Transformation Model Research Articles

The transition from extractive to sustainable economies represents a complex challenge for post-mining regions in Indonesia. This study explores the emergence and development of locally initiated tourism villages in Membalong and Tanjung Rusa Villages, Belitung, as a model of socio-ecological transformation from tin-extractive landscapes toward creative tourism economies. Employing a qualitative-descriptive approach through in-depth interviews and a literature review, this study analyzes the processes of social mobilization, tourism narrative construction, and landscape regeneration undertaken by local communities to transform mining voids (kolong or camui) and environmental degradation into valuable tourism assets. The transformation model is supported by five pillars : (1) revitalization of mining voids into tourism lakes and ecological parks, (2) diversification of eco-friendly local products (pandan thorn handicrafts and natural culinary), (3) integration of traditional gardening and fishing practices as coastal tourism attractions, (4) preservation of hospitable coastal Malay culture as social capital for welcoming tourists, and (5) capacity building in homestay management and tour guiding aligned with SAPTA PESONA principles (represents Indonesia's seven tourism hospitality standards: safety, order, cleanliness, beauty, friendliness, memorable experiences, and comfort) integrated with enhanced English communication competencies to serve international tourist mobility. Despite its success, the model faces critical callenges : dependency on external financing, managerial capacity gaps, limited foreign language proficiency, inadequate marketing communication strategies, and risks of pseudo-empowerment in governing village enterprises (BUMDes) and tourism awareness groups (Pokdarwis). This study contributes to community-based tourism (CBT) theory by demonstrating how environmental rehabilitation can simultaneously cultivate socio-economic and communicative capacities at the grassroots leve. The findings provide valuable practical models for other post-mining regions in Indonesia to develop inclusive, competitive, and sustainable tourism destinations.

Purpose How can small and medium-sized enterprises (SMEs) effectively digitalize their sustainability reporting processes through a structured, maturity-based adoption model? The purpose of this study is to provide a clear roadmap for advancing sustainability reporting capabilities in alignment with digital transformation. Design/methodology/approach This study builds on a systematic literature review of 32 research publications indexed in Scopus, comprising 22 journal articles and 10 conference papers published between 2014 and 2024. This study employed an inductive thematic analysis process to develop a conceptual model. Findings The study identified a four-stage digital maturity model that helps progress enterprises in the digitalization of sustainability reporting, such as initiation, development, integration and optimization. A coding framework was established to categorize the literature into three themes: digital transformation and maturity models, organizational enablers and resource capabilities, and external pressure and sustainability compliance. The model demonstrates that digital advancement enhances sustainability disclosures’ accuracy, efficiency and strategic value. While larger firms typically progress more rapidly, SMEs often face barriers such as limited resources and technological capabilities. Research limitations/implications This study offers a conceptual framework for understanding the digitalization in sustainability reporting; it is limited by its theoretical nature and lack of empirical validation. Further empirical research is needed to validate the framework across different enterprises sizes, sectors and geographies. Practical implications The maturity model serves as a diagnostic tool that enterprises can use to assess their current stage of digital maturity and identify areas for improvement. Social implications The study provides a new model for understanding digital transition in sustainability reporting, which advances the theoretical disclosure by proposing a novel approach to understanding the integration of digital tools in sustainability reporting practices. Originality/value This conceptual approach enables the development of theoretical foundation for future empirical research and provides a practical guide for enterprises and policymakers seeking to advance sustainability reporting through digital transition.

Abstract Unlike the rural transition models in other Global South countries, China's rural transformation is deeply embedded within a distinctive institutional framework. Villages in Southern China’s metropolitan hinterlands demonstrate a unique dual-track model of transformation incorporating both state-led and grassroots innovation. Based on the dominant actors in the initial phase of rural transformation, this study selects two typical villages with different driving forces—Guinan (grassroots-innovation) and Yongmo (state-led)—to empirically examine their transformation pathways through the lens of Actor-Network Theory (ANT). Key findings reveal that, first, the rural transformation model evolves from being dominated by a single type of actor in the initial phase to a multi-actor configuration characterized by negotiation, symbiosis, and co-evolution as diverse stakeholders become involved. Second, the stability of the actor-network relies on three key mechanisms: shared obligatory passage points (OPPs), effective processes of translation, and the strategic anchoring of non-human actors. Third, intermediary actors among the key players are particularly crucial, serving essential roles in linking urban and rural resources, building social capital, and enhancing rural resilience. Furthermore, the Chinese government’s role as a super-actor expands the explanatory power of ANT in contexts marked by a strong state and relatively weak communities, offering localized theoretical insights and practical implications for rural transformation in the Global South.

Abstract In the context of transit-oriented development (TOD), the comprehensive commercial development around metro stations has become a new trend. However, increased commercial attractions can alter passenger behavior, intensifying space congestion and flow conflicts within metro stations. Consequently, commercial service areas can become critical risk zones. This study explored a recognition and evaluation method for consumption behavior based on passenger trajectory data from TOD metro stations. First, the characteristics of both transfer and consumption behaviors are detailed, distinguishing between strong- and weak-purpose consumption. A dynamic model of the “transfer–consumption–transfer” behavior process is also developed. Second, a machine learning-based method for recognizing and evaluating consumption behavior, grounded in passenger trajectory analysis, is proposed. This method employs machine learning method to detect and track passenger movements. Simultaneously, a coordinate transformation model is constructed to correct data deviations from the pixel-to-world coordinate system. Several analytical indicators, including deviation angles, distances, and conflict points, are introduced to mathematically describe the consumption behavior characteristics at TOD metro stations. Finally, the proposed machine learning-based method is applied to a comprehensive metro station in Shanghai, China. The experimental results validate the effectiveness of the proposed method.

Technology is causing unprecedented disruption that requires organisations to implement digital transformation processes. These processes are aimed at integrating technologies, redesigning their business models and, at the same time, adapting the skills of their employees and incorporating sustainability into their processes. This research aims to conceptualise a methodology for implementing Sustainable Digital Transformation (SDT) processes based on the “Working with People” (WWP) model. The model integrates three key dimensions and aligns with project management and organisational change approaches. For the purpose of this article, empirical experiences of technology adoption implemented in five large Spanish companies with an international presence are analysed. The companies were selected because they were undergoing a strategic digital transformation process aimed at implementing a digital and sustainable culture. The results show that the WWP model, aligned with IPMA project management and ADKAR organisational change approaches, is a useful tool for articulating the implementation of a Sustainable Digital Transformation, highlighting the importance of people. The model is replicable for other companies, facilitating sustainable success in digital transformation from a practical perspective of holistic and sustainable digital transformation based on the WWP model. This study addresses a key research gap in the field of digital transformation: the lack of integrative methodologies that combine technological innovation, human development, and sustainability. The proposed Working with People-based Sustainable Digital Transformation (WWP–SDT) model provides companies with a practical framework to align digital adoption with cultural change and long-term sustainable impact.

This study investigates the implementation of Islamic Religious Education (IRE) in the era of Industrial Revolution 4.0, focusing on its opportunities, challenges, and adaptive strategies. Using a qualitative descriptive approach, data were collected through interviews, observations, and document analysis in selected madrasahs and Islamic higher education institutions in Banten Province. The results show that digital transformation opens vast opportunities for interactive, student-centered, and technology-integrated learning in IRE. However, teachers still face challenges related to limited digital literacy, unequal infrastructure, and the risk of declining spiritual interaction between educators and learners. The study finds that institutions have developed adaptive strategies, including digital literacy training, the integration of Islamic values into technology-based curricula, and the adoption of hybrid learning models that combine technological innovation with spiritual guidance. The findings highlight that the success of IRE in the digital era depends not only on mastering technology but also on maintaining the spiritual and ethical foundations of Islamic pedagogy. Therefore, this study proposes a model of value-based digital transformation, positioning technology as an instrument (wasilah) to strengthen, rather than replace, the moral mission of Islamic education.

Welding of DP590 steel and 6061 aluminum alloy presents considerable challenges, due to their significant differences in physical properties. This study successfully achieved well joining using friction stir lap diffusion welding (FSDLW) under low heat input conditions with the the addition of a Zn interlayer, achieving a maximum strength of 153.5 N/mm at 1900 rpm and 30 mm/min. By ultrasonic-assisted friction stir lap diffusion welding (Ua-FSDLW), joints were welded well at lower rotational speeds. Under low heat input conditions, a continuous, uniform, and thin intermetallic compound (IMC) layer formed at the interface, increasing the tensile shear strength to 345.6 N/mm at 1800 rpm and 50 mm/min, which was an improvement of approximately 125.14% compared to FSLDW. The fracture mode transitioned from interfacial peeling to steel substrate tearing. The strengthening mechanisms induced by ultrasonic assistance were revealed by establishing physical models for interface layer formation and transformation in FSDLW and Ua-FSDLW.

This paper explores how Artificial Intelligence, Strategic Foresight and Competitive Intelligence convergence can significantly contribute to enhancing Morocco’s national competitiveness, transformational growth, and consequently, placing the new development model within a new AI-driven paradigm by converging different intelligences to serve competitiveness, performance, sovereignty, resilience, and Morocco's strategic and digital transformation.To this end, we adopted a mixed approach, combining a literature review, semi-structured interviews with experts and professionals in these fields, a benchmark of best practices for converging this triptych of AI, strategic foresight and competitive intelligence, and conceptual and systemic modelling that operationalizes this convergence across the entire Moroccan foresight and intelligence ecosystem. Thus, we proposed the LOUMAR Framework as part of a holistic, inclusive and convergent-oriented approach designed to harness the largely untapped synergistic potential between artificial intelligence, strategic foresight and competitive intelligence. This strategic and integrative model is based on six key levers, primarily focusing on Leadership and strategic vision, Organizing the National augmented foresight and intelligence ecosystem, Utilizing AI-enhanced competitive intelligence, Modelling AI-driven strategic foresight, Advancing AI-powered and intelligence-driven transformation, Resilience, realignment and continuous evaluation. Furthermore, we have outlined Morocco's main achievements and strengths with regards to this tripartite convergence, the constraints and points for improvement, the opportunities to be seized and the perspectives and development paths to follow, through the implementation of a strategic and digital transformation model that drives transformational growth and boosts Morocco’s augmented competitiveness.

This study addresses knowledge gaps in long-term integrated landscape and seascape approaches (ILSA) by examining the facilitation of future-scaping —a participatory method for co-visioning futures and setting actionable goals—in the Xinshe “Forest–River–Farmlands–Ocean” Eco-Agriculture Initiative in eastern coastal Taiwan. Drawing on facilitator perspectives since 2016, we show how future-scaping tools helped the Dipit and pateRungan Indigenous tribes, government agencies, and the local school articulate 2026 and 2050 visions and translate them into 19 priority objectives. The process revealed shared aspirations for ecological integrity, sustainable agriculture, cultural revival, youth return, and equitable governance; fostered inclusive knowledge weaving; and enabled adaptive shifts in the initiative’s concluding phase. Lessons learned are discussed through the Problems, People, and Process dimensions of the Xinshe ILSA’s 5P + S model, emphasizing structured flexibility to bridge aspiration–implementation gaps, sustained inclusivity across knowledge systems, and facilitation as a key boundary function. As a model for rural transformation, the Xinshe ILSA affirms co-produced, iterative approaches as vital for navigating nexus challenges and advancing toward living in harmony with nature.

This study developed and validated a technology-enabled framework to optimize China's international communication. It aimed to address challenges of high cultural discount and weak technological adaptation hindering China's external discourse. The methodology involved an audience adaptation algorithm using K-means clustering to profile audiences and a bidirectional encoder representations from transformers (BERT) model for generating culturally sensitive messages. A random forest model was then built to predict communication effectiveness, followed by a controlled experiment on Twitter and TikTok data. Results showed the technology-optimized strategy more than doubled value recognition and interaction rates, with the prediction model achieving high accuracy (mean square error = 0.047, R^2 = 0.89). Sub-audience engagement rose by over 95%. This work provides a scalable technical solution that uses artificial intelligence to significantly enhance the precision and impact of international communication, enabling a shift from broadcasting to value-driven, adaptive engagement.

Transformative biorefinery model for biomass valorization into biofuel and renewable platform chemicals

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that leads to memory loss, cognitive decline, and behavioral changes, without a known cure. Neuroimages are often collected alongside the covariates at baseline to forecast the prognosis of the patients. Identifying regions of interest within the neuroimages associated with disease progression is thus of significant clinical importance. One major complication in such analysis is that the domain of the brain area in neuroimages is irregular. Another complication is that the time to AD is interval-censored, as the event can only be observed between two revisit time points. To address these complications, we propose to model the imaging predictors via bivariate splines over triangulation and incorporate the imaging predictors in a flexible class of semiparametric transformation models. The regions of interest can then be identified by maximizing a penalized likelihood. A computationally efficient expectation-maximization algorithm is devised for parameter estimation. An extensive simulation study is conducted to evaluate the finite-sample performance of the proposed method. An illustration with the AD Neuroimaging Initiative dataset is provided.

A proposed AI application for enhancing the quality of life of people with disabilities in Saudi Arabia.

This paper takes Dong Sheng and Li Shi, a classic play of Liyuan Opera, as the in-depth research object. Through close text reading and dramatic narratology analysis, it reveals that the play takes "awakening of passion and desire - conflict with Confucian ethics - liberation of human nature" as its implicit narrative thread. While maintaining the entertaining nature of a marketplace comedy, it realizes the critical reconstruction of feudal ethics. The study finds that the transformation of Dong Siwei in the play—from a Confucian scholar "afraid of ethics and laws" to a brave man "breaking through Confucian ethics"—is not only a typical manifestation of individual emotional strength breaking through rational discipline, but also an artistic projection of the awakening of humanistic consciousness among the citizen class in the mid-to-late Ming Dynasty. By deconstructing and reconstructing the "talented scholar and beautiful lady" model, the play completes a modern reflection on traditional moral ethics amid the dramatic tension of the confrontation between passion and reason, providing an important model for the contemporary transformation of classical dramas. Its innovative value lies in the organic integration of the entertaining function of marketplace comedies and the ideological depth of humanistic criticism, pioneering a new paradigm of "conveying reason through passion" in classical dramas.

Purpose of the study: This study aimed to develop a strategic entrepreneurship plan for students at Al-Fithrah Islamic Boarding School, as an effort to strengthen entrepreneurship education within the pesantren ecosystem by aligning the institution’s vision and mission with actionable and sustainable strategies for student entrepreneurial development. Methodology: Employing a developmental research approach based on the Borg and Gall model, the study followed 10 systematic stages from research and information gathering through product development, field testing, revision, and dissemination. Data were collected through expert validation questionnaires and evaluation instruments completed by caregivers, ustadz, and students, ensuring they met standards of feasibility, practicality, and effectiveness. Main Findings: The plan’s feasibility is supported by strong alignment with institutional values, comprehensive internal and external environmental analyses, the formulation of measurable objectives, and coherence between strategies and operational action plans. Practicality was demonstrated through the development and use of a strategic planning guidebook that accommodated diverse educational backgrounds and teaching experiences, helping stakeholders address implementation challenges. The plan proved effective through the creation of a clear strategy map and structured business planning pathway, enabling students to systematically build entrepreneurship competencies across the introduction, reinforcement, and expansion stages. Novelty/Originality of this study: The formulation of a pesantren-based strategic entrepreneurship planning model that integrates religious learning, character education, and entrepreneurial practice, an approach rarely addressed in previous studies. This integration provides a transformative model for Islamic boarding schools seeking to cultivate spiritually grounded yet economically empowered young entrepreneurs.

Purpose. To clarify the essence and consequences of digital transformation of employment in the context of post-industrial transformations, to identify key factors influencing the structure, forms, and dynamics of employment in the digital economy, and to subsequently develop a methodological approach to analyzing these changes based on cognitive modeling. Methodology. The study uses cognitive modeling as an interdisciplinary tool for analyzing the transformation of employment in the digital economy. The methodological basis is a systematic approach to identifying and formalizing complex cause-and-effect relationships between economic, technological, socio-demographic, and institutional factors that determine changes in the structure of the labor market. The modeling process uses an expert analytical knowledge base that provides a representative reflection of current transformation trends, in particular the dynamics of the formation of new forms of employment (gig economy, remote work) and the gradual reduction of traditional forms of labor participation. This approach allows for the effective study of insufficiently structured or poorly formalized processes characteristic of rapid digitalization. Findings. A cognitive model of employment transformation has been developed, which visualizes the interrelationships between the main factors of digitalization and structural changes in the labor market. The key influences of technological progress on changes in labor demand, professional mobility, the dynamics of the emergence of new professions, and the transformation of labor relations have been identified. The model allows forming scenarios for the development of employment in the digital economy and adapt labor market policies to new challenges. Originality. The feasibility of using cognitive modeling as a tool for studying employment transformation is substantiated, which provides a deeper understanding of structural changes in the labor market in the context of digitalization. A conceptual model has been developed that combines technological, social and economic aspects of post-industrial changes and allows predicting their impact on employment, identifying hidden relationships and creating a basis for making strategic management decisions. Practical value. The results of the study can be used to improve employment management strategies in the context of the digital transformation of the economy. The proposed cognitive model of the digital transformation of employment allows us to identify key factors that determine the dynamics of changes in the structure of the labor market. Along with this, it can be used to assess the risks and potential of automation in various sectors of the economy, to model employment development scenarios depending on the level of digitalization. The practical application of the results of cognitive modeling will allow us to form more effective employment policy directions that can ensure flexibility, social sustainability and competitiveness of the national labor market in the long term.

The rapid growth of the Internet of Things (IoT) has revolutionised industries but also introduced critical security threats, making robust Intrusion Detection Systems (IDS) essential. Traditional signature-based IDS struggles with evolving threats, while AI-driven approaches, such as machine learning (ML) and deep learning (DL), show promise but face challenges in terms of scalability and adaptability. Large Transformer Models (LTMs) offer a novel solution by enhancing anomaly detection, automating threat analysis, and improving real-time IoT security through advanced contextual understanding. In this research, we propose an LTM-based IDS for real-time detection of IoT attacks. Integrating LTMs into IoT security can improve intelligence, automation, and threat mitigation. We propose transformer-based deep learning models such as Fine-Tuned Bidirectional Encoder Representations from Transformers Model (BERT), Distilled Bidirectional Encoder Representations from Transformers (DistilBERT), and Robustly Optimised BERT Pretraining (RoBERTa). Attack categories in the RT_IoT2022 dataset were encoded into numerical labels, followed by comprehensive data preprocessing, including random sampling and handling of missing values. To improve interpretability, the data was transformed into text format to ensure compatibility with BERT-based models. Subsequently, the dataset was split and converted into the Hugging Face Dataset format, allowing for seamless integration with Natural Language Processing (NLP) models for IoT attack detection. Then, we apply fine-tuning multiple transformer architectures, including BERT, DistilBERT, and RoBERTa, for IoT attack classification, optimising hyperparameters for efficient learning. The BERT model demonstrated strong performance, achieving its lowest training loss of 0.0211 at 34_{th} epoch and the lowest validation loss of 0.0677 at 49_{th} epoch. These results indicate effective learning and good generalisation capability.

The object of this research is the system of sustainable innovation management in motor transport enterprises (MTEs). The problem addressed concerns the lack of integrated frameworks that combine sustainability, innovation, and audit mechanisms, which limits adaptability, resilience, and strategic alignment in MTEs, particularly under volatile markets and post-conflict recovery conditions in Ukraine. The research develops and establishes an audit-integrated conceptual model based on the viable systems approach (VSA) and the viable system model (VSM). The model aligns strategic, tactical, and operational subsystems with dual-loop regulation, combining deviation-based operational control with disturbance-based adaptive control. Internal audit functions are embedded to ensure accountability, transparency, continuous monitoring, and systemic integration of sustainable innovations. A comparative analysis with existing approaches, including the triple bottom line concept, circular economy principles, ISO 14001, and ESG frameworks, was conducted using the fuzzy analytic hierarchy process (Fuzzy-AHP). This method enabled a multi-criteria expert evaluation under conditions of uncertainty and provided quantitative validation of the advantages of the proposed VSM-based model. The results confirmed that the model ensures comprehensive systemic integration, positions innovation as a structural driver of development, enhances organizational resilience, and institutionalizes internal audit as a governance mechanism. The practical significance is the applicability of the model in ecological modernization, digital transformation, and post-conflict recovery of transport enterprises, particularly in developing economies with resource constraints. In practice, it supports managers and policymakers in designing adaptive strategies, embedding audit into business processes, and improving resilience, competitiveness, and sustainability performance.

High-dimensional interval-censored failure time data occur in many areas and many methods have been proposed for their regression analysis. However, these methods may fail or not perform well when the available information is limited. To address this, we propose two transfer learning estimation procedures that can take into account multiple so-called source data under the framework of semiparametric linear transformation models, which are commonly used and well-known for their flexibility. The first one is a data-driven source detection procedure that allows one to classify the source data into two groups, positive and negative transfers, and perform the transfer learning estimation based on the combination of all of the positive transfers. Then a model-averaging approach is developed with the adaptive weights to source datasets determined based on their relevance to the target task. The asymptotic properties of the resulting estimators including the consistency are provided. An extensive simulation is conducted and demonstrates the superior performance of the proposed methods in terms of estimation accuracy and predictive capability. Finally they are applied to a breast cancer data that motivated this study.

Background: Examination protocoling is a resource-intensive task. Various artificial intelligence (AI) approaches have been investigated to automate this process. Objective: To evaluate performance of traditional machine-learning (ML) models, bidirectional encoder representations from transformers (BERT) models, and large language models (LLMs) for automated CT and MRI protocoling. Evidence Acquisition: MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, and Google Scholar databases were searched through July 2025 for studies reporting performance of an AI-based technique in assigning protocols for CT or MRI requisitions. Accuracy results were separately extracted for all models tested in each study and pooled using random-effects meta-analysis. AI approaches were compared using Welch t tests. Common sources of error were qualitatively summarized. Evidence Synthesis: The final analysis included 23 studies, comprising 1,196,259 imaging requisitions. Requisition subspecialties included body imaging (n=4), musculoskeletal imaging (n=3), neuroradiology (n=6), thoracic imaging (n=1), and multiple subspecialties (n=9). Sixteen studies evaluated traditional ML models, eight evaluated BERT models, and five evaluated LLMs. Task-specific model fine-tuning was performed in three studies for traditional ML models, all studies for BERT models, and one study for LLMs. Overall pooled protocoling accuracy was 85% (95% CI: 83-87%). Pooled accuracy was 83% (95% CI: 80-85%) for traditional ML models, 87% (95% CI: 85-89%) for BERT models, and 86% (95% CI: 83-89%) for LLMs; these pooled accuracies were not significantly different between any pairwise combination of the three AI approaches (all p > .05). Among 30 distinct models (14 traditional ML models, nine BERT models, seven LLMs), the top ten performing models comprised two traditional ML models, six BERT models (including the top performing model [BioBert; accuracy, 93%]), and two LLMs. Common sources of error included ambiguous requisition text, data imbalance yielding incorrect protocol assignments for low-volume protocols, presence of multiple clinically reasonable protocols for given requisitions, and difficulty handling requisitions containing terms strongly associated with disparate protocols. Conclusion: The top performing AI models for automated CT and MRI protocoling included predominantly finetuned BERT models. Clinical Impact: AI tools show strong potential to help streamline radiologist workflows, possibly through hybrid AI-radiologist approaches. Fine-tuned LLMs warrant further exploration.