Enterprise Information Systems最新文献

{"title":"Enterprise Information Systems: 24th International Conference, ICEIS 2022, Virtual Event, April 25–27, 2022, Revised Selected Papers","authors":"","doi":"10.1007/978-3-031-39386-0","DOIUrl":"https://doi.org/10.1007/978-3-031-39386-0","url":null,"abstract":"","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"43 4","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50988072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Ledger and Decentralised Technology Adoption for Smart Digital Transition in Collaborative Enterprise","authors":"Bokolo Anthony Jnr","doi":"10.1080/17517575.2021.1989494","DOIUrl":"https://doi.org/10.1080/17517575.2021.1989494","url":null,"abstract":"","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"17 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60134638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The severity and effects of Cyber-breaches in SMEs: a machine learning approach","authors":"Ignacio Fernandez De Arroyabe, J. C. F. D. Arroyabe","doi":"10.1080/17517575.2021.1942997","DOIUrl":"https://doi.org/10.1080/17517575.2021.1942997","url":null,"abstract":"","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"17 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17517575.2021.1942997","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60134525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing blockchain in information systems: a review","authors":"Yang Lu","doi":"10.1080/17517575.2021.2008513","DOIUrl":"https://doi.org/10.1080/17517575.2021.2008513","url":null,"abstract":"ABSTRACT Blockchain, as a distributed ledger and decentralized database, has the potential to form a secured system of value exchange. Due to its attractive features, blockchain has drawn a lot of attention. As the pioneer of blockchain, Bitcoin has been implemented for more than a decade, and it reflects high levels of stability and reliability. It is foreseeable that blockchain will be implemented and applied to daily activities among institutions, businesses, and personnel. We conduct a study consisting of operating mechanisms, core technologies, research directions, applications, and drawbacks. This paper links that research to blockchain-based information systems.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47338769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Malware detection for IoT devices using hybrid system of whitelist and machine learning based on lightweight flow data","authors":"Masataka Nakahara, Norihiro Okui, Yasuaki Kobayashi, Yutaka Miyake, A. Kubota","doi":"10.1080/17517575.2022.2142854","DOIUrl":"https://doi.org/10.1080/17517575.2022.2142854","url":null,"abstract":"ABSTRACT For the security of IoT devices, the number and type of devices are generally large, so it is important to collect data efficiently and detect threats in a lightweight way. In this paper, we propose the architecture for malware detection, a method to detect malware using flow information, and a method to decrease the amount of transmission data between the servers in this architecture. We evaluate the performance of malware detection and the amount of data before and after the data reduction. And show that the performance of malware detection is maintained even though the amount of data is reduced.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45747338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain technology in industry 4.0","authors":"Ling Li","doi":"10.1080/17517575.2022.2095535","DOIUrl":"https://doi.org/10.1080/17517575.2022.2095535","url":null,"abstract":"Industry 4.0 was initially introduced during the Hannover Fair in 2011. In 2013, it was officially announced as a German strategic initiative to take a pioneering role in industries currently revolutionising the manufacturing sector. Industrie 4.0, also called Industry 4.0, symbolises the beginning of the Fourth Industrial Revolution. Industry 4.0 represents the current trend of automation technologies in the manufacturing industry, and it mainly includes enabling technologies such as the cyber-physical systems (CPS), Internet of Things (IoT), and cloud computing (Gorkhali 2022; Karnik et al. 2022; Li 2020; Li & Zhou, 2021; Sigov et al. 2022; Xu 2020). Various technologies can be used to implement Industry 4.0. These technologies include CPS, IoT, cloud computing, industrial information integration, and blockchain technology. World Economic Forum (2015) predicted that by 2027 10% of global GDP will be stored on blockchain technology. In recent years, the interest in studying the role to be played by blockchain in the manufacturing sector has been increasing. As a result, some companies have started integrating the blockchain concept into manufacturing practices (Gorkhali, Li, Shrestha 2020; Xu, Lu, and Li 2021). Potential applications of blockchain in Industry 4.0 include promoting resilience, scalability, security, and autonomy, as well as the usage of blockchain to timestamp sensor data. According to a recent study, Industry 4.0 and blockchain will significantly impact future enterprise information systems. This special issue (SI) aims to allow researchers and practitioners to share the most recent advances in Industry 4.0-related blockchain technologies from enterprise information systems perspectives. To foster a coherent, cumulative body of knowledge regarding blockchain technology in Industry 4.0, this SI presents eight articles authored by scholars from China, Hungary, Thailand, the US, and other countries. In addition, all authors were asked to respond to at least two rounds of peer review to prepare for this issue. In the paper entitled ‘Building trust of Blockchain-based Internet-of-Thing services using public key infrastructure’, Viriyasitavat et al. (2022) introduce a generic architecture design that incorporates Public Key Infrastructure (PKI) to establish the trust in BIoT services. In the paper entitled ‘A novel service level agreement model using blockchain and smart contract for cloud manufacturing in industry 4.0’, Tan et al. (2021) proposed a method to facilitate data security. Szabó, Ternai, and Fodor (2022), in their paper entitled ‘Affordances in blockchain-based financial recommendations concerned with life events and personalities’, aim to discover affordances in blockchain when designing an AI-based financial recommendation system as a decision support system. Bi et al. (2022), in their paper ‘Security and safety assurance of collaborative manufacturing in industry 4.0’, considered that Industry 4.0 provides an ideal plat","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42233791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-enabled IoT penetration testing: state-of-the-art and research challenges","authors":"C. Greco, G. Fortino, B. Crispo, K. Choo","doi":"10.1080/17517575.2022.2130014","DOIUrl":"https://doi.org/10.1080/17517575.2022.2130014","url":null,"abstract":"ABSTRACT Internet of Things (IoT) is gaining importance as its applications are found in many critical infrastructure sectors (e.g., Industry 4.0, healthcare, transportation, and commercial facilities). This reinforces the importance of investigating the security risks associated with IoT deployment. Hence, in this paper, we perform a comprehensive review of the literature on penetration testing of IoT devices and systems. Specifically, a total of 99 articles published between 2015 and 2021 was reviewed to identify existing and potential IoT penetration testing applications and proposed approaches. We finally provide recent advances of AI-enabled penetration testing methods that can notably be performed at the network edge.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48545830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Job satisfaction and turnover decision of employees in the Internet sector in the US","authors":"Victor Chang, Yeqing Mou, Qi Xu, Yue Xu","doi":"10.1080/17517575.2022.2130013","DOIUrl":"https://doi.org/10.1080/17517575.2022.2130013","url":null,"abstract":"ABSTRACT This paper proposes that high value on the work-life balance, compensation, career opportunity and fitness of culture and management style would improve job satisfaction. A turnover risk prediction model based on the random forest is constructed to understand the turnover risk feature and identify risk. Using a sample of 17,724 online reviews of employees from Glassdoor, the positive effect of antecedents, the job satisfaction variable as a mediator, and the unemployment rate variable as a moderator is verified. Finally, job satisfaction is identified as the most important feature for predicting turnover based on the random forest algorithm.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44701066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Customer opinions mining through social media: insights from sustainability fraud crisis - Volkswagen emissions scandal","authors":"Juling Ding, Maowei Xu, Y. K. Tse, Kuo-Yi Lin, Minhao Zhang","doi":"10.1080/17517575.2022.2130012","DOIUrl":"https://doi.org/10.1080/17517575.2022.2130012","url":null,"abstract":"ABSTRACT Social media has emerged as a vital tool to advance two-way communication between companies and customers. This paper uses 29,764 tweets to investigate a sustainability fraud crisis, the Volkswagen emissions scandal. We provide a Tweet Analytic Framework comprising three approaches: cluster analysis, sentiment analysis, and time series analysis. This paper explores public opinions regarding the Volkswagen emissions scandal in two stages and reveals the typical crisis development trend, the strong condemnation and negative sentiment, and significant public concerns. This paper can yield important insights for understanding how customers’ opinions change, thereby improving the effectiveness of managing sustainability fraud crises.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45444999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy protection against attack scenario of federated learning using internet of things","authors":"Kusum Yadav, Elham Kariri, Shoayee Alotaibi, W. Viriyasitavat, G. Dhiman, Amandeep Kaur","doi":"10.1080/17517575.2022.2101025","DOIUrl":"https://doi.org/10.1080/17517575.2022.2101025","url":null,"abstract":"ABSTRACT Laws and regulations for privacy protection have been promulgated one after another, and the phenomenon of data islands has become a significant bottleneck hindering the development of big data and artificial intelligence technologies. From the perspective of the historical development, concepts, and architecture classification of federated learning, the technical advantages of federated learning are explained using Internet of Things. Simultaneously, numerous attack methods and classifications of federated learning systems are examined, as well as the distinctions between different federated learning encryption algorithms. Finally, it reviews research in the subject of federal learning privacy protection and security mechanisms, as well as identifies difficulties and opportunities.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48024107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}