CHAPTER 3: METHOD
3.5 Pilot Test
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The intention to click “Like” for the posts was assessed using the model proposed by Chin et al. (2015). The instrument included 3 items to measure Facebook users’ intention to click the “Like” button for the posts. Participants were asked to rate their agreement with the statements on a seven-point Likert-type scale (ranging from 1 = “Strongly disagree” to 7 =
“Strongly agree”). The statements are shown below:
1. I am very willing to click “Like” after reading this post;
2. I would like to click “Like” for this post;
3. I do not think I should click “Like” for this post.
3.5 Pilot Test
Before testing the hypotheses, a pilot test was conducted to confirm the design of the stimulus materials and the questionnaire. 45 participants (9 groups of stimulus materials, 5 participants in each group) took part in the pilot test. The website address (URL) of the mock-up Corporate Facebook Page and questionnaire was sent to the participants. After viewing the mock-up Facebook Page, they were directed to the online questionnaire to measure their perceived corporate reputation of the restaurant, purchase intention and intention to click “Like” for the posts. The questionnaires were collected and the data was entered into the SPSS. Analysis indicated that the scales for corporate reputation along the 7 dimensions (food and service quality, brand affect, self-congruence, brand awareness, brand association, brand trust and overall brand reputation), purchase intention and intention to click “Like” for posts were reliable (α value > 0.7). The corporate reputation measurement scale was reliable with Cronbach’s α = 0.859, the intention to purchase scale was reliable with α = 0.752, and the intention to click “Like” for posts scale was reliable with α = 0.925.
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4.1 Effect of Content on Corporate Reputation
An analysis of variance (ANOVA) was conducted to test Hypothesis 1. It aimed at examining and comparing the effects of corporate-generated content, identifiable consumer-generated content and unidentifiable consumer-generated content on corporate reputation. The corporate reputation variable was created from a 24-item seven-point Likert scale by accumulating the score of each item. The scale was reliable, α = 0.961, n = 270.
According to the results of ANOVA, corporate-generated content, identifiable consumer-generated content and unidentifiable consumer-generated content had significantly different effects on corporate reputation, F(2, 87) = 6.49, df = 2, p < 0.05. Thus, fisher least significant difference (LSD) test was then conducted to further investigate the differences of the effects (figure 2).
Supporting H1a, participants in Group 2 (the identifiable consumer-generated content condition) reported that the identifiable consumer-generated content had significantly more positive effect on corporate reputation, M = 5.28, SD = 1.01, relative to participants in Group 1 (the corporate-generated content condition), M = 4.77, SD = 0.51, F(2, 87) = 6.49, df = 2, p
< 0.05. Next, participants in Group 2 reported that the identifiable consumer-generated content had significantly more positive effect on corporate reputation, M = 5.28, SD = 1.01, relative to participants in Group 3 (the unidentifiable consumer-generated content condition), M = 4.53, SD = 0.89, F(2, 87) = 6.49, df = 2, p < 0.05, supporting H1b.
Regarding the comparison between the effect of corporate-generated content and unidentifiable consumer-generated content on corporate reputation, participants in Group 1 reported that the corporate-generated content had more positive effect on corporate reputation, M = 4.77, SD = 051, than unidentifiable consumer-generated content (participants in Group
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statistics, corporate-generated content had stronger effect on corporate reputation than unidentifiable consumer-generated content, yet the effect was not significant according to LSD. Therefore, H1c is not supported.Figure 2. Effects of corporate Facebook page content on corporate reputation.
Note. Group 1 = Corporate-generated content without showing number of “Likes”;
Group 2 = Identifiable consumer-generated content without showing number of “Likes”;
Group 3 = Unidentifiable consumer-generated content without showing number of “Likes”.
4.2 Effect of Content on Intention to Purchase
An analysis of variance was conducted to test Hypothesis 2, examining the effect of corporate-generated content, identifiable consumer-generated content and unidentifiable consumer-generated content on purchase intention. The purchase intention variable was created from a 4-item seven-point Likert scale by accumulating the score of each item. The scale was reliable, α = 0.892, n = 270. According to the results of ANOVA, corporate-generated content, identifiable consumer-generated content and unidentifiable
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Effects of Corporate Facebook Page Content on Corporate Reputation
Group 1 Group 2 Group 3 2
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consumer-generated content had significantly different effects on purchase intention, F(2, 87)
= 4.23, df = 2, p < 0.05. Thus, fisher least significant difference (LSD) test was then conducted to further investigate the differences of the effects (figure 3).
Participants in Group 2 reported that the identifiable consumer-generated content had more positive effect on purchase intention, M = 5.40, SD = 1.14, relative to the corporate-generated content in Group 1, M = 5.15, SD = 0.55, F(2, 87) = 4.23, df = 2, p = 0.296 > 0.05. Descriptive analysis shows that identifiable consumer-generated content had stronger effect on intention to purchase than corporate-generated content. However, ANOVA shows the effect to be not significant. H2a is not supported.
Supporting H2b, participants in Group 2 reported that the identifiable consumer-generated content had significantly more positive effect on purchase intention, M = 5.40, SD = 1.14, relative to the unidentifiable consumer-generated content in Group 3, M = 4.72, SD = 0.97, F(2, 87) = 4.23, df = 2, p < 0.05.
H2c is not supported as participants in Group 1 reported that the corporate-generated content had more positive effect on purchase intention, M = 5.15, SD = 0.55, relative to the unidentifiable consumer-generated content in Group 3, M = 4.72, SD = 0.97, F(2, 87) = 4.23, df = 2, p = 0.072 > 0.05. According to descriptive statistics, corporate-generated content had
stronger effect on purchase intention than unidentifiable consumer-generated content, yet the effect was not significant on ANOVA.
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Figure 3. Effects of corporate Facebook page content on purchase intention.
Note. Group 1 = Corporate-generated content without showing number of “Likes”;
Group 2 = Identifiable consumer-generated content without showing number of “Likes”;
Group 3 = Unidentifiable consumer-generated content without showing number of “Likes”.
4.3 Effect of Content on Intention to Click “Like” for Posts
An analysis of variance was conducted to test Hypothesis 3, examining and comparing the effect of corporate-generated content, identifiable consumer-generated content and unidentifiable consumer-generated content on intention to click “Like” for the posts. The intention to click “Like” for posts variable was created from a 3-item seven-point Likert scale by accumulating the score of each item. The scale was reliable, α = 0.934, n = 270. According to the results of ANOVA, corporate-generated content, identifiable consumer-generated content and unidentifiable consumer-generated content did not have significantly different effects on intention to click “Like” for posts, F(2, 87) = 1.82, df = 2, p = 0.167 > 0.05. H3 is not supported.
Participants in Group 2 reported that identifiable consumer-generated content had more positive effect on intention to click “Like” for posts (figure 4), M = 4.78, SD = 1.54, than
Effects of Corporate Facebook Page Content on Intention to Purchase
Group 1 Group 2 Group 3 2
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corporate-generated content reported by participants in Group 1, M = 4.39, SD = 1.22, than unidentifiable consumer-generated content reported by participants in Group 3, M = 4.14, SD
= 1.10. In line with Hypothesis 3, identifiable consumer-generated content did have stronger effect on intention to click “Like” for posts than corporate-generated content and unidentifiable consumer-generated content on descriptive analysis, yet the positive effect was not significant on ANOVA.
Figure 4. Effects of corporate Facebook page content on intention to click “Like” for posts.
Note. Group 1 = Corporate-generated content without showing number of “Likes”;
Group 2 = Identifiable consumer-generated content without showing number of “Likes”;
Group 3 = Unidentifiable consumer-generated content without showing number of “Likes”.
4.4 Effect of Number of Likes
4.4.1 Effect of Number of Likes on Corporate-generated Content
An analysis of variance (ANOVA) was conducted to test Hypothesis 4a. According to the results of ANOVA, corporate-generated content with no information of number of likes (Group 1), few likes (Group 4) and many likes (Group 7) had significantly different effects
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Effects of Corporate Facebook Page Content on Intention to click "Like"
Group 1 Group 2 Group 3 2
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on corporate reputation, F(2, 87) = 11.8, df = 2, p < 0.05. Thus, LSD test was then conducted to further investigate the differences of the effects (figure 5).
Participants in Group 7 reported that the corporate-generated content with many likes had significantly more positive effect on corporate reputation, M = 5.20, SD = 0.59, than participants in Group 1 (the corporate-generated content with no information of number of likes condition), M = 4.77, SD = 0.51, F(2, 87) = 11.8, df = 2, p < 0.05, and participants in Group 4 (the corporate-generated content with few likes condition), M = 4.39, SD = 0.79, F(2, 87) = 11.8, df = 2, p < 0.001. In sum, the corporate-generated content with more “Likes” have more positive effect on corporate reputation than the other groups, supporting H4a.
Figure 5. Effects of corporate-generated content with “Likes” on corporate reputation.
Note. Group 1 = Corporate-generated content without showing number of “Likes”;
Group 4 = Corporate-generated content with “Few Likes”;
Group 7 = Corporate-generated content with “Many Likes”.
Next, an analysis of variance (ANOVA) was conducted to test Hypothesis 4b. According to the results of ANOVA, corporate-generated content with no information of number of likes
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Effects of Corporate-generated Content with "Likes" on Corporate Reputation
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(Group 1), few likes (Group 4) and many likes (Group 7) had significantly different effects on purchase intention, F(2, 87) = 16.2, df = 2, p < 0.05. Thus, LSD test was then conducted to further investigate the differences of the effects (figure 6).
Participants in Group 7 reported that the corporate-generated content with many likes had significantly more positive effect on purchase intention, M = 5.43, SD = 0.56, than participants in Group 1 (the corporate-generated content with no information of number of likes condition), M = 5.15, SD = 0.55, F(2, 87) = 16.2, df = 2, p < 0.05, and participants in Group 4 (the corporate-generated content with few likes condition), M = 4.41, SD = 0.95, F(2, 87) = 11.8, df = 2, p < 0.001. In sum, the corporate-generated content with more “Likes” have more positive effect on purchase intention than the other groups, supporting H4b.
Figure 6. Effects of corporate generated-content with “Likes” on purchase intention.
Note. Group 1 = Corporate-generated content without showing number of “Likes”;
Group 4 = Corporate-generated content with “Few Likes”;
Group 7 = Corporate-generated content with “Many Likes”.
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Effects of Corporate-generated Content with "Likes" on Purchase Intention
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An analysis of variance (ANOVA) was conducted to test Hypothesis 4c. According to the results of ANOVA, corporate-generated content with no information of number of likes (Group 1), few likes (Group 4) and many likes (Group 7) did not have significant different effects on intention to click “Like” for posts, F(2, 87) = 0.80 df = 2, p = 0.923 > 0.05. Since the effect was not significant, H4c is not supported.
4.4.2 Effect of Number of Likes on Identifiable Consumer-generated Content
Regarding the effect of number of likes on identifiable consumer-generated content, an analysis of variance (ANOVA) was conducted to test Hypothesis 5a. According to the results of ANOVA, identifiable consumer-generated content with no information of number of likes (Group 2), few likes (Group 5) and many likes (Group 8) had significantly different effects on corporate reputation, F(2, 87) = 14.3, df = 2, p < 0.05. Thus, LSD test was then conducted to further investigate the differences of the effects (figure 7).
Participants in Group 8 reported that the identifiable consumer-generated content with many likes had significantly more positive effect on corporate reputation, M = 5.70, SD = 0.68, than participants in Group 2 (the identifiable consumer-generated content with no information of number of likes condition), M = 5.28, SD = 1.01, F(2, 87) = 14.3, df = 2, p <
0.001, and participants in Group 5 (the identifiable consumer-generated content with few likes condition), M = 5.03, SD = 0.42, F(2, 87) = 11.8, df = 2, p < 0.001. In sum, the identifiable consumer-generated content with more “Likes” have more positive effect on corporate reputation than the other groups, supporting H5a.
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Figure 7. Effects of identifiable consumer-generated content with “Likes” on corporate
reputation.
Note. Group 2 = Identifiable consumer-generated content without showing number of
“Likes”;
Group 5 = Identifiable consumer-generated content with “Few Likes”;
Group 8 = Identifiable consumer-generated content with “Many Likes”.
After that, an analysis of variance (ANOVA) was conducted to test Hypothesis 5b.
According to the results of ANOVA, identifiable consumer-generated content with no information of number of likes (Group 2), few likes (Group 5) and many likes (Group 8) had significantly different effects on purchase intention, F(2, 87) = 5.33, df = 2, p <0.05. Thus, LSD test was then conducted to further investigate the differences of the effects (figure 8).
Participants in Group 8 reported that the identifiable consumer-generated content with many likes had significantly more positive effect on corporate reputation, M = 5.94, SD = 1.01, than participants in Group 2 (the identifiable consumer-generated content with no information of number of likes condition), M = 5.40, SD = 1.14, F(2, 87) = 5.33, df = 2, p <
0.05, and participants in Group 5 (the identifiable consumer-generated content with few likes 5.28
Effects of Identifiable Consumer-generated Content with "Likes" on Corporate Reputation
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consumer-generated content with more “Likes” have more positive effect on purchase intention than the other groups, supporting H5b.Figure 8. Effects of identifiable consumer-generated content with “Likes” on purchase
intention.
Note. Group 2 = Identifiable consumer-generated content without number of “Likes”;
Group 5 = Identifiable consumer-generated content with “Few Likes”;
Group 8 = Identifiable consumer-generated content with “Many Likes”.
Testing Hypothesis 5c, an analysis of variance (ANOVA) was conducted. According to the results, identifiable consumer-generated content with no information of number of likes (Group 2), few likes (Group 5) and many likes (Group 8) did not have significant different effects on intention to click “Like” for posts, F(2, 87) = 0.81, df = 2, p = 0.447 > 0.05. Since the effect was not significant, H5c is not supported.
5.4
Effects of Identifiable Consumer-generated Content with "Likes" on Purchase Intention
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4.4.3 Effect of Number of Likes on Unidentifiable Consumer-generated Content
An analysis of variance (ANOVA) was conducted to test H6a. According to the results, unidentifiable consumer-generated content with no information of number of likes (Group 3), few likes (Group 6) and many likes (Group 9) had significantly different effects on corporate reputation, F(2, 87) = 5.23, df = 2, p < 0.05. Thus, LSD test was then conducted to further investigate the differences of the effects (figure 9).
Participants in Group 9 reported that the unidentifiable consumer-generated content with many likes had significantly more positive effect on corporate reputation, M = 5.11, SD = 0.72, than participants in Group 3 (the unidentifiable consumer-generated content with no information of number of likes condition), M = 4.53, SD = 0.89, F(2, 87) = 5.23, df = 2, p <
0.05, and participants in Group 6 (the unidentifiable consumer-generated content with few likes condition), M = 4.41, SD = 1.06, F(2, 87) = 11.8, df = 2, p < 0.01. In sum, the unidentifiable consumer-generated content with more “Likes” have more positive effect on corporate reputation than the other groups, supporting H6a.
Figure 9. Effects of unidentifiable consumer-generated content with “Likes” on corporate reputation.
Note. Group 3 = Unidentifiable consumer-generated content without number of “Likes”;
Group 6 = Unidentifiable consumer-generated content with “Few Likes”;
Group 9 = Unidentifiable consumer-generated content with “Many Likes”.
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Effects of Unidentifiable Consumer-generated Content with "Likes" on Corporate Reputation
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According to the results of ANOVA, unidentifiable consumer-generated content with no information of number of likes (Group 3), few likes (Group 6) and many likes (Group 9) did not have significantly different effects on purchase intention, F(2, 87) = 0.79, df = 2, p = 0.456 > 0.05. Thus, H6b is not supported.
ANOVA was conducted to test H6c. According to the results of ANOVA, unidentifiable consumer-generated content with no information of number of likes (Group 3), few likes (Group 6) and many likes (Group 9) did not have significant different effects on intention to click “Like” for posts, F(2, 87) = 2.76, df = 2, p = 0.069 > 0.05. H6c is also not supported.
Still, descriptive analysis was conducted, trying to compare the differences of the effects.
To conclude, H1a, H1b, H2b, H4a, H4b, H5a, H5b and H6a are supported (table 3). The other hypotheses are not supported since the effect was not significant according to ANOVA.
However, it is still meaningful to look at the descriptive statistics (figure 10, 11 and 12), showing that the results are in line with the direction of the hypotheses.
Figure 10. Perceived corporate reputation of content on Corporate Facebook Page.
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Figure 11. Purchase intention of content on Corporate Facebook Page.
Figure 12. Intention to click “Like” for posts on Corporate Facebook Page.
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Table 3
Hypotheses supported
H1a On corporate Facebook Pages, positive consumer-generated content is perceived to have more positive effect on corporate reputation than corporate-generated content.
H1b On Corporate Facebook Pages, positive identifiable consumer-generated content is perceived to have more positive effect on corporate reputation than positive unidentifiable consumer-generated content.
H2b On Corporate Facebook Pages, positive identifiable consumer-generated content is perceived to have more positive effect on purchase intention than positive unidentifiable consumer-generated content.
H4a Corporate-generated content with more “likes” is perceived to have more positive effect on corporate reputation than that with less “likes”.
H4b Corporate-generated content with more “likes” is perceived to have more positive effect on purchase intention than that with less “likes”.
H5a Positive identifiable consumer-generated content with more “likes” is perceived to have more positive effect on corporate reputation than that with less “likes”.
H5b Positive identifiable consumer-generated content with more “likes” is perceived to have more positive effect on purchase intention than that with less “likes”.
H6a Positive unidentifiable consumer-generated content with more “likes” is perceived to have more positive effect on corporate reputation than that with less “likes”.
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To investigate the effects of official information (company-generated content) and consumer reviews (consumer-generated content) on restaurant Corporate Facebook Pages on corporate reputation, purchase intention and intention to click “Like” for posts from the perspectives of warranting principle and herd behavior theory, seven hypotheses were developed and statistical analysis were performed using ANOVA technique and descriptive statistics.
The results support H1a and H1b, with positive identifiable consumer-generated content having more significant positive effect on corporate reputation than corporate-generated content and unidentifiable consumer-generated content. This supports previous literature regarding the warranting principle, as proposed by Walther et al. (2009), Lillqvist and Louhiala-Salminen (2014) and DeAndrea (2014). Walther et al. (2009) tested the effects of self-generated information against others-generated information and found that others-generated statements about the target have higher warranting value and can influence viewers’ impressions of the target more than self-generated information. Lillqvist and Louhiala-Salminen (2014) suggested that followers’ posts on corporate Facebook Pages represent a type of warranting - positive comments by consumers are more likely to affect impressions positively. DeAndrea et al. (2015) examined how viewers’ evaluations of a target on websites are influenced by user-generated content. The results indicate that the less people are confident that user-generated reviews are truly produced by third-party reviewers, the less people trust those reviews. The results of this research clearly confirm that identifiable others-generated statements about the target have higher warranting value and can influence corporate reputation more than self-generated information. The warranting value decreases as viewers become less confident that the user-generated information is truly produced by
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third-party consumers or reviewers. Identifiable consumer-generated content thus has more positive effect on corporate reputation than unidentifiable consumer-generated.
On the subject of the comparison of the effect of corporate-generated content and unidentifiable consumer-generated content, H1c is not supported as the effect was not significant enough. This may due to the fact that when participants or the general consumers read some information that looks very official on the corporate Facebook Pages, they naturally perceive the content as generated by the corporations. They may not spend too much time evaluating the profile pictures or names of the users to determine their true identity, instead they focus more on the content itself. This explains the reason why the effect of corporate-generated content and unidentifiable consumer-generated content on corporate reputation is not significantly different.
Regarding the effects of number of likes on corporate-generated content, H4a and H4b are supported. Corporate-generated content with the more number of likes has more positive effect on corporate reputation and purchase intention than the other groups. This shows the importance of the target to gather more “likes” for their posts on corporate Facebook Pages.
Corporate reputation and purchase intentions are positively affected by the number of likes of corporate-generated content on the pages. Although some of the effects are not found to be significant on ANOVA, it is still meaningful to look at the descriptive statistics. The content with more likes does have stronger effect on corporate reputation than the content with no information of the number of likes and content with fewer likes. What is specifically deserved to be mentioned is that the unidentifiable consumer-generated content with “many likes” has stronger effect on corporate reputation, M = 5.11, SD = 0.72, than corporate-generated content with no information of “likes”, M = 4.77, SD = 0.51. Although consumers may find the content generated by unidentifiable consumers less trustworthy than corporate-generated content, they may still choose to trust the information which is agreed by
Corporate reputation and purchase intentions are positively affected by the number of likes of corporate-generated content on the pages. Although some of the effects are not found to be significant on ANOVA, it is still meaningful to look at the descriptive statistics. The content with more likes does have stronger effect on corporate reputation than the content with no information of the number of likes and content with fewer likes. What is specifically deserved to be mentioned is that the unidentifiable consumer-generated content with “many likes” has stronger effect on corporate reputation, M = 5.11, SD = 0.72, than corporate-generated content with no information of “likes”, M = 4.77, SD = 0.51. Although consumers may find the content generated by unidentifiable consumers less trustworthy than corporate-generated content, they may still choose to trust the information which is agreed by