Keywords
happiness, satisfaction, COVID-19, bibliometrics analysis, publications
This article is included in the Research on Research, Policy & Culture gateway.
happiness, satisfaction, COVID-19, bibliometrics analysis, publications
Happiness and satisfaction are increasingly influencing a growing movement of quality-of-life studies, and are no longer a single metric, or overly tied to infrastructural development. Rather, they are more holistic, biopsychosocial constructs that look into quality of life, satisfaction, psychological wellness, and salient features of the landscape, of city design, and of spaces in which people interact at work and play. However, the coronavirus disease (COVID-19) pandemic has potentially resulted in shifts among the general population in terms of how happiness and satisfaction are perceived (O’Donnell, Wilson, Bosch, & Borrows, 2020). Such a change is multifactorial and does not merely result from the physical infection and the fear that ensues. More structurally, there have been ripple effects from the psychological consequences of multiple lockdowns, movement restrictions, and quarantine orders imposed across varying degrees in various parts of the world. These have resulted in stagnant or declining economic growth and high levels of opportunities lost, inevitably increasing unemployment rates globally (Mele & Magazzino, 2021). Employment aside, such measures have resulted in high levels of isolation, reduced social interaction with family and friends, and detachment from society, which have been demonstrated to have a detrimental effect on the population. This is especially so in two crucial sectors – the young, for whom the inalienable right to receiving a quality universal education has been impaired by online schooling and connectivity issues; and the elderly, who become increasingly isolated by COVID-19 lockdowns and have more physical susceptibility to the adverse consequences of the pandemic per se.
Hence, it is crucial that we quantify and collate the research work performed by institutions around the world during the COVID-19 pandemic that focus on effect of COVID-19 lockdowns on happiness and satisfaction, as evidence-based policies are crucial to provide scientifically sound recommendations that inform governmental policies on this issue. This is even more so as economies and countries gradually open up. As countries transform post-pandemic and continue creating spaces for individuals in cities to work, play, and conduct their business, it is crucial that we can keep our pulse on trends in quality of life, urban design, connectedness, happiness, and psychological wellbeing. Individual studies, though with good reliability and validity, are less generalizable; hence bibliometric analysis has emerged as a new tool to perform big picture analysis on the evidence at large.
Bibliometric analysis is crucial as a new analytical technique to map existing literature that revolves around a particular theme of research, and can be important in assessing trends (Deng, Wang, Chen, & Wang, 2020; Derviş, 2019; Kawuki, Yu, & Musa, 2020b; Kutluk & Danis, 2021; Musa, El-Sharief, Musa, Musa, & Akintunde, 2021; Odone et al., 2020; Sun & Yuan, 2020). It identifies suitable research hotspots based on historical trends which can encompass diverse domains both quantitatively and qualitatively. Also, bibliometric analysis can assist with research retrospection (Falagas, Karavasiou, & Bliziotis, 2006; Zhang et al., 2021), allowing analysis, visualization and evaluation of scientific research teams. Hence, connections can be established more clearly between authors, frameworks, methodologies, and translational practice. (Song et al., 2019). Moreover, research trends, topics, and relative importance on publication in particular areas can be ascertained (Ellegaard & Wallin, 2015; Herrera-Viedma et al., 2020). Bibliometric tools have assisted greatly in looking at research trends in various fields across the spectrum including Ebola (Kawuki, Yu, & Musa, 2020b; Yi, Yang, & Sheng, 2016), timeframe-specific COVID-19 research (Furstenau et al., 2021; Lou et al., 2020), malaria (Fu et al., 2015), and childhood obesity (Kawuki et al., 2020a). As the pandemic has shifted global priorities drastically, however, there has been no bibliometric research that examines trends before and after, and does a suitable comparison.
Thus, this analysis’s specific objectives include identifying the scientific research growth, publication, and citation trends across time for COVID- 19 affecting happiness and satisfaction. This study looks at these bibliometric parameters pre- and during COVID-19 to identify if there have been disruptions in research output or shifts in research priorities given the cataclysmic changes the world has experienced (Zambrano, Alvarez, & Caballero, 2021). Hence, this bibliometric analysis aims to shine the spotlight on the most active authors, journals, the highest-contributing countries and institutions, and the most proactive funding organizations involved in the field of happiness and satisfaction. Word-cloud and conceptual structure map methods allow more illustrative depictions of the research corpus of happiness and satisfaction research both prior to and in the light of the COVID-19 pandemic, allowing us to make comparisons and contrasts parsimoniously.
The study adopted the bibliometric method to quantitatively and qualitatively analyze documents indexed in the Scopus database. The study period of the current research was divided into two phases, before the COVID-19 pandemic (covering 1998 documents) and during COVID-19 (2020 until December 18, 2021).
On December 18, 2021, the Scopus database was comprehensively searched for relevant publications on happiness and satisfaction (search query used: TITLE-ABS-KEY “happiness” AND “satisfaction”). Only articles published in English were retrieved. A total of 3069 documents were extracted from Scopus. Bibliometric indicators include the year of publications, authors, region, subject areas, countries, institutions, journals, country collaboration. Authorship productivity was presented in the final analysis.
The metadata on the effects of the COVID-19 Pandemic on happiness and satisfaction was exported from Scopus. Bibliometrix, with an R package was used to perform comprehensive science mapping analysis (Aria & Cuccurullo, 2017) and VOSviewer.Var1.6.6 was used to developed bibliometric maps between documents to examine their characteristics (Van Eck & Waltman, 2010).
The data search result included 1998 articles before COVID-19 and 1071 during COVID-19, with 21.66 and 30.4 average citations per document, respectively. The types of documents included were 2593 articles (84.50%) and 129 conference papers (4.20%), among others (refer to Table 1).
The annual scientific production included the number of articles before the COVID-19 period (year 2019) 449 (14.63%) and during the COVID-19 period (year 2021) 541 (17.63%) (refer to Table 2).
Year | Articles |
---|---|
2014 | 85 |
2015 | 331 |
2016 | 386 |
2017 | 346 |
2018 | 401 |
2019 | 449 |
2020* | 519 |
2021* | 541 |
2022* | 11 |
There was a range of 2.06-16.01 mean total citations per year (2015-2019) before the COVID-19 period (21.06%) and 39.06 mean total citations per year in 2020 during the COVID-19 period (refer to Table 3).
Year | N | MeanTCperArt | MeanTCperYear | CitableYears |
---|---|---|---|---|
2014 | 85 | 0 | 0 | 7 |
2015 | 331 | 12.41087613 | 2.068479355 | 6 |
2016 | 386 | 11.03108808 | 2.206217617 | 5 |
2017 | 346 | 23.91618497 | 5.979046243 | 4 |
2018 | 401 | 30.56109726 | 10.18703242 | 3 |
2019 | 449 | 32.02895323 | 16.01447661 | 2 |
2020* | 519 | 39.05587669 | 19.52793834 | 2 |
2021* | 541 | 22.69500924 | 22.69500924 | 1 |
2022* | 11 | 0.545454545 | 0 |
The source local impact was computed by the H index before the COVID-19 pandemic (refer to Figure 1). Advances in Intelligent Systems and Computing had a h-index of 7, followed by a h-index of 5 in Lecture Notes in Computer Science, and h=4 in ACM International Conference Proceeding Series.
The source local impact was computed by H index for the period during the COVID-19 pandemic (refer to Figure 2). The h-index was 4 for Proceedings of the International Conference on Industrial Engineering and Operations Management, followed by 3 in Pervasive Health, whereby the others ranged between 1 and 2.
A total of 1,139 sources contributed to happiness and satisfaction research before the COVID-19 period. The topmost influential publications are listed in Table 4. Journal of Happiness Studies had 102 articles, followed by Social Indicators Research which had 77 articles.
A total of 643 sources contributed to happiness and satisfaction research during the COVID-19 period. The topmost influential publications are listed in Table 5. Journal of Happiness Studies had 53 articles followed by International Journal of Environmental Research and Public Health with 46 articles.
Table 6 lists the top 10 most influential publications before the COVID-19 pandemic, including their TC per Year and Normalized TC. Table 7 displayed the top ten most influential publications during to the COVID-19 pandemic, including their TC per Year and Normalized TC, as shown in Table 5.
A total of 5220 authors contributed to happiness and satisfaction research before the COVID-19 period. The topmost relevant authors are listed in Table 8. Veenhoven R contributed 15 articles, with 9.92 articles fractionalized, followed by Diener E who contributed 14 articles, with 4.79 articles fractionalized.
Authors | Articles | Articles Fractionalized |
---|---|---|
Veenhoven R | 15 | 9.92 |
Diener E | 14 | 4.79 |
Oishi S | 14 | 4.55 |
Okulicz-Kozaryn A | 11 | 6.17 |
Na Na | 10 | 10.00 |
Holder Md | 8 | 3.75 |
Tay L | 8 | 2.46 |
Abdel-Khalek Am | 7 | 4.50 |
Kim J | 7 | 1.97 |
Lyubomirsky S | 7 | 2.12 |
A total of 3520 authors contributed to happiness and satisfaction research during COVID-19. The topmost relevant authors are listed in Table 9. Veenhoven R contributed 8 articles, with 3.12 articles fractionalized, followed by Ravina-Ripoll R who contributed 7 articles, with 2.00 articles fractionalized.
University of California was the most relevant affiliation before the COVID-19 pandemic (number of publications [NP] = 42), followed by University of Michigan (NP = 32) and Erasmus University Rotterdam (NP = 26) (refer to Figure 3).
University of California was the most relevant affiliation during the COVID-19 pandemic (NP = 19), followed by University of Toronto (NP = 18) and both University of Pennsylvania and Zhejiang University (NP = 14) (refer to Figure 4).
The USA was the most productive country (NP = 341), followed by the United Kingdom (NP = 119), China (NP = 79), and Korea (NP = 73) as the most influential countries before the COVID-19 pandemic. Meanwhile, the US was the most productive country based on the multiple country publications metric (NP = 54), followed by the United Kingdom (NP = 47) and Netherlands (NP = 29) (refer to Table 10).
The USA was the most productive country (NP = 145), followed by China (NP = 81), Spain (NP = 52), and Korea (NP = 47) as the most influential countries during the COVID-19 pandemic. Meanwhile, the US was the most productive country based on the multiple country publications metric (NP = 34), followed by Spain (NP = 21) and China (NP = 19) (refer to Table 11).
Using a multiple correspondence analysis (MCA) pre- and post-COVID 19, as shown in Figure 5, a total of 39 keywords were divided into one color, hence explaining the concept of research effects of pre-COVID-19 on happiness and satisfaction. This contrasts with Figure 6, where 42 keywords were divided into two colors: red, with 23 keywords; and blue, with 19 keywords. This hence explaining the concept of research effects of during COVID-19 on happiness and satisfaction. Both groups demonstrated different keywords that explain the concept/s of research on happiness and satisfaction pre- and during COVID-19.
The relation between affiliations, countries, and “keywords plus” occurrence on the effects of the COVID-19 pandemic on happiness and satisfaction are presented in the three fields plot in Figure 7. The keyword visualization is seen in Figure 7a and Figure 7b, with the word cloud in Figure 8a and 8b. Before the COVID-19 pandemic, “female” is the most frequent keyword with 2430 instances, followed by “male” with 2340, then “happiness” with 2290. During the COVID-19 pandemic, “happiness” is the most frequent keyword with 1230 instances, followed by “female” with 1220, then “male” with 1080.
The analysis of social networks between researchers before the COVID-19 pandemic with three or more publications was considered and had 140 authors; only network maps with 11 items are shown in four clusters with links (links = 18 and total link strength = 42) as shown in Figure 9(a). Figure 9(b) demonstrates collaborative ties among countries during the COVID-19 pandemic and mental health research. Authors who published at least three articles in the dataset (n = 61) were included. Overall collaboration is presented in nine different clusters with distinct colors, and the thickness of the line between two countries that contributed to happiness and satisfaction research represents the strength of research collaboration. The distance between the two countries reflects how much the two countries are closely related to the research field. For example, the top three countries were the USA (links = 52 and total link strength = 300), followed by United Kingdom (links = 42, total link strength = 171) and Australia (links = 33 and total link strength = 114). Figure 9(c) showcased which organizations were related, and 26 organizations that meet the thresholds presented in 2 cluster with links (links = 4 and total link strength = 10).
The analysis of social networks between researchers during COVID-19 pandemic with three or more publications was considered and had 68 authors; only network maps with 14 items are shown in 11 clusters with links (links = 17 and total link strength = 20) as shown in Figure 10(a). Figure 10(b) demonstrates collaborative ties among countries during the COVID-19 pandemic and mental health research. Authors who published at least three articles in the dataset (n = 62) were included. Overall collaboration is presented in 37 different clusters with distinct colors, and the thickness of the line between two countries that contributed to happiness and satisfaction research represents the strength of research collaboration. The distance between the two countries reflects how much the two countries are closely related to the research field. For example, the top three countries were the USA (links = 43 and total link strength = 164), followed by Spain (links = 30, total link strength = 81) and Germany (links = 32 and total link strength = 63). Figure 10(c) showcased which organizations were related, and 14 organizations that meet the thresholds presented in one cluster with links (links = 6 and total link strength = 18).
There was a wider range of trend topics pre-COVID 19. Topics ranged across the dimensions, from economic (poverty; productivity) to mental health (suicide; psychological resilience; depression) to general quality of life studies (life satisfaction; happiness; wellbeing); hence no one theme predominated (refer to Figure 11). Post-COVID 19, the topics were more circumscribed, with 2021 topics being relevant to the times (China; patient satisfaction; controlled clinical study) reflecting global anxieties and research priorities (refer to Figure 12). Observing the thematic evolution using author’s keywords pre- and post-COVID 19, some trends emerged out of the literature. Pre-COVID, the themes ranged across the spectrum, from adolescents, social media, to China (refer to Figure 13). Post-COVID-19, the themes coalesced into a few broad keywords, and various themes expanded into separate strands. For instance, the “mental health” keyword evolved into two separate strands of “mental health” and “social support.” The “happiness” keyword evolved into two strands of “subjective well-being” and “happiness” (refer to Figure 14).
This study presents a bibliometric overview of the COVID-19 pandemic and publications related to happiness and satisfaction. Overall, the analysis suggests that themes have become more concise and more wellbeing-related in the post-pandemic landscape. It is interesting to note though that the three articles with the highest citation numbers were all related to happiness and satisfaction in an engineering or technical setting, and they were all from 2021. Due to the usual life cycle of research publication in journals, it is most certain that all three articles were submitted pre-COVID, so are more reflective of the earlier research landscape. The article with the highest impact was a conference series paper focusing primarily on urban quality of life as a response to various urban issues and challenges; however, it was not specific to COVID-19. The article with the second highest level of impact focuses on an elucidation of a human facial expression classification system to test video games using the K-Nearest Neighbor (KNN) classification method and using the Indonesia Mixed Emotion Dataset (IMED) as training data and trial data, incorporating several processes, namely preprocessing, feature extraction, and classification of facial expressions. Again, this research article did not have any correlations with COVID-19 either. The third article with the highest number of citations is a book chapter focusing on psychological impact of design, namely on empirical case studies in city regeneration of post-industrial sites. Notably it is interesting that two of the three top-cited articles focus on the latest evidence in regenerating the urban landscape, despite not being pandemic-specific. Hence, in the new post-COVID urban landscape, it is imperative that cities are replanned and designed smartly to allow for suitable ventilation, physical distancing, increased cyclist, and pedestrian mobility, and higher environmental efficiency and sustainability, as would be suitable in a post-COVID urban landscape.
We can observe too that there are very little differences in author productivity pre- and during COVID-19. The most productive authors were the same ten people pre- and during COVID-19, with roughly similar numbers of articles. This suggests that the during COVID-19 productivity most probably reflects work performed prior to the commencement of the pandemic; hence it will possibly take a few more years for the literature on happiness and satisfaction directly pertaining to the pandemic to be reflected in the bibliometric analysis. Another postulation is that due to this ten authors’ primacy in this field, they would still accrue similar numbers of authorship during the pandemic as they would be in advisory or consultancy rather than main authorship roles for papers produced by their happiness or satisfaction research units.
The best place to hence observe a difference is in thematic transformation keywords pre- and during COVID. Pre-COVID, the themes ranged across the spectrum, from adolescents, social media, to China. During COVID-19, the themes coalesced into a few broad keywords. The mental health keyword evolved into two separate strands of mental health and social support.
This suggested that the latter is a key component of preservation of good mental health. The “happiness” keyword evolved into two strands of “subjective well-being” and “happiness,” suggesting that in the pandemic, individuals’ experiences of happiness and unhappiness were very individualized as different nations and regions were subjected to widely varying levels of lockdown despite having similar epidemiological characteristics. It would be interesting to further perform bibliometric analyses on particular topics within the overall ambit of happiness and see if different trends emerged.
This research is crucial in that it is the first study utilizing novel bibliometric methodologies examining the relationship between both the pre- and during the COVID-19 period regarding happiness and satisfaction. No doubt limitations are inherent in bibliometric methodology; only English publications were able to be extracted in this project, and other databases, such as Google Scholar, PubMed, Web of Science, and Chinese databases were not included. Nevertheless, Scopus nevertheless retains primacy as one of the largest peer reviewed databases extant and is a highly valid primary search source. Also, bibliometric analyses cannot adequately take into account false-positive and false-negative results. Moreover, top-cited articles in this bibliometric analysis were ranked based on the total citation score. No doubt this metric is accepted in publishing and research as a reasonable judge of a paper’s impact; however, self-citation may be a mechanism that artificially inflates the overall citation numbers and the h-index.
This bibliometric study uniquely allows us to observe, with comparisons pre- and during the pandemic, the state of affairs in happiness and satisfaction research across a designated time period, and casts light on the prominent articles, authors, publishing journals, countries, and funding agencies in happiness and satisfaction research. This study demonstrates how themes have evolved over the pandemic, despite the static nature of authors involved, and signals a potential paradigm shift in the priorities of the research community involved in happiness and satisfaction, away from the multifarious foci, towards more focused research addressing the recovery of the world at large from the calamitous economic, social, and psychological consequences of COVID-19. To this end, it is hence crucial that international agencies and research units with expertise or interest in this field offer grants to academicians and researchers who can expedite practical solutions to improve happiness and satisfaction across all strata of society. This bibliometric analysis also underscores the importance of multinational and multiagency collaborations in resolving issues of our times. Despite its precipitous consequences, the sudden shift to universal online working has significantly loosened the barriers to international collaboration, allowing agencies, universities, governments, and individuals to collaborate real-time to share knowledge and expertise in solving the greatest and most pressing issues of our pandemic times, one of which indubitably will be the promotion of higher levels of happiness and satisfaction.
Zenodo: Happiness and satisfaction research pre and during COVID-19 pandemic: A Bibliometric analysis of global scientific literature. https://doi.org/10.5281/zenodo.7607045 (Wider, 2023).
This project contains the following underlying data:
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Views | Downloads | |
---|---|---|
F1000Research | - | - |
PubMed Central
Data from PMC are received and updated monthly.
|
- | - |
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Yes
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
Partly
References
1. Adegoke K, Giwa S, Adegoke O, Maxakato N: Systematic mapping on the evaluation of electrochemical CO2 conversion to fuels/chemicals/value-added products and way forward for breakthroughs in electrocatalysis. Scientific African. 2023; 20. Publisher Full TextCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: My field of study is mechanical engineering and I have published extensively using bibliometric analysis in different areas of my field of study.
Is the work clearly and accurately presented and does it cite the current literature?
No
Is the study design appropriate and is the work technically sound?
No
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
No
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
No
References
1. Weiner H: [Is the "biopsychosocial model" a helpful construct?].Psychother Psychosom Med Psychol. 1994; 44 (3-4): 73-83 PubMed AbstractCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: Scientometrics, bibliometric visualisation, theory of science, research policy studies.
Alongside their report, reviewers assign a status to the article:
Invited Reviewers | ||
---|---|---|
1 | 2 | |
Version 1 13 Jun 23 |
read | read |
Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list:
Sign up for content alerts and receive a weekly or monthly email with all newly published articles
Already registered? Sign in
The email address should be the one you originally registered with F1000.
You registered with F1000 via Google, so we cannot reset your password.
To sign in, please click here.
If you still need help with your Google account password, please click here.
You registered with F1000 via Facebook, so we cannot reset your password.
To sign in, please click here.
If you still need help with your Facebook account password, please click here.
If your email address is registered with us, we will email you instructions to reset your password.
If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance.
Comments on this article Comments (0)