A Resource-Based View of How Innovation Intermediaries Support Digital Manufacturing: A Case Study from Singapore's Aerospace Industry DOI
Yanan Wang, Guendalina Anzolin,

Eoin O'Sullivan

et al.

Published: Jan. 1, 2024

Language: Английский

On the emergence of interdisciplinary scientific fields: (how) does it relate to science convergence? DOI Creative Commons
Philipp Baaden, Michael Rennings, John Marcus

et al.

Research Policy, Journal Year: 2024, Volume and Issue: 53(6), P. 105026 - 105026

Published: May 21, 2024

Interdisciplinary scientific fields, such as synthetic biology, bioinformatics, and human brain science, often emerge at the intersection of existing disciplines. This fundamental process is described in literature streams 'science convergence' 'evolution new fields'. However, despite their empirical relevance potential for science convergence to accelerate evolution these two concepts have been developed separately up this point. In study, we therefore investigate interplay between by first conducting a systematic review on examine its underlying dynamics. We then integrate concept into current understanding evolutionary leading theoretical conceptualization typology different pathways interdisciplinary fields. The exhibit varying levels research activities stages process. apply cases illustrating how an early emphasis drive field. essence, our related proxies enable policymakers other actors understand gives rise

Language: Английский

Citations

6

Is fair carbon mitigation practicable in China? Insights from digital technology innovation and carbon inequality DOI
Senmiao Yang, Jianda Wang, Miaomiao Tao

et al.

Environmental Impact Assessment Review, Journal Year: 2024, Volume and Issue: 108, P. 107608 - 107608

Published: July 23, 2024

Language: Английский

Citations

6

Innovation intermediaries and emerging digital technologies DOI
Ana Colovic, Annalisa Caloffi, Federica Rossi

et al.

Technovation, Journal Year: 2024, Volume and Issue: 133, P. 103022 - 103022

Published: April 30, 2024

Language: Английский

Citations

5

Understanding Innovation and Sustainability in Digital Organizations: A Mixed-Method Approach DOI Open Access
Sabrina Schork, Dilan Özdemir-Kaluk,

Cudi Zerey

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(2), P. 415 - 415

Published: Jan. 8, 2025

In recent years, organizations have increasingly turned to digital transformation (D) drive innovation (I) and sustainability (S). However, the rapid growth of studies on this topic, particularly since 2019, has made it challenging clearly define these concepts, operationalize their constructs, understand positive organizational impacts. This article addresses gaps through a systematic literature review (SLR) that combines quantitative qualitative analysis. study begins with comprehensive bibliometric analysis 3099 articles published between 2010 2023 in Scopus database containing terms “digital,” “innovation,” “sustainability” (or “sustainable”). It then covers detailed 20 high-quality from VHB-JOURQUAL-C-ranked journals or higher. By employing an inductive–deductive approach, authors identify consistent conflicting definitions, diverse constructs for each D-I-S concept, numerous effects. provides structured overview existing definitions introduces model distinguish environmental, organizational, solution levels. Additionally, presents visual framework direct mediated effects organizations. Overall, insights underscore critical role advancing sustainability, offering valuable guidance researchers practitioners alike.

Language: Английский

Citations

0

Pursuing a corporate sustainable identity: Green governance strategy, hybrid vehicle development, knowledge and sustainability performance DOI
Akrum Helfaya, Phuong Bui

Journal of Innovation & Knowledge, Journal Year: 2025, Volume and Issue: 10(2), P. 100660 - 100660

Published: Jan. 30, 2025

Language: Английский

Citations

0

Organizing Ecosystems for the Greater Good: An Integrative Review DOI
A. Raizada, Sabyasachi Sinha

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 145148 - 145148

Published: Feb. 1, 2025

Language: Английский

Citations

0

Innovation intermediaries in the digital transformation process. A comparative case study of research and technology organisations in the US and the UK DOI Creative Commons
Guendalina Anzolin,

Eoin O'Sullivan

Technovation, Journal Year: 2025, Volume and Issue: 142, P. 103200 - 103200

Published: March 1, 2025

Language: Английский

Citations

0

Bio-inspired design: leveraging nature for enhanced ecosystem services DOI
Ruaa M. Ismail, Naglaa A. Megahed, Merhan M. Shahda

et al.

Architectural Engineering and Design Management, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 32

Published: March 25, 2025

Urbanization and built-up areas negatively impact ecosystems, contributing to climate change, pollution, biodiversity loss. In response, the interdisciplinary framework of Ecosystem Services (ES) has gained prominence in sustainability resilience agendas. The potential bio-inspired design (BID) provide or improve ES built environment is investigated this study. Its originality stems from attempt bridge gap literature by examining relationship between BID environments. By focusing on specific ES, establishing evaluation criteria, analyzing different strategies at micro, meso, macro urban scales, research highlights influence ES. For example, green roofs have a direct, high regulation, while sponge cities medium regulation. This study identifies purification, energy as most relevant due their integrability, available technologies, ability be executed various scales. findings are synthesized into matrix, linking methods assessing based established criteria.

Language: Английский

Citations

0

Improving decision-making and stakeholder engagement at project governance using digital technology for sustainable infrastructure projects DOI
Roksana Jahan Tumpa, Leila Moslemi Naeni

Smart and Sustainable Built Environment, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Purpose Achieving sustainable development goals requires efficient decision-making and stakeholder engagement in infrastructure projects. This research aims to investigate how at the project governance level can be advanced using digital technology improve sustainability performance Design/methodology/approach Grounded acceptance model, this qualitative study explored perceptions of professionals facilitating within Seventeen semi-structured interviews were conducted with purposively selected data analyzed inductive thematic analysis. Findings Digital enables evidence-based aligned by providing real-time data, optimizing analysis enhancing authenticity while reducing resource time pressure. It promotes offering integrated, collaborative centralized platforms which foster transparency, collaboration, mitigate risk greenwashing modern slavery streamlining communication siloed engagement. However, human oversight remains essential prevent technological misinterpretation. Practical implications provides valuable insights for management seeking integrate into demonstrates enhance environmental, social economic dimensions projects, helping them remain competitive a dynamic environment. Social presents reliable, up date required informed decision-making, enabling socially choices. reduces risks erroneous decision benefit broader communities addressing challenges, fostering resilience well-being. Originality/value Despite slow adoption Australian crucial. addresses gap comprehensive understanding level.

Language: Английский

Citations

0

Integrating Bioengineering and Machine Learning: A Multi-Algorithm Approach to Enhance Agricultural Sustainability and Resource Efficiency DOI Creative Commons

Senthil G. A,

R. Prabha,

R.M. Asha

et al.

BIO Web of Conferences, Journal Year: 2025, Volume and Issue: 172, P. 02001 - 02001

Published: Jan. 1, 2025

The novel research incorporates high-level machine learning algorithms for optimizing agricultural performance regarding sustainability and resource efficiencies. By using random forests SVMs, this work successfully achieved 92% prediction accuracy crop yields an 89% classification of regions, thereby highly enhancing the decision-making power farmers policymakers. With over 10,000 historical records, forest model established a hypothesis that maize could be increased by almost 25% in ideal conditions. At same time, SVM identified more strongly within high-productivity areas yield increase 15% targeted crops. Furthermore, Convolutional Neural Networks processed nearly 5,000 satellite images to register precision rate up 94% early stress resulting reduction loss 30%. Reinforcement Learning was used also reduce water use irrigation 20% without impacting crops while schedules adapt real-time data concerning environment toward helping meet goals. Network (CNN) stands out as best algorithm context due its exceptional detection symptoms, achieving accuracy. Findings have indicated multi-algorithm approach not only promotes predictive capabilities optimization but raises food safety with threats agriculture.

Language: Английский

Citations

0