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

Kautsarina, Kautsarina; Hidayanto, Achmad Nizar; Anggorojati, Bayu; Abidin, Zaenal; Phusavat, Kongkiti

doi:10.1016/j.dib.2020.105588

Cited by 27 publications

(20 citation statements)

References 11 publications

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“…Although less widely used than the first type, PLS is unique in that it requires fewer samples, does not require data to conform to a normal distribution, can process both reflective and formative indicators, is not affected by multiple collinearities, and has strong predictive power. It is also widely used in management, economics, health behavior, and other fields, and some scholars have applied it to extend and test the theory of planned behavior [ 13 ]. Each endogenous latent variable had a path coefficient ( R 2 ) that tested the fit of the model.…”

Section: Methodsmentioning

confidence: 99%

Investigation and influencing factors of the behavioral intention of nurses voluntarily participating in the care of older adults with disabilities

Cui

Sun

Wang

et al. 2023

International Journal of Nursing Sciences

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Section: Methodsmentioning

confidence: 99%

Investigation and influencing factors of the behavioral intention of nurses voluntarily participating in the care of older adults with disabilities

Cui

Sun

Wang

et al. 2023

International Journal of Nursing Sciences

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“…(4) Kautsarina et al (2020) This study aims to understand 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.…”

Section: Methodsmentioning

confidence: 99%

Cleaner production through open innovation in Indonesian batik small and medium enterprises (SME)

Rumanti

Sunaryo

Wiratmadja

et al. 2020

TQM

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PurposeThe purpose of this paper is to design a research model and analyze the relationship between open innovation and cleaner production. The paper maps and characterizes the conditions of open innovation against cleaner production in Indonesian batik small and medium enterprise (SME), particularly in Java and Madura. The mapping process is executed by classifying the batik SME into four quadrants. The diagram is a quadrant in which there are four parts to distinguish each of the ability of batik SMEs in understanding and achieving cleaner production through open innovation. This research will obtain a new method or model that can be applied by organizations to achieve cleaner production through an open innovation. The data is obtained from 182 batik SMEs located in Laweyan, Madura and Lasem (in Java Island, Indonesia).Design/methodology/approachOne of the problems in batik SME is the waste management from the dyeing and wax removal process. In the first stages of this research, a number of initial models were elaborated as a reference, then the results of the elaboration became a new research model. The research model that has been produced is then tested using data from respondents. Based on the test results, the model can be stated valid or not. In this study, the model is valid after testing data from 182 respondents, because all outer loading for all indicators is above 0.7. The composite reliability and AVE values of all constructs were above 0.7 and 0.5. Based on the validated research model, the data is statistically processed by using the Structural Equation Modeling (SEM). By using the SEM method and statistical software SMART PLS 3.0this research can be supported to achieve the research objectives.FindingsBased on data testing and processing, open innovation climate could predict a sustained relationship to open innovation with an accuracy rate of 0.466 and influence rate of 0.427, whereas open innovation could predict a sustained relationship to cleaner production with an accuracy rate of 0.183 and influence rate of 0.324. The relationships between open innovation climate and open innovation; including open innovation toward cleaner production, are statistically significant because all prediction values and accuracy in the model have met the criteria for measurement parameters based on the value of R2, p value and T-statistics to be stated as a significant relationship.Research limitations/implicationsThis research provides an overview of the influence and importance of open innovation in creating an environmentally friendly production process in the context of cleaner production. Cleaner production on batik SMEs can be achieved through open innovation, both for inbound open innovation and outbound open innovation. Open innovation comprehensively provides support for batik SMEs in achieving cleaner production. Open innovation can be run well and optimally if it gets support from a conducive climate open innovation. Furthermore, the implementation of cleaner production could be a guideline for the owner to minimize the waste from batik SME production, both for natural and synthetic dyes. Some limitations in these study include the absence of influence from the existing stakeholders on batik SMEs on the implementation process of open innovation; the use of the cross-sectional approach that results in the unavailability of further analysis regarding the dynamics or improvements that occur in attaining cleaner production through open innovation; and finally providing no analysis of the differences in characteristics at each location of batik SMEs.Originality/valueThe implementation of cleaner production model is considered as one of the new methods and references in conjunction with reducing the negative impact of waste toward the environment, particularly in the traditional textile industry which is limited in waste management capability.

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“…Participation in government social media accounts can help provide knowledge and tips to the public about the latest information security threats, so it can positively influence their information security behavior through perceived severity, perceived vulnerability, self-efficacy, and response efficiency [54]. Research conducted by Kautsarina et al [55] also stated that the result directly influences government engagement, privacy, perceived behavioral control, and the implementation of proactive security behaviors. Other variables have a positive and significant impact on the performance of positive security behaviors, indicating their role as mediators.…”

Section: B Related Workmentioning

confidence: 99%

Differences in Information Security Behavior of Smartphone Users in Indonesia Using Pearsonâ€™s Chi-square and Post Hoc Test

Candiwan¹,

Sudirman²,

Sari³

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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The risk of data theft is still a negative impact of smartphone technology development that harms its users. One cause of data theft is information security behavior. Therefore, this study was conducted to determine information security behavior and its differences among smartphone users in Indonesia based on the demographic variables (i.e., gender, generation, educational background, and operating system) and four approaches, namely Avoiding Harmful Behavior, Settings, and Add-on Utilities, Preventive Behavior, and Disaster/Data Recovery. The data obtained from 400 respondents were processed using Pearson's chi-square and post hoc tests. The results showed that there are significant differences between the demographic variables and approaches. It was revealed that men behave better than women in terms of adopting settings and add-on utilities, preventive behavior, and disaster/data recovery. On the other hand, men tend to have riskier behavior than women in avoiding harmful behavior. Based on generation, it was found that Generation Z exhibits more secure behavior than Generation Y in terms of settings and add-on utilities regarding remote device locking. Meanwhile, Generation Z has better behavior than Generations X and Y in preventive behavior as they uninstall/remove unused applications. Furthermore, undergraduate users behave better than high schoolers in avoiding harmful behaviors such as sharing PIN/passwords. Lastly, iOS users were found to have better data recovery behavior than Android users in automatically backing up data in the cloud. These results can be considered when designing security education, training, and awareness programs to improve information security.

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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)

Cited by 27 publications

References 11 publications

Investigation and influencing factors of the behavioral intention of nurses voluntarily participating in the care of older adults with disabilities

Investigation and influencing factors of the behavioral intention of nurses voluntarily participating in the care of older adults with disabilities

Cleaner production through open innovation in Indonesian batik small and medium enterprises (SME)

Differences in Information Security Behavior of Smartphone Users in Indonesia Using Pearsonâ€™s Chi-square and Post Hoc Test

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