Search Results (218)

Climate change is one of the most pressing global environmental challenges, driven by the accumulation of greenhouse gases in the atmosphere. Industrial processes, particularly plastic injection molding, are major contributors due to their high energy demand, raw material use, and waste generation. This study quantifies the carbon footprint of plastic injection molding operations and identifies emission hotspots to support alignment with sustainability objectives. A greenhouse gas inventory was developed for the production processes of Petka Mold Industry in Adana, Türkiye, covering 1 January–31 December 2023. The assessment followed the ISO 14064-1:2019 standard and included emissions from direct fuel consumption, purchased electricity, refrigerant leaks, company vehicles, employee commuting, business travel, purchased goods, and waste transportation. Carbon dioxide, methane, and nitrous oxide were calculated in carbon dioxide equivalent units. This research represents the first comprehensive carbon footprint study in the plastic mold sector integrating all categories (Categories 1–6). In addition, uncertainty and materiality analyses were applied to ensure robustness and transparency, an approach rarely adopted in similar industrial contexts. While most previous studies are limited to Categories 1–3, this work expands the boundaries to all categories, offering a pioneering model for industrial applications. The total corporate GHG emissions for 2023 were calculated as 3922.75 metric tons of CO₂e. Among the categories, purchased raw materials and end-of-life product stages were the most significant contributors, whereas transport and auxiliary services had smaller shares. The results provide a reliable baseline for developing action plans and pursuing emission reduction targets. By combining full category coverage with rigorous assessment tools, this study contributes methodological novelty to corporate carbon accounting and establishes a foundation for future progress toward carbon neutrality. Full article

Big data analytics has become a cornerstone of modern industries, driving advancements in business intelligence, competitive intelligence, and data-driven decision-making. This study applies Neural Topic Modeling (NTM) using the BERTopic framework and N-gram-based textual content analysis to examine job postings related to big data analytics in real-world contexts. A structured analytical process was conducted to derive meaningful insights into workforce trends and skill demands in the big data analytics domain. First, expertise roles and tasks were identified by analyzing job titles and responsibilities. Next, key competencies were categorized into analytical, technical, developer, and soft skills and mapped to corresponding roles. Workforce characteristics such as job types, education levels, and experience requirements were examined to understand hiring patterns. In addition, essential tasks, tools, and frameworks in big data analytics were identified, providing insights into critical technical proficiencies. The findings show that big data analytics requires expertise in data engineering, machine learning, cloud computing, and AI-driven automation. They also emphasize the importance of continuous learning and skill development to sustain a future-ready workforce. By connecting academia and industry, this study provides valuable implications for educators, policymakers, and corporate leaders seeking to strengthen workforce sustainability in the era of big data analytics. Full article

Creating applications to manually populate and modify knowledge graphs is a complex task. In this paper, we propose a novel approach for designing user interfaces for this purpose, based on existing SHACL constraint files. Our method consists of taking SHACL constraints and creating multi-form web applications. The novelty of the approach is to treat the editing of knowledge graphs via multi-form application interaction as a business process. This enables user interface modeling, such as modeling of application control flows by integrating ontology-based business process management components. Additionally, because our application models are themselves knowledge graphs, we demonstrate how they can leverage OWL reasoning to verify logical consistency and improve the user experience. Full article

Consumer choices are largely influenced by sustainability, necessitating SMEs from the agri-food sector to strategically address sustainability and innovate their business models. Nonetheless, the challenge for such sustainable leadership lies in maintaining an equilibrium between innovation, sustainability, and financial performance. This study examined how strategic leadership fosters sustainability-oriented innovation within SMEs exemplified by the wine industry. A survey involving 354 German wineries served to analyze a multi-dimensional concept of innovation clusters (early adopters, pragmatists, pioneers, skeptics, conservatives), type of innovation, sustainability orientation, strategic ambitions, and business performance. Exploring the adoption of fungus-resistant grape varieties (FRV) allowed investigating how sustainability transitions to meet EU Green Deal targets are shaped by strategic groups involving strategic positioning and innovation clusters. There was a correlation between stronger sustainability orientation with greater innovation (Means up to 4.39). As per the findings, it was observed that high scores (p < 0.001, η² = 0.144–0.160) in market and process innovation were obtained by early adopters and pioneers. These innovation champions excel in economic and social sustainability (p < 0.001) but nonetheless were found to be financially underperforming (Means 1.97–2.18). Innovations that were applied enhanced innovation scores (η² = 0.128) but did not improve immediate performance. The strongest performance (Mean 2.60) was reported by skeptics though they fared poor in terms of sustainability and innovation. It was also noted that early adopters and pioneers (44–45%) were leading in FRV adoption, while a lag was observed within premium-oriented organizations. These insights may motivate SMEs in their quest for strategic sustainability and allow fine-tuning political and societal measures to achieve a sustainable transition and quantified Green Deal ambitions. It was concluded that long-term positioning was improved by sustainability-driven innovation, however, it would involve short-term performance trade-offs for SMEs. Political support should motivate the sustainable leadership champions to also safeguard profitability. Full article

Throughout history, cultural heritage has accumulated, and is often embodied in monuments, structures, and notable figures. Cultural heritage preservation and management also include digitalization, allowing tangible monuments to be managed as digital inventory with “digital twins”. This provides innovative ways to experience and interact with the real world, in particular by using modern mobile devices. The digitalization of monuments opens new ways to produce decorative items based on the shape of the monuments. Usually, decorative items are produced by craft businesses, family-run for generations, with specialized skills in metal and stone processing. We developed and tested a methodological proposal for frugal innovation: how to produce decorative items with minimal costs based on digital twins, which are particularly in demand in tourism-driven countries like Croatia. A micro-business with three employees, specializing in “metal art,” aims to innovate and expand by producing small-scale replicas of cultural heritage objects, such as busts, statues, monuments, or profiles. A method has been developed to create replicas in the desired material and at a desired scale, faithfully reproducing the original—whether based on a physical object, 3D model, or photograph. The results demonstrate that this sustainable frugal innovation can be successfully implemented using affordable tools and licenses. Full article

This study presents a data-driven framework that integrates lean management and digital business process modelling to enhance sustainability in textile manufacturing. Conducted in a company producing industrial safety textiles from Peru, this research applies lean tools within a digital BPM structure supported by real-time data tracking. The integrated approach led to increased production efficiency (from 79% to 86%), reduced setup times, and improved operational agility. The digital infrastructure empowered operators and supported informed decision-making. This work contributes to Industrial Engineering, Business Administration, and MIS by offering a holistic model that bridges lean principles with Industry 4.0 technologies. The findings, though context-specific, provide actionable insights for manufacturers aiming for smart and sustainable operations. Future research should validate the proposed framework across diverse industrial contexts and assess its longitudinal impact on lean performance outcomes. Full article

While integrated Artificial Intelligence and Business Analytics (AI-BA) represents a significant advancement in marketing analytics and greatly influences firms’ innovations, there is a considerable gap in current research regarding its impact on technological innovation. This study addresses this gap by exploring how AI-BA affects data-driven and technological innovation, considering the mediating roles of integration capabilities and digital platforms. A theoretical model has been developed based on the dynamic capability view (DCV) and organizational information processing theory (OIPT). The model has been validated using data from enterprises in Saudi Arabia, and Partial Least Squares Structural Equation Modeling (PLS-SEM) has been employed for analysis. The findings demonstrate that AI-BA directly enhances both technological and data-driven innovation. Additionally, it was discovered that data-driven innovation, integration capabilities, and digital platforms mediate these effects, thereby enhancing technological innovation within the respective industries. These findings provide both theoretical and practical insights into the relationship between AI-BA, data-driven innovation, and technological innovation. They enrich the existing literature and provide actionable guidance for practitioners aiming to align their AI-BA with improved technological innovation outcomes. Full article

The intersection of Artificial Intelligence (AI) and education is revolutionizing learning and teaching in this digital era, with Generative AI and large language models (LLMs) providing even greater possibilities for the future. The digital transformation of language education demands innovative approaches that combine pedagogical rigor with explainable AI (XAI) principles, particularly for low-resource languages. This paper presents a novel methodology that integrates Business Process Model and Notation (BPMN) with Multi-Agent Systems (MAS) to create transparent, workflow-driven language tutors. Our approach uniquely embeds XAI through three mechanisms: (1) BPMN’s visual formalism that makes agent decision-making auditable, (2) Retrieval-Augmented Generation (RAG) with verifiable knowledge provenance from textbooks of the National Institute of Languages of Luxembourg, and (3) human-in-the-loop validation of both content and pedagogical sequencing. To ensure realism in learner interaction, we integrate speech-to-text and text-to-speech technologies, creating an immersive, human-like learning environment. The system simulates intelligent tutoring through agents’ collaboration and dynamic adaptation to learner progress. We demonstrate this framework through a Luxembourgish language learning platform where specialized agents (Conversational, Reading, Listening, QA, and Grammar) operate within BPMN-modeled workflows. The system achieves high response faithfulness (0.82) and relevance (0.85) according to RAGA metrics, while speech integration using Whisper STT and Coqui TTS enables immersive practice. Evaluation with learners showed 85.8% satisfaction with contextual responses and 71.4% engagement rates, confirming the effectiveness of our process-driven approach. This work advances AI-powered language education by showing how formal process modeling can create pedagogically coherent and explainable tutoring systems. The architecture’s modularity supports extension to other low-resource languages while maintaining the transparency critical for educational trust. Future work will expand curriculum coverage and develop teacher-facing dashboards to further improve explainability. Full article

Background: The service life and availability of electronic components are steadily declining, whereas the operational lifespan of industrial devices that incorporate such components often extends over several decades. This disparity creates a mismatch between the durability of individual components and the longevity of the overall systems in which they are embedded. Obsolescence Management (OM) addresses this issue by establishing a structured and controlled process aimed at anticipating and mitigating the impacts of component and product obsolescence. As defined by the international standard International Electrotechnical Commission [IEC] 62402:2019, obsolescence refers to the transition of an (electronic) item from availability to unavailability by the manufacturer, in accordance with the original specification. To implement proactive OM, obsolescence managers require data that are comprehensible, accurate, complete, trustworthy, secure, and discoverable. In this context, Data Governance (DG) offers a promising approach to enhance data literacy and intelligence within OM. Methods: This study employed a sequential mixed-methods design, integrating qualitative and quantitative approaches including a Systematic Literature Review (SLR), Expert Interviews (EIs), Focus Groups (FGs), Content Analysis (CA), and Workshops (WKSHs), within a case study informed by Design Science Research (DSR). Results: This paper proposes a DG structure tailored to support OM through data integration and business intelligence methods, drawing on established DG reference frameworks within an SME. The proposed structure encompasses a set of processes and knowledge domains recognized as best practices in the field. Furthermore, we present a model designed to facilitate the implementation of DG in OM and to assess the quality of the data required. This enables more reliable obsolescence processes across key functional areas such as product management, procurement, and product development, ultimately supporting data-driven and accurate decision-making. Full article

Activities in business processes primarily depend on human behavior for completion. Due to human agency, the behavior underlying individual activities may occur in multiple phases and can vary in execution. As a result, the execution duration and nature of such activities may exhibit complex multimodal characteristics. Phase-type distributions are useful for analyzing the underlying behavioral structure, which may consist of multiple sub-activities. The phenomenon of delayed start is also common in such activities, possibly due to the minimum task completion time or prerequisite tasks. As a result, the distribution of durations or certain components does not start at zero but has a minimum value, and the probability below this value is zero. When using phase-type models to fit such distributions, a large number of phases are often required, which exceed the actual number of sub-activities. This reduces the interpretability of the parameters and may also lead to optimization difficulties due to overparameterization. In this paper, we propose a smooth-delayed phase-type mixture model that introduces delay parameters to address the difficulty of fitting this kind of distribution. Since durations shorter than the delay should have zero probability, such hard truncation renders the parameter not estimable under the Expectation–Maximization (EM) framework. To overcome this, we design a soft-truncation mechanism to improve model convergence. We further develop an inference framework that combines the EM algorithm, Bayesian inference, and Sequential Least Squares Programming for comprehensive and efficient parameter estimation. The method is validated on a synthetic dataset and two real-world datasets. Results demonstrate that the proposed approach maintains a suitable performance comparable to purely data-driven methods while providing good interpretability to reveal the potential underlying structure behind human-driven activities. Full article

This study aims to systematically reveal, from the perspective of organizational scale, the differences between large and small architectural design organizations in China in terms of characteristics of production activities, technological capabilities and innovation levels, resource integration capabilities, and client groups, and to quantify the priority order of clients’ attention to architectural design products, thereby providing a reference for industry structure optimization and strategic decision making. This research combines case analysis and comparative study to construct a four-dimensional comparative framework. The results show that large design organizations, leveraging their advantages in technological research and development as well as resource integration, focus on large-scale complex projects, technology-driven projects, cultural landmark projects, and multi-stakeholder collaborative projects, primarily serving government agencies and large enterprises. In contrast, small design organizations excel in flexibility, concentrating on small-scale simple projects, specialized niche projects, localized projects, and short-cycle, low-budget projects, serving individual owners and small businesses. Furthermore, this study adopts the Analytic Hierarchy Process (AHP) to establish an evaluation model. Twenty experts from architectural design organizations, construction organizations, and research institutions were invited to score the survey questionnaires, and quantitative weight analysis was performed. The research findings provide support for the optimization of the industry. Full article

The rapid proliferation of Internet of Things (IoT) devices has led to a growing ecosystem of Cloud Platforms designed to manage, process, and analyze IoT data. Selecting the optimal IoT Cloud Platform is a critical decision for businesses and developers, yet it presents a significant challenge due to the diverse range of features, pricing models, and architectural nuances. This manuscript presents a comprehensive, feature-driven review of twelve prominent IoT Cloud Platforms, including AWS IoT Core, IoT on Google Cloud Platform, and Microsoft Azure IoT Hub among others. We meticulously analyze each platform across nine key features: Security, Scalability and Performance, Interoperability, Data Analytics and AI/ML Integration, Edge Computing Support, Pricing Models and Cost-effectiveness, Developer Tools and SDK Support, Compliance and Standards, and Over-The-Air (OTA) Update Capabilities. For each feature, platforms are quantitatively scored (1–10) based on an in-depth assessment of their capabilities and offerings at the time of research. Recognizing the dynamic nature of this domain, we present our findings in a two-dimensional table to provide a clear comparative overview. Furthermore, to empower users in their decision-making process, we introduce a novel, web-based tool for evaluating IoT Cloud Platforms, called the “IoT Cloud Platforms Selector”. This interactive tool allows users to assign personalized weights to each feature, dynamically calculating and displaying weighted scores for each platform, thereby facilitating a tailored selection process. This research provides a valuable resource for researchers, practitioners, and organizations seeking to navigate the complex landscape of IoT Cloud Platforms. 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

This theoretical essay aims to analyze some of the socio-economic innovations introduced by Platform Capitalism Specifically, it focuses on two main aspects: first, the digital platform as a radical organizational innovation. Digital platforms represent a structural novelty in the market economy, signaling a new organization of production and labor. Second, the essay examines the role of platforms in directly generating value through the concept of “network value”. To this end, it explores the function of “business intelligence” as a strategic and competitive tool. Finally, the paper discusses the key issues associated with platform capitalism, which could threaten its social sustainability and contribute to economic and financial instability. These issues include the increasing commodification of everyday activities, the devaluation of paid labor in favor of free production driven by platform users (the so-called prosumers), and the emergence of proprietary and financial monopolies. Hence, digital platforms do not inherently ensure comprehensive social and environmental sustainability unless supported by targeted economic policy interventions. Conclusively, it is emphasized that defining robust social welfare frameworks—which account for emerging value creation processes—is imperative. Simultaneously, policymakers must incentivize the proliferation of cooperative platforms capable of fostering experimental circular economy models aligned with ecological sustainability. Full article

Enterprise architecture (EA) provides a strategic foundation for aligning business processes, IT infrastructure, and organizational strategy, enabling firms to navigate uncertainty and complexity. In developing economies, small and medium-sized enterprises (SMEs) face significant challenges in maintaining financial resilience and sustainable growth amidst frequent disruptions. This study investigates how EA-driven change events affect SME financial performance by activating three key adaptive mechanisms: improvisational capability, flexible IT systems, and organizational culture. A novel classification of EA change triggers is proposed to guide adaptive responses. Using survey data from 291 Pakistani SMEs collected during the COVID-19 crisis, the study employs structural equation modeling (SEM) to validate the conceptual model. The results indicate that improvisational capability and flexible IT systems significantly enhance financial performance, while the mediating role of organizational culture is statistically insignificant. This study contributes to EA and sustainability literature by integrating a typology of EA triggers with adaptive capabilities theory and testing their effects in a real-world crisis context. Full article