[1] Liu Xueyu, Mei Shue, Zhong Weijun,. Firm review revelation policy consideringsocial ties among consumers [J]. Journal of Southeast University (English Edition), 2021, 37 (1): 98-103. [doi:10.3969/j.issn.1003-7985.2021.01.013]
Copy
Firm review revelation policy consideringsocial ties among consumers(
)
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 37
- Issue:
- 2021 1
- Page:
- 98-103
- Research Field:
- Economy and Management
- Publishing date:
- 2021-03-20
Info
- Title:
- Firm review revelation policy consideringsocial ties among consumers
- Author(s):
- Liu Xueyu; Mei Shu’e; Zhong Weijun
- School of Economics and Management, Southeast University, Nanjing 211189, China
- PACS:
- C934
- DOI:
- 10.3969/j.issn.1003-7985.2021.01.013
- Abstract:
- An optimization model is proposed to analyze the optimal review revelation policies and consumer online social network management strategies of e-commerce firms. The results show that displaying friend reviews to consumers does not necessarily increase firms’ profits. Only when positive reviews account for a large proportion of all the reviews and when the cost of showing friend reviews is not high, can showing friend reviews be more profitable than not showing such information. The distribution of social ties among consumers can affect firms’ profits. Even in the case that showing friend reviews to consumers is more profitable, an increase in the proportion of strong ties is not necessarily beneficial to firms. Only when the proportion of positive reviews is large enough, can firms’ profits increase with the increase in the proportion of strong ties among consumers. Moreover, the degree of consumer distrust in the average quality rating can also affect firms’ strategies for managing consumer online social networks. As the degree of consumer distrust in the average quality rating rises, firms are more likely to obtain higher profits by taking measures to increase the proportion of strong ties among consumers on their websites.
References:
[1] Chevalier J A, Mayzlin D. The effect of word of mouth on sales: Online book reviews[J]. Journal of Marketing Research, 2006, 43(3): 345-354. DOI:10.1509/jmkr.43.3.345.
[2] Li X X, Hitt L M. Self-selection and information role of online product reviews[J]. Information Systems Research, 2008, 19(4): 456-474. DOI:10.1287/isre.1070.0154.
[3] Zhu F, Zhang X M. Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics[J]. Journal of Marketing, 2010, 74(2): 133-148. DOI:10.1509/jmkg.74.2.133.
[4] Weinburger M C, Dillon W R, Olson J. The effects of unfavorable product information[J]. Advances in Consumer Research, 1980, 7: 528-532.
[5] Herr P M, Kardes F R, Kim J. Effects of word-of-mouth and product-attribute information on persuasion: An accessibility-diagnosticity perspective[J]. Journal of Consumer Research, 1991, 17(4): 454-462. DOI:10.1086/208570.
[6] Mizerski R W. An attribution explanation of the disproportionate influence of unfavorable information[J]. Journal of Consumer Research, 1982, 9(3): 301-310. DOI:10.1086/208925.
[7] Yi C, Jiang Z J, Li X P, et al. Leveraging user-generated content for product promotion: The effects of firm-highlighted reviews[J]. Information Systems Research, 2019, 30(3): 711-725. DOI:10.1287/isre.2018.0807.
[8] Li X X, Connecticut U O. Revealing or non-revealing: The impact of review disclosure policy on firm profitability[J]. MIS Quarterly, 2017, 41(4): 1335-1345. DOI:10.25300/misq/2017/41.4.14.
[9] Chen Y B, Xie J H. Online consumer review: Word-of-mouth as a new element of marketing communication mix[J]. Management Science, 2008, 54(3): 477-491. DOI:10.1287/mnsc.1070.0810.
[10] Liu Y, Feng J, Liao X W. When online reviews meet sales volume information: Is more or accurate information always better?[J].Information Systems Research, 2017, 28(4): 723-743. DOI:10.1287/isre.2017.0715.
[11] Sun Y C, Dong X J, McIntyre S. Motivation of user-generated content: Social connectedness moderates the effects of monetary rewards[J].Marketing Science, 2017, 36(3): 329-337. DOI:10.1287/mksc.2016.1022.
[12] Zhang Y C, Godes D. Learning from online social ties[J]. Marketing Science, 2018, 37(3): 425-444. DOI:10.1287/mksc.2017.1076.
[13] Brown J J, Reingen P H. Social ties and word-of-mouth referral behavior[J]. Journal of Consumer Research, 1987, 14(3): 350-362. DOI:10.1086/209118.
[14] Bitter S, Grabner-Kr�E4;uter S. Consequences of customer engagement behavior: When negative Facebook posts have positive effects[J]. Electronic Markets, 2016, 26(3): 219-231. DOI:10.1007/s12525-016-0220-7.
[15] Wang J C, Chang C H. How online social ties and product-related risks influence purchase intentions: A Facebook experiment[J]. Electronic Commerce Research and Applications, 2013, 12(5): 337-346. DOI:10.1016/j.elerap.2013.03.003.
[16] Wu R H, Qiu C. Seller manipulation of consumer reviews under competition [C]// Proceedings of the Annual Hawaii International Conference on System Sciences. Koloa, HI, USA, 2016: 3575-3583.
[17] Boleslavsky R, Cotton C S, Gurnani H. Demonstrations and price competition in new product release[J]. Management Science, 2017, 63(6): 2016-2026. DOI:10.1287/mnsc.2016.2449.
[18] Hu N, Pavlou P A, Zhang J. On self-selection biases in online product reviews[J]. MIS Quarterly, 2017, 41(2): 449-471. DOI:10.25300/misq/2017/41.2.06.
[19] Kornish L J, Li Q P. Optimal referral bonuses with asymmetric information: Firm-offered and interpersonal incentives[J]. Marketing Science, 2010, 29(1): 108-121. DOI:10.1287/mksc.1080.0484.
Memo
- Memo:
-
Biographies: Liu Xueyu(1994—), female, Ph.D. candidate; Mei Shu’e(corresponding author), female, doctor, professor, meishue@seu.edu.cn.
Foundation item: The National Social Science Foundation of China(No.17BGL196).
Citation: Liu Xueyu, Mei Shu’e, Zhong Weijun.Firm review revelation policy considering social ties among consumers[J].Journal of Southeast University(English Edition), 2021, 37(1):98-103.DOI:10.3969/j.issn.1003-7985.2021.01.013.