Thermomechanical environment characterisation in injection moulding and its relation to the mechanical properties of talc-filled polypropylene

This study is focused on the establishment of relationships between the injection moulding processing conditions, the applied thermomechanical environment (TME) and the tensile properties of talc-filled polypropylene,adopting a new extended concept of thermomechanical indices (TMI). In this approach, TMI are calculated from computational simulations of the moulding process that characterise the TME during processing, which are then related to the mechanical properties of the mouldings. In this study, this concept is extended to both the filling and the packing phases, with new TMI defined related to the morphology developed during these phases. A design of experiments approach based on Taguchi orthogonal arrays was adopted to vary the injection moulding parameters (injection flow rate, injection temperature, mould wall temperature and holding pressure), and thus, the TME. Results from analysis of variance for injection-moulded tensile specimens have shown that among the considered processing conditions, the flow rate is the most significant parameter for the Young’s modulus; the flow rate and melt temperature are the most significant for the strain at break; and the holding pressure and flow rate are the most significant for the stress at yield. The yield stress and Young’s modulus were found to be governed mostly by the thermostress index (TSI, related to the orientation of the skin layer), whilst the strain at break depends on both the TSI and the cooling index (CI, associated to the crystallinity degree of the core region). The proposed TMI approach provides predictive capabilities of the mechanical response of injection-moulded components, which is a valuable input during their design stage.

An empirical assessment of customer satisfaction and quality of servisse: Comparing Servqual and Servperf

This paper assesses the validity and reliability of two instruments measuring quality of service, the SERVPERF and SERVQUAL scales, replicated in a novel cultural settings, a Portuguese energy company. To provide insights and strategies for managerial intervention, a relation between customers’ satisfaction and quality of service is established. The empirical study suggests a superior convergent and predictive validity of SERVPERF scale to measure quality of service in this settings when comparing to SERVQUAL. The main differences of this study with previous ones, are that this one resorts on a confirmatory factor analysis, the validation of the instruments is performed by using the same measures suggested by their creators and extends the line of research to a novel cultural settings, a Portuguese energy company. Concerning the relationship between service quality and customers’ satisfaction, all of the quality of service attributes correlate almost equally to the satisfaction ones, with a lower weight concerning tangibles.

An empirical assessment of customer satisfaction and quality of service: comparing SERVQUAL and SERVPERF

This paper assesses the validity and reliability of two instruments measuring quality of service, the SERVPERF and SERVQUAL scales, replicated in a novel cultural settings, a Portuguese energy company. To provide insights and strategies for managerial intervention, a relation between customers’ satisfaction and quality of service is established. The empirical study suggests a superior convergent and predictive validity of SERVPERF scale to measure quality of service in this settings when comparing to SERVQUAL. The main differences of this study with previous ones, are that this one resorts on a confirmatory factor analysis, the validation of the instruments is performed by using the same measures suggested by their creators and extends the line of research to a novel cultural settings, a Portuguese energy company. Concerning the relationship between service quality and customers’ satisfaction, all of the quality of service attributes correlate almost equally to the satisfaction ones, with a lower weight concerning tangibles.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.