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This article aims to optimise energy use and consumption by integrating Lean Six Sigma methodology with the ISO 50001 energy management system standard in an Irish dairy plant operation.

This work utilised Lean Six Sigma methodology to identify methods to measure and optimise energy consumption. The authors use a single descriptive case study in an Irish dairy as the methodology to explain how DMAIC was applied to reduce energy consumption.

The replacement of heavy oil with liquid natural gas in combination with the new design of steam boilers led to a CO₂ footprint reduction of almost 50%.

A further longitudinal study would be useful to measure and monitor the energy management system progress and carry out more case studies on LSS integration with energy management systems across the dairy industry.

The novelty of this study is the application of LSS in the dairy sector as an enabler of a greater energy-efficient facility, as well as the testing of the DMAIC approach to meet a key objective for ISO 50001 accreditation.

This paper aims to seek answers to the question: What are the relevant factors that allow not-for-profit innovation networks to successfully transition new technologies from proof-of-concept to commercialisation?

This question is examined using the knowledge-based view and network orchestration theory. Data are collected from 35 interviews with managers and engineers working within seven centres that comprise the High Value Manufacturing Catapult (HVMC). These centres constitute a not-for-profit innovation network where suppliers, customers and competitors collaborate to help transition new technologies across the “Valley of Death” (the gap between establishing a proof of concept and commercialisation).

Network orchestration theory suggests that a hub firm facilitates the exchange of knowledge amongst network members (knowledge mobility), to enable these members to profit from innovation (innovation appropriability). The hub firm ensures positive network growth, and also allows for the entry and exit of network members (network stability). This study of not-for-profit innovation networks suggests the role of a network orchestrator is to help ensure that intellectual property becomes a public resource that enhances the productivity of the domestic economy. The authors observed how network stability was achieved by the HVMC's seven centres employing a loosely-coupled hybrid network configuration. This configuration however ensured that new technology development teams, comprised of suppliers, customers and competitors, remained tightly-coupled to enable co-development of innovative technologies. Matching internal technical and sectoral expertise with complementary experience from network members allowed knowledge to flow across organisational boundaries and throughout the network. Matrix organisational structures and distributed decision-making authority created opportunities for knowledge integration to occur. Actively moving individuals and teams between centres also helped to diffuse knowledge to network members, while regular meetings between senior management ensured network coordination and removed resource redundancies.

The study contributes to knowledge-based theory by moving beyond existing understanding of knowledge integration in firms, and identified how knowledge is exchanged and aggregated within not-for-profit innovation networks. The findings contribute to network orchestration theory by challenging the notion that network orchestrators should enact and enforce appropriability regimes (patents, licences, copyrights) to allow members to profit from innovations. Instead, the authors find that not-for-profit innovation networks can overcome the frictions that appropriability regimes often create when exchanging knowledge during new technology development. This is achieved by pre-defining the terms of network membership/partnership and setting out clear pathways for innovation scaling, which embodies newly generated intellectual property as a public resource. The findings inform a framework that is useful for policy makers, academics and managers interested in using not-for-profit networks to transition new technologies across the Valley of Death.

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

Minimizing the impact on the surrounding environment and maximizing the use of production raw materials while ensuring that the relevant processes and services can be delivered within the specified time are the contents of enterprise supply chain management in the green financial system.

With the continuous development of China's economy and the continuous deepening of the concept of sustainable development, how to further upgrade the enterprise supply chain management is an urgent need to solve. How to maximize the utilization of resources in the supply chain needs to be realized from the whole process of raw material purchase, transportation and processing.

It was proved that digital twin technology had a partial intermediary role in the role of supply chain big data analysis capability on corporate finance, market, operation and other performance.

This paper focused on describing how digital twin technology could be applied to big data analysis of enterprise supply chain under the green financial system and proved its usability through experiments. The experimental results showed that the indirect effect of the path big data analysis capability digital twin technology enterprise financial performance was 0.378. The indirect effect of the path big data analysis capability digital twin technology enterprise market performance was 0.341. The indirect effect of the path big data analysis capability digital twin technology enterprise operational performance was 0.374.

Industry 4.0 has been identified as a key cornerstone to modernise economies where man and machines complement each other seamlessly to achieve synergies in decision-making and productivity for contributing to SDG 8: Decent Work and Economic Growth and SDG 9: Industry, Innovation and Infrastructure. The integration of Industry 4.0 remains a challenge for the developing world, depending on their current status in the industrial revolution journey from its predecessors 1.0, 2.0 and 3.0. This chapter reviews reported findings in literature to highlight how robotics and automated systems can pave the way to implementing and applying the principles of Industry 4.0 for developing countries like Mauritius, where data collection, processing and analysis for decision-making and prediction are key components to be integrated or designed into industrial processes centred heavily on the use of artificial intelligence (AI) and machine learning techniques. Robotics has not yet found its way into the various industrial sectors in Mauritius, although it has been an important driver for Industry 4.0 across the world. The inherent barriers and transformations needed as well as the potential application scenarios are discussed.

This study aims to explore and understand the challenges and opportunities presented by the rising demand for organic products in the context of toddy consumption and marketing.

This research examines consumer behaviour and decision-making patterns using decision tree analysis. A survey questionnaire based on established theories was distributed to individuals above the legal drinking age of 23 in Kerala, India, using purposive and random sampling.

The study found that people's fondness for toddy shop food plays a crucial role in their food choices. When the fondness is low, subjective norms can override personal preferences. But when the fondness is high, individual perceptions take precedence.

Using machine learning techniques, we created a compass to guide marketing strategies and cultural preservation efforts in toddy shops by considering the complex factors that influence consumer decisions.

This study aims to explore the impact of tourists’ perceptions of two rural destination attractiveness dimensions on tourists’ environmentally responsible behavioral intentions (ERBI). Further, the mediating effects of tourists’ green self-identity on the relationship between the perception of rural destination attractiveness and ERBI are investigated.

This study collected survey data from 188 tourists who had visiting experiences in rural attractions located in the Guangdong Province of China. Partial least squares structural equation modeling (PLS-SEM) was used to test the proposed hypotheses.

The results found that rural destination specialty fresh food attractiveness perceived by tourists was positively associated with their ERBI. Moreover, tourists’ green self-identity positively mediated the perception of rural destination attractiveness and ERBI.

This study explains how the tourists’ perceptions of two rural destination attractiveness dimensions influence their ERBI. By exploring the mediating role of tourists’ green self-identity, this study also emphasizes the transforming mechanism from tourists’ perceived experience to their ERBI. The study provides insights into nature-based tourism destination management and sustainability practices.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The current study is an attempt to investigate the residential satisfaction and prioritize effective components on residents' satisfaction based on household surveys conducted in eight Mehr housing complexes in Mazandaran province located in different counties of this region.

In the current work, using software of SmartPLS 3, second-order confirmatory factor analysis has been employed to evaluate and rank influential factors on residents' satisfaction.

As a result of descriptive analysis, 51.8% of the respondents were highly satisfied with Mehr housing complexes. Moreover, the results showed that there was the highest level of satisfaction (76.3%) with the security, while the lowest one (34.4%) was related to satisfaction with the facilities of the housing complexes. The standardized coefficients obtained showed that the components of physical characteristics (0.901), facility (0.863), neighborhood relationship (0.810), visual quality (0.774), security (0.737) and environmental health (0.715) have the most influence on the satisfaction of the residents, respectively.

This paper proved that migration has a significant effect on the level of residents' satisfaction, in multicultural cities. Therefore, it is crucial to promote social interaction and involvement among different ethnic groups in residential complexes that can result in intimacy, hence satisfying sociocultural needs, improving neighborhood relationships and consequent satisfaction of residents in Mehr housing projects in Iran.

Vocational education and entrepreneurship are constantly increasing in research fields. However, what is the current state of vocational education and entrepreneurial research? Where will the area go next? These questions are still unanswered; thus, this study tries to map the research landscape of the study area to get insights and provide directions for future research.

This research collected extant literature on vocational education and entrepreneurship using Scopus scientific database. Bibliometric analysis has been performed to extract insights from 175 documents published in the study area. Content analysis on the extant literature has also been committed to getting contextual information and developing an integrated research framework for future researchers.

The bibliometric analysis revealed that training, career choice, curriculum, self-employment, student psychology, better job opportunity, learning environment and innovation are the most discussed in the vocational education and entrepreneurship literature. Developed nation’s strong presence, indicated by the number of publications in the field.

This study significantly contributes to entrepreneurship by disclosing advances in the literature and some of the most active research fronts in this sector, delivering insights that have yet to be wholly appreciated or appraised. The study also developed an integrated framework that could benefit various vocations, education and entrepreneurship stakeholders.