Identification and ranking of obstacles to the use of big data in Iran's insurance industry
Abstract
Due to the increase in the volume of data used in today's organizations, big data plays a very important role in the competitiveness of organizations, their survival, and the provision of optimal services. The adoption and use of any new technology or tool faces obstacles, and identifying these obstacles makes the right decisions to be made to reduce the risks of failure of new technology adoption. Therefore, this research has identified and prioritized the obstacles to using big data in Iran's insurance industry. This research is considered an applied research in terms of its purpose. First, by reviewing the background, 26 obstacles to the use of big data in Iran's insurance industry were identified in the form of four main obstacles. Confirmatory factor analysis was used to analyze the data and the best-worst method was used to prioritize the obstacles. The statistical population of the research was made up of insurance experts in Qazvin province. The main barriers identified are: technology, capital and expertise, data and information, and organization. The findings showed that technological barriers are the most important obstacles in using big data in Iran's insurance industry. Identifying existing obstacles and creating a hierarchical structure of obstacles will help policymakers and senior decision makers of the insurance industry to have a more appropriate prioritization for the implementation of programs and main strategies for the use of new technologies, especially the use and analysis of big data.

Introduction
Theoretically, there are many discussions about the effect of information technology on the efficiency of the organization and it is clear that the use of information technology helps the competitiveness of enterprises (Mehta, Hall & Byrd, 2014; Kohli & Devaraj, 2008). In a situation where the flow of information in the internet and virtual networks is established at a high speed and intensity, the level of information of people as the main contacts of this data flow is affected by this process and we can expect the emergence of new ideas and creativity from each individual and employee. (Bondaruk and Roll, 2009) and creating a new structure for current organizations in the global community is inevitable and worthy of serious reflection and revision. To create a proper perspective on big data and its importance, imagine a society where the population is increasing exponentially, but its public services and infrastructure cannot respond to the population growth and manage it. Such a situation is happening in the field of data. Therefore, it requires the development of technical infrastructure for data management and its growth in areas such as collection, storage, search, sharing and analysis.
In addition to the existing hardware in every job system, which must be updated in line with the turbulent era we are in, the software related to every job and profession also needs to be reviewed and updated. As mentioned in the cycle of knowledge management in the organization, the stage after revealing knowledge is to institutionalize it in the organization and create a correct perception of it. Perception is a process dependent on humans and affected by their mental background and inner self (Moeinian, 2012). Therefore, bulk data or big data is one of the topics that has been the focus of managers and thinkers due to the high volume and speed of data exchange in today's world. Although many studies have been conducted in recent years about various methods of data storage and analysis, especially in the field of health and treatment, marketing and customer behavior, but in the insurance industry it has received less attention and is a relatively unknown topic for managers of organizations (Azizi and Khademi). , 2013).
As the competitive market becomes more complex day by day, decision makers of organizations depend more and more on big data for organizational use, performance trend recognition, product performance optimization and inventory management optimization (Hazen et al., 2014). Therefore, it is very necessary to guarantee the quality and validity of the data used, and the lack of adequate knowledge of the data may put organizational decisions at risk and cause the organization to suffer losses (Vars et al., 2011). The use of big data can facilitate organizations' ability to create a clear picture of customers and their demand for customer orientation (Manika et al., 2011). The discussion of big data analysis is the latest application of information technology, this discussion is even more important when it becomes a powerful tool, also, big data can provide large amounts of data that could not even be imagined in previous years (Lusher et al. al., 2014).
In order to maintain the market, insurance organizations are changing from a service-oriented model to a customer-oriented model. One of the ways to do this transfer is to use customer categories. Big data helps them segment customer groups into specific categories. Therefore, reasons such as the change in the business model, the introduction of internet insurance platforms, the huge growth of the volume of data, and the very diverse and changing needs of customers have made the use of big data in the insurance industry a fundamental requirement (Hussein and Prieto, 2016; Mansouri). and Fucordi, 1401). Big data is used for customer attraction and retention, risk assessment, fraud detection and prevention, cost reduction, personalized service and optimal pricing, influencing internal processes, and many other insurance services (Pathak, 2021).
As all businesses must manage some level of risk, this is also critical for insurance organizations. For example, insurance institutions are required to analyze liquidity risk in different situations. Big data-based analytics can help measure these risks in real-time. Like many competitive organizations, the customers of insurance organizations are also free to change their insurer whenever they want, if the insurance organization has the right to know the reason for the cancellation of its customers and take action to improve its situation. Also, due to the increase in the number of insurance transactions, they will witness less fraud, so that in case of increased security risks, it provides a higher level of security.
Considering the importance of the issue that the process of big data analysis is growing rapidly at the global level and the perspective and benefits of big data are of great help to large businesses, it is still in the acceptance and identification of the influential components in the implementation of big data. There is a gap, especially in the insurance industry. Despite the countless benefits of big data for organizations, achieving acceptance of big data analysis and examining the obstacles in this way is still a challenge for large organizations and insurance companies (Li, Peng & Xing, 2019; Moktadir et al., 2019; Chen & Zhang, 2014). ; Rahman, 2016). The purpose of this research is to identify and rank the existing obstacles to the use of big data in insurance organizations. Identifying these obstacles will help insurance organizations to better understand how to increase competition and be able to design better strategies for the organization's progress, and finally, by understanding how to use big data in order to lead to a sustainable competitive advantage, reduce the risks in this field. 

Research background
The term big data was first coined in 2001 by Doug Laney at the Gartner Institute to refer to data that is increasing in volume, velocity, and variety. According to his definition, big data includes large volumes of data that are used with modern processing and storage methods for a better understanding of the world and a more accurate decision-making process. In recent years, the development process of big data has grown dramatically.
Wiltz, Atkins and Steiner (2022) in a study by examining the theoretical foundations, first extracted 69 obstacles in the use of big data analysis for small and medium-sized companies, and then by collecting field data from experts, they concluded that 21 main obstacles in Applying big data analytics to UK SMEs. Govindan et al. (2022) prioritized barriers to adoption of blockchain technology-based platforms from a balanced scorecard perspective in the healthcare industry using a structural approach. The results showed that financial issues, security issues, lack of expertise and knowledge, and unclear government policies are the most important barriers to the adoption of blockchain technology-based platforms in the healthcare industry.
Saeed, Arshad, and Zhang (2022) examined the challenges and possible solutions for using the Internet of Things in the Indian insurance industry with a systematic review of the background and 15 main obstacles were identified in this regard, which according to the authors, these obstacles also exist in the use of big data. . In another study, the capabilities of big data analytics for healthcare in Bangladesh were investigated by polling 51 experts and using the DEMATEL technique. The findings identified 11 key factors such as the use of advanced analytical techniques that can be critical in managing big data in the healthcare sector (Morad et al., 2022).
Mansouri and Foukardi (1401) modeled the barriers to the adoption of big data analysis in Iran's insurance industry by using comprehensive structural interpretive modeling and by using Micmac analysis. In this research, 10 main obstacles to the acceptance of big data analysis in Iran's insurance industry were identified. Qutobi and Pertabian (2018) investigated the opportunities and challenges of using big data in a study. According to the obtained results, factors such as the lack of expert and experienced IT personnel and high investment costs are considered as obstacles to the use of big data in the industry.
Moqtadar et al. (2019) conducted a study titled Big Data Analytics Barriers in Bangladesh Insurance Industry. The most important data-related barriers are technology-related barriers, and the five barriers are: lack of infrastructure, complexity of data integration, data privacy, lack of access to BDA tools, and high investment costs. These findings can help insurance industry managers in understanding the true nature of barriers and possible benefits of using BDA and making policy regarding the adoption of BDA in production supply chains.
Amankwah and Adomaki (2019), by analyzing big data and business problems in data-rich environments, have expressed how different types of access to big data and different capabilities of big data analysis can change the conditions of companies that are facing failure. . Torsilla and Romo (2018) have investigated the obstacles to using big data during a research. In this research, they have investigated the statistical role of the issues raised by the obstacles to the use of big data and have finally proposed a name for data training in which all the activities derived from the knowledge related to this source of information have been mentioned. They have introduced organizational obstacles and obstacles related to attracting capital among the most important obstacles to using big data.
Ghasem Aghaei (2020), in an article, has examined the positive and negative role of the influential factors of large data in the use of big data analysis, in which he has expressed a solution to increase the scope of using big data analysis in both positive and negative aspects. Negatively indicated. It also states that at different levels of data security concern, work complexity, access and data recognition, the impact of using big data is different.
Alherti et al. (2017) conducted a study titled identifying big data barriers in insurance companies. While sitting on these vast troves of data, many insurance companies are not ready to take advantage of this new strategic resource. Access to big data requires overcoming a number of barriers in the areas of technology, people and insurance companies. To overcome these obstacles, a comprehensive, social and technical approach is required. This study recommends specific tactics to address big data barriers, which include changes in technology infrastructure, focus on privacy protection, big data promotion and development of analytical skills, and creating a clear organizational vision regarding big data.
Rahmati Monfared (2014) in a study identified and ranked the obstacles affecting the electronic insurance of Iran Insurance Company. The research findings of eight main obstacles include the lack of a comprehensive electronic program for the sale of insurance policies, the lack of electronic infrastructure for paying insurance premiums, the lack of standardization of insurance risk, the lack of appropriate communication and telecommunication infrastructures, the lack of belief of managers in the possibility of electronic insurance, lack of public trust and confidence. compared to electronic activities, and the general lack of familiarity of insurance policyholders with electronic insurance. Kazemi, Fayazi and Mirzadeh (2007), in a research, investigated the barriers to using information technology in Iran's insurance industry. These obstacles are placed in 6 main groups, including managerial, human, cultural, organizational, technological, and environmental factors, and finally, the results of the research have shown that environmental and cultural factors are the most important obstacles affecting the use of information technology in Iran's insurance industry.
Thorp and Gray (2015) conducted a study titled Big Data and its obstacles in the private sector. Big data is envisioned as having the potential to transform the private sector by providing large amounts of data to support service delivery. Critics argue that the transformative potential of big data is inhibited by privacy requirements that limit the sharing of health information. The results obtained from this study show that the lack of suitable internet bandwidth, the existence of network and telecommunication problems, and the incompatibility of systems with users are among the obstacles to the use of big data.
Sagirouglu and Sinans (2013) conducted a study titled Big Data Review. Big data is a term for massive data sets that are large, diverse, and complex in structure with storage, analysis, and visualization problems for further processing or results. The process of investigating massive amounts of data to uncover hidden patterns and hidden correlations is known as big data analytics. This useful information for companies or organizations by helping to gain richer and deeper insight and gain a competitive advantage. For this reason, the implementation of big data should be analyzed as precisely as possible. This study provides an overview of the content, scope, examples, methods, benefits and challenges of big data and addresses privacy concerns about it.
By reviewing previous studies, the most important obstacles to using big data in various organizations were identified. Table 1 shows the main and secondary barriers to using big data.

Research Methodology
This research, in terms of its purpose, is an exploratory and applied type of research, because it identifies the obstacles to the use of big data from the perspective of insurance experts and using its results, it is possible to remove the obstacles to the implementation of big data in the insurance industry. In order to collect data and identify obstacles, the theoretical foundations and background of the research were first examined, and a list of potential obstacles mentioned in previous researches was identified in Table No. 1. Then, for the final confirmation and meaningful examination of the identified obstacles, questionnaires and confirmatory factor analysis were used to ask for opinions from experts. In the final step, to determine the importance of each obstacle, the best and worst fuzzy methods were used and experts were asked for their opinions.
Considering that field data collection was done in two stages, the statistical population of this research is divided into two main categories. In the first category, the statistical population consisted of all managers and experts of different insurances in Qazvin province, who were selected by random sampling and 200 people. Also, in the prioritization section, the statistical population consisted of managers of provincial branches of insurances, which was used using judgmental sampling from the perspective of 10 insurance experts in Qazvin province. Table 2 shows the demographic characteristics of the samples.

findings
In order to identify the obstacles to the use of big data in Iran's insurance industry, valid scientific articles were reviewed between 2015 and 2022. By searching for related keywords in reliable domestic and foreign scientific databases, 26 barriers were identified in the form of four main categories of barriers: technology, capital and expertise, data and information, and organization as potential barriers to using big data in Iran's insurance industry. In the next step, confirmatory factor analysis (construct validity) was used to ensure the correctness of the identified barriers and to validate the barriers. Before testing the effectiveness of the identified factors, the Kolmogorov-Smirnov test was performed to check the normality of the data. As shown in Table 2, the significance level for all constructs is greater than 5%, and therefore all data are normally distributed at the 5% significance level. In addition, in Table 3, the data related to the mean and standard deviation of each of the variables, as well as the Cronbach's alpha value to measure the validity of the questionnaire are specified, and as can be seen, the research questionnaire has a good reliability.

Table 3- Reliability measurement and the number of secondary barriers

The results of the factor analysis of the measurement scale of the research variables are presented in Figure 1. The factor load of observation is greater than 0.6 in all cases, which shows that the correlation between hidden variables and visible variables is acceptable. The value of the t-value statistic is greater than 1.96, which indicates that the observed correlations are significant.

Figure 1- Confirmatory factor analysis of the research questionnaire (standard and meaningful mode)

Considering that all barriers are extracted from previous research, there is content validity for all of them. In this study, the ERLS goodness-of-fit index was used according to the study of Do, Yalsinkaya and Bestiller (2016), because this index produces unbiased estimates for normal and non-normal data. The chi square value of ERLS is equal to 232.22. Other goodness-of-fit indices are shown in Table 4, all of which indicate a good fit.

Table 4- The fit indices of the variable measurement model

The results of the confirmatory factor analysis show that all 26 obstacles identified in the form of four main obstacles are effective on the use of big data in Iran's insurance industry. Figure 2 shows the hierarchical structure of obstacles to the use of big data in Iran's insurance industry.


Figure 2- Hierarchical structure of obstacles to the use of big data in Iran's insurance industry

After the final confirmation of the obstacles to using big data in Iran's insurance industry, the best-worst (BWM) method has been used to rank them (Rezaei, 2015). For this purpose, first the weight of the main obstacles was obtained.
Step 1- Determining the set of obstacles: In this step, we consider the obstacles of technology, capital and expertise, data and information and organization (C_1, C_2, C_3, C_4) to determine the weight or decision.
Step 2- Determining the best (in other words, the most desirable and important) and worst (the most undesirable and unimportant) obstacles. In this section, the decision maker generally specifies the best and worst obstacles. No comparison is made in this section. Based on the opinion of experts, C1 was selected as the best obstacle and C3 as the worst obstacle.
Step 3- Determining the degree of preference of the best barrier against other barriers using numbers 1 to 9. The results of the best barrier compared to the rest of the barriers can be as follows:
where a_Bj is the preference of the best barrier (B) over barrier j. Obviously, a_BB=1. Table 5 shows the pairwise comparison of the best barrier with other major barriers.
Table 5- Comparison of the main obstacles with the best obstacle

Step 4- Determining the degree of preference of obstacles to the worst obstacle using numbers 1 to 9. In this case, the result vector can be as follows:
where a_jw is the preference of barrier j over the worst barrier (W). Obviously, a_ww=1. Table 6 shows the degree of preference of obstacles compared to the worst obstacle.

Table 6- Comparison of the main obstacles with the worst obstacle

Step 5- Finding optimal weights (〖w_1〗^*,〖w_2〗^*,〖w_3〗^*,〖w_4〗^*): The optimal weight for each obstacle is unique, so that for each pair of w_B/w_j and w_j/w_w we will have the relation 3:
To satisfy these conditions for all j, we need to find a solution such that the absolute value of the maximum difference |w_B/w_j -a_Bj | and |w_j/w_w = a_jw | be minimized Considering that the weights are non-negative and summable, the following problem can be stated (Relation 4):
Problem 4 can be expressed as problem 5:
By converting problem 5 to problem 6 and solving it by Lingo software, the optimal weights (〖w_1〗^*,〖w_2〗^*,〖w_3〗^*,〖w_4〗^*) of the main obstacles were obtained.
The result of solving model 6 showed that technological barriers with a normalized weight of 0.435 have the highest priority; The barrier of capital and expertise with a weight of 0.327 is in the second priority; The organizational barrier with a normal weight of 0.133 is in the third priority; And finally, the barrier of data and information with a similar weight of 0.105 has the lowest priority. The comparison is fully consistent when relation 7 holds for all j's.
In this regard, a_Bj, a_iW, and a_BW will be the priorities of the best obstacle over obstacle j, the priority of obstacle j over the worst obstacle, and the priority of the best obstacle over the worst obstacle, respectively. Using the compatibility index (CI) according to table 7 (Rezaei, 2015) and equation 8, the compatibility rate is calculated, which is in the range [0 and 0.1], and the closer it is to zero, the comparisons are more compatible and stable, and the more If it is closer to one, the comparisons are less consistent and stable.

Table 7- Compatibility index (CI) in the best-worst method

In this study, the value of ξ^* equal to 0.208 was obtained. As a result, according to Table 4, because the preference of the best element to the worst element is equal to 6, so the incompatibility rate is equal to:
The inconsistency rate of the comparisons made is 0.056, which is smaller than 0.1, and therefore the comparisons made can be trusted.
In the second step of the best-worst method, the set of sub-obstacles related to each obstacle is compared and prioritized in pairs in its respective cluster, according to the steps taken to determine the weight of the main obstacles. Tables 8 to 11 show the comparison of the best and worst secondary barriers in each group of main barriers.

Table 8- Comparison of technological sub-barriers with the best and worst barrier

Table 9- Comparison of sub-barriers of capital and expertise with the best and worst barrier

Table 10 - Comparison of data and information sub-barriers with the best and worst barrier

Table 11- Comparison of sub-obstacles of data and information with the best and worst element

Based on tables 8 to 11, the weights of all 26 obstacles identified in the use of big data in Iran's insurance industry were determined. To determine the final priority of the barriers to using big data in the insurance industry with the best-worst method, it is enough to weight the barriers based on each sub-barrier in the weight of the main barriers. The results of the calculation and the weights of the obstacles are listed in Table 12:

Table 12- The final priority of obstacles to the use of big data in the insurance industry by the best-worst method

Therefore, the obstacle of lack of basic facilities with a weight of 0.339 is the first priority. The obstacle of not having access to big data analysis tools with a weight of 0.122 is the second priority. The obstacle of lack of expert and experienced IT personnel and high investment costs with weights of 0.113 and 0.088 are in the third and fourth priority. Lack of suitable internet bandwidth with a weight of 0.070 is also the fifth most important obstacle.

Conclusion and suggestions
In this research, to identify and rank the barriers to using big data in Iran's insurance industry, 26 barriers were first identified by examining the related profiles in the form of four main groups of barriers including technology, capital and expertise, data and information, and organizational. Then, these obstacles were finally approved by asking opinions from managers and industry experts and using confirmatory factor analysis. In the end, all obstacles were prioritized using the best-worst method. In prioritizing the main barriers to using big data in the insurance industry, technological barriers with a normalized weight of 0.435 have the highest priority. The barrier of capital and expertise with a weight of 0.327 is in the second priority. The organizational barrier with a normal weight of 0.133 is in the third priority. Data and information barrier with a weight of 0.105 has the lowest priority. Considering that the technology factor is of the highest importance, it can be acknowledged that due to the advancement of technology, especially information and communication technology, the increase in the amount of data required for processing, the high dependence of all businesses on information and communication technology, and its application In all the growing industries of the world, it is obvious that the most important factor as the main obstacles to the use of big data in the insurance industry is important, and the attention and emphasis on it is obvious to the activists of this field. In the study of Qutubi and Pertabian (2018), the technological barrier has the highest priority, and therefore it is consistent with the results of the present study.
In the prioritization of the secondary obstacles of technology, the obstacle of lack of basic facilities with a normal weight of 0.320 is ranked first. The obstacles of lack of access to big data analysis tools, lack of suitable internet bandwidth, existence of network and telecommunication problems, unwillingness to implement new technology, and lack of compatibility of systems with users are also in the second to sixth priorities, respectively. The compatibility rate obtained for this group of barriers is equal to 0.017, so the comparisons can be trusted. In the study of Khoshnoud Mansourkhani et al. (2017), the lack of basic facilities was ranked as the first obstacle, and for this reason, it is in line with the results of the present study. Regarding the fact that the lack of basic facilities has the most weight among the known factors, it can be acknowledged that almost most technology-based needs are somehow derived from the existence and provision of basic facilities, and above all, the provision of said facilities , is a prerequisite for other factors.
In the prioritization of the sub-obstacles of capital and expertise, based on the obtained special vector, the obstacle of lack of expert and experienced IT personnel with a normal weight of 0.346 ranks first. Further, the obstacles of high investment costs, lack of necessary facilities for the development of big data analysis tools, lack of funds, lack of relevant training courses for employees, and lack of experts are also in the next priorities. The consistency rate is also 0.018, which shows the consistency of the weighting results. In the study of Amankwah and Adomaki (2019), the lack of budget was ranked as the fourth priority, and in this sense, it is consistent with the results of the present study. Regarding the first priority, under the title of lack of expert and experienced IT personnel, it can be said, because the power Human resources in every industry and field cannot be overlooked, therefore the importance of hiring and employing expert and experienced IT personnel is obvious to everyone and it should be implemented based on merit selection.
In the prioritization of the sub-obstacles of data and information, the obstacle of lack of support systems with a normal weight of 0.290 is in the first place, followed by the obstacles of complexity in data integration, lack of determining the registration method of registered and non-registered statistical items, data quality, The ability to implement data, the lack of determining the method of quality control and data correction, and data security are placed in the second to seventh priorities. The compatibility rate for these obstacles is equal to 0.022, which shows the reliability of the comparisons. In Torsilla and Romo's (2018) study, the data security barrier was placed as the last priority, and in this sense, it is in line with the results of the present study. Regarding the first priority under the title of lack of support systems, it is necessary to mention that one of the solutions to attract customers' satisfaction and gain their trust is possible by providing support systems. Therefore, the importance of this issue to remove the secondary barriers of data and information is undeniable.
In the prioritization of organizational sub-obstacles, the obstacle of lack of coherent law in data sharing in the organization with a weight of 0.250 is the first priority. In the following, the obstacle of the absence of the educational system is the second priority, the weak communication network in the organization's structure is the third priority, the lack of budget for software and hardware equipment is the fourth priority, the existence of financial restrictions for the use of information technology is the fifth priority, the attitudes of People's different thinking regarding big data is the sixth priority, and finally, the obstacle of time limitation is the seventh priority. For this group of obstacles, the compatibility rate of 0.078 was obtained, which indicates the compatibility of the findings. Regarding the first priority of the lack of a consistent law in data sharing in the organization, it can be acknowledged that in the absence of a regular law and proper planning in this area, the sub-obstacles of the organization will appear one after the other and will be out of the management's control.
Regarding the sub-obstacles of data and information, it is suggested to the managers and assistants active in the insurance industry, to be diligent by creating and providing support systems and removing the complexity in data integration, in order to remove the obstacles to the advancement of organizational data and information. By determining the registration method of registered and non-registered statistical items and increasing the quality of data presentation, they will be able to take a big step in removing the mentioned obstacles. By employing specialized, expert and capable people in data implementation and using a quality control and information correction method, it will be helped in some way in the field of removing the secondary obstacles of data and information. Finally, it is suggested that they try to upgrade and improve their work process by using a system that provides security for users' and organization's data.
It is suggested that future researchers do a systematic review of the background in order to identify all the effective obstacles in using big data. In addition, by using qualitative research methods and tools such as the Delphi method and data base theory, similar studies can be conducted and obstacles that have probably not been investigated so far can be identified. After identifying the obstacles, users and other researchers are suggested to conduct studies to identify solutions to overcome the obstacles.

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