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Publicações

Publicações por CEGI

2024

Customer Preferences for Delivery Service Attributes in Attended Home Delivery

Autores
Amorim, P; Dehoratius, N; Eng-Larsson, F; Martins, S;

Publicação
MANAGEMENT SCIENCE

Abstract
Retailers face increasing competitive pressure to determine how best to deliver products purchased online to the end customer. Grocery retailers often require attended home delivery where the customer must be present to receive the delivery. For attended home delivery to function, the retailer and customer must agree on a delivery time slot that works for both parties. Using online data from a grocery retailer, we observe customer preferences for three delivery service attributes associated with each time slot: speed, precision, and timing. We define speed as the expected time between the placement of an order and its delivery, precision as the duration of the offered time slot, and timing as the availability of choices across times of the day and days of the week. We show that customers not only value speed as an attribute of delivery service but that precision and timing are also key drivers of the customer's time slot selection process. We also observe substantial customer heterogeneity in the willingness of customers to pay for time slots. Customers that differ in their loyalty to the retailer, basket value, basket size, and basket composition exhibit distinct differences in their willingness to pay. We show that retailers with the capability to tailor their time slot offerings to specific customer segments have the potential to generate approximately 9% more shipping revenue than those who cannot. Our findings inform practitioners seeking to design competitive fulfillment strategies and academics customer behavior in the attended home context.

2024

Gamification in Mobile Ticketing Systems: A Review

Autores
Ferreira, MC; Gouveia, D; Dias, TG;

Publicação
Lecture Notes in Networks and Systems

Abstract
This review is an analysis of the literature on public transport and mobile ticketing systems and their gamification. The review is divided into three main topics: (i) Behavioral Change in relation to Public Transport, (ii) Gamification, and (iii) Gamification in Public Transport and Mobile Ticketing. This study shows the diversity of the theme of gamification applied to the transport sector and demonstrates its potential to attract and retain more customers for more sustainable means of transport. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

2024

Analyzing Quality of Service and Defining Marketing Strategies for Public Transport: The Case of Metropolitan Area of Porto

Autores
Ferreira, MC; Peralo, G; Dias, TG; Tavares, RS;

Publicação
Lecture Notes in Networks and Systems

Abstract
The aim of this work is to determine, based on a market research, the level of passenger satisfaction with public transport services, in order to support better marketing decisions. This survey involves dimensions such as the level of satisfaction with timetables and frequency, vehicle conditions, driver attitudes and behavior, fares and information made available to passengers. The study was applied to the case of public transport in the Porto Metropolitan Area, Portugal, and aims to help define recommendations to improve the quality of service and define more effective marketing strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

2024

Citizen engagement with sustainable energy solutions- understanding the influence of perceived value on engagement behaviors

Autores
Banica, B; Patrício, L; Miguéis, V;

Publicação
ENERGY POLICY

Abstract
Citizen engagement with Sustainable Energy Solutions (SES) is considered essential for the current energy transition, since decarbonization requires individuals to shift from passive consumers to citizens actively involved with the energy system. However, citizen engagement research has remained peripheral and scattered, particularly in what regards the drivers of engagement behaviors. To address this challenge, this study examines how different forms of perceived value of SES (utilitarian, social, and environmental) influence different types of citizen engagement behaviors (information seeking, proactive managing, sharing feedback, helping other users, and advocating). To this end, we developed a quantitative study in the context of a H2020 EU project, with a sample of 456 citizens from the city of Alkmaar (the Netherlands). Our findings show that the utilitarian value of SES has a significant effect on all the engagement behaviors, except for sharing feedback. Social value has a significant influence on the more socially related engagement behaviors, such as sharing feedback, helping other users, and advocating. Finally, environmental value has an indirect effect on information seeking, proactive managing, and advocating, but only when mediated through awareness of consequences. The implications of this study should allow SES providers to design more relevant offerings and policymakers to develop better citizen engagement strategies.

2024

Machine learning and cointegration for structural health monitoring of a model under environmental effects

Autores
Rodrigues, M; Miguéis, VL; Felix, C; Rodrigues, C;

Publicação
EXPERT SYSTEMS WITH APPLICATIONS

Abstract
Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage.

2024

Machine learning models for short-term demand forecasting in food catering services: A solution to reduce food waste

Autores
Rodrigues, M; Miguéis, V; Freitas, S; Machado, T;

Publicação
JOURNAL OF CLEANER PRODUCTION

Abstract
Food waste is responsible for severe environmental, social, and economic issues and therefore it is imperative to prevent or at least minimize its generation. The main cause of food waste is poor demand forecasting and so it is essential to improve the accuracy of the tools tasked with these forecasts. The present work proposes four models meant to help food catering services predict food demand accurately and thus avoid overproducing or underproducing. Each model is based on a different machine learning technique. Two baseline models are also proposed to mimic how food catering services estimate future demand and to infer the added value of employing machine learning in this context. To verify the impact of the proposed models, they were tested on data from the three different canteens chosen as case studies. The results show that the models based on the random forest algorithm and the long short-term memory neural network produced the best forecasts, which would lead to a 14% to 52% reduction in the number of wasted meals. Furthermore, by basing their decisions on these forecasts, the food catering services would be able to reduce unmet demand by 3% to 16% when compared with the forecasts of the baseline models. Thus, employing machine learning to forecast future demand can be very beneficial to food catering services. These forecasts can increase the service level of food services and reduce food waste, mitigating its environmental, social, and economic consequences.

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