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  • Name

    Pedro Campos
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2010
001
Publications

2019

Digital Piracy: Factors that Influence the Intention to Pirate – A Structural Equation Model Approach

Authors
Meireles, R; Campos, P;

Publication
International Journal of Human–Computer Interaction

Abstract

2019

Innovation and Employment: An Agent-Based Approach

Authors
Neves, F; Campos, P; Silva, S;

Publication
JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION

Abstract
While the effects of innovation on employment have been a controversial issue in economic literature for several years, this economic puzzle is particularly relevant nowadays. We are witnessing tremendous technological developments which threaten to disrupt the labour market, due to their potential for significantly automating human labour. As such, this paper presents a qualitative study of the dynamics underlying the relationship between innovation and employment, using an agent-based model developed in Python. The model represents an economy populated by firms able to perform either Product Innovation (leading to the discovery of new tasks, which require human labour) or Process Innovation (leading to the automation of tasks previously performed by humans). The analysis led to three major conclusions, valid in this context. The first takeaway is that the Employment Rate in a given economy is dependent on the automation potential of the tasks in that economy and dependent on the type of innovation performed by firms in that economy (with Product Innovation having a positive effect on employment and Process Innovation having a negative effect). Second, in any given economy, if firms' propensity for product and process innovation, as well as the automation potential of their tasks are stable over time, the Employment Rate in that economy will tend towards stability over time. The third conclusion is that higher levels of Process Innovation and lower levels of Product Innovation, lead to a more intense decline of wage shares and to a wider gap between employee productivity growth and wage growth.

2019

Centrality and community detection: a co-marketing multilayer network

Authors
Fernandes, A; Goncalves, PCT; Campos, P; Delgado, C;

Publication
Journal of Business and Industrial Marketing

Abstract
Purpose: Based on the data obtained from a questionnaire of 595 people, the authors explore the relative importance of consumers, checking whether socioeconomic variables influence their centrality, detecting the communities within the network to which they belong, identifying consumption patterns and checking whether there is any relationship between co-marketing and consumer choices. Design/methodology/approach: A multilayer network is created from data collected through a consumer survey to identify customers’ choices in seven different markets. The authors focus the analysis on a smaller kinship and cohabitation network and apply the LART network community detection algorithm. To verify the association between consumers’ centrality and variables related to their respective socioeconomic profile, the authors develop an econometric model to measure their impact on consumer’s degree centrality. Findings: Based on 595 responses analysing individual consumers, the authors find out which consumers invest and which variables influence consumers’ centrality. Using a smaller sample of 70 consumers for whom they know kinship and cohabitation relationships, the authors detect communities with the same consumption patterns and verify that this may be an adequate way to establish co-marketing strategies. Originality/value: Network analysis has become a widely used technique in the extraction of knowledge on consumers. This paper’s main (and novel) contribution lies in providing a greater understanding on how multilayer networks represent hidden databases with potential knowledge to be considered in business decisions. Centrality and community detection are crucial measures in network science which enable customers with the highest potential value to be identified in a network. Customers are increasingly seen as multidimensional, considering their preferences in various markets. © 2019, Emerald Publishing Limited.

2019

Sequence and Network Mining of Touristic Routes Based on Flickr Geotagged Photos

Authors
Silva, A; Campos, P; Ferreira, CA;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Information provided by geotagged photos allow us to know where and when people have been, supporting a better understanding about tourist’s movement patterns across a destination. The aim of this paper is to study tourists’ movement patterns during their staying in Porto through the analysis of geotagged photos in order to fulfill marketing segmentation in an innovative way. For that purpose, the SPADE algorithm was used to find sequence patterns of tourists paths based on the time and location of the photos collected. Then, the K-Mode clustering algorithm was applied to these sequences in order to find identical behaviors in terms of paths followed by tourists. At the same time, in order to understand the influence of the different attractions on tourists’ paths, we performed a Social Network Analysis of the touristic attractions (spots, museums, streets, monuments, etc.). Based on the time and location of the photos collected, along with personal information, it was possible to understand tourists’ frequent movements across the city and to identify market segments based on a hybrid strategy. © 2019, Springer Nature Switzerland AG.

2018

An agent-based model for detection in economic networks

Authors
Brito, J; Campos, P; Leite, R;

Publication
Communications in Computer and Information Science

Abstract
The economic impact of fraud is wide and fraud can be a critical problem when the prevention procedures are not robust. In this paper we create a model to detect fraudulent transactions, and then use a classification algorithm to assess if the agent is fraud prone or not. The model (BOND) is based on the analytics of an economic network of agents of three types: individuals, businesses and financial intermediaries. From the dataset of transactions, a sliding window of rows previously aggregated per agent has been used and machine learning (classification) algorithms have been applied. Results show that it is possible to predict the behavior of agents, based on previous transactions. © 2018, Springer International Publishing AG, part of Springer Nature.

Supervised
thesis

2017

Novas perspetivas do Business Intelligence: criação de novos indicadores de desempenho

Author
Tânia Patrícia Serra Veloso

Institution
UP-FEP

2017

Mining multi-layered networks - applications to the portuguese urban system and EU domains

Author
João Marcelo Fernandes Costa

Institution
UP-FEP

2017

Utilização de Business Intelligence com Smart Connected Products: Estudo qualitativo

Author
Luís Eduardo Santos Costa Nogueira

Institution
UP-FEP

2017

Promoções para atratividade de clientes no Mercado de Retalho Desportivo

Author
Pedro Filipe Gonçalves Martins

Institution
UP-FEP

2017

Search Engine Marketing: Diferenças entre nicho e não nicho.

Author
Rui Emanuel Almeida Caprichoso

Institution
UP-FEP