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Today many firms are pushed towards digitalization to ensure business continuity and their survival due to COVID-19. Therefore, this study aims to investigate the emerging knowledge management models in the era of digitalization and disruption.

The authors have adopted a semi-structured approach composed of qualitative data collection from 37 business executives from India representing different industry sectors. The authors adopted a three-layer coding process (axial, open and selective) to develop a framework grounded in organizational information processing theory.

Scanning the business environment leads to understand the status of current and potential business through intelligence of information, whereas better planning and execution can be achieved through employing and using the information intelligently that fits to the overall and strategic objective of the business. Overall, the business continuity can be obtained by information prosperity across the business by engaging diverse stakeholders. According to the findings, these aspects lead to the effective implementation of digital knowledge to ensure business continuity in uncertain business environment.

The study offers the insights for managing and executing the knowledge in digital platforms, where they can think of developing a system architecture on the basis of degree of uncertainty and information processing requirements for combining the knowledge.

The present study is unique, where it offers the meaningful visions to the designers and users of virtual knowledge management systems.

The purpose of this study is to highlight the importance of Internet of Things (IoT) in India. The purpose also includes providing insights regarding policy framing for IoTs with a focus on regulation and governance.

A qualitative approach has been taken here for understanding the issues of IoT policy for India so far as regulations and governance are concerned.

This study highlights that the draft IoT policy of India, 2015 is to be improved. Attention is to be focused on regulation and governance for addressing security and privacy issues among other issues. For proper enablement of IoT technology, arrangements of funds are essential.

IoT-related technologies in India have immense potential for the industries. This study implies the importance of security and privacy issues. If these issues are properly addressed, the industries will flourish further.

The study provides insights covering how usage of IoT technology would help the industry to grow up, how research and development organizations would be able to strengthen IoT technology for further development and to what extent it will improve the human daily activities.

IoT would influence lives of millions of people of India. IoT-related policies would have huge social implication in terms of human–device interactions and communications. This research is a contemporary study on the implication of IoT policy toward the future growth of IoT-enabled devices in India.

The Government of India is expected to frame a comprehensive IoT policy with the help of draft IoT policy of 2015. This paper has taken a unique attempt to provide effective inputs to develop a comprehensive IoT policy for India.

Mobile crowd sensing (MCS) is a new paradigm enabled by Internet of Things (IoT) in which sensor-rich ubiquitous devices collect and share the data over a large geography. Human behaviour attributes (perception, comprehension and projection) play a key role in the decision-making process for sharing and processing the data. This study aims to understand how situation awareness plays an important role in MCS in an IoT ecosystem.

A systematic literature review was conducted by following a rigorous search protocol that identified a total of 470 peer-reviewed research papers. These papers were further filtered and finally 31 relevant papers were selected.

The major issues and concerns arising due to human participation in the MCS system were identified. Further, probable strategies were explored to deal with the challenges resulting due to certain human behaviour attributes.

This study provides the recommendations to address the major challenges related to the MCS system, which in turn may enhance the adoption of emerging smart technology-driven services.

The study is original and is based on the existing literature and its interpretation.

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.

In current times, organizations operating in emerging economies are providing digital services to its citizen the internet. Literature indicates that digital services are facing major challenges with respect to its adoption among users groups due to the perceived risks.

With the use of generalized analytic network process (GANP), prioritization of different dimensions of risk has been done on the basis of an empirical survey among user groups in India.

The result indicates that dimensions like privacy risk, performance risk and financial risk are more important risks across digital services models. However, physical risk, social risk, psychological risk and time risk are comparatively less important risks across digital services. This research also finds out that the end users are reluctant to provide their personal information.

The sample size is relatively small which limits generalizability of results beyond India. However, an application of GANP has been showcased for empirical research.

The research outcome can help managers in deciding which dimensions of risk are more important for digital service delivery and thus facilitate adoption.

This paper focused on the different facets of risk perceived by consumers, toward the digital services available in smart cities. Perceived risk dimensions such as privacy risk, performance risk, financial risk, physical risk, social risk, psychological risk and time risk have shown that there is a need to prioritize these risks to the digital services which is offered to the residents of the smart cities.

This paper aims to enlighten stakeholders about critical success factors (CSFs) in developing intelligent autonomous systems (IASs) by integrating artificial intelligence (AI) with robotics. It suggests a prioritization hierarchy model for building sustainable ecosystem for developing IASs.

This paper is based on the existing literature and on the opinion of 15 experts. All the experts have minimum of eight years of experience in AI and related technologies. The CSF theory is used as a theoretical lens and total interpretative structure modelling (TISM) is used for the prioritization of CSFs.

Developing countries like India could leverage IASs and associated technologies for solving different societal problems. Policymakers need to develop basic policies regarding data collection, standardized hardware, skilled manpower, funding and start-up culture that can act as building blocks in undertaking sustainable ecosystem for developing IASs and implementing national AI strategy. Clear-cut regulations need to be in place for the proper functioning of the ecosystem. Any technology that can function properly in India has better chances of working at the global level considering the size of the population.

This paper had all its experts from India only, and that makes the limitation of this paper, as there is a possibility that some of the factors identified may not hold same significance in other countries.

Stakeholders will understand the critical factors that are important in developing sustainable ecosystem for IASs and what should be the possible order of activities corresponding to each CSF.

The paper is the first of its kind that has used the CSF theory and TISM methodology for the identification and prioritization of CSFs in developing IASs. Further, eight significant factors, that is, emerging economy multinational enterprises (EMNEs), governance, utility, manpower, capital, software, data and hardware, have come up as the most important factors in integrating AI with robotics in India.

The Web 3.0 has been hugely enabled by smartphones and new generation mobile applications. With the growing adoption of smartphones, the use of mobile applications has grown exponentially and so has the development of mobile applications. This study is an attempt to understand the issues and challenges faced in the mobile applications domain using discussions made on Twitter based on mining of user generated content.

The study uses 89,908 unique tweets to understand the nature of the discussions. These tweets are analyzed using descriptive, content and network analysis. Further using transaction cost economics, the findings are reviewed to develop practice insights about the ecosystem.

Findings indicate that the discussions are mostly skewed toward a positive polarity and positive user experiences. The tweeters are predominantly application developers who are interacting more with marketers and less with individual users.

Most of these applications are for individual use (B2C) and not for enterprise usage. There are very few individual users who contribute to these discussions. The predominant users are application reviewers or bloggers of review websites who use the recently developed applications and discuss their thoughts on the same.

The results may be useful in varied domains which are planning to expand their reach to a larger audience using mobile applications and for marketers who primarily focus on promotional content.

The domain of mobile applications on social media is still restricted to promotions and digital marketing and may solely be used for the purpose of link building by application developers. As such, the discussions could provide inputs towards mobile phone manufacturers and ecosystem providers on what are the real issues these communities are facing while developing these applications.

The study uses mixed research methodology for mining experiences in the domain of mobile application developers using social media analytics and transaction cost economics. The discussion on the findings provides inputs for policy-making and possible intervention areas.

Social media platforms play a key role in information propagation and there is a need to study the same. This study aims to explore the impact of the number of close communities (represented by cliques), the size of these close communities and its impact on information virality.

This study identified 6,786 users from over 11 million tweets for analysis using sentiment mining and network science methods. Inferential analysis has also been established by introducing multiple regression analysis and path analysis.

Sentiments of content did not have a significant impact on the information virality. However, there exists a stagewise development relationship between communities of close friends, user reputation and information propagation through virality.

This paper contributes to the theory by introducing a stagewise progression model for influencers to manage and develop their social networks.

There is a gap in the existing literature on the role of the number and size of cliques on information propagation and virality. This study attempts to address this gap.

The pandemic COVID-19 brought in large challenges globally among the workforce. There were reports of how employee layoffs and pay-cuts were gradually becoming prominent across industries based on media reports. However, there were no attempts to develop a typology of challenges faced by the workforce.

This study mined user-generated content from Twitter to bring out a typology of challenges due to the sudden turbulence that is faced from the pandemic. A case study has also been conducted by taking in-depth interviews in the academic sector to deep dive into the nature of these problems.

The study findings indicate that these challenges are basically stemming from challenges surrounding infrastructure readiness, digital readiness, changing nature of deliverables, workforce demand versus supply problems and challenges surrounding job losses.

There is a need to explore the linkages through inferential research infrastructure readiness, digital readiness, changing nature of deliverables, workforce demand versus supply problems and challenges surrounding job losses on employee welfare during pandemics.

The authors provide inductive insights based on a data-driven research methodology surrounding the sudden challenges faced and possible mechanisms to address these issues faced by a stressed workforce catering to multiple stakeholders.

This paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).

A semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.

The review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.

Given the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.