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This study examines the relationship between economic policy uncertainty (EPU) and managers' ex ante strategic choice on firms’ fixed and variable costs structure, i.e. cost rigidity and the moderating effect of government contracts and political connections.

Using a sample of 4,162 US firms during 2003–2019 and EPU measure from Baker et al. (2016), the authors examine the association between EPU and cost rigidity using multivariate regression analysis. The authors also examine the moderating effects of government customers and political connections using the subsampling method.

This study finds that increases in EPU leads to higher cost rigidity, suggesting that managers tend to look ahead and make an ex ante commitment to invest more in fixed costs to avoid congestion costs in anticipation of future product demand during EPU. The study also finds that the presence of government customers and political connections moderates the need for adopting greater cost rigidity.

This study measures firms' cost rigidity based on archival data. Future studies could utilize managers' cost structure choices using firms' internal management cost structure forecasts data to measure cost rigidity to examine the relationship between cost rigidity and EPU.

This study demonstrates that managers tend to make a proactive commitment to invest in fixed inputs when facing demand uncertainty from EPU to avoid congestion costs. This study also highlights the value of having government contracts and political connections by demonstrating that managers are less concerned about the congestion costs, hence weakening the impact of EPU on cost rigidity when they have government as major customers and/or political connections.

This study extends the management accounting literature by documenting that cost rigidity is related to EPU and that the relationship between cost rigidity and EPU also depends on whether the firm has government as major customers and/or political connections or not.

This study identifies whether the Internet search index can be used as effective enough data to identify agricultural and livestock product demand and compare the accuracy of the prediction of major agricultural and livestock products purchases between these prediction models using artificial neural network, linear regression and a decision tree.

Artificial neural network, linear regression and decision tree algorithms were used in this study to compare the accuracy of the prediction of major agricultural and livestock products purchases. The analysis data were studied using 10-fold cross validation.

First, the importance of the Internet search index among the 20 explanatory variables was found to be high for most items, so the Internet search index can be used as a variable to explain agricultural and livestock products purchases. Second, as a result of comparing the accuracy of the prediction of six agricultural and livestock purchases using three models, beef was the most predictable, followed by radishes, chicken, Chinese cabbage, garlic and dried peppers, and by model, a decision tree shows the highest accuracy of prediction, followed by linear regression and an artificial neural network.

This study is meaningful in that it analyzes the purchase of agricultural and livestock products using data from actual consumers' purchases of agricultural and livestock products. In addition, the use of data mining techniques and Internet search index in the analysis of agricultural and livestock purchases contributes to improving the accuracy and efficiency of agricultural and livestock purchase predictions.

The purpose of this paper is to analyze the characteristics of closed car-sharing services and present the relevant factors to provide reasonable service locations. Car-sharing service locations have been arranged to allow customers to easily access locations. However, the particular car-sharing service in this study, enforced through an agreement between residents of the House of Commons, is operated as a closed service, limited to public housing residents. This closed car-sharing service in the House of Commons differs from typical services in that, after use, the users return the vehicle to the same parking location. In addition, determination of the general car-sharing locations is based on the profits of the service operator. The service can be stopped as there are a number of other alternatives for mobility. However, if the closed car-sharing service in the House of Commons is canceled, there is no other alternative for existing users. Therefore, the site selection for the initial introduction of the House of Commons in a closed car-sharing service is a very important issue.

This study uses nine months of data from 39 complexes for location analysis for public housing residents in urban areas. The LH Happy Carsharing is a closed private service, used by people residing within approximately 50 rental LH-apartment complexes. As closed car sharing is unfavorable in comparison to other car-sharing services, it is important to demonstrate that the service can succeed in securing a sufficient amount of users. To analyze the viability and usage of the service, the authors have considered ten potential variables.

On the basis of the regression analysis result, the adopted significant variables are the number of households, the percentage of residents in the 40’s age group and the number of households with registered vehicles.

In general, for car-sharing research, specific user groups are not generally identified. However, LH Happy Carsharing is specifically targeted to residents in rental apartments. The LH Happy Carsharing is a private service, used by people residing within approximately 50 rental LH Apartment complexes. Since closed carsharing is unfavorable in comparison to other carsharing services, it is important to demonstrate that the service can succeed in securing a sufficient amount of users. Therefore, this study aims to analyze the characteristics of private carsharing and present the relevant factors to provide reasonable service locations.

Guided by a synthesis of social norms theory (SNT), the social identity model of deindividuation effects (SIDE) and information cascades theory (ICT), this study aims to unveil the mechanism underlying the role of social norms in shaping consumer responses to woke advertising in the algorithmic social media environment.

This paper analyzed 125,481 unique comments on a woke campaign, which represented the dynamic social norms condition in which the prominence of popularity information sets a social norm that can be passed on through a sequential commenting process. Also, this paper conducted an experiment with two conditions, namely, static social norms condition, representing a situation in which the prominence of popularity information sets a social norm through a non-sequential commenting process; without social norms condition, epitomizing the situation in which there is no popularity information that can set a social norm.

The results revealed that when evaluating a social media-based woke ad, depersonalized consumers in a dynamic social norms condition were more likely to be influenced by the prevailing norms than those in a static social norms condition were.

Through the lens of ICT, this research extends SNT and SIDE by detailing the procedure regarding how perceived social norms shape the formation of consumer opinions in a sequential fashion.

The purpose of this paper is to make an overview of 474 publications and 512 patents of FTL from 1987 to 2020 in order to provide a conclusive and comprehensive analysis for researchers in this field, as well as a preliminary knowledge of FTL for interested researchers.

Firstly, the FTL algorithms are classified and its functions are introduced in detail. Secondly, the structures of the publications are analyzed in terms of the fundamental information and the publication of the most productive countries/regions, institutions and authors. After that, co-citation networks of institutions, authors and papers illustrated by VOS Viewer are given to show the relationship among those and the most influential of them is further analyzed. Then, the characteristics of the patent are analyzed based on the basic information and classification of the patent and the most productive inventors. In order to obtain research hotspots and trends in this field, the time-line review and citation burst detection of keywords carried out by Cite Space are made to be visual. Finally, based on the above analysis, it draws some other important conclusions and the development trend of this field.

The research on FTL algorithm is still the top priority in the future, and how to improve the performance of SSD in the era of big data is one of the research hotspots.

This paper makes a comprehensive analysis of FTL with the method of bibliometrics, and it is valuable for researchers can quickly grasp the hotspots in this area.

This article draws the structural characteristics of the publications in this field and summarizes the research hotspots and trends in this field in recent years, aiming to inspire new ideas for researchers.