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Unpredictable economic landscapes have led to a continuous escalation in global economic policy uncertainty (EPU). Improving risk management and sustainability in an environment with high macro risk is critical for business development. This study aims to explore the impact of corporate sustainable development on corporate tax risk.

After using a sample of companies that were A-share listed on the Shanghai and Shenzhen stock exchanges from 2011 to 2021, this paper applies ordinary least squares and a moderate effect model.

Better environmental, social and governance (ESG) performance can weaken corporate tax risk by improving green innovation capability, reputation and information transparency. Meanwhile, the restraining effect of ESG on tax risk was more significant amid high EPU. These impacts were amplified amid higher market competition, lower tax supervision and a lower degree of corporate digital transformation.

The findings emphasize the need for the government to establish a healthy business and tax environment so that enterprises can improve sustainable development and increase their risk management abilities, especially post-COVID-19.

This study guides enterprises and the entirety of society to in paying attention to and promoting ESG practices, which can enhance enterprise tax management.

This study expands the research on the economic consequences of sustainable development and the factors influencing corporate tax risk and EPU.

As technology becomes more widely accessible, there is a growing concern about internet gaming disorder (IGD) around the world. The aim of this study is to evaluate the prevalence of IGD and also assess the effects of depression, anxiety, stress and insomnia levels on the IGD of youths in Bangladesh.

A cross-sectional quantitative study design was used to collect data from the youths of different locations in Bangladesh between October 21, 2023 and January 15, 2024. A total of 501 samples were collected using the convenience sampling technique. The following measurement scales were Internet Gaming Disorder Scale short form, depression, anxiety and stress scales and insomnia severity index, which were used to assess scores for IGD, psychological distress and insomnia, respectively.

The study found that the prevalence of IGD was 9.8%. IGD was shown to be significantly associated with depression, anxiety and stress. Aside from that, IGD and insomnia had a significant association, as did a friendly family environment.

The generalizability of the results could be improved by conducting additional studies with a more diverse sample, such as the general population or a different age group.

The study will help the government reduce the prevalence of IGD, improving the mental and physical health of youth.

No research has been conducted on youth and different professions in Bangladesh. There has also been very little research on the prevalence of gaming addiction and mental health.

The advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.

This study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.

The authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.

These results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.

The digital evolution of production and logistics systems requires seamless integration of various functions. Cyber-physical systems (CPS) can streamline operations, reduce costs and optimise freight transport dynamics. Therefore, CPS adoption is essential to transform existing logistics infrastructure by connecting logistical equipment to cyberspace. This research addresses the critical issue of low CPS adoption in logistics. By analysing the barriers, the study proposes effective strategies to increase CPS integration significantly.

This study used the technology-organisation-environment (TOE) framework and identified 18 barriers to CPS adoption in freight logistics. The barriers were then analysed using the fuzzy data-driven multi-criteria decision-making (MCDM) method, i.e. fuzzy-based decision-making trial and evaluation laboratory (Fuzzy-DEMATEL).

The computational analysis of the proposed framework shows that “the complexity of CPS and lagged data-driven logistics infrastructure” (T₁) is a major technological inhibitor to CPS adoption. The “high cost of CPS technology” (O₈) and a “lack of top management support” (O₃) are the top causal inhibitors at the organisational level. “Lack of data governance and regulatory framework” (E₂) is the main environmental inhibitor.

The causal mapping of the TOE-based barriers helps policymakers and logisticians to develop strategies or follow recommended adoption strategies to improve CPS adoption in logistics.

This study is a pioneering effort to unveil the adoption barriers of CPS in the freight logistics industry and suggest strategies for fostering adoption. This research could provide valuable insights for logistics organisations to support CPS-driven logistics modernisation.

This study aims to fabricate a novel electromagnetic interference (EMI) shielding composite aerogel with both thermal insulation and high temperature warning functions.

An emerging bio-based polypyrrole (PPy) gel/Fe₃O₄/calcium alginate (PFC) EMI shielding composite aerogel was prepared by freeze-drying and in situ polymerization method. First, Fe₃O₄/calcium alginate (CA) aerogel was obtained by freeze-drying the Fe₃O₄/CA mixture. Then, PPy/Fe₃O₄/CA was obtained by synthesizing PPy on the surface of CA/Fe₃O₄ aerogel through in situ polymerization. Finally, PPy/Fe₃O₄/CA was immersed in porphyrin solution (cross-linking agent) to get the final PFC EMI shielding composite aerogel.

Due to the matched impedance between Fe₃O₄ and PPy, the EMI shielding performance of PFC composite aerogel can reach up to −8 dB. In addition, the PFC EMI shielding composite aerogel also shows excellent self-extinguishing and thermal insulation properties. After leaving the flame, the burning PFC aerogel is quickly extinguished. When the PFC aerogel is placed on the heating plate at 230 °C, the temperature on the side of the aerogel away from the heating plate is only 90.3 °C after 5 min of heating. The electrical resistance of the PFC composite aerogel can be reduced from 3.62 × 104 O to 5 × 102 O to trigger the warning light after 3 s of exposure to the alcohol lamp flame. This reversible thermal resistance response characteristic can be used to give an early warning signal when the PFC encounters high temperature or flame.

This work provides a novel strategy for designing a multifunctional EMI shielding composite aerogel with repeatable high temperature warning performance. This PFC composite aerogel shows potential applications in the prevention of material combustion in high temperature electromagnetic environments.

The few previous researches on the impact of calf compression garments (CG) on running performance while assessing physiological and perceptual factors. Therefore, this study investigated how the clothing pressure of two types of Calf CG, CG1 and CG2, affects muscle fatigue and activation during running.

Five healthy amateur runners(three female and two male)were recruited for a 30-min running trial. They wear a Calf CG on their right leg (CG group), but not on their left leg(CON group). After obtaining the clothing pressure of Calf CG on the gastrocnemius lateral head (GL), gastrocnemius medial head (GM) and tibialis anterior(TA) of the right leg, surface electromyography (sEMG)of four muscles of GL, GM, TA and rectus femoris (RF) of the left and right legs were measured during running, and heart rate, cardiopulmonary rate, and human RPE were also measured. Blood bleed oxygen before and after the running trial were measured. The root mean square (RMS) of the characteristic values was selected as an index for the analysis of sEMG signals, and the data were analyzed using statistical and computational methods.

The results showed that the indexes of heart rate, blood oxygen, and RPE were significantly increased, indicating that the subjects had reached the fatigue level. The comparison of mean clothing pressure at GL, GM and TA locations reveals that the TA location consistently exhibits the highest pressure for both types of CG. When wearing CG1, the mean clothing pressure at the GL and GM test points is greater than that of CG2(CG1-GL = 0.2059 kPa > CG2-GL = 0.148 kPa; CG1-GM = 0.1633 kPa > CG2-GM = 0.127 kPa). This is attributed to the double-layered fabric on the sides of CG1, which precisely covers the GL and GM areas, thereby resulting in higher mean clothing pressure at these locations compared to CG2. Conversely, the mean clothing pressure at the TA location for CG1 is lower than that for CG2(CG1-TA = 0.3852 kPa < CG2-TA = 0.426 kPa). The pressure exerted by the CG1 on the lower limb test areas has both positive and negative effects, though neither are statistically significant. The pressure exerted by CG2 alleviates fatigue at the directly affected locations GL and GM, but exerts excessive pressure on TA, resulting in a negative effect. Additionally, CG2 pressure alleviates fatigue at the indirectly affected location RF on the same side. Based on the specific clothing pressure data, it is concluded that when the pressure at the GM location is 0.127 kPa, 30 min of running has a fatigue-relieving effect. However, the pressure should not be excessively high, at 0.1633 kPa it exhibits an insignificant adverse effect. At the TA location, a garment pressure mean between 0.3852 and 0.426 kPa does not alleviate fatigue after 30 min of running, and the negative effect becomes more pronounced as the pressure increases. The pressure exerted by the CG at GL, GM, TA and RF locations shows significant changes from the previous time period during the 15–18 min interval after running. Therefore, in the design of CG, attention should be paid to the changes in clothing pressure effects on muscles during this specific time period.

The few previous researches on the impact of calf compression garments (CG) on running performance while assessing physiological and perceptual factors. Therefore, this study investigated how the clothing pressure of two types of Calf CG, CG1 and CG2, affects muscle fatigue and activation during running.

Efficiency and quality are primary factors for the survival of health systems. The evaluation of the efficiency of the healthcare system is a crucial component of promoting long-term health policy actions. Healthcare capacity indicators provide a basis for evaluating and comparing the performance of different healthcare organizations. Intrinsic quality indicators are Donabedian (1980)’s structural and process elements of quality of healthcare. This study aims to integrate capacity and intrinsic quality indicators of healthcare while measuring the efficiency of provinces by using radial and non-radial efficiency measurement techniques.

Efficiency analysis performed in Turkey from 2015 to 2020 by performing input-oriented radial, nonradial, and super-efficiency estimates for 81 provinces of Turkey by incorporating capacity and intrinsic quality indicators into the different model specifications.

Radial and nonradial efficiency results have an increasing trend over the study years obtained from the efficiency models showing high average scores obtained from the models that include intrinsic quality of care indicators. Statistically significant mean rank differences are observed between different radial efficiency models for all study years (p < 0.001). Negative and moderate level correlations were observed between radial efficiency results and quality of care indicators (r < 0.70).

Under long-term centralized health policies, increases in efficiency result in decreased intrinsic quality of care indicators. A better synthesis of health system capacity and intrinsic healthcare quality indicators is necessary to generate evidence-based health systems.

The importance of mental wellbeing and the need for organizations to address it is increasing in the post-pandemic context. Although Artificial Intelligence (AI) is increasingly being adopted in HRM functions, its adoption and utility for enabling mental wellbeing is limited. Building on the Open System Theory (OST) and adopting the technology-in-practice lens, the authors examined the roles of human and technology agencies in enabling mental wellbeing.

The study was conducted in two stages; in Stage 1, the authors adopted a case methodology approach to examine the feasibility of a technology company's offerings to assess mental wellbeing. In Stage 2, the authors followed the grounded theory approach and interviewed 22 key stakeholders and HR leaders of diverse organizations. The authors used Gioia's approach to analyze the data.

The study demonstrates the interdependence and inseparability of human activity, technological capability and structured context. Specifically, the authors observe that AI adoption is pushing the boundaries of how organizations could support employees' mental health and wellbeing. These technological advancements and adoption are likely to facilitate the evolution of agentic practices, routines and structures.

This study carries two important implications. While the advent of cutting-edge technologies appears to affect employees' mental wellbeing, the study findings indicate the assistive role of technology in supporting mental wellbeing and facilitating changes in organizational practices. Second, the ontology of technology-in-practice shows how human–machine agencies gain newer relevance from the interactions that unite them. Specifically, per OST, technology (from an external context) can potentially change how mental wellbeing practices in organizations are managed. The authors extend the existing literature by suggesting that both human agents and internal contexts effectively limit the potential of technology agents to change existing structures significantly.

The authors address the need for more research on the technology-management interface, and the boundaries of technology-enabled wellbeing at work. While AI-HRM scholarship has primarily relied on micro-level psychological theories to examine impact and outcomes, the authors borrow from the macro-level theories, such as the OST and the technology-in-practice to explain how AI is shifting the boundaries of human and machine agencies for enabling mental wellbeing.

The objective of this study is to systematically identify, categorize and assess the driving factors and interdependencies associated with various types of healthcare waste. The study specifically focuses on waste that has been managed or is recommended for treatment through the application of Lean Six Sigma (LSS) methodologies.

To accomplish the study’s objectives, interpretive structural modeling (ISM) was utilized. This analytical tool aided in quantifying the driving power and dependencies of each form of healthcare waste, referred to as “enablers,” as well as their related variables. As a result, these enablers were classified into four distinct categories: autonomous, dependent, linkage and drivers or independents.

In the healthcare sector, the “high cost” (HC) emerges as an autonomous variable, operating with substantial independence. Conversely, variables such as skill wastage, poor service quality and low patient satisfaction are identified as dependent variables. These are distinguished by their low driving power and high dependency. On the flip side, variables related to transportation, production, processing and defect waste manifest strong driving forces and minimal dependencies, categorizing them as independent factors. Notably, inventory waste (IW) is highlighted as a salient issue within the healthcare domain, given its propensity to engender additional forms of waste.

Employing the ISM model, along with comprehensive case study analyses, provides a detailed framework for examining the complex hierarchies of waste existing within the healthcare sector. This methodological approach equips healthcare leaders with the tools to accurately pinpoint and eliminate unnecessary expenditures, thereby optimizing operational efficiency and enhancing patient satisfaction. Of particular significance, the study calls attention to the key role of IW, which often acts as a trigger for other forms of waste in the sector, thus identifying a crucial area requiring focused intervention and improvement.

This research reveals new insights into how waste variables are structured in healthcare, offering a useful guide for managers looking to make their waste-reduction strategies more efficient. These insights are highly relevant not just for healthcare providers but also for the administrators and researchers who are helping to shape the industry. Using the classification and ranking model developed in this study, healthcare organizations can more easily spot and address common types of waste. In addition, the model serves as a useful tool for practitioners, helping them gain a deeper, more detailed understanding of how different factors are connected in efforts to reduce waste.