Search Results (65)

Background: Developing and cultivating mindfulness exerts a positive effect on psychological and cognitive performance. Sharpening the skill requires continuous mindfulness-based training (MT), which can be challenging for people leading busy lives. Therefore, the current study examined whether trait mindfulness can be improved by a flexible and brief MT program of self-breathing using a pre–post intervention design. Methods: Trait mindfulness was assessed using the Japanese version of the Five Facet Mindfulness Questionnaire (FFMQ) before the intervention (pre), after 2 weeks (during), and 4 weeks after the intervention started (post). Data were analyzed using the Friedman test followed by the Dunn–Bonferroni correction. Results: The study sample consisted of 22 healthy participants aged from 20 to 60 years with no previous experience with yoga or meditation equivalent to MT. The mean number of days of MT practice was 26.4, and 11 participants had interruptions. The median values of pre-, during-, and post-total FFMQ scores were 115.5, 123, and 129, respectively. Significant differences were observed in the total pre and post (p < 0.001) and during and post (p = 0.002) FFMQ scores, though a medium effect was found (r = 0.30) only between the pre and post scores. Of the five sub-scales of FFMQ, significant differences were observed only between pre and post Observing (p = 0.01), Nonreactivity (p < 0.001), and Describing (p = 0.01), and during and post Nonjudging (p = 0.016), and Nonreactivity (p = 0.025). Conclusions: Our findings suggest that the simple, brief, and flexible self-breathing method employed in this study has a substantial effect on fostering trait mindfulness and, therefore, can be adopted by people with hectic daily schedules. Full article

Given the high prevalence of overweight and obesity amongst educators, this study investigated the feasibility of the 16-week Health4LIFE weight loss intervention for primary school educators living with overweight/obesity in low-income settings in Cape Town, South Africa. The research comprised two sub-studies, a pilot randomised controlled trial testing the intervention (10 intervention, n = 79 and 10 control schools, n = 58), and an investigation of the perceptions of participating educators and principals. Feasibility outcomes included reach, applicability, acceptability, implementation integrity, and a hypothesis-generating signal of effect on lifestyle factors and weight. The intervention consisted of a wellness day, weight loss manual, and text messages. Results indicated acceptable reach, with positive feedback on intervention components from principals and educators. Implementation was largely successful, though three schools dropped out due to scheduling issues. Barriers included interruption of teaching time and busy school schedules. The intervention group (n = 42) showed favourable shifts in belief patterns, stages of change, and lifestyle behaviours, with a trend towards weight loss. Control group (n = 43) changes were limited to dietary intake. The triangulation of results supported the intervention’s feasibility in terms of primary and secondary outcomes. Recommendations for enhancement include adding in-person follow-up sessions and an app-based element to potentially increase impact on lifestyle indicators and weight loss. Full article

This paper aims to evaluate corporate governance in relation to enterprise performance indicators in order to enhance it. The intention is not only to align with the interests of shareholders, but also to foster competitive, sustainable, and inclusive growth. For this purpose, the leading energy producer in each of the five countries—Denmark, Estonia, Latvia, Lithuania, and Sweden—was investigated to evaluate their corporate governance performance. An analysis was conducted, employing regression analysis, Pearson correlation, and descriptive statistics. The influence of corporate governance on the performance of chosen enterprises was examined, utilising specifically developed models. The findings reveal that the corporate governance variables are diverse, and financial metrics exhibit significant variability, reflecting the complexity of the energy industry. The research results confirm that larger and more varied boards positively impact the performance of state-owned power suppliers and increase their net income. The presence of independent members was also found to contribute to the net income growth of state-owned power suppliers. However, the study indicated that the frequency of audit meetings does not necessarily increase earnings. Still, larger audit committees can contribute to CG decision-making processes concerning debt management. The results also implied the need to consider the qualifications of the board members and its composition for proper power interruption management to minimise the frequency and duration of power outages. Therefore, it must be of pivotal focus for respective corporate governance duties. In this respect, the need for more specific and regular assessments was also found to be justified regarding industry-specific challenges related to power system disruptions. Customer-centric strategies should deserve relevant attention as well. The enforcement of the management audit function could be a solution. Consequently, assessing the governance structures and decision-making processes must be systematic for energy producers due to the business dynamics leading to the revaluation of the evolving challenges and possible solutions aimed at the competitive and sustainable development of the energy sector. Full article

How can organisations be forensically ready? As organisations are bound to be criticised in the digitally developing world, they must ensure that they are forensically ready. The readiness of digital forensics ensures compliance in an organisation’s legal, regulatory, and operational structure. Several digital forensic investigative methods and duties are based on specific technological designs. The present study is the first to address the core principles of digital forensic studies, namely, reconnaissance, reliability, and relevance. It reassesses the investigative duties and establishes eight separate positions and their obligations in a digital forensics’ investigation. A systematic literature review revealed a gap in the form of a missing comprehensive direction for establishing a digital forensic framework for ethical purposes. Digital forensic readiness refers to the ability of a business to collect and respond to digital evidence related to security incidents at low levels of cost and interruption to existing business operations. This study established a digital forensic framework through a systematic literature review to ensure that organisations are forensically ready to conduct an efficient forensic investigation and to cover ethical aspects. Furthermore, this study conducted a focus group evaluation through focus group discussions to provide insights into the framework. Lastly, a roadmap was provided for integrating the system seamlessly into zero-knowledge data collection technologies. Full article

Background: Supply chain networks (SCNs) have been interrupted by the COVID-19 pandemic, leaving them open to financial losses. SCs have been impacted by the pandemic, necessitating the adoption of sustainable practices and dynamic capacities to ensure resilience and performance. Several studies have focused on this subject, offering insights into the importance of sustainable supply-chain management, corporate governance, big data management activities, and digital technology in minimising the consequences of the pandemic and fostering sustainability. Methods: This study suggests an analytical framework for assessing environmentally friendly procedures and dynamic capacities to assure performance in a disruptive environment. Results: The following are some of the important details and contributions in this article: (1) developed a conceptual framework for assessing dynamic capacities and sustainable behaviours considering COVID-19, (2) concentrates on financial ratios during COVID-19, and (3) established drivers for sustainable practices and competencies during disruption and unpredictable business settings. Conclusions: The suggested model can assist practitioners in creating and implementing sustainable supply chain (SC) activities and tracking and assessing their effects on the sustainability of businesses. So, the proposed model can assist managers in creating and implementing sustainable supply-chain activities and tracking and analysing their effects on the sustainability of businesses. Full article

The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User Plane Function (UPF) to the edge of the network. However, cross-UPF handover during communication between the UE and the remote server will cause TCP/IP session interruption and affect continuity of delay-sensitive real-time communication continuity. Information-Centric Networks (ICNs) separate identity and location, and their ability to route based on identity can effectively handle mobility. Therefore, based on the 5G-ICN architecture, we propose a seamless mobility support method based on router buffered data (BDMM), making full use of the ICN’s identity-based routing capabilities to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. We design a dynamic buffer resource allocation strategy (DBRAS) that can adjust the buffer resource allocation results in time according to network traffic changes and business types to solve the problem of unreasonable buffer resource allocation. Finally, experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, our method also has good performance in average handover delay. Full article

This study examined the efficacy of artificial intelligence (AI) technologies in predictive risk assessment and their contribution to ensuring business continuity. This research aimed to understand how different AI components, such as natural language processing (NLP), AI-powered data analytics, AI-driven predictive maintenance, and AI integration in incident response planning, enhance risk assessment and support business continuity in an environment where businesses face a myriad of risks, including natural disasters, cyberattacks, and economic fluctuations. A cross-sectional design and quantitative method were used to collect data for this study from a sample of 360 technology specialists. The results of this study show that AI technologies have a major impact on business continuity and predictive risk assessment. Notably, it was discovered that NLP improved the accuracy and speed of risk assessment procedures. The integration of AI into incident response plans was particularly effective, greatly decreasing company interruptions and improving recovery from unforeseen events. It is advised that businesses invest in AI skills, particularly in fields such as NLP for automated risk assessment, data analytics for prompt risk detection, predictive maintenance for operational effectiveness, and AI-enhanced incident response planning for crisis management. Full article

Monitoring production systems is a key element for identifying waste and production efficiency, and for this purpose, the calculation of the Key Performance Indicator (KPI) Overall Equipment Effectiveness (OEE) is validly recognized in the scientific literature. The collection and analysis of the cause of the interruption of the plants is particularly useful in this sense. The use of Internet of Things (IoT) technology in order to automate data collection for the purpose of calculating the OEE and the causes of interruption is effective. Furthermore, the existing literature lacks research studies that aim to improve the data quality of important process data that cannot be collected automatically. This study proposes the use of IoT technologies to request targeted and intelligent information inputs from the operators directly involved in the process, improving the completeness and accuracy of the information through the real-time and smart combination of manual and automated data. The Business Process Model and Notation (BPMN) methodology was used to analyze and redesign the collection data process and define the architectural model with a deep knowledge of the specific process. The proposed architecture, designed for application to a plastic injection molding production line, comprises several elements: the telemetry of the injection molding machine, an intervention request system, an intervention tracking system, and a human–system interface. Furthermore, a dashboard was developed using the Power BI software, 2.122.746.0 version, to analyze the information collected. Reducing the randomness of manual data makes it possible to direct production efficiency efforts more effectively, helping to reduce waste and production costs. Reducing production costs appears to be strongly linked to reducing environmental impacts, and future studies will be able to quantify the benefits obtained from the solution in terms of environmental impact. Full article

Supply chain agility has become a key success factor for businesses trying to handle upheavals and uncertainty in today’s quickly changing business environment. Proactive risk reduction is essential for achieving this agility. To facilitate real-time risk prevention and improve agility, this research study proposes an innovative strategy that makes use of machine learning as well as predictive analytics approaches. Traditional supply chain risk management frequently uses post-event analysis as well as historical data, which restricts its ability to address real-time interruptions. This research, on the other hand, promotes a futuristic methodology that uses predictive analytics to foresee possible disruptions. Based on contextual and historical data, machine learning models can be trained to find patterns and correlations as well as anomalies that point to imminent dangers. Organizations can identify risks as they arise and take preventative measures by incorporating these models into a real-time monitoring system. This study examines numerous predictive analytics methods, showing how they can be used to spot supply chain risks. These methods include time series analysis and anomaly detection as well as natural language processing. Additionally, risk assessment models are continuously improved and optimized using machine learning algorithms, assuring their accuracy and adaptability in changing contexts. This research clarifies the symbiotic relationship among predictive analytics and machine learning as well as supply chain agility using a synthesis of theoretical discourse and practical evidence. Case studies from various sectors highlight the usefulness and advantages of the suggested strategy. The advantages of this novel technique include improved risk visibility and quicker response times as well as the capacity to quickly modify operations. The development of a holistic framework that incorporates predictive analytics and machine learning into risk management procedures, setting the path for real-time risk identification as well as mitigation, is one of the theoretical contributions. On the practical side, the case studies offered in this paper show the actual benefits as well as the adaptability of the proposed approach across a wide range of businesses. Full article

Sanitary requirements, geopolitical crises, and other factors that increase price volatility have an impact on the organization of markets and changes in investment policies and business strategies. The COVID-19 pandemic interrupted the trade of chicken meat, due to the drastic reduction in the circulation of goods, interrupted the supply of production chains, changed consumption habits, and made it difficult to reorganize business due to the slow resumption of operations by suppliers of inputs and in distribution logistics. The magnitude of these impacts has not been studied despite the high relevance of this economic dimension and the managerial implications for sector governance and trade management. The purpose of this study was to evaluate the economic impact of the COVID-19 pandemic on the production costs and competitiveness of the Brazilian chicken meat production chain. The methodology consisted of the detailed collection of information and data on private and social prices carried out using the Policy Analysis Matrix (PAM) method. The competitiveness coefficients and policy effects in the Brazilian broiler production chain before (2015) and during (2022) the COVID-19 pandemic were quantified and compared. Generally, the significant increases in the production costs of chicken meat (30.49%) caused a decrease in total factor productivity (−19.54%), a reduction in gross revenue, and lower tax collection. The pandemic has reduced the profitability of the chicken production chain in Brazil by 32.31%, reduced the competitiveness of exports, and worsened other economic indicators of the production chain. To the best of our knowledge, no other study has investigated the impacts of the COVID-19 pandemic on the competitiveness of the Brazilian chicken meat production chain. The PAM method allows for prices paid and received to be updated in real terms in projects representative of Brazil, the world leader in exports. This information is important for both national and international stakeholders. Additionally, this model is applicable to other meats traded in the international market, as it provides greater precision in business management and can estimate the impacts of risks on the availability or quality of food and health crises with robust results. Full article

The challenges of the global business environment foster small medium-sized enterprises (SMEs) to continuously improve their performance in the level of vulnerability to possible impacts and interruptions in their operations that may affect their sustainability. Resilience strategies and ambidextrous capabilities have become important determinants of organizational performance, which has developed as an emerging area of interest in supply chain management in recent years. SMEs are one of the major contributing sectors to the Malaysian economy. Therefore, SMEs have been forced to survive in the current market situation to ensure higher economic growth and competitiveness. The resilience strategies and ambidexterity capabilities are important determinants of SMEs’ performance. As such, this study aims to examine the relationship between proactive resilience strategies, ambidextrous capabilities, and the performance of SMEs in the manufacturing sector, drawing on the dynamic capabilities perspective. A quantitative research design is adopted, a structured survey questionnaire is used, and data are collected from 351 SMEs in the manufacturing sector. Partial least squares structural equation modeling (PLS-SEM), Smart PLS 3.0 is used to test both direct and mediating results. The findings of this study suggested that proactive resilience strategies may have a significant influence on organizational performance of SMEs. Ambidextrous capabilities also act as a strong mediator between proactive resilience strategies and organizational performance. These findings contribute to the dynamic capabilities literature by highlighting the importance of proactive resilience strategies and ambidextrous capabilities in enhancing the positive impact on organizational performance in SMEs. This study provides a plausible explanation of two important management mechanisms for enhancing organizational performance sustainability. The relationships between proactive resilience strategies, ambidextrous capabilities, and organizational performance are malleable. This study also suggests that fostering formal and informal relationships might hold the key to the sustainable performance of SMEs in the long term. This study’s practical contributions are improving the knowledge and performance of supply chain systems for SMEs in the manufacturing sector and enhancing their competitive power in domestic and international markets. Full article

This systematic literature review explores the digital transformation (DT) and cybersecurity implications for achieving business resilience. DT involves transitioning organizational processes to IT solutions, which can result in significant changes across various aspects of an organization. However, emerging technologies such as artificial intelligence, big data and analytics, blockchain, and cloud computing drive digital transformation worldwide while increasing cybersecurity risks for businesses undergoing this process. This literature survey article highlights the importance of comprehensive knowledge of cybersecurity threats during DT implementation to prevent interruptions due to malicious activities or unauthorized access by attackers aiming at sensitive information alteration, destruction, or extortion from users. Cybersecurity is essential to DT as it protects digital assets from cyber threats. We conducted a systematic literature review using the PRISMA methodology in this research. Our literature review found that DT has increased efficiency and productivity but poses new challenges related to cybersecurity risks, such as data breaches and cyber-attacks. We conclude by discussing future vulnerabilities associated with DT implementation and provide recommendations on how organizations can mitigate these risks through effective cybersecurity measures. The paper recommends a staged cybersecurity readiness framework for business organizations to be prepared to pursue digital transformation. Full article

The classical economic growing quantity (EGQ) model is a key concept in the inventory control problems research literature. The EGQ model is commonly employed for the purpose of inventory control in the management of growing items, such as fish and farm animals, within industries such as livestock, seafood, and aviculture. The economic order quantity (EOQ) model assumes that customer demand is satisfied without interruption in each cycle; however, this assumption is not always true for some companies as they do not have continuous operations, except for item storage, during non-working times such as weekends, natural idle periods, or spare time. In this study, we extend the traditional EGQ model by incorporating the concept of working and non-working periods, resulting in the development of a new model called discontinuous economic growing quantity (DEGQ). Unlike the conventional EGQ model, the DEGQ model considers the presence of intermittent operational periods, in which the firm is actively engaged in its activities, and non-working periods, during which only storage-related operations occur. By incorporating this discontinuity, the DEGQ model provides a more accurate representation of real-world scenarios where businesses operate in a non-continuous manner, thus enhancing the effectiveness of inventory control and management strategies. The study aims to obtain the optimal number of periods in each cycle and the optimal slaughter age for the breeding items, and, subsequently, to find the optimal order size to minimize the total cost. Finally, we propose an optimal analytical procedure to determine the optimal solutions. This procedure entails finding the optimal number of periods using a closed-form equation and determining the optimal slaughter age by exhaustively searching the entire range of possible growth times. Full article

In a smart parking system, the license plate recognition service controls the car’s entry and exit and plays the core role in the parking lot system. When the Internet is interrupted, the parking lot’s business will also be interrupted. Hence, we proposed an Edge-Cloud-Dew architecture for the mobile industry in order to tackle this critical problem. The architecture has an innovative design, including LAN-level deployment, Platform-as-a-Dew Service (PaaDS), the dew version of license plate recognition, and the dew type of machine learning model training. Based on these designs, the architecture presents many benefits, such as: (1) reduced maintenance and deployment issues and increased dew service reliability and sustainability; (2) effective release of the network constraint on cloud computing and increase in the horizontal and vertical scalability of the system; (3) enhancement of dew computing to resolve the heavy computing process problem; and (4) proposal of a dew type of machine learning training mechanism without requiring periodic retraining, but with acceptable accuracy. Finally, business owners can reduce their burdens when introducing machine learning technology. Our research goal is to make parking systems smarter in edge computing through the integration of cloud and dew architecture technology. Full article

The COVID-19 outbreak has significantly impacted supply chains and has caused several supply chain disruptions in almost all industries worldwide. Moreover, increased transportation costs, labor shortages, and insufficient storage facilities have all led to food loss during the pandemic, and this disruption has affected the logistics in the food value chain. As a result, we examine the food supply chain, which is one of the key industries COVID-19 has detrimentally affected, impacting, indeed, on the entire business process from the supplier all the way to the customer. Retail businesses are thus facing supply issues, which affect consumer behavior by creating stress regarding the availability of food. This has a negative impact on the amount of food that is available as well as its quality, freshness, safety, access to markets, and affordability. This study examines the impact of COVID-19 on the United Arab Emirates food distribution systems and how consumer behavior changed in reaction to interruptions in the food supply chain and the food security problem. Hypothesis testing was used in the study’s quantitative methodology to assess consumer behavior, and participants who were consumers were given a descriptive questionnaire to ascertain whether the availability and security of food had been impacted. The study used JASP 0.17.2 software to develop a model of food consumption behavior and to reveal pertinent connections between each construct. Results show that consumer food stress and consumption behavior are directly impacted by food access, food quality and safety, and food pricing. Furthermore, food stress has an impact on how consumers behave when it comes to consumption. Food stress, however, is not significantly influenced by food supply. Full article