Mid-sized Enterprises Research Articles

The sustainable development of small and midsize enterprises in China on green reverse logistics

In response to the increasing global emphasis on sustainability and the specific challenges faced by small and medium-sized enterprises (SMEs) in China, this study explores the integration of green reverse logistics within these enterprises to enhance their sustainability and competitiveness. The aim of this study is to understand the relationship between reverse logistics, green logistics, and sustainable development. Data analysis was conducted utilizing a combination of descriptive statistics and correlation analysis. A survey of 311 participants examined SMEs’ performance in reverse logistics practices and their initiatives in green logistics and sustainable development. The empirical findings reveal significant progress in reverse logistics practices among SMEs, reducing environmental impact and improving resource efficiency. Moreover, a notable positive correlation was identified between reverse logistics promotion and advancements in green logistics and sustainable development. SMEs’ investment in reverse logistics is closely linked to their efforts in environmentally conscious and sustainable supply chain management. These insights benefit SMEs and supply chain practitioners and offer a valuable reference for future research and practical applications in this field.

Testing technological Kuznets curve implications on achieving sustainable development goal 10 in seven Asian countries

This study investigates the relationship between technological innovations, financial development, and income inequality implications on achieving Sustainable Development Goal 10 (SDG10) in seven Asian economies via testing the inverted U-shaped hypothesis of technological innovation on income inequality in the concept of technological Kuznets curve hypothesis. The significant contribution of this study is introducing the Technological Kuznets Curve (TKC) as an extension to the Kuznets curve (KC) framework to capture the impact of technological innovation on income inequality. The PMG/ARDL was applied to estimate the coefficients to accomplish the study's objective. The panel cointegration tests confirm that all variables are cointegrated with income inequality. The empirical findings validate the KC hypothesis and demonstrate that technological innovations contribute to reducing income inequality within the Kuznets curve framework in the long run. Financial development (money supply) was negatively correlated with income inequality. The study further validates the TKC hypothesis, revealing an inverted U-shaped relationship between technological innovations and income inequality. Financial development, measured by domestic credit to the private sector, also shows a negative correlation with income inequality within the TKC framework. However, the money supply demonstrates a positive link with income inequality. The study recommended the necessity to prioritize policies that support technological innovation through subsidies, encouragement for research and development, and collaborations between the public and private sectors will greatly lower income inequality and support achieving Sustainable Development Goal 10 (SDG10). Financial development significantly facilitates access to financial services and contributes to diminishing income inequality by building an effective and inclusive banking system (mobile banking services for poor communities), easing access to credit for emerging and small and midsize enterprises (SMEs), and money circulation. Governments should strategically allocate resources toward programs and initiatives that directly address income inequality and promote inclusive growth.

Breaking the data barrier: a review of deep learning techniques for democratizing AI with small datasets

Justifiably, while big data is the primary interest of research and public discourse, it is essential to acknowledge that small data remains prevalent. The same technological and societal forces that generate big datasets also produce a more significant number of small datasets. Contrary to the notion that more data is inherently superior, real-world constraints such as budget limitations and increased analytical complexity present critical challenges. Quality versus quantity trade-offs necessitate strategic decision-making, where small data often leads to quicker, more accurate, and cost-effective insights. Concentrating AI research, particularly in deep learning (DL), on big datasets exacerbates AI inequality, as tech giants such as Meta, Amazon, Apple, Netflix and Google (MAANG) can easily lead AI research due to their access to vast datasets, creating a barrier for small and mid-sized enterprises that lack similar access. This article addresses this imbalance by exploring DL techniques optimized for small datasets, offering a comprehensive review of historic and state-of-the-art DL models developed specifically for small datasets. This study aims to highlight the feasibility and benefits of these approaches, promoting a more inclusive and equitable AI landscape. Through a PRISMA-based literature search, 175+ relevant articles are identified and subsequently analysed based on various attributes, such as publisher, country, utilization of small dataset technique, dataset size, and performance. This article also delves into current DL models and highlights open research problems, offering recommendations for future investigations. Additionally, the article highlights the importance of developing DL models that effectively utilize small datasets, particularly in domains where data acquisition is difficult and expensive.

A methodology for evaluating feature selection and clustering methods with project-specific requirements

This paper describes a methodology for ranking feature selection and clustering methods with user-specific preferences and taking data properties into account. For a better understanding of this paper, the developed methodology is referred to as the Two Machine Learning Procedures, Preferences and Properties (2ML3P) methodology. The 2ML3P methodology aims to support users from multiple domains, such as engineers, who have little expertise in machine learning (ML). It is also independent from the disciplinary core competencies of the manufacturer, with a strong focus on employability in small and mid-sized enterprises (SME). The foundation of the methodology to evaluate the combination of the two machine learning classes is described. It focuses on a range of feature selection and clustering methods, their limitations, and their challenges. The paper covers the concept phase by defining the inputs, such as the specific characteristics of machine learning classes or the properties of the production data and the user preferences. With applied methodologies such as the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), the preferences of the user as valid input are integrated. The scientific contribution of this methodology is the approach to include user preferences and specific data properties in the selection process of two ML methods. As digitalisation progresses, making data-driven decisions in the domains of production and logistics is a goal for many SMEs. This methodology can support a data-driven decision-aid model by providing a guided method, which requires relatively little ML knowledge on the part of the engineer. It allows the user(s) to select the best suited combination of ML methods for a clustering use case.

Conceptualizing Multi-Level Legal Systems to Address Global Food Security: The Hard Law-Soft Law Interface of International Law and Corporate Social Responsibility

This Article argues that the Zero Hunger Sustainable Development Goal (SDG) has been threatened by a combination of the COVID-19 pandemic, the Russian invasion of Ukraine, and climate change. Given that global food security is a well-recognized and fundamental human right worthy of protection, this Article examines the existing legal regimes—from the international, through the regional and national to organizational levels. Recognizing the critical role of commercial activities and enterprises in global food security, this Article argues that there is a need for direct, purposeful engagement by companies of all sizes, from multinational corporations ("MNCs") to small and midsize enterprises (“SMEs”) to address global food security. This Article demonstrates that it can be done as a form of corporate social responsibility (“CSR”), a global policy with national and organizational implementations. It then examines India and Indonesia as two jurisdictions where CSR laws have been enacted to address the specific needs of those societies and especially those members of society without sufficient resources to address their own needs. After a brief review of the legislation, a range of potential reforms are developed which would support a return to the zero hunger trajectory.

Effects of Technological Advancement on the Functioning of Small and Midsize Enterprises (SMEs) in Khyber Pakhtunkhwa, Pakistan

Technological transformation aims to investigate small and medium-sized businesses' capacity to implement novel strategies that will boost production while endangering worker efficiency. Managers of SMEs in Khyber Pakhtunkhwa (KP), the province of Pakistan, made up a population sample of 273 respondents, who were compelled to do new research. The main statistical methods that are mandated are methods for linear regression. It is hypothesized that the medium-sized firm in KP's north and south areas is affected by the technological shift. The coefficient's determination (R2 = 0.101) shows that technical change accounts for 10.1% of the success observed in SME performance. When the forced p-value (0.000) matches the significance level of 0.05, it is considered statistically significant. results stated that the performance of SMEs in KP was positively and significantly impacted by technological progress.

An empirical Study on the Impact of Mid-Sized Enterprises’ ESG Awareness and Rating on Firm Performance

An empirical Study on the Impact of Mid-Sized Enterprises’ ESG Awareness and Rating on Firm Performance

CODAS–Hamming–Mahalanobis Method for Hierarchizing Green Energy Indicators and a Linearity Factor for Relevant Factors’ Prediction through Enterprises’ Opinions

As enterprises look forward to new market share and supply chain opportunities, innovative strategies and sustainable manufacturing play important roles for micro-, small, and mid-sized enterprises worldwide. Sustainable manufacturing is one of the practices aimed towards deploying green energy initiatives to ease climate change, presenting three main pillars—economic, social, and environmental. The issue of how to reach sustainability goals within the sustainable manufacturing of pillars is a less-researched area. This paper’s main purpose and novelty is two-fold. First, it aims to provide a hierarchy of the green energy indicators and their measurements through a multi-criteria decision-making point of view to implement them as an alliance strategy towards sustainable manufacturing. Moreover, we aim to provide researchers and practitioners with a forecasting method to re-prioritize green energy indicators through a linearity factor model. The CODAS–Hamming–Mahalanobis method is used to obtain preference scores and rankings from a 50-item list. The resulting top 10 list shows that enterprises defined nine items within the economic pillar as more important and one item on the environmental pillar; items from the social pillar were less important. The implication for MSMEs within the manufacturing sector represents an opportunity to work with decision makers to deploy specific initiatives towards sustainable manufacturing, focused on profit and welfare while taking care of natural resources. In addition, we propose a continuous predictive analysis method, the linearity factor model, as a tool for new enterprises to seek a green energy hierarchy according to their individual needs. The resulting hierarchy using the predictive analysis model presented changes in the items’ order, but it remained within the same two sustainable manufacturing pillars: economic and environmental.

Identifying and prioritizing the barriers to green innovation in SMEs and the strategies to counteract the barriers: An interval-valued intuitionistic fuzzy approach

Increasing environmental burdens and the importance of innovation in the presently competitive environment have encouraged small- and mid-size enterprises (SMEs) to adopt and implement green innovation practices. This process, however, is faced with many limitations and obstacles. The purpose of this study is to propose a framework to identify barriers to green innovation in SMEs and to provide solutions/strategies to overcome such barriers. Using the systematic literature review (SLR) method and the opinions of 20 experts, the study primarily identified 29 sub-categories of barriers falling under seven main categories including “management”, “organization”, “human resources”, “financial resources”, “external cooperation”, “customers, markets or competitors”, and “the external environment.” Similarly, 22 strategies were discovered that could help to counteract the barriers identified. To determine the weights of the barriers, the Intuitionistic Fuzzy Multiplicative Best-Worst Method (IFMBWM) was used. The strategies, too, were ranked through the Interval-Valued Intuitionistic Fuzzy MULTIMOORA (IVIF-MULTIMOORA) method. Then, a sensitivity analysis and a comparative analysis were applied to confirm the robustness of the proposed method and ensure the consistency of the obtained ranking of alternatives. As the results revealed, the optimal and effective solutions that could be used by policy-makers/managers to overcome the barriers to green innovation were “simplifying authorization and licensing procedures”, “effective environmental laws and regulations”, “shared responsibility throughout the supply chain”, “a supportive organizational culture”, and “merit-based selection of managers.”

Big data in the business environment: an analysis of its contributions to Competitiveness. A Literature Review

In the era of Industry 4.0, characterized by transformative technological advancements reshaping manufacturing processes, big data has become a common practice in business intelligence. It encompasses the use of data with advanced analytics techniques and plays an important role in business aspects and customer choice. In this context, the primary goal of this research is to comprehend the relationship between big data and the competitiveness of businesses. The research is based on a review of 83 articles published on the Web of Science in the period 2016 and 2023. Through cluster analysis, four groups of research categories are identified in this area (big data and AI in Industry 4.0, analysis of data for decision-making, big data and business innovation, and Internet of Things as a data source). The practical implications of this research are pertinent to organizational management activities involving innovation processes and decision-making, with direct implications for small and midsize enterprises competitiveness. On a theoretical level, the identified categories provide a framework for future research in understanding the connection between big data and competitiveness in the context of industry 4.0.

Enhancing SME innovation across European regions: Success factors in EU-funded open innovation networks

This study examines the funding allocations to Open Innovation (OI) networks involving small and mid-size enterprises (SMEs) and other organizational actors by the European Union's Horizon 2020 Fast Track to Innovation program (2018–2021). Using a unique dataset of program application information at the participant level, the technological, organizational and locational characteristics of SMEs' OI innovation networks are analyzed. Success in securing funding is found to be linked to host regional innovation performance and to technological distance among SMEs and their partners, while organizational distance has a curvilinear effect. Counter to program objectives, partner heterogeneity has some mixed effects. Insights are offered for SMEs on strategic positioning and to policymakers on the design of future innovation policy initiatives.

An innovative blockchain-based system for human resources digitalization with traceable relationship management

Small and Midsize Enterprises (SMEs) have faced innovative changes in their organizational structure, especially in human resources (HR) and employee management: these digital structural changes have transformed their business practices and strategies. The employees with new technical skills have also been impactful in this digitalization. The HR departments in SMEs have become increasingly pivotal in recruiting new employees and supporting existing employees throughout the organization. ITSvil in Salerno (Italy) has developed the Easywork project based on the “Fenice” microservices architecture. This system supports the HR departments in efficiently managing HR activities through digitalization. The first step in acquiring new resources is formulating the employment contract, which is essential for stipulating agreements on specific job requirements. Blockchain technology and smart contracts are among recent technological innovations impacting this process. Writing contracts in digital codes to simplify and effectively manage the legal relationship involving bureaucratic tasks and cost management is now possible. The new digital system makes employment contract processing transparent, traceable, and reliable. This research presents the design and development of a new microservice compatible and integrable process with the “Fenice” system based on blockchain technology. The proposed digital system aims to make HR management activities more transparent and traceable.

The Key Impact of Management Modes for Innovation in SMEs

This study assists in obtaining a better knowledge of managerial and organizational factors favoring different types of innovation, highlighting the critical role of creativity, risk-taking, and performance management for operational innovation. Also, this study aims to simultaneously investigate interactions of exploration and exploitation innovation to show how managers and organizations are usually treated in exploration and exploitation innovation. A sample of 755 valid questionnaires from small and mid-size enterprises (SMEs) from diverse firms in the global south were employed and analyzed using the partial least square structural equation model to examine the proposed research model and assumptions. This study shows that creativity, risk-taking, and performance management are positively and significantly affect operational innovation in global south SMEs. From a strategic point of view, our findings demonstrate that SMEs must create innovation in order to maintain their performance. Managers of SMEs would be aware of the influence of various types of innovation on various aspects of business performance, such as marketing, operations, and finance. On a management level, the research findings assist SMEs in achieving high performance levels with their innovation projects. In addition, this research helps readers to develop a better knowledge of how organizations combine operating innovations and exploration.

Bridging conflicting frames in policies for digital transformation

Abstract Despite several policies aimed at igniting digital transformation, many regions lag behind. We investigate this discrepancy by postulating that the political consensus on accelerating Industry 4.0 adoption among micro-, small- and mid-sized enterprises (MSMEs) needs to consider the heterogeneity of entrepreneurial and social fabrics. Best practices in policy-making exist, but their deployment requires adaptations. The present paper delves into such adaptations and on the mechanics behind how a ‘model’ is modified locally. We do so by illustrating the results of action research on a regional policy initiative. Our findings point to one important set of factors and mechanisms conducive to the adaptation of a policy model to the peculiarities of a locale via the alignment of different cognitive frames: co-design processes. We elaborate on the role of different actors, the relevance of ‘hybrid’ actors, and the nature of universities as platforms

Sustainable practices for the efficient use of green energy in Ciudad Juárez SMEs

Carbon emissions have been increasing due to economic growth and development. Fossil-based energy is one of the main factors of environmental pollution. In Mexico, 77.24% of fossil-based energy is from stationary source combustion, 15.7% from industry processes and commercial activities, 6.64% from mobile sources, and 0.05% from agriculture and livestock activities. Specifically, manufacturing operations use 33.4%, of the total consumption of electricity and natural gas. Because the production of green energy is from naturally regenerating sources and does not emit greenhouse gases or compounds, decreases the environmental impact, and because they are able to apply to manufacturing operations, it is pertinent the efforts in this sense. The article presents the PRISMA 2020 as a methodology for searching between distinct databases and current research with the objective of identifying variables and their measurements so micro, small, and mid-size enterprises in Ciudad Juarez, Chihuahua, Mexico can develop collaboration strategies towards a sustainable manufacturing environment. The literature review resulted in the identification of six green energy indicators and their measurements.

Bond Issuance as Reputational Signal: Debunking the Negative Perception of Additional Liability

This paper examines the determinants of bond issuance in the Chinese market and the influence of capital structure—in particular direct debt finance—on firm performance and the cost of debt. The results reveal that institutional factors in the Chinese market, in particular the involvement of the financial authority permission process during bond issuance, enhance the credibility of firms that are able to successfully issue bonds. Empirical analysis of Chinese listed manufacturing firms over the period from 2010 to 2021 demonstrates that firms with higher outstanding levels of bonds perform better and face lower costs of both bond and nonbond direct finance. We interpret this as bond issuance approval serving as a signal to markets of an implicit government guarantee on firms that are approved to issue bonds. The agency problem is analyzed using propensity-score matching and Logit analysis, revealing a trade-off between the principal–agent conflict and conflicts of interest among different shareholders when power is very concentrated through CEO duality: the CEO simultaneously serves as the chairman of the board. In large firms, as measured by total assets, the cost-reducing effect of the principal–agent problem being mitigated by CEO duality outweighs the agency costs arising from conflicts of interest between large and small shareholders, leading to an increased likelihood of successful bond issuance. However, in large firms, as measured by market capitalization, where share ownership is likely more diversified, this effect diminishes. In conclusion, this paper posits that policymakers ought to investigate strategies for granting preferential treatment to high-growth, small to mid-sized enterprises, enabling them to secure funding through direct debt financing.

The interplay of entrepreneurial motivations, job attractiveness and family-owned SMEs growth: evidence from China Pakistan Economic Corridor region

PurposeThe purpose of this study is to provide a nuanced explanation of the linkage between entrepreneurial motivations, job attractiveness and growth of family-owned small and mid-sized enterprises (SMEs) using expectancy and institutional theories.Design/methodology/approachThe data was collected from small family business owners and job seekers in the same companies during interview time using a simple random technique.FindingsThe study found that three EMs among small business owners play a pivotal role in family SME business growth in underdeveloped trade regions like China–Pakistan Economic Corridor. These firms are interested in investing in seaport-related commerce, restaurants or hotels and real estate business.Practical implicationsThe government, small family business owners, universities and regional youth can use this applied research for their benefits alike.Originality/valueThe study contributes in multiple ways. First, the authors brought a unique context in the emerging economies context of an informal economy like Pakistan. Second, the authors have uniquely tested the moderating role of job attractiveness in the least developed regions. Finally, the authors have integrated family SMEs’ expectancy theory and institutional perspective.

Multi-agent collaborative conceptual design method for robotic manufacturing systems in small- and mid-sized enterprises

Robotic manufacturing systems are essential in the post-pandemic world owing to their high level of flexibility and automation during a labor crisis. However, designing a suitable robotic manufacturing system for small- and mid-sized enterprises (SMEs) is challenging, considering both their financial burdens and the current supply chain disruptions of component suppliers. To address these challenges, this study proposes a distributed multi-agent collaborative conceptual design method involving designers and suppliers to assist SMEs in implementing robotic manufacturing systems. First, we propose a common data model that enables knowledge interaction between different agents during collaborative conceptual design. Subsequently, based on the proposed data model, the agent-based collaborative conceptual design process is developed, which enables different agents to communicate, interact, and negotiate with each other according to their experiences and knowledge. Third, an integrative algorithm based on 2-additive fuzzy measures, Choquet integral, and stochastic multi-criteria acceptability analysis is implemented to support the multi-agent decision-making process to robustly select architecture alternatives. As a case study, a real industrial design project of a robotic manufacturing system required by our industrial partner, is adopted to demonstrate the effectiveness of the proposed method.

Dark side of blockchain technology adoption in SMEs: an Indian perspective

PurposeThe purpose of this paper is to understand the dark side of blockchain technology (BCT) adoption in small and mid-size enterprises. The focus of the authors is to decode the intricate relationship among the selected variables missing in the existing literature.Design/methodology/approachA focused group approach is initiated by the authors to identify the barriers. Total interpretive structural modeling, Matrice d'impacts croisés multiplication appliquée á un classment, that is, matrix multiplication applied to classification and decision-making trial and evaluation laboratory are used to analyze the complex relationships among identified barriers.FindingsThis study finds that implementation of BCT reduces maintenance cost by withdrawing manual effort, as BCT has better capability to quantify the internal status of the system (observability characteristic). The observability characteristic of BCT provides high compatibility to the system. This study also finds that the compatibility of BCT with the organization reduces implementation cost and facilitates project management. The findings of this study recommend analyzing maintenance cost and compatibility of BCT before implementing it. Small and mid-size enterprises can select complex BCT depending on the sophistication level of IT usage and IT project management capabilities.Research limitations/implicationsThis study comes with various limitations, where the model developed by the authors may not be conclusive, as it is based exclusively on expert opinion. The samples collected may not help in validating the model statistically. Though the model has its limitations, it can still be considered as a nascent initiative for further investigation using structural equation modeling.Originality/valueThe outcomes of the theoretical and managerial contributions of the study can be categorized into three levels. This study can be used both by the industrialists and researchers to understand the barriers and the recovery methods thereafter. Suggestions that serve as future directives are also discussed by the authors.

Role of innovativeness in medium-size enterprises competitiveness: A case study of Kenyan medium-sized enterprises

Innovativeness has been termed as the most crucial of the elements in today’s globalized and competitive environment. Firms focusing on innovativeness achieve not only competitiveness but also are able to sustain them for a longer period of time. The relevance of Mid-sized enterprises’ competitiveness and their sustainability cannot be overemphasized for Kenya’s macroeconomic development, especially in this competitive global economy. Despite the concerted effort with a focus on Mid-sized enterprises, researchers question the role of innovativeness in promoting the competitive advantage of the Kenyan economy and the role of innovativeness in the ultimate competitiveness of Mid-sized enterprises. Thus, the focus of this study was to fill the gap. A descriptive and exploratory research design was used. The study used the stratified random sampling technique to ensure representation according to the various sectors. Statistical Package for Social Sciences (SPSS) was used for analysis. The results show enough evidence that innovativeness positively and significantly influences the competitiveness of Mid-sized enterprises in Kenya. The study provides managers with information regarding the processes of shaping strategy and decision-making, which has implications for management stressing the importance of encouraging EO capabilities and focusing efforts on innovativeness to improve firm competitiveness advantage.