This experience report explores the implementation of two specialized roles, Product Guardian and Security Master, within Alpha, a telecommunications solution provider. These roles were introduced to address challenges related to knowledge gaps, code quality, and security integration in Large Scale Agile (LSA) development. A qualitative case study was conducted with experienced LSA software professionals. The results highlight how Alpha cultivated a culture of continuous learning, team growth, and psychological safety, which proved instrumental in enhancing overall team outcomes. The report provides in-depth reflections on the role of leadership practices in fostering team autonomy, risk-taking, and double-loop learning, and it shares key lessons that offer practical insights for other organizations seeking to scale agile in complex, high-security projects. The report provides actionable recommendations for integrating security, fostering team collaboration, and managing complexity in LSA teams. These findings will be of interest to academics and practitioners seeking to optimize decisions and leadership strategies in LSA environments.

Background: Depression is one of the most prevalent mental health disorders globally, affecting approximately 280 million people and frequently going undiagnosed or misdiagnosed. The growing ubiquity of wearable devices enables continuous monitoring of activity levels, providing a new avenue for data-driven detection and severity assessment of depression. However, existing machine learning models often exhibit lower performance when distinguishing overlapping subtypes of depression and frequently lack explainability, an essential component for clinical acceptance. Objective: This study aimed to develop and evaluate an interpretable machine learning framework for detecting depression and classifying its severity using wearable-actigraphy data, while addressing common challenges such as imbalanced datasets and limited model transparency. Methods: We used the Depresjon dataset and applied Adaptive Synthetic Sampling (ADASYN) to mitigate class imbalance. We extracted multiple statistical features (eg, power spectral density mean and autocorrelation) and demographic attributes (eg, age) from the raw activity data. Five machine learning algorithms (logistic regression, support vector machines, random forest, XGBoost, and neural networks) were assessed via accuracy, precision, recall, F1-score, specificity, and Matthew correlation constant. We further used Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to elucidate prediction drivers. Results: XGBoost achieved the highest overall accuracy of 84.94% for binary classification and 85.91% for multiclass severity. SHAP and LIME revealed power spectral density mean, age, and autocorrelation as top predictors, highlighting circadian disruptions' role in depression. Conclusions: Our interpretable framework reliably identifies depressed versus nondepressed individuals and differentiates mild from moderate depression. The inclusion of SHAP and LIME provides transparent, clinically meaningful insights, emphasizing the potential of explainable artificial intelligence to enhance early detection and intervention strategies in mental health care.

In the ever-evolving landscape of business, organizations continually embark on projects, necessitating adept and expeditious management to introduce new products to the market ahead of competitors. This pursuit mandates a remarkable amalgamation of agility and dexterity within multidisciplinary teams and social sustainability. The goal of this study is to investigate the nexus between social sustainability and large-scale agile project management. We studied three Swedish software companies using in-depth interviews. Our findings highlight the importance of trust, clear communication, and a culture of learning for project success. Additionally, psychological safety, strong leadership, and decentralized decision-making emerged as important factors influencing social sustainability. This research offers practical guidance for fostering social sustainability within large-scale agile projects.

Agile software development (ASD) methods have increasingly been used in large-scale software development projects. While ASD emphasizes the importance of social interactions between practitioners for continuous reflection and knowledge sharing, these learning activities become incredibly challenging in large-scale projects. Drawing on well-established theoretical concepts, we posit that learning in large-scale ASD projects requires a suitable environment that empowers practitioners to openly and frequently engage in social interactions, which are essential for reflection and knowledge sharing. We hypothesize that several team-level factors shape individuals’ perceptions about the learning environment and learning activities in their projects, ultimately influencing their learning behavior. To test our model, we collected survey responses from practitioners working in large-scale ASD projects in five Swedish companies (N = 159). The data was analyzed using confirmatory factor analysis (CFA) and structural equation modeling (SEM). The results show that team structure plays a crucial role in promoting team cohesion and reflexivity, which, alongside knowledge sharing, contribute to the learning process. Our study provides ASD development research with a theoretically informed understanding of the interrelationship between the learning environment and learning activities in large-scale ASD. Our results guide practitioners in fostering suitable learning environments and enhancing learning in large-scale ASD. 

Process debt (PD) in agile software development represents inefficiencies that undermine team performance and job satisfaction. This study investigates the quantitative impact of PD on job satisfaction within agile teams, surveying 191 participants from two software development organizations. Our research examines five PD types: Process Unsuitability, Roles Debt, Synchronization Debt, Documentation Debt, and Infrastructure Debt. Using multiple regression analysis, our model explains approximately 33.8 % of the variance in job satisfaction. Among the five PD types, Process Unsuitability and Roles Debt emerged as statistically significant predictors of reduced job satisfaction. These findings indicate that certain forms of PD have a measurable negative impact on developers’ perceptions of their work environment. By identifying which PD types most strongly influence job satisfaction, this research offers empirically grounded insights that can inform targeted interventions. Understanding and addressing the most impactful PD categories may help organizations refine agile processes, thereby mitigating the detrimental effects of process inefficiencies on job satisfaction. 

Non-Technical Debt (NTD) is a common challenge in agile software development, manifesting in four critical forms:•Process Debt arises from inefficient or outdated workflows that hinder agility and adaptability. Examples include misaligned processes, poor synchronization across teams, and unclear role definitions, all of which can slow progress.•Social Debt stems from suboptimal team dynamics or organizational culture, such as poor communication, lack of trust, or fixed silos. These issues hinder collaboration, increase misunderstandings, and often result in costly rework.•People Debt refers to issues with people and their competence. It reflects challenges related to human resources and expertise, such as inadequate training, hiring delays, or overworked teams. This form of debt limits an organization’s ability to retain skilled, motivated personnel and meet increasing demands.•Organizational debt arises from outdated structures, policies, or practices that no longer align with the organization’s goals. Such rigidity limits innovation, hinders adaptability, and prevents the pursuit of operational excellence.The NODLA project11 (2021–2024), a collaboration between Karlstad University and four leading Swedish industrial partners, reveals how various debt types disrupt large-scale Agile Software Development (ASD) environments. Through extensive surveys, in-depth interviews, and statistical analyses involving a diverse group of software professionals, we identified key drivers of NTD and their impacts. Our findings emphasize:•Well-structured, highly cohesive teams learn faster, adapt more effectively,and innovate consistently.•Psychological safety, fostered by proactive leadership, is essential for innovation, experimentation, and keeping employees.•Inefficient processes and unclear roles contribute significantly to drops in job satisfaction, productivity, and team morale.•Social fragmentation, particularly in remote and hybrid settings, breeds rework, delays, and increased costs.•Neglected human resource needs, such as delayed hiring or insufficient training, limit an organization’s ability to meet growing demands.This white paper distils these insights into practical, evidence-based strategies, such as refining team composition, clarifying roles, fostering psychological safety, streamlining workflows, and embracing failure as a learning tool. By implementing these strategies, organizations can reduce NTD, reclaim agility, and unlock their teams’ full potential.

Deep learning-based models have revolutionized medical diagnostics by using Big Data to enhance disease diagnosis and clinical decision-making. However, their significant computational demands and opaque decision making processes, often characterized as ’black-box’ systems, pose major challenges in time-critical and resource constrained healthcare settings. To address these issues, this study explores the application of randomized machine learning models, specifically Extreme Learning Machines (ELMs) and Random Vector Functional Link (RVFL) networks, in medical diagnostics. These models introduce stochasticity into their training processes, reducing computational complexity and training times while maintaining accuracy. Furthermore, we integrate Explainable AI techniques namely Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) to explain the decision-making rationale of ELMs and RVFL. Performance evaluations on genitourinary cancers and coronary artery disease datasets demonstrate that RVFL outperforms traditional deep learning models, achieving superioraccuracyof88.29%withacomputationaloverhead of 6.22 seconds for genitourinary cancers, and an accuracy of 81.64% with a computational time of 0.0308 seconds for coronary artery disease. This research highlights the potential of randomized models in enhancing efficiency and transparency in medical diagnosis, thereby accelerating better treatment outcomes and advocating for more accessible and interpretable AI solutions in healthcare. 

Agile methods have become a standard practice within software industry, with organizations increasingly adopting large-scale agile (LSA) frameworks. However, as these frameworks are implemented across multiple teams and organizational functions, new challenges emerge, particularly in maintaining alignment, coherence, and collaboration across teams. One crucial element in addressing these challenges is fostering of a culture of continuous learning and psychological safety, with the objective of optimizing team performance and ensuring project success. Despite the importance of this topic, there is a significant gap in existing literature regarding antecedents of psychological safety and its impact on team learning and performance in LSA environments. This study aims to investigate impact of high-quality relationships and psychological safety on learning from failures and, consequently, on team performance in LSA context. An online survey of 167 software professionals in Sweden was conducted to test a conceptual model that is developed based on existing literature. The hypotheses were analyzed using partial least squares structural equation modeling. The results demonstrate strong positive correlation between the presence of high-quality relationships, psychological safety, and capacity to learn from failures and team performance. Specifically, the formation of high-quality relationships has been demonstrated to significantly enhance psychological safety, which in turn facilitates learning from failures and leads to improved team performance. These findings offer valuable insights for both practitioners and researchers, highlighting the importance of cultivating relational dynamics and a psychologically safe environment in LSA projects. Furthermore, the study offers guidance for future research, regarding the scalability and generalizability of these findings.

This study investigates the impact of perceived organizational support (POS) on employees’ intentions to continue using business analytics (BA) tools. By integrating Organizational Support Theory (OST) and technology adoption models, the research highlights the critical roles of technical and top management support in influencing perceived compatibility and usefulness, which drive BA continuance intentions. Data were collected between August and October 2021 from employees across various industries in Ireland, Finland, and Sweden who used BA tools in their work. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data, the findings reveal significant relationships between the organizational support types and continuance intentions, with technical support being particularly crucial for long-term use. This study extends OST by emphasizing the importance of technical support and confirms the relevance of perceived compatibility and usefulness in technology continuance. 

PurposeThis study aims to examine the factors that influence citizens intention to continue using e-government services in Pakistan.Design/methodology/approachData were collected through an online survey of 641 Pakistani citizens. The responses were analyzed using partial least squares structural equation modeling.FindingsThis study shows that disposition to trust positively correlates with both trust in the internet and trust in government. Notably, citizens' satisfaction, perceived usefulness, confirmation of expectations and perceived risk significantly influence their intention to continue using e-government services. Trust in the internet emerged as a significant predictor of continuance intention, while trust in government did not show a significant direct effect.Practical implicationsThe findings provide valuable insights for policymakers and practitioners working on e-government initiatives in developing countries. The study emphasizes the importance of building trust, enhancing user satisfaction and addressing perceived risks to encourage sustained use of e-government services. Recommendations include improving digital literacy, enhancing data security measures and developing user-centric e-government platforms.Originality/valueThis study contributes to the existing literature by focusing on the unique context of Pakistan, a developing country with specific socio-cultural and technological challenges. By integrating the Expectation-Confirmation Model with trust factors, the research offers a comprehensive framework for understanding e-government service continuance in developing nations. This study's empirical findings, based on a substantial sample size and rigorous analysis, provide actionable insights for policymakers and practitioners in Pakistan and similar contexts.