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)

Mining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. After (...) precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately. (shrink)

The paper proposes a research model explaining the sequential mediation effect of job embeddedness and work engagement between ethical leadership and career satisfaction. The model also examines whether JE heightens WENG, a factor indirectly influenced by ethical practices ending in employee satisfaction. The study used a time-lagged data collection procedure and survey responses of 247 hotel workers in China. Data were analyzed through structural equation modeling. The results showed that EL directly and indirectly contributes to employee CS. The present empirical (...) framework extends the hospitality industry literature by explaining the precise mechanism through which EL generates CS among hospitality workers in China. The paper offers theoretical and practical implications and future research directions. (shrink)

Presently, there is armed conflict in Darfur, Sudan. Armed conflict has adversely affected the social, economic and educational development of Darfur, Sudan. Purpose of this study is to know the impact of armed conflict on managerial behavior of the principals of secondary schools in Darfur, Sudan. In this study, the impact of armed conflict on performance of the secondary school principals was analyzed in perspective of their managerial behavior. Based on the findings of the study significant impact of armed conflict (...) was found on the managerial behavior of the principals. It was found that principals coordinated with the staff in activities of the school but were frightened and unable to perform their administrative and managerial duties freely and independently. There was communication gap between the principals, teachers and parents. The principals did not possess the skills to maintain school budget & finance properly. It was concluded that the principals need administrative and managerial training. Government of Sudan may establish academies of Educational Administration & Management to impart training to the principals of secondary schools and to take steps to control the existing armed conflict. (shrink)

Knowledge hiding has become an alarming issue for the organizations. Knowledge hiding is an employee’s intentional attempt to conceal knowledge requested by others at the workplace. Employee knowledge hiding significantly influences an organization’s effective functioning. This research is an attempt to extend previous work on antecedents of knowledge hiding. Drawing on conservation of resources theory, it is proposed that receiving poor treatment by organizations in the form of organizational dehumanization creates psychological distress among employees toward the organization. Distress among workers (...) in turn intervenes the path and increases the likelihood of engaging in knowledge hiding behaviors. An employee’s felt obligation for constructive change may moderate the relationship between organizational dehumanization and employee psychological distress. Data for the current study were collected from 245 employees of the telecommunication sector in three-time lags. The results support the direct and indirect effect of organizational dehumanization on employee knowledge hiding behaviors through the mediation of psychological distress. The results also support the moderation of FOCC between organizational dehumanization and psychological distress. Furthermore, the findings of the study may help organizational practitioners and managers about the value of effective organizational climate and practices for better organizational functioning through knowledge sharing and providing insight into undesirable repercussions of organizational dehumanization. Implications for organizations and practitioners are discussed. (shrink)

In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the (...) above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset. (shrink)

Over the past couple of decades, research on the perks of corporate social responsibility has reported that it is a source of competitive advantage and can increase the bottom-line performance of the organization. However, a somewhat small proportion of this research is focused on the ‘greenwash’ side, which posits that not all CSR would lead to positive impacts. By extending this line of research, the current study is aimed at investigating the differential impacts of CSR by developing a scale for (...) the three-dimensional model of CSR attributions coined by Ahmad in 2017, that is, Embedded, P-Social, and P-Instrumental CSR, and testing the same with counterproductive sustainability behaviors. The attribution theory was used to explain the relationship between the study variables. Data (N = 311) were collected from the tourism and hospitality sectors. It was found that Embedded and P-Social CSR practices would be attributed to more genuine CSR and would lead to a reduction in counterproductive sustainability behaviors. Furthermore, the P-Instrumental CSR would be attributed to more selfish and instrumental practices and hence would be considered as greenwashing, which will consequently lead to enhancing counterproductive sustainability behaviors. The study further tested the mediating role of employees' organizational trust, which was also supported by the results. Several theoretical and practical implications are discussed. (shrink)

Business Ethics: A European Review, EarlyView.

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence. Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast (...) numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted. (shrink)

Background and ObjectivesThe purpose of this study was to draw the attention toward the implications of COVID-19 and the related restrictions imposed worldwide especially in Pakistan. The primary objective was to highlight the levels of psychological distress, anxiety, family violence, suicidality, and well-being due to COVID-19 and the secondary objective was to associate it to social demographic factors.Materials and MethodsIt is designed as a cross-sectional study by employing an online questionnaire in the English language and obtaining responses using a snowball (...) sampling technique. We used three validated measures including Kessler Psychological Distress Scale, Generalized Anxiety Disorder index and World Health Organization Well-Being Index.ResultsA sample of 420 participants was recruited from across Pakistan, with most participants were females, students and belonging to Punjab. Nearly one-fourth of the participants scored above the minimum value set for moderate or high psychological distress. There was a higher prevalence of distress among females and resident of province Punjab. The majority of individuals reported that they were living with their family and more than half were neutral regarding their satisfaction with their living conditions. 40.5% believed that the lockdown has had a negative impact on their mental health. 31.4% have reported that they themselves have experienced abuse from a family member. 48.6% scored high on the GAD-7 scale and low wellbeing score was found among 80.2%. Students were found to be more vulnerable to mental illness and anxiety.ConclusionWith the lockdown restrictions, psychosocial distress has become prevalent in Pakistan. (shrink)

Authentic leadership has appeared as a significant field of research. Building on social exchange theory that explicates how individuals mutually mechanize reciprocation and eventually establish a trust-based relationship, we postulated a positive relationship between authentic leadership and followers' organizational citizenship behaviors (OCB). Based on a two-wave time-lagged design, the data were obtained from 270 employees working in the private banking sector of Pakistan. We found that authentic leadership is positively associated with subordinate’s OCB, as well as, leads to a higher (...) level of both affective and cognitive-based followers' trust. The results also indicated that both affective and cognitive-based trust positively mediated the relationship between authentic leadership and OCB. The implication for theory and practice are discussed. (shrink)

A personalized recommender system is broadly accepted as a helpful tool to handle the information overload issue while recommending a related piece of information. This work proposes a hybrid personalized recommender system based on affinity propagation, namely, APHPRS. Affinity propagation is a semisupervised machine learning algorithm used to cluster items based on similarities among them. In our approach, we first calculate the cluster quality and density and then combine their outputs to generate a new ranking score among clusters for the (...) personalized recommendation. In the first phase, user preferences are collected and normalized as items rating matrix. This generated matrix is then clustered offline using affinity propagation and kept in a database for future recommendations. In the second phase, online recommendations are generated by applying the offline model. Negative Euclidian similarity and the quality of clusters are used together to select the best clusters for recommendations. The proposed APHPRS system alleviates problems such as sparsity and cold-start problems. The use of affinity propagation and the hybrid recommendation technique used in the proposed approach helps in improving results against sparsity. Experiments reveal that the proposed APHPRS performs better than most of the existing recommender systems. (shrink)