FACTORS INFLUENCING DESTINATION IMAGE THROUGH SOCIAL MEDIA IN THE PRE-PURCHASE PERIOD OF TOURISM IN PAKISTAN

Due to the technological advancements, people start depending on social media for various matters. As social media provides a tool for developing an image of the destination, it became an essential component in the process of decision-making regarding traveling. This study aims to investigate the influence of user-generated content (UGC), Information Quality (IQ) and Tourist’s Motivation (TM) on destination image through social media in the pre-trip period of tourism in Pakistan. The empirical analysis was conducted by using the survey method through online Google forms. Data was collected from social media (Facebook) users who were the members of the Facebook tourism groups in Pakistan. The findings of the study revealed that UGC does not significantly affect the destination image in the pre-purchase period. Whereas, information quality and tourist’s motivation significantly affect the destination image of Pakistani tourists. The research concludes that tourist’s motivation before experiencing tourism and the information they have exposed to plays a significant role in developing a destination image in the tourism market. Furthermore, this research contributes to the tourism sector of Pakistan by providing information regarding factors developing destination image. This information may help in developing a positive image of the destinations in Pakistan. Also, this study contributes by providing the tourism sector an understanding of the tourist’s behaviors in the pre-purchase period of traveling in Pakistan

FACTORS INFLUENCING DESTINATION IMAGE THROUGH SOCIAL MEDIA IN THE PRE-PURCHASE PERIOD OF TOURISM IN PAKISTAN

Due to the technological advancements, people start depending on social media for various matters. As social media provides a tool for developing an image of the destination, it became an essential component in the process of decision-making regarding traveling. This study aims to investigate the influence of user-generated content (UGC), Information Quality (IQ) and Tourist’s Motivation (TM) on destination image through social media in the pre-trip period of tourism in Pakistan. The empirical analysis was conducted by using the survey method through online Google forms. Data was collected from social media (Facebook) users who were the members of the Facebook tourism groups in Pakistan. The findings of the study revealed that UGC does not significantly affect the destination image in the pre-purchase period. Whereas, information quality and tourist’s motivation significantly affect the destination image of Pakistani tourists. The research concludes that tourist’s motivation before experiencing tourism and the information they have exposed to plays a significant role in developing a destination image in the tourism market. Furthermore, this research contributes to the tourism sector of Pakistan by providing information regarding factors developing destination image. This information may help in developing a positive image of the destinations in Pakistan. Also, this study contributes by providing the tourism sector an understanding of the tourist’s behaviors in the pre-purchase period of traveling in Pakistan

Serious Games in Science Education. A Systematic Literature Review

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Mapping Tools for Open Source Intelligence with Cyber Kill Chain for Adversarial Aware Security

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INVESTIGATING THE DETERMINANTS OF FACULTY JOB SATISFACTION UNDER THE MODERATING ROLE OF LIFE SATISFACTION: A STUDY OF PRIVATE SECTOR UNIVERSITIES OF PAKISTAN

 Current study investigates the determinants of faculty job satisfaction in private sector universities of Pakistan. Furthermore, this study inquires the moderating role of life satisfaction on faculty job satisfaction. Approximately 500 questionnaires were distributed among the faculty members of W category private sector universities, out of 500 questionnaires, 430 were received and 396 were found to be completely filled as per criteria. After applying the statistical tools of SPSS 23, it was confirmed that motivational and hygienic factors have significant and positive relationship with faculty job satisfaction. Moreover, the results proved that impact of life satisfaction did not moderate the relationship of faculty job satisfaction with motivational factors as well as hygienic factors. The results of the study can be generalized to other universities as well as other sectors of the Pakistan. The framework of the study can be applied to compare the faculty job satisfaction level of public and private sector universities

INVESTIGATING THE DETERMINANTS OF FACULTY JOB SATISFACTION UNDER THE MODERATING ROLE OF LIFE SATISFACTION: A STUDY OF PRIVATE SECTOR UNIVERSITIES OF PAKISTAN

 Current study investigates the determinants of faculty job satisfaction in private sector universities of Pakistan. Furthermore, this study inquires the moderating role of life satisfaction on faculty job satisfaction. Approximately 500 questionnaires were distributed among the faculty members of W category private sector universities, out of 500 questionnaires, 430 were received and 396 were found to be completely filled as per criteria. After applying the statistical tools of SPSS 23, it was confirmed that motivational and hygienic factors have significant and positive relationship with faculty job satisfaction. Moreover, the results proved that impact of life satisfaction did not moderate the relationship of faculty job satisfaction with motivational factors as well as hygienic factors. The results of the study can be generalized to other universities as well as other sectors of the Pakistan. The framework of the study can be applied to compare the faculty job satisfaction level of public and private sector universities

The Impact of Children on Parental Purchasing Behavior

Children have a major influence on their parents' purchases of similar products. Children are a dominant market for marketers to consider when making strategy choices since they are part of a family unit. This market is active in three ways: first, it is a large market in and of itself; second, it is a key influencer in facilitating purchasing decisions; and third, it is a possible future market. The focus of this research is on children's second position. They become the focal point of family expectations, and parents are more receptive to their recommendations when making purchases. This research study explains the effect of children on parental purchase behavior in the twin cities of Islamabad and Rawalpindi using a survey as a data collection method. Parents are among the participants in this research. The relationship between parental purchase behavior (PPB) and age of child (AOC), importance of child (IOC), product category (PC), communication pattern (CP), and family orientation is revealed by multiple regression analysis (FO). The age of the child (AOC), family orientation (FO), and Product Category (PC) are the three most significant factors that affect parental buying behavior. The paper concludes that children have a major influence on their parents' decisions

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals' sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger, an Android-based application that highlights the issue of leaking smartphone users' privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies

An effective deep learning approach for the classification of Bacteriosis in peach leave

Bacteriosis is one of the most prevalent and deadly infections that affect peach crops globally. Timely detection of Bacteriosis disease is essential for lowering pesticide use and preventing crop loss. It takes time and effort to distinguish and detect Bacteriosis or a short hole in a peach leaf. In this paper, we proposed a novel LightWeight (WLNet) Convolutional Neural Network (CNN) model based on Visual Geometry Group (VGG-19) for detecting and classifying images into Bacteriosis and healthy images. Profound knowledge of the proposed model is utilized to detect Bacteriosis in peach leaf images. First, a dataset is developed which consists of 10000 images: 4500 are Bacteriosis and 5500 are healthy images. Second, images are preprocessed using different steps to prepare them for the identification of Bacteriosis and healthy leaves. These preprocessing steps include image resizing, noise removal, image enhancement, background removal, and augmentation techniques, which enhance the performance of leaves classification and help to achieve a decent result. Finally, the proposed LWNet model is trained for leaf classification. The proposed model is compared with four different CNN models: LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The proposed model obtains an accuracy of 99%, which is higher than LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The achieved results indicate that the proposed model is more effective for the detection of Bacteriosis in peach leaf images, in comparison with the existing models