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Digital technology has been widely applied in zakat collection. Millennials, who are now dominating the productive phase and at their peak carrier path, are the potential target for zakat collection as their number reached 31.3% of the Indonesian population. On the other hand, public and private zakat institutions have attempted to optimize the country’s zakat potential, reaching 233.6tn rupiahs, through development of a digital platform for zakat collection. However, the gap between the actual collection of zakat with its potential is still large. This study aims to analyse the factors affecting millennials in paying zakat through direct payment or through digital platform of private or public zakat institutions.

Multinomial logistic regression method, which signifies the contribution of this study, is used to analyse factors influencing millennials in their zakat payment. In addition, cross-tabulation is used to classify the characteristics of respondents. Respondents are selected conveniently through a digital questionnaire distributed in February–March 2021. Respondents are also selected purposively based on their experience in paying zakat through direct, private or public zakat institutions, which are consisted of 50 respondents per each category; thus, the total becomes 150 respondents.

Based on the results, three variables, namely, education, accessibility and age, are found to have a significant influence on zakat payment through online platforms provided by private zakat institutions. Meanwhile, variables that influence zakat payment through online platforms provided by public zakat institutions are education, accessibility and income. This study also finds that millennials have the highest probability to select online platforms provided by private zakat institutions as a channel of their zakat payment. However, the overall result shows that millennials tend to pay directly to the mustahik (zakat recipients) rather than via online platforms, presumably because of their limited zakat literacy.

The purposive sampling technique used to determine the research samples limits the generalization of the study.

This paper establishes a new approach in analysing millennials preference in their zakat payment with digital inclusiveness. The use of a multinomial logistic approach, which has not been widely applied in such research, strengthens the analysis that is relevant to the need of both private and public zakat institutions to analyse determinants of millennials in paying their zakat through online platform. This study can be used as a reference to formulate a more effective marketing strategy for zakat collection. This paper also serves as an estimate of the preference with some selected typical characteristics of millennials by using a multinomial logistic approach.

Formal payment through the zakat institution theoretically is more preferable than direct payment to mustahik (zakat recipients) in the zakat campaign. However, based on this research, despite digital marketing and platforms having been well-used by both private and public zakat institutions, the millennials still prefer direct zakat payment than through online platforms. The findings of this research suggest the importance of strengthening zakat literacy through a more effective digital marketing strategy of zakat institutions which target the millennials.

This study fills the gap in the literature on how millennials choose their zakat payment method, whether through digital platforms developed by private and public zakat institutions or directly to the targeted zakat recipients. The use of multinomial logistic regression approach adds the novelty of this research.

The objective of this paper is to develop a model that can predict financial distress amongst public listed companies in Malaysia using the logistic regression analysis.

The logistic regression analysis used in this paper is geared towards developing a model that can predict financial distress amongst public listed companies in Malaysia.

The results prove that five financial ratios have been found to be significant and useful for corporate failure prediction in Malaysia. The overall predictive accuracy is 91.5 percent and this demonstrates that the logistic regression analysis used is a reliable technique for financial distress prediction. In addition, the predictive accuracy of the model in this paper is higher than that of previous studies, which utilised discriminant analysis rather than the method adopted in this research.

The economic crisis mostly began to affect Malaysia's economic standing in July 1997 causing many companies to fall into financial distress, as they were unable to cope with the unexpected downturn. A financial distress prediction model is therefore required to act as a predictor of Malaysian public listed companies' well‐being prior to a financial crisis and to gauge the warning signals of the onset of a downturn in order to strategize their survival techniques during this phase. This study focuses on public listed companies in Malaysia, thus the model adopted is tailored to suit the given context.

In this chapter, we analyze donor behavior based on the general segmentation bases. In particular, we study the behavior of the individual donor group's support for higher education. There has been very little research to date that discriminates the donor behavior of individual donors on the bases of their donation levels. The existing literature is limited to a general treatment of donor behavior using one of the available classical statistical discriminant techniques.

We investigate the individual donor behavior using both classical statistical techniques and a mathematical programming formulation. The study entails classifying individual donors based on their donation levels, a response variable. We use individuals’ income levels, savings, and age as predictor variables. For this study, we use the characteristics of a real dataset to simulate multiple datasets of donors and their characteristics. The results of a simulation experiment show that the weighted linear programming model consistently outperforms standard statistical approaches in attaining lower APparent Error Rates (APERs) for 100 replications in each of the three correlation cases.

Examines some of the more important types of model used in analysing logistics problems. Discusses some of the developments in computers, computer networks and software which are making the systems approach more feasible and desirable.

The purpose of this paper is to evaluate the interaction between inventory availability (scarcity) and pricing levels (price-leadership (PL)), and its effect on product returns in the internet retail supply chain. Specifically, this paper investigates how supply chain managers can use inventory (seller-induced scarcity) and pricing (PL) levers to control product returns.

Empirical data of sales and product returns from an internet retailer is analyzed to identify the scale of the effect that product scarcity and PL has on product returns. These factors are considered in developing a sales-return process model which is used with empirical data in a simulation study. The study evaluates changes in product returns for different policy settings related to PL and inventory levels. Findings of the simulation study are validated using statistical analysis of empirical data.

PL and seller-induced product scarcity affect the rate of product returns; however, the scale of this effect depends on inventory and pricing decisions. The results identify an inflection boundary based on scarcity and PL levels which reverses this effect. This reversal is explained by underlying principles at play regarding buyers’ valuation of the sale and corresponding product attributes.

Supply chain managers in internet retail can leverage lower inventory under the seller-induced scarcity approach to improve revenues. However, reducing inventory levels beyond a threshold is counterproductive, due to an associated increase in product returns. Similarly, setting market competitive prices (PL) can help reduce product returns. Under the seller-induced scarcity condition, this effect is reversed for inventory levels below a threshold. Retailers can implement the methodology developed in this paper to identify the inventory-price threshold that can help increase revenues while keeping the rate of product returns at a manageable level.

This research extends prior work regarding the role of product scarcity and pricing on product returns and develops a deeper understanding of how these factors can be managed to control product returns in the internet retail setting.

The purpose of this paper is to investigate the substitutability of corporate hedging and diversification in the real estate investment trusts (REITs) industry. The authors hypothesize that, relative to diversified firms, focused firms are more likely to be associated with hedging. The role of firm size is also analyzed.

The logistic regression approach is utilized to analyze the probability of hedging and the panel regression approach is used to examine the amount of hedging.

The authors find that, relative to diversified firms, firms focused on a single property type are more likely to engage in hedging. However, this finding is significant only for smaller firms, which implies a non-linear relation between hedging and firm size. The evidence is not as strong when firm focus is measured by geographic concentration. In terms of hedging amount, smaller firms’ average hedge ratio is greater than that of larger firms. For either small or large firms group, hedging amounts increase with firm focus measured by either property or geographic concentration and increase with firm sizes.

The results imply that, relative to diversified REITs, REITs focused on a single property type are more likely to engage in hedging. However, this finding is significant only for smaller firms, which implies a non-linear relation between hedging and firm size. The evidence is not as strong when firm focus is measured by geographic concentration, suggesting that geographic concentration is perceived to be less risky than property type concentration. For either small or large firms group, hedging amounts increase with firm focus measured by either property or geographic concentration and increase with firm sizes, which implies that hedging amount does not depend on firm size. The sample period is limited to the years 2010 to 2013 because some data needs to be manually collected.

The results imply that REITs consider both property diversification and hedging in managing their risk.

The research represents an early attempt to investigate the relation between corporate hedging and diversification. The investigation into the REIT industry has several advantages such as a lower likelihood of using derivatives for speculation.

The present paper seeks to address the issue of MIS where developments in IT have had a significant impact on competencies.

This paper explores this notion which has received little coverage in IS literature to date, with a focus on the field of accounting and management control. Many authors have voiced their uncertainty about how the control function will evolve in the future. In effect, IS developments challenge controllers' legitimacy if the latters' know‐how fails to keep up with technological developments. This paper analyses the makeup of controllers' competencies and, in particular, the need for the latter to be able to use ERP systems. It proposes the concept of technological contingency as a means to understand evolutions in ERP controllers' competencies in comparison with traditional controllers.

Technological progress broadens controllers' competencies, and ERP plays the role of a medium through which increased contingency takes place. Organisations of a certain size are compelled to implement ERP, leading to management controllers having to adjust their skills set. The study provides an ERP model to be used by controllers.

The antecedents of the evolution can be more developed in another study.

The managerial impact is considerable. It is crucial for French university programmes to rapidly develop greater focus on ERP training, and job and skill referentials need to be updated in organisations, especially promotion, valorisation, evaluation, and career development systems. Being an IS specialist in a large corporation can confer real legitimacy as it becomes an imperative. For business organisations, interpersonal relations have changed completely; communication takes place through integrated tools and there is less face to face, but on the other hand relationships are pre‐ordained by IS tools. The way economic, accounting, and financial knowledge is disseminated will also change as it is communicated more explicitly.

To the best of the authors' knowledge, studies on management controllers' skills in an ERP environment are nonexistent. This is the first study on this subject.

The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

The purpose of this paper is to develop some robust approaches to estimate the logistic regression profile parameters in order to decrease the effects of outliers on the performance of T² control chart. In addition, the performance of the non-robust and the proposed robust control charts is evaluated in Phase II.

In this paper some, robust approaches including weighted maximum likelihood estimation, redescending M-estimator and a combination of these two approaches (WRM) are used to decrease the effects of outliers on estimating the logistic regression parameters as well as the performance of the T² control chart.

The results of the simulation studies in both Phases I and II show the better performance of the proposed robust control charts rather than the non-robust control chart for estimating the logistic regression profile parameters and monitoring the logistic regression profiles.

In many practical applications, there are outliers in processes which may affect the estimation of parameters in Phase I and as a result of deteriorate the statistical performance of control charts in Phase II. The methods developed in this paper are effective for decreasing the effect of outliers in both Phases I and II.

This paper considers monitoring the logistic regression profile in Phase I under the presence of outliers. Also, three robust approaches are developed to decrease the effects of outliers on the parameter estimation and monitoring the logistic regression profiles in both Phases I and II.

The purpose of this paper is to make out a complete solution to logistic outsourcing problem in a medium‐scale organization by using consistent fuzzy preference relation (CFPR) and vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) method.

The initial approach to this research was to develop a comprehensive framework for logistic outsourcing problem and selection of most appropriate third party logistic (3PL) provider.

It has been found that the organization should outsource logistic activities. The alternatives (3PL providers) have also been ranked and the fifth 3PL provider has been termed as best third party logistic provider.

The parameters selected for this study and developed framework are applicable only to a medium‐scale organization manufacturing automobile parts in northern India.

This is probably the first time that an attempt has been made to apply the two‐phase methodology approach, using CFPR and VIKOR, to analyze a multi‐criteria logistic outsourcing problem. A case is provided which demonstrates how to solve logistic outsourcing, a multi‐criteria decision‐making problem.