mahmood alborzi

The‏ aim‏ of‏ this‏ research‏ is‏ to‏ design‏ a‏ digital‏ marketing‏ model‏ in‏ small‏ and‏ medium-sized‏ industries‏ in‏ Iran‏ using‏ the‏ meta-synthesis‏ method.‏ The‏ present‏ research‏ was‏ conducted‏ as‏ a‏ qualitative‏ research.‏ Accordingly,‏ in‏ order‏ to‏ design‏ a‏ comprehensive‏ model‏ of‏ digital‏ marketing,‏ the‏ meta-synthesis‏ qualitative‏ research‏ method‏ of‏ Sandelowski‏ and‏ Barroso‏ (2007)‏ was‏ used.‏ Thus,‏ after‏ identifying‏ the‏ categories,‏ concepts‏ and‏ codes‏ of‏ digital‏ marketing‏ and‏ evaluating‏ its‏ validity‏ and‏ reliability,‏ the‏ conceptual‏ model‏ of‏ the‏ research‏ was‏ formed. Finally,‏ the‏ present‏ research‏ model‏ consists‏ of‏ 6‏ categories,‏ 18‏ components‏ and‏ 54‏ indicators‏ that‏ can‏ represent‏ the‏ digital‏ marketing‏ model‏ in‏ small‏ and‏ medium-sized‏ industries‏ in‏ Iran.‏ The‏ results‏ of‏ the‏ research‏ show‏ that‏ all‏ six‏ categories;‏ 1.‏ Marketing‏ strategy,‏ 2.‏ Reaching‏ and‏ interacting‏ with‏ target‏ customers,‏ 3.‏ Converting‏ visitors‏ into‏ customers,‏ 4.‏ Engaging‏ customers,‏ 5.‏ Appropriate‏ digital‏ tools,‏ and‏ 6.‏ Digital‏ content‏ marketing‏ play‏ a‏ significant‏ role‏ in‏ the‏ "digital‏ marketing"‏ model‏ for‏ Iranian‏ small‏ and‏ medium-sized‏ industries,‏ and‏ the‏ designed‏ model‏ is‏ the‏ most‏ appropriate‏ digital‏ marketing‏ model‏ in‏ terms‏ of‏ the‏ cultural,‏ organizational,‏ and‏ structural‏ conditions‏ existing‏ in‏ Iranian‏ small‏ and‏ medium-sized‏ industries.

This study explores the role of machine learning in managing knowledge of customer experience. Given the importance of high-quality knowledge for organizational innovation, this research aim is to address existing research gaps and propose a novel model for leveraging customer experiences in the fintech sector using machine learning. The main research question is to identify the key components and effective elements in developing a knowledge management of customer experience model using machine learning. Two secondary questions focus on identifying the most relevant knowledge management model for developing the knowledge management of customer experience model and assessing the suitability of machine learning capabilities for interpreting customer perceptions.The research methodology is design science. Conceptual and structural equation models were developed, and hypotheses were tested and validated through model fitting. The findings led to the creation of a framework for future research and the development of the APO model into a seven-layer APO-CEM model, which includes preprocessing, coding, thematic categorization, and improved decision tree accuracy. The model was positioned and validated within the fintech ecosystem. Results confirm the model's effectiveness in enhancing growth, productivity, and customer satisfaction and demonstrate that machine learning can effectively measure and improve the quality of knowledge of customer experience through the cultivation of customer insights.

Rapidly expanding domains such as the Internet of Things require sophisticated approaches to securing interconnected devices against cyber threats. The following study intends to fill in a crucial gap in the state of effective intrusion detection systems for the Internet of Things based on a comparison and analysis of various hyperparameter optimization approaches to improve existing and future detection systems. In other words, our main goal was to investigate and compare various hyperparameter optimization strategies to find and assess the most effective way to improve the performance of deep learning -based IDS. Our methodology was comprised of the following comparative optimization analysis used to compare a hybrid optimization approach against stand-alone implementation of Harmony Search and Bayesian Optimization. The analysis was done quantitatively based on IDS trained and tested on simulated Internet of Things network data, and IDS performance was evaluated by the following metrics : accuracy, precision, recall, and F1 score. The comparison of results showed that the hybrid optimization demonstrated the best performance indicators in terms of accuracy at 99.74%, precision at 99.7%, recall at 99.72%, and F1 score at 99.71%. The results of the study confirm the efficiency of implementing multiple optimization approaches and reveal the potential effectiveness of such combination for effective hyperparameter optimization of deep learning -based IDS in the Internet of Things environment.

Nowadays, demand response is recognized as an important element in the reliability of smart grid. Smart home energy management systems, which prioritize the start-up of electrical appliances according to the necessity of use and efficiency, play a vital role in the effectiveness of load response strategies in residential areas. Considering the sensor technologies, clarification on electricity consumption details helps to optimally monitor how the appliances are used. In this research, an unsupervised machine learning model was proposed for the clustering of home appliances to manage the bills of customers based on their inherent characteristics. Due to the small number of clusters, it becomes possible to manage electricity consumption. The hierarchical clustering method was used to classify appliances into three clusters. The first cluster is the appliances that are turned on at the discretion of the consumers immediately, the second cluster is the appliances that can be turned on according to the schedule and their usage can be postponed and the third cluster is appliances that are preferred by a limited number of consumers. The silhouette coefficient was developed as a measure of the hierarchical clustering model performance, where the average silhouette coefficient of 0.56 indicates the satisfaction of the model. Based on the results, it was found that the proposed clustering method can rationally classify different types of home appliances by selecting the appropriate characteristics since the appliances in a cluster are very similar to each other and can help users understand the operating conditions of the appliances.

The purpose of this research is to create a system dynamics model to manage the life cycle of start-up businesses based on the identified factors that are effective in the failure and success of these businesses and to examine the effects of these factors in different circles. The method of this research is grounded theory in the identification of factors and in the modeling part it is based on system dynamics. According to the studies, the success rate of startups around the world is very low, less than 10%. Therefore, identifying the factors affecting the success and failure of start-ups and designing a dynamic model based on these factors can lead to the management of start-ups and increase the probability of success. In order to extract failure and success factors in this research, 25 interviews were conducted with the activists of start-up businesses in Tehran, within the framework of the constructionist grounded theory method. The identified factors included 87 concepts, 32 categories and 7 general categories. To design the model, first, causal loops diagrams were drawn in different areas and then a model, based on system dynamics, was designed which included 13 stocks of effective factors. The resulting model was checked with numerous tests, the results of which revealed the possibility of predicting the growth or failure of startups through modeling and determining the relevant coefficients.

Over fifty years have passed since the introduction of the information technology strategy. This concept has changed drastically due to variations in the basic concepts of strategy, both theoretically and practically. The present study is aimed to examine the historical process of the formation of this concept. Therefore, using the systematic review method and compendium content analysis, a comprehensive search was conducted on different databases. After refining the retrieved documents, they were classified into six categories: methodology of information systems, formal planning, IT strategic planning (including strategic alignment, searching and discovering IT opportunities in the domain of competitive advantage, IT portfolio management, and development of technology policy and architecture), IT capability development, IT micro strategy development and IT platform as a strategy. Then, by studying selected texts, conceptual codes were extracted in accordance with the problem question. The codes are categorized into different categories. Then, five concepts of IT strategy were identified based on basic theories related to each category: IT strategy as a plan, as supporting businesses, as a pattern, as a business transformer, and as a perspective. Results revealed that the two concepts of IT strategy as a plan and as supporting businesses are rarely studied nowadays. Few studies have focused on IT strategy as a pattern, including the experimental examination of the results of this concept in various organizational contexts and the investigation of dynamic capabilities. The concept of IT strategy as a business transformer focuses on the integration of business strategy.

Oil and natural gas are the main industries in the energy market and play a critical role in the global economy. This industry is improved due to technological advancements, and new technologies are applied for better exploitation and increased profitability. When dealing with shared oil reservoirs, the importance of this issue becomes more apparent. This paper describes a SWOT carried out to plan the strategy for the transfer of intelligent oil fields technology in Iran. Several potential solutions may be pursued to enhance the transfer and implementation of intelligent technology in Iranian oil fields. These may involve the establishment of oil field development contracts with neighboring countries to manage shared fields collaboratively, the avoidance of conflicts of interest in equipment supply contracts, the implementation of legal requirements mandating the adoption of intelligent field technology by oil companies, the provision of investment incentives, and the hiring of information technology and network specialists. By implementing these measures, it is anticipated that the overall efficiency and effectiveness of oil field operations in Iran will be significantly improved.

Despite the many studies conducted in knowledge management and experience management, providing a model in knowledge management of customer experience is an issue that needs to be addressed. The paper was conducted with two 1. Providing a model of customer experience knowledge management and 2. its effectiveness evaluation in the Fintech ecosystem from the experts’ perspective. The research is a descriptive and analytic survey type. The research method is Grounded Theory. In order to achieve the first purpose, 48 articles were selected, studied, and opinionated by purposefully sampling people. After reading and analyzing articles, customer knowledge experience concepts were extracted and categorized based on the phenomena, context, cause, intervention, strategy, and consequence. Then, the relationship between the concepts was designed as a model. Based on those propositions, the customer knowledge experience management model was provided, and the fitting test was tested and approved. In order to achieve the second purpose, a questionnaire was designed based on four components, including 1- Understanding the importance of customer experience knowledge management, 2- the role of tacit knowledge in customer experience, 3- the effectiveness of customer experience knowledge, and 4- Applying knowledge of customer experience. The results showed that from the point of view of experts, the effectiveness of customer experience knowledge management in the Fintech ecosystem leads to improving marketing performance, better management of products and services, and increased customer satisfaction.

The corporate landscape is highly affected by two market factors, namely digitalization and sustainability. These two driving forces have been the topic of several studies on how they change management methods, businesses, and society in general. However, the point that these two trends meet each other has been mostly neglected by research studies. Modern organizations and corporations are dealing with the adopting digital transformation issue as a new strategic paradigm. The present study attempts to elaborate on the relationship between digital transformation and sustainability. Therefore, through an in-depth review of the relevant literature, critical factors and their indicators were determined, and based on the proposed conceptual model, six hypotheses were developed. Then, a questionnaire was designed and distributed among 120 Iranian experts, managers, and consultants, and 97 complete questionnaires were returned. Reliability, Content Validity Ratio (CVR), and Content Validity Index (CVI) of the questionnaire were calculated, and the hypotheses were tested through Structural Equation Modeling (SEM) using SmartPLS Software. The results showed that digital transformation significantly affected an organization's sustainability aspects through operation, customer, business model, technology, workforce, and collaboration. Digital transformation and sustainability should constitute integral parts of organizational strategy. Considering that business practices affect the environment, society, and economy, digital transformation can influence the business sustainability. Digital technologies transform markets and create novel paradigms in the industry. In addition, they present new solutions to organizations to cope with sustainability issues. Due to this importance for organizations as consumers and other stakeholders, they are sensitive to the effects of business on brand value, revenues, and company valuation.

The aim of this study was to provide a model of supply chain management enablers of government agencies with a causal mapping approach. This study is based on interpretive philosophy and with a research approach in soft operations. This research has been done in terms of purpose, foundation and in terms of nature and form of implementation as a cross-sectional survey.

In the first step, previous research has been used to identify the factors; In the second step, the causal mapping method is used to present the pattern of factor relationships. Data analysis was performed with UCinet5 software and NetDraw6 software. The statistical population of this study includes experts of government agencies who have been selected by purposive sampling. Data collection tools are taking notes, interviewing and holding workshops. To achieve the objectives of the research by analyzing interviews with experts, a set of supply chain management indicators were identified.

Based on the network model of supply chain management indicators in government agencies, 19 categories and 294 relationships have been identified. The Senior Management Support Index is at the heart of the cluster model relationship. Government support along with senior management support in government institutions can pave the way for achieving supply chain management goals. In addition to these factors, the IT infrastructure in the organization must be available and the strategic path of the supply chain must be determined.

Among the types of supply chain risks disruptions are risks that resulting from natural disasters, sanctions, transportation problems and equipment failure.
These risks can seriously disrupt the flow of materials, information and cash flow. This study proposes a hybrid model for managing, evaluating and rating disorders. In this research, by presenting a mathematical model with disruption parameter, supply chain disruption risk assessment is investigated. Initially, the relationships between the disturbances are formulated by fuzzy DEMATEL technique, and the DEMATEL output as a weighted parameter, and then the model is solved using meta-heuristic algorithms, genetic and local search methods. Finally, the disruptions are evaluated and ranked based on the costs incurred in the chain, and then the number of appropriate suppliers for each disruption is determined.

With global warming and energy shortages, smart grids have become a significant issue in the power grid. Demand response is one of the basic factors of smart grids. To enhance the efficiency of demand response, an intelligent home appliance control system is essential, which prioritizes the start-up of electrical appliances according to the necessity of use and efficiency. To properly manage the demand response, utilities use different signals such as price. One of the pricing methods that can be considered is different pricing for electrical appliance clusters. In this article, appliances are clustered by the K-means and hierarchical clustering based on the characteristics of the appliances themselves, such as the appliances’ extent of consumption, the type of use of home appliances, how home appliances work, the ability to change the working conditions of home appliances, home appliances usage time, etc. It seems that the K-means clustering method outperforms the hierarchical method in this issue, due to its lower value of DB coefficient. In this method, home appliances were classified into three clusters. The silhouette coefficient was developed as a measure of the K-means clustering model performance, where the average silhouette coefficient of 0.6 indicates the satisfactory value of the model. Based on the results, it was found that the proposed clustering method can rationally classify different types of home appliances by selecting the appropriate characteristics since the appliances in a cluster are very similar to each other and can help users understand the operating conditions of the appliances.

Economical, environmental, and social issues are significant challenges for industries and governments nowadays. The spare parts impose high inventory costs on the companies and require human resources, energy, and budget for the repair operations. These issues justify integrating repair, and inventory management decisions to reduce costs. Since the system is interacting with the environment, incorporating the sustainability dimensions with network design and planning decisions help managers to make more reliable decisions. We investigated the social and environmental dimensions to cover the sustainability dimensions of the spare part supply chain. These attributes contribute to industry-oriented properties in real-world problems. This paper investigates a multi-objective model to minimize costs while maximizing sustainability in a repairable spare part supply chain. Life cycle assessment (LCA) is utilized to assess social and environmental dimensions. Finally, the model is solved using NSGA-II with a priority-based encoding and decoding procedure. The findings shed light on contributing to formulating the spare part supply chain sustainability which integrates the network design and planning decisions resulting in more reliable outcomes.

Today, Environmental protection and sustainable development are two critical issues. Sustainability is now playing a significant role in almost every aspect of life. Information and communication technology is an integral element in the global economy and international development, with extensive applications in almost all industries. Similarly, software development is an essential component of a rapidly evolving technology community. It is also an important starting point for reducing resource consumption and carbon emissions. Nevertheless, compared to hardware, the software has received inconsiderable attention. The aim of this study is to identify, rank, and determine the importance of factors influencing green software development using a hybrid approach. Five criteria and four subcritical were identified from qualitative content analysis of relevant studies and interviews with twelve academic and industry experts. FDEMATEL was employed to determine the interaction and interrelationships among the identified criteria and sub-criteria while utilizing FANP to calculate the weights of the criteria and their relevant sub-criteria to determine their priorities. The following ranked first to fifth as the most influential factors: operational factor “$ 0.2153 $”, infrastructure factor “$ 0.2046 $”, technological factor “$ 0.2006 $”, individual factor “$ 0.1945 $”, and the organizational factor “$ 0.1849 $”. Among the criteria, knowledge, and awareness had the greatest weight of “$ 0.2908 $“, and ethical factors had the lowest weight of “$ 0.2308 $”. Given the insignificant difference between the highest and lowest factors, it can be concluded that all factors are almost equally effective. And it can be concluded developing green software requires the creation of a trusted ecosystem.

Startups as a new model of business development, have been considered by governments and investors around the world. Also startups have played an important role in economic development of countries. The success rate of startups around the world is low, so in recent years efforts have been made to identify the factors behind their failure and success. The purpose of this article is to investigate these factors in the Iranian business ecosystem by conducting interviews with activists in this field. In this research, By conducting 25 interviews, 7 general categories, 32 categories and 87 concepts were extracted. General categories include having the suitable business model, fund-raising and support, Analyze and managing the market, the founder's managerial ability, product / service characteristics, team characteristics, and legal issues. The results show that a number of effective factors, are independent of the society hosting business and also have been mentioned in numerous global studies, but a number of others are depending on the culture and conditions of the society. So in order to improve the performance and success rate of startups, in addition to global efforts in this field, it is important to know the culture and community-related factors

This research was done with the aim of providing a dynamic model of factors affecting the effective implementation of the civil service law in the field of human resources measures.

The research approach in terms of method is mix method.  In the qualitative part, with the theoretical sampling method, 46 scientific documents in the field of civil service law and implementation of public policy were content analyzed and effective factors in the implementation of civil service law in the form of three main dimensions, 29 components and 108 indicators after screening. Two rounds of fuzzy Delphi were explained with the participation of 11 experts. The statistical population of the quantitative part includes executive and academic experts who are proficient in the civil service law, which are 11 informed and knowledgeable experts in the field of non-probability (targeted) sampling in the form of a snowball. Civil Service Law were selected and their opinions and views were used in order to screen the research indicators in Delphi technique and analyze the trend of influential variables in the design of the dynamic model of effective implementation of the Civil Service Law in the field of human resources in the Venzym software environment.

The dynamic analysis of the variables showed that the accumulation variables include infrastructure, government, society, organizational structure and human resources. Flow variables also include human and financial capacities, organizational strategy, political factors, legal, cultural and social factors, recruitment and employment, service compensation and improvement of human resources, and by extracting the flow chart and simulating data over 5 years, Four dynamic hypotheses were presented for the effective implementation of the civil service law.

Originality:

 In terms of concept, for the first time with a qualitative and comprehensive approach, the factors affecting the implementation of the civil service law in the field of human resources management are explained, and in terms of methodology, modeling using the system dynamics method in this field is another new aspect. in this research. In terms of practical value, this research, by identifying the important feedback cycles of intervening factors in law enforcement, by presenting a scenario, has provided the context to pay attention to strengthening positive circles and planning to break negative reinforcing circles in effective law enforcement.

The purpose of this study is to help increase the productivity of public agencies, in today's highly competitive and ever-changing world, by finding factors that affect the attraction of capable, committed and competent human resources. Obviously, the resulting model will be effective in helping to achieve development, which is one of the goals of the 1404 vision document, by helping to attract specialized and qualified human resources. To achieve this purpose, the indicators were collected using literature review and the opinions of 20 experts in public administration, human resource management and senior managers of public agencies using” Expert Panel technique”. Quantitative data were collected through the distribution of 180 questionnaires in the “Administrative and Employment organization of the whole country”, as the custodian of employment in IRAN. Statistical analysis of research data is done by EXCEL and Lisrel software and through confirmatory factor analysis. Also, fuzzy Dimatel analysis is performed to find the relationship between these factors. The findings of the analysis confirm the impact of the four mentioned factors on human resource recruitment and show that three factors out of the four (environmental factors, organizational factors and individual competencies) have a cause role and one dimension (Job related factors) have the effect role on recruitment. This means that human resource management can use this plan to improve recruit of human resources, by identifying the causal factors and facilitating them without making a direct effort on effective factor.

The belief of the stakeholders who provide tourism services in the destination plays an important role in the sustainability of destinations. The values ​​of variables affecting these beliefs are always ambiguous and the causal relationships between them are uncertain. If we want to consider the reality of the dynamics in their opinions, the complexity of the issue will increase. For this purpose, we used fuzzy logic, Bayesian belief networks and opinion dynamics models. It is implemented on the data of Iranian stakeholders with the aim of attracting Chinese tourists. The data was collected by the questionnaire during the years 2019, 2020 and 2021 and is related to 540 stakeholders. Finally, not only an agent-based simulation is provided to visualize trends and how they evolve or co-evolve, but the possibility of predicting defective and desirable trends is provided to prevent or strengthen them, respectively.

One of the important issues that organizations face is the issue of effective decision making in different stages of the organization''''''''''''''''''''''''''''''''s activities. It is the right or wrong decision that determines the survival or death of the organization and in today''''''''''''''''''''''''''''''''s world determines the fate of the organization. Because of the importance of decision making in organizations, the need for methods to evaluate the effectiveness of decisions in organizations increases. Therefore, in this research, a comprehensive model for measuring the effectiveness of decisions is presented through an extensive review of the subject literature using the theme analysis method. After identifying the indicators of effective decision, questionnaires were designed and distributed among a group of experts in the organization who were selected by purposeful judgment, and based on their opinions, the model for evaluating the effectiveness of decisions in three consecutive stages using the Delphi method. Fuzzy was finalized. The model has 1 dimension, 4 components and 24 indicators and can be used as a suitable framework for evaluating the effectiveness of decisions made in different organizations. Finally, in order to determine the appropriateness of the proposed model and its compliance with the needs and requirements of each organization, using the Mikhailov hierarchical analysis method, the coefficients of importance of each of the dimensions and indicators in the model in the Mashhad Social Security Organization And the results were reported.

Organizations are trying to improve performance in providing smart services. An intelligent Q&A system is a virtual assistant that is able to interact with users. The purpose of this article is to investigate whether the characteristics of the Q&A system can improve the performance of the tax organization.

First, the question and answer system on the web platform is provided to the taxpayers. The tool used to evaluate the components and sub-components was a researcher-made questionnaire. Formal validity measurement tool was used to assess the validity and Cronbach's alpha calculation method was used for reliability. Using Cochran's sampling formula, 384 taxpayers were determined as the statistical sample size. The data are then analyzed by modeling the structural equations of least squares to evaluate the model.

Features related to intelligent question and answer systems have a positive effect on improving organizational performance.

This study shows the importance and positive effect of organizational investment in accepting factors related to service intelligence and the effects of new technologies based on artificial intelligence to strengthen the client-organization relationship.

The alignment of marketing and business strategy and their relationship with the organization’s financial performance are research domains which are still investigated despite having a long history. This is due to the different aspects of the topic and the different results in various organizational contexts and time periods. Although marketing and business plans are similar, there are differences between their characteristics. The present study attempted to analyze the relationship between the alignment of marketing and business strategies with the organization’s financial performance in a five-year period from 2014 to 2018 in a group of companies registered in Tehran Stock Exchange. The configuration theory in strategic management was used to develop the conceptual model. The marketing strategy was divided into the configurations of aggressive, mass, minimizer, and value-related, and the business strategy was divided into the four configurations of prospector, low cost defenders, differentiated defenders, and analyzers. Research hypotheses involved the relationship between the combination of configurations and the organization’s financial performance. One of the well-known tools for identifying marketing strategy is the self-typing paragraph approach. The Miles framework was used to identify the business strategy. To measure the performance, a questionnaire was used with six factors including sales growth volume, market share status, net profit growth status per share, operating profit margin status, net profit margin, and P/E ratio. The list of the Iranian stock exchange companies was used to select the companies. These companies were filtered based on three conditions. The first condition was the existence of an independent marketing department in these companies, the second condition was the existence of a competitive or semi-competitive environment, and the third condition was the companies’ lack of a joint venture investor. Thus, 108 companies cooperated with the research. First, marketing and business strategies and organization’s financial performance were identified. Then, the companies were divided into two groups including aligned and non-aligned groups. After that, the mean test was used for the statistical comparison of the performance of the aligned and non-aligned states. As the results revealed, the mean performance of the aligned states significantly differed from that of the non-aligned states. In the field of marketing strategy, the post hoc test showed that the mean performance of the aggressive strategy was higher than in other strategies. The value-related and mass strategies had a higher mean performance than the minimizer strategies but were not different from each other. In the field of business strategy, the prospector strategy and the differentiated defenders had higher mean performances than the low-cost defenders and analyzer. In general, the statistical results rejected the equality of mean performance in different combinations of business and marketing strategy.According to the research findings, four bodies were identified. The first body was the combination of the low-cost defenders with the minimizer strategy. In this body, the company's main focus was on maintaining the market share. These businesses reduce their risks by delaying the introduction of the product in the market until the product is established by others in the market. Adequate quality, low price and very wide distribution strategy are pursued by them. They generally focus on the product line and use the least amount of professional marketing staff. The second body was the combination of the differentiated defenders with the value-related strategy. In this body, the ratio of operating profit to sales was high. Also, the integrated chain of communication with customers was active, and the company used a relatively conservative and somewhat risky approach to activities. The company paid only some attention to market share and focused on maintaining the market. The company paid attention to capacity building, as compared to its competitors, and implemented the market-product strategy. The company considered its marketing strategy combination to be innovative, and high-quality products and the main value were in close relationship with the customer. They conducted mediocre marketing research and generally demanded a fair price. Their distribution strategy was somewhat selective, and they used moderate advertisement. In fact, the focus was on customer orientation, and there was a tendency for niches markets. The Third body was the combination of the analyzers with the mass marketing strategy. In this body, the ratio of the operating profit to sales was high. Also, an integrated chain of communication with customers and suppliers was active, and the company used a relatively conservative view. The company paid some attention to market share and focused on maintaining the market. There was a lot of focus on market-product strategy, but serious attention was also paid to cost reduction. The company was dominated by the engineering-oriented view. An extensive data analysis was performed every day for optimal decision making and long-term planning. These businesses generally produce high-quality products that follow market innovation and use very wide distribution channels. The fourth body was the combination of the prospectors with the aggressive strategy. In this body, the operating profit margin was taken into consideration, the integrated chain of communication with the customers and suppliers was active, and the company used a very risky view. The company paid serious attention to the market share. It was a leader in terms of innovation, diversity and capacity development, compared to its competitors, and fully implements the market-product strategy. The development of new markets was a priority and the company had an entrepreneurial outlook. These businesses usually produce innovative and high-quality products, and have a very close relationship with the customer and conduct a very extensive marketing research. This research is done to identify and encourage market segments where buyers tend to pay high prices. Buyers in these markets are selected with a selective distribution strategy and are contacted with through intense and focused advertising. These results indicate the proximity of marketing and business strategies and the direct effect of marketing strategy on the organization’s performance. The research contributes to managers’ knowledge of successful and high-performance alignments. Therefore, organizations can intensify marketing activities and reinforce their alignment skills.

The oil industry is one of the most significant strategic industries in countries with oil reserves. For this reason, politicians desire to know how to choose a condition with maximum exploitation but minimum cost. In recent years, most of the experts have been paying attention to intelligent oil fields owing to their advantages. Intelligent oil fields and operational integrated systems can reduce the number of drilling operations and as a result, lower the costs of drilling. On the other hand, intelligent equipment will decrease water production from the field, improve the quality of the produced oil, and reduce the costs of water production. This paper focuses on generalizing production data values and exploitation costs of the oil field, from the conventional field to an intelligent one. To this end, a model of the system dynamic was developed in VENSIM software to assess the effective factors on intelligent oil fields, the value of reducing water production, and lowering drilling cost in the intelligent state. Eventually, it was found out that the costs of drilling and water production are dependent on the price variation of each oil barrel, the percentage of the lack of equipment, and the power of current suppliers. The variation of each factor has a remarkable effect on the costs of drilling and water production with oil.

Today, recognizing and retaining customers is one of the major challenges of customer-oriented organizations, especially in the field of banking, which has attracted the attention of many researchers. With the increasing growth of customers and the use of electronic devices that have led to the production of large volumes of data, customer behavior analysis can be considered as a competitive factor for them. In this paper, considering the varied density and data growth that leads to computational overhead, a combined approach is used of the RFM method , density-based clustering algorithm and Map-Reduce( which is an efficient and low-cost framework for running synchronous algorithms.) it is used. The results show that the proposed algorithm is more accurate than VDMR-DBSCAN. Also, the output of the algorithm is in the form of 5 clusters, the results of which can help managers in identifying valuable customers, and This method leads to increased revenue and reduced unnecessary costs that occur due to lack of recognition and incorrect segmentation of customers.

Energy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The most important objective of the present study is to minimize the energy consumption of sensors and increase the IoT network's lifetime by applying multi-objective optimization algorithms when selecting cluster heads and routing between cluster heads for transferring data to the base station. In the present article, after distributing the sensor nodes in the network, the type-2 fuzzy algorithm has been employed to select the cluster heads and also the genetic algorithm has been used to create a tree between the cluster heads and base station. After selecting the cluster heads, the normal nodes become cluster members and send their data to the cluster head. After collecting and aggregating the data by the cluster heads, the data is transferred to the base station from the path specified by the genetic algorithm. The proposed algorithm was implemented with MATLAB simulator and compared with LEACH, MB-CBCCP, and DCABGA protocols, the simulation results indicate the better performance of the proposed algorithm in different environments compared to the mentioned protocols. Due to the limited energy in the sensor-based IoT and the fact that they cannot be recharged in most applications, the use of multi-objective optimization algorithms in the design and implementation of routing and clustering algorithms has a significant impact on the increase in the lifetime of these networks.

The advancement of technology and its impact on organizations has complicated the decision-making process; as a result, ethical decision-making has become a vital discourse in information technology organizations. This empirical study is an attempt to present a model of individual ethical decision-making in the area of information technology with an emphasis on big data. We used ethical scenarios and conducted quantitative methods to analyze data that were collected from 163 IT experts. Results show that the variables "locus of control and ego strength" affect an individual’s ethical behavior and "self-esteem and self-efficacy" affect "ego strength". Moreover, the variable "perceived probability of disclosure", which has been proposed by authors, has an effect on ethical intention. We conclude by stressing practical solutions to reduce ethical risks.