Search Results (17)

Technological innovation plays a vital role in enhancing the economic growth and sustainability of the forestry sector. However, research on the nature, dynamics, and impact of such innovations, particularly within small and medium-sized enterprises (SMEs), remains limited. The forestry sector is often characterised by low levels of technological advancement and a traditionally conservative attitude toward change. Limited expertise, financial constraints, and ownership structures further influence the potential for innovation. This study examines the development of innovation among SMEs in Lithuania’s forestry sector and its contribution to sustainable forest management. Forestry innovations are understood as new processes, products, or services introduced by forest owners and managers to improve management efficiency and sustainability. The study employed the method of a structured questionnaire survey to evaluate technological, organisational, and financial aspects of innovation adoption among small and medium-sized enterprises in the forestry sector. Drawing on comparative survey data from 2005 and 2024, the study analyses the types of innovations implemented by forestry enterprises, the factors driving or hindering their adoption, and the evolving trends in innovation application. The results reveal a significant shift toward digitalisation and technology-based management practices, suggesting that Lithuanian forestry enterprises are gradually transitioning toward a more innovation-driven model. These developments appear to be influenced by the EU Green Deal policy framework, evolving innovation support mechanisms, and broader socio-economic changes. Nonetheless, technological transformation introduces new challenges, including the need for workforce upskilling and enhanced adaptability to rapidly changing market conditions. Full article

The changes introduced under the European Green Deal policy affect many areas of life. They also have significant consequences for the functioning of small and medium-sized enterprises. The authors put forward the thesis that one of the key categories of costs in the case of such firms, which significantly influences their decision to commence, continue or cease operations, is the cost of purchasing electricity and gas. Analysing data from the Central Statistical Office in Poland for the years 2015–2025 and constructing an econometric model on this basis, the authors found arguments that the cost of purchasing electricity (as opposed to the cost of purchasing gas) may probably indeed play the role attributed to it. However, the detected relationships are of a very complex nature and only the ML model, Random Forest, was able to identify them (linear and non-linear OLS regression models were not appropriate here). Although Random Forest is a predictive method and does not identify structural causality, the findings may be important for decision-makers assessing the scale of the challenges that small and medium-sized enterprises will have to face in the coming years. Moreover, the findings constitute a significant argument in favour of support instruments (e.g., contracts for difference, long-term PPAs for SMEs, support for energy efficiency and self-generation) for the aforementioned category of entities. Full article

Sales forecasting in make-to-order (MTO) production is particularly challenging for small- and medium-sized enterprises (SMEs) due to high product customization, volatile demand, and limited historical data. This study evaluates the practical feasibility and accuracy of statistical and machine learning (ML) forecasting methods in MTO settings across three manufacturing sectors: electrical equipment, steel, and office supplies. A cross-industry benchmark assesses models such as ARIMA, Holt–Winters, Random Forest, LSTM, and Facebook Prophet. The evaluation considers error metrics (MAE, RMSE, and sMAPE) as well as implementation aspects like computational demand and interpretability. Special attention is given to data sensitivity and technical limitations typical in SMEs. The findings show that ML models perform well under high volatility and when enriched with external indicators, but they require significant expertise and resources. In contrast, simpler statistical methods offer robust performance in more stable or seasonal demand contexts and are better suited in certain cases. The study emphasizes the importance of transparency, usability, and trust in forecasting tools and offers actionable recommendations for selecting a suitable forecasting configuration based on context. By aligning technical capabilities with operational needs, this research supports more effective decision-making in data-constrained MTO environments. Full article

As an important pillar of the national economy, the green transformation of SMEs is the key to promoting sustainable economic development. However, SMEs generally face issues such as information opacity and high operational risks, which make it difficult for them to obtain traditional financing support, thereby hindering green development. Green Supply Chain Finance has opened up new financing channels for SMEs, but the accuracy of credit risk evaluation remains a bottleneck that limits its widespread application. This paper constructs a credit risk evaluation index system that integrates multiple sources of information, covering factors such as the situations of SMEs themselves, stakeholder feedback, and expert ratings. It compares and analyzes the performance of the genetic algorithm-optimized random forest model (GA-RF), the BP neural network, the support vector machine, and the logistic regression model in credit risk evaluation. The empirical results indicate that the GA-RF model is significantly better than the other models in terms of accuracy, precision, and F1 score, and has the highest AUC value, making it more effective in identifying credit risk. In addition, the GA-RF model reveals that the asset–liability ratio, the time of establishment, the growth rate of operating revenue, the time of collection of accounts receivable, the return on net assets, and daily shipments are the key indicators affecting the credit risk assessment. Full article

In the context of digitalization, this study investigated the financing mechanisms and preferences of technology-driven small and medium-sized enterprises (TDSMEs) listed on the National Equities Exchange and Quotations (NEEQ) in China. Its primary objective was to identify the factors influencing financing decisions and to elucidate how TDSMEs choose their financing options in a rapidly evolving digital environment. To achieve this goal, we constructed a panel regression model using financial data from 41 TDSMEs (2017–2023), identifying the key determinants of financing decisions while examining the impact of regional heterogeneity and validating the model’s robustness. The empirical findings indicated that various independent variables, including a firm’s capital structure, significantly influenced both internal and external financing. Additionally, six machine learning (ML) algorithms were employed to predict financing preferences. Among them, the random forest (RF) model achieved the best financing preferences performance, with an average F1 score of 0.814, indicating its robust predictive capability for TDSMEs’ financing preferences. To further validate the proposed models, we conducted a case study on a TDSME newly recognized in 2024 (named TS Pharmaceutical). Both the Lasso and RF models demonstrated outstanding predictive accuracy, confirming the practicality of the ML models. These results provide valuable insights into navigating the ever-changing digital financing landscape, offering recommendations for policymakers and financial institutions to better support TDSMEs. The key innovation of this study lies in its novel integration of conventional panel regression analysis and ML techniques, thereby bridging the gap between digital transformation and financing strategies while contributing both theoretically and practically to the field. Full article

As a representative of potential “hidden champions”, a concept originating in Germany, specialized and innovative Little Giant Enterprises (LGEs) have become exemplary models for small and medium-sized enterprises (SMEs) in China. These enterprises are regarded as crucial support for realizing the strategy of building a strong manufacturing country and addressing the weaknesses in key industrial areas. This paper begins by examining urban agglomerations, which serve as the main spatial carriers for industrial restructuring and high-quality development in manufacturing. Based on data from LGEs in the Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations from 2019 to 2023, the study employs the Random Forest (RF) and Multi-scale Geographically Weighted Regression (MGWR) methods to conduct a comparative analysis of their spatial patterns and influencing factors. The results are as follows: (1) LGEs exhibit spatial clustering in both the YRD and PRD regions. Enterprises in the YRD form a “one-axis-three-core” pattern within a distance of 65 km, while enterprises in the PRD present a “single-axis” pattern within a distance of 30 km, with overall high clustering intensity. (2) The YRD is dominated by traditional manufacturing and supplemented by high-tech services. In contrast, the PRD has a balanced development of high-tech manufacturing and services. Enterprises in different industries are generally characterized by a “multi-point clustering” characteristic, of which the YRD displays a multi-patch distribution and the PRD a point–pole distribution. (3) Factors such as industrial structure, industrial platforms, and logistics levels significantly affect enterprise clustering and exhibit scale effects differences between the two urban clusters. Factors such as industrial platforms, logistics levels, and dependence on foreign trade show positive impacts, while government fiscal expenditure shows a negative impact. Natural geographical location factors exhibit opposite effects in the two regions but are not the primary determinants of enterprise distribution. Each region should leverage its own strengths, improve urban coordination and communication mechanisms within the urban cluster, strengthen the coordination and linkage of the manufacturing industry chain upstream and downstream, and promote high-tech industries, thereby enhancing economic resilience and regional competitiveness. Full article

The objective of this research is to present a qualitative methodology for the empirical investigation of enterprises’ responses to economic shocks. Annual balance sheets and income statements of nearly 26,000 Hungarian small- and medium-sized enterprises (SMEs) in the production sector have been examined. A data-driven resilience metric is introduced, based on annual sales growth fluctuations in response to idiosyncratic economic disturbances. Accordingly, Logistic Regression and Random Forest classification of company-year observations have been conducted. Non-parametric statistical tests based on pair-matching suggest that while resilience against economic downturns is critical for short-term survival, it does not necessarily translate to any enhanced long-term development or prosperity. This study demonstrates that companies exposed to economic setbacks tend to lag behind compared to control pairs and illuminate the aftermath of resilient shock reactions at the population level. Our findings suggest that enterprises that have experienced an economic shock should be considered vulnerable and monitored regardless of their shock reaction history as part of a sustainable national economic strategy to foster overall competitiveness and productivity and maintain supply chains. Full article

Challenges related to effective supply and demand planning and inventory management impose critical planning issues for many small and medium-sized enterprises (SMEs). In recent years, data-driven methods in machine learning (ML) algorithms have provided beneficial results for many large-scale enterprises (LSE). However, ML applications have not yet been tested in SMEs, leaving a technological gap. Limited recourse capabilities and financial constraints expose the risk of implementing an insufficient enterprise resource planning (ERP) setup, which amplifies the need for additional support systems for data-driven decision-making. We found the forecasts and determination of inventory management policies in SMEs are often based on subjective decisions, which might fail to capture the complexity of achieving performance goals. Our research aims to utilize the leverage of ML models for SMEs within demand and inventory management by considering various key performance indicators (KPI). The research is based on collaboration with a Danish SME that faced issues related to forecasting and inventory planning. We implemented the following ML models: Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Random Forest (RF), Wavelet-ANN (W-ANN), and Wavelet-LSTM (W-LSTM) for forecasting purposes and reinforcement learning approaches, namely Q-learning and Deep Q Network (DQN) for inventory management. Results demonstrate that predictive ML models perform superior concerning the statistical forecasting approaches, but not always if we focus on industrial KPIs. However, when ML models are solely considered, the results indicate careful consideration must be regarded, given that model evaluation can be perceived from an academic and managerial perspective. Secondly, Q-learning is found to yield preferable economic results in terms of inventory planning. The proposed models can serve as an extension to modern ERP systems by offering a data-driven approach to demand and supply planning decision-making. Full article

Supply chain finance is an effective way to solve the financial problems of small and medium-sized manufacturing enterprises, and the assessment of credit risk is one of the key issues in supply chain financing. However, traditional credit risk assessment models cannot truly reflect the credit status of financing companies. In recent years, scholars working in this field have proposed using machine learning methods to predict the credit risk of supply chain enterprises, achieving good results. Nonetheless, there is no consensus on which approach is the most suitable for manufacturing companies. This study took small and medium-sized manufacturing enterprises as the research object, selected risk evaluation indicators according to the characteristics of the small and medium-sized manufacturing enterprises, and built a credit risk evaluation system. On this basis, we selected SMEs on China’s stock market from 2015 to 2020 as the sample data and evaluated corporate credit risk based on four commonly used machine learning algorithms. Then, combined with the evaluation results, a partial dependence plot method was used to visually analyze the important indicators. The results showed that a credit risk evaluation system for supply chain finance for manufacturing SMEs could be composed of the profile of the financing companies, the asset status of the financing companies, the profile of the core companies, and the operation of supply chains. The use of a random forest algorithm made it possible to more accurately assess the credit risk of manufacturing supply chain finance. Since the impacts of different indicators on the evaluation results were quite different, supply chain enterprises and financial service institutions should formulate corresponding strategies according to specific situations. Full article

Agricultural and forested landscapes in Africa are changing rapidly in response to socio-economic and environmental pressures. Integrated landscape approaches provide an opportunity for a more holistic and coordinated resource management strategy through the engagement of multiple stakeholders. Despite their influence as landscape actors, participation of private businesses in such initiatives has thus far been limited. This study focuses on the Kalomo District in southern Zambia, which provides an example of a rural landscape characterized by high levels of poverty, low agricultural productivity, and widespread deforestation and forest degradation. The study applied a value-chain analysis approach to better understand how the production of four locally important commodities (maize, tobacco, cattle, and charcoal) impacts land use, local livelihoods, and environmental objectives in this landscape, focusing on the role and influence of private sector actors. Data were collected through focus group discussions and key informant semi-structured interviews. Qualitative content analysis was employed to analyze the data and contextualize the findings. Results indicate three key potential entry points for increased private sector engagement: (1) improving water security for smallholders; (2) empowering small and medium-sized enterprises (SMEs) as private sector actors; and (3) collective planning for sustainable landscape activities with deliberate measures to involve private sector actors. We discuss options for optimizing benefits from the identified entry points. Full article

Small–medium-sized forest enterprises (SMFEs) have historically played an essential role in developing countries’ economies worldwide because most businesses start as small businesses, and government support and knowledge-based recourse are critical to the sustainable development of SMFEs and local economies. The current studies examined the effects of the Khyber Pakhtunkhwa (KPK) government’s (Pakistan) support (GS) and entrepreneur knowledge (EK) on the development of small–medium-sized forest enterprises (SD) and their contribution to the local economic development (LED) of the region. Primary data were collected from 350 SMFEs in KPK, Pakistan. The model was developed by using a structural equation model (SEM) to investigate the impact of GS, EK, and SMFEs on the growth, SG, and sustainable development of the local economy. This study concludes that EK and GS could increase growth in SMFE businesses and contribute to LED. On the other hand, crediting loans and equipping businesses with training could not directly affect SMFE businesses and LED growth. The government needs to use natural resources and the SMFE communities as leaders among suppliers in the local market for the sustainable development of LED and SMFEs, alongside focusing on preserving and taking initiatives to develop. This study discusses several practical implications for policymakers, business owners, and academics, with recommendations for future research. Full article

This article presents how selected factors related to forest biomass affect enterprise development in rural areas. The study used a multivariate analysis of variance (ANOVA), as well as the AHP operational research method. The following factors were selected for analysis: conifer timber harvesting, sales of renewable fuel in the form of briquettes to selected customers, and the number of the given company’s regular customers. Their selection was determined by the fact that using plant material for energy purposes has become significantly more popular in recent years. This particularly includes forest biomass, which is increasingly used as an energy commodity in the Polish heating industry. Forest biomass is a biodegradable raw material generated in the form of waste during wood production and processing, as well as during sanitation cutting. The study was conducted using a diagnostic survey method with a survey questionnaire in the first quarter of 2020. It included 614 owners of small and medium-sized enterprises operating in various rural areas across all of Poland’s voivodeships. The study was conducted using the CATI method. Analyses defining the dependence of specific factors on the examined parameters and supporting the priority nature of the given actions may show the development of particular pro-ecological actions in a given area. In one case, the critical level of significance determining the assignment of the analyzed factor to a specific homogeneous group was below 0.05. This means that there was a correlation between the sales of renewable fuel in the form of briquettes to selected customers and the number of enterprises in the voivodeship. Therefore, due to the sales of renewable fuel in the form of briquettes to selected customers, the greatest development prospects for wood industry companies existed in the Małopolskie, Mazowieckie, Śląskie and Wielkopolskie Voivodeships. Full article

This case study introduces an innovation and development concept for agile software tools for the improvement of the productivity and customer experience of forest services. This need was recognized in the context of the opening of forest data and the development of service platforms for a forest-based bioeconomy in Finland. The forest services that were studied covered a continuum from a single type of work, e.g., soil preparation and young stand management through timber procurement, to comprehensive forest property management services. The study concentrated on the needs of micro-, small, and medium-sized enterprises (SMEs), which provide either retail- or business to business (B2B) services as sub-contractors. In addition, the challenges and bottlenecks in service processes detected by other stakeholders were considered. The prevailing service processes were conceptually modelled in order to search for opportunities for improvements in business and ecosystem services, i.e., agile software concepts. For example, we examined whether it would be possible to create opportunities for flexible operational models for precision, resilience, and protection of valuable microsites in forests. These software concepts were developed and evaluated in co-operation with the stakeholders in a co-creative workshop. The technological feasibility and commercial viability of the concepts, as well as the desirability for the customer were considered. The results of this business development process—i.e., agile software concepts and their anticipated benefits—were provided for further evaluation. In addition to the practical implications of this kind of innovation process tested, the potential of these kinds of agile tools for the further development of knowledge-intensive service processes was further discussed. Full article

Research Highlights: This study reviews the available literature on processed non-timber forest products (NTFPs) in order to comprehensively identify relevant factors enabling or constraining their potential to contribute to rural development. Background and Objectives: NTFPs, such as wild foods, medicinal plants, and raw materials for handicrafts, make significant contributions to rural livelihoods. NTFPs can help fulfil households’ subsistence and consumption needs, serve as a safety-net in times of crises, and provide cash income. In particular, the processing of NTFPs has often been suggested to positively influence sustainable economic development in rural areas. However, despite rising interest and recognition of the potential contributions of such industries as key sources of employment and their strategic role in overall growth strategies of developing countries, many NTFP processing enterprises remain in the informal sector and an in-depth understanding of the underlying factors is lacking. This review aims to identify enabling and constraining factors affecting NTFP processing enterprises. Materials and Methods: Using systematic review methodology, studies investigating commercialized, processed NTFPs and their economic impacts have been identified and the current evidence base with regard to NTFP processing and small and medium sized enterprise (SME) development synthesized. Results: Despite the diverse nature of NTFPs, a number of constraining and enabling factors affecting NTFP processing and commercialization were identified. The former includes aspects such as the lack of resource access (finances, skills, technologies, etc.), market information, and basic infrastructure; the latter, amongst others, the role of key entrepreneurs; and cooperation across the value chain, amongst producers, and among members of the institutional environment or an abundant resource base. Moving from small-scale NTFP commercialization in local markets to more mature NTFP value chains reaching export markets, the increasing role of cooperation and having a supportive institutional framework in place, becomes apparent. Conclusions: Overall, successful NTFP processing strongly depends on the socio-economic and environmental context in question, requiring a holistic approach tailored to the respective context and value chain. Full article

Small-to-medium-sized forest enterprises (SMFEs) offer numerous benefits to rural communities and society as a whole. Less attention has been paid to the sustainability of SMFEs in terms of improving the livelihood of rural communities. This study aims to assess the impact of SMFEs in Khyber-Pakhtunkhwa (KPK), Pakistan, and evaluate their potential role in reducing poverty and promoting rural livelihoods. Primary data were collected from 350 household heads and analyzed using econometric methodologies: The ordinary least squares (OLS) and ordered logit model. Household income, a wealth index, and five capitals of sustainable livelihood have been considered to gauge the impact of SMFEs. The results of the study reveal that there is a strong positive association between SMFEs and improvement in a rural community’s livelihood. The results further showed that households engaged in SMFE-related activities earn 3% more income and possess about 24% more assets. These findings are robust for various dimensions of sustainable livelihood and show positive effects of SMFEs on livelihood assets. This study continues the discussion on several practical implications along with recommendations for future research. Full article