Digital Commons Network^™

Enhancing Market Acceptance Of Sports Rehabilitation Services: A Perspective Of Public Health Literacy And Information Acquisition, Na Li

Dissertations and Theses Collection (Open Access)

This article takes public health literacy as its point of departure, constructs a theoretical model of “Health Literacy–Health Information Acquisition–Market Acceptance”, and introduces rehabilitation experience and service accessibility as moderating variables, in order to systematically examine the formation pathways and underlying mechanisms of market acceptance in the sports rehabilitation service sector.

This study adopts a combination of questionnaire survey and empirical analysis, collecting data from public samples across multiple regions and conducting statistical tests. The findings indicate that, Firstly, public health literacy has a statistically significant positive influence on the market acceptance of sports rehabilitation services. As the level of …

The Impact Of Mortality Awareness On Task And Relationship Leadership Styles, Sang Yeol Lee

Dissertations and Theses Collection (Open Access)

In current organizational management practice, the question of how leaders can transform the universal awareness of human finitude into constructive leadership is an issue that embodies both profound humanistic concern and significant managerial implications. Based on transcendence management theory and leadership style theory, this study explores how mortality awareness affects leaders’ task orientation and relationship orientation styles, revealing its underlying mechanisms and boundary conditions. By conducting a three-wave, matched-pair questionnaire survey involving 203 enterprise managers and their subordinates, the theoretical hypotheses were tested. The findings are as follows: First, mortality awareness has a significant direct positive effect on both task-oriented …

How Do Investors View Mandatory Esg Disclosure? Evidence From China’S A-Share Market, Suk Wah Inessa Lee

Dissertations and Theses Collection (Open Access)

This study investigates the valuation effects of China's 2024 transition from voluntary to mandatory ESG disclosure across the Shanghai, Shenzhen, and Beijing stock exchanges. Using a two-stage event study surrounding the policy's draft and final confirmation, this research examines how investors price a regulatory reform affecting over 51% of A-share market capitalization. Anchored in Information Asymmetry Theory, the study evaluates whether the mandate acts as an information-enhancing mechanism or a compliance cost shock. The results indicate that mandated firms experience significant negative cumulative abnormal returns, suggesting investors primarily interpret the policy as a compliance-driven cost shock — consistent with the …

The Impact Of De-Familization Of Governance Structures On Corporate Performance During The Intergenerational Succession Of Family Businesses, Jing Qiao

Dissertations and Theses Collection (Open Access)

Intergenerational succession poses a vital challenge for family firms, with only 10% of global family businesses surviving to the third generation. Chinese family firms, growing alongside reform and opening-up, now face a critical succession period as founders average over 60 years old.

Based on 1932 A-share listed family firms from 2007 to 2024, this study theoretically and empirically examines how intergenerational succession affects corporate performance, investigates the mediating role of governance de-familization (management, ownership, control rights), and tests the moderating effect of intergenerational differences.

First, succession significantly harms financial and market performance: ROE falls by 1.330% and Tobin’s Q drops …

From Ancient Harmony To Modern Sustainability: Assessing The Coupling Of Taoist “Harmony Between Humanity And Nature” With Esg And Its Impact On Firm Innovation And Performance, Biyi Wang

Dissertations and Theses Collection (Open Access)

Against the backdrop of intensifying global ecological crises, the ESG evaluation system has become a core metric for corporate sustainable development worldwide. However, the Western-dominated ESG system lacks crucial cultural dimensions in its application within Chinese corporate practices. The ecological wisdom embedded in the Taoist concept of “Harmony between Humanity and Nature” offers a significant philosophical foundation for the ESG localization. Extant research has focused on the influence of Confucian culture on ESG, with insufficient exploration of the modern managerial value of “Harmony between Humanity and Nature”. A scientific coupling framework linking this concept with ESG indicators has yet to …

Compliance Supervision And Firm Performance: An Empirical Study On Digital Transformation Of The Pharmaceutical Manufacturing Industry, Jun Ma

Dissertations and Theses Collection (Open Access)

The biopharmaceutical industry, characterized by stringent global regulatory oversight, faces a pivotal theoretical tension regarding the nexus between regulatory compliance and corporate performance. The traditional perspectives often frame compliance as an “operational burden”, while the emerging viewpoints posit it as a “strategic asset”. Grounded in an integrated framework of the Resource-Based View (RBV) and Dynamic Capability (DC), this study investigates the moderating role of digital transformation in the relationship between Good Manufacturing Practice (GMP) compliance and corporate performance.

Employing a mixed-method design, this paper conducted two complementary studies. Study 1 adopted a grounded theory based on interviews with nine industry …

Agentspec: Customizable Runtime Enforcement For Safe And Reliable Llm Agents, Haoyu Wang, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

Agents built on LLMs are increasingly deployed across diverse domains, automating complex decision-making and task execution. However, their autonomy introduces safety risks, including security vulnerabilities, legal violations, and unintended harmful actions. Existing mitigation methods, such as model-based safeguards and early enforcement strategies, fall short in robustness, interpretability, and adaptability. To address these challenges, we propose AgentSpec, a lightweight domain-specific language for specifying and enforcing runtime constraints on LLM agents. With AgentSpec, users define structured rules that incorporate triggers, predicates, and enforcement mechanisms, ensuring agents operate within predefined safety boundaries. We implement AgentSpec across multiple domains, including code execution, embodied agents, …

Hypersiniel: Guaranteed Output Delivery Comes (Almost) Free In Private Delegation Of Zksnarks, Yunbo Yang, Yuejia Cheng, Junkai Liang, Kailun Wang, Xuanming Liu, Xiaoguo Li, Jianfei Sun, Jiachen Shen, Xiaolei Dong, Zhenfu Cao, Meng Hao, Guomin Yang, Deng, Robert H., Kui Ren

Research Collection School Of Computing and Information Systems

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness.Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to …

Bridging Draft Policy Misalignment: Group Tree Optimization For Speculative Decoding, Shijing Hu, Jingyang Li, Zhihui Lu, Pan Zhou

Research Collection School Of Computing and Information Systems

Speculative decoding accelerates large language model (LLM) inference by letting a lightweight draft model propose multiple tokens that the target model verifies in parallel. Yet existing training objectives optimize only a single greedy draft path, while decoding follows a tree policy that re-ranks and verifies multiple branches. This draft policy misalignment limits achievable speedups. We introduce Group Tree Optimization (GTO), which aligns training with the decoding-time tree policy through two components: (i) Draft Tree Reward, a sampling-free objective equal to the expected acceptance length of the draft tree under the target model, directly measuring decoding performance; (ii) Group-based Draft Policy …

Dragging With Geometry: From Pixels To Geometry-Guided Image Editing, Xinyu Pu, Hongsong Wang, Jie Gui, Pan Zhou

Research Collection School Of Computing and Information Systems

Interactive point-based image editing serves as a controllable editor, enabling precise and flexible manipulation of image content. However, most drag-based methods operate primarily on the 2D pixel plane with limited use of 3D cues. As a result, they often produce imprecise and inconsistent edits, particularly in geometry-intensive scenarios such as rotations and perspective transformations. To address these limitations, we propose a novel geometry-guided drag-based image editing method—GeoDrag, which addresses three key challenges: 1) incorporating 3D geometric cues into pixel-level editing, 2) mitigating discontinuities caused by geometry-only guidance, and 3) resolving conflicts arising from multi-point dragging. Built upon a unified displacement …

Dreamcs: Geometry-Aware Text-To-3d Generation With Unpaired 3d Reward Supervision, Xiandong Zou, Ruihao Xia, Hongsong Wang, Pan Zhou

Research Collection School Of Computing and Information Systems

While text-to-3D generation has attracted growing interest, existing methods often struggle to produce 3D assets that align well with human preferences. Current preference alignment techniques for 3D content typically rely on hardly-collected preference-paired multi-view 2D images to train 2D reward models, when then guide 3D generation — leading to geometric artifacts, such as the Janus face problem and geometric incompleteness, due to their inherent 2D bias. To address these limitations, we construct 3D-MeshPref, the first large-scale unpaired 3D preference dataset, featuring diverse 3D meshes annotated by a large language model and refined by human evaluators. We then develop RewardCS, the …

From Spatial To Actions: Grounding Vision-Language-Action Model In Spatial Foundation Priors, Zhengshen Zhang, Hao Li, Yalun Dai, Zhengbang Zhu, Lei Zhou, Chenchen Liu, Dong Wang, Francis E. H. Tay, Sijin Chen, Ziwei Liu, Yuxiao Liu, Xinghang Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptability. Recent 3D integration techniques for VLAs either require specialized sensors and transfer poorly across modalities, or inject weak cues that lack geometry and degrade vision-language alignment. In this work, we introduce FALCON (From Spatial to Action), a novel paradigm that injects rich 3D spatial tokens into the action head. FALCON leverages spatial foundation models to deliver strong geometric priors from RGB alone, and includes an Embodied Spatial Model that can optionally fuse depth, or pose …

Smu Launches Longevity Societies And Economies Institute To Advance Knowledge And Innovation For Singapore’S Longevity Transition, Singapore Management University

SMU Press Releases and News

Singapore Management University (SMU) launched the SMU Longevity Societies and Economies Institute (LSEI) which will focus on the economic and societal transitions needed for economies and societies to continue thriving despite an ageing population. The new institute will consolidate SMU’s ageing-related research and drive an interdisciplinary agenda to build resilient and opportunity-rich longevity societies and economies.

Price Discovery On Decentralized Exchanges, Agostino Capponi, Ruizhe Jia, Shihao Yu

Research Collection Lee Kong Chian School Of Business

Decentralized exchanges (DEXs) allow traders to express their willingness to pay for quick execution through a public priority fee bidding mechanism. We provide evidence that high-fee DEX trades are more informative and contribute more to price discovery. Using address-level blockchain transaction data, we show that informed traders persistently bid higher fees to secure early execution, revealing a strong willingness to pay for execution priority. Further, analysis of Ethereum mempool data demonstrates that informed traders employ a “jump bidding” strategy, placing high initial bids to deter potential competitors.

Advancing The Understanding Of Palliative Care In Singapore: Knowledge, Attitudes, Receptiveness, And The Moderating Role Of Media Information-Seeking Preferences, Su Lin Yeo, Angel Lee, Raymond Han Lip Ng, May O. Lwin, Yumin Lin

Research Collection Lee Kong Chian School Of Business

Introduction: Despite the growing demand for palliative care, this specialized medical care remains severely underutilized, with almost 50% of countries worldwide lacking access. Given that misperceptions and stigma continue to hinder progress in healthcare policy, this study aims to examine the relationships between palliative care knowledge, attitudes, and receptiveness to better understand how public health communication can enhance awareness and understanding of the topic in Singapore. Applying the adapted knowledge-attitude-practice (KAP) model, it further extends theory by examining the moderating role of media information-seeking between knowledge and receptiveness.Methods: Mixed-mode surveys involving 1,226 participants (926 online and 300 in-person respondents), representing …

Impending-Exit Period And Employee Performance: Rethinking Human Capital Disruption, Yea Hee Ko, Charlie O. Trevor

Research Collection Lee Kong Chian School Of Business

The well-established disruptive effects of employee turnover on firms have typically been attributed to post-exit dynamics, such as losses of human and social capital. Little is known, however, about leavers’ pre-exit job performance, which, if declining in sufficient magnitude as separation nears, may drive some of this disruption. Drawing on career concerns research, we argue that impending exit weakens incentives to improve future career prospects at the firm, thereby resulting in reduced performance. Our analysis reveals strikingly large negative relationships, as job performance during the impending-exit period declines by 53.9% and 79.8% across two performance measures. Additionally, we predict and …

The Robin Hood Effect In Transgressions Against Firms: How Political Ideology Shapes Consumer Justifications, Jason D. Lin, Anat Keinan, Hannah H. Chang, Donald R. Lehmann

Research Collection Lee Kong Chian School Of Business

Consumer transgressions against firms involve moral violations in which consumers take advantage of loopholes in companies’ return and satisfaction-guaranteed policies or the lack of strict enforcement against password sharing, piracy, shoplifting, wardrobing, coupon stacking, promotion abuse, and fraud. Drawing on the timeless tale of Robin Hood, the legendary outlaw who stands up against injustice, the authors demonstrate that consumers use a Robin Hood justification to legitimize their transgressions against firms. Building on moral foundations theory, this research demonstrates that political ideology impacts consumers’ transgression behavior and the perceived morality of these practices. Six studies show that under certain conditions in …

Ai Models As Cultural Beings: Investigating Ai Cultural Biases And The Impact Of Cultural Alignment On Human-Ai Creative Collaboration, Choon Ngee Tan, Meng Han, Roy Y. J. Chua, Chi-Ying Cheng

Research Collection Lee Kong Chian School Of Business

Existing research on AI cultural biases predominantly focuses on Western models, overlooking critical gaps in non-Western models. We conduct a comparative analysis of AI models – ChatGPT (U.S. developed) and ErnieBot (China developed) – from different cultures to investigate how corresponding cultural biases manifest in their outputs. Additionally, we examine how cultural alignment between human users and AI models impacts their collaborative creative performance and the underlying psychological mechanisms. In Study 1, multi-choice prompt with zero-shot technique was used to evaluate cultural biases in four widely used AI models – ChatGPT-3.5/4, ErnieBot-3.5/4 – comparing their responses to established cultural psychometric …

Woulda, Shoulda, Coulda? The Impact Of Predictive, Prescriptive, And Prospective Expectations On Stakeholder Reactions, Yuri Mishina, Maxine Yu, David Gomulya

Research Collection Lee Kong Chian School Of Business

When and why might stakeholders react to firm activities in ways that might be different than, or even contradictory to, what we might expect based on the extant research? We draw on expectancy violation theory (EVT) and bring in the notion of heuristics and future-oriented expectations to examine this question, using a sample of investor reactions to earnings surprises from 2013 to 2019. We find that, in addition to comparing earnings to consensus earnings estimates, investors appear to compare the earnings surprises to the firm’s past performance and to its peers. Importantly, their expectations regarding future interactions with the firm …

Fixed Effects Estimation Of Spatial Panel Model With Missing Responses: An Application To Us State Tax Competition, Xiaoyu Meng, Zhenlin Yang

Research Collection School Of Economics

We consider estimation and inferences for general spatial panel data models with randomly missing observations on responses. It allows for unobserved spatiotemporal heterogeneity, time-varying endogenous and contextual spatial interactions, time-varying cross-sectional error dependence, and serial correlation. A general M-estimation method is proposed for model estimation and a novel corrected plug-in method is proposed for model inference. Both take into account the estimation of fixed effects. Asymptotic properties of the proposed methods are studied, and finite sample properties are investigated. An empirical application is given using U.S. state tax competition data. The proposed methods apply to matrix exponential spatial specification and …

Genuinely Unbalanced Spatial Panel Data Models: Fixed Effects M-Estimation And Inference, Xiaoyu Meng, Zhenlin Yang

Research Collection School Of Economics

We consider spatial panel data models with genuine unbalancedness arising from the non-presence of some spatial units in certain time periods. General M-estimation methods are proposed for model estimation, which take into account the estimation of the incidental fixed effects parameters and allow for spatiotemporal heteroskedasticity and high-order time-varying spatial effects. Corrected plug-in methods are proposed for standard error estimation. The proposed estimation and inference methods are rigorously studied for their asymptotic properties and finite sample performance. An application to China’s provincial FDI inflows shows that properly accounting for genuine unbalancedness uncovers significant positive spatial spillovers that are masked when …

Conflogger: Enhance Systems’ Configuration Diagnosability Through Configuration Logging, Shiwen Shan, Yintong Huo, Yuxin Su, Zhining Wang, Dan Li, Zibin Zheng

Research Collection School Of Computing and Information Systems

Modern configurable systems offer customization via intricate configuration spaces, yet such flexibility introduces pervasive configuration-related issues such as misconfigurations and latent softwarebugs. Existing diagnosability supports focus on post-failure analysis of software behavior to identify configuration issues, but none of these approaches look into whether the software clue sufficient failure information for diagnosis. To fill in the blank, we propose the idea of configuration logging to enhance existing logging practices at the source code level. We develop ConfLogger, the first tool that unifies configuration-aware static taint analysis with LLM-based log generation to enhance software configuration diagnosability. Specifically, our method 1) identifies …

Propaganda Ai: An Analysis Of Semantic Divergence In Large Language Models, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) can exhibit concept-conditioned semantic divergence: common high-level cues (e.g., ideologies, public figures) elicit unusually uniform, stance-like responses that evade token-trigger audits. This behavior falls in a blind spot of current safety evaluations, yet carries major societal stakes, as such concept cues can steer content exposure at scale. We formalize this phenomenon and present RAVEN (Response Anomaly Vigilance), a black-box audit that flags cases where a model is simultaneously highly certain and atypical among peers by coupling semantic entropy over paraphrastic samples with cross-model disagreement. In a controlled LoRA fine-tuning study, we implant a concept-conditioned stance using …

Where Did It Go Wrong? Attributing Undesirable Llm Behaviors Via Representation Gradient Tracing, Zhe Li, Wei Zhao, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their deployment is frequently undermined by undesirable behaviors such as generating harmful content, factual inaccuracies, and societal biases. Diagnosing the root causes of these failures poses a critical challenge for AI safety. Existing attribution methods, particularly those based on parameter gradients, often fall short due to prohibitive noisy signals and computational complexity. In this work, we introduce a novel and efficient framework that diagnoses a range of undesirable LLM behaviors by analyzing representation and its gradients, which operates directly in the model's activation space to provide a semantically meaningful signal linking …

Reducing Class-Wise Performance Disparity Via Margin Regularization, Beier Zhu, Kesen Zhao, Jiequan Cui, Qianru Sun, Yuan Zhou, Xun Yang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Deep neural networks often exhibit substantial disparities in class-wise accuracy, even when trained on class-balanced data—posing concerns for reliable deployment. While prior efforts have explored empirical remedies, a theoretical understanding of such performance disparities in classification remains limited. In this work, we present Margin Regularization for performance disparity Reduction (MR2 ), a theoretically principled regularization for classification by dynamically adjusting margins in both the logit and representation spaces. Our analysis establishes a margin-based, class-sensitive generalization bound that reveals how per-class feature variability contributes to error, motivating the use of larger margins for “hard” classes. Guided by this insight, MR2 optimizes …

Real-Time Motion-Controllable Autoregressive Video Diffusion, Kesen Zhao, Jiaxin Shi, Beier Zhu, Junbao Zhou, Xiaolong Shen, Yuan Zhou, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) approaches. Existing AR video diffusion models are limited to simple control signals or text-to-video generation, and often suffer from quality degradation and motion artifacts in few-step generation. To address these challenges, we propose AR-Drag, the first RL-enhanced few-step AR video diffusion model for real-time image-to-video generation with diverse motion control. We first fine-tune a base I2V model to support basic motion control, then further improve it via reinforcement learning with a trajectory-based reward model. Our design preserves the …

Llmqua: Practical Backdoor Injection On Large Language Model Quantization, Xiangxiang Chen, Peixin Zhang, Jun Sun, Jin Song Dong, Wenhai Wang, Jingyi Wang

Research Collection School Of Computing and Information Systems

Quantization is widely used to enable local deployment of large language models (LLMs) on resource-constrained devices. Recent work (e.g., QuRA) shows quantization can be exploited via rounding manipulation to implant backdoors. However, such an attack has been evaluated only on small models and does not directly apply to LLMs due to three key constraints: (1) limited poisoning data from small, task-agnostic calibration sets; (2) layer-wise quantization restricting adversarial access to global representations; and (3) lack of gradient access in quantization pipelines, blocking gradient-based attacks.We propose LLMQuA, a practical quantization-phase backdoor attack tailored to the LLM setting. LLMQuA (i) injects backdoors …

Prompting Frameworks For Large Language Models: A Survey, Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang

Research Collection School Of Computing and Information Systems

Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where “prompt” plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: (1) temporal lag of training data, and (2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power …

Who You Explain To Matters: Learning By Explaining To Conversational Agents With Different Pedagogical Roles, Zhengtao Xu, Junti Zhang, Anthony Tang, Yi-Chieh Lee

Research Collection School Of Computing and Information Systems

Conversational agents are increasingly used in education for learning support. An application is “learning by explaining”, where learners explain their understanding to an agent. However, existing research focuses on single roles, leaving it unclear how different pedagogical roles influence learners’ interaction patterns, learning outcomes and experiences. We conducted a between-subjects study (N=96) comparing agents with three pedagogical roles (Tutee, Peer, Challenger) and a control condition while learning an economics concept. We found that different pedagogical roles shaped learning dynamics, including interaction patterns and experiences. Specifically, the Tutee agent elicited the most cognitive investment but led to high pressure. The Peer …

Reasoning On Time-Series For Financial Technical Analysis, Kelvin J. L. Koa, Jan Chen, Yunshan Ma, Huanhuan Zheng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches between domains: the stock price inputs and outputs lie in the time-series domain, while the reasoning step should be in natural language. In this work, we introduce Verbal Technical Analysis (VTA), a novel framework that combine verbal and latent reasoning to produce stock time-series forecasts that are both accurate and interpretable. To reason over time-series, we convert stock price data into textual annotations and …