Publication activity
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
Research across participating journals · All insights follow your collection filters.
Publication activity alongside incoming citations.
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Leading affiliations and themes in this collection.
Full distributions & methodology ↓Independent sources. Clear time scales.
Crossref and OpenAlex · separate totals for the same publication cohorts
Hover over a year or focus the chart and use arrow keys to inspect values.
Latest lifetime totals, grouped by the cited article’s year. These are not citations received in that year.
OpenAlex · incoming citation links grouped by citing-work year
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.
Articles by publication year
Latest lifetime totals · grouped by publication year
Latest lifetime totals · grouped by publication year
By year of the citing work · 2017–2026
2026 is year to date. Crossref and OpenAlex lifetime charts share a citation scale. Unknown observations appear as gaps; known zero stays zero. Annual receipts use available OpenAlex histories and may not sum to lifetime totals. 0 undated publications excluded from publication-year charts.
Crossref citation coverage: 3 / 3 publications (100.0%). Latest observation: 21 Oct 2026, 07:22 UTC. Means and percentages use records with known counts.
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
Observation range: 21 Oct 2026 07:23 UTC — 22 Oct 2026 01:37 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
Citations are the latest recorded totals for articles published in each year, not citations received during that year. Crossref and OpenAlex remain separate and share the same citation scale. 2026 is incomplete; newer articles have had less time to receive citations.
0 undated publications excluded. Unknown citation counts are shown as —, never as zero. Partial coverage can understate totals; expand each table for known / total records.
| Publication year | Journal / scope | Publications | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 0 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 0 | 3 | 3/3 | 3/3 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 3 / 3 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-21T07:23:04.561994+00:00 — 2026-10-22T01:37:57.387881+00:00. Older observations may be retained after failed or incomplete refreshes.
| Citation year | Journal / scope | OpenAlex · Citations received by year | Publications | Annual history known |
|---|---|---|---|---|
| 2017 | All selected publications | 0 | 3 | 3/3 |
| 2018 | All selected publications | 0 | 3 | 3/3 |
| 2019 | All selected publications | 0 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 0 | 3 | 3/3 |
| 2022 | All selected publications | 0 | 3 | 3/3 |
| 2023 | All selected publications | 0 | 3 | 3/3 |
| 2024 | All selected publications | 0 | 3 | 3/3 |
| 2025 | All selected publications | 0 | 3 | 3/3 |
| 2026 | All selected publications | 0 | 3 | 3/3 |
Counts represent citation links, not unique citing papers across a journal: one paper citing two selected articles contributes two links. Older citations outside this window are excluded, so annual sums need not equal lifetime totals. OpenAlex methodology ↗
Publications by publication year. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2025 | 3 |
Latest Crossref totals for the selected publications; not citations received in the selected years.
Unknown counts are excluded from the mean, median and cited-publication rate.
Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.
| Journal / publisher | Publications | Crossref citations | OpenAlex citations | OpenAlex coverage | Crossref mean | Crossref median | Crossref cited %* | Crossref coverage |
|---|---|---|---|---|---|---|---|---|
| International Journal of Business Ecosystem & Strategy Bussecon International Academy | 2 | 0 | 0 | 2/2 | 0.0 | 0.0 | 0.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
Childhood obesity is a growing global public health concern, particularly in rural settings, where socio-cultural factors contribute to the prevalence of bullying. Obese learners often face severe psychosocial challenges due to bullying, which negatively impacts their academic performance, self-esteem, and mental well-being. Despite the increasing awareness of bullying, limited research focuses on its educational and psychosocial effects on obese learners in rural secondary schools. This study explores the experiences of obese learners subjected to bullying and examines how these experiences affect their educational outcomes and emotional health. Grounded in Bandura’s Social Learning Theory, which emphasizes how behaviours such as bullying are learned through observation and reinforcement, the study adopts an interpretive paradigm to gain in-depth insights into the lived experiences of affected learners. A qualitative research design was employed, using in-depth interviews, discussions, and observations to collect data from five public rural secondary schools. Participants included ten obese learners in grades 10 and 11 who had experienced bullying and four teachers who interacted with these learners. Purposive sampling ensured the selection of participants with relevant experiences, while thematic analysis was used to identify key patterns emerging from the data. Findings indicate that bullying has significant negative psychological effects, leading to anxiety, depression, social withdrawal, and diminished self-esteem. Academic performance is also affected, as many bullied obese learners experience absenteeism, lack of concentration, and reduced motivation to engage in learning. To address these challenges, schools should implement anti-bullying policies that specifically target weight-based discrimination and foster an inclusive school culture. Psychoeducational support programs should be introduced to provide counselling and resilience training for obese learners. This study highlights the severe academic and psychosocial consequences of bullying among obese learners in rural secondary schools. Addressing these issues requires targeted interventions that promote a supportive learning environment, empower affected learners, and discourage bullying behaviours.
Cyberbullying is the most common form of violence and aggression that affects the wellbeing of learners during their schooling days. Globally, cyberbullying is increasingly gaining recognition among the social scientists. It is locally becoming a phenomenon that is problematic threatening the mental health of learners. The phenomenon of cyberbullying violates the human rights of learners as outlined in the Constitution of South Africa. Bullying is a phenomenon that is destructive in nature and brings out the culture of violence in learning environment. Cyberbullying is a complex social issue and has close association with mental health illness/problems. It has consequence which includes, trauma, drop in school performance, school dropout and suicide. Cyberbullying is not a new concept among social workers because of their involvement with the phenomenon daily. Cyberbullying involves the use of social media, technology and texts to inflict harm on the victims. The study adopted the qualitative research approach, and this approach was used to provide the knowledge about the destructive nature of cyberbullying on learners in schools. The primary goal of this study was to develop an in-depth knowledge on the destruction or learners in schools. Face-to-face, semi-structured interviews was used in the study with the adoption of purposive technique of sampling. The Interview guide was utilized as a data collection instrument exploratory research designs was employed. The researcher obtained permission from the participants prior to data collection and sought permission for the audio-recording of the participants. The study applied the thematic content as a method of data analysis. The study employed two theoretical framework which were ecological approach and the strength-based approach. The study found that emotional, psychological and educational effects are some of the destructive effects of bullying which puts too much pressure on learners.
The rapid advancement of technology has changed the world's civilization rapidly and provided many benefits to humans in various activities. Every individual, from children to adults will adapt to technology because daily life is very interchangeable to the use of technology, especially the internet to access social media, watch movies to find the latest news that spreads quickly. This study aims to identify the influence of Empathy, Self-Control and Parenting Style on adolescent Cyberbullying Behavior on Instagram social media. This study uses a quantitative method with a sampling technique, namely purposive sampling involving as many as 104 adolescent Instagram users as respondents. The instrument in this study is in the form of a questionnaire that is distributed through Google Form. The data of this study was tested using SPSS (Statistical Product and Service Solutions) software. The results of this study show that Empathy has a positive and significant influence on Cyberbullying behavior.Self-Control has a negative but not significant influence on Cyberbullying behavior. Parenting has a positive but also insignificant influence on Cyberbullying behavior. The results of this study indicate that the empathy factor is more dominant in influencing adolescent cyberbullying behavior when compared to self-control and parenting style.
Cyberbullying is the most common form of violence and aggression that affects the wellbeing of learners during their schooling days. Globally, cyberbullying is increasingly gaining recognition among the social scientists. It is locally becoming a phenomenon that is problematic threatening the mental health of learners. The phenomenon of cyberbullying violates the human rights of learners as outlined in the Constitution of South Africa. Bullying is a phenomenon that is destructive in nature and brings out the culture of violence in learning environment. Cyberbullying is a complex social issue and has close association with mental health illness/problems. It has consequence which includes, trauma, drop in school performance, school dropout and suicide. Cyberbullying is not a new concept among social workers because of their involvement with the phenomenon daily. Cyberbullying involves the use of social media, technology and texts to inflict harm on the victims. The study adopted the qualitative research approach, and this approach was used to provide the knowledge about the destructive nature of cyberbullying on learners in schools. The primary goal of this study was to develop an in-depth knowledge on the destruction or learners in schools. Face-to-face, semi-structured interviews was used in the study with the adoption of purposive technique of sampling. The Interview guide was utilized as a data collection instrument exploratory research designs was employed. The researcher obtained permission from the participants prior to data collection and sought permission for the audio-recording of the participants. The study applied the thematic content as a method of data analysis. The study employed two theoretical framework which were ecological approach and the strength-based approach. The study found that emotional, psychological and educational effects are some of the destructive effects of bullying which puts too much pressure on learners.
Childhood obesity is a growing global public health concern, particularly in rural settings, where socio-cultural factors contribute to the prevalence of bullying. Obese learners often face severe psychosocial challenges due to bullying, which negatively impacts their academic performance, self-esteem, and mental well-being. Despite the increasing awareness of bullying, limited research focuses on its educational and psychosocial effects on obese learners in rural secondary schools. This study explores the experiences of obese learners subjected to bullying and examines how these experiences affect their educational outcomes and emotional health. Grounded in Bandura’s Social Learning Theory, which emphasizes how behaviours such as bullying are learned through observation and reinforcement, the study adopts an interpretive paradigm to gain in-depth insights into the lived experiences of affected learners. A qualitative research design was employed, using in-depth interviews, discussions, and observations to collect data from five public rural secondary schools. Participants included ten obese learners in grades 10 and 11 who had experienced bullying and four teachers who interacted with these learners. Purposive sampling ensured the selection of participants with relevant experiences, while thematic analysis was used to identify key patterns emerging from the data. Findings indicate that bullying has significant negative psychological effects, leading to anxiety, depression, social withdrawal, and diminished self-esteem. Academic performance is also affected, as many bullied obese learners experience absenteeism, lack of concentration, and reduced motivation to engage in learning. To address these challenges, schools should implement anti-bullying policies that specifically target weight-based discrimination and foster an inclusive school culture. Psychoeducational support programs should be introduced to provide counselling and resilience training for obese learners. This study highlights the severe academic and psychosocial consequences of bullying among obese learners in rural secondary schools. Addressing these issues requires targeted interventions that promote a supportive learning environment, empower affected learners, and discourage bullying behaviours.
The rapid advancement of technology has changed the world's civilization rapidly and provided many benefits to humans in various activities. Every individual, from children to adults will adapt to technology because daily life is very interchangeable to the use of technology, especially the internet to access social media, watch movies to find the latest news that spreads quickly. This study aims to identify the influence of Empathy, Self-Control and Parenting Style on adolescent Cyberbullying Behavior on Instagram social media. This study uses a quantitative method with a sampling technique, namely purposive sampling involving as many as 104 adolescent Instagram users as respondents. The instrument in this study is in the form of a questionnaire that is distributed through Google Form. The data of this study was tested using SPSS (Statistical Product and Service Solutions) software. The results of this study show that Empathy has a positive and significant influence on Cyberbullying behavior.Self-Control has a negative but not significant influence on Cyberbullying behavior. Parenting has a positive but also insignificant influence on Cyberbullying behavior. The results of this study indicate that the empathy factor is more dominant in influencing adolescent cyberbullying behavior when compared to self-control and parenting style.
Source coverage refers to this result set; a publication may occur in several sources. Sync times refer to contributing journals.
Missing abstracts: 0 · Missing authors: 0 · Missing publication dates: 0 · Dates without day precision: 0
| Source | Most recent successful journal sync | Latest attempt |
|---|---|---|
| crossref | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |
| ojs-rest | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 06:46 UTC |
| oai-pmh | 20 Oct 2026 19:55 UTC · ijrbs | complete · ijrbs · 20 Oct 2026 19:50 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |