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: 4 / 4 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 02:08 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.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 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 |
|---|---|---|---|---|---|
| 2019 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 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 | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 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 | 0/0 | 0/0 |
| 2025 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 10 | 2 | 2/2 | 2/2 |
| 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 | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 2 | 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 4 / 4 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): 2026-10-21T07:23:00.681494+00:00 — 2026-10-22T02:08:13.753269+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 | 4 | 4/4 |
| 2018 | All selected publications | 0 | 4 | 4/4 |
| 2019 | All selected publications | 0 | 4 | 4/4 |
| 2020 | All selected publications | 1 | 4 | 4/4 |
| 2021 | All selected publications | 1 | 4 | 4/4 |
| 2022 | All selected publications | 4 | 4 | 4/4 |
| 2023 | All selected publications | 3 | 4 | 4/4 |
| 2024 | All selected publications | 1 | 4 | 4/4 |
| 2025 | All selected publications | 3 | 4 | 4/4 |
| 2026 | All selected publications | 1 | 4 | 4/4 |
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 |
|---|---|
| 2019 | 2 |
| 2022 | 1 |
| 2025 | 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 | 4 | 5 | 14 | 4/4 | 1.25 | 1.5 | 75.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
Individuals are becoming more aware of the negative effects of the rapid fashion industry and are shifting to second-hand clothes. Due to its potential to minimize clothing waste, purchasing second-hand garments is reflected as environmentally friendly. Using an online questionnaire survey, this research examines the factors influencing consumers’ purchasing intention of second-hand clothing via the mediation of individualism and product attitude by applying the Statistical Package for the Social Sciences (SPSS) 25.0 and the Analysis of Moment Structures (AMOS) 25.0 to analyze the data collected information. The proposed models are expanded by additional construct factors to evaluate their influence on purchase intention. The results indicate that online second-hand clothing purchase intention (PI) is partially explained by the theory of planned behavior (TPB) with product attitude (PA) and individualism (IN). Among the additional factors encompassed, consumer perceived value (CPV) and consumer perceived risk (CPR) negatively impacted the consumer’s intention to buy second-hand clothing. Additionally, this study validates the effectiveness of the integrated model of TPB in interpreting customers’ buying intentions for second-hand clothing. This study also yielded theoretical and managerial implications, as well as proposals for further research.
The purpose of this paper is to present a science-based narrative of the impact of the Ebola Virus Disease (EVD) on the supply chains of the major commodities of Liberia. Scientific literatures from appropriate journals, newspapers, trade data of applicable ministries and governmental agencies, data on Ebola from websites of World Health Organization (WHO), Food and Agricultural Organization (FAO), Center for Disease and Control and Prevention (CDC), World Bank, the International Monetary Funds (IMF) and other relevant institutions are used extensively while also employing a market chain approach to represent the overall supply chains of these products. The review reveals, among others, the significant disruption to the flow of goods and the decrease in output of the main export commodities of Liberia. It also shows a fall in the real GDP growth rate of the Mano River Basin (MRB) countries during the Ebola years. The paper identifies that the specific mechanisms through which the supply chains were disrupted were as a result of fear and government’s regulation. Additionally, it provides a practical conduit for the diversification of the rubber industry. Given the complex web of supply chains of just a single product, this effort is in no way an exhaustive review on the impact of the EVD on supply chains of commodities dealt with herein, let alone the overall impact of EVD on the country as a whole. Obviously, this review is also limited in terms of scope and extent. This review is a useful introduction to investigators who might want to commit to research in this particular aspect of the impact of the EVD vis-a-vis its impact on supply chains in Liberia or on a broader level, the MRB Countries.
Natural Rubber (NR) is an important agricultural product utilized in various sectors. NR is one of the world's biggest industries and a critical component in many industries such as automotive, manufacturing, and medicine. Liberia is a top producer of NR with over 127 years of production history. However, since 2014, Liberia's NR net production has decreased by 0.7% year on year and has fallen behind Sri Lanka. NR production in Liberia has experienced scant performance due to many factors. In this project, we employ mixed-method research to examine the factors influencing the decline of the Liberian NR supply chain and propose solutions that will further stimulate the industry's growth. We conduct an MCDM study among 34 smallholder rubber farmers from Grand Bassa, Margibi and Bong counties. Their grading of ten policy objectives and recommendations are programmed using Fuzzy AHP.
This study attempts to clarify and evaluate the factors that impact on cotton sector production in Cameroon and differentiate producers `behavior. A description of producers ‘behaviour is made in order to identify their main characteristics, which should shed light on the relationships between cotton textile production , the main producers` characteristics price and non-price variables. An inventory of ongoing sectoral agricultural policies is presented and suggestions are made for formulating alternative policies. The next section presents both a review of agricultural policies and the cotton sector in Cameroon. The third and fourth sections are literature review and cotton belt while the fifth presents the methodology. Discussion and interpretation of the empirical findings is followed by the conclusion and policy recommendations.
Individuals are becoming more aware of the negative effects of the rapid fashion industry and are shifting to second-hand clothes. Due to its potential to minimize clothing waste, purchasing second-hand garments is reflected as environmentally friendly. Using an online questionnaire survey, this research examines the factors influencing consumers’ purchasing intention of second-hand clothing via the mediation of individualism and product attitude by applying the Statistical Package for the Social Sciences (SPSS) 25.0 and the Analysis of Moment Structures (AMOS) 25.0 to analyze the data collected information. The proposed models are expanded by additional construct factors to evaluate their influence on purchase intention. The results indicate that online second-hand clothing purchase intention (PI) is partially explained by the theory of planned behavior (TPB) with product attitude (PA) and individualism (IN). Among the additional factors encompassed, consumer perceived value (CPV) and consumer perceived risk (CPR) negatively impacted the consumer’s intention to buy second-hand clothing. Additionally, this study validates the effectiveness of the integrated model of TPB in interpreting customers’ buying intentions for second-hand clothing. This study also yielded theoretical and managerial implications, as well as proposals for further research.
Natural Rubber (NR) is an important agricultural product utilized in various sectors. NR is one of the world's biggest industries and a critical component in many industries such as automotive, manufacturing, and medicine. Liberia is a top producer of NR with over 127 years of production history. However, since 2014, Liberia's NR net production has decreased by 0.7% year on year and has fallen behind Sri Lanka. NR production in Liberia has experienced scant performance due to many factors. In this project, we employ mixed-method research to examine the factors influencing the decline of the Liberian NR supply chain and propose solutions that will further stimulate the industry's growth. We conduct an MCDM study among 34 smallholder rubber farmers from Grand Bassa, Margibi and Bong counties. Their grading of ten policy objectives and recommendations are programmed using Fuzzy AHP.
This study attempts to clarify and evaluate the factors that impact on cotton sector production in Cameroon and differentiate producers `behavior. A description of producers ‘behaviour is made in order to identify their main characteristics, which should shed light on the relationships between cotton textile production , the main producers` characteristics price and non-price variables. An inventory of ongoing sectoral agricultural policies is presented and suggestions are made for formulating alternative policies. The next section presents both a review of agricultural policies and the cotton sector in Cameroon. The third and fourth sections are literature review and cotton belt while the fifth presents the methodology. Discussion and interpretation of the empirical findings is followed by the conclusion and policy recommendations.
The purpose of this paper is to present a science-based narrative of the impact of the Ebola Virus Disease (EVD) on the supply chains of the major commodities of Liberia. Scientific literatures from appropriate journals, newspapers, trade data of applicable ministries and governmental agencies, data on Ebola from websites of World Health Organization (WHO), Food and Agricultural Organization (FAO), Center for Disease and Control and Prevention (CDC), World Bank, the International Monetary Funds (IMF) and other relevant institutions are used extensively while also employing a market chain approach to represent the overall supply chains of these products. The review reveals, among others, the significant disruption to the flow of goods and the decrease in output of the main export commodities of Liberia. It also shows a fall in the real GDP growth rate of the Mano River Basin (MRB) countries during the Ebola years. The paper identifies that the specific mechanisms through which the supply chains were disrupted were as a result of fear and government’s regulation. Additionally, it provides a practical conduit for the diversification of the rubber industry. Given the complex web of supply chains of just a single product, this effort is in no way an exhaustive review on the impact of the EVD on supply chains of commodities dealt with herein, let alone the overall impact of EVD on the country as a whole. Obviously, this review is also limited in terms of scope and extent. This review is a useful introduction to investigators who might want to commit to research in this particular aspect of the impact of the EVD vis-a-vis its impact on supply chains in Liberia or on a broader level, the MRB Countries.
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 |