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Service workers and customer relations. A study in Industrial Marketing Management (February 2016) investigated the impact of service workers鈥 (e.g., waiters and waitresses) personal resources on the quality of the firm鈥檚 relationship with customers. The study focused on four types of personal resources: flexibility in dealing with customers(x1), service worker reputation(x2), empathy for the customer(x3), and service worker鈥檚 task alignment(x4). A multiple regression model was employed used to relate these four independent variables to relationship quality (y). Data were collected for n = 220 customers who had recent dealings with a service worker. (All variables were measured on a quantitative scale, based on responses to a questionnaire.)

a) Write a first-order model for E(y) as a function of the four independent variables. Refer to part

Which 尾 coefficient measures the effect of flexibility(x1)on relationship quality (y), independently of the other

b) independent variables in the model?

c) Repeat part b for reputation(x2), empathy(x3), and task alignment(x4).

d) The researchers theorize that task alignment(x4)鈥渕oderates鈥 the effect of each of the other x鈥檚 on relationship quality (y) 鈥 that is, the impact of eachx, x1,x2, orx3on y depends on(x4). Write an interaction model for E(y) that matches the researchers鈥 theory.

e) Refer to part d. What null hypothesis would you test to determine if the effect of flexibility(x1)on relationship quality (y) depends on task alignment(x4)?

f) Repeat part e for the effect of reputation(x2)and the effect of empathy(x3).

g) None of the t-tests for interaction were found to be 鈥渟tatistically significant鈥. Given these results, the researchers concluded that their theory was not supported. Do you agree?

Short Answer

Expert verified

a) The equation is y=y=0+1x1+2x2+3x3+4x4+.

b) The coefficient that measures changes in y for a given 1-unit increase in flexibility is measured by.

c) According to the equation the coefficients associated with reputation (x2), empathy (x3) , and task alignment (x4) are1,2, and 3 respectively.

d) Ey=0+1x1+2x2+3x3+4x4+5x1x4+6x2x4+7x3x4+

e) The null hypothesis would be H0:5=0against the alternate hypothesis Ha:50;

f) To test whether the effect of reputation (x2) and task alignment (x4) interact, the value of 6is tested. Mathematically, the null hypothesis would belocalid="1651190898603" H0:6=0; against the alternate hypothesislocalid="1651190913791" Ha:60

g) To test whether the effect of empathy (x3) and task alignment (x4) interact, the value of 7is tested. Mathematically, the null hypothesis would be localid="1651190931488" H0:7=0; against the alternate hypothesislocalid="1651190948413" Ha:70

Step by step solution

01

First-order model equation

The first-order model equation here isEy=0+1x1+2x2+3x3+4x4+

Where,x1= flexibility in dealing with customers

x2= service worker reputation

x3= empathy for the customer

x4= service worker鈥檚 task alignment

02

Interpretation of  β coefficient

The coefficient that measures changes in y for a given 1-unit increase in flexibility is measured by1 .

03

Clarification of β coefficient

The coefficients associated with different variables represent the changes in y due to a 1-unit change in the respective variable.

Therefore, according to the equation the coefficients associated with reputation (x2) , empathy (X3), and task alignment (x4) are 2,3 , and 4 respectively.

04

Interaction model

The interaction model where task alignment (x4) impacts the relationship of each (x1, X2, and X3) can be written as,

Ey=0+1x1+2x2+3x3+4x4+5x1x4+6x2x4+7x3x4+

Here the added variables x1x4,a, x2x4and x3x4represent the interaction between x1and x4, x2and x4, x3 and x4respectively.

05

Significance of β5

To test whether the effect of flexibility x1 and x4 task alignmentinteract, the value is tested

Mathematically,

The null hypothesis would be H0:5=0against the alternate hypothesis;

06

Importance of β6

To test whether the effect of reputation (x2) and task alignment (x4) interact, the value is tested

Mathematically,

The null hypothesis would be H0:6=0; against the alternate hypothesis Ha:60

To test whether the effect of empathy (x3) and task alignment (x4) interact, the value is tested

Mathematically,

The null hypothesis would be H0:7=0; against the alternate hypothesis Ha:70

07

 Conclusion about the interaction model

When it is concluded from the t-test that none of the tests are statistically significant for interaction then it can be said that the researcher鈥檚 theory that there are some interactions in the model is not true.

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Most popular questions from this chapter

Consider a multiple regression model for a response y, with one quantitative independent variable x1 and one qualitative variable at three levels.

a. Write a first-order model that relates the mean response E(y) to the quantitative independent variable.

b. Add the main effect terms for the qualitative independent variable to the model of part a. Specify the coding scheme you use.

c. Add terms to the model of part b to allow for interaction between the quantitative and qualitative independent variables.

d. Under what circumstances will the response lines of the model in part c be parallel?

e. Under what circumstances will the model in part c have only one response line?

Consider fitting the multiple regression model

Ey=0+1x1+2x2+3x3+4x4+5x5

A matrix of correlations for all pairs of independent variables is given below. Do you detect a multicollinearity problem? Explain.


Question: Refer to Exercise 12.82.

a. Write a complete second-order model that relates E(y) to the quantitative variable.

b. Add the main effect terms for the qualitative variable (at three levels) to the model of part a.

c. Add terms to the model of part b to allow for interaction between the quantitative and qualitative independent variables.

d. Under what circumstances will the response curves of the model have the same shape but different y-intercepts?

e. Under what circumstances will the response curves of the model be parallel lines?

f. Under what circumstances will the response curves of the model be identical?

State casket sales restrictions. Some states permit only licensed firms to sell funeral goods (e.g., caskets, urns) to the consumer, while other states have no restrictions. States with casket sales restrictions are being challenged in court to lift these monopolistic restrictions. A paper in the Journal of Law and Economics (February 2008) used multiple regression to investigate the impact of lifting casket sales restrictions on the cost of a funeral. Data collected for a sample of 1,437 funerals were used to fit the model. A simpler version of the model estimated by the researchers is E(y)=0+1x1+2x2+3x1x2, where y is the price (in dollars) of a direct burial, x1 = {1 if funeral home is in a restricted state, 0 if not}, and x2 = {1 if price includes a basic wooden casket, 0 if no casket}. The estimated equation (with standard errors in parentheses) is:

y^=1432 + 793x1- 252x2+ 261x1x2, R2= 0.78

(70) (134) (109)

  1. Calculate the predicted price of a direct burial with a basic wooden casket at a funeral home in a restricted state.

  2. The data include a direct burial funeral with a basic wooden casket at a funeral home in a restricted state that costs \(2,200. Assuming the standard deviation of the model is \)50, is this data value an outlier?

  3. The data also include a direct burial funeral with a basic wooden casket at a funeral home in a restricted state that costs \(2,500. Again, assume that the standard deviation of the model is \)50. Is this data value an outlier?

Question: Shopping on Black Friday. Refer to the International Journal of Retail and Distribution Management (Vol. 39, 2011) study of shopping on Black Friday (the day after Thanksgiving), Exercise 6.16 (p. 340). Recall that researchers conducted interviews with a sample of 38 women shopping on Black Friday to gauge their shopping habits. Two of the variables measured for each shopper were age (x) and number of years shopping on Black Friday (y). Data on these two variables for the 38 shoppers are listed in the accompanying table.

  1. Fit the quadratic model, E(y)=0+1x+2x2, to the data using statistical software. Give the prediction equation.
  2. Conduct a test of the overall adequacy of the model. Use =0.01.
  3. Conduct a test to determine if the relationship between age (x) and number of years shopping on Black Friday (y) is best represented by a linear or quadratic function. Use =0.01.
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