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This study aims to examine the predictive power of childhood adversity and severity of positive symptoms on suicidality, controlling for selected sociodemographics factors, among hospitalized patients diagnosed with schizophrenia in Jordan.

This study used a descriptive-explorative design. The study was conducted at two major psychiatric hospitals in Jordan. The targeted sample was 66 patients diagnosed with schizophrenia. Data was collected using a structured format in the period February–April 2024.

A two-step multiple hierarchical regression analysis was conducted. In the first model, childhood adversity and the severity of positive symptoms were entered. In the second model, sociodemographic variables were entered. The analysis revealed that the first model (F = 5.35, p = 0.007) was statistically significant. The second model (F = 717, p < 0.001) was statistically significant. Furthermore, the analysis revealed that childhood adversity was not a significant predictor for suicidality. However, positive symptoms and patients’ demographics (age, number of hospitalizations and length of being diagnosed with schizophrenia) were significant predictors of suicidality. The analysis revealed that childhood adversity was not a significant predictor of suicidality. However, positive symptoms and patients’ demographics (age, number of hospitalizations and length of being diagnosed with schizophrenia) were significant predictors of suicidality.

One limitation of this study is related to the sample and the setting where there were only 66 patients recruited from governmental hospitals within inpatient wards. Thus, the upcoming studies should include more participants from private hospitals and different hospital settings including outpatient and emergency departments.

The research provides empirical insights that positive symptoms, age hospitalization and schizophrenia diagnosis length were significant predictors of suicidality. At the same time, childhood adversity was not a significant predictor of suicidality.

The current research contributes to expanding mental health studies. Moreover, this study enlarges the body of knowledge in the academic world and clinical settings. It supports the disciplines of psychology, mental health and social sciences by increasing knowledge of the complicated relationships among childhood adversity, positive symptoms and suicidality.

This paper fulfills an identified need to study childhood adversity with comorbid psychiatric disorders such as schizophrenia, as well as psychiatric mental health covariates.

This work aims to empirically investigate the effects of financial development on crop productivity of India.

Time series data such as crop production index, International Monetary Fund’s (IMF) financial development index, gross domestic product (GDP) per capita, arable land, rural population, trade openness and physical capital from 1980 to 2020 was used. The autoregressive distributed lag (ARDL) bounds testing approach of cointegration was used to determine the long-run equilibrium relationship between the selected time series. Also, ARDL long- and short-run coefficients were estimated to examine the effects of selected variables on crop productivity. Furthermore, to establish the robustness of results, long-run estimators such as fully modified least squares and the dynamic least squares were also used. Finally, using the vector error-correction model, causality between the selected time series was examined.

The ARDL cointegration test confirmed the existence of long-run equilibrium relationship among agricultural productivity, financial development, capital formation, GDP per capita, arable land, rural population and trade openness. The estimated long-run elasticities from all the three techniques and the short-run elasticities of ARDL have consistently suggested that the elasticity of financial development is higher (1.55% and 1.40%, respectively) in explaining the crop productivity of India. The short-run causality estimates indicated the presence of positive bidirectional causality between crop productivity and financial development and seven positive unidirectional causal relationships between the selected variables.

Agricultural credit being an important non-land input and essential for overall growth and sustenance of agricultural sector, the policymakers should ensure the overall development of its financial sector which will reduce the intermediation, informational and other transactional costs associated with agricultural credit. This will possibly result in timely availability and access to adequate and low-cost credit from institutional sources.

Though extensive research is available on the effects of financial development on economic growth, limited research is available concerning the impact of financial development on crop productivity, especially for an emerging economy like India. For India, predominantly studies have investigated the impact of farm credit on crop productivity but have not exclusively examined the effects of financial development on agricultural productivity. Therefore, this study not only adds to the empirical literature but also provides new evidence on the nexus between financial development and crop productivity by examining the effects of financial development on crop productivity using the composite financial development index developed by the IMF using the ARDL bounds test for cointegration and other econometric estimators.

The purpose of this study is to explore the interfacial adhesion between superhydrophobic coatings FC-X (X = 1%, 2%, 3%, 4% and 5%) and the concrete substrate, along with the impact of FC-X on the water repellency characteristics of the concrete substrate.

One synthetic step was adopted to prepare novel F-SiO₂ NP hybrid fluororesin coating. The impact of varying mass fractions of F-SiO₂ NPs on the superhydrophobicity of FC-X was analyzed and subsequently confirmed through water contact angle (WCA) measurements. Superhydrophobic coatings were simply applied to the concrete substrate using a one-step spraying method. The interfacial adhesion between FC-X and the concrete substrate was analyzed using tape pasting tests and abrasion resistance measurements. The influence of FC-X on the water repellency of the concrete substrate was investigated through measurements of water absorption, impermeability and electric flux.

FC-4% exhibits excellent superhydrophobicity, with a WCA of 157.5° and a sliding angle of 2.3°. Compared to control sample, FC-X exhibits better properties, including chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

This study offers a thorough investigation into the practical implications of enhancing the durability and water repellency of concrete substrates by using superhydrophobic coatings, particularly FC-4%, which demonstrates exceptional superhydrophobicity alongside remarkable chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

Through the examination of the interfacial adhesion between FC-X and the concrete substrate, along with an assessment of FC-X’s impact on the water repellency of the concrete, this paper provides valuable insights into the practical application of superhydrophobic coatings in enhancing the durability and performance of concrete materials.

This study aims to explore the pandemic's opportunities for enhancing the environmental practices of the food and beverages green supply chains and its effect on the supply chains' viability by exploring the relationship between fear and uncertainty of COVID-19, food and beverages green supply chain management (F&B-GSCM) and supply chains’ viability based on the two dimensions (robustness and resilience) and examine the moderating effect of innovative technology adoption like big data analysis (BDA) capabilities and blockchain technologies (BCT) on this relationship.

This study adopted partial least squares structural equation modeling (PLS-SEM) on a sample of 362 F&B small and medium enterprises (SMEs)’ managers in the Egyptian market for data analysis and hypothesis testing.

The empirical results show that the fear and uncertainty of the pandemic have a significant positive effect on green supply chain management (GSCM). Also, BDA moderates the relationship between fear and uncertainty of COVID-19 and GSCM. However, BCT do not moderate that relationship. Similarly, GSCM positively affects supply chain viability dimensions (robustness and resilience). In addition, F&B-GSCM significantly mediates the relationship between fear and uncertainty of COVID-19 and supply chain viability dimensions (robustness and resilience).

Food and beverages (F&B) managers could develop a consistent strategy for applying BCT and BDA to provide clear information and focus on their procedures to meet their stakeholders' needs during COVID-19. Governments and managers should develop a consistent strategy to apply food and beverages supply chains (F&B SCs)' green practices to achieve F&B SCs' resilience and robustness, especially during the pandemic.

The Egyptian F&B SCs have been linked directly with many European countries as a main source of many basic food and agriculture products, which have been affected lately by the pandemic. Based on the “social-cognitive,” “stakeholder” and “resource-based view” theories, this study sheds light on the optimistic side of the COVID-19 pandemic, as it also brings the concepts of F&B-GSCM, SC resilience, SC robustness and innovative technologies back into the light, which helps in solving F&B SC issues and helps to achieve their viability.

How much variance in firm performance can be attributed to firms’ corporate political activities (CPA)? Under what conditions does CPA contribute to firm performance? To theorize and empirically tackle these questions, we build on the resource-based view (RBV) to theorize how CPA might improve or hinder firm performance, and specifically examine the direct relationship between firms’ investments in lobbying activities and their performance. We also expect firm growth rate to moderate the relationship between lobbying and performance.

We empirically test our hypotheses using large-scale longitudinal panel data from publicly traded US firms from 2008 to 2018.

Our analyses support our predictions of the double-edged sword effect of lobbying on firm performance. Moreover, our results show that this effect is steeper for firms with higher growth rates.

Our study contributes meaningful insights to strategy scholarship on the influence of nonmarket strategies, highlighting the relevance of firm-specific conditions in shaping the performance outcomes of such strategies. In particular, we make a contribution by identifying a nonlinear relationship between lobbying and firm performance, which is amplified in fast-growing firms compared to stagnant ones.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

The research enterprise within higher education is becoming more competitive as funding agencies require more collaborative research projects, higher-level of accountability and competition for limited resources. As a result, research analytics has emerged as a field, like many other areas within higher education to act as a data-informed unit to better understand how research institutions can effectively grow their research strategy. This is a new and emerging field within higher education.

As businesses and other industries are embracing recent advances in data technologies such as cloud computing and big data analytic tools to inform decision making, research administration in higher education is seeing a potential in incorporating advanced data analytics to improve day-to-day operations and strategic advancement in institutional research. This paper documents the development of a survey measuring research administrators’ perspectives on how higher education and other research institutions perceive the use of data and analytics within the research administration functions. The survey development process started with composing a literature review on recent developments in data analytics within the research administration in the higher education domain, from which major components of data analytics in research administration were conceptualized and identified. This was followed by an item matrix mapping the evidence from literature with corresponding, newly drafted survey items. After revising the initial survey based on suggestions from a panel of subject matter experts to review, a pilot study was conducted using the revised survey instrument and validated by employing the Rasch measurement analysis.

After revising the survey based on suggestions from the subject matter experts, a pilot study was conducted using the revised survey instrument. The resultant survey instrument consists of six dimensions and 36 survey items with an establishment of reasonable item fit, item separation and reliability. This survey protocol is useful for higher educational institutions to gauge research administrators’ perceptions of the culture of data analytics use in the workplace. Suggestions for future revisions and potential use of the survey were made.

Very limited scholarly work has been published on this topic. The use of data-informed and data-driven approaches with in research strategy within higher education is an emerging field of study and practice.

Given the current friction and wear challenges faced by automobile parts and bearings, this study aims to identify a novel texture for creating anti-friction and wear-resistant surfaces. This includes detailing the preparation process with the objective of mitigating friction and wear in working conditions.

Femtosecond laser technology was used to create a mango-shaped texture on the surface of GCr15 bearing steel. The optimized processing technology of the texture surface was obtained through adjusting the laser scanning speed. The tribological behavior of the laser-textured surface was investigated using a reciprocating tribometer.

The friction coefficient of the mango-shaped texture surface is 25% lower than that of the conventional surface, this can be attributed to the reduced contact area between the friction ball and the micro-textured surface, leading to stress concentration at the extrusion edge and a larger stress distribution area on the contact part of the ball and disk compared to the conventional surface and the function of the micro-texture in storing wear chips during the sliding process, thereby reducing secondary wear.

The mango-shaped textured surface in this study demonstrates effective solutions for some of the friction and wear issues, offering significant benefits for equipment operation under light load conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0127/

Green finance aims to promote sustainable financial activities, environmental conservation and ecological balance. This study examines how renewable energy consumption (REN), technological innovation (TEC) and green finance (GRF) influence CO2 emissions in Vietnam from 2000 to 2022.

We utilize a novel three-stage methodology including quantile-on-quantile regression, wavelet coherence and wavelet-quantile regression to explore the relationship in the structure of intercorrelation in terms of quantile, time and frequency.

The findings show that Vietnam will increase environmental quality for higher green development. Specifically, there is a negative influence of TEC, REN and GRF on CO2 emissions across different quantiles and timescales.

The study recommends policies that support green development and reduce carbon emissions, such as increasing the use of renewable energy and conducting well-planned research to achieve a carbon-free, sustainable environment.

This article looks into the effects of GRF, TEC and REN on CO2 emissions in Vietnam. Some studies argue that green development in underdeveloped nations is insufficient to reduce CO2 emissions, thereby limiting the sample to a few advanced economies. Adopting diverse methodologies demonstrates the varied and intricate nature of understanding CO2 drivers. Additionally, our work makes detailed policy implications for Vietnam to meet its net-zero emission target and achieve sustainable development by 2050.

The study seeks evidence on the asymmetric effects of broad money growth on inflation in the short run and long run, in the context of emerging markets and developing economies (EMDEs).

Using a panel dataset of 122 EMDEs (by distinguishing between inflation-targeting and non-inflation-targeting EMDEs), we employ the nonlinear counterpart of the autoregressive distributed lag framework, which provides evidence of asymmetric dynamics between money growth and inflation in EMDEs.

In consonance with the quantity theory of money, we find a long-run relationship between money growth and inflationary outcomes. We also find that the response of inflation is higher to a tightening episode in the monetary policy stance than to a loosening episode. The study also provides evidence that adopting the inflation targeting framework in EMDEs has led to a significant reduction in the inflation rates along with ensuring a higher magnitude of transmission from money supply growth to inflationary outcomes.

To the best of our knowledge, the present study is one of the first attempts to evaluate the differential impact of broad money growth on inflationary outcomes, using a panel dataset of EMDEs. As a result of inherent differences in the financial structures of EMDEs vis-à-vis advanced nations, there is an imperative need to assess the dynamics of pass-through from money supply to inflation to gain an understanding of the mechanism of monetary transmission in these economies.