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Psychological factors of using adaptive cruise control
Published in Don Harris, Engineering Psychology and Cognitive Ergonomics Volume Five, 2017
Neville A. Stanton, Mark S. Young
Structural equation modelling techniques were applied to the psychological variables using EQS software on an IBM PC. To test the model the correlations between three indicator variables for each of these constructs were calculated. There are 110 cases with complete data under each of the two conditions. The EQS structural equation program was used to test the fit of the model to variance/covariance matrix for each condition separately. In this model all the paths are significant at p <.05 and there are no additional paths between the latent variables that significantly improve the fit. The overall model fit is adequate (X2 = 197.34, df=132, p<.001, CFI = 0.901, RMSEA = 0.068, CI.90 0.068 – 0.0.84). Both Well-being and Mental Workload had a significant association with Situational Awareness. The path coefficients have the same interpretation as standardised regression coefficients. A one standard deviation increase in Well-being is associated with 0.53 standard deviation decrease in Situational Awareness. The effect of Mental Workload is slightly less (-0.45) and such that an increase in Mental Workload is associated with a decrease in Situational Awareness. Neither Locus of Control or Mental Model has a significant association with Situational Awareness. An external Locus of Control is associated with increased Trust (0.28). An increase in Trust is also associated with an increase in Situational Awareness (0.53). Finally it should be noted that neither Well-being nor Mental Workload have direct influence on Trust. Any effects are mediated via Situational Awareness.
Information Technology From the Child's Perspective
Published in Betty A. Collis, Gerald A. Knezek, Kwok-Wing Lai, Keiko T. Miyashita, Willem J. Pelgrum, Tjeerd Plomp, Takashi Sakamoto, Children and Computers in School, 2013
Gerald A. Knezek, Keiko T. Miyashita, Takashi Sakamoto
During 1993, the authors attempted to formulate a probable causal model among nine computer use, learning disposition, and background variables gathered from subjects over consecutive years, in two nations (Knezek, Miyashita, & Sakamoto, 1994). The attributes involved were age, gender, computer exposure, motivation, study habits, empathy, creative tendencies, computer importance, and computer enjoyment. Two regression analysis techniques were employed. Both were aimed at producing path coefficients that show probable directions of influence among the variables included in the model.
Overview on Structural Equation Modeling
Published in Sergey V. Samoilenko, Kweku-Muata Osei-Bryson, Creating Theoretical Research Frameworks Using Multiple Methods, 2017
Sergey V. Samoilenko, Kweku-Muata Osei-Bryson
Basically, this approach aims to assess how well the causal structure embedded in the path model works when applied to the data set. Running the analysis of the structural model yields the path coefficients between the constructs in the model. The significance of the path coefficients is then evaluated by running a bootstrap or jackknife procedure to estimate standard errors. Once t values for each path have been obtained, the significance level of each path is established using a two-tailed t-distribution table.
Examining the Impact of Psychological, Social, and Quality Factors on the Continuous Intention to Use Virtual Meeting Platforms During and beyond COVID-19 Pandemic: A Hybrid SEM-ANN Approach
Published in International Journal of Human–Computer Interaction, 2023
Mohammed A. Al-Sharafi, Mostafa Al-Emran, Ibrahim Arpaci, Gonçalo Marques, Abdallah Namoun, Noorminshah A. Iahad
The structural or inner model captures the effect of the relationships between the evaluated constructs. Therefore, evaluating the structural model involves testing the hypotheses underlying the proposed relationships between the constructs (Hair et al., 2017; Ramayah et al., 2018). In PLS-SEM, a structural model can be evaluated using path coefficients, t-values, p-values, and coefficient of determination (R2). Based on this premise, the three hypotheses projected in this study were tested using the path coefficient (ß) criterion. The standardized path coefficient value ranges between −1 and +1, where values close to +1 are reflections of the positive and strong relationship between every two constructs, while −1 indicates otherwise. When a path coefficient value is used to assess the significance level of relationships, the existence of t-values that are higher than a specified critical value indicates the significance of the coefficient at a specific error probability. For instance, t-value > 1.96 implies a significance level with a p-value < 0.05 (Hair et al., 2017; Ramayah et al., 2018).
Determinants of Trust in Health Information Technology: An Empirical Investigation in the Context of an Online Clinic Appointment System
Published in International Journal of Human–Computer Interaction, 2020
Heng Xie, Gayle Prybutok, Xianghui Peng, Victor Prybutok
The structural equation model shows the results of path coefficient estimates and the coefficients of determination, R-square. The path coefficient indicates the direct standardized strength of the relationship between constructs in the structural equation model. R-square measures the proportion of the variance in the dependent construct that is predictable from independent constructs. In the model, we hypothesized that six trusting base constructs have a positive influence on trusting beliefs. In addition, we proposed that subjective norm has both direct and indirect influences on trusting intention. According to Figure 2, the results indicate that the research model has a good fit with the R-square values. The R-square on trusting beliefs is 0.759, and the R-square on trusting intention is 0.630. High R-square values support the assumption that trust formation could explain patients’ health information technology acceptance.
Deconstructing persuasiveness of strategies in behaviour change systems using the ARCS model of motivation
Published in Behaviour & Information Technology, 2019
Rita Orji, Derek Reilly, Kiemute Oyibo, Fidelia A. Orji
The structural model shows the relations between the ARCS constructs and the persuasiveness of individual strategies. To measure the strength of the relationships between the constructs in the structural models, we calculated the path coefficient (β), and the significance of the path coefficient (p) (Hair, Ringle, and Sarstedt 2011), which are the established criteria. Path coefficients measure the influence of one variable on another. The individual path coefficients (β) and their corresponding level of significance (p) obtained from our models are summarised in Table 4. We also calculate the coefficient of determination (R2) which shows the explanatory power of the models.