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The purpose of this research is to examine how pre-scandal associations and scandal types interactively influence consumer judgment and negative electronic word-of-mouth (eWOM).

Drawing from cognitive dissonance theory and associative memory network model, the online experiments (Amazon Mechanical Turk; Nexperiment 1 = 146 and Nexperiment 2 = 189) were conducted to examine the effects of positive pre-scandal associations (performance vs pro-social) and scandal types (performance-related vs -unrelated) on consumer blame and eWOM toward scandalized athletes. Data were analyzed by employing t-test (experiment 1), Multivariate Analysis of Covariance (MANCOVA) and PROCESS Model 8 (experiment 2) to test the hypotheses.

The findings highlight that positive pre-scandal association demonstrated both protecting and backfiring effects depending on the types of scandals. Specifically, when performance-related scandals emerged, consumers made more negative blame judgment of athletes with salient performance association, relative to pro-social association. Inversely, when performance-unrelated scandals occurred, athletes with salient pro-social association were more likely to be blamed. Regarding eWOM, consumers generate more negative eWOM when athletes with pre-performance associations are involved with performance-related scandals. This pattern of the result was not observed when athletes' pro-social association and performance-unrelated scandals were prominent.

The current work adds consumers' negative eWOM toward scandalized athletes to the literature as a predictor of how athletes' pre-scandal association with consumers and scandal types are related. The findings indicate that consumers feel greater dissonance and generate more negative eWOM when athletes' pre-scandal associations and scandal types are closely related.

Marketers and brand managers are subject to reputational crises when their endorsers are involved in scandals. To effectively manage such crises, it is imperative to understand (1) the underlying mechanisms through which consumers process negative information surrounding morally tainted endorsers, and (2) how these mechanisms affect consumer behavior in the context of athlete scandals.

Drawing on attribution theory and the moral reasoning strategy framework, we investigate the impact of attribution on moral reasoning strategies, and the impact of such strategies on consumers' responses to scandalized athletes and endorsements.

Overall, our results demonstrate that the same scandal can be evaluated differently, depending on its information, including the consensus, distinctiveness, and consistency of the scandal. The results of Study 1 show that in the context of an on-field scandal, individuals engage in a sequential cognitive process in which they go through attribution, the choice of a moral reasoning strategy, and ultimately a response. The results of Study 2 reveal that in the context of an off-field scandal, attribution directly influences consumers' responses.

We extend the existing literature on the moral reasoning of athlete scandals by suggesting that attribution is a determinant of moral reasoning choice in the context of on-field scandals. We also extend the sports marketing and consumer behavior literature by suggesting that consumers' diverse reactions to athlete scandals depend on their attribution patterns and moral reasoning choices.

This paper estimates and forecasts yield curve of korea bond market using a three factor term structure model based on the Nelson-Siegel model. The Nelson-Siegel model is in-terpreted as a model of level, slope and curvature and has the flexibility required to match the changing shape of the yield curve. To estimate this model, we use the two-step estima-tion procedure as in Diebold and Li. Estimation results show our model is Quite flexible and gives a very good fit to data.

To see the forecasting ability of our model, we compare the RMSEs (root mean square error) of our model to random walk (RW) model and principal component model for out-of sample period as well as in-sample period. we find that our model has better forecasting performances over principal component model but shows slight edge over RW model especially for long run forecasting period. Considering that it is difficult for any model to show better forecasting ability over the RW model in out-of-sample period, results suggest that our model is useful for practitioners to forecast yields curve dynamics.

This study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.

The study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.

The study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.

The study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.

To the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

The speed of information transfer, discovery, and evaluation is accelerating the timeline for young researchers to establish their scholarly identity. A clear scholarly identity is an essential element in developing and maintaining a scholar’s reputation. The paper aims to discuss this issue.

Texas A&M University is developing workflows and programmatic support for graduate students as they navigate a labyrinth of tools, such as ORCID iD, to establish and manage their identity and reputation.

This paper discusses the evolution of a collaboration between the Texas A&M University Libraries (A&M UL) and the Office of Research and Graduate Studies in the College of Veterinary Medicine & Biomedical Sciences to educate graduate-level students about scholarly identity and reputation.

The collaboration between the Texas A&M UL and the Texas A&M College of Veterinary Medicine & Biomedical Sciences Office of Research and Graduate Studies evolved into an intentional practice to assist emerging researchers as they navigate the labyrinth of standards and research information systems to establish their scholarly identity.

The purpose of this paper is to examine the relationships between researcher characteristics and their use of metadata in their ResearchGate profiles.

This paper reports on one part of a larger study that examined researchers’ use of and engagement with research information management systems (RIMSs). The study’s design included qualitative, semi-structured interviews with 15 researchers and a survey completed by 412 researchers. Detailed reports of findings from the interviews and survey can be found elsewhere. This paper reports on the part of the study that analyzed the use of metadata elements in the ResearchGate profiles of 126 survey participants.

Most researchers shared metadata related to their research rather than their teaching or service. Statistical analyses revealed statistically significant relationships between researchers’ metadata use and their participation levels in RIMSs, as well as between metadata use and researchers’ seniority.

The study’s findings help to identify researchers’ priorities for different metadata elements, as well as to construct profile metadata templates for each specific participation level.

The restructuring of shipping and shipbuilding companies in the midst of rapidly shrinking global shipping demand has become a prominent issue in Korea. In shipping finance, loan syndication featuring many creditors surges as the preferred option. However, increasing the numbers of creditors in the syndicate results in two opposite effects. First is the beneficial effect from their enhanced monitoring power. On the other hand, there is the adverse effect resulting from increased difficulty in coordination when syndicate members increase, particularly in bankruptcy. Our aim of this paper is to analyze the role of finance in the shipping and shipbuilder markets, and determine the theoretical optimal number of creditors for the shipping finance syndicate based on Bolton and Scharfstein (1996). The two issues above result from moral hazard and non-verifiability: coordination among many creditors for collection of bonds in case of default, and the enhancement of monitoring private benefit exploitation by the ship-owner during default. Considering the two conflicting forces result from an increase in creditor membership, we draw conclusions on determining the optimal number of creditors by considering trade-offs between these two factors: More creditors are preferred when the monitoring effect dominates. Otherwise, less creditors are preferred.

In this paper, we analyze the effect of investor attention level on expected return in the Korean stock market by investor type. We find that the risk-adjusted excess returns in the next period are significantly higher when the institutional and foreign investor’s attention is high. In other words, investment strategies that buy stocks in higher attention groups and sell those in lower attention groups provide significant excess returns. This result is in contrast to the argument that the market operates more competitively and moves more efficiently as the number of investors increases due to the increased investor attention. Next, we examine how the degree of attention of institutional, individual, and foreign investors affects each other. The analysis reveals that the attention of individual investors affects the attention of institutional investors in the next period, and vice versa. In addition, as a result of group analysis according to the size of company and stock price, we find that the investor's attention affects the market differently depending on the type of investors and stock price level.

There have been several document ranking methods to calculate the conceptual distance or closeness between a Boolean query and a document. Though they provide good retrieval effectiveness in many cases, they do not support effective weighting schemes for queries and documents and also have several problems resulting from inappropriate evaluation of Boolean operators. We propose a new method called Knowledge‐Based Extended Boolean Model (kb‐ebm) in which Salton's extended Boolean model is incorporated. kb‐ebm evaluates weighted queries and documents effectively, and avoids the problems of the previous methods. kb‐ebm provides high quality document rankings by using term dependence information from is‐a hierarchies The performance experiments show that the proposed method closely simulates human behaviour.

This research looks into hedge strategies to resolve foreign exchange-related risks, generated when investing in overseas financial assets, as an example of quantity risk. If an investor has information with no uncertainty over the volume and there is only a price risk he want to hedge, an investor will be able to reduce or eliminate risks by using relative derivative securities such as forwards or futures contracts. However, if there are uncertainties over the volume of hedging targets that is called quantity risk, it is impossible to set the optimal hedge ratio with the traditional method without considering the presence of quantity risk. In this paper, we theoretically draw an optimal hedge ratio which is estimated via minimal variance criterion under static hedge structure. We also analyze its hedge performance and the impact of change in covariance on the optimal hedge ratio and variance of investment return denominated as its own country currency. For theoretical approach, we review the impact that overseas financial assets’ yield and exchanges rates distribution will have on optimal hedge ratio through simple numerical analysis. Empirical analysis is carried out by using the stock indices of the U.S., Europe and Asian countries, and the results indicate that hedge strategies taken with quantity risk for all markets produced better hedging performance than the strategies taken without quantity risk. Since there is a need for systematic research on risks involving foreign exchanges that occur in the event of foreign investments aimed to develop the domestic financial industry, we hope that our research serve as a stepping-stone for further research.