect Effect Service Assurance Environmental Cue Post Adoption 0.248** H4 Supported Total Effect Service Assurance Environmental Cue Post Adoption 0.742* Note(s): *p < 0.05, **p < 0.01, ***p < 0.001 χ 2 (73) = 174.48 (p < 0.001), RMR = 0.029, SRMR = 0.0391, GFI = 0.906, AGFI = 0.864, NFI = 0.923, RFI = 0.904, IFI = 0.954, TLI = 0.942, CFI = 0.953, and RMSEA = 0.08 (90% CI [0.065, 0.095]) To test the moderating role of technology expertise, a multi-group analysis was performed by dividing respondents into high and low technology expertise groups based on median-split criteria. As shown in Table 8 , the moderating effect was partially supported. Specifically, technology expertise significantly strengthened the relationship between environmental cues and post-adoption evaluation (Δβ = 0.395, p < 0.01), supporting H7. However, its moderating effects on the relationships between service assurance and environmental cues (Δβ = 0.109) and between service assurance and post-adoption evaluation (Δβ = −0.185) were not significant, indicating that these effects are relatively stable across user groups. In addition, a significant indirect effect of environmental cues was observed between service assurance and post-adoption evaluation among users with high technology expertise (β = 0.375, p < 0.05), whereas this mediating effect was not significant for the low technology expertise group. The model comparison results confirmed marginally significant overall difference between the groups (χ²(3) = 7.04, p = 0.071). Table 8 Moderation Effect Analysis Relationship Standardized Direct Effect (β) Hypotheses Exo. (ξ) Endo. (η) Low High Difference Service Assurance Environmental Cue 0.593* 0.702* 0.109 H5 Not Supported Environmental Cue Post Adoption 0.139 0.534* 0.395** H7 Supported Service Assurance Post Adoption 0.562* 0.377** -0.185 H6 Not Supported Note(s): *p < 0.05, **p < 0.01, ***p < 0.001 Model Comparison: χ 2 (3) = 7.04 (p = 0.071), NFI Delta1 = 0.003, IFI Delta2 = 0.003, RFI rho1=-0.000, TLI rho2=-0.000 The overall SEM results for the direct, mediating, and moderating effects are presented in Fig. 2 . To summarize, the direct relationships among service assurance, environmental cues, and post-adoption evaluation were significant. Environmental cues significantly mediated the relationship between service assurance and post-adoption evaluation. Technology expertise moderated the relationship between environmental cues and post-adoption evaluation, such that users with high technology expertise demonstrated a strong and significant positive relationship between these factors, whereas the relationship was insignificant for users with low technology expertise. However, the moderating effects of technology expertise on the relationships between service assurance and environmental cues, as well as between service assurance and post-adoption evaluation, were not significant. 6. Implications 6.1. Theoretical Implications This study makes several theoretical contributions to the growing body of literature on SST and SSK by extending the theory of technology continuance (Liao et al., 2009 ) to a service quality–based experiential framework. Whereas prior studies have primarily emphasized users’ perceptions of usefulness, ease of use, and satisfaction as predictors of continuance intention (Foroughi et al., 2023 ; Rahi et al., 2021 ), the present study highlights service assurance and environmental cues as key experiential factors influencing users’ post-adoption evaluations in SST environments. By doing so, this research expands TCT beyond its traditional focus on cognitive appraisal to encompass the affective and contextual dimensions of the SST experience. Second, this study empirically verifies the mediating role of environmental cues in the relationship between service assurance and post-adoption evaluation. Although previous research has acknowledged the influence of servicescape and atmospheric design on customer perceptions and behavioral intentions (Baker et al., 1994 ; Bitner, 1992 ; Lin & Hsieh, 2011 ; Youssef et al., 2025 ), limited attention has been given to how such environmental attributes interact with service reliability and assurance in technology-mediated contexts. The results suggest that environmental cues serve as an experiential bridge that translates perceived service reliability into favorable post-adoption attitudes. This finding complements recent discussions emphasizing the integration of physical and digital atmospherics in smart service environments (Stead et al., 2021 ; Wong et al., 2022 ). Third, the moderating role of technology expertise advances the theoretical understanding of user heterogeneity in technology continuance behavior. While studies on technology readiness (Parasuraman, 2000 ) and personal innovativeness (Agarwal & Prasad, 1998 ) have examined individual differences in technology adoption, empirical exploration of how users’ technological capability alters experiential evaluation remains scarce. This study demonstrates that users with higher technology expertise derive stronger affective and evaluative benefits from environmental cues, supporting the notion that technological self-efficacy amplifies experiential value in SST settings (Shih, 2006 ; Thatcher & Perrewé, 2002 ). This contributes to the ongoing scholarly efforts to contextualize individual-level technology differences within the TCT framework (Foroughi et al., 2023 ; Khayer & Bao, 2019 ). Finally, by incorporating multidimensional constructs such as service assurance, environmental cues, and technology expertise, this study contributes to the emerging human–technology interaction perspective in service science (Singh & Yadav, 2025 ; Vakulenko et al., 2019 ). The findings align with recent calls to investigate how smart environments and user characteristics jointly shape post-adoption outcomes in digital service ecosystems. Thus, this research provides a contextually grounded extension of TCT that connects service quality theory, servicescape research, and technology acceptance literature, offering a more holistic understanding of user continuance behavior in SST/SSK environments. 6.2. Practical Implications The findings of this study also provide several practical implications for service managers and practitioners implementing SSTs and SSKs in hospitality, retail, and transportation sectors. First, the results highlight the critical role of service assurance in shaping users’ perceptions of technological reliability and their subsequent evaluations. Managers should design SST systems that ensure prompt and consistent service recovery when technical errors occur. As emphasized by Collier et al. ( 2017 ) and Meuter et al. ( 2000 ), users expect a swift and empathetic resolution even in self-service settings, where human contact is minimized. Providing clear guidance on how customers can seek assistance—whether through digital help functions, on-site attendants, or remote support—can strengthen perceptions of reliability and trust. Second, environmental cues such as spatial design, cleanliness, and aesthetic appeal were found to mediate users’ post-adoption evaluations. This indicates that technological functionality alone is insufficient; the servicescape remains a vital touchpoint for user satisfaction (Bitner, 1992 ; Youssef et al., 2025 ; Wong et al., 2022 ). Service providers should therefore invest in enhancing both physical and digital atmospherics—through intuitive kiosk interfaces, ambient lighting, sound design, and maintenance of clean, modern facilities—to communicate professionalism and technological competence. Third, this study reveals that users’ technology expertise significantly moderates how they respond to environmental cues. For technologically confident users, the design and ambiance of the service setting significantly enhance satisfaction, whereas low-expertise users rely more on human or system-based reassurance. Accordingly, managers should segment users based on their technology proficiency and tailor their support strategies: offering simplified tutorials, intuitive UI/UX features, or hybrid service options for low-expertise users, while enabling greater autonomy and efficiency for high-expertise users. Such differentiated approaches can enhance both inclusivity and user retention (Foroughi et al., 2023 ; Nam et al., 2023 ). Lastly, the study suggests that continuous monitoring of user feedback on SST experiences can guide adaptive improvement of smart service environments. Integrating real-time analytics or AI-driven feedback systems would allow service providers to detect friction points in user interaction and maintain high-quality experiences over time (Singh & Yadav, 2025 ; Stead et al., 2021 ). Overall, these insights emphasize that the success of SST/SSK adoption depends not only on technological sophistication but also on how effectively service environments and human factors are harmonized. Organizations that manage both technological reliability and experiential design are more likely to sustain user engagement and loyalty in the evolving landscape of smart service ecosystems. 7. Conclusion This study extends the theory of technology continuance by integrating service assurance, environmental cues, and technology expertise to explain users’ post-adoption evaluation of SSTs and SSKs. The findings confirm that service assurance and environmental cues significantly influence post-adoption evaluations, with environmental cues mediating the link between perceived reliability and user satisfaction. Moreover, technology expertise moderates the effect of environmental cues on post-adoption evaluation, revealing that technologically proficient users derive stronger experiential value from well-designed smart environments. Theoretically, this research contributes to SST literature by bridging service quality, servicescape, and technology continuance perspectives. It highlights the experiential dimension of post-adoption behavior and underscores the importance of individual technological capability in shaping users’ evaluation processes. By situating service assurance and environmental cues within the TCT framework, the study advances understanding of how users continuously interpret and evaluate technology-mediated services. Practically, the results suggest that organizations should strengthen reliability and user confidence through robust service assurance mechanisms, while simultaneously enhancing the physical and digital environment to improve the overall service experience. Segmenting users based on technological proficiency and providing differentiated levels of support can foster inclusivity and sustain engagement in diverse user groups. Despite its contributions, this study is limited to a single-country context and self-reported measures. Future research could employ cross-cultural comparisons or longitudinal approaches to examine how evolving user expertise and digital maturity shape the continuance of smart service adoption over time. Declarations Author Contribution Sangyung Lee conceived the overall research idea, collected and analyzed the data, and conducted the literature review and discussion of implications. Yeong-Hyeon Choi (co-first author) contributed to drafting the abstract and introduction, participated in the literature review and discussion of implications, and checked the references. Yeong Ju Rhee contributed to developing the implications and conclusion, reviewed the manuscript, ensured consistency in formatting, and served as the corresponding author. Data Availability The anonymized dataset supporting the findings of this study is openly available in the Figshare repository at the following link: https://figshare.com/s/a7de9a8aa7d6fee34109. The dataset includes only fully anonymized survey responses with no identifying information. References Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7993661","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":588752800,"identity":"5505eda2-16a7-41b4-aa39-fec234c6546a","order_by":0,"name":"Sangyung Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDADPgYGxgeSDRAOYwMxWtgYGJgNSNbCJgFTileLfPvZw6952+7YtbE3H6uw3HEnsYH98APGmXtwazE4k5dmzdv2LLmN51jaDckzzxIbeNIMGDc8w6OFIcfMmLftcDKbRI7ZDcm2w4kNDDnAgDiAx2H9b6Ba5N9/KwBr4X+DXwvDjRzjx0AtdmwSPGwMYC0SQFs24NFicOONGeOcc4cT2HjSjCUkzxw2bpN4ZnBwBl6H5Rh/eFN22J6f/fDDz5I7Dsv28yc/fNiDz2HA6JDiYWAA+hoYlxIM4AhiwK8BqPDjDwYGexCL8QMBpaNgFIyCUTAyAQBGCVX7wpge7QAAAABJRU5ErkJggg==","orcid":"","institution":"Jeonbuk National University","correspondingAuthor":true,"prefix":"","firstName":"Sangyung","middleName":"","lastName":"Lee","suffix":""},{"id":588752802,"identity":"4897e8c6-2f03-43a2-864f-44474262f492","order_by":1,"name":"Yeong-Hyeon Choi","email":"","orcid":"","institution":"Hanyang University","correspondingAuthor":false,"prefix":"","firstName":"Yeong-Hyeon","middleName":"","lastName":"Choi","suffix":""},{"id":588752806,"identity":"9426a1b7-d5ce-4e6c-b3a2-6ef139efdaa8","order_by":2,"name":"Young Ju Rhee","email":"","orcid":"","institution":"Sungshin Women's University","correspondingAuthor":false,"prefix":"","firstName":"Young","middleName":"Ju","lastName":"Rhee","suffix":""}],"badges":[],"createdAt":"2025-10-31 02:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7993661/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7993661/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102578308,"identity":"66f49df6-a713-474e-a25b-21448f5bc563","added_by":"auto","created_at":"2026-02-13 08:41:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135955,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7993661/v1/ec222be5262500e11745725e.png"},{"id":102578348,"identity":"77d205bb-20e5-4f79-8090-6803d27c215f","added_by":"auto","created_at":"2026-02-13 08:41:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81208,"visible":true,"origin":"","legend":"\u003cp\u003eSEM Output\u003c/p\u003e\n\u003cp\u003eNote(s): *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7993661/v1/c79b60a4fce27deb91a39947.png"},{"id":102578393,"identity":"2b542319-40d0-4fb2-929e-bd295b01fb41","added_by":"auto","created_at":"2026-02-13 08:41:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1322358,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7993661/v1/54896725-ef4d-4466-8044-9f3442c7e4c2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"User Evaluation of Technology-Enabled Self-Service Kiosks: Service Assurance, Environmental Cues, and Technology Expertise","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe rapid proliferation of self-service technologies (SSTs) and self-service kiosks (SSKs) has fundamentally transformed the nature of service encounters across industries such as retail, hospitality, and food service. These systems enable customers to perform transactions autonomously, offering convenience, efficiency, and operational scalability. Beyond simple automation, recent advances in artificial intelligence and smart interfaces have positioned SSTs and SSKs as essential components of digital service ecosystems (Singh \u0026amp; Yadav, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As firms increasingly adopt these technologies to enhance customer experience and reduce labor dependency, understanding the determinants of users\u0026rsquo; sustained engagement with such systems has become a pressing research priority.\u003c/p\u003e \u003cp\u003eDespite extensive research on initial technology adoption, relatively fewer studies have examined users\u0026rsquo; post-adoption evaluations of SSTs and SSKs\u0026mdash;how consumers assess, confirm, and internalize their ongoing experiences after repeated use (Foroughi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rahi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Traditional models such as the technology acceptance (Davis, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) and the expectation\u0026ndash;confirmation theory (Bhattacherjee, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) offer useful foundations but tend to focus on functional efficiency rather than the broader experiential and environmental dimensions that shape customer value. The technology continuance theory (TCT) (Liao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) extends these perspectives by explaining continued usage as a function of both cognitive confirmation and affective satisfaction. However, in the context of SSTs, users\u0026rsquo; evaluations are often intertwined with their perceptions of service reliability and environmental quality, which influence confidence, comfort, and satisfaction during automated service encounters (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lin \u0026amp; Hsieh, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Youssef et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies reveal a paradigmatic shift in SST/SSK research\u0026mdash;from focusing solely on speed and cost reduction to emphasizing emotional engagement, design aesthetics, and customer delight (Collier \u0026amp; Barnes, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lee, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Stead et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, technological competence and digital literacy have emerged as crucial individual differences moderating these perceptions (Guan et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Users with greater technological expertise demonstrate stronger confidence and adaptability when interacting with automated systems, while less experienced users rely more heavily on service assurance and environmental cues for trust and satisfaction (Galdolage, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Parasuraman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to develop and empirically test an extended TCT framework that incorporates service assurance and environmental cues as key experiential antecedents of users\u0026rsquo; post-adoption evaluations in SST and SSK contexts. Furthermore, the study investigates the moderating role of technology expertise in shaping how users interpret and integrate these service experiences. By doing so, this research not only advances theoretical understanding of technology continuance behavior but also provides practical insights into how organizations can design more inclusive and engaging self-service environments. Finally, this study contributes to the broader literature by mapping recent research trends and theoretical developments surrounding SSTs and SSKs, offering a consolidated perspective on the evolving dynamics of human\u0026ndash;technology interaction in automated service contexts.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Self-Service Kiosk\u003c/h2\u003e \u003cp\u003eThe emergence of SST has transformed traditional service encounters by allowing customers to participate directly in service production and delivery without human employees. Among SST forms, SSKs have become especially prominent across retail, hospitality, transportation, and foodservice industries, offering greater efficiency, convenience, and autonomy (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Vakulenko et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). SSKs enable users to perform transactions such as ordering, payment, and check-in with minimal staff interaction, thereby streamlining operations and reducing labor costs (Chau et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Yet the increasing replacement of face-to-face encounters with automated systems raises new questions about how customers perceive, evaluate, and emotionally respond to technology-mediated services (Collier et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEarly research on SST primarily emphasized utilitarian benefits, such as speed, accuracy, and operational efficiency. Lee et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) demonstrated that the service quality delivered through SSKs has both direct and indirect effects on consumers\u0026rsquo; retail patronage intentions via reliability, problem solving, and personal interaction. Similarly, Joshi and Joshi (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) applied the SSTQUAL scale to measure quality perceptions in app-based transport services, showing that reliability and responsiveness significantly predict satisfaction and behavioral intention. These findings established that kiosk performance and service assurance remain critical even when human interaction is minimized.\u003c/p\u003e \u003cp\u003eOver time, researchers began to explore the experiential and emotional dimensions of SSK use. Vakulenko et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) conducted a systematic review of SSK literature and identified multiple value categories\u0026mdash;functional, emotional, and social\u0026mdash;that collectively shape customer experience. Their follow-up work (Vakulenko et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) proposed an integrative framework of customer value in SSKs, distinguishing between instrumental (e.g., convenience, control) and affective (e.g., enjoyment, trust) value creation. This evolution reflects a broader shift in service research from technology acceptance toward value co-creation and experiential consumption. Lee (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) likewise confirmed that consumers\u0026rsquo; technology readiness, expressed through optimism and innovativeness, significantly affects adoption motives and satisfaction, while age moderates these relationships. Another study by Lee (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e) demonstrated that utilitarian and hedonic values derived from SST experiences positively influence perceived service quality and satisfaction, emphasizing that technology-driven encounters must deliver both functional efficiency and emotional gratification.\u003c/p\u003e \u003cp\u003eThe conceptual foundation of perceived value in SSKs aligns with Zeithaml (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), who defined customer value as the trade-off between perceived benefits and costs. Kumar and Mittal (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) extended this principle to technology-based banking, showing that consistent service performance and reliability underpin satisfaction in digital interfaces. Galdolage (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) further highlighted the dual importance of performance and convenience, identifying features such as usefulness, efficiency, and information richness as determinants of customer choice and satisfaction with SSTs. These insights underscore that effective SSK design must integrate technological capability with perceptual factors that reduce uncertainty and enhance confidence.\u003c/p\u003e \u003cp\u003eThe servicescape concept (Bitner, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) has increasingly been applied to SSK settings, illustrating how environmental cues shape customers\u0026rsquo; sensory, cognitive, and emotional responses. Guan et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that servicescape attributes\u0026mdash;cleanliness, layout, and modern aesthetics\u0026mdash;positively influence customers\u0026rsquo; attitudes toward SST usage, with employee and core service quality moderating the effect. Youssef et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) extended this notion in smart fast-food contexts, introducing the Smart Servicescape model in which aesthetics, functionality, symbolism, and security jointly enhance customer inspiration, satisfaction, and loyalty. Halstead and Richards (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) integrated servicescape theory with SST research, arguing that well-designed interfaces, interactivity, and sensory stimulation enhance both cognitive evaluations and affective experiences. Roy (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) likewise proposed that perceptions of the retailer\u0026rsquo;s servicescape moderate consumers\u0026rsquo; interactive kiosk adoption behavior, underscoring the importance of the physical and ambient environment in shaping acceptance of automated services.\u003c/p\u003e \u003cp\u003eRecent qualitative investigations have deepened understanding of how customers cognitively process new kiosk environments. Stead et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) employed an ethnographic schema-elicitation technique to reveal how consumers activate and modify schemas when navigating new SST-enabled servicescapes, highlighting the role of mental adaptation in technology experiences. Wong et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) advanced this work by conceptualizing smart service quality (SSQ) through five technology-infused dimensions\u0026mdash;s-servicescape, s-assurance, s-responsiveness, s-reliability, and s-empathy\u0026mdash;reflecting the fusion of physical and digital service elements in smart restaurants. Their findings suggest that as automation becomes integral to hospitality, service quality must be reframed through the lens of technological immersion and experiential restoration.\u003c/p\u003e \u003cp\u003eScholars have also examined how social and emotional factors influence responses to SSKs. Collier and Barnes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) emphasized the hedonic nature of self-service experiences, showing that enjoyment and fun\u0026mdash;not only efficiency\u0026mdash;predict customer delight. Nam et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) focused on elderly users and found that time pressure and perceived difficulty evoke negative emotions toward SST use, calling for inclusive and user-friendly designs. Ivkov et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) similarly explored hospitality students\u0026rsquo; willingness to implement service robots, finding that expectations of business outcomes outweigh empathy or social influence, revealing generational and professional nuances in technology acceptance. Chau et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) added a cognitive dimension by examining how automation alters the service encounter script, demonstrating that explicit, system-driven communication in unmanned encounters can unintentionally reduce consumer satisfaction during service failures. Collier et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) complemented this by studying SST failure recovery and showing that customers prefer varying levels of employee intervention depending on social context and self-monitoring within the servicescape.\u003c/p\u003e \u003cp\u003eExpanding beyond individual interactions, Singh and Yadav (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) conducted a two-decade review of in-store technology research and proposed a conceptual framework emphasizing omnichannel integration, smart retail strategy, and evolving consumer expectations. Their synthesis reinforces that SSKs are part of a broader transformation in physical retail where digital interfaces merge with spatial design to shape customer journeys. Zine et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) contributed from a service-system design perspective, demonstrating how flexibility and customer participation in technology-based services improve assurance and experience quality. Similarly, Joshi and Joshi (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Galdolage (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) confirmed that customers value SSKs offering consistency, efficiency, and minimal physical exertion, supporting the role of automation in enhancing perceived convenience and control.\u003c/p\u003e \u003cp\u003eCollectively, the literature positions SSKs as multidimensional service interfaces that combine functional performance, environmental design, and emotional engagement to co-create value for both customers and service providers. As Vakulenko et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Wong et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) suggest, the effectiveness of SSKs lies not merely in technological advancement but in how seamlessly they integrate into users\u0026rsquo; experiential, affective, and spatial contexts. Contemporary research thus converges on the view that the long-term success of SSK implementation depends on balancing service assurance, environmental aesthetics, and user inclusion, ensuring that technology enhances rather than replaces the human essence of service encounters.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the key research trends identified in recent studies on SST and SSK.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey Research Trends\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImplications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContext\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficiency to Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFocus has shifted from efficiency and cost reduction to enhancing hedonic value, emotion, and delight in SST experiences. The design of SSTs should emphasize engagement and enjoyment rather than mere operational speed.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCollier \u0026amp; Barnes, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetail; smart dining; hospitality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eServicescape Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical and ambient elements, including cleanliness, layout, and aesthetics, shape user attitudes, satisfaction, and usage intentions. Investing in smart servicescape design that enhances aesthetics, functionality, and security is essential for positive user experiences.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGuan et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Halstead \u0026amp; Richards, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Youssef et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBanking halls; fast-food kiosks; multi-channel services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer Value Frameworks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUtilitarian, hedonic, and social values together determine satisfaction, loyalty, and continued use of SSKs. Effective kiosk design should balance convenience, control, enjoyment, and trust to maximize user value.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVakulenko et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-industry (retail, transport, hospitality)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmart Service Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eService quality has evolved into the concept of smart service quality, encompassing elements such as s-servicescape and s-assurance in technology-rich contexts. Integrating high-quality interfaces with responsive human support enhances the overall service experience.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJoshi \u0026amp; Joshi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSmart restaurants; retail kiosks; mobility services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenerational and Inclusion Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology readiness, age, and digital literacy significantly influence users\u0026rsquo; adoption motives and emotional responses toward SST. Developing inclusive designs that minimize time pressure and provide user assistance helps reduce digital barriers.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLee (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e); Nam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetail; fast-food; general consumers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFailure and Recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffective service recovery depends on the social context, as unmanned encounters require distinct communication strategies. Calibrating staff involvement and kiosk message tone can improve user satisfaction following service failures.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChau et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Collier et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTicketing kiosks; grocery self-checkout; hybrid services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance and Information Richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeatures such as usefulness, speed, clarity, and guidance reduce user friction and strengthen the willingness to use SSTs. Providing clear instructions, timely feedback, and efficient error recovery enhances overall usability.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGaldolage (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e); Kumar \u0026amp; Mittal, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOnline/onsite SST; banking; retail\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrategic and Systemic Perspectives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSKs now operate as integral components of omnichannel ecosystems, highlighting the need for integrated theoretical approaches and longitudinal research. Aligning kiosk design with the broader customer journey and flexible service systems ensures consistent service experiences.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingh \u0026amp; Yadav, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zine et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOmnichannel retail; manufacturing service systems\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Technology Continuance Theory and Proposed Extended Model\u003c/h2\u003e \u003cp\u003eThe theory of technology continuance, introduced by Liao et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), integrates the technology acceptance model (Davis, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Davis et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) and the expectation\u0026ndash;confirmation model (Bhattacherjee, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) to explain how users decide whether to continue using a technology after initial adoption. TCT posits that users\u0026rsquo; confirmation of expectations and their perceived usefulness jointly determine satisfaction, which subsequently shapes continuance intention. Beyond simple acceptance, TCT extends the understanding of post-adoption behavior by emphasizing how users\u0026rsquo; cumulative experiences and perceived benefits influence long-term engagement. Later research further refined the theory by incorporating constructs such as habit, technostress, and emotional attachment (e.g., Foroughi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rahi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), showing that sustained technological use is both a rational and affective process.\u003c/p\u003e \u003cp\u003eOriginally developed for information systems, TCT has been widely applied to various technology-mediated service environments such as mobile banking, e-commerce, and SSTs. These studies commonly demonstrate that users\u0026rsquo; continuance intention depends not only on system performance but also on experiential quality and service reliability (Khayer \u0026amp; Bao, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous literature particularly stresses that post-adoption behavior is shaped by users\u0026rsquo; holistic evaluation of their service experience rather than by technical ease of use alone (Lee, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Vakulenko et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In SST and SSK contexts, users interpret technology through a service lens, where physical, emotional, and social dimensions jointly determine satisfaction and loyalty (Collier \u0026amp; Barnes, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Youssef et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, integrating service-related factors into TCT offers a more realistic explanation of how technology-driven service experiences shape continued use.\u003c/p\u003e \u003cp\u003eIn the context of SSKs, research consistently finds that users\u0026rsquo; post-adoption evaluations are grounded in their perceptions of both service assurance and environmental cues (Guan et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Vakulenko et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These antecedents jointly represent the functional and emotional value dimensions described by Zeithaml (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Service assurance captures users\u0026rsquo; confidence in the reliability, responsiveness, and competence of service providers or systems (Brady \u0026amp; Cronin, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Meuter et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Parasuraman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). In the digitalized retail context, Wong et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reframed this construct as SSQ\u0026mdash;comprising smart responsiveness, assurance, and reliability\u0026mdash;demonstrating that even in automated environments, perceived human-like assurance remains crucial for satisfaction. Similarly, environmental cues, derived from Bitner\u0026rsquo;s (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) servicescape framework, encompass perceptions of ambient conditions, spatial layout, and aesthetics that shape emotional reactions and behavioral intentions. Recent SST research (Nam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Youssef et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) reinforces that the smart servicescape\u0026mdash;including visual clarity, functionality, and design symbolism\u0026mdash;significantly affects users\u0026rsquo; comfort, trust, and willingness to reuse kiosks. These findings suggest that experiential dimensions of SSTs are integral to sustaining user engagement, complementing the functional mechanisms emphasized in TCT.\u003c/p\u003e \u003cp\u003eBuilding on this logic, the proposed model extends TCT by positioning service assurance and environmental cues as key antecedents of post-adoption evaluation. Unlike traditional TCT studies that focus on cognitive appraisal (confirmation \u0026rarr; satisfaction \u0026rarr; continuance), the extended framework incorporates experiential and contextual factors that shape emotional satisfaction and behavioral reinforcement. This perspective aligns with the shift in SST literature from purely utilitarian to hedonic and affective experiences (Collier et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vakulenko et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It also reflects current theoretical trends emphasizing that the post-adoption stage is an experiential process where users\u0026rsquo; functional reliability judgments are intertwined with aesthetic and sensory impressions (Stead et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Youssef et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe post-adoption evaluation construct in this study represents a global, integrative assessment of the SST experience. Rather than separating satisfaction and continuance intention, this unified factor captures users\u0026rsquo; cognitive and affective appraisal of whether SSTs continue to deliver expected value and enjoyment (Davis et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Liao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This approach is consistent with the evolution of TCT in recent studies (Foroughi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rahi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which treat post-adoption as a higher-order evaluation encompassing perceived usefulness, satisfaction, and loyalty. In SSK contexts, where technology-mediated encounters blend service quality and design cues, such a unified construct offers stronger explanatory power for understanding user retention and brand attachment.\u003c/p\u003e \u003cp\u003eFurthermore, this study introduces technology expertise as a moderating variable that conditions how users interpret service and environmental cues. Technology expertise refers to an individual\u0026rsquo;s perceived knowledge and capability to effectively use technological systems (Compeau \u0026amp; Higgins, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Kim \u0026amp; Gupta, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Thatcher \u0026amp; Perrew\u0026eacute;, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Users with higher expertise tend to evaluate SST environments more confidently, translating technical and aesthetic cues into positive post-adoption judgments. It is conceptually related to technology readiness (Parasuraman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and personal innovativeness (Agarwal \u0026amp; Prasad, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), yet it focuses more on the applied competence that shapes users\u0026rsquo; real-world interactions with self-service interfaces. As found in Lee (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e) and Nam et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), individual differences in digital literacy, confidence, and readiness significantly moderate user satisfaction and continuance intention toward SSTs. Therefore, technology expertise provides a meaningful extension to TCT by integrating personal capability into the cognitive\u0026ndash;experiential framework of post-adoption evaluation.\u003c/p\u003e \u003cp\u003eIn summary, the proposed extended TCT framework situates users\u0026rsquo; continuance evaluation of SSTs within a broader experiential and individual-difference context. By linking functional reliability (service assurance), emotional comfort (environmental cues), and technological competence (expertise), this model advances understanding of how users form enduring post-adoption evaluations of SSKs. This integration bridges the traditional IS perspective of TCT with the evolving service-dominant logic of technology-mediated consumer experiences, offering a comprehensive theoretical foundation for explaining sustained SST use.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Hypotheses Development","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Service Assurance and Post-Adoption Evaluation\u003c/h2\u003e \u003cp\u003eService assurance refers to customers\u0026rsquo; perceptions of a service provider\u0026rsquo;s reliability, responsiveness, and competence in delivering technology-based services (Brady \u0026amp; Cronin, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Cronin \u0026amp; Taylor, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Meuter et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Parasuraman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Rooted in the SERVQUAL framework (Parasuraman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), assurance reflects users\u0026rsquo; confidence in the service provider\u0026rsquo;s knowledge and problem-solving ability, while responsiveness emphasizes dependable support. Later studies viewed service quality hierarchically, positioning interaction quality\u0026mdash;employees\u0026rsquo; behavior and expertise\u0026mdash;as central to user evaluation (Brady \u0026amp; Cronin, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn SST and SSK contexts, assurance remains vital because users still expect reliability and support even when interaction occurs via technology. Meuter et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) found that perceived support and problem-solving capacity strongly influence satisfaction, while Zeithaml et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) showed that assurance and responsiveness drive behavioral intentions. Recent findings reaffirm these effects in smart environments: smart assurance\u0026mdash;the perceived reliability and security of automated systems\u0026mdash;significantly enhances user comfort and satisfaction (Wong et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Youssef et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Lee (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) also demonstrated that perceived reliability in SST use strengthens overall satisfaction and continuance intention.\u003c/p\u003e \u003cp\u003eFrom the TCT perspective (Liao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), perceived reliability functions as post-use confirmation, reinforcing users\u0026rsquo; confidence that the technology continues to meet expectations. Such confirmation enhances satisfaction and perceived usefulness, fostering favorable post-adoption evaluations. Accordingly, service assurance is expected to strengthen both users\u0026rsquo; perceptions of the service environment and their overall evaluation of SST experiences. Accordingly, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH1: Service assurance positively influences environmental cues in self-service kiosk services.\u003c/p\u003e \u003cp\u003eH2: Service assurance positively influences users\u0026rsquo; post-adoption evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Environmental Cues and Post-Adoption Evaluation\u003c/h2\u003e \u003cp\u003eEnvironmental cues refer to the physical and atmospheric elements of a service setting that shape users\u0026rsquo; sensory and emotional responses during service encounters (Baker et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Bitner, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Lin \u0026amp; Hsieh, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Originating from Bitner\u0026rsquo;s (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) servicescape framework, this concept explains how ambient conditions, spatial layout, and design symbols influence both cognitive and affective evaluations. A clean, modern, and aesthetically pleasing environment communicates quality and professionalism, thereby enhancing perceived value and satisfaction (Turley \u0026amp; Milliman, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Baker et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) demonstrated that environmental design and cleanliness affect customers\u0026rsquo; quality inferences and store image, while Wakefield and Blodgett (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) confirmed that atmospheric appeal fosters satisfaction and behavioral intentions in service settings.\u003c/p\u003e \u003cp\u003eIn SST and SSK contexts, environmental cues function as critical signals of technological modernity and operational reliability. Lin and Hsieh (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) validated this through the SSTQUAL scale, identifying the physical environment dimension as a strong predictor of satisfaction and behavioral intention. More recent studies reinforce this view in smart service environments. Youssef et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) revealed that smart servicescape elements\u0026mdash;such as aesthetics, functionality, and financial security\u0026mdash;significantly enhance customer inspiration, satisfaction, and loyalty. Similarly, Wong et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) emphasized that smart service quality depends on the synergy between physical design and digital interfaces, highlighting that a well-designed servicescape elevates users\u0026rsquo; confidence and immersion. Nam et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further observed that environmental appeal and convenience in SSKs enhance satisfaction, particularly among users with higher technology readiness and digital literacy.\u003c/p\u003e \u003cp\u003eFrom the perspective of TCT (Liao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), these environmental perceptions serve as experiential confirmations\u0026mdash;users interpret positive physical and aesthetic cues as evidence that the technology consistently meets or exceeds their expectations. Favorable environments thereby reinforce perceived usefulness and satisfaction, strengthening post-adoption evaluations. Moreover, environmental cues may act as a mediating mechanism between service assurance and post-adoption evaluation: reliable service provision often manifests through well-managed physical settings that signal efficiency, care, and modernity (Stead et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, environmental cues are expected to both directly enhance users\u0026rsquo; post-adoption evaluations and mediate the influence of service assurance within the extended TCT framework. Accordingly, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH3: Environmental cues positively influence users\u0026rsquo; post-adoption evaluation.\u003c/p\u003e \u003cp\u003eH4: Environmental cues mediate the relationship between service assurance and post-adoption evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Moderating Role of Technology Expertise\u003c/h2\u003e \u003cp\u003eTechnology expertise refers to an individual\u0026rsquo;s perceived knowledge and capability to effectively understand and use technological systems (Compeau \u0026amp; Higgins, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Kim \u0026amp; Gupta, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Thatcher \u0026amp; Perrew\u0026eacute;, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Prior studies conceptualize it as a personal capability encompassing users\u0026rsquo; familiarity, knowledge, and experience with technology, which in turn shapes perceptions of usefulness and ease of use (Shih, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Thatcher et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Venkatesh et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Conceptually, it is closely related to technology readiness and personal innovativeness, reflecting individuals\u0026rsquo; confidence and enthusiasm toward engaging with new technologies (Agarwal \u0026amp; Prasad, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Parasuraman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Vakulenko et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn SST/SSK settings, higher expertise reduces perceived barriers and fosters favorable evaluations\u0026mdash;especially when systems provide clear guidance and information richness (Galdolage, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Joshi \u0026amp; Joshi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u0026mdash;whereas low-expertise users lean more on contextual cues such as service assurance and the servicescape. Recent work shows that technology-oriented or digitally literate users respond more positively to smart service quality and well-designed servicescapes, with stronger satisfaction and reuse intentions (Guan et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lee, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Nam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Stead et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Youssef et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin the extended TCT framework (Liao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), technology expertise is expected to moderate the strength of the relationships among key experiential factors in the SST context. First, users with higher technology expertise are more capable of recognizing how reliable and competent service delivery enhances the surrounding service environment. Thus, the effect of service assurance on environmental cues is likely to be stronger for users with higher technological orientation. Second, technologically confident users are also more likely to translate positive service assurance into favorable post-adoption evaluations, as they can better appreciate the value of a dependable and well-managed SST experience. Finally, users with high technology expertise are expected to derive greater satisfaction and perceived value from well-designed environmental cues, strengthening the relationship between environmental cues and post-adoption evaluation. Accordingly, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH5: Technology expertise moderates the relationship between service assurance and environmental cues, such that the effect is stronger for users with higher technology expertise.\u003c/p\u003e \u003cp\u003eH6: Technology expertise moderates the relationship between service assurance and post-adoption evaluation, such that the effect is stronger for users with higher technology expertise.\u003c/p\u003e \u003cp\u003eH7: Technology expertise moderates the relationship between environmental cues and post-adoption evaluation, such that the effect is stronger for users with higher technology expertise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Summary of the Proposed Model\u003c/h2\u003e \u003cp\u003eIn summary, this study proposes an extended TCT framework in which users\u0026rsquo; post-adoption evaluation of SSTs is influenced by both service-related and environmental factors. Service assurance and environmental cues serve as core experiential antecedents shaping users\u0026rsquo; overall assessment of SST services, while technology expertise represents an individual-level moderator that explains variations in how users interpret and evaluate their experiences. The proposed conceptual framework is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Methodology","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Operational Definition\u003c/h2\u003e \u003cp\u003eThe constructs employed in this study were operationalized by adapting and synthesizing conceptual definitions from prior literature on service quality, servicescape, and technology continuance behavior, and applying them to the context of SSTs and SSKs. Specifically, items measuring service assurance were derived from the SERVQUAL framework and subsequent studies emphasizing reliability, responsiveness, and competence in technology-enabled services (Brady \u0026amp; Cronin, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Meuter et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Parasuraman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Environmental cues were operationalized based on the servicescape theory, reflecting the physical and ambient attributes of service settings such as cleanliness, modernity, and employee appearance (Baker et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Bitner, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Lin \u0026amp; Hsieh, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Finally, post-adoption evaluation was defined using the TCT framework (Liao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), capturing users\u0026rsquo; holistic satisfaction and comparative preferences toward SST-based services after continued use (Davis et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the operational definitions and corresponding reference studies used in this research. All measurement items were rephrased to fit the context of retail environments using self-service technologies, ensuring conceptual equivalence with prior validated scales. Before data collection, the questionnaire was reviewed by three academic experts in service management and information systems to ensure content validity and linguistic clarity. Minor wording adjustments were made to enhance readability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOperational Definition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelevant studies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eService Assurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStores using self-service technology will actively resolve problems when they arise for customers.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eBrady \u0026amp; Cronin, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Meuter et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Parasuraman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1988\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStores using self-service technology will be capable of properly delivering services to customers.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStores using self-service technology will have employees who are always willing to help customers.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEven when they are busy, employees in stores using self-service technology will respond to customers\u0026rsquo; requests.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStores using self-service technology will be able to handle issues related to refunds, mistakes, and safety.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployees in stores using self-service technology will be polite and courteous to customers.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployees in stores using self-service technology will have sufficient knowledge to answer customers\u0026rsquo; inquiries.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEnvironmental Cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStores using self-service technology will have clean buildings and modern facilities.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBaker et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Bitner, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Lin \u0026amp; Hsieh, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStores using self-service technology will have well-maintained ancillary facilities such as parking lots.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployees in stores using self-service technology will be neatly dressed.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePost Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe services provided by stores using self-service technology are overall satisfactory.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDavis et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Liao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe use of self-service technology has improved the overall quality of service.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI would be willing to revisit stores using self-service technology in the future.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompared to other stores, I would prefer to use stores that adopt self-service technology in the future.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Data Collection\u003c/h2\u003e \u003cp\u003eData for this study were collected through a paper-based survey administered in June 2023 targeting Korean consumers with prior experience using SSTs such as SSKs in restaurants, banks, and airports. Participants were recruited through convenience sampling, focusing on adults who had interacted with SST/SSK systems within the previous six months. The survey aimed to capture users\u0026rsquo; perceptions of service assurance, environmental cues, post-adoption evaluations, and technology expertise, as well as basic demographic information. Data were collected anonymously, and participation was voluntary. In total, 218 valid responses were obtained and used for analysis after screening for missing or incomplete responses. This sample size satisfies the recommended minimum criteria for structural equation modeling (SEM) (Hair et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), ensuring adequate statistical power for the hypothesized model.\u003c/p\u003e \u003cp\u003eDemographic characteristics of the respondents are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The majority of participants were in their twenties (34.9%) and thirties (17.4%), reflecting the population segments most familiar with technology-based services in Korea. The gender distribution was relatively balanced, with 52.8% male and 47.2% female respondents. Most participants had completed college or university education (75.2%), while 16.5% held postgraduate degrees. In terms of technology expertise, measured by the item \u0026ldquo;People seek me out for explanations of the latest technologies,\u0026rdquo; responses were widely distributed, indicating variability in users\u0026rsquo; self-assessed technological capability. This diversity allowed for a meaningful examination of moderating effects based on technology expertise within the proposed model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Profile\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTech Expertise -People seek me out for explanations of the latest technologies.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege/University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduate School (Master\u0026rsquo;s/PhD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the descriptive statistics of all measurement items used in the study, including their mean values, standard deviations, skewness, and kurtosis. The mean scores for all items ranged between 2.95 and 3.69, indicating generally positive perceptions of SST experiences among respondents. Standard deviations ranged from 0.74 to 0.98, suggesting moderate variability in responses. All skewness and kurtosis values fell within the acceptable threshold range of \u0026plusmn;\u0026thinsp;2.0 (Hair et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), confirming that the data approximate a normal distribution suitable for SEM. Overall, these results indicate that the measurement items exhibit no serious deviations from normality and are appropriate for subsequent confirmatory factor and structural analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Measurement Items\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eService Assurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEnvironmental Cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePost Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Reliability and Validity Analysis\u003c/h2\u003e \u003cp\u003eA confirmatory factor analysis (CFA) was conducted to assess the reliability and validity of the measurement model. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, all standardized factor loadings exceeded the recommended threshold of 0.70 (Hair et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), demonstrating adequate indicator reliability. The values of composite reliability (CR) ranged from 0.870 to 0.917, and Cronbach\u0026rsquo;s α values ranged from 0.866 to 0.918, both surpassing the acceptable criterion of 0.70 (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Nunnally \u0026amp; Bernstein, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). In addition, average variance extracted (AVE) values ranged between 0.613 and 0.699, indicating satisfactory convergent validity. The overall model fit indices also met the recommended thresholds. These values indicate an acceptable model fit according to the criteria suggested by Hu and Bentler (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and Hair et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability and Convergent Validity Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor Loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage variance extracted (AVE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComposite reliability (CR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCronbach α\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eService Assurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEnvironmental Cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePost Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote(s): χ\u003csup\u003e2\u003c/sup\u003e(73)\u0026thinsp;=\u0026thinsp;174.48 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RMR\u0026thinsp;=\u0026thinsp;0.029, SRMR\u0026thinsp;=\u0026thinsp;0.0391, GFI\u0026thinsp;=\u0026thinsp;0.906, AGFI\u0026thinsp;=\u0026thinsp;0.864, NFI\u0026thinsp;=\u0026thinsp;0.923, RFI\u0026thinsp;=\u0026thinsp;0.904, IFI\u0026thinsp;=\u0026thinsp;0.954, TLI\u0026thinsp;=\u0026thinsp;0.942, CFI\u0026thinsp;=\u0026thinsp;0.953, and RMSEA\u0026thinsp;=\u0026thinsp;0.08 (90% CI [0.065, 0.095])\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiscriminant validity was evaluated using the Fornell\u0026ndash;Larcker criterion. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the square root of each AVE (diagonal values) was greater than the corresponding inter-construct correlations (off-diagonal values), confirming discriminant validity (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). This indicates that each construct is empirically distinct and captures unique aspects of the SST experience.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant Validity Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Service Assurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Environmental Cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Post Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote(s): The off-diagonal matrix shows the correlation between the factors. The numbers of the diagonal are the squared root of AVE.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Structural Equation Modeling Analysis\u003c/h2\u003e \u003cp\u003eA SEM analysis was conducted to test the hypothesized relationships among the constructs within the extended TCT framework. The overall model demonstrated a satisfactory fit to the data. All indices met or exceeded the recommended thresholds (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hair et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), indicating a good model fit. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the direct effects of service assurance on environmental cues (β\u0026thinsp;=\u0026thinsp;0.668, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and post-adoption evaluation (β\u0026thinsp;=\u0026thinsp;0.494, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were both significant, supporting the proposed relationships. Environmental cues also exerted a significant positive influence on post-adoption evaluation (β\u0026thinsp;=\u0026thinsp;0.371, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The indirect effect of service assurance on post-adoption evaluation through environmental cues (β\u0026thinsp;=\u0026thinsp;0.248, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was statistically significant, confirming the mediating role of environmental cues. These results collectively validate that users\u0026rsquo; perceptions of reliable and competent service (service assurance) foster favorable environmental impressions, which subsequently enhance their overall evaluations of SSK experiences.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect, Indirect, and Total Effect\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRelationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandardized Effect (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExo. (ξ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMed. (ηₘ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEndo. (η)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eService Assurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnvironmental Cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.668*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH1 Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental Cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.371**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH3 Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eService Assurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.494*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH2 Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eService Assurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnvironmental Cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.248**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eH4 Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eService Assurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnvironmental Cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.742*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNote(s): *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e(73)\u0026thinsp;=\u0026thinsp;174.48 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RMR\u0026thinsp;=\u0026thinsp;0.029, SRMR\u0026thinsp;=\u0026thinsp;0.0391, GFI\u0026thinsp;=\u0026thinsp;0.906, AGFI\u0026thinsp;=\u0026thinsp;0.864, NFI\u0026thinsp;=\u0026thinsp;0.923, RFI\u0026thinsp;=\u0026thinsp;0.904, IFI\u0026thinsp;=\u0026thinsp;0.954, TLI\u0026thinsp;=\u0026thinsp;0.942, CFI\u0026thinsp;=\u0026thinsp;0.953, and RMSEA\u0026thinsp;=\u0026thinsp;0.08 (90% CI [0.065, 0.095])\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo test the moderating role of technology expertise, a multi-group analysis was performed by dividing respondents into high and low technology expertise groups based on median-split criteria. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the moderating effect was partially supported. Specifically, technology expertise significantly strengthened the relationship between environmental cues and post-adoption evaluation (Δβ\u0026thinsp;=\u0026thinsp;0.395, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting H7. However, its moderating effects on the relationships between service assurance and environmental cues (Δβ\u0026thinsp;=\u0026thinsp;0.109) and between service assurance and post-adoption evaluation (Δβ = \u0026minus;0.185) were not significant, indicating that these effects are relatively stable across user groups. In addition, a significant indirect effect of environmental cues was observed between service assurance and post-adoption evaluation among users with high technology expertise (β\u0026thinsp;=\u0026thinsp;0.375, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas this mediating effect was not significant for the low technology expertise group. The model comparison results confirmed marginally significant overall difference between the groups (χ\u0026sup2;(3)\u0026thinsp;=\u0026thinsp;7.04, p\u0026thinsp;=\u0026thinsp;0.071).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModeration Effect Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRelationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eStandardized Direct Effect (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExo. (ξ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndo. (η)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eService Assurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental Cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.593*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.702*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH5 Not Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.534*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.395**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH7 Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eService Assurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.562*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.377**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH6 Not Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNote(s): *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eModel Comparison: χ\u003csup\u003e2\u003c/sup\u003e(3)\u0026thinsp;=\u0026thinsp;7.04 (p\u0026thinsp;=\u0026thinsp;0.071), NFI Delta1\u0026thinsp;=\u0026thinsp;0.003, IFI Delta2\u0026thinsp;=\u0026thinsp;0.003, RFI rho1=-0.000, TLI rho2=-0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe overall SEM results for the direct, mediating, and moderating effects are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. To summarize, the direct relationships among service assurance, environmental cues, and post-adoption evaluation were significant. Environmental cues significantly mediated the relationship between service assurance and post-adoption evaluation. Technology expertise moderated the relationship between environmental cues and post-adoption evaluation, such that users with high technology expertise demonstrated a strong and significant positive relationship between these factors, whereas the relationship was insignificant for users with low technology expertise. However, the moderating effects of technology expertise on the relationships between service assurance and environmental cues, as well as between service assurance and post-adoption evaluation, were not significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Implications","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Theoretical Implications\u003c/h2\u003e \u003cp\u003eThis study makes several theoretical contributions to the growing body of literature on SST and SSK by extending the theory of technology continuance (Liao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) to a service quality\u0026ndash;based experiential framework. Whereas prior studies have primarily emphasized users\u0026rsquo; perceptions of usefulness, ease of use, and satisfaction as predictors of continuance intention (Foroughi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rahi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the present study highlights service assurance and environmental cues as key experiential factors influencing users\u0026rsquo; post-adoption evaluations in SST environments. By doing so, this research expands TCT beyond its traditional focus on cognitive appraisal to encompass the affective and contextual dimensions of the SST experience.\u003c/p\u003e \u003cp\u003eSecond, this study empirically verifies the mediating role of environmental cues in the relationship between service assurance and post-adoption evaluation. Although previous research has acknowledged the influence of servicescape and atmospheric design on customer perceptions and behavioral intentions (Baker et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Bitner, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Lin \u0026amp; Hsieh, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Youssef et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), limited attention has been given to how such environmental attributes interact with service reliability and assurance in technology-mediated contexts. The results suggest that environmental cues serve as an experiential bridge that translates perceived service reliability into favorable post-adoption attitudes. This finding complements recent discussions emphasizing the integration of physical and digital atmospherics in smart service environments (Stead et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThird, the moderating role of technology expertise advances the theoretical understanding of user heterogeneity in technology continuance behavior. While studies on technology readiness (Parasuraman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and personal innovativeness (Agarwal \u0026amp; Prasad, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) have examined individual differences in technology adoption, empirical exploration of how users\u0026rsquo; technological capability alters experiential evaluation remains scarce. This study demonstrates that users with higher technology expertise derive stronger affective and evaluative benefits from environmental cues, supporting the notion that technological self-efficacy amplifies experiential value in SST settings (Shih, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Thatcher \u0026amp; Perrew\u0026eacute;, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This contributes to the ongoing scholarly efforts to contextualize individual-level technology differences within the TCT framework (Foroughi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Khayer \u0026amp; Bao, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, by incorporating multidimensional constructs such as service assurance, environmental cues, and technology expertise, this study contributes to the emerging human\u0026ndash;technology interaction perspective in service science (Singh \u0026amp; Yadav, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Vakulenko et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The findings align with recent calls to investigate how smart environments and user characteristics jointly shape post-adoption outcomes in digital service ecosystems. Thus, this research provides a contextually grounded extension of TCT that connects service quality theory, servicescape research, and technology acceptance literature, offering a more holistic understanding of user continuance behavior in SST/SSK environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e6.2. Practical Implications\u003c/h2\u003e \u003cp\u003eThe findings of this study also provide several practical implications for service managers and practitioners implementing SSTs and SSKs in hospitality, retail, and transportation sectors. First, the results highlight the critical role of service assurance in shaping users\u0026rsquo; perceptions of technological reliability and their subsequent evaluations. Managers should design SST systems that ensure prompt and consistent service recovery when technical errors occur. As emphasized by Collier et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Meuter et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), users expect a swift and empathetic resolution even in self-service settings, where human contact is minimized. Providing clear guidance on how customers can seek assistance\u0026mdash;whether through digital help functions, on-site attendants, or remote support\u0026mdash;can strengthen perceptions of reliability and trust.\u003c/p\u003e \u003cp\u003eSecond, environmental cues such as spatial design, cleanliness, and aesthetic appeal were found to mediate users\u0026rsquo; post-adoption evaluations. This indicates that technological functionality alone is insufficient; the servicescape remains a vital touchpoint for user satisfaction (Bitner, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Youssef et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Service providers should therefore invest in enhancing both physical and digital atmospherics\u0026mdash;through intuitive kiosk interfaces, ambient lighting, sound design, and maintenance of clean, modern facilities\u0026mdash;to communicate professionalism and technological competence.\u003c/p\u003e \u003cp\u003eThird, this study reveals that users\u0026rsquo; technology expertise significantly moderates how they respond to environmental cues. For technologically confident users, the design and ambiance of the service setting significantly enhance satisfaction, whereas low-expertise users rely more on human or system-based reassurance. Accordingly, managers should segment users based on their technology proficiency and tailor their support strategies: offering simplified tutorials, intuitive UI/UX features, or hybrid service options for low-expertise users, while enabling greater autonomy and efficiency for high-expertise users. Such differentiated approaches can enhance both inclusivity and user retention (Foroughi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lastly, the study suggests that continuous monitoring of user feedback on SST experiences can guide adaptive improvement of smart service environments. Integrating real-time analytics or AI-driven feedback systems would allow service providers to detect friction points in user interaction and maintain high-quality experiences over time (Singh \u0026amp; Yadav, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Stead et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, these insights emphasize that the success of SST/SSK adoption depends not only on technological sophistication but also on how effectively service environments and human factors are harmonized. Organizations that manage both technological reliability and experiential design are more likely to sustain user engagement and loyalty in the evolving landscape of smart service ecosystems.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study extends the theory of technology continuance by integrating service assurance, environmental cues, and technology expertise to explain users\u0026rsquo; post-adoption evaluation of SSTs and SSKs. The findings confirm that service assurance and environmental cues significantly influence post-adoption evaluations, with environmental cues mediating the link between perceived reliability and user satisfaction. Moreover, technology expertise moderates the effect of environmental cues on post-adoption evaluation, revealing that technologically proficient users derive stronger experiential value from well-designed smart environments.\u003c/p\u003e \u003cp\u003eTheoretically, this research contributes to SST literature by bridging service quality, servicescape, and technology continuance perspectives. It highlights the experiential dimension of post-adoption behavior and underscores the importance of individual technological capability in shaping users\u0026rsquo; evaluation processes. By situating service assurance and environmental cues within the TCT framework, the study advances understanding of how users continuously interpret and evaluate technology-mediated services. Practically, the results suggest that organizations should strengthen reliability and user confidence through robust service assurance mechanisms, while simultaneously enhancing the physical and digital environment to improve the overall service experience. Segmenting users based on technological proficiency and providing differentiated levels of support can foster inclusivity and sustain engagement in diverse user groups.\u003c/p\u003e \u003cp\u003eDespite its contributions, this study is limited to a single-country context and self-reported measures. Future research could employ cross-cultural comparisons or longitudinal approaches to examine how evolving user expertise and digital maturity shape the continuance of smart service adoption over time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSangyung Lee conceived the overall research idea, collected and analyzed the data, and conducted the literature review and discussion of implications. Yeong-Hyeon Choi (co-first author) contributed to drafting the abstract and introduction, participated in the literature review and discussion of implications, and checked the references. Yeong Ju Rhee contributed to developing the implications and conclusion, reviewed the manuscript, ensured consistency in formatting, and served as the corresponding author.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe anonymized dataset supporting the findings of this study is openly available in the Figshare repository at the following link: https://figshare.com/s/a7de9a8aa7d6fee34109. The dataset includes only fully anonymized survey responses with no identifying information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgarwal, R., \u0026amp; Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. \u003cem\u003eInformation Systems Research, 9\u003c/em\u003e(2), 204-215. https://doi.org/10.1287/isre.9.2.204\u003c/li\u003e\n\u003cli\u003eBaker, J., Grewal, D., \u0026amp; Parasuraman, A. (1994). 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Designing flexible service systems: Application to machine tools. \u003cem\u003eJournal of Intelligent Manufacturing, 27\u003c/em\u003e(6), 1237-1259. https://doi.org/10.1007/s10845-014-0947-y\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"self-service technology, self-service kiosk, service assurance, environmental cues, technology expertise, Technology Continuance Theory (TCT), post-adoption evaluation","lastPublishedDoi":"10.21203/rs.3.rs-7993661/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7993661/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates how service assurance and environmental cues influence users\u0026rsquo; post-adoption evaluation of self-service technologies (SSTs) and self-service kiosks (SSKs), incorporating the moderating role of technology expertise within the technology continuance theory (TCT) framework. Although prior research has examined initial technology acceptance, post-adoption evaluations in automated service environments remain underexplored. Data were collected from Korean consumers with prior experience using SSTs/SSKs, and structural equation modeling (SEM) was applied to test the proposed model. The results revealed that service assurance significantly affects both environmental cues and post-adoption evaluations, while environmental cues also exert a significant mediating effect between service assurance and post-adoption evaluation. Moreover, technology expertise moderates the relationship between environmental cues and post-adoption evaluation, indicating that users with higher technological proficiency perceive greater experiential value from well-designed SST environments. This study extends the TCT by integrating service quality and servicescape perspectives, offering a comprehensive understanding of how users\u0026rsquo; cognitive and experiential factors interact in technology-mediated services. The findings also suggest practical strategies for managers to enhance customer experience by improving service reliability, optimizing physical and digital atmospherics, and tailoring user support based on technology expertise. Overall, this research provides both theoretical advancement and managerial insights into sustaining positive user engagement in the evolving landscape of smart service environments.\u003c/p\u003e","manuscriptTitle":"User Evaluation of Technology-Enabled Self-Service Kiosks: Service Assurance, Environmental Cues, and Technology Expertise","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 08:39:04","doi":"10.21203/rs.3.rs-7993661/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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