Meta-analysis of publications on the impact of digital marketing strategies on small and medium enterprise performance
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Received January 2, 2025;Accepted November 7, 2025;Published November 19, 2025
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Author(s)Kosovare UkshiniLink to ORCID Index: https://orcid.org/0000-0003-2759-9466
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Roberta BajramiLink to ORCID Index: https://orcid.org/0000-0001-6540-7627
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Arta Hoti ArifajLink to ORCID Index: https://orcid.org/0000-0001-7076-8072
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DOIhttp://dx.doi.org/10.21511/im.21(4).2025.10
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Article InfoVolume 21 2025, Issue #4, pp. 130-146
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Creative Commons Attribution 4.0 International License
Type of the article: Research Article
Abstract
This study synthesises findings from 56 research articles published between 2010 and 2024 to examine how digital marketing affects the performance of small and medium-sized enterprises (SMEs). Using a random-effects model estimated with restricted maximum likelihood (REML), the analysis shows a clear and moderately strong positive relationship (r = 0.3715, 95% CI [0.3443, 0.3884], p < 0.0001). The lack of variability across studies is remarkable, with an I² value of only 0.09%. This suggests a strong consistency in the observed relationship, even though the studies used different contexts and research methods.
Mixed-effects moderation analyses show that the impact of digital marketing varies depending on industry structure. Specifically, service-sector companies see greater performance improvements than small and medium-sized manufacturing businesses. In contrast, the geographic setting, the research design, and the type of performance metric used do not significantly change the observed relationship. To assess the possibility of publication bias, the study used Egger’s regression test and funnel plot analysis. Neither method showed any signs of systematic bias.
The combined results suggest that digital marketing is a strong and broadly effective way to improve the performance of small and medium-sized enterprises (SMEs), especially in service-based industries where digital interactions with customers are crucial. However, the lack of diversity requires further investigation, as it could indicate hidden similarities in how the studies were designed or how they measured things. Future research would benefit from more detailed studies over time, as well as a closer look at how different sectors or organizations might affect the results of digital marketing.
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JEL Classification (Paper profile tab)M31, L25, O33
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References56
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Tables12
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Figures3
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- Figure 1. Forest plot of effect sizes (Individual r with 95% CI)
- Figure 2a. Funnel plot (z vs SE) for publication bias
- Figure 2b. Funnel plot (r vs SE_z) for publication bias
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- Table 1. Studies by industry
- Table 2. Studies by geography
- Table 3. Studies by design
- Table 4. Studies by performance metric
- Table 5. Random-effects meta-analysis results (k = 56; τ² estimator: REML)
- Table 6. Industry moderator
- Table 7. Geographic moderator
- Table 8. Study design moderator
- Table 9. Performance metric moderator (QM = 3.2761, p = 0.3516)
- Table 10. Regression test for funnel-plot asymmetry
- Table A1. Summary of articles
- Table A2. Dataset for meta-analysis
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Conceptualization
Kosovare Ukshini, Roberta Bajrami
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Data curation
Kosovare Ukshini
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Project administration
Kosovare Ukshini
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Writing – original draft
Kosovare Ukshini, Roberta Bajrami
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Investigation
Roberta Bajrami
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Methodology
Roberta Bajrami
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Supervision
Roberta Bajrami
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Writing – review & editing
Roberta Bajrami, Arta Hoti Arifaj
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Formal Analysis
Arta Hoti Arifaj
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Validation
Arta Hoti Arifaj
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Visualization
Arta Hoti Arifaj
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Conceptualization
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