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Forces of globalization have driven international convergence in corporate governance and accounting considerably over the past few decades. Nevertheless, despite the global trend, convergence of corporate governance and financial reporting remains a subject of debate. This research monograph critically examines whether China’s convergence with Anglo-American corporate governance principles and the International Financial Reporting Standards (IFRS) is likely to produce the expected outcomes of improving the transparency and comparability of accounting information in Chinese firms. In this chapter, we discuss the motivation for and the significance of the study; describe the issues associated with the adoption of internationally acceptable standards and principles in China; explain the theoretical framework used to inform the study and research methodology; and present the aim and objectives of the monograph.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

Although the early conversations of microcelebrity centered on Anglo-centric theories and context despite the varied backgrounds and cultural context of microcelebrity, this compilation of chapters seeks to assess and reframe the applications and uptake of microcelebrity around the world. Each of the chapters in this anthology contribute to expand the theoretical concept and contextualize the history and cultural affairs of those who are famous online. The case studies provide examples of how a microcelebrity emerges to fame because of their exposure and interaction within a group of niche users, a specific online community, or a specific cultural and geographical context through the social networks that emerge online. Academic scholarship on microcelebrity has crossed methodologies, disciplines and platforms demonstrating the wide appeal as the influence of these figures are on the rise. As preparation for the reader, this chapter offers a brief history of current scholarship, with an emphasis on shifting knowledge production away from an Anglo and Global North perspective. The introduction chapter serves as a road map for the reader breaking down each of the three sections of the book – norms, labors, and activism. Lastly, the co-editors have outlined different ways to read the text group chapters according to reader interest.

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

The leisure industry is colossally impacted by varied types of crisis. Assessing the volatility; an attempt is made towards disaster planning and a response system. This chapter indicates an all-inclusive integrated approach to deal with disasters and narrates conceptual and latest factual findings in the space of disaster management. An efficient and self-equipped attraction demands a competent and efficient disaster management system in place.

This chapter devises measures to deal with the capacity of a destination during pandemic and proposes recovering strategies for the leisure business. Destination governance and disaster management techniques are well explored in the proposed chapter.

An imperative study of this nature will determine the role of cultural perceptions of varied risk and threats in a pandemic scenario. Innovative practices of disaster governance and Post-disaster recovery strategies are crucial mechanisms for the sustenance of tourism and hospitality sector.

The conceptual ideas and outcomes obtained in this chapter helps policy makers not only to find new strategies to placate the negative impacts of COVID-19 on the organic image of tourist destinations but also assists in accelerating the recovery timeframe just after the pandemic.

In this chapter, we investigate whether the option contract can coordinate a supply chain when supply chain members have fairness concerns. Specifically, we consider a supply chain consisting of a manufacturer and a retailer where the two members can be either rational or fair-minded, and explore the condition under which the supply chain can be coordinated using an option contract. We follow the traditional newsvendor model by assuming that the market demand is stochastic with a cumulative distribution function and the retail price is exogenous. Under the option contract, the manufacturer’s decision variables include its option price and its exercise price, and the retailer is to decide its order quantity. We derive the equilibrium results for four different scenarios, that is, (i) both the two members are rational, (ii) the supplier is rational but the retailer is fair-minded, (iii) the supplier is fair-minded but the retailer is rational, and (iv) both are fair-minded. While the option contract can coordinate the supply chain when either of the two members is rational, we also find that when both the two members are rational, the option contract can coordinate the supply chain only under some specific conditions. Furthermore, we investigate whether the two members will suffer the disadvantageous or the advantageous inequality in the equilibrium and find some interesting findings.

Multinational enterprises (MNEs) own and control technological resources and capabilities that make them critical actors in accelerating the transition toward net zero. Even beyond the energy sector, stakeholders are putting increasing pressure on MNEs to reduce the carbon intensity of their operations, that is, to improve their carbon performance. While there is unambiguous evidence that national climate policy is a critical catalyst for long-term carbon performance improvements, there is limited research on how MNEs’ carbon strategies react to climate policies. This chapter reviews the concepts, drivers, and strategies connected to carbon performance in the broader sustainability and management literature to clarify potential complementarities to international business (IB). The authors then highlight how MNEs will face increasing institutional complexity along two dimensions: (1) the structural diversity of institutional environments and (2) institutional dynamism, primarily reflected by public policy. The proposed conceptual framework maps these two dimensions to national and subnational levels, and the authors present two data sources that allow the quantitative analysis of country differences in the diversity and dynamism of national climate policy. The authors conclude that there are ample opportunities for IB researchers to explore MNEs’ strategic reactions to climate policy and to inform policymakers about the consequences of national climate policy in the global economy.

Sustainable development calls for a larger share of intermittent renewable energy. To mitigate this intermittency, Compressed Air Energy Storage (CAES) technology was introduced. This technology can be made more sustainable by recovering the heat of the compression phase and reusing it during the discharge phase, resulting in an adiabatic CAES without the need for burning of fossil fuels. The key process parameters of CAES are temperature, pressure ratios, and the mass flow rates of air and thermal fluids. The variation in these parameters during the charge and discharge phases significantly influences the performance of CAES plants. In this chapter, the transient thermodynamic behavior of the system under various operating conditions is analyzed and the impact of heat recovery on the discharge phase energy efficiency, power generation, and CO₂ emissions is studied. Simulations are carried out over the air pressure range from 2,500 to 7,000 kPa for a 65 MW system over a five-hour discharge duration. It is also assumed that the heat loss in the air storage and the hot thermal fluid tank is insignificant and standby duration does not impact the status of the system. This result shows that the system exergy and the generated power are more sensitive to pressure change at higher pressures. This work also reveals that every 10°C increase on the temperature of the stored air can lead to a 0.83% improvement in the energy efficiency. The result of the transient thermodynamic model is used to estimate the reduction in CO₂ emissions in CAES systems. According to the obtained result, a 65 MW ACAES plant can reduce about 17,794 tons of CO₂ emission per year compared to a traditional CAES system with the same capacity.