JMIR Publications

ABSTRACT

Background:

Despite extensive research into technology users' privacy concerns, a critical gap remains in understanding why individuals adopt varying standards for data protection across different contexts. The emergence of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR), and Big Data has created rapidly evolving and complex privacy landscapes. However, privacy is frequently treated as a static construct, failing to capture the fluid and context-dependent nature of user concerns. This oversimplification has led to fragmented research, inconsistent findings, and a limited ability to address the nuanced challenges posed by these technologies. Understanding these dynamics is particularly crucial in fields such as digital health and informatics, where sensitive data and user trust are central to technology adoption and ethical innovation.

Objective:

This study synthesizes existing research on privacy behaviors in emerging technologies, focusing on IoT, AI, AR, and Big Data. The primary objectives are to (1) identify the psychological antecedents, outcomes, and theoretical frameworks that explain privacy behavior, (2) assess whether traditional online privacy literature—such as studies on e-commerce and social networking—applies to these advanced technologies, and (3) advocate for a context-dependent approach to understanding privacy concerns.

Methods:

A systematic literature review was conducted following PRISMA 2020 guidelines. Searches were performed across three electronic databases (ScienceDirect, SAGE, and EBSCO) to identify empirical studies related to privacy behaviors in emerging technologies. A total of 521 studies were initially retrieved, of which 179 met the inclusion criteria after screening and eligibility assessment. Studies were included if they (1) examined privacy behaviors within IoT, AI, AR, or Big Data contexts, (2) were empirical, peer-reviewed research, and (3) were published in English. Non-empirical papers, review articles, and studies focusing solely on privacy-enhancing technologies were excluded. Data extraction focused on psychological antecedents, privacy outcomes, and theoretical frameworks. Risk of bias was minimized through predefined inclusion criteria and independent screening by two reviewers. Data synthesis was performed using a narrative approach.

Results:

Findings indicate that contextual dimensions—including data sensitivity, recipient transparency, and transmission principles—are often overlooked despite their significant influence on privacy concerns. Of the 179 included studies, 54.2% explicitly analyzed information sensitivity, yet only 28.4% considered transmission principles. Furthermore, traditional privacy theories, developed for earlier technologies such as e-commerce and social media, frequently fail to address the complexities of modern, context-contingent privacy concerns. Results suggest that privacy behaviors in AI, IoT, AR, and Big Data settings deviate from established models, necessitating updated frameworks that better capture context-dependent dynamics.

Conclusions:

This study advances the understanding of user privacy by emphasizing the critical role of contextual factors in data-sharing scenarios. Findings challenge static interpretations of privacy and highlight the necessity of frameworks that address dynamic, context-dependent behaviors. Limitations include potential selection bias and the heterogeneous methodologies of included studies. Practical implications include guiding healthcare providers, policymakers, and technology developers toward privacy strategies that build trust, enhance data protection, and support the ethical development of digital health practices.