Emerging Science Journal (ESJ)
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Adaptive Strategies: Algorithmic Analysis of Pre- and Post-Pandemic Manager-Frontline Employee Communication Model in Restaurants
Get PDFThe aim of this study is to determine the impact of the COVID-19 pandemic on an independent restaurant chain in Puebla, Mexico, through longitudinal censuses with employees who had direct contact with diner guests (frontline employees) in 2019 and 2023. The survey data was analyzed through the algorithm Synthetic Minority Over-sampling Technique (SMOTE) to balance sample sizes and Information Gain to determine the dependence between the study variables. The results revealed the health crisis has led to changes in pivotal factors that drove better performance from frontline employees and managers in restaurants in Mexico. After the pandemic, frontline employees began to favor standard compliance due to the reinforcement of hygiene protocols. However, there are implications for the structure of teams and work styles on account of the decline in the number of frontline employees and the creation of multifunctional positions. Regarding the manager's factors, Linear Communication, which consists of providing clear and timely information, has become more relevant. While, there has been a decrease in Dynamic Communication oriented towards dialogue and Productive Communication focused on participation and the achievement of objectives. COVID-19 has accentuated the vulnerability of female frontline employees, who, since the pandemic, occupy multifunctional positions; the number of employees has decreased in greater proportion than the number of restaurants that have closed, impacting team dynamics; the most affected restaurants are located in automotive industrial parks, while restaurants in tourist or commercial areas have managed to maintain their workforce. The selected independent restaurant chain has focused on the functional aspects of the restaurant, as denoted by their current transactional leadership style and hierarchical organizational culture. However, there is an important theoretical implication related to the post-pandemic context, characterized by the presence of multifunctional roles and the pressure to meet customer service standards. Frontline employees have adopted individualistic behaviors, negatively impacting collaborative teamwork. These finding challenges existing literature that often emphasizes the positive aspects of employee empowerment and multifunctionality; it suggests that, under certain conditions, these factors may inadvertently hinder team cohesion and collaborative efforts. This study highlights the need to capitalize on Mexican restaurants by not only valuing the strategies from senior management but also by incorporating interpersonal perspectives from frontline employees to improve the organization in procedural and relational terms to adapt to future crises. Doi: 10.28991/ESJ-2024-08-05-021 Full Text: PD
Factors Influencing Social Media Platform Engagement among Thai Students: A Quantitative Study
Get PDFThis study delves into the complex landscape of social media utilization among undergraduate students in higher education institutions in Thailand, investigating the pivotal factors that shape their engagement with these platforms. Employing a quantitative research approach, the investigation utilizes a meticulously crafted multi-stage sampling methodology coupled with a robust data collection process. Through applying multi-correlation and multiple-regression analyses, the research unveils significant insights into the determinants of social media usage among Thai youth. Notably, motivation for social media use, access, creativity, and participation through these platforms emerge as substantive predictors. This aligns seamlessly with existing research, underscoring the critical roles played by motivation and accessibility in influencing online engagement. The resultant predictive equation is a pragmatic instrument for comprehending and forecasting social media engagement patterns among Thai undergraduate students. The findings underscore the importance of motivation, access, and creativity as driving forces behind social media utilization. This research equips educators, policymakers, and researchers with valuable insights, emphasizing the imperative of fostering responsible and effective use of social media within this demographic. The study's contribution to the academic landscape is noteworthy because it sheds light on unexplored facets such as cultural dynamics, peer networks, and individual traits, enriching our understanding of the intricate social media landscape among Thai undergraduate students. Doi: 10.28991/ESJ-2024-08-02-011 Full Text: PD
Examining the Role of Technostress Creators and Inhibitors on Academics Burnout
Get PDFRecent studies have focused on examining the impact of technostress (TS) on academics and students. However, there remains a paucity of studies examining the influence of TS on burnout among academics. This study aims to explore the influence of TS on academics' feelings of burnout and to examine the mitigating role of TS inhibitors on burnout among academics when using online learning technology. A web-based survey was designed and used to collect data from 115 academics in Malaysian universities. The data were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. The research findings reveal that academic burnout is significantly influenced by technology insecurity, technology invasion, and technology uncertainty. However, the impact of technology overload is only partially significant, while technology complexity does not exert a significant influence on academic burnout. Moderation tests reveal that literacy and involvement facilitation significantly moderate the relationship between technology uncertainty and insecurity, reducing burnout feelings. This study extends existing literature by providing empirical evidence to explain the relationship between TS and the academic burnout construct. Furthermore, it demonstrates the mitigating role of TS inhibitors on the burnout construct. Additionally, it offers potential strategies for alleviating burnout among academics, particularly in Malaysian university contexts. Doi: 10.28991/ESJ-2024-SIED1-012 Full Text: PD
Challenges of Women Healthcare Workers during COVID-19
Get PDFObjectives: The study aimed to identify the psychological and social challenges faced by Emirati women working in the medical field during COVID-19, as well as the extent to which these challenges differed depending on the nature of their work, the variable of their social status, and the extent to which these challenges were related to the study's primary variables. Methodology: For this study, a questionnaire for assessing psychological and social issues was developed, and after obtaining psychometric features and proving validity and stability, it was used on a sample of 150 Emirati women. Results:The statistical data show that the study sample in the field of psychological issues suffers anxiety, dread, tension, and agitation. In addition, there are social difficulties to consider, such as social stigma, loss of family and professional relations, absence from home, trouble maintaining a work-life balance, and strained social relationships with patients. The findings also revealed a lack of statistical significance with the variables of the place of residence and social status on the study sample's varying challenges, as well as the presence of a statistically significant relationship between the study variables (age, shift time, housing, work sector, number of years of experience) and the challenges. Doi: 10.28991/ESJ-2024-08-04-017 Full Text: PD
Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis
Get PDFAccurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions highly influence cooling demand, leveraging external air data and user metrics offers a promising approach to estimate a building's hourly cooling energy usage. This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. By leveraging weather conditions, building characteristics, and operational parameters, we aim to predict cooling consumption across multiple systems (Cooling Ceiling, Ventilation, Free Cooling, and Total Cooling). Data from four weather stations, encompassing diverse features relevant to the European Central Bank (ECB) building's cooling consumption in Frankfurt, were employed. Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. Models. The results consistently demonstrate the superiority of the Random Forest model across different weather stations and feature sets. This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices. Doi: 10.28991/ESJ-2024-08-06-01 Full Text: PD
Optimizing STEM Education via e-Learning: Addressing Challenges and Strategic Planning for Graduate Employment Outcomes
Get PDFThis study fills a significant vacuum in the literature and offers insightful information, especially pertinent to the Arabic and Gulf areas. In support of the socioeconomic development and diversification initiatives in these areas, it encourages the creation of educational policies and practices that can improve student job chances. Educators and policymakers play a crucial role in implementing these recommendations, ensuring that effective STEM education equips future generations with the knowledge and skills to navigate and secure digital environments, mitigating risks posed by cyber threats. By preparing students to excel in STEM disciplines, they ensure that the future workforce is ready to contribute to and shape a safer, more innovative future. Today's STEM workforce requires continuous education to stay abreast of advancements in STEM fields. Online delivery of STEM courses enables higher education institutions to collaborate effectively with industry partners, minimizing disruptions to productivity. This study aims to evaluate the enhancement of students' STEM knowledge and skills through e-learning platforms using Structural Equation Modeling (SEM). The dataset comprises 212 participants and 15 indicators, analyzed using measurement and structural models. Results indicate significant findings supporting hypotheses: E-Learning (E.L.) improving Student employment opportunities (SEO) (B=0.539, t=8.884, p=0.000) and E.L. improving STEM (B=0.465, t=8.849, p=0.000), validating H1 and H2, respectively. H3 explores how possessing STEM knowledge mediates E-Learning's impact on SEO, revealing a notable indirect relationship (B=0.082, t=2.303, p=0.021). Integrating E-Learning with STEM fosters a robust digital infrastructure, addressing talent shortages and improving human resource management amidst global technological advances. Online STEM courses are increasingly recognized as a viable solution to national STEM challenges, overcoming traditional adoption barriers. Doi: 10.28991/ESJ-2024-SIED1-014 Full Text: PD
Innovative Technology for Managing Biofuel Production from Timber Industry Waste
Get PDFThe relevance of this study is determined by the growing worldwide interest in renewable energy sources against the backdrop of depleting fossil fuel reserves. This study aims to develop an innovative technology for managing biofuel production from wood waste, including a set of interrelated economic and mathematical models focused on maximizing the fuel and energy efficiency of biofuels depending on the location of waste generation, feedstock moisture content, and distance to the biofuel production site. This technology should also combine the main directions of international research in the field of environmental responsibility of countries in terms of carbon dioxide (CO2) emissions and the Paris Climate Agreement. The methodological basis of the research comprises the authors' innovative technology based on a set of interconnected economic and mathematical models and managerial decision-making systems, methods for nonlinear programming, system analysis, an information approach to the analysis of systems, accepted technological processes, norms, and standards established in the international practice of the timber industry. This innovative technology was implemented in practice using the capabilities of the MathCad and MS Excel software products. The article determines the optimal operating parameters of timber industry enterprises at which the specific thermal energy of the produced biofuel exceeds by at least 15% the thermal energy spent on processing this biofuel as an energy carrier. Wood waste biofuel production is profitable if the distance for feedstock transportation to the production site does not exceed 80 km and the relative humidity of the raw materials does not exceed 60%. Doi: 10.28991/ESJ-2024-08-03-03 Full Text: PD
Cannibalism and Harvesting in Tritrophic Chains: Insights from Mathematical and Artificial Neural Network Analysis
Get PDFIn this study, we introduce a novel tri-trophic food chain model that integrates cannibalism among basal prey and harvesting behaviors in the top predator, aiming to understand ecosystem dynamics comprehensively. Objectives encompass assessing system boundedness, computing fixed points, and determining stability characteristics using mathematical frameworks. The Routh-Hurwitz criteria and Lyapunov function are employed for local and global stability analyses of coexistence equilibrium points. Graphical interpretations elucidate relationships among pivotal parameters: prey growth rate, cannibalism intensity, and predator predation rate. Phase portraits and time series solutions illustrate parameter impacts. To enhance analytical depth and predictive capabilities, we utilize artificial neural networks (ANNs). Methods include connecting ANNs to computational proficiency for insights into the model's behavior over time. Findings demonstrate system boundedness, computed fixed points, and stability characteristics. Graphical interpretations reveal parameter impacts on system dynamics. ANNs offer predictive insights into model behavior. This study's novelty lies in integrating cannibalism and harvesting behaviors into a tri-trophic food chain model, employing mathematical analyses and ANNs to understand ecosystem dynamics comprehensively. Improvements include predictive capabilities and deeper analytical insights. Doi: 10.28991/ESJ-2024-08-04-02 Full Text: PD
Tax Incentives for Economic Growth in the Russian Far East: Broad vs. Targeted Stimuli
Get PDFThe purpose of this study is to justify the choice of tax incentive policy instruments aimed at the economic development of the Russian Far East, which is facing acute demographic and environmental problems. To model the dynamics of the region's real economic system, this study employed a mathematical model based on actual data from 2010 to 2021, covering economic, technological, and socio-ecological aspects. Using the versatile AnyLogic 8.0 platform for agent-based and system dynamics modeling, experiments on alternative tax incentive policy options involving both broad tax incentives and targeted economic development measures were conducted. Specifically, a 50% investment tax deduction for residents in special economic zones in the Russian Far East was implemented. The experimental results show that, despite comparable population dynamics, targeted stimulation of growth poles through public-private partnership programs outperforms broad tax incentives for economic entities in the Russian Far East. This is evident in higher economic growth rates in the region, particularly during the experimental period, except for 2040–2050, where adverse demographic trends constrain growth in both scenarios. The theoretical significance of the application of this method has shown that it allows us to obtain new significant results in the subject area of research due to the consideration of the complex interaction of factors of influence both at the micro- and macro-level, primarily behavioral factors that are fundamentally important for understanding the action of taxes. The practical implications of this study lie in defining the parameters of tax policy to target and stimulate growth poles in regions serving as hubs for generating and disseminating new technologies. The planned perspective is to encourage population growth and ensure sustained economic development in Russia's Far East. It is advisable to explore comprehensive tax and budgetary regulations that simultaneously address economic, socio-demographic, and environmental issues in the region. Doi: 10.28991/ESJ-2024-08-03-021 Full Text: PD
Role of Two-Way Asymmetrical Communication in Sustaining Public Relations
Get PDFInternet technology's worldwide success and adoption have provided organizations with direct access to their constituents and customers. Especially, organizations relying on online platforms provide comparatively better services and have strong relations with their clients. This research also focused on relevant phenomena in the United Arab Emirates banking sector organizations. The researchers employed a cross-sectional design and randomly selected a sample of n=400 individuals. Results revealed a significant impact of customer support services on providing product information (p>0.008) and service quality (p>0.000). Further, the effect of service quality on Artificial Intelligence also remained significant (p>0.000). Besides, Artificial Intelligence is also found significantly impact the Public Relations of Emirati banks (p>0.006). Finally, the mediating impact of communication skills on Artificial Intelligence and Public Relations remained significant (p>0.088). Moreover, the Artificial Neural Network (ANN) revealed the Sum of Square Values at 568.19, the Overall Relative Error value at 0.813, and the accuracy level at 18.7% (training). While, regarding the testing, the Sum of Square Values remained at 256.80 and the Average Overall Relative Error value remained at 0.861, indicating an overall accuracy of 13.9%. Thus, it is concluded that the importance of two-way communication can be determined because it helps determine and understand the customers' needs and demands. The more an organization understands its customers, the more it fulfills their expectations, indicating the importance of two-way communication. Finally, this research recommends more studies regarding AI-enabled Emotional Intelligence in other sectors to dig out in-depth results. Doi: 10.28991/ESJ-2024-08-03-020 Full Text: PD