Introduction: In the midst of the Fourth Industrial Revolution, which was initiated by the World Economic Forum in January 2016, the insurtech sector, or insurance technology, emerged with various new digital technologies. The ecosystem of the insurtech industry consists of users, insurance companies, digital technology firms, and government organizations. Platform-based business models have emerged as a key strategy for insurtech companies to offer innovative products and services. Despite the growing market interest in insurtech and the continuous increase in new insurtech services, there have been limited studies conducted on insurance platforms so far. Given the importance of the topic of adopting digital insurance technologies, conducting research to identify the factors influencing users' acceptance of digital insurance platform services and providing a framework to enhance the adoption of these platforms is necessary and essential. So far, most studies have focused on examining the factors affecting the acceptance of digital insurance platform services from the customers' perspective, while research on these factors from the viewpoint of insurance service providers has been very limited. Therefore, conducting a study to evaluate the perspectives of industry experts on the factors influencing users' acceptance of digital insurance platform services can reveal the main aspects related to the successful implementation of these services on both sides (i. e., customers and insurers). This research addresses an important gap in the studies by providing a framework specifically designed for the acceptance of platform-based business models in the insurtech sector, and for this reason, it is significant. The objectives of this research are: 1. Identifying the factors influencing user acceptance of digital insurance platform services in the insurtech sector. 2. Providing a framework for the acceptance of business models based on digital insurance platforms in the insurtech sector. Methods: The present research is applied in terms of its objective. The method of this study is qualitative. This method was chosen based on the nature of the topic, which involves an in-depth examination of the experiences and opinions of industry experts regarding the factors influencing the adoption of these platforms. The statistical population of this research includes experts and specialists in the insurtech field and the insurance industry. Purposeful sampling was used in this study. Additionally, snowball sampling was employed to identify the samples. Data were collected through in-depth interviews. The design of the interview questions was based on a review of the research background and theoretical literature to ensure that all key and important aspects of the research topic were adequately covered. In this study, interviews were conducted with 14 insurance industry experts. The interviews continued until theoretical saturation was achieved. The criteria for selecting experts included having at least five years of work experience in the insurance industry and familiarity with and mastery of insurtech and insurance platforms. In this research, thematic analysis was used to analyze the data. The coding of the collected data was carried out accurately using Nvivo software. The Cohen's kappa coefficient was used to measure reliability in this study. For this purpose, two interviews were randomly selected and given to a coder other than the researcher. After the coding of the interviews by this coder, the Cohen's kappa coefficient for these two interviews was found to be 0. 64 through the software.

The insurance industry is one of the main indicators of development. This research was conducted with the aim of The impact of insurtech components on insurance startups is given with a grounded theory approach. The research method of this research is based on qualitative approach and data method, focusing on interviews. Therefore, it is considered part of exploratory research. In this research, the systematic design of Corbin and Strauss was used, and based on this design, three stages of open coding, axial coding, and selective coding were performed to analyze the collected qualitative data.The method of data collection in this research is through interviews, a library method. The participants in the qualitative stage include 15 experts, including senior and executive managers and middle startups active in the insurance industry, with an average of 10 years of work experience and with a master's degree and a doctorate in In the field of information technology, as well as senior managers and senior experts of information technology units of Central Insurance and insurance companies with an average of 15 years of work experience in the fields of insurance and information technology. The selection of the research sample is non-probable and purposeful.The results of the research showed that 12 influential variables by experts include, identification of existing opportunities, market knowledge, competitive environment, information exchange, diversity and innovation, customer knowledge, risk management, pricing strategy, investment participation, advertising, information technology and organizational technology resources. It was confirmed

Today, the use of insurance technologies to increase value and improve businesses can be considered a competitive advantage for insurance companies. Given the importance and role of these technologies in the growth and development of insurance, the purpose of the present study is to present the insurance technology marketing model for start-up businesses in the insurance industry. The research method of this study is mixed-methods analysis. In the qualitative stage, using content analysis and open and axial coding, the components and indicators are identified and then, by selective coding, the relationships between the concept, dimensions, and components are specified. In the quantitative stage, the identified components have been described using the descriptive-analytical method, analysis method, path and confirmatory factor analyses. The dimensions and components identified in the qualitative analysis of ‘, Insurtech’,insurance technology model for insurance startups were: 1) identifying opportunities, 2) marketing, 3) competitive environment, 4) information exchange, 5) diversity and innovation, 6) customer knowledge, 7) risk management, 8) pricing strategy, and 9) participation in investment, advertising, enterprise technology resources, and information technology. In addition to identifying relevant and effective components, the way they relate to each other is specified. The results of the model also show that the components of information exchange and marketing and competitive environment affect organizational technology and information technology.

Objective The development of insurance technology in the Iranian insurance industry and its acceptance by customers increase insurance penetration and reduce violations and frauds, as well as costs. Despite the growth in the number of internet and app users, Insurance startups have a small share in the premiums produced. The necessity to conduct this research is quite clear owing to the importance of technology acceptance in the insurance industry and the role of startups in countries’ GDP. An investigation of readiness to accept InsurTech is a critical challenge that should be considered when implementing any type of technology. Therefore, the success of insurance startups is not limited to the industry’ s authority, but it also includes identifying effective factors in users’ acceptance. In addition, if this technology is not utilized with all its features and technical capabilities, it will not achieve efficiency. These features are effective when they are accepted. This research mainly aims to design a model for insurance technology acceptance using interpretive structural modeling (ISM). Methodology This research uses grounded theory and interpretive structural modeling to design a technology acceptance model in the insurance industry. The grounded theory and 43 in-depth interviews with experts were used to identify factors affecting insurance technology acceptance. The interviews were conducted from December 2019 to May 2020 until theoretical saturation was achieved. Then, the thematic analysis was used to determine, analyze, and explain models within the data, and a confirmatory technique was applied to validate qualitative data. In the next stage, interpretive structural modeling was utilized to design the model and identify the constructs and relationships. The validity of the qualitative findings was assessed by interviewees during the interviews, and transcriptions of interview contents were reviewed carefully and immediately after each interview. The accuracy of concepts and themes extracted in terms of coordination with the reality was verified by comparing and reviewing interview texts. In addition, the impact of the themes obtained by the grounded theory was assessed using the structural-interpretive questionnaire distributed among ten interviewees. Findings The initial technology acceptance model was designed by interpretive structural modeling. According to the influence-dependence power diagram, support, legitimization, cultural development, and performance have a high influence power and low dependence power, and they are placed at the quadrant of independent variables. Moreover, price, cooperation, trust, and purchase advantages have a high dependency power and low influence power, and they are considered dependent variables. Constraints and complexity also have similar influence power and dependence power, and thus, they are linkage variables. Finally, among the variables, support with the highest influence power and lowest dependence power has the greatest effect on capacitation, while the trust with the lowest influence power and highest dependence power is the first level or dependent variable. It should be noted that none of the variables was laid in the autonomous quadrant. Since the variables of support, legitimization, cultural development, and performance have high driving power and influence power, these variables should be considered by the industry’ s authority to create a supportive atmosphere toward InsurTechs. They should provide appropriate resources, and boost infrastructures for sustainable development of InsurTechs. On the other hand, the variables of price, cooperation, trust, and purchase advantages have a high dependency power, and InsurTech companies should help to develop and increase national insurance penetration by presenting up-to-date marketing techniques, interacting with insurers concerning the attraction of customer trust in the insurance services and resolving their anxiety about their purchase, and creating attractiveness in customer purchase by providing unique advantages and more appropriate prices and conditions relative to traditional companies. According to the results, the variables of constraints and complexity have a high driving power. In other words, they give and receive high effects, and any change in these variables leads to fundamental changes. Conclusion Resolving legal barriers by industry’ s authority, providing suitable insurance services by startups, and facilitating the process are necessary activities. Acceptance of new technologies and cooperation among insurance companies and startups are the first step that should be considered by InsurTechs firmly toward the acceptance of technology by employees and customers. Furthermore, attracting the trust and interest of top managers of insurance companies is an important task of insurance startups so that they provide a context to customers’ further participation by ensuring the protection of their information. Cooperation among insurance companies and insurance startups in providing appropriate price and conditions, facilitating the access of InsurTechs, as business partners, to the real premium, and creating a platform to present unified web services by the industry’ s authority and insurer companies jointly are required for the acceptance of this technology. Support of the insurance industry in legitimizing and fostering the infrastructure for insurance startups decreases constraints. On the other hand, trust and cooperation of insurance companies with InsurTechs increase the customers’ acceptance of the insurance technology and use of purchase advantages achieved by this strategy. The results indicate that support has the greatest impact on capacitation, while trust is the most affected variable.

BACKGROUND AND OBJECTIVES: The insurance industry has a great influence on the dynamics and progress of the economy in countries.  The penetration of technologies in the structure of insurance organizations has converted the nature of the insurance business. In this research,  by using of the business ecosystem, the effective factors of insurtech have been tried to be investigated on the blocks of the business landscape that make up the business model of insurance companies, as well as the influence and effectiveness of these factors. METHODS: In this research, after revisal the literature, the effective agents of insurtech on the business ecosystem of the insurance industry were  gathered and provided to the experts in the form of a questionnaire. The statistical society of the research consists of information technology directores and insurance industry research and development managers and insurance industry experts who are related to insurance technology.  In this platform, the opinion questionnaire of experts was completed by 15 experts in this field. After examining the answers of the experts to the questionnaire, 26 sub-criteria affecting the business environment of the insurance industry were determined. Then, the Dematel questionnaire was completed by 8 experts in the insurance industry, and effective factors were identified and ranked by using the combination of network analysis and Dimtel methods.FINDINGS: Based on the results of Dematel analysis method, the criteria of key resources, effective activities, customer orientation, customer sections and channels affect the system. Also, income flow criteria, key partners, value proposition and cost structure are affected by the system. The results of the network analysis method have shown that at the level of the main criteria, the criterion of key partners by a weight of 0.206 ranks first, value proposition by a weight of 0.192 ranks second, cost structure by a weight of 0.185 ranks third, key resources by a weight of 0.125 ranks fourth, customer orientation by The weight of 0.099 ranks fifth, key activities by a weight of 0.097 ranks sixth, channels by a weight of 0.044 rank seventh, revenue stream by a weight of 0.037 ranks eighth, and customer segments by a weight of 0.015 ranks ninth.CONCLUSION: Insurance companies should update their business environment to take advantage of Insurtech opportunities. The directors of insurance companies should pay most attention to the company's key partners first and then to the value proposition and cost structure.

Background and Objective: The development of new financial markets, such as insurtech, has become a focus for researchers and policymakers. This study aims to identify the challenges faced by insurance companies in Iran in adopting insurtech.METHODS: The research method was qualitative and data was collected through theoretical study, literature review, and interviews with experts. Qualitative content analysis was conducted using MAXQDA software. The credibility of the analysis was assessed using data-oriented reliability. The study population included managers of insurtech businesses in Iran.Findings: Through interviews, consensus was reached on various challenges. After coding, 52 initial indicators were identified, leading to the identification of 21 subcomponents and 12 main components. The credibility of the coding was confirmed using the Kappa test. The identified dimensions and factors included culture, integration, training, conflict of interests, financial issues, management structure, infrastructure, regulations, neglect of innovation and new products, lack of investment, taxes, and contracts. The experts predicted the strongest consensus among conflict of interests, management structure, regulations, infrastructure, culture, and training.Conclusion: The challenges and barriers to adopting insurtech in Iran can be categorized into macro and micro levels. At the macro level, there are obstacles related to laws, regulations, monopolies, and infrastructure that require government attention. At the micro level, obstacles include organizational interests, culture, structure, infrastructure, and training that should be addressed by organizational managers. Without proper implementation of these factors, insurtech activities cannot thrive in Iran. The proposed framework can assist managers and policymakers in addressing these challenges.

As FinTech startups grow and develop, the nature of risks that need to be managed also changes. The insurance industry as one area of technology application plays a crucial role in supporting and enhancing the security of these companies. The purpose of this research is to identify the influencing factors and discover a paradigm model for insurance coverage of FinTech startups as an innovative product. The research method is qualitatively based on the Grounded Theory Method. The qualitative part includes semi-structured interviews with fourteen experts. The research conclusions show that the causal conditions provide requirements for the core category. Insurance strategies have an impact when context conditions are present. On the other hand, the COVID-19 crisis is known as the intervening condition. Ultimately, the use of strategies also yields consequences.

The rapid advancements in emerging technologies—particularly artificial intelligence (AI), blockchain, and the Internet of Things (IoT)—have propelled the insurance industry toward unprecedented innovations and positioned InsurTech as a driving force in the reconfiguration of the insurance value chain. This study aimed to design and develop a product model for Iran’s insurance industry based on these transformative technologies. Employing a mixed-methods research approach (qualitative and quantitative), thestudy first extracted the initial indicators and components through systematic content analysis and semi-structured interviews with 15 experts in the insurance and information technology sectors. These qualitative data were analyzed using grounded theory via MAXQDA software. In the quantitative phase, a conceptual model was validated using a researcher-made questionnaire administered to a sample of 384 insurance industry customers. Data analysis was conducted using Structural Equation Modeling (PLS-SEM) via SmartPLS software. The findings revealed that emerging technologies significantly impact the performance of the insurance industry through four key components: AI-driven innovation in insurance products, blockchain-based transparency and security, IoT-based risk management and personalized services, and enhanced operational efficiency of insurance companies. The results suggest that by improving personalization, reducing costs, increasing transparency, and optimizing processes, these technologies not onlyenhance the operational and competitive efficiency of insurance firms but also improve customer experience and the overall sustainability of the industry. The study’s recommendations emphasize the development of innovative products, the strengthening of digital infrastructure, and the adoption of mobile applications for customer-oriented services and targeted advertising