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: 5 / 5 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 07:22 UTC — 22 Oct 2026 04:26 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 |
|---|---|---|---|---|---|
| 2014 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 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 | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 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 | 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 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): 2026-10-21T07:22:45.278845+00:00 — 2026-10-22T04:26:20.164863+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 | 2 | 5 | 5/5 |
| 2024 | All selected publications | 4 | 5 | 5/5 |
| 2025 | All selected publications | 2 | 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 |
|---|---|
| 2014 | 1 |
| 2016 | 1 |
| 2019 | 1 |
| 2022 | 1 |
| 2023 | 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 Finance & Banking Studies SSBFNET | 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 | 3 | 2 | 8 | 3/3 | 0.67 | 0 | 33.3 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
Halal packaged food products have attracted much attention in ASEAN where its largest member, Indonesia, is the world’s most populous Muslim country and is a major market in the region. For a non-Muslim country like Thailand, Islam also has a significant role in being the second popular religion after Buddhism. Although the Islamic population in Thailand is not as great as in Islamic countries, Thailand plays a substantial part in halal business as being recognized as a food production base exporting halal food worldwide. Therefore, the major objective of this study is to identify and analyze the significant factors that influence purchase intention of Thai and Indonesian Muslims toward halal packaged food products from non-Muslim manufacturers. This study used a survey to collect data consisting of 200 Thai respondents and 200 Indonesian respondents. The results indicated that product ingredients significantly influenced consumers’ intention to purchase both in Thailand and Indonesia while halal certification only significantly influenced consumers’ intention to purchase in Indonesia.
Tax policy is one of the most important policy in consideration of investment development in certain industry. Research by Newlon (1987), Swenson (1994) and Hines (1996) concluded that tax rate is one of the most important thing considered by investors in a foreign direct investment. One of tax policy could be used to attract foreign direct investment is income tax incentives. The attractiveness of income tax incentives to a foreign direct investment is as much as the attractiveness to a domestic investment (Anwar and Mulyadi, 2012).
 In this paper, we have conducted a study of income tax incentives in food and agriculture industry; where we conduct a thorough study of income tax incentives and corporate performance in Indonesian and Australian food and agriculture industry. Our research show that there is a significant influence of income tax incentives to corporate performance. Based on our study, we conclude that the significant influence of income tax incentives to Indonesian corporate performance somewhat in a higher degree than the Australian peers. We have also concluded that Indonesian government provide a relatively more interesting income tax incentives compare to Australian government. However, an average method of net income –a method applied in Australia– could be considered by Indonesian government to avoid a market price fluctuation in this industry.
<p>Tax policy is one of the most important policy in consideration of investment development in certain industry. Research by Newlon (1987), Swenson (1994) and Hines (1996) concluded that tax rate is one of the most important thing considered by investors in a foreign direct investment. One of tax policy could be used to attract foreign direct investment is income tax incentives. The attractiveness of income tax incentives to a foreign direct investment is as much as the attractiveness to a domestic investment (Anwar and Mulyadi, 2012). In this paper, we have conducted a study of income tax incentives in food and agriculture industry; where we conduct a thorough study of income tax incentives and corporate performance in Indonesian and Australian food and agriculture industry. Our research show that there is a significant influence of income tax incentives to corporate performance. Based on our study, we conclude that the significant influence of income tax incentives to Indonesian corporate performance somewhat in a higher degree than the Australian peers. We have also concluded that Indonesian government provide a relatively more interesting income tax incentives compare to Australian government. However, an average method of net income –a method applied in Australia– could be considered by Indonesian government to avoid a market price fluctuation in this industry.</p>
This study aims to find out how the impact of the Covid-19 pandemic on the survival of MSMEs in East Jambi District from the operational and financial side, the role and support of the government and the private sector in helping MSMEs, as well as the operational, financial and marketing strategies for the direction of business transformation from MSMEs to be able to endure. This study uses a descriptive quantitative approach, which is measured using descriptive statistical methods with excel and SPSS 25.0. The design of this research is a survey (as primary data). The results of this study are the Covid-19 pandemic has an impact on decreasing turnover, profits, and the number of buyers. 36% of respondents informed that cash adequacy is only sufficient for the next 1-3 months. The majority of MSMEs have also never received assistance from the government and the private sector. The operational and financial strategies carried out are cost efficiency by 38% and increasing operational hours by 18%, as well as marketing strategies by promoting to customers via social media by 30% and doing online sales by 27%. MSMEs in East Jambi Sub-district carry out operational and financial strategies so that they survive and continue to develop during the Covid-19 pandemic. MSMEs conduct cost efficiency as much as 38%, 30% of MSMEs promote to customers through social media such as Whatsapp, Facebook, and Instagram and 27% of MSMEs sell through online channels to customers in their marketing strategy. In addition, it is informed that the estimated turnover and profits of MSMEs in 2022 are fixed. The findings in this study provide an overview of the condition of MSMEs in East Jambi District due to the impact of the Covid 19 Pandemic in terms of operational and financial business sustainability and provide an overview of Government policies and assistance from the private sector to assist affected MSMEs.
Small and medium food enterprises (SMFEs) have the potential to increase sustainable competitiveness through innovation. Previous research proves that one of the factors that hinder the implementation of SMFE innovation is the limited capability of entrepreneurs to manage their human resources. Research on human resources in SMEs is more focused on employees and the relationship with company performance but is still very limited on the capabilities of SMFE entrepreneurs. This research fills the gap by improving the capabilities of SMFE entrepreneurs in managing human resources (HRM) through training. Before the training, there was a training need analysis (TNA) through a Focus Group Discussion (FGD) with the head of the processed food section of the Malang Regency Industry and Trade Office and five SMFE representatives. The output from TNA is the acquisition of 14 training topics. Participants of the training were 35 SMFE entrepreneurs in Malang district that were selected purposively. Data were collected using a questionnaire with a Likert scale of 5 (score 1 for strongly disagreeing and score 5 for strongly agreeing on statements from 14 training topics) before and after training. The data were analyzed using nonparametric statistics (Wicoxon Rank Sign Test) because it was not normally distributed. The findings of the study were the knowledge of SMFE entrepreneurs increased significantly for all topics related to HRM after training. The greatest increase in knowledge was how to build a good working relationship with employees. Two other indicators that also highly increased were the process of selecting new employees and motivating employees, the facts of which were carried out simply and not systematically.
This study aims to find out how the impact of the Covid-19 pandemic on the survival of MSMEs in East Jambi District from the operational and financial side, the role and support of the government and the private sector in helping MSMEs, as well as the operational, financial and marketing strategies for the direction of business transformation from MSMEs to be able to endure. This study uses a descriptive quantitative approach, which is measured using descriptive statistical methods with excel and SPSS 25.0. The design of this research is a survey (as primary data). The results of this study are the Covid-19 pandemic has an impact on decreasing turnover, profits, and the number of buyers. 36% of respondents informed that cash adequacy is only sufficient for the next 1-3 months. The majority of MSMEs have also never received assistance from the government and the private sector. The operational and financial strategies carried out are cost efficiency by 38% and increasing operational hours by 18%, as well as marketing strategies by promoting to customers via social media by 30% and doing online sales by 27%. MSMEs in East Jambi Sub-district carry out operational and financial strategies so that they survive and continue to develop during the Covid-19 pandemic. MSMEs conduct cost efficiency as much as 38%, 30% of MSMEs promote to customers through social media such as Whatsapp, Facebook, and Instagram and 27% of MSMEs sell through online channels to customers in their marketing strategy. In addition, it is informed that the estimated turnover and profits of MSMEs in 2022 are fixed. The findings in this study provide an overview of the condition of MSMEs in East Jambi District due to the impact of the Covid 19 Pandemic in terms of operational and financial business sustainability and provide an overview of Government policies and assistance from the private sector to assist affected MSMEs.
Small and medium food enterprises (SMFEs) have the potential to increase sustainable competitiveness through innovation. Previous research proves that one of the factors that hinder the implementation of SMFE innovation is the limited capability of entrepreneurs to manage their human resources. Research on human resources in SMEs is more focused on employees and the relationship with company performance but is still very limited on the capabilities of SMFE entrepreneurs. This research fills the gap by improving the capabilities of SMFE entrepreneurs in managing human resources (HRM) through training. Before the training, there was a training need analysis (TNA) through a Focus Group Discussion (FGD) with the head of the processed food section of the Malang Regency Industry and Trade Office and five SMFE representatives. The output from TNA is the acquisition of 14 training topics. Participants of the training were 35 SMFE entrepreneurs in Malang district that were selected purposively. Data were collected using a questionnaire with a Likert scale of 5 (score 1 for strongly disagreeing and score 5 for strongly agreeing on statements from 14 training topics) before and after training. The data were analyzed using nonparametric statistics (Wicoxon Rank Sign Test) because it was not normally distributed. The findings of the study were the knowledge of SMFE entrepreneurs increased significantly for all topics related to HRM after training. The greatest increase in knowledge was how to build a good working relationship with employees. Two other indicators that also highly increased were the process of selecting new employees and motivating employees, the facts of which were carried out simply and not systematically.
Halal packaged food products have attracted much attention in ASEAN where its largest member, Indonesia, is the world’s most populous Muslim country and is a major market in the region. For a non-Muslim country like Thailand, Islam also has a significant role in being the second popular religion after Buddhism. Although the Islamic population in Thailand is not as great as in Islamic countries, Thailand plays a substantial part in halal business as being recognized as a food production base exporting halal food worldwide. Therefore, the major objective of this study is to identify and analyze the significant factors that influence purchase intention of Thai and Indonesian Muslims toward halal packaged food products from non-Muslim manufacturers. This study used a survey to collect data consisting of 200 Thai respondents and 200 Indonesian respondents. The results indicated that product ingredients significantly influenced consumers’ intention to purchase both in Thailand and Indonesia while halal certification only significantly influenced consumers’ intention to purchase in Indonesia.
<p>Tax policy is one of the most important policy in consideration of investment development in certain industry. Research by Newlon (1987), Swenson (1994) and Hines (1996) concluded that tax rate is one of the most important thing considered by investors in a foreign direct investment. One of tax policy could be used to attract foreign direct investment is income tax incentives. The attractiveness of income tax incentives to a foreign direct investment is as much as the attractiveness to a domestic investment (Anwar and Mulyadi, 2012). In this paper, we have conducted a study of income tax incentives in food and agriculture industry; where we conduct a thorough study of income tax incentives and corporate performance in Indonesian and Australian food and agriculture industry. Our research show that there is a significant influence of income tax incentives to corporate performance. Based on our study, we conclude that the significant influence of income tax incentives to Indonesian corporate performance somewhat in a higher degree than the Australian peers. We have also concluded that Indonesian government provide a relatively more interesting income tax incentives compare to Australian government. However, an average method of net income –a method applied in Australia– could be considered by Indonesian government to avoid a market price fluctuation in this industry.</p>
Tax policy is one of the most important policy in consideration of investment development in certain industry. Research by Newlon (1987), Swenson (1994) and Hines (1996) concluded that tax rate is one of the most important thing considered by investors in a foreign direct investment. One of tax policy could be used to attract foreign direct investment is income tax incentives. The attractiveness of income tax incentives to a foreign direct investment is as much as the attractiveness to a domestic investment (Anwar and Mulyadi, 2012).
 In this paper, we have conducted a study of income tax incentives in food and agriculture industry; where we conduct a thorough study of income tax incentives and corporate performance in Indonesian and Australian food and agriculture industry. Our research show that there is a significant influence of income tax incentives to corporate performance. Based on our study, we conclude that the significant influence of income tax incentives to Indonesian corporate performance somewhat in a higher degree than the Australian peers. We have also concluded that Indonesian government provide a relatively more interesting income tax incentives compare to Australian government. However, an average method of net income –a method applied in Australia– could be considered by Indonesian government to avoid a market price fluctuation in this industry.
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 |