In this paper, we theoretically discuss and empirically show how IT capability spurs companies towards greener strategies. Based on the data of listed Chinese manufacturing companies from 2008 to 2018, using a panel regression model, the results show that: (1) corporate IT capability can promote green technological innovation. (2) Compared with state-owned enterprises (SOEs), the IT effect is more significant for non-state-owned enterprises (non-SOEs); compared with regions with weak environmental regulation, the IT effect is more significant in regions with strong environmental regulation. (3) Additionally, we found that the promotion of IT software capability is stronger than IT hardware capability, and they have a synergistic effect on green technological innovation. Overall, our findings offer a new point view for a deeper understanding of green technological innovation, and provide microscopic evidence for the objective evaluation of corporate IT capability.
{"title":"The Impact of IT Capability on Corporate Green Technological Innovation: Evidence from Manufacturing Companies in China","authors":"Yulian Peng, Jianqing Zhou, Miaoxin Lin, Dawei Feng","doi":"10.1142/s021964922250068x","DOIUrl":"https://doi.org/10.1142/s021964922250068x","url":null,"abstract":"In this paper, we theoretically discuss and empirically show how IT capability spurs companies towards greener strategies. Based on the data of listed Chinese manufacturing companies from 2008 to 2018, using a panel regression model, the results show that: (1) corporate IT capability can promote green technological innovation. (2) Compared with state-owned enterprises (SOEs), the IT effect is more significant for non-state-owned enterprises (non-SOEs); compared with regions with weak environmental regulation, the IT effect is more significant in regions with strong environmental regulation. (3) Additionally, we found that the promotion of IT software capability is stronger than IT hardware capability, and they have a synergistic effect on green technological innovation. Overall, our findings offer a new point view for a deeper understanding of green technological innovation, and provide microscopic evidence for the objective evaluation of corporate IT capability.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131453304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-29DOI: 10.1142/s0219649222500629
M. Therasa, G. Mathivanan
Question answering system is a more eminent research area because of its vast usage in recent years, which can be modelled to solve the deep learning-related limitations. More number of research works have been presented in this question answering field, where most of the systems adopt deep learning as the major contribution. Question answering system focusses on satisfying the users in getting relevant answers regarding a certain question in natural language. This paper presents the incremental question answering system using optimised deep learning. The proposed model covers two-step feature extraction, feature dimension reduction, and deep learning-based classification. From the benchmark dataset collected from a public source, the initial process is to extract the features using word-to-vector. Further, Principle Component Analysis (PCA) is adopted for reducing the dimension of the feature vector. These dimension-reduced features are used for incremental question answering systems by the Optimised Deep Neural Network (O-DNN). Here, the testing weight of the DNN is updated by the Modified Deer Hunting Optimisation Algorithm (M-DHOA) for handling the incremental data. Various implementation details in the algorithms produce better results, which shows the superior performance of the proposed method over existing systems.
{"title":"Development of Novel Incremental Question Answering System Using Optimised Deep Belief Network","authors":"M. Therasa, G. Mathivanan","doi":"10.1142/s0219649222500629","DOIUrl":"https://doi.org/10.1142/s0219649222500629","url":null,"abstract":"Question answering system is a more eminent research area because of its vast usage in recent years, which can be modelled to solve the deep learning-related limitations. More number of research works have been presented in this question answering field, where most of the systems adopt deep learning as the major contribution. Question answering system focusses on satisfying the users in getting relevant answers regarding a certain question in natural language. This paper presents the incremental question answering system using optimised deep learning. The proposed model covers two-step feature extraction, feature dimension reduction, and deep learning-based classification. From the benchmark dataset collected from a public source, the initial process is to extract the features using word-to-vector. Further, Principle Component Analysis (PCA) is adopted for reducing the dimension of the feature vector. These dimension-reduced features are used for incremental question answering systems by the Optimised Deep Neural Network (O-DNN). Here, the testing weight of the DNN is updated by the Modified Deer Hunting Optimisation Algorithm (M-DHOA) for handling the incremental data. Various implementation details in the algorithms produce better results, which shows the superior performance of the proposed method over existing systems.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123883469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-28DOI: 10.1142/s0219649222500654
Ammar Ashraf Bin Narul Akhla, Thong Chee Ling, A. S. Shibghatullah, C. S. Mon, A. Cherukuri, Chaw Lee Yen, Lee Chiw Yi
Real-time information (RTI) is defined as any up-to-date information collected which is immediately made available for the users. RTI is often used in transportation such as location tracking and navigation purposes which can affect the travel experience of travellers. The main objective of this study is to conduct a systematic review of published literature as an evidence regarding the impact of RTI on travellers. To date, there is a lack of comprehensive and structured study in reviewing the impacts of RTI for travellers. Three main research questions drive this review study: (i) what are the purpose, methodology and findings mentioned in literature that involved the impact of RTI? (ii) what elements are impacted by the availability of RTI? and (iii) does RTI help to improve transportation experience? Thirty-one relevant articles are included in the systematic review through a comprehensive literature search strategy which discards irrelevant literature. The 31 articles are significant to the study as they present the general situation of RTI which possesses impact elements for travellers. Based on the review results, three main elements were identified: traveller’s behaviour, traveller’s waiting time and traveller’s path and route choice. Most of the findings from the literature consistently revealed the positive impact of RTI to travellers. This opens to various possibilities and opportunities for the development and improvement of RTI, especially in the transportation field of the future. This study contributes both practically and theoretically for the future research in the utilisation and availability of RTI in the transportation field.
{"title":"Impact of Real-Time Information for Travellers: A Systematic Review","authors":"Ammar Ashraf Bin Narul Akhla, Thong Chee Ling, A. S. Shibghatullah, C. S. Mon, A. Cherukuri, Chaw Lee Yen, Lee Chiw Yi","doi":"10.1142/s0219649222500654","DOIUrl":"https://doi.org/10.1142/s0219649222500654","url":null,"abstract":"Real-time information (RTI) is defined as any up-to-date information collected which is immediately made available for the users. RTI is often used in transportation such as location tracking and navigation purposes which can affect the travel experience of travellers. The main objective of this study is to conduct a systematic review of published literature as an evidence regarding the impact of RTI on travellers. To date, there is a lack of comprehensive and structured study in reviewing the impacts of RTI for travellers. Three main research questions drive this review study: (i) what are the purpose, methodology and findings mentioned in literature that involved the impact of RTI? (ii) what elements are impacted by the availability of RTI? and (iii) does RTI help to improve transportation experience? Thirty-one relevant articles are included in the systematic review through a comprehensive literature search strategy which discards irrelevant literature. The 31 articles are significant to the study as they present the general situation of RTI which possesses impact elements for travellers. Based on the review results, three main elements were identified: traveller’s behaviour, traveller’s waiting time and traveller’s path and route choice. Most of the findings from the literature consistently revealed the positive impact of RTI to travellers. This opens to various possibilities and opportunities for the development and improvement of RTI, especially in the transportation field of the future. This study contributes both practically and theoretically for the future research in the utilisation and availability of RTI in the transportation field.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126608966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-28DOI: 10.1142/s0219649222500642
R. Roshan, O. Rishi
The drastic growth of smart city has considerably gained attention around the world in the international policies and systematic literature. Numerous specialists should include diverse opinions owing to the hurdles to the design of smart cities in India. Thus, these experts have also offered their opinions regarding public, agriculture, industry and academia-fields, which help in developing the smart cities. Generally, more limitations have to be faced with offering energy optimisation and superior performance in Internet of Things (IoT)-enabled smart cities. In wireless sensor networks (WSNs) and IoT, the sensors or IoT devices or nodes are often grouped into clusters that result in selecting the cluster head, which gathers information from the entire nodes in cluster and plainly transmits with the base station. This paper makes an attempt on the development of smart cities in India using the hybrid meta-heuristic-based multi-objective cluster head selection model. The proposed model focusses on the design and development of new smart city model applicable for India by considering a multi-objective function using the constraints like distance, delay, energy, load and temperature of the IoT devices. The optimisation of these variables during the smart city development model by IoT is accomplished by a new hybrid Deer Hunting-Tunicate Swarm Optimisation (DH-TSO) algorithm. The performance of the proposed model is verified through a comparative analysis using various state-of-the-art optimisation models by concerning the number of alive nodes, and normalised energy, and thus ensures the overall lifetime of the network.
{"title":"Design and Development of Multi-Objective Hybrid Clustering Framework for Smart City in India Using Internet of Things","authors":"R. Roshan, O. Rishi","doi":"10.1142/s0219649222500642","DOIUrl":"https://doi.org/10.1142/s0219649222500642","url":null,"abstract":"The drastic growth of smart city has considerably gained attention around the world in the international policies and systematic literature. Numerous specialists should include diverse opinions owing to the hurdles to the design of smart cities in India. Thus, these experts have also offered their opinions regarding public, agriculture, industry and academia-fields, which help in developing the smart cities. Generally, more limitations have to be faced with offering energy optimisation and superior performance in Internet of Things (IoT)-enabled smart cities. In wireless sensor networks (WSNs) and IoT, the sensors or IoT devices or nodes are often grouped into clusters that result in selecting the cluster head, which gathers information from the entire nodes in cluster and plainly transmits with the base station. This paper makes an attempt on the development of smart cities in India using the hybrid meta-heuristic-based multi-objective cluster head selection model. The proposed model focusses on the design and development of new smart city model applicable for India by considering a multi-objective function using the constraints like distance, delay, energy, load and temperature of the IoT devices. The optimisation of these variables during the smart city development model by IoT is accomplished by a new hybrid Deer Hunting-Tunicate Swarm Optimisation (DH-TSO) algorithm. The performance of the proposed model is verified through a comparative analysis using various state-of-the-art optimisation models by concerning the number of alive nodes, and normalised energy, and thus ensures the overall lifetime of the network.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126543064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-27DOI: 10.1142/s0219649222500691
Xi Song
This paper studies the relationship between corporate social responsibility (CSR) and stock price volatility based on data of Chinese A-share listed companies from 2010 to 2018, and further analyses the path and underlying mechanism of CSR that affects stock price volatility. With China’s gradual transition to a sustainable development model, both public and the government are paying increasing attention to CSR performance. At the same time, the Chinese government takes a more serious attitude towards systemic financial risks, emphasising the importance of controlling systemic risks such as an abnormal stock price on many public occasions. In this context, CSR and stock price volatility have received unprecedented attention, and it’s of great value for both industry and the government to explore the impact of CSR on stock price volatility.
{"title":"The Effect of Corporate Social Responsibility on Stock Price Volatility - Evidence from Chinese Listed Companies","authors":"Xi Song","doi":"10.1142/s0219649222500691","DOIUrl":"https://doi.org/10.1142/s0219649222500691","url":null,"abstract":"This paper studies the relationship between corporate social responsibility (CSR) and stock price volatility based on data of Chinese A-share listed companies from 2010 to 2018, and further analyses the path and underlying mechanism of CSR that affects stock price volatility. With China’s gradual transition to a sustainable development model, both public and the government are paying increasing attention to CSR performance. At the same time, the Chinese government takes a more serious attitude towards systemic financial risks, emphasising the importance of controlling systemic risks such as an abnormal stock price on many public occasions. In this context, CSR and stock price volatility have received unprecedented attention, and it’s of great value for both industry and the government to explore the impact of CSR on stock price volatility.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114069567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-27DOI: 10.1142/s0219649222500678
Guanghui Wei
The task scheduling is one of the core problems of cloud computing and aims to assign tasks reasonably, realise the optimal scheduling strategy and improve the operating efficiency of overall cloud computing system. For the shortcomings of traditional particle swarm optimisation (PSO) algorithm in total completion time and average completion time, a quadratic particle swarm optimisation (QPSO) algorithm is proposed. Using the proposed algorithm, people can find a scheduling result with the short total completion time of task and also ensuring the short average completion time of task. Finally, the research made a simulation experiment with Cloud Sim. Experiment results show that in the same condition setting, the algorithm proposed is superior to the traditional PSO algorithm. When the number of tasks increases, the comprehensive scheduling performance of QPSO is more than 20% higher than that of PSO.
{"title":"Quadratic Particle Swarm Optimisation Algorithm for Task Scheduling Based on Cloud Computing Server","authors":"Guanghui Wei","doi":"10.1142/s0219649222500678","DOIUrl":"https://doi.org/10.1142/s0219649222500678","url":null,"abstract":"The task scheduling is one of the core problems of cloud computing and aims to assign tasks reasonably, realise the optimal scheduling strategy and improve the operating efficiency of overall cloud computing system. For the shortcomings of traditional particle swarm optimisation (PSO) algorithm in total completion time and average completion time, a quadratic particle swarm optimisation (QPSO) algorithm is proposed. Using the proposed algorithm, people can find a scheduling result with the short total completion time of task and also ensuring the short average completion time of task. Finally, the research made a simulation experiment with Cloud Sim. Experiment results show that in the same condition setting, the algorithm proposed is superior to the traditional PSO algorithm. When the number of tasks increases, the comprehensive scheduling performance of QPSO is more than 20% higher than that of PSO.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132566216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-27DOI: 10.1142/s021964922250071x
Rajasekhar Batchu, H. Seetha
In the internet era, network-based services and connected devices are growing with many users, thus it became an increase in the number of cyberattacks. Distributed Denial of Service (DDoS) attacks are the type of cyberattacks increasing their strength and impact on the victim. Effective detection of such attacks through a DDoS Detection System is relatively essential research. Although machine learning techniques have grown in popularity in the field of cybersecurity over the last several years, the change in the attack patterns in recent days shows the need for developing a robust DDoS prediction model. Therefore, we suggested a DDoS prediction system using a two-stage hybrid methodology. Initially, features are extracted by the unsupervised Deep Sparse Autoencoder (DSAE) using Elastic Net regularisation with optimum hyperparameters. Further, several learning models are tuned to classify attacks based on the extracted feature sets. Finally, the models’ performance is analysed with extracted features in balanced and imbalanced data scenarios. The experimental outcomes show that the suggested model outperforms current approaches. The model was evaluated on the CICIDS-2017 and CICDDoS-2019 datasets and achieved an accuracy of 99.98% and 99.99%, respectively.
{"title":"A Hybrid Detection System for DDoS Attacks Based on Deep Sparse Autoencoder and Light Gradient Boost Machine","authors":"Rajasekhar Batchu, H. Seetha","doi":"10.1142/s021964922250071x","DOIUrl":"https://doi.org/10.1142/s021964922250071x","url":null,"abstract":"In the internet era, network-based services and connected devices are growing with many users, thus it became an increase in the number of cyberattacks. Distributed Denial of Service (DDoS) attacks are the type of cyberattacks increasing their strength and impact on the victim. Effective detection of such attacks through a DDoS Detection System is relatively essential research. Although machine learning techniques have grown in popularity in the field of cybersecurity over the last several years, the change in the attack patterns in recent days shows the need for developing a robust DDoS prediction model. Therefore, we suggested a DDoS prediction system using a two-stage hybrid methodology. Initially, features are extracted by the unsupervised Deep Sparse Autoencoder (DSAE) using Elastic Net regularisation with optimum hyperparameters. Further, several learning models are tuned to classify attacks based on the extracted feature sets. Finally, the models’ performance is analysed with extracted features in balanced and imbalanced data scenarios. The experimental outcomes show that the suggested model outperforms current approaches. The model was evaluated on the CICIDS-2017 and CICDDoS-2019 datasets and achieved an accuracy of 99.98% and 99.99%, respectively.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127515649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-21DOI: 10.1142/s0219649222500617
Hela Limam, Amal Zouhair, W. Oueslati
Support vector machine (SVM) is a machine learning method widely used in solving binary data classification problems due to its performance. Nevertheless, in practical problems of classification, there are often cases of the presence of more than two classes of objects in the original dataset. The paper considers a solution to the problem of SVM multiclass with the aim to increase the data classification quality based on a new way of hybridisation between SVM and [Formula: see text]-nearest neighbour (KNN) algorithms. The first phase of the approach is called the filtering phase. At this level, the feature space is split into two classes separated by a hyperplane. In the next step called review, we generate a second hyperplane, then we calculate the distance between each test pattern and the second hyperplane in the feature space using e.g. the KNN function. The result of the two phases is three classes instead of two produced by the conventional SVM. For evaluation purposes, dataset experiments are conducted on seven benchmark datasets that have high dimensionality and large size. Numerical experiments show that the 3SVM approach can improve not only the accuracy compared to other multiclass SVM approaches, but also the precision, recall, and [Formula: see text]-score.
{"title":"A New Hybrid Multiclass Approach Based on KNN and SVM","authors":"Hela Limam, Amal Zouhair, W. Oueslati","doi":"10.1142/s0219649222500617","DOIUrl":"https://doi.org/10.1142/s0219649222500617","url":null,"abstract":"Support vector machine (SVM) is a machine learning method widely used in solving binary data classification problems due to its performance. Nevertheless, in practical problems of classification, there are often cases of the presence of more than two classes of objects in the original dataset. The paper considers a solution to the problem of SVM multiclass with the aim to increase the data classification quality based on a new way of hybridisation between SVM and [Formula: see text]-nearest neighbour (KNN) algorithms. The first phase of the approach is called the filtering phase. At this level, the feature space is split into two classes separated by a hyperplane. In the next step called review, we generate a second hyperplane, then we calculate the distance between each test pattern and the second hyperplane in the feature space using e.g. the KNN function. The result of the two phases is three classes instead of two produced by the conventional SVM. For evaluation purposes, dataset experiments are conducted on seven benchmark datasets that have high dimensionality and large size. Numerical experiments show that the 3SVM approach can improve not only the accuracy compared to other multiclass SVM approaches, but also the precision, recall, and [Formula: see text]-score.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124649621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-20DOI: 10.1142/s0219649222500666
S. Abraham, Binsu C. Kovoor
Visual saliency models mimic the human visual system to gaze towards fixed pixel positions and capture the most conspicuous regions in the scene. They have proved their efficacy in several computer vision applications. This paper provides a comprehensive review of the recent advances in eye fixation prediction and salient object detection, harnessing deep learning. It also provides an overview on multi-modal saliency prediction that considers audio in dynamic scenes. The underlying network structure and loss function for each model are explored to realise how saliency models work. The survey also investigates the inclusion of specific low-level priors in deep learning-based saliency models. The public datasets and evaluation metrics are succinctly introduced. The paper also makes a discussion on the key issues in saliency modeling along with some open problems and growing research directions in the field.
{"title":"Visual Saliency Modeling with Deep Learning: A Comprehensive Review","authors":"S. Abraham, Binsu C. Kovoor","doi":"10.1142/s0219649222500666","DOIUrl":"https://doi.org/10.1142/s0219649222500666","url":null,"abstract":"Visual saliency models mimic the human visual system to gaze towards fixed pixel positions and capture the most conspicuous regions in the scene. They have proved their efficacy in several computer vision applications. This paper provides a comprehensive review of the recent advances in eye fixation prediction and salient object detection, harnessing deep learning. It also provides an overview on multi-modal saliency prediction that considers audio in dynamic scenes. The underlying network structure and loss function for each model are explored to realise how saliency models work. The survey also investigates the inclusion of specific low-level priors in deep learning-based saliency models. The public datasets and evaluation metrics are succinctly introduced. The paper also makes a discussion on the key issues in saliency modeling along with some open problems and growing research directions in the field.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125235972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-20DOI: 10.1142/s0219649222500605
K. Keoy, C. Thong, C. A. Kumar, Yung Jing Koh, Su Mon Chit, Luqman Lee, Japos Genaro, C. Kwek
Technology greatly supports people’s daily lives such as education, business, medical, and many other aspects. It can be noted that the higher education institutions’ students rely on technological support and university assistance for their studies during the COVID-19 pandemic. Technological enablement is the primary determinant for entrepreneurial initiation that received attention from scholars. The focus areas include how governmental support, entrepreneurial intention, entrepreneurial education and technological enablement (mediator factor) can influence the entrepreneurial initiation. Empirical studies showed the direct and indirect impacts of the contributing factors in a particular area. However, is it the same effect of the factors for different countries? This study conducted a self-administered questionnaire to collect topic-related information from higher education institutions in Malaysia and the Philippines. A formative-reflective model, PLS-MGA, was used to analyse the direct and indirect impacts alongside the mediating factor, technological enablement. The results showed that entrepreneurial competencies, Entrepreneurial Education System, Entrepreneurial Education Mechanism, and Entrepreneurial Intention positively and significantly impact entrepreneurial success in both regions. However, the result also demonstrated that the impact of technological enablement on entrepreneurial success is more significant in Malaysia than in the Philippines. With such findings, policymakers and institutions in both countries can understand the insight and importance of technological enablement in stimulating entrepreneurship and its perceived success. Hence, they can implement supportive strategies and necessary policies to ensure technology adoption, success in shaping students’ entrepreneurial mindset and achieving the perceived outcome.
{"title":"An Investigation on the Impact of Technological Enablement on the Success of Entrepreneurial Adoption Among Higher Education Students: A Comparative Study","authors":"K. Keoy, C. Thong, C. A. Kumar, Yung Jing Koh, Su Mon Chit, Luqman Lee, Japos Genaro, C. Kwek","doi":"10.1142/s0219649222500605","DOIUrl":"https://doi.org/10.1142/s0219649222500605","url":null,"abstract":"Technology greatly supports people’s daily lives such as education, business, medical, and many other aspects. It can be noted that the higher education institutions’ students rely on technological support and university assistance for their studies during the COVID-19 pandemic. Technological enablement is the primary determinant for entrepreneurial initiation that received attention from scholars. The focus areas include how governmental support, entrepreneurial intention, entrepreneurial education and technological enablement (mediator factor) can influence the entrepreneurial initiation. Empirical studies showed the direct and indirect impacts of the contributing factors in a particular area. However, is it the same effect of the factors for different countries? This study conducted a self-administered questionnaire to collect topic-related information from higher education institutions in Malaysia and the Philippines. A formative-reflective model, PLS-MGA, was used to analyse the direct and indirect impacts alongside the mediating factor, technological enablement. The results showed that entrepreneurial competencies, Entrepreneurial Education System, Entrepreneurial Education Mechanism, and Entrepreneurial Intention positively and significantly impact entrepreneurial success in both regions. However, the result also demonstrated that the impact of technological enablement on entrepreneurial success is more significant in Malaysia than in the Philippines. With such findings, policymakers and institutions in both countries can understand the insight and importance of technological enablement in stimulating entrepreneurship and its perceived success. Hence, they can implement supportive strategies and necessary policies to ensure technology adoption, success in shaping students’ entrepreneurial mindset and achieving the perceived outcome.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126415433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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