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This study aims to explore the spillover effects of data breaches from a consumer perspective in the e-commerce context. Specifically, we investigate how an online retailer’s data breach affects consumers’ privacy risk perceptions of competing firms, and further how it affects shopping intention for the competitors. We also examine how the privacy risk contagion effect varies depending on the characteristics of competitors and their competitive responses.

We conducted two scenario-based experiments with surveys. To assess the spillover effects and the moderating effects, we employed an analysis of covariance. We also performed bootstrapping-based mediation analyses using the PROCESS macro.

We find evidence for the privacy risk contagion effect and demonstrate that it negatively influences consumers’ shopping intention for a competing firm. We also find that a competitor’s cybersecurity message is effective in avoiding the privacy risk contagion effect and the competitor even benefits from it.

While previous studies have examined the impacts of data breaches on customer perceptions of the breached firm, our study focuses on customer perceptions of the non-breached firms. To the best of the authors’ knowledge, this study is one of the first to provide empirical evidence for the negative spillover effects of a data breach from a consumer perspective. More importantly, this study empirically demonstrates that the non-breached competitor’s competitive response is effective in preventing unintended negative spillover in the context of the data breach.

This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.

Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.

The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.

The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.

The study aims to investigate market reactions associated with the JUST 100 rankings published by JUST Capital, a non-profit organization, as well as differences in financial reporting quality and performance between selected firms and their industry peers.

This study uses a sample of 431 firms selected as the 100 America’s Most Just Companies between 2016 and 2020 by JUST Capital. This study performs both an event study to determine whether the rankings are useful to investors and cross-sectional regression analyses on the characteristics of selected firms compared to their peers.

This study finds that investors react positively to selected firms around the time of the release of the JUST 100 rankings, suggesting that the rankings are decision-useful. This study also finds that selected firms exhibit higher accounting quality and financial performance than their peers.

Rankings may not be free from bias because of JUST Capital’s ownership of an exchange-traded fund.

The findings validate the rankings as well as the methodology used by JUST Capital, as they show market participants value firms that engage in socially responsible actions through their commitment to positively impact five key stakeholder groups: employees, customers, communities, environment and shareholders.

To the best of the authors’ knowledge, this is the first study that shows the importance of the JUST 100 rankings for investment decisions. Considering the growing push for companies to disclose environmental, social and governance (ESG) activities, this study provides evidence to support ESG disclosure regulations.

Findings in the literature indicate leading financial analysts attract high levels of market attention and provide more accurate earnings forecasts prior to becoming all-star analysts. Furthermore, these analysts significantly impact the investment decisions of other market participants and thus the market price of assets. Therefore, this study examines the information role of leading financial analysts and identifies two significant conclusions. First, the positive outcomes of these analyst leaders are more informative and attract more followers. Second, informational herding by followers of these analysts is not as naïve as suggested in previous studies, as followers who smartly use information from analyst leaders tend to perform better. We also find that analysts who practice smart learning by studying and selectively employing analyst-leader decisions achieve better career outcomes.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

This article provides the first empirical study on the effects of the COVID-19 pandemic on sustainability reporting in US financial institutions using institutional, stakeholder and legitimacy theories.

The study used the independent sample t-test and Mann–Whitney U test throughout as well as OLS, random effects, fixed effects and heteroskedasticity corrected model to test the impact of the COVID-19 pandemic on sustainability reporting in the US financial sector. A sample from all listed US financial firms was used after controlling for both the Refinitiv Eikon sector classification and the NAICS sector classification.

Using U Mann–Whitney test and independent sample t-test the study revealed that the average ESG score for the pre-COVID19 period is 53% compared with 62.3% for the COVID-19 period, indicating that the sustainability reporting during COVID-19 is much higher compared with the pre-pandemic period. The findings of regression analysis also confirm that the US financial companies increased their sustainability reporting during the COVID-19 pandemic.

This study is an early attempt to look at how the COVID-19 epidemic has affected financial reporting procedures, although it is focused only on one area and other entity-related factors like stock market implications, company governance, internal audit practice, etc could have been considered.

This research offers useful recommendations for policymakers to create standards for regulators on the significance of raising sustainability awareness. The findings are crucial for accounting regulators as they work to implement COVID-19 and enforce required integrated reporting rules and regulations.

The study provides the first empirical evidence on the impact of the COVID-19 pandemic on sustainability reporting, by examining how US financial institutions approach the topic of sustainability during the COVID-19 pandemic and assessing the pandemic's current consequences on sustainability.

The purpose of this article is to explore the relationship between the attributes of Olympic mascots and their impact on sponsorship effectiveness. Based on a multiattribute model and the introduction of engagement theory and the meaning transfer model, this article uses the 2022 Beijing Winter Olympics mascot “Bing Dwen Dwen” as the research object to empirically analyze the effects and mechanisms of the mascot's attributes on preference, event engagement, sponsorship enterprise trust and sponsorship enterprise attitude, ultimately constructing a sponsorship effectiveness model.

The survey method was used to examine 238 respondents' emotions and attitudes towards companies participating in sponsoring Olympic mascots.

The study found that the main attributes of the mascot include visual and emotional factors, both of which have a positive impact on preference, with emotional factors having a greater influence than visual factors. Visual and emotional factors indirectly affect engagement through preference. Preference and engagement play a completely mediating role in the effect of mascot attributes on sponsorship enterprise trust and sponsorship enterprise attitude.

This study provides practical recommendations for managers to achieve marketing success in sports sponsorship through mascots.

This paper provides a measurement tool for the study of mascot attributes and important support for subsequent research in sponsorship marketing.

This research analyzes the alignment of story framing between Samarco and news media following the dam disaster in Minas Gerais in November 2015. Drawing on framing theory as the underlying impetus, the study seeks to answer five major questions: RQ1: How did Samarco frame the mining tragedy in the aftermath of the dam collapse? RQ2: How did the news media frame the mining tragedy in the aftermath of the dam collapse? RQ3: Did the frames presented by Samarco and news media coincide? RQ4: Did the frames presented by Samarco and news media contradict? RQ5: What can be observed about the information flow and interaction between news media and the general public on social media? From a methods perspective, the study uses comparative textual analysis and NodeXL social network visualization to analyze the discourse around Samarco and information flow on social media in the aftermath of the tragedy. The results show that, while some social media content served as a forum for expressions of empathy toward survivors, social media content on Twitter mostly delivered a one-sided and positive view of the firm’s actions.

This study aims to explore the impact of carbon neutral announcements on “stock market value” of publicly listed companies in China.

The event study approach is adopted. Market, market-adjusted, Carhart four-factor model and a cross-sectional regression model are employed to examine the impacts of carbon neutral announcements on “stock market value” of Chinese companies based on data from 188 carbon neutral announcements.

Carbon neutral announcements positively impact Chinese shareholder value. Carbon neutral announcements at the strategic level have a more positive and significant impact on Chinese stock market value. Innovative carbon neutral announcements do not significantly cause Chinese stock market reactions. Companies have more positive and significant stock market reactions when the companies make carbon neutral announcements that reflect high supply chain network resilience and heterogeneity and strong supply chain network relationships.

The findings uncover the business value of carbon neutral activities and provide operations managers in developing countries insights into how to improve enterprises' market value by actively implementing carbon neutral activities.

This paper is the first trial to apply an event study to examine the relationship between carbon neutral announcements and Chinese stock market value from the perspective of announcement level and type and supply chain networks. This paper introduces corporate reputation theory and enriches the application of corporate reputation theory in the field of low-carbon environmental protections and supply chains.

This study aims to evaluate the presence of abnormal returns due to stock splits or reverse stock splits in the Brazilian capital market context.

The event study technique was used on data from 518 events that occurred in a 30-year period (1987–2016), comprising 167 stock splits and 351 reverse stock splits.

The results revealed the occurrence of abnormal returns around the time the shares began trading stock splits or reverse stock splits at a statistical significance level of 5%. The main conclusion is that stock split and reverse stock split operations represent opportunities for extraordinary gains and may serve as a reference for investment strategies in the Brazilian stock market.

This study innovates by including reverse stock splits, as the existing literature focuses on stock splits, and by testing two distinct “zero” dates that of the ordinary general meeting that approved the share alteration and the “ex” date of the alteration, when the shares were effectively traded, reverse split or split.