The purpose of this paper is to develop a comprehensive maturity model for command and control. For this purpose, the inductive research method has been used to design the maturity model. Thus, firstly, the most prominent maturity models in command and control, including the conceptual maturity model, the headquarters maturity model, the NATO model, and the maturity model of integrated command and control centers (ICCCs) were analyzed. Among the studied models, the NATO model is more comprehencive, so we chose it as a basis for developing our model. Then, the stages and variables of maturity were analyzed using the focus group method. The findings of the research show that some important characteristics of the command and control maturity  have been neglected in the basic model. Therefore, the innovation of this research is to add new features to achieve a more comprehensive model. The result is a model containing 5 levels and 20 variables. Validation of the model has been done through comparative study and focus group method, which should be further strengthened by case studies and survey researches.

Introduction: This study aimed to explain the causal model of marital boredom based on emotional and social maturity among married people in Shiraz, Iran. Material & Methods: This descriptive and correlational study was conducted on 362 married people (270 women and 92 men) married for 10 years and were selected by convenience sampling method. The data were collected using the Paynes' Marital Boredom Self-Report Scale (1996), Yashvir and Bhargava's Emotional Maturity Scale (EMS) (1991), and Rao's Social Maturity Scale (RSMS) (1973). In addition to descriptive statistics (mean, standard deviation, and correlation matrix), inferential statistics (structural equation model) were used to analyze the data of the aforementioned scales collected from the sample population by AMOS software. (Ethic Code: ETHICS-2107-1014) Findings: The results showed that emotional maturity predicts marital boredom negatively and significantly (P<0. 01, β, =-0. 69),however, social maturity is not a significant predictor of marital boredom (P<0. 01, β, =-0. 09). After calculating the model by considering the output correction indicators of AMOS software, this model was modified and fitted with eight indicators. In general, the results of this analysis confirmed a good model fit. Discussion & Conclusion: The causal model showed that emotional maturity is able to predict marital boredom negatively and significantly. However, social maturity is not a significant predictor of marital boredom.

Background and Objectives: In recent decades, Artificial Intelligence (AI) has emerged as a pivotal technology in enhancing Human Resource Management (HRM) processes. However, many organizations have yet to fully exploit this technology's potential, which may be due to low levels of organizational and informational maturity. This study examines the impact of organizational, informational, and human resource maturity levels on managers' and experts' perceptions of AI efficiency in HRM. The innovation of this research lies in combining three maturity models organizational, informational, and human resource and examining their combined effects on AI productivity in HRM.Methodology: To address this gap, this research employs a descriptive-survey approach, collecting data from 120 HR managers and experts in knowledge-based companies at the Science and Technology Park. The sample size was determined using the Morgan table, and the sampling method was judgmental. Data analysis methods include multiple regression and Structural Equation Modeling (SEM).Findings: The results indicate that human resource maturity, with an impact coefficient of 0.361, has the most significant effect on the perception of AI efficiency, while informational and organizational maturity follow with coefficients of 0.296 and 0.240, respectively.Conclusion: This study recommends that organizations should pay special attention to the simultaneous development of organizational, informational, and human resource maturity to effectively leverage AI.

Increasing penetration of mobile technologies,is caused the emergence and implementation of mobile government, in some countries. Measureing the progress of implementing mobile government maturity model is possible by maturity model. In this article, key elements and concepts of e-Government maturity models and mobile government will be studied. Then, elements and components extracted from each model and analyzed by metha-synthesis method. For proposing a customized m-government model, the requirements for implementing the mobile government based on experiences in other countries is extracted. Meanwhile, existing limitations of mobile technologies in terms of the maturity model is considerd. For customization the model, the requirements in Iranian organizations, is extracted by the study of upstream documents. Finally, maturity model for mobile government introduced and its components with regard to the above requirements are explaind. Validity of this model is confirmed by a questionnaire and survey analysis with experts opinions and using appropriate statistical tests.

With the advent of the digital age, the need for governments and companies to use information technology to optimize performance, Business process smartening and provide remote services has increased. Thus, information technology and cyber security and information have also found a special place in the digital arena. Accordingly, one of the most serious dangers that governments face, which can also undermine national security, is cyber-attacks. These attacks cover a wide range of targets, one of the main of which is to damage critical infrastructure. Therefore, the sustainability of critical infrastructure in the face of such threats is crucial. This study, considering that the security of vital infrastructure is one of the most important factors in ensuring national security and passive defense, seeks to obtain the indicators of security of critical infrastructure through a comparative study method using library resources. In this study, 10 of the most important models of cyber security and information security maturity have been analyzed. The results of this study indicate that the studied models have a total of 93 indicators. Cyber security and information security maturity models are significantly similar,Therefore, some of the counted indicators overlap. Overlapping indicators were identified and classified into 17 groups. The results show that the "incident management" index with a frequency of 11 is the most important index in securing critical infrastructure, as well as physical security, monitoring, access-identity control, security policies and other indicators in They are next.

This paper specifies and prioritizes factors and variables affecting knowledge management and develops a Knowledge Management maturity model. The developed model has a practical application and shows weaknesses and strengths of a particular organization in comparison with ideal and desirable position in each variable.Using a questionnaire, extracted factors and variables were confirmed and then prioritized by experts. Binomial test, Friedman, chi square and weight mean test were used to analyze the gathered data. At the end the model was tested in two organizations.

To effectively leverage artificial intelligence (AI), organizations need a comprehensive understanding of their AI maturity level. Assessing AI maturity can serve as the first step in developing a digital transformation roadmap and AI strategies. This study aims to present a comprehensive framework for evaluating organizational AI maturity. The research methodology employed is a systematic literature review. Through this process, after screening and assessing the quality of scientific articles and consulting reports, 31 selected documents were analyzed. The findings indicate that key dimensions of AI maturity include strategy and leadership, organization and culture, data, technology and infrastructure, operations, decision-making, ethics and regulations, security, and privacy. Additionally, it was found that existing maturity models primarily focus on technical and managerial aspects, with less attention given to social, legal, and ethical dimensions. By conducting a comparative analysis of various AI maturity models, this study proposes a comprehensive framework for assessing organizational readiness in adopting and developing AI, which can serve as a guideline for policymakers and organizational managers.