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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.

Digital platforms (DP) are transforming service delivery and affecting associated actors. The position of DPs is impacted by the regulations. However, emerging economies often lack the regulatory environment to support DPs. This paper aims to explore the regulatory developments for DPs using the multi-level perspective (MLP).

The paper explores regulatory developments of ride-hailing platforms (RHPs) in India and their impacts. This study uses qualitative interview data from platform representatives, bureaucrats, drivers, experts and policy documents.

Regulatory developments in the ride-hailing space cannot be explained as a linear progression. The static institutional assumptions, especially without considering the multi-actors and multi-levels in policy formulation, do not serve associated actors adequately in different times and spaces. The RHPs regulations must consider the perspective of new RHPs and the support available to them. Non-consideration of short- and long-term perspectives of RHPs may have unequal outcomes for established and new RHPs.

This research has implications for the digital economy regulatory ecosystem, DPs and implications for policymakers. Though the data from legal documents and qualitative interviews is adequate, transactional data from the RHPs and interviews with judiciary actors would have been insightful.

The study provides insights into critical aspects of regulatory evolution, governance and regulatory impact on the DPs’ ecosystem. The right balance of regulations according to the business models of DPs allows DPs to have space for growth and development of the platform ecosystem.

This research shows the interactions in the digital space and how regulations can impact various actors. A balanced policy can guide the paths of DPs to have equal opportunities.

DP regulations have a complex structure. The paper studies regulatory developments of DPs and the impacts of governance and controls on associated players and platform ecosystems.

The study aims to explore and test the integrated relationships between university reputation and its key antecedents. In doing so, theoretically derived antecedents of university reputation were examined. The study reports the complex interplay among image, quality, value, satisfaction and attachment and their subsequent effect on reputation.

A quantitative method was used to achieve research objectives. Data collected from students enrolled in major private university in Northern India were analysed to test the proposed model directly and indirectly using structural equation modelling (SEM).

The findings confirmed most of the hypothesised relationships. Prominently, image construct was found to be significantly affecting students' quality perceptions along with satisfaction, attachment, value and importantly reputation. The study found evidence for the impact of students' attachment on university reputation. Findings also indicated the presence of several indirect relationships among the considered dimensions.

Current research offers implications for universities that are met with the perpetual challenge of survival in the competitive higher education (HE) marketplace. Findings from the study not only help build theory on university reputation but make essential contribution towards guiding managers in developing effective strategies by building reputation via concentrating on the most crucial determinants.

Although research in HE marketing is growing, effects of student attachment towards building reputation has not garnered attention, which is theoretically a vital construct. The paper presents new framework to realise university reputation with the help of integrated relationships among select dimensions in the setting of an emerging HE market.