Journal of Business Economics and Management

Considering the nature, complexity and importance of the current managerial challenges, there is a need for a systematic study that offers guidance to their holistic analysis, and no overarching structure to guide this type of research could have been identified to date. This article lays the groundwork for a comprehensive examination of managerial challenges. Based on the review of 78 empirical works, the key practices in studying managerial challenges are synthesized and presented as a reference framework. The framework is designed using the 5W and 1H method. It offers an up-to-date understanding of the substance of managerial challenges, which contributes to both the theoretical understanding and practical execution of managerial work.

Corruption remains a significant constraint for firms in Europe, despite ongoing institutional reforms. The main goal of this paper is to obtain a list of firm-level variables that can serve as predictors of corruption perception using a machine learning approach. Drawing on agency and institutional theory, we analyse firm-level data from European firms from the World Bank Enterprise Survey (WBES). We employ a Random Forest classifier, which is well-suited for high-dimensional, categorical survey data, capturing non-linear relationships and interactions often missed by traditional models. The model achieves strong predictive performance (ROC AUC = 0.755; Accuracy = 79%). Results show that the most important prediction factors of corruption perception include firm age, size, ownership concentration, legal form, external financial audits, bribery experiences, sector, country group (EU vs. WB), innovation activity, and informal sector competition. The findings support the design of risk-based audits and encourage reforms to reduce informality through streamlined registration processes. The study contributes methodologically by applying machine learning to the field of political economy and expands theoretical insights into firm-level institutional barriers. It is one of the first research to apply Random Forest to firm-level corruption perception in both EU and Western Balkans.

Enterprise resource planning (ERP) systems are evolving to support organisations in addressing their climate impact. Yet, there is a paucity of empirical and cross-sectoral data on how these solutions can mitigate organisations’ negative climate impact through changes in business models. By focusing on a dataset of ERP-related patents published between 2020 and 2024, within a climate change classification, this study aims to shed light on how ERP-based solutions can enable business model transformation to improve environmental performance and to investigate Industry 4.0 technologies that facilitate such mitigations. Through a pragmatic inductive approach employing mixed methods, the study uncovers three main areas of business model transformation for climate change mitigation: production optimisation, sustainability management and monitoring, and supply chain performance improvement. While most of the examined patents prioritise production optimisation, the findings reveal the emergence of novel applications designed to enhance organisational sustainability management and monitoring. Furthermore, the research emphasises unexploited opportunities to enhance ERPs through the integration of Industry 4.0 technologies. This study provides a substantial contribution to the existing literature by focusing on a significant yet underexplored area: ERP-based solutions designed to enable business model transformation to mitigate climate change, with implications for researchers, organisational adopters, and system developers.

Given that structural efficiency serves as a significant instrument, this paper applies a novel approach to measure structural efficiency levels within Chinese banks from the perspective of potential improvement. To further investigate the patterns of structural efficiency, the overall structural efficiency is disaggregated into a series of variable-specific structural efficiencies. It reveals that fixed assets and non-interest incomes constitute the primary sources of structural inefficiency during the study period. Furthermore, the structural efficiencies of small-medium commercial banks surpass those of large state-owned commercial banks, although the efficiency gap between the two types of banks has narrowed. Based on the variable-specific structural efficiencies, this paper further explores the structural efficiency patterns within the Chinese banking sector.

This research empirically explores the influences of ageing on China’s GDP growth, incorporating Artificial Intelligence (AI) as a moderating factor. Specifically, industrial robot penetration was used as a proxy for AI adoption. This research selects panel data in 31 provinces of China (2000–2022). The nonlinear association between ageing population and GDP growth is examined using panel threshold regression models, while threshold variables are ageing and AI adoption, respectively. To verify the robustnes, the old-age dependency ratio is utilized as a proxy of ageing population. According to the findings, GDP growth is initially negatively affected by ageing population. However, when AI adoption surpasses a critical threshold, this negative effect is significantly mitigated. This finding highlights the importance of AI adoption in managing the economic challenges brought by ageing. Therefore, some valuable recommendations have been put forward to support inclusive and sustainable economic development. These include greater investment in research and expansion concerning AI, promoting AI-driven robotics in key sectors, and offering targeted skilling programs for elderly employees. Further suggestions are to invest in digital infrastructures and the industry of ageing, as well as to leverage and develop elderly human capital.