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This study aims to analyze the interaction between environmental, social and governance (ESG) practices and digital capabilities in promoting business model innovation (BMI) in family firms. Specifically, it researches how ESG practices influence BMI in family firms, breaking down this influence into its ESG components.

We used microdata from the Flash Eurobarometer 486 survey, conducted by the European Commission in 2020, which provides detailed data on the challenges and obstacles faced by European businesses. The survey included telephone interviews with key managers from 2,483 family-owned businesses across 27 EU countries.

The analysis found that the environmental, social and governance dimensions of ESG significantly enhance business model innovation in family firms. Additionally, the interaction between environmental practices and digital capabilities significantly enhances business model innovation in family firms, while the interactions between social or governance practices and digital capabilities do not show significant effects.

The study supports the theoretical framework that integrates ESG practices into business model innovation, providing empirical evidence for the concept of sustainable business models. It emphasizes the importance of environmental sustainability, social engagement and robust governance in driving innovation.

Family business managers can use the findings to guide their innovation strategies by integrating ESG practices with digital capabilities. Policymakers can also benefit from understanding the importance of supporting ESG practices and digitalization in family businesses, fostering a regulatory environment that encourages sustainable innovation.

This research expands the theoretical understanding of how ESG practices and digital capabilities interact to foster BMI, particularly in family firms. By breaking down ESG practices into environmental, social and governance components, the study offers a detailed view of their interaction with digital capabilities.

In the last few years, environmental issues have become a matter of survival. In this sense, e-waste management is among the major problems since it may be a way of mitigating mineral depletion. In this context, the literature lacks e-waste supply chain studies that systematically map supply chain challenges and risks concerning material recovery.

Given this context, the authors' paper conducted a systematic literature review (SLR) to build a framework to identify the constructs of e-waste supply chain risk management.

The paper revealed the theoretical relationship between important variables to achieve e-waste supply chain risk management via a circular economy (CE) framework. These variables include reverse logistics (RL), closed-loop supply chains (CLSC), supply chain risk management, supply chain resilience and smart cities.

The literature contributions of this paper are as follows: (1) a complete list of the risks of the e-waste supply chains, (2) the techniques being used to identify, assess and mitigate e-waste supply chain risks and (3) the constructs that form the theoretical framework of e-waste supply chain risk management. In addition, the authors' results address important literature gaps identified by researchers and serve as a guide to implementation.

Entrepreneurial marketing (EM) literature has evolved recently, but more understanding is needed on how the seven dimensions of EM impact causal and effectual entrepreneurial behavior and, similarly, how entrepreneurial behavior influences the results of all dimensions of the EM construct. This study investigates the association and mutual influence between EM and entrepreneurship.

This study uses a qualitative strategy, addressing gaps due to its low incidence and employs theoretical replication, which is practically unexplored. It investigates two cases in Brazil: small companies (eight cases selected by literal replication) and a structured network of companies (one case selected by theoretical replication), predicting a positive influence of EM in the first case and a negative or neutral influence in the second.

The influence of EM on entrepreneurship is context-dependent and varies according to the empirical object. In turn, the impact of entrepreneurship on the results of the EM dimensions is more stable, primarily causal and varies slightly between structures.

First, by studying how the dimensions of EM impact causal/effectual behavior, this study broadens the understanding of the area, which was previously focused on only a few dimensions. Second, by investigating the impact of entrepreneurship on EM outcomes, this study sheds light on the influence of and differences in causal/effectual behavior in each of the seven dimensions. Finally, it extends the understanding of EM and entrepreneurship in small businesses and a structured network by identifying similarities and distinctions hitherto unexplored.

This research analyses the impact of customers and government regulations on the implementation of socio-environmental practices in certifying organic agricultural products. It explores the dyad’s relationship between the focal company and its suppliers in the application of socio-environmental practices.

This study uses a quantitative methodology through a survey approach, with a sample of 206 agro-extractivists from the acai berry supply chain. The data are evaluated using regression analysis.

The main results reveal that customer pressure positively influences the implementation of social and environmental practices, but suggest a non-significant relationship between government regulations and the impact on environmental practices implementation. Social and environmental practices are positively related to operational performance. A moderating effect of organic certification is found in the relationship between customer pressure and the application of environmental practices.

The main contributions are exploring the use of socio-environmental practices in an emerging economy and organic certification as a moderating variable, revealing an “institutional void” that may hamper the enforcement of government regulations.

The purpose of this study is to evaluate and enhance sustainable manufacturing practices across various industries, focusing on environmental, economic and social dimensions, to promote a comprehensive understanding and implementation of sustainability, thereby improving overall industry performance and fostering long-term ecological and economic health.

The study uses multi-criteria decision-making-multivariate analysis technique to examine sustainable manufacturing practices (SMPs) in the Indian manufacturing sector. It identifies 11 SMP criteria through literature review and expert recommendations. Data are collected through questionnaires, expert committees and interviews. The study focuses on four key industries: automobile, steel, textile and plastic. Techniques like principal component analysis (PCA), technique for order preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) are used to rank and assess performance.

The Indian automobile industry has shown the most effective SMPs compared to steel, textile and plastic sectors. The automobile sector is the benchmark for sustainable measures, emphasizing the importance of green practices for environmental, social and economic performance. Recommendations extend beyond the automobile sector to cement, electronics and construction.

The research emphasizes the importance of SMPs across various industries, focusing on economic, environmental and social considerations. It advocates for a holistic approach that enhances resource efficiency and minimizes ecological footprint.

The study uses ranking methods like PCA-integrated TOPSIS and COPRAS to evaluate performance in different industries, focusing on the benchmarked automobile sector. The research offers valuable insights and advocates for the widespread adoption of sustainable policies beyond the studied sectors.

The primary objective of this study is to understand how gamification elements, perceived teacher support and boredom relate to academic performance and how these relationships are mediated by perceived enjoyment while pointing out such influence on educational outcomes.

A quantitative survey methodology was conducted with 350 Lebanese university students specializing in digital marketing. This study used structural equation modeling (SEM) to analyze the data and provide exciting insights into the complex ties between variables.

The results indicate that well-implemented gamification elements significantly increased perceived enjoyment and positively influenced academic performance. Furthermore, perceived teacher support enhanced the effectiveness of gamification by increasing student engagement and enjoyment. Conversely, boredom negatively affects perceived enjoyment and academic performance, underscoring the need for well-designed gamification strategies that sustain interest and motivation.

Structural equation modeling and other quantitative tools excel at discovering connections but may not reveal the origins of the patterns they uncover. Given the complexity of causation, quantitative studies examining the mediating role of subjective satisfaction may gain more insight using a mixed or qualitative approach. Although the data supplied by the 350 responders were interesting, the sample size was insufficient to make any definitive conclusions. These findings may not be generalizable because Lebanon’s student bodies are diverse. The ability to detect tiny changes in the target variables requires researchers to consider how much time and energy they can dedicate to gathering data while structuring their investigations.

This study contributes to understanding gamification as a powerful tool for innovation in education and reshaping learning into motivating, engaging and sustaining productive experiences to improve educational quality. Therefore, our recommendations shed light on such improvements' impact on society. In this vein, we enrich this path by highlighting the crucial role of teachers and decision-makers in developing new professional programs.

This study demonstrates the importance of perceived enjoyment in the transformative gamification process in education. This study emphasizes the value of effective gamification implementation supported by teachers as a powerful tool for enhancing learning experiences and improving the quality of education.

With the current wave of modernization in the dairy industry, the global dairy market has seen significant shifts. Making the most of inventory planning, machine learning (ML) maximizes the movement of commodities from one site to another. By facilitating waste reduction and quality improvement across numerous components, it reduces operational expenses. The focus of this study was to analyze existing dairy supply chain (DSC) optimization strategies and to look for ways in which DSC could be further improved. This study tends to enhance the operational excellence and continuous improvements of optimization strategies for DSC management

Preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for systematic reviews are served as inspiration for the study's methodology. The accepted protocol for reporting evidence in systematic reviews and meta-analyses is PRISMA. Health sciences associations and publications support the standards. For this study, the authors relied on descriptive statistics.

As a result of this modernization initiative, dairy sector has been able to boost operational efficiency by using cutting-edge optimization strategies. Historically, DSC researchers have relied on mathematical modeling tools, but recently authors have started using artificial intelligence (AI) and ML-based approaches. While mathematical modeling-based methods are still most often used, AI/ML-based methods are quickly becoming the preferred method. During the transit phase, cloud computing, shared databases and software actually transmit data to distributors, logistics companies and retailers. The company has developed comprehensive deployment, distribution and storage space selection methods as well as a supply chain road map.

Many sorts of environmental degradation, including large emissions of greenhouse gases that fuel climate change, are caused by the dairy industry. The industry not only harms the environment, but it also causes a great deal of animal suffering. Smaller farms struggle to make milk at the low prices that large farms, which are frequently supported by subsidies and other financial incentives, set.

This paper addresses a need in the dairy business by giving a primer on optimization methods and outlining how farmers and distributors may increase the efficiency of dairy processing facilities. The majority of the studies just briefly mentioned supply chain optimization.

This study aims to develop a novel diagnostic methodology for implementing ISO13485:2016 and test its applicability to improve quality management systems (QMS) in the medical devices industry context.

First, a literature review on the topic was conducted. Second, insights gained from the literature and expert interviews were employed to develop the new maturity assessment methodology. Subsequently, the methodology was tested on a medical device manufacturer. Next, based on the evaluation of the intervention, actions were recommended to improve the QMS.

Research findings have developed a maturity assessment methodology comprising 52 certifiable requirements structured into four macro-requirements derived from ISO 13485:2016. Findings show that the methodology is valuable for aiding QMS implementation, and the diagnosed maturity levels corresponded with the company’s empirical perceptions of the requirement’s maturity.

Empirical evidence validates the significance and practical utility of the proposed methodology, as evidenced by the company’s attainment of FDA (US Food and Drug Administration) approval after the intervention. Findings suggest that the methodology could be replicated within the medical products industry or adapted to assess other QMS, leveraging the organizational alignment with the international regulations of the sector and the ISO 9000 requirements.

The developed methodology fills existing gaps in both literature and practice within the medical devices industry, providing a valuable contribution by addressing the limited research on diagnostic methodologies designed for ISO 13485:2016 implementation. The article assists medical device enterprises in addressing QMS maturity levels as a metric for evaluating QMS requirements, which is an underexplored avenue in existing QMS evaluation approaches.

This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance.

In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employee’s performance meets expectations or needs improvement.

All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce.

The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the model’s effectiveness across various contexts.

The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment.

Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance.

This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach.

This paper employs the Biondi and Lapsley (2014) accounting information transparency model to investigate whether effective transparency can be achieved through digital platforms for popular reporting. In order to address the limitations in previous research, which predominantly targets non-expert users, this is attained by focusing on the perception of local government auditors. By engaging auditors who are experts in accounting, the study aims to investigate if any cognitive biases in the design of online platforms could impact the achievement of a sophisticated understanding with shared meanings among citizens.

The study adopts an explorative and inductive approach, semi-structured interviews and an online survey among the Italian local governments’ Professional Auditors’ Association (ANCREL). Auditors assessed the OpenBDAP (https://openbdap.rgs.mef.gov.it/) platform in facilitating the comparison and analysis of the budgets and financial statements of Italian public sector organisations.

The results present a nuanced assessment of OpenBDAP, highlighting a general consensus on its ability to enhance the access and understanding of financial data and the appreciation of the utility of infographics in understanding financial data. Therefore, a clear challenge emerges in achieving active engagement of stakeholders. Despite expectations expressed during interviews with the Italian National Accounting Office regarding the design of custom APIs to meet user needs, our findings indicate the potential presence of cognitive biases in the design of the online platform as key obstacles.

By extending the application of the Biondi and Lapsley (2014) model on digital platforms for popular reporting to local government auditors, this study highlights the potential presence of cognitive bias in the design of online platforms that may impact the achievement of effective transparency, an aspect not previously identified in existing research. Finally, it suggests that these popular reporting platforms may evolve beyond mere transparency tools, assuming a broader role as potent learning instruments. This transformation could help to address the inherent complexity of accounting information, which is the real obstacle to achieving transparency.