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Background Information

Business entrants in to the market usually have a big challenge to surmount. The case usually becomes even more difficult when the enterprise is getting into an industry already filled with the same business. It is there very prudent for any particular business enterprise getting into the market to have well laid down strategies to ensure they have their right foot forward so as to remain afloat in the market.

Furphy is a new business that just got into the market not more than 15 years ago. It is an Australian small brewery producing beer known as pale ale beer. Its operations are mostly in Melbourne and Victoria cities. It sells its products directly to the consumers who buy in small quantities and to retailers who buy in large quantities for the purpose of reselling. These retailers are bar, restaurants and pub owners. Despite being a small company its fortunes has been rising. Just in the previous year they recorded 3 million litres in production. This huge production within a very short period of time was due to the increase in demand for pale ale beer. However these good fortunes have recently been threatened by pending competition which is being brought by small other breweries that have also started similar businesses. For this reason the company has employed the services of beautiful data which is a market research company to conduct a survey in order to understand its customers better and there by consolidate their customer base. 

  • Intention to repurchase descriptive statistics

descriptive statistics for repurchase intention

Mean

7.665

Standard Error

0.063161077

Median

7.6

Mode

7.2

Standard Deviation

0.893232513

Sample Variance

0.797864322

Kurtosis

0.584037705

Skewness

-0.206346633

Range

5.6

Minimum

4.3

Maximum

9.9

Sum

1533

Count

200

Confidence Level(95.0%)

0.124550899

Table 1.1

It can be seen from the descriptive statistics about intention to repurchase score than the mean score was 7.66. Given that the score was rated from 1 to 10 with 1 indicating little intention to repurchase and 10 indicating high intention to repurchase, it can be concluded that majority of the customers had higher intentions of repurchasing products from Furphy company. The mode which is usually indicates the number that appears most in a dataset is 7.2. this also confirms that the majority of customers indeed have high intentions to repurchase from the company. 

  • Summary of whether one would recommend or not

 

Figure 1.2

The graph above gives a pictorial view of the responses made by 200 respondents during the survey when they were asked whether they would recommend Furphy beer to other customers. As can be viewed from the graph, 101 respondents indicated that they were willing to recommend Furphy products to other new customers. However 99 out of the 200 respondents indicated otherwise, that they were not willing to recommend Furphy products to other customers. Since the number of those that are not willing to recommend the beer to other customers is such a big portion of Furphy customers, there is need to investigate to find the reasons why. This is because this can be a potential group of customers that the company can lose to its competitors if nothing is done immediately.

Analysis of Repurchasing Intention at Furphy Brewery

In order to retain customers in any business, there must be salient features of selling that would always make them coming back to the same company to buy the same product. These features can be termed as factors influencing repurchasing intention. There may be more than one feature that influences repurchasing intention yet they differ in the degree with which they influence the intention. These features in for statistical analysis purposes are known as independent variables while the variable which is influenced such as “repurchasing intention” is known as dependent variable. In this case some of the independent variables that influence repurchasing intention include the following;

  • Beer quality
  • Brand image
  • Advertising
  • Delivery speed
  • Cost of Shipping

In order to determine whether there exists a relationship between the listed above independent variables and dependent variable, a correlation test was run and results were as in the tables that follow;

Correlation test between purchase intention and quality of pale ale beer

Repurchase Int

Quality

Repurchase Int

1

Quality

0.433371527

1

Table 2.1.1

Correlations are normally measured in terms of significance and direction. A value of 1 usually means a perfect positive correlation between the variables tested. A value of zero on the other hand usually indicates no correlation while -1 usually mean a perfect correlation but in the negative direction. The correlation test between repurchase intention and quality gives a Pearson correlation value of .43 which means that there is a significant relationship between quality and repurchase intention. The correlation is not only significant but also in the positive direction. This means that customers consider quality of pale ale beer before they consider buying again.

Correlation test between purchase intention and advertising of pale ale beer

Repurchase Int

Advert

Repurchase Int

1

Advert

0.237037785

1

Table 2.1.2

A value of 1 in correlation analysis usually means a perfect positive correlation between the variables tested. A value of zero on the other hand usually indicates no correlation while -1 usually mean a perfect correlation but in the negative direction. The correlation test between “repurchase intention” and “advertising” gives a Pearson correlation value of .24 which means that there is a significant relationship between “advertising” and “repurchase intention”. The correlation is not only significant but also in the positive direction. This means that adverts serve an important role by luring customers in form of providing vital information about the product such as the quality, uses and price. In this regard it can be concluded that Furphy customers are somehow influenced by advertisements.

Correlation test between purchase intention and brand image of pale ale beer

Factors Influencing Repurchasing Intention

Repurchase Int

Brand Image

Repurchase Int

1

Brand Image

0.33800493

1

Table 2.1.3 

As can be observed from table 2.1.3, the Pearson correlation results for correlation test between intention to repurchase and brand image is .33. Since a value of 1 in correlation analysis usually means a perfect positive correlation between the variables tested, a value of zero on the other hand usually indicates no correlation while -1 usually mean a perfect correlation but in the negative direction, then it can be said that the correlation test between “repurchase intention” and “brand image” is significant since it gives a Pearson correlation value of .33 which means that there is a significant relationship between “advertising” and “repurchase intention”. The correlation is not only significant but also in the positive direction. This means that brand image serve an important role by luring customers to buy.

Repurchase Int

Shipping Speed

Repurchase Int

1

Shipping Speed

0.425081995

1

Table 2.1.4

As can be observed from table 2.1.4, the Pearson correlation results for correlation test between intention to repurchase and shipping speed is .43. Since a value of 1 in correlation analysis usually means a perfect positive correlation between the variables tested, a value of zero on the other hand usually indicates no correlation while -1 usually mean a perfect correlation but in the negative direction, then it can be said that the correlation test between “repurchase intention” and “shipping speed” is significant since it gives a Pearson correlation value of .43 which means that there is a significant relationship between “advertising” and “shipping”. The correlation is not only significant but also in the positive direction. This is an indication that customers do value and consider the time taken for the products to reach them. 

Variables that could have an effect on repurchasing intention are as listed below;

  • Beer quality
  • Brand image
  • Advertising
  • Delivery speed
  • Cost of shipping

Having listed the above variables as those that influence repurchase intention, it is however important to note that they differently influence the repurchase intention. Some could have a significant influence while others could have a weak influence on this dependent variable. To establish the strength of influence, a multi-regression analysis is conducted to come up with multi-regression models as follows;

R Square

0.426075

Adjusted R Square

0.414302

Standard Error

0.683599

Observations

200

ANOVA

df

SS

MS

F

Significance F

Regression

4

67.64998

16.9125

36.19134

1.31E-22

Residual

195

91.12502

0.467308

Total

199

158.775

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

2.752286

0.412907

6.665627

2.64E-10

1.937948

3.566623

1.937948

3.56662311

Quality

0.289221

0.035481

8.15144

4.28E-14

0.219245

0.359196

0.219245

0.35919618

Advert

-0.03296

0.055145

-0.59778

0.55068

-0.14172

0.075793

-0.14172

0.0757931

Brand Image

0.255826

0.055977

4.570226

8.63E-06

0.145428

0.366223

0.145428

0.36622273

Shipping Speed

0.372359

0.069297

5.373381

2.19E-07

0.235691

0.509027

0.235691

0.50902654


Table 2.2

From the table above, we get the regression model below. 

Multi-regression equation given above illustrate the correlation between “repurchase intention” which is the dependent variable and the other four independent variables which include “quality”, “brand image” and lastly “shipping speed”. The regression model is not that perfect and therefore not the best in terms of establishing the relationship between the two sets of variables. This is confirmed by an R squared value of 0.426. This means that only 42.6% of the variation in repurchase intention can be influenced by the independent variables. To add on, it can be observed that the variable “advert” has the weakest influence on the dependent variable. It has a coefficient of -.03. On the other hand, the variable that has got the highest influence compared to the rest is shipping speed since it has a coefficient of .37. This indicates that a unit change in it causes a resultant 37% change on the dependent variable “repurchase intention”.

Correlation Test Results

Regression model of the two variables (quality and brand image)

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.5836648

R Square

0.3406646

Adjusted R Square

0.33397092

Standard Error

0.72897246

Observations

200

ANOVA

df

SS

MS

F

Significance F

Regression

2

54.08903

27.0445

50.89

1.52E-18

Residual

197

104.686

0.5314

Total

199

158.775

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

3.587492

0.407504

8.80357

6.7E-16

2.783863

4.39112

2.78386

4.39112

Quality

0.309413

0.037619

8.22497

2.6E-14

0.235227

0.3836

0.23522

0.38360

Brand Image

0.311546

0.0461

6.75799

1.5E-10

0.220633

0.4024

0.22063

0.40246

Table 2.3

The regression model is as below, 

The multi-regression equation given above illustrates the correlation between “repurchase intention” which is the dependent variable and the other two independent variables which include “quality”, and “brand image”.  The regression model is not that perfect and therefore not the best in terms of establishing the relationship between the two sets of variables. This is confirmed by an R squared value of 0.583. This means that only 58.3% of the variation in repurchase intention can be influenced by the independent variables. To add on, it can be observed that the two variables, “quality” and “brand image” have got equal influence influence on the dependent variable. They both have a coefficient of .31. This indicates that a unit change in them causes a resultant 31% change on the dependent variable “repurchase intention”.

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.50585693

R Square

0.25589124

Adjusted R Square

0.24450182

Standard Error

0.43566579

Observations

200

ANOVA

df

SS

MS

F

Significance F

Regression

3

12.79328

4.264427

22.46745

1.51E-12

Residual

196

37.20172

0.189805

Total

199

49.995

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

3.47140144

0.24437

14.20554

5.95E-32

2.98947

3.953333

2.98947

3.953333

Quality

-0.1459922

0.022489

-6.49162

6.81E-10

-0.19034

-0.10164

-0.19034

-0.10164

Advert

0.00932603

0.034563

0.269827

0.787577

-0.05884

0.077489

-0.05884

0.077489

Brand Image

-0.1642182

0.035317

-4.64982

6.1E-06

-0.23387

-0.09457

-0.23387

-0.09457

Table 3.1

The regression model is as below, 

Multi-regression equation given above illustrate the correlation between “recommending” which is the dependent variable and the other three independent variables which include “quality”, “brand image” and lastly “advert”. The regression model is not that perfect and therefore not the best in terms of establishing the relationship between the two sets of variables. This is confirmed by an R squared value of 0.25. This means that only 25% of the variation in the dependent variable “recommending” can be influenced by the independent variables. To add on, it can be observed that the variable “advert” has the weakest influence on the dependent variable. It has a coefficient of .009. On the other hand, the variable that has got the highest influence compared to the rest is “advert” since it has a coefficient of .16. This indicates that a unit change in it causes a resultant 16% change on the dependent variable “recommending”.

First quarter

Second quarter

Third quarter

Q3    64.6X-128742
    
    gradient=64.6
    x=2018
    128742
Q3 =    1620.8

Fourth quarter


From the above quarters, the summary for 2018 quarterly projection is as in the table below.

2018

Q1

1751.98

Q2

2092.90

Q3

1620.80

Q4

1746.36

Table 4.1

Conclusion

It can be concluded that about a half of the customers of the pale ale beer were reluctant to recommend this product to other customers. This was confirmed by a huge 47% against 53% who indicated that they cannot recommend the beer to others. If this is anything to go by then the company must have a cause to worry because this is quite a big number. The study recommends that further research should be conducted by the company on this special group to understand their reasons why they are not willing to recommend the product so that the company can act from a point of information. It also came out that customers pay more attention on the shipping speed than any other factor for them to consider repurchasing the same product. This means that the company should improve on their delivery service, that is from the time a customer places an order to the time the goods are delivered to his or her door step.

Cite This Work

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My Assignment Help. (2022). Factors That Determine Repurchasing Intention At Furphy Brewery, Essay.. Retrieved from https://myassignmenthelp.com/free-samples/mis771-descriptive-analytics-and-visualisation/predicting-variables-file-A970FF.html.

"Factors That Determine Repurchasing Intention At Furphy Brewery, Essay.." My Assignment Help, 2022, https://myassignmenthelp.com/free-samples/mis771-descriptive-analytics-and-visualisation/predicting-variables-file-A970FF.html.

My Assignment Help (2022) Factors That Determine Repurchasing Intention At Furphy Brewery, Essay. [Online]. Available from: https://myassignmenthelp.com/free-samples/mis771-descriptive-analytics-and-visualisation/predicting-variables-file-A970FF.html
[Accessed 21 July 2024].

My Assignment Help. 'Factors That Determine Repurchasing Intention At Furphy Brewery, Essay.' (My Assignment Help, 2022) <https://myassignmenthelp.com/free-samples/mis771-descriptive-analytics-and-visualisation/predicting-variables-file-A970FF.html> accessed 21 July 2024.

My Assignment Help. Factors That Determine Repurchasing Intention At Furphy Brewery, Essay. [Internet]. My Assignment Help. 2022 [cited 21 July 2024]. Available from: https://myassignmenthelp.com/free-samples/mis771-descriptive-analytics-and-visualisation/predicting-variables-file-A970FF.html.

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