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This study evaluates the effect of the media image content of business to business (B2B) organizations to accelerate social media engagement. It highlights the importance of strategically designing image content for business marketing strategies.

This study designed a computation extensive research model based upon the stimulus-organism-response (SOR) theory using 39,139 Facebook posts of 125 organizations selected from Fortune 500 firms. Attributes from images and text were estimated using deep learning models. Subsequently, inferential analysis was established with ordinary least squares regression. Further machine learning algorithms, like support vector regression, k-nearest neighbour, decision tree and random forest, are used to analyze the significance and robustness of the proposed model for predicting engagement metrics.

The results indicate that the social media (SM) image content of B2B firms significantly impacts their social media engagement. The visual and linguistic attributes are extracted from the image using deep learning. The distinctive effect of each feature on social media engagement (SME) is empirically verified in this study.

This research presents practical insights formulated by embedding marketing, advertising, image processing and statistical knowledge of SM analytics. The findings of this study provide evidence for the stimulating effect of image content concerning SME. Based on the theoretical implications of this study, marketing and media content practitioners can enhance the efficacy of SM posts in engaging users.

Blockchain technology has fascinated researchers and industry professionals. Since its birth, the attention for blockchain has been exponentially increasing, however, most of the industries are still skeptical in adoption for value creation. The purpose of this study is to analyze the actual level of implementation and diffusion of blockchain technology within the logistics and transportation industry by comparing and using the collective intelligence of academic literature and industry practices of implementation of blockchain in this domain.

This study uses the methodology of systematic literature review along with inductive reasoning. The systematic literature review of academic and industry frontiers together has brought a bigger and real picture into consideration.

The results highlight that, within the transportation sector, currently there is a very low diffusion of blockchain, although applications show immense promises for the future. The various application where blockchain technology can make a significant impact are also identified.

Due to the early stage of experimentation with blockchain technology, high-quality data which is relevant to the optimized usage of this technology in the logistics and transportation industry is not available.

The study will help the practitioners in identifying additional avenues in which they could implement blockchain for the effectiveness, efficiency and growth of the logistics and transportation industry.

The analysis of mixed sources of information for undertaking systematic literature review by assessing academic and trade publications is a novelty of this study.