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Publicações

Publicações por CEGI

2023

ARE THE TRENDS OF EDUCATION AND TRAINING SYSTEMS IN EUROPEAN COUNTRIES IMPROVING AND CONVERGING?

Autores
Camanho, A; Stumbriene, D; Barbosa, F; Jakaitiene, A;

Publicação
EDULEARN Proceedings - EDULEARN23 Proceedings

Abstract

2023

Benefit-of-the-Doubt Composite Indicators and Use of Weight Restrictions

Autores
Camanho, S; Zanella, A; Moutinho, V;

Publicação
Lecture Notes in Economics and Mathematical Systems

Abstract

2023

Data Envelopment Analysis: A Review and Synthesis

Autores
Camanho, S; D’Inverno, G;

Publicação
Lecture Notes in Economics and Mathematical Systems

Abstract

2023

Internal Benchmarking for Efficiency Evaluations Using Data Envelopment Analysis: A Review of Applications and Directions for Future Research

Autores
Piran, FS; Camanho, S; Silva, MC; Lacerda, DP;

Publicação
Lecture Notes in Economics and Mathematical Systems

Abstract

2023

Curbing Dropout: Predictive Analytics at the University of Porto

Autores
Blanquet, L; Grilo, J; Strecht, P; Camanho, A;

Publicação
Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao

Abstract
This study explores data mining techniques for predicting student dropout in higher education. The research compares different methodological approaches, including alternative algorithms and variations in model specifications. Additionally, we examine the impact of employing either a single model for all university programs or separate models per program. The performance of models with students grouped according to their position on the program study plan was also tested. The training datasets were explored with varying time series lengths (2, 4, 6, and 8 years) and the experiments use academic data from the University of Porto, spanning the academic years from 2012 to 2022. The algorithm that yielded the best results was XGBoost. The best predictions were obtained with models trained with two years of data, both with separate models for each program and with a single model. The findings highlight the potential of data mining approaches in predicting student dropout, offering valuable insights for higher education institutions aiming to improve student retention and success. © 2023 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.

2023

How Startups and Entrepreneurs Survived in Times of Pandemic Crisis: Implications and Challenges for Managing Uncertainty

Autores
Silva E.; Beirão G.; Torres A.;

Publicação
Journal of Small Business Strategy

Abstract
The recent pandemic crisis has greatly impacted startups, and some changes are expected to be long-lasting. Small businesses usually have fewer resources and are more vulnerable to losing customers and investors, especially during crises. This study investigates how startups’ business processes were affected and how entrepreneurs managed this sudden change brought by the COVID-19 outbreak. Data were analyzed using qualitative research methods through in-depth interviews with the co-founders of eighteen startups. Results show that the three core business processes affected by the COVID-19 crisis were marketing and sales, logistics and operations, and organizational support. The way to succeed is to be flexible, agile, and adaptable, with technological knowledge focusing on digital channels to find novel opportunities and innovate. Additionally, resilience, self-improvement, education, technology readiness and adoption, close relationship with customers and other stakeholders, and incubation experience seem to shield startups against pandemic crisis outbreaks.

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