Result of Experiment 2

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6.4.3 Result of Experiment 2

 Result for Proposition 1-A:

As mentioned before, we want to know that would a person’s awareness of the potential benefits of the service increase after the process of anchoring effect happens. For this proposition, we use the subjective opinions from the phase 7’s open question, “Do you think your understanding of the potential benefits increases after the process? Why?” for the data analysis. The raw data shows in Table 6.4.4, and we found that the opinions can be summarized into 3 categories:

1. Agree: Through the provided anchors and the process of giving the counter values, the subjects thought their understanding of the features and functions of the service increased and they could well imagine the potential benefits and value brought by the service. The descriptions were clear to help them imagine the service and they had wondered the value of each attribute and the service deeply during the process of giving the counter values.

2. Partially agree: Through the provided anchors and the process of giving the counter values, the subjects thought their understanding of the features and functions of the service increased and could preliminarily imagine the potential benefits and value brought by the service. However, the imaginations were obscure because they thought they couldn’t judge the value precisely until they really use the service. Besides, if there are pictures, videos or prototypes, maybe they could imagine the service more clearly.

3. Disagree: The subjects thought they got nothing from the whole process because they believed there is a big gap between the expected value and

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experience and the real value and experience. They believed they could only judge the value of the service after using it and the expected value means nothing to them.

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Table 6.4.4 The data of the opinions from subjects

Subject Opinion Reason

Subject1

Agree It helps me more understand the service. The descriptions of the functions are clear.

Subject2

Agree The descriptions of the functions help me more understand the service.

Subject3

Agree The concrete introductions of the functions help me more understand the service.

Subject4

Agree It helps me more understand the service. The descriptions are clear enough for me to imagine the picture of the service and the potential benefits for trip planning.

Subject5

Disagree It doesn’t help. The descriptions are not clear.

Subject6

Disagree It doesn’t help. I can’t judge the value if I only have the text descriptions.

Subject7

Partially agree I can get the big picture, but I can’t make sure that the value in use and my expectation are the same.

Subject8

Agree Although the text descriptions might be different from value in use, it helps me more understand the service.

Subject9

Agree It helps me more understand the service. The descriptions help me judge whether the functions match my needs. But it does not help me judge the value in use.

Subject10

Agree Those details help me well understand every attribute’s content and the value.

Subject11 Partially agree It helps me more understand the service with a minimum level.

However, it does not help for judging the value of the service.

Subject12 Partially agree It helps me more understand the service with a minimum level.

If there are screenshots or videos of the service, it would give more help.

Subject13 Partially agree It helps me more understand the service with a minimum level,

but it does not help me judge the value in use.

Subject14 Partially agree The descriptions of the functions help me more understand the

service with a minimum level, but I need more details to judge the value.

Subject15

Agree The descriptions are clear enough for me to understand the service and judge the value.

Subject16 Partially agree It helps me more understand the service with a minimum level.

If there are screenshots or videos of the service, it would give

‧ service and judge the value.

Subject18

Agree The descriptions are clear enough for me to understand the functions of the service and think whether I need it or not.

Subject19

Agree The descriptions are clear enough for me to understand the functions of the service relate to my needs and imagine the benefits.

Subject20 Partially agree It helps me more understand the service with a minimum level.

If there are screenshots or videos of the service, it would give more help.

Subject21

Agree The descriptions are clear enough for me to understand the functions of the service and think whether I need it or not.

Subject22

Agree The descriptions are clear enough for me to understand the functions of the service and some of them do match my needs.

Subject23

Agree The descriptions are clear enough for me to understand the functions of the service.

Subject24 Partially agree It helps me more understand the functions of the service with a

minimum level, but it does not help me judge the value in use.

Subject25

Agree It helps me more understand the service.

Subject26 Partially agree The attribute name helps me get a big picture of the service, but

the descriptions are too long and I don’t have patience to read them.

Subject27 Partially agree I can get the big picture, but I afraid of that the value in use and

my expectation are different

Subject28

Agree The descriptions are clear enough for me to more understand the service.

Subject29

Agree The descriptions are clear enough for me to judge that the value of every attribute and whether it matches my needs or not. This process makes me more understand the value of service to me.

Subject30

Disagree It doesn’t help. There are too many ways to instantiate the

descriptions of the service attributes.

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According to Table 6.4.5, we can observe that 17 subjects expressed agree, 10 subjects expressed partially agree and 3 subjects expressed disagree. That means 57% of them expressed agree, 33% of them expressed partially agree and only 10% of them expressed disagree (see Figure 6.4.2). To sum up, we can say 90%

of the subjects’ thoughts support the proposition 1-A.

Figure 6.4.2 Pie Chart of the percentage

As a result, we almost can justify that our proposition 1-A is established.

However, to be more rigorous, we further use another statistic method to do the examination. Hence, we gave the score 3 to represent for “agree”, 2 to represent

“partially agree” and 1 to represent for “disagree”, and then we built a null hypothesis and an alternative hypothesis as shown in Figure 6.4.3.

Figure 6.4.3 Hypothesis of the testing for awareness of the potential benefits

Agree 57%

Partially agree

33%

Disagree 10%

Agree Partially agree Disagree

𝐻0: 𝜇 < 2, 𝑡ℎ𝑒 𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠of the potential benefits of the service does not increase.

𝐻1: 𝜇 ≥ 2, 𝑡ℎ𝑒 𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 𝑜𝑓 𝑡ℎ𝑒 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑏𝑒𝑛𝑒𝑓𝑖𝑡𝑠 𝑜𝑓 𝑡ℎ𝑒 𝑠𝑒𝑟𝑣𝑖𝑐𝑒 𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒.

μ = popultion mean.

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Since we have 30 simple random samples with unknown standard deviation, we decided to use One-Samples T test as the method to verify the hypothesis. We use SPSS to run the One-Sample T test and the consequence is shown in Table 6.4.6.

Table 6.4.6 One-Sample T test for awareness of the potential benefits

One-Sample Test hypothesis H0 is rejected and it means there is statistically significant evidence to support our proposition 1-A, once the process of anchoring effect happens, the awareness of the potential benefits of the service will increase.

 Result for Proposition 1-B:

To examine the proposition 1-B, would a person’s willingness to try the new service increases after the process of anchoring effect happened, we use the difference between the Readiness to Change score before the anchoring process and after the anchoring process to represent the change of willingness to try the new service. To obtain a Readiness to Change score, we first need to sum items from each subscale and divide by 3 to get the mean for each subscale. Then sum the means from the contemplation and action subscales and subtract the precontemplation mean (C + A – PC = Readiness to Change score) (DiClemente et al., 2004), because the statements of the contemplation and action subscales are positive for deciding to use the service and the statements of the precontemplation subscale are negative for deciding to use the service Table 6.4.7 shows the consequence of Readiness to Change score.

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Table 6.4.7 Readiness to Change score

Subject Before anchoring After anchoring Increment(A-B) Increased Opinion

Subject1

4.67 5.67 1.00 1 Agree

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From the result, we can see that 80% of the subjects’ willingness to try the service increased and the average increment is 0.81, which is above 0. That means most of the subjects became more willing to try the service after the anchoring process. This result is reasonable because we can see that these subjects with the positive change all agreed or partially agreed that the process let them understood more about the service. The other 20% of the subjects’

willingness to try the service remained constant or decreased, and their opinions were “disagree” or “partially agree”. Thus, we can observe that most for the people who partially agreed their understanding of the service increased their willingness to try the service increased too. However, there are still a few people who partially agreed, their willingness to try the service remained constant or decreased. For those who partially agreed and the willingness decreased, we infer that although their understanding of the service increased, but the increased understanding made them more sure that they don’t have the needs to use the service. For those who partially agreed and willingness remained constant, we infer the reason why their willingness didn’t increase might be that although they had a strong faith in value in use so no matter how much understanding of the service increased, their willingness won’t change. For those whose opinions were “disagree”, it is reasonable because they couldn’t judge the value or only got an obscure value of the service. Therefore, we can infer that on the average, people’s willingness to try the service increase through the anchoring process. To be more rigorous, we also built a hypothesis for proposition 1-B (Figure 6.4.4) and ran a One Sample T test to test the hypothesis.

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Figure 6.4.4 Hypothesis of the testing for awareness of the potential benefits

Table 6.4.8 One-Sample T test for Increment of willingness to try the service

One-Sample Test Test Value = 0

t df Sig. (1-tailed)

3.425 29 0.001

The result (Table 6.4.8) shows that the t value is 3.425 and its significance level is 0.001 (p-value = 0.001), which is below 0.01. Therefore, the null hypothesis H0 is rejected and it means there is statistically significant evidence to support our proposition 1-B, once the process of anchoring effect happens, the willingness to try the service will increase.

Furthermore, in the micro view, we used some descriptive statistics and ran an Independent-Samples T test and we have an interesting finding that the users and the service providers have the different means of readiness score to change (see Table 6.4.9, Figure 6.4.5 and Table 6.4.10).

Table 6.4.9 Descriptive statistics for users and service providers

Descriptive statistics

μ = popultion mean of increment.

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Figure 6.4.5 Hypothesis for difference between the increment of users and service providers

Table 6.4.10 Independent-Samples T test for difference between the increment of users and service providers

Independent-Samples Test

t df Sig. (2-tailed)

2.193 28 0.037

We can see that the t value is 2.193 and its significance level is 0.037 (p-value = 0.037), which is below 0.05. Therefore, the null hypothesis H0 is rejected and it means there is statistically significant difference of readiness to change score between the users and service providers. And from the descriptive statistics, we can say that the mean of users’ change of willingness to try the service is much higher than the service providers. We will discuss and infer the reason in section 6.4.4.

 Result for Proposition 2:

To examine the proposition 2, would a person’s motivation toward positive end-state affect the willingness to try the service, first we need transfer the result of threshold on the 7-point Likert scale that ranged from strongly high to strongly low. We gave the score 7 to option “strongly high”, 6 to option “high”, 5 to option “a little high”, 4 to “normal”, 3 to option “a little low”, 2 to option

𝐻0: 𝑇ℎ𝑒𝑟𝑒 𝑖𝑠 𝑛𝑜 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑡ℎ𝑒 𝑖𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡 𝑜𝑓 𝑢𝑠𝑒𝑟𝑠 𝑎𝑛𝑑 𝑝𝑟𝑜𝑣𝑖𝑑𝑒𝑟𝑠.

𝐻₁: 𝐻₀ 𝑖𝑠 𝑛𝑜𝑡 𝑡𝑟𝑢𝑒.

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“low”, and 1 to option “strongly low”. Then we added all the values of threshold of service attributes to represent each subject’s level of motivation toward positive end-state. We could say that a person’s motivation toward positive end-state is more promotion-concerned with the higher value; and a person’s motivation toward positive end-state is more prevention-concerned with the lower value. And we did a simply classification using the median of sum of the motivation, which is 28, as the critical value to classify the subjects. The organized data is shown in Table 6.4.11.

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Table 6.4.11 Data of Threshold of service’s value for the service attributes

Subject

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Sum

Prevention-concerned /Promotion-concerned

Increment of Readiness to Change

Score

Subject1 2 2 3 4 4 3 2 2 4 2 28 Promotion-concerned 1.00

Subject2 2 4 2 3 2 3 2 4 4 2 28 Promotion-concerned 0.66

Subject3 3 4 3 3 4 2 2 3 4 2 30 Prevention-concerned 0.34

Subject4 3 4 3 3 4 3 4 4 4 4 36 Prevention-concerned 2.00

Subject5 3 3 4 2 3 2 2 2 3 3 27 Promotion-concerned -0.33

Subject6 2 3 2 3 3 1 1 3 4 1 23 Promotion-concerned 0.00

Subject7 3 2 2 1 3 2 1 2 1 1 18 Promotion-concerned 3.33

Subject8 1 3 2 2 4 2 3 4 4 3 28 Promotion-concerned 1.66

Subject9 2 2 5 6 2 1 1 2 5 1 27 Promotion-concerned 1.00

Subject10 4 5 3 2 2 2 4 4 3 4 33 Prevention-concerned 1.00

Subject11 2 4 2 2 3 2 2 4 4 4 29 Prevention-concerned 1.33

Subject12 5 4 3 3 3 3 4 3 4 2 34 Prevention-concerned -1.00

Subject13 4 5 3 3 5 4 3 2 4 3 36 Prevention-concerned -0.67

Subject14 5 4 2 2 4 4 3 4 4 2 34 Prevention-concerned 0.33

Subject15 2 3 1 1 2 2 1 4 1 1 18 Promotion-concerned 1.33

Subject16 2 1 3 2 6 3 1 1 1 3 23 Promotion-concerned 0.67

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Subject17 2 1 3 1 2 1 1 3 1 1 16 Promotion-concerned 1.00

Subject18 3 2 2 3 2 1 1 3 4 4 25 Promotion-concerned 3.00

Subject19 3 3 2 2 1 1 2 2 3 3 22 Promotion-concerned 1.00

Subject20 4 4 2 2 3 2 2 3 4 3 29 Prevention-concerned 0.33

Subject21 2 2 2 2 4 3 4 5 4 3 31 Prevention-concerned 1.00

Subject22 3 1 3 1 2 5 5 3 2 6 31 Prevention-concerned 0.67

Subject23 5 4 3 2 5 2 2 2 2 2 29 Prevention-concerned 1.33

Subject24 6 6 3 2 1 4 4 4 3 2 35 Prevention-concerned 0.33

Subject25 4 2 4 2 3 2 2 2 3 4 28 Promotion-concerned 3.00

Subject26 3 2 2 2 3 3 2 2 1 3 23 Promotion-concerned 0.00

Subject27 3 2 2 2 4 4 3 4 3 6 33 Prevention-concerned 1.00

Subject28 2 2 3 1 3 3 2 3 2 3 24 Promotion-concerned 1.33

Subject29 2 3 2 1 1 4 4 2 4 1 24 Promotion-concerned 1.33

Subject30 6 5 6 3 5 5 2 1 6 4 43 Prevention-concerned -3.67

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From the descriptive statistics (see Figure 6.4.12) for the classification result, we can see that the people who are more promotion-concerned has the higher increment of willingness to change than the people who are more prevention-concerned. To be more rigorous to the result, we use another statistic method, Spearman's rank correlation coefficient, to examine the relationship between threshold of service attributes’ value and increment of Readiness to Change Score (see Table 6.4.13).

Table 6.4.12 Descriptive statistics for prevention and promotion concerned

Descriptive statistics

Table 6.4.13 Spearman's rank correlation coefficient for motivation toward positive end-state and Increment of Readiness to Change Score

Spearman's rho

Variables: Motivation(Threshold) & Increment of Readiness to Change Score

Correlation Coefficient Sig. (2-tailed) N

- 0.530 0.003 30

From Table 6.4.13, we can see the Spearman’s correlation coefficient’s value is -0.530 (p-value = 0.003, which is below 0.01) and that means there is a statistically significant evidence supports that these two variables react in the contrary way. That is, if an individual’s threshold for service attribute’s quality is high (which means more prevention-concerned), the increment of willingness

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through the anchoring process decreases; if an individual’s threshold for service attribute’s quality is low (which means more promotion-concerned), the increment of willingness through the anchoring process increases. This result well supports the position 2-A and proposition 2-B.

Again, in the micro view, we used some descriptive statistics and ran an Independent-Samples T test and we found that that the users and the service providers have the different means of motivation (see Table 6.4.14, Figure 6.4.6 and Table 6.4.15).

Table 6.4.14 Descriptive statistics for motivation of users and service providers

Descriptive statistics

Groups N

Mean

Std.

Deviation

Std. Error Mean motivation toward

positive end-state

Users 14

25.36

5.37 1.43

Service

Providers 16

30.63

5.56 1.39

Figure 6.4.6 Hypothesis for difference between the motivation of users and service providers

𝐻₀: 𝑇ℎ𝑒𝑟𝑒 𝑖𝑠 𝑛𝑜 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑡ℎ𝑒 𝑚𝑜𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑢𝑠𝑒𝑟𝑠 𝑎𝑛𝑑 𝑝𝑟𝑜𝑣𝑖𝑑𝑒𝑟𝑠.

𝐻1: 𝐻0 𝑖𝑠 𝑛𝑜𝑡 𝑡𝑟𝑢𝑒.

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Table 6.4.15 Independent-Samples T test for difference between the motivation of users and service providers

Independent-Samples Test

t df Sig. (2-tailed)

-2.63 28 0.014

We can see that the t value is -2.63 and its significance level is 0.014 (p-value = 0.014), which is below 0.05. Therefore, the null hypothesis H0 is rejected and it means there is statistically significant difference of motivation toward positive end-state between the users and service providers. And from the descriptive statistics, we can see that the mean of users’ motivation toward positive end-state is lower than the service providers’, which means the users are more promotion–

concerned than the service providers. We will discuss this phenomenon and infer the reason of it in the later section 6.4.4.

 Result for Proposition 3:

To justify proposition 3, whether level of anchoring effect is able to represent motivation toward positive end-state of a specific target, we first calculated all the adjustment indexes (see Table 6.4.16). Then we used the all subjects’

threshold of service attributes’ values and the related adjustment indexes to run a Spearman's rank correlation coefficient to examine the relationship between them (see Table 6.4.17).

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Table 6.4.16 Data of adjustment index for the service attributes

Subject

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Subject1 0.125 0.5 0.5 0.125 0.125 0.125 0 0 0.625 0.125

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Table 6.4.17 Spearman's rank correlation coefficient for motivation toward positive end-state and Adjustment Index

Spearman's rho

Variables: Preference Level & AI

Correlation Coefficient Sig. (2-tailed) N

-0.131 0.023 300

From Table 6.4.17, we can see the Spearman’s correlation coefficient’s value is -0.131 (p-value = 0.023, which is below 0.05) and that means there is a statistically significant evidence supports that these two variables react in the contrary way. That is, if the value motivation toward positive end-state increases (which means more prevention-concerned and a high threshold to be satisfied with the quality of the attribute), the adjustment index decreases (which means the level of anchoring effect is smaller); if the value motivation toward positive end-state decreases (which means more promotion-concerned and a low threshold to be satisfied with the quality of the attribute), the adjustment index increases (which means the level of anchoring effect is bigger). This result supports the position 3, “the level of anchoring effect is able to represent motivation toward positive end-state of a specific target, for example, an attribute of a service”, since we can say that a big adjustment index is accompanied by the promotion-concerned attitude of an attribute and a small adjustment index is accompanied by the promotion-concerned attitude.

 Other results:

To find other implication of these data from the mciro view, we use the profile of the subjects to categorize the data and try to explain the findings in the later section.

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First, we used the role of the subjects as the point of view (see Figure 6.4.8) and found that the users’ mean of threshold is lower than the service providers’, which means users are more promotion-concerned while the service providers are more prevention-concerned. And we also observed that the users’ mean of increment of willingness is higher than the service providers’.

Figure 6.4.8 Difference between the users and service providers

Second, we used their background, business and engineering, as the point of view (see Figure 6.4.9) and found that the business-related subjects’ mean of threshold is lower than the engineering-related subjects’, which means business-related subjects are more promotion-concerned while the engineering-related subjects are more prevention-concerned. And we also observed that the business-related subjects’ mean of increment of willingness is higher than the engineering-related subjects’.

25.36

Mean of threshold Mean of increment of willingness

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Figure 6.4.9 Difference between business-related and engineering-related

Third, we used gender as the point of view (see Figure 6.4.10) and found that the females’ mean of threshold is lower than the males’, which means females are more promotion-concerned while the males’ are more prevention-concerned.

And we also observed that the females’ mean of increment of willingness is higher than the males’.

27.00

30.71

0.98 0.48

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00

Business Engineering

Mean of threshold Mean of increment of willingness

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Figure 6.4.10 Difference between Female and Male

Last, we followed the suggestion of URICA questionnaire to use readiness to change score to categorize the subjects. The suggested standards for the original 12-item URICA are that scores between -2 to 8 are categorized into pre-contemplation, scores between 8 and 11 were categorized into contemplation and scores between 11 and 14 were categorized into preparation to action.

However, as mentioned before, we used a modified 9-item version of URICA without maintenance subscale. The range of 9-item’s readiness score is -3 to 9.

To generate the categorization standards for 9-item version, we used linear interpolation to transfer the old boundaries. The new standards are that scores below 4.5 will be categorized into pre-contemplation, scores above 4.5 and below 6.75 will be categorized into contemplation and scores above 6.75 will be categorized into preparation to action. The categorized result is displayed in Table 6.4.18. We can see that before anchoring process, there are 14 subjects in

27.17 28.83

1.00 0.68

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00

Female Male

Mean of threshold Mean of increment of willingness

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pre-contemplation stage, 14 subjects in contemplation stage and 2 subjects in preparation to action stage; and after anchoring process, there are 7 subjects in pre-contemplation stage, 14 subjects in contemplation stage and 9 subjects in preparation to action stage (see Figure 6.4.11).

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Table 6.4.18 Behavior Change Stage of Subjects

Subject

Subject1 4.67 Contemplation 5.67 Contemplation

Subject2 2.67 Pre-contemplation 3.33 Pre-contemplation

Subject3 5.33 Contemplation 5.67 Contemplation

Subject4 5.33 Contemplation 7.33 Preparation to Action

Subject5 4.33 Pre-contemplation 4.00 Pre-contemplation

Subject6 6.67 Contemplation 6.67 Contemplation

Subject7 5.00 Contemplation 8.33 Preparation to Action

Subject8 5.67 Contemplation 7.33 Preparation to Action

Subject9 6.67 Contemplation 7.67 Preparation to Action

Subject10 4.00 Pre-contemplation 5.00 Contemplation

Subject11 4.00 Pre-contemplation 5.33 Contemplation

Subject12 7.00 Preparation to Action 6.00 Contemplation

Subject13 6.00 Contemplation 5.33 Contemplation

Subject14 4.33 Pre-contemplation 4.67 Contemplation

Subject15 7.00 Preparation to Action 8.33 Preparation to Action

Subject16 6.33 Contemplation 7.00 Preparation to Action

Subject17 6.67 Contemplation 7.67 Preparation to Action

Subject18 1.33 Pre-contemplation 4.33 Pre-contemplation

Subject19 6.00 Contemplation 7.00 Preparation to Action

Subject20 3.00 Pre-contemplation 3.33 Pre-contemplation

Subject21 4.00 Pre-contemplation 5.00 Contemplation

Subject22 4.00 Pre-contemplation 4.67 Contemplation

Subject23 5.67 Contemplation 7.00 Preparation to Action

Subject24 5.33 Contemplation 5.67 Contemplation

Subject25 3.33 Pre-contemplation 6.33 Contemplation

Subject26 4.00 Pre-contemplation 4.00 Pre-contemplation

Subject27 4.67 Contemplation 5.67 Contemplation

Subject28 4.33 Pre-contemplation 5.67 Contemplation

Subject29 3.00 Pre-contemplation 4.33 Pre-contemplation

Subject30 3.67 Pre-contemplation 0.00 Pre-contemplation

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Figure 6.4.11 Difference between before anchoring and after anchoring

We also used the behavior change stages as the point of view to see the threshold and increment of willingness(see Figure 6.4.11) and found that the pre-contemplators’ mean of threshold is the highest, contemplators’ mean is less than pre-contemplators’, and mean of the subjects in the preparation to action subjects in the preparation to action stage is the lowest.

Before anchoring After anchoring

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Figure 6.4.12 Difference between different behavior change stages

在文檔中 運用錨定理論於創新服務特色之心理偏好分析 - 政大學術集成 (頁 111-135)