The number of comments/reviews for movies is enormous and cannot be processed manually. Therefore, machine learning techniques are used to efficiently process the user’s opinion. This research work proposes a deep neural network with seven layers for movie reviews’ sentiment analysis. The model consists of an input layer called the embedding layer, which represents the dataset as a sequence of numbers called vectors, and two consecutive layers of 1D-CNN for extracting features. A global max-pooling layer is used to reduce dimensions. (...) A dense layer for classification and a dropout layer are also used to reduce overfitting and improve generalization error in the neural network. A fully connected layer is the last layer to predict between two classes. Two movie review datasets are used and widely accepted by the research community. The first dataset contains 25,000 samples, half positive and half negative, whereas the second dataset contains 50,000 specimens of movie reviews. Our neural network model performs sentiment classification among positive and negative movie reviews called binary classification. The model achieves 92% accuracy on both datasets, which is more efficient than traditional machine learning models. (shrink)

Women entrepreneurs innovate, initiate, engage, and run business enterprises to contribute the domestic development. Women entrepreneurs think and start taking risks of operating enterprises and combine various factors involved in production to deal with the uncertain business environment. Entrepreneurship and technological innovation play a crucial role in developing the economy by creating job opportunities, improving skills, and executing new ideas. It has a significant impact on the income of the household. The study focused on investigating the role of women’s entrepreneurship (...) and innovation technologies in contributing to household income in the challenging situation of the pandemic COVID-19. The paper emphasized identifying the determinants of female entrepreneurial contribution toward household income. This study collected data from selected rural and urban areas of district Faisalabad through a self-administered questionnaire. Investigators interviewed female entrepreneurs and chose them through the snowball sampling technique from a population of purposively selected female-run businesses. Interviews were conducted with women entrepreneurs to gather relevant information for the survey investigation at their workplaces and home. The effects of various factors, including age, education, family size, income from other sources, time allocated to entrepreneurial activity, firm size, and location were estimated empirically using an ordered logit model. The study findings exhibited a positive and significant role of respondents’ education, family size, time allocated to entrepreneurial activities, and firm size. The survey outcomes also indicated that the contribution of entrepreneurial income to household income in the rural areas is significantly higher than that in urban areas. This study signifies that regulations against gender discrimination in public and private institutions are helpful. Besides, encouraging an environment for entrepreneurial culture among women in the country would increase family income. The study’s findings and policy implications directly link to Sustainable Development Goal 5 of Gender Equality and SDG 8 related to decent work and economic growth. (shrink)

In this study, we implemented a new numerical method known as the Chebyshev Pseudospectral method for solving nonlinear delay differential equations having fractional order. The fractional derivative is defined in Caputo manner. The proposed method is simple, effective, and straightforward as compared to other numerical techniques. To check the validity and accuracy of the proposed method, some illustrative examples are solved by using the present scenario. The obtained results have confirmed the greater accuracy than the modified Laguerre wavelet method, the (...) Chebyshev wavelet method, and the modified wavelet-based algorithm. Moreover, based on the novelty and scientific importance, the present method can be extended to solve other nonlinear fractional-order delay differential equations. (shrink)

Research suggests that ethical leadership influences employees’ behavior and organizational functioning. Thus, the purpose of this study is to investigate the role of ethical leadership in the improvement of employees’ innovative performance. Specifically, this research developed and tested a framework about the intermediating mechanism of social capital in the relationship between ethical leadership and employees’ innovative performance. The current study integrated assumptions of social learning and social exchange theories, which postulate that the leadership has a direct effect on the employees’ (...) conduct, principally through role modeling and the reciprocal landscape. Data were collected through in-person administered questionnaire-based survey from 297 employees working in the manufacturing sectors of Pakistan. The conceptual model of our research was analyzed applying Structural Equation Modeling (SEM). The results exhibited that the ethical leadership plays an important role in fostering employees’ innovative performance. The present study also found that social capital plays an intermediating role between the ethical leadership and employees’ innovative performance relationship. The current study contributed to the leadership and innovation literature and provided insights in examining the ethical leadership-employees’ innovative performance relationship through the intermediating mechanism of social capital. (shrink)

Islamic financial institutions (IFIs) are emerging as prominent players in the financial world and are increasingly known for their conservative socially responsible investment (SRI). Being the Shari'a regulators and monitors of IFIs, the Shari'a departments are expected to implement the Islamic perspective of SRI – drawn from Shari'a principles – in their respective institutions. The purpose of this paper is to develop an SRI framework applicable to IFIs and other Shari'a compliant entities and assess its applicability within Shari'a departments of (...) two Islamic banks. This paper involves cross-case analysis based on interviews with Shari'a department officials in two settings differentiated by their respective independence. The proposed framework consists of required, expected and desired SRI aspects as applicable to IFIs. The findings reveal that the required aspects are uniformly observed by the two cases. There are, however, variations when it comes to observing the expected and desired ethical SRI aspects that may be driven by the independence of the Shari'a boards. This inconsistency and non-adherence of expected and desired aspects may lead to reputational risks in the long run. (shrink)

Islamic Financial Institutions have recently witnessed remarkable growth driven by their holistic business model. The key differentiator of IFIs is their Shari’a-based business proposition which often requires some financial sacrifices, e.g. being ethical, responsible and philanthropic. It also requires them to refrain from investments in tobacco, alcohol, pornography or earning interest. For IFIs’ sponsors and managers, however, the key motivational factor for entering the Islamic financial market is not the achievement of Shari’a objectives through the holistic business model, but rather (...) the urge to tap this highly profitable market where customers are inclined to pay a premium for Shari’a compliance. In order for IFIs to be accepted by the market, their financial instruments need to be approved by Shari’a scholars, known for their integrity and expertise in Shari’a. One can therefore expect potential tensions between IFIs’ managers and Shari’a scholars. The purpose of this research is to probe the hidden struggle between managers and Shari’a scholars in pursuit of their respective objectives. The study investigates this phenomenon using grounded theory as a methodological framework based on data collected from three IFIs from two countries. The findings reveal that Shari’a scholars and managers of IFIs have divergent objectives, which creates incongruence of objectives at the strategic level. The findings illustrate the tension and latent struggle for Shari’a compliance, which has been termed as ‘Fatwa Repositioning’ resulting in four possible consequences: deep, reasonable, minimum and superficial Shari’a compliance. Fatwa Repositioning is the core category of this study, which exhibits how managers and Shari’a scholars struggle to position the Shari’a compliance of their institutions so as to best serve their respective objectives. Interestingly, Shari’a scholars are seemingly not always in control of what they are supposed to be controlling, i.e. Shari’a compliance. (shrink)

Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “_port performance index (PPI)_”, to determine the overall performance and utilization of inland waterway ports based on six criteria,_ port facility, port availability, port economics, port service, port connectivity, and port environment_. Unlike existing literature, which mainly ranks ports based on quantitative factors, this study utilizes a Bayesian (...) Network (BN) model that focuses on both quantitative and qualitative factors to rank a port. The assessment of inland waterway port performance is further analyzed based on different advanced techniques such as sensitivity analysis and belief propagation. Insights drawn from the study show that all the six criteria are necessary to predict PPI. The study also showed that port service has the highest impact while port economics has the lowest impact among the six criteria on PPI for inland waterway ports. (shrink)

Efficient identification of influential nodes is one of the essential aspects in the field of complex networks, which has excellent theoretical and practical significance in the real world. A valuable number of approaches have been developed and deployed in these areas where just a few have used centrality measures along with their concerning deficiencies and limitations in their studies. Therefore, to resolve these challenging issues, we propose a novel effective distance-based centrality algorithm for the identification of influential nodes in concerning (...) networks. EDBC algorithm comprises factors such as the power of K-shell, degree nodes, effective distance, and numerous levels of neighbor’s influence or neighborhood potential. The performance of the proposed algorithm is evaluated on nine real-world networks, where a susceptible infected recovered epidemic model is employed to examine the spreading dynamics of each node. Simulation results demonstrate that the proposed algorithm outperforms the existing techniques such as eigenvector, betweenness, closeness centralities, hyperlink-induced topic search, H-index, K-shell, page rank, profit leader, and gravity over a valuable margin. (shrink)

The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron, recurrent neural network, and autoregressive integrated moving average on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock (...) market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA-MLP and ARIMA-RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root-mean-square error and mean absolute error. Apart from this, ARIMA-MLP outperformed the ARIMA-RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA-RNN beat the ARIMA-MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time-series forecasting. (shrink)

The use of slang, abusive, and offensive language has become common practice on social media. Even though social media companies have censorship polices for slang, abusive, vulgar, and offensive language, due to limited resources and research in the automatic detection of abusive language mechanisms other than English, this condemnable act is still practiced. This study proposes USAD, a lexicon-based intelligent framework to detect abusive and slang words in Perso-Arabic-scripted Urdu Tweets. Furthermore, due to the nonavailability of the standard dataset, we (...) also design and annotate a dataset of abusive, offensive, and slang word Perso-Arabic-scripted Urdu as our second significant contribution for future research. The results show that our proposed USAD model can identify 72.6% correctly as abusive or nonabusive Tweet. Additionally, we have also identified some key factors that can help the researchers improve their abusive language detection models. (shrink)

Proposals to release genetically engineered organisms in the wild raise complex ethical issues related to their safe and equitable implementation. While there is broad agreement that community and public engagement is vital to decision‐making in this context, more discussion is needed about who should be engaged in such activities and in what ways. This article identifies Indigenous peoples as key stakeholders in decisions about gene‐editing in the wild and argues that engagement activities need not only include Indigenous peoples but also (...) be designed, conducted, and analyzed in ways that confront longstanding power imbalances that dismiss Indigenous expertise. We offer specific recommendations to guide deliberative activities to not only be inclusive of Indigenous peoples but also to empower their diverse, situated knowledges. We call on those committed to the inclusive design of broad public deliberation to pursue strategies that shift dominant power dynamics to include Indigenous communities in more meaningful ways. (shrink)

Given that personal source of information is preferred by the customers over company-generated marketing communications, promoting advocacy behavior among customers is of much importance for every organization. Literature suggests that an organization's corporate social responsibility (CSR) activities significantly influence individual behavior. However, the advocacy behavior of customers (ADB), from a CSR perspective, did not receive due attention. To address this literature gap, the current study attempts to explore the relationship among CSR and ADB with the intervening role of brand admiration (...) (BA). To collect the data, customers of a large fast-food service organization in Pakistan were approached. The proposed relationships were tested through the structural equation modeling (SEM) technique. The statistical evidence verifies a positive link between CSR and ADB, while BA mediated this relationship. These findings will be supportive to the fast-food business to realize the critical role of CSR for a meaningful customer-company relationship in the face of competition. (shrink)

There are two types of stock price manipulation examined in the theoretical literature: insider trading, which involves private information that is true and the public spreading of fraudulent false information. While there is a large empirical literature on insider trading, this is the first empirical article to examine the impact of false, fraudulent public information on stock prices and trading volume. We find that such false information, even after being denied by a credible source such as the SEC, generates both (...) abnormal returns and abnormal trading volume. We also find that the effects of the false information on security returns and volume can be persistent for at least 2 weeks. In addition, we show that perpetrators of false news attacks can make potentially large profits from such market manipulations. (shrink)

Computer vision and human–computer interaction are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object’s class. The primary goals of this research were to investigate (...) and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context, PASCAL VOC, and Kitti. We evaluated our system’s accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects. (shrink)

The purpose of this article is to look at how migration and culture interact to shape the migration landscape in Southeast Asian countries. Within the scope of migration study, there has been a lack of attention paid to the importance of culture. Scholars may have lost sight of the importance of culture due to a sustained and continuous concentration on socioeconomic concerns. The research claims that one of the aspects that influences migration decision-making is culture. To back up our claim, (...) we performed qualitative research in Malaysia, Thailand, and Brunei. Through the lens of acculturation, enculturation, and deculturation, the study highlights a vital problem of whether migration influences culture or the other way around or whether migration itself becomes a part of a culture. (shrink)

Based on the Lotka–Volterra system, a pest-natural enemy model with nonlinear feedback control as well as nonlinear action threshold is introduced. The model characterizes the implementation of comprehensive prevention and control measures when the pest density reaches the nonlinear action threshold level depending on the pest density and its change rate. The mortality rate of the pest is a saturation function that strictly depends on their density while the release of natural enemies is also a nonlinear pulse term depending on (...) the density of real-time natural enemies. The exact impulsive and phase sets are given. The definition and properties of the Poincaré map corresponding to the pulse points on the phase set are provided. We investigate the existence and stability of boundary and interior order-1 periodic solution. The theoretical analysis developed in the present paper combined with nonlinear controlling measures as well as nonlinear action threshold methods and techniques laid the foundation for the establishment and analysis of other state-dependent feedback control models. (shrink)

With the advancement of scientific collaboration in the 20th century, researchers started collaborating in many research areas. Researchers and scientists no longer remain solitary individuals; instead, they collaborate to advance fundamental understandings of research topics. Various bibliometric methods are used to quantify the scientific collaboration among researchers and scientific communities. Among these different bibliometric methods, the co-authorship method is one of the most verifiable methods to quantify or analyze scientific collaboration. In this research, the initial study has been conducted to (...) analyze interdisciplinary research activities in the computer science domain. The ACM has classified the computer science fields. We selected the Journal of Universal Computer Science for experimentation purposes. The J.UCS is the first Journal of Computer Science that addresses a complete ACM topic. Using J.UCS data, the co-authorship network of the researcher up to the 2nd level was developed. Then the co-authorship network was analyzed to find interdisciplinary among scientific communities. Additionally, the results are also visualized to comprehend the interdisciplinary among the ACM categories. A whole working web-based system has been developed, and a forced directed graph technique has been implemented to understand IDR trends in ACM categories. Finally, the IDR values between the categories are computed to quantify the collaboration trends among the ACM categories. It was found that “Artificial Intelligence” and “Information Storage and Retrieval”, “Natural Language Processing and Information Storage and Retrieval”, and “Human-Computer Interface” and “Database Applications” were found the most overlapping areas by acquiring an IDR score of 0.879, 0.711, and 0.663, respectively. (shrink)

The adoption of mobile payment and mobile banking is low in several countries, despite its associated benefits. The present study examines the impact of Pakistani consumers’ financial skills and digital literacy on their intention to adopt m-payment/m-banking using the Technology Acceptance Model. The data were collected from 454 individual smartphone users residing in Punjab province via an online and offline questionnaire survey. Structural equation modeling was used to analyze the consumers’ data. The results endorse that their financial skills have no (...) association with intention to adopt but through perceived usefulness; their digital literacy bridges a strong association with intention and through perceived ease of use. Furthermore, this study discusses the theoretical and practical implications of the findings, as well as limitations and future directions. (shrink)

Academic frauds, dishonesty and cheating are pervasive in Pakistan, but thus far less systematic research has been undertaken on the effectiveness of the policies designed for countering academic dishonesty. Generally, the success of a policy depends on a good design and appropriate implementation. The design aspect of the policies to counter academic dishonesty in Pakistan is studied elsewhere, the purpose of this article is to empirically examine the implementation stages of the same policies. In doing so, the study utilizes a (...) panel data set comprising of 60 cross-sections and 06 time series observations (2012–2017). Results of the rigorous panel regression models shows that leniency in the prescribed punishments and an exhaustive belief on the efficacy of traditional examination surveillance methods are counterproductive. The study however failed to substantiate the direct relationship of discrimination and academic dishonesty in the sample. It is therefore concluded that prescribed punishments must be awarded in letter and spirit and that there is need for coordination at different stages of the implementation. Further, the study also recommends that traditional forms of examination surveillance must be augmented using modern technology. (shrink)

According to the United Nations, the Rohingya people are the most persecuted minority group in the world. The atrocities perpetrated by Myanmar authorities could by any reckoning be called ethnic cleansing. This paper delves into the level of discrimination against the Rohingya population perpetrated by Myanmar authorities in myriad of ways. A team of researchers interviewed 37 victims. The pattern of persecution goes back to 1948 – the year when the country achieved independence from their British colonizers. Today, this population (...) group is the single largest “stateless” community after Palestinians in the world. Their “statelessness” or lack of citizenship increases their vulnerability owing to the lack of entitlements to any legal protection from the government. Without citizenship, they are deprived of basic rights such as access to health services, education and employment. The illiteracy rate among the Rohingya, for example, is a staggering 80 percent. However, so far, no unified responses either from the ASEAN or the EU were provided to the crisis. As a result, the level of discrimination against and brutality towards them kept escalating. (shrink)

Terrorism is a global phenomenon that constantly challenges human survival. Based on the social structure, human beings adopt different strategies to overcome its negative consequences on their mind and behavior. Coping strategies and those processes essential for adjustment and survival illustrate how individuals perceive, consider, deal with, and realize a stressful situation in the era of terrorism. The study focuses on exploring coping strategies and avoidance of terrorism impacts. This research study was qualitatively designed to explore the coping strategies adaptation (...) and their influence on the former FATA individuals during the wave of terrorism in the region. The study was undertaken in five districts of erstwhile FATA, including Dara Adam Khel, Orakzai Agency, Khyber Agency, and North and South Waziristan agency. A sample size of twenty-five (25) participants was purposively selected and interviewed, ten (10) focused group discussions (FGDs) were done from each district, and the other 50 participants were chosen for ten (10) FGDs. The collected data were analyzed via the thematic analysis technique. The findings revealed that coping strategies included four different phases: the welcome, resistance, acceptance, surrender, and flee phases for overcoming threats of terrorism. Furthermore, it was also found that fear of terrorism had strongly influenced the individuals’ physical, socio-economic, psychological, emotional stress and fear, education, and social integration. The study recommended that social integration and solidarity among the people, as well as their cooperation with the government, can eliminate the threat of terrorism. (shrink)

This work compares Autometrics with dual penalization techniques such as minimax concave penalty and smoothly clipped absolute deviation under asymmetric error distributions such as exponential, gamma, and Frechet with varying sample sizes as well as predictors. Comprehensive simulations, based on a wide variety of scenarios, reveal that the methods considered show improved performance for increased sample size. In the case of low multicollinearity, these methods show good performance in terms of potency, but in gauge, shrinkage methods collapse, and higher gauge (...) leads to overspecification of the models. High levels of multicollinearity adversely affect the performance of Autometrics. In contrast, shrinkage methods are robust in presence of high multicollinearity in terms of potency, but they tend to select a massive set of irrelevant variables. Moreover, we find that expanding the data mitigates the adverse impact of high multicollinearity on Autometrics rapidly and gradually corrects the gauge of shrinkage methods. For empirical application, we take the gold prices data spanning from 1981 to 2020. While comparing the forecasting performance of all selected methods, we divide the data into two parts: data over 1981–2010 are taken as training data, and those over 2011–2020 are used as testing data. All methods are trained for the training data and then are assessed for performance through the testing data. Based on a root-mean-square error and mean absolute error, Autometrics remain the best in capturing the gold prices trend and producing better forecasts than MCP and SCAD. (shrink)

Originally published in 1981, this book considers one particular area of econometrics- the linear model- where significant recent advances have been made. It considers both single and multiequation models with varying co-efficients, explains the various theories and techniques connected with these and goes on to describe the various applications of the models. Whilst the detailed explanation of the models will interest primarily econometrics specialists, the implications of the advances outlined and the applications of the models will intrest a wide range (...) of economists. (shrink)

Statistical distributions have great applicability for modeling data in almost every applied sector. Among the available classical distributions, the inverse Weibull distribution has received considerable attention. In the practice of distribution theory, numerous methods have been studied and suggested/introduced to increase the flexibility level of the traditional probability distributions. In this paper, we implement different distribution methods to obtain five new different versions of the inverse Weibull model. The new modifications of the inverse Weibull model are called the logarithm transformed-inverse (...) Weibull, a flexible reduced logarithmic-inverse Weibull, the weighted TX-inverse Weibull, a new generalized-inverse Weibull, and the alpha power transformed extended-inverse Weibull distributions. To illustrate the flexibility and applicability of the new modifications of the inverse Weibull model, a biomedical data set is analyzed. The data set consists of 108 observations and represents the mortality rate of the COVID-19-infected patients. The practical application shows that the new generalized-inverse Weibull is the best modification of the inverse Weibull distribution. (shrink)

Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This study proposed the Sigmoid Fish Swarm Optimization algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The (...) proposed SiFSO algorithm’s performance is tested against state-of-the-art particle swarm optimization algorithms in Q-modularity and normalized mutual information. The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms. (shrink)

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For (...) the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset. (shrink)

Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system, and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or (...) not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes dataset using accuracy and mean squared error as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP-SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes. (shrink)

The purpose of this research is to highlight the relationship between green product attributes and consumer trust that influence consumers’ decision to purchase green products in the context of Pakistan. This study contributes to determining quantitatively how green product attributes such as physical, perceptual, and reflexive attributes influence consumers’ trust to purchase a green product and investigates the mediating role of green marketing. Data was collected from different industrial sectors through a survey questionnaire. We employed Structural Equation Modeling using the (...) SMART-PLS software to check the reliability and validity of the constructs, and to test the hypotheses. This study reveals variations in terms of shaping the sustainable consumers’ buying behavior by modifying product attributes and green marketing strategies that are in congruence with the proposed hypotheses of this study. In the end, the findings and interpretations of the results are given which can guide the managers to develop effective green marketing campaigns in reshaping the purchase intentions of consumers toward their green products. (shrink)

This research explores the effect of servant leadership on prosocial rule-breaking (PSRB) and the mediating mechanism of public service motivation (PSM) between the association of servant leadership and PSRB. The said phenomenon is examined in the civil service context of Pakistan during the continuing crises of the COVID-19 pandemic, a situation where the traditional civil service policy and rule system has become highly complicated for passionate employees’ service performance and efficiency, and where servant leadership has received greater attention for inspiring (...) the attitudinal and behavioral outcomes of frontline workers during the pandemic. Data were collected from 546 frontline workers of the corona relief tiger force. The findings of the study revealed that servant leadership has a significant effect on PSRB and PSM, and that PSM significantly promotes PSRB. The results also revealed that servant leadership has a significant impact on PSRBviaengendering PSM. (shrink)

Web search engines usually keep users’ profiles for multiple purposes, such as result ranking and relevancy, market research, and targeted advertisements. However, user web search history may contain sensitive and private information about the user, such as health condition, personal interests, and affiliations that may infringe users’ privacy since a user’s identity may be exposed and misused by third parties. Numerous techniques are available to address privacy infringement, including Private Information Retrieval protocols that use peer nodes to preserve privacy. Previously, (...) we have proved that PIR protocols are vulnerable to the QuPiD Attack. In this research, we proposed NN-QuPiD Attack, an improved version of QuPiD Attack that uses an Artificial Neural Network based model to associate queries with their original users. The results show that the NN-QuPiD Attack gave 0.512 Recall with the Precision of 0.923, whereas simple QuPiD Attack gave 0.49 Recall with the Precision of 0.934 with the same data. (shrink)

The world is looking toward organizations for social responsibility to contribute to a sustainable environment. Employees’ organizational citizenship behavior for the environment is a voluntary environmental-oriented behavior that is important for organizations’ environmental performance. Based on social learning theory, this study examined the effects of responsible leadership in connection with OCBE by using a sample of 520 employees in the manufacturing and service sectors in China including engine manufacturing, petroleum plants, banking, and insurance sector organizations. Further, the roles of psychological (...) ownership and employee environmental commitment were used as mediators and moderators simultaneously. The direct, mediation, and moderation model results exposed a positive relationship between responsible leadership and OCBE via employee psychological ownership and employee environmental commitment. The study also revealed that the indirect effect is stronger when employees hold a higher employee environmental commitment. The theoretical and practical implications for environmental sustainability in respect of organizations as well as future research directions are discussed. (shrink)

Jama’at-i-Islami is one of the most prominent religious parties of Pakistan that also take active part in the politics of the country. The party is credited with the introduction of Islamic element in the political and constitutional set up of Pakistan. This paper highlights the efforts of the party for the enforcement of Islamic Constitution soon after the creation of Pakistan up to the enforcement of the Constitution of 1956. The style, ideas and politics of the party regarding the Islamic (...) Constitution in Pakistan from 1947 to 1956 are the main focus of the discussion. The second part of the paper deals with the contribution of the Jama’at in the Ahmadiyah issue. How reluctantly the party was involved in the issue and in what way could it win the sympathy of the people as a main leading force of the campaign against Ahmadiya influence in 1953 have been examined in the course of the discussion. (shrink)

The COVID-19 pandemic that began in 2019 has created an acute fear of economic crisis, and people have experienced the state of perceived job insecurity. Several measures were taken to control this deadly pandemic, but it still affected the majority of global operational activities. This study addresses the United Nation’s Sustainable Development Goal number 8 that relates to decent work and economic growth. This quantitative study examines the impact of fear associated with economic crisis and perceived job insecurity on mental (...) health with the moderating effect of surface and deep acting. Surface acting is displaying fake emotions, and deep acting is modifying inner feelings according to the required emotions. This study used sample data from private-sector employees and applied SmartPLS for structural model assessment. As many organizations took more challenging decisions to sustain their business operations, the study therefore analyzes the impact of the pandemic on private sector employees. The two main findings of the study are: surface acting moderates the relationships of fear of economic crisis and perceived job insecurity with mental health and declines the impact of both on mental health, while deep acting negatively moderates the relationships of fear of economic crisis and perceived job insecurity with mental health and improved mental health even in the presence of both. The study highlighted the importance of deep acting at workplaces to sustain employees’ mental and psychological stability. Organizations could introduce emotional labor strategies and strengthen the mental health of their employees against the underlying fear of economic crisis and perceived job insecurity. (shrink)

This study examined the relationship between urban culture and street vending. Prior research on this topic is limited and inconclusive. Therefore, we have proposed an integrated model to test the positive effect of urban culture on street vending using multiple mediations of consumption patterns, resistance, and microfinance. We tested a sample of 425 responses that reflect the public opinion in Baghdad, Iraq. These responses were collected between September and November 2018. A partial least squares–based structural equation modeling is employed to (...) test the validity of measurement models and the significance of the entire structural model, predictive power, and mediation analysis. We found that resistance mediates the effect of urban culture on street vending; low-income consumption and resistance sequentially mediate the effect of urban culture on street vending, while resistance mediates the effect of a lack of microfinance on street vending. The direct impact of culture on street vending is not significant, and a lack of microfinance positively influences the pervasiveness of trading on streets. This study contributes to the extant literature as it proposed and tested a novel and comprehensive model to analyze the relationship between urban culture and street vending, simultaneously examining the effects of culture, consumption, resistance, and microfinance on street vending. (shrink)

Although substantial evidence suggests receiving social support has positive implications for well-being, less is known about how providing support can confer benefits, particularly for Asian American and Latinx individuals who are more likely to come from interdependent cultures that emphasize family obligation. Asian American and Latinx college students reported on anxiety before taking part in a modified laboratory task that elicited a physiological stress response as measured by total cortisol output. They were randomly assigned to write a supportive note to (...) a family member, a supportive note to a close friend, or about their day in a control condition after the mild lab stressor and reported on psychosocial well-being. Those who provided support to a family member experienced higher self-esteem compared to those in the control condition. However, there was variation in Asian American and Latinx participants’ physiological stress response. The findings suggest that providing support to close others, particularly family members, can be differentially meaningful for individuals from diverse backgrounds. (shrink)

Workplace productivity is badly affected by many negative factors such as narcissism, and sadism. In addition, paranoia and antagonism play an important role in increasing workplace incivility. Through emotional intelligence, such negative behaviors could be addressed by managers and their junior colleagues. The current study aims to investigate the parallel mediating role of paranoia, antagonism, and emotional intelligence on the relationship between narcissism, sadism, and workplace incivility. A survey approach was used. Primary data was collected in PLS-SEM. The population of (...) the study was all faculty members in higher education institutions in the Khyber Pakhtunkhwa region. A measurement model and structural model were developed. The measurement model demonstrated that convergent and discriminant validities were established. The structural model's findings revealed that narcissism, antagonism, and emotional intelligence were not mediated between narcissism and workplace incivility. Similarly, emotional intelligence did not play any mediating role between sadism and workplace incivility. This implied that emotional intelligence has no role in decreasing or reducing workplace uncivil behavior. (shrink)

Internet of Things is expanding and evolves into all aspects of the society. Research and developments in the field of IoT have shown the possibility of producing huge volume of data and computation among different devices of the IoT. The data collected from IoT devices are transferred to a central server which can further be retrieved and accessed by the service providers for analyzing, processing, and using. Industrial Internet of Health Things is the expansion of the Internet of Health Things (...) which plays an important role in observing, consulting, monitoring, and treatment process of remote exchange data processes. The linkage of computation and interoperability are supported through various intelligent sensors, controllers, and actuators. The role of parallel computing for efficient and Intelligent Industrial Internet of Health Things is obvious to analyze and process different healthcare situations. A detailed overview of this existing literature is needed through which the research community will provide new solutions for efficient healthcare with the help of IoT based on parallel computing. Therefore, the current study presents a detailed overview of the existing literature for facilitating IIoHT. (shrink)

Tax payments stimulate business enterprises to choose tax management through tax avoidance activities, which is the legal practice to reduce the amount of tax payable. In developing economies, taxation is considered more critical for budget and revenues of a country. This paper investigates whether various business strategies influence corporate tax avoidance decisions of firms by adopting business strategies. Besides, it explores how gender diversity can ease this relationship. This study has chosen a sample of organizations from non-financial sector in Pakistan. (...) The time frame is 5 years, including once a year. The present model employed a generalized moment method and tested the proposed hypothesis to draw the results. The study has taken the size, leverage, and business profitability as control variables of firms. The study outcomes by using the GMM method demonstrate that the presence of female directors reduces tax avoidance behavior in prospector companies. This study provides insight into future research for stakeholders, government officials, tax authorities, and policymakers. The findings offer valuable recommendations and practical insights and implications. The findings provide future directions for research to test different frameworks to attain beneficial results to promote the responsibility of tax payment culture. (shrink)

Fuzzy sets and fuzzy logics are used to model events with imprecise, incomplete, and uncertain information. Researchers have developed numerous methods and techniques to cope with fuzziness or uncertainty. This research intends to introduce the novel concepts of complex neutrosophic relations and its types based on the idea of complex neutrosophic sets. In addition, these concepts are supported by suitable examples. A CNR discusses the quality of a relationship using the degree of membership, the degree of abstinence, and the degree (...) of nonmembership. Each of these degrees is a complex number from the unit circle in a complex plane. The real part of complex-valued degrees represents the amplitude term, while the imaginary part represents the phase term. This property empowers CNRs to model multidimensional variables. Moreover, some interesting properties and useful results have also been proved. Furthermore, the practicality of the proposed concepts is verified by an application, which discusses the use of the proposed concepts in statistical decision-making. Additionally, a comparative analysis between the novel concepts of CNRs and the existing methods is carried out. (shrink)

For efficient energy distribution, microgrids provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics, are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. Thus, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumption matching at (...) MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. Therefore, in this work, we develop an end-to-end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. These features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state-of-the-art methods. (shrink)