Pub Date : 2022-04-30DOI: 10.1142/s021964922250023x
Saramsh Kharel, K. Anup, Niranjan Devkota, U. R. Paudel
Managing knowledge in the field of tourism and the hospitality industry will carry significant importance as most people are not aware of knowledge management (KM) implications. As the importance of knowledge management is not well captured in the tourism sector of Nepal, this study aims to identify the awareness level of knowledge management among the tourism entrepreneurs in Nepal and suggest managerial implications for the same. The primary data were collected through 276[Formula: see text]questionnaire surveys. Tourism entrepreneurs saw the benefits of KM for tourism development despite the costs and challenges it poses. Awareness of knowledge management of entrepreneurs differs according to the people, process, technology, organization structure, and the organization culture dimension. It was further influenced by the demographic characteristics of the tourism entrepreneurs. Enterprises are in more need of knowledge management awareness and several amendments in tourism development policies and programs. Therefore, this study recommends increasing entrepreneurs’ awareness of knowledge management by the joint effort of tourism enterprises and the Nepal Tourism Board. Various knowledge management seminars (integrating tourism experts with tourism entrepreneurs) and training programs should be conducted to manage knowledge effectively.
{"title":"Entrepreneurs' Level of Awareness on Knowledge Management for Promoting Tourism in Nepal","authors":"Saramsh Kharel, K. Anup, Niranjan Devkota, U. R. Paudel","doi":"10.1142/s021964922250023x","DOIUrl":"https://doi.org/10.1142/s021964922250023x","url":null,"abstract":"Managing knowledge in the field of tourism and the hospitality industry will carry significant importance as most people are not aware of knowledge management (KM) implications. As the importance of knowledge management is not well captured in the tourism sector of Nepal, this study aims to identify the awareness level of knowledge management among the tourism entrepreneurs in Nepal and suggest managerial implications for the same. The primary data were collected through 276[Formula: see text]questionnaire surveys. Tourism entrepreneurs saw the benefits of KM for tourism development despite the costs and challenges it poses. Awareness of knowledge management of entrepreneurs differs according to the people, process, technology, organization structure, and the organization culture dimension. It was further influenced by the demographic characteristics of the tourism entrepreneurs. Enterprises are in more need of knowledge management awareness and several amendments in tourism development policies and programs. Therefore, this study recommends increasing entrepreneurs’ awareness of knowledge management by the joint effort of tourism enterprises and the Nepal Tourism Board. Various knowledge management seminars (integrating tourism experts with tourism entrepreneurs) and training programs should be conducted to manage knowledge effectively.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"1079 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122896089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-29DOI: 10.1142/s021964922240010x
Mohammad Khalid Sohail, A. Raheman, Javid Iqbal, M. Sindhu, Abdul Staar, Muhammad Mushafiq, Humaira Afzal
Pair trading strategy is a well-known profitable strategy in stock, forex, and commodity markets. As most of the world stock markets declined during COVID-19 period, therefore this study is going to observe whether this strategy is still profitable after COVID-19 pandemic. One of the powerful algorithms of DBSCAN under the umbrella of unsupervised machine learning is applied and three clusters were formed by using market and accounting data. The formation of these three clusters was based on book value per share, earning per share, classification of sector, market capitalisation and with other factors formed from PCA on the returns of daily data of six months of the 80 sample firms for year 2019–2020. An average of [Formula: see text] average excess monthly return with Sharpe ratio of [Formula: see text] and Treynor ratio of [Formula: see text] is to be observed in COVID-19 pandemic period. However, the result of risk-adjusted performance under Jensen’s alpha is observed to be insignificant. The policy implication of this study, for different portfolios and fund managers is suggested to use machine learning approach to get positive and higher returns for their clients.
{"title":"Are Pair Trading Strategies Profitable During COVID-19 Period?","authors":"Mohammad Khalid Sohail, A. Raheman, Javid Iqbal, M. Sindhu, Abdul Staar, Muhammad Mushafiq, Humaira Afzal","doi":"10.1142/s021964922240010x","DOIUrl":"https://doi.org/10.1142/s021964922240010x","url":null,"abstract":"Pair trading strategy is a well-known profitable strategy in stock, forex, and commodity markets. As most of the world stock markets declined during COVID-19 period, therefore this study is going to observe whether this strategy is still profitable after COVID-19 pandemic. One of the powerful algorithms of DBSCAN under the umbrella of unsupervised machine learning is applied and three clusters were formed by using market and accounting data. The formation of these three clusters was based on book value per share, earning per share, classification of sector, market capitalisation and with other factors formed from PCA on the returns of daily data of six months of the 80 sample firms for year 2019–2020. An average of [Formula: see text] average excess monthly return with Sharpe ratio of [Formula: see text] and Treynor ratio of [Formula: see text] is to be observed in COVID-19 pandemic period. However, the result of risk-adjusted performance under Jensen’s alpha is observed to be insignificant. The policy implication of this study, for different portfolios and fund managers is suggested to use machine learning approach to get positive and higher returns for their clients.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128170859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-29DOI: 10.1142/s021964922240007x
Manar Alsaid, Nayana Pampapura Madali
The widespread transmission of misinformation regarding the COVID-19 pandemic on social media has become a severe concern for various reasons such as containing the spread of the virus, taking preventive measures, and so on. According to the recent studies, misinformation and conspiracy theories spread on social media have hampered efforts to limit the infection, which has been exacerbated in some instances by politicians and celebrities. Misunderstandings about COVID-19 and wearing a mask sparked much debate. As time went on, a sizable portion of the population continued to refuse to wear masks, owing to extrinsic considerations, such as politics, ideology, personal views, and health concerns. In this study, we look at the concerns surrounding three Twitter hashtags (#masks, #maskup, and #maskoff) in order to understand better how social noise can lead to unintended misinformation. Sentiment analysis, topic modelling, and contextual analysis were used to compare and contrast two datasets relevant to these hashtags, one gathered in 2020 and the other in 2021. According to sentiment analysis, people’s emotions differed between hashtags, and the majority of tweets were based on social media users’ personal opinions. Topic modelling results revealed the prevalence of social noise leading to the unintended spread of misinformation on Twitter. The content analysis results show that while the #maskoff hashtag is used to resist masking influenced by factors, such as misinformation, conspiracy theories, and ideology, the #masks and #maskup hashtags were generally positive and used more to raise awareness of the benefits of wearing masks.
{"title":"Social Noise and the Impact of Misinformation on COVID-19 Preventive Measures: Comparative Data Analysis Using Twitter Masking Hashtags","authors":"Manar Alsaid, Nayana Pampapura Madali","doi":"10.1142/s021964922240007x","DOIUrl":"https://doi.org/10.1142/s021964922240007x","url":null,"abstract":"The widespread transmission of misinformation regarding the COVID-19 pandemic on social media has become a severe concern for various reasons such as containing the spread of the virus, taking preventive measures, and so on. According to the recent studies, misinformation and conspiracy theories spread on social media have hampered efforts to limit the infection, which has been exacerbated in some instances by politicians and celebrities. Misunderstandings about COVID-19 and wearing a mask sparked much debate. As time went on, a sizable portion of the population continued to refuse to wear masks, owing to extrinsic considerations, such as politics, ideology, personal views, and health concerns. In this study, we look at the concerns surrounding three Twitter hashtags (#masks, #maskup, and #maskoff) in order to understand better how social noise can lead to unintended misinformation. Sentiment analysis, topic modelling, and contextual analysis were used to compare and contrast two datasets relevant to these hashtags, one gathered in 2020 and the other in 2021. According to sentiment analysis, people’s emotions differed between hashtags, and the majority of tweets were based on social media users’ personal opinions. Topic modelling results revealed the prevalence of social noise leading to the unintended spread of misinformation on Twitter. The content analysis results show that while the #maskoff hashtag is used to resist masking influenced by factors, such as misinformation, conspiracy theories, and ideology, the #masks and #maskup hashtags were generally positive and used more to raise awareness of the benefits of wearing masks.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115607168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-29DOI: 10.1142/s0219649222500253
Fariba Sarhangnia, Nona Ali Asgharzadeholiaee, Milad Boshkani Zadeh
Link Prediction (LP) is one of the critical problems in Online Social Networks (OSNs) analysis. LP is a technique for predicting forthcoming or missing links based on current information in the OSN. Typically, modelling an OSN platform is done in a single-layer scheme. However, this is a limitation which might lead to incorrect descriptions of some real-world details. To overcome this limitation, this paper presents a multilayer model of OSN for the LP problem by analysing Twitter and Foursquare networks. LP in multilayer networks involves performing LP on a target layer benefitting from the structural information of the other layers. Here, a novel criterion is proposed, which calculates the similarity between users by forming intralayer and interlayer links in a two-layer network (i.e. Twitter and Foursquare). Particularly, LP in the Foursquare layer is done by considering the two-layer structural information. In this paper, according to the available information from the Twitter and Foursquare OSNs, a weighted graph is created and then various topological features are extracted from it. Based on the extracted features, a database with two classes of link existence and no link has been created, and therefore the problem of LP has become a two-class classification problem that can be solved by supervised learning methods. To prove the better performance of the proposed method, Katz and FriendLink indices as well as SEM-Path algorithm have been used for comparison. Evaluations results show that the proposed method can predict new links with better precision.
链路预测(Link Prediction, LP)是在线社交网络(Online Social network, osn)分析中的关键问题之一。LP是一种基于OSN中当前信息预测即将到来或缺失的链路的技术。通常，OSN平台的建模是在单层方案中完成的。然而，这是一个限制，可能会导致对一些现实世界细节的不正确描述。为了克服这一局限性，本文通过对Twitter和Foursquare网络的分析，提出了面向LP问题的多层OSN模型。多层网络中的LP涉及利用其他层的结构信息在目标层上执行LP。在这里，我们提出了一个新的标准，它通过在两层网络(即Twitter和Foursquare)中形成层内和层间链接来计算用户之间的相似性。其中，Foursquare层的LP是通过考虑两层结构信息来实现的。本文根据Twitter和Foursquare的可用osn信息，创建一个加权图，然后从中提取各种拓扑特征。基于提取的特征，创建了一个有链路存在和无链路两类的数据库，从而LP问题变成了一个可以用监督学习方法解决的两类分类问题。为了证明该方法具有更好的性能，我们使用Katz和FriendLink指标以及SEM-Path算法进行了比较。评价结果表明，该方法能较好地预测新链接。
{"title":"A Novel Multilayer Model for Link Prediction in Online Social Networks Based on Reliable Paths","authors":"Fariba Sarhangnia, Nona Ali Asgharzadeholiaee, Milad Boshkani Zadeh","doi":"10.1142/s0219649222500253","DOIUrl":"https://doi.org/10.1142/s0219649222500253","url":null,"abstract":"Link Prediction (LP) is one of the critical problems in Online Social Networks (OSNs) analysis. LP is a technique for predicting forthcoming or missing links based on current information in the OSN. Typically, modelling an OSN platform is done in a single-layer scheme. However, this is a limitation which might lead to incorrect descriptions of some real-world details. To overcome this limitation, this paper presents a multilayer model of OSN for the LP problem by analysing Twitter and Foursquare networks. LP in multilayer networks involves performing LP on a target layer benefitting from the structural information of the other layers. Here, a novel criterion is proposed, which calculates the similarity between users by forming intralayer and interlayer links in a two-layer network (i.e. Twitter and Foursquare). Particularly, LP in the Foursquare layer is done by considering the two-layer structural information. In this paper, according to the available information from the Twitter and Foursquare OSNs, a weighted graph is created and then various topological features are extracted from it. Based on the extracted features, a database with two classes of link existence and no link has been created, and therefore the problem of LP has become a two-class classification problem that can be solved by supervised learning methods. To prove the better performance of the proposed method, Katz and FriendLink indices as well as SEM-Path algorithm have been used for comparison. Evaluations results show that the proposed method can predict new links with better precision.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130641962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-29DOI: 10.1142/s0219649222500198
Yawar Abbas, A. Martinetti, Lex Frunt, Jeroen Klinkers, M. Rajabalinejad, L. V. Dongen
While there is a clear consensus in the literature on the need to share lessons learned, it remains unclear how to properly do so. This paper addresses this point and offers insight into how best to incorporate tacitly held social preferences for developing knowledge-sharing strategies. A descriptive survey was conducted to analyse the knowledge sharing practices for lessons learned within the railway sector. Eight variables are investigated that are derived from the four LEAF features: learnability, embraceability, applicability, and findability. This study revealed that for learnability, storytelling and discussion with colleagues are preferred ways to share personal experiences. Trust and the creation of a learning culture emerged as key aspects of embraceability. With regard to applicability, a process-related knowledge-sharing focus for intraorganisational and a content-related focus for interorganisational knowledge domains are preferred. Better technological findability is identified as a key area of improvement. Finally, novel dependencies are established using the chi-square test between key LEAF features.
{"title":"Investigating Interdependencies Between Key Features of Lessons Learned: An Integral Approach for Knowledge Sharing","authors":"Yawar Abbas, A. Martinetti, Lex Frunt, Jeroen Klinkers, M. Rajabalinejad, L. V. Dongen","doi":"10.1142/s0219649222500198","DOIUrl":"https://doi.org/10.1142/s0219649222500198","url":null,"abstract":"While there is a clear consensus in the literature on the need to share lessons learned, it remains unclear how to properly do so. This paper addresses this point and offers insight into how best to incorporate tacitly held social preferences for developing knowledge-sharing strategies. A descriptive survey was conducted to analyse the knowledge sharing practices for lessons learned within the railway sector. Eight variables are investigated that are derived from the four LEAF features: learnability, embraceability, applicability, and findability. This study revealed that for learnability, storytelling and discussion with colleagues are preferred ways to share personal experiences. Trust and the creation of a learning culture emerged as key aspects of embraceability. With regard to applicability, a process-related knowledge-sharing focus for intraorganisational and a content-related focus for interorganisational knowledge domains are preferred. Better technological findability is identified as a key area of improvement. Finally, novel dependencies are established using the chi-square test between key LEAF features.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126710652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1142/s0219649222500204
Abedal-Kareem Al-Banna, E. Edirisinghe, H. Fang, W. Hadi
Stuttering is a neurodevelopmental speech disorder wherein people suffer from disfluency in speech generation. Recent research has applied machine learning and deep learning approaches to stuttering disfluency recognition and classification. However, these studies have focussed on small datasets, generated by a limited number of speakers and within specific tasks, such as reading. This paper rigorously investigates the effective use of eight well-known machine learning classifiers, on two publicly available datasets (FluencyBank and SEP-28k) to automatically detect stuttering disfluency using multiple objective metrics, i.e. prediction accuracy, recall, precision, F1-score, and AUC measures. Our experimental results on the two datasets show that the Random Forest classifier achieves the best performance, with an accuracy of 50.3% and 50.35%, a recall of 50% and 42%, a precision of 42% and 46%, and an F1 score of 42% and 34%, against the FluencyBank and SEP-28K datasets, respectively. Moreover, we show that the machine learning-based approaches may not be effective in accurate stuttering disfluency evaluation, due to diverse variations in speech rate, and differences in vocal tracts between children and adults. We argue that the use of deep learning approaches and Automatic Speech Recognition (ASR) with language models may improve outcomes, specifically for large scale and imbalanced datasets.
{"title":"Stuttering Disfluency Detection Using Machine Learning Approaches","authors":"Abedal-Kareem Al-Banna, E. Edirisinghe, H. Fang, W. Hadi","doi":"10.1142/s0219649222500204","DOIUrl":"https://doi.org/10.1142/s0219649222500204","url":null,"abstract":"Stuttering is a neurodevelopmental speech disorder wherein people suffer from disfluency in speech generation. Recent research has applied machine learning and deep learning approaches to stuttering disfluency recognition and classification. However, these studies have focussed on small datasets, generated by a limited number of speakers and within specific tasks, such as reading. This paper rigorously investigates the effective use of eight well-known machine learning classifiers, on two publicly available datasets (FluencyBank and SEP-28k) to automatically detect stuttering disfluency using multiple objective metrics, i.e. prediction accuracy, recall, precision, F1-score, and AUC measures. Our experimental results on the two datasets show that the Random Forest classifier achieves the best performance, with an accuracy of 50.3% and 50.35%, a recall of 50% and 42%, a precision of 42% and 46%, and an F1 score of 42% and 34%, against the FluencyBank and SEP-28K datasets, respectively. Moreover, we show that the machine learning-based approaches may not be effective in accurate stuttering disfluency evaluation, due to diverse variations in speech rate, and differences in vocal tracts between children and adults. We argue that the use of deep learning approaches and Automatic Speech Recognition (ASR) with language models may improve outcomes, specifically for large scale and imbalanced datasets.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127861737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1142/s0219649222500150
Yanfang Ma, Xuezhen Liu, Xi Deng
Financing difficulties are common among the small-, medium- and micro-enterprises (SMMEs). Although supply chain financing alleviates the problems of SMMEs, such as narrow financing channels, intractable financing and expensive financing, however, due to the centralised storage and management of data, the authenticity of data cannot be guaranteed. The credit of the core enterprises in the supply chain cannot penetrate the SMMEs in upstream and downstream. This paper establishes a blockchain pass-through model for supply chain financing by improving the PBFT consensus algorithm based on blockchain’s decentralised and tamper-evident characteristics and the pass-through of SMMEs’ assets in the supply chain. The model improves the circulation efficiency of the supply chain; moreover, it enables the credit of core enterprises to the upstream and downstream, solving the financing dilemma of SMMEs.
{"title":"Blockchain Token Model for Supply Chain Financing of SMMEs","authors":"Yanfang Ma, Xuezhen Liu, Xi Deng","doi":"10.1142/s0219649222500150","DOIUrl":"https://doi.org/10.1142/s0219649222500150","url":null,"abstract":"Financing difficulties are common among the small-, medium- and micro-enterprises (SMMEs). Although supply chain financing alleviates the problems of SMMEs, such as narrow financing channels, intractable financing and expensive financing, however, due to the centralised storage and management of data, the authenticity of data cannot be guaranteed. The credit of the core enterprises in the supply chain cannot penetrate the SMMEs in upstream and downstream. This paper establishes a blockchain pass-through model for supply chain financing by improving the PBFT consensus algorithm based on blockchain’s decentralised and tamper-evident characteristics and the pass-through of SMMEs’ assets in the supply chain. The model improves the circulation efficiency of the supply chain; moreover, it enables the credit of core enterprises to the upstream and downstream, solving the financing dilemma of SMMEs.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134287569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1142/s0219649222500046
Bing Chen, Ding Liu, Ting Zhang
The current Internet of Things (IoT) technology has entered a relatively mature development stage, and more and more IoT devices can readily access the Internet. However, along with this, the IoT system still faces fragile security of device nodes, easy data tampering, and low system stability. To this end, this paper proposes a smart contract-based security model for IoT systems. The proposal is based on the super ledger Fabric blockchain platform having decentralised, tamper-proof, and programmable features. These features achieve credible authentication of IoT device nodes on the one hand and tamper-proof data storage on the other hand. Further, with these features, we gain a trustworthy environment for enhancing the security of the whole IoT system.
{"title":"A Blockchain-Based Security Model for IoT Systems","authors":"Bing Chen, Ding Liu, Ting Zhang","doi":"10.1142/s0219649222500046","DOIUrl":"https://doi.org/10.1142/s0219649222500046","url":null,"abstract":"The current Internet of Things (IoT) technology has entered a relatively mature development stage, and more and more IoT devices can readily access the Internet. However, along with this, the IoT system still faces fragile security of device nodes, easy data tampering, and low system stability. To this end, this paper proposes a smart contract-based security model for IoT systems. The proposal is based on the super ledger Fabric blockchain platform having decentralised, tamper-proof, and programmable features. These features achieve credible authentication of IoT device nodes on the one hand and tamper-proof data storage on the other hand. Further, with these features, we gain a trustworthy environment for enhancing the security of the whole IoT system.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133950023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1142/s0219649222400044
M. Nawaz, Saif-Ur-Rehman Khan, Bashir Ahmad, Javed Iqbal, Inayat ur-Rehman
Context: From the past few years, Application Programming Interface (API) is widely used for mobile- and web-based application developments. Software developers can integrate third-party services into their projects to achieve their development goals efficiently using APIs; however, with the rapid increase in the number of APIs, the manual selection of Mashup-oriented API is becoming more difficult for the developer. Objective: In the COVID-19 pandemic, everyone wants an update about the latest Standard Operating Procedures (SOPs) and the latest information on COVID-19. Additionally, a software developer wants to develop an application that provides the SOPs and latest information of COVID-19; a developer can add these functionalities into an application using COVID-19-based APIs. Moreover, the current work aims at proposing a COVID-19-based API recommendation system for the developers. Method: In this study, we propose a COVID-19-based API recommendation system for developers. The recommendation system takes a developer query as input and recommends top-3 APIs and supported features, which help the developer during software development. Furthermore, the proposed COVID-19-based API recommendation system ensures the maximum participation of the developers by validating the recommended APIs and recommendation system from the expert developers using research questionnaires. Results: Additionally, the proposed COVID-19-based API recommendation system’s output is validated by expert developers and evaluated on 120 expert developers’ queries. In addition, experiment results show that single value decomposition achieves better prediction. Conclusion: We conclude that it is significantly important to recommend APIs along with supported features to the developer for project development, and future work is needed to take more developer’s queries also to build Integrated Development Environment for the developers.
{"title":"CAPIRS: COVID-19-Based Application Programming Interface Recommendation System for the Developers","authors":"M. Nawaz, Saif-Ur-Rehman Khan, Bashir Ahmad, Javed Iqbal, Inayat ur-Rehman","doi":"10.1142/s0219649222400044","DOIUrl":"https://doi.org/10.1142/s0219649222400044","url":null,"abstract":"Context: From the past few years, Application Programming Interface (API) is widely used for mobile- and web-based application developments. Software developers can integrate third-party services into their projects to achieve their development goals efficiently using APIs; however, with the rapid increase in the number of APIs, the manual selection of Mashup-oriented API is becoming more difficult for the developer. Objective: In the COVID-19 pandemic, everyone wants an update about the latest Standard Operating Procedures (SOPs) and the latest information on COVID-19. Additionally, a software developer wants to develop an application that provides the SOPs and latest information of COVID-19; a developer can add these functionalities into an application using COVID-19-based APIs. Moreover, the current work aims at proposing a COVID-19-based API recommendation system for the developers. Method: In this study, we propose a COVID-19-based API recommendation system for developers. The recommendation system takes a developer query as input and recommends top-3 APIs and supported features, which help the developer during software development. Furthermore, the proposed COVID-19-based API recommendation system ensures the maximum participation of the developers by validating the recommended APIs and recommendation system from the expert developers using research questionnaires. Results: Additionally, the proposed COVID-19-based API recommendation system’s output is validated by expert developers and evaluated on 120 expert developers’ queries. In addition, experiment results show that single value decomposition achieves better prediction. Conclusion: We conclude that it is significantly important to recommend APIs along with supported features to the developer for project development, and future work is needed to take more developer’s queries also to build Integrated Development Environment for the developers.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125645713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1142/s0219649222500228
L. Reis, J. Fernandes, Sérgio Evangelista Silva, Alana Deusilan Sester Pereira
Knowledge Management Implementation (KMI) can be analysed from three perspectives: Knowledge Management System (KMS) implementation, Knowledge Management Processes (KMPs) implementation and Organisational Outcomes (OO). Quality Function Deployment (QFD), conceived within the scope of quality, represents a method capable of bringing significant contributions to the knowledge field of KMI. The QFD method stands out as a comprehensive approach to improve the quality of products and services, focussing on customer requirements. The literature presents a scarcity of studies that discuss the KMI implementation process, addressing these three perspectives. Furthermore, studies that address QFD in the context of KM are more focussed on the KMS implementation perspective. In this context, this research proposes an innovative approach adopting QFD in order to structure the KMI, encompassing the three perspectives presented (KMS implementation, KMP implementation and OO). In this context, QFD is seen as a method of integrating and operationalising KMI activities. To validate this approach, we applied the case study in an academic support department at a Brazilian public university. As a result, it was possible to verify that the QFD helps in the operationalisation of the KMS and KMP and also improves OO. Still, it was observed that the approach to knowledge management seems to be easy to apply in the academic setting and has produced good results in the service offered in the department where it was applied.
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