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Although healthcare and healthy living are integral to the Sustainable Development Goals (SDGs) for 2030, the coronavirus epidemic has dealt a devastating blow to these efforts. As governments and policymakers were compelled to shift their focus to lockdowns, sustenance, procurement, and distribution of vaccines, the momentum for health initiatives slowed, and the already fragile health systems of emerging markets were subjected to additional shocks. However, in many underserved regions of the globe, the introduction of technology has greatly facilitated the distribution and adoption of healthcare services.

This chapter highlights mini-cases from four emerging nations: Bangladesh, Nigeria, Vietnam, and the Philippines. Although the countries are emerging, each one of them are in a distinct stage of development and face a unique set of healthcare-related challenges. The chapter showcases how four different organizations based in these countries leveraged the use of technology to take healthcare services to underserved populations. In doing so, they addressed the key challenges of imparting healthcare: geographic accessibility, availability, financial accessibility, and acceptability.

This chapter concludes with a discussion of the implications of expanding healthcare industries leading to increased healthcare waste. To prevent mass population exposure to hazardous substances, the emergence of intelligent healthcare waste collection and disposal systems will be an absolute necessity. Hence, with the development of healthcare services, governments and policymakers need to mechanize smart waste management systems to safeguard humans, animals, and the environment.

This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business.

The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning; fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables; and sixth, providing appropriate strategies based on the proposed model.

According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models.

The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries; therefore, generalizing the results to other business centers should be used with caution.

Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Business owners must accept the items returned by the customers for any reasons, and the conditions for accepting returned items and the deadline for accepting the returned items must be clearly communicated to the customers. Store owners must consider a discount for a certain amount of purchase from the store. They have to use an exponential rule to increase the discount when the amount of purchase is increased to encourage customers for more purchase. The managers of large stores must try to quickly deliver the ordered items, and they should use equipped and new transporting vehicles and skilled and friendly workforce for delivering the items. It is recommended that the types of services, the rules for prizes, the discount, the rules for accepting the returned items and the method of distributing the items must be prepared and shown in the store for all the customers to see. The special services and reward rules of the store must be communicated to the customers using new media such as social networks. To predict the customer behaviors based on the data, the future researchers should use the boosting method because it increases efficiency and accuracy of prediction. It is recommended that for predicting the customer behaviors, particularly their churning status, the ANN method be used. To extract and select the important and effective variables influencing customer behaviors, the discriminant analysis method can be used which is a very accurate and powerful method for predicting the classes of the customers.

The current study tries to fill this gap by considering five basic and important variables besides RFM in stores, i.e. prize, discount, accepting returns, delay in distribution and the number of items, so that the business owners can understand the role services such as prizes, discount, distribution and accepting returns play in retraining the customers and preventing them from churning. Another innovation of the current study is the comparison of machine-learning methods with their boosting and bagging versions, especially considering the fact that previous studies do not consider the bagging method. The other reason for the study is the conflicting results regarding the superiority of machine-learning methods in a more accurate prediction of customer behaviors, including churning. For example, some studies introduce ANN (Huang et al., 2010; Hung and Wang, 2004; Keramati et al., 2014; Runge et al., 2014), some introduce support vector machine ( Guo-en and Wei-dong, 2008; Vafeiadis et al., 2015; Yu et al., 2011) and some introduce DT (Freund and Schapire, 1996; Qureshi et al., 2013; Umayaparvathi and Iyakutti, 2012) as the best predictor, confusing the users of the results of these studies regarding the best prediction method. The current study identifies the best prediction method specifically in the field of store businesses for researchers and the owners. Moreover, another innovation of the current study is using discriminant analysis for selecting and filtering variables which are important and effective in predicting churners and non-churners, which is not used in previous studies. Therefore, the current study is unique considering the used variables, the method of comparing their accuracy and the method of selecting effective variables.

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

The research data were limited to only one e-clothing store.

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

This paper aims to fill the noticeably fragmented zakat literature repertoire by empirically exploring stakeholders’ views toward zakat management performance issues based on a selected zakat institution (ZI) operating on a corporatized platform with corporate administrative style.

A quantitative approach using a questionnaire survey distributed to Muslims in the State which ZI is operating was adopted. A total of 448 usable responses are used in the analysis covering descriptive and mean difference.

The results indicate that managerial reform configuration in terms of corporatization has been viewed positively by stakeholders, translated into a comforting agreement score toward ZI’s improved management performance (collection, disbursement and reporting). Such perceptions are, however, observably sensitive to demographic factors of gender and employment type. The survey also document evidence that the corporatization exercise itself had improved respondents’ confidence toward ZI being the zakat administrator in the State.

The research contributes to the public policy debate with respect to corporatized ZI’s management performance from the stakeholders’ perspective. The results are arguably informative at various levels, forming a basis for reality check and policy inputs for various stakeholders, including (but not limited to) the ZI itself, zakat payers and asnafs, particularly in designing relevant and necessary administrative strategies and relevant policy formulation in addressing the performance and accountability issues in ZIs.

This paper aims to explore the asymmetric effect of liquidity risk (LR) and Shariah board size on bank financial stability for a panel of Islamic banks (IBs) based in Gulf Cooperation Council (GCC) and Southeast Asian countries over the 2006–2019 period.

This paper uses the asymmetric nonlinear autoregressive distributed lag (NARDL) error correction model insofar as it allows assessing not only whether IBs with large boards outperform their peers with reduced boardrooms but also unveiling the potential asymmetries between LR and stability.

The findings show that while increasing the number of the Shariah board members does not impact the financial stability of IBs in both the short and long runs its decrease appears to enhance their stability in the long run. The findings also show that a hike, as well as a fall in LR, significantly influences the stability in the long run, which underlines the role that LR plays in bank financial stability.

A prominent line of future research may consist in extending the country sample to cover more representative full-fledged IBs based on different regions, which allows the breakdown of the sample into GCC-based and non-GCC-based IBs. Doing so is interesting in terms of governance implications. Another extension would consist in considering additional sources of risk to stability.

IBs should enhance their expertise, which helps them diversify their funding strategy and cater for liquidity solutions. They also must establish a better Shariah governance framework to contain their risk-taking behavior that ultimately contributes to achieving financial stability.

This paper contributes to the empirical literature in Islamic banking by performing a model that simultaneously accounts for both short- and long-run asymmetries in the relationship between the financial stability of full-fledged IBs, the LR and the size of the Shariah supervisory board.

This study aims to identify the determinants of customers' green purchasing behaviors. First, the study examines the relationship between green self-identity (GSI) and green peer influence (GPI) on green purchase behavior (GPB). Second, it examines the relationships between both GSI and GPI, and purchasing behavior mediated by green consumption values (functional value (FV) and social value (SV)). Third, it investigates the moderating effect of customer disidentification (CDI) on the relationships between GSI and both green consumption values. Finally, it investigates the indirect relationships between GSI and purchasing behavior moderated by CDI.

A quantitative study is conducted using a survey of 204 Egyptian buyers of organic food products. AMOS and Hayes's PROCESS macro are used to test the hypotheses under investigation.

The customer's peer influence and GSI are found to have a positive impact on green purchasing behavior. Additionally, the mediating impact of values and the moderating impact of CDI are also confirmed.

This study helps organic food companies in identifying the determinants of customers' green purchasing behavior. The results of the study will guide the efforts of green marketing professionals in promoting green products in the Egyptian market.

Since the notion of green consumption is still in its infancy, there is a need for further exploration on the green consumption concept to better understand customers' predictors of that type of consumption; accordingly, the current research was conducted.

This paper aims to present the fatigue life behaviour of upper arm suspension. The main objectives are to predict the fatigue life of the component and to identify the critical location. In this analysis, three aluminium alloys were used for the suspension, and their fatigue life was compared to select the suitable material for the suspension arm.

CAD model was prepared using Solid Works software, and finite element analysis was done using ANSYS 14.0 software by importing the Parasolid file to ANSYS. The model is subjected to loading and boundary conditions; the authors consider a vertical force with constant amplitude applied at the bushing that connected to the tire, the others two bushing that connected to the body of the car are constraint. Tetrahedral elements given enhanced results as compared to other types of elements; therefore, the elements (TET 10) are used. The maximum principal stress was considered in the linear static analysis, and fatigue analysis was done using strain life approach.

Life and damage are evaluated and the critical location was considered at node 63,754. From the fatigue analysis, aluminium alloys 7175-T73 (Al 90%-Zn 5.6%-Mg 2.5% -… …) and 2014-T6 (Al 93.5%-Cu 4.4%-Mg 0.5%… …) present a similar behaviour as compared to 6061-T6 (Al 97.9%-Mg 1.0%-Si 0.6%… … .); in this case of study, these lather are considered to be the materials of choice to manufacture the suspension arms; but 7175-T73 aluminium alloys remain the material with a better resistance to fatigue.

By the finite element analysis method and assistance of ANSYS software, it is able to analyse the different car components from varied aspects such as fatigue, and consequently save time and cost. For further research, the experimental works under controlled laboratory conditions should be done to determine the validation of the result from the software analysis.

The onset of a crisis demands that businesses respond quickly and effectively. So, it might be helpful to examine the effect of business model innovation and how to increase its impact on better crisis management. This study aims to discuss the aforementioned objectives.

The present study is applied in terms of aim and a quantitative descriptive survey regarding the data collection method. The structural equation model with the partial least squares approach and Smart PLS 3 software was used for the structural analysis of the questionnaire.

The findings revealed that business model innovation could lead to better crisis management. In addition, the components of entrepreneurial capability, resilience and business performance played a mediating role.

Some factors may mediate the effect of business model innovation on crisis management. Thus, future research can investigate them and identify their impact.

The present study suggests that managers should re-examine business model processes and make them innovative to improve crisis management.

The present study examines the factors that affect crisis management with an emphasis on innovation, assesses the impact of mediating factors in this regard and attempts to provide a model to facilitate better crisis management.

This paper aims to study the role of ethics in the social innovation process and its effect on entrepreneurial passion. It explores the factors that encourage social entrepreneurs to innovate by examining the concepts of harmonious and obsessive passion and ethics.

The database consists of 97 entrepreneurs who benefited from the services offered by the support organizations for social entrepreneurs. The data are analyzed using the partial least squares structural equation modeling.

The results show that Islamic ethics has a positive effect on social innovation. This effect can be further amplified by harmonious passion (HP). Such passion certainly strengthens social entrepreneurs throughout the innovation process and consolidates the implementation phase of their projects.

This study highlights the importance of ethics in the process of social innovation. Ethics acts directly or through HP to stimulate social innovation. This passion enables taking actions and favors the creation of innovative social projects.

These findings add value to the previous literature by introducing ethics into the entrepreneurial passion theory and exploring new factors that promote social innovation.