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: 2 / 2 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: 22 Oct 2026 01:09 UTC — 22 Oct 2026 02:14 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.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 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 |
|---|---|---|---|---|---|
| 2016 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2016 | All selected publications | 23 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2016 | All selected publications | 53 | 2 | 2/2 | 2/2 |
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 2 / 2 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): 2026-10-22T01:09:49.558698+00:00 — 2026-10-22T02:14:40.908520+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 | 2 | 2/2 |
| 2018 | All selected publications | 6 | 2 | 2/2 |
| 2019 | All selected publications | 3 | 2 | 2/2 |
| 2020 | All selected publications | 7 | 2 | 2/2 |
| 2021 | All selected publications | 9 | 2 | 2/2 |
| 2022 | All selected publications | 7 | 2 | 2/2 |
| 2023 | All selected publications | 9 | 2 | 2/2 |
| 2024 | All selected publications | 6 | 2 | 2/2 |
| 2025 | All selected publications | 4 | 2 | 2/2 |
| 2026 | All selected publications | 1 | 2 | 2/2 |
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 |
|---|---|
| 2016 | 2 |
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 Research in Business and Social Science SSBFNET | 2 | 23 | 53 | 2/2 | 11.5 | 11.5 | 100.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
The aim of this research is to understand the factors behind smartphone purchase decisions of consumers. Nowadays companies make use of various strategies in order to attract new customers, retain existing customers and differentiate their products from those of their competitors. Perhaps, the most important and effective strategy to influence consumer behavior in the product selection is emphasizing the “brand name” of the products. Our aim in this paper is to investigate how smartphone brands can influence consumers’ buying decisions. Brand equity is a set of brand assets and liabilities linked to a brand name and symbol, which add to or subtract from the value provided by a product or service. It enhances the customer’s ability to interpret and process information, improves confidence in the purchase decision and affects the quality of the user experience. Using this construct widely discussed in the literature, we use and build our hypothesis based on Aaker model about the brand equity, including perceived quality, brand awareness, brand association and brand loyalty. The study involved a questionnaire administered to 171 smartphone consumers between December 2015 and March 2016. The consumers were chosen by convenience sampling among the students from a prestigious university in the Istanbul district of Turkey. Our findings indicate that a majority of the smartphone buyers’ decisions are mainly influenced by brand loyalty and brand awareness. Perceived quality and brand association do not seem to influence purchase decisions for the sample of this study.
This research aims to analyze the effect of brand trust and brand loyalty to consumer purchase intentions in the Smartphone market. All the residents of ?stanbul who use mobile phones are the population of the research. Due to the fact that it is very hard to reach all the population, simple sampling method has been used for data collection and 185 participants have been reached. Structural Equation Model has been used to explain the relationship among brand trust, brand loyalty and purchase intentions. SPSS for Windows 17.0 program and AMOS 20 program are used to analyze the data. Results show that both brand trust and brand loyalty have a positive effect on consumer’s purchase intentions. Also it had been determined that brand trust has a positive effect on brand loyalty. Moreover, brand trust has been found as the most effective factor for smartphones brands in the purchase intensions.
This research aims to analyze the effect of brand trust and brand loyalty to consumer purchase intentions in the Smartphone market. All the residents of ?stanbul who use mobile phones are the population of the research. Due to the fact that it is very hard to reach all the population, simple sampling method has been used for data collection and 185 participants have been reached. Structural Equation Model has been used to explain the relationship among brand trust, brand loyalty and purchase intentions. SPSS for Windows 17.0 program and AMOS 20 program are used to analyze the data. Results show that both brand trust and brand loyalty have a positive effect on consumer’s purchase intentions. Also it had been determined that brand trust has a positive effect on brand loyalty. Moreover, brand trust has been found as the most effective factor for smartphones brands in the purchase intensions.
The aim of this research is to understand the factors behind smartphone purchase decisions of consumers. Nowadays companies make use of various strategies in order to attract new customers, retain existing customers and differentiate their products from those of their competitors. Perhaps, the most important and effective strategy to influence consumer behavior in the product selection is emphasizing the “brand name” of the products. Our aim in this paper is to investigate how smartphone brands can influence consumers’ buying decisions. Brand equity is a set of brand assets and liabilities linked to a brand name and symbol, which add to or subtract from the value provided by a product or service. It enhances the customer’s ability to interpret and process information, improves confidence in the purchase decision and affects the quality of the user experience. Using this construct widely discussed in the literature, we use and build our hypothesis based on Aaker model about the brand equity, including perceived quality, brand awareness, brand association and brand loyalty. The study involved a questionnaire administered to 171 smartphone consumers between December 2015 and March 2016. The consumers were chosen by convenience sampling among the students from a prestigious university in the Istanbul district of Turkey. Our findings indicate that a majority of the smartphone buyers’ decisions are mainly influenced by brand loyalty and brand awareness. Perceived quality and brand association do not seem to influence purchase decisions for the sample of this study.
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 |