Structural Equation Modeling (SEM) to Test Sustainable Development in University 4.0 in the Ultra-Smart Society Era

Rosak-Szyrocka, Joanna; Tiwari, Sunil

doi:10.3390/su152316167

Cited by 6 publications

(4 citation statements)

References 83 publications

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“…It can, therefore, be concluded that the created hypothetical model confirms the fact that it is applicable in this form, as it shows a good degree of agreement with real data. This statement is confirmed also by values of other indicators: χ2 = 507.962, df = 468, p = 0.098, and it is in accordance with the theory presented in [40,43,47,60,61].…”

Section: The Model Of Cybersecuritysupporting

confidence: 88%

“…Within this questionnaire, the basic pillars of CS threats and their sub-pillars were defined based on a brainstorming session of 15 specialists (more detailed in Section 2.2). The evaluation and analysis of the obtained data are based on the application of the structural equation modelling method (SEM) and confirmatory factor analysis [38][39][40][41][42][43][44][45][46][47]. Mathematical modelling using structural equations finds its application first in psychological research (in psychometrics), gradually the range of SEM applications expands marketing, strategic management, organizational research, management information systems, and operational management [38][39][40].…”

Section: The Problem Statement and Research Questionsmentioning

confidence: 99%

“…Mathematical modelling using structural equations finds its application first in psychological research (in psychometrics), gradually the range of SEM applications expands marketing, strategic management, organizational research, management information systems, and operational management [38][39][40]. Currently, the SEM is successfully used in logistics controlling [39,40], operational management [38,40], economics and finance [41], and many others [42,43]. One of the disadvantages of SEM is that it cannot test the direction of the relationships between variables [44]; however, this was not necessary in the research described.…”

Section: The Problem Statement and Research Questionsmentioning

confidence: 99%

“…FA belongs to the oldest statistical methods used to discover and describe latent variables (originally given only by the sample covariance between a set of indicators) [45]. It finds its wide application even today [39][40][41][42][43], it is especially appreciated by researchers conducting studies related to measurement [41][42][43] and developing models of interdependencies between explanatory entities (factors) and variables that need to be explained (indicators) [46]. Based on the common variance, which is shared among the set of indicators, the covariances that significantly differed from zero are observed between them.…”

Section: The Research Toolmentioning

confidence: 99%

See 3 more Smart Citations

Application of Structural Equation Modelling to Cybersecurity Risk Analysis in the Era of Industry 4.0

Gombár,

Vagaská,

Korauš

et al. 2024

Mathematics

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In the current digital transformation to Industry 4.0, the demands on the ability of countries to react responsibly and effectively to threats in the field of cyber security (CS) are increasing. Cyber safety is one of the pillars and concepts of Industry 4.0, as digitization brings convergence and integration of information technologies (IT) and operational technologies (OT), IT/OT systems, and data. Collecting and connecting a large amount of data in smart factories and cities poses risks, in a broader context for the entire state. The authors focus attention on the issue of CS, where, despite all digitization, the human factor plays a key role—an actor of risk as well as strengthening the sustainability and resilience of CS. It is obvious that in accordance with how the individuals (decision-makers) perceive the risk, thus they subsequently evaluate the situation and countermeasures. Perceiving cyber threats/risks in their complexity as a part of hybrid threats (HT) helps decision-makers prevent and manage them. Due to the growing trend of HT, the need for research focused on the perception of threats by individuals and companies is increasing. Moreover, the literature review points out a lack of methodology and evaluation strategy. This study presents the results of the research aimed at the mathematical modelling of risk perception of threats to the state and industry through the disruption of CS. The authors provide the developed factor model of cyber security (FMCS), i.e., the model of CS threat risk perception. When creating the FMCS, the researchers applied SEM (structural equation modelling) and confirmatory factor analysis to the data obtained by the implementation of the research tool (a questionnaire designed by the authors). The pillars and sub-pillars of CS defined within the questionnaire enable quantification in the perception of the level of risk of CS as well as differentiation and comparison between the analyzed groups of respondents (students of considered universities in SK and CZ). The convergent and discriminant validity of the research instrument is verified, and its reliability is confirmed (Cronbach’s alpha = 0.95047). The influence of the individual pillars is demonstrated as significant at the significance level of α = 5%. For the entire research set N = 964, the highest share of risk perception of CS threats is achieved by the DISRIT pillar (disruption or reduction of the resistance of IT infrastructure).

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