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Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

Virtual try-on (VTO) technology offers an opportunity for fashion and beauty brands to provide enriched self-explorative experiences. The increased popularity of VTOs makes it urgent to understand the drivers and consequences of the exploration of styles in VTO contexts (herein called self-explorative engagement). Notably, little is known about the antecedent and outcomes of the personalized self-explorative experience central to VTOs. This paper aims to fill this knowledge gap.

An online quasi-experiment (N = 500) was conducted in the context of fashion and beauty VTOs. Participants were asked to virtually try on sunglasses or lipsticks and subsequently answer a questionnaire measuring the key constructs: self-presence (i.e. physical similarity and identification), self-explorative engagement (i.e. exploration of styles in VTO context), brand cognitive processing and brand attitude. The authors analyze the data with structural equation modeling via maximum likelihood estimation in LISREL.

The experience of self-presence during consumers’ use of VTOs in augmented reality environments has a positive effect on self-explorative engagement. Furthermore, a mediation analysis reveals that self-explorative engagement improves brand attitude via brand cognitive processing. The results are confirmed for two popular fashion and beauty brands.

Grounded in extended self theory, to the best of the authors’ knowledge, this is the first study to show that a realistic VTO experience encourages self-extension via a process starting from the exploration of styles and results in increased brand cognitive processing and more positive brand attitudes. The exploration of styles is enabled by self-presence.