A Bibliographic Analysis of Research Trends on Privacy in Technology Adoption: Information Synthesis Perspective

Abstract

This study is to explore information synthesis on research topics and emerging trends in privacy within the context of technology adoption. A search for the terms privacy and technology adoption in the Web of Science database yielded information on 2910 publications from 2005 to 2025. The analysis was conducted using CiteSpace, incorporating cluster analysis, timeline analysis, and burst detection to identify key patterns and developments. Fifteen sub-areas of privacy related to technology adoption were identified, including health information exchange, blockchain adoption, artificial intelligence, Internet banking, smart home devices, location-based services, mobile commerce, ubiquitous commerce adoption, tracing apps, metaverse adoption, and facial recognition payment. Timeline analysis provided insights into the growth or decline of these research clusters over time. Based on the findings, a framework was developed to illustrate key insights and their interconnections, offering guidance for future research. The study concludes by discussing its implications, limitations, and recommendations for further research.

1. Introduction

In today’s digital era, individuals generate and share vast amounts of personal data while using various information technology services. These include Internet activities, social networking services, location-based services, and electronic commerce, typically accessed through computers, smartphones, and tablets. At the same time, companies can collect and analyze personal data using advanced commercial data analytics technologies, enabling them to process large volumes of information rapidly and apply it to various business strategies.

However, the ease of accessing personal data also increases individuals’ vulnerability to privacy breaches and unethical information practices. As technology evolves—driven by cloud computing, big data, artificial intelligence (AI), the Internet of Everything (IoE), and blockchain—concerns over personal privacy have become increasingly urgent [1]. The use of these technologies to analyze personal data raises critical questions regarding data security and privacy protection. Despite efforts by scholars and policymakers to develop legal, commercial, and technological safeguards, the misuse and abuse of personal information persist [2].

Privacy has emerged as a crucial research area, particularly in the rapidly expanding field of information technology. Previous studies have examined privacy related issues across various domains, including bibliometric analyses of consumer privacy research trends from a marketing perspective [2], a conceptual framework on privacy concerns in tourism using topic modeling [3], security and privacy in computer systems [4], privacy protection in healthcare data [5], the application of machine learning in blockchain [6], and privacy information disclosure in AI-enabled IoT environments [7].

A review of the existing literature reveals that a substantial number of studies on technology adoption and privacy have been published in international journals, particularly in the management and technology fields. However, most bibliometric studies on privacy and information technology have focused on specific domains rather than providing a broad, integrated perspective on privacy concerns in technology adoption. For example, these studies focus primarily on consumer-oriented marketing literature [2] or are limited to the tourism sector [3]. Furthermore, studies on emerging technologies [6,7] often lack cross-domain integration and tend to overlook the social and behavioral implications of privacy in technology adoption contexts. Even after an extensive search of the Web of Science database, there appears to be a lack of comprehensive studies addressing privacy concerns from this perspective. The present bibliometric research addresses this gap by analyzing the current state of technology adoption across various privacy-related domains.

This study systematically examines privacy research in the context of technology adoption, with the following objectives: (1) to analyze general trends in privacy research related to technology adoption, (2) to identify co-citation clusters within this research field, (3) to determine landmark studies that have significantly influenced privacy research in technology adoption, and (4) to highlight emerging topics and suggest future research directions. By identifying key research trends and emerging topics, this study aims to enhance understanding of privacy concerns while offering valuable insights and opportunities for further exploration.

Figure 1 illustrates the research process. The study begins with a literature review covering privacy research, privacy in technology adoption, and bibliometric analysis. Next, the data collection phase describes the method of retrieving relevant publications from the Web of Science database and analyzes general trends in privacy research. The data analysis phase includes cluster analysis, timeline analysis, and burst detection analysis. Finally, the study concludes by summarizing the findings, discussing implications, identifying limitations, and providing recommendations for future research.

2. Literature Review

2.1. Privacy in Technology Adoption

Privacy was first defined by Warren and Brandeis [8] as “the right to be left alone,” emphasizing an individual’s right to be free from external interference or intrusion. In the context of information technology, privacy is defined as the right to control the collection and use of personal information. Growing concerns over personal privacy stem from individuals’ increasing difficulty in determining when and where their data is collected, how it is used, and the security of sharing information with companies or websites [9].

As information technology has advanced, the concept of privacy has expanded from traditional notions of personal privacy to a broader framework known as information privacy. This refers to the right to control one’s personal data in an increasingly interconnected digital environment. The rise of the Internet, smartphones, and social media has heightened privacy concerns, prompting extensive research into how information technology affects individuals’ privacy.

The increasing concern over privacy is closely linked to the rapid development of digital networks and information technologies, significantly influencing the adoption of these technologies. Since the early 2000s, there has been growing interest in privacy issues, particularly in relation to the exchange of information between Internet users and providers of online products, services, and data. The introduction of emerging digital technologies—such as cloud computing, big data, AI, IoE, and blockchain—has accelerated research into privacy and security in the digital age. As these continue to evolve, understanding and addressing privacy concerns has become an essential area of study.

2.2. Related Studies on Privacy in Technology Adoption

Technological innovation, particularly in information technology, has been focal point of research for decades. One of the most influential scholars in this field, Rogers [10], defined innovation as “an idea, practice, or object that is perceived as new by an individual or other unit of adoption.” According to Rogers, novelty is subjective—it depends on how the adopter perceives an idea rather than its inherent characteristics. Thus, if an individual regards an idea as new, it qualifies as an innovation for that person.

Numerous studies have examined privacy within the context of information technology adoption. Dinev and Hart [9] investigated the relationship between perceived vulnerability, perceived ability to control, and Internet privacy concerns, particularly focusing on information access and misuse. Their findings indicate that individuals with a heightened perception of vulnerability are more concerned about how their information is accessed and used. In a comparative study of social networking platforms (Facebook and MySpace), Dwyer et al. [11] analyzed the impact of privacy concerns and trust on information sharing and relationship formation. More recently, Chatterjee and Kharagpur [12] examined the moderating effect of perceived privacy risk on users’ intentions to continue using AI-enabled services, while Salih et al. [13] found that security and privacy risks significantly influence users’ behavioral intentions to adopt IoT technologies.

Privacy research in information technology has explored various domains, including Internet privacy concerns, privacy calculation models in e-commerce, factors influencing the decision to discontinue social networking site (SNS) use, purchase intentions for location-based services based on privacy-trust-behavioral intention models, IoT adoption, and privacy control mechanisms related to self-disclosure on platforms such as Facebook. The diversity of research topics underscores the complexity of privacy concerns in technology adoption and the need for ongoing studies to address emerging challenges.

2.3. Bibliometric Analysis

Bibliometric analysis is a quantitative method that applies mathematical and statistical techniques to examine a large body of literature in a specific research field. It analyzes various aspects of academic publications, such as the year of publication, author, contributions, institutional affiliations, citation impact, and research trends.

Several open—source software programs facilitate citation and network analysis using databases such as Web of Science, Scopus, and PubMed. Popular tools for bibliometric analysis include CiteSpace, VOSviewer, BibExcel, and Network Workbench. In this study, privacy-related articles on technology adoption were analyzed using CiteSpace and data extracted from the Web of Science database.

A review of bibliographic studies on privacy research reveals several key contributions. Shu and Liu [1] analyzed trends in consumer privacy research using 23,171 articles from Web of Science. Their study employed co-citation analysis, keyword co-occurrence analysis, and structural variation analysis to identify emerging research topics, including privacy calculus, privacy ethics, privacy-enhancing technologies, privacy-related coping strategies, and contemporary privacy challenges.

Amin et al. [6] systematically analyzed 38 studies on privacy information disclosure in AI-enabled IoT environments. Their research extracted privacy-related articles from five databases (ABI/INFORM, ScienceDirect, EBSCOhost, Web of Science, and Google Scholar) covering the period from 2010 to 2022. They identified key topics related to information disclosure behavior in IoT applications, including healthcare, wearable IoT, home IoT, and voice assistants, and proposed a research framework for future studies.

Sharma et al. [2] investigated privacy concerns in the tourism industry using 1073 papers published between 1979 and 2022. Their study introduced a conceptual framework linking public services and cybersecurity, sustainable tourism and governance, and customer behavior and tourism marketing in response to increasing technological adoption in tourism.

Ali et al. [3] analyzed 1017 papers on computer privacy research published between 1976 and 2020. Their study identified key privacy-related keywords used in research over the past 40 years, including computer privacy, data privacy, personal information protection, and social networking.

Ouyang et al. [4] examined privacy protection in healthcare data using CiteSpace, analyzing 479 papers published in Web of Science between 2012 and 2023. Meanwhile, Valencia-Arias et al. [5] investigated research trends related to machine learning in blockchain, analyzing 166 papers from Scopus and Web of Science.

Their findings revealed emerging research areas such as cloud computing, intrusion detection, distributed learning, smart contracts, edge computing, and IoT applications. Notably, cloud computing, intrusion detection, and distributed learning have seen significant traction in recent years, reflecting a growing interest in these areas.

3. Methodology

3.1. Research Questions

This study aims to examine trends in privacy research on technology adoption. Specifically, it seeks to identify key research patterns, major co-citation clusters, influential studies, and emerging topics in this research area. The study addresses the following research questions:

RQ1. 

How have privacy research trends evolved in the context of technology adoption?

RQ2. 

What are the main co-citation clusters in privacy research on technology adoption?

RQ3. 

Which studies have been most influential in privacy research on technology adoption?

RQ4. 

What emerging topics have been identified, and what are the future research directions in privacy research on technology adoption?

3.2. Data Collection

This study aims to analyze the intellectual structure of privacy research in technology adoption literature, providing deeper insights into the field. The datasets were retrieved from the Web of Science, which includes the SCI(E)/SSCI/A&HCI databases’ comprehensive collection of over 12 million high-quality scholarly journal articles published worldwide.

To gather relevant records, the keywords “privacy” AND “technology adoption” were used in the title, abstract, and author keywords field. The search was limited to original research articles published in English-language journals between 2005 and 2025 (as of 2 February 2025). Studies not focused on privacy or technology adoption (e.g., reviews, editorials, conference abstracts) were excluded. After removing duplicates and verifying relevance through manual screening of titles and abstracts, a total of 2910 documents were retained, yielding 135,020 reference records for analysis.

Figure 2 presents the chronological distribution of articles on privacy in technology adoption published between 2005 and 2025. The number of publications reached 100 articles in 2016 and has increased rapidly since then. In 2024 alone, 569 articles related to privacy information technology adoption were published.

Table 1. Most prolific regions, affiliations, and authors in privacy research related to technology adoption.

Regions	Freq.	Affiliations	Freq.	Authors	Freq.
USA	753	State University System of Florida	63	Dwivedi, Y.K.	13
China	388	Indian Institute of Management (IIM) System	54	Esmaeilzadeh, P.	10
India	307	Indian Institute of Technology (IIT)System	41	Ziefle, M.	9
England	237	University of Texas System	39	Pal, D.	9
Australia	200	University of North Carolina	35	Prybutok, V.	9
Germany	163	University of California System	33	OOI, K.B.	8
Saudi Arabia	161	University of Toronto	33	Rana, N.P.	8
Canada	157	University of London	29	Chatterjee, S.	8
South Korea	132	King Saud University	28	Oliveira, T.	7
Malaysia	125	Symbiosis International University	28	Venkatesh, V.	7

summarizes the most prolific regions, affiliations, and authors in privacy research related to technology adoption. The United States leads with 753 publications, followed by China (388), India (307), England (237), and Australia (200). Among institutions, the State University System of Florida has the highest number of publications (63), followed by the Indian Institute of Management (IIM) System (54), the Indian Institute of Technology (IIT) System (41), the University of Texas System (39), and the University of North Carolina (35). In terms of individual contributions, Dwivedi is the most prolific author, with 13 publications, followed by Esmaeilzadeh (10), Ziefle (9), Pal (9), and Prybutok (9).

Table 2. Publication title and field.

Publication Titles	Freq.	Fields (Web of Science Categories)	Freq.
IEEE Access	72	Computer Science-Information Systems	597
Computers in Human Behavior	48	Business	394
Journal of Medical Internet Research	48	Information Science & Library Science	308
Sustainability	41	Management	267
Technological Forecasting and Social Change	36	Health Care Sciences & Services	251
Sensors	32	Telecommunications	243
Technology in Society	30	Medical Informatics	229
Journal of Retailing and Consumer Services	29	Engineering Electrical Electronic	227
International Journal of Medical Informatics	24	Computer Science, Interdisciplinary Applications	123
HELIYON	23	Computer Science, Theory & Methods	111

presents the most frequently published journals and research fields. IEEE Access has the highest number of articles (72), followed by Computers in Human Behavior (48), Journal of Medical Internet Research (48), Sustainability (41), and Technological Forecasting and Social Change (36). Regarding research fields, Computer Science-Information Systems is the most prominent category, with 697 articles, followed by Business (394), Information Science & Library Science (308), Management (267), and Health Care Sciences & Services (251). These categories are based on Web of Science classifications.

3.3. Statistical Methods

This study utilized CiteSpace 6.4.R1 (Chaomei Chen, Drexel University, Philadelphia, PA, USA; https://citespace.podia.com/) for bibliometric analysis, a widely used tool in scientometric research. CiteSpace enables the visualization of structural and dynamic patterns trends within a scientific field and is particularly valuable in disciplines such as computer science, sociology, and management science [1].

A major advantage of CiteSpace is its ability to directly process Web of Science data, extracting detailed metadata such as authors (AU), abstracts (AB), and references (CR) to generate co-citation networks and insightful visualizations.

This study employs three statistical methods: cluster analysis, timeline analysis, and burst detection analysis.

First, cluster analysis is an effective method for understanding the intellectual structure of a research area and identifying its sub-domains. This technique calculates the proximity of nodes within a network and groups them into clusters based on shared characteristics. Using CiteSpace, co-citation analysis visually maps the intellectual landscape by organizing frequently cited authors into cluster nodes and integrating co-citation relationships into a cohesive network.

The statistical analysis of clustering consists of betweenness centrality, modularity Q, and silhouette value, which are key parameters for validating the network. Betweenness centrality measures how much a node dominates communication pathways, indicating its role in linking thematic clusters [14]. A node with the highest centrality value is crucial in connecting preceding and following clusters, making it an intellectual turning point [1]. In other words, betweenness centrality reflects the strength of links between nodes and their influence in the network. A high betweenness value signifies a node’s impact. Similarly, citation burst refers to the rapid increase in a publication’s citation count, indicating its growing influence. Modularity (Q) measures cluster quality, assessing how well-separated clusters are. It ranges from 0 to 1, with higher values indicating more distinct [15]. A value of 1 suggests a perfectly divided network with clear boundaries between clusters. Silhouette value evaluates cohesion, separation, and homogeneity of clusters, with values between −1 and 1. A value close to 1 indicates high precision in clustering, while a value near −1 suggests weak connectivity among cluster members [16].

Second, timeline analysis tracks how clusters evolve and connect over time. This method helps identify which clusters have been actively studied at different periods. Even if a cluster is currently the largest, a recent decline in its size may indicate reduced relevance. Conversely, a smaller cluster showing recent growth in nodes and connections may signal an emerging topic requiring closer examination in the future.

Finally, burst detection analysis is a computational technique that identifies sudden changes in research activity [17]. In CiteSpace, the sigma score of a node integrates betweenness centrality and citation burstness, highlighting influential references. CiteSpace visualizations represent citation history as tree rings, where each ring to citations received in a given year. If a citation burst is detected, the corresponding ring is colored red. Otherwise, the rings are shaded in a gradient from blue to orange, indicating variations in citation intensity over time [18].

4. Analysis Results

4.1. Cluster Analysis

This study employs CiteSpace software to perform cluster analysis, determining the optimal number of clusters without researcher intervention. The results are presented in Figure 3, revealing 15 distinct clusters. The document co-citation network on privacy in technology adoption comprises 1117 nodes and 4332 links, with an overall network density of 0.007. The weighted mean silhouette score is 0.9089, and the modularity value is 0.8031. Network density quantifies how well-connected a network is, where a value of 1.00 represents a highly centralized and connected structure [16]. The modularity and silhouette values indicate that the clustering results are highly reliable.

Table 3 presents attributes of each cluster, including cluster number (label: log-likelihood ratio), size, silhouette value, average publication year, citing papers, and coverage. Cluster size denotes the total number of citing articles, while the mean publication year reflects the recency of research in that cluster. Each cluster label represents a frontier topic within the domain of privacy in technology adoption. The major research themes within the clusters include health information exchange, blockchain adoption, artificial intelligence, Internet banking, smart home devices, and location-based services, among others.

Cluster #0, labeled “Health information exchange,” is the largest cluster, comprising 117 studies. It has a silhouette value of 0.812 and a mean publication year of 2017. The most cited article in this cluster, authored by Chopdar et al. [19], examines factors influencing the adoption of contact-tracing apps in response to the COVID-19 pandemic. These apps function as technological tools designed to rapidly trace and notify users of potential exposure, aiding efforts to contain the virus’s spread.

Cluster #1, labeled “Blockchain adoption,” is the second-largest cluster, containing 95 citing references. It has a silhouette value of 0.905 and a mean publication year of 2018. The most cited article in this cluster, authored by Uddin et al. [20], explores the challenges and solutions related to blockchain adoption in IoT. The study highlights the need to balance transparency and privacy, emphasizing the risk of data leakage when processing information on blockchain nodes.

Cluster #2, labeled “Artificial intelligence,” is the third-largest cluster, consisting of 92 studies. It has a high silhouette value of 0.919 and a mean publication year of 2020. The most cited article in this cluster, published by Singh et al. [21], investigates the adoption intentions of online shopping assistants in e-commerce interactions. The study identifies key factors influencing acceptance, including anthropomorphism, attitude, ease of use, enjoyment, privacy, trust, and usefulness.

Cluster #3, labeled “Internet banking,” is the fourth-largest cluster, with 89 citing references. It has a silhouette value of 0.876 and a mean publication year of 2011. The most cited article in this cluster, authored by Hanafizadeh and Khedmatgozar [22], examines the role of perceived risk in shaping customers’ awareness and adoption of Internet banking. Their findings suggest that awareness of Internet banking mitigates perceived risks across various dimensions, including time, financial, performance, social, security, and privacy concerns.

Cluster #4, labeled “Smart home device,” is the fifth-largest cluster, comprising 82 studies. It has a silhouette value of 0.852 and a mean publication year of 2016. The most cited article in this cluster, authored by Sergueeva et al. [23] explores the factors influencing privacy in the adoption of wearable technology devices (WTDs) for personal health management. The study employs the unified theory of acceptance and use of technology 2 (UTAUT2) model and examines the Privacy–Personalization Paradox.

Cluster #5, labeled “Location-based service,” is the sixth largest cluster, consisting of 63 citing references. It has a silhouette value of 0.985 and a mean publication year of 2014. The most cited article in this cluster, authored by Zhou and Lu [24], investigates how five personality traits—extraversion, agreeableness, openness to experience, conscientiousness, and neuroticism—affect user adoption of mobile commerce. Additionally, Zhu [25,26] highlights privacy risks and concerns associated with the adoption of location-based services [27].

Cluster #6, labeled “Mobile commerce,” is the seventh-largest cluster, containing 63 studies. It has a silhouette value of 0.985 and a mean publication year of 2014. The most cited article in this cluster, authored by Lu et al. [28], examines mobile payment satisfaction and continuous usage. Their study finds that post-usage privacy protection directly impacts users’ intention to continue using mobile payments. Additionally, another study by Lu et al. [29] reveals that privacy plays a significant role in influencing users’ intention to continue mobile shopping on smartphone platforms.

Cluster #7, titled “Ubiquitous commerce adoption,” is the eighth-largest cluster, consisting of 51 studies. It has a silhouette value of 0.915 and a mean publication year of 2004. The most cited article in this cluster, authored by Sheng et al. [30], explores how personalization and context affect customers’ privacy concerns and their intention to adopt ubiquitous commerce (u-commerce) applications.

Cluster #8, labeled “Tracing app,” is the ninth-largest cluster, comprising 51 studies. It has a silhouette value of 0.915 and a mean publication year of 2020. The most cited article in this cluster, authored by Alkhalifah and Bukar [31], investigates the factors influencing the adoption of COVID-19 contact-tracing applications. The study applies the Technology Acceptance Model (TAM), Task-Technology Fit (TTF), and Privacy Calculus Theory (PCT), emphasizing the significance of privacy concerns, as these applications require the collection of both personal and location data.

Additionally, the tenth-largest cluster (#9) consists of 47 studies with a silhouette value of 0.995. The eleventh-largest cluster (#10) follows with 45 studies and a silhouette value of 0.946, while the twelfth-largest cluster (#11) contains 40 studies with a silhouette value of 0.979. The thirteenth-largest cluster (#12) includes 22 studies with a silhouette value of 0.961, followed by the fourteenth-largest cluster (#13) with 20 studies and a silhouette value of 0.977. Finally, the fifteenth-largest cluster (#14) comprises 11 studies with a silhouette value of 0.964. The major citing articles for these clusters are listed in Table 3.

Figure 3. Results of cluster analysis.

Figure 3. Results of cluster analysis.

Table 3. Representative papers of main clusters.

Custer #	Size	Silhouette	Average Year	Citing Papers	Coverage %
#0 Health information exchange	117	0.812	2017	Chopdar, P.K. [19]. Adoption of COVID-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator.	21
				Alam, M.Z. [32]. Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach.	17
				Ashrafi, D.M. [33]. Okay google, good to talk to you ... examining the determinants affecting users’ behavioral intention for adopting voice assistants: Does technology self-efficacy matter?.	17
				Bu, F. [34]. Motivating information system engineers’ acceptance of privacy by design in China: an extended UTAUT model.	13
				Colak, H. [35]. How ready are we? Acceptance of internet of things (IoT) technologies by consumers.	12
#1 Blockchain adoption	95	0.905	2018	Uddin, M.A. [20]. A survey on the adoption of blockchain in IoT: Challenges and solutions.	13
				Rana, N.P. [36]. Analysis of challenges for blockchain adoption within the Indian public sector: An interpretive structural modeling approach.	13
				Moraes, K.K. [37]. Overcoming technological barriers for blockchain adoption in supply chains: A diffusion of innovation (DOI)-informed framework proposal.	11
				Mayer, A.H. [38]. Fogchain: A fog computing architecture integrating blockchain and internet of things for personal health records.	10
				Mukherjee, A.A. [39]. Application of blockchain technology for sustainability development in agricultural supply chain: Justification framework.	10
#2 Artificial intelligence	92	0.919	2020	Singh, C. [21]. Investigating the acceptance intentions of online shopping assistants in e-commerce interactions: Mediating role of trust and effects of consumer demographics.	19
				Wiangkham, A. [40]. Exploring the drivers for the adoption of metaverse technology in engineering education using PLS-SEM and ANFIS.	16
				Ashrafi, D.M. [33]. Okay google, good to talk to you ... examining the determinants affecting users’ behavioral intention for adopting voice assistants: Does technology self-efficacy matter?.	14
				Acikgoz, F. [41]. Consumer engagement with AI-powered voice assistants: A behavioral reasoning perspective.	13
				Molinillo, S. [42]. Impact of perceived value on intention to use voice assistants: The moderating effects of personal innovativeness and experience.	12
#3 Internet banking	89	0.876	2011	Hanafizadeh, P. [22]. The mediating role of the dimensions of the perceived risk in the effect of customers’ awareness on the adoption of internet banking in Iran.	10
				Li, H. [43]. Examining individuals’ adoption of healthcare wearable devices: an empirical study from privacy calculus perspective.	9
				Baillette, P. [44]. Bring your own device in organizations: Extending the reversed it adoption logic to security paradoxes for CEOs and end users.	9
				Baillette, P. [45]. BYOD-related innovations and organizational change for entrepreneurs and their employees in SMEs: The identification of a twofold security paradox.	8
				Giovanis, A.N. [46]. An extension of tam model with IDT and security/privacy risk in the adoption of internet banking services in Greece.	7
#4 Smart home device	82	0.852	2016	Sergueeva, K. [23]. Understanding the barriers and factors associated with consumer adoption of wearable technology devices in managing personal health.	14
				Pal, D. [47]. The future of smartwatches: Assessing the end-users’ continuous usage using an extended expectation-confirmation model.	13
				Pal, D. [48]. Prohibitive factors to the acceptance of internet of things (IoT) technology in society: A smart-home context using a resistive modeling approach.	12
				Peng, C. [49]. Determinants and cross-national moderators of wearable health tracker adoption: A meta-analysis.	11
				Pal, D. [50]. Antecedents of trust and the continuance intention in IoT-based smart products: The case of consumer wearables.	11
#5 Location-based service	76	0.958	2008	Zhou, T. [24]. The effects of personality traits on user acceptance of mobile commerce.	14
				Zhou, T. [51]. The effect of interactivity on the flow experience of mobile commerce user.	14
				Zhou, T. [26]. Examining continuous usage of location-based services from the perspective of perceived justice.	13
				Zhou, T. [25]. An empirical examination of user adoption of location-based services.	13
				Alaiad, A. [52]. The determinants of home healthcare robots adoption: An empirical investigation.	9
				Zhou, T. [53]. The impact of privacy concern on user adoption of location-based services.	9
#6 Mobile commerce	63	0.985	2014	Lu, J. [28]. How do post-usage factors and espoused cultural values impact mobile payment continuation?	13
				Lu, J. [29]. Comparison of mobile shopping continuance intention between China and USA from an espoused cultural perspective.	12
				Ooi, K. [54]. Unfolding the privacy paradox among mobile social commerce users: A multi-mediation approach.	11
				Chopdar, P.K. [55]. Mobile shopping apps adoption and perceived risks: A cross-country perspective utilizing the unified theory of acceptance and use of technology.	8
				Palos-Sanchez, P. [56]. The effect of internet searches on afforestation: The case of a green search engine.	8
#7 Ubiquitous commerce adoption	51	0.915	2004	Sheng, H. [30]. An experimental study on ubiquitous commerce adoption: Impact of personalization and privacy concerns.	13
				Katos, V. [57]. Modeling corporate wireless security and privacy.	11
				Dinev, T. [58]. Is there an on-line advertisers’ dilemma? A study of click fraud in the pay-per-click model.	10
				Xu, H. [59]. The role of push-pull technology in privacy calculus: The case of location-based services.	9
				Carter, L. [60]. The utilization of e-government services: Citizen trust, innovation and acceptance factors.	8
#8 Tracing app	51	0.915	2020	Alkhalifah, A. [31]. Examining the prediction of COVID contact-tracing app adoption using an integrated model and hybrid approach analysis.	17
				Thenoz, E. [61]. The adoption of contact-tracing applications and the integration of a health pass: A prosocial rationality in the privacy calculus?	12
				Trkman, M. [62]. The roles of privacy concerns and trust in voluntary use of governmental proximity tracing applications.	12
				Kuo, K. [63]. Antecedents predicting digital contact tracing acceptance: A systematic review and meta-analysis.	11
				Chopdar, P.K. [19]. Adoption of COVID-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator.	10
#9 Own health	47	0.995	2009	Steele, R. [64]. Personal health record architectures: Technology infrastructure implications and dependencies.	8
				Gartrell, K. [65]. Testing the electronic personal health record acceptance model by nurses for managing their own health a cross-sectional survey.	8
				Gartrell, K. [66]. Electronic personal health record use among nurses in the nursing informatics community.	6
				Gaylin, D.S. [67]. Public attitudes about health information technology, and its relationship to healthcare quality, costs, and privacy.	5
				Blechman, E.A. [68]. Strategic value of an unbound, interoperable PHR platform for rights-managed care coordination.	5
#10 Metaverse adoption	45	0.946	2022	Wiangkham, A. [40]. Exploring the drivers for the adoption of metaverse technology in engineering education using PLS-SEM and ANFIS.	13
				Abumalloh, R.A. [69]. The adoption of metaverse in the retail industry and its impact on sustainable competitive advantage: Moderating impact of sustainability commitment.	11
				Gupta, R. [70]. Are we ready for metaverse adoption in the service industry? Theoretically exploring the barriers to successful adoption.	10
				Nadeem, W. [71]. What drives metaverse retail environments' (non)usage? A behavioral reasoning theory perspective.	8
				Mahmoud, A.B. [72]. Exploring the public’s beliefs, emotions and sentiments towards the adoption of the metaverse in education: A qualitative inquiry using big data.	8
#11 Social cognitive theory	40	0.979	2012	Ratten, V. [73]. Cloud computing technology innovation advances: A set of research propositions.	20
				Ratten, V. [74]. A cross-cultural comparison of online behavioral advertising knowledge, online privacy concerns and social networking using the technology acceptance model and social cognitive theory.	20
				Ratten, V. [75]. International consumer attitudes toward cloud computing: A social cognitive theory and technology acceptance model perspective.	16
				Senarathna, I. [76]. Security and privacy concerns for Australian SMEs cloud adoption: Empirical study of metropolitan vs. regional SMEs.	8
				Lim, N. [77]. Cloud computing: The beliefs and perceptions of Swedish school principals.	6
#12 Facial recognition payment	22	0.961	2021	Yu, T. [78]. Convenient or risky? Investigating the behavioral intention to use facial recognition payment in smart hospitals.	16
				Yu, T. [79]. Acceptance of or resistance to facial recognition payment: A systematic review.	14
				Chen, H. [80]. Bridging the intention-behavior gap in facial recognition payment from an innovation resistance perspective: A mixed-method approach.	8
				Hwang, J. [81]. An integrated model of artificial intelligence (AI) facial recognition technology adoption based on perceived risk theory and extended TPB: A comparative analysis of US and South Korea.	8
				Wang, M. [82]. Exploring college students’ risk perception and acceptance intention of facial recognition technology in China.	7
				Lyu, T. [83]. Understanding people’s intention to use facial recognition services: The roles of network externality and privacy cynicism.	7
#13 Learning analytics	20	0.997	2014	Hassan, A.M. [84]. Urban transition in the era of the internet of things: Social implications and privacy challenges.	4
				Burhan, M. [85]. IoT elements, layered architectures and security issues: A comprehensive survey.	4
				Fortino, G. [86]. Using trust and local reputation for group formation in the cloud of things.	4
				Chaurasia, S.S. [87]. Big data academic and learning analytics: Connecting the dots for academic excellence in higher education.	4
				Karampela, M. [88]. Personal health data: A systematic mapping study.	3
#14 Cross country analysis	11	0.964	2013	Tavares, J. [89]. Electronic health record portal adoption: A cross-country analysis.	5
				Ho, S.M. [90]. Trust or consequences? Causal effects of perceived risk and subjective norms on cloud technology adoption.	4
				Saadi, M.R. [91]. Prioritization of citizens’ preferences for using mobile government services the analytic hierarchy process (AHP) approach.	3
				Kim, Y. [92]. A study on the adoption of IoT smart home service: Using value-based adoption model.	2

Table 3. Representative papers of main clusters.

Custer #	Size	Silhouette	Average Year	Citing Papers	Coverage %
#0 Health information exchange	117	0.812	2017	Chopdar, P.K. [19]. Adoption of COVID-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator.	21
				Alam, M.Z. [32]. Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach.	17
				Ashrafi, D.M. [33]. Okay google, good to talk to you ... examining the determinants affecting users’ behavioral intention for adopting voice assistants: Does technology self-efficacy matter?.	17
				Bu, F. [34]. Motivating information system engineers’ acceptance of privacy by design in China: an extended UTAUT model.	13
				Colak, H. [35]. How ready are we? Acceptance of internet of things (IoT) technologies by consumers.	12
#1 Blockchain adoption	95	0.905	2018	Uddin, M.A. [20]. A survey on the adoption of blockchain in IoT: Challenges and solutions.	13
				Rana, N.P. [36]. Analysis of challenges for blockchain adoption within the Indian public sector: An interpretive structural modeling approach.	13
				Moraes, K.K. [37]. Overcoming technological barriers for blockchain adoption in supply chains: A diffusion of innovation (DOI)-informed framework proposal.	11
				Mayer, A.H. [38]. Fogchain: A fog computing architecture integrating blockchain and internet of things for personal health records.	10
				Mukherjee, A.A. [39]. Application of blockchain technology for sustainability development in agricultural supply chain: Justification framework.	10
#2 Artificial intelligence	92	0.919	2020	Singh, C. [21]. Investigating the acceptance intentions of online shopping assistants in e-commerce interactions: Mediating role of trust and effects of consumer demographics.	19
				Wiangkham, A. [40]. Exploring the drivers for the adoption of metaverse technology in engineering education using PLS-SEM and ANFIS.	16
				Ashrafi, D.M. [33]. Okay google, good to talk to you ... examining the determinants affecting users’ behavioral intention for adopting voice assistants: Does technology self-efficacy matter?.	14
				Acikgoz, F. [41]. Consumer engagement with AI-powered voice assistants: A behavioral reasoning perspective.	13
				Molinillo, S. [42]. Impact of perceived value on intention to use voice assistants: The moderating effects of personal innovativeness and experience.	12
#3 Internet banking	89	0.876	2011	Hanafizadeh, P. [22]. The mediating role of the dimensions of the perceived risk in the effect of customers’ awareness on the adoption of internet banking in Iran.	10
				Li, H. [43]. Examining individuals’ adoption of healthcare wearable devices: an empirical study from privacy calculus perspective.	9
				Baillette, P. [44]. Bring your own device in organizations: Extending the reversed it adoption logic to security paradoxes for CEOs and end users.	9
				Baillette, P. [45]. BYOD-related innovations and organizational change for entrepreneurs and their employees in SMEs: The identification of a twofold security paradox.	8
				Giovanis, A.N. [46]. An extension of tam model with IDT and security/privacy risk in the adoption of internet banking services in Greece.	7
#4 Smart home device	82	0.852	2016	Sergueeva, K. [23]. Understanding the barriers and factors associated with consumer adoption of wearable technology devices in managing personal health.	14
				Pal, D. [47]. The future of smartwatches: Assessing the end-users’ continuous usage using an extended expectation-confirmation model.	13
				Pal, D. [48]. Prohibitive factors to the acceptance of internet of things (IoT) technology in society: A smart-home context using a resistive modeling approach.	12
				Peng, C. [49]. Determinants and cross-national moderators of wearable health tracker adoption: A meta-analysis.	11
				Pal, D. [50]. Antecedents of trust and the continuance intention in IoT-based smart products: The case of consumer wearables.	11
#5 Location-based service	76	0.958	2008	Zhou, T. [24]. The effects of personality traits on user acceptance of mobile commerce.	14
				Zhou, T. [51]. The effect of interactivity on the flow experience of mobile commerce user.	14
				Zhou, T. [26]. Examining continuous usage of location-based services from the perspective of perceived justice.	13
				Zhou, T. [25]. An empirical examination of user adoption of location-based services.	13
				Alaiad, A. [52]. The determinants of home healthcare robots adoption: An empirical investigation.	9
				Zhou, T. [53]. The impact of privacy concern on user adoption of location-based services.	9
#6 Mobile commerce	63	0.985	2014	Lu, J. [28]. How do post-usage factors and espoused cultural values impact mobile payment continuation?	13
				Lu, J. [29]. Comparison of mobile shopping continuance intention between China and USA from an espoused cultural perspective.	12
				Ooi, K. [54]. Unfolding the privacy paradox among mobile social commerce users: A multi-mediation approach.	11
				Chopdar, P.K. [55]. Mobile shopping apps adoption and perceived risks: A cross-country perspective utilizing the unified theory of acceptance and use of technology.	8
				Palos-Sanchez, P. [56]. The effect of internet searches on afforestation: The case of a green search engine.	8
#7 Ubiquitous commerce adoption	51	0.915	2004	Sheng, H. [30]. An experimental study on ubiquitous commerce adoption: Impact of personalization and privacy concerns.	13
				Katos, V. [57]. Modeling corporate wireless security and privacy.	11
				Dinev, T. [58]. Is there an on-line advertisers’ dilemma? A study of click fraud in the pay-per-click model.	10
				Xu, H. [59]. The role of push-pull technology in privacy calculus: The case of location-based services.	9
				Carter, L. [60]. The utilization of e-government services: Citizen trust, innovation and acceptance factors.	8
#8 Tracing app	51	0.915	2020	Alkhalifah, A. [31]. Examining the prediction of COVID contact-tracing app adoption using an integrated model and hybrid approach analysis.	17
				Thenoz, E. [61]. The adoption of contact-tracing applications and the integration of a health pass: A prosocial rationality in the privacy calculus?	12
				Trkman, M. [62]. The roles of privacy concerns and trust in voluntary use of governmental proximity tracing applications.	12
				Kuo, K. [63]. Antecedents predicting digital contact tracing acceptance: A systematic review and meta-analysis.	11
				Chopdar, P.K. [19]. Adoption of COVID-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator.	10
#9 Own health	47	0.995	2009	Steele, R. [64]. Personal health record architectures: Technology infrastructure implications and dependencies.	8
				Gartrell, K. [65]. Testing the electronic personal health record acceptance model by nurses for managing their own health a cross-sectional survey.	8
				Gartrell, K. [66]. Electronic personal health record use among nurses in the nursing informatics community.	6
				Gaylin, D.S. [67]. Public attitudes about health information technology, and its relationship to healthcare quality, costs, and privacy.	5
				Blechman, E.A. [68]. Strategic value of an unbound, interoperable PHR platform for rights-managed care coordination.	5
#10 Metaverse adoption	45	0.946	2022	Wiangkham, A. [40]. Exploring the drivers for the adoption of metaverse technology in engineering education using PLS-SEM and ANFIS.	13
				Abumalloh, R.A. [69]. The adoption of metaverse in the retail industry and its impact on sustainable competitive advantage: Moderating impact of sustainability commitment.	11
				Gupta, R. [70]. Are we ready for metaverse adoption in the service industry? Theoretically exploring the barriers to successful adoption.	10
				Nadeem, W. [71]. What drives metaverse retail environments' (non)usage? A behavioral reasoning theory perspective.	8
				Mahmoud, A.B. [72]. Exploring the public’s beliefs, emotions and sentiments towards the adoption of the metaverse in education: A qualitative inquiry using big data.	8
#11 Social cognitive theory	40	0.979	2012	Ratten, V. [73]. Cloud computing technology innovation advances: A set of research propositions.	20
				Ratten, V. [74]. A cross-cultural comparison of online behavioral advertising knowledge, online privacy concerns and social networking using the technology acceptance model and social cognitive theory.	20
				Ratten, V. [75]. International consumer attitudes toward cloud computing: A social cognitive theory and technology acceptance model perspective.	16
				Senarathna, I. [76]. Security and privacy concerns for Australian SMEs cloud adoption: Empirical study of metropolitan vs. regional SMEs.	8
				Lim, N. [77]. Cloud computing: The beliefs and perceptions of Swedish school principals.	6
#12 Facial recognition payment	22	0.961	2021	Yu, T. [78]. Convenient or risky? Investigating the behavioral intention to use facial recognition payment in smart hospitals.	16
				Yu, T. [79]. Acceptance of or resistance to facial recognition payment: A systematic review.	14
				Chen, H. [80]. Bridging the intention-behavior gap in facial recognition payment from an innovation resistance perspective: A mixed-method approach.	8
				Hwang, J. [81]. An integrated model of artificial intelligence (AI) facial recognition technology adoption based on perceived risk theory and extended TPB: A comparative analysis of US and South Korea.	8
				Wang, M. [82]. Exploring college students’ risk perception and acceptance intention of facial recognition technology in China.	7
				Lyu, T. [83]. Understanding people’s intention to use facial recognition services: The roles of network externality and privacy cynicism.	7
#13 Learning analytics	20	0.997	2014	Hassan, A.M. [84]. Urban transition in the era of the internet of things: Social implications and privacy challenges.	4
				Burhan, M. [85]. IoT elements, layered architectures and security issues: A comprehensive survey.	4
				Fortino, G. [86]. Using trust and local reputation for group formation in the cloud of things.	4
				Chaurasia, S.S. [87]. Big data academic and learning analytics: Connecting the dots for academic excellence in higher education.	4
				Karampela, M. [88]. Personal health data: A systematic mapping study.	3
#14 Cross country analysis	11	0.964	2013	Tavares, J. [89]. Electronic health record portal adoption: A cross-country analysis.	5
				Ho, S.M. [90]. Trust or consequences? Causal effects of perceived risk and subjective norms on cloud technology adoption.	4
				Saadi, M.R. [91]. Prioritization of citizens’ preferences for using mobile government services the analytic hierarchy process (AHP) approach.	3
				Kim, Y. [92]. A study on the adoption of IoT smart home service: Using value-based adoption model.	2

4.2. Timeline Analysis

The timeline analysis effectively illustrates the temporal relationships between research fronts and their foundational documents. When visualized through an appropriate graphical user interface within a document database, this analysis facilitates the identification of research topics, experts, centers of excellence, and the disciplinary origins of foundational studies. In CiteSpace, node colors further indicate the temporal evolution of research: blue nodes represent early studies, green nodes indicate mid-period research, and yellow/orange nodes denote recent studies. More importantly, for technology forecasting, this approach aids in detecting the potential emergence of new research fronts [93].

The timeline analysis reveals that research activity remains ongoing in clusters such as blockchain, artificial intelligence, the metaverse, and facial recognition payment, indicating their continued relevance in the future. The mobile commerce cluster has shown a sustained focus on privacy-related research since 2010, with a notable increase in activity after 2020. In contrast, clusters such as ubiquitous commerce, Internet banking, and location-based services, which have been subjects of research since 2000, demonstrate a historical connection between privacy studies and technology adoption. However, research in these areas has stagnated in recent years.

Figure 4 presents a timeline of privacy research in technology adoption, highlighting key contributions from Dwivedi et al. [94], Dhagarra et al. [95], McLean and Osei-Frimpong [96], Hair et al. [97], Venkatesh et al. [98], Li et al. [43], and Braun and Clarke [99].

4.3. Burst Detection Analysis

Typically, the number of citations a document receives increases gradually over time as its reputation grows, making older documents more likely to be cited than recent ones. However, in certain cases, driven by specific circumstances, environments, or issues, documents accumulate a high number of citations within a short period. Burst detection analysis is a method used to identify such documents and determine turning points in research. This visualization technique represents data in a time-series format based on clusters extracted from the cluster analysis.

Figure 5 presents the burst detection analysis of privacy in technology adoption, along with a summary of the top 25 references exhibiting the strongest citation bursts. In this visualization, blue bars indicate periods of ordinary citation activity, whereas red bars highlight periods during which a reference experienced a rapid increase in citations—representing a citation burst. The five papers with the highest turning-point scores are as follows:

The highest turning point value in the burst detection analysis is 22.79, corresponding to Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology (MIS Quarterly) by Venkatesh et al. [100]. This paper was intensely cited over approximately five years, from 2013 to 2017, leading to a high research transition point score. It is an extension of the UTAUT theory proposed by Venkatesh et al. [101], which examines how performance expectancy, effort expectancy, social influence, and facilitating conditions influence behavioral intentions and technology. The UTAUT2 theory was developed by incorporating hedonic motivation, price value, and habit into the UTAUT framework. It has been widely cited in privacy-related studies on technology adoption, particularly in research on location-based applications [102], blockchain adoption [36], and the privacy-personalization paradox in facial recognition systems [103].

The second-highest turning point score, 15.16, belongs to Hair et al. [97] for their study When to use and how to report the results of PLS-SEM (European Business Review). This paper has been actively cited since 2023, through the adoption of the partial least square structural equation modeling (PLS-SEM) methodology.

With a turning point score of 14.2, the third-highest ranking study is Examining individuals’ adoption of healthcare wearable devices: An Empirical study from privacy calculus perspective (International Journal of Medical Informatics) by Li et al. [43]. Cited extensively from 2017 to 2021, this study examines the factors influencing the adoption of healthcare wearable devices from a privacy calculus perspective. It empirically analyzed analyzes how information sensitivity, personal innovativeness, legislative protection, perceived prestige, perceived informativeness, and functional congruence impact perceived privacy risk, perceived benefits, adoption intention, and actual adoption.

The fourth-highest turning point score, 10.23, is attributed to Unified Theory of Acceptance and Use of Technology: A synthesis and the road ahead (Journal of the Association for Information Systems). By Venkatesh et al. [98]. Cited extensively between 2016 and 2019, and this paper synthesizes existing research on UTAUT from 2003 to 2014, conducting a theoretical analysis of the model and its extensions while outlining future research directions. The study introduces a research model investigating the impact of transformational leadership on post-adaptive use and job performance, with facilitating conditions, behavioral intentions, habits, and transactional leadership identified as influencing factors.

The fifth-highest turning point score, 9.82, is associated with Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model (International Journal of Medical Informatics) by Hoque and Sorwar [104]. Cited extensively from 2017 to 2019, this study examines the effects of performance expectancy, effort expectancy, social influence, facilitating conditions, technology anxiety, and resistance to change on behavioral intentions and the usage of mobile health services. The study employs an extended UTAUT model to assess these influences.

The works of Oliveira et al. [105], Yang et al. [106], Hair et al. [107], Marston et al. [108], and Baptista and Oliveira [109] show turning-point scores of 9 or higher. The remaining studies include those by Smith et al. [110], Kim and Shin [111], Gao et al. [112], Alalwan et al. [113], Henseler et al. [114], Sicari et al. [115], Zheng et al. [116], Johnson and Verdicchio [117], Zhao et al. [118], Rauschnabel et al. [119], Saberi et al. [120], and Hair et al. [121].

Figure 5. Burst detection analysis of privacy in technology adoption [9,30,43,95,97,98,100,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121].

Figure 5. Burst detection analysis of privacy in technology adoption [9,30,43,95,97,98,100,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121].

4.4. Framework for Research

Based on the findings, a research framework has been developed to illustrate the key results and their interconnections, providing a foundation for future studies on privacy in technology adoption. The framework (Figure 6) highlights how individuals’ privacy concerns influence their intention to adopt information technology.

Major theories on privacy concerns in the adoption of new information technologies include the concern for information privacy (CFIP), the privacy-trust-intention model, the privacy calculus model, the technology acceptance model (TAM), and UTAUT/UTAUT2. The CFIP framework consists of four key dimensions: data collection, improper access, errors, and secondary use. The privacy-trust-intention model examines the relationship between privacy, trust, and intention, while the privacy calculus model evaluates the trade-off between privacy risks and benefits in shaping user intentions. Specifically, in the privacy calculus model, users are more likely to disclose personal information when they perceive greater benefits, but concerns over third-party access can deter disclosure. The TAM framework encompasses perceived ease of use, perceived usefulness, and user attitudes toward technology adoption. The UTAUT extends this by incorporating performance expectancy, effort expectancy, social influence, and facilitating conditions. UTAUT2 further refines the model by adding hedonistic motivation, price value, and habit as additional influencing factors. Beyond these models, other theories addressing privacy concerns in technology adoption include innovation diffusion theory (IDT), the privacy paradox, the theory of planned behavior (TPB), social cognitive theory, and the expectation-confirmation model. Various antecedents influence privacy concerns, such as prior privacy and IT experience, anxiety, self-efficacy, morality, privacy control, personality traits, subjective and social norms, privacy awareness, perceived vulnerability, personalization, privacy policies, and regulatory frameworks.

5. Discussions

This study systematically examines research on privacy in technology adoption to identify key trends and topics. To achieve this, a bibliometric analysis was conducted using research papers from the Web of Science database. This dataset encompasses a wide range of interdisciplinary studies, ensuring a comprehensive overview of the evolving landscape of privacy research within technology adoption. A total of 2910 papers published between 2005 and 2025 (as of February 2) were analyzed, with cluster analysis, timeline analysis, and research transition point detection performed using CiteSpace for visualization. The use of CiteSpace allows for robust visualization and identification of intellectual structures, enabling a nuanced understanding of research dynamics and emerging themes. The key findings are as follows:

First, the intellectual structure of privacy research in technology adoption over the past two decades can be classified into 15 clusters, showing how this field has evolved. Earlier studies focused on Internet banking, location-based services, ubiquitous commerce, and personal health information exchange, whereas recent research increasingly examines emerging technologies such as the metaverse, facial recognition, AI, and blockchain. The largest cluster remains health information exchange, followed by blockchain adoption, AI, and Internet banking. These trends indicate a shift from traditional online services to complex, immersive digital environments, highlighting growing privacy concerns in emerging technologies. Overall, this provides a clear answer to RQ1, showing both historical and current research directions.

Second, the timeline analysis reveals that active research clusters include blockchain, artificial intelligence, the metaverse, and facial recognition payment, whereas clusters such as ubiquitous commerce, Internet banking, and location-based services have shown stagnation since the early 2000s. This finding addresses RQ2 by identifying which technology areas have maintained or lost research momentum over time.

Next, the burst detection analysis highlights key turning points in privacy research. The most significant turning point corresponds to “Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology” by Venkatesh et al. [100]. The UTAUT2 model, an extension of the original UTAUT theory, has been extensively cited in privacy research on technology adoption. UTAUT2 incorporates additional variables, including hedonic motivation, price value, and habit, alongside performance expectancy, effort expectancy, social influence, and facilitating condition from the original UTAUT model. Several highly cited papers in privacy research on technology adoption using the UTAUT framework “Unified Theory of Acceptance and Use of Technology: A synthesis and the road ahead” by Venkatesh et al. [98] and “Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model” by Hoque and Sorwar [104]. Additionally, the PLS-SEM methodology is frequently used in this research domain, with studies such as “When to use and how to report the results of PLS-SEM” by Hair et al. [97] and “Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective” by Li et al. [43] being widely referenced. The burst detection analysis identifies the most influential studies in privacy research on technology adoption, with Venkatesh et al.’s work on UTAUT/UTAUT2 as a key turning point. This finding addresses RQ3 by identifying the studies that have significantly influenced research trends and guided empirical investigations.

Finally, this study proposes a framework outlining key findings and their interconnections, offering a foundation for future research on privacy in technology adoption. Theoretical models commonly used in privacy research include CFIP, privacy-trust-intention model, the privacy calculus model, the privacy paradox, the TAM by Davis et al. [122], and UTAUT/UTAUT2 by Venkatesh et al. [100,101]. Additional frameworks such as IDT, TPB, social cognitive theory, and expectation-confirmation model are frequently applied. Antecedents influencing privacy concerns include privacy experience, anxiety, self-efficacy, privacy control, morality, personality traits, subjective norms, and privacy awareness, among others. By synthesizing these findings, this study addresses RQ4 by highlighting emerging topics and offering directions for future research, such as investigating privacy in AI-driven, metaverse-based, and blockchain-enabled technologies.

6. Concluding Remarks

This study offers several theoretical contributions to the field of privacy research in technology adoption. First, it provides a comprehensive analysis and visualization of the intellectual structure of the field over the past two decades. By mapping core research topics, the study highlights key areas of scholarly interest and influential contributions. The cluster analysis reveals major thematic areas and identifies the most impactful works within each, while the timeline analysis distinguishes between emerging trends and declining research topics. Notably, privacy research in areas such as blockchain, AI, metaverse, and facial recognition-based payment systems has gained increasing scholarly attention in recent years.

Second, the burst detection analysis identifies seminal works that serve as foundational references, marking turning points in the development of the field. This curated set of pivotal literature provides researchers—especially early-career scholars—with essential guidance in navigating the complex landscape of privacy studies.

Third, by examining how privacy-related variables interact with key constructs from technology adoption models (e.g., perceived usefulness, perceived risk, trust), the study offers theoretical extensions to frameworks such as TAM and UTAUT, thus bridging privacy research with established adoption theories.

Finally, this study emphasizes the interdisciplinary nature of privacy research by integrating findings from fields such as information systems, communication, psychology, law, and management. This highlights the need for a more holistic and collaborative approach in future theoretical development.

This study also provides valuable practical insights for organizations operating within the rapidly changing digital environment. As digital technologies such as AI, IoT, and blockchain continue to evolve, organizations face increasing challenges in complying with privacy regulations. The growing complexity of personal information protection necessitates proactive measures by companies to anticipate and address these concerns. In particular, as personal data is increasingly collected and stored in centralized locations—via cloud computing and IoT—risks of hacking and data breaches are rising. Organizations must identify potential security vulnerabilities and implement appropriate privacy policies and security measures. Specifically, companies developing technologies such as blockchain, AI, metaverse, and facial recognition-based payment systems must establish robust privacy risk management models. Integrating privacy-by-design principles and adopting security frameworks will help mitigate privacy risks and enhance consumer trust in these technologies. In conclusion, embedding data ethics and privacy consciousness at every level of the organization will bolster resilience and drive long-term innovation centered on privacy. Additionally, understanding emerging privacy concerns in technologies such as AI, blockchain, and the metaverse can guide public policy, helping regulators design effective privacy protections and prioritize areas requiring oversight. These insights can also inform digital education, providing a basis for curricula that enhance data literacy, privacy awareness, and ethical technology use.

However, this study has certain limitations. First, the analysis relied primarily on online database searches, which may have excluded relevant studies from conference proceedings or unpublished works. Second, this study primarily focuses on privacy concerns in technology adoption while overlooking other influential factors, such as individual, technological, policy, and cultural differences. These contextual variables may significantly shape user attitudes toward privacy and technology adoption, highlighting the need for further research to develop a more holistic understanding of privacy behavior in digital environments. While this study focuses on privacy research trends and theoretical frameworks, future research should examine international issues, cross-national practices, and global governance models to provide a more comprehensive understanding of privacy management across borders.