Will artificial intelligence boost crowdfunding? Understanding backers’ contribution intentions through the technology acceptance model in an emerging market context

Will artificial intelligence boost crowdfunding? 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Understanding backers’ contribution intentions through the technology acceptance model in an emerging market context HIBAT-ALLAH EZZAHID This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8485936/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research examines how artificial intelligence (AI) enabled features shape backers’ contribution intention in crowdfunding campaigns within an emerging market context. It focuses on the differentiated effects of AI-driven marketing mechanisms, such as interaction, information quality, accessibility, entertainment, and customization, and investigates the moderating role of AI familiarity. We conducted a quantitative study based on a survey data from 424 potential crowdfunding backers in Morocco, the hypothesis are tested through partial least squares structural equation modeling (PLS-SEM). The findings reveal that AI-enabled features influence contribution intention in a non-uniform manner. Customization emerges as the strongest positive driver, followed by entertainment, accessibility, and information quality, highlighting the importance of personalized, engaging, and credible AI-generated content. In contrast, intensive AI interaction exerts a negative effect, suggesting potential user discomfort. However, this effect is mitigated among individuals with higher AI familiarity, confirming its significant moderating role. Among the control variables, only age significantly affects contribution intention. JEL Classification : M1; M3 Crowdfunding Artificial intelligence Contribution intention Quantitative study Morocco Figures Figure 1 1. Introduction Crowdfunding has become a rapidly growing form of financing, enabling entrepreneurs to collect funds from a large audience to bring their projects to life without depending on traditional financial institutions. Therefore, prior research suggests that backers are faced to information issue that allow them to evaluate the crowdfunding campaign when deciding to contribute (Hoegen et al.,2018; Mochkabadi and Volkmann, 2020 ; Butticè and Ughetto, 2021 ). From this perspective, AI can enhance the user experience by facilitating personalized interactions, providing relevant information, increasing platform accessibility, and delivering real-time customer support, thereby boosting backer engagement (Pytkowska and Korynski, 2017 ). A central stream of research in this area has concentrated on two main areas: the drivers of participation among backers and project creators, and the determinants of crowdfunding campaign success campaigns (Baah-Beeprah, 2023; De la Pallière et al., 2021 ; Ezzahid and Meghouar, 2023 ; Gerber and Hui, 2013; Meghouar et al., 2024). Within the stream on backer-related motivations, prior works have primarily studied how entrepreneurial signals influence individuals’ contribution decisions (Kleinert and Mochkabadi, 2021; Vismara, 2018 ). Grounded in signaling theory (Connelly et al., 2011 ; Spence, 2002 ), this research highlights the role of signals related to venture characteristics, campaign features, and team attributes in shaping funding outcomes. Nevertheless, existing studies tend to focus on the investor-centric perspective, particularly within equity crowdfunding, which limits the understanding of backer behavior across alternative crowdfunding models such reward-based and donation-based platforms. Although artificial intelligence is increasingly embedded across digital platforms; contributing to enhanced information quality, more sophisticated recommendation systems, and advances predictive analytics, only a very limited number of studies have examined AI within the crowdfunding literature (Gregoriades and Themistocleous, 2025 ; Behl et al., 2021 ; Korzyński et al., 2021). Existing studies focuses mostly on technical or platform-centered perspectives (Behl et al., 2021 ; Korzyński et al., 2021; Ye at al., 2024), offering limited insight into how AI-driven functionalities influence decision making. Consequently, the integration of AI into the theoretical frameworks traditionally used to explain backer behavior is still nasent, pointing to a significant gap in the literature. Adopting an innovation-oriented perspective, this study seeks to explain how AI-enabled marketing mechanisms shape backers’ intention to contribute to crowdfunding campaigns in the Moroccan context. Based on the technology acceptance model (TAM), this research examines the effects of key AI-related dimensions; namely, interaction, information quality, accessibility, entertainment, and customization on contribution intention. To the best of our knowledge, these relationships have not yet been empirically investigated within crowdfunding ecosystem in Morocco. Building on the identified gaps, a key question arises regarding how backers interpret and respond to AI-enabled features when evaluating crowdfunding campaigns. While prior research has demonstrated the role of signaling mechanisms and motivational drivers in shaping contribution behavior, the increasing integration of AI fundamentally transforms the way information is generated, communicated, and perceived by backers. However, empirical evidence stills limited as to whether, and to what extent AI-driven marketing efforts strengthen backers’ willingness to contribute, particularly in emerging markets such as Morocco, where digital financial technologies are still in a phase of gradual adoption. Accordingly, this study seeks to answer the following research question: To what extent AI-driven marketing dimensions influence backers’ intention to contribute to crowdfunding campaigns in the Moroccan context? By examining this question, the research extends existing theoretical frameworks and advances understanding of backer behavior in an increasingly AI-enables crowdfunding environment. To answer this question, we did a quantitative study via 424 with potential backers in Morocco. Building on TAM, we examine whether and how AI marketing efforts affect the contribution intention. The findings reveal that interaction, information quality, accessibility, entertainment, and customization significantly influence contribution intention. Moreover, AI familiarity moderates several of these relationships, indicating that individuals with greater exposure to AI are more responsive to its value-enhancing features. Demographic variables exhibit differentiated effects on contribution intention, underscoring the heterogeneous nature of backers in AI-enabled crowdfunding environments. To address this research question, this research adopts a quantitative study based on survey data collected from 424 potential backers in Morocco. The findings reveal that the customization stands out as the most influential positive determinant of contribution intention. In addition, entertainment, accessibility, and information quality display significant positive effects. By contrast, highly AI-driven interaction may generate discomfort among certain users, thereby exerting a negative influence on contribution intention. Importantly, this adverse effect is attenuated among individuals with higher levels of AI familiarity, highlighting the moderating role of familiarity with AI technologies. Regarding the control variables, age emerges as the demographic factor exerting a significant effect on contribution intention. This research offers several contributions. First, it enriches the literature on technology acceptance in crowdfunding by highlighting the specific impact of AI on backers’ intention to contribute. Second, by focusing on the emerging Moroccan context, it provides an important contextual perspective for local crowdfunding stakeholders. Finally, it offers valuable insights for policymakers and institutions aiming to support crowdfunding campaigns and assist project creators in integrating AI technologies into their campaigns. This research offers several contributions. First, it enriches research on technology acceptance in crowdfunding by examining the impact of AI in shaping backers’ contribution intentions. Second, by focusing on the Moroccan context as an emerging market, the study provides a contextualized perspective that enriches current understanding and its particularly relevant for local crowdfunding ecosystems. Finally, the findings generate practical implications for policymakers and institutional actors seeking to support crowdfunding development. The remainder of the paper is structured as follows. First, we present a detailed literature review on crowdfunding and AI-driven marketing dimensions. Secondly, we present the data and discuss the empirical findings. Finally, we conclude with the study’s implications for theory and practice, its limitations, and future research avenues. 2. Literature review From the above, it is evident that success of crowdfunding campaigns depends not only on the innovativeness of a project, but also on how effectively the project is described and communicated to potential backers. In this regard, the integration of AI into crowdfunding campaigns has broadened opportunities for communication and interaction between project creators and backers, thereby enhancing trust and improving overall user experience. By learning pattern recognition capabilities, AI enables the prediction of products and services that are likely to appeal specific customer groups (Mehta et al., 2022 ), identifies which crowdfunding campaigns are riskier than others (Mahbub and Zhuang, 2022), and serves as a decision-support tool for effective and efficient decision-making process (Aggarwal et al., 2020). In the context of crowdfunding, AI has been increasingly employed to predict campaign success and failure (Guidotti et al., 2018 ; Kaminski and Hopp, 2020 ). Based on recent advances in the analysis of backer decision-making, Gregoriades and Themistocleous ( 2025 ) investigate the sources of vulnerability affecting both campaigners’ and backers’ choices by developing two binary classification models: one based on textual features and the other combining categorical, numerical, and textual information. Overall, the authors highlight several counterintuitive patterns. For instance, textual terms such as “stretch goals”, which simultaneously convey ambition and potential risk, positively correlate with success, whereas more explicit disclosures of risk tend to activate concerns that would otherwise remain latent among backers. Viewed broadly, these findings suggest the use of counterfactual explanations in generating actionable insights, offering guidance on how unsuccessful projects may be redesigned to improve their chances of success, while also suggesting new policies, such as behavioral nudges, aimed at protecting backers from points of vulnerability. Second, Bai et al. ( 2024 ) examined the effects of AI-assisted disclosures, specifically those generated through ChatGPT on crowdfunding performance using Kickstarter as empirical data. By conceptualizing the introduction of ChatGPT as an exogenous shock, the authors analyze how AI-generated campaign narratives alter funding dynamics and project outcomes. Their findings indicate that AI support substantially improves fundraising performance. These positive effects are especially salient among creators from non-English-speaking backgrounds, minority groups, and first-time project creators, suggesting that AI can reduce information frictions and partially level the playing field for disadvantaged creators. At the same time, the study identifies important limitations. In institutional setting marked by weaker governance structures or lower levels of social capital, the use of AI-generated content is associated with substantially lower project delivery rates. This pattern raises concerns about the potential of AI tools to unintentionally amplify the visibility of lower-quality projects, thereby leading to inefficient resource allocation. Overall, the study advances the emerging literature on generative AI in financial communication by highlighting the risks of AI generated disclosures in crowdfunding environments. Third, Ye et al. ( 2024 ) have focused on the challenges faced by small businesses seeking funding through online crowdfunding platforms, noting that over 40% of campaigns, particularly those originating from low socio-economic areas, fail to secure any financial support. Drawing on recent advances in AI, the authors developed machine learning models to identify the textual and structural features most strongly associated with campaign success. Their most effective model predicts fundraising outcomes with an accuracy of 81% relying primarily on information embedded in campaign narratives. Notably, the study shows that enhancing only three narratives components with the support of a large language model substantially enhances campaign attractiveness, with 83% of human evaluators rating the revised campaigns more favorably and significantly higher likelihood of securing financial contributions. Finally, and complementary line of inquiry, a recent large-scale study conducted in the United States investigated how AI-powered chatbot marketing efforts (CMEs) contribute to the development of customer-brand relationships and shape online behavioral intentions (Cheng and Jiang, 2021). Based on survey data from 1,072 consumers who interacted with chatbot services from 30 industry-leading brands, the findings identify interaction, information, accessibility, entertainment, and customization as the central components shaping effective chatbot marketing. The study further shows that CMEs exert significant direct effects on the perceived quality of communication with chatbot agents, while indirectly enhancing customer-brand relationships and subsequent customer responses. Nevertheless, the extent to which these AI marketing efforts influence backers’ intentions within crowdfunding environments remains largely unexplored. This gap underscores the relevance of the present study, which seeks to advance understanding of backers’ acceptance of AI-enabled features in crowdfunding contexts. In this study, we adapt the conceptual structure of CMEs identified in prior work to examine the intention to contribute to crowdfunding through which AI shaper backer responses. Building on the CME classification, we conceptualize how these features shape backer responses within crowdfunding platforms. This framework enables an objective assessment of backer reactions to AI marketing initiatives and provides a more granular understanding of how such strategies differentially influence contribution intentions in crowdfunding contexts. Having established that AI-enabled communication mechanisms shape backer responses through distinct behavioral pathways, it becomes necessary to situate these dynamics within broader theoretical frameworks on technology adoption. In this regard, the TAM provides a well-established foundation for explaining how individuals evaluate and engage with technological interfaces. Originally developed to explain users’ acceptance of information systems, the TAM, introduced by Davis (1989), highlights two central constructs: perceived usefulness and perceived ease of use. Perceived usefulness refers to the extent to which individuals believe that a technology enhances task performance, whereas perceived ease of use reflects the degree to which the technology is perceived as effortless to operate. Applied to the crowdfunding context, TAM offers a relevant lens for understanding how specific AI-driven marketing dimensions, namely interaction, information quality, accessibility, entertainment and customization, influence backers’ intention to contribute to campaigns. Regarding the interaction, facilitated by AI, it is a key aspect of marketing efforts in crowdfunding, as it enhances communication between project creators and backers, and contributes to a more satisfying funding experience. Through AI agents, backers can receive quick responses, facilitating real-time exchanges and reducing perceived psychological distance from the project. This immediacy fosters a sense of closeness and trust, which is particularly important in crowdfunding contexts where personal relationships between backers and creators are typically absent (Godey et al., 2016 ). Such perceived proximity plays a critical role in encouraging engagement, as backers often seek meaningful connections with the projects they choose to support. Accordingly, to Gefen and Straub (2004), regular communication and immediate responses promote greater trust in online interaction; an effect that becomes even more salient in crowdfunding settings characterized by high uncertainty. Furthermore, the capacity of AI agents to stimulate human conversation and dynamically adapt responses to user queries reinforces this sense of proximity, making the interaction experience smoother and more engaging (Huang and Rust, 2018 ). In addition, research by Han et al. (2020) has shown that the quality of AI interactions can directly influence users’ perceived usefulness, due to reduced uncertainty and increased transparency of the information provided. Effective AI interaction is perceived as an added value making the crowdfunding experience more convenient and reassuring, as it helps backers make informed decisions. By enabling instant and personalized communication, AI interactions strengthen backer engagement by offering more immersive user experience, which positively influences their perception of the campaign’s usefulness. As a result, this dimension of interaction is expected to positively influence backers’ intention to contribute by improving their perception of the usefulness and reliability of crowdfunding campaigns. H1. Perceived interaction with AI agents positively influences backers’ intention to contribute to crowdfunding campaigns. One of the central roles of AI in crowdfunding lies in its capacity to provide accurate, relevant, and personalized information to potential backers. Through big data analysis, thereby increasing their engagement and trust in the projects (Sadek et al., 2015 ). Indeed, the ability of AI to process large volumes of data allows to identify the specific informational needs of backers and to provide directly relevant information, which reduces uncertainty and enhances transparency (Bhimani and Willcocks, 2014 ). In the context of crowdfunding, information accuracy and relevance are particularly critical, as they shape perceptions of project credibility and feasibility. Prior research indicates campaigns providing clear, comprehensive, and detailed information are more likely to foster trust and stimulate financial support from backers (Luo and Toubia, 2015 ). Through real-time access to project updates and responding to frequently questions, AI enhances the perceived usefulness of crowdfunding platforms, helping backers make more informed decisions and feel more involved in the support process. In addition, when information provision is aligned with individual preferences, users experience a stronger sense of control and satisfaction, which enhances trust in crowdfunding campaigns (Yuan et al., 2017 ). H2. Perceived information quality positively influences backers’ intention to contribute to crowdfunding campaigns. Accessibility, ensured by AI, is a key factor in the marketing efforts of crowdfunding campaigns. According to Sultan and Wong ( 2019 ), the accessibility of AI technologies allows users to quickly address concerns or access specific information at any time, enhancing perceived service quality and promoting user satisfaction. This increased availability is particularly important in crowdfunding, where potential backers may require immediate answers to confirm their support decisions. By providing continuous access to information and support, AI removes temporal barriers that could hinder backer engagement and reinforces their sense of trust and transparency toward the project. Moreover, the 24/7 availability of AI services positively influences the perceived ease of use of the technology. As shown by Xu et al., ( 2017 ), AI systems that offer continuous accessibility facilitate the user experience by providing consistent support and reducing the effort required to obtain information. This accessibility allows backers to feel reassured, knowing they can obtain the necessary information and receive immediate assistance when needed, which enhances their perception of the system’s efficiency and user-friendliness. Indeed, by facilitating access to information and making the interaction process more convenient, AI-driven accessibility helps strengthen backers’ intention to contribute. H3. Perceived accessibility positively influences backers’ intention to contribute to crowdfunding campaigns. Entertainment refers to the hedonic dimension through which useful and credible information is delivered in an engaging and enjoyable manner, thereby enhancing perceived value and strengthening individuals’ intention to adopt digital tools (Chung et al., 2018 ). In digital environments, entertainment goes beyond mere amusement and functions as a strategic mechanism that captures attention, sustains user engagement, and facilitates information processing. Similar to other social media contexts, individuals increasingly seek pleasure, enjoyment, and emotional simulation when interacting with AI powered systems (Chung et al., 2018 ). In the context of AI-enabled platforms, entertainment is often embedded through interactive features, conversational agents, personalized content, and dynamic visual elements; which collectively enhance the user experience. These hedonic attributes reduce cognitive effort, increase perceived ease of use, and foster positive affect toward the platform, ultimately reinforcing favorable ease of use, and foster positive affect toward the platform, ultimately reinforcing favorable attitudes and behavioral intentions. Prior research in digital and social media marketing suggest that entertainment-oriented content plays a critical role in shaping brand perceptions by generating positive-emotions, increasing brand awareness, and strengthening purchase intentions (Kim and Ko, 2010 ). Applied to AI-driven crowdfunding platforms, entertainment may contribute to higher levels of engagement by making campaign information more accessible, appealing, and emotionally resonant (Chen et al., 2016 ; Jiao et al., 2023 ; Xiang et al., 2019 ) By combining informative content with enjoyable and interactive AI features, platforms can enhance backers’ involvement and motivation to explore, evaluate, and support projects (Gregoriades et al., 2025; Jiao et al., 2023 ). Consequently, entertainment emerges as a key experiential component that complements informational and functional aspects of AI, influencing backers’ intentions to adopt and interact with AI-enabled crowdfunding tools (Behl et al., 2021 ; Gregoriades et al., 2025; Jiao et al., 2023 ). H4. Perceived entertainment positively influences backers’ intention to contribute to crowdfunding campaigns. Customization refers to tailoring reward options, pricing, and campaign content to fit backers’ preferences and needs. In reward-based crowdfunding, higher levels of customization, through price or product differentiation in reward tiers, strengthen the positive impact of early contributions on campaign success, indicating that more customized options make campaigns more attractive and encourage additional contributions (Wei et al., 2023). Large-scale studies of Kickstarter data show that reward personalization has a positive effect on willingness to participate: increasing personalization initially enhances perceived fit, expected benefits, and thus contribution intention, but excessive choice and complexity eventually reduce willingness to back (Wang et al., 2024). Customization also operates through cognitive and affective pathways. By making rewards feel more personally relevant and aligned with backers’ lifestyles or identities (eg., life-related rather than purely artistic rewards), personalization amplifies expectancy that contributions will generate values outcomes, thereby reinforcing funding intention (Wang et al., 2024). In parallel, customized offers can be a persuasive cue that signals creator effort, responsiveness, and market orientation, which strengthens perceived campaign quality and reduces uncertainty (Shneor et al., 2019; Shneor et al., 2021; Wang et al., 2019). Overall, customization enhances perceived value, relevance, and trust, which together foster more favorable attitude toward campaigns and translate into stronger intentions to contribute financially. H5. Perceived customization positively influences backers’ intention to contribute to crowdfunding campaigns. A growing body of research explores how user familiarity with artificial intelligence (AI) systems shapes interactions, trust, and outcomes across various contexts. Indeed, familiarity with AI systems often increases user comfort, trust, and willingness to interact (Wang et al., 2024; Arce-Urriza et al., 2025 ). In the context of crowdfunding platforms, AI familiarity can play a crucial role between perceived AI interaction and backers’ contribution intention. Baroni et al ( 2022 ) developed an extended TAM that explicitly incorporates AI familiarity as a construct, alongside perceived usefulness, ease of use, trust in AI, and collaborative intention. Their findings indicate that users’ familiarity with AI systems enhances their trust and perceived quality of AI outputs, which in turn positively influences both behavioral intention and willingness to contribute or collaborate within Ai-powered platforms. Similarly, Belanche et al ( 2019 ) found that users with greater familiarity with AI, exhibit stronger effects of perceived usefulness and attitude on adoption intentions, highlighting the importance in shaping user acceptance and engagement (Belanche et al., 2019 ). On the other hand, Zhang et al., ( 2020 ) confirm that crowd familiarity closely related to AI familiarity, significantly boosts user trust and readiness, which are essential for actual donation behavior on crowdfunding platforms. Their findings indicate that technological utilization and social collaboration are more effective when users are already comfortable with the digital environment, reinforcing the moderating role of familiarity in the pathway from accessibility to contribution (Zhang et al., 2020 ). Additionally, Kelly et al., ( 2023 ) show in a systematic literature that perceived usefulness, effort expectancy, and trust are key predictors of AI acceptance, but these are more influential among users with higher AI familiarity. This suggest that as users become familiar with AI, the accessibility of AI features more strongly translates into actual behavioral intentions and platform engagement (Kelly et al., 2023 ). H6.1. AI familiarity moderates the relationship between perceived interaction with AI agents and backers’ intention to contribute to crowdfunding campaigns. H6.2. AI familiarity moderates the relationship between perceived accessibility of AI-enabled features and backers’ intention to contribute to crowdfunding campaigns. 3. Methods The purpose of our research, as well as the nature of the data required, justified the adoption of quantitative study. To this end, an online survey was administrated to a sample of backers and potential backers in Morocco. The data collection process spanned six weeks. A total of 450 completed questionnaires were received. After data screening for incomplete responses and inconsistencies, 424 valid questionnaires were retained for analysis. The empirical analysis relies on data collected through a self-administrated survey instrument. Despite its relevance, this method entails a potential limitation related to common method variance (Podsakoff et al., 2024 ). To address this concern, we implemented several procedural remedies, including assuring respondents of complete anonymity and structuring the questionnaire so that the measurement of variables was dispersed across different sections. The collected data were modeled using the partial least squares structural equation modeling (PLS-SEM) method in order to assess the effects between latent constructs while adjusting for measurement errors in the structural model (Hair et al., 2021 ). The SmartPLS software applies the PLS method based on structural equation modeling (SEM), which analyzes second-generation multivariate data. This method is widely used in management sciences. It allows the study of direct, indirect, and total affects between several variables (Roussel et al., 2002), and takes measurement errors into account (Fornell and Bookstein, 1982 ). Table 1 below presents the main criteria for evaluating the measurement model. Overall, in our cleaned sample of 424 respondents, 70% were female and 37% were male. Regarding age, 56% of respondents were between 25 and 34 years old, 30% were between 35 and 44 years old, and the remainder were distributed across the 18–24 and 45 + age groups. Based on educational background, the majority of respondents held a PhD degree (63%). Respondents with a Master’s degree represented 18% of the sample, followed by those with a Bachelor’s degree (5.9%), engineering qualifications (4%), MBA degrees (4%), and a baccalaureate (4%). Only 0.71% of respondents were PhD candidates. 4. Results and discussion 4.1 Measurement model Expect for AI familiarity, all other items used a five-point Likert-Type scale, ranging from strongly disagree (1) to strongly agree (5). This study adopted 24 questions from previous research (Lin, 2007; Cheng et al., 2015; Godey et al., 2016; Chung et al., 2018; Cheng and Jiang, 2021) to measure acceptance of AI by potential backers in Morocco and contribution intention with five independent variables (Cronbach’s α was 0,839 for AI perceived interaction, 0,698 for AI information quality, 0,863 for AI accessibility, 0,784 for entertainment, 0,887 for customization), and dependent variable (Cronbach’s α was 0,744 for contribution intention). Based on the reliability tests, all constructs showed acceptable internal consistency. Cronbach’s alpha exceeded the recommended threshold of 0,70 indicating that the items used to measure each construct were reliable. To verify the unidimensionality of the constructs, we examined the outer loadings of the measurement model. Two items related to the constructs information quality and accessibility were removed due to poor representative quality. The model was tested according to the previously mentioned criteria. The results presented in table 1 indicate that the composite reliability values range from 0,70 to above 0,90, which reflects acceptable to excellent reliability according to the recommended thresholds. Similarly, the average variance extracted (AVE) for each construct exceeds 50% indicating satisfactory convergent validity. Discriminant validity is also confirmed, as the squared AVE values are higher than the correlations between latent variables, and each item’s loading is higher on its associated construct than on other constructs. The results regarding the assessment of the measurement model are presented below (1,2,3). Table 1. Construct reliability and validity Constructs Cronbach’s Alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted Interaction 0,839 2,064 0,856 0,670 Information quality 0,698 0,716 0,868 0,766 Accessibility 0,863 0,922 0,911 0,774 Entertainment 0,784 0,951 0,886 0,705 Customization 0,887 0,900 0,922 0,748 Contribution intention 0,744 0,875 0,847 0,614 Familiarity N/A N/A N/A N/A Ease of use N/A N/A N/A N/A Table 2. Latent construct correlations ACCES CUSTO ENTER INFO INTER INTEN ACCES 1 0,148 0,156 0,136 0,549 0,254 CUSTO 0,148 1 0,808 0,933 0,231 0,980 ENTER 0,156 0,808 1 0,841 0,227 0,854 INFO 0,136 0,933 0,841 1 0,167 0,940 INTER 0,549 0,231 0,227 0,167 1 0,269 INTEN 0,254 0,980 0,854 0,940 -0,278 1 Table 3. Loadings and cross-loadings of the latent variables ACCES CUSTOM ENTER INFO INTEN INTER ACCES1 0,856 ACCES2 0,900 ACCES3 0,883 CUSTOM1 0,812 CUSTOM2 0,928 CUSTOM3 0,803 CUSTOM4 0,910 ENTER1 0,986 ENTER2 0,131 ENTER3 0,927 ENTER4 0,986 INFO2 0,901 INFO3 0,849 INTEN1 0,942 INTEN2 0,897 INTEN3 0,246 INTEN4 0,839 INTER1 0,946 INTER2 0,771 INTER3 0,696 4.2 Evaluation of the structural model and hypothesis testing Regarding the coefficient of determination (R²), and following Chin’s (1998) guideline, the variable contribution intention shows a value of …, which is higher than the recommended threshold of 0.50. This indicates excellent support for the nomological validity of the model (see table…). Table 4. Coefficients of Determination of the Endogenous Variable R² R² adjusted Contribution intention 0,985 0,985 The quality of the structural model can also be assessed through the average of the coefficients of determination, known as “Goodness of Fit” (GOF) index, calculated on the dependent variable. In our case, GOF = √(0.985 × 0.712), yielding a value of 0,837. This index exceeds the recommended threshold of 0,30, which suggests that the model fits the data well and allows us to proceed with the analysis. Overall, the conditions of validity and reliability of the research model are satisfactorily met. The measurement scales of the latent variables have been evaluated as valid and reliable, which enables us to move on the next step: testing the research hypothesis. Table 5 presents the effects of the main independent variables on contribution intention. The results show that interaction has a significant but negative effect on contribution intention, leading to the rejection of the corresponding hypothesis. In contrast, information quality (β = 0.088, p = 0.003), accessibility (β = 0.136, p = 0.005), entertainment (β = 0.135, p = 0.000), and customization (β = 0.783, p = 0.000) display positive and significant effects, supporting their respective hypotheses. Regarding the moderating effects, Table 6 indicates that familiarity significantly moderates the relationship between interaction and contribution intention (β = 0.050, p = 0.024), thereby confirming the associated hypothesis. However, the moderating effect of familiarity on the relationship between accessibility and contribution intention is not significant (β = –0.052, p = 0.090), resulting in the rejection of this hypothesis. Finally, Table 7 shows the effects of the control variables. Among them, only age has a significant influence on contribution intention (β = 0.014, p = 0.049). Gender, education level, project type, and ease of use do not exhibit significant effects, and therefore their corresponding hypotheses are rejected. Table 5. Results of the causal path relationships Relation Path Coefficient for the Original Sample Sample Mean Standard Deviation T-value P-Value Result Interaction → Contribution intention -0,033 -0,032 0,011 2,912 0,004 Rejected Information quality → Contribution intention 0,088 0,093 0,030 2,956 0,003 Accepted Accessibility → Contribution intention 0,136 0,128 0,049 2,788 0,005 Accepted Entertainment → Contribution intention 0,135 0,135 0,016 8,392 0,000 Accepted Customization→ Contribution intention 0,783 0,778 0,028 27,808 0,000 Accepted Table 6. Results of the moderating effect of familiarity Relation Path Coefficient for the Original Sample Sample Mean Standard Deviation T-value P-Value Result Interaction× Familiarity → Contribution intention Accessibility× Familiarity → Contribution intention 0,050 -0,052 0,047 -0,046 0,022 0,030 2,265 1,696 0,024 0,090 Accepted Rejected Table 7. Results of indirect effects of the control variables on contribution intention Relation Path Coefficient for the Original Sample Sample Mean Standard Deviation T-value P-Value Result Gender → Contribution intention 0,044 0,0411 0,026 1,728 0,084 Rejected Age → Contribution intention 0,014 0,015 0,007 1,967 0,049 Accepted Education level → Contribution intention 0,006 0,009 0,010 0,619 0,536 Rejected Project type → Contribution intention -0,023 -0,020 0,012 1,936 0,053 Rejected Ease of use → Contribution intention -0,005 -0,006 0,009 0,607 0,544 Rejected 4.3 Discussion The findings provide nuanced insights into how AI-driven marketing mechanisms shape backers’ contribution intentions in crowdfunding contexts. Overall, the results confirm that AI does not influence contribution intention uniformly, but rather through differentiated functional and experiential pathways. While most AI dimensions positively affect contribution intention, the role of interaction emerges as more complex and context-dependent. 4.3.1 The dominant role of customization Among all predictors, customization exhibits by far the strongest positive effect on contribution intention (β = 0.783, p < 0.001), underscoring personalization as the primary mechanism through which AI enhances crowdfunding performance. By tailoring reward structures, pricing options, and campaign content to backers’ preferences, AI-enabled customization increases perceived relevance and value, thereby strengthening engagement and motivation to contribute. This finding is consistent with prior research showing that higher levels of customization in reward-based crowdfunding, particularly through differentiated reward ties, enhance campaign attractiveness and stimulate additional contributions (Wei et al., 2023). In line with large-scale empirical evidence from kickstarter, customization appears to operate by improving perceived fit and expected benefits, which are key drivers of willingness to participate in crowdfunding campaigns (Wang et al., 2024). Customization acts as a persuasive signal of creator effort, responsiveness, and market orientation, which strengthens perceived campaign quality and reduces uncertainty among backers (Shneor et al., 20219; Shneor et al., 2021; Wang et al., 2019). 4.3.2 Information quality and accessibility as trust-enhancing mechanisms Consistent with H2 and H3, information quality and accessibility both positively influence contribution intention. These findings reinforce the notion that AI’s capacity to process large volumes of data and deliver accurate, timely, and relevant information plays a critical role in reducing uncertainty, one of the main barriers to crowdfunding participation. High-quality information enhances perceptions of project credibility and feasibility, which are key determinants of trust in online funding environments (Luo and Toubia, 2015). Similarly, AI-driven accessibility, through 24/7 availability and instant responses, lowers cognitive and temporal costs for backers, thereby improving perceived ease of use. These results align with TAM predictions and extend them to crowdfunding contexts by showing that ease of access and information clarity jointly support contribution intentions, even when ease of use itself does not emerge as a significant direct predictor. 4.3.3 Entertainment as an experiential catalyst The positive and significant effect of entertainment confirms the importance of hedonic dimensions in AI-enabled crowdfunding. Entertainment enhances emotional engagement and sustains attention, making campaign exploration more enjoyable and immersive. This supports prior research suggesting that effective responses generated through interactive and entertaining AI features can strengthen behavioral intentions in digital environments (Chung et al., 2018; Kim and Ko, 2010). In crowdfunding, where decisions are often driven by both rational evaluation and emotional resonance, entertainment appears to act a complementary mechanism that simplifies the impact of informational content. By reducing cognitive effort and fostering positive affect, entertainment indirectly reinforces both perceived usefulness and perceived ease of use thereby encouraging contribution behavior. 4.3.4 The paradoxical effect of interaction Contrary to H1, AI perceived interaction shows a significant but negative direct effect on contribution intention. This finding suggests that increased interaction with AI agent may, under certain conditions, generate skepticism or discomfort rather than trust. One possible explanation lies in the perceived authenticity gap: excessive or highly visible AI interaction may signal automation at the expense of human involvement, which can undermine emotional connection in contexts where relational trust is critical. This result resonates with emerging concerns in the literature that AI-driven communication, while efficient, may activate perceptions of manipulation or impersonality, particularly in high-stakes decisions such as financial contributions (Huang and Rust, 2018). In the Moroccan context, where interpersonal trust and social proximity play an important cultural role, AI interaction that is perceived as overly mechanical may weaken rather than strengthen contribution intention. 4.3.5 The moderating role of AI familiarity The moderation analysis provides important clarification of this paradox. AI familiarity significantly moderates the relationship between interaction and contribution intention, transforming interaction into a positive force for users who are more accustomed to AI systems. This finding supports extended TAM frameworks that emphasize familiarity as a key condition for trust formation and AI acceptance (Baroni et al., 2022; Kelly et al., 2022). For familiar users, AI interaction is more likely to be interpreted as efficient, supportive, and reliable, thereby reducing uncertainty and enhancing perceived usefulness. In contrast, for less familiar users, interaction may amplify perceived risk or confusion. Interestingly, AI familiarity does not moderate the accessibility-intention relationship, suggesting that basic access to information benefits users regardless of their prior AI experience. This distinction underscores that interaction is a cognitively and emotionally demanding AI feature, while accessibility functions as a more universally valued utility. 4.3.6 Control variables and demographic effects Among the control variables, age emerges as the only significant predictor of contribution intention. This result suggests generational differences in openness toward AI-enabled crowdfunding. With older respondents potentially exhibiting greater financial capacity or more structured decision-making processes. Gender, education level, project type, and perceived ease of use do not exert significant effects, indicating that AI acceptance in crowdfunding transcends traditional demographic boundaries once experiential and informational factors are accounted for. 5. Conclusion The objective of this research was to examine the influence of artificial intelligence (AI)-enabled marketing dimensions on backers’ contribution intentions in crowdfunding campaigns. Focusing on an emerging market, this study adopts a quantitative approach to analyze the effects of several AI-driven dimensions, namely interaction, information quality, accessibility, entertainment, and customization, as well as the moderating role of AI familiarity. The empirical findings obtained from a sample of 424 potential backers in Morocco confirm that customization is the strongest positive determinant of contribution intention. Entertainment, accessibility, and information quality also exert significant positive effects, emphasizing the importance of engaging, user-friendly, credible AI-generated content in facilitating decision-making. In contrast, AI-enabled interaction exhibits a more nuanced effect, as intensive or highly visible interaction may generate discomfort among certain backers. However, this negative effect is mitigated for users with higher levels of familiarity with AI technologies. Among the control variables, age emerges as the only demographic factor that significantly influences contribution intention. This research contributes to the literature on crowdfunding and digital innovation by examining the impact of AI-enabled mechanisms on contribution intention. By demonstrating the differentiated effects of AI dimensions and highlighting the conditional role of AI familiarity, this study moves beyond a simplistic view of technology as beneficial and provides a more nuanced understanding of AI adoption in crowdfunding platforms, particularly within an emerging market context. The findings also provide valuable implications for platform operators, project creators, and policymakers. They suggest that AI should be mainly leveraged to enhance personalization, content quality, and accessibility, rather than to intensify direct interaction with backers. Project creators are encouraged to focus on customized recommendations and engaging AI-generated content, especially when targeting users who are familiar with AI technologies. Additionally, initiatives aimed at improving AI literacy may help reduce resistance to AI-driven features and foster greater acceptance among backers. Finally, this study presents several limitations that open perspectives for future research. First, the focus on a single emerging market may limit the generalizability of the findings. Future studies could extend the analysis to cross-country comparisons between developed and emerging economies. Second, the study relies on self-reported intentions rather than actual contribution behavior; longitudinal or experimental research studies could provide deeper insights into behavioral outcomes. Finally, future research may consider additional moderating variables such as cultural factors, to enrich understanding of AI adoption in crowdfunding contexts. Declarations Human ethics and consent to participate All participants were informed about the purpose of the study and the voluntary nature of their participation. Informed consent was obtained from all participants prior to their involvement in the study. Participants were assured of confidentiality and anonymity, and they were informed that they could withdraw from the study at any time without any consequences. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution H.E. conceptualized the study, designed the research methodology, collected the data, performed the data analysis, and interpreted the results. H.E. wrote the original draft of the manuscript and revised it critically for important intellectual content. H.E. approved the final version of the manuscript. Data Availability Yes. 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(2024). Using Artificial Intelligence to Unlock Crowdfunding Success for Small Businesses. arXiv preprint arXiv:2407.09480 . Yuan, M., Cheng, X., Zhang, T., Wang, Y., Xu, L., Cheng, C., … and Chen, W. (2017, April). Crowdfunding assisted cellular system analysis and application. In International Conference on 5G for Future Wireless Networks (pp. 69–78). Cham: Springer International Publishing. Zhang, Y., Xiong, F., Xie, Y., Fan, X., and Gu, H. (2020). The impact of artificial intelligence and blockchain on the accounting profession. Ieee Access , 8 , 110461–110477. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-8485936","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601126036,"identity":"e9957e0d-de52-4369-9868-4487629a2d6c","order_by":0,"name":"HIBAT-ALLAH EZZAHID","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYJACZgYDZjkGBsbGAzARCWK0GAO1NJCihYE5sQHIIE4LP//xh58LCqzT17YfBtry57C9wQHmg7d5GOzycGmRbDiQLD3DID1325nEhgOMbYcTNxxgS7bmYUguxqXF4GDDAWkeg8O52w6AtDQcTjA4wGMmzcNwAOxUbMD+MGPzb6CWdLPzD2EO4/+GV4sBGzMbyJYEsxtAWxjYDjNuOMDDhleLxBk2Nmseg3TDbTeAtiS2pSfOPMxmbDnHIBmnFv7+449v8/yxljc7n/7wwYc/1vZ8x5sf3nhTYYdTCypIYGgGRxPQwUSpB4M64pWOglEwCkbBiAEAAH1aSrvL1Q8AAAAASUVORK5CYII=","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":true,"prefix":"","firstName":"HIBAT-ALLAH","middleName":"","lastName":"EZZAHID","suffix":""}],"badges":[],"createdAt":"2025-12-31 05:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8485936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8485936/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104072901,"identity":"ba767b43-3677-4984-8e8c-3702c19cc1a7","added_by":"auto","created_at":"2026-03-06 12:21:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch Model (Summary of hypothesis)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8485936/v1/9c69e1d255b586504107cd33.png"},{"id":104402875,"identity":"5e082f43-9769-4740-a761-2ab5afefc1d5","added_by":"auto","created_at":"2026-03-11 12:16:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1885071,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8485936/v1/035fc440-b090-4079-add6-3698e621d010.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Will artificial intelligence boost crowdfunding? Understanding backers’ contribution intentions through the technology acceptance model in an emerging market context","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCrowdfunding has become a rapidly growing form of financing, enabling entrepreneurs to collect funds from a large audience to bring their projects to life without depending on traditional financial institutions. Therefore, prior research suggests that backers are faced to information issue that allow them to evaluate the crowdfunding campaign when deciding to contribute (Hoegen et al.,2018; Mochkabadi and Volkmann, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Buttic\u0026egrave; and Ughetto, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). From this perspective, AI can enhance the user experience by facilitating personalized interactions, providing relevant information, increasing platform accessibility, and delivering real-time customer support, thereby boosting backer engagement (Pytkowska and Korynski, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA central stream of research in this area has concentrated on two main areas: the drivers of participation among backers and project creators, and the determinants of crowdfunding campaign success campaigns (Baah-Beeprah, 2023; De la Palli\u0026egrave;re et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ezzahid and Meghouar, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gerber and Hui, 2013; Meghouar et al., 2024). Within the stream on backer-related motivations, prior works have primarily studied how entrepreneurial signals influence individuals\u0026rsquo; contribution decisions (Kleinert and Mochkabadi, 2021; Vismara, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Grounded in signaling theory (Connelly et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Spence, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), this research highlights the role of signals related to venture characteristics, campaign features, and team attributes in shaping funding outcomes. Nevertheless, existing studies tend to focus on the investor-centric perspective, particularly within equity crowdfunding, which limits the understanding of backer behavior across alternative crowdfunding models such reward-based and donation-based platforms.\u003c/p\u003e \u003cp\u003eAlthough artificial intelligence is increasingly embedded across digital platforms; contributing to enhanced information quality, more sophisticated recommendation systems, and advances predictive analytics, only a very limited number of studies have examined AI within the crowdfunding literature (Gregoriades and Themistocleous, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Behl et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Korzyński et al., 2021). Existing studies focuses mostly on technical or platform-centered perspectives (Behl et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Korzyński et al., 2021; Ye at al., 2024), offering limited insight into how AI-driven functionalities influence decision making. Consequently, the integration of AI into the theoretical frameworks traditionally used to explain backer behavior is still nasent, pointing to a significant gap in the literature.\u003c/p\u003e \u003cp\u003eAdopting an innovation-oriented perspective, this study seeks to explain how AI-enabled marketing mechanisms shape backers\u0026rsquo; intention to contribute to crowdfunding campaigns in the Moroccan context. Based on the technology acceptance model (TAM), this research examines the effects of key AI-related dimensions; namely, interaction, information quality, accessibility, entertainment, and customization on contribution intention. To the best of our knowledge, these relationships have not yet been empirically investigated within crowdfunding ecosystem in Morocco.\u003c/p\u003e \u003cp\u003eBuilding on the identified gaps, a key question arises regarding how backers interpret and respond to AI-enabled features when evaluating crowdfunding campaigns. While prior research has demonstrated the role of signaling mechanisms and motivational drivers in shaping contribution behavior, the increasing integration of AI fundamentally transforms the way information is generated, communicated, and perceived by backers. However, empirical evidence stills limited as to whether, and to what extent AI-driven marketing efforts strengthen backers\u0026rsquo; willingness to contribute, particularly in emerging markets such as Morocco, where digital financial technologies are still in a phase of gradual adoption. Accordingly, this study seeks to answer the following research question: To what extent AI-driven marketing dimensions influence backers\u0026rsquo; intention to contribute to crowdfunding campaigns in the Moroccan context? By examining this question, the research extends existing theoretical frameworks and advances understanding of backer behavior in an increasingly AI-enables crowdfunding environment.\u003c/p\u003e \u003cp\u003eTo answer this question, we did a quantitative study via 424 with potential backers in Morocco. Building on TAM, we examine whether and how AI marketing efforts affect the contribution intention. The findings reveal that interaction, information quality, accessibility, entertainment, and customization significantly influence contribution intention. Moreover, AI familiarity moderates several of these relationships, indicating that individuals with greater exposure to AI are more responsive to its value-enhancing features. Demographic variables exhibit differentiated effects on contribution intention, underscoring the heterogeneous nature of backers in AI-enabled crowdfunding environments.\u003c/p\u003e \u003cp\u003eTo address this research question, this research adopts a quantitative study based on survey data collected from 424 potential backers in Morocco. The findings reveal that the customization stands out as the most influential positive determinant of contribution intention. In addition, entertainment, accessibility, and information quality display significant positive effects. By contrast, highly AI-driven interaction may generate discomfort among certain users, thereby exerting a negative influence on contribution intention. Importantly, this adverse effect is attenuated among individuals with higher levels of AI familiarity, highlighting the moderating role of familiarity with AI technologies. Regarding the control variables, age emerges as the demographic factor exerting a significant effect on contribution intention.\u003c/p\u003e \u003cp\u003eThis research offers several contributions. First, it enriches the literature on technology acceptance in crowdfunding by highlighting the specific impact of AI on backers\u0026rsquo; intention to contribute. Second, by focusing on the emerging Moroccan context, it provides an important contextual perspective for local crowdfunding stakeholders. Finally, it offers valuable insights for policymakers and institutions aiming to support crowdfunding campaigns and assist project creators in integrating AI technologies into their campaigns.\u003c/p\u003e \u003cp\u003eThis research offers several contributions. First, it enriches research on technology acceptance in crowdfunding by examining the impact of AI in shaping backers\u0026rsquo; contribution intentions. Second, by focusing on the Moroccan context as an emerging market, the study provides a contextualized perspective that enriches current understanding and its particularly relevant for local crowdfunding ecosystems. Finally, the findings generate practical implications for policymakers and institutional actors seeking to support crowdfunding development.\u003c/p\u003e \u003cp\u003eThe remainder of the paper is structured as follows. First, we present a detailed literature review on crowdfunding and AI-driven marketing dimensions. Secondly, we present the data and discuss the empirical findings. Finally, we conclude with the study\u0026rsquo;s implications for theory and practice, its limitations, and future research avenues.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eFrom the above, it is evident that success of crowdfunding campaigns depends not only on the innovativeness of a project, but also on how effectively the project is described and communicated to potential backers. In this regard, the integration of AI into crowdfunding campaigns has broadened opportunities for communication and interaction between project creators and backers, thereby enhancing trust and improving overall user experience. By learning pattern recognition capabilities, AI enables the prediction of products and services that are likely to appeal specific customer groups (Mehta et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), identifies which crowdfunding campaigns are riskier than others (Mahbub and Zhuang, 2022), and serves as a decision-support tool for effective and efficient decision-making process (Aggarwal et al., 2020).\u003c/p\u003e \u003cp\u003eIn the context of crowdfunding, AI has been increasingly employed to predict campaign success and failure (Guidotti et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kaminski and Hopp, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Based on recent advances in the analysis of backer decision-making, Gregoriades and Themistocleous (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) investigate the sources of vulnerability affecting both campaigners\u0026rsquo; and backers\u0026rsquo; choices by developing two binary classification models: one based on textual features and the other combining categorical, numerical, and textual information. Overall, the authors highlight several counterintuitive patterns. For instance, textual terms such as \u0026ldquo;stretch goals\u0026rdquo;, which simultaneously convey ambition and potential risk, positively correlate with success, whereas more explicit disclosures of risk tend to activate concerns that would otherwise remain latent among backers. Viewed broadly, these findings suggest the use of counterfactual explanations in generating actionable insights, offering guidance on how unsuccessful projects may be redesigned to improve their chances of success, while also suggesting new policies, such as behavioral nudges, aimed at protecting backers from points of vulnerability.\u003c/p\u003e \u003cp\u003eSecond, Bai et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) examined the effects of AI-assisted disclosures, specifically those generated through ChatGPT on crowdfunding performance using Kickstarter as empirical data. By conceptualizing the introduction of ChatGPT as an exogenous shock, the authors analyze how AI-generated campaign narratives alter funding dynamics and project outcomes. Their findings indicate that AI support substantially improves fundraising performance. These positive effects are especially salient among creators from non-English-speaking backgrounds, minority groups, and first-time project creators, suggesting that AI can reduce information frictions and partially level the playing field for disadvantaged creators. At the same time, the study identifies important limitations. In institutional setting marked by weaker governance structures or lower levels of social capital, the use of AI-generated content is associated with substantially lower project delivery rates. This pattern raises concerns about the potential of AI tools to unintentionally amplify the visibility of lower-quality projects, thereby leading to inefficient resource allocation. Overall, the study advances the emerging literature on generative AI in financial communication by highlighting the risks of AI generated disclosures in crowdfunding environments.\u003c/p\u003e \u003cp\u003eThird, Ye et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have focused on the challenges faced by small businesses seeking funding through online crowdfunding platforms, noting that over 40% of campaigns, particularly those originating from low socio-economic areas, fail to secure any financial support. Drawing on recent advances in AI, the authors developed machine learning models to identify the textual and structural features most strongly associated with campaign success. Their most effective model predicts fundraising outcomes with an accuracy of 81% relying primarily on information embedded in campaign narratives. Notably, the study shows that enhancing only three narratives components with the support of a large language model substantially enhances campaign attractiveness, with 83% of human evaluators rating the revised campaigns more favorably and significantly higher likelihood of securing financial contributions.\u003c/p\u003e \u003cp\u003eFinally, and complementary line of inquiry, a recent large-scale study conducted in the United States investigated how AI-powered chatbot marketing efforts (CMEs) contribute to the development of customer-brand relationships and shape online behavioral intentions (Cheng and Jiang, 2021). Based on survey data from 1,072 consumers who interacted with chatbot services from 30 industry-leading brands, the findings identify interaction, information, accessibility, entertainment, and customization as the central components shaping effective chatbot marketing. The study further shows that CMEs exert significant direct effects on the perceived quality of communication with chatbot agents, while indirectly enhancing customer-brand relationships and subsequent customer responses. Nevertheless, the extent to which these AI marketing efforts influence backers\u0026rsquo; intentions within crowdfunding environments remains largely unexplored. This gap underscores the relevance of the present study, which seeks to advance understanding of backers\u0026rsquo; acceptance of AI-enabled features in crowdfunding contexts.\u003c/p\u003e \u003cp\u003eIn this study, we adapt the conceptual structure of CMEs identified in prior work to examine the intention to contribute to crowdfunding through which AI shaper backer responses. Building on the CME classification, we conceptualize how these features shape backer responses within crowdfunding platforms. This framework enables an objective assessment of backer reactions to AI marketing initiatives and provides a more granular understanding of how such strategies differentially influence contribution intentions in crowdfunding contexts. Having established that AI-enabled communication mechanisms shape backer responses through distinct behavioral pathways, it becomes necessary to situate these dynamics within broader theoretical frameworks on technology adoption. In this regard, the TAM provides a well-established foundation for explaining how individuals evaluate and engage with technological interfaces.\u003c/p\u003e \u003cp\u003eOriginally developed to explain users\u0026rsquo; acceptance of information systems, the TAM, introduced by Davis (1989), highlights two central constructs: perceived usefulness and perceived ease of use. Perceived usefulness refers to the extent to which individuals believe that a technology enhances task performance, whereas perceived ease of use reflects the degree to which the technology is perceived as effortless to operate. Applied to the crowdfunding context, TAM offers a relevant lens for understanding how specific AI-driven marketing dimensions, namely interaction, information quality, accessibility, entertainment and customization, influence backers\u0026rsquo; intention to contribute to campaigns.\u003c/p\u003e \u003cp\u003eRegarding the interaction, facilitated by AI, it is a key aspect of marketing efforts in crowdfunding, as it enhances communication between project creators and backers, and contributes to a more satisfying funding experience. Through AI agents, backers can receive quick responses, facilitating real-time exchanges and reducing perceived psychological distance from the project. This immediacy fosters a sense of closeness and trust, which is particularly important in crowdfunding contexts where personal relationships between backers and creators are typically absent (Godey et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such perceived proximity plays a critical role in encouraging engagement, as backers often seek meaningful connections with the projects they choose to support. Accordingly, to Gefen and Straub (2004), regular communication and immediate responses promote greater trust in online interaction; an effect that becomes even more salient in crowdfunding settings characterized by high uncertainty. Furthermore, the capacity of AI agents to stimulate human conversation and dynamically adapt responses to user queries reinforces this sense of proximity, making the interaction experience smoother and more engaging (Huang and Rust, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, research by Han et al. (2020) has shown that the quality of AI interactions can directly influence users\u0026rsquo; perceived usefulness, due to reduced uncertainty and increased transparency of the information provided. Effective AI interaction is perceived as an added value making the crowdfunding experience more convenient and reassuring, as it helps backers make informed decisions. By enabling instant and personalized communication, AI interactions strengthen backer engagement by offering more immersive user experience, which positively influences their perception of the campaign\u0026rsquo;s usefulness. As a result, this dimension of interaction is expected to positively influence backers\u0026rsquo; intention to contribute by improving their perception of the usefulness and reliability of crowdfunding campaigns.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1. Perceived interaction with AI agents positively influences backers\u0026rsquo; intention to contribute to crowdfunding campaigns.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOne of the central roles of AI in crowdfunding lies in its capacity to provide accurate, relevant, and personalized information to potential backers. Through big data analysis, thereby increasing their engagement and trust in the projects (Sadek et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Indeed, the ability of AI to process large volumes of data allows to identify the specific informational needs of backers and to provide directly relevant information, which reduces uncertainty and enhances transparency (Bhimani and Willcocks, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the context of crowdfunding, information accuracy and relevance are particularly critical, as they shape perceptions of project credibility and feasibility. Prior research indicates campaigns providing clear, comprehensive, and detailed information are more likely to foster trust and stimulate financial support from backers (Luo and Toubia, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Through real-time access to project updates and responding to frequently questions, AI enhances the perceived usefulness of crowdfunding platforms, helping backers make more informed decisions and feel more involved in the support process. In addition, when information provision is aligned with individual preferences, users experience a stronger sense of control and satisfaction, which enhances trust in crowdfunding campaigns (Yuan et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2. Perceived information quality positively influences backers\u0026rsquo; intention to contribute to crowdfunding campaigns.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAccessibility, ensured by AI, is a key factor in the marketing efforts of crowdfunding campaigns. According to Sultan and Wong (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the accessibility of AI technologies allows users to quickly address concerns or access specific information at any time, enhancing perceived service quality and promoting user satisfaction. This increased availability is particularly important in crowdfunding, where potential backers may require immediate answers to confirm their support decisions. By providing continuous access to information and support, AI removes temporal barriers that could hinder backer engagement and reinforces their sense of trust and transparency toward the project. Moreover, the 24/7 availability of AI services positively influences the perceived ease of use of the technology. As shown by Xu et al., (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), AI systems that offer continuous accessibility facilitate the user experience by providing consistent support and reducing the effort required to obtain information. This accessibility allows backers to feel reassured, knowing they can obtain the necessary information and receive immediate assistance when needed, which enhances their perception of the system\u0026rsquo;s efficiency and user-friendliness. Indeed, by facilitating access to information and making the interaction process more convenient, AI-driven accessibility helps strengthen backers\u0026rsquo; intention to contribute.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3. Perceived accessibility positively influences backers\u0026rsquo; intention to contribute to crowdfunding campaigns.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEntertainment refers to the hedonic dimension through which useful and credible information is delivered in an engaging and enjoyable manner, thereby enhancing perceived value and strengthening individuals\u0026rsquo; intention to adopt digital tools (Chung et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In digital environments, entertainment goes beyond mere amusement and functions as a strategic mechanism that captures attention, sustains user engagement, and facilitates information processing. Similar to other social media contexts, individuals increasingly seek pleasure, enjoyment, and emotional simulation when interacting with AI powered systems (Chung et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the context of AI-enabled platforms, entertainment is often embedded through interactive features, conversational agents, personalized content, and dynamic visual elements; which collectively enhance the user experience. These hedonic attributes reduce cognitive effort, increase perceived ease of use, and foster positive affect toward the platform, ultimately reinforcing favorable ease of use, and foster positive affect toward the platform, ultimately reinforcing favorable attitudes and behavioral intentions. Prior research in digital and social media marketing suggest that entertainment-oriented content plays a critical role in shaping brand perceptions by generating positive-emotions, increasing brand awareness, and strengthening purchase intentions (Kim and Ko, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Applied to AI-driven crowdfunding platforms, entertainment may contribute to higher levels of engagement by making campaign information more accessible, appealing, and emotionally resonant (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jiao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xiang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) By combining informative content with enjoyable and interactive AI features, platforms can enhance backers\u0026rsquo; involvement and motivation to explore, evaluate, and support projects (Gregoriades et al., 2025; Jiao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, entertainment emerges as a key experiential component that complements informational and functional aspects of AI, influencing backers\u0026rsquo; intentions to adopt and interact with AI-enabled crowdfunding tools (Behl et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gregoriades et al., 2025; Jiao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4. Perceived entertainment positively influences backers\u0026rsquo; intention to contribute to crowdfunding campaigns.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCustomization refers to tailoring reward options, pricing, and campaign content to fit backers\u0026rsquo; preferences and needs. In reward-based crowdfunding, higher levels of customization, through price or product differentiation in reward tiers, strengthen the positive impact of early contributions on campaign success, indicating that more customized options make campaigns more attractive and encourage additional contributions (Wei et al., 2023). Large-scale studies of Kickstarter data show that reward personalization has a positive effect on willingness to participate: increasing personalization initially enhances perceived fit, expected benefits, and thus contribution intention, but excessive choice and complexity eventually reduce willingness to back (Wang et al., 2024). Customization also operates through cognitive and affective pathways. By making rewards feel more personally relevant and aligned with backers\u0026rsquo; lifestyles or identities (eg., life-related rather than purely artistic rewards), personalization amplifies expectancy that contributions will generate values outcomes, thereby reinforcing funding intention (Wang et al., 2024). In parallel, customized offers can be a persuasive cue that signals creator effort, responsiveness, and market orientation, which strengthens perceived campaign quality and reduces uncertainty (Shneor et al., 2019; Shneor et al., 2021; Wang et al., 2019). Overall, customization enhances perceived value, relevance, and trust, which together foster more favorable attitude toward campaigns and translate into stronger intentions to contribute financially.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5. Perceived customization positively influences backers\u0026rsquo; intention to contribute to crowdfunding campaigns.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA growing body of research explores how user familiarity with artificial intelligence (AI) systems shapes interactions, trust, and outcomes across various contexts. Indeed, familiarity with AI systems often increases user comfort, trust, and willingness to interact (Wang et al., 2024; Arce-Urriza et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the context of crowdfunding platforms, AI familiarity can play a crucial role between perceived AI interaction and backers\u0026rsquo; contribution intention. Baroni et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) developed an extended TAM that explicitly incorporates AI familiarity as a construct, alongside perceived usefulness, ease of use, trust in AI, and collaborative intention. Their findings indicate that users\u0026rsquo; familiarity with AI systems enhances their trust and perceived quality of AI outputs, which in turn positively influences both behavioral intention and willingness to contribute or collaborate within Ai-powered platforms. Similarly, Belanche et al (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that users with greater familiarity with AI, exhibit stronger effects of perceived usefulness and attitude on adoption intentions, highlighting the importance in shaping user acceptance and engagement (Belanche et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the other hand, Zhang et al., (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) confirm that crowd familiarity closely related to AI familiarity, significantly boosts user trust and readiness, which are essential for actual donation behavior on crowdfunding platforms. Their findings indicate that technological utilization and social collaboration are more effective when users are already comfortable with the digital environment, reinforcing the moderating role of familiarity in the pathway from accessibility to contribution (Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, Kelly et al., (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) show in a systematic literature that perceived usefulness, effort expectancy, and trust are key predictors of AI acceptance, but these are more influential among users with higher AI familiarity. This suggest that as users become familiar with AI, the accessibility of AI features more strongly translates into actual behavioral intentions and platform engagement (Kelly et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6.1. AI familiarity moderates the relationship between perceived interaction with AI agents and backers\u0026rsquo; intention to contribute to crowdfunding campaigns.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eH6.2. AI familiarity moderates the relationship between perceived accessibility of AI-enabled features and backers\u0026rsquo; intention to contribute to crowdfunding campaigns.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Methods","content":"\u003cp\u003eThe purpose of our research, as well as the nature of the data required, justified the adoption of quantitative study. To this end, an online survey was administrated to a sample of backers and potential backers in Morocco. The data collection process spanned six weeks. A total of 450 completed questionnaires were received. After data screening for incomplete responses and inconsistencies, 424 valid questionnaires were retained for analysis. The empirical analysis relies on data collected through a self-administrated survey instrument. Despite its relevance, this method entails a potential limitation related to common method variance (Podsakoff et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To address this concern, we implemented several procedural remedies, including assuring respondents of complete anonymity and structuring the questionnaire so that the measurement of variables was dispersed across different sections.\u003c/p\u003e \u003cp\u003eThe collected data were modeled using the partial least squares structural equation modeling (PLS-SEM) method in order to assess the effects between latent constructs while adjusting for measurement errors in the structural model (Hair et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The SmartPLS software applies the PLS method based on structural equation modeling (SEM), which analyzes second-generation multivariate data. This method is widely used in management sciences. It allows the study of direct, indirect, and total affects between several variables (Roussel et al., 2002), and takes measurement errors into account (Fornell and Bookstein, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below presents the main criteria for evaluating the measurement model.\u003c/p\u003e \u003cp\u003eOverall, in our cleaned sample of 424 respondents, 70% were female and 37% were male. Regarding age, 56% of respondents were between 25 and 34 years old, 30% were between 35 and 44 years old, and the remainder were distributed across the 18\u0026ndash;24 and 45\u0026thinsp;+\u0026thinsp;age groups. Based on educational background, the majority of respondents held a PhD degree (63%). Respondents with a Master\u0026rsquo;s degree represented 18% of the sample, followed by those with a Bachelor\u0026rsquo;s degree (5.9%), engineering qualifications (4%), MBA degrees (4%), and a baccalaureate (4%). Only 0.71% of respondents were PhD candidates.\u003c/p\u003e"},{"header":"4. Results and discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Measurement model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExpect for AI familiarity, all other items used a five-point Likert-Type scale, ranging from strongly disagree (1) to strongly agree (5). This study adopted 24 questions from previous research (Lin, 2007; Cheng et al., 2015; Godey et al., 2016; Chung et al., 2018; Cheng and Jiang, 2021) to measure acceptance of AI by potential backers in Morocco and contribution intention with \u0026nbsp;five independent variables (Cronbach\u0026rsquo;s \u0026alpha; was 0,839 for AI perceived interaction, \u0026nbsp; 0,698 for AI information quality, 0,863 for AI accessibility, 0,784 for entertainment, 0,887 for customization), and dependent variable (Cronbach\u0026rsquo;s \u0026alpha; was 0,744 for contribution intention). Based on the reliability tests, all constructs showed acceptable internal consistency. Cronbach\u0026rsquo;s alpha exceeded the recommended threshold of 0,70 indicating that the items used to measure each construct were reliable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo verify the unidimensionality of the constructs, we examined the outer loadings of the measurement model. Two items related to the constructs information quality and accessibility were removed due to poor representative quality. The model was tested according to the previously mentioned criteria. The results presented in table 1 indicate that the composite reliability values range from 0,70 to above 0,90, which reflects acceptable to excellent reliability according to the recommended thresholds. Similarly, the average variance extracted (AVE) for each construct exceeds 50% indicating satisfactory convergent validity. Discriminant validity is also confirmed, as the squared AVE values are higher than the correlations between latent variables, and each item\u0026rsquo;s loading is higher on its associated construct than on other constructs. The results regarding the assessment of the measurement model are presented below (1,2,3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Construct reliability and validity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstructs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026rsquo;s Alpha\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite reliability (rho_a)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite reliability (rho_c)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage variance extracted\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2,064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInformation quality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccessibility\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntertainment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCustomization\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContribution intention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0,614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamiliarity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEase of use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eN/A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Latent construct correlations \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eACCES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCUSTO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eENTER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINFO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTEN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eACCES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0,148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0,156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0,549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0,254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCUSTO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0,148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0,808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0,933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0,231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0,980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eENTER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0,156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0,808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0,841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0,227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0,854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINFO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0,933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0,841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0,167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0,940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0,549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0,231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0,227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0,167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0,269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTEN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0,254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0,980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0,854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0,940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-0,278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Loadings and cross-loadings of the latent variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eACCES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCUSTOM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eENTER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINFO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTEN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eACCES1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,856\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eACCES2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,900\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eACCES3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,883\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCUSTOM1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,812\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCUSTOM2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,928\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCUSTOM3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,803\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCUSTOM4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,910\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eENTER1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,986\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eENTER2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,131\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eENTER3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,927\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eENTER4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,986\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINFO2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,901\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINFO3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,849\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTEN1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,942\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTEN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,897\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTEN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,246\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTEN4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,839\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTER1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,946\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTER2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,771\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTER3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,696\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Evaluation of the structural model and hypothesis testing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the coefficient of determination (R\u0026sup2;), and following Chin\u0026rsquo;s (1998) guideline, the variable contribution intention shows a value of \u0026hellip;, which is higher than the recommended threshold of 0.50. This indicates excellent support for the nomological validity of the model (see table\u0026hellip;). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Coefficients of Determination of the Endogenous Variable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2; adjusted\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContribution intention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,985\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,985\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe quality of the structural model can also be assessed through the average of the coefficients of determination, known as \u0026ldquo;Goodness of Fit\u0026rdquo; (GOF) index, calculated on the dependent variable. In our case, GOF = \u0026radic;(0.985 \u0026times; 0.712), yielding a value of 0,837. This index exceeds the recommended threshold of 0,30, which suggests that the model fits the data well and allows us to proceed with the analysis. Overall, the conditions of validity and reliability of the research model are satisfactorily met. The measurement scales of the latent variables have been evaluated as valid and reliable, which enables us to move on the next step: testing the research hypothesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 presents the effects of the main independent variables on contribution intention. The results show that interaction has a significant but negative effect on contribution intention, leading to the rejection of the corresponding hypothesis. In contrast, information quality (\u0026beta; = 0.088, p = 0.003), accessibility (\u0026beta; = 0.136, p = 0.005), entertainment (\u0026beta; = 0.135, p = 0.000), and customization (\u0026beta; = 0.783, p = 0.000) display positive and significant effects, supporting their respective hypotheses.\u003c/p\u003e\n\u003cp\u003eRegarding the moderating effects, Table 6 indicates that familiarity significantly moderates the relationship between interaction and contribution intention (\u0026beta; = 0.050, p = 0.024), thereby confirming the associated hypothesis. However, the moderating effect of familiarity on the relationship between accessibility and contribution intention is not significant (\u0026beta; = \u0026ndash;0.052, p = 0.090), resulting in the rejection of this hypothesis.\u003c/p\u003e\n\u003cp\u003eFinally, Table 7 shows the effects of the control variables. Among them, only age has a significant influence on contribution intention (\u0026beta; = 0.014, p = 0.049). Gender, education level, project type, and ease of use do not exhibit significant effects, and therefore their corresponding hypotheses are rejected.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Results of the causal path relationships\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath Coefficient for the Original Sample\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eInteraction \u0026rarr; Contribution intention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e-0,033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0,032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0,011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2,912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eRejected\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eInformation quality \u0026rarr; Contribution intention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0,088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0,093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0,030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2,956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0,003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAccepted\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eAccessibility \u0026rarr; Contribution intention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0,128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0,049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2,788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAccepted\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEntertainment \u0026rarr; Contribution intention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0,135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0,135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e8,392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAccepted\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCustomization\u0026rarr; Contribution intention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0,783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0,778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0,028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e27,808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAccepted\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Results of the moderating effect of familiarity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath Coefficient for the Original Sample\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction\u0026times; Familiarity\u0026nbsp;\u003c/strong\u003e\u0026rarr;\u003cstrong\u003e\u0026nbsp;Contribution intention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAccessibility\u0026times; Familiarity\u0026nbsp;\u003c/strong\u003e\u0026rarr;\u003cstrong\u003e\u0026nbsp;Contribution intention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0,050\u003c/p\u003e\n \u003cp\u003e-0,052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0,047\u003c/p\u003e\n \u003cp\u003e-0,046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,022\u003c/p\u003e\n \u003cp\u003e0,030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2,265\u003c/p\u003e\n \u003cp\u003e1,696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0,024\u003c/p\u003e\n \u003cp\u003e0,090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAccepted\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRejected \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Results of indirect effects of the control variables on contribution intention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath Coefficient for the Original Sample\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u0026nbsp;\u003c/strong\u003e\u0026rarr;\u003cstrong\u003e\u0026nbsp;Contribution intention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0,044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0,0411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1,728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0,084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eRejected\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u0026rarr;\u003cstrong\u003e\u0026nbsp;Contribution intention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0,015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1,967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0,049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAccepted\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u0026nbsp;\u003c/strong\u003e\u0026rarr;\u003cstrong\u003e\u0026nbsp;Contribution intention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0,006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0,009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0,619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0,536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eRejected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProject type\u0026nbsp;\u003c/strong\u003e\u0026rarr;\u003cstrong\u003e\u0026nbsp;Contribution intention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-0,023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0,020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1,936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0,053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eRejected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEase of use\u0026nbsp;\u003c/strong\u003e\u0026rarr;\u003cstrong\u003e\u0026nbsp;Contribution intention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-0,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0,006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0,607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0,544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eRejected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Discussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings provide nuanced insights into how AI-driven marketing mechanisms shape backers\u0026rsquo; contribution intentions in crowdfunding contexts. Overall, the results confirm that AI does not influence contribution intention uniformly, but rather through differentiated functional and experiential pathways. While most AI dimensions positively affect contribution intention, the role of interaction emerges as more complex and context-dependent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.1 The dominant role of customization\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong all predictors, customization exhibits by far the strongest positive effect on contribution intention (\u0026beta; = 0.783, p \u0026lt; 0.001), underscoring personalization as the primary mechanism through which AI enhances crowdfunding performance. By tailoring reward structures, pricing options, and campaign content to backers\u0026rsquo; preferences, AI-enabled customization increases perceived relevance and value, thereby strengthening engagement and motivation to contribute. This finding is consistent with prior research showing that higher levels of customization in reward-based crowdfunding, particularly through differentiated reward ties, enhance campaign attractiveness and stimulate additional contributions (Wei et al., 2023). In line with large-scale empirical evidence from kickstarter, customization appears to operate by improving perceived fit and expected benefits, which are key drivers of willingness to participate in crowdfunding campaigns (Wang et al., 2024). Customization acts as a persuasive signal of creator effort, responsiveness, and market orientation, which strengthens perceived campaign quality and reduces uncertainty among backers (Shneor et al., 20219; Shneor et al., 2021; Wang et al., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.2 Information quality and accessibility as trust-enhancing mechanisms\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsistent with H2 and H3, information quality and accessibility both positively influence contribution intention. These findings reinforce the notion that AI\u0026rsquo;s capacity to process large volumes of data and deliver accurate, timely, and relevant information plays a critical role in reducing uncertainty, one of the main barriers to crowdfunding participation. High-quality information enhances perceptions of project credibility and feasibility, which are key determinants of trust in online funding environments (Luo and Toubia, 2015). Similarly, AI-driven accessibility, through 24/7 availability and instant responses, lowers cognitive and temporal costs for backers, thereby improving perceived ease of use. These results align with TAM predictions and extend them to crowdfunding contexts by showing that ease of access and information clarity jointly support contribution intentions, even when ease of use itself does not emerge as a significant direct predictor.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.3 Entertainment as an experiential catalyst\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe positive and significant effect of entertainment confirms the importance of hedonic dimensions in AI-enabled crowdfunding. Entertainment enhances emotional engagement and sustains attention, making campaign exploration more enjoyable and immersive. This supports prior research suggesting that effective responses generated through interactive and entertaining AI features can strengthen behavioral intentions in digital environments (Chung et al., 2018; Kim and Ko, 2010). In crowdfunding, where decisions are often driven by both rational evaluation and emotional resonance, entertainment appears to act a complementary mechanism that simplifies the impact of informational content. By reducing cognitive effort and fostering positive affect, entertainment indirectly reinforces both perceived usefulness and perceived ease of use thereby encouraging contribution behavior.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.4 The paradoxical effect of interaction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContrary to H1, AI perceived interaction shows a significant but negative direct effect on contribution intention. This finding suggests that increased interaction with AI agent may, under certain conditions, generate skepticism or discomfort rather than trust. One possible explanation lies in the perceived authenticity gap: excessive or highly visible AI interaction may signal automation at the expense of human involvement, which can undermine emotional connection in contexts where relational trust is critical. This result resonates with emerging concerns in the literature that AI-driven communication, while efficient, may activate perceptions of manipulation or impersonality, particularly in high-stakes decisions such as financial contributions (Huang and Rust, 2018). In the Moroccan context, where interpersonal trust and social proximity play an important cultural role, AI interaction that is perceived as overly mechanical may weaken rather than strengthen contribution intention.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.5 The moderating role of AI familiarity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe moderation analysis provides important clarification of this paradox. AI familiarity significantly moderates the relationship between interaction and contribution intention, transforming interaction into a positive force for users who are more accustomed to AI systems. This finding supports extended TAM frameworks that emphasize familiarity as a key condition for trust formation and AI acceptance (Baroni et al., 2022; Kelly et al., 2022). For familiar users, AI interaction is more likely to be interpreted as efficient, supportive, and reliable, thereby reducing uncertainty and enhancing perceived usefulness. In contrast, for less familiar users, interaction may amplify perceived risk or confusion. Interestingly, AI familiarity does not moderate the accessibility-intention relationship, suggesting that basic access to information benefits users regardless of their prior AI experience. This distinction underscores that interaction is a cognitively and emotionally demanding AI feature, while accessibility functions as a more universally valued utility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.6 Control variables and demographic effects\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the control variables, age emerges as the only significant predictor of contribution intention. This result suggests generational differences in openness toward AI-enabled crowdfunding. With older respondents potentially exhibiting greater financial capacity or more structured decision-making processes. Gender, education level, project type, and perceived ease of use do not exert significant effects, indicating that AI acceptance in crowdfunding transcends traditional demographic boundaries once experiential and informational factors are accounted for.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe objective of this research was to examine the influence of artificial intelligence (AI)-enabled marketing dimensions on backers\u0026rsquo; contribution intentions in crowdfunding campaigns. Focusing on an emerging market, this study adopts a quantitative approach to analyze the effects of several AI-driven dimensions, namely interaction, information quality, accessibility, entertainment, and customization, as well as the moderating role of AI familiarity. The empirical findings obtained from a sample of 424 potential backers in Morocco confirm that customization is the strongest positive determinant of contribution intention. Entertainment, accessibility, and information quality also exert significant positive effects, emphasizing the importance of engaging, user-friendly, credible AI-generated content in facilitating decision-making. In contrast, AI-enabled interaction exhibits a more nuanced effect, as intensive or highly visible interaction may generate discomfort among certain backers. However, this negative effect is mitigated for users with higher levels of familiarity with AI technologies. Among the control variables, age emerges as the only demographic factor that significantly influences contribution intention.\u003c/p\u003e \u003cp\u003eThis research contributes to the literature on crowdfunding and digital innovation by examining the impact of AI-enabled mechanisms on contribution intention. By demonstrating the differentiated effects of AI dimensions and highlighting the conditional role of AI familiarity, this study moves beyond a simplistic view of technology as beneficial and provides a more nuanced understanding of AI adoption in crowdfunding platforms, particularly within an emerging market context. The findings also provide valuable implications for platform operators, project creators, and policymakers. They suggest that AI should be mainly leveraged to enhance personalization, content quality, and accessibility, rather than to intensify direct interaction with backers. Project creators are encouraged to focus on customized recommendations and engaging AI-generated content, especially when targeting users who are familiar with AI technologies. Additionally, initiatives aimed at improving AI literacy may help reduce resistance to AI-driven features and foster greater acceptance among backers.\u003c/p\u003e \u003cp\u003eFinally, this study presents several limitations that open perspectives for future research. First, the focus on a single emerging market may limit the generalizability of the findings. Future studies could extend the analysis to cross-country comparisons between developed and emerging economies. Second, the study relies on self-reported intentions rather than actual contribution behavior; longitudinal or experimental research studies could provide deeper insights into behavioral outcomes. Finally, future research may consider additional moderating variables such as cultural factors, to enrich understanding of AI adoption in crowdfunding contexts.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cb\u003eHuman ethics and consent to participate\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll participants were informed about the purpose of the study and the voluntary nature of their participation. Informed consent was obtained from all participants prior to their involvement in the study. Participants were assured of confidentiality and anonymity, and they were informed that they could withdraw from the study at any time without any consequences.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.E. conceptualized the study, designed the research methodology, collected the data, performed the data analysis, and interpreted the results. H.E. wrote the original draft of the manuscript and revised it critically for important intellectual content. H.E. approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eYes. I used research data in this study.The data that support the findings of this study were generated by the author. Due to ethical restrictions and the confidentiality of participants, the data are not publicly available. Anonymized data may be made available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAggarwal, R., Lee, M. J., Osting, B., and Singh, H. (2021). Improving funding operations of equity-based crowdfunding platforms. \u003cem\u003eProduction and Operations Management\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(11), 4121\u0026ndash;4139.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArce-Urriza, M., Chocarro, R., Cortinas, M., and Marcos-Matas, G. (2025). From familiarity to acceptance: The impact of Generative Artificial Intelligence on consumer adoption of retail chatbots. \u003cem\u003eJournal of Retailing and Consumer Services\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e, 104234.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaah-Peprah, P. 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(2020). The impact of artificial intelligence and blockchain on the accounting profession. \u003cem\u003eIeee Access\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 110461\u0026ndash;110477.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Crowdfunding, Artificial intelligence, Contribution intention, Quantitative study, Morocco","lastPublishedDoi":"10.21203/rs.3.rs-8485936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8485936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research examines how artificial intelligence (AI) enabled features shape backers’ contribution intention in crowdfunding campaigns within an emerging market context. It focuses on the differentiated effects of AI-driven marketing mechanisms, such as interaction, information quality, accessibility, entertainment, and customization, and investigates the moderating role of AI familiarity. We conducted a quantitative study based on a survey data from 424 potential crowdfunding backers in Morocco, the hypothesis are tested through partial least squares structural equation modeling (PLS-SEM). The findings reveal that AI-enabled features influence contribution intention in a non-uniform manner. Customization emerges as the strongest positive driver, followed by entertainment, accessibility, and information quality, highlighting the importance of personalized, engaging, and credible AI-generated content. In contrast, intensive AI interaction exerts a negative effect, suggesting potential user discomfort. However, this effect is mitigated among individuals with higher AI familiarity, confirming its significant moderating role. Among the control variables, only age significantly affects contribution intention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification\u003c/strong\u003e: M1; M3\u003c/p\u003e","manuscriptTitle":"Will artificial intelligence boost crowdfunding? Understanding backers’ contribution intentions through the technology acceptance model in an emerging market context","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 12:21:44","doi":"10.21203/rs.3.rs-8485936/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e5e83637-ad94-4c5b-bc59-602fd23ea431","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-05T15:28:29+00:00","index":20,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T13:29:10+00:00","index":19,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-06T12:21:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 12:21:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8485936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8485936","identity":"rs-8485936","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}