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This study aims to empirically test how sponsored video customization (i.e. the degree to which a sponsored video is customized for a sponsoring brand) affects video shares differently depending on influencer characteristics (i.e. mega influencer and expert influencer) and brand characteristics (i.e. brand establishment and product involvement).

This study uses a unique real-world data set that combines coded variables (e.g. customization) and objective video performance (e.g. sharing) of 365 sponsored videos to test the hypotheses. A negative binomial model is used to analyze the data set.

This study finds that the effect of video customization on video shares varies across contexts. Video customization positively affects shares if they are made for well-established brands and high-involvement products but negatively influences shares if they are produced by mega and expert influencers.

This study extends the influencer marketing literature by focusing on a new media modality – sponsored video. Drawing on the multiple inference model and the persuasion knowledge theory, this study teases out different conditions under which video customization is more or less likely to foster audience engagement, which both influencers and brands care about. The chosen research setting may limit the generalizability of the findings of this study.

The findings suggest that mega and expert influencers need to consider if their endorsement would backfire on a highly customized video. Brands that aim to engage customers with highly-customized videos should gauge their decision by taking into consideration their years of establishment and product involvement. For video-sharing platforms, especially those that are planning to expand their businesses to include “matching-making services” for brands and influencers, the findings provide theory-based guidance on optimizing such matches.

This paper fulfills an urgent research need to study how brands and influencers should produce sponsored videos to achieve optimal outcomes.

Despite talent management’s (TM) importance for improving organizations' competitiveness and resilience, the pandemic highlighted the weakness of organizational-level TM strategies. Therefore, the objective of this study is to investigate the moderating impact of HR analytics on the relationship between TM and its individual outcomes (talent motivation and quality of hires) and subsequently, their impact on organizational outcomes (talent retention).

The structural equation modeling (SEM) technique was used to analyze 219 online questionnaires administered to HR managers from European companies.

A positive relationship exists between TM activities and talent motivation as well as the quality of hires. Furthermore, HR analytics positively moderates these relationships. Finally, talent motivation and the quality of hires are positively related to talent retention.

This study offers several contributions to theory, as it analyzes TM from an individual perspective and provides further empirical confirmation of the potential benefits of HR analytics and additional grounding to the contingency theory.

Our results will allow practitioners to better orient their HR investments, with positive effects for their organizations and their employees.

This study demonstrates that HR analytics can help organizations adopt a human-centric approach to TM, thus increasing the chances for talents to fully express their potential.

This study takes a step forward toward considering TM outcomes from an individual perspective, responding to new generations' need to pay more attention to their individualities. HR analytics can be a suitable tool to do so, as it can provide insights and suggestions based on the actual organizational context, making TM a more data-driven process.