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Data modeling positive security behavior implementation among smart device users in Indonesia: A partial least squares structural equation modeling approach (PLS-SEM)

机译：印度尼西亚智能设备用户之间的数据建模积极安全行为实施：部分最小二乘结构方程建模方法（PLS-SEM）

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The article presents raw inferential statistical data related to understanding the positive security behaviors of smart device users in Indonesia, which was used to determine whether the studied variables were direct or mediating factors. The factors explored include government efforts, technology provider support, privacy concerns, trust, perceived behavioral control, attitudes, and subjective norms. The theory of planned behavior was adopted to develop the proposed model for implementing positive security behaviors. Structured questionnaires were distributed via an online survey to consumers currently using a smartphone or using a smartphone and some other smart device. Furthermore, the respondents were from 19 provinces in Indonesia. The quantitative research method was used to analyze the data. Reliability and validity were confirmed. Structural equation modeling (SEM) using the Smart PLS software version 3 was used to present data. SEM path analysis identified estimates of the relationships of the primary constructs in the data. The outcomes obtained from this dataset demonstrate a direct influence between government efforts, privacy, and perceived behavioral control and performing positive security behaviors. Other variables had positive and significant influences on implementing positive security behaviors, indicating their roles as mediation variables. This data is useful for reference and consideration in the improvement of smart device users’ security behaviors. This data can also provide valuable insights to countries with characteristics that are similar to those of Indonesia.
机译：本文提供了与了解印度尼西亚智能设备用户的积极安全行为有关的原始推断统计数据，该统计数据用于确定研究的变量是直接因素还是中介因素。探索的因素包括政府的努力，技术提供商的支持，隐私问题，信任，感知的行为控制，态度和主观规范。采用计划行为理论来发展提出的实施积极安全行为的模型。通过在线调查向当前使用智能手机或使用智能手机和某些其他智能设备的消费者分发了结构化问卷。此外，受访者来自印度尼西亚的19个省。定量研究方法用于分析数据。信度和效度得到确认。使用Smart PLS软件版本3的结构方程模型（SEM）表示数据。 SEM路径分析确定了数据中主要结构之间关系的估计。从该数据集获得的结果表明，政府的努力，隐私和感知的行为控制与执行积极的安全行为之间存在直接影响。其他变量对实施积极的安全行为也具有积极和重大的影响，表明它们作为调解变量的作用。这些数据对于改进智能设备用户的安全行为具有参考和考虑作用。该数据还可以为特征类似于印度尼西亚的国家提供有价值的见解。

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