Pivotal Domain Research Articles

The Road to Explainable Graph Neural Networks

GOAL: Graph Neural Networks (GNNs) are being increasingly adopted in various real-world applications, including drug and material discovery [16, 17, 27], recommendation engines [29], congestion modeling on road networks [5], and weather forecasting [11]. However, similar to other deeplearning models, GNNs are considered black boxes due to their limited capacity to provide explanations for their predictions. This lack of interpretability poses a significant barrier to their adoption in critical domains such as healthcare, finance, and law enforcement, where transparency and trustworthiness are essential for decision-making processes. In these pivotal domains, understanding the rationale behind model predictions is crucial not only for compliance with interpretability requirements but also for identifying potential vulnerabilities and gaining insights to refine the model further. How do we make GNNs interpretable? This is a central motivating question driving my research pursuits.

Research on the Impact and Solution Strategies of Blockchain Technology on Data Security and Transparency in Enterprise Digital Transformation

Abstract: This research delves into the profound impact of blockchain technology on enhancing data security and transparency during enterprises digital transformation. By dissecting blockchains core attributesdecentralization, immutability, and advanced securitythis study elucidates how it offers pragmatic solutions to prevalent challenges such as data breaches and opaque information processing. Leveraging an extensive literature review, meticulous case analysis, and expert insights, the article comprehensively illustrates blockchains diverse applications in ensuring data integrity and enhancing transparency across critical sectors, including financial services, supply chain management, and healthcare. Furthermore, the study identifies and deliberates on existing research gaps in blockchain technologys application within digital transformation, proposing a systematic framework to deepen comprehension of its role in this process. Moreover, it conducts an in-depth analysis of the technical, operational, and environmental hurdles hindering blockchain adoption, offering a series of action strategies to enable enterprises to harness their potential effectively amidst the swiftly evolving digital landscape. Through this comprehensive inquiry, the article underscores the strategic significance of blockchain technology in augmenting corporate operational transparency and data security. Additionally, it elucidates how blockchain can bolster corporate governance, compliance, and stakeholder trust. Overall, this synthesis provides a thorough review and profound insights into research findings, underscoring blockchains pivotal role in addressing contemporary digital challenges while furnishing invaluable strategic guidance for industry innovation and advancement. The research outcomes hold substantial implications for practitioners and researchers, signaling the transformative potential of blockchain technology in reshaping enterprise operations and charting a course for future exploration in this pivotal domain.

Mapping Green Supply Chain Practices: An Extensive Bibliometric Review with the SPAR-4-SLR and TCCM Framework

Purpose: The growing global awareness of environmental sustainability and the threat of climate change has prompted organisations to embrace green supply chain practices (GSCP) to minimise their ecological footprint and improve their competitive edge. The primary objective of this study is to offer a comprehensive, insightful analysis of the GSCP utilising the TCCM (theory, context, characteristics, and methods) framework. Design/Methodology/Approach: This study conducts an extensive bibliometric analysis, incorporating the TCCM framework and SPAR-4-SLR (Scientific Procedures and Rationales for Systematic Literature Reviews) protocol, to delve into the dynamic field of GSCP spanning 2004 to 2023. Findings: This article offers a nuanced exploration of the intricacies of GSCP research dynamics, shedding light on influential articles, distinguished authors, prominent academic institutions, publication sources and nine key thematic research clusters that have had a substantial impact and propelled this pivotal domain forward. TCCM framework is used to construct an all-encompassing citation network, elucidating five significant domains guided by the theoretical framework. These domains include social and economic theories, resource and strategy theories, innovation and change theories, systems and interdisciplinary theories, and management and organisation theories. Every subcategory within the GSCP realm represents a distinct and unique facet and is supported by an individual theoretical framework and scholarly contribution. Practical Implications: The findings from the bibliometric review yield significant insights for researchers and green supply-chain practitioners. This article aims to guide future research endeavours and policy formulation by evaluating the existing knowledge base and pinpointing diverse research avenues. It underscores the importance of interdisciplinary collaboration among academics from different domains to address the complex aspects of sustainability in supply-chain operations, thus highlighting the multidisciplinary character of green supply-chain research. Originality/Value: To the best of our knowledge, this review study is the first to present a bibliometric analysis of GSCP using the TCCM framework and the SPAR-4-SLR protocol.

The Impact of River Landform Changes Caused by Geohazards on the Economic Development of Ecotourism in Sabah, Malaysia

The impact of river landform changes caused by geohazards on the economic development of ecotourism in Sabah, Malaysia is a highly significant and crucial topic that necessitates extensive analysis, comprehension, and thorough understanding in order to comprehend the multifaceted consequences and ramifications. It is imperative to explore and scrutinize the intricate interplay between the alterations in river landforms induced by geohazards, such as natural disasters and environmental upheavals, and their profound influence on the economic growth and prosperity of the burgeoning ecotourism sector in Sabah, Malaysia. This comprehensive investigation holds immense value in unraveling the intricate dynamics and intricacies inherent in this intricate and complex relationship. By delving deep into the intricate mechanisms and multifarious aspects that underlie the interdependence of geohazards-induced river landform transformations and the economic development of ecotourism in Sabah, Malaysia, researchers and stakeholders alike can gain invaluable insights that can pave the way towards well-informed decision-making, sustainable growth, and effective management strategies. Consequently, the expansion of knowledge and understanding in this pivotal domain can potentially lead to the formulation and implementation of robust measures, policies, and initiatives that can mitigate the adverse effects of geohazards on river landforms, foster the resilience and adaptability of the ecotourism industry, and ultimately cultivate a harmonious balance between economic progress and environmental conservation in Sabah, Malaysia.

Cross-cultural design in costume: case study on totemic symbols of China and Thailand

Cross-cultural design has emerged as a pivotal domain of significance within the context of globalization. In the field of cross-cultural design, designers are tasked with addressing user requirements and identity characteristic contexts across diverse cultural backgrounds, aiming to achieve enhanced service delivery and cultural dissemination outcomes. Nonetheless, the landscape of contemporary fashion design research exhibits a noticeable dearth in studies that effectively integrate with cross-cultural design. This study selects the iconic cultural symbols of the Chinese loong (dragon) and Thai naga as case subjects, embarking upon research that employs costume design as a medium and bridge for cross-cultural design and communication. The research methodology integrates qualitative and quantitative approaches, including field investigations, participatory research, and Analytic Hierarchy Process (AHP) analysis, thereby substantiating theoretical constructs through empirical investigation. The study proposes that cross-cultural costume design, undertaken with the purpose of cross-cultural communication, can be conceptualized as a cyclical process involving multiple encoding and decoding iterations. The research elaborates on how costume, functioning as a non-verbal language, serve as a medium for facilitating cross-cultural interactions. Furthermore, the design extraction of cultural symbols is approached through a four-tiered framework. By articulating its research perspective, methodologies, and design cases, this study contributes valuable insights to researchers and practitioners engaged in cross-cultural design and related fields.

Secondary school randomized controlled interventions with a minimum dual approach to health and the links to BMI in pupils: A systematic review and meta-analysis within the project From Science 2 School

Introduction & Purpose Health behavior offers a pivotal domain of health for managing current global crises, including physical inactivity and overweight/obesity (Schroeder, 2007). However, adults have limited capacity for adopting healthy behavior into their lifestyle (Prochaska & Velicer, 1997). Schools offer an ideal setting for implementing health interventions considering the breadth of children that can be reached and the amount of time children spend in school (Tanous et al., 2022). The present study hypothesized that appropriately planned physical activity (PA) intervention for pupils and during compulsory secondary school time results in healthier body weight management. Methods A systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a published protocol (Tanous et al., 2022), and a trial registration was completed on the International Register of Systematic Reviews (PROSPEO: CRD4202347770). The databases searched (12/2021) included PubMed, Education Source, and EMBASE. Only school-based randomized controlled trial interventions on PA and/or diet published in English or German were included. The study population included pupils (10-19 years of age) enrolled in secondary schools and without major nutritional deficit or unstable health condition. The study sample was refined using exclusion criteria: (i) no outcome on body weight, body mass index (BMI), or self-reported, (ii) intervention groups only targeting overweight and/or obese pupils, (iii) interventions outside of regular school hours or implemented beyond the school setting, (iv) total intervention duration less than eight weeks, (v) branches of schools specializing on physical exercise/sports in general, and (vi) no control group. Title/abstract and full-text screening, data appraisal, and summary data extraction were completed by two reviewers. Results A total of 5,945 articles were identified by the search process, including 23 studies and 9,441 pupils (of which 4,787 males and 4,654 females) eligible for the analysis. PA interventions ranged from eight weeks to eight months and included jumping (n = 2), strength exercises (n = 3), cardiovascular endurance (n = 4), or a combination (n = 2). For PA intervention implemented in the secondary level school setting for at least eight weeks duration, a random effect size of -0.12 units (95% CI [-0.26. 0.02]) of BMI (kg/m2) was found with low statistical heterogeneity (I2 = 0%; p = 0.60). Discussion Due to the behavioral constraints of the school setting, children are denied from achieving the minimum level of health protection, which adversely contributes to the poor global health status with a major impact. Previous meta-analyses identified a small PA intervention effect for improving BMI in pupils (-0.02 kg/m2 and -0.05 kg/m2; Guerra et al., 2013; Harris et al., 2009) with considerable heterogeneity (I2 = 77% and 54%, respectively). The findings of the present investigation revealed a more precise effect measure than previous meta-analyses (Guerra et al., 2013; Harris et al., 2009) regarding the exclusive power of the secondary school setting over PA levels and the BMI health improvement possibility for pupils (-0.12 kg/m2). Conclusion PA intervention in secondary schools contributes to limited, yet beneficial body weight management for pupils in the ongoing global health crisis (Schroeder, 2007; Tanous et al., 2022). Considering the lack of formal organization at homes, schools are urged to achieve 60 minutes of PA daily.

Dental Caries Segmentation using Deformable Dense Residual Half U-Net for Teledentistry System

Clinical practitioners’ workload and challenges are significantly reduced by classifying, predicting, and localizing lesions or dental caries. In recent research, a high-reliability diagnostic system within deep learning models has been implemented in a clinical teledentistry system. In order to construct an efficient, precise, and lightweight deep learning architecture, it is dynamically structured. In this paper, we present an efficient, accurate, and lightweight deep learning architecture for augmenting spatial locations and improving the transformation modeling abilities of fixed-structure CNNs. Deformable Dense Residual (DDR) enhances the efficacy of the residual convolution block by optimizing its structure, thereby mitigating model redundancy and ameliorating the challenge of vanishing gradients encountered during the training stages. DDR Half U-Net presents notable advancements to the simplified U-Net framework across three pivotal domains: the encoder, decoder, and loss function. Specifically, the encoder integrates deformable convolutions, thereby enhancing the model's capacity to discern features of diverse scales and configurations. In the decoder, a sophisticated arrangement of dense residual connections facilitates the fusion of low-level and high-level features, contributing to comprehensive feature extraction. Moreover, the utilization of a weight-adaptive loss function ensures equitable consideration of both caries and non-caries samples, thereby promoting balanced optimization during training.

Unveiling the nexus: organization inclusion, ethical virtues and organizational citizenship behavior in the hospitality sector

PurposeThe purpose of this study is to examine the intricate dynamics within the hospitality service sector, which predominantly employs low-wage and low-skilled workers. These employees frequently encounter challenges related to breaches in their psychological contracts. Despite their critical role in customer service, their experiences are often overlooked in organizational inclusion research. We investigate the relationship between employees’ perceptions of organizational inclusion and their assessments of organizational ethical virtues (OEVs), considering the potential moderating effect of psychological contract breach. Furthermore, it explores how these factors influence customer- and organizationdirected organizational citizenship behaviors (OCBs).Design/methodology/approachOur research design incorporates a two-stage moderated-mediation model to test our proposed hypotheses empirically. A two-source sample of 451 European hotel managers and employees extends the inquiry with the proposed model. Structural Equation Modeling (SEM) was employed to analyze the proposed relationships.FindingsFindings reveal that organization inclusion is positively associated with OEV and employee-perceived OEV mediates an indirect link between organization inclusion on customer-oriented OCB. Moreover, psychological contract breach (P CB) attenuates the association between organization inclusion and OEV at the first stage and OEV, and OCB at the second stage.Originality/valueThe findings robustly corroborate our proposed model. The study findings culminate in a discussion accentuating the extensive implications of our findings for both research and practicality within the hospitality sector. Anchored in empirical revelations, we delineate avenues for future exploration in this pivotal domain.

Конфликт интересов. Рецензия на практическое пособие для муниципального управления. М., 2023. 377 с.

The development and improvement of the institution of conflict of interest prevention necessitates a collaborative effort between government agencies, public organizations, and the expert community. This is the only way to create an effective anti-corruption system and safeguard the interests of citizens and society at large. Municipal governance represents a pivotal domain of state activity, exerting a profound impact on the quality of life of citizens. However, instances of conflict of interest, corruption, and abuse are pervasive, eroding trust in the authorities. The book, entitled Conflict of Interest. A Practical Guide for Municipal Administration, is of great relevance, both in theoretical and practical terms. In the modern world, the issue of conflict of interest is becoming increasingly significant and requires a comprehensive approach to resolution. The authors of the book effectively integrate theoretical aspects of conflict of interest analysis with practical guidance for specialists in municipal administration. In their handbook, the authors address the issue of combating corruption at the municipal level, emphasizing the necessity for effective action by authorities and public organizations in this domain. It is essential that the legislation be transparent and effective in order to prevent and punish abuses.

Harvesting the power of the oceans: Advancements, challenges, and the promising future of wave energy

This dissertation explores the pivotal domain of wave energy, delving into its historical evolution, technological advancements, and economic prospects. In response to escalating environmental challenges and the imperative to address climate change, the pursuit of sustainable and ecologically benign energy sources has gained prominence. Wave energy emerges as a compelling frontier within the realm of renewable energy, tapping into the perpetual undulations of oceans. The study traces the conceptual antecedents of wave energy from the 19th century to its current status as a burgeoning renewable energy source. Significant strides in technology during the 1990s and 2010s marked a shift towards optimizing energy conversion efficiencies, exploring novel materials, and refining wave energy converter designs. The dissertation analyzes diverse wave energy converter types, their operational principles, and their potential applications. Despite challenges such as environmental impact, operational reliability, and high capital costs, the unwavering commitment of researchers positions Wave Energy as a sanguine contender in the renewable energy landscape. The economic outlook for wave energy is promising, with global potential estimated at 2 billion kilowatts, fostering socio-economic benefits and contributing to carbon emission reduction. By presenting a comprehensive examination of wave energy, this dissertation underscores its significance in the pursuit of sustainable, environmentally friendly, and reliable energy solutions.

Real-World-Time Data and RCT Synergy: Advancing Personalized Medicine and Sarcoma Care through Digital Innovation.

This manuscript examines the synergistic potential of prospective real-world/time data/evidence (RWTD/E) and randomized controlled trials (RCTs) to enrich healthcare research and operational insights, with a particular focus on its impact within the sarcoma field. Through exploring RWTD/E's capability to provide real-world/time, granular patient data, it offers an enriched perspective on healthcare outcomes and delivery, notably in the complex arena of sarcoma care. Highlighting the complementarity between RWTD/E's expansive real-world/time scope and the structured environment of RCTs, this paper showcases their combined strength, which can help to foster advancements in personalized medicine and population health management, exemplified through the lens of sarcoma treatment. The manuscript further outlines methodological innovations such as target trial emulation and their significance in enhancing the precision and applicability of RWTD/E, underscoring the transformative potential of these advancements in sarcoma care and beyond. By advocating for the strategic incorporation of prospective RWTD/E into healthcare frameworks, it aims to create an evidence-driven ecosystem that significantly improves patient outcomes and healthcare efficiency, with sarcoma care serving as a pivotal domain for these developments.

Infrastructure-assisted 3D detection networks based on camera-lidar early fusion strategy

Target detection in autonomous driving is a pivotal domain of the current research focus. The process fundamentally relies on on-board sensors tasked to identify proximate objects. However, long-range perception instability and the partial obstruction by traffic participants further complicate the challenge. These factors collectively affected the effectiveness of targeted detection from the cooperative vehicle-infrastructure system (CVIS) and urgently need to be addressed. Starting from infrastructure-side assisted detection, we propose a 3D detection network based on multi-sensor sensing. Our approach consists of three sub-networks: Diversity Balanced Feature Fusion Network (MRBNeXt), Early Multimodal Fusion Network (VBRFusion), and TwoStage Lightweight Detection Network (TSL). MRBNeXt focuses on extracting raw images fused into multilevel semantic representations to address the drawbacks of needing a rich feature-level representation; VBRFusion proposes a two-branch structure that acts on the point cloud voxelization to aggregate high-dimensional features. The point features are mapped to the sampled graphical semantic features via the coordinate features to complete the early fusion and thus improve the feature quality of a single modality. In the proposed area network, TSL enhances the sensory field processing of multidimensional features using the context-aware module in a two-stage approach to achieve fast target recognition at different scale levels. Finally, we perform comparative ablation experiments on the DAIR-V2X vehicle-infrastructure dataset. The results validated our approach and demonstrated its effectiveness and enhancement in detection accuracy at the infrastructure end compared to current state-of-the-art methods. This improvement significantly boosted the performance of 3D target detection tasks in complex traffic scenarios and provided a more robust justification for the subsequent development of vehicle-side-infrastructure-side collaborative 3D target detection.

A systematic literature review on patient-reported outcome domains and measures in nonsurgical efficacy trials related to chronic pain associated with endometriosis: an urgent call to action.

Endometriosis, a common cause for chronic pelvic pain, significantly affects quality of life, fertility, and overall productivity of those affected. Therapeutic options remain limited, and collating evidence on treatment efficacy is complicated. One reason could be the heterogeneity of assessed outcomes in nonsurgical clinical trials, impeding meaningful result comparisons. This systematic literature review examines outcome domains and patient-reported outcome measures (PROMs) used in clinical trials. Through comprehensive search of Embase, MEDLINE, and CENTRAL up until July 2022, we screened 1286 records, of which 191 were included in our analyses. Methodological quality (GRADE criteria), information about publication, patient population, and intervention were assessed, and domains as well as PROMs were extracted and analyzed. In accordance with IMMPACT domain framework, the domain pain was assessed in almost all studies (98.4%), followed by adverse events (73.8%). By contrast, assessment of physical functioning (29.8%), improvement and satisfaction (14.1%), and emotional functioning (6.8%) occurred less frequently. Studies of a better methodological quality tended to use more different domains. Nevertheless, combinations of more than 2 domains were rare, failing to comprehensively capture the bio-psycho-social aspects of endometriosis-associated pain. The PROMs used showed an even broader heterogeneity across all studies. Our findings underscore the large heterogeneity of assessed domains and PROMs in clinical pain-related endometriosis trials. This highlights the urgent need for a standardized approach to both, assessed domains and high-quality PROMs ideally realized through development and implementation of a core outcome set, encompassing the most pivotal domains and PROMs for both, stakeholders and patients.

Material Attribute Estimation as Part of Telecommunication Augmented Reality, Virtual Reality, and Mixed Reality System: Systematic Review

The integration of material attribute estimation (MAE) within augmented reality, virtual reality, and mixed reality telecommunication systems stands as a pivotal domain, evolving rapidly with the advent of the Tactile Internet. This unifying implementation process has the potential for improvements in the realism and interactivity of immersive environments. The interaction between MAE and the haptic Internet could lead to significant advances in haptic feedback systems, enabling more accurate and responsive user experiences. This systematic review is focused on the intersection of MAE and the Tactile Internet, aiming to find an implementation path between these technologies. Motivated by the potential of the haptic Internet to advance telecommunications, we explore its potential to advance the analysis of material attributes within AR, VR, and MR applications. Through an extensive analysis of current research approaches, including machine learning methods, we explore the possibilities of integrating the TI into MAE. By exploiting haptic and visual properties stored in the materials of 3D objects and using them directly during rendering in remote access scenarios, we propose a conceptual framework that combines data capture, visual representation, processing, and communication in virtual environments.

Research on the Current Situation and Calculation Method of Carbon Emissions Assessment for Building Curtain Walls

Curtain wall systems stand out as a pivotal domain within the construction sector’s endeavors towards energy efficiency and carbon mitigation. To refine the evaluation framework for carbon emissions within this industry, this paper explores the calculation and assessment method for building curtain walls. The article first reviews the current research status regarding carbon emissions from materials and the impact of curtain walls on buildings in the operational stage. Based on lifecycle theory, the carbon emissions from building curtain walls are divided into six stages: material acquisition, processing and production, installation and construction, transportation, use and maintenance, and dismantling. On this basis, this paper proposes a method for calculating carbon emissions from building curtain walls. Following that, a case study is conducted using a specific glass curtain wall project for illustrative analysis. The results indicate that the carbon emissions from the material acquisition stage constitute approximately 90% of the total, serving as the primary source of carbon emissions for glass curtain walls. Furthermore, the scientific application of photovoltaics can significantly reduce the carbon emission levels of building curtain walls. Finally, an analysis was conducted on the current issues existing in the evaluation of carbon emissions.

AirLock: Unlock in-air via hand rotation recognition

This paper proposes AirLock, an artificial intelligence solution meticulously crafted to elevate user authentication security in network-aware environments. With a focus on enhancing safety and convenience within indoor spaces, AirLock ingeniously employs conventional multi-antenna commercial wireless systems as a supplementary security bolster. The system makes substantial contributions in two pivotal domains. Firstly, it adeptly utilizes amplitude differences between antenna pairs to extricate individual biometrics and seamlessly eliminate multipath noise from signals. Secondly, it boasts a Seq2Seq attention network structure fortified with a precise coding strategy for authentication, ensuring swift mapping of behavior characteristics to an authenticated user. Across diverse real-world scenarios, AirLock impressively achieves an average AAR of 96.5%.

Effective Synthesis of High-Integrity mRNA Using In Vitro Transcription.

mRNA vaccines are entering a period of rapid development. However, their synthesis is still plagued by challenges related to mRNA impurities and fragments (incomplete mRNA). Most impurities of mRNA products transcribed in vitro are mRNA fragments. Only full-length mRNA transcripts containing both a 5'-cap and a 3'-poly(A) structure are viable for in vivo expression. Therefore, RNA fragments are the primary product-related impurities that significantly hinder mRNA efficacy and must be effectively controlled; these species are believed to originate from either mRNA hydrolysis or premature transcriptional termination. In the manufacturing of commercial mRNA vaccines, T7 RNA polymerase-catalyzed in vitro transcription (IVT) synthesis is a well-established method for synthesizing long RNA transcripts. This study identified a pivotal domain on the T7 RNA polymerase that is associated with erroneous mRNA release. By leveraging the advantageous properties of a T7 RNA polymerase mutant and precisely optimized IVT process parameters, we successfully achieved an mRNA integrity exceeding 91%, thereby further unlocking the immense potential of mRNA therapeutics.

Real-Time Human Pose Estimation Using Machine Learning

Human pose estimation is a pivotal domain within computer vision, underpinning applications from motion capture in cinematic production to sophisticated user interfaces in desktop devices. This research delineates the implementation of real-time human pose estimation within web browsers utilizing TensorFlow.js and the PoseNet model. PoseNet, an advanced machine learning model optimized for browser-based execution, facilitates precise pose detection sans specialized hardware. The primary aim of this study is to integrate PoseNet with TensorFlow.js, achieving efficient real-time pose estimation directly in the browser by leveraging JavaScript, thereby ensuring seamless user interaction and broad accessibility. A modular system architecture is designed, focusing on optimization strategies such as model quantization, asynchronous processing, and on-device computation to enhance performance and privacy preservation. In conclusion, this research establishes a robust framework for deploying PoseNet in web environments, underscoring its potential to revolutionize human-computer interaction within browser-based applications. Our findings contribute significantly to the field of computer vision and machine learning, offering insights into the practical deployment of pose estimation models on widely accessible platforms. Keywords — ReaReal-time Pose Estimation, TensorFlow.js, PoseNet, Machine Learning, Computer Vision, Browser-based Pose Detection, Human-Computer Interaction, Multi-person Tracking, On-device Computation, Asynchronous Processing, Cross-browser Compatibility, Performance Optimization, Privacy-preserving AI, Web-based Machine Learning, Motion Capture, Fitness Tracking, Interactive , Virtual Reality Interfaces, Deep LearningLearning

Integration of Smart Cities and Building Information Modeling (BIM) for a Sustainability Oriented Business Model to Address Sustainable Development Goals

The construction industry, business models, and smart cities are recognized as pivotal domains with profound implications for fostering sustainability, prompting extensive research endeavors. However, there remains a dearth of interdisciplinary integration within this sphere aimed at fostering sustainable development. Nevertheless, current studies suggest that research in this area could provide theoretical and practical guidance for the sustainable transformation of society and make a positive contribution to the realization of the Sustainable Development Goals (SDGs). Therefore, this paper aims to utilize an innovative mixed research approach combining macro-quantitative bibliometric analysis with subsequent micro-qualitative content examination based on the SDGs to explore the relationship between BIM and smart cities in promoting a sustainability-oriented business model, which provides a comprehensive understanding of the overall situation and development of research topics in the field and contributes to the improvement of the SDGs. The results show that, during the last 13 years (from the year 2011 to 2023), the period from the year 2011 to 2016 was the initial stage of the field, followed by a rapid growth after the year 2018, of which “BIM”, “Smart City”, “Business Model”, “Building Life Cycle”, “Urban Management”, and “Business Model Innovation” are the keywords representing the current research hotspots. The circular economy model that has been developed since 2021 has contributed to life cycle stages, including “briefing stages” and “procurement stages”. As such, the “whole life cycle”, “strategic urban planning frameworks”, and “sustainable business models” have become future research trends, whilst real-world applications such as “smart tourism”, “e-government”, and “green building” have emerged. Further, the key partnerships of “city managers”, “corporate enterprises”, and “public participation” for smart cities contribute to the achievement of SDGs 8 and 17 in terms of integrating urban information technology and urban infrastructure, policy regulation, knowledge-sharing, improving economic efficiency, and promoting sustainable economic growth.

Marketing innovations and digital technologies: A systematic review, proposed framework, and future research agenda

AbstractIn recent years, scholars and practitioners have increasingly recognized the profound impact of digital technologies (DTs) on reshaping organizations' value propositions and their ability to create, communicate, and deliver value to stakeholders. Given this heightened attention, it is imperative to comprehensively examine how DTs affect marketing innovations. This study reports the results of a systematic review of empirical research on DTs' role in marketing innovations. Specifically, it identifies a comprehensive list of technologies and investigates their relevance in enabling innovative approaches within marketing research, strategy formulation (segmentation, targeting, and positioning), and implementation (product, promotion, place, and price). The study synthesizes emerging themes in the literature, pinpoints research gaps, proposes a 3As framework for classifying DTs in marketing, and delineates future research directions in this pivotal domain.