Pub Date : 2022-06-22DOI: 10.1142/s0219649222500538
Mengfei Lin, Depeng Zhang, Si Liu, Yanpin Huang
Purpose — Emotion is one of the key factors affecting creativity. In the field of marketing research, researchers generally begin to explore how to make rational use of customers” negative emotions to contribute to companies’ innovation process. However, the existing views are still divergent. Design/methodology/approach — To explore the relationship between customers’ negative emotions and creativity, we construct a research model from the perspective of self-determination Theory and Resource Preservation Theory, Based on this model, we conducted an empirical study with 401 participants. Findings: — We found that there is an inverse U-shaped relation between negative emotion and creativity. And we further verified the mediating role of customer intrinsic motivation and the moderating role of innovation self-efficacy. Originality/value — The understanding of the nonlinear relationship between emotion and creativity may provide valuable theoretical contributions to the research of creativity, and provide practical guidance for the design of innovative activities.
{"title":"U-Shaped Relation between Negative Emotions and Customer Creativity in Corporate Innovation Context","authors":"Mengfei Lin, Depeng Zhang, Si Liu, Yanpin Huang","doi":"10.1142/s0219649222500538","DOIUrl":"https://doi.org/10.1142/s0219649222500538","url":null,"abstract":"Purpose — Emotion is one of the key factors affecting creativity. In the field of marketing research, researchers generally begin to explore how to make rational use of customers” negative emotions to contribute to companies’ innovation process. However, the existing views are still divergent. Design/methodology/approach — To explore the relationship between customers’ negative emotions and creativity, we construct a research model from the perspective of self-determination Theory and Resource Preservation Theory, Based on this model, we conducted an empirical study with 401 participants. Findings: — We found that there is an inverse U-shaped relation between negative emotion and creativity. And we further verified the mediating role of customer intrinsic motivation and the moderating role of innovation self-efficacy. Originality/value — The understanding of the nonlinear relationship between emotion and creativity may provide valuable theoretical contributions to the research of creativity, and provide practical guidance for the design of innovative activities.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122618078","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 : 2022-06-20DOI: 10.1142/s0219649222500472
A. Salamai
Over the last decade, collaboration and secure information-sharing (SIS) have been studied in the context of supply chain management (SCM) to determine their influence on improving a business’s performance and profitability. Collaboration refers to the firms working together to accomplish a particular objective, whereas SIS is a vital technology which permits the firms and the enablers of a supply chain to be integrated. In this paper, these aspects and their impacts on SCM are reviewed. A conceptual model with a set of hypotheses for measuring the effects of collaboration and information-sharing on SCs, which demonstrate their effective roles in SCM, is proposed.
{"title":"A Review of Collaboration and Secure Information-Sharing for Supply Chain Management","authors":"A. Salamai","doi":"10.1142/s0219649222500472","DOIUrl":"https://doi.org/10.1142/s0219649222500472","url":null,"abstract":"Over the last decade, collaboration and secure information-sharing (SIS) have been studied in the context of supply chain management (SCM) to determine their influence on improving a business’s performance and profitability. Collaboration refers to the firms working together to accomplish a particular objective, whereas SIS is a vital technology which permits the firms and the enablers of a supply chain to be integrated. In this paper, these aspects and their impacts on SCM are reviewed. A conceptual model with a set of hypotheses for measuring the effects of collaboration and information-sharing on SCs, which demonstrate their effective roles in SCM, is proposed.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122838620","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 : 2022-06-02DOI: 10.1142/s0219649222400275
Ying Xu
English for Science and Technology (EST), as a special language style, is widely used in the field of Science and Technology. For this kind of articles, the requirements of translation quality are relatively high. Therefore, this paper studies a quality evaluation method of Sci-Tech English translation for cross-cultural communication. As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future. This paper also describes the evaluation of machine translation quality with and automatic evaluation process with machine learning technology. The evaluation index of EST translation quality is selected according to the selection principle and expert consultation method. Then, the weight of the index is calculated by using the analytic hierarchy process. Finally, the translation quality evaluation is given by using the fuzzy comprehensive evaluation, glass-box and black-box evaluation with machine learning method. The results show that under the application of the research method, the evaluation results are completely corresponding to the actual competition results of four competitors, which proves the effectiveness of the research method.
{"title":"The Quality Evaluation Method of Sci-Tech English Translation for Intercultural Communication","authors":"Ying Xu","doi":"10.1142/s0219649222400275","DOIUrl":"https://doi.org/10.1142/s0219649222400275","url":null,"abstract":"English for Science and Technology (EST), as a special language style, is widely used in the field of Science and Technology. For this kind of articles, the requirements of translation quality are relatively high. Therefore, this paper studies a quality evaluation method of Sci-Tech English translation for cross-cultural communication. As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future. This paper also describes the evaluation of machine translation quality with and automatic evaluation process with machine learning technology. The evaluation index of EST translation quality is selected according to the selection principle and expert consultation method. Then, the weight of the index is calculated by using the analytic hierarchy process. Finally, the translation quality evaluation is given by using the fuzzy comprehensive evaluation, glass-box and black-box evaluation with machine learning method. The results show that under the application of the research method, the evaluation results are completely corresponding to the actual competition results of four competitors, which proves the effectiveness of the research method.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127896780","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 : 2022-05-30DOI: 10.1142/s0219649222500411
P. Vijaya, M. Selvi
The personalised learning is growing rapidly with the help of mobile and online technology. The e-learning recommendation scheme provides the suggestion concerning the courses to the students from numerous countries without past information of the courses online. The accuracy is an important issue in the e-learning course recommendation method. Hence, in this paper, Fuzzy-c-means clustering (FCM) and collaborative filtering are applied in the E-Khool user log data for effective e-learning recommendation system. The training phase and testing phase are the two phases of the devised method. During training, the relationship among the data in clustering is determined using the weighted cosine similarity and the data clustering is carried out with the help of FCM. During testing, the rating of the course is calculated using collaborative filtering. At last, the deep RNN classifier is used to evaluate prediction measure of the course recommendation. The devised e-learning recommendation method based on FCM and collaborative filtering offered a higher accuracy of 0.97 and less mean square error of 0.00115, respectively.
{"title":"An Approach Using E-Khool User Log Data for E-Learning Recommendation System","authors":"P. Vijaya, M. Selvi","doi":"10.1142/s0219649222500411","DOIUrl":"https://doi.org/10.1142/s0219649222500411","url":null,"abstract":"The personalised learning is growing rapidly with the help of mobile and online technology. The e-learning recommendation scheme provides the suggestion concerning the courses to the students from numerous countries without past information of the courses online. The accuracy is an important issue in the e-learning course recommendation method. Hence, in this paper, Fuzzy-c-means clustering (FCM) and collaborative filtering are applied in the E-Khool user log data for effective e-learning recommendation system. The training phase and testing phase are the two phases of the devised method. During training, the relationship among the data in clustering is determined using the weighted cosine similarity and the data clustering is carried out with the help of FCM. During testing, the rating of the course is calculated using collaborative filtering. At last, the deep RNN classifier is used to evaluate prediction measure of the course recommendation. The devised e-learning recommendation method based on FCM and collaborative filtering offered a higher accuracy of 0.97 and less mean square error of 0.00115, respectively.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117295299","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 : 2022-05-28DOI: 10.1142/s0219649222500344
A. A. Fadelelmoula
The purpose of this paper is to evaluate the effects of certain motivational factors on driving the continuance usage intention of mobile government-to-employees services (MG2ES). These services have been frequently overlooked by the extant IT adoption literature in determining the predictors that drive the user’s continuance intention to adopt them. To respond to this lack, an integrated model incorporating factors from several IT adoption theories was developed. These factors were divided into two categories, namely, m-service-centric and user-centric ones. Both categories were specified as direct antecedents of the MG2ES continuance intention. A structured questionnaire-based survey was carried out to empirically examine the hypothesised relationships between the model constructs. The target population of this survey was employees of Saudi’s public sector. The analysis of the collected data (i.e. 194 valid responses) was conducted using the structural equation modelling (SEM) approach. The results demonstrated that only two m-service-centric factors (i.e. m-service strength and effort expectancy) and one user-centric factor (i.e. attitude towards the MG2ES usage) are having positive impacts on the continuance intention to use MG2ES. These findings provide valuable insights and clarifications to the key MG2ES stakeholders about the aspects that motivate such intention, including augmenting the MG2ES strength, implementing effective design mechanisms to reduce the MG2ES usage efforts, delivering more compatible services, and acquiring effective tools for improving information sharing.
{"title":"Exploring the Contributing Factors of the Continuance Intention to Use the Mobile Government-to-Employees Services","authors":"A. A. Fadelelmoula","doi":"10.1142/s0219649222500344","DOIUrl":"https://doi.org/10.1142/s0219649222500344","url":null,"abstract":"The purpose of this paper is to evaluate the effects of certain motivational factors on driving the continuance usage intention of mobile government-to-employees services (MG2ES). These services have been frequently overlooked by the extant IT adoption literature in determining the predictors that drive the user’s continuance intention to adopt them. To respond to this lack, an integrated model incorporating factors from several IT adoption theories was developed. These factors were divided into two categories, namely, m-service-centric and user-centric ones. Both categories were specified as direct antecedents of the MG2ES continuance intention. A structured questionnaire-based survey was carried out to empirically examine the hypothesised relationships between the model constructs. The target population of this survey was employees of Saudi’s public sector. The analysis of the collected data (i.e. 194 valid responses) was conducted using the structural equation modelling (SEM) approach. The results demonstrated that only two m-service-centric factors (i.e. m-service strength and effort expectancy) and one user-centric factor (i.e. attitude towards the MG2ES usage) are having positive impacts on the continuance intention to use MG2ES. These findings provide valuable insights and clarifications to the key MG2ES stakeholders about the aspects that motivate such intention, including augmenting the MG2ES strength, implementing effective design mechanisms to reduce the MG2ES usage efforts, delivering more compatible services, and acquiring effective tools for improving information sharing.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123738530","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 : 2022-05-28DOI: 10.1142/s0219649222500368
S. Nithya, Arun Sahayadhas
Fake news plays a major role by broadcasting misinformation, which influences people’s knowledge or perceptions and distorts their decision-making and awareness. Online forums and social media have stimulated the broadcast of fake news by embedding it with truthful information. Thus, fake news has evolved into the main challenge of better impact in the information-driven community for intense fakesters. The detection of fake news articles that is generally found by considering the quality of the information in their news feeds under uncertain authenticity calls for automated tools. However, designing such tools is a major problem because of the multiple faces of fakesters. This paper offers a new text-analytics-driven method for detecting fake news to reduce the risks impacted by the consumption of fake news. The methodology for improved fake news detection focusses on four phases: (a) pre-processing, (b) feature extraction, (c) optimal feature selection and (d) classification. The pre-processing of the text data will be initially done by stop word removal, blank space removal and stemming. Further, the feature extraction is performed by term frequency-inverse document frequency, and grammatical analysis is done using mean, Q25, Q50, Q75, Max, Min and standard deviation. Then, the optimal feature selection is developed, which minimises the number of input variables. It is intended to reduce the number of input variables to improve the model’s performance by minimising the computational cost of modelling. An improved meta-heuristic algorithm called successive position-based barnacles mating optimisation is used for optimal feature selection and classification. As the main contribution, the influence of deep learning is employed, which employs optimised long short-term memory. Finally, the result shows the superiority in terms of different significant measures by the proposed model over other methods for fake news detection experimentally done on a publicly available benchmark dataset.
{"title":"Automated Fake News Detection by LSTM Enabled with Optimal Feature Selection","authors":"S. Nithya, Arun Sahayadhas","doi":"10.1142/s0219649222500368","DOIUrl":"https://doi.org/10.1142/s0219649222500368","url":null,"abstract":"Fake news plays a major role by broadcasting misinformation, which influences people’s knowledge or perceptions and distorts their decision-making and awareness. Online forums and social media have stimulated the broadcast of fake news by embedding it with truthful information. Thus, fake news has evolved into the main challenge of better impact in the information-driven community for intense fakesters. The detection of fake news articles that is generally found by considering the quality of the information in their news feeds under uncertain authenticity calls for automated tools. However, designing such tools is a major problem because of the multiple faces of fakesters. This paper offers a new text-analytics-driven method for detecting fake news to reduce the risks impacted by the consumption of fake news. The methodology for improved fake news detection focusses on four phases: (a) pre-processing, (b) feature extraction, (c) optimal feature selection and (d) classification. The pre-processing of the text data will be initially done by stop word removal, blank space removal and stemming. Further, the feature extraction is performed by term frequency-inverse document frequency, and grammatical analysis is done using mean, Q25, Q50, Q75, Max, Min and standard deviation. Then, the optimal feature selection is developed, which minimises the number of input variables. It is intended to reduce the number of input variables to improve the model’s performance by minimising the computational cost of modelling. An improved meta-heuristic algorithm called successive position-based barnacles mating optimisation is used for optimal feature selection and classification. As the main contribution, the influence of deep learning is employed, which employs optimised long short-term memory. Finally, the result shows the superiority in terms of different significant measures by the proposed model over other methods for fake news detection experimentally done on a publicly available benchmark dataset.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124361910","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 : 2022-05-27DOI: 10.1142/s021964922250037x
V. Tank, S. Mahajan
Voice quality enhancement is a significant method for any speech communication model. Speech Enhancement (SE) and noise reduction approaches can significantly improve the perceptual voice quality of a hands-free communication system and increase the recognition rates of automatic speech recognition systems. Speech communications in real-world cases require high-performance enhancement techniques for addressing the distortions, which can corrupt the intelligibility and quality of the speech signal. Recent portable devices generally incorporate several microphones that can be easily used for improving signal quality. This paper plans to present a novel dual-channel SE model using the coherence function and heuristic concepts. The adaptive coherence function relates to the dual-microphone SE approach suitable for smartphones with primary and reference microphones. With this improved signal, the enhancement is performed by optimising denoising using Discrete Wavelet Transform (DWT) by Adaptive wind speed-based Deer Hunting Optimization Algorithm (AWS-DHOA). The considered objective function depends on the quality measure called Perceptual Evaluation of Speech Quality (PESQ) score. From the results, the RMSE of the proposed model using AWS-DHOA is 39.8%, 45.5%, 53.8% and 45.5% minimised than GWO-CFD, WOA-CFD, CSA-CFD, and RDA-CFD, respectively, on considering the babble noise. Finally, the comparative analysis confirmed that the proposed method improves speech quality and intelligibility by comparing diverse algorithms when different noise types corrupt the speech.
{"title":"Automated Dual-Channel Speech Enhancement Using Adaptive Coherence Function with Optimised Discrete Wavelet Transform","authors":"V. Tank, S. Mahajan","doi":"10.1142/s021964922250037x","DOIUrl":"https://doi.org/10.1142/s021964922250037x","url":null,"abstract":"Voice quality enhancement is a significant method for any speech communication model. Speech Enhancement (SE) and noise reduction approaches can significantly improve the perceptual voice quality of a hands-free communication system and increase the recognition rates of automatic speech recognition systems. Speech communications in real-world cases require high-performance enhancement techniques for addressing the distortions, which can corrupt the intelligibility and quality of the speech signal. Recent portable devices generally incorporate several microphones that can be easily used for improving signal quality. This paper plans to present a novel dual-channel SE model using the coherence function and heuristic concepts. The adaptive coherence function relates to the dual-microphone SE approach suitable for smartphones with primary and reference microphones. With this improved signal, the enhancement is performed by optimising denoising using Discrete Wavelet Transform (DWT) by Adaptive wind speed-based Deer Hunting Optimization Algorithm (AWS-DHOA). The considered objective function depends on the quality measure called Perceptual Evaluation of Speech Quality (PESQ) score. From the results, the RMSE of the proposed model using AWS-DHOA is 39.8%, 45.5%, 53.8% and 45.5% minimised than GWO-CFD, WOA-CFD, CSA-CFD, and RDA-CFD, respectively, on considering the babble noise. Finally, the comparative analysis confirmed that the proposed method improves speech quality and intelligibility by comparing diverse algorithms when different noise types corrupt the speech.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125972333","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 : 2022-05-27DOI: 10.1142/s0219649222500393
D. Kwon, Pilwon Jeong, Doohee Chung
Artificial intelligence-based investment services (robo-advisors) are becoming increasingly commercialized. Robo-advisors are expected to expand further due to the enhancement of accessibility to investment for general investors through customized portfolio selection and automated transactions established upon the artificial intelligence-based algorithm. This study comprehensively investigates factors that influence acceptance intention of and resistance to robo-advisors using a combined model of technology acceptance model and innovation resistance model. The model was examined through conducting a choice-based conjoint analysis of 158 users with investment experience and age ranging from 20s to 60s. The independent variables of the research for robo-advisors are transparency, customization, social presence, and user control. The effects of the independent variables on acceptance intention and innovation resistance are analyzed, respectively, through mediator variables of perceived usefulness, perceived complexity, and perceived safety. This study indicates the fundamental factors for the promotion of the domestic robo-advisor market based on the analysis of further advanced overseas robo-advisor markets. The significance of this study derives from providing implications on the direction of development for companies or financial institutions in the sphere of robo-advisors.
{"title":"An Empirical Study of Factors Influencing the Intention to Use Robo-Advisors","authors":"D. Kwon, Pilwon Jeong, Doohee Chung","doi":"10.1142/s0219649222500393","DOIUrl":"https://doi.org/10.1142/s0219649222500393","url":null,"abstract":"Artificial intelligence-based investment services (robo-advisors) are becoming increasingly commercialized. Robo-advisors are expected to expand further due to the enhancement of accessibility to investment for general investors through customized portfolio selection and automated transactions established upon the artificial intelligence-based algorithm. This study comprehensively investigates factors that influence acceptance intention of and resistance to robo-advisors using a combined model of technology acceptance model and innovation resistance model. The model was examined through conducting a choice-based conjoint analysis of 158 users with investment experience and age ranging from 20s to 60s. The independent variables of the research for robo-advisors are transparency, customization, social presence, and user control. The effects of the independent variables on acceptance intention and innovation resistance are analyzed, respectively, through mediator variables of perceived usefulness, perceived complexity, and perceived safety. This study indicates the fundamental factors for the promotion of the domestic robo-advisor market based on the analysis of further advanced overseas robo-advisor markets. The significance of this study derives from providing implications on the direction of development for companies or financial institutions in the sphere of robo-advisors.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130422307","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 : 2022-05-26DOI: 10.1142/s021964922250040x
K. J. Kumar, Richa Sharma
Energy industries are the pioneers in exploiting the knowledge management (KM) for meeting the challenges. Renewable energy industries are emerging to meet the energy security and climate changes and challenges. Therefore, it was of interest to study how the Indian renewable energy (RE) industries are able to exploit the KM practices to boost their organisation performance. Pilot study was undertaken to study the prevalence of the knowledge management (KM) practices in Indian renewable energy industries through the questionnaire and the measurement of Knowledge Management Performance Index (KMPI) value. The questionnaire was modified based on the outcomes of pilot study. The same qualitative analysis and quantitative analysis were done for the pilot study, and the KMPI value was also determined. The relation of all KM concepts, viz. KM creation, KM storage, KM transfer, KM exploitation and KM dissemination was the construct. This study provides one of first insights of KM performance in promoting new and renewable energy technologies. The clarity on the knowledge and technological gap, process of extracting and disseminating information, difficulty in accessing skilled labour, lack of collaborative R and D and research activities and storage of knowledge were found to be major issues in the exploitation of KM in RE industries in India.
{"title":"Studies on the Role of Knowledge Management in Performance Enhancement and Promotion of Renewable Energy Industries in India","authors":"K. J. Kumar, Richa Sharma","doi":"10.1142/s021964922250040x","DOIUrl":"https://doi.org/10.1142/s021964922250040x","url":null,"abstract":"Energy industries are the pioneers in exploiting the knowledge management (KM) for meeting the challenges. Renewable energy industries are emerging to meet the energy security and climate changes and challenges. Therefore, it was of interest to study how the Indian renewable energy (RE) industries are able to exploit the KM practices to boost their organisation performance. Pilot study was undertaken to study the prevalence of the knowledge management (KM) practices in Indian renewable energy industries through the questionnaire and the measurement of Knowledge Management Performance Index (KMPI) value. The questionnaire was modified based on the outcomes of pilot study. The same qualitative analysis and quantitative analysis were done for the pilot study, and the KMPI value was also determined. The relation of all KM concepts, viz. KM creation, KM storage, KM transfer, KM exploitation and KM dissemination was the construct. This study provides one of first insights of KM performance in promoting new and renewable energy technologies. The clarity on the knowledge and technological gap, process of extracting and disseminating information, difficulty in accessing skilled labour, lack of collaborative R and D and research activities and storage of knowledge were found to be major issues in the exploitation of KM in RE industries in India.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127560656","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 : 2022-05-25DOI: 10.1142/s0219649222500460
Y. Jing, Zhou Mingfang, Yafang Chen
The evaluation system of education effect is an important part of the whole teaching process, and the establishment of the evaluation system of college English teaching effect is an important work to test the effect of college English teaching. The traditional evaluation model is widely used and cannot be applied to a variety of teaching situations. Therefore, this paper proposes an evaluation model of college English education effect based on big data analysis. This paper determines the selection principle of the evaluation index of college English education effect, and on this basis, selects the evaluation index factors of college English education effect (experts, students and teachers), calculates the weight and membership matrix of the evaluation index, and outputs the comprehensive evaluation results of college English education effect, which realizes the construction of the evaluation model of college English education effect. The results show that: under the background of the experimental subjects (senior one and senior two), the evaluation errors of English education effect meet the needs of colleges and universities, which proves that the construction model is effective and feasible, and provides the basis and support for the reform of college English education. The range of assessment errors is between 0.78% and 1.44%, all consistent with the demands of the evaluation of the English education effect which demonstrates that the model is successful.
{"title":"Evaluation Model of College English Education Effect Based on Big Data Analysis","authors":"Y. Jing, Zhou Mingfang, Yafang Chen","doi":"10.1142/s0219649222500460","DOIUrl":"https://doi.org/10.1142/s0219649222500460","url":null,"abstract":"The evaluation system of education effect is an important part of the whole teaching process, and the establishment of the evaluation system of college English teaching effect is an important work to test the effect of college English teaching. The traditional evaluation model is widely used and cannot be applied to a variety of teaching situations. Therefore, this paper proposes an evaluation model of college English education effect based on big data analysis. This paper determines the selection principle of the evaluation index of college English education effect, and on this basis, selects the evaluation index factors of college English education effect (experts, students and teachers), calculates the weight and membership matrix of the evaluation index, and outputs the comprehensive evaluation results of college English education effect, which realizes the construction of the evaluation model of college English education effect. The results show that: under the background of the experimental subjects (senior one and senior two), the evaluation errors of English education effect meet the needs of colleges and universities, which proves that the construction model is effective and feasible, and provides the basis and support for the reform of college English education. The range of assessment errors is between 0.78% and 1.44%, all consistent with the demands of the evaluation of the English education effect which demonstrates that the model is successful.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128492028","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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