The role of individual differences in cyber dating abuse perpetration
- March, Evita, Grieve, Rachel, Clancy, Elizabeth, Klettke, Bianca, Van Dick, Rolf, Hernandez Bark, Alina
- Authors: March, Evita , Grieve, Rachel , Clancy, Elizabeth , Klettke, Bianca , Van Dick, Rolf , Hernandez Bark, Alina
- Date: 2021
- Type: Text , Journal article
- Relation: Cyberpsychology, Behavior, and Social Networking Vol. 24, no. 7 (2021), p. 457-463
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- Description: There is a growing research interest in cyber dating abuse (CDA). CDA includes abusive online behavior toward a current or former intimate partner, such as aggression, control, harassment, and humiliation. Despite the potential overlap and reciprocal relationship of CDA and intimate partner violence, there remains considerable paucity in research exploring predictors of this abusive online behavior. In the current study, we adopt the General Aggression Model framework and explore the role of gender, hegemonic masculinity, vulnerable narcissism, and sexual aggression myths to predict perpetration of CDA. Participants (N = 415, 51 percent women; Mage = 32.68 years) were recruited via social media advertisements and completed an anonymous, confidential online questionnaire. The questionnaire comprised the Conformity to Masculine Roles Norms Inventory, the Hypersensitive Narcissism Scale, the Acceptance of Modern Myths About Sexual Aggression Scale, and a modified Cyber Aggression in Relationships Scale. A hierarchical regression analysis indicated that hegemonic masculinity, vulnerable narcissism, and sexual aggression myths were all significant positive predictors of perpetrating CDA. As gender was a significant predictor until the inclusion of these variables, a multiple mediation analysis was performed, indicating that both hegemonic masculinity and sexual aggression myths fully mediated the relationship between gender and perpetrating CDA. These results add to the growing body of research exploring how CDA emerges as a behavior and highlight possible implications for management and intervention. © Copyright 2021, Mary Ann Liebert, Inc., publishers 2021.
- Authors: March, Evita , Grieve, Rachel , Clancy, Elizabeth , Klettke, Bianca , Van Dick, Rolf , Hernandez Bark, Alina
- Date: 2021
- Type: Text , Journal article
- Relation: Cyberpsychology, Behavior, and Social Networking Vol. 24, no. 7 (2021), p. 457-463
- Full Text:
- Reviewed:
- Description: There is a growing research interest in cyber dating abuse (CDA). CDA includes abusive online behavior toward a current or former intimate partner, such as aggression, control, harassment, and humiliation. Despite the potential overlap and reciprocal relationship of CDA and intimate partner violence, there remains considerable paucity in research exploring predictors of this abusive online behavior. In the current study, we adopt the General Aggression Model framework and explore the role of gender, hegemonic masculinity, vulnerable narcissism, and sexual aggression myths to predict perpetration of CDA. Participants (N = 415, 51 percent women; Mage = 32.68 years) were recruited via social media advertisements and completed an anonymous, confidential online questionnaire. The questionnaire comprised the Conformity to Masculine Roles Norms Inventory, the Hypersensitive Narcissism Scale, the Acceptance of Modern Myths About Sexual Aggression Scale, and a modified Cyber Aggression in Relationships Scale. A hierarchical regression analysis indicated that hegemonic masculinity, vulnerable narcissism, and sexual aggression myths were all significant positive predictors of perpetrating CDA. As gender was a significant predictor until the inclusion of these variables, a multiple mediation analysis was performed, indicating that both hegemonic masculinity and sexual aggression myths fully mediated the relationship between gender and perpetrating CDA. These results add to the growing body of research exploring how CDA emerges as a behavior and highlight possible implications for management and intervention. © Copyright 2021, Mary Ann Liebert, Inc., publishers 2021.
The spectrum of big data analytics
- Authors: Sun, Zhaohao , Huo, Yanxia
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Computer Information Systems Vol. 61, no. 2 (2021), p. 154-162
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- Description: Big data analytics is playing a pivotal role in big data, artificial intelligence, management, governance, and society with the dramatic development of big data, analytics, artificial intelligence. However, what is the spectrum of big data analytics and how to develop the spectrum are still a fundamental issue in the academic community. This article addresses these issues by presenting a big data derived small data approach. It then uses the proposed approach to analyze the top 150 profiles of Google Scholar, including big data analytics as one research field and proposes a spectrum of big data analytics. The spectrum of big data analytics mainly includes data mining, machine learning, data science and systems, artificial intelligence, distributed computing and systems, and cloud computing, taking into account degree of importance. The proposed approach and findings will generalize to other researchers and practitioners of big data analytics, machine learning, artificial intelligence, and data science. © 2019 International Association for Computer Information Systems.
- Authors: Sun, Zhaohao , Huo, Yanxia
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Computer Information Systems Vol. 61, no. 2 (2021), p. 154-162
- Full Text:
- Reviewed:
- Description: Big data analytics is playing a pivotal role in big data, artificial intelligence, management, governance, and society with the dramatic development of big data, analytics, artificial intelligence. However, what is the spectrum of big data analytics and how to develop the spectrum are still a fundamental issue in the academic community. This article addresses these issues by presenting a big data derived small data approach. It then uses the proposed approach to analyze the top 150 profiles of Google Scholar, including big data analytics as one research field and proposes a spectrum of big data analytics. The spectrum of big data analytics mainly includes data mining, machine learning, data science and systems, artificial intelligence, distributed computing and systems, and cloud computing, taking into account degree of importance. The proposed approach and findings will generalize to other researchers and practitioners of big data analytics, machine learning, artificial intelligence, and data science. © 2019 International Association for Computer Information Systems.
Tracing the Pace of COVID-19 research : topic modeling and evolution
- Liu, Jiaying, Nie, Hansong, Li, Shihao, Ren, Jing, Xia, Feng
- Authors: Liu, Jiaying , Nie, Hansong , Li, Shihao , Ren, Jing , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: Big Data Research Vol. 25, no. (2021), p.
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- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren and Feng Xia" is provided in this record**
- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc.
- Authors: Liu, Jiaying , Nie, Hansong , Li, Shihao , Ren, Jing , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: Big Data Research Vol. 25, no. (2021), p.
- Full Text:
- Reviewed:
- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren and Feng Xia" is provided in this record**
- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc.
A survey on context awareness in big data analytics for business applications
- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
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- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
- Full Text:
- Reviewed:
- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
Community-diversified influence maximization in social networks
- Li, Jianxin, Cai, Taotao, Deng, Ke, Wang, Xinjue, Sellis, Timos, Xia, Feng
- Authors: Li, Jianxin , Cai, Taotao , Deng, Ke , Wang, Xinjue , Sellis, Timos , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Information Systems Vol. 92, no. (2020), p.
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- Description: To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find k nodes such that, if a message is initiated and spread by the k nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets. © 2020 Elsevier Ltd
- Authors: Li, Jianxin , Cai, Taotao , Deng, Ke , Wang, Xinjue , Sellis, Timos , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Information Systems Vol. 92, no. (2020), p.
- Full Text:
- Reviewed:
- Description: To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find k nodes such that, if a message is initiated and spread by the k nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets. © 2020 Elsevier Ltd
Data-driven computational social science : A survey
- Zhang, Jun, Wang, Wei, Xia, Feng, Lin, Yu-Ru, Tong, Hanghang
- Authors: Zhang, Jun , Wang, Wei , Xia, Feng , Lin, Yu-Ru , Tong, Hanghang
- Date: 2020
- Type: Text , Journal article
- Relation: Big Data Research Vol. 21, no. (2020), p. 1-22
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- Description: Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on datadriven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.
- Authors: Zhang, Jun , Wang, Wei , Xia, Feng , Lin, Yu-Ru , Tong, Hanghang
- Date: 2020
- Type: Text , Journal article
- Relation: Big Data Research Vol. 21, no. (2020), p. 1-22
- Full Text:
- Reviewed:
- Description: Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on datadriven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.
Diarrhoeal disease surveillance in Papua New Guinea : findings and challenges
- Abdad, Mohammad, Soli, Kevin, Pham, Bang, Bande, Grace, Maure, Tobias, Jonduo, Marinjo, Kisa, Debbie, Rai, Glennis, Phuanukoonnon, Suparat, Siba, Peter, Horwood, Paul, Greenhill, Andrew
- Authors: Abdad, Mohammad , Soli, Kevin , Pham, Bang , Bande, Grace , Maure, Tobias , Jonduo, Marinjo , Kisa, Debbie , Rai, Glennis , Phuanukoonnon, Suparat , Siba, Peter , Horwood, Paul , Greenhill, Andrew
- Date: 2020
- Type: Text , Journal article
- Relation: Western Pacific Surveillance and Response Vol. 11, no. 1 (Jan-Mar 2020), p. 6
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- Description: Diarrhoeal diseases are among the leading causes of morbidity and mortality in the Western Pacific Region. However, data on the major causes of infectious diarrhoea are limited in many countries within the Region, including Papua New Guinea. In 2013-2014, we conducted surveillance for acute diarrhoeal illness in four provinces in Papua New Guinea. One rural health clinic from each province participated in the surveillance activity. Samples were sent to central laboratories and batch analysed for bacterial and viral gastrointestinal pathogens that are commonly associated with diarrhoea. Across the four sites, the most commonly detected pathogens were Shigella spp., Campylobacter spp. and rotavirus. In this paper, we report the results of the surveillance activity and the challenges that we faced. The lessons learnt may be applicable to other parts of the Region with a similar socioeconomic status.
- Authors: Abdad, Mohammad , Soli, Kevin , Pham, Bang , Bande, Grace , Maure, Tobias , Jonduo, Marinjo , Kisa, Debbie , Rai, Glennis , Phuanukoonnon, Suparat , Siba, Peter , Horwood, Paul , Greenhill, Andrew
- Date: 2020
- Type: Text , Journal article
- Relation: Western Pacific Surveillance and Response Vol. 11, no. 1 (Jan-Mar 2020), p. 6
- Full Text:
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- Description: Diarrhoeal diseases are among the leading causes of morbidity and mortality in the Western Pacific Region. However, data on the major causes of infectious diarrhoea are limited in many countries within the Region, including Papua New Guinea. In 2013-2014, we conducted surveillance for acute diarrhoeal illness in four provinces in Papua New Guinea. One rural health clinic from each province participated in the surveillance activity. Samples were sent to central laboratories and batch analysed for bacterial and viral gastrointestinal pathogens that are commonly associated with diarrhoea. Across the four sites, the most commonly detected pathogens were Shigella spp., Campylobacter spp. and rotavirus. In this paper, we report the results of the surveillance activity and the challenges that we faced. The lessons learnt may be applicable to other parts of the Region with a similar socioeconomic status.
Differences in personality and the sharing of managerial tacit knowledge: an empirical analysis of public sector managers in Malaysia
- Abdul Manaf, Halimah, Harvey, William, Armstrong, Steven, Lawton, Alan
- Authors: Abdul Manaf, Halimah , Harvey, William , Armstrong, Steven , Lawton, Alan
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Knowledge Management Vol. 24, no. 5 (2020), p. 1177-1199
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- Description: Purpose: This study aims to identify differences in knowledge-sharing mechanisms and personality among expert, typical and novice managers within the Malaysian public sector. Strengthening knowledge sharing function is essential for enabling public institutions around the world to be more productive. Design/methodology/approach: This quantitative study involves 308 employees from management and professional groups within 98 local authorities in the Malaysian local government. Stratified random sampling techniques were used and the sampling frame comprised 1,000 staff using postal surveys. Data analyses were carried out using analysis of variance and correlations to test the research hypotheses. Findings: The findings reveal that expert managers are more proactive in sharing their knowledge, particularly those with the personality traits of conscientiousness and openness. These two personality traits were also related to expert behaviours such as thoroughness, responsibility and persistence, which led to work competency and managerial success. Originality/value: This study provides theoretical insights into how managerial tacit knowledge differs and can accumulate, depending on the personality traits of middle managers. The paper shows the different mechanisms of knowledge sharing, tacit knowledge and personality among expert, typical and novice managers. Practically, this study is important for guiding senior managers in their attempts to identify the most appropriate personalities of their middle managers. This study found that the expert group was higher in conscientiousness, openness and overall personality traits compared with the typical and novice groups. The paper also highlights the value of sharing managerial tacit knowledge effectively. © 2020, Emerald Publishing Limited.
- Authors: Abdul Manaf, Halimah , Harvey, William , Armstrong, Steven , Lawton, Alan
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Knowledge Management Vol. 24, no. 5 (2020), p. 1177-1199
- Full Text:
- Reviewed:
- Description: Purpose: This study aims to identify differences in knowledge-sharing mechanisms and personality among expert, typical and novice managers within the Malaysian public sector. Strengthening knowledge sharing function is essential for enabling public institutions around the world to be more productive. Design/methodology/approach: This quantitative study involves 308 employees from management and professional groups within 98 local authorities in the Malaysian local government. Stratified random sampling techniques were used and the sampling frame comprised 1,000 staff using postal surveys. Data analyses were carried out using analysis of variance and correlations to test the research hypotheses. Findings: The findings reveal that expert managers are more proactive in sharing their knowledge, particularly those with the personality traits of conscientiousness and openness. These two personality traits were also related to expert behaviours such as thoroughness, responsibility and persistence, which led to work competency and managerial success. Originality/value: This study provides theoretical insights into how managerial tacit knowledge differs and can accumulate, depending on the personality traits of middle managers. The paper shows the different mechanisms of knowledge sharing, tacit knowledge and personality among expert, typical and novice managers. Practically, this study is important for guiding senior managers in their attempts to identify the most appropriate personalities of their middle managers. This study found that the expert group was higher in conscientiousness, openness and overall personality traits compared with the typical and novice groups. The paper also highlights the value of sharing managerial tacit knowledge effectively. © 2020, Emerald Publishing Limited.
High esteem and hurting others online : trait sadism moderates the relationship between self-esteem and internet trolling
- March, Evita, Steele, Genevieve
- Authors: March, Evita , Steele, Genevieve
- Date: 2020
- Type: Text , Journal article
- Relation: Cyberpsychology, behavior and social networking Vol. 23, no. 7 (2020), p. 441-446
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- Description: Internet trolling is commonly defined as disruptive online behavior, intended to provoke and distress others for amusement. Previous research has shown that gender (specifically, male), trait psychopathy, and trait sadism significantly predict engaging in trolling. In this study, we sought to replicate and extend previous research by exploring the role of self-esteem in predicting trolling, and possible interactions between self-esteem and personality. Participants (n = 400, 67.5 percent women, average age = 24.97 years [SD = 8.84]) completed an online questionnaire, including measures of psychopathy, sadism, self-esteem, and trolling behaviors. Results corroborated previous research showing gender (male) to be a significant predictor of trolling, and trait psychopathy and sadism to be significant positive predictors. Although self-esteem had no additional value on top of trait psychopathy and sadism in explaining trolling, there was a significant interaction between self-esteem and trait sadism. A moderation analysis indicated a positive relationship between self-esteem and trolling, but only when trait sadism was high. These results portray the troll as a callous individual may enjoy causing psychological harm, particularly if their self-esteem is high. These results contribute to building the psychological profile of trolls and provide future directions for research exploring trolling behaviors.
- Authors: March, Evita , Steele, Genevieve
- Date: 2020
- Type: Text , Journal article
- Relation: Cyberpsychology, behavior and social networking Vol. 23, no. 7 (2020), p. 441-446
- Full Text:
- Reviewed:
- Description: Internet trolling is commonly defined as disruptive online behavior, intended to provoke and distress others for amusement. Previous research has shown that gender (specifically, male), trait psychopathy, and trait sadism significantly predict engaging in trolling. In this study, we sought to replicate and extend previous research by exploring the role of self-esteem in predicting trolling, and possible interactions between self-esteem and personality. Participants (n = 400, 67.5 percent women, average age = 24.97 years [SD = 8.84]) completed an online questionnaire, including measures of psychopathy, sadism, self-esteem, and trolling behaviors. Results corroborated previous research showing gender (male) to be a significant predictor of trolling, and trait psychopathy and sadism to be significant positive predictors. Although self-esteem had no additional value on top of trait psychopathy and sadism in explaining trolling, there was a significant interaction between self-esteem and trait sadism. A moderation analysis indicated a positive relationship between self-esteem and trolling, but only when trait sadism was high. These results portray the troll as a callous individual may enjoy causing psychological harm, particularly if their self-esteem is high. These results contribute to building the psychological profile of trolls and provide future directions for research exploring trolling behaviors.
- Grieve, Rachel, March, Evita, Watkinson, Jarrah
- Authors: Grieve, Rachel , March, Evita , Watkinson, Jarrah
- Date: 2020
- Type: Text , Journal article
- Relation: Computers in Human Behavior Vol. 102, no. (Jan 2020), p. 144-150
- Full Text: false
- Reviewed:
- Description: This study was the first to delineate the role of grandiose narcissism and vulnerable narcissism, in addition to self-esteem and self-monitoring, in predicting authentic self-presentation on Facebook. Facebook users (N = 155) answered questions about their personality as well as the persona they present on Facebook, and Euclidean distances quantified the congruence between the two personas. Self-monitoring (ability to modify self-presentation) was included as a control variable in regression analysis. As hypothesised, grandiose narcissism predicted more congruent presentation between the true self and the Facebook self, while vulnerable narcissism predicted a greater difference between the two personas. In contrast to predictions, self-esteem was not associated with congruence between the two selves; however, a follow-up moderation analysis revealed a significant self-esteem vulnerable narcissism interaction. Specifically, for individuals with average and low levels of self-esteem, there is more incongruence between the true self and the Facebook self as a function of increased vulnerable narcissism. Given the psychological benefits associated with authentic self-presentation on Facebook, these findings inform understanding of the negative affective processes of vulnerable narcissists and their self-presentation on this popular social networking medium.
OKC-enabled online knowledge integration: role of group heterogeneity and group interaction process
- Qiu, Jiangnan, Xu, Liwei, Zuo, Min, Wang, Jingxian, Weadon, Helen
- Authors: Qiu, Jiangnan , Xu, Liwei , Zuo, Min , Wang, Jingxian , Weadon, Helen
- Date: 2020
- Type: Text , Journal article
- Relation: Information Technology and People Vol. 34, no. 1 (2020), p. 336-359
- Full Text: false
- Reviewed:
- Description: Purpose: Online knowledge integration has been an important concern of the online knowledge community as it can lead to various positive outcomes of online knowledge coproduction. This paper identifies online knowledge integration factors by considering group heterogeneity and group interaction process. Design/methodology/approach: Based on the categorization-elaboration model (CEM) and interactive team cognition (ITC) theory, a research model that reflects the antecedent's factors and mediating factors of online knowledge integration was developed and empirically examined based on data collected from 2,339,836 data extracted from Wikipedia. Findings: Group interaction process plays an essential mediator role in online knowledge integration. Group knowledge heterogeneity negatively influences online knowledge integration and group experience heterogeneity positively, and they both positively promote online knowledge integration through group interaction process with different paths. Research limitations: Our research concerns the OKC context in one setting (Wikipedia). We expect that the results will generalize to other OKC platforms. Practical implications: The findings of the study could assist the online knowledge community's organizers to understand the motivational mechanisms of online knowledge integration. Group interaction process could be regarded as the key role to promote group wisdom and maintain group independence. Social implications: We advance the understanding of the online knowledge integration and gain a richer understanding of the importance of group interaction independence for online knowledge integration based on the agreement of group wisdom. It suggested keeping group interaction independence is an important aspect for highly online knowledge integration among heterogeneity groups. Originality/value: This study extends CEM and ITC theory to the domain of knowledge integration context and finds the mechanism between group heterogeneity and online knowledge integration by introducing the group interaction process. © 2020, Emerald Publishing Limited.
Random walks : a review of algorithms and applications
- Xia, Feng, Liu, Jiaying, Nie, Hansong, Fu, Yonghao, Wan, Liangtian, Kong, Xiangjie
- Authors: Xia, Feng , Liu, Jiaying , Nie, Hansong , Fu, Yonghao , Wan, Liangtian , Kong, Xiangjie
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 4, no. 2 (2020), p. 95-107
- Full Text:
- Reviewed:
- Description: A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this article, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together. © 2017 IEEE.
- Authors: Xia, Feng , Liu, Jiaying , Nie, Hansong , Fu, Yonghao , Wan, Liangtian , Kong, Xiangjie
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 4, no. 2 (2020), p. 95-107
- Full Text:
- Reviewed:
- Description: A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this article, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together. © 2017 IEEE.
Rapid health data repository allocation using predictive machine learning
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Health Informatics Journal Vol. 26, no. 4 (2020), p. 3009-3036
- Full Text:
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- Description: Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. © The Author(s) 2020.
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Health Informatics Journal Vol. 26, no. 4 (2020), p. 3009-3036
- Full Text:
- Reviewed:
- Description: Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. © The Author(s) 2020.
- Wells, Jonathan, Aryal, Sunil, Ting, Kai
- Authors: Wells, Jonathan , Aryal, Sunil , Ting, Kai
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 8 (2020), p. 3203-3216
- Full Text: false
- Reviewed:
- Description: Existing distance metric learning methods require optimisation to learn a feature space to transform data—this makes them computationally expensive in large datasets. In classification tasks, they make use of class information to learn an appropriate feature space. In this paper, we present a simple supervised dissimilarity measure which does not require learning or optimisation. It uses class information to measure dissimilarity of two data instances in the input space directly. It is a supervised version of an existing data-dependent dissimilarity measure called me. Our empirical results in k-NN and LVQ classification tasks show that the proposed simple supervised dissimilarity measure generally produces predictive accuracy better than or at least as good as existing state-of-the-art supervised and unsupervised dissimilarity measures. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
Social media markers to identify fathers at risk of postpartum depression : a machine learning approach
- Shatte, Adrian, Hutchinson, Delyse, Fuller-Tyszkiewicz, Matthew, Teague, Samantha
- Authors: Shatte, Adrian , Hutchinson, Delyse , Fuller-Tyszkiewicz, Matthew , Teague, Samantha
- Date: 2020
- Type: Text , Journal article
- Relation: Cyberpsychology, Behavior, and Social Networking Vol. 23, no. 9 (2020), p. 611-618
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- Description: Postpartum depression (PPD) is a significant mental health issue in mothers and fathers alike; yet at-risk fathers often come to the attention of health care professionals late due to low awareness of symptoms and reluctance to seek help. This study aimed to examine whether passive social media markers are effective for identifying fathers at risk of PPD. We collected 67,796 Reddit posts from 365 fathers, spanning a 6-month period around the birth of their child. A list of "at-risk"words was developed in collaboration with a perinatal mental health expert. PPD was assessed by evaluating the change in fathers' use of words indicating depressive symptomatology after childbirth. Predictive models were developed as a series of support vector machine classifiers using behavior, emotion, linguistic style, and discussion topics as features. The performance of these classifiers indicates that fathers at risk of PPD can be predicted from their prepartum data alone. Overall, the best performing model used discussion topic features only with a recall score of 0.82. These findings could assist in the development of support and intervention tools for fathers during the prepartum period, with specific applicability to personalized and preventative support tools for at-risk fathers. © Copyright 2020, Mary Ann Liebert, Inc., publishers 2020.
- Authors: Shatte, Adrian , Hutchinson, Delyse , Fuller-Tyszkiewicz, Matthew , Teague, Samantha
- Date: 2020
- Type: Text , Journal article
- Relation: Cyberpsychology, Behavior, and Social Networking Vol. 23, no. 9 (2020), p. 611-618
- Full Text:
- Reviewed:
- Description: Postpartum depression (PPD) is a significant mental health issue in mothers and fathers alike; yet at-risk fathers often come to the attention of health care professionals late due to low awareness of symptoms and reluctance to seek help. This study aimed to examine whether passive social media markers are effective for identifying fathers at risk of PPD. We collected 67,796 Reddit posts from 365 fathers, spanning a 6-month period around the birth of their child. A list of "at-risk"words was developed in collaboration with a perinatal mental health expert. PPD was assessed by evaluating the change in fathers' use of words indicating depressive symptomatology after childbirth. Predictive models were developed as a series of support vector machine classifiers using behavior, emotion, linguistic style, and discussion topics as features. The performance of these classifiers indicates that fathers at risk of PPD can be predicted from their prepartum data alone. Overall, the best performing model used discussion topic features only with a recall score of 0.82. These findings could assist in the development of support and intervention tools for fathers during the prepartum period, with specific applicability to personalized and preventative support tools for at-risk fathers. © Copyright 2020, Mary Ann Liebert, Inc., publishers 2020.
The gene of scientific success
- Kong, Xiangjie, Zhang, Jun, Zhang, Da, Bu, Yi, Ding, Ying, Xia, Feng
- Authors: Kong, Xiangjie , Zhang, Jun , Zhang, Da , Bu, Yi , Ding, Ying , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: ACM Transactions on Knowledge Discovery from Data Vol. 14, no. 4 (2020), p.
- Full Text:
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- Description: This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard work. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our article presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other. © 2020 ACM.
- Authors: Kong, Xiangjie , Zhang, Jun , Zhang, Da , Bu, Yi , Ding, Ying , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: ACM Transactions on Knowledge Discovery from Data Vol. 14, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard work. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our article presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other. © 2020 ACM.
Using radar plots for performance benchmarking at patient and hospital levels using an Australian orthopaedics dataset
- Morales-Silva, Daniel, McPherson, Cameron, Pineda-Villavicencio, Guillermo, Atchison, Rory
- Authors: Morales-Silva, Daniel , McPherson, Cameron , Pineda-Villavicencio, Guillermo , Atchison, Rory
- Date: 2020
- Type: Text , Journal article
- Relation: Health Informatics Journal Vol. 26, no. 3 (2020), p. 2119-2137
- Full Text:
- Reviewed:
- Description: This study will highlight the diagnostic potential that radar plots display for reporting on performance benchmarking from patient admissions to hospital for surgical procedures. Two drawbacks of radar plots – the presence of missing information and ordering of indicators – are addressed. Ten different orthopaedic surgery procedures were considered in this study. Moreover, twelve outcome indicators were provided for each of the 10 surgeries of interest. These indicators were displayed using a radar plot, which we call a scorecard. At the hospital level, we propose a facile process by which to consolidate our 10 scorecards into one. We addressed the ordering of indicators in our scorecards by considering the national median of the indicators as a benchmark. Furthermore, our the consolidated scorecard facilitates concise visualisation and dissemination of complex data. It also enables the classification of providers into potential low and high performers that warrant further investigation. In conclusion, radar plots provide a clear and effective comparative tool for discerning multiple outcome indicators against the benchmarks of patient admission. A case study between two top and bottom performers on a consolidated scorecard (at hospital level) showed that medical provider charges varied more than other outcome indicators. © The Author(s) 2020.
- Authors: Morales-Silva, Daniel , McPherson, Cameron , Pineda-Villavicencio, Guillermo , Atchison, Rory
- Date: 2020
- Type: Text , Journal article
- Relation: Health Informatics Journal Vol. 26, no. 3 (2020), p. 2119-2137
- Full Text:
- Reviewed:
- Description: This study will highlight the diagnostic potential that radar plots display for reporting on performance benchmarking from patient admissions to hospital for surgical procedures. Two drawbacks of radar plots – the presence of missing information and ordering of indicators – are addressed. Ten different orthopaedic surgery procedures were considered in this study. Moreover, twelve outcome indicators were provided for each of the 10 surgeries of interest. These indicators were displayed using a radar plot, which we call a scorecard. At the hospital level, we propose a facile process by which to consolidate our 10 scorecards into one. We addressed the ordering of indicators in our scorecards by considering the national median of the indicators as a benchmark. Furthermore, our the consolidated scorecard facilitates concise visualisation and dissemination of complex data. It also enables the classification of providers into potential low and high performers that warrant further investigation. In conclusion, radar plots provide a clear and effective comparative tool for discerning multiple outcome indicators against the benchmarks of patient admission. A case study between two top and bottom performers on a consolidated scorecard (at hospital level) showed that medical provider charges varied more than other outcome indicators. © The Author(s) 2020.
A mediating effect on erp km model for the performance of oil and gas sector in klang valley: A preliminary study
- Ma’arif, Muhamad, Satar, N. S. M., Singh, D. S. V., Motahar, S. M.
- Authors: Ma’arif, Muhamad , Satar, N. S. M. , Singh, D. S. V. , Motahar, S. M.
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Advanced Trends in Computer Science and Engineering Vol. 8, no. 1.4 S1 (2019), p. 463-468
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- Description: The development of information technology and the internet has created a borderless business environment and increased market competition. Driving globalization trends, information technology facilitates the organization in the aspect of the decision-making process, increasing productivity with cost-effective and fast delivery to meet customer needs. This article presents a conceptual study of ERP KM model and proposes a direction for further investigation. In this study, a literature review on Incentive as mediating effects in ERP KM model against operational and financial performance was analyzed. In order to achieve this target, to maintain the competitive advantage, oil and gas industry players implement Knowledge Management (KM) on Enterprise Resource Planning (ERP) systems. However, most studies focus only on the implementation and improvement of the ERP process flows as compared to KM concepts. This paper covers literary studies related to KM and ERP as well as merging these two concepts to form the appropriate ERP KM model for the oil and gas sector in Klang Valley, Malaysia. The new model of ERP KM Rizam 2019 introduced in this study will be tested for its effectiveness in the oil and gas sector especially in the Klang Valley. It was found that the mediating effect ‘Incentives’ in addition to KM is expected to have a positive relationship on operational and financial performance compared to the direct influences of ERP usage on performance. © 2019, World Academy of Research in Science and Engineering. All rights reserved.
- Authors: Ma’arif, Muhamad , Satar, N. S. M. , Singh, D. S. V. , Motahar, S. M.
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Advanced Trends in Computer Science and Engineering Vol. 8, no. 1.4 S1 (2019), p. 463-468
- Full Text:
- Reviewed:
- Description: The development of information technology and the internet has created a borderless business environment and increased market competition. Driving globalization trends, information technology facilitates the organization in the aspect of the decision-making process, increasing productivity with cost-effective and fast delivery to meet customer needs. This article presents a conceptual study of ERP KM model and proposes a direction for further investigation. In this study, a literature review on Incentive as mediating effects in ERP KM model against operational and financial performance was analyzed. In order to achieve this target, to maintain the competitive advantage, oil and gas industry players implement Knowledge Management (KM) on Enterprise Resource Planning (ERP) systems. However, most studies focus only on the implementation and improvement of the ERP process flows as compared to KM concepts. This paper covers literary studies related to KM and ERP as well as merging these two concepts to form the appropriate ERP KM model for the oil and gas sector in Klang Valley, Malaysia. The new model of ERP KM Rizam 2019 introduced in this study will be tested for its effectiveness in the oil and gas sector especially in the Klang Valley. It was found that the mediating effect ‘Incentives’ in addition to KM is expected to have a positive relationship on operational and financial performance compared to the direct influences of ERP usage on performance. © 2019, World Academy of Research in Science and Engineering. All rights reserved.
A qualitative analysis of internet trolling
- March, Evita, Marrington, Jessica
- Authors: March, Evita , Marrington, Jessica
- Date: 2019
- Type: Text , Journal article
- Relation: Cyberpsychology, Behavior, and Social Networking Vol. 22, no. 3 (2019), p. 192-197
- Full Text: false
- Reviewed:
- Description: Internet trolling is receiving increasing research attention and exploration; however, disagreement and confusion surround definitions of the behavior. In the current study, 379 participants (60 percent women) completed an online questionnaire providing qualitative responses to the following: How do you define Internet trolling? What kind of behaviors constitutes Internet trolling? Does Internet trolling differ from Internet cyberbullying? Have you ever been trolled online, and if so how did it feel? Word frequency analyses indicated that Internet trolling is most commonly characterized as an abusive aggressive behavior. Responses also highlight the subjective nature of humor in trolling depending on whether an individual has trolled. Interestingly, the groups that indicated trolling as a "bullying" behavior were the groups who had never been trolled. Results of the current study highlight the need to differentiate between "kudos" trolling and Cyber Abuse. © 2019, Mary Ann Liebert, Inc.
A simulated annealing-based maximum-margin clustering algorithm
- Seifollahi, Sattar, Bagirov, Adil, Borzeshi, Ehsan, Piccardi, Massimo
- Authors: Seifollahi, Sattar , Bagirov, Adil , Borzeshi, Ehsan , Piccardi, Massimo
- Date: 2019
- Type: Text , Journal article
- Relation: Computational Intelligence Vol. 35, no. 1 (2019), p. 23-41
- Full Text:
- Reviewed:
- Description: Maximum-margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing-based algorithm that is able to mitigate the issue of local minima in the maximum-margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k-means++ and SVM at each step of the annealing process. More precisely, k-means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.
- Authors: Seifollahi, Sattar , Bagirov, Adil , Borzeshi, Ehsan , Piccardi, Massimo
- Date: 2019
- Type: Text , Journal article
- Relation: Computational Intelligence Vol. 35, no. 1 (2019), p. 23-41
- Full Text:
- Reviewed:
- Description: Maximum-margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing-based algorithm that is able to mitigate the issue of local minima in the maximum-margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k-means++ and SVM at each step of the annealing process. More precisely, k-means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.