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<journal-id journal-id-type="publisher-id">Front. Hum. Dyn.</journal-id>
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<journal-title>Frontiers in Human Dynamics</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Hum. Dyn.</abbrev-journal-title>
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<issn pub-type="epub">2673-2726</issn>
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<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-id pub-id-type="doi">10.3389/fhumd.2025.1730153</article-id>
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<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Attitudes toward mobile payment: a comparison study of Gulf States</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Al-Shamali</surname> <given-names>Sarah</given-names></name>
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<contrib contrib-type="author">
<name><surname>Almutairi</surname> <given-names>Shihanah</given-names></name>
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</contrib>
<contrib contrib-type="author">
<name><surname>Albalushi</surname> <given-names>Ammar</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<name><surname>Alsaber</surname> <given-names>Ahmad</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author">
<name><surname>Nafea</surname> <given-names>Rania</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<name><surname>AlReshaid</surname> <given-names>Faisal</given-names></name>
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<aff id="aff1"><label>1</label><institution>College of Business and Economics, American University of Kuwait</institution>, <city>Safat</city>, <country country="kw">Kuwait</country></aff>
<aff id="aff2"><label>2</label><institution>Business Management Department, Majan University College</institution>, <city>Ruwi</city>, <country country="om">Oman</country></aff>
<aff id="aff3"><label>3</label><institution>Al Yamamah University</institution>, <city>Khobar</city>, <country country="sa">Saudi Arabia</country></aff>
<author-notes>
<corresp id="c001"><label>&#x0002A;</label>Correspondence: Sarah Al-Shamali, <email xlink:href="mailto:salshamali1@auk.edu.kw">salshamali1@auk.edu.kw</email></corresp>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-23">
<day>23</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>7</volume>
<elocation-id>1730153</elocation-id>
<history>
<date date-type="received">
<day>22</day>
<month>10</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>23</day>
<month>12</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>29</day>
<month>12</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2026 Al-Shamali, Almutairi, Albalushi, Alsaber, Nafea and AlReshaid.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Al-Shamali, Almutairi, Albalushi, Alsaber, Nafea and AlReshaid</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-23">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<p>The main objective of the current study is to determine the adoption of mobile payment platforms within the Gulf Cooperation Council (GCC) region, focusing on Kuwait, Oman, and Bahrain. The research addresses the critical issue of perceived security (PS) and its impact on user trust and attitudes toward mobile payment adoption. This research examines the differences in adoption between three GCC nations, providing insights into the security variables that influence user perceptions and trust. Using a survey distributed to 859 respondents, the research employs Structural Equation Modeling (SEM) to analyze the relationships between security measures, cultural factors, perceived security, trust, and user attitudes. The findings reveal that perceived security significantly impacts both user attitudes and trust toward mobile payment usage. Security technology protection (STP) and security rules and policies (SRP) enhance perceived security but do not directly influence trust. Security responsibility commitment (SRC) and coverage of mobile payment context (CMPC) positively affect perceived security, while uncertainty avoidance significantly influences both perceived security and trust. These results underscore the importance of robust security measures and cultural considerations in promoting mobile payments. Previous research has highlighted the importance of security measures in fostering trust and encouraging the use of mobile payments, but there is a lack of empirical data on how more specific security-related factors vary across emerging markets. The study&#x00027;s implications suggest that service providers should focus on enhancing security perceptions and addressing cultural factors to build trust and encourage the use of their systems. This research contributes to the existing literature by providing empirical data on the determinants of mobile payment adoption in emerging markets and offering valuable insights for policymakers and stakeholders in the GCC&#x00027;s digital economy and market entry.</p></abstract>
<kwd-group>
<kwd>attitudes</kwd>
<kwd>digital payment platforms</kwd>
<kwd>Gulf Cooperation Council (GCC)</kwd>
<kwd>mobile payment</kwd>
<kwd>security</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. Funding for this paper was attained by an internal grant from the American University of Kuwait.</funding-statement>
</funding-group>
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<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Digital Impacts</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<label>1</label>
<title>Introduction</title>
<p>Mobile payment platforms have become an integral component of contemporary commerce, enabling consumers to initiate and complete transactions through smartphones and related digital interfaces. While mobile payments offer convenience and can support more efficient and potentially more sustainable financial ecosystems, their continued diffusion depends on whether users perceive these services as safe and trustworthy. Prior research consistently shows that users&#x00027; evaluations of security and trust are central determinants of favorable responses to mobile payment platforms, including more positive attitudes toward their usage.</p>
<p>Security remains particularly salient because mobile payments involve the transmission and storage of sensitive personal and financial data. Users are more likely to engage with mobile payment services when they believe that transactions are protected and that providers act reliably and responsibly. In other words, users&#x00027; perceived security (PS) is a foundational mechanism through which technical and organizational safeguards translate into trust and ultimately shape attitudes toward mobile payment usage.</p>
<p>These concerns are highly relevant in the Gulf Cooperation Council (GCC), where governments and service providers are accelerating digital service delivery and encouraging cashless transactions. However, consumer confidence in digital transactions is uneven, and concerns about privacy, fraud, and system vulnerabilities can undermine trust in payment services. Moreover, although GCC countries share important cultural and regional characteristics, they also differ in the maturity of their digital ecosystems, payment infrastructures, and user experiences. Accordingly, understanding whether established security&#x02013;trust mechanisms generalize across GCC markets&#x02014;and identifying which security-related drivers matter most in each context&#x02014;has both theoretical and practical value.</p>
<p>Despite the growing body of mobile payment research, two gaps remain. First, many studies examine &#x0201C;security&#x0201D; at a general level, offering limited empirical insight into how specific security-related features and commitments differentially influence perceived security and trust. Distinguishing among dimensions such as security technology protection (STP), security rules and policies (SRP), and security responsibility commitment (SRC) is important because these elements may shape user perceptions through different psychological pathways. Second, cross-country evidence in emerging markets&#x02014;particularly within the GCC&#x02014;remains limited, and much of the existing work relies on single-country designs. This constrains generalizability and makes it difficult for researchers and practitioners to know whether validated models travel across national contexts within the region.</p>
<p>To address these gaps, this study extends a previously validated mobile payment adoption framework by testing it in a cross-country GCC context. Using survey data collected from 859 respondents in Kuwait, Oman, and Bahrain, we apply PLS-SEM to examine how security-related factors, coverage of the mobile payment context, and uncertainty avoidance shape perceived security and trust, and how these mediators influence users&#x00027; attitudes toward mobile payment usage. By adopting a multi-country design and employing a more granular conceptualization of security-related antecedents, this research contributes: (1) comparative evidence on the generalizability of a security&#x02013;trust mechanism across GCC markets, (2) a clearer understanding of which security dimensions influence trust directly vs. indirectly through perceived security, and (3) actionable implications for mobile payment providers and policymakers seeking to accelerate trustworthy digital payments in the GCC.</p>
<p>The remainder of this paper reviews the relevant literature and develops the hypotheses, describes the methodology and data, presents the results, and concludes with a discussion, implications, and limitations.</p>
</sec>
<sec id="s2">
<label>2</label>
<title>Literature review</title>
<p>Mobile payment adoption has commonly been examined through technology adoption perspectives such as TAM/UTAUT, which posit that users form evaluative judgments about a technology, such as attitudes toward using it, based on beliefs about its expected outcomes and the conditions enabling its use. In payment settings, however, these evaluations are strongly shaped by uncertainty and perceived risk, because transactions involve sensitive financial and personal information. Accordingly, adoption research increasingly emphasizes that attitudes toward using digital payment technologies depend not only on functional benefits, but also on whether users believe the system is secure and dependable.</p>
<p>Trust theory further explains why security-related beliefs are central in mobile payment contexts. Trust reduces perceived vulnerability and facilitates engagement when users cannot fully observe or control the service environment. In mobile payments, users infer trustworthiness from cues such as the platform&#x00027;s technical protections, formal procedures, and visible commitments to safeguarding users&#x00027; assets and data. These cues influence perceived security, which in turn supports the development of trust, ultimately shaping users&#x00027; attitudes toward mobile payment usage.</p>
<p>Finally, cultural dimensions&#x02014;particularly uncertainty avoidance&#x02014;help explain why the strength of security&#x02013;trust mechanisms may vary across countries. In higher uncertainty-avoidance contexts, users typically exhibit lower tolerance for ambiguous or risky outcomes and therefore rely more heavily on safeguards and trust-building cues when evaluating technologies. For GCC markets that share regional similarities but differ in digital ecosystem maturity and consumer experience, integrating uncertainty avoidance provides a culturally grounded rationale for cross-country comparison. Building on these perspectives, the present study models perceived security and trust as central mechanisms linking security-related antecedents, contextual coverage, and uncertainty avoidance to attitudes toward mobile payment usage (<xref ref-type="fig" rid="F1">Figure 1</xref>).</p>
<fig position="float" id="F1">
<label>Figure 1</label>
<caption><p>Conceptual model.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fhumd-07-1730153-g0001.tif">
<alt-text content-type="machine-generated">Flowchart illustrating the relationship between security measures, payment culture, perceived security, trust, and users&#x00027; attitude. Security measures include technology protection, rules and policies, and responsibility commitment. Payment culture involves coverage of mobile payment context and uncertainty avoidance. Arrows indicate influences on perceived security and trust, which in turn affect users' attitude.</alt-text>
</graphic>
</fig>
<p>To begin, security measures are one of the most fundamental aspects that shapes user attitudes toward the adoption of online mobile payment platforms, which surged due to COVID-19 (<xref ref-type="bibr" rid="B26">Hardiyanto et al., 2023</xref>). A robust perception of security is crucial for building trust among users, which in turn affects their willingness to adopt mobile payment systems (<xref ref-type="bibr" rid="B18">Chaw et al., 2024</xref>). Research indicates that users are more likely to trust mobile payment services when they perceive that their transactions are secure. For instance, <xref ref-type="bibr" rid="B55">Yunita and Andajani (2021)</xref> emphasize that consumers&#x00027; perceptions of security guarantees are vital for fostering trust in electronic payment systems, suggesting that enhanced security measures can lead to increased user confidence in mobile payments. Similarly, <xref ref-type="bibr" rid="B53">Yan and Pan (2015)</xref> highlight that a lack of trust in service providers can deter users from engaging with mobile payment solutions, reinforcing the necessity of trust-building mechanisms to facilitate adoption.</p>
<p>Moreover, perceived security risks also play a critical role in shaping user attitudes. Studies have shown that users often associate mobile payments with potential security threats, which can lead to reluctance in adopting these technologies. For example, <xref ref-type="bibr" rid="B24">Hampshire (2017)</xref> found that many consumers perceive mobile payment solutions as insecure compared to traditional payment methods. This sentiment is echoed by <xref ref-type="bibr" rid="B57">Zhao (2015)</xref> who discuss the importance of addressing security concerns to increase user trust. Hence, the importance of robust security systems is crucial for their adoption. The role of security is especially important in the Gulf Cooperation Council (GCC) region. In his review, <xref ref-type="bibr" rid="B46">Radcliffe (2018)</xref> writes that Kuwaiti e-government services have reported numerous risks to their systems which has resulted in security and privacy concerns among citizens which may prevent them from trusting, and therefore using, e-government services. This is concerning as in 2021 the Kuwait government launched &#x0201C;Sahel&#x0201D;, a platform that provides access to a vast array of electronic services across numerous ministries, as bill payments, license renewals, and civil ID services. This concern about online transactions is not just restricted to e-government services; e-commerce in the region has similarly grappled with security issues due to a lack of professional IT personnel, negatively impacting the industry.</p>
<p>Considering cultural or context-specific factors that may influence user attitudes toward mobile payment platforms is also important. As mobile payment systems become more accessible, users are more likely to perceive them as convenient, which positively influences users&#x00027; continuance intentions (<xref ref-type="bibr" rid="B55">Yunita and Andajani, 2021</xref>). Literature highlights that perceived convenience mediates the relationship between perceived risk and the intention to use mobile payments, suggesting that when users find the coverage mobile payment options readily available and easy to use, their likelihood of adoption increases (<xref ref-type="bibr" rid="B47">Richard et al., 2023</xref>). During the COVID-19 pandemic, increased accessibility to mobile payment options led to a more favorable attitude among users, as they sought convenient solutions for transactions during times of social distancing (<xref ref-type="bibr" rid="B25">Handayati and Trisnawati, 2023</xref>).</p>
<p>It has been found that uncertainty avoidance also significantly influences mobile payment adoption. For example, in Italy, a high uncertainty avoidance culture, social influence plays a crucial role in adoption intentions, while tradition remains a significant barrier (<xref ref-type="bibr" rid="B39">Migliore et al., 2022</xref>). Similarly, Hua et al. (2015) report that uncertainty avoidance is an antecedent of consumer trust in mobile payment adoption. It also moderates the relationship between perceived usefulness, subjective norms, and adoption intentions in both China and Pakistan (<xref ref-type="bibr" rid="B1">Akhtar et al., 2018</xref>). In another study it was found to significantly increases resistance to mobile banking innovations, particularly regarding image and risk barriers (<xref ref-type="bibr" rid="B3">AlMutairi and Yen, 2022</xref>). In Vietnam, uncertainty avoidance and collectivism impact performance expectancy, behavioral intention, and consumer trust in mobile payment adoption (<xref ref-type="bibr" rid="B40">Nguyen et al., 2022</xref>).</p>
<p>Developing countries vary greatly in their growth and adoption of m-payment (<xref ref-type="bibr" rid="B13">Aslam et al., 2017</xref>). The governments of the GCC in particular are heavily promoting technology and internet usage which support mobile payment systems. For example, Oman&#x00027;s &#x0201C;Digital Oman Strategy&#x0201D; focuses on internet infrastructure and digital literacy<xref ref-type="fn" rid="fn0003"><sup>1</sup></xref>, and public-private partnerships have fostered fintech innovation, supporting mobile payment adoption (see text footnote <xref ref-type="fn" rid="fn0003">1</xref>). Similarly, Bahrain&#x00027;s &#x0201C;Economic Vision 2030&#x0201D; outlines economic diversification with a strong digital economy focus (<xref ref-type="bibr" rid="B10">Alsarraf and Almutairi, 2025</xref>; Bahrain Economic Vision 2030). Initiatives include a national broadband network and e-government services for mobile payments (<xref ref-type="bibr" rid="B14">Bahrain Telecommunications Regulatory Authority, 2020</xref>). <xref ref-type="bibr" rid="B31">Khan and Al-harby (2022)</xref> highlight that digital payments are becoming increasingly dominant in the financial technology sector, especially in Saudi Arabia. According to the Fintech Saudi 2021 survey, 69% of respondents use digital payments, making it the most significant part of Fintech adoption in the country (<xref ref-type="bibr" rid="B5">Al-Okaily et al., 2025</xref>). This trend is also seen in other GCC countries like the UAE, Bahrain, and Kuwait, where over 60% of the population use electronic payment methods. With this being said, financial service security concerns are also very real. The &#x0201C;Let&#x00027;s Be Aware&#x0201D; (Diraya) campaign was launched by the Central Bank of Kuwait (CBK) in collaboration with local banks and Kuwait Banking Association, due to cyber fraud. The campaign aims to foster financial literacy and educate customers about fraudulent emails and links. Hence, although there is heavy promotion of technology, in parallel there is also a concern for security (<xref ref-type="bibr" rid="B34">Kuwait Times, 2025</xref>).</p>
<p>With these strategic initiatives in mind, our research is highly relevant and important, especially considering the direction of GCC governments to promote digital economies and technological advancements. Understanding consumer attitudes toward digital platforms in these countries can inform better policy and strategy development, supporting regional digital goals (<xref ref-type="bibr" rid="B59">Zulfiqar et al., 2025</xref>). Though financial technology such as digital payments is increasing in the GCC region, it is also in the infancy stage (<xref ref-type="bibr" rid="B31">Khan and Al-harby, 2022</xref>). Therefore, the evaluation of the use of various technologies in the financial sector, the adoption of technology from the side of both supply and demand, are still important for relevant stakeholders (<xref ref-type="bibr" rid="B8">Alsabah et al., 2025</xref>).</p>
<p>Major knowledge gaps in the literature still exist. The first is a lack of empirical data on differences between countries, in addition to the limited investigation into the antecedents of perceived security (PS) and trust, as well as research objectives that delve deeply into the security variables of mobile payment platforms. Few existing studies have used empirical data to discuss why mobile payment adoption differs between countries, particularly within emerging regions. A prior study by <xref ref-type="bibr" rid="B11">Al-Shamali et al. (2025)</xref> successfully developed and validated a model explaining the roles of perceived security, trust, and security measures on mobile payment adoption, but its focus was limited to the Kuwaiti market. That research established the instrument&#x00027;s reliability but called for further research to test its generalizability. This current study answers that call by building directly upon that established framework, extending the same methodology to a multi-national context by collecting new data from Kuwait, Oman, and Bahrain to perform a comparative cross-cultural validation. Doing so will provide online mobile platforms with nuanced insights into mobile payment adoption among such countries. The security features provided by mobile payment platforms are paramount for their users. Therefore, this research examines various security variables (e.g. security technology and protection) and incorporates them in different roles, such as antecedents (e.g. perceived security) and independent factors (e.g. security rules and policies) within a conceptual model (see <xref ref-type="fig" rid="F1">Figure 1</xref>).</p>
<p>As such, based on the preceding review, the study hypotheses are formulated next to operationalize the proposed relationships in <xref ref-type="fig" rid="F1">Figure 1</xref>. We begin by outlining the theoretical role of perceived security and trust as central mechanisms, and then develop hypotheses for each antecedent in the proposed model.</p>
</sec>
<sec id="s3">
<label>3</label>
<title>Conceptual framework and hypothesis development</title>
<list list-type="simple">
<list-item><p>(a) Perceived security (PS)</p></list-item>
</list>
<p>Defined as a user&#x00027;s subjective belief in safety, perceived security gauges the extent to which a consumer trusts a technology or service to adequately protect their financial and personal assets. This psychological construct is a critical determinant of trust, which in turn affects acceptance and sustained use. <xref ref-type="bibr" rid="B32">Kim et al. (2010)</xref> emphasize that consumers&#x00027; perceptions of security guarantees are crucial in building trust in electronic payment systems, suggesting that enhanced security measures can lead to increased user trust in mobile payments. Similarly, <xref ref-type="bibr" rid="B15">Bulsara and Pandya (2019)</xref> argue that trust among customers toward mobile payment systems is guaranteed. Perceived security strongly affects mobile payment adoption (<xref ref-type="bibr" rid="B6">Alotaibi and Alghamdi, 2022</xref>). Factors like security, ease of use, usefulness, and trust are intertwined with adoption (<xref ref-type="bibr" rid="B27">Hee et al., 2020</xref>). The perceived level of security exposure during adoption significantly influences users&#x00027; intentions (<xref ref-type="bibr" rid="B35">Lai and Liew, 2021</xref>). These studies collectively underscore the significance of perceived security in shaping user attitudes and intentions toward mobile payment platforms.</p>
<p><italic>Hypothesis 1: Perceived security affects users&#x00027; attitudes toward mobile payment usage</italic>.</p>
<p>Perceived security significantly impacts trust in mobile payment services (<xref ref-type="bibr" rid="B56">Zhang et al., 2019</xref>; <xref ref-type="bibr" rid="B6">Alotaibi and Alghamdi, 2022</xref>), and users&#x00027; intention to continue using them (<xref ref-type="bibr" rid="B27">Hee et al., 2020</xref>; <xref ref-type="bibr" rid="B24">Hampshire, 2017</xref>). Thus, maintaining high perceived security is crucial for fostering trust and promoting sustained usage (<xref ref-type="bibr" rid="B56">Zhang et al., 2019</xref>; <xref ref-type="bibr" rid="B6">Alotaibi and Alghamdi, 2022</xref>).</p>
<p><italic>Hypothesis 3: Perceived security affects trust toward mobile payment usage</italic>.</p>
<list list-type="simple">
<list-item><p>(b) Trust (TR)</p></list-item>
</list>
<p>Trust emerges as a critical factor influencing user attitudes toward mobile payment systems (<xref ref-type="bibr" rid="B43">Pelegrin, 2021</xref>; <xref ref-type="bibr" rid="B52">Xin et al., 2015</xref>; <xref ref-type="bibr" rid="B45">Qasim and Abu-Shanab, 2015</xref>; <xref ref-type="bibr" rid="B42">Park et al., 2018</xref>; <xref ref-type="bibr" rid="B41">Olaleye et al., 2022</xref>; <xref ref-type="bibr" rid="B51">Wu and Tang, 2022</xref>). Studies consistently demonstrate that trust in service providers directly correlates with users&#x00027; willingness to adopt mobile payment solutions (<xref ref-type="bibr" rid="B20">Fan et al., 2018</xref>). The importance of trust is further reinforced by <xref ref-type="bibr" rid="B29">Hossain (2019)</xref>, who notes that a lack of trust can hinder the acceptance of mobile payment systems. Thus, trust serves to alleviate user concerns surrounding perceived risks while bolstering the perceived utility and advantages, resulting in stronger intentions for both initial and repeated use (<xref ref-type="bibr" rid="B44">Putritama, 2019</xref>; <xref ref-type="bibr" rid="B54">Yeboah et al., 2020</xref>). Based on this:</p>
<p><italic>Hypothesis 2: Trust affects users&#x00027; attitude toward mobile payment usage</italic>.</p>
<p><italic>Security measures</italic></p>
<list list-type="simple">
<list-item><p>(a) Security technology protection (STP)</p></list-item>
</list>
<p>Security is a paramount concern for users when adopting mobile payment systems. The mobile payment security technology protection (STP) in this paper refers to the technical measures taken by these platforms to safeguard users&#x00027; privacy information and property. <xref ref-type="bibr" rid="B33">Kousaridas et al. (2008)</xref> writes that technical protection related to privacy, integrity and stability is conducive to improve users&#x00027; PS and trust. Similarly, <xref ref-type="bibr" rid="B20">Fan et al. (2018)</xref> reports that STP significantly impacts PS and trust in mobile payment systems. Once gained, perceived safety and trustworthiness from users, positively influence the intention to use mobile payment platforms. These findings stress the critical role of STP in shaping user perceptions and trust, influencing system adoption and usage (<xref ref-type="bibr" rid="B49">Su et al., 2021</xref>; <xref ref-type="bibr" rid="B38">Liu and Peng, 2020</xref>; <xref ref-type="bibr" rid="B56">Zhang et al., 2019</xref>). Based on this, we hypothesize the following:</p>
<p><italic>Hypothesis 4a: Security technology protection influences the perceived security of mobile payment usage</italic>.</p>
<p><italic>Hypothesis 4b: Security technology protection influences trust toward mobile payment usage</italic>.</p>
<list list-type="simple">
<list-item><p>(b) Security rules and policies (SRP)</p></list-item>
</list>
<p>For this research, Security rules and policies (SRP) consist of the transaction procedures, security statements and other administrative rules of mobile payment, such as authentication, validation, and revision rules. Users&#x00027; PS and trust in mobile payment systems are enhanced when SRP is clearly defined, reducing their concerns about privacy and fraud (<xref ref-type="bibr" rid="B6">Alotaibi and Alghamdi, 2022</xref>; <xref ref-type="bibr" rid="B22">Ghaisani et al., 2022</xref>; <xref ref-type="bibr" rid="B37">Lisana, 2022</xref>). This confidence is reinforced by strong SRP, which evidently increases trust in these platforms (<xref ref-type="bibr" rid="B20">Fan et al., 2018</xref>). This trust, in turn, acts as the primary driver of mobile payment adoption intentions, synthesized from an evaluation of the service&#x00027;s security, mobility, customization options, and reputation (<xref ref-type="bibr" rid="B55">Yunita and Andajani, 2021</xref>). Therefore, given its profound influence on trust, the security feature of such systems is a prerequisite for service consumption (<xref ref-type="bibr" rid="B30">Hwang et al., 2021</xref>). Thus, we hypothesize the following:</p>
<p><italic>Hypothesis 5a: Security rules and policies influence the perceived security of mobile payment usage</italic>.</p>
<p><italic>Hypothesis 5b: Security rules and policies influence trust toward mobile payment usage</italic>.</p>
<list list-type="simple">
<list-item><p>(c) Security responsibility commitment (SRC)</p></list-item>
</list>
<p>Security responsibility commitment (SRC) refers to the security measures and cooperation agreements made by mobile payment platforms to protect users&#x00027; information and property. SRC is a major factor that determines the user&#x00027;s PS when they engage with platforms (<xref ref-type="bibr" rid="B20">Fan et al., 2018</xref>). A robust security responsibility commitment enhances trust and positively impacts users&#x00027; PS (<xref ref-type="bibr" rid="B7">AlReshaid et al., 2025</xref>; <xref ref-type="bibr" rid="B22">Ghaisani et al., 2022</xref>). State-of-the-art encryption tools and third-party certification enhance trust in platforms by reducing uncertainties (<xref ref-type="bibr" rid="B50">Thakur and Srivastava, 2014</xref>). Thus, emphasizing security responsibility in mobile payment services is vital for building user trust and encouraging platform usage (<xref ref-type="bibr" rid="B6">Alotaibi and Alghamdi, 2022</xref>). Hence, we developed the following:</p>
<p><italic>Hypothesis 6a: Security responsibility commitment influences the perceived security of mobile payment usage</italic>.</p>
<p><italic>Hypothesis 6b: Security responsibility commitment influences trust toward mobile payment usage</italic>.</p>
<p><italic>Payment infrastructure and culture</italic></p>
<list list-type="simple">
<list-item><p>(a) Coverage of mobile payment context (CMPC)</p></list-item>
</list>
<p>The coverage or useability of mobile payment platforms refers to how available they are, and how often they can be used. The availability of platforms influence users&#x00027; perceived security and trust, which in turn affect their attitude toward mobile payment use (<xref ref-type="bibr" rid="B20">Fan et al., 2018</xref>). <xref ref-type="bibr" rid="B58">Zhou (2011)</xref> also reports how widespread these platforms is beneficial to enhance customers&#x00027; trust in them. Based on this, we hypothesize the following:</p>
<p><italic>Hypothesis 7a: Coverage of mobile payment context influences perceived security of mobile payment usage</italic>.</p>
<p><italic>Hypothesis 7b: Coverage of mobile payment context influences trust toward mobile payment usage</italic>.</p>
<list list-type="simple">
<list-item><p>(b) Uncertainty avoidance (UA)</p></list-item>
</list>
<p><xref ref-type="bibr" rid="B36">Liljander et al. (2006)</xref> elucidate that cultures with high uncertainty avoidance (UA) require more specific details, structures, and features, exhibiting lower tolerance for the unknown. In contrast, cultures with low UA demonstrate less emphasis on efficiency and structure, yet they exhibit a greater willingness to experiment with unexpected technologies due to their open-minded approach toward innovation. Similarly, <xref ref-type="bibr" rid="B52">Xin et al. (2015)</xref> report it also significantly influences trust in mobile payment systems. Users with low uncertainty avoidance are more inclined to accept risk and show greater trust. Yet, issues of privacy and transaction security increase the feeling of uncertainty, especially among users who are skeptical of technology (<xref ref-type="bibr" rid="B17">Chang et al., 2021</xref>). However, reducing uncertainties can enhance trust in service providers (<xref ref-type="bibr" rid="B42">Park et al., 2018</xref>). Trust plays a vital role in addressing uncertainties and security concerns associated with mobile payments, thus influencing user adoption and continuance intention (<xref ref-type="bibr" rid="B48">Sembiring and Aruan, 2020</xref>).</p>
<p><italic>Hypothesis 8a: High uncertainty avoidance influences the perceived security of mobile payment usage</italic>.</p>
<p><italic>Hypothesis 8b: High uncertainty avoidance influences trust toward mobile payment usage</italic>.</p>
</sec>
<sec id="s4">
<label>4</label>
<title>Methods</title>
<p>This study employed a quantitative, cross-sectional survey design to examine the proposed relationships among security-related factors, perceived security, trust, and attitudes toward mobile payment usage across three GCC countries (Kuwait, Oman, and Bahrain). Data were collected via an online questionnaire. The following subsections describe the sample and data collection procedure, measurement operationalization, and the analytical approach used to test the measurement and structural models.</p>
<sec>
<label>4.1</label>
<title>Sample</title>
<p>The data collection methodology involved distributing a survey link primarily using snowball sampling, in three GCC countries. Mobile payment platforms familiar to respondents in each country were included in the surveys. The study involved 859 respondents in total from Kuwait, Oman, and Bahrain as shown in <xref ref-type="table" rid="T1">Table 1</xref>.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Demographic characteristics of respondents in Kuwait, Oman and Bahrain.</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Demographic characteristic</bold></th>
<th valign="top" align="center"><bold>Kuwait (N = 534)</bold></th>
<th valign="top" align="center"><bold>Oman (<italic>N</italic> = 194)</bold></th>
<th valign="top" align="center"><bold>Bahrain (<italic>N</italic> = 131)</bold></th>
<th valign="top" align="center"><bold>Total (<italic>N</italic> = 859)</bold></th>
<th valign="top" align="center"><bold><italic>p-value</italic></bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>Gender</bold></td>
</tr>
<tr>
<td valign="top" align="left">Male</td>
<td valign="top" align="center">246.0 (46.1%)</td>
<td valign="top" align="center">81.0 (41.8%)</td>
<td valign="top" align="center">54.0 (41.2%)</td>
<td valign="top" align="center">381.0 (44.4%)</td>
<td valign="top" align="center">0.430<sup>a</sup></td>
</tr>
<tr>
<td valign="top" align="left">Female</td>
<td valign="top" align="center">288.0 (53.9%)</td>
<td valign="top" align="center">113.0 (58.2%)</td>
<td valign="top" align="center">77.0 (58.8%)</td>
<td valign="top" align="center">478.0 (55.6%)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><bold>Education</bold></td>
</tr>
<tr>
<td valign="top" align="left">Less than high school</td>
<td valign="top" align="center">17.0 (3.2%)</td>
<td valign="top" align="center">2.0 (1.0%)</td>
<td valign="top" align="center">0.0 (0.0%)</td>
<td valign="top" align="center">19.0 (2.2%)</td>
<td valign="top" align="center">&#x0003C; 0.001<sup>a</sup></td>
</tr>
<tr>
<td valign="top" align="left">High school</td>
<td valign="top" align="center">104.0 (19.5%)</td>
<td valign="top" align="center">27.0 (13.9%)</td>
<td valign="top" align="center">29.0 (22.1%)</td>
<td valign="top" align="center">160.0 (18.6%)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">Diploma</td>
<td valign="top" align="center">83.0 (15.5%)</td>
<td valign="top" align="center">50.0 (25.8%)</td>
<td valign="top" align="center">6.0 (4.6%)</td>
<td valign="top" align="center">139.0 (16.2%)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">Bachelor</td>
<td valign="top" align="center">278.0 (52.1%)</td>
<td valign="top" align="center">101.0 (52.1%)</td>
<td valign="top" align="center">67.0 (51.1%)</td>
<td valign="top" align="center">446.0 (51.9%)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">Post-graduate</td>
<td valign="top" align="center">52.0 (9.7%)</td>
<td valign="top" align="center">14.0 (7.2%)</td>
<td valign="top" align="center">29.0 (22.1%)</td>
<td valign="top" align="center">95.0 (11.1%)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><bold>Salary</bold></td>
</tr>
<tr>
<td valign="top" align="left">Very low&#x02014;I can&#x00027;t cover all my basic needs</td>
<td valign="top" align="center">264.0 (49.4%)</td>
<td valign="top" align="center">130.0 (67.0%)</td>
<td valign="top" align="center">61.0 (46.6%)</td>
<td valign="top" align="center">455.0 (53.0%)</td>
<td valign="top" align="center">&#x0003C; 0.001<sup>a</sup></td>
</tr>
<tr>
<td valign="top" align="left">Low&#x02014;I can cover only my basic needs with difficulty</td>
<td valign="top" align="center">112.0 (21.0%)</td>
<td valign="top" align="center">30.0 (15.5%)</td>
<td valign="top" align="center">44.0 (33.6%)</td>
<td valign="top" align="center">186.0 (21.7%)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">Moderate&#x02014;I can cover my basic needs and some extras occasionally</td>
<td valign="top" align="center">86.0 (16.1%)</td>
<td valign="top" align="center">22.0 (11.3%)</td>
<td valign="top" align="center">14.0 (10.7%)</td>
<td valign="top" align="center">122.0 (14.2%)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">High&#x02014;I can comfortably cover my needs and save a small portion</td>
<td valign="top" align="center">42.0 (7.9%)</td>
<td valign="top" align="center">5.0 (2.6%)</td>
<td valign="top" align="center">4.0 (3.1%)</td>
<td valign="top" align="center">51.0 (5.9%)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">Very high&#x02014;I can cover all my needs with ease and invest or save significantly</td>
<td valign="top" align="center">30.0 (5.6%)</td>
<td valign="top" align="center">7.0 (3.6%)</td>
<td valign="top" align="center">8.0 (6.1%)</td>
<td valign="top" align="center">45.0 (5.2%)</td>
<td/>
</tr></tbody>
</table>
<table-wrap-foot>
<p><sup>a</sup>Pearson&#x00027;s Chi-squared test was used to evaluate the statistical significance of differences in demographic characteristics across the three countries.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec>
<label>4.2</label>
<title>Common method bias assessment</title>
<p>Data was collected using a self-report, cross-sectional instrument, because of this, several procedural and statistical procedures were taken to mitigate and test for Common-Method Variance (CMV). Procedurally, the survey incorporated anonymity assurances, randomized item ordering, psychological separation between predictor and outcome constructs, and varied scale anchors to reduce respondent consistency bias.</p>
<p>First, Harman&#x00027;s single-factor test indicated that the first unrotated factor accounted for 27.4% of the variance, well below the 40&#x02013;50% threshold typically associated with CMV concerns. Second, full collinearity VIFs were calculated for all constructs. All VIF values ranged from 1.42 to 2.61, below the recommended cutoff of 3.3, indicating that neither vertical nor lateral collinearity threatens the model and that CMV is unlikely to bias the results. Third, a marker-variable analysis was performed using an unrelated cognitive tendency item embedded in the dataset. Correlations between the marker variable and all study constructs were close to zero, and CMV-adjusted structural path coefficients remained virtually identical to the original estimates, confirming that CMV does not substantively impact the findings.</p>
</sec>
<sec>
<label>4.3</label>
<title>Measures</title>
<p>The survey instrument used in this study was adopted from <xref ref-type="bibr" rid="B11">Al-Shamali et al. (2025)</xref>, which validated the constructs from <xref ref-type="bibr" rid="B20">Fan et al. (2018)</xref>. <xref ref-type="bibr" rid="B11">Al-Shamali et al. (2025)</xref> confirmed the instrument&#x00027;s reliability (Cronbach&#x00027;s a &#x0003E; 0.7 for all constructs) and both convergent and discriminant validity (AVE &#x0003E; 0.5). The survey consists of the same items, evaluated on a 5-point Likert scale.</p>
</sec>
</sec>
<sec sec-type="results" id="s5">
<label>5</label>
<title>Results</title>
<p>Gender distribution was relatively balanced across countries, with females slightly outnumbering males; however, this difference was not statistically significant (<italic>p</italic> = 0.430; see <xref ref-type="table" rid="T1">Table 1</xref>). Educational attainment varied significantly among the three countries (<italic>p</italic> &#x0003C; 0.001), with Bahrain showing the highest proportion of postgraduate respondents (22.1%) compared to Kuwait (9.7%) and Oman (7.2%). Salary levels also differed significantly (<italic>p</italic> &#x0003C; 0.001). Respondents in Oman reported the highest proportion of &#x0201C;Very Low&#x0201D; income, indicating difficulty in meeting basic needs (67.0%), followed by Kuwait (49.4%) and Bahrain (46.6%). In contrast, a higher percentage of respondents in Kuwait reported &#x0201C;High&#x0201D; or &#x0201C;Very High&#x0201D; income levels (13.5% combined) compared to Oman (6.2%) and Bahrain (9.2%). These findings highlight notable differences in the socioeconomic and educational contexts across the three countries.</p>
<p>The Structural Equation Modeling (SEM) approach utilized in the proposed conceptual framework presents an advanced analytical method, allowing for the simultaneous examination of interconnections among observed variables, latent constructs, and their indicators. SEM enables the assessment of hypothesized relationships, encompassing direct, indirect, and mediating effects, while also gauging the significance and strength of pathways, thereby assisting in the validation of theories (<xref ref-type="bibr" rid="B9">Alsaber et al., 2025</xref>). <xref ref-type="fig" rid="F2">Figure 2</xref> illustrates the SEM used to examine the relationships between security measures, cultural factors, perceived security, trust, and attitudes toward mobile payment adoption. Independent variables&#x02014;Security Technology Protection (STP), Security Rules and Policies (SRP), Security Responsibility Commitment (SRC), Coverage of Mobile Payment Context (CMPC), and Uncertainty Avoidance (UA)&#x02014;influence two mediating constructs, Perceived Security (PS) and Trust (TR). The standardized path coefficients show the strength of these relationships, with notable effects such as CMPC on PS (0.284) and PS on Attitude (0.393). The <italic>R</italic><sup>2</sup> values indicate the explained variance, with PS (<italic>R</italic><sup>2</sup> = 0.528), TR (<italic>R</italic><sup>2</sup> = 0.621), and Attitude (<italic>R</italic><sup>2</sup> = 0.520), highlighting the model&#x00027;s predictive power. Observed variables (e.g., PS1, PS2, PS3) exhibit strong loadings, confirming construct validity.</p>
<fig position="float" id="F2">
<label>Figure 2</label>
<caption><p>Node diagram with loading.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fhumd-07-1730153-g0002.tif">
<alt-text content-type="machine-generated">Diagram illustrating the relationships between various factors affecting perceived security (PS), trust (TR), and attitude (AT). Five main factors are depicted: Security Technology Protection (STP), Security Rules and Policies (SRP), Security Responsibility Commitment (SRC), Coverage of Mobile Payment Context (CMPC), and Uncertainty Avoidance (UA). Arrows indicate causal paths with weights, showing how these factors influence PS and TR, which in turn affect AT. Each factor has specific indicators connected to it, shown with respective loadings.</alt-text>
</graphic>
</fig>
<p>While the structural model confirms the universal importance of trust across the GCC, a granular analysis reveals that the specific drivers of this trust vary significantly by nation, reflecting distinct market trends and consumer priorities. In Oman, the analysis identifies Security Rules and Policies (SRP) as the dominant antecedent of confidence, whereas the path from Security Technology Protection (STP) was non-significant. This finding suggests that Omani users, operating in a developing digital ecosystem, prioritize institutional governance over technical features. For policymakers and providers in Oman, this implies that adoption strategies should focus less on app functionality and more on visible regulatory compliance and error-reversal guarantees to overcome high uncertainty avoidance.</p>
<p>Conversely, the Bahraini market exhibits a sophisticated &#x0201C;fintech-hub&#x0201D; behavior where Security Responsibility Commitment (SRC) emerges as the strongest predictor. This indicates that Bahraini consumers are highly sensitive to data rights and vendor accountability. Consequently, platforms entering Bahrain must differentiate themselves through transparent privacy agreements and user-control features rather than just operational reliability. Finally, Kuwait represents a balanced market where Security Rules and Policies remain the primary driver, yet unlike Oman, technical protections (STP) remain significant. This reflects a mature user base that demands both a seamless technical experience and the safety net of strong regulations.</p>
<p>Collectively, these findings demonstrate that while &#x0201C;security&#x0201D; is the goal across the GCC, the specific lever to pull&#x02014;governance in Oman, privacy in Bahrain, and a hybrid approach in Kuwait&#x02014;must be localized. In the context of the Gulf region, which is characterized by high uncertainty avoidance, these results indicate that user behavior is not driven primarily by convenience, but by risk reduction. The strong standardized coefficients suggest that for GCC users, trust is not an inherent trait but a conditional outcome that must be manufactured through visible security guarantees. Consequently, the dominant behavioral driver across Kuwait, Oman, and Bahrain is the psychological assurance of safety, without which the functional benefits of mobile payments fail to influence user attitudes.</p>
<p>To bridge the gap between statistical significance and practical application, the strongest paths in the structural model offer immediate directives for industry stakeholders in the GCC. First, the dominant influence of Perceived Security on Trust suggests that for mobile payment providers, security is not merely a technical requirement but a primary marketing asset. In practice, this means that customer acquisition strategies in the GCC should pivot from highlighting &#x0201C;speed and convenience&#x0201D; to highlighting &#x0201C;safety and reliability.&#x0201D; Marketing campaigns that explicitly visualize security features are likely to be more effective than those focusing solely on lifestyle benefits.</p>
<p>Second, the significant role of Security Responsibility Commitment (SRC) in driving Perceived Security has implications for platform development and User Interface (UI) design. The data suggests that users look for visible commitments from the provider. Therefore, developers should integrate &#x0201C;responsibility cues&#x0201D; &#x02014;such as clear fraud protection guarantees, visible money-back policies, and immediate access to support&#x02014;directly into the transaction screens, rather than burying them in terms of service.</p>
<p>Finally, the impact of Security Rules and Policies (SRP) highlights a critical role for policymakers and regulators. Since users derive security perceptions from the existence of clear rules, regulatory bodies in Kuwait, Oman, and Bahrain should continue to prioritize public awareness campaigns that educate citizens on the legal frameworks protecting their digital assets. For service providers, displaying certification badges and regulatory compliance markers on their landing pages is a data-backed strategy to lower the barrier to entry for risk-averse adopters.</p>
<sec>
<label>5.1</label>
<title>Measurement model assessment</title>
<p><xref ref-type="table" rid="T2">Table 2</xref> presents the outer model summary for the PLS-PM model, confirming the reliability and validity of the constructs. All constructs exhibit strong internal consistency, with Cronbach&#x00027;s alpha values ranging from 0.724 (SRC) to 0.871 (AT), and composite reliability (&#x003C1;c) values exceeding the recommended threshold of 0.7, ranging from 0.844 (SRC) to 0.921 (AT), as recommended by <xref ref-type="bibr" rid="B23">Hair et al. (2017)</xref>. Convergent validity is established, as Average Variance Extracted (AVE) values for all constructs surpass the 0.5 threshold, ranging from 0.622 (UA) to 0.795 (AT), consistent with <xref ref-type="bibr" rid="B21">Fornell and Larcker&#x00027;s (1981)</xref> criteria. Item loadings are robust, ranging from 0.683 (UA3) to 0.905 (AT2), indicating that each item effectively measures its respective construct, meeting the recommendations of <xref ref-type="bibr" rid="B23">Hair et al. (2017)</xref>. Variance inflation factor (VIF) values are below the critical threshold of 5, demonstrating no significant multicollinearity among the items, with VIF values ranging from 1.373 (SRC1) to 2.527 (AT2), as suggested by <xref ref-type="bibr" rid="B19">Diamantopoulos and Siguaw (2006)</xref>. Constructs such as AT, PS, and CMPC display particularly strong reliability and validity, supporting their inclusion in the model. These results validate the measurement model&#x00027;s robustness, ensuring its appropriateness for subsequent structural analysis.</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>Outer model summary table for PLS-PM model.</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Construct</bold></th>
<th valign="top" align="center"><bold>Items</bold></th>
<th valign="top" align="center"><bold>Loading</bold></th>
<th valign="top" align="center"><bold>VIF</bold></th>
<th valign="top" align="center"><bold>Cronbach&#x00027;s alpha</bold></th>
<th valign="top" align="center"><bold>Composite reliability (rho_a)</bold></th>
<th valign="top" align="center"><bold>Composite reliability (rho_c)</bold></th>
<th valign="top" align="center"><bold>Average variance extracted (AVE)</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left" rowspan="3">AT</td>
<td valign="top" align="center">AT1</td>
<td valign="top" align="center">0.892</td>
<td valign="top" align="center">2.270</td>
<td valign="top" align="center">0.871</td>
<td valign="top" align="center">0.872</td>
<td valign="top" align="center">0.921</td>
<td valign="top" align="center">0.795</td>
</tr>
<tr>
<td valign="top" align="center">AT2</td>
<td valign="top" align="center">0.905</td>
<td valign="top" align="center">2.527</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="center">AT3</td>
<td valign="top" align="center">0.877</td>
<td valign="top" align="center">2.195</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left" rowspan="3">CMPC</td>
<td valign="top" align="center">CMPC1</td>
<td valign="top" align="center">0.867</td>
<td valign="top" align="center">1.938</td>
<td valign="top" align="center">0.805</td>
<td valign="top" align="center">0.808</td>
<td valign="top" align="center">0.885</td>
<td valign="top" align="center">0.72</td>
</tr>
<tr>
<td valign="top" align="center">CMPC2</td>
<td valign="top" align="center">0.876</td>
<td valign="top" align="center">1.999</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="center">CMPC3</td>
<td valign="top" align="center">0.800</td>
<td valign="top" align="center">1.518</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left" rowspan="3">PS</td>
<td valign="top" align="center">PS1</td>
<td valign="top" align="center">0.873</td>
<td valign="top" align="center">2.037</td>
<td valign="top" align="center">0.847</td>
<td valign="top" align="center">0.847</td>
<td valign="top" align="center">0.907</td>
<td valign="top" align="center">0.766</td>
</tr>
<tr>
<td valign="top" align="center">PS2</td>
<td valign="top" align="center">0.880</td>
<td valign="top" align="center">2.117</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="center">PS3</td>
<td valign="top" align="center">0.872</td>
<td valign="top" align="center">1.986</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left" rowspan="3">SRC</td>
<td valign="top" align="center">SRC1</td>
<td valign="top" align="center">0.770</td>
<td valign="top" align="center">1.373</td>
<td valign="top" align="center">0.724</td>
<td valign="top" align="center">0.729</td>
<td valign="top" align="center">0.844</td>
<td valign="top" align="center">0.644</td>
</tr>
<tr>
<td valign="top" align="center">SRC2</td>
<td valign="top" align="center">0.814</td>
<td valign="top" align="center">1.478</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="center">SRC3</td>
<td valign="top" align="center">0.823</td>
<td valign="top" align="center">1.432</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left" rowspan="3">SRP</td>
<td valign="top" align="center">SRP1</td>
<td valign="top" align="center">0.817</td>
<td valign="top" align="center">1.465</td>
<td valign="top" align="center">0.778</td>
<td valign="top" align="center">0.78</td>
<td valign="top" align="center">0.871</td>
<td valign="top" align="center">0.692</td>
</tr>
<tr>
<td valign="top" align="center">SRP2</td>
<td valign="top" align="center">0.822</td>
<td valign="top" align="center">1.685</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="center">SRP3</td>
<td valign="top" align="center">0.855</td>
<td valign="top" align="center">1.787</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left" rowspan="2">STP</td>
<td valign="top" align="center">STP1</td>
<td valign="top" align="center">0.880</td>
<td valign="top" align="center">1.489</td>
<td valign="top" align="center">0.728</td>
<td valign="top" align="center">0.73</td>
<td valign="top" align="center">0.88</td>
<td valign="top" align="center">0.786</td>
</tr>
<tr>
<td valign="top" align="center">STP2</td>
<td valign="top" align="center">0.893</td>
<td valign="top" align="center">1.489</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left" rowspan="3">TR</td>
<td valign="top" align="center">TR1</td>
<td valign="top" align="center">0.859</td>
<td valign="top" align="center">1.852</td>
<td valign="top" align="center">0.814</td>
<td valign="top" align="center">0.815</td>
<td valign="top" align="center">0.89</td>
<td valign="top" align="center">0.729</td>
</tr>
<tr>
<td valign="top" align="center">TR2</td>
<td valign="top" align="center">0.874</td>
<td valign="top" align="center">2.047</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="center">TR3</td>
<td valign="top" align="center">0.828</td>
<td valign="top" align="center">1.636</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left" rowspan="5">UA</td>
<td valign="top" align="center">UA1</td>
<td valign="top" align="center">0.794</td>
<td valign="top" align="center">1.905</td>
<td valign="top" align="center">0.847</td>
<td valign="top" align="center">0.852</td>
<td valign="top" align="center">0.891</td>
<td valign="top" align="center">0.622</td>
</tr>
<tr>
<td valign="top" align="center">UA2</td>
<td valign="top" align="center">0.814</td>
<td valign="top" align="center">1.934</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="center">UA3</td>
<td valign="top" align="center">0.683</td>
<td valign="top" align="center">1.448</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="center">UA4</td>
<td valign="top" align="center">0.833</td>
<td valign="top" align="center">2.089</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="center">UA5</td>
<td valign="top" align="center">0.810</td>
<td valign="top" align="center">1.890</td>
<td/>
<td/>
<td/>
<td/>
</tr></tbody>
</table>
</table-wrap>
<p>Further, <xref ref-type="table" rid="T3">Table 3</xref> presents the loadings and cross-loadings for the model, providing evidence of discriminant validity. Each indicator exhibits its highest loading on the construct it is intended to measure, with all primary loadings exceeding the recommended threshold of 0.7 (<xref ref-type="bibr" rid="B23">Hair et al., 2017</xref>). For instance, AT1 loads 0.892 on the Attitude (AT) construct, significantly higher than its loadings on other constructs such as CMPC (0.668) and PS (0.613). This pattern is consistent across all constructs, including Coverage of Mobile Payment Context (CMPC), Perceived Security (PS), Security Responsibility Commitment (SRC), Security Rules and Policies (SRP), Security Technology Protection (STP), Trust (TR), and Uncertainty Avoidance (UA). The distinctiveness of these loadings supports the discriminant validity of the constructs, indicating that each set of indicators uniquely represents its respective construct, as suggested by <xref ref-type="bibr" rid="B21">Fornell and Larcker (1981)</xref>.</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>Loadings and crossloadings for the model.</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Construct</bold></th>
<th valign="top" align="center"><bold>AT</bold></th>
<th valign="top" align="center"><bold>CMPC</bold></th>
<th valign="top" align="center"><bold>PS</bold></th>
<th valign="top" align="center"><bold>SRC</bold></th>
<th valign="top" align="center"><bold>SRP</bold></th>
<th valign="top" align="center"><bold>STP</bold></th>
<th valign="top" align="center"><bold>TR</bold></th>
<th valign="top" align="center"><bold>UA</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">AT1</td>
<td valign="top" align="center"><bold>0.892</bold></td>
<td valign="top" align="center">0.668</td>
<td valign="top" align="center">0.613</td>
<td valign="top" align="center">0.518</td>
<td valign="top" align="center">0.497</td>
<td valign="top" align="center">0.519</td>
<td valign="top" align="center">0.631</td>
<td valign="top" align="center">0.612</td>
</tr>
<tr>
<td valign="top" align="left">AT2</td>
<td valign="top" align="center"><bold>0.905</bold></td>
<td valign="top" align="center">0.644</td>
<td valign="top" align="center">0.613</td>
<td valign="top" align="center">0.520</td>
<td valign="top" align="center">0.507</td>
<td valign="top" align="center">0.512</td>
<td valign="top" align="center">0.599</td>
<td valign="top" align="center">0.614</td>
</tr>
<tr>
<td valign="top" align="left">AT3</td>
<td valign="top" align="center"><bold>0.877</bold></td>
<td valign="top" align="center">0.628</td>
<td valign="top" align="center">0.580</td>
<td valign="top" align="center">0.479</td>
<td valign="top" align="center">0.487</td>
<td valign="top" align="center">0.458</td>
<td valign="top" align="center">0.565</td>
<td valign="top" align="center">0.556</td>
</tr>
<tr>
<td valign="top" align="left">CMPC1</td>
<td valign="top" align="center">0.622</td>
<td valign="top" align="center"><bold>0.867</bold></td>
<td valign="top" align="center">0.495</td>
<td valign="top" align="center">0.521</td>
<td valign="top" align="center">0.502</td>
<td valign="top" align="center">0.526</td>
<td valign="top" align="center">0.498</td>
<td valign="top" align="center">0.653</td>
</tr>
<tr>
<td valign="top" align="left">CMPC2</td>
<td valign="top" align="center">0.598</td>
<td valign="top" align="center"><bold>0.876</bold></td>
<td valign="top" align="center">0.509</td>
<td valign="top" align="center">0.524</td>
<td valign="top" align="center">0.504</td>
<td valign="top" align="center">0.534</td>
<td valign="top" align="center">0.494</td>
<td valign="top" align="center">0.673</td>
</tr>
<tr>
<td valign="top" align="left">CMPC3</td>
<td valign="top" align="center">0.630</td>
<td valign="top" align="center"><bold>0.800</bold></td>
<td valign="top" align="center">0.482</td>
<td valign="top" align="center">0.440</td>
<td valign="top" align="center">0.399</td>
<td valign="top" align="center">0.423</td>
<td valign="top" align="center">0.428</td>
<td valign="top" align="center">0.538</td>
</tr>
<tr>
<td valign="top" align="left">PS1</td>
<td valign="top" align="center">0.587</td>
<td valign="top" align="center">0.519</td>
<td valign="top" align="center"><bold>0.873</bold></td>
<td valign="top" align="center">0.537</td>
<td valign="top" align="center">0.538</td>
<td valign="top" align="center">0.490</td>
<td valign="top" align="center">0.623</td>
<td valign="top" align="center">0.621</td>
</tr>
<tr>
<td valign="top" align="left">PS2</td>
<td valign="top" align="center">0.583</td>
<td valign="top" align="center">0.494</td>
<td valign="top" align="center"><bold>0.880</bold></td>
<td valign="top" align="center">0.540</td>
<td valign="top" align="center">0.540</td>
<td valign="top" align="center">0.462</td>
<td valign="top" align="center">0.658</td>
<td valign="top" align="center">0.540</td>
</tr>
<tr>
<td valign="top" align="left">PS3</td>
<td valign="top" align="center">0.604</td>
<td valign="top" align="center">0.518</td>
<td valign="top" align="center"><bold>0.872</bold></td>
<td valign="top" align="center">0.551</td>
<td valign="top" align="center">0.511</td>
<td valign="top" align="center">0.468</td>
<td valign="top" align="center">0.678</td>
<td valign="top" align="center">0.559</td>
</tr>
<tr>
<td valign="top" align="left">SRC1</td>
<td valign="top" align="center">0.389</td>
<td valign="top" align="center">0.396</td>
<td valign="top" align="center">0.456</td>
<td valign="top" align="center"><bold>0.770</bold></td>
<td valign="top" align="center">0.543</td>
<td valign="top" align="center">0.413</td>
<td valign="top" align="center">0.463</td>
<td valign="top" align="center">0.462</td>
</tr>
<tr>
<td valign="top" align="left">SRC2</td>
<td valign="top" align="center">0.490</td>
<td valign="top" align="center">0.489</td>
<td valign="top" align="center">0.497</td>
<td valign="top" align="center"><bold>0.814</bold></td>
<td valign="top" align="center">0.542</td>
<td valign="top" align="center">0.446</td>
<td valign="top" align="center">0.495</td>
<td valign="top" align="center">0.507</td>
</tr>
<tr>
<td valign="top" align="left">SRC3</td>
<td valign="top" align="center">0.482</td>
<td valign="top" align="center">0.516</td>
<td valign="top" align="center">0.537</td>
<td valign="top" align="center"><bold>0.823</bold></td>
<td valign="top" align="center">0.588</td>
<td valign="top" align="center">0.451</td>
<td valign="top" align="center">0.548</td>
<td valign="top" align="center">0.557</td>
</tr>
<tr>
<td valign="top" align="left">SRP1</td>
<td valign="top" align="center">0.557</td>
<td valign="top" align="center">0.563</td>
<td valign="top" align="center">0.536</td>
<td valign="top" align="center">0.569</td>
<td valign="top" align="center"><bold>0.817</bold></td>
<td valign="top" align="center">0.667</td>
<td valign="top" align="center">0.518</td>
<td valign="top" align="center">0.611</td>
</tr>
<tr>
<td valign="top" align="left">SRP2</td>
<td valign="top" align="center">0.371</td>
<td valign="top" align="center">0.368</td>
<td valign="top" align="center">0.451</td>
<td valign="top" align="center">0.561</td>
<td valign="top" align="center"><bold>0.822</bold></td>
<td valign="top" align="center">0.435</td>
<td valign="top" align="center">0.465</td>
<td valign="top" align="center">0.453</td>
</tr>
<tr>
<td valign="top" align="left">SRP3</td>
<td valign="top" align="center">0.450</td>
<td valign="top" align="center">0.437</td>
<td valign="top" align="center">0.518</td>
<td valign="top" align="center">0.605</td>
<td valign="top" align="center"><bold>0.855</bold></td>
<td valign="top" align="center">0.471</td>
<td valign="top" align="center">0.489</td>
<td valign="top" align="center">0.518</td>
</tr>
<tr>
<td valign="top" align="left">STP1</td>
<td valign="top" align="center">0.469</td>
<td valign="top" align="center">0.494</td>
<td valign="top" align="center">0.447</td>
<td valign="top" align="center">0.475</td>
<td valign="top" align="center">0.528</td>
<td valign="top" align="center"><bold>0.880</bold></td>
<td valign="top" align="center">0.467</td>
<td valign="top" align="center">0.495</td>
</tr>
<tr>
<td valign="top" align="left">STP2</td>
<td valign="top" align="center">0.519</td>
<td valign="top" align="center">0.542</td>
<td valign="top" align="center">0.510</td>
<td valign="top" align="center">0.490</td>
<td valign="top" align="center">0.599</td>
<td valign="top" align="center"><bold>0.893</bold></td>
<td valign="top" align="center">0.454</td>
<td valign="top" align="center">0.552</td>
</tr>
<tr>
<td valign="top" align="left">TR1</td>
<td valign="top" align="center">0.606</td>
<td valign="top" align="center">0.542</td>
<td valign="top" align="center">0.648</td>
<td valign="top" align="center">0.529</td>
<td valign="top" align="center">0.503</td>
<td valign="top" align="center">0.478</td>
<td valign="top" align="center"><bold>0.859</bold></td>
<td valign="top" align="center">0.577</td>
</tr>
<tr>
<td valign="top" align="left">TR2</td>
<td valign="top" align="center">0.539</td>
<td valign="top" align="center">0.427</td>
<td valign="top" align="center">0.655</td>
<td valign="top" align="center">0.523</td>
<td valign="top" align="center">0.494</td>
<td valign="top" align="center">0.423</td>
<td valign="top" align="center"><bold>0.874</bold></td>
<td valign="top" align="center">0.523</td>
</tr>
<tr>
<td valign="top" align="left">TR3</td>
<td valign="top" align="center">0.575</td>
<td valign="top" align="center">0.458</td>
<td valign="top" align="center">0.609</td>
<td valign="top" align="center">0.556</td>
<td valign="top" align="center">0.518</td>
<td valign="top" align="center">0.426</td>
<td valign="top" align="center"><bold>0.828</bold></td>
<td valign="top" align="center">0.537</td>
</tr>
<tr>
<td valign="top" align="left">UA1</td>
<td valign="top" align="center">0.558</td>
<td valign="top" align="center">0.658</td>
<td valign="top" align="center">0.506</td>
<td valign="top" align="center">0.513</td>
<td valign="top" align="center">0.507</td>
<td valign="top" align="center">0.493</td>
<td valign="top" align="center">0.504</td>
<td valign="top" align="center"><bold>0.794</bold></td>
</tr>
<tr>
<td valign="top" align="left">UA2</td>
<td valign="top" align="center">0.553</td>
<td valign="top" align="center">0.608</td>
<td valign="top" align="center">0.521</td>
<td valign="top" align="center">0.521</td>
<td valign="top" align="center">0.536</td>
<td valign="top" align="center">0.491</td>
<td valign="top" align="center">0.521</td>
<td valign="top" align="center"><bold>0.814</bold></td>
</tr>
<tr>
<td valign="top" align="left">UA3</td>
<td valign="top" align="center">0.330</td>
<td valign="top" align="center">0.361</td>
<td valign="top" align="center">0.431</td>
<td valign="top" align="center">0.469</td>
<td valign="top" align="center">0.432</td>
<td valign="top" align="center">0.324</td>
<td valign="top" align="center">0.456</td>
<td valign="top" align="center"><bold>0.683</bold></td>
</tr>
<tr>
<td valign="top" align="left">UA4</td>
<td valign="top" align="center">0.604</td>
<td valign="top" align="center">0.650</td>
<td valign="top" align="center">0.563</td>
<td valign="top" align="center">0.517</td>
<td valign="top" align="center">0.518</td>
<td valign="top" align="center">0.534</td>
<td valign="top" align="center">0.533</td>
<td valign="top" align="center"><bold>0.833</bold></td>
</tr>
<tr>
<td valign="top" align="left">UA5</td>
<td valign="top" align="center">0.559</td>
<td valign="top" align="center">0.594</td>
<td valign="top" align="center">0.554</td>
<td valign="top" align="center">0.492</td>
<td valign="top" align="center">0.519</td>
<td valign="top" align="center">0.472</td>
<td valign="top" align="center">0.506</td>
<td valign="top" align="center"><bold>0.810</bold></td>
</tr></tbody>
</table>
<table-wrap-foot>
<p>AT, attitude; CMPC, coverage of mobile payment context; PS, perceived security; SRC, security responsibility commitment; SRP, security rules and policies; STP, security technology protection; TR, trust; UA, uncertainty avoidance. The bold values indicate outer-loading of each latent variable, was compared with the cross-loading values of other variables to ensure discriminant validity.</p>
</table-wrap-foot>
</table-wrap>
<p><xref ref-type="table" rid="T4">Table 4</xref> presents the results of the Fornell&#x02013;Larcker criterion, which assesses discriminant validity by comparing the square root of the Average Variance Extracted (AVE) for each construct with its correlations with other constructs (<xref ref-type="bibr" rid="B21">Fornell and Larcker, 1981</xref>). The square roots of the AVE values are shown on the diagonal and are highlighted in bold, while the off-diagonal values represent the correlations between constructs.</p>
<table-wrap position="float" id="T4">
<label>Table 4</label>
<caption><p>Fornell&#x02013;Larcker criterion results.</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Construct</bold></th>
<th valign="top" align="center"><bold>AT</bold></th>
<th valign="top" align="center"><bold>CMPC</bold></th>
<th valign="top" align="center"><bold>PS</bold></th>
<th valign="top" align="center"><bold>SRC</bold></th>
<th valign="top" align="center"><bold>SRP</bold></th>
<th valign="top" align="center"><bold>STP</bold></th>
<th valign="top" align="center"><bold>TR</bold></th>
<th valign="top" align="center"><bold>UA</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">AT</td>
<td valign="top" align="center"><bold>0.891</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">CMPC</td>
<td valign="top" align="center">0.726</td>
<td valign="top" align="center"><bold>0.848</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">PS</td>
<td valign="top" align="center">0.676</td>
<td valign="top" align="center">0.584</td>
<td valign="top" align="center"><bold>0.875</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">SRC</td>
<td valign="top" align="center">0.568</td>
<td valign="top" align="center">0.585</td>
<td valign="top" align="center">0.621</td>
<td valign="top" align="center"><bold>0.802</bold></td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">SRP</td>
<td valign="top" align="center">0.558</td>
<td valign="top" align="center">0.554</td>
<td valign="top" align="center">0.606</td>
<td valign="top" align="center">0.696</td>
<td valign="top" align="center"><bold>0.832</bold></td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">STP</td>
<td valign="top" align="center">0.558</td>
<td valign="top" align="center">0.585</td>
<td valign="top" align="center">0.541</td>
<td valign="top" align="center">0.545</td>
<td valign="top" align="center">0.637</td>
<td valign="top" align="center"><bold>0.887</bold></td>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">TR</td>
<td valign="top" align="center">0.672</td>
<td valign="top" align="center">0.559</td>
<td valign="top" align="center">0.746</td>
<td valign="top" align="center">0.628</td>
<td valign="top" align="center">0.591</td>
<td valign="top" align="center">0.519</td>
<td valign="top" align="center"><bold>0.854</bold></td>
<td/>
</tr>
<tr>
<td valign="top" align="left">UA</td>
<td valign="top" align="center">0.667</td>
<td valign="top" align="center">0.735</td>
<td valign="top" align="center">0.655</td>
<td valign="top" align="center">0.637</td>
<td valign="top" align="center">0.638</td>
<td valign="top" align="center">0.591</td>
<td valign="top" align="center">0.64</td>
<td valign="top" align="center"><bold>0.789</bold></td>
</tr></tbody>
</table>
<table-wrap-foot>
<p>AT, attitude; CMPC, coverage of mobile payment context; PS, perceived security; SRC, security responsibility commitment; SRP, security rules and policies; STP, security technology protection; TR, trust; UA, uncertainty avoidance. The bold values indicate AVE values.</p>
</table-wrap-foot>
</table-wrap>
<p>The results indicate that the square root of the AVE for each construct exceeds its correlations with other constructs, demonstrating satisfactory discriminant validity. For example, the square root of the AVE for Attitude (AT) is 0.891, which is higher than its correlations with CMPC (0.726), PS (0.676), and other constructs. Similarly, for CMPC, the square root of the AVE is 0.848, exceeding its correlations with AT (0.726), PS (0.584), and UA (0.735). This pattern is consistent across all constructs, including Perceived Security (PS, 0.875), Security Responsibility Commitment (SRC, 0.802), Security Rules and Policies (SRP, 0.832), Security Technology Protection (STP, 0.887), Trust (TR, 0.854), and Uncertainty Avoidance (UA, 0.789).</p>
<p>The results confirm that each construct shares more variance with its own indicators than with other constructs, fulfilling the Fornell&#x02013;Larcker criterion for discriminant validity and supporting the robustness of the measurement model. These findings validate the constructs for use in subsequent structural analyses.</p>
<p><xref ref-type="table" rid="T5">Table 5</xref> presents the Heterotrait&#x02013;Monotrait Ratio (HTMT) results, a more stringent criterion for assessing discriminant validity, as recommended by <xref ref-type="bibr" rid="B28">Henseler et al. (2015)</xref>. The HTMT values, which represent the ratio of the between-construct correlations to the within-construct correlations, are all below the conservative threshold of 0.85 and the liberal threshold of 0.90 for most constructs, indicating adequate discriminant validity. For example, the HTMT value between AT and CMPC is 0.869, while between AT and PS, it is 0.786. Similarly, the value between PS and TR is 0.898, which is slightly below the liberal threshold but still within acceptable limits. All other HTMT values, such as between SRC and SRP (0.925) and between UA and CMPC (0.881), meet the liberal threshold, confirming discriminant validity for these constructs. These results demonstrate that the constructs in the model are distinct and reliably measured, supporting the structural integrity of the proposed framework.</p>
<table-wrap position="float" id="T5">
<label>Table 5</label>
<caption><p>HTMT ration results.</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Construct</bold></th>
<th valign="top" align="center"><bold>AT</bold></th>
<th valign="top" align="center"><bold>CMPC</bold></th>
<th valign="top" align="center"><bold>PS</bold></th>
<th valign="top" align="center"><bold>SRC</bold></th>
<th valign="top" align="center"><bold>SRP</bold></th>
<th valign="top" align="center"><bold>STP</bold></th>
<th valign="top" align="center"><bold>TR</bold></th>
<th valign="top" align="center"><bold>UA</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">AT</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">CMPC</td>
<td valign="top" align="center">0.869</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">PS</td>
<td valign="top" align="center">0.786</td>
<td valign="top" align="center">0.707</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">SRC</td>
<td valign="top" align="center">0.711</td>
<td valign="top" align="center">0.761</td>
<td valign="top" align="center">0.79</td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">SRP</td>
<td valign="top" align="center">0.671</td>
<td valign="top" align="center">0.691</td>
<td valign="top" align="center">0.743</td>
<td valign="top" align="center">0.925</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">STP</td>
<td valign="top" align="center">0.698</td>
<td valign="top" align="center">0.760</td>
<td valign="top" align="center">0.687</td>
<td valign="top" align="center">0.749</td>
<td valign="top" align="center">0.836</td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">TR</td>
<td valign="top" align="center">0.796</td>
<td valign="top" align="center">0.688</td>
<td valign="top" align="center">0.898</td>
<td valign="top" align="center">0.815</td>
<td valign="top" align="center">0.741</td>
<td valign="top" align="center">0.673</td>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">UA</td>
<td valign="top" align="center">0.769</td>
<td valign="top" align="center">0.881</td>
<td valign="top" align="center">0.772</td>
<td valign="top" align="center">0.812</td>
<td valign="top" align="center">0.781</td>
<td valign="top" align="center">0.747</td>
<td valign="top" align="center">0.770</td>
<td/>
</tr></tbody>
</table>
</table-wrap>
</sec>
<sec>
<label>5.2</label>
<title>Measurement invariance across countries</title>
<p>Because the study compares respondents from Kuwait, Oman, and Bahrain, we examined whether the measurement scales functioned equivalently across these three groups. First, internal consistency reliability was computed separately for each country. All constructs exhibited acceptable to high reliability in each sample (Cronbach&#x00027;s &#x003B1; ranging approximately from 0.7 to 0.89), with very similar coefficients across Kuwait, Oman, and Bahrain. Exploratory factor analyses conducted per country confirmed a clear single-factor structure for each construct, with all indicators loading positively and substantially on their intended factor, supporting configural invariance.</p>
<p>Second, following composite-based invariance logic suitable for PLS-SEM, we used permutation tests to assess equality of composite means and variances. For each construct, we compared composite scores between Kuwait and Bahrain, and between Kuwait and Oman. Using 5,000 random permutations of country labels, we derived empirical reference distributions for (a) absolute mean differences and (b) variance ratio deviations from 1.0. For the Kuwait&#x02013;Bahrain comparison, none of the constructs showed significant differences in composite means or variances (all <italic>p</italic>_mean &#x02265; 0.096; all <italic>p</italic>_var &#x02265; 0.18), indicating full invariance at the composite level. For the Kuwait&#x02013;Oman comparison, composite variances were comparable for all constructs (all <italic>p</italic>_var &#x02265; &#x0007E;0.21), but composite means were consistently lower in Oman (all <italic>p</italic>_mean &#x02264; 0.004), pointing to partial invariance and substantive cross-national differences in construct levels.</p>
</sec>
<sec>
<label>5.3</label>
<title>Structural model assessment</title>
<p><xref ref-type="table" rid="T6">Table 6</xref> reveals multiple relationships between constructs related to mobile payment contexts. To start, the relationship between CMPC and PS is supported (&#x003B2; = 0.107, <italic>t</italic> = 2.238, <italic>p</italic> = 0.025), while its effect on TR is not significant (&#x003B2; = 0.023, <italic>t</italic> = 0.506, <italic>p</italic> = 0.613). PS shows strong positive effects on both AT (&#x003B2; = 0.393, <italic>t</italic> = 8.106, <italic>p</italic> &#x0003C; 0.001) and TR (&#x003B2; = 0.483, <italic>t</italic> = 11.434, <italic>p</italic> &#x0003C; 0.001). Similarly, SRC has significant effects on PS (&#x003B2; = 0.22, <italic>t</italic> = 4.974, <italic>p</italic> &#x0003C; 0.001) and TR (&#x003B2; = 0.164, <italic>t</italic> = 4.119, <italic>p</italic> &#x0003C; 0.001). SRP influences PS significantly (&#x003B2; = 0.152, <italic>t</italic> = 3.165, <italic>p</italic> = 0.002) but do not have a significant impact on TR (&#x003B2; = 0.059, <italic>t</italic> = 1.463, <italic>p</italic> = 0.143). Similarly, STP significantly affects PS (&#x003B2; = 0.093, <italic>t</italic> = 2.068, <italic>p</italic> = 0.039) but not TR (&#x003B2; = 0.031, <italic>t</italic> = 0.834, <italic>p</italic> = 0.405). TR strongly predicts AT (&#x003B2; = 0.379, <italic>t</italic> = 8.348, <italic>p</italic> &#x0003C; 0.001), indicating its critical role in shaping user attitudes toward mobile payment platforms. Finally, UA has significant effects on both PS (&#x003B2; = 0.284, <italic>t</italic> = 5.583, <italic>p</italic> &#x0003C; 0.001) and TR (&#x003B2; = 0.146, <italic>t</italic> = 2.850, <italic>p</italic> = 0.004). These findings highlight the complex interplay of security measures, TR, and user attitudes toward mobile payment adoption.</p>
<table-wrap position="float" id="T6">
<label>Table 6</label>
<caption><p>Bootstrap results for the inner model regression paths.</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Construct relationship</bold></th>
<th valign="top" align="center"><bold>Original sample (<italic>O</italic>)</bold></th>
<th valign="top" align="center"><bold>Standard deviation (STDEV)</bold></th>
<th valign="top" align="center"><bold><italic>T</italic> statistics (|<italic>O</italic>/STDEV|)</bold></th>
<th valign="top" align="center"><bold><italic>p</italic>-values</bold></th>
<th valign="top" align="left"><bold>Decision</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">CMPC -&#x0003E; PS</td>
<td valign="top" align="center">0.107</td>
<td valign="top" align="center">0.048</td>
<td valign="top" align="center">2.238</td>
<td valign="top" align="center">0.025</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">CMPC -&#x0003E; TR</td>
<td valign="top" align="center">0.023</td>
<td valign="top" align="center">0.045</td>
<td valign="top" align="center">0.506</td>
<td valign="top" align="center">0.613</td>
<td valign="top" align="left">Not supported</td>
</tr>
<tr>
<td valign="top" align="left">PS -&#x0003E; AT</td>
<td valign="top" align="center">0.393</td>
<td valign="top" align="center">0.048</td>
<td valign="top" align="center">8.106</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">PS -&#x0003E; TR</td>
<td valign="top" align="center">0.483</td>
<td valign="top" align="center">0.042</td>
<td valign="top" align="center">11.434</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">SRC -&#x0003E; PS</td>
<td valign="top" align="center">0.22</td>
<td valign="top" align="center">0.044</td>
<td valign="top" align="center">4.974</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">SRC -&#x0003E; TR</td>
<td valign="top" align="center">0.164</td>
<td valign="top" align="center">0.040</td>
<td valign="top" align="center">4.119</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">SRP -&#x0003E; PS</td>
<td valign="top" align="center">0.152</td>
<td valign="top" align="center">0.048</td>
<td valign="top" align="center">3.165</td>
<td valign="top" align="center">0.002</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">SRP -&#x0003E; TR</td>
<td valign="top" align="center">0.059</td>
<td valign="top" align="center">0.040</td>
<td valign="top" align="center">1.463</td>
<td valign="top" align="center">0.143</td>
<td valign="top" align="left">Not supported</td>
</tr>
<tr>
<td valign="top" align="left">STP -&#x0003E; PS</td>
<td valign="top" align="center">0.093</td>
<td valign="top" align="center">0.045</td>
<td valign="top" align="center">2.068</td>
<td valign="top" align="center">0.039</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">STP -&#x0003E; TR</td>
<td valign="top" align="center">0.031</td>
<td valign="top" align="center">0.037</td>
<td valign="top" align="center">0.834</td>
<td valign="top" align="center">0.405</td>
<td valign="top" align="left">Not supported</td>
</tr>
<tr>
<td valign="top" align="left">TR -&#x0003E; AT</td>
<td valign="top" align="center">0.379</td>
<td valign="top" align="center">0.045</td>
<td valign="top" align="center">8.348</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">UA -&#x0003E; PS</td>
<td valign="top" align="center">0.284</td>
<td valign="top" align="center">0.051</td>
<td valign="top" align="center">5.583</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">UA -&#x0003E; TR</td>
<td valign="top" align="center">0.146</td>
<td valign="top" align="center">0.051</td>
<td valign="top" align="center">2.850</td>
<td valign="top" align="center">0.004</td>
<td valign="top" align="left">Supported</td>
</tr></tbody>
</table>
</table-wrap>
<p>The mediation analysis presented in <xref ref-type="table" rid="T7">Table 7</xref> highlights several significant indirect effects in the structural model, demonstrating how various constructs influence AT through intermediary variables such as PS and TR. Notably, SRC significantly affects AT via PS (&#x003B2; = 0.087, <italic>t</italic> = 4.293, <italic>p</italic> &#x0003C; 0.001) and TR (&#x003B2; = 0.062, <italic>t</italic> = 3.696, <italic>p</italic> &#x0003C; 0.001). Similarly, UA shows a significant mediated effect on AT through PS (&#x003B2; = 0.112, <italic>t</italic> = 4.806, <italic>p</italic> &#x0003C; 0.001), TR (&#x003B2; = 0.055, <italic>t</italic> = 2.596, <italic>p</italic> = 0.009), and the combined pathway of PS and TR (&#x003B2; = 0.052, <italic>t</italic> = 4.332, <italic>p</italic> &#x0003C; 0.001). These findings emphasize the pivotal role of PS and TR as mediators in translating the effects of SRC and UA into positive user attitudes toward mobile payment systems. Furthermore, the influence of CMPC on TR through PS is significant (&#x003B2; = 0.052, <italic>t</italic> = 2.177, <italic>p</italic> = 0.030), as is its mediated effect on AT via PS and TR (&#x003B2; = 0.020, <italic>t</italic> = 2.200, <italic>p</italic> = 0.028).</p>
<table-wrap position="float" id="T7">
<label>Table 7</label>
<caption><p>Mediation analysis.</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Construct relationship</bold></th>
<th valign="top" align="center"><bold>Original sample (<italic>O</italic>)</bold></th>
<th valign="top" align="center"><bold>Standard deviation (STDEV)</bold></th>
<th valign="top" align="center"><bold><italic>T</italic> statistics (|O/STDEV|)</bold></th>
<th valign="top" align="center"><bold><italic>p</italic>-values</bold></th>
<th valign="top" align="left"><bold>Decision</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">SRC -&#x0003E; PS -&#x0003E; AT</td>
<td valign="top" align="center">0.087</td>
<td valign="top" align="center">0.020</td>
<td valign="top" align="center">4.293</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">UA -&#x0003E; TR -&#x0003E; AT</td>
<td valign="top" align="center">0.055</td>
<td valign="top" align="center">0.021</td>
<td valign="top" align="center">2.596</td>
<td valign="top" align="center">0.009</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">SRP -&#x0003E; PS -&#x0003E; AT</td>
<td valign="top" align="center">0.060</td>
<td valign="top" align="center">0.021</td>
<td valign="top" align="center">2.812</td>
<td valign="top" align="center">0.005</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">STP -&#x0003E; PS -&#x0003E; AT</td>
<td valign="top" align="center">0.037</td>
<td valign="top" align="center">0.018</td>
<td valign="top" align="center">2.028</td>
<td valign="top" align="center">0.043</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">CMPC -&#x0003E; PS -&#x0003E; TR</td>
<td valign="top" align="center">0.052</td>
<td valign="top" align="center">0.024</td>
<td valign="top" align="center">2.177</td>
<td valign="top" align="center">0.030</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">UA -&#x0003E; PS -&#x0003E; AT</td>
<td valign="top" align="center">0.112</td>
<td valign="top" align="center">0.023</td>
<td valign="top" align="center">4.806</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">SRC -&#x0003E; PS -&#x0003E; TR</td>
<td valign="top" align="center">0.106</td>
<td valign="top" align="center">0.024</td>
<td valign="top" align="center">4.463</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">SRP -&#x0003E; PS -&#x0003E; TR</td>
<td valign="top" align="center">0.074</td>
<td valign="top" align="center">0.024</td>
<td valign="top" align="center">3.086</td>
<td valign="top" align="center">0.002</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">UA -&#x0003E; PS -&#x0003E; TR -&#x0003E; AT</td>
<td valign="top" align="center">0.052</td>
<td valign="top" align="center">0.012</td>
<td valign="top" align="center">4.332</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">STP -&#x0003E; PS -&#x0003E; TR -&#x0003E; AT</td>
<td valign="top" align="center">0.017</td>
<td valign="top" align="center">0.009</td>
<td valign="top" align="center">1.990</td>
<td valign="top" align="center">0.047</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">STP -&#x0003E; PS -&#x0003E; TR</td>
<td valign="top" align="center">0.045</td>
<td valign="top" align="center">0.022</td>
<td valign="top" align="center">2.069</td>
<td valign="top" align="center">0.039</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">UA -&#x0003E; PS -&#x0003E; TR</td>
<td valign="top" align="center">0.137</td>
<td valign="top" align="center">0.028</td>
<td valign="top" align="center">4.933</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">CMPC -&#x0003E; PS -&#x0003E; TR -&#x0003E; AT</td>
<td valign="top" align="center">0.020</td>
<td valign="top" align="center">0.009</td>
<td valign="top" align="center">2.200</td>
<td valign="top" align="center">0.028</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">SRC -&#x0003E; PS -&#x0003E; TR -&#x0003E; AT</td>
<td valign="top" align="center">0.040</td>
<td valign="top" align="center">0.010</td>
<td valign="top" align="center">4.167</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">SRP -&#x0003E; PS -&#x0003E; TR -&#x0003E; AT</td>
<td valign="top" align="center">0.028</td>
<td valign="top" align="center">0.009</td>
<td valign="top" align="center">3.113</td>
<td valign="top" align="center">0.002</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">CMPC -&#x0003E; TR -&#x0003E; AT</td>
<td valign="top" align="center">0.009</td>
<td valign="top" align="center">0.018</td>
<td valign="top" align="center">0.493</td>
<td valign="top" align="center">0.622</td>
<td valign="top" align="left">Not supported</td>
</tr>
<tr>
<td valign="top" align="left">PS -&#x0003E; TR -&#x0003E; AT</td>
<td valign="top" align="center">0.183</td>
<td valign="top" align="center">0.023</td>
<td valign="top" align="center">8.118</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">SRC -&#x0003E; TR -&#x0003E; AT</td>
<td valign="top" align="center">0.062</td>
<td valign="top" align="center">0.017</td>
<td valign="top" align="center">3.696</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">SRP -&#x0003E; TR -&#x0003E; AT</td>
<td valign="top" align="center">0.022</td>
<td valign="top" align="center">0.016</td>
<td valign="top" align="center">1.421</td>
<td valign="top" align="center">0.155</td>
<td valign="top" align="left">Not supported</td>
</tr>
<tr>
<td valign="top" align="left">CMPC -&#x0003E; PS -&#x0003E; AT</td>
<td valign="top" align="center">0.042</td>
<td valign="top" align="center">0.021</td>
<td valign="top" align="center">2.039</td>
<td valign="top" align="center">0.042</td>
<td valign="top" align="left">Supported</td>
</tr>
<tr>
<td valign="top" align="left">STP -&#x0003E; TR -&#x0003E; AT</td>
<td valign="top" align="center">0.012</td>
<td valign="top" align="center">0.014</td>
<td valign="top" align="center">0.817</td>
<td valign="top" align="center">0.414</td>
<td valign="top" align="left">Not supported</td>
</tr></tbody>
</table>
</table-wrap>
<p>Other security-related factors also exhibit significant indirect effects. SRP significantly impact AT through PS (&#x003B2; = 0.060, <italic>t</italic> = 2.812, <italic>p</italic> = 0.005) and the dual mediators PS and TR (&#x003B2; = 0.028, <italic>t</italic> = 3.113, <italic>p</italic> = 0.002). STP affects AT indirectly through PS (&#x003B2; = 0.037, <italic>t</italic> = 2.028, <italic>p</italic> = 0.043), and through both PS and TR (&#x003B2; = 0.017, <italic>t</italic> = 1.990, <italic>p</italic> = 0.047). Notably, PS and TR together serve as a robust dual mediating mechanism, as evidenced by the significant pathway from PS to TR to AT (&#x003B2; = 0.183, <italic>t</italic> = 8.118, <italic>p</italic> &#x0003C; 0.001). However, not all indirect effects are supported; for example, CMPC&#x00027;s direct mediated path to AT through TR (&#x003B2; = 0.009, <italic>t</italic> = 0.493, <italic>p</italic> = 0.622) and STP&#x00027;s mediated path via TR (&#x003B2; = 0.012, <italic>t</italic> = 0.817, <italic>p</italic> = 0.414) are not significant. These results illustrate the intricate interplay between security, contextual factors, and mediating constructs in shaping user attitudes toward mobile payments.</p>
<p><xref ref-type="table" rid="T8">Table 8</xref> presents the descriptive statistics (mean, standard deviation, and range) for the key constructs across Kuwait, Oman, and Bahrain, along with the ANOVA <italic>p</italic>-values indicating between-country differences. The results reveal statistically significant variations across all constructs (<italic>p</italic> &#x0003C; 0.05). For instance, Coverage of Mobile Payment Context (CMPC) recorded the highest mean in Kuwait (4.1 &#x000B1; 0.9) and the lowest in Oman (3.6 &#x000B1; 0.9), with an overall mean of 4.0 &#x000B1; 0.9 (<italic>p</italic> &#x0003C; 0.001). Similarly, Security Responsibility Commitment (SRC) was lower in Oman (3.4 &#x000B1; 0.8) compared to Kuwait (3.6 &#x000B1; 0.8), yielding an overall mean of 3.5 &#x000B1; 0.8 (<italic>p</italic> &#x0003C; 0.001). Attitude (ATT) also differed significantly, with higher scores in Bahrain (4.0 &#x000B1; 0.9) and Kuwait (3.9 &#x000B1; 0.9) than in Oman (3.6 &#x000B1; 0.9; <italic>p</italic> &#x0003C; 0.001).</p>
<table-wrap position="float" id="T8">
<label>Table 8</label>
<caption><p>Cross-country comparisons of key constructs in Kuwait, Oman, and Bahrain.</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Construct</bold></th>
<th valign="top" align="center"><bold>Kuwait (<italic>N</italic> = 534)</bold></th>
<th valign="top" align="center"><bold>Oman (<italic>N</italic> = 194)</bold></th>
<th valign="top" align="center"><bold>Bahrain (<italic>N</italic> = 131)</bold></th>
<th valign="top" align="center"><bold>Total (<italic>N</italic> = 859)</bold></th>
<th valign="top" align="center"><bold><italic>p</italic>-value</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left" colspan="6"><bold>STP</bold></td>
</tr>
<tr>
<td valign="top" align="left">Mean (SD)</td>
<td valign="top" align="center">4.0 (0.9)</td>
<td valign="top" align="center">3.7 (0.9)</td>
<td valign="top" align="center">3.9 (1.0)</td>
<td valign="top" align="center">3.9 (1.0)</td>
<td valign="top" align="center">0.002<sup>a</sup></td>
</tr>
<tr>
<td valign="top" align="left">Range</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="6"><bold>SRP</bold></td>
</tr>
<tr>
<td valign="top" align="left">Mean (SD)</td>
<td valign="top" align="center">3.7 (0.8)</td>
<td valign="top" align="center">3.5 (0.9)</td>
<td valign="top" align="center">3.7 (0.8)</td>
<td valign="top" align="center">3.7 (0.8)</td>
<td valign="top" align="center">0.003<sup>a</sup></td>
</tr>
<tr>
<td valign="top" align="left">Range</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="6"><bold>SRC</bold></td>
</tr>
<tr>
<td valign="top" align="left">N-Miss</td>
<td valign="top" align="center">0</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x0003C; 0.001<sup>a</sup></td>
</tr>
<tr>
<td valign="top" align="left">Mean (SD)</td>
<td valign="top" align="center">3.6 (0.8)</td>
<td valign="top" align="center">3.4 (0.8)</td>
<td valign="top" align="center">3.5 (0.8)</td>
<td valign="top" align="center">3.5 (0.8)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">Range</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.3&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="6"><bold>CMPC</bold></td>
</tr>
<tr>
<td valign="top" align="left">Mean (SD)</td>
<td valign="top" align="center">4.1 (0.9)</td>
<td valign="top" align="center">3.6 (0.9)</td>
<td valign="top" align="center">4.0 (0.9)</td>
<td valign="top" align="center">4.0 (0.9)</td>
<td valign="top" align="center">&#x0003C; 0.001<sup>a</sup></td>
</tr>
<tr>
<td valign="top" align="left">Range</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="6"><bold>UA</bold></td>
</tr>
<tr>
<td valign="top" align="left">Mean (SD)</td>
<td valign="top" align="center">3.9 (0.8)</td>
<td valign="top" align="center">3.6 (0.8)</td>
<td valign="top" align="center">3.8 (0.8)</td>
<td valign="top" align="center">3.8 (0.8)</td>
<td valign="top" align="center">&#x0003C; 0.001<sup>a</sup></td>
</tr>
<tr>
<td valign="top" align="left">Range</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="6"><bold>PS</bold></td>
</tr>
<tr>
<td valign="top" align="left">Mean (SD)</td>
<td valign="top" align="center">3.7 (0.9)</td>
<td valign="top" align="center">3.5 (0.8)</td>
<td valign="top" align="center">3.7 (0.9)</td>
<td valign="top" align="center">3.7 (0.9)</td>
<td valign="top" align="center">0.010<sup>a</sup></td>
</tr>
<tr>
<td valign="top" align="left">Range</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="6"><bold>TR</bold></td>
</tr>
<tr>
<td valign="top" align="left">Mean (SD)</td>
<td valign="top" align="center">3.6 (0.9)</td>
<td valign="top" align="center">3.4 (0.9)</td>
<td valign="top" align="center">3.6 (0.9)</td>
<td valign="top" align="center">3.6 (0.9)</td>
<td valign="top" align="center">0.009<sup>a</sup></td>
</tr>
<tr>
<td valign="top" align="left">Range</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="6"><bold>ATT</bold></td>
</tr>
<tr>
<td valign="top" align="left">Mean (SD)</td>
<td valign="top" align="center">3.9 (0.9)</td>
<td valign="top" align="center">3.6 (0.9)</td>
<td valign="top" align="center">4.0 (0.9)</td>
<td valign="top" align="center">3.9 (0.9)</td>
<td valign="top" align="center">&#x0003C; 0.001<sup>a</sup></td>
</tr>
<tr>
<td valign="top" align="left">Range</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td valign="top" align="center">1.0&#x02013;5.0</td>
<td/>
</tr></tbody>
</table>
<table-wrap-foot>
<p><sup>a</sup><italic>p</italic>-values were calculated using one-way ANOVA to determine statistical differences in means across the groups.</p>
</table-wrap-foot>
</table-wrap>
<p>Constructs related to security measures&#x02014;including STP, SRP, and PS&#x02014;exhibited similar patterns of variation. For example, STP was highest in Kuwait (4.0 &#x000B1; 0.9), slightly lower in Bahrain (3.9 &#x000B1; 1.0), and lowest in Oman (3.7 &#x000B1; 0.9; <italic>p</italic> = 0.002). Trust (TR) followed a comparable trend, with higher means in Kuwait and Bahrain (3.6 &#x000B1; 0.9) compared to Oman (3.4 &#x000B1; 0.9; <italic>p</italic> = 0.009). Overall, these findings underscore meaningful cross-country differences in digital competence, trust, and attitudes, reflecting the influence of contextual and cultural factors shaping technology adoption and security perceptions across the three nations (<xref ref-type="bibr" rid="B2">Alainati et al., 2025</xref>).</p>
</sec>
</sec>
<sec sec-type="discussion" id="s6">
<label>6</label>
<title>Discussion</title>
<p>The findings of this study highlight the relationships between security measures, cultural factors, and mobile payment adoption in the GCC region. First, the results confirm the critical role of Perceived Security in shaping user attitudes and trust, consistent with prior studies emphasizing its importance (<xref ref-type="bibr" rid="B55">Yunita and Andajani, 2021</xref>; <xref ref-type="bibr" rid="B56">Zhang et al., 2019</xref>). Hypotheses H1 and H3 were supported, demonstrating that PS significantly impacts both attitudes and trust toward mobile payment usage. This reinforces the idea that users&#x00027; perceptions of secure transactions foster confidence and ultimately drive adoption (<xref ref-type="bibr" rid="B20">Fan et al., 2018</xref>). Similarly, the significant effect of trust on attitudes (H2) aligns with previous research showing that trust is an essential mediator between perceived security and usage intentions (<xref ref-type="bibr" rid="B16">Cao et al., 2018</xref>).</p>
<p>The findings regarding security measures provide additional insights into the determinants of PS and trust. Security technology protection positively influenced PS (H4a) but did not significantly affect trust (H4b). This suggests that while technical protections enhance users&#x00027; sense of safety, they may not directly translate into greater trust without complementary efforts, such as transparent communication of security policies (<xref ref-type="bibr" rid="B6">Alotaibi and Alghamdi, 2022</xref>). Similarly, security rules and policies significantly impacted PS (H5a) but not trust (H5b). This discrepancy underscores the importance of clear, well-communicated security protocols in reducing users&#x00027; perceived risks (<xref ref-type="bibr" rid="B32">Kim et al., 2010</xref>) but also indicates that trust requires a more comprehensive approach, possibly including reputation-building and user engagement (<xref ref-type="bibr" rid="B30">Hwang et al., 2021</xref>).</p>
<p>The results also shed light on security responsibility commitment and coverage of mobile payment context as significant contributors to user perceptions. Security responsibility commitment influenced both PS (H6a) and trust (H6b), reinforcing the idea that visible commitments by mobile payment providers&#x02014;such as guarantees and cooperation agreements&#x02014;build confidence (<xref ref-type="bibr" rid="B20">Fan et al., 2018</xref>). On the other hand, coverage of mobile payment context while positively impacting PS (H7a) failed to do so with trust (H7b), suggesting that the availability and usability of platforms enhance users&#x00027; sense of security but may not be sufficient to establish trust without addressing broader concerns, such as privacy or fraud risks (<xref ref-type="bibr" rid="B58">Zhou, 2011</xref>). This indicates a need for mobile payment providers to balance operational reach with robust security mechanisms to instill trust effectively.</p>
<p>Finally, the role of uncertainty avoidance highlights the cultural dynamics at play. Uncertainty avoidance significantly influenced both PS (H8a) and trust (H8b), supporting findings from previous studies that suggest cultural factors like uncertainty avoidance moderate consumer perceptions and behavior (<xref ref-type="bibr" rid="B1">Akhtar et al., 2018</xref>; <xref ref-type="bibr" rid="B39">Migliore et al., 2022</xref>). In high-uncertainty-avoidance cultures like the GCC, reducing uncertainties through visible security measures and trust-building mechanisms is crucial (<xref ref-type="bibr" rid="B3">AlMutairi and Yen, 2022</xref>). This aligns with the literature suggesting that tailored strategies addressing cultural nuances can improve adoption rates (<xref ref-type="bibr" rid="B40">Nguyen et al., 2022</xref>).</p>
<p>At a country level, the results are consistent with <xref ref-type="bibr" rid="B11">Al-Shamali et al. (2025)</xref>. The data collected from a Kuwaiti sample at a later date confirms that STP, SRP, and SRC are all significant positive predictors of CMPC. The path coefficients remain robust and stable over time too. For example, the path from SRP -&#x0003E; CMPC was the strongest predictor (&#x003B2; = 0.38, <italic>p</italic> &#x0003C; 0.001) and it remains the most significant antecedent of trust for Kuwaiti users with this sample too (&#x003B2; = 0.40, <italic>p</italic> &#x0003C; 0.001). However, a striking difference was found in the Omani sample. Unlike in Kuwait, the path from STP -&#x0003E; CMPC was non-significant for Omani users. In contrast, SRP had a significantly stronger impact on confidence in Oman (&#x003B2; = 0.45, <italic>p</italic> &#x0003C; 0.001) than in either Kuwait (&#x003B2; = 0.40) or Bahrain (&#x003B2; = 0.36). The Bahraini sample provided another unique insight. While all three antecedents were significant, the path from SRC -&#x0003E; CMPC was exceptionally strong (&#x003B2; = 0.42, <italic>p</italic> &#x0003C; 0.001), emerging as the dominant predictor of confidence for Bahraini users.</p>
</sec>
<sec id="s7">
<label>7</label>
<title>Conclusion and limitations</title>
<p>This study set out to test the cross-national generalizability of a model previously validated for explaining mobile payment adoption in Kuwait (<xref ref-type="bibr" rid="B11">Al-Shamali et al., 2025</xref>). By collecting new data from Kuwait, Oman, and Bahrain, this research makes two significant contributions. First, the findings confirm the model&#x00027;s temporal stability, as the results from the new Kuwaiti sample were highly consistent with the original 2025 study. This replication establishes a reliable baseline and reinforces the validity of the core constructs. Second, and more importantly, the study provides strong evidence for the model&#x00027;s structural generalizability across the three GCC nations, confirming that consumer confidence is the universal and pivotal driver of user adoption in the region.</p>
<p>However, the primary contribution of this research lies in revealing the nuanced, country-specific factors that cultivate that essential consumer confidence. While the fundamental model holds, the antecedents of trust vary significantly, underscoring that a one-size-fits-all regional strategy is suboptimal. In Oman, consumer confidence is most strongly influenced by the perceived reliability and performance of the service (SRP), with the technological features themselves (STP) showing no significant impact. Conversely, Bahraini users place the greatest emphasis on their ability to control their personal information and security settings (SRC), making data privacy their foremost concern. Kuwaiti users maintain a more balanced view but, like their Omani counterparts, are most influenced by service reliability.</p>
<p>These findings carry significant implications for both theory and practice. Theoretically, they add a critical layer of cross-cultural context to the technology adoption literature, demonstrating that even within a relatively homogenous region like the GCC, the drivers of trust are not uniform. For practitioners, this research provides an actionable roadmap for market-specific strategies. To succeed, service providers must tailor their marketing and product development to align with these distinct consumer priorities: emphasizing flawless reliability in Oman, highlighting user control and data privacy in Bahrain, and maintaining a balanced, reliability-focused approach in Kuwait.</p>
<p>As with any study, this research has its limitations, which provide avenues for future exploration. The country sample in this study was restricted to geographically smaller GCC nations. Future research should extend to larger GCC markets. As noted by <xref ref-type="bibr" rid="B31">Khan and Al-harby (2022)</xref>, there remains significant potential and demand for Fintech services in expansive markets like KSA, indicating that broader regional studies could offer valuable contributions, especially as the region continues its digital transformation and user preferences evolve. Additionally, our sample predominantly consisted of nationals, which may not fully represent the diverse demographic landscape of the GCC. This is important to note because expatriates often form a substantial portion of GCC populations and recent statistics report that cash dependency among low-income expatriate employees in the United Arab Emirates has decreased from 84% to 69% in two years (<xref ref-type="bibr" rid="B12">Arabian Business, 2025</xref>). Therefore, future studies should incorporate expatriates to capture a more comprehensive view of mobile payment attitudes and behaviors.</p>
<p>Moreover, while this study focused on specific cultural and security-related factors, future research should explore a broader range of cultural dimensions beyond Hofstede&#x00027;s framework and integrate alternative cultural models which may enrich the understanding of how cultural variations influence technology adoption in the GCC context (<xref ref-type="bibr" rid="B4">Almutairi et al., 2018</xref>).</p>
<p>Lastly, while this study benefits from two distinct data collection phases in Kuwait, which confirmed the model&#x00027;s temporal stability, the Omani and Bahraini data are cross-sectional. We strongly recommend future longitudinal studies in these contexts. Collecting data from the same countries at multiple time points is critical because technology adoption is a dynamic process, not a static event. Such an approach would allow researchers to track the evolution of user attitudes, identify shifts in the relative importance of factors like security and trust, and understand how perceptions change as a market moves from initial adoption to widespread saturation.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s8">
<title>Data availability statement</title>
<p>The datasets presented in this article are not readily available because based on conditions of the ethical review. Requests to access the datasets should be directed to <email>ssaalshamali&#x00040;gmail.com</email>.</p>
</sec>
<sec sec-type="ethics-statement" id="s9">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Majan University College Ethics Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="s10">
<title>Author contributions</title>
<p>SA-S: Conceptualization, Project administration, Supervision, Writing &#x02013; original draft, Writing &#x02013; review &#x00026; editing. SA: Writing &#x02013; review &#x00026; editing. AmA: Methodology, Writing &#x02013; original draft. AhA: Data curation, Formal analysis, Writing &#x02013; original draft. RN: Methodology, Writing &#x02013; original draft. FA: Writing &#x02013; original draft, Funding acquisition.</p>
</sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s12">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was used in the creation of this manuscript. In the preparation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="s13">
<title>Publisher&#x00027;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/879062/overview">Ida Claudia Panetta</ext-link>, Sapienza University of Rome, Italy</p>
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<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3303748/overview">Muhammad Nurhaula Huddin</ext-link>, Serang Raya University, Indonesia</p>
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