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: 21 Oct 2026 07:23 UTC — 22 Oct 2026 01:59 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 |
|---|---|---|---|---|---|
| 2026 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2026 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2026 | All selected publications | 1 | 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): 2026-10-21T07:23:05.172460+00:00 — 2026-10-22T01:59:18.546293+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 | 2 | 2/2 |
| 2018 | All selected publications | 0 | 2 | 2/2 |
| 2019 | All selected publications | 0 | 2 | 2/2 |
| 2020 | All selected publications | 0 | 2 | 2/2 |
| 2021 | All selected publications | 0 | 2 | 2/2 |
| 2022 | All selected publications | 0 | 2 | 2/2 |
| 2023 | All selected publications | 0 | 2 | 2/2 |
| 2024 | All selected publications | 0 | 2 | 2/2 |
| 2025 | All selected publications | 0 | 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 |
|---|---|
| 2026 | 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 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 | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
This study examines how Artificial Intelligence (AI) adoption in digital marketing enhances the competitiveness of small retail businesses (SRBs) in Cape Town, South Africa. Guided by an integrated Technology–Organisation–Environment (TOE) and Unified Theory of Acceptance and Use of Technology (UTAUT) framework, a systematic review following PRISMA 2020 guidelines was conducted across eight databases, resulting in the inclusion of 37 empirical and conceptual studies published between 2015 and 2025. Findings show that AI adoption remains modest, with only 39% of small businesses planning to develop internal AI capabilities by 2025. The most common entry points are ChatGPT, text-to-image generators, predictive analytics systems, and WhatsApp-based chatbots. Businesses using these tools reported an average 11% increase in sales and a 28% reduction in marketing costs within six months. However, adoption is constrained by infrastructure challenges (patchy broadband, power cuts, high cloud costs), organisational limitations (low digital literacy, managerial scepticism, fear of automation), and socio-cultural barriers (language diversity and customer mistrust of chatbots). This study provides the first Cape Town-focused synthesis linking AI adoption to SME competitiveness through the TOE–UTAUT framework. It recommends policy measures to improve digital infrastructure and data governance, practical initiatives to strengthen AI literacy and ethical awareness, and theoretical extensions to contextualise socio-cultural dimensions of AI use. Collectively, these insights contribute to advancing inclusive, trust-based digital transformation in Cape Town’s small business ecosystem.
This paper aims to evaluate how the type of streamer (human vs. AI) influences consumer desire in live-streaming commerce through socio-cognitive and motivational mechanisms. Building on the CASA framework, Self-Determination Theory (SDT), and the Model of Goal-Directed Behavior (MGB), this study examines whether human and AI streamers differentially generate perceived empathy, which then activates intrinsic and extrinsic motivations leading to consumer desire. This paper also aims to test whether personal involvement and skepticism moderate the empathy–motivation pathway. A between-subjects experimental design was used to randomly assign participants to view identical livestreaming scripts presented by either a human or a humanoid AI streamer. Data from all 277 Chinese livestreaming participants were analyzed using PLS-SEM. The key findings show that (i) perceived empathy is significantly higher for human streamers than AI streamers, (ii) perceived empathy significantly boosts intrinsic motivation and consumer desire, with a smaller effect on extrinsic motivation, and (iii) intrinsic motivation, but not extrinsic motivation, is a significant predictor of consumer desire. Additionally, personal involvement strengthens the link between empathy and intrinsic motivation, while skepticism weakens the motivational pathways—especially in AI-based interactions. This research reveals that empathy is the main psychological link between streamer identity and consumer desire, and that intrinsic motivation is the dominant mechanism driving consumer engagement in live commerce, grounded in warmth and relatedness. The findings also indicate that emotional authenticity and the fulfillment of psychological needs remain crucial determinants of consumer desire, even in AI-mediated retail. Practical implications include enhancing empathetic communication and balancing the use of human and AI streamers in digital commerce.
This paper aims to evaluate how the type of streamer (human vs. AI) influences consumer desire in live-streaming commerce through socio-cognitive and motivational mechanisms. Building on the CASA framework, Self-Determination Theory (SDT), and the Model of Goal-Directed Behavior (MGB), this study examines whether human and AI streamers differentially generate perceived empathy, which then activates intrinsic and extrinsic motivations leading to consumer desire. This paper also aims to test whether personal involvement and skepticism moderate the empathy–motivation pathway. A between-subjects experimental design was used to randomly assign participants to view identical livestreaming scripts presented by either a human or a humanoid AI streamer. Data from all 277 Chinese livestreaming participants were analyzed using PLS-SEM. The key findings show that (i) perceived empathy is significantly higher for human streamers than AI streamers, (ii) perceived empathy significantly boosts intrinsic motivation and consumer desire, with a smaller effect on extrinsic motivation, and (iii) intrinsic motivation, but not extrinsic motivation, is a significant predictor of consumer desire. Additionally, personal involvement strengthens the link between empathy and intrinsic motivation, while skepticism weakens the motivational pathways—especially in AI-based interactions. This research reveals that empathy is the main psychological link between streamer identity and consumer desire, and that intrinsic motivation is the dominant mechanism driving consumer engagement in live commerce, grounded in warmth and relatedness. The findings also indicate that emotional authenticity and the fulfillment of psychological needs remain crucial determinants of consumer desire, even in AI-mediated retail. Practical implications include enhancing empathetic communication and balancing the use of human and AI streamers in digital commerce.
This study examines how Artificial Intelligence (AI) adoption in digital marketing enhances the competitiveness of small retail businesses (SRBs) in Cape Town, South Africa. Guided by an integrated Technology–Organisation–Environment (TOE) and Unified Theory of Acceptance and Use of Technology (UTAUT) framework, a systematic review following PRISMA 2020 guidelines was conducted across eight databases, resulting in the inclusion of 37 empirical and conceptual studies published between 2015 and 2025. Findings show that AI adoption remains modest, with only 39% of small businesses planning to develop internal AI capabilities by 2025. The most common entry points are ChatGPT, text-to-image generators, predictive analytics systems, and WhatsApp-based chatbots. Businesses using these tools reported an average 11% increase in sales and a 28% reduction in marketing costs within six months. However, adoption is constrained by infrastructure challenges (patchy broadband, power cuts, high cloud costs), organisational limitations (low digital literacy, managerial scepticism, fear of automation), and socio-cultural barriers (language diversity and customer mistrust of chatbots). This study provides the first Cape Town-focused synthesis linking AI adoption to SME competitiveness through the TOE–UTAUT framework. It recommends policy measures to improve digital infrastructure and data governance, practical initiatives to strengthen AI literacy and ethical awareness, and theoretical extensions to contextualise socio-cultural dimensions of AI use. Collectively, these insights contribute to advancing inclusive, trust-based digital transformation in Cape Town’s small business ecosystem.
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 |