Data Collection Practices Research Articles

Data Privacy Concerns and their Impact on Consumer Trust in Digital Marketing

The rise of digital marketing has transformed consumer-brand interactions through personalized strategies and targeted ads driven by extensive consumer data. However, increased data collection has sparked privacy concerns, affecting consumer trust. This study examines how dataprivacy concerns impact consumer trust, engagement, and willingness to share information in digital marketing. Key privacy issues include over-collection, lack of transparency, unauthorized sharing, and data breaches. Using a mixed-methods approach, with quantitative surveys of 600 participants and qualitative interviews with 30 individuals, the study measures privacy concerns, trust, and engagement levels. Findings reveal a strong negative correlation between privacy concerns and trust, with transparency in data practices and regulatory compliance (GDPR,CCPA) identified as critical trust factors. Brands that prioritize transparency, data control, and compliance foster greater consumer trust, addressing the privacy- personalization paradox and aligning with consumer expectations for ethical data use. This study offers insights into privacy-trust dynamics and provides recommendations for privacy-centered marketing strategies. Keywords:Data Privacy ,Consumer Trust, User Consent ,Data Transparency, Data Collection Practices, Third-Party Data Sharing ,Data Breaches, Privacy Regulations (e.g., GDPR, CCPA).

Insights from an Experiment Crowdsourcing Data from Thousands of US Amazon Users: The importance of transparency, money, and data use

Data generated by users on digital platforms are a crucial resource for advocates and researchers interested in uncovering digital inequities, auditing algorithms, and understanding human behavior. Yet data access is often restricted. How can researchers both effectively and ethically collect user data? This paper shares an innovative approach to crowdsourcing user data to collect otherwise inaccessible Amazon purchase histories, spanning 5 years, from more than 5,000 U.S. users. We developed a data collection tool that prioritizes participant consent and includes an experimental study design. The design allows us to study multiple important aspects of privacy perception and user data sharing behavior, including how socio-demographics, monetary incentives and transparency can impact share rates. Experiment results (N=6,325) reveal both monetary incentives and transparency can significantly increase data sharing. Age, race, education, and gender also played a role, where female and less-educated participants were more likely to share. Our study design enables a unique empirical evaluation of the 'privacy paradox', where users claim to value their privacy more than they do in practice. We set up both real and hypothetical data sharing scenarios and find measurable similarities and differences in share rates across these contexts. For example, increasing monetary incentives had a 6 times higher impact on share rates in real scenarios. In addition, we study participants' opinions on how data should be used by various third parties, again finding that gender, age, education, and race have a significant impact. Notably, the majority of participants disapproved of government agencies using purchase data yet the majority approved of use by researchers. Overall, our findings highlight the critical role that transparency, incentive design, and user demographics play in ethical data collection practices, and provide guidance for future researchers seeking to crowdsource user generated data.

Progress in improving low-cost measurement of feeding behaviors and diets

Information on infant and young child feeding practices and on the diets of vulnerable population groups is needed for global and national monitoring and for evaluating the impacts of interventions. Several population-level indicators have been developed to meet this need, but research to support selection of best data collection practices has been lacking. This supplement brings together a set of recent studies that address measurement issues with the objective of advancing knowledge of best practices, with a focus on low-cost measurement methods. Topics include social desirability bias, recall bias, and the cognitive validity of specific approaches and wording for asking questions on diets. The studies in the supplement provide examples of methods and results that can improve measurement, but also identify gaps and areas where additional work is required.

Monitoring stress-induced brittle rock mass damage for preventative support maintenance

Stress-induced brittle fracturing near an excavation boundary results in a volume increase, known as bulking. Excessive bulking places added demand on the rock support, which, if not detected and addressed through preventative support maintenance (i.e., proactively added reinforcement), can cause the support to fail, leading to a safety hazard and costly production delays for underground mining operations. For caving mines, these project risks are exacerbated during cave establishment due to the large abutment stress from undercutting that redistributes and concentrates stresses near excavations critical for production. This paper reports the findings from research conducted to develop and improve geotechnical monitoring practices to support preventative support maintenance in deep mining operations. This research uses a unique geotechnical monitoring database collected for the Deep Mill Level Zone panel cave mine. The data was collected across a large footprint during the mine's ramp-up period and represents an initial step toward best practices for data collection at cave mines operating in high-stress environments. Borehole camera surveys supplemented by multi-point borehole extensometers have been used to determine the depth of stress fracturing in pillar walls as a function of the distance away from the undercut. Convergence measurements and LiDAR scanning are used to characterize the corresponding rock mass bulking. The results show that the interpretation of monitoring data can be used to identify the long-term depth of stress fracturing and bulking trends in response to undercut advances. These show that direct measures of stress-induced fracturing damage provide an early indication of excavations vulnerable to bulking and that LiDAR scanning is an effective method for capturing the onset of bulking and anticipating local areas likely to experience greater deformation demand as bulking progresses. Proactive and strategic geotechnical monitoring based on the long-term depth of stress-induced fracturing trends is proposed to assist with preventative support maintenance practices.

Workshop summaries from the 2024 voice AI symposium, presented by the Bridge2AI-voice consortium.

The 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools. Each workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience. Key outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes. The symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.

Privacy, confidentiality and ethical concerns in audio ai assistants: A comparative study of North American, European, and Asian Markets

This study examines the ethical, privacy, and confidentiality issues with audio AI assistants in the markets of North America, Europe, and Asia. Understanding the ramifications of data collecting, storage, and usage is crucial as the use of technologies like Google Assistant and Amazon Alexa grows. The study highlights significant regulatory differences between the areas; North America offers a fragmented landscape, and many Asian nations are still developing their data protection legislation, while Europe leads with strong frameworks like the General Data Protection Regulation (GDPR). Key challenges identified include a lack of user awareness regarding privacy rights, extensive data collection practices, unauthorized listening, data breaches, and cultural differences influencing attitudes toward data protection. The findings reveal that these challenges impact user trust and hinder the responsible innovation of AI technologies. Recommendations for improvement include establishing global regulations to promote consistency in data protection, adopting best practices such as privacy-by-design principles, and enhancing user education initiatives. Additionally, the study identifies critical areas for future research, particularly the role of AI assistants in sensitive sectors like healthcare and education, where privacy concerns may differ significantly. Overall, this comparative analysis provides a foundational understanding of audio AI assistants' ethical landscape and underscores the importance of addressing these issues to foster a secure and trustworthy digital environment.

INTEGRATING ADVANCED TECHNOLOGIES IN RETAIL: A CONCEPTUAL FRAMEWORK FOR ENHANCING CONSUMER EXPERIENCE AND TRUST

This study explores the integration of advanced technologies, such as Artificial Intelligence (AI), Augmented Reality (AR), and Big Data Analytics, in retail to enhance personalized consumer experiences while addressing privacy concerns. This research investigates the personalization-privacy paradox, where consumers value tailored recommendations but remain wary of data collection practices. Through a comprehensive literature review, this study developed a conceptual framework for responsibly integrating these technologies in retail. The framework illustrates how AI personalizes shopping experiences, AR enhances consumer engagement, and Big Data improves operational efficiency while also considering privacy issues. The key findings reveal that balancing personalized services with transparent data practices is essential for building consumer trust. This study emphasizes the importance of transparency and ethical data handling in mitigating privacy concerns and fostering a more consumer-centric retail environment. These insights contribute to retail strategies and provide practical guidance for leveraging cutting-edge technologies without compromising privacy, thereby highlighting the need for a balanced approach that maximizes the benefits of innovation while safeguarding consumer trust.

Advancing data-driven decision-making for human papillomavirus (HPV)

Abstract Background HPV is among the most important infections associated with cancers in men and women, including cervical cancer which is the second most common cancer affecting women aged 15-44 years in the EU. Yet, data on vaccine uptake, HPV infection rates and incidence of related cancers is lacking across Europe, obstructing policymakers’ ability to build adequate prevention strategies towards the EU’s goal of eliminating HPV-related cancers. Methods Vaccines Europe analysed the landscape of data collection practices for HPV, highlighting discrepancies across Europe as well as successful initiatives. Results Our analysis reveals stark gaps in data collection across Europe. Some countries do not report disease levels and vaccination coverage for HPV. When reporting does occur, methods and timelines often differ between countries. Limited data availability exists for vaccination uptake in boys and young adults. This lack of standardisation hinders the comparability and reliability of collected data. It also impedes the integration and analysis of data within and across countries, hindering the development of effective elimination strategies. Concrete actions are needed at EU and national level to further develop and strengthen data collection, sharing and use in decision-making. Our research calls on strengthening ECDC’s role, promoting best practice sharing between countries, ensuring sustained resource allocation for data systems/surveillance, establishing electronic vaccination registries, an EU implementation roadmap to support countries in achieving the objectives of Europe’s Beating Cancer Plan, and translating data into policy recommendations. The Council Recommendation on vaccine-preventable cancers provides a framework for EU Member States to establish electronic vaccination registries and the ECDC to develop an HPV dashboard by the end of 2024. Key messages • Current data collection practices leave Europeans more vulnerable in the fight against cancer. • Robust data can guide policy and support elimination strategies for vaccine-preventable cancers.

Community Provider Perspectives on an Autism Learning Health Network: A Qualitative Study.

Although autism is highly prevalent, no single care center has enough patients to produce generalizable knowledge of optimal care; this slows the pace of quality improvement research. The Autism Care Network (ACNet) is a learning health network (LHN) dedicated to developing the most effective approach to care for autistic children and adolescents through integrating clinical and research data. Given that most autistic patients receive care in the community, expanding ACNet to include community providers is essential to improve autism care. Our objectives were to: (1) understand the current data collection practices, learning needs, capacity, and overall interest of community clinicians in participating in an autism LHN; (2) identify their perspectives on participating in a LHN and ways in which their engagement and interest can be cultivated. Participants were purposively sampled from community physicians who participated in ASD-focused educational programming. In-depth semi-structured interviews were conducted. Analysis of 29 participant interviews yielded five primary themes: Navigating Administrative Challenges, Improving Data Collection Practices, Increasing Provider Confidence and Competence, Breaking Down Silos, and System and Societal Barriers to Achieving Best Practices. This study provides a rich and nuanced understanding of the experiences of community providers regarding the challenges of ASD care provision in the community. Overall, these findings suggest that LHNs have the potential to address several of the issues in community autism care highlighted by community providers.

24 Community provider perspectives on an autism learning health network: A qualitative study

Abstract Background Although autism is highly prevalent, no single care centre has a sufficient number of patients to produce generalizable knowledge; this slows the pace of quality improvement research. Learning health networks (LHNs) offer a promising solution to improve the quality and efficiency of healthcare by integrating clinical and research data. The Autism Care Network (ACNet) is an LHN composed of 20 clinics across the United States and Canada dedicated to developing the most effective approach to care for children with autism. In Canada, the majority of autistic patients receive a majority of their ongoing care in the community. Therefore, to make meaningful improvements in the continuum of care for patients and their families, it is essential that the ACNet be extended to include community physicians. Objectives The primary objectives were to (1) understand the current data collection practices, learning needs, clinic capacity, areas of improvement, and overall interest of community physicians in participating in a LHN and (2) identify community physicians’ perspectives on the benefits and disadvantages of participating in a LHN and ways in which their engagement in a LHN can be supported. Design/Methods In-depth semi-structured interviews were conducted participants who were purposively sampled from community providers who had previously participated in autism-focused educational programming. The interview guide was developed by the three ACNet sites based on experience with LHNs and working with community physicians. Data were analyzed using an interpretative phenomenological analysis approach with a social constructivist paradigm. Results Analysis of 29 interviews identified 5 primary themes: (1) “Navigating Administrative Challenges,” which highlighted the lack of time, resources, and administrative capacity in the community; (2) “Improving Data Collection Practices”, which emphasized the barriers to consistently collecting information from patients and families; (3) “Increasing Provider Confidence and Competence,” which explored the challenges of navigating the everchanging landscape of community autism care; (4) “Breaking Down Silos”, which focused on fragmented care and the lack of communication between allied health resources, diagnostic hubs, and community physicians; and (5) “Systemic and Societal Barriers to Achieving Best Practices,” which explored the systemic barriers that impact autism care for physicians and families. Conclusion This study provides an understanding of the experiences of community physicians regarding the challenges of community-based autism care. LHNs have the potential to address several of the issues highlighted by community physicians in autism care. Additionally, LHNs can play an important role in improving community care in fields beyond autism.

Back to the Future: Revisiting Barometric Levelling

Abstract. Barometric levelling, traditionally employed in aviation, parachute jumping, and mountain climbing, has undergone a transformative shift with the technological advancement of state-of-the-art atmospheric pressure sensors. This evolution has rendered these sensors more accurate and affordable, broadening their applications within the realm of Intelligent Systems. The integration of barometric altimeter modules into electronic devices, coupled with GNSS and mapping systems, has significantly enhanced their versatility, making them complementary components in various consumer electronics like smartphones, watches, sports bands, bicycle computers, and motor vehicle tracking devices. Moreover, they play a pivotal role in emerging technologies such as drone mapping projects and modelling. This because of their capabilities of measuring elevations in GNSS denied areas such as caves, canyons, and tunnels. The significance of barometric levelling lies in its capacity to determine relative elevation between two points through the measurement of atmospheric pressure. This technique leverages the fact that atmospheric pressure decreases with increasing altitude, enabling the calculation of elevation differences by comparing pressure readings at different locations.In this study, 25 elevations within a 3 km radius were independently measured using barometric levelling, highlighting its practical usage. These elevations ranged from 2 m to 140 m. Readings of time of measurements, temperature, air pressure, and humidity were taken at each of these stations and were synchronized with the readings obtained from a fixed reference station of known elevation. The measurements at this reference station were carried out with an instrument with data logging capabilities, able to record relevant weather data at pre-established intervals of time without human intervention. The results of these measurements were compared to fixed reference stations of known elevation, and the subsequent differences were analysed. The study employed a portable weather monitoring instrumentation with data logging capabilities, emphasizing the importance of automated data collection practices. The comparison of the 25 elevation readings with true elevations, determined by conventional surveying methods, revealed an RMSE of +/- 0.49 meters, affirming the accuracy of barometric levelling.Additionally, a distance-dependent test demonstrated a predictable decrease in positional accuracy as the distance from a reference base station increased, providing valuable insights into the system's limitations. The barometric levelling tests presented here offer results that can inform future applications. The conclusions and recommendations derived from this study provide guidance for optimizing the use of barometric levelling within the broader landscape of Intelligent Systems.

Social Class, the Overlooked Element of Diversity within Higher Education: An Analysis of Policy Documents and Data Collection Practices by Russell Group Universities in the United Kingdom

Investigating the data that universities collect on the characteristics of staff and students offers an insight into which aspects of diversity they prioritise. We sent Freedom of Information requests to 24 Russell Group institutions asking them to report the data they collect on the class background and protected characteristics of staff and students. We also reviewed university equality policy documents using content analysis. Analysis showed that while all institutions collect data on the protected characteristics of staff and students, and 18 universities collect data on the class background of students, just one university collects data on the class background of staff. This is despite 10 institutions recognising class background as being an important component of diversity within their policies. We propose recommendations on what data universities should collect on the class background of staff, and how they can use these data to support intersectional diversity.

Engaging Data Literacies in Displacement

Data-driven humanitarianism is changing the face of aid. More data potentially enables quicker and more efficient evidence-based responses to situations of conflict and disaster. Yet the proliferation of data also challenges traditional lines of accountability, exacerbates the drive toward extractive relations and processes while deepening communication barriers and asymmetric relations between humanitarians and affected communities. This article reflects on critical data literacy as a transformative method in the context of the datafication of the humanitarian sector. It draws on research carried out with internally displaced persons (IDPs) in Nigeria and South Sudan as part of a collaborative international project examining the practice and ethics of data collection and use. The article discusses the project’s participatory ethos, its engagement of IDPs with the project over time and the importance of developing co-produced tools of critical data literacy together with IDPs. Reflecting on the significance of our findings for humanitarian practitioners as well as for academics working in the field of humanitarianism and displacement, the article argues for a collective commitment to engaging with affected communities while cautioning against viewing data literacy as an easy fix to empowerment challenges, both in the conduct of humanitarian work and in the implementation of research.

Case C-80/23: A Quest for Clarity. The Law Enforcement Directive 2016/680, a Bulgarian Court and Case C-205/21

Abstract This contribution examines the legal intricacies surrounding law enforcement data collection practices in Bulgaria within the framework of the Law Enforcement Directive 2016/680 (LED) and relevant Court of Justice of the European Union (CJEU) rulings, particularly cases C-205/21 and C-80/23. The analysis underscores challenges in interpreting the concept of “strict necessity” and ensuring compliance with its provisions, in particular, Art. 10. Key findings reveal ambiguities in the LED’s application, particularly concerning judicial review and the scope of data collection. The subsequent Case C-80/23 further seeks clarification on the strict necessity standard and the scope of judicial review in the collection of biometric and genetic data. The outcome of both cases has broader implications for EU member states, highlighting the need for legislative alignment and underscoring the complexities and challenges in balancing effective law enforcement with the protection of fundamental rights.

The Impact of Machine Learning Algorithms and Big Data on Privacy in Data Collection and Analysis

In the era of rapid technological advancements, machine learning (ML) and big data analytics have become pivotal in harnessing vast amounts of data for insights, efficiency, and innovation across various sectors. However, the widespread collection and analysis of data raise significant privacy concerns, highlighting the delicate balance between leveraging technology for societal benefits and safeguarding individual privacy. This article delves into the complexities of data collection and analysis practices, emphasizing the potential for privacy breaches through methods such as location tracking, browsing habits analysis, and the creation of detailed personal profiles. It discusses the implications of ML algorithms capable of de-anonymizing data, despite measures like data anonymization and encryption aimed at protecting privacy. The article also examines the existing legal frameworks, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), designed to enhance privacy protection, alongside the ethical considerations for developers and companies in using ML and big data. Furthermore, it explores future outlooks, including developments in technologies like federated learning and differential privacy, that promise enhanced privacy protection. The conclusion calls for a concerted effort among policymakers, technologists, and the public to engage in ongoing dialogue and develop solutions that ensure the ethical use of ML and big data while upholding privacy rights.

Social Determinants of Health: Importance, Benefits to Communities, and Best Practices for Data Collection and Utilization

Social Determinants of Health: Importance, Benefits to Communities, and Best Practices for Data Collection and Utilization

Applications of Machine Learning in Sweet-Spots Identification: A Review

Summary The identification of sweet spots, areas within a reservoir with the highest production potential, has been revolutionized by the integration of machine learning (ML) algorithms. This review explores the advancements in sweet-spot identification techniques driven by ML, analyzing 122 research papers published in OnePetro, Elsevier, ScienceDirect, SpringerLink, GeoScienceWorld, and MDPI databases within the last 10 years. The review provides a comprehensive analysis of ML applications in sweet-spot identification and highlights best practices in data collection, preprocessing, feature engineering, model selection, training, validation, optimization, and evaluation. The paper categorizes and discusses the different data types used in ML algorithms into six groups, analyzes the combinations of frequently used data types for training and validation, and visualizes the distribution of input parameters and features within each of the six main categories. It also examines the frequency of target variables used in these models. In addition, it discusses various supervised and unsupervised ML algorithms and highlights key studies offering valuable insights for researchers.

Longitudinal Changes in Lifestyle Behaviors and Cardiovascular Health During the Transition to Fatherhood: The Dad Bod Study Rationale and Design.

Despite the importance of the transition to fatherhood as a critical life stage among young adult men, much remains unknown about the factors predictive of ideal cardiovascular health (CVH) and how CVH is impacted as young men face new roles and responsibilities associated with fatherhood. To address this gap, the Dad Bod Study is a prospective, longitudinal and observational study designed to examine how fatherhood affects young men's CVH. A total of 125, first-time prospective fathers (men, 19-39 years) will be enrolled and followed over 1.5 years. Metrics of the American Heart Association's "Life's Essential 8" as well as demographic, social, and psychosocial factors will be collected at four time points ((baseline (during the pregnant partner's 2nd trimester) 1-month postpartum, 6-months postpartum, and 1-year postpartum). The primary aims are to measure predictors of CVH among first-time fathers and describe longitudinal changes in CVH. A secondary aim is to identify best practices for recruitment, retention, and remote data collection in this population. The Dad Bod Study offers a novel examination of CVH among first-time fathers, exploring how new paternal roles and responsibilities impact cardiovascular health. Findings may provide key insights into critical CVH behaviors and risk factors to monitor, preserve, and improve as young men transition to fatherhood.

Streamlining the Conduct of Cancer Clinical Trials: New Standard Data Collection Practices for National Cancer Institute Late Phase Clinical Studies.

The increase in the complexity of cancer clinical trials over the past several decades has led to a dramatic growth in trial cost and operational burden. The extent and frequency of data collection, particularly in late phase trials which enroll many participants, have been major contributors to this problem. The Clinical Trials and Translational Research Advisory Committee of the National Cancer Institute (NCI) recently assessed the impact of these stressors on the NCI National Clinical Trials Network (NCTN) and recommended that data collection in late phase NCTN trials be limited to data elements essential to address the primary and secondary objectives of the trial. The purpose of this commentary is to describe the rationale for this recommendation, progress towards implementation, and the development of new streamlined standard practices for data collection for late phase NCTN trials effective January 1, 2025.

Ethical Implications of Cybersecurity Breaches and Data Privacy Violations

The increasing reliance on digital technology has led to a surge in cybersecurity breaches and data privacy violations. These incidents raise significant ethical concerns, impacting individuals, organizations, and society as a whole. This paper explores the ethical ramifications of such breaches, focusing on issues like privacy intrusion, potential harm to individuals, and the erosion of trust. It further examines the ethical obligations of organizations in data collection, storage, and security practices. Finally, the paper discusses potential solutions and approaches to mitigate these ethical concerns and promote a more responsible digital ecosystem Keywords:Cybersecurity, Data Privacy, Ethical Implications, Privacy Intrusion, Trust, Data Security