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, 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 07:22 UTC — 22 Oct 2026 01:41 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 |
|---|---|---|---|---|---|
| 2023 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 6 | 3 | 3/3 | 3/3 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 13 | 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 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): 2026-10-21T07:22:49.812430+00:00 — 2026-10-22T01:41:21.452674+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 | 0 | 3 | 3/3 |
| 2019 | All selected publications | 0 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 0 | 3 | 3/3 |
| 2022 | All selected publications | 0 | 3 | 3/3 |
| 2023 | All selected publications | 2 | 3 | 3/3 |
| 2024 | All selected publications | 4 | 3 | 3/3 |
| 2025 | All selected publications | 4 | 3 | 3/3 |
| 2026 | All selected publications | 3 | 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 |
|---|---|
| 2023 | 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 | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 2 | 6 | 13 | 2/2 | 3.0 | 3.0 | 100.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
The aim of this paper is to find out whether there is a relationship between the intensity of using Tiktok, utilitarian value, hedonic value, and impulse buying. TikTok is a social commerce with a live shopping service that provides convenience in online shopping and allows direct interaction with sellers while browsing social media without switching applications. This study uses the stimulus-organism-response (SOR) theory, dividing the stimulus into internal and external factors. Internal factors include utilitarian value and hedonic value, while external factors are the intensity of use of the TikTok application. Respondents in this study are married women who use Tiktok. This research is causality research with a total sample of 120 respondents. The data collection method uses a questionnaire and is analyzed using SEM-PLS. The findings of this study indicate that the intensity of TikTok use significantly affects impulse purchases. However, utilitarian and hedonic values have no significant effect on impulse purchases. For the mediating effect, the impulse to buy impulsively mediates the relationship between the intensity of TikTok use and the hedonic value of impulse buying. However, it does not mediate the utilitarian value of impulse buying.
Psychological components of Online Customer's Shopping Experience (OCSE) on attitude loyalty and online impulsive buying in e-commerce. The research approach used is quantitative. The population was all Indonesian people who used and bought e-commerce products. The sample was determined using a non-probability sampling method with a convenience sampling technique. The number of samples is 470 respondents. Research data were analyzed using AMOS 24 software with Structural Equation Model (SEM) modeling. The results of SEM analysis showed that informativeness has a positive and significant effect on online impulsive buying, convenience has a positive and significant impact on online impulsive buying and attitude loyalty, and attitude loyalty has a positive and significant impact on online impulsive buying.
The e-commerce industry significantly expanded during a negative economic recession. This is because, during the COVID-19 epidemic, many countries enacted social limitations that caused various economic activities to diminish in many sectors and resulted in layoffs in various businesses because there was no production activity. Through consumer attributes such as impulsive buying tendencies, shopping enjoyment preferences, moods, and personal situations, this study aims to understand the factors contributing to impulsive buying. The SEM statistical method and PLS software were used to test the research model empirically. Sampling with a purpose was chosen. Two hundred legitimate respondents were used as samples to test the assumptions. First, the study found that impulsive buying patterns, moods, and situations all positively influence impulsive purchases. Second, other elements, such spending patterns, are not significant. This demonstrates that customers can enjoy looking up information about the desired goods, but it hasn't yet led customers to make impulsive purchases at online shops.
Psychological components of Online Customer's Shopping Experience (OCSE) on attitude loyalty and online impulsive buying in e-commerce. The research approach used is quantitative. The population was all Indonesian people who used and bought e-commerce products. The sample was determined using a non-probability sampling method with a convenience sampling technique. The number of samples is 470 respondents. Research data were analyzed using AMOS 24 software with Structural Equation Model (SEM) modeling. The results of SEM analysis showed that informativeness has a positive and significant effect on online impulsive buying, convenience has a positive and significant impact on online impulsive buying and attitude loyalty, and attitude loyalty has a positive and significant impact on online impulsive buying.
The aim of this paper is to find out whether there is a relationship between the intensity of using Tiktok, utilitarian value, hedonic value, and impulse buying. TikTok is a social commerce with a live shopping service that provides convenience in online shopping and allows direct interaction with sellers while browsing social media without switching applications. This study uses the stimulus-organism-response (SOR) theory, dividing the stimulus into internal and external factors. Internal factors include utilitarian value and hedonic value, while external factors are the intensity of use of the TikTok application. Respondents in this study are married women who use Tiktok. This research is causality research with a total sample of 120 respondents. The data collection method uses a questionnaire and is analyzed using SEM-PLS. The findings of this study indicate that the intensity of TikTok use significantly affects impulse purchases. However, utilitarian and hedonic values have no significant effect on impulse purchases. For the mediating effect, the impulse to buy impulsively mediates the relationship between the intensity of TikTok use and the hedonic value of impulse buying. However, it does not mediate the utilitarian value of impulse buying.
The e-commerce industry significantly expanded during a negative economic recession. This is because, during the COVID-19 epidemic, many countries enacted social limitations that caused various economic activities to diminish in many sectors and resulted in layoffs in various businesses because there was no production activity. Through consumer attributes such as impulsive buying tendencies, shopping enjoyment preferences, moods, and personal situations, this study aims to understand the factors contributing to impulsive buying. The SEM statistical method and PLS software were used to test the research model empirically. Sampling with a purpose was chosen. Two hundred legitimate respondents were used as samples to test the assumptions. First, the study found that impulsive buying patterns, moods, and situations all positively influence impulsive purchases. Second, other elements, such spending patterns, are not significant. This demonstrates that customers can enjoy looking up information about the desired goods, but it hasn't yet led customers to make impulsive purchases at online shops.
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 |