Digital Commons Network^™

Unlocking Private Equity Investing, Yin Wang, Steve Balaban

Asian Management Insights

Myths and market shifts to take note of.

Sustainability And Climate-Related Regulations And Due Diligence, Adolf Peter

Asian Management Insights

Why does it matter?

The Reit Man For The Job, John Lim

Asian Management Insights

An interview with John Lim, Co-Founder of ARA Asset Management. Deciding to pursue a lifelong entrepreneurial dream when he turned 45, John Lim went on to reshape the Asian real estate investment trust (REIT) industry.

Hard Lessons, Stronger Banks, Gloria Yang Yu

Asian Management Insights

The enduring wisdom of the Savings and Loan crisis veterans.

Digital Platforms, Mobility, And Cities, Shauna Brail

Asian Management Insights

The rise of the digital platform economy and urban mobility requires the reshaping of urban policy.

Singapore Awakened: How Success – And Flourishing – Shape Family, Seow Hon Tan

Research Collection Yong Pung How School Of Law

Assoc. Prof. (Dr.) Tan Seow Hon delivered the keynote address at Cultivate SG’s second annual conference, “Unfiltered – The Family on Trial”, on 17 November 2025. In her speech titled “Singapore Awakened: How Success – and the Alternative of Flourishing – Shape Family”, Dr Tan reflects on the “Singapore Dream”, the narrow mindset of success in contrast with the concept of flourishing, and how these impact marriage and family. She concludes by offering some thoughts on how to move from success to flourishing.

Research On Building Sustainable Competitive Advantage Of Mutual Fund Management Companies In The Era Of Pan-Asset Management, Li Ren

Dissertations and Theses Collection (Open Access)

In the context of China’s “big asset management” era, public mutual fund management companies face intensifying competition and an urgent need to build sustainable core competitiveness. Existing research has not clearly identified their key resources or explained how these resources interact with strategic management capabilities to generate competitive advantage. Drawing on the resource-based view and strategic management theory, this study develops a conceptual framework of core competitiveness for public mutual fund management companies. It proposes a three-dimensional resource classification system—valuable, rare, and inimitable resources—tailored to the mutual fund industry and incorporates strategic management capability as a mediating mechanism through which …

Research On The Impact Of Strategic Orientation, Employees’ Environmental Identity, And Use Of Artificial Intelligence On Corporate Green Innovation, Hao Yang

Dissertations and Theses Collection (Open Access)

Based on social cognitive theory and integrating the perspective of technology enablement, this study constructs a moderated mediation model to systematically explore how environmental policy orientation and environmental market orientation influence corporate green innovation through environmental identity, and to examine the boundary role of AI application intensity. Through empirical analysis of 313 teams and 939 valid questionnaires, the following conclusions are drawn: First, both environmental policy orientation and environmental market orientation independently and significantly promote green innovation. Second, environmental identity plays a partial mediating role between the two orientations and green innovation, indicating that strategies need to be internalized as …

Research On The Impact Of Supply Chain Digitization On Innovation Performance: Based On Quasi-Natural Experiments And Longitudinal Case Study, Binbin Chen

Dissertations and Theses Collection (Open Access)

The Chinese government prioritizes supply chain digital transformation. Pilot policies for supply chain digitalization will accelerate digital development across industrial and supply chains. While existing research offers substantial empirical evidence on supply chain digitalization, its impact on corporate innovation performance requires further investigation. Based on this, the study constructs a quasi-natural experiment using data from A-share listed companies and appropriate econometric models, demonstrating that supply chain digitalization significantly enhances corporate innovation performance. This effect is statistically and economically significant, providing a foundation for future research. The study also identifies the mechanisms through which supply chain digitalization improves corporate innovation performance, …

The Lexicographic Preference And The Tops-Only Property, Xinning Geng

Dissertations and Theses Collection (Open Access)

This paper studies the structure of the lexicographic preference domain in the context of strategy proof Random Social Choice Functions (RSCFs). Classical sufficient conditions for the tops-only property—namely, the Interior and Exterior Properties—do not hold in lexicographic domains. Nevertheless, these domains still exhibit the tops-only property. We first provide a structural explanation of why the Interior and Exterior Properties fail in this setting. We then introduce two new structural conditions, Property 1 and Property 2, and show that any two-dimensional lexicographic preference domain satisfying these conditions is a tops-only domain. Finally, we prove that the two-dimensional lexicographic domain meets both …

Research On The Influencing Factors Of Bim Technology Adoption In Construction Enterprises, Meng Zhao

Dissertations and Theses Collection (Open Access)

The construction industry plays a pivotal role in national economic development, yet it faces significant challenges, including inefficiencies, limited informatization, and fragmented management. Building Information Modeling (BIM) has emerged as a transformative digital technology, offering the potential to revolutionize construction enterprises by enhancing collaboration, reducing costs, and improving project lifecycle management. Despite increasing government support and global advancements, BIM adoption in China, particularly during the construction phase, remains inconsistent and insufficiently explored. This dissertation adopts a mixed-methods approach, combining both qualitative and quantitative research. The qualitative component, through interviews, identifies the primary barriers to BIM adoption during the construction phase, …

Symbolic Execution Engine For Dynamic Analysis Of System Software, Pansilu Madhura Bhashana Pitigala Arachchillage

Dissertations and Theses Collection (Open Access)

System software, like any regular software, is prone to errors. It plays a specific role in a computer system by managing the underlying hardware and providing a platform to execute the application software. Defective or vulnerable system software can be exploited by attackers to compromise the entire system. Therefore, the system software must be studied and thoroughly analyzed to evaluate its security. However, due to the inherent complexity and its close interactions with the hardware, analyzing system software is a challenging task. As a result, there is a lack of tools and techniques capable of effectively analyzing system software.

This …

Graph Perturbations For Robust Knowledge Discovery And Retrieval, Hanhua Xiao

Dissertations and Theses Collection (Open Access)

Graph perturbation, rooted in classical perturbation theory, studies how small topology edits, i.e., adding or deleting edges, affects graph properties (e.g., density, centrality). This fundamental problem underpins applications like bioinformatics, privacy preservation and system defense. While much prior work targets perturbations that influence global graph statistics or model outputs, comparatively little addresses robustness for knowledge discovery and information retrieval. In these settings, graphs are attributed: nodes carry real-world semantics (e.g., locations, people) and edges encode interactions or relationships. This thesis proposes new formulations and algorithms that generate and leverage graph perturbations to make knowledge discovery and retrieval more robust. Specifically, …

Enhancing Multi-View, Multi-Modal Sensing, Perception And Actuation For Edge Intelligence, Dhanuja Tharith Wanniarachchige

Dissertations and Theses Collection (Open Access)

Artificial Intelligence of Things (AIoT) technologies have ushered in exciting new advances in intelligent sensing, perception, and actuation for many real-world cyberphysical systems (CPS) applications. These technologies have had a formidable impact in domains such as large-scale video surveillance, autonomous transportation and robotics, precision healthcare, and industrial automation. In these applications, sensors and actuators are often collocated with processing nodes, and such nodes are typically interconnected via wireless networks. Vision-based machine intelligence, exemplified by tasks such as object detection, object tracking, and activity analysis, is a very common enabler of such CPS applications. Efficient execution of Deep Neural Network (DNN) …

Scaling Up Cooperative Multi-Agent Reinforcement Learning, Minghong Geng

Dissertations and Theses Collection (Open Access)

Multi-agent systems (MAS) involve multiple autonomous agents that coordinate their actions to achieve shared or competing objectives in dynamic environments. Over the past decade, multi-agent reinforcement learning (MARL) has emerged as a powerful paradigm for enabling collaborative behaviors among autonomous agents within MAS to solve complex tasks. This dissertation discusses a critical scalability gap that exists between current MARL capabilities and real-world deployment requirements. Most existing MARL research focuses on small-scale laboratory problems, often struggling to coordinate large agent populations and facing challenges with extended decision-making horizons. In contrast, many real-world applications demand coordination among hundreds or thousands of agents …

The Link Between Intergenerational Social Mobility Of Cohorts And Individual Self-Rated Health: A Multi-Country Analysis., Jacinth J. X. Tan, Gregory T. H. Hng, Faith A. Wee

Research Collection School of Social Sciences

This research examined the potential health consequences of increased opportunities for social mobility. Specifically, we used indices of intergenerational social mobility of cohorts—the average level of change in economic status relative to one’s parents experienced across individuals within a cohort—as proxy measures of individuals’ exposure to opportunities for social mobility and investigated links to their self-rated health (SRH). We combined recent multi-country data on cohort social mobility from the World Bank with multi-country individual SRH data from the World Values Survey waves six (Ncountries=44, Nrespondents=48,955) and seven (Ncountries=41, Nrespondents=37,051). Multilevel modeling analyses revealed that at the aggregate, SRH had negative …

Competitive Stress And Life History Mismatch, Norman P. Li, Amy J. Y. Lim

Research Collection School of Social Sciences

Simulations, correlational studies, and laboratory experiments have recently shown that cues to high-resource competition may be steering individuals toward increasingly less favorable attitudes toward reproduction and slower life-history strategies. This work suggests that, alongside extrinsic mortality, it may be fruitful to examine the role of competitive stress cues in shaping life-history strategies.

The Impact Of Sanctions On Github Developers And Activities, Youmei Fan, Ani Hovhannisyan, Hideaki Hata, Christoph Treude, Raula G. Kula

Research Collection School Of Computing and Information Systems

The GitHub platform has fueled the creation of truly global software, enabling contributions from developers across various geographical regions of the world. As software becomes more entwined with global politics and social regulations, it becomes similarly subject to government sanctions. In 2019, GitHub restricted access to certain services for users in specific locations but rolled back these restrictions for some communities (e.g., the Iranian community) in 2021. We conducted a largescale empirical study, collecting approximately 156 thousand user profiles and their 41 million activity points from 2008 to 2022, to understand the response of developers. Our results indicate that many …

Sketch-Sparsenet: Sparse Convolution Framework For Sketch Recognition, Jingru Yang, Jin Wang, Yang Zhou, Guodong Lu, Yu Sun, Huan Yu, Heming Fang, Zhihui Li, Shengfeng He

Research Collection School Of Computing and Information Systems

In free-hand sketch recognition, state-of-the-art methods often struggle to extract spatial features from sketches with sparse distributions, which are characterized by significant blank regions devoid of informative content. To address this challenge, we introduce a novel framework for sketch recognition, termed Sketch-SparseNet. This framework incorporates an advanced convolutional component: the Sketch-Driven Dilated Deformable Block (SD3B). This component excels at extracting spatial features and accurately recognizing free-hand sketches with sparse distributions. The SD3B component innovatively bridges gaps in the blank areas of sketches by establishing spatial relationships among disconnected stroke points through adaptive reshaping of convolution kernels. These kernels are deformable, …

Sustainable Llm Inference For Edge Ai: Evaluating Quantized Llms For Energy Efficiency, Output Accuracy, And Inference Latency, Erik Johanne Husom, Arda Goknil, Merve Astekin, Lwin Khin Shar, Andre Kasen, Sagar Sen, Benedikt Andreas Mithassel, Ahmet Soylu

Research Collection School Of Computing and Information Systems

Deploying Large Language Models (LLMs) on edge devices presents significant challenges due to computational constraints, memory limitations, inference speed, and energy consumption. Model quantization has emerged as a key technique to enable efficient LLM inference by reducing model size and computational overhead. In this study, we conduct a comprehensive analysis of 28 quantized LLMs from the Ollama library, which applies by default Post-Training Quantization (PTQ) and weight-only quantization techniques, deployed on an edge device (Raspberry Pi 4 with 4GB RAM). We evaluate energy efficiency, inference performance, and output accuracy across multiple quantization levels and task types. Models are benchmarked on …

Enhancing Spatial Understanding In Mixed-Reality Presentations, Nam-Dang Vo, Van-Vinh Thai, Nam-Hoi Do, Viet-Tham Huynh, Anthony Tang, Khan-Duy Le

Research Collection School Of Computing and Information Systems

Mixed reality (MR) presentations often involve a presenter wearing a head-mounted display (HMD) and an audience watching via a large display, making it difficult for audiences to perceive spatial relationships between the presenter and virtual objects. We report two experiments testing three design variations: (1) scene camera placement (audience-aligned vs. opposite), (2) overlaying the presenter’s first-person view, and (3) highlighting objects in the presenter’s view. Results show that audience-aligned cameras and object highlighting improve spatial understanding, while combining third- and first-person views can further aid perception. We derive design guidelines for configuring MR presentations to better support audience comprehension.

Disc: Decentralized Identity System With Self-Sovereign Credential Aggregation, Yang Yang, Wai Keung Ching, Minming Huang, Supachate Innet, Guomin Yang, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

The evolution of decentralized identity (DID) and self-sovereign identity (SSI) frameworks, as endorsed by W3C Verifiable Credentials (VC) and eIDAS 2.0, underscores the need for secure, efficient, and privacy-preserving credential management. However, existing credential systems often depend on centralized issuers, lack efficient aggregation mechanisms, or fail to ensure unlinkability across authentication sessions. To address these challenges, we propose DISC (Decentralized Identity System with Self-Sovereign Credential Aggregation), a novel credential system that enables multi-authority credential issuance, user-controlled credential aggregation, and unlinkable authentication. DISC allows users to aggregate credentials from multiple issuers while maintaining constant-size authentication tokens and supporting batch verification for …

Interpersonal Capacity For Trustworthiness: A Reflective Framework For Navigating Entrustment, Roberto Angelo Moreira Vale

Dissertations and Theses Collection (Open Access)

This dissertation advances understanding of how trust shapes salient cross-cultural encounters, drawing on a constructivist grounded theory analysis of 220 interview accounts. Across these narratives, trust-related dynamics consistently appeared alongside 16 other contextual, interpersonal, and intrapersonal factors. Notably, these dynamics were identified in interviews with participants from all GLOBE cultural clusters, suggesting a degree of cross-cultural breadth that warrants further empirical testing. Together, these patterns indicate that it is not cultural difference alone that makes an encounter meaningful, but how individuals interpret and respond to the possibility or risk of entrusting and being entrusted. Salience emerged at moments when participants …

Addressing Sparsity For Knowledge Graph Completion: Data And Model Perspectives, Ran Liu

Dissertations and Theses Collection (Open Access)

Knowledge graphs (KGs) are powerful tools for structuring factual knowledge into relational triples, yet their practical utility is often adversely affected by data sparsity. Many entities and relations are associated with only a few observations, which limits the quality of learned embeddings and weakens generalization in downstream tasks. The problem of sparsity led to two interrelated challenges. Firstly, it restricts the informativeness of training samples: positive examples are scarce, and conventional negative sampling often produces trivial or redundant negatives that resulting in limited guidance. Secondly, in few-shot relation learning scenarios, sparsity worsens distribution shifts between training and test relations, as …

Smu To Build Open Legal Database With Support From Ministry Of Law, Singapore Management University

SMU Press Releases and News

The Singapore Management University (SMU) will be developing Singapore’s first open empirical legal database with support from the Ministry of Law (MinLaw). Housed at SMU’s Centre for Digital Law, the three-year project will compile and release machine-readable datasets containing key statistical information about Singapore court decisions, statutes, legal scholarship and court structures. Named Singapore Open Legal Informatics Database (SOLID), the database will be freely accessible to researchers worldwide through a public website and Application Programming Interface (API), positioning Singapore alongside leading jurisdictions around the world that have invested in building up their legal data infrastructure.

Blue Bird: Building The Right Path, Yuanto Kusnadi, Wee Kiat Lim

Research Collection School Of Accountancy

How Indonesia's leading mobility provider pursued twin transformations involving sustainable growth and digitalisation.

Price Dislocations: Insights From Trade Repository Data, Albert Menkveld, Lucas Saru, Shihao Yu

Research Collection Lee Kong Chian School Of Business

This paper identifies price dislocation events in EuroSTOXX 50 futures, i.e., periods marked by high absolute returns. Combining public limit order book data with confidential trade repository data collected under the European Market Infrastructure Regulation (EMIR), we analyze market conditions around such dislocations. We find that price dislocations are accompanied by an increase in trading volume, and in the number of trades. EMIR data enables us to identify who participates in these trades, which allows us to tell if the volume increase is driven by fewer investors trading more, i.e., a more concentrated market, or by more investors participating. The …

How Behavioral Science Can Improve The Return On Ai Investments, David De Cremer, Shane Schweitzer, Jack Mcguire, Devesh Narayanan

Research Collection Lee Kong Chian School Of Business

Many AI projects fail because leaders treat adoption as a tech purchase instead of a behavioral change problem. People resist tools that disrupt routines, overreact to visible AI errors, and prefer familiar human judgment. As a result, even good systems fail to gain purchase. Leaders can address this problem by applying “Behavioral Human-Centered AI” across the AI adoption cycle. In the design phrase, companies should co-design with diverse users, add purposeful friction where it improves scrutiny, require beta tests with subgroup results and behavioral input. During adoption, they should frame AI as an augmenter, disclose limits and safeguards, use explainability …

Building Confidence For Class Participation, Tamas Makany, Ivy Seow

Research Collection Lee Kong Chian School Of Business

What happens to students’ critical thinking when half the class filters their thoughts through AI? During a recent debate on AI policy in education, one student mentioned they routinely run their ideas through ChatGPT before speaking up. When I asked who else did the same, more than half the class raised their hands.

Damslnet: Dual-Attention Multi-Scale Lightweight Network For Plant Disease Classification, Linfan Deng, Juan Qin, Kun Li, Jinhua Zhu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Accurately identifying crop diseases plays a crucial role in advancing intelligent and modern agricultural production. Deep learning techniques have performed robust performance in classifying plant disease images. However, current studies face the challenge that many plant disease datasets are generated in controlled environments, leading to reduced model performance in real-world agricultural settings. This paper aims to provide a lightweight model that can accurately classify plant diseases in natural environments. Specifically, this paper investigates the Dual-Attention Multi-Scale Lightweight Network (DAMSLNet), which combines dual-attention-based multi-scale feature extraction and deep information fusion, to classify plant diseases. At the front end, the model employs …