This study examined the impact of security champion and security training on protection behaviour... more This study examined the impact of security champion and security training on protection behaviour in the context of IT service oriented SMEs in Bangladesh. Drawing upon protection motivation theory, this study examined the influence of security training on threat appraisal and influence of security training on coping appraisal which leads to protection behaviour via protection motivation. Data was collected from six different IT service oriented organizations with a sample size of 147 by survey questionnaire. Data was analysed using partial least squares (PLS) technique and result shows that perceived value of data, security training and threat appraisal are strong predictors of threat appraisal, protection motivation and protection behaviour. Theoretical contribution and practical implications of this research are also discussed.
Lecture notes in electrical engineering, Sep 24, 2019
Music is the covered up arithmetical exercise of a mind oblivious that it is figuring. Music not ... more Music is the covered up arithmetical exercise of a mind oblivious that it is figuring. Music not being just an extravagant language for human emotion is also a key itself for identifying the human emotion. Researches indicate that music causes stimulation through specific brain circuits to produce emotions. Listening to a piece of music can manipulate a person to feel joyous or brooding according to the emotion included in the music. But the perennial challenge is to examine the correlation between music and the subsequent effect on emotion. This music emotion recognition (MER) system can be used for simplistic music information retrieval. In this paper using (Music Information Retrieval) MIR Toolbox in Matlab, eight distinct features were extracted from 100 songs of various genres and similar emotions were clustered into four categories using the Russell’s Two Dimensional Emotion Model. Mapping the extracted features into the four emotion classes, several machine-learning classifiers were trained. A set of unknown songs were used to validate the recognition accuracy. Along with the common features like pitch, timbre, rhythm etc. roll-off and brightness were also used. Roll-off showed a great priority in Random Forest feature ranking. With all these features combined, a highest prediction accuracy of 75% was found from artificial neural network (ANN) among the others classifiers like Support Vector Machine (SVM), linear discriminant, and Ensemble learner.
Bulletin of Electrical Engineering and Informatics
Food delivery methods are at the top of the list in today's world. People's attitudes tow... more Food delivery methods are at the top of the list in today's world. People's attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classification techniques are used: extreme gradient boosting (XGB), random forest classifier (RFC), decision tree classifier (DTC), and multi nominal Naive Bayes (MNB). Three deep learning (DL) models are used: convolutional neural network (CNN), long term short memory (LSTM), and recurrent neural network (RNN). The XGB model exceeds all four machine learning (ML) algorithms with an accuracy ...
Security has always been a significant concern since the dawn of human civilization. That is why ... more Security has always been a significant concern since the dawn of human civilization. That is why we build houses to keep ourselves and our belongings safe. And we do not hesitate to spend a lot on front-door locks and install CCTV cameras to monitor security threats. This paper presents an innovative automatic Front Door Security (FDS) algorithm that uses Human Activity Recognition (HAR) to detect four different security threats at the front door from a real-time video feed with 73.18% accuracy. The activities are recognized using an innovative combination of GoogleNet-BiLSTM hybrid network. This network receives the video feed from the CCTV camera and classifies the activities. The proposed algorithm uses this classification to alert any attempts to break the door by kicking, punching, or hitting. Furthermore, the proposed FDS algorithm is effective in detecting gun violence at the front door, which further strengthens security. This Human Activity Recognition (HAR)-based novel FDS algorithm demonstrates the potential of ensuring better safety with 71.49% precision, 68.2% recall, and an F1-score of 0.65. INDEX TERMS Intelligent surveillance, real-time security, deep learning, hybrid networks, sequence folding, video-frame feature vector.
Bulletin of Electrical Engineering and Informatics
As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuatio... more As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator ...
2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS)
Day by Day, the trend of using social media(SM) has increased among the people. With the increasi... more Day by Day, the trend of using social media(SM) has increased among the people. With the increasing rate, the use of Banglish(Merge of Bangla and English) and shortcut words has also being increased. In this research, Banglish and Shortcut words have been fully converted to English. For this type of conversation, we have used the CNNs method and the method consist of multiple layer and these layers are connected to each other. This method is considered to be the best method because it does not need any feature extraction. The convolutional neuron network is used in many areas such as image and pattern recognition, speech recognition, natural language processing and video analysis. In this research we have used CNN method because we have decided to use computer vision as some of the words are close to each other and at first we need to convert all the shortcut words into images for pre-processing the data. With the help of CNN method searched for the Banglish and shortcut words that people use for their daily conversation. At first we find the different representative of a single word and then we converted those shortcut and Banglish words to main word using CNNs method.
PurposeThis study proposes a research model to identify the relevant constructs of employee resis... more PurposeThis study proposes a research model to identify the relevant constructs of employee resistance and symbolic adoption in pre-implementation stage of enterprise resource planning systems in manufacturing industries, drawing suitable support from the existing body of literature. The proposed model is a combination of the status quo bias theory and absorptive capacity theory to measure employee resistance that negatively lead to symbolic adoption of a user.Design/methodology/approachThis research used a self-administered questionnaire to survey 221 participants from five organizations in the manufacturing industry, all working towards deploying enterprise resource planning (ERP) systems.FindingsThe results show that factors contributing to status quo bias and absorptive capacity impact end-user grumbling. Furthermore, end-user grumbling affects symbolic adoption substantially.Practical implicationsThis study provides researchers, practitioners and ERP vendors a broader overview ...
Indonesian Journal of Electrical Engineering and Computer Science, 2021
The present study has been conducted to examine whether skills and general technology-related val... more The present study has been conducted to examine whether skills and general technology-related value (GTV) required to operate the internet of things (IoT). This study also investigates is there any effect of technophilia to adopt IoT. The research method we use in this quantitative study was the sample survey. For investigating results, 352 surveys were conducted where 26 surveys were led through online and 292 surveys were distributed to different age groups. The proposed model was examined using partial least square structural equation model where the results revealed that IoT skills and General knowledge on technology directly contribute to technophilia which covers behavioural, emotional, and cognitive aspects. That is if people have a fascination for new technologies then they are willing to use IoT.
Emerging Technologies in Data Mining and Information Security, 2021
Massive advancement of network traffic has been observed lately since the significant usage of on... more Massive advancement of network traffic has been observed lately since the significant usage of online application and cloud administration. Thus, various routing issues need to be managed without increasing the latency to ensure even traffic. Software-defined networking (SDN) is developed to address the above-mentioned issue. SDN allows the network’s programmable features and responses gradually. The suggested system addresses the concerns raised due to multipath steering through SDN. The application of OpenFlow convention and Ryu controller is done to implement SDN with the help of the Mininet-WiFi emulator. The SDN network is designed using a graphical UI program, and the module is implemented with Python programming along with OpenFlow based-Ryu controller. The breadth-first search (BFS) and depth-first search (DFS) schemes are portrayed to calculate the briefest path in the system. Finally, a round trip time (RTT) is observed to ensure the most suitable search algorithm among th...
In the current competitive job market, information is the most powerful tool. As a job, the seeke... more In the current competitive job market, information is the most powerful tool. As a job, the seeker looks for a job, and he must have the insight of what kind of competition he is about to face. This information will allow the job seeker to improve himself from the rest in the market. To determine the demand for any field of job among job seekers, with the help of the unsupervised k-means machine learning algorithm, the data of job interests can be clustered in different groups based on their kinds. The visual representation of the clusters in a scatter plot gives the information on which variety of jobs are in more or less demand among job seekers with the density of the groups. This study provides insight into the current job market.
Music is the covered up arithmetical exercise of a mind oblivious that it is figuring. Music not ... more Music is the covered up arithmetical exercise of a mind oblivious that it is figuring. Music not being just an extravagant language for human emotion is also a key itself for identifying the human emotion. Researches indicate that music causes stimulation through specific brain circuits to produce emotions. Listening to a piece of music can manipulate a person to feel joyous or brooding according to the emotion included in the music. But the perennial challenge is to examine the correlation between music and the subsequent effect on emotion. This music emotion recognition (MER) system can be used for simplistic music information retrieval. In this paper using (Music Information Retrieval) MIR Toolbox in Matlab, eight distinct features were extracted from 100 songs of various genres and similar emotions were clustered into four categories using the Russell’s Two Dimensional Emotion Model. Mapping the extracted features into the four emotion classes, several machine-learning classifiers were trained. A set of unknown songs were used to validate the recognition accuracy. Along with the common features like pitch, timbre, rhythm etc. roll-off and brightness were also used. Roll-off showed a great priority in Random Forest feature ranking. With all these features combined, a highest prediction accuracy of 75% was found from artificial neural network (ANN) among the others classifiers like Support Vector Machine (SVM), linear discriminant, and Ensemble learner.
International Journal of Industrial Management, 2020
Bangladesh observes the production of approximately 400,000 metric tonnes of e-waste every year. ... more Bangladesh observes the production of approximately 400,000 metric tonnes of e-waste every year. It is expected that it will witness a 20% increase annually. In order to implement e-waste recycling, it is essential to uncover the influencing factors. In this study, electronic shop owners act as one of the major players responsible for the aggregation of e-waste in the country. Based on Value-Belief-Norm Theory, Self Determination Theory and synthesizing relevant literature in the body of knowledge, this paper presents an integrated conceptual model consisting Altruistic values, Egoistic values, Biosphric values, Environmental belief, Personal norm, Intrinsic motivation and Extrinsic motivation, which are predicted to influence e-waste separation intention in Bangladesh. From a practical point of view, this research sheds light on key determinants that affect separation intention, in which practitioners need to consider when developing strategies concerning e-waste management and pol...
PurposeFirms' knowledge-processing capabilities have a central role in achieving innovation p... more PurposeFirms' knowledge-processing capabilities have a central role in achieving innovation performance and competitive advantage. Absorptive capacity capabilities and innovation are viewed as essential for enterprise success. Absorptive capacity is deemed as a highly important organizational capability to recognize value and assimilate both external and internal knowledge in order to enhance firm innovation. The aim of this study is to determine if innovation performance can be improved through absorptive capacity (knowledge acquisition, dissemination and utilization), when it is supported by internal (firm experience) and external knowledge sources (R&D cooperation and contracted R&D).Design/methodology/approachA quantitative methodology based on employing a structured questionnaire was used for data collection. The proposed research model and its associated hypotheses are tested by using Partial Least Squares (PLS) structural equation modelling (SEM) on a data set of 248 manu...
Purpose Building upon the theory of planned behaviour and the entrepreneurial event model, the pu... more Purpose Building upon the theory of planned behaviour and the entrepreneurial event model, the purpose of this paper is to test the effects of the following covariates in predicting entrepreneurial intention among tourism students in Bangladesh, namely, attitude, subjective norm, perceived behavioural control (PBC), perceived desirability and perceived feasibility. Design/methodology/approach A total of 137 private university students participated in the study by means of questionnaire. The hypotheses were tested using partial least squares (PLS) analysis. Findings Findings indicate that attitude and subjective norm significantly influence perceived desirability. It was also found that subjective norm and PBC positively influence perceived feasibility. Interestingly also, both perceived desirability and perceived feasibility predict entrepreneurial intention. Originality/value The study proves the robustness of the integration of the two intent models in explaining entrepreneurial i...
Design Solutions for User-Centric Information Systems
This study examined the impact of security champion and security training on protection behaviour... more This study examined the impact of security champion and security training on protection behaviour in the context of IT service oriented SMEs in Bangladesh. Drawing upon protection motivation theory, this study examined the influence of security training on threat appraisal and influence of security training on coping appraisal which leads to protection behaviour via protection motivation. Data was collected from six different IT service oriented organizations with a sample size of 147 by survey questionnaire. Data was analysed using partial least squares (PLS) technique and result shows that perceived value of data, security training and threat appraisal are strong predictors of threat appraisal, protection motivation and protection behaviour. Theoretical contribution and practical implications of this research are also discussed.
This study examined the impact of security champion and security training on protection behaviour... more This study examined the impact of security champion and security training on protection behaviour in the context of IT service oriented SMEs in Bangladesh. Drawing upon protection motivation theory, this study examined the influence of security training on threat appraisal and influence of security training on coping appraisal which leads to protection behaviour via protection motivation. Data was collected from six different IT service oriented organizations with a sample size of 147 by survey questionnaire. Data was analysed using partial least squares (PLS) technique and result shows that perceived value of data, security training and threat appraisal are strong predictors of threat appraisal, protection motivation and protection behaviour. Theoretical contribution and practical implications of this research are also discussed.
Lecture notes in electrical engineering, Sep 24, 2019
Music is the covered up arithmetical exercise of a mind oblivious that it is figuring. Music not ... more Music is the covered up arithmetical exercise of a mind oblivious that it is figuring. Music not being just an extravagant language for human emotion is also a key itself for identifying the human emotion. Researches indicate that music causes stimulation through specific brain circuits to produce emotions. Listening to a piece of music can manipulate a person to feel joyous or brooding according to the emotion included in the music. But the perennial challenge is to examine the correlation between music and the subsequent effect on emotion. This music emotion recognition (MER) system can be used for simplistic music information retrieval. In this paper using (Music Information Retrieval) MIR Toolbox in Matlab, eight distinct features were extracted from 100 songs of various genres and similar emotions were clustered into four categories using the Russell’s Two Dimensional Emotion Model. Mapping the extracted features into the four emotion classes, several machine-learning classifiers were trained. A set of unknown songs were used to validate the recognition accuracy. Along with the common features like pitch, timbre, rhythm etc. roll-off and brightness were also used. Roll-off showed a great priority in Random Forest feature ranking. With all these features combined, a highest prediction accuracy of 75% was found from artificial neural network (ANN) among the others classifiers like Support Vector Machine (SVM), linear discriminant, and Ensemble learner.
Bulletin of Electrical Engineering and Informatics
Food delivery methods are at the top of the list in today's world. People's attitudes tow... more Food delivery methods are at the top of the list in today's world. People's attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classification techniques are used: extreme gradient boosting (XGB), random forest classifier (RFC), decision tree classifier (DTC), and multi nominal Naive Bayes (MNB). Three deep learning (DL) models are used: convolutional neural network (CNN), long term short memory (LSTM), and recurrent neural network (RNN). The XGB model exceeds all four machine learning (ML) algorithms with an accuracy ...
Security has always been a significant concern since the dawn of human civilization. That is why ... more Security has always been a significant concern since the dawn of human civilization. That is why we build houses to keep ourselves and our belongings safe. And we do not hesitate to spend a lot on front-door locks and install CCTV cameras to monitor security threats. This paper presents an innovative automatic Front Door Security (FDS) algorithm that uses Human Activity Recognition (HAR) to detect four different security threats at the front door from a real-time video feed with 73.18% accuracy. The activities are recognized using an innovative combination of GoogleNet-BiLSTM hybrid network. This network receives the video feed from the CCTV camera and classifies the activities. The proposed algorithm uses this classification to alert any attempts to break the door by kicking, punching, or hitting. Furthermore, the proposed FDS algorithm is effective in detecting gun violence at the front door, which further strengthens security. This Human Activity Recognition (HAR)-based novel FDS algorithm demonstrates the potential of ensuring better safety with 71.49% precision, 68.2% recall, and an F1-score of 0.65. INDEX TERMS Intelligent surveillance, real-time security, deep learning, hybrid networks, sequence folding, video-frame feature vector.
Bulletin of Electrical Engineering and Informatics
As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuatio... more As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator ...
2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS)
Day by Day, the trend of using social media(SM) has increased among the people. With the increasi... more Day by Day, the trend of using social media(SM) has increased among the people. With the increasing rate, the use of Banglish(Merge of Bangla and English) and shortcut words has also being increased. In this research, Banglish and Shortcut words have been fully converted to English. For this type of conversation, we have used the CNNs method and the method consist of multiple layer and these layers are connected to each other. This method is considered to be the best method because it does not need any feature extraction. The convolutional neuron network is used in many areas such as image and pattern recognition, speech recognition, natural language processing and video analysis. In this research we have used CNN method because we have decided to use computer vision as some of the words are close to each other and at first we need to convert all the shortcut words into images for pre-processing the data. With the help of CNN method searched for the Banglish and shortcut words that people use for their daily conversation. At first we find the different representative of a single word and then we converted those shortcut and Banglish words to main word using CNNs method.
PurposeThis study proposes a research model to identify the relevant constructs of employee resis... more PurposeThis study proposes a research model to identify the relevant constructs of employee resistance and symbolic adoption in pre-implementation stage of enterprise resource planning systems in manufacturing industries, drawing suitable support from the existing body of literature. The proposed model is a combination of the status quo bias theory and absorptive capacity theory to measure employee resistance that negatively lead to symbolic adoption of a user.Design/methodology/approachThis research used a self-administered questionnaire to survey 221 participants from five organizations in the manufacturing industry, all working towards deploying enterprise resource planning (ERP) systems.FindingsThe results show that factors contributing to status quo bias and absorptive capacity impact end-user grumbling. Furthermore, end-user grumbling affects symbolic adoption substantially.Practical implicationsThis study provides researchers, practitioners and ERP vendors a broader overview ...
Indonesian Journal of Electrical Engineering and Computer Science, 2021
The present study has been conducted to examine whether skills and general technology-related val... more The present study has been conducted to examine whether skills and general technology-related value (GTV) required to operate the internet of things (IoT). This study also investigates is there any effect of technophilia to adopt IoT. The research method we use in this quantitative study was the sample survey. For investigating results, 352 surveys were conducted where 26 surveys were led through online and 292 surveys were distributed to different age groups. The proposed model was examined using partial least square structural equation model where the results revealed that IoT skills and General knowledge on technology directly contribute to technophilia which covers behavioural, emotional, and cognitive aspects. That is if people have a fascination for new technologies then they are willing to use IoT.
Emerging Technologies in Data Mining and Information Security, 2021
Massive advancement of network traffic has been observed lately since the significant usage of on... more Massive advancement of network traffic has been observed lately since the significant usage of online application and cloud administration. Thus, various routing issues need to be managed without increasing the latency to ensure even traffic. Software-defined networking (SDN) is developed to address the above-mentioned issue. SDN allows the network’s programmable features and responses gradually. The suggested system addresses the concerns raised due to multipath steering through SDN. The application of OpenFlow convention and Ryu controller is done to implement SDN with the help of the Mininet-WiFi emulator. The SDN network is designed using a graphical UI program, and the module is implemented with Python programming along with OpenFlow based-Ryu controller. The breadth-first search (BFS) and depth-first search (DFS) schemes are portrayed to calculate the briefest path in the system. Finally, a round trip time (RTT) is observed to ensure the most suitable search algorithm among th...
In the current competitive job market, information is the most powerful tool. As a job, the seeke... more In the current competitive job market, information is the most powerful tool. As a job, the seeker looks for a job, and he must have the insight of what kind of competition he is about to face. This information will allow the job seeker to improve himself from the rest in the market. To determine the demand for any field of job among job seekers, with the help of the unsupervised k-means machine learning algorithm, the data of job interests can be clustered in different groups based on their kinds. The visual representation of the clusters in a scatter plot gives the information on which variety of jobs are in more or less demand among job seekers with the density of the groups. This study provides insight into the current job market.
Music is the covered up arithmetical exercise of a mind oblivious that it is figuring. Music not ... more Music is the covered up arithmetical exercise of a mind oblivious that it is figuring. Music not being just an extravagant language for human emotion is also a key itself for identifying the human emotion. Researches indicate that music causes stimulation through specific brain circuits to produce emotions. Listening to a piece of music can manipulate a person to feel joyous or brooding according to the emotion included in the music. But the perennial challenge is to examine the correlation between music and the subsequent effect on emotion. This music emotion recognition (MER) system can be used for simplistic music information retrieval. In this paper using (Music Information Retrieval) MIR Toolbox in Matlab, eight distinct features were extracted from 100 songs of various genres and similar emotions were clustered into four categories using the Russell’s Two Dimensional Emotion Model. Mapping the extracted features into the four emotion classes, several machine-learning classifiers were trained. A set of unknown songs were used to validate the recognition accuracy. Along with the common features like pitch, timbre, rhythm etc. roll-off and brightness were also used. Roll-off showed a great priority in Random Forest feature ranking. With all these features combined, a highest prediction accuracy of 75% was found from artificial neural network (ANN) among the others classifiers like Support Vector Machine (SVM), linear discriminant, and Ensemble learner.
International Journal of Industrial Management, 2020
Bangladesh observes the production of approximately 400,000 metric tonnes of e-waste every year. ... more Bangladesh observes the production of approximately 400,000 metric tonnes of e-waste every year. It is expected that it will witness a 20% increase annually. In order to implement e-waste recycling, it is essential to uncover the influencing factors. In this study, electronic shop owners act as one of the major players responsible for the aggregation of e-waste in the country. Based on Value-Belief-Norm Theory, Self Determination Theory and synthesizing relevant literature in the body of knowledge, this paper presents an integrated conceptual model consisting Altruistic values, Egoistic values, Biosphric values, Environmental belief, Personal norm, Intrinsic motivation and Extrinsic motivation, which are predicted to influence e-waste separation intention in Bangladesh. From a practical point of view, this research sheds light on key determinants that affect separation intention, in which practitioners need to consider when developing strategies concerning e-waste management and pol...
PurposeFirms' knowledge-processing capabilities have a central role in achieving innovation p... more PurposeFirms' knowledge-processing capabilities have a central role in achieving innovation performance and competitive advantage. Absorptive capacity capabilities and innovation are viewed as essential for enterprise success. Absorptive capacity is deemed as a highly important organizational capability to recognize value and assimilate both external and internal knowledge in order to enhance firm innovation. The aim of this study is to determine if innovation performance can be improved through absorptive capacity (knowledge acquisition, dissemination and utilization), when it is supported by internal (firm experience) and external knowledge sources (R&D cooperation and contracted R&D).Design/methodology/approachA quantitative methodology based on employing a structured questionnaire was used for data collection. The proposed research model and its associated hypotheses are tested by using Partial Least Squares (PLS) structural equation modelling (SEM) on a data set of 248 manu...
Purpose Building upon the theory of planned behaviour and the entrepreneurial event model, the pu... more Purpose Building upon the theory of planned behaviour and the entrepreneurial event model, the purpose of this paper is to test the effects of the following covariates in predicting entrepreneurial intention among tourism students in Bangladesh, namely, attitude, subjective norm, perceived behavioural control (PBC), perceived desirability and perceived feasibility. Design/methodology/approach A total of 137 private university students participated in the study by means of questionnaire. The hypotheses were tested using partial least squares (PLS) analysis. Findings Findings indicate that attitude and subjective norm significantly influence perceived desirability. It was also found that subjective norm and PBC positively influence perceived feasibility. Interestingly also, both perceived desirability and perceived feasibility predict entrepreneurial intention. Originality/value The study proves the robustness of the integration of the two intent models in explaining entrepreneurial i...
Design Solutions for User-Centric Information Systems
This study examined the impact of security champion and security training on protection behaviour... more This study examined the impact of security champion and security training on protection behaviour in the context of IT service oriented SMEs in Bangladesh. Drawing upon protection motivation theory, this study examined the influence of security training on threat appraisal and influence of security training on coping appraisal which leads to protection behaviour via protection motivation. Data was collected from six different IT service oriented organizations with a sample size of 147 by survey questionnaire. Data was analysed using partial least squares (PLS) technique and result shows that perceived value of data, security training and threat appraisal are strong predictors of threat appraisal, protection motivation and protection behaviour. Theoretical contribution and practical implications of this research are also discussed.
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Papers by Imran Mahmud