Consumer Privacy Research Articles

Evaluating the Impact of Data Protection Compliance on AI Development and Deployment in the U.S. Health sector

This paper evaluates the impact of data protection Compliance on artificial intelligence development and deployment in the U.S. Health sector. As AI technologies develop quickly, worries about data security and privacy have increased, especially in delicate industries like healthcare. HIPAA, or the Health Insurance Portability and Accountability Act, and CCPA, or California Consumer Privacy Act, are two important U.S. laws that are examined in this paper along with their comparison to the European Union's General Data Protection Regulation, or GDPR. The intersection of AI and data protection regulations is also examined. The analysis emphasizes the necessity for privacy-by-design principles and the development of openness and accountability, and it shows how these policies affect AI developers, healthcare providers, and other stakeholders. Through an analysis of the opportunities and obstacles posed by the CCPA and HIPAA, the paper offers useful guidance for managing the regulatory environment and promoting innovation in AI development. The study reports that observing data protection rules is not only required by law but also strategically crucial for fostering public confidence and guaranteeing the moral application of AI in healthcare. The paper finally concludes that maintaining a sustainable growth of AI applications in the U.S. healthcare industry requires striking a balance between technological advancement and stringent data privacy procedures and compliance as AI continues to advance.

Machine Unlearning in Digital Healthcare: Addressing Technical and Ethical Challenges

The ``Right to be Forgotten," as outlined in regulatory frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), allows individuals to request the deletion of their personal data from deployed machine learning models. This provision ensures that individuals can maintain control over their personal information. In the digital health era, this right has become a critical concern for both patients and healthcare providers. To facilitate the effective removal of personal data from machine learning models, the concept of ``machine unlearning" has been introduced. This paper highlights the technical and ethical challenges associated with machine unlearning in digital healthcare. By examining current unlearning methodologies and their limitations, we propose a roadmap for future research and development in this field.

To Partner or Not to Partner? The Partnership Between Platforms and Data Brokers in Two-Sided Markets

Data have become a vital competitive asset for online advertising platforms, such as Facebook and Snapchat. These platforms often partner with data brokers to enhance targeting capabilities, which raises important consumer privacy concerns. This study develops a game-theoretic model to analyze the economic dynamics of partnerships between competing platforms and a data broker in a two-sided market. Our findings reveal that heightened consumer privacy concerns can encourage platforms to collaborate with data brokers rather than discourage them, as these concerns may strategically soften price competition on the advertiser side. However, when both platforms partner with a data broker, a prisoner’s dilemma may arise, even if the broker significantly improves targeting capabilities. From a policy perspective, such partnerships can harm consumer surplus in a purely ad-sponsored model, where users are not charged. Conversely, they may enhance consumer surplus in a mixed revenue model that combines advertising with subscriptions. These insights suggest that policymakers should consider regulations on platform-data broker partnerships to protect consumer interests, particularly regarding privacy protections that platforms may not prioritize under competitive pressures.

Consumer private data collection strategies for AI-enabled products

The increasing use of artificial intelligence (AI) to enhance products and services has enabled personalized offerings and smarter functionalities through the analysis of consumer data. However, privacy concerns present significant challenges to the effective utilization and commercialization of AI-enabled products. To address these concerns, firms must carefully navigate consumer data privacy and develop appropriate data collection strategies to support future product intelligence, particularly with AI technologies like ChatGPT. This study examines two primary data collection approaches: the uniform policy strategy and the option menu strategy. A mathematical model is constructed to assess these strategies, considering factors such as information externalities and heterogeneous consumer privacy concerns. By comparing firm profits, consumer surplus, and social welfare under both strategies, the study finds that the option menu strategy becomes optimal when there are considerable differences in privacy concerns across consumer groups or when even smaller differences exist, but consumers place a high value on personalized services. These insights offer guidance to firms and policymakers in formulating appropriate data collection strategies for AI-enabled products.

REGULAÇÃO E PROTEÇÃO DE DADOS PESSOAIS NA INDÚSTRIA DE TELECOMUNICAÇÕES: A IMPLEMENTAÇÃO DA LEI GERAL DE PROTEÇÃO DE DADOS E A GESTÃO DA VIOLAÇÃO DE SIGILO

The present work aims to analyze Brazilian legislation aimed at protecting personal data and violations of these standards, especially in the telecommunications sector. For its idealization, consultations were carried out in articles, bibliographies, jurisprudence, legislation and informative websites based on a qualitative approach with a special focus on the analysis of the General Data Protection Law (LGPD) and the Marco Civil da Internet. The study examines how the sharing of personal data by mobile phone operators compromises consumer privacy, highlighting the responsibilities of companies and the consequences of data leaks. Despite efforts to adapt to standards, the confidentiality of personal data is constantly violated, as private telephone companies do not comply with legislative legalities to protect fundamental rights. This highlights the essential role of regulatory bodies, such as the National Telecommunications Agency (ANATEL), in ensuring data protection and building a relationship of trust between companies and consumers.

The role of data governance in enhancing cybersecurity resilience for global enterprises

Data governance plays a critical role in enhancing cybersecurity resilience for global enterprises. In the face of increasingly sophisticated cyber threats, coupled with rising regulatory demands such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), enterprises must implement robust data governance frameworks to safeguard data integrity, availability, and confidentiality (Ramirez et al., 2008). Effective data governance involves a combination of policies, procedures, and technologies that align with an organization’s overall cybersecurity strategy (Khatri & Brown, 2010). This paper provides a comprehensive analysis of the critical intersection between data governance and cybersecurity, emphasizing the link between governance structures, risk management processes, and technological innovations in mitigating evolving cyber threats (Weber et al., 2009). Additionally, we explore the essential role of data stewardship, stakeholder responsibilities, and the deployment of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), in fostering organizational resilience (Arora & Pedersen, 2017). Through case studies and best practices, the study presents a conceptual framework that enables global enterprises to adapt to the fast-changing cybersecurity landscape while maintaining compliance with international data regulations. By addressing key elements such as access control, data classification, and real-time monitoring, this research underscores how comprehensive data governance frameworks serve as a foundation for enhanced cybersecurity resilience in today’s digital economy (Da Veiga & Eloff, 2007).

Application of intuitionistic fuzzy set in social life cycle impact assessment

Abstract Purpose Intuitionistic fuzzy set (IFS) has been found promising for handling ambiguity forms of uncertainty in social data. This paper undertakes an application of an intuitionistic fuzzy set in the social life cycle impact assessment (S-LCIA) of a public building project. This work proposes to combine an intuitionistic fuzzy set (IFS) with multi-attribute decision-making in converting qualitative data into quantitative social impacts of the building project. This research utilises IFS to accommodate and handle experts’ imprecise cognitions in S-LCIA to facilitate the identification and prioritisation of the most pressing indicators accompanying social impacts in a project. Methods Data were collected using questionnaire(s), structured interviews, and through targeted focus group sessions. Intuitionistic fuzzy set (IFS) approach was used to model the obtained data from structured interviews. Using IFS, this research also accounts for missing or ambiguous data that emerged during the data elicitation process. In combining IFS with multi-attribute decision-making techniques, the social impacts of selected stakeholders were evaluated using fuzzy set and IFS approaches. Sensitivity analysis was then used to test the robustness of the results and ranking was conducted based on each social stakeholder subcategory. Results and discussion Results revealed that approximately 23% of missing datapoints in the public case study building were incorporated in the S-LCIA using IFS. The issues of highest priority in each of the considered subcategory in the public case study building project according to IFS are (i) consumer privacy by adopting more functional planning (0.27), (ii) public commitment to sustainability (0.33), and (iii) education provided in the local community by having an indigenous botanical element for children (0.39), respectively. Conversely, the FS technique inferred that the issue of highest priority in the consumer stakeholder is the feedback mechanism (0.26). The overall degree of correlation between the IFS and FS is only 0.234, revealing that IFS provides a different perspective from conventional FS when used in modelling social data. Conclusion The IFS method provides an objective and systematic approach for dealing with a heterogeneous scope of imprecise and inexact social data in achieving holistic social life cycle assessment results. Furthermore, expansive stakeholder involvement would rely on a robust approach for improving social well-being in public buildings, thereby leaving no one behind in accomplishing a sustainable world.

Issues of personal data protection when using cloud technologies

It is indicated that in today’s digital world, where the amount of stored and processed data is growing at a frantic pace, issues of personal data protection are becoming increasingly important. With the proliferation of cloud technologies that allow companies and individuals to store and process information on remote servers, a new spectrum of threats to data privacy and security is emerging. The article examines the issue of personal data protection when using cloud technologies, in particular, the need to integrate legal and technological aspects to ensure comprehensive protection. The impact of international regulatory acts, such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Protection Act), on cloud data protection processes is analyzed. Particular attention is paid to problems arising from differences in the legal regimes of different countries, which complicate data management at the international level. The article examines in detail modern technical solutions, such as data encryption and multi­factor authentication, which are key elements in protecting confidential information. These technologies have been found to provide a high level of protection, but also have their vulnerabilities. Based on this, it is proposed to invest in the latest technologies, in particular quantum encryption and artificial intelligence, to improve the effectiveness of data protection. Practical cases of data leaks are separately considered, which emphasize the importance of proper configuration of security systems and regular monitoring. Analysis of the results of empirical research shows the need to adapt business processes to changes in the regulatory environment and introduce new technologies to reduce risks. Based on the received data, recommendations for organizations are formulated, which include reviewing data protection policies, investing in modern technologies and training employees. The conclusions of the article emphasize the need for a comprehensive approach to ensuring the protection of personal data in cloud environments. This involves taking into account both modern technologies and current legal requirements to ensure adequate data protection and minimize possible risks.

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.

Consumer privacy decision-making in digital era: an integrative review and taxonomy

PurposeIn the digital age, personalized services and accurate recommendations enhance the customer experience and streamline shopping. However, increasing concerns about personal privacy have led to resistance from consumers, necessitating a balance between providing high-quality online services and safeguarding personal data. The aim of this paper is to offer a comprehensive review of the fragmented literature on consumer privacy decision-making and to identify key issues worth exploring in future research.Design/methodology/approachAlthough previous studies have analyzed the antecedents and outcomes of privacy decisions, they have often been conducted in a fragmented manner. There remains a lack of a holistic understanding of the factors influencing privacy decisions, including their boundaries. Therefore, we build on the Theory of Planned Behavior to combine consumer privacy decision-making with a graphically conceptual framework used in a similar scoping methodology. We attempt to dissect the antecedent, moderator and outcome variables that influence consumer privacy decision-making, ultimately providing a comprehensive framework for understanding these dynamics.FindingsBased on the Theory of Planned Behavior, we analyze the entire process of consumer privacy decision-making in terms of antecedent, moderating and outcome variables. The results indicate that consumer privacy decision-making is not an isolated behavior or a single choice but a complex, multi-level dynamic process. The factors influencing consumer privacy decisions primarily encompass five aspects: individual characteristics, information, organization, platform and interaction management, leading to various outcomes in both behavioral and perceptual dimensions. Furthermore, the process is constrained by multiple moderating variables, such as information sensitivity, platform knowledge and prior experience.Originality/valueWe build on the Theory of Planned Behavior to combine consumer privacy decision-making with a graphically conceptual framework used in a similar scoping methodology. We dissect the antecedent, moderator and outcome variables that influence consumer privacy decision-making, aiming to provide a comprehensive framework for understanding these processes.

High subjective socioeconomic status positively predicts the willingness to disclose privacy

In the digital era, consumers' information has become a vital marketing asset. Despite the potential risks associated with compromising privacy, some consumers willingly permit companies to collect their data. As consumers with more resources may face more significant consequences if their privacy is compromised, it is important to understand the privacy disclosure intent of consumers with high socioeconomic status. We conducted two studies, in the United States (n = 284) and China (n = 303). We found that high subjective (but not objective) socioeconomic status positively predicted participants' tendency to report their personal information in the consumer survey, with this relationship being mediated by higher levels of interpersonal trust. This is the first study to examine the relationship between subjective socioeconomic status and personal information disclosure. As such, it contributes to expanding existing research domains while also offering valuable insights into marketing strategies aimed at safeguarding consumer privacy.

A Review on Privacy Protection Techniques in Smart Grid Applications

: The extensive use of electricity and the increasing number of consumers challenge matching power consumption with the power generated. Having a traditional way of power generation and distribution, power is also widely fetched through renewable energy sources. So, to have improved efficiency and reliable means of the power source, to be able to integrate multiple sources of power generation like PV Cells, Solar Power, and Wind Power into the existing standards of the power source, precise calculations of the power consumption in the multisource environment, provision to scale up the smart and electric vehicle and most importantly, to reduce the carbon emissions, several attempts have been made to convert the traditional grids into smart grids. A tiny step in the smart grid's development is the smart metering infrastructure, in which smart meters are deployed through the consumer end. Through smart meters, it is possible to establish the link, either through wireless media or wired connections, between the consumer and the grid. Once the smart meters are deployed through the Advanced Metering Infrastructure (AMI), the meters remain active round the clock, giving a window to hackers. Through this window, utility bill manipulations, payment transaction information, and other significant data can be accessed by unethical approaches and threaten the consumer's privacy. This review-research paper discusses various methods presented by distinct authors to address the issues related to customer privacy protection in the smart grid.

Consumer Data Collection Strategies in Two-Sided Platforms: The Role of Data Ownership Assignment and Privacy Concerns

Recognizing data as the new oil of the digital economy, two-sided platforms have invested heavily in collecting consumer data. However, issues surround privacy concerns and conflicts over data ownership. More importantly, should consumers retain ownership of their personal data? This paper models a two-sided platform under different data ownership structures and explores the effect of data ownership assignment, consumer privacy concerns, and network externalities on Consumer Data Collection (CDC) strategies. Game theoretic results suggest that, with a low data collection compensation, leaving data ownership with consumers increases the platform's inclination toward investing in CDC strategies when the cost of personal data protection is low and the loss of consumer data ownership is high. However, in cases with high data collection compensation, this inclination diminishes, indicating a decreased desire for CDC investment. Interestingly, when data ownership is assigned to consumers, heightened consumer privacy concerns lead to consumers providing less personal data, but the platform may be more incentivized to invest in CDC. Further, allowing consumers to hold on to data does not always increase consumer surplus, as the latter depends on the trade-off between the costs of data ownership shift (i.e., both personal data protection costs and data collection compensation) and the benefits attached to the data. When data collection compensation is relatively low, conflicting views on data ownership may arise between the platform and consumers.

Johnny Still Can't Opt-out: Assessing the IAB CCPA Compliance Framework

The privacy laws and regulations that govern the collection, sharing, and selling of online data are changing. In the U.S., California adopted the California Consumer Privacy Act (CCPA) and the California Privacy Rights Act (CPRA), and twelve other U.S. states have adopted similar laws. Industry has responded by developing technical standards for collecting and disseminating consent information, such as the IAB CCPA Compliance Framework. While publishers are adopting this framework and the IAB is extending it to cover privacy laws in other U.S. states, recent work has observed that opt-out signals are not being honored under the framework. In this study, we take a deep dive into the IAB CCPA Compliance Framework to measure end-to-end flows of consent information and better understand why opt-out signals are not being honored. Using data crawled from top websites under different experimental conditions, we examine overall adoption of the framework, the flow of consent information from publishers to third parties and between third parties, and finally the reach of opt-out signals. Our results uncover numerous issues with the adoption and implementation of the framework that prevent users' consent choices from being honored by third parties.

Designing a Legal Regime for Personal Data Protection in the Course of Businesses’Process of Collecting Consumers’Data: Learning from the California Consumer Privacy Act

Designing a Legal Regime for Personal Data Protection in the Course of Businesses’Process of Collecting Consumers’Data: Learning from the California Consumer Privacy Act

Consumer interaction and privacy: the impact of in-feed lead generation ads on self-disclosure intention

PurposeAmidst the growing emphasis on privacy protection, this study aims to investigate how online interaction introduced in Lead Generation Ads (LGAs) affects consumers' Self-Disclosure Intention (SDI), particularly in the context of the Chinese advertising market.Design/methodology/approachThis research employs two scenario-based online surveys to analyse in depth the impact of LGAs on consumers' SDI. The first study collects valid feedback from 220 consumers through an online questionnaire to assess the direct effect of perceived interactivity on SDI. The second study, using an experimental design with a sample of 265 participants, further explores the mediating roles of perceived control and perceived vulnerability in the relationship between perceived interactivity and SDI and examines the moderating effect of privacy invasion experience (PIE).FindingsThis study reveals the significant and positive influence of perceived interactivity on SDI, with perceived control acting as a mediator that enhances this effect. Conversely, perceived vulnerability weakens the positive impact of perceived interactivity on SDI. Additionally, we explore the moderating role of PIE and find that it significantly influences the relationship between perceived interactivity and SDI. These findings underscore the importance of considering consumer privacy sensitivity, particularly in the design of interactive marketing strategies and within highly interactive advertising environments.Research limitations/implicationsOur research uncovers consumer privacy attitudes and behaviours in the Chinese market, providing insights into its unique dynamics of privacy and information disclosure. However, the geographical and cultural specificity of our study may limit its generalizability. Future studies should expand into various cultural and market contexts, considering the impact of digital technologies on consumer interactions and information disclosure, thereby enhancing the depth and applicability of global marketing strategies.Practical implicationsAdvertising platforms should explore online interactive communication methods to enhance consumers' perceived interactions and alleviate privacy concerns. Also, platforms should be designed with system security in mind to prevent the leakage and misuse of consumer data, thus increasing consumers' SDI.Social implicationsThe study provides insights for marketers on designing more effective and privacy-sensitive online advertising strategies in the Chinese market. Understanding the factors influencing consumers’ willingness to share personal information can lead to more effective engagement in digital marketing campaigns.Originality/valueBy integrating interactivity theory with privacy computing theory, this research provides a new perspective on the role of online interaction in consumer privacy protection and information disclosure decisions. The findings not only enrich the theoretical frameworks of interactive marketing and privacy protection but also offer empirical support for marketing practitioners in regard to designing interactive advertising strategies, especially balancing consumer privacy protection with the enhancement of shopping intentions.

I genuinely recognize you: how technological engagement and privacy increase the online purchase intent of consumers

Purpose The purpose of the study was to explore the purchase intention of online consumers by proposing and validating a model supported by exhaustive reviews from top-rated journals, where digital technology, consumer privacy, consumer engagement and online advertising were the extracted constructs influencing consumer learning on digital platforms and finally evaluating the purchase intention of online consumers. Design/methodology/approach A questionnaire representing these constructs was then sent to the 470 respondents on LinkedIn, and then designations like business heads, managers and faculty from educational institutions were selected using a stratified sampling technique and, finally, PLS-SEM robust computation standards aided in research model assessment and validation. Findings Results predicted that the variance explained by individual independent constructs defines consumer privacy as a priority for companies, followed by online advertising, consumer engagement and digital technology while measuring the final purchase intent for online consumption. Also, with dynamism in consumer sentiments and a rapidly changing technological environment, the consumer’s digital behaviour may differ in the coming future in relation to their online purchase intent. Research limitations/implications Current research anticipates that the final online purchase intent of consumers has been vividly covered by our independent constructs, but an unexplained R2 of 31% still promotes prospects related to the existing research. Furthermore, India has a huge rural population that, with a lack of money, has a complex behavioural mindset due to religious issues. Practical implications It is important to note that in a real-time market, a better understanding of the duality of persuasive and smart technology and the evaluation of the performance of social media helps in deciding the final online consumer intent. Originality/value The need for digital transformation has become an essential necessity for companies while managing the expectations and needs of the fastest-growing online consumers.

The Emerging Challenges of Customer Relationship Management with Customer Data Platforms (CDP): Constraints and Perspectives

Customers today are increasingly demanding a personalized shopping experience, coupled with stringent data security measures and prompt responses. As the landscape of online privacy evolves, particularly with the anticipated sunset of third-party cookies by 2024, businesses face a critical imperative to revamp their customer data management strategies. The emergence of Customer Data Platforms (CDPs) stands out as a pivotal solution in this evolving ecosystem. This article undertakes a detailed critique of CDPs, starting with insights gathered from both users and industry experts, and subsequently examining consumer perceptions and behaviors in the digital realm. At the heart of contemporary consumer expectations lies the desire for tailor-made interactions that reflect their preferences and behaviors. This demand necessitates robust data management tools capable of aggregating, analyzing, and activating customer data across multiple touchpoints. CDPs have emerged as a central tool in this endeavor, offering businesses a unified platform to integrate disparate data sources and derive actionable insights in real-time. By leveraging comprehensive customer profiles built from first-party data, CDPs enable businesses to deliver personalized experiences that foster customer loyalty and drive revenue growth. Moreover, the impending demise of third-party cookies, compounded by stringent data protection regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act), underscores the urgency for businesses to adopt privacy-centric data practices. CDPs play a crucial role in this paradigm shift by empowering businesses to ethically collect and utilize customer data, thereby enhancing transparency and trust. However, challenges persist, including the complexity of integrating CDPs into existing infrastructures and ensuring compliance with evolving regulatory frameworks. From a consumer standpoint, attitudes towards data privacy are evolving, with an increasing emphasis on transparency and control over personal information. Consumers are more inclined to engage with brands that demonstrate a commitment to data security and ethical data usage practices. This shift necessitates a nuanced approach from businesses using CDPs, emphasizing not only personalization but also data protection and consent management. While CDPs offer substantial benefits in navigating the complexities of modern data management and meeting consumer expectations for personalized experiences, their successful implementation hinges on addressing technical, regulatory, and ethical considerations. By doing so, businesses can not only adapt to the evolving landscape of digital privacy but also foster enduring customer relationships grounded in trust and mutual value.

Consumer Privacy Expectations and the Impact on Buying Intentions

Based on past research and attitudinal theories, this study intends to investigate the effect of attitudes about data privacy protection and disclosure and the perception of corporate data privacy performance on future purchase intention. Additionally, it seeks to understand the relationship between purchase behavior intensity, personal data privacy concerns and privacy expectations (attitude). A total of 271 usable surveys were gathered from United States consumers and analyzed by two multiple linear regression models and correlation analysis. The results show that attitude (data privacy protection and disclosure responsibility) and perceptions about corporate data privacy performance predict purchase intention. Furthermore, past purchase behavior intensity and personal data privacy concerns influence corporate data privacy responsibility attitudes. This study extends the current data privacy literature by understanding how data privacy expectations (attitude) and data privacy performance perceptions impact purchase intent and are influenced by personal data privacy concerns and purchase behavior.

Impact of Digital Fashion Shows on Consumer Engagement with Fashion Brands in Nigeria

Purpose: The aim of the study was to analyze the impact of digital fashion shows on consumer engagement with fashion brands in Nigeria. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Digital fashion shows have significantly enhanced consumer engagement with fashion brands in Nigeria by offering a dynamic, accessible online experience. These virtual events have allowed brands to reach a broader audience, increasing their visibility and facilitating direct interaction with consumers. Real-time feedback during these shows has enabled brands to fine-tune their marketing strategies and product offerings to align with consumer preferences. The use of augmented reality technologies has further personalized the shopping experience, leading to increased consumer interest and brand loyalty. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), uses and gratifications theory (UGT) & engagement theory may be used to anchor future studies on impact of digital fashion shows on consumer engagement with fashion brands in Nigeria. Fashion brands should leverage a combination of VR, AR, and live-streaming to create more engaging and immersive experiences. By investing in these technologies and incorporating high-quality digital content, brands can enhance consumer interaction and foster deeper emotional connections. Policymakers and industry bodies can work together to establish standards for digital content, technology integration, and consumer privacy protection, which will enhance the overall consumer experience and build trust in digital fashion shows.