Driving User Engagement through Gamification: An Empirical Design of Public Cultural Cloud Platform

Driving User Engagement through Gamification: An Empirical Design of Public Cultural Cloud Platform | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Driving User Engagement through Gamification: An Empirical Design of Public Cultural Cloud Platform Lei Xu, Meng Wang, Ya Chen, Yuwen Hua This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8356239/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Under the background of digital transformation in public cultural services, this study explores the integration of the gamification design concept into the National Public Cultural Cloud Platform to enhance user participation and engagement. Based on the Octalysis framework and Kano model, our previous research summarized five design dimensions and identified 23 key design factors and their optimization priorities. This study conducts UI design for the overall interface of the National Public Culture Cloud Platform, and constructs a structural equation model to explore whether gamification elements have high applicability and effectiveness in actual use. Subsequently, fuzzy-set Qualitative Comparative Analysis is employed to identify the configuration relationships affecting actual use of the gamified Public Cultural Cloud Platform. The results indicate that core gamification elements can significantly improve user satisfaction and usage intention, while attractive gamification design may generate cognitive load and have negative effects. User behavior is jointly influenced by gamification design, user perceptions, and attitudes, forming three distinct configuration paths. It is recommended that subsequent optimization prioritize both foundational usability and gamification design to enhance platform effectiveness. The study integrates multidisciplinary methods, providing an innovative paradigm for the digitalization of public culture. Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing Social science/Science technology and society Public culture cloud platform Gamification Structural equation model Fuzzy-set Qualitative Comparative Analysis User behavior Figures Figure 1 Figure 2 Figure 3 1 Introduction The Public Cultural Cloud Platform refers to a new public cultural service network model formed by applying modern information technologies such as cloud computing, cloud storage, and big data to the field of public cultural services (Chen et al., 2018 ). Since its establishment in 2017, the National Public Cultural Cloud Platform has actively responded to national policies encouraging locally tailored cultural cloud platforms. It delivers services through computers, mobile phones, and public cultural all-in-one machines. As a centralized hub for resources, activities, and services in public cultural services, the National Public Cultural Cloud features nine major modules including “Reading Books” “Enjoying Activities” “'Learning Skills” and “Booking Venues”. This enhances the richness, convenience, and choice in digital public cultural services while strengthening the influence of public culture among the people. However, with the expansion of the user base, changes in user demographics, and the increasing sophistication of needs, public cultural cloud platforms struggle to effectively meet user demands in areas such as resources, services, systems, and interface design and organization. There remains significant room for improvement, particularly in interface layout and design (Wei & Wang, 2020 ). Specifically, existing research reveals that among users of public cultural cloud apps, the 31–40 age group constitutes the largest segment, followed by the 18–30 age group—indicating that young and middle-aged adults form the primary user base (Liu & Wei, 2020 ). Nevertheless, issues including unsystematic and unintuitive interface design, overly complex interaction flows, and insufficiently engaging and joyful emotional immersion experiences have significantly hindered the platform's promotion and usage among this core demographic (Chen, 2020 ). Gamification design, as an effective means to enhance user engagement and participation, seeks to influence user behavior by providing game-like experiences and motivations (Koivisto & Hamari, 2019 ) within information systems such as education (Oliveira et al., 2023 ), social networking (Hristova & Lieberoth, 2021 ), healthcare (Yang et al., 2024 ), and commerce (Maddalena et al., 2023 ), offering valuable insights for public platform design. Internationally, the implementation of gamification in public cultural platforms is characterized by diversity and multi-dimensionality, emphasizing task design and reward mechanisms, incorporating rich narrative backgrounds, scenarios (Tan & Ng, 2024 ), and other gamification elements, alongside tangible rewards such as points, badges, leaderboards, and virtual currency to attract and incentivize user participation (Hamari et al., 2014 ). The integration of emerging technologies, such as mobile augmented reality (Kotsopoulos et al., 2024 ) and artificial intelligence (Hutson & Hutson, 2024 ), further enables interactive and personalized gamified experiences for cultural information and services. In contrast, optimizations of China’s public cultural platforms have primarily focused on system and interface functionality, resource integration, and utilization. These efforts largely concentrate on 'foundational performance' aspects like navigation systems (Cao & Wang, 2023 ), platform service quality (Qian & Chen, 2017 ), and interface design (Wei & Zhang, 2022 ), with comparatively limited exploration or implementation of systematic gamification design, revealing a gap relative to international practices (Wanyan & Tong, 2022 ). However, gamification design also carries inherent negative impacts, such as intensified competition (Hakulinen et al., 2013 ), difficulties in task evaluation (Domínguez et al., 2013 ), gamification-induced cognitive load (McDaniel et al., 2012 ), and drop-off effects (Berkling & Thomas, 2013 ). Consequently, it remains uncertain whether gamified user experiences can effectively adapt to the public cultural service context. This uncertainty is critical because, compared to commercial online platforms, many citizens exhibit lower motivation when engaging with public cultural services (Prestopnik & Tang, 2015 ). Therefore, investigating the actual behavioral impact of gamification in public cultural services is imperative. To address these research gaps, our prior study developed a measurement scale based on the Octalysis framework and Kano model (Kano, 1984 ) to evaluate gamified user experiences in public cultural platforms. This identified optimization priorities for 23 gamification design factors by determining user-preferred elements (Wang et al., 2023 ). Building on this foundation, the current study employs the Technology Acceptance Model (TAM) and cognitive load theory to examine how these gamification elements influence actual user behavior. We propose the following research questions: (1) How does gamification design manifest in public cultural platforms? (2) How do gamification elements influence user experience in public cultural platforms? (3) Can combinations of these elements incentivize practical usage of public cultural cloud platforms? To address these research questions, this study employs the Octalysis framework, Technology Acceptance Model, and cognitive load theory. Due to the Kano model ranking the gamification elements suitable for the National Public Culture Cloud Platform based on must-be, attractive, one-dimensional, and indifference requirements, the must-be, one-dimensional, and attractive gamification designs that have a high impact on user satisfaction are selected for gamification UI design of the National Public Culture Cloud Platform. Through contextual user experience testing and questionnaire surveys, we formulate research hypotheses, analyze the practical effectiveness of gamification designs, and establish a structural equation model (SEM) of user perceptions toward the gamified UI to examine whether gamification incentivizes platform usage. Finally, using fuzzy-set Qualitative Comparative Analysis (fsQCA), we investigate the configurational effects of variables on actual platform usage and propose gamification design recommendations to enhance public engagement with the National Public Cultural Cloud, offering actionable insights for advancing digital transformation in China's public cultural platforms. 2 Theoretical foundations 2.1 Octalysis Framework The Octalysis framework, proposed by Chou, Y. K. (2019) in Actionable Gamification , adopts a human-centric rather than function-oriented design philosophy. It posits that every human behavior stems from one or more core drives that influence behavioral patterns and decision-making, thereby enabling corresponding motivational mechanisms. The framework identifies eight core drives: Meaning, Accomplishment, Empowerment, Ownership, Social Influence, Scarcity, Unpredictability, and Avoidance. According to the characteristics and preliminary research conclusions of the public cultural cloud platform (Wang et al., 2023 ), the research summarized five design elements: tasks, human-computer interaction, user feedback, rewards, and socialization, which are classified as the core drives of the Octalysis framework, forming five design dimensions including task, interaction, feedback, rewards, and community interaction design. User experience feedback is collected through UI design to optimize the existing platform. This matrix determines the specific distribution of gamification design factors, allowing us to prioritize optimization improvements following the sequence of must-be, one-dimensional, attractive, and indifferent requirement, as detailed in Appendix A. These research findings are then used to inform the gamification design of the national public cultural platforms. 2.2 Technology Acceptance Model The Technology Acceptance Model is proposed by Davis ( 1985 ) as a theoretical model for analyzing the factors that affect users’ acceptance of new technologies or information systems, with the aim of exploring the key factors that influence users’ acceptance of information system technology. This model analyzes the relationship between behavior attitude, usage intention, and actual behavior by defining perceived usefulness, perceived ease of use, and external variables, and emphasizes that perceived usefulness and perceived ease of use are the two main factors that affect user’s behavioral intention. Subsequently, many scholars have conducted improved research on the TAM model, evolving from TAM, TAM2 (Venkatesh & Davis, 2000 ), UTAUT (Venkatesh et al., 2003 ) to TAM3 (Venkatesh & Bala, 2008 ). As a theoretical model, TAM is applicable to various information systems or new technologies, but the degree of influence of various variables on user willingness to use varies in different industries and application scenarios. Therefore, it is necessary to combine the characteristics of specific research objects to obtain targeted feedback on user’s behavioral intention. TAM is widely applied in gamified information system design. Vanduhe et al. ( 2020 ) employ TAM and Technology Facilitation Theory (TFT) to investigate higher education instructors’ acceptance and behavioral continuity intentions toward gamified training via the Moodle Gamified Training Platform. Viet et al. (2024) explore user acceptance of gamified e-banking platforms, revealing that gamification positively influences digital banking users’ perceived value, attitudes, and behavioral intention. Aydin ( 2015 ) develop an extended TAM incorporating intrinsic motivations (e.g., enjoyment and fun) to demonstrate user attitudes and continuance intentions toward gamified systems. As a category of information systems, cloud platforms’ interface navigation layouts, functional modules, and data resource designs directly impact user adoption willingness and perceptions. According to the TAM, external variables can influence users' perceptions of usefulness and ease of use, which in turn directly or indirectly affect behavioral attitudes and intentions. There is a significant positive correlation between usage intention and actual usage behavior (Davis, 1989 ). Therefore, this study adopts the TAM framework to examine user behavioral intentions and perceptions regarding the gamified UI version of the National Public Cultural Cloud. 2.3 Cognitive Load Theory Cognitive Load Theory (CLT) is proposed by John Sweller ( 2011 ), where Cognitive Load (CL) refers to the total amount of mental resources imposed on an individual’s cognitive system during a specific task, primarily categorized into extraneous CL, intrinsic CL, and germane CL (Paas & Van Merriënboer, 1994 ). Its core idea is that by optimizing task design and information presentation methods, the effort required for individuals to complete tasks can be reduced, thereby improving task efficiency. When users interact with unfamiliar webpages, platforms, or information systems, they often face challenges due to limitations in comprehension abilities because an individual’s cognitive resources and working memory capacity are limited; when the total information processing exceeds these resources, it leads to cognitive overload, affecting learning and problem-solving efficiency. The CLT has been widely applied in various platforms and information systems, such as Im et al. ( 2014 ) extracting platform interface design principles to minimize cognitive dissonance on online learning platforms; Huang and Johnson ( 2009 ) use CLT for instructional game design to enhance users’ learning effectiveness and experience; Yu et al. ( 2019 ) design a mobile learning platform to improve learners’ satisfaction and reduce their CL in EFL courses. As one of the manifestations of human-computer interaction, the path, elements, hierarchy, and interface design of the platform system's user interface will directly affect the level of CL, thereby reflecting the user’s work status (Chalmers, 2003 ). For example, on public cultural cloud platforms, excessive visual elements (e.g., graphics, colors, animations, and multimedia) coupled with cumbersome page-redirects and navigation functions may induce significant cognitive load. Therefore, this study incorporates CL as a factor influencing users’ willingness to adopt gamification. 3 Research model and hypotheses development Based on Fig. 1 , this study selects must-be requirements, one-dimensional requirements, and attractive requirements as core latent variables. Since indifferent requirements have minimal impact on user needs and satisfaction, they are excluded from the model construction. Using the TAM as the theoretical framework while incorporating CLT as a supplementary component, the study establishes a user perception model for gamification design in the National Public Cultural Cloud platform (see Fig. 2 ). According to TAM, user usage behavior is determined by behavioral intention, which in turn is jointly influenced by attitude toward use and perceived usefulness. Attitude toward use itself is shaped by both perceived usefulness and perceived ease of use. Whether gamification design can effectively enhance user satisfaction and efficiency when using the Public Cultural Cloud platform, and whether it enables users to effortlessly experience platform functionalities, directly impacts actual usage. CLT further indicates that when platform services exceed users’ cognitive capacity or operational abilities, technology overload and cognitive overload may occur, disrupting user engagement with services and potentially leading to abandonment. This study hypothesizes that must-be, one-dimensional, and attractive gamification designs significantly influence user satisfaction attitudes and usage behaviors. Validation of these hypotheses would demonstrate the reliability of the constructed indicator system. The model ultimately reveals a complete pathway through which gamification design elements affect user behavior by influencing user perceptions and CL. 3.1 Gamification Design and User Perception Derived from the Kano classification prioritization in prior research, must-be gamification elements include: competition & leaderboards, points, feedback, reward redemption, push notifications, personal homepage, email & messages, and onboarding tutorials. One-dimensional gamification elements comprise: secret tasks & exclusive rewards, personal collection services, badges & achievements, and daily check-ins. Attractive gamification elements consist of: experience & level, rewards, protagonist, and storytelling & contextualization. Points, levels, and leaderboards themselves do not undermine users’ intrinsic motivation in non-game contexts; rather, they serve as viable means to facilitate specific user behaviors within non-game environments (Mekler et al., 2013 ). Thiel ( 2016 ) focus on reward-based and social gamification, demonstrating that while not all individuals consider gamified participation appropriate, rewards and social factors positively influence user engagement, with social gamification exhibiting greater potential for motivating civic participation. Hassan et al. ( 2019 ) explore the impact of incentive feedback on users in gamification and find that gamification effects are positively correlated with emotional feedback experience, personalized design is positively correlated with emotional and information feedback experience, the use of social networks and other platform systems is positively correlated with social feedback experience, emotional feedback is closely related to user perception and willingness to continue using, and information feedback affects user continued use. Sun et al. ( 2017 ) study the impact of badges and narrative design in gamification elements on the perceptual ability and autonomy of elderly people, and results indicate that gamification optimization can enhance their intrinsic motivation to use information systems, thereby producing positive effects in the information technology learning experience. In gamified learning environments, Topu ( 2023 ) observes that both active and reflective learners exhibit high behavioral, emotional, and cognitive engagement alongside lower cognitive load. In the realm of public services, gamification design in online banking plays significant roles in driving valuable customer behaviors, making complex operations user-friendly and efficient, stimulating interaction with new products, and fostering customer loyalty (Rodrigues et al., 2016 ). In summary, based on the explanation of the elements, the following hypotheses are reasonably proposed: H1a: Must-be gamification design positively influences perceived usefulness of the platform system. H1b: Must-be gamification design positively influences perceived ease of use of the platform system. H1c: Must-be gamification design positively influences CL during platform system usage. H2a: One-dimensional gamification design positively influences perceived usefulness of the platform system. H2b: One-dimensional gamification design positively influences perceived ease of use of the platform system. H2c: One-dimensional gamification design positively influences CL during platform system usage. H3a: Attractive gamification design positively influences perceived usefulness of the platform system. H3b: Attractive gamification design positively influences perceived ease of use of the platform system. H3c: Attractive gamification design positively influences CL during platform system usage. 3.2 Platform System and User Perception According to Davis et al. ( 1989 ), this study defines perceived usefulness as users’ subjective experience after using the platform system, specifically reflected in their perception of convenience regarding service and resource utilization within the National Public Cultural Cloud platform. For instance, gamification design may help users complete resource retrieval tasks more efficiently and enhance learning productivity. Perceived ease of use refers to the level of difficulty users encounter when selecting functions or locating resources within the platform system, along with their anticipated perception of operational convenience. Drawing upon the evaluative and causal dimensions of the cognitive load structural model proposed by Paas et al. (1994), we define CL as the increased mental burden on memory or comprehension during platform usage, resulting from complex functionalities, novel gamification designs, and excessive additional information processing that exceed users’ cognitive capacity or expected thresholds. The characteristics of information systems, including platform system features, system accessibility, and platform system user experience, directly affect users’ perception of the usefulness and ease of use of information systems (Lederer et al., 2000 ). Furthermore, ease of use positively influences usefulness, but this relationship is irreversible (Davis, 1985 ; Venkatesh & Davis, 2000 ). Overly complex platform webpage design imposes excessive CL on users, severely impairing the system’s ease of use (Albers & Patton, 2006 ). When users attempt to learn software applications or platform systems involving multi-element interactions, their intricate structures and functionalities increase CL and render the systems difficult to comprehend. Darejeh et al. ( 2021 ) use narrative elements to assist users in providing introductions to software systems. Electronic learning systems with narrative elements can reduce users’ CL and make it easier for them to learn new systems. Based on these, the following hypotheses are proposed: H4: CL during platform system usage negatively influences perceived ease of use. H5: Perceived ease of use of the platform system positively influences perceived usefulness. 3.3 User perception and attitude, behavioral intention Following Venkatesh et al., this study defines attitude as users’ degree of preference toward the gamified National Public Cultural Cloud platform, while behavioral intention refers to users' propensity for actions such as information browsing and resource retrieval, reflecting the potential likelihood of future platform usage. Research on the TAM and its integrated frameworks consistently demonstrates significant positive correlations: perceived ease of use and perceived usefulness both show significant positive relationships with attitude, whereas perceived usefulness exhibits significant positive relationships with both attitude toward adopting application systems and usage intention (Raza et al., 2017 ). Adams et al. ( 1992 ) demonstrate that while both perceived usefulness and perceived ease of use are significant determinants of system usage, the relationship between perceived usefulness and user adoption is stronger. Younas et al. find that defects in online learning systems can increase users’ CL, especially poor interface design, ultimately affecting users’ willingness and attitude to use (2021). Based on these, the following hypotheses are proposed: H6: Perceived usefulness positively influences attitude toward the platform system. H7: Perceived ease of use positively influences attitude toward the platform system. H8: Perceived usefulness of the platform system positively influences behavioral intention. H9: CL during platform system usage negatively influences behavioral intention. 3.4 Attitude, behavioral intention, and actual use Actual usage refers to users’ concrete utilization behaviors of the platform system, such as e-reading, venue reservations, and live-stream viewing. Fishbein and Ajzen ( 1975 ) contend that behavioral intention constitutes a necessary step toward performing any actual behavior, with attitude being a key factor influencing behavior intention. Joo et al. ( 2018 ) find that satisfaction significantly positively influences students’ continuance intention toward Korean MOOC courses. Current empirical studies have consistently confirmed a significant positive correlation between attitude and behavioral intention (Chen et al., 2012 ; Alraimi et al., 2015 ; Wang et al.,2019). Behavioral intention establishes users’ initial motivation and inclination to engage with the platform system, subsequently shaping their usage behaviors (Wei et al., 2021 ). In the context of this study, user satisfaction attitude refers to the belief that the National Public Culture Cloud Platform can better meet their learning, communication, or cultural content experience needs through gamified design. Therefore, users who are more satisfied with the national public cultural cloud platform are more likely and willing to use it in their actual lives because they believe that the platform will help them achieve their expectations of learning and experiencing cultural content. Based on these, the following hypotheses are proposed: H10: Attitude toward the platform system positively influences behavioral intention. H11: Behavioral intention toward the platform system positively influences actual use. 4 Methodology 4.1 Research context We select the National Public Cultural Cloud platform ( https://www.culturedc.cn ) as the empirical context for this study. As a national-level public cultural cloud platform integrating resources from provinces, municipalities, and autonomous regions, it optimally satisfies citizens' digital public cultural needs. Based on Kano classification, we employ must-be, one-dimensional, and attractive gamification design to carry out UI gamification concept design for the National Public Culture Cloud. Because gamification elements such as contextualization, protagonist, and personal homepage are highly relevant to digital cultural travel and cultural communication forums, we refer to the travel software Tripadvisor and the Chinese gaming community NGA ( https://ngabbs.com ) and cultural communication forum Baidu Tieba ( https://tieba.baidu . com). The gamification design of the National Public Culture Cloud is completed using UI design software Pixso and iH5 game production platform. The specific UI gamification design concept diagram is shown in Appendix B. The gamified UI design for the National Public Cultural Cloud prioritizes both visual impact and operability, implementing high-contrast color schemes that avoid adjacent hues to enhance interface readability. Multiple distinctive font styles are blended throughout the interface to ensure typographic clarity, while simplified information hierarchies and layout designs improve usability through streamlined structures. For icons and content, highly recognizable light skeuomorphic graphics integrated with textual elements strengthen wayfinding functionality, assisting children, elderly users, and individuals with disabilities in navigating the platform effortlessly. Building upon this foundational optimization of the homepage layout, we implement gamification designs specifically for the “Reading Books” and “Booking Venues” sections, select Journey to the West as an example in the “Reading Books” module to design an interactive mini game called Dreaming of Journey to the West , with gamified elements such as tasks, achievements, and ratings. In the “Booking Venues” module, Jinshan Temple is selected as an example to design a venue storytelling guide, with game elements such as commentary, interactive Q&A, storytelling, and contextualization. Finally, two unique sections, “Personal Homepage” and “Cultural Communication Forum” are designed, involving gamified elements such as check-in, rewards, points, message prompts, and interactive forums, to facilitate users’ use of information resources while meeting their free interaction needs between users and the platform, as well as between users themselves. 4.2 Data collection and Measures This study employs a dual-mode data collection approach combining online and offline questionnaires. From July 12 to August 16, 2024, we distribute online surveys via a China’s prominent questionnaire platform Wenjuanxing ( https://www.wjx.cn ), primarily targeting adolescent and middle-aged demographics (questionnaire items see Table 1 ). To address digital literacy considerations, offline surveys are conducted for minors and senior citizens, featuring detailed questionnaire explanations and demonstrations of gamified UI prototypes to evaluate participants’ perceptions of the gamified National Public Cultural Cloud version. To ensure participant alignment with the target platform, we provide the official National Public Cultural Cloud link and confirm prior platform usage. To optimize our sample, we add screening questions to test participants’ understanding of gamification. Ultimately, 450 questionnaires are collected, with 439 valid responses retained after rigorous verification. Appendix C presents participants’ demographic characteristics. Table 1 Variables and Measurement Items Variables Indicators Questionnaire Items Scale Source Must-be gamification design M1 I feel a sense of achievement when appearing on leaderboards and competitive rankings Cao, et al., 2023 M2 Multiple pathways are available to earn reward points Lewis, et al., 2016 M3 The National Public Cultural Cloud provides immediate and clear feedback on user actions Hobert & Berens, 2023 M4 The reward catalog features exquisite diverse items, covering various point brackets and multiple categories of physical rewards Kim, 2015 M5 The cultural content pushed by the platform consistently engages my interest Wu, 2022 M6 Personal homepage enhances usage flexibility within the cloud platform Cheng, 2024 M7 Email and message notifications demonstrate human-centered design Kunkel, et al., 2023 M8 The concise onboarding tutorial efficiently facilitates platform exploration and mastery Batooli, et al., 2018 One-dimensional gamification design O1 Diverse task elements with moderate difficulty levels Justin & Joy, 2024 O2 Hidden tasks and exclusive rewards create novel and engaging experiences Pishchanska, et al., 2022 O3 The personal collection service enables efficient archiving of curated resources, ensuring seamless future access Silva, et al., 2021 O4 Being awarded badges and achievements provides profound fulfillment Werbach & Hunter, 2020 O5 Daily check-in rewards with points incentivize consistent platform engagement Koutropoulos, 2012 Attractive gamification design A1 Gaining XP bonuses from learning activities provides profound satisfaction Dicheva, et al., 2019 A2 Earning points or virtual rewards for task completion fosters tangible accomplishment Goh, et al., 2017 A3 Assuming protagonist role in resource interaction creates narrative immersion O’Connor, et al., 2020 A4 Gamified contextualization and storytelling of resources delivers compelling novelty Odinokaya, et al., 2024 Perceived usefulness PU1 Gamified mechanics (e.g., narrative scenarios and mini-games) enhance platform utility Luo, 2023 ; Davis & Bagozzi, 1989 PU2 I can efficiently accomplish tasks with high accuracy within the gamified system PU3 The platform enables operational fluency during interaction processes PU4 Information and services acquisition efficiency is significantly improved PU5 Resource accessibility and comprehensiveness are substantially enhanced Perceived ease of use PEOU1 Navigating information through the gamified platform is intuitive Luo, 2024 ; Li & Zhu, 2022 PEOU2 Learning gamified platform operations requires minimal effort PEOU3 Achieving proficiency in gamified platform usage is straightforward PEOU4 Mini-games provide moderately challenging learning experiences PEOU5 Resource discovery via categorical navigation is efficient Cognitive load CL1 Mastering gamified platform functionalities demands substantial effort Su, 2016 ; Chalmers, 2003 CL2 Previously accessed resources are often difficult to relocate CL3 Search failures for target resources occur frequently, usage history tracking function is inadequate CL4 Resource sharing mechanisms are non-intuitive CL5 Activity enrollment and venue subscription processes are unclear Attitude AT1 I have positive attitudes toward the gamified platform Wixom & Todd, 2005 ; Dishaw & Strong, 1999 AT2 Interacting with gamified elements induces relaxation and enjoyment AT3 The interface of gamified platform demonstrates refined and user-friendly design AT4 Overall, I highly endorse this gamified platform Behavioral intention BI1 I am willing to try the gamified platform in the future BI2 I intend to regularly engage with gamification mechanics (e.g., daily check-ins and task systems) BI3 I plan to utilize the platform for learning resources and cultural participation (e.g., Read books, Participate in activities, Book venues) BI4 I would recommend this platform to others Actual use U1 After gamification optimization, I will frequently access the National Public Cultural Cloud U2 After gamification optimization, I will frequently obtain the latest cultural information, search for talent training, venues, events and other information, and conduct cultural and tourism product transactions U3 After gamification optimization, I will utilize the cloud platform to address my cultural needs U4 I intend to experience gamification mechanics and participate in digital cultural exchange 5 Data analysis and results Structural Equation Modeling (SEM) is used to analyze the complex interrelationships between observed variables and latent variables. It is categorized into two types: Covariance-Based Structural Equation Modeling (CB-SEM) and Partial Least Squares Structural Equation Modeling (PLS-SEM). If the research objective is theory testing and confirmation, CB-SEM is the appropriate method; conversely, if the research objective is prediction and theory development, PLS-SEM is the suitable approach (Dash & Paul, 2021 ). In this study, the questionnaire sample size is sufficient, user responses exhibit high credibility, and the data satisfies the assumption of normal distribution. According to the recommendations of Ringle et al. ( 2009 ), when dealing with data meeting the normality assumption, CB-SEM should be prioritized as this method was originally developed for parametric tests. Based on this, this study will employ SPSS 27.0 and Amos 27 statistical software to conduct systematic reliability and validity tests on the questionnaire scales and to empirically verify the proposed theoretical model hypotheses. 5.1 SEM Results This study conducts rigorous reliability and validity tests, with results showing all indicators met or significantly exceeded established standards, thus confirming the questionnaire’s reliability and validity. Specifically, all Corrected Item-Total Correlation (CITC) values are greater than 0.4, and deleting any item would have reduced the Cronbach’s alpha coefficient of its respective factor. The overall Cronbach’s alpha coefficient for the questionnaire is as high as 0.954, and the alpha coefficients for all factors exceed 0.85. Content validity is deemed good as the items are derived from well-established literature. Exploratory Factor Analysis (EFA) extract nine factors consistent with the questionnaire’s design, cumulatively explaining 69.532% of the variance. Standardized factor loadings and Average Variance Extracted (AVE) values are all greater than 0.5, while Composite Reliability (CR) values all exceed 0.7, demonstrating excellent convergent validity. The square roots of the AVE for each factor are greater than its correlation coefficients with other factors, establishing discriminant validity. A Kaiser-Meyer-Olkin (KMO) value of 0.946 and a significant Bartlett’s test of sphericity further supported the questionnaire’s ideal construct validity. Following the reliability and validity tests, a SEM is established based on the theoretical model using Amos 27, as depicted in Fig. 3 . All fit indices for the hypothesized model fall within ideal ranges, meeting all recommended thresholds, indicating that the overall model fit is satisfactory and that the hypothesized model meets statistical requirements (Table 2 ). The R²values for CL, perceived usefulness, perceived ease of use, attitude, behavioral intention, and actual use are 0.214, 0.457, 0.495, 0.410, 0.419, and 0.293, indicating that the model has good explanatory and predictive power. Table 2 Model Fitting Results Fit Metrics Target Threshold Result Value Assessment 𝑥 2 /𝑑𝑓(D/ 𝑑𝑓) 0.85 0.896 Passed AGFI > 0.80 0.884 Passed NFI > 0.80 0.904 Passed CFI > 0.80 0.975 Passed RMSEA ≤ 0.5 0.027 Passed An analysis of the relationships between the elements was conducted by examining the significance levels using p values. The p value reflects the significance level between variables, with a threshold of 0.05: p value < 0.05 indicates an acceptable significance level (*); < 0.01 indicates a good significance level (**); and 0.05). The p values for all other hypotheses are less than 0.05, and they have been validated. Specifically, except for H1a which shows an acceptable significance level, and H4 and H5 which show a good significance level, the p values for the other 13 hypotheses are all less than 0.001, indicating a very high significance level (***) (see Table 3 ). Furthermore, the standardized path coefficients showed a negative relationship for the paths between CL and must-be gamification design, one-dimensional gamification design, perceived ease of use, and behavioral intention. All other standardized path coefficients indicated positive relationships. Table 3 Test results of the SEM model Hypotheses Path Coefficient t-Statistic p Hypothesis Testing H1a Must-be gamification design → Perceived usefulness 0.14 2.491 * Supported H1b Must-be gamification design → Perceived ease of use 0.295 5.013 *** Supported H1c Must-be gamification design → CL -0.212 3.252 *** Supported H2a One-dimensional gamification design → Perceived usefulness 0.259 4.693 *** Supported H2b One-dimensional gamification design → Perceived ease of use 0.186 3.205 *** Supported H2c One-dimensional gamification design → CL -0.27 4.161 *** Supported H3a Attractive gamification design → Perceived usefulness 0.218 3.842 *** Supported H3b Attractive gamification design → Perceived ease of use 0.334 5.864 *** Supported H3c Attractive gamification design → CL -0.094 1.502 0.133 Not Supported H4 CL → Perceived ease of use -0.15 3.065 ** Supported H5 Perceived ease of use → Perceived usefulness 0.17 2.923 ** Supported H6 Perceived usefulness → Attitude 0.295 4.479 *** Supported H7 Perceived ease of use → Attitude 0.479 7.737 *** Supported H8 Perceived usefulness → Behavioral intention 0.318 5.531 *** Supported H9 CL → Behavioral intention -0.141 3.211 *** Supported H10 Attitude → Behavioral intention 0.29 5.941 *** Supported H11 Behavioral intention → Actual use 0.572 9.41 *** Supported Building upon the original model, a mediation effect between gamification design and user usage intention is constructed, investigating whether the three types of gamification design can directly enhance users' intention to use, and exploring the chain mediation relationships within the influence pathways (see Appendix D). The mediation effects in this study are analyzed using Amos 27, employing the bias-corrected nonparametric percentile bootstrap method to test confidence intervals. With 5000 bootstrap resamples, 95% confidence intervals are calculated, providing good statistical significance. The study finds significant direct effects between must-be, one-dimensional, and attractive gamification designs and behavioral intention. This indicates that implementing gamification design can enhance users’ intention to use, thereby promoting actual usage of the Public Cultural Cloud Platform. Conversely, gamification design does not directly influence CL to achieve positive feedback effects; instead, it indirectly promotes usage only when it fulfills requirements for usefulness and ease of use. This conclusion is also supported by the research of Limayem et al. ( 2007 ). Must-be and one-dimensional gamification designs primarily enhance user satisfaction and strengthen usage intention through perceived usefulness and perceived ease of use (or a chain mediation involving both). Attractive gamification design, however, primarily maximizes user attraction and usage through perceived usefulness, perceived ease of use, and their combined effects. 5.2 Reanalysis of the data using fsQCA This study employs purposive sampling to determine the analytical sample, ensuring the relevance and representativeness of case selection (Campbell et al., 2020 ). The sample selection adheres to the following principles: First, ensuring sufficient homogeneity within the overall case base, meaning selected cases are comparable in their fundamental characteristics. Second, maintaining maximum heterogeneity within this base, including both positive cases with high scores in gamification design, user perception, attitude, and actual use, and negative cases with low scores, to avoid sample homogenization. Finally, controlling the proportional relationship between the number of cases and the condition variables to prevent the common problem of limited diversity encountered in QCA research. Based on these principles, through screening valid questionnaires and data cleaning, 75 representative samples are ultimately selected from the National Public Cultural Cloud Platform’s gamified version satisfaction survey for configurational analysis. 5.2.1 Calibration The fsQCA method requires calibration of the original data to determine the membership scores of variables within the [0, 1] interval (Fu et al., 2021 ). The calibration points (full in point, crossover point, and full out point) for the eight condition variables and one outcome variable are set at the 95%, 50%, and 5% (Zschoch, 2011 ), respectively, of the descriptive statistics of the case samples. Cases are with the suffix “fs”. Notably, the calibration for user non actual use behavior is achieved by taking the negation of the set of user actual use behavior. 5.2.2 Necessity analysis Prior to conducting the configurational analysis, we need to perform necessary analysis on each condition variable to determine whether the outcome variable (user actual use) is a subset of any single condition variable (Zhang & Du, 2019 ). According to the commonly adopted standard, a necessary condition is identified if its consistency score is ≥ 0.9 and its coverage is > 0.5 (Kaiser & Rice, 1974 ). This study utilizes the fsQCA 4.1 software to perform the necessary condition analysis. As shown in Table 4 , the consistency scores for all condition variables fell below the threshold of 0.9, indicating that no single condition constitutes a necessary condition for either user actual use (USE) or non actual use (~ USE). Table 4 Necessity testing Conditional variables Actual use (USE) Non actual use (~ USE) Consistency Coverage Consistency Coverage Must-be gamification design (M) 0.732 0.711 0.537 0.493 ~ Must-be gamification design (~ M) 0.479 0.523 0.686 0.707 One-dimensional gamification design (O) 0.753 0.717 0.546 0.491 ~ One-dimensional gamification design (~ O) 0.466 0.521 0.686 0.724 Attractive gamification design (A) 0.722 0.750 0.499 0.489 ~ Attractive gamification design (~ A) 0.508 0.518 0.745 0.717 Perceived usefulness (PU) 0.714 0.722 0.541 0.516 ~ Perceived usefulness (~ PU) 0.521 0.546 0.708 0.700 Perceived ease of use (PEOU) 0.651 0.706 0.522 0.534 ~ Perceived ease of use (~ PEOU) 0.571 0.559 0.713 0.659 Cognitive load (CL) 0.560 0.558 0.721 0.678 ~ Cognitive load (~ CL) 0.677 0.720 0.530 0.532 Attitude (AT) 0.758 0.748 0.511 0.475 ~ Attitude (~ AT) 0.468 0.504 0.729 0.740 Behavioral intention (BI) 0.675 0.720 0.524 0.528 ~ Behavioral intention (~ BI) 0.557 0.554 0.722 0.677 Note: Adding“~”before the condition indicates that it is absent. 5.2.3 Sufficiency analysis Sufficiency analysis aims to identify which combinations of must-be, one-dimensional, and attractive gamification design elements with user perception, attitude, and behavioral intention constitute sufficient conditions for user actual use. The study sets a case frequency threshold of 1 and a raw consistency threshold of 0.8. Combinations with consistency above the threshold were assigned a value of 1, while those below are assigned 0. To prevent simultaneous subset relations between the outcome and its negation (Greckhamer, 2016 ), the PRI consistency threshold is set to 0.70 (Greckhamer et al., 2018 ). After constructing the truth table and performing standardized parsing, five distinct configurations of gamification design leading to user actual use are obtained (see Table 5 ). This study follows the conventions for presenting QCA results proposed by Fiss ( 2011 ): ● and U denote the presence and absence of core conditions, respectively; ● and ⊗ denote the presence and absence of peripheral conditions, respectively; and blanks indicate that the condition is not decisive in the configuration—its presence or absence does not affect the outcome. The analysis results show that both the consistency scores for individual solution configurations and the overall solution consistency exceed the threshold of 0.8. The overall solution coverage reached 0.534, indicating that these five path configurations have a high explanatory power for user actual use behavior. Table 5 Actual use path and configuration Conditional variables Actual use (Outcome variable: USEfs) Configuration 1 (L1a) Configuration 2 (L1b) Configuration 3 (L1c) Configuration 4 (L2) Configuration 5 (L3) Must-be gamification design (Mfs) ● ● ● ● ⊗ One-dimensional gamification design (Ofs) ● ● ● ● ⊗ Attractive gamification design (Afs) ● ● ● ● Perceived usefulness (PUfs) ● ● ● ● ● Perceived ease of use (PEOUfs) ● ● ● ● ⊗ Cognitive load (CLfs) ⊗ ⊗ ⊗ ⊗ Attitude (ATfs) ● ● ● ● Behavioral intention (BIfs) ● ● ● ● Raw coverage 0.425 0.403 0.449 0.412 0.157 Unique coverage 0.030 0.008 0.054 0.017 0.030 Consistency 0.897 0.941 0.914 0.940 0.947 Solution coverage 0.534 Solution consistency 0.880 This study also conducts a configurational analysis on user non actual use behavior. The raw consistency threshold is set to 0.8, the case frequency threshold is set to 1, and the PRI consistency threshold is set to 0.75. After constructing the truth table, five configuration paths leading to non actual use are ultimately obtained, as shown in Table 6 . Both the consistency scores of the individual solution configurations and the overall solution consistency exceed 0.9, while the overall solution coverage is 0.397. This indicates that these five configurations possess substantial explanatory power for non actual use. Table 6 Non actual use path and configuration Conditional variables Non actual use (Outcome variable: ~USEfs) Configuration 1 (NL1a) Configuration 2 (NL1b) Configuration 3 (NL2) Configuration 4 (NL3) Configuration 5 (NL4) Must-be gamification design (Mfs) ⊗ ● ⊗ ⊗ One-dimensional gamification design (Ofs) ⊗ ⊗ ⊗ ⊗ ⊗ Attractive gamification design (Afs) ⊗ ⊗ ⊗ ⊗ ⊗ Perceived usefulness (PUfs) ● ● ⊗ ⊗ ⊗ Perceived ease of use (PEOUfs) ⊗ ⊗ ● ● ⊗ Cognitive load (CLfs) ● ● ● ● Attitude (ATfs) ⊗ ⊗ ⊗ ⊗ ● Behavioral intention (BIfs) ⊗ ⊗ ⊗ ● ● Raw coverage 0.291 0.293 0.215 0.215 0.201 Unique coverage 0.015 0.013 0.012 0.022 0.014 Consistency 0.942 0.943 0.923 0.973 0.968 Solution coverage 0.397 Solution consistency 0.926 To ensure the robustness of the research findings, this study systematically tests the data. Common robustness testing methods in QCA research include adjusting calibration thresholds, modifying case frequency thresholds, varying consistency levels, adding or deleting condition variables or case samples (Kaiser & Rice, 1974 ). Given the limited sample size in this study, the robustness test is conducted by increasing the consistency threshold (Landers et al., 2017 ): the consistency threshold is raised from 0.80 to 0.85. Using the consistency and coverage of the new configurations as the test criteria, the truth table is reconstructed and the standardized analysis is performed. The test results show that after raising the consistency threshold, the five path configurations based on the intermediate solution remain unchanged. The combinations of factors within each path show no significant alterations, and the consistency and coverage metrics exhibit only minor fluctuations. This result fully confirms the robustness and reliability of the present study. 6 Discussion and implications 6.1 Result Discussion This study, through hypothesis testing, verifies that gamified design optimization can motivate users to utilize public cultural platforms. The research indicates that the gamified UI design of the National Public Cultural Cloud is concise, aesthetically pleasing, and aligns with user habits, thereby enhancing users' perceived usefulness and perceived ease of use, ultimately increasing platform usage frequency and user retention. Regarding the relationship between gamification design and user perception, one-dimensional gamification design exerts the strongest influence on perceived usefulness. Cardador et al. ( 2017 ) and Landers et al. ( 2017 ) both find that game elements related to tasks, rewards, and progress (such as points, badges, achievements, virtual rewards, etc.) enhance users’ perception of the system’s usefulness. This study also confirms that gamified “Easter egg” designs based on tasks and rewards are significantly effective in motivating users (Takbiri et al., 2023 ) and providing learning support (Schmidt et al., 2023 ). Simultaneously, gamified personal collection services facilitate resource collection and sharing for users, and game elements related to personalization and freedom of choice (Marques et al., 2023 ) are also important factors influencing perceived usefulness. Attractive gamification design has the most significant impact on perceived ease of use. The narrativization and contextualization of cultural resources transform user experiences from one-sided resource learning into immersive engagement (Marques et al., 2023 ), simplifying the learning process. Aguilos and Fuchs (2022) note that some students exhibit varying preferences regarding level and experience point designs. In this study, the gamified UI design ensures platform ease of use by granting experience point bonuses, points, and additional rewards upon task completion and social discussions, while seamlessly integrating the experience and point mechanisms into resource content and personal profiles. In summary, this study provides empirical support for hypotheses H1a, H1b, H2a, H2b, H3a, and H3b. Regarding the relationship between gamification design and CL, the findings support hypotheses H1c and H2c. The gamification elements in the National Public Cultural Cloud platform effectively reduce users’ CL. This aligns with the conclusions of studies by Su ( 2016 ), Shaban and Pearson ( 2020 ), and Darejeh ( 2021 ). For instance, concise tutorial guides, personalized information feeds, and notification feedback particularly accommodate elderly users and those less proficient with digital technology. The gamified task design alleviates users’ CL when learning cultural content, making the learning process easier and more enjoyable. This can also be explained by the strong positive correlation between flow experience and CL (Chang et al., 2017 ). However, the negative correlation between attractive gamification design and users’ CL (H3c) is not supported. It is speculated that attractive gamification design elements might be overly novel or complex, potentially causing users difficulties in understanding operations or creating attentional distraction due to excessive elements, thereby generating additional CL (Li et al., 2025 ; Šola et al., 2024 ). Gamification elements like interactive learning and immersion, while capable of promoting intrinsic motivation and learning behaviors to some extent, may also induce extraneous CL (Skulmowski & Xu, 2022 ). Regarding the relationship between perceived usefulness, perceived ease of use, and other factors, ease of use positively influences usefulness. When users first interact with the National Public Cultural Platform, the gamification design—through its tutorial guides, simple platform navigation, and enhanced human-computer interaction facilitated by narrative and social mechanisms (Zhang et al., 2023 )—improves efficiency in accessing information and services, thereby strengthening perceptions of platform usefulness (supporting H5). This aligns with findings by Mizher and Alwreikat ( 2023 ) using the TAM. If platform operations prove overly complex, users are unlikely to perceive it as useful and may seek alternative services; conversely, greater ease of use promotes actual use (supporting H4). Perceived usefulness and perceived ease of use exhibit a positive feedback relationship with attitude, which is consistent with findings by Queiro-Ameijeiras et al. ( 2025 ) and Nugroho ( 2024 ), thereby supporting hypotheses H6 and H7. Notably, perceived ease of use exerts a more significant influence on attitude than perceived usefulness, primarily because perceived usefulness affects user satisfaction while perceived ease of use influences the holistic value perception of the gamified platform system (Jamil et al., 2019 ). Specifically, the selection of gamification elements needs to consider the user needs of each resource section of the platform and the overall attributes of the public cultural cloud platform. Each module requires different gamification strategies and interface designs, and the ease of use of gamification elements will directly affect the choice of gamification strategies (Acosta-Medina et al., 2021 ), ultimately enabling the system’s gamification design and interface aesthetics to positively impact practical utility (Cheng, 2021 ). Regarding factors influencing behavioral intention, attitude positively affects behavioral intention, perceived usefulness positively affects behavioral intention, while CL negatively affects behavioral intention. The influence of perceived usefulness is most significant, indicating that if users subjectively believe that gamification design is helpful for the public to access the latest cultural resources, search for talent training, venues, events, and conduct daily needs such as cultural and tourism product transactions on public cultural platforms, they will be more inclined to use the platform. This conclusion (supporting H9) aligns with views proposed and validated by Denden et al. ( 2022 ). Attitude significantly and positively influences intention to use the Public Cultural Cloud, suggesting that users’ interest in or preference for the gamification design process and outcomes generates positive emotional responses that motivate usage. Asiri ( 2019 ) confirms attitude as a fundamental predictor of gamification behavioral intention, thus supporting H8. Finally, both perceived ease of use and CL are significant factors influencing behavioral intention. This indicates that users who perceive the gamification designs as straightforward and easy to operate, while not generating extraneous CL, exhibit stronger behavioral intention. Conversely, CL may arise from design complexity, poor content organization or presentation, or insufficient user receptiveness to gamification. Research on the negative effects of gamification provides support for H10, as evidenced by examples such as anxiety and stress induced by competition and leaderboards, fear of negative evaluation (Chen et al., 2022 ), difficult gamification mechanics (Toda et al., 2017), and incomprehensible game rules (Campos et al., 2015 ), all of which undermine users’ behavioral intention. Consequently, gamification design should prioritize reducing extraneous CL while enhancing germane CL to improve platform behavioral intention. Furthermore, behavioral intention demonstrates a significant positive influence on actual use (supporting H11), indicating that stronger intention corresponds to a higher likelihood of actual engagement with the cloud platform. The fsQCA results indicate that the five solution paths can be further summarized into three configuration types: comprehensive gamification type, one-dimensional + low-load type, and attractive + low-load type. These reveal complex combinations of various gamification design elements, user perceptions, attitudes, and usage behaviors, with high systematicity and dynamism. Comprehensive gamification type: Configurations L1a, L1b, and L1c all demonstrate that diverse combinations of gamification can promote users’ actual usage of the National Public Cultural Cloud Platform. Building on satisfying the platform’s usefulness and ease of use, gamification design combines various conditions, such as interesting mechanisms, high-quality cultural content, efficient real-time feedback, clear demonstration of practical benefits, and user-friendly interactive operations. This combination significantly reduces users’ CL, increases users’ satisfaction and behavioral intention, which in turn stimulates actual use. One-dimensional + low-load type: Configuration L2 indicates that when using must-be gamification designs, supplemented with some one-dimensional gamification elements, users believe that moderate gamification is useful and easy to operate. For example, incorporating only elements like points and leaderboards, where users earn points through daily check-ins and tasks that can be exchanged for tangible and virtual rewards. These straightforward gamification elements reduce the risk of CL on users, effectively enhancing users’ satisfaction and behavioral intention. This combination naturally encourages active usage of the Public Cultural Cloud Platform. Attractive + low-load type: Configuration L3 demonstrates that even without employing must-be or one-dimensional gamification designs, by solely utilizing attractive gamification elements with the highest user appeal while ensuring usefulness, users still develop curiosity toward gamification elements that present operational challenges and moderate complexity. Examples include virtual reality narratives featuring story-driven scenarios and contextual protagonists. Although this places certain demands on users’ digital literacy, the high immersion and sense of accomplishment delivered by attractive gamification directly enhance users’ satisfaction and behavioral intention. Consequently, this approach effectively attracts more younger demographics to experience the gamified National Public Cultural Cloud Platform. 6.2 Theoretical implications This study explores the digital services of the National Public Culture Cloud platform based on a gamification theoretical framework, making some theoretical implications. First, the research systematically introduces gamification design theory into the relatively underexplored field of public cultural services. Although gamification is widely recognized in information technology as an effective means to enhance user engagement, its application in digital platforms for public culture remains insufficiently explored. This study fills this gap, not only expanding the application boundaries of gamification theory but, more importantly, providing a novel theoretical perspective and methodological foundation for design innovation in public cultural cloud platforms. Second, through an in-depth analysis of the specific implementation of gamification design within the National Public Culture Cloud platform, this study aims to extract and construct a systematic set of design principles and methodological frameworks based on gamification concepts specifically applicable to public cultural cloud platforms. These outcomes are expected to provide direct theoretical guidance and practical references for the optimization, upgrading, and future development of similar platforms. Finally, by integrating the Kano model, the Octalysis framework, the TAM, and CLT, this research promotes the extension and refinement of these theories, enriching the theoretical foundation for the digital transformation of public cultural services. Model results indicate that the gamification design of the National Public Culture Cloud platform generally achieves user satisfaction, fostering enthusiasm for using the platform and engaging with public culture. Most gamification design elements positively influence perceived usefulness and perceived ease of use, thereby promoting actual user behavior to some extent. However, the impact of attractive gamification design on CL is not significant and may even exhibit negative effects. Furthermore, fsQCA reveals that there is not a single configuration path that affects the actual use of gamified platforms, including multiple configurations with or without certain conditions that can lead to the ideal result. 6.3 Practical implications In fsQCA, configurations NL1a and NL1b reveal that under conditions lacking one-dimensional and attractive gamification elements, despite the platform’s high perceived usefulness, its low ease of use imposes significant CL on users. Regardless of whether must-be gamification elements are incorporated, users’ attitude and behavioral intention remain low. Configuration NL2 shows that when one-dimensional and attractive gamification elements are absent and perceived usefulness is low, implementing must-be gamification may make the platform seem easier to use. However, since users perceive these must-be elements as lacking substantial utility within the platform system, they still experience CL, ultimately discouraging platform usage. Configurations NL4 and NL5 demonstrate that when gamification design is essentially omitted, users perceive insufficient platform usefulness. Even if the interface of the National Public Cultural Cloud Platform meets usability, users have some behavioral intention, the improvement in attitude is not significant. This underscores the necessity of combining both fundamental platform optimization and gamification enhancements. ໿Integrating these findings with empirical results from SEM, this study proposes fundamental optimization suggestions and five dimensions of gamification optimization suggestions, providing practical guidance for the improvement of public cultural platform functions and user experience enhancement. 6.3.1 Fundamental optimization suggestions First, diversified personalized classification should be implemented. While the platform’s primary classification based on geographical regions has taken initial shape, it currently lacks refined strategies addressing user differentiation, particularly showing deficiencies in areas like children’s cognitive stratification and user behavioral profiling. It is recommended that platform administrators incorporate a developmental psychology perspective (Baltes et al., 1980 ) to refine age-dimensional classification design. This should be integrated with user interests, occupations, and behavioral trajectories to construct a personalized intelligent classification and recommendation system (Passalacqua et al., 2021 ) using AI technology. Furthermore, by guiding users to create custom tags, generate reading lists, and engage in social recommendations, user participation in content co-creation can be encouraged, enabling the synergistic evolution of professional content classification and user-generated content. Secondly, constructing comprehensive information resources is crucial for enhancing the platform's content value and user stickiness. Currently, multiple sections of the platform suffer from resource gaps and outdated updates, increasingly failing to meet the growing demand for diverse cultural content. It is recommended that the National Public Cultural Cloud Platform adhere to the principle of co-creation and sharing, achieving resource interoperability with institutions at all levels—such as libraries, cultural centers, and art museums (Yarrow et al., 2008 )—to enrich content supply and seamlessly integrate online and offline cultural service chains. Simultaneously, the platform should diversify information presentation formats through multimedia integration and timely update mechanisms, providing comprehensive, promptly responsive, and clearly articulated cultural information. This will strengthen users' perception of the platform's authority and completeness. Third, regarding streamlined platform experience, the increasing of tabs and functional modules increasingly exposes users to the risk of CL. The platform should strengthen hierarchical and modular strategies in UI design by adopting a unified visual language, simplifying navigation paths, and highlighting critical information to reduce user barriers to entry. Furthermore, for presenting cultural content, gamification design must maintain a balance between “entertainment value” and “professional depth” to avoid oversimplification of content or superficiality in form. To this end, lightweight interactive mechanisms—such as knowledge quizzes, virtual explorations, and achievement badges—may be introduced. These transform in-depth cultural content into tangible gaming experiences, enhancing user immersion and boosting learning motivation. Finally, at the service adaptation level, platform design should account for the diverse needs of children, elderly users, and individuals with disabilities by incorporating accessibility features such as color vision deficiency modes, voice navigation, and text-to-speech functions (Bercaru & Popescu, 2024 ). This not only aligns with the universal service principle of public cultural platforms but also contributes to building a more equitable digital cultural ecosystem. 6.3.2 Gamification optimization suggestions The gamification design optimization of the National Public Culture Cloud Platform can be carried out systematically around five core dimensions: tasks, interactions, feedback, rewards, and community interactions. In task design, a diversified task system needs to be built, including core tasks that integrate situational narratives and role-playing (Wahyuni et al, 2025 ; Liu et al, 2025 ), extended ancillary tasks, regular lightweight tasks, and randomly triggered hidden Easter egg tasks. At the same time, using AI user profiling technology, tasks of adaptive difficulty should be precisely pushed based on user ability and behavior data, achieving a personalized immersive experience. The optimization of interaction design focuses on enhancing exploration freedom and identity recognition. By providing a simple tutorial guide, the entry barrier is lowered. A dynamic points ranking and multi-dimensional identity system (UID/level/title) are established based on cultural contributions (such as discussion, translation, and resource supplement). Users are allowed to customize their personal homepage and resource collection, with popular resources flexibly displayed in a modular form to stimulate exploration interest. The feedback mechanism should strengthen immediacy and sense of value. A dynamic progress bar should be used to visually display task completion and resource update status. Hierarchical visual cues (such as highlighting important information) and a badge achievement system should be designed. The feedback frequency and form should be customized based on user preferences, meeting interaction needs in different scenarios, and effectively enhancing users' sense of achievement and participation in the feedback loop. The reward system should go beyond a single points-based model and expand into a structure that includes both virtual rewards (such as experience points, exclusive titles) and physical rewards (such as cultural products, industry co-branded merchandise, and vouchers). In particular, unique rewards should be tied to hidden tasks and special cultural contributions (such as providing in-depth reviews or high-quality translations), creating highly attractive incentives. Community interaction design emphasizes the creation of an open and shared ecosystem, upgrading features such as live comment streams (Zimmer et al., 2020 ) and community forums (Bista et al., 2014 ) to promote real-time communication and the exchange of cultural perspectives among users. An intelligent resource recommendation system based on user interests should be developed. Resource integration ought to be deepened by linking libraries, museums, and other institutional collections, connecting events, venues, and cultural services to build a national-level cultural resource center. Community interaction, rewards, and interaction designs should work in synergy to achieve the visual connection and collaborative distribution of cross-industry co-branded resources. This should reduce users' CL while achieving an engaging, resource-integrated, and sustainable public cultural service effect. 7 Conclusion and Limitations 7.1 Conclusion This study innovatively introduces gamification design elements into the Public Cultural Cloud Platform. Using the Octalysis framework as the theoretical foundation, it delves into the motivational effects of gamification design on user usage behavior. Drawing on prior research findings, the high-satisfaction must-be, one-dimensional, and attractive gamification designs identified via the Kano model are applied to optimize the cloud platform. By incorporating design elements from gamified platforms, cultural communication forums, and cultural tourism platforms, the platform interface underwent gamification transformation through UI design. User experience questionnaires are employed to collect feedback data, and a SEM is constructed to verify the applicability and effectiveness of the gamification elements in UI design practice. Furthermore, the fsQCA method is utilized to uncover the configurational relationships influencing usage behavior on the gamified platform. Based on the research results, this paper proposes recommendations focusing on both fundamental and gamification optimization. Each gamification design possesses distinct characteristics, and their synergistic effects effectively enhance user stickiness and usage intention. This provides a valuable reference framework and practical guidance for gamifying the Public Cultural Cloud Platform and similar platforms. 7.2 Limitations This study still has several limitations, which also point the way for future research. First, the empirical investigation explored the relationships between must-be, one-dimensional and attractive gamification designs and user perception, CL, attitude, behavioral intention, and actual use, as well as the multiple chain mediation effects between these three types of gamification design and behavioral intention. However, the influence of moderating variables on these relationships is not considered. Subsequent research could select specific moderating variables, such as age, gender, or geographical region, to investigate their potential impact on the effectiveness of gamification within the Public Cultural Cloud Platform. Second, as a public welfare platform, the Public Cultural Cloud needs to pay special attention to the needs of specific user groups when deepening the application of gamification design: How to ensure gamification design exerts a positive guiding effect on minors, and how to optimize design to enhance accessibility for vulnerable groups such as the elderly and people with disabilities. These issues are worth exploring in depth. Future research should focus on strengthening content orientation and usage safety for younger users within the game mechanism design, while simultaneously improving usage adaptability for elderly users. This will foster the development of a more inclusive public cultural service platform. Declarations Competing interests The authors declare no competing interests. Ethical approval According to Article 32 of the National Guidelines for Scientific Research Ethics Review, studies involving human data or anonymized information—where there is no harm to participants, no sensitive personal information or commercial interests, and no relevance to life science or medical topics—are eligible for exemption from ethical review. This provision is intended to alleviate unnecessary burdens on researchers and foster the development of life science and medical research. Specifically, Article 32, Item 2 allows for exemption from ethical review for research utilizing anonymized data. Our study fully complies with this criterion, as it involved a voluntary questionnaire, ensuring complete anonymization of participant responses. The data collected is non-sensitive, non-medical, and unrelated to life science research. After thorough consideration, Institute of Science and Technology Information of Jiangsu University, China confirmed on June 15, 2024, that ethical approval is waived for this study, classifying it under minimal risk research. Consequently, no specific ethical approval number has been assigned. Additionally, the study adheres to the ethical standards set forth in the 1964 Declaration of Helsinki. Informed consent From July 12 to August 16, 2024, participants filled out a consent form before participating in the questionnaire survey. Online informed consent was obtained through the questionnaire platform Wenjuanxing ( https://www.wjx.cn ). Written informed consent for participation in this study was obtained from all legal guardians or close relatives of the participants, as well as from the participants themselves, prior to their involvement. The consent clearly outlined the study’s purpose, data collection scope, and agreement to the publication of the research results. Participants were assured that their anonymity and confidentiality would be maintained, and that the collected data would be used exclusively for research analysis and not disclosed to third parties. They were informed that participation was voluntary, and they could withdraw at any time without any consequences. As the study involved non-interventional research, participants were assured that there were no risks associated with their participation. Author Contribution Lei Xu: Conceptualization, Investigation, Methodology, Software, Validation, Formal analysis, Visualization, Writing – original draft, Writing – review & editing.Meng Wang: Conceptualization, Supervision, Funding acquisition, Writing – original draft. Ya Chen: Investigation, Methodology, Validation, Writing – review & editing, Project administration.Yuwen Hua: Data Curation, Software, Visualization.All authors reviewed the manuscript. Acknowledgement This work is supported by the National Social Science Fund Post-support Project of China (grant number 24FTQB003); the Major Project of Philosophy and Social Science Research Jiangsu Province (grant number 2024SJZD131). Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Acosta-Medina JK, Torres-Barreto ML, Cárdenas-Parga AF (2021) Students' preference for the use of gamification in virtual learning environments. Australasian J Educational Technol 37(4):145–158. https://doi.org/10.14742/ajet.6512 Adams DA, Nelson RR, Todd PA (1992) Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Q 227–247. https://doi.org/10.2307/249577 Aguilos V, Fuchs K (2022, July) The perceived usefulness of gamified e-learning: A study of undergraduate students with implications for higher education. In Frontiers in Education. 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Supplementary Files AppendixA.docx AppendixB.docx AppendixC.docx AppendixD.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers invited by journal 25 Mar, 2026 Editor assigned by journal 09 Jan, 2026 Editor invited by journal 06 Jan, 2026 Submission checks completed at journal 03 Jan, 2026 First submitted to journal 03 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8356239","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":612781866,"identity":"8ee94a88-6e38-456d-84f5-87267083c08d","order_by":0,"name":"Lei Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIie3QMQrCMBSA4ZRCXSJ1fEXoDYQngY71Kg1CXYoILh0bhHTpAQTv4Rzp0KUHqFtLL9ADiNpZkMbNId/8ft5LCDGMv2SJ/pG+fHcmRDfoJXaJtLaZV5QnBnqJE8Nc2hybnVxQncA9JwzBcSJy7yQBEvqrbCKBJlm3SOneunDZHsiWBWoiQRi3RABHe8lzBKL4VSMJQCFy6d0kUL0kjr0singBlmYCdV8yohQDysdPRo23uDkXvfVU/qaqum5IQ38y+bzzt3HDMAzjizfBeEGK6/l11QAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangsu University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Xu","suffix":""},{"id":612781868,"identity":"8e75adb8-ccf0-4d80-ab46-a1787357d2e2","order_by":1,"name":"Meng Wang","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Wang","suffix":""},{"id":612781870,"identity":"5afc9285-134b-4702-9231-771e4914f8af","order_by":2,"name":"Ya Chen","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Chen","suffix":""},{"id":612781871,"identity":"b6152d6c-4490-4c7c-8eff-a5c3f6f4c76a","order_by":3,"name":"Yuwen Hua","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yuwen","middleName":"","lastName":"Hua","suffix":""}],"badges":[],"createdAt":"2025-12-14 06:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8356239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8356239/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105555028,"identity":"e1876c7e-98be-433c-98bc-ee5a46d27b11","added_by":"auto","created_at":"2026-03-27 10:48:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100318,"visible":true,"origin":"","legend":"\u003cp\u003eUser satisfaction distribution matrix for gamification design factors\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8356239/v1/6ac418c127d4c4d0d22d2436.png"},{"id":105555026,"identity":"ad0adcee-d014-467c-8639-0a4da96d7067","added_by":"auto","created_at":"2026-03-27 10:48:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107023,"visible":true,"origin":"","legend":"\u003cp\u003eUser Perception Model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8356239/v1/f1a45845191d554900d86412.png"},{"id":105567310,"identity":"1dfa547b-5849-42b8-95bc-dc9a43b4920e","added_by":"auto","created_at":"2026-03-27 12:58:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":114188,"visible":true,"origin":"","legend":"\u003cp\u003eResults of User Perception Model\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8356239/v1/d1aa13bea15fdcb69db7d5a9.png"},{"id":105570430,"identity":"f774c394-f892-458e-a5cb-37b415ac1322","added_by":"auto","created_at":"2026-03-27 13:16:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1734547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8356239/v1/aa66261c-b127-4e86-800f-6375e500396d.pdf"},{"id":105555031,"identity":"7f449a94-a9fc-4a1f-ab96-ec133b30fd98","added_by":"auto","created_at":"2026-03-27 10:48:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20200,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-8356239/v1/18ae8098cbcce0618244cd4c.docx"},{"id":105555032,"identity":"0c4afb1a-ebbb-46c8-b2fd-28547ad42fe4","added_by":"auto","created_at":"2026-03-27 10:48:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4969402,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixB.docx","url":"https://assets-eu.researchsquare.com/files/rs-8356239/v1/535a20bbf5c9528b927c81cb.docx"},{"id":105555030,"identity":"9d458f08-5924-4cc4-bd5b-ee5a4226b95a","added_by":"auto","created_at":"2026-03-27 10:48:25","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":21942,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixC.docx","url":"https://assets-eu.researchsquare.com/files/rs-8356239/v1/d9258bce62f89aeca909ae93.docx"},{"id":105555029,"identity":"53f06b65-f0b1-48c7-b974-d146a9f5d7a1","added_by":"auto","created_at":"2026-03-27 10:48:25","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":27705,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixD.docx","url":"https://assets-eu.researchsquare.com/files/rs-8356239/v1/a9b75402994d0a472897c576.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Driving User Engagement through Gamification: An Empirical Design of Public Cultural Cloud Platform","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe Public Cultural Cloud Platform refers to a new public cultural service network model formed by applying modern information technologies such as cloud computing, cloud storage, and big data to the field of public cultural services (Chen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Since its establishment in 2017, the National Public Cultural Cloud Platform has actively responded to national policies encouraging locally tailored cultural cloud platforms. It delivers services through computers, mobile phones, and public cultural all-in-one machines. As a centralized hub for resources, activities, and services in public cultural services, the National Public Cultural Cloud features nine major modules including \u0026ldquo;Reading Books\u0026rdquo; \u0026ldquo;Enjoying Activities\u0026rdquo; \u0026ldquo;'Learning Skills\u0026rdquo; and \u0026ldquo;Booking Venues\u0026rdquo;. This enhances the richness, convenience, and choice in digital public cultural services while strengthening the influence of public culture among the people. However, with the expansion of the user base, changes in user demographics, and the increasing sophistication of needs, public cultural cloud platforms struggle to effectively meet user demands in areas such as resources, services, systems, and interface design and organization. There remains significant room for improvement, particularly in interface layout and design (Wei \u0026amp; Wang, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specifically, existing research reveals that among users of public cultural cloud apps, the 31\u0026ndash;40 age group constitutes the largest segment, followed by the 18\u0026ndash;30 age group\u0026mdash;indicating that young and middle-aged adults form the primary user base (Liu \u0026amp; Wei, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nevertheless, issues including unsystematic and unintuitive interface design, overly complex interaction flows, and insufficiently engaging and joyful emotional immersion experiences have significantly hindered the platform's promotion and usage among this core demographic (Chen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGamification design, as an effective means to enhance user engagement and participation, seeks to influence user behavior by providing game-like experiences and motivations (Koivisto \u0026amp; Hamari, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) within information systems such as education (Oliveira et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), social networking (Hristova \u0026amp; Lieberoth, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), healthcare (Yang et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and commerce (Maddalena et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), offering valuable insights for public platform design. Internationally, the implementation of gamification in public cultural platforms is characterized by diversity and multi-dimensionality, emphasizing task design and reward mechanisms, incorporating rich narrative backgrounds, scenarios (Tan \u0026amp; Ng, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and other gamification elements, alongside tangible rewards such as points, badges, leaderboards, and virtual currency to attract and incentivize user participation (Hamari et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The integration of emerging technologies, such as mobile augmented reality (Kotsopoulos et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and artificial intelligence (Hutson \u0026amp; Hutson, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), further enables interactive and personalized gamified experiences for cultural information and services. In contrast, optimizations of China\u0026rsquo;s public cultural platforms have primarily focused on system and interface functionality, resource integration, and utilization. These efforts largely concentrate on 'foundational performance' aspects like navigation systems (Cao \u0026amp; Wang, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), platform service quality (Qian \u0026amp; Chen, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and interface design (Wei \u0026amp; Zhang, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with comparatively limited exploration or implementation of systematic gamification design, revealing a gap relative to international practices (Wanyan \u0026amp; Tong, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, gamification design also carries inherent negative impacts, such as intensified competition (Hakulinen et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), difficulties in task evaluation (Dom\u0026iacute;nguez et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), gamification-induced cognitive load (McDaniel et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and drop-off effects (Berkling \u0026amp; Thomas, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Consequently, it remains uncertain whether gamified user experiences can effectively adapt to the public cultural service context. This uncertainty is critical because, compared to commercial online platforms, many citizens exhibit lower motivation when engaging with public cultural services (Prestopnik \u0026amp; Tang, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, investigating the actual behavioral impact of gamification in public cultural services is imperative. To address these research gaps, our prior study developed a measurement scale based on the Octalysis framework and Kano model (Kano, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) to evaluate gamified user experiences in public cultural platforms. This identified optimization priorities for 23 gamification design factors by determining user-preferred elements (Wang et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Building on this foundation, the current study employs the Technology Acceptance Model (TAM) and cognitive load theory to examine how these gamification elements influence actual user behavior. We propose the following research questions:\u003c/p\u003e \u003cp\u003e(1) How does gamification design manifest in public cultural platforms?\u003c/p\u003e \u003cp\u003e(2) How do gamification elements influence user experience in public cultural platforms?\u003c/p\u003e \u003cp\u003e(3) Can combinations of these elements incentivize practical usage of public cultural cloud platforms?\u003c/p\u003e \u003cp\u003eTo address these research questions, this study employs the Octalysis framework, Technology Acceptance Model, and cognitive load theory. Due to the Kano model ranking the gamification elements suitable for the National Public Culture Cloud Platform based on must-be, attractive, one-dimensional, and indifference requirements, the must-be, one-dimensional, and attractive gamification designs that have a high impact on user satisfaction are selected for gamification UI design of the National Public Culture Cloud Platform. Through contextual user experience testing and questionnaire surveys, we formulate research hypotheses, analyze the practical effectiveness of gamification designs, and establish a structural equation model (SEM) of user perceptions toward the gamified UI to examine whether gamification incentivizes platform usage. Finally, using fuzzy-set Qualitative Comparative Analysis (fsQCA), we investigate the configurational effects of variables on actual platform usage and propose gamification design recommendations to enhance public engagement with the National Public Cultural Cloud, offering actionable insights for advancing digital transformation in China's public cultural platforms.\u003c/p\u003e"},{"header":"2 Theoretical foundations","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Octalysis Framework\u003c/h2\u003e \u003cp\u003eThe Octalysis framework, proposed by Chou, Y. K. (2019) in \u003cem\u003eActionable Gamification\u003c/em\u003e, adopts a human-centric rather than function-oriented design philosophy. It posits that every human behavior stems from one or more core drives that influence behavioral patterns and decision-making, thereby enabling corresponding motivational mechanisms. The framework identifies eight core drives: Meaning, Accomplishment, Empowerment, Ownership, Social Influence, Scarcity, Unpredictability, and Avoidance. According to the characteristics and preliminary research conclusions of the public cultural cloud platform (Wang et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the research summarized five design elements: tasks, human-computer interaction, user feedback, rewards, and socialization, which are classified as the core drives of the Octalysis framework, forming five design dimensions including task, interaction, feedback, rewards, and community interaction design. User experience feedback is collected through UI design to optimize the existing platform.\u003c/p\u003e \u003cp\u003eThis matrix determines the specific distribution of gamification design factors, allowing us to prioritize optimization improvements following the sequence of must-be, one-dimensional, attractive, and indifferent requirement, as detailed in Appendix A. These research findings are then used to inform the gamification design of the national public cultural platforms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Technology Acceptance Model\u003c/h2\u003e \u003cp\u003eThe Technology Acceptance Model is proposed by Davis (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) as a theoretical model for analyzing the factors that affect users\u0026rsquo; acceptance of new technologies or information systems, with the aim of exploring the key factors that influence users\u0026rsquo; acceptance of information system technology. This model analyzes the relationship between behavior attitude, usage intention, and actual behavior by defining perceived usefulness, perceived ease of use, and external variables, and emphasizes that perceived usefulness and perceived ease of use are the two main factors that affect user\u0026rsquo;s behavioral intention. Subsequently, many scholars have conducted improved research on the TAM model, evolving from TAM, TAM2 (Venkatesh \u0026amp; Davis, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), UTAUT (Venkatesh et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) to TAM3 (Venkatesh \u0026amp; Bala, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). As a theoretical model, TAM is applicable to various information systems or new technologies, but the degree of influence of various variables on user willingness to use varies in different industries and application scenarios. Therefore, it is necessary to combine the characteristics of specific research objects to obtain targeted feedback on user\u0026rsquo;s behavioral intention.\u003c/p\u003e \u003cp\u003eTAM is widely applied in gamified information system design. Vanduhe et al. (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) employ TAM and Technology Facilitation Theory (TFT) to investigate higher education instructors\u0026rsquo; acceptance and behavioral continuity intentions toward gamified training via the Moodle Gamified Training Platform. Viet et al. (2024) explore user acceptance of gamified e-banking platforms, revealing that gamification positively influences digital banking users\u0026rsquo; perceived value, attitudes, and behavioral intention. Aydin (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) develop an extended TAM incorporating intrinsic motivations (e.g., enjoyment and fun) to demonstrate user attitudes and continuance intentions toward gamified systems. As a category of information systems, cloud platforms\u0026rsquo; interface navigation layouts, functional modules, and data resource designs directly impact user adoption willingness and perceptions. According to the TAM, external variables can influence users' perceptions of usefulness and ease of use, which in turn directly or indirectly affect behavioral attitudes and intentions. There is a significant positive correlation between usage intention and actual usage behavior (Davis, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Therefore, this study adopts the TAM framework to examine user behavioral intentions and perceptions regarding the gamified UI version of the National Public Cultural Cloud.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Cognitive Load Theory\u003c/h2\u003e \u003cp\u003eCognitive Load Theory (CLT) is proposed by John Sweller (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), where Cognitive Load (CL) refers to the total amount of mental resources imposed on an individual\u0026rsquo;s cognitive system during a specific task, primarily categorized into extraneous CL, intrinsic CL, and germane CL (Paas \u0026amp; Van Merri\u0026euml;nboer, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Its core idea is that by optimizing task design and information presentation methods, the effort required for individuals to complete tasks can be reduced, thereby improving task efficiency. When users interact with unfamiliar webpages, platforms, or information systems, they often face challenges due to limitations in comprehension abilities because an individual\u0026rsquo;s cognitive resources and working memory capacity are limited; when the total information processing exceeds these resources, it leads to cognitive overload, affecting learning and problem-solving efficiency. The CLT has been widely applied in various platforms and information systems, such as Im et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) extracting platform interface design principles to minimize cognitive dissonance on online learning platforms; Huang and Johnson (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) use CLT for instructional game design to enhance users\u0026rsquo; learning effectiveness and experience; Yu et al. (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) design a mobile learning platform to improve learners\u0026rsquo; satisfaction and reduce their CL in EFL courses. As one of the manifestations of human-computer interaction, the path, elements, hierarchy, and interface design of the platform system's user interface will directly affect the level of CL, thereby reflecting the user\u0026rsquo;s work status (Chalmers, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). For example, on public cultural cloud platforms, excessive visual elements (e.g., graphics, colors, animations, and multimedia) coupled with cumbersome page-redirects and navigation functions may induce significant cognitive load. Therefore, this study incorporates CL as a factor influencing users\u0026rsquo; willingness to adopt gamification.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Research model and hypotheses development","content":"\u003cp\u003eBased on Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, this study selects must-be requirements, one-dimensional requirements, and attractive requirements as core latent variables. Since indifferent requirements have minimal impact on user needs and satisfaction, they are excluded from the model construction. Using the TAM as the theoretical framework while incorporating CLT as a supplementary component, the study establishes a user perception model for gamification design in the National Public Cultural Cloud platform (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to TAM, user usage behavior is determined by behavioral intention, which in turn is jointly influenced by attitude toward use and perceived usefulness. Attitude toward use itself is shaped by both perceived usefulness and perceived ease of use. Whether gamification design can effectively enhance user satisfaction and efficiency when using the Public Cultural Cloud platform, and whether it enables users to effortlessly experience platform functionalities, directly impacts actual usage. CLT further indicates that when platform services exceed users\u0026rsquo; cognitive capacity or operational abilities, technology overload and cognitive overload may occur, disrupting user engagement with services and potentially leading to abandonment. This study hypothesizes that must-be, one-dimensional, and attractive gamification designs significantly influence user satisfaction attitudes and usage behaviors. Validation of these hypotheses would demonstrate the reliability of the constructed indicator system. The model ultimately reveals a complete pathway through which gamification design elements affect user behavior by influencing user perceptions and CL.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Gamification Design and User Perception\u003c/h2\u003e \u003cp\u003eDerived from the Kano classification prioritization in prior research, must-be gamification elements include: competition \u0026amp; leaderboards, points, feedback, reward redemption, push notifications, personal homepage, email \u0026amp; messages, and onboarding tutorials. One-dimensional gamification elements comprise: secret tasks \u0026amp; exclusive rewards, personal collection services, badges \u0026amp; achievements, and daily check-ins. Attractive gamification elements consist of: experience \u0026amp; level, rewards, protagonist, and storytelling \u0026amp; contextualization. Points, levels, and leaderboards themselves do not undermine users\u0026rsquo; intrinsic motivation in non-game contexts; rather, they serve as viable means to facilitate specific user behaviors within non-game environments (Mekler et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Thiel (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) focus on reward-based and social gamification, demonstrating that while not all individuals consider gamified participation appropriate, rewards and social factors positively influence user engagement, with social gamification exhibiting greater potential for motivating civic participation. Hassan et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) explore the impact of incentive feedback on users in gamification and find that gamification effects are positively correlated with emotional feedback experience, personalized design is positively correlated with emotional and information feedback experience, the use of social networks and other platform systems is positively correlated with social feedback experience, emotional feedback is closely related to user perception and willingness to continue using, and information feedback affects user continued use. Sun et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) study the impact of badges and narrative design in gamification elements on the perceptual ability and autonomy of elderly people, and results indicate that gamification optimization can enhance their intrinsic motivation to use information systems, thereby producing positive effects in the information technology learning experience. In gamified learning environments, Topu (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observes that both active and reflective learners exhibit high behavioral, emotional, and cognitive engagement alongside lower cognitive load. In the realm of public services, gamification design in online banking plays significant roles in driving valuable customer behaviors, making complex operations user-friendly and efficient, stimulating interaction with new products, and fostering customer loyalty (Rodrigues et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In summary, based on the explanation of the elements, the following hypotheses are reasonably proposed:\u003c/p\u003e \u003cp\u003eH1a: Must-be gamification design positively influences perceived usefulness of the platform system.\u003c/p\u003e \u003cp\u003eH1b: Must-be gamification design positively influences perceived ease of use of the platform system.\u003c/p\u003e \u003cp\u003eH1c: Must-be gamification design positively influences CL during platform system usage.\u003c/p\u003e \u003cp\u003eH2a: One-dimensional gamification design positively influences perceived usefulness of the platform system.\u003c/p\u003e \u003cp\u003eH2b: One-dimensional gamification design positively influences perceived ease of use of the platform system.\u003c/p\u003e \u003cp\u003eH2c: One-dimensional gamification design positively influences CL during platform system usage.\u003c/p\u003e \u003cp\u003eH3a: Attractive gamification design positively influences perceived usefulness of the platform system.\u003c/p\u003e \u003cp\u003eH3b: Attractive gamification design positively influences perceived ease of use of the platform system.\u003c/p\u003e \u003cp\u003eH3c: Attractive gamification design positively influences CL during platform system usage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Platform System and User Perception\u003c/h2\u003e \u003cp\u003eAccording to Davis et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), this study defines perceived usefulness as users\u0026rsquo; subjective experience after using the platform system, specifically reflected in their perception of convenience regarding service and resource utilization within the National Public Cultural Cloud platform. For instance, gamification design may help users complete resource retrieval tasks more efficiently and enhance learning productivity. Perceived ease of use refers to the level of difficulty users encounter when selecting functions or locating resources within the platform system, along with their anticipated perception of operational convenience. Drawing upon the evaluative and causal dimensions of the cognitive load structural model proposed by Paas et al. (1994), we define CL as the increased mental burden on memory or comprehension during platform usage, resulting from complex functionalities, novel gamification designs, and excessive additional information processing that exceed users\u0026rsquo; cognitive capacity or expected thresholds. The characteristics of information systems, including platform system features, system accessibility, and platform system user experience, directly affect users\u0026rsquo; perception of the usefulness and ease of use of information systems (Lederer et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Furthermore, ease of use positively influences usefulness, but this relationship is irreversible (Davis, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Venkatesh \u0026amp; Davis, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Overly complex platform webpage design imposes excessive CL on users, severely impairing the system\u0026rsquo;s ease of use (Albers \u0026amp; Patton, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). When users attempt to learn software applications or platform systems involving multi-element interactions, their intricate structures and functionalities increase CL and render the systems difficult to comprehend. Darejeh et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) use narrative elements to assist users in providing introductions to software systems. Electronic learning systems with narrative elements can reduce users\u0026rsquo; CL and make it easier for them to learn new systems. Based on these, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH4: CL during platform system usage negatively influences perceived ease of use.\u003c/p\u003e \u003cp\u003eH5: Perceived ease of use of the platform system positively influences perceived usefulness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 User perception and attitude, behavioral intention\u003c/h2\u003e \u003cp\u003eFollowing Venkatesh et al., this study defines attitude as users\u0026rsquo; degree of preference toward the gamified National Public Cultural Cloud platform, while behavioral intention refers to users' propensity for actions such as information browsing and resource retrieval, reflecting the potential likelihood of future platform usage. Research on the TAM and its integrated frameworks consistently demonstrates significant positive correlations: perceived ease of use and perceived usefulness both show significant positive relationships with attitude, whereas perceived usefulness exhibits significant positive relationships with both attitude toward adopting application systems and usage intention (Raza et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Adams et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) demonstrate that while both perceived usefulness and perceived ease of use are significant determinants of system usage, the relationship between perceived usefulness and user adoption is stronger. Younas et al. find that defects in online learning systems can increase users\u0026rsquo; CL, especially poor interface design, ultimately affecting users\u0026rsquo; willingness and attitude to use (2021). Based on these, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH6: Perceived usefulness positively influences attitude toward the platform system.\u003c/p\u003e \u003cp\u003eH7: Perceived ease of use positively influences attitude toward the platform system.\u003c/p\u003e \u003cp\u003eH8: Perceived usefulness of the platform system positively influences behavioral intention.\u003c/p\u003e \u003cp\u003eH9: CL during platform system usage negatively influences behavioral intention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Attitude, behavioral intention, and actual use\u003c/h2\u003e \u003cp\u003eActual usage refers to users\u0026rsquo; concrete utilization behaviors of the platform system, such as e-reading, venue reservations, and live-stream viewing. Fishbein and Ajzen (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) contend that behavioral intention constitutes a necessary step toward performing any actual behavior, with attitude being a key factor influencing behavior intention. Joo et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) find that satisfaction significantly positively influences students\u0026rsquo; continuance intention toward Korean MOOC courses. Current empirical studies have consistently confirmed a significant positive correlation between attitude and behavioral intention (Chen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Alraimi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al.,2019). Behavioral intention establishes users\u0026rsquo; initial motivation and inclination to engage with the platform system, subsequently shaping their usage behaviors (Wei et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the context of this study, user satisfaction attitude refers to the belief that the National Public Culture Cloud Platform can better meet their learning, communication, or cultural content experience needs through gamified design. Therefore, users who are more satisfied with the national public cultural cloud platform are more likely and willing to use it in their actual lives because they believe that the platform will help them achieve their expectations of learning and experiencing cultural content. Based on these, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH10: Attitude toward the platform system positively influences behavioral intention.\u003c/p\u003e \u003cp\u003eH11: Behavioral intention toward the platform system positively influences actual use.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Methodology","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Research context\u003c/h2\u003e \u003cp\u003eWe select the National Public Cultural Cloud platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.culturedc.cn\u003c/span\u003e\u003cspan address=\"https://www.culturedc.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as the empirical context for this study. As a national-level public cultural cloud platform integrating resources from provinces, municipalities, and autonomous regions, it optimally satisfies citizens' digital public cultural needs. Based on Kano classification, we employ must-be, one-dimensional, and attractive gamification design to carry out UI gamification concept design for the National Public Culture Cloud. Because gamification elements such as contextualization, protagonist, and personal homepage are highly relevant to digital cultural travel and cultural communication forums, we refer to the travel software Tripadvisor and the Chinese gaming community NGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngabbs.com\u003c/span\u003e\u003cspan address=\"https://ngabbs.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and cultural communication forum Baidu Tieba (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tieba.baidu\u003c/span\u003e\u003cspan address=\"https://tieba.baidu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. com). The gamification design of the National Public Culture Cloud is completed using UI design software Pixso and iH5 game production platform. The specific UI gamification design concept diagram is shown in Appendix B. The gamified UI design for the National Public Cultural Cloud prioritizes both visual impact and operability, implementing high-contrast color schemes that avoid adjacent hues to enhance interface readability. Multiple distinctive font styles are blended throughout the interface to ensure typographic clarity, while simplified information hierarchies and layout designs improve usability through streamlined structures. For icons and content, highly recognizable light skeuomorphic graphics integrated with textual elements strengthen wayfinding functionality, assisting children, elderly users, and individuals with disabilities in navigating the platform effortlessly. Building upon this foundational optimization of the homepage layout, we implement gamification designs specifically for the \u0026ldquo;Reading Books\u0026rdquo; and \u0026ldquo;Booking Venues\u0026rdquo; sections, select \u003cem\u003eJourney to the West\u003c/em\u003e as an example in the \u0026ldquo;Reading Books\u0026rdquo; module to design an interactive mini game called \u003cem\u003eDreaming of Journey to the West\u003c/em\u003e, with gamified elements such as tasks, achievements, and ratings. In the \u0026ldquo;Booking Venues\u0026rdquo; module, Jinshan Temple is selected as an example to design a venue storytelling guide, with game elements such as commentary, interactive Q\u0026amp;A, storytelling, and contextualization. Finally, two unique sections, \u0026ldquo;Personal Homepage\u0026rdquo; and \u0026ldquo;Cultural Communication Forum\u0026rdquo; are designed, involving gamified elements such as check-in, rewards, points, message prompts, and interactive forums, to facilitate users\u0026rsquo; use of information resources while meeting their free interaction needs between users and the platform, as well as between users themselves.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Data collection and Measures\u003c/h2\u003e \u003cp\u003eThis study employs a dual-mode data collection approach combining online and offline questionnaires. From July 12 to August 16, 2024, we distribute online surveys via a China\u0026rsquo;s prominent questionnaire platform \u003cem\u003eWenjuanxing\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wjx.cn\u003c/span\u003e\u003cspan address=\"https://www.wjx.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), primarily targeting adolescent and middle-aged demographics (questionnaire items see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To address digital literacy considerations, offline surveys are conducted for minors and senior citizens, featuring detailed questionnaire explanations and demonstrations of gamified UI prototypes to evaluate participants\u0026rsquo; perceptions of the gamified National Public Cultural Cloud version. To ensure participant alignment with the target platform, we provide the official National Public Cultural Cloud link and confirm prior platform usage. To optimize our sample, we add screening questions to test participants\u0026rsquo; understanding of gamification. Ultimately, 450 questionnaires are collected, with 439 valid responses retained after rigorous verification. Appendix C presents participants\u0026rsquo; demographic characteristics.\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\u003eVariables and Measurement Items\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuestionnaire Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScale Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eMust-be gamification design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI feel a sense of achievement when appearing on leaderboards and competitive rankings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCao, et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultiple pathways are available to earn reward points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLewis, et al., 2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe National Public Cultural Cloud provides immediate and clear feedback on user actions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHobert \u0026amp; Berens, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe reward catalog features exquisite diverse items, covering various point brackets and multiple categories of physical rewards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKim, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe cultural content pushed by the platform consistently engages my interest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWu, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePersonal homepage enhances usage flexibility within the cloud platform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCheng, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmail and message notifications demonstrate human-centered design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKunkel, et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe concise onboarding tutorial efficiently facilitates platform exploration and mastery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBatooli, et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eOne-dimensional gamification design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiverse task elements with moderate difficulty levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJustin \u0026amp; Joy, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHidden tasks and exclusive rewards create novel and engaging experiences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePishchanska, et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe personal collection service enables efficient archiving of curated resources, ensuring seamless future access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSilva, et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeing awarded badges and achievements provides profound fulfillment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWerbach \u0026amp; Hunter, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaily check-in rewards with points incentivize consistent platform engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKoutropoulos, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAttractive gamification design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGaining XP bonuses from learning activities provides profound satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDicheva, et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarning points or virtual rewards for task completion fosters tangible accomplishment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGoh, et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssuming protagonist role in resource interaction creates narrative immersion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eO\u0026rsquo;Connor, et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGamified contextualization and storytelling of resources delivers compelling novelty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdinokaya, et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePerceived usefulness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGamified mechanics (e.g., narrative scenarios and mini-games) enhance platform utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLuo, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Davis \u0026amp; Bagozzi, 1989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI can efficiently accomplish tasks with high accuracy within the gamified system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe platform enables operational fluency during interaction processes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInformation and services acquisition efficiency is significantly improved\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResource accessibility and comprehensiveness are substantially enhanced\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePerceived ease of use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNavigating information through the gamified platform is intuitive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLuo, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li \u0026amp; Zhu, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLearning gamified platform operations requires minimal effort\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAchieving proficiency in gamified platform usage is straightforward\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMini-games provide moderately challenging learning experiences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResource discovery via categorical navigation is efficient\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCognitive load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMastering gamified platform functionalities demands substantial effort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSu, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chalmers, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreviously accessed resources are often difficult to relocate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSearch failures for target resources occur frequently, usage history tracking function is inadequate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResource sharing mechanisms are non-intuitive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity enrollment and venue subscription processes are unclear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAttitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI have positive attitudes toward the gamified platform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eWixom \u0026amp; Todd, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Dishaw \u0026amp; Strong, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1999\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInteracting with gamified elements induces relaxation and enjoyment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe interface of gamified platform demonstrates refined and user-friendly design\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall, I highly endorse this gamified platform\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBehavioral intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI am willing to try the gamified platform in the future\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI intend to regularly engage with gamification mechanics (e.g., daily check-ins and task systems)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI plan to utilize the platform for learning resources and cultural participation (e.g., Read books, Participate in activities, Book venues)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI would recommend this platform to others\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eActual use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter gamification optimization, I will frequently access the National Public Cultural Cloud\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter gamification optimization, I will frequently obtain the latest cultural information, search for talent training, venues, events and other information, and conduct cultural and tourism product transactions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter gamification optimization, I will utilize the cloud platform to address my cultural needs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI intend to experience gamification mechanics and participate in digital cultural exchange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5 Data analysis and results","content":"\u003cp\u003eStructural Equation Modeling (SEM) is used to analyze the complex interrelationships between observed variables and latent variables. It is categorized into two types: Covariance-Based Structural Equation Modeling (CB-SEM) and Partial Least Squares Structural Equation Modeling (PLS-SEM). If the research objective is theory testing and confirmation, CB-SEM is the appropriate method; conversely, if the research objective is prediction and theory development, PLS-SEM is the suitable approach (Dash \u0026amp; Paul, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, the questionnaire sample size is sufficient, user responses exhibit high credibility, and the data satisfies the assumption of normal distribution. According to the recommendations of Ringle et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), when dealing with data meeting the normality assumption, CB-SEM should be prioritized as this method was originally developed for parametric tests. Based on this, this study will employ SPSS 27.0 and Amos 27 statistical software to conduct systematic reliability and validity tests on the questionnaire scales and to empirically verify the proposed theoretical model hypotheses.\u003c/p\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 SEM Results\u003c/h2\u003e\n \u003cp\u003eThis study conducts rigorous reliability and validity tests, with results showing all indicators met or significantly exceeded established standards, thus confirming the questionnaire\u0026rsquo;s reliability and validity. Specifically, all Corrected Item-Total Correlation (CITC) values are greater than 0.4, and deleting any item would have reduced the Cronbach\u0026rsquo;s alpha coefficient of its respective factor. The overall Cronbach\u0026rsquo;s alpha coefficient for the questionnaire is as high as 0.954, and the alpha coefficients for all factors exceed 0.85. Content validity is deemed good as the items are derived from well-established literature. Exploratory Factor Analysis (EFA) extract nine factors consistent with the questionnaire\u0026rsquo;s design, cumulatively explaining 69.532% of the variance. Standardized factor loadings and Average Variance Extracted (AVE) values are all greater than 0.5, while Composite Reliability (CR) values all exceed 0.7, demonstrating excellent convergent validity. The square roots of the AVE for each factor are greater than its correlation coefficients with other factors, establishing discriminant validity. A Kaiser-Meyer-Olkin (KMO) value of 0.946 and a significant Bartlett\u0026rsquo;s test of sphericity further supported the questionnaire\u0026rsquo;s ideal construct validity.\u003c/p\u003e\n \u003cp\u003eFollowing the reliability and validity tests, a SEM is established based on the theoretical model using Amos 27, as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. All fit indices for the hypothesized model fall within ideal ranges, meeting all recommended thresholds, indicating that the overall model fit is satisfactory and that the hypothesized model meets statistical requirements (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The R\u0026sup2;values for CL, perceived usefulness, perceived ease of use, attitude, behavioral intention, and actual use are 0.214, 0.457, 0.495, 0.410, 0.419, and 0.293, indicating that the model has good explanatory and predictive power.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModel Fitting Results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFit Metrics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTarget Threshold\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResult Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAssessment\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e𝑥\u003csup\u003e2\u003c/sup\u003e/𝑑𝑓(D/ 𝑑𝑓)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAn analysis of the relationships between the elements was conducted by examining the significance levels using p values. The p value reflects the significance level between variables, with a threshold of 0.05: p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates an acceptable significance level (*); \u0026lt; 0.01 indicates a good significance level (**); and \u0026lt;\u0026thinsp;0.001 indicates a very high significance level (***). The path coefficient between charismatic gamified design and CL is not significant (p\u0026thinsp;=\u0026thinsp;0.133\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The p values for all other hypotheses are less than 0.05, and they have been validated. Specifically, except for H1a which shows an acceptable significance level, and H4 and H5 which show a good significance level, the p values for the other 13 hypotheses are all less than 0.001, indicating a very high significance level (***) (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, the standardized path coefficients showed a negative relationship for the paths between CL and must-be gamification design, one-dimensional gamification design, perceived ease of use, and behavioral intention. All other standardized path coefficients indicated positive relationships.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTest results of the SEM model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHypotheses\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath Coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypothesis Testing\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMust-be gamification design \u0026rarr; Perceived usefulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMust-be gamification design \u0026rarr; Perceived ease of use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMust-be gamification design \u0026rarr; CL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne-dimensional gamification design \u0026rarr; Perceived usefulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne-dimensional gamification design \u0026rarr; Perceived ease of use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH2c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne-dimensional gamification design \u0026rarr; CL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttractive gamification design \u0026rarr; Perceived usefulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttractive gamification design \u0026rarr; Perceived ease of use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH3c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttractive gamification design \u0026rarr; CL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot Supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCL \u0026rarr; Perceived ease of use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived ease of use \u0026rarr; Perceived usefulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived usefulness \u0026rarr; Attitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived ease of use \u0026rarr; Attitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e7.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived usefulness \u0026rarr; Behavioral intention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCL \u0026rarr; Behavioral intention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude \u0026rarr; Behavioral intention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBehavioral intention \u0026rarr; Actual use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e9.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBuilding upon the original model, a mediation effect between gamification design and user usage intention is constructed, investigating whether the three types of gamification design can directly enhance users\u0026apos; intention to use, and exploring the chain mediation relationships within the influence pathways (see Appendix D). The mediation effects in this study are analyzed using Amos 27, employing the bias-corrected nonparametric percentile bootstrap method to test confidence intervals. With 5000 bootstrap resamples, 95% confidence intervals are calculated, providing good statistical significance. The study finds significant direct effects between must-be, one-dimensional, and attractive gamification designs and behavioral intention. This indicates that implementing gamification design can enhance users\u0026rsquo; intention to use, thereby promoting actual usage of the Public Cultural Cloud Platform. Conversely, gamification design does not directly influence CL to achieve positive feedback effects; instead, it indirectly promotes usage only when it fulfills requirements for usefulness and ease of use. This conclusion is also supported by the research of Limayem et al. (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Must-be and one-dimensional gamification designs primarily enhance user satisfaction and strengthen usage intention through perceived usefulness and perceived ease of use (or a chain mediation involving both). Attractive gamification design, however, primarily maximizes user attraction and usage through perceived usefulness, perceived ease of use, and their combined effects.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 Reanalysis of the data using fsQCA\u003c/h2\u003e\n \u003cp\u003eThis study employs purposive sampling to determine the analytical sample, ensuring the relevance and representativeness of case selection (Campbell et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The sample selection adheres to the following principles: First, ensuring sufficient homogeneity within the overall case base, meaning selected cases are comparable in their fundamental characteristics. Second, maintaining maximum heterogeneity within this base, including both positive cases with high scores in gamification design, user perception, attitude, and actual use, and negative cases with low scores, to avoid sample homogenization. Finally, controlling the proportional relationship between the number of cases and the condition variables to prevent the common problem of limited diversity encountered in QCA research. Based on these principles, through screening valid questionnaires and data cleaning, 75 representative samples are ultimately selected from the National Public Cultural Cloud Platform\u0026rsquo;s gamified version satisfaction survey for configurational analysis.\u003c/p\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e5.2.1 Calibration\u003c/h2\u003e\n \u003cp\u003eThe fsQCA method requires calibration of the original data to determine the membership scores of variables within the [0, 1] interval (Fu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The calibration points (full in point, crossover point, and full out point) for the eight condition variables and one outcome variable are set at the 95%, 50%, and 5% (Zschoch, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), respectively, of the descriptive statistics of the case samples. Cases are with the suffix \u0026ldquo;fs\u0026rdquo;. Notably, the calibration for user non actual use behavior is achieved by taking the negation of the set of user actual use behavior.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e5.2.2 Necessity analysis\u003c/h2\u003e\n \u003cp\u003ePrior to conducting the configurational analysis, we need to perform necessary analysis on each condition variable to determine whether the outcome variable (user actual use) is a subset of any single condition variable (Zhang \u0026amp; Du, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to the commonly adopted standard, a necessary condition is identified if its consistency score is \u0026ge;\u0026thinsp;0.9 and its coverage is \u0026gt;\u0026thinsp;0.5 (Kaiser \u0026amp; Rice, \u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e). This study utilizes the fsQCA 4.1 software to perform the necessary condition analysis. As shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the consistency scores for all condition variables fell below the threshold of 0.9, indicating that no single condition constitutes a necessary condition for either user actual use (USE) or non actual use (~\u0026thinsp;USE).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNecessity testing\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eConditional variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eActual use (USE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNon actual use (~\u0026thinsp;USE)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoverage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoverage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMust-be gamification design (M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Must-be gamification design (~\u0026thinsp;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne-dimensional gamification design (O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ One-dimensional gamification design (~\u0026thinsp;O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttractive gamification design (A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Attractive gamification design (~\u0026thinsp;A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived usefulness (PU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Perceived usefulness (~\u0026thinsp;PU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived ease of use (PEOU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Perceived ease of use (~\u0026thinsp;PEOU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive load (CL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Cognitive load (~\u0026thinsp;CL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude (AT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Attitude (~\u0026thinsp;AT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBehavioral intention (BI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Behavioral intention (~\u0026thinsp;BI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: Adding\u0026ldquo;~\u0026rdquo;before the condition indicates that it is absent.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e5.2.3 Sufficiency analysis\u003c/h2\u003e\n \u003cp\u003eSufficiency analysis aims to identify which combinations of must-be, one-dimensional, and attractive gamification design elements with user perception, attitude, and behavioral intention constitute sufficient conditions for user actual use. The study sets a case frequency threshold of 1 and a raw consistency threshold of 0.8. Combinations with consistency above the threshold were assigned a value of 1, while those below are assigned 0. To prevent simultaneous subset relations between the outcome and its negation (Greckhamer, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), the PRI consistency threshold is set to 0.70 (Greckhamer et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). After constructing the truth table and performing standardized parsing, five distinct configurations of gamification design leading to user actual use are obtained (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). This study follows the conventions for presenting QCA results proposed by Fiss (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e): ● and U denote the presence and absence of core conditions, respectively; ● and \u0026otimes; denote the presence and absence of peripheral conditions, respectively; and blanks indicate that the condition is not decisive in the configuration\u0026mdash;its presence or absence does not affect the outcome. The analysis results show that both the consistency scores for individual solution configurations and the overall solution consistency exceed the threshold of 0.8. The overall solution coverage reached 0.534, indicating that these five path configurations have a high explanatory power for user actual use behavior.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eActual use path and configuration\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eConditional variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eActual use (Outcome variable: USEfs)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration 1 (L1a)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration 2 (L1b)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration 3 (L1c)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration 4 (L2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration 5 (L3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMust-be gamification design (Mfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne-dimensional gamification design (Ofs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttractive gamification design (Afs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived usefulness (PUfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived ease of use (PEOUfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive load (CLfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude (ATfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBehavioral intention (BIfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRaw coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnique coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolution coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolution consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThis study also conducts a configurational analysis on user non actual use behavior. The raw consistency threshold is set to 0.8, the case frequency threshold is set to 1, and the PRI consistency threshold is set to 0.75. After constructing the truth table, five configuration paths leading to non actual use are ultimately obtained, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Both the consistency scores of the individual solution configurations and the overall solution consistency exceed 0.9, while the overall solution coverage is 0.397. This indicates that these five configurations possess substantial explanatory power for non actual use.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" style=\"width: 970.363px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNon actual use path and configuration\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003cth style=\"width: 236px; height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eConditional variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 690px; height: 35px;\" colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eNon actual use (Outcome variable: ~USEfs)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003cth style=\"width: 142px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eConfiguration 1 (NL1a)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 143px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eConfiguration 2 (NL1b)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eConfiguration 3 (NL2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eConfiguration 4 (NL3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eConfiguration 5 (NL4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr style=\"height: 36px;\"\u003e\n \u003ctd style=\"width: 236px; height: 36px;\" align=\"left\"\u003e\n \u003cp\u003eMust-be gamification design (Mfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px; height: 36px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px; height: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 36px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 36px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 36px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35.9564px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35.9564px;\" align=\"left\"\u003e\n \u003cp\u003eOne-dimensional gamification design (Ofs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px; height: 35.9564px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px; height: 35.9564px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35.9564px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35.9564px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35.9564px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eAttractive gamification design (Afs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003ePerceived usefulness (PUfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003ePerceived ease of use (PEOUfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eCognitive load (CLfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 143px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eAttitude (ATfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eBehavioral intention (BIfs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eRaw coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eUnique coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eSolution coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 690px; height: 35px;\" colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 236px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eSolution consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 690px; height: 35px;\" colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo ensure the robustness of the research findings, this study systematically tests the data. Common robustness testing methods in QCA research include adjusting calibration thresholds, modifying case frequency thresholds, varying consistency levels, adding or deleting condition variables or case samples (Kaiser \u0026amp; Rice, \u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e). Given the limited sample size in this study, the robustness test is conducted by increasing the consistency threshold (Landers et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e): the consistency threshold is raised from 0.80 to 0.85. Using the consistency and coverage of the new configurations as the test criteria, the truth table is reconstructed and the standardized analysis is performed. The test results show that after raising the consistency threshold, the five path configurations based on the intermediate solution remain unchanged. The combinations of factors within each path show no significant alterations, and the consistency and coverage metrics exhibit only minor fluctuations. This result fully confirms the robustness and reliability of the present study.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"6 Discussion and implications","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Result Discussion\u003c/h2\u003e \u003cp\u003eThis study, through hypothesis testing, verifies that gamified design optimization can motivate users to utilize public cultural platforms. The research indicates that the gamified UI design of the National Public Cultural Cloud is concise, aesthetically pleasing, and aligns with user habits, thereby enhancing users' perceived usefulness and perceived ease of use, ultimately increasing platform usage frequency and user retention.\u003c/p\u003e \u003cp\u003eRegarding the relationship between gamification design and user perception, one-dimensional gamification design exerts the strongest influence on perceived usefulness. Cardador et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Landers et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) both find that game elements related to tasks, rewards, and progress (such as points, badges, achievements, virtual rewards, etc.) enhance users\u0026rsquo; perception of the system\u0026rsquo;s usefulness. This study also confirms that gamified \u0026ldquo;Easter egg\u0026rdquo; designs based on tasks and rewards are significantly effective in motivating users (Takbiri et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and providing learning support (Schmidt et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Simultaneously, gamified personal collection services facilitate resource collection and sharing for users, and game elements related to personalization and freedom of choice (Marques et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) are also important factors influencing perceived usefulness. Attractive gamification design has the most significant impact on perceived ease of use. The narrativization and contextualization of cultural resources transform user experiences from one-sided resource learning into immersive engagement (Marques et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), simplifying the learning process. Aguilos and Fuchs (2022) note that some students exhibit varying preferences regarding level and experience point designs. In this study, the gamified UI design ensures platform ease of use by granting experience point bonuses, points, and additional rewards upon task completion and social discussions, while seamlessly integrating the experience and point mechanisms into resource content and personal profiles. In summary, this study provides empirical support for hypotheses H1a, H1b, H2a, H2b, H3a, and H3b.\u003c/p\u003e \u003cp\u003eRegarding the relationship between gamification design and CL, the findings support hypotheses H1c and H2c. The gamification elements in the National Public Cultural Cloud platform effectively reduce users\u0026rsquo; CL. This aligns with the conclusions of studies by Su (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Shaban and Pearson (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Darejeh (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, concise tutorial guides, personalized information feeds, and notification feedback particularly accommodate elderly users and those less proficient with digital technology. The gamified task design alleviates users\u0026rsquo; CL when learning cultural content, making the learning process easier and more enjoyable. This can also be explained by the strong positive correlation between flow experience and CL (Chang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the negative correlation between attractive gamification design and users\u0026rsquo; CL (H3c) is not supported. It is speculated that attractive gamification design elements might be overly novel or complex, potentially causing users difficulties in understanding operations or creating attentional distraction due to excessive elements, thereby generating additional CL (Li et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Šola et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Gamification elements like interactive learning and immersion, while capable of promoting intrinsic motivation and learning behaviors to some extent, may also induce extraneous CL (Skulmowski \u0026amp; Xu, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the relationship between perceived usefulness, perceived ease of use, and other factors, ease of use positively influences usefulness. When users first interact with the National Public Cultural Platform, the gamification design\u0026mdash;through its tutorial guides, simple platform navigation, and enhanced human-computer interaction facilitated by narrative and social mechanisms (Zhang et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u0026mdash;improves efficiency in accessing information and services, thereby strengthening perceptions of platform usefulness (supporting H5). This aligns with findings by Mizher and Alwreikat (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) using the TAM. If platform operations prove overly complex, users are unlikely to perceive it as useful and may seek alternative services; conversely, greater ease of use promotes actual use (supporting H4). Perceived usefulness and perceived ease of use exhibit a positive feedback relationship with attitude, which is consistent with findings by Queiro-Ameijeiras et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Nugroho (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), thereby supporting hypotheses H6 and H7. Notably, perceived ease of use exerts a more significant influence on attitude than perceived usefulness, primarily because perceived usefulness affects user satisfaction while perceived ease of use influences the holistic value perception of the gamified platform system (Jamil et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Specifically, the selection of gamification elements needs to consider the user needs of each resource section of the platform and the overall attributes of the public cultural cloud platform. Each module requires different gamification strategies and interface designs, and the ease of use of gamification elements will directly affect the choice of gamification strategies (Acosta-Medina et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), ultimately enabling the system\u0026rsquo;s gamification design and interface aesthetics to positively impact practical utility (Cheng, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding factors influencing behavioral intention, attitude positively affects behavioral intention, perceived usefulness positively affects behavioral intention, while CL negatively affects behavioral intention. The influence of perceived usefulness is most significant, indicating that if users subjectively believe that gamification design is helpful for the public to access the latest cultural resources, search for talent training, venues, events, and conduct daily needs such as cultural and tourism product transactions on public cultural platforms, they will be more inclined to use the platform. This conclusion (supporting H9) aligns with views proposed and validated by Denden et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Attitude significantly and positively influences intention to use the Public Cultural Cloud, suggesting that users\u0026rsquo; interest in or preference for the gamification design process and outcomes generates positive emotional responses that motivate usage. Asiri (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) confirms attitude as a fundamental predictor of gamification behavioral intention, thus supporting H8. Finally, both perceived ease of use and CL are significant factors influencing behavioral intention. This indicates that users who perceive the gamification designs as straightforward and easy to operate, while not generating extraneous CL, exhibit stronger behavioral intention. Conversely, CL may arise from design complexity, poor content organization or presentation, or insufficient user receptiveness to gamification. Research on the negative effects of gamification provides support for H10, as evidenced by examples such as anxiety and stress induced by competition and leaderboards, fear of negative evaluation (Chen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), difficult gamification mechanics (Toda et al., 2017), and incomprehensible game rules (Campos et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), all of which undermine users\u0026rsquo; behavioral intention. Consequently, gamification design should prioritize reducing extraneous CL while enhancing germane CL to improve platform behavioral intention. Furthermore, behavioral intention demonstrates a significant positive influence on actual use (supporting H11), indicating that stronger intention corresponds to a higher likelihood of actual engagement with the cloud platform.\u003c/p\u003e \u003cp\u003eThe fsQCA results indicate that the five solution paths can be further summarized into three configuration types: comprehensive gamification type, one-dimensional\u0026thinsp;+\u0026thinsp;low-load type, and attractive\u0026thinsp;+\u0026thinsp;low-load type. These reveal complex combinations of various gamification design elements, user perceptions, attitudes, and usage behaviors, with high systematicity and dynamism.\u003c/p\u003e \u003cp\u003eComprehensive gamification type: Configurations L1a, L1b, and L1c all demonstrate that diverse combinations of gamification can promote users\u0026rsquo; actual usage of the National Public Cultural Cloud Platform. Building on satisfying the platform\u0026rsquo;s usefulness and ease of use, gamification design combines various conditions, such as interesting mechanisms, high-quality cultural content, efficient real-time feedback, clear demonstration of practical benefits, and user-friendly interactive operations. This combination significantly reduces users\u0026rsquo; CL, increases users\u0026rsquo; satisfaction and behavioral intention, which in turn stimulates actual use.\u003c/p\u003e \u003cp\u003eOne-dimensional\u0026thinsp;+\u0026thinsp;low-load type: Configuration L2 indicates that when using must-be gamification designs, supplemented with some one-dimensional gamification elements, users believe that moderate gamification is useful and easy to operate. For example, incorporating only elements like points and leaderboards, where users earn points through daily check-ins and tasks that can be exchanged for tangible and virtual rewards. These straightforward gamification elements reduce the risk of CL on users, effectively enhancing users\u0026rsquo; satisfaction and behavioral intention. This combination naturally encourages active usage of the Public Cultural Cloud Platform.\u003c/p\u003e \u003cp\u003eAttractive\u0026thinsp;+\u0026thinsp;low-load type: Configuration L3 demonstrates that even without employing must-be or one-dimensional gamification designs, by solely utilizing attractive gamification elements with the highest user appeal while ensuring usefulness, users still develop curiosity toward gamification elements that present operational challenges and moderate complexity. Examples include virtual reality narratives featuring story-driven scenarios and contextual protagonists. Although this places certain demands on users\u0026rsquo; digital literacy, the high immersion and sense of accomplishment delivered by attractive gamification directly enhance users\u0026rsquo; satisfaction and behavioral intention. Consequently, this approach effectively attracts more younger demographics to experience the gamified National Public Cultural Cloud Platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Theoretical implications\u003c/h2\u003e \u003cp\u003eThis study explores the digital services of the National Public Culture Cloud platform based on a gamification theoretical framework, making some theoretical implications. First, the research systematically introduces gamification design theory into the relatively underexplored field of public cultural services. Although gamification is widely recognized in information technology as an effective means to enhance user engagement, its application in digital platforms for public culture remains insufficiently explored. This study fills this gap, not only expanding the application boundaries of gamification theory but, more importantly, providing a novel theoretical perspective and methodological foundation for design innovation in public cultural cloud platforms. Second, through an in-depth analysis of the specific implementation of gamification design within the National Public Culture Cloud platform, this study aims to extract and construct a systematic set of design principles and methodological frameworks based on gamification concepts specifically applicable to public cultural cloud platforms. These outcomes are expected to provide direct theoretical guidance and practical references for the optimization, upgrading, and future development of similar platforms. Finally, by integrating the Kano model, the Octalysis framework, the TAM, and CLT, this research promotes the extension and refinement of these theories, enriching the theoretical foundation for the digital transformation of public cultural services. Model results indicate that the gamification design of the National Public Culture Cloud platform generally achieves user satisfaction, fostering enthusiasm for using the platform and engaging with public culture. Most gamification design elements positively influence perceived usefulness and perceived ease of use, thereby promoting actual user behavior to some extent. However, the impact of attractive gamification design on CL is not significant and may even exhibit negative effects. Furthermore, fsQCA reveals that there is not a single configuration path that affects the actual use of gamified platforms, including multiple configurations with or without certain conditions that can lead to the ideal result.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Practical implications\u003c/h2\u003e \u003cp\u003eIn fsQCA, configurations NL1a and NL1b reveal that under conditions lacking one-dimensional and attractive gamification elements, despite the platform\u0026rsquo;s high perceived usefulness, its low ease of use imposes significant CL on users. Regardless of whether must-be gamification elements are incorporated, users\u0026rsquo; attitude and behavioral intention remain low. Configuration NL2 shows that when one-dimensional and attractive gamification elements are absent and perceived usefulness is low, implementing must-be gamification may make the platform seem easier to use. However, since users perceive these must-be elements as lacking substantial utility within the platform system, they still experience CL, ultimately discouraging platform usage. Configurations NL4 and NL5 demonstrate that when gamification design is essentially omitted, users perceive insufficient platform usefulness. Even if the interface of the National Public Cultural Cloud Platform meets usability, users have some behavioral intention, the improvement in attitude is not significant. This underscores the necessity of combining both fundamental platform optimization and gamification enhancements.\u003c/p\u003e \u003cp\u003e໿Integrating these findings with empirical results from SEM, this study proposes fundamental optimization suggestions and five dimensions of gamification optimization suggestions, providing practical guidance for the improvement of public cultural platform functions and user experience enhancement.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e6.3.1 Fundamental optimization suggestions\u003c/h2\u003e \u003cp\u003eFirst, diversified personalized classification should be implemented. While the platform\u0026rsquo;s primary classification based on geographical regions has taken initial shape, it currently lacks refined strategies addressing user differentiation, particularly showing deficiencies in areas like children\u0026rsquo;s cognitive stratification and user behavioral profiling. It is recommended that platform administrators incorporate a developmental psychology perspective (Baltes et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) to refine age-dimensional classification design. This should be integrated with user interests, occupations, and behavioral trajectories to construct a personalized intelligent classification and recommendation system (Passalacqua et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) using AI technology. Furthermore, by guiding users to create custom tags, generate reading lists, and engage in social recommendations, user participation in content co-creation can be encouraged, enabling the synergistic evolution of professional content classification and user-generated content.\u003c/p\u003e \u003cp\u003eSecondly, constructing comprehensive information resources is crucial for enhancing the platform's content value and user stickiness. Currently, multiple sections of the platform suffer from resource gaps and outdated updates, increasingly failing to meet the growing demand for diverse cultural content. It is recommended that the National Public Cultural Cloud Platform adhere to the principle of co-creation and sharing, achieving resource interoperability with institutions at all levels\u0026mdash;such as libraries, cultural centers, and art museums (Yarrow et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u0026mdash;to enrich content supply and seamlessly integrate online and offline cultural service chains. Simultaneously, the platform should diversify information presentation formats through multimedia integration and timely update mechanisms, providing comprehensive, promptly responsive, and clearly articulated cultural information. This will strengthen users' perception of the platform's authority and completeness.\u003c/p\u003e \u003cp\u003eThird, regarding streamlined platform experience, the increasing of tabs and functional modules increasingly exposes users to the risk of CL. The platform should strengthen hierarchical and modular strategies in UI design by adopting a unified visual language, simplifying navigation paths, and highlighting critical information to reduce user barriers to entry. Furthermore, for presenting cultural content, gamification design must maintain a balance between \u0026ldquo;entertainment value\u0026rdquo; and \u0026ldquo;professional depth\u0026rdquo; to avoid oversimplification of content or superficiality in form. To this end, lightweight interactive mechanisms\u0026mdash;such as knowledge quizzes, virtual explorations, and achievement badges\u0026mdash;may be introduced. These transform in-depth cultural content into tangible gaming experiences, enhancing user immersion and boosting learning motivation.\u003c/p\u003e \u003cp\u003eFinally, at the service adaptation level, platform design should account for the diverse needs of children, elderly users, and individuals with disabilities by incorporating accessibility features such as color vision deficiency modes, voice navigation, and text-to-speech functions (Bercaru \u0026amp; Popescu, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This not only aligns with the universal service principle of public cultural platforms but also contributes to building a more equitable digital cultural ecosystem.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e6.3.2 Gamification optimization suggestions\u003c/h2\u003e \u003cp\u003eThe gamification design optimization of the National Public Culture Cloud Platform can be carried out systematically around five core dimensions: tasks, interactions, feedback, rewards, and community interactions.\u003c/p\u003e \u003cp\u003eIn task design, a diversified task system needs to be built, including core tasks that integrate situational narratives and role-playing (Wahyuni et al, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu et al, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), extended ancillary tasks, regular lightweight tasks, and randomly triggered hidden Easter egg tasks. At the same time, using AI user profiling technology, tasks of adaptive difficulty should be precisely pushed based on user ability and behavior data, achieving a personalized immersive experience.\u003c/p\u003e \u003cp\u003eThe optimization of interaction design focuses on enhancing exploration freedom and identity recognition. By providing a simple tutorial guide, the entry barrier is lowered. A dynamic points ranking and multi-dimensional identity system (UID/level/title) are established based on cultural contributions (such as discussion, translation, and resource supplement). Users are allowed to customize their personal homepage and resource collection, with popular resources flexibly displayed in a modular form to stimulate exploration interest.\u003c/p\u003e \u003cp\u003eThe feedback mechanism should strengthen immediacy and sense of value. A dynamic progress bar should be used to visually display task completion and resource update status. Hierarchical visual cues (such as highlighting important information) and a badge achievement system should be designed. The feedback frequency and form should be customized based on user preferences, meeting interaction needs in different scenarios, and effectively enhancing users' sense of achievement and participation in the feedback loop.\u003c/p\u003e \u003cp\u003eThe reward system should go beyond a single points-based model and expand into a structure that includes both virtual rewards (such as experience points, exclusive titles) and physical rewards (such as cultural products, industry co-branded merchandise, and vouchers). In particular, unique rewards should be tied to hidden tasks and special cultural contributions (such as providing in-depth reviews or high-quality translations), creating highly attractive incentives.\u003c/p\u003e \u003cp\u003eCommunity interaction design emphasizes the creation of an open and shared ecosystem, upgrading features such as live comment streams (Zimmer et al., \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and community forums (Bista et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) to promote real-time communication and the exchange of cultural perspectives among users. An intelligent resource recommendation system based on user interests should be developed. Resource integration ought to be deepened by linking libraries, museums, and other institutional collections, connecting events, venues, and cultural services to build a national-level cultural resource center. Community interaction, rewards, and interaction designs should work in synergy to achieve the visual connection and collaborative distribution of cross-industry co-branded resources. This should reduce users' CL while achieving an engaging, resource-integrated, and sustainable public cultural service effect.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"7 Conclusion and Limitations","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Conclusion\u003c/h2\u003e \u003cp\u003eThis study innovatively introduces gamification design elements into the Public Cultural Cloud Platform. Using the Octalysis framework as the theoretical foundation, it delves into the motivational effects of gamification design on user usage behavior. Drawing on prior research findings, the high-satisfaction must-be, one-dimensional, and attractive gamification designs identified via the Kano model are applied to optimize the cloud platform. By incorporating design elements from gamified platforms, cultural communication forums, and cultural tourism platforms, the platform interface underwent gamification transformation through UI design. User experience questionnaires are employed to collect feedback data, and a SEM is constructed to verify the applicability and effectiveness of the gamification elements in UI design practice. Furthermore, the fsQCA method is utilized to uncover the configurational relationships influencing usage behavior on the gamified platform. Based on the research results, this paper proposes recommendations focusing on both fundamental and gamification optimization. Each gamification design possesses distinct characteristics, and their synergistic effects effectively enhance user stickiness and usage intention. This provides a valuable reference framework and practical guidance for gamifying the Public Cultural Cloud Platform and similar platforms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Limitations\u003c/h2\u003e \u003cp\u003eThis study still has several limitations, which also point the way for future research. First, the empirical investigation explored the relationships between must-be, one-dimensional and attractive gamification designs and user perception, CL, attitude, behavioral intention, and actual use, as well as the multiple chain mediation effects between these three types of gamification design and behavioral intention. However, the influence of moderating variables on these relationships is not considered. Subsequent research could select specific moderating variables, such as age, gender, or geographical region, to investigate their potential impact on the effectiveness of gamification within the Public Cultural Cloud Platform.\u003c/p\u003e \u003cp\u003eSecond, as a public welfare platform, the Public Cultural Cloud needs to pay special attention to the needs of specific user groups when deepening the application of gamification design: How to ensure gamification design exerts a positive guiding effect on minors, and how to optimize design to enhance accessibility for vulnerable groups such as the elderly and people with disabilities. These issues are worth exploring in depth. Future research should focus on strengthening content orientation and usage safety for younger users within the game mechanism design, while simultaneously improving usage adaptability for elderly users. This will foster the development of a more inclusive public cultural service platform.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e According to Article 32 of the National Guidelines for Scientific Research Ethics Review, studies involving human data or anonymized information\u0026mdash;where there is no harm to participants, no sensitive personal information or commercial interests, and no relevance to life science or medical topics\u0026mdash;are eligible for exemption from ethical review. This provision is intended to alleviate unnecessary burdens on researchers and foster the development of life science and medical research. Specifically, Article 32, Item 2 allows for exemption from ethical review for research utilizing anonymized data. Our study fully complies with this criterion, as it involved a voluntary questionnaire, ensuring complete anonymization of participant responses. The data collected is non-sensitive, non-medical, and unrelated to life science research. After thorough consideration, Institute of Science and Technology Information of Jiangsu University, China confirmed on June 15, 2024, that ethical approval is waived for this study, classifying it under minimal risk research. Consequently, no specific ethical approval number has been assigned. Additionally, the study adheres to the ethical standards set forth in the 1964 Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003eFrom July 12 to August 16, 2024, participants filled out a consent form before participating in the questionnaire survey. Online informed consent was obtained through the questionnaire platform \u003cem\u003eWenjuanxing\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wjx.cn\u003c/span\u003e\u003cspan address=\"https://www.wjx.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Written informed consent for participation in this study was obtained from all legal guardians or close relatives of the participants, as well as from the participants themselves, prior to their involvement. The consent clearly outlined the study\u0026rsquo;s purpose, data collection scope, and agreement to the publication of the research results. Participants were assured that their anonymity and confidentiality would be maintained, and that the collected data would be used exclusively for research analysis and not disclosed to third parties. They were informed that participation was voluntary, and they could withdraw at any time without any consequences. As the study involved non-interventional research, participants were assured that there were no risks associated with their participation.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLei Xu: Conceptualization, Investigation, Methodology, Software, Validation, Formal analysis, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.Meng Wang: Conceptualization, Supervision, Funding acquisition, Writing \u0026ndash; original draft. Ya Chen: Investigation, Methodology, Validation, Writing \u0026ndash; review \u0026amp; editing, Project administration.Yuwen Hua: Data Curation, Software, Visualization.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work is supported by the National Social Science Fund Post-support Project of China (grant number 24FTQB003); the Major Project of Philosophy and Social Science Research Jiangsu Province (grant number 2024SJZD131).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcosta-Medina JK, Torres-Barreto ML, C\u0026aacute;rdenas-Parga AF (2021) Students' preference for the use of gamification in virtual learning environments. 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Canadian Journal of Political Science/Revue canadienne de science politique, 44(3), 743\u0026ndash;746. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0008423911000709\u003c/span\u003e\u003cspan address=\"10.1017/S0008423911000709\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Public culture cloud platform, Gamification, Structural equation model, Fuzzy-set Qualitative Comparative Analysis, User behavior","lastPublishedDoi":"10.21203/rs.3.rs-8356239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8356239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnder the background of digital transformation in public cultural services, this study explores the integration of the gamification design concept into the National Public Cultural Cloud Platform to enhance user participation and engagement. Based on the Octalysis framework and Kano model, our previous research summarized five design dimensions and identified 23 key design factors and their optimization priorities. This study conducts UI design for the overall interface of the National Public Culture Cloud Platform, and constructs a structural equation model to explore whether gamification elements have high applicability and effectiveness in actual use. Subsequently, fuzzy-set Qualitative Comparative Analysis is employed to identify the configuration relationships affecting actual use of the gamified Public Cultural Cloud Platform. The results indicate that core gamification elements can significantly improve user satisfaction and usage intention, while attractive gamification design may generate cognitive load and have negative effects. User behavior is jointly influenced by gamification design, user perceptions, and attitudes, forming three distinct configuration paths. It is recommended that subsequent optimization prioritize both foundational usability and gamification design to enhance platform effectiveness. The study integrates multidisciplinary methods, providing an innovative paradigm for the digitalization of public culture.\u003c/p\u003e","manuscriptTitle":"Driving User Engagement through Gamification: An Empirical Design of Public Cultural Cloud Platform","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 10:48:18","doi":"10.21203/rs.3.rs-8356239/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"217190378418000593436605423298937107413","date":"2026-03-26T05:33:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278273275315144289457707136885727306739","date":"2026-03-25T13:35:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194328572147855692207485699522243821723","date":"2026-03-25T11:21:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-25T10:10:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-09T08:48:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-06T20:06:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-03T09:35:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-01-03T09:27:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b6379403-86a9-4340-965d-555886c50c0e","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65205424,"name":"Business and commerce/Information systems and information technology"},{"id":65205425,"name":"Physical sciences/Mathematics and computing"},{"id":65205426,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-03-27T10:48:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 10:48:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8356239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8356239","identity":"rs-8356239","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}