J. Enterp. Inf. Manag.最新文献

{"title":"Investigating medical professionals' continuance intention of the cloud-based e-learning system: an extension of expectation-confirmation model with flow theory","authors":"Yung-Ming Cheng","doi":"10.1108/jeim-12-2019-0401","DOIUrl":"https://doi.org/10.1108/jeim-12-2019-0401","url":null,"abstract":"PurposeThe purpose of this study is to propose an integrated model based on expectation–confirmation model (ECM), flow theory and human–organization–technology fit framework to examine whether human, organizational and technology factors as antecedents to medical professionals' beliefs can affect their continuance intention of the cloud-based e-learning system.Design/methodology/approachSample data for this study were collected from medical professionals at five hospitals in Taiwan. A total of 500 questionnaires were distributed, and 368 (73.6%) useable questionnaires were analyzed using structural equation modeling in this study.FindingsSynthetically speaking, human, organizational and technology factors, as antecedents to medical professionals' continuance intention of the cloud-based e-learning system have been examined, and the results strongly support the research model with all hypothesized links being significant.Originality/valueParticularly, it is worth mentioning that the application of capturing both ECM and flow theory for completely explaining three types of factors (i.e. human, organizational and technology factors) as external variables to medical professionals' cloud-based e-learning continuance intention is well documented, that is, information systems (IS) and nonIS determinants are simultaneously evaluated, and extrinsic and intrinsic motivators are both taken into consideration in this study's theoretical development of medical professionals' cloud-based e-learning continuance intention to acquire a more comprehensive and robust analysis.","PeriodicalId":390951,"journal":{"name":"J. Enterp. Inf. Manag.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130563658","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}

{"title":"Sustainable supply chain management under big data: a bibliometric analysis","authors":"Xinyi Zhang, Yanni Yu, Ning Zhang","doi":"10.1108/jeim-12-2019-0381","DOIUrl":"https://doi.org/10.1108/jeim-12-2019-0381","url":null,"abstract":"PurposeThis study aims to provide a literature review and bibliometric analysis of sustainable supply chain management using big data. We reviewed the literature on sustainable supply chain management under big data from 2012 to 2019 and extracted 777 articles.Design/methodology/approachWe conducted quantitative analysis and data network visualization of the chosen literature, including authors, journals, countries, research institutions and citations.FindingsWe discovered that the development of this interdisciplinary field has gained increasing popularity among researchers around the world, such as China and the US publishing the most articles and Western states having more cooperation, which indicates this research topic is growing in significance globally.Originality/valueScientific and technological revolutions such as big data have been incorporated in various industries. Modern supply chain management has also been combined with the advances in data science to achieve sustainability goals. No studies have reviewed the sustainable supply chain management based on big data. This study fills this gap.","PeriodicalId":390951,"journal":{"name":"J. Enterp. Inf. Manag.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126228045","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}

{"title":"An empirical examination of the moderating role of age and gender in consumer mobile banking use: a cross-national, quantitative study","authors":"Mohamad Merhi, K. Hone, A. Tarhini, Nisreen Ameen","doi":"10.1108/jeim-03-2020-0092","DOIUrl":"https://doi.org/10.1108/jeim-03-2020-0092","url":null,"abstract":"PurposeDespite the benefits of mobile banking services in an increasingly digitised world, adoption rates remain unsatisfactory. The present cross-national study examines age- and gender-dependent variations of consumer intentions and use of mobile banking services.Design/methodology/approachThe study analyses consumer mobile banking use by integrating factors such as with trust, security and privacy and it examines the effects of these factors among two demographic factors including age and gender. 897 Lebanese and British mobile banking users completed a survey. Data was analysed by partial least squares-structural equations modelling.FindingsConsumer behavioural intention was significantly moderated by age through its relationship with facilitating conditions and trust among Lebanese respondents, and performance expectancy, effort expectancy, hedonic motivation, price value and habit among their British counterparts. As for gender, a significant moderating effect was evidenced in the Lebanese, but not the British sample, on the level of performance expectancy, effort expectancy, facilitating conditions, price value and perceived security.Originality/valueThe findings provide evidence of the applicability of the new factors proposed in this research. The reflection of the influence of these demographic factors in a cross-national context provides insights into mobile banking adoption variation between different countries.","PeriodicalId":390951,"journal":{"name":"J. Enterp. Inf. Manag.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115512539","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}

{"title":"Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation","authors":"Hamza Saleem, Yongjun Li, Zulqurnain Ali, Muhammad Ayyoub, Yu Wang, Aqsa Mehreen","doi":"10.1108/jeim-03-2020-0119","DOIUrl":"https://doi.org/10.1108/jeim-03-2020-0119","url":null,"abstract":"PurposeThis paper aims to investigate the use of big data (BDU) in predicting technological innovation, supply chain and SMEs' performance and whether technological innovation mediates the association between BDU and firm performance. Additionally, this research also seeks to explore the moderating effect of information sharing in the association between BDU and technological innovation.Design/methodology/approachUsing survey methods and structural associations in AMOS 24.0., the proposed model was tested on SME managers recruited from the largest economic and manufacturing hub of China, Pearl River Delta.FindingsThe findings suggest that BDU is positively related to technological innovation (product and process) and organizational outcomes (e.g., supply chain and SMEs performance). Technological innovation (i.e., product and process) significantly mediates the association between BDU and organizational outcomes. Moreover, information sharing positively moderates the association between BDU and technological innovations.Practical implicationsThis research provides deeper insights into how BDU is useful for SME managers in achieving the firm’s goals. Particularly, SME managers can bring technological innovation into their business processes, overcome the challenges of forecasting, and generate dynamic capabilities for attaining the best SMEs’ performance. Additionally, BDU with information sharing enables SMEs reduce their risk and decrease production costs in their manufacturing process.Originality/valueFirms always need to adopt new ways to enhance their productivity using available resources. This is the first study that contributes to big data and performance management literature by exploring the moderating and mediation mechanism of information sharing and technological innovation respectively using RBVT. The study and research model enhances our insights on BDU, information sharing, and technological innovation as valuable resources for organizations to improve supply chain performance, which subsequently increases SME productivity. This gap was overlooked by previous researchers in the domain of big data.","PeriodicalId":390951,"journal":{"name":"J. Enterp. Inf. Manag.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130504719","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}

{"title":"Image editing-based data augmentation for illumination-insensitive background subtraction","authors":"Dimitrios Sakkos, Edmond S. L. Ho, Hubert P. H. Shum, Garry Elvin","doi":"10.1108/jeim-02-2020-0042","DOIUrl":"https://doi.org/10.1108/jeim-02-2020-0042","url":null,"abstract":"PurposeA core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA 2019, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly but also features semantic transformations of illumination which enhance the generalisation of the model.Design/methodology/approachIn our pilot study published in SKIMA 2019, the proposed framework successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. In this paper, we further enhance the data augmentation framework by proposing new variations in image appearance both locally and globally.FindingsExperimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place.Originality/valueSuch data augmentation allows us to effectively train an illumination-invariant deep learning model for BGS. We further propose a post-processing method that removes noise from the output binary map of segmentation, resulting in a cleaner, more accurate segmentation map that can generalise to multiple scenes of different conditions. We show that it is possible to train deep learning models even with very limited training samples. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation","PeriodicalId":390951,"journal":{"name":"J. Enterp. Inf. Manag.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117099117","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}

{"title":"Why people need privacy? The role of privacy fatigue in app users' intention to disclose privacy: based on personality traits","authors":"Jie Tang, U. Akram, Wenjing Shi","doi":"10.1108/jeim-03-2020-0088","DOIUrl":"https://doi.org/10.1108/jeim-03-2020-0088","url":null,"abstract":"PurposeMobile Applications (App) privacy has become a prominent social problem. Compared with privacy concerns, this study examines a relatively novel concept of privacy fatigue and explores its effect on the users’ intention to disclose their personal information via mobile Apps. In addition, the personality traits are proposed as antecedents that will induce the personal perception of privacy fatigue and privacy concerns differently.Design/methodology/approachData were collected from 426 respondents. Structure equation modeling was used to test the hypotheses.FindingsThe findings describe that App users’ intention toward personal information disclosure is determined by privacy fatigue and privacy concerns, but the former has a greater impact. With minor exceptions, the two factors are also influenced by different personality traits. Specifically, neuroticism has positive effects on privacy fatigue, but agreeableness and extraversion have presented the opposite results on the two variables.Practical implicationsThis research is very scarce to examine the joint effects of privacy fatigue, privacy concerns and personality traits on App users’ disclosing intention. In doing so, these results will be of benefit to App providers and platform managers and can be the basis for a variety of follow-up studies.Originality/valueWhile previous research just focuses on privacy concerns, this study explores the critical roles of privacy fatigue and opens up a new avenue of emotion-attitude analysis that can further increase the specificity and richness of users’ privacy research. Additionally, implications for personality traits as antecedents in the impact of App users’ privacy emotions and attitudes are discussed.","PeriodicalId":390951,"journal":{"name":"J. Enterp. Inf. Manag.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134143637","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}

{"title":"Big data management and environmental performance: role of big data decision-making capabilities and decision-making quality","authors":"Q. Nisar, Nadia Nasir, Samia Jamshed, S. Naz, Mubashar Ali, Shahzad Ali","doi":"10.1108/jeim-04-2020-0137","DOIUrl":"https://doi.org/10.1108/jeim-04-2020-0137","url":null,"abstract":"PurposeThis study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the Chinese public and private hospitals. It also examined the moderating effect of big data governance that was almost ignored in previous studies.Design/methodology/approachThe target population consisted of managerial employees (IT experts and executives) in hospitals. Data collected using a survey questionnaire from 752 respondents (374 respondents from public hospitals and 378 respondents from private hospitals) was subjected to PLS-SEM for analysis.FindingsFindings revealed that data management challenges (leadership focus, talent management, technology and organizational culture for big data) are significant antecedents for big data decision-making capabilities in both public and private hospitals. Moreover, it was also found that big data decision-making capabilities played a key role to improve the decision-making quality (effectiveness and efficiency), which positively contribute toward environmental performance in public and private hospitals of China. Public hospitals are playing greater attention to big data management for the sake of quality decision-making and environmental performance than private hospitals.Practical implicationsThis study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.Originality/valueThe proposed model provides an insight look at the dynamic capabilities theory in the domain of big data management to tackle the environmental issues in hospitals. The current study is the novel addition in the literature, and it identifies that big data capabilities are envisioned to be a game-changer player in effective decision-making and to improve the environmental performance in health sector.","PeriodicalId":390951,"journal":{"name":"J. Enterp. Inf. Manag.","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122700173","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}

{"title":"Pathological test type and chemical detection using deep neural networks: a case study using ELISA and LFA assays","authors":"Marzia Hoque Tania, M. Kaiser, Kamal Abuhassan, M. A. Hossain","doi":"10.1108/jeim-01-2020-0038","DOIUrl":"https://doi.org/10.1108/jeim-01-2020-0038","url":null,"abstract":"Purpose The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals has created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of an image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology, etc. Design/methodology/approach The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training Deep Learning (DL) models on thousands of images of these tests using transfer learning, this paper i) classifies the type of the assay, and ii) classifies the colourimetric results. Findings This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time. Originality/value To the best of our knowledge, this is the first attempt to provide Colourimetric Assay Type Classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.","PeriodicalId":390951,"journal":{"name":"J. Enterp. Inf. Manag.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124620245","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}

{"title":"Optical character recognition (OCR) using partial least square (PLS) based feature reduction: an application to artificial intelligence for biometric identification","authors":"Zainab Akhtar, Jong Weon Lee, Muhammad Attique Khan, M. Sharif, S. Khan, Naveed Riaz","doi":"10.1108/jeim-02-2020-0076","DOIUrl":"https://doi.org/10.1108/jeim-02-2020-0076","url":null,"abstract":"PurposeIn artificial intelligence, the optical character recognition (OCR) is an active research area based on famous applications such as automation and transformation of printed documents into machine-readable text document. The major purpose of OCR in academia and banks is to achieve a significant performance to save storage space.Design/methodology/approachA novel technique is proposed for automated OCR based on multi-properties features fusion and selection. The features are fused using serially formulation and output passed to partial least square (PLS) based selection method. The selection is done based on the entropy fitness function. The final features are classified by an ensemble classifier.FindingsThe presented method was extensively tested on two datasets such as the authors proposed and Chars74k benchmark and achieved an accuracy of 91.2 and 99.9%. Comparing the results with existing techniques, it is found that the proposed method gives improved performance.Originality/valueThe technique presented in this work will help for license plate recognition and text conversion from a printed document to machine-readable.","PeriodicalId":390951,"journal":{"name":"J. Enterp. Inf. Manag.","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115901098","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}

{"title":"External diffusion of B2B e-procurement and firm financial performance: role of information transparency and supply chain coordination","authors":"Nripendra Kumar, Kunal K. Ganguly","doi":"10.1108/jeim-02-2020-0060","DOIUrl":"https://doi.org/10.1108/jeim-02-2020-0060","url":null,"abstract":"PurposeThe purpose of this paper is to understand the process through which external diffusion of business-to-business (B2B) e-procurement impacts firm performance. The research model has been developed to empirically examine the role of information transparency and supply chain coordination in improving the firm financial performance by external diffusion of e-procurement.Design/methodology/approachThe survey is conducted in India with a target population of purchasing professionals working on the B2B e-procurement platform. The measurement model was first tested by using confirmatory factor analysis for reliability and validity, then structural equation modeling (SEM) was used to test the hypotheses of the research model using AMOS 22. The phantom model approach has been used for testing multiple mediators.FindingsThe result of the study highlights the importance of information transparency and supply chain coordination in enhancing the firm financial performance by external diffusion of e-procurement. The results establish the role of information transparency in enhancing firm performance by improving supply chain coordination. The results also indicate that supply chain coordination mediates the relationship between external diffusion of e-procurement and firm financial performance.Originality/valueThis is the first study that has focused on the external diffusion of e-procurement and its impact on firm performance. Also, this study attempted to understand the process through which external diffusion of e-procurement impacts the firm financial performance.","PeriodicalId":390951,"journal":{"name":"J. Enterp. Inf. Manag.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128347388","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}