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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.

The main objective of this study is identification of the key factors in planning digital content marketing (DCM) strategy in line with the corporate's main marketing objectives in the B2B sector.

In order to identify the different content types and their corresponding marketing goals, content analysis method was served to analyze the content of Instagram pages of 24 top-ranked corporates from three different industries. SPSS version 22 was used to investigate the significant difference levels and the mean ranks of identified content types.

The findings uncovered the twelve content types which are commonly published by the corporates in line with their main marketing goals in the B2B sector. Furthermore, the results revealed the most valuable content types from the B2B audiences' viewpoint and the most efficient content types in persuading audiences to participate in conversations.

This study sheds some light on the ambiguous facets of DCM in the B2B sector, and its findings is useful as the starting point for the scholars who intend to investigate the various aspects of DCM and for the practitioners who work in the related fields.

This research offers a novel contribution to using Instagram as a DCM platform in the B2B sector. Also it contributes to identifying the main factors in communicating to B2B audiences through DCM.

Despite the significant potential of digital content marketing (DCM) to establish public and professional awareness, especially in uncertain situations, no previous research has investigated how to plan business-to-business DCM to help firms and society get through a crisis. Thus, this study aims to offer an integrative framework for providing valuable information for managing uncertainty, particularly during the pandemic crisis.

Through the lens of business awareness, this research explores relevant content types that can help firms and society get through the pandemic crisis. For this, the systematic review of 52 articles appearing in publication outlets for more than one decade (2010 to 2021) was conducted.

Based on the findings from the literature review, this paper identified two main categories of valuable content types for firms and society during the pandemic, namely, business-centered content types to enhance industrial environment awareness and human-centered content types to raise emotional awareness during the pandemic crisis.

To the best of the authors’ knowledge, this research delivers the first scientific article that focuses on presenting an integrative framework for providing valuable content types helping firms and society to manage uncertainty.