- Fong, George, Corbett, Jennifer, Thompson, Helen, Feely, Paul, Fong, Barbara, Turville, Kylie, Taylor, Meghan
- Authors: Fong, George , Corbett, Jennifer , Thompson, Helen , Feely, Paul , Fong, Barbara , Turville, Kylie , Taylor, Meghan
- Date: 2011
- Type: Text , Dataset
- Full Text: false
- Description: Lateral Plains and the University of Ballarat’s Centre for eCommerce and Communications were commissioned by Yarra Ranges Council to undertake research to extend understanding of how better information communication technology (ICT) infrastructure and its use can lead to greater levels of innovation across the municipality. The online survey was completed between the period- March, 2011- April, 2011 275 valid responses were received. An ICT survey in online and hardcopy formats was used to gather an evidence base to support ICT. Summary available online. Qualitatitve data may be available by contacting CeCC.
Great South Coast ICT survey, 2011
- Thompson, Helen, Fong, George
- Authors: Thompson, Helen , Fong, George
- Date: 2011
- Type: Text , Dataset
- Full Text:
- Description: A combination of qualitative and quantitative research methods were utilised to collect information from across the Great South Coast(GSC) region Victoria which included (5 municipalities:- Warrnambool City and the Shires of Corangamite, Glenelg, Moyne and Southern Grampians) and were aimed at information regarding telecommunications and broadband access and services, barriers and usage at local levels. Data collection methods included key stakeholder interviews, the online survey, case studies and spatial mapping of the responses and feedback garnered mainly from the surveys. Anticipated NBN access infrastructure has also been mapped.The adopted consultation and research methodology was designed to assess demand and support from business operators, local residents and other stakeholders for next generation broadband for the GSC region. The online survey was a major instrument for gathering data in the period to July 2011. The largest contributions to the 598 valid responses came from Warrnambool (n=166), Hamilton (n=94), Camperdown (n=29) and Portland (n=23). Summary available online. Qualitative data may be available by contacting CeCC.
- Authors: Thompson, Helen , Fong, George
- Date: 2011
- Type: Text , Dataset
- Full Text:
- Description: A combination of qualitative and quantitative research methods were utilised to collect information from across the Great South Coast(GSC) region Victoria which included (5 municipalities:- Warrnambool City and the Shires of Corangamite, Glenelg, Moyne and Southern Grampians) and were aimed at information regarding telecommunications and broadband access and services, barriers and usage at local levels. Data collection methods included key stakeholder interviews, the online survey, case studies and spatial mapping of the responses and feedback garnered mainly from the surveys. Anticipated NBN access infrastructure has also been mapped.The adopted consultation and research methodology was designed to assess demand and support from business operators, local residents and other stakeholders for next generation broadband for the GSC region. The online survey was a major instrument for gathering data in the period to July 2011. The largest contributions to the 598 valid responses came from Warrnambool (n=166), Hamilton (n=94), Camperdown (n=29) and Portland (n=23). Summary available online. Qualitative data may be available by contacting CeCC.
Intelligent energy prediction techniques for fog computing networks
- Farooq, Umar, Shabir, Muhammad, Javed, Muhammad, Imran, Muhammad
- Authors: Farooq, Umar , Shabir, Muhammad , Javed, Muhammad , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Soft Computing Vol. 111, no. (2021), p.
- Full Text:
- Reviewed:
- Description: Energy Efficiency is a key concern for future fog-enabled Internet of Things (IoT). Since Fog Nodes (FNs) are energy-constrained devices, task offloading techniques must consider the energy consumption of the FNs to maximize the performance of IoT applications. In this context, accurate energy prediction can enable the development of intelligent energy-aware task offloading techniques. In this paper, we present two energy prediction techniques, the first one is based on the Recursive Least Square (RLS) filter and the second one uses the Artificial Neural Network (ANN). Both techniques use inputs such as the number of tasks and size of the tasks to predict the energy consumption at different fog nodes. Simulation results show that both techniques have a root mean square error of less than 3%. However, the ANN-based technique shows up to 20% less root mean square error as compared to the RLS-based technique. © 2021 Elsevier B.V.
- Authors: Farooq, Umar , Shabir, Muhammad , Javed, Muhammad , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Soft Computing Vol. 111, no. (2021), p.
- Full Text:
- Reviewed:
- Description: Energy Efficiency is a key concern for future fog-enabled Internet of Things (IoT). Since Fog Nodes (FNs) are energy-constrained devices, task offloading techniques must consider the energy consumption of the FNs to maximize the performance of IoT applications. In this context, accurate energy prediction can enable the development of intelligent energy-aware task offloading techniques. In this paper, we present two energy prediction techniques, the first one is based on the Recursive Least Square (RLS) filter and the second one uses the Artificial Neural Network (ANN). Both techniques use inputs such as the number of tasks and size of the tasks to predict the energy consumption at different fog nodes. Simulation results show that both techniques have a root mean square error of less than 3%. However, the ANN-based technique shows up to 20% less root mean square error as compared to the RLS-based technique. © 2021 Elsevier B.V.
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.
- 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.
- 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.
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.
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
- 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.
- 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.
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
- 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.
- 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.
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
- 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.
- 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.
Business analytics-based enterprise information systems
- Sun, Zhaohao, Strang, Kenneth, Firmin, Sally
- Authors: Sun, Zhaohao , Strang, Kenneth , Firmin, Sally
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Computer Information Systems Vol. 57, no. 2 (2017), p. 169-178
- Full Text:
- Reviewed:
- Description: Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.
- Authors: Sun, Zhaohao , Strang, Kenneth , Firmin, Sally
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Computer Information Systems Vol. 57, no. 2 (2017), p. 169-178
- Full Text:
- Reviewed:
- Description: Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.
A global optimisation approach to classification in medical diagnosis and prognosis
- Bagirov, Adil, Rubinov, Alex, Yearwood, John, Stranieri, Andrew
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
Integrated generalized zero-shot learning for fine-grained classification
- Shermin, Tasfia, Teng, Shyh, Sohel, Ferdous, Murshed, Manzur, Lu, Guojun
- Authors: Shermin, Tasfia , Teng, Shyh , Sohel, Ferdous , Murshed, Manzur , Lu, Guojun
- Date: 2022
- Type: Text , Journal article
- Relation: Pattern Recognition Vol. 122, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets. © 2021 Elsevier Ltd
- Authors: Shermin, Tasfia , Teng, Shyh , Sohel, Ferdous , Murshed, Manzur , Lu, Guojun
- Date: 2022
- Type: Text , Journal article
- Relation: Pattern Recognition Vol. 122, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets. © 2021 Elsevier Ltd
Matching algorithms : fundamentals, applications and challenges
- Ren, Jing, Xia, Feng, Chen, Xiangtai, Liu, Jiaying, Sultanova, Nargiz
- Authors: Ren, Jing , Xia, Feng , Chen, Xiangtai , Liu, Jiaying , Sultanova, Nargiz
- Date: 2021
- Type: Text , Journal article , Review
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 5, no. 3 (2021), p. 332-350
- Full Text:
- Reviewed:
- Description: Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory's basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching. © 2017 IEEE. **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, Xia Feng, Nargiz Sultanova" is provided in this record**
- Authors: Ren, Jing , Xia, Feng , Chen, Xiangtai , Liu, Jiaying , Sultanova, Nargiz
- Date: 2021
- Type: Text , Journal article , Review
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 5, no. 3 (2021), p. 332-350
- Full Text:
- Reviewed:
- Description: Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory's basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching. © 2017 IEEE. **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, Xia Feng, Nargiz Sultanova" is provided in this record**
Applying Turner's three-process theory of power to the study of power relations in a troubled information systems implementation
- Ye, Michelle, de Salas, Kristy, Ollington, Nadia, McKay, Judy
- Authors: Ye, Michelle , de Salas, Kristy , Ollington, Nadia , McKay, Judy
- Date: 2017
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 21, no. (2017), p. 1-25
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- Description: This paper explores the nature and exercise of power in an interpretive case study of a troubled information systems (IS) implementation in a university in the Asia Pacific region using Turner's Three-Process Theory of Power based on Social Identity Theory and Self-Categorisation Theory. The findings demonstrate the value of Turner's theoretical lens as well as its insufficiency for explaining all power related activities. This research has led to the development of an extended Three-Process Theory of Power by adding the alternative components that emerged from the data in the case study in relation to the nature and exercises of power. Based on the findings, we further recommend specific guidelines for IS theoreticians and practitioners including advice to project managers on a range of key issues. Thus, this paper contributes theorising the sources of power and tactical applications of power in given situations, particularly in IS implementation projects. © 2017 Ye, de Salas, Ollington & McKay.
- Authors: Ye, Michelle , de Salas, Kristy , Ollington, Nadia , McKay, Judy
- Date: 2017
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 21, no. (2017), p. 1-25
- Full Text:
- Reviewed:
- Description: This paper explores the nature and exercise of power in an interpretive case study of a troubled information systems (IS) implementation in a university in the Asia Pacific region using Turner's Three-Process Theory of Power based on Social Identity Theory and Self-Categorisation Theory. The findings demonstrate the value of Turner's theoretical lens as well as its insufficiency for explaining all power related activities. This research has led to the development of an extended Three-Process Theory of Power by adding the alternative components that emerged from the data in the case study in relation to the nature and exercises of power. Based on the findings, we further recommend specific guidelines for IS theoreticians and practitioners including advice to project managers on a range of key issues. Thus, this paper contributes theorising the sources of power and tactical applications of power in given situations, particularly in IS implementation projects. © 2017 Ye, de Salas, Ollington & McKay.
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
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- 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.
Group decision making in health care : A case study of multidisciplinary meetings
- Sharma, Vishakha, Stranieri, Andrew, Burstein, Frada, Warren, Jim, Daly, Sharon, Patterson, Louise, Yearwood, John, Wolff, Alan
- Authors: Sharma, Vishakha , Stranieri, Andrew , Burstein, Frada , Warren, Jim , Daly, Sharon , Patterson, Louise , Yearwood, John , Wolff, Alan
- Date: 2016
- Type: Text , Journal article
- Relation: Journal of Decision Systems Vol. 25, no. (2016), p. 476-485
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- Description: Abstract: Recent studies have demonstrated that Multi-Disciplinary Meetings (MDM) practiced in some medical contexts can contribute to positive health care outcomes. The group reasoning and decision-making in MDMs has been found to be most effective when deliberations revolve around the patient’s needs, comprehensive information is available during the meeting, core members attend and the MDM is effectively facilitated. This article presents a case study of the MDMs in cancer care in a region of Australia. The case study draws on a group reasoning model called the Reasoning Community model to analyse MDM deliberations to illustrate that many factors are important to support group reasoning, not solely the provision of pertinent information. The case study has implications for the use of data analytics in any group reasoning context. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
- Authors: Sharma, Vishakha , Stranieri, Andrew , Burstein, Frada , Warren, Jim , Daly, Sharon , Patterson, Louise , Yearwood, John , Wolff, Alan
- Date: 2016
- Type: Text , Journal article
- Relation: Journal of Decision Systems Vol. 25, no. (2016), p. 476-485
- Full Text:
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- Description: Abstract: Recent studies have demonstrated that Multi-Disciplinary Meetings (MDM) practiced in some medical contexts can contribute to positive health care outcomes. The group reasoning and decision-making in MDMs has been found to be most effective when deliberations revolve around the patient’s needs, comprehensive information is available during the meeting, core members attend and the MDM is effectively facilitated. This article presents a case study of the MDMs in cancer care in a region of Australia. The case study draws on a group reasoning model called the Reasoning Community model to analyse MDM deliberations to illustrate that many factors are important to support group reasoning, not solely the provision of pertinent information. The case study has implications for the use of data analytics in any group reasoning context. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
An empirical evaluation of the potential of public e-procurement to reduce corruption
- Neupane, Arjun, Soar, Jeffrey, Vaidya, Kishor
- Authors: Neupane, Arjun , Soar, Jeffrey , Vaidya, Kishor
- Date: 2014
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 18, no. 2 (2014), p. 21-44
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- Description: One of the significant potential benefits of e-procurement technology is reducing opportunities for corruption in public procurement processes. The authors identified anticorruption capabilities of e-procurement through an extensive literature review and a theoretical model representing the impact of three latent variables: monopoly of power, information asymmetry, and transparency and accountability upon the dependent variable, the intent-to-adopt e-procurement. This research was guided by the Principal- Agent theory and collected the perceptions of 46 government officers of the potential of public e-procurement to reduce corruption in public procurement processes. Results were analysed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings suggest that the intent-to-adopt e-procurement has a positive and significant relationship with the independent variables that might inform developing countries in strategies to combat corruption in public procurement.
- Authors: Neupane, Arjun , Soar, Jeffrey , Vaidya, Kishor
- Date: 2014
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 18, no. 2 (2014), p. 21-44
- Full Text:
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- Description: One of the significant potential benefits of e-procurement technology is reducing opportunities for corruption in public procurement processes. The authors identified anticorruption capabilities of e-procurement through an extensive literature review and a theoretical model representing the impact of three latent variables: monopoly of power, information asymmetry, and transparency and accountability upon the dependent variable, the intent-to-adopt e-procurement. This research was guided by the Principal- Agent theory and collected the perceptions of 46 government officers of the potential of public e-procurement to reduce corruption in public procurement processes. Results were analysed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings suggest that the intent-to-adopt e-procurement has a positive and significant relationship with the independent variables that might inform developing countries in strategies to combat corruption in public procurement.
Enabling situational awareness of business processes
- Zhao, Xiaohui, Yongchareon, Sira, Cho, Nam-Wook
- Authors: Zhao, Xiaohui , Yongchareon, Sira , Cho, Nam-Wook
- Date: 2021
- Type: Text , Journal article
- Relation: Business Process Management Journal Vol. 27, no. 3 (2021), p. 779-795
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- Description: Purpose: The purpose of this research is to explore the ways of integrating situational awareness into business process management for the purpose of realising hyper automated business processes. Such business processes will help improve their customer experiences, enhance the reliability of service delivery and lower the operational cost for a more competitive and sustainable business. Design/methodology/approach: Ontology has been deployed to establish the context modelling method, and the event handling mechanisms are developed on the basis of event calculus. An approach on performance of the proposed approach has been evaluation by checking the cost savings from the simulation of a large number of business processes. Findings: In this research, the authors have formalised the context presentation for a business process with a focus on rules and entities to support context perception; proposed a system architecture to illustrate the structure and constitution of a supporting system for intelligent and situation aware business process management; developed real-time event elicitation and interpretation mechanisms to operationalise the perception of contextual dynamics and real-time responses; and evaluated the applicability of the proposed approaches and the performance improvement to business processes. Originality/value: This paper presents a framework covering process context modelling, system architecture and real-time event handling mechanisms to support situational awareness of business processes. The reported research is based on our previous work on radio frequency identification-enabled applications and context-aware business process management with substantial extension to process context modelling and process simulation. © 2021, Emerald Publishing Limited.
- Authors: Zhao, Xiaohui , Yongchareon, Sira , Cho, Nam-Wook
- Date: 2021
- Type: Text , Journal article
- Relation: Business Process Management Journal Vol. 27, no. 3 (2021), p. 779-795
- Full Text:
- Reviewed:
- Description: Purpose: The purpose of this research is to explore the ways of integrating situational awareness into business process management for the purpose of realising hyper automated business processes. Such business processes will help improve their customer experiences, enhance the reliability of service delivery and lower the operational cost for a more competitive and sustainable business. Design/methodology/approach: Ontology has been deployed to establish the context modelling method, and the event handling mechanisms are developed on the basis of event calculus. An approach on performance of the proposed approach has been evaluation by checking the cost savings from the simulation of a large number of business processes. Findings: In this research, the authors have formalised the context presentation for a business process with a focus on rules and entities to support context perception; proposed a system architecture to illustrate the structure and constitution of a supporting system for intelligent and situation aware business process management; developed real-time event elicitation and interpretation mechanisms to operationalise the perception of contextual dynamics and real-time responses; and evaluated the applicability of the proposed approaches and the performance improvement to business processes. Originality/value: This paper presents a framework covering process context modelling, system architecture and real-time event handling mechanisms to support situational awareness of business processes. The reported research is based on our previous work on radio frequency identification-enabled applications and context-aware business process management with substantial extension to process context modelling and process simulation. © 2021, Emerald Publishing Limited.
Understanding personal use of the Internet at work: An integrated model of neutralization techniques and general deterrence theory
- Cheng, Lijiao, Li, Wenli, Zhai, Qingguo, Smyth, Russell
- Authors: Cheng, Lijiao , Li, Wenli , Zhai, Qingguo , Smyth, Russell
- Date: 2014
- Type: Text , Journal article
- Relation: Computers in Human Behavior Vol. 38, no. (September 2014 2014), p. 220-228
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- Description: This paper examines the influence of neutralization techniques, perceived sanction severity, perceived detection certainty and perceived benefits of using the Internet for personal purposes on intention to use the Internet at work for personal use. To do so, we draw on a conceptual framework integrating neutralization theory and general deterrence theory. The study finds that both neutralization techniques and perceived benefits have a positive effect on personal use of the Internet. Perceived detection certainty is found to have a negative effect on personal use of the Internet, while the effect of perceived sanctions severity on personal use of the Internet is not significant. The effect of neutralization and perceived benefits are much stronger than perceived detection certainty. The findings suggest that people may think more about neutralization and perceived benefits than they do about costs, when deciding whether to use the Internet at work for personal purposes.
- Description: C1
- Authors: Cheng, Lijiao , Li, Wenli , Zhai, Qingguo , Smyth, Russell
- Date: 2014
- Type: Text , Journal article
- Relation: Computers in Human Behavior Vol. 38, no. (September 2014 2014), p. 220-228
- Full Text:
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- Description: This paper examines the influence of neutralization techniques, perceived sanction severity, perceived detection certainty and perceived benefits of using the Internet for personal purposes on intention to use the Internet at work for personal use. To do so, we draw on a conceptual framework integrating neutralization theory and general deterrence theory. The study finds that both neutralization techniques and perceived benefits have a positive effect on personal use of the Internet. Perceived detection certainty is found to have a negative effect on personal use of the Internet, while the effect of perceived sanctions severity on personal use of the Internet is not significant. The effect of neutralization and perceived benefits are much stronger than perceived detection certainty. The findings suggest that people may think more about neutralization and perceived benefits than they do about costs, when deciding whether to use the Internet at work for personal purposes.
- Description: C1
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
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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:
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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.