Abstract
With the advent of Information and Communication Technologies in recent decades, organizations face several challenges today. Adopting Digital Transformation (DT) offers numerous opportunities for Small and Medium Enterprises (SMEs) to improve their efficiency and operations, reaching new markets, shareholders, and customers. However, there are potential risks associated with this process. With Digital Transformation (DT), the radius of connectivity and interconnection between devices and systems increases in Mozambique and worldwide, creating more significant space cyberattacks. As Small and Medium-sized Enterprises (SMEs) connect to the digital world and move forward with adopting innovative digital technologies, they become more vulnerable to digital security risks. Hence, managing digital security risks effectively is crucial to realizing the benefits of Digital Transformation (DT). This position paper proposes to present the research work that will culminate in the proposal to develop a framework that fits Mozambican Small and Medium Enterprises (SMEs) through a Design Science Research (DSR) methodology, which can help to assist Mozambican Small and Medium Enterprises (SMEs) in the Digital Transformation (DT) process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Seaport cranes operate continuously and consume large amounts of energy while aiming to minimise containerships’ berthing time. Although previous studies have contributed to addressing the crane scheduling problem, most have focused exclusively on loading time, often overlooking the aspect of energy consumption. Furthermore, crane activity is typically modelled in a simplified manner—commonly assuming a fixed cycle duration or constant energy usage when handling a container—without accounting for the impact of variable container masses. In this study, an energy-aware quay crane scheduling formulation for container terminals is proposed, highlighting the importance of integrating an energy model into the scheduling problem. The optimisation problem is formulated as a Mixed Integer Linear Programming (MILP) model. The objective is to minimise total energy costs by reordering the sequence in which containers are handled, while respecting precedence constraints defined by the ship’s stowage plan. Two solution methods—a MILP approach solved using CPLEX and a genetic algorithm (GA)—are compared. The results indicate that, for larger containerships, the genetic algorithm provides a more efficient solution method. Moreover, incorporating detailed energy consumption models for electric cranes may significantly reduce energy costs during containership handling operations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
This project was developed to improve the cost estimation process of new products within the Product Development Department of a furniture manufacturer. This work involved developing a methodology using Machine Learning (ML) models trained on products' existing data to predict the cost of new innovative ones based on similarities and given data. The ML models used were Linear Regression (LR), Light Gradient-Boosting Machine (LGBM), Random Forest (RF), and Support Vector Machine (SVM). The proposed methodology considers the estimation of the total cost of producing a product, which encompasses both material and operational costs. Throughout this project, several analyses were developed to identify and evaluate different independent variables that could explain the behaviour of these two cost components. The suitability of the different variables was studied by applying several ML models, and a set of functions that return an estimate of the cost as a function of these predictor variables was obtained. The proposed approach, which incorporates ML models into more complex variables to predict, resulted in a 19.29% reduction in estimation error.

Abstract
Despite the recognised potential of horizontal collaboration in logistics to reduce inefficiencies, and the increasing academic interest in this topic, in practice many initiatives fail. One of the main reasons for this failure is the poor strategy planning and governance organisation. This paper addresses this gap proposing a comprehensive conceptual framework to support the design and implementation of a common strategy for the stakeholders of such partnerships. The research employs qualitative methods, drawing on interviews and the case analysis of existent initiatives. The proposed framework involves the main phases of the strategic formulation, deciding the stakeholder engagement, strategic formulation, operational implementation, and business model elaboration. It serves as a road map for stakeholders to avoid common mistakes and accelerate the deployment of cooperative partnerships.

Abstract
Software ecosystems have emerged as a paradigm to structure software products, communities and business models, in a form inspired by the natural ecosystems. Mobility solutions are also evolving from individual vehicles to soft mobility services based on electric vehicles. This paper aims to address the creation of a software platform to support an ecosystem of mobility solutions—the Intelligent Mobility Ecosystem, based on connected electric vehicles. It follows the paradigm of software ecosystems, in which a technological platform provides the functionalities needed to create solutions within the ecosystem. The work being carried out is part of the A-Mover project, which aims to develop a connected electric motorcycle and electronic services to support driving and use of the vehicle in individual and business contexts. The aim is to develop a set of functionalities around the vehicle to create specific mobility solutions. The concept of a software ecosystem is reviewed below and the proposed architecture for the software platform that will support the ecosystem is described. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
As competitive and cooperative dynamics gain prominence in games, they present unique opportunities to study player behavior. This paper explores the orientations of different player types, as categorized by Bartles Taxonomy, through the lens of a Game With A Purpose (GWAP) called BartleZ. Bartle's Taxonomy identifies four distinct player types Achievers, Explorers, Socializers, and Killers. This study delves into how these different types approach competitive and cooperative gameplay, through structured dilemmas in BartleZ. Results with 45 participants, reveal that player orientations significantly influence engagement and decision-making. Achievers balanced both strategies; Explorers favored cooperation; Socializers consistently chose cooperation; and Killers preferred competition but adapted in some contexts. Overall, players leaned toward cooperation early on, with a shift toward competition as complexity increased. Our findings pinpoint the importance of tailoring GWAP mechanics with diverse player motivations, enhancing both engagement and problem-solving effectiveness.