Partial least squares path modeling (PLS-PM) has become very popular in recent years, for measuring concepts that depend on different aspects and that are based on different types of relationships. PLS-PM represents a useful tool to explore relationships and to analyze the influence of the different aspects on the complex phenomenon analyzed. In particular, the use of higher-order constructs has allowed researchers to extend the application of PLS-PM to more advanced and complex models. In this work, our attention is focused on higher-order constructs that include reflective or formative relationships. Even if the dispute between formative models and reflective models is not exactly recent, it is still alive in current literature, for the most part within the context of structural equation models. This paper focuses attention on theoretical and mathematical differences between formative and reflective measurement models within the context of the PLS-PM approach. A simulation study is proposed in order to show how these approaches fit well in different modeling situations. The approaches have been compared using empirical application in a sustainability context. The findings from the simulation and the empirical application can help researchers to estimate and to use the higher-order PLS-PM approach in reflective and formative type models.

Purpose: To evaluate the accuracy of computer-aided dental implant positions obtained with mucosal-supported templates as compared to Three-Dimensional (3D) planning. Materials and methods: One-hundred implants were inserted into 14 edentulous patients using the All-on-4/6 protocol after surgical virtual planning with RealGUIDE, 3DIEMME, and Geomagic software. After 6 months, three-dimensional neck (V) and apex (S) spatial coordinates of implants and angle inclination displacements as compared to virtual plans were evaluated. Results: The S maxilla coordinates revealed a significant discrepancy between clinical and virtual implant positions (p-value = 0.091). The V coordinates showed no significant differences (p-value = 0.71). The S (p-value = 0.017) and V (p-value = 0.038) mandible coordinates showed significant discrepancies between the clinical and virtual positions of the screws. Implant evaluation showed a 1-mm in average of the horizontal deviation in the V point and a 1.6-mm deviation in the S point. A mean 5° angular global deviation was detected. The multivariate permutation test of the S (p-value = 0.02) confirmed the difference. Greater errors in the mandible were detected as compared to the maxilla, and a higher S discrepancy was found in the posterior jaw compared to the anterior section of both the mandible and maxilla. Conclusions: Computer-aided surgery with mucosal-supported templates is a predictable procedure for implant placement. Data showed a discrepancy between the actual dental implant position as compared to the virtual plan, but this was not statistically significant. However, the horizontal and angle deviations detected indicated that flap surgery should be used to prevent implant positioning errors due to poor sensitivity and accuracy in cases of severe jaw atrophy.

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To analyse the level of passenger satisfaction of a public local transport service, after an explorative factorial analysis, a Structural Equation Model was adopted. The main goal of this paper is to verify how much some service characteristics could influence the perceived quality of the service. The passengers are found very sensitive to the level of the service organization and to the way the service is delivered (punctuality and regularity, and short waiting time). The safety and reliability of buses, the level of comfort and cleanness and the professionalism and courtesy of staff had, also, a big weight to determinate of the customer satisfaction. Applying a further analysis on different sub-samples, based on individual characteristics, we found that the model above identified is full invariant for different residence area and frequency of use of the service, and partially invariant for age groups, employment status, time slot of use and reason of use. The test of invariance the structural coefficients failed considering gender or education level of the passengers