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Despite the fact this is already known over how sports spectators interpret sponsorship content, less is known about participants’ sentiments toward sponsors, which are sometimes crucial to the survival of participation sporting events. Various researchers have examined at how spectators recognize or recall sponsors, but very few have explored it from the viewpoint of sport participants. This research portrays a shift in focus from spectator-based to participant-based marketing by studying the differences over how participants and spectators perceive sport sponsorship, recognizing the current knowledge gap regarding the process of sponsorship in participant-based sports. Sponsorship previous research has focused on the effectiveness of sport sponsorships utilizing variables such sponsor recognition, desire to sponsors’ products and anticipated sponsorship advantages (Koronios and Dimitropoulos, 2020). The examination of sport sponsorship from the standpoint of the sport spectator is something that many of the past studies have in common. The application of past research analysing sport sponsorship from a distinct perspective: that of the sport participant, distinguishes the present study. In addition, the present study explores the distinctions in sponsorship effectiveness between spectators and participants, a topic which hasn't been addressed at before in sponsorship literature.

This study depicts a move away from spectator-based to participant-based marketing, utilizing contemporary implicit physiological variables to measure participants’ and spectators’ awareness of and attitude toward sponsors, and it proposes a model of their purchase intentions toward real sponsors of a martial event. A total of 1,332 questionnaires were collected and analysed.

Factors such as sport involvement, social media use, sincerity and beliefs of sponsorship were found to have a significant effect on purchase intention of spectators and participants. And crucial differences in the significance of various antecedent factors between spectators and participants were detected in the formation of positive purchase and word-of-mouth intentions.

This paper gives prominence to contemporary methodological advances in the field of implicit social cognition and their relevance to sponsorship. This is the first sponsorship research which examines the effectiveness of sponsorship variables under the implicit–explicit variables dichotomy.

The purpose of this paper is to investigate the information-based microstructure theory’s effectiveness in explaining short-term disturbances in currency prices by determining whether the price discovery process in the US dollar (USD) and South African rand (ZAR)-USD/ZAR spot market is led by an individual market agent, around an exogenous news event.

The influence of central bank intervention-related events on USD/ZAR volatility is investigated through the application of Brown-Forsythe variance equality tests on individual dealer and market quotes. Furthermore, the study applies bivariate Granger-causality tests to individual dealers’ USD/ZAR spot rate quotes, in an effort to determine whether certain dealers can be established as price leaders around an exogenous news event.

The study finds significant evidence to suggest the USD/ZAR market price leadership of Nomura forex (FX) prior to the public announcement of a South African Reserve Bank intervention-related news event. This finding supports microstructure theory’s assertions regarding the existence of foreign-exchange market characteristics such as trader heterogeneity and private information.

The paper is conducted on a sample of eight USD/ZAR market agents, of which six are offshore dealers, and only two are located locally. Although these proportions are somewhat relatable to the locations of rand turnover, it would still be interesting to investigate the existence of price leadership solely amongst South African authorised FX dealers.

The results suggest the existence and price relevance of private information, as well as the heterogeneous nature of USD/ZAR market participants, based on informational asymmetries. The outcomes of the paper are useful to market participants, researchers, and central banks alike.

Though the study does not impugn the body of work related to the orthodox macroeconomic approaches to exchange rate determination, it seems apparent that much more microstructure-related research still has to be conducted in the context of emerging market currencies. It is this void that the current study has attempted to provide for in contribution to literature.

The purpose of this paper is to analyze the relationship between the factors influencing investors sentiment and investment decision-making (DM) of the individual investors. This paper proposes a unique conceptual framework that incorporates the herding, market and awareness factors that are leading to investor sentiment (IS) and decision-making process of the individual investors.

This study has conducted a questionnaire-based survey to collect data from 875 individual investors through the convenience sampling method. Structural equation modeling was used to evaluate the relationship between factors, namely, market effect, herd behavior, media, social interaction and advocate recommendation that influences IS and DM.

The present study found that market effect and herding are the most significantly influencing factors of investors sentiment. Among the sources of awareness, the internet has the lowest influence when compared to media, social interaction and advocate recommendation.

This study will help individual investors to avoid the problems faced while making an investment decision. The study could help investors to select a suitable investment aid and avoid repeating expensive errors, which arise due to investors’ sentiment. It is recommended to increase the awareness regarding investors’ sentiment among individuals, so as to increase their understanding about the financial settings and to make them confident while investing. The present study also sheds light upon the behavior of Indian individual investors so that policymakers can take appropriate measures to provide the proper guidance. Policymakers can conduct awareness campaigns to increase investors’ knowledge on the market condition and to enhance proper investment DM among them.

To best of the authors’ knowledge, previous studies have focused on limited factors at a time. The present study has investigated how factors influencing investors sentiment, namely, market factors (MF), herding as well as awareness would influence investment DM among individual investors in India. The influence of these factors has never been studied simultaneously in the context of Indian individual investors’ DM.

This study aims to develop and test a methodical approach to assessing the effectiveness of business models in fast fashion.

The approach allows one to identify the key features of companies' adaptation strategies during the COVID-19 pandemic. The paper analyzes the economic profitability of the fast-fashion industry and establishes business opportunity trends. The author identifies a set of general characteristics of business related to supply and demand before and during the pandemic crisis. The study relies on descriptive statistics and statistical processing methods, including regression analysis, arithmetic mean and the indicator of market economy dynamics.

The study found that the fast-fashion business model allows companies to adapt to the new market norms because of its dual strategy: demand-driven transparency and supply-driven sustainability, within the logic of consumption. The results indicate that focus on sustainability and transparent stakeholder engagement helps companies to remain relevant in the fashion industry.

The results of the study can be used by general managers of companies, marketers and administrative managers to select strategies and make decisions under the current and future fashion industry demands in markets at different developmental stages.

The purpose of this paper is to use prediction markets to forecast an agricultural event: United States Department of Agriculture's number of cattle on feed (COF). Prediction markets are increasingly popular forecast tools due to their flexibility and proven accuracy to forecast a diverse array of events.

During spring 2008, a market was constructed comprised of student traders in which they bought and sold contracts whose value was contingent on the number of COF to be reported on April 18, 2008. During a nine‐week period, students were presented three types of contracts to forecast the number of COF. To estimate forecasts a uniform price sealed bid auction mechanism was used.

The results showed that prediction markets forecasted 11.5 million head on feed, which was about 1.6 percent lower than the actual number of COF (11.684 million). The prediction market also fared slightly worse than analysts' predictions, which on average suggested there would be about 11.795 million head (an over‐estimate of about 1 percent).

The contribution of this study was not to provide conclusive evidence on the efficacy of using prediction markets to forecast COF, but rather to present an empirical example that will spark interest among agricultural economists on the promises and pitfalls of a research method that has been relatively underutilized in the agricultural economics literature.

The purpose of this paper is to provide a selective review of literature and presents a conceptual framework in journal and institution rankings. Several streams of ranking literature are analyzed.

The authors provide a conceptual framework to analyze the literature of journal and school ranking. Thus, several streams of ranking literature are analyzed to support the conceptual framework.

Through the lens of a context-driven framework, the authors point to originality, utility, and timeliness as aspects that contribute to the recent increase of the ranking literature. Finally, the authors discuss other issues that arise within ranking due to subjective biases, institutional preferences and difficulties establishing weighting measurements, as well as the future direction of ranking.

The authors propose a context-based ranking framework to analyze rankings as factors that influence the environment may ultimately affect the usefulness of these rankings. It also implies that ranking of a journal or institution is a relative measure, as the context in which rankings are derived may change over time. Ultimately, the relative benchmarks used in the ranking will change as newer, more relevant metrics are developed.

The conceptual framework is new and provides a useful benchmark to understand ranking of journals and school.

This article investigates the determinants of cross-section variation of initial public offerings' (IPOs) first-day returns in a sample of 710 issues across seven emerging markets between 2013 and 2017.

Ordinary least squares regression (OLS) and the semi-parametric quantile regression (QR) technique are employed. QR enables to analyse beyond the explanatory variables' relative mean effect at various points in the endogenous variable distribution. Furthermore, parameter estimates under QR are robust to the existence of outliers and long tails in the data distribution.

Underpricing varies across countries with an average of 78%. According to the OLS results, independent variables explain 26% of the variation of IPOs' first-day returns. Findings show that employing QR is important, given the non-normality of the data and because each quantile is associated with a different effect of explanatory variables.

In addition to firm-specific, market-specific and issue-specific factors, the paper extends IPOs' underpricing literature through studying the impact of country-specific characteristics, largely neglected by literature, on IPO underpricing.

What is the role of analysts in reducing agency problems and information asymmetry between stockholders and managers? The purpose of this paper is to confirm the analyst’s role by examining his or her influence on CEO compensation structure.

The major population for this study consists of publicly traded corporations of the S & P 1500 for which data on CEO compensation is available from Standard & Poor’s Execucomp database, along with the proxy statements of these firms. Regression analysis is used to test hypotheses about the effect of analyst coverage on CEO compensation.

The evidence shows that CEOs of firms with greater analyst coverage or higher analyst coverage quality (analyst coverage index) have higher pay-for-performance (Delta), more compensation incentives to increase firm risk (Vega), more total compensation, and more excess compensation. Even after controlling for the effect of other types of corporate governance, including internal governance and institutional holdings, analysts’ activities still have an incremental effect on CEO compensation structure.

The authors findings may be useful to investors who use analyst coverage to evaluate the firm’s CEO compensation, as it suggests that investors may reference the information about analyst coverage of firms to craft appropriate CEO compensation structures.

The authors results contribute by showing that the extra effect of analyst activities on CEO compensation structure exists, even after controlling for other types of governance mechanisms, such as internal governance and institutional investors’ holdings.

This study aims to explore the dependence structure among Chinese firms across the emerging 5G industry at different stages and to provide some strategic insights for market participants.

This study adopt macroeconomic fundamentals and the log-returns of 45 listed firms in the Chinese 5G industry to construct the weighted adjacency matrix by measuring the correlation parameters and then use the triangulated maximally filtered graph (TMFG) algorithm to construct the dependence network. It analyses the topological structure of the constructed networks to obtain the dependence characteristics for each firm in the whole industrial supply chain at different levels.

The empirical results provide a comprehensive and concise snapshot of the industrial structure, across the whole 5G industry at different levels, rather than just a “one-to-one” pattern. Specifically, the dependence characteristics of different firms are heterogeneous, and most firms are highly connected with partners in the whole industrial supply chain, whereas a few firms that are weakly connected. Those closely connected firms are usually in the midstream. In addition, compared with firms at different levels, downstream firms usually have closer dependencies and stronger influence capabilities.

Regulators not only should promote stability development for those firms most intensely connected with whole industry chain but also protect those firms with weak link in the whole industry chain. Investors should better understand the embedded ties among different firms to obtain effective market information and can select multiple firms with fewer connections as backup to conduct joint investment for risk mitigation. Mangers should give priority to the central players/firms in the whole industrial supply chain and establish the alliances with closely connected firms.

This study contributes to both the information system and operation management literature by constructing a new network method, Copula-TMFG, to capture the dependence structure among Chinese firms in 5G industry, empirically providing some strategic insights for 5G industry stakeholders, such as regulators, investors and managers.

Prediction markets are techniques to aggregate dispersed public opinions via market mechanisms to predict uncertain future events’ outcome. Many experiments have shown that prediction markets outperform other traditional forecasting methods in terms of accuracy. Logarithmic market scoring rules (LMSR) is one of the most simple and widely used market mechanisms; however, market makers have to confront crucial design decisions including the setting of the parameter “b” or the “liquidity parameter” in the price functions. As the liquidity parameter has significant effects on the market performance, this paper aims to provide a comprehensive basis for the setting of the parameter.

The analyses include the effects of the liquidity parameter on the forecast standard error and the amount of time for the market price to converge to the true value. These experiments use artificial prediction markets, the proposed simulation models that mimic real prediction markets.

The simulation results indicate that prediction market’s forecast standard error decreases as the value of the liquidity parameter increases. Moreover, for any given number of traders in the market, there exists an optimal liquidity parameter value that yields appropriate price adaptability and leads to the fastest price convergence.

Understanding these tradeoffs, the market makers can effectively determine the liquidity parameter value under various objectives on the standard error, the time to convergence and cost.