Leveraging AI-powered social media platforms to enhance customer engagement and drive sales growth in Uganda’s SMEs

In the digital age, Artificial Intelligence (AI) has transformed social media platforms into powerful marketing tools, particularly for small and medium-sized enterprises (SMEs) seeking to improve customer engagement and drive sales. This study investigates the role of AI-powered features such as chatbots, content recommendation algorithms, targeted advertising, and predictive analytics on platforms like Facebook, WhatsApp, TikTok, and Instagram in enhancing brand visibility and influencing sales performance among SMEs in Uganda. In an extended Technology Acceptance Model (TAM), the study employed a mixed-methods approach, collecting survey data from 155 SMEs operators across Kampala, Gulu, Mbale, and Mbarara. Using multiple regression analysis, the findings reveal that attitude towards AI tools and customer engagement are the strongest predictors of sales growth, followed by perceived usefulness and ease of use. The study highlights the growing relevance of AI in the Ugandan SME ecosystem and underscores the need for digital skills development, localized AI tools, and inclusive policy support to maximize AI’s impact on business growth. This study concludes with practical recommendations and proposes future research directions to bridge digital disparities and expand understanding of AI’s transformative potential in emerging markets.

1 Introduction

In the era of digital transformation, social media has emerged as a critical marketing tool for businesses of all sizes, especially small and medium-sized enterprises (SMEs). In Uganda, where traditional advertising is often costly and less effective for reaching younger, tech-savvy consumers, social media platforms such as Facebook, WhatsApp, Instagram, and TikTok have revolutionized the marketing landscape (Ransbotham, Gerbert, and Reeves 2021). These platforms allow businesses to connect directly with customers, build relationships, and increase visibility in ways previously unimaginable (Kapoor and Nerur 2022). SMEs, which form over 90% of Uganda’s private sector and are vital for employment and economic growth, increasingly rely on digital marketing to overcome resource constraints and expand market reach (Uganda Investment Authority 2023).
A recent shift has seen the integration of Artificial Intelligence (AI) into these social media platforms, further amplifying their impact. AI-driven tools such as content recommendation, algorithms, predictive analytics, automated chatbots, and intelligent ad targeting allow for a deeper level of engagement with customers. According to Chatterjee et al. (2021), these AI features can improve marketing efficiency by personalizing content delivery and responding to customer behaviour in real time. Through automating complex tasks and offering data-driven insights, AI empowers SMEs to make smarter decisions, optimize their marketing strategies, and reduce operational costs. The influence of AI-powered social media is especially significant for SMEs operating in developing economies like Uganda, where marketing budgets are limited, and customer acquisition can be challenging. AI-enhanced personalization allows businesses to deliver the right message to the right audience at the right time, which is critical for conversion and retention (Kumar et al. 2022). For instance, AI algorithms on Facebook and Instagram can analyse a user’s behaviour and show them ads that match their interests, increasing the likelihood of purchase.
Similarly, WhatsApp Business APIs can automate customer service conversations, improving responsiveness and customer satisfaction (Murphy et al. 2021). Research by Mehta et al. (2022) highlights that AI chatbots improve engagement quality and can mimic human interaction to a level where customers are often unaware, they’re interacting with a machine. For Ugandan SMEs, where staffing is lean, such tools present a cost-effective alternative to support and allow small businesses to compete with larger firms. Despite the potential, the adoption and integration of AI-powered social media tools among Ugandan SMEs remain uneven. Barriers such as digital illiteracy, infrastructure limitations, and cost-related concerns persist (UNCTAD 2022). Moreover, many SMEs still lack awareness of how to leverage AI tools effectively. This study contributes to a growing body of knowledge aimed at supporting digital inclusion and economic growth in the region (Tuten and Solomon 2021). By analysing the use of key AI functionalities like targeted advertising, chatbots, content recommendation systems, and predictive analytics across popular platforms like Facebook, WhatsApp, TikTok, and Instagram, this study seeks to provide practical insights for entrepreneurs, marketers, and policymakers. The findings are expected to inform strategies that can foster wider adoption of AI-powered digital marketing tools, ultimately contributing to the competitiveness and sustainability of SMEs in Uganda.
The goal of this article is to analyse the role of Artificial Intelligence (AI)-powered social media marketing tools in enhancing sales performance among small and medium-sized enterprises (SMEs) in Uganda. The study focuses on how AI-driven mechanisms such as chatbots, predictive analytics, targeted advertising, and content recommendation algorithms affect customer engagement and ultimately improve SME sales outcomes.

2 Literature review

2.1 Artificial intelligence in social media marketing

Artificial Intelligence (AI) has emerged as a transformative force in marketing, particularly in social media, where it facilitates hyper-personalized, data-driven interactions between businesses and consumers. Social media platforms like Facebook, Instagram, TikTok, and WhatsApp integrate AI technologies such as machine learning, natural language processing, and image recognition to automate content delivery, understand user intent, and optimize marketing outcomes (Kapoor et al. 2022; Kietzmann et al. 2018). These platforms leverage AI to analyse vast amounts of user-generated data in real-time, enabling businesses to engage customers more effectively and respond to trends instantly. For example, Facebook’s AI engine utilizes behavioural tracking to recommend relevant ads and content to users, enhancing both visibility and engagement (Meta Business 2022). TikTok’s For You Page algorithm uses deep learning to push highly tailored video content, significantly increasing the potential for virality and brand exposure, even for small and medium-sized enterprises (SMEs) with limited budgets (Montag et al. 2021). In Uganda, where mobile penetration is rising and social platforms are increasingly used for business, these AI-driven tools offer affordable and innovative marketing solutions, helping SMEs compete with larger players (Ndagire and Bwire 2023).
Kocisova and Starchon (2023), social media metrics such as reach, impressions, engagement rate, and sentiment analysis serve as key indicators for assessing marketing performance and optimizing future campaigns. They argue that integrating quantitative metrics with qualitative insights enhances strategic decision-making and helps marketers justify the return on investment (ROI) from digital campaigns. It therefore underscores the need for a structured measurement framework that connects marketing activities with tangible business outcomes such as customer retention and sales growth. Building on this foundation, the present study explores how artificial intelligence-powered tools can further strengthen social media measurement by offering predictive analytics and automation features that refine performance tracking among SMEs in emerging markets.

2.2 Customer engagement and personalization through AI

Customer engagement refers to the quality and frequency of interactions between a brand and its consumers, often determining the level of customer loyalty and eventual sales outcomes. AI-powered social media tools significantly enhance engagement by enabling real-time, personalized communication. Chatbots, for instance, have become a vital customer service tool on WhatsApp Business and Facebook Messenger, providing instant responses to common queries, resolving issues promptly, and even guiding users through the sales funnel (Davenport et al. 2020; Chatterjee et al. 2021). These AI assistants operate 24/7, increasing responsiveness and reducing customer service costs for SMEs. Moreover, recommendation algorithms personalize the user experience by suggesting content and products that match individual preferences, which is proven to boost click-through rates and conversion (Tuten and Solomon 2021). Studies by Osei-Frimpong et al. (2020) indicate that personalized customer experiences driven by AI contribute significantly to emotional engagement and purchasing decisions, particularly in Sub-Saharan Africa, where digital trust is growing. Additionally, predictive analytics help businesses anticipate consumer behaviour and tailor messages, promotions, and product recommendations, accordingly, making marketing more proactive rather than reactive (Chen et al. 2021).

2.3 AI tools driving sales performance

The integration of AI into social media marketing also contributes directly to enhanced sales performance, especially for SMEs. AI tools such as targeted advertising algorithms, predictive customer scoring, and sentiment analysis enable businesses to focus their marketing budgets more effectively and reach high-intent buyers. On platforms like Facebook and Instagram, SMEs can use AI-based ad managers to segment audiences by demographics, interests, behaviour, and even purchasing intent, resulting in highly efficient campaigns (Dwivedi et al. 2021). According to a report by McKinsey & Company (2022), AI-enabled marketing can increase sales productivity by up to 20% through better lead targeting and message timing. In Uganda, many SMEs use AI-driven insights to make data-informed decisions regarding which products to promote, when to launch campaigns, and which audience segments to prioritize (Nabukeera and Namubiru 2022).
Artificial intelligence (AI) tools such as chatbots, recommendation algorithms, and predictive analytics have transformed how businesses engage with consumers on digital platforms. These technologies enhance customer experiences by providing real-time interaction, personalized content, and seamless service delivery. Belhamri and Belboula (2024) highlight that the perceived anthropomorphism of conversational agents significantly influences users’ sense of social presence, emotional connection, and behavioural intention toward a brand. Their findings suggest that the more human-like and responsive AI systems appear, the greater the likelihood of fostering customer trust and engagement. This aligns with contemporary perspectives in digital marketing, where AI-powered tools not only automate interactions but also strengthen brand relationships and customer loyalty. The article examines how AI-driven social media marketing tools influence customer engagement and sales performance among SMEs in Uganda, emphasizing their potential to humanize digital communication and enhance behavioural outcomes.
Measuring the effectiveness of social media marketing has become a critical aspect of modern marketing performance analysis. Effective measurement allows firms to evaluate engagement, brand visibility, and conversion outcomes derived from online interactions. According to Kocisova and Starchon (2023), developing comprehensive marketing metrics is essential for assessing social media strategies, as it enables organizations to connect digital engagement indicators such as reach, impressions, and user interaction with overall marketing objectives and business growth. Their study emphasizes that a systematic evaluation of online performance metrics strengthens data-driven decision-making and helps marketers justify investments in social media campaigns within competitive markets.
While Marketing Science & Inspirations (MSI) has published research on social media metrics (Kocisova and Starchon 2023) and on the role of AI conversational agents in shaping user engagement and behavioural intention (Belhamri Belboula 2024), there remains a notable gap in understanding how these digital innovations function within small and medium-sized enterprises (SMEs), particularly in emerging economies. This study extends the field by examining the application of AI-powered social media marketing tools such as automated chatbots, predictive analytics, and recommendation algorithms in the context of Ugandan SMEs. Specifically, it explores how attitudes toward AI adoption and levels of customer engagement mediate the relationship between AI-driven marketing initiatives and sales performance.
With focus on an emerging market setting, this research article contributes to filling the contextual void in MSI literature, which has largely centered on developed economies (Nahan-Suomela 2020). Furthermore, it advances theoretical discourse by integrating perspectives from technology adoption and relationship marketing theories to explain how digital intelligence tools influence consumer behaviour and organizational outcomes. The findings are expected to enrich MSI’s body of knowledge by providing empirical evidence that highlights the transformative role of AI in enhancing marketing efficiency, customer relationships, and overall business growth among SMEs from developing economies.

2.4 Theoretical framework

The framework of this study is rooted in the Technology Acceptance Model (TAM), extended to incorporate AI-driven capabilities in digital marketing, particularly as they relate to social media platforms. The model hypothesizes that AI-powered tools influence customer engagement, which in turn drives sales growth. Specifically, four interrelated AI tools are central to the framework: (1) Chatbots that improve customer service responsiveness and satisfaction; (2) Content recommendation algorithms that enhance relevance and engagement; (3) Predictive analytics that optimize campaign timing and product promotion; and (4) Targeted advertising systems that improve audience reach and conversion rates.
Each of these tools is expected to enhance brand visibility, personalize customer interactions, and increase sales. Building on the work of Venkatesh and Davis (2000) and more recent models adapted for AI and digital platforms (Ramsbotham et al. 2021), the framework creates a direct path from AI integration to sales performance. In Ugandan context, where SMEs often face constraints in human resources and financial capital, AI-powered platforms offer cost-effective marketing tools that are likely to build strong customer relationships and achieve sales growth.

3 Research methodology

3.1 Study design

The study adopted a quantitative cross-sectional research design to examine how AI-powered features on social media platforms influence customer engagement and sales performance among small and medium-sized enterprises (SMEs) in Uganda. This design was selected due to its effectiveness in capturing the current adoption levels, usage patterns, and perceived outcomes of AI-driven social media marketing strategies. A structured survey method was employed to collect standardized data from a wide respondent base, allowing for statistical analysis and generalizability (Saunders et al. 2019; Alzougool 2021).
The study population consisted of registered SMEs in Uganda actively using social media platforms such as Facebook, Instagram, WhatsApp, or TikTok for marketing purposes. Using data from the Uganda Registration Services Bureau (URSB) and recent reports from the Uganda Communications Commission (UCC), SMEs were purposively selected from urban business hubs including Kampala, Gulu, Mbale, and Mbarara, where social media usage is relatively high (UCC 2023). The targeted sample size was 155 SMEs, and a stratified purposive sampling technique was used to ensure representation across sectors such as retail, hospitality, fashion, and agribusiness.

3.2 Instrument development

Data was collected using a self-administered structured questionnaire comprising of closed-ended items rated on a five-point Likert scale (1=Strongly disagree to 5=Strongly agree). The questionnaire had five sections: (1) Demographics (size, sector, years of operation), (2) Adoption of AI-powered tools (use of chatbots, targeted ads), (3) Customer engagement indicators (response time, personalization), (4) Sales performance metrics (sales growth, repeat purchase rate), (5) Perceived effectiveness of AI features.
Measurement items were adapted from validated scales in prior studies including Dwivedi et al. (2021) on digital marketing adoption, Chatterjee et al. (2021) on AI-enabled consumer engagement, and Tuten and Solomon (2021) on social media marketing performance. To ensure contextual relevance, the instrument was pre-tested with 10 SME owners and 3 academic experts in marketing and digital technologies in Uganda. To ensure the validity and reliability of the questionnaire, Content validity was established through expert review, while construct validity was evaluated using exploratory factor analysis (EFA) during data analysis. Cronbach’s alpha was calculated for internal consistency reliability, with a threshold of 0.70 considered acceptable (Hair et al. 2019).
Items falling below acceptable factor loadings (0.40) or internal consistency were dropped or revised accordingly.

WordPress Tables
Table 1: Showing construct items and their references
Source: Authors

3.3 Sample and data collection

The survey was conducted over eleven weeks period (April to June 2025). Data collectors distributed printed questionnaires and administered digital versions using Google Forms to accommodate respondent preferences. Before participation, respondents received a consent form explaining the research purpose, anonymity assurance, and voluntary nature of participation. Only SMEs with at least one year of experience in social media marketing were included. For this study, we employed a convenience (non-random) sampling method to gather data on AI-powered social media platforms and how they enhance customer engagement and drive sales growth in Uganda’s SMEs. A total of 155 questionnaires were distributed to respondents at several SMEs points and locations from April to June 2025. Out of these, 148 responses were collected. Seven questionnaires were deemed invalid due to incomplete responses. Therefore, only 148 questionnaires were analysed with a response rate of 95%. The data collection targeted individuals aged 18 and older, ensuring a diverse representation of leveraging AI-powered social media platforms to enhance customer engagement and drive sales growth in Uganda’s SMEs. The questionnaires were distributed in four cities: Kampala, Gulu, Mbale and Mbarara, to capture a broad regional perspective. Participants were thoroughly informed about the study’s objectives and their rights, including the option to decline participation or withdraw at any stage. The completion time for each questionnaire was approximately ten minutes, allowing respondents to provide thoughtful and considered answers. This approach aimed to ensure a reliable and representative sample while accommodating participants’ convenience and privacy. Collected data was coded and entered to SPSS version 27 and analysed using both descriptive and inferential statistics. Descriptive analysis summarized demographic characteristics, AI tool usage frequency, and general perceptions. Inferential analysis employed multiple regression analysis to test the relationship between the adoption of AI-powered social media features (independent variables) and two key dependent variables: customer engagement and sales performance. Assumptions of normality, linearity, and multicollinearity were tested using skewness/kurtosis statistics, variance inflation factors (VIFs), and tolerance values (Hair et al. 2019; Field 2022).

3.4 Importance of the hypotheses to be tested

Formulating hypotheses is an essential component of empirical research as it provides a clear, testable statement that connects theoretical constructs to observable phenomena. In this study, hypotheses are used to examine the relationship between AI-driven social media marketing tools and the sales performance of SMEs. They guide the analysis by translating the conceptual framework into measurable propositions that can be statistically verified. Testing these hypotheses helps determine whether the theoretical assumptions drawn from the Technology Acceptance Model (TAM) and innovation diffusion theory hold true in the Ugandan SME context.
Specifically, the hypotheses help to:
a) Establish causal relationships between AI use, customer engagement, and sales outcomes.
b) Validate whether perceived usefulness and ease of use influence SMEs’ adoption of AI-powered marketing tools.
c) Provide empirical evidence to support or refute claims about the effectiveness of AI in improving sales performance.

3.5 Formulation of research hypotheses

Based on the theoretical background and literature review, the following hypotheses are formulated for testing:
• Ha1: Adoption of Artificial Intelligence (AI)-powered tools in social media marketing has a significant positive effect on SME sales performance in Uganda.
• Ha2: Perceived usefulness of AI tools positively influences SME operators’ attitude towards using AI in social media marketing.
• Ha3: Perceived ease of use of AI-powered marketing tools significantly enhances SMEs’ willingness to adopt AI-driven social media strategies.
• Ha4: Customer engagement mediated by AI applications (such as chatbots, targeted advertising, and predictive analytics) significantly improves sales growth among SMEs.
• Ha5: The attitude of SME operators towards AI use positively mediates the relationship between AI tool adoption and sales performance.

3.6 Hypothesis testing procedure

The hypotheses were tested using multiple regression and mediation analysis. Statistical significance was evaluated at a 5% level (p<0.05). The dependent variable is Sales Performance, while the independent variables include AI Adoption, Perceived Usefulness, Ease of Use, Customer Engagement, and Attitude toward AI. Mediation effects were examined using Baron and Kenny’s (1986) approach or Hayes’ PROCESS Macro in SPSS. The testing validated that the theoretical relationships proposed by the Technology Acceptance Model (TAM) and prior AI-marketing literature hold in Uganda’s SME context.

4 Empirical results

4.1 Descriptive statistics

The study involved a total of 155 SME participants operating in urban centres of Uganda, specifically Kampala, Gulu, Mbale and Mbarara. The demographic data revealed that 54.7% of the respondents were male and 45.3% female. The majority (36%) were aged between 31 and 40 years, while 28% fell in the 41-50 age group. Most respondents held at least a diploma (42%), followed by bachelor’s degree holders (34%), and only 10% had postgraduate qualifications. Regarding business size, 63% of the SMEs employed fewer than 20 workers, aligning with Uganda’s official SME classification. Descriptive analysis showed high adoption of AI-powered social media tools. 73% of businesses reported using at least one AI feature on Facebook or Instagram, while 49% reported using WhatsApp Business chatbots. The mean score for perceived usefulness of AI tools in marketing was 3.84 (SD=0.76), perceived ease of use was 3.69 (SD=0.72), attitude toward AI-powered tools was 3.91 (SD=0.81), and perceived customer engagement outcomes averaged 3.88 (SD=0.67). The overall sales growth score over the past 12 months (self-reported) averaged 3.55 (SD=0.89) on a 5-point scale, indicating a moderate to high influence of AI on performance. Cronbach’s alpha values ranged from 0.83 to 0.91, confirming internal reliability.

4.2 Hypothesis testing and regression results

To test the proposed hypotheses, multiple linear regression analysis was conducted using SPSS version 27. The dependent variable was sales growth, while the independent variables were perceived usefulness, perceived ease of use, attitude toward AI tools, and perceived customer engagement. The model was statistically significant (F (4, 145)=32.87, p<0.001) with an adjusted R²=0.61, indicating that approximately 61% of the variance in sales growth can be explained by the predictors. The regression coefficients are summarized below.

WordPress Tables
Table 2: Showing the coefficients of the regression model
Source: Authors

All four predictors had positive effects on sales growth. The strongest predictor was attitude toward AI tools, followed by customer engagement, suggesting that SMEs that perceive AI tools favourably and actively engage customers via these platforms tend to experience higher sales performance.

4.3 Discussion

The findings from this study confirm that AI-powered social media tools play a pivotal role in driving customer engagement and enhancing sales performance among SMEs in Uganda. The significant and positive relationship between perceived usefulness and sales aligns with the foundational assumptions of the Technology Acceptance Model (TAM), which states that users are more likely to adopt technologies they believe will enhance performance (Venkatesh and Bala 2008). In this context, Ugandan SMEs appear to recognize that AI tools such as predictive advertising, automated messaging, and real-time insights can offer tangible business value. These tools enable cost-effective targeting and communication, particularly in environments where traditional marketing budgets are limited. Thus, AI allows resource-constrained firms to achieve scale and efficiency previously only accessible to larger corporations. Interestingly, perceived usefulness and ease of use were both significant, aligning with the Technology Acceptance Model (Davis 1989), but they were not as strong as attitudinal or behavioural factors. This suggests that while the functional value of AI tools matters, the belief systems and proactive use behaviours of SME owners may play an even larger role in translating technology into growth. These results offer actionable insights for both SMEs and policymakers: enhancing awareness, digital skills, and positive narratives around AI can substantially accelerate its impact in Uganda’s SME sector. Moreover, attitude emerged as the strongest predictor of sales growth, underlining the importance of managerial perception and openness toward emerging technologies. This reinforces findings by Ransbotham et al. (2021), who argued that technology adoption success often hinges less on the tool itself and more on the mindset of those implementing it. In Uganda, where formal digital training opportunities for SME operators remain sparse, a positive attitude toward AI can act as a catalyst for experimentation and self-directed learning. It suggests that policymakers and private stakeholders must invest in shaping positive narratives and offering real-world success stories of AI-driven growth within local business communities. Creating a community of early adopters or AI ambassadors among SMEs could help normalize usage and reduce psychological resistance, which is often a barrier in technologically underserved contexts. The significance of customer engagement as a driver of sales confirms existing global studies that view engagement as a bridge between brand exposure and transactional outcomes (Chatterjee et al. 2021; Tuten and Solomon 2021). In Uganda’s highly competitive and fragmented market environment, where consumer trust and loyalty are still developing in the digital space, AI-enabled engagement tactics such as automated replies, personalized product suggestions, and emotion-aware content may serve as critical differentiators. These tools help SMEs build relationships that feel authentic and responsive, which is especially important for first-time digital buyers or rural customers unfamiliar with online transactions. Moreover, by analysing customer sentiment and behavioural data, AI helps businesses continuously refine their marketing messages and offerings, making campaigns more adaptive and responsive to emerging consumer trends. Additionally, the study reveals that perceived ease of use also contributes significantly to AI adoption and sales outcomes, although it is not the dominant factor. This finding reflects the growing technological familiarity among Ugandan SME operators, especially in urban regions where digital infrastructure is improving. However, ease of use could become a more pressing barrier in rural areas or among businesses led by older entrepreneurs with limited digital exposure. This points to the need for context-sensitive training programs that simplify the onboarding process for AI-based platforms. Tools must be accessible not just linguistically, but also in terms of user interface and backend complexity. Thus, designers of AI marketing tools should prioritize localized, simplified interfaces and provide vernacular-based tutorials that resonate with Ugandan entrepreneurs. Finally, these results have broader implications for SME resilience and competitiveness in Sub-Saharan Africa. As consumer preferences shift toward mobile-first, socially mediated buying behaviours, businesses that fail to adapt will likely be marginalized. AI tools are no longer futuristic luxuries but necessary instruments for competitive positioning, customer retention, and sales forecasting. With proper institutional support, SMEs in Uganda could leverage AI not just for marketing, but for strategic decision-making across inventory management, customer service, and product development. Therefore, AI adoption through social media platforms can serve as a strategic equalizer, reducing the digital divide and giving local entrepreneurs a stronger foothold in both domestic and global markets.

5 Recommendations

Based on the findings, the government, in collaboration with universities, private tech firms, and development partners, should design subsidized, sector-specific digital literacy programs to strengthen SME owners’ and managers’ capacity to adopt and effectively use AI-powered marketing tools. Such programs should focus on practical skills data analytics, social media automation, and AI ethics and be integrated into existing entrepreneurship development initiatives. The Ministry of ICT and innovation hubs should promote collaboration between global technology providers and local SMEs to co-create affordable, contextually relevant AI solutions. These partnerships should encourage knowledge transfer, shared innovation labs, and the development of vernacular-based chatbots and localized AI interfaces that reflect Uganda’s market realities. This approach would ensure that AI tools are not only technologically sophisticated but also culturally and economically suitable for local business environments. Finally, policymakers should develop national guidelines and standards for ethical, transparent, and inclusive AI marketing practices, ensuring that data privacy, algorithmic fairness, and consumer protection are prioritized. A national AI oversight body could be mandated to monitor compliance, promote responsible innovation, and prevent exploitative or biased use of automated marketing systems. Clear governance mechanisms would enhance public trust and encourage sustainable AI integration in digital business ecosystems.

5.1 Conclusion

This study set out to examine the role of AI-powered social media platforms in enhancing customer engagement and driving sales growth among SMEs in Uganda. Drawing from the extended Technology Acceptance Model (TAM) and empirical insights from 155 SME operators, the research confirmed that AI features such as chatbots, content recommendation systems, predictive analytics, and targeted advertising significantly improve business outcomes. The regression analysis revealed that attitude toward AI tools and customer engagement are the strongest predictors of sales growth, followed by perceived usefulness and ease of use. These findings suggest that while technological functionality is essential, the beliefs, behaviours, and adaptability of business owners play an equally critical role in the successful integration of AI into SME operations. The study also highlights the unique opportunity AI offers for SMEs in Uganda to overcome resource limitations, tap into broader markets, and build personalized relationships with customers at scale. As digital transformation deepens in Africa, and consumer behaviour continues shifting toward mobile and online platforms, SMEs that leverage AI-integrated tools are likely to gain a sustained competitive advantage. However, challenges such as digital skills gaps, tool accessibility, and infrastructure disparities still limit widespread adoption and must be addressed through coordinated support from the private sector, government, and development partners.

5.2 Implications

For SME owners, the findings emphasize the importance of cultivating a positive and proactive mindset toward digital innovation. Business leaders should explore and experiment with affordable AI tools embedded within platforms like Facebook Business Suite, WhatsApp Business, and Instagram to automate routine marketing tasks, improve responsiveness, and track campaign performance. Investing in basic digital literacy and social media strategy training will also empower entrepreneurs to maximize the return on AI-enabled platforms. For technology providers and social media companies, the study underscores the need to design localized and intuitive AI tools tailored to small business contexts in emerging economies. Simplified user interfaces, AI tutorials in local languages, and feature sets aligned with common SME needs (e.g., inventory alerts, customer chat templates) can improve uptake and user satisfaction. For government agencies and policymakers, there is a clear call to support digital inclusion initiatives targeting SMEs. This includes offering training subsidies, integrating AI use cases into the national entrepreneurship curriculum, and promoting AI-readiness frameworks within Uganda’s national SME development strategy. Additionally, investment in affordable internet access, particularly in peri-urban and rural regions, will be essential to ensure equitable participation in the digital economy. Collectively, these recommendations can accelerate the diffusion of AI-driven social media marketing across Uganda’s SME landscape, enabling firms to not only survive but thrive in a rapidly digitizing marketplace.

5.3 Limitations and future research

While this study provides valuable insights into the impact of AI-powered social media platforms on customer engagement and sales growth among SMEs in Uganda, several limitations must be acknowledged. First, the study relied primarily on self-reported data from SME owners and managers, which may be subject to social desirability bias or overestimation of actual tool usage and sales performance. Although care was taken to ensure the reliability of responses, objective performance data (e.g., profit margins, conversion rates) would strengthen the empirical foundation of future studies. Secondly, the sample was drawn predominantly from urban areas such as Kampala, Gulu, Mbale and Mbarara, where digital infrastructure is relatively more developed. As such, the findings may not fully represent SMEs operating in rural and underserved regions, where internet access, digital literacy, and exposure to AI tools are significantly lower. Future studies should expand coverage to include rural SMEs and informal enterprises to better understand the digital divide in AI adoption and its implications for inclusive economic development. Thirdly, this study focused on four AI-driven factors perceived usefulness, ease of use, attitude, and customer engagement without incorporating other moderating variables that may influence sales outcomes, such as firm size, industry sector, or owner’s digital competency. Future research could adopt structural equation modelling (SEM) to test more complex, multi-path relationships and identify which contextual factors amplify or weaken the effect of AI tools on business performance. Lastly, given the rapid evolution of AI technologies, particularly generative AI (e.g., AI content creation, voice assistants), future research should explore how these advanced tools are being adopted and perceived in emerging markets. Longitudinal studies could also be employed to examine the sustainability of AI impacts on SME growth over time and whether early adopters achieve long-term competitive advantages compared to late adopters.

Poznámky/Notes

This paper is an output of the research project VEGA 1/0109/23 Framework for systematisation of digital transformation in organisations with the focus on marketing and business processes.

Literatúra/List of References

1. Alzougool, B., 2021. Quantitative research methods in social sciences. Routledge, 2021.
2. Baron, R. M. and Kenny, D. A., 1986. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. In: Journal of Personality and Social Psychology. 1986, 51(6), 1173-1182. ISSN 1939-1315. Available at: <https://doi.org/10.1037/0022-3514.51.6.1173>
3. Belhamri, Z. and Belboula, I., 2024. The impact of perceived anthropomorphism of the conversational agent (CA) on social presence, flow, and users’ behavioral intention. In: Marketing Science & Inspirations. 2024, 19(4), 45-59. ISSN 1338-7944. Available at: <https://msijournal.com/impact-perceived-anthropomorphism/>
4. Chatterjee, S., Nguyen, B., Ghosh, S. K., Bhattacharjee, K. K. and Chaudhuri, S., 2021. Adoption of artificial intelligence integrated tools in social media marketing: Impact on consumer engagement. In: Journal of Business Research. 2021, 129, 902-914. ISSN 1873-7978. Available at: <https://doi.org/10.1016/j.jbusres.2020.11.059>
5. Chen, H., Chiang, R. H. and Storey, V. C., 2021. Business intelligence and analytics: From big data to big impact. In: MIS Quarterly. 2021, 36(4), 1165-1188. ISSN 2162-9730. Available at: <https://doi.org/10.2307/41703503>
6. Davenport, T., Guha, A., Grewal, D. and Bressgott, T., 2020. How artificial intelligence will change the future of marketing. In: Journal of the Academy of Marketing Science. 2020, 48, 24-42. ISSN 1552-7824. Available at: <https://doi.org/10.1007/s11747-019-00696-0>
7. Davis, F. D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. In: MIS Quarterly. 1989, 13(3), 319-340. ISSN 2162-9730. Available at: <https://doi.org/10.2307/249008>
8. Dwivedi, Y. K., Kapoor, K. K. and Chen, H., 2021. Social media marketing and artificial intelligence: Opportunities and challenges. In: Information Systems Frontiers. 2021, 23, 1055-1072. ISSN 1572-9419. Available at: <https://doi.org/10.1007/s10796-020-10000-1>
9. Field, A., 2022. Discovering statistics using IBM SPSS statistics. Sage Publications, 2022. ISBN 9781529630008.
10. Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E., 2019. Multivariate data analysis. Cengage Learning, 2019. ISBN 978-1-4737-5654-0.
11. Kapoor, K., Dwivedi, Y. K., Piercy, N. F. and Reynolds, N., 2022. Artificial intelligence in social media marketing: A systematic literature review and research agenda. In: Journal of Business Research. 2022, 142, 1-20. ISSN 1873-7978. Available at: <https://doi.org/10.1016/j.jbusres.2022.01.062>
12. Kietzmann, J., Paschen, J. and Treen, E., 2018. Artificial intelligence in advertising: How marketers can leverage AI. In: Journal of Advertising Research. 2018, 58(3), 263-267. ISSN 1740-1909. Available at: <https://doi.org/10.2501/JAR-2018-035>
13. Kocisova, L. and Starchon, P., 2023. The role of marketing metrics in social media: A comprehensive analysis. In: Marketing Science & Inspirations. 2023, 18(2), 40-49. ISSN 1338-7944. Available at: <https://msijournal.com/the-role-of-marketing-metrics-in-social-media/>
14. Kumar, V., Rajan, B., Gupta, S. and Pozza, I. D., 2022. Customer engagement in the age of artificial intelligence. In: Journal of the Academy of Marketing Science. 2022, 50, 14-33. ISSN 1552-7824. Available at: <https://doi.org/10.1007/s11747-021-00811-3>
15. McKinsey & Company, 2022. The state of AI in marketing: Unlocking sales growth with AI. McKinsey & Company. 2022. [online]. [cit. 2025-03-25]. Available at: <https://www.mckinsey.com>
16. Mehta, S., Saxena, S. and Sharma, P., 2022. Chatbots and AI-powered personalization in social media marketing: Effects on customer engagement. In: Journal of Retailing and Consumer Services. 2022, 66, 102930. ISSN 1873-1384. Available at: <https://doi.org/10.1016/j.jretconser.2021.102930>
17. Meta Business, 2022. AI-driven advertising solutions for small and medium businesses. Meta. 2022. [online]. [cit. 2025-03-25]. Available at: <https://www.meta.com/business>
18. Montag, C., Yang, H. and Elhai, J. D., 2021. Artificial intelligence and the future of social media marketing. In: Current Opinion in Psychology. 2021, 41, 18-23. ISSN 2352-2518. Available at: <https://doi.org/10.1016/j.copsyc.2021.04.008>
19. Murphy, J., Li, X. and MacDonald, R., 2021. WhatsApp business APIs and SMEs: Enhancing customer experience with AI chatbots. In: International Journal of Information Management. 2021, 58, 102314. ISSN 1873-4707. Available at: <https://doi.org/10.1016/j.ijinfomgt.2020.102314>
20. Nabukeera, M. and Namubiru, F., 2022. Digital marketing adoption and performance outcomes among SMEs in Uganda. In: Uganda Journal of Business and Economics. 2022, 5(2), 45-60.
21. Nahan-Suomela, R., 2020. Networking and entrepreneurial orientation in internationalization of small-and medium sized enterprises: A two-case study of Ostrobothnia company in Finland. In: Marketing Science & Inspirations. 2020, 15(3), 36-47. ISSN 1338-7944. Available at: <https://ideas.repec.org/a/cub/journm/v15y2020i3p36-47.html>
22. Ndagire, E. and Bwire, J., 2023. Social media marketing practices of SMEs in urban Uganda: Adoption and challenges. In: African Journal of Management Research. 2023, 10(1), 1-15. ISSN 2458-7435.
23. Osei-Frimpong, K., Wilson, A. and Lemke, F., 2020. Engagement and personalization in social media marketing: Evidence from emerging markets. In: Journal of Retailing and Consumer Services. 2020, 54, 102030. ISSN 1873-1384. Available at: <https://doi.org/10.1016/j.jretconser.2020.102030>
24. Ransbotham, S., Gerbert, P. and Reeves, M., 2021. Artificial intelligence and the future of work. In: MIT Sloan Management Review. 2021, 62(2), 1-13. ISSN 1532-8937. Available at: <https://sloanreview.mit.edu>
25. Saunders, M., Lewis, P. and Thornhill, A., 2019. Research methods for business students. Pearson Education, 2019. ISBN 9781292208787.
26. Tuten, T. L. and Solomon, M. R., 2021. Social media marketing. Sage Publications, 2021. ISBN 978-1-5297-3199-6.
27. Uganda Communications Commission (UCC), 2023. Annual report 2023: Trends in ICT and SME adoption. UCC. 2023. [online]. [cit. 2025-03-25]. Available at: <https://www.ucc.co.ug>
28. Uganda Investment Authority, 2023. SME sector report 2023: Opportunities and challenges. UIA. [online]. [cit. 2025-03-25]. Available at: <https://www.ugandainvest.go.ug>
29. UNCTAD, 2022. Digital economy report 2022: Promoting inclusion in developing countries. United nations conference on trade and development. [online]. [cit. 2025-03-25]. Available at: <https://unctad.org>
30. Venkatesh, V. and Bala, H., 2008. Technology acceptance model 3 and a research agenda on interventions. In: Decision Sciences. 2008, 39(2), 273-315. ISSN 1540-5915. Available at: <https://doi.org/10.1111/j.1540-5915.2008.00192.x>
31. Venkatesh, V. and Davis, F. D., 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. In: Management Science. 2000, 46(2), 186-204. ISSN 1526-5501. Available at: <https://doi.org/10.1287/mnsc.46.2.186.11926>

Kľúčové slová/Key words

artificial intelligence (AI), social media marketing, small and medium-sized enterprises (SMEs), customer engagement, sales performance, digital marketing adoption, emerging markets, digital transformation
umelá inteligencia (AI), marketing na sociálnych médiách, malé a stredné podniky (MSP), zapojenie zákazníkov, výkonnosť predaja, zavádzanie digitálneho marketingu, rozvíjajúce sa trhy, digitálna transformácia

JEL klasifikácia/JEL Classification

L26, M15, M31, O33

Résumé

Využitie sociálnych médií poháňaných umelou inteligenciou na zvýšenie angažovanosti zákazníkov a podporu rastu predaja v ugandských MSP
V digitálnej ére umelá inteligencia (AI) premenila platformy sociálnych médií na výkonné marketingové nástroje, najmä pre malé a stredné podniky (MSP), ktoré sa snažia zlepšiť interakciu so zákazníkmi a zvýšiť predaj. Táto štúdia skúma aká je úloha funkcií poháňaných umelou inteligenciou, ako sú chatboty, algoritmy odporúčania obsahu, cielená reklama a prediktívna analýza na platformách ako Facebook, WhatsApp, TikTok a Instagram, pri zvyšovaní viditeľnosti značky a ovplyvňovaní predajných výkonov na príklade MSP v Ugande. V rozšírenom modeli prijatia technológie (TAM) štúdia použila kombinovaný prístup, pričom zhromaždila údaje z prieskumu od 155 prevádzkovateľov MSP v Kampale, Gulu, Mbale a Mbarare. Na základe viacnásobnej regresnej analýzy zistenia ukazujú, že postoj k nástrojom AI a angažovanosť zákazníkov sú najsilnejšími prediktormi rastu predaja, nasledované vnímanou užitočnosťou a jednoduchosťou používania. Štúdia zdôrazňuje rastúci význam umelej inteligencie v ugandskom ekosystéme malých a stredných podnikov a podčiarkuje potrebu rozvoja digitálnych zručností, lokalizovaných nástrojov umelej inteligencie a inkluzívnej politickej podpory s cieľom maximalizovať vplyv umelej inteligencie na rast podnikania. Štúdia končí praktickými odporúčaniami a navrhuje smery budúceho výskumu s cieľom prekonať digitálne rozdiely a rozšíriť pochopenie transformatívneho potenciálu umelej inteligencie na rozvíjajúcich sa trhoch.

Recenzované/Reviewed

10. August 2025 / 20. August 2025