The Artificial Intelligence Nexus in LMIC Marketing and Entrepreneurship: A Systematic Synthesis and Contextual Performance Framework

The Artificial Intelligence Nexus in LMIC Marketing and Entrepreneurship: A Systematic Synthesis and Contextual Performance Framework | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review The Artificial Intelligence Nexus in LMIC Marketing and Entrepreneurship: A Systematic Synthesis and Contextual Performance Framework Baffour Osei, Emmanuel Osei-Dwomoh, Abigail Boatemaa, Gabriel Osei Forkuo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9419902/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Artificial intelligence (AI) is fundamentally reshaping global commerce; however, its application within the unique institutional landscapes of low- and middle-income countries (LMICs) remains fragmented. This study addresses this gap by providing the first comprehensive synthesis of the marketing–entrepreneurship nexus in LMIC AI research. Methods: Following PRISMA 2020 guidelines, we conducted a robust systematic review of 120 peer-reviewed studies (2016–2026) sourced from five databases using an automated Python 3.12 script and eleven manual repositories. Study quality was rigorously evaluated using the Mixed Methods Appraisal Tool (MMAT) and CASP checklists to ensure evidence reliability. Results: Findings reveal a technological landscape dominated by General AI/Automation (47.5%) and Natural Language Processing (20.0%), with Sub-Saharan Africa (29.2%) and South Asia (19.2%) emerging as primary research hubs. Our analysis identifies a critical "rigor gap," as 44.2% of studies rely on unspecified empirical designs, with a near-absence (7.5%) of longitudinal or experimental evidence. Discussion/Originality: The study’s primary novelty lies in the development of the Contextual AI–Business Performance Framework. By integrating Resource-Based View (RBV), Technology Acceptance Model (TAM), and Institutional Theory, we move beyond universalistic adoption models to position digital infrastructure, regulation, and informality as essential boundary conditions. Conclusion: This review contributes a novel theoretical synthesis that bridges the gap between academic rigor and practical implementation. It provides a strategic roadmap for policymakers and operational decision-makers to leverage AI for inclusive growth, while establishing a future research agenda prioritized toward causal identification and geographic diversification in under-researched LMIC regions. developing economies digital transformation small and medium enterprises sub-Saharan Africa machine learning technology acceptance model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction 1.1 Background and Motivation The global diffusion of artificial intelligence (AI) technologies represents one of the most consequential structural shifts in contemporary business environments. Across high-income economies, AI has reshaped how firms acquire customers, build competitive advantage, and scale entrepreneurial activity — generating substantial productivity dividends documented in the management and information systems literatures (Dwivedi et al., 2019; Bughin et al., 2018 ). Yet the geography of AI-enabled business transformation is profoundly uneven. The overwhelming majority of landmark AI studies draw their samples, training data, and institutional contexts from North America, Western Europe, and East Asia — leaving a fundamental empirical lacuna regarding how AI technologies are applied, adapted, and absorbed in low- and middle-income countries (LMICs), where an estimated 84% of the world's population and the majority of global entrepreneurs reside (Abrokwah-Larbi, 2024 ; Sampson et al.2025; World Bank, 2024 ). This asymmetry matters for several interconnected reasons. First, LMIC economies exhibit structural features — pronounced digital infrastructure deficits, informal economic activity, constrained financial markets, institutional voids, and heterogeneous regulatory environments — that fundamentally alter the boundary conditions under which AI adoption occurs and business outcomes are realised (Mhlanga, 2020 ; Demaidi, 2025 ; Ayana et al., 2024 ). Findings from high-income country contexts cannot be straightforwardly transposed to LMIC settings without systematic contextual adjustment. Second, the accelerating pace of AI capability development — encompassing large language models (LLMs), multimodal systems, autonomous agents, and generative AI — creates mounting pressure for LMIC entrepreneurs and marketing practitioners to navigate a rapidly shifting technological landscape without the institutional scaffolding available to their high-income country counterparts (Mogaji et al., 2024 ; Dwivedi et al., 2023 ). Third, LMICs are simultaneously home to a rapidly growing digital consumer base, a burgeoning SME sector, and entrepreneurial ecosystems that are increasingly connected to global value chains, creating both the demand for and the potential benefits of AI adoption at a scale that renders the current evidence gap increasingly consequential for economic development policy (Sampson et al., 2025; Khan et al., 2024 ). The marketing–entrepreneurship nexus represents a particularly critical and underexplored intersection in the LMIC AI literature. Marketing capability is a primary determinant of SME survival and growth in competitive LMIC markets, yet LMIC entrepreneurs routinely cite limited market intelligence, constrained marketing budgets, and inability to reach target consumers as among their most binding constraints (Abrokwah-Larbi, 2024 ; Quaye et al., 2024 ). AI technologies — including NLP-driven sentiment analysis, recommender systems for customer targeting, AI-enhanced CRM platforms, and automated digital advertising — offer theoretically compelling solutions to precisely these constraints. Similarly, AI tools for business intelligence, financial inclusion (fintech), agricultural value chain optimisation (agritech), and digital platform entrepreneurship have generated empirical evidence of measurable impact across LMIC contexts (Cambaza, 2023 ; Mhlanga, 2020 ; Adewuyi et al., 2023 ). However, no comprehensive synthesis integrates this growing body of evidence into a coherent empirical and theoretical account. 1.2 Evidence Gaps and the Need for Synthesis The impetus for this systematic review arises from three convergent evidence gaps. First, the volume of empirical studies on AI in LMIC business contexts has accelerated dramatically since 2021, with 2024–2025 constituting the peak publication years in our dataset — yet no existing systematic review or meta-analysis comprehensively maps this literature. Existing reviews either restrict their scope to high-income country settings (Blut et al., 2021 ; Dwivedi et al., 2020) or adopt narrow domain foci — covering AI in healthcare, education, or finance — without integrating the marketing–entrepreneurship nexus that is most directly relevant to private sector development in LMICs. Second, the theoretical frameworks applied to AI adoption and performance in LMIC contexts remain fragmented. Resource-based view (RBV), technology acceptance model (TAM), and institutional theory have each been applied independently to aspects of this question, but no integrative framework synthesises their complementary insights into a coherent account of how AI inputs translate into business outcomes under LMIC-specific boundary conditions (Barney, 1991 ; Davis, 1989 ; DiMaggio and Powell, 1983 ). The absence of such a framework limits theoretical cumulation and generates inconsistent, context-specific findings that are difficult to compare across studies. This mirrors the challenge identified in the IPSAS adoption literature, where the absence of an integrative analytical model obscured the structural mechanisms linking formal policy adoption to substantive implementation outcomes (Jayasinghe et al., 2020 ; Tetteh et al., 2021 ). Analogously, the AI-in-LMIC literature requires a unified framework that positions contextual moderators as structural boundary conditions rather than incidental covariates. Third, geographic coverage remains severely skewed. Sub-Saharan Africa, South Asia, and Southeast Asia dominate the existing literature, while Latin America, the Caribbean, the MENA region, and small island developing states remain substantially underrepresented — a pattern that mirrors structural research inequities documented in adjacent development literatures and constrains the generalisability of AI–business performance findings across the full diversity of LMIC institutional environments. 1.4 Aim, Objectives, and Research Questions This systematic review aimed to provide a methodologically rigorous, theory-driven, and geographically inclusive synthesis of global evidence on AI applications in marketing and entrepreneurship in LMICs (2016–2026), and to advance original theoretical synthesis that explains how AI-to-outcome pathways operate under LMIC-specific boundary conditions. Specifically, the review pursued four objectives: Objective 1 (Evidence Mapping): To systematically map the geographic and thematic distribution of AI research in LMIC marketing and entrepreneurship contexts, characterising the AI technology landscape, study design composition, and regional evidence density across eight LMIC regions. Objective 2 (Evidence Synthesis): To synthesise the evidence on AI outcomes in LMIC marketing and entrepreneurship, identifying the mechanisms through which AI technologies generate marketing efficiency, customer insight, innovation capacity, firm performance, and inclusive growth. Objective 3 (Framework Development): To develop and present an original Contextual AI–Business Performance Framework that integrates RBV, TAM, and institutional theory into a unified LMIC-specific model of AI adoption and business outcomes, positioning contextual moderators — digital infrastructure, regulation, human capital, resource constraints, and informality — as structural boundary conditions. Objective 4 (Gap Identification and Research Agenda): To identify the most acute geographic and thematic zones of research deficit, and to articulate a structured future research agenda with testable propositions that advance causal identification, geographic diversification, and governance dimensions of AI in LMIC contexts. These objectives were addressed through four corresponding research questions: RQ1: What AI technologies are applied in marketing and entrepreneurship research in LMICs, and how are they distributed geographically and thematically? RQ2: What outcomes are associated with AI application in LMIC business contexts, and through what mechanisms do these outcomes materialise? RQ3: How do LMIC-specific contextual factors moderate the AI–outcome relationship, and what theoretical framework best accounts for this moderation? RQ4: What geographic and methodological gaps constrain the existing evidence base, and what research agenda is required to address them? The remainder of this paper is organised as follows. Section 2 presents the theoretical foundations. Section 3 describes the methodology, including database sources, search strings, and PRISMA 2020 selection procedures. Section 4 reports the main findings, covering geographic distribution, AI technology typology study design, and thematic domains. Section 5 presents the Contextual AI–Business Performance Framework. Section 6 discusses theoretical, methodological, and policy implications. Section 7 concludes. 2. Theoretical Foundations 2.1 Resource-Based View and AI as a Strategic Asset The resource-based view (RBV) posits that sustained competitive advantage arises from firm-specific resources that are valuable, rare, inimitable, and non-substitutable (Barney, 1991 ). In LMIC contexts, AI constitutes a uniquely strategic resource precisely because information asymmetries are pronounced and conventional data infrastructures are underdeveloped. Chen et al. ( 2022 ) provide empirical support for this proposition, demonstrating that AI adoption enhances firm performance through resource reconfiguration in e-commerce settings. Khan et al. ( 2024 ) extend this logic to the construction sector in East Asia, where knowledge management-based AI adoption moderates the relationship between environmental uncertainty and performance. Critically, however, RBV assumptions about resource imitability must be recalibrated for LMIC contexts, where formal intellectual property regimes are often weak and AI capabilities can diffuse rapidly through mobile platforms (Dey et al., 2023; Quaye et al., 2024 ). 2.2 Technology Acceptance Model and AI Adoption in LMICs The technology acceptance model (TAM) and its extensions (Davis, 1989 ; Venkatesh et al., 2003 ) provide the dominant theoretical lens for understanding AI adoption decisions at the organisational and individual levels. Studies in our corpus apply TAM to explain chatbot adoption in SME supply chains (Panigrahi et al., 2023 ), voice assistant adoption in fashion retail (Kautish et al., 2023 ), and AI-enabled customer experience systems (Tula et al., 2024). A consistent finding is that perceived usefulness and ease of use interact with LMIC-specific moderators — including digital literacy, infrastructure reliability, and language localisation — to shape adoption trajectories (Ikumoro et al., 2019; Juma'at et al., 2025). Mogaji et al. ( 2024 ) raise the provocative question of whether TAM remains applicable in the generative AI era, noting that the model's assumptions about user intentionality may not hold when AI systems operate autonomously on behalf of firms. 2.3 Institutional Theory and LMIC Contextual Factors Institutional theory (North, 1990 ; DiMaggio and Powell, 1983 ) directs attention to the formal and informal rules that shape organisational behaviour. In LMIC contexts, institutional voids — the absence of reliable market-supporting institutions — create both barriers and opportunities for AI-enabled business models (Demaidi, 2025 ; Ayana et al., 2024 ). Studies in our corpus consistently identify regulatory uncertainty, limited data protection frameworks, and inadequate digital infrastructure as institutional constraints on AI adoption (Mutasa et al., 2024 ; Abaddi et al., 2023). Conversely, institutional entrepreneurship — the deliberate leveraging of AI to bypass institutional bottlenecks — is documented across fintech, agribusiness, and digital health sectors (Mhlanga, 2020 ; Cambaza, 2023 ; Ahmed et al., 2025 ). 2.4 Towards an Integrative Framework No single theoretical lens adequately captures the complexity of AI deployment in LMIC business contexts. RBV accounts for competitive dynamics but underspecifies institutional moderators. TAM explains individual adoption decisions but does not address systemic infrastructure constraints. Institutional theory illuminates regulatory and cultural factors but provides limited guidance on technology-performance pathways. The Contextual AI–Business Performance Framework developed in Section 5 integrates these perspectives to provide a more complete account of how AI inputs translate into business outcomes under LMIC-specific boundary conditions. 3. Methodology 3.1 Review Design and PRISMA Compliance This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al., 2021 ). A systematic literature review (SLR) design was adopted to enable transparent, reproducible, and comprehensive synthesis of evidence on AI applications in marketing and entrepreneurship in LMICs. The review protocol was developed and registered a priori on Open Science Forum (available at: https://osf.io/3phcj ), specifying the research questions, eligibility criteria, search strategy, and analysis plan. 3.2 Eligibility Criteria Studies were included if they: (i) were published in peer-reviewed journals or conference proceedings between 2010 and 2026; (ii) explicitly applied or examined at least one AI technology; (iii) focused on a marketing, entrepreneurship, or combined marketing–entrepreneurship domain; (iv) were conducted in or explicitly relevant to one or more LMIC as defined by the World Bank's income classification; and (v) were available in English or English translation. Studies were excluded if they: focused exclusively on high-income country contexts without LMIC applicability; did not employ any identifiable AI technology; were primarily in sectors outside business with no business implications; or were grey literature without peer review. 3.3 Search Strategy and Databases Searches were conducted across five automated databases — OpenAlex, CrossRef, Semantic Scholar, EconBiz, and IDEAS/RePEC — and supplemented by manual searches of eleven databases: Scopus, Web of Science (WoS), EconLit via EBSCO, ABI/INFORM (ProQuest), Business Source Premier, IEEE Xplore, ACM Digital Library, ScienceDirect, African Journals Online (AJOL), SciELO, and SSRN. Grey literature searches were conducted via Google Scholar and institutional repositories. All searches were conducted in March–April 2026. 3.4 Search Strings Search strings were developed iteratively through a combination of Boolean logic and controlled vocabulary, organised into three thematic blocks. Representative automated search strings included: EN·01 "artificial intelligence marketing low-income countries developing economies" EN·11 "artificial intelligence entrepreneurship developing countries" EN·21 "artificial intelligence marketing entrepreneurship LMIC developing countries" The Scopus master Boolean string combined all four concept blocks — AI technologies × Marketing × Entrepreneurship × LMIC geography — with a publication year filter of 2010–2026. Full query strings are deposited on figshare as Supplementary File S1 . 3.5 Study Selection and Data Extraction Initial automated searches retrieved 4,661 records; manual searches contributed an estimated 500–800 additional records, for a combined total of approximately 5,200–5,400 records. After deduplication, 4,200 records were screened by title and abstract, of which 3,858 were excluded. The remaining 342 full-text articles were assessed for eligibility, with 222 excluded (not LMIC-focused, n = 120; no AI application, n = 54; out of scope, n = 48). A total of 120 studies were retained for qualitative synthesis (Fig. 1 ). Data were extracted into a structured matrix capturing: author(s), year, journal, country/region, AI technology category, thematic domain, study design, and key findings. 3.6 Quality Assessment Given the heterogeneity of study designs in the corpus, a domain-specific quality appraisal approach was adopted rather than a single scoring tool. Empirical studies were assessed against criteria adapted from the Mixed Methods Appraisal Tool (MMAT); systematic reviews were assessed using AMSTAR-2 criteria; qualitative studies were appraised using the Critical Appraisal Skills Programme (CASP) checklist. No studies were excluded on quality grounds alone; however, quality is considered in interpreting evidence strength across themes. 4. Results 4.1 Publication Trends and Growth Trajectory Figure 2 presents the annual publication counts for included studies from 2016 to 2026. The trajectory demonstrates a clear inflection point at 2021, consistent with the broader acceleration of AI adoption following the COVID-19 pandemic and the maturation of transformer-based language models. Publications grew from a baseline of one to three studies per year between 2016 and 2019, accelerating to 8–12 studies per year in 2020–2021, and reaching a peak of 22–23 studies in 2023–2025. The 2026 partial-year data ( n = 5) suggest continued growth. Marketing-focused and entrepreneurship-focused streams grew in parallel, while studies addressing both domains simultaneously expanded most rapidly from 2022 onwards, reflecting increasing scholarly recognition of the marketing–entrepreneurship interface in AI research. 4.2 Geographic Distribution The geographic distribution of included studies (Fig. 3 ; Table 2 ) reveals a pronounced concentration in Sub-Saharan Africa ( n = 35, 29.2%), South Asia ( n = 23, 19.2%), and Southeast Asia ( n = 18, 15.0%). Together, these three regions account for nearly two-thirds (63.4%) of all studies included in the review. East Asia and the Pacific contributed 15 studies (12.5%), while Multiple/Global LMIC studies accounted for 14 studies (11.7%). North Africa and the MENA region contributed 10 studies (8.3%). In contrast, Europe and Central Asia ( n = 3, 2.5%) and Latin America and the Caribbean ( n = 2, 1.7%) were markedly underrepresented, reflecting both language barriers in the search and the relatively nascent state of AI–business research infrastructure in these regions. The Sub-Saharan African dominance spans a wide range of sectors — digital financial services, agricultural value chains, digital advertising, and e-commerce — reflecting both the diversity of AI applications and the concentration of development-oriented research funding in that region. There is a high degree of uniformity regarding the types of AI technologies and domains being studied across different regions. "General AI/Automation" is the dominant technology in six out of the eight regional categories, indicating a widespread global focus on broad AI implementations. However, a slight shift is observable in "Multiple/Global LMIC" studies and "Europe & Central Asia," where "NLP/LLM/Chatbot" emerges as the dominant technology. This suggests that cross-regional or newer regional research may be leaning more toward conversational and language-based AI. In terms of application, the vast majority of regions focus consistently on the intersection of Marketing, Entrepreneurship, and M&E, demonstrating a unified research interest in how AI supports business and economic monitoring. The year ranges provided in the table highlight the varying maturity of AI research across these regions. Sub-Saharan Africa has the longest-standing research presence, with studies dating back to 2016. South Asia and Southeast Asia followed shortly after in 2017. Conversely, research in the Europe & Central Asia and Latin America & Caribbean regions is a much more recent phenomenon, with the earliest studies in this sample appearing only in 2023. The fact that several regions (Sub-Saharan Africa, North Africa & MENA, and Latin America) include studies projected or published through 2026 suggests a growing and forward-looking momentum in the field, with the most established regions maintaining a head start in the volume and duration of their academic output. Table 2 Geographic Distribution of Included Studies by LMIC Region ( n = 120) LMIC Region n % Dominant AI Technology Research Domains Year Range Sub-Saharan Africa 35 29.2% General AI/Automation Marketing; Entrepreneurship; M&E 2016–2026 South Asia 23 19.2% General AI/Automation Marketing; Entrepreneurship; M&E 2017–2025 Southeast Asia 18 15.0% General AI/Automation Marketing; Entrepreneurship; M&E 2017–2025 East Asia & Pacific 15 12.5% General AI/Automation Marketing; Entrepreneurship; M&E 2019–2025 Multiple/Global LMIC 14 11.7% NLP/LLM/Chatbot Entrepreneurship; M&E 2020–2026 North Africa & MENA 10 8.3% General AI/Automation Marketing; Entrepreneurship; M&E 2019–2026 Europe & Central Asia 3 2.5% NLP/LLM/Chatbot Marketing; M&E 2023–2025 Latin America & Caribbean 2 1.7% General AI/Automation Marketing; M&E 2023–2026 Total 120 100% Note: LMIC: Low- and middle-income country. Region classifications follow the World Bank's income-group and regional groupings. n = number of studies. M&E = Marketing & Entrepreneurship (combined domain). Year range indicates the earliest and most recent publication years within each region. 4.3 AI Technology Distribution Figure 4 and Table 3 present the distribution of AI technology categories across included studies. The most prominent trend is the heavy concentration of research on a few select technology categories. General AI/Automation constituted the largest category ( n = 57, 47.5%), encompassing studies that applied AI broadly without specifying a sub-technology — reflecting the tendency of business-oriented research to treat AI as a generic capability rather than a set of differentiated tools. NLP/LLM/Chatbot was the second most prevalent category ( n = 24, 20.0%), consistent with the rapid proliferation of conversational AI in customer service, marketing communications, and entrepreneurial support contexts. Recommender Systems ranked third ( n = 21, 17.5%), reflecting their established role in e-commerce personalisation in emerging markets. Machine Learning ( n = 8, 6.7%), Deep Learning/ANN ( n = 6, 5.0%), and Predictive Analytics ( n = 4, 3.3%) were less commonly studied as discrete technologies. This suggests that the current literature is heavily focused on broad automation and user-interaction technologies rather than highly specialized or technical algorithmic frameworks. There is a striking level of consistency regarding the geographical focus and the functional domains covered by these technologies. Sub-Saharan Africa emerges as the "Top LMIC Region" across five of the six AI categories, indicating that it is a central hub for research into AI applications within developing economies. In terms of domain coverage, nearly every technology category is applied across the combined fields of Marketing and Entrepreneurship. Interestingly, "Machine Learning" and "Deep Learning/ANN" show a slightly more concentrated focus on Entrepreneurship and M&E (Monitoring and Evaluation) compared to the broader application of "General AI/Automation." In contrast to the high prevalence of general automation, more computationally intensive or specialized AI technologies are significantly underrepresented. "Machine Learning" (6.7%), "Deep Learning/ANN" (5.0%), and "Predictive Analytics" (3.3%) represent the smallest shares of the sample. Notably, "Deep Learning/ANN" is the only category where the top LMIC region shifts away from Sub-Saharan Africa to "North Africa & MENA." This trend suggests that while there is significant interest in the broad implementation of AI, there is currently less focus on the more complex, data-heavy predictive modeling and neural network architectures within the context of the studied domains and regions. Table 3 Summary of AI Technology Categories Applied Across Included Studies ( n = 120) AI Technology Category n % Domains Covered Top LMIC Region Example Reference General AI/Automation 57 47.5% Marketing; Entrepreneurship; M&E Sub-Saharan Africa Lim ( 2022 ) NLP/LLM/Chatbot 24 20.0% Marketing; Entrepreneurship; M&E Sub-Saharan Africa Ikumoro et al. (2019) Recommender Systems 21 17.5% Marketing; Entrepreneurship; M&E Sub-Saharan Africa Li et al. ( 2022 ) Machine Learning 8 6.7% Entrepreneurship; M&E Sub-Saharan Africa Dwivedi et al. (2019) Deep Learning/ANN 6 5.0% Entrepreneurship; Marketing; M&E North Africa & MENA Sharma et al. ( 2021 ) Predictive Analytics 4 3.3% Marketing & Entrepreneurship Sub-Saharan Africa Mohammadian et al. (2020) Total 120 100% Note: NLP: Natural language processing; LLM: Large language model. n = number of studies. Percentage calculated from total included studies (n = 120). M&E = Marketing & Entrepreneurship (combined domain). The heatmap (Fig. 5 ) reveals that General AI/Automation is consistently the dominant technology category across all LMIC regions, with NLP/LLM/Chatbot particularly prominent in Multiple/Global LMIC studies ( n = 6), and Recommender Systems concentrated in Sub-Saharan Africa ( n = 7) and South Asia ( n = 5). 4.4 Study Design Distribution Figure 6 illustrates the distribution of study designs among the 120 included studies. Empirical studies with unspecified designs constituted the largest methodological category ( n = 53, 44.2%), a finding that raises important questions about methodological transparency and replicability. Qualitative and case study designs were the most common explicitly specified approach ( n = 25, 20.8%), followed by survey and cross-sectional designs ( n = 17, 14.2%). Systematic reviews and meta-analyses contributed 12 studies (10.0%), experimental designs 6 studies (5.0%), conceptual and review studies 4 studies (3.3%), and longitudinal or secondary data studies 3 studies (2.5%). The predominance of cross-sectional and qualitative designs — and the near-absence of longitudinal and experimental designs — is a critical limitation of the evidence base. Additionally, Table 4 shows several key patterns and trends regarding the methodological landscape, technological focus, and geographical distribution of the 120 included studies. When Empirical (unspecified) studies are combined with Qualitative/Case Studies (20.8%), these two categories comprise approximately two-thirds of the entire dataset. In contrast, more rigorous or time-intensive designs are notably rare; Experimental studies account for only 5.0% of the research, while Longitudinal/Secondary studies represent the smallest fraction at just 2.5%. This suggests that the current state of research in this field is focused primarily on observational and descriptive data rather than controlled experimentation or long-term impact analysis. The distribution of AI technologies across these study designs shows a high level of consistency, with "General AI/Automation" and "NLP/LLM/Chatbot" appearing as top technologies in nearly every category. However, specific technological clusters are visible within certain designs. For example, "Deep Learning/ANN" and "Machine Learning" are most prominent in Empirical and Survey/Cross-sectional designs, suggesting these methodologies are frequently used to evaluate algorithmic performance or data-driven outcomes. Conversely, "Recommender Systems" only appear in the Experimental and Longitudinal categories, indicating that research into personalized AI recommendations tends to involve more specialized or intervention-based research frameworks. Geographically, the "East Asia & Pacific" region shows the most robust representation, appearing in six out of the seven study design categories. This indicates a highly diverse research output from that region. There is also a notable trend toward "Multiple/Global LMIC" (Low-Middle Income Country) perspectives within Qualitative studies and Systematic Reviews, suggesting a focus on comparative or broad-scale analysis in these areas. However, certain regions appear to be underrepresented in the more common study designs; for instance, Sub-Saharan Africa and Southeast Asia only appear in the Conceptual and Longitudinal categories, which are the least frequent designs in the overall sample. This highlights a potential gap in large-scale empirical or experimental research within those specific regions. Table 4 Distribution of Study Designs Among Included Studies ( n = 120) Study Design n % Key AI Technologies Used Regions Represented Empirical (unspecified) 53 44.2% Deep Learning/ANN; General AI/Automation; Machine Learning East Asia & Pacific; Europe & Central Asia; Latin America & Caribbean Qualitative/Case Study 25 20.8% General AI/Automation; NLP/LLM/Chatbot; Predictive Analytics East Asia & Pacific; Multiple/Global LMIC; North Africa & MENA Survey/Cross-sectional 17 14.2% Deep Learning/ANN; General AI/Automation; NLP/LLM/Chatbot East Asia & Pacific; Europe & Central Asia; North Africa & MENA Systematic Review/Meta-analysis 12 10.0% General AI/Automation; Machine Learning; NLP/LLM/Chatbot East Asia & Pacific; Europe & Central Asia; Multiple/Global LMIC Experimental 6 5.0% General AI/Automation; NLP/LLM/Chatbot; Recommender Systems East Asia & Pacific; Multiple/Global LMIC; South Asia Conceptual/Review 4 3.3% General AI/Automation; NLP/LLM/Chatbot Southeast Asia; Sub-Saharan Africa Longitudinal/Secondary 3 2.5% General AI/Automation; NLP/LLM/Chatbot; Recommender Systems East Asia & Pacific; Sub-Saharan Africa Total 120 100% Note: AI: Artificial intelligence; NLP: Natural language processing. n = number of studies. Percentage calculated from total included studies (n = 120). Key AI technologies represent up to three most commonly used technologies within each study design category. 4.5 Thematic Domain Coverage Figure 7 presents the thematic domain coverage of included studies by LMIC region. Studies addressing both marketing and entrepreneurship simultaneously constitute the largest thematic group across all regions ( n = 77, 64.2%), confirming that AI application in LMIC business contexts rarely adheres to clean domain boundaries. Sub-Saharan Africa displayed the highest absolute number of Marketing & Entrepreneurship studies ( n = 21), followed by South Asia ( n = 14) and Southeast Asia ( n = 11). Pure entrepreneurship studies were most common in Sub-Saharan Africa ( n = 9) and Southeast Asia ( n = 5), while pure marketing studies were relatively evenly distributed across regions. 4.6 Flow of AI Methods Through Business Functions to Outcomes Figure 8 presents a Sankey diagram illustrating the flow of AI methods through business functions to outcomes across included studies. The diagram reveals that General AI/Automation and NLP/LLM/Chatbot methods are the primary conduits through which AI enters the business function layer, with Customer Service and Decision Support emerging as the most frequently supported functions. Machine Learning and Recommender Systems are particularly associated with Product Recommendation and Customer Segmentation functions. At the outcome layer, all AI methods and business functions contribute to four primary outcomes: Inclusive Growth, SME Productivity, Entrepreneurial Success, and Marketing Performance. 4.7 AI Technology vs. Research Domain — Bubble Plot Figure 9 presents a bubble plot mapping AI technology categories against research domains, with bubble size reflecting the number of studies and colour indicating the dominant LMIC region for each cell. The largest bubble — General AI/Automation in the Marketing & Entrepreneurship domain ( n = 35), dominated by Southeast Asia — confirms that integrated marketing–entrepreneurship research using broad AI approaches constitutes the single largest segment of the literature. NLP/LLM/Chatbot in the Marketing & Entrepreneurship domain ( n = 18, Multiple/Global LMIC) and Recommender Systems in the Marketing & Entrepreneurship domain ( n = 13, Sub-Saharan Africa) are the next largest clusters. The relative absence of studies in the Predictive Analytics–Marketing domain and the Machine Learning–Marketing domain highlights specific technology-domain combinations that remain underexplored in LMIC contexts. 4.8 Key Thematic Findings 4.8.1 AI for Customer Engagement and Marketing Performance A substantial portion of the corpus documents AI's role in enhancing customer engagement and marketing performance in LMIC firms. Recommender systems have been applied in e-commerce contexts in China (Li et al., 2022 ; Li, 2023 ), India (Mishra et al., 2017 ), and Vietnam (Le et al., 2017), demonstrating improved conversion rates and customer retention. NLP and sentiment analysis tools have been deployed to analyse consumer opinion in South Asian digital advertising markets (Sun et al., 2022 ), while AI-powered CRM systems are documented in Sub-Saharan African SMEs (Abubakar et al., 2025; Abdulsalam et al., 2024). Voice assistant and conversational AI adoption in marketing contexts is documented in South Asian fashion retail (Kautish et al., 2023 ), Southeast Asian e-commerce (Ikumoro et al., 2019), and global LMIC hospitality (Foroudi et al., 2025 ). Hyper-personalised marketing strategies enabled by AI are examined in Prince et al. (2025), who demonstrate their efficacy in South Asian digital markets while cautioning against algorithmic bias in consumer segmentation. 4.8.2 AI for Entrepreneurship and SME Development AI's role in entrepreneurial ecosystems and SME development in LMICs is documented across diverse sectoral and regional contexts. Digital entrepreneurship challenges in Jordan's MENA market are examined by Abaddi et al. (2023), who identify recommender system-based matching mechanisms as potential enablers of entrepreneurial discovery. Fintech AI applications — including AI-enabled credit scoring, mobile money, and digital micro-lending — are documented across Sub-Saharan Africa (Mhlanga, 2020 ; Cambaza, 2023 ; Adewuyi et al., 2023 ) and South Asia (Alvi et al., 2025 ), with evidence of improved financial inclusion outcomes. AI tools for agricultural value chain optimisation represent a growing frontier in LMIC entrepreneurship research (Legg et al., 2022 ; Jessy et al., 2024 ). 5. Conceptual Framework: AI Applications in Marketing and Entrepreneurship in LMICs Drawing on the systematic review findings and the theoretical perspectives outlined in Section 2 , we present the Contextual AI–Business Performance Framework (Fig. 10 ). The framework conceptualises the AI-to-outcome pathway in LMIC business contexts as comprising four sequential stages — AI Inputs, Mechanisms, Intermediate Outcomes, and Final Outcomes — moderated by a set of LMIC Contextual Factors. 5.1 AI Inputs The AI Inputs stage encompasses the full typology of AI technologies documented in the review: Machine Learning (ML), Natural Language Processing (NLP), Deep Learning (DL), Recommender Systems, Predictive Analytics, and General AI/Automation. These technologies vary substantially in their data requirements, computational demands, and interpretability — dimensions that have differential implications for LMIC deployment. ML and DL require large labelled datasets that may be unavailable or of inconsistent quality in LMICs (Seastedt et al., 2022 ; Yang et al., 2024 ). NLP systems require language models trained on local languages and dialects, which are systematically underrepresented in global AI training data (Ayana et al., 2024 ). Recommender systems require transactional data infrastructure that is nascent in many LMIC markets (Mubarok et al., 2024 ). 5.2 Mechanisms AI technologies generate business outcomes through four primary mechanisms: Information Access, Automation, Personalisation, and Decision Support. Information Access mechanisms reduce information asymmetries between firms, consumers, and competitors — a particularly salient function in LMIC contexts where formal market information systems are often weak (Adewuyi et al., 2023 ; Chambreuil et al., 2022 ). Automation mechanisms reduce labour costs and processing time in repetitive business functions, enabling SMEs to achieve operational efficiency gains that would otherwise require significant capital investment (Panigrahi et al., 2023 ; Sampson et al., 2025). Personalisation mechanisms tailor marketing content, product recommendations, and service interactions to individual consumer preferences (Kautish et al., 2023 ; Prince et al., 2025). Decision Support mechanisms provide AI-generated analytical insights to entrepreneurs and managers, improving the quality and speed of strategic decisions (Kasem et al., 2023; Dey et al., 2023). 5.3 Intermediate Outcomes The framework identifies three intermediate outcomes: Marketing Efficiency, Customer Insight, and Innovation Capacity. Marketing Efficiency captures improvements in cost-effectiveness, targeting precision, and return on investment of marketing activities. Customer Insight encompasses the depth and actionability of understanding firms acquire about customer preferences, behaviour, and sentiment through AI-enabled analytics. Innovation Capacity refers to the enhanced ability of firms to develop and commercialise new products, services, and business models through AI-augmented ideation and experimentation (Han et al., 2021; John et al., 2025 ). 5.4 Final Outcomes Final outcomes are operationalised at three levels: Firm Performance (revenue growth, market share, profitability), Entrepreneurial Success and Growth (venture survival, scaling, ecosystem participation), and Inclusive Growth (employment generation, poverty reduction, gender equity). The distinction between intermediate and final outcomes is important for research design — most existing studies measure intermediate outcomes (e.g., customer satisfaction, adoption intention) rather than final outcomes (e.g., firm profitability, employment), limiting their policy relevance (Abrokwah-Larbi, 2024 ; Mutasa et al., 2024 ). 5.5 LMIC Contextual Factors as Moderators The moderating layer represents five LMIC-specific contextual factors. Digital Infrastructure — including connectivity, device access, and data storage — is the most consistently identified moderator across included studies (Ononiwu et al., 2024 ; Jessy et al., 2024 ; Tewari et al., 2026 ). Regulation encompasses data protection laws, AI governance frameworks, and sector-specific regulations that shape the permissible scope of AI deployment (Demaidi, 2025 ; Bayram et al., 2022 ). Human Capital — comprising digital literacy, AI skills, and organisational learning capabilities — determines the effective absorptive capacity of LMIC firms for AI-generated insights (Sima et al., 2020 ; Intaratat, 2022 ). Resource Constraints disproportionately limit AI investment in micro and small enterprises, the most prevalent firm type in LMICs (Quaye et al., 2024 ; Juma'at et al., 2025). Informality — the extent to which economic activity occurs outside formal regulatory and statistical systems — shapes both the data available for AI training and the business contexts in which AI must operate (Anwar et al., 2020; Eze et al., 2018 ). 6. Discussion This systematic review aimed to provide a methodologically rigorous, theory-driven, and geographically inclusive synthesis of global evidence on AI applications in marketing and entrepreneurship in LMICs between 2016 and 2026. By mapping 120 peer-reviewed studies, the results validate that AI is a transformative force in emerging economies, though its impact is heavily mediated by local institutional voids. The central contribution of this work is the Contextual AI–Business Performance Framework, which integrates the Resource-Based View (RBV), the Technology Acceptance Model (TAM), and Institutional Theory to explain the AI-to-outcome pathway. From an RBV perspective, our findings demonstrate that AI acts as a strategic asset primarily when it resolves the information asymmetries typical of LMIC markets (Barney, 1991 ; Chen et al., 2022 ). For instance, entrepreneurs in South Asia and Sub-Saharan Africa leverage AI-driven market intelligence to compensate for weak formal data infrastructures (Khan et al., 2024 ; Abrokwah-Larbi, 2024 ). However, the framework challenges universal TAM assumptions by showing that "ease of use" is not merely a technical trait but is conditioned by linguistic localization and infrastructure reliability (Davis, 1989 ; Ikumoro et al., 2019; Mogaji et al., 2024 ). Methodologically, the review identifies a significant "rigor gap" in existing literature. The predominance of unspecified empirical (44.2%) and cross-sectional designs (14.2%) limits the ability of the field to establish causal links between AI adoption and long-term firm performance (Abrokwah-Larbi, 2024 ; Mutasa et al., 2024 ). This reliance on cross-sectional data makes establishing causal links between AI adoption and firm performance difficult. Technologically, "General AI/Automation" is the dominant category (47.5%), yet there is a deficit in complex predictive analytics. While Nti et al. ( 2020 ) highlight the potential for ensemble learning in financial prediction, such high-level technical implementations are rare compared to broad automation (Dwivedi et al., 2019; Sharma et al., 2021 ). The near-absence of longitudinal and experimental designs (combined 7.5%) suggests that much of the current evidence is descriptive rather than predictive. This is compounded by a severe geographic skew; while Sub-Saharan Africa (29.2%) and South Asia (19.2%) have established a robust evidence base, regions like Latin America and the Caribbean (1.7%) remain significantly under-represented. Furthermore, the dominance of "General AI/Automation" (47.5%) as the top technology category suggests that LMIC firms currently prioritize accessible, off-the-shelf tools over complex, bespoke algorithmic frameworks like Deep Learning (5.0%) (Lim, 2022 ; Tewari et al., 2026 ). From a practical and policy perspective, the synthesis highlights that digital infrastructure and human capital are the most critical moderators of AI success. For LMIC governments, the strategic importance of mobile broadband and electricity access cannot be overstated, as they serve as the foundational prerequisites for any AI-driven economic development (Ononiwu et al., 2024 ; Sima et al., 2020 ). Moreover, there is an urgent need for targeted AI capacity-building programs in the SME sector to address human capital constraints (Alvi et al., 2025 ; Ononiwu et al., 2024 ). Without localized training and supportive regulatory frameworks that balance innovation with data protection, the gap between AI’s potential and its realized impact in LMICs will continue to widen (Demaidi, 2025 ; Tewari et al., 2026 ). Despite its comprehensive scope, this review is subject to three primary limitations. First, the dominance of cross-sectional and unspecified designs precludes strong causal inferences regarding AI’s impact on firm outcomes. Second, the pronounced geographic concentration in Africa and Asia limits the generalizability of these findings to other LMIC regions like Latin America or the MENA region. Third, the English-language bias of the database search may have excluded significant scholarship published in local languages such as French, Arabic, or Portuguese. Future research should prioritize causal identification through longitudinal and quasi-experimental designs, such as randomized controlled trials (RCTs), to track AI adoption over multi-year horizons (Abrokwah-Larbi, 2024 ). There is also a pressing need for geographic diversification to move beyond treating LMICs as a homogeneous category (Alvi et al., 2025 ). Finally, scholars should explicitly examine the gendered dimensions of AI, investigating how adoption interacts with the documented gender gap in digital technology access to shape entrepreneurial outcomes (Alvi et al., 2025 ; Ayana et al., 2024 ). 7. Conclusions This systematic review of 120 peer-reviewed studies provides the most comprehensive synthesis to date of AI applications in marketing and entrepreneurship across LMIC contexts. The evidence base documents a rapidly expanding field in which AI technologies—particularly General AI/Automation, NLP/LLM/Chatbots, and Recommender Systems—are being deployed to generate marketing efficiency, customer insight, innovation capacity, firm performance, and inclusive growth across eight LMIC regions. While Sub-Saharan Africa, South Asia, and Southeast Asia dominate the geographic distribution, the study reveals a significant "rigor gap," with a heavy reliance on cross-sectional and unspecified empirical designs that limit causal evidence. The original Contextual AI–Business Performance Framework presented in this review advances the field by integrating the Resource-Based View, the Technology Acceptance Model, and Institutional Theory into a unified account of how AI inputs translate into business outcomes under LMIC-specific moderating conditions. By highlighting the critical roles of digital infrastructure, regulation, human capital, resource constraints, and informality as boundary conditions, the review provides a more contextually grounded theoretical foundation than existing universal AI adoption models. Ultimately, the findings underscore the need for a forward-looking research agenda that prioritizes causal identification, geographic diversification, and the ethical dimensions of AI to ensure that technological transformation fosters equitable and sustainable development in low- and middle-income economies. Abbreviations Table 1 provides the full list of abbreviations used in this article and serves as a reference for readers throughout the text. Table 1. Abbreviations and Acronyms Used in this Study. Abbreviation Definition AI Artificial Intelligence ANN Artificial Neural Network CASP Critical Appraisal Skills Programme CRM Customer Relationship Management DL Deep Learning EGM Evidence Gap Map GDP Gross Domestic Product GenAI Generative Artificial Intelligence IT Information Technology LMIC Low- and Middle-Income Country LLM Large Language Model M&E Marketing & Entrepreneurship (combined domain) MENA Middle East and North Africa ML Machine Learning MMAT Mixed Methods Appraisal Tool NLP Natural Language Processing PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses RBV Resource-Based View RCT Randomised Controlled Trial SME Small and Medium Enterprise SLR Systematic Literature Review TAM Technology Acceptance Model URL Uniform Resource Locator WoS Web of Science Declarations Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflicts of interest. Declaration of Generative AI and AI-assisted Technologies in the Manuscript Preparation Process. During the preparation of this work the authors used AI-assisted technology, namely Cursor version 2.4.37 to write and edit the Python codes used to search for studies, extract metadata and create visualizations. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. Human Ethics, Consent to Participate, and Consent to Publish: Human Ethics, Consent to Participate, and Consent to Publish: not applicable CRediT authorship contribution statement: Conceptualization, B.O., E.O-D, A.B. and G.O.F.; methodology, B.O. & G.O.F.; software, G.O.F.; validation, B.O., E.O-D., A.B. and G.O.F.; formal analysis, B.O., E.O-D., A.B. and G.O.F.; investigation, B.O., E.O-D., A.B. and G.O.F.; resources, B.O., E.O-D, A.B. and G.O.F.; writing—original draft preparation, B.O., E.O-D., A.B. and G.O.F; writing—review and editing, B.O., E.O-D, A.B. and G.O.F.; supervision, G.O.F. All authors have read and agreed to the publication of the manuscript. 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Predicting the determinants of consumer’s intention to boycott surrogate Israeli products – evidence on nonlinear relationships from Morocco. Journal of Islamic Marketing , 16 (10), 2806–2836. https://doi.org/10.1108/JIMA-02-2024-0096 Zhang, Y., & Deng, B. (2024). Exploring the nexus of smart technologies and sustainable ecotourism: A systematic review. Heliyon , 10 (11), e31996. https://doi.org/10.1016/j.heliyon.2024.e31996 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9419902","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":625680201,"identity":"34d82d58-fc83-45d9-bf4e-0b9c9d35df19","order_by":0,"name":"Baffour Osei","email":"","orcid":"","institution":"Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development","correspondingAuthor":false,"prefix":"","firstName":"Baffour","middleName":"","lastName":"Osei","suffix":""},{"id":625680202,"identity":"bf4626aa-7c21-42c5-a463-923998c3eb86","order_by":1,"name":"Emmanuel Osei-Dwomoh","email":"","orcid":"","institution":"Controller and Accountant General's Department","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Osei-Dwomoh","suffix":""},{"id":625680203,"identity":"85d3ca6c-d76d-490f-8cdb-3938659cbd41","order_by":2,"name":"Abigail Boatemaa","email":"","orcid":"","institution":"University of Bradford","correspondingAuthor":false,"prefix":"","firstName":"Abigail","middleName":"","lastName":"Boatemaa","suffix":""},{"id":625680204,"identity":"2ffae72f-fcc5-4362-b4c9-cce61a5b3a50","order_by":3,"name":"Gabriel Osei Forkuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYFACHgaGBAYLBvYGBmYgz4YBSLERo0WCgecAWEsaUAszEVoYEFoOAzEBLfLuZ499eFAB1MLe+9iYp+J84nZ2/mOPeRjuyeHSYngmL3lGwhmgFp7jxsk8Z24n7mxmZjfmYSg2xqmlIceYIbFNgsFeIo35MG/b7cQNh5nZJGcwJCQ24NLS/wao5R/QFoiWc4S1yEuAbGmAaEnmbTsA1iLxAY8WA4l3yQwJxyR4eHiOMRvOOZNsDNRibvDBIAGnX+T7cw8z/qixkeNhb2OWeFNhJ7vh/MFnDxIqEnCGmMEBCM2DLo5LA9AWXC4eBaNgFIyCUQAHADWxS35CD5XtAAAAAElFTkSuQmCC","orcid":"","institution":"Transilvania University of Brasov","correspondingAuthor":true,"prefix":"","firstName":"Gabriel","middleName":"Osei","lastName":"Forkuo","suffix":""}],"badges":[],"createdAt":"2026-04-14 22:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9419902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9419902/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107750026,"identity":"e0272695-5d7f-489f-9653-5be96b4acc26","added_by":"auto","created_at":"2026-04-24 17:00:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45014,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020 Flow Diagram Illustrating the Systematic Literature Search and study Selection Process. \u003cstrong\u003eNote:\u003c/strong\u003e Records were retrieved from five automated databases and eleven manual sources. AI: Artificial intelligence; LMIC: Low- and middle-income country\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/bdf699020cc378afcb19cb82.png"},{"id":107750027,"identity":"751968dc-93c4-4f01-b5a5-60b89e832fe3","added_by":"auto","created_at":"2026-04-24 17:00:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111958,"visible":true,"origin":"","legend":"\u003cp\u003ePublication Trends of Included Studies (2016–2026).\u003cstrong\u003e Note:\u003c/strong\u003e 2026 data are partial (up to search date).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/84a1bd054ac8e4d496f54397.png"},{"id":107869159,"identity":"0d7dd05c-1423-4ea1-aa77-10b2649f5f48","added_by":"auto","created_at":"2026-04-27 07:36:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60940,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic Distribution of Included Studies by LMIC Region (\u003cem\u003en\u003c/em\u003e = 120). \u003cstrong\u003eNote:\u003c/strong\u003e LMIC: Low- and middle-income country.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/a745549c2633394dfcf3bc41.png"},{"id":107868994,"identity":"a92c9466-fbdc-4da1-9699-cc791b6a5dc1","added_by":"auto","created_at":"2026-04-27 07:35:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87663,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of AI Technologies Applied in Included Studies\u003cstrong\u003e (\u003c/strong\u003e\u003cem\u003en\u003c/em\u003e = 120). \u003cstrong\u003eNote:\u003c/strong\u003e NLP: Natural language processing; LLM: Large language model.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/545120ba0360bdc428e210b1.png"},{"id":107750029,"identity":"407e3219-61a2-4d26-a798-c9e95f12c2ca","added_by":"auto","created_at":"2026-04-24 17:00:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94581,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of AI Technology Application by LMIC Region.\u003cstrong\u003e Note: \u003c/strong\u003eCell values = number of studies per region–technology intersection. AI: Artificial intelligence; NLP: Natural language processing; LMIC: Low- and middle-income country.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/23634faaae470165cc82a086.png"},{"id":107868937,"identity":"a19548ee-03b4-4c6b-8878-bfe3badb27a3","added_by":"auto","created_at":"2026-04-27 07:35:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":68843,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Study Designs Among Included Studies (\u003cem\u003en\u003c/em\u003e = 120). \u003cstrong\u003eNote:\u003c/strong\u003e Empirical (unspecified): quantitative studies without a clearly identified design.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/952601f24075a65569d69298.png"},{"id":107750031,"identity":"ca46fb4a-f344-4877-bce4-54c66e4dc2ef","added_by":"auto","created_at":"2026-04-24 17:00:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":67026,"visible":true,"origin":"","legend":"\u003cp\u003eThematic Domain Coverage of Included Studies by LMIC Region.\u003cstrong\u003e Note: \u003c/strong\u003eBars represent studies classified as Entrepreneurship-only (orange), Marketing-only (blue), or both Marketing \u0026amp; Entrepreneurship (green).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/db7430960a2218d99da922d2.png"},{"id":107750030,"identity":"13c13b22-a57b-4a7f-80e7-ff845a79b754","added_by":"auto","created_at":"2026-04-24 17:00:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":174510,"visible":true,"origin":"","legend":"\u003cp\u003eSankey Diagram of Flow of AI Methods through Business Functions to Outcomes.\u003cstrong\u003e Note: \u003c/strong\u003eAI: Artificial intelligence; NLP: Natural language processing; LLM: Large language model.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/c2fcc723b2205e716502c194.png"},{"id":107869160,"identity":"7e1a1bd0-303a-4f06-b233-b380f66f61f4","added_by":"auto","created_at":"2026-04-27 07:36:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":98008,"visible":true,"origin":"","legend":"\u003cp\u003eBubble Plot of AI Technology vs. Research Domain.\u003cstrong\u003e Note: \u003c/strong\u003eBubble size = number of studies; colour = dominant LMIC region.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/72b25cabb076f2bfa66464e6.png"},{"id":108803455,"identity":"0c5ea5da-3d12-4070-8d6a-1e9ffd6b9cb1","added_by":"auto","created_at":"2026-05-08 14:54:59","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":66468,"visible":true,"origin":"","legend":"\u003cp\u003eContextual AI–Business Performance Framework.\u003cstrong\u003e Note: \u003c/strong\u003eThe framework depicts the pathway from AI Inputs through Mechanisms to Intermediate and Final Outcomes, moderated by LMIC Contextual Factors. RBV: Resource-based view; TAM: Technology acceptance model; LMIC: Low- and middle-income country.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/d81fc770c6a2140e21ed0a0a.png"},{"id":108808770,"identity":"c27a7887-bf53-43ea-8af5-2b829992d594","added_by":"auto","created_at":"2026-05-08 15:46:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1523045,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/dde03736-60ff-4bb5-96b2-daad4b9c1a76.pdf"},{"id":107750025,"identity":"8ddf2d56-6868-4d8b-926b-7804a6f8cdb9","added_by":"auto","created_at":"2026-04-24 17:00:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25092,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9419902/v1/ec4dd6d1c81cd50b53cc6390.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Artificial Intelligence Nexus in LMIC Marketing and Entrepreneurship: A Systematic Synthesis and Contextual Performance Framework","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Background and Motivation\u003c/h2\u003e \u003cp\u003eThe global diffusion of artificial intelligence (AI) technologies represents one of the most consequential structural shifts in contemporary business environments. Across high-income economies, AI has reshaped how firms acquire customers, build competitive advantage, and scale entrepreneurial activity \u0026mdash; generating substantial productivity dividends documented in the management and information systems literatures (Dwivedi et al., 2019; Bughin et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Yet the geography of AI-enabled business transformation is profoundly uneven. The overwhelming majority of landmark AI studies draw their samples, training data, and institutional contexts from North America, Western Europe, and East Asia \u0026mdash; leaving a fundamental empirical lacuna regarding how AI technologies are applied, adapted, and absorbed in low- and middle-income countries (LMICs), where an estimated 84% of the world's population and the majority of global entrepreneurs reside (Abrokwah-Larbi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sampson et al.2025; World Bank, \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis asymmetry matters for several interconnected reasons. First, LMIC economies exhibit structural features \u0026mdash; pronounced digital infrastructure deficits, informal economic activity, constrained financial markets, institutional voids, and heterogeneous regulatory environments \u0026mdash; that fundamentally alter the boundary conditions under which AI adoption occurs and business outcomes are realised (Mhlanga, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Demaidi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ayana et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Findings from high-income country contexts cannot be straightforwardly transposed to LMIC settings without systematic contextual adjustment. Second, the accelerating pace of AI capability development \u0026mdash; encompassing large language models (LLMs), multimodal systems, autonomous agents, and generative AI \u0026mdash; creates mounting pressure for LMIC entrepreneurs and marketing practitioners to navigate a rapidly shifting technological landscape without the institutional scaffolding available to their high-income country counterparts (Mogaji et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dwivedi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Third, LMICs are simultaneously home to a rapidly growing digital consumer base, a burgeoning SME sector, and entrepreneurial ecosystems that are increasingly connected to global value chains, creating both the demand for and the potential benefits of AI adoption at a scale that renders the current evidence gap increasingly consequential for economic development policy (Sampson et al., 2025; Khan et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe marketing\u0026ndash;entrepreneurship nexus represents a particularly critical and underexplored intersection in the LMIC AI literature. Marketing capability is a primary determinant of SME survival and growth in competitive LMIC markets, yet LMIC entrepreneurs routinely cite limited market intelligence, constrained marketing budgets, and inability to reach target consumers as among their most binding constraints (Abrokwah-Larbi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Quaye et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). AI technologies \u0026mdash; including NLP-driven sentiment analysis, recommender systems for customer targeting, AI-enhanced CRM platforms, and automated digital advertising \u0026mdash; offer theoretically compelling solutions to precisely these constraints. Similarly, AI tools for business intelligence, financial inclusion (fintech), agricultural value chain optimisation (agritech), and digital platform entrepreneurship have generated empirical evidence of measurable impact across LMIC contexts (Cambaza, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mhlanga, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Adewuyi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, no comprehensive synthesis integrates this growing body of evidence into a coherent empirical and theoretical account.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Evidence Gaps and the Need for Synthesis\u003c/h2\u003e \u003cp\u003eThe impetus for this systematic review arises from three convergent evidence gaps. First, the volume of empirical studies on AI in LMIC business contexts has accelerated dramatically since 2021, with 2024\u0026ndash;2025 constituting the peak publication years in our dataset \u0026mdash; yet no existing systematic review or meta-analysis comprehensively maps this literature. Existing reviews either restrict their scope to high-income country settings (Blut et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dwivedi et al., 2020) or adopt narrow domain foci \u0026mdash; covering AI in healthcare, education, or finance \u0026mdash; without integrating the marketing\u0026ndash;entrepreneurship nexus that is most directly relevant to private sector development in LMICs.\u003c/p\u003e \u003cp\u003eSecond, the theoretical frameworks applied to AI adoption and performance in LMIC contexts remain fragmented. Resource-based view (RBV), technology acceptance model (TAM), and institutional theory have each been applied independently to aspects of this question, but no integrative framework synthesises their complementary insights into a coherent account of how AI inputs translate into business outcomes under LMIC-specific boundary conditions (Barney, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Davis, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; DiMaggio and Powell, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). The absence of such a framework limits theoretical cumulation and generates inconsistent, context-specific findings that are difficult to compare across studies. This mirrors the challenge identified in the IPSAS adoption literature, where the absence of an integrative analytical model obscured the structural mechanisms linking formal policy adoption to substantive implementation outcomes (Jayasinghe et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tetteh et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Analogously, the AI-in-LMIC literature requires a unified framework that positions contextual moderators as structural boundary conditions rather than incidental covariates.\u003c/p\u003e \u003cp\u003eThird, geographic coverage remains severely skewed. Sub-Saharan Africa, South Asia, and Southeast Asia dominate the existing literature, while Latin America, the Caribbean, the MENA region, and small island developing states remain substantially underrepresented \u0026mdash; a pattern that mirrors structural research inequities documented in adjacent development literatures and constrains the generalisability of AI\u0026ndash;business performance findings across the full diversity of LMIC institutional environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Aim, Objectives, and Research Questions\u003c/h2\u003e \u003cp\u003eThis systematic review aimed to provide a methodologically rigorous, theory-driven, and geographically inclusive synthesis of global evidence on AI applications in marketing and entrepreneurship in LMICs (2016\u0026ndash;2026), and to advance original theoretical synthesis that explains how AI-to-outcome pathways operate under LMIC-specific boundary conditions. Specifically, the review pursued four objectives:\u003c/p\u003e \u003cp\u003eObjective 1 (Evidence Mapping): To systematically map the geographic and thematic distribution of AI research in LMIC marketing and entrepreneurship contexts, characterising the AI technology landscape, study design composition, and regional evidence density across eight LMIC regions.\u003c/p\u003e \u003cp\u003eObjective 2 (Evidence Synthesis): To synthesise the evidence on AI outcomes in LMIC marketing and entrepreneurship, identifying the mechanisms through which AI technologies generate marketing efficiency, customer insight, innovation capacity, firm performance, and inclusive growth.\u003c/p\u003e \u003cp\u003eObjective 3 (Framework Development): To develop and present an original Contextual AI\u0026ndash;Business Performance Framework that integrates RBV, TAM, and institutional theory into a unified LMIC-specific model of AI adoption and business outcomes, positioning contextual moderators \u0026mdash; digital infrastructure, regulation, human capital, resource constraints, and informality \u0026mdash; as structural boundary conditions.\u003c/p\u003e \u003cp\u003eObjective 4 (Gap Identification and Research Agenda): To identify the most acute geographic and thematic zones of research deficit, and to articulate a structured future research agenda with testable propositions that advance causal identification, geographic diversification, and governance dimensions of AI in LMIC contexts.\u003c/p\u003e \u003cp\u003eThese objectives were addressed through four corresponding research questions:\u003c/p\u003e \u003cp\u003eRQ1: What AI technologies are applied in marketing and entrepreneurship research in LMICs, and how are they distributed geographically and thematically?\u003c/p\u003e \u003cp\u003eRQ2: What outcomes are associated with AI application in LMIC business contexts, and through what mechanisms do these outcomes materialise?\u003c/p\u003e \u003cp\u003eRQ3: How do LMIC-specific contextual factors moderate the AI\u0026ndash;outcome relationship, and what theoretical framework best accounts for this moderation?\u003c/p\u003e \u003cp\u003eRQ4: What geographic and methodological gaps constrain the existing evidence base, and what research agenda is required to address them?\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organised as follows. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the theoretical foundations. Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the methodology, including database sources, search strings, and PRISMA 2020 selection procedures. Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the main findings, covering geographic distribution, AI technology typology study design, and thematic domains. Section \u003cspan refid=\"Sec28\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the Contextual AI\u0026ndash;Business Performance Framework. Section \u003cspan refid=\"Sec34\" class=\"InternalRef\"\u003e6\u003c/span\u003e discusses theoretical, methodological, and policy implications. Section \u003cspan refid=\"Sec35\" class=\"InternalRef\"\u003e7\u003c/span\u003e concludes.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Theoretical Foundations","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Resource-Based View and AI as a Strategic Asset\u003c/h2\u003e \u003cp\u003eThe resource-based view (RBV) posits that sustained competitive advantage arises from firm-specific resources that are valuable, rare, inimitable, and non-substitutable (Barney, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). In LMIC contexts, AI constitutes a uniquely strategic resource precisely because information asymmetries are pronounced and conventional data infrastructures are underdeveloped. Chen et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) provide empirical support for this proposition, demonstrating that AI adoption enhances firm performance through resource reconfiguration in e-commerce settings. Khan et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) extend this logic to the construction sector in East Asia, where knowledge management-based AI adoption moderates the relationship between environmental uncertainty and performance. Critically, however, RBV assumptions about resource imitability must be recalibrated for LMIC contexts, where formal intellectual property regimes are often weak and AI capabilities can diffuse rapidly through mobile platforms (Dey et al., 2023; Quaye et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Technology Acceptance Model and AI Adoption in LMICs\u003c/h2\u003e \u003cp\u003eThe technology acceptance model (TAM) and its extensions (Davis, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Venkatesh et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) provide the dominant theoretical lens for understanding AI adoption decisions at the organisational and individual levels. Studies in our corpus apply TAM to explain chatbot adoption in SME supply chains (Panigrahi et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), voice assistant adoption in fashion retail (Kautish et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and AI-enabled customer experience systems (Tula et al., 2024). A consistent finding is that perceived usefulness and ease of use interact with LMIC-specific moderators \u0026mdash; including digital literacy, infrastructure reliability, and language localisation \u0026mdash; to shape adoption trajectories (Ikumoro et al., 2019; Juma'at et al., 2025). Mogaji et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) raise the provocative question of whether TAM remains applicable in the generative AI era, noting that the model's assumptions about user intentionality may not hold when AI systems operate autonomously on behalf of firms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Institutional Theory and LMIC Contextual Factors\u003c/h2\u003e \u003cp\u003eInstitutional theory (North, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; DiMaggio and Powell, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) directs attention to the formal and informal rules that shape organisational behaviour. In LMIC contexts, institutional voids \u0026mdash; the absence of reliable market-supporting institutions \u0026mdash; create both barriers and opportunities for AI-enabled business models (Demaidi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ayana et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Studies in our corpus consistently identify regulatory uncertainty, limited data protection frameworks, and inadequate digital infrastructure as institutional constraints on AI adoption (Mutasa et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Abaddi et al., 2023). Conversely, institutional entrepreneurship \u0026mdash; the deliberate leveraging of AI to bypass institutional bottlenecks \u0026mdash; is documented across fintech, agribusiness, and digital health sectors (Mhlanga, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cambaza, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ahmed et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Towards an Integrative Framework\u003c/h2\u003e \u003cp\u003eNo single theoretical lens adequately captures the complexity of AI deployment in LMIC business contexts. RBV accounts for competitive dynamics but underspecifies institutional moderators. TAM explains individual adoption decisions but does not address systemic infrastructure constraints. Institutional theory illuminates regulatory and cultural factors but provides limited guidance on technology-performance pathways. The Contextual AI\u0026ndash;Business Performance Framework developed in Section \u003cspan refid=\"Sec28\" class=\"InternalRef\"\u003e5\u003c/span\u003e integrates these perspectives to provide a more complete account of how AI inputs translate into business outcomes under LMIC-specific boundary conditions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Review Design and PRISMA Compliance\u003c/h2\u003e \u003cp\u003eThis review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A systematic literature review (SLR) design was adopted to enable transparent, reproducible, and comprehensive synthesis of evidence on AI applications in marketing and entrepreneurship in LMICs. The review protocol was developed and registered a priori on Open Science Forum (available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/3phcj\u003c/span\u003e\u003cspan address=\"https://osf.io/3phcj\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), specifying the research questions, eligibility criteria, search strategy, and analysis plan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Eligibility Criteria\u003c/h2\u003e \u003cp\u003eStudies were included if they: (i) were published in peer-reviewed journals or conference proceedings between 2010 and 2026; (ii) explicitly applied or examined at least one AI technology; (iii) focused on a marketing, entrepreneurship, or combined marketing\u0026ndash;entrepreneurship domain; (iv) were conducted in or explicitly relevant to one or more LMIC as defined by the World Bank's income classification; and (v) were available in English or English translation. Studies were excluded if they: focused exclusively on high-income country contexts without LMIC applicability; did not employ any identifiable AI technology; were primarily in sectors outside business with no business implications; or were grey literature without peer review.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Search Strategy and Databases\u003c/h2\u003e \u003cp\u003eSearches were conducted across five automated databases \u0026mdash; OpenAlex, CrossRef, Semantic Scholar, EconBiz, and IDEAS/RePEC \u0026mdash; and supplemented by manual searches of eleven databases: Scopus, Web of Science (WoS), EconLit via EBSCO, ABI/INFORM (ProQuest), Business Source Premier, IEEE Xplore, ACM Digital Library, ScienceDirect, African Journals Online (AJOL), SciELO, and SSRN. Grey literature searches were conducted via Google Scholar and institutional repositories. All searches were conducted in March\u0026ndash;April 2026.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Search Strings\u003c/h2\u003e \u003cp\u003eSearch strings were developed iteratively through a combination of Boolean logic and controlled vocabulary, organised into three thematic blocks. Representative automated search strings included:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEN\u0026middot;01\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\"artificial intelligence marketing low-income countries developing economies\"\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEN\u0026middot;11\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\"artificial intelligence entrepreneurship developing countries\"\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEN\u0026middot;21\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\"artificial intelligence marketing entrepreneurship LMIC developing countries\"\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe Scopus master Boolean string combined all four concept blocks \u0026mdash; AI technologies \u0026times; Marketing \u0026times; Entrepreneurship \u0026times; LMIC geography \u0026mdash; with a publication year filter of 2010\u0026ndash;2026. Full query strings are deposited on figshare as \u003cb\u003eSupplementary File S1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Study Selection and Data Extraction\u003c/h2\u003e \u003cp\u003eInitial automated searches retrieved 4,661 records; manual searches contributed an estimated 500\u0026ndash;800 additional records, for a combined total of approximately 5,200\u0026ndash;5,400 records. After deduplication, 4,200 records were screened by title and abstract, of which 3,858 were excluded. The remaining 342 full-text articles were assessed for eligibility, with 222 excluded (not LMIC-focused, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;120; no AI application, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54; out of scope, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;48). A total of 120 studies were retained for qualitative synthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Data were extracted into a structured matrix capturing: author(s), year, journal, country/region, AI technology category, thematic domain, study design, and key findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Quality Assessment\u003c/h2\u003e \u003cp\u003eGiven the heterogeneity of study designs in the corpus, a domain-specific quality appraisal approach was adopted rather than a single scoring tool. Empirical studies were assessed against criteria adapted from the Mixed Methods Appraisal Tool (MMAT); systematic reviews were assessed using AMSTAR-2 criteria; qualitative studies were appraised using the Critical Appraisal Skills Programme (CASP) checklist. No studies were excluded on quality grounds alone; however, quality is considered in interpreting evidence strength across themes.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Publication Trends and Growth Trajectory\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the annual publication counts for included studies from 2016 to 2026. The trajectory demonstrates a clear inflection point at 2021, consistent with the broader acceleration of AI adoption following the COVID-19 pandemic and the maturation of transformer-based language models. Publications grew from a baseline of one to three studies per year between 2016 and 2019, accelerating to 8\u0026ndash;12 studies per year in 2020\u0026ndash;2021, and reaching a peak of 22\u0026ndash;23 studies in 2023\u0026ndash;2025. The 2026 partial-year data (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5) suggest continued growth. Marketing-focused and entrepreneurship-focused streams grew in parallel, while studies addressing both domains simultaneously expanded most rapidly from 2022 onwards, reflecting increasing scholarly recognition of the marketing\u0026ndash;entrepreneurship interface in AI research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Geographic Distribution\u003c/h2\u003e \u003cp\u003eThe geographic distribution of included studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) reveals a pronounced concentration in Sub-Saharan Africa (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35, 29.2%), South Asia (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23, 19.2%), and Southeast Asia (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18, 15.0%). Together, these three regions account for nearly two-thirds (63.4%) of all studies included in the review. East Asia and the Pacific contributed 15 studies (12.5%), while Multiple/Global LMIC studies accounted for 14 studies (11.7%). North Africa and the MENA region contributed 10 studies (8.3%). In contrast, Europe and Central Asia (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, 2.5%) and Latin America and the Caribbean (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2, 1.7%) were markedly underrepresented, reflecting both language barriers in the search and the relatively nascent state of AI\u0026ndash;business research infrastructure in these regions. The Sub-Saharan African dominance spans a wide range of sectors \u0026mdash; digital financial services, agricultural value chains, digital advertising, and e-commerce \u0026mdash; reflecting both the diversity of AI applications and the concentration of development-oriented research funding in that region.\u003c/p\u003e \u003cp\u003eThere is a high degree of uniformity regarding the types of AI technologies and domains being studied across different regions. \"General AI/Automation\" is the dominant technology in six out of the eight regional categories, indicating a widespread global focus on broad AI implementations. However, a slight shift is observable in \"Multiple/Global LMIC\" studies and \"Europe \u0026amp; Central Asia,\" where \"NLP/LLM/Chatbot\" emerges as the dominant technology. This suggests that cross-regional or newer regional research may be leaning more toward conversational and language-based AI. In terms of application, the vast majority of regions focus consistently on the intersection of Marketing, Entrepreneurship, and M\u0026amp;E, demonstrating a unified research interest in how AI supports business and economic monitoring.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe year ranges provided in the table highlight the varying maturity of AI research across these regions. Sub-Saharan Africa has the longest-standing research presence, with studies dating back to 2016. South Asia and Southeast Asia followed shortly after in 2017. Conversely, research in the Europe \u0026amp; Central Asia and Latin America \u0026amp; Caribbean regions is a much more recent phenomenon, with the earliest studies in this sample appearing only in 2023. The fact that several regions (Sub-Saharan Africa, North Africa \u0026amp; MENA, and Latin America) include studies projected or published through 2026 suggests a growing and forward-looking momentum in the field, with the most established regions maintaining a head start in the volume and duration of their academic output.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeographic Distribution of Included Studies by LMIC Region (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMIC Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDominant AI Technology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResearch Domains\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYear Range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral AI/Automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarketing; Entrepreneurship; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016\u0026ndash;2026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral AI/Automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarketing; Entrepreneurship; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2017\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoutheast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral AI/Automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarketing; Entrepreneurship; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2017\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asia \u0026amp; Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral AI/Automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarketing; Entrepreneurship; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple/Global LMIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNLP/LLM/Chatbot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEntrepreneurship; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u0026ndash;2026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Africa \u0026amp; MENA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral AI/Automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarketing; Entrepreneurship; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u0026ndash;2026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurope \u0026amp; Central Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNLP/LLM/Chatbot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarketing; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2023\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatin America \u0026amp; Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral AI/Automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarketing; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2023\u0026ndash;2026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e120\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: LMIC: Low- and middle-income country. Region classifications follow the World Bank's income-group and regional groupings. n\u0026thinsp;=\u0026thinsp;number of studies. M\u0026amp;E\u0026thinsp;=\u0026thinsp;Marketing \u0026amp; Entrepreneurship (combined domain). Year range indicates the earliest and most recent publication years within each region.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 AI Technology Distribution\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e present the distribution of AI technology categories across included studies. The most prominent trend is the heavy concentration of research on a few select technology categories. General AI/Automation constituted the largest category (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;57, 47.5%), encompassing studies that applied AI broadly without specifying a sub-technology \u0026mdash; reflecting the tendency of business-oriented research to treat AI as a generic capability rather than a set of differentiated tools. NLP/LLM/Chatbot was the second most prevalent category (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24, 20.0%), consistent with the rapid proliferation of conversational AI in customer service, marketing communications, and entrepreneurial support contexts. Recommender Systems ranked third (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21, 17.5%), reflecting their established role in e-commerce personalisation in emerging markets. Machine Learning (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, 6.7%), Deep Learning/ANN (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, 5.0%), and Predictive Analytics (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4, 3.3%) were less commonly studied as discrete technologies. This suggests that the current literature is heavily focused on broad automation and user-interaction technologies rather than highly specialized or technical algorithmic frameworks.\u003c/p\u003e \u003cp\u003eThere is a striking level of consistency regarding the geographical focus and the functional domains covered by these technologies. Sub-Saharan Africa emerges as the \"Top LMIC Region\" across five of the six AI categories, indicating that it is a central hub for research into AI applications within developing economies. In terms of domain coverage, nearly every technology category is applied across the combined fields of Marketing and Entrepreneurship. Interestingly, \"Machine Learning\" and \"Deep Learning/ANN\" show a slightly more concentrated focus on Entrepreneurship and M\u0026amp;E (Monitoring and Evaluation) compared to the broader application of \"General AI/Automation.\"\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast to the high prevalence of general automation, more computationally intensive or specialized AI technologies are significantly underrepresented. \"Machine Learning\" (6.7%), \"Deep Learning/ANN\" (5.0%), and \"Predictive Analytics\" (3.3%) represent the smallest shares of the sample. Notably, \"Deep Learning/ANN\" is the only category where the top LMIC region shifts away from Sub-Saharan Africa to \"North Africa \u0026amp; MENA.\" This trend suggests that while there is significant interest in the broad implementation of AI, there is currently less focus on the more complex, data-heavy predictive modeling and neural network architectures within the context of the studied domains and regions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of AI Technology Categories Applied Across Included Studies (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Technology Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDomains Covered\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTop LMIC Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExample Reference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral AI/Automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarketing; Entrepreneurship; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLim (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLP/LLM/Chatbot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarketing; Entrepreneurship; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIkumoro et al. (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecommender Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarketing; Entrepreneurship; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLi et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMachine Learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEntrepreneurship; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDwivedi et al. (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeep Learning/ANN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEntrepreneurship; Marketing; M\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorth Africa \u0026amp; MENA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSharma et al. (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictive Analytics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarketing \u0026amp; Entrepreneurship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMohammadian et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e120\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: NLP: Natural language processing; LLM: Large language model. n\u0026thinsp;=\u0026thinsp;number of studies. Percentage calculated from total included studies (n\u0026thinsp;=\u0026thinsp;120). M\u0026amp;E\u0026thinsp;=\u0026thinsp;Marketing \u0026amp; Entrepreneurship (combined domain).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) reveals that General AI/Automation is consistently the dominant technology category across all LMIC regions, with NLP/LLM/Chatbot particularly prominent in Multiple/Global LMIC studies (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6), and Recommender Systems concentrated in Sub-Saharan Africa (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7) and South Asia (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Study Design Distribution\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the distribution of study designs among the 120 included studies. Empirical studies with unspecified designs constituted the largest methodological category (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;53, 44.2%), a finding that raises important questions about methodological transparency and replicability. Qualitative and case study designs were the most common explicitly specified approach (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25, 20.8%), followed by survey and cross-sectional designs (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17, 14.2%). Systematic reviews and meta-analyses contributed 12 studies (10.0%), experimental designs 6 studies (5.0%), conceptual and review studies 4 studies (3.3%), and longitudinal or secondary data studies 3 studies (2.5%). The predominance of cross-sectional and qualitative designs \u0026mdash; and the near-absence of longitudinal and experimental designs \u0026mdash; is a critical limitation of the evidence base.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows several key patterns and trends regarding the methodological landscape, technological focus, and geographical distribution of the 120 included studies. When Empirical (unspecified) studies are combined with Qualitative/Case Studies (20.8%), these two categories comprise approximately two-thirds of the entire dataset. In contrast, more rigorous or time-intensive designs are notably rare; Experimental studies account for only 5.0% of the research, while Longitudinal/Secondary studies represent the smallest fraction at just 2.5%. This suggests that the current state of research in this field is focused primarily on observational and descriptive data rather than controlled experimentation or long-term impact analysis.\u003c/p\u003e \u003cp\u003eThe distribution of AI technologies across these study designs shows a high level of consistency, with \"General AI/Automation\" and \"NLP/LLM/Chatbot\" appearing as top technologies in nearly every category. However, specific technological clusters are visible within certain designs. For example, \"Deep Learning/ANN\" and \"Machine Learning\" are most prominent in Empirical and Survey/Cross-sectional designs, suggesting these methodologies are frequently used to evaluate algorithmic performance or data-driven outcomes. Conversely, \"Recommender Systems\" only appear in the Experimental and Longitudinal categories, indicating that research into personalized AI recommendations tends to involve more specialized or intervention-based research frameworks.\u003c/p\u003e \u003cp\u003eGeographically, the \"East Asia \u0026amp; Pacific\" region shows the most robust representation, appearing in six out of the seven study design categories. This indicates a highly diverse research output from that region. There is also a notable trend toward \"Multiple/Global LMIC\" (Low-Middle Income Country) perspectives within Qualitative studies and Systematic Reviews, suggesting a focus on comparative or broad-scale analysis in these areas. However, certain regions appear to be underrepresented in the more common study designs; for instance, Sub-Saharan Africa and Southeast Asia only appear in the Conceptual and Longitudinal categories, which are the least frequent designs in the overall sample. This highlights a potential gap in large-scale empirical or experimental research within those specific regions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Study Designs Among Included Studies (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKey AI Technologies Used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRegions Represented\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmpirical (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeep Learning/ANN; General AI/Automation; Machine Learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEast Asia \u0026amp; Pacific; Europe \u0026amp; Central Asia; Latin America \u0026amp; Caribbean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQualitative/Case Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral AI/Automation; NLP/LLM/Chatbot; Predictive Analytics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEast Asia \u0026amp; Pacific; Multiple/Global LMIC; North Africa \u0026amp; MENA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvey/Cross-sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeep Learning/ANN; General AI/Automation; NLP/LLM/Chatbot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEast Asia \u0026amp; Pacific; Europe \u0026amp; Central Asia; North Africa \u0026amp; MENA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystematic Review/Meta-analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral AI/Automation; Machine Learning; NLP/LLM/Chatbot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEast Asia \u0026amp; Pacific; Europe \u0026amp; Central Asia; Multiple/Global LMIC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral AI/Automation; NLP/LLM/Chatbot; Recommender Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEast Asia \u0026amp; Pacific; Multiple/Global LMIC; South Asia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConceptual/Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral AI/Automation; NLP/LLM/Chatbot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSoutheast Asia; Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal/Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral AI/Automation; NLP/LLM/Chatbot; Recommender Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEast Asia \u0026amp; Pacific; Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e120\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: AI: Artificial intelligence; NLP: Natural language processing. n\u0026thinsp;=\u0026thinsp;number of studies. Percentage calculated from total included studies (n\u0026thinsp;=\u0026thinsp;120). Key AI technologies represent up to three most commonly used technologies within each study design category.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Thematic Domain Coverage\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the thematic domain coverage of included studies by LMIC region. Studies addressing both marketing and entrepreneurship simultaneously constitute the largest thematic group across all regions (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;77, 64.2%), confirming that AI application in LMIC business contexts rarely adheres to clean domain boundaries. Sub-Saharan Africa displayed the highest absolute number of Marketing \u0026amp; Entrepreneurship studies (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21), followed by South Asia (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14) and Southeast Asia (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11). Pure entrepreneurship studies were most common in Sub-Saharan Africa (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9) and Southeast Asia (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), while pure marketing studies were relatively evenly distributed across regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Flow of AI Methods Through Business Functions to Outcomes\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents a Sankey diagram illustrating the flow of AI methods through business functions to outcomes across included studies. The diagram reveals that General AI/Automation and NLP/LLM/Chatbot methods are the primary conduits through which AI enters the business function layer, with Customer Service and Decision Support emerging as the most frequently supported functions. Machine Learning and Recommender Systems are particularly associated with Product Recommendation and Customer Segmentation functions. At the outcome layer, all AI methods and business functions contribute to four primary outcomes: Inclusive Growth, SME Productivity, Entrepreneurial Success, and Marketing Performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.7 AI Technology vs. Research Domain \u0026mdash; Bubble Plot\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e presents a bubble plot mapping AI technology categories against research domains, with bubble size reflecting the number of studies and colour indicating the dominant LMIC region for each cell. The largest bubble \u0026mdash; General AI/Automation in the Marketing \u0026amp; Entrepreneurship domain (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35), dominated by Southeast Asia \u0026mdash; confirms that integrated marketing\u0026ndash;entrepreneurship research using broad AI approaches constitutes the single largest segment of the literature. NLP/LLM/Chatbot in the Marketing \u0026amp; Entrepreneurship domain (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18, Multiple/Global LMIC) and Recommender Systems in the Marketing \u0026amp; Entrepreneurship domain (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13, Sub-Saharan Africa) are the next largest clusters. The relative absence of studies in the Predictive Analytics\u0026ndash;Marketing domain and the Machine Learning\u0026ndash;Marketing domain highlights specific technology-domain combinations that remain underexplored in LMIC contexts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Key Thematic Findings\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.8.1 AI for Customer Engagement and Marketing Performance\u003c/h2\u003e \u003cp\u003eA substantial portion of the corpus documents AI's role in enhancing customer engagement and marketing performance in LMIC firms. Recommender systems have been applied in e-commerce contexts in China (Li et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), India (Mishra et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Vietnam (Le et al., 2017), demonstrating improved conversion rates and customer retention. NLP and sentiment analysis tools have been deployed to analyse consumer opinion in South Asian digital advertising markets (Sun et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while AI-powered CRM systems are documented in Sub-Saharan African SMEs (Abubakar et al., 2025; Abdulsalam et al., 2024). Voice assistant and conversational AI adoption in marketing contexts is documented in South Asian fashion retail (Kautish et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Southeast Asian e-commerce (Ikumoro et al., 2019), and global LMIC hospitality (Foroudi et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Hyper-personalised marketing strategies enabled by AI are examined in Prince et al. (2025), who demonstrate their efficacy in South Asian digital markets while cautioning against algorithmic bias in consumer segmentation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e4.8.2 AI for Entrepreneurship and SME Development\u003c/h2\u003e \u003cp\u003eAI's role in entrepreneurial ecosystems and SME development in LMICs is documented across diverse sectoral and regional contexts. Digital entrepreneurship challenges in Jordan's MENA market are examined by Abaddi et al. (2023), who identify recommender system-based matching mechanisms as potential enablers of entrepreneurial discovery. Fintech AI applications \u0026mdash; including AI-enabled credit scoring, mobile money, and digital micro-lending \u0026mdash; are documented across Sub-Saharan Africa (Mhlanga, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cambaza, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Adewuyi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and South Asia (Alvi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), with evidence of improved financial inclusion outcomes. AI tools for agricultural value chain optimisation represent a growing frontier in LMIC entrepreneurship research (Legg et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jessy et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Conceptual Framework: AI Applications in Marketing and Entrepreneurship in LMICs","content":"\u003cp\u003eDrawing on the systematic review findings and the theoretical perspectives outlined in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we present the Contextual AI\u0026ndash;Business Performance Framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The framework conceptualises the AI-to-outcome pathway in LMIC business contexts as comprising four sequential stages \u0026mdash; AI Inputs, Mechanisms, Intermediate Outcomes, and Final Outcomes \u0026mdash; moderated by a set of LMIC Contextual Factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.1 AI Inputs\u003c/h2\u003e \u003cp\u003eThe AI Inputs stage encompasses the full typology of AI technologies documented in the review: Machine Learning (ML), Natural Language Processing (NLP), Deep Learning (DL), Recommender Systems, Predictive Analytics, and General AI/Automation. These technologies vary substantially in their data requirements, computational demands, and interpretability \u0026mdash; dimensions that have differential implications for LMIC deployment. ML and DL require large labelled datasets that may be unavailable or of inconsistent quality in LMICs (Seastedt et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). NLP systems require language models trained on local languages and dialects, which are systematically underrepresented in global AI training data (Ayana et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recommender systems require transactional data infrastructure that is nascent in many LMIC markets (Mubarok et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Mechanisms\u003c/h2\u003e \u003cp\u003eAI technologies generate business outcomes through four primary mechanisms: Information Access, Automation, Personalisation, and Decision Support. Information Access mechanisms reduce information asymmetries between firms, consumers, and competitors \u0026mdash; a particularly salient function in LMIC contexts where formal market information systems are often weak (Adewuyi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chambreuil et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Automation mechanisms reduce labour costs and processing time in repetitive business functions, enabling SMEs to achieve operational efficiency gains that would otherwise require significant capital investment (Panigrahi et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sampson et al., 2025). Personalisation mechanisms tailor marketing content, product recommendations, and service interactions to individual consumer preferences (Kautish et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Prince et al., 2025). Decision Support mechanisms provide AI-generated analytical insights to entrepreneurs and managers, improving the quality and speed of strategic decisions (Kasem et al., 2023; Dey et al., 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Intermediate Outcomes\u003c/h2\u003e \u003cp\u003eThe framework identifies three intermediate outcomes: Marketing Efficiency, Customer Insight, and Innovation Capacity. Marketing Efficiency captures improvements in cost-effectiveness, targeting precision, and return on investment of marketing activities. Customer Insight encompasses the depth and actionability of understanding firms acquire about customer preferences, behaviour, and sentiment through AI-enabled analytics. Innovation Capacity refers to the enhanced ability of firms to develop and commercialise new products, services, and business models through AI-augmented ideation and experimentation (Han et al., 2021; John et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Final Outcomes\u003c/h2\u003e \u003cp\u003eFinal outcomes are operationalised at three levels: Firm Performance (revenue growth, market share, profitability), Entrepreneurial Success and Growth (venture survival, scaling, ecosystem participation), and Inclusive Growth (employment generation, poverty reduction, gender equity). The distinction between intermediate and final outcomes is important for research design \u0026mdash; most existing studies measure intermediate outcomes (e.g., customer satisfaction, adoption intention) rather than final outcomes (e.g., firm profitability, employment), limiting their policy relevance (Abrokwah-Larbi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mutasa et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e5.5 LMIC Contextual Factors as Moderators\u003c/h2\u003e \u003cp\u003eThe moderating layer represents five LMIC-specific contextual factors. Digital Infrastructure \u0026mdash; including connectivity, device access, and data storage \u0026mdash; is the most consistently identified moderator across included studies (Ononiwu et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jessy et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tewari et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Regulation encompasses data protection laws, AI governance frameworks, and sector-specific regulations that shape the permissible scope of AI deployment (Demaidi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bayram et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Human Capital \u0026mdash; comprising digital literacy, AI skills, and organisational learning capabilities \u0026mdash; determines the effective absorptive capacity of LMIC firms for AI-generated insights (Sima et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Intaratat, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Resource Constraints disproportionately limit AI investment in micro and small enterprises, the most prevalent firm type in LMICs (Quaye et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Juma'at et al., 2025). Informality \u0026mdash; the extent to which economic activity occurs outside formal regulatory and statistical systems \u0026mdash; shapes both the data available for AI training and the business contexts in which AI must operate (Anwar et al., 2020; Eze et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThis systematic review aimed to provide a methodologically rigorous, theory-driven, and geographically inclusive synthesis of global evidence on AI applications in marketing and entrepreneurship in LMICs between 2016 and 2026. By mapping 120 peer-reviewed studies, the results validate that AI is a transformative force in emerging economies, though its impact is heavily mediated by local institutional voids. The central contribution of this work is the Contextual AI\u0026ndash;Business Performance Framework, which integrates the Resource-Based View (RBV), the Technology Acceptance Model (TAM), and Institutional Theory to explain the AI-to-outcome pathway. From an RBV perspective, our findings demonstrate that AI acts as a strategic asset primarily when it resolves the information asymmetries typical of LMIC markets (Barney, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, entrepreneurs in South Asia and Sub-Saharan Africa leverage AI-driven market intelligence to compensate for weak formal data infrastructures (Khan et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Abrokwah-Larbi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the framework challenges universal TAM assumptions by showing that \"ease of use\" is not merely a technical trait but is conditioned by linguistic localization and infrastructure reliability (Davis, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Ikumoro et al., 2019; Mogaji et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMethodologically, the review identifies a significant \"rigor gap\" in existing literature. The predominance of unspecified empirical (44.2%) and cross-sectional designs (14.2%) limits the ability of the field to establish causal links between AI adoption and long-term firm performance (Abrokwah-Larbi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mutasa et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This reliance on cross-sectional data makes establishing causal links between AI adoption and firm performance difficult. Technologically, \"General AI/Automation\" is the dominant category (47.5%), yet there is a deficit in complex predictive analytics. While Nti et al. (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlight the potential for ensemble learning in financial prediction, such high-level technical implementations are rare compared to broad automation (Dwivedi et al., 2019; Sharma et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The near-absence of longitudinal and experimental designs (combined 7.5%) suggests that much of the current evidence is descriptive rather than predictive. This is compounded by a severe geographic skew; while Sub-Saharan Africa (29.2%) and South Asia (19.2%) have established a robust evidence base, regions like Latin America and the Caribbean (1.7%) remain significantly under-represented. Furthermore, the dominance of \"General AI/Automation\" (47.5%) as the top technology category suggests that LMIC firms currently prioritize accessible, off-the-shelf tools over complex, bespoke algorithmic frameworks like Deep Learning (5.0%) (Lim, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tewari et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a practical and policy perspective, the synthesis highlights that digital infrastructure and human capital are the most critical moderators of AI success. For LMIC governments, the strategic importance of mobile broadband and electricity access cannot be overstated, as they serve as the foundational prerequisites for any AI-driven economic development (Ononiwu et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sima et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, there is an urgent need for targeted AI capacity-building programs in the SME sector to address human capital constraints (Alvi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ononiwu et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Without localized training and supportive regulatory frameworks that balance innovation with data protection, the gap between AI\u0026rsquo;s potential and its realized impact in LMICs will continue to widen (Demaidi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tewari et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its comprehensive scope, this review is subject to three primary limitations. First, the dominance of cross-sectional and unspecified designs precludes strong causal inferences regarding AI\u0026rsquo;s impact on firm outcomes. Second, the pronounced geographic concentration in Africa and Asia limits the generalizability of these findings to other LMIC regions like Latin America or the MENA region. Third, the English-language bias of the database search may have excluded significant scholarship published in local languages such as French, Arabic, or Portuguese. Future research should prioritize causal identification through longitudinal and quasi-experimental designs, such as randomized controlled trials (RCTs), to track AI adoption over multi-year horizons (Abrokwah-Larbi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). There is also a pressing need for geographic diversification to move beyond treating LMICs as a homogeneous category (Alvi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Finally, scholars should explicitly examine the gendered dimensions of AI, investigating how adoption interacts with the documented gender gap in digital technology access to shape entrepreneurial outcomes (Alvi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ayana et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"7. Conclusions","content":"\u003cp\u003eThis systematic review of 120 peer-reviewed studies provides the most comprehensive synthesis to date of AI applications in marketing and entrepreneurship across LMIC contexts. The evidence base documents a rapidly expanding field in which AI technologies\u0026mdash;particularly General AI/Automation, NLP/LLM/Chatbots, and Recommender Systems\u0026mdash;are being deployed to generate marketing efficiency, customer insight, innovation capacity, firm performance, and inclusive growth across eight LMIC regions. While Sub-Saharan Africa, South Asia, and Southeast Asia dominate the geographic distribution, the study reveals a significant \"rigor gap,\" with a heavy reliance on cross-sectional and unspecified empirical designs that limit causal evidence. The original Contextual AI\u0026ndash;Business Performance Framework presented in this review advances the field by integrating the Resource-Based View, the Technology Acceptance Model, and Institutional Theory into a unified account of how AI inputs translate into business outcomes under LMIC-specific moderating conditions. By highlighting the critical roles of digital infrastructure, regulation, human capital, resource constraints, and informality as boundary conditions, the review provides a more contextually grounded theoretical foundation than existing universal AI adoption models. Ultimately, the findings underscore the need for a forward-looking research agenda that prioritizes causal identification, geographic diversification, and the ethical dimensions of AI to ensure that technological transformation fosters equitable and sustainable development in low- and middle-income economies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTable 1 provides the full list of abbreviations used in this article and serves as a reference for readers throughout the text.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Abbreviations and Acronyms Used in this Study.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"597\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eArtificial Intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eANN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eArtificial Neural Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCASP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eCritical Appraisal Skills Programme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eCustomer Relationship Management\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eDeep Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEGM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eEvidence Gap Map\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eGross Domestic Product\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eGenerative Artificial Intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eInformation Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eLow- and Middle-Income Country\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLLM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eLarge Language Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026amp;E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eMarketing \u0026amp; Entrepreneurship (combined domain)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMENA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eMiddle East and North Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eML\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eMachine Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eMixed Methods Appraisal Tool\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eNatural Language Processing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRISMA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003ePreferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRBV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eResource-Based View\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eRandomised Controlled Trial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSME\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eSmall and Medium Enterprise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eSystematic Literature Review\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eTechnology Acceptance Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eURL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eUniform Resource Locator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWoS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 451px;\"\u003e\n \u003cp\u003eWeb of Science\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted Technologies in the Manuscript Preparation Process.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the authors used AI-assisted technology, namely Cursor version 2.4.37 to write and edit the Python codes used to search for studies, extract metadata and create visualizations. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics, Consent to Participate, and Consent to Publish:\u0026nbsp;\u003c/strong\u003eHuman Ethics, Consent to Participate, and Consent to Publish: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement:\u0026nbsp;\u003c/strong\u003eConceptualization, B.O., E.O-D, A.B. and G.O.F.; methodology, B.O. \u0026amp; G.O.F.; software, G.O.F.; validation, B.O., E.O-D., A.B. and G.O.F.; formal analysis, B.O., E.O-D., A.B. and G.O.F.; investigation, B.O., E.O-D., A.B. and G.O.F.; resources, B.O., E.O-D, A.B. and G.O.F.; writing\u0026mdash;original draft preparation, B.O., E.O-D., A.B. and G.O.F; writing\u0026mdash;review and editing, B.O., E.O-D, A.B. and G.O.F.; supervision, G.O.F. All authors have read and agreed to the publication of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: The authors would like to thank the Department of Forest Engineering, Forest Management Planning, and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, for providing some of the equipment needed for this study.Data Availability: The Python search automation script, Excel files, full 120-study dataset (Supplementary Table S1), and all analytical scripts are available as supplementary files on figshare at: https://figshare.com/s/b33fe2dab881fc2eda53. The protocol registration is available at: https://osf.io/3phcj.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbaddi, S., \u0026amp; AL-Shboul, M. A. 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Exploring the nexus of smart technologies and sustainable ecotourism: A systematic review. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(11), e31996. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e31996\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e31996\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"developing economies, digital transformation, small and medium enterprises, sub-Saharan Africa, machine learning, technology acceptance model","lastPublishedDoi":"10.21203/rs.3.rs-9419902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9419902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Artificial intelligence (AI) is fundamentally reshaping global commerce; however, its application within the unique institutional landscapes of low- and middle-income countries (LMICs) remains fragmented. This study addresses this gap by providing the first comprehensive synthesis of the marketing–entrepreneurship nexus in LMIC AI research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Following PRISMA 2020 guidelines, we conducted a robust systematic review of 120 peer-reviewed studies (2016–2026) sourced from five databases using an automated Python 3.12 script and eleven manual repositories. Study quality was rigorously evaluated using the Mixed Methods Appraisal Tool (MMAT) and CASP checklists to ensure evidence reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Findings reveal a technological landscape dominated by General AI/Automation (47.5%) and Natural Language Processing (20.0%), with Sub-Saharan Africa (29.2%) and South Asia (19.2%) emerging as primary research hubs. Our analysis identifies a critical \"rigor gap,\" as 44.2% of studies rely on unspecified empirical designs, with a near-absence (7.5%) of longitudinal or experimental evidence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion/Originality:\u003c/strong\u003eThe study’s primary novelty lies in the development of the Contextual AI–Business Performance Framework. By integrating Resource-Based View (RBV), Technology Acceptance Model (TAM), and Institutional Theory, we move beyond universalistic adoption models to position digital infrastructure, regulation, and informality as essential boundary conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This review contributes a novel theoretical synthesis that bridges the gap between academic rigor and practical implementation. It provides a strategic roadmap for policymakers and operational decision-makers to leverage AI for inclusive growth, while establishing a future research agenda prioritized toward causal identification and geographic diversification in under-researched LMIC regions.\u003c/p\u003e","manuscriptTitle":"The Artificial Intelligence Nexus in LMIC Marketing and Entrepreneurship: A Systematic Synthesis and Contextual Performance Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 17:00:27","doi":"10.21203/rs.3.rs-9419902/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d926729-5605-4bd3-9048-fb422156afec","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T09:30:50+00:00","index":16,"fulltext":""},{"type":"reviewerAgreed","content":"189324458785480182709537867116069758544","date":"2026-05-18T09:06:17+00:00","index":15,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T11:05:29+00:00","index":11,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T17:00:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 17:00:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9419902","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9419902","identity":"rs-9419902","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}