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Eco-innovation products, which means achieving more efficient and responsible use of resources and reducing the detrimental impact on the environment, can win a competitive advantage for the enterprises. But it is not easy to implement due to the high cost of eco-innovative technologies development, the uncertainty of market needs and return risk of investment. Many enterprises seek collaborations from their upstream suppliers to jointly carry out eco-innovation, such as Apple, IBM and Nike. A unique feature of collaboration is that efforts by one party enhance the marginal value of the other party's efforts. However, the collaboration will make the partner know the eco-innovation technology and prompt the partner to encroach the market to sell competitive products by herself. Motivated by this observation, this paper considers the optimal collaboration strategy on eco-innovation between upstream and downstream supply chain member and the optimal encroachment strategy of upstream supplier in a supply chain.

This paper models a supply chain wherein a supplier provides products or materials for her manufacturer and cooperates with her manufacturer in eco-innovation. Also, the supplier could encroach on the market to sell similar products by herself. Then this paper uses game theory and mathematical modeling to do relative analysis.

The analysis reveals several interesting insights. First, eco-innovation collaboration makes supplier encroachment no longer only rely on the encroachment cost. The delayed realized eco-innovation efficiency information also plays a vital role. Second, different from previous research, the authors find the manufacturer's preference for supplier encroachment depends on the uncertainty of eco-innovation efficiency and potential market demand. Third, both partial and full encroachment strategies of the supplier can effectively improve the eco-innovation level.

The paper is the first to take the interplay between collaboration and encroachment into account in a supply chain. The results caution enterprises and policymakers to take vertical collaboration and delayed realized information into account in the competitive supply chain before making any operational decisions. Furthermore, the authors propose that governmental intervention aimed at stimulating supplier encroachment in appropriate circumstances can contribute to the improved environmental performance of products.

The purpose of this paper is to explore the dynamic relationship between formal and non-formal processes of accountability in a public services context.

The paper presents a case study of the impact of the Health and Social Care Act (2012) on the practices of Health and Wellbeing Board (HWB) members. It draws upon multiple data sources, including in-depth interviews with the members, comprehensive archival data published by the HWB (2011–2019), and observations of HWB public meetings. We utilise the concept of dynamic duality (Li, 2008) to further theorise the relationship between formal and non-formal processes of accountability and how they mutually transform one another.

The case illustrates the role of formal and non-formal processes of accountability at a HWB in England. Moreover, the case study reveals the relationship and interaction between the formal and non-formal accountability processes and how they change and transform each other over time. We find that whilst non-formal accountability processes were strengthened by a historical legacy of partnership working, over time the dynamics at play led to the development of formal accountability processes through more sophisticated performance systems, which in turn transformed non-formal accountability processes.

The paper presents a more holistic conceptualisation than articulated in prior accountability literature, dynamic duality, on the relationship between formal and non-formal accountability processes. Through application of this conceptualisation to a HWB in England, the paper spotlights the inter-relationship between formal and non-formal processes of accountability, and how they have the potential to transform each over an extended time-period.

This paper aims to enhance the predictability of stock returns. Existing studies have used investor sentiment to forecast stock returns. However, it is unclear whether high-frequency intraday investor sentiment can enhance the forecasting performance of low-frequency stock returns.

Thus, we employ the MIDAS model and the high-frequency intraday sentiment extracted from the Internet stock forum to forecast Chinese A-shares returns at daily frequency.

The results illustrate that high-frequency sentiment data are better than daily sentiment data in predicting daily stock returns, and the sentiment in non-trading hours has been proved superior to those in trading hours.

First, our study adds to the growing literature on investor sentiment. We are the first to construct a proxy for high-frequency investor sentiment using intraday postings collected from Chinese Internet stock forum. Second, we confirm that sentiment in non-trading hours has a stronger predictive ability than those in trading hours. Third, we also contribute to the performance comparison of MIDAS-class models. The good performance of U-MIDAS is confirmed in our empirical applications.

Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.

First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.

Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.

Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.

The highlights of the paper are as follows:

1. A new seasonal grey buffer operator is constructed.

2. The impact of shock perturbations on seasonal data trends is effectively mitigated.

3. A novel seasonal grey forecasting approach with multi-method fusion is proposed.

4. Seasonal electricity consumption is successfully predicted by the novel approach.

5. The way to adjust China's power system flexibility in the future is analyzed.

A new seasonal grey buffer operator is constructed.

The impact of shock perturbations on seasonal data trends is effectively mitigated.

A novel seasonal grey forecasting approach with multi-method fusion is proposed.

Seasonal electricity consumption is successfully predicted by the novel approach.

The way to adjust China's power system flexibility in the future is analyzed.

The purpose of this paper is to investigate how complementors’ marketing capability and technology capability affect their performance. Drawing on social capital theory, the authors examine platform network centrality as a mediator and platform reputation as a moderator of the relationships between these two capabilities and complementor performance.

This study collects data by questionnaire from 154 Chinese firms adopting e-commerce platforms. Hierarchical multiple regression is used to test the hypotheses of this study.

This study finds that complementors’ marketing capability and technology capability positively affect performance by increasing their platform network centrality. Moreover, platform reputation positively moderates the relationship between platform network centrality and complementor performance, and it strengthens the mediating role of platform network centrality.

This paper emphasizes the critical role of marketing capability and technology capability on complementor performance. It explores the improvement path of complementor performance from the perspective of network position, which is a key element for complementors to effectively leverage their capabilities to build competitive advantage.

This study assesses distress in the lives of farming population in Ghana's northern interior. Whereas recent studies addressing mental health in Africa produce evidentially sound analyses of the correlated role of food and water insecurity, this study engages with how and why worries over educational costs feature so prominently in a measure of psychosocial well-being. While it is significant that education identifiably figures in escalating the experience with distress, my contention is that what is more important is putting that escalation into the context of why. I use Narotzky and Besnier's framework for understanding the economy as the interactions between crisis, value, and hope to situate a mixed methods study concerned with how distress is proximately shaped by the interactive socioeconomic features of daily life (paying school fees, buying food, cultivating maize, etc.) but ultimately shaped by structural features of the political economy that direct people's objectives amid chronic crisis. My proposition is that because education is where value resides, it is a primary vehicle by which people are acting in crisis in a hopeful manner. In this way, this study leverages the tools of economic anthropology within debates about how to best assess the ways in which health and illness are shaped by socioeconomic realms.

This study aims to investigate patterns of information use among undergraduate engineers as they progress through their academic programs. The primary objective was to discern how second and fourth-year students differ in their use of different types of information while performing specific tasks, namely, conducting labs, composing reports and undertaking projects.

The research used an online questionnaire to collect data, focusing on the comparative analysis of second and fourth-year engineering students’ information use. The analytical framework comprised a chi-square test, residual analysis and exploratory data analysis, for evaluating statistical significance and identifying trends over time.

The results demonstrated a statistically significant difference in information use between second and fourth year undergraduates. Notably, fourth year students exhibited a preference for disciplinary genres, such as journal articles, patents and technical reports. This coincided with a decline in fourth year students’ use of educational genres, including textbooks and instructors’ handouts, notes and slides. These shifts in information use were observed consistently across all three tasks.

The uniqueness of the study resides in its innovative approach to exploring information use by investigating the relationship between genres and tasks over the course of students’ academic programs. The research introduces a novel approach for visualizing changes in information use. By describing the evolving preferences of undergraduate students from novice to emerging professional, this study contributes valuable insights into the nuanced ways in which information is used throughout the levels of engineering education.

It is a complex and dynamic process to provide high-quality rural infrastructure. However, there lacks a holistic performance evaluation method for rural infrastructure provision that reflects changing rural social needs and takes a village as a whole. This study aims to develop a holistic and dynamic performance evaluation model for rural infrastructure in Mainland China.

This study established an evaluation index system by combining the lifecycle theory and the economy, efficiency, effectiveness and equity (4E) theory. This study developed an evaluation model by using the analytic network process (ANP) and matter-element analysis theory (MEAT). The model was validated by two representative villages in Mainland China.

The developed model can reflect dynamic social needs and effectively evaluate the overall infrastructure provision performance of a village. The weight of indicators reflects the changes in Mainland China’s contemporary rural social needs, with particular emphasis on the impact and output performance. The evaluation result shows that the overall performance of the representative villages was excellent but had a tendency toward good. Although the output performance was excellent, different input, process and impact performances resulted in different downgrade trends.

This study provides a theoretical basis for disaggregating the complex issue of the performance of rural infrastructure provision. The results can be used by relevant authorities to make a holistic and dynamic evaluation of the performance of rural infrastructure provision and timely revise planning and management policies.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.