Search Results (94)

This study aims to assess the perceived quality of telecommunication services in Portugal and examine how governance practices influence organizational performance, addressing the lack of empirical evidence on service quality gaps in the Portuguese telecommunications sector. Specifically, it investigates the alignment between customers’ expectations and perceptions of service delivery among major telecommunications providers in northern Portugal. A convenience sample of 119 subscribers was collected through an online questionnaire disseminated via social media and email. The survey measured service quality across the five SERVQUAL dimensions (tangibles, reliability, responsiveness, assurance, and empathy), and sociodemographic variables (gender, age, and education) were recorded to explore their influence on customer satisfaction and perceived quality. Results reveal a consistent gap between expectations (6.51) and perceptions (5.54), particularly in reliability and responsiveness, despite generally positive evaluations of tangibility and assurance. Sociodemographic factors significantly influenced satisfaction levels and perceptions of service quality, highlighting the importance of tailored governance strategies. These findings demonstrate that effective governance and quality management are interdependent drivers of sustainable competitiveness in technology-intensive sectors. By identifying specific quality gaps and their drivers, this study provides actionable insights for improving service delivery. Enhancing organizational reliability, responsiveness, and empathy—supported by transparent communication and data-driven decision-making—is essential for improving customer trust, operational resilience, and long-term performance. By integrating continuous quality assessment into administrative strategy, telecommunications firms can enhance service excellence and contribute to broader goals of sustainable economic development and digital transformation. Full article

Smart supply chains, enabled by Industry 4.0 technologies, are increasingly recognized as key drivers of competitiveness, leveraging data across the value chain to enhance visibility, responsiveness, and resilience, while supporting better planning, optimized resource utilization, and agile customer service. Effective data management has thus become a strategic capability, fostering operational performance, innovation, and long-term value creation. However, existing research and practice remain fragmented, often focusing on isolated functions such as production, logistics, or quality, the most data-intensive and critical domains in smart manufacturing, without comprehensively addressing data acquisition, storage, integration, analysis, and visualization across all supply chain phases. This article addresses these gaps through a systematic literature review of 55 peer-reviewed studies published between 2020 and 2025, conducted following PRISMA guidelines using Scopus and Web of Science. Contributions are categorized into reviews, frameworks/models, and empirical studies, and the analysis examines how data is collected, integrated, and leveraged across the entire supply chain. By adopting a holistic perspective, this study provides a comprehensive understanding of data management in smart manufacturing supply chains, highlights current practices and persistent challenges, and identifies key avenues for future research. Full article

Value co-creation is essential for the success and sustainability of Small and Medium Enterprises (SMEs), enabling them to integrate resources and knowledge from multiple stakeholders, such as customers, suppliers, and universities, to develop innovative offerings. However, research drawing on Service-Dominant Logic (SDL) and Resource-Based View (RBV) has devoted limited attention to how entrepreneurial ecosystem cooperation and marketing innovation jointly shape SME value co-creation, particularly in smaller and peripheral economies. This study examines the impact of entrepreneurial ecosystems (EEs) on value co-creation in SMEs, focusing on the moderating role of marketing innovation. EEs provide SMEs with access to new knowledge, technologies, and financial resources, which support innovation and enhance their competitiveness. Using microdata from the Portuguese Community Innovation Survey (CIS) 2020 and logistic regression models, we investigate how cooperation with key stakeholders (universities, customers, suppliers, consultants, competitors and government agencies) affects the likelihood that SMEs engage in value co-creation with users. The results show that ecosystem cooperation significantly contributes to value co-creation, with cooperation with universities, customers and suppliers exerting the strongest effects. Marketing innovation further strengthens the association between ecosystem cooperation and value co-creation, especially for knowledge-intensive and market-oriented partners. Theoretically, the study extends SDL by applying its multi-actor value co-creation perspective to entrepreneurial ecosystem configurations and specifying how cooperation with distinct actors activates co-creation mechanisms in SMEs. It extends RBV by conceptualising ecosystem cooperation as an externally orchestrated bundle of strategic resources and by positioning marketing innovation as a dynamic capability that shapes the returns to such cooperation. The findings also provide practical guidance for SMEs and policymakers seeking to design ecosystems and marketing strategies that support collaborative innovation in the knowledge economy. Full article

Digital transformation has reshaped how cruise firms acquire, engage and retain customers. However, existing research rarely integrates these behavioural dimensions within a unified analytical framework. This study applies a hybrid regression–Fuzzy Cognitive Mapping (FCM) approach to examine how acquisition channels, engagement indicators and online reputation metrics jointly shape website performance and digital innovation among leading global cruise operators. Using multi-source web-analytics data, regression models identify the direct predictive effects of organic, paid, referral and email channels, while FCM captures their non-linear feedback dynamics. Results reveal that visibility does not equate to engagement: organic and referral traffic drive exposure but not depth, whereas authority and reputation mediate engagement–performance relationships. Scenario simulations reveal asymmetric responses within the digital ecosystem. Consequently, balanced, knowledge-driven channel diversification emerges as a key strategic advantage. The findings extend the Knowledge-Based View (KBV) by conceptualising behavioural analytics as organisational knowledge resources that enable adaptive learning and digital innovation. The proposed framework contributes to both tourism analytics and information systems research, offering a scalable model for understanding how data-intensive service firms convert behavioural information into strategic knowledge and sustainable competitive advantage. Full article

In contemporary e-commerce, enterprises coordinate transactions, supply chains, and customer interactions within platform-based, data-intensive ecosystems. Integrated office application (IOA) serves as the operational backbone of these ecosystems by unifying communication, content management, workflow automation, and analysis across procurement, fulfillment, and after-sales service processes. As e-commerce processes become fully digitized, employees’ daily interactions with IOA directly impact service quality, operational efficiency, and sustainability outcomes. However, the micro-mechanisms by which IOA attributes translate into sustainable work practices are under-explored in the e-commerce literature. This study aims to explore how system quality, information quality, and collaboration quality influence user perceptions (perceived ease of use and usefulness), social influences (subjective norms), and satisfaction, thus jointly driving user intention and IOA-enabled sustainable behaviors. By integrating the Technology Acceptance Model, the Theory of Planned Behavior, and the IS Success Model, this research elaborates in a human-centered way on how an e-commerce enterprise’s system support can promote corporate and individual sustainability through employees’ adoption and continuous effective use. Full article

Adaptive forest and fire management in parks and protected areas is becoming increasingly complex as climate change alters the frequency and intensity of disturbances (wildfires, pest and disease outbreaks, etc.), while park visitation and the number of people living adjacent to publicly managed lands continues to increase. Evidence-based, climate-adaptive forest and fire management practices are critical for the responsible stewardship of public resources and require the continued availability of long-term ecological monitoring data. The US National Park Service has been collecting long-term fire monitoring plot data since 1998, and has continued to add monitoring plots, but these data are housed in databases with limited access and minimal analytic capabilities. To improve the availability and decision support capabilities of this monitoring dataset, we created the Trends in Forest Fuels Dashboard (TFFD), which provides an implementation framework from data collection to web visualization. This easy-to-use and updatable tool incorporates data from multiple years, plot types, and locations. We demonstrate our approach at Rocky Mountain National Park using the ArcGIS Online (AGOL) software platform, which hosts TFFD and allows for efficient data visualizations and analyses customized for the end user. Adopting interactive, web-hosted tools such as TFFD allows the National Park Service to more readily leverage insights from long-term forest monitoring data to support decision making and resource allocation in the context of environmental change. Our approach translates to other data-to-decision workflows where customized visualizations are often the final steps in a pipeline designed to increase the utility and value of collected data and allow easier integration into reporting and decision making. This work provides a template for similar efforts by offering a roadmap for addressing data availability, cleaning, storage, and interactivity that may be adapted or scaled to meet a variety of organizational and management use cases. Full article

Customer loyalty is crucial for (while fueled by) business sustainability. Loyal customers advocate for a company’s offer and sustainable practices, while their steady support generates stable revenue stream, lower acquisition costs, and predictable cash flows that enable long-term business viability. Such a stable revenue stream is especially critical in periods of intense competition or macroeconomic disruption (e.g., COVID-19 pandemic) which profoundly influenced consumer behavior. In this context, the purpose of the current paper is to test the BG/NBD prediction model for its potential validation as a practical tool in estimating the buying behavior of customers of a self-service car washing company before and within the COVID-19 pandemic period. To achieving this, transaction data of the company’s customers was retrieved from the company’s internal information system and used as input for BG/NBD model. The model proved its effectiveness in estimating the total number of repeated transactions for the year 2020 based on the 2019 data at total customer base and at loyal customer level. Loyal customers were considered from the behavioral loyalty perspective only and defined as customers which used the company’s services at least once in both years. In the estimation of the repeated transactions frequencies, the model’s prediction accuracy increases with higher frequencies of loyal customers. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

In a Kubernetes environment, the resource allocation for Pods has a direct impact on both performance and cost. When resource sizes are determined based on user experience, under-provisioning can lead to performance degradation and execution instability, while over-provisioning can result in resource waste and increased costs. To address these issues, this study proposes an adaptive scheduling method that employs the Design of Experiments (DoE) approach to determine the optimal resource size for each application with minimal experimentation and integrates the results into a custom Kubernetes scheduler. Experiments were conducted in a Kubernetes-based cloud environment using five applications with diverse workload characteristics, including CPU-intensive, memory-intensive, and AI inference workloads. The results show that the proposed method improved the performance score—calculated as the harmonic mean of execution time and cost—by an average of approximately 1.5 times (ranging from 1.15 to 1.59 times) compared with the conventional maximum resource allocation approach. Moreover, for all applications, the difference in mean scores before and after optimal resource allocation was statistically significant (p-value < 0.05). The proposed approach demonstrates scalability for achieving both resource efficiency and service-level agreement (SLA) compliance across various workload environments. Full article

The paper advances the theory of queuing networks by presenting generalized product-form solutions that explicitly take into account the service intensity depending on the number of customers in the network nodes, including the presence of multiple service channels and multi-threaded nodes. This represents a significant extension of the classical results on the Jackson network by integrating graph-theoretic methods, including basic subgraphs with service rates depending on the number of requests. The originality of the article is in the combination of stationary and non-stationary approaches to modeling service networks within a single approach. In particular, acyclic networks with deterministic service time and non-stationary Poisson input flow are considered. Such systems present a significant difficulty, which is noted in well-known works. A stationary model of an open queuing network with service intensity depending on the number of customers in the network nodes is constructed. The stationary network model is related to the problem of marine linear navigation along a strictly defined route and schedule. A generalization of the product theorem with a new form of stationary distribution is developed for it. It is shown that even a small increase in the service intensity with a large number of requests in a queuing network node can significantly reduce its average value. A non-stationary model of an acyclic queuing network with deterministic service time in network nodes and a non-stationary Poisson input flow is constructed. The non-stationary model is associated with irregular (tramp) sea transportation. The intensities of non-stationary Poisson flows in acyclic networks are represented by product formulas using paths between the initial node and other network nodes. The parameters of Poisson distributions of the number of customers in network nodes are calculated. The simplest formulas for calculating such queuing networks are obtained for networks in the form of trees. Full article

The quality of RF signal coverage of mobile networks, and for example, the parameters of LTE signals at many places, is not reliable enough for intensive data transfer. This fact causes mobile service customers to look for and to apply additional local devices or systems for amplification of the initially supplied RF signal level to improve the quality of the service. This local improvement covers a small area around the customer’s residence. This paper shares some useful results of design, analyses, and optimization for effective performance of the tranceiving antenna for LTE (4G) signals. Full article

In today’s fast-moving digital economy, service start-ups are reshaping industries; however, they face intense uncertainty, limited resources, and fierce competition. This study introduces an Artificial Intelligence (AI)-powered process modeling framework designed to give these ventures a competitive edge by combining big data analytics, machine learning, and Business Process Model and Notation (BPMN). While past models often overlook the dynamic, human-centered nature of service businesses, this research fills that gap by integrating AI-Driven Ideation, AI-Augmented Content, and AI-Enabled Personalization to fuel innovation, agility, and customer-centricity. Expert insights, gathered through a two-stage fuzzy Delphi method and validated using DEMATEL, reveal how AI can transform start-up processes by offering real-time feedback, predictive risk management, and smart customization. This model does more than optimize operations; it empowers start-ups to thrive in volatile, data-rich environments, improving strategic decision-making and even health and safety governance. By blending cutting-edge AI tools with process innovation, this research contributes a fresh, scalable framework for digital-age entrepreneurship. It opens exciting new pathways for start-up founders, investors, and policymakers looking to harness AI’s full potential in transforming how new ventures operate, compete, and grow. Full article

In response to escalating societal and environmental expectations, corporate social responsibility (CSR) has evolved into a strategic imperative rather than a voluntary or peripheral activity. This study investigates the effect of employees’ CSR perceptions on job performance, with corporate reputation (CR) examined as a mediating variable. Drawing on Social Identity and Social Exchange Theories, the research explores how CSR, as an element of business innovation and sustainable organizational practices, fosters internal stakeholder engagement and performance enhancement. Data were collected from five-star hotel employees in İstanbul/Türkiye, a service sector context where customer satisfaction is highly dependent on frontline employee behavior. Using PROCESS Macro for SPSS 27, the findings reveal that CSR perceptions significantly and positively influence employee performance both directly and indirectly through the enhancement of CR. This mediating effect underscores the role of CSR not only as an ethical framework but also as an internal mechanism that strengthens employee commitment and output. The study contributes to CSR and the organizational behavior literature by empirically validating that internal CSR perceptions shape strategic outcomes such as employee performance, especially within high-contact service environments. Theoretical implications emphasize CSR’s integrative function in reputation-building and performance systems, while practical insights recommend embedding socially responsible practices into HR and internal communication strategies to achieve sustainable outcomes and societal well-being. These findings offer meaningful contributions to the scope of business innovation by linking CSR with strategic performance indicators in labor-intensive industries. Full article

This paper deals with queueing models, in which the number of customers is described by a (inhomogeneous, in general) birth–death process. Depending on the choice of the type of intensities for the arrival and service of customers, the system can either have impatience (in which, as the queue length increases, the intensities of arrival decrease and the intensities of service increases) or attraction (in which, on the contrary, as the queue length increases, the intensities of the arrival of customers increase and service intensities decrease). In this article, various types of such models are considered, and their transient and limiting characteristics are computed. Furthermore, the rate of convergence and related bounds are also dealt with. Several numerical examples illustrate the proposed procedures. Full article

Software as a service (SaaS) is a major service model for delivering software to end users through the cloud. SaaS platforms provide their users with cost-efficient, flexible and scalable services that can be available on demand, anytime, and anywhere. Moreover, SaaS empowers software providers to establish recurring revenue and create profitable businesses. However, SaaS can endure high customer turnover due to reasons such as serving a wide range of customers, intense competition and rapid evolution of technology. Maintaining a regular customer base and keeping users engaged is crucial for the survival of a SaaS business. Thus, it is crucial for SaaS providers to identify both the reasons behind users’ engagement and churn of their app towards taking proper actions to retain them in the long term. SaaS data regarding user behavior, subscriptions and system performance can be utilized for deriving insights and identifying patterns to support decision-making for SaaS providers. To this end, the aim of this survey is to review research in data-driven decision support systems in SaaS, identifying current gaps and challenges and highlighting directions for future improvements towards the development of more efficient and intelligent systems. Full article