Digital Commons Network^™

Dynamic Spatial Panel Data Models With Interactive Fixed Effects: M-Estimation And Inference Under Fixed Or Relatively Small T, Liyao Li, Ke Miao, Zhenlin Yang

Research Collection School Of Economics

We propose an M-estimation method for dynamic spatial panel data models with interactive fixed effects based on (relatively) short panels. Unbiased estimating functions are constructed by adjusting the concentrated conditional quasi scores, given the initial values and with the factor loadings being concentrated out, to account for the effects of conditioning and concentration. Solving the estimating equations gives the M-estimators of the common parameters and common factors. Under fixed T, n-consistency and joint asymptotic normality of the M-estimators are established. Under T = o(n), the M-estimators of the common parameters are shown to be nT-consistent and asymptotically normal. For inference, …

Brand Switching Or Behavior Change: The 2023 Bud Light Boycott's Impact On Alcohol Purchases, Aljoscha Janssen

Research Collection School Of Economics

I study how the 2023 Bud Light boycott affected overall alcohol demand using household-level panel data and a set of difference-in-differences designs. Focusing on households that, before April 2023, regularly purchased Bud Light, I find a large drop in Bud Light volume (34%–37%), partial switching into other beer (＋70 to ＋90 ounces per month), and a net decline in total ethanol purchases of about 3–4 fl-oz per month, or roughly 5.5%–7.5% of pre-boycott intake. I detect no compensatory increase in wine or spirits. Scaled nationally, the quantity response is equivalent to roughly 150 million standard drinks per year; using CDC …

The Uncomfortable Truths About Inclusive Growth In Singapore, Kong Weng Ho

Research Collection School Of Economics

In a commentary, SMU Associate Professor of Economics (Education) Ho Kong Weng said that the Ministry of Finance’s Occasional Paper provides food for thought on the way forward on social mobility, growth and redistribution. Assoc Prof Ho stated that these macro statistics suggest Singapore’s socioeconomic compact has performed well, and suggests that sustaining inclusive growth is a multifaceted effort that cannot be boiled down to one single metric, whether income, wealth, inequality or mobility. He noted that pursuing ever-lower Gini coefficients through heavy regulation risks deadweight losses without guaranteeing better mobility outcomes. He added that Singapore’s approach of tempering market-driven …

The Case For Ai Authorship In Copyright Law, Cheng Lim Saw, Duncan Lim

Research Collection Yong Pung How School Of Law

Today, with generative AI, literary and artistic works can be created almost effortlessly. There is at present intense debate as to whether works generated by AI – broadly categorised as “AI-assisted” and “AI-generated” works – ought to attract copyright protection. AI-assisted works are those that involve some degree of human intervention. Where AI-generated works are concerned, however, such works are created autonomously by the AI itself with minimal (de minimis) input from an identifiable human being. Presently, it is generally accepted that AI-generated works do not attract copyright protection for want of a human author. This article examines whether it …

A Panel Quantile Model Via Correlated Random Effects Approach For Testing Pecking Order Theory, Zongwu Cai, Meng Shi, Wuqing Wu, Yue Zhao

Student Publications

This article investigates the relative importance of internal and external sources of funds in financing activities across different levels of investment activities by proposing a panel data quantile regression model with correlated random effects, accounting for heteroscedasticity in both firm-specific individuals and distribution of investment. A new estimation method, which takes the influence of disturbance into account, is proposed by using the integrated quasi-likelihood function for the conditional quantile model and Laplace approximation. The large sample theory for the proposed estimator and the corresponding asymptotic χ² test are investigated. A Monte Carlo simulation is conducted to examine the finite …

Atomic Steps Toward Sustainable Learning With Open Educational Resources (Oer), Eng Ling Lynn Yeo

Research Collection Library

Open Educational Resources (OER) are gaining momentum globally as one of the tools to equitable and sustainable education. In an academic landscape shaped by rapid digital change and budget constraints, libraries must adapt to support sustainable learning.

This lightning talk will share how SMU Libraries has taken atomic, strategic steps to introduce and encourage OER adoption, aligning collection strategies with institutional goals for sustainability. It will highlight initiatives such as faculty workshops, participation in a regional OER special interest group, and efforts to integrate OER discovery into existing library systems. It will also share examples of leveraging existing collection workflows …

Between ‘Technology Futures’: How Standards Are Transforming Digital Trade, Craig Atkinson

Centre for Digital Law

Digital trade relies on technical standards. As information technologies advance, international digital standards make the technologies used in trade – or ‘tradetech’ – more interoperable, scalable, and secure.

A wide range of tradetech use cases depend on digital standards, including those for document formats, electronic signatures, digital identity, and cybersecurity. They also encompass system specifications for cross-border payments, electronic transferable records (ETRs), transportation/logistics, trade facilitation, and financial compliance.

Most digital standards are voluntary and are produced by international Standards Development Organisations (SDOs) alongside a growing number of industry-led consortia.

Leading The Change: Staff-Driven Ai Transformation In Smu Libraries’ Collection Team, Siew Khim Lim, Fion Goh

Research Collection Library

Why do libraries need to use AI? It is crucial for Libraries to stay relevant in this digital age by improving efficiency, access, and user experience. By adopting AI, libraries can better manage growing digital collections, provide innovative services, and ensuring they remain essential as hubs for knowledge and learning in an AIdriven world.

Paid Search Marketing Vs. Search Engine Optimization: Analytical Models Of Search Marketing Based On Search Engine Quality, Kai Li, Chunyang Shen, Mei Lin, Zhangxi Lin

Research Collection School Of Computing and Information Systems

As search engines are leading revenue growth in online marketing, search marketing has become a popular area of academic research. Although search engine advertising has interested researchers for decades and much has been learned, one thing that puzzles scholars is why search engine optimization companies are tolerated rather than excluded from the market, even though they capture a significant share of the advertising market. In this paper, we shed light on this phenomenon and establish an analytical model based on organic search quality. Through analysis of the model, we were able to draw several intriguing conclusions. First, there is no …

Do Comments And Expertise Still Matter? An Experiment On Programmers’ Adoption Of Ai-Generated Javascript Code, Changwen Li, Christoph Treude, Ofir Turel

Research Collection School Of Computing and Information Systems

This paper investigates the factors influencing programmers’ adoption of AI-generated JavaScript code recommendations within the context of lightweight, function-level programming tasks. It extends prior research by (1) utilizing objective (as opposed to the typically self-reported) measurements for programmers’ adoption of AI-generated code and (2) examining whether AI-generated comments added to code recommendations and development expertise drive AI-generated code adoption. We tested these potential drivers in an online experiment with 173 programmers. Participants were asked to answer some questions to demonstrate their level of development expertise. Then, they were asked to solve a LeetCode problem without AI support. After attempting to …

Internationalization Struggles And Student Mobility: Ethnic Exclusion And Racism In Philippine Higher Education, Yasmin Y. Ortiga

Research Collection School of Social Sciences

There is an extensive literature on student mobility that seeks to understand the nature of, rationales for and consequences of internationalisation in higher education. This scholarship primarily focusses on students studying in the ‘west’. This paper shows how ‘atypical’ student destinations also illuminate the contradictions of internationalisation, revealing the challenges faced by higher education settings beyond academic centres in the west. Drawing on the case of the Philippines, I discuss how private universities actively recruited international students to maximise profits from student tuition. Yet, these efforts led to a backlash among Filipino educators and students, prompting administrators to institute policies …

Auseinandersetzung: Heidegger's Black Notebooks And Intercultural Engagement, Steven Burik

Research Collection School of Social Sciences

Heidegger’s relation to Asian philosophy should be guided by the notion of Auseinandersetzung. Often translated as con-frontation, this term plays a pivotal role in understanding Heidegger’s engagements with Asian philosophy. Auseinandersetzung (and related terms) can help us realise the changed perspectives on our own thinking that we derive from engagements with culturally and/or historically other types of thinking. Heidegger warns us against facile endeavours of comparing and contrasting, which, as a form of comparative philosophy, he sees as a kind of dialectics unworthy of true thinking. Thinking through the term Auseinandersetzung allows us to reflect more meaningfully on comparative philosophy.

Memoryart: Enhancing Llms Via Multi-Memory Models With Adaptive Resonance Theory For Healthcare Agents, Renke Dai, Hebin Hu, Jiahui Zhang, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Though promising in healthcare consultation applications, large language models (LLMs) face critical limitations in retaining and utilizing long-term memory across multiturn interactions. In particular, existing memory enhancing paradigms are constrained by limited context windows and embedding-based retrieval, often failing to maintain task relevance and still suffering from memory prototype collapse in multi-turn healthcare consultation. To address these challenges, we propose a cognitively-inspired memory framework named MemoryART, which is grounded in Adaptive Resonance Theory (ART)—a cognitive and learning theory of how humans and animals adapt to dynamic environments. MemoryART employs three memory modules—working memory, episodic memory, and semantic memory to support …

Clique Annealing: Semi-Supervised Community Detection Under Crystallization Kinetics, Ling Cheng, Jiashu Pu, Ruicheng Liang, Qian Shao, Hezhe Qiao, Feida Zhu

Research Collection School Of Computing and Information Systems

Semi-supervised community detection seeks to find a specified community type when only few communities are labeled. Existing "select-then-refine" pipelines often start from mis-aligned cores and rely on Reinforcement-Learning or Generative Adversarial Network, increasing computational cost and limiting scalability. We address these issues with a unified energy framework under crystallization kinetics that jointly models energy, structure, and growth. Based on this perspective, we propose CLique ANNealing (CLANN), which first employs Nucleus Proposer to select candidate clique as community core under four physics-inspired criteria. A learning-free Transitive Annealer then iteratively merges neighboring cliques and repositions the nucleus, enabling spontaneous, scalable community growth. …

Potent But Stealthy: Rethink Profile Pollution Against Sequential Recommendation Via Bi-Level Constrained Reinforcement Paradigm, Jiajie Su, Zihan Nan, Yunshan Ma, Xiaobo Xia, Xiaohua Feng, Weiming Liu, Xiang Chen, Xiaolin Zheng, Chaochao Chen

Research Collection School Of Computing and Information Systems

Sequential Recommenders, which exploit dynamic user intents through interaction sequences, are vulnerable to adversarial attacks. While existing attacks primarily rely on data poisoning, they require large-scale user access or fake profiles, thus lacking practicality. In this paper, we focus on the Profile Pollution Attack that subtly contaminates partial user interactions to induce targeted mispredictions. Previous PPA methods suffer from two limitations, i.e., i) overreliance on sequence horizon impact restricts fine-grained perturbations on item transitions, and ii) holistic modifications cause detectable distribution shifts. To address these challenges, we propose a constrained reinforcement driven attack CREAT that synergizes a bi-level optimization framework …

Prosocial Ceos And Accounting Manipulation, Mei Feng, Weili Ge, Zhejia Ling, Wei Ting Loh

Research Collection School Of Accountancy

This paper examines the association between chief executive officers’ (CEOs’) prosocial tendency and their firms’ likelihood of accounting manipulation. We measure CEOs’ prosocial tendency based on their involvement with charitable organizations. We find that prosocial CEOs are less likely to engage in accounting manipulation, as proxied by material non-reliance restatements and SEC or DOJ enforcement actions. The effect is more pronounced when CEOs are involved with charities that directly aim to improve the welfare of others and when they face stronger incentives to misreport. These results continue to hold in analyses of changes in CEOs’ prosocial tendency around turnover events. …

Sports And Corporate Innovation, Bing Qin

Dissertations and Theses Collection (Open Access)

Driven by the digital economy and intensifying global competition, corporate innovation has become a core driver of sustainable growth. Topmanagement members are key strategic decision-makers, and their individual characteristics critically influence the formulation and implementation of innovation strategies. While existing studies have examined the impact of topmanagement’s cognitive and psychological traits on innovation performance, significant gaps remain in understanding how their routine behavioral preferences—particularly running, a globally prevalent exercise—affect innovation performance through psychological mechanisms. The systematic relationship between running preferences, top management’s psychological traits, and organizational innovation outcomes remains underexplored.

Against this backdrop, the study investigates the mechanismthroughwhich top management’s …

Inside Out: Improving Large Model Safety, Wei Zhao

Dissertations and Theses Collection (Open Access)

While Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) are at the frontier of current advancements in artificial intelligence, demonstrating remarkable capabilities across diverse applications, there are growing concerns about their reliability and security. LLMs remain vulnerable to adversarial attacks through carefully crafted prompts that circumvent safety mechanisms, while MLLMs face additional security challenges stemming from their multimodal nature. Despite considerable efforts in reinforcement learning from human feedback (RLHF) and supervised fine-tuning, existing safeguards have proven inadequate in addressing these critical vulnerabilities. This inadequacy stems from the fact that these models are inherently blackboxes that do not provide …

Visual Analytics For Interpretable Quantum Computing, Shaolun Ruan

Dissertations and Theses Collection (Open Access)

Quantum computing has entered a stage of increasing practicality. Many quantum hardware vendors such as IBM, Rigetti, Honeywell, and IonQ now enable experiments on real devices in the Noisy Intermediate-Scale Quantum (NISQ) era. These platforms show computational advantages in domains such as optimization, machine learning, and materials science. However, they remain limited by hardware noise and the absence of human-interpretable information. Existing visual metaphors, such as the Bloch Sphere for single-qubit states or circuit schematics for algorithm design, struggle to convey multi-qubit entanglement or measurement probabilities in ways accessible to human reasoning. Likewise, the rise of variational quantum circuits and …

Constrained Reinforcement Learning: From Single-Agent Safety To Multi-Agent Coordination, Hao Jiang

Dissertations and Theses Collection (Open Access)

Real-world decision-making systems such as autonomous driving and largescale ride-pooling must operate under strict safety and resource constraints. Traditional Reinforcement Learning (RL) methods, while powerful in simulation, often fail to guarantee such constraints, limiting their real-world deployment. The fundamental challenge lies in integrating constraint satisfaction with long-term reward optimization, especially when outcomes are stochastic and interdependent across multiple agents.

This dissertation advances the field of Constrained Reinforcement Learning (CRL) from both single-agent safety and multi-agent coordination perspectives. In the single-agent setting, we introduce a Reward Penalty framework that augments the state space with cumulative cost and penalizes only trajectories that …

The Impact Of Executives’ Social Networks And Digital Capabilities On Firm Performance In Dynamic Environments, Lijun Men

Dissertations and Theses Collection (Open Access)

With the acceleration of digital transformation, improving firm performance in complex and dynamic market environments has become a central challenge for companies. This study examines the effects of executives’ social networks and firms’ digital capabilities on firm performance. Further, it investigates the moderating roles of digital capabilities and environmental dynamism in these relationships. Using panel data for Chinese listed firms from 2005 to 2024, we conduct empirical analyses with fixed-effects models. The results indicate that executives’ political, business, and academic networks all contribute positively to firm performance, underscoring the importance of executives’ social networks in resource acquisition, information flows, and …

The Impact Of Corporate Mergers And Acquisitions On Performance From The Perspective Of Growth Pressure, Jiandong Wang

Dissertations and Theses Collection (Open Access)

Against the backdrop of economic volatility and intensified industry competition, firms generally face multidimensional growth pressures, including historical growth pressure, social growth pressure, and market competition pressure. Growth pressure is not only related to firms’ operational stability, but also profoundly influences their strategic choices, particularly merger and acquisition (M&A) behaviour.Grounded in performance feedback theory, this study constructs an integrated analytical framework encompassing growth pressure, M&A strategic decisions, and M&A performance, and systematically examines how different types of growth pressure affect firms’ M&A decisions, as well as how M&A performance varies across different firm characteristics and different types of M&A.

The …

Automatic Generation Of Introductory Programming Exercises With Large Language Models, Nguyen Binh Duong Ta, Hua Gia Phuc Nguyen, Gottipati Swapna

Research Collection School Of Computing and Information Systems

Despite recent advances in code generation made possible by large language models (LLMs), programming is still an essential skill that computing students need to master now and in the foreseeable future. In learning programming, frequent practices with exercises set at an appropriate difficulty and knowledge level is of crucial importance for students. However, it’s not a trivial task for instructors to create many good quality exercises customized for each student. Programming problems found on Internet sources such as LeetCode are mostly too challenging for novice programmers with no prior coding knowledge. Recent work in AI-enabled education has been leveraging LLMs …

Choice-Based Crowdshipping For Next-Day Delivery Services: A Dynamic Task Display Problem, Alp Arslan, Firat Kilci, Shih-Fen Cheng, Archan Misra

Research Collection School Of Computing and Information Systems

This paper studies integrating the crowd workforce into next-day home delivery services. In this setting, both crowd drivers and contract drivers collaborate in making deliveries. Crowd drivers have limited capacity and can choose not to deliver if the presented tasks do not align with their preferences. The central question addressed is: How can the platform minimize the total task fulfilment cost, which includes payouts to crowd drivers and additional payouts to contract drivers for delivering the unselected tasks by customizing task displays to crowd drivers? To tackle this problem, we formulate it as a finite-horizon Stochastic Decision Problem, capturing crowd …

Can We Read Ai’S Mind? A Quest For Transparency, Santhosh Kumar Ravindran, Estera Kot, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Although Artificial Intelligence (AI) systems are playing an increasing role in critical domains such as healthcare, finance, and autonomous systems, their decision-making processes remain largely opaque. This paper examines the challenges of AI transparency, addressing the “black box” problem using Explainable AI (XAI) techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). It also examines the ethical, regulatory, and societal implications of AI opacity and proposes a Comprehensive AI Observability (CAO) Framework that integrates deep explainability, provenance tracking, and real-time monitoring to enhance AI accountability. By bridging technical solutions with governance structures, this research emphasizes the …

Actor-Critic For Continuous Action Chunks: A Reinforcement Learning Framework For Long-Horizon Robotic Manipulation With Sparse Reward, Jiarui Yang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Existing reinforcement learning (RL) methods struggle with long-horizon robotic manipulation tasks, particularly those involving sparse rewards. While action chunking is a promising paradigm for robotic manipulation, using RL to directly learn continuous action chunks in a stable and data-efficient manner remains a critical challenge. This paper introduces AC3 (Actor-Critic for Continuous Chunks), a novel RL framework that learns to generate high-dimensional, continuous action sequences. To make this learning process stable and dataefficient, AC3 incorporates targeted stabilization mechanisms for both the actor and the critic. First, to ensure reliable policy improvement, the actor is trained with an asymmetric update rule, learning …

Leveraging Large Language Models For Career Mobility Analysis: A Study Of Gender, Race, And Job Change Using Us Online Resume Profiles, Palakorn Achananuparp, Ye Xu, Yao Lu, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

We present a large-scale analysis of career mobility of college-educated U.S. workers using online resume profiles to investigate how gender, race, and job change options are associated with upward mobility. This study addresses key research questions of how the job changes affect their upward career mobility, and how the outcomes of upward career mobility differ by gender and race. We address data challenges – such as missing demographic attributes, missing wage data, and noisy occupation labels – through various data processing and Artificial Intelligence (AI) methods. In particular, we develop a large language models (LLMs) based occupation classification method known …

Dual-Lora And Quality-Enhanced Pseudo Replay For Multimodal Continual Food Learning, Xinlan Wu, Bin Zhu, Feng Han, Pengkun Jiao, Jingling Chen

Research Collection School Of Computing and Information Systems

Food analysis has become increasingly critical for health-related tasks such as personalized nutrition and chronic disease prevention. However, existing large multimodal models (LMMs) in food analysis suffer from catastrophic forgetting when learning new tasks, requiring costly retraining from scratch. To address this, we propose a novel continual learning framework for multimodal food learning, integrating a Dual-LoRA architecture with Quality-Enhanced Pseudo Replay. We introduce two complementary low-rank adapters for each task: a specialized LoRA that learns task-specific knowledge with orthogonal constraints to previous tasks’ subspaces, and a cooperative LoRA that consolidates shared knowledge across tasks via pseudo replay. To improve the …

Design Principles For Customer-Engaging Digital Service Systems: An Action Research Study, Keng Leng Siau, Xiaofeng Chen, Xin Tan

Research Collection School Of Computing and Information Systems

Digital services represent a business approach employed by organizations to operate in the digital environment. However, systematic development guidelines for developing quality digital service systems are lacking in the literature. The authors identified four general challenges for developing and implementing customer-engaging digital service systems (CEDSS). By employing the method of canonical action research in a digital service system project, they derived 10 design principles for developing high-quality CEDSS. They empirically evaluated the design principles in the development project and through follow-up focus group sessions. The design principles provide applicable and actionable guidelines for the development of CEDSS.

Towards Secure Program Partitioning For Smart Contracts With Llm’S In-Context Learning, Ye Liu, Yuqing Niu, Chengyan Ma, Ruidong Han, Wei Ma, Yi Li, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging because they stem from inherent data confidentiality issues rather than straightforward implementation bugs. To tackle this by preventing sensitive information leakage, we present PARTITIONGPT, the first LLM-driven approach that combines static analysis with the in-context learning capabilities of large language models (LLMs) to partition smart contracts into critical (privileged) and normal codebases, guided by a few annotated sensitive data variables. We evaluated PARTITIONGPT on 18 annotated smart contracts containing 99 sensitive functions. The results demonstrate that PARTITIONGPT successfully generates …