Solution Manual for Business Research Methods 9th Edition By Zikmund

Solution Manual for Business Research Methods 9th Edition By Zikmund

Case 6

Knowing the Way

Objective: The purpose of this case is to allow students to analyze data.

Summary: The Swamp Palace museum is an interactive museum that teaches visitors the ways of life on the swamps of the southern United States. It contains over 100 exhibits demonstrating the ecology of the swamp and the habits of the animals and insects inhabiting it. Additionally, there are restaurants, swimming, and thrill rides in the forty plus acres and miles of pathways.

The park was originally government-funded, but now it is self-supporting. While there are enough visitors, the park has struggled just to break even. The Swamp Palace sought the help of Marketivity Group, and exploratory research results are provided. The park commissioned Marketivity to undertake a further study addressing several research questions (see question 3 below for the research questions). Marketivity implemented a quasi-experimental design and students are asked to analyze the data in the questions below.

Questions:

1. Run frequencies on Gender, Others, and Age. Are any problems evident with coding? Take any necessary corrective actions.

Gender
Frequency Percent Valid Percent Cumulative Percent
Valid Male 65 32.7 32.7 32.7
Female 116 58.3 58.3 91.0
3.00 18 9.0 9.0 100.0
Total 199 100.0 100.0

Coding problem is evident by the 18 respondents coded with a 3 (1=Male and 2=Female)

Age

Frequency Percent Valid Percent Cumulative Percent
Valid 1.00 42 21.1 21.6 21.6
2.00 77 38.7 39.7 61.3
3.00 56 28.1 28.9 90.2
4.00 14 7.0 7.2 97.4
5.00 5 2.5 2.6 100.0
Total 194 97.5 100.0
Missing System 5 2.5
Total 199 100.0

Frequencies suggest a younger clientele. Perhaps the 5 missing observations are 18 year olds? Notice in the wording to the age question, 1 = less than 18 and 2 = 19-24, this means that someone who is exactly 18 could be confused.

Others
Frequency Percent Valid Percent Cumulative Percent
Valid Alone 42 21.1 21.9 21.9
1 35 17.6 18.2 40.1
2 64 32.2 33.3 73.4
3 32 16.1 16.7 90.1
>=4 19 9.5 9.9 100.0
Total 192 96.5 100.0
Missing System 7 3.5
Total 199 100.0

Nothing out of the ordinary and no explanation for the 7 missing responses is evident although some respondents hesitate to give their age in a given situation.

2. Compute a composite scale for the 5 SQ items and the 4 VAL items. Compute a coefficient alpha for each of the resulting service quality and value scales.

The composites (SQT and VALT, respectively) can be created with a formula in Excel or using the compute function in SPSS. Coefficient α for SQT is .92 and for VALT is .93. The descriptive statistics for the composites can be seen in the table below:
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
SQT 184 5.00 25.00 20.3967 4.03945
VALT 194 4.00 20.00 17.1907 3.22389
Valid N (listwise) 179

3. Perform an appropriate test of each research question RQ1, RQ2, RQ3, RQ4, and RQ5.

1. Do patrons who use a mobile phone navigation app report higher service quality and have an improved experience relative to those who do not?

All should compare scores on SQT for those with and without the device. Here, those without report slightly higher SQT. Experience could be captured either by the VALT score or perhaps by the feelings. GOODF represents the sum of the feelings scale after reverse coding so that higher scores mean more positive feelings. Perceived value is slightly higher for those without the app. Feelings however, are slightly more positive for those with the app. Each of the differences is significant as indicated by an independent samples t-test. More advanced students may realize the opportunity to use a multivariate analysis of variance – however, the students at this point should not be expected to realize this opportunity.

Group Statistics
Wayf N Mean Std. Deviation Std. Error Mean
SQT No 86 21.0698 3.87083 .41740
Yes 96 19.8646 4.12341 .42084
VALT No 90 17.7333 2.97858 .31397
Yes 101 16.7426 3.36646 .33498
GOODF No 73 15.5890 5.49200 .64279
Yes 82 20.5854 5.91285 .65296

2. Do patrons who use the mobile phone app have a greater likelihood of upgrading to a season pass?
This calls for a cross-tabulation with a chi-square test. The results are shown below. The test is insignificant suggesting that customers with an app are not more likely to upgrade than are others. The expected value for the Yes-Yes combination is 30.3 which is almost equal to the observed count of 29.

Wayf * UPGRADE Crosstabulation
Count
UPGRADE Total
No Yes Defer
Wayf No 33 32 9 74
Yes 33 29 11 73
Total 66 61 20 147
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square .341a 2 .843
Likelihood Ratio .341 2 .843
N of Valid Cases 147
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 9.93.

3. Do patrons who use a coupon report more positive price perceptions?
This can be answered with an independent samples t-test as shown below. In this case, the .2 difference in means is not significant suggesting no difference in price perceptions based on coupon usage.
Group Statistics
Groupn N Mean Std. Deviation Std. Error Mean
PriceP Yes 98 3.6122 1.39685 .14110
No 99 3.4141 1.16067 .11665

4. Do patrons who use the mobile phone app have a greater likelihood of upgrading to a season pass?
This is the same as number 2. (Mistake in that the same RQ is included twice)

5. What factors contribute to improved value perceptions?
A number of analyses could address potential relationships with value perceptions. The key is that VALT is the dependent variable. Here, a regression analysis uses metric variables available to explore the question. The results suggests that service quality is the key variable in explaining variance in VALT.

Other analyses could use the GLM to examine the effect of nominal variables such as coupon usage or gender (after changing the miscodes to missing). A number of combinations are possible but the clearest findings that emerge are greater value perceptions for those that use a coupon but lower value perceptions for those who use the app.

4. List an additional research question that can be addressed with a one-way ANOVA. Conduct the test.

Several are possible all in the same style as: Do customer feelings vary based on the use of a coupon? Here, the categorical variable coupon predicts the customer feeling variable (interval).

5. List an additional research question that can be addressed with a GLM model. Conduct the test.

Again, several are possible. Here is one that involves an analysis comparable to a full-factorial experimental design – if the coupon and app are being examined in a field experiment, this is the result. The analysts could include some interval/ratio variables as covariates, but none are included in this analysis:

Tests of Between-Subjects Effects
Dependent Variable:VALT
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model 572.887a 3 190.962 25.567 .000
Intercept 56494.366 1 56494.366 7563.668 .000
Wayf 41.960 1 41.960 5.618 .019
Groupn 518.120 1 518.120 69.368 .000
Wayf * Groupn 2.302 1 2.302 .308 .579
Error 1396.736 187 7.469
Total 58537.000 191
Corrected Total 1969.623 190
a. R Squared = .291 (Adjusted R Squared = .279)

The overall model is significant as indicated by the F of over 25 (p < .001). The results show significantly greater value without the app and significantly greater value with the coupon. However, the interaction is not significant.

6. Summarize the implications for the decision statements that arise from the tests above. Make sure you cover whether the park should invest in the navigation system and coupon technologies.

The results do not support further investment in the app. The app does not produce positive results for service quality, for value or for upgrade. On the other hand, the coupon shows promise based on improved value perceptions for those who use a coupon.