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One of the crucial issues in the contemporary finance is the prediction of the volatility of financial assets. In this paper, the authors are interested in modelling the stochastic volatility of the MAD/EURO and MAD/USD exchange rates.

For this purpose, the authors have adopted Bayesian approach based on the MCMC (Monte Carlo Markov Chain) algorithm which permits to reproduce the main stylized empirical facts of the assets studied. The data used in this study are the daily historical series of MAD/EURO and MAD/USD exchange rates covering the period from February 2, 2000, to March 3, 2017, which represent 4,456 observations.

By the aid of this approach, the authors were able to estimate all the random parameters of the stochastic volatility model which permit the prediction of the future exchange rates. The authors also have simulated the histograms, the posterior densities as well as the cumulative averages of the model parameters. The predictive efficiency of the stochastic volatility model for Morocco is capable to facilitate the management of the exchange rate in more flexible exchange regime to ensure better targeting of monetary and exchange policies.

To the best of the authors’ knowledge, the novelty of the paper lies in the production of a tool for predicting the evolution of the Moroccan exchange rate and also the design of a tool for the monetary authorities who are today in a proactive conception of management of the rate of exchange. Cyclical policies such as monetary policy and exchange rate policy will introduce this type of modelling into the decision-making process to achieve a better stabilization of the macroeconomic and financial framework.

A progressive hybrid censoring scheme (PHCS) becomes impractical for ensuring dependable outcomes when there is a low likelihood of encountering a small number of failures prior to the predetermined terminal time T. The generalized progressive hybrid censoring scheme (GPHCS) efficiently addresses to overcome the limitation of the PHCS.

In this article, estimation of model parameter, survival and hazard rate of the Unit-Lindley distribution (ULD), when sample comes from the GPHCS, have been taken into account. The maximum likelihood estimator has been derived using Newton–Raphson iterative procedures. Approximate confidence intervals of the model parameter and their arbitrary functions are established by the Fisher information matrix. Bayesian estimation procedures have been derived using Metropolis–Hastings algorithm under squared error loss function. Convergence of Markov chain Monte Carlo (MCMC) samples has been examined. Various optimality criteria have been considered. An extensive Monte Carlo simulation analysis has been shown to compare and validating of the proposed estimation techniques.

The Bayesian MCMC approach to estimate the model parameters and reliability characteristics of the generalized progressive hybrid censored data of ULD is recommended. The authors anticipate that health data analysts and reliability professionals will get benefit from the findings and approaches presented in this study.

The ULD has a broad range of practical utility, making it a problem to estimate the model parameters as well as reliability characteristics and the significance of the GPHCS also encourage the authors to consider the present estimation problem because it has not previously been discussed in the literature.

This paper aims to formulate a hedonic pricing model for Japanese rice wine, sake, via hierarchical Bayesian modeling estimated using an efficient Markov chain Monte Carlo (MCMC) method. Using the estimated model, the authors examine how producing regions, rice breeds and taste characteristics affect sake prices.

The datasets in the estimation consist of cross-sectional observations of 403 sake brands, which include sake prices, taste indicators, premium categories, rice breeds and regional dummy variables. Data were retrieved from Rakuten, Japan’s largest online shopping site. The authors used the Bayesian estimation of the hedonic pricing model and used an ancillarity–sufficiency interweaving strategy to improve the sampling efficiency of MCMC.

The estimation results indicate that Japanese consumers value sweeter sake more, and the price of sake reflects the cost of rice preprocessing only for the most-expensive category of sake. No distinctive differences were identified among rice breeds or producing regions in the hedonic pricing model.

To the best of the authors’ knowledge, this study is the first to estimate a hedonic pricing model of sake, despite the rich literature on alcoholic beverages. The findings may contribute new insights into consumer preference and proper pricing for sake breweries and distributors venturing into the e-commerce market.

Several inferential procedures are advocated in the literature. The most commonly used techniques are the frequentist and the Bayesian inferential procedures. Bayesian methods afford inferences based on small data sets and are especially useful in studies with limited data availability. Bayesian approaches also help incorporate prior knowledge, especially subjective knowledge, into predictions. Considering the increasing difficulty in data acquisition, the application of Bayesian techniques can be hugely beneficial to managers, especially in analysing limited data situations like a study of expert opinion. Another factor constraining the broader application of Bayesian statistics in business was computational power requirements and the availability of appropriate analytical tools. However, with the increase in computational power, connectivity and the development of appropriate software programmes, Bayesian applications have become more attractive. This chapter attempts to unravel the applications of the Bayesian inferential procedure in marketing management.

This study aims to explore the relationship between promoter share pledging and the company’s dividend payout policy in India. Furthermore, this study also analyses the moderating impact of family involvement in business on the association between share pledging and dividend payout.

A sample of 236 companies from the S&P Bombay Stock Exchange Sensitive (BSE) 500 Index (2014–2023) has been analysed through fixed-effects panel data regression. For additional testing, robustness checks include alternative measures of dividend payout and promoter share pledging, as well as alternative methodologies such as Bayesian regression. Lastly, to address potential endogeneity, instrumental variables with a two-stage least squares (IV-2SLS) methodology have been implemented.

Upholding the agency perspective, a significantly negative impact of promoter share pledging on corporate dividend payouts in India has been uncovered. Moreover, family involvement in business moderates this relationship, highlighting that the negative association between promoter share pledging and dividend payouts is more pronounced in family companies. The findings are consistent throughout the robustness testing.

The present study represents a pioneering endeavour to empirically analyse the link between promoter share pledging and dividend payouts in India. It enhances the theoretical underpinnings of the agency relationship, particularly by substantiating the existence of Type II agency conflicts between majority and minority shareholders. The findings of this research bear significant implications for investors, researchers and policymakers, particularly in light of the widespread prevalence of promoter-controlled entities in India.

This paper aims to explore the impact of cycle superposition on bank liquidity risk under different levels of financial openness so that banks can better manage their liquidity risk. Meanwhile, it can also provide some ideas for banks in other emerging economies to better cope with the shocks of the global financial cycle.

Employing the monthly data of 16 commercial banks in China from 2005 to 2021 and based on the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model, the authors first examine whether the cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. Subsequently, the authors investigate the impact of different levels of financial openness on cycle superposition amplification. Finally, the shock of the financial cycle of the world's major economies on the liquidity risk of Chinese banks is also empirically analyzed.

Cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. However, there are significant differences under different levels of financial openness. Compared with low financial openness, in the period of high financial openness, the magnifying effect of cycle superposition is strengthened in the short term but obviously weakened in the long run. In addition, the authors' findings also demonstrate that although the United States is the main shock country, the influence of other developed economies, such as Japan and Eurozone countries, cannot be ignored.

Firstly, the cycle superposition index is constructed. Secondly, the authors supplement the literature by providing evidence that the association between cycle superposition and bank liquidity risk also depends on financial openness. Finally, the dominant countries of the global financial cycle have been rejudged.

This study aims to examine the results of structural equation modelling in applying unified theory of acceptance and use of technology in adopting Islamic Fintech among millennials in Malaysia via measurement and structural models.

A total of 418 valid responses have been obtained from Malaysians who are using Islamic Fintech. Before the data is analysed into measurement and structural modelling preliminary analysis such as common method bias has been conducted.

All the requirements for model fit in this study have been achieved. Four exogenous constructs are performance expectancy, effort expectancy, social influence and facilitating condition. The mediating construct is behavioural intention, whereas the endogenous variable is user adoption. All exogenous constructs show significant p-values except for effort expectancy.

This study offers important implications, specifically for the digital economy that is currently making its way throughout every aspect of human life, namely, social, religious, financial transaction, entertainment and others. The impact of the digital economy can be traced through the emergence of Fintech. The adoption of Islamic Fintech is one of the least discussed areas academically, therefore, this study is considered necessary to explore the prediction of consumer behaviour in Islamic Fintech adoption as a part of the digital economy in Malaysia.

This study fills the perceived gap in the existing financial technology literature by assessing Islamic financial technology adoption via measurement and structural modelling.

This study aims to improve the use of digitalization in facilities management (FM) for shopping complex facilities in the post-COVID-19 era. The resumption of economic activities, especially in shopping complexes, poses challenges for FM with throngs of shoppers. To tackle these challenges, enhanced and innovative FM practices are necessary.

The study used a qualitative research approach, incorporating case studies, interviews, observations and documentation. It focused on super-regional shopping complexes in the Klang Valley, Malaysia, selecting two complexes for qualitative data collection. Supplementary data were gathered from various sources, including government policy publications, websites, books, journal papers and archival records.

The research provides valuable insights into FM innovations and the application of FM digitalization in shopping complexes after the COVID-19 pandemic. It also addresses challenges faced by FM teams during this period. Recommendations for implementing FM digitalization in super-regional shopping complexes post-COVID-19 include developing skilled personnel, defining appropriate work scopes, strategies and policies, using cost-effective software, and increasing occupant awareness. The involvement of outsourced service providers is advised, emphasizing their understanding of the organization’s business model and innovative approaches.

The findings offer new perspectives on the characteristics of FM digitalization in the commercial sector during business disruptions caused by the pandemic. The proposed strategies are grounded in real industry implementations, aiming to enhance the FM digitalization approach for improved business performance.

Acquainting organisations regarding the concepts of Total Quality Management (TQM) and its implementation is one measure that effectively improves their global position and performance. Kaizen is one of the concepts of TQM, which focuses on low-cost organisational transformational methods and often saves consuming significant resources (time, capital, etc.). Using Kaizen in organisational transformation sets efficient guidelines to improve processes agility and leanness and increase manufacturing productivity. Hence, this study aims to identify the key success factors in Kaizen projects and presents a score function that measures the readiness level of organisations to implement Kaizen projects.

A literature review first extracts the key success factors in Kaizen projects. Afterwards, the selected factors are screened via the fuzzy Delphi method using expert opinions from the manufacturing sector of an emerging economy. Subsequently, their importance is cross-examined by the Bayesian best–worst Method (BBWM). The BBWM is one of the most recent multiple criteria decision-making (MCDM) methods that lead to stable, dynamic and robust pairwise comparisons. After analysing the weights of the key factors, a score function is designed so that organisations can understand how much they are ready to launch Kaizen projects.

According to the findings, “Training and education” and “Employee attitude” played an important role in the success of Kaizen projects. The literature extracted 22 success factors of Kaizen projects, and 10 factors were eliminated through the fuzzy Delphi method. Twelve success factors in Kaizen projects were evaluated and investigated through the BBWM. Matching to this method, “Training and education” and “Employee attitude” weighed 0.119 and 0.112, relatively. Furthermore, “Support from senior management” was the least important factor.

To the best knowledge of the authors, this is the first research in which the success factors of Kaizen projects have been identified and analysed through an integrated multi-layer decision-making framework. Although some studies have investigated the key success factors of Kaizen projects and analysed them through statistical approaches, research that examines the success factors of Kaizen projects through MCDM methods is yet to be reported. Moreover, the score function that measures the level of readiness of each organisation for the successful implementation of Kaizen projects is a unique contribution to this research.