Abstract Paper aims This study investigates integrating digital transformation, human factors, business process management, and emerging technologies to improve organisational efficiency and employee well-being. The research aims to develop a conceptual model that optimises digital processes while reducing the cognitive load on employees. Originality The research fills a gap in the literature by emphasising the intersection of human factors and digital transformation. It introduces a human-centric approach that balances operational efficiency with employee well-being, which has been underexplored in previous studies. Research method A systematic literature review was conducted using Scopus and Web of Science databases to identify relevant studies. Content analysis was used to extract criteria for each domain, and Structural Equation Modelling (SEM) was applied to analyse complex relationships between digital transformation and human factors. Main findings The results indicate that integrating digital tools into organisational processes optimises workflows and decision-making while mitigating cognitive overload. The proposed model prioritises employee engagement, usability, and well-being alongside technological advancement. Implications for theory and practice This study contributes to the theoretical understanding of digital transformation by integrating human factors. The findings provide a structured pathway for organisations to enhance operational efficiency while safeguarding employee well-being, offering a balanced approach to digitalisation that can be applied in real-world scenarios.

Abstract: Paper aims The research aims to investigating the impact of implementing Internet of Things (IoT) using Bayesian networks in the supply chain of manufacturing of Indian auto components enterprises to achieve enhanced productivity and reduced failure rates. Originality The research's originality lies in exploring IoT's impact with Bayesian Networks in Indian auto component manufacturing, showcasing Industry 4.0 applications. Research method The research utilizes Bayesian Network analysis to investigate IoT's impact in Indian auto component manufacturing supply chains, validating findings through Industry 4.0-based IoT implementation and a pilot study. Main findings Implementing IoT in Indian auto component manufacturing enhanced industry performance, productivity, and reduced failure rates with Industry 4.0 technologies. Implications for theory and practice The research offers theoretical insights into IoT and Industry 4.0's impact on the automotive industries and practical solutions for practitioners

Abstract Paper aims Assess the sustainability of sugarcane production in Sofala Province, Mozambique, using the SustenAgro Index approach and the Entropy Weight Method. Originality This paper provides several original contributions. First it enhances our understanding about the sustainability of sugarcane production in Mozambique, a topic that has been few studied. Second, it proposes a robust and comprehensive sustainability assessment framework based on the SustenAgro Index tailored to Mozambique. Finally, an innovative solution using an entropy approach is employed to determine the weights of criteria. Research method An intentional sample of 30 sugarcane producers from the districts of Nhamatanda and Búzi was selected. The sustainability indicators and dimensions were weighted using the entropy method, and the sustainability index was determined using the SustenAgro Index approach. Main findings Sugarcane production systems present positive sustainability scores. The social dimension has highest contribution to the sustainability index, followed by the economic and environmental dimension. Inefficient water management and the considerable distance between production fields and the sugar factory, significantly impacts the sustainability of sugarcane production. Implications for theory and practice This article presents a reliable framework for assessing sustainability in sugarcane production, leading policymakers and stakeholders to prioritize critical factors in designing policies and interventions.

Abstract Paper aims This study aims to explore the relationships between barriers to agile methodologies, focusing on identifying reinforcement mechanisms between these barriers and effective mitigation strategies. Originality This study contributes original insights by examining the interconnectedness of barriers to agile adoption and introducing reinforcement mechanisms as a novel concept. The findings add both theoretical depth and practical value to the existing literature on agile methodology implementation. Research method The research is based on qualitative evidence gathered from five projects. A comprehensive literature review was conducted, followed by in-depth content analysis of interviews using the N-VIVO® software. A coding schema was developed to systematically analyze the data and uncover key insights. Main findings The study identified four distinct reinforcement mechanisms that exacerbate the challenges of transitioning from traditional to agile methodologies. Additionally, the research highlights specific mitigation strategies that facilitate pattern recognition and suggest appropriate interventions for different stages of agile implementation. Implications for theory and practice The identification of reinforcement mechanisms and corresponding mitigation strategies provides practical guidance for organizations aiming to implement agile methodologies. This framework can help managers recognize patterns of resistance and apply targeted solutions during different phases of the agile transition.

Abstract Paper aims Elucidating the barriers for an active participation of field workers in Operational Experience Feedback (OEF) processes and identifying potential ways forward. Originality Although the literature on OEF already addresses its challenges and strategies, we identified an opportunity for delving deeper into the (inter)subjective issues involved. Research method We conduct a review of the technical-scientific literature on OEF and elaborate a discussion in light of the theory of Psychodynamics of Work (PDW). Main findings Silence and disengagement in OEF can be the result of field workers and managers resorting to defensive strategies against the risks of questionings and critiques, which are nevertheless necessary for discussing and deliberating issues reported via OEF. The deliberation gap (i.e., the exclusion of field workers from deliberation of issues they report) can be an important element in the distrust and distance between field and management. Implications for theory and practice In order to strengthen OEF processes, we propose the development of collective resources that shall enable stakeholders dealing with questionings more constructively. For this purpose, we suggest strategies that consider the expectations on OEF results, performance evaluation criteria, and the conditions for field workers to participate in the deliberation of issues reported.

Abstract Paper aims To explore the pioneering application of the QFD methodology in cartographic production in Brazil, focusing on aligning customer expectations with technical specifications in the development of orthoimage products. Originality This study represents a novel approach to employing the QFD methodology in the Brazilian cartographic sector, highlighting its potential to transform customer requirements (VOC) into prioritized technical attributes, an approach not widely used in this context. Research method The research employs a QFD-based approach, integrating qualitative and quantitative analyses to translate customer expectations into technical specifications. The methodology was applied in the design of orthoimage maps and extracts, with a focus on customer needs and operational requirements. Main findings Nine customer expectations (WHATs) related to image information were identified and addressed through fifteen technical descriptors (HOWs) using the QFD methodology. The study highlights the effectiveness of QFD while emphasizing the need for clearer definitions of cartographic products, scale, and operational requirements to improve the translation of VOC into VOE. Implications for theory and practice This study demonstrates the feasibility and challenges of applying the QFD methodology in cartographic production. It provides insights into bridging the gap between customer expectations and technical production, emphasizing the need for structured approaches in geoinformation projects. The findings can guide future research and practice in improving cartographic product development, particularly in aligning user needs with technical capabilities.

Abstract Paper aims This research explores proposed integrated policies and supports for university spin-offs by considering the growth level of the spin-offs. Originality According to the literature, different types of support are needed to make spin-offs become established companies. However, the literature lacks clarity in addressing the specific types of support required at each stage of spin-off growth. Research method This research employs a qualitative research method in which the data collection is based on nine interviews with the founders of the spin-offs, the inventor, the director of the technology transfer office, the head of the university incubator, the manager of the university technopark, and the director of university’s company. Main findings Each level of spin-offs’ growth has to be supported by specific policies and supports. There are different types of support expected at the pre-incubation stage, which are Intellectual Property Rights (IPRs) protection, patent incentives, royalties, matching funds between university and industry, and the Technology Transfer Office as a matchmaker of inventor and startup founders. Implications for theory and practice This study provides a theoretical contribution to the policy framework for university spin-offs and offers practical guidance for university management and incubator managers.

Abstract Paper aims This article introduces a novel hybrid methodology in order to optimise dual-axis photovoltaic tracking systems in three Argentinian provinces by combining artificial intelligence, swarm intelligence and the productive chain. It identifies the most suitable strategy by balancing fixed-panel worst-case scenarios with continuous-tracking best-case scenarios and incorporating the decision makers’ preferences. Originality Firstly, the novel research methods listed below combine mathematical modelling and graphical analysis, and highlighting their complementarity and distinct contributions. Secondly, theoretical, methodological and practical gaps are identified and addressed in Argentina and other under-explored regions. This offers decision-makers a viable interim solution. Research method Firstly, it involves the novel mathematical modelling, simulation, optimisation, comparison of dual-axis solar tracking in fixed and mobile cases using multi-criteria techniques, while also validating across provinces and extreme scenarios. Secondly, it consists of a novel hybrid multi-criteria optimisation model combining particle swarm optimisation with constriction factor and a fuzzy-guided feedback metaheuristic system. It is for dynamic boundary-reflected constraints, the Analytic Hierarchy Process, and radial basis function neural networks. Thirdly, this survey is based on data obtained through the present line of research, including government and meteorological station data, manufacturer data and independent research. Main findings This methodology improves energy efficiency by 10–27% and economic performance by 40–110% compared to fixed panels, depending on regional and technical conditions. Implications for theory and practice This novel, scalable hybrid methodology combines the aforementioned research methods (theory) with support for decision-making in the planning of renewable energy projects in constrained economies (practice).

Abstract Paper aims This study aims to explore the relationship between Total Quality Management (TQM), Technology Management (TM), and sustainable performance in manufacturing. Originality The originality of this study lies in its integrated approach, combining TQM and TM to assess their collective impact on Corporate Sustainability Performance (CSP). While prior research has examined these concepts separately, this study provides a comprehensive framework that highlights their synergies in driving sustainability. Research method This study employs a mixed-methods approach, combining surveys and expert interviews. The quantitative phase assesses TQM and TM practices' impact on sustainability in manufacturing firms, while qualitative interviews provide deeper insights into key success factors, challenges, and mechanisms driving the adoption of these strategies. Main findings The findings indicate that TQM and TM collectively enhance CSP by improving operational efficiency, reducing waste and emissions, fostering sustainable innovation, and promoting a culture of continuous improvement and employee involvement. These findings highlight the need to integrate quality management and technology for sustainability goals. Implications for theory and practice Theoretically, this study enriches the understanding of how TQM and TM interact to drive sustainable performance. Practically, it provides organizations with actionable strategies to align quality management and technology for long-term sustainability.

Abstract Paper aims This study seeks to investigate the accuracy of machine learning algorithms for estimation of the effort required for software development in the manufacturing sector to identify the most effective algorithms according to the nature and complexity of the data and the number of available attributes. Originality This work distinguishes itself from other studies in the field of effort prediction by utilizing a data repository that consists exclusively of projects from the manufacturing sector. This approach ensures that the specific characteristics of manufacturing projects are reflected in the predictions, addressing a gap in the existing literature. Another notable contribution of this study is the comparative analysis of various machine learning algorithms assessed under different dimensionality scenarios (three and five variables). Although this factor is crucial for enhancing effort estimation accuracy, it has received limited attention in the literature. Research method The investigated techniques in this work were (i) Support Vector Regression, (ii) Gradient Boosting Machines (GBM), (iii) eXtreme Gradient Boosting (XGBoost), (iv) Random Forest (RF), (v) Extreme Learning Machine (ELM); and (vi) Linear Regression (LR). Performance measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) were used to compare the results achieved by each model, considering a dataset of 230 records originating from various countries. Main findings The comparison among machine learning models revealed significant performance variations depending on the number of variables and the evaluation metrics adopted. GBM stood out for its robustness in complex scenarios, while SVR achieved the lowest mean absolute error. ELM, in turn, proved effective with fewer variables but showed sensitivity to outliers and less stability in more complex contexts. Among all the techniques evaluated, XGB yielded the worst performance across all parameters. Implications for theory and practice This study contributes by applying these models to the manufacturing sector and comparing scenarios with three and five variables. The results support a more informed selection of models based on project complexity and data dimensionality. The more research conducted in this area, the stronger the theoretical and practical conclusions can be drawn.