PurposeThe coronavirus disease (COVID-19) pandemic unprecedentedly shocks the market. Little is known about the impact of COVID-19 on brand engagement across country-of-origin (COO) and country-of-market (COM). To address the gap, this study examines how the spread of the COVID-19 affects consumer brand engagement on social media for global brands through the mechanisms of the COO and consumer animosity.Design/methodology/approachThe authors collect consumer engagement activity data from Facebook for eight global smartphone brands and match it with the COVID-19 statistics. Ordinary least square (OLS) models are used to estimate the impact on global brands brought by the spread of the COVID-19.FindingsThe results show that consumer brand engagement decreases for all brands in a COM as the number of confirmed COVID-19 new cases increases in the COM. Consumer brand engagement decreases for a brand across all COM as the number of confirmed COVID-19 new cases increases in the brand’s COO. If a brand’s COO is imputed for the pandemic, its consumer brand engagement will receive additional negative impacts across all COM.Originality/valueThis study enriches the COO literature by showing how the spread of a pandemic affects consumer brand engagement via COO and discovers the moderating role of consumer animosity.

Identifying the right influencers for brands is often the starting point for a successful influencer campaign. However, influencer identification is understudied, and most previous studies have only discussed visible characteristics of influencers and their social networks, overlooking content-based metrics. Combining interdisciplinary theories and techniques from marketing, linguistics, and computer science, we propose a data-driven automated text analysis framework to identify characteristics of effective influencers using influencer posts. Specifically, we propose a model that incorporates influencer personality traits captured by natural language processing, accounting for traditional covariates, such as network structure and follower engagement. In addition, we use a dataset that attributes influencer social media activities to customer purchases to address fake engagement and showcase our automated textual analysis. The proposed framework can help marketers develop influencer profiles and predict optimal influencers for their campaigns.

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