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Table of Contents

Improvement in Customer Service in Healthcare - Part 7


6. Methodology

This research paper is concerned with the improvements in the customer relationship management strategy of healthcare organizations. To approximate the effect of customer relationship management strategies that are implemented by these organizations, a service quality assessment is made using the SERVQUAL model. The study is conducted in healthcare organizations in (name of place here). The information collected came from healthcare organizations found within a (indicate here) kilometer radius from the central town of (name the area here).

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The data collected includes information that is collected from healthcare customers of these healthcare organizations. The method of analysis is through principal component analysis. The outcome is analysed using the analytical hierarchical process. The conceptual framework for the data analysis is shown in the succeeding illustration.

Figure 5 Conceptual Framework for CRM Strategies in Healthcare Organizations

The study utilized a questionnaire for acquiring data. The dimensions of SERVQUAL that are used are five. These five dimensions or factors are Tangibles, Reliability, Responsiveness, Assurance, and Empathy. These factors are further divided into 10 statements that are meant to determine the service quality of healthcare institutions that are under study. The five dimensions are in line with the quality dimensions that were mentioned in the study conducted by Gronroos in 1982. These dimensions directly measure service quality and are very relevant in the case of service quality measurements for healthcare organizations.

The SERVQUAL model states that the 10 statements are divided into two distinct parts. The first of the two parts is developed to measure customer expectations. A second part of the SERVQUAL model is the determination of the perception of the customers. In addition, there is a portion wherein demographic information is collected. The demographic part of the SERVQUAL approach provides information about the respondents. The information that is provided includes age, gender, educational achievement, type of employment, years employed, years of patronage of the healthcare institution, modes of payment, annual personal revenues, annual personal expenditures, frequency of healthcare service purchase, among others. The establishment of demographic data enables us to better understand the type of respondents and how these types of respondents perceive the services that they avail from healthcare institutions.

Another set of respondents were identified for the study. These included experts from the healthcare (medical) field, which include physicians, nurses, physical therapists, pharmacists, and other professional personnel that are front line participants in the provision of healthcare and healthcare products. These respondents were also asked to provide demographic information including age, gender, educational achievement, type of employment, years employed, years of patronage of the healthcare institution, modes of payment, annual personal revenues, annual personal expenditures, years of healthcare provision practice, among others. Similar to the first set of respondents, the purpose of acquiring demographic data is to establish a better understanding of the type of healthcare professional respondents and how these types of respondents perceive the services that they dispense in their respective healthcare institutions.

A questionnaire was developed to acquire these segments of information. The questionnaire was developed with the supervisor and tested for validity. After the test of validity and usability, the questionnaire was found to be sufficient. Initial test respondents acknowledge the usability of the questionnaire by pointing the applicability of the statements used. The SERVQUAL model was found to be appropriate for this study because the structure of the SERVQUAL model has led to the development of research questions that determine both consumer perception of service and quality in hospitals and other healthcare institutions. However, because of limitations on resources and time, a convenience sampling technique was applied to acquire the number of respondents necessary for determining statistically significant results.

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The study included the dispersion of (include number of respondents here) in a period of five working days. As much as possible, the respondents were asked to answer the questionnaires immediately. However, some of the respondents were not able to answer immediately due to unspecified reasons. The questionnaires sent to the experts from the medical field (i.e. front line healthcare professionals) took considerable time as well, since these professionals had very busy schedules. At the end of the data collection period, a total number of (include number here) respondents were accessed. A total number of (include number here) questionnaires were dispensed and from these, a total number of (include number here) valid responses were acquired.

The SERVQUAL model is preferred for this study because of its ability to evaluate the difference between the consumers’ expectations and the consumers’ perception regarding the quality of service received from healthcare institutions. In this study, we relate the quality of service delivered by healthcare institutions as a direct indication of the success of the customer relationship management strategy that the healthcare institution is implementing.

The SERVQUAL model uses a 7-point scale that shows how much the respondents agree with or disagrees to the statement. The extremes are the numerals 1 for strongly disagree and numerals 7 for strongly agree. This scale is similar in construction to the level of perception and expectation of customers. A higher number corresponds to a higher level of perception and expectations. Service quality is determined by taking the arithmetic difference between the scores for quality perception and quality expectations. In formula form, that is:

Service Quality = Quality Perception - Quality Expectation

Service quality scores measure the difference between what the customers perceive and what the customers expect. The more positive the score is, the higher the level of quality of service that the healthcare institution has provided the customer. The lower the service quality score is, the lower the quality of service provided by the same institution. In the SERVQUAL model, both elements of customer satisfaction and service quality are represented as functions related to customer perception and customer expectations.

The base case is when service quality is at the satisfactory levels only. At this level, the perception of the customer about the quality of the service and the expectation of the same customer about the quality of the same service is the same. We are interested in cases where in the perception and expectations of the consumer regarding the healthcare service is not the same. This follows the disconfirmation paradigm. The disconfirmation paradigm follows the principles of discrepancy theories. The principle of the disconfirmation paradigm says that the judgements made by consumers regarding their levels of satisfaction or dissatisfaction are based on the difference between the levels of service quality perceived and expected. A positive disconfirmation is an indication that the consumer’s level of satisfaction has increased. A negative disconfirmation says the reverse, that is, the level of satisfaction of the consumer has decreased. The disconfirmation paradigm is the principle framework that was used to develop the questionnaire by which data for this research was acquired.

The questionnaire was distributed to customers of the various healthcare organizations within the (name of place here) for the purpose of acquiring information leading to the level of customer satisfaction and quality of the services rendered in the healthcare institution that they are attending. The questionnaire included questions that lead to the collection of data pertaining to customer loyalty, service improvements, customer relations, and use of information technology. This is the first part of the task of data gathering, with the respondents chosen at random. A total number of (indicate how many there were) respondents were provided with questionnaires. A total number of (indicate how many there were) questionnaires were returned. A total number of (indicate how many there were) of the total number of questionnaires distributed were valid for analysis. This number represents about 90% of the total number or respondents.

Another set of questions were posted to healthcare professionals and categorized as expert answers. This set of questions completes the data gathering tasks because this provides an insight into the users of the information collected from healthcare customers. These experts also deploy the customer relationship management strategies of the healthcare organization and are the immediate recipients of feedbacks from their clients. . A total number of (indicate how many there were) respondents were provided with questionnaires. A total number of (indicate how many there were) questionnaires were returned. A total number of (indicate how many there were) of the total number of questionnaires distributed were valid for analysis. This number represents about 90% of the total number or respondents.

After the data collection task was completed, a principal component analysis was conducted and applied to the variables that were selected based on the SERVQUAL framework explained in the preceding section of this report. The variables that are determined are based on their contribution to the overall efficiency of the final customer relationship management framework.

After the principal component analysis identifies the critical elements, the customer relationship management framework will be identified per healthcare institution based on the respondents and experts responses to the distributed questionnaires. This step is undertaken because principal component analysis will enable the categorization of the variables. When the variables are categorized and the volume of data is reduced to a manageable level, a better strategy can be developed to ensure that the healthcare institution applies a workable customer relationship management strategy.

The expert response questionnaire was designed to enable the researcher to find the factors that are critical in managing customer relationship management strategies. This tool was conceived to expose the relevant factors of effective customer relationship management strategies from a set of multi-behavioral factors, into simplified single factors. Single factors will approximate other factors that behave similar to it, making the analysis direct to the point and more accurate.

Lastly, the quality of the services offered by the healthcare institution was measured using principal component analysis. The data for this step was gathered using the same questionnaire handed out to random patients patronizing the said healthcare institution.

The statistical tool called SPSS was used for data analysis. SPSS was selected because the study involved the management and analysis of quantitative data. Quantitative data is analyzed using SPSS, a statistical tool that can develop descriptive and inferential results. There are other statistical software packages that are available in the public domain for use in these types of researches. However, the researchers believe that the use of SPSS possess greater advantages in the form of user-friendliness and provides output that is easily readable.

SPSS enabled the researchers to develop descriptive statistics of the data collected. Descriptive statistics include the determination of the mean, the standard deviation of the data, the level of skewness and kurtosis. The mean is the average of all the quantitative values that were acquired. The mean represents the general distribution of the discrete observations of the data set. The data collected from the questionnaires indicate that the distribution of the data set follows a general bell-shape, which is an indication of normal distribution.

Standard deviation is the measurement of the average amount of variability in the data set. The variability is measured between the values collected and the computed mean. The skewness of the data is a measure of the asymmetrical probability distribution of the data set. The asymmetrical probability may be to the right or to the left. The asymmetrical value can also be zero, which means that there is no skewness observed in the data set. Distribution values can be heavily concentrated around the calculated mean. When this happens, the distribution will have a very high peak. When the values have high standard deviation, the values are dispersed and the distribution has a low peak. When this occurs, kurtosis happens. A normal distribution is calculated to have a kurtosis equal to 3.0.

In addition, the research sought to verify if some SERVQUAL variables are related using factor analysis. Factor analysis requires the regrouping of similar attributes into one single dimension. The single dimension that is used for grouping similar attributes should be consistent with the SERVQUAL model to be valid for use. If the single dimension groupings are found to be consistent, then it will be used as an appropriate variable in determining the level of service quality in healthcare institutions.

A simple regression methodology was employed to determine the statistical significance of the variables under study. These variables were identified as those that affect both levels of customer satisfaction and service quality in the healthcare organizations under study. Afterwards, an analytical hierarchal process was used to determine the essential factors that affect the two constructs. The results of the analyses are shown in the succeeding section of this research report.

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The factors were extracted with at a degree of 0.5. At this level of extraction, all the principal components were analyzed under their normal curves of distribution. The results indicate that of the five components, about 29% of the total variances were represented in the first component. About 17% of the total variances were represented in the second component. The fifth component represented approximately 76% of the total cumulative variances. This validates the significance of the five components of the study that was identified in the preceding section of the report.

Lastly, information acquired from the respondents of this study was treated with utmost confidentiality. No information was modified. The data collected was analyzed using widely accepted statistical techniques and procedures.

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