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.     

Effects of financial constraints on export performance of firms during the global financial crisis: microeconomic evidence from Korea

This study uses a manufacturing firm-level panel data set of South Korea for 2006–2013 to investigate the effect of financial constraints on the export performance of firms, with particular emphasis on the corporate ownership structure. The empirical results show that foreign multinational corporation (MNC) subsidiaries are not affected by financial constraint during both crisis and noncrisis periods, implying advantages of foreign ownership. However, domestic firms suffer more from financial constraints on exports during crisis years. In particular, domestic firms without parent firms are financially constrained during both crisis and noncrisis periods. However, those with parent firms do not experience financial constraints during noncrisis periods, although they too suffer from them during crisis periods. Thus, parent–subsidiary linkage among domestic firms plays an important role in alleviating financial constraints on export activity in noncrisis years but not as much during crisis years. Therefore, domestic parent firms exhibit less resilience to the global financial crisis, in comparison to foreign MNC parent firms.     

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.     

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.     

跨越竞争丛林与范式重构：全球治理改革进程中的疫苗产业创新治理

疫苗作为新冠疫情防控的最关键手段之一，已在各国抗疫中发挥积极影响。疫苗研发、生产、注射意愿以及全球分配的公平性与可及性等一系列问题日益受到世界关注。分析全球治理改革中，疫苗作为一种全球公共产品的创新治理问题，讨论疫苗产业发展特征与创新规律演化，剖析西方国家疫苗产业创新治理困境以及“竞争丛林”逻辑，阐释中国行动方案及对全球治理改革的贡献。最终提出疫苗产业创新治理机制对推动全球治理改革的政策含义。     

Multicultural managers and competitive advantage: Evidence from elite football teams

Successful organizations have dynamic capabilities that are aligned with their competitive environments. As competitive environments globalize, the dynamic capabilities driven by managers with multicultural backgrounds are increasingly relevant. These managers are more likely to seize cognitively distant opportunities than monocultural managers. Using panel analysis of multilevel data from 355 elite football (soccer) teams, we find support for a moderated relationship among the degree of globalization in the competitive environment, the multicultural background of team managers, and team performance. When the competitive environment is highly global, teams with multicultural managers outperform teams with monocultural managers. When the competitive environment is less global, the reverse is true. The same effect holds when tested both cross-sectionally and longitudinally.     

全球价值链视角下我国远洋渔业国际竞争力影响因素研究

文章基于全球价值链视角，结合远洋渔业的产业特性，构建了远洋渔业国际竞争力概念框架及其影响因素理论模型。进而基于我国74 家远洋渔业企业的问卷调查横截面数据，运用多元回归分析，深入解剖了影响我国远洋渔业经济租金、国际话语权、国际在位权、企业就业贡献四个国际竞争力维度的影响因素。研究发现：（1）远洋渔业企业越是进行海外远洋渔业基地建设、基地建设功能越健全，水产品国内消费市场越兴盛，远洋渔业企业冷冻、冷藏等储藏能力越强，企业越是能获得持续盈利优势。（2）远洋渔业设备技术研发、远洋渔业金融政策、政府间远洋渔业争议协商机制和海上通信系统研发，有助于提高我国远洋渔业国际组织影响力。（3）企业船上加工技术、企业水产品出口能力、海外直接投资正向显著影响企业履约能力。（4）企业远洋渔船规模、企业有自己独特的海洋与岸上联动发展模式，促进了企业社会就业贡献。     

Away from home: How young Chinese consumers travel with global brands?

This interpretive study investigates how a group of young Chinese students consume global brands of American origins, in China and in the UK. More specifically, this research examines how meanings attached to global food brands travel abroad with consumers and investigates the relationship between brand consistency and brand meanings across national boundaries. Findings from a thematic analysis of focus group interviews conducted over a nine-month period, reveal that some brand meanings are context and culture specific (contextual meanings) while other meanings travel with consumers across borders (core meanings). Theoretically, this study shows how global brands provide a platform of structural meanings, ideas and practices that are global and globalising in themselves, allowing a degree of fluidity and adaptation in relation to the local context of consumption.     

Cross-border exchanges and volatility forecasting

We test for the performance of a series of volatility forecasting models (GARCH 1,1; EGARCH 1,1; CGARCH) in the context of several indices from the two oldest cross-border exchanges (Euronext; OMX). Our findings overall indicate that the EGARCH (1,1) model outperforms the other two, both before and after the outbreak of the global financial crisis. Controlling for the presence of feedback traders, the accuracy of the EGARCH (1,1) model is not affected, something further confirmed for both the pre and post crisis periods. Overall, ARCH effects can be found in the Euronext and OMX indices, with our results further indicating the presence of significant positive feedback trading in several of our tests.     

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.