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: 68 / 68 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 00:14 UTC — 21 Oct 2026 07:23 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.
68 / 68 with data · 0 missing or unmatched. Percentages use all 68 selected publications.
68 / 68 with data · 0 missing or unmatched. Percentages use all 68 selected publications.
68 / 68 with data · 0 missing or unmatched. Percentages use all 68 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 |
|---|---|---|---|---|---|
| 2017 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2018 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2019 | All selected publications | 13 | 13 | 13/13 | 13/13 |
| 2020 | All selected publications | 16 | 16 | 16/16 | 16/16 |
| 2021 | All selected publications | 15 | 15 | 15/15 | 15/15 |
| 2022 | All selected publications | 7 | 7 | 7/7 | 7/7 |
| 2023 | All selected publications | 6 | 6 | 6/6 | 6/6 |
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2017 | All selected publications | 3 | 2 | 2/2 | 2/2 |
| 2018 | All selected publications | 12 | 3 | 3/3 | 3/3 |
| 2019 | All selected publications | 46 | 13 | 13/13 | 13/13 |
| 2020 | All selected publications | 80 | 16 | 16/16 | 16/16 |
| 2021 | All selected publications | 63 | 15 | 15/15 | 15/15 |
| 2022 | All selected publications | 23 | 7 | 7/7 | 7/7 |
| 2023 | All selected publications | 20 | 6 | 6/6 | 6/6 |
| 2024 | All selected publications | 18 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 0 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2017 | All selected publications | 6 | 2 | 2/2 | 2/2 |
| 2018 | All selected publications | 14 | 3 | 3/3 | 3/3 |
| 2019 | All selected publications | 84 | 13 | 13/13 | 13/13 |
| 2020 | All selected publications | 112 | 16 | 16/16 | 16/16 |
| 2021 | All selected publications | 105 | 15 | 15/15 | 15/15 |
| 2022 | All selected publications | 27 | 7 | 7/7 | 7/7 |
| 2023 | All selected publications | 30 | 6 | 6/6 | 6/6 |
| 2024 | All selected publications | 17 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2026 | All selected publications | 0 | 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 68 / 68 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): 2026-10-21T00:14:26.916150+00:00 — 2026-10-21T07:23:07.436063+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 | 1 | 68 | 68/68 |
| 2018 | All selected publications | 0 | 68 | 68/68 |
| 2019 | All selected publications | 5 | 68 | 68/68 |
| 2020 | All selected publications | 14 | 68 | 68/68 |
| 2021 | All selected publications | 42 | 68 | 68/68 |
| 2022 | All selected publications | 60 | 68 | 68/68 |
| 2023 | All selected publications | 65 | 68 | 68/68 |
| 2024 | All selected publications | 101 | 68 | 68/68 |
| 2025 | All selected publications | 71 | 68 | 68/68 |
| 2026 | All selected publications | 41 | 68 | 68/68 |
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 |
|---|---|
| 2017 | 2 |
| 2018 | 3 |
| 2019 | 13 |
| 2020 | 16 |
| 2021 | 15 |
| 2022 | 7 |
| 2023 | 6 |
| 2024 | 2 |
| 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 | 3 | 8 | 10 | 3/3 | 2.67 | 3 | 66.7 | 3/3 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 65 | 262 | 390 | 65/65 | 4.03 | 3 | 84.6 | 65/65 · 100.0% |
* Percentage among publications with a known citation count.
Online banking has become a vital instrument for delivering quality and easily reached banking services at the lowest possible time with ensuring affordability both for customers and service providers. Although this banking has huge potentials and benefits, the successful adoption of online banking remains a significant challenge in the context of an emerging economy like Bangladesh. The purpose of this empirical study is to investigate the factors influencing the online banking services adoption in Bangladesh by using the extended Technology Acceptance Model (TAM) with government support and risk factors. A questionnaire survey method was used to collect data from 380 banking customers. To analyse the collected survey data and test the set of hypothesis, a structural equation modelling (SEM) method was used with the help of SmartPLS3 package. The results illustrate that perceived ease of use, perceived usefulness, attitude towards use, government support positively influence the intention to use as well as the intention to use positively impact on intention to continue to the adoption of online banking services. However, the relationship between risk and intention to use was statistically insignificant. Finally, the findings may assist banking professionals and programmers to develop and design successful policies regarding online banking services to boost this service industry in the future.
Ready-made garments (RMG) are one of the most critical sectors in the economy of the South Asian region in terms of the labor force employed and export earnings. This research study aims to determine the Corporate Social Responsibility Stakeholders dimension and its influence on textile firms Performance. The study used organizational legitimacy as mediating variable between the CSR stakeholders and firms’ performances. The research study was used in the quantitative analysis approach to determine the cause and effect of the relationship between CSR and Textile firm’s financial and non-financial performance. Though the study collected primary data & secondary data from 250 respondents using survey questionnaires, the researcher obtained secondary data by analyzing the audited annual and sustainability reports of various RMG companies. We have collected data by conducting a focus group interview forming a team of employers, top-level managers, and CSR officers. We asked them all the questions, filled it, tapped it, reserved it for the interpretations. We have surveyed 67 industries, but it enabled us to collect the data from the 50 sectors—the data collected from 2016 April to 2018 December. Our study has some limitations in that the sample size is small compared to the other research. SPSS-23 & MS-Excel were used to analyze the collected data. CSR practices benefitted RMG companies in terms of long-term sustainable development by increasing the firm’s financial and non-financial performance of the RMG sector.
In this paper, we investigate the definition of Fintech and measure the extent of the impact of Fintech variables on the Cybersecurity as the dependent variable. The hypotheses developed by the authors which are based on research through the statistical results of the research variables and as all correlation coefficients were a positive relationship and at the level of significance 0.01 which made the authors accept the correlation hypotheses, and for the results of the influence factor between the research variables it also had a positive effect when the level of 0.05 for all sections of the independent variable, as we have also noticed that the influence of the independent variable of financial technology when linked to all of its sections with Cybersecurity. We find the influence of the influence coefficient significantly increased to 0.908, which explains that there is a complementarity between the sections of the independent variable.
Numerous studies have addressed the different context of mHealth services among diverse user groups. But due to a lack of understanding the insight of factors affecting the mHealth adoption, it’s crucial need to conduct a systematic review on this issue. The objective of this study was to synthesize the present understanding of the influential factors of mHealth adoption. We performed a systematic literature search on eight electronically reputed scientific databases from 2010 to March 2019, such as Science Direct, Springer, IEEE Xplore, JMIR, Taylor & Francis, Emerald, Mary Ann Liebert and Google Scholar. This was accomplished by gathering data including authors, countries, years, target population, sample size, models/theories, and key influential factors. Primarily, a total of 2969 potentially relatable papers were found, of which 50 met the inclusion criteria. It was found that cross-sectional approach, survey methods and structural equation modeling (SEM) were the most explored research methodologies whereas PLS-SEM was found to be the largest used analysis tools. From the analysis, a total of ninety-four influential factors were clearly recognized and the findings represent that the following 15 factors appeared most recurrently and significantly; perceived usefulness, perceived ease of use, social-influence, subjective norms, self-efficacy, trust, facilitating conditions, technology anxiety, performance expectancy, effort expectancy, cost, attitude, resistance to change, perceived privacy and security, and perceived behavioral control. The research results have significant theoretical and practical implications for mHealth services providers, researchers and policy makers with regards to the Sustainable Development Goals (SDGs) allied to healthcare.
The 21st century has witnessed a rapid surge in industrial pollution, accompanied by the depletion of natural resources, prompting a collective call to action. As a result, the greening of corporations and ecological sustainability has emerged as a matter of paramount importance for decision-makers worldwide. This study, thereby, aims to examine the implementation of environmentally responsible (Green) human resource management practices in the health sector and their influence on organizational sustainability performance. Methodologically, a descriptive approach was employed, utilizing a questionnaire survey to collect data from 250 healthcare employees within Pakistan's healthcare sector. The study's statistical outcomes indicate that green hiring positively influences economic performance but not social and environmental performance. However, green training, involvement, performance management, and compensation are all positively related to organizational sustainability performance, encompassing social, economic, and environmental aspects in Pakistan’s healthcare sector. By summing up, this study concludes the significance of customizing green HR practices for enhancing sustainability performance in the healthcare sector. It underscores the crucial role played by green human resource management practices and their impact on organizational sustainability performance in a developing country context, specifically in Pakistan.
The research aims to explore the impact of digital platform integration and reconstruction capabilities on organizational agility (mediator) and ESG performance of small and medium size enterprises of Pakistan. The study also examines the moderating role of technology uncertainty between digital platform capabilities and organizational agility that further leads to ESG performance. Data is collected from 300 firms while industries include; IT, Manufacturing, Textiles, Electronics, and Automobile in eight big cities of Pakistan and the respondents are middle and senior managers. Data was analyzed using the software; SPSS (reliability analysis) and R Studio (version 4.5.2), Structural Equation modeling (SEM) and regression Analysis. The study used Resource-Based View and Dynamic Capability Theory to see the role of digital platform capabilities, organization agility and uncertain technology conditions’ on ESG. The findings of this research are that such organizations with agility perform better on the ESG dimensions. First, this study takes a capability perspective to explore the impact of digital platform integration and reconstruction capability on Organizational agility, which extends the related studies taking digital perspective. Second, the inner mechanism between organizational agility and firm ESG performance is revealed where organizational agility works as a bridge for ESG performance. Third, the relationship between digital platform capabilities and organizational agility is exposed by the moderation of technology uncertainty. Where technology uncertainty positively moderates the relationship between digital platform capabilities and organizational agility.
Purpose of this research is to examine the relationship between organizational agility (OA) and the Environmental, Social, and Governance (ESG) performance of firms. Specifically, it seeks to understand the organizational agility and agility in various industries of Pakistani firms including some control variables e.g. firm size, firm age, industry and city. Data is collected from 300 firms of Pakistan and the respondents are middle and senior managers. R studio is used to analyze data (version 4.5.2). The analysis techniques include; descriptive statistical, correlation, reliability, validity, Common Method Variance (CMV), Structural Equation modeling (SEM) and regression (including control variables). Data is collected from the industries of IT, Manufacturing, Textiles, Electronics, and Automobile, in eight cities of Pakistan. Results show that there exists a positive relationship between organizational agility and firm ESG Performance. This study contributes to the literature by the importance of organizational agility as a dynamic capability in achieving ESG goals, providing new directions for future research and practical recommendations for policymakers and industry leaders.
This paper aims to evaluate how the type of streamer (human vs. AI) influences consumer desire in live-streaming commerce through socio-cognitive and motivational mechanisms. Building on the CASA framework, Self-Determination Theory (SDT), and the Model of Goal-Directed Behavior (MGB), this study examines whether human and AI streamers differentially generate perceived empathy, which then activates intrinsic and extrinsic motivations leading to consumer desire. This paper also aims to test whether personal involvement and skepticism moderate the empathy–motivation pathway. A between-subjects experimental design was used to randomly assign participants to view identical livestreaming scripts presented by either a human or a humanoid AI streamer. Data from all 277 Chinese livestreaming participants were analyzed using PLS-SEM. The key findings show that (i) perceived empathy is significantly higher for human streamers than AI streamers, (ii) perceived empathy significantly boosts intrinsic motivation and consumer desire, with a smaller effect on extrinsic motivation, and (iii) intrinsic motivation, but not extrinsic motivation, is a significant predictor of consumer desire. Additionally, personal involvement strengthens the link between empathy and intrinsic motivation, while skepticism weakens the motivational pathways—especially in AI-based interactions. This research reveals that empathy is the main psychological link between streamer identity and consumer desire, and that intrinsic motivation is the dominant mechanism driving consumer engagement in live commerce, grounded in warmth and relatedness. The findings also indicate that emotional authenticity and the fulfillment of psychological needs remain crucial determinants of consumer desire, even in AI-mediated retail. Practical implications include enhancing empathetic communication and balancing the use of human and AI streamers in digital commerce.
This paper explores the relationship between environmental, social, and governance performance (ESGP) and firm value in both emerging and developed economies, with the moderating impact of environmental uncertainty and competitive advantage. Using secondary unbalanced data spanning from 2011 to 2022, we examine both the direct and non-linear effects of ESG performance on firm value, employing advanced econometric estimation techniques such as two-step system GMM (2sGMM) and two-stage least square (2SLS). The analysis finds that: (1) ESG performance positively influences firm value in both Chinese and US banks, and the results remain valid after robustness test; (2) The results also confirm that ESG performance non-linearly influence firm value across the samples, with an inverted U-shape relationship; (3) Additionally, we further investigate the moderating effects of environmental uncertainty and competitive advantage, uncovering that environmental uncertainty dampens the ESGP-firm value relationship, while competitive advantage further amplifies it. This study contributes to the understanding of how ESG performance can act as strategic assets that align managerial and stakeholder interests, and improve firm performance, by underpinning agency, stakeholder, and the resource-based view theory. Practical insights are also provided for policymakers, regulators, and financial institutions regarding the use of ESGP as a competitive tool in uncertain and dynamic market environments.
The 21st century has witnessed a rapid surge in industrial pollution, accompanied by the depletion of natural resources, prompting a collective call to action. As a result, the greening of corporations and ecological sustainability has emerged as a matter of paramount importance for decision-makers worldwide. This study, thereby, aims to examine the implementation of environmentally responsible (Green) human resource management practices in the health sector and their influence on organizational sustainability performance. Methodologically, a descriptive approach was employed, utilizing a questionnaire survey to collect data from 250 healthcare employees within Pakistan's healthcare sector. The study's statistical outcomes indicate that green hiring positively influences economic performance but not social and environmental performance. However, green training, involvement, performance management, and compensation are all positively related to organizational sustainability performance, encompassing social, economic, and environmental aspects in Pakistan’s healthcare sector. By summing up, this study concludes the significance of customizing green HR practices for enhancing sustainability performance in the healthcare sector. It underscores the crucial role played by green human resource management practices and their impact on organizational sustainability performance in a developing country context, specifically in Pakistan.
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 |