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The direction of causality between job satisfaction and job performance (known as the holy grail of industrial psychologists) is undetermined and related research findings in different organizational contexts are mixed. Based on the ample literature, mainly from Western countries, on the relationship between job satisfaction and job performance, a non-recursive bow pattern model was utilized to investigate the direct relationship between these two variables in an Asia–Pacific higher education system.

This study is quantitative in approach and survey in design. Additionally, to meet the statistical requirements of non-recursive bow pattern analysis, the authors added welfare as a theory-driven instrumental variable to introduce exogenous variability. Using the efficient partial least squares (PLSe2) estimator, the authors fitted the model to the data collected from 2008 academics affiliated with Malaysian public universities and polytechnics.

The results showed that while job satisfaction is considerably influenced by welfare, it is not a significant predictor of job performance directly. In addition, a meaningful positive correlation between the disturbance terms of job satisfaction and job performance was observed, suggesting the existence of other factors that could increase both job satisfaction and job performance. The findings' theoretical and practical implications are discussed, and a list of theory-driven evidenced-based policies in this regard is provided.

This is the first study to test a non-recursive bow pattern model and examine the holy grail of industrial psychology based on the PLSe2 methodology, as a parametric approach to partial least squares (PLS), in a higher education context. This study also provides higher education researchers with the advantages of the PLSe2 method, especially in causal-predictive modeling, in the context of applied higher education research.

Sustainability has become a priority for companies due to pressure from multiple stakeholders. In an overly competitive market, shareholders push for economic results, allowing lean manufacturing to establish itself as dominant paradigm in manufacturing. However, concerns grow regarding how lean implementation can allow companies to achieve sustainable development goals, or, if the resources required for a successful lean implementation can result in a detriment of environmental and social performance. This paper intends to help close the knowledge gap regarding the effects of lean manufacturing on sustainable performance from a triple bottom line perspective, and how operational, environmental and social outcomes interact between themselves.

Two models for the interaction between lean and sustainability were proposed. The first is called the “sand-cone” model, which poses that performance improvements derived from lean are cumulative on each one of the sustainability dimensions. The second is called the “trade-offs” approach. In this case, the resources required to improve one dimension of sustainability clash with those required by the others. Data were gathered from a sample of 133 Colombian metalworking companies and processed using structural equations models.

The results support the cumulative “sand-cone”, which follows a sequence of operational-environmental-social improvement in the presence of lean. For the “trade-offs” model, partial evidence suggests that they can occur in detriment of social performance.

The “sand-cone” and “trade-offs” are empirically tested for the first time in the context of sustainability, providing further knowledge into its interaction with lean manufacturing. The models’ results contribute to practitioners by providing a tested path for companies to improve their performance in a cumulative sequence that will provide better long-term results.

The purpose of this paper is to investigate the effect of corruption level in steering the business–government relations (BGRs) in developing countries. It also examines the moderating effect of firm size.

Using robust tobit and probit models, this study tests the response behavior of 9787 firms from 23 African countries to their government's policy and regulations and the direct effect of corruption control level in their response decisions. The authors also perform several other additional analyses to ensure the robustness of the findings, including change analysis, two-stage model and recursive bivariate model.

The result shows that corruption level is among the significant factors that drive BGRs exponentially. The finding points out that, there is a strong alliance of business and government in more corrupt countries. Moreover, the impact of corruption level exacerbates when the firm is bigger.

Managers should focus more on activities that create long-term sustainable advantage. Valuable time of the senior managers should not waste on negotiating government policies to earn a short term advantages.

It is evident that legal and transparent government alliances can lead to economic rent for firms. However, it is important to note that any alliance based on corruption and illegality is short-lived and ultimately detrimental to long-term prosperity. Therefore, it is crucial for firms to prioritize ethical business practices and build relationships with governments that prioritize transparency and accountability.

Given the detrimental impact of corruption on economic progress, it is crucial for Africa policy-makers to prioritize reforms aimed at reducing its adverse effect. By implementing ethical and transparent business practices, countries can attract more investment and promote economic growth.

This study contributes to the existing literature on the passive form of political connectivity/activity and to what extend corruption level affect the political activities of firms.

This paper investigates the relationship between health and labour market participation considering the potential role played by the presence of children and elderly persons (with/without disabilities) in Italian households.

The authors use longitudinal data from the European Union Statistics on Income and Living Conditions and full-information maximum likelihood to estimate a two-equation model (one equation for labour force participation and one for health status) with instruments to address the endogeneity of the labour force participation choice. The model is estimated separately by gender.

The authors find that while the presence of children, elderly persons or both is positively associated with the health status of both genders, the presence of disabled elderly persons exerts a negative role. As for participation, interesting differences emerge. The presence of children discourages women's participation but is positively associated with men's labour force participation. Interestingly, a caring role for elderly persons without disability emerges for both genders when the presence of children is combined with that of elderly people. Gender differences are also at work for the role of childcare services and elderly and/or disabled home care/assistance.

The findings indicate a possible caring role for elderly persons without disabilities, neutralizing the effect of the presence of children on the labour force participation of both genders. The results also suggest that greater coverage of care services should increase the active participation of women in the labour market.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

This article summarizes the international scientific research output of global forest product models, infers future research trends and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.

In 1999, Joseph Buongiorno, a scholar at the University of Wisconsin in the United States of America, proposed the global forest products model (GFPM), which was first applied to research in the global forestry sector. GFPM is a recursive dynamic model based on five assumptions: macroeconomics, local equilibrium, dynamic equilibrium, forest product conversion flow and trade inertia. Using a certain year from 1992 to present as the base period, it simulates and predicts changes in prices, production and import and export trade indicators of 14 forest products in 180 countries (regions) through computer programs. Its advantages lie in covering a wide range of countries and a wide variety of forest products. The data mainly include forest resource data, forest product trade data, and other economic data required by the model, sourced from the Food and Agriculture Organization (FAO) of the United Nations and the World Bank, respectively.

Compared to international quantitative and modeling research in the field of forest product production and trade, China's related research is not comprehensive and in-depth, and there is not much quantitative and mathematical modeling research, resulting in a significant gap. This article summarizes the international scientific research output of global forest product models, infers future research trends, and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.

On the basis of summarizing and analyzing the international scientific research output of GFPM, sorting out the current research status and progress at home and abroad, this article discusses potential research expansion directions in 10 aspects, including the types, yield and quality of domestic forest product production, international trade of forest products, and external impacts on the forestry system, in order to provide new ideas for global forest product model research in China.

The authors construct an index of economic policy uncertainty (EPU) for Greece using textual analysis and analyze its role in the 10-year Greek economic crisis.

To identify the causal relationship between various measures of economic activity and EPU in Greece, the authors use a sophisticated “shock-based” structural vector autoregressive identification scheme. Additionally, the authors use two additional models to ensure the robustness of the results.

EPU is negatively associated with domestic economic activity and economic sentiment, and positively with bond credit spreads. EPU is also estimated to have prolonged the crisis even in periods when macroeconomic imbalances were cured. The results are robust across various model specifications and different proxies of economic activity.

Brunnermeier (2017) observed that uncertainty may be central to understanding the evolution of the Greek crisis. Yet little attention has been paid to policy uncertainty in the existing long and growing literature on the Greek crisis. The authors attempt to fill this gap.

New generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.

Based on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.

The results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.

The number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.

The influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.

This paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

This conceptual article presents a schematic for use with extended cybernetic recursion in living systems and applies it to the issue of hyper vigilance as a demonstration of its utility.

The test-operate-test-exit (TOTE) schematic of Miller et al. (1960) is critically evaluated along with other schematics, including those of ordered cybernetics, and a new schematic is proposed, a recursive test-operate-test (rTOT), which emphasizes teleological purpose and hierarchical structure. The background psychophysiology of trauma is reviewed and then rTOT is applied to hyper vigilance, a cardinal component of post-traumatic stress disorder (PTSD).

Once the schematic was developed, it was applied to the behavior of hyper vigilance. Other applications are suggested.

As demonstrated, the rTOT schematic has potentially wide application because of its pragmatic and detailed structure.

The rTOT requires careful consideration of teleological purposes for its application and is simple enough, but also complex enough, for relevant utilization. Its compact nature and adjustable hierarchy scope are good mini-max complexity solutions for cybernetic, information modeling schematics.

The revealed teleological purpose of the trauma adaptation of hyper vigilance presents significant alternative formulation options for prevention and intervention.

While the rTOT schematic is derived from previous schematics, it is original in its emphasis on information processing, the teleological aspects of extended recursion and on the provision of a hierarchical structure for those recursions. It is considerably more compact than other schematics associated with the ordered cybernetics literature. The explication of the adaptation model for post-trauma consequences is significantly enhanced by the rTOT application.

The main purpose of this study is to examine the impact of agricultural internet information (AII) acquisition on climate-resilient variety adoption among rice farmers in the Jianghan Plain region of China. Additionally, it explores the influencing channels involved in this process.

Based on survey data for 877 rice farmers from 10 counties in the Jianghan Plain, China, this paper used an econometric approach to estimate the impact of AII acquisition on farmers’ adoption of climate-resilient varieties. A recursive bivariate Probit model was used to address endogeneity issues and obtain accurate estimates. Furthermore, three main influencing mechanisms were proposed and tested, which are broadening information channels, enhancing social interactions and improving agricultural skills.

The results show that acquiring AII can overall enhance the likelihood of farmers adopting climate-resilient varieties by 36.8%. The three influencing channels are empirically confirmed. Besides, educational attainment, income and peer effects can facilitate farmers’ acquisition of AII, while climate conditions and age significantly influence the adoption of climate-resilient varieties.

Practical recommendations are put forward to help farmers build climate resilience, including investing in rural internet infrastructures, enhancing farmers’ digital literacy and promoting the dissemination of climate-resilient information through diverse internet platforms.

Strengthening climate resilience is essential for sustaining the livelihoods of farmers and ensuring national food security; however, the role of internet information has received limited attention. To the best of the authors’ knowledge, this study is the first to examine the casual relationship between internet information and climate resilience, which fills the research gap.