Abstract
This study aimed to identify the constructs related to customer experience that underpin e-commerce, as well as their interconnections, to develop a comprehensive conceptual model based on theories-in-use. A quantitative approach was employed through a survey of 441 respondents. Data analysis was conducted using partial least squares structural equation modeling. The research findings revealed that there are a total of 11 constructs: customer experience, customer satisfaction, customer loyalty, word-of-mouth, trust, perceived risk, security and privacy, web content, perceived price, perceived value, and service quality. Furthermore, twelve relationships were established between these constructs, which led to the development of a holistic conceptual model. The identified constructs and the relationships between them are hierarchized, which has practical implications for businesses. It allows them to concentrate on operational activities and formulate and implement strategies that are valued by consumers and supported by empirical evidence. The originality and value of this research lie in the conception and development of a comprehensive e-commerce model, which includes eleven constructs and twelve relationships. It also highlights the pivotal role of the customer experience.

Abstract
The advancement of technology has led to a growing demand for autonomy across various sectors. A key aspect of achieving autonomous navigation through intricate environments is path planning, initially confined to 2D spaces but rapidly evolving to address the complexities of 3D environments. Despite the widespread adoption of RRT-based planners, their inherent lack of optimality has encouraged researchers to find refinements. This paper transposes an existing algorithm developed for 2D environments to 3D, leveraging a heuristic to optimize the generated paths in terms of path length, memory consumed, and execution time. Along with this scalability to 3D scenarios, a modification was introduced that trades off some execution time for a substantial improvement in path length. The results obtained from a series of simulated experimental tests prove the efficacy of the proposed method in 3D environments, demonstrating reduced memory consumption and execution time compared to conventional approaches.

Abstract
The Eurasian grapevine (Vitis vinifera L.) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and inspection activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) and Machine Learning (ML) methods have emerged as a classification alternative to deal with the scarcity of ampelographs and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The classification pipeline of the 31 studies found in the literature was described, highlighting its pros and cons. Most of the studies used DL-based models trained with leaf images acquired in a controlled environment at a maximum distance of 1.2 metres to classify grape varieties. In addition, there is a large gap between practical applications and the datasets used: a great lack of varieties, limited data acquired in the field and a lack of tests on plants under adverse conditions. Potential directions for improving this area of research were also presented.

Abstract
Collaborative robots, or cobots, are increasingly used by manufacturing companies to meet the demands for greater flexibility and to adapt to the trend of mass customisation in production. When considering the adoption of cobots, companies enter a critical decision-making phase. This study aims to identify the relevant decision factors for adopting collaborative robots (cobots) in manufacturing medium-sized enterprises (SMEs) in Portugal, using a combined framework of Technology-Organisation-Environment (TOE), Diffusion of Innovations (DOI) theory, and Institutional Theory. Data was collected through an online survey distributed to Portuguese manufacturing companies, yielding 78 valid responses. Analysis conducted using SmartPLS 4 revealed that top management support, resource availability, and industry pressure significantly influence the adoption decision. However, factors such as the relative advantage of cobots, compatibility with existing processes, organisational innovativeness, human resources quality, and external support did not significantly impact SMEs' adoption of cobots. These findings enhance the understanding of technology management, specifically the process of adopting cobots in manufacturing. The insights from this study help managers focus on the key factors critical for successful cobot adoption, supporting decision-makers in making more informed choices.

Abstract
The concept of sustainable fashion is becoming more relevant in today's society. The purpose of this research is to identify the determinants of the purchase intention of sustainable fashion, and the relationship between price and the purchase of sustainable fashion. A questionnaire was administered, which made it possible to define the concept of sustainable fashion, to use PLS-SEM to identify the determinants, and to apply linear regression models and t-tests of two independent samples (two-tailed test). The concept of sustainable fashion comprises the dimensions of manufacturing with a reduced environmental impact, consuming second-hand fashion products, manufacturing in an environmentally friendly way, reusing fashion products, manufacturing to last longer, manufacturing according to fair trade principles, using recycled materials, and manufacturing from organic materials. The PLS-SEM results show that purchase intention is determined by consumer knowledge, environmental beliefs, and willingness to pay more. The research also revealed that there is a non-linear (quadratic or exponential) relationship between the price of the product and the price increase that consumers are willing to pay and that they value the dimensions of sustainable fashion differently. The purchase intention determinants of consumers and non-consumers of sustainable fashion are identical, yet the dimensions of sustainable fashion are valued differently by each group.

Abstract
Integration of introductory programming into higher education programs beyond computer science has lead to an increase in the failure and drop out rates of programming courses. In this context, programming instructors have explored new methodologies by introducing dynamic elements in the teaching-learning process, such as automatic code evaluation systems and gamification. Even though these methods have shown to be successful in improving students' engagement, they do not address all the existing problems and new strategies should be explored. In this work, we propose a new approach that combines the strengths of the Kumon method for personalized learning and progressive skill acquisition with the ability of online judge systems to provide automated assessment and immediate feedback. This approach has been used in teaching Programming I to students in several bachelor degrees and led to a 10% increase in exam approval rates compared to the baseline editions in which our Kumon-inspired methodology was not implemented. © Ivone Amorim, Pedro Baltazar Vasconcelos, and João Pedro Pedroso;