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This paper aims to present a continuous-time workforce planning model in which workforce flow occurs in terms of internal and external recruitment considering human resource strategies (HRS). The proposed model is a linear optimal control model in which promotions occur by inside appointment and outside employment of the system considering a cost leadership or a differentiation strategy and whether organizations have an internal or an external recruitment orientation. In other words, in the model and its solution procedure, this paper could determine any arbitrary function for the demand of the workforce with each HRS.

The proposed model contains five main sections, namely, applicants, newcomers, workforce who are doing sensitive-simple jobs, expert workforce and supervisors (or managers) that have a different orientation in different HRS. Each of these sections has a target value that this paper tries to attain it by applying appropriate control variables, such as recruitment, layoff, degradation, promotion and retirement. To reach this purpose, this paper formulated an optimal control problem using a linear system transition equation with a quadratic cost function.

Based on the proposed model, it was found that the optimal control model can interpret the managerial aspects. This model could be useful for different firms with different types of workforce demands. This paper has tried to have a comprehensive view of different flows of the workforce in an organization that concern to workforce planning.

Despite the considerable amount of research published, and the importance of following a human resources strategy from organizational strategy, in the knowledge, there is no comprehensive study dedicated to human resources strategy and workforce planning by optimal control models for workforce planning.

Workforce planning must answer how many workforces, in which positions, and talents, and when each organization is needed. To find the requirements workforce, organizations need to know the organizational position and talents pools. Clarifying the number of workforces required in each pool requires attention to workforce flows, including hiring, promotion, degradation, horizontal movement, and exiting the organization. It is a dynamic issue and must be addressed over several periods over a specific duration, which adds to the complexity. According to the talent management presented in this research, all the above complex questions are answered by applying the optimal control (OC) model according to talent management presented in this research.

This research presents a dynamic model by using a linear-quadratic optimal control model, which was solved by Pontryagin's maximum principle, to achieve an optimal number of workforce requirements for each of the positions of nursing services manager, supervisor, head nurses and nurses in the health sector according to the required talents in each position.

The results have shown that the target value of workforce numbers has been achieved in the planning period, and the validation test and sensitivity analysis justified the model by reaching the workforce planning targets.

This study provides a dynamic model for achieving quantitative workforce planning targets; the model presented in this manuscript has included an important qualitative factor, namely workforce talents. According to the authors' review, there is no comprehensive research devoted to workforce planning through optimal control models by attention to workforces skills.

The purpose of this paper is to explore the relationship between effective strategies to improve the quality and quality management of allocated resources for the successful implementation of the strategies. For this purpose, three quality management resources (human, organizational and technological) and eight different strategies related to quality are considered.

The paper employs the fuzzy analytic network process (FANP) to prioritize and model the interactions between eight strategies, the three types of resources (human, organizational and technological) needed for effective strategy implementation and the ability to enhance quality. Then, Goal Programming (GP) is formulated by the output of the FANP to identify the extent to which each single strategy is inhibited by a lack of (or overloaded by) resources.

The first three priorities of strategies identified by the FANP include continuous management of quality system, continuous use of human knowledge and continuous approach toward target, and the order of resources is as follows: human resources, organizational resources and technological resources. The results obtained showed the largest share of human resources and its crucial role in improving the quality of the products. The contribution of organizational resources amounts to half of the contribution of human resources.

The main contribution of this paper is to employ the FANP to prioritize, whereas in prior studies in this area, priorities were conducted as definitive, and uncertainty in the opinion of experts was not considered. In this paper, the FANP–GP combined method is used.

One of the most important ways to pay attention to sustainable economic development is to invest in green technology and alter the energy consumption structure (ECS) in countries. Changing the ECS can be important in two ways: first, it increases the diversity of energy consumption and reduces energy dependence on other countries. Second, the use of highly polluted nonrenewable energy sources (such as oil and coal) is reduced, leading to the transfer of energy to natural gas with less carbon emissions or renewable energy. To this end, the authors examined the asymmetric effects of eco-innovation on the US ECS from 1980 to 2019. This paper aims to address this issue.

In this research, the nonlinear autoregressive distributed lag (ARDL) (NARDL) model is used and the results are compared with the linear ARDL model.

The ARDL results also confirm the positive effects of oil prices and GDP per capita in the long run. On the other hand, short-term and long-term Wald test results confirm the nonlinear effects of eco-innovation (LPATENT) on US ECS. These results indicate that 1% positive shock in LPATENTˆ+ increases the ECS by 0.179, while 1% negative fluctuations (LPATENTˆ-) leads to a decrease (−0.085) in the ECS. However, the ARDL results, in general, show the positive effects of LPATENT on the ECS in long run. Evidence suggests that ignoring nonlinear effects can lead to inaccurate results. Policy suggestions for environmental technology innovation are presented in the results.

This research has innovations in various aspects so that the previous studies in this field have examined the effects of environmental innovation on renewable or nonrenewable energy consumption, and so far no study has been done on the ECS. In this research, the Shannon–Wiener index has been used to calculate the ECS.

The purpose of this study is to examine oil price shocks on US shale oil supply and energy security during the period 2000q1–2020q4.

In this study, the Shannon–Wiener index was used to calculate energy security, and then a structural vector autoregression (VAR) was applied to measure the effect of oil price shocks.

The results of the variance decomposition indicate that oil prices account for about 20% of changes in US shale oil production, while it explains only about 3% of changes in energy security. Finally, historical decomposition confirms the results of impulse response functions.

The novelty of this study is that so far, no study has examined the effect of oil price shock on shale oil production and energy security in the USA using the structural VAR model. This study also used the latest Shannon–Wiener index as a measure of energy security in the USA. The reason for selecting this index is that, in addition to considering the share of the total consumption of each primary energy, the share of energy imports from each country as well as the political risk of energy exporting countries to the USA are also included.

This study aims to measure the effect of customers’ technology readiness and the quality of electronic services on customer satisfaction.

The statistical sample included 410 respondents from 24 branches of a private bank. The sampling method was questionnaire. Because of the structural and organizational similarity of private banks and the elimination of nuisance variables, a bank with the most branches and customers has been selected. To test the model, equation modeling was performed to test the hypotheses. Data were collected through a self-developed structured questionnaire, which served as the measurement tool as well.

The results of the study showed that technology readiness has a significant and positive effect on customer satisfaction through the quality of self-service. Moreover, the intermediate role of perceived value in this regard was confirmed; however, the role of trust was not confirmed.

Previous studies have considered technology readiness as an effective factor in the quality of self-service and customer satisfaction. In this study, apart from quality of service in self-service banking and customer satisfaction, two variables of trust and perceived value have been investigated. An attempt has also been made to address some questions, including “what the effect of customer technology readiness on perceived value of self-services as well as customer satisfaction is?” and “how it is possible to improve self-service quality in modern banking based on customer expectations?” or “what effects variables such as trust and perceived value have on customer satisfaction?” Having a glance at the studies done before, it can be understood that so far, there has been no study done using a mixture of these variables, yet societies’ demands for self-service operations grow day by day. It is, therefore, mandatory to study the prerequisites associated with any actions before one is taken. The paper contributes in the following way: trust and perceived value are added to the the study because of their role in customer satisfaction. In addition, for the first time, variables have been studied, which had never been under focus in any studies in developing countries before.

The purpose of this paper is to apply fuzzy spectrum in order to collect the vague and imprecise data and to employ the fuzzy U control chart in variable sample size using fuzzy rules. This approach is improved and developed by providing some new rules.

The fuzzy operating characteristic (FOC) curve is applied to investigate the performance of the fuzzy U control chart. The application of FOC presents fuzzy bounds of operating characteristic (OC) curve whose width depends on the ambiguity parameter in control charts.

To illustrate the efficiency of the proposed approach, a practical example is provided. Comparing performances of control charts indicates that OC curve of the crisp chart has been located between the FOC bounds, near the upper bound; as a result, for the crisp control chart, the probability of the type II error is of significant level. Also, a comparison of the crisp OC curve with OC_avg curve and FOC_α curve approved that the probability of the type II error for the crisp chart is more than the same amount for the fuzzy chart. Finally, the efficiency of the fuzzy chart is more than the crisp chart, and also it timely gives essential alerts by means of linguistic terms. Consequently, it is more capable of detecting process shifts.

This research develops the fuzzy U control chart with variable sample size whose output is fuzzy. After creating control charts, performance evaluation in the industry is important. The main contribution of this paper is to employs the FOC curve for evaluating the performance of the fuzzy control chart, while in prior studies in this area, the performance of fuzzy control chart has not been evaluated.

The USA is one of the largest oil producers in the world. For this purpose, the authors model and predict the US conventional and unconventional oil production during the period 2000–2030.

In this research, the system dynamics (SD) model has been used. In this model, economic, technical, geopolitical, learning-by-doing and environmental (social costs of carbon) issues are considered.

The results of the simulation, after successfully passing the validation test, show that the US unconventional oil production rate under the optimistic scenario (high oil prices) in 2030 is about 12.62 million barrels/day (mb/day), under the medium oil price scenario is about 11.4 mb/day and under the pessimistic scenario (low oil price) is about 10.18 mb/day. The results of US conventional oil production forecasting under these three scenarios (high, medium and low oil prices) show oil production of 4.62, 4.26 and 3.91 mb/day, respectively.

The contribution of this study is important in several respects: First, by modeling SD that technical, economic, proven reserves and technology factors are considered, this paper models US conventional and unconventional oil production separately. In this modeling, nonlinear relationships and feedback loops are presented to better understand the relationships between variables. Second, given the importance of environmental issues, the modeling of social costs of CO₂ emissions per barrel of oil is also presented and considered as a part of oil production costs. Third, conventional and unconventional US oil production by 2030 is forecast separately, the results of this study could help policymakers to develop unconventional oil and plan for energy self-sufficiency.