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Antecedences and consequences of customer satisfaction in mobile telecom services using PLS-SEM and NCA: the moderating role of involvement with ICTs
No full textPurpose: This study aims to examine the antecedents [such as service quality (call), network quality (internet), perceived value, and corporate image] and consequences [such as electronic word of mouth (eWOM), customer loyalty and postpurchase intention] of customer satisfaction with mobile telecommunication services, as well as the moderating role of customer involvement with Information and Communication Technologies (ICTs) between customer satisfaction, eWOM, customer loyalty and postpurchase intention. Design/methodology/approach: The authors collected 295 usable mobile service user questionnaires and analyzed the data with partial least square-structural equation modeling and necessary condition analysis (NCA). Findings: Service quality (call), perceived value and corporate image significantly affect and are necessary conditions for customer satisfaction. In contrast, network quality (internet) is a necessary condition but does not have a significant effect on customer satisfaction. In addition, customer satisfaction influences eWOM, customer loyalty and postpurchase intentions but these are not necessary conditions. Finally, the results demonstrated that customer engagement with ICTs moderates the relationship between customer satisfaction and eWOM. Originality/value: To the best of the authors’ knowledge, this is the first study to include the service quality of mobile phone calls and the network quality of the internet connection in the formation of customer satisfaction and its subsequent influence on eWOM, customer loyalty, and postpurchase intention through dual partial least squares structural equation modeling and NCA methodological approaches. In addition, the results indicate that customer engagement with ICTs moderates the relationship between customer satisfaction and eWOM.27460764
A Data-Driven Assessment of Redesign Initiatives in Financial Management Processes
No full textBusiness Process Redesign (BPR) is a fundamental approach to enhancing efficiency, compliance, and digital transformation in public sector operations. Despite extensive theoretical advancements, its application in real-world settings remains limited. This study addresses this gap by applying the BPR Assessment Framework to business processes within the Greek Public Financial Management (PFM) domain, specifically analyzing workflows from the Greek Customs Service and the Financial and Economic Crime Unit (S.D.O.E.). This research employs a structured methodology that integrates internal process metrics with clustering techniques to systematically classify processes based on their redesign potential. The findings reveal that a significant proportion of public sector workflows demonstrate high redesign capacity, highlighting opportunities for efficiency gains and improved regulatory compliance. Furthermore, this study identifies key challenges, such as organizational resistance and technological constraints, that impact BPR implementation. By demonstrating the framework’s applicability in a complex, operational environment, this study provides actionable insights for policymakers and practitioners. Specifically, the results show how structured process evaluation enables targeted redesign initiatives that streamline administrative workflows, enhance compliance with financial regulations, and support digital transformation in public administration. These insights contribute to advancing BPR practices by bridging the gap between theoretical development and real-world application, offering a replicable methodology for improving public sector efficiency.16317
Benchmarking Efficiency in Mediterranean Ports: A DEA-Based Analysis of Connectivity and Operational Performance
No full textThis study investigates the operational performance of major Mediterranean ports through a tailored Data Envelopment Analysis (DEA) framework. Recognizing the underrepresentation of these ports in existing benchmarking studies, this research emphasizes both connectivity and efficiency. Utilizing advanced DEA methodologies—Constant Returns to Scale (CCR), Variable Returns to Scale (BCC) and Window Analysis—the study evaluates efficiency trends over time, providing actionable insights for enhancement. Key input variables such as terminal size, berth length and equipment count are analyzed alongside output metrics like annual container throughput to ensure a comprehensive assessment of port performance. The findings reveal significant efficiency disparities among Mediterranean ports, with transshipment hubs like Tanger Med and Piraeus achieving optimal efficiency scores due to strategic investments and infrastructure upgrades. Conversely, many ports operate below optimal levels, indicat ing opportunities for technical and managerial improvements. This research contributes substantially to the field by introducing a novel benchmarking framework tailored to the unique geopolitical dynamics of the Mediterranean region. It highlights the critical role of connectivity, infrastructure and technology in driving efficiency while offering a valuable foundation for policymakers and port authorities to implement targeted strategies that enhance competitiveness and foster sustainable growth.837844Proceedings of the 27th International Conference on Enterprise Information System
A Comparative Evaluation of Time-Series Forecasting Models for Energy Datasets
No full textTime series forecasting plays a critical role across numerous domains such as finance, energy, and healthcare. While traditional statistical models have long been employed for this task, recent advancements in deep learning have led to a new generation of state-of-the-art (SotA) models that offer improved accuracy and flexibility. However, there remains a gap in understanding how these forecasting models perform under different forecasting scenarios, especially when incorporating external variables. This paper presents a comprehensive review and empirical evaluation of seven leading deep learning models for time series forecasting. We introduce a novel dataset that combines energy consumption and weather data from 24 European countries, allowing us to benchmark model performance across various forecasting horizons, granularities, and variable types. Our findings offer practical insights into model strengths and limitations, guiding future applications and research in time series forecasting.14
Inverted VEA for worst-practice benchmarking: with an application to distress prediction of European banks
No full textIn this paper we introduce managerial preferences in the assessment of worst-practices by means of Value Efficiency Analysis (VEA). Our model involves the choice of a Decision Making Unit (DMU) being on the worst-practice frontier, that has the least desirable input/output structure by view of a Decision Maker (DM). The method then assesses all DMUs based on the worst favorable sets of input/output weights for the chosen DMU. The scores of the associated linear program, referred to as Inverted VEA, are larger than or equal to the respective Inverted DEA scores. Higher (lower) differences between Inverted DEA and Inverted VEA scores highlight DMUs with an input–output bundle that is farther (closer) to the least desirable ones. This aids central management to identify DMUs which should be marked for closer monitoring and inspection or put through a restructuring process. We illustrate the usefulness of the method by applying it to assess the relative financial distress of 33 major European banks that were evaluated by the European Banking Authority in the 2018 stress test.347147149
Artificial Intelligence for Software Engineering: The Journey so far and the Road ahead
No full textArtificial intelligence and recent advances in deep learning architectures, including transformer networks and large language models, change the way people think and act to solve problems. Software engineering, as an increasingly complex process to design, develop, test, deploy, and maintain large-scale software systems for solving real-world challenges, is profoundly affected by many revolutionary artificial intelligence tools in general, and machine learning in particular. In this roadmap for artificial intelligence in software engineering, we highlight the recent deep impact of artificial intelligence on software engineering by discussing successful stories of applications of artificial intelligence to classic and new software development challenges. We identify the new challenges that the software engineering community has to address in the coming years to successfully apply artificial intelligence in software engineering, and we share our research roadmap towards the effective use of artificial intelligence in the software engineering profession, while still protecting fundamental human values.
We spotlight three main areas that challenge the research in software engineering: the use of generative artificial intelligence and large language models for engineering large software systems, the need of large and unbiased datasets and benchmarks for training and evaluating deep learning and large language models for software engineering, and the need of a new code of digital ethics to apply artificial intelligence in software engineering
Digital HRM Practices and Perceived Digital Competence: An Analysis of Organizational Culture’s Role
No full textThis study explores the relationship between digital human resource management (HRM)
practices, organizational culture, and employees’ perceived digital competence within
Greek organizations. While digitalization has become a central priority in human resource
management (HRM), there is still limited understanding of how cultural context shapes
the effectiveness of digital HR interventions. Using a quantitative approach, data were
collected via an online questionnaire from 257 employees across various sectors. The
research employed the method of Partial Least Squares Structural Equation Modeling (PLSSEM) and Multi-Group Analysis (MGA) to examine the structural relationships between
digital HRM practices—such as e-learning, onboarding, and performance management—
and digital competence, taking into account different organizational culture profiles. The
results show that digital HRM practices have a positive, but modest, impact on employees’
digital skills, with e-learning emerging as the most influential factor. Importantly, the effect
of HRM practices varies significantly according to the cultural environment: supportive
and innovative cultures foster stronger development of digital competence compared
to hierarchical settings. The findings underline the necessity for organizations to adapt
digital HR strategies to their specific cultural context and not to rely solely on technological
solutions. This research contributes to the growing literature by demonstrating the interplay
between technology and culture in shaping employees’ digital capabilities and suggests
that a balanced focus on both is essential for successful digital transformation.533
Applied Federated Model Personalization in the Industrial Domain: A Comparative Study
No full textThe time-consuming nature of training and deploying complicated Machine and Deep Learning (DL) models for a variety of applications continues to pose significant challenges in the field of Machine Learning (ML). These challenges are heightened in the federated domain, where optimizing models for individual nodes is particularly difficult. Many methods have been developed to tackle this problem, aiming to reduce training expenses and time while maintaining efficient optimisation. Three suggested strategies to tackle this challenge include Active Learning, Knowledge Distillation, and Local Memorization. These methods enable the adaptive finetuning of the leveraged AI models allowing for model personalization with local data, thereby improving the effectiveness of current models. The present study delves into the fundamental principles of these three approaches and proposes an advanced Federated Learning System that utilises different Personalization methods towards improving the accuracy of AI models and enhancing user experience in real-time NG-IoT applications, investigating the efficacy of these techniques in the local and federated domain. The results of the original and optimised models are then compared in both local and federated contexts using a comparison analysis. The analysis reveals promising results for optimizing and personalizing models using the proposed techniques.63192321
A qualitative parameter for beta changes
No full textIn this study, we explore the ‘qualitative’ aspect of a stock using an innovative parameter known as b∗. We propose a method to construct two portfolios, DtU (Downside to Upside Risk) and UtD (Upside to Downside Risk), based on variations in the coefficient b∗ which we consider as a ‘qualitative’ parameter. Our analysis spans from January 1992 to July 2018, encompassing both the bullish period until the early 2000s and the 2008 financial crisis. We employ various models to investigate their effectiveness in capturing systematic risk within the portfolios’ constituents. While most models detect beta changes, their outcomes yield mixed results concerning the extent and direction of these changes, highlighting the challenge investors face in constructing beta-based portfolios. Additionally, our findings indicate that stocks in the DtU portfolio, exhibiting positive b∗ changes, demonstrate strong fundamentals, lower post-earnings announcement drift compared to UtD stocks, and a reduced arbitrage to explained risk (AR/ER) ratio. Notably, both DtU and UtD portfolios exhibit lower AR/ER ratios than previously documented in the literature. Finally, we evaluate the performance of an out-of-sample trading strategy designed to capture these ‘qualitative’ beta changes. Our results reveal positive and statistically significant risk-adjusted returns for the DtU portfolio, not adequately captured by recently proposed asset pricing models. These outcomes exhibit robustness across various analyses and checks.10310446
By-production modeling of technical and environmental inefficiency in Brazilian dairy farms
No full textThis study develops a by-production stochastic frontier model to assess farm technical and environmental inefficiency, applied to a panel of Brazilian dairy farms observed from 2014 to 2021. The model specifies a good output (mainly milk production) and a bad output (CO2-equivalent emissions) equation, capturing the dependence between the inefficiency terms and between the noise terms across the two equations using two distinct copulas. A Bayesian estimation framework is introduced for parameter estimation. Results indicate that milk production is primarily driven by feed inputs, while emissions are mostly influenced by herd size. Technical inefficiency averages 0.066, while environmental inefficiency averages 0.061, reflecting relatively high efficiency levels in both dimensions. Strong upper tail dependence between the inefficiency terms and between the noise terms across the two equations highlights the interconnected nature of these processes, suggesting shared inefficiency drivers and external shocks.39212660