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The purpose of this paper is to develop a “good fit” for garments for customer satisfaction, comfort, and functionality as well as a manufacturer's success and reputation.

This paper reviews and evaluates garments on a live fit model and makes recommendations for the acceptance or modification of the garment for production. As more manufacturing, product development, and designing responsibilities continue to take place globally, alternatives to the traditional fit analysis are under consideration.

Fit analysis using live and three‐dimensional scan models as an alternative to the traditional fit analysis are under consideration.

This paper evaluates garments on a live fit model and makes recommendations for the acceptance or modification of the garment for production.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

Means, medians and SD for available socio‐economic status (SES) black‐white differences are here substituted for those of IQ in a between‐groups model published by the author over a decade ago. The goodness of fit of the SES variables used is compared with that for the earlier IQ data. Even when SES variables are relatively successful this can be viewed as additional evidence of the importance of IQ differences to black‐white differences in delinquency.

The purpose of this paper was to assess the causal relationship(s) between moral hazard and adverse selection of public–private partnership (PPP) construction projects. Structural equation modelling (SEM) was used to explore the cause and effect relationship between moral hazard and adverse selection problems in PPP construction projects in Ghana. The study produced a framework to predict, estimate and depict the complex causal relationships (i.e. the directionality) between moral hazard and adverse selection.

To test the proposed framework, a quantitative methodology was used, in which, data were collected using research questionnaires that targeted a sample of 280 PPP stakeholders in Ghana. In total, 210 useable questionnaires were retrieved, representing a response rate of 75 per cent.

The interrelationships between the eight causes and the nine effects of moral hazard and adverse selection were established using the model. The tested framework showed the degree of association and isolation of the unobserved variables on the indicator factors. Confirmatory factor analysis (CFA) was used to evaluate the fit of items to latent constructs. Because the fit of each model was good and the item loadings were adequate, it was assumed that the indicators of the different variables factors were fitting. Furthermore, a diagnostic fit analysis was conducted using the robust maximum likelihood method to test the statistical significance of the parameter estimates.

This novel research is one of the few studies investigating the causal relationships between moral hazard and adverse selection of PPP construction projects. The research concluded with future studies that seek to validate the model developed in other countries and/or other industries.

The research findings will serve as a guide for construction stakeholders in the PPP sector on the causes and effects of adverse selection and moral hazard and how to mitigate these.

Outsourcing is recognized as one of the critical factors for efficient execution of pharmaceutical supply chain management (PSCM), and many pharmaceutical companies engage in international outsourcing of services (IOS) to survive in global highly competitive business. Since the key success factors for both domestic & international alliances are partnership characteristics and strategic fit management, but there is no empirical research on this issue in Thai pharmaceutical partnership offshore outsourcing. Therefore, this survey of Thai and foreign companies, both contract providers (CPs) and contract manufacturers (CMs), seeks to indicate significant relationships among both outsourcing strategic fit and partnership types, including outsourcing performance outcome. This research is two-fold. First, the partnership types (Type I, II, & III), the strategic fit types (low fit, moderate fit, and good fit), and their correlations are analyzed. And second, their outsourcing performance (company revenues and growth rates) are presented. The results showed that the most of the Thai pharmaceutical outsourcing manufacturing are classified as the partnership Type II, as well as the moderate strategic fit, and strongly support the relationship between the two models. Both of the companies’ revenue and growth rate could predict the companies’ performances outcome for each of partnership and strategic fit types. However, it is not necessary that the most integrative type of partnership, Type III, will be always the best, because it depends also on the strategic fit between each pair of partners as well.

This research aims to integrate the unified theory of acceptance and usage of technology (UTAUT) with task technology fit to explain users' behavioral intention of using library mobile applications in university libraries.

By integrating the unified theory of acceptance and usage of technology (UTAUT) and the moderator of task-technology fit, this research proposes a library mobile applications usage intention model. The study data come from a convenience sample of 363 undergraduate and graduate students. A structural equation modelling (SEM) technique was conducted to identify causal relationships.

Results showed that the UTAUT model fits the data well. The empirical data reveal that performance expectancy, effort expectancy, social influence, and facilitating conditions determine users' behavioral intention of using library mobile applications. As a determinant in the UTAUT model, the moderating effect of task-technology fit is also significant. Moreover, individuals with different levels of task-technology fit will strengthen or weaken the relationships of determinants in the intention to use library mobile applications in university libraries.

This study addresses self-reported behavioral intentions as part of the survey; as a result, the data may have introduced inaccuracies. The implications of the proposed library mobile applications success model are discussed.

University librarians should reinforce the efficiency of library mobile applications to influence users' willingness to use such applications.

This study combines both the constructs of the UTAUT model and task-technology fit to consider intentions to use library mobile applications in university libraries.

The purpose of this study was to explore participants’ fit perceptions of customized garments and investigate the factors that affected the customized fit.

Because different patternmaking methods generate varied fit results, eight customized dresses were developed for four fit models aged 18–25 using two different made-to-measure (MTM) systems (Gerber AccuMark and Telestia Creator). The authors designed a questionnaire to assess the fit of the final garments on each of the four models. A total of 12 participants (four fit models and eight experts) attended a live evaluation meeting and completed the questionnaire. The quantitative and qualitative data of the participants’ fit perceptions were collected and analyzed.

The authors compared the fit outputs of the two MTM systems and determined the fit issues from the participants’ perceptions. The results showed that the participants evaluated the customized fit mainly according to the ease, seam placement and wrinkle occurrence. The majority of fit models and experts preferred Dress B created using Telestia Creator to Dress A developed using Gerber AccuMark. The participants’ fit perceptions also revealed that many factors, such as the MTM systems, participants’ fit preferences, models’ body characteristics, fabric and ease, impacted the fit results. In addition, the experts perceived more fit issues than the models.

The fit output comparison of the two MTM systems could be valuable for pattern makers to use the systems. The research results would also be useful for apparel companies to conduct a fit evaluation and improve their customization processes.

The purpose of this study is threefold: (1) to propose partial least squares path modeling (PLS-PM) as a way to estimate models containing composites of composites and to compare the performance of the PLS-PM approaches in this context, (2) to provide and evaluate two testing procedures to assess the overall fit of such models and (3) to introduce user-friendly step-by-step guidelines.

A simulation is conducted to examine the PLS-PM approaches and the performance of the two proposed testing procedures.

The simulation results show that the two-stage approach, its combination with the repeated indicators approach and the extended repeated indicators approach perform similarly. However, only the former is Fisher consistent. Moreover, the simulation shows that guidelines neglecting model fit assessment miss an important opportunity to detect misspecified models. Finally, the results show that both testing procedures based on the two-stage approach allow for assessment of the model fit.

Analysts who estimate and assess models containing composites of composites should use the authors’ guidelines, since the majority of existing guidelines neglect model fit assessment and thus omit a crucial step of structural equation modeling.

This study contributes to the understanding of the discussed approaches. Moreover, it highlights the importance of overall model fit assessment and provides insights about testing the fit of models containing composites of composites. Based on these findings, step-by-step guidelines are introduced to estimate and assess models containing composites of composites.

The purpose of this paper is to propose methods to improve the least square error polynomial fitting of multi-input nonlinear sources that suffer from strong correlating inputs.

The polynomial fitting is improved by amplitude normalization, reducing the order of the model, utilizing Chebychev polynomials and finally perturbing the correlating controlling voltage spectra. The fitting process is estimated by the reliability figure and the condition number.

It is shown in the paper that perturbing one of the controlling voltages reduces the correlation to a large extend especially in the cross-terms of the multi-input polynomials. Chebychev polynomials reduce the correlation between the higher-order spectra derived from the same input signal, but cannot break the correlation between correlating input and output voltages.

Optimal perturbations are sought in a separate optimization loop, which slows down the fitting process. This is due to the fact that each nonlinear source that suffers from the correlation needs a different perturbation.

The perturbation, harmonic balance run and refitting of an individual nonlinear source inside a device model is new and original way to characterize and fit polynomial models.

This study seeks to investigate the multidimensionality person‐group (PG) fit. It first aims to examine values‐based, personality‐based, and KSA‐based fit as distinct PG fit dimensions. It then also aims to examine fit as an aggregate construct (each dimension combines to form a latent PG fit construct), and as a superordinate construct (an overarching assessment of compatibility drives the individual fit dimensions). It also aims to propose that the distinct dimensions or the overall perception predict commitment to team, employee voice, and knowledge sharing, resulting in a final outcome of employee task performance.

Data were collected using longitudinal survey methodology from three different sources (793 employees, their supervisors and the Human Resources department) in a manufacturing firm in Korea. The various models were evaluated using structural equation modeling.

The distinct dimensions model, in which values‐based fit predicted commitment to the team, personality‐based fit predicted voice behaviors, and KSA‐based fit predicted knowledge sharing, was mostly supported. Each of these intermediary factors predicted supervisors' ratings of individual task performance. Although each dimension had unique impact on the outcomes, results suggested that a superordinate PG construct might be driving the more specific fit assessments. The aggregate model was not supported.

This study is the first to show how different dimensions of PG fit may differentially influence affect and behavior, to predict task performance. It also shows the first evidence for PG fit as a superordinate multidimensional construct. Results provide a basis for new knowledge regarding the multi‐faceted relationship between fit perceptions and outcomes.