Big data
Sadegh Tayebi; Alaedin Etemad Ahari; Fariba Hanifi
Abstract
The research aims to apply big data in providing an effective model of education in serving the knowledge workers of the municipality. The research method was integrated research (quantitative and qualitative). The components and dimensions of the subject were examined in the form of documentary studies ...
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The research aims to apply big data in providing an effective model of education in serving the knowledge workers of the municipality. The research method was integrated research (quantitative and qualitative). The components and dimensions of the subject were examined in the form of documentary studies and interviews and identified in the form of educational content with thematic analysis technique. To analyze the qualitative data, the theme analysis method was used using ATLAS TI software, and genetic algorithm and meta-heuristic methods were used in MINITAB software. The research tool (data collection) was the qualitative part of a semi-structured interview with 12 elites, experts, and qualified specialists of Karaj municipality. The sampling method in the qualitative part was non-probability and non-homogeneous purposeful type dependent on the criterion and in the quantitative part, it was simply random. Finally, the proposed model of in-service training for employees was designed and validated. 6 comprehensive themes (planning (comprehensive implementation), learner, teachers, content, educational environment, and infrastructure) were identified in the form of a paradigm model. The results showed that the VIS algorithm had the best performance. Algorithms CNSGA-II and MISA are almost ranked second and have shown almost similar performances. NSGA-II algorithm is ranked next. The NNIA algorithm is in the next position in terms of performance, and the worst performance is assigned to the NRGA algorithm. Organizational innovation based on big data and organizational training improves the performance of knowledge workers and creativity.
Big data
Mohammad Ali Ghazi Kelahroodi; Farshad Faezy Razi; Younos Vakil Alroaia
Abstract
This research provides a data-driven model of electronic banking customer experience using digital marketing knowledge. The study is applied-developmental research, and it is a cross-sectional survey research. A semi-structured interview and a Likert scale questionnaire were used to collect data. The ...
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This research provides a data-driven model of electronic banking customer experience using digital marketing knowledge. The study is applied-developmental research, and it is a cross-sectional survey research. A semi-structured interview and a Likert scale questionnaire were used to collect data. The statistical population in the qualitative section includes banking industry experts. Using targeted method, 15 experts participated in this section. The statistical population is one million people (active customers of electronic banking) and the sample was calculated based on the Cochran table of 384 people. To analyze the data in the qualitative part, the foundation data analysis method was used in MAXQDA, and for the validation and presentation of the final model, the structural equation modeling method and SMARTPLS software were used. Based on the designed model, 6 categories for causal factors (proper decision-making, time management, digitalization effects, cost management, business trends, and relationship management), 2 categories for background conditions (banking industry and digital economy), 2 categories for intervening conditions (individual factors and environmental factors), 4 categories for strategy (digital tools, trust building and training, digital differentiation and digital platform), 3 categories for outcomes (prosperity of the banking industry, customer satisfaction, and economic productivity) became. Banks are an important pillar of the economy and the strategies they adopt will affect the recovery of the economy after the pandemic. Digitization is one of the important options for banks in order to provide the best and most reliable solutions to customers in their current business with the bank.
Big data
Mona Abrofarakh; Kambiz Shahroodi; Narges Delafrooz
Abstract
This research aims to design a data-driven value-creation model for insurance policyholders. It uses a mixed methodology (qualitative-quantitative). The statistical population was university professors in insurance and marketing and senior managers of Iran's insurance industry, including Asia Insurance, ...
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This research aims to design a data-driven value-creation model for insurance policyholders. It uses a mixed methodology (qualitative-quantitative). The statistical population was university professors in insurance and marketing and senior managers of Iran's insurance industry, including Asia Insurance, and Alborz Insurance. The saturation was reached with 12 university professors. To identify the influential factors in the data-driven value creation model for insurance policyholders, the Delphi technique was used in the form of theoretical consensus. The interpretative structural method was used for modeling. The studied structures to design and explain the value creation model for insurance policyholders in Iran's insurance industry include factors related to employees, policyholders, training, organization, management, and branding. Based on structural-interpretive modeling calculations, it was determined that the factors related to employees are external independent variables unaffected by any variable in the model. The factors related to employees and training are endogenous independent variables, and the factors related to the brand are dependent. Also, the factors related to insurance policyholders plays a mediating role. Researchers believe that the more organizations can gain a better understanding of customer needs, as well as the activities of competitors and factors affecting market conditions and distributing information at all levels of the organization, the more ability they will have to survive in the competitive market.
