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. Its primary objectives are to identify the psychological antecedents, outcomes, and theoretical frameworks that explain privacy behavior, and to evaluate whether insights from traditional online privacy literature—such as those from e-commerce and social networking sites—apply to these advanced technologies. Additionally, the study advocates for a context-dependent approach to understanding privacy.

Methods:

In this study, we find that privacy is a context-dependent and fluid concept. A systematic literature review of 179 studies was conducted to synthesize psychological antecedents, outcomes, and theoretical frameworks related to privacy behaviors in emerging technologies. The review followed established guidelines, utilizing leading research databases. Studies were screened for relevance to privacy behaviors, focus on emerging technologies, and empirical grounding, with methodological details analyzed to assess the applicability of traditional privacy findings—e.g., in contexts such as e-commerce and social networking sites—to current cutting-edge technologies.

Results:

The systematic review reveals significant gaps in the existing privacy literature regarding emerging technologies such as IoT, AI, AR, and Big Data. Contextual dimensions, including data sensitivity, recipient transparency, and transmission principles, are frequently overlooked despite their critical role in shaping privacy concerns and behaviors. The findings also highlight that privacy theories developed for traditional technologies often fail to account for the unique complexities of cutting-edge contexts. By synthesizing psychological antecedents, behavioral outcomes, and theoretical frameworks, this study underscores the need for a context-contingent approach to privacy research.

Conclusions:

This study advances the understanding of user privacy by emphasizing the critical role of contextual factors in data-sharing scenarios, particularly in the age of ubiquitous and emerging health-related technologies. The findings challenge static interpretations of privacy and highlight the need for tailored frameworks that address dynamic, context-dependent privacy behaviors. Practical implications include guiding healthcare providers, policymakers, and technology developers toward context-sensitive strategies that build trust, enhance data protection, and support the ethical development of digital health practices.