Predicting future consumer purchases in grocery retailing with the condensed Poisson lognormal model

To identify the effect of marketing actions on consumer purchasing, analysts must disentangle the dynamic component of purchasing from expected period-to-period stochastic fluctuations. This is done by comparing marketplace observations to the conditional expectation of future purchasing. Current methods of deriving the conditional expectation contain systematic bias and rely on certain unrealistic modelling assumptions. We therefore propose a new model to predict future consumer purchases in grocery retailing. The new model is a mixture of Erlang-2, Poisson and lognormal distributions or a condensed Poisson lognormal model (CPLN). Using two grocery retailing datasets from the UK, we demonstrate that the CPLN model predicts future consumer purchases well with error of 7% and 9%, respectively. Compared with the previous benchmark model, the condensed Negative Binominal Distribution (CNBD), the CPLN model reduces error by 50% (7% versus 14%) and 67% (9% versus 27%), respectively. Theoretical and practical implications for retailers are discussed.     

Staging a comeback? The influencing mechanism of tourist crowding perception on adaptive behavior

An increasing number of studies have recently elucidated the serious negative effects of tourist crowding. However, how tourists cope with crowding and adopt adaptive responses remains unclear. Thus, this study qualitatively identified the mechanism of tourist crowding perception of adaptive behavior from interviewees and then quantitatively validated the mechanism through the mediation of tourist fatigue and negative emotions, as well as the moderation of peer emotion contagion. Tourists were more likely to return to a destination and engage in temporal replacement behavior through the mediating effect of tourist fatigue, but it was also found that peer emotional contagion interferes with two paths that explain how tourist crowding perception influences tourist emotion change or state variation, impacting adaptive behavior responses. Consequently, we recommend diverting tourists, focusing on alleviating fatigue and negative emotions and channeling the peers’ negative emotions to mitigate the negative impact of tourist crowding.     

Media,reputational risk,and bank loan contracting

We investigate how reputational risk arising from traditional and online media coverage of Corporate Social Irresponsibility (CSI) conducts affects the cost of borrowing and what are the factors that can mitigate or amplify this effect. First, we find that negative media attention increases bank loan costs and show that this result is robust to endogeneity concerns and alternative measures of key variables. Next, we find that the impact of negative media attention on bank loan costs is more severe if the misconduct involves borrowers with prior high Corporate Social Responsibility (CSR) reputations, while it is smaller when prior lending relationships exist between the lead arranger and the borrower.     

Economic uncertainty,central bank digital currency,and negative interest rate policy

The COVID-19 outbreak has brought unprecedented social attention to economic uncertainty and negative interest rate policy (NIRP). How does uncertainty affect economic activity, and how effective is a NIRP based on central bank digital currency (CBDC)? To answer the two questions, we constructed a dynamic stochastic general equilibrium (DSGE) model that accommodates sticky prices and wages. The results indicated: (i) Economic uncertainty has substantially reduced investment, output, wage, and loans, which increases unemployment risk. In the short term, it has triggered impulsive consumption by households, while consumption has fallen into a slump in the long run. (ii) After suffering an uncertainty shock, the economy entered short-term stagflation and long-term deflation. The short-term stagflation was mainly caused by resident wage adjustment, and the long-term deflation was due to the decline in effective demand caused by unemployment risk. (iii) CBDC could eliminate the zero lower bound (ZLB) constraint, thereby improving the effectiveness of NIRP. Compared with traditional currency, CBDC-based NIRP could more effectively smooth macroeconomic fluctuations and alleviate the negative impact of an uncertainty shock, which is more conducive to restoring market confidence and promoting economic recovery.     

Central bank digital currency and the effectiveness of negative interest rate policy: A DSGE analysis

Implementing a negative interest rate policy (NIRP) in the traditional fiat system is less effective than desired because of the zero lower bound (ZLB) constraint on interest rates and the cash barrier. Would this problem be solved if a new form of currency was introduced, i.e., central bank digital currency (CBDC), in the economy? To answer this question, we construct a dynamic stochastic general equilibrium (DSGE) model to analyze the effectiveness of NIRP upon the introduction of CBDC. The results suggest that: (i) The CBDC can eliminate the ZLB constraint and stabilize the economic fluctuations caused by NIRP. (ii) The central bank can implement NIRP by directly adjusting the interest rate of digital currency to stimulate consumption, investment, and output and to accelerate macroeconomic recovery. (iii) Welfare analysis shows that the central bank can effectively choose different NIRP rules according to the economic objectives.