Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy: Volume 110A

Purpose: This chapter aims to identify and evaluate the various components of business model disclosures in an Integrated Report and ascertain how the notion of business model is perceived among practitioners.

Need for the Study: According to previous research, the International Integrated Reporting Council’s (IIRC) objective of improving corporate reporting by encouraging organisations to disclose their business model has not found the desired recognition. Therefore, the study elaborates on the various components of business model reporting and their implications on corporate reporting in general.

Methodology: A review of literature was conducted to identify and analyse research based on business models and their disclosures in integrated reporting. A narrative review was undertaken for selected literature.

Findings: The findings suggest that most large-sized organisations use integrated reporting for impression management and are not inclined to disclose too much about their business models for fear of competition. There is still a lack of clear understanding of what a business model should entail.

Practical Implication: This study adds to the research on business model disclosures in integrated reporting. Voluntary disclosure and a better understanding of such disclosures will prepare organisations of all sizes and industries for an event when Integrated Reporting becomes statutory.

Design/Methodology/Approach: This chapter applies tests associated with the adaptive market hypothesis (AMH) and Johansen cointegration test. AMH acknowledges the views of the efficient market hypothesis and behavioural finance approach.

Purpose: Cryptocurrencies are considered a new asset class by multiasset portfolio managers. Hence, we examine the AMH and cointegration in the cryptocurrency market to know whether select cryptocurrencies can be diversified.

Findings: We find that cryptocurrencies are efficient and there is a long-run relationship among constituent series, and there is no short-run causality derived from bitcoin, Ethereum and litecoin to bitcoin, while stellar and Dogecoin have short-run causality to bitcoin.

Originality/Value: This chapter is different from the existing one as this is the first study in which the AMH and Johansen cointegration test are applied to check the efficiency and relationship of Bitcoin, Ethereum, and Monero, Stellar, litecoin and Dogecoin.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

Introduction: Governance is the management of various actions to improve human capacities and boost the efficiency with which services are delivered to the general public. The study analyses the relationship between two variables: geographic location and desire to switch to an E-governance system. The study also aims to explore the relevance of artificial intelligence (AI) in supporting E-Governance.

Objectives of study: (1) To investigate the notion of e-governance and the many approaches available. (2) To research various government E-Governance initiatives and raise awareness about the difficulties and opportunities facing India’s e-government system. (3) To study the acceptance of E-governance by the public from rural and urban districts.

Methodology: This study will use a descriptive research approach as its research strategy. Primary data was collected to check for the preference for an acceptance rate of E-Governance based on geographic location (URBAN and RURAL). The current investigation is conducted on 200 respondents from Northern India’s selected urban and rural districts.

Finding and implications: The report summarises the significance of e-governing system adoption in India and offers ways to improve the operation of these systems in the future. The results of the test show that both factors are highly significant. The study recommends that future research integrate our search for scientific studies with a search for non-scientific publications, as journal and conference publications may lag behind the most recent breakthroughs in the implications of AI use in public administration.

Purpose: Thanks to the Fourth Industrial Revolution and the digital economy, digital banking has become an attractive business trend. Moreover, the spreading of the Covid-19 virus worldwide over the past two years has boosted the digitalisation of banking services. The development of digital banking is now becoming an uncontroversial issue that will attract the concern of scholars, bank managers, and policy-makers.

Methodology: As an emerging country with a young population having significant digital appliance joy, Vietnam will be a perfect case study to research the development of digital banking. Besides, digital banks, as well as the appliances of artificial intelligence (AI) in the banking sector, have appeared in Vietnam’s banking system at several different levels.

Findings: Moreover, most commercial banks in Vietnam are now in the race to complete their digital services to provide innovative digital banking services that add more value to their clients. Hence, the chapter will describe the overall picture of Vietnam’s current digital banking market.

Implications: Based on the crucial features of the operations of several digital banks and the appliances of AI in the digital banking sector in Vietnam during the chosen period, the author would like to give information on the potential of the Vietnamese digital banking market and suggest the key policies which the Vietnamese government should consider to support the digital transformation of the banking sector in Vietnam.

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.

Purpose: In this study, an attempt has been made to examine the compliance unit’s role in mediating the electronic government’s role in money laundering. E-government is clarified as the application of Information technology to encourage access and transfer of all aspects of government amenities and operations that impact transparency and accountability for the benefit of the people, trades, workforces and other stakeholders. The current study aims to assess whether the e-government can lessen or counterbalance the risks related to money laundering in the country and the mediating role of the compliance unit in reducing money laundering.

Methodology: This study practices structural modelling to assess the direct linkage between e-government and anti-money laundering and the indirect path between e-government and anti-money laundering that passes through the compliance unit as a mediator.

Findings: The findings prove that the compliance unit fully mediates the relationship between E-government and anti-money laundering. The direct path shows an insignificant relationship between e-government and money laundering, but this association becomes significant when the compliance unit is brought as a mediator.

Originality: This study directs that e-government runs on a sustainable ICT platform to improve transparency and accountability of all aspects of government facilities and actions for sustainable economic goals and help to diminish money laundering by enhancing transparency and accountability of government administration.

Introduction: Interest and action concerning fiscal accountability have surged around the world in recent years, especially among Sub-Saharan African countries, because decision-making in the region has traditionally been shrouded in secrecy, with the general public having almost no access to knowledge on the management of public funds. Limited fiscal transparency has led to government fiscal crises where citizens have begun to call for better governance and participation in public funds.

Purpose: This study examines the impact of e-governance on the overall fiscal performance in SSA, while the specific objectives include the effect of e-governance on the central government’s primary balance and public external debt stock.

Methodology: The study employs annual data across 43 SSA countries to analyse the study from 2000 to 2018 using the panel-corrected standard error (PCSE) method for estimating the models. Overall fiscal performance is generated through principal component analysis (PCA), which involves a linear combination of public external debt stock and central government primary balance.

Findings: The results reveal that there is clear evidence of the effectiveness of e-governance on the overall fiscal performance, even though this is not the same for the public external debt stock in SSA, despite the success recorded in the region’s ICT and telecommunication sectors in recent times. In addition, all other control variables impact fiscal performance except population growth.

Purpose: The aim of this chapter is to provide a quantitative literature review on machine learning (ML) and artificial intelligence (AI) in the Insurance Sector.

Need for the Study: The current study maps the literature regarding AI and ML in the insurance sector through bibliometric tools to identify the significant gaps in the available literature, considerable insights that emerged, and a scientific evaluation of AI and ML in the Insurance sector.

Methodology: The VOS viewer method was used to conduct the depth and quantitative analysis of the AI and ML in Insurance. The study of 450 articles has been retrieved through the Scopus database from 2012 to 2021. The implication of performance analysis methods has helped to explore influential journals, authors, countries, Keywords, and affiliations, elevating the literature in AI and the Insurance Sector.

Finding: This study conducts an exploratory analysis and identifies the prominent authors, sources, countries, affiliations, and articles using modern bibliometric analysis (BA) tools. The geographic scattering of the study indicates that the USA and the UK have highly influential publications and contribute to AI and Insurance. East and Southern Asia countries are far behind.

Practical Implication: Furthermore, this chapter can be used as a reference paper to explore the new field of study in the insurance sector using AI. The search criteria were set in the study to limit the sample published papers/articles included in Scopus data based on the AI and ML in Insurance.

Purpose: The study’s objective is to look at the link between money laundering and economic and financial performance, emphasising the effectiveness of the literature and possible later research directions using science mapping, which allows for scientific knowledge analysis.

Need for the Study: This study is related to a better understanding of the field’s historical evolution in terms of publications.

Methodology: This study used bibliometric approaches to analyse a sample of 660 studies from the Web of Science between 1994 and 2022, concentrating on keywords, author, paper, journal, and subject analysis. This study focused on performance analysis and scientific mapping of articles using the R package.

Findings: The empirical results indicated that the research field’s primary issues include corporate governance, fraud, machine learning, fraud detection, financial fraud, financial statement, corruption, earnings management, ethics, governance, financial reporting, bankruptcy, internal control, or performance. M. S. Beasly, D. B. Farber, E. M. Fich, R. Romano, and A. Shivdasani are the most well-known authors on the issue of money laundering and financial and economic performance. At the same time, the most typical journals are the Journal of Business Ethics, Journal of Money Laundering Control, Accounting Review, Journal of Financial Economics, and Journal of Corporate Finance.

Practical Implications: This study will act as a guide for researchers of various fields to evaluate the development of scientific publications in a particular theme over time, especially for those who are in the field of money laundering and financial performance.

Purpose: This conceptual paper aims to identify how financial inclusion relates to sustainability and the level of sustainable development.

Methodology: The paper used discourse analysis to identify how financial inclusion relates to sustainability and the level of sustainable development.

Finding: The paper argued that granting access to basic formal financial services contributes to greater sustainable development by ensuring that access to finance is guaranteed sustainably, and basic financial services are provided sustainably and based on sustainability principles to yield a lasting impact for sustainable development. The paper also argued that financial inclusion increases the level of sustainable development because financial inclusion increases the economic opportunities and social welfare of banked adults while it only provides limited benefits for the environment. This approach links financial inclusion to sustainable development by adopting sustainability principles in offering basic financial services to banked adults.

Implication: Consequently, a synergy between financial inclusion and sustainable development is needed. The synergy should be based on sustainability principles, requiring policies integrating financial inclusion into the sustainable development agenda.

Originality: This paper is the first to identify the relationship between the financial inclusion agenda, the sustainable development agenda and the sustainability agenda.

Introduction: One of the main goals of the 2030 Agenda for Sustainable Development is to represent gender equality due to its essential role in sustainable progress. At the same time, the balance between women and men in management is explicitly mentioned as a desideratum, given that more women in leadership roles positively impact business performance and sustainability.

Purpose: The study investigates the dynamic relationship between gender inequalities in management positions and sustainable competitiveness. Our contribution is twofold: we examine this interrelationship and its causality.

Methodology: We used panel data of 350 observations for 2012–2021, and we employ a Vector Auto-Regression model and Granger causality method to examine the relationship between the gender gap in management and sustainable competitiveness. The panel VAR for analysing the impulse response function was enriched using Monte Carlo simulations with 5% and 95%.

Findings: The results highlighted that a bidirectional causality between the gender gap in management and sustainable competitiveness is manifested in the European countries. Our results are similar to other studies found in the literature, with gender equality and sustainability positively associated. As an element of originality, our study demonstrates that gender equality in management contributes to sustainable performance, and, on the other hand, a more competitive and sustainable environment contributes to eliminating the gap between men and women in management.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

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.