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Social media (SM) networks offer a golden opportunity for firms that particularly engage in international activities to set up sustainable customer relationships and improve competitiveness. The purpose of this study is to examine the influence of SM adoption on the export intensity (EI) of firms listed on Borsa Istanbul (BIST) for the years 2010–2020. The authors use social media index (SMI) to measure SM adoption and firm size (FSize) as a moderator on exploring the interaction of SM and EI.

Using a sample of 150 firms listed on the BIST Industrials Index, this study explores how the adoption of SM affects EI by using panel data analysis over the period of 2010–2020.

The results indicate that the SMI has a positive and significant effect on the EI. FSize positively moderates the interaction of SMI and EI, indicating that large firms benefit more from the SM in increasing export performance. The findings reflect high potential of EI improvement through adopting right SM policies in emerging markets.

The sample covers only public companies listed on the BIST Industrials Index. Future studies may extend the coverage and include multiple emerging markets to draw generalized results for the export-oriented firms. This research also analyzes solely four SM networks, i.e. Facebook, Instagram, Twitter and YouTube. However, there are many other SM networks that firms use in online marketing in foreign markets. Finally, this research did not discuss the potential factors that could influence the use of SM in emerging market firms.

This study denotes the significant role of SM adoption on the EI of firms in an emerging market setting from the perspective of resource-based view. It presents an insightful approach in understanding the mission played by SM networks in enhancing the EI of Turkish firms. Policymakers may use the findings to develop public support programs to promote the adoption and implementation of the SM among exporting firms in emerging markets.

The study provides evidence on the effects of SM adoption on the EI from the perspective of emerging countries. It also helps to gain a deeper understanding of how different SM platforms contribute to the internationalization of firms.

Although the association between sustainability assurance (SA) quality and firm value has been examined in previous studies, the moderating relationship is novel in this study and highlights the effect of corporate environmental sustainability performance (CESP) on the relationship between SA quality and firm value. This study aims to examine whether such an effect is strengthened or weakened by eco-efficiency, as measured by ISO 14001 certification, aggregate CESP score and each individual dimension of CESP (emission reduction [ER], resource reduction [RR] and product innovation [PI]).

The sample includes 40 companies in Euronext Paris with the largest market capitalisations (the Cotation Assistée en Continu 40 [CAC 40] index) from 2010 to 2020. The authors apply the feasible generalised least squares regression technique to estimate all the regression models. Because observed associations may be biased by reverse causation or self-selection, the authors use the instrumental variable approach and Heckman two-stage estimation.

The results show that SA quality had a positive and significant effect on firm value. Second, the authors demonstrate that CESP, as assessed by ISO 14001 certification, has a stronger interaction with assurance quality and acting as a moderator variable. Using the ASSET4 scores, an alternative proxy for CESP, the authors find inconsistent evidence regarding the impact of CESP attributes. The CESP and ER scores are homogeneous and have a positive effect on firm value. However, the PI and RR CESP attributes are not homogenous and do not have the same interactive effect on firm value. The results are robust to the use of an instrumental variable approach and the Heckman two-stage estimation procedure.

Policy implications: Regulators may be interested in the findings when considering current and future assurance requirements for sustainability reporting, and shareholders when considering SA as an investment choice criterion. The insights into and enhanced understanding of the incentives for obtaining high SA quality can help policymakers develop effective policies and initiatives for SA. Considering the possible improvements in sustainability performance when obtaining a high level of sustainability verification, governments need to consider mandating SA.

Firms receive clear confirmation of the importance of investing in SA quality. Financial markets do not evaluate SA dichotomously but reward companies with higher SA quality because of the greater credibility it provides. Firms should allocate a significant percentage of their annual budgets and other relevant resources to environmental training and development programmes to improve and maintain environmental performance. If they care about environmental issues, they must announce this by issuing sustainability reports and seeking assurance of the information disclosed. High-quality assurance not only has a significant effect on investors’ investment reliability judgements but also the perceived credibility of environmental performance fully moderates the effect of assurance on these judgements.

This study has social implications; the authors find that the French market rewards firms that provide a high-quality assurance to guarantee the integrity of their sustainability reports. Therefore, by incorporating environmental sustainability into their financial goals, a better assurance ultimately will urge firms to move from green washing to strategic goals, which is beneficial for society. Further, firms that focus on sustainability as part of their business strategy may attract employees who engage in green behaviours at work and create a friendlier and productive environment because it gives meaning to the work they do and keeps them engaged to the level needed to perform their jobs capably.

This study contributes to the literature by re-examining the relationship between SA quality and firm value. It also provides new evidence on the moderating effect of CESP on the SA quality–firm value nexus. Specifically, it explores the joint effect of credibility and eco-efficiency on market confidence in sustainability information.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

This paper aims to explore the relationship between corporate corruption risk and environmental, social and governance (ESG) reporting and if this relationship is moderated by business ethics.

Data from a sample of 347 European firms selected from the ESG Index between 2010 and 2020 were used to test the model using panel data and multiple regressions. This paper considered the feasible generalized least squares estimation for linear panel data models. A multiple regression model is used to analyze the moderating effect of business ethics on the association between corporate corruption risk and ESG reporting. For robustness analyses, the authors included the alternative measure of the dependent variable, and they applied the simultaneous equation model for the endogeneity test.

The empirical results reveal a negative relationship between corporate corruption risk and ESG reporting. Furthermore, the findings suggest that business ethics positively moderate the relationship between corporate corruption risk and ESG reporting.

This paper presents an enormous contribution to the various economic agents involved in the company. The results could attract the attention of socially responsible investors and, above all, corporate citizens. Moreover, the managers of corrupt companies could take into account the results of this study by being more committed to an optimized transparency strategy on ESG reporting.

To the best of the authors’ knowledge, this is the first study to investigate the moderating role of business ethics on the relationship between corporate corruption risk and ESG reporting in the European context. It is also the first study documenting that business ethics reinforce the relationship between firm corruption and nonfinancial information transparency. This study fills a research gap as it expands the existing literature, which generally focuses on the impact of corporate corruption on ESG reporting.

Validation of the Teachers' Sense of Efficacy Scale (TSES) for use with teachers in Macao (SAR) was undertaken to determine its usefulness as a measure of teacher self-efficacy for inclusive education. This paper discusses the results found by analyzing various versions of the TSES and TSES-C in a Chinese format with 200 pre-service teachers in Macao (SAR). Psychometric analyses were undertaken to investigate the validity of the existing scales and the three and two factor solutions. The results indicated a preferred 9-item version that produced improved factor loadings and reliabilities. The use of a relatively quick and short scale to measure such a complex phenomenon as teacher self-efficacy is discussed. Issues are raised regarding generalizability of scales and the impact of culture, demographics, and edifying issues that may impact on the usefulness of such scales.

An integrated intuitionistic fuzzy (IF) modelling-based framework for examining the performance analysis of a packaging unit (PU) in three different stages has been proposed.

For the series and parallel configuration of PU, a mathematical model based on the intuitionistic fuzzy Lambda–Tau (IFLT) approach was developed in order to calculate various reliability parameters at various spreads. For determining membership and non-membership function-based reliability parameters for the top event, AND/OR gate transitions expression was employed.

For 15%–30% spread, unit’s availability for the membership function falls by 0.006442%, and it falls even more by 0.014907% with an increase in spread from 30% to 45%. In contrast, for 15%–30% spread, the availability of non-membership function-based systems reduces by 0.007491% and further diminishes. Risk analysis has presented applying an emerging approach called intuitionistic fuzzy failure mode and effect analysis (IFFMEA). For each of the stated failure causes, the output values of the intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED)-based IFFMEA have been tabulated. Failure causes like HP1, MT6, FB9, EL16, DR23, GR27, categorized under subsystems, namely hopper, motor, fluidized bed dryer, distributor, grader and bin, respectively, with corresponding IFFMEA output scores 1.0975, 1.0190, 0.8543, 1.0228, 0.9026, 1.0021, were the most critical one to contribute in the system’s failure.

The limitation of the proposed framework lies in the fact that the results obtained for both reliability and risk aspects mainly depend on the correctness of raw data provided by the experts. Also, an approximate model of PU is obtained from plant experts to carry performance analysis, and hence more attention is required in constructing the model. Under IFLT, reliability parameters of PU have been calculated at various spreads to study and analyse the failure behaviour of the unit for both membership and non-membership function in the IFS of [0.6,0.8]. For both membership- and non-membership-based results, availability of the considered system shows decreasing trend. To improve the performance of the considered system, risk assessment was carried using IFFMEA technique, ranking all the critical failure causes against IFHWED score value, on which more attention should be paid so as to avoid sudden failure of unit.

The livelihood of millions of farmers and workers depends on sugar industries. So perpetual running of these industries is very important from this viewpoint. On the basis of findings of reliability parameters, the maintenance manager could frame a correct maintenance policy for long-run availability of the sugar mills. This long-run availability will generate revenue, which, in turn, will ensure the livelihood of the farmers.

Mathematical modelling of the considered unit has been done applying basic expressions of AND/OR gate. IFTOPSIS approach has been implemented for ranking result comparison obtained under IFFMEA approach. Eventually, sensitivity analysis was also presented to demonstrate the stability of ranking of failure causes of PU.

In recent decades, emerging market multinational enterprises (EMNEs) have predominantly adopted a big step internationalization strategy to expand their business overseas. This study aims to examine the effect of big step internationalization on the speed of subsequent foreign direct investment (FDI) expansion for EMNEs. The authors also investigate the potential boundary conditions.

The authors use the random effects generalized least squares (GLS) regression following a hierarchical approach to analyze the panel data set conducted by a sample of publicly listed Chinese firms from 2001 to 2012.

The findings indicate that implementing big step internationalization in the initial stages accelerates the speed of subsequent FDI expansion. Notably, the authors find that this effect is more pronounced for firms that opt for acquisitions as the entry mode in their first big step internationalization and possess a board of directors with strong political connections to their home country’s government. In contrast, the board of director’s international experience negatively moderates this effect.

This study provides insights into our scholarly and practical understanding of EMNEs’ big step internationalization and subsequent FDI expansion speed, which offers important implications for firms’ decision-makers and policymakers.

This study extends the internationalization theory, broadens the international business literature on the consequences of big step internationalization and deepens the theoretical and practical understanding of foreign expansion strategies in EMNEs.

This article aims to examine the related effects between out-of-class activities, mediated by in-class activities, on the perception of course and teacher performance in a flipped classroom institutionalized setting.

The authors argue that institutionalized out-of-class (content) and in-class (learning) activities positively impact course and teaching quality perception. This study used a sample of 978 responses from MBA students to conduct a path model analysis to test four hypotheses developed from literature from flipped classroom proposing the positive relationship of out-of-class activities in in-class activities and its influence on the course and teaching performance.

The findings reported that out-class and in-class activities and educator performance influenced course performance perception. In-class activities mediated the out-of-class activities’ impact and directly impacted educator’s and course performance. Educator performance is positively correlated with course performance. Overall, executives have a positive perception on institutionalized flipped classroom for MBA courses as an effective provision form. The flipped classroom is able to mobilize their experiences and enrich learners’ educational experience.

Only one unit of the higher education organization was studied, and the authors do not consider indirect effects of the environment on variable’s relationships nor the indirect effects whose would be a suggested for future studies.

The present study provides new insights on flipped classroom. This study evidenced that flipped classroom planned and standardized in an institutional level positively impacts the outcome within the context of executive education.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

The purpose of this paper aims to design a robust wind turbine emulator (WTE) based on a three-phase induction motor (3PIM).

The 3PIM is driven by a soft voltage source inverter (VSI) controlled by a specific space vector modulation. By adjusting the appropriate vector sequence selection, the desired VSI output voltage allows a real wind turbine speed emulation in the laboratory, taking into account the wind profile, static and dynamic behaviors and parametric variations for theoretical and then experimental analysis. A Mexican hat profile and a sinusoidal profile are therefore used as the wind speed system input to highlight the electrical, mechanical and electromagnetic system response.

The simulation results, based on relative error data, show that the proposed reactive power control method effectively estimates the flux and the rotor time constant, thus ensuring an accurate trajectory tracking of the wind speed for the wind emulation application.

The proposed architecture achieves its results through the use of mathematical theory and WTE topology combine with an online adaptive estimator and Lyapunov stability adaptation control methods. These approaches are particularly relevant for low-cost or low-power alternative current (AC) motor drives in the field of renewable energy emulation. It has the advantage of eliminating the need for expensive and unreliable position transducers, thereby increasing the emulator drive life. A comparative analysis was also carried out to highlight the online adaptive estimator fast response time and accuracy.