Conference Agenda

We are pleased to submit our manuscript, "New Product Performance in Nanostores: Technology Adoption, Innovation Capability, and Product Intelligence in Emerging Markets," for consideration in the LACCEI conference proceedings. This study represents the first multi-country hierarchical linear modeling (HLM) analysis of new product performance determinants in informal microenterprises (nanostores) across seven emerging markets (Jordan, Israel, Saudi Arabia, UAE, Morocco, Honduras, and Colombia; N=1,412). Building on our exploratory LACCEI 2023 pilot study of 143 Honduran nanostores, this research addresses three critical gaps: (1) validating performance determinants across diverse institutional contexts using multilevel methodology that accounts for neighborhood-level clustering effects ignored in prior single-level studies, (2) examining the theoretically ambiguous moderating role of competitive intensity, and (3) conducting comparative analysis across MENA and Latin American contexts. Our analysis reveals that innovation capability (γ = 0.254, p < .01) substantially outweighs technology adoption (γ = 0.038, p < .05) in driving new product performance, while product intelligence shows no significant direct effect. Notably, competitive intensity positively moderates the technology-performance relationship (γ = 0.274, p < .05), though this pattern reverses across regions (amplification in Latin America, suppression in MENA). The exceptionally high ICC (0.617) confirms the necessity of multilevel modeling for nanostore research. We believe these findings offer significant theoretical and practical contributions to informal retail innovation literature. This manuscript has not been previously published nor is it under consideration elsewhere. We appreciate your time and consideration.

Digital transformation is a critical factor for the competitiveness of Small and Medium-sized Enterprises (SMEs). Information and Communication Technologies (ICT) drive development and enable new business models; however, in rural areas, this process faces specific challenges related to infrastructure, organizational culture, and access to technologies. This paper presents a Digital Maturity Model tailored for rural SMEs, grounded on the Technology-Organization-Environment (TOE) framework and adapted to reflect the degree of control, priority, and impact of each dimension. The model integrates a hierarchical weighting system that balances contextual limitations with the areas where SMEs have greater agency, providing realistic diagnoses and feasible digitalization pathways. Assessment items are structured across five maturity levels, each defined through description, observable elements, typical tools and processes, and advancement guidelines. In addition, the model incorporates an Empathy Map-based workshop approach to ensure contextual relevance and human-centered evaluation. The result is a practical yet theoretically grounded tool that supports rural SMEs in their digital transition and contributes to the academic discourse on digital maturity in underserved contexts.

This study investigates how relational capability, perceived quality, and perceived AI benefits influence operational performance in nanostores across Honduras and Colombia, addressing AI perception dynamics in resource-constrained Latin American retail contexts. The research is framed by an integrated theoretical model addressing limitations of traditional technology acceptance theories by incorporating Resource-Based View and Institutional Theory lenses, positioning relational capability as a networked resource contingent on institutional context. A cross-sectional dataset of 608 nanostores (204 Honduras, 404 Colombia) was analyzed using Multi-Group Structural Equation Modeling with Maximum Likelihood Estimation. Measurement validity was assessed through confirmatory factor analysis and invariance testing. Relational capability (β = 0.34, p < 0.001) and perceived AI benefits (β = 0.41, p < 0.001) significantly predict operational performance, while perceived quality exerts a moderate effect (β = 0.23, p < 0.01). Multi-group analysis reveals relational capability is more influential in Colombia, whereas perceived quality is more salient in Honduras. Contextual factors explain additional variance (R² = 0.42 Colombia, 0.36 Honduras). The cross-sectional design limits causal inference; common method variance was non-significant. Future research should incorporate longitudinal designs and objective metrics. Results suggest context-specific AI adoption strategies: Colombian nanostores benefit from ecosystem-driven approaches emphasizing supplier networks, while Honduran ones require quality-centered strategies focusing on service reliability. Policymakers should tailor digital transformation initiatives to local institutional realities and develop data privacy frameworks for low-resource settings. This study advances technology adoption theory by introducing relational capability as a critical networked resource in informal contexts and provides empirical evidence of cross-national heterogeneity in AI perception–performance relationships in informal retail.

This research examines determinants of artificial intelligence (AI) adoption and its impact on operational performance in nanostores across seven emerging markets, addressing a critical literature gap focused on formal SMEs in developed economies. By investigating perceptual and contextual drivers in informal, resource-constrained retail, it provides one of the first large-scale quantitative studies in this context. Building on exploratory work in Honduras, this cross-sectional study analyzes data from 1,412 nanostores in Jordan, Israel, Saudi Arabia, UAE, Morocco, Honduras, and Colombia (collected August 2024–November 2025). Partial Least Squares Structural Equation Modeling (PLS-SEM; R² = 0.617) reveals perceived AI benefits (β = 0.394, p < 0.001) and environmental interaction (β = 0.132, p < 0.05) as significant predictors of operational performance, while perceived quality has no direct effect. Digital infrastructure accessibility emerges as a key contextual determinant, with multi-group analysis showing stronger effects in Middle Eastern than in Latin American markets. Results indicate that effective AI integration requires addressing perceptual barriers (benefit demonstration) and structural constraints (infrastructure/ecosystem support). Findings advocate context-sensitive strategies: prioritizing benefit communication and ecosystem linkages in Latin America, alongside targeted infrastructure investments. This multi-regional evidence guides inclusive digital transformation in informal retail.

This systematic review examined the influence of machine learning (ML) and smart warehousing (SW) on the global logistics sector through the analysis of literature published between 2022 and 2025, following PRISMA guidelines and the PICOC framewor k. A tota l of 40 peer- reviewed articles indexed in EBSCO, PROQUEST, REDALYC, ScienceDirect, Scopus, and Web of Science were analyzed. The findings indicate that ML enhances predictive processes such as demand forecasting and route planning, while SW transforms ph ys ical operations through automation and Internet of Things (IoT) integration. The synergy between both technologies leads to substa ntial improvements in operational efficiency, including cost reductions ranging from 15% to 25% and accuracy levels exceeding 99.5%. However, their implementation faces significant barriers, including high investment requirements, a shortage of specialized talent, an d technological integration challenges— particularly for SMEs and in developing regions, especially in Latin America . The study concludes that the strategic adoption of ML and SW is essential for logistics digital transformation.