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This paper aims to underscore how the digitization of content and the convergence in the telecommunications sector has prompted a wave of consolidation between telecom and content players.

Using interdisciplinary insights from competition policy and business strategy, the paper draws attention to the interplay between business model innovation and merger control in the converged telecoms sector.

Technological innovation and business model innovation led to the emergence of over-the-top (OTT) services. This innovation in turn led to two key effects, first, successful commercialization of content and the emergence of the “smart pipes” that in turn has led to the second effect, which is increased mergers and acquisitions (M&As) in the converged telecommunications sector. Emergence of OTT with big data as a key advantage challenged the strategy and business models of the more established players, such as the AT&T, Time Warner, Liberty Global and Fox, which in turn led to the current trend of M&As in the sector.

This paper makes the following key contributions to the literature on M&As between the fixed/mobile and content players. First, it elucidates how the existing market players can benefit from competition policy, such as merger remedies to enter new and related markets. Second, it advocates that the US and the European competition authorities while assessing these M&As, take due account of innovation in business models, as business model innovation not only promotes innovation in the market but also enhances consumer welfare, considering that it offers the merged firm economies of scale and scope to offer better-quality goods and services at subsidized prices.

Purpose: This chapter aims to analyse and highlight the current landscape of performance management (PM) systems, and the benefits of integrating modern technology such as smart analytics (SA) and artificial intelligence (AI) into PM systems. The chapter discusses the application of AI in PM tasks which successively simplify many offline PM tasks.

Methodology: To carry out this analysis, a systematic literature review was performed. The review covers literature detailing PM components as well as research concerned with the integration of SA and AI into PM systems.

Findings: This study uncovers the merits of using SA and AI in PM. SA technology provides organisations with a clear direction for improvement, rather than simply state failure in performance. AI can be used to automate redundant tasks while retaining the human element of decision-making. AI also helps reduce the time required to take action on feedback.

Significance: The findings of this research provide insights into the use of SA and AI to make PM tasks fast, scalable, and error-free.

In a dynamic and uncertain business environment, it is necessary for companies to adapt to be capable of addressing the changing circumstances and ensure business continuity. Hence, companies are employing advanced Industry 4.0 (I4.0) technologies. This study aims to explore the role of advanced I4.0 technologies in facilitating companies’ development towards supply chain resilience (SCR).

The study adopted a structured approach of Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) to identify 234 articles. Under PRISMA, we adopted a three-phase methodology of planning, conducting and reporting the review results. The data of these articles were synthesized using a Scopus database to investigate the relationship between I4.0 technologies and SCR.

The study’s findings map the technologies appropriate in different supply chain nodes and bridge the literature gap in the intersection of I4.0 technologies and SCR. The study results indicate the role of I4.0 technologies role in achieving resilience in key functions of an organization through an I4.0 technologies-enabled sourcing, manufacturing, distribution and return resilience (I-SMDRR) framework. The I-SMDRR framework also depicts the relationship being moderated by internal and external risk factors.

The study offers a unique framework by mapping sourcing, manufacturing, distribution and return resilience through I4.0 technologies. Additionally, the review delineates the theory-mapped research gaps helpful for future researchers. In summary, this systematic review of the literature identifies the components that lead to developing SCR.

In the cloud-computing environment, privacy preservation and enabling security to the cloud data is a crucial and demanding task. In both the commercial and academic world, the privacy of important and sensitive data needs to be safeguarded from unauthorized users to improve its security. Therefore, several key generations, encryption and decryption algorithms are developed for data privacy preservation in the cloud environment. Still, the outsourced data remains with the problems like minimum data security, time consumption and increased computational complexity. The purpose of this research study is to develop an effective cryptosystem algorithm to secure the outsourced data with minimum computational complexity.

A new cryptosystem algorithm is proposed in this paper to address the above-mentioned concerns. The introduced cryptosystem algorithm has combined the ElGamal algorithm and hyperchaotic sequence, which effectively encrypts the outsourced data and diminishes the computational complexity of the system.

In the resulting section, the proposed improved ElGamal cryptosystem (IEC) algorithm performance is validated using the performance metrics like encryption time, execution time, decryption time and key generation comparison time. The IEC algorithm approximately reduced 0.08–1.786 ms of encryption and decryption time compared to the existing model: secure data deletion and verification.

The IEC algorithm significantly enhances the data security in cloud environments by increasing the power of key pairs. In this manuscript, the conventional ElGamal algorithm is integrated with the pseudorandom sequences for a pseudorandom key generation for improving the outsourced cloud data security.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

The COVID-19 outbreak and severe weather challenges have disrupted the supply chains of various industries across the globe, including the automotive industry. The purpose of this study is to investigate the outbound vehicle logistics practices of a Chinese automotive firm to achieve value chain optimization to meet the order fulfillment through the feasibility of using blockchain technology (BCT).

To identify the research gap and to formulate the research questions, a detailed literature review was conducted. To compare and contrast the Chinese automotive outbound logistics process, we interviewed Original Equipment Manufacturers (OEMs), vehicle logistics service providers and local IT vendors. To study the feasibility of BCT in the case companies, we conducted interviews with the experts as part of the in-depth empirical case study. Case analysis has emerged as the research themes.

In-depth data analysis suggested three important areas that needed focus from OEMs (1) handling distribution constraints; (2) ability to monitor quality; and (3) good communication with network partners and receiving real-time alerts, especially during peak demand periods. Our findings confirm that OEMs can address all these challenges in the presence of BCT alliance frameworks with automotive supply chain partners.

The scope of the research is restricted to the Chinese automotive OEMs. This research can be extended to other countries practicing the same level of outbound vehicle logistics. Data collected in this research were generated from a data sheet which is subjective to some extent.

This research provides practical insights for practitioners on the feasibility of using BCT on outbound logistics which can optimize order fulfillment and performance.

The paper adds insights into the feasibility of using BCT in automotive vehicle logistics to increase performance and compares the practices from China with developed economies.

Over the next decade, humanity is going to face big environmental problems, and considering these serious issues, businesses are adopting environmentally responsible practices. To put forward specific measures to achieve a more prosperous environmental future, this study aims to develop an environment-based perspective framework by integrating the Internet of Things (IoT) technology into a sustainable automotive supply chain (SASC).

The study presents a conceptual environmental framework – based on 29 factors constituting four stakeholders' rectifications – that holistically assess the SASC operations as part of the ReSOLVE model utilizing IoT. Then, experts from the SASC, IoT and sustainability areas participated in two rigorous rounds of a Delphi study to validate the framework.

The results indicate that the conceptual environmental framework proposed would help companies enhance the connectivity between major IoT tools in SASC, which would help develop congruent strategies for inducing sustainable growth.

This study adds value to existing knowledge on SASC sustainability and digitalization in the context where the SASC is under enormous pressure, competitiveness and increased variability.

Innovation and entrepreneurship are regarded as the key drivers to steer the engine of economic development in any nation. As a result, to understand the context and process of innovation and entrepreneurship there has been a steady rise in scientific literature and empirical studies. The purpose of this paper is to study the trends and progress of academic research on innovation and entrepreneurship in India by identifying the key articles, journals, authors and institutions.

Scientometric methods especially bibliometrics is used, for measuring the maturity of this research field in the country. The paper studies the research landscape in innovation and entrepreneurship in India by doing a bibliometric analysis using data from publications indexed in the Scopus database from the year 2000 to 2018. The study takes a multidisciplinary review of the literature in innovation and entrepreneurship research in India and could be used as a reference for future studies in this theme.

The study finds an increase in the scholarly studies in innovation and entrepreneurship in India in the last decade. It was also found that a large number of publications were joint-authored and collaborations between Indian and foreign universities is happening. The paper also highlights the authorship patterns, top journals and the most cited papers.

A major limitation of this study is that it has considered publications which are indexed in Scopus. This paper has contributed by highlighting the growth of studies in the field of innovation and entrepreneurship in the Indian context. The results can be used by future studies in this area as a starting point to highlight the nature of this research area.

The study attempts to present a trend analysis of published literature on innovation and entrepreneurship in India.