Abstract
Os Cadernos de Investigação do Mestrado em Negócio Eletrónico publicam anualmente os artigos científicos desenvolvidos pelos alunos, em colaboração com os seus orientadores de Dissertação/Projeto/Estágio, no âmbito da unidade curricular Metodologias de Investigação e Comunicação Científica, incluindo os alunos da turma de mobilidade Erasmus.

Abstract
O mundo pós-COVID-19 é claramente mais digital do que no passado e cursos como o Mestrado em Negócio Electrónico (MNE) são relevantes para capacitar os profissionais do nosso país para pensarem as actividades realizadas on-line ou suportadas em algum momento no digital. Esta revista apresenta trabalhos de investigadores-júnior realizados no contexto da unidade curricular Metodologias de Investigação e Comunicação Científica, que contam com a orientação de professores/investigadores-senior da escola de Ciências Empresariais (ISCAP) do Politécnico do Porto e de outras escolas/faculdades do ensino superior em Portugal.

Abstract
A terceira edição dos Cadernos de Investigação do Mestrado em Negócio Eletrónico (MNE) testemunha o contínuo amadurecimento deste ciclo de estudos como polo de reflexão académica e científica. Este volume reúne 21 artigos de jovens investigadores que, sob orientação de docentes-investigadores, exploram os fenómenos mais relevantes que moldam o atual panorama do negócio eletrónico.

Abstract

Abstract
Artificial Intelligence (AI) integration in supply chain systems is growing, and with it grows its potential impact on inter-organisational collaborative networks. We review existing literature on how different AI archetypes (Reflexive, Anticipatory, Supervisory, Prescriptive) could support Collaborative Supply Chain Management (CSCM) activities, and how they impact information sharing, collaborative decision-making, and trust among supply chain partners at different integration levels. Adopting a sociotechnical perspective, we synthesise existing literature and map the archetypes along four levels of AI integration, varying in scope and decision autonomy. The results are conceptual frameworks demonstrating how AI impacts collaboration dynamics as it evolves from a decision-support tool to an autonomous coordination agent. Findings show differentiated effects along archetypes and integration levels, with implications for CSCM governance, transparency, and resilience. We contribute to the discussion on human-AI collaboration in CSCM and offer a baseline for research on the human-centric values of Industry 5.0.

Abstract
Influential spreaders are key nodes in networks that maximize or control the spreading processes. Many real-world systems are represented as weighted networks, and several indexing methods, such as weighted betweenness, closeness, k-shell decomposition, voterank, and mixed degree decomposition, among others, have been proposed to identify these influential nodes. However, these methods often face limitations such as high computational cost, non-monotonic rankings, and reliance on tunable parameters. To address these issues, this paper introduces a new tunable parameter-free method, Normalized Strength-Degree Centrality (nsd), which efficiently combines a node's normalized degree and strength to measure its influence across various network structures. Experimental results on eleven real and synthetic weighted networks show that nsd outperforms the existing methods in accurately identifying influential spreaders, strongly correlating to the Weighted Susceptible-Infected-Recovered (WSIR) model. Additionally, nsd is a parameter-free method that does not require time-consuming preprocessing to estimate rankings.