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 / 5 publications (80.0%). 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 — 23 Oct 2026 06:54 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.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 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 |
|---|---|---|---|---|---|
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 3 | 3 | 2/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 0 | 3 | 2/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 0 | 3 | 2/3 | 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 5 / 5 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): 2026-10-21T07:23:06.731939+00:00 — 2026-10-23T06:54:04.158376+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 | 5 | 5/5 |
| 2018 | All selected publications | 0 | 5 | 5/5 |
| 2019 | All selected publications | 0 | 5 | 5/5 |
| 2020 | All selected publications | 0 | 5 | 5/5 |
| 2021 | All selected publications | 0 | 5 | 5/5 |
| 2022 | All selected publications | 0 | 5 | 5/5 |
| 2023 | All selected publications | 0 | 5 | 5/5 |
| 2024 | All selected publications | 0 | 5 | 5/5 |
| 2025 | All selected publications | 0 | 5 | 5/5 |
| 2026 | All selected publications | 0 | 5 | 5/5 |
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 |
|---|---|
| 2024 | 1 |
| 2025 | 1 |
| 2026 | 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 Finance & Banking Studies SSBFNET | 1 | — | 0 | 1/1 | — | — | — | 0/1 · 0.0% |
| International Journal of Research in Business and Social Science SSBFNET | 2 | 0 | 0 | 2/2 | 0.0 | 0.0 | 0.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
This paper searches the impact of trade credit on firms’ performance in 15 sub-Saharan African (SSA) countries. In particular, we also examine whether trade credit can advance in an environment of higher financial inclusion rate. We achieve this by using a fixed effect estimation model and solving endogeneity by GMM and IV-2SLS methods by utilizing firm level data from the Bloomberg terminal from 2010 to 2023. Baseline results summarize that firm performance grows with the level of trade credit finance. We find a firm’s industry and SSA blocs heterogeneity the trade credit-firm performance nexus. Additionally, financial inclusion alone is positive and when interacted with trade credit it becomes significant. The major implication among others is that trade credit facilitates more firm performance in a situation of higher financial inclusion level.
The coal industry in Indonesia is a capital-intensive sector characterized by long operating cycles and high dependence on global commodity price fluctuations. These conditions create substantial liquidity needs, making efficient working capital management crucial. This study aims to examine the effect of Supply Chain Finance (SCF) on firm performance in Indonesia’s coal production sub-industry. SCF in this study is measured using the Cash Conversion Cycle (CCC) and its components, Days Sales Outstanding (DSO), Days Inventory Outstanding (DIO), and Days Payable Outstanding (DPO). Firm performance is proxied by Return on Assets (ROA), while the control variables include firm size (SIZE), leverage (LEV), and sales growth (SALEG). The main hypothesis proposes that SCF has a positive effect on firm performance. This research employs secondary data from 19 coal production companies listed on the Indonesia Stock Exchange (IDX) during the 2020-2024 period. The analysis is conducted using panel data regression with the Random Effect Model (REM) approach, estimated through the Generalized Least Square (XTGLS) method.The results show that SCF has no significant effect on firm performance, while leverage (LEV) and sales growth (SALEG) have a negative and significant effect. The coefficient of determination (R²) of 27.06% indicates that the model explains 27.06% of the variation in firm performance. These findings suggest that SCF efficiency in the coal industry does not directly enhance profitability, likely due to the long cash conversion cycle and high capital requirements inherent in this sector.
This paper aims to analyse the challenges and outcomes of “selling-to-finish” by small and medium enterprises (SMEs) in South Africa. “Selling-to-finish” is a global problem that threatens SMEs everywhere. It entails an unconventional business strategy that deviates from the main goal of most businesses, which is to make profit. In South Africa, “selling-to-finish” in SMEs has a detrimental effect on the country’s economic development, growth, and efforts to combat poverty. The misunderstanding of “selling-to-finish” hinders the growth of SMEs. This is because SME owenrs mistakenly believe that having more clients who buy products and service equates to selling for a profit without doing the necessary calculations. “Selling-to-finish” puts at risk not only the expansion and advancement of SMEs but also the achievement of Sustainable Development Goal (SDG) 1, which is to eradicate poverty and National Development Plan (NDP) vision 2030, which is to build a good economy and provides employment to all citizens. This chapter employed a desktop-based qualitative research. The paper argues that improper application of selling-to-finish causes major downfall of SMEs in South Africa. It found that the achievement of economic growth, development, and poverty reduction is seriously jeopardized by “selling-to-finish” in SMEs. As a result of “selling-to-finish”, SMEs face difficulties such as financial unsustainability, cash flow problems, decreased investment capacity, and competitive disadvantage. It recommends that SME owners should prepare well-informed monthly, quarterly, or annual cash flow statements. It goes on to advise SMEs to contract with outside firms to ensure that their cash flow figures are accurately prepared.
As the 3D printing industry continues to gain momentum in South Africa's SME sector, issues about their financing and financial management need to be investigated. This is due to the capital-intensive nature of the business as well as the financing challenges encountered by 3D printing SMMEs. This study aimed to contribute to research on financial management practices and sources of finance as predictors of performance for 3D printing small and medium enterprises (SMMEs) in terms of profitability and increase in client base. The study adopted the interpretivist research paradigm, exploratory research design, inductive research approach and qualitative research method to achieve the objectives of the study. A total of twenty (20) 3D printing firms participated in the study. Employees and managers were purposefully selected to participate in the study. The participants comprised finance managers, senior managers and managers of the twenty (20) 3D printing firms that participated in the study. Structured interviews served as the instrument for data collection based on the objectives of the study. Data gathered from interviews were transcribed and analysed using thematic analysis. The study revealed that effective working capital management practices boost the profitability of 3D printing SMMEs, particularly through adopting no-credit policies and hiring competent finance officers. These practices also help expand the client base by ensuring timely delivery of products and services, with customer relationship management being a strategic approach. The study also revealed that liquidity management practices significantly boost the profitability of 3D printing firms by ensuring sufficient cash flow to meet financial obligations, managing financial risks, and ensuring timely product delivery, thereby enhancing customer satisfaction and contributing to growth in the customer base. The study recommends the need for 3D printing SMMEs to prioritise cash flow management and develop robust systems to monitor and control working capital components, including inventory, receivables, and payables.
Research Originality: This study investigates the relationship between inventory management and profitability within the textiles industry in Bangladesh, emphasizing the key factors that significantly enhance the industry's profitability. Research Objectives: This analysis investigates how various factors, including total assets, total equity, total revenue, share capital, retained earnings, finished items, work in progress, and raw materials, impact profit after tax (PAT). Research Methods: The data spans from 2011 to 2023 and encompasses 46 textile companies listed on the Dhaka Stock Exchange (DSE). To conduct this study, different models like Pooled Ordinary Least Square (POLS), Second Stage Least Square (2SLS), Generalized Method of Moments (GMM) are utilized. Empirical Results: Through the application of techniques including Pooled Ordinary Least Squares (POLS), Two-Stage Least Squares (2SLS), and the Generalized Method of Moments (GMM), we have established that total assets exhibit a significant negative correlation with PAT in both the POLS and 2SLS models. On the other hand, there is a notable positive correlation between total revenue and retained earnings on PAT across all 2SLS, POLS, and GMM models. The share capital shows a negative correlation with PAT in all three models: 2SLS, POLS, and GMM. The main independent variable, which is our fundamental focus, shows that finished goods have a positive correlation with PAT; however, this correlation lacks significance. The raw materials exhibited a negative correlation with PAT, and this relationship is significant. The correlation between Packing Materials and PAT is notably positive. This study highlights the importance of strategies that could facilitate sustainable growth in the textile industry.
Research Originality: This study investigates the relationship between inventory management and profitability within the textiles industry in Bangladesh, emphasizing the key factors that significantly enhance the industry's profitability. Research Objectives: This analysis investigates how various factors, including total assets, total equity, total revenue, share capital, retained earnings, finished items, work in progress, and raw materials, impact profit after tax (PAT). Research Methods: The data spans from 2011 to 2023 and encompasses 46 textile companies listed on the Dhaka Stock Exchange (DSE). To conduct this study, different models like Pooled Ordinary Least Square (POLS), Second Stage Least Square (2SLS), Generalized Method of Moments (GMM) are utilized. Empirical Results: Through the application of techniques including Pooled Ordinary Least Squares (POLS), Two-Stage Least Squares (2SLS), and the Generalized Method of Moments (GMM), we have established that total assets exhibit a significant negative correlation with PAT in both the POLS and 2SLS models. On the other hand, there is a notable positive correlation between total revenue and retained earnings on PAT across all 2SLS, POLS, and GMM models. The share capital shows a negative correlation with PAT in all three models: 2SLS, POLS, and GMM. The main independent variable, which is our fundamental focus, shows that finished goods have a positive correlation with PAT; however, this correlation lacks significance. The raw materials exhibited a negative correlation with PAT, and this relationship is significant. The correlation between Packing Materials and PAT is notably positive. This study highlights the importance of strategies that could facilitate sustainable growth in the textile industry.
This paper searches the impact of trade credit on firms’ performance in 15 sub-Saharan African (SSA) countries. In particular, we also examine whether trade credit can advance in an environment of higher financial inclusion rate. We achieve this by using a fixed effect estimation model and solving endogeneity by GMM and IV-2SLS methods by utilizing firm level data from the Bloomberg terminal from 2010 to 2023. Baseline results summarize that firm performance grows with the level of trade credit finance. We find a firm’s industry and SSA blocs heterogeneity the trade credit-firm performance nexus. Additionally, financial inclusion alone is positive and when interacted with trade credit it becomes significant. The major implication among others is that trade credit facilitates more firm performance in a situation of higher financial inclusion level.
The coal industry in Indonesia is a capital-intensive sector characterized by long operating cycles and high dependence on global commodity price fluctuations. These conditions create substantial liquidity needs, making efficient working capital management crucial. This study aims to examine the effect of Supply Chain Finance (SCF) on firm performance in Indonesia’s coal production sub-industry. SCF in this study is measured using the Cash Conversion Cycle (CCC) and its components, Days Sales Outstanding (DSO), Days Inventory Outstanding (DIO), and Days Payable Outstanding (DPO). Firm performance is proxied by Return on Assets (ROA), while the control variables include firm size (SIZE), leverage (LEV), and sales growth (SALEG). The main hypothesis proposes that SCF has a positive effect on firm performance. This research employs secondary data from 19 coal production companies listed on the Indonesia Stock Exchange (IDX) during the 2020-2024 period. The analysis is conducted using panel data regression with the Random Effect Model (REM) approach, estimated through the Generalized Least Square (XTGLS) method.The results show that SCF has no significant effect on firm performance, while leverage (LEV) and sales growth (SALEG) have a negative and significant effect. The coefficient of determination (R²) of 27.06% indicates that the model explains 27.06% of the variation in firm performance. These findings suggest that SCF efficiency in the coal industry does not directly enhance profitability, likely due to the long cash conversion cycle and high capital requirements inherent in this sector.
This paper aims to analyse the challenges and outcomes of “selling-to-finish” by small and medium enterprises (SMEs) in South Africa. “Selling-to-finish” is a global problem that threatens SMEs everywhere. It entails an unconventional business strategy that deviates from the main goal of most businesses, which is to make profit. In South Africa, “selling-to-finish” in SMEs has a detrimental effect on the country’s economic development, growth, and efforts to combat poverty. The misunderstanding of “selling-to-finish” hinders the growth of SMEs. This is because SME owenrs mistakenly believe that having more clients who buy products and service equates to selling for a profit without doing the necessary calculations. “Selling-to-finish” puts at risk not only the expansion and advancement of SMEs but also the achievement of Sustainable Development Goal (SDG) 1, which is to eradicate poverty and National Development Plan (NDP) vision 2030, which is to build a good economy and provides employment to all citizens. This chapter employed a desktop-based qualitative research. The paper argues that improper application of selling-to-finish causes major downfall of SMEs in South Africa. It found that the achievement of economic growth, development, and poverty reduction is seriously jeopardized by “selling-to-finish” in SMEs. As a result of “selling-to-finish”, SMEs face difficulties such as financial unsustainability, cash flow problems, decreased investment capacity, and competitive disadvantage. It recommends that SME owners should prepare well-informed monthly, quarterly, or annual cash flow statements. It goes on to advise SMEs to contract with outside firms to ensure that their cash flow figures are accurately prepared.
As the 3D printing industry continues to gain momentum in South Africa's SME sector, issues about their financing and financial management need to be investigated. This is due to the capital-intensive nature of the business as well as the financing challenges encountered by 3D printing SMMEs. This study aimed to contribute to research on financial management practices and sources of finance as predictors of performance for 3D printing small and medium enterprises (SMMEs) in terms of profitability and increase in client base. The study adopted the interpretivist research paradigm, exploratory research design, inductive research approach and qualitative research method to achieve the objectives of the study. A total of twenty (20) 3D printing firms participated in the study. Employees and managers were purposefully selected to participate in the study. The participants comprised finance managers, senior managers and managers of the twenty (20) 3D printing firms that participated in the study. Structured interviews served as the instrument for data collection based on the objectives of the study. Data gathered from interviews were transcribed and analysed using thematic analysis. The study revealed that effective working capital management practices boost the profitability of 3D printing SMMEs, particularly through adopting no-credit policies and hiring competent finance officers. These practices also help expand the client base by ensuring timely delivery of products and services, with customer relationship management being a strategic approach. The study also revealed that liquidity management practices significantly boost the profitability of 3D printing firms by ensuring sufficient cash flow to meet financial obligations, managing financial risks, and ensuring timely product delivery, thereby enhancing customer satisfaction and contributing to growth in the customer base. The study recommends the need for 3D printing SMMEs to prioritise cash flow management and develop robust systems to monitor and control working capital components, including inventory, receivables, and payables.
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 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |