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This study presents the results of a field research designed to assess the seven bases of supervisory influence over respondents in industrial organisations working in Saudi Arabia. These seven bases of influence are: referent, experience, reward, coercive, legitimate, connection and information. The sample is composed of Saudi subjects as well as non‐Saudi subjects. The respondents ranked the seven methods of influence according to their importance. Results pertaining to factor analysis indicate that culture can decide the significance of an influence method over another. This points to the fact that Saudi participants and non‐Saudi participants have different sets of power bases.

A study is described involving the use of questionnaires to investigate the situational factors (economy, government, technology, competition, culture, demography) that affect the banking industry in Saudi Arabia. The results reveal that culture and technology are viewed as less important than the government and the economy. It is advisable for banking strategists to start thinking about the importance of culture, demography, and technology in Saudi Arabia. Considering the rise in Islamic awareness, bankers should in some way create an atmosphere to benefit from this trend and change it to a positive factor. More attention should be directed towards integrating technology, culture and demography.

Contends that managers from Saudi Arabia and other Middle East nations, as well as from other areas of the world, are becoming more conscious of the critical role that managerial values can play in today’s ever widening and increasingly competitive marketplace. Alignment of personal and organizational values has become a focal issue for many management theories and studies in recognition of the importance of value congruence. An organization’s productivity, success and/or failure can hinge on the degree to which the different levels and functions of its management share values. Reports on the results of a survey aimed at determining whether Saudi managers share common values, using several demographic variables to help clarify findings. Makes use of research on North American managers which is adjusted the better to fit the Muslim‐based culture of Saudi Arabia. A total of 144 Saudi managers of differing levels and functions participated in this survey. Finds overall that Saudi managers of all levels and functions generally shared the same values ‐ with one exception: marital status.

The intelligence in the Internet of Things (IoT) can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment. The computational latency incurred by the cloud-only solution can be significantly brought down by the fog computing layer, which offers a computing infrastructure to minimize the latency in service delivery and execution. For this purpose, a task scheduling policy based on reinforcement learning (RL) is developed that can achieve the optimal resource utilization as well as minimum time to execute tasks and significantly reduce the communication costs during distributed execution.

To realize this, the authors proposed a two-level neural network (NN)-based task scheduling system, where the first-level NN (feed-forward neural network/convolutional neural network [FFNN/CNN]) determines whether the data stream could be analyzed (executed) in the resource-constrained environment (edge/fog) or be directly forwarded to the cloud. The second-level NN ( RL module) schedules all the tasks sent by level 1 NN to fog layer, among the available fog devices. This real-time task assignment policy is used to minimize the total computational latency (makespan) as well as communication costs.

Experimental results indicated that the RL technique works better than the computationally infeasible greedy approach for task scheduling and the combination of RL and task clustering algorithm reduces the communication costs significantly.

The proposed algorithm fundamentally solves the problem of task scheduling in real-time fog-based IoT with best resource utilization, minimum makespan and minimum communication cost between the tasks.

The purpose of this paper examines how the challenge of poverty can be effectively addressed by broadly adopting Islamic value based cooperative model (IVCM) where the members come together to overcome and eventually eradicate the curse of poverty for themselves and for their future generations.

The elimination of poverty by adopting IVCM and its impact on the cooperative members, as well as its effects on major macroeconomic variables, are examined on the theoretical ground by using the general equilibrium model of demand and supply-side variables.

The IVCM for the elimination of poverty reveals that the poverty gap can be eliminated through resource mobilization, as well as by creating new and additional income, wealth and resources through collaborative efforts. Through cooperative organizations based on Islamic values and principles, eventually, the entire poverty pool can enjoy income-earning opportunities through employment or self-employment, as well as promoting skills and education, leading to breaking the vicious cycle of poverty.

Cooperatives in general and Islamic cooperatives, in particular, are not new in the discourse about poverty. Indeed, there are cooperatives throughout the Muslim world and beyond and there are many studies related to cooperatives and their role in development. However, this might be the first theoretical contribution that models the role and impact of cooperatives in a macroeconomic framework, and thus, advances the scientific repertoire of knowledge and understanding about the related discourse by developing a rigorous mathematical model.

This paper aims to investigate the macroeconomic effects of Qard-al-Hasan (QH) as a tool of monetary policy (MP) and its effectiveness in achieving full employment and price stability in the economy.

QH-based MP and its effects on major macroeconomic variables are examined on theoretical ground by using the standard aggregate output and aggregate expenditure model within the framework of Islamic economic principles.

QH-based MP positively influences real sectors of the economy and increases output, and the economy returns to full employment. QH provides the lowest possible borrowing costs across the economy and thus triggers rightward shift in aggregate supply curve and thus increases output and lowers price level. In addition, increase in output eliminates excess demand or shortages and thus maintains price stability. Furthermore, QH-based MP also increases exportable surplus and exports, decreases imports as well as increases inflow of funds and foreign currency reserves with the Central Bank and thus makes MP more effective.

QH-based MP is usually expansionary MP, and as such, it can be argued that there is a probability that QH-based MP may lead to higher inflation rate. However, in this study, it has been shown with real world data in Table II, that 23 countries in Group 1 have pursued zero or negative interest rate policy and their experiences mitigate such probability.

This is, perhaps, the first paper that presents a complete model of QH as a tool of MP with fully explained transmission mechanism. This is new contribution in the literature of Islamic finance where theoretical model on QH is systematically developed and applied as an effective tool of MP in attaining full employment and price stability. This model of QH-based MP can unfold a new horizon of uninterrupted economic growth, full employment and price stability by increasing output and employment, as well as by eliminating excess demand or shortages.

The aim of this paper is to study the effect of deterministic roughness and small elastic deformation of surface on flow rates, load capacity and coefficient of friction in Rayleigh step bearing under thin film lubrication.

Reynolds equation, pressure-density relationship, pressure-viscosity relationship and film thickness equation are discretized using finite difference method. Progressive mesh densification (PMD) method is applied to solve the related equations iteratively.

The nature and shape of roughness play a significant role in pressure generation. It has been observed that square roughness dominates the pressure generation for all values of minimum film thickness. Deformation more than 100 nm in bounding surfaces influences the film formation and pressure distribution greatly. Divergent shapes of film thickness in step zone causes a delay of pressure growth and reduces the load capacity with decreasing film thickness. The optimum value of film thickness ratio and step ratios have been found out for the maximum load capacity and minimum coefficient of friction, which are notably influenced by elastic deformation of the surface.

It is expected that these findings will help in analysing the performance parameters of a Rayleigh step bearing under thin film lubrication more accurately. It will also help the designers, researchers and manufacturers of bearings.

Most of the previous studies have been limited to sinusoidal roughness and thick film lubrication in Rayleigh step bearing. Effect of small surface deformation due to generated pressure in thin film lubrication is significant, as it influences the performance parameters of the bearing. Different wave forms such as triangular, sawtooth, sinusoidal and square formed during finishing operations behaves differently in pressure generation. The analysis of combined effect of roughness and small surface deformation has been performed under thin film lubrication for Rayleigh step bearing using PMD as improved methods for direct iterative approach.

This study examines the potential of contracts as one of the supply chain coordination mechanisms under competitive conditions. It also investigates a two-echelon supply chain model with two manufacturers and two retailers to develop a competitive structure in grey stochastic demand.

Supply chain demand is considered as a stochastic phenomenon depending on the selling price of the product. Also, products can be replaced by market manufacturers. Each retailer faces the pricing of products from two manufacturers, leading to competition between downstream retailers. In the present study, the duopoly supply chain model was presented based on the wholesale price contract, revenue-sharing contract and quantity discount contract separately.

Grey optimization and analysis of their coordination were presented. The results showed the high performance of revenue-sharing contracts in the supply chain. Thus, manufacturers will give the next priority to quantity discount contracts.

Ordering is the main factor contributing to competitive decision-making. Meanwhile, decision-making along with ordering and pricing will be required due to the nature of the demand.

Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.

The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.

The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.

There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.

1. Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

2. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

3. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

4. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).