Using the Social Influence of Electronic Word-of-Mouth for Predicting Product Sales: The Moderating Effect of Review or Reviewer Helpfulness and Product Type

Round 1

Reviewer 1 Report

This study aims to explore the moderating effect of review or reviewer helpfulness and product type (experience or search goods) on the relationship between eWOM and product sales. While this study has some interesting points, I have several major concerns for the study. My comments are as follows.

First, the research framework is not clear. This study is based on social exchange theory to explore social influence of eWOM. However, I cannot find the variables regarding social exchange in the research framework. In the Introduction, the authors argued that “Helpful reviews increase the sites’ business value, and providing helpful reviews tends to result in attracting consumers seeking information and greater potential value to customers. Consequently, eWOM factors such as volume, valence, helpfulness help reducing social risk in purchasing through e-commerce by highlighting social engagement and the quality of recommendations for each product (Chen and Tseng, 2011).” (p. 2) Thus, this state that review helpfulness positively affects product sales. Why do the authors test the moderating effect of review helpfulness in the model? Similarly, reviewer helpfulness has the same problem. In addition, the moderating effect of product type is also doubtful. Why do the authors test the moderating effect of search goods on the relationship between the volume of reviews and product sales, as H3 proposed? (p. 7)

Second, literature review is weak. Why do the authors select average number of reviews X, average review depth, average review rating, and average review extremity as the independent variables in the model? The authors need to clearly justify the research framework of the study.

Moreover, in the hypothesis development, why do the authors propose the hypotheses (e.g., H3, H4, H7, H8) regarding the moderating effect for search and experience goods rather than for product type (search goods vs. experience goods)?

Third, in the data collection, the authors need to explain how to judge search goods and experience goods in the study. For example, why Consumer Electronics is an experience good? Home improvement is a search good? (see table 1, p. 10) Moreover, the reliability and validity are not clear in the study.

Fourth, in the Results and Discussion, the authors argued that “Table 4 shows that hypothesis H1 (review volume with helpfulness), H2 (review volume with helpfulness and search goods), H4 (review extremity with helpfulness and search goods), H5 (volume with reviewer helpfulness), H7 (review rating with reviewer helpfulness) are accepted.” (p. 12) Table 4 is the result of regression analysis. It does not test the moderating effect here. Thus, I suspect this argument.

Finally, I cannot see the important implications in the study (p. 16-17). I think this study lacks the proper research framework and the research gap is also unclear. Thus, the contribution of the study is not significant.

Author Response

Please see the attachment.

 

Author Response File: Author Response.docx

Reviewer 2 Report

The aim of the study is to suggest the moderating effect of review or reviewer helpfulness and product type (experience or search goods) on the relationship between eWOM and product sales using the prediction power of Business Intelligence (BI) methods for each subsample of products which is created according to high or low review and reviewer helpfulness.

Strengths and weaknesses:

The aim and purposes of the manuscript are clearly defined, the manuscript is well structured, but the manuscript is very descriptive, repeating the same ideas many times. The methodology is adequate to investigate the type of subject that is analysed and the results of the study are presented and discussed. Implications to research and to practice are presented as well as the limitations of the study.

eWom and product data used are old, they were created between 2011 and 2016.

Figures and tables should be presented more carefully and aesthetically more beautiful. Repeat the headings of tables 2 and 4 in the two pages.

The following improvements should also be considered:

Line 123: “Ngo and Sinha, 2014” is missing in References;

Line 161: “Duan et al., 2006” is missing in References. There is “Duan et al.” reference that is of 2008;

Line 257: “Van Der Heide et al., 2013” is missing in References;

Line 504: “Kwon and Sung, 2012” is missing in References;

Lines 796 and 797: “Teng S, Khong KW, Goh WW, Chong, AYL (2014) Examining the antecedents of persuasive eWOM 796 messages in social media. Online Information Review 38(6):746 – 682 is not mentioned in the text.

Author Response

Please see the attachment.

 

Author Response File: Author Response.docx

Reviewer 3 Report

This is an interesting and original article. The subject of the article is very important of Electronic Word-of-mouth for Predicting Product Sales. The research is interesting and the conclusions clearly formulated. This article is recommendable.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

My review comments:

The authors do not adequately answer my first round questions. The research framework is still unclear. The authors argued that “Among eWOM variables, the effects of volume and valence on sales have been most commonly investigated (Chintagunta et al, 2010; Duan et al., 2008). Our study attempts to extend this line of eWOM studies of volume and valence by suggesting moderators of review or reviewer helpfulness, which represent review quality and thus affect sales.” However, in my opinion, the volume and valence of review may affect review helpfulness and reviewer helpfulness, which in turn increase product sales. Moreover, the review may have positive or negative contexts. Thus, if the contexts are negative, even though the volume is large, product sales may decrease.

Moreover, the authors combine review helpfulness, reviewer helpfulness and product type to develop the hypotheses, such as H3, H4, H7, and H8. However, the arguments for developing these hypotheses are weak. Why do experience goods and review helpfulness have a negative moderating effect, as H4 proposed? In my opinion, review helpfulness is more important when consumers buy experience goods. Moreover, the arguments of H5 and H6 are not adequate for developing the moderating effect, and the arguments for developing H7 and H8 are missing.

In the data collection, the authors still do not explain the measurement of search goods and experience goods in the study. For example, why Consumer Electronics is an experience good? Home improvement is a search good? (see table 1, p. 10) Moreover, the measurements of review helpfulness and reviewer helpfulness are not clear. How to divide the sample? The subsample is not equal.

In the Results and Discussion, the authors argued that “Table 4 shows that hypothesis H1 (review volume with helpfulness), H2 (review volume with helpfulness and search goods), H4 (review extremity with helpfulness and search goods), H5 (volume with reviewer helpfulness), H7 (review rating with reviewer helpfulness) are accepted.” (p. 12) Table 4 is the result of Regression analysis, but not the result of moderating effect.

Finally, I think the research gap and research framework are not clear. Thus, the contribution of the study is not significant.

Author Response

Please see the attachment.

Author Response File: Author Response.docx