Trust In Technology Research Articles

Adaptable robots, ethics, and trust: a qualitative and philosophical exploration of the individual experience of trustworthy AI

AbstractMuch has been written about the need for trustworthy artificial intelligence (AI), but the underlying meaning of trust and trustworthiness can vary or be used in confusing ways. It is not always clear whether individuals are speaking of a technology’s trustworthiness, a developer’s trustworthiness, or simply of gaining the trust of users by any means. In sociotechnical circles, trustworthiness is often used as a proxy for ‘the good’, illustrating the moral heights to which technologies and developers ought to aspire, at times with a multitude of diverse requirements; or at other times, no specification at all. In philosophical circles, there is doubt that the concept of trust should be applied at all to technologies rather than their human creators. Nevertheless, people continue to intuitively reason about trust in technologies in their everyday language. This qualitative study employed an empirical ethics methodology to address how developers and users define and construct requirements for trust throughout development and use, through a series of interviews. We found that different accounts of trust (rational, affective, credentialist, norms based, relational) served as the basis for individual granting of trust in technologies and operators. Ultimately, the most significant requirement for user trust and assessment of trustworthiness was the accountability of AI developers for the outputs of AI systems, hinging on the identification of accountable moral agents and perceived value alignment between the user and developer’s interests.

Unlocking the potential of blockchain technology in China’s supply chain: a survey of industry professionals

PurposeThis study aims to identify the essential elements impacting the adoption of blockchain technology (BCT) in supply chain management (SCM) by integrating the technology acceptance and information system success (ISS) models.Design/methodology/approachQuestionnaire-based data was collected from 236 supply chain professionals from Beijing. The proposed research framework was evaluated using structural equation modeling (SEM) by using SPSS 23 and AMOS 24 software.FindingsThe empirical findings specify the positive influence of total quality on perceived usefulness and compatibility. Further, perceived ease of use positively influences perceived usefulness, compatibility and behavioral intention. Moreover, perceived usefulness positively impacts compatibility and behavioral intention. Compatibility positively influences behavioral intention. Finally, technology trust was found to be a significant moderator between perceived usefulness and behavioral intention and between perceived ease of use and adoption intention to use BCT in SCM.Originality/valueThis study empirically develops the second-order construct of total quality, representing the ISS model. Furthermore, this study established how the ISS and technology acceptance models influence behavioral intention through compatibility. Finally, this study confirmed the moderating role of technology trust among perceived ease of use, perceived usefulness and behavioral intention.

AiWATERS: an artificial intelligence framework for the water sector

The presence of Artificial Intelligence (AI) and Machine Learning (ML) applications has led to its widespread adoption across diverse domains. AI is making its way into industry, beyond research and academia. Concurrently, the water sector is undergoing a digital transformation. Water utilities in the United States are at different stages in their journey of digital transformation, and the decision makers in water sector, who are non-expert stakeholders in AI applications, need to better understand this technology to make informed decisions. While AI has numerous benefits to offer, there are also many challenges related to data, model development, knowledge integration and ethical concerns that should be considered before implementing it for real world applications. Civil engineering is a licensed profession where critical decision making is involved. Therefore, trust in any decision support technology is critical for its acceptance in real-world applications. Therefore, this research proposes a framework called aiWATERS (Artificial Intelligence for the Water Sector) which can serve as a guide for the water utilities to successfully implement AI in their system. Based on this framework, we conduct pilot interviews and surveys with various small, medium, and large water utilities in the United States (US) to capture their current state of AI implementation and identify the challenges faced by them. The research findings reveal that most of the water utilities in the United States are at an early stage of implementing AI as they face concerns regarding the black box nature, trustworthiness, and sustainability of AI technology in their system. The aiWATERS framework is intended to help the utilities navigate through these issues in their journey of digital transformation.

Integrating connected autonomous shuttle buses as an alternative for public transport – A simulation-based study

The concept of connected autonomous shuttles is gaining recognition for its potential to revolutionize traditional public transport by offering a safer and more consistent travel experience. Despite technological advancements facilitating their integration into current traffic systems, research in this area is still exploratory, with public acceptance and trust in autonomous technology posing significant challenges. This paper contributes to the field by validating the benefits of deploying connected autonomous shuttles and examining their impact on ridership via an immersive experience. We introduce a VR-enabled co-simulation strategy to analyze the effects of replacing a traditional bus service line with connected shuttles in Melbourne, Australia. Our case study results reveal that while autonomous shuttles can reduce travel time because of optimized vehicle motion, their traffic efficiency is affected by fleet size, with the optimal fleet size identified as four in the study area. Furthermore, we observed a mismatch between participants’ stated intentions and actual boarding behavior, indicating that the attractive appearance of this novel mobility mode may not necessarily enhance ridership. Our work offers an alternative approach to simulation studies in futuristic public transportation and complement existing literature in the field.

From video to vital signs: using personal device cameras to measure pulse rate and predict blood pressure using explainable AI

In this study, we address the need for low-cost, rapid, and reliable methods for blood pressure measurement to facilitate timely diagnosis and treatment of hypertension. We aim to enhance understanding and trust in the novel remote photoplethysmography technology, specifically as deployed by Lifelight®\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circledR$$\\end{document}, for vital sign monitoring. Lifelight®\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circledR$$\\end{document} utilizes remote photoplethysmography to extract physiologically meaningful features from video recordings on personal devices. This study describes the accurate calculation of pulse rates and the extraction of morphological and time series features from the remote photoplethysmography signal for blood pressure prediction. Unlike common approaches, distinct model types are employed for predicting systolic and diastolic blood pressure. Evaluation on an independent clinical laboratory’s dataset demonstrates the efficacy of the proposed method. The pulse rate accuracy, measured as root mean square error, is 1.9 beats per minute. The model for systolic blood pressure prediction shows a mean error of 1.2 mmHg and a standard deviation of 15.9 mmHg. The model for diastolic prediction shows a mean error of -\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}3.2 mmHg and a standard deviation of 13.1 mmHg. This research establishes the validity of remote photoplethysmography technology by showcasing accurate pulse rate calculations and effective feature extraction for blood pressure prediction. The results indicate the potential for widespread adoption of remote photoplethysmography in measuring blood pressure and other vital signs.

Trustworthy Artificial Intelligence for Social Governance

As AI technology continues to evolve, it plays an increasingly significant role in everyday life and social governance. However, the frequent occurrence of issues such as algorithmic bias, privacy breaches, and data leaks has led to a crisis of trust in AI among the public, presenting numerous challenges to social governance. Establishing technical trust in AI, reducing uncertainties in AI development, and enhancing its effectiveness in social governance have become a consensus among policymakers and researchers. By comparing different types of AI, the paper proposes and conceptualizes the idea of trustworthy AI, then discusses its characteristics and its value and impact pathways in social governance. The analysis addresses how mismatches in technological trust can affect social stability and the advancement of AI strategies. The paper highlights the potential of trustworthy AI to improve the efficiency of social governance and solve complex social problems.

Cognitive Reapportionment and the Art of Letting Go: A Theoretical Framework for the Allocation of Decision Rights

ABSTRACT We have entered an era where, in addition to us humans, systems can also think. It is imperative to decide how decision rights and authorities are allocated and distributed across interconnected co-cognitors: things that think. To address this question vital to the redesign of organizations, we reconceptualize cognitive reapportionment as the dynamic reallocation of decision rights and authorities across human and system co-cognitors capable of independent decision making. We articulate the Helix Model of Decision Journeys, wherein decision journeys, comprised of decision elements allocable to human or system co-cognitors, interweave and integrate business processes. The dynamic reapportionment of cognitive responsibilities across co-cognitors is dependent upon the type of scientific reasoning—deduction, induction, or abduction—at each decision element. We further propose two intertwined mechanisms for the art of letting go that facilitate the adoption of the Helix model. First, the nexus of omnipresent system co-cognitors collectively needs to evoke trust. Only when Technology Trust Thresholds are exceeded can decision making be reorganized and control relinquished to system co-cognitors. Second, the world of omnipresent cognitors will compel us to redesign, reprogram, and remix our workflow and business processes. Only through a purposeful effort to Redesign in the Remix Regime can the deduction-induction-abduction nature of decision tasks serve as the basis for their reapportionment.

Blockchain Technology Adoption Through the UTAUT Model

This paper studies the adoption of blockchain technology under the scope of the Unified Theory of Acceptance and Use of Technology (UTAUT). Previous results on Management Information Systems (MIS) research are divergent about the significance of UTAUT variables in explaining the adoption behaviour of blockchain technology. The paper focuses on this specific concern and tries to contribute to existing studies by testing the model in a specific context (Tunisia) and by considering the individual variable “trust in technology” as a mediating one. For this aim, a structural equation approach is adopted among 95 Tunisian professional respondents operating in technology-based sectors. The findings stipulate the importance of facilitating conditions and performance expectancy as drivers of the adoption intention. Additionally, the study reveals that trust in technology is significant in its mediating role in influencing the intention of adoption with the facilitating conditions and the social influence constructs. Moreover, the paper uncovers a direct relationship with the same variable. These findings provide valuable insights for both researchers and practitioners in understanding the factors that influence blockchain technology adoption in the Tunisian context and stress the indirect role of trust in technology with which decision-makers should be concerned.

Quantum Technologies: a Hermeneutic Technology Assessment Approach

This paper develops a hermeneutic technology assessment of quantum technologies. It offers a “vision assessment” of quantum technologies that can eventually lead to socio-ethical analysis. Section 2 describes this methodological approach and in particular the concept of the hermeneutic circle applied to technology. Section 3 gives a generic overview of quantum technologies and their impacts. Sections 4 and 5 apply the hermeneutic technology assessment approach to the study of quantum technologies. Section 5 proposes distinguishing three levels in the analysis of the creation and communication of social meanings to quantum technologies: (a) fictions, (b) popularization, and (c) scientific journalism. Section 6 analyzes the results and defines some lines of action to increase social acceptance and trust in quantum technologies. The aim of this paper is to contribute to the debate on quantum technologies by enhancing the reflection on them and their potential, as well as illustrating the complexity of technological innovation and the need to shape it.

Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles.

The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional "one size fits all" approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger's personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human-Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.

Effects of trust and customer perceived value on the acceptance of urban air mobility as public transportation

Urban air mobility (UAM) is expected to become an innovative mode of public transportation. Although multiple factors may facilitate the adoption of UAM, its implementation may be difficult owing to the lack of customer acceptance and usage intentions. This study proposes a research model to explore the effects of trust and customer perceived value on the acceptance of UAM as public transportation. In this study, empirical data from 573 respondents were collected via an online survey. Additionally, thirteen hypotheses on trust and customer-perceived value were presented, and a relationship model was conceptualized and analyzed using structural equation modeling based on the partial least squares method. The results indicated that all thirteen hypotheses proposed in this study were statistically supported and trust in technology and customer perceived value was found to positively influence the intention to use UAM as public transport. Notably, functional, emotional, and social values were found to considerably affect trust in technology and customer-perceived value; specifically, functional value had the greatest impact in this context. The results of this study validate the importance of trust and customer-perceived value, which should be prioritized in the adoption of UAM as a public transportation service.

Patients' experiences and perspectives regarding the use of digital technology to support exercise-based cardiac rehabilitation: a qualitative interview study.

Despite the well-known benefits of exercise-based cardiac rehabilitation for the secondary prevention of cardiovascular disease, participation in cardiac rehabilitation programmes and adherence to secondary prevention recommendations remain limited. Digital technologies have the potential to address low participation and adherence but attempts at implementing digital health interventions in real-life clinical practice frequently encounter various barriers. Studies about patients' experiences and perspectives regarding the use of digital technology can assist developers, researchers and clinicians in addressing or pre-empting patient-related barriers. This study was therefore conducted to investigate the experiences and perspectives of cardiac rehabilitation patients in Austria with regard to using digital technology for physical activity and exercise. Twenty-five current and former cardiac rehabilitation patients (18 men and 7 women, age range 39 to 83) with various cardiac conditions were recruited from a clinical site in Salzburg, Austria. Semi-structured qualitative interviews were audio-recorded and transcribed verbatim. The analysis followed a descriptive phenomenological approach, applying the framework analysis method. The sample was diverse, including interviewees who readily used digital devices to support their physical activity, exercise and health monitoring, and interviewees who did not. Simplicity, convenience and accessibility were highlighted as important facilitators for the use of digital technology, while annoyance with digital devices, concerns about becoming dependent on them, or simply a preference to not use digital technology were commonly stated reasons for non-use. Interviewees' views on data protection, data sharing and artificial intelligence revealed wide variations in individuals' prior knowledge and experience about these topics, and a need for greater accessibility and transparency of data protection regulation and data sharing arrangements. These findings support the importance that is attributed to user-centred design methodologies in the conceptualisation and design of digital health interventions, and the imperative to develop solutions that are simple, accessible and that can be personalised according to the preferences and capabilities of the individual patient. Regarding data protection, data sharing and artificial intelligence, the findings indicate opportunity for information and education, as well as the need to offer patients transparency and accountability in order to build trust in digital technology and digital health interventions.

Is it all about fun? Self-service technology acceptance in Germany

ABSTRACT The introduction of self-service technology (SST), particularly self-checkout and self-scan services, has gained increasing attention from German retail. But to date, end-customer SST use has not developed extensively. The majority of German customers still prefer to use the traditional checkout service counter. The purpose of this paper is to explore how trust, fun, and usefulness can attract inexperienced prospective adopters and experienced customers to use SST. For this, our study investigated the intention to use an SST as a holistic system (self-scan and self-checkout) and considered trust in the SST technology for the first time. Also the interplay between fun and perceived usefulness has not been investigated before. The data were collected using an online questionnaire and in front of a German hypermarket that offers SST. The sample (n = 222) consists of both inexperienced and experienced customers. The data were analyzed using structural equation modeling in SmartPLS4. Results demonstrate fun is a decisive factor for a holistic SST system that incorporates the task of self-scanning and self-checkout. This also holds for trust and perceived usefulness. With regard to experienced and prospective users, the results show different effects in the importance of both groups. Fun is crucial for prospective users. For experienced users, benefits and trust become more important for long-term usage. Trust is an essential aspect for both user types. Managerial implications are discussed.

Artificial Intelligence Methods in Natural Language Processing: A Comprehensive Review

The rapid evolution of Artificial Intelligence (AI) since its inception in the mid-20th century has significantly influenced the field of Natural Language Processing (NLP), transforming it from a rule-based system to a dynamic and adaptive model capable of understanding the complexities of human language. This paper aims to offer a comprehensive review of the various applications and methodologies of AI in NLP, serving as a detailed guide for future research and practical applications. In the early sections, the paper elucidates the indispensable role of AI in NLP, highlighting its transition from symbolic reasoning to a focus on machine learning and deep learning, and its extensive applications in sectors such as healthcare, transportation, and finance. It emphasizes the symbiotic relationship between AI and NLP, facilitated by platforms like AllenNLP, which aid in the development of advanced language understanding models. Further, the paper explores specific AI techniques employed in NLP, including machine learning, Naive Bayes, and Support Vector Machines, and identifies pressing challenges and avenues for future research. It delves into the applications of AI in NLP, showcasing its transformative potential in tasks such as machine translation, facilitated by deep learning methods, and the development of chatbots and virtual assistants that have revolutionized human-technology interaction. The paper also highlights other fields impacted by AI techniques, including text summarization, sentiment analysis, and named entity recognition, emphasizing the efficiency and accuracy brought about by the integration of AI in these areas. In conclusion, the paper summarizes the remarkable advancements and persistent challenges in NLP, such as language ambiguity and contextual understanding, and underscores the need for diverse and representative labeled data for training. Looking forward, it identifies promising research avenues including Explainable AI, Few-shot and Zero-shot Learning, and the integration of NLP with other data modalities, aiming for a holistic understanding of multimodal data. The paper calls for enhanced robustness and security in NLP systems, especially in sensitive applications like content moderation and fake news detection, to foster trust and reliability in AI technologies. It advocates for continual learning in NLP models to adapt over time without losing previously acquired knowledge, paving the way for a future where AI and NLP work synergistically to understand and generate human language more effectively and efficiently.

My colleague is an AI! Trust differences between AI and human teammates

Purpose The purpose of this study was to investigate trust within human-AI teams. Trust is an essential mechanism for team success and effective human-AI collaboration. Design/methodology/approach In an online experiment, the authors investigated whether trust perceptions and behaviours are different when introducing a new AI teammate than when introducing a new human teammate. A between-subjects design was used. A total of 127 subjects were presented with a hypothetical team scenario and randomly assigned to one of two conditions: new AI or new human teammate. Findings As expected, perceived trustworthiness of the new team member and affective interpersonal trust were lower for an AI teammate than for a human teammate. No differences were found in cognitive interpersonal trust and trust behaviours. The findings suggest that humans can rationally trust an AI teammate when its competence and reliability are presumed, but the emotional aspect seems to be more difficult to develop. Originality/value This study contributes to human–AI teamwork research by connecting trust research in human-only teams with trust insights in human–AI collaborations through an integration of the existing literature on teamwork and on trust in intelligent technologies with the first empirical findings on trust towards AI teammates.

Exploring Vulnerabilities and Attack Vectors Targeting Pacemaker Devices in Healthcare

This technical paper investigates the vulnerabilities and potential threats posed by emerging technologies, specifically Bluetoothenabled patient pacemakers. With the advancements in healthcare technology, pacemakers now utilize Bluetooth connectivity for real-time monitoring and data transmission, offering patients and healthcare providers an important convenience. However, this technology also introduces significant security risks, leaving these life-sustaining devices susceptible to malicious attacks. Through an in-depth analysis of existing research, real-life incidents, and vulnerabilities identified by experts in the field, this paper will underscore the critical vulnerabilities present in pacemaker systems. Examples, including findings from researchers such as Billy Rios, Jonathon Butts, and Marie Moe, demonstrate the potential severity of these vulnerabilities. From remote control manipulation to unauthorized access to sensitive medical data, the threats posed by these vulnerabilities are substantial and potentially life-threatening. Moreover, this paper outlines advanced mitigation strategies essential for protecting patient pacemakers against these security risks. Recommendations include end-to-end encryption, whitelist device pairing, intrusion detection systems, and regular firmware updates, highlight the collaborative efforts required from patients, healthcare providers, and manufacturers to mitigate these risks effectively. This paper’s findings underscore the urgent need for robust cybersecurity measures in the design, implementation, and maintenance of pacemaker systems. Addressing these vulnerabilities is key for ensuring patient safety, maintaining privacy, and building trust in healthcare technology. The implications of this research extend beyond pacemaker security, emphasizing the broader importance of cybersecurity in medical devices and the importance of ongoing research and regulatory initiatives to protect patient health.

From Cash to Central Bank Digital Currency: Can Gen Z Adopt It?

This study aims to examine the intention of youth in Indonesia to adopt Central Bank Digital Currency (CBDC). A sample of 285 respondents was analyzed using SEM-PLS and in-depth interviews. The results of the study found that involvement facilitation, institutional trust, and technology trust had a significant effect on youth's intention to use CBDC. These findings emphasize the importance of building trust in technology and institutions, as well as increasing public confidence in the ability of central banks to launch and regulate CBDC systems. Empirical evidence also shows that facilitation and literacy provided by the authorities need to be a significant focus in addressing potential barriers to technology use among the public. Furthermore, there are maximum literacy facilities and consultation given can increase public confidence in CBDC provider authorities to continue to develop technology to the fullest.

Blockchain technology and supply chain performance: The role of trust and relational capabilities

Blockchain technology (BCT) has become a focal point of attention within the field of supply chain management (SCM), characterized as a transformative and disruptive technology capable of instilling transparency and trust. While parallels between BCT and the internet have been drawn, its practical application in SCM remains in an early stage, resulting in a limited body of literature and a pronounced debate with divergent perspectives concerning its impact in this SCM domain. Drawing upon the Relational View (RV) and Dynamic Capabilities View (DCV), this study aims to address this debate by investigating the role of BCT in enhancing supply chain performance. The study utilizes semi-structured interviews with 31 industry experts, followed by thematic analysis, to unveil the underlying mechanisms through which BCT influences supply chain performance. Our findings underscore the pivotal role of trust, facilitated by BCT features, in enhancing SC performance, emphasizing the interdependence of trust in technology, platforms, and SC members. Moreover, our study uncovers the substantial influence of supply chain uncertainty on the association between BCT and trust, acknowledging its pertinence in the modern business landscape. Additionally, we present a conceptual model that elucidates the relationships among BCT, trust, relational capabilities, and supply chain performance. Our qualitative investigation suggests that trust, relational capabilities, and dynamic capabilities sequentially mediate the association between BCT and supply chain performance. This research, while reaffirming the applicability of the original RV model in the context of BCT, contributes to a nuanced perspective by identifying additional associations within the relational view. Specifically, we identify knowledge sharing, effective governance, and resource complementarity as mediating factors in the relationship between relation-specific assets and relational rent. Our study provides practical advice to managers and practitioners. It stresses the need to understand how BCT impacts supply chain performance and the crucial role of trust. We also recommend nurturing relational capabilities in supply chain networks and considering the context-specific benefits of BCT, especially in uncertain environments. This understanding enables informed decision-making to tackle challenges and leverage opportunities.

Enhancing transparency in AI-powered customer engagement

This paper addresses the critical challenge of building consumer trust in AI-powered customer engagement by emphasising the necessity for transparency and accountability. Despite the potential of AI to revolutionise business operations and enhance customer experiences, widespread concerns about misinformation and the opacity of AI decision-making processes hinder trust. Surveys highlight a significant lack of awareness among consumers regarding their interactions with AI, alongside apprehensions about bias and fairness in AI algorithms. The paper advocates for the development of explainable AI models that are transparent and understandable to both consumers and organisational leaders, thereby mitigating potential biases and ensuring ethical use. It underscores the importance of organisational commitment to transparency practices beyond mere regulatory compliance, including fostering a culture of accountability, prioritising clear data policies and maintaining active engagement with stakeholders. By adopting a holistic approach to transparency and explainability, businesses can cultivate trust in AI technologies, bridging the gap between technological innovation and consumer acceptance, and paving the way for more ethical and effective AI-powered customer engagements.

A Conceptual Development of Digital Trade Adoption among Manufacturing Companies in China: A Technology Acceptance Model Approach

The proliferation of digital technologies has brought about transformative changes in various sectors, including manufacturing. In the context of China, a prominent manufacturing hub, the adoption of digital trade practices has emerged as a critical determinant of competitiveness and sustainability for manufacturing companies. This paper proposes a conceptual framework for understanding the factors influencing the adoption of digital trade among manufacturing firms in China, employing the Technology Acceptance Model (TAM) as a theoretical lens. Drawing upon extant literature and empirical insights, this conceptual paper identifies key constructs within the TAM framework, such as perceived usefulness, perceived ease of use, attitude toward digital trade, and behavioral intention, and adapts them to the specific context of digital trade adoption in the Chinese manufacturing sector. It explores how factors such as organizational readiness, technological infrastructure, government policies, and cultural factors influence the perceived usefulness and ease of use of digital trade platforms among manufacturing companies in China. Moreover, this paper extends the traditional TAM model by incorporating additional constructs such as trust in technology, perceived risk, and organizational innovativeness, which are deemed essential in understanding the complexities of digital trade adoption in a rapidly evolving technological landscape. It underscores the significance of trust-building mechanisms, risk mitigation strategies, and organizational culture in facilitating the successful implementation of digital trade initiatives within manufacturing enterprises in China. The proposed conceptual framework contributes to both theoretical and practical understandings of digital trade adoption by elucidating the interplay between technological factors, organizational dynamics, and contextual influences within the Chinese manufacturing context. By offering insights into the determinants of digital trade adoption and the mechanisms for enhancing acceptance and utilization of digital trade platforms, this paper aims to inform policymakers, industry practitioners, and scholars engaged in the promotion and facilitation of digital transformation within the manufacturing sector in China and beyond.