“Customer Reviews or Vlogger Reviews?” The Impact of Cross-Platform UGC on the Sales of Experiential Products on E-Commerce Platforms
Abstract
:1. Introduction
- Which of the following influences experiential product sales more: e-commerce platform online reviews or third-party platform product evaluations?
- What are the mechanisms that lead to differential impacts of cross-platform UGC on sales of experiential products?
- Integrating a cross-platform perspective and ELM theory. Through this integration, we develop a novel model that examines the underlying mechanisms influencing the impact of cross-platform UGC on product sales. This integration offers a new theoretical framework for understanding the persuasive effects of UGC in a multi-platform environment.
- Investigating the intrinsic mechanisms underlying the differential impact of cross-platform UGC on product sales. Through empirical analysis of cross-platform UGC data, this study explores the differential effects of e-commerce platform UGC and third-party platform UGC on product sales, along with the underlying causes for such differences, which were not clear in previous literature. By delving into this mechanism, our study enhances the understanding of how different types of UGC can vary in their effects on sales, with a specific focus on the influence of cross-platform UGC in the context of e-commerce. This study extends the existing literature in the intersecting domain of ELM and UGC by considering the diversified influence of cross-platform UGC on consumer information processing paths within the ELM process.
- Unveiling valuable insights for effective sales promotion strategies through cross-platform UGC utilization. We explore the moderating role of price in the relationships between cross-platform UGC and product sales. Furthermore, this research offers valuable insights to practitioners regarding sales promotion strategies through effective management practices of cross-platform UGC.
2. Relevant Literature
2.1. Effect of User-Generated Content on Online Sales
3.2.2. Impact of Peripheral Path UGC on Product Sales
3.2.3. Mediating Effect of Purchase Intention
3.2.4. Moderating Effect of Product Price
4. Research Methodology
4.1. Variable Definition and Measurement
4.2. Sample Selection and Data Collection
4.3. Online Review Inconsistency and Text Sentiment Analysis
4.3.1. Sentiment Polarity Analysis
4.3.2. Tone and Online Review Inconsistency Measures
5. Data Analysis and Results
5.1. Descriptive Statistics and Correlation Analysis
5.2. Main Effect Analysis
5.3. Analysis of the Mediating Effects of Purchase Intention
5.4. Analysis of the Moderating Effects of Product Price
6. Discussion
- Both the text features (Char, Svar) of online reviews from the central path and the third-party platform evaluation quantity (Tugc) from the peripheral path significantly impact product sales, with the central path having greater explanatory power. Moreover, online reviews from the central path are the primary source of information for consumers’ purchase decisions, which supports previous research findings [16,20]. Additionally, integrating cross-platform information from both paths is advantageous for boosting sales.
- Review length positively impacts sales, while review inconsistency has a negative impact. The quantity of third-party platform product evaluations has a positive impact on sales. An interesting phenomenon is that there is a significant positive correlation between review length and product sales, likely due to the perceived popularity it generates. Moreover, as illustrated in Table 5, third-party evaluations can, to some extent, weaken the negative impact of review inconsistency and enhance the positive effect of review length on sales. This is due to the fact that the number of third-party evaluations in the peripheral path can reduce information asymmetry in the central path and improve product sales.
- Purchase intention mediates the relationship between review length, review inconsistency, the quantity of third-party platform product evaluations, and sales. Moreover, purchase intention fully mediates the relationship between the number of third-party evaluations and sales, indicating that third-party platform UGC can only indirectly affect product sales by influencing consumer purchase intention. However, obtaining third-party platform evaluation information incurs additional costs for consumers. As such, consumers typically only purchase a product when they have a strong purchase intention. Therefore, Tugc may be more effective for non-rational or impulsive consumers in facilitating their purchasing decisions.
- Product price positively moderates the relationship between review inconsistency and the number of third-party platform evaluations on sales but not review length and sales. Higher-priced products raise consumers’ perceived risks, leading to more cautious purchase decisions and a greater reliance on cross-platform UGC information. The length of online reviews is unrelated to the product price and thus is not sensitive to the influence of price factors.
7. Conclusions and Implications
7.1. Theoretical Implications
7.2. Practical Implications
7.3. General Conclusions
- UGC originating from the e-commerce platform has a stronger impact on product sales than the UGC from the third-party platform. Moreover, the cross-platform UGC has a stronger impact on sales than the UGC from any single platform.
- UGC on e-commerce platforms can impact sales directly and through purchase intention, whereas third-party UGC only influences sales through purchase intention. Additionally, product price can strengthen the positive relationship between the number of third-party UGC and sales.
8. Limitations and Future Research Directions
- Unravel the mechanisms at the emotional level. Advances in auto-emotion-detection AI technologies may aid in uncovering and analyzing hidden emotions within UGC videos. Future research can employ these technologies, along with neuromarketing techniques (Neuromarketing technologies refer to the application of neuroscientific methods and techniques in marketing research and practice. These technologies aim to understand and measure consumers’ cognitive and emotional responses, subconscious processes, and neural activities when they engage with marketing stimuli. For more about neuromarketing, see, for example, [21,22,109,110].), to gain a deeper understanding of consumers’ emotional responses to the emotions expressed in both textual and video UGC. This approach enables a more comprehensive examination of the emotional dynamics present in cross-platform UGC.
- Broaden the range of products as well as the content type under investigation. By including multiple product types (not limited to the liquid foundation) and content types (i.e., UGC and MGC), future studies may consider how UGC and MGC from multiple platforms affect product sales differently. Many other experiential products can also be examined to generalize our findings.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
UGC | User-generated content |
ELM | Elaboration likelihood model |
NLP | Natural language processing |
VIF | Variance inflation factor |
eWOM | Electronic word-of-mouth |
MGC | Marketer-generated content |
Appendix A
No. | Steps | Explanations | Tools or Methods |
---|---|---|---|
1 | Building corpus | Building an adaptive corpus for online reviews of cosmetics in the e-commerce industry | Sentiment kernel file sentiment.marshal.3 of SnowNLP library |
2 | Word segmentation | The most basic step in text processing and analysis, which means dividing complete sentences into meaningful words | Segmentation tool jieba library; Marking rules: ICTCLAS tagging method of Chinese Academy of Sciences |
3 | Stop word filtering | Removing words unrelated to sentiment words, such as “one”, “prepare”, “several degrees”, etc. that appear in sample reviews | Self-built stop word table based on Baidu stop word table |
4 | Text feature weight calculation | Feature weight coefficients can not only represent the importance of features, but also represent the correlation, expression ability, and other aspects of features | TF-IDF algorithm |
5 | Text feature vectorization | Converting natural language text words into digital variables that machines can execute | Vector Space Model (VSM) |
6 | Text classification | Distinguishing text words into positive sentiment words and negative sentiment words | Naive Bayes Classifier (NBC) model; SnowNLP sentiment lexicon |
References
- Yang, X.; Liu, Y.; Dong, J.; Li, S. Impact of streamers’ characteristics on sales performance of search and experience products: Evidence from Douyin. J. Retail. Consum. Serv. 2023, 70, 103155. [Google Scholar] [CrossRef]
- Nelson, P. Information and consumer behavior. J. Political Econ. 1970, 78, 311–329. [Google Scholar] [CrossRef]
- Basu, S. Information search in the internet markets: Experience versus search goods. Electron. Commer. Res. Appl. 2018, 30, 25–37. [Google Scholar] [CrossRef]
- Lu, B.; Chen, Z. Live streaming commerce and consumers’ purchase intention: An uncertainty reduction perspective. Inf. Manag. 2021, 58, 103509. [Google Scholar] [CrossRef]
- Susan, M.M.; David, S. What makes a helpful online review? A study of customer reviews on amazon. com. MIS Q. 2010, 34, 185–200. [Google Scholar]
- Huang, A.H.; Chen, K.; Yen, D.C.; Tran, T.P. A study of factors that contribute to online review helpfulness. Comput. Hum. Behav. 2015, 48, 17–27. [Google Scholar] [CrossRef]
- Daugherty, T.; Eastin, M.S.; Bright, L. Exploring consumer motivations for creating user-generated content. J. Interact. Advert. 2008, 8, 16–25. [Google Scholar] [CrossRef]
- Hong, H.; Xu, D.; Wang, G.A.; Fan, W. Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decis. Support Syst. 2017, 102, 1–11. [Google Scholar] [CrossRef]
- Saura, J.R.; Dwivedi, Y.K.; Palacios-Marqués, D. Online User Behavior and User-Generated Content. Front. Psychol. 2022, 13, 895467. [Google Scholar] [CrossRef]
- Chen, Y.; ** the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis. J. Bus. Res. 2021, 135, 758–773. [Google Scholar] [CrossRef]
- Hong, W.; Yu, Z.; Wu, L.; Pu, X. Influencing factors of the persuasiveness of online reviews considering persuasion methods. Electron. Commer. Res. Appl. 2020, 39, 100912. [Google Scholar] [CrossRef]
- Cheng, M.; **, X. What do Airbnb users care about? An analysis of online review comments. Int. J. Hosp. Manag. 2019, 76, 58–70. [Google Scholar] [CrossRef]
- Rauschnabel, P.A.; Felix, R.; Hinsch, C. Augmented reality marketing: How mobile AR-apps can improve brands through inspiration. J. Retail. Consum. Serv. 2019, 49, 43–53. [Google Scholar] [CrossRef]
- ** logistics for customer satisfaction and repeat purchasing behavior: Evidence from China. Sustainability 2019, 11, 5626. [Google Scholar] [CrossRef] [Green Version]
- Chevalier, J.A.; Mayzlin, D. The effect of word of mouth on sales: Online book reviews. J. Mark. Res. 2006, 43, 345–354. [Google Scholar] [CrossRef] [Green Version]
- Li, K.; Chen, Y.; Zhang, L. Exploring the influence of online reviews and motivating factors on sales: A meta-analytic study and the moderating role of product category. J. Retail. Consum. Serv. 2020, 55, 102107. [Google Scholar] [CrossRef]
- Lee, S.; Choeh, J.Y. Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Syst. Appl. 2014, 41, 3041–3046. [Google Scholar] [CrossRef]
- Bosman, D.J.; Boshoff, C.; Van Rooyen, G.J. The review credibility of electronic word-of-mouth communication on e-commerce platforms. Manag. Dyn. J. S. Afr. Inst. Manag. Sci. 2013, 22, 29–44. [Google Scholar]
- Cheung, C.M.; ** cart use. J. Bus. Res. 2010, 63, 986–992. [Google Scholar] [CrossRef]
- Chen, Y.; Chai, Y.; Liu, Y.; Xu, Y. Analysis of review helpfulness based on consumer perspective. Tsinghua Sci. Technol. 2015, 20, 293–305. [Google Scholar] [CrossRef] [Green Version]
- Faul, F.; Erdfelder, E.; Buchner, A.; Lang, A.G. Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behav. Res. Methods 2009, 41, 1149–1160. [Google Scholar] [CrossRef] [Green Version]
- Cheng, W.; Huang, J.; **e, J. Facades of conformity: A values-regulation strategy links employees’ insecure attachment styles and task performance. Curr. Psychol. 2022, 1–17. [Google Scholar] [CrossRef]
- Davis, A.K.; Piger, J.M.; Sedor, L.M. Beyond the numbers: Measuring the information content of earnings press release language. Contemp. Account. Res. 2012, 29, 845–868. [Google Scholar] [CrossRef]
- Young, K.H.; Young, L.Y. Estimation of regressions involving logarithmic transformation of zero values in the dependent variable. Am. Stat. 1975, 29, 118–120. [Google Scholar]
- Rodgers, J.L.; Nicewander, W.A. Thirteen ways to look at the correlation coefficient. Am. Stat. 1988, 42, 59–66. [Google Scholar] [CrossRef]
- Mansfield, E.R.; Helms, B.P. Detecting multicollinearity. Am. Stat. 1982, 36, 158–160. [Google Scholar]
- Sun, Y.; Dong, X.; McIntyre, S. Motivation of user-generated content: Social connectedness moderates the effects of monetary rewards. Mark. Sci. 2017, 36, 329–337. [Google Scholar] [CrossRef]
- Yu, Y.; Duan, W.; Cao, Q. The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decis. Support Syst. 2013, 55, 919–926. [Google Scholar] [CrossRef]
- Timoshenko, A.; Hauser, J.R. Identifying customer needs from user-generated content. Mark. Sci. 2019, 38, 1–20. [Google Scholar] [CrossRef]
- Petty, R.E.; Kasmer, J.A.; Haugtvedt, C.P.; Cacioppo, J.T. Source and message factors in persuasion: A reply to Stiff’s critique of the elaboration likelihood model. Commun. Monogr. 1987, 54, 233–249. [Google Scholar] [CrossRef]
- Booth-Butterfield, S.; Welbourne, J. The elaboration likelihood model. In The Persuasion Handbook: Developments in Theory and Practice; Sage: Thousand Oaks, CA, USA, 2002; pp. 153–173. [Google Scholar]
- Malthouse, E.C.; Calder, B.J.; Kim, S.J.; Vandenbosch, M. Evidence that user-generated content that produces engagement increases purchase behaviours. J. Mark. Manag. 2016, 32, 427–444. [Google Scholar] [CrossRef]
- López, M.; Sicilia, M. Determinants of E-WOM influence: The role of consumers’ internet experience. J. Theor. Appl. Electron. Commer. Res. 2014, 9, 28–43. [Google Scholar] [CrossRef] [Green Version]
- Pilelienė, L.; Alsharif, A.H.; Alharbi, I.B. Scientometric analysis of scientific literature on neuromarketing tools in advertising. Balt. J. Econ. Stud. 2022, 8, 1–12. [Google Scholar] [CrossRef]
- Alsharif, A.H.; Salleh, N.Z.M.; Al-Zahrani, S.A.; Khraiwish, A. Consumer Behaviour to Be Considered in Advertising: A Systematic Analysis and Future Agenda. Behav. Sci. 2022, 12, 472. [Google Scholar] [CrossRef] [PubMed]
Study | Platform Type | Content Type | Sentiment Analysis | Mechanism | ||
---|---|---|---|---|---|---|
EP | TP | MGC | UGC | |||
Song et al. [16] | ✓ | ✓ | ✓ | ✓ | - | - |
Gu et al. [15] | ✓ | ✓ | - | ✓ | - | - |
Chung et al. [57], Zhou and Guo [19] | - | ✓ | - | ✓ | - | - |
Chen et al. [58] | ✓ | - | - | ✓ | ✓ | - |
Goh et al. [59], Liao and Huang [53] | - | ✓ | ✓ | ✓ | - | - |
Yi et al. [17], Alzate et al. [20], Li et al. [28] | ✓ | - | - | ✓ | - | - |
Our study | ✓ | ✓ | - | ✓ | ✓ | ✓ |
Variables | Symbols | Path | Variable Type | Measurement | Data Source |
---|---|---|---|---|---|
Online Review Length | Char | Central Path | Independent | Average character count of consumer online reviews for each product [72,73] | E-commerce Platform |
Online Review Inconsistency | Svar | Independent | Sentiment variance of consumer online reviews for each product [14] | E-commerce Platform | |
Number of Third-party Platform Product Evaluations | Tugc | Peripheral Path | Independent | Number of product evaluations published on the third-party platform [16,72] | Third-party Platform |
Purchase Intention | Pintent | - | Mediating | Number of users who add the product into their favorites [28,94] | E-commerce Platform |
Product Price | Price | - | Moderating | Product’s price in RMB yuan | E-commerce Platform |
Product Sales | Sales | - | Dependent | Monthly sales volume of the product | E-commerce Platform |
Store Rating | Store | - | Control | Average of store description, service, and logistics ratings [95] | E-commerce Platform |
Variables | Maximum | Minimum | Mean | SD | Median |
---|---|---|---|---|---|
Sales | 100,000.00 | 142.00 | 3432.09 | 9280.58 | 782.50 |
Char | 118.94 | 17.33 | 41.43 | 16.77 | 36.95 |
Svar | 0.21 | 0.00 | 0.07 | 0.04 | 0.07 |
Tugc | 1000.00 | 1.00 | 246.41 | 308.26 | 102.50 |
Pintent | 5,426,310.00 | 312.00 | 248,902.22 | 663,702.37 | 48,441.50 |
Price | 2300.00 | 29.90 | 226.81 | 243.33 | 139.00 |
Store | 4.90 | 4.60 | 4.81 | 0.04 | 4.80 |
Variables | Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|---|---|
1. Sales | 6.92 | 1.43 | ||||||
2. Char | 3.68 | 0.35 | 0.41 ** | |||||
3. Svar | 0.07 | 0.04 | −0.39 ** | −0.37 ** | ||||
4. Tugc | 4.48 | 1.70 | 0.21 ** | −0.06 | −0.08 | |||
5. Pintent | 10.79 | 1.91 | 0.62 ** | 0.24 ** | −0.27 ** | 0.27 ** | ||
6. Price | 5.08 | 0.78 | −0.11 | 0.06 | 0.24 ** | 0.08 | 0.10 | |
7. Store | 1.76 | 0.01 | −0.11 | 0.11 | −0.00 | −0.07 | −0.02 | 0.33 ** |
Variables | Sales | ||||||
---|---|---|---|---|---|---|---|
M1 | M2 | M3 | |||||
Coefficient | VIF | Coefficient | VIF | Coefficient | VIF | ||
Independent Variable (Central Path) | Char | 1.360 *** | 1.170 | 1.437 *** | 1.179 | ||
Svar | −10.393 *** | 1.156 | −9.536 *** | 1.168 | |||
Independent Variable (Peripheral Path) Control Variable | Tugc | 0.169 *** | 1.005 | 0.169 *** | 1.019 | ||
Store | −29.777 ** | 1.013 | −19.497 | 1.005 | −27.265 ** | 1.017 | |
Adjusted | 0.25 | 0.05 | 0.29 | ||||
F-value | 34.4 *** | 8.2 *** | 31.3 *** |
Variables | Pintent | Sales | |||||
---|---|---|---|---|---|---|---|
M4 | M5 | ||||||
Coefficient | VIF | Coefficient | VIF | ||||
Independent Variable (Central Path) | Char | 1.074 *** | 1.179 | 1.036 *** | 1.226 | ||
Svar | −9.433 ** | 1.168 | −6.010 *** | 1.208 | |||
Independent Variable (Peripheral Path) | Tugc | 0.305 *** | 1.019 | 0.055 | 1.108 | ||
Control Variable | Store | −6.299 | 1.017 | −24.911 ** | 1.018 | ||
Mediating Variable | Pintent | 0.374 *** | 1.208 | ||||
Adjusted | 0.16 | 0.51 | |||||
F-value | 15.37 *** | 59.99 *** |
Route Description | Coefficients | 95% CI | p-Value |
---|---|---|---|
Char → Pintent → Sales | 0.401 | [0.144, 0.680] | 0.0024 ** |
Svar → Pintent → Sales | −3.525 | [−5.704, −1.340] | 0.0024 ** |
Tugc → Pintent → Sales | 0.114 | [0.070, 0.160] | <2 × 10 *** |
Variables | Sales | ||||||
---|---|---|---|---|---|---|---|
M6 | M7 | M8 | |||||
Coefficient | VIF | Coefficient | VIF | Coefficient | VIF | ||
Independent Variable (Central Path) | Char | 1.443 *** | 1.370 | 1.566 *** | 1.233 | 1.484 *** | 1.206 |
Svar | −8.837 *** | 1.290 | −8.341 *** | 1.294 | −8.776 *** | 1.284 | |
Independent Variable (Peripheral Path) Control Variable | Tugc | 0.175 *** | 1.042 | 0.176 *** | 1.042 | 0.177 *** | 1.043 |
Store | −23.187 * | 1.176 | −24.576 * | 1.152 | −21.280 * | 1.164 | |
Moderating Variable | Price | −0.106 | 1.302 | −0.047 | 1.293 | −0.120 | 1.261 |
Char × Price | −0.103 | 1.236 | |||||
Moderating Items | Svar × Price | −6.783 ** | 1.077 | ||||
Tugc × Price | 0.124 * | 1.015 | |||||
Adjusted | 0.29 | 0.31 | 0.30 | ||||
F-value | 21.01 *** | 23.05 *** | 22.19 *** |
Hypothesis Related Questions | Hypothesis | Results |
---|---|---|
How does the central path of UGC affect sales? | H1: The length of online reviews positively influences product sales. | Supported |
H2: The inconsistency of online reviews negatively influences product sales. | Supported | |
How does the peripheral path of UGC affect sales? | H3: The number of third-party platform product evaluations positively influences product sales. | Supported |
What is the mediating role of purchase intention in the relationship between cross-platform UGC and sales? | H4a: Purchase intention mediates the relationship between review length and sales. | Supported |
H4b: Purchase intention mediates the relationship between review inconsistency and sales. | Supported | |
H4c: Purchase intention mediates the relationship between the number of third-party platform product evaluations and sales. | Supported | |
What is the moderating role of the product price in the relationship between cross-platform UGC and sales? | H5a: The effect of review length on sales is positively moderated by product price. | Not supported |
H5b: The effect of review inconsistency on sales is positively moderated by product price. | Supported | |
H5c: The effect of the number of third-party platform product evaluations on sales is positively moderated by product price. | Supported |
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Share and Cite
Jia, Y.; Feng, H.; Wang, X.; Alvarado, M. “Customer Reviews or Vlogger Reviews?” The Impact of Cross-Platform UGC on the Sales of Experiential Products on E-Commerce Platforms. J. Theor. Appl. Electron. Commer. Res. 2023, 18, 1257-1282. https://doi.org/10.3390/jtaer18030064
Jia Y, Feng H, Wang X, Alvarado M. “Customer Reviews or Vlogger Reviews?” The Impact of Cross-Platform UGC on the Sales of Experiential Products on E-Commerce Platforms. Journal of Theoretical and Applied Electronic Commerce Research. 2023; 18(3):1257-1282. https://doi.org/10.3390/jtaer18030064
Chicago/Turabian StyleJia, Yiwu, Haolin Feng, **n Wang, and Michelle Alvarado. 2023. "“Customer Reviews or Vlogger Reviews?” The Impact of Cross-Platform UGC on the Sales of Experiential Products on E-Commerce Platforms" Journal of Theoretical and Applied Electronic Commerce Research 18, no. 3: 1257-1282. https://doi.org/10.3390/jtaer18030064