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Video game streaming (VGS) is emerging as an extremely popular, highly interactive, inordinately subscribed and very dynamic form of digital media. Incorporated environmental elements, gratifications and user pre-existing attitudes in VGS, this paper presents the development of an extended model of uses and gratification theory (EUGT) for predicting users' behavior in novel technological context.

The proposed model was empirically tested in VGS context due to its popularity, interactivity and relevance. Data collected from 308 VGS users and structural equation modeling (SEM) was employed to assess the hypotheses. Multi-model comparison technique was used to assess the explanatory power of EUGT.

The findings confirmed three significant types elements in determining VGS viewers' engagement, including gratifications (e.g. involvement), environmental cues (e.g. medium appeal) and user predispositions (e.g. pre-existing attitudes). The results revealed that emerging technologies provide potential opportunities for new motives and gratifications, and highlighted the significant of pre-existing attitudes as a mediator in the gratification-uses link.

This study is one of its kind in tackling the criticism on UGT of considering media users too rational or active. The study achieved this objective by considering environmental impacts on user behavior which is largely ignored in recent UGT studies. Also, by incorporating users pre-existing attitudes into UGT framework, this study conceptualized and empirically verified the higher explanatory power of EUGT through a novel multi-modal approach in VGS. Compared to other rival models, EUGS provides a more robust explanation of users' behavior. The findings contribute to the literature of UGT, VGS and users' engagement.

In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.

Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.

Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.

The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.

The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.

This study aims to provide a comprehensive analysis of various approaches to the residential property market evolution modelling and to examine the macroeconomic fundamentals that have shaped this market development in Ukraine in recent years.

The study uses a comprehensive data set encompassing relevant macroeconomic indicators and historical apartment prices. Multifactor linear regression (MLR) and ridge regression (RR) models are constructed to identify the impact of multiple predictors on apartment prices. Additionally, the ARIMAX model integrates time series analysis and external factors to enhance modelling and forecasting accuracy.

The investigation reveals that MLR and RR yield accurate predictions by considering a range of influential variables. The hybrid ARIMAX model further enhances predictive performance by fusing external indicators with time series analysis. These findings underscore the effectiveness of a multidimensional approach in capturing the complexity of housing price dynamics.

This research contributes to the real estate modelling and forecasting literature by providing an analysis of multiple linear regression, RR and ARIMAX models within the specific context of property price prediction in the turbulent Ukrainian real estate market. This comprehensive analysis not only offers insights into the performance of these methodologies but also explores their adaptability and robustness in a market characterized by evolving dynamics, including the significant influence of external geopolitical factors.

Amidst the COVID-19 pandemic, education has shifted to online teaching and learning. Interactivity is a crucial tool used to make online education effective. This study empirically examines the role of interactivity in higher education and its influence on students' behavioral outcomes, specifically focusing on soft skills and personality upgradation.

A quasi-experimental research design was carried out for post-graduate students undergoing a business communication course from four major institutions. For analysis, t-test, confirmatory factor analysis (CFA) and partial least squares structural equation modeling (PLS-SEM) have been employed. Experimental research has established the causal relationship between interactivity, personality and soft skill upgradation (SSU).

It was found that the theoretical structural model has a rational model-fit validity. Resultantly, practitioners may use prior knowledge of virtual community (VC) members to enhance web interactivity, thereby increasing social identity and social bonds in a group for more meaningful and effective delivery of online courses.

The major limitations lie in its context-dependent nature, predominantly influenced by the pandemic-induced mandatory online learning. The study's cross-sectional design also inhibits its ability to assess goal-directed behaviors over time, necessitating further longitudinal research.

The study is one of the pioneering pieces of research that examines the role of pre-defined grouping and enhanced web interactivity in VCs in the context of online learning, especially during the COVID-19 pandemic. Integrating theories of web interactivity, social bond theory (SBT) and social identity theory (SIT) provides a novel understanding of cognitive and social influences that drive meaningful online discussions and their impacts on knowledge enhancement and personality development. Its findings have implications for the design of effective online learning environments and e-learning pedagogy, contributing to the growing domain of information and communication technology (ICT)-enabled education.

This study aims to examine the asymmetric effects of average temperature (TP) and rainfall (RF) on the Moroccan food security, measured by the food production index (FPI), using annual data from 1961 to 2020.

The study uses the Climate Change and Food Security Framework (CCFS) developed by the Food and Agriculture Organization (FAO) and employs the nonlinear auto-regressive distributed lag (NARDL) model and various econometric techniques to show the effects of climate variability in the short and long-term. It also examines if the impacts on Moroccan food security are asymmetric by analyzing the positive and negative partial sums of mean temperature and rainfall.

The study shows that RF has a long-term relationship with FPI, with increased RF leading to increased FPI and decreased RF leading to decreased FPI. FPI responds more strongly and persistently to a positive shock in RF than to an adverse shock. The study also identifies an asymmetric relationship between FPI and RF, with increased TP enhancing food output in the long run and a decrease reducing food production in the long run.

The current study could have some limitations. For instance, there are several other non-climate factors that might potentially impact food security. In particular, CO2 emissions which from the literature is a key variable that represent climate change impact on food security, was not included. The present research has not included those factors mainly because adding more variables to the model reduces the degree of freedom available to estimate the parameters, resulting in inaccurate results.

This paper contributes to the food security literature by utilizing the latest asymmetry methodology to decompose climate changes into their positive and negative trends and examining the contrasting impacts food production.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources.

Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms.

Research indicates that integral control parameters (K_i) affect outputs substantially more than proportional control parameters (K_p). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676.

This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.

Despite the wide use of quantitative assessment to identify the relationship between green logistics (GL) practices and the sustainability performance (SP) of firms, results of these studies are inconsistent. A lack of theoretical foundation has been cited as a potential reason for these contradictory findings. This study aims to explore the relationship between GL practices and SP qualitatively and to provide a theoretical foundation for this link.

Following a multi-methodology approach, the authors used the grounded theory method (GTM) to investigate perceived relationships through qualitative analysis and adopted the system thinking (ST) approach to identify causal relationships using causal loop diagrams (CLDs).

The authors identified different sustainability practices under three major categories: logistics capabilities, resource-related practices and people-related practices. This analysis showed the relationships among these practices are non-linear. Based on the results, the authors developed three propositions and introduced a theoretical foundation for the relationship between GL practices and SP.

Managerial personnel can use the theoretical foundation provided by this study when making decisions on GL practices adoption. This theoretical foundation suggests applying a holistic approach that can help optimize SP by selecting suitable practices. On the other hand, researchers can use a multi-methodology approach suggested by this study to explore complex social issues.

This study contributes to the knowledge from a methodology perspective as no previous studies have been conducted to identifying the relationship between GL practices and SP by combining GTM and ST approaches. This combination can be extended to build system dynamics models for sustainable logistics impacts bringing novelty to the research field of sustainable logistics.

The purpose of this research is to systematically scrutinize the influence of macroeconomic determinants on trade openness, through the lens of various trade theories, with a particular focus on the economies of the GIPSI countries – Greece, Ireland, Portugal, Spain and Italy.

This study investigates the macroeconomic factors influencing trade openness in the GIPSI economies from 1995 to 2020. Methods include stepwise regression (SR) for model selection, Pedroni panel cointegration test and panel regression results. The analysis uses advanced panel regressions, including FMOLS, Panel OLS and FEM. The long-term dynamics were tested using Pedroni cointegration, while Granger causality testing was used to examine the causal direction between the trade openness ratio and trade determinant.

The results show both long-term and short-term relationships between trade openness and (1) foreign direct investment, (2) labor force participation rate, (3) trade reserves and (4) trade balance. The researchers also detected unidirectional and bidirectional causality relationships between trade openness and these four factors. The study also revealed that trade reserves (TR) emerge as the most influential determinant of trade openness, and per capita income does not exhibit economic significance concerning the trade openness of GIPSI economies.

This research is conducted within the context of the GIPSI nations (Greece, Ireland, Portugal, Spain and Italy). As such, the outcomes may not be universally applicable to other economic systems due to the distinct institutional settings and governance structures across different economic groups. Future investigations may explore the relationship between trade openness and its determinants by incorporating different variables.

To the best of the authors' knowledge, this is the first study investigating the theory that suggested trade drivers drive the trade openness of GIPSI countries context. By focusing on GIPSI countries, the study offers a unique perspective on the dynamics of trade openness in economies that have experienced financial crises and stringent austerity measures.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.