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 00:49 UTC — 21 Oct 2026 07:22 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 |
|---|---|---|---|---|---|
| 2015 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2015 | All selected publications | 8 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2015 | All selected publications | 15 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
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): 2026-10-21T00:49:28.576123+00:00 — 2026-10-21T07:22:57.100450+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 | 3 | 3 | 3/3 |
| 2018 | All selected publications | 2 | 3 | 3/3 |
| 2019 | All selected publications | 2 | 3 | 3/3 |
| 2020 | All selected publications | 1 | 3 | 3/3 |
| 2021 | All selected publications | 0 | 3 | 3/3 |
| 2022 | All selected publications | 5 | 3 | 3/3 |
| 2023 | All selected publications | 1 | 3 | 3/3 |
| 2024 | All selected publications | 0 | 3 | 3/3 |
| 2025 | All selected publications | 4 | 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 ↗
Records with full publication dates only. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2015-01 | 1 |
| 2022-04 | 1 |
| 2024-08 | 1 |
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 Research in Business and Social Science SSBFNET | 3 | 9 | 18 | 3/3 | 3.0 | 1 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
In modern age, positive psychological capital is as important as financial capital for organizational development and sustainability. Efficacy, hope, optimism, and resilience are the dimensions of positive psychological capital. Emotional labor is defined by Hochschild as control of self emotions at work by workers who have a high degree of contact with coworkers within the organization or with external clients, so as to create an expression, voice or body gesture which is acceptible to the clients. Emotional labor is analysized under three components: surface acting, deep acting and naturally felt emotions. This study is aimed to investigate the impact of positive psychological capital on emotional labor. So a research is conducted with the aim of exploring the relationship between the sub-dimensions of psychological capital and emotional labour.
The study examines how surface acting and deep acting affect emotional exhaustion and job performance in the hotel industry and whether emotional intelligence has a moderating effect on the relationship. The study was conducted using a questionnaire survey, and the data were analyzed using a structural equation model. The main findings of the study demonstrate that surface acting has no effect on emotional exhaustion while deep acting has a negative effect on emotional exhaustion. This indicates that deep acting not only produces better service performance but also reduces emotional exhaustion. Moreover, surface acting and deep acting both have a positive effect on job performance, showing that both acting skills are all about demonstrating a better job performance at work. Furthermore, emotional intelligence has a moderating effect on the relationships between surface acting and job performance and deep acting and job performance, this indicates that employees with higher emotional intelligence are more likely to perform a more effective outcome of acting on job performance, regardless of whether it is surface acting or deep acting. But this phenomenon only occurs when employees are not experiencing emotional exhaustion. If employees are already experiencing emotional exhaustion, emotional intelligence does not have any moderation effect on job performance.
This study aims to examine the effects of deep acting on employees employed at the racecourse. The objective of this study is to examine the effects of deep acting, a type of emotional labour, on the well-being and job performance of service employees in the hotel industry in South Africa. Additionally, the study intends to provide effective coping strategies to minimise any negative consequences of emotional labour. This study employs a qualitative methodology, utilising purposive sampling to choose eight participants from the racecourse industry. The data was gathered using semi-structured in-depth interviews, enabling a thorough investigation of the participants' experiences and perceptions. Thematic analysis was employed to examine the data and discover significant themes and patterns pertaining to the practice of deep acting and its effects. According to the study, sales assistants reported that deep acting was less stressful than surface acting. The practice of deep acting had a positive impact on the job performance and communication abilities of the sales associates, hence contributing to their professional development. Notwithstanding these advantages, certain individuals experienced a sense of being underappreciated by their employers due to the emotional work they performed. The results indicate that engaging in deep acting promotes a heightened sense of empathy towards clients and improves the performance of sales associates as a result of their cumulative work experience. This research emphasises the necessity for organisations, especially those in the hospitality sector, to acknowledge and endorse the emotional labour carried out by their employees. By acknowledging the beneficial aspects of deep acting and providing appropriate coping mechanisms, employers can help alleviate potential negative impacts on employee well-being. Acquiring this comprehension can result in improved management techniques that foster employee contentment and productivity. This study adds to the scarce body of scholarship on emotional work in the South African setting, particularly within the racecourse business. This study presents innovative perspectives on the impact of deep acting on service employees and provides practical approaches for efficiently managing emotional labour. The research highlights the significance of maintaining a balance between employees' self-management and their regulation of client interactions, which ultimately improves the overall quality of customer service and staff satisfaction.
This study aims to examine the effects of deep acting on employees employed at the racecourse. The objective of this study is to examine the effects of deep acting, a type of emotional labour, on the well-being and job performance of service employees in the hotel industry in South Africa. Additionally, the study intends to provide effective coping strategies to minimise any negative consequences of emotional labour. This study employs a qualitative methodology, utilising purposive sampling to choose eight participants from the racecourse industry. The data was gathered using semi-structured in-depth interviews, enabling a thorough investigation of the participants' experiences and perceptions. Thematic analysis was employed to examine the data and discover significant themes and patterns pertaining to the practice of deep acting and its effects. According to the study, sales assistants reported that deep acting was less stressful than surface acting. The practice of deep acting had a positive impact on the job performance and communication abilities of the sales associates, hence contributing to their professional development. Notwithstanding these advantages, certain individuals experienced a sense of being underappreciated by their employers due to the emotional work they performed. The results indicate that engaging in deep acting promotes a heightened sense of empathy towards clients and improves the performance of sales associates as a result of their cumulative work experience. This research emphasises the necessity for organisations, especially those in the hospitality sector, to acknowledge and endorse the emotional labour carried out by their employees. By acknowledging the beneficial aspects of deep acting and providing appropriate coping mechanisms, employers can help alleviate potential negative impacts on employee well-being. Acquiring this comprehension can result in improved management techniques that foster employee contentment and productivity. This study adds to the scarce body of scholarship on emotional work in the South African setting, particularly within the racecourse business. This study presents innovative perspectives on the impact of deep acting on service employees and provides practical approaches for efficiently managing emotional labour. The research highlights the significance of maintaining a balance between employees' self-management and their regulation of client interactions, which ultimately improves the overall quality of customer service and staff satisfaction.
The study examines how surface acting and deep acting affect emotional exhaustion and job performance in the hotel industry and whether emotional intelligence has a moderating effect on the relationship. The study was conducted using a questionnaire survey, and the data were analyzed using a structural equation model. The main findings of the study demonstrate that surface acting has no effect on emotional exhaustion while deep acting has a negative effect on emotional exhaustion. This indicates that deep acting not only produces better service performance but also reduces emotional exhaustion. Moreover, surface acting and deep acting both have a positive effect on job performance, showing that both acting skills are all about demonstrating a better job performance at work. Furthermore, emotional intelligence has a moderating effect on the relationships between surface acting and job performance and deep acting and job performance, this indicates that employees with higher emotional intelligence are more likely to perform a more effective outcome of acting on job performance, regardless of whether it is surface acting or deep acting. But this phenomenon only occurs when employees are not experiencing emotional exhaustion. If employees are already experiencing emotional exhaustion, emotional intelligence does not have any moderation effect on job performance.
In modern age, positive psychological capital is as important as financial capital for organizational development and sustainability. Efficacy, hope, optimism, and resilience are the dimensions of positive psychological capital. Emotional labor is defined by Hochschild as control of self emotions at work by workers who have a high degree of contact with coworkers within the organization or with external clients, so as to create an expression, voice or body gesture which is acceptible to the clients. Emotional labor is analysized under three components: surface acting, deep acting and naturally felt emotions. This study is aimed to investigate the impact of positive psychological capital on emotional labor. So a research is conducted with the aim of exploring the relationship between the sub-dimensions of psychological capital and emotional labour.
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 |