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: 3 / 3 publications (100.0%). Latest observation: 21 Oct 2026, 00:21 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 00:22 UTC — 22 Oct 2026 02:21 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.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 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 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 4 | 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 | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 7 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 10 | 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 | 0 | 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 3 / 3 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): 2026-10-21T00:22:02.303418+00:00 — 2026-10-22T02:21:29.960358+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 | 3 | 3/3 |
| 2018 | All selected publications | 2 | 3 | 3/3 |
| 2019 | All selected publications | 1 | 3 | 3/3 |
| 2020 | All selected publications | 3 | 3 | 3/3 |
| 2021 | All selected publications | 4 | 3 | 3/3 |
| 2022 | All selected publications | 4 | 3 | 3/3 |
| 2023 | All selected publications | 0 | 3 | 3/3 |
| 2024 | All selected publications | 1 | 3 | 3/3 |
| 2025 | All selected publications | 1 | 3 | 3/3 |
| 2026 | All selected publications | 0 | 3 | 3/3 |
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 |
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 | 3 | 4 | 17 | 3/3 | 1.33 | 0 | 33.3 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
The proactively evolved banking regulations in the Indian Banking sector under the authorative directive of the Reserve bank of India (RBI) has often brought about a change in the business strategy, capital structure and operations of the banks in the Indian banking sector. During these events of continuous change and adoption of Basel norms, we analyse the efficiency of the Indian banking sector with using Data Envelopment Analysis across three economic eras andacross the different ownership structures. The determinants of efficiency are selected on the basis of intermediation approach. We also attempt to identify whether the inefficiency arises from managerial incompetence or improper size and resource allocation. From our analysis, we identify the main cause of inefficiency in the Indian Banking sector to be arising out of improper size allocation.
The object of this study is to explore the effect of cultural distance on both the long run and short run performance of cross border mergers and acquisitions undertaken by Indian acquiring firms. We utilize buy and hold returns (BHAR), cumulative abnormal returns (CAR) and cross-sectional regression analysis in our study. Adopting the traditional Hofstede measure of cultural distance and other pertinent variables, commonly used to measure cultural differences, we document a negative and statistically significant influence of cultural distance on Indian cross-border M&As and corroborate some of other findings reported in prior research. Also, we find that the BHAR is nevertheless higher when the acquisitions are friendly, paid for 100% cash, and the acquiring firm is large, older and belongs to a business group. The inclusion of the variable ‘business group’ along with industry relatedness and acquirer size provides valuable insights into the Indian cross border acquisition landscape, wherein business groups dominate to a great extent.
<p>The proactively evolved banking regulations in the Indian Banking sector under the authorative directive of the Reserve bank of India (RBI) has often brought about a change in the business strategy, capital structure and operations of the banks in the Indian banking sector. During these events of continuous change and adoption of Basel norms, we analyse the efficiency of the Indian banking sector with using Data Envelopment Analysis across three economic eras andacross the different ownership structures. The determinants of efficiency are selected on the basis of intermediation approach. We also attempt to identify whether the inefficiency arises from managerial incompetence or improper size and resource allocation. From our analysis, we identify the main cause of inefficiency in the Indian Banking sector to be arising out of improper size allocation.</p>
The object of this study is to explore the effect of cultural distance on both the long run and short run performance of cross border mergers and acquisitions undertaken by Indian acquiring firms. We utilize buy and hold returns (BHAR), cumulative abnormal returns (CAR) and cross-sectional regression analysis in our study. Adopting the traditional Hofstede measure of cultural distance and other pertinent variables, commonly used to measure cultural differences, we document a negative and statistically significant influence of cultural distance on Indian cross-border M&As and corroborate some of other findings reported in prior research. Also, we find that the BHAR is nevertheless higher when the acquisitions are friendly, paid for 100% cash, and the acquiring firm is large, older and belongs to a business group. The inclusion of the variable ‘business group’ along with industry relatedness and acquirer size provides valuable insights into the Indian cross border acquisition landscape, wherein business groups dominate to a great extent.
<p>The proactively evolved banking regulations in the Indian Banking sector under the authorative directive of the Reserve bank of India (RBI) has often brought about a change in the business strategy, capital structure and operations of the banks in the Indian banking sector. During these events of continuous change and adoption of Basel norms, we analyse the efficiency of the Indian banking sector with using Data Envelopment Analysis across three economic eras andacross the different ownership structures. The determinants of efficiency are selected on the basis of intermediation approach. We also attempt to identify whether the inefficiency arises from managerial incompetence or improper size and resource allocation. From our analysis, we identify the main cause of inefficiency in the Indian Banking sector to be arising out of improper size allocation.</p>
The proactively evolved banking regulations in the Indian Banking sector under the authorative directive of the Reserve bank of India (RBI) has often brought about a change in the business strategy, capital structure and operations of the banks in the Indian banking sector. During these events of continuous change and adoption of Basel norms, we analyse the efficiency of the Indian banking sector with using Data Envelopment Analysis across three economic eras andacross the different ownership structures. The determinants of efficiency are selected on the basis of intermediation approach. We also attempt to identify whether the inefficiency arises from managerial incompetence or improper size and resource allocation. From our analysis, we identify the main cause of inefficiency in the Indian Banking sector to be arising out of improper size allocation.
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 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |
| ojs-rest | 21 Oct 2026 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:14 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 00:22 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |