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Articles 1861 - 1890 of 8696
Full-Text Articles in Entire DC Network
Impact Of Geographical Diversification And Limited Attention On Private Equity Fund Returns, Victor Hock Keong Ong
Impact Of Geographical Diversification And Limited Attention On Private Equity Fund Returns, Victor Hock Keong Ong
Dissertations and Theses Collection (Open Access)
This study analyzes the effect of geographical diversification on global private equity (PE) fund returns. I find that there is a negative correlation between geographical diversification and PE fund returns. To establish the causality between geographical diversification and PE fund returns, I employ an instrument variable analysis where the instrument used is the stock market capitalization value of the host country where the PE fund is based. My results apply to Net IRR, multiple and DPI as dependent variables used to proxy for PE fund returns in the main regression model. A one standard deviation increase in geographical diversification results …
Two Singapore Public Healthcare Ai Applications For National Screening Programs And Other Examples, Andy Wee An Ta, Han Leong Goh, Christine Ang, Lian Yeow Koh, Ken Poon, Steven M. Miller
Two Singapore Public Healthcare Ai Applications For National Screening Programs And Other Examples, Andy Wee An Ta, Han Leong Goh, Christine Ang, Lian Yeow Koh, Ken Poon, Steven M. Miller
Research Collection School Of Computing and Information Systems
This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation-wide screening programs. The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases, targeting the rapidly increasing number of adults in the country with diabetes. In the second example, the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients—especially which elderly patients with complex conditions—have a high risk of being readmitted as an inpatient multiple times in the …
Ergo: Event Relational Graph Transformer For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Kunquan Deng, Mukai Li, Kun Wang, Jing Shao, Yan Zhang
Ergo: Event Relational Graph Transformer For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Kunquan Deng, Mukai Li, Kun Wang, Jing Shao, Yan Zhang
Research Collection School Of Computing and Information Systems
Document-level Event Causality Identification (DECI) aims to identify event-event causal relations in a document. Existing works usually build an event graph for global reasoning across multiple sentences. However, the edges between events have to be carefully designed through heuristic rules or external tools. In this paper, we propose a novel Event Relational Graph TransfOrmer (ERGO) framework1 for DECI, to ease the graph construction and improve it over the noisy edge issue. Different from conventional event graphs, we define a pair of events as a node and build a complete event relational graph without any prior knowledge or tools. This naturally …
Interactive Contrastive Learning For Self-Supervised Entity Alignment, Kaisheng Zeng, Zhenhao Dong, Lei Hou, Yixin Cao, Minghao Hu, Jifan Yu, Xin Lv, Lei Cao, Xin Wang, Haozhuang Liu, Yi Huang, Jing Wan, Juanzi Li
Interactive Contrastive Learning For Self-Supervised Entity Alignment, Kaisheng Zeng, Zhenhao Dong, Lei Hou, Yixin Cao, Minghao Hu, Jifan Yu, Xin Lv, Lei Cao, Xin Wang, Haozhuang Liu, Yi Huang, Jing Wan, Juanzi Li
Research Collection School Of Computing and Information Systems
Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without the use of pre-aligned entity pairs. The current state-of-the-art (SOTA) selfsupervised EA approach draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and contrastive loss, and suffers from two shortcomings. Firstly, it puts unidirectional emphasis on pushing sampled negative entities far away rather than pulling positively aligned pairs close, as is done in the well-established supervised EA. Secondly, it advocates the minimum information requirement for self-supervised EA, while we argue that self-described KG’s side information (e.g., entity name, relation name, …
Stitching Weight-Shared Deep Neural Networks For Efficient Multitask Inference On Gpu, Zeyu Wang, Xiaoxi He, Zimu Zhou, Xu Wang, Qiang Ma, Xin Miao, Zhuo Liu, Lothar Thiele, Zheng. Yang
Stitching Weight-Shared Deep Neural Networks For Efficient Multitask Inference On Gpu, Zeyu Wang, Xiaoxi He, Zimu Zhou, Xu Wang, Qiang Ma, Xin Miao, Zhuo Liu, Lothar Thiele, Zheng. Yang
Research Collection School Of Computing and Information Systems
Intelligent personal and home applications demand multiple deep neural networks (DNNs) running on resourceconstrained platforms for compound inference tasks, known as multitask inference. To fit multiple DNNs into low-resource devices, emerging techniques resort to weight sharing among DNNs to reduce their storage. However, such reduction in storage fails to translate into efficient execution on common accelerators such as GPUs. Most DNN graph rewriters are blind for multiDNN optimization, while GPU vendors provide inefficient APIs for parallel multi-DNN execution at runtime. A few prior graph rewriters suggest cross-model graph fusion for low-latency multiDNN execution. Yet they request duplication of the shared …
Interactive Video Corpus Moment Retrieval Using Reinforcement Learning, Zhixin Ma, Chong-Wah Ngo
Interactive Video Corpus Moment Retrieval Using Reinforcement Learning, Zhixin Ma, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Known-item video search is effective with human-in-the-loop to interactively investigate the search result and refine the initial query. Nevertheless, when the first few pages of results are swamped with visually similar items, or the search target is hidden deep in the ranked list, finding the know-item target usually requires a long duration of browsing and result inspection. This paper tackles the problem by reinforcement learning, aiming to reach a search target within a few rounds of interaction by long-term learning from user feedbacks. Specifically, the system interactively plans for navigation path based on feedback and recommends a potential target that …
Towards Understanding The Faults Of Javascript-Based Deep Learning Systems, Lili Quan, Qianyu Guo, Xiaofei Xie, Sen Chen, Xiaohong Li, Yang Liu
Towards Understanding The Faults Of Javascript-Based Deep Learning Systems, Lili Quan, Qianyu Guo, Xiaofei Xie, Sen Chen, Xiaohong Li, Yang Liu
Research Collection School Of Computing and Information Systems
Quality assurance is of great importance for deep learning (DL) systems, especially when they are applied in safety-critical applications. While quality issues of native DL applications have been extensively analyzed, the issues of JavaScript-based DL applications have never been systematically studied. Compared with native DL applications, JavaScript-based DL applications can run on major browsers, making the platform- and device-independent. Specifically, the quality of JavaScript-based DL applications depends on the 3 parts: the application, the third-party DL library used and the underlying DL framework (e.g., TensorFlow.js), called JavaScript-based DL system. In this paper, we conduct the first empirical study on the …
Transplantfix: Graph Differencing-Based Code Transplantation For Automated Program Repair, Deheng Yang, Xiaoguang Mao, Liqian Chen, Xuezheng Xu, Yan Lei, David Lo, Jiayu He
Transplantfix: Graph Differencing-Based Code Transplantation For Automated Program Repair, Deheng Yang, Xiaoguang Mao, Liqian Chen, Xuezheng Xu, Yan Lei, David Lo, Jiayu He
Research Collection School Of Computing and Information Systems
Automated program repair (APR) holds the promise of aiding manual debugging activities. Over a decade of evolution, a broad range of APR techniques have been proposed and evaluated on a set of real-world bug datasets. However, while more and more bugs have been correctly fixed, we observe that the growth of newly fixed bugs by APR techniques has hit a bottleneck in recent years. In this work, we explore the possibility of addressing complicated bugs by proposing TransplantFix, a novel APR technique that leverages graph differencing-based transplantation from the donor method. The key novelty of TransplantFix lies in three aspects: …
Right To Know, Right To Refuse: Towards Ui Perception-Based Automated Fine-Grained Permission Controls For Android Apps, Vikas Kumar Malviya, Chee Wei Leow, Ashok Kasthuri, Naing Tun Yan, Lwin Khin Shar, Lingxiao Jiang
Right To Know, Right To Refuse: Towards Ui Perception-Based Automated Fine-Grained Permission Controls For Android Apps, Vikas Kumar Malviya, Chee Wei Leow, Ashok Kasthuri, Naing Tun Yan, Lwin Khin Shar, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
It is the basic right of a user to know how the permissions are used within the Android app’s scope and to refuse the app if granted permissions are used for the activities other than specified use which can amount to malicious behavior. This paper proposes an approach and a vision to automatically model the permissions necessary for Android apps from users’ perspective and enable fine-grained permission controls by users, thus facilitating users in making more well-informed and flexible permission decisions for different app functionalities, which in turn improve the security and data privacy of the App and enforce apps …
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System, Xianjie Zhang, Yu Liu, Wenjun Li, Chen Gong
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System, Xianjie Zhang, Yu Liu, Wenjun Li, Chen Gong
Research Collection School Of Computing and Information Systems
Electricity demands are increasing significantly and the traditional power grid system isfacing huge challenges. As the desired next-generation power grid system, smart grid can providesecure and reliable power generation, and consumption, and can also realize the system’s coordinatedand intelligent power distribution. Coordinating grid power distribution usually requiresmutual communication between power distributors to accomplish coordination. However, the powernetwork is complex, the network nodes are far apart, and the communication bandwidth is oftenexpensive. Therefore, how to reduce the communication bandwidth in the cooperative power distributionprocess task is crucially important. One way to tackle this problem is to build mechanismsto selectively send out …
The Differential Effects Of Csr And Csi On Consumer Willingness To Pay: Implications For Service Providers And Retailers, Sabine Benoit, Julia Hartmann, Christina Sichtmann, Martin Wetzels
The Differential Effects Of Csr And Csi On Consumer Willingness To Pay: Implications For Service Providers And Retailers, Sabine Benoit, Julia Hartmann, Christina Sichtmann, Martin Wetzels
Research Collection Lee Kong Chian School Of Business
Service providers and retailers reselling branded have the discretion to set and adapt prices according to customers’ willingness to pay (WTP). Research often notes markup effects, such that WTP increases in response to corporate social responsibility (CSR) and markdown effects, lowering their WTP for corporate social irresponsibility (CSI). Theory suggests attitude changes to (negative) CSI are stronger than to (positive) CSR, but the extent and whether this difference holds for WTP and across various product types are unknown. Using experimental data, an incentive-compatible measure, and an actual purchase, this article reports on three studies that show that consumers mark up …
Explanation Guided Contrastive Learning For Sequential Recommendation, Lei Wang, Ee-Peng Lim, Zhiwei Liu, Tianxiang Zhao
Explanation Guided Contrastive Learning For Sequential Recommendation, Lei Wang, Ee-Peng Lim, Zhiwei Liu, Tianxiang Zhao
Research Collection Lee Kong Chian School Of Business
Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user adoptions. Nevertheless, the existing contrastive learning-based methods fail to ensure that the positive (or negative) sequence obtained by some random augmentation (or sequence sampling) on a given anchor user sequence remains to be semantically similar (or different). When the positive and negative sequences turn out to be false positive and false negative respectively, it may lead to degraded recommendation performance. In this work, we address the above problem by proposing Explanation Guided Augmentations …
Building Up A Culture Of Respect, Siow-Heng Ong
Building Up A Culture Of Respect, Siow-Heng Ong
Research Collection Lee Kong Chian School Of Business
Recently, we have become more acutely aware of a variety of undesirable workplace circumstances and practices in Singapore. personal time; discriminatory workplace practices against members of various categories of minority groups; and bias against women staff.
Unsupervised Video Hashing With Multi-Granularity Contextualization And Multi-Structure Preservation, Yanbin Hao, Jingru Duan, Hao Zhang, Bin Zhu, Pengyuan Zhou, Xiangnan He
Unsupervised Video Hashing With Multi-Granularity Contextualization And Multi-Structure Preservation, Yanbin Hao, Jingru Duan, Hao Zhang, Bin Zhu, Pengyuan Zhou, Xiangnan He
Research Collection School Of Computing and Information Systems
Unsupervised video hashing typically aims to learn a compact binary vector to represent complex video content without using manual annotations. Existing unsupervised hashing methods generally suffer from incomplete exploration of various perspective dependencies (e.g., long-range and short-range) and data structures that exist in visual contents, resulting in less discriminative hash codes. In this paper, we propose aMulti-granularity Contextualized and Multi-Structure preserved Hashing (MCMSH) method, exploring multiple axial contexts for discriminative video representation generation and various structural information for unsupervised learning simultaneously. Specifically, we delicately design three self-gating modules to separately model three granularities of dependencies (i.e., long/middle/short-range dependencies) and densely …
Adaptive Structural Similarity Preserving For Unsupervised Cross Modal Hashing, Liang Li, Baihua Zheng, Weiwei Sun
Adaptive Structural Similarity Preserving For Unsupervised Cross Modal Hashing, Liang Li, Baihua Zheng, Weiwei Sun
Research Collection School Of Computing and Information Systems
Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts. However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lack of sufficient annotations for the remaining types of relations. In this paper, we propose a general approach to learn relation prototypes from unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient training data. We learn relation prototypes as an implicit factor between entities, which reflects the meanings of relations as well …
Structured Reflection Increases Intentions To Reduce Other People’S Health Risks During Covid-19, Jairo Ramos, Marrissa D. Grant, Stephan Dickert, Kimin Eom, Alex Flores, Gabriela M. Jiga-Boy, Tehila Kogut, Marcus Mayorga, Eric J. Pedersen, Beatriz Pereira, Enrico Rubaltelli, K Sherman David, Paul Slovic, Västjäll. Daniel, Leaf Van Boven
Structured Reflection Increases Intentions To Reduce Other People’S Health Risks During Covid-19, Jairo Ramos, Marrissa D. Grant, Stephan Dickert, Kimin Eom, Alex Flores, Gabriela M. Jiga-Boy, Tehila Kogut, Marcus Mayorga, Eric J. Pedersen, Beatriz Pereira, Enrico Rubaltelli, K Sherman David, Paul Slovic, Västjäll. Daniel, Leaf Van Boven
Research Collection School of Social Sciences
People believe they should consider how their behavior might negatively impact other people, Yet their behavior often increases others’ health risks. This creates challenges for managing public health crises like the COVID-19 pandemic. We examined a procedure wherein people reflect on their personal criteria regarding how their behavior impacts others’ health risks. We expected structured reflection to increase people's intentions and decisions to reduce others’ health risks. Structured reflection increases attention to others’ health risks and the correspondence between people's personal criteria and behavioral intentions. In four experiments during COVID-19, people (N = 12,995) reported their personal criteria about how …
Mando: Multi-Level Heterogeneous Graph Embeddings For Fine-Grained Detection Of Smart Contract Vulnerabilities, Huu Hoang Nguyen, Nhat Minh Nguyen, Chunyao Xie, Zahra Ahmadi, Daniel Kudenko, Thanh Nam Doan, Lingxiao Jiang
Mando: Multi-Level Heterogeneous Graph Embeddings For Fine-Grained Detection Of Smart Contract Vulnerabilities, Huu Hoang Nguyen, Nhat Minh Nguyen, Chunyao Xie, Zahra Ahmadi, Daniel Kudenko, Thanh Nam Doan, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Learning heterogeneous graphs consisting of different types of nodes and edges enhances the results of homogeneous graph techniques. An interesting example of such graphs is control-flow graphs representing possible software code execution flows. As such graphs represent more semantic information of code, developing techniques and tools for such graphs can be highly beneficial for detecting vulnerabilities in software for its reliability. However, existing heterogeneous graph techniques are still insufficient in handling complex graphs where the number of different types of nodes and edges is large and variable. This paper concentrates on the Ethereum smart contracts as a sample of software …
Editing Out-Of-Domain Gan Inversion Via Differential Activations, Haorui Song, Yong Du, Tianyi Xiang, Junyu Dong, Jing Qin, Shengfeng He
Editing Out-Of-Domain Gan Inversion Via Differential Activations, Haorui Song, Yong Du, Tianyi Xiang, Junyu Dong, Jing Qin, Shengfeng He
Research Collection School Of Computing and Information Systems
Despite the demonstrated editing capacity in the latent space of a pretrained GAN model, inverting real-world images is stuck in a dilemma that the reconstruction cannot be faithful to the original input. The main reason for this is that the distributions between training and real-world data are misaligned, and because of that, it is unstable of GAN inversion for real image editing. In this paper, we propose a novel GAN prior based editing framework to tackle the out-of-domain inversion problem with a composition-decomposition paradigm. In particular, during the phase of composition, we introduce a differential activation module for detecting semantic …
Made In Singapore, Pao-Li Chang, Tran Bao Phuong Nguyen
Made In Singapore, Pao-Li Chang, Tran Bao Phuong Nguyen
Research Collection School Of Economics
In this paper, we characterize the position of Singapore in global value chains and identify Singapore’s key upstream and downstream trade partners. We trace how the position of Singapore in global value chains has changed in the past two decades: whether it has moved upstream or downstream, how involved it is in global value chains, how its trend compares with the other major Asian exporters (China, Japan, Korea, Taiwan and Hong Kong), and which key sectors of Singapore play a major role in these global trade networks.
Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yiu Lim Lui, Peter C. B. Phillips, Jun Yu
Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yiu Lim Lui, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new test has stable size properties unlike conventional test statistics that typically lead to size distortion and inconsistency in the presence of strongly dependent equation errors. The new procedure can be used to consistently time-stamp the origination and termination of an explosive episode under similar conditions of long memory errors. Simulations are conducted to assess the finite …
On The Optimal Forecast With The Fractional Brownian Motion, Xiaohu Wang, Chen Zhang, Jun Yu
On The Optimal Forecast With The Fractional Brownian Motion, Xiaohu Wang, Chen Zhang, Jun Yu
Research Collection School Of Economics
This paper examines the performance of alternative forecasting formulae with the fractional Brownian motion based on a discrete and finite sample. One formula gives the optimal forecast when a continuous record over the infinite past is available. Another formula gives the optimal forecast when a continuous record over the finite past is available. Alternative discretiza-tion schemes are proposed to approximate these formulae. These alternative discretization schemes are then compared with the conditional expectation of the target variable on the vector of the discrete and finite sample. It is shown that the conditional expectation delivers more accurate forecasts than the discretization-based …
Equivariance And Invariance Inductive Bias For Learning From Insufficient Data, Tan Wang, Qianru Sun, Sugiri Pranata, Karlekar Jayashree, Hanwang Zhang
Equivariance And Invariance Inductive Bias For Learning From Insufficient Data, Tan Wang, Qianru Sun, Sugiri Pranata, Karlekar Jayashree, Hanwang Zhang
Research Collection School Of Computing and Information Systems
We are interested in learning robust models from insufficient data, without the need for any externally pre-trained model checkpoints. First, compared to sufficient data, we show why insufficient data renders the model more easily biased to the limited training environments that are usually different from testing. For example, if all the training "swan" samples are "white", the model may wrongly use the "white" environment to represent the intrinsic class "swan". Then, we justify that equivariance inductive bias can retain the class feature while invariance inductive bias can remove the environmental feature, leaving only the class feature that generalizes to any …
Automatic Pull Request Title Generation, Ting Zhang, Ivana Clairine Irsan, Ferdian Thung, Donggyun Han, David Lo, Lingxiao Jiang
Automatic Pull Request Title Generation, Ting Zhang, Ivana Clairine Irsan, Ferdian Thung, Donggyun Han, David Lo, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Pull Requests (PRs) are a mechanism on modern collaborative coding platforms, such as GitHub. PRs allow developers to tell others that their code changes are available for merging into another branch in a repository. A PR needs to be reviewed and approved by the core team of the repository before the changes are merged into the branch. Usually, reviewers need to identify a PR that is in line with their interests before providing a review. By default, PRs are arranged in a list view that shows the titles of PRs. Therefore, it is desirable to have a precise and concise …
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System, Xianjie Zhang, Yu Liu, Wenjun Li, Chen Gong
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System, Xianjie Zhang, Yu Liu, Wenjun Li, Chen Gong
Research Collection School Of Computing and Information Systems
Electricity demands are increasing significantly and the traditional power grid system is facing huge challenges. As the desired next-generation power grid system, smart grid can provide secure and reliable power generation, and consumption, and can also realize the system’s coordinated and intelligent power distribution. Coordinating grid power distribution usually requires mutual communication between power distributors to accomplish coordination. However, the power network is complex, the network nodes are far apart, and the communication bandwidth is often expensive. Therefore, how to reduce the communication bandwidth in the cooperative power distribution process task is crucially important. One way to tackle this problem …
Of Landlords And Tenants: Property In The Midst Of A Pandemic, Seng Wei, Edward Ti
Of Landlords And Tenants: Property In The Midst Of A Pandemic, Seng Wei, Edward Ti
Research Collection Yong Pung How School Of Law
Described as the crisis of our generation, the coronavirus pandemic has had a profound effect on consumption behaviour, in turn devastating businesses globally. Marking a departure from the sanctity of contract and causing perceived incursions to accrued legal rights, the Coronavirus Act 2020 and related legislation provide business tenancies protection, among others, against forfeiture for non-payment of rent. While this regulation of commercial tenancies appears to be justified on the basis of pragmatic utility, I suggest that Honoré’s incident of ownership prohibiting harmful use also allows for these emergency laws to be vindicated from a property perspective. This provides an …
Holding Residential Property On Inter Vivos Trusts In Singapore: Transfers Of Equity Interests, Vincent Ooi
Holding Residential Property On Inter Vivos Trusts In Singapore: Transfers Of Equity Interests, Vincent Ooi
Research Collection Yong Pung How School Of Law
Following amendments to the ACD regime in Singapore, transferring equity interests to and from a trust with no beneficial owners will attract ACD, as will the exercise of a power of appointment by a trustee to grant equity interests to a beneficiary. Renunciation of interests in a bare trust will also attract ACD. Together with the introduction of ABSD (Trust), it is now impractical to use trusts to hold residential properties for succession planning purposes. Remaining options are to gift the properties without any strings attached or bequeath the properties in a will and risk subsequent changes to death taxation.
Misunderstood Menu Metrics: Side-Length Food Sizing Leads To Quantity Underestimation And Overeating, Thomas Allard, Stefano Puntoni
Misunderstood Menu Metrics: Side-Length Food Sizing Leads To Quantity Underestimation And Overeating, Thomas Allard, Stefano Puntoni
Research Collection Lee Kong Chian School Of Business
This research highlights consumers' failure to understand food sizing communicated using side-length metrics (e.g., 12-inch pizza, 8-inch cake, 2-inch cookie), which are ubiquitous in menus and online interfaces. A series of studies show that describing food size options using side-length metrics leads to food quantity underestimation and food intakes misaligned with consumers' objectives. This robust effect arises because of a linearization heuristic where people do not adequately adjust for the exponential difference in the surface area associated with linear changes in side-length metrics. Choice architecture interventions that replace side-length information with metrics varying linearly with quantities (e.g., surface area, numbers …
“No More Uncle” And Gender-Neutral Marketing, Singapore Management University
“No More Uncle” And Gender-Neutral Marketing, Singapore Management University
Perspectives@SMU
How Asian men’s beauty care is shaping conception of masculinity and gender-neutral marketing
Broken Effects? How To Reduce False Positives In Panel Regressions, Xina Li, Phebo D. Wibbens
Broken Effects? How To Reduce False Positives In Panel Regressions, Xina Li, Phebo D. Wibbens
Research Collection Lee Kong Chian School Of Business
Many published papers in the management field have used statistical methods that, according to the latest insights in econometrics, can lead to elevated rates of false positives: results that appear “significant, ” whereas they are not. The question is how problematic these less robust econometric analyses are in practice for management research. This paper presents simulations and an empirical replication to investigate two widespread but now largely discredited practices in panel data analysis: nonclustered standard errors and random effects (RE). The simulations indicate that these two practices can lead to strongly elevated rates of false positives in typical empirical settings …
What You See Is What You Get? Measuring Companies' Projected Employer Image Attributes Via Companies' Employment Webpages, Christian P. Theurer, Philipp Schapers, Andranik Tumasjan, Isabell M. Welpe, Filip Lievens
What You See Is What You Get? Measuring Companies' Projected Employer Image Attributes Via Companies' Employment Webpages, Christian P. Theurer, Philipp Schapers, Andranik Tumasjan, Isabell M. Welpe, Filip Lievens
Research Collection Lee Kong Chian School Of Business
Information on a company's employment webpage sends signals about the employer image the company intends to project to applicants. Nonetheless, we know little about the content of recruitment signals sent via company employment webpages. This study develops a method to measure companies' projected employer image attributes based on their employment webpages. Specifically, we analyze companies' projected employer image attributes by applying computer-aided text analysis (CATA) to the employment webpages of 461 Fortune 500 companies (i.e., more than 11,100 individual pages). Our results show that projected employer image attributes remain relatively stable over time. Moreover, we find relatively low levels of …