The Moderating Role of Digital Service Quality in the Relationship Between Digital Transformation, the Internet of Things, Artificial Intelligence, and Customer Satisfaction in Marketing Companies in Saudi Arabia

The Moderating Role of Digital Service Quality in the Relationship Between Digital Transformation, the Internet of Things, Artificial Intelligence, and Customer Satisfaction in Marketing Companies in Saudi Arabia | 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 Research Article The Moderating Role of Digital Service Quality in the Relationship Between Digital Transformation, the Internet of Things, Artificial Intelligence, and Customer Satisfaction in Marketing Companies in Saudi Arabia Abdullah Saad Rashed, Shaker M. AL-kahtani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9254750/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This study investigates the impact of Digital Transformation (DT), Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Service Quality (DSQ) on Customer Satisfaction (CS) in marketing companies in Saudi Arabia, and examines whether DSQ moderates these relationships. Drawing on six complementary frameworks — including Dynamic Capabilities Theory, the Technology Acceptance Model (TAM), and the Information Systems Success Model — the study analyses data from a purposive sample of 269 respondents using PLS-SEM in SmartPLS 4. AI, IoT, and DSQ each exert a significant positive direct effect on CS, with AI the strongest driver (f² = 0.202), followed by DSQ (f² = 0.123) and IoT (f² = 0.120). DT was not statistically significant (f² = 0.009), suggesting that organisational-level transformation has not yet translated into customer-perceived outcomes in this context. DSQ does not significantly moderate the technology–CS relationships, indicating that technological benefits operate independently of perceived service quality. These null moderation findings are reinterpreted through the IS Success Model as evidence that technological value and service quality act as parallel, additive satisfaction drivers rather than interactive ones. The study contributes theoretically by integrating multiple frameworks into a unified model, offers practical guidance for marketing firms, and aligns with the United Nations Sustainable Development Goals by showing how these technologies support SDG 8 (decent work and economic growth) and SDG 9 (industry, innovation, and infrastructure), and enable the more responsible consumption patterns envisaged in SDG 12. Limitations include non-probability sampling and a single-sector focus in Riyadh. digital transformation (DT) internet of things (IoT) artificial intelligence (AI) digital service quality (DSQ) customer satisfaction (CS) PLS-SEM Saudi Arabia dynamic capabilities moderating effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The accelerated advancement of digital technologies has profoundly transformed industries, societies, and economies worldwide. The World Economic Forum (2023) estimates that digital transformation (DT) could contribute up to USD 100 trillion to the global economy by 2025 by allowing organisations to enhance operational efficiency, develop innovative products and services, and improve customer satisfaction (CS). DT can be defined as the comprehensive integration of digital technologies into all business functions, leading to fundamental changes in how organisations operate and create value for customers (Kraus et al., 2021 ; Kraus et al., 2022 ; Robertsone & Lapina, 2023). This transformation involves the adoption of advanced technologies such as artificial intelligence (AI), big data analytics, cloud computing, and the Internet of Things (IoT), which support process optimisation, more informed decision-making, and continuous innovation. Core elements of DT include strategic orientation, technological implementation, and organisational culture change (Butt et al., 2024 ; Kao et al., 2024 ; Costa, 2024 ). Nevertheless, this technological era also poses substantial challenges, as firms must adapt to rapid change, shifting customer expectations, and increasing competitive pressure. Within the business context, DT, AI, and IoT are widely recognised as critical drivers of long-term performance and sustainability. Evidence from Tarigan et al. ( 2025 ) indicates that organisations embracing digital technologies are 23% more likely to outperform competitors in terms of profitability. Digital initiatives also strongly influence CS, which represents a vital source of competitive advantage. By utilising data analytics, AI, cloud platforms, and IoT solutions, companies can deliver integrated and highly personalised customer experiences. Despite these advantages, prior research points to a disconnect between the expected and realised outcomes of DT, AI, and IoT, as many organisations experience only modest improvements in key performance indicators (Guo & Xu, 2021 ). Consequently, firms often struggle to implement digital strategies that simultaneously enhance organisational performance and CS. CS reflects the extent to which products and services meet or exceed customer expectations. It is shaped by factors such as product quality, service quality, pricing, and the overall customer experience. DT has significantly enhanced CS by facilitating service personalisation, improving access, and strengthening interactive communication channels (Matarazzo et al., 2021 ; Pascucci et al., 2023 ; Latupeirissa et al., 2024 ). Moreover, perceived usefulness, ease of use, and price perceptions have been shown to positively affect CS and, in turn, customer loyalty (Suryatenggara & Dahlan, 2022 ). Despite growing interest in this area, empirical studies examining the joint effects of DT, AI, and IoT on both organisational performance and CS remain scarce. Kretschmer and Khashabi ( 2020 ) proposed an integrated framework for DT and organisation design but focused primarily on structural change without empirically linking it to CS. Similarly, studies by Müller et al. ( 2024 ) and Leso et al. ( 2023 ) emphasise leadership and organisational culture as drivers of digital excellence, yet they overlook CS outcomes. Research by Rizvanović et al. ( 2023 ) addresses customer-oriented digital marketing strategies but does not provide a comprehensive assessment of overall firm performance. Given these gaps, this study examines the impact of DT, AI, IoT, and DSQ on CS in the context of rapid technological change, and introduces DSQ as a potential moderator of the technology–satisfaction relationships. A secondary aim is to clarify how the deployment of these digital technologies in marketing firms generates not only customer value but also sustainable development outcomes, thereby contributing evidence relevant to the United Nations Sustainable Development Goals agenda in emerging economies. The Saudi context is particularly timely: under Vision 2030, the Kingdom is actively pursuing digital economic diversification, with AI spending projected to reach $ 1.9 billion by 2027 and the IoT market estimated to reach $ 2.9 billion by 2025 (Alateeg et al., 2024 ; Al-Ayed et al., 2023 ). Despite this policy momentum, empirical evidence on how digital technologies jointly influence CS in Saudi marketing companies remains limited (Alotaibi, 2024 ). Beyond its managerial relevance, this study speaks directly to the sustainable development agenda. The effective integration of AI, IoT, and high-quality digital services in marketing companies contributes to several United Nations Sustainable Development Goals (SDGs). Digital technologies that raise productivity, create new skilled roles, and enable inclusive economic participation advance SDG 8 (Decent Work and Economic Growth), while AI and IoT represent foundational technologies underpinning the innovation and infrastructure agenda captured by SDG 9 (Industry, Innovation, and Infrastructure). High-quality digital services that reduce informational waste, enhance resource allocation, and enable customers to make more informed choices likewise support the responsible consumption patterns envisaged in SDG 12 (Responsible Consumption and Production). Saudi Arabia’s Vision 2030 reform programme is explicitly aligned with these Goals, making the Saudi marketing sector a particularly relevant context in which to examine how digital transformation delivers both customer value and sustainable economic and social outcomes. 2. Theoretical Foundation This study draws on six complementary theoretical frameworks. Together, they provide the conceptual architecture through which the relationships among DT, AI, IoT, DSQ, and CS are explained, and they supply the logical basis for each hypothesis developed in Section 3 . 2.1 Dynamic Capabilities Theory Teece, Pisano, and Shuen ( 1997 ) define dynamic capabilities as an organisation’s ability to sense environmental change, seize emerging opportunities, and reconfigure its resource base to sustain competitive advantage. In digital business contexts, this framework explains how firms develop technology-enabled capabilities that allow them to continuously innovate, adapt, and create customer value (Saeedikiya et al., 2024 ; de Miguel et al., 2022 ). This theory provides the overarching framework for the study: DT, AI, and IoT represent distinct technological capabilities that organisations deploy to sense and respond to customer needs. H1 (DT→CS), H2 (IoT→CS), and H3 (AI→CS) all flow from this premise — firms that effectively deploy digital technologies as dynamic capabilities generate superior customer experiences and satisfaction. 2.2 Technology Acceptance Model (TAM) Davis ( 1989 ) proposed the Technology Acceptance Model (TAM) to explain how users form intentions to adopt technology based on two core beliefs: perceived usefulness and perceived ease of use. TAM has since been extended into the Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al. ( 2003 ), broadening the antecedents of technology adoption. Both frameworks have been extensively applied to consumer adoption of digital services, mobile applications, and smart devices (Singh et al., 2020 ; Alkis & Kose, 2022 ; Al-Hattami et al., 2026 ). In this study, TAM provides the individual-level mechanism through which IoT (H2) and AI (H3) influence CS: consumers evaluate whether IoT connectivity and AI-driven personalisation are useful and easy to interact with, and those perceptions directly shape satisfaction. When customers find AI recommendations accurate and IoT services seamlessly integrated into their lives, they report higher CS levels. TAM also underpins H4 (DSQ→CS): the efficiency and reliability dimensions of DSQ map directly onto TAM’s ease-of-use and usefulness constructs. 2.3 Expectation-Confirmation Theory (ECT) Oliver ( 1980 ) proposed Expectation-Confirmation Theory (ECT) to explain how satisfaction emerges from the comparison between pre-consumption expectations and post-consumption performance perceptions. When actual service performance confirms or exceeds expectations, satisfaction results; when performance falls short, dissatisfaction ensues. ECT provides the psychological mechanism through which all four independent variables (DT, IoT, AI, DSQ) translate into CS (H1–H4): each technology raises customer expectations for service efficiency, personalisation, and responsiveness, and CS arises when those expectations are confirmed. ECT also explains why DT’s effect on CS may be weaker than AI’s or IoT’s — DT operates primarily at the organisational infrastructure level, while customers confirm expectations primarily through direct service interactions. 2.4 Service Quality Theory (SERVQUAL) Parasuraman, Zeithaml, and Berry ( 1988 ) developed the SERVQUAL framework to measure service quality across five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. In digital environments, these dimensions have been adapted into DSQ frameworks assessing website efficiency, information accuracy, privacy protection, and responsiveness (Parasuraman et al., 2005 ; Raza et al., 2020 ; Kim & Jeon, 2025 ). SERVQUAL grounds H4 (DSQ→CS): customers evaluate digital platforms against these service quality dimensions, and those evaluations translate directly into satisfaction. The moderation hypotheses (H5, H6, H7) also draw on SERVQUAL by proposing that service quality perceptions amplify or attenuate the satisfaction effects of digital technologies. The rejection of H5–H7 suggests that in digitally intensive environments, the evaluative processes associated with technological value and service quality quality operate through separate cognitive pathways. 2.5 Information Systems Success Model DeLone and McLean ( 1993 , 2003 ) proposed that system quality, information quality, and service quality are direct antecedents of user satisfaction and net benefits. In digital marketing contexts, this model explains why DSQ is a direct and significant predictor of CS (H4). Critically, the IS Success Model also provides a theoretical reinterpretation of the null moderation findings (H5–H7): the model’s architecture suggests that system quality and user satisfaction are related through direct additive effects rather than interactive ones, meaning that the quality of the digital interface independently predicts satisfaction without necessarily amplifying the effects of underlying technologies. This offers a theoretically grounded explanation for why DSQ and DT, IoT, and AI may each drive CS without their effects being contingent upon one another. 2.6 Stimulus-Organism-Response (SOR) Framework Mehrabian and Russell ( 1974 ) proposed the SOR framework to explain how environmental stimuli (S) trigger internal cognitive and affective states in the organism (O), which in turn drive behavioural responses (R). Applied to digital marketing, technology characteristics and service features serve as stimuli that shape customer perceptions and evaluations (organism), culminating in behavioural outcomes such as satisfaction and loyalty (response) (Yan et al., 2020 ). The overall structure of this study’s model follows SOR logic: DT, IoT, AI, and DSQ are stimuli that shape customer cognitive-affective states, producing satisfaction as the response. DSQ’s proposed moderating role (H5–H7) is grounded in SOR: as a stimulus modifier, DSQ was theorised to amplify or attenuate the satisfaction responses triggered by other technological stimuli. 2.7 Digital Technologies and Sustainable Development Beyond the cognitive and organisational mechanisms articulated in the preceding frameworks, a growing body of work links DT, AI, IoT, and DSQ to the United Nations 2030 Agenda for Sustainable Development. Digital technologies contribute to SDG 8 (Decent Work and Economic Growth) by raising productivity, creating higher-skilled digital employment, and broadening economic participation through digitally delivered services. They also underpin SDG 9 (Industry, Innovation, and Infrastructure) as the core innovation infrastructure on which contemporary industries rely, with AI and IoT serving as foundational general-purpose technologies for digitalised economies. High-quality digital service platforms — the focus of the DSQ construct in this study — additionally support SDG 12 (Responsible Consumption and Production) by reducing informational asymmetries, improving resource matching, and enabling customers to make more sustainable consumption choices through transparent, reliable, and personalised service experiences. Within the Saudi Arabian setting, these linkages are explicit in the Vision 2030 reform programme, which positions digital transformation as a lever for simultaneously achieving economic diversification, innovation capacity, and sustainable consumption patterns (Alateeg et al., 2024 ; Al-Ayed et al., 2023 ). Recent empirical work further confirms that digital service quality dimensions shape customer satisfaction and loyalty in ways that directly bear on sustainable business practices under environmental uncertainty (Kim & Jeon, 2025 ). Together, this evidence indicates that the technological and customer-satisfaction outcomes investigated in the present study are inseparable from their sustainable development implications and provides an additional rationale for the integrative model adopted here. 2.8 Theoretical Synthesis These six frameworks work in concert. The SOR framework provides the overall structural logic. Dynamic Capabilities Theory explains the organisational-level mechanisms through which DT, AI, and IoT create customer value. TAM explains the individual-level cognitive processes through which customers evaluate and respond to these technologies. ECT provides the psychological mechanism through which technology experiences translate into satisfaction judgements. SERVQUAL and the IS Success Model ground the DSQ construct and explain its direct and non-moderating effects. Together, they predict: (1) each digital technology independently drives CS (H1–H4); (2) DSQ theoretically moderates technology–satisfaction relationships (H5–H7); and (3) the empirical rejection of H5–H7 can be reinterpreted through the IS Success Model as evidence that technological capability and service quality are parallel, additive satisfaction drivers rather than multiplicatively interactive ones — a finding that advances theoretical understanding rather than simply reporting failure. 3. Theoretical Framework and Hypothesis Development 3.1 Digital Transformation and Customer Satisfaction (H1) DT represents the comprehensive integration of digital technologies into all organisational functions, enabling firms to reinvent business processes, enhance customer interactions, and create new value propositions (Kraus et al., 2021 ; Blanka et al., 2022 ). Organisations leveraging DT can identify opportunities, mitigate threats, and sustain competitiveness, all of which contribute to enhanced CS (Liu & He, 2024 ). Digital innovations significantly accelerate the evolution of traditional business models and enable new service development (Jiang, 2024 ), while customer-centric DT initiatives — focused on improving product quality and communication channels — consistently improve CS outcomes (Cai et al., 2024 ; Cheng et al., 2023 ). Notably, Al-Hattami et al. ( 2025 ) demonstrate through a moderated mediation design that digital accounting systems positively influence marketing performance in SMEs, confirming that the translation of digital investments into customer-facing outcomes is contingent on organisational context and implementation quality. Based on Dynamic Capabilities Theory, ECT, and the foregoing empirical evidence, the following hypothesis is proposed: H1: Digital Transformation (DT) has a positive and significant direct impact on Customer Satisfaction (CS) in marketing companies in Saudi Arabia. 3.2 Internet of Things and Customer Satisfaction (H2) IoT enables interconnected communication among devices and has emerged as a fundamental driver of contemporary innovation, with applications spanning smart transportation, urban development, supply chain management, and modern marketing (Brous et al., 2020 ; Lee & Lee, 2018 ). IoT technologies provide substantial advantages related to consumption efficiency and enhanced CS by enabling real-time monitoring, data-driven personalisation, and improved service responsiveness (Eslami et al., 2024 ; Ren et al., 2024 ). Adopting a consumer-oriented approach, IoT positively affects technology acceptance and adoption behaviour (Shahzad et al., 2024 ), with consumers evaluating whether potential benefits outweigh privacy, risk, and trust concerns (Goad et al., 2021 ; Lim & Rasul, 2022 ; Taneja & Ali, 2021 ). Grounded in TAM and Dynamic Capabilities Theory, the following hypothesis is proposed: H2: The Internet of Things (IoT) has a positive and significant direct impact on Customer Satisfaction (CS) in marketing companies in Saudi Arabia. 3.3 Artificial Intelligence and Customer Satisfaction (H3) AI has become a transformative force in sales and marketing, reshaping how organisations engage with customers (Luo et al., 2021 ; Singh et al., 2020 ). By leveraging large-scale datasets, AI systems customise interactions and recommendations according to individual preferences and behaviours (Rafieian & Yoganarasimhan, 2023 ), enabling dynamic pricing models, AI-powered chatbots, virtual assistants, and predictive CRM systems (Ledro et al., 2022 ; Paschen et al., 2020 ). This heightened personalisation fosters feelings of recognition and appreciation, significantly improving CS and loyalty (Rane, 2023 ). AI’s ability to analyse customer feedback in real time and continuously improve service quality reinforces its role as a critical CS driver (Gao et al., 2022 ; Ameen et al., 2021 ). Based on TAM, ECT, and Dynamic Capabilities Theory, the following hypothesis is proposed: H3: Artificial Intelligence (AI) has a positive and significant direct impact on Customer Satisfaction (CS) in marketing companies in Saudi Arabia. 3.4 Digital Service Quality, Customer Satisfaction, and Moderation (H4–H7) DSQ is broadly defined as customers’ overall assessment of the effectiveness and excellence of services provided in the digital marketplace (Nagar & Ghai, 2020 ). It has been operationalised through dimensions including efficiency, reliability, responsiveness, privacy, and communication (Parasuraman et al., 2005 ; Raza et al., 2020 ). Recent empirical evidence confirms that digital service quality dimensions — particularly system quality, information quality, and service quality — significantly predict customer satisfaction and brand loyalty in banking and marketing contexts (Kim & Jeon, 2025 ). Factors such as attentiveness, responsiveness, friendliness, and accessibility are key drivers of CS (Singh et al., 2020 ), while trust and consumer uncertainty play important roles in shaping online CS outcomes (Gupta & Bhatt, 2021 ). High DSQ reduces uncertainty in digital interactions and enhances trust, ease of use, and perceived value, all of which translate into greater CS. Grounded in SERVQUAL, the IS Success Model, and TAM, the following direct effect hypothesis is proposed: H4: Digital Service Quality (DSQ) has a positive and significant direct impact on Customer Satisfaction (CS) in marketing companies in Saudi Arabia. Beyond its direct effect, DSQ is theorised to moderate the relationships between DT, IoT, AI, and CS. Drawing on the SOR framework, DSQ functions as a stimulus modifier: superior digital service environments may amplify the positive effects of advanced technologies on CS by enhancing the overall customer experience, reducing friction, and strengthening trust. Conversely, poor DSQ may attenuate these effects by introducing uncertainty and dissatisfaction that overwhelm technological benefits. Based on these arguments, the following moderation hypotheses are proposed: H5: DSQ moderates the relationship between DT and CS, such that higher DSQ strengthens the positive effect of DT on CS. H6: DSQ moderates the relationship between IoT and CS, such that higher DSQ strengthens the positive effect of IoT on CS. H7: DSQ moderates the relationship between AI and CS, such that higher DSQ strengthens the positive effect of AI on CS. 4. Methodology 4.1 Data and Sample The study targeted marketing and manufacturing firms in Saudi Arabia that rely on digital platforms in their marketing practices, including AI, DT, IoT, and DSQ. A purposive non-probability sampling approach was adopted to ensure inclusion of respondents relevant to the study’s aims (Shinya et al., 2023 ). Data were gathered through structured questionnaires administered electronically via Google Forms, with 305 participants initially invited. Of the distributed questionnaires, 282 were returned, and after screening for missing data and confirming data normality, 269 responses were retained for analysis (Wah & Ng, 2024 ). This sample size satisfies established SEM guidelines recommending a minimum of 250 observations (Gaskin et al., 2025 ). Non-probability purposive sampling was selected to closely align the sample with the research objectives. The study acknowledges the inherent limitations of this approach, particularly the risk of overrepresenting respondents with higher levels of technical expertise, which may restrict the generalisability of results across populations with differing degrees of digital proficiency (Reedy et al., 2023 ). Future research is encouraged to employ probability-based sampling to enhance representativeness. Additionally, variations in respondents’ comprehension of advanced concepts such as AI, DT, and IoT may have affected responses. 4.2 Questionnaire and Data Collection Data were collected using revised questionnaires administered to marketing and production companies in Saudi Arabia. Responses were measured on a five-point Likert scale, ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). The questionnaire comprised 20 items across five constructs (DT, IoT, AI, DSQ, CS), with four items per construct. Data collection was conducted over a two-month period from September to October 2024. Given the characteristics of the target sample, the survey was distributed electronically via Google Forms, with links shared through email and WhatsApp groups. 4.3 Analytical Approach SmartPLS 4 (Cheah et al., 2024 ) was utilised to assess both the measurement and structural models (Sarstedt et al., 2024 ). Partial Least Squares Structural Equation Modelling (PLS-SEM) was selected due to its effectiveness in analysing complex research frameworks, handling multivariate datasets, and examining moderation effects (Magno et al., 2024 ; Hair & Alamer, 2022 ). The analysis followed a two-step procedure: the measurement model was first evaluated for validity and reliability (Dos Santos & Cirillo, 2023 ), followed by structural model analysis to test the proposed hypotheses (Cheung et al., 2024 ). Principal component analysis (PCA) was conducted to assess construct unidimensionality (Baharum et al., 2023 ), and confirmatory factor analysis (CFA) verified that the 20 observed indicators adequately represented the five latent variables (Sureshchandar, 2023 ). Internal consistency reliability was assessed using Cronbach’s alpha, with values above 0.7 considered acceptable (Malkewitz et al., 2023 ). Discriminant validity was examined using the HTMT criterion (Dirgiatmo, 2023 ) and the Fornell–Larcker criterion (Hair et al., 2022). 4.4 Common Method Bias Because all constructs were collected from the same respondents at a single point in time via a single online survey, common method bias (CMB) represents a potential threat to validity. Harman’s single-factor test was conducted as a procedural remedy. The results showed that the first unrotated factor accounted for less than 50% of the total variance, providing initial evidence that CMB does not pose a serious threat to the study’s conclusions. Future research should consider multi-source data collection or marker variable approaches to further address this concern (Carrion-Bosquez et al., 2025 ). 5. Results 5.1 Measurement Model Table 1 reports the EFA and CFA results. All factor loadings exceed 0.80, confirming indicator reliability. All constructs achieved Cronbach’s alpha values above 0.85, indicating high internal consistency. Table 1 Measurement Model: EFA, CFA, Reliability, and Validity Construct Item Factor Loading Outer Weights VIF PCA % Cronbach’s α t-value p-value CR (rho_a) CR (rho_c) AVE AI AI1 0.852 0.279 2.263 76.574 0.864 44.385 0.000 0.865 0.907 0.709 AI2 0.859 0.279 2.386 41.389 0.000 AI3 0.850 0.300 2.171 47.046 0.000 AI4 0.807 0.331 1.650 33.596 0.000 CS CS1 0.871 0.305 3.043 76.803 0.884 50.397 0.000 0.886 0.920 0.743 CS2 0.909 0.293 3.936 73.336 0.000 CS3 0.878 0.286 2.684 62.338 0.000 CS4 0.786 0.275 1.803 26.902 0.000 DSQ DSQ1 0.873 0.291 2.473 76.668 0.889 52.850 0.000 0.889 0.923 0.751 DSQ2 0.889 0.284 3.261 61.723 0.000 DSQ3 0.884 0.281 3.032 56.501 0.000 DSQ4 0.820 0.300 1.875 31.668 0.000 DT DT1 0.888 0.287 3.503 76.487 0.905 65.953 0.000 0.906 0.934 0.779 DT2 0.896 0.283 3.802 68.514 0.000 DT3 0.886 0.275 3.107 51.965 0.000 DT4 0.859 0.288 2.643 43.981 0.000 IoT IoT1 0.826 0.304 2.039 75.946 0.855 38.551 0.000 0.857 0.902 0.697 IoT2 0.808 0.280 1.991 30.511 0.000 IoT3 0.864 0.317 2.644 45.581 0.000 IoT4 0.842 0.295 2.503 35.123 0.000 Note. Factor loadings (EFA) and t-values (CFA) are identical — the duplicate CFA loading column has been removed. Construct-level statistics (PCA %, Cronbach’s α, CR, AVE) appear on the first item row of each construct. All factor loadings > 0.80; all Cronbach’s α > 0.85; all CR (rho_c) > 0.90; all AVE > 0.69, satisfying convergent validity thresholds. DT = Digital Transformation; IoT = Internet of Things; AI = Artificial Intelligence; DSQ = Digital Service Quality; CS = Customer Satisfaction. Table 2 presents HTMT ratios for discriminant validity. While most values fall within acceptable ranges, several ratios between closely related constructs — particularly DT and AI (0.892), IoT and DT (0.883), and IoT and AI (0.871) — approach the conservative 0.85 threshold. All interaction term pairs also show HTMT values above 0.85. These elevated values suggest that although the constructs are theoretically distinct, their empirical boundaries partially overlap within the marketing context of this sample. This is acknowledged as a study limitation and should motivate future research to develop more discriminant measurement instruments. Table 2 Discriminant Validity — HTMT Ratios AI AI CS DSQ DT IoT DSQ×DT DSQ×IoT DSQ×AI CS 0.819 DSQ 0.860 0.810 DT 0.892 0.824 0.879 IoT 0.871 0.816 0.838 0.883 DSQ×DT 0.834 0.808 0.865 0.825 0.855 DSQ×IoT 0.840 0.795 0.867 0.823 0.855 0.851 DSQ×AI 0.854 0.817 0.874 0.825 0.864 0.853 0.866 Note. Values below 0.90 (conservative threshold) indicate acceptable discriminant validity for most pairs. Elevated values between DT, IoT, and AI, and across interaction terms, are acknowledged as a limitation. Table 3 Fornell–Larcker Criterion AI AI CS DSQ DT IoT 0.842 CS 0.809 0.862 DSQ 0.848 0.808 0.867 DT 0.791 0.738 0.789 0.883 IoT 0.837 0.797 0.819 0.777 0.835 Note. Bold diagonal values = square root of AVE. All diagonal values exceed off-diagonal correlations, confirming discriminant validity. All inter-construct correlations remain below 0.85. Table 4 Cross Loadings Item AI CS DSQ DT IoT AI1 0.852 0.635 0.677 0.648 0.729 AI2 0.859 0.636 0.680 0.670 0.686 AI3 0.850 0.682 0.666 0.676 0.658 AI4 0.807 0.753 0.815 0.665 0.738 CS1 0.720 0.871 0.750 0.668 0.718 CS2 0.709 0.909 0.714 0.644 0.679 CS3 0.706 0.878 0.674 0.640 0.667 CS4 0.650 0.786 0.645 0.589 0.684 DSQ1 0.763 0.705 0.873 0.665 0.711 DSQ2 0.751 0.687 0.889 0.691 0.691 DSQ3 0.707 0.680 0.884 0.698 0.715 DSQ4 0.716 0.725 0.820 0.680 0.720 DT1 0.720 0.660 0.689 0.888 0.691 DT2 0.708 0.650 0.686 0.896 0.674 DT3 0.649 0.632 0.686 0.886 0.657 DT4 0.712 0.662 0.726 0.859 0.719 IoT1 0.644 0.675 0.654 0.625 0.826 IoT2 0.673 0.623 0.641 0.670 0.808 IoT3 0.748 0.705 0.719 0.673 0.864 IoT4 0.729 0.656 0.721 0.630 0.842 Note. Bold values (on diagonal of construct-item matrix) exceed all cross-loadings, confirming convergent validity. 5.2 Model Fit and Predictive Relevance Table 5 Model Fit Indices (SRMR, R², Q²) Construct SRMR R² Q² (Stone–Geisser) CS 0.062 0.738 0.539 Note. SRMR = 0.062 0, confirming predictive relevance ( DeLone & McLean, 2003 ). The CS construct recorded an R² value of 0.738, indicating that DT, IoT, AI, DSQ, and the three interaction terms collectively explain 73.8% of the variance in CS — a result classified as substantial according to Hair et al. (2022). The Stone–Geisser Q² value of 0.539 exceeds zero, confirming strong predictive relevance and the model’s effectiveness in predicting CS outcomes. 5.3 Hypothesis Testing Table 6 presents the bootstrapped path coefficients (5,000 subsamples), Cohen’s f² effect sizes, and incremental R² (ΔR²) values for all seven hypotheses. Effect sizes were calculated arithmetically using ΔR² = t² ÷ (t² + df_error), where df_error = n − k − 1 = 261, and f² = ΔR² ÷ (1 − R²_full = 0.262). Cohen’s ( 1988 ) benchmarks classify f² ≥ 0.35 as large, ≥ 0.15 as medium, ≥ 0.02 as small, and 1.96 and p < 0.05. Table 6 Hypothesis Testing Results with Effect Sizes Hyp. Path β 2.5% 97.5% S.D. t-value p-value ΔR² f² Magnitude Decision H1 DT → CS 0.056 −0.080 0.201 0.072 0.778 0.437 0.002 0.009 Negligible Not Supported H2 IoT → CS 0.227 0.076 0.382 0.078 2.914 0.004 0.032 0.120 Small Supported H3 AI → CS 0.251 0.121 0.375 0.066 3.819 < 0.001 0.053 0.202 Medium Supported H4 DSQ → CS 0.254 0.086 0.423 0.086 2.950 0.003 0.032 0.123 Small Supported H5 DSQ×DT → CS −0.066 −0.177 0.040 0.055 1.208 0.227 0.006 0.021 Negligible Not Supported H6 DSQ×IoT → CS 0.101 −0.032 0.251 0.071 1.424 0.155 0.008 0.029 Small Not Supported H7 DSQ×AI → CS −0.101 −0.221 0.027 0.063 1.598 0.110 0.010 0.037 Small Not Supported Note. Bootstrap subsamples = 5,000. ΔR² = t² ÷ (t² + 261); f² = ΔR² ÷ 0.262. The t-value for H1 was arithmetically corrected from the originally reported 2.786 to 0.778 (β ÷ S.D. = 0.056 ÷ 0.072); the 95% CI spanning zero independently confirms non-significance. f² benchmarks ( Cohen, 1988 ): < 0.02 negligible; ≥ 0.02 small; ≥ 0.15 medium; ≥ 0.35 large. Table 7 Moderation Model Comparison — Incremental R² Model Predictors Included R² ΔR² Interpretation Baseline (main effects only) DT, IoT, AI, DSQ → CS 0.715 — Direct effects only Full model (with moderation) + DSQ×DT, DSQ×IoT, DSQ×AI 0.738 0.023 Interaction terms add negligible variance Note. Baseline R² = R²_full − ΣΔR²_interactions = 0.738 − (0.006 + 0.008 + 0.010) = 0.715. The three interaction terms together contribute only ΔR² = 0.023 (2.3% incremental variance), confirming that DSQ moderation produces no meaningful additional explanatory power beyond the main effects. H1 (DT→CS, β = 0.056, t = 0.778, p = 0.437, f² = 0.009): The corrected t-value is 0.778, obtained arithmetically as β ÷ S.D. = 0.056 ÷ 0.072. The 95% CI (− 0.080 to 0.201) independently confirms non-significance. The f² of 0.009 is negligible by Cohen’s ( 1988 ) benchmarks, indicating that DT explains essentially no unique variance in CS. This is not a power artefact: with 269 respondents and 5,000 bootstrap subsamples, the study has sufficient power to detect small effects. Accordingly, H1 is not supported. H2 (IoT→CS, β = 0.227, t = 2.914, p = 0.004, f² = 0.120): IoT positively and significantly predicts CS, with a 95% CI (0.076 to 0.382) entirely above zero. The small effect size (f² = 0.120) confirms practical as well as statistical significance. H2 is supported. H3 (AI→CS, β = 0.251, t = 3.819, p < 0.001, f² = 0.202): AI exerts the strongest and only medium-magnitude effect on CS in the model. The f² of 0.202 crosses the medium threshold (Cohen, 1988 ), confirming AI as the dominant, practically significant driver of CS. The 95% CI (0.121 to 0.375) is narrow, indicating precise estimation. H3 is supported. H4 (DSQ→CS, β = 0.254, t = 2.950, p = 0.003, f² = 0.123): DSQ has a significant positive effect on CS with a small-to-medium effect size nearly matching AI’s in magnitude, confirming the practical importance of digital service quality as a CS driver alongside AI. H4 is supported. H5 (DSQ×DT→CS, β = −0.066, t = 1.208, p = 0.227, f² = 0.021): The moderation effect of DSQ on the DT–CS relationship is neither statistically significant nor practically meaningful (f² = 0.021, negligible). H5 is not supported. H6 (DSQ×IoT→CS, β = 0.101, t = 1.424, p = 0.155, f² = 0.029): The moderation effect of DSQ on the IoT–CS relationship is not statistically significant (p = 0.155) and the effect size is small (f² = 0.029). H6 is not supported. H7 (DSQ×AI→CS, β = −0.101, t = 1.598, p = 0.110, f² = 0.037): The moderation effect of DSQ on the AI–CS relationship is not statistically significant (p = 0.110) and the effect size is small (f² = 0.037). The negative direction of β suggests a potential suppression dynamic — at higher DSQ levels, AI’s effect on CS is marginally attenuated rather than amplified — though this pattern does not reach significance. H7 is not supported. As shown in Table 7 , the three interaction terms (H5–H7) collectively contribute only ΔR² = 0.023 to CS explanatory power beyond the baseline model (R² = 0.715). This incremental gain of 2.3% is negligible and confirms that the null moderation findings are genuine rather than artefacts of insufficient statistical power. Taken together, the results indicate that DSQ independently drives CS but does not amplify or attenuate the effects of DT, AI, or IoT on CS in this sample. Moderation Interaction Plots. Figures 4 , 5 , and 6 present the interaction plots for H5, H6, and H7 respectively. The parallel slopes across high and low DSQ conditions visually confirm the absence of significant moderation: the effect of each predictor on CS does not meaningfully differ as a function of DSQ level, consistent with the null findings reported in Table 6 . 6. Discussion 6.1 Direct Effects on Customer Satisfaction The results indicate a strong and statistically significant positive relationship between AI and CS (β = 0.251, p < 0.001). AI applications such as personalised recommendations, predictive analytics, chatbots, and AI-driven CRM substantially enhance CS in Saudi marketing companies. AI improves CS by enabling real-time personalisation, faster response times, and more accurate anticipation of customer needs, consistent with TAM (Davis, 1989 ) and ECT (Oliver, 1980 ). This finding aligns with prior research demonstrating that AI-driven personalisation and automation positively influence CS and loyalty (Luo et al., 2021 ; Rafieian & Yoganarasimhan, 2023 ; Ameen et al., 2021 ). Gao et al. ( 2022 ) similarly found that AI-enabled interactions significantly enhance perceived service value. The current study extends these findings to the Saudi marketing context, confirming AI as a critical CS driver in emerging digital economies. The findings reveal a strong and positive effect of DSQ on CS (β = 0.254, p = 0.003). High DSQ reduces uncertainty in digital interactions and enhances trust, ease of use, and perceived value, consistent with SERVQUAL (Parasuraman et al., 1988 ) and the IS Success Model (DeLone & McLean, 2003 ). Recent empirical evidence from Kim and Jeon ( 2025 ) confirms that digital service quality dimensions — particularly brand image, customer orientation, and system quality — significantly influence customer satisfaction and brand loyalty in banking environments, which closely mirror the Saudi marketing context of the present study. This result also aligns with earlier studies emphasising DSQ as a key determinant of online CS (Raza et al., 2020 ; Singh et al., 2020 ; Gupta & Bhatt, 2021 ) and confirms that DSQ functions as a substitute for face-to-face service encounters in digital environments (Khan, 2021 ). The non-significant effect of DT on CS (β = 0.056, t = 0.778, p = 0.437) is a theoretically important finding. It aligns with Guo and Xu ( 2021 ), who argued that many DT initiatives fail to deliver strong customer-facing outcomes unless explicitly designed around service personalisation and responsiveness. Consistent with ECT, DT operates primarily at the organisational infrastructure level — improving internal processes and service delivery capabilities — while customers confirm expectations through direct service interactions rather than through transformation per se. Kretschmer and Khashabi ( 2020 ) similarly note that DT’s performance benefits depend on how well organisational design adapts to the transformation, a condition that may not yet be fully met in the Saudi marketing context. Al-Hattami et al. ( 2025 ) further demonstrate in an SME context that digital systems’ effects on marketing performance are mediated and moderated by implementation conditions, suggesting that the pathway from digital investment to customer outcomes is indirect and context-dependent rather than automatic. Alateeg et al. ( 2024 ) and Al-Ayed et al. ( 2023 ) highlight that while Saudi firms have made significant digital investments under Vision 2030, translating these investments into tangible customer experiences remains an ongoing challenge. The present finding suggests that DT in Saudi marketing companies is still operating more as an internal efficiency driver than as a customer-facing value generator. IoT technologies enhance CS (β = 0.227, p = 0.004) by enabling real-time monitoring, data-driven personalisation, and improved service responsiveness. Customers benefit from seamless connected experiences that improve convenience and perceived control. This finding is consistent with Eslami et al. ( 2024 ) and Ren et al. ( 2024 ), who found that customer perceptions of IoT services — including price perception, service perception, and integration behaviour — significantly and positively influence satisfaction and loyalty. It also aligns with Shahzad et al. ( 2024 ), who confirmed that perceived usefulness and ease of use mediate technology acceptance in digital contexts. Unlike studies focusing mainly on privacy and security concerns (Goad et al., 2021 ), the current results highlight the positive experiential value of IoT when implemented effectively in marketing contexts. 6.2 Moderation Effects and Theoretical Reinterpretation The moderating effects of DSQ on the DT–CS (H5), IoT–CS (H6), and AI–CS (H7) relationships were not statistically significant. These null findings, however, carry important theoretical implications and should not be treated merely as negative results. Drawing on the IS Success Model (DeLone & McLean, 1993 , 2003 ), this study reinterprets them as evidence that technological value and service quality operate as parallel, additive drivers of CS rather than as interactive ones. Customers appear to evaluate technological benefits (AI personalisation, IoT connectivity, DT infrastructure) and service quality (DSQ) through separate cognitive channels — consistent with ECT’s framework in which different attributes independently confirm or disconfirm distinct expectations. This finding contrasts with Gupta and Bhatt ( 2021 ), who suggested that DSQ amplifies the benefits of digital initiatives. However, it aligns with Guo and Xu ( 2021 ), who noted that DT alone does not guarantee improved CS unless explicitly designed around customer-facing outcomes. The SOR framework’s prediction that DSQ would moderate technology–satisfaction relationships was not supported, suggesting that in highly digitised marketing environments, customers may have sufficiently high baseline expectations for both technology and service quality that variations in DSQ no longer alter how they respond to individual technologies. This is a theoretically interesting finding that merits further investigation. 7. Conclusion, Limitations, and Future Research 7.1 Conclusion This study examined the impact of DT, AI, IoT, and DSQ on CS within marketing companies in Saudi Arabia, along with the moderating role of DSQ. Using PLS-SEM, the study provides robust empirical evidence that IoT, AI, and DSQ each have a significant, positive direct effect on CS. Among these factors, AI and DSQ emerged as the strongest drivers, highlighting the importance of personalised, data-driven interactions and high-quality digital interfaces. IoT also significantly contributes to CS by enabling real-time connectivity, automation, and improved service responsiveness. By contrast, DT did not exert a statistically significant direct effect on CS (corrected: t = 0.778, p = 0.437, H1 not supported), suggesting that organisational-level digital transformation in the Saudi marketing context has not yet translated into tangible customer-perceived outcomes — a finding consistent with the broader literature on the implementation gap in DT (Guo & Xu, 2021 ; Kretschmer & Khashabi, 2020 ) and with the evolving stage of Saudi Arabia’s Vision 2030 digital transformation journey (Alateeg et al., 2024 ; Al-Ayed et al., 2023 ). A key contribution lies in the theoretical reinterpretation of the null moderation findings. Contrary to the SOR-based expectation that DSQ would amplify technological effects on CS, the IS Success Model offers a more fitting explanation: technological capability and service quality are parallel, additive satisfaction drivers operating through independent cognitive channels. This advances theoretical understanding beyond simple hypothesis rejection and suggests that organisations should invest in AI, IoT, DT, and DSQ simultaneously but independently, rather than assuming that improving one will enhance the customer impact of the others. The study contributes theoretically by integrating six complementary frameworks — Dynamic Capabilities Theory, TAM, ECT, SERVQUAL, the IS Success Model, and the SOR Framework — into a unified model that captures both direct and moderation effects. It extends the literature by providing firm-level PLS-SEM evidence from an emerging economy context and by contextualising DT theory within Saudi Arabia’s Vision 2030 reform environment. 7.2 Practical Implications For marketing directors and digital strategy managers, the strong positive effects of AI and IoT on CS justify investment in AI-driven personalisation, predictive analytics, and smart connected systems as strategic priorities. The non-significant direct effect of DT on CS indicates that broad digital transformation programmes, while strategically necessary for long-term competitiveness, do not automatically translate into improved customer perceptions. Managers should ensure that DT initiatives are explicitly aligned with customer-facing service outcomes — personalisation, responsiveness, and experience enhancement — rather than focused solely on internal efficiency gains. Consistent with Alotaibi ( 2024 ), who found that AI adoption in Saudi institutions improves CS when ease of use is emphasised, firms should pair technology investments with user-centric design principles. The significant direct impact of DSQ confirms the importance of reliable, secure, and user-friendly digital platforms, and should be managed as a parallel strategic priority alongside AI and IoT adoption, consistent with evidence-based guidelines for the Saudi digital economy under Vision 2030 (Alateeg et al., 2024 ). These implications also carry a sustainable development dimension. By strengthening AI-driven personalisation, IoT-enabled service responsiveness, and digital service quality, marketing companies contribute directly to SDG 8 (Decent Work and Economic Growth) through productivity gains and the creation of higher-skilled digital roles, to SDG 9 (Industry, Innovation, and Infrastructure) through continued investment in digital innovation infrastructure, and to SDG 12 (Responsible Consumption and Production) by enabling more informed, efficient, and responsible customer decisions. Policymakers, in turn, can treat digital service quality standards and digital literacy initiatives as instruments for translating private-sector technological investment into broader sustainable economic and social returns, reinforcing the Vision 2030 reform agenda. 7.3 Limitations and Future Research Several limitations should be acknowledged. First, the study employed non-probability purposive sampling in a single sector and national context, limiting generalisability. Future research should apply probability-based sampling and explore diverse industries and cross-national settings. Second, the cross-sectional design precludes causal inference over time; longitudinal studies would better capture the dynamic evolution of digital technology effects on CS. Third, elevated HTMT values among DT, IoT, and AI suggest that future studies should develop more discriminant measurement instruments for these constructs in Arabic-language GCC contexts. Fourth, the non-significant finding for H1 (DT→CS) suggests that DT’s customer-facing impact in Saudi marketing firms may be mediated by variables not included in the current model, such as implementation quality, employee digital skills, or customer digital literacy — all of which merit investigation in future research. Fifth, future research might incorporate additional moderating variables — such as regulatory quality, digital literacy, or cultural orientation — to identify boundary conditions on the technology–CS chain. Declarations Clinical Trial Number Not applicable. Ethics Approval This research was approved by the Ethics Committee of Al-Razi University, Sana’a, Yemen. The study adhered to the ethical standards outlined in the Declaration of Helsinki (2013) for research involving human participants, ensuring informed consent, voluntary participation, confidentiality, and the right to withdraw. Consent to Participate Participants were provided with written informed consent forms that explained the study’s objectives, their rights, and responsibilities. As the data were gathered through a survey, this acted as written consent. Participants were assured they could withdraw from the study at any time without consequence and that their participation was completely voluntary. Consent to Publish Not applicable, as the study does not contain any identifiable images or personal information. Competing Interests The authors declare no competing interests. Funding This study was funded by Prince Sattam Bin Abdulaziz University, under funding number 1447/2026. Author Contribution A.S.R. contributed to conceptualisation, data collection, formal analysis, and writing — original draft. S.M.A. contributed to supervision, theoretical framework, validation, and writing — review and editing. All authors read and approved the final manuscript. Data Availability The data that support the findings of this study were generated through a self-administered survey. The datasets are not publicly available due to respondent confidentiality but are available from the corresponding author (S.M. AL-Kahtani; [email protected] ) upon reasonable request. References Aguiar-Costa LM, Cunha CA, Silva WK, Abreu NR. 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Manag Decis Econ. 2023;44(5):2520–39. Yan Y, Huang C, Wang Q, Hu B. Data mining of customer choice behaviour in IoT within relationship network. Int J Inf Manag. 2020;50:566–74. Yao Q, Tang H, Boadu F, Xie Y. Digital transformation and firm sustainable growth. J Knowl Econ. 2023;14(4):4929–53. Yesica Y, Sitorus T, Purwanto E. Pengaruh tata kelola perusahaan yang baik dan tanggung jawab sosial perusahaan terhadap kinerja keuangan. J Bus Appl Manage. 2020;13(2):191–205. Zhang P, Yang H, Chen C, Wang T, Jia X. The impact of population aging on corporate digital transformation. Technol Forecast Soc Chang. 2025;214:124070. Zhang W, Chu J, Zhang T, Wang Y. Identifying the factors influencing enterprise digital transformation intention. Bus Process Manage J. 2023;29(7):2107–28. Additional Declarations No competing interests reported. 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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-9254750","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":639604272,"identity":"bac3f79c-7c29-423d-91ed-e058fcac5bbf","order_by":0,"name":"Abdullah Saad Rashed","email":"","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"Saad","lastName":"Rashed","suffix":""},{"id":639604273,"identity":"71edc0fb-4cad-47c7-b2c9-627c37fee840","order_by":1,"name":"Shaker M. AL-kahtani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYDACCQZmECUjIQHhyoHIAw+I0MID1MLYAOQag7UkkKCFIRFEMODTIj+7+bHBxz12PJKzm58/+LnDIn1+2OGHQFvs5HQbsGsxuHPMOHHGs2QeaZljho29ZyRyN95OMwBqSTY2O4BDi0SC8WGeA8w8chIJhg28bUAtsxNAWg4kbsOhRX5G+ufDfw7UA7Wkf2z82yaRbjg7/QNeLQw3coyTGQ4c5pGWyDFsBtqSIC+dg98Wgxs5xYY9B47zSM7IKZwt2yZhuEE6p+BAggFuvwAdtlnix4FqOYkb6Rs+vm2rk5efnb75w4cKOzlcWrDYC1ZpQKxysL0NpKgeBaNgFIyCkQAAaNxhsiWIzG0AAAAASUVORK5CYII=","orcid":"","institution":"Al-Razi University, Sana’a, Republic of Yemen","correspondingAuthor":true,"prefix":"","firstName":"Shaker","middleName":"M.","lastName":"AL-kahtani","suffix":""}],"badges":[],"createdAt":"2026-03-28 18:38:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9254750/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9254750/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109316310,"identity":"3b421ef4-a190-4906-a178-0c6e08479ca3","added_by":"auto","created_at":"2026-05-15 12:25:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual Framework\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9254750/v1/54dae57e024e172b0675952f.png"},{"id":109316284,"identity":"932f9791-0026-483f-bdbe-134c4373dfea","added_by":"auto","created_at":"2026-05-15 12:25:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePLS Structural Model with Direct and Moderation Path Coefficients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9254750/v1/bbb0d81a753ee9fc5c12768c.png"},{"id":109405725,"identity":"1795e341-c7da-4529-9ea9-6996de930cf3","added_by":"auto","created_at":"2026-05-17 13:19:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFull Structural Model with Standardised Path Coefficients (β) and t-Values\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9254750/v1/902810d895974099c409daa5.png"},{"id":109316301,"identity":"5ab8770f-6503-4fab-9ed1-1d41121d2740","added_by":"auto","created_at":"2026-05-15 12:25:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction Plot: DSQ Moderating DT–CS (H5 — Not Supported)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9254750/v1/e8e0a06704cdd4b2c8aa7f91.png"},{"id":109316311,"identity":"1a8848ba-49c9-476b-aa17-2973023db07e","added_by":"auto","created_at":"2026-05-15 12:25:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":53823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction Plot: DSQ Moderating IoT–CS (H6 — Not Supported)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9254750/v1/cd42114e5ca5c38c05ee1c4e.png"},{"id":109316312,"identity":"2153509c-5578-4c14-92ce-79e3ce65f9fa","added_by":"auto","created_at":"2026-05-15 12:25:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":57608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction Plot: DSQ Moderating AI–CS (H7 — Not Supported)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9254750/v1/fb351d306fd4137c63219d95.png"},{"id":109405855,"identity":"7d55f03a-a74e-4b54-8026-080ae0578d6d","added_by":"auto","created_at":"2026-05-17 13:20:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":772126,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9254750/v1/22f8a772-b7ca-4fc7-a0fd-584f117a03fd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Moderating Role of Digital Service Quality in the Relationship Between Digital Transformation, the Internet of Things, Artificial Intelligence, and Customer Satisfaction in Marketing Companies in Saudi Arabia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe accelerated advancement of digital technologies has profoundly transformed industries, societies, and economies worldwide. The World Economic Forum (2023) estimates that digital transformation (DT) could contribute up to USD 100 trillion to the global economy by 2025 by allowing organisations to enhance operational efficiency, develop innovative products and services, and improve customer satisfaction (CS). DT can be defined as the comprehensive integration of digital technologies into all business functions, leading to fundamental changes in how organisations operate and create value for customers (Kraus et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kraus et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Robertsone \u0026amp; Lapina, 2023). This transformation involves the adoption of advanced technologies such as artificial intelligence (AI), big data analytics, cloud computing, and the Internet of Things (IoT), which support process optimisation, more informed decision-making, and continuous innovation. Core elements of DT include strategic orientation, technological implementation, and organisational culture change (Butt et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kao et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Costa, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, this technological era also poses substantial challenges, as firms must adapt to rapid change, shifting customer expectations, and increasing competitive pressure.\u003c/p\u003e \u003cp\u003eWithin the business context, DT, AI, and IoT are widely recognised as critical drivers of long-term performance and sustainability. Evidence from Tarigan et al. (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) indicates that organisations embracing digital technologies are 23% more likely to outperform competitors in terms of profitability. Digital initiatives also strongly influence CS, which represents a vital source of competitive advantage. By utilising data analytics, AI, cloud platforms, and IoT solutions, companies can deliver integrated and highly personalised customer experiences. Despite these advantages, prior research points to a disconnect between the expected and realised outcomes of DT, AI, and IoT, as many organisations experience only modest improvements in key performance indicators (Guo \u0026amp; Xu, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, firms often struggle to implement digital strategies that simultaneously enhance organisational performance and CS.\u003c/p\u003e \u003cp\u003eCS reflects the extent to which products and services meet or exceed customer expectations. It is shaped by factors such as product quality, service quality, pricing, and the overall customer experience. DT has significantly enhanced CS by facilitating service personalisation, improving access, and strengthening interactive communication channels (Matarazzo et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pascucci et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Latupeirissa et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, perceived usefulness, ease of use, and price perceptions have been shown to positively affect CS and, in turn, customer loyalty (Suryatenggara \u0026amp; Dahlan, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite growing interest in this area, empirical studies examining the joint effects of DT, AI, and IoT on both organisational performance and CS remain scarce. Kretschmer and Khashabi (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) proposed an integrated framework for DT and organisation design but focused primarily on structural change without empirically linking it to CS. Similarly, studies by M\u0026uuml;ller et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Leso et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasise leadership and organisational culture as drivers of digital excellence, yet they overlook CS outcomes. Research by Rizvanović et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) addresses customer-oriented digital marketing strategies but does not provide a comprehensive assessment of overall firm performance. Given these gaps, this study examines the impact of DT, AI, IoT, and DSQ on CS in the context of rapid technological change, and introduces DSQ as a potential moderator of the technology\u0026ndash;satisfaction relationships. A secondary aim is to clarify how the deployment of these digital technologies in marketing firms generates not only customer value but also sustainable development outcomes, thereby contributing evidence relevant to the United Nations Sustainable Development Goals agenda in emerging economies. The Saudi context is particularly timely: under Vision 2030, the Kingdom is actively pursuing digital economic diversification, with AI spending projected to reach \u003cspan\u003e$\u003c/span\u003e1.9\u0026nbsp;billion by 2027 and the IoT market estimated to reach \u003cspan\u003e$\u003c/span\u003e2.9\u0026nbsp;billion by 2025 (Alateeg et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Al-Ayed et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite this policy momentum, empirical evidence on how digital technologies jointly influence CS in Saudi marketing companies remains limited (Alotaibi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond its managerial relevance, this study speaks directly to the sustainable development agenda. The effective integration of AI, IoT, and high-quality digital services in marketing companies contributes to several United Nations Sustainable Development Goals (SDGs). Digital technologies that raise productivity, create new skilled roles, and enable inclusive economic participation advance SDG 8 (Decent Work and Economic Growth), while AI and IoT represent foundational technologies underpinning the innovation and infrastructure agenda captured by SDG 9 (Industry, Innovation, and Infrastructure). High-quality digital services that reduce informational waste, enhance resource allocation, and enable customers to make more informed choices likewise support the responsible consumption patterns envisaged in SDG 12 (Responsible Consumption and Production). Saudi Arabia\u0026rsquo;s Vision 2030 reform programme is explicitly aligned with these Goals, making the Saudi marketing sector a particularly relevant context in which to examine how digital transformation delivers both customer value and sustainable economic and social outcomes.\u003c/p\u003e"},{"header":"2. Theoretical Foundation","content":"\u003cp\u003eThis study draws on six complementary theoretical frameworks. Together, they provide the conceptual architecture through which the relationships among DT, AI, IoT, DSQ, and CS are explained, and they supply the logical basis for each hypothesis developed in Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Dynamic Capabilities Theory\u003c/h2\u003e \u003cp\u003eTeece, Pisano, and Shuen (\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) define dynamic capabilities as an organisation\u0026rsquo;s ability to sense environmental change, seize emerging opportunities, and reconfigure its resource base to sustain competitive advantage. In digital business contexts, this framework explains how firms develop technology-enabled capabilities that allow them to continuously innovate, adapt, and create customer value (Saeedikiya et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; de Miguel et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This theory provides the overarching framework for the study: DT, AI, and IoT represent distinct technological capabilities that organisations deploy to sense and respond to customer needs. H1 (DT\u0026rarr;CS), H2 (IoT\u0026rarr;CS), and H3 (AI\u0026rarr;CS) all flow from this premise \u0026mdash; firms that effectively deploy digital technologies as dynamic capabilities generate superior customer experiences and satisfaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Technology Acceptance Model (TAM)\u003c/h2\u003e \u003cp\u003eDavis (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) proposed the Technology Acceptance Model (TAM) to explain how users form intentions to adopt technology based on two core beliefs: perceived usefulness and perceived ease of use. TAM has since been extended into the Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), broadening the antecedents of technology adoption. Both frameworks have been extensively applied to consumer adoption of digital services, mobile applications, and smart devices (Singh et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Alkis \u0026amp; Kose, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Al-Hattami et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). In this study, TAM provides the individual-level mechanism through which IoT (H2) and AI (H3) influence CS: consumers evaluate whether IoT connectivity and AI-driven personalisation are useful and easy to interact with, and those perceptions directly shape satisfaction. When customers find AI recommendations accurate and IoT services seamlessly integrated into their lives, they report higher CS levels. TAM also underpins H4 (DSQ\u0026rarr;CS): the efficiency and reliability dimensions of DSQ map directly onto TAM\u0026rsquo;s ease-of-use and usefulness constructs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Expectation-Confirmation Theory (ECT)\u003c/h2\u003e \u003cp\u003eOliver (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) proposed Expectation-Confirmation Theory (ECT) to explain how satisfaction emerges from the comparison between pre-consumption expectations and post-consumption performance perceptions. When actual service performance confirms or exceeds expectations, satisfaction results; when performance falls short, dissatisfaction ensues. ECT provides the psychological mechanism through which all four independent variables (DT, IoT, AI, DSQ) translate into CS (H1\u0026ndash;H4): each technology raises customer expectations for service efficiency, personalisation, and responsiveness, and CS arises when those expectations are confirmed. ECT also explains why DT\u0026rsquo;s effect on CS may be weaker than AI\u0026rsquo;s or IoT\u0026rsquo;s \u0026mdash; DT operates primarily at the organisational infrastructure level, while customers confirm expectations primarily through direct service interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Service Quality Theory (SERVQUAL)\u003c/h2\u003e \u003cp\u003eParasuraman, Zeithaml, and Berry (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) developed the SERVQUAL framework to measure service quality across five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. In digital environments, these dimensions have been adapted into DSQ frameworks assessing website efficiency, information accuracy, privacy protection, and responsiveness (Parasuraman et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Raza et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kim \u0026amp; Jeon, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). SERVQUAL grounds H4 (DSQ\u0026rarr;CS): customers evaluate digital platforms against these service quality dimensions, and those evaluations translate directly into satisfaction. The moderation hypotheses (H5, H6, H7) also draw on SERVQUAL by proposing that service quality perceptions amplify or attenuate the satisfaction effects of digital technologies. The rejection of H5\u0026ndash;H7 suggests that in digitally intensive environments, the evaluative processes associated with technological value and service quality quality operate through separate cognitive pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Information Systems Success Model\u003c/h2\u003e \u003cp\u003eDeLone and McLean (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) proposed that system quality, information quality, and service quality are direct antecedents of user satisfaction and net benefits. In digital marketing contexts, this model explains why DSQ is a direct and significant predictor of CS (H4). Critically, the IS Success Model also provides a theoretical reinterpretation of the null moderation findings (H5\u0026ndash;H7): the model\u0026rsquo;s architecture suggests that system quality and user satisfaction are related through direct additive effects rather than interactive ones, meaning that the quality of the digital interface independently predicts satisfaction without necessarily amplifying the effects of underlying technologies. This offers a theoretically grounded explanation for why DSQ and DT, IoT, and AI may each drive CS without their effects being contingent upon one another.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Stimulus-Organism-Response (SOR) Framework\u003c/h2\u003e \u003cp\u003eMehrabian and Russell (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) proposed the SOR framework to explain how environmental stimuli (S) trigger internal cognitive and affective states in the organism (O), which in turn drive behavioural responses (R). Applied to digital marketing, technology characteristics and service features serve as stimuli that shape customer perceptions and evaluations (organism), culminating in behavioural outcomes such as satisfaction and loyalty (response) (Yan et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The overall structure of this study\u0026rsquo;s model follows SOR logic: DT, IoT, AI, and DSQ are stimuli that shape customer cognitive-affective states, producing satisfaction as the response. DSQ\u0026rsquo;s proposed moderating role (H5\u0026ndash;H7) is grounded in SOR: as a stimulus modifier, DSQ was theorised to amplify or attenuate the satisfaction responses triggered by other technological stimuli.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Digital Technologies and Sustainable Development\u003c/h2\u003e \u003cp\u003eBeyond the cognitive and organisational mechanisms articulated in the preceding frameworks, a growing body of work links DT, AI, IoT, and DSQ to the United Nations 2030 Agenda for Sustainable Development. Digital technologies contribute to SDG 8 (Decent Work and Economic Growth) by raising productivity, creating higher-skilled digital employment, and broadening economic participation through digitally delivered services. They also underpin SDG 9 (Industry, Innovation, and Infrastructure) as the core innovation infrastructure on which contemporary industries rely, with AI and IoT serving as foundational general-purpose technologies for digitalised economies. High-quality digital service platforms \u0026mdash; the focus of the DSQ construct in this study \u0026mdash; additionally support SDG 12 (Responsible Consumption and Production) by reducing informational asymmetries, improving resource matching, and enabling customers to make more sustainable consumption choices through transparent, reliable, and personalised service experiences. Within the Saudi Arabian setting, these linkages are explicit in the Vision 2030 reform programme, which positions digital transformation as a lever for simultaneously achieving economic diversification, innovation capacity, and sustainable consumption patterns (Alateeg et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Al-Ayed et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Recent empirical work further confirms that digital service quality dimensions shape customer satisfaction and loyalty in ways that directly bear on sustainable business practices under environmental uncertainty (Kim \u0026amp; Jeon, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Together, this evidence indicates that the technological and customer-satisfaction outcomes investigated in the present study are inseparable from their sustainable development implications and provides an additional rationale for the integrative model adopted here.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Theoretical Synthesis\u003c/h2\u003e \u003cp\u003eThese six frameworks work in concert. The SOR framework provides the overall structural logic. Dynamic Capabilities Theory explains the organisational-level mechanisms through which DT, AI, and IoT create customer value. TAM explains the individual-level cognitive processes through which customers evaluate and respond to these technologies. ECT provides the psychological mechanism through which technology experiences translate into satisfaction judgements. SERVQUAL and the IS Success Model ground the DSQ construct and explain its direct and non-moderating effects. Together, they predict: (1) each digital technology independently drives CS (H1\u0026ndash;H4); (2) DSQ theoretically moderates technology\u0026ndash;satisfaction relationships (H5\u0026ndash;H7); and (3) the empirical rejection of H5\u0026ndash;H7 can be reinterpreted through the IS Success Model as evidence that technological capability and service quality are parallel, additive satisfaction drivers rather than multiplicatively interactive ones \u0026mdash; a finding that advances theoretical understanding rather than simply reporting failure.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Theoretical Framework and Hypothesis Development","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Digital Transformation and Customer Satisfaction (H1)\u003c/h2\u003e \u003cp\u003eDT represents the comprehensive integration of digital technologies into all organisational functions, enabling firms to reinvent business processes, enhance customer interactions, and create new value propositions (Kraus et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Blanka et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Organisations leveraging DT can identify opportunities, mitigate threats, and sustain competitiveness, all of which contribute to enhanced CS (Liu \u0026amp; He, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Digital innovations significantly accelerate the evolution of traditional business models and enable new service development (Jiang, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while customer-centric DT initiatives \u0026mdash; focused on improving product quality and communication channels \u0026mdash; consistently improve CS outcomes (Cai et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, Al-Hattami et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) demonstrate through a moderated mediation design that digital accounting systems positively influence marketing performance in SMEs, confirming that the translation of digital investments into customer-facing outcomes is contingent on organisational context and implementation quality. Based on Dynamic Capabilities Theory, ECT, and the foregoing empirical evidence, the following hypothesis is proposed:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH1: Digital Transformation (DT) has a positive and significant direct impact on Customer Satisfaction (CS) in marketing companies in Saudi Arabia.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Internet of Things and Customer Satisfaction (H2)\u003c/h2\u003e \u003cp\u003eIoT enables interconnected communication among devices and has emerged as a fundamental driver of contemporary innovation, with applications spanning smart transportation, urban development, supply chain management, and modern marketing (Brous et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee \u0026amp; Lee, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). IoT technologies provide substantial advantages related to consumption efficiency and enhanced CS by enabling real-time monitoring, data-driven personalisation, and improved service responsiveness (Eslami et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ren et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Adopting a consumer-oriented approach, IoT positively affects technology acceptance and adoption behaviour (Shahzad et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with consumers evaluating whether potential benefits outweigh privacy, risk, and trust concerns (Goad et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lim \u0026amp; Rasul, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Taneja \u0026amp; Ali, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Grounded in TAM and Dynamic Capabilities Theory, the following hypothesis is proposed:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH2: The Internet of Things (IoT) has a positive and significant direct impact on Customer Satisfaction (CS) in marketing companies in Saudi Arabia.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Artificial Intelligence and Customer Satisfaction (H3)\u003c/h2\u003e \u003cp\u003eAI has become a transformative force in sales and marketing, reshaping how organisations engage with customers (Luo et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By leveraging large-scale datasets, AI systems customise interactions and recommendations according to individual preferences and behaviours (Rafieian \u0026amp; Yoganarasimhan, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), enabling dynamic pricing models, AI-powered chatbots, virtual assistants, and predictive CRM systems (Ledro et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Paschen et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This heightened personalisation fosters feelings of recognition and appreciation, significantly improving CS and loyalty (Rane, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). AI\u0026rsquo;s ability to analyse customer feedback in real time and continuously improve service quality reinforces its role as a critical CS driver (Gao et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ameen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on TAM, ECT, and Dynamic Capabilities Theory, the following hypothesis is proposed:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH3: Artificial Intelligence (AI) has a positive and significant direct impact on Customer Satisfaction (CS) in marketing companies in Saudi Arabia.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Digital Service Quality, Customer Satisfaction, and Moderation (H4\u0026ndash;H7)\u003c/h2\u003e \u003cp\u003eDSQ is broadly defined as customers\u0026rsquo; overall assessment of the effectiveness and excellence of services provided in the digital marketplace (Nagar \u0026amp; Ghai, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It has been operationalised through dimensions including efficiency, reliability, responsiveness, privacy, and communication (Parasuraman et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Raza et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recent empirical evidence confirms that digital service quality dimensions \u0026mdash; particularly system quality, information quality, and service quality \u0026mdash; significantly predict customer satisfaction and brand loyalty in banking and marketing contexts (Kim \u0026amp; Jeon, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Factors such as attentiveness, responsiveness, friendliness, and accessibility are key drivers of CS (Singh et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while trust and consumer uncertainty play important roles in shaping online CS outcomes (Gupta \u0026amp; Bhatt, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). High DSQ reduces uncertainty in digital interactions and enhances trust, ease of use, and perceived value, all of which translate into greater CS. Grounded in SERVQUAL, the IS Success Model, and TAM, the following direct effect hypothesis is proposed:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH4: Digital Service Quality (DSQ) has a positive and significant direct impact on Customer Satisfaction (CS) in marketing companies in Saudi Arabia.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBeyond its direct effect, DSQ is theorised to moderate the relationships between DT, IoT, AI, and CS. Drawing on the SOR framework, DSQ functions as a stimulus modifier: superior digital service environments may amplify the positive effects of advanced technologies on CS by enhancing the overall customer experience, reducing friction, and strengthening trust. Conversely, poor DSQ may attenuate these effects by introducing uncertainty and dissatisfaction that overwhelm technological benefits. Based on these arguments, the following moderation hypotheses are proposed:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH5: DSQ moderates the relationship between DT and CS, such that higher DSQ strengthens the positive effect of DT on CS.\u003c/em\u003e \u003c/p\u003e\u003cp\u003e \u003cem\u003eH6: DSQ moderates the relationship between IoT and CS, such that higher DSQ strengthens the positive effect of IoT on CS.\u003c/em\u003e \u003c/p\u003e\u003cp\u003e \u003cem\u003eH7: DSQ moderates the relationship between AI and CS, such that higher DSQ strengthens the positive effect of AI on CS.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Methodology","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data and Sample\u003c/h2\u003e \u003cp\u003eThe study targeted marketing and manufacturing firms in Saudi Arabia that rely on digital platforms in their marketing practices, including AI, DT, IoT, and DSQ. A purposive non-probability sampling approach was adopted to ensure inclusion of respondents relevant to the study\u0026rsquo;s aims (Shinya et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Data were gathered through structured questionnaires administered electronically via Google Forms, with 305 participants initially invited. Of the distributed questionnaires, 282 were returned, and after screening for missing data and confirming data normality, 269 responses were retained for analysis (Wah \u0026amp; Ng, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This sample size satisfies established SEM guidelines recommending a minimum of 250 observations (Gaskin et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNon-probability purposive sampling was selected to closely align the sample with the research objectives. The study acknowledges the inherent limitations of this approach, particularly the risk of overrepresenting respondents with higher levels of technical expertise, which may restrict the generalisability of results across populations with differing degrees of digital proficiency (Reedy et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Future research is encouraged to employ probability-based sampling to enhance representativeness. Additionally, variations in respondents\u0026rsquo; comprehension of advanced concepts such as AI, DT, and IoT may have affected responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Questionnaire and Data Collection\u003c/h2\u003e \u003cp\u003eData were collected using revised questionnaires administered to marketing and production companies in Saudi Arabia. Responses were measured on a five-point Likert scale, ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;strongly agree\u0026rdquo;). The questionnaire comprised 20 items across five constructs (DT, IoT, AI, DSQ, CS), with four items per construct. Data collection was conducted over a two-month period from September to October 2024. Given the characteristics of the target sample, the survey was distributed electronically via Google Forms, with links shared through email and WhatsApp groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Analytical Approach\u003c/h2\u003e \u003cp\u003eSmartPLS 4 (Cheah et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) was utilised to assess both the measurement and structural models (Sarstedt et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Partial Least Squares Structural Equation Modelling (PLS-SEM) was selected due to its effectiveness in analysing complex research frameworks, handling multivariate datasets, and examining moderation effects (Magno et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hair \u0026amp; Alamer, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The analysis followed a two-step procedure: the measurement model was first evaluated for validity and reliability (Dos Santos \u0026amp; Cirillo, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), followed by structural model analysis to test the proposed hypotheses (Cheung et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) was conducted to assess construct unidimensionality (Baharum et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and confirmatory factor analysis (CFA) verified that the 20 observed indicators adequately represented the five latent variables (Sureshchandar, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Internal consistency reliability was assessed using Cronbach\u0026rsquo;s alpha, with values above 0.7 considered acceptable (Malkewitz et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Discriminant validity was examined using the HTMT criterion (Dirgiatmo, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the Fornell\u0026ndash;Larcker criterion (Hair et al., 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Common Method Bias\u003c/h2\u003e \u003cp\u003eBecause all constructs were collected from the same respondents at a single point in time via a single online survey, common method bias (CMB) represents a potential threat to validity. Harman\u0026rsquo;s single-factor test was conducted as a procedural remedy. The results showed that the first unrotated factor accounted for less than 50% of the total variance, providing initial evidence that CMB does not pose a serious threat to the study\u0026rsquo;s conclusions. Future research should consider multi-source data collection or marker variable approaches to further address this concern (Carrion-Bosquez et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Measurement Model\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reports the EFA and CFA results. All factor loadings exceed 0.80, confirming indicator reliability. All constructs achieved Cronbach\u0026rsquo;s alpha values above 0.85, indicating high internal consistency.\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\u003eMeasurement Model: EFA, CFA, Reliability, and Validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\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\u003eOuter Weights\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePCA %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCR (rho_a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCR (rho_c)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e73.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e52.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e61.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e56.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e65.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e68.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIoT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIoT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIoT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIoT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIoT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e35.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eNote. Factor loadings (EFA) and t-values (CFA) are identical \u0026mdash; the duplicate CFA loading column has been removed. Construct-level statistics (PCA %, Cronbach\u0026rsquo;s α, CR, AVE) appear on the first item row of each construct. All factor loadings\u0026thinsp;\u0026gt;\u0026thinsp;0.80; all Cronbach\u0026rsquo;s α\u0026thinsp;\u0026gt;\u0026thinsp;0.85; all CR (rho_c)\u0026thinsp;\u0026gt;\u0026thinsp;0.90; all AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.69, satisfying convergent validity thresholds. DT\u0026thinsp;=\u0026thinsp;Digital Transformation; IoT\u0026thinsp;=\u0026thinsp;Internet of Things; AI\u0026thinsp;=\u0026thinsp;Artificial Intelligence; DSQ\u0026thinsp;=\u0026thinsp;Digital Service Quality; CS\u0026thinsp;=\u0026thinsp;Customer Satisfaction.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents HTMT ratios for discriminant validity. While most values fall within acceptable ranges, several ratios between closely related constructs \u0026mdash; particularly DT and AI (0.892), IoT and DT (0.883), and IoT and AI (0.871) \u0026mdash; approach the conservative 0.85 threshold. All interaction term pairs also show HTMT values above 0.85. These elevated values suggest that although the constructs are theoretically distinct, their empirical boundaries partially overlap within the marketing context of this sample. This is acknowledged as a study limitation and should motivate future research to develop more discriminant measurement instruments.\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\u003eDiscriminant Validity \u0026mdash; HTMT Ratios\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDSQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIoT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDSQ\u0026times;DT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDSQ\u0026times;IoT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDSQ\u0026times;AI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.819\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIoT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ\u0026times;DT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ\u0026times;IoT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ\u0026times;AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote. Values below 0.90 (conservative threshold) indicate acceptable discriminant validity for most pairs. Elevated values between DT, IoT, and AI, and across interaction terms, are acknowledged as a limitation.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eFornell\u0026ndash;Larcker Criterion\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=\"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 \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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDSQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIoT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIoT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote. Bold diagonal values\u0026thinsp;=\u0026thinsp;square root of AVE. All diagonal values exceed off-diagonal correlations, confirming discriminant validity. All inter-construct correlations remain below 0.85.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eCross Loadings\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=\"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 \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\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDSQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIoT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIoT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIoT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIoT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIoT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote. Bold values (on diagonal of construct-item matrix) exceed all cross-loadings, confirming convergent validity.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Model Fit and Predictive Relevance\u003c/h2\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\u003eModel Fit Indices (SRMR, R\u0026sup2;, Q\u0026sup2;)\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\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ\u0026sup2; (Stone\u0026ndash;Geisser)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote. SRMR\u0026thinsp;=\u0026thinsp;0.062\u0026thinsp;\u0026lt;\u0026thinsp;0.08, indicating acceptable model fit. R\u0026sup2; = 0.738 indicates substantial predictive accuracy (Hair et al., 2022). Q\u0026sup2; = 0.539\u0026thinsp;\u0026gt;\u0026thinsp;0, confirming predictive relevance (\u003c/em\u003eDeLone \u0026amp; McLean, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe CS construct recorded an R\u0026sup2; value of 0.738, indicating that DT, IoT, AI, DSQ, and the three interaction terms collectively explain 73.8% of the variance in CS \u0026mdash; a result classified as substantial according to Hair et al. (2022). The Stone\u0026ndash;Geisser Q\u0026sup2; value of 0.539 exceeds zero, confirming strong predictive relevance and the model\u0026rsquo;s effectiveness in predicting CS outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Hypothesis Testing\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the bootstrapped path coefficients (5,000 subsamples), Cohen\u0026rsquo;s f\u0026sup2; effect sizes, and incremental R\u0026sup2; (ΔR\u0026sup2;) values for all seven hypotheses. Effect sizes were calculated arithmetically using ΔR\u0026sup2; = t\u0026sup2; \u0026divide; (t\u0026sup2; + df_error), where df_error\u0026thinsp;=\u0026thinsp;n \u0026minus; k\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;=\u0026thinsp;261, and f\u0026sup2; = ΔR\u0026sup2; \u0026divide; (1\u0026thinsp;\u0026minus;\u0026thinsp;R\u0026sup2;_full\u0026thinsp;=\u0026thinsp;0.262). Cohen\u0026rsquo;s (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) benchmarks classify f\u0026sup2; \u0026ge; 0.35 as large, \u0026ge; 0.15 as medium, \u0026ge; 0.02 as small, and \u0026lt;\u0026thinsp;0.02 as negligible. A path is considered statistically significant when t\u0026thinsp;\u0026gt;\u0026thinsp;1.96 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\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\u003eHypothesis Testing Results with Effect Sizes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyp.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS.D.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eΔR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ef\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMagnitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDT \u0026rarr; CS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNegligible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIoT \u0026rarr; CS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI \u0026rarr; CS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSQ \u0026rarr; CS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSQ\u0026times;DT \u0026rarr; CS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNegligible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSQ\u0026times;IoT \u0026rarr; CS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSQ\u0026times;AI \u0026rarr; CS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eNote. Bootstrap subsamples\u0026thinsp;=\u0026thinsp;5,000. ΔR\u0026sup2; = t\u0026sup2; \u0026divide; (t\u0026sup2; + 261); f\u0026sup2; = ΔR\u0026sup2; \u0026divide; 0.262. The t-value for H1 was arithmetically corrected from the originally reported 2.786 to 0.778 (β\u0026thinsp;\u0026divide;\u0026thinsp;S.D. = 0.056\u0026thinsp;\u0026divide;\u0026thinsp;0.072); the 95% CI spanning zero independently confirms non-significance. f\u0026sup2; benchmarks (\u003c/em\u003eCohen, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1988\u003c/span\u003e\u003cem\u003e): \u0026lt; 0.02 negligible; \u0026ge; 0.02 small; \u0026ge; 0.15 medium; \u0026ge; 0.35 large.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eModeration Model Comparison \u0026mdash; Incremental R\u0026sup2;\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictors Included\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline (main effects only)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDT, IoT, AI, DSQ \u0026rarr; CS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect effects only\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull model (with moderation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+ DSQ\u0026times;DT, DSQ\u0026times;IoT, DSQ\u0026times;AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInteraction terms add negligible variance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote. Baseline R\u0026sup2; = R\u0026sup2;_full\u0026thinsp;\u0026minus;\u0026thinsp;ΣΔR\u0026sup2;_interactions\u0026thinsp;=\u0026thinsp;0.738 \u0026minus; (0.006\u0026thinsp;+\u0026thinsp;0.008\u0026thinsp;+\u0026thinsp;0.010)\u0026thinsp;=\u0026thinsp;0.715. The three interaction terms together contribute only ΔR\u0026sup2; = 0.023 (2.3% incremental variance), confirming that DSQ moderation produces no meaningful additional explanatory power beyond the main effects.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eH1 (DT\u0026rarr;CS, β\u0026thinsp;=\u0026thinsp;0.056, t\u0026thinsp;=\u0026thinsp;0.778, p\u0026thinsp;=\u0026thinsp;0.437, f\u0026sup2; = 0.009): The corrected t-value is 0.778, obtained arithmetically as β\u0026thinsp;\u0026divide;\u0026thinsp;S.D. = 0.056\u0026thinsp;\u0026divide;\u0026thinsp;0.072. The 95% CI (\u0026minus;\u0026thinsp;0.080 to 0.201) independently confirms non-significance. The f\u0026sup2; of 0.009 is negligible by Cohen\u0026rsquo;s (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) benchmarks, indicating that DT explains essentially no unique variance in CS. This is not a power artefact: with 269 respondents and 5,000 bootstrap subsamples, the study has sufficient power to detect small effects. Accordingly, H1 is not supported.\u003c/p\u003e \u003cp\u003eH2 (IoT\u0026rarr;CS, β\u0026thinsp;=\u0026thinsp;0.227, t\u0026thinsp;=\u0026thinsp;2.914, p\u0026thinsp;=\u0026thinsp;0.004, f\u0026sup2; = 0.120): IoT positively and significantly predicts CS, with a 95% CI (0.076 to 0.382) entirely above zero. The small effect size (f\u0026sup2; = 0.120) confirms practical as well as statistical significance. H2 is supported.\u003c/p\u003e \u003cp\u003eH3 (AI\u0026rarr;CS, β\u0026thinsp;=\u0026thinsp;0.251, t\u0026thinsp;=\u0026thinsp;3.819, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, f\u0026sup2; = 0.202): AI exerts the strongest and only medium-magnitude effect on CS in the model. The f\u0026sup2; of 0.202 crosses the medium threshold (Cohen, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), confirming AI as the dominant, practically significant driver of CS. The 95% CI (0.121 to 0.375) is narrow, indicating precise estimation. H3 is supported.\u003c/p\u003e \u003cp\u003eH4 (DSQ\u0026rarr;CS, β\u0026thinsp;=\u0026thinsp;0.254, t\u0026thinsp;=\u0026thinsp;2.950, p\u0026thinsp;=\u0026thinsp;0.003, f\u0026sup2; = 0.123): DSQ has a significant positive effect on CS with a small-to-medium effect size nearly matching AI\u0026rsquo;s in magnitude, confirming the practical importance of digital service quality as a CS driver alongside AI. H4 is supported.\u003c/p\u003e \u003cp\u003eH5 (DSQ\u0026times;DT\u0026rarr;CS, β = \u0026minus;0.066, t\u0026thinsp;=\u0026thinsp;1.208, p\u0026thinsp;=\u0026thinsp;0.227, f\u0026sup2; = 0.021): The moderation effect of DSQ on the DT\u0026ndash;CS relationship is neither statistically significant nor practically meaningful (f\u0026sup2; = 0.021, negligible). H5 is not supported.\u003c/p\u003e \u003cp\u003eH6 (DSQ\u0026times;IoT\u0026rarr;CS, β\u0026thinsp;=\u0026thinsp;0.101, t\u0026thinsp;=\u0026thinsp;1.424, p\u0026thinsp;=\u0026thinsp;0.155, f\u0026sup2; = 0.029): The moderation effect of DSQ on the IoT\u0026ndash;CS relationship is not statistically significant (p\u0026thinsp;=\u0026thinsp;0.155) and the effect size is small (f\u0026sup2; = 0.029). H6 is not supported.\u003c/p\u003e \u003cp\u003eH7 (DSQ\u0026times;AI\u0026rarr;CS, β = \u0026minus;0.101, t\u0026thinsp;=\u0026thinsp;1.598, p\u0026thinsp;=\u0026thinsp;0.110, f\u0026sup2; = 0.037): The moderation effect of DSQ on the AI\u0026ndash;CS relationship is not statistically significant (p\u0026thinsp;=\u0026thinsp;0.110) and the effect size is small (f\u0026sup2; = 0.037). The negative direction of β suggests a potential suppression dynamic \u0026mdash; at higher DSQ levels, AI\u0026rsquo;s effect on CS is marginally attenuated rather than amplified \u0026mdash; though this pattern does not reach significance. H7 is not supported.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the three interaction terms (H5\u0026ndash;H7) collectively contribute only ΔR\u0026sup2; = 0.023 to CS explanatory power beyond the baseline model (R\u0026sup2; = 0.715). This incremental gain of 2.3% is negligible and confirms that the null moderation findings are genuine rather than artefacts of insufficient statistical power. Taken together, the results indicate that DSQ independently drives CS but does not amplify or attenuate the effects of DT, AI, or IoT on CS in this sample.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eModeration Interaction Plots.\u003c/b\u003e Figures\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e present the interaction plots for H5, H6, and H7 respectively. The parallel slopes across high and low DSQ conditions visually confirm the absence of significant moderation: the effect of each predictor on CS does not meaningfully differ as a function of DSQ level, consistent with the null findings reported in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Direct Effects on Customer Satisfaction\u003c/h2\u003e \u003cp\u003eThe results indicate a strong and statistically significant positive relationship between AI and CS (β\u0026thinsp;=\u0026thinsp;0.251, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). AI applications such as personalised recommendations, predictive analytics, chatbots, and AI-driven CRM substantially enhance CS in Saudi marketing companies. AI improves CS by enabling real-time personalisation, faster response times, and more accurate anticipation of customer needs, consistent with TAM (Davis, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) and ECT (Oliver, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). This finding aligns with prior research demonstrating that AI-driven personalisation and automation positively influence CS and loyalty (Luo et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rafieian \u0026amp; Yoganarasimhan, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ameen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Gao et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) similarly found that AI-enabled interactions significantly enhance perceived service value. The current study extends these findings to the Saudi marketing context, confirming AI as a critical CS driver in emerging digital economies.\u003c/p\u003e \u003cp\u003eThe findings reveal a strong and positive effect of DSQ on CS (β\u0026thinsp;=\u0026thinsp;0.254, p\u0026thinsp;=\u0026thinsp;0.003). High DSQ reduces uncertainty in digital interactions and enhances trust, ease of use, and perceived value, consistent with SERVQUAL (Parasuraman et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) and the IS Success Model (DeLone \u0026amp; McLean, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Recent empirical evidence from Kim and Jeon (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) confirms that digital service quality dimensions \u0026mdash; particularly brand image, customer orientation, and system quality \u0026mdash; significantly influence customer satisfaction and brand loyalty in banking environments, which closely mirror the Saudi marketing context of the present study. This result also aligns with earlier studies emphasising DSQ as a key determinant of online CS (Raza et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gupta \u0026amp; Bhatt, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and confirms that DSQ functions as a substitute for face-to-face service encounters in digital environments (Khan, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe non-significant effect of DT on CS (β\u0026thinsp;=\u0026thinsp;0.056, t\u0026thinsp;=\u0026thinsp;0.778, p\u0026thinsp;=\u0026thinsp;0.437) is a theoretically important finding. It aligns with Guo and Xu (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who argued that many DT initiatives fail to deliver strong customer-facing outcomes unless explicitly designed around service personalisation and responsiveness. Consistent with ECT, DT operates primarily at the organisational infrastructure level \u0026mdash; improving internal processes and service delivery capabilities \u0026mdash; while customers confirm expectations through direct service interactions rather than through transformation per se. Kretschmer and Khashabi (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) similarly note that DT\u0026rsquo;s performance benefits depend on how well organisational design adapts to the transformation, a condition that may not yet be fully met in the Saudi marketing context. Al-Hattami et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) further demonstrate in an SME context that digital systems\u0026rsquo; effects on marketing performance are mediated and moderated by implementation conditions, suggesting that the pathway from digital investment to customer outcomes is indirect and context-dependent rather than automatic. Alateeg et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Al-Ayed et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlight that while Saudi firms have made significant digital investments under Vision 2030, translating these investments into tangible customer experiences remains an ongoing challenge. The present finding suggests that DT in Saudi marketing companies is still operating more as an internal efficiency driver than as a customer-facing value generator.\u003c/p\u003e \u003cp\u003eIoT technologies enhance CS (β\u0026thinsp;=\u0026thinsp;0.227, p\u0026thinsp;=\u0026thinsp;0.004) by enabling real-time monitoring, data-driven personalisation, and improved service responsiveness. Customers benefit from seamless connected experiences that improve convenience and perceived control. This finding is consistent with Eslami et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Ren et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who found that customer perceptions of IoT services \u0026mdash; including price perception, service perception, and integration behaviour \u0026mdash; significantly and positively influence satisfaction and loyalty. It also aligns with Shahzad et al. (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who confirmed that perceived usefulness and ease of use mediate technology acceptance in digital contexts. Unlike studies focusing mainly on privacy and security concerns (Goad et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the current results highlight the positive experiential value of IoT when implemented effectively in marketing contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Moderation Effects and Theoretical Reinterpretation\u003c/h2\u003e \u003cp\u003eThe moderating effects of DSQ on the DT\u0026ndash;CS (H5), IoT\u0026ndash;CS (H6), and AI\u0026ndash;CS (H7) relationships were not statistically significant. These null findings, however, carry important theoretical implications and should not be treated merely as negative results. Drawing on the IS Success Model (DeLone \u0026amp; McLean, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), this study reinterprets them as evidence that technological value and service quality operate as parallel, additive drivers of CS rather than as interactive ones. Customers appear to evaluate technological benefits (AI personalisation, IoT connectivity, DT infrastructure) and service quality (DSQ) through separate cognitive channels \u0026mdash; consistent with ECT\u0026rsquo;s framework in which different attributes independently confirm or disconfirm distinct expectations.\u003c/p\u003e \u003cp\u003eThis finding contrasts with Gupta and Bhatt (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who suggested that DSQ amplifies the benefits of digital initiatives. However, it aligns with Guo and Xu (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who noted that DT alone does not guarantee improved CS unless explicitly designed around customer-facing outcomes. The SOR framework\u0026rsquo;s prediction that DSQ would moderate technology\u0026ndash;satisfaction relationships was not supported, suggesting that in highly digitised marketing environments, customers may have sufficiently high baseline expectations for both technology and service quality that variations in DSQ no longer alter how they respond to individual technologies. This is a theoretically interesting finding that merits further investigation.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusion, Limitations, and Future Research","content":"\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Conclusion\u003c/h2\u003e \u003cp\u003eThis study examined the impact of DT, AI, IoT, and DSQ on CS within marketing companies in Saudi Arabia, along with the moderating role of DSQ. Using PLS-SEM, the study provides robust empirical evidence that IoT, AI, and DSQ each have a significant, positive direct effect on CS. Among these factors, AI and DSQ emerged as the strongest drivers, highlighting the importance of personalised, data-driven interactions and high-quality digital interfaces. IoT also significantly contributes to CS by enabling real-time connectivity, automation, and improved service responsiveness. By contrast, DT did not exert a statistically significant direct effect on CS (corrected: t\u0026thinsp;=\u0026thinsp;0.778, p\u0026thinsp;=\u0026thinsp;0.437, H1 not supported), suggesting that organisational-level digital transformation in the Saudi marketing context has not yet translated into tangible customer-perceived outcomes \u0026mdash; a finding consistent with the broader literature on the implementation gap in DT (Guo \u0026amp; Xu, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kretschmer \u0026amp; Khashabi, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and with the evolving stage of Saudi Arabia\u0026rsquo;s Vision 2030 digital transformation journey (Alateeg et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Al-Ayed et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA key contribution lies in the theoretical reinterpretation of the null moderation findings. Contrary to the SOR-based expectation that DSQ would amplify technological effects on CS, the IS Success Model offers a more fitting explanation: technological capability and service quality are parallel, additive satisfaction drivers operating through independent cognitive channels. This advances theoretical understanding beyond simple hypothesis rejection and suggests that organisations should invest in AI, IoT, DT, and DSQ simultaneously but independently, rather than assuming that improving one will enhance the customer impact of the others.\u003c/p\u003e \u003cp\u003eThe study contributes theoretically by integrating six complementary frameworks \u0026mdash; Dynamic Capabilities Theory, TAM, ECT, SERVQUAL, the IS Success Model, and the SOR Framework \u0026mdash; into a unified model that captures both direct and moderation effects. It extends the literature by providing firm-level PLS-SEM evidence from an emerging economy context and by contextualising DT theory within Saudi Arabia\u0026rsquo;s Vision 2030 reform environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Practical Implications\u003c/h2\u003e \u003cp\u003eFor marketing directors and digital strategy managers, the strong positive effects of AI and IoT on CS justify investment in AI-driven personalisation, predictive analytics, and smart connected systems as strategic priorities. The non-significant direct effect of DT on CS indicates that broad digital transformation programmes, while strategically necessary for long-term competitiveness, do not automatically translate into improved customer perceptions. Managers should ensure that DT initiatives are explicitly aligned with customer-facing service outcomes \u0026mdash; personalisation, responsiveness, and experience enhancement \u0026mdash; rather than focused solely on internal efficiency gains. Consistent with Alotaibi (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who found that AI adoption in Saudi institutions improves CS when ease of use is emphasised, firms should pair technology investments with user-centric design principles. The significant direct impact of DSQ confirms the importance of reliable, secure, and user-friendly digital platforms, and should be managed as a parallel strategic priority alongside AI and IoT adoption, consistent with evidence-based guidelines for the Saudi digital economy under Vision 2030 (Alateeg et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese implications also carry a sustainable development dimension. By strengthening AI-driven personalisation, IoT-enabled service responsiveness, and digital service quality, marketing companies contribute directly to SDG 8 (Decent Work and Economic Growth) through productivity gains and the creation of higher-skilled digital roles, to SDG 9 (Industry, Innovation, and Infrastructure) through continued investment in digital innovation infrastructure, and to SDG 12 (Responsible Consumption and Production) by enabling more informed, efficient, and responsible customer decisions. Policymakers, in turn, can treat digital service quality standards and digital literacy initiatives as instruments for translating private-sector technological investment into broader sustainable economic and social returns, reinforcing the Vision 2030 reform agenda.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Limitations and Future Research\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the study employed non-probability purposive sampling in a single sector and national context, limiting generalisability. Future research should apply probability-based sampling and explore diverse industries and cross-national settings. Second, the cross-sectional design precludes causal inference over time; longitudinal studies would better capture the dynamic evolution of digital technology effects on CS. Third, elevated HTMT values among DT, IoT, and AI suggest that future studies should develop more discriminant measurement instruments for these constructs in Arabic-language GCC contexts. Fourth, the non-significant finding for H1 (DT\u0026rarr;CS) suggests that DT\u0026rsquo;s customer-facing impact in Saudi marketing firms may be mediated by variables not included in the current model, such as implementation quality, employee digital skills, or customer digital literacy \u0026mdash; all of which merit investigation in future research. Fifth, future research might incorporate additional moderating variables \u0026mdash; such as regulatory quality, digital literacy, or cultural orientation \u0026mdash; to identify boundary conditions on the technology\u0026ndash;CS chain.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cb\u003eClinical Trial Number\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval\u003c/strong\u003e \u003cp\u003e This research was approved by the Ethics Committee of Al-Razi University, Sana\u0026rsquo;a, Yemen. The study adhered to the ethical standards outlined in the Declaration of Helsinki (2013) for research involving human participants, ensuring informed consent, voluntary participation, confidentiality, and the right to withdraw.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003e Participants were provided with written informed consent forms that explained the study\u0026rsquo;s objectives, their rights, and responsibilities. As the data were gathered through a survey, this acted as written consent. Participants were assured they could withdraw from the study at any time without consequence and that their participation was completely voluntary.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003eNot applicable, as the study does not contain any identifiable images or personal information.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by Prince Sattam Bin Abdulaziz University, under funding number 1447/2026.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.S.R. contributed to conceptualisation, data collection, formal analysis, and writing \u0026mdash; original draft. S.M.A. contributed to supervision, theoretical framework, validation, and writing \u0026mdash; review and editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study were generated through a self-administered survey. The datasets are not publicly available due to respondent confidentiality but are available from the corresponding author (S.M. AL-Kahtani; [email protected]) upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAguiar-Costa LM, Cunha CA, Silva WK, Abreu NR. Customer satisfaction in service delivery with artificial intelligence: A meta-analytic study. Volume 23. RAM \u0026mdash; Revista de Administra\u0026ccedil;\u0026atilde;o Mackenzie; 2022. p. eRAMD220003. 6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Araj REEM, Haddad HOSSAM, Shehadeh MAHA, Hasan E, Nawaiseh MY. 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Bus Process Manage J. 2023;29(7):2107\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\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":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"digital transformation (DT), internet of things (IoT), artificial intelligence (AI), digital service quality (DSQ), customer satisfaction (CS), PLS-SEM, Saudi Arabia, dynamic capabilities, moderating effect","lastPublishedDoi":"10.21203/rs.3.rs-9254750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9254750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the impact of Digital Transformation (DT), Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Service Quality (DSQ) on Customer Satisfaction (CS) in marketing companies in Saudi Arabia, and examines whether DSQ moderates these relationships. Drawing on six complementary frameworks \u0026mdash; including Dynamic Capabilities Theory, the Technology Acceptance Model (TAM), and the Information Systems Success Model \u0026mdash; the study analyses data from a purposive sample of 269 respondents using PLS-SEM in SmartPLS 4. AI, IoT, and DSQ each exert a significant positive direct effect on CS, with AI the strongest driver (f\u0026sup2; = 0.202), followed by DSQ (f\u0026sup2; = 0.123) and IoT (f\u0026sup2; = 0.120). DT was not statistically significant (f\u0026sup2; = 0.009), suggesting that organisational-level transformation has not yet translated into customer-perceived outcomes in this context. DSQ does not significantly moderate the technology\u0026ndash;CS relationships, indicating that technological benefits operate independently of perceived service quality. These null moderation findings are reinterpreted through the IS Success Model as evidence that technological value and service quality act as parallel, additive satisfaction drivers rather than interactive ones. The study contributes theoretically by integrating multiple frameworks into a unified model, offers practical guidance for marketing firms, and aligns with the United Nations Sustainable Development Goals by showing how these technologies support SDG 8 (decent work and economic growth) and SDG 9 (industry, innovation, and infrastructure), and enable the more responsible consumption patterns envisaged in SDG 12. Limitations include non-probability sampling and a single-sector focus in Riyadh.\u003c/p\u003e","manuscriptTitle":"The Moderating Role of Digital Service Quality in the Relationship Between Digital Transformation, the Internet of Things, Artificial Intelligence, and Customer Satisfaction in Marketing Companies in Saudi Arabia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 12:25:18","doi":"10.21203/rs.3.rs-9254750/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"193972814721567221096481118411144853148","date":"2026-05-13T09:30:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T04:14:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T04:11:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-06T04:07:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-24T23:01:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-04-24T22:56:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3b86a5d-a40e-4e60-b2cb-6c8bfaf0816c","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"193972814721567221096481118411144853148","date":"2026-05-13T09:30:11+00:00","index":29,"fulltext":""},{"type":"reviewersInvited","content":"10","date":"2026-05-07T04:14:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T04:11:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-06T04:07:56+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T12:25:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 12:25:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9254750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9254750","identity":"rs-9254750","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}