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: 7 / 7 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 07:45 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.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 selected publications.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 selected publications.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 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 |
|---|---|---|---|---|---|
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 4 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 2 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2022 | All selected publications | 7 | 2 | 2/2 | 2/2 |
| 2023 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 5 | 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 7 / 7 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): 2026-10-21T07:22:53.450278+00:00 — 2026-10-22T07:45:45.739168+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 | 7 | 7/7 |
| 2018 | All selected publications | 0 | 7 | 7/7 |
| 2019 | All selected publications | 0 | 7 | 7/7 |
| 2020 | All selected publications | 0 | 7 | 7/7 |
| 2021 | All selected publications | 0 | 7 | 7/7 |
| 2022 | All selected publications | 0 | 7 | 7/7 |
| 2023 | All selected publications | 3 | 7 | 7/7 |
| 2024 | All selected publications | 3 | 7 | 7/7 |
| 2025 | All selected publications | 6 | 7 | 7/7 |
| 2026 | All selected publications | 1 | 7 | 7/7 |
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 |
|---|---|
| 2021 | 1 |
| 2022 | 2 |
| 2023 | 1 |
| 2024 | 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 | 1 | 1 | 2/2 | 0.5 | 0.5 | 50.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 5 | 6 | 12 | 5/5 | 1.2 | 1 | 80.0 | 5/5 · 100.0% |
* Percentage among publications with a known citation count.
This study aims to investigate the implementation of supply chain agility in mediating the relationship between absorptive capacity and competitive advantage. The object of this study was some cafes and modern restaurants that have over 500 Instagram followers, while the subjects of this study were individuals that manage the cafes and restaurants. The data of three variables of this study were collected using a valid and reliable questionnaire. The data were analyzed using Structural Equation Modeling (SEM) which is a statistical analysis technique that incorporates factor analysis and simultaneous equations. The analysis was performed using AMOS 22.0. The results indicated that absorptive capacity has a positive and significant effect on supply chain agility, while supply chain agility has a positive and significant effect on competitive advantage, absorptive capacity has a positive and significant effect on competitive advantage, and supply chain agility has a positive and significant effect in mediating the relationship between absorptive capacity and competitive advantage.
The objective of this study was to determine and analyze the impact of TQM, Transformational Leadership (TL), and Executive Ability (EA) on improving sustainable competitive advantage (SCA) in MSMEs in Banten Province based on local leading potential. The method of distributing questionnaires is used in this study to collect primary data. This study's population consisted of all owners and managers of MSMEs in Banten Province. In contrast, the sample in this study is formed of the owners and managers of MSMEs of Local Leading Potential in Banten Province. Purposive sampling was being used in the study, as well as Warp PLS V. 7.0 and SPSS Version 25 test equipment. The findings of this study were as follows: (1) TQM had a significant positive effect on TL and SCA, but not on EA. (2) TL has a statistically significant positive effect on SCA and EA. (3) EA has a statistically significant positive effect on SCA. (4) TL mediates the effect of TQM on SCA and TQM on EA, and (5) EA does not mediate the effect of TQM on SCA. VAF (Variance Accounted For) has been used to calculate mediation effects.
Supply chain management is the process of integrating critical business procedures throughout the supply chain to generate value for stakeholders and customers. Total Quality Management is a system that emphasises quality and focuses on ensuring customer satisfaction. Customer satisfaction is a very important instrument for operational performance and business continuity. Therefore, this study aims to determine and analyse the effect of supply chain management (SCM) and total quality management (TQM) on operational performance (OP) mediated by competitive advantage (CA). Sample collection was carried out by distributing questionnaires, using purposive sampling technique, which is one of the non-probability sampling categories. The data analysis method uses structural equation model-partial least square (SEM-PLS). The results stated that the strategy of implementing supply chain management and total quality management together can increase competitive advantage. Increased competitive advantage can improve operational performance. The suggested policy implications are that the company can continue to maintain good relations with partners, produce products according to customer desires, competitive prices and timely products.
The delivery system for Base Transceiver System (BTS) to strengthen the telecommunication network throughout Indonesia maritime country is needed to speed up the process due to uneven distribution of sufficient telecommunications infrastructure. PT. Gunadhya Rajawali Logistik (GRL) is a third-party logistics company that focuses on transportation and warehouse management and delivery for telecommunication equipment tools. Unfortunately, in delivering BTS installation part throughout Indonesia, PT. GRL received several warning letters and had to pay a penalty from one of customers, for being late in reporting the delivery proof. This study aims to analyze the root causes of late submission of Soft Copy of Proof of Delivery (SCPOD) to avoid penalty given by the customer and determine suitable solutions to improve the process. The approach used in this study is lean six sigma with the help of Define, Measure, Analyze, Improve, and Control (DMAIC) roadmap. According to the interview with the employees related, the root causes of late submission of SCPOD are irresponsible drivers, indiscipline drivers in carrying out deliveries, and lack of data management in the transportation system. To be able to solve this problem, several proposed solutions can be applied. The proposed solutions to overcome the root causes that should be applied by the company are to hire a document control, retraining the Standard Operating Procedure (SOP) for drivers, utilize transportation management system application, and apply incentive systems for drivers. In the future, it is recommended for PT. GRL to have a minimum standard in recruiting the drivers for example only recruit who possess a minimum of high school education to ease the completion of administrative tasks.
Dynamic capabilities in the supply chain play an essential role in improving an organization's operational performance. This research aims to see the influence of supply chain dynamic capabilities, including collaboration, integration, agility, and responsive capabilities, on the operational performance of food and beverage MSMEs in Sleman, Indonesia. The sample of this study is food and beverage MSMEs located in Sleman, Indonesia, which apply supply chain management capabilities such as collaboration, integration, agility, and responsive capabilities. The MSMEs must have been operated for more than three years, starting from 2019, so MSMEs already have a more robust supply chain network. Data from sixty food and beverage MSMEs was collected in 2024 using a set of questionnaires tested for validity using Pearson’s correlation and reliability using Cronbach’s Alpha. Data processing in this research uses SPSS 25 software with an analysis tool, namely multiple regression analysis. This analysis helps prove whether or not there is an influence between two or more independent variables on the dependent variable. The results of this research show that collaboration capabilities, integration capabilities, agility capabilities, and responsive capabilities partially have a significant positive effect on operational performance. Increasing collaboration capabilities between MSMEs and internal and external parties must be maintained to improve value from the MSMEs themselves. According to the hypothesis test results, the integration capability variable significantly influences the operational performance of MSMEs in Sleman, Indonesia. The agility capability variable significantly influences the operational performance of MSMEs in Sleman, Indonesia. The responsiveness significantly influences the operational performance of MSMEs in Sleman, Indonesia.
Dynamic capabilities in the supply chain play an essential role in improving an organization's operational performance. This research aims to see the influence of supply chain dynamic capabilities, including collaboration, integration, agility, and responsive capabilities, on the operational performance of food and beverage MSMEs in Sleman, Indonesia. The sample of this study is food and beverage MSMEs located in Sleman, Indonesia, which apply supply chain management capabilities such as collaboration, integration, agility, and responsive capabilities. The MSMEs must have been operated for more than three years, starting from 2019, so MSMEs already have a more robust supply chain network. Data from sixty food and beverage MSMEs was collected in 2024 using a set of questionnaires tested for validity using Pearson’s correlation and reliability using Cronbach’s Alpha. Data processing in this research uses SPSS 25 software with an analysis tool, namely multiple regression analysis. This analysis helps prove whether or not there is an influence between two or more independent variables on the dependent variable. The results of this research show that collaboration capabilities, integration capabilities, agility capabilities, and responsive capabilities partially have a significant positive effect on operational performance. Increasing collaboration capabilities between MSMEs and internal and external parties must be maintained to improve value from the MSMEs themselves. According to the hypothesis test results, the integration capability variable significantly influences the operational performance of MSMEs in Sleman, Indonesia. The agility capability variable significantly influences the operational performance of MSMEs in Sleman, Indonesia. The responsiveness significantly influences the operational performance of MSMEs in Sleman, Indonesia.
Supply chain management is the process of integrating critical business procedures throughout the supply chain to generate value for stakeholders and customers. Total Quality Management is a system that emphasises quality and focuses on ensuring customer satisfaction. Customer satisfaction is a very important instrument for operational performance and business continuity. Therefore, this study aims to determine and analyse the effect of supply chain management (SCM) and total quality management (TQM) on operational performance (OP) mediated by competitive advantage (CA). Sample collection was carried out by distributing questionnaires, using purposive sampling technique, which is one of the non-probability sampling categories. The data analysis method uses structural equation model-partial least square (SEM-PLS). The results stated that the strategy of implementing supply chain management and total quality management together can increase competitive advantage. Increased competitive advantage can improve operational performance. The suggested policy implications are that the company can continue to maintain good relations with partners, produce products according to customer desires, competitive prices and timely products.
In general, this research aims to analyze and determine the effect of the application of lean manufacturing which is focused on eliminating waste and the influence of the physical work environment on labor productivity. This research used primary data through the distribution of questionnaires conducted to employees of Company X with a total of 230 respondents from sections in departments directly related to research variables. Data analysis methods using the Structural Equation Model (SEM) measured by the Partial Least Square (PLS) model, and data processing using SmartPLS 4.0 Software. Based on the results of calculations, data analysis shows that the application of principles related to lean manufacturing, which in this research is focused on the process of eliminating waste has a positive effect on the productivity of the workforce. The creation of a physical work environment that meets standards also positively affects the productivity of the workforce.
The first objective of this study is to analyze the effect of external supply chain flexibility (inbound supplier flexibility and outbound logistics flexibility) on product innovation performance. The second objective is to analyze the effect of external supply chain flexibility on product innovation performance with the lead supplier influence and normative integration as moderating variables. There are six hypotheses in this study: 1) the inbound supplier flexibility affects the product innovation performance, 2) the outbound logistics flexibility affects the product innovation performance, 3) the lead supplier influence moderates the relationship between the inbound supplier flexibility and the product innovation performance, 4) the lead supplier influence moderates the relationship between the outbound logistics flexibility and the product innovation performance, 5) the normative integration moderates the relationship between the inbound supplier flexibility and the product innovation performance, and 6) the normative integration moderates the relationship between the outbound logistics flexibility and product innovation performance. The sample of this study is one hundred fashion SMEs in Sleman Regency, Special Region of Yogyakarta, Indonesia. This study uses census as the sampling method. This study uses primary data obtained by distributing questionnaires to the respondents. The researchers analyzed the data with PLS. The results show that inbound supplier flexibility and outbound logistics flexibility positively affect product innovation performance. The lead supplier influence negatively moderates the effect of inbound supplier flexibility on product innovation performance in fashion. The lead supplier influence does not moderate the effect of outbound logistics flexibility on product innovation performance. Normative integration does not moderate the relationship among the variables of the study.
This study aims to investigate the implementation of supply chain agility in mediating the relationship between absorptive capacity and competitive advantage. The object of this study was some cafes and modern restaurants that have over 500 Instagram followers, while the subjects of this study were individuals that manage the cafes and restaurants. The data of three variables of this study were collected using a valid and reliable questionnaire. The data were analyzed using Structural Equation Modeling (SEM) which is a statistical analysis technique that incorporates factor analysis and simultaneous equations. The analysis was performed using AMOS 22.0. The results indicated that absorptive capacity has a positive and significant effect on supply chain agility, while supply chain agility has a positive and significant effect on competitive advantage, absorptive capacity has a positive and significant effect on competitive advantage, and supply chain agility has a positive and significant effect in mediating the relationship between absorptive capacity and competitive advantage.
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 |