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Financial asset return series usually exhibit nonnormal characteristics such as high peaks, heavy tails and asymmetry. Traditional risk measures like standard deviation or variance are inadequate for nonnormal distributions. Value at Risk (VaR) is consistent with people's psychological perception of risk. The asymmetric Laplace distribution (ALD) captures the heavy-tailed and biased features of the distribution. VaR is therefore used as a risk measure to explore the problem of VaR-based asset pricing. Assuming returns obey ALD, the study explores the impact of high peaks, heavy tails and asymmetric features of financial asset return data on asset pricing.

A VaR-based capital asset pricing model (CAPM) was constructed under the ALD that follows the logic of the classical CAPM and derive the corresponding VaR-β coefficients under ALD.

ALD-based VaR exhibits a minor tail risk than VaR under normal distribution as the mean increases. The theoretical derivation yields a more complex capital asset pricing formula involving β coefficients compared to the traditional CAPM.The empirical analysis shows that the CAPM under ALD can reflect the β-return relationship, and the results are robust. Finally, comparing the two CAPMs reveals that the β coefficients derived in this paper are smaller than those in the traditional CAPM in 69–80% of cases.

The paper uses VaR as a risk measure for financial time series data following ALD to explore asset pricing problems. The findings complement existing literature on the effects of high peaks, heavy tails and asymmetry on asset pricing, providing valuable insights for investors, policymakers and regulators.

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

This study investigates the role of consumer cosmopolitanism on consumer attitudes and purchase intentions (PIs) towards foreign and local brands.

The responses were collected on a structured questionnaire through a consumer survey. The data were then analysed through structural equation modelling (SEM).

The results depict the positive influence of consumer cosmopolitanism on consumer attitudes towards foreign brands, which positively influences PIs towards foreign brands and negatively influences the PIs of local brands. Further, the mediating role of perceived quality was observed in explaining the consumer preference towards foreign and domestic brands.

Finally, the study concludes by providing implications for marketing scholars and managers of global and local brands.

The paper examines the underlying mechanisms related to consumer cosmopolitanism and its role in influencing the foreign and local brand purchase.

The paper examined the impact of brand-identity expressiveness and perceived brand value that was neglected in prior literature, in addition to perceived brand quality and prestige, on behavioral intention. Research also tests if certain consumer values moderate this relationship.

Mall-interception technique was used. Also, partial least square-structural equation modelling (PLS-SEM) was used to analyze data and test research hypotheses.

Results indicate that perceived brand globalness (PBG) and perceived brand localness (PBL) are significantly positively associated with perceived quality and prestige. Besides, the inclusion of brand-identity expressiveness and perceived brand value was supported. PBL showed a greater association with brand identity expressiveness compared to PBG, different from previous findings. Furthermore, the total indirect effect of PBG on behavioral intentions was significant through the routes of perceived quality and prestige, compared to a weak and non-significant effect for PBL through the quality route.

This study proposes a comprehensive model testing additional pathways through which global and local brands can boost their preferences.

The purpose of this study is to enhance the understanding of the marketing placebo effect (MPE) by proposing and empirically testing a model of antecedents and consequences of MPE for reduced-sugar labeled products in the food industry.

An online survey was conducted on a sample of 409 consumers to collect data on their health consciousness, sugar-induced anxiety, self-congruity, fresh start mindset and MPE of reduced front-of-pack sugar labeling in food products. Structural equation modeling (SEM) was used to analyze the data and test the hypotheses.

The results highlight the sugar-induced anxiety as the most pronounced determinant for the proposed placebo effect. Health consciousness was observed to indirectly influence the MPE via mediators (sugar-induced anxiety and self-congruity). Furthermore, the supporting role of the fresh start mindset moderates the relationships between health consciousness, sugar-induced anxiety, self-congruity and the MPE.

This study is one of the few to investigate the moderating effects of having a fresh start mindset on the MPE of reduced-sugar labeled products. Moreover, the study contributes to the growing body of research on the indirect effects of health consciousness on consumer behavior, highlighting the important role of emotional (anxiety) and self-congruity factors in shaping the MPE toward reduced-sugar labeled products.

By understanding the complex interplay between the variables of the antecedents and consequences of MPE for reduced-sugar labeled products, which engenders consumer attitude and belief about sugar intake, marketers and policymakers can develop more effective campaign strategies to promote such products and, consequently, a healthy diet and lifestyle.

This study is one of the few to investigate the moderating effects of the fresh start mindset on the MPE of reduced-sugar labeled products. Moreover, the study contributes to the growing body of research on the indirect effects of health consciousness on consumer behavior, highlighting the critical role emotional (i.e. anxiety) and cognitive (i.e. self-congruity) factors play in shaping the outcome of the MPE of reduced-sugar labeling in products.

This paper aims to formally conceptualize service mega-disruptions as any far-reaching and unforeseen general environmental stressor or threat that impacts a service organization’s ability to provide a desired level of service. The authors differentiate sudden large-scale general environmental threats from traditional service failures in scope and scale of impact via number of customers and sectors affected and duration and speed of the disruption.

This paper draws from service recovery theory to build a conceptual model of service mega-disruptions. The resulting conceptual model maps service failure recovery strategies against a service mega-disruption recovery approach to examine consumer response to changes in service value. This work further articulates additional research needs including conceptualization, measurement and methods as traditional drivers of service recovery and the value of the service experience change in response to service mega-disruptions.

This work proposes a research agenda to investigate whether service mega-disruptions can bypass the need for service recovery due to a consumer self-moderating process. As past research shows, the less control a service provider has over a failure, the more customers attribute fault to the situation and transfer blame away from an organization. This paper suggests that this self-moderating process disrupts the need for service providers to court forgiveness for a failure with perceptions of similarity and controllability providing an alternate pathway to customer forgiveness. Similarly, it is suggested that service mega-disruptions play a role in transforming service ecosystems into tighter, more contractual systems with less agency for service providers and poorer ability to adjust to market conditions. The duration and longevity of effects on service providers’ control, agency and ability to adjust following a service mega-disruption must be researched further.

This paper builds theory to develop a conceptual model of service mega-disruptions and their role in customer engagement and reshaping the service ecosystem. This paper culminates in the proposition of a research agenda that aims to build research capacity among services marketing scholars as service providers’ coordination and market conditions are challenged by service mega-disruptions.