Ana Fernandes

Existing literature presents multiple perspectives on quality within organizational contexts. Although these perspectives may differ, they universally emphasize the importance of meeting customer expectations regarding products/services. Consequently, organizations are dedicated to addressing customer requirements to foster elevated satisfaction levels. This study aims to assess customer satisfaction in water laboratories and develop a predictive model using artificial neural networks to improve service quality. A methodology was devised, integrating principles from thermodynamics with logic programming for knowledge representation and reasoning. Data were collected from 412 participants of both genders, aged 22 to 79 years old, using a questionnaire covering six specific areas, i.e., customer service, quality of service provided, support documentation, technical support, billing and payment, and online services and tools. While customer opinions were largely positive, the study identified areas for improvement, including clarity and effectiveness in responses to inquiries, reliability of results, clarity of analysis reports, usefulness of test interpretation guidelines, inclusion of legal information, billing options, and online services. Differences in satisfaction were noted based on socio-demographic factors such as age and academic qualifications. The findings offer a framework (an ANN-based model) for future evaluations and improvements in services, highlighting the importance of addressing specific customer needs to enhance satisfaction.

The degradation of available water quality is a consequence of pollution, environmental degradation, and/or climate change resulting from human activities. Designing strategies to foster the adoption of sustainable behaviors requires an awareness within populations regarding water quality, water availability, and the proper consumption of water. The goal of the present work was to assess the population awareness of sustainable water usage practices. The data collection involved administering a questionnaire to a sample of 1723 participants, encompassing individuals of both genders and aged between 15 and 84 years. The questionnaire includes five specific themes (i.e., water quality, water availability, water resources, water saving, and water reuse). The outcomes indicate that despite a higher awareness of sustainable water usage practices, participants reveal low levels of awareness regarding water reuse. Furthermore, measures of participants’ overall awareness and the potential for improving it are presented. These metrics are crucial in enabling the grouping of participants according to similar characteristics, thereby streamlining the design of targeted interventions.

The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis aiming to ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. Logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health.

In the literature, several definitions of quality can be found in the context of organizations. However, all of them are related to customer satisfaction with the products or services offered by companies. Thus, organizations are increasingly committed to meet customers’ requests, aiming to promote high levels of satisfaction. This study aims to evaluate the levels of satisfaction of water laboratory customers and to establish a predictive model for customers’ satisfaction assessment. To achieve this goal, artificial intelligence methods have been used. A questionnaire was used to collect data and applied to a cohort including 253 customers. The results showed most of the customers rating the global performance of the laboratory as positive. However, this study revealed that clarity of answers to customers’ questions, reliability of the results, and presentation of analytical results contributed most to customers’ dissatisfaction. The model presented in this study, based on artificial neural networks, exhibited good performance in the prediction of the customers’ satisfaction and contributed to establish improvement measures to promote their satisfaction.

The actual COVID-19 pandemic crisis brought new challenges for all companies, forcing them to adapt new working methods in order to avert/minimize infection. Monitoring employee satisfaction is a very difficult task, but one that is paramount in the current pandemic crisis. To respond to this challenge, a workable problem-solving methodology had to be developed and tested that examined the dynamics between Artificial Intelligence, Logic Programming, and Entropy for Knowledge Representation and Reasoning. Such formalisms are in line with an Artificial Neural Network approach to computing, where the ultimate goal is to assess the satisfaction of employees in Water Analysis Laboratories while considering its development and management. The model was trained and tested with real world data collected through questionnaires that had an overall accuracy of greater than 90%.