Abstract
Background:
Worldwide, there is a growing concern of Internet Gaming Disorder (IGD). IGD is an emerging problem in India, the top ranked country in gaming app downloads and the second largest Internet-using country in the world. Especially post pandemic, young adults spend longer hours on Internet and online gaming. This study was done to estimate the prevalence of IGD and to find out the association of IGD with sociodemographic and Internet gaming pattern characteristics.
Materials and Methods:
This cross-sectional study investigated the prevalence and predictors of IGD among 353 young adults aged 18–25 years in Tamil Nadu using an online self-reported questionnaire. Internet Gaming Disorder Questionnaire-Short Form (IGDQ-SF) assessing all nine DSM-5 criteria for IGD was used. Data were analyzed using the SPSS version 21. Categorical data are presented as frequency and proportions. Bivariate analysis and multiple logistic regression were done to identify the predictor variables of IGD. P value < 0.05 is considered statistically significant.
Results:
Out of 353 young adult participants, 3.4% had IGD. Multiple logistic regression analysis revealed that age of starting gaming, money spent on Internet, buying and playing online games, amount of time spent online on weekends, and type of game played were strongly associated with IGD (P < 0.05).
Conclusion:
Understanding the prevalence of IGD and its causes and associated factors remains limited, underscoring the need for additional research and the implementation of preventive measures.
Keywords: Internet addiction, Internet gaming disorder, problematic gaming, young adults
INTRODUCTION
The Internet has become an integral part of everyday life for many people, especially young adults. Despite its many advantages, hyperconnectivity raises questions about excessive online use, particularly in relation to Internet Gaming Disorder (IGD).
According to WHO, gaming disorder is defined in ICD-11 as “a pattern of gaming behaviour (“digital-gaming” or “video-gaming”) characterized by impaired control over gaming, increasing priority given to gaming over other activities to the extent that gaming takes precedence over other interests and daily activities, and continuation or escalation of gaming despite the occurrence of negative consequence”.[1] In addition to psychological issues such as anxiety, depression, and social isolation, IGD can cause physical concerns like eye strain, musculoskeletal pain, and sleep disturbances. Relationship stress, responsibility neglect, and a drop in performance at work or at school are common outcomes.[2] These concerns highlight the need for further research, particularly in regions like India, which currently boasts the second-largest Internet-using population in the world.[3]
A meta-analysis of 53 studies from 17 different countries between 2009 and 2019 yielded IGD prevalence estimates of 3.05% (confidence interval: [2.38, 3.91]).[4] Studies conducted in India report prevalence rates varying from 3.6% to 14.84% among college students.[5,6,7,8] Furthermore, the COVID-19 pandemic exacerbated these concerns. The shift to online learning in lockdown resulted in increased unstructured time and reliance on digital interactions, potentially intensifying gaming engagement and fueling IGD susceptibility.[9]
Although IGD is an important health problem in the digital world, there is a dearth of studies published from India. Understanding the prevalence of IGD and its correlates is crucial for designing effective prevention and intervention strategies. Hence, this study was designed with the following objectives: (1) To estimate the prevalence of IGD among young adults and (2) to find out the association of IGD with sociodemographic and Internet gaming pattern characteristics of the study participants.
MATERIALS AND METHODS
This cross-sectional study investigated the prevalence and predictors of IGD among young adults aged 18 to 25 years across Tamil Nadu, India, from June 2022 to December 2022. The sample size was calculated as 353 using Open-Epi software, based on an IGD prevalence of 9.1% reported in a previous study,[10] with 95% CI and 3% margin of error. Institutional Ethics Committee clearance was obtained (REF: IEC/1515/MEIII/21). Young adults who were active Internet users (>2 hrs/day), able to comprehend English, and who had indulged in online gaming within the past year were included after providing informed consent. An open-linked web-based questionnaire describing the study objectives and informed consent was developed. Using a snow ball sampling technique, the Google form link was rolled out to the eligible contacts of the investigators and from there on to their potential contacts through e-mails, Whatsapp, and other social media until the required sample size was achieved. The potential selection bias and limited representativeness of the sample, with use of snowball sampling, are acknowledged.
The self-reported questionnaire had three sections: Sociodemographic information and gaming profile, including participant’s age, sex, education, occupation, and money spent on Internet, time spent in gaming over weekdays and weekends, most played game, and type of gameplay. Socioeconomic status was assessed using the Modified BG Prasad Scale updated for 2023. Internet Gaming Disorder Questionnaire-Short Form scale (IGDQ-SF), a validated instrument assessing all nine DSM-5 criteria for IGD, was used. Scores ranged from 9 to 45, with a higher score indicating greater severity.[11] Participants endorsing at least five criteria as ‘5: Very Often’ are taken as having IGD. Data were analyzed using the SPSS version 21. Categorical variables are presented as frequency and proportions. Bivariate associations between variables and IGD were examined using Chi-square tests. Logistic regression analysis was performed to identify predictors of IGD. P value < 0.05 is considered statistically significant.
RESULTS
Sociodemographic profile
The sociodemographic profile of the study participants is presented in Table 1. In this study, the mean age of the study participants was 20 years. The majority of the respondents (62.6%) were in the age group of 18–21 years. Sex distribution was almost equal. Around 81.6% of them were students, and most of them belong to class 1 socioeconomic status (according to modified B.G Prasad scale updated for 2023).
Table 1.
Frequency distribution of sociodemographic variables (n=353)
| Variables | n (%) | 95% CI |
|---|---|---|
| Age group (Years) | ||
| 18-21 | 221 (62.6) | 56.7 – 67.7 |
| 22-25 | 132 (37.4) | 32.3 – 43.3 |
| Sex | ||
| Male | 182 (51.6) | 46.4 – 56.7 |
| Female | 171 (48.4) | 43.3 – 53.6 |
| Working status | ||
| Student | 288 (81.6) | 77.3 – 85.3 |
| Employed | 65 (18.4) | 14.7 – 22.7 |
| Socioeconomic status (as per Modified BG Prasad’s scale updated for year 2023) | ||
| Class 1 | 242 (68.6) | 63.7 – 73.1 |
| Class 2 | 56 (15.9) | 11.9 – 19.6 |
| Class 3 | 32 (9.1) | 6.2 – 12.2 |
| Class 4 | 23 (6.5) | 4.2 – 9.1 |
| Money spent on internet per month (Rupees) | ||
| 0-500 | 319 (90.4) | 87.3 – 93.2 |
| >500 | 34 (9.6) | 6.8 – 12.7 |
Distribution of Internet gaming pattern
Around 10.2% of the study participants reported spending money to purchase online games. The majority (73.4%) of participants have started online gaming in their adulthood. More than 80% of the participants were into online gaming for up to 2 hours/day during weekdays and up to 4 hrs/day on weekends. There was a mixed genre of online games played, like adventure games (33.7%), board/puzzle games (19.5%), and shooting/racing games (46.7%), but the majority (72.8%) reported that they play single game intensively [Table 2].
Table 2.
Distribution of Internet gaming pattern variables (n=353)
| Variables | n (%) | 95% CI |
|---|---|---|
| Spending money to purchase games online | ||
| Yes | 36 (10.2) | 7.1 – 13.3 |
| No | 317 (89.8) | 86.7 – 92.9 |
| Age at which started playing online games (Years) | ||
| <10 | 33 (9.3) | 6.5 – 12.5 |
| 10-19 | 61 (17.3) | 13.9 – 21.6 |
| >19 | 259 (73.4) | 68.5 – 77.9 |
| Playing online games per day during weekdays (Hours) | ||
| 0-2 | 304 (86.1) | 82.2 – 89.8 |
| >2 | 49 (13.9) | 10.2 – 17.8 |
| Playing online games per day during weekends (Hours) | ||
| 0-4 | 305 (86.4) | 83 – 89.8 |
| >4 | 48 (13.6) | 10.2 – 17 |
| Most played online games genre | ||
| Adventure/Simulation/Strategy | 119 (33.7) | 29.2 – 39.1 |
| Board games/Puzzle | 69 (19.5) | 15.3 – 24.4 |
| Shooting/Racing | 165 (46.7) | 40.8 – 51.8 |
| Type of gameplay | ||
| Single game intensively | 257 (72.8) | 67.7 – 76.8 |
| Multiple games | 96 (27.2) | 23.2 – 32.3 |
| Device used | ||
| Desktop/Laptop/Tab | 37 (10.5) | 7.6 – 13.4 |
| Mobile phone | 316 (89.5) | 86.6 – 92.4 |
| Average Internet used daily (GB) | ||
| 0-1 | 163 (46.2) | 40.8 – 51.9 |
| 1.01-2 | 157 (44.5) | 39.4 – 50.2 |
| >2 | 33 (9.3) | 6.5 – 12.5 |
Prevalence and predictors of Internet gaming disorder
Out of 353 young adults who participated in the study, 12 participants (3.4%) had IGD [Figure 1]. On applying Chi-square test, daily Internet use, money spent on Internet and purchasing online games, age at which they started online gaming, hours spent in gaming, and type of game play were found to have a significant association with IGD [Table 3].
Figure 1.
Prevalence of Internet gaming disorder (N = 353)
Table 3.
Association of Internet gaming disorder with sociodemographic and Internet gaming pattern characteristics (n=353)
| Variables | Internet Gaming Disorder |
Chi-square value | P | |
|---|---|---|---|---|
| Absent n (%) | Present n (%) | |||
| Age group (Years) | ||||
| 18-21 | 212 (95.9) | 9 (4.1) | 0.815 | 0.367 |
| 22-25 | 129 (97.7) | 3 (2.3) | ||
| Sex | ||||
| Male | 173 (95.1) | 9 (4.9) | 2.733 | 0.098 |
| Female | 168 (98.2) | 3 (1.8) | ||
| Working status | ||||
| Student | 279 (96.9) | 9 (3.1) | 0.359 | 0.549 |
| Employed | 62 (95.4) | 3 (4.6) | ||
| Socioeconomic status (as per Modified BG Prasad’s scale updated for year 2023) | ||||
| Class 1 | 231 (95.5) | 11 (4.5) | 3.347 | 0.341 |
| Class 2 | 55 (98.2) | 1 (1.8) | ||
| Class 3 | 32 (100) | 0 (0) | ||
| Class 4 | 23 (100) | 0 (0) | ||
| Money spent on Internet per month (Rupees) | ||||
| 0-500 | 314 (98.4) | 5 (1.6) | 33.851 | <0.001 |
| >500 | 27 (79.4) | 7 (20.6) | ||
| Spending money to purchase games online | ||||
| Yes | 27 (75) | 9 (25) | 56.959 | <0.001 |
| No | 314 (99.1) | 3 (0.9) | ||
| Age at which started playing online games (Years) | ||||
| <10 | 30 (90.9) | 3 (9.1) | 20.814 | <0.001 |
| 10-19 | 54 (88.5) | 7 (11.5) | ||
| >19 | 257 (99.2) | 2 (0.8) | ||
| Playing online games per day during weekdays (Hours) | ||||
| 0-2 | 298 (98) | 6 (2) | 13.557 | <0.001 |
| >2 | 43 (87.8) | 6 (12.2) | ||
| Playing online games per day during weekends (Hours) | ||||
| 0-4 | 302 (99) | 3 (1) | 39.864 | <0.001 |
| >4 | 39 (81.3) | 9 (18.8) | ||
| Most played online games genre | ||||
| Adventure/Simulation/Strategy | 115 (96.6) | 4 (3.4) | 3.484 | 0.175 |
| Board games/Puzzle | 69 (100) | 0 (0) | ||
| Shooting/Racing | 157 (95.2) | 8 (4.8) | ||
| Type of game play | ||||
| Single game intensively | 255 (99.2) | 2 (0.8) | 19.772 | <0.001 |
| Multiple games | 86 (89.6) | 10(10.4) | ||
| Device used | ||||
| Desktop/Laptop/Tab | 34 (91.9) | 3 (8.1) | 2.791 | 0.095 |
| Mobile phone | 307 (97.2) | 9 (2.8) | ||
| Average Internet used daily (GB) | ||||
| 0-1 | 162 (99.4) | 1 (0.1) | 36.084 | <0.001 |
| 1.01-2 | 153(97.5) | 4 (2.5) | ||
| >2 | 26 (78.8) | 7 (21.2) | ||
*Note: Figures in parentheses represent row-wise percentages
Predictors of IGD are presented in Table 4. Multiple logistic regression analysis shows that money spent on Internet in purchasing/playing online games, age at which started online gaming, time spent in online gaming in weekends, and type of game play were significantly associated with IGD (P < 0.05).
Table 4.
Logistic regression analysis for predictors of Internet gaming disorder (n=353)
| Variables | Internet Gaming Disorder |
Crude OR (95% CI) | Adjusted OR (95% CI) | |
|---|---|---|---|---|
| Present n=12 (%) | Absent n=341 (%) | |||
| Money spent on Internet per month (Rupees) | ||||
| 0-500 | 5 (1.6) | 314 (98.4) | 1 | 1 |
| >500 | 7 (20.6) | 27 (79.4) | 16.28 (4.84 – 54.77)* | 18.34(1.46 – 230.49)* |
| Spending money to purchase games online | ||||
| Yes | 9 (25) | 27 (75) | 34.89 (8.91 –136.54)* | 10.09 (1.19 – 85.39)* |
| No | 3 (0.9) | 314 (99.1) | 1 | 1 |
| Age at which started playing online games (Years) | ||||
| <10 | 3 (9.1) | 30 (90.9) | 12.85 (2.06 – 80.00)* | 11.75(0.77 – 178.98) |
| 10-19 | 7 (11.5) | 54 (88.5) | 16.66 (3.37 – 82.39)* | 18.01(1.66 – 195.19)* |
| >19 | 2 (0.8) | 257 (99.2) | 1 | 1 |
| Playing online games per day during weekdays (Hours) | ||||
| 0-2 | 6 (2) | 298 (98) | 1 | 1 |
| >2 | 6 (12.2) | 43 (87.8) | 6.93 (2.13 – 22.46)* | 0.39 (0.02 – 5.39) |
| Playing online games per day during weekends (Hours) | ||||
| 0-4 | 3 (1) | 302 (99) | 1 | 1 |
| >4 | 9 (18.8) | 39 (81.3) | 23.23 (6.03 – 89.48)* | 20.12(1.75 – 230.71)* |
| Type of game play | ||||
| Single game intensively | 2 (0.8) | 255 (99.2) | 1 | 1 |
| Multiple games | 10(10.4) | 86 (89.6) | 14.82 (3.19 – 68.99)* | 9.63 (1.03 – 90.18)* |
| Average Internet used daily (GB) | ||||
| 0-1 | 1 (0.1) | 162 (99.4) | 1 | 1 |
| 1.01-2 | 4 (2.5) | 153 (97.5) | 4.23 (0.47 – 38.31) | 1.91 (0.07 – 49.69) |
| >2 | 7 (21.2) | 26 (78.8) | 43.61 (5.15 –369.15)* | 13.53(0.43 – 422.09) |
Note: Figures in parentheses represent row-wise percentages; *P<0.05
DISCUSSION
The majority of the study participants were students (81.6%) and were of age 18–21 years (62.6%). In the current study, the prevalence of IGD was found to be 3.4%. This is similar to the reported global pooled prevalence of 3.05% from a systematic review and meta-analysis from 53 studies by Stevens et al.[3] conducted between 2009 and 2019. A meta-analytic review done in the South East Asian region by Chia DXY et al. revealed a pooled IGD prevalence of 10.1% (95% CI: 7.3%–13.8%).[12] In India, multiple studies done in various populations report varied prevalence. A cross-sectional study done among engineering students from Ernakulum district aged 18–25 years reported an IGD prevalence of 2.2%, whereas a similar study done among youngsters of Bihar had an IGD prevalence of 26.8%.[13,14] This varied prevalence can be explained by the difference in study tool and diagnostic criteria used in each study.
Internet usage from younger age shows an increased risk of gaming disorder. In this study, it is found that online gaming from early adolescence is significantly associated with IGD and it is also an independent predictor of IGD (P < 0.05; AOR: 1.662–195.19). Many studies similarly report that gaming from early age of life is positively associated with increased risk of problematic gaming in adulthood.[15,16]
Excessive spending on Internet usage and online gaming indicate lack of control over online activities, especially gaming. The present study also observed that money spent on Internet per month and money spent in playing or purchasing games are significant predictors of IGD (P < 0.05). Many studies have reported similar association between money spent and IGD score.[17,18,19] Monetization in gaming allows one to stay in the game longer. The recent trend in gaming industry is free-to-play games integrating microtransactions of money which includes cosmetic upgrades, pay-to-win features, and loot boxes with randomized rewards. This will not only increase the risk of IGD but result in financial harm to the individual.[20]
On further analysis, we also found that time spent on online gaming especially during weekends and multiple game plays are significantly associated with increased risk of IGD. Time spent in gaming as a risk factor of IGD is well supported by many research findings especially in weekends because of the preference of multiplayer online gaming.[17,19,21] In a study by Liu Y et al.,[22] it was observed that respondents with >6 hrs of gaming activity per week had significantly higher odds (OR: 2.46 for 6–10 h and 3.44 for >10 h) of becoming new cases of IGD. This will also have a negative impact on their productivity and social life. According to 2023 consumer engagement with games report, globally 64% of gamers expressed the need for diversity in games.[23] Constantly switching between games is also a risk for problematic gaming.
This study thus identifies age, money spent on Internet usage and gaming, time spent, and multiple gaming as significant predictors of IGD. But there are certain limitations for this study. The use of snowball sampling in this study may have resulted in selection bias and reduced sample representativeness. Moreover, the reliance on self-reported data introduces the possibility of recall bias. These limitations should be taken into account when interpreting the results.
CONCLUSION
Since its inclusion in the ICD-11, IGD has gained increased attention, particularly with the rise in online gaming among young people. However, there remains limited understanding of its prevalence, causes, risk factors, and related consequences. Further research is necessary to explore the multifaceted factors contributing to gaming addiction. Additionally, development of a standardized evaluation tool and implementation of prevention and treatment strategies for IGD is crucial. Policy makers should establish regulations on age restrictions, gaming time, and expenditure, while also promoting digital literacy and encouraging responsible gaming practices.
Key messages
IGD is an emerging public health concern, particularly with increasing popularity of online gaming among youth. Addressing risk factors like age of onset, time, spending, and gaming preferences are essential. Policymakers should promote digital literacy and responsible gaming practices and enforce regulations on in-game purchases and gaming time.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
REFERENCES
- 1.Gaming disorder. Available from: https://www.who.int/standards/classifications/frequently-asked-questions/gaming-disorder . [Last accessed on 2025 Jan 09]
- 2.Düll L, Müller A, Steins-Loeber S. Negative consequences experienced by individuals with gaming disorder symptoms: A systematic review of available longitudinal studies. Curr Addict Rep. 2024;11:528–50. [Google Scholar]
- 3.Asia internet stats by country and population statistics. Available from: https://www.internetworldstats.com/asia.htm . [Last accessed on 2024 Apr 03]
- 4.Stevens MW, Dorstyn D, Delfabbro PH, King DL. Global prevalence of gaming disorder: A systematic review and meta-analysis. Aust N Z J Psychiatry. 2020;55:553–68. doi: 10.1177/0004867420962851. [DOI] [PubMed] [Google Scholar]
- 5.Basu S, Sharma P, Rustagi R, Sharma R, Sharma N. Measuring addiction to internet gaming among Indian medical students: Development and preliminary psychometric properties of a new scale. Ind Psychiatry J. 2020;29:33–7. doi: 10.4103/ipj.ipj_114_20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Khan S, Narasannavar A, Angolkar M. Prevalence of internet gaming disorder among engineering student of engineering institute of Belagavi City, Karnataka, India. Int J Community Med Public Health. 2021;9:175–80. [Google Scholar]
- 7.Chiang CL, Zhang MW, Ho RC. Prevalence of internet gaming disorder in medical students: A meta-analysis. Front Psychiatry. 2022;12:760911. doi: 10.3389/fpsyt.2021.760911. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Singh S, Dahiya N, Singh AB, Kumar R, Balhara YP. Gaming disorder among medical college students from India: Exploring the pattern and correlates. Ind Psychiatry J. 2019;28:107–14. doi: 10.4103/ipj.ipj_96_18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Gopali L, Dhital R, Koirala R, Shrestha T, Bhusal S, Rimal R, et al. Effect of COVID-19 pandemic on internet gaming disorder among general population: A systematic review and meta-analysis. PLoS Global Public Health. 2023;3:e0001783. doi: 10.1371/journal.pgph.0001783. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Khan S, Narasannavar A, Angolkar M. Prevalence of internet gaming disorder among engineering student of engineering institute of Belagavi City, Karnataka, India. Int J Community Med Public Health. 2021;9:175–80. [Google Scholar]
- 11.Schivinski B, Brzozowska-Woś M, Buchanan EM, Griffiths MD, Pontes HM. Psychometric assessment of the internet gaming disorder diagnostic criteria: An item response theory study. Addict Behav Rep. 2018;8:176–84. doi: 10.1016/j.abrep.2018.06.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Chia DX, Ng CW, Kandasami G, Seow MY, Choo CC, Chew PK, et al. Prevalence of internet addiction and gaming disorders in Southeast Asia: A meta-analysis. Int J Environ Res Public Health. 2020;17:2582. doi: 10.3390/ijerph17072582. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Palal NJ, Mohandas S, Mathew MM, Raj AS, Mohan AV, Bari A, et al. Prevalence of Internet gaming disorder among engineering students aged 18–25 years: A cross-sectional study from Ernakulam district. Amrita J Med. 2022;18:112–6. [Google Scholar]
- 14.Kumar RS, Bharti DA. Prevalence of internet gaming disorder (IGD) among youngsters. Indian J Clin Psychol. 2022;49:70–6. [Google Scholar]
- 15.Nakayama H, Matsuzaki T, Mihara S, Kitayuguchi T, Higuchi S. Relationship between problematic gaming and age at the onset of habitual gaming. Pediatr Int. 2020;62:1275–81. doi: 10.1111/ped.14290. [DOI] [PubMed] [Google Scholar]
- 16.Beard CL, Haas AL, Wickham RE, Stavropoulos V. Age of Initiation and Internet Gaming Disorder: The Role of Self-Esteem. Cyberpsychol Behav Soc Netw. 2017;20:397–401. doi: 10.1089/cyber.2017.0011. [DOI] [PubMed] [Google Scholar]
- 17.Li Y, Tang Y, Huang S, Tan L, Huang Q, Chen X, et al. Role of gaming devices associated with internet gaming disorder in China: Cross-sectional study. JMIR Serious Games. 2023;11:e40130. doi: 10.2196/40130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Paik S-H, Cho H, Chun J-W, Jeong J-E, Kim D-J. Gaming device usage patterns predict internet gaming disorder: Comparison across different gaming device usage patterns. Int J Environ Res Public Health. 2017;14:1512. doi: 10.3390/ijerph14121512. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Rho MJ, Lee H, Lee T-H, Cho H, Jung DJ, Kim D-J, et al. Risk factors for internet gaming disorder: Psychological factors and internet gaming characteristics. Int J Environ Res Public Health. 2018;15:40. doi: 10.3390/ijerph15010040. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Costes JM, Bonnaire C. Spending money in free-to-play games: Sociodemographic characteristics, motives, impulsivity and internet gaming disorder specificities. Int J Environ Res Public Health. 2022;19:15709. doi: 10.3390/ijerph192315709. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Severo RB, Soares JM, Affonso JP, Giusti DA, de Souza Junior AA, de Figueiredo VL, et al. Prevalence and risk factors for internet gaming disorder. Braz J Psychiatry. 2020;42:532–5. doi: 10.1590/1516-4446-2019-0760. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Liu Y, Gong R, Yu Y, Xu C, Yu X, Chang R, et al. Longitudinal predictors for incidence of internet gaming disorder among adolescents: The roles of time spent on gaming and depressive symptoms. J Adolesc. 2021;92:1–9. doi: 10.1016/j.adolescence.2021.06.008. [DOI] [PubMed] [Google Scholar]
- 23. Available from: https://newzoo.com/resources/trend-reports/newzoo-global-games-market-report-2023-free-version . [Last accessed on 2024 Mar 29]