درخت حوزه‌های تخصصی

Purpose: This research aims to design and explain the process model of content marketing with the approach of strengthening the customers' purchase intention in the field of Keshavarzi bank services.Method: This research is applied in terms of purpose, exploratory in terms of approach, and mixed (qualitative-quantitative) in terms of data analysis. In the qualitative phase, a group of experts was considered as the statistical population including senior managers of the Keshavarzi Bank, university professors in the field of marketing, and marketing consultants familiar with the banking industry. In this phase, using a targeted non-probability sampling method, 11 experts were selected and interviewed in depth. In the quantitative phase, the customers of the Keshavarzi bank in Ahvaz were considered as the population, and out of them, 400 people were selected as a statistical sample based on the available random sampling method. The main tool of data collection in the qualitative phase was in-depth and unstructured interviews with experts. In the quantitative phase, a closed and researcher-made questionnaire consisting of 42 items was used as the main tool for data collection. In the qualitative phase, the data from the questionnaire was analyzed based on the grounded theory method (open, central, and selective coding). Content validity and reliability were obtained with Cronbach's alpha coefficient of 0.7. In the quantitative phase, SPSS and LISREL software were used to perform descriptive and inferential analyses.Findings: The findings showed that the content marketing process model included 14 categories  including causal factors (emphasis on ethics in the design of advertising message; emphasis of advertising message on competitive advantages; persuasive power of advertising message; the power of advertising message to create needs in the customer), contextual and environmental factors (adjustment of the message with the culture of the society; the general inclination of the society to use social media), the main category (attracting the customer's attention to the content of the promotional message and the services of the Keshavarzi bank), Intervening factors (use of specialized media to spread advertising messages; following the example of world-class banks in digital content production), strategic factors (stimulating customers to pass the promotional message by word of mouth; systematic and viral dissemination of the promotional message through digital social media), the consequences (increasing the intention to purchase the services of the Keshavarzi bank; strengthening the brand of the Keshavarzi bank; increasing loyalty to the Keshavarzi bank).Conclusion: The results of the research led to the design of a content marketing process model with the approach of strengthening the purchase intention of customers in the field of Keshavarzi bank services (based on the structure of the paradigm model). The hypothesized relationships of the model were tested and verified in a large community.

Fog computing is considered a promising solution to minimize processing and networking demands of the Internet of things (IoT) devices. In this work, a model based on the energy consumption evaluation criteria is provided to address the deployment issue in fog computing. Numerous factors, including processing loads, communication protocols, the distance between each connection of fog nodes, and the amount of traffic that is exchanged, all have an impact on the re-search system's overall energy consumption. The power consumption for implementing each com-ponent on the fog node as well as the power consumption for information exchange between the fog nodes are taken into account when calculating each fog node's energy use. Each fog node's energy consumption is closely correlated to how its resources are used, and as a result, to the average normalized resource utilization of a fog node. When the dependent components are spread across two distinct fog nodes, the transfer energy is taken into account in the computations. The sum of the energy used for transmission and the energy used for computational resources is the entire amount of energy consumed by a fog node. The goal is to reduce the energy consumption of the fog network while deploying components using a novel metaheuristic method.  Therefore, this work presents an enhanced water strider algorithm (EWSA) to address the problem of deploying application components with minimum energy consumption. Simulation experiments with two scenarios have been conducted based on the proposed EWSA algorithm. The results show that the EWSA algorithm achieved better performance with 0.01364 and 0.01004 optimal energy consumption rates.