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The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.

This study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.

With sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.

Future research could use the same method to study other types of sponsors, such as title sponsors or other product categories.

Sponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.

Prior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.

Nowadays businesses tend to compete with rivals by improving capability to meet customer demands. One of the key to improve logistics efficiency of a firm is to select appropriate supplier. In the past, to select the most suitable supplier, most people evaluated performance by using average performance or variance from historical data but did not mentioned skewness. In other words, skewness impact on supplier performance is ignored by researchers and buyers. In fact, supplier with greatest average performance does not confirm to be the most suitable one because of uncertainties which make its performance skew either to the left or right, i.e., lower or higher than expectation. Therefore, this empirical study aims to discover and determine the important role of skewness on supplier selection problem. After identifying influential criteria on supplier selection, we analyze skewness effect on suppliers’ performance in each criterion by surveying real data of suppliers’ performances. Skewness effect can be rated in 3 levels; no effect, moderately effect, and highly effect. The results show that, there is only one criterion with no skewness effect, which is price. Criteria which have high skewed performance, for both of medium-sized and large-sized buyers, are lead time, product quality and reliability, and on-time delivery. Also, skewness has higher effect on suppliers’ performance of medium-sized buyers than large-sized buyers. The conclusion surprisingly shows that, skewness is the best index to distinguish between good and bad suppliers, while mean is the worst index.

The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed industries, the rapidly developing fund industry in China has received very little attention. This study aims to examine the performance of open-end securities investment funds investing in Chinese domestic equity during the period May 2003 to September 2020. Specifically, applying a non-parametric bootstrap methodology from the literature on fund performance, the authors investigate the role of skill versus luck in this rapidly evolving investment funds industry.

This study evaluates the performance of Chinese equity securities investment funds from 2003–2020 using a bootstrap methodology to distinguish skill from luck in performance. The authors consider unconditional and conditional performance models.

The bootstrap methodology incorporates non-normality in the idiosyncratic risk of fund returns, which is a major drawback in “conventional” performance statistics. The evidence does not support the existence of “genuine” skilled fund managers. In addition, it indicates that poor performance is mainly attributable to bad stock picking skills.

The authors find that the top-ranked funds with positive abnormal performance are attributed to “good luck” not “good skill” while the negative abnormal performance of bottom funds is mainly due to “bad skill.” Therefore, sensible advice for most Chinese equity investors would be against trying to “pick winners funds” among Chinese securities investment funds but it would be recommended to avoid holding “losers.” At the present time, investors should consider other types of funds, such as index/tracker funds with lower transactions. In addition, less risk-averse investors may consider Chinese hedge funds [Zhao (2012)] or exchange-traded fund [Han (2012)].

The paper makes several contributions to the literature. First, the authors examine a wide range (over 50) of risk-adjusted performance models, which account for both unconditional and conditional risk factors. The authors also control for the profitability and investment risks in Fama and French (2015). Second, the authors select the “best-fit” model across all risk-adjusted models examined and a single “best-fit” model from each of the three classes. Therefore, the bootstrap analysis, which is mainly based on the selected best-fit models, is more precise and robust. Third, the authors reduce the possibility that findings may be sample-period specific or may be a survivor (upward) biased. Fourth, the authors consider further analysis based on sub-periods and compare fund performance in different market conditions to provide more implications to investors and practitioners. Fifth, the authors carry out extensive robustness checks and show that the findings are robust in relation to different minimum fund histories and serial correlation and heteroscedasticity adjustments. Sixth, the authors use higher frequency weekly data to improve statistical estimation.

Blockchain technology enhances information management in healthcare supply chains by securing healthcare information and providing medical resource traceability. However, there is no decision framework to support blockchain implementation for managing information, especially in emerging economies’ healthcare supply chains. This paper develops a hierarchical decision model for implementing blockchain technology for information management in emerging economies’ healthcare supply chains.

This study uses 20 health supply chain experts in Ghana to rank 17 decision criteria for implementing blockchain for healthcare information management using the best-worst method (BWM) multi-criteria decision technique.

The results show that “security” and “privacy,” “infrastructural facility” and “presence of training facilities” are the top three critical factors impacting blockchain adoption in the health supply chain for healthcare information management. Other sub-factors are prioritized.

To implement blockchain effectively to enhance information management in the healthcare supply chain, health institutions, blockchain technology providers and state authorities should concentrate on the highly critical factors extracted from the study.

This is the first study that develops a hierarchical decision model for implementing blockchain technology in emerging economies' health supply chains.

The strategic location of city logistics facilities may help to establish more efficient urban logistics systems, reduce social and environmental costs of urban freight transport, and improve urban traffic conditions. In addition, it may allow a number of shippers or freight carriers to jointly operate freight vehicles and terminals or information systems while allowing them to have the capability to provide higher levels of services to their customers. This paper considers the problem of selecting a location for a city logistics facility while considering linguistic factors. This paper identifies the important factors in the selection of a location for a city logistics facility by performing a case study which applies the analytic hierarchy process method on data from Chongqing, China. The optimal location in Chongqing is then determined by using the fuzzy synthetic evaluation method. The results of this paper are expected to help municipal governments select appropriate locations for city logistics facilities and quantify the advantages and disadvantages of alternative locations.

The purpose of this study is to analyze and forecast the financial sustainability and resilience of commercial banks of Bangladesh in response to the negative effects of COVID-19 pandemic.

Eighteen publicly listed commercial banks of Dhaka Stock Exchange (DSE) have been taken as a sample for this study. To measure the riskiness of banks' credit portfolio, nine industries of DSE have been considered to determine probable loss of revenue arising from the COVID-19 pandemic shock. Moreover, two commonly used multiple-criteria-decision-making (MCDM) tools namely TOPSIS method and HELLWIG method have been used for analyzing the data.

Based on the performance scores under TOPSIS and HELLWIG method, banks are categorized into three groups (six banks each) namely top resilient, moderate resilient and low resilient. It is found that EBL and DBBL are the most resilient banks, and ONEBANK is the worst resilient bank in Bangladesh in managing the COVID-19 pandemic shock.

This study concludes that banks with low capital adequacy, low liquidity ratio, low performance and higher NPLs are more vulnerable to the shocks caused by the COVID-19 pandemic. The management of commercial banks should emphasize on maintaining higher capital base and reducing default loans.

Resilience of the Bangladeshi banking sector under any adverse economic event has been examined by only using stress testing approach. This study is empirical evidence where both TOPSIS and HELLWIG MCDM methods have been used to make the result conclusive.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

The paper aims to enrich previous findings for an emerging banking industry such as Vietnam, reporting the difference between the parametric and nonparametric methods when measuring cost efficiency. The purpose of the study is to assess the consistency in issuing policies to improve the cost efficiency of Vietnamese commercial banks.

The cost efficiency of banks is assessed through the data envelopment analysis (DEA) and the stochastic frontier analysis (SFA). Next, five tests are conducted in succession to analyze the differences in cost efficiency measured by these two methods, including the distribution, the rankings, the identification of the best and worst banks, the time consistency and the determinants of efficiency frontier. The data are collected from the annual financial statements of Vietnamese banks during 2005–2017.

The results show that the cost efficiency obtained under the SFA models is more consistent than under the DEA models. However, the DEA-based efficiency scores are more similar in ranking order and stability over time. The inconsistency in efficiency characteristics under two different methods reminds policy makers and bank administrators to compare and select the appropriate efficiency frontier measure for each stage and specific economic conditions.

This paper shows the need to control for heterogeneity over banking groups and time as well as for random noise and outliers when measuring the cost efficiency.