Risk management in commercial processes is among the most important procedures affecting the competitiveness of small and medium-sized enterprises (SMEs), their innovativeness and potential contribution to global sustainable development goals (SDGs). The ecosystem of commercial processes is the prerequisite to manage risk faced by SMEs. Commercial risk assessment and management using elements of artificial intelligence, big data, and machine learning technologies could be developed and maintained as external services for a group of SMEs allowing to share costs and benefits. This paper aims to provide a conceptual framework of commercial risk assessment and management solution based on elements of artificial intelligence. This conceptualization is done on the background of scientific literature, policy documents, and risk management standards. Main building blocks of the framework in terms of commercial risk categories, data sources and workflow phases are presented in the article. Business companies, state policy, and academic research focused recommendations on the further development of the framework and its implementation are elaborated.

As supply chains (SCs) become more complex globally, businesses are looking for efficient business analytics (BA), business intelligence (BI), and artificial intelligence (AI) tools for managing supply-chain risk. The tools and methodologies proposed by the supply-chain risk management (SCRM) literature are mostly based on experts’ judgments, their knowledge, and past data. The expert evaluation-based approach could be partly or fully replaced by AI solutions, increasing objectivity, impartiality, and impersonality, reducing sources of human mistakes, biases, and inefficiencies in SCRM. However, the transition from BA to AI in SCRM is not a self-contained process; though attractive as a vision, it is not straightforward as a management or implementation process. The purpose of this research is to explore and define the conceptual grounds for transitioning from BA to AI in SCRM. The conceptual SCRM structure, its AI suitability, and implementation terms are defined theoretically based on a literature review. A single, in-depth business case study is employed to explore the theoretically defined terms of AI-based SCRM implementation. The proposed conceptual AI-suitable SCRM structure is defined by five principal building blocks: risk events, risk-event indicators, data-processing rules and algorithms, analytical techniques, and risk event probability forecasts. The study concludes that the business environment meets AI-based SCRM-implementation terms of data existence and access. Since data on risk events and negative outcomes are limited for machine learning, experts’ experience and knowledge might be utilised to build initial rules and data-processing algorithms for AI.

The Quality of Life (QoL) in the local place concept anticipates that overall individually perceived QoL is being constituted both by objective local living conditions as well as subjective individual experience and evaluation of these local place's external QoL factors. However, the mathematical-statistical research based scientific evidences about some certain context related direction and strength of relationships between objective local place related conditions (as a cause side) and subjective QoL perception (as an effect side) are lacking. By taking into account specific characteristics, i.e. the extent and nature of the data available to measure the objective and subjective QoL, the article explores methodological possibilities to model statistically probable cause-effect relationships established and empirically observed in Lithuania municipalities’. Such statistical probability based cause-effect models would be extremely valuable in building and justifying empirical observation based and any single municipality local place context relevant QoL improvement strategies

Covid - 19 has affected global value chains (GVCs) and firms’ behaviours within GVCs to a large extent. Firms need to find flexible solutions to stabilize production and rethink value chain governance and supplier relationships. Thus, GVCs recover from the initial shock, and the GVCs reconfiguration becomes essential for managers. We reviewed recent literature focusing on the GVCs, the role of governance and Covid – 19 effects on it as well as the regionalization trend. Thus, this study aims to give empirical evidence on the reconfiguration of GVCs, particularly the changes in the structure of suppliers in the global apparel industry, by using the Bloomberg Supply Chain Analysis tool in the period of 2017-2021. We analysed the structural changes in selected leading apparel multinational companies’ GVCs - Industria de Diseno Textil, S.A. (Inditex), Hennes &Mauritz AB (H & M) and adidas AG. The findings provide insights on the current state of the theoretically discussed and widely expected regionalization trend in GVCs. The study concludes that two out of three cases provide evidence indicating an ongoing trend toward GVCs regionalization. The contribution of this study consists of empirical evidence of the changes in GVCs supplier structure in the apparel industry in response to pandemic global business environment. Practical implications and recommendations for businesses and policy makers are related to the revealed theories and testable models by using Bloomberg’s Supply Chain Analysis data and linking causes and effects of the GVCs regionalization processes.

Artificial intelligence (AI) has recently become an integral part of the financial industry. Wide opportunities for AI are seen in tasks associated with credit scoring. However, the expected potential and benefits of AI-based credit scoring and the overall path to digitalised lending could be challenged by the fact that a relatively large number of SMEs in Europe are credit invisible due to their financing patterns, that limit the availability of credit history and other creditworthiness sensitive data needed to build and train AI models. The research aims to estimate the nature and availability of credit scoring relevant SMEs data in different European countries. Research methods respective to the above-stated aim are the following: scientific literature review, descriptive, and country to country comparative analysis of the data from the Survey on the Access to Finance of Enterprises (SAFE). The survey data indicate that the share of credit invisible SMEs changes in the range of 1030%, but there is no direct relationship with the characteristics of the country. The initiatives to score the creditworthiness of SMEs should consider the share of SMEs that do not use services of the traditional financing sector, which means that data about its creditworthiness are limited. The whole population of SMEs could be covered by AI models incorporating both conventional financial data from credit visible SMEs processed by machine learning as well as unstructured non-financial data (describing credit invisible SMEs) processed by deep learning techniques and methods. Thus, we claim that despite the huge potential of AI incorporation into creditworthiness evaluation, the use of AI is not a magic pill, enhancing easier SME’s access to finance, unless its use is aligned to the available data. Accordingly to the accessible data, different AI tools and techniques should be applied in credit scoring applications.

Purpose: The purpose of this study is to explore the development of entrepreneurship competences (EC) shaped by self-directed experiential learning (SDEL) within business and entrepreneurship studies internships. Design/methodology/approach: The investigation applied deductive qualitative research guided by SDEL framework. Students' weekly reflections on entrepreneurship internships were analyzed to identify enabling and constraining behavior, affecting EC development. Seventeen undergraduate students provided their written consent to the use of their reflections for this study. Lists of keywords were then inductively derived from the coded data to characterize empirically confirmed SDEL patterns. Keyword distribution was analyzed throughout the reflections on EntreComp-defined competences. Findings: The study identified distinct patterns through which self-directed learning (SDL) would either enable or limit the progression across Kolb's experiential learning (EL) phases in the development of EC. SDL manifested unevenly across EL phases, with strengths in dealing with concrete experience and reflective observation, along with notable limitations in engaging with conceptualization and experimentation. Contextual factors, task design and learner readiness had significantly shaped these outcomes. Originality/value: The research advances a theoretical understanding of SDEL by mapping the emergence of its enabling and limiting elements in the SDL-EL interaction. Lists of keywords, assigned to the SDEL framework, propose a replicable tool for analyzing reflective texts, offering a way to anticipate, support and evaluate learning behaviors in educational contexts. The findings occurred in a natural learning environment, as students were not prompted to consider SDL or EL concepts. The disclosures represent authentic and spontaneous manifestations of SDEL occurrence in actuality.