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This study aims to examine the existence of herding and the impact of economic and political factors in the Shariah-compliant stocks of Gulf Cooperation Council markets, namely, Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates. This study also seeks to explore the existence of herding under market stress and cross-stocks herding between Shariah-compliant and conventional stocks.

The data period is from 1 January 2016 to 31 December 2021. Panel data regression and panel quantile regression are used to examine herding.

The results show that herding tends to exist in Shariah stocks before the pandemic but is more pronounced in both types of stocks during the pandemic. The empirical evidence shows that economic factors are significant to herding before and during pandemic, whereas the political factors are only shown to be significant before COVID-19. Conventional stocks are correlated to the herding of Shariah stocks but the Shariah stocks have no significant impact on the herding of conventional stocks. Panel quantile regression shows that herding exists in extreme conditions but not all markets perform similarly.

The results of this study imply that the political factor can lead investors to herd. This political factor represents information that is used by investors to herd, consistent with the prediction of information-based theory of herding. Hence, policymakers and regulators need to be wary of any change in the political factors as they may cause movement in stock prices that deviate from fundamental value because of investor herding.

This study examines the impact of firm-specific information and macroeconomic variables on market overreaction of US and Chinese winner and loser portfolio before and during COVID-19.

The firm-specific information includes firm size, volume, volatility, return of asset (ROA), return of equity (ROE), earning per share (EPS) and quick ratio while the macroeconomic variables are export rate, import rate, real GDP, nominal GDP, FDI, IPI and unemployment rate. Besides, one-third of the top performance stocks are categorized as winner portfolio while one-third of lowest performance stocks are categorized as loser portfolio. This study uses AECR to indicate stock return and measure market overreaction. GAECR is used to determine contrarian profit. The data range of pre-COVID-19 is from 1-Jan-2015 to 31-Dec-2019 while the period of COVID-19 is from 1-Jan-2020 to 31-Dec-2020.

In pre-COVID-19, firm-specific information (volatility, ROA, ROE and EPS) and macroeconomic variables are found to be correlated to stock return in US and Chinese portfolios except Chinese winner portfolio. Nonetheless, the impact of firm-specific information has vanished and macroeconomic variables are significant to stock return in COVID-19. It shows that investors rely on the economic indicators to trade in turbulent period due to emergence of COVID-19 as a disruption in market. Furthermore, US and Chinese portfolios are overreacted during COVID-19. Chinese loser portfolio has higher tendency of overreaction than US loser portfolio while US winner portfolio has higher tendency of overreaction than Chinese winner portfolio.

The results of this study assists academician, practitioners and investors on understanding and create awareness to the existence of market overreaction and the determinants that can cause the phenomenon.

This study aims to analyze the effects of performance expectancy, effort expectancy, social influence and facilitating conditions on the behavioral intention of using mobile health applications, especially during and after the COVID-19 pandemic.

A survey was developed using an online survey platform and distributed to Indonesian consumers for three weeks, and 149 usable responses were obtained. The principal component analysis, linear regression and analysis of variance tests were performed to test the validity and reliability of the measurement model and the hypothesized relationships among constructs.

Surprisingly, unlike previous studies on IT adoption, the findings show that social influence has no significant impact on behavioral intention. Facilitating conditions have a very weak to almost no significant impact on behavioral intention to use mobile health applications.

This research is conducted during pandemic COVID-19 where using mobile health apps is a must. In the future this research can be expanded as comparison study after the pandemic COVID-19 stated.

The result implies that digital technologies adoption intention is strongly affected by performance expectancy and effort expectancy, with performance expectancy as the most significant predictor. Nonetheless, the interaction of performance expectancy, effort expectancy, social influence and facilitating conditions influences behavioral intention significantly. Therefore, social influence and facilitating conditions are still important even with very insignificant effects.

To improve consumers’ behavioral intention to use mobile health applications, application providers should promote mobile health applications as useful telemedicine tools by primarily focusing on the application performance and usage experience.