You ruined our love story,but I just cannot hate you—A moderation–mediation analysis of past experienced brand love and brand hate

The development of the consumer–brand relationship is crucial for brands as it reflects how well a consumer is emotionally connected with the brand. However, due to unacceptable behaviour, brands have become susceptible to negative consumer–brand relationships. Given the recent importance of the negative consumer–brand relationship and its consequences, little is known about the role of previously experienced brand love. Studies support the link between strongly remembered events and experiences, customer knowledge, brand association, and consumer congruence with brands in creating long-lasting influence and deep emotion towards the brand. The study examines moderation–mediation analysis of past experienced brand love and brand hate. This research, anchored in consumer brand relationship literature, builds on an analysis of data from 207 respondents. We conducted a research survey in a South-Western European country and performed the SPSS Hayes Process macro 58 to analyse the moderating role of past experienced brand love alongside the mediating role of brand hate to test our hypotheses. The moderation results show that past experienced brand love significantly moderated the link between brand hate causes (corporate wrongdoings) and brand hate. However, there is no significant moderation influence of past experienced brand love on the consequences of brand hate causes. The study also demonstrates that brand hate mediates the link between corporate wrongdoings and violations of expectations with negative word of mouth, consumer complaints, and patronage reduction/cessation. The current study is unique in that it highlights new avenues in existing research by extending the domain in consumer–brand relationships. The findings of the study have theoretical and empirical implications for brand managers.     

Aggregate productivity and inefficient cropping patterns in Uganda

Journal of Productivity Analysis - This paper measures the impact of inefficient spatial distribution of crops on aggregate agricultural productivity in Uganda. By combining village level data on...     

Are electronic government innovations helpful to deter corruption? Evidence from across the world

Electronic government innovations have been a critical development in public administration in recent years. Many countries have implemented e-government policies to enhance efficiency and transparency and combat corruption. This paper examines the impact of e-government on corruption using longitudinal data for more than 170 countries from 2002 to 2020. The empirical results suggest that e-government serves as a deterrent to corrupt activities. We analyse which e-government domains affect corruption, which types of corruption are more affected by e-government and the circumstances under which e-government is more effective in reducing corruption. The empirical results suggest that online service completion and e-participation are important features of e-government as an anticorruption tool. Evidence suggests that e-participation reduces corrupt legislature activities, public sector theft, executive bribery, and corrupt exchanges. The potential of e-government to deter corruption is higher in countries where corruption is moderate or high and economic development is lower. Higher levels of GDP per capita, foreign direct investment, and political rights are also associated with lower levels of corruption.     

DIRECTED SEARCH WITH PHANTOM VACANCIES

When vacancies are filled, the job ads often remain, creating phantom vacancies. Older listings more likely represent phantoms. We assume job seekers direct their search based on listing age. Forming a match with an age-a vacancy creates an age-a phantom with probability β and generates an externality affecting vacancies aged a and older. Thus, the externality decreases with the match's listing age. Relative to efficient behavior, job seekers overapply to younger listings. We calibrate using U.S. data. The contribution of phantoms to inefficiency is large, but, given their existence, the planner cannot improve much on the directed search allocation.     

Working time reduction and employment in a finite world*

We study the consequences of a working time reduction (WTR) in a growth model with efficiency wages and an essential natural resource (natural capital), and in which technical progress cannot reduce the resource content of final production to zero. We show that if natural capital is scarce enough, a WTR increases the long-term levels of the hourly wage and employment. A numerical analysis of the transitory impacts of a WTR confirms that when natural capital is scarcer, a WTR increases employment more and the hourly wage less, with a less negative initial impact on output.     

An interview with Luis Raúl Pericchi

Luis Raúl Pericchi Guerra was born in Caracas, Venezuela, on 11 March 1952. He completed a B.S. in Mathematics in 1975 at the Universidad Simón Bolívar in Caracas, an M.S. in Statistics at the University of California Berkeley in 1978 and a Ph.D. in Statistics at Imperial College London in 1981. After graduating from Imperial College, Luis Raúl went back to Universidad Simón Bolívar. There, he played a key role in the developing of graduate programmes in Statistics and single handedly built an internationally recognised group focused on Bayesian statistics. In 2001, he moved to the Universidad de Puerto Rico in Rio Piedras to become the Chair of the Mathematics Department. At Universidad de Puerto Rico, he was instrumental in the establishment of a Ph.D. track in Computational Mathematics and Statistics. Luis Raúl has published over 120 papers in statistical and domain-specific journals, making significant contributions to several areas of Bayesian statistics (especially in the areas of model selection and Bayesian robustness) and their application (especially in hydrology). He is a Fellow of the American Statistical Association, the International Society for Bayesian Analysis, the John Simon Guggenheim Memorial Foundation and an Elected Member of the International Statistical Institute. This conversation took place over multiple sessions during the 2022 O'Bayes meeting in Santa Cruz, California, and the months that followed.     

Procyclical volatility in Chinese stock markets

Review of Quantitative Finance and Accounting - We investigate the macroeconomic determinants of stock market volatility in China using the two-component GARCH-MIDAS model of Engle et al....     

Public overspending in higher education

We study the trade-off between governmental investments in pretertiary and tertiary education from an efficiency point of view. We develop a model comprising agents with different incomes and abilities, public and private schools, and public universities that select applicants based on an admission exam. Reallocating governmental resources from tertiary to pretertiary education may positively affect aggregate production and human capital if some conditions are satisfied. For instance, in an economy with a high proportion of credit-constrained students, a reallocation of expenditure toward public schools benefits many students, compensating for the negative effect of a decrease in public university investments. We also quantitatively investigate the optimal allocation of public investment between pretertiary and tertiary education, and we find that a 10% increase in productivity of public investments in pretertiary education could increase the optimal GDP between 2.1% and 3%.     

Inference in difference-in-differences: How much should we trust in independent clusters?

We analyze the challenges for inference in difference-in-differences (DID) when there is spatial correlation. We present novel theoretical insights and empirical evidence on the settings in which ignoring spatial correlation should lead to more or less distortions in DID applications. We show that details, such as the time frame used in the estimation, the choice of the treated and control groups, and the choice of the estimator, are key determinants of distortions due to spatial correlation. We also analyze the feasibility and trade-offs involved in a series of alternatives to take spatial correlation into account. Given that, we provide relevant recommendations for applied researchers on how to mitigate and assess the possibility of inference distortions due to spatial correlation.