Environmental Uncertainty Research Articles

Unveiling factors and contingencies influencing exhaustion in professional esports players: evidence from China

PurposeThe rapid growth of professional esports has highlighted the lack of a universally recognised governing body to standardise operations and competition rules. This absence presents many challenges. A key concern is the well-being of professional esports players (e-pro-players), who often suffer from exhaustion. This study aims to examine the factors contributing to exhaustion among e-pro-players.Design/methodology/approachUsing the conservation of resources theory, we developed a framework to explain the factors leading to e-pro-players’ exhaustion and the conditions under which it occurs. We tested this framework with 126 responses in a dyadic survey from e-pro-players and their coaches in China. Additionally, we gathered qualitative insights from 50 interviews with esports stakeholders to provide more context for our quantitative findings.FindingsOur study found that e-pro-players’ intrinsic motivation to engage in training reduces their exhaustion, while their struggle to cope with uncertainty in esports environments (intolerance of uncertainty) increases it. The effect of intrinsic motivation is weaker for those who believe their talent for playing esports is fixed (entity belief) but stronger for those with high relational identification with their coaches. Additionally, the link between uncertainty intolerance and exhaustion is stronger in players with strong entity beliefs.Originality/valueOur study sheds light on the factors contributing to e-pro-players’ exhaustion within the partially regulated professional esports environment, a phenomenon that significantly influences their overall well-being. Through the identification and examination of these factors and the conditions under which they affect exhaustion, we deepen the understanding of the drivers of exhaustion for e-pro-players who operate in an industry lacking standardised regulations.

How Does Secure-base Leadership Influence Employee Well-being?

To improve their operations in fast-paced business environment, organizations need leaders who embed the required changes into corporate strategy and implement it accordingly to maintain competitive edge. However, organizational change is disruptive and have a significant impact on employee well-being, unfortunately, the literature does not provide a comprehensive answer on what kind of leadership can be more effective to support employee well-being in environmental uncertainty. To ensure a smooth transition in dynamic environment, among several influential leadership styles, recent research found that employee well-being in the organization can be improved through secure-base leadership. Since, secure-base leadership recognizes the value of employees and provide them with secure-base support in organization. However, the literature is silent on the attributes of secure-base leadership. Therefore, to explore the attributes of secure-base leadership and how these attributes affect employee overall well-being, the study conducted a thematic analysis, data was purposively collected via interview from eight respondents. The study found that secure-base leadership is characterized by the elements such as (1) autonomy to make decisions (2) maintaining healthy work life balance (3) open communication (4) handling conflicts (5) recognizing and appreciating employees (6) promoting employees wellbeing and mental health (7) collaboration and teamwork and (8) working on a challenging project, among others. All these attributes are positively related to employee overall well-being, for example secure-base leaders fulfil employee psychological needs by providing autonomy through trust and empowerment, fostering competence through feedback and development opportunities, and promoting relatedness through open communication and interpersonal support. Fulfilment of employee psychological needs positively affect intrinsic motivation which in turn lead to higher level of job satisfaction and employee overall well-being. Findings of the current study enriches the literature on the relationship between secure-base leadership and employees well-beings. Further, the study shed light on key attributes possess by secure-base leaders, therefore, HR manager should formulate selection criteria for mangers in which priority should be given to candidates with secure-base leadership attributes.

Governance and Leadership in Enhancing SME Resilience in Ghana: A Systematic Literature Review

Small and medium enterprises (SMEs) are especially susceptible to pandemics. Consequently, resilience and adaptability to shocks from pandemics like COVID-19 are crucial. Nonetheless, although SMEs have strategies to cope with these, there is still a dearth of research on the nature and effectiveness of these coping strategies. Thus, it remains unclear how leadership and governance frameworks reduce SMEs’ vulnerability and increase their resilience to pandemic risk. This academic article provides a systematic literature review on the role of governance and leadership in enhancing the resilience of small and medium enterprises (SMEs) in Ghana. The review explores existing literature on SME resilience, environmental uncertainty, and the impact of governance frameworks. The article also focuses on how leadership practices in the Ghanaian context contribute to building SME resilience in times of crisis, such as the COVID-19 pandemic. The study analysis revealed that there are opportunities for SMEs to adopt better governance and leadership practices that foster long-term resilience. Governance practices, such as formal structures, board diversity, and leadership, enable SMEs to respond to crises and navigate challenging situations. Moreover, leadership styles that prioritize governance procedures, especially transformational leadership, empower SMEs to maintain agility and innovation, thereby securing their survival and growth in a rapidly evolving environment. The review highlights the need for stronger governance frameworks and leadership development programs that equip SME owner-managers with the tools to navigate environmental uncertainties.

Small green housing makes a big difference: the impact of natural environmental uncertainty on consumers' willingness to purchase green housing

PurposeThe residential sector is a principal contributor to global energy consumption, underscoring the critical importance of promoting green housing initiatives to mitigate energy use and environmental degradation. The prevalence of uncertainty in the natural environment, exemplified by phenomena like extreme weather events, highlights the urgent need for adaptive strategies and sustainable practices to mitigate the impact on human communities and ecosystems. Against this backdrop, this paper presents a theoretical framework examining the influence of natural environmental uncertainty on consumers' willingness to purchase green housing.Design/methodology/approachThrough three experiments, this study modeled the mechanism by which the natural environment uncertainty affects consumers' willingness to purchase green housing, and then verified the mediating effect of the threat of ontological security and the moderating effect of the degree of consumers' natural connectedness.FindingsThis paper concludes (1) natural environmental uncertainty exerts a significant positive impact on the willingness to purchase green housing, with the threat to ontological security serving as a pivotal mediating variable; (2) the degree of natural connectedness significantly moderates the effect of ontological security threats on the purchasing intent for green housing.Originality/valueThis research contributes to the marketing literature by offering a novel perspective on the impact of natural environmental uncertainty on consumer behavior, augmenting the body of knowledge concerning the determinants of green housing purchase intentions, and provides new ideas for marketers.

Digital transformation and supply chain concentration: evidence from China

ABSTRACT Excessive supply chain concentration (SCC) introduces various risks, and the factors contributing to SCC are complex and have been rarely explored. This study examines the economic impact of digital transformation on supply chains by analysing data from China’s A-share listed companies (2010–2021) using regression models. The findings demonstrate that digital transformation significantly reduces supply chain concentration and increases the firms’ influence within the supply chain. The mechanism analysis shows that digital transformation makes firms more attractive to suppliers and customers, eases financing constraints, boosts firm reputation, and reduces SCC. The impact is more pronounced in firms facing higher environmental uncertainty or those positioned upstream in the supply chain. Except for blockchain, all five dimensions of digital transformation help to reduce SCC, thereby improving total factor productivity and economic growth. Digital transformation also creates a spillover effect, raising expectations for digital transformation and productivity among suppliers and customers. These findings have significant implications for understanding digital transformation practices and advancing supply chain management. The conclusions remain robust after various tests, including changes to the measures of variables and controls for reverse causality using lagged treatment and instrumental variables.

Sustainable Strategies and Value Creation in the Food and Beverage Sector: The Case of Large Listed European Companies

Food and beverage in Europe represents a central sector in terms of contribution to GDP, employment, and the trade balance. At the same time, it has significant environmental, economic, and social implications, making the adoption of sustainable strategies of vital importance for companies in the sector. This study explores how sustainable business practices can create long-term strategic value, with a particular focus on the food and beverage sector. The research analyzes the relationship between sustainability and business strategy, focusing on how companies can thrive in the context of environmental uncertainties, social fluctuations, and economic interconnectedness. Specifically, the study aims to identify the key sustainability practices and strategies adopted by leading food and beverage companies and determine how these practices affect their ESG performance (environmental, social, and governance) results. The study uses a reasoned selection of case studies from one of the primary CSR regulatory categories—listed companies with considerable size (excluding tobacco companies and those with a capitalization of less than EUR 10 billion)—comparing companies’ sustainability reports and the latest 2023 integrated annual report to assess every aspect of each company, from market position to sustainability policies and sustainable reporting.

Forging a path to sustainability: the impact of Fintech on corporate ESG performance

Emerging development of Fintech has raised widespread attention to its important role in shaping corporate ESG performance despite little in-depth research. We study the impact of Fintech on corporate ESG performance using a comprehensive dataset covering major China’s firms over the past decade. The Fintech development at the city level is measured via an entropy method based on information gathering from firms and households. Drawn on a panel data estimation with two-way fixed effects, our findings show that corporate ESG performance improves with Fintech development. Promotion of green innovation, mitigation of financial mismatch, and environmental uncertainty are found to serve as the three mediating channels that drive the relationship between Fintech and corporate ESG performance. The positive impact of Fintech is shown to be more pronounced in firms with non-high-tech, non-high-polluting, and labor-intensive features. Further analysis indicates that Fintech strengthens in the enterprises’ green value through perspectives of green total factor productivity and green investment.

Conceptual advances in petrophysical inversion techniques: The synergy of machine learning and traditional inversion models

Petrophysical inversion techniques are essential for estimating reservoir properties such as porosity, permeability, and fluid saturation, which are critical for optimizing hydrocarbon recovery and reservoir management. Traditionally, deterministic and stochastic inversion methods have relied on well log data, core samples, and seismic information to generate models of subsurface properties. However, these techniques face limitations in handling large datasets, nonlinear relationships, and uncertainties in complex reservoir environments. Recent advances in machine learning (ML) offer an opportunity to enhance these traditional methods by providing a data-driven approach capable of learning patterns and refining inversion models. This review explores the potential synergy between traditional petrophysical inversion techniques and machine learning algorithms to create a new paradigm for reservoir parameter estimation and analysis. Machine learning’s ability to process large volumes of diverse data and capture nonlinear relationships complements the physics-based constraints of traditional inversion methods. By combining these two approaches, hybrid models can provide improved accuracy in predicting reservoir properties and handling data gaps or uncertainties. The review discusses specific case studies where machine learning-assisted inversion models have been successfully applied, such as the estimation of porosity and permeability, seismic inversion for lithology identification, and fluid saturation modeling. These hybrid models demonstrate enhanced predictive accuracy, faster computational efficiency, and real-time adaptability, transforming reservoir characterization and management. The integration of machine learning with traditional inversion techniques represents a major advancement in petrophysical analysis. This synergy holds the potential to revolutionize reservoir parameter estimation, making it more accurate, robust, and responsive to real-time data, ultimately improving decision-making in reservoir engineering and maximizing hydrocarbon recovery. Keywords: Petrophysical Inversion, Machine Learning, Traditional Inversion, Models.

The Impact of Environmental Uncertainty on Supply Chain Risks in Vietnamese Textile Enterprises

The Impact of Environmental Uncertainty on Supply Chain Risks in Vietnamese Textile Enterprises

The meaning and use of weapons in an English remand prison

Purpose Weapon use is as risky in prison as it is in the community, but the type, use and meaning of weapons differ between these settings. Consequently, knowledge about community-based weapon violence may not generalise to prison contexts. The purpose of this study is to understand the meaning and use of weapons in a prison setting. Design/methodology/approach Using a framework for understanding weapon selection derived from a community setting, six prisoners in a remand setting in England who had a history of weapon possession and use in prison discussed their selection and use of weapons in prison. Findings Respondents described a hyperviolent milieu for some in which access to weapons was essential and wherein the official consequences of weapon carrying were outweighed by the potential costs of victimisation. Weapons served a variety of purposes for prisoners. At the individual level, they reduced the uncertainty of a hyperviolent environment, and they were used to construct and manage a violent identity as an aggressive precaution against victimisation. Originality/value This study develops the literature on weapon decision-making, extending it into a novel setting and addresses a significant gap in the prison research literature about the meaning and utility of weapons in a custodial setting. The use of a community-derived framework for understanding weapon carrying translated well into a prison environment and offers support for the synthesis of community and prison models of violence but distinctive features of the prison environment, such as how weapons are acquired, limits the fit of the model to a prison setting.

Prescribed-Time Dynamic Positioning Control for USV with Lumped Disturbances, Thruster Saturation and Prescribed Performance Constraints

This work studies the dynamic positioning (DP) control issue of unmanned surface vessels subjected to thruster saturation, error constraints, and lumped disturbances composed of time-varying marine environmental disturbances and model parameter uncertainties. Combining the disturbance-accurate estimation technique and the prescribed performance control strategy, a novel prescribed-time DP control scheme is established to address this challenging problem. In particular, the prescribed-time lumped disturbance observer is designed to accurately estimate external marine disturbances, which guarantees that the estimation error converges to zero within a prescribed time. Subsequently, a prescribed performance control strategy is proposed to guarantee that the positioning errors of DP surface vessels with thruster saturation constraints meet the error constraints requirements within a prescribed time. Furthermore, an anti-windup compensator is presented to mitigate the thruster saturation and improve the robustness of the DP control system. The stability analysis demonstrates that all positioning errors of the closed-loop system can converge to predefined performance constraints within a prescribed time. Finally, the numerical simulation confirms the efficacy and superiority of the proposed PTDP scheme.

Strategic nature of corporate social responsibility, competitive performance, innovation capability and environmental uncertainty

PurposeThe goal of this work is to evaluate how corporate social responsibility (CSR) affects competitive performance in Nigeria's banking industry, with innovation capability acting as a mediator and environmental uncertainty as a moderating factor.Design/methodology/approachThe banking industry in Nigeria served as the site for the empirical investigation. Employees at deposit money institutions received a questionnaire. Direct and mediating effects and the moderating role were thus examined utilizing a final sample of 267 cases using consistent partial least squares structural equation modeling with ADANCO 2.2.1.FindingsThe data shows that CSR has both a significant strategic impact on innovation capability and a competitive innovation capability. In contrast, the outcome shows a strong effect of CSR's strategic character on performance in the marketplace. Furthermore, evidence for mediating and moderating effects was provided.Research limitations/implicationsThe study was restricted to Nigerian banking institutions. Additionally, data on competitive performance were acquired from employees' perspectives, while considering the competitive performance of their rivals.Originality/valueThe primary contribution of this paper is the empirical investigation of the mediating impact of innovation capability and the moderating function of environmental uncertainty in banking organizations that use a CSR strategy to attain competitive performance.

Customer Requirement Importance Determination Method Based on Uncertain Language Information Entropy

To reflect the uncertainty of the objective environment in an effective way, a customer demand selection method based on information entropy of uncertain language is proposed. First, the customer representative uses uncertain language variables to represent the competitiveness of the company’s products and those of its competitors. Secondly, the method of information entropy was used to determine the competitive priority of each customer demand item. Finally, taking the improvement of the PE crusher as an example, the practicability and reliability of the proposed method are illustrated.

NHPP Software Reliability Model with Rayleigh Fault Detection Rate and Optimal Release Time for Operating Environment Uncertainty

Software is used in diverse settings and depends on development and testing environments. Software development should improve the reliability, quality, cost, and stability of software, making the software testing period crucial. We proposed a software reliability model (SRM) that considers the uncertainty of software environments and the fault detection rate function as a Rayleigh distribution, with an explicit mean value function solution in the model. The goodness-of-fit of the proposed model relative to several existing nonhomogeneous Poisson process (NHPP) SRMs is presented based on three software application failure datasets. Further, a cost model is also presented that addresses the error-removal risk level and required time. The optimal testing release policy for minimizing the expected total cost (ETC) is also determined for NHPP SRMs. The impact of the software environment is studied by varying it, and the optimal release times and minimum ETCs are compared. The goodness-of-fit comparison confirmed that the proposed model has more accurate prediction values than other models. Further, whereas the existing models applied to the cost model do not change after a certain operation period, the proposed model yields changes in release time even for long operating periods.

Probing an LSTM-PPO-Based reinforcement learning algorithm to solve dynamic job shop scheduling problem

With the growth of personalized demand and the continuous improvement in social productivity, the large-scale and few-variety centralized production model is gradually transitioning towards a personalized model of small batches and multiple varieties, which makes the manufacturing process of the job shop increasingly complex. Furthermore, disruptive events such as machinery failures and rush orders in the job shop increase the uncertainty and variability of the production environment. Traditional scheduling methods are usually based on fixed rules and heuristic algorithms, which are difficult to adapt to constantly changing production environments and demands. This may lead to inaccurate scheduling decisions and hinder the optimal allocation of job shop resources. To solve the dynamic job shop scheduling problem (JSP) more effectively, this paper proposes a Reinforcement Learning (RL) optimization algorithm integrating long short-term memory (LSTM) neural network and proximal policy optimization (PPO). It can dynamically adjust scheduling strategies according to the changing production environment, achieving comprehensive status awareness of the job shop environment to make optimal scheduling decisions. First, a state-aware network framework based on LSTM-PPO is proposed to achieve real-time perception of job shop state changes. Then, the state and action space of the job shop are described within the context of the state-aware network framework. Finally, an experimental environment is established to verify the algorithm’s effectiveness. Training the LSTM-PPO algorithm makes it feasible to achieve better performance than other scheduling methods. By comparing the initial planning time with the actual completion time of the rescheduling decision under different dynamic disturbances, the efficiency of the proposed algorithm is verified for the dynamic JSP

United we stand, divided we fall: The impact of top management team stability on corporate litigation

Escalating commercial litigation in emerging markets arouses growing concerns for listed companies, while certain corporate governance practices are proved to be effective in curbing litigation risks. Using panel data from China’s A-share listed companies, we demonstrate that a stable top management team is a cornerstone in reducing a firm’s exposure to litigation risk. We find that this inhibitory effect is achieved by mitigating agency problems, strengthening internal controls, and cultivating firm-specific human capital. Further analyses reveal that this effect is more pronounced when TMTs have more company ownership, exhibit stronger managerial discretion, and maintain a higher proportion of female executives, or when they experience greater environmental uncertainty. Additionally, a strong legal environment and institutional monitoring can both serve as substitutes for top management team stability in reducing corporate litigation risk. Our study contributes to the literature by providing empirical evidence of the positive impact of top management team stability on corporate risk management.

Environmental Justice Specialization and Corporate ESG Performance: Evidence from China Environmental Protection Court

In order to implement the dual-carbon strategy and achieve sustainable economic development, it is essential to guarantee environmental protection through the establishment of an effective environmental rule of law. This study employs a quasi-natural experiment, namely the establishment of environmental protection courts in China’s intermediate people’s courts, to investigate the impact on the ESG performance of A-share listed companies from 2010 to 2022. A double-difference model is utilized for this purpose. This study reveals that the specialization of environmental justice is an effective means of promoting the ESG performance of enterprises. The results of mechanism tests indicate that the specialization of environmental justice has a positive impact on the ESG performance of enterprises, primarily by enhancing external supervision and garnering greater media attention and analyst interest. Furthermore, heterogeneity analysis reveals that the influence of environmental justice specialization on corporate ESG performance is particularly pronounced in eastern regions, contexts characterized by high environmental uncertainty and heavily polluting enterprises. These findings offer invaluable insights into the development of environmental justice and the advancement of sustainable economic growth.

Travel Agency Companies Under Environmental Uncertainty: Multiple Strategic Orientation and Performance

The global pandemic has significantly impacted the economy, tourism industry, and its supporting industries. Many market areas of the tourism industry, including hotels, restaurants, and travel agents, have reached a point of saturation. To better understand how environmental uncertainty affects travel agency performance, this research will investigate the organizational strategic orientation's mediating impact. A questionnaire is used to gather the data, which is subsequently subjected to PLS-SEM analysis. The population consisted of travel agent’s manager across Indonesia, from which 75 were selected using purposive sampling. According to the findings, technology uncertainty affects the strategic orientation that organizations choose, and this includes entrepreneurial and market orientations, which might affect the learning orientation and organizational performance of travel agency organizations

Applications of distance measure between dual hesitant fuzzy sets in medical diagnosis and weighted dual hesitant fuzzy sets in making decision

This paper introduces a novel distance measure for dual hesitant fuzzy sets (DHFS) and weighted dual hesitant fuzzy sets (WDHFS), with a rigorous proof of the triangular inequality to ensure its mathematical validity. The proposed measure extends the normalized Hamming, generalized, and Euclidean distance measures to dual hesitant fuzzy elements (DHFE), offering a broader framework for handling uncertainty in fuzzy environments. Additionally, the utilization of a score function is shown to simplify the computation of these distance measures. The practical relevance of the proposed measure is demonstrated through its application in medical diagnosis and decision-making processes. A comparative analysis between the newly introduced distance measure denoted as χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi$$\\end{document}, and an existing measure, χ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi _1$$\\end{document} is performed to underscore the superiority and potential advantages of the new approach in real-world scenarios.

Supply Chain Environmental Uncertainty, Competitive Advantage, and Operational Performance in Manufacturing Industry

Goal: This study investigates the impact of Supply Chain Environmental Uncertainty (SCEU) on competitive advantage and operational performance in manufacturing companies. The mediating role of competitive advantage and the moderating role of supply chain environmental uncertainty in the relationship between competitive advantage and operational performance were examined. Design/ Methodology/ Approach: This paper used quantitative approach to confirm the conceptual model. Data was collected through questionnaires from directors/operational managers in 121 large and medium-scale manufacturing companies in Southeast Sulawesi Indonesia. The analysis employs Generalized Structured Component Analysis (GSCA) to test the direct, mediating, and moderating effects of supply chain environmental uncertainty. Results: The findings demonstrate a significant positive influence of supply chain environmental uncertainty on competitive advantage and operational performance. However, competitive advantage does not significantly affect operational performance. The study reveals that competitive advantage is perfect mediator between supply chain environmental uncertainty and operational performance, while supply chain environmental uncertainty is a moderating predictor between competitive advantage and operational performance. Practical Implications: The research has implications for manufacturing company managers adapting to supply chain environmental uncertainty through supplier, customer, and technology considerations. It emphasizes the importance of adapting to supply chain environmental uncertainty to enhancing competitive advantage and operational performance in the manufacturing industry. Efforts to improve operational performance and strategically integrate competitive advantage should consider the business environment, including suppliers, customers, and technology. Limitations: This research is limited to large and medium-scale manufacturing industries, hindering generalization to other industries, particularly small-scale ones. Additionally, researchers can consider additional contextual factors such as supply chain integration, supply chain agility, and total quality management. Originality/Value: This study expands the theoretical framework of supply chain environmental uncertainty, competitive advantage, and operational performance through empirical testing of a theoretical model. The findings support the proposed model's validity and highlight the mediation role of competitive advantage and supply chain environmental uncertainty, providing a reference for future theory development and model building.