Chang Hyun Sohn | Computational Fluid Dynamic | Best Researcher Award

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Prof Chang Hyun Sohn | Computational Fluid Dynamic | Best Researcher Award

Professor, Kyungpook National University, South Korea

Dr. Chang-Hyun Sohn is a distinguished Professor of Mechanical Engineering at Kyungpook National University (KNU), South Korea. 🎓 With expertise in Computational Fluid Dynamics (CFD), Flow-Induced Vibration, and Particle Image Velocimetry (PIV), he has made significant contributions to thermal-fluid sciences. 🌊 He has served as a Visiting Professor at the University of Cambridge and the University of Tennessee and previously worked at the Agency for Defense Development (ADD), contributing to small jet engine development. ✈️ His extensive research output includes 134 journal papers, 64 conference proceedings, 37 books & reports, and 5 patents. 📚 Recognized with prestigious awards, he has held leadership roles in KSME, KASE, and KSCFE. 🔬 His influence spans academia, industry, and engineering societies, making him a pioneer in fluid dynamics research. 🌍

Profile

scholar

Education 🎓

Ph.D. in Mechanical Engineering, KAIST, South Korea (1991) 🏆 Focused on thermal-fluid flow and CFD modeling, advancing numerical simulations in fluid dynamics. 💡M.E. in Mechanical Engineering, KAIST, South Korea (1985) 📊 Specialized in computational modeling and flow analysis, contributing to advanced engineering applications. 🚀B.E. in Mechanical Engineering, Kyungpook National University, South Korea (1983) 🔧 Developed a strong foundation in mechanical systems, thermodynamics, and aerodynamics, shaping future research in flow dynamics. 🌪️

Professional Experience 🏢

Professor, Kyungpook National University (1994 – Present) 👨‍🏫 Leading fluid dynamics research and mentoring future engineers. 🎯Team Manager, Agency for Defense Development (ADD) (1991 – 1994) 🛩️ Spearheaded small jet engine development and military propulsion technology. 💨Visiting Professor, University of Cambridge (1996 – 1997) 🇬🇧 Collaborated on aerodynamic research in turbulence and flow modeling. 📈Visiting Professor, University of Tennessee (2005 – 2006) 🇺🇸 Advanced CFD applications in thermal-fluid sciences. 🔥Vice Dean, College of Engineering, KNU (2007 – 2008) 📌 Strengthened academic programs in mechanical and automotive engineering. 🏗️Director, Industrial-University Consortium Center (2007 – 2008) 🔄 Enhanced industry-academic collaboration for applied mechanical research. 🏭

Awards & Honors 🏆

Outstanding Paper Award, Korean Society for Computational Fluid Engineering (2010) 📜 Recognized for excellence in CFD-based thermal-fluid research. 🔥Best Paper Award, Korean Society of Mechanical Engineers (2010) ✨ Acknowledged for groundbreaking contributions to mechanical engineering innovations. 🚗Advisor of Winning Team, National Fluid Engineering Competition (2010) 🏅 Mentored students in a national-level fluid mechanics challenge. 🎯Outstanding Portfolio Instructor, KNU (2010) 👏 Honored for exceptional teaching in mechanical and aerospace engineering. 📖Invited Speaker, IBCAST (2016) & FMFP (2017) 🎤 Shared insights on fluid mechanics, CFD, and turbulence modeling in global conferences. 🌎

Research Focus 🔬

Computational Fluid Dynamics (CFD) 🖥️ Developing high-precision simulations for thermal-fluid flows, aerodynamics, and turbulence modeling. 🌪️Particle Image Velocimetry (PIV) Measurement 📸 Enhancing fluid flow visualization techniques for experimental validation of CFD models. 💡Flow-Induced Vibration (FIV) 🔊 Investigating structural interactions with fluid flow for safer, more efficient engineering systems. 🏗️Aerospace & Automotive Applications 🚀 Designing advanced propulsion systems, aerodynamic vehicles, and jet engines. ✈️Thermal-Fluid System Optimization ⚡ Improving cooling systems, energy efficiency, and industrial heat transfer mechanisms. 🔥

Publications

Investigating the Power Extraction of Applying Hybrid Pitching Motion on a Wing with Leading and Trailing Flaps

Enhanced Power Extraction via Hybrid Pitching Motion in an Oscillating Wing Energy Harvester with Leading Flap

Wetting performance analysis of porosity distribution in NMC111 layered electrodes in lithium-ion batteries using the Lattice Boltzmann Method

Reduction of delivery pressure fluctuations in a gerotor pump

Numerically Investigating the Energy-Harvesting Performance of an Oscillating Flat Plate with Leading and Trailing Flaps

Conclusion

Dr. Chang-Hyun Sohn is an outstanding candidate for the Best Researcher Award, given his exceptional contributions to CFD, leadership in mechanical engineering, and innovation in applied research. His strong publication record, international impact, and industry collaborations make him highly suitable for this prestigious recognition. Further engagement in cutting-edge fields like AI-enhanced CFD and sustainability applications could further strengthen his position as a global leader in the field.

Xiaolin Yang | CImage analysis | Best Researcher Award

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Dr. Xiaolin Yang | Image analysis | Best Researcher Award

Dr at China university of mining and technology, China

Xiaolin Yang is a skilled Business Analyst and Postdoctoral Researcher at Henan Investment Group. With a solid background in mineral process engineering, his expertise spans industry research, project management, and production optimization. Xiaolin holds a Bachelor’s and a Ph.D. in Mineral Process Engineering from the China University of Mining and Technology, specializing in mineral processing, machine learning, and image analysis. His dedication to academic excellence and practical application makes him a valuable asset in the mineral industry.

Publication Profile

scopus

Education🎓 

.Bachelor of Mineral Process Engineering | China University of Mining and Technology, 2015–2019 | Focus: Mineral separation methods and equipment. Doctor of Mineral Process Engineering | China University of Mining and Technology, 2019–2024 | Research areas: Mineral processing, machine learning, image analysis. Xiaolin’s academic journey emphasized innovation in mineral separation, blending engineering with data science to improve mineral processing efficiency and accuracy.

Experience💼 

Postdoctoral Researcher | Henan Investment Group, 2024–Present | Xiaolin’s role involves comprehensive industry research, preparing assessment reports, and offering investment insights and recommendations. His project management tasks focus on feasibility assessments and evaluating the effectiveness of production processes, aiming to optimize industrial production and implement innovative solutions in mineral processing.

Awards and Honors🏆 

Published Author | Xiaolin has authored notable academic articles, such as in Journal of Materials Research and Technology (2021), Energy (2022), and Expert Systems with Applications (2024). His work, recognized for its significance in mineral processing and machine learning, highlights his expertise in utilizing advanced algorithms for practical industry challenges.

Research Focus🔍

Research Interests | Xiaolin’s research delves into mineral processing, machine learning applications, and image analysis. His studies, including deep learning for ash determination in coal flotation, explore novel algorithms to enhance mineral processing accuracy, bridging engineering and artificial intelligence for industrial optimization.

Publication  Top Notes

Multi-scale neural network for accurate determination of ash content in coal flotation concentrate

Authors: Yang, X., Zhang, K., Thé, J., Tan, Z., Yu, H.

Journal: Expert Systems with Applications, 2025, 262, 125614

Description: This paper presents a multi-scale neural network model that accurately determines ash content in coal flotation concentrate using froth images, leveraging deep learning to enhance mineral processing efficiency.

STATNet: One-stage coal-gangue detector for real industrial applications

Authors: Zhang, K., Wang, T., Yang, X., Tan, Z., Yu, H.

Journal: Energy and AI, 2024, 17, 100388

Description: The STATNet model is introduced as a coal-gangue detection system using a one-stage deep learning algorithm, tailored for industrial application with a focus on real-time processing.

COFNet: Predicting surface area of covalent-organic frameworks

Authors: Wang, T., Yang, X., Zhang, K., Tan, Z., Yu, H.

Journal: Chemical Physics Letters, 2024, 847, 141383

Description: COFNet utilizes deep learning to predict the specific surface area of covalent-organic frameworks, combining structural image analysis with statistical features for accurate predictions.

Enhancing coal-gangue detection with GAN-based data augmentation

Authors: Zhang, K., Yang, X., Xu, L., Tan, Z., Yu, H.

Journal: Energy, 2024, 287, 129654

Description: This study employs GAN-based data augmentation and a dual attention mechanism to improve coal-gangue object detection, aiming to refine accuracy in complex industrial environments.

Multi-step carbon price forecasting using hybrid deep learning models

Authors: Zhang, K., Yang, X., Wang, T., Tan, Z., Yu, H.

Journal: Journal of Cleaner Production, 2023, 405, 136959

Description: A hybrid deep learning model for multi-step forecasting of carbon prices is proposed, integrating multivariate decomposition to enhance predictive reliability.

PM2.5 and PM10 concentration forecasting with spatial–temporal attention networks

Authors: Zhang, K., Yang, X., Cao, H., Tan, Z., Yu, H.

Journal: Environment International, 2023, 171, 107691

Description: This article introduces a spatial–temporal attention mechanism for PM2.5 and PM10 forecasting, using convolutional neural networks with residual learning to tackle air quality predictions.

Ash determination of coal flotation concentrate using hybrid deep learning model

Authors: Yang, X., Zhang, K., Ni, C., Tan, Z., Yu, H.

Journal: Energy, 2022, 260, 125027

Description: This work features a hybrid model that utilizes deep learning and attention mechanisms to determine ash content in coal flotation, contributing to process optimization.

Influence of cation valency on flotation of chalcopyrite and pyrite

Authors: Yang, X., Bu, X., Xie, G., Chehreh Chelgani, S.

Journal: Journal of Materials Research and Technology, 2021, 11, pp. 1112–1122

Description: This comparative study explores how different cation valencies affect chalcopyrite and pyrite flotation, contributing to better separation techniques in mineral processing.

Conclusion

Xiaolin Yang is a compelling candidate for the Best Researcher Award. His strengths in applying AI and image analysis to mineral processing reflect a unique skill set that is highly relevant for advancing research and industry practices. With further interdisciplinary work and expanded research visibility, Xiaolin is well-positioned to make impactful contributions and earn recognition in his field.

Wei-Zhi Wu | mathemarical foundations of AI | Best Researcher Award

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Prof. Wei-Zhi Wu | mathemarical foundations of AI | Best Researcher Award

Professor at Zhejiang Ocean Univeristy, China

Wei-Zhi Wu, Ph.D., is a distinguished Professor of Mathematics at Zhejiang Ocean University in Zhoushan, China. With a prolific career in applied mathematics, Dr. Wu specializes in granular computing, data mining, and the mathematical foundations of artificial intelligence. He has contributed to over 200 articles in esteemed journals, as well as four key monographs. His expertise has earned him repeated recognition on Elsevier’s Most Cited Chinese Researchers list (2014-2023), as well as among the Top 100,000 Scientists globally, with a remarkable 2% percentile ranking in both career and annual categories. Dr. Wu also holds prominent editorial roles in various international academic journals.

Publication Profile

scholar

Education🎓

B.Sc. in Mathematics – Zhejiang Normal University, Jinhua, China, 1986 M.Sc. in Mathematics – East China Normal University, Shanghai, China, 1992 Ph.D. in Applied Mathematics – Xi’an Jiaotong University, Xi’an, China, 2002

Experience🖊️ 

Professor of Mathematics – School of Information Engineering, Zhejiang Ocean University, Zhoushan, Chin Extensive Publications – Authored 200+ articles and 4 monographs in mathematics, computing, and AI Editorial Board Membership – Serves on multiple prestigious international journals, contributing to mathematical and AI research dissemination Research Leader – Notable for pioneering efforts in granular computing, data mining, and AI foundations

Awards and Honors🏆

Most Cited Chinese Researchers – Featured in Elsevier’s list (2014-2023) mTop Global Scientist – Ranked in the Top 100,000 Scientists worldwide, with a career-long and single-year ranking in the top 2%  Prolific Author – Renowned for influential monographs and extensive publication record Editorial Distinction – Serves as an editorial board member for multiple top-tier international journals

Research Focus🌍

Granular Computing – Explores and applies granular structures in computational systems  Data Mining – Develops and advances data mining techniques for complex data analysis Mathematics of AI – Examines foundational mathematical principles underpinning artificial intelligence algorithms  Interdisciplinary Applications – Integrates applied mathematics into practical AI and computing solutions

Publication  Top Notes

  • 粗糙集理论与方法 (Rough Set Theory and Methods)
    Authors: 张文修, 吴伟志, 梁吉业, 李德玉
    Publisher: 科学出版社 (Science Press)
    Citations: 620*
    Year: 2001
  • Generalized Fuzzy Rough Sets
    Authors: W.Z. Wu, J.S. Mi, W.X. Zhang
    Journal: Information Sciences, Vol. 151, pp. 263-282
    Citations: 769
    Year: 2003
  • Constructive and Axiomatic Approaches of Fuzzy Approximation Operators
    Authors: W.Z. Wu, W.X. Zhang
    Journal: Information Sciences, Vol. 159(3), pp. 233-254
    Citations: 554
    Year: 2004
  • Approaches to Knowledge Reduction Based on Variable Precision Rough Set Model
    Authors: J.S. Mi, W.Z. Wu, W.X. Zhang
    Journal: Information Sciences, Vol. 159(3-4), pp. 255-272
    Citations: 525
    Year: 2004
  • Granular Computing and Knowledge Reduction in Formal Contexts
    Authors: W.Z. Wu, Y. Leung, J.S. Mi
    Journal: IEEE Transactions on Knowledge and Data Engineering, Vol. 21(10), pp. 1461-1474
    Citations: 432
    Year: 2009
  • Knowledge Acquisition in Incomplete Information Systems: A Rough Set Approach
    Authors: Y. Leung, W.Z. Wu, W.X. Zhang
    Journal: European Journal of Operational Research, Vol. 168(1), pp. 164-180
    Citations: 414
    Year: 2006
  • Neighborhood Operator Systems and Approximations
    Authors: W.Z. Wu, W.X. Zhang
    Journal: Information Sciences, Vol. 144(1), pp. 201-217
    Citations: 284
    Year: 2002
  • On Characterizations of (I, T)-Fuzzy Rough Approximation Operators
    Authors: W.Z. Wu, Y. Leung, J.S. Mi
    Journal: Fuzzy Sets and Systems, Vol. 154(1), pp. 76-102
    Citations: 279
    Year: 2005
  • Knowledge Reduction in Random Information Systems via Dempster–Shafer Theory of Evidence
    Authors: W.Z. Wu, M. Zhang, H.Z. Li, J.S. Mi
    Journal: Information Sciences, Vol. 174(3-4), pp. 143-164
    Citations: 267
    Year: 2005
  • A Rough Set Approach for the Discovery of Classification Rules in Interval-Valued Information Systems
    Authors: Y. Leung, M.M. Fischer, W.Z. Wu, J.S. Mi
    Journal: International Journal of Approximate Reasoning, Vol. 47(2), pp. 233-246
    Citations: 258
    Year: 2008

Conclusion

Dr. Wei-Zhi Wu is a highly accomplished researcher whose work demonstrates both depth and breadth across mathematics, data mining, and AI. His robust research profile, substantial publications, international recognition, and leadership roles affirm his suitability for the Best Researcher Award. Given his impactful contributions to foundational AI research, awarding him could encourage further advances in mathematical applications within AI and inspire other scholars in related fields.

Jinxia Zhang | Defect detection | Best Researcher Award

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Assoc Prof Dr. Jinxia Zhang | Defect detection | Best Researcher Award

 Associate Professor at Southeast University, China

Assoc Prof Dr. Jinxia Zhang is an Associate Professor at Southeast University, Nanjing, China, specializing in saliency detection, visual attention, computer vision, and deep learning. With a Ph.D. in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology, he has extensive experience in artificial intelligence research. His journey includes time as a visiting scholar at Harvard Medical School and numerous prestigious research projects funded by national foundations. Assoc Prof Dr. Jinxia Zhang leads key AI initiatives, driving innovations in multimodal understanding, defect analysis, and object detection. His academic and professional contributions have positioned him as a prominent researcher in visual computing and AI.

Publication Profile

scholar

Education 🎓

Assoc Prof Dr. Jinxia Zhang  earned his M.Sc. and Ph.D. in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology in 2015. His doctoral research laid a foundation for his interest in artificial intelligence, particularly in areas like visual attention and computer vision. Prior to his postgraduate work, he completed his B.Sc. in Computer Science and Technology at the same institution in 2009, where he developed a solid understanding of computational theories and applications. His education has provided him with both theoretical knowledge and practical skills that are central to his current research on AI and deep learning.Assoc Prof Dr. Jinxia Zhang  is currently an Associate Professor at Southeast University, Nanjing, a role he has held since 2019. From 2016 to 2019, he served as a Lecturer at the same university, where he significantly contributed to AI teaching and research. His early career included a prestigious stint as a Visiting Scholar at Harvard Medical School, USA, between 2012 and 2014, where he collaborated on cutting-edge AI-driven healthcare projects. His international exposure and academic roles have enriched his teaching and research, particularly in computer vision and AI, making him a key figure in the field.

Awards and Honors  🏆

Assoc Prof Dr. Jinxia Zhang  has received numerous accolades for his research excellence and contributions to the field of AI. He was awarded the National Natural Science Foundation of China grant in 2018 for his project on salient object detection. In 2017, he secured the Jiangsu Natural Science Foundation Grant for his innovative research on visual cognitive characteristics. Additionally, his work in defect diagnosis for photovoltaic modules was recognized as part of the National Key Research and Development Plan. These prestigious awards underscore his pioneering contributions in artificial intelligence and computer vision research.

Research Focus  🔬

Assoc Prof Dr. Jinxia Zhang ‘s research focuses on the intersection of visual attention, saliency detection, and deep learning within artificial intelligence. He leads projects on multimodal understanding and e-commerce applications, and is a Principal Investigator for research into AI-based fruit and vegetable recognition. His earlier work in defect diagnosis for photovoltaic modules and salient object detection in complex scenes has been supported by prominent grants. His innovative approach combines perceptual grouping and visual attention to develop cutting-edge solutions in computer vision, making significant advancements in how machines perceive and interact with visual data.

Conclusion

The candidate demonstrates an impressive body of work across several domains of artificial intelligence, particularly in salient object detection, visual cognition, and multimodal learning. Their academic achievements, project leadership, and dedication to advancing AI make them a strong contender for the Best Researcher Award. By continuing to broaden their industry collaborations and expanding the scope of their research impact, they can become a globally recognized leader in AI and computer vision.

Publication  Top Notes

  • Towards the Quantitative Evaluation of Visual Attention Models (2015)
    • Citation: 75
    • Journal: Vision Research
    • Key Contributors: Z. Bylinskii, E.M. DeGennaro, R. Rajalingham, H. Ruda, J. Zhang, J.K. Tsotsos
    • Highlights: Focuses on quantitative approaches to evaluate visual attention models, essential for improving saliency detection.
  • A Novel Graph-Based Optimization Framework for Salient Object Detection (2017)
    • Citation: 63
    • Journal: Pattern Recognition
    • Key Contributors: J. Zhang, K.A. Ehinger, H. Wei, K. Zhang, J. Yang
    • Highlights: Presents a new graph-based optimization method for enhancing the accuracy of salient object detection.
  • Salient Object Detection by Fusing Local and Global Contexts (2020)
    • Citation: 60
    • Journal: IEEE Transactions on Multimedia
    • Key Contributors: Q. Ren, S. Lu, J. Zhang, R. Hu
    • Highlights: This paper integrates both local and global visual contexts to refine salient object detection in multimedia applications.
  • Inter-Hour Direct Normal Irradiance Forecast with Multiple Data Types and Time-Series (2019)
    • Citation: 36
    • Journal: Journal of Modern Power Systems and Clean Energy
    • Key Contributors: T. Zhu, H. Zhou, H. Wei, X. Zhao, K. Zhang, J. Zhang
    • Highlights: Introduces a time-series forecasting model for direct normal irradiance, benefiting renewable energy systems.
  • Winter is Coming: How Humans Forage in a Temporally Structured Environment (2015)
    • Citation: 35
    • Journal: Journal of Vision
    • Key Contributors: D. Fougnie, S.M. Cormiea, J. Zhang, G.A. Alvarez, J.M. Wolfe
    • Highlights: Examines human visual foraging behavior in dynamically changing environments.
  • Domain Adaptation for Epileptic EEG Classification Using Adversarial Learning and Riemannian Manifold (2022)
    • Citation: 25
    • Journal: Biomedical Signal Processing and Control
    • Key Contributors: P. Peng, L. Xie, K. Zhang, J. Zhang, L. Yang, H. Wei
    • Highlights: This paper explores domain adaptation techniques to improve epileptic EEG classification through adversarial learning.
  • A Lightweight Network for Photovoltaic Cell Defect Detection in Electroluminescence Images (2024)
    • Citation: 23
    • Journal: Applied Energy
    • Key Contributors: J. Zhang, X. Chen, H. Wei, K. Zhang
    • Highlights: Develops a lightweight neural network for detecting defects in photovoltaic cells using knowledge distillation.
  • Salient Object Detection via Deformed Smoothness Constraint (2018)
    • Citation: 21
    • Journal: IEEE International Conference on Image Processing (ICIP)
    • Key Contributors: X. Wu, X. Ma, J. Zhang, A. Wang, Z. Jin
    • Highlights: Proposes a deformed smoothness constraint approach for improving salient object detection.
  • Character Recognition via a Compact Convolutional Neural Network (2017)
    • Citation: 20
    • Conference: International Conference on Digital Image Computing
    • Key Contributors: H. Zhao, Y. Hu, J. Zhang
    • Highlights: Develops a compact CNN for robust character recognition in natural scene images.
  • A Prior-Based Graph for Salient Object Detection (2014)
    • Citation: 23
    • Conference: IEEE International Conference on Image Processing (ICIP)
    • Key Contributors: J. Zhang, K.A. Ehinger, J. Ding, J. Yang
    • Highlights: Uses a prior-based graph model to enhance the performance of salient object detection algorithms.

Michele Paulin | Concordia University | Best Researcher Award

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Dr. Michele Paulin | Concordia University | Best Researcher Award

Dr. Concordia University, Canada

MICHÈLE PAULIN, former RBC Professorship holder in Strategic Relationship Marketing (2010-2020), holds a law degree (L.L.B.) from Université de Sherbrooke, an Executive MBA from John Molson School of Business, and a Ph.D. from UQÀM. She has received numerous accolades, including an award from the Journal of Service Management (2014) and the MCB University Press Award for Excellence (2001). Dr. Paulin has over 120 publications and presentations across prestigious journals and conferences. Her research focuses on immersive experiential learning, humanistic and sustainable management practices, service design and management, and self-determination motivation practices. She also serves as an expert and reviewer in various capacities.

Professional Profiles:

Scopus

Google scholar

Meet Dr. Michèle Paulin: A Leader in Strategic Relationship Marketing 🌟

Dr. Michèle Paulin, who held the RBC Professorship in Strategic Relationship Marketing from 2010 to 2020, boasts an impressive academic and professional background. She holds a law degree (L.L.B.) from Université de Sherbrooke, an MBA from the John Molson School of Business, and a Ph.D. from UQÀM. Her expertise and contributions have been widely recognized, including receiving a highly recommended award from the Journal of Service Management in 2014 at Service Frontiers and the Award for Excellence from MCB University Press—International Journal of Bank Marketing in 2001. 🏆

Funded Projects and Academic Contributions 📚

Dr. Paulin’s research has been extensively supported by prestigious funding bodies such as SSHRC, FQRSC, and internal grants from the Luc Beauregard Centre, ARRE, CASA Grants, and FRDP. Her prolific academic output includes over 120 publications and scientific presentations in renowned journals and conferences, including the Journal of Consumer Behaviour, Humanistic Management Journal, Universal Journal of Management, Journal of Humanistic Psychology, Motivation Science, and many more. 📰

Focus on Experiential Learning and Sustainable Practices 🌱

Dr. Paulin’s projects are characterized by their focus on immersive experiential learning, humanistic and sustainable management practices, service design, and self-determination motivation practices. Her innovative approach has significantly influenced the fields of service design and management. 🚀

A Respected Expert and Reviewer 🧐

In addition to her research and teaching, Dr. Paulin serves as an expert and reviewer for various academic and professional organizations. Her broad expertise and commitment to excellence have made her a respected figure in the academic community. 🌍 

✍️Publications Top Note :

Is it fashionable to swap clothes? The moderating role of culture
Armouch, F., Paulin, M., Laroche, M.
Journal of Consumer Behaviour, 2024

🌟 This article explores the cultural influences on the popularity of clothing swaps, providing insights into sustainable fashion practices and consumer behavior.

Integrated Self-Determined Motivation and Charitable Causes: The Link to Eudaimonia in Humanistic Management
Ferguson, R.J., Schattke, K., Paulin, M., Dong, W.
Humanistic Management Journal, 2024

🌟 Investigating the relationship between self-determined motivation and charitable engagement, this paper delves into how these factors contribute to human well-being and eudaimonia.

Hofstede’s individual-level indulgence dimension: Scale development and validation
Heydari, A., Laroche, M., Paulin, M., Richard, M.-O.
Journal of Retailing and Consumer Services, 2021, 62, 102640

🌟 This research validates a scale for Hofstede’s indulgence dimension, providing a tool for better understanding consumer behavior across different cultures.

Persuasions by Corporate and Activist NGO Strategic Website Communications: Impacts on Perceptions of Sustainability Messages and Greenwashing
Ferguson, R.J., Schattke, K., Paulin, M.
Humanistic Management Journal, 2021, 6(1), pp. 117–131

🌟 Examining how corporate and activist communications shape perceptions of sustainability, this article sheds light on the effectiveness of green marketing strategies.

Working Together in Montréal to Improve Veterans’ Well-Being: A Canadian Perspective
Fewster, B., Brais, H., Gregory, S., Paulin, M.
Journal of Humanistic Psychology, 2019, pp. 1–21

🌟 This study focuses on collaborative efforts to enhance the well-being of veterans in Montréal, highlighting the importance of community and support systems.

The social context for value co-creations in an entrepreneurial network: Influence of interpersonal attraction, relational norms and partner trustworthiness
Ferguson, R., Schattke, K., Paulin, M.
International Journal of Entrepreneurial Behaviour and Research, 2016, 22(2), pp. 199–214

🌟 Investigating the dynamics of value co-creation in entrepreneurial networks, this paper explores the roles of interpersonal attraction, relational norms, and trust.

Gaining Millennial women’s support for a fashion show: Influence of fashion experiences, gender identity and cause-related Facebook appeals
Salman, A., Ferguson, R.J., Paulin, M., Schattke, K.
Journal of Global Fashion Marketing, 2016, 7(2), pp. 132–146

🌟 This article examines how fashion experiences and gender identity influence Millennial women’s support for cause-related fashion shows.

Development of market Mavenism traits: Antecedents and moderating effects of culture, gender, and personal beliefs
Kiani, I., Laroche, M., Paulin, M.
Journal of Business Research, 2016, 69(3), pp. 1120–1129

🌟 Analyzing the development of market mavenism traits, this research looks at the impacts of culture, gender, and personal beliefs on consumer behavior.

Self-determination theory, social media and charitable causes: An in-depth analysis of autonomous motivation
Ferguson, R., Gutberg, J., Schattke, K., Paulin, M., Jost, N.
European Journal of Social Psychology, 2015, 45(3), pp. 298–307

🌟 This study explores how self-determination theory applies to social media and charitable causes, emphasizing the role of autonomous motivation in prosocial behavior.

Organizational Culture in a Professional Business to Business Service Context: Implications for Business Performance and Long-Term Relationships in Mexican Commercial Banking
Paulin, M., Perrien, J., Ferguson, R.
Developments in Marketing Science: Proceedings of the Academy of Marketing Science, 2015, pp. 478