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This paper aims to propose an approach for hate speech detection against politicians in Arabic community on social media (e.g. Youtube). In the literature, similar works have been presented for other languages such as English. However, to the best of the authors’ knowledge, not much work has been conducted in the Arabic language.

This approach uses both classical algorithms of classification and deep learning algorithms. For the classical algorithms, the authors use Gaussian NB (GNB), Logistic Regression (LR), Random Forest (RF), SGD Classifier (SGD) and Linear SVC (LSVC). For the deep learning classification, four different algorithms (convolutional neural network (CNN), multilayer perceptron (MLP), long- or short-term memory (LSTM) and bi-directional long- or short-term memory (Bi-LSTM) are applied. For extracting features, the authors use both Word2vec and FastText with their two implementations, namely, Skip Gram (SG) and Continuous Bag of Word (CBOW).

Simulation results demonstrate the best performance of LSVC, BiLSTM and MLP achieving an accuracy up to 91%, when it is associated to SG model. The results are also shown that the classification that has been done on balanced corpus are more accurate than those done on unbalanced corpus.

The principal originality of this paper is to construct a new hate speech corpus (Arabic_fr_en) which was annotated by three different annotators. This corpus contains the three languages used by Arabic people being Arabic, French and English. For Arabic, the corpus contains both script Arabic and Arabizi (i.e. Arabic words written with Latin letters). Another originality is to rely on both shallow and deep leaning classification by using different model for extraction features such as Word2vec and FastText with their two implementation SG and CBOW.

Polymer substrates with micron size roughness features have been found to play an important role in the mechanical performance of thin functional films which are used extensively in stretchable and flexible micro electromechanical systems. The purpose of this study is to report the stretchability and flexibility limits of micro size silver nano platelet films on a soft polymer substrate having two different orientations of micro grating with respect to the applied load.

Parallel and perpendicular micro gratings on the surface of a soft polymer substrate polydimethylsiloxane were patterned using a carefully machined master aluminum block and thin aluminum foils. Silver nano platelet-based films were rod coated on the substrate surface containing the micro gratings. These films were dried in ambient air and were tested for their stretchability and flexibility limits using homemade tools. Finite element modeling has also been performed and was found to support the experimental observations.

Experiments indicate that stretchability of silver nano platelet-based thin films tends to increase when the grating orientation remains parallel to the axis of the applied load, while its flexibility improves when the orientation becomes perpendicular to the loading axis.

The effect of grating orientation with respect to the applied load was investigated. The experiments show that micro grating roughness features are capable of enhancing the mechanical performance of nano platelet-based silver films on a soft polymer substrate and can be used in various stretchable and flexible micro electro mechanical device applications.

This study aims to investigate two important research questions. First, this research examines the impact of real earnings management on investment inefficiency of the non-financial listed firms in Pakistan. Second, this research further explores the moderating role of short-term debt on the nexus between real earnings management and investment inefficiency. This study attempts to highlight an important research problem i.e. the jinx of real earnings management from the context of an emerging economy.

This study employs the data from non-financial listed firms in Pakistan over the period from 2008 to 2018. The study uses panel data methodologies with firm and year fixed-effects to examine the proposed hypotheses. The results are robust to the use of sensitivity analysis, different estimation techniques and endogeneity issues (using two-stage least squares (2SLS) and generalized method of moments (GMM) techniques).

The results of the research are twofold. First, consistent with the theoretical arguments, the findings reveal that real earnings management increases investment inefficiency and results in over-investments by the firms. Second, short-term debt attenuates the relationship between real earnings management and investment inefficiency. It implies that a higher level of short-term debt weakens the adverse effects of real earnings management on the investment efficiency of the firm.

This study offers original findings on the issues pertaining to the quality of accounting and financial reporting in an emerging economy like Pakistan, where the implementation of regulations is weak in the corporate world and management frequently exploits shareholders' wealth for the short-term benefits.

Academic scholars have tested students’ entrepreneurial intention (SEI) through the Theory of Planned Behavior (TPB) and Social Cognitive Theory (SCT). Still, the link between entrepreneurial intention and career is missing in previous studies. An extensive literature review developed the rationale that existing theories in the entrepreneurial discipline have limitations in linking entrepreneurial intention with career. This research is conducted to develop a comprehensive model for the relationship between entrepreneurial university support, student entrepreneurial intention, behavior and career. Stimulus-Organism-Behavior-Consequence (SOBC) paradigm from organizational behavior research is borrowed to entrepreneurship literature.

The cross-sectional data was collected from Pakistani university students enrolled in business incubators. A sample of 100 responses was tested with a partial least square–structural equation modelling approach.

The study established that by the underpinning of SOBC, entrepreneurial university support influences students’ entrepreneurial intention. It is also found that the students’ entrepreneurial intention strongly influences their entrepreneurial behavior, leading them to entrepreneurship careers.

The policies influencing students’ entrepreneurial intention and behavior can be developed using the SOBC paradigm. Higher education institutions can improve students’ entrepreneurial intentions and behavior to lead them to entrepreneurship careers.

This research introduced the SOBC paradigm in entrepreneurial intention and behavior literature. SOBC underpinning explored a new dimension of entrepreneurial intention and behavior literature.

The purpose of this paper is to evaluate the level of job satisfaction among faculty members of the health sciences program at a Saudi higher education institution; and predict the influence of various factors on overall job satisfaction. However, this study is quite different since it intended to evaluate the level of job satisfaction of faculty members using a self-structured questionnaire and ascertained the various factors influencing the overall job satisfaction of Saudi academics.

An exploratory study design was adopted and Academic Job Satisfaction (AJS) survey was administered to 943 faculty members of the health sciences program through an online system. A total of 850 faculty members responded to 47 items and one global rating item (overall job satisfaction) using a five-point ordinal scale.

The level of job satisfaction of health sciences’ faculty members on all dimensions of AJS is observed to be high (>3.5) except salary, which is shown as medium (2.5–3.49). Regression analysis indicates the factors other than Imam Abdulrahman Bin Faisal University (IAU) administrative policies and interpersonal relationships are significant predictors of overall job satisfaction; and salary is the most significant predictor of overall job satisfaction among health sciences’ faculty members.

This study adds a value to the existing literature by exploring the factors influencing job satisfaction of health sciences’ faculty members working in Saudi Universities. This would aid policy makers to focus on these factors, thereby improve and maintain job satisfaction among healthcare academics.

The purpose of this study is to extend the existing literature on knowledge management, which generally focuses on knowledge sharing. The model of this article explains how knowledge creation and team performance can be increased through the integration of social and technological factors

To empirically test the model, multi-wave and multi-source data were collected from 80 teams whose members use social media as a tool for communication and interaction.

The analysis results provide insights into some interesting findings. The results show transactive memory system (TMS) as an important factor that can significantly contribute to knowledge creation in teams. Especially, the TMS strengthens the significant positive effect of enterprise social media (ESM) and insignificant positive effect of knowledge complementarity on knowledge creation. Furthermore, knowledge creation is found to be a significant predictor of team performance

Much of the knowledge management literature focuses on the ways to increase the quantity of accessible knowledge to organization members. Such knowledge management studies are more relevant to knowledge exchange among individual employees, teams and organizations. However, this study takes a nuanced approach to explore how knowledge creation can be increased in teams by implementing a knowledge integration mechanism. A general model of knowledge creation is proposed, but the strength of this model lies in the moderating effect of TMS which strengthens the effect of knowledge complementarity and ESM on knowledge creation in teams which eventually increases team performance.

The study has two aims: first, it aimed to investigate the impact of contextual factors (such as information quality, service quality, system quality, trust in applications (app) and COVID-19 health anxiety) on the intention to use the Mobile Payment (MP) app, and subsequently, the actual use of the app. Second, the aim of this study is whether the COVID-19 threat has a moderating influence on the relationship between customers' intent to use MP app and the actual use of MP app.

The data are collected through an online survey from 341 Mobile Banking (MB) app users from Pakistan to empirically analyze the relationship between service quality, system quality, information quality, trust in the app, COVID-19 health anxiety and COVID-19 threat, intentions to use MB-app and actual use of MB-app.

The empirical analysis of the data collected from MB-app users from Pakistan shows that service quality, system quality, information quality, trust in the app and COVID-19 health anxiety positively related to intentions to use MB-app, consequently affect the actual use of MB-app. Furthermore, the results demonstrate that the COVID-19 threat positively moderates the relationship between intentions to use MB-app and actual use of MB-app.

Although, prior research established a positive impact of mobile apps on customer service and consumer satisfaction. Yet, it is not clear which factors influence customers to adopt MB-app. This study contributes to the research on MB-apps based on adaptive structuration theory and examines the technological factors and contextual factors that collectively explain when and how individuals decide to adopt MB-app.

This study aims to examine the impacts of climate change (CC), measured average annual rainfall, average annual temperature and carbon dioxide (CO₂e) on cereal production (CPD) in Bangladesh by using the annual dataset from 1988–2014, with the incorporation of cereal cropped area (CCA), financial development (FD), energy consumption (EC) and rural labor force as important determinants of CPD.

This study used an auto-regressive distributive lag (ARDL) model and several econometric approaches to validate the long- and short-term cointegration and the causality directions, respectively, of the scrutinized variables.

Results of the bounds testing approach confirmed the stable long-term connections among the underlying variables. The estimates of the ARDL model indicated that rainfall improves CPD in the short-and long-term. However, CO₂e has a significantly negative impact on CPD both in the short-and long-term. Results further showed that temperature has an adverse effect on CPD in the short-term. Among other determinants, CCA, FD and EC have significantly positive impacts on CPD in both cases. The outcomes of Granger causality indicated that a significant two-way causal association is running from all variables to CPD except temperature and rainfall. The connection between CPD and temperature is unidirectional, showing that CPD is influenced by temperature. All other variables also have a valid and significant causal link among each other. Additionally, the findings of variance decomposition suggest that results are robust, and all these factors have a significant influence on CPD in Bangladesh.

These findings have important policy implications for Bangladesh and other developing countries. For instance, introduce improved cereal crop varieties, increase CCA and familiarizes agricultural credits through formal institutions on relaxed conditions and on low-interest rates could reduce the CPD’s vulnerability to climate shocks.

To the best of the authors’ knowledge, this study is the first attempt to examine the short- and long-term impacts of CC on CPD in Bangladesh over 1988–2014. The authors used various econometrics techniques, including the ARDL approach, the Granger causality test based on the vector error correction model framework and the variance decomposition method.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

This article investigates the long-run impact of remittance inflows on agricultural productivity (AGP) in emerging Asian economies (Bangladesh, Sri Lanka, Malaysia, India, Nepal, Philippines, Pakistan, and Vietnam), employing a panel dataset from 2000 to 2018.

This study initially applies cross-sectional dependence (CSD), second-generation unit root, Pedroni, and Westerlund panel co-integration techniques. Next, it uses the augmented mean group (AMG) and common correlated effect mean group (CCEMG) methods to investigate the long-term impact of remittance inflows on AGP while controlling for several other important determinants of agricultural growth, such as cultivated area, fertilizers, temperature change, credit, and labor force.

The empirical findings are as follows: The results first revealed the existence of CSD and long-term co-integration between AGP and its determinants. Second, remittance inflows significantly boosted AGP, indicating that remittance inflows played a crucial role in improving AGP. Third, global warming (changes in temperature) negatively impacts AGP. Finally, additional critical elements, for instance, cultivated area, fertilizers, credit, and labor force, positively affect AGP.

This study suggests that policymakers of emerging Asian economies should develop an exclusive remittance-receiving system and introduce remittance investment products to utilize foreign funds and mitigate agricultural production risks effectively.

This is the first empirical examination of the long-term impact of remittance flows on agricultural output in emerging Asian economies. This study utilized robust estimation methods for panel data sets, such as the Pedroni, Westerlund, AMG, and CCEMG tests.