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: 22 Oct 2026 01:10 UTC — 22 Oct 2026 07:56 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 | 0 | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | 4 | 2 | 2/2 | 2/2 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2017 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 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): 2026-10-22T01:10:47.272045+00:00 — 2026-10-22T07:56:40.951261+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 | 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 | 2 | 5 | 5/5 |
| 2022 | All selected publications | 3 | 5 | 5/5 |
| 2023 | All selected publications | 0 | 5 | 5/5 |
| 2024 | All selected publications | 0 | 5 | 5/5 |
| 2025 | All selected publications | 2 | 5 | 5/5 |
| 2026 | All selected publications | 1 | 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 |
| 2017 | 2 |
| 2023 | 1 |
| 2025 | 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 |
|---|---|---|---|---|---|---|---|---|
| Bussecon Review of Social Sciences Bussecon International Academy | 1 | 2 | 1 | 1/1 | 2.0 | 2 | 100.0 | 1/1 · 100.0% |
| International Journal of Business Ecosystem & Strategy Bussecon International Academy | 1 | 1 | 1 | 1/1 | 1.0 | 1 | 100.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 3 | 5 | 10 | 3/3 | 1.67 | 1 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
In today’s business world, social media tools (i.e. facebook, twitter, Linkedin, etc.) begin to play an important role in reaching and communicating with customers for businesses. However, the importance of using social media can’t be still understood by SMEs so far. Businesses use different social media tools in various purposes in their life. In this research we aim to investigate the ideas of SMEs about social media and their aims of using social media tools. Data were collected from SMEs operating in Burdur by simple random sampling. The data obtained from the 84 questionnaires was entered to computer and statistical analysis was carried out by using SPSS 20 statistical program. In this study, reliability analysis was used firstly. Then, some descriptive analyses were carried out about using social media for SMEs. Finally, a frequency table regarding the social media usage in SMEs was given. As a result, it was seen that 82.1% of SMEs participating in this study have a social network page. Secondly, SMEs are using facebook social media network mostly. In addition, SMEs have expressed that they attend all statements involved in the use of social media scale.
This study explores the influence of digital marketing strategies on brand loyalty among young airline passengers, specifically targeting Millennials and Gen Z individuals aged 18-35 years old. With the increasing reliance on digital channels such as social media, email marketing, and search engine marketing, it is crucial to understand how these strategies impact the customer-brand relationship, brand trust, and ultimately, brand loyalty. The research employs a quantitative approach, gathering data from 230 participants who are frequent users of digital platforms and have recent travel experiences with airlines. The findings reveal that social media marketing has the most significant impact on the customer-brand relationship, followed by email and search engine marketing. Additionally, the study confirms that brand trust acts as a critical mediator between digital marketing activities and brand loyalty. These results highlight the importance of tailored digital marketing strategies for airlines aiming to engage and retain young passengers in a highly competitive industry.
In recent years, the use of mobile devices in the marketing world is increasing parallel with technological advances. The main problem of this research is to determine agencies’ use of what type of mobile tools for what type of purposes in the marketing process. The aim of this research is to identify Mobile Marketing (MM) applications used by group A travel agencies, and to describe the attitudes towards MM applications of agencies. According to related law, it is only group A agencies give all agency services. Therefore, it is thought that MM is more widely used by those agencies. Thus, this research was made only on the group A agencies. The population of study was consisted of 675 groups A agencies’ representatives deployed in the European and Asian sides of Istanbul. A questionnaire was used as data collection tool. Questionnaire form consisted of two parts. In the first part, questions concerning MM and in the second part, questions regarding demographic issues to managers and travel agents took place. The results were given as descriptive statistics (frequencies, percentages, mean and standard deviation) on the tables. T-test and One Way ANOVA analysis were performed for the differences of mean among groups. In the study it was concluded that agencies use mobile phones mostly and mobile computers for increasing sales firstly and direct marketing. Thus, the main hypothesis established was confirmed partly.
In recent years, the aviation industry has transformed significantly, driven by the increasing importance of digital technologies and social media for communication and marketing. The COVID-19 pandemic accelerated this transformation, posing unprecedented challenges to airlines. The present study investigates how Turkish Airlines, Qatar Airways, and Emirates adapted their social media strategies before, during, and after the pandemic. The research has a threefold focus: (i) assessing pre-pandemic social media strategies, (ii) examining strategy evolution during the pandemic, and (iii) comparing the effectiveness of these strategies before and after COVID-19. By exploring how these airlines leveraged social media to navigate pandemic-related disruptions and maintain vital connections with passengers, this study provides valuable insights. These findings hold significant implications for aviation stakeholders, marketing professionals, and researchers, shedding light on the evolving role of social media in crisis communication and marketing. The study uncovered interesting changes in how airlines use Twitter. After the pandemic, they started using more emojis, and their messages shifted from promoting services to expressing hope during the pandemic and back to promotions afterward. They played with visuals and hashtags. Pricing-wise, they offered discounts for students, different ticket options, and bundled services. In promotions, they focused on being environmentally and socially responsible, user convenience, and specific events. They mainly shared updates on flights, achievements, and travel advice. To engage people, they used quizzes, replied directly to customers, and targeted specific groups. For marketing managers, the study suggests being more customer-focused, emphasizing environmental and social responsibility, offering diverse pricing, celebrating milestones, and creating engaging content. Overall, the findings provide practical insights for adjusting marketing strategies in the ever-changing world of airlines, especially considering the challenges brought by COVID-19.
Kullanımları dünya genelinde yaygınlaşan sosyal medya siteleri ciro yaratarak karlı birer işletme haline gelebilmek için günümüzde reklam destekli iş modelleri kullanmaktadır. Kullanıcıların ücretsiz olarak kullanmaya alıştığı bu platformlara erişim için ücret ödemek istemeyecekleri varsayımıyla reklam destekli modeller tercih edilmektedir. Bu çalışma içerisinde sosyal medya sitelerinin en yaygın kullanılanı olan Facebook üzerinde gösterilen reklamlara kullanıcıların tutumları ölçümlenmiş ve bu tutum ve tutumu oluşturan faktörlerin Facebook reklamlarını görmemek için para ödemeye etkisi test edilmiştir. Ödeme eğiliminde olan ve olmayan kullanıcılar arasında farklılar irdelenmiştir. Literatürde kabul görmüş ölçekler yardımıyla hazırlanan anket formu İnternet üzerinden Facebook kullanıcılarına dağıtılmış, elde edilen 327 soru formu bir ön-analiz sürecinden geçirilerek düşük kaliteli ve tam doldurulmamış anketler ayıklanarak 281 kullanılabilir soru formuna ulaşılmıştır. Verilerin analizinde yapısal eşitlik modellemesi kullanılmıştır. Gerçekleştirilen analiz sonuçlarına göre Facebook reklamlarına olan tutum ve tutumun öncülleri reklamları görmemek için ödeme niyetine olumsuz yönde etki etmektedir.
This study explores the influence of digital marketing strategies on brand loyalty among young airline passengers, specifically targeting Millennials and Gen Z individuals aged 18-35 years old. With the increasing reliance on digital channels such as social media, email marketing, and search engine marketing, it is crucial to understand how these strategies impact the customer-brand relationship, brand trust, and ultimately, brand loyalty. The research employs a quantitative approach, gathering data from 230 participants who are frequent users of digital platforms and have recent travel experiences with airlines. The findings reveal that social media marketing has the most significant impact on the customer-brand relationship, followed by email and search engine marketing. Additionally, the study confirms that brand trust acts as a critical mediator between digital marketing activities and brand loyalty. These results highlight the importance of tailored digital marketing strategies for airlines aiming to engage and retain young passengers in a highly competitive industry.
In recent years, the aviation industry has transformed significantly, driven by the increasing importance of digital technologies and social media for communication and marketing. The COVID-19 pandemic accelerated this transformation, posing unprecedented challenges to airlines. The present study investigates how Turkish Airlines, Qatar Airways, and Emirates adapted their social media strategies before, during, and after the pandemic. The research has a threefold focus: (i) assessing pre-pandemic social media strategies, (ii) examining strategy evolution during the pandemic, and (iii) comparing the effectiveness of these strategies before and after COVID-19. By exploring how these airlines leveraged social media to navigate pandemic-related disruptions and maintain vital connections with passengers, this study provides valuable insights. These findings hold significant implications for aviation stakeholders, marketing professionals, and researchers, shedding light on the evolving role of social media in crisis communication and marketing. The study uncovered interesting changes in how airlines use Twitter. After the pandemic, they started using more emojis, and their messages shifted from promoting services to expressing hope during the pandemic and back to promotions afterward. They played with visuals and hashtags. Pricing-wise, they offered discounts for students, different ticket options, and bundled services. In promotions, they focused on being environmentally and socially responsible, user convenience, and specific events. They mainly shared updates on flights, achievements, and travel advice. To engage people, they used quizzes, replied directly to customers, and targeted specific groups. For marketing managers, the study suggests being more customer-focused, emphasizing environmental and social responsibility, offering diverse pricing, celebrating milestones, and creating engaging content. Overall, the findings provide practical insights for adjusting marketing strategies in the ever-changing world of airlines, especially considering the challenges brought by COVID-19.
Kullanımları dünya genelinde yaygınlaşan sosyal medya siteleri ciro yaratarak karlı birer işletme haline gelebilmek için günümüzde reklam destekli iş modelleri kullanmaktadır. Kullanıcıların ücretsiz olarak kullanmaya alıştığı bu platformlara erişim için ücret ödemek istemeyecekleri varsayımıyla reklam destekli modeller tercih edilmektedir. Bu çalışma içerisinde sosyal medya sitelerinin en yaygın kullanılanı olan Facebook üzerinde gösterilen reklamlara kullanıcıların tutumları ölçümlenmiş ve bu tutum ve tutumu oluşturan faktörlerin Facebook reklamlarını görmemek için para ödemeye etkisi test edilmiştir. Ödeme eğiliminde olan ve olmayan kullanıcılar arasında farklılar irdelenmiştir. Literatürde kabul görmüş ölçekler yardımıyla hazırlanan anket formu İnternet üzerinden Facebook kullanıcılarına dağıtılmış, elde edilen 327 soru formu bir ön-analiz sürecinden geçirilerek düşük kaliteli ve tam doldurulmamış anketler ayıklanarak 281 kullanılabilir soru formuna ulaşılmıştır. Verilerin analizinde yapısal eşitlik modellemesi kullanılmıştır. Gerçekleştirilen analiz sonuçlarına göre Facebook reklamlarına olan tutum ve tutumun öncülleri reklamları görmemek için ödeme niyetine olumsuz yönde etki etmektedir.
In today’s business world, social media tools (i.e. facebook, twitter, Linkedin, etc.) begin to play an important role in reaching and communicating with customers for businesses. However, the importance of using social media can’t be still understood by SMEs so far. Businesses use different social media tools in various purposes in their life. In this research we aim to investigate the ideas of SMEs about social media and their aims of using social media tools. Data were collected from SMEs operating in Burdur by simple random sampling. The data obtained from the 84 questionnaires was entered to computer and statistical analysis was carried out by using SPSS 20 statistical program. In this study, reliability analysis was used firstly. Then, some descriptive analyses were carried out about using social media for SMEs. Finally, a frequency table regarding the social media usage in SMEs was given. As a result, it was seen that 82.1% of SMEs participating in this study have a social network page. Secondly, SMEs are using facebook social media network mostly. In addition, SMEs have expressed that they attend all statements involved in the use of social media scale.
In recent years, the use of mobile devices in the marketing world is increasing parallel with technological advances. The main problem of this research is to determine agencies’ use of what type of mobile tools for what type of purposes in the marketing process. The aim of this research is to identify Mobile Marketing (MM) applications used by group A travel agencies, and to describe the attitudes towards MM applications of agencies. According to related law, it is only group A agencies give all agency services. Therefore, it is thought that MM is more widely used by those agencies. Thus, this research was made only on the group A agencies. The population of study was consisted of 675 groups A agencies’ representatives deployed in the European and Asian sides of Istanbul. A questionnaire was used as data collection tool. Questionnaire form consisted of two parts. In the first part, questions concerning MM and in the second part, questions regarding demographic issues to managers and travel agents took place. The results were given as descriptive statistics (frequencies, percentages, mean and standard deviation) on the tables. T-test and One Way ANOVA analysis were performed for the differences of mean among groups. In the study it was concluded that agencies use mobile phones mostly and mobile computers for increasing sales firstly and direct marketing. Thus, the main hypothesis established was confirmed partly.
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 |