Pub Date : 2023-02-08DOI: 10.1108/idd-06-2022-0060
Edoardo Ramalli, B. Pernici
Purpose Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments. Design/methodology/approach This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study. Findings The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata. Originality/value The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.
{"title":"Knowledge graph embedding for experimental uncertainty estimation","authors":"Edoardo Ramalli, B. Pernici","doi":"10.1108/idd-06-2022-0060","DOIUrl":"https://doi.org/10.1108/idd-06-2022-0060","url":null,"abstract":"\u0000Purpose\u0000Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.\u0000\u0000\u0000Design/methodology/approach\u0000This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.\u0000\u0000\u0000Findings\u0000The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.\u0000\u0000\u0000Originality/value\u0000The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.\u0000","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46025773","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 : 2023-02-03DOI: 10.1108/idd-06-2022-0054
Huyen Nguyen, Haihua Chen, Jiangping Chen, Kate Kargozari, Junhua Ding
Purpose This study aims to evaluate a method of building a biomedical knowledge graph (KG). Design/methodology/approach This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed. Findings With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG. Originality/value The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.
{"title":"Construction and evaluation of a domain-specific knowledge graph for knowledge discovery","authors":"Huyen Nguyen, Haihua Chen, Jiangping Chen, Kate Kargozari, Junhua Ding","doi":"10.1108/idd-06-2022-0054","DOIUrl":"https://doi.org/10.1108/idd-06-2022-0054","url":null,"abstract":"\u0000Purpose\u0000This study aims to evaluate a method of building a biomedical knowledge graph (KG).\u0000\u0000\u0000Design/methodology/approach\u0000This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed.\u0000\u0000\u0000Findings\u0000With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG.\u0000\u0000\u0000Originality/value\u0000The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.\u0000","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47999259","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 : 2023-01-12DOI: 10.1108/idd-05-2022-0037
William A. Ellegood, Jason M. Riley
Purpose This study aims to understand how informational factors influence online purchase intention when considering secondhand books. Design/methodology/approach A conceptual model linking book condition, description, delivery cost, picture, sellers’ rating and delivery date to purchase intention was developed and tested by using structural equation modeling. Survey data from 234 respondents was used to analyze both direct and mediating relationships. Findings The examination demonstrates how book condition, delivery cost and sellers’ rating influence consumers’ purchase intention. Book condition directly and indirectly influenced purchase intention, while delivery cost and sellers’ rating were significant only when including the mediating variable delivery date. Originality/value This work clarifies where resources should be allocated when offering secondhand books online. Sellers should dedicate time to include informational factors such as book condition, delivery cost and sellers’ rating. Contra wise, there is little value expounding on the book’s description or providing a high-quality picture when selling online.
{"title":"How informational factors affect consumers when purchasing secondhand books online","authors":"William A. Ellegood, Jason M. Riley","doi":"10.1108/idd-05-2022-0037","DOIUrl":"https://doi.org/10.1108/idd-05-2022-0037","url":null,"abstract":"\u0000Purpose\u0000This study aims to understand how informational factors influence online purchase intention when considering secondhand books.\u0000\u0000\u0000Design/methodology/approach\u0000A conceptual model linking book condition, description, delivery cost, picture, sellers’ rating and delivery date to purchase intention was developed and tested by using structural equation modeling. Survey data from 234 respondents was used to analyze both direct and mediating relationships.\u0000\u0000\u0000Findings\u0000The examination demonstrates how book condition, delivery cost and sellers’ rating influence consumers’ purchase intention. Book condition directly and indirectly influenced purchase intention, while delivery cost and sellers’ rating were significant only when including the mediating variable delivery date.\u0000\u0000\u0000Originality/value\u0000This work clarifies where resources should be allocated when offering secondhand books online. Sellers should dedicate time to include informational factors such as book condition, delivery cost and sellers’ rating. Contra wise, there is little value expounding on the book’s description or providing a high-quality picture when selling online.\u0000","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44217774","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 : 2023-01-11DOI: 10.1108/idd-03-2021-0026
N. Butt, N. Warraich
Purpose The multitasking phenomenon has been prevailing in the technology-driven information environment. People are engaged in multitasking to process information and deal with personal and professional information tasks. This study aims to explore the external predictors of multitasking information behavior (MIB) of library and information science (LIS) professionals from Pakistan. Design/methodology/approach This is a quantitative study based on a questionnaire survey, and data was collected through Google Form; the link was shared via e-mail and WhatsApp to get maximum responses. The sampling includes the LIS professionals working in Higher Education Commission-recognized universities of Khyber Pakhtunkhwa (KPK), Pakistan. A total of 126 responses were received from 41 universities of KPK. Findings Pearson correlation and regression were applied by using SPSS for data analysis. The findings revealed that time pressure (TP) is a good predictor of multitasking because when professionals got tasks with deadlines, they try to perform multiple activities at a time. Therefore, TP is a predictor of human MIB. This study also revealed that the work environment is a less significant predictor of MIB, and the use of multiple information resources is a weak predictor of MIB. It is noted that individual covariates were not predictors of the multitasking information. However, TP was the most significant predictor among all the contextual and individual factors predicting MIB. Research limitations/implications This research line is significant because MIB is a new dimension of human information behavior among LIS professionals. The findings are beneficial for LIS professionals to increase their work productivity and performance by rationalizing the significant predictors. Originality/value To the best of the authors’ knowledge, no such study is available that highlighted the MIB among LIS professionals. Therefore, this study will highlight external factors’ effects on LIS professionals’ MIB. This study will contribute to the literature on libraries and information management as this study describes the LIS professionals’ behavior.
{"title":"Examining the predictors of multitasking information behavior among library and information science professionals in Pakistan","authors":"N. Butt, N. Warraich","doi":"10.1108/idd-03-2021-0026","DOIUrl":"https://doi.org/10.1108/idd-03-2021-0026","url":null,"abstract":"\u0000Purpose\u0000The multitasking phenomenon has been prevailing in the technology-driven information environment. People are engaged in multitasking to process information and deal with personal and professional information tasks. This study aims to explore the external predictors of multitasking information behavior (MIB) of library and information science (LIS) professionals from Pakistan.\u0000\u0000\u0000Design/methodology/approach\u0000This is a quantitative study based on a questionnaire survey, and data was collected through Google Form; the link was shared via e-mail and WhatsApp to get maximum responses. The sampling includes the LIS professionals working in Higher Education Commission-recognized universities of Khyber Pakhtunkhwa (KPK), Pakistan. A total of 126 responses were received from 41 universities of KPK.\u0000\u0000\u0000Findings\u0000Pearson correlation and regression were applied by using SPSS for data analysis. The findings revealed that time pressure (TP) is a good predictor of multitasking because when professionals got tasks with deadlines, they try to perform multiple activities at a time. Therefore, TP is a predictor of human MIB. This study also revealed that the work environment is a less significant predictor of MIB, and the use of multiple information resources is a weak predictor of MIB. It is noted that individual covariates were not predictors of the multitasking information. However, TP was the most significant predictor among all the contextual and individual factors predicting MIB.\u0000\u0000\u0000Research limitations/implications\u0000This research line is significant because MIB is a new dimension of human information behavior among LIS professionals. The findings are beneficial for LIS professionals to increase their work productivity and performance by rationalizing the significant predictors.\u0000\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, no such study is available that highlighted the MIB among LIS professionals. Therefore, this study will highlight external factors’ effects on LIS professionals’ MIB. This study will contribute to the literature on libraries and information management as this study describes the LIS professionals’ behavior.\u0000","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43303870","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 : 2023-01-10DOI: 10.1108/idd-02-2022-0016
Manpreet Singh, Urvashi Tandon, A. Mittal
Purpose The purpose of this paper is to identify the antecedents of continued usage intentions in the connected devices ecosystem in health care by analyzing the users' and physicians' expectations in a new ecosystem where one prefers to connect digitally rather than physically. Design/methodology/approach This is a unique study in which data was collected from 242 doctors and 215 end-users to gauge the expectations from the connected devices in health care. Further, these responses were hypothesised using UTAUT-2 and ECT theories to analyze general users’ and professional users’ or doctors’ expectations for continued usage in connected devices ecosystem in the health-care ecosystem. Findings Performance expectancy, social influence, facilitating conditions and price value emerged as significant predictors of satisfaction in both user groups. But habit and hedonic motivation reflected an insignificant impact on user satisfaction. Surprisingly, effort expectancy emerged as a significant factor for end-user satisfaction, and this became insignificant for professional user satisfaction. Satisfaction was positively related to continued usage for both user groups, and app quality has a positive impact on all the predictors. Practical implications To the best of the authors’ knowledge, this is the first comparative study to understand the factors which influence consumer behavior leading to a holistic model and can be imbibed for creating a better customer experience in an era where we are more comfortable connecting digitally rather than physically. Originality/value This study has used the Unified Theory of Acceptance and Use of Technology-2 model and expectation confirmation theory to analyze the key factors influencing the intentions for continued usage of devices in the Internet of Medical Devices setup.
{"title":"Modeling users’ and practitioners’ intention for continued usage of the Internet of Medical Devices (IoMD): an empirical investigation","authors":"Manpreet Singh, Urvashi Tandon, A. Mittal","doi":"10.1108/idd-02-2022-0016","DOIUrl":"https://doi.org/10.1108/idd-02-2022-0016","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to identify the antecedents of continued usage intentions in the connected devices ecosystem in health care by analyzing the users' and physicians' expectations in a new ecosystem where one prefers to connect digitally rather than physically.\u0000\u0000\u0000Design/methodology/approach\u0000This is a unique study in which data was collected from 242 doctors and 215 end-users to gauge the expectations from the connected devices in health care. Further, these responses were hypothesised using UTAUT-2 and ECT theories to analyze general users’ and professional users’ or doctors’ expectations for continued usage in connected devices ecosystem in the health-care ecosystem.\u0000\u0000\u0000Findings\u0000Performance expectancy, social influence, facilitating conditions and price value emerged as significant predictors of satisfaction in both user groups. But habit and hedonic motivation reflected an insignificant impact on user satisfaction. Surprisingly, effort expectancy emerged as a significant factor for end-user satisfaction, and this became insignificant for professional user satisfaction. Satisfaction was positively related to continued usage for both user groups, and app quality has a positive impact on all the predictors.\u0000\u0000\u0000Practical implications\u0000To the best of the authors’ knowledge, this is the first comparative study to understand the factors which influence consumer behavior leading to a holistic model and can be imbibed for creating a better customer experience in an era where we are more comfortable connecting digitally rather than physically.\u0000\u0000\u0000Originality/value\u0000This study has used the Unified Theory of Acceptance and Use of Technology-2 model and expectation confirmation theory to analyze the key factors influencing the intentions for continued usage of devices in the Internet of Medical Devices setup.\u0000","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45295109","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 : 2023-01-04DOI: 10.1108/idd-09-2022-0091
Dazhi Yang, C. Snelson, Shi Feng
Purpose This paper aims to identify computational thinking (CT) in 4th to 6th grade students in the context of project-based problem-solving while engaged in an after-school program. Design/methodology/approach This case study approach was selected due to its suitability for answering “how” or “why” questions about real-world phenomena within a set context (Creswell and Poth, 2018; Yin, 2018). This was an appropriate fit given the context of an after-school program and the research question asked how to identify learners’ demonstrated CT through project-based learning hands-on activities and problem-solving in a naturalistic environment. Findings Results show that heuristics, algorithms and conditional logic were observed more than other components of CT such as data collection, simulations and modeling. Descriptions of common activities in a naturalistic learning environment are presented to illustrate how the students practiced CT over time, which could help readers develop an understanding of CT in conjunction with hands-on problem-solving activities in elementary students. Identifying and classifying CT in this study focused on students’ learning process. Originality/value This study contributes to the challenging field of evaluating CT while focusing on observable behaviors and problem-solving activities with various degrees of teacher’s facilitation instead of final artifacts. Implications for researchers and educators interested in integrating CT in K-12 learning and its assessment are discussed.
{"title":"Identifying computational thinking in students through project-based problem-solving activities","authors":"Dazhi Yang, C. Snelson, Shi Feng","doi":"10.1108/idd-09-2022-0091","DOIUrl":"https://doi.org/10.1108/idd-09-2022-0091","url":null,"abstract":"\u0000Purpose\u0000This paper aims to identify computational thinking (CT) in 4th to 6th grade students in the context of project-based problem-solving while engaged in an after-school program.\u0000\u0000\u0000Design/methodology/approach\u0000This case study approach was selected due to its suitability for answering “how” or “why” questions about real-world phenomena within a set context (Creswell and Poth, 2018; Yin, 2018). This was an appropriate fit given the context of an after-school program and the research question asked how to identify learners’ demonstrated CT through project-based learning hands-on activities and problem-solving in a naturalistic environment.\u0000\u0000\u0000Findings\u0000Results show that heuristics, algorithms and conditional logic were observed more than other components of CT such as data collection, simulations and modeling. Descriptions of common activities in a naturalistic learning environment are presented to illustrate how the students practiced CT over time, which could help readers develop an understanding of CT in conjunction with hands-on problem-solving activities in elementary students. Identifying and classifying CT in this study focused on students’ learning process.\u0000\u0000\u0000Originality/value\u0000This study contributes to the challenging field of evaluating CT while focusing on observable behaviors and problem-solving activities with various degrees of teacher’s facilitation instead of final artifacts. Implications for researchers and educators interested in integrating CT in K-12 learning and its assessment are discussed.\u0000","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49174558","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 : 2022-12-22DOI: 10.1108/idd-05-2022-0038
Meenal Arora, Anshika Prakash, Saurav Dixit, A. Mittal, Swati Singh
Purpose This study aims to analyze the existing literature in human resource analytics and highlights the future research agenda and trends in the same context. It deals with evaluating regional distribution, identifying key authors, publications, journals and keyword occurrences while examining current literature. Design/methodology/approach A total of 127 articles exported from the Scopus database were systematically analyzed using bibliometric analysis through VOSviewer, including performance analysis and science mapping of the literature studied. Findings This research postulates the inconsistency between the number of publications and citations received by an author. There was an increase in collaborative research over the years. Human Resource Management Review was regarded as the most influential journal with maximum citation. Maximum publications came from Asian countries. The study revealed that the author with maximum citation were mostly the first authors of the most cited documents. Practical implications This research may be beneficial for both researchers and human resource (HR) practitioners because it identifies the research gaps and research needs in the HR analytics domain. Besides, this study recognizes the patterns in HR analytics literature that helps researchers better understand the subject area. Originality/value This research incorporates bibliometric analysis for analyzing HR analytics literature to establish a more exhaustive and systematic understanding of the research area. This research contributes to the existing body of literature and assists fellow researchers in future studies.
{"title":"A critical review of HR analytics: visualization and bibliometric analysis approach","authors":"Meenal Arora, Anshika Prakash, Saurav Dixit, A. Mittal, Swati Singh","doi":"10.1108/idd-05-2022-0038","DOIUrl":"https://doi.org/10.1108/idd-05-2022-0038","url":null,"abstract":"\u0000Purpose\u0000This study aims to analyze the existing literature in human resource analytics and highlights the future research agenda and trends in the same context. It deals with evaluating regional distribution, identifying key authors, publications, journals and keyword occurrences while examining current literature.\u0000\u0000\u0000Design/methodology/approach\u0000A total of 127 articles exported from the Scopus database were systematically analyzed using bibliometric analysis through VOSviewer, including performance analysis and science mapping of the literature studied.\u0000\u0000\u0000Findings\u0000This research postulates the inconsistency between the number of publications and citations received by an author. There was an increase in collaborative research over the years. Human Resource Management Review was regarded as the most influential journal with maximum citation. Maximum publications came from Asian countries. The study revealed that the author with maximum citation were mostly the first authors of the most cited documents.\u0000\u0000\u0000Practical implications\u0000This research may be beneficial for both researchers and human resource (HR) practitioners because it identifies the research gaps and research needs in the HR analytics domain. Besides, this study recognizes the patterns in HR analytics literature that helps researchers better understand the subject area.\u0000\u0000\u0000Originality/value\u0000This research incorporates bibliometric analysis for analyzing HR analytics literature to establish a more exhaustive and systematic understanding of the research area. This research contributes to the existing body of literature and assists fellow researchers in future studies.\u0000","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49146918","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 : 2022-11-14DOI: 10.1108/idd-04-2022-0027
Marina Bagić Babac
Purpose Social media allow for observing different aspects of human behaviour, in particular, those that can be evaluated from explicit user expressions. Based on a data set of posts with user opinions collected from social media, this paper aims to show an insight into how the readers of different news portals react to online content. The focus is on users’ emotions about the content, so the findings of the analysis provide a further understanding of how marketers should structure and deliver communication content such that it promotes positive engagement behaviour. Design/methodology/approach More than 5.5 million user comments to posted messages from 15 worldwide popular news portals were collected and analysed, where each post was evaluated based on a set of variables that represent either structural (e.g. embedded in intra- or inter-message structure) or behavioural (e.g. exhibiting a certain behavioural pattern that appeared in response to a posted message) component of expressions. The conclusions are based on a set of regression models and exploratory factor analysis. Findings The findings show and theorise the influence of social media content on emotional user engagement. This provides a more comprehensive understanding of the engagement attributed to social media content and, consequently, could be a better predictor of future behaviour. Originality/value This paper provides original data analysis of user comments and emotional reactions that appeared on social media news websites in 2018.
{"title":"Emotion analysis of user reactions to online news","authors":"Marina Bagić Babac","doi":"10.1108/idd-04-2022-0027","DOIUrl":"https://doi.org/10.1108/idd-04-2022-0027","url":null,"abstract":"\u0000Purpose\u0000Social media allow for observing different aspects of human behaviour, in particular, those that can be evaluated from explicit user expressions. Based on a data set of posts with user opinions collected from social media, this paper aims to show an insight into how the readers of different news portals react to online content. The focus is on users’ emotions about the content, so the findings of the analysis provide a further understanding of how marketers should structure and deliver communication content such that it promotes positive engagement behaviour.\u0000\u0000\u0000Design/methodology/approach\u0000More than 5.5 million user comments to posted messages from 15 worldwide popular news portals were collected and analysed, where each post was evaluated based on a set of variables that represent either structural (e.g. embedded in intra- or inter-message structure) or behavioural (e.g. exhibiting a certain behavioural pattern that appeared in response to a posted message) component of expressions. The conclusions are based on a set of regression models and exploratory factor analysis.\u0000\u0000\u0000Findings\u0000The findings show and theorise the influence of social media content on emotional user engagement. This provides a more comprehensive understanding of the engagement attributed to social media content and, consequently, could be a better predictor of future behaviour.\u0000\u0000\u0000Originality/value\u0000This paper provides original data analysis of user comments and emotional reactions that appeared on social media news websites in 2018.\u0000","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47647838","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 : 2022-11-04DOI: 10.1108/idd-02-2022-0015
Clement Ola Adekoya, Isioma Alexis Ureki, Adesola Victoria Alade
Purpose The quest for sustainable food security (SFS) is fundamental to United Nations Sustainable Development Goals. In furtherance of their pivotal role in providing the required information resources in support of education and research, libraries are expected to assist the economy in ensuring SFS. The purpose of this study is to investigate how libraries provide information to support research in agriculture towards the attainment of SFS in Nigeria. Design/methodology/approach Descriptive research design was used for the study. Interview and questionnaire were used as the instruments of data collection. Findings This study found that the extent of use of library information resources for SFS in Nigeria is high. Libraries, though facing some challenges, contribute significantly to the attainment of food security in Nigeria. It was recommended that libraries should intensify efforts to embark on media literacy programmes and provide information resources for research on agriculture and food production with a view to actualising food security goals specified in Sustainable Development Goals. Libraries should be well-funded to acquire the relevant information resources to aid research into food security and end hunger and poverty across the world. Practical implications This study suggests having better sponsored libraries that can perform as required in advancing agricultural information needs. Originality/value This study is a creative attempt to know how libraries can contribute to SFS through the provision of information to farmers and lecturers in agriculture.
{"title":"Attainment of sustainable food security in Nigeria: the role of libraries","authors":"Clement Ola Adekoya, Isioma Alexis Ureki, Adesola Victoria Alade","doi":"10.1108/idd-02-2022-0015","DOIUrl":"https://doi.org/10.1108/idd-02-2022-0015","url":null,"abstract":"\u0000Purpose\u0000The quest for sustainable food security (SFS) is fundamental to United Nations Sustainable Development Goals. In furtherance of their pivotal role in providing the required information resources in support of education and research, libraries are expected to assist the economy in ensuring SFS. The purpose of this study is to investigate how libraries provide information to support research in agriculture towards the attainment of SFS in Nigeria.\u0000\u0000\u0000Design/methodology/approach\u0000Descriptive research design was used for the study. Interview and questionnaire were used as the instruments of data collection.\u0000\u0000\u0000Findings\u0000This study found that the extent of use of library information resources for SFS in Nigeria is high. Libraries, though facing some challenges, contribute significantly to the attainment of food security in Nigeria. It was recommended that libraries should intensify efforts to embark on media literacy programmes and provide information resources for research on agriculture and food production with a view to actualising food security goals specified in Sustainable Development Goals. Libraries should be well-funded to acquire the relevant information resources to aid research into food security and end hunger and poverty across the world.\u0000\u0000\u0000Practical implications\u0000This study suggests having better sponsored libraries that can perform as required in advancing agricultural information needs.\u0000\u0000\u0000Originality/value\u0000This study is a creative attempt to know how libraries can contribute to SFS through the provision of information to farmers and lecturers in agriculture.\u0000","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43161895","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 : 2022-11-03DOI: 10.1108/idd-07-2022-0063
Reza Edris Abadi, M. Ershadi, S. T. A. Niaki
Purpose The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems. Design/methodology/approach Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system. Findings This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method. Research limitations/implications In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods. Originality/value Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.
{"title":"A clustering approach for data quality results of research information systems","authors":"Reza Edris Abadi, M. Ershadi, S. T. A. Niaki","doi":"10.1108/idd-07-2022-0063","DOIUrl":"https://doi.org/10.1108/idd-07-2022-0063","url":null,"abstract":"\u0000Purpose\u0000The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.\u0000\u0000\u0000Design/methodology/approach\u0000Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.\u0000\u0000\u0000Findings\u0000This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.\u0000\u0000\u0000Research limitations/implications\u0000In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.\u0000\u0000\u0000Originality/value\u0000Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.\u0000","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47319983","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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