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This study explores the response of consumer confidence in policy uncertainty in the Japanese context. The study also considers the dynamism of stock market behavior and financial stress and its impact on consumer confidence, which has remained unaddressed in the literature. The role of these control variables has important implications for policy discussions, particularly when other countries can learn from Japanese experiences.

The nonlinear autoregressive distributed lag model postulated by Shin et al. (2014) was used for studying the asymmetric response of consumer confidence to policy uncertainty. This method has improved estimates compared to traditional linear cointegration methods.

The findings confirm the asymmetric impact of policy uncertainty on the consumer confidence index in Japan. The impact of the rise in policy uncertainty is greater than that of a fall in asymmetry on consumer confidence in Japan. Furthermore, the Wald test confirmed asymmetric behavior.

The contribution of this study is threefold. First, this study contributes to the extant literature by analyzing the asymmetric response of consumer confidence to policy uncertainty, controlling for both the financial stress and stock price indices. Second, to test the robustness of the exercise, the study utilized different frequencies of observations. Third, this study is the first to utilize the concept of Arbatli et al. (2017) to formulate a combined index of uncertainty based on economic policy uncertainty index, along with uncertainty indices such as fiscal, monetary, trade and exchange rate policies to study the overall impact of policy uncertainty.

This research's main objective is to investigate the relationship between consumption expenditure and consumer confidence in the USA and to study their effects on US economic revivalism during and after the coronavirus disease 2019 (COVID-19) shock.

The authors use Michigan's monthly Consumer Sentiment Index and its five components from January 1978 to April 2020. The study is unique in quantifying the potential variations in US consumer confidence due to COVID-19 under different scenarios, by providing a projection until December 2021. It also estimates the time needed for recovery and offers guidance to policymakers on ways to contain the negative impacts of COVID-19 on the economy by restoring consumer confidence.

All scenarios show a gradual recovery of consumer confidence and consumption expenditure. This study recommends expansionary policies to encourage consumption expenditure to generate additional demand and boost economic growth and job creation.

Though this study is limited to the US consumer confidence index, it offers significant implications for marketers, customers and policymakers of other developed economies. The authors recommend expansionary economic policies to boost consumer confidence, raise economic growth and result in job creation.

The study is unique in quantifying the potential variations in US consumer confidence due to COVID-19 under different scenarios; by providing a projection until December 2021. It also estimates the time needed for recovery and guidance for policymakers on ways to contain the COVID-19 shock negative impacts on the economy by restoring consumer confidence.

The purpose of this paper is twofold. First, the study examines the prolonged effect of policy-related economic uncertainty on hotel operating performance, particularly the room demand (occupancy). Second, the study attempts to explain why occupancy drops when the perceived economic uncertainty is high by studying the mediating effect of consumer sentiment in the relationship between economic policy uncertainty and hotel demand.

This quantitative study uses secondary data – US economic policy uncertainty (EPU) index, University of Michigan's index of consumer sentiment (ICS), and property-level hotel operating data from three states of the US – California, Florida and New York. Data were analyzed using random effect regression and structural equation modeling. Robustness tests were conducted to enhance the reliability of the research findings.

Random-effects regression analysis reveals that policy-related economic uncertainty has a negative and lead-lag effect on hotel occupancy, average daily rate and revenue per available room (RevPAR). Structural equation modeling results show that the relationship between economic policy uncertainty and hotel occupancy is significantly mediated by consumer sentiment. Robustness test results support the findings from the main analysis.

This study offers valuable implications for the hotel professionals in regard to anticipating the economic impact of policy-related uncertainty on hotel industry and understanding how consumer sentiment affects demand at such crises times. Moreover, the study suggests potential course of actions to deal with declining room demand at times of uncertainty.

This empirical study explores how economic policy uncertainty affects hotel performance at the property level and explains the mediating effect of consumer sentiment on hotel room demand. The study provides a first-hand evidence of how consumer sentiment relates to the perception of economic uncertainty and leads to decline in consumer demand. In that regard, findings of the study have valuable implications for hospitality industry practitioners and relevant policymakers.

The purpose of this paper is to investigate the impact of a consumer confidence shock on GDP and different types of consumer spending during a slack state as well as a non-slack state of an economy.

The authors use the US quarterly data from 1960Q1 to 2014Q4 and apply Jorda’s (2005) local projection method to compute the impulse responses of macroeconomic variables to a consumer confidence shock. The local projection method allows us to include non-linearities in the response function.

In general, the response of output, following a consumer confidence shock, is similar in slack and non-slack states and indicate that an unfavorable confidence shock is contractionary. However, the intensity and duration of impact of a confidence shock on different components of spending are state dependent. Overall, a negative confidence shock appears to have a stronger impact on non-slack time than on a slack time.

Policy makers should be careful about undertaking a policy action that may affect consumer confidence adversely, particularly during an economic good time. An adverse confidence shock can trigger a downfall in a well-functioning economy and the dampening effect may last for several quarters before the economy rebounds.

US economy is subject to fluctuations; however, the literature on the impact of confidence shock in different economic states is limited. The incremental contribution of this paper is that it investigates how the consumers respond to the confidence shock in a state-dependent model. Furthermore, the authors use a more robust and alternative estimation method that tackles any non-linear problems.

Seasonal fluctuation interference often affects the relational analysis of economic time series. The main purpose of this paper is to propose a new grey relational model for relational analysis of seasonal time series and apply it to identify and eliminate the influence of seasonal fluctuation of retail sales of consumer goods in China.

First, the whole quarterly time series is divided into four groups by data grouping method. Each group only contains the time series data in the same quarter. Then, the new series of four-quarters are used to establish the grey correlation model and calculate its correlation coefficient. Finally, the correlation degree of factors in each group of data was calculated and sorted to determine its importance.

The data grouping method can effectively reflect the correlation between time series in different quarters and eliminate the influence of seasonal fluctuation.

In this paper, the main factors influencing the quarterly fluctuations of retail sales of consumer goods in China are explored by using the grouped grey correlation model. The results show that the main factors are different from quarter to quarter: in the first quarter, the main factors are money supply, tax and per capita disposable income of rural residents. In the second quarter are money supply, fiscal expenditure and tax. In the third quarter are money supply, fiscal expenditure and per capita disposable income of rural residents. In the fourth quarter are money supply, fiscal expenditure and tax.

This paper successfully realizes the application of grey relational model in quarterly time series and extends the applicable scope of grey relational model.

Purpose: Investors and portfolio managers can earn profitably when they correctly predict when stock prices will go up or down. For this reason, it is crucial to know the effect levels of the factors that affect stock prices. In addition to macroeconomic factors, the psychological behavior of investors also affects stock prices. Therefore, the study aims to reveal the different sensitivity levels of the stock index against macroeconomic and psychological factors.

Design/Methodology/Approach: In this study, dollar rate (USD), euro rate (EURO), time deposit interest rate (IR), gold price (GOLD), industrial production index (IPI), and consumer price index (CPI) (inflation (INF)) were used as macroeconomic factors, while Consumer Confidence Index (CCI) and VIX Fear Index (VIX) were used as psychological factors. In addition, the BIST-100 index, which is listed in Borsa Istanbul, was used as the stock index. The sensitivity of the stock index to macroeconomic and psychological factors was investigated using the Multivariate Adaptive Regression Spline (MARS) method using data from January 2012 to October 2020.

Findings: In the analyses performed using the MARS method, the coefficients of INF, USD, EURO, IR, CCI, and VIX Index were found to be statistically significant and effective on the stock index. Among these variables, INF has the highest effect on stocks. It is followed by USD, IR, EURO, CCI, and VIX. GOLD and IPI variables did not show statistical significance in the model. The most important difference of the MARS model from other regressions is that each factor’s effect on the stock index is analyzed by separating it according to the value of the factor. According to the results obtained from the MARS model: (1) it has been determined that USD, EURO, IR, and CPI have both positive and negative effects on the stock market index and (2) CCI and VIX have been found to have negative effects on stocks. These results provide essential information about how investors who plan to invest in the stock index should take into consideration different macroeconomic and psychological values.

Originality/value: This study contributes to the literature as it is one of the first studies to examine the effects of factors affecting the stock index by decomposing it according to the values it takes. Also, this study provides additional information by listing the factors affecting the stock index in order of importance. These results will help investors, portfolio managers, company executives, and policy-makers understand the stock markets.

This paper is about the effects of unemployment on consumption behaviour through “job security” in Switzerland. Based on a behavioural model of consumption the paper establishes the links between job security and consumption empirically. In a second step, perceived “job security” as reported in the Swiss Consumer Survey is then connected with the labour market. The paper finds that the record high level of unemployment since 1991 has mainly caused the observed deterioration of the perceived “job security”. Two different scenarios of unemployment rates are then developed to show the quantitative effects unemployment had on perceived “job security” and finally through this measure of consumer confidence on consumption expenditures. In conclusion the unusually high number of unemployed have acted as a psychological shock to change the subjective assessment of “job security” to such a degree that significant changes in consumer behaviour have resulted.

This study aims to develop a parsimonious model to estimate US aggregate hotel industry revenue using domestic trips, consumer confidence index, international inbound trips, personal consumption expenditure and number of hotel rooms as predictor variables. Additionally, the study applied the model in six sub-segments of the hotel industry – luxury, upper upscale, upscale, upper midscale, midscale and economy.

Using monthly aggregate data from the past 22 years, the study adopted the auto-regressive distribute lags (ARDL) approach in developing the estimation model. Unit root analysis and cointegration test were further utilized. The model showed significant utility in accurately estimating aggregate hotel industry and sub-segment revenue.

All predictor variables except number of rooms showed significant positive influences on aggregate hotel industry revenue. Substantial variations were noted regarding estimating sub-segment revenue. Consumer confidence index positively affected all sub-segment revenues, except for upper upscale hotels. Inbound trips by international tourists and personal consumption expenditure positively influenced revenue for all sub-segments but economy hotels. Domestic trips by US residents added significant explanatory power to only upper upscale, upscale and economy hotel revenue. Number of hotel rooms only had significant negative effect on luxury and upper upscale hotel sub-segment revenues.

Hotel operators can make marketing and operating decisions regarding pricing, inventory allocation and strategic management based on the revenue estimation models specific to their segments.

It is the first study that adopted the ARDL bound approach and analyzed the predictive capacity of macroeconomic variables on aggregate hotel industry and sub-segment revenue.

Develops an original 12‐step management of technology protocol and applies it to 51 applications which range from Du Pont’s failure in Nylon to the Single Online Trade Exchange for Auto Parts procurement by GM, Ford, Daimler‐Chrysler and Renault‐Nissan. Provides many case studies with regards to the adoption of technology and describes seven chief technology officer characteristics. Discusses common errors when companies invest in technology and considers the probabilities of success. Provides 175 questions and answers to reinforce the concepts introduced. States that this substantial journal is aimed primarily at the present and potential chief technology officer to assist their survival and success in national and international markets.