DATA TABULATION AND INTERPRETATION4.0 Introduction

This chapter focuses on the presentation of the field outcomes into frequency tables, pie charts and graphs, analysis of the presented data and interpretation of the finding in line of theories by previous scholars. Field findings on structured questionnaires were compiled, inputted and analysed with the aid of SPSS 20 computer software. The frequency tables, pie charts bar graphs were generated with the use of excel and SPSS 20software.

4.1 Response rate

Response rate is the percentage of respondents who responded to a survey that was administered to them (Laiho and Martin, 2015). Folwer (2016) asserts that relatively low response rate shows little relationship to the original target population and its unlikely that results from such sample will provide any credible statistics about the characteristics of the population. 20% and below response rate is too low and 80% is a de facto standard (Johnson and Owens, 2016). The response rate after administering 211 structured questionnaires was 74% as 157 questionnaires were returned and correctly completed. This was because the researcher visited employees and companies through appointments. The researcher also visited companies and employees on their free time which led to a good response rate of 74%. Table 4.1 shows a detailed response rate base.

### Table 4.1 response rate

Distributed to Number of questionnaires distributed Number of questionnaires returned Percentage response rate (%)

## Employees 21 21 100

## Companies 190 136 71

### Overall response rate 211 157 74

## Source: Primary data

From the tabulated data, employees had a response rate of 100% because they were readily available at Probe Market Research and during the time of research there was little to no absenteeism of employees. The companies had a response rate of 71% and a nonresponse rate of 29% due to work pressures. Johnson and Owen (2016) concludes that a response rate greater than 20% ensure credibility in the sample results hence meaning that there was credibility in the research findings.

4.2.1 Age distribution4.2.1 Pie chart: Age of respondents

## lefttop

### Source: SPSS 20 output

Respondents in the age range 18-30years constituted the highest percentage of 40% with a modal frequency of 63 and the lowest respondents were over 60 years. From the pie chart it can be concluded that the Probe Market is more popular among employees aged 30 years and below. There are relatively less employees of Probe market aged 60 years and above as the employees in that age range constitute 5% of the respondents.

4.2.2 Gender distribution for corporate respondents

## Gender

Frequency Percent Valid Percent Cumulative Percent

### Valid Males 93 68.4 68.4 68.4

### Females 43 31.6 31.6 100.0

Total 136 100.0 100.0 Frequency table 4.2 depicts that males were relatively the most respondents in sample constituting roughly 68.4% and female respondents were constituting 31.6%. It can be concluded that Probe markets corporate customers consist more of males than females.

### Gender distribution for employees

## Gender

Frequency Percent Valid Percent Cumulative Percent

### Valid Males 16 76.2 76.2 76.2

### Females 5 23.8 23.8 100.0

Total 21 100.0 100.0 Frequency table above depicts that males were relatively the most respondents in sample constituting roughly 76.2% for the questionnaires distributed to employees and female respondents were constituting 23.8%. It can be concluded that Probe market Research is mostly filled with males than females. The gender parity is most likely cascading from the fact that males perform more than females as far as research is concerned.

4.2.3 Period that employees served Probe Market Research

## Period

Frequency Percent Valid Percent Cumulative Percent

### Valid 1-3years 10 47.6 47.6 47.6

4-7years 5 23.8 23.8 71.4

### above 7 years 6 28.6 28.6 100.0

Total 21 100.0 100.0 Source output Spss 20

The table above shows that employees who were at Probe market from 1-3years were only 10 whilst those who were employed for 4-7years were only 5. Employees who were at probe market were for more that 7years were 6 which is 28.6% of the sample size.

Period that corporate customers have been in operation

## Period

Frequency Percent Valid Percent Cumulative Percent

### Valid 1-5years 80 58.8 58.8 58.8

6-10years 21 15.4 15.4 74.3

10-15years 14 10.3 10.3 84.6

15-20years 15 11.0 11.0 95.6

20+ years 6 4.4 4.4 100.0

## Total 136 100.0 100.0

Customers that operated 1-5 years were 58.8% of the customers of Probe market. Customers that were involved with probe for 6-10 years were 21 in number hence being 15.4%. Those who were Probe customers for 10-15years were 14 being 10.3%. 15-20 years was occupied by a small percentage which was 11%. Lastly as shown by the table customers who had the least number were those who had been involved with Probe Market for more than 20 years and they were only 6 at 4.4%

4.3 Reliability test

Cronbach`s alpha test was employed to unpack the ability of the Likert scale incorporated in the questionnaire to measure internal consistency. Cronbach (1951) theorised that a Cronbach`s alpha coefficient above 0.7 validates that the multiple questions were consistent in measuring the variable. Table 4.5 provides a summary of the reliability test result of the six variables in the three research objectives.

Descriptive table Based on Cronbach`s Alpha test

### Reliability Statistics

### Cronbach’s Alpha N of Items

.717 7

### Source: SPSS 20 output

Cronbachs alpha test is used to test the reliability of the questions and results and the scale is reliable if results ranges from 0.7 to 1. The above results of 0.717 were obtained which shows that information collected by the researcher from the respondents was reliable.

Results of reliability testing to determine whether customer value influences sales revenue

### Reliability Statistics

### Cronbach’s Alpha N of Items

.717 7

### Source: SPSS 20 output

The reliability testing was done to see if the responses given by the customers were reliable and the result came between the range of 0.7 and 1 which posits that the results are reliable.

Results of reliability testing to determine whether customer satisfaction influences sales revenue

The responses given were tested and the result derived was 0.955 which shows that information given was reliable

### Reliability Statistics

### Cronbach’s Alpha N of Items

.955 7

### Source: SPSS 20 output

4.4 Correlation AnalysisCorrelation analysis was employed to determine the degree to which the association between the two concepts of the main research objective are monotonic. Table 4.6 shows the Spearman correlation coefficients between the three research objectives variables. That is, the intensity of association between employee productivity and sales revenue, customer value and sales revenue and customer satisfaction and sales revenue.

Table 4.4.0 Spearman correlation coefficients

## Correlations

## EMP SR CUV SR CUS SR

Spearman’s rho EMP Correlation Coefficient 1.000 .581** .526** .493** .573** .682**

Sig. (2-tailed) . .000 .000 .000 .000 .000

### N 157 157 157 157 157 157

SR Correlation Coefficient .581** 1.000 .791** .644** .759** .720**

Sig. (2-tailed) .000 . .000 .000 .000 .000

### N 157 157 157 157 157 157

CUV Correlation Coefficient .526** .791** 1.000 .576** .688** .712**

Sig. (2-tailed) .000 .000 . .000 .000 .000

### N 157 157 157 157 157 157

SR Correlation Coefficient .493** .644** .576** 1.000 .628** .561**

Sig. (2-tailed) .000 .000 .000 . .000 .000

### N 157 157 157 157 157 157

CS Correlation Coefficient .573** .759** .688** .628** 1.000 .802**

Sig. (2-tailed) .000 .000 .000 .000 . .000

### N 157 157 157 157 157 157

SR Correlation Coefficient .682** .720** .712** .561** .802** 1.000

Sig. (2-tailed) .000 .000 .000 .000 .000 .

### N 157 157 157 157 157 157

**. Correlation is significant at the 0.01 level (2-tailed).

### Source: SPSS 20 output

4.4.1 The extent of the relationship between employee productivity and sales revenue.Table 4.4.0 shows a Spearman correlation coefficient of 0.581 between service responsiveness and repeat purchase. The correlation coefficient of 0.581 shows that there is strong relationship between the two variables as posted by (Bewick. V, 2003). Moreover, the positive coefficient indicates that there is a positive linear monotonic relationship between employee productivity and repeat purchase. An improvement in employee productivity standards will result in increase in repeat purchase among Probe customers.

4.4.2 The extent of the relationship between customer value and sales revenue.Table 4.4.0 presents a Spearman correlation coefficient of 0.576 between customer value and increased purchases. The positive and magnitude of the coefficient points out existence of a positive and strong relationship between the cited two variables (McDonald, JH 2015). Hence, any action by Probe to enhance value for customers will directly and significantly magnify increased purchase. When Probe market place strategies to increase customer value it will increase sales revenue by 57.6%.

4.4.3 The extent of the relationship and direction of the relationship between customer satisfaction and sales revenue.Table 4.4.0 depicts a Spearman correlation coefficient of 0.715 between customer satisfaction and continual business. The positive coefficient states presence of a positive and very strong association between customer satisfaction and continual business. Hence, any customer satisfaction strategy implemented will enhance continuity of the business for Probe market by 71.7%. Furthermore, discriminant validity was assessed by inspecting the Spearman correlation coefficients in the correlation table 4.6. DeVellis (2013) asserts that correlation coefficient below 0.8 proves discriminant validity and values greater than 0.8 are too related indicating multi collinearity. Therefore, table 4.6 shows variables in the conceptual framework are distinct as evidence by correlation coefficient values ranging from 0.493 to 0.791.

4.5 Regression analysisBewick, V. (2003) states that regression analysis was done to ascertain the causal relationship between variables in the research objectives as Spearman correlation coefficient provided evidence that the variables were related and distinct. The regression analysis also involves quantifying the cause-and-effect relationship between the independent and dependent variables. Table 4.7 shows the summary of the regression analysis.