Search Results (58)

Artificial Intelligence (AI) is a transformational force reshaping business processes, financial decision-making, and enabling firms to create, deliver and capture value more effectively. While large corporations in South Asian countries, particularly Bangladesh, India, Pakistan and Sri Lanka have started leveraging AI to drive Business Model Innovation (BMI), Small and Medium Enterprises (SMEs) continue to face significant challenges. These include limited infrastructure, poor bandwidth penetration, unreliable electricity, weak institutional capacity and governance immaturity, along with ethics and compliance concerns. These challenges hinder SMEs from fully exploiting AI-driven BMI and reduce their financial resilience and competitiveness in increasingly digital and globalised markets. This paper examines how South Asian countries are adopting AI technologies in SMEs by comparing patterns and variations in adoption, capability, ethics, risks, compliance, and financial outcomes. The paper proposes a tailored, actionable framework, called TRIAD (Target, Restructure, Integrate, Accelerate, and Democratise)-AI, designed to address technical, organisational and institutional challenges that shape AI-driven BMI across South Asian SMEs and to meet regional and global SME needs. The framework integrates the best practices from global AI leaders such as China, Estonia and Singapore, emphasising responsible AI adoption through robust ethics and compliance standards, and risk management, and offering practical guidance for South Asian SMEs. By adopting this framework, South Asian countries can gain a competitive advantage, enhance operational efficiency, support GDP growth across the region and ensure adherence to all relevant international AI standards for responsible, sustainable, and financially sound innovation. Full article

In traction networks of mining enterprises, ensuring selective and sensitive protection remains an urgent task, especially in conditions of frequent starts of electric transport and possible cases of short circuits, lack of reliable grounding and increased spreading resistance. Standard methods—maximum current protection (MCP) and differential current protection (DCP)—demonstrate limited efficiency at operating currents less than 800 A, which is typical for remote sections of the contact network. The objective of this study is to develop and experimentally verify a method for adjusting the parameters of current and impulse protection, ensuring reliable shutdown of accidents at low values of short-circuit current without the need to replace equipment. The proposed method is based on transient processes modeled using differential equations and the introduction of a dynamic sensitivity coefficient reflecting the dependence of the setting on the circuit time constant. Universal response characteristics were constructed in normalized coordinates for BAT-49 and VAB-43 switches and RDSh-I and RDSh-II relays. Experiments have confirmed that the application of the method allows for reducing the tripping threshold to 600–650 A, increasing the selectivity of protection to 95% and reducing the probability of false tripping by more than two times compared to MCP/DCP. The response time remained within 35–45 ms, which meets the requirements for high-speed systems. The developed method is adapted to different network sections using the relative coordinates of the energy consumer on the supply section of the traction network and does not require complex digital equipment. This makes it especially effective in field conditions, where it is impossible to upgrade the protection using intelligent adaptive systems. Full article

Micro, Small, and Medium Enterprises (MSMEs) are vital drivers of economic growth in Kenya, yet they face persistent barriers, including limited capacity, financial exclusion, and weak market integration. This study assessed the potential of universities as strategic hubs for MSME capacity building through a collaborative initiative between Egerton University and the KCB Foundation. Using the International Labour Organization’s Start and Improve Your Business (SIYB) methodology, 481 entrepreneurs from Egerton, Njoro, and Gilgil were trained in a business development bootcamp. This study evaluated the training effectiveness, participant demographics, confidence in skill application, networking outcomes, and satisfaction levels. The results showed high participant confidence (over 95% across all regions), strong financial management uptake (85%), and mobile banking adoption (70%). Gilgil led in inclusivity and peer engagement, while Njoro showed stronger gender representation. However, logistical challenges caused 25% absenteeism in rural areas, and only 23% accessed post-training mentorship. These findings underscore the transformative role of HEIs in fostering sustainable entrepreneurship through localized, inclusive, and industry-aligned training. Policy recommendations include hybrid delivery models, tiered curricula for diverse skill levels, and institutionalized mentorship through public–private partnerships. This case demonstrates the value of embedding entrepreneurship support within university mandates to advance national MSME development agendas. Full article

Businesses are influenced by the cyclical nature of economic development and distinct stages in the corporate life cycle. Accurate early-warning mechanisms are crucial to mitigating bankruptcy risk, enabling timely rescue measures. This article analyses the reliability of various bankruptcy prediction models, including those by Kliestik et al., Poznanski, the modified Zmijewski, Jakubik–Teply, and Virag–Hajdu, across corporate life cycle stages. Reliability was assessed using five metrics: accuracy, balanced accuracy, F1 and F2 scores, and the Matthews correlation coefficient (MCC). The sample included over 5000 SMEs from Central Europe, with financial data from 2022. The findings reveal a U-shaped trend in financial distress risk, with start-ups and declining enterprises facing the highest risks. The results indicate that the Kliestik et al. model shows consistent reliability across all life cycle stages, while the Poznanski model shows more variability. Conversely, the Virag–Hajdu model exhibits significant variability in reliability, with its best performance observed during the Decline stage. The modified Zmijewski and Jakubik–Teply models show lower MCC values overall, with the modified Zmijewski model performing better at predicting the financial distress of mature shake-out firms compared to other stages. Full article

In order to realise the efficient maintenance of industrial machines, Small and Medium-sized Enterprises (SMEs) need a system that utilises digital technology to handle everything from data collection to the visualisation of the collected data in an integrated manner. In this paper, an integrated monitoring platform using external sensor devices is proposed and implemented for the purpose of preventive maintenance of industrial machines. The proposed system performs edge processing to calculate features effective for monitoring on the sensor device, collects only the obtained features, and visualises them on a web server. In order to determine the features required by edge processing, a cycle waveform cut-out algorithm was proposed. As an evaluation experiment, the proposed system was used to detect the loosening of bolts on the support side of a ball screw. The results of the analysis showed that the dispersion value immediately after the start of uniform motion from the right end to the left end was valid, so the system was implemented as edge processing in the sensor device. In wireless transmission experiments on a testbed, an average of 20 consecutive cycles were used to achieve a 99.9% correct response rate and high detection accuracy, demonstrating the usefulness of the proposed system. Full article

In the field of industrial big data, the key issue in discovering data value lies not in overcoming the bottlenecks formed by analysis methods and data mining algorithms but in the difficulty of providing data element resources that meet business analysis needs. Due to the surge in data volume and the increasing reliance of enterprises on data-driven decision-making, future data management strategies are constantly evolving to meet higher quality and efficiency requirements. Data metadata resources that meet business analysis needs require high-quality data integration, standardization, and metadata management. The key is to ensure the consistency and availability of data to support accurate analysis and decision-making. By leveraging automation and machine learning, organizations can more effectively integrate and manage data metadata resources, thereby improving data quality and analytical capabilities. The multi-value chain data space is a digital ecological platform for organizing and managing industrial big data. Research on optimizing the supply of its business data resources is a significant topic. This paper studies the evaluation index system of data quality and data utility, constructs an evaluation matrix of business data resources, and addresses the issues of data sparsity and cold start in evaluation calculations through a data quality-utility-based evaluation model of business data resources. It investigates a business data resource algorithm based on collaborative filtering, forming a recommendation set of similar data quality-utility data resources to provide to data analysis users. Finally, using actual production datasets, the paper validates the business data resource evaluation model, compares the performance and effectiveness of three business data resource recommendation algorithms based on collaborative filtering, empirically demonstrates the recommendation accuracy and stability performance of the combined improved data quality-utility collaborative filtering algorithm (CFA-DQU), and provides technical research recommendations for optimization of business data resources. Full article

As the concept of “dual carbon” deepens, the ESG rating system has emerged as a means of measuring corporate value and providing information for investment decisions. However, the standards set by different rating agencies vary, leading to discrepancies in ESG ratings. Confucian culture, as an informal institution, may indirectly influence these rating discrepancies by shaping corporate behavior. Therefore, this paper takes traditional culture as the starting point to explore the intrinsic relationship between Confucian culture and corporate ESG rating divergence, with the aim of providing empirical support for improving China’s ESG rating system. This study focuses on non-financial listed companies in the Shanghai and Shenzhen A-shares from 2010 to 2022, analyzing the relationship between the extent of Confucian cultural influence on companies and ESG rating divergence. The research findings indicate the following: (1) There is a positive correlation between Confucian culture and corporate ESG rating divergence. (2) The impact of Confucian culture on ESG rating divergence is significantly greater in state-owned enterprises (SOEs) than in non-state-owned enterprises. (3) This influence is more pronounced in highly polluting industries compared to non-highly polluting industries. (4) The effect is more significant in companies with older CEOs than younger CEOs. (5) This influence is more evident in companies required to disclose social responsibility information compared to those that do so voluntarily. After conducting a series of robustness checks, the conclusions of the paper remain robust. Full article

Extracting the economic value by the integrated utilization of space in old communities is crucial for encouraging independent participation from enterprises and residents, reducing reliance on government leadership and fiscal investment. This study starts from the active perspective of spatial assets and constructs a value activation framework for old communities by balancing “endogenous demand” and “exogenous opportunities”. By enhancing the “economic value” through the “use value”, five methods for value extraction and overall project utilization paths are proposed, guided by a dynamic “cost-revenue” balance. Using multi-source data, we identify the spatial assets of 1096 old communities in central Guangzhou and apply a market comparison method for an economic value assessment. Additionally, this study offers recommendations on the timing and project portfolios for regeneration efforts, along with strategies for establishing a coordinating implementation entity and fund account. This research provides strategic insights for advancing the regeneration of old communities by tapping into their macro-level economic potential. Full article

As global warming becomes increasingly serious, humans start to consider how to coexist with the natural environment. People become more and more aware of environmental protection and sustainable development. Therefore, in the pursuit of economic growth, it has become a consensus that enterprises should be responsible for the social and ecological environment. Regarding the manufacturing of electronic devices, as long as both component production quality and assembly quality are ensured, consumers can be provided with high-quality, safe, and efficient products. In light of this trend, enhancing product availability and reliability can reduce costs and carbon emissions resulting from repairing or replacing components, thus becoming a vital factor for corporate and environmental sustainability. Accordingly, enterprises enhance their economic benefits as well as have the effects of energy conservation and waste reduction by extending products’ service lifetime and increasing their added value. According to several studies, it takes a long time to retrieve electronic products’ lifetime data. Moreover, acquiring complete samples is often challenging. Consequently, when analyzing real cases, samples are usually collected using censoring techniques. The type-I right censoring data is suitable for industrial processes. Thus, this study utilized type-I right censoring sample data to estimate the lifetime performance index. It usually takes a large amount of time to access lifetime data for electronic products and it is often impossible to obtain complete samples since the size of the sample is usually small. Hence, to avoid misjudgment caused by sampling errors, this study followed suggestions from existing research and applied fuzzy tests built on confidence intervals to establish a fuzzy evaluation model for the lifetime performance index. This model helps relevant electronic industries not only evaluate the lifetime of their electronic components but also instantly seize opportunities for improvement. Full article

The concept of Industry 5.0 is novel from many points of view, as it fosters the transition to a sustainable, human-centric, resilient European industry. To reach this ambitious goal, it is necessary to act simultaneously on many fronts, starting from guaranteeing basic human rights (e.g., privacy, independence, and dignity) and paying attention to the circular economy and energy efficiency. Despite being difficult to adopt in its general formulation, this concept can be scaled up to specific fields, thus producing increased value with repercussions to the whole industrial process. The winemaking industry puts Italy at the forefront globally, as it is also among the key components of the whole national agrifood/agritech value chain. However, the Italian winemaking industry is quite fragmented, with a heterogeneous mix of small and medium enterprises (SMEs) and with large companies having opposite approaches to the production process, both in terms of involvement of human resources and seasonality of efforts, due to the existing climate differences nationwide. This fact makes the adoption of common practices even harder but makes the benefits of projects promoting this process innovation more tangible. In such a framework, CANTINA 5.0 seeks to fill in this important gap, promoting the Industry 5.0 principles in a selected group of SMEs and large companies from two different Italian areas featuring different climate conditions and different seasonality, hence characterized by different wine harvesting periods and types of wine production. The present article deals with the description of this paradigm in its single parts, including the use of questionnaires and smart tools to detect the health and well-being state of factory workers and winemakers, the use of well-grounded (including gas chromatography/mass spectrometry) and novel (e.g., based on the Internet-of-Things) environmental monitoring tools applied to the cellars/production departments, and the sensory analysis of the end-products, also leveraging the chemical and emotional characteristics of wines produced using the Industry 5.0 approach. Full article

An environmental, social and governance (ESG) evaluation system can focus on the value of enterprises more comprehensively and better scrutinize the development premise of enterprise. As a novel investment concept, both domestic and foreign investors widely acknowledge the significance of ESG. With the implementation of “carbon peak”, “carbon neutral” and other national strategies, an increasing number of transportation enterprises in China’s international commercial ports have started to focus on the role of ESG evaluation. This not only facilitates self-examination and correction within enterprises but also helps in adjusting the strategic direction toward sustainable development. This shift toward ESG evaluation is crucial for promoting environmental sustainability and corporate social responsibility within the transportation industry. In this regard, this study aims to evaluate the sustainable development performance of China’s international commercial ports based on ESG elements. A data envelopment analysis (DEA) is considered to be a non-parametric performance evaluation method that can effectively solve for multi-criteria decision-making units, so this study mainly selects the DEA model for the performance evaluation. This study conducted research to select eight benchmarking companies within the industry and found that efficient units excelled in their ability to complete capacity levels with high quality and quantity at ports. In contrast, less efficient units scored lower in the domain of social responsibility. Full article

This work aims to conduct an analysis to find opportunities for the implementation of software incorporating the concept of digital twins for foundry work. Examples of implementations and their impact on the work of enterprises are presented, as is a definition and history of the concept of a digital twin. The outcome of this work is the implementation of software that involves a digital copy of the author’s device, created by the “Łukasiewicz” Research Network at the Krakow Institute of Technology. The research problem of this scientific work is to reduce the number of necessary physical tests on real objects in order to find a solution that saves time and energy when testing the thermal expansion of known and new metal alloys. This will be achieved by predicting the behavior of the sample in a digital environment and avoiding causing it to break in reality. Until now, after an interruption, the device often continued to operate and collect data even though no current was flowing through the material, which could be described as inefficient testing. The expected result will be based on the information and decisions obtained by predicting values with the help of a recurrent neural network. Ultimately, it is intended to predict the condition of the sample after a set period of time. Thanks to this, a decision will be made, based on which the twin will know whether it should automatically end its work, disconnect the power or call the operator for the necessary interaction with the device. The described software will help the operator of a real machine, for example, to operate a larger number of workstations at the same time, without devoting all their attention to a process that may last even for hours. Additionally, it will be possible to start work on selecting the chemical composition of the next material sample and plan its testing in advance. The machine learning handles model learning and value prediction with the help of artificial neural networks that were created in Python. The application uses historical test data, additionally retrieves current information, presents it to the user in a clear modern form and runs the provided scripts. Based on these, it decides on the further operation of the actual device. Full article

Industrial ecology addresses newer business models that improve flows of energy, water, and materials, mimicking interconnections found in natural systems. Businesses can function interdependently to extend the life cycle of resources by setting up systems to repurpose waste or transfer a byproduct of manufacturing as an input for creating another product. Although the extant literature focuses on the role of businesses in closed-loop processes, governments can catalyse sustainable entrepreneurship to transition to a circular economy. There is a limited understanding of how public–private partnerships can facilitate this shift in small and medium enterprises. Multiple case studies were conducted to examine industrial ecology projects that were spearheaded by a state grant scheme in Australia. The long-term progress in establishing initiatives across commercial and industrial projects was monitored. The findings show government incentives to start up projects facilitate conditions to develop technology and other capabilities for responsible production and consumption. This study extends the theory of innovation ecosystems into practice. The model demonstrates that sustainable value for business and society can be realized through financial support and collaboration that enables entrepreneurship and drives circularity across cities and regions. Full article

Our story starts in April 2020, in the early stages of the UK’s first national COVID-19 lockdown. A multidisciplinary team of researchers and artists began a collaboration with Migrateful, a charity that runs cookery classes led by refugees, asylum seekers, and migrants struggling to integrate and access employment. Teaching classes and sharing their cuisine and stories helps the chefs develop their confidence and sense of belonging, and food is central to the enterprise. The focus of the project was a series of interactive online cookery classes delivered by Migrateful chefs, with ongoing involvement from the researchers and artists. In this paper, we weave together the research team’s reflections on the project with commentary from the participants and artists. We outline our methods and our learning from the collaboration and explain how it inspired new ways of thinking about refugee representation, food and belonging, co-creative storytelling, and virtual engagement. We discuss the ways in which Migrateful’s model helps to support the production of counter-narratives that value, foreground, and amplify migrants’ perspectives and voices while acknowledging the tensions involved in adapting this model to the virtual space. We emphasise the power dynamics inherent in engaging and researching with marginalised people and their stories while considering whether artistic involvement and creation may help to navigate some of these challenges, and we address how the virtual environment affected the potential for collaborative storytelling, interaction, and engagement levels among participants. Together, these reflections form a ‘recipe’ for what we hope to be a more meaningful and ethical model of engagement activity that builds on this learning. Full article

With the continuous improvement of people’s income level and consumption level, the demand for fresh products is driven by the strong demand, and at the same time, low-carbon and green development puts forward new requirements for the cold chain logistics industry. Starting from the perspective of considering the green cost, by constructing a distribution path optimization model of fresh cold chain products considering green cost with the optimal total distribution cost as the research objective, energy saving and emission reduction are integrated into the path optimization problem so that we can explore how to protect the environment while realizing the benefits of the enterprise, and the model is solved by using the ant colony algorithm. By observing the cold chain logistics distribution path arrangement before and after optimization, it is found that the fresh cold chain product distribution path optimization considering green cost can effectively reduce the transportation cost, refrigeration cost, carbon emission cost, and cargo damage cost in the distribution process. Under the optimal distribution strategy, the total cost is reduced by about 16.6% compared to the original route, and the environmental cost is reduced while reducing the distribution cost. It shows that this strategy can improve transportation efficiency and customer satisfaction while saving resources and protecting the environment. And this study comprehensively considers the actual operation of logistics enterprises, so this study has a certain significance of reference value for the green transformation of enterprises. It further promotes the sustainable development of the cold chain industry and reduces the distribution costs of cold chain logistics companies. It also provides a certain degree of inspiration and reference for other cold chain logistics companies to realize the unification of economic and environmental benefits in actual operation. Full article