Challenges Facing Health Insurance

Published Date: 02 Nov 2017

The insurance industry was in serious trouble long before the global financial crisis. The current crisis has just helped to worsen a situation that has already been bad. The industry and especially general business has been on the path to self-destruction (IRA report 2011). The general insurance business cannot be sustainable with underwriting losses that have continued to worsen every year and over-reliance on the cushioning effect of investment income especially in an unstable environment. What is happening has a sobering effect on all players to see the essence of going back to basics in terms of underwriting as the surest way to survival (Kuria, 2009). If the industry were to collapse, it would have serious implications for the insuring public, the insurance industry and on the entire economy. Urgent action is, therefore, necessary to forestall such an eventuality.

2.2.1 Prudent Underwriting;

Poor underwriting may is attributed to insurance companies not following the clear guidelines or ways on which a person is supposed to analyse a risk in insurance Underwriting goes a long way to how insurance companies manage risk. Insurance companies which don’t assess and classify the risk correctly risk being hit by huge claims which may contribute to high losses (Mecedo, 2009)

2.2.2 Premium undercutting/ Pricing of premiums

Price undercutting means that the premium charged towards a risk is lower than the actual and recommended charge. Premium undercutting is very rampant within the insurance sector. Mr Tom Gichuhu the AKI director has been quotes saying "We will implore IRA (Insurance Regulatory Authority) to go a step further and prescribe underwriting guidelines for medical insurance," added Gichuhi. Motor insurance class was also a serial loss making class like medical until recently when the IRA announced new measures to reign in fraud and careless losses under this niche (Okulo, 2012).

Unhealthy competition within the industry may contribute to price undercutting. Premium undercutting is charging a premium to a risk which is not consumerate with the risk Stiff competition which is characterised by few products has led to premium undercutting which mean that insurance companies will have insufficient premiums to pay claims because wrong premiums have been used (IRA report 2011).

2.2.3 Health insurance regulation

The insurance business in Kenya is regulated under the insurance act Cap 487; the insurance act is very silent on administration of Private health Insurance. And it does not give clear guidelines on how to manage private health Insurance few amendments were introduces in the year 2004 following the collapse of several Health Management Organisations (HMO),s. The amendments were quickly introduces as a measure to stop the loophole because clients did not have a recourse.

According to the World Bank working paper number 193 (2010) most players in the private health insurance sector are of the opinion that current law fails to cover most of the important aspects of health insurance and therefore to create a level playing field, and that this hinders the development of innovative risk pooling mechanisms. The report continues to say that Insurance Regulatory Authority (IRA) has no particular expertise in matters of health insurance (premium determination, benefits design, control of adverse selection, alternative methods of provider payment). Matters governing health insurance are dealt with by the same examiners that deal with other casualty insurance. There is no mechanism for resolving appeals if an insured party alleges that a medical benefit was unfairly denied (IRA report 2011).

2.2.4 ICT capacity

Martin et al (1995) points out that in an increasing competitive world, IT is critical to development of more effective operational and management processes. To serve customers well, companies need to be proficient in half a dozen areas: reduce cycle time, reduce asset levels (such as inventory and people), faster development of new products, improved customer service, increasing empowerment of employees and increased knowledge sharing and learning. IT is critical resource in accomplishing all these goals. Cook (2005) contends that one of the greatest drivers of change is the range of possibilities opened by the increased use of technology, from buying products or services online to using internet to pay bills, this has revolutionized organization’s interface with the customers and this enhances the quality of service. Insurance details can be captured and underwritten electronically as well as shared between players in mitigating instances of fraud.

Laudon (2007) argues that companies with reputation for high quality service can charge a premium for their products and services. Information systems have a major contribution to make in this drive for quality. In the service industry like insurance, quality strategies are generally enabled by superior information systems. Companies achieve quality by benchmarking to set strict standards for products and services and then measuring performance against those standards. It can be used to reduce costs of stationery that could be used in physically completing forms and forwarding them to the insurance company, enhance product and service quality, improve customer service, integrate supplier and customer operations and enable organization learning. It can also be used to create new market opportunities. Companies capturing and applying information at each point of contact with customers will be better off than those that do so only at one or a few points. Information systems may be used to tighten linkages with suppliers and develop intimacy with customers. Through use of internet, the customers’ bargaining power has grown and can easily find a lowest cost provider on the web; it has also created opportunities for building brands and large customer bases that are willing to pay a premium for the brand.

There is a notable lack of skills and expertise in health management and information and communication technology (ICT) across both the private and public sector for health insurance, including regulatory bodies such as the IRA (IRA Report, 2011). Health insurance training is very briefly and superficially covered in the curriculum of professional insurance courses offered at the College of Insurance and other institutions. The courses available are the Diploma in Insurance and Advanced Diploma in Insurance (ACII). In these courses, health insurance is covered in only one paper Private Medical Insurance) (Barnes, O'Hanlon, Feeley, McKeon, Gitonga, and Decker, 2010).

2.2.5 Fraud

Fraud remains a major challenge in the insurance industry affecting the Insurance companies, policy holders and beneficiaries, financial success of the insurance company .Insurance fraud is an intentional deception committed by applicants, policyholders, claimants, service providers, agents, brokers, company employees. for financial gain may occur during the process of buying, using and underwriting insurance covers. Usually motivated by greed or financial distress

According to the (IRA report 2011) the level of insurance fraud in Kenya is high with figures showing that an estimated 25%of insurance industry income is fraudulently claimed. 30%of the motor insurance claims are fraudulent. And that 35% of all medical claims are fraudulent, with the establishment of Insurance Fraud Investigation Unit (IFIU) in 2011, as a unit under criminal investigation unit to work with IRA; the unit will go a long way to ensure that they Receive reports of suspected insurance fraud. Investigate Insurance Fraud related crimes, Coordinate investigation with other law enforcement state agencies. Prepare comprehensive investigative reports for criminal investigation and administrative action. and arrest and prosecute suspects in court

2.2.6 Claims Settlement Process

The essence of insurance is to compensate when an insured peril strikes. There is an agreement whereby one party (insurer), in return for a consideration (premium), undertakes to pay the other party (insured) a sum of money or its equivalent in kind on the happening of a specified event which is contrary to the insured’s financial interest. This settlement should be done within a reasonable time to ensure that the insured continues with their daily operations. That is to say, the insured is put where they were before the loss occurred. According to Campbell (2000), most insurance services are activated when there has been an unexpected, possibility, or unpleasant occurrence. If the occurrence never happens, there is no opportunity for a customer, broker, or agent to seek for insurers’ services. Therefore the insurer should ensure that when a disaster strikes, the service to the customer must exceed expectation as this is the only time the customer can weigh the services being offered. He further stated that insurance is based on trust and that most successful businesses require commitment and integrity from both sides. The way an insurer handles a claim will determine the continued loyalty of their customers (Canner, 2007).

Munguti (2006) explains that claims service is often regarded as the "fulfilment of a promise", the acid test, the evidence of the product the customer bought. Through an efficient claims settlement service, the insurer creates confidence among the insuring public and other service providers. According to the insurance Act (revised 2007) every insurer shall in respect of all claims arising from policies of insurance issued by it, pay the claims within ninety days of admission of liability and settlement of the amount due and establishment of the identity of the claimant. Extension of the period within which to settle the claim can only be done by the commissioner of insurance/chief executive of Insurance Regulatory Authority

2.2.7 Hospital charges

Escalating hospital bills and high incidence of fraud are eroding the margins of medical insurers, making them shy away big schemes being rolled out by the government and companies for workers. Firms that underwrite medical insurance said they have been forced to turn down mega deals because medical claims consistently exceed the segment’s premiums. (Business daily 2012) "The reality about medical insurance is that we cannot manage costs since the firms have no control of what the hospitals charge and where clients want to get treatment," said Lydia Kibaara, the general manager in charge of medical insurance at Jubilee Insurance said. A growing number of clients, she added, are today visiting hospitals for preventable ailments, piling to their costs.

2.3 Effects of the challenges on business performance

The effect of business performance of any insurance company is depended on the strategies which the insurance will apply

2.3.1 Low Profitability

Increased fraud shrinks profitability of insurance companies. It has been estimated that fraudulent claims cost the property and casualty insurance industry billions of dollars annually around the world (StaSoft, 2011). According to the National Insurance Crime Bureau (NICB) (2012), fraud is the second most costly white-collar crime in America behind tax evasion. The residual effects of fraudulent insurance claims are felt not only by individuals and companies who see not only rising premiums, but also insurance companies continually experiencing greater pressure to reduce overall claims expenses in order to increase their profitability (Kaur and Kapoor, 2007). The increasingly high prevalence of fraud is one of the main contributing factors driving up the combined ratios of insurance companies in most countries today. Fraud rates used to be in the low single digits and were viewed as "the cost of doing business" but the escalating levels into the double digits, insurance companies are looking for new approaches and technologies to reduce this costly expenditure (StaSoft, 2011).

Most insurance companies have some put in place some mechanisms to help fight against this escalating costs of frauds. Some have use mechanism like rules-based manual process or a rules engine application that attempts to identify fraudulent claims. However, the challenges facing the current mechanisms include the fact that the current systems may catch some of the fraudulent claims, but typically not enough of them hence still exposing the insurance company (Kaur and Kapoor, 2007). These systems can also catch them early enough in the process to be able to take actionable steps to reduce or recover the losses. The subtle nuances of fraud have become increasingly complex and well-executed fraud schemes are constantly and rapidly evolving (StaSoft, 2011). Improving relationships with partners and managing fraud are seen as the essential measures aimed at reducing claims costs among insurance companies. However, the improving relationships may prove less effective owing to the high bargaining power of intermediaries while managing frauds appears a little contradictory to companies’ stated intentions not to focus as much on claims risks.

2.3.2 Loss Ratio

The loss ratio.is the ratio of total losses incurred which includes paid and reserved in claims plus adjustment expenses divided by the total premiums earned (Rudolf, 2001). Claims represent an insurance company’s biggest expense, with claims pay-outs and loss-adjustment expenses accounting for up to 80 per cent of an insurance company’s revenue. One way to reduce these expenses is through claims recovery. Unfortunately, opportunities for claims recovery, such as through salvage and subrogation, are often obscured by the sheer volume of claims data available. In addition, many recovery opportunities are missed simply because the indicator for a possible recovery is hidden in the claims narrative (Kaur and Kapoor, 2007).

The management of subrogation rules and regulations has become quite complex over the years. This complexity, together with overworked and understaffed teams, has led to a steady increase in time-consuming investigations and ineffective recovery processes (Rudolf, 2001). Unfortunately, this has resulted in missed opportunities for recovery that could have considerable implications for an insurer’s overall profitability. In addition, loss expenses have risen, as thorough subrogation investigations can be both lengthy and costly. And poor subrogation rates result in higher premiums that can reduce new business sales and lower your retention rates (Kaur and Kapoor, 2007).

2.3.3 Operations expenses

Healthcare is important to everyone. Receiving quality care when needed to ensure a long and healthy life is a basic tenet of life. Healthcare insurance providers struggle to deliver quality care at a reasonable price. Insurers struggle to provide a fair and affordable funding solution to ensure they core their exposures (Oracle, 2011). Healthcare Insurers are operating in a volatile and fluid environment as governments enact new legislation that could alter the way benefits are funded and delivered in different countries. The levels of sophistication on human life have changed the operational expenses for insurance companies hence affecting their financial performance (Oracle, 2011).

Healthcare Insurers are continuously challenged to determine their organizational effectiveness. Despite modern processes and systems, indirect costs are increasing. To reduce these costs many organizations are introducing shared service centres’, centralizing certain operations either in the front or back-office. The economies of scale outweigh the overhead of such centralized operations, but the overhead and other types of indirect costs still need to be allocated. Profitability and cost management solutions help ensure the business relevance of shared service centres’. Profitability and cost management solutions can also help establish whether these shared service centres’ should be placed within the organization or should be outsourced.

2.3.4 Claims Paid

Majority of businesses prefer to rely on the simpler methods of managing claims costs that do not require significant investment in operational infrastructure, while only a handful companies intend to invest in underwriting and claims management systems (Oracle, 2011).

2.4 Theoretical review

Kombo and Tromp (2006) define a theory as a reasoned statement or group of statements which are supported by evidence meant to explain phenomena. This study will attempt to use two theories discussed below to explain the relationship among phenomena in health Insurance. The theories are intended to guide research work and interpretation of the findings.

2.4.1 Probability Theory

Probability theory is concerned with measuring the likelihood that something will happen and making predictions on the basis of likelihood. The theory deals with random events and is based on the premises that although some events appear to be matter of chance they actually occur with regularity over a large number of trials. According to this theory the likelihood of an event is assigned a numerical value between 0 and 1 and those that are impossible assigned value of 0 and those that are inevitable assigned a value of 1Events that may or may not happen are assigned value between o and I with higher value assigned to those estimated to have a greater likelihood or probability of occurring. Vaughan and Vaughan ( 2008) The theory argues that by combining a sufficiently number of homogenous exposure units, the insurer is able to make predictions for the group as a whole by using the theory of probability. Under health Insurance if the insurer can predict future losses with absolute precision it would not face any possibility of loss, it would collect each individual shares of the total losses and expenses as they occur. If predictions are not accurate the premiums that the insurer has charged may be inadequate. To manage expenses in operation and use these funds (Vaughan and Vaughan. 2008)

2.4.2 Expected utility (EU) theory

Under expected utility (EU) theory, insurance demand is a choice between an uncertain loss that occurs with a probability when uninsured and a certain loss like paying a premium (Manning and Marquis, 1996). EU theory assumes that people are risk averse and make choices between taking a risk that has different implications on wealth. At the time of insurance choice, consumers are uncertain whether they will be ill or not, and of the related financial consequences. Insurance reduces this uncertainty. Through insurance, they can level out their income over two different states, ill/not ill, which makes the aggregate outcome relatively certain. This certainty allows the insured to reach a higher utility in case of illness than those without insurance. Accordingly, the insurance demand reflects individuals’ risk aversion and demand for certainty, implying that the more risk averse individuals are, the more insurance coverage they will buy (Begget et al, 2000).

This theory is silent about the level of consumers ‘income and its impact on the insurance choice. Using longitudinal data from the UK, Proper (1993) examined the demand for private health insurance that covers care in a private sector that exists alongside a public health care system which is free to patients. She finds that private health insurance enrolment can be explained by demographics, income and the quality of care in the public and private sector. Based on data from the US, Phelps (1973) finds that the insurance demand correlates with income, and is positively related with other variables that tend to be linked with income, such as education level, urban areas and white households. Using time-series data, he identifies a positive relationship between insurance demand, and user fee levels, and with higher mean level of illness; and a negative association between insurance demand and premium level (Phelps, 1973). Findings from these studies are consistent with consumer theory, implying that insurance is a normal good. EU theory has been criticized. Laboratory studies have shown that the model’s prediction of choice behaviour is poor, and additional factors need to be included such as the societal context about prudent behaviour or regret considerations (Schoemaker, 1982). Individuals’ insurance decisions may not only be affected by risk aversion but also by the access motive of insurance.

The access motive reflects the gains from the availability of medical care that would otherwise be unaffordable for the poor. Gaining higher access to care when insured may cause the poor to insure if they are unable to obtain needed health care when uninsured. Without insurance, the poor would not have enough money and time to save for an expensive health care procedure, and lending institutions may be reluctant to lend money when the ability of the patient is limited to repay these loans (Nyman, 1999). Despite these critiques, EU theory is most commonly used in models of decision-making under risk (Marquis and Holmer, 1996). However, other theories have emerged that aim to account for these weaknesses.

2.4.3 State-dependent utility theory

State-dependent utility theory suggests that consumers’ utility level and tastes are influenced by their state, such as their health or socio-economic status. Accordingly, people may have different degrees of risk aversion, which could influence their insurance decision and the magnitude of their expected insurance pay-off. Most people insure when they are healthy. A healthy person might optimistically expect to remain healthy in the near future, which has implications on the insurance choice. The resulting insurance coverage may be below full loss coverage, if the anticipated insurance pay-off is below the real loss in case of illness. Hence, the anticipated need for medical care given the current state, and the magnitude of the related insurance pay-off in case of sickness will affect individuals’ insurance demand (Phelps, 1973).

Manning and Marquis (1996) estimate insurance demand by adding the value of medical care to the value of risk avoided in the purchaser’s utility function. At the end of the RAND study, participants were asked to select from hypothetical insurance plans with different co-insurance rates. Results suggest that enrolment in a hypothetical insurance is not affected by household income and premium levels but rather by the expected pay-off individuals will receive when sick (Manning and Marquis, 1996). The poor may expect less payoff when sick, which could influence their insurance decision. They may anticipate purchasing single tablets of medicine from a market vendor for self-treatment, not covered by insurance. Also, the richer may not enrol in CBHI because the magnitude of the expected pay-off from CBHI is not ‘good enough’ for them. They might prefer to pay user fees or purchase private insurance coverage allowing them to use more expensive hospital care.

Figure 1. Conceptual framework

Independent Variables

Challenges

Prudent Underwriting

Price Undercutting

Health Regulation

ICT capacity

Claim settlement process

Fraud

High Hospital charges

Government policy

Association of Kenya Insurers

Insurance Regulatory Authority

Business Performance

Low Profitability

High Loss Ratio

Claims Paid

Operations expenses

Dependent variable

Intervening/Moderating variables

Fig 1.1: challenges facing insurance companies in the provision of private medical insurance

CHAPTER THREE

RESEARCH METHODOLOGY

3.0 Introduction

Research methodology is an approach and a set of supporting methods and guidelines to be used as a framework for doing design research (Blessing & Chakrabarti 2009). Rusell (2000) explains that research methodology applies to ways the researcher comes close to problems and seeks answers to those problems. The author further argues that the success in the research depends on whether the researcher specifies what to find out and the best way to do it. According to Mugenda and Mugenda (2003), research methodology includes research design, population and sample, data collection procedures, data analysis procedures and measurement of variables.

This chapter outlines the methodology that will be used in the research study. It describes the type of research design to be used, the population of the research study, target population, sample size, sampling design, and finally pre-testing of the research study. It further described the data collection instruments to be used, procedures to be employed in collecting the research data, data analysis and presentation of the research findings.

3.1 Research Design

A research design according to Kumar (2005) is a plan, structure and strategy of investigation so conceived as to obtain answers to research questions or problems. Chandran (2004) describes research design as an understanding of conditions for collection and analysis of data in a way that combines their relationships with the research to the economy of procedures. Kombo and Tromp (2006) suggest that research design deals with the detailing of procedures that are adopted to carry out a study. A research design has two functions according to Kumar (2005). Through the research design one can conceptualize the research design and ensure that the procedures are adequate to obtain valid, objective and accurate answers to the research questions. Patton (2002) recommends that a combination of both qualitative and quantitative method be employed to enrich the research.

There are four types of research design. These are exploratory, descriptive, observation and the experimental research designs (Chandran, 2004). According to Chandran (2004), exploratory design addresses the need certain inquiries focus on questions that require answers in order to understand people, events and situations. It is also referred to as formulative design as it provides new insights, familiarity and discovery of new ideas to the researcher especially in situations that require deeper investigations.

According to Luck and Pocock (2000), an experimental research design may be defined as the actual trail of a proposed course of action or other hypotheses under consideration. Chandran (2004) on the other hand states that this design is appropriate to research situations that involve two or more concepts that are related and there is a need to study the relationship between them. The method requires formulation of a hypothesis and testing of the same and often involves an experimental and control group. This method is complex, time consuming to implement and can be very costly (Chandran, 2004).

Chandran (2004) articulates that descriptive design is appropriate to describe and portray characteristics of an event, situation, a group of people, community or a population. It leads to a profile development of a situation or a community of people by acquiring complete and possibly accurate information through interaction between the investigator and informants via questionnaires and interviews.

The survey which falls under descriptive research design is also referred to as the ex-post facto method and is another common research design. According to Kerlinger (2005), this method does not offer the researcher control over the data collected in terms of manipulation of the variables of the study. The researcher reports the data as collected and this ensures that there is no bias introduced in the data collected.

In observation research design the researcher does not verbally communicate or interact with the subject of study but simply observes them. The method is cheap, takes a relatively short time to implement and can cover a large population.

In order to meet the required goal of the research study, the researcher will apply the descriptive research design. Descriptive designs are used in preliminary and exploratory studies Bougie (2009) to allow the researcher to gather information, summarize, present and interpret it for the purpose of classification (Orodho, 2002). This design has been chosen upon because the researcher aims to build a profile about effects of challenges facing insurance companies in the provision of private medical insurance and their effect on business performance in Kenya.

Lee (2007) observes that descriptive survey research is intended to produce statistical information which is useful in the information researched. The descriptive research design is preferred in this study because it allows for analysis of different variables at the same time and thus will enable the researcher identify effects of challenges facing insurance companies in the provision of private health insurance and their effect on business performance in Kenya. The use of the descriptive research design will lead to a better understanding of the phenomenon being studied and help to view issues and problems from the perspective of those being studied.

This research is intended to assess effects of challenges facing insurance companies in the provision of Private Health insurance and their effect on business performance in Kenya. Kombo and Tromp (2006) confirmed that, the major purpose of a descriptive research is description of the state of affairs as it exists. Kerlinger (2005) points out that, descriptive studies are not only restricted to fact findings, but may often result in the formulation of important principles of knowledge and solution to significant problems.

3.2 Population

Population is the aggregate of all that conforms to a given specification (Mugenda & Mugenda 2003). In other words, population refers to an entire group of individuals, events or objects having a common observable characteristic. The population of the research study will be insurance companies providing Private health insurance services in Kenya. The number of licensed insurance companies providing medical services stood at 20 as at 31st December, 2012

3.3 Target population

Mugenda and Mugenda (2003) define target population as the total population the researcher would preferably generalize the results. It is impractical to select a representative sample from the target population because it is difficult to identify individual members. The target population for this study will be employees from the 20 identified insurance companies.

3.4 Sample size

At times dealing with all the numbers even of the smaller accessible population would involve a tremendous amount of time and resources. It’s therefore advisable for the researcher to further select a given number of members from the accessible population (Kothari 2004). According to Mugenda and Mugenda (2003), 10% of accessible population is enough to conduct descriptive study. For the purpose of this research, the researcher will use all the 20 organizations providing health insurance. The study will target the health underwriting and claims departments care management and marketing department.. From the records of the concerned insurance companies, there were 724 employees in these departments as at 31st December, 2012.

To reduce duplication of data, the study will use purposive sampling technique to select four respondents from each organization. These shall include the underwriting manager, claims manager, the care management and marketing manager. The researcher picked on this method because of these selected respondents’ responsibility in the whole business of health insurance. These will bring the sample size to 72 respondents.

3.4.2 Sample and Sampling Design

According to Breakwell (2006), sampling is the process of selecting a number of individuals for a study in such a way that the individual represents a larger group from which they are selected. Sampling procedures provide a valid alternative to a census where it is impossible to survey the whole population.

According to Breakwell (2006), there are two types of sampling design; probability and non-probability sampling. Probability sampling is based on the concept of random selection, which is a procedure that assures that each population is given an equal chance of selection. The non-probability sampling is non-random and subjective in that each member does not have an equal chance of being selected.

According to Mugenda and Mugenda (2003), with probability sampling, every member of the population has an equal chance of being selected. The advantage of this method of sampling include the selection of sample members being unbiased and the general acceptance that the method is fair.

The random sample may be the most important type of sample. A random sample allows a known probability that each elementary unit will be chosen. For this reason it is sometimes known as probability sampling. Examples of random sampling are stratified sampling, simple random sampling, snowball sampling, systematic sampling, convenience sampling among others (Kumar, 2005). A judgment sample on the other hand is obtained according to the discretion of someone who is familiar with the relevant characteristics of the population (Kumar, 2005).

Convenience sampling is a non-probability sampling technique where subjects are selected because of their convenient accessibility and proximity to the researcher. The subjects are selected just because they are easier to recruit for the study. Convenience sampling can be applied in part or as a whole. The advantages of convenience sampling are that it saves time, money and effort (Kumar, 2005). The disadvantage of using convenience sampling is sampling bias and that the sample is not representative of the entire population. For the purpose of this purposive sampling method will be employed since the researcher understands they key personnel to provide the needed information for this study.

3.5 Types of data

According to Mugenda and Mugenda (2003) there are two types of data namely, primary and secondary data. The two types of data will be essential for the purpose of this research study and will help in the process of addressing the research problem under study. Primary data refers to the information a researcher obtains from the field, while secondary data refers to the information a researcher obtains from research articles, books, and journals (Mugenda & Mugenda, 2003). Primary data will be collected using a questionnaire which will contain both open and closed ended questions. Secondary data on the other hand will be collected from the journal, annual publications at the Association of Kenya Insurers (AKI) and Insurance regulatory Authority (IRA).

According to Kombo and Tromp (2006), research is categorized into two broad categories, quantitative and qualitative research. The quantitative approach is seen as an objective that is relating to phenomenon or conditions independent of individual thought and perceptible to all observers and relies heavily on statistics and figures, while qualitative approach is seen as subjective, relating to experience or knowledge as conditioned by personal mental characteristics or states and preferring language and description (Lee 2007). Qualitative approach involves the examination of perceptions in order to gain an understanding of social and human activities. This research study will focus on both quantitative and qualitative data

3.6 Data Collection Instruments

According to Mugenda and Mugenda (2003), in social science research, the most commonly used instruments of data collection are questionnaires, interview schedules, observational forms, standardized test and content analysis.

The most common research instrument is the questionnaire. However, there are disadvantages in using this method since the researcher cannot probe further questions to get further information and cannot control respondents that fill the questionnaire and the response rates (Breakwell, 2006). Other disadvantages are that it may confuse respondents to the nature of information required, it may discourage the respondents to the extent of discarding the questionnaire and it may leave important information required in the research study (Mugenda & Mugenda 2003). However, the advantages of the questionnaire are that it saves time and it is economical in terms of money, it is convenient in that respondents can respond based on the contents and it is easier to administer.

The interview is another form of the most frequently used methods of collecting qualitative data. Structured interview and in-depth interviews are the two types of interviews used in research evaluation by the researchers. In structured interviews emphasis is on obtaining answers to carefully phrased questions while in in-depth interviews, the interviewers seek to encourage free and open responses and there may be a trade-off between comprehensive coverage of topics and in-depth exploration of a more limited set of questions. However, this method requires a substantial amount of pre-planning, the quality and usefulness of the information is highly dependent upon the quality of the questions asked and there is a possibility that the presence of the researcher may influence the way a respondent answers various questions hence introducing biasness in the responses.

According to Patton (2002) an in-depth interview is a dialogue between a skilled interviewer and an interviewee. Its goal is to elicit rich, detailed material that can be used in analysis (such interviews are best conducted face to face, although in some situations telephone interviewing can be successful). Patton (2002) asserts that the quality of the information obtained is largely dependent on the interviewer’s skills and personality. In-depth interviews also encourage capturing of respondents’ perceptions in their own words, a very desirable strategy in qualitative data collection. This allows the evaluator to present the meaningfulness of the experience from the respondent’s perspective. In-depth interviews are conducted with individuals or with a small group.

However, in-depth interview method has disadvantages. It is expensive and time consuming and needs well qualified and highly trained interviewers. The interviewee may distort information through recall error, selective perceptions and desires to please the interviewer. Flexibilities may result in inconsistencies across interviews.

Standardized tests are tests administered and scored under a consistent set of procedures. Uniform conditions of administration are necessary to make it possible to compare results across individuals being studied. The advantages of this data collection method is that it can provide measures of many characteristics of people, allows comparability of common measures across research populations, many tests can be administered to groups which saves time. The disadvantages on the other hand include reactive effects such as social desirability can occur, the test may not be appropriate for a local or unique population, open ended questions and probing is not available (Mugenda and Mugenda 2003).

According to Lee (2007) observation can take three forms; unobtrusive where no is aware that you are observing, participant where the researcher actually takes part and participates in the research activity and obtrusive where the individuals under study are aware that the researcher is actually observing them. The advantages of using observation as a method of data collection is that it collects data on actual versus self-reported behavior or perceptions and it is also real time. The disadvantage on the other hand include observer bias, interpretation and coding challenges, sampling problems, labour intensive and low response rate.

Content analysis is a summarizing, quantitative analysis of messages that relies on the scientific method (including attention to objectivity, inter-subjectivity, a priori design, reliability, validity, generalisability, replicability, and hypothesis testing) and is not limited as to the types of variables that may be measured or the context in which the messages are created or presented (Lee, 2007).

3.6.1 Data Collection Procedure

The research shall be done in such a way that the researcher prepares a questionnaire to collect data from the respondents. The researcher will seek permission from the management of the sampled health insurance providers and afterwards get a letter from Daystar University postgraduate department as a confirmation of the purpose of the research. The researcher will then distribute the questionnaires and a brief introduction for the purpose of the research to the insurance stakeholder’s staff and collected the questionnaires once the respondents have finished filling them in.

3.6.2 Pre-testing

According to Orodho (2002), for a questionnaire to provide useful results, the questions must be both valid and reliable. Reliability measures the relevance of the questions included in the questionnaire. Validity refers to ability of the instrument to test what it is supposed to. Be tested. Pre-testing enables the researcher to receive important feedback on how questions are to be recorded or restructured. The questionnaire needs to be pre-tested under field conditions before it is ready for the field (Lewin, 2005). It is very important for the researcher to pretest research instruments to enhance clarity of the instruments to be used. The purpose of enhancing clarity is to ensure collection of accurate information and to correct any deficiencies revealed during pre-testing exercise (Mugenda & Mugenda 1999). The researcher will pre-tested the questionnaire on employees in one Private Health insurance companies who shall however not form part of the main data collection sample. The three shall come from one insurance company.

3.7 Ethical Considerations

Breakwell (2006) states that ethics are norms governing human conducts which have a significant impact on human welfare. It involves making a judgment about right and wrong behavior. Bryman (2007) states that it is the responsibility of the researcher to carefully assess the possibility of harm to research participants. The possibility of harm should be minimized. The author further states that the researcher must take all reasonable precautions to ensure that the respondents are in no way directly harmed or adversely affected as a result of their participation in a research project. The researcher will ensure that the questionnaires do not require the respondent’s names or details that may reveal their identity. Confidentiality will therefore be upheld for all respondents. The names of the respondents shall not be disclosed.

3.8 Data Analysis Procedure

Data analysis is the process of bringing order, structure and meaning to the mass of information collected. It involves examining what has been collected and making deductions and inferences (Kombo and Tromp, 2006). The studies will employee descriptive statistics to analyze the data obtained. Descriptive statistics involve the collection, organization and analysis of all data relating to some population or sample under study.

For quantitative data analysis and processing, Kumar (2005) prescribes the following steps: data editing to ensure that the data is free from inconsistencies and any incompleteness. After cleaning, the data is coded. Coding of data follows the following steps: developing a code book, pre-testing code book, coding the data and verifying the coded data. Once the data was coded, selection of a few instruments and recording the responses to identify any discrepancies in coding and finally content analysis which was the process used for analyzing qualitative data.

According to Breakwell (2006) descriptive research design is commonly represented by use of frequency charts, bar graphs, and pie charts to tabulate the information gathered appropriately. Statistical Package for Social Sciences (SPSS) will be used to analyze the data. This package is known for its efficiency and ability to handle large amounts of data. Given its wide spectrum for statistical procedures purposefully designed for social science, it developed appropriate holding frame to come up with reliable results according to the responses in the questionnaires.

3.9 Conclusion

This chapter has outlined the methodology that will be used in the research. The chapter describes the type of research design that will be used, the target population, sample size, sampling design and pre-testing, research analysis and presentation of the research findings. It further describes the procedures that will be used in collecting the research data as well as the data collection instruments. It has also spelt out an indication of how the collected data will be analyzed as well as the output.