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: 11 / 11 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 — 23 Oct 2026 07:29 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.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 selected publications.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 selected publications.
11 / 11 with data · 0 missing or unmatched. Percentages use all 11 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 |
|---|---|---|---|---|---|
| 2021 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2026 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 7 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2026 | All selected publications | 0 | 4 | 4/4 | 4/4 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 9 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2026 | All selected publications | 0 | 4 | 4/4 | 4/4 |
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 11 / 11 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): 2026-10-21T07:22:59.241773+00:00 — 2026-10-23T07:29:42.571907+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 | 11 | 11/11 |
| 2018 | All selected publications | 0 | 11 | 11/11 |
| 2019 | All selected publications | 0 | 11 | 11/11 |
| 2020 | All selected publications | 0 | 11 | 11/11 |
| 2021 | All selected publications | 1 | 11 | 11/11 |
| 2022 | All selected publications | 2 | 11 | 11/11 |
| 2023 | All selected publications | 2 | 11 | 11/11 |
| 2024 | All selected publications | 1 | 11 | 11/11 |
| 2025 | All selected publications | 1 | 11 | 11/11 |
| 2026 | All selected publications | 5 | 11 | 11/11 |
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 |
|---|---|
| 2021 | 2 |
| 2024 | 2 |
| 2025 | 3 |
| 2026 | 4 |
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 | 6 | 3 | 3 | 6/6 | 0.5 | 0.0 | 33.3 | 6/6 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 5 | 7 | 9 | 5/5 | 1.4 | 0 | 40.0 | 5/5 · 100.0% |
* Percentage among publications with a known citation count.
This research empirically investigates consumers’ perception of real-time marketing as a marketing tool that is increasingly being used by businesses. Based on the review of literature, the paper aims to delve into real-time marketing and explore the elements of real-time marketing that influence consumers' perception of RTM campaigns. This study adopted a quantitative methodology. A total of 201 consumers completed an online survey in Gauteng, South Africa. To identify underlying factors that influence the consumer's perception of various real-time marketing campaigns, exploratory factor analysis was conducted. Descriptive statistic was used to provide a further summary of the basic characteristics of the sample. The research findings indicate that consumers value real-time marketing communication campaigns that offer value. Consumers' perception of value includes any real-time marketing campaign that offers some financial gain in the form of immediate and personalized discounts. This research provides retailers with a greater understanding of what consumers perceive as valuable and what they regard as clutter when it comes to real-time marketing campaigns
Social media has become a vital tool for entrepreneurs, enabling them to expand their reach, enhance brand visibility, and engage directly with customers. Platforms like Facebook, Instagram, and LinkedIn provide entrepreneurs with cost-effective ways to access global markets, promote their products, and foster meaningful relationships with consumers. While social media has facilitated the growth of entrepreneurship by lowering barriers to entry, challenges such as managing online presence, handling negative feedback, and addressing privacy concerns persist. This study aims to systematically review existing literature to explore how entrepreneurs can strategically leverage social media for growth, examining its economic benefits, challenges, and best practices. Findings from the review reveal that social media offers entrepreneurs substantial advantages, including increased brand awareness, enhanced customer engagement, and improved sales performance through direct sales features and promotional campaigns. Additionally, social media enables networking, resource acquisition, and business model innovation. However, challenges related to platform management, algorithm changes, and privacy risks must be navigated. Based on these findings, the study recommends that entrepreneurs develop a clear social media strategy, actively engage with customers, stay updated with trends, and leverage data analytics to optimise their marketing efforts. Building brand communities and fostering customer loyalty through personalised communication and consistent interaction can further support business growth.
The internet and online space have created numerous platforms for users to communicate and interact, where each platform provides access to different sources of information. The purpose of the study was to investigate the online ideal self and its influence on brand attitude and attachment towards consumer purchase intention. A unique conceptual model was developed to test the proposed hypotheses of the study. Social media is one of the leading communication platforms that have emerged in the online space. Social media allows consumers and brands to interact in an informal manner from anywhere in the world. The study adopted a quantitative approach utilizing the survey method. Data was collected from 300 consumers. The data was then processed in SPSS 24 for descriptive statistics and AMOS 24 for hypotheses testing. A key finding suggests that social media consumers’ attachment is considerably dependent on their attitudes towards those brands. Brands can use social media to receive real-time feedback from customers as well as track this feedback. Implications emerged from the findings and further research direction is proposed.
This study explored the influence of electronic word-of-mouth (e-WOM) on the online purchasing behaviour of Generation Z and Millennial students in South Africa, specifically identifying key e-WOM factors influencing consumer decisions and examining the role of demographic differences in shaping these behaviours. A quantitative, cross-sectional survey was conducted with 288 students and lecturers from Durban University of Technology. Data analysis was performed using SPSS software and regression analysis to examine the causal relationships between e-WOM factors and consumer buying behaviour. Key findings indicate that e-WOM significantly influences consumer buying behaviour, with factors such as e-WOM volume, e-WOM adoption and usage emerging as strong predictors. The influence of e-WOM was consistent across age groups and gender. The consistent influence of e-WOM on both Generation Z and Millennials, regardless of gender, suggests that unified e-WOM strategies can be developed for these generational cohorts, resulting in more efficient and cost-effective marketing campaigns. Based on these findings, digital marketers must focus on increasing the volume of e-WOM, particularly by encouraging customer reviews and enhancing social media engagement and leveraging user-generated content. Businesses can benefit from these approaches by enhancing brand trust, improving consumer decision-making and ultimately driving sales performance. Future studies could incorporate a mixed methods approach to provide a more comprehensive understanding of the phenomenon.
The study seeks to ascertain the factors of electronic word of mouth (e-WOM) influencing consumer buying behaviour. This research aims to contribute to the ongoing debate about the factors of e-WOM that influence consumer buying decisions while making an online purchase. Structural equation modelling (SEM) was used to measure and analyse the relationships between observed and latent variables. The results reveal that e-WOM valence is the most influential e-WOM factor during online purchase decisions. Marketers must pay close attention to negative e-WOMs, monitor unfavourable evaluations and use negative feedback to improve product and service quality.
This study explored the complex decision-making process and the factors used by people, influencers, and brands when selecting social media platforms. The various factors include target demographics, values, content style, and overall marketing strategy. The study aimed to assess the importance of aligning personal preferences, user fit, and creative vision in selecting which social media platforms to use for a brand. To build a stronger level of engagement, content must be designed in a way that creates value for individual consumers. The Uses and Gratification Theory (UGT) provided a framework for understanding the motivations of millennials seeking a specific type of content on social media and how brands select social media platforms. A mixed-methods approach was used for the study to obtain the perspectives of both social media content creators and customers. The target population comprised millennials in the greater Durban area. Purposive sampling was used to select a sample of 400 participants for quantitative data and 10 content creators for qualitative data collection. Data was analysed both qualitatively and quantitatively using thematic analysis and SPSS version 28.0, respectively. The results contribute to a deeper understanding of the dynamic interplay between personal preferences, user fit, and creative vision in shaping the digital landscape. The study findings suggest that influencer marketing can be a powerful tool for promoting brand preference. Brands should carefully select influencers whose followers align with their target audience and ensure they have a strong following and the trust of their followers. The findings emphasise the effectiveness of diverse content types and customer appreciation posts. Businesses should tailor their content strategies to suit their brand identity and connect with their audience effectively. Different social media platforms offer unique features and formats.
The study seeks to ascertain the factors of electronic word of mouth (e-WOM) influencing consumer buying behaviour. This research aims to contribute to the ongoing debate about the factors of e-WOM that influence consumer buying decisions while making an online purchase. Structural equation modelling (SEM) was used to measure and analyse the relationships between observed and latent variables. The results reveal that e-WOM valence is the most influential e-WOM factor during online purchase decisions. Marketers must pay close attention to negative e-WOMs, monitor unfavourable evaluations and use negative feedback to improve product and service quality.
This study explores the impact of digital marketing on consumer decision-making within Durban’s grocery retail sector, with a particular focus on Spar South Beach. Employing a quantitative descriptive survey approach, primary data was gathered through structured questionnaires distributed to 278 shoppers, complemented by secondary data from academic and government sources. Findings indicate that digital marketing significantly influences purchasing behaviors, with 78.6% of respondents acknowledging that Spar’s digital content influenced their decision to shop there. Social media was identified as the primary engagement channel, and digital loyalty rewards were strong predictors of repeat purchase intentions. Reliability analysis showed excellent scale consistency (Cronbach’s ? = 0.941, KMO = 0.943), and results revealed notable differences across age groups, with younger consumers exhibiting greater responsiveness to digital marketing efforts. Gender and educational attainment did not significantly moderate responses. These insights highlight the importance of leveraging social media campaigns, targeted promotions, and digital loyalty programs to enhance customer engagement and drive conversions, while also considering generational preferences. The study provides valuable, context-specific insights into the South African grocery retail industry, emphasizing the role of digital touchpoints in transitioning consumers from engagement to purchase.
The rapid development of digital technologies has significantly transformed the grocery retail industry, allowing retailers to utilize digital and social media platforms to enhance customer engagement, increase brand visibility, and drive sales growth. This study assesses the effectiveness of SPAR’s digital and social media marketing strategies in Durban, South Africa. Guided by the Technology Acceptance Model (TAM), the research employed a quantitative methodology, analyzing survey data collected from 278 customers at the South Beach Super Spar. Reliability analysis indicated a high level of internal consistency (Cronbach’s Alpha = 0.883). Non-parametric statistical tests, including the Kruskal-Wallis H and Mann–Whitney U tests, were used to evaluate customer perceptions of various digital channels. The results indicate that social media is the most effective platform for SPAR, with 58.4% of respondents identifying it as their primary mode of engagement. Participants overwhelmingly agreed that SPAR's social media channels effectively promote offers; however, perceptions of the mobile app and WhatsApp marketing strategies were mixed and showed significant variation among user groups. These findings underscore the importance of social media in increasing brand awareness and customer engagement, while also highlighting potential areas for enhancing the usability and impact of SPAR’s mobile application and WhatsApp marketing efforts. This study offers valuable guidance for grocery retailers aiming to strengthen customer engagement in competitive urban environments such as Durban by providing insights into digital marketing performance in emerging markets.
Instagram has emerged not only as a social networking platform but also as a vital digital marketing tool for fashion brands. However, missed marketing opportunities persist due to factors such as limited computer literacy, unfamiliarity with digital tools, and resistance to new media. The aim of the study is to explore the opportunities for leveraging Instagram as an effective marketing tool within the fashion industry in Durban. Specifically, it seeks to identify how fashion businesses can utilise Instagram to enhance brand visibility, engage with their target audience, and maximize promotional efforts to drive success in the digital marketplace. This quantitative study employed a purposive sampling method and focused on descriptive analysis through a structured online questionnaire distributed to 130 fashion businesses in Durban. The findings underscore Instagram's efficacy in enabling fashion businesses to effectively showcase products, engage visually with their target audience, and amplify brand awareness. The platform's visual-centric nature inherently supports robust brand marketing and promotion, particularly in the fashion sector. Recommendations highlight the imperative for fashion brands to strategically leverage Instagram to enhance visibility and augment brand perception. Success on Instagram necessitates a well-coordinated marketing approach to ensure impactful brand promotion and engagement with followers.
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 |