Telematics and Informatics Reports最新文献

From broadcast to mobile: Cross-channel effects in home shopping platforms 从广播到移动：家庭购物平台的跨渠道效应

IF 4.7

Telematics and Informatics Reports

Pub Date : 2026-01-17 DOI: 10.1016/j.teler.2026.100295

Yonghee Kim , Sungjin Yoo , Jaehyun Kim

This paper examines the interaction between television-based home shopping and mobile applications in Korea’s evolving digital market. While previous research has primarily focused on integrating physical and online channels, there is limited understanding of how broadcast media connect with digital platforms, such as mobile apps. In response to the growth of mobile commerce and the ongoing policy debates surrounding TV transmission fees, this study addresses two questions: (1) Can exposure to TV home shopping lead to greater use of the mobile app through mechanisms such as trust formation? (2) How do TV exposure and mobile usage work together to shape user retention? Using monthly panel data from four major home shopping companies in Korea between April 2021 and December 2023, we apply fixed-effects models to identify cross-channel effects. We find that higher TV viewership predicts increased mobile engagement, and that sustained app use, in turn, supports user retention. TV and mobile appear to function as sequential complements rather than substitutes. In this "reverse showrooming" pattern, television builds product credibility and trust through live demonstrations, while mobile apps provide transaction convenience and personalized engagement. This challenges assumptions about media displacement and demonstrates how broadcast and digital platforms can serve distinct yet coordinated functions across the customer journey. By extending channel integration theory to broadcast-digital settings, the study highlights how the nature of TV—its mass reach, regulatory standards, and low-effort exposure—can drive digital activity. Practically, the results indicate that firms and policymakers should consider integrated approaches to managing and regulating cross-channel shopping platforms.

本文考察了韩国不断发展的数字市场中基于电视的家庭购物和移动应用程序之间的相互作用。虽然之前的研究主要集中在整合实体和在线渠道，但对广播媒体如何与数字平台（如移动应用程序）连接的理解有限。为了应对移动商务的增长和围绕电视传输费用的持续政策辩论，本研究解决了两个问题：(1)通过信任形成等机制，电视家庭购物是否会导致移动应用程序的更多使用？(2)电视曝光和手机使用如何共同影响用户留存率？利用2021年4月至2023年12月韩国四大家庭购物公司的月度面板数据，我们应用固定效应模型来识别跨渠道效应。我们发现，更高的电视收视率预示着更高的手机用户粘性，而持续的应用使用反过来又会支持用户留存率。电视和手机的作用似乎是互为补充，而不是相互替代。在这种“反向展厅”模式中，电视通过现场演示建立产品的可信度和信任度，而移动应用提供交易便利和个性化参与。这挑战了关于媒体替代的假设，并展示了广播和数字平台如何在客户旅程中提供不同但协调的功能。通过将频道整合理论扩展到广播数字设置，该研究强调了电视的本质——它的广泛覆盖、监管标准和低成本曝光——如何推动数字活动。实际上，研究结果表明，企业和政策制定者应该考虑综合方法来管理和规范跨渠道购物平台。

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引用次数: 0

The effects of loneliness, self-esteem, perceived risk, and perceived threat on users’ willingness to use AI chatbots 孤独、自尊、感知风险和感知威胁对用户使用人工智能聊天机器人意愿的影响

IF 4.7

Telematics and Informatics Reports

Pub Date : 2026-01-13 DOI: 10.1016/j.teler.2026.100291

Min Zhou, Yuxin Hu

Artificial intelligence (AI) has emerged as a transformative force, reshaping social relationships and actively participating in social interaction. AI chatbots such as Apple Siri, XiaoIce, and Xeva are specifically designed to foster emotional engagement and simulate close relationships with users, extending human social relationships to human-machine interactions. A survey study was conducted to examine the effects of loneliness, self-esteem, perceived risk, and perceived threat on users’ willingness to use AI chatbots from the perspective of parasocial relationship theory. The findings showed that loneliness positively affects users' willingness to use AI chatbots, while perceived risk and perceived threat negatively affect users’ willingness. Furthermore, parasocial relationships serve as a mediating mechanism in these dynamics. To enhance the quality of human-AI interactions, it is crucial to innovate AI technologies to better meet users' social needs while maintaining appropriate boundaries to foster healthy parasocial relationships.

人工智能（AI）已成为一股变革力量，重塑社会关系，积极参与社会互动。苹果Siri、小冰和Xeva等人工智能聊天机器人专门设计用于培养情感参与，模拟与用户的亲密关系，将人类的社交关系扩展到人机交互。一项调查研究从副社会关系理论的角度研究了孤独、自尊、感知风险和感知威胁对用户使用人工智能聊天机器人意愿的影响。研究结果表明，孤独感对用户使用人工智能聊天机器人的意愿有积极影响，而感知风险和感知威胁对用户的意愿有消极影响。此外，副社会关系在这些动态中起着中介作用。为了提高人类与人工智能交互的质量，创新人工智能技术以更好地满足用户的社交需求，同时保持适当的边界，以培养健康的副社会关系，这一点至关重要。

引用次数: 0

AI without borders: The rise of cross-disciplinary machine learning 无国界人工智能：跨学科机器学习的兴起

IF 4.7

Telematics and Informatics Reports

Pub Date : 2026-01-10 DOI: 10.1016/j.teler.2026.100294

Aji Prasetya Wibawa , Roni Herdianto , Anik Nur Handayani , Agung Bella Putra Utama , Aripriharta , Felix Andika Dwiyanto , Rafal Drezewski

This literature review thoroughly analyzes Machine Learning (ML) algorithms, their applications in many fields, current developments, and interdisciplinary viewpoints. An in-depth analysis of academic literature demonstrates the significant influence of machine learning on research, industry, and society. It effectively performs several tasks, including predictive modeling, image recognition, natural language processing, and autonomous systems. Although machine learning has great potential to transform decision-making and foster innovation, it faces challenges such as data accuracy and bias, model explainability, scalability, and ethical concerns that require careful study. Future research should focus on multidisciplinary cooperation, transparent governance frameworks, and responsible deployment of AI to enable equitable and ethical usage of ML technology. It is crucial to explore new trends like federated learning, quantum machine learning, human-centric ML, and advancements in explainable and interpretable ML models. These efforts are essential for utilizing ML to create a positive societal impact and promote innovation in various fields.

这篇文献综述全面分析了机器学习（ML）算法，它们在许多领域的应用，当前的发展，以及跨学科的观点。对学术文献的深入分析证明了机器学习对研究、工业和社会的重大影响。它有效地执行多项任务，包括预测建模、图像识别、自然语言处理和自主系统。尽管机器学习在改变决策和促进创新方面具有巨大潜力，但它面临着数据准确性和偏见、模型可解释性、可扩展性以及需要仔细研究的伦理问题等挑战。未来的研究应侧重于多学科合作、透明的治理框架和负责任的人工智能部署，以实现机器学习技术的公平和道德使用。探索新的趋势，如联邦学习、量子机器学习、以人为中心的机器学习，以及可解释和可解释的机器学习模型的进步，是至关重要的。这些努力对于利用机器学习创造积极的社会影响和促进各个领域的创新至关重要。

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引用次数: 0

Advancing solar radiation prediction with explainable AI and ensemble learning techniques 利用可解释的人工智能和集成学习技术推进太阳辐射预测

IF 4.7

Telematics and Informatics Reports

Pub Date : 2026-01-10 DOI: 10.1016/j.teler.2026.100292

Iftekharul Islam , Shamanta Sharmin Sristy , Raunaq Jahan , Md. Motiur Rahman Tareq , Mahmudul Hasan , Md. Palash Uddin

The core of sustainable development is empowering future generations with clean, renewable energy. Accurate prediction of solar radiation is imperative for optimizing the efficacy of renewable energy sources and is a critical component of achieving sustainable energy independence. This study examines solar radiation prediction by employing Machine Learning (ML), Deep Learning (DL) algorithms, and Explainable AI (XAI) methods to ensure interpretability. We propose an ensemble ML model named SLRK (Stacking of Linear Regression, Random Forest, and K-nearest Neighbors). Our proposed model outperforms all comparative ML and DL algorithms in terms of regression accuracy and performance metrics, achieving an

R^{2}

score of 98%, MSE of 0.0020, RMSE of 0.0475, and MAE of 0.0201. Other ML models yielded

R^{2}

values from 55% to 97% with MSE values exceeding 0.0043 and RMSE values above 0.0481, and DL models achieved

R^{2}

between 91% and 93% with MSE in the range of 0.0053 to 0.0070 and RMSE between 0.0730 and 0.0837. The t-test confirms that most algorithms demonstrate statistical significance in predicting solar radiation. In addition, XAI techniques, including SHAP, LIME, Shapash, and ELi5, were used to exhibit the impact of temperature, humidity, day of the year, and time of day on solar radiation predictions. These explainability results enhance trust in the model’s predictions and provide practical insights for real-world applications, such as optimal solar panel placement and energy grid optimization. Key challenges remain in improving the model’s robustness under changing climatic conditions and enhancing generalizability across diverse geographic regions, which constitute important directions for future research.

可持续发展的核心是为子孙后代提供清洁的可再生能源。太阳辐射的准确预测是优化可再生能源效率的必要条件，也是实现可持续能源独立的关键组成部分。本研究通过使用机器学习（ML）、深度学习（DL）算法和可解释人工智能（XAI）方法来检查太阳辐射预测，以确保可解释性。我们提出了一个名为SLRK （Stacking of Linear Regression, Random Forest, and K-nearest Neighbors）的集成ML模型。我们提出的模型在回归精度和性能指标方面优于所有比较ML和DL算法，实现R2评分为98%，MSE为0.0020，RMSE为0.0475，MAE为0.0201。其他ML模型的R2在55% ~ 97%之间，MSE大于0.0043，RMSE大于0.0481；DL模型的R2在91% ~ 93%之间，MSE在0.0053 ~ 0.0070，RMSE在0.0730 ~ 0.0837。t检验证实，大多数算法在预测太阳辐射方面具有统计显著性。此外，XAI技术（包括SHAP、LIME、Shapash和ELi5）用于显示温度、湿度、一年中的天数和一天中的时间对太阳辐射预测的影响。这些可解释性结果增强了对模型预测的信任，并为现实世界的应用提供了实际的见解，例如最佳的太阳能电池板放置和能源网优化。关键的挑战仍然是提高模型在变化的气候条件下的鲁棒性和增强不同地理区域的普遍性，这是未来研究的重要方向。

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引用次数: 0

Influencing factors of user acceptance and adoption of e-government AI assistants: An empirical study based on the extended UTAUT2 model 电子政务人工智能助手用户接受与采用的影响因素——基于扩展UTAUT2模型的实证研究

IF 4.7

Telematics and Informatics Reports

Pub Date : 2026-01-09 DOI: 10.1016/j.teler.2026.100293

Xueyao Li , Chenyang Wang , Zisong Ma

In the context of digital government transformation, e-government AI assistants represent a critical component in the intelligent upgrading of government services, where understanding user adoption behavior becomes essential for enhancing service effectiveness. Building upon the extended UTAUT2 model, this study incorporates Trust in AI (TIAI) and Perceived Privacy Protection (PPP) to construct a comprehensive framework examining the adoption mechanisms of government service applications. The research specifically investigates how performance expectancy, effort expectancy, and social influence shape users' behavioral intentions and actual usage patterns. Through PLS-SEM analysis of 513 valid questionnaire responses, the study reveals several key findings. Social influence emerges as the most significant predictor of usage intention (β=0.277, p < 0.001), while perceived privacy protection demonstrates particularly strong predictive power (β=0.235, p < 0.001) along with the highest performance score (76.026). The analysis also identifies meaningful positive effects from effort expectancy (β=0.125, p < 0.05) and habit (β=0.100, p < 0.05) on usage intention. Regarding actual usage behavior, the results show significant influences from facilitating conditions (β=0.229, p < 0.001), trust in AI (β=0.273, p < 0.001), and usage intention itself (β=0.397, p < 0.001). These findings not only contribute to the theoretical understanding of technology adoption within digital government transformation but also offer practical insights for optimizing the design and implementation of e-government AI assistants, revealing the dominance of social influence in collectivist contexts like China, privacy protection measures, and trust-building features in driving user adoption.

在数字化政府转型的背景下，电子政务人工智能助手是政府服务智能升级的关键组成部分，了解用户采用行为对于提高服务效率至关重要。本研究以扩展的UTAUT2模型为基础，结合人工智能信任（TIAI）和感知隐私保护（PPP），构建了一个考察政府服务应用采用机制的综合框架。该研究特别调查了性能期望、努力期望和社会影响如何影响用户的行为意图和实际使用模式。通过对513份有效问卷的PLS-SEM分析，本研究揭示了几个关键发现。社会影响是使用意愿的最显著预测因子（β=0.277, p < 0.001），而感知到的隐私保护表现出特别强的预测能力（β=0.235, p < 0.001），以及最高的表现得分（76.026）。分析还发现，努力预期（β=0.125, p < 0.05）和习惯（β=0.100, p < 0.05）对使用意图有显著的积极影响。在实际使用行为方面，结果显示便利条件（β=0.229, p < 0.001）、对人工智能的信任（β=0.273, p < 0.001）和使用意图本身（β=0.397, p < 0.001）对实际使用行为有显著影响。这些发现不仅有助于对数字政府转型中技术采用的理论理解，还为优化电子政务人工智能助手的设计和实施提供了实践见解，揭示了在中国等集体主义背景下社会影响的主导地位，隐私保护措施和信任建设特征在推动用户采用方面的作用。

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引用次数: 0

The evolution of blockchain in smart buildings with IoT integration and future prospects b区块链在物联网智能建筑中的演变及未来展望

IF 4.7

Telematics and Informatics Reports

Pub Date : 2026-01-06 DOI: 10.1016/j.teler.2025.100283

Sunawar Khan , Tehseen Mazhar , Tariq Shahzad , Muhammad Usman Tariq , Tariq Ali , Muhammad Ayaz , Weiwei Jiang , Habib Hamam

The adoption of Internet of Things (IoT) sensors in smart buildings introduces complex challenges around data privacy, integrity, interoperability, and governance. Blockchain—particularly permissioned and hybrid designs—offers immutability, fine-grained access control, and auditable workflows that can strengthen compliance and trust in building operations. This article synthesizes architectural patterns and operational trade-offs for integrating blockchain with smart-building IoT, focusing on latency-aware data placement (off-chain buffering with on-chain anchoring), identity and consent management, and verifiable telemetry for control loops. Across databases (IEEE Xplore, ScienceDirect, Springer, ACM & Wiley), 553 records were identified; Step 1 excluded 311. After duplicate removal and title/keyword screening (Step 2), 375 papers remained with 178 excluded; abstract screening (Step 3) yielded 336 papers with 39 excluded; introduction/conclusion criteria (Step 4) retained 264 with 72 excluded; full-text review (Step 5) resulted in 131 included studies. Consistently with Table 3, counts progressed from 553 (initial) to 375 (title/keyword), 336 (abstract), and 131 (final full-text). We summarize verified design choices and open issues—cross-chain interoperability, light-node feasibility, and empirical benchmarking for off-chain computing—to guide robust, efficient, and modifiable IoT infrastructures.

智能建筑中物联网（IoT）传感器的采用带来了数据隐私、完整性、互操作性和治理方面的复杂挑战。区块链-特别是许可和混合设计-提供不变性，细粒度访问控制和可审计的工作流程，可以加强建筑运营的合规性和信任。本文综合了区块链与智能建筑物联网集成的架构模式和操作权衡，重点关注延迟感知数据放置（链下缓冲与链上锚定）、身份和同意管理，以及控制回路的可验证遥测。通过数据库（IEEE explore, ScienceDirect， b施普林格，ACM & Wiley），确定了553条记录；步骤1排除311。经过重复删除和标题/关键词筛选（步骤2），剩下375篇论文，其中178篇被排除；摘要筛选（步骤3）共336篇，排除39篇；介绍/结论标准（步骤4）保留264项，排除72项；全文综述（步骤5）纳入131项研究。与表3一致，计数从553（首字母）到375（标题/关键字）、336（摘要）和131（最终全文）。我们总结了经过验证的设计选择和开放问题-跨链互操作性，轻节点可行性和脱链计算的经验基准-以指导稳健，高效和可修改的物联网基础设施。

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引用次数: 0

Shaping attitudes toward artificial intelligence: A convergence of two models 塑造对人工智能的态度：两个模型的融合

IF 4.7

Telematics and Informatics Reports

Pub Date : 2026-01-04 DOI: 10.1016/j.teler.2026.100290

Christian Montag , Cheng-Zhong Xu , Raian Ali

The AI revolution is impacting societies around the globe. Therefore, there is an urgent need to assess individual differences in attitudes toward AI to better understand how people perceive this technology being added to a growing number of products and services.

The present short paper aims to bring together two recent frameworks called IMPACT framework and the Five-Factor-Model, which explicitly have been developed to understand AI attitude formation. Insights from these models can provide researchers with a comprehensive model to guide their research. Comparing and providing a synthesis of both theories help to see where independent working research groups come to same conclusions and where differences occur. Finally, we provide insights into the current situation of AI attitude measurement, whereas AI attitudes are the focus of both theoretical models discussed.

人工智能革命正在影响全球社会。因此，迫切需要评估个人对人工智能的态度差异，以更好地了解人们如何看待这项技术被添加到越来越多的产品和服务中。本短文旨在汇集两个最近的框架，称为IMPACT框架和五因素模型，这两个框架是为了理解人工智能态度形成而明确开发的。从这些模型中获得的见解可以为研究人员提供一个全面的模型来指导他们的研究。对两种理论进行比较和综合，有助于了解独立的研究小组在哪些地方得出相同的结论，在哪些地方出现了差异。最后，我们提供了对人工智能态度测量现状的见解，而人工智能态度是两个理论模型讨论的重点。

引用次数: 0

B-PhishQR—A blockchain-based framework for secure QR code verification against phishing attacks b - phishqr -一个基于区块链的安全QR码验证框架，用于防止网络钓鱼攻击

IF 4.7

Telematics and Informatics Reports

Pub Date : 2026-01-03 DOI: 10.1016/j.teler.2025.100289

Rouwa Yalda, Arshad Khan, Narayan Nepal

QR phishing, or “quishing”, is an emerging cyber threat that uses malicious QR codes to trick users into revealing sensitive information. The QR code scanning increased by 433% between 2021 and 2023, while phishing attacks exploded by 270% in 2024 alone; these increased statistics make urgent measures against quishing necessary. To secure QR code interactions, this work introduces a centralised system that integrates per-code AES-GCM encryption with key derivation (HKDF), real-time revocation checks, and blockchain-based integrity verification. The design employs a secure, three-tier service-oriented architecture that logically separates cryptographic services to protect sensitive operations and enables online verification via RESTful APIs.

The Key contributions to this paper are: (1) A distributed three-tier architecture separating Blockchain Server, Key Management Server, and Verifier components, (2) Cryptographic key derivation ensuring the compromise of one QR code does not affect others, (3) Real-time revocation mechanism using distributed blockchain propagation for immediate response against compromised codes, and (4) mobile-optimised web interface along with validation of enterprise-scale performance. The experimental results show that the proposed system successfully prevents quishing attacks without sacrificing performance, making it suitable for enterprise deployment.

This approach enhances users’ trust and presents an efficient countermeasure for quishing attacks, ensuring the integrity of data through cryptographic verification, real-time revocation, and authentication of QR code across diverse environments.

二维码网络钓鱼是一种新兴的网络威胁，它使用恶意二维码诱骗用户泄露敏感信息。二维码扫描在2021年至2023年间增长了433%，而网络钓鱼攻击仅在2024年就激增了270%；这些增加的统计数字使采取紧急措施防止犯罪成为必要。为了确保QR码的交互，这项工作引入了一个集中的系统，该系统将每个代码的AES-GCM加密与密钥派生（HKDF）、实时撤销检查和基于区块链的完整性验证集成在一起。该设计采用安全的三层面向服务的体系结构，逻辑上分离加密服务以保护敏感操作，并通过RESTful api支持在线验证。本文的主要贡献有：(1)分离区块链服务器、密钥管理服务器和验证器组件的分布式三层架构，(2)加密密钥派生确保一个QR码的泄露不会影响其他QR码，(3)使用分布式区块链传播的实时撤销机制对泄露的代码进行即时响应，以及(4)移动优化的web界面以及企业级性能验证。实验结果表明，该系统在不牺牲性能的情况下成功地阻止了攻击，适合企业部署。这种方法增强了用户的信任，并提供了一种有效的解决攻击的对策，通过加密验证、实时撤销和不同环境下的QR码认证来确保数据的完整性。

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引用次数: 0

The impact of the perceived quality of NDMA’s misinformation correction in Pakistan on public perception during the COVID-19 pandemic: The mediating roles of engagement, sentiment, and trust COVID-19大流行期间巴基斯坦NDMA错误信息纠正的感知质量对公众认知的影响：参与、情绪和信任的中介作用

IF 4.7

Telematics and Informatics Reports

Pub Date : 2025-12-23 DOI: 10.1016/j.teler.2025.100288

Arslan Amin, Yu Hong

This study examines how the National Disaster Management Authority of Pakistan (NDMA) addressed COVID-19 misinformation and its effect on public perception, focusing on the mediating roles of public engagement, sentiment, and trust in government. The study applies trust-building communication theory and social media engagement theory to investigate how corrective communication strategies influence public responses in a politically sensitive, low–digital literacy environment. Data were collected through an online survey of 560 social media users who interacted with NDMA’s official posts. Structural equation modeling was used to test the proposed relationships. The results demonstrated that perceived quality of misinformation correction promotes engagement and positive sentiment, which subsequently strengthens trust and improves public perception. Trust functions as a central pathway through which emotional and participatory responses enhance confidence in and compliance with governmental guidance. The findings suggest that governmental crisis communication should combine factual corrections with transparent and emotionally resonant messaging, encourage interactive public participation, and monitor sentiment in real time to address emerging concerns. Such strategies can improve institutional trust, counter misinformation, and enhance public cooperation in future crises.

本研究考察了巴基斯坦国家灾害管理局（NDMA）如何应对COVID-19错误信息及其对公众认知的影响，重点关注公众参与、情绪和对政府信任的中介作用。本研究运用信任建立传播理论和社交媒体参与理论来研究在政治敏感、低数字素养的环境下，纠正性传播策略如何影响公众反应。数据是通过对560名与NDMA官方帖子互动的社交媒体用户进行在线调查收集的。采用结构方程模型对提出的关系进行检验。结果表明，错误信息纠正的感知质量促进了参与和积极情绪，从而增强了信任，提高了公众感知。信任是情感和参与性反应增强对政府指导的信心和遵守的核心途径。研究结果表明，政府危机沟通应将事实纠正与透明和情感共鸣的信息传递结合起来，鼓励公众互动参与，并实时监控情绪，以解决新出现的问题。这些战略可以改善机构信任，反击错误信息，并在未来危机中加强公共合作。

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引用次数: 0

Understanding Algorithmic Personalization in Instagram Ads: The Role of Perceived Creativity, Authenticity & Need for Cognition in Shaping Generation Z’s Purchase Intentions 理解Instagram广告中的算法个性化：感知创意、真实性和认知需求在塑造Z世代购买意愿中的作用

IF 4.7

Telematics and Informatics Reports

Pub Date : 2025-12-16 DOI: 10.1016/j.teler.2025.100287

Inam Ul Haq , Bilal Mazhar , Fatima Maqsood , Mandeep Pokharel , Hannan Khan Tareen , Muhammad Maaz ul Hassan

Algorithmic personalization has transformed the dynamic of consumer engagement with social media advertising, yet its underlying psychological mechanisms remain underexplored. Drawing upon the Elaboration Likelihood Model (ELM) and the Theory of Planned Behavior (TPB), this study examines how personalized Instagram advertisements shape purchase intention through perceived creativity and authenticity, and how Need for Cognition (NFC) moderates these relationships. Data from 458 Pakistani Generation Z users were analyzed using covariance-based structural equation modeling (CFI = 0.997, RMSEA = 0.016, SRMR = 0.024). The results show that personalization significantly enhances perceived creativity (β = 0.578, p < 0.001) and authenticity (β = 0.664, p < 0.001), both of which positively influence purchase intention trough indirect effects (β = 0.195 and β = 0.258, respectively). NFC further strengthens the personalization-intention link (β = 0.142, p < 0.001). These findings extend ELM by situating algorithmic personalization within central-route processing and complement the TPB by demonstrating how creativity and authenticity function as attitudinal mechanisms shaping purchase intention in algorithmically mediated digital advertising.

算法个性化已经改变了消费者与社交媒体广告互动的动态，但其潜在的心理机制仍未得到充分探索。利用精化似然模型（ELM）和计划行为理论（TPB），本研究探讨了个性化Instagram广告如何通过感知创造力和真实性来塑造购买意愿，以及认知需求（NFC）如何调节这些关系。采用基于协方差的结构方程模型对458名巴基斯坦Z世代用户的数据进行分析（CFI = 0.997, RMSEA = 0.016, SRMR = 0.024）。结果表明，个性化显著提高了感知创造力（β = 0.578, p < 0.001）和真实性（β = 0.664, p < 0.001），两者均通过间接效应正向影响购买意愿（β = 0.195和β = 0.258）。NFC进一步强化了个性化与意向之间的联系（β = 0.142, p < 0.001）。这些发现通过将算法个性化置于中央路径处理中来扩展ELM，并通过展示创造力和真实性如何在算法介导的数字广告中作为塑造购买意愿的态度机制来补充TPB。

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引用次数: 0