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The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

Various family problems have been identified as risk factors for juvenile delinquency. While providing training on skills (e.g., interpersonal relationship skills) to tackle these family problems, delinquency-prevention programs often overlook the fact that these training components fall under the umbrella of home economics education (HEE). Moreover, they often fail to see the relevance of the entire range of HEE (ranging from cooking, handyman work skills, financial management, to child development and interpersonal relationship skills) in reducing family problems. There is also a scarcity of research examining the relationship between HEE and these family problems.

To fill this void, this study explores the relationships between HEE and three key familial problems – conflict in marital/romantic relationships, ineffective parenting behavior, and family financial conditions. This study utilized data collected from a sample of adults (N = 280) with college or graduate education. Data were analyzed using ordinary least squares (OLS) regression models available in SPSS 26. F tests were used to evaluate the fitness of models.

The results show that HEE is significantly and positively associated with respondents’ use of negotiation in solving conflict in marital/romantic relationships. It also has a marginally significant and positive association with family financial conditions. Specifically, HEE on financial management is significantly and positively associated with family financial conditions. The author concludes that the role of HEE in reducing family problems deserves more research attention.

Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.

This paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.

In this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.

Firstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.

Following the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.

Drawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.

The results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.

The conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

The formation of international joint ventures (IJVs) is one of the prevalent approaches for Western companies to conduct business in China. Yet, doing business in China is difficult for many firms, partially because of the institutional voids that are created by weak formal institutions. The paper aims to focus on the role of guanxi (an informal institution and a company capability that fills such institutional void) in the formation and management of IJVs.

A systematic literature review on the role of guanxi in IJV formation and management is conducted based on papers published in top international business journals between 2005 and 2020 – in total, 47 papers are included in the review.

The findings of the study are presented in four themes, namely, the role of guanxi as social capital in IJV formation, the role of trust in guanxi-based IJVs, the role of control mechanisms in balancing high reliance on trust in guanxi-based IJVs and the role of guanxi in managing inconsistencies in the regulatory environment. The analysis also reveals that guanxi has a positive effect on the development of IJVs if control between the parent firms is well balanced; otherwise, guanxi can trigger opportunism and leads to failures.

The study unravels how guanxi leads to successful outcomes in IJV formation and management, which assist managers who operate IJVs with their decision-making.

To the best of the authors’ knowledge, no previous paper has critically analysed the literature on IJVs using a guanxi perspective at micro (personal), meso (business) and (macro) governmental levels. This approach allows for providing more nuanced view of the role of guanxi in the formation and management of IJVs and aligns more closely with managerial decision-making.

Under the “dual carbon” framework, the article explores the equilibrium points among the government, agricultural enterprises and village committees, and uses sensitivity analysis to reveal the dynamic factors affecting these stakeholders, thereby proposing methods to enhance agricultural disaster resilience.

The article uses MATLAB to construct a game model for the three parties with interests: agribusiness, government and village council. It examines the stability of strategies among these entities. Through graphical simulation, the paper analyzes the sensitivity of agricultural enterprises carbon emissions and village committees’ rent-seeking behaviors in the decision-making process, focusing on significant factors such as government carbon tax and regulatory policies.

A single government reward and punishment mechanism is insufficient to influence the strategic choices of enterprises and village committees. The cost of rent-seeking does not affect the strategic choices of enterprises and village committees. A key factor influencing whether the village committee engages in rent-seeking is the level of labor income of the village committee as an “intermediary”.

This paper focuses on the dynamic game between three stakeholders (the government, agricultural enterprises and village committees), seeking dynamic equilibrium and conducting sensitivity analysis through visualization to provide the government with optimal policy recommendations.

Purpose: The primary purpose is to discuss the productivity and digitalisation interaction at the theoretical level, analyse the productivity and digitalisation differences between the European Union (EU)-14 and EU-13 countries, and evaluate the digitalisation impact on the manufacturing sector labour productivity of the EU countries.

Need for study: The average added value created per capita in new EU countries (EU-13) is one-third lower than in old EU countries (EU-14). To increase productivity, manufacturing companies must adapt to modern trends and take advantage of industrial digitisation opportunities. Digitisation can improve production efficiency, reduce costs, and improve product quality, allowing continuous monitoring and analysis of production data, enabling informed decisions and faster problem-solving.

Methodology: Analysis of scientific literature, comparing viewpoints, insights, and conclusions. The empirical study includes calculating rates of change of indicators, differences between EU-14 and EU-13, and structural analysis. The impact of digitisation on the productivity of EU countries is studied by creating a correlation matrix and using regression analysis: ordinary least square models.

Findings: EU-13 countries are behind EU-14 in labour productivity and manufacturing digitalisation. Digitalisation positively impacts productivity per employee. A faster increase in digitisation, industrial robot use, and e-commerce sales could significantly increase productivity in EU-13, reducing productivity differences between countries.

Practical implications: This study highlights the need for policy promoting digitisation innovation, particularly in EU-13 countries, to be implemented by both national and EU-based economic development and regional and cohesion institutions.

Tingting Qi's chapter titled, “Moving toward Decentralization? Changing Education Governance in China After 1985,” provides the historical and policy context for the volume. This chapter integrates the post-1978 Chinese educational reforms into the socioeconomic context of China. The special contribution of this chapter is that it explores the complexity of educational decentralization in China through an in-depth analysis of changes in education finance, administration, and curriculum. Qi reviews prior studies, government documents, laws, and regulations related to Chinese education reform since 1978 within the context of education decentralization in China. Qi also demonstrates that China's educational policy reforms are moving China toward “centralized decentralization” because decentralization is driven by a common, centralized national goal of economic modernization. The chapter presents evidence that “centralized decentralization” is a strategic maneuver that maintains centralized control while providing the reform legitimacy of decentralization. By focusing on decentralization as the core of Chinese educational policy reforms, this chapter situates the following chapters within the social, cultural, and political context of post-1978 China.

The purpose of this paper is to investigate the effects of surface topography, including surface roughness, waviness and taper, on the cavitation of liquid film lubricated mechanical seals (LFL-MS).

A universal governing equation considering cavitation is established, and an equivalent relative density is defined to characterize the cavitation degree. The equation is discretized by the finite volume method and solved by the Gauss–Seidel relaxation scheme.

Results indicate that both radial length and a circumferential width of the cavitation zone and cavitation degree are affected significantly by the waviness amplitude and taper, but the effect of surface roughness is limited.

Effect mechanism of surface topography on the cavitation of LFL-MS is investigated and cavitation degree is reflected by an equivalent relative density. The results further help to comprehensively explore the cavitation mechanism.