Search results

The endowment effect is arguably one of the most robust phenomena documented in economics, behavioral decision theory and consumer research. However, the endowment effect has traditionally been studied as a fairly static phenomenon at the transactional level of analysis.

This paper documents this “contagious” endowment effect using lab experiments and such field data as eBay transactions and discuss the managerial implication of these findings.

This study suggests that the endowment effect is not limited to the level of the specific object, but can manifest itself with the more abstract class of objects to which a specific object happens to belong.

A logical next step would be to examine the boundary conditions – how similar does the subsequent object have to be for the endowment effect to transfer over to it? A related aspect would be whether there are boundary conditions arising from the quality of the endowment.

The effects reported here probably underlie the success of the many types of “bait and switch” schemes that have been used by the more unsavory type of marketer. As such, these findings might have implications for policy in the area of consumer protection.

This paper argues for and presents evidence consistent with the notion that the endowment effect is dynamic and can be transferred from one transaction to another and refer to this generalization of the endowment effect to other, similar products as “contagious endowment.”

The purpose of this paper is to explore the spillover effects of offensive commenting in online community from the lens of emotional and behavioral contagion. Specifically, it examines the contagion of swearing – a linguistic mannerism that conveys high-arousal emotion – based upon two mechanisms of contagion: mimicry and social interaction effect.

The study performs a series of mixed-effect logistic regressions to investigate the contagious potential of offensive comments collected from YouTube in response to Donald Trump’s 2016 presidential campaign videos posted between January and April 2016.

The study examines non-random incidences of two types of swearing online: public and interpersonal. Findings suggest that a first-level (a.k.a. parent) comment’s public swearing tends to trigger chains of interpersonal swearing in the second-level (a.k.a. child) comments. Meanwhile, among the child-comments, a sequentially preceding comment’s swearing is contagious to the following comment only across the same swearing type. Based on the findings, the study concludes that offensive comments are contagious and have impact on shaping the community-wide linguistic norms of online user interactions.

The study discusses the ways in which an individual’s display of offensiveness may influence and shape discursive cultures on the internet. This study delves into the mechanisms of text-based contagion by differentiating between mimicry effect and social interaction effect. While online emotional contagion research to this date has focused on the difference between positive and negative valence, internet research that specifically looks at the contagious potential of offensive expressions remains sparse.

Predicting future outbreaks and understanding how they are spreading from location to location can improve patient care provided. Recently, mining social media big data provided the ability to track patterns and trends across the world. This study aims to analyze social media micro-blogs and geographical locations to understand how disease outbreaks spread over geographies and to enhance forecasting of future disease outbreaks.

In this paper, the authors use Twitter data as the social media data source, influenza-like illnesses (ILI) as disease epidemic and states in the USA as geographical locations. They present a novel network-based model to make predictions about the spread of diseases a week in advance utilizing social media big data.

The authors showed that flu-related tweets align well with ILI data from the Centers for Disease Control and Prevention (CDC) (p < 0.049). The authors compared this model to earlier approaches that utilized airline traffic, and showed that ILI activity estimates of their model were more accurate. They also found that their disease diffusion model yielded accurate predictions for upcoming ILI activity (p < 0.04), and they predicted the diffusion of flu across states based on geographical surroundings at 76 per cent accuracy. The equations and procedures can be translated to apply to any social media data, other contagious diseases and geographies to mine large data sets.

First, while extensive work has been presented utilizing time-series analysis on single geographies, or post-analysis of highly contagious diseases, no previous work has provided a generalized solution to identify how contagious diseases diffuse across geographies, such as states in the USA. Secondly, due to nature of the social media data, various statistical models have been extensively used to address these problems.

Existing research has theoretically modeled conditional correlations between the long-term interest rates as a function of macroeconomic variable. In line with it, the purpose of this paper is to explore whether conditional correlations can be a new signal to predict recessions. Furthermore, this paper also tries to investigate among the four factors – the time difference of the beginning and the end of recessions, financial integration (FI), and trade integration (TI) – which factors drive the direction of change in conditional correlations. Finally, this paper is to explain the implication for Korea trade.

This study uses a probit regression model for 33 country during the period from 1972 to 2015. To measure the time-varying interest rates conditional correlations, a VAR(1)-DBEKK-GARCH(1,1) model is adopted due to its statistical advantages. Furthermore, the authors also construct the four measures – time difference of the beginning of recessions (BEG), time difference of the end of recessions (END), FI, and TI. The authors first study the predictive power of correlations in both in and out-samples test, and study which factors determine the different behavior of interest rate co-movements using the four measures.

The empirical results show that the conditional correlations between the long-term interest rates of the USA and individual countries contain information about recessions a few quarters ahead which term spreads of neither individual countries nor the USA conveyed in. However, there is a heterogeneity of the significance and direction of interest rate correlations. A further research reveals that especially the heterogeneous degree of TI leads to the different overlapped recession period of individual countries with the USA, resulting in heterogeneous behavior of interest rates among countries.

As a limitation of this paper, the forecasting power of interest rate correlations is not always significant in all countries. Despite this, the study has a profound implication that for those countries where the US accounts for the high proportion of trade, increase in conditional correlations can be a signal for future recessions. Especially, given a considerable portion of trade in GDP and the more sensitive trade activity of Korea to a contagious recession than a domestic recession, the conditional correlation measure is particularly useful for Korean policy makers.

Although many papers model interest rate co-movement as a function of macroeconomic condition, this paper provides the first evidence to show interest rate co-movement precede the macro shocks empirically. Furthermore, this paper determines the precise channel through which TI affects the time-varying behavior of interest rate co-movements before recessions.

The purpose of this paper is to examine the effects of share repurchase announcements on the stock price of rival firms in the same industry in Vietnam during 2010–2017.

Both event study and t-test are employed to test the effects of share repurchase announcements on rival firms. In addition, cross-sectional analysis by ordinary least square regression is also applied for investigating the heterogeneous effects due to information transfer.

The finding shows that stock repurchase announcements result in a positive and significant valuation effect for both announcing firms and rival firms in Vietnam. Furthermore, the degree of signal to the industry is conditional on the degree of signal about the announcing firms as a contagious effect. Intra-industry effects are more favorable when profit performance of rival firms is good and when leverage of rival firms is low.

Rival firms can seize opportunities surrounding share repurchase announcements in the same industry in Vietnam. However, due to firm characteristics, intra-industry effects of stock repurchases differ among industries.

By examining different methods, the paper attributes valuable results to investigate the stock price behavior of rival firms in the same industry when firms announce stock repurchase in Vietnam.

Twitter usage during Gezi Park Protests, a significant large-scale connective action, is analyzed to reveal meaningful findings on individual and group tweeting characteristics. Subsequent to the Arab Spring in terms of its timing, the Gezi Park Protests began by the spread of news on construction plans to build a shopping mall at a public park in Taksim Square in Istanbul on May 26, 2013. Though started as a small-scale local protest, it emerged into a series of multi-regional social protests, also known as the Gezi Park demonstrations. The paper aims to discuss these issues.

The authors sought answers to three important research questions: whether Twitter usage is reflective of real life events, what Twitter is actually used for, and is Twitter usage contagious? The authors have collected streamed data from Twitter. As a research methodology, the authors followed social media analytics framework proposed by Fan and Gordon (2014), which included three consecutive processes; capturing, understanding, and presenting. An analysis of 54 million publicly available tweets and 3.5 million foursquare check-ins, which account to randomly selected 1 percent of all tweets and check-ins posted from Istanbul, Turkey between March and September 2013 are presented.

A perceived lack of sufficient media coverage on events taking place on the streets is believed to result in Turkish protestors’ use of Twitter as a medium to share and get information on ongoing and planned demonstrations, to learn the recent news, to participate in the debate, and to create local and global awareness.

Data collection via streamed tweets comes with certain limitations. Twitter restricts data collection on publicly available tweets and only allows randomly selected 1 percent of all tweets posted from a specific region. Therefore, the authors’ data include only tweets of publicly available Twitter profiles. The generalizability of the findings should be regarded with concerning this limitation.

The authors conclude that Twitter was used mainly as a platform to exchange information to organize street demonstrations.

The authors conclude that Twitter usage reflected Street movements on a chronological level. Finally, the authors present that Twitter usage is contagious whereas tweeting is not necessarily.

This study aims to analyze the contagious effects of economic policy uncertainties in the USA on the economies of its important trading partners, such as Japan, Canada, Mexico and the Eurozone.

In the study using the uncertainty index created by Baker et al. (2016), the interaction between variables was analyzed with structural VAR (SVAR) models.

According to the results obtained from the analysis, economic policy uncertainties in the USA had significant effects on the economies of its high-volume trading partners. The internal debt crisis experienced in the Eurozone after the 2008 crisis caused the European Central Bank to respond to the economic policy uncertainties in the USA with contractionary monetary policies, unlike other countries. In addition to these results, Mexico, which has a more fragile economic structure than other countries in the analysis, was more impacted by increasing uncertainties, as expected.

The present study aimed to bring a new perspective to the literature by evaluating the contagiousness of local uncertainty in the globalizing world and the monetary policies implemented as a precaution against this situation on an empirical plane.

This study examines the extent to which gold and silver bubbles are correlated and which metal’s bubble spills over to the other. In addition, the overlap in bubble-like episodes for the two metals is demonstrated and the influence of crises (global financial crises, European debt crisis and the COVID-19 pandemic) on the development of these episodes is compared.

This study proposes a two-step approach. In the first step, price bubbles are identified based on the backward sup augmented Dickey–Fuller of Phillips et al. (2015a, 2015b) and modified by Phillips and Shi (2018). In the second step, the correlation in the contagion effect of the bubbles between the two precious metal prices is measured using a nonparametric regression with a time-varying coefficient approach developed by Greenaway-McGrevy and Phillips (2016).

The findings suggest that the safe-haven property of gold and silver during financial market turbulence induces excessive price increases beyond their fundamental values. Furthermore, the results indicate that bubbles are contagious among precious metal markets and flow mainly from gold to silver; these findings are associated with the period after 2005, particularly during the global financial crisis. A contagious bubble effect is not found between gold and silver during the coronavirus disease 2020 pandemic.

The results suggest that financial market participants should consider portfolio weights in precious markets in light of the bubble correlation between gold and silver, especially during crises.

To the best of the authors’ knowledge, this is the first study that explores the correlation of bubble-like episodes between gold and silver.

This paper aims to examine the contagion effects of Greece, Ireland, Portugal, Spain and Italy (GIPSI) and US stock markets on seven Eurozone and six non-Eurozone stock markets.

In this paper, a dynamic conditional correlation (DCC) model popularly known as DCC-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model given by Engle (2002) is applied to estimate the DCCs across sample markets.

Analyzing the Eurozone crisis period, the empirical results suggest that among GIPSI stock markets, Spain, Italy, Portugal and Ireland appear to be most contagious for Eurozone and non-Eurozone markets. The study finds that France, Belgium, Austria and Germany in Eurozone and UK, Sweden and Denmark in non-Eurozone are strongly hit by the contagion shock.

The findings of the study have significant implications for the concerned regulatory authorities, as it may provide an important direction for further policy research with regard to financial integration in the European Union (EU). From global investors’ perspective, the EU-based diversification strategies seem to be inefficient especially during Eurozone crisis.

To the best of the authors’ knowledge, this is the first study that examines the issue of financial contagion of Eurozone crisis for a large basket of stock markets of European countries comprising seven Eurozone and six non-Eurozone markets for the period 2009-2012. The study uses the Markov regime switching model to identify crisis period and utilizes the DCC estimates of DCC-GARCH to examine the patterns of financial contagion. The finding of this study is quite interesting and is different in several ways than existing studies in the literature.

The purpose of this paper is to address the following question: Can a bank capitalize on its well-established self-service technologies (SSTs) in order to entice customers to adopt a newly introduced SST, namely, mobile banking? More specifically, it proposes an integrative model that simultaneously investigates the transference effects of attitudes, trust and the contagious influences of social pressures on mobile banking adoption intentions.

Structural equation modeling is applied to data collected from banks’ clients who are actually non-users of mobile banking.

The results indicate that attitude toward and trust in mobile banking along with coercive, normative and mimetic pressures are key antecedents to mobile banking adoption intentions. In addition, attitudes toward automated teller machines (ATMs) and online banking significantly predict attitude toward mobile banking. The results also support the effects of trust in ATMs as well as trust in online banking on trust in mobile banking. Moreover, predicted differences in the relative effects of attitude and trust are supported. Particularly, attitude toward online banking has a stronger impact on attitude toward mobile banking compared to the impact of attitude toward ATMs. In the same vein, the effect of trust in online banking on mobile banking is significantly stronger than the effect of trust in ATMs.

The study’s results hint at some practical and worthwhile guidelines for banks that can be leveraged in communication campaigns aiming at boosting the adoption rates of mobile banking. Banks can take advantage of the transference effects of the established attitudes toward and trusting beliefs in their mature SSTs as well as the contagious social influences in inducing the adoption of a newly introduced SST.

The present study represents a first step toward generating new insights into the role of the joint effects of attitudes, trust and social influences in the adoption of a new SST.