Aim/Purpose The primary purpose of this study is to provide a cost-effective and artificial intelligence enabled security solution for IoT enabled healthcare ecosystem. It helps to implement, improve, and add new attributes to healthcare services. The paper aims to develop a method based on an artificial neural network technique to predict suspicious devices based on bandwidth usage. Background COVID has made it mandatory to make medical services available online to every remote place. However, services in the healthcare ecosystem require fast, uninterrupted facilities while securing the data flowing through them. The solution in this paper addresses both the security and uninterrupted services issue. This paper proposes a neural network based solution to detect and disable suspicious devices without interrupting critical and life-saving services. Methodology This paper is an advancement on our previous research, where we performed manual knowledge-based intrusion detection. In this research, all the experiments were executed in the healthcare domain. The mobility pattern of the devices was divided into six parts, and each one is assigned a dedicated slice. The security module regularly monitored all the clients connected to slices, and machine learning was used to detect and disable the problematic or suspicious devices. We have used MATLAB’s neural network to train the dataset and automatically detect and disable suspicious devices. The different network architectures and different training algorithms (Levenberg–Marquardt and Bayesian Framework) in MATLAB software have attempted to achieve more precise values with different properties. Five iterations of training were executed and compared to get the best result of R=99971. We configured the application to handle the four most applicable use cases. We also performed an experimental application simulation for the assessment and validation of predictions. Contribution This paper provides a security solution for the IoT enabled healthcare system. The architectures discussed suggest an end-to-end solution on the sliced network. Efficient use of artificial neural networks detects and block suspicious devices. Moreover, the solution can be modified, configured and deployed in many other ecosystems like home automation. Findings This simulation is a subset of the more extensive simulation previously performed on the sliced network to enhance its security. This paper trained the data using a neural network to make the application intelligent and robust. This enhancement helps detect suspicious devices and isolate them before any harm is caused on the network. The solution works both for an intrusion detection and prevention system by detecting and blocking them from using network resources. The result concludes that using multiple hidden layers and a non-linear transfer function, logsig improved the learning and results. Recommendations Everything from offices, schools, colleges, and e-consultation is
{"title":"Security as a Solution: An Intrusion Detection System Using a Neural Network for IoT Enabled Healthcare Ecosystem","authors":"Anshul Jain, Tanya Singh, Satyendra Kumar Sharma","doi":"10.28945/4838","DOIUrl":"https://doi.org/10.28945/4838","url":null,"abstract":"Aim/Purpose The primary purpose of this study is to provide a cost-effective and artificial intelligence enabled security solution for IoT enabled healthcare ecosystem. It helps to implement, improve, and add new attributes to healthcare services. The paper aims to develop a method based on an artificial neural network technique to predict suspicious devices based on bandwidth usage. Background COVID has made it mandatory to make medical services available online to every remote place. However, services in the healthcare ecosystem require fast, uninterrupted facilities while securing the data flowing through them. The solution in this paper addresses both the security and uninterrupted services issue. This paper proposes a neural network based solution to detect and disable suspicious devices without interrupting critical and life-saving services. Methodology This paper is an advancement on our previous research, where we performed manual knowledge-based intrusion detection. In this research, all the experiments were executed in the healthcare domain. The mobility pattern of the devices was divided into six parts, and each one is assigned a dedicated slice. The security module regularly monitored all the clients connected to slices, and machine learning was used to detect and disable the problematic or suspicious devices. We have used MATLAB’s neural network to train the dataset and automatically detect and disable suspicious devices. The different network architectures and different training algorithms (Levenberg–Marquardt and Bayesian Framework) in MATLAB software have attempted to achieve more precise values with different properties. Five iterations of training were executed and compared to get the best result of R=99971. We configured the application to handle the four most applicable use cases. We also performed an experimental application simulation for the assessment and validation of predictions. Contribution This paper provides a security solution for the IoT enabled healthcare system. The architectures discussed suggest an end-to-end solution on the sliced network. Efficient use of artificial neural networks detects and block suspicious devices. Moreover, the solution can be modified, configured and deployed in many other ecosystems like home automation. Findings This simulation is a subset of the more extensive simulation previously performed on the sliced network to enhance its security. This paper trained the data using a neural network to make the application intelligent and robust. This enhancement helps detect suspicious devices and isolate them before any harm is caused on the network. The solution works both for an intrusion detection and prevention system by detecting and blocking them from using network resources. The result concludes that using multiple hidden layers and a non-linear transfer function, logsig improved the learning and results. Recommendations Everything from offices, schools, colleges, and e-consultation is ","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69308658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paul Kariuki, Lizzy O Ofusori, Prabhakar Rontala Subramanniam, M. Okpeku, Maria L Goyayi
Aim/Purpose The paper's objective is to examine the challenges of using the mobile phone to mine location data for effective contact tracing of symptomatic, pre-sympto-matic, and asymptomatic individuals and the implications of this technology for public health governance Background The COVID-19 crisis has created an unprecedented need for contact tracing across South Africa, requiring thousands of people to be traced and their details captured in government health databases as part of public health efforts aimed at breaking the chains of transmission Contact tracing for COVID-19 requires the identification of persons who may have been exposed to the virus and fol-lowing them up daily for 14 days from the last point of exposure Mining mo-bile phone location data can play a critical role in locating people from the time they were identified as contacts to the time they access medical assistance In this case, it aids data flow to various databases designated for COVID-19 work Methodology The researchers conducted a review of the available literature on this subject drawing from academic articles published in peer-reviewed journals, research re-ports, and other relevant national and international government documents re-porting on public health and COVID-19 Document analysis was used as the primary research method, drawing on the case studies Contribution Contact tracing remains a critical strategy in curbing the deadly COVID-19 pan-demic in South Africa and elsewhere in the world However, given increasing concern regarding its invasive nature and possible infringement of individual liberties, it is imperative to interrogate the challenges related to its implementa-tion to ensure a balance with public governance The research findings can thus be used to inform policies and practices associated with contact tracing in South Africa Findings The study found that contact tracing using mobile phone location data mining can be used to enforce quarantine measures such as lockdowns aimed at miti-gating a public health emergency such as COVID-19 However, the use of tech-nology can expose the public to criminal activities by exposing their locations From a public governance point of view, any exposure of the public to social ills is highly undesirable Recommendations for Practitioners In using contact tracing apps to provide pertinent data location caution needs to be exercised to ensure that sensitive private information is not made public to the extent that it compromises citizens' safety and security The study recom-mends the development and implementation of data use protocols to support the use of this technology, in order to mitigate against infringement of individ-ual privacy and other civil liberties Recommendations for Researchers Researchers should explore ways of improving digital applications in order to improve the acceptability of the use of contact tracing technology to manage pandemics such as COVID-19, paying attention to ethical consideratio
{"title":"Challenges in Contact Tracing by Mining Mobile Phone Location Data for COVID-19: Implications for Public Governance in South Africa","authors":"Paul Kariuki, Lizzy O Ofusori, Prabhakar Rontala Subramanniam, M. Okpeku, Maria L Goyayi","doi":"10.28945/4736","DOIUrl":"https://doi.org/10.28945/4736","url":null,"abstract":"Aim/Purpose The paper's objective is to examine the challenges of using the mobile phone to mine location data for effective contact tracing of symptomatic, pre-sympto-matic, and asymptomatic individuals and the implications of this technology for public health governance Background The COVID-19 crisis has created an unprecedented need for contact tracing across South Africa, requiring thousands of people to be traced and their details captured in government health databases as part of public health efforts aimed at breaking the chains of transmission Contact tracing for COVID-19 requires the identification of persons who may have been exposed to the virus and fol-lowing them up daily for 14 days from the last point of exposure Mining mo-bile phone location data can play a critical role in locating people from the time they were identified as contacts to the time they access medical assistance In this case, it aids data flow to various databases designated for COVID-19 work Methodology The researchers conducted a review of the available literature on this subject drawing from academic articles published in peer-reviewed journals, research re-ports, and other relevant national and international government documents re-porting on public health and COVID-19 Document analysis was used as the primary research method, drawing on the case studies Contribution Contact tracing remains a critical strategy in curbing the deadly COVID-19 pan-demic in South Africa and elsewhere in the world However, given increasing concern regarding its invasive nature and possible infringement of individual liberties, it is imperative to interrogate the challenges related to its implementa-tion to ensure a balance with public governance The research findings can thus be used to inform policies and practices associated with contact tracing in South Africa Findings The study found that contact tracing using mobile phone location data mining can be used to enforce quarantine measures such as lockdowns aimed at miti-gating a public health emergency such as COVID-19 However, the use of tech-nology can expose the public to criminal activities by exposing their locations From a public governance point of view, any exposure of the public to social ills is highly undesirable Recommendations for Practitioners In using contact tracing apps to provide pertinent data location caution needs to be exercised to ensure that sensitive private information is not made public to the extent that it compromises citizens' safety and security The study recom-mends the development and implementation of data use protocols to support the use of this technology, in order to mitigate against infringement of individ-ual privacy and other civil liberties Recommendations for Researchers Researchers should explore ways of improving digital applications in order to improve the acceptability of the use of contact tracing technology to manage pandemics such as COVID-19, paying attention to ethical consideratio","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69308871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aim/Purpose: This paper reports a case study of organizational transition from a non-competitive selection method to a novel bidding method for the selection of consultants in the Architectural and Engineering (A/E) industry. Background: Public procurement agencies are increasingly relying on external consultants for the design of construction projects. Consultant selection can be based on either competitive bidding, or quality-based criteria, or some combination between these two approaches. Methodology: Different sources of information were reviewed: internal documents, and quantitative data from the enterprise software platform (ERP). In addition, informal and unstructured interviews were conducted with relevant officials. Contribution: As there are mixed opinions in the scientific literature regarding the use of competitive bidding for the selection of consultants in the A/E industry, this paper contributes a detailed review of a transition to a competitive selection method and provides a financial and qualitative comparison between the two methods. In addition, the method implemented is novel, as it delegates most of the responsibility of hiring and managing consultants to one main contractor. Findings: While the new selection method was intended to reduce bureaucratic overload, it has unexpectedly also succeeded to reduce costs as well. Recommendations for Practitioners: It may be more efficient and profitable to adopt the selection method described in this study. Recommendation for Researchers: Similar methods can be applied to other industries successfully. Impact on Society: Our method was applied in a public organization and resulted in a better outcome, both financial and managerial. Adopting this approach can benefit public budgets. Future Research: The selection, data storage, and analysis methods are interrelated components. Future analysis of these components can help better shape the consultant selection process.
{"title":"Transition to a Competitive Consultant Selection Method: A Case Study of a Public Agency in Israel","authors":"A. Mitelman, Yahel Giat","doi":"10.28945/4891","DOIUrl":"https://doi.org/10.28945/4891","url":null,"abstract":"Aim/Purpose: This paper reports a case study of organizational transition from a non-competitive selection method to a novel bidding method for the selection of consultants in the Architectural and Engineering (A/E) industry. Background: Public procurement agencies are increasingly relying on external consultants for the design of construction projects. Consultant selection can be based on either competitive bidding, or quality-based criteria, or some combination between these two approaches. Methodology: Different sources of information were reviewed: internal documents, and quantitative data from the enterprise software platform (ERP). In addition, informal and unstructured interviews were conducted with relevant officials. Contribution: As there are mixed opinions in the scientific literature regarding the use of competitive bidding for the selection of consultants in the A/E industry, this paper contributes a detailed review of a transition to a competitive selection method and provides a financial and qualitative comparison between the two methods. In addition, the method implemented is novel, as it delegates most of the responsibility of hiring and managing consultants to one main contractor. Findings: While the new selection method was intended to reduce bureaucratic overload, it has unexpectedly also succeeded to reduce costs as well. Recommendations for Practitioners: It may be more efficient and profitable to adopt the selection method described in this study. Recommendation for Researchers: Similar methods can be applied to other industries successfully. Impact on Society: Our method was applied in a public organization and resulted in a better outcome, both financial and managerial. Adopting this approach can benefit public budgets. Future Research: The selection, data storage, and analysis methods are interrelated components. Future analysis of these components can help better shape the consultant selection process.","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69309105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aim/Purpose: This study aims at investigating the factors that influence students’ continuous intention to use Moodle, as an exemplar of learning management systems (LMSs), in the post-adoption phase. Background: Higher education institutions (HEIs) have invested heavily in learning management systems (LMSs), such as Moodle and BlackBoard, as these systems enhance students’ learning and improve their interactions with the educational systems. While most studies on LMSs have focused on the pre-adoption or acceptance phases of this technology, the determinant factors that influence students’ continuance intention to use LMSs have received less attention in the information systems (IS) literature. Methodology: The theoretical model for this study was primarily drawn from the expectation-confirmation model (ECM). A total of 387 Kuwaiti students, from a private American University in the State of Kuwait, participated in this study. Partial least squares (PLS) was employed to analyze the data. Contribution: This study contributes to the existing scientific knowledge in different ways. First, this study extends the expectation confirmation model (ECM) by integrating factors that are important to students’ continuous intention to use LMSs, including system interactivity, effort expectancy, attitude, computer anxiety, self-efficacy, subjective norms, and facilitating conditions. Second, this study adds on a Kuwaiti literature context by focusing on the continuous intention to use LMSs, which is, to the best of our knowledge, the first study that extends and empirically assesses the applicability of the ECM in the LMSs context in a developing country – Kuwait. Third, this study conceptually and empirically differentiates between satisfaction and attitude, as two separate affect constructs, which were taken as interchangeable factors in ECM, and were disregarded by a large number of prior ECM studies concerned with continuous use intention. Finally, this study aims to assist HEIs, faculty members, and systems’ developers in understanding the main factors that influence students’ continuance use intention of LMSs. Findings: While subjective norms were not significant, the results mainly showed that students’ continuous intention to use Moodle is significantly influenced by performance expectancy, effort expectancy, attitude, satisfaction, self-efficacy and facilitating conditions. The study’s results also confirmed that satisfaction and attitude are two conceptually and empirically different constructs, conflicting with the views that these constructs can be taken as interchangeable factors in the ECM. Recommendations for Practitioners: This study offers several useful practical implications. First, given the significant influence of system interactivity on performance expectancy and satisfaction, faculty members should modify their teaching approach by enabling communication and interaction among instructors, students, and peers using the LMS. Second,
{"title":"Students’ Continuance Intention to Use Moodle: An Expectation-Confirmation Model Approach","authors":"Ahmad A. Rabaa'i, Shareef Abu ALmaati, Xiaodi Zhu","doi":"10.28945/4842","DOIUrl":"https://doi.org/10.28945/4842","url":null,"abstract":"Aim/Purpose: This study aims at investigating the factors that influence students’ continuous intention to use Moodle, as an exemplar of learning management systems (LMSs), in the post-adoption phase. Background: Higher education institutions (HEIs) have invested heavily in learning management systems (LMSs), such as Moodle and BlackBoard, as these systems enhance students’ learning and improve their interactions with the educational systems. While most studies on LMSs have focused on the pre-adoption or acceptance phases of this technology, the determinant factors that influence students’ continuance intention to use LMSs have received less attention in the information systems (IS) literature. Methodology: The theoretical model for this study was primarily drawn from the expectation-confirmation model (ECM). A total of 387 Kuwaiti students, from a private American University in the State of Kuwait, participated in this study. Partial least squares (PLS) was employed to analyze the data. Contribution: This study contributes to the existing scientific knowledge in different ways. First, this study extends the expectation confirmation model (ECM) by integrating factors that are important to students’ continuous intention to use LMSs, including system interactivity, effort expectancy, attitude, computer anxiety, self-efficacy, subjective norms, and facilitating conditions. Second, this study adds on a Kuwaiti literature context by focusing on the continuous intention to use LMSs, which is, to the best of our knowledge, the first study that extends and empirically assesses the applicability of the ECM in the LMSs context in a developing country – Kuwait. Third, this study conceptually and empirically differentiates between satisfaction and attitude, as two separate affect constructs, which were taken as interchangeable factors in ECM, and were disregarded by a large number of prior ECM studies concerned with continuous use intention. Finally, this study aims to assist HEIs, faculty members, and systems’ developers in understanding the main factors that influence students’ continuance use intention of LMSs. Findings: While subjective norms were not significant, the results mainly showed that students’ continuous intention to use Moodle is significantly influenced by performance expectancy, effort expectancy, attitude, satisfaction, self-efficacy and facilitating conditions. The study’s results also confirmed that satisfaction and attitude are two conceptually and empirically different constructs, conflicting with the views that these constructs can be taken as interchangeable factors in the ECM. Recommendations for Practitioners: This study offers several useful practical implications. First, given the significant influence of system interactivity on performance expectancy and satisfaction, faculty members should modify their teaching approach by enabling communication and interaction among instructors, students, and peers using the LMS. Second,","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69309338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohamed Emran Hossain, Imran Mahmud, Rozhan M Idrus
Aim/Purpose: The main aim of this study is to identify the factors that influence the continuance intention of use of innovative systems by non-academic employees of a private university and associated academic institutions in Bangladesh. Background: The targeted academic institutions have introduced many new online services aimed at improving students’ access to information and services, including a new online library, ERP or online forum, and the jobs-tracking system (JTS). This research is focused only on the JTS for two reasons. First, it is one of the most crucial systems for the Daffodil Family, as it enables efficient working across many institutes spread across the country and abroad. Second, it is employed in a wide variety of organisational institutes, not just the university. This study aims to discover negative factors that lead to a decrease in users’ intentions to continue using the system. The ultimate goal is to improve the motivation among administrative staff to use technology-related innovation by reducing or eliminating the problems. Methodology: G* power analysis was employed to determine the expected sample size. A questionnaire survey was conducted of 211 users of a new job tracking system from a private university in Bangladesh, to collect data for testing the suggested research model. The data was analysed using the structural equation technique, which is a powerful multivariate analysis mechanism. Contribution: This research contributes to the body of literature and helps better understand users’ continuance intention in the post-implementation phase of the JTS. It complements the micro-level examinations of continuance intention of using IT, by building on our understanding of the phenomenon at the individual level. Specifically, this study examines the role of technostress where organisations invest in IT to make their users more comfortable with innovative and new technologies like the JTS. Findings: This research develops a theoretical advancement of the expectation-confirmation theory, with implications for IT managers and senior management dealing with IT-related behaviour. All proposed hypotheses were supported. Specifically, the predictors of exhaustion – work overload, work–life balance, and role ambiguity – are significant. The core factors for satisfaction, perceived usefulness, and confirmation, are also found to be significant. Finally, satisfaction and exhaustion significantly influence continuance intention, in both positive and negative ways. Recommendations for Practitioners: This study gives an idea about some of the difficulties that people face when implementing new and innovative IT, particularly in academia in Bangladesh. It offers insights into strategies the management may want to follow when implementing new technology like the JTS. This study suggests strategies to increase satisfaction and reduce technostress among new users to enhance organisational support for change. Recommendation
{"title":"Modelling End Users’ Continuance Intention to Use Information Systems in Academic Settings: Expectation-Confirmation and Stress Perspective","authors":"Mohamed Emran Hossain, Imran Mahmud, Rozhan M Idrus","doi":"10.28945/4841","DOIUrl":"https://doi.org/10.28945/4841","url":null,"abstract":"Aim/Purpose: The main aim of this study is to identify the factors that influence the continuance intention of use of innovative systems by non-academic employees of a private university and associated academic institutions in Bangladesh. Background: The targeted academic institutions have introduced many new online services aimed at improving students’ access to information and services, including a new online library, ERP or online forum, and the jobs-tracking system (JTS). This research is focused only on the JTS for two reasons. First, it is one of the most crucial systems for the Daffodil Family, as it enables efficient working across many institutes spread across the country and abroad. Second, it is employed in a wide variety of organisational institutes, not just the university. This study aims to discover negative factors that lead to a decrease in users’ intentions to continue using the system. The ultimate goal is to improve the motivation among administrative staff to use technology-related innovation by reducing or eliminating the problems. Methodology: G* power analysis was employed to determine the expected sample size. A questionnaire survey was conducted of 211 users of a new job tracking system from a private university in Bangladesh, to collect data for testing the suggested research model. The data was analysed using the structural equation technique, which is a powerful multivariate analysis mechanism. Contribution: This research contributes to the body of literature and helps better understand users’ continuance intention in the post-implementation phase of the JTS. It complements the micro-level examinations of continuance intention of using IT, by building on our understanding of the phenomenon at the individual level. Specifically, this study examines the role of technostress where organisations invest in IT to make their users more comfortable with innovative and new technologies like the JTS. Findings: This research develops a theoretical advancement of the expectation-confirmation theory, with implications for IT managers and senior management dealing with IT-related behaviour. All proposed hypotheses were supported. Specifically, the predictors of exhaustion – work overload, work–life balance, and role ambiguity – are significant. The core factors for satisfaction, perceived usefulness, and confirmation, are also found to be significant. Finally, satisfaction and exhaustion significantly influence continuance intention, in both positive and negative ways. Recommendations for Practitioners: This study gives an idea about some of the difficulties that people face when implementing new and innovative IT, particularly in academia in Bangladesh. It offers insights into strategies the management may want to follow when implementing new technology like the JTS. This study suggests strategies to increase satisfaction and reduce technostress among new users to enhance organisational support for change. Recommendation ","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69309325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aim/Purpose: The aim of this study is to determine the variables which affect the intention to use Near Field Communication (NFC)-enabled smart wearables (e.g., smartwatches, rings, wristbands) payments. Background: Despite the enormous potential of wearable payments, studies investigating the adoption of this technology are scarce. Methodology: This study extends the Technology Acceptance Model (TAM) with four additional variables (Perceived Security, Trust, Perceived Cost, and Attractiveness of Alternatives) to investigate behavioral intentions to adopt wearable payments. The moderating role of gender was also examined. Data collected from 311 Kuwaiti respondents were analyzed using Structural Equation Modeling (SEM) and multi-group analysis (MGA). Contribution: The research model provided in this study may be useful for academics and scholars conducting further research into m-payments adoption, specifically in the case of wearable payments where studies are scarce and still in the nascent stage; hence, addressing the gap in existing literature. Further, this study is the first to have specifically investigated wearable payments in the State of Kuwait; therefore, enriching Kuwaiti context literature. Findings: This study empirically demonstrated that behavioral intention to adopt wearable payments is mainly predicted by attractiveness of alternatives, perceived usefulness, perceived ease of use, perceived security and trust, while the role of perceived cost was found to be insignificant. Recommendations for Practitioners: This study draws attention to the importance of cognitive factors, such as perceived usefulness and ease of use, in inducing users’ behavioral intention to adopt wearable payments. As such, in the case of perceived usefulness, smart wearable devices manufacturers and banks enhance the functionalities and features of these devices, expand on the financial services provided through them, and maintain the availability, performance, effectiveness, and efficiency of these tools. In relation to ease of use, smart wearable devices should be designed with an easy to use, high quality and customizable user interface. The findings of this study demonstrated the influence of trust and perceived security in motivating users to adopt wearable payments, Hence, banks are advised to focus on a relationship based on trust, especially during the early stages of acceptance and adoption of wearable payments. Recommendation for Researchers: The current study validated the role of attractiveness of alternatives, which was never examined in the context of wearable payments. This, in turn, provides a new dimension about a determinant factor considered by customers in predicting their behavioral intention to adopt wearable payments. Impact on Society: This study could be used in other countries to compare and verify the results. Additionally, the research model of this study could also be used to investigate other m-payments methods, such as
{"title":"Understanding the Determinants of Wearable Payment Adoption: An Empirical Study","authors":"Ahmad A. Rabaa'i, Xiaodi Zhu","doi":"10.28945/4746","DOIUrl":"https://doi.org/10.28945/4746","url":null,"abstract":"Aim/Purpose: The aim of this study is to determine the variables which affect the intention to use Near Field Communication (NFC)-enabled smart wearables (e.g., smartwatches, rings, wristbands) payments. Background: Despite the enormous potential of wearable payments, studies investigating the adoption of this technology are scarce. Methodology: This study extends the Technology Acceptance Model (TAM) with four additional variables (Perceived Security, Trust, Perceived Cost, and Attractiveness of Alternatives) to investigate behavioral intentions to adopt wearable payments. The moderating role of gender was also examined. Data collected from 311 Kuwaiti respondents were analyzed using Structural Equation Modeling (SEM) and multi-group analysis (MGA). Contribution: The research model provided in this study may be useful for academics and scholars conducting further research into m-payments adoption, specifically in the case of wearable payments where studies are scarce and still in the nascent stage; hence, addressing the gap in existing literature. Further, this study is the first to have specifically investigated wearable payments in the State of Kuwait; therefore, enriching Kuwaiti context literature. Findings: This study empirically demonstrated that behavioral intention to adopt wearable payments is mainly predicted by attractiveness of alternatives, perceived usefulness, perceived ease of use, perceived security and trust, while the role of perceived cost was found to be insignificant. Recommendations for Practitioners: This study draws attention to the importance of cognitive factors, such as perceived usefulness and ease of use, in inducing users’ behavioral intention to adopt wearable payments. As such, in the case of perceived usefulness, smart wearable devices manufacturers and banks enhance the functionalities and features of these devices, expand on the financial services provided through them, and maintain the availability, performance, effectiveness, and efficiency of these tools. In relation to ease of use, smart wearable devices should be designed with an easy to use, high quality and customizable user interface. The findings of this study demonstrated the influence of trust and perceived security in motivating users to adopt wearable payments, Hence, banks are advised to focus on a relationship based on trust, especially during the early stages of acceptance and adoption of wearable payments. Recommendation for Researchers: The current study validated the role of attractiveness of alternatives, which was never examined in the context of wearable payments. This, in turn, provides a new dimension about a determinant factor considered by customers in predicting their behavioral intention to adopt wearable payments. Impact on Society: This study could be used in other countries to compare and verify the results. Additionally, the research model of this study could also be used to investigate other m-payments methods, such as ","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69308601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anantha Raj A. Arokiasamy, K. Nguyen, Hoa Thi Nhu Nguyen
Aim/Purpose: This paper aimed to examine the impact of knowledge management on firm innovativeness of multinational enterprises (MNEs) via the mediating role of innovative culture in Malaysia. Background: Inadequate management practices and growing competition among MNEs operating in developing nations, notably in Malaysia, have hindered their organizational success. Although several studies have shown that knowledge management has a substantial impact on MNEs’ success, it is not apparent if innovation at the company level has a direct impact on their performance. Thus, there is no definitive evidence between knowledge management with business innovativeness and organizational success. Methodology: This study adopted a quantitative approach based on a cross-sectional survey and descriptive design to gather the data in a specific period. A convenient sampling approach was used to select 296 respondents from Malaysia-dependent MNEs of different industries. One of the advantages of this study methodology is that the sample targeted many fields. Afterward, SPSS AMOS 24.0 software package analysis was performed to test the hypotheses. Contribution: The study contributes to knowledge management and firm innovativeness literature through advancing innovative culture as a mediating factor that accounts for the link between these two constructs, especially from an emerging economy perspective. The research findings also offer managerial implications for organizations in their quest to improve firm innovativeness. Findings: The results support that innovative culture significantly affects MNEs’ performance. Innovative culture enhances the capability of MNEs to be innovative that finally leads to the superior performance of firm innovativeness. Recommendations for Practitioners: According to this research, companies that exhibit an innovative culture, the acquisition of new information, the conversion of tacit knowledge into explicit knowledge, the application of knowledge, and the safeguarding of knowledge, all have a positive effect on their innovativeness. This means that for organizations to run an innovative MNE in Malaysia, a creative culture must be fostered since the current study has shown how it is seen as a catalyst that facilitates learning, transformation, and implementation of relevant knowledge. Recommendation for Researchers: Future studies should be carried out in other sectors aside from the manufacturing sector using the same scales used to measure knowledge management. Furthermore, a comparative analysis of knowledge management and firm innovativeness using innovative culture as a mediator should be researched in other developing economies. Impact on Society: While the main aim of this study was to better understand how and why MNEs operate the way they do, it had an indirect impact on the business and political tactics taken by CEOs and managers working in MNEs in developing countries, as this research has shown. Future Research
{"title":"The Impact of Knowledge Management on Firm Innovativeness via Mediating Role of Innovative Culture – The Case of MNEs in Malaysia","authors":"Anantha Raj A. Arokiasamy, K. Nguyen, Hoa Thi Nhu Nguyen","doi":"10.28945/4873","DOIUrl":"https://doi.org/10.28945/4873","url":null,"abstract":"Aim/Purpose: This paper aimed to examine the impact of knowledge management on firm innovativeness of multinational enterprises (MNEs) via the mediating role of innovative culture in Malaysia. Background: Inadequate management practices and growing competition among MNEs operating in developing nations, notably in Malaysia, have hindered their organizational success. Although several studies have shown that knowledge management has a substantial impact on MNEs’ success, it is not apparent if innovation at the company level has a direct impact on their performance. Thus, there is no definitive evidence between knowledge management with business innovativeness and organizational success. Methodology: This study adopted a quantitative approach based on a cross-sectional survey and descriptive design to gather the data in a specific period. A convenient sampling approach was used to select 296 respondents from Malaysia-dependent MNEs of different industries. One of the advantages of this study methodology is that the sample targeted many fields. Afterward, SPSS AMOS 24.0 software package analysis was performed to test the hypotheses. Contribution: The study contributes to knowledge management and firm innovativeness literature through advancing innovative culture as a mediating factor that accounts for the link between these two constructs, especially from an emerging economy perspective. The research findings also offer managerial implications for organizations in their quest to improve firm innovativeness. Findings: The results support that innovative culture significantly affects MNEs’ performance. Innovative culture enhances the capability of MNEs to be innovative that finally leads to the superior performance of firm innovativeness. Recommendations for Practitioners: According to this research, companies that exhibit an innovative culture, the acquisition of new information, the conversion of tacit knowledge into explicit knowledge, the application of knowledge, and the safeguarding of knowledge, all have a positive effect on their innovativeness. This means that for organizations to run an innovative MNE in Malaysia, a creative culture must be fostered since the current study has shown how it is seen as a catalyst that facilitates learning, transformation, and implementation of relevant knowledge. Recommendation for Researchers: Future studies should be carried out in other sectors aside from the manufacturing sector using the same scales used to measure knowledge management. Furthermore, a comparative analysis of knowledge management and firm innovativeness using innovative culture as a mediator should be researched in other developing economies. Impact on Society: While the main aim of this study was to better understand how and why MNEs operate the way they do, it had an indirect impact on the business and political tactics taken by CEOs and managers working in MNEs in developing countries, as this research has shown. Future Research","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69309495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fahad Awad Sawaean, Khairul A. M. Ali, Ahmad Awad Alenezi
Aim/Purpose: This study aimed to investigate the impact of innovation management and learning orientation as the mechanisms playing the role of an intermediate relationship between entrepreneurial leadership and organisational performance of small and medium enterprises (SMEs) in Kuwait. Background: SMEs are currently among the principal economic instruments in most industrialised and developing countries. The contribution of SMEs can be viewed from various perspectives primarily related to the crucial role they play in developing entrepreneurial activities, employment generation, and improving innovativeness. Developing countries, including Kuwait and other countries, in the Gulf Cooperation Council (GCC), have recognised the key role played by SMEs as a strong pillar of growth. Consequently, many governments have formulated policies and programmes to facilitate the growth and success of SMEs. Unfortunately, the organisational performance of SMEs in developing countries, particularly in Kuwait, remains below expectations. The lagged growth could be due to a lack of good managerial practices and increasing competition that negatively impact their performance. Numerous researchers discovered the positive effect of entrepreneurial leadership on SMEs’ performance. However, a lack of clarity remains regarding the direct impact of entrepreneurial leadership on SMEs’ performance, especially in developing countries. Therefore, the nexus between entrepreneurial leadership and organisational performance is still indecisive and requires further studies. Methodology: This study adopted a quantitative approach based on a cross-sectional survey and descriptive design to gather data within a specific period. The data were collected by distributing a survey questionnaire to Kuwaiti SMEs’ owners and Chief Executive Officers (CEOs) via online and on-hand instruments. A total of 384 useable questionnaires were obtained. Moreover, the partial least square-structural equation modelling (PLS-SEM) analysis was performed to test the hypotheses. Contribution: The current study contributed to the existing literature by developing a moderated mediation model integrating entrepreneurial leadership, innovation management, and learning orientation. The study also investigated their effect on the organisational performance of SMEs. The study findings also bridged the existing significant literature gap regarding the role of these variables on SMEs’ performance in developing countries, particularly in Kuwait, due to the dearth of studies linking these variables in this context. Furthermore, this study empirically confirmed the significant effect of innovation management and learning orientation as intermediate variables in strengthening the relationship between entrepreneurial leadership and organisational performance in the settings of Kuwait SMEs, which has not been verified previously. Findings: The study findings showed the beneficial and significant impact of entrepr
{"title":"Entrepreneurial Leadership and Organisational Performance of SMEs in Kuwait: The Intermediate Mechanisms of Innovation Management and Learning Orientation","authors":"Fahad Awad Sawaean, Khairul A. M. Ali, Ahmad Awad Alenezi","doi":"10.28945/4887","DOIUrl":"https://doi.org/10.28945/4887","url":null,"abstract":"Aim/Purpose: This study aimed to investigate the impact of innovation management and learning orientation as the mechanisms playing the role of an intermediate relationship between entrepreneurial leadership and organisational performance of small and medium enterprises (SMEs) in Kuwait. Background: SMEs are currently among the principal economic instruments in most industrialised and developing countries. The contribution of SMEs can be viewed from various perspectives primarily related to the crucial role they play in developing entrepreneurial activities, employment generation, and improving innovativeness. Developing countries, including Kuwait and other countries, in the Gulf Cooperation Council (GCC), have recognised the key role played by SMEs as a strong pillar of growth. Consequently, many governments have formulated policies and programmes to facilitate the growth and success of SMEs. Unfortunately, the organisational performance of SMEs in developing countries, particularly in Kuwait, remains below expectations. The lagged growth could be due to a lack of good managerial practices and increasing competition that negatively impact their performance. Numerous researchers discovered the positive effect of entrepreneurial leadership on SMEs’ performance. However, a lack of clarity remains regarding the direct impact of entrepreneurial leadership on SMEs’ performance, especially in developing countries. Therefore, the nexus between entrepreneurial leadership and organisational performance is still indecisive and requires further studies. Methodology: This study adopted a quantitative approach based on a cross-sectional survey and descriptive design to gather data within a specific period. The data were collected by distributing a survey questionnaire to Kuwaiti SMEs’ owners and Chief Executive Officers (CEOs) via online and on-hand instruments. A total of 384 useable questionnaires were obtained. Moreover, the partial least square-structural equation modelling (PLS-SEM) analysis was performed to test the hypotheses. Contribution: The current study contributed to the existing literature by developing a moderated mediation model integrating entrepreneurial leadership, innovation management, and learning orientation. The study also investigated their effect on the organisational performance of SMEs. The study findings also bridged the existing significant literature gap regarding the role of these variables on SMEs’ performance in developing countries, particularly in Kuwait, due to the dearth of studies linking these variables in this context. Furthermore, this study empirically confirmed the significant effect of innovation management and learning orientation as intermediate variables in strengthening the relationship between entrepreneurial leadership and organisational performance in the settings of Kuwait SMEs, which has not been verified previously. Findings: The study findings showed the beneficial and significant impact of entrepr","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69309058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Al-Hunaiyyan, Andrew Thomas Bimba, Salah Alsharhan
Aim/Purpose This study describes a conceptual model, based on the principles of concept algebra that can provide intelligent academic advice using adaptive, knowledge-based feedback. The proposed model advises students based on their traits and academic history. The system aims to deliver adaptive advice to students using historical data from previous and current students. This data-driven approach utilizes a cognitive knowledge-based (CKB) model to update the weights (values that indicate the strength of relationships between concepts) that exist between student’s performances and recommended courses. Background A research study conducted at the Public Authority for Applied Education and Training (PAAET), a higher education institution in Kuwait, indicates that students’ have positive perceptions of the e-Advising system. Most students believe that PAAET’s e-Advising system is effective because it allows them to check their academic status, provides a clear vision of their academic timeline, and is a convenient, user-friendly, and attractive online service. Student advising can be a tedious element of academic life but is necessary to fill Adaptive e-Advising System 248 the gap between student performance and degree requirements. Higher education institutions have prioritized assisting undecided students with career decisions for decades. An important feature of e-Advising systems is personalized feedback, where tailored advice is provided based on students' characteristics and other external parameters. Previous e-Advising systems provide students with advice without taking into consideration their different attributes and goals. Methodology This research describes a model for an e-Advising system that enables students to select courses recommended based on their personalities and academic performance. Three algorithms are used to provide students with adaptive course selection advice: the knowledge elicitation algorithm that represents students' personalities and academic information, the knowledge bonding algorithm that combines related concepts or ideas within the knowledge base, and the adaptive e-Advising model that recommends relevant courses. The knowledge elicitation algorithm acquires student and academic characteristics from data provided, while the knowledge bonding algorithm fuses the newly acquired features with existing information in the database. The adaptive e-Advising algorithm provides recommended courses to students based on existing cognitive knowledge to overcome the issues associated with traditional knowledge representation methods. Contribution The design and implementation of an adaptive e-Advising system are challenging because it relies on both academic and student traits. A model that incorporates the conceptual interaction between the various academic and student-specific components is needed to manage these challenges. While other e-Advising systems provide students with general advice, these earlier models are to
{"title":"A Cognitive Knowledge-based Model for an Academic Adaptive e-Advising System","authors":"A. Al-Hunaiyyan, Andrew Thomas Bimba, Salah Alsharhan","doi":"10.28945/4633","DOIUrl":"https://doi.org/10.28945/4633","url":null,"abstract":"Aim/Purpose This study describes a conceptual model, based on the principles of concept algebra that can provide intelligent academic advice using adaptive, knowledge-based feedback. The proposed model advises students based on their traits and academic history. The system aims to deliver adaptive advice to students using historical data from previous and current students. This data-driven approach utilizes a cognitive knowledge-based (CKB) model to update the weights (values that indicate the strength of relationships between concepts) that exist between student’s performances and recommended courses. Background A research study conducted at the Public Authority for Applied Education and Training (PAAET), a higher education institution in Kuwait, indicates that students’ have positive perceptions of the e-Advising system. Most students believe that PAAET’s e-Advising system is effective because it allows them to check their academic status, provides a clear vision of their academic timeline, and is a convenient, user-friendly, and attractive online service. Student advising can be a tedious element of academic life but is necessary to fill Adaptive e-Advising System 248 the gap between student performance and degree requirements. Higher education institutions have prioritized assisting undecided students with career decisions for decades. An important feature of e-Advising systems is personalized feedback, where tailored advice is provided based on students' characteristics and other external parameters. Previous e-Advising systems provide students with advice without taking into consideration their different attributes and goals. Methodology This research describes a model for an e-Advising system that enables students to select courses recommended based on their personalities and academic performance. Three algorithms are used to provide students with adaptive course selection advice: the knowledge elicitation algorithm that represents students' personalities and academic information, the knowledge bonding algorithm that combines related concepts or ideas within the knowledge base, and the adaptive e-Advising model that recommends relevant courses. The knowledge elicitation algorithm acquires student and academic characteristics from data provided, while the knowledge bonding algorithm fuses the newly acquired features with existing information in the database. The adaptive e-Advising algorithm provides recommended courses to students based on existing cognitive knowledge to overcome the issues associated with traditional knowledge representation methods. Contribution The design and implementation of an adaptive e-Advising system are challenging because it relies on both academic and student traits. A model that incorporates the conceptual interaction between the various academic and student-specific components is needed to manage these challenges. While other e-Advising systems provide students with general advice, these earlier models are to","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46016651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aim/Purpose This article proposes a methodology for selecting the initial sets for clustering categorical data. The main idea is to combine all the different values of every single criterion or attribute, to form the first proposal of the so-called multiclusters, obtaining in this way the maximum number of clusters for the whole dataset. The multiclusters thus obtained, are themselves clustered in a second step, according to the desired final number of clusters. Background Popular cluster methods for categorical data, such as the well-known K-Modes, usually select the initial sets by means of some random process. This fact introduces some randomness in the final results of the algorithms. We explore a different application of the clustering methodology for categorical data that overcomes the instability problems and ultimately provides a greater clustering efficiency. Methodology For assessing the performance of the proposed algorithm and its comparison with K-Modes, we apply both of them to categorical databases where the response variable is known but not used in the analysis. In our examples, that response variable can be identified to the real clusters or classes to which the observations belong. With every data set, we perform a two-step analysis. In the first step we perform the clustering analysis on data where the response variable (the real clusters) has been omitted, and in the second step we use that omitted information to check the efficiency of the clustering algorithm (by comparing the real clusters to those given by the algorithm). Contribution Simplicity, efficiency and stability are the main advantages of the multicluster method. A Multicluster Approach to Selecting Initial Sets for Clustering of Categorical Data 228 Findings The experimental results attained with real databases show that the multicluster algorithm has greater precision and a better grouping effect than the classical Kmodes algorithm. Recommendations for Practitioners The method can be useful for those researchers working with small and medium size datasets, allowing them to detect the underlying structure of the data in an intuitive and reasonable way. Recommendations for Researchers The proposed algorithm is slower than K-Modes, since it devotes a lot of time to the calculation of the initial combinations of attributes. The reduction of the computing time is therefore an important research topic. Future Research We are concerned with the scalability of the algorithm to large and complex data sets, as well as the application to mixed data sets with both quantitative and qualitative attributes.
{"title":"A Multicluster Approach to Selecting Initial Sets for Clustering of Categorical Data","authors":"Carlos Santos-Mangudo, Antonio J. Heras","doi":"10.28945/4643","DOIUrl":"https://doi.org/10.28945/4643","url":null,"abstract":"Aim/Purpose This article proposes a methodology for selecting the initial sets for clustering categorical data. The main idea is to combine all the different values of every single criterion or attribute, to form the first proposal of the so-called multiclusters, obtaining in this way the maximum number of clusters for the whole dataset. The multiclusters thus obtained, are themselves clustered in a second step, according to the desired final number of clusters. Background Popular cluster methods for categorical data, such as the well-known K-Modes, usually select the initial sets by means of some random process. This fact introduces some randomness in the final results of the algorithms. We explore a different application of the clustering methodology for categorical data that overcomes the instability problems and ultimately provides a greater clustering efficiency. Methodology For assessing the performance of the proposed algorithm and its comparison with K-Modes, we apply both of them to categorical databases where the response variable is known but not used in the analysis. In our examples, that response variable can be identified to the real clusters or classes to which the observations belong. With every data set, we perform a two-step analysis. In the first step we perform the clustering analysis on data where the response variable (the real clusters) has been omitted, and in the second step we use that omitted information to check the efficiency of the clustering algorithm (by comparing the real clusters to those given by the algorithm). Contribution Simplicity, efficiency and stability are the main advantages of the multicluster method. A Multicluster Approach to Selecting Initial Sets for Clustering of Categorical Data 228 Findings The experimental results attained with real databases show that the multicluster algorithm has greater precision and a better grouping effect than the classical Kmodes algorithm. Recommendations for Practitioners The method can be useful for those researchers working with small and medium size datasets, allowing them to detect the underlying structure of the data in an intuitive and reasonable way. Recommendations for Researchers The proposed algorithm is slower than K-Modes, since it devotes a lot of time to the calculation of the initial combinations of attributes. The reduction of the computing time is therefore an important research topic. Future Research We are concerned with the scalability of the algorithm to large and complex data sets, as well as the application to mixed data sets with both quantitative and qualitative attributes.","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45279003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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