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  ETHICAL CONSIDERATIONS AND SOCIETAL IMPACTS OF AI ADOPTION IN SMEs WITHIN EMERGING MARKETS

  This paper examines the ethical considerations and societal implications of AI adoption by small and medium enterprises (SMEs) in emerging markets. Drawing on Stakeholder Theory, Diffusion of Innovation, and the Technology-Organization-Environment framework, it proposes a comprehensive conceptual model that places ethical principles, fairness, ... This paper examines the ethical considerations and societal implications of AI adoption by small and medium enterprises (SMEs) in emerging markets. Drawing on Stakeholder Theory, Diffusion of Innovation, and the Technology-Organization-Environment framework, it proposes a comprehensive conceptual model that places ethical principles, fairness, accountability, and inclusivity at its core. The discussion highlights the complex interplay of technological, organizational and societal dimensions, illustrating how AI can enhance competitiveness while potentially exacerbating inequalities and raising privacy, bias and transparency concerns. By integrating ethical and societal factors into a single framework, this study addresses a critical gap in current research, offering guidance for SME leaders, policymakers and researchers. The propositions suggest that ethical leadership fosters stakeholder trust, and that inclusive policies are essential to prevent AI-driven inequalities. This framework can serve as a roadmap for responsible AI adoption, informing capacity-building initiatives, regulatory guidance and future empirical studies. Ultimately, the paper invites further research to validate and refine its concepts and to explore cross-industry and cross-regional variations, ensuring that AI’s benefits are realized while mitigating its ethical and societal risks.

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  COMMERCIAL SECRET MANAGEMENT IN TERMS OF SHADOW DIGITAL ECONOMY

  The article explores trade secret protection management in the context of the shadow digital economy (SDE). The study aims to identify threats to trade secrets associated with SDE and propose effective protection methods. It emphasizes that the economic value of trade secrets lies in their confidentiality, making them vulnerable to data leaks, ... The article explores trade secret protection management in the context of the shadow digital economy (SDE). The study aims to identify threats to trade secrets associated with SDE and propose effective protection methods. It emphasizes that the economic value of trade secrets lies in their confidentiality, making them vulnerable to data leaks, cyberattacks, and other risks, particularly in the digital environment. The authors examine the nature of trade secret in terms of SDE spread. The paper presents methods of trade secret protection, including documentary, economic-organizational, and information-analytical types. Special attention is paid to the combined use of patent protection and encryption techniques. The study concludes with key findings, highlighting the need for enhanced cybersecurity, international cooperation to combat cyber espionage, and improved organizational measures. This work significantly contributes to the understanding of commercial secret management and offers valuable recommendations for businesses operating in the digital economy.

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  APPLICATION OF MACHINE LEARNING ALGORITHMS IN PREDICTING CUSTOMER LOYALTY TOWARDS GROCERY RETAILERS

  Retailers strive for customer loyalty in the sense of repeat purchases, but also as a high proportion of purchases (compared to competitors) and willingness to recommend to other customers. This paper examines customer loyalty in the grocery sector as a three-dimensional construct and shows how machine learning techniques can be useful in its ... Retailers strive for customer loyalty in the sense of repeat purchases, but also as a high proportion of purchases (compared to competitors) and willingness to recommend to other customers. This paper examines customer loyalty in the grocery sector as a three-dimensional construct and shows how machine learning techniques can be useful in its study. Price characteristics of the retailer (price level, value for money, price dynamics, price communication and price dispersion) and non-price characteristics of the retailer (general product range, retailer's private label product range, store design and atmosphere, service level and location) are included in the model as predictor variables. Using the data collected through the primary research conducted in Croatia, 433 samples were divided into 10 independent predictor variables and one dependent variable (customer loyalty), a prediction was created using supervised machine learning classification algorithms. The Random Forest classifier proves to be the best choice overall, with ROC_AUC value of 0.790, a high accuracy of 0.915 and an F1 score of 0.954, reflecting both precision and responsiveness. The application of the SHapley Additive exPlanations analysis additionally enables the interpretation of the results, highlighting the influence of features on the accuracy of the prediction. The results indicate that price dynamics and service level are the most important features for the model predictions, followed by value for money and price communication.