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This study aims to conceptualise a customer-centric model based on an online customer experience (OCE) construct, mediated by e-loyalty (EL) and e-trust (ET), to improve the continuous usage intention (CUI) of Indian digital banks from Generation Y and Z perspectives.

This study used an online survey method to gather data from a sample of 466 digital banking users, from which usable questionnaires were obtained. The obtained data were subjected to thorough analysis using PLS-SEM to further study the research hypotheses.

The main factors that determine digital banks’ OCE are perceived enjoyment, e-service quality, information quality and e-convenience. Additionally, relevant constructs were evaluated using an importance-performance map analysis.

This study used convenience sampling for the urban population using digital banking; therefore, the outcome may be generalised to a limited extent. It would be valuable to imitate studies in other countries to strengthen digital banking further.

There is a lack of research on digital banking and OCE in India; thus, this study helps rectify this issue while providing valuable insights. This study differs from others in that it examines the connections between OCE, EL, ET and the bottom line of financial institutions, using these factors as dependent variables instead of traditional measures.

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.

This study aimed at exploring the differential effects of different corporate governance (CG) indicators on risk management practices in Islamic financial institutions (IFIs) and conventional financial institutions (CFIs) of Pakistan. It also investigated the moderating role of institutional quality (IQ) in shaping the effects of CG practices on financial institutions of Pakistan.

A sample of 57 financial institutions including commercial banks, insurance companies and Modarba companies over the period 2006–2017 is used to carry out the empirical analysis. The authors applied the robust two-step system-generalized method of moments estimator, which is also called the dynamic panel data estimator. They also built the PCA-based composite index of CG and IQ by using different indicators to investigate the moderating role of IQ. They used three proxies for risk taking, five for CG and one for Shari’ah governance. To test the validity of the instruments, they applied the Arellano and Bond’s (1991) AR (1) and AR (2) tests and the J-statistic of Hansen (1982).

The results provided strong evidence that several individual characteristics of CG and the composite index are significantly related to the operational risk, the liquidity risk and the Z-score (a proxy for solvency risk). The results also revealed that IQ significantly and substantially contributes in reducing the level of risks. Finally, the estimation results indicated that the effects of CG on risk management are significantly different at IFIs and CFIs. This differential impact is mainly attributed to the fundamental differences in business models, operational strategies and contractual obligations of both types of institutions.

The findings of this study are important for enhancing our understanding of how CG relates to risk taking in Islamic and conventional financial services industries and how good quality institutions are important for formulating the governance effects on the risk-taking behavior of financial institutions. The findings suggest that a suitable size of board should be chosen to manage the risk effectively. As the findings show that the risk-taking behavior of IFIs differs from that of CFIs, the regulators and international standard setting bodies should tailor the regulatory frameworks accordingly.

This paper is different from the existing studies in four aspects. First, to the best of the authors’ knowledge, this is the first empirical investigation in Pakistan, which does the comparison of IFIs and CFIs while examining the impacts of CG on risk management. Second, the paper constructs the composite index of CG by considering several different indicators of governance and examines the combined effect of governance indicators on risk management process. Third, this paper adds to the growing literature on the role of IQ by investigating whether it acts as a moderator between CG structures and risk management and if yes, then whether this moderating role is different for IFIs and CFIs. Finally, the paper builds upon the existing research work on the CG effects for different types of financial institutions by proposing a single regression based analytical framework for comparing the effects across two different types of institutions, harvesting the benefits of higher degrees of freedom and avoiding/minimizing the measurement error.

This study investigates the influence of e-marketing risks on a Corporation’s Reputation (CR) resulting from its online marketing of products and services.

A comprehensive analysis was conducted to enhance the company’s e-marketing strategies and bolster its reputation in the market. This involved an investigation into key factors of e-marketing risks, such as customer confidence, product quality, marketing fraud, credibility and customer knowledge and proficiency in using online platforms. These factors have directly impacted the company’s reputation, including aspects such as product/service quality, attractiveness, performance and commitment to social responsibility.

Its finding indicates that customers' lack of confidence in e-marketing has a strong impact on CR, followed by product quality and credibility. The absence of consumer awareness about e-marketing websites and e-fraud frequently negatively affects the organizational reputation.

To enhance the corporation’s reputation, it is recommended that companies provide educational resources on online shopping, including guidance on using the company’s website, comparing prices and other services that facilitate online purchases. This will help to support the credibility of e-marketing and enhance customer trust.

This research is an exploration of how e-marketing has affected a Corporation’s Reputation. It provides modern knowledge about the dynamic interplay between digital strategies and brand perception. Investigating this relationship provides valuable insights into the evolving landscape of consumer trust in the digital age. By analysing the various ways in which e-marketing influences a company’s reputation, innovative approaches can be developed to enhance its online presence and build lasting customer trust.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This chapter sought to answer the following questions: (a) what does special education means for students with intellectual disability?, (b) what is being done, and (c) how do we maintain tradition? The answers, while complicated, suggest special education for students with intellectual disability historically and currently involves attention to what, how, and where, with the how being the key elements of special education for students with intellectual disability. This chapter discussed the what, how, and where for students with intellectual disability in a historical and current framework while also providing evidence-based practices for students with intellectual disability to implement to maintain the tradition of high-quality services.

Purpose: The research determines how the COVID-19 pandemic affected India’s burgeoning education technology sector and how the new normal responded to this advancement worldwide.

Need for Study: India’s education sector saw a boom in Ed-Tech funding during the pandemic. The Indian Education industry adopted technology as a partner and succeeded in being called as the Ed-Tech capital of the world. The country has a strategic edge in online education that, if explored and researched upon, can be deployed for market growth at a global level.

Research Methodology: The researcher prioritised rigorous original quantitative and qualitative methods of investigation on technology applications in educational contexts during COVID-19 pandemic and drew its conclusions after conducting a comprehensive literature review and collecting and analysing the data from numerous journals and published expert articles on the sector.

Findings: The study found that despite the global pandemic, the Ed-Tech industry was an expanding marketplace for start-ups in India. After the pandemic, the market expanded rapidly, and by 2025, it is expected to have quadrupled in size, expenditure, and investment. It is expected that the global e-learning business, which the outbreak of the COVID-19 pandemic has bolstered, will grow to over 400 billion US dollars by 2026.

Practical Implications: The study sets forth the various Ed-Tech categories, outlining their user bases, growth projections, and the innovative technologies employed in developing these products. The research’s long-term investment projections are envisioned to aid consultants in positioning themselves for profitable operations in the era of digital Ed-Tech disruption worldwide.

This study explores the potential of computational design processes in creating contextually responsive envelopes for high-rise residential buildings in the Middle East. This includes considering both physical constraints and social preferences, with a focus on balancing sunlight exposure, privacy and views.

A two-phase simulation study analyzed various exterior envelope systems in Baghdad high-rise buildings. The first phase examined two commonly used exterior envelopes – fully glazed and window-based – to assess sunlight exposure, privacy and views. In the second phase, a multi-objective optimization process was applied to derive contextually optimized design solutions addressing the challenges identified in the first phase.

The study reveals that contextually optimized design solutions significantly improved direct sunlight exposure and privacy while maintaining satisfactory views. Although fully glazed exterior envelopes provided better-uninterrupted views, the optimized solutions offered more balanced performance across all factors, demonstrating the potential of computational design processes in creating contextually responsive building envelopes.

This paper emphasizes the importance of considering both physical and social contexts in the development of algorithms for architecture in the Middle East. This paper supports a progressive interpretation of traditional building references and demonstrates how computational design processes can create contextually responsive building envelopes that satisfy social needs and provide better-performing buildings for inhabitants.

This study aims to examine investors' reactions to bad financial news (IRBFN) based on complex financial accounting disclosures (CFAD) as well as how investors' herding behavior influences investor reactions in United Arab Emirates (UAE) project-based organizations (PBOs).

The primary data collection was furnished via online questionnaires, and 310 completed questionnaires were analyzed using structural equation modelling (SEM), moderation analysis, multiple regression simulations and path analysis.

The study shows that four out of the five CFAD dimensions observed – investors’ relations (IR), board and management structure, transparency disclosure and other disclosure channels – have a direct influence on investor's reactions to bad financial news, with the exception of “external auditing and audit service”. In addition, investor herding has a moderation impact on the relationship between CFAD and IRBFN.

There is a possibility that the broad view of the results may be limited by the size of the research sample. The paper's findings should therefore be authenticated at an intercontinental level with the same conceptual framework in other nations.

The purpose of modeling stakeholders' decision-making process is to improve their decisions and to control their reactions that may negatively affect PBOs in the UAE.

This research contributes to planned behavior theory and agency theory in the UAE context, both of which are empirically tested.

China's Belt and Road Initiative (BRI) is the most ambitious investment strategy for infrastructural development belonging to the significant potential for stimulating regional economic growth in Asia, Europe and Africa. This study aims to investigate the impact of infrastructure on spurring inward foreign direct investment (FDI) within the purview of human capital, GDP per capita, foreign aid, trade, domestic investment, population and institutional quality in BRI countries.

In doing so, the authors analyze panel data from 2000 to 2019 within the framework of the system generalized method of movement (GMM) approach for 66 BRI countries from Europe, Asia, Africa and the Middle East.

The investigated results demonstrate that aggregate and disaggregate infrastructure indices, e.g. transport, telecommunications, financial and energy infrastructures, are the driving forces in attracting foreign direct investment (FDI) in the BRI countries. In addition, control variables (i.e. institutional quality, human capital, trade, domestic investment, foreign aid and GDP per capita) play an essential role in spurring FDI inflows.

The authors’ study uniquely investigates both the pre- (2000–2012) and post- (2013–2019) BRI scenarios using the aggregate and disaggregate infrastructural components from the perspectives of full and clustered sample regions, such as Asia, Europe, Africa and the Middle East. The study provides several policy implications.