social media and luxury a systematic literature review

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Social media and luxury: A systematic literature review

2021, International Journal of Management Reviews

Related Papers

Delphine Veissiere

International Journal of Marketing Studies

cesare amatulli

People and companies worldwide are by now involved and attached to web 2.0 technologies and in particular to social media platforms. In this context, these digital technologies and social media have modified and improved the way to communicate and collaborate between companies and customers in all sectors through by implementing effective interaction mechanisms. However, the approach to social media and digital technologies in sectors such as luxury has been slower. Therefore, the main aim of this study is firstly to systematically examine and review the current studies conducted on the related area of ​​social media and luxury marketing. Secondly, in addition to providing an overview of the main themes and trends covered by the relevant literature, this review has focused on the consumption of luxury and the dual dimension that it can take in a social media context. Third, a systematic review of the literature on academic research on social media marketing in luxury brands was cond...

Journal of Theoretical and Applied Electronic Commerce Research

Amadea Agapie

As consumers spend more time on social media, brands can take advantage of this opportunity to better serve and communicate with their followers. Still, given the characteristics of luxury, luxury brands may be reluctant to use social media. To determine the extent to which Millennials and Generation Z consumers perceive compatibility between social media marketing and luxury brands, a purposive sampling technique was employed. We collected data from 303 Romanian visitors of the Facebook page entitled Do You Like Luxury? This page, with postings from luxury producers, was specially created and sponsored in order to raise interest in completing the questionnaires. For data analysis, we used statistical tests, including confirmatory factorial analysis, regression, and independent-samples t-test. Our research shows that, for Millennials and Generation Z, luxury brands are compatible with social media apps as marketing channels. Even in the case of inadequacy, managers can still use the...

Sophie Visvikis-siest

Creative Commons Attribution Non-Commercial License

Doç. Dr. Merve Çelik Varol

The main purpose of this search, to examine the effects of social media on luxury consumption. Online survey form created for the purpose, between the dates of 1-6 June 2016 social media has been applied to the 208 user. The Program SPSS 22.0 statistics of the survey data is realized through the evaluation of the achieved. Impact of social media on luxury consumption in determining the level of the participants, the average and standard deviation statistics were used, descriptive characteristics of the participants to examine the effects of social media on luxury consumption in terms of the kruskal-Wallis H and Mann-Whitney u tests were used. As a result of the research , the impact of social media on a low level of participants, the effects of luxury consumption on social media sites increased with the increment of income level and it was concluded that used. The effects of social media on luxury consumption, individuals ' gender, age, educational status, and the social media sites they use most in the research that does not vary according to other results that can be reached. SOSYAL MEDYANIN LÜKS TÜKETİM ÜZERİNDEKİ ETKİLERİ ÖZ Bu araştırmanın temel amacı, sosyal medyanın lüks tüketim üzerindeki etkilerini incelemektir. Amaç doğrultusunda oluşturulan çevrimiçi anket formu, 1-6 Haziran 2016 tarihleri arasında 208 sosyal medya kullanıcısına uygulanmıştır. Anketlerden ulaşılan verilerin değerlendirilmesi bilgisayar ortamında SPSS 22.0 istatistik programı aracılığıyla gerçekleştirilmiştir. Katılımcıların lüks tüketimleri üzerinde sosyal medyanın etki düzeyinin belirlenmesinde ortalama ve standart sapma istatistiklerinden, katılımcıların tanımlayıcı özellikleri açısından sosyal medyanın lüks tüketim üzerindeki etkilerini incelemek için Kruskal Wallis H ve Mann Whitney U testlerinden faydalanılmıştır. Araştırma sonucunda, sosyal medyanın katılımcılar üzerindeki etkisinin düşük düzeyde olduğu, sosyal medyanın lüks tüketim üzerindeki etkilerinin gelir düzeyi ve sosyal medya sitelerinde kullanılan süre artışıyla birlikte arttığı sonucuna varılmıştır. Sosyal medyanın lüks tüketim üzerindeki etkilerinin, bireylerin cinsiyetlerine, yaşlarına, eğitim durumları ve en fazla kullandıkları sosyal medya sitelerine göre farklılık göstermediği araştırmada ulaşılan diğer sonuçlardır.

Gilberto Sarfati

Jianqiong Xu

The advances in communication media have changed the way people search or share information. Social media are the two-way forms of communication that provide new opportunities to users to interact with each other on platforms to share ideas. This study analyses customer expectations and interactions through integrating social media and luxury fashion brands. Also, by identifying the advantages that can be obtained by businesses from social media, this paper develops a structural model that contributes to address the gap and current knowledge of consumer buying behaviour in luxury product purchases under the effect of social media. The research also suggests that it is important to understand and interact with customer expectations via the combination of social media and traditional marketing media to develop effective marketing communications. The paper concludes with some strategies that practitioners and researchers can adopt to develop effective marketing communication strategies...

Sekandar Riaz

jyoti peswani

Global Fashion Management Conference

Bruno Godey

RELATED PAPERS

Stratigraphic Evolution of Foreland Basins

Steve Dorobek

Revista Virtual de Química

Clayton Alves

Sherab Jatsho

Maria Lúcia De Castro Gomes

Lenguas Y Literaturas Indoamericanas

Arkadiusz Nocon

HANDE ORTAY

Tomlin Paul

Hans-martin Jäck

Tạp chí Y học Việt Nam

Phạm Văn Toàn

International Journal of Electronics

Eric Vannerson

arXiv (Cornell University)

Ryszard Nest

American Economic Journal: Economic Policy

Michael Devereux

ndzana jean calvin

Journal of Functional Analysis

Marta Calanchi

Treviso Tango , Lidia Ferrari

International Journal of Industrial Ergonomics

Ankit Kumar

Journal of health disparities research and practice

Eileen Rillamas-Sun

Mayya Gogina

International journal of advanced research

daniel wamalwa

Judit Besora

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

To read this content please select one of the options below:

Please note you do not have access to teaching notes, social media marketing in luxury brands: a systematic literature review and implications for management research.

Management Research Review

ISSN : 2040-8269

Article publication date: 3 April 2018

Issue publication date: 5 June 2018

Digital technologies and social media have improved the connectivity and collaboration between firms and customers in all sectors. However, in the luxury sector, the approach to social media and digital technologies has been slower than in other industries. The purpose of this paper is to review the academic literature on social media marketing in luxury brands to highlight the current state of the art, the addressed key research themes and the implications for management research and practice.

Design/methodology/approach

A systematic literature review of academic research on social media marketing has been conducted to gather, examine and synthetize studies related to luxury brands. By following a review protocol based on both automatic and manual search on the Scopus database, all relevant studies on luxury brands were identified and analyzed.

A critical conceptualization of social media marketing in luxury brands has been provided and the emerging key research themes have been categorized into four main areas.

Originality/value

Academic literature about social media marketing activities in luxury firms is very limited and existing studies focus only on certain aspects, contexts or single cases. In contrast, the value of this study, for both academics and practitioners, lies in providing, for the first time, a comprehensive and critical systematization of social media marketing academic literature in the field of luxury brands.

• Systematic review
• Co-creation
• Competitive advantage
• Social media marketing
• Digital luxury
• Luxury brand

Arrigo, E. (2018), "Social media marketing in luxury brands: A systematic literature review and implications for management research", Management Research Review , Vol. 41 No. 6, pp. 657-679. https://doi.org/10.1108/MRR-04-2017-0134

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

• Systematic Review
• Open access
• Published: 12 May 2024

Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis

• Mingxuan Du 1 ,
• Chengjia Zhao 2 ,
• Haiyan Hu 1 ,
• Ningning Ding 1 ,
• Jiankang He 1 ,
• Wenwen Tian 1 ,
• Wenqian Zhao 1 ,
• Xiujian Lin 1 ,
• Gaoyang Liu 1 ,
• Wendan Chen 1 ,
• ShuangLiu Wang 1 ,
• Pengcheng Wang 3 ,
• Dongwu Xu 1 ,
• Xinhua Shen 4 &
• Guohua Zhang 1  

BMC Psychology volume  12 , Article number:  263 ( 2024 ) Cite this article

360 Accesses

Metrics details

A growing number of studies have reported that problematic social networking use (PSNU) is strongly associated with anxiety symptoms. However, due to the presence of multiple anxiety subtypes, existing research findings on the extent of this association vary widely, leading to a lack of consensus. The current meta-analysis aimed to summarize studies exploring the relationship between PSNU levels and anxiety symptoms, including generalized anxiety, social anxiety, attachment anxiety, and fear of missing out. 209 studies with a total of 172 articles were included in the meta-analysis, involving 252,337 participants from 28 countries. The results showed a moderately positive association between PSNU and generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO) respectively (GA: r  = 0.388, 95% CI [0.362, 0.413]; SA: r  = 0.437, 95% CI [0.395, 0.478]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]), and there were different regulatory factors between PSNU and different anxiety subtypes. This study provides the first comprehensive estimate of the association of PSNU with multiple anxiety subtypes, which vary by time of measurement, region, gender, and measurement tool.

Peer Review reports

Introduction

Social network refers to online platforms that allow users to create, share, and exchange information, encompassing text, images, audio, and video [ 1 ]. The use of social network, a term encompassing various activities on these platforms, has been measured from angles such as frequency, duration, intensity, and addictive behavior, all indicative of the extent of social networking usage [ 2 ]. As of April 2023, there are 4.8 billion social network users globally, representing 59.9% of the world’s population [ 3 ]. The usage of social network is considered a normal behavior and a part of everyday life [ 4 , 5 ]. Although social network offers convenience in daily life, excessive use can lead to PSNU [ 6 , 7 ], posing potential threats to mental health, particularly anxiety symptoms (Rasmussen et al., 2020). Empirical research has shown that anxiety symptoms, including generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO), are closely related to PSNU [ 8 , 9 , 10 , 11 , 12 ]. While some empirical studies have explored the relationship between PSNU and anxiety symptoms, their conclusions are not consistent. Some studies have found a significant positive correlation [ 13 , 14 , 15 ], while others have found no significant correlation [ 16 , 17 , 18 , 19 ]. Furthermore, the degree of correlation varies widely in existing research, with reported r-values ranging from 0.12 to 0.80 [ 20 , 21 ]. Therefore, a systematic meta-analysis is necessary to clarify the impact of PSNU on individual anxiety symptoms.

Previous research lacks a unified concept of PSNU, primarily due to differing theoretical interpretations by various authors, and the use of varied standards and diagnostic tools. Currently, this phenomenon is referred to by several terms, including compulsive social networking use, problematic social networking use, excessive social networking use, social networking dependency, and social networking addiction [ 22 , 23 , 24 , 25 , 26 ]. These conceptual differences hinder the development of a cohesive and systematic research framework, as it remains unclear whether these definitions and tools capture the same underlying construct [ 27 ]. To address this lack of uniformity, this paper will use the term “problematic use” to encompass all the aforementioned nomenclatures (i.e., compulsive, excessive, dependent, and addictive use).

Regarding the relationship between PSNU and anxiety symptoms, two main perspectives exist: the first suggests a positive correlation, while the second proposes a U-shaped relationship. The former perspective, advocating a positive correlation, aligns with the social cognitive theory of mass communication. It posits that PSNU can reinforce certain cognitions, emotions, attitudes, and behaviors [ 28 , 29 ], potentially elevating individuals’ anxiety levels [ 30 ]. Additionally, the cognitive-behavioral model of pathological use, a primary framework for explaining factors related to internet-based addictions, indicates that psychiatric symptoms like depression or anxiety may precede internet addiction, implying that individuals experiencing anxiety may turn to social networking platforms as a coping mechanism [ 31 ]. Empirical research also suggests that highly anxious individuals prefer computer-mediated communication due to the control and social liberation it offers and are more likely to have maladaptive emotional regulation, potentially leading to problematic social network service use [ 32 ]. Turning to the alternate perspective, it proposes a U-shaped relationship as per the digital Goldilocks hypothesis. In this view, moderate social networking usage is considered beneficial for psychosocial adaptation, providing individuals with opportunities for social connection and support. Conversely, both excessive use and abstinence can negatively impact psychosocial adaptation [ 33 ]. In summary, both perspectives offer plausible explanations.

Incorporating findings from previous meta-analyses, we identified seven systematic reviews and two meta-analyses that investigated the association between PSNU and anxiety. The results of these meta-analyses indicated a significant positive correlation between PSNU and anxiety (ranging from 0.33 to 0.38). However, it is evident that these previous meta-analyses had certain limitations. Firstly, they focused only on specific subtypes of anxiety; secondly, they were limited to adolescents and emerging adults in terms of age. In summary, this systematic review aims to ascertain which theoretical perspective more effectively explains the relationship between PSNU and anxiety, addressing the gaps in previous meta-analyses. Additionally, the association between PSNU and anxiety could be moderated by various factors. Drawing from a broad research perspective, any individual study is influenced by researcher-specific designs and associated sample estimates. These may lead to bias compared to the broader population. Considering the selection criteria for moderating variables in empirical studies and meta-analyses [ 34 , 35 ], the heterogeneity of findings on problematic social network usage and anxiety symptoms could be driven by divergence in sample characteristics (e.g., gender, age, region) and research characteristics (measurement instrument of study variables). Since the 2019 coronavirus pandemic, heightened public anxiety may be attributed to the fear of the virus or heightened real life stress. The increased use of electronic devices, particularly smartphones during the pandemic, also instigates the prevalence of problematic social networking. Thus, our analysis focuses on three moderators: sample characteristics (participants’ gender, age, region), measurement tools (for PSNU and anxiety symptoms) and the time of measurement (before COVID-19 vs. during COVID-19).

The present study was conducted in accordance with the 2020 statement on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 36 ]. To facilitate transparency and to avoid unnecessary duplication of research, this study was registered on PROSPERO, and the number is CRD42022350902.

Literature search

Studies on the relationship between the PSNU and anxiety symptoms from 2000 to 2023 were retrieved from seven databases. These databases included China National Knowledge Infrastructure (CNKI), Wanfang Data, Chongqing VIP Information Co. Ltd. (VIP), Web of Science, ScienceDirect, PubMed, and PsycARTICLES. The search strings consisted of (a) anxiety symptoms, (b) social network, and (c) Problematic use. As shown in Table  1 , the keywords for anxiety are as follows: anxiety, generalized anxiety, social anxiety, attachment anxiety, fear of missing out, and FoMO. The keywords for social network are as follows: social network, social media, social networking site, Instagram, and Facebook. The keywords for addiction are as follows: addiction, dependence, problem/problematic use, excessive use. The search deadline was March 19, 2023. A total of 2078 studies were initially retrieved and all were identified ultimately.

Inclusion and exclusion criteria

Retrieved studies were eligible for the present meta-analysis if they met the following inclusion criteria: (a) the study provided Pearson correlation coefficients used to measure the relationship between PSNU and anxiety symptoms; (b) the study reported the sample size and the measurement instruments for the variables; (c) the study was written in English and Chinese; (d) the study provided sufficient statistics to calculate the effect sizes; (e) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, they were coded by the first measurement. In addition, studies were excluded if they: (a) examined non-problematic social network use; (b) had an abnormal sample population; (c) the results of the same sample were included in another study and (d) were case reports or review articles. Two evaluators with master’s degrees independently assessed the eligibility of the articles. A third evaluator with a PhD examined the results and resolved dissenting views.

Data extraction and quality assessment

Two evaluators independently coded the selected articles according to the following characteristics: literature information, time of measurement (before the COVID-19 vs. during the COVID-19), sample source (developed country vs. developing country), sample size, proportion of males, mean age, type of anxiety, and measurement instruments for PSNU and anxiety symptoms. The following principles needed to be adhered to in the coding process: (a) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, it was coded by the first measurement; (b) if multiple studies used the same data, the one with the most complete information was selected; (c) If studies reported t or F values rather than r , the following formula \( r=\sqrt{\frac{{t}^{2}}{{t}^{2}+df}}\) ; \( r=\sqrt{\frac{F}{F+d{f}_{e}}}\) was used to convert them into r values [ 37 , 38 ]. Additionally, if some studies only reported the correlation matrix between each dimension of PSNU and anxiety symptoms, the following formula \( {r}_{xy}=\frac{\sum {r}_{xi}{r}_{yj}}{\sqrt{n+n(n-1){r}_{xixj}}\sqrt{m+m(m-1){r}_{yiyj}}}\) was used to synthesize the r values [ 39 ], where n or m is the number of dimensions of variable x or variable y, respectively, and \( {r}_{xixj} \) or \( {r}_{yiyj}\) represents the mean of the correlation coefficients between the dimensions of variable x or variable y, respectively.

Literature quality was determined according to the meta-analysis quality evaluation scale developed [ 40 ]. The quality of the post-screening studies was assessed by five dimensions: sampling method, efficiency of sample collection, level of publication, and reliability of PSNU and anxiety symptom measurement instruments. The total score of the scale ranged from 0 to 10; higher scores indicated better quality of the literature.

Data analysis

All data were performed using Comprehensive Meta Analysis 3.3 (CMA 3.3). Pearson’s product-moment coefficient r was selected as the effect size index in this meta-analysis. Firstly, \( {\text{F}\text{i}\text{s}\text{h}\text{e}\text{r}}^{{\prime }}\text{s} Z=\frac{1}{2}\times \text{ln}\left(\frac{1+r}{1-r}\right)\) was used to convert the correlation coefficient to Fisher Z . Then the formula \( SE=\sqrt{\frac{1}{n-3}}\) was used to calculate the standard error ( SE ). Finally, the summary of r was obtained from the formula \( r=\frac{{e}^{2z}-1}{{e}^{2z}+1}\) for a comprehensive measure of the relationship between PSNU and anxiety symptoms [ 37 , 41 ].

Although the effect sizes estimated by the included studies may be similar, considering the actual differences between studies (e.g., region and gender), the random effects model was a better choice for data analysis for the current meta-analysis. The heterogeneity of the included study effect sizes was measured for significance by Cochran’s Q test and estimated quantitatively by the I 2 statistic [ 42 ]. If the results indicate there is a significant heterogeneity (the Q test: p -value < 0.05, I 2  > 75) and the results of different studies are significantly different from the overall effect size. Conversely, it indicates there are no differences between the studies and the overall effect size. And significant heterogeneity tends to indicate the possible presence of potential moderating variables. Subgroup analysis and meta-regression analysis were used to examine the moderating effect of categorical and continuous variables, respectively.

Funnel plots, fail-safe number (Nfs) and Egger linear regression were utilized to evaluate the publication bias [ 43 , 44 , 45 ]. The likelihood of publication bias was considered low if the intercept obtained from Egger linear regression was not significant. A larger Nfs indicated a lower risk of publication bias, and if Nfs < 5k + 10 (k representing the original number of studies), publication bias should be a concern [ 46 ]. When Egger’s linear regression was significant, the Duval and Tweedie’s trim-and-fill was performed to correct the effect size. If there was no significant change in the effect size, it was assumed that there was no serious publication bias [ 47 ].

A significance level of P  < 0.05 was deemed applicable in this study.

Sample characteristics

The PRISMA search process is depicted in Fig.  1 . The database search yielded 2078 records. After removing duplicate records and screening the title and abstract, the full text was subject to further evaluation. Ultimately, 172 records fit the inclusion criteria, including 209 independent effect sizes. The present meta-analysis included 68 studies on generalized anxiety, 44 on social anxiety, 22 on attachment anxiety, and 75 on fear of missing out. The characteristics of the selected studies are summarized in Table  2 . The majority of the sample group were adults. Quality scores for selected studies ranged from 0 to 10, with only 34 effect sizes below the theoretical mean, indicating high quality for the included studies. The literature included utilized BSMAS as the primary tool to measure PSNU, DASS-21-A to measure GA, IAS to measure SA, ECR to measure AA, and FoMOS to measure FoMO.

Flow chart of the search and selection strategy

Overall analysis, homogeneity tests and publication bias

As shown in Table  3 , there was significant heterogeneity between PSNU and all four anxiety symptoms (GA: Q  = 1623.090, I 2  = 95.872%; SA: Q  = 1396.828, I 2  = 96.922%; AA: Q  = 264.899, I 2  = 92.072%; FoMO: Q  = 1847.110, I 2  = 95.994%), so a random effects model was chosen. The results of the random effects model indicate a moderate positive correlation between PSNU and anxiety symptoms (GA: r  = 0.350, 95% CI [0.323, 0.378]; SA: r  = 0.390, 95% CI [0.347, 0.431]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]).

Figure  2 shows the funnel plot of the relationship between PSNU and anxiety symptoms. No significant symmetry was seen in the funnel plot of the relationship between PSNU and GA and between PSNU and SA. And the Egger’s regression results also indicated that there might be publication bias ( t  = 3.775, p  < 0.001; t  = 2.309, p  < 0.05). Therefore, it was necessary to use fail-safe number (Nfs) and the trim and fill method for further examination and correction. The Nfs for PSNU and GA as well as PSNU and SA are 4591 and 7568, respectively. Both Nfs were much larger than the standard 5 k  + 10. After performing the trim and fill method, 14 effect sizes were added to the right side of the funnel plat (Fig.  2 .a), the correlation coefficient between PSNU and GA changed to ( r  = 0.388, 95% CI [0.362, 0.413]); 10 effect sizes were added to the right side of the funnel plat (Fig.  2 .b), the correlation coefficient between PSNU and SA changed to ( r  = 0.437, 95% CI [0.395, 0.478]). The correlation coefficients did not change significantly, indicating that there was no significant publication bias associated with the relationship between PSNU and these two anxiety symptoms (GA and SA).

Funnel plot of the relationship between PSNU and anxiety symptoms. Note: Black dots indicated additional studies after using trim and fill method; ( a ) = Funnel plot of the PSNU and GA; ( b ) = Funnel plot of the PSNU and SA; ( c ) = Funnel plot of the PSNU and AA; ( d ) = Funnel plot of the PSNU and FoMO

Sensitivity analyses

Initially, the findings obtained through the one-study-removed approach indicated that the heterogeneities in the relationship between PSNU and anxiety symptoms were not attributed to any individual study. Nevertheless, it is important to note that sensitivity analysis should be performed based on literature quality [ 223 ] since low-quality literature could potentially impact result stability. In the relationship between PSNU and GA, the 10 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.402, 95% CI [0.375, 0.428]); In the relationship between PSNU and SA, the 8 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.431, 95% CI [0.387, 0.472]); In the relationship between PSNU and AA, the 5 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.367, 95% CI [0.298, 0.433]); In the relationship between PSNU and FoMO, the 11 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.508, 95% CI [0.470, 0.544]). The revised estimates indicate that meta-analysis results were stable.

Moderator analysis

The impact of moderator variables on the relation between psnu and ga.

The results of subgroup analysis and meta-regression are shown in Table  4 , the time of measurement significantly moderated the correlation between PSNU and GA ( Q between = 19.268, df  = 2, p  < 0.001). The relation between the two variables was significantly higher during the COVID-19 ( r  = 0.392, 95% CI [0.357, 0.425]) than before the COVID-19 ( r  = 0.270, 95% CI [0.227, 0.313]) or measurement time uncertain ( r  = 0.352, 95% CI [0.285, 0.415]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). The relation was significantly higher when PSNU was measured with the BSMAS ( r  = 0.373, 95% CI [0.341, 0.404]) compared to others ( r  = 0.301, 95% CI [0.256, 0.344]).

The moderating effect of the GA measurement was significant ( Q between = 60.061, df  = 5, p  < 0.001). Specifically, when GA measured by the GAD ( r  = 0.398, 95% CI [0.356, 0.438]) and the DASS-21-A ( r  = 0.433, 95% CI [0.389, 0.475]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the STAI ( r  = 0.232, 95% CI [0.187, 0.276]).

For the relation between PSNU and GA, the moderating effect of region, gender and age were not significant.

The impact of moderator variables on the relation between PSNU and SA

The effects of the moderating variables in the relation between PSNU and SA were shown in Table  5 . The results revealed a gender-moderated variances between the two variables (b = 0.601, 95% CI [ 0.041, 1.161], Q model (1, k = 41) = 4.705, p  = 0.036).

For the relation between PSNU and SA, the moderating effects of time of measurement, region, measurement of PSNU and SA, and age were not significant.

The impact of moderator variables on the relation between PSNU and AA

The effects of the moderating variables in the relation between PSNU and AA were shown in Table  6 , region significantly moderated the correlation between PSNU and AA ( Q between = 6.410, df  = 2, p  = 0.041). The correlation between the two variables was significantly higher in developing country ( r  = 0.378, 95% CI [0.304, 0.448]) than in developed country ( r  = 0.242, 95% CI [0.162, 0.319]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). Specifically, when AA was measured by the GPIUS-2 ( r  = 0.484, 95% CI [0.200, 0.692]) and the PMSMUAQ ( r  = 0.443, 95% CI [0.381, 0.501]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the BSMAS ( r  = 0.248, 95% CI [0.161, 0.331]) and others ( r  = 0.313, 95% CI [0.250, 0.372]).

The moderating effect of the AA measurement was significant ( Q between = 17.283, df  = 2, p  < 0.001). The correlation was significantly higher when measured using the ECR ( r  = 0.386, 95% CI [0.338, 0.432]) compared to the RQ ( r  = 0.200, 95% CI [0.123, 0.275]).

For the relation between PSNU and AA, the moderating effects of time of measurement, region, gender, and age were not significant.

The impact of moderator variables on the relation between PSNU and FoMO

The effects of the moderating variables in the relation between PSNU and FoMO were shown in Table  7 , the moderating effect of the PSNU measurement was significant ( Q between = 8.170, df  = 2, p  = 0.017). Among the sub-dimensions, the others was excluded because there was only one sample. Specifically, when measured using the FoMOS-MSME ( r  = 0.630, 95% CI [0.513, 0.725]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the FoMOS ( r  = 0.472, 95% CI [0.432, 0.509]) and the T-S FoMOS ( r  = 0.557, 95% CI [0.463, 0.639]).

For the relationship between PSNU and FoMO, the moderating effects of time of measurement, region, measurement of PSNU, gender and age were not significant.

Through systematic review and meta-analysis, this study established a positive correlation between PSNU and anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out), confirming a linear relationship and partially supporting the Social Cognitive Theory of Mass Communication [ 28 ] and the Cognitive Behavioral Model of Pathological Use [ 31 ]. Specifically, a significant positive correlation between PSNU and GA was observed, implying that GA sufferers might resort to social network for validation or as an escape from reality, potentially alleviating their anxiety. Similarly, the meta-analysis demonstrated a strong positive correlation between PSNU and SA, suggesting a preference for computer-mediated communication among those with high social anxiety due to perceived control and liberation offered by social network. This preference is often accompanied by maladaptive emotional regulation, predisposing them to problematic use. In AA, a robust positive correlation was found with PSNU, indicating a higher propensity for such use among individuals with attachment anxiety. Notably, the study identified the strongest correlation in the context of FoMO. FoMO’s significant association with PSNU is multifaceted, stemming from the real-time nature of social networks that engenders a continuous concern about missing crucial updates or events. This drives frequent engagement with social network, thereby establishing a direct link to problematic usage patterns. Additionally, social network’s feedback loops amplify this effect, intensifying FoMO. The culture of social comparison on these platforms further exacerbates FoMO, as users frequently compare their lives with others’ selectively curated portrayals, enhancing both their social networking usage frequency and the pursuit for social validation. Furthermore, the integral role of social network in modern life broadens FoMO’s scope, encompassing anxieties about staying informed and connected.

The notable correlation between FoMO and PSNU can be comprehensively understood through various perspectives. FoMO is inherently linked to the real-time nature of social networks, which cultivates an ongoing concern about missing significant updates or events in one’s social circle [ 221 ]. This anxiety prompts frequent engagement with social network, leading to patterns of problematic use. Moreover, the feedback loops in social network algorithms, designed to enhance user engagement, further intensify this fear [ 224 ]. Additionally, social comparison, a common phenomenon on these platforms, exacerbates FoMO as users continuously compare their lives with the idealized representations of others, amplifying feelings of missing out on key social experiences [ 225 ]. This behavior not only increases social networking usage but also is closely linked to the quest for social validation and identity construction on these platforms. The extensive role of social network in modern life further amplifies FoMO, as these platforms are crucial for information exchange and maintaining social ties. FoMO thus encompasses more than social concerns, extending to anxieties about staying informed with trends and dynamics within social networks [ 226 ]. The multifaceted nature of FoMO in relation to social network underscores its pronounced correlation with problematic social networking usage. In essence, the combination of social network’s intrinsic characteristics, psychological drivers of user behavior, the culture of social comparison, and the pervasiveness of social network in everyday life collectively make FoMO the most pronouncedly correlated anxiety type with PSNU.

Additionally, we conducted subgroup analyses on the timing of measurement (before COVID-19 vs. during COVID-19), measurement tools (for PSNU and anxiety symptoms), sample characteristics (participants’ region), and performed a meta-regression analysis on gender and age in the context of PSNU and anxiety symptoms. It was found that the timing of measurement, tools used for assessing PSNU and anxiety, region, and gender had a moderating effect, whereas age did not show a significant moderating impact.

Firstly, the relationship between PSNU and anxiety symptoms was significantly higher during the COVID-19 period than before, especially between PSNU and GA. However, the moderating effect of measurement timing was not significant in the relationship between PSNU and other types of anxiety. This could be attributed to the increased uncertainty and stress during the pandemic, leading to heightened levels of general anxiety [ 227 ]. The overuse of social network for information seeking and anxiety alleviation might have paradoxically exacerbated anxiety symptoms, particularly among individuals with broad future-related worries [ 228 ]. While the COVID-19 pandemic altered the relationship between PSNU and GA, its impact on other types of anxiety (such as SA and AA) may not have been significant, likely due to these anxiety types being more influenced by other factors like social skills and attachment styles, which were minimally impacted by the epidemic.

Secondly, the observed variance in the relationship between PSNU and AA across different economic contexts, notably between developing and developed countries, underscores the multifaceted influence of socio-economic, cultural, and technological factors on this dynamic. The amplified connection in developing countries may be attributed to greater socio-economic challenges, distinct cultural norms regarding social support and interaction, rising social network penetration, especially among younger demographics, and technological disparities influencing accessibility and user experience [ 229 , 230 ]. Moreover, the role of social network as a coping mechanism for emotional distress, potentially fostering insecure attachment patterns, is more pronounced in these settings [ 231 ]. These findings highlight the necessity of considering contextual variations in assessing the psychological impacts of social network, advocating for a nuanced understanding of how socio-economic and cultural backgrounds mediate the relationship between PSNU and mental health outcomes [ 232 ]. Additionally, the relationship between PSNU and other types of anxiety (such as GA and SA) presents uniform characteristics across different economic contexts.

Thirdly, the significant moderating effects of measurement tools in the context of PSNU and its correlation with various forms of anxiety, including GA, and AA, are crucial in interpreting the research findings. Specifically, the study reveals that the Bergen Social Media Addiction Scale (BSMAS) demonstrates a stronger correlation between PSNU and GA, compared to other tools. Similarly, for AA, the Griffiths’ Problematic Internet Use Scale 2 (GPIUS2) and the Problematic Media Social Media Use Assessment Questionnaire (PMSMUAQ) show a more pronounced correlation with AA than the BSMAS or other instruments, but for SA and FoMO, the PSNU instrument doesn’t significantly moderate the correlation. The PSNU measurement tool typically contains an emotional change dimension. SA and FoMO, due to their specific conditional stimuli triggers and correlation with social networks [ 233 , 234 ], are likely to yield more consistent scores in this dimension, while GA and AA may be less reliable due to their lesser sensitivity to specific conditional stimuli. Consequently, the adjustment effects of PSNU measurements vary across anxiety symptoms. Regarding the measurement tools for anxiety, different scales exhibit varying degrees of sensitivity in detecting the relationship with PSNU. The Generalized Anxiety Disorder Scale (GAD) and the Depression Anxiety Stress Scales 21 (DASS-21) are more effective in illustrating a strong relationship between GA and PSNU than the State-Trait Anxiety Inventory (STAI). In the case of AA, the Experiences in Close Relationships-21 (ECR-21) provides a more substantial correlation than the Relationship Questionnaire (RQ). Furthermore, for FoMO, the Fear of Missing Out Scale - Multi-Social Media Environment (FoMOS-MSME) is more indicative of a strong relationship with PSNU compared to the standard FoMOS or the T-S FoMOS. These findings underscore the importance of the selection of appropriate measurement tools in research. Different tools, due to their unique design, focus, and sensitivity, can reveal varying degrees of correlation between PSNU and anxiety disorders. This highlights the need for careful consideration of tool characteristics and their potential impact on research outcomes. It also cautions against drawing direct comparisons between studies without acknowledging the possible variances introduced by the use of different measurement instruments.

Fourthly, the significant moderating role of gender in the relationship between PSNU and SA, particularly pronounced in samples with a higher proportion of females. Women tend to engage more actively and emotionally with social network, potentially leading to an increased dependency on these platforms when confronting social anxiety [ 235 ]. This intensified use might amplify the association between PSNU and SA. Societal and cultural pressures, especially those related to appearance and social status, are known to disproportionately affect women, possibly exacerbating their experience of social anxiety and prompting a greater reliance on social network for validation and support [ 236 ]. Furthermore, women’s propensity to seek emotional support and express themselves on social network platforms [ 237 ] could strengthen this link, particularly in the context of managing social anxiety. Consequently, the observed gender differences in the relationship between PSNU and SA underscore the importance of considering gender-specific dynamics and cultural influences in psychological research related to social network use. In addition, gender consistency was observed in the association between PSNU and other types of anxiety, indicating no significant gender disparities.

Fifthly, the absence of a significant moderating effect of age on the relationship between PSNU and various forms of anxiety suggests a pervasive influence of social network across different age groups. This finding indicates that the impact of PSNU on anxiety is relatively consistent, irrespective of age, highlighting the universal nature of social network’s psychological implications [ 238 ]. Furthermore, this uniformity suggests that other factors, such as individual psychological traits or socio-cultural influences, might play a more crucial role in the development of anxiety related to social networking usage than age [ 239 ]. The non-significant role of age also points towards a potential generational overlap in social networking usage patterns and their psychological effects, challenging the notion that younger individuals are uniquely susceptible to the adverse effects of social network on mental health [ 240 ]. Therefore, this insight necessitates a broader perspective in understanding the dynamics of social network and mental health, one that transcends age-based assumptions.

Limitations

There are some limitations in this research. First, most of the studies were cross-sectional surveys, resulting in difficulties in inferring causality of variables, longitudinal study data will be needed to evaluate causal interactions in the future. Second, considerable heterogeneity was found in the estimated results, although heterogeneity can be partially explained by differences in study design (e.g., Time of measurement, region, gender, and measurement tools), but this can introduce some uncertainty in the aggregation and generalization of the estimated results. Third, most studies were based on Asian samples, which limits the generality of the results. Fourth, to minimize potential sources of heterogeneity, some less frequently used measurement tools were not included in the classification of measurement tools, which may have some impact on the results of heterogeneity interpretation. Finally, since most of the included studies used self-reported scales, it is possible to get results that deviate from the actual situation to some extent.

This meta-analysis aims to quantifies the correlations between PSNU and four specific types of anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out). The results revealed a significant moderate positive association between PSNU and each of these anxiety symptoms. Furthermore, Subgroup analysis and meta-regression analysis indicated that gender, region, time of measurement, and instrument of measurement significantly influenced the relationship between PSNU and specific anxiety symptoms. Specifically, the measurement time and GA measurement tools significantly influenced the relationship between PSNU and GA. Gender significantly influenced the relationship between PSNU and SA. Region, PSNU measurement tools, and AA measurement tools all significantly influenced the relationship between PSNU and AA. The FoMO measurement tool significantly influenced the relationship between PSNU and FoMO. Regarding these findings, prevention interventions for PSNU and anxiety symptoms are important.

Data availability

The datasets are available from the corresponding author on reasonable request.

Abbreviations

• Problematic social networking use
• Generalized anxiety
• Social anxiety
• Attachment anxiety

Fear of miss out

Bergen Social Media Addiction Scale

Facebook Addiction Scale

Facebook Intrusion Questionnaire

Generalized Problematic Internet Use Scale 2

Problematic Mobile Social Media Usage Assessment Questionnaire

Social Network Addiction Tendency Scale

Brief Symptom Inventory

The anxiety subscale of the Depression Anxiety Stress Scales

Generalized Anxiety Disorder

The anxiety subscale of the Hospital Anxiety and Depression Scale

State-Trait Anxiety Inventory

Interaction Anxiousness Scale

Liebowitz Social Anxiety Scale

Social Anxiety Scale for Social Media Users

Social Anxiety for Adolescents

Social Anxiety Subscale of the Self-Consciousness Scale

Social Interaction Anxiety Scale

Experiences in Close Relationship Scale

Relationship questionnaire

Fear of Missing Out Scale

FoMO Measurement Scale in the Mobile Social Media Environment

Trait-State Fear of missing Out Scale

Rozgonjuk D, Sindermann C, Elhai JD, Montag C. Fear of missing out (FoMO) and social media’s impact on daily-life and productivity at work: do WhatsApp, Facebook, Instagram, and Snapchat Use disorders mediate that association? Addict Behav. 2020;110:106487.

Article   PubMed   Google Scholar  

Mieczkowski H, Lee AY, Hancock JT. Priming effects of social media use scales on well-being outcomes: the influence of intensity and addiction scales on self-reported depression. Social Media + Soc. 2020;6(4):2056305120961784.

Article   Google Scholar  

Global digital population as of April. 2023 [ https://www.statista.com/statistics/617136/digital-population-worldwide/ ].

Marengo D, Settanni M, Fabris MA, Longobardi C. Alone, together: fear of missing out mediates the link between peer exclusion in WhatsApp classmate groups and psychological adjustment in early-adolescent teens. J Social Personal Relationships. 2021;38(4):1371–9.

Marengo D, Fabris MA, Longobardi C, Settanni M. Smartphone and social media use contributed to individual tendencies towards social media addiction in Italian adolescents during the COVID-19 pandemic. Addict Behav. 2022;126:107204.

Müller SM, Wegmann E, Stolze D, Brand M. Maximizing social outcomes? Social zapping and fear of missing out mediate the effects of maximization and procrastination on problematic social networks use. Comput Hum Behav. 2020;107:106296.

Sun Y, Zhang Y. A review of theories and models applied in studies of social media addiction and implications for future research. Addict Behav. 2021;114:106699.

Boustead R, Flack M. Moderated-mediation analysis of problematic social networking use: the role of anxious attachment orientation, fear of missing out and satisfaction with life. Addict Behav 2021, 119.

Hussain Z, Griffiths MD. The associations between problematic social networking Site Use and Sleep Quality, attention-deficit hyperactivity disorder, Depression, anxiety and stress. Int J Mental Health Addict. 2021;19(3):686–700.

Gori A, Topino E, Griffiths MD. The associations between attachment, self-esteem, fear of missing out, daily time expenditure, and problematic social media use: a path analysis model. Addict Behav. 2023;141:107633.

Marino C, Manari T, Vieno A, Imperato C, Spada MM, Franceschini C, Musetti A. Problematic social networking sites use and online social anxiety: the role of attachment, emotion dysregulation and motives. Addict Behav. 2023;138:107572.

Tobin SJ, Graham S. Feedback sensitivity as a mediator of the relationship between attachment anxiety and problematic Facebook Use. Cyberpsychology Behav Social Netw. 2020;23(8):562–6.

Brailovskaia J, Rohmann E, Bierhoff H-W, Margraf J. The anxious addictive narcissist: the relationship between grandiose and vulnerable narcissism, anxiety symptoms and Facebook Addiction. PLoS ONE 2020, 15(11).

Kim S-S, Bae S-M. Social Anxiety and Social Networking Service Addiction Proneness in University students: the Mediating effects of Experiential Avoidance and interpersonal problems. Psychiatry Invest. 2022;19(8):702–702.

Zhao J, Ye B, Yu L, Xia F. Effects of Stressors of COVID-19 on Chinese College Students’ Problematic Social Media Use: A Mediated Moderation Model. Front Psychiatry 2022, 13.

Astolfi Cury GS, Takannune DM, Prates Herrerias GS, Rivera-Sequeiros A, de Barros JR, Baima JP, Saad-Hossne R, Sassaki LY. Clinical and Psychological Factors Associated with Addiction and Compensatory Use of Facebook among patients with inflammatory bowel disease: a cross-sectional study. Int J Gen Med. 2022;15:1447–57.

Balta S, Emirtekin E, Kircaburun K, Griffiths MD. Neuroticism, trait fear of missing out, and Phubbing: the mediating role of state fear of missing out and problematic Instagram Use. Int J Mental Health Addict. 2020;18(3):628–39.

Boursier V, Gioia F, Griffiths MD. Do selfie-expectancies and social appearance anxiety predict adolescents’ problematic social media use? Comput Hum Behav. 2020;110:106395.

Worsley JD, McIntyre JC, Bentall RP, Corcoran R. Childhood maltreatment and problematic social media use: the role of attachment and depression. Psychiatry Res. 2018;267:88–93.

de Bérail P, Guillon M, Bungener C. The relations between YouTube addiction, social anxiety and parasocial relationships with YouTubers: a moderated-mediation model based on a cognitive-behavioral framework. Comput Hum Behav. 2019;99:190–204.

Naidu S, Chand A, Pandaram A, Patel A. Problematic internet and social network site use in young adults: the role of emotional intelligence and fear of negative evaluation. Pers Indiv Differ. 2023;200:111915.

Apaolaza V, Hartmann P, D’Souza C, Gilsanz A. Mindfulness, compulsive Mobile Social Media Use, and derived stress: the mediating roles of self-esteem and social anxiety. Cyberpsychology Behav Social Netw. 2019;22(6):388–96.

Demircioglu ZI, Goncu-Kose A. Antecedents of problematic social media use and cyberbullying among adolescents: attachment, the dark triad and rejection sensitivity. Curr Psychol (New Brunsw NJ) 2022:1–19.

Gao Q, Li Y, Zhu Z, Fu E, Bu X, Peng S, Xiang Y. What links to psychological needs satisfaction and excessive WeChat use? The mediating role of anxiety, depression and WeChat use intensity. BMC Psychol. 2021;9(1):105–105.

Article   PubMed   PubMed Central   Google Scholar  

Malak MZ, Shuhaiber AH, Al-amer RM, Abuadas MH, Aburoomi RJ. Correlation between psychological factors, academic performance and social media addiction: model-based testing. Behav Inform Technol. 2022;41(8):1583–95.

Song C. The effect of the need to belong on mobile phone social media dependence of middle school students: Chain mediating roles of fear of missing out and maladaptive cognition. Sichuan Normal University; 2022.

Tokunaga RS, Rains SA. A review and meta-analysis examining conceptual and operational definitions of problematic internet use. Hum Commun Res. 2016;42(2):165–99.

Bandura A. Social cognitive theory of mass communication. Media effects. edn.: Routledge; 2009. pp. 110–40.

Valkenburg PM, Peter J, Walther JB. Media effects: theory and research. Ann Rev Psychol. 2016;67:315–38.

Slater MD. Reinforcing spirals: the mutual influence of media selectivity and media effects and their impact on individual behavior and social identity. Communication Theory. 2007;17(3):281–303.

Ahmed E, Vaghefi I. Social media addiction: A systematic review through cognitive-behavior model of pathological use. 2021.

She R, han Mo PK, Li J, Liu X, Jiang H, Chen Y, Ma L, fai Lau JT. The double-edged sword effect of social networking use intensity on problematic social networking use among college students: the role of social skills and social anxiety. Comput Hum Behav. 2023;140:107555.

Przybylski AK, Weinstein N. A large-scale test of the goldilocks hypothesis: quantifying the relations between digital-screen use and the mental well-being of adolescents. Psychol Sci. 2017;28(2):204–15.

Ran G, Li J, Zhang Q, Niu X. The association between social anxiety and mobile phone addiction: a three-level meta-analysis. Comput Hum Behav. 2022;130:107198.

Fioravanti G, Casale S, Benucci SB, Prostamo A, Falone A, Ricca V, Rotella F. Fear of missing out and social networking sites use and abuse: a meta-analysis. Comput Hum Behav. 2021;122:106839.

Moher D, Liberati A, Tetzlaff J, Altman DG, Group* P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–9.

Card NA. Applied meta-analysis for social science research. Guilford; 2015.

Peterson RA, Brown SP. On the use of beta coefficients in meta-analysis. J Appl Psychol. 2005;90(1):175.

Hunter JE, Schmidt FL. Methods of meta-analysis: correcting error and bias in research findings. Sage; 2004.

Zhang Y, Li S, Yu G. The relationship between self-esteem and social anxiety: a meta-analysis with Chinese students. Adv Psychol Sci. 2019;27(6):1005–18.

Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Introduction to meta-analysis. Wiley; 2021.

Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.

Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34.

Light RJ, Pillemer DB. Summing up: the science of reviewing research. Harvard University Press; 1984.

Rosenthal R. Meta-Analytic Procedures for Social Science Research Sage Publications: Beverly Hills, 1984, 148 pp. Educational Researcher 1986;15(8):18–20.

Rothstein HR, Sutton AJ, Borenstein M. Publication bias in meta-analysis. Publication bias meta‐analysis: Prev Assess Adjustments 2005:1–7.

Duval S, Tweedie R. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta‐analysis. Biometrics. 2000;56(2):455–63.

Al-Mamun F, Hosen I, Griffiths MD, Mamun MA. Facebook use and its predictive factors among students: evidence from a lower- and middle-income country, Bangladesh. Front Psychiatry 2022, 13.

Schou Andreassen C, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, Pallesen S. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behaviors: J Soc Psychologists Addict Behav. 2016;30(2):252–62.

Arikan G, Acar IH, Ustundag-Budak AM. A two-generation study: The transmission of attachment and young adults’ depression, anxiety, and social media addiction. Addict Behav 2022, 124.

Arpaci I, Karatas K, Kiran F, Kusci I, Topcu A. Mediating role of positivity in the relationship between state anxiety and problematic social media use during the COVID-19 pandemic. Death Stud. 2022;46(10):2287–97.

Brailovskaia J, Margraf J. Facebook Addiction Disorder (FAD) among German students-A longitudinal approach. PLoS ONE 2017, 12(12).

Brailovskaia J, Margraf J. The relationship between burden caused by coronavirus (Covid-19), addictive social media use, sense of control and anxiety. Comput Hum Behav. 2021;119:106720–106720.

Brailovskaia J, Margraf J. Addictive social media use during Covid-19 outbreak: validation of the Bergen Social Media Addiction Scale (BSMAS) and investigation of protective factors in nine countries. Curr Psychol (New Brunsw NJ) 2022:1–19.

Brailovskaia J, Krasavtseva Y, Kochetkov Y, Tour P, Margraf J. Social media use, mental health, and suicide-related outcomes in Russian women: a cross-sectional comparison between two age groups. Women’s Health (London England). 2022;18:17455057221141292–17455057221141292.

PubMed   Google Scholar  

Chang C-W, Huang R-Y, Strong C, Lin Y-C, Tsai M-C, Chen IH, Lin C-Y, Pakpour AHH, Griffiths MDD. Reciprocal relationships between Problematic Social Media Use, problematic gaming, and psychological distress among University students: a 9-Month Longitudinal Study. Front Public Health 2022, 10.

Charzynska E, Sussman S, Atroszko PA. Profiles of potential behavioral addictions’ severity and their associations with gender, personality, and well-being: A person-centered approach. Addict Behav 2021, 119.

Chen C-Y, Chen IH, Pakpour AH, Lin C-Y, Griffiths MD. Internet-related behaviors and psychological distress among Schoolchildren during the COVID-19 School Hiatus. Cyberpsychology Behav Social Netw. 2021;24(10):654–63.

Da Veiga GF, Sotero L, Pontes HM, Cunha D, Portugal A, Relvas AP. Emerging adults and Facebook Use: the validation of the Bergen Facebook Addiction Scale (BFAS). Int J Mental Health Addict. 2019;17(2):279–94.

Dadiotis A, Bacopoulou F, Kokka I, Vlachakis D, Chrousos GP, Darviri C, Roussos P. Validation of the Greek version of the Bergen Social Media Addiction Scale in Undergraduate Students. EMBnetjournal 2021, 26.

Fekih-Romdhane F, Jahrami H, Away R, Trabelsi K, Pandi-Perumal SR, Seeman MV, Hallit S, Cheour M. The relationship between technology addictions and schizotypal traits: mediating roles of depression, anxiety, and stress. BMC Psychiatry 2023, 23(1).

Flynn S, Noone C, Sarma KM. An exploration of the link between adult attachment and problematic Facebook use. BMC Psychol. 2018;6(1):34–34.

Fung XCC, Siu AMH, Potenza MN, O’Brien KS, Latner JD, Chen C-Y, Chen IH, Lin C-Y. Problematic use of internet-related activities and Perceived Weight Stigma in Schoolchildren: a longitudinal study across different epidemic periods of COVID-19 in China. Front Psychiatry 2021, 12.

Gonzalez-Nuevo C, Cuesta M, Muniz J, Postigo A, Menendez-Aller A, Kuss DJ. Problematic Use of Social Networks during the First Lockdown: User Profiles and the Protective Effect of Resilience and Optimism. Journal of Clinical Medicine 2022, 11(24).

Hou X-L, Wang H-Z, Hu T-Q, Gentile DA, Gaskin J, Wang J-L. The relationship between perceived stress and problematic social networking site use among Chinese college students. J Behav Addictions. 2019;8(2):306–17.

Hussain Z, Wegmann E. Problematic social networking site use and associations with anxiety, attention deficit hyperactivity disorder, and resilience. Computers Hum Behav Rep. 2021;4:100125.

Imani V, Ahorsu DK, Taghizadeh N, Parsapour Z, Nejati B, Chen H-P, Pakpour AH. The mediating roles of anxiety, Depression, Sleepiness, Insomnia, and Sleep Quality in the Association between Problematic Social Media Use and Quality of Life among patients with Cancer. Healthcare 2022, 10(9).

Islam MS, Sujan MSH, Tasnim R, Mohona RA, Ferdous MZ, Kamruzzaman S, Toma TY, Sakib MN, Pinky KN, Islam MR et al. Problematic smartphone and Social Media Use among Bangladeshi College and University students amid COVID-19: the role of Psychological Well-Being and Pandemic related factors. Front Psychiatry 2021, 12.

Islam MS, Jahan I, Dewan MAA, Pontes HM, Koly KN, Sikder MT, Rahman M. Psychometric properties of three online-related addictive behavior instruments among Bangladeshi school-going adolescents. PLoS ONE 2022, 17(12).

Jahan I, Hosen I, Al Mamun F, Kaggwa MM, Griffiths MD, Mamun MA. How has the COVID-19 pandemic impacted Internet Use behaviors and facilitated problematic internet use? A Bangladeshi study. Psychol Res Behav Manage. 2021;14:1127–38.

Jiang Y. Problematic social media usage and anxiety among University Students during the COVID-19 pandemic: the mediating role of Psychological Capital and the moderating role of academic burnout. Front Psychol. 2021;12:612007–612007.

Kim M-R, Oh J-W, Huh B-Y. Analysis of factors related to Social Network Service Addiction among Korean High School Students. J Addictions Nurs. 2020;31(3):203–12.

Koc M, Gulyagci S. Facebook addiction among Turkish college students: the role of psychological health, demographic, and usage characteristics. Cyberpsychology Behav Social Netw. 2013;16(4):279–84.

Lin C-Y, Namdar P, Griffiths MD, Pakpour AH. Mediated roles of generalized trust and perceived social support in the effects of problematic social media use on mental health: a cross-sectional study. Health Expect. 2021;24(1):165–73.

Lin C-Y, Imani V, Griffiths MD, Brostrom A, Nygardh A, Demetrovics Z, Pakpour AH. Temporal associations between morningness/eveningness, problematic social media use, psychological distress and daytime sleepiness: mediated roles of sleep quality and insomnia among young adults. J Sleep Res 2021, 30(1).

Lozano Blasco R, Latorre Cosculluela C, Quilez Robres A. Social Network Addiction and its impact on anxiety level among University students. Sustainability 2020, 12(13).

Marino C, Musetti A, Vieno A, Manari T, Franceschini C. Is psychological distress the key factor in the association between problematic social networking sites and poor sleep quality? Addict Behav 2022, 133.

Meshi D, Ellithorpe ME. Problematic social media use and social support received in real-life versus on social media: associations with depression, anxiety and social isolation. Addict Behav 2021, 119.

Mitropoulou EM, Karagianni M, Thomadakis C. Social Media Addiction, Self-Compassion, and Psychological Well-Being: a structural equation Model. Alpha Psychiatry. 2022;23(6):298–304.

Ozimek P, Brailovskaia J, Bierhoff H-W. Active and passive behavior in social media: validating the Social Media Activity Questionnaire (SMAQ). Telematics Inf Rep. 2023;10:100048.

Phillips WJ, Wisniewski AT. Self-compassion moderates the predictive effects of social media use profiles on depression and anxiety. Computers Hum Behav Rep. 2021;4:100128.

Reer F, Festl R, Quandt T. Investigating problematic social media and game use in a nationally representative sample of adolescents and younger adults. Behav Inform Technol. 2021;40(8):776–89.

Satici B, Kayis AR, Griffiths MD. Exploring the Association between Social Media Addiction and relationship satisfaction: psychological distress as a Mediator. Int J Mental Health Addict 2021.

Sediri S, Zgueb Y, Ouanes S, Ouali U, Bourgou S, Jomli R, Nacef F. Women’s mental health: acute impact of COVID-19 pandemic on domestic violence. Archives Womens Mental Health. 2020;23(6):749–56.

Shabahang R, Shim H, Aruguete MS, Zsila A. Oversharing on Social Media: anxiety, Attention-Seeking, and Social Media Addiction Predict the breadth and depth of sharing. Psychol Rep 2022:332941221122861–332941221122861.

Sotero L, Ferreira Da Veiga G, Carreira D, Portugal A, Relvas AP. Facebook Addiction and emerging adults: the influence of sociodemographic variables, family communication, and differentiation of self. Escritos De Psicología - Psychol Writings. 2019;12(2):81–92.

Stockdale LA, Coyne SM. Bored and online: reasons for using social media, problematic social networking site use, and behavioral outcomes across the transition from adolescence to emerging adulthood. J Adolesc. 2020;79:173–83.

Wang Z, Yang H, Elhai JD. Are there gender differences in comorbidity symptoms networks of problematic social media use, anxiety and depression symptoms? Evidence from network analysis. Pers Indiv Differ. 2022;195:111705.

White-Gosselin C-E, Poulin F. Associations Between Young Adults’ Social Media Addiction, Relationship Quality With Parents, and Internalizing Problems: A Path Analysis Model. 2022.

Wong HY, Mo HY, Potenza MN, Chan MNM, Lau WM, Chui TK, Pakpour AH, Lin C-Y. Relationships between Severity of Internet Gaming Disorder, Severity of Problematic Social Media Use, Sleep Quality and Psychological Distress. Int J Environ Res Public Health 2020, 17(6).

Yam C-W, Pakpour AH, Griffiths MD, Yau W-Y, Lo C-LM, Ng JMT, Lin C-Y, Leung H. Psychometric testing of three Chinese online-related addictive Behavior instruments among Hong Kong University students. Psychiatr Q. 2019;90(1):117–28.

Yuan Y, Zhong Y. A survey on the use of social networks and mental health of college students during the COVID-19 pandemic. J Campus Life Mental Health\. 2021;19(3):209–12.

Google Scholar  

Yurdagul C, Kircaburun K, Emirtekin E, Wang P, Griffiths MD. Psychopathological consequences related to problematic Instagram Use among adolescents: the mediating role of body image dissatisfaction and moderating role of gender. Int J Mental Health Addict. 2021;19(5):1385–97.

Zhang W, Pu J, He R, Yu M, Xu L, He X, Chen Z, Gan Z, Liu K, Tan Y, et al. Demographic characteristics, family environment and psychosocial factors affecting internet addiction in Chinese adolescents. J Affect Disord. 2022;315:130–8.

Zhang L, Wu Y, Jin T, Jia Y. Revision and validation of the Chinese short version of social media disorder. Mod Prev Med. 2021;48(8):1350–3.

Zhang X, Fan L. The influence of anxiety on colleges’ life satisfaction. Chin J Health Educ. 2021;37(5):469–72.

Zhao M, Wang H, Dong Y, Niu Y, Fang Y. The relationship between self-esteem and wechat addiction among undergraduate students: the multiple mediating roles of state anxiety and online interpersonal trust. J Psychol Sci. 2021;44(1):104–10.

Zhao J, Zhou Z, Sun B, Zhang X, Zhang L, Fu S. Attentional Bias is Associated with negative emotions in problematic users of Social Media as measured by a dot-probe Task. Int J Environ Res Public Health 2022, 19(24).

Atroszko PA, Balcerowska JM, Bereznowski P, Biernatowska A, Pallesen S, Schou Andreassen C. Facebook addiction among Polish undergraduate students: validity of measurement and relationship with personality and well-being. Comput Hum Behav. 2018;85:329–38.

Chen Y, Li R, Zhang P, Liu X. The moderating role of state attachment anxiety and avoidance between social anxiety and social networking sites Addiction. Psychol Rep. 2020;123(3):633–47.

Chen B, Zheng X, Sun X. The relationship between problematic social media use and online social anxiety: the roles of social media cognitive overload and dispositional mindfulness. Psychol Dev Educ. 2023;39(5):743–51.

Chentsova VO, Bravo AJ, Mezquita L, Pilatti A, Hogarth L, Cross-Cultural AS. Internalizing symptoms, rumination, and problematic social networking site use: a cross national examination among young adults in seven countries. Addict Behav 2023, 136.

Chu X, Ji S, Wang X, Yu J, Chen Y, Lei L. Peer phubbing and social networking site addiction: the mediating role of social anxiety and the moderating role of Family Financial Difficulty. Front Psychol. 2021;12:670065–670065.

Dempsey AE, O’Brien KD, Tiamiyu MF, Elhai JD. Fear of missing out (FoMO) and rumination mediate relations between social anxiety and problematic Facebook use. Addict Behav Rep. 2019;9:100150–100150.

PubMed   PubMed Central   Google Scholar  

Yildiz Durak H, Seferoglu SS. Modeling of variables related to problematic social media usage: Social desirability tendency example. Scand J Psychol. 2019;60(3):277–88.

Ekinci N, Akat M. The relationship between anxious-ambivalent attachment and social appearance anxiety in adolescents: the serial mediation of positive Youth Development and Instagram Addiction. Psychol Rep 2023:332941231159600–332941231159600.

Foroughi B, Griffiths MD, Iranmanesh M, Salamzadeh Y. Associations between Instagram Addiction, academic performance, social anxiety, Depression, and life satisfaction among University students. Int J Mental Health Addict. 2022;20(4):2221–42.

He L. Influence mechanism and intervention suggestions on addiction of social network addiction. Gannan Normal University; 2021.

Hu Y. The influencing mechanism of type D personality on problematic social networking sites use among adolescents and intervention research. Central China Normal University; 2020.

Jia L. A study of the relationship between neuroticism, perceived social support, social anxiety and problematic social network use in high school students. Harbin Normal University; 2022.

Lee-Won RJ, Herzog L, Park SG. Hooked on Facebook: the role of social anxiety and need for Social Assurance in Problematic Use of Facebook. Cyberpsychology Behav Social Netw. 2015;18(10):567–74.

Li H. Social anxiety and internet interpersonal addiction in adolescents and countermeasures. Central China Normal University; 2022.

Lin W-S, Chen H-R, Lee TS-H, Feng JY. Role of social anxiety on high engagement and addictive behavior in the context of social networking sites. Data Technol Appl. 2019;53(2):156–70.

Liu Y. The influence of family function on social media addiction in adolescents: the chain mediation effect of social anxiety and resilience. Hunan Normal University; 2021.

Lyvers M, Salviani A, Costan S, Thorberg FA. Alexithymia, narcissism and social anxiety in relation to social media and internet addiction symptoms. Int J Psychology: J Int De Psychologie. 2022;57(5):606–12.

Majid A, Yasir M, Javed A, Ali P. From envy to social anxiety and rumination: how social media site addiction triggers task distraction amongst nurses. J Nurs Adm Manag. 2020;28(3):504–13.

Mou Q, Zhuang J, Gao Y, Zhong Y, Lu Q, Gao F, Zhao M. The relationship between social anxiety and academic engagement among Chinese college students: a serial mediation model. J Affect Disord. 2022;311:247–53.

Ruggieri S, Santoro G, Pace U, Passanisi A, Schimmenti A. Problematic Facebook use and anxiety concerning use of social media in mothers and their offspring: an actor-partner interdependence model. Addict Behav Rep. 2020;11:100256–100256.

Ruiz MJ, Saez G, Villanueva-Moya L, Exposito F. Adolescent sexting: the role of body shame, Social Physique anxiety, and social networking site addiction. Cyberpsychology Behav Social Netw. 2021;24(12):799–805.

She R, Kit Han Mo P, Li J, Liu X, Jiang H, Chen Y, Ma L, Tak Fai Lau J. The double-edged sword effect of social networking use intensity on problematic social networking use among college students: the role of social skills and social anxiety. Comput Hum Behav. 2023;140:107555.

Stănculescu E. The Bergen Social Media Addiction Scale Validity in a Romanian sample using item response theory and network analysis. Int J Mental Health Addict 2022.

Teng X, Lei H, Li J, Wen Z. The influence of social anxiety on social network site addiction of college students: the moderator of intentional self-regulation. Chin J Clin Psychol. 2021;29(3):514–7.

Tong W. Influence of boredom on the problematic mobile social networks usage in adolescents: multiple chain mediator. Chin J Clin Psychol. 2019;27(5):932–6.

Tu W, Jiang H, Liu Q. Peer victimization and adolescent Mobile Social Addiction: mediation of social anxiety and gender differences. Int J Environ Res Public Health 2022, 19(17).

Wang S. The influence of college students self-esteem, social anxiety and fear of missing out on the problematic mobile social networks usage. Huaibei Normal University; 2021.

Wang X. The impact of peer relationship and social anxiety on secondary vocational school students’ problematic social network use and intervention study. Huaibei Normal University; 2022.

Wegmann E, Stodt B, Brand M. Addictive use of social networking sites can be explained by the interaction of internet use expectancies, internet literacy, and psychopathological symptoms. J Behav Addictions. 2015;4(3):155–62.

Yang W. The relationship between the type of internet addiction and the personality traits in college students. Huazhong University of Science and Technology; 2004.

Yang Z. The relationship between social variables and social networking usage among shanghai working population. East China Normal University; 2013.

Zhang C. The relationship between perceived social support and problematic social network use among junior high school students: a chain mediation model and an intervention study. Hebei University; 2022.

Zhang J, Chang F, Huang D, Wen X. The relationship between neuroticism and the problematic mobile social networks use in adolescents: the mediating role of anxiety and positive self-presentation. Chin J Clin Psychol. 2021;29(3):598–602.

Zhang Z. College students’ loneliness and problematic social networking use: Chain mediation of social self-efficacy and social anxiety. Shanghai Normal University; 2019.

Zhu B. Discussion on mechanism of social networking addiction——Social anxiety, craving and excitability. Liaoning Normal University; 2017.

Blackwell D, Leaman C, Tramposch R, Osborne C, Liss M. Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Pers Indiv Differ. 2017;116:69–72.

Chen A. From attachment to addiction: the mediating role of need satisfaction on social networking sites. Comput Hum Behav. 2019;98:80–92.

Chen Y, Zhong S, Dai L, Deng Y, Liu X. Attachment anxiety and social networking sites addiction in college students: a moderated mediating model. Chin J Clin Psychol. 2019;27(3):497–500.

Li J. The relations among problematic social networks usage behavior, Childhood Trauma and adult attachment in University students. Hunan Agricultural University; 2020.

Liu C, Ma J-L. Adult attachment orientations and social networking site addiction: the Mediating effects of Online Social Support and the fear of missing out. Front Psychol. 2019;10:2629–2629.

Mo S, Huang W, Xu Y, Tang Z, Nie G. The impact of medical students’ attachment anxiety on the use of problematic social networking sites during the epidemic. Psychol Monthly. 2022;17(9):1–4.

Teng X. The effect of attachment anxiety on problematic mobile social network use: the role of loneliness and self-control. Harbin Normal University; 2021.

Worsley JD, Mansfield R, Corcoran R. Attachment anxiety and problematic social media use: the Mediating Role of Well-Being. Cyberpsychology Behav Social Netw. 2018;21(9):563–8.

Wu Z. The effect of adult attachment on problematic social network use: the chain mediating effect of loneliness and fear of missing out. Jilin University; 2022.

Xia N. The impact of attachment anxiety on adolescent problem social networking site use: a moderated mediation model. Shihezi University; 2022.

Young L, Kolubinski DC, Frings D. Attachment style moderates the relationship between social media use and user mental health and wellbeing. Heliyon 2020, 6(6).

Bakioglu F, Deniz M, Griffiths MD, Pakpour AH. Adaptation and validation of the online-fear of missing out inventory into Turkish and the association with social media addiction, smartphone addiction, and life satisfaction. BMC Psychol. 2022;10(1):154–154.

Bendayan R, Blanca MJ. Spanish version of the Facebook Intrusion Questionnaire (FIQ-S). Psicothema. 2019;31(2):204–9.

Blachnio A, Przepiorka A. Facebook intrusion, fear of missing out, narcissism, and life satisfaction: a cross-sectional study. Psychiatry Res. 2018;259:514–9.

Casale S, Rugai L, Fioravanti G. Exploring the role of positive metacognitions in explaining the association between the fear of missing out and social media addiction. Addict Behav. 2018;85:83–7.

Chen Y, Zhang Y, Zhang S, Wang K. Effect of fear of’ missing out on college students negative social adaptation: Chain¬ - mediating effect of rumination and problematic social media use. China J Health Psychol. 2022;30(4):581–6.

Cheng S, Zhang X, Han Y. Relationship between fear of missing out and phubbing on college students: the chain intermediary effect of intolerance of uncertainty and problematic social media use. China J Health Psychol. 2022;30(9):1296–300.

Cui Q, Wang J, Zhang J, Li W, Li Q. The relationship between loneliness and negative emotion in college students: the chain-mediating role of fear of missing out and social network sites addiction. J Jining Med Univ. 2022;45(4):248–51.

Ding Q, Wang Z, Zhang Y, Zhou Z. The more gossip, the more addicted: the relationship between interpersonal curiosity and social networking sites addiction tendencies in college students. Psychol Dev Educ. 2022;38(1):118–25.

Fabris MA, Marengo D, Longobardi C, Settanni M. Investigating the links between fear of missing out, social media addiction, and emotional symptoms in adolescence: the role of stress associated with neglect and negative reactions on social media. Addict Behav. 2020;106:106364.

Fang J, Wang X, Wen Z, Zhou J. Fear of missing out and problematic social media use as mediators between emotional support from social media and phubbing behavior. Addict Behav. 2020;107:106430.

Gao Z. The study on the relationship and intervention among fear of missing out self-differentiation and problematic social media use of college students. Yunnan Normal University; 2021.

Gioia F, Fioravanti G, Casale S, Boursier V. The Effects of the Fear of Missing Out on People’s Social Networking Sites Use During the COVID-19 Pandemic: The Mediating Role of Online Relational Closeness and Individuals’ Online Communication Attitude. Front Psychiatry 2021, 12.

Gu X. Study on the Inhibitory Effect of Mindfulness Training on Social Media Addiction of College Students. Wuhan University; 2020.

Gugushvili N, Taht K, Schruff-Lim EM, Ruiter RA, Verduyn P. The Association between Neuroticism and problematic social networking sites Use: the role of fear of missing out and Self-Control. Psychol Rep 2022:332941221142003–332941221142003.

Hou J. The study on FoMO and content social media addiction among young people. Huazhong University of Science and Technology; 2021.

Hu R, Zhang B, Yang Y, Mao H, Peng Y, Xiong S. Relationship between college students’ fear of missing and wechat addiction: a cross-lagged analysis. J Bio-education. 2022;10(5):369–73.

Hu G. The relationship between basic psychological needs satisfaction and the use of problematic social networks by college students: a moderated mediation model and online intervention studies. Jiangxi Normal University; 2020.

Jiang Y, Jin T. The relationship between adolescents’ narcissistic personality and their use of problematic mobile social networks: the effects of fear of missing out and positive self-presentation. Chin J Special Educ 2018(11):64–70.

Li J. The effect of positive self-presentation on social networking sites on problematic use of social networking sites: a moderated mediation model. Henan University; 2020.

Li J, Zhang Y, Zhang X. The impact of Freshmen Social Exclusion on problematic Social Network Use: a Moderated Mediation Model. J Heilongjiang Vocat Inst Ecol Eng. 2023;36(1):118–22.

Li M. The relationship between fear of missing out and social media addiction among middle school students——The moderating role of self-control. Kashi University; 2022.

Li R, Dong X, Wang M, Wang R. A study on the relationship between fear of missing out and social network addiction. New Educ Era 2021(52):122–3.

Li Y. Fear of missing out or social avoidance? The influence of peer exclusion on problematic social media use among adolescents in Guangdong Province and Macao. Guangzhou University; 2020.

Ma J, Liu C. The effect of fear of missing out on social networking sites addiction among college students: the mediating roles of social networking site integration use and social support. Psychol Dev Educ. 2019;35(5):605–14.

Mao H. A follow-up study on the mechanism of the influence of university students’ Qi deficiency quality on WeChat addiction. Hunan University of Chinese Medicine; 2021.

Mao Y. The effect of dual filial piety to the college students ’internet social dependence: the mediation of maladaptive cognition and fear of missing out. Huazhong University of Science and Technology; 2021.

Moore K, Craciun G. Fear of missing out and personality as predictors of Social networking sites usage: the Instagram Case. Psychol Rep. 2021;124(4):1761–87.

Niu J. The relationship of college students’ basic psychological needs and social media dependence: the mediating role of fear of missing out. Huazhong University of Science and Technology; 2021.

Pi L, Li X. Research on the relationship between loneliness and problematic mobile social media usage: evidence from variable-oriented and person-oriented analyses. China J Health Psychol. 2023;31(6):936–42.

Pontes HM, Taylor M, Stavropoulos V. Beyond Facebook Addiction: the role of cognitive-related factors and Psychiatric Distress in Social networking site addiction. Cyberpsychol Behav Soc Netw. 2018;21(4):240–7.

Quaglieri A, Biondi S, Roma P, Varchetta M, Fraschetti A, Burrai J, Lausi G, Marti-Vilar M, Gonzalez-Sala F, Di Domenico A et al. From Emotional (Dys) Regulation to Internet Addiction: A Mediation Model of Problematic Social Media Use among Italian Young Adults. Journal of Clinical Medicine 2022, 11(1).

Servidio R, Koronczai B, Griffiths MD, Demetrovics Z. Problematic smartphone Use and Problematic Social Media Use: the predictive role of Self-Construal and the Mediating Effect of Fear Missing Out. Front Public Health 2022, 10.

Sheldon P, Antony MG, Sykes B. Predictors of problematic social media use: personality and life-position indicators. Psychol Rep. 2021;124(3):1110–33.

Sun C, Li Y, Kwok SYCL, Mu W. The relationship between intolerance of uncertainty and problematic Social Media Use during the COVID-19 pandemic: a serial mediation model. Int J Environ Res Public Health 2022, 19(22).

Tang Z. The relationship between loneliness and problematic social networks use among college students: the mediation of fear of missing out and the moderation of social support. Jilin University; 2022.

Tomczyk Ł, Selmanagic-Lizde E. Fear of missing out (FOMO) among youth in Bosnia and Herzegovina — Scale and selected mechanisms. Child Youth Serv Rev. 2018;88:541–9.

Unal-Aydin P, Ozkan Y, Ozturk M, Aydin O, Spada MM. The role of metacognitions in cyberbullying and cybervictimization among adolescents diagnosed with major depressive disorder and anxiety disorders: a case-control study. Clinical Psychology & Psychotherapy; 2023.

Uram P, Skalski S. Still logged in? The Link between Facebook Addiction, FoMO, Self-Esteem, Life satisfaction and loneliness in social media users. Psychol Rep. 2022;125(1):218–31.

Varchetta M, Fraschetti A, Mari E, Giannini AM. Social Media Addiction, fear of missing out (FoMO) and online vulnerability in university students. Revista Digit De Investigación en Docencia Universitaria. 2020;14(1):e1187.

Wang H. Study on the relationship and intervention between fear of missing and social network addiction in college students. Yunnan Normal University; 2021.

Wang M, Yin Z, Xu Q, Niu G. The relationship between shyness and adolescents’ social network sites addiction: Moderated mediation model. Chin J Clin Psychol. 2020;28(5):906–9.

Wegmann E, Oberst U, Stodt B, Brand M. Online-specific fear of missing out and internet-use expectancies contribute to symptoms of internet-communication disorder. Addict Behav Rep. 2017;5:33–42.

Wegmann E, Brandtner A, Brand M. Perceived strain due to COVID-19-Related restrictions mediates the Effect of Social needs and fear of missing out on the risk of a problematic use of Social Networks. Front Psychiatry 2021, 12.

Wei Q. Negative emotions and problematic social network sites usage: the mediating role of fear of missing out and the moderating role of gender. Central China Normal University; 2018.

Xiong L. Effect of social network site use on college students’ social network site addiction: A moderated mediation model and attention bias training intervention study. Jiangxi Normal University; 2022.

Yan H. The influence of college students’ basic psychological needs on social network addiction: The intermediary role of fear of missing out. Wuhan University; 2020.

Yan H. The status and factors associated with social media addiction among young people——Evidence from WeChat. Chongqing University; 2021.

Yang L. Research on the relationship of fear of missing out, excessive use of Wechat and life satisfaction. Beijing Forestry University; 2020.

Yin Y, Cai X, Ouyang M, Li S, Li X, Wang P. FoMO and the brain: loneliness and problematic social networking site use mediate the association between the topology of the resting-state EEG brain network and fear of missing out. Comput Hum Behav. 2023;141:107624.

Zhang C. The parental rejection and problematic social network sites with adolescents: the chain mediating effect of basic psychological needs and fear of missing out. Central China Normal University; 2022.

Zhang J. The influence of basic psychological needs on problematic mobile social networks usage of adolescent: a moderated mediation model. Liaocheng University; 2020.

Zhang Y, Chen Y, Jin J, Yu G. The relationship between fear of missing out and social media addiction: a cross-lagged analysis. Chin J Clin Psychol. 2021;29(5):1082–5.

Zhang Y, Jiang W, Ding Q, Hong M. Social comparison orientation and social network sites addiction in college students: the mediating role of fear of missing out. Chin J Clin Psychol. 2019;27(5):928–31.

Zhou J, Fang J. Social network sites support and addiction among college students: a moderated mediation model. Psychology: Techniques Appl. 2021;9(5):293–9.

Andreassen CS, Torsheim T, Brunborg GS, Pallesen S. Development of a Facebook addiction scale. Psychol Rep. 2012;110(2):501–17.

Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, Pallesen S. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behav. 2016;30(2):252.

Elphinston RA, Noller P. Time to face it! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychology Behav Social Netw. 2011;14(11):631–5.

Caplan SE. Theory and measurement of generalized problematic internet use: a two-step approach. Comput Hum Behav. 2010;26(5):1089–97.

Jiang Y. Development of problematic mobile social media usage assessment questionnaire for adolescents. Psychology: Techniques Appl. 2018;6(10):613–21.

Wang X. College students’ social network addiction tendency: Questionnaire construction and correlation research. Master’s thesis Southwest University; 2016.

Derogatis LR. Brief symptom inventory 18. Johns Hopkins University Baltimore; 2001.

Lovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression anxiety stress scales (DASS) with the Beck Depression and anxiety inventories. Behav Res Ther. 1995;33(3):335–43.

Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7.

Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica. 1983;67(6):361–70.

Spielberger CD, Gonzalez-Reigosa F, Martinez-Urrutia A, Natalicio LF, Natalicio DS. The state-trait anxiety inventory. Revista Interamericana de Psicologia/Interamerican Journal of Psychology 1971, 5(3&4).

Marteau TM, Bekker H. The development of a six-item short‐form of the state scale of the Spielberger State—trait anxiety inventory (STAI). Br J Clin Psychol. 1992;31(3):301–6.

Leary MR. Social anxiousness: the construct and its measurement. J Pers Assess. 1983;47(1):66–75.

Liebowitz MR. Social phobia. Modern problems of pharmacopsychiatry 1987.

Alkis Y, Kadirhan Z, Sat M. Development and validation of social anxiety scale for social media users. Comput Hum Behav. 2017;72:296–303.

La Greca AM, Stone WL. Social anxiety scale for children-revised: factor structure and concurrent validity. J Clin Child Psychol. 1993;22(1):17–27.

Fenigstein A, Scheier MF, Buss AH. Public and private self-consciousness: Assessment and theory. J Consult Clin Psychol. 1975;43(4):522.

Mattick RP, Clarke JC. Development and validation of measures of social phobia scrutiny fear and social interaction anxiety. Behav Res Ther. 1998;36(4):455–70.

Peters L, Sunderland M, Andrews G, Rapee RM, Mattick RP. Development of a short form Social Interaction anxiety (SIAS) and Social Phobia Scale (SPS) using nonparametric item response theory: the SIAS-6 and the SPS-6. Psychol Assess. 2012;24(1):66.

Brennan KA, Clark CL, Shaver PR. Self-report measurement of adult attachment: an integrative overview. Attachment Theory Close Relationships. 1998;46:76.

Wei M, Russell DW, Mallinckrodt B, Vogel DL. The experiences in Close Relationship Scale (ECR)-short form: reliability, validity, and factor structure. J Pers Assess. 2007;88(2):187–204.

Bartholomew K, Horowitz LM. Attachment styles among young adults: a test of a four-category model. J Personal Soc Psychol. 1991;61(2):226.

Przybylski AK, Murayama K, DeHaan CR, Gladwell V. Motivational, emotional, and behavioral correlates of fear of missing out. Comput Hum Behav. 2013;29(4):1841–8.

Xiaokang S, Yuxiang Z, Xuanhui Z. Developing a fear of missing out (FoMO) measurement scale in the mobile social media environment. Libr Inform Service. 2017;61(11):96.

Bown M, Sutton A. Quality control in systematic reviews and meta-analyses. Eur J Vasc Endovasc Surg. 2010;40(5):669–77.

Turel O, Qahri-Saremi H. Problematic use of social networking sites: antecedents and consequence from a dual-system theory perspective. J Manage Inform Syst. 2016;33(4):1087–116.

Chou H-TG, Edge N. They are happier and having better lives than I am: the impact of using Facebook on perceptions of others’ lives. Cyberpsychology Behav Social Netw. 2012;15(2):117–21.

Beyens I, Frison E, Eggermont S. I don’t want to miss a thing: adolescents’ fear of missing out and its relationship to adolescents’ social needs, Facebook use, and Facebook related stress. Comput Hum Behav. 2016;64:1–8.

Di Blasi M, Gullo S, Mancinelli E, Freda MF, Esposito G, Gelo OCG, Lagetto G, Giordano C, Mazzeschi C, Pazzagli C. Psychological distress associated with the COVID-19 lockdown: a two-wave network analysis. J Affect Disord. 2021;284:18–26.

Yang X, Hu H, Zhao C, Xu H, Tu X, Zhang G. A longitudinal study of changes in smart phone addiction and depressive symptoms and potential risk factors among Chinese college students. BMC Psychiatry. 2021;21(1):252.

Kuss DJ, Griffiths MD. Social networking sites and addiction: ten lessons learned. Int J Environ Res Public Health. 2017;14(3):311.

Ryan T, Chester A, Reece J, Xenos S. The uses and abuses of Facebook: a review of Facebook addiction. J Behav Addictions. 2014;3(3):133–48.

Elhai JD, Levine JC, Dvorak RD, Hall BJ. Non-social features of smartphone use are most related to depression, anxiety and problematic smartphone use. Comput Hum Behav. 2017;69:75–82.

Jackson LA, Wang J-L. Cultural differences in social networking site use: a comparative study of China and the United States. Comput Hum Behav. 2013;29(3):910–21.

Ahrens LM, Mühlberger A, Pauli P, Wieser MJ. Impaired visuocortical discrimination learning of socially conditioned stimuli in social anxiety. Soc Cognit Affect Neurosci. 2014;10(7):929–37.

Elhai JD, Yang H, Montag C. Fear of missing out (FOMO): overview, theoretical underpinnings, and literature review on relations with severity of negative affectivity and problematic technology use. Brazilian J Psychiatry. 2020;43:203–9.

Barker V. Older adolescents’ motivations for social network site use: the influence of gender, group identity, and collective self-esteem. Cyberpsychology Behav. 2009;12(2):209–13.

Krasnova H, Veltri NF, Eling N, Buxmann P. Why men and women continue to use social networking sites: the role of gender differences. J Strateg Inf Syst. 2017;26(4):261–84.

Palmer J. The role of gender on social network websites. Stylus Knights Write Showc 2012:35–46.

Vannucci A, Flannery KM, Ohannessian CM. Social media use and anxiety in emerging adults. J Affect Disord. 2017;207:163–6.

Primack BA, Shensa A, Sidani JE, Whaite EO, yi Lin L, Rosen D, Colditz JB, Radovic A, Miller E. Social media use and perceived social isolation among young adults in the US. Am J Prev Med. 2017;53(1):1–8.

Twenge JM, Campbell WK. Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Prev Med Rep. 2018;12:271–83.

Download references

This research was supported by the Social Science Foundation of China (Grant Number: 23BSH135).

Author information

Authors and affiliations.

School of Mental Health, Wenzhou Medical University, 325035, Wenzhou, China

Mingxuan Du, Haiyan Hu, Ningning Ding, Jiankang He, Wenwen Tian, Wenqian Zhao, Xiujian Lin, Gaoyang Liu, Wendan Chen, ShuangLiu Wang, Dongwu Xu & Guohua Zhang

School of Education, Renmin University of China, 100872, Beijing, China

Chengjia Zhao

School of Media and Communication, Shanghai Jiao Tong University, Dongchuan Road 800, 200240, Shanghai, China

Pengcheng Wang

Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, 313002, Huzhou, China

Xinhua Shen

You can also search for this author in PubMed   Google Scholar

Contributions

GZ, XS, XL and MD prepared the study design, writing - review and editing. MD and CZ wrote the main manuscript text. MD and HH analyzed data and edited the draft. ND, JH, WT, WZ, GL, WC, SW, PW and DX conducted resources and data curation. All authors have approved the final version of the manuscript.

Corresponding authors

Correspondence to Xinhua Shen or Guohua Zhang .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Du, M., Zhao, C., Hu, H. et al. Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis. BMC Psychol 12 , 263 (2024). https://doi.org/10.1186/s40359-024-01705-w

Download citation

Received : 25 January 2024

Accepted : 03 April 2024

Published : 12 May 2024

DOI : https://doi.org/10.1186/s40359-024-01705-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Fear of missing out
• Meta-analysis

BMC Psychology

ISSN: 2050-7283

• General enquiries: [email protected]

The Linkage Between Digital Transformation and Organizational Culture: Novel Machine Learning Literature Review Based on Latent Dirichlet Allocation

• Open access
• Published: 21 May 2024

Cite this article

You have full access to this open access article

• Tobias Reisberger   ORCID: orcid.org/0009-0003-0190-7368 1 ,
• Philip Reisberger   ORCID: orcid.org/0009-0004-4678-4151 1 ,
• Lukáš Copuš   ORCID: orcid.org/0000-0002-9502-830X 1 ,
• Peter Madzík   ORCID: orcid.org/0000-0002-1655-6500 1 &
• Lukáš Falát   ORCID: orcid.org/0000-0002-2597-7059 2  

Organizational culture is a crucial component of innovation in company success, particularly in the setting of the information economy. The purpose of this research is to conduct a bibliometric analysis in order to identify dominant research topics, their potential shifts, and recent developments in the fields of organizational culture and digital transformation. It demonstrates a machine learning–supported method for identifying and segmenting the current state of this research field. The literature was identified from the Scopus database through a search query. The analyzed amount of papers (3065) was published in 1619 sources (journals, proceedings, books, etc.) with various research impacts. Identifying the dominant research topics resulted in eight topics: Social Media Connectivity; Digital Innovation Ecosystems; Socio-economic Sustainability; Digital Workforce Transformation; Digital Competence and Cultural Transformation; Knowledge, Culture, and Innovation; Data and Resource Management; and Digital Transformation Maturity. The results showed a shift in the research field on organizational culture related to digital transformation towards the subject area of business, management, and accounting, with increasing research interest and impact for the Digital Workforce Transformation as well as for the Knowledge, Culture, and Innovation topics.

Avoid common mistakes on your manuscript.

Introduction

In recent years, the world has gone through many events that have changed how we live, relax, work, or communicate. These changes are still resonating in the business environment, for example, in the transition to partial or complete work from home and bring several challenges that organizations have to deal with (Yang et al., 2023 ). One of the crucial areas is the socialization of employees and the formation and maintenance of organizational values expressed by the organizational culture (Noto et al., 2023 ).

Organizational culture has been well-researched since the early 1980s (O’Reilly et al., 1991 ; Schein, 1985 ). The focus originated in American-based qualitative studies and shifted over time towards a more international perspective (Cameron & Quinn, 1999 ; Denison & Mishra, 1995 ; Hofstede, 1998 ), as well as adopting a more quantitative viewpoint with many published papers (O’Reilly et al., 2014 ). Several different areas of organizational culture have already been analyzed, including performance, motivation, leadership, and innovation, among many others (Affes & Affes, 2022 ; Aasi & Rusu, 2017 ; Abu Bakar et al., 2021 ). One of the up-to-date research areas is the topic of digitalization.

The advent of automation and digitalization and the resulting digital transformation in recent history have significantly impacted many markets and organizations and influenced the behaviors and expectations of customers. Digital transformation is driven by several external factors, including the rapid growth and adoption of new technologies that foster e-commerce, big data, a changing competitive landscape, and altered consumption behavior, driven by better-informed, connected, and more empowered customers (Verhoef et al., 2021 ). It provides many challenges and opportunities, including relevant impacts on organizational culture (Alloghani et al., 2022 ). In recent years, the impact of the COVID-19 pandemic has had a significant influence on organizational culture (Daum & Maraist, 2021 ; Spicer, 2020 ).

Even before the pandemic, the fast development of digital technologies, including automation, smart technology, artificial intelligence (AI), and robots, cloud computing, and the Internet of Things (IoT) is radically altering the nature of work and of organizations (Nimawat & Gidwani, 2021 ). The combination of technological advancements was coined as the Fourth Industrial Revolution or Industry 4.0 by Klaus Schwab in late 2015 (Schwab, 2015 ). The speed and scope of current technological changes are prompting concerns about the extent to which new technologies will fundamentally alter organizational cultures, workplaces, or completely replace workers (Acemoglu & Autor, 2011 ; Brynjolfsson & McAfee, 2014 ; Frey & Osborne, 2017 ).

These Industry 4.0 developments and an agile workforce are all components of a global digital transformation that changed the workplace dynamics and led to significant changes in organizations and employee behavior. Due to the unexpected interruption brought on by the coronavirus pandemic, working from anywhere has become the new standard for millions of people worldwide (Özkazanç-Pan & Pullen, 2020 ).

The combination of these two driving forces will have a lasting effect on the formation and effectiveness of organizational culture in the future (Kniffin et al., 2021 ; Trenerry et al., 2021 ). However, the number and range of publications in recent years on organizational culture, digital transformation, Industry 4.0, and COVID-19 make it necessary to provide a structured overview of the published literature.

Firstly, this paper shall give an overview of the research being conducted on organizational culture and digital transformation and identify the main research areas, authors and journals. The methods utilized are outlined, along with the applied bibliometric tools. Secondly, this paper aims to provide an overview of the status quo of research by identifying the different research clusters with its critical analysis.

Literature Review

Research on organizational culture and digital transformation.

Over time, the concept of organizational culture has been the center of attention for many researchers. It has been the main focus of study of several scientific works, especially in management and business (Mohelska & Sokolova, 2018 ; Streimikiene et al., 2021 ; Vallejo, 2011 ).

The concept of organizational culture has been studied from different angles, with researchers exploring the role that organizational culture can play and which factors impact organizational culture (Guzal-Dec, 2016 ; Polyanska et al., 2019 ; Zeng & Luo, 2013 ).

A high number of researchers agree with Schein’s ( 1985 ) model, which asserts that there are three levels at which an organizational culture may be conceptualized: fundamental presumptions and beliefs, norms and values, and cultural artifacts (Chatman & O’Reilly, 2016 ). From the perspective of the organization and its working environment, organizational culture emerges from behavior in which basic assumptions and beliefs are shared and seen as given by organizational members (Schein, 1985 ).

Academics primarily focused on organizational culture’s definition, connotation, structural components and type categorization in the 1980s; most of this research was qualitative (Cui et al., 2018 ). Even though there was no universal agreement on the meanings of organizational culture at the time, Schein’s framework (Schein, 1992 ) was somewhat representational in the academic world. Research on organizational culture then evolved from mainly qualitative research to quantitative studies in the 1990s (Cameron & Quinn, 1999 ; Denison & Mishra, 1995 ; Hofstede, 1998 , 2001 ; O’Reilly et al., 2014 ). According to Cui et al. ( 2018 ), contemporary views of organizational culture are seen as a key factor for success, promoting organizational effectiveness and performance (Gregory et al., 2009 ), organizational innovation (Hogan & Coote, 2014 ), and organizational identity (Ravasi & Schultz, 2006 ). Organizational culture is now considered a key component of innovation in company success, particularly in the setting of the information economy (Büschgens et al., 2013 ). Cartwright identifies nine relevant factors that drive the cultural transformation in organizations that enable successful business practices (Cartwright, 1999 ).

Organizational culture has two basic academic foundations: sociology (organizations have culture) and anthropology (organizations are cultures). The sociological position has become dominant in recent years (Cameron & Quinn, 1999 ). Based on this, there are two opposing viewpoints regarding the possibility of managing organizational culture — the functionalist and symbolist view (Schueber, 2009 ). The functionalist perspective regards culture as an organizational variable (Alvesson, 1993 ), and it can be determined by management (Meek, 1988 ; Silverzweig & Allen, 1976 ). According to the functionalist perspective, culture is seen as something that the organization possesses and can be controlled (Barley et al., 1988 ; Smircich, 1983 ). The symbolist viewpoint regards culture as a representation of what an organization is rather than anything it has . This implies major challenges in controlling or managing organizational culture (Morgan, 1986 ; Smircich, 1983 ). Functionalists would argue that the culture should be changed to fit the strategy, whereas symbolists would propose that the strategy should be adjusted to the organization’s culture (Ogbonna, 1992 ; Senior, 1997 ). In this paper, the functionalist view is supported by implications of the results.

Digitalization is defined as “the transformation of business models as a result of fundamental changes to core internal processes, customer interfaces, products and services, as well as the use of information and communications technologies” (Isensee et al., 2020 ). However, digitalization and digital transformation are quite different. A company may embark on several digitalization initiatives, from automating procedures to retraining staff members to utilize computers. On the other hand, businesses cannot conduct digital transformation as projects. Instead, this more general phrase refers to a client-centered strategic business transformation that calls for adopting digital technology and organizational changes across all departments (Verhoef et al., 2021 ).

An executive’s view that does not distinguish between digitalization and digital transformation could lead to an insufficient strategic focus (Li & Shao, 2023 ). Digital transformation efforts will often involve several digitalization projects, which require management sponsorship and the willingness to change existing structures and practices. Various papers have studied the challenges that may arise from organizational culture when adopting new technologies and structures, e.g., agile practices (Anwar et al., 2016 ; Ghimire et al., 2020 ; Raharjo & Purwandari, 2020 ), technology adoption (Melitski et al., 2010 ), or even Green Supply Chain Management (El Baz & Iddik, 2021 ). As the business becomes primarily customer-driven, digital transformation necessitates improving how well the organization manages change (Anghel, 2019 ).

Industry 4.0 began in the twenty-first century with the development of cyber-physical systems (CPS), the Internet of Things (IoT), the Internet of Services, smart factories, and cloud computing. It continues today (Hermann et al., 2016 ; Kagermann et al., 2013 ; Liao et al., 2017 ; Xu et al., 2018 ). It is characterized as a combination of CPS and IoT in the manufacturing industry, which can have repercussions for value creation, company growth, work organization, and downstream businesses (Kagermann et al., 2013 ; Kiel et al., 2017 ). The advent of Industry 4.0 involves significant changes for organizations and societies and has various effects on nations, businesses, industries, and society (Schwab, 2015 ). Industry 4.0 implementation is a complicated process involving horizontal, vertical and seamless integration and will rely on the synergies between the business and stakeholders from many functional domains (Müller, 2019a , 2019b ; Wang et al., 2016 ). In particular, many organizations fail to capture their Industry 4.0 vision and strategy throughout the change process (Schumacher et al., 2016a ). Other important factors that hinder the application of a successful digital transformation towards a functional Industry 4.0 concept are fear of uncertainty and wrong expectation of requirements (Balasingham, 2016 ). Willingness to adopt this technology is another reason to fail (Adebanjo et al., 2021 ). Organizations aiming to incorporate and adopt digital transformation into their operational procedures must recognize and assess important critical factors (Nimawat & Gidwani, 2021 ).

Organizational communication and collaboration styles have changed due to globalization, advancements in information and communication technologies (ICTs), an increase in hybrid work models and the rise of computer-mediated groups (Sharma et al., 2022 ). With the knowledge economy, digital culture, and recent technological innovations, new working styles have quietly emerged in organizations (Powell et al., 2004 ). Then, the spreading of the coronavirus and the required shift in transition to remote working acted as a catalyst for how organizations operate and employees engage. The drastic changes in the workplace naturally affected employees and spurred changes in their behavior and attitudes (Caligiuri et al., 2020 ). The corresponding research topic of COVID-19-related impacts and the implications on digital transformation in the context of organizational culture is relatively new. Many partial aspects that have gained new relevance during the corona pandemic have already received attention in the research community over the past 20 years.

Therefore, this study aims to conduct a bibliometric analysis in order to identify dominant research topics, their potential shifts, and recent developments in the fields of organizational culture and digital transformation. The most significant research articles or authors and their related relationships can be found using the scientific computer-aided review process known as bibliometric analysis. It can help to forecast the possible direction of such identified fields and is widely applied in academic research (Diem & Wolter, 2013 ). This method aids in providing a thorough overview of the subject as well as visually summarizing its patterns and trends (Baker et al., 2020 ; Zhou et al., 2020 ).

Overview of Bibliometric Reviews

The topic of organizational culture has had a large number of contributors in the past decades. Several articles were published on organizational culture as bibliometric studies (Cicea et al., 2022 ). Only a few reviews were conducted on digital transformation in organizations related to organizational culture (e.g., as digitalization). Table 1 lists a few publications on these topics.

Overview of Systematic Reviews

Apart from bibliometric literature reviews, many authors have conducted systematic literature reviews on various research areas relating to organizational culture and digital transformation. As seen in the following non-conclusive overview in Table  2 and Table  3 , researchers have focused their attention on heterogeneous study fields like performance-orientation, entrepreneurship, Industry 4.0, agile practices, work-from-anywhere, SMEs, and many others. This broad overview indicates that the topic of organizational culture plays a very relevant role in recent research, especially in the context of digital transformation.

The provided overview on digital transformation research mainly focuses on functional areas and its application. The center of research is the implementation, readiness, adoption, as well as barriers, opportunities, and challenges. Additionally, research fields like examining potential directions (Belinski et al., 2020 ; Kamble et al., 2018 ; Pagliosa et al., 2019 ; Piccarozzi et al., 2018 ; Schneider, 2018 ; Sony & Naik, 2020 ); implementation, readiness and adoption (Çınar et al., 2021 ; Pacchini et al., 2019 ; Sung & Kim, 2021 ); barriers, opportunities, and challenges to the adoption and implementation of Industry 4.0 (Bajic et al., 2021 ; Raj et al., 2020 ); and sustainability (de Sousa Jabbour et al., 2018 ; Luthra & Mangla, 2018 ) are analyzed.

The main focus areas, among many others, which are influenced by digital transformation are agile and collaborative teamwork and management (Kerber & Buono, 2004 ; Huang et al., 2003 ; Sheppard, 2020 ; Parry & Battista, 2019 ; Singer-Velush et al. 2020 ; Hamouche, 2020 ), adaptive business culture in dynamic , supportive , environments , with focus on employee well-being , work design , open innovation , workforce effectiveness (Am et al., 2020 ; Ngoc Su et al., 2021 ; Baker et al., 2006 ; Žižek et al., 2021 ; Parry & Battista, 2019 ; Bélanger et al., 2013 ; Carnevale & Hatak, 2020 ), and recent technological developments (Ågerfalk et al., 2020 ; Bloom et al., 2015 ; Bondarouk & Ruël, 2009 ; Johnson et al., 2020 ; Spreitzer et al., 2017 ; Wiggins et al., 2020 ).

Research Gap

The research mentioned in the aforementioned literature review sought to examine several factors of organizational culture and digital transformation. However, reviews of literature based solely on a systematic or bibliometric methodology have significant drawbacks. Studies of systematic literature reviews are frequently in-depth and typically handle only a small number of documents. As a result, the findings are more constrained (Moher et al., 2015 ; Page et al., 2021 ). Contrarily, bibliometric reviews are concentrated on a wider range of the studied areas. They mostly reveal major trends as an outcome (Cobo et al., 2011 ; van Eck & Waltman, 2010 ). Using machine learning to find latent patterns in textual data is one of the most popular study methods in the field of bibliometric review (Han, 2020 ; Mariani & Baggio, 2022 ). Automated processing is used to analyze the scientific publications for our study. It employs an advanced machine learning–based methodology to extract topics from the scientific literature. This paper contributes to the existing literature by answering the following research questions:

Research Question 1 (RQ1) . How has the organizational culture — digital transformation relationship evolved over time?

The number of publications on digital transformation is growing, and organizational culture is a well-established research area with years of academic work. Consequently, a bibliometric analysis of the growth of the top journals, articles, and most cited publications may be able to provide relevant insights.

Research Question 2 (RQ2) . What are the dominant research topics on organizational culture and digital transformation?

The total number of publications on the subject of this study is rapidly increasing. Therefore, we may apply machine learning to extract particular study ideas from a large body of published scientific literature.

Research Methodology

This paper aims to establish the trends of research papers in the field of organizational culture research with a focus on digital transformation. The authors conducted the review of the literature using bibliometric analysis and a machine learning method.

Researchers often undertake bibliometric analysis with the main goal to determine the body of knowledge on a certain subject, to provide an assessment of the research already conducted, and to develop networking structures for the scientific community. Five steps ( study design , data collection , data analysis , data visualization , and interpretation of results ) represent the workflow of science mapping and were used to apply the bibliometric approach and network analysis (Aria & Cuccurullo, 2017 ).

The review usually starts by determining the database that contains the input data. The only source for this paper are the bibliographic records from the Scopus database as data collection input. This source has been considered reliable in prior works. Scopus, developed by Elsevier B.V., is the largest database of scientific peer-review literature hosting more than 27,950 journal published articles (Elsevier, 2023 ). It was chosen for this study as it is the largest and most relevant scientific database in the world, covering most of the publications available. This includes consistent repositories of documents as well as additional information such as country of all the authors, citations per document, and further information that is relevant in terms of quality and quantity for the study.

The search query was developed after identifying the research area. This was done by splitting the topic into three fields of research. The first set was organization with the corresponding synonyms followed by culture (second set). The third was digital transformation and its phases digitization and digitalization following Verhoef et al. ( 2021 ) and its synonyms including Industry 4.0 . The database was queried using additional synonyms and alternative spellings to increase the study’s coverage.

To collect these articles, the combination of the following keywords was selected:

Digital transformation , digitalization , digitalisation , digitization , digitisation combined with Industry 4.0 search terms fourth industrial revolution , 4IR , 4-IR , industry 4.0 and the organizational culture related keyword organisation *, organization *, firm , company , corporate , enterprise , business and culture .

The search criteria were then determined. The authors used the title , abstract and keywords from the articles provided by the Scopus database (TITLE-ABS-KEY). This resulted in 3077 identified papers. The search query and result are shown in Table  4 . The search was conducted on March 30, 2023.

After collecting the data, all documents with no abstracts were removed. The authors also removed all documents with abstracts defined as: “[No abstract available]”. After this removal, the dataset consisted of 3065 documents. The applied dataset was made up of the following eight variables: authors, title, year, source, cited by, abstract, authors keywords, index keywords. A total of 139 documents were tagged as Review . In addition, to answer the research question RQ1, we joined our dataset with a dataset that defined individual subject areas for each journal. Thanks to such an expanded dataset, we were able to better structure the results.

Topic Modelling

In order to be able to answer research question RQ2, we needed to perform an analysis of the sentific field. There are several ways to conduct a literature review. Instead of the standard literature review process, we decided to carry out the literature review based on machine learning. This way of analyzing the scientific field allowed us to assess a much larger number of documents and thus make the literature review more relevant. Our review based on machine learning analyzed 3065 document abstracts in total.

Before the actual process of identifying individual research topics in the selected area, it was necessary to perform text preprocessing and then divide the analyzed documents into individual topics. Data preprocessing included several steps which are common in text analytics. After removing some special characters, we removed punctuation, further removed numbers and stopwords defined in the tm package in R. In addition, we defined other custom stopwords that were removed from the corpus of abstracts. Then we then removed the extra spaces and stemmed the words in the document. The last step was to delete custom stopwords Footnote 1 specific to our area of interest. In this case, these were words that were irrelevant to our field of research and, in our opinion, did not add value to the resulting analysis. We defined these words based on the frequency analysis of stemmed words from the corpus of analyzed abstracts. The mentioned procedures were performed in the R programming language using the tm and SnowballC packages. After removing the specific stopwords, we finally removed the extra spaces. Subsequently, a document-term matrix (dtm) was created, which contained the frequencies of all individual words in every document. Since the dtm itself also contained low-frequency words, we removed words that appeared in less than 0.5% of the abstracts in the resulting matrix. The resulting dtm contained 1108 words.

After preprocessing the text of the abstracts, we proceeded to structure the abstracts into research topics. We implemented the mentioned process, also called topic modeling, using the Latent Dirichlet Allocation method, also known as LDA (Blei et al., 2003 ). LDA is a probabilistic generative process, the result of which is a set of topics that represent the composition of the entire space into individual parts. Based on the words in individual documents, the so-called latent topical structure is created, while latent topics are a mixture of several documents. Based on the posterior estimates of the hidden variables, we can estimate the structure of the latent topics. Hidden variables in our case represent latent topical structure (Blei & Lafferty, 2009 ).

Topic modeling using LDA was implemented in the R programming language using the topicmodels library. Topic modeling itself assumes the number of topics into which the entire space needs to be divided. There are several approaches for finding the number of topics. Since the approach based on the evaluation of statistical criteria resulted in a large number of topics, we decided to prefer an expert approach. This approach consisted in manually assessing the interpretability of the most frequent words in individual alternatives. As part of the testing itself for a suitable number of topics, we gradually manually evaluated solutions with the number of topics k  = {6, 7, 8, 9, 10, 11, 12}.

To quantify the parameters of the LDA model, we used Gibbs sampling (Gelfand, 2000 ; Griffiths & Steyvers, 2004 ; Grün & Hornik, 2011 ). For parameter quantification, we used 2000 iterations, taking into account only every 200th observation for a higher degree of independence between. For each k, we repeated the process five times, always recording only the best solution. Based on the results of the expert analysis, we chose a solution with the number of topics k  = 8. Finally, we realized the visualization of topics, which was performed using the ldavis package (Sievert & Shirley, 2014 ).

Development of Related Research Papers

The direct or indirect role of organizational culture in various processes of digital transformation has been the subject of a lot of research. The studies that formed the basis for our analysis were identified from the Scopus bibliometric database through a search query, which is presented in the “ Research Methodology ” section. The data was collected on March 30, 2023, while on this date, 3065 valid documents were registered in the mentioned database. A significant increase in the number of studies has only been noticeable since 2018. Still, it must be said that studies investigating the links between organizational culture and digitalization appeared sporadically even before that. Figure  1 shows an overview of the annual development of published papers and the number of citations related to the given papers. We can notice that in the last 5 years, research has an exponential character (measured through the number of published papers per year), but at the same time, this research area is interesting for academics (measured through the absolute number of citations).

Development of published papers related to organizational culture and digital transformation

The analyzed amount of papers were published in 1619 sources (journals, proceedings, books, etc.) with various research impacts. Table 5 shows the ranking of the sources that had the greatest impact on research on organizational culture and digital transformation in terms of the total number of citations. The research impact is primarily dominated by journals that directly or indirectly deal with the business environment, which is natural considering the nature of the papers. Of the ten listed top influential papers, as many as seven are from the last 5 years, which indicates that since 2018, research interest and the research impact of the given topic have grown dramatically.

Each analyzed document in our dataset was assigned to one of the 28 subject areas used by the Scopus database for their classification. Such an assignment took place based on pairing information about the journal in which the given article is located with the categorization of the journal according to the subject areas of the Scopus database. Figure  2 shows an overview of research interest and research impact for the individual subject areas.

Overview of research impact and research interest of subject areas

Until 2019, ENGI (engineering) was the most frequent category, while a dramatic increase in papers in the BUSI (business, management, and accounting) group can be seen in the last four years. This increase has caused BUSI to be the subject area with the most outstanding research impact and research interest. No such significant changes were recorded in the other subject areas. Possible reasons for the increased interest of researchers in the field of BUSI in the topic of organizational culture and digital transformation are indirectly indicated by some current studies. For example, the study by Priyanto et al. ( 2023 ) emphasizes the importance of proactively modernizing a business to maintain a competitive edge. The need to increase the competitive edge was also pointed out in the study by Troise et al. ( 2022 ), in which the authors examined the relationships between SMEs’ agility (measured by digital technologies capability, relational capability, and innovation capability) and the effects of agility on three outcomes (financial performance, product and process innovation). These studies and many others (Alomari, 2021 ; Carvalho et al., 2020 ; Chaurasia et al., 2020 ; Tessarini Junior & Saltorato, 2021 ) emphasize the managerial aspect of digitalization, which could explain the dramatic increase in research interest and research impact that we have seen over the last 4 years.

These results are also confirmed by a more detailed analysis of the development of the annual number in the five most numerous subject areas (Fig.  3 ). In the left part, we can see the absolute number of articles in the given subject areas, while the dominance of BUSI is visible mainly in the last three years. However, comparing the share of papers in particular subject areas is very interesting (right part of Fig.  3 ). We see that the increase in the BUSI subject area is continuous, while the share of SOCI (social sciences) and COMP (computer science) is decreasing in the long term. Areas such as ENGI and DECI (decision science) maintain a relatively constant share. According to the long-term trend, it can be assumed that the share of the BUSI subject area will grow in research on topics related to organizational culture and digital transformation in the coming years.

Development of papers in top 5 subject areas — absolute numbers (left) and share (right)

Topics Identification and Their Development

By analyzing the abstracts of the individual papers, it was possible to categorize documents into thematically related clusters using LDA. Such clusters contain papers with the occurrence of the same terms and are called topics. The individual steps of extracting topics from the analyzed dataset are listed in the “ Topic Modelling ” section. To choose the number of topics, several experiments were carried out with the aim of identifying such a constellation in which the individual topics would be well interpretable and, at the same time, sufficiently distinguishable from each other. The number of topics k  = 8 was selected by expert assessment according to these criteria. The results and a brief description of the topics via the top-5 most frequent terms can be found in Fig.  4 as an intertopic distance map between two principal components (PC).

Intertopic distance map

Eight identified topics were analyzed with regard to the most frequented words, and at the same time, the most cited articles in the given topic were also used for their better characterization. This allowed these topics to be named and briefly characterized:

Social Media Connectivity (Topic-1)

This topic includes various aspects of digital and social media, as well as online platforms and the cultural impacts of digital technologies. The Social Media Connectivity topic focuses on main areas like the rise of social media (Munar, 2012 ; van Dijck, 2013 ), its platforms (Mikos, 2016 ; Morris, 2015 ), as well as structural change (Kim, 2020 ; Peukert, 2019 ). The articles of topic-1 explore a wide range of subjects in particular such as social media strategies, digital engagement with heritage, digital storytelling, cultural globalization, and the transformative effects of digital technological change. There are many different inter-organizational subcultures present within organizations that are dealing with convergence and cooperation across media platforms. According to Erdal ( 2009 ), cooperation between those cultures is frequently linked to competition. It is the topic with the most significant research interest (measured through the number of papers), and at the same time, it is the topic with the highest research impact (measured through the number of citations). There are 458 related papers in this topic with a sum of all citations of 91% (based on a 6000 citation strip).

Digital Innovation Ecosystems (Topic-2)

This topic captures the overarching theme of digital transformation across various domains. It emphasizes the integration of digital technologies, innovation processes and the development of ecosystems to drive transformative change in industries and organizations with regard to culture. Regarding the function of organizational culture throughout this transformation process, two alternative viewpoints may be seen. When individuals are empowered to use their problem-solving skills, their capacity for learning and their sense of responsibility, a culture may result in a workforce that is people-centered and engaged driving the integration of digital technologies. On the other hand, there is a culture that focuses primarily on promoting this technology for the purpose of managing or substituting processes neglecting the input and use of people (Rossini et al., 2021 ). The main subjects of this topic include healthcare (Jacob et al., 2020 ), manufacturing (Reinhardt et al., 2020 ), and a digital transformation focus of information systems and organizational practices (Ulas, 2019 ). Additionally, the challenges for the organization and management in rapidly changing environments are analyzed (Granlund & Taipaleenmäki, 2005 ). This topic has a relatively considerable research interest with 419 papers published, but its research impact is average with 51%.

Socio-economic Sustainability (Topic-3)

The Socio-economic Sustainability topic captures the intersection of digital transformation, sustainability and socio-economic considerations across a wide variety of domains such as urban development (Anttiroiko, 2016 ), corporate responsibility and sustainability (Etter et al., 2019 ; Lăzăroiu et al., 2020 ), technology management (Tasleem et al., 2019 ), and organizational practices with regard to culture, among others. In the case of sustainable performance, all forms of organizational culture — based on the types defined by Quinn and Spreitzer ( 1991 ) — have a positive effect on sustainable performance (Gebril Taha & Espino-Rodríguez, 2020 ). There is also a strong correlation between organizational culture and eco-innovation (Reyes-Santiago et al., 2017 ). Furthermore, the sharing economy and its cultural effects towards consumption and ownership are analyzed (Dabbous & Tarhini, 2021 ). The third topic has an average research interest, counting 367 papers and a slightly below-average research impact of 42% compared to the other topics.

Digital Workforce Transformation (Topic-4)

Digital Workforce Transformation highlights the themes of digital transformation with the focus of organizational resilience, leadership, and the impact of technology on work culture and employee well-being. The main focus is on the employee-work relationship, including subjects like leadership (Cortellazzo et al., 2019 ; Guzmán et al., 2020 ), employee well-being (Coldwell, 2019 ; Theurer et al., 2018 ), and resilience (McFadden et al., 2015 ). In particular, the implications on cultural organizational characteristics, operations, digital transformation, and financial planning of COVID-19 for work, workers, and organizations are analyzed (Kniffin et al., 2021 ; Obrenovic et al., 2020 ). As a result of the COVID-19 pandemic, many organizations have changed their mode of operation. They adopted a pure work from home model or make use of a hybrid mode of operation. Establishing a communicative work from home culture will result in increased employee satisfaction (Fay & Kline, 2011 ; Mandal et al., 2023 ). Organizations have to educate their employees concerning these new processes and technologies. Individuals dislike change, so organizations must coordinate training and awareness programs to demonstrate the advantages of new digital platforms and related technologies (Mandal et al., 2023 ). Regarding research interest, this topic is average with 381 papers, and its research impact is slightly below average with 42%.

Digital Competence and Cultural Transformation (Topic-5)

This topic refers to the concepts of competence in the digital era, cultural transformation, innovation, and sustainability. These articles explore different aspects of digital transformation (Suárez-Guerrero et al., 2016 ), the impact of digital competence on various sectors (Konttila et al., 2019 ), cultural factors in innovation and enterprise, and the intersection of technology and culture (Mohelska & Sokolova, 2018 ). The role of leadership in the transformation of organizational culture is also a focus of analysis (Sá & Serpa, 2020 ). From the point of view of research interest, this is a minor topic (355 papers) that simultaneously has a relatively small research impact (33%).

Knowledge, Culture and Innovation (Topic-6)

Knowledge, Culture, and Innovation captures the common themes of knowledge management (Gandini, 2016 ; Yeh et al., 2006 ), organizational culture (Dubey et al., 2019 ), innovation, and the transformative effects (Ungerman et al., 2018 ) of digitalization across various sectors. Digital innovation is linked to organizational culture by the digital capabilities of an organization (Zhen et al., 2021 ). The capabilities required by management in dynamic environments are examined in particular (Karimi & Walter, 2015 ). Research interest, counting 388 papers, as well as research impact, with 56%, of this topic are both average.

Data and Resource Management (Topic-7)

The Data and Resource Management topic encompasses the concepts of digitalization, Industry 4.0, data management, quality management, organizational culture and the impact of technology on various industries (Durana et al., 2019 ; Gunasekaran et al., 2019 ; Sony et al., 2020 ). These titles explore different aspects of implementing Industry 4.0, including the utilization of big data (Chiang et al., 2017 ), improving organizational performance through digital transformation (Ananyin et al., 2018 ) and the role of data-driven decision-making in different sectors. A number of relevant factors for Industry 4.0 implementation like the development of Industry 4.0-specific know-how, securing financial resources, integration of employees into the implementation process, and the establishment of an open-minded and flexible corporate culture are analyzed. (Veile et al., 2020 ). The research interest of this topic is the smallest of all with only 315 papers, and its research impact is also relatively small with 34%.

Digital Transformation Maturity (Topic-8)

This topic covers the concepts of digital transformation, Industry 4.0, maturity models, organizational culture, and the impact of technology on business strategies and performance (Gajsek et al., 2019 ; Teichert, 2019 ). These titles explore various aspects of digitalization, technology implementation, strategic management, organizational resilience, and the adoption factors of Industry 4.0 in the manufacturing industry (Kohnová et al., 2019 ). The analysis shows that factors like organizational identity, dematerialization, and collaboration play a key role in the digital transformation (Tronvoll et al., 2020 ). The size of research interest of this topic is average (382 papers), but its research impact is among the largest (of 80%).

These topics are sufficiently distinguishable from each other not only from an interpretive point of view but also within the position in the intertopic distance map (Fig.  4 ). In the coordinates of two principal components, almost all topics are relatively isolated, meaning they are sufficiently distinguishable from each other. In one case, however, a statistical similarity was identified, namely for topic-2 Digital Innovation Ecosystems and topic-8 Digital Transformation Maturity (Fig.  4 top left). This finding suggests that there is some interrelationship between the two topics. After a closer examination of the articles from both topics, it was found that topic-2 and topic-8 share a rather similar basis of content. The central point of investigation in these articles is the identification of various (success) factors and challenges that arise for organizations and their cultures during the phase of digital transformation (AlBar & Hoque, 2019 ; Cichosz et al., 2020 ; Shardeo et al., 2020 ). Topic-2 builds on this common foundation by focusing on systems and functional aspects. There, the organization’s implementation, integration, and management of tools and data (ERP, big data) is examined. Additionally, this topic focuses on the organization’s life cycle, evolution, business models, and processes like DevOps and Agile development (Gupta et al., 2019 ; Jacob et al., 2020 ; Nascimento et al., 2019 ). On the other hand, the majority of the articles in topic-8 focus on a perspective with regard to the organizational readiness of the organization towards changes related to Industry 4.0, including the impacts those changes will have on culture, the implications for strategy, and the general organization’s maturity through the examination of maturity models (Ganzarain & Errasti, 2016 ; Mittal et al., 2018 ; Santos & Martinho, 2020 ; Schumacher et al., 2016a , b ).

The eight topics identified are not static and their development may change over time. To capture such changes, we analyzed the share of papers (research interest) and the share of citations (research impact) of papers in the last 10 years. We did not analyze the absolute numbers but their relative share primarily to avoid the risk of distortion caused by the exponential increase in the number of articles and citations. The results can be found in Fig.  5 .

Development of research interest (top) and research impact (bottom) in last 10 years

Several findings can be seen in Fig.  5 . The first of them is a marked decrease in topic-1 both from the point of view of research interest and the point of view of research impact. As mentioned earlier, this topic is currently one of the most important. However, trend analysis shows that its importance is declining relatively quickly. It is gradually being replaced by topics with higher research interest (e.g., topic-4) or research impact (e.g., topic-6).

The downward trend of topic-1 Social Media Connectivity can be explained with the growing maturity of this research field. In the early start of the new millennium, the rise of social networks and communication platforms like Facebook, Twitter, Instagram, Whatsapp, and other social media services and applications changed the way of communication and collaboration. As of 2023, this field of research is established and many papers have been published and cited already. Based on our search query, there were 458 papers identified with over 5400 citations in total from 1997 to 2023.

The second finding is the gradual emergence of new topics. These are topics that almost or did not exist 10 years ago. The most significant representative of such topics is topic-4, which almost did not exist in 2013, but is currently one of the most important topics. The upward trend of topic-4 Digital Workforce Transformation is strongly connected with the emergence of new working modes and cultural shifts within the organizational landscape due to COVID-19 pandemic related effects. The rise of topic-4 with a strong focus on the employee-work relationship and employee well-being is relatively new. This was triggered with the start of the worldwide pandemic (COVID-19). The worldwide pandemic had a significant impact on how people worked and communicated. This remote work model has many implications on a number of different fields like organizational culture, collaboration, employee motivation, and productivity, among many others. Thus, the requirement for employees and the organizations to adapt to this new work reality open up many new research fields. The growing topic-6 Knowledge, Culture, and Innovation combines knowledge management, organizational culture, and innovation in regard to the transformative effects of digitalization across various sectors. This topic recently gained special attention because the world economy is facing challenges during the pandemic caused by less international business and trade and increased costs (Amirul et al., 2023 ). Competitive advantages through knowledge management, knowledge sharing, and innovation are the key to deal with the (project) uncertainty many companies face (Borodako et al., 2023 ).

The third finding is that increasing research interest does not necessarily increase research impact. For example, we can mention topic-5 Digital Competence and Cultural Transformation , which is gradually gaining research interest, but its research impact is the smallest of all. However, it should be noted here that research impact is based on processing the number of citations, which can generally have a time delay.

A more detailed characterization of topics is also possible by comparing them to the analyzed subject areas. Figure  6 shows the decomposition of individual topics into subject areas. The basis for this decomposition was the papers themselves.

Decomposition of topics to subject areas

Several findings can be seen in Fig.  6 . Topic-1, which currently dominates research impact and research interest, but has a negative trend, is most associated with papers from the SOCI subject area. If we compare these results with the analysis of subject areas (Fig.  2 ), we can conclude that there are two parallel phenomena — a decrease in interest in both SOCI and topic-1. This topic played a key role in the past, but its outlook, as well as the outlook of organizational culture research in relation to digital transformation in the SOCI subject area, is negative. On the other hand, we can see that the BUSI subject area is most prominently represented in topic-6. By comparing the development of BUSI and the development of topic-6, we can also notice parallel phenomena — in this case, however, with a positive trend. Both topic-6 and the BUSI subject area have been growing in recent years, and it is assumed that this could be the case in the following years as well. In the past the focus of research has been on identification and introduction as well as adaptation of new technologies that drive the trend of digital transformation. With this established foundation, nowadays, the research shifts more towards the application and impacts of these technologies in organizations and its consequences on innovation-orientation, knowledge generation and sharing as well as cultural effects (Kronblad et al., 2023 ). This can be seen with the strengthening of topic-6. Other topics appear more heterogeneous from the point of view of subject areas, and the papers that fall into them are from different subject areas.

This article begins with a brief review of organizational culture research in relation to digital transformation. Later, an overview of the research area was presented based on the 3065 publications listed and identified in the Scopus database. To answer research question 1, we have identified the key journals, papers and authors and have shown the development of publications over time. Research interest and research impact of the given topic have grown dramatically since 2018. According to research areas, from 2004 until 2023, the share of papers (research impact) as well as the share of citations (research interest) is mainly contributed to the subject area of BUSI (with a share of more than 25%). The dominance of BUSI has been visible mainly in the last 3 years.

The identification of the dominant research topics (research question 2) resulted in eight topics: Social Media Connectivity , Digital Innovation Ecosystems , Socio-economic Sustainability , Digital Workforce Transformation , Digital Competence and Cultural Transformation , Knowledge, Culture and Innovation , Data and Resource Management , and Digital Transformation Maturity . The topic with the most significant research interest (measured by the number of papers) and the highest research impact (measured by the number of citations) is Social Media Connectivity (topic-1). This is because of the strong role of this topic in the past. The outlook is declining for this topic as well as the related subject area SOCI. Two rising topics were identified. In recent years Digital Workforce Transformation (topic-4) and Knowledge, Culture, and Innovation (topic-6) gained strong interest. Both are from the area of BUSI.

To fulfil the aims of the article, following the completion of the literature review, we were able to identify a number of research topics that are distinct due to the methodology that we have utilized. As a result of their development over time, some of these topics are also relatively new; for instance, as of 2013, topic-4 ( Digital Workforce Transformation ) did not exist at all. In light of the fact that the topics have developed over time, it is clear that the most important areas influencing culture have been transformed under the conditions brought about by digital transformation.

Implications

Firstly, this study demonstrated a machine learning–supported method for identifying and segmenting the current state of this research field. This method, as used in this paper, can be applied to other fields to obtain a systematic overview of research topics.

Secondly, organizational culture has been a field of research for many years and research on digital transformation is constantly growing. The interrelation of these two research areas is relatively new, and their findings will have a lasting effect on the formation and effectiveness of organizational culture in the future.

With the increased interest in Digital Workforce Transformation and Knowledge, Culture, and Innovation , we could identify a shift in the research field on organizational culture in relation to digital transformation towards the subject area of BUSI. Those two rising topics show a need to focus on the impact of technology on work culture and employee well-being, as well as on knowledge management and innovation in relation to organizational culture.

The long-term trend of the share development of the BUSI subject area indicates that this area will also grow continuously in the future. From 2019 onwards, the constant increase of papers published per year implies that additional distinct new topics will be established in this field of research. These and other future trends will help researchers to focus on relevant topics and areas for their work.

A possible explanation for this shift in research could derive from the impact technological changes have on businesses today. The work-related requirements during the COVID-19 pandemic acted as a catalyst for many technological advancements due to the necessity to work instantly remote, changing many processes and all communication to digital. This growing importance of technology for every business could lead to an increased relevance and importance for management practice as well as for researchers. An additional cause for organizations to reevaluate matters related to knowledge and innovation is the pervasive integration and accessibility of AI technology in routine business operations. The alignment of current processes, particularly the innovation process within organizations, with this novel capability will be a subject of interest for managers and researchers as well.

Following the functionalist perspective on organizational culture, the management of organizations can attempt to control and change culture (Alvesson, 1993 ). The introduction of these two topics has significant implications for management practice. A strong organizational culture that is people-centered is essential for successful knowledge-driven organizational innovation. As a result, managers must pay special attention to the factors that influence work culture, address the challenges that arise during the transformation, and understand and improve their organization’s digital capabilities.

Managers can focus their efforts on a variety of areas to foster an adaptable, innovative, and supportive work culture while effectively leveraging technology for digital transformation. Enhanced emphasis is placed on the behavior and collaboration of the team and managers, while these recommendations also encompass measures pertaining to the structure and processes.

The delegation of decision-making authority and work ownership responsibility to employees by managers is a critical structural element. Utilizing data to facilitate well-informed decision-making can provide support for this. Establishing a work environment that offers adequate resources and support, including tools, training, and assistance in adjusting to digital transformations and fostering innovation, is an additional critical element (Veile et al., 2020 ). Furthermore, it is beneficial to measure and communicate progress by assessing the impact of digital transformation on work culture, employee well-being, knowledge management, and innovation on a regular basis. The manager should be willing to make the necessary cultural changes to align, adapt, and evolve organizational culture in the digital age (Cortellazzo et al., 2019 ).

During digital transformation, an open and productive organizational culture will be fostered through the promotion of a flexible and inclusive work environment that actively solicits employee feedback and input, with a focus on employee well-being (Coldwell, 2019 ). Managers who set a good example and encourage their employees’ continuous learning and skill development, as well as cross-functional collaboration, will be better able to promote an adaptive organizational culture in an increasingly digital and competitive landscape (Sá & Serpa, 2020 ). Creating a culture that values innovation and encourages employees to come up with new ideas and solutions, as well as celebrating successful innovations, can help managers create a people-oriented work culture that is essential for organizational innovation (Karimi & Walter, 2015 ). This can be seen in the increased interest in the area on Knowledge, Culture, and Innovation by organizations as well as by researchers.

Limitations and Future Research

This study has a number of limitations, which can be mainly attributed to the way the analysis was conducted. The focus of this study is on an automated bibliometric analysis of the literature. While the quantitative focus has many advantages, it also has some limitations. The main advantage includes the possibility to process and analyze a large number of papers via automation and machine learning techniques. A total of 3065 papers were analyzed. This approach — in comparison to a standard systematic literature review — does not analyze the papers manually. Therefore, some relevant documents could be missing, as well as some irrelevant ones might be included. The authors have selected a search query that yields highly relevant search results. Thus, it is assumed that the share of notable articles that are missing is very small and therefore neglectable and does not have a significant impact on the results.

The applied dataset covers most of the important publications, but all the data comes from just one database (Scopus). This is not comprehensive, and some relevant articles (or journals) could be excluded. In addition, some information may be missing because the source of analysis is not the full text of the articles. Another limitation comes from the fact that the primary focus in the topic modeling are the abstracts of the relevant papers and not the whole text. The analysis of the full text could potentially provide a more extensive understanding, but at the same time, it would take much longer.

We decided on the expert approach by determining the number of topics, as the statistical approach resulted in a large number of topics. This may be of a subjective nature, but it resulted in eight well interpretable and sufficiently distinguishable topics. The title, abstract, and keywords of each topic’s top-30 papers (based on citation count) were used to name each topic. This results in subjective topic names but helps to sum up each topic with a generalized distinct phrase.

This study suggests a number of possible future directions for additional research. It is recommended to extend the data sources to other databases than Scopus as well as the search query. This could result in capturing an increased number of relevant papers. In this research two developing, fast growing topics (topic-4 and topic-6) were identified. Further research should concentrate on examining this trend and focusing on those topics.

Future research could concentrate on finding various organizational culture types that reflect and favor those two emerging topics. Considering Quinn and Rohrbaugh’s CVF (Cameron & Quinn, 1999 ; Quinn & Rohrbaugh, 1983 ), the characteristics of the adhocracy culture type may align with the aspects connected to Digital Workforce Transformation and Knowledge, Culture and Innovation as this culture type values innovation and flexibility. This can be supported through the systematic research and cultural audits in organizations.

Data Availability

The data and code that support the findings of this study are available from the corresponding author upon request.

cultur, digit, studi, research, technolog, busi, industri, organ, organiz, use, transform, practic, compani, paper, result, factor, perform, effect, find, implement, author, analysi, provid, differ, organis, approach, base, adopt, identifi, impact, improv, literatur, support, relat, increas, focus, success, level, structur, present, purpos, aim, relationship, influenc, understand, method, enterpris, signific, firm, articl, includ, limit, publish, framework, context, contribut, corpor, show, requir, sector, case, review, futur, within, creat, examin, key, explor, right, current, propos, institut, collect, main, howev, reserv, natur, analyz, implic, discuss, consid, concept, mani, construct, investig, achiev, conduct, among, becom, toward, exist, respons, applic, enabl, theori, affect, issu, survey, assess, opportun, three, interview, adapt, indic, appli, perspect, area, suggest, critic, determin, specif, high, aspect, field, build, form, order, evalu, direct, establish, relev, offer, object, various, methodolog, address, problem, enhanc, addit, part, empir, initi, scienc, associ, analyt, reveal, term, theoret, test, springer, possibl, generat, complex, big, open, continu, switzerland, particip, academ, state, mediat, originalityvalu, designmethodologyapproach, across, solut,advanc, content, regard, characterist, highlight, analys, therefor, higher, interest, access, allow, emerald, advantag, face, make, better, year, insight, goal, select, trend, function, small, element, due, must, conceptu, view, systemat, action, chapter, combin, play, accord, question, describ, questionnair, sever, valid, larg, general, thus, â€, major, recent, type, technic, mean, concern, moder, topic, facilit,sampl, gap, respond, way, attent, outcom, stage, scientif, final, expect, repres, creation, report, still, variabl, especi, techniqu, ensur, compar, number, carri, practition, necessari, exampl, defin, second, copyright, document, compon, subject, common, obtain, demonstr, evid, drive, link, depend, exclus, principl, multipl, essenti, observ, quantit, format, revolut, effort, reflect, four, negat, recommend, made, idea, top, ltd, awar, five, regul, standard, rapid, previous, statist, take, strong, introduc, european, journal, foster, sinc, conclus, featur, basi, driver, equat, digitalis, special, best, comprehens, hand, help, forc, given, consist, align, uniqu, total, explain, overal, materi, refer, gain, furthermor, remain, taylor, whether, moreov, imag, conclud, origin, hypothes, consider, think, similar, russian, attribut, fundament, ieee, clear, bring, caus, around, encourag, period, live, shape, step, start, deploy, name, crisi, intent, contemporari, produc, particular, today, protect, satisfact, ident, accept, six, despit, progress, paradigm, theme, appropri, although, elsevi, argu, datadriven, attract, seek, complet, scholar, search, deal, china, maintain, act, respect, introduct, pattern, serv, less, acceler, indepth, predict, crucial, style, detail, procedur, extend, limitationsimpl, phase, emphas, togeth, greater, abl, central, via, confirm, novel, draw, correl, databas, rate, emot, primari, basic, wide, degre, give, machin, legal, domin, thing, map, basel, record, turn, interpret, south, transfer, cover, mdpi, along, leverag, pressur, move, hospit, decad, least, expand, evolv, fourth, holist, now, informa, rang, other, reliabl, solv, excel, site, uncertainti, henc, partial, littl, without, contain, balanc, prefer, real, cours, overcom, alreadi, india, prepar, sale, actor, instrument, valuabl, beyond, past, center, histori, fact, regress, prevent, preserv, assist, deliv, low, definit, mine, substanti, extens, answer, close, known, third, taken, contextu, popular, employees’, index, fit, deriv, locat, embrac, text, scenario, outlin, certain, ongo, desir, independ, transpar, avoid, proceed, realiz, illustr, visual, promis, inc, reach, usag, algorithm, identif, consult, feder, gather, whole, prioriti, russia, altern, constant, occur, shown, actual, proactiv, seem, europ, matter, resist, express, igi, appear, sociotechn, light, extent, germani, done, hybrid, upon, just, read, receiv, driven, german, cycl, suitabl, mainten, fulli, look, long, bodi, ground, attempt, broad, compris, varieti, indonesia, frame, african, rise, home, weak, proper, financ, keep, maker, dissemin, properti, senior, mitig, next, difficulti, captur, correspond, flow, begin, code, overview, stimul, squar, prove, volum, reduct, full, american, choic, malaysia, intend, llc, eight, tri, occup, diffus, vari, under, numer, extract, organization’, anoth, len, rule, indian, aid, know, joint, socioeconom, lower, summar, classifi, fast, experiment, exhibit, paramet, brought, widespread, understood, nowaday, mix, embed, africa, built, provis, sociolog, good, comparison, adjust, behind, quick, adequ, channel, instead, verifi, indirect, seven, primarili, soft, safe, company’, pose, handl, themat, routin, therebi, interconnect, reform, assumpt, either, constitut, utilis, believ, prior, john, separ, come, segment, item, assum, suffici, minim, whose, sem, plssem, outsid, seri, huge, restrict, wast, classif, updat, translat, obstacl, frequent, hold, version, interfac, discov, almost, represent, equal, wherea, hypothesi, presenc, simpl, robust, alway, categor, claim, score, like, print, interdisciplinari, ten, australia, note, italian, bibliometr, lie, america, underpin, synthesi, wiley, promin, alter, typic, stori, fuzzi, simultan, fulfil, estim, pursu, correct, return, manner, narrat, becam, besid, contrast, ration, inspir, replac, hinder, imper, detect, thought, son, faculti, convers, asia, profound, pilot, acknowledg, maxim, configur, urgent, argument, hard, sensit, gmbh, charact, larger, rich, wider, elabor, highest, shed, phenomena, deep, necess, mutual, mass, option, trigger, expans, poor, extant, domest, today’, concentr, demograph, reinforc, clarifi, anticip, eas, expos, deeper, most, editor, devot, middl, crosssect, usual, nine, ultim, manifest, scopus, calcul, vulner, andor, run, massiv, tension, ideal, old, retriev, first, singapor, ambigu, list, conscious, inher, insid, ministri, rethink, serious, compos, stay, modifi, per, encount, rare, attain, circumst, date, recognis, enter, near, spss, explicit, held, incent, unpreced, largest, stronger, insuffici, lack, nevertheless, word, longer, input, decreas, conting, accur, tendenc, preval, match, tackl, undertaken, sciencebusi, amongst, mention, easili, reader, chosen, prosper, elimin, coupl, hope, authors’, get, later, everyday, dedic, encompass, thrive, miss, acm, refin, interdepend, guarante, precis, except, random, accomplish, latest, easi, vast, prevail.

Aasi, P., & Rusu, L. (2017). Facing the digitalization challenge: Why organizational culture matters and how it influences IT governance performance. In N. Paspallis, M. Raspopoulos, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information systems development: Advances in methods, tools and management (ISD2017 Proceedings). Larnaca.

Google Scholar  

Abu Bakar, M. R., Mat Razali, N. A., Wook, M., Ismail, M. N., & Tengku Sembok, T. M. (2021). The mediating role of cloud computing and moderating influence of digital organizational culture towards enhancing SMEs performance. In H. Badioze Zaman, et al. (Eds.), Advances in visual informatics. IVIC 2021. Lecture Notes in Computer Science (vol 13051, pp. 447–458). Springer. https://doi.org/10.1007/978-3-030-90235-3_39

Acemoglu, D., & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. Handbook of Labor Economics, 4 , 1043–1171. https://doi.org/10.1016/s0169-7218(11)02410-5

Article   Google Scholar  

Adebanjo, D., Laosirihongthong, T., Samaranayake, P., & Teh, P.-L. (2021). Key enablers of Industry 4.0 development at firm level: Findings from an emerging economy. IEEE Transactions on Engineering Management, 70 (2), 400–416. https://doi.org/10.1109/tem.2020.3046764

Affes, W., & Affes, H. (2022). Business model and firm performance in Tunisian firms: A mediated moderation analysis. Journal of the Knowledge Economy, 13 (4), 2822–2839. https://doi.org/10.1007/s13132-021-00836-4

Ågerfalk, P. J., Conboy, K., & Myers, M. D. (2020). Information systems in the age of pandemics: COVID-19 and beyond. European Journal of Information Systems, 29 (3), 203–207. https://doi.org/10.1080/0960085x.2020.1771968

Alankarage, S., Chileshe, N., Rameezdeen, R., Edwards, D. J., & Samaraweera, A. (2021). Exploring BIM-triggered organisational and professional culture change: A systematic literature review. Construction Innovation, 23 (1), 229–247. https://doi.org/10.1108/ci-04-2021-0084

AlBar, A. M., & Hoque, Md. R. (2019). Factors affecting cloud ERP adoption in Saudi Arabia: An empirical study. Information Development, 35 (1), 150–164. https://doi.org/10.1177/0266666917735677

Alloghani, M., Thron, C., & Subair, S. (2022). Past achievements and future promises of digital transformation: A literature review. In M. Alloghani, C. Thron, & S. Subair. (Eds.), Artificial intelligence for data science in theory and practice. Studies in computational intelligence (vol 1006, pp. 27–39). Springer, Cham. https://doi.org/10.1007/978-3-030-92245-0_2

Alomari, K. M. (2021). Identifying critical success factors in designing effective and efficient supply chain structures: A literature review. Uncertain Supply Chain Management, 9 (2), 447–456. https://doi.org/10.5267/j.uscm.2021.1.006

Alvesson, M. (1993). Cultural perspectives on organizations . Cambridge University Press.

Am, E. N., Affandi, A., Udobong, A., Sarwani, S., & Hernawan, H. (2020). Implementation of human resource management in the adaptation period for new habits. International Journal of Educational Administration, Management, and Leadership, 1 (1), 19–26. https://doi.org/10.51629/ijeamal.v1i1.4

Amirul, S. R., Ahmad, S. N. B., & Nasip, S. (2023). Organisational culture and dynamic marketing capabilities in the digital age of pandemic crisis. In B. Alareeni, & A. Hamdan. (Eds.), Impact of artificial intelligence, and the fourth industrial revolution on business success. ICBT 2021. Lecture Notes in Networks and Systems (vol 485). Springer, Cham. https://doi.org/10.1007/978-3-031-08093-7_21

Ananyin, V., Zimin, K., Lugachev, M., Gimranov, R., & Skripkin, K. (2018). Digital organization: Transformation into the new reality. Business Informatics, 2018 (2), 45–54. https://doi.org/10.17323/1998-0663.2018.2.45.54

Anghel, D. (2019). The ground rules for managers and leaders in the change management process of digitization. Quality - Access to Success, 20 (3), 37–42.

Anttiroiko, A.-V. (2016). City-as-a-platform: The rise of participatory innovation platforms in Finnish cities. Sustainability, 8 (9), 922. https://doi.org/10.3390/su8090922

Anwar, A., Kamel, A. A., & Ahmed, E. (2016, May). Agile Adoption Case Study, Pains, Challenges & Benefits. AMECSE ‘16: Proceedings of the 2nd Africa and Middle East Conference on Software Engineering. Association for Computing Machinery,  60–65. https://doi.org/10.1145/2944165.2944175

Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11 (4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Assoratgoon, W., & Kantabutra, S. (2023). Toward a sustainability organizational culture model. Journal of Cleaner Production, 400 , 136666. https://doi.org/10.1016/j.jclepro.2023.136666

Baek, P., Chang, J., & Kim, T. (2019). Organizational culture now and going forward. Journal of Organizational Change Management, 32 (6), 650–668. https://doi.org/10.1108/jocm-05-2018-0121

Bajic, B., Rikalovic, A., Suzic, N., & Piuri, V. (2021). Industry 4.0 implementation challenges and opportunities: A managerial perspective. IEEE Systems Journal, 15 (1), 546–559. https://doi.org/10.1109/jsyst.2020.3023041

Baker, E., Avery, G. C., & Crawford, J. (2006). Home alone: The role of technology in telecommuting. Information Resources Management Journal, 19 (4), 1–22. https://doi.org/10.4018/irmj.2006100101

Baker, H. K., Kumar, S., & Pandey, N. (2020). Thirty years of small business economics: A bibliometric overview. Small Business Economics, 56 , 487–517. https://doi.org/10.1007/s11187-020-00342-y

Balasingham, K. (2016). Industry 4.0: Securing the future for German manufacturing companies. Retrieved 12 April 2023, from http://essay.utwente.nl/70665/1/Balasingham_BA_MA.pdf

Barley, S. R., Meyer, G. C., & Gash, D. W. (1988). Cultures of culture: Academic, practitioners and the pragmatics of normative control. Administrative Science Quarterly, 33 , 24–60.

Bélanger, F., Watson-Manheim, M. B., & Swan, B. R. (2013). A multi-level socio-technical systems telecommuting framework. Behaviour & Information Technology, 32 (12), 1257–1279. https://doi.org/10.1080/0144929x.2012.705894

Belinski, R., Peixe, A. M. M., Frederico, G. F., & Garza-Reyes, J. A. (2020). Organizational learning and Industry 4.0: Findings from a systematic literature review and research agenda. Benchmarking: An International Journal, 27 (8), 2435–2457. https://doi.org/10.1108/bij-04-2020-0158

Betto, F., Sardi, A., Garengo, P., & Sorano, E. (2022). The evolution of balanced scorecard in healthcare: A systematic review of its design, implementation, use, and review. International Journal of Environmental Research and Public Health, 19 (16), 10291. https://doi.org/10.3390/ijerph191610291

Bibby, L., & Dehe, B. (2018). Defining and assessing Industry 4.0 maturity levels – Case of the defence sector. Production Planning & Control, 29 (12), 1030–1043. https://doi.org/10.1080/09537287.2018.1503355

Blei, D. M., & Lafferty, J. D. (2009). Topic models. In A. N. Srivastava & M. Sahami (Eds.), Text mining: Classification, clustering, and applications (pp. 71–93). Chapman & Hall/CRC.

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3 , 993–1022.

Bloom, N., Liang, J., Roberts, J., & Ying, Z. J. (2015). Does working from home work? Evidence from a Chinese experiment. Quarterly Journal of Economics, 130 (1), 165–218. https://doi.org/10.3386/w18871

Bondarouk, T. V., & Ruël, H. J. M. (2009). Electronic human resource management: Challenges in the digital era. The International Journal of Human Resource Management, 20 (3), 505–514. https://doi.org/10.1080/09585190802707235

Borodako, K., Berbeka, J., Rudnicki, M., & Łapczyński, M. (2023). The impact of innovation orientation and knowledge management on business services performance moderated by technological readiness. European Journal of Innovation Management, 26 (7), 674–695. https://doi.org/10.1108/ejim-09-2022-0523

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton and Company.

Büschgens, T., Bausch, A., & Balkin, D. B. (2013). Organizational culture and innovation: A meta-analytic review. Journal of Product Innovation Management, 30 (4), 763–781. https://doi.org/10.1111/jpim.12021

Caligiuri, P., Cieri, H., Minbaeva, D., Verbeke, A., & Zimmermann, A. (2020). International HRM insights for navigating the COVID-19 pandemic: Implications for future research and practice. Journal of International Business Studies, 51 (5), 697–713. https://doi.org/10.1057/s41267-020-00335-9

Cameron, K. S., & Quinn, R. E. (1999). Diagnosing and changing organizational culture: Based on the competing values framework . Addison-Wesley Publishing.

Carnevale, J. B., & Hatak, I. (2020). Employee adjustment and well-being in the era of COVID-19: Implications for human resource management. Journal of Business Research, 116 , 183–187. https://doi.org/10.1016/j.jbusres.2020.05.037

Cartwright, J. (1999). Cultural transformation: Nine factors for continuous business improvement . Prentice Hall.

Carvalho, A. M., Sampaio, P., Rebentisch, E., Carvalho, J. Á., & Saraiva, P. (2020). The influence of operational excellence on the culture and agility of organizations: Evidence from industry. International Journal of Quality & Reliability Management, 38 (7), 1520–1549. https://doi.org/10.1108/ijqrm-07-2020-0248

Chatman, J. A., & O’Reilly, C. A. (2016). Paradigm lost: Reinvigorating the study of organizational culture. Research in Organizational Behavior, 36 , 199–224. https://doi.org/10.1016/j.riob.2016.11.004

Chaurasia, S. S., Kaul, N., Yadav, B., & Shukla, D. (2020). Open innovation for sustainability through creating shared value-role of knowledge management system, openness and organizational structure. Journal of Knowledge Management, 24 (10), 2491–2511. https://doi.org/10.1108/jkm-04-2020-0319

Chen, Y., Liu, L., Li, W., Xie, Z., & Wei, C. (2023). Microfoundations of dynamic capabilities: A systematic review and a multilevel framework. Management Decision, 61 (6), 1717–1753. https://doi.org/10.1108/md-05-2022-0615

Chiang, L., Lu, B., & Castillo, I. (2017). Big data analytics in chemical engineering. Annual Review of Chemical and Biomolecular Engineering, 8 (1), 63–85. https://doi.org/10.1146/annurev-chembioeng-060816-101555

Chung, N., Lee, H., Lee, S. J., & Koo, C. (2015). The influence of tourism website on tourists’ behavior to determine destination selection: A case study of creative economy in Korea. Technological Forecasting and Social Change, 96 , 130–143. https://doi.org/10.1016/j.techfore.2015.03.004

Cicea, C., Țurlea, C., Marinescu, C., & Pintilie, N. (2022). Organizational culture: A concept captive between determinants and its own power of influence. Sustainability, 14 (4), 2021. https://doi.org/10.3390/su14042021

Cichosz, M., Wallenburg, C. M., & Knemeyer, A. M. (2020). Digital transformation at logistics service providers: Barriers, success factors and leading practices. The International Journal of Logistics Management, 31 (2), 209–238. https://doi.org/10.1108/ijlm-08-2019-0229

Çınar, Z. M., Zeeshan, Q., & Korhan, O. (2021). A framework for Industry 4.0 readiness and maturity of smart manufacturing enterprises: A case study. Sustainability, 13 (12), 6659. https://doi.org/10.3390/su13126659

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62 (7), 1382–1402. https://doi.org/10.1002/asi.21525

Coldwell, D. A. L. (2019). Negative influences of the 4th industrial revolution on the workplace: Towards a theoretical model of entropic citizen behavior in toxic organizations. International Journal of Environmental Research and Public Health, 16 (15), 2670. https://doi.org/10.3390/ijerph16152670

Cortellazzo, L., Bruni, E., & Zampieri, R. (2019). The role of leadership in a digitalized world: A review. Frontiers in Psychology, 10 (1), 1938. https://doi.org/10.3389/fpsyg.2019.01938

Cui, Y., Liu, Y., & Mou, J. (2018). Bibliometric analysis of organisational culture using CiteSpace. South African Journal of Economic and Management Sciences, 21 (1).  https://doi.org/10.4102/sajems.v21i1.2030

Dabbous, A., & Tarhini, A. (2021). Does sharing economy promote sustainable economic development and energy efficiency? Evidence from OECD countries. Journal of Innovation & Knowledge, 6 (1), 58–68. https://doi.org/10.1016/j.jik.2020.11.001

Daum, D. L., & Maraist, C. C. (2021). The importance of culture in the era of COVID-19. Industrial and Organizational Psychology, 14 (1–2), 160–162. https://doi.org/10.1017/iop.2021.40

de Sousa Jabbour, A. B. L., Jabbour, C. J. C., Foropon, C., & Godinho Filho, M. (2018). When titans meet – Can Industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technological Forecasting and Social Change, 132 , 18–25. https://doi.org/10.1016/j.techfore.2018.01.017

Denison, D. R., & Mishra, A. K. (1995). Toward a theory of organizational culture and effectiveness. Organization Science, 6 (2), 204–223. https://doi.org/10.1287/orsc.6.2.204

Diem, A., & Wolter, S. C. (2013). The use of bibliometrics to measure research performance in education sciences. Research in Higher Education, 54 (1), 86–114. https://doi.org/10.1007/s11162-012-9264-5

Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Fosso Wamba, S., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210 , 120–136. https://doi.org/10.1016/j.ijpe.2019.01.023

Durana, P., Kral, P., Stehel, V., Lazaroiu, G., & Sroka, W. (2019). Quality culture of manufacturing enterprises: A possible way to adaptation to Industry 4.0. Social Sciences, 8 (4), 124. https://doi.org/10.3390/socsci8040124

El Baz, J., & Iddik, S. (2021). Green supply chain management and organizational culture: A bibliometric analysis based on Scopus data (2001–2020). International Journal of Organizational Analysis, 30 , 156–179. https://doi.org/10.1108/ijoa-07-2020-2307

Elsevier. (2023). Scopus content coverage guide. Retrieved 10 June 2023, from https://www.elsevier.com/__data/assets/pdf_file/0007/69451/ScopusContentCoverageGuideWEB.pdf

Erdal, I. J. (2009). Cross-media (re)production cultures. convergence. The International Journal of Research into New Media Technologies, 15 (2), 215–231. https://doi.org/10.1177/1354856508105231

Etter, M., Fieseler, C., & Whelan, G. (2019). Sharing economy, sharing responsibility? Corporate social responsibility in the digital age. Journal of Business Ethics, 159 (4), 935–942. https://doi.org/10.1007/s10551-019-04212-w

Fay, M. J., & Kline, S. L. (2011). Coworker relationships and informal communication in high-intensity telecommuting. Journal of Applied Communication Research, 39 (2), 144–163. https://doi.org/10.1080/00909882.2011.556136

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114 (1), 254–280. https://doi.org/10.1016/j.techfore.2016.08.019

Gajsek, B., Marolt, J., Rupnik, B., Lerher, T., & Sternad, M. (2019). Using maturity model and discrete-event simulation for Industry 4.0 implementation. International Journal of Simulation Modelling, 18 (3), 488–499. https://doi.org/10.2507/ijsimm18(3)489

Gandini, A. (2016). The reputation economy. Understanding knowledge work in digital society. Palgrave Macmillan . https://doi.org/10.1057/978-1-137-56107-7

Ganzarain, J., & Errasti, N. (2016). Three stage maturity model in SME’s toward Industry 4.0. Journal of Industrial Engineering and Management, 9 (5), 1119–1128. https://doi.org/10.3926/jiem.2073

Garro Abarca, V. M., Palos-Sanchez, P. R., & Rus-Arias, E. (2020). Working in virtual teams: A systematic literature review and a bibliometric analysis. IEEE Access, 8 , 168923–168940. https://doi.org/10.1109/access.2020.3023546

Gebril Taha, M., & Espino-Rodríguez, T. F. (2020). The impact of the organizational culture on hotel outsourcing and sustainable performance an empirical application in the Egyptian hotel sector. Sustainability, 12 (22), 9687. https://doi.org/10.3390/su12229687

Gelfand, A. E. (2000). Gibbs sampling. Journal of the American Statistical Association, 95 (452), 1300–1304. https://doi.org/10.2307/2669775

Ghimire, D., Charters, S., & Gibbs, S. (2020, January). Scaling Agile Software Development Approach in Government Organization in New Zealand. ICSIM ‘20: Proceedings of the 3rd International Conference on Software Engineering and Information Management , 100–104. https://doi.org/10.1145/3378936.3378945

Granlund, M., & Taipaleenmäki, J. (2005). Management control and controllership in new economy firms - A life cycle perspective. Management Accounting Research, 16 (1), 21–57. https://doi.org/10.1016/j.mar.2004.09.003

Gregory, B. T., Harris, S. G., Armenakis, A. A., & Shook, C. L. (2009). Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes. Journal of Business Research, 62 (7), 673–679. https://doi.org/10.1016/j.jbusres.2008.05.021

Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101 (Supplement 1), 5228–5235. https://doi.org/10.1073/pnas.0307752101

Grün, B., & Hornik, K. (2011). Topicmodels: An R package for fitting topic models. Journal of Statistical Software, 40 (13), 1–30. https://doi.org/10.18637/jss.v040.i13

Gunasekaran, A., Subramanian, N., & Ngai, W. T. E. (2019). Quality management in the 21st century enterprises: Research pathway towards Industry 4.0. International Journal of Production Economics, 207 (1), 125–129. https://doi.org/10.1016/j.ijpe.2018.09.005

Gupta, S., Qian, X., Bhushan, B., & Luo, Z. (2019). Role of cloud ERP and big data on firm performance: A dynamic capability view theory perspective. Management Decision, 57 (8), 1857–1882. https://doi.org/10.1108/md-06-2018-0633

Guzal-Dec, D. (2016). The role of local authorities in developing pro-ecological organizational culture of the communal offices located in areas of natural value (the example of Lubelskie Voivodeship). Economics and Environment, 57 (2), 235–248.

Guzmán, V. E., Muschard, B., Gerolamo, M., Kohl, H., & Rozenfeld, H. (2020). Characteristics and skills of leadership in the context of Industry 4.0. Procedia Manufacturing, 43 (1), 543–550. https://doi.org/10.1016/j.promfg.2020.02.167

Hamouche, S. (2020). COVID-19 and employees’ mental health: Stressors, moderators and agenda for organizational actions. Emerald Open Research, 2 (15), 15. https://doi.org/10.35241/emeraldopenres.13550.1

Han, X. (2020). Evolution of research topics in LIS between 1996 and 2019: An analysis based on latent Dirichlet allocation topic model. Scientometrics, 125 (3), 2561–2595. https://doi.org/10.1007/s11192-020-03721-0

Hermann, M., Pentek, T., & Otto, B. (2016, January). Design principles for Industrie 4.0 scenarios. 2016 49th Hawaii International Conference on System Sciences (HICSS) , 3928–3937. https://doi.org/10.1109/hicss.2016.488

Hofstede, G. (1998). Identifying organizational subcultures: An empirical approach. Journal of Management Studies, 35 (1 January 1998), 0022–2380.

Hofstede, G. (2001). Culture consequences: Comparing values, behaviors, institutions and organisations across nations (2nd ed.). Sage Publication Inc.

Hogan, S. J., & Coote, L. V. (2014). Organizational culture, innovation, and performance: A test of Schein’s model. Journal of Business Research, 67 (8), 1609–1621. https://doi.org/10.1016/j.jbusres.2013.09.007

Huang, W. W., Wei, K.-K., Watson, R. T., & Tan, B. C. Y. (2003). Supporting virtual team-building with a GSS: An empirical investigation. Decision Support Systems, 34 (4), 359–367. https://doi.org/10.1016/s0167-9236(02)00009-x

Isensee, C., Teuteberg, F., Griese, K.-M., & Topi, C. (2020). The relationship between organizational culture, sustainability, and digitalization in SMEs: A systematic review. Journal of Cleaner Production, 275 (1), 122944. https://doi.org/10.1016/j.jclepro.2020.122944

Jacob, C., Sanchez-Vazquez, A., & Ivory, C. (2020). Social, organizational, and technological factors impacting clinicians’ adoption of mobile health tools: Systematic literature review. JMIR Mhealth and Uhealth, 8 (2), e15935. https://doi.org/10.2196/15935

Johnson, A., Dey, S., Nguyen, H., Groth, M., Joyce, S., Tan, L., Glozier, N., & Harvey, S. B. (2020). A review and agenda for examining how technology-driven changes at work will impact workplace mental health and employee well-being. Australian Journal of Management, 45 (3), 402–424. https://doi.org/10.1177/0312896220922292

Kagermann, H., Wahlster, W., & Helbig, J. (2013, April). Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative Industrie 4.0. Final report of the Industrie 4.0 Working Group. Retrieved March 29, 2023, from https://www.din.de/resource/blob/76902/e8cac883f42bf28536e7e8165993f1fd/recommendations-for-implementing-industry-4-0-data.pdf

Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt Industry 4.0 in Indian manufacturing industry. Computers in Industry, 101 , 107–119. https://doi.org/10.1016/j.compind.2018.06.004

Kar, S., Yadav, M., & Panda, T. K. (2023). Inclusive organizational behaviour – The dynamic rules of building new workplaces. VINE Journal of Information and Knowledge Management Systems . https://doi.org/10.1108/vjikms-05-2022-0155

Karimi, J., & Walter, Z. (2015). The Role of Dynamic Capabilities in Responding to Digital Disruption: A Factor-Based Study of the Newspaper Industry. Journal of Management Information Systems, 32 (1), 39–81. https://doi.org/10.1080/07421222.2015.1029380

Kerber, K. W., & Buono, A. F. (2004). Leadership challenges in global virtual teams: Lessons from the field. SAM Advanced Management Journal, 69 (4), 4.

Kiel, D., Müller, J. M., Arnold, C., & Voigt, K.-I. (2017). Sustainable industrial value creation: Benefits and challenges of Industry 4.0. International Journal of Innovation Management, 21 (08), 1740015. https://doi.org/10.1142/s1363919617400151

Kim, R. Y. (2020). The impact of COVID-19 on consumers: Preparing for digital sales. IEEE Engineering Management Review, 48 (3), 212–218. https://doi.org/10.1109/EMR.2020.2990115

Kitsios, F., Kamariotou, M., & Talias, M. A. (2020). Corporate sustainability strategies and decision support methods: A bibliometric analysis. Sustainability, 12 (2), 521. https://doi.org/10.3390/su12020521

Kniffin, K. M., Narayanan, J., Anseel, F., Antonakis, J., Ashford, S. P., Bakker, A. B., Bamberger, P., Bapuji, H., Bhave, D. P., Choi, V. K., Creary, S. J., Demerouti, E., Flynn, F. J., Gelfand, M. J., Greer, L. L., Johns, G., Kesebir, S., Klein, P. G., Lee, S. Y., & Ozcelik, H. (2021). COVID-19 and the workplace: Implications, issues, and insights for future research and action. American Psychologist, 76 (1), 63–77. https://doi.org/10.1037/amp0000716

Kohnová, L., Papula, J., & Salajová, N. (2019). Internal factors supporting business and technological transformation in the context of Industry 4.0. Business: Theory and Practice, 20 , 137–145. https://doi.org/10.3846/btp.2019.13

Konttila, J., Siira, H., Kyngäs, H., Lahtinen, M., Elo, S., Kääriäinen, M., Kaakinen, P., Oikarinen, A., Yamakawa, M., Fukui, S., Utsumi, M., Higami, Y., Higuchi, A., & Mikkonen, K. (2019). Healthcare professionals’ competence in digitalisation: A systematic review. Journal of Clinical Nursing, 28 (5–6), 745–761. https://doi.org/10.1111/jocn.14710

Kronblad, C., Pregmark, J. E., & Berggren, R. (2023). Difficulties to digitalize: Ambidexterity challenges in law firms. Journal of Service Theory and Practice, 33 (2), 217–236. https://doi.org/10.1108/jstp-05-2022-0120

Lăzăroiu, G., Ionescu, L., Andronie, M., & Dijmărescu, I. (2020). Sustainability management and performance in the urban corporate economy: A systematic literature review. Sustainability, 12 (18), 7705. https://doi.org/10.3390/su12187705

Leyva-Duarte, J. E., Chávez Martínez, J. D. J., Pinedo-de-Anda, F. J., & Niebla-Zatarain, J. C. (2019). Bibliometric analysis of organizational culture in business economics of Web of Science, 1980–2018. Nova Scientia, 11 (22), 478–500. https://doi.org/10.21640/ns.v11i22.1810

Leyva-Duarte, J. E., De la Garza Carranza, M. T., Chávez Martínez, J. D. J., Pinedo-de-Anda, F. J., Niebla Zatarain, J. C., & González Farías, J. P. (2020). Organizational culture in the hospitality industry a bibliometric analysis and systematic literature review. Independent Journal of Management & Production, 11 (4), 1140–1162. https://doi.org/10.14807/ijmp.v11i4.1089

Li, G., & Shao, Y. (2023). How do top management team characteristics affect digital orientation? Exploring the internal driving forces of firm digitalization. Technology in Society, 74 , 102293. https://doi.org/10.1016/j.techsoc.2023.102293

Liao, Y., Deschamps, F., Loures, E. D. F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. International Journal of Production Research, 55 (12), 3609–3629. https://doi.org/10.1080/00207543.2017.1308576

Luthra, S., & Mangla, S. K. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117 , 168–179. https://doi.org/10.1016/j.psep.2018.04.018

Mandal, S., Das, P., Menon, G. V., & Amritha, R. (2023). Enablers of work from home culture: an integrated empirical framework. Benchmarking: An International Journal, 30 (4), 1231–1258. https://doi.org/10.1108/bij-08-2021-0476

Mariani, M., & Baggio, R. (2022). Big data and analytics in hospitality and tourism: A systematic literature review. International Journal of Contemporary Hospitality Management, 34 (1), 231–278. https://doi.org/10.1108/ijchm-03-2021-0301

McFadden, P., Campbell, A., & Taylor, B. (2015). Resilience and burnout in child protection social work: Individual and organisational themes from a systematic literature review. British Journal of Social Work, 45 (5), 1546–1563. https://doi.org/10.1093/bjsw/bct210

Meek, V. L. (1988). Organizational culture: Origins and weaknesses. Organization Studies, 9 (4), 453–473. https://doi.org/10.1177/017084068800900401

Melitski, J., Gavin, D., & Gavin, J. (2010). Technology adoption and organizational culture in public organizations. International Journal of Organization Theory & Behavior, 13 (4), 546–568. https://doi.org/10.1108/ijotb-13-04-2010-b005

Mikos, L. (2016). Digital media platforms and the use of TV content: Binge watching and video-on-demand in Germany. Media and Communication, 4 (3), 154–161. https://doi.org/10.17645/mac.v4i3.542

Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems, 49 , 194–214. https://doi.org/10.1016/j.jmsy.2018.10.005

Mkoba, E. S., & Marnewick, C. (2022). Organisational culture attributes influencing the adoption of agile practices: A systematic literature review. Journal of Information Systems Engineering and Management, 7 (1), 11690. https://doi.org/10.55267/iadt.07.11690

Mohelska, H., & Sokolova, M. (2018). Management approaches for Industry 4.0 - The organizational culture perspective. Technological and Economic Development of Economy, 24 (6), 2225–2240. https://doi.org/10.3846/tede.2018.6397

Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., & Stewart, L. A. (2015). Preferred Reporting Items for Systematic Review and meta-analysis Protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4 (1), 1–9. https://doi.org/10.1186/2046-4053-4-1

Montini, P., de Araújo Pinho, C. M., de Oliveira, R. M., Costa, I., & Napolitano, D. M. (2020). Evaluation of the relationship between lean philosophy and organizational culture: A bibliometric review [Avaliação da relação da Filosofia Lean e a Cultura Organizacional: uma revisão bibliométrica]. Research, Society and Development, 9 (11), e059119386. https://doi.org/10.33448/rsd-v9i11.938

Morgan, G. (1986). Images of an organization. Sage Publications.

Morris, J. W. (2015). Curation by code: Infomediaries and the data mining of taste. European Journal of Cultural Studies, 18 (4–5), 446–463. https://doi.org/10.1177/1367549415577387

Müller, J. M. (2019a). Assessing the barriers to Industry 4.0 implementation from a workers’ perspective. IFAC-PapersOnLine, 52 (13), 2189–2194. https://doi.org/10.1016/j.ifacol.2019.11.530

Müller, J. M. (2019b). Antecedents to digital platform usage in Industry 4.0 by established manufacturers. Sustainability, 11 (4), 1121. https://doi.org/10.3390/su11041121

Munar, A. M. (2012). Social media strategies and destination management. Scandinavian Journal of Hospitality and Tourism, 12 (2), 101–120. https://doi.org/10.1080/15022250.2012.679047

Nascimento, D. L. M., Alencastro, V., Quelhas, O. L. G., Caiado, R. G. G., Garza-Reyes, J. A., Rocha-Lona, L., & Tortorella, G. (2019). Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context. Journal of Manufacturing Technology Management, 30 (3), 607–627. https://doi.org/10.1108/jmtm-03-2018-0071

Ngoc, Su., & D., Luc Tra, D., Thi Huynh, H. M., Nguyen, H. H. T., & O’Mahony, B. (2021). Enhancing resilience in the Covid-19 crisis: Lessons from human resource management practices in Vietnam. Current Issues in Tourism, 24 (22), 1–17. https://doi.org/10.1080/13683500.2020.1863930

Nidhi, & Arti,. (2020). Impact of organisational culture on work-life balance a bibliometric analysis and growth in research. European Journal of Molecular & Clinical Medicine, 7 (8), 5308–5319.

Nimawat, D., & Gidwani, B. D. (2021). Identification of cause and effect relationships among barriers of Industry 4.0 using decision-making trial and evaluation laboratory method. Benchmarking: An International Journal, 28 (8), 2407–2431. https://doi.org/10.1108/bij-08-2020-0429

Noto, G., Marisca, C., & Barresi, G. (2023). Adapting management control to virtual teams: Evidence from a natural experiment. Qualitative Research in Accounting & Management . https://doi.org/10.1108/qram-04-2022-0066

Obrenovic, B., Du, J., Godinic, D., Tsoy, D., Khan, M. A. S., & Jakhongirov, I. (2020). Sustaining enterprise operations and productivity during the COVID-19 pandemic: “Enterprise effectiveness and sustainability model.” Sustainability, 12 (15), 5981. https://doi.org/10.3390/su12155981

Ogbonna, E. (1992). Managing organisational culture: Fantasy or reality? Human Resource Management Journal, 3 (2), 42–54. https://doi.org/10.1111/j.1748-8583.1992.tb00309.x

O’Reilly, C. A., Chatman, J., & Caldwell, D. F. (1991). People and organizational culture: A profile comparison approach to assessing person-organization fit. Academy of Management Journal, 34 (3), 487–516. https://doi.org/10.2307/256404

O’Reilly, C. A., Caldwell, D. F., Chatman, J. A., & Doerr, B. (2014). The promise and problems of organizational culture. Group & Organization Management, 39 (6), 595–625. https://doi.org/10.1177/1059601114550713

Özkazanç-Pan, B., & Pullen, A. (2020). Gendered labour and work, even in pandemic times. Gender, Work & Organization, 27 (5), 675–676. https://doi.org/10.1111/gwao.12516

Pacchini, A. P. T., Lucato, W. C., Facchini, F., & Mummolo, G. (2019). The degree of readiness for the implementation of Industry 4.0. Computers in Industry, 113 , 103125. https://doi.org/10.1016/j.compind.2019.103125

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., & McGuinness, L. A. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. British Medical Journal, 372 (71), n71. https://doi.org/10.1136/bmj.n71

Pagliosa, M., Tortorella, G., & Ferreira, J. C. E. (2019). Industry 4.0 and lean manufacturing. Journal of Manufacturing Technology Management, 32 (5), 543–569. https://doi.org/10.1108/jmtm-12-2018-0446

Parry, E., & Battista, V. (2019). The impact of emerging technologies on work: a review of the evidence and implications for the human resource function. Emerald Open Research, 1 , 5. https://doi.org/10.12688/emeraldopenres.12907.1

Peukert, C. (2019). The next wave of digital technological change and the cultural industries. Journal of Cultural Economics, 43 (2), 189–210. https://doi.org/10.1007/s10824-018-9336-2

Piccarozzi, M., Aquilani, B., & Gatti, C. (2018). Industry 4.0 in management studies: A systematic literature review. Sustainability, 10 (10), 1–24, 3821. https://doi.org/10.3390/su10103821

Polyanska, A., Zapukhliak, I., & Diuk, O. (2019). Culture of organization in conditions of changes as an ability of efficient transformations: The case of gas transportation companies in Ukraine. Oeconomia Copernicana, 10 (3), 561–580. https://doi.org/10.24136/oc.2019.027

Powell, A., Piccoli, G., & Ives, B. (2004). Virtual teams: A review of current literature and directions for future research. ACM SIGMIS Database, 35 (1), 6–36. https://doi.org/10.1145/968464.968467

Priyanto, P., Murwaningsari, E., & Augustine, Y. (2023). Exploring the relationship between robotic process automation, digital business strategy and competitive advantage in banking industry. Journal of System and Management Sciences, 13 (3), 290–305. https://doi.org/10.33168/JSMS.2023.0320

Quinn, R. E., & Rohrbaugh, J. (1983). A spatial model of effectiveness criteria: Towards a competing values approach to organizational analysis. Management Science, 29 (3), 363–377. https://doi.org/10.1287/mnsc.29.3.363

Quinn, R. E., & Spreitzer, G. M. (1991). The psychometrics of the competing values culture instrument and an analysis of the impact of organizational culture on quality of life. Research in Organizational Change and Development, 5 , 115–142.

Raharjo, T., & Purwandari, B. (2020). Agile project management challenges and mapping solutions. Proceedings of the 3rd International Conference on Software Engineering and Information Management , 123–129. https://doi.org/10.1145/3378936.3378949

Raj, A., Dwivedi, G., Sharma, A., de Sousa, Lopes, Jabbour, A. B., & Rajak, S. (2020). Barriers to the adoption of Industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224 (1), 107546. https://doi.org/10.1016/j.ijpe.2019.107546

Ravasi, D., & Schultz, M. (2006). Responding to organizational identity threats: Exploring the role of organizational culture. Academy of Management Journal, 49 (3), 433–458. https://doi.org/10.5465/amj.2006.21794663

Reinhardt, I. C., Oliveira, D. J. C., & Ring, D. D. T. (2020). Current perspectives on the development of Industry 4.0 in the pharmaceutical sector. Journal of Industrial Information Integration, 18 , 100131. https://doi.org/10.1016/j.jii.2020.100131

Reis, N. R., Ferreira, M. P., Santos, J. C., & Serra, F. R. (2013). A bibliometric study of the cultural models in international business research [Um estudo bibliométrico dos modelos culturais na pesquisa em negócios internacionais]. BASE - Revista De Administração E Contabilidade Da Unisinos, 10 (4), 340–354. https://doi.org/10.4013/base.2013.104.04

Reyes-Santiago, M., & d. R., Sánchez-Medina, P. S., & Díaz-Pichardo, R. (2017). Eco-innovation and organizational culture in the hotel industry. International Journal of Hospitality Management, 65 , 71–80. https://doi.org/10.1016/j.ijhm.2017.06.001

Rossini, M., Cifone, F. D., Kassem, B., Costa, F., & Portioli-Staudacher, A. (2021). Being lean: How to shape digital transformation in the manufacturing sector. Journal of Manufacturing Technology Management, 32 (9), 239–259. https://doi.org/10.1108/jmtm-12-2020-0467

Sá, M. J., & Serpa, S. (2020). The COVID-19 pandemic as an opportunity to foster the sustainable development of teaching in higher education. Sustainability, 12 (20), 1–16, 8525. https://doi.org/10.3390/su12208525

Sahoo, P., Saraf, P. K., & Uchil, R. (2022). Identification of critical success factors for leveraging Industry 4.0 technology and research agenda: A systematic literature review using PRISMA protocol. Asia-Pacific Journal of Business Administration . https://doi.org/10.1108/apjba-03-2022-0105

Santos, R. C., & Martinho, J. L. (2020). An Industry 4.0 maturity model proposal. Journal of Manufacturing Technology Management, 31 (5), 1023–1043. https://doi.org/10.1108/jmtm-09-2018-0284

Schein, E. H. (1985). Organizational Culture and Leadership. Jossey-Bass Publishers.

Schein, E. H. (1992). Organizational Culture and Leadership. Jossey-Bass Publishers.

Schneider, W. (2018). Psychosocial ramifications of digitalization [Psychosoziale Folgen der Digitalisierung]. Psychotherapeut, 63 (4), 291–300. https://doi.org/10.1007/s00278-017-0186-8

Schueber, M. (2009). Understanding organisational culture in a development NGO in Nepal by applying academic theory to witnessed organisational behaviour. Omertaa: Journal for Applied Anthropology. Retrieved April 4, 2023, from https://www.omertaa.org/archive/omertaa0050.pdf

Schumacher, A., Erol, S., & Sihn, W. (2016a). Strategic Guidance Towards Industry 4.0 - A Three-Stage Process Model. In D. Dimitrov & T. Oosthuizen (Eds.), Proceedings of the International Conference on Competitive Manufacturing - Resource Efficiency for Global Competitiveness (pp. 495–501).

Schumacher, A., Erol, S., & Sihn, W. (2016b). A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP, 52 (1), 161–166. https://doi.org/10.1016/j.procir.2016.07.040

Schwab, K. (2015). The Fourth Industrial Revolution: What it means and how to respond. Retrieved 11 June 2023, from https://www.foreignaffairs.com/articles/2015-12-12/fourth-industrial-revolution

Senior, B. (1997). Organisational Change. Pitman Publishing.

Shardeo, V., Patil, A., & Madaan, J. (2020). Critical success factors for blockchain technology adoption in freight transportation using fuzzy ANP - modified TISM approach. International Journal of Information Technology & Decision Making, 19 (6), 1549–1580. https://doi.org/10.1142/s0219622020500376

Sharma, G. M., Pandiya, B., Anand, I.M., Oberai, H., & Chauhan, S. (2022). Virtual team leadership: A Bibliometric Analysis. Res Militaris , 12 (6). Retrieved March 14, 2023, from https://resmilitaris.net/index.php/resmilitaris/article/view/3184

Sheppard, B. (2020). A guide to thriving in the post-COVID-19 workplace. Retrieved 9 June 2023, from https://www.weforum.org/agenda/2020/05/workers-thrive-covid-19-skills

Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. In J. Chuang, S. Green, M. Hearst, J. Heer, & P. Koehn (Eds.), Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces (pp. 63–70). Association for Computational Linguistics. https://doi.org/10.3115/v1/W14-3110

Silverzweig, S., & Allen, R. F. (1976). Changing the corporate culture. Sloan Management Review, 17 , 33–49.

Sindakis, S., Kitsios, F., Aggarwal, S., & Kamariotou, M. (2022). Entrepreneurial strategies and family firm culture in the Arab world: A systematic literature review. Journal of Small Business and Enterprise Development, 29 (7), 994–1016. https://doi.org/10.1108/jsbed-03-2022-0143

Singer-Velush, N., Sherman, K., & Anderson, E. (2020). Microsoft analyzed data on its newly remote workforce. Harvard Business Review. Retrieved June 3, 2023, from https://hbr.org/2020/07/microsoft-analyzeddata-on-its-newly-remote-workforce

Smircich, L. (1983). Concepts of culture and organizational analysis. Administrative Science Quarterly, 28 (3), 339–358.

Sony, M., Antony, J., & Douglas, J. A. (2020). Essential ingredients for the implementation of Quality 4.0. The TQM Journal, 32 (4), 779–793. https://doi.org/10.1108/tqm-12-2019-0275

Sony, M., & Naik, S. (2020). Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model. Technology in Society, 61 , 101248. https://doi.org/10.1016/j.techsoc.2020.101248

Spicer, A. (2020). Organizational culture and COVID-19. Journal of Management Studies, 57 (8), 1737–1740. https://doi.org/10.1111/joms.12625

Spreitzer, G. M., Cameron, L., & Garrett, L. (2017). Alternative work arrangements: Two images of the new world of work. Annual Review of Organizational Psychology and Organizational Behavior, 4 (1), 473–499. https://doi.org/10.1146/annurev-orgpsych-032516-113332

Streimikiene, D., Mikalauskiene, A., Digriene, L., & Kyriakopoulos, G. (2021). Assessment of the role of a leader in shaping sustainable organizational culture. Amfiteatru Economic, 23 (57), 486–503. https://doi.org/10.24818/EA/2021/57/483

Suárez-Guerrero, C., Lloret-Catalá, C., & Mengual-Andrés, S. (2016). Teachers’ perceptions of the digital transformation of the classroom through the use of tablets: A study in Spain. Comunicar, 24 (49), 81–89. https://doi.org/10.3916/c49-2016-08

Sung, W., & Kim, C. (2021). A study on the effect of change management on organizational innovation: Focusing on the mediating effect of  members’ innovative behavior. Sustainability, 13 (4), 2079. https://doi.org/10.3390/su13042079

Tasleem, M., Khan, N., & Nisar, A. (2019). Impact of technology management on corporate sustainability performance. International Journal of Quality & Reliability Management, 36 (9), 1574–1599. https://doi.org/10.1108/ijqrm-01-2018-0017

Teichert, R. (2019). Digital transformation maturity: A systematic review of literature. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67 (6), 1673–1687. https://doi.org/10.11118/actaun201967061673

Teravainen, V., Suominen, A., & Kahkonen, K. (2017). Positioning organizational culture studies between the construction industry and other industries. In M. Buser, G. Lindahl, & C. Räisänen (Eds.), 9th Nordic Conference on Construction Economics and Organization (pp. 428–441).

Tessarini Junior, G., & Saltorato, P. (2021). Workforce agility: A systematic literature review and a research agenda proposal. Innovar, 31 (81), 155–167. https://doi.org/10.15446/innovar.v31n81.95582

Theurer, C. P., Tumasjan, A., & Welpe, I. M. (2018). Contextual work design and employee innovative work behavior: When does autonomy matter? PLoS ONE, 13 (10), e0204089. https://doi.org/10.1371/journal.pone.0204089

Trenerry, B., Chng, S., Wang, Y., Suhaila, Z. S., Lim, S. S., Lu, H. Y., & Oh, P. H. (2021). Preparing workplaces for digital transformation: An integrative review and framework of multi-level factors. Frontiers in Psychology, 12 . https://doi.org/10.3389/fpsyg.2021.620766

Troise, C., Corvello, V., Ghobadian, A., & O’Regan, N. (2022). How can SMEs successfully navigate VUCA environment: The role of agility in the digital transformation era. Technological Forecasting and Social Change, 174 , 121227. https://doi.org/10.1016/j.techfore.2021.121227

Tronvoll, B., Sklyar, A., Sörhammar, D., & Kowalkowski, C. (2020). Transformational shifts through digital servitization. Industrial Marketing Management . https://doi.org/10.1016/j.indmarman.2020.02.005

Ulas, D. (2019). Digital transformation process and SMEs. Procedia Computer Science, 158 (1), 662–671. https://doi.org/10.1016/j.procs.2019.09.101

Ungerman, O., Dedkova, J., & Gurinova, K. (2018). The impact of marketing innovation on the competitiveness of enterprises in the context of Industry 4.0. Journal of Competitiveness, 10 (2), 132–148. https://doi.org/10.7441/joc.2018.02.09

Vallejo, M. C. (2011). A model to study the organizational culture of the family firm. Small Business Economics, 36 , 47–64.

van Dijck, J. (2013). The culture of connectivity. A critical history of social media. Oxford Academic . https://doi.org/10.1093/acprof:oso/9780199970773.001.0001

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84 (2), 523–538. https://doi.org/10.1007/s11192-009-0146-3

van Nunen, K., Li, J., Reniers, G., & Ponnet, K. (2018). Bibliometric analysis of safety culture research. Safety Science, 108 , 248–258. https://doi.org/10.1016/j.ssci.2017.08.011

Veile, J. W., Kiel, D., Müller, J. M., & Voigt, K.-I. (2020). Lessons learned from Industry 4.0 implementation in the German manufacturing industry. Journal of Manufacturing Technology Management, 31 (5), 977–997. https://doi.org/10.1108/jmtm-08-2018-0270

Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122 , 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022

Wang, S., Wan, J., Li, D., & Zhang, C. (2016). Implementing smart factory of Industrie 4.0: An outlook. International Journal of Distributed Sensor Networks, 12 (1), 3159805. https://doi.org/10.1155/2016/3159805

Wiggins, M. W., Auton, J., Bayl-Smith, P., & Carrigan, A. (2020). Optimising the future of technology in organisations: A human factors perspective. Australian Journal of Management, 45 (3), 449–467. https://doi.org/10.1177/0312896220918915

Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56 (8), 2941–2962. https://doi.org/10.1080/00207543.2018.1444806

Yang, E., Kim, Y., & Hong, S. (2023). Does working from home work? Experience of working from home and the value of hybrid workplace post-COVID-19. Journal of Corporate Real Estate, 25 (1), 50–76. https://doi.org/10.1108/jcre-04-2021-0015

Yeh, Y., Lai, S., & Ho, C. (2006). Knowledge management enablers: A case study. Industrial Management & Data Systems, 106 (6), 793–810. https://doi.org/10.1108/02635570610671489

Yun, J. J., Zhao, X., Jung, K., & Yigitcanlar, T. (2020). The culture for open innovation dynamics. Sustainability, 12 (12), 5076. https://doi.org/10.3390/su12125076

Zeng, K., & Luo, X. (2013). Impact of ownership type and firm size on organizational culture and on the organizational culture effectiveness linkage. Journal of Business Economics and Management, 14 (Supplement 1), 96–111. https://doi.org/10.3846/16111699.2012.754373

Zhen, Z., Yousaf, Z., Radulescu, M., & Yasir, M. (2021). Nexus of digital organizational culture, capabilities, organizational readiness, and innovation: Investigation of SMEs operating in the digital economy. Sustainability, 13 (2), 720. https://doi.org/10.3390/su13020720

Zhou, W., Luo, D., Fang, H., Gou, X., & Jin, C. (2020). Bibliometric overview and retrospective analysis of fund performance research between 1966 and 2019. Economic Research-Ekonomska Istraživanja, 33 (1), 1510–1537. https://doi.org/10.1080/1331677x.2020.1755879

Žižek, S. Š, Mulej, M., & Potočnik, A. (2021). The sustainable socially responsible society: Well-Being Society 6.0. Sustainability, 13 (16), 9186. https://doi.org/10.3390/su13169186

Download references

Open access funding provided by The Ministry of Education, Science, Research and Sport of the Slovak Republic in cooperation with Centre for Scientific and Technical Information of the Slovak Republic This paper was funded by Faculty of Management, Comenius University Bratislava, Slovakia, and supported by the project VEGA 1/0614/23 Preparedness of companies for the challenges associated with Industry 4.0 in terms of business processes and business process management.

Author information

Authors and affiliations.

Faculty of Management, Comenius University Bratislava, Bratislava, Slovakia

Tobias Reisberger, Philip Reisberger, Lukáš Copuš & Peter Madzík

Faculty of Management Science and Informatics, University of Žilina, Žilina, Slovakia

Lukáš Falát

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Tobias Reisberger .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Reisberger, T., Reisberger, P., Copuš, L. et al. The Linkage Between Digital Transformation and Organizational Culture: Novel Machine Learning Literature Review Based on Latent Dirichlet Allocation. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02027-3

Download citation

Received : 14 August 2023

Accepted : 17 April 2024

Published : 21 May 2024

DOI : https://doi.org/10.1007/s13132-024-02027-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Organizational culture
• Digital transformation
• Industry 4.0
• Machine learning
• Latent Dirichlet allocation
• Literature review
• Find a journal
• Publish with us
• Track your research