Singapore Management University

ABSTRACT We propose a new and direct measure of investor attention using search frequency in Google (Search Volume Index (SVI)). In a sample of Russell 3000 stocks from 2004 to 2008, we find that SVI (1) is correlated with but different from existing proxies of investor attention; (2) captures investor attention in a more timely fashion and (3) likely measures the attention of retail investors. An increase in SVI predicts higher stock prices in the next 2 weeks and an eventual price reversal within the year. It also contributes to the large first‐day return and long‐run underperformance of IPO stocks.

We investigate whether individual experiences of macroeconomic shocks affect financial risk taking, as often suggested for the generation that experienced the Great Depression. Using data from the Survey of Consumer Finances from 1960 to 2007, we find that individuals who have experienced low stock market returns throughout their lives so far report lower willingness to take financial risk, are less likely to participate in the stock market, invest a lower fraction of their liquid assets in stocks if they participate, and are more pessimistic about future stock returns. Those who have experienced low bond returns are less likely to own bonds. Results are estimated controlling for age, year effects, and household characteristics. More recent return experiences have stronger effects, particularly on younger people.

Using research designs patterned after randomized experiments, many recent economic studies examine outcome measures for treatment groups and comparison groups that are not randomly assigned. By using variation in explanatory variables generated by changes in state laws, government draft mechanisms, or other means, these studies obtain variation that is readily examined and is plausibly exogenous. This paper describes the advantages of these studies and suggests how they can be improved. It also provides aids in judging the validity of inferences they draw. Design complications such as multiple treatment and comparison groups and multiple pre- or post-intervention observations are advocated.

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for four different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.

According to the hedonic treadmill model, good and bad events temporarily affect happiness, but people quickly adapt back to hedonic neutrality. The theory, which has gained widespread acceptance in recent years, implies that individual and societal efforts to increase happiness are doomed to failure. The recent empirical work outlined here indicates that 5 important revisions to the treadmill model are needed. First, individuals' set points are not hedonically neutral. Second, people have different set points, which are partly dependent on their temperaments. Third, a single person may have multiple happiness set points: Different components of well-being such as pleasant emotions, unpleasant emotions, and life satisfaction can move in different directions. Fourth, and perhaps most important, well-being set points can change under some conditions. Finally, individuals differ in their adaptation to events, with some individuals changing their set point and others not changing in reaction to some external event. These revisions offer hope for psychologists and policy-makers who aim to decrease human misery and increase happiness.

“Grand challenges” are formulations of global problems that can be plausibly addressed through coordinated and collaborative effort. In this Special Research Forum, we showcase management research that examines societal problems that individuals, organizations, communities, and nations face around the world. We develop a framework to guide future research to provide systematic empirical evidence on the formulation, articulation, and implementation of grand challenges. We highlight several factors that likely enhance or suppress the attainment of collective goals, and identify representative research questions for future empirical work. In so doing, we aspire to encourage management scholars to engage in tackling broader societal challenges through their collaborative research and collective insight.

This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.

The impacts of COVID-19 on workers and workplaces across the globe have been dramatic. This broad review of prior research rooted in work and organizational psychology, and related fields, is intended to make sense of the implications for employees, teams, and work organizations. This review and preview of relevant literatures focuses on (a) emergent changes in work practices (e.g., working from home, virtual teamwork) and (b) emergent changes for workers (e.g., social distancing, stress, and unemployment). In addition, potential moderating factors (demographic characteristics, individual differences, and organizational norms) are examined given the likelihood that COVID-19 will generate disparate effects. This broad-scope overview provides an integrative approach for considering the implications of COVID-19 for work, workers, and organizations while also identifying issues for future research and insights to inform solutions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

ABSTRACT Using corporate social responsibility (CSR) ratings for 23,000 companies from 114 countries, we find that a firm's CSR rating and its country's legal origin are strongly correlated. Legal origin is a stronger explanation than “doing good by doing well” factors or firm and country characteristics (ownership concentration, political institutions, and globalization): firms from common law countries have lower CSR than companies from civil law countries, with Scandinavian civil law firms having the highest CSR ratings. Evidence from quasi‐natural experiments such as scandals and natural disasters suggests that civil law firms are more responsive to CSR shocks than common law firms.

This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the non-linear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models and a DSGE model that was solved with a second-order perturbation method.

Individual entrepreneurial intent is a key construct in research on new business formation. However, neither a clear or consistent definition of nor a uniform and reliable way to measure individual entrepreneurial intent has yet emerged. Several management scholars have highlighted the impediment this constitutes to the advancement of entrepreneurship research. This paper first seeks to clarify the construct of individual entrepreneurial intent and then reports the development and validation of a reliable and internationally applicable individual entrepreneurial intent scale.

In this article we propose a new multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model with time-varying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. Whereas each conditional-variance term is assumed to follow a univariate GARCH formulation, the conditional-correlation matrix is postulated to follow an autoregressive moving average type of analog. Our new model retains the intuition and interpretation of the univariate GARCH model and yet satisfies the positive-definite condition as found in the constant-correlation and Baba–Engle–Kraft–Kroner models. We report some Monte Carlo results on the finite-sample distributions of the maximum likelihood estimate of the varying-correlation MGARCH model. The new model is applied to some real data sets.

A recursive test procedure is suggested that provides a mechanism for testing explosive behavior, date stamping the origination and collapse of economic exuberance, and providing valid confidence intervals for explosive growth rates. The method involves the recursive implementation of a right-side unit root test and a sup test, both of which are easy to use in practical applications, and some new limit theory for mildly explosive processes. The test procedure is shown to have discriminatory power in detecting periodically collapsing bubbles, thereby overcoming a weakness in earlier applications of unit root tests for economic bubbles. An empirical application to the Nasdaq stock price index in the 1990s provides confirmation of explosiveness and date stamps the origination of financial exuberance to mid-1995, prior to the famous remark in December 1996 by Alan Greenspan about irrational exuberance in the financial market, thereby giving the remark empirical content. How do we know when irrational exuberance has unduly escalated asset values? (Alan Greenspan, 1996) Experience can be a powerful teacher. The rise and fall of internet stocks, which created and then destroyed $8 trillion of shareholder wealth, has led a new generation of economists to acknowledge that bubbles can occur. (Alan Krueger, 2005)

The authors highlight the need for and develop a framework for engagement by reviewing the relevant literature and analyzing popular-press articles. They discuss the definitions of the focal constructs—customer engagement (CE) and employee engagement (EE)—in the engagement framework, capture these constructs’ multidimensionality, and develop and refine items for measuring CE and EE. They validate the proposed framework with data from 120 companies over two time periods, and they develop strategies to help firms raise their levels of CE and EE to improve performance. They also observe that the influence of EE on CE is moderated by employee empowerment, type of firm (business-to-business [B2B] vs. business-to-consumer [B2C]), and nature of industry (manufacturing vs. service); in particular, this effect is stronger for B2B (vs. B2C) firms and service (vs. manufacturing) firms. The authors find that although both CE and EE positively influence firm performance, the effect of CE on firm performance is stronger. Furthermore, the effect of CE and EE on performance is enhanced for B2B (vs. B2C) and for service (vs. manufacturing) firms.

BACKGROUND: With online health information becoming increasingly popular among patients, concerns have been raised about the impact of patients' Internet health information-seeking behavior on their relationship with physicians. Therefore, it is pertinent to understand the influence of online health information on the patient-physician relationship. OBJECTIVE: Our objective was to systematically review existing research on patients' Internet health information seeking and its influence on the patient-physician relationship. METHODS: We systematically searched PubMed and key medical informatics, information systems, and communication science journals covering the period of 2000 to 2015. Empirical articles that were in English were included. We analyzed the content covering themes in 2 broad categories: factors affecting patients' discussion of online findings during consultations and implications for the patient-physician relationship. RESULTS: We identified 18 articles that met the inclusion criteria and the quality requirement for the review. The articles revealed barriers, facilitators, and demographic factors that influence patients' disclosure of online health information during consultations and the different mechanisms patients use to reveal these findings. Our review also showed the mechanisms in which online information could influence patients' relationship with their physicians. CONCLUSIONS: Results of this review contribute to the understanding of the patient-physician relationship of Internet-informed patients. Our main findings show that Internet health information seeking can improve the patient-physician relationship depending on whether the patient discusses the information with the physician and on their prior relationship. As patients have better access to health information through the Internet and expect to be more engaged in health decision making, traditional models of the patient-provider relationship and communication strategies must be revisited to adapt to this changing demographic.

The principal purpose of this study is to investigate the association between the efficiency of value added (VA) by the major components of a firm's resource base (physical capital, human capital and structural capital) and three traditional dimensions of corporate performance: profitability, productivity, and market valuation. Data are drawn from a sample of 75 publicly traded firms from South Africa from business sectors heavily reliant on intellectual capital. Empirical analysis is conducted using correlation and linear multiple regression analysis. Findings from the empirical analysis indicate that associations between the efficiency of VA by a firm's major resource bases and profitability, productivity and market valuation are generally limited and mixed. Overall, the empirical findings suggest that physical capital remains the most significant underlying resource of corporate performance in South Africa despite efforts to increase the nation's intellectual capital base.

Recent work on econometric detection mechanisms has shown the effectiveness of recursive procedures in identifying and dating financial bubbles in real time. These procedures are useful as warning alerts in surveillance strategies conducted by central banks and fiscal regulators with real‐time data. Use of these methods over long historical periods presents a more serious econometric challenge due to the complexity of the nonlinear structure and break mechanisms that are inherent in multiple‐bubble phenomena within the same sample period. To meet this challenge, this article develops a new recursive flexible window method that is better suited for practical implementation with long historical time series. The method is a generalized version of the sup augmented Dickey–Fuller (ADF) test of Phillips et al. (“Explosive behavior in the 1990s NASDAQ: When did exuberance escalate asset values?” International Economic Review 52 (2011), 201–26; PWY) and delivers a consistent real‐time date‐stamping strategy for the origination and termination of multiple bubbles. Simulations show that the test significantly improves discriminatory power and leads to distinct power gains when multiple bubbles occur. An empirical application of the methodology is conducted on S&P 500 stock market data over a long historical period from January 1871 to December 2010. The new approach successfully identifies the well‐known historical episodes of exuberance and collapses over this period, whereas the strategy of PWY and a related cumulative sum (CUSUM) dating procedure locate far fewer episodes in the same sample range.

This study draws on depth interviews with 49 managers in customer firms and 55 managers in supplier firms and on discussions with 21 managers in two focus groups to propose a new way of thinking about customer solutions. Extant literature and suppliers interviewed for this study view a solution as a customized and integrated combination of goods and services for meeting a customer's business needs. In contrast, customers view a solution as a set of customer–supplier relational processes comprising (1) customer requirements definition, (2) customization and integration of goods and/or services and (3) their deployment, and (4) postdeployment customer support, all of which are aimed at meeting customers' business needs. The relational process view can help suppliers deliver more effective solutions at profitable prices. In addition, field research suggests that the effectiveness of a solution depends not only on supplier variables but also on several customer variables. Supplier variables include contingent hierarchy, documentation emphasis, incentive externality, customer interactor stability, and process articulation. Customer variables include adaptiveness to supplier offerings and political and operational counseling that a customer provides to a supplier. Several of these variables underscore the importance of suppliers developing social capital with customers. The authors discuss implications for solution suppliers and identify areas for further research.

In turbulent environments, enterprise agility, that is, the ability of firms to sense environmental change and respond readily, is an important determinant of firm success. We define and deconstruct enterprise agility, delineate enterprise agility from similar concepts in the business research literature, explore the underlying capabilities that support enterprise agility, explicate the enabling role of information technology (IT) and digital options, and propose a method for measuring enterprise agility. The concepts in this paper are offered as foundational building blocks for the overall research program on enterprise agility and the enabling role of IT.

Academic research relies extensively on macroeconomic variables to forecast the U.S. equity risk premium, with relatively little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the predictive ability of technical indicators with that of macroeconomic variables. Technical indicators display statistically and economically significant in-sample and out-of-sample predictive power, matching or exceeding that of macroeconomic variables. Furthermore, technical indicators and macroeconomic variables provide complementary information over the business cycle: technical indicators better detect the typical decline in the equity risk premium near business-cycle peaks, whereas macroeconomic variables more readily pick up the typical rise in the equity risk premium near cyclical troughs. Consistent with this behavior, we show that combining information from both technical indicators and macroeconomic variables significantly improves equity risk premium forecasts versus using either type of information alone. Overall, the substantial countercyclical fluctuations in the equity risk premium appear well captured by the combined information in technical indicators and macroeconomic variables. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1838 . This paper was accepted by Wei Jiang, finance.