Digital Commons Network^™

Gender And Professional Identities In Businesswomen’S Negotiation, Chi-Ying Cheng, Amy J. Lim, Yi Wen Tan, Fiona Lee

Research Collection School of Social Sciences

Gender roles and expectations for women have been shown to account for why women tend to negotiate ineffectively in business settings. Drawing from the psychological literature on multiple identities, this paper examines how individual differences in perceived compatibility between gender and professional identities–captured by the construct Gender-Professional Identity Integration (G-PII)–shape businesswomen’s negotiation behaviors. Two studies examined how G-PII interacts with identity cues and cue valence to influence negotiation outcomes. We found that those who perceived their gender and professional identities as compatible (high G-PII) exhibited an “assimilation” effect–they negotiate more effectively when their professional identity was primed by professional identity …

Gaming The Marketplace: Consumer Opportunism And Beliefs About Societal Hierarchy, Shilpa Madan, Aparna Labroo, Constantine S. Katsikeas

Research Collection Lee Kong Chian School Of Business

Consumers often behave opportunistically, taking more than what fair marketplace exchange warrants (e.g., taking excess samples, returning used items). While each individual transgression may be minor, cumulatively they can undermine firm profitability. Thus, addressing consumer opportunism is an important managerial concern. We identify a novel antecedent of consumer opportunism: consumers’ acceptance of societal hierarchy (i.e., power distance belief [PDB]), including firms’ higher position in it than consumers. Nine studies (plus two supplementary studies) employing archival, correlational, and experimental data provide converging evidence that, in marketplace interactions, this belief evokes a need to feel clever (i.e., to feel smart and knowledgeable …

Practical On-Device Intelligence For Resource-Constrained Embedded And Wearable Devices, Xiao Ma

Dissertations and Theses Collection (Open Access)

Embedded and wearable devices increasingly rely on deep neural networks to support intelligent sensing applications directly on the device. However, building practical on-device intelligence remains challenging throughout the deployment lifecycle. Before deployment, models must remain accurate despite severe memory, computation, and energy budgets, and a single device often needs to support multiple sensing applications without deploying a separate model for each application. After deployment, the device continues to operate in changing real-world environments, where shifts in data distribution can degrade the model’s performance over time, so the model must be able to adapt to such shifts efficiently.

The first challenge …

Dynamics Of High-Growth Young Firms And The Role Of Venture Capitalists, Yoshiki Ando

Research Collection School Of Economics

Motivated by the substantial growth and upfront investments of venture capital (VC)-backed firms observed in administrative US Census data, this study develops a life-cycle firm dynamics model. In the model, startups choose the source of financing from VC, angel investors, or banks, depending on their growth potential, and invest in innovation. The calibrated model explains the life-cycle dynamics of firms with different sources of financing and suggests that venture capitalists' managerial advice accounts for around 22% of the growth in VC-backed firms. A counterfactual economy without VC financing would experience an aggregate consumption loss of around 0.46%.

Semiparametric Cointegrating Rank Selection For Curved Cross-Section Time Series, Peter C. B. Phillips

Research Collection School Of Economics

Cointegrating rank selection is studied in a function space reduced rank regression where the data are time series of cross-section curves. Consistent cointegrating rank estimation is developed using information criteria extended to curve time series environments. The asymptotic theory involves two-parameter Gaussian processes that generalise the standard limit processes involved in cointegrating regressions. Simulations provide evidence of the effectiveness of consistent rank selection by the BIC criterion and the tendency of AIC to overestimate order as in standard lag order selection in autoregression, as well as in reduced rank regression with multiple time series.

Connectivity And Selective Rural Migration, Lin Ma, Yuan Mei, Qunfeng Wu, Mingzhi Xu

Research Collection School Of Economics

How does infrastructure shape rural development? Using household panel data from a nationally representative sample of Chinese villages, matched to high-resolution highway maps, we find that road expansion between 2000 and 2015 operates mainly through reallocation, with less productive farmers scaling down or exiting and more productive farmers remaining and expanding. We quantify the aggregate and distributional consequences in a dynamic spatial equilibrium model with heterogeneous households and endogenous migration. Improved connectivity not only accelerates urbanization but also increases aggregate rural output by 2.2%–5.3% in the long run, even as the rural workforce declines.

Casual Killing In The Home: Ecological Citizenship And The Micro-Geopolitics Of Mosquito Cohabitation, Orlando Woods, Junxi Qian

Research Collection College of Integrative Studies

This article explores how the visceral acts of being bitten by and killing mosquitoes can underpin the formation of ecological citizenship. This form of citizenship is premised on human-nature entanglement and raises questions concerning how the rights and responsibilities stemming from such entanglement become distributed across the public and private domains. As a space in which mosquito cohabitation is contested daily, the home is a key site through which ecological citizenship can be forged and theoretical debates concerning urban political ecology can be extended. Human-mosquito entanglements in the home can reveal the limits of public pest management strategies, the partiality …

Misplay Or Malice? Players’ Interpretations Of Poor Gameplay In Competitive Team-Based Multiplayer Online Games, Valerie Yu, Benjamin H. Detenber, Sonny Rosenthal

Research Collection College of Integrative Studies

Research on perceptions of gaming toxicity has focused on its verbal forms, while gameplay-related forms remain relatively understudied. Furthermore, assessments of gameplay actions as toxic may depend on situational considerations. The present study used a controlled experiment to examine player interpretations of gameplay sabotage using pre-recorded gameplay scenarios from the game, League of Legends. We found that the presence of gameplay sabotage elicited stronger negative emotions and triggered greater player-reported intentions to retaliate and correct the offending player than in comparable instances of unintentional poor gameplay. Further exploratory analyses suggested that participants rated poor gameplay as less acceptable in more …

From Open Banking To Banking-As-A-Service: Regulatory Challenges In The Evolution Of Financial Intermediation, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

Over the past decade, financial innovation has moved from open banking, centred on consumer-permissioned data sharing, to banking-as-a-service (BaaS), which modularises core banking functions through application programming interfaces. This shift allows fintechs and non-financial platforms to embed financial products seamlessly, reducing transaction costs and fostering innovation. Yet the 2024 collapse of Synapse in the United States exposed the fragility of this model when intermediaries operate outside robust oversight, leaving consumers without recourse and revealing liability fragmentation and regulatory blind spots. This paper distinguishes BaaS from open banking, open finance, and embedded finance, and maps leading global models – from bank-led …

Efficient And Universal Watermarking For Llm-Generated Code Detection, Boquan Li, Zirui Fu, Mengdi Zhang, Peixin Zhang, Jun Sun, Xingmei Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty and the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Watermarking is broadly considered a promising solution and has been successfully applied to identify LLM-generated text. However, existing efforts on code are far from ideal, suffering from limited universality and excessive time and memory consumption. In this work, we propose a plugand- play watermarking approach for AI-generated code detection, named ACW …

Hvi-Cidnet+: Beyond Extreme Darkness For Low-Light Image Enhancement, Kangbiao Shi, Xiaowen Ma, Yixu Feng, Tao Hu, Peng Wu, Guansong Pang, Qingsen Yan

Research Collection School Of Computing and Information Systems

Low-Light Image Enhancement (LLIE) aims to recover visually pleasing content and details from degraded low-light images. However, existing RGB-based methods often suffer from color bias and brightness artifacts due to inherent high color sensitivity. Although the HSV color space can decouple brightness and color, it introduces noticeable red and black noise artifacts. To address these challenges, we adopt the Horizontal/Vertical-Intensity (HVI) color space for LLIE, which is defined by the HV color map and learnable intensity. The former enforces small distances for red coordinates to alleviate red noise artifacts, while the latter adaptively compresses low-light regions to suppress black noise …

Task-Aligned Haze Removal With Semantic-Aware Fusion And Contrast Self-Correction, Jinbin Wang, Aiping Yang, Guosong Jiang, Wenlong Yu, Dongwei Ren, Qinghua Hu

Research Collection School Of Computing and Information Systems

Adverse haze conditions introduce complex degradations that obscure scene details and distort structural cues critical for object detection, posing persistent challenges for vision‐based sensing systems. Although existing haze removal methods have achieved notable improvements in visual clarity, their optimisation objectives are often misaligned with downstream detection requirements, leading to limited detection performance in real‐world scenarios. To address this issue, this work proposes a task‐aligned weakly supervised haze removal framework, termed Dehaze4Detection, which explicitly aligns low‐level restoration with high‐level detection objectives. The framework incorporates a Semantic‐Aware Multi‐Scale Fusion Module (SMFM) that embeds pixel‐level semantic knowledge into the dehazing process, enabling selective …

Parameterized Approximation Schemes For Fair Clustering In Doubling Metrics, Xiaoliang Wu, Ting Liang, Zhize Li, Qilong Feng

Research Collection School Of Computing and Information Systems

Fair clustering has garnered considerable attention, with various fairness notions proposed to ensure equitable representation across demographic groups. In this paper, we focus on the k-center problem in bounded doubling metrics under two popular fairness requirements: group fairness and data summarization fairness, referred to Group Fair k-Center (Gf-k-Cen) and Data Summarization Fair k-Center (Dsf-k-Cen), respectively. Both fairness notions extend classical clustering formulation by associating each data point with a demographic label. Motivated by recent advances in parameterized approximation results for fair clustering, we investigate whether these problems admit Fixed-Parameter Tractable (FPT) approximation schemes in bounded doubling metrics. The previous algorithms …

Corruption Is Futile: Hardware-Assisted Multi-User Searchable Encryption, Feng Li, Chengyan Ma, Xiaoguo Li, Pengfei Wu, Lisha Yao, Jianting Ning, Guomin Yang, Jianfeng Ma, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Searchable encryption enables writers to upload encrypted data to an untrusted cloud server, while allowing authorized readers to perform keyword searches over the encrypted content without compromising data privacy. The multi writer/multi-reader (M/M) setting better reflects real-world de ployment requirements because it aligns with the collaborative nature of many modern applications, but introduces substantial challenges in both efficiency and security. The state-of-the art scheme, delegatable searchable encryption (DSE), achieves optimal search time and forward privacy in the M/M setting. Nevertheless, DSE provides only limited security guarantees: search tokens generated by readers reveal the update count of queried keywords, and the …

Medical Firms’ Online Q&A Interactions: Effect Of Language Style On Investor Engagement, Runyu Wang, Keng Siau, Zili Zhang

Research Collection School Of Computing and Information Systems

PurposeThis study aims to investigate how language styles used by medical firms on digital question and answer (Q&A) platforms influence investor engagement. It examines the linguistic characteristics of managerial responses and compares their effects across industries, with a focus on the distinctive demands of medical communication.Design/methodology/approachWe analyze a panel dataset of 1,223 firm-month observations, constructed from 13,549 individual responses provided by 70 medical firms on a major Chinese web-based firm-investor Q&A platform over a 24-month period (2020–2021). Content analysis is used to assess linguistic features, such as first-person usage, uncertainty, understandability and concreteness. A two-way fixed effects model with clustered …

Modeling And Forecasting Intraday Spot Volatility, Adam Clements, Daniel P. A. Preve

Research Collection School Of Economics

We propose a multiple-equation regression-based method for modeling and forecasting intraday spot volatility. In this approach, intraday intervals are treated as individual time series, deviating from the common practice of treating the data as one continuous sample. Our empirical study, which spans more than two decades and encompasses six US blue-chip stocks, employs the recent OK volatility estimator developed by Li, Wang, and Zhang (2024) to expose the dynamics of latent intraday spot volatility over time. We demonstrate that the proposed method effectively captures the intricate dynamics of intraday spot volatility and find strong evidence that it outperforms a competing …

Securing A Calibrated Marketing Budget, Junqiu Jiang, Kapil R. Tuli, Nirmalya Kumar

Research Collection Lee Kong Chian School Of Business

This study investigates the sociopolitical processes underlying the development and approval of marketing budgets in large multinational corporations. While prior research has focused extensively on optimizing the level and allocation of marketing budgets, little attention has been paid to the internal organizational dynamics that shape the marketing budgeting process. Drawing on the theories-in-use of both CMOs and CEOs, this study examines how these executives codevelop a calibrated marketing budget (CMKB)—a budget that aligns expected performance with allocated resources through an iterative, participative process. Field data show that CMOs deploy a set of signals to assuage CEO concerns related to goal …

The Anatomy Of Earnings Conference Calls: An Integrative Framework For Management Research, Matthew P. Mount, Gokhan Ertug, Wei Shi, Tengjian Zou

Research Collection Lee Kong Chian School Of Business

Over the last decade, there has been an explosion in the use of diverse data sources by management scholars to observe and capture managerial and organizational constructs that have historically been difficult to access. This surge has been driven by the growing availability of rich, multi-modal data—textual, image, and audio (Luo, Jia, Ouyang, & Fang, 2024)—together with advances in analytical techniques to process and analyze data, such as computer-aided text analysis (Harrison, Thurgood, Boivie, & Pfarrer, 2019), machine learning (Choudhury, Wang, Carlson, & Khanna, 2019; Harrison, Josefy, Kalm, & Krause, 2023), and deep learning (Gouvard, Goldberg, & Srivastava, 2023). These …

Emerging Library Leaders’ Summer School For Asia-Pacific (Ellssa): Smu Libraries Flagship Leadership Development Program For Librarians, Rajendra Munoo, Sumita Govindan

Research Collection Library

Strong leadership skills are becoming more important for librarians as they deal with rapid digital change in a VUCA world, rising user expectations, and a workforce made up of different generations. The Emerging Library Leaders’ Summer School for Asia-Pacific (ELLSSA) is Singapore Management University (SMU) Libraries’ main leadership development program, created to prepare early‑career librarians and high‑potential staff for future leadership roles across the Asia‑Pacific region and beyond. Recognised by the International Federation of Library Associations and Institutions (IFLA) as one of only a small number of leadership programs in the region, ELLSSA helps address gaps in leadership development within …

Using Individual Vs. Group Evaluation To Incentivize Effort For Heterogeneous Groups, Prasart Jongjaroenkamol

Research Collection School Of Accountancy

This study uses an analytical model to examine how individual and group performance evaluations affect motivation among agents with different abilities. While group evaluations encourage collaboration, they also introduce uncertainty. The results reveal a non-monotonic relationship between the weight assigned to group evaluations and motivation: motivation increases at moderate weights but decreases when the weight is too low or high. The study also identifies the optimal weighting between evaluation types to maximize motivation and examines factors influencing these allocations. These insights demonstrate how evaluation design shapes effort incentives and inform performance measurement and assessment practices in organizational and educational contexts.

Larger Is Not Always Better: Exploring Small Open-Source Language Models In Logging Statement Generation, Renyi Zhong, Yichen Li, Guangba Yu, Wenwei Gu, Jinxi Kuang, Yintong Huo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Developers use logging statements to create logs that document system behavior and aid in software maintenance. As such, high-quality logging is essential for effective maintenance; however, manual logging often leads to errors and inconsistency. Recent methods emphasize using large language models (LLMs) for automated logging statement generation, but these present privacy and resource issues, hindering their suitability for enterprise use. This paper presents the first large-scale empirical study evaluating small open-source language models (SOLMs) for automated logging statement generation. We evaluate four prominent SOLMs using various prompt strategies and parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA) and Retrieval-Augmented Generation …

Activity Transition Graph Generation: How Far Are We?, Jiakun Liu, Peixin Zhang, Han Hu, Yonghui Liu, Wei Minn, Ferdian Thung, Shahar Maoz, Eran Toch, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Android applications (i.e., apps) are indispensable nowadays and are getting bigger and bigger with an increasing number offunctionalities. To understand how to access functionalities in an app, prior studies proposed tools to model the transitionsbetween functionalities with the activity transition graph (ATG). ATG is an important data structure and has been used forvarious Android app analyses, including app design, understanding, and testing. However, there is no benchmarking work onATG generation. It is still unclear whether the transitions identified by tools are correct and how many transitions are missed.To fill this gap, we manually identified all transitions in 98 applications to …

Constrained Assortment Optimization Under The Mixed-Logit Model, Hoang Giang Pham, Tien Mai

Research Collection School Of Computing and Information Systems

In this paper, we study the assortment optimization problem under the mixed-logit customer choice model. While assortment optimization has been a central topic in revenue management for decades, the mixed-logit model is widely regarded as one of the most general and flexible frameworks for modeling and predicting customer purchasing behavior. The assortment optimization problem is known to be NP-hard to be approximated to any constant factor, even in the unconstrained case. To address this challenge, we first explore the submodularity properties of a simplified version of the objective function to derive novel semi-constant factor approximation solutions for assortment problems under …

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks For In-Home Activity Learning Of Mild Cognitive Impairment, Seng Khoon Teh, Ah-Hwee Tan, Kar Way Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

In-home spatiotemporal data, such as the movement trajectory data and the spatial time series data, contains potential predictive utility for detection of geriatric conditions including Mild Cognitive Impairment (MCI), frailty, and cognitive frailty. However, few have explored spatiotemporal learning models for learning and fusion of such disparate spatiotemporal data, owing to the lack of a generalized machine learning model that can jointly model these different spatiotemporal data types. This work reports a multimodal spatiotemporal machine learning model based on a class of self-organizing neural networks that can integrate different spatiotemporal data types for MCI detection. Specifically, Episodic Memory Adaptive Resonance …

Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, …

Co-Matching: Towards Human–Model Collaborative Legal Case Matching, Chen Huang, Xinwei Yang, Yang Deng, Wenqiang Lei, Jiancheng Lv, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recent efforts have aimed to improve AI models in legal case matching by integrating legal domain knowledge. However, successful legal case matching requires the tacit knowledge of legal practitioners, which is difficult to verbalize and encode into models. This emphasizes the crucial role of involving legal practitioners in high-stakes legal case matching. To address this, we propose a collaborative matching framework called Co-Matching, which encourages both the model and the legal practitioner to participate in the matching process, integrating tacit knowledge. Unlike existing methods that rely solely on the model, Co-Matching allows both the legal practitioner and the model to …

New Algorithms For Fully-Dynamic K-Center With Outliers, Junyu Huang, Zhize Li, Zhen Zhang, Xujia Li, Jianxin Wang, Qilong Feng

Research Collection School Of Computing and Information Systems

In this paper, we study the fully dynamic k-center with outliers problem, where points are inserted and deleted over time and the goal is to maintain an approximate clustering while discarding up to z outliers. Existing algorithms typically rely on radius guessing to maintain cluster representations, leading to update and query times that depend explicitly on the aspect ratio Δ. We propose a layered-sampling framework that avoids radius guessing by maintaining a hierarchy of sampled structures, which can separate most inliers from potential outliers and refine the remaining uncertain points. The resulting algorithm achieves Õ(k²/ε⁴) update and query time independent …

The Allure Of A Slow Life Strategy: How Mating Context And Ecological Harshness Shape Mate Preferences, Lynn K. L. Tan, Nicole Ruiying Chen, Kenneth Tan, Norman P. Li

Research Collection School of Social Sciences

Although mate preferences have been extensively studied across various factors such as physical attractiveness or social status, their connection to life history strategy remains underexplored. Specifically, do individuals prefer mates who display signs of a fast or slow strategy? And what might qualify these preferences? Drawing from life history theory, we suggest that mate preferences are sensitive to cues of prospective mates' life history strategy, and that these preferences are malleable and shaped by ecological conditions. We hypothesize that in general, individuals will favor slow (versus fast) strategists as a mate. This is because modern environments are generally defined by …

Cyberbullying Perpetration: A Systematic Review Of Global Prevalence, Profiles, Antecedents, Consequences, And Interventions, Ysabel A. Guevarra, K. T. A. Sandeeshwara Kasturiratna, Charlotte K. Y. Lee, Andree Hartanto

Research Collection School of Social Sciences

The anonymity afforded by cyberspace enables individuals to engage in harmful online behaviours, most notably cyberbullying perpetration, which has become a pressing social and psychological concern worldwide. Despite extensive research on its antecedents, outcomes and interventions, insights into cyberbullying perpetration remain scattered, highlighting the value of a comprehensive synthesis to capture its multifaceted nature. This systematic review synthesised findings from 54 meta-analyses, comprising 314 effect sizes (Sample size: Range = 3273–1,479,614, Mdn = 46,604) across diverse populations worldwide. Results indicate that approximately one in ten individuals engage in peer cyberbullying, with even higher prevalence in partner-directed forms such as cyber …

Income Tax Over-Withholding And Household Investment Decisions, Xi Novia Chen, Jungbae Kim, Ben Lourie, Chenqi Zhu

Research Collection School Of Accountancy

Over three-quarters of U.S. taxpayers over-withhold taxes, leading to tax refunds. This study explores how over-withholding impacts stock investments by comparing individuals' investment patterns from wages and tax refunds. We find that while some portion of tax refunds are promptly invested, the investment rate is lower than that of wages. This suggests that over-withholding, which alters the label and timing of wages into refunds, influences investment behavior. Our cross-sectional analysis indicates that the differential propensity to invest wages versus tax refunds are more pronounced for individuals with automatic investment setups and lower financial sophistication. These findings underscore the importance of …