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This research aims to understand how organizational workplace meetings surrounding the COVID-19 pandemic impacted logistics Airmen across the United States Air Force and how these meetings impacted their risk seeking behavior on social media.

This survey research tested an extended Planned Risk Information Risk Seeking Model (PRISM) with organizational meetings as an antecedent to determine if current meetings influenced an Airman's perceived behavioral control, attitude toward seeking, subjective norms, knowledge sufficiency and intention to seek information regarding COVID-19.

Results of the CFA showed that the expanded PRISM model had good model fit. Additionally, using a custom dialog PROCESS macro in SPSS, it was found that perceptions of existing meetings were directly, positively related to attitude toward seeking, subjective norms and perceived behavioral control, and indirectly related to knowledge sufficiency threshold and information seeking. Theoretical and managerial implications are discussed.

This research adds to the limited body of knowledge of crisis communication and effectively expands the PRISM model to include an antecedent that helps explain information seeking during times of uncertainty.

The objective of this study is to investigate how country risk, different political actions from the government and bureaucratic behavior influence the activities in industry supply chains (SCs) in emerging markets. The main objective of this study is to investigate the influence of these external stakeholders’ elements to the demand-side and supply-side drivers and barriers for improving competitiveness of Ready-Made Garment (RMG) industry in the way of analyzing supply chain. Considering the phenomenon of recent change in the RMG business environment and the competitiveness issues this study uses the principles of stakeholder and resource dependence theory and aims to find out some factors which influence to make an efficient supply chain for improving competitiveness. The RMG industry of Bangladesh is the case application of this study. Following a positivist paradigm, this study adopts a two phase sequential mixed-method research design consisting of qualitative and quantitative approaches. A tentative research model is developed first based on extensive literature review. Qualitative field study is then carried out to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. A survey is carried out with sample of top and middle level executives of different garment companies of Dhaka city in Bangladesh and the collected quantitative data are analyzed by partial least square-based structural equation modeling. The findings support eight hypotheses. From the analysis the external stakeholders’ elements like bureaucratic behavior and country risk have significant influence to the barriers. From the internal stakeholders’ point of view the manufacturers’ and buyers’ drivers have significant influence on the competitiveness. Therefore, stakeholders need to take proper action to reduce the barriers and increase the drivers, as the drivers have positive influence to improve competitiveness.

This study has both theoretical and practical contributions. This study represents an important contribution to the theory by integrating two theoretical perceptions to identify factors of the RMG industry’s SC that affect the competitiveness of the RMG industry. This research study contributes to the understanding of both external and internal stakeholders of national and international perspectives in the RMG (textile and clothing) business. It combines the insights of stakeholder and resource dependence theories along with the concept of the SC in improving effectiveness. In a practical sense, this study certainly contributes to the Bangladeshi RMG industry. In accordance with the desire of the RMG manufacturers, the research has shown that some influential constructs of the RMG industry’s SC affect the competitiveness of the RMG industry. The outcome of the study is useful for various stakeholders of the Bangladeshi RMG industry sector ranging from the government to various private organizations. The applications of this study are extendable through further adaptation in other industries and various geographic contexts.

The authors examine the relation between noise trading in equity markets and stochastic volatility by estimating a two-factor jump diffusion model. Their analysis shows that contemporaneous price deviations in the derivatives market are statistically significant in explaining movements in index futures prices and option-market volatility measures.

To understand the impact noise may have in the S&P 500 derivatives market, the authors first measure and evaluate the influence noise exerts on futures prices and then investigate its influence on option volatility.

In the period from 1996 to 2003, this study finds significant changes in the volatility and mean reversion in the noise level and a significant increase in its relation to implied volatility in option prices. The results are consistent with a bubble in technology stocks that occurred with significant increases in noise trading.

This study provides estimates for this model during the periods preceding and during the technology bubble. The study analysis shows that the volatility and mean reversion in the noise level are much stronger during the bubble period. Furthermore, the relation between noise trading and implied volatility in the futures market was of a significantly larger magnitude during this period. The study results support the importance of noise trading in market bubbles.

Bloomfield, O'Hara and Saar (2009) find that noise traders lower bid–ask spreads and improve liquidity through increases in trading volume and market depth. Such improved market conditions could have positive effects on market quality, and this impact could be evidenced by lower implied volatility when noise traders are more active. Indeed, the results in this study indicate that the level and characteristics of noise trading are fundamentally different during the technology bubble, and this noise trading activity has a larger impact during this period on implied volatility in the options market.

This paper uniquely analyzes derivatives on the S&P 500 Index in order to detect the presence and influence of noise traders. The authors derive and implement a two-factor jump diffusion noise model. In their model, noise rectifies the difference of analysts' opinions, market information and beliefs among traders. By incorporating a reduced-form temporal expression of heterogeneities among traders, the model is rich enough to capture salient time-series characteristics of equity prices (i.e. stochastic volatility and jumps). A singular feature of the authors’ model is that stochastic volatility represents the random movements in asset prices that are attributed to nonmarket fundamentals.

The purpose of this paper is to examine the effect of falling commodity prices on farm debt usage of corn and soybean farms, and how this debt usage differs based on the financial leverage of the farm.

Using panel data on farms surveyed at least twice in the Agricultural Resource Management Survey (ARMS) from 1996 to 2015, this paper uses a difference-in-differences approach to measure the effect of low commodity price shocks on financially vulnerable farms. To account for the correlation in the error structure between the three dependent variables (real estate debt, non-real estate debt, and interest payments) we use a seemingly unrelated regression approach.

Following a commodity price shock, financially vulnerable farms (debt-to-asset ratio greater than 40 percent) were found to increase their non-real estate debt when compared with non-financially vulnerable farms. Off-farm business income was found to help farms reduce real estate debt and interest payments in the face of these shocks.

Data consist of corn and soybean farms surveyed more than once in the ARMS from 1996 to 2015 and are not representative of all US farms, but have similar characteristics to US commercial farms.

The results indicate that financially vulnerable commercial crop farms respond to lower prices by taking on non-real estate debt, increasing financial stress. Well-targeted federal programs could prevent further financial stress for this group.

This is the first paper to use unbalanced panel data from ARMS to examine how farm debt use responds to commodity prices. This paper can inform policymakers about the financial risks to farms resulting from the current low-price environment.