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Article

Revealing the Intellectual Structure and Evolution of Digital Addiction Research: An Integrated Bibliometric and Science Mapping Approach

by
Turgut Karakose
1,
Tijen Tülübaş
1 and
Stamatios Papadakis
2,*
1
Faculty of Education, Kutahya Dumlupinar University, 43100 Kutahya, Turkey
2
Department of Education, University of Crete, 74100 Rethymno, Greece
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(22), 14883; https://doi.org/10.3390/ijerph192214883
Submission received: 17 October 2022 / Revised: 9 November 2022 / Accepted: 9 November 2022 / Published: 12 November 2022
(This article belongs to the Section Mental Health)
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Abstract

:
The current study uses “digital addiction” as an umbrella term to refer to addiction to the Internet, social media, smartphones, digital devices, video games, or similar digital technologies. The study aims to investigate the scientific landscape of the digital addiction research field through combining bibliometric and science mapping analysis and to facilitate insight into the development and evolution of this knowledge base. Data for the analysis were extracted from the Scopus database, which covers a wide range of journal articles, with 429 articles addressing digital addiction included in the analysis. The science mapping analysis was performed over three consecutive time periods using SciMAT software to observe the thematic evolution. The results indicated that addictive behavior was the most significant theme across the three time periods. Cross-sectional studies addressing the risk factors or outcomes of addiction among adults and different sexes were prevalent during the first period (1997–2012), while interest moved to the addictive behavior of adolescents and students during the second period (2013–2017). Research during the third period (2018–2022) was driven by smartphone and social media addiction, and resilience as a preventive factor garnered more research interest than previously studied risk factors, which may indicate a perspective change by researchers. Implications are suggested for future investigations of digital addiction.

1. Introduction

Recent decades have witnessed a breakthrough in communication and information technologies, and many types of digital media (i.e., mediums of broadcasting any digitized information through a screen or/and a speaker for the purposes of communication, entertainment, or dissemination of information) have become an indispensable part of everyday life [1]. The adoption of digital technologies—namely the electronic tools; devices; systems; and resources used to generate, store, or process data such as the Internet and smartphones—have influenced people and societies in significant ways [2]. This process of digitalization is considered to be as revolutionary as the invention of the wheel, the steam engine, or even electricity [3], which are all examples of things once believed to be impossible having before they became a reality in life [4]. Yet, despite offering a wide range of possibilities and significant convenience to almost every aspect of modern-day life, there is a growing consensus that digital technologies have also created certain serious challenges and adverse effects on human relations, as well as both mental and physical health [5,6]. It is increasingly considered that usage of smartphones, social media, video games, and other such innovations can be as addictive as tobacco smoking, taking nonprescribed drugs or illicit substances, or gambling [7,8]. Some even claim that digital media is a powerful “new drug” that presents a high risk of addiction [9]. Indeed, as Meng et al. [10] recently stated, the global prevalence of smartphone addiction has reached nearly 27%, whilst Internet addiction has reached almost 15%, and game addiction is at 6%, indicating the potential addictive nature of using digital technologies.

1.1. Digital Addiction

Behavioral addictions due to the overuse of digital technologies have become of serious concern to both scholars and practitioners since the early 1990s, although the phenomenon was then recognized only as Internet addiction but covered addiction to any kind of online activity [11]. However, with the introduction of various digital devices (e.g., smartphone, tablet personal computers, and gaming consoles) and affiliated digital activities (e.g., social media, video games, and cybergames), the scope of such addictions has consolidated into a much broader term—“digital addiction” [8,12].
Digital addiction is an overarching term that refers to the compulsive, prolonged, and uncontrollable use of any digital device or media with deleterious effects on the psychological or physical health of users [10,13] and thus incorporates addiction to the Internet, social media, smartphones, video games, or any other related digital technology devices [6,8]. Digital addiction does not necessarily entail the use of the Internet, since digital devices can also be used in an addictive manner without requiring an Internet connection (e.g., addiction to offline gaming, excessive use of offline mobile apps, etc.) [12,14]. However, the Internet, as a channel used to access digital material, can facilitate addiction through enabling users to instantly access whatever content they want from virtually any place and at any time [9].
Although digital addiction is a relatively new term in the scientific world, scholars have defined its core features based on those of addictive behavior. Some scholars contend that digital addiction occurs through habit formation and self-control problems, just as seen in other behavioral addictions such as gambling or substance abuse [5,15]. The combination of these two forces often results in preoccupation with the digital media/device, repeated unsuccessful attempts to stop or reduce usage, greater utilization than expected or warranted, deleterious effects on school or work life, damage caused to relationships or social life, and even lying about the level of actual usage, all of which characterize typical addictive behaviors [9,16]. In other words, digital addiction “suggest[s] an increasing trend of compulsive behavior amongst users of technological devices, recognizing that over-exposure to and over-use of technology can result in a dependence on digital devices, leading to behavioral symptoms similar to any addictive disorder” [4] (p. 18). However, in the case of digital addiction, the instant availability of the content being craved through the digital medium, without necessarily requiring any intermediary dealer, makes it an even more complex syndrome [9].
Digital addiction has now become a global concern, as research increasingly reveals its serious consequences on both the physical and psychological well-being of users, with impairments seen in social life (e.g., social isolation, neglect of social activities, and social conflict) [17]; emotional impairment (e.g., anxiety, depression, and restlessness) [18,19]; physical impairment (e.g., poor eyesight, backache, and weight gain/loss) [20,21]; cognitive disorders (e.g., poor concentration and changes in brain function) [22,23]; and decreased performance and productivity at school or work [24,25]. Thus, digital addiction has attracted significant interest from both scholars and practitioners across a variety of fields such as psychology, sociology, neuroscience, and education.

1.2. The Purpose of the Study

The influence of technological revolution and digitalization on people and society have urged systematic studies to develop a broader conceptualization of digital addiction and to provide empirical evidence to support its detection, prevention, and treatment [10]. Incremental interest in digital addiction has aggregated a significant collection of intellectual contributions to this research field during the last two decades [11]. Nonetheless, to the researchers’ knowledge, the intellectual structure and the thematic evolution of this field have yet to be investigated. In order to address this gap in the literature, the current study aims to investigate the scientific landscape of digital addiction as a research field through combining bibliometric and science mapping analysis and facilitate insight into the development and evolution of this knowledge base. The study particularly aims to reveal (1) the volume and growth trajectory of scholarships on digital addiction, (2) journals and authors that have evidenced the greatest citation impact on digital addiction, (3) the intellectual structure and evolution of the digital addiction knowledge base, and (4) the themes that have attracted the most attention on digital addiction.

2. Materials and Methods

2.1. Study Design

The current study combines the bibliometric performance analysis and scientific mapping methods to determine the bibliometric performance of the digital addiction research field, to reveal its dynamic and structural features, and to delineate its cognitive and conceptual architecture [26,27].

2.2. Data Search and Identification

Bibliometric studies often use online databases such as Web of Science (WoS), PubMed, and Scopus as data sources. Scopus is defined as one of the optimum databases used for bibliometrics [28], since it lists more journals than WoS and covers almost all publications listed in the WoS database [29]. This helps to reduce the risk of missing documents published that would be valuable to the analysis. Therefore, data for the current study were searched for and extracted from the Scopus database. Data collection and analysis were conducted following a three-step procedure: (1) searching and defining data, (2) extracting and cleaning data, and (3) analyzing data [30]. The selection process of the 429 articles included in the final analysis is reported according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidance [31], as illustrated in Figure 1.
The following inclusion/exclusion criteria in Table 1 were applied to select the data for analysis.
The following keyword string was used to perform a keyword search on the Scopus database on 9 July 2022:
TITLE (“digital addiction” OR “internet addiction” OR “social media addiction” OR “smartphone addiction” OR “mobile phone addiction” OR “digital media addiction” OR “game addiction” OR “virtual addiction” OR “technology addiction” OR “computer game addiction” OR “gaming addiction” OR “mobile addiction” OR “online shopping addiction” OR “cybersex addiction” OR “gadget addiction” OR “mobile apps addiction” OR “sms addiction” OR “selfie addiction”)
The keywords were selected after an in-depth review of the relevant literature and the approval of three field experts. A total of 3270 documents were listed after the initial search. During the data selection, all three authors of the manuscript individually scanned through the titles and keywords of these 3270 articles yielded during the first cycle of search. Next, they skimmed through the abstracts of selected 659 articles. Finally, they organized a peer-debriefing to discuss over which articles to include in or exclude from the analysis and agreed upon 565 articles. The abstracts of the remaining 565 articles were read, and 136 were excluded, because they were incorrectly populated and/or were considered to be outside the scope. The authors also conferred with two other scholars when they could not reach an agreement. In the end, a total of 429 research articles were selected to be included in the study.

2.3. Data Extraction and Analysis

The bibliometric data of each article selected for inclusion in the analysis (title, author names, keywords, abstract, citations, publication date, and journal name, plus other information such as country of origin) were transferred to SciMAT (Science Mapping Analysis Tool) software-version 1.1.04. Using SciMAT, keywords found to be representative of the same concept (e.g., adolescent and adolescents and self control and self-control) were manually grouped in order to increase the quality of the thematic analysis [32,33].
First, a bibliometric performance analysis was performed to determine the temporal distribution of related articles, the accumulated number of articles, the average number of citations per article, the most cited articles, the most cited authors, and the most influential journals and countries [34]. Then, the SciMAT software tool was used to perform a science mapping analysis [32], which is a useful method to determine the conceptual structure, evolution, and research trajectory of a particular research field and delineates the structural and dynamic aspects of scientific research in that particular field of study [26,35]. SciMAT is considered a particularly powerful tool, since it allows for the analysis and monitoring of a research field’s evolution over sequential time periods [32,36,37,38]. In the current study, four steps were followed to conduct a science mapping analysis using SciMAT software [26,32,35,39,40,41]: (i) identification of digital addiction research themes, (ii) visualization of research themes and thematic network, (iii) identification of thematic areas, and (iv) performance analysis.
(i) Identification of digital addiction research themes: A standardized network of co-occurring keywords was formed based on the keywords extracted from the retrieved articles. Next, in order to identify the themes of the published research, a clustering algorithm was applied to the normalized network of cooccurring keywords, with closely related keywords making up each cluster or theme. Thus, the conceptual subfields of digital addiction as a research field were identified, and their thematic evolution was revealed.
(ii) Visualization of research themes and thematic network: A graphical representation of the identified research themes was created using two different tools, strategic diagram and thematic network. The current study employed a clustering algorithm to identify and illustrate the research themes. The themes emerged from the cluster-based analysis were presented in a four-quadrant, two-dimensional strategic diagram based on the density (y-axis) and centrality (x-axis) values. Density represents the capacity of themes in a research field to persist and develop over time. In addition, density relates to the internal relations of the themes. Themes with increased relationships among themselves shift upwards towards the top of the strategic diagram. Density is mathematically formulated as “d = 100 (Σeij/w)”, where “i” and “j” represent the keywords of the theme, and “w” represents the number of keywords in the theme. Centrality measures the degree to which a cluster interacts with other clusters or the strength of its relationship. In other words, centrality relates to the external relations of a theme. Therefore, as the relationship of a theme with other themes increases, the themes shift to the right-hand side of the strategic diagram. Centrality is mathematically formulated as “c = 10 × Σekh”, where “k” represents a keyword belonging to any one theme, and “h” represents a keyword belonging to another theme. Centrality reveals that a cluster or network is an important crossing point and has therefore a critical role in highlighting and helping to understand the relationship between themes. Density, on the other hand, measures the internal strength of the relationship—that is, the strength of the relationship between keywords within a theme. Centrality and density values enable a research field to be represented in a strategic biaxial diagram incorporating four different categories.
Accordingly, in the current study, the research themes revealed through conceptual science mapping analysis based on a network of cooccurring keywords are presented in a strategic diagram (see Figure 2a) and is comprised of four groupings [32]: (a) motor themes (Q1): high centrality and density (themes related to the development and structuring of the research field, i.e., they show high progress and are the most significant themes for that field of research; (b) basic and transversal themes (Q2): high centrality and low density (themes not that well-developed but related to the research field but still tend to be motor themes due to their high centrality); (c) emerging or declining themes (Q3): low centrality and density (themes emerging or already disappeared, which can be determined through an in-depth qualitative analysis); and (d) highly developed and isolated themes (Q4): low centrality and high density (despite being highly specialized and environmental themes, they may be no longer deemed important or lack the appropriate background due to newly emerged concepts in the field).
The thematic network structure (see Figure 2b) was used to present how strategic themes emerge alongside other subthemes related to the research field, with each thematic network tagged according to the most important (most central) keyword in the associated theme. The keywords are interconnected, with the volume of the spheres being proportional to the number of articles corresponding to each keyword. The size of the circles in the thematic networks correspond to the number of articles, whilst the thickness of the lines shows the strength of their relationship.
The evolution of research themes, time periods, origins, and interrelationships developed over different time periods are presented in the thematic evolution map (see Figure 2c). A theme may belong to a different thematic area or may not be a continuation of any other theme depending on their interrelationship. Solid lines on the thematic map indicate that the same keywords as the theme names are shared between the themes, whilst the dashed lines indicate that common words are shared apart from the theme labels. The thickness of the lines varies according to the degree of the relationship, whilst the size of the circles corresponds to the number of articles.
(iii) Identification of thematic areas: The data were divided into three consecutive time periods so as to analyze the evolution of the digital addiction research field. The conceptual links between themes from the different time periods were explored using an inclusion index, which is based on the equation: Ii = #(U ∩ V)/min(#U, #V) [42,43]. The thematic evolution map was created through forming conceptual links (common keywords) from the U theme to the V theme. A thematic connection between the U theme and the V theme shows that both themes have common elements, as well as reflecting their evolution over time. As the number of common keywords between periodical clusters increases, their evolution becomes more evident.
(iv) Performance analysis: The number of articles, total citations, and different h-index values [44,45] were used as bibliometric indicators in order to measure the contribution (scientific impact) of each thematic area that emerged from the analysis to the whole digital addiction research field.
An analysis made over different time periods is suggested to save the data from uniformity and is used to obtain comparative results [33]. Hence, the articles selected for analysis were evaluated over three consecutive time periods that were formed based on the number of articles published over the whole period of analysis (1997–2022). Period 1 was comprised of the years 1997–2012 due to the lower number of articles published during the earlier years, whilst Period 2 was comprised of the years 2013–2017, and Period 3 was comprised of the years 2018–2022.

3. Results

3.1. Overall Bibliometric Analysis

A performance-based bibliometric analysis was first conducted in order to evaluate the digital addiction research field. Using the bibliometric indicators, the distributions of the articles according to their year of publication, the accumulated number of articles, numbers of citations per article, most productive/cited authors, most productive/cited journals, and most productive countries were determined [45].

3.1.1. Publication Trends

The distribution of the 429 articles included in the current study by year of publication, the accumulated number of articles, and a graphical representation of the average number of citations per article are presented in Figure 3. The red line illustrates the yearly citation rates, whilst the grey line refers to the number of publications accumulated per year, and the green columns show the annual distribution of the articles.
As can be seen in Figure 3, research interest in the digital addiction research field increased incrementally from 1997 to 2022, and in general, the number of articles increased during the last years. As can be clearly seen, the number of articles published on digital addiction peaked during 2020 and 2021, which may indicate scholars’ interest in the prevailing influence of increased exposure to digital media during the COVID-19 pandemic. The growth of research into digital addiction also has parallels with the advancement and accelerating prevalence of digital technologies in the current age.

3.1.2. Most Influential Authors

The top 10 most productive research scientists who contributed to the digital addiction literature are listed in Table 2 based on the total number of citations received by their published articles. Within the scope of the 429 articles analyzed, the number of authors that contributed to the articles totaled 1177, with some authors also having been involved in more than one of the published studies.
As can be seen in Table 2, Young, one of the first authors to address Internet addiction and who devised the first questionnaire to measure the phenomenon in 1998, contributed to the field with the highest citation rate (n = 3219). Ko followed Young with the highest number of articles published (n = 7) and second-highest number of citations (n = 1072).

3.1.3. Most Influential Journals

The top 10 journals that published the highest number of articles on digital addiction between 1997 and 2022 are listed in Table 3, based on the total number of articles published.
As shown in Table 3, many of the articles on digital addiction were published in the Cyberpsychology, Behavior, and Social Networking journal; previously known as Cyberpsychology and Behavior, which emerged as one of the most influential journals in the field. When the publications of these two journal names are considered cumulatively, they represent the highest number of articles published in the digital addiction research field, followed by Children and Youth Services and Turkish Online Journal of Educational Technology with 25 and 10 publications, respectively.

3.1.4. Most Cited Articles

The top 10 articles that received the highest number of citations among the 429 articles included in the analysis are listed in Table 4.
As can be seen in Table 4, the most influential article published within the digital addiction field of research was one of the first publications in the field with a focus on Internet addiction. The second article with the highest number of citations, on the other hand, presented a new scale on gaming addiction, which is widely used to measure levels of gaming addiction. It is also notable, however, that the articles published between 2010 and 2022 have, to date, received much less interest compared to those published prior to 2010.

3.1.5. Most Productive Countries

The 10 most productive countries that contributed to the digital addiction research field are listed in Table 5.
As can be seen from Table 5, Turkey emerged as the country having published the most articles on digital addiction. As previously mentioned, the Turkish Online Journal of Educational Technology was among the top three most productive journals on digital addiction, which correlates to Turkey’s leading position in Table 5. After Turkey, China, the United States, and South Korea are shown to hold pioneering positions in the advancement of digital technologies. It is also noteworthy that Table 5 presents a global view of the interest and contribution to the digital addiction field of research.

3.2. Science Mapping and Performance Analysis

In this section, the articles obtained from the Scopus database were analyzed using SciMAT software, and the findings are reported as: (i) period-based thematic analyses, (ii) overlapping graph analyses, and (iii) evolution map analyses.

3.2.1. Scientific Evolution Structure

Period 1 (1997–2012)

Nine themes emerged from the analysis of the 70 articles reviewed in the scope of Period 1. The strategic diagram and the performance measures for the themes that emerged during the first period are presented in Figure 4.
A total of nine main themes emerged during the first period (1997–2012). Addictive Behavior, Male, Questionnaire, and Adult were found to be the motor themes, which contributed the most to the development of the digital addiction research field. The Motivation theme emerged as a highly advanced and isolated theme, which is defined as having a strong basis on its own but not providing an appropriate or significant background for this field of research. The Internet Addiction, Adolescent Behavior, and Child themes were shown to be among the emerging/declining themes, which may have emerged during the period yet remained as weak or disappearing themes. The Sex Difference theme was included as a basic and transversal theme, which means that, despite being related to the research field being studied, it had not developed sufficiently during the time period. The theme shown to be of the most significant importance during Period 1 was Addictive Behavior, which was a motor theme represented by 40 published articles.
Cluster networks were examined (see Figure 5) in order to determine the related subthemes that emerged during the first period (1997–2012). Accordingly, it was determined that the main theme of Addictive Behavior (1, 1) was strongly related to the subthemes of High School Student, Human, Adolescent, Anxiety Disorder, College Students, Computers, Cross-Sectional Studies, and Emotions. Strong relations were also observed between these subthemes as well. To illustrate, studies on High School Student [46], Human [47], Adolescent [48], Anxiety Disorder [49], College Students [50], Computers [51], Cross-Sectional Studies [52], and Emotions [53] support the current study’s findings with regards to the subthemes shown in the Addictive Behavior cluster network.
The analysis also revealed that the main theme of Male (0.89, 0.89) had strong associations with the subthemes of Female, Depression, Reference Values, Risk Factors, School, Sex Factors, Universities, and Video Games. Existing studies on Female [54], Depression [55], Reference Values [56], Risk Factors [57], School [58], Sex Factors [59], Universities [49], and Video Games [60] illustrate the current study’s findings with regards to the Male cluster network.
The main theme of Questionnaire (0.78, 0.78) was found to have associations with Self Control, Social Networking, Parents, Social Network, Social Media Addiction, Cultural Factor, Happiness, and Satisfaction subthemes. Studies on Self-Control [61], Social Networking [62], Parents [46], Social Network [63], Cultural Factor [61], Happiness [64], and Satisfaction [65] illustrate these results with regards to the Questionnaire cluster network.
The analysis revealed that the main theme of Adult (0.56, 0.56) was associated with the subthemes of Psychological Stress, Anxiety, Young Adult, Aggression, Impulse Behavior, Impulsiveness, Culture, and Interpersonal Communication. To illustrate these results, studies on Psychological Stress [54], Anxiety [49], Young Adult [66], Aggression [62], Impulsive Behavior [67], and Culture [68] can be listed as examples.

Period 2 (2013–2017)

Nine themes emerged from the analysis of the 94 articles reviewed within the scope of Period 2. The strategic diagram and the performance measures for the themes that emerged during the second period are presented in Figure 6.
The most significant theme that emerged during the second period (2013–2017) was Addictive Behavior, represented by 25 published articles. During this period, the themes were gathered in only two regions of the strategic diagram. The Sex Factors, Addictive Behavior, Psychopathology, Adolescent, and Students themes emerged as motor themes. However, the themes of Smartphone Addiction, Internet Addiction, Game Addiction, and Teenagers were among those that were emerging or declining during the second period.
The cluster networks (see Figure 7) were examined in order to determine the subthemes associated with the motor themes that emerged during the second period (2013–2017). Accordingly, the main theme of Sex Factor (1, 1) was found to be associated with the subthemes of Socioeconomic Factors, Young Adult, Social Media Addiction, Male, Female, Adult, and Sex Difference. Studies on Socioeconomic Factors [69], Young Adult [70], Social Media Addiction [71], Male [72], Female [73], Adult [74], and Sex Difference [69] are representative of some of the published articles that illustrate these findings with regards to the Sex Factor cluster network.
The main theme of Addictive Behavior (0.89, 0.78) was determined to have strong associations with the Risk Factor, School, Anxiety Disorder, Cross-Sectional Studies, Emotions, Human, Medical Students, and Questionnaire subthemes. To illustrate these results, the studies on Risk Factor [75], School [76], Anxiety Disorder [77], Cross-Sectional Studies [78], Emotions [59], Human [79], Medical-Students [78], and Questionnaire [71] can be listed.
The main theme of Psychopathology was found to be strongly associated with the Social Adaptation, Social Skill, Psychology, Personality, Aggression, Impulsive Behavior, Mental Disease, and Impulsiveness subthemes. These studies on Social Adaptation [62], Social Skill [80], Psychology [74], Personality [79], Aggression [77], Impulsive Behavior [72], Mental Disease [74], and Impulsiveness [81] can be given as illustrative of the findings with regards to the Psychopathology cluster network.
The Students (0.67, 0.67) main theme was found to be associated with the subthemes of Smartphone Use, Interpersonal Communication, Online System, Surveys, Universities, Child–Parent Relation, Education, and Parent–Child Relations. To illustrate these results with regards to the Students cluster network, these studies on Smartphone Use [82], Interpersonal Communication, Online System [83], Surveys [72], Universities [84], Child–Parent [79], Education [85], and Parent–Child Relations [70] may be listed.
The Adolescent (0.56, 0.56) main theme was determined to be associated with the subthemes of Culture, Mental Disorders, Video Games, Adolescent Behavior, Reproducibility, Social Behavior, Child Psychology, and Cultural Factor. These studies on Culture [86], Mental Disorders [87], Video Games [73], Adolescent Behavior [88], Reproducibility [86], Social Behavior [86], Child Psychology [87], and Cultural Factor [73] are some that illustrate these results with regards to the Adolescent cluster network.

Period 3 (2018–2022)

A total of 12 themes emerged from the analysis of the 265 published articles reviewed within the scope of Period 3. The strategic diagram and the performance values for the themes that emerged during the third period are presented in Figure 8.
Among the 12 main themes that emerged during the third period covering the years between 2018 and 2022, the Emotions theme was the most significant, represented by 45 published articles. The themes that emerged during the third period were observed to gather in only two regions of the strategic diagram. The Emotions, Male, Social Media Addiction, Addictive Behavior, Smartphone Addiction, Adolescent, and Resilience themes were found to be the motor themes, whilst Social Networking (Online), Loneliness, Young Adult, Digital Addiction, and Psychological Well-Being were among the emerging/declining themes during the third period.
Cluster networks (see Figure 9) of the motor themes that emerged during the third period (2018–2022) were examined in order to determine their associated subthemes. The main theme of Emotions (1, 1) was found to have a strong association with the Internet Addiction Disorder, Stress, Human, Students, Internet Addiction, Female, Internet, and Psychology subthemes. Studies on Internet Addiction Disorder [63], Stress [89], Human [90], Students [91], Internet Addiction [92], Female [93], Internet [94], and Psychology [95] are some of the articles published that support these findings with regards to the Emotions cluster network.
The Male (0.92, 0.83) theme, on the other hand, was found to be associated with the subthemes of Socioeconomic Factor, Socioeconomics, Adolescent Behavior, Sex Difference, Impulsive Behavior, and Impulsiveness. These studies on Socioeconomic Factor [96], Socioeconomics [97], Adolescent Behavior [98], Sex Difference [99], and Impulsive Behavior [97] illustrate these findings with regards to the Male cluster network.
The main theme of Social Media Addiction (0.83, 0.75) was found to be associated with an Internet Gaming Disorder, Mental Stress, Universities, Adult, Child, Child–Parent Relation, Psychological Stress, and Gaming Addiction subthemes. These studies on Internet Gaming Disorder [95], Mental Stress [100], Universities [101], Adult [102], Child [103], Child–Parent Relation [104], Psychological Stress [105], and Gaming Addiction [106] illustrate these findings with regards to the Social Media Addiction cluster network.
The main theme of Addictive Behavior (0.75, 0.58) was determined to be associated with the subthemes of Personality Inventory, Cultural Factor, Anxiety Disorder, Sex Factors, Personality, Reproducibility, Interpersonal Relations, and Child Psychology themes. These studies on Personality Inventory [107], Cultural Factor [102], Anxiety Disorder [108], Sex Factors [102], Personality [95], Reproducibility [109], Interpersonal Relations [110], and Child Psychology [98] support the findings with regards to the Addictive Behavior cluster network.
The main theme of Smartphone Addiction (0.67, 0.67) was determined to have associations with the Nursing Students, Mobile Phone, Surveys, Smartphone, Nomophobia, Academic Self-Efficacy, and Satisfaction subthemes. These studies on Nursing Students [111], Mobile Phone [112], Surveys [113], Smartphone [114], Nomophobia [115], Academic Self-Efficacy [116], and Satisfaction [117] support the findings with regards to the Smartphone Addiction cluster network.
The main theme of Resilience (0.5, 0.92) was found to be associated with the subthemes of Psychological Resilience, Anxiety, Cross-Sectional Study, Depression, Game Addiction, Life Satisfaction, Academic Achievement, and Children. Studies on Psychological Resilience [118], Anxiety [119], Cross-Sectional Study [120], Depression [121], Game Addiction [122], Life Satisfaction [123], Academic Achievement [124], and Children [125] support these findings with regards to the Resilience cluster network.

3.2.2. Overlapping Map

An overlapping-items graph shows the number of keywords used within each period, as well as those that were newly appeared, lost, or reused in the subsequent period [126]. When the overlapping map presented in Figure 10a is scrutinized, it can be seen that a total of 87 keywords emerged during the first period of analysis (1997–2012) and that 20 of these keywords were not used during the second period, whilst 67 of them were. During the second period of analysis (2013–2017), on the other hand, a total of 91 keywords emerged, 82 of which were used during the subsequent third period, whilst nine of them were not. The third period (2018–2022) showed a total of 117 keywords having been used, with 24 having been used for the first time during the second period and 35 emerging during the third period. In addition, it was determined that the similarity index remained at a similar level (from 0.6 to 0.65) between the time periods.
The overlapping-items graph (see Figure 10a) revealed that the terminology related to digital addiction has been increasingly consolidated with each passing year and that new keywords have been introduced to the field. Viewed from left to right, the number of keywords shown in Figure 10a increased from 87 during the first period (left) to 117 during the third period (right). This significant increase in the number of keywords used reveals that studies on digital addiction became more diversified over time as the keywords used increased cumulatively. The increase in the number of keywords added during each period indicate that studies on digital addiction were constantly developing, whilst the reserved words indicate that the terms used in this research field were being constantly updated.

3.2.3. Thematic Evolution Structure

The thematic evolution map (see Figure 10b) illustrates the pattern of development in knowledge domains and the relationship between digital addiction-focused research topics over the three periods of analysis. The size of the spheres shown on the map relate to the number of published articles, whilst the thickness of the lines connecting the spheres relate to the correlation between the themes emerged during the analysis periods [32,41].
As shown in the thematic evolution map, nine themes emerged during the first period (1997–2012), which constituted 16.32% of the articles included in the analysis. Six of these themes survived during other periods, and one disappeared without making any further connection. The Addictive Behavior theme that emerged during the first period continued its existence across all three periods and also connected with the Adolescent, Internet Addiction, and Game Addiction themes in the second period. The Questionnaire theme was exchanged with the Addictive Behavior, Sex Factor, and Internet Addiction themes during the second period. The Internet Addiction theme survived during the second period and also connected with the Student and Smartphone themes. The Male theme was exchanged with the Sex Factor theme during the second period and continued as the Male theme during the third period. The Adult theme was observed to evolve into the Sex Factor and Psychopathology themes, the Child theme evolved to become the Adolescent and Sex Factor themes, and the Sex Difference theme to the Sex Factor, Students, and Teenagers themes. The Adolescent Behavior theme was exchanged with Adolescent, Students, and Smartphone Addiction themes, whilst the Motivation theme was exchanged with the Students, Smartphone Addiction, and Game Addiction themes.
During the second period (2013–2017), which constituted 21.91% of the analyzed articles, nine themes emerged. Whilst two of these themes were from the first period, seven of them appeared for the first time during the second period. All of the themes that emerged during the second period were connected with those from either the first or third periods. The Adolescent theme from the second period was shown to have connections with the Addictive Behavior, Male, and Adolescent themes, and the Adolescent theme also continued to exist during the third period. The Sex Factor theme was exchanged with the Male, Social Media Addiction, and Young Adult themes. The Student theme was exchanged with the Emotions, Social Media Addiction, Adolescent, Smartphone Addiction, Young Adult, and Loneliness themes. The Internet Addiction theme evolved into the Emotions, Adolescent, Social Networking (Online), Loneliness, and Psychological Well-Being themes, while the Psychopathology theme evolved into the Addictive Behavior, Emotions, and Male themes. The Smartphone Addiction theme continued to exist in the third period and was also connected with the Social Networking (Online) and Resilience themes. The Game Addiction theme was exchanged with the Resilience theme during the third period, whilst the Teenagers theme was exchanged with the Young Adult, Loneliness, and Digital Addiction themes during the third period.
The third period (2015–2021) was comprised of 61.77% of the analyzed articles, and 12 themes emerged during this period. Among these themes, Addictive Behavior and Smartphone Addiction were transferred from the previous (second) period, whilst 10 new themes emerged during the third period. The themes that emerged for the first time during the third period were Emotions, Male, Social Media Addiction, Adolescent, Resilience, Social Networking (Online), Young Adult, Loneliness, Digital Addiction, and Psychological Well-Being. All the themes that emerged during the third period were observed to be connected with themes from the second period.

4. Discussion

The current study investigated the conceptual architecture and intellectual evolution of the digital addiction research field. Using the term digital addiction as an umbrella term incorporating addiction to any type of digital media such as computers, smartphones, video games, and movies, regardless of whether or not an Internet connection is required, the study yielded results that were not only representative of this broader scope of digital addiction as a research field but also delineated the evolving research perspective on the addictive nature of digitalization in contemporary society, moving from Internet addiction to video/online gaming addiction and, more recently, to social media/social networking addiction.
The period-based science mapping analysis in the current study helped reveal the changing research trajectories and topical trends that contributed to the development of the digital addiction knowledge base. To begin with, during the first period of analysis, which constituted research articles published between 1997 and 2012, addictive behavior was found to be the central theme of research. This is not a surprising result considering that research in this area during this initial period was still in its infancy, attempting to establish a theoretical and empirical footing for the investigation of this newer variant of addictive behavior, as opposed to gambling or tobacco smoking addiction or substance abuse [11,127]. Borrowing from the general addiction literature, scholars were inclined to uncover risk factors or outcomes of digital addiction (i.e., initially termed as Internet addiction) as a new type of addictive behavior, which developed in parallel to the digital revolution and the associated accelerating influence on society in general.
The goal of this early research was to describe, diagnose, and treat addiction to Internet-based technologies [128]. More specifically, scholars used previously defined behavioral addiction frameworks to differentiate normal from addictive use of this new media type [129,130]. Digital addiction was in fact distinguished from other behavioral addictions such as gambling or tobacco smoking [131,132], because these technologies have quickly become integral to many aspects of modern daily life, and result not only in harm but numerous benefits as well in people’s everyday lives. In the same vein, establishing valid and reliable criteria to measure addiction to digital media attracted significant debate not only among scholars but also practitioners (e.g., psychologists, psychiatrists, and neuroscientists) [127]. This was a significant concern during this initial period, since the field lacked a solid theoretical and empirical footing. To address this void, Young [133] made the first attempt to develop one of the most widely used questionnaires to measure Internet addiction (the Internet addiction diagnostic questionnaire) based on findings with regards to gambling addiction. All of these developments in the literature may go towards explaining how the Questionnaire emerged as an influential theme during this initial period.
Another reason for the prevalence of the Questionnaire theme could be that many of the published studies that focused on the addictive-behavior theme adopted a cross-sectional study design, and the data for these studies were often gathered by way of administering surveys or questionnaires [134,135]. Cross-sectional studies particularly aim to investigate the prevalence of a disease or pathological problem within a given population and to detect relationships between exposures and outcomes [136]. Thus, they are considered useful for understanding the etiology of health problems and the generation of hypotheses, which therefore may explain the adoption of cross-sectional design for the investigation of digital addiction as a pathological problem. Indeed, closer scrutiny of the prominent themes and subthemes that emerged during the first period indicated a research focus on the cross-sectional investigation of psychological variables underlying digital addiction, such as anxiety, depression, emotions, stress, aggression, self-control, and impulsiveness in different groups such as adults, students, adolescents, and sex (i.e., males vs. females). Yet, cross-sectional designs can be criticized for precluding causal interpretations between exposures and outcomes, as it is impossible to determine the stability of the exposure, to identify whether or not the exposure preceded the outcome(s), and to detect confounding variables [134,137]. As such, it can be logically assumed that research published during this first period had attempted to observe correlations between digital addiction and psychological problems such as loss of control, depression, stress, impulsiveness, or anxiety but were found to be insufficient in determining causal relations between digital addiction and various variables [131]. As Young eloquently explained [132], these studies were unlikely to offer evidence as to whether people became addicted in order to escape feelings of sadness, for instance, or whether the addiction itself was the cause or trigger of such negative feelings. As prompted by Basel et al. [12], it was difficult to determine whether digital addiction was a symptom of an underlying mental health problem or a condition that caused the issue.
During the first period of analysis, two other prominent themes that guided research were found to be males and adults. Research on males was found to have mostly addressed the risk factors for university or school-aged males being addicted to video games in comparison to their female counterparts considering the subthemes associated with the male theme. Earlier evidence that Internet or technology use by males and females had distinctive characteristics [138,139] may have guided scholars towards the comparative investigation of digital addiction between two sexes. With regards to the theme of adults, its associated subthemes revealed a research interest in addiction-related psychological variables such as stress, impulsiveness, depression, anxiety, or aggression in particular to adult or young adult groups.
As for the second period of analysis, which was comprised of the years 2013–2017, the results showed a maintained research interest in addictive behavior and comparison between male and female users, as well as investigation into the psychopathology of addiction. The initial interest of scholars in adult or young adult groups seemingly moved to the addictive impact of digital media on adolescents and students, who were defined as one of the groups most vulnerable to digital addiction [127,139,140]. Internet addiction, which initially appeared as an emerging theme during the first period, was accompanied by smartphone and game addiction during the second period, which indicated the broadening scope of digital addiction research published between 2013 and 2017. Yet, these themes did not take their place among the motor themes, which indicated that scholars were inclined to develop a better understanding into this addictive behavior, especially within adolescent and student populations. Digital technologies have not only changed the way in which adolescents interact, play, communicate, and learn, but also, their excessive use of technology may have alleviated concerns about its potential damage to their mental, physiological, and psychological development [141,142]. It is even emphasized that “desire for digital media is in fact exquisitely aligned with the biology of the teen brain and our evolutionary heritage” and adolescents’ hunger for human connectedness, their appetite for adventure, and desire for information turns digital media into a natural allure for them [143] (p. 127). In light of these findings, future investigations of digital addiction could address behavioral addictions of teenagers or children and particularly focus on newer forms of addictive media such as smartphones or social media as a means of engaging in addictive activities such as chatting, gaming, and shopping [144].
The third period of analysis comprised research articles that were published between 2018 and the first quarter of 2022. Social media addiction and smartphone addiction were two prominent themes noted during the third period. Recent evidence has shown that more than 4 billion people are now users of the Internet, mobile phones, and social media [10,145]. Given the widespread use of smartphones and social media during the past decade for dealing with the demands of work, socialization, and for pleasure, smartphone or social media dependency has prominently been added to academic discussions regarding digital addiction [5,131]. As Hoehe and Thibaut [1] recently pointed out, there is increasing public concern that people are now interacting more through their digital devices than they are in person and that smartphones are changing the social fabric of communities through decreased human capacity for empathy, introspection, creativity, and productivity. As the current study’s results indicate, this has also been notably of concern to scholars, having guided research through the impact of smartphone or social media addiction over the past 5 years. This finding was also supported by Meng et al.’s [10] study in that research on social media and smartphone addiction peaked during 2020–2021. One particular reason for this increase could be the influence of the COVID-19 pandemic, which increased the risks for digital addiction due to school and university-aged students, as well as many adults, forcibly having stayed at home under quarantine conditions, lockdowns, and a general worldwide extended period of remote working/schooling. The pandemic not only accelerated peoples’ exposure to the Internet and digital media but also induced psychological problems, which, in return, increased the risks for increased digital addiction [10,17,146].
The current study also revealed that research during the third period focused on the theme of resilience. Resilience is broadly defined as a person’s capability to adapt to adverse situations in a positive manner, and previous research showed that the resilience of adolescents or students could decrease the likelihood of their developing behavioral addictions such as alcohol or gambling addiction [139,147]. This may have boosted research interest in the relationship between digital addiction and resilience. A focus on resilience is also significant, since it might suggest a different perspective in the investigation of digital addiction, in that previous studies mostly focused on the risk factors or outcomes of digital addiction, whilst the focus of resilience research is more on the protective factors that can reduce addiction behaviors and/or their negative consequences [147,148]. As suggested previously (e.g., [144,149]), the knowledge of risk factors for digital addiction would not suffice for a thorough understanding of digital addiction, but the knowledge of protective factors should also be developed in order to help those at risk of addiction both to stay safe despite risk experiences and to thrive or cope with negative consequences in the case of addiction. Therefore, future studies focusing on protective factors such as resilience in the face of addiction to different types of digital media could contribute greatly to the theory of digital addiction.
Another finding that deserves particular attention was that digital addiction was only observed as a standalone term during the third period and that it was an emerging theme. This may indicate the conceptual recognizance of digital addiction as an overarching term to refer to addiction to any type of digital media, yet future studies are still warranted for its further development. Given that the variety of digital technologies is constantly increasing, it is likely that several types of digital addiction have yet to emerge, but as Lin et al. [131] suggested, handling all these types of addiction as a generalized pathology would help to develop a broader and overarching framework for the prevention, diagnosis, and treatment of digital addiction.

5. Conclusions

The current study offered a different perspective into the digital addiction research field through revealing the intellectual and conceptual evolution of this knowledge base. The findings not only determined the current status of this field of study but also offered a guideline for future studies through revealing the insufficiently illuminated or underdeveloped aspects of digital addiction as a field of study. Thus, the current study contributed to the understanding of digital addiction as the pathology of the current and, most probably, the coming era. Another strength of the study was its coverage of research on not only Internet addiction but on any type of addiction resulting from the excessive use of various novel technologies, which enabled the development of a broader and cumulative understanding into the investigation of these affiliated types of addiction.
The current study also presents certain limitations. First, the study may have missed some of the research published on digital addiction despite the wide coverage of journals and articles listed on the Scopus database, and the inclusion of a larger scope of research thanks to the co-word analysis. More specifically, the current study addressed articles with a particular focus on ‘addiction’ and, thus, used ‘addiction’ as the primary keyword during the data search process while excluding ‘problematic use’ of digital media/devices. Despite its increasing popularity in the literature, digital addiction has not yet been included in the Diagnostic and Statistical Manual (DSM) or in the International Classification of Diseases (ICD), so there is the likelihood that the present analysis might have missed some research that addressed such behaviors as problematic use of digital media. Furthermore, despite being a review study, the current study did not present the findings of previous research but only mapped the scientific evolution of the research field. Therefore, future reviews of digital addiction could be conducted using different methods such as a meta-analysis, meta-synthesis, or systematic literature review. Similarly, future science mapping studies could help to observe the evolution of the main themes emerged during the third period of the current study’s analysis (2018–2022) over the next couple of years.

Author Contributions

Conceptualization, T.K.; methodology, T.K. and T.T.; formal analysis, T.K. and T.T.; data curation, T.K. and T.T.; writing—original draft preparation, T.K., T.T. and S.P.; writing, T.K., T.T. and S.P.; review and editing, T.K., T.T. and S.P.; and supervision, T.K. and S.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data used are publicly available; no identifying information was collected or included. All the data used in this research was accessed through the Scopus database.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hoehe, M.R.; Thibaut, F. Going Digital: How Technology Use May Influence Human Brains and Behavior. Dialogues Clin. Neurosci. 2022, 22, 93–97. [Google Scholar] [CrossRef] [PubMed]
  2. Urbach, N.; Röglinger, M. Introduction to Digitalization Cases: How Organizations Rethink their Business for the Digital Age. In Digitalization Cases: How Organizations Rethink Their Business for the Digital Age; Urbach, N., Röglinger, M., Eds.; Springer: Cham, Denmark, 2019; pp. 1–12. [Google Scholar] [CrossRef]
  3. Egard, H.; Hansson, K. The Digital Society Comes Sneaking in. An Emerging Field and Its Disabling Barriers. Disabil. Soc. 2021. [Google Scholar] [CrossRef]
  4. Rugai, J.; Hamiliton-Ekeke, J.-T. A Review of Digital Addiction: A Call for Safety Education. Educ. e-Learn. Res. 2016, 3, 17–22. [Google Scholar]
  5. Allcott, H.; Gentzkow, M.; Song, L. Digital Addiction. Am. Econ. Rev. 2022, 112, 2424–2463. [Google Scholar] [CrossRef]
  6. Singh, A.K.; Singh, P.K. Digital Addiction: A Conceptual Overview. Libr. Philos. Pract. 2019, p. 3538. Available online: https://digitalcommons.unl.edu/libphilprac/3538?utm_source=digitalcommons.unl.edu%2Flibphilprac%2F3538&utm_medium=PDF&utm_campaign=PDFCoverPages (accessed on 2 August 2022).
  7. Alter, A. Irresistible: The Rise of Addictive Technology and the Business of Keeping Us Hooked; Penguin Books: New York, NY, USA, 2018; ISBN 9780698402638. [Google Scholar]
  8. Christakis, D.A. The Challenges of Defining and Studying “Digital Addiction” in Children. JAMA 2019, 321, 2277–2278. [Google Scholar] [CrossRef]
  9. Dresp-Langley, B.; Hutt, A. Digital Addiction and Sleep. Int. J. Environ. Res. Public Health 2022, 19, 6910. [Google Scholar] [CrossRef]
  10. Meng, S.-Q.; Cheng, J.-L.; Li, Y.-Y.; Yang, X.-Q.; Zheng, J.-W.; Chang, X.-W.; Shi, Y.; Chen, Y.; Lu, L.; Sun, Y.; et al. Global Prevalence of Digital Addiction in General Population: A Systematic Review and Meta-analysis. Clin. Psychol. Rev. 2022, 92, 102128. [Google Scholar] [CrossRef]
  11. Lopez-Fernandez, O. Generalised versus Specific Internet Use-Related Addiction Problems: A Mixed Methods Study on Internet, Gaming, and Social Networking Behaviours. Int. J. Environ. Res. Public Health 2018, 15, 2913. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Basel, A.M.; McAlaney, J.; Skinner, T.; Pleva, M.; Ali, R. Defining Digital Addiction: Key Features from the Literature. Psihologija 2020, 53, 237–253. [Google Scholar] [CrossRef]
  13. Al-Khani, A.M.; Saquib, J.; Rajab, A.M.; Khalifa, M.A.; Almazrou, A.; Saquib, N. Internet Addiction in Gulf Countries: A Systematic Review and Meta-analysis. J. Behav. Addict. 2021, 10, 601–610. [Google Scholar] [CrossRef]
  14. Karakose, T.; Polat, H.; Papadakis, S. Examining Teachers’ Perspectives on School Principals’ Digital Leadership Roles and Technology Capabilities during the COVID-19 Pandemic. Sustainability 2021, 13, 13448. [Google Scholar] [CrossRef]
  15. Bernheim, B.D.; Rangel, A. Addiction and Cue-Triggered Decision Processes. Am. Econ. Rev. 2004, 94, 1558–1590. [Google Scholar] [CrossRef]
  16. World Health Organization. Addictive Behaviours: Gaming Disorder. Available online: https://www.who.int/features/qa/gaming-disorder/en/ (accessed on 22 October 2022).
  17. Király, O.; Potenza, M.N.; Stein, D.J.; King, D.L.; Hodgins, D.C.; Saunders, J.B.; Griffiths, M.D.; Gjoneska, B.; Billieux, J.; Brand, M.; et al. Preventing Problematic Internet Use during the COVID-19 Pandemic: Consensus Guidance. Compr. Psychiatry 2020, 100, 152180. [Google Scholar] [CrossRef]
  18. Jain, A.; Sharma, R.; Gaur, K.L.; Yadav, N.; Sharma, P.; Sharma, N.; Khan, N.; Kumawat, P.; Jain, G.; Maanju, M.; et al. Study of Internet Addiction and Its Association with Depression and Insomnia in University Students. J. Fam. Med. Prim. Care 2020, 9, 1700–1706. [Google Scholar] [CrossRef]
  19. Kandasamy, S.; Abdulrahuman, M.B.; Shyamala, J. A Study on Anxiety Disorder Among College Students with Internet Addiction. Int. J. Community Med. Public Health 2019, 6, 1695–1700. [Google Scholar] [CrossRef]
  20. Hinojo-Lucena, F.-J.; Aznar-Díaz, I.; Cáceres-Reche, M.-P.; Trujillo-Torres, J.-M.; Romero-Rodríguez, J.-M. Problematic Internet Use as a Predictor of Eating Disorders in Students: A Systematic Review and Meta-Analysis Study. Nutrients 2019, 11, 2151. [Google Scholar] [CrossRef]
  21. Raouf, S.Y.A.; Gabr, H.M.; Al-Wutayd, O.; Al-Batanony, M.A. Video Game Disorder and Mental Wellbeing among University Students: A Cross-Sectional Study. Pan Afr. Med. J. 2022, 41, 89. [Google Scholar] [CrossRef]
  22. Kuss, D.J.; Griffiths, M.D.; Karila, L.; Billieux, J. Internet Addiction: A Systematic Review of Epidemiological Research for the Last Decade. Curr. Pharm. Des. 2014, 20, 4026–4052. [Google Scholar] [CrossRef] [Green Version]
  23. Shaw, M.; Black, D.W. Internet Addiction: Definition, Assessment, Epidemiology and Clinical Management. CNS Drugs 2008, 22, 353–365. [Google Scholar] [CrossRef]
  24. Alrobai, A.; McAlaney, J.; Phalp, K.; Ali, R. Exploring the Risk Factors of Interactive E-Health Interventions for Digital Addiction. Int. J. Sociotechnol. Knowl. Dev. 2016, 8, 1. [Google Scholar] [CrossRef]
  25. Ha, J.-H.; Yoo, H.-J.; Cho, I.-H.; Chin, B.; Shin, D.; Kim, J.-H. Psychiatric Comorbidity Assessed in Korean Children and Adolescents Who Screen Positive for Internet Addiction. J. Clin. Psychiatry 2006, 67, 821–826. [Google Scholar] [CrossRef] [PubMed]
  26. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F. Science Mapping Software Tools: Review, Analysis, and Cooperative Study Among Tools. J. Assoc. Inf. Sci. Technol. 2011, 62, 1382–1402. [Google Scholar] [CrossRef]
  27. MacFadden, I.; Santana, M.; Vázquez-Cano, E.; López-Meneses, E. A Science Mapping Analysis of ‘Marginality, Stigmatization and Social Cohesion’ in WoS (1963–2019). Qual. Quant. 2021, 55, 275–293. [Google Scholar] [CrossRef]
  28. Cañadas, D.C.; Perales, A.B.; Belmonte, M.D.P.C.; Martínez, R.G.; Carreño, T.P. Kangaroo Mother Care and Skin-to-Skin Care in Preterm Infants in the Neonatal Intensive Care Unit: A Bibliometric Analysis. Arch. Pédiatrie 2021, 29, 90–99. [Google Scholar] [CrossRef] [PubMed]
  29. Mongeon, P.; Paul-Hus, A. The Journal Coverage of Web of Science and Scopus: A Comparative Analysis. Scientometrics 2016, 106, 213–228. [Google Scholar] [CrossRef]
  30. Hallinger, P.; Kulophas, D. The Evolving Knowledge Base on Leadership and Teacher Professional Learning: A Bibliometric Analysis of the Literature, 1960–2018. Prof. Dev. Educ. 2019, 46, 521–540. [Google Scholar] [CrossRef]
  31. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Ann. Intern. Med. 2009, 151, 264–269. [Google Scholar] [CrossRef] [Green Version]
  32. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F. SciMAT: A New Science Mapping Analysis Software Tool. J. Assoc. Inf. Sci. Technol. 2012, 63, 1609–1630. [Google Scholar] [CrossRef]
  33. López-Robles, J.R.; Cobo, M.J.; Gutiérrez-Salcedo, M.; Martínez-Sánchez, M.A.; Gamboa-Rosales, N.K.; Herrera-Viedma, E. 30th Anniversary of Applied Intelligence: A Combination of Bibliometrics and Thematic Analysis Using SciMAT. Appl. Intell. 2021, 51, 6547–6568. [Google Scholar] [CrossRef]
  34. Zupic, I.; Čater, T. Bibliometric Methods in Management and Organization. Organ. Res. Methods 2015, 18, 429–472. [Google Scholar] [CrossRef]
  35. Martínez, M.A.; Cobo, M.J.; Herrera, M.; Herrera-Viedma, E. Analyzing the Scientific Evolution of Social Work Using Science Mapping. Res. Soc. Work Pract. 2015, 25, 257–277. [Google Scholar] [CrossRef]
  36. Batagelj, V.; Cerinšek, M. On Bibliographic Networks. Scientometrics 2013, 96, 845–864. [Google Scholar] [CrossRef] [Green Version]
  37. Chen, C. Science Mapping: A Systematic Review of the Literature. J. Data Inf. Sci. 2017, 2, 1–40. [Google Scholar] [CrossRef] [Green Version]
  38. Garfield, E. Scientography: Mapping the Tracks of Science. Curr. Contents: Soc. Behav. Sci. 1994, 7, 5–10. [Google Scholar]
  39. Callon, M.; Courtial, J.P.; Laville, F. Co-word Analysis as a Tool for Describing the Network of Interactions between Basic and Technological Research: The Case of Polymer Chemistry. Scientometrics 1991, 22, 155–205. [Google Scholar] [CrossRef]
  40. Coulter, N.; Monarch, I.; Konda, S. Software Engineering as Seen Through Its Research Literature: A Study in Co-word Analysis. J. Am. Soc. Inf. Sci. Technol. 1998, 49, 1206–1223. [Google Scholar] [CrossRef]
  41. Murgado-Armenteros, E.M.; Gutiérrez-Salcedo, M.; Torres-Ruiz, F.J.; Cobo, M.J. Analysing the Conceptual Evolution of Qualitative Marketing Research through Science Mapping Analysis. Scientometrics 2015, 102, 519–557. [Google Scholar] [CrossRef]
  42. Börner, K.; Chen, C.; Boyack, K.W. Visualizing Knowledge Domains. In Annual Review of Information Science & Technology; Cronin, B., Ed.; Information Today, Inc./American Society for Information Science and Technology: Medford, NJ, USA, 2005; Volume 37, pp. 179–255, ISBN-13: 978-0938734550. [Google Scholar]
  43. Sternitzke, C.; Bergmann, I. Similarity Measures for Document Mapping: A Comparative Study on the Level of an Individual Scientist. Scientometrics 2009, 78, 113–130. [Google Scholar] [CrossRef]
  44. Alonso, S.; Cabrerizo, F.J.; Herrera-Viedma, E.; Herrera, F. H-Index: A Review Focused in Its Variants, Computation and Standardization for Different Scientific Fields. J. Informetr. 2009, 3, 273–289. [Google Scholar] [CrossRef] [Green Version]
  45. Hirsch, J.E. An Index to Quantify an Individual’s Scientific Research Output. Proc. Natl. Acad. Sci. USA 2005, 102, 16569–16572. [Google Scholar] [CrossRef] [Green Version]
  46. Jeong, E.J.; Kim, D.H. Social Activities, Self-Efficacy, Game Attitudes, and Game Addiction. Cyberpsychology Behav. Soc. Netw. 2011, 14, 213–221. [Google Scholar] [CrossRef] [PubMed]
  47. Iskender, M.; Akin, A. Self-compassion and Internet addiction. Turk. Online J. Educ. Technol. 2011, 10, 215–221. [Google Scholar]
  48. Isiklar, A. Examination of the Variables Predicting Internet Addiction in Adolescence. Energy Educ. Sci. Tech. 2012, 4, 1417–1424. [Google Scholar]
  49. Mehroof, M.; Griffiths, M.D. Online Gaming Addiction: The Role of Sensation Seeking, Self-Control, Neuroticism, Aggression, State Anxiety, and Trait Anxiety. Cyberpsychology Behav. Soc. Netw. 2010, 13, 313–316. [Google Scholar] [CrossRef] [PubMed]
  50. Hsiao, H.-C.; Chung, H.-N.; Tu, Y.-L. A Study on Internet Addiction, College Students’ Personal Characteristics Issues and Cyber Behaviors. Int. J. Learn. 2010, 17, 285–294. [Google Scholar] [CrossRef]
  51. Tsai, C.-C.; Lin, S.S.J. Analysis of Attitudes toward Computer Networks and Internet Addiction of Taiwanese Adolescents. Cyberpsychology Behav. 2001, 4, 373–376. [Google Scholar] [CrossRef] [Green Version]
  52. Guo, J.; Chen, L.; Wang, X.; Liu, Y.; Chui, C.H.K.; He, H.; Qu, Z.; Tian, D. The Relationship Between Internet Addiction and Depression among Migrant Children and Left-Behind Children in China. Cyberpsychology Behav. Soc. Netw. 2012, 15, 585–590. [Google Scholar] [CrossRef]
  53. Ko, C.-H.; Yen, J.-Y.; Yen, C.-F.; Chen, C.-S.; Wang, S.-Y. The Association between Internet Addiction and Belief of Frustration Intolerance: The Gender Difference. Cyberpsychology Behav. 2008, 11, 273–278. [Google Scholar] [CrossRef]
  54. Lam, L.T.; Peng, Z.-W.; Mai, J.-C.; Jing, J. Factors Associated with Internet Addiction among Adolescents. Cyberpsychology Behav. 2009, 12, 551–555. [Google Scholar] [CrossRef]
  55. Yen, J.-Y.; Yen, C.-F.; Wu, H.-Y.; Huang, C.-J.; Ko, C.-H. Hostility in the Real World and Online: The Effect of Internet Addiction, Depression, and Online Activity. Cyberpsychology Behav. Soc. Netw. 2011, 14, 649–655. [Google Scholar] [CrossRef]
  56. Xiuqin, H.; Huimin, Z.; Mengchen, L.; Jinan, W.; Ying, Z.; Ran, T. Mental Health, Personality, and Parental Rearing Styles of Adolescents with Internet Addiction Disorder. Cyberpsychology Behav. Soc. Netw. 2010, 13, 401–406. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Lin, M.-P.; Ko, H.-C.; Wu, J.Y.-W. Prevalence and Psychosocial Risk Factors Associated with Internet Addiction in a Nationally Representative Sample of College Students in Taiwan. Cyberpsychology Behav. Soc. Netw. 2011, 14, 741–746. [Google Scholar] [CrossRef] [PubMed]
  58. Canan, F.; Ataoglu, A.; Nichols, L.A.; Yildirim, T.; Ozturk, O. Evaluation of Psychometric Properties of the Internet Addiction Scale in a Sample of Turkish High School Students. Cyberpsychology Behav. Soc. Netw. 2010, 13, 317–320. [Google Scholar] [CrossRef] [PubMed]
  59. Fioravanti, G.; Dèttore, D.; Casale, S. Adolescent Internet Addiction: Testing the Association between Self-Esteem, the Perception of Internet Attributes, and Preference for Online Social Interactions. Cyberpsychology Behav. Soc. Netw. 2012, 15, 318–323. [Google Scholar] [CrossRef]
  60. Skoric, M.M.; Teo, L.L.C.; Neo, R.L. Children and Video Games: Addiction, Engagement, and Scholastic Achievement. Cyberpsychology Behav. 2009, 12, 567–572. [Google Scholar] [CrossRef]
  61. Hur, M.H. Demographic, Habitual, and Socioeconomic Determinants of Internet Addiction Disorder: An Empirical Study of Korean Teenagers. Cyberpsychology Behav. 2006, 9, 514–525. [Google Scholar] [CrossRef]
  62. Floros, G.; Siomos, K.; Stogiannidou, A.; Giouzepas, I.; Garyfallos, G. The Relationship between Personality, Defense Styles, Internet Addiction Disorder, and Psychopathology in College Students. Cyberpsychology Behav. Soc. Netw. 2014, 17, 672–676. [Google Scholar] [CrossRef]
  63. Zhou, S.X.; Leung, L. Gratification, Loneliness, Leisure Boredom, and Self-Esteem as Predictors of SNS-Game Addiction and Usage Pattern among Chinese College Students. Cyberpsychology Behav. Soc. Psychol. Learn 2012, 2, 34–48. [Google Scholar] [CrossRef] [Green Version]
  64. Akın, A. The Relationships between Internet Addiction, Subjective Vitality, and Subjective Happiness. Cyberpsychology Behav. Soc. Netw. 2012, 15, 404–410. [Google Scholar] [CrossRef]
  65. Ko, C.-H.; Yen, J.-Y.; Yen, C.-F.; Lin, H.-C.; Yang, M.J. Factors Predictive for Incidence and Remission of Internet Addiction in Young Adolescents: A Prospective Study. Cyberpsychology Behav. 2007, 10, 545–551. [Google Scholar] [CrossRef]
  66. De Berardis, D.; D’Albenzio, A.; Gambi, F.; Sepede, G.; Valchera, A.; Conti, C.M.; Fulcheri, M.; Cavuto, M.; Ortolani, C.; Salerno, R.M.; et al. Alexithymia and Its Relationships with Dissociative Experiences and Internet Addiction in a Nonclinical Sample. Cyberpsychology Behav. 2009, 12, 67–69. [Google Scholar] [CrossRef] [PubMed]
  67. Lemmens, J.S.; Valkenburg, P.M.; Peter, J. Development and Validation of a Game Addiction Scale for Adolescents. Media Psychol. 2009, 12, 77–95. [Google Scholar] [CrossRef]
  68. Wan, C.-S.; Chiou, W.-B. Psychological Motives and Online Games Addiction: A Test of Flow Theory and Humanistic Needs Theory for Taiwanese Adolescents. Cyberpsychology Behav. 2006, 9, 317–324. [Google Scholar] [CrossRef] [PubMed]
  69. Ak, Ş.; Koruklu, N.; Yılmaz, Y.A. Study on Turkish Adolescent’s Internet Use: Possible Predictors of Internet Addiction. Cyberpsychology Behav. Soc. Netw. 2013, 16, 205–209. [Google Scholar] [CrossRef] [PubMed]
  70. Chi, X.; Lin, L.; Zhang, P. Internet Addiction Among College Students in China: Prevalence and Psychosocial Correlates. Cyberpsychology Behav. Soc. Netw. 2016, 19, 567–573. [Google Scholar] [CrossRef]
  71. Hawi, N.S.; Samaha, M. The Relations among Social Media Addiction, Self-Esteem, and Life Satisfaction in University Students. Soc. Sci. Comput. Rev. 2017, 35, 576–586. [Google Scholar] [CrossRef]
  72. Tsimtsiou, Z.; Haidich, A.B.; Spachos, D.; Kokkali, S.; Bamidis, P.; Dardavesis, T.; Arvanitidou, M. Internet Addiction in Greek Medical Students: An Online Survey. Acad. Psychiatry 2015, 39, 300–304. [Google Scholar] [CrossRef]
  73. Brunborg, G.S.; Hanss, D.; Mentzoni, R.A.; Pallesen, S. Core and Peripheral Criteria of Video Game Addiction in the Game Addiction Scale for Adolescents. Cyberpsychology Behav. Soc. Netw. 2015, 18, 280–285. [Google Scholar] [CrossRef] [Green Version]
  74. Cheng, C.; Sun, P.; Mak, K.K. Internet Addiction and Psychosocial Maladjustment: Avoidant Coping and Coping Inflexibility as Psychological Mechanisms. Cyberpsychology Behav. Soc. Netw. 2015, 18, 539–546. [Google Scholar] [CrossRef]
  75. Rehbein, F.; Baier, D. Family-, Media-, and School-Related Risk Factors of Video Game Addiction: A 5-Year Longitudinal Study. J. Media Psychol. 2013, 25, 118–128. [Google Scholar] [CrossRef]
  76. Lau, J.T.; Gross, D.L.; Wu, A.; Cheng, K.-M.; Lau, M. Incidence and Predictive Factors of Internet Addiction among Chinese Secondary School Students in Hong Kong: A Longitudinal Study. Curr. Pharm. Des. 2017, 52, 657–667. [Google Scholar] [CrossRef] [PubMed]
  77. Lim, J.-A.; Gwak, A.R.; Park, S.M.; Kwon, J.-G.; Lee, J.-Y.; Jung, H.Y.; Sohn, B.K.; Kim, J.W.; Kim, D.J.; Choi, J.S. Are Adolescents with Internet Addiction Prone to Aggressive Behavior? The Mediating Effect of Clinical Comorbidities on the Predictability of Aggression in Adolescents with Internet Addiction. Cyberpsychology Behav. Soc. Netw. 2015, 18, 260–267. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Berner, J.E.; Santander, J.; Contreras, A.M.; Gómez, T. Description of Internet Addiction Among Chilean Medical Students: A Cross-Sectional Study. Acad. Psychiatry 2014, 38, 11–14. [Google Scholar] [CrossRef]
  79. Yao, M.Z.; He, J.; Ko, D.M.; Pang, K. The Influence of Personality, Parental Behaviors, and Self-Esteem on Internet Addiction: A Study of Chinese College Students. Cyberpsychology Behav. Soc. Netw. 2014, 17, 104–110. [Google Scholar] [CrossRef] [PubMed]
  80. You, S.; Kim, E.; Lee, D. Virtually Real: Exploring Avatar Identification in Game Addiction Among Massively Multiplayer Online Role-Playing Games (MMORPG) Players. Games Cult. 2017, 12, 56–71. [Google Scholar] [CrossRef]
  81. Irles, D.L.; Gomis, R.M. Impulsiveness and Video Game Addiction. Salud Drog. 2016, 16, 33–40. [Google Scholar]
  82. Bian, M.; Leung, L. Linking Loneliness, Shyness, Smartphone Addiction Symptoms, and Patterns of Smartphone Use to Social Capital. Soc. Sci. Comput. Rev. 2015, 33, 61–79. [Google Scholar] [CrossRef] [Green Version]
  83. Yang, H.-L.; Wu, W.-P. The Effect of Flow Frequency on Internet Addiction to Different Internet Usage Activities. Int. J. Inf. Commun. 2017, 13, 28–39. [Google Scholar] [CrossRef] [Green Version]
  84. Dalbudak, E.; Evren, C.; Aldemir, S.; Coskun, K.S.; Ugurlu, H.; Yildirim, F.G. Relationship of Internet Addiction Severity with Depression, Anxiety, and Alexithymia, Temperament and Character in University Students. Cyberpsychology Behav. Soc. Netw. 2013, 16, 272–278. [Google Scholar] [CrossRef] [Green Version]
  85. Kim, P.W.; Kim, S.Y.; Shim, M.; Im, C.H.; Shon, Y.-M. The Influence of An Educational Course on Language Expression and Treatment of Gaming Addiction for Massive Multiplayer Online Role-Playing Game (MMORPG) Players. Comput. Educ. 2013, 63, 208–217. [Google Scholar] [CrossRef]
  86. Fioravanti, G.; Casale, S. Evaluation of the Psychometric Properties of the Italian Internet Addiction Test. Cyberpsychology Behav. Soc. Netw. 2015, 18, 120–128. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Sung, M.; Shin, Y.-M.; Cho, S.-M. Factor Structure of the Internet Addiction Scale and Its Associations with Psychiatric Symptoms for Korean Adolescents. Community Ment. Health J. 2014, 50, 612–618. [Google Scholar] [CrossRef] [PubMed]
  88. Lai, C.-M.; Mak, K.-K.; Cheng, C.; Watanabe, H.; Nomachi, S.; Bahar, N.; Young, K.S.; Ko, H.-C.; Kim, D.; Griffiths, M.D. Measurement Invariance of the Internet Addiction Test among Hong Kong, Japanese, and Malaysian Adolescents. Cyberpsychology Behav. Soc. Netw. 2015, 18, 609–617. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  89. Gökçearslan, Ş.; Uluyol, Ç.; Şahin, S. Smartphone Addiction, Cyberloafing, Stress and Social Support among University Students: A Path Analysis. Child. Youth Serv. Rev. 2018, 91, 47–54. [Google Scholar] [CrossRef]
  90. Zhang, M.W.; Lim, R.B.; Lee, C.; Ho, R. Prevalence of Internet Addiction in Medical Students: A Meta-analysis. Acad. Psychiatry 2018, 42, 88–93. [Google Scholar] [CrossRef]
  91. Neverkovich, S.D.; Bubnova, I.S.; Kosarenko, N.N.; Sakhieva, R.G.; Sizova, Z.M.; Zakharova, V.L.; Sergeeva, M.G. Students’ Internet Addiction: Study and Prevention. Eurasia J. Math. Sci. Technol. Educ. 2018, 14, 1483–1495. [Google Scholar] [CrossRef]
  92. Karakose, T. Assessing the Relationships between Internet Addiction, Depression, COVID-19-Related Fear, Anxiety, and Suspicion among Graduate Students in Educational Administration: A Structural Equation Modeling Analysis. Sustainability 2022, 14, 5356. [Google Scholar] [CrossRef]
  93. Chun, J. Conceptualizing Effective Interventions for Smartphone Addiction Among Korean Female Adolescents. Child. Youth Serv. Rev. 2018, 84, 35–39. [Google Scholar] [CrossRef]
  94. Arpaci, I.; Kesici, Ş.; Baloğlu, M. Individualism and Internet Addiction: The Mediating Role of Psychological Needs. Internet Res. 2018, 28, 293–314. [Google Scholar] [CrossRef]
  95. Hawi, N.S.; Samaha, M.; Griffiths, M.D. The Digital Addiction Scale for Children: Development and Validation. Cyberpsychology Behav. Soc. Netw. 2019, 22, 771–778. [Google Scholar] [CrossRef]
  96. Cheung, T.; Lee, R.L.; Tse, A.C.; Do, C.W.; So, B.C.; Szeto, G.P.; Lee, P.H. Psychometric Properties and Demographic Correlates of the Smartphone Addiction Scale-Short Version Among Chinese Children and Adolescents in Hong Kong. Cyberpsychology Behav. Soc. Netw. 2019, 22, 714–723. [Google Scholar] [CrossRef] [PubMed]
  97. He, Z.-H.; Li, M.-D.; Ma, X.-Y.; Liu, C.-J. Family Socioeconomic Status and Social Media Addiction in Female College Students: The Mediating Role of Impulsiveness and Inhibitory Control. J. Genet. Psychol. 2021, 182, 60–74. [Google Scholar] [CrossRef]
  98. Jiang, Q.; Huang, X.; Tao, R. Examining Factors Influencing Internet Addiction and Adolescent Risk Behaviors among Excessive Internet Users. Health Commun. 2018, 33, 1434–1444. [Google Scholar] [CrossRef]
  99. Huang, Y.; Jung, H.-R.; Lim, C.H. Effects of Smartphone Addiction on Radiology Students’ Interpersonal Satisfaction. Med. Legal. Update 2020, 20, 1971–1975. [Google Scholar] [CrossRef]
  100. Hasan, A.A.-H.; Jaber, A.A. Prevalence of Internet Addiction, Its Association with Psychological Distress, Coping Strategies Among Undergraduate Students. Nurse Educ. Today 2019, 81, 78–82. [Google Scholar] [CrossRef]
  101. Soomro, K.A.; Zai, S.A.Y.; Hina, Q.A. Investigating the Impact of University Students’ Smartphone Addiction on their Satisfaction with Classroom Connectedness. Educ. Inf. Technol. 2019, 24, 3523–3535. [Google Scholar] [CrossRef]
  102. Albursan, I.S.; Al Qudah, M.F.; Dutton, E.; Hassan, E.M.A.H.; Bakhiet, S.F.A.; Alfnan, A.A.; Aljomaa, S.S.; Hammad, H.I. National, Sex and Academic Discipline Difference in Smartphone Addiction: A Study of Students in Jordan, Saudi Arabia, Yemen and Sudan. Community Ment. Health J. 2019, 55, 825–830. [Google Scholar] [CrossRef] [PubMed]
  103. Nam, C.R.; Lee, D.H.; Lee, J.Y.; Choi, A.R.; Chung, S.J.; Kim, D.-J.; Bhang, S.-Y.; Kwon, J.-G.; Kweon, Y.-S.; Choi, J.-S. The Role of Resilience in Internet Addiction Among Adolescents between Sexes: A Moderated Mediation Model. J. Clin. Med. 2018, 7, 222. [Google Scholar] [CrossRef] [Green Version]
  104. Mun, I.B.; Lee, S. How Does Parental Smartphone Addiction Affect Adolescent Smartphone Addiction?: Testing the Mediating Roles of Parental Rejection and Adolescent Depression. Cyberpsychology Behav. Soc. Netw. 2021, 24, 399–406. [Google Scholar] [CrossRef]
  105. Kim, N.; Sung, J.Y.; Park, J.Y.; Kong, I.D.; Hughes, T.L.; Kim, D.-K. Association between Internet Gaming Addiction and Leukocyte Telomere Length in Korean Male Adolescents. Soc. Sci. Med. 2019, 222, 84–90. [Google Scholar] [CrossRef]
  106. Seok, H.J.; Lee, J.M.; Park, C.-Y.; Park, J.Y. Understanding Internet Gaming Addiction Among South Korean Adolescents through Photovoice. Child. Youth Serv. Rev. 2018, 94, 35–42. [Google Scholar] [CrossRef]
  107. Miskulin, I.; Simic, I.; Pavlovic, N.; Kovacevic, J.; Fotez, I.; Kondza, G.; Palenkic, H.; Bilic-Kirin, V.; Kristic, M.; Miskulin, M. Personality Traits of Croatian University Students with Internet Addiction. Behav. Sci. 2022, 12, 173. [Google Scholar] [CrossRef]
  108. Kim, K.; Yee, J.; Chung, J.E.; Kim, H.J.; Han, J.M.; Kim, J.H.; Lee, K.E.; Gwak, H.S. Smartphone Addiction and Anxiety in Adolescents–A Cross-sectional Study. Am. J. Health Behav. 2021, 45, 895–901. [Google Scholar] [CrossRef] [PubMed]
  109. Rosenthal, S.R.; Cha, Y.; Clark, M.A. The Internet Addiction Test in a Young Adult US Population. Cyberpsychology Behav. Soc. Netw. 2018, 21, 661–666. [Google Scholar] [CrossRef] [PubMed]
  110. Sun, Y.; Wilkinson, J.S. Parenting Style, Personality Traits, and Interpersonal Relationships: A Model of Prediction of Internet Addiction. Int. J. Commun. 2020, 14, 2163–2185. [Google Scholar]
  111. Osorio-Molina, C.; Martos-Cabrera, M.B.; Membrive-Jiménez, M.J.; Vargas-Roman, K.; Suleiman-Martos, N.; Ortega-Campos, E.; Gómez-Urquiza, J.L. Smartphone Addiction, Risk Factors and Its Adverse Effects in Nursing Students: A Systematic Review and Meta-analysis. Nurse Educ. Today 2021, 98, 104741. [Google Scholar] [CrossRef]
  112. Xie, X.; Chen, W.; Zhu, X.; He, D. Parents’ Phubbing Increases Adolescents’ Mobile Phone Addiction: Roles of Parent-Child Attachment, Deviant Peers, and Gender. Child. Youth Serv. Rev. 2019, 105, 104426. [Google Scholar] [CrossRef]
  113. Erdem, C.; Uzun, A.M. Smartphone Addiction Among Undergraduates: Roles of Personality Traits and Demographic Factors. Technol. Knowl. 2020, 27, 579–597. [Google Scholar] [CrossRef]
  114. Durak, H.Y. Investigation of Nomophobia and Smartphone Addiction Predictors among Adolescents in Turkey: Demographic Variables and Academic Performance. Soc. Sci. J. 2019, 56, 492–517. [Google Scholar] [CrossRef]
  115. Anshari, M.; Alas, Y.; Sulaiman, E. Smartphone Addictions and Nomophobia Among Youth. Vulnerable Child. Youth Stud. 2019, 14, 242–247. [Google Scholar] [CrossRef]
  116. Li, L.; Gao, H.; Xu, Y. The Mediating and Buffering Effect of Academic Self-Efficacy on the Relationship between Smartphone Addiction and Academic Procrastination. Comput. Educ. 2020, 159, 104001. [Google Scholar] [CrossRef]
  117. Sun, R.; Gao, Q.; Xiang, Y.; Chen, T.; Liu, T.; Chen, Q. Parent–Child Relationships and Mobile Phone Addiction Tendency among Chinese Adolescents: The Mediating Role of Psychological Needs Satisfaction and the Moderating Role of Peer Relationships. Child. Youth Serv. Rev. 2020, 116, 105113. [Google Scholar] [CrossRef]
  118. Bilgin, O.; Taş, İ. Effects of Perceived Social Support and Psychological Resilience on Social Media Addiction among University Students. Univers. J. Educ. Res. 2018, 6, 751–758. [Google Scholar] [CrossRef] [Green Version]
  119. Kumar, G.; Dash, P.; Jnaneswar, A.; Suresan, V.; Jha, K.; Ghosal, S. Impact of Internet Addiction during COVID-19 on Anxiety and Sleep Quality among College Students of Bhubaneswar City. J. Educ. Health Promot. 2022, 11, 156. [Google Scholar] [CrossRef] [PubMed]
  120. Al Shawi, A.F.; Hameed, A.K.; Shalal, A.I.; Abd Kareem, S.S.; Majeed, M.A.; Humidy, S.T. Internet Addiction and Its Relationship to Gender, Depression and Anxiety Among Medical Students in Anbar Governorate-West of Iraq. Community Health Equity Res. Policy 2022, 42, 253–256. [Google Scholar] [CrossRef]
  121. Yang, X.; Zhou, Z.; Liu, Q.; Fan, C. Mobile Phone Addiction and Adolescents’ Anxiety and Depression: The Moderating Role of Mindfulness. J. Child. Fam. Stud. 2019, 28, 822–830. [Google Scholar] [CrossRef]
  122. Lee, J.Y.; Ko, D.W.; Lee, H. Loneliness, Regulatory Focus, Inter-Personal Competence, and Online Game Addiction: A Moderated Mediation Model. Internet Res. 2019, 29, 381–394. [Google Scholar] [CrossRef]
  123. Kil, N.; Kim, J.; McDaniel, J.T.; Kim, J.; Kensinger, K. Examining Associations Between Smartphone Use, Smartphone Addiction, And Mental Health Outcomes: A Cross-Sectional Study of College Students. Health Promot. Perspect. 2021, 11, 36–44. [Google Scholar] [CrossRef]
  124. Gorjinpour, F.; Tavana, Z. Relationship between Attachment Styles and Internet Addiction and Its Influence on Female High School Students Academic Achievement. J. Health Sci. Surveill. Syst. 2022, 10, 50–55. [Google Scholar]
  125. Lazarinis, F.; Alexandri, K.; Panagiotakopoulos, C.; Verykios, V.S. Sensitizing Young Children on Internet Addiction and Online Safety Risks through Storytelling in a Mobile Application. Educ. Inf. Technol 2020, 25, 163–174. [Google Scholar] [CrossRef]
  126. Salazar-Concha, C.; Ficapal-Cusí, P.; Boada-Grau, J.; Camacho, L.J. Analyzing the Evolution of Technostress: A Science Mapping Approach. Heliyon 2021, 7, 06726. [Google Scholar] [CrossRef] [PubMed]
  127. Young, K.S. Internet Addiction: A New Clinical Phenomenon and Its Consequences. Am. Behav. Sci. 2004, 48, 402–415. [Google Scholar] [CrossRef] [Green Version]
  128. Montag, C.; Walla, P. Carpe Diem Instead Of Losing Your Social Mind: Beyond Digital Addiction and Why We All Suffer From Digital Overuse. Cogent Psychol. 2016, 3, 1157281. [Google Scholar] [CrossRef]
  129. Duke, E.; Montag, C. Smartphone Addiction and Beyond: Initial Insights on an Emerging Research Topic and Its Relationship to Internet Addiction. In Internet Addiction: Neuroscientific Approaches and Therapeutical Implications Including Smartphone Addiction, 2nd ed.; Montag, C., Reuter, M., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 359–379. ISBN 978-3-319-46275-2. [Google Scholar]
  130. Young, K.S.; Yue, X.D.; Ying, L. Prevalence Estimates and Etiologic Models of Internet Addiction. In Internet Addiction: A Handbook and Guide to Evaluation and Treatment; Young, K.S., Abreu, C.N., Eds.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2011; pp. 3–18. [Google Scholar] [CrossRef]
  131. Lin, Y.-H.; Lin, S.-H.; Yang, C.C.H.; Kuo, T.B.J. Psychopathology of Everyday Life in the 21st century: Smartphone Addiction. In Internet Addiction: Neuroscientific Approaches and Therapeutical Implications Including Smartphone Addiction, 2nd ed.; Montag, C., Reuter, M., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 3–18. ISBN 978-3-319-46275-2. [Google Scholar]
  132. Young, K.S. The Evolution of Internet Addiction Disorder. In Internet Addiction: Neuroscientific Approaches and Therapeutical Implications Including Smartphone Addiction, 2nd ed.; Montag, C., Reuter, M., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 3–18. ISBN 978-3-319-46275-2. [Google Scholar]
  133. Young, K.S. Internet Addiction: The Emergence of a New Clinical Disorder. Cyberpsychology Behav. 1998, 1, 237–244. [Google Scholar] [CrossRef] [Green Version]
  134. Kesmodel, U.S. Cross-sectional Studies–What Are They Good For? Acta Obstet. Gynecol. Scand. 2018, 97, 388–393. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  135. Olsen, C.; St George, D.M.M. Cross-sectional Study Design and Data Analysis. Coll. Entr. Exam. Board 2004, 26, 2006. [Google Scholar]
  136. Wang, X.; Cheng, Z. Cross-sectional Studies: Strengths, Weaknesses, and Recommendations. Chest 2020, 158, S65–S71. [Google Scholar] [CrossRef]
  137. Levin, K.A. Study Design III: Cross-sectional Studies. Evid. Based Dent. 2006, 7, 24–25. [Google Scholar] [CrossRef]
  138. Billieux, J.; van der Linden, M.; d’Acremont, M.; Ceschi, G.; Zermatten, A. Does Impulsivity Relate to Perceived Dependence on and Actual Use of the Mobile Phone? Appl. Cogn. Psychol. 2007, 21, 527–537. [Google Scholar] [CrossRef]
  139. Nam, S.-J.; Hwang, H. Consumers’ Needs for Public Education and Corporate Participation Regarding Child Internet Addiction: Based on the Risk Perception Attitude Framework. J. Consum. Aff. 2019, 53, 1220–1233. [Google Scholar] [CrossRef]
  140. Kandell, J.J. Internet Addiction on Campus: The Vulnerability of College Students. Cyberpsychology Behav. 1998, 1, 11–17. [Google Scholar] [CrossRef]
  141. Giedd, J. The Digital Revolution and Adolescent Brain Evolution. J. Adolesc. Health 2012, 51, 101–105. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  142. Twenge, J.M. Have Smartphones Destroyed a Generation? Atlantic 2017, 9, 2017. [Google Scholar]
  143. Giedd, J. Adolescent Brain and The Natural Allure of Digital Media. Dialogues Clin. Neurosci. 2020, 22, 127–133. [Google Scholar] [CrossRef]
  144. Choi, S.W.; Kim, D.J.; Choi, J.S.; Ahn, H.; Choi, E.J.; Song, W.Y.; Kim, S.; Young, H. Comparison of Risk and Protective Factors Associated with Smartphone Addiction and Internet Addiction. J. Behav. Addict. 2015, 4, 308–314. [Google Scholar] [CrossRef]
  145. Kemp, S. Digital 2020 Reports. 2020. Available online: https://wearesocial.com/digital-2020 (accessed on 5 August 2020).
  146. Li, Y.-Y.; Sun, Y.; Meng, S.-Q.; Bao, Y.-P.; Cheng, J.-L.; Chang, X.-W.; Ran, M.-S.; Sun, Y.-K.; Kosten, T.; Strang, J.; et al. Internet Addiction Increases in the General Population during COVID-19: Evidence from China. Am. J. Addict. 2021, 30, 389–397. [Google Scholar] [CrossRef]
  147. Robertson, T.W.; Yan, Z.; Rapoza, K.A. Is Resilience a Protective Factor of Internet Addiction? Comput. Hum. Behav. 2018, 78, 255–260. [Google Scholar] [CrossRef]
  148. Zhou, P.; Zhang, C.; Liu, J.; Wang, Z. The Relationship between Resilience and Internet Addiction: A Multiple Mediation Model through Peer Relationship and Depression. Cyberpsychology Behav. Soc. Netw. 2017, 20, 634–639. [Google Scholar] [CrossRef]
  149. Wisniewski, P.; Jia, H.; Wang, N.; Zheng, S.; Xu, H.; Rosson, M.B.; Carroll, J.M. Resilience Mitigates the Negative Effects of Adolescent Internet Addiction and Online Risk Exposure. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea, 13–18 April 2015; pp. 4029–4038. [Google Scholar]
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Figure 1. PRISMA flow diagram.
Figure 1. PRISMA flow diagram.
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Figure 2. (a) Strategic diagram, (b) thematic network structure, and (c) thematic evolution structure [32].
Figure 2. (a) Strategic diagram, (b) thematic network structure, and (c) thematic evolution structure [32].
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Figure 3. Distribution of publications and citations by year (1997–2022).
Figure 3. Distribution of publications and citations by year (1997–2022).
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Figure 4. Period 1: (a) Strategic diagram and (b) performance analysis.
Figure 4. Period 1: (a) Strategic diagram and (b) performance analysis.
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Figure 5. Period 1: Thematic network structures.
Figure 5. Period 1: Thematic network structures.
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Figure 6. Period 2: (a) Strategic diagram and (b) performance analysis.
Figure 6. Period 2: (a) Strategic diagram and (b) performance analysis.
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Figure 7. Period 2: Thematic network structures.
Figure 7. Period 2: Thematic network structures.
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Figure 8. Period 3: (a) Strategic diagram and (b) performance analysis.
Figure 8. Period 3: (a) Strategic diagram and (b) performance analysis.
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Figure 9. Period 3: Thematic network structures.
Figure 9. Period 3: Thematic network structures.
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Figure 10. (a) Overlapping map and (b) thematic evolution map.
Figure 10. (a) Overlapping map and (b) thematic evolution map.
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Table
Table 1. Inclusion/exclusion criteria.
Table 1. Inclusion/exclusion criteria.
CriteriaIncludedExcludedRationale
LanguageEnglishOther languagesInternationally used and researchers’ ability to understand
Document typeJournal articlesBooks, book chapters, conference proceedings Targeting of refereed, high-quality publications
DatabaseScopusOther databases (e.g., Web of Science, PubMed, etc.)Broader coverage of journals/articles
Table
Table 2. Top 10 authors most cited in digital addiction.
Table 2. Top 10 authors most cited in digital addiction.
RankAuthorTC *TPh-İndex
1Young, Kimberly, S.3219529
2Ko, Chihhung1072752
3Yen, Chengfang1070656
4Yen, J. Y.1070645
5Leung, Louis918535
6Lemmens, Jeroen, S.592113
7Peter, Jochen592153
8Valkenburg, Patti, M.592164
9Chen, Chengchung486231
10Hawi, Nazir, S.483514
* TC: total citations; TP: total publications. Data retrieved from Scopus, 9 July 2022.
Table
Table 3. Top 10 journals in terms of the number of publications on digital addiction.
Table 3. Top 10 journals in terms of the number of publications on digital addiction.
RankJournal NameTP *TCSJRScopus Quartile
1Cyberpsychology, Behavior, and Social Networking4423391.15Q1
2Children and Youth Services Review255460.80Q1
3Cyberpsychology and Behavior256852n/an/a
4Turkish Online Journal of Educational Technology10126n/an/a
5Behavior and Information Technology84030.70Q1
6Social Science Computer Review85601.50Q1
7Computers and Education77693.68Q1
8Universal Journal of Educational Research737n/an/a
9International Journal of Adolescence and Youth7570.85Q1
10Contemporary Educational Technology6380.72Q1
* TP: Total publications; TC: Total citations; SJR: Scientific Journal Ranking Data retrieved from Scopus, 9 July 2022.
Table
Table 4. Top 10 articles cited in digital addiction.
Table 4. Top 10 articles cited in digital addiction.
RankArticle NameJournal NameAuthor(s)YearTC *
1Internet addiction: The emergence of a new clinical disorderCyberpsychology and BehaviorYoung, K. S.19982608
2Development and validation of a game addiction scale for adolescentsMedia PsychologyLemmens, J. S.; Valkenburg, P. M.; Peter, J.2009592
3Internet addiction: A new clinical phenomenon and its consequencesAmerican Behavioral ScientistYoung, K. S.2004578
4Internet addiction, usage, gratification, and pleasure experience: The Taiwan college students’ caseComputers and EducationChou, C.; Hsiao, M.-C.2000410
5Shyness and locus of control as predictors of Internet addiction and Internet useCyberpsychology and BehaviorChak, K.; Leung, L.2004402
6Internet addiction on campus: The vulnerability of college studentsCyberpsychology and BehaviorKandell, J. J.1998381
7Online gaming addiction: The role of sensation seeking, self-control, neuroticism, aggression, state anxiety, and trait anxietyCyberpsychology, Behavior, and Social NetworkingMehroof, M.; Griffiths, M.D.2010361
8Prevalence of Internet addiction and correlations with family factors among South Korean adolescentsAdolescencePark, S.-K.; Kim, J.-Y.; Cho, C.-B.2008303
9Factors Associated with Internet Addiction among AdolescentsCyberpsychology and BehaviorLam, L.-T.; Peng, Z.-W.; Mai, J.-C.; Jing, J.2009301
10Family factors of Internet addiction and substance use experience in Taiwanese adolescentsCyberpsychology and BehaviorYen, J.-Y.; Yen C.-F.; Chen, C.-C.; Chen, S.-H.; Ko, C.-H.2007287
(*) Data retrieved from Scopus, 9 July 2022.
Table
Table 5. Top 10 countries with most publications in digital addiction.
Table 5. Top 10 countries with most publications in digital addiction.
RankCountryTP *TC
1Turkey911420
2China552153
3United States494877
4South Korea391230
5Taiwan372962
6Hong Kong241675
7Russian Federation2198
8Iran18219
9Malaysia1792
10India13355
(*) Data retrieved from Scopus, 9 July 2022.
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Karakose, T.; Tülübaş, T.; Papadakis, S. Revealing the Intellectual Structure and Evolution of Digital Addiction Research: An Integrated Bibliometric and Science Mapping Approach. Int. J. Environ. Res. Public Health 2022, 19, 14883. https://doi.org/10.3390/ijerph192214883

AMA Style

Karakose T, Tülübaş T, Papadakis S. Revealing the Intellectual Structure and Evolution of Digital Addiction Research: An Integrated Bibliometric and Science Mapping Approach. International Journal of Environmental Research and Public Health. 2022; 19(22):14883. https://doi.org/10.3390/ijerph192214883

Chicago/Turabian Style

Karakose, Turgut, Tijen Tülübaş, and Stamatios Papadakis. 2022. "Revealing the Intellectual Structure and Evolution of Digital Addiction Research: An Integrated Bibliometric and Science Mapping Approach" International Journal of Environmental Research and Public Health 19, no. 22: 14883. https://doi.org/10.3390/ijerph192214883

APA Style

Karakose, T., Tülübaş, T., & Papadakis, S. (2022). Revealing the Intellectual Structure and Evolution of Digital Addiction Research: An Integrated Bibliometric and Science Mapping Approach. International Journal of Environmental Research and Public Health, 19(22), 14883. https://doi.org/10.3390/ijerph192214883

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