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Formulation of the Research Problem, Part V

Michael Colapietro, Robert Contreras, Donald Dennis, Ericka Gutierrez, Russell Girdler, Diana Velez

QNT/561 » Applied Business Research and Statistics

February 28, 2015

Heidi Carty

Formulation of the Research Problem

Customer satisfaction is very important in the new home building trade. Builders compete to have the highest customer satisfaction rating which translates into sales. Builders are judged by independent survey service providers (JD Powers, 2015). Intermountain West Builders needs to have strong customer satisfaction scores to be competitive in the marketplace. The company is conducting a research study regarding price and home quality based on customer satisfaction scores. The research broken down into two variables that need to be studied, a research question is formed, and finally a hypothesis is reached (McClave, Benson, & Sincich, 2011).

The Variables

Intermountain West Builders goal is to stay competitive therefore need to increase sales and have a competitive advantage over their competitors. They have focused their attention on the following two variables: price and age. A dependent variable responds to the independent variable. The independent variable is home quality and the dependent variable is age. Age of the buyer will have an effect on the price of the home. Young people still do not have family and this result to less household when it comes to house equipment’s and occupants. This will result in buying a small and less expensive house. Likewise, if the customer is of older age (35+) he or she will buy a more expensive and larger house because of a larger family occupant.

Background and Business Problem

Intermountain West Builders is a company with a history of quality built homes. This strong background helps propel them as a premier builder of quality homes. One aspect of this company is its personable approach to each customer. No customer is excluded or discounted despite the size of this company. However, recent trends suggest customer satisfaction is not as high as the perception suggests. The problem would take value from the company and further alienate the customer’s perception of both satisfaction and quality homes built. The company business model of customer satisfaction should be reviewed based on feedback generated from surveys.

Research Question

The two variables in question are home age of the customer and the price. If the customer is older, they are more likely to pay a higher price for a bigger home. This principal applies to many other applications, be it purchase of house, any household item, grocery item, or any other service. Moreover, a house is a large investment, where it is difficult for a company to convince the customer to purchase and close the deal. Intermountain West Builders needs to focus on providing best quality at affordable prices. A company can always procure materials at the lowest prices, by bargaining with different suppliers. In addition, the business can offer a product of high quality at competitive prices.

The Hypotheses

The research question discussed was based on price and age. The question inquired was, “Is there a correlation between prices (DV) based on age of the customer?” Based on the research question, Intermountain West Builders have formed two hypotheses:

1. Fair home prices and good customer satisfaction will lead to higher customer service scores.

2. There is a correlation between home price and customer age.

a. The better quality home, the higher the price will become.

Introduction values on home purchases are not expected to equal the cost nor the price of such purchase. Cost is for labor and materials, whereas price is the amount you pay for goods or services. Price can affect value, but will never determine the value (Folger, n.d.). Quality can be defined in many ways, based on neighborhood characteristics, structural attributes, as well as features within (Joseph, 2014) the home. According to Goodman, 2013, most people will compensate builders more for better quality, because price is strongly correlated with encountered problems and the expectations of the client (Kerr, 2015).

Correlation between price and age

A stream of marketing research indicates that price often have effect on the age bracket of the customer purchasing a home. Most elderly people view their home as an important asset of investment. They view it as the asset their children will inherit and hope it will increase in value. This leads to high number of older people buying more homes than the younger generation. They will buy more homes and much expensive home than the younger people do (Kerr, 2015).

Price and how it affects customer service

Establishing is paramount to the selling price a new home. The price charged to clients will have a direct effect on the success of any construction business. The basic rules of pricing are:

· All prices must cover costs and profits.

· The most effective way to lower prices is to lower costs.

· Review prices frequently to assure that they reflect the dynamics of cost, market demand, response to the competition, and profit objectives.

· Prices must be established to assure sales

Before setting a price, construction businesses must know the costs of running the business. If the price for the home does not cover costs the company will exhaust precious financial resources, and the business will ultimately fail. After setting a proper price, a product can be floated in market for selling.

Research price and how it affects customer service

The price/quality relationship refers to the perception by most consumers that a relatively high price is a sign of good quality. Older people understand the value of the home better than the young people thus they will spend more on the quality rather than quality. The belief in this relationship is most important with complex products that are hard to test, and experiential products that cannot be tested until used. The greater the uncertainty surrounding a product, the more consumers depend on the price/quality hypothesis and the greater premium the consumers are prepared to pay.

The relationship between customer satisfaction and customer service

The relationship between customer satisfaction and customer serves is pronounced. High customer service equals higher customer satisfaction. A quote from Demand Media states “one of the most important business traits is learning how to please your customers” sums the relationship between customers service and customer satisfaction (Holt, 2015). With high customer serves comes high customer loyalty as a study from Dr. Emel Kursunluoglu shows an increase of 43% in customer loyalty (Kursunluoglu, 2011). With high customer service comes high customer satisfaction relating to high customer loyalty and an increase in business with the strongest form of advertising, word of mouth increasing (Kerr, 2015).

Quality and long lasting effects on customer service scores

Quality is a continued investment that is part of a continuous improvement plan. Quality built into the construction of a new home will impact the end buyer if each and every defect is addressed along the way in order to minimize their frequency. The construction business finds the importance tracking and maintaining data related to defects, defect frequency of occurrence, defect origins, defect severity, and defect repair costs (Rosenfield 109). As a result, the quality built home will help to improve customer satisfaction as the new owner finds minimal or zero repairs due to a defect from the construction process. The only repairs might come from routine care of the home to ensure prolonged satisfaction from a well-built home. A quality built home is a long lived in home!

Effects of poor overall service

The effects of poor overall service directly affect the customers’ perceptions in relation to pricing and quality. Akkemans and Voss (2013) demonstrated this principal in correlating the effects of the customer satisfaction levels relative to delays and rework in the supply chain. The negative effects are furthered bolstered by Ahmed and Kangari, whose research leads to the conclusion that construction businesses lag behind other industries that have implemented total quality management (TQM). Given that customer satisfaction is a key component of TQM, the client’s perspective of receiving a fair price with an acceptable level of quality are wanting within the construction industry as a whole. Finally, Eriksson (2010) tackles the issue of poor service and the effects by taking a detailed look at the construction industry by striving to improve the industry from the client-side versus the more traditional supply-side approach. All these articles lend credence to the effects of poor overall service in the construction industry.

Sampling Element

Intermountain Builders determined that the best sampling element is the survey method. The primary reasoning behind the decision was budgetary constraints and the time needed to gather the necessary data. The survey is a cost effective solution requiring little outlay of capital, especially if an electronic survey service is utilized (i.e. Survey Monkey). Basic services from electronic surveys are often free and Intermountain Builders can apply existing client contact information from in-house data stores, gather public records contact information, and purchase realtor contact information quickly to round out the survey targeted participants.

Stratified Sampling Method

The final method of sampling used by the company is a survey plan. This survey is a questioner based on a number of questions that rate quality, customer satisfaction and price of the customer’s home. This survey is a rating system based on stratified sample. Stratified sample is a sampling technique in which the entire target population is divided into different subgroups and then randomly selects the final subjects proportionally from the different strata (About education,2015). By subgrouping the population specific areas of interest can be identified in the research.

Descriptive Statistics

Amount Spent to Buy the Home (in Thousands)

We need some additional analysis here as to the conclusion of the descriptive statistics. The distribution is normally distributed.

Central Tendency:	Mean =133.15 ($ in thousands)
Dispersion:	Standard deviation = 3.4462 ($ in thousands)
Count:	100
Min/Max:	Min 75; Max 174 ($ in thousands)

Inferential Statistics

The researcher is trying to find the correlation between two research variables, the amount spent on the house purchase and the number of rooms in the purchased home. In this study, the sum paid to buy the house is an independent variable whereas the number of rooms in the home is the dependent variable. As mentioned in the week 4’s research paper the researcher randomly selected 500-population samples to avoid any data collection bias or sampling errors to reach a decisive outcome. Samples are measured in the units or numbers. Dependent and independent variables were determined that are unrelated to the number of rooms the buyer purchased for the homes. Therefore, the researcher decided the amount spent on the acquisition of the home is an independent variable and number of rooms in the home is a dependent variable because of the customer’s needs; customers bought the home\ but there was not a dependency on the number of rooms in the home.

Therefore, the researcher performs the regression slope analysis to test the following hypothesis and to reach a definitive decision that shows that there is a relationship between the selected variables or not. (There is not a significant linear relationship between the amount spent on the house purchase and the age of the purchaser)

(There is an important linear relationship between amount spent on the house purchase and age of the purchaser in the purchased home). Below is the regression analysis:

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.079999084
R Square	0.006399853
Adjusted R Square	0.004404672
Standard Error	3.656416932
Observations	500
ANOVA
	df	SS	MS	F	Significance F
Regression	1	42.88438082	42.88438082	3.207655515	0.073901618
Residual	498	6657.953619	13.36938478
Total	499	6700.838

	Coefficients	Standard Error	t Stat	P-value
Intercept	6.108605675	0.368080592	16.59583747	1.47971E-49
X Variable 1	-0.138121065	0.077119829	-1.790992885	0.073901618

Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
5.385423388	6.831787962	5.385423388	6.831787962
-0.2896414	0.013399269	-0.2896414	0.013399269

Based on the calculation for the regression, the analysis indicates that there is very weak positive correlation between the two research variables and correlation coefficient value 0.080. Thus strengthening researcher findings that there is very weak positive correlation between two research variables. Furthermore, when the researcher evaluates the data and identifies that coefficient of determination of 0.004404 proves that approximate 99.99% of the data cannot be explained by the collected data. One step further, the researcher tries to strengthen the findings that there is no relationship between research variables, regression hypothesis test was performed, and the results are mentioned in the previous calculations. Analysis of the slop of the dependent variable for significance shows that t-stat for the hypothesis is – 1.79099, and p-value is 0..73902. Therefore, there is significant evidence that for the confidence interval of 99%, 95% or 90%, or for the significance level of 0.01, 0.05, and 0.10 the null hypothesis can be rejected.

Conclusion

Answers to the research questions

Donald: Per Appendix C, the scatter graph shows no correlation between the prices of home and the ages of those home buyers. Older people are not to be assumed for purchasing a more expensive home. Younger people are also not to be assumed to buy less expensive homes. Those home buyers between the ages of 25 and 45 show significant evidence that each age group is well represented in each home price group; furthering our conclusion rejecting the null hypothesis.

Ericka Summary of the results of testing the null hypothesis:

Ninety-nine percent of the data collected could not be explained therefore the null hypothesis was rejected. The regression slope analysis was used to test the null hypothesis which demonstrated a weak positive correlation between the two variables. In addition, the multiple coefficient of determination .079999 also concluded there to be a weak relationship between the amount spent on the house and the number of rooms in the purchased home.

Donald Research questions answers:

Ninety-nine percent of the data collected could not be explained therefore the null hypothesis was rejected. The regression slope analysis was used to test the null hypothesis which demonstrated a weak positive correlation between the two variables. In addition, the multiple coefficient of determination .079999 also concluded there to be a weak relationship between the amount spent on the house and the number of rooms in the purchased home.

1. Donald: Answers to the research questions (this is critical). 

2. Diana: Conclusions that were derived from this study (Paragraph needed, who wants this one?)

1. An inconclusive result (i.e., no significant results were found) is acceptable for this project.  Should that be the case, suggest potential future research efforts and a new research questions that might provide more definitive results.

Robert: Recommendations:

The research data is very clear and direct in regards to the research questions. However, because the relationship proves to be weak, although positive, it would suggest the parameters may need to be widened or the variables be revisited. The research project is clear in what it is attempting to discoverer and has sound objective. It could be that some other data points to consider might be economic background, size of family, number of years married, financial plans for retirement, or any other variable that may help to discover the end result. The age factor was an excellent place to compare what one demographic might pay or perceive quality of home from that of another. An older age bracket may suggest, as the paper notes, different objectives and goals or the importance of investment. That being said, the study has enough data to be tested by others or for the research team to adjust variables for other test points.

3. Michael: Observations (reflection) on the business problem and its solution 

Michael Observations:

The business problem Intermountain West Builders is faced with reflects the research the company conducted. The problem, poor customer satisfaction relates to lower price of their home. Intermountain West Builders designed research to confirm this thought building a hypothesis with research questions around this thinking. The research broke the hypothesis down into two variables that are the research questions;

1. Fair home prices and good customer satisfaction will lead to higher customer service scores.

2. There is a correlation between home price and customer age.

a. The better quality home, the higher the price will become.

The research question was “Is there a correlation between prices (DV) based on age of the customer?” The solution raise customer satisfaction and price of the home could be raised. If customer satisfaction is low or remains unchanged prices of the home cannot be raised

Russ: Research challenges and future proactive steps

WIP…

Appendix A

Raw data used in the analysis

Appendix B

Charts and Tables

Descriptive Statistics on Sales

Mean133.15

Standard Error0.907290338

Median130

Mode130

Standard Deviation17.34462

Sample Variance300.8358586

Kurtosis

14.51105606

SkewnessNaN

Range95

Minimum95

Maximum160

Sum13,315

Count100

Confidence Level(95.0%)8.27

Descriptive Statistics on Age

Mean33.91

Standard Error0.653675699

Median35

Mode25

Standard Deviation6.536756988

Sample Variance42.72919192

Kurtosis-1.41022686

Skewness0.095178927

Range20

Minimum25

Maximum45

Sum3391

Count100

Confidence Level(95.0%)1.297034402

Appendix C

Descriptive Statistics

Appendix D

Home Price vs. Ages

Regression Statistics

Multiple R0.052191723

R Square0.002723976

Adjusted R Square-0.00745231

Standard Error9.106647674

Observations100

ANOVA

dfSSMSFSignificance F

Regression122.1988784422.198880.2676787920.606058973

Residual988127.24112282.93103

Total998149.44

CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%

Intercept45.296480774.8344974419.3694292.82949E-1535.7025779454.89038435.7025779454.8903836

X Variable 1-0.0724411910.1400163-0.517380.606058973-0.3502989810.2054166-0.3502989810.2054166

$0$20$40$60$80$100$120$140$160$1800510152025Home Price vs. Age

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