Understanding the operators’ cloud change errors based on cognitive abilities and personality traits: An investigation integrated with quantitative and qualitative methods

IF 2.5 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL International Journal of Industrial Ergonomics Pub Date : 2024-03-23 DOI:10.1016/j.ergon.2024.103571
Wei Zhang , Changxu Wu , Jiahao Yu , Shuo Peng
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Abstract

While cloud services make industrial data convenient, they also expose it to cloud incidents. Operators' error in cloud change activities is a leading factor for cloud incidents, which have received relatively less attention in cloud security research. This study conducted a two-stage research process using an integrated approach to explore the stable individual factors related to cloud change errors. First, in the qualitative research, content analysis based on interviews and historical documents was conducted to extract the operator's cognitive abilities and personality traits and develop hypotheses. Five cognitive abilities and six personality traits were extracted. Second, quantitative research based on an experiment was conducted to test relationships between operators' different types of cloud change errors and 1) cognitive ability and 2) personality traits, respectively. Results of error type comparisons suggested that operators generated more uncorrected errors than corrected errors and more operational errors than omission errors in cloud change activities. The multivariate Poisson regression analysis suggested that cognitive abilities of sustained attention, divided attention, and long-term memory negatively predicted the number of operators' total errors, uncorrected errors, and operational errors. Regarding personality traits, with the increase in resilience capacity and carefulness and the decrease in self-esteem, the number of different types of errors reduced, except for omission errors. Working memory and risk-taking propensity were also significant predictors of the number of uncorrected errors with negative and positive coefficients, respectively. Logical reasoning, emotional stability, and sense of responsibility were not observed as predictors of cloud change errors. The present findings have several implications for the industry and cloud providers to enhance industrial cloud data security regarding human cognitive abilities and personality traits.

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基于认知能力和个性特征了解操作员的云变化错误:结合定量和定性方法的调查
云服务在为工业数据提供便利的同时,也将其暴露在云事故面前。操作员在云变更活动中的错误是导致云事件的主要因素,而这在云安全研究中受到的关注相对较少。本研究采用综合方法,分两个阶段进行研究,探索与云变更错误相关的稳定个体因素。首先,在定性研究中,基于访谈和历史文献进行内容分析,提取操作员的认知能力和人格特质,并提出假设。共提取了五种认知能力和六种人格特质。其次,基于实验的定量研究分别检验了操作员不同类型的云变化错误与 1) 认知能力和 2) 人格特质之间的关系。错误类型比较结果表明,操作员在云变化活动中产生的未纠正错误多于纠正错误,操作错误多于遗漏错误。多元泊松回归分析表明,持续注意、分散注意和长期记忆等认知能力对操作员的总错误数、未纠正错误数和操作错误数有负向预测作用。在人格特质方面,随着应变能力和细心程度的提高以及自尊心的降低,除遗漏错误外,不同类型的错误数量均有所减少。工作记忆和冒险倾向也是未纠正错误数量的重要预测因素,其系数分别为负数和正数。逻辑推理、情绪稳定和责任感都不能预测云变化错误。本研究结果对工业界和云提供商在人类认知能力和人格特质方面加强工业云数据安全有若干启示。
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来源期刊
International Journal of Industrial Ergonomics
International Journal of Industrial Ergonomics 工程技术-工程:工业
CiteScore
6.40
自引率
12.90%
发文量
110
审稿时长
56 days
期刊介绍: The journal publishes original contributions that add to our understanding of the role of humans in today systems and the interactions thereof with various system components. The journal typically covers the following areas: industrial and occupational ergonomics, design of systems, tools and equipment, human performance measurement and modeling, human productivity, humans in technologically complex systems, and safety. The focus of the articles includes basic theoretical advances, applications, case studies, new methodologies and procedures; and empirical studies.
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