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 02:02 UTC — 23 Oct 2026 07:25 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 | 2 | 2 | 2/2 | 2/2 |
| 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 | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 7 | 2 | 2/2 | 2/2 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 10 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2014 | All selected publications | 23 | 2 | 2/2 | 2/2 |
| 2015 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2016 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2018 | All selected publications | 31 | 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 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): 2026-10-22T02:02:23.112414+00:00 — 2026-10-23T07:25:33.655880+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 | 2 | 5 | 5/5 |
| 2018 | All selected publications | 4 | 5 | 5/5 |
| 2019 | All selected publications | 2 | 5 | 5/5 |
| 2020 | All selected publications | 7 | 5 | 5/5 |
| 2021 | All selected publications | 16 | 5 | 5/5 |
| 2022 | All selected publications | 8 | 5 | 5/5 |
| 2023 | All selected publications | 4 | 5 | 5/5 |
| 2024 | All selected publications | 8 | 5 | 5/5 |
| 2025 | All selected publications | 3 | 5 | 5/5 |
| 2026 | All selected publications | 2 | 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 | 2 |
| 2016 | 1 |
| 2018 | 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 | 5 | 19 | 58 | 5/5 | 3.8 | 3 | 100.0 | 5/5 · 100.0% |
* Percentage among publications with a known citation count.
In recent years, with consumers’ widespread preference for shopping in Private Shopping Clubs (PSCs) on the internet, there has been a remarkable increase in impulse purchases with the attractive opportunities and smart strategies of PSCs stimulating consumers’ impulse buying behavior. Within the PSC framework, the purpose of this article is to investigate the moderator effect of brand awareness and brand loyalty on the relationship between online impulse buying behavior and perceived low price, browsing behavior and time pressure. The study created and tested five hypotheses using data collected in Turkey. Results indicate that browsing behavior, time pressure and perceived low price do influence online impulse buying behavior. A hierarchical regression analysis was also used to analyze the moderating role of brand awareness and brand loyalty on impulse buying behavior and both variables were found to have a moderating role. The results provide substantial information on strategy development for internet retailers.
Definition of store image is argued to be useful if it predicts phenomena such as satisfaction, loyalty, customer retention and other attitudinal and behavioral outcomes. Previous researches show that customers are affected by the store attributes and consider them to assess perceived benefits and attitudes. One of the biggest challenges for companies is to build a positive image. Knowing the alternatives which could be used to form a positive image is an important issue. A positive image which is created in the customer’s mind about store is seen to have a strong and positive influence on satisfaction. A satisfied customer is likely to be loyal to the store. Therefore, image and customer satisfaction gain importance in the stores which aim to survive. This study focuses on store image, customer satisfaction and the relationship between them. The study primarily explains these two concepts, and then provides a literature review on the researches related with them. The study presents researches in which store image and customer satisfaction are separately discussed, thus it also shows other variables which can be influential on these concepts. Implications for both store image theory and practices are discussed. The literature review reveals that store image plays an important role in customer satisfaction. Since most of the studies in literature are based on retail industry, in this study ‘store’ concept mostly refers to ‘retail store’.
Impulse buying is a common phenomenon of consumption today and its getting great attention from consumer researchers as well as marketers. It is important for companies to have a good understanding of factors triggering impulse buying. The main aim of this study is to identify the determinants influencing Turkish consumers’ impulse buying behavior in supermarkets. 593 surveys were taken into analysis which were collected between the dates 5-10 December, 2017. Regression analysis, t-test and ANOVA test were conducted to test the hypotheses. While some independent variables such as sales promotion and credit card usage found to be the affecting factors of impulse buying; variables such as income level is found to have no relation with impulse purchasing.
The study’s research model suggests that store design and ease of shopping are associated with customer retention through a mediated pathway in which store design and ease of shopping influence perceived store satisfaction, which in turn, influences customer retention. This survey was administered to two separate clothing stores offered to either females or males (in total, 533 participants). Using structural equation modelling methodology, data was analysed to explain the interrelations among the variables in the model. The results of an empirical study of a sample of store shoppers revealed that store design and ease of shopping influence customer retention in an indirect way through customer perception of satisfaction with the store. In building store satisfaction that is centred on customer retention, store design and shopping ease differ in their relative influences. This difference is high for females, but for men as low as to be considered negligible in males. As a result, improving customers’ perceptions of store design and ease of shopping is a way to ensure store satisfaction support customer retention. The results of the study provide a new insight into the relationships by suggesting indirect effects of shopping ease and store design on consumer retention by their impacts on store satisfaction, rather than direct effects.
Power has long been shifting from manufacturers to retailers in marketing channels and there is an intensive competition in the retail sector. Retailers need to follow changing consumer trends and technological developments closer and design their marketing strategies accordingly. Thus studies about retailing has become more important than ever. The purpose of this conceptual study is to examine the reflections of changing consumer trends and technological developments on retailing. Within the study, it is observed that; (i) in retailing, understanding how consumers behave as shoppers in different channels is an important key to success, (ii) retailing should be adapted to changing consumer trends, (iii) technologies used in retailing are grouped under three categories which are smart technologies, biometric technologies and technologies developed for different dimensions of reality, these technologies provide substantial benefits for retailers and consumers, but high switching and implementation costs, (iv) consumers’ approach towards these technologies and consumers’ worries about privacy of personal information should also be considered while evaluating these technologies. In the conclusion part of the study, some recommendations are made for retailers. This innovative study is expected to contribute to retailers and the retailing literature.
Impulse buying is a common phenomenon of consumption today and its getting great attention from consumer researchers as well as marketers. It is important for companies to have a good understanding of factors triggering impulse buying. The main aim of this study is to identify the determinants influencing Turkish consumers’ impulse buying behavior in supermarkets. 593 surveys were taken into analysis which were collected between the dates 5-10 December, 2017. Regression analysis, t-test and ANOVA test were conducted to test the hypotheses. While some independent variables such as sales promotion and credit card usage found to be the affecting factors of impulse buying; variables such as income level is found to have no relation with impulse purchasing.
In recent years, with consumers’ widespread preference for shopping in Private Shopping Clubs (PSCs) on the internet, there has been a remarkable increase in impulse purchases with the attractive opportunities and smart strategies of PSCs stimulating consumers’ impulse buying behavior. Within the PSC framework, the purpose of this article is to investigate the moderator effect of brand awareness and brand loyalty on the relationship between online impulse buying behavior and perceived low price, browsing behavior and time pressure. The study created and tested five hypotheses using data collected in Turkey. Results indicate that browsing behavior, time pressure and perceived low price do influence online impulse buying behavior. A hierarchical regression analysis was also used to analyze the moderating role of brand awareness and brand loyalty on impulse buying behavior and both variables were found to have a moderating role. The results provide substantial information on strategy development for internet retailers.
Power has long been shifting from manufacturers to retailers in marketing channels and there is an intensive competition in the retail sector. Retailers need to follow changing consumer trends and technological developments closer and design their marketing strategies accordingly. Thus studies about retailing has become more important than ever. The purpose of this conceptual study is to examine the reflections of changing consumer trends and technological developments on retailing. Within the study, it is observed that; (i) in retailing, understanding how consumers behave as shoppers in different channels is an important key to success, (ii) retailing should be adapted to changing consumer trends, (iii) technologies used in retailing are grouped under three categories which are smart technologies, biometric technologies and technologies developed for different dimensions of reality, these technologies provide substantial benefits for retailers and consumers, but high switching and implementation costs, (iv) consumers’ approach towards these technologies and consumers’ worries about privacy of personal information should also be considered while evaluating these technologies. In the conclusion part of the study, some recommendations are made for retailers. This innovative study is expected to contribute to retailers and the retailing literature.
Definition of store image is argued to be useful if it predicts phenomena such as satisfaction, loyalty, customer retention and other attitudinal and behavioral outcomes. Previous researches show that customers are affected by the store attributes and consider them to assess perceived benefits and attitudes. One of the biggest challenges for companies is to build a positive image. Knowing the alternatives which could be used to form a positive image is an important issue. A positive image which is created in the customer’s mind about store is seen to have a strong and positive influence on satisfaction. A satisfied customer is likely to be loyal to the store. Therefore, image and customer satisfaction gain importance in the stores which aim to survive. This study focuses on store image, customer satisfaction and the relationship between them. The study primarily explains these two concepts, and then provides a literature review on the researches related with them. The study presents researches in which store image and customer satisfaction are separately discussed, thus it also shows other variables which can be influential on these concepts. Implications for both store image theory and practices are discussed. The literature review reveals that store image plays an important role in customer satisfaction. Since most of the studies in literature are based on retail industry, in this study ‘store’ concept mostly refers to ‘retail store’.
The study’s research model suggests that store design and ease of shopping are associated with customer retention through a mediated pathway in which store design and ease of shopping influence perceived store satisfaction, which in turn, influences customer retention. This survey was administered to two separate clothing stores offered to either females or males (in total, 533 participants). Using structural equation modelling methodology, data was analysed to explain the interrelations among the variables in the model. The results of an empirical study of a sample of store shoppers revealed that store design and ease of shopping influence customer retention in an indirect way through customer perception of satisfaction with the store. In building store satisfaction that is centred on customer retention, store design and shopping ease differ in their relative influences. This difference is high for females, but for men as low as to be considered negligible in males. As a result, improving customers’ perceptions of store design and ease of shopping is a way to ensure store satisfaction support customer retention. The results of the study provide a new insight into the relationships by suggesting indirect effects of shopping ease and store design on consumer retention by their impacts on store satisfaction, rather than direct effects.
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 |