Managing a Global Retail Brand in Different Markets: Meta-Analyses of Customer Responses to Service Encounters

This study investigates how retailers can leverage their brand to shape customers’ satisfaction with service encounters. It develops and tests hypotheses about how brand, store, and consumer factors moderate customer responses to experience clues during retail service encounters. Six meta-regression analyses synthesize and compare results from 842 satisfaction equations describing customers’ encounters with a global retailer operating 400 stores in 32 countries. The results show how customers weigh their perceptions of service encounters differently depending on brand, store, and consumer factors. In markets where customers believe the retailer has high holistic brand quality, they place less weight on experience clues within the store. In markets where customers believe the retailer’s service brand promise, they place more weight on in-store experience clues. In markets where the retailer promises utilitarian value, customers weigh functional experience clues more heavily. In markets with an online purchasing channel, the effect of experience clues common to offline and online store environments is magnified, and unique clues are diminished. In addition, customers heavily weigh experience clues that fit their goals. In general, retail success factors include high brand quality (which makes customers more forgiving), a service brand promise that is mirrored in the store image (which makes customers attend to the experience clues aligned with them), and the careful monitoring and managing of retail touchpoints (to customize experience clues to each market). In this way, retailers can use customer-based strategies to effectively design and manage their global retail brand in different markets.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition

This work presents key insights on the model development strategies used in our cross-learning-based retail demand forecast framework. The proposed framework outperforms state-of-the-art univariate models in the time series forecasting literature. It has achieved 17th position in the accuracy track of the M5 forecasting competition, which is among the top 1% of solutions.     

Digital marketing communication in global marketplaces: A review of extant research,future directions,and potential approaches

Digital marketing communication, that is, communication through digital or electronic media among businesses and consumers, is growing rapidly, especially during the COVID-19 era. We propose a framework for analyzing digital marketing communication along four major dyads, business-to-consumer (B2C), business-to-business (B2B), consumer-to-consumer (C2C), and consumer-to-business (C2B). We review and summarize, for researchers and practitioners, the literature during 2000–2021 in these dyads along four major components: goals; channels, media, and platforms; content; and responses. We find that extant research in digital marketing communication pertains mostly to a specific, national level rather than a global level, despite the porousness of national boundaries for digital marketing. We derive important insights, identify key research gaps and questions in each of the dyads along these dimensions. We suggest approaches to address these research questions under three major components: substantive issues, data, and methods. These approaches can offer the insights that managers need to better formulate digital marketing strategies in local and global contexts.     

The impacts of value chain upgrading on the export of processed food

Upgrading in global value chains (GVCs) is an important path for developing countries to move along to capture higher benefits. Several qualitative studies of GVCs have identified two main upgrading types: economic (product, process, functional, and inter-sectoral) and social upgrading. The upgrading concept is widespread in productive sectors such as processed food, which has become a key export for developing countries. However, they have confronted multiple product standards required by global buyers. This study uses a case study of Thailand’s processed food exports (TPFEs). Although Thailand is a leading exporter of processed food, the country needs to upgrade in many areas related to production to meet global product standards and requirements. If Thailand fails to comply with global product standards, it will lose its export competitiveness. This study uses a gravity model to evaluate the impact of economic and social upgrading (EUP and SUP) on TPFEs. Our results show that upgrading types are significant in TPFEs, particularly for exports to developed countries. Process upgrading has a negative impact on TPFEs because of increased production costs to comply with product standards. However, process upgrading can lead to increased producers’ and exporters’ knowledge about how to comply with international standards. Consequently, process upgrading exhibits a lagged positive effect on TPFEs.     

Global and Extreme Dependence Between Investor Sentiment and Stock Returns in European Markets

The authors investigate the global and extreme dependence structure between investor sentiment and stock returns in 7 European stock markets (Belgium, France, Germany, Greece, the Netherlands, Portugal, and the UK), over the period 1985–2015. Global dependence refers to the correlation of changes in sentiment and stock returns over the whole range of these 2 variables, and extreme dependence refers to the local correlation of high (i.e. asymptotic) changes in sentiment and high stock returns. Using copula models and a bootstrap procedure, 6 statistical tests are performed for this purpose. Among the results of the tests, the authors highlight those that provide evidence of contemporaneous lower extreme dependence and contemporaneous upper extreme independence between sentiment and returns. As policy implications, these results suggest that financial stability can be promoted if regulators consider the impact of their decisions on investor sentiment. Also, the results seem to support the arguments in favor of short selling ban during turmoil periods. Finally, overall, the results are relevant for both investors and regulators and reinforce the importance of considering investor sentiment to better understand the behavior of financial markets.     

Global norm of national treatment for patent uncertainties: A longitudinal comparison between the US and China

Sporadic studies on the global norm of national treatment for patent uncertainties (NTPU) urge for insights of changes as well as for clarification to discrepancy. This global norm has been a concern for policy makers and practitioners for over a century, as a socially and strategically more significant matter than before for multilateral cooperation given the active technology transfer across borders. To fill in the void and extend prior studies, we examine the global compliance of NTPU from the perspective of patent pendency and granting by addressing three relevant questions: (1) Is NTPU upheld within countries? (2) How does NTPU diverge across countries? (3) How does NTPU change, as an outcome, over time? Based on the institutional theory, lagged regression modeling and longitudinal comparison of US and Chinese patenting, our findings reveal that: (1) NTPU is overall upheld because equality in pendency is demonstrated in both countries and in US granting, and foreigners are even favored for Chinese granting. (2) NTPU is comparatively divergent between the countries in pendency and granting due to national variations. (3) Regressive and progressive changes in NTPU are evidenced since both countries provide equal or higher granting, but longer pendency than before. Our findings contribute to theories by providing new insights to the global norm of national treatment and institutional theory from the perspective of patent uncertainties. We make novel empirical contribution to address NTPU changes of the top patent filing countries and methodological contribution to the longitudinal comparative study. The results also provide implications that concern policy makers and practitioners to handle patent uncertainties across borders.     

Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.     

Culture,financial crisis and the demand for property,accident and health insurance in the OECD countries

Culture has been known to play an important role in explaining differences in consumption behaviour across countries. Yet, we know very little how it affects spending on non-life insurance products. This paper attempts to shed some light on how cultural characteristics impact the demand for property, accident and health insurance, focusing on the OECD countries in the period 2000–2017. We find, via the system generalized method of moment estimations, that cultural characteristics such as individualism, long-term orientation, masculinity and uncertainty avoidance were the drivers of the expenditure on property insurance, whereas long-term orientation, uncertainty avoidance and hypometropia explained accident and health insurance spending across the OECD countries. In the presence of the global financial crisis, cultural effects on property insurance spending turned out to be relatively minor, with the exception of individualism. These findings provide valuable information for non-life insurance companies, consumers and policy makers in the OECD countries.     

Extreme risk spillovers between crude palm oil prices and exchange rates

This study is the first attempt to examine the extreme risk spillovers between Malaysian crude palm oil (CPO) and foreign exchange currencies of the three largest CPO importers: India, the European Union and China throughout the global financial crisis. Using daily data of three currencies, CPO spot and futures from 2000 to 2018, our results show: First, before the crisis, the unexpected change in foreign exchange rates is the primary driver of risk spillover to the CPO market. Second, during the crisis, the extreme movement of CPO spot returns is dominant in the Malaysian exchange rates relative to the euro. Third, after the crisis, the spillover flows from the CPO market to the foreign exchange market. Overall, our findings show the importance of CPO pricing dynamics in mitigating foreign exchange risk over the crisis period. This paper contributes to the extant literature by recognizing the effect of risk spillover on the targeted foreign exchange rate for portfolio allocation.