Effects of Pricing Decisions on Service Delivery Penetration among Insurance Companies in Kenya

Effects of Pricing Decisions on Service Delivery Penetration among Insurance Companies in Kenya

Chege Simon Maina¹ and Dr. Maria Mung’ara, (PhD)²

¹Mount Kenya University, P.O Box 342-00100, Thika Kenya

²School of Social Sciences, Business Department, Mount Kenya University

DOI: https://dx.doi.org/10.47772/IJRISS.2023.7011052

Received: 24 October 2023; Revised: 02 November 2023; Accepted: 06 November 2023; Published: 06 December 2023

ABSTRACT

Insurance firms have long been regarded as a reliable source of safety nets, with their services enabling investment and cutting down on the reserves necessary for individuals and businesses to weather the financial storms of life. The goal of this research determines how much insurance companies in Kenya’s pricing decisions affect their bottom lines. The following specific goals serves as the basis for this research: The goals of this study were to establish that technology adoption has an effect on the organizational performance of insurance companies in Kenya; analyze the effect of competition on the organizational performance of insurance companies in Kenya; establish that the influence of target customers on the organizational performance of insurance companies in Kenya; and analyze the effect of economic conditions on the organizational performance of insurance companies in Kenya. Price theory by Stigler, market segmentation theory by Tedlow, and the resource-based theory by Rumelt formed the basis of the research. Because of the complementary nature of the insights they yield, the study employs a mixed methodology approach. Specifically, a descriptive research strategy was used for this study. The research took place at insurance companies in Nairobi City County. There were 55 insurance companies used as the unit of analysis. Insurance Underwriting Managers and finance managers who oversee the sale and pricing of insurance policies served as the study’s sampling frame because of their central role in informing the study’s focus. A total of 110 participants were used for the study, all of whom were selected through a census. Primary data was gathered through the use of questionnaires. Descriptive statistics was used to analyze the gathered data. In order to accomplish the goals of the research, we used the mean and standard deviation. All of the quantitative information gathered was analyzed using SPSS version 24. Multiple regression analysis was used to model the association between the independent and dependent variables. The study found there was a significant relationship between technological adoption, competition, target clients, economic conditions and organizational performance. The study concluded and recommended that technological adoption, competition, target clients, economic conditions should be enhanced by the insurance companies to improve organization performance.

Key Words– Competition, Economic Condition, Performance, Target Clientele and Technology Adoption

INTRODUCTION

1.1 Background of the Study

Insurance has long been recognized as a means by which individuals and businesses can feel more at ease about their financial futures by lowering the upfront cost of preparing for the possibility of loss due to accidents or other perils (Ackah & Owusu, 2012). The adoption of technology in the insurance industry has been a subject of considerable interest and debate. Numerous studies have examined the impact of technology adoption on the organizational performance of insurance companies. Gupta et al. (2022) found that insurance companies that invest in advanced technologies, such as artificial intelligence and data analytics, tend to experience enhanced operational efficiency and improved customer service. These technologies enable insurers to streamline their processes, reduce administrative costs, and offer more personalized services to their clients. The competitive landscape of the insurance industry has undergone significant changes in recent years. Studies by Vnukova et al. (2020) have shown that increased competition has led insurance companies to innovate and improve their services. This has resulted in better product offerings and pricing strategies, ultimately contributing to improved organizational performance. In a competitive environment, insurance companies are driven to differentiate themselves through better customer service, more attractive policies, and innovative distribution channels. The importance of understanding and targeting the right customer segments cannot be overstated in the insurance sector. Research by Sthembiso and Nzama (2023) demonstrates that insurance companies that identify and cater to the specific needs and preferences of their target customers tend to outperform their peers. Tailoring insurance products to meet the unique demands of different customer segments not only enhances customer satisfaction but also leads to increased loyalty and retention.

There are currently 53 insurance companies, 191 insurance brokers, and 6,483 insurance agents operating in Kenya (IRA, 2017). 53 companies brought in a total of Kes 207.7B in revenue, and the underwriting profit was Kes 15.5B, or about 11.7% of total revenue (IRA, 2017). Non-life insurance premiums account for 60% of this sector’s revenue. Most Kenyans also do not understand the ins and outs of insurance, which makes it difficult for them to make informed decisions about whether or not to purchase coverage (Kiama, 2015). Customers have a mental and emotional distrust of the industry and its agents due to reports of agents providing incorrect policy interpretation and, even worse, misappropriating clients’ premiums (Mwiti, 2016).

The dynamics in the growth of the industry is affected by the strong cultural systems amongst the Kenyan communities. The setting is that whenever a member of the society encounters a misfortune, the community assists. The cost of the misfortune is transferred from an individual to the whole community whereby members gather and raise funds towards settlement of bills (Makroi, 2017). As opposed to the insurance option, this system is inherently expensive, inefficient and leaves behind those who were involved sometimes-traumatized (Makori, 2017). The insurance industry has also been slow to respond to changing market dynamics in technology (Faurie, 2014). In most cases, the products distribution and claims payment system is still manual and the industry still rely on products that were introduced over 80 years ago from Europe that have little or no relevance to Kenyans (Basant and Chandra, 2015). There has been failure to innovate new products that resonate with Kenyans.

1.2 Statement of the Problem

Insurance companies have been forced to strategically adapt to the ever-changing business environment over the years (Reavis, 2012). Consequently, insurance firms adapt their marketing strategies to address the industry’s unique challenges (Yaari, 2015). Although insurance has been around for centuries, few Kenyans have it and the industry as a whole is seeing a decline in gross premiums in real terms (Makori, 2017). Some developed nations have seen declining premium increases, while emerging markets have seen average increases that are hardly noticeable (Mwiti, 2016). As an example, comparing Kenya to South Africa, where insurance industry performance is estimated to be 14.2%, we see that Kenya has a significantly lower 2.8% (McKinsey, 2014; KPMG, 2014). Kenya is home to 55 different insurance providers. This is the same as Kenya’s banking sector, which serves a population of 1 million people at a ratio of 1:1. However, pricing decisions continue to have a negligible effect on insurance industry performance, at below 3.0 percent, which

is lower than the average of 3.8 percent in Africa (KMPG, 2014).

There is a dearth of resources on the topic of how to improve organizational performance in Kenya through marketing strategies. Factors influencing Nairobi’s SMEs to use insurance were analyzed by Kamara and Makori (2017). Kodia (2017) investigated what factors led to the successful adoption of life assurance products in Kenya. Kamau (2013) looked into what could be causing low insurance organization performance in Kenya. In addition, Mwiti (2016) both honed in on the nature of competition among Kenya’s life insurance firms. It was yet to determine whether or not pricing decisions have any effect on the operational effectiveness of insurance firms in Kenya, despite the increasing focus on these topics.

THEORETICAL FRAMEWORK

Three theories—price theory, market segmentation theory, and resource-based theory—served as our guides in this investigation.

2.1.1 The Theory of Price by Stigler in 1978

According to the supply and demand theory of pricing developed by economist George Stigler in 1978, the price of a good or service is determined by the market.

This theory is of importance to the study because the insurance industry uses price theory to determine the selling price that brings supply and demand as close to equilibrium as possible; as a result, it lends support to the organizational performance and economic conditions variables in this study.

2.1.2 Theory of Market Segmentation by Tedlow in 1990

Market segmentation theory was first developed by Tedlow in 1990, the theory is widely used to guide marketing strategy in the financial sector because it allows for segmentation based on benefit sought while still providing high-quality products, services, and results-based services.

The customer-centric nature of the market segmentation theory makes it applicable to this investigation. It helps insurance companies zero in on what their clients actually want, and then weigh whether or not it makes business sense to come up with pricing decisions that reflect that. Insurance company executives are thus better able to adapt to the needs of their customers and carve out dominant positions in niche markets.

2.1.3 Resource Based Theory by Wernerfelt in 1984

The resource-based theory, pioneered in the 1980s by Wernerfelt (1984), is now one of the most popular methods for analyzing long-term pricing choices.

The ability of resource-based theory on a set of resources like technological adoption to accomplish a stretch task of an operational activity to improve organizational performance is crucial to this investigation. Therefore, it lends credence to the study’s technology adoption outcome variable.

RESEARCH METHODOLOGY

3.2 Research Design

A cross-sectional survey approach was utilized for this investigation. Due to its focus on establishing the actual situation regarding pricing decisions on the organizational performance of insurance companies, a cross-sectional survey was appropriate method to employ. In this kind of research, participants were chosen with a focus on one or more of the variables of interest.  Research of this sort was used to provide a descriptive account of a community’s features, but it was not used to establish causal links between factors. This strategy was frequently used to draw conclusions about potential connections or to collect preliminary data to bolster follow-up investigation and experimentation. Adoption of technology, levels of competition, potential customers, and economic conditions was the study’s five independent variables. Organizational effectiveness in insurance firms served as the study’s dependent variable.

3.2 Location of the Study

The insurance agencies in Nairobi County, Kenya, served as the research sites. It was convenient that the research was taking place in a place with a thriving economy and a lot of business activity. Kenya, whose capital is Nairobi, is in Africa. The County of Nairobi is one of Kenya’s 47 counties. Nairobi County is the third-smallest of Kenya’s 47 counties, but its population makes it the most populous city in the country. After Kenya’s eight provinces were divided into 47 counties in 2013, Nairobi County was established along the same borders as Nairobi Province.

3.3 Sampling Techniques and Sample Size

Since insurance underwriter managers and finance managers who oversaw policy sales and pricing have a direct impact on the study’s central hypothesis, they were the primary population sampled. Element(s) clearly associated with the population from which a sample is drawn constitute a sampling frame. As long as the sample was selected scientifically, it was usually sufficient to draw conclusions about the entire population if only 10% of the population is studied (Orodho, 2012).

Because of the specifics of the investigation and the relatively small size of the sample, the researcher employs a census of all insurance companies, which was broken down by line of business (services). As a result, we need to carefully select respondents/the unit of inquiry who can provide detailed information pertinent to our research questions. Due to the small size of the target population, this type of sampling was necessary because it allows researchers to more accurately represent the population’s composition. The proportion of the population represented by each subgroup was used to establish the total number of participants. As a result, all 110 participants of the population were surveyed for this study.

3.4 Research Instruments

The study’s primary research tool was a set of questionnaires, and because of their predetermined format, the study’s results were consistent across participants. This was due to the fact that a large number of respondents are guaranteed when using a structured questionnaire, requiring little effort on the part of the researcher (the individuals who answer the questions). The questionnaire had four parts: Part A, which collected general information about respondents; Part B, which collected data on the use of technology by respondents; Part C, which collected data on the competition; Part D, which collected data on the respondents’ intended customers; Part E, which collected data on economic conditions; and Part F, which collected data on the organizations’ performance.

3.5 Data Analysis and Presentation

The researcher used SPSS version 24 to analyze the quantitative information while the qualitative information was analyzed thematically.

RESEARCH FINDINGS

4.1 Response Rate

Table 4.1 aimed to get the response rate from the sample size of the respondents whom participated in this research, ad their results are as follows;

Based on the findings the researcher gave out 110 questionnaires to the insurance underwriter managers and finance managers who work in the insurance companies in Nairobi County. There were 80 (72.7%) of the questionnaires were fully filled and returned by the participants while 30 (27.3%) of the questionnaires were partially filled/not filled/ unreturned by the participants. Howell (2013) postulated that 50% response rate is adequate while 60% return as good and 70% and above return rate as very good. Hence the return rate of tis research was very good with a return rate of 72.7% which was above the stated 70%.

4.2 Technology Adoption and Organizational Performance

Table 4.4 establishes the influence of technology adoption on the pricing decisions on the organizational performance of insurance companies in Kenya.

As per the field research findings the participants agreed that through technology adoption there had been business improvements at the work place, this was evidenced by (Means Score = 4.111; Standard Deviation = 0.889). Through technology adoption there had been cost reunions in distribution of information was agreed with (Means Score = 4.216; Standard Deviation = 0.784). Majority of the respondents strongly agreed that through technology adoption, the level of quality insurance, process was effective this was evident as shown by (Means Score = 4.712; Standard Deviation = 0.288). Other participants agreed that through technology adoption there had been improved insurance operations at the workplace with (Means Score = 4.382; Standard Deviation = 0.618). Majority of the participants strongly agreed that through technology adoption there had been improved level of competitive environment, this was shown by (Means Score = 4.735; Standard Deviation = 0.265).

An overall average Means Score of 4.431 this shows that the majority of the respondents agreed to the statements on the influence of technology adoption on the pricing decisions on the organizational performance of insurance companies in Kenya.  In their own opinion the majority of the respondents stated that technology adoption significantly influences organizational performance of their insurance companies.

4.3 Competition and Organizational Performance

Table 4.5 analyzes the influence of competition on the pricing decisions on the organizational performance of insurance companies in Kenya.

Based on the survey findings of this research the respondents strongly agreed that lowering of service prices in the insurance company had increased sales, this was shown by (Means Score = 4.523; Standard Deviation = 0.477). The participants of this research agreed that the level of competition among insurance companies had increased service level and was revealed by (Means Score = 4.121; Standard Deviation = 0.879). Other participants with (Means Score = 4.629; Standard Deviation = 0.371) strongly agreed that the insurance company’s competition was based on customer’s loyalty. Majority of the respondents agreed that the level of cost leadership among insurance companies had increased sales, this was revealed by (Means Score = 4.017; Standard Deviation = 0.983). Based on the findings the majority of the participants strongly agreed that the level of market Focus on the Clientele among insurance companies had increased service level, as evidenced by (Means Score = 4.951; Standard Deviation = 0.049).

With an overall average Means Score of 4.448 shows that the majority agreed to the parameters on the influence of competition on the pricing decisions on the organizational performance of insurance companies in Kenya. On their own opinion the majority of the participants agreed that competition significantly impacts organizational performance of insurance companies in Kenya.

4.5 Target Clientele and Organizational Performance

Table 4.6 determines the influence of target clientele on the pricing decisions on the organizational performance of insurance companies in Kenya.

Based on the survey research findings the majority of the respondents strongly agreed that through mass marketing had reached the targeted clientele, this was evident as shown by (Means Score = 4.461; Standard Deviation = 0.239). Product Differentiated Marketing had increased sales in their insurance companies’ services was also strongly agreed by the majority of the respondents as it evident as shown by (Means Score = 4.831; Standard Deviation = 0.169). Majority of the respondents with (Means Score = 4.915; Standard Deviation = 0.085) strongly agreed that the company made sure the target market was reached. The level of email marketing had increased sales in their insurance company services was strongly agreed by the majority as shown by (Means Score = 4.825; Standard Deviation = 0.175). Majority of the respondents strongly agreed that the level of telemarketing had increased sales in their insurance companies’ services, this was shown by (Means Score = 4.830; Standard Deviation = 0.170).

The statements on the influence of target clientele on the pricing decisions on the organizational performance of insurance companies in Kenya were strongly agreed as it was shown by overall average Means Score of 4.832. On their own opinion the majority of the participants agreed that targeted clientele influences organizational performance of insurance companies in Kenya.

4.6 Economic Conditions and Organizational Performance

Table 4.7 analyzes the influence of the economic conditions on the pricing decisions on the organizational performance of insurance companies in Kenya.

As per the survey research findings the participants strongly agreed that economic conditions in the insurance company affected their monetary policies, this was shown by (Means Score = 4.722; Standard Deviation = 0.278). Other participants strongly agreed that unemployment levels affected their sale services and this was presented by (Means Score = 4.891; Standard Deviation = 0.109). Daily dynamics of exchange rate in the country affected their sale services was also strongly agreed by the majority, this was evident and was shown by (Means Score = 4.928; Standard Deviation = 0.072). The insurance company had improved their policies due to financial turmoil was strongly agreed by the majority and was shown by (Means Score = 4.625; Standard Deviation = 0.375). The insurance company had improved ways in buying power to the clients was agreed by the majority as it was shown by (Means Score = 4.219; Standard Deviation = 0.781).

An overall average Means Score of 4.677 showed that the majority of the respondents strongly agreed that economic conditions on the pricing decisions impacts organizational performance of insurance companies in Kenya. On their own opinion the majority of the respondents agreed that economic conditions of a State significantly influence organizational performance of insurance companies in Kenya.

CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusions

The study concludes that, technology improves workplace operations, reduces information distribution costs, makes the insurance process successful, improves workplace insurance operations, and makes insurance companies more competitive. Technology adoption should be effective because innovative devices can improve organizational performance.

This study concludes that lowering insurance company service prices increases sales, competition improves service, insurance companies should compete on customer loyalty, cost leadership increases sales, and market focus on clients improves service. Insurance companies thrive in competitive markets. Insurance competition lowers prices and increases options, creativity, and quality. Product and labour markets compete. Companies must increase pay and benefits in a tight labour market.

The study concludes that mass marketing reaches the targeted clientele, product differentiation marketing increases insurance sales, the insurance company should reach the target market, email marketing increases sales, and telemarketing increases sales. From product development and naming to promotional channel selection, the target market affects marketing strategy. They buy insurance due to demographics and behavior. Finding its best customers helps an insurance company target its market. Non-target customers are accepted but not prioritized.

The study concludes that insurance company economic conditions influenced their monetary policies; that the country’s exchange rate affects sales services; financial turmoil improves the insurance company’s policies; the insurance company improves client purchasing; that economic conditions on pricing decisions affects Kenyan insurance companies’ organizational performance; and that a State’s economy affects Kenyan insurance companies’ organizational performance. The economy’s formation can affect any business. Business success depends on economics. After that, the company’s economy matters most. Business success follows economic improvement.

5.2 Recommendation

This study found that technology adoption improves organizational performance. Since digitalization has changed organizational performance over time, insurance companies should use digital technology, especially social media and online marketing platforms. This improves insurance efficiency and customer delivery. This research also found that digitization reduces costs by eliminating the need for many employees to market a company’s product. Insurance companies should use this strategy to market their services to their target clientele.

This study found that influence of pricing decisions (technological adoption, competition, target clientele, and economic conditions) improves organizational performance. Pricing strategies should match insurance firms’ market positioning. This ensures insurance companies deliver quality products. However, insurance pricing should be revised to attract customers and generate revenue. This ensures firm profitability and organizational efficiency.

5.3 Suggestion for Further Research

This study was limited to the influence of pricing decisions on the organizational performance of insurance companies in Kenya. The study was also limited to the following independent variables technology adoption, competition, target clientele and economic conditions. Hence the study suggests the same research topic to be done but with different variables such as regulatory environment, customer satisfaction and product portfolio diversification in Kenyan Insurance companies. This will help us gain knowledge of if other pricing decisions significantly influence organizational performance of insurance companies.

REFERENCES

1. Ackah, C., & Owusu, A. (2012). Assessing the Knowledge of and Attitude Towards Insurance in Ghana. International Research Conference on Microinsurance, Twente.
2. Basant, R. & Chandra, P. (2015). Building Technological Capabilities in a Liberalizing Developing Economy. Firm Strategies and Public Policy, 11 (4-5): 399 – 421.
3. Faurie, J. (2014). A Look into the Future of the Insurance Industry. Journal of Financial Services Marketing, 8(1): 6-9.
4. Gupta, S., Ghardallou, W., Pandey, D. K., & Sahu, G. P. (2022, December). Artificial intelligence adoption in the insurance industry: Evidence using the technology–organization–environment framework. Research in International Business and Finance, 63, 101757. https://doi.org/10.1016/j.ribaf.2022.101757.
5. IRA (2017). National Survey on Enterprises Perception of Insurance in Kenya. Retrieved from http://www.ira.go.ke/
6. Kamara, J. M and Makori, M. (2017). Determinants of Uptake of Insurance Services Among Small and Medium Enterprises in Nairobi City County, Kenya. The Strategic Journal of Business & Change Management, 4(2): 1054 – 1072.
7. Kamau, G. M. (2013). Factors Contributing to Low Insurance Influence of pricing decisions in Kenya. International Journal of Social Sciences and Entrepreneurship, 1(2): 463 – 469.
8. Kiama, N. (2015). Promoting Insurance Influence of pricing decisions in Kenya. Journal of Targeting, Measurement and Analysis for Marketing. 12(1): 82 – 89.
9. Kodia, K. J.G. (2017) The Determinants of Successful Uptake of Life Assurance Products: A Literature Review. Australian Journal of Business Science Design & Literature, 10(1): 33 – 39.
10. KPMG (2014). The South African Insurance Industry Survey 2014. Retrieved from kpmg.com.
11. McKinsey, J. (2014). Global Insurance Pool0 (4^th Edition). New York: Cengage Learning.
12. Mwiti, L. (2016). How Rogue Insurance Agents Mislead Clients in Race for Life Cover Commissions. Journal of Business and Management, 2(1): 17–19.
13. Orodho, J. A. (2012). Research Methods: A Comprehensive Guide. Fictional Publishing.
14. Reavis, M. W. (2012). Insurance Concepts & Coverage Property, Liability. Life, Health and Risk Management (1^st Edition). Canada: Friesen Press.
15. Rogers, E. (2012). Diffusion of Innovations (3^rd Edition). Glencoe: Free Press.
16. Sthembiso Msomi, T., & Nzama, S. (2023, April 5). Analyzing firm-specific factors affecting the financial performance of insurance companies in South Africa. Insurance Markets and Companies, 14(1), 8–21. https://doi.org/10.21511/ins.14(1).2023.02
17. Tedlow, R (1990). New and Improved: The Story of Mass Marketing in America. Basic Books, N.Y.
18. Vnukova, N., Opeshko, N., & Mamedova, E. (2020, December 25). Identifying changes in insurance companies’ competitiveness on the travel services market. Insurance Markets and Companies, 11(1), 53–60. https://doi.org/10.21511/ins.11(1).2020.06
19. Wernerfelt, B (2014). A Resource-Based View of the Firm. Strategic Management Journal, 5(2): 171 – 180.
20. Yaari, M. (2015). Uncertain Lifetime, Life Insurance and the Theory of the Consumer. Review of Economic Studies, 32, 137-150.

TABLES

Table 4. 1: Response Rate

Source: (Field Research Data, 2023)

Table 4. 2: Technology Adoption and Organizational Performance

Source: (Field Research Data, 2023)

Table 4. 3: Competition and Organizational Performance

Source: (Field Research Data, 2023)

Table 4. 4: Target Clientele and Organizational Performance

Source: (Field Research Data, 2023)

Table 4.5: Economic Conditions and Organizational Performance

Source: (Field Research Data, 2023)