review-article

Recommendation networks of homogeneous products on an E-commerce platform: : Measurement and competition effects

Authors:

Zihang TuAuthors Info & Claims

Volume 201, Issue C

https://doi.org/10.1016/j.eswa.2022.117128

Published: 01 September 2022 Publication History

Highlights

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This paper studies the competition effect of product recommendation networks.

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Three network attributes, centrality, connection, and scale are measured.

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Their impacts on market competition in a homogeneous product market are tested at the global-network level.

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A panel data of 320,981 package tour products was used to estimate the model.

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Empirical results confirm the effect of three attributes on market competition.

Abstract

Extant studies have focused on the sales benefits of product recommendation networks; however, the competition effects of such networks have been overlooked. Understanding the competition effects of product recommendation networks is important within an e-commerce platform because maintaining a proper level of competition is critical to the platform ecosystems. This research adopts a data-driven approach and social network analysis, elaborating upon three structural properties of recommendation networks – centrality, connection, and scale – and operationalizes them as evenness of distribution, clustering of structure, and network size at the global-network level. The competition effects of the three proposed network variables were tested based on a daily panel dataset of 320,981 package tour products from Tuniu.com, a top travel service platform in China featuring homogeneous products. Empirical results confirmed that the three proposed structural network properties increase market competition in recommendation networks of homogeneous products. This research contributes to our understanding of the impact of recommendation networks on market competition in the context of homogeneous product markets and offers practical implications of substition recommendation for platform governance. Platform operators can leverage product recommendation networks to alleviate market competition and encourage the entry of sellers with new, innovative products that contribute to the e-commerce platform ecosystems.

References

[1]

Z. Abbasi-Moud, S. Hosseinabadi, M. Kelarestaghi, F. Eshghi, CAFOB: Context-aware fuzzy-ontology-based tourism recommendation system, Expert Systems with Applications 199 (2022).

Digital Library

[2]

Z. Abbasi-Moud, H. Vahdat-Nejad, J. Sadri, Tourism recommendation system based on semantic clustering and sentiment analysis, Expert Systems with Applications 167 (2021).

Digital Library

[3]

P. Adamopoulos, A. Ghose, A. Tuzhilin, Heterogeneous demand effects of recommendation strategies in a mobile application: Evidence from econometric models and machine-learning instruments, MIS Quarterly 45 (1) (2022) 101–150.

[4]

G. Adomavicius, J.C. Bockstedt, S.P. Curley, J. Zhang, Do recommender systems manipulate consumer preferences? A study of anchoring effects, Information Systems Research 24 (4) (2013) 956–975.

Digital Library

[5]

AnalysisConsulting. (2019). Quarterly monitoring and analysis of china's online vacation travel market in Q4, 2018. In. https://www.analysys.cn/article/detail/20019196.

[6]

C. Anderson, The long tail: Why the future of business is selling less of more, Hyerion Press, New York, 2006.

Digital Library

[7]

D. Baum, M. Spann, The interplay between online consumer reviews and recommender systems: An experimental analysis, International Journal of Electronic Commerce 19 (1) (2014) 129–162.

[8]

J. Bobadilla, F. Ortega, A. Hernando, A. Gutiérrez, Recommender systems survey, Knowledge-Based Systems 46 (1) (2013) 109–132.

[9]

Brin, S., Page, L., Motwani, R., & Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. In: Stanford InfoLab.

[10]

E. Brynjolfsson, Y.J. Hu, M.D. Smith, From niches to riches: Anatomy of the long tail, MIT Sloan Management Review 47 (4) (2006) 67–71.

[11]

R. Burke, Hybrid recommender systems: Survey and experiments, User Modeling and User-Adapted Interaction 12 (4) (2002) 331–370.

Digital Library

[12]

E. Carmi, G. Oestreicher-singe, U. Stettner, A. Sundararajan, Is Oprah contagious? The depth of diffusion of demand shocks in a product network, MIS Quarterly 41 (1) (2017) 207–222.

[13]

L. Chen, J. Cao, Y. Wang, W. Liang, G. Zhu, Multi-view graph attention network for travel recommendation, Expert Systems with Applications 191 (2022).

Digital Library

[14]

P. Chen, S. Wu, J. Yoon, The impact of online recommendations and consumer feedback on sales, In the Proceedings of International Conference on Information Systems , 2004.

[15]

Y.H. Chen, Y.B. Lu, B. Wang, Z. Pan, How do product recommendations affect impulse buying? An empirical study on WeChat social commerce, Information & Management 56 (2) (2019) 236–248.

Digital Library

[16]

I.Y. Choi, M.G. Oh, J.K. Kim, Y.U. Ryu, Collaborative filtering with facial expressions for online video recommendation, International Journal of Information Management 36 (3) (2016) 397–402.

Digital Library

[17]

D. Cosley, S.K. Lam, I. Albert, J.A. Konstan, J. Riedl, Is seeing believing?: How recommender system interfaces affect users' opinions, in: In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , 2003, pp. 585–592.

[18]

D. Fleder, K. Hosanagar, Blockbuster culture's next rise or fall: The impact of recommender systems on sales diversity, Management Science 55 (5) (2009) 697–712.

[19]

M. Franceschet, Pagerank: Standing on the shoulders of giants, Communications of the ACM 54 (6) (2011) 92–101.

[20]

M. Ghasemaghaei, K. Hassanein, I. Benbasat, Assessing the design choices for online recommendation agents for older adults: Older does not always mean simpler information technology, MIS Quarterly 43 (1) (2019) 329–346.

[21]

J. Goldenberg, G. Oestreichersinger, S. Reichman, The quest for content: How user-generated links can facilitate online exploration, Journal of Marketing Research 49 (4) (2012) 452–468.

[22]

M. Gorgoglione, U. Panniello, A. Tuzhilin, Recommendation strategies in personalization applications, Information & Management 56 (6) (2019).

[23]

G. Häubl, K.B. Murray, Preference construction and persistence in digital marketplaces: The role of electronic recommendation agents, Journal of Consumer Psychology 13 (1–2) (2003) 75–91.

[24]

J. Hair, R. Anderson, R. Tatham, W. Black, Multivariate data analysis, 7th ed., Prentice Hall, NJ, 2010.

[25]

K. Han, R.J. Kauffman, B.R. Nault, Returns to information technology outsourcing, Information Systems Research 22 (4) (2011) 824–840.

[26]

Y.D. Handarkho, Social experience vs. social technology in enhancing the intention to use social commerce: A case study of Indonesia, Journal of Enterprise Information Management 34 (3) (2021) 860–883.

[27]

Hanneman, R. A., & Riddle, M. (2011). Concepts and measures for basic network analysis. In J. Scott & P. J. Carrington (Eds.), The SAGE Handbook of Social Network Analysis (pp. 340-369).

[28]

J. He, W.W. Chu, A social network-based recommender system, Data Mining for Social Network Data, 1st, Springer, Boston, MA, 2010, pp. 47–74.

[29]

J. He, G. Liu, X. Tan, T. Li, Research on collaborative recommendation of dynamic medical services based on cloud platforms in the industrial interconnection environment, Technological Forecasting and Social Change 170 (2021).

[30]

J.O. Hinz, D.K.J. Eckert, The impact of search and recommendation systems on sales in electronic commerce, Business & Information Systems Engineering 2 (2) (2010) 67–77.

[31]

K. Hosanagar, R. Krishnan, L. Ma, Recomended for you: The impact of profit incentives on the relevance of online recommendations, In Proceedings of International Conference on Information Systems (2008) , 2008, https://aisel.aisnet.org/icis2008/31.

[32]

H.J. Huang, J. Yang, B. Zheng, Demand effects of product similarity network in e-commerce platform, Electronic Commerce Research 21 (2) (2021) 297–327.

[33]

I. Im, J. Jun, W. Oh, S.O. Jeong, Deal-seeking versus brand-seeking: Search behaviors and purchase propensities in sponsored search platforms, MIS Quarterly 40 (1) (2016) 187–203.

[34]

W. Jabr, E. Zheng, Know yourself and know your enemy: An analysis of firm recommendations and consumer reviews in a competitive environment, MIS Quarterly 38 (3) (2014) 635–654.

[35]

D. Jannach, M. Jugovac, Measuring the business value of recommender systems, ACM Transactions on Management Information Systems (TMIS) 10 (4) (2019) 1–23.

[36]

N. Jing, T. Jiang, J. Du, V. Sugumaran, Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website, Electronic Commerce Research 18 (1) (2018) 159–179.

[37]

R.S. Khemani, D.M. Shapiro, Glossary of industrial organisation economics and competition law, Directorate for Financial, Fiscal and Enterprise Affairs, OECD, France Paris, 1993.

[38]

D. Lee, K. Hosanagar, How do product attributes and reviews moderate the impact of recommender systems through purchase stages?, Management Science 67 (1) (2020) 524–546.

[39]

B. Leem, H. Chun, An impact of online recommendation network on demand, Expert Systems with Applications 41 (4) (2014) 1723–1729.

[40]

L. Li, J. Chen, S. Raghunathan, Recommender system rethink: Implications for an electronic marketplace with competing manufacturers, Information Systems Research 29 (4) (2018) 1003–1023.

[41]

Z. Lin, K.Y. Goh, C.S. Heng, The demand effects of product recommendation networks: An empirical analysis of network diversity and stability, MIS Quarterly 41 (2) (2017) 397–426.

[42]

Z. Lin, Q. Wang, E-commerce product networks, word-of-mouth convergence, and product sales, Journal of the Association for Information Systems 19 (1) (2018) 23–39.

[43]

Z.J. Lin, An Empirical Investigation of User and System Recommendations in E-commerce, Decision Support Systems 68 (2014) 111–124.

[44]

Q.H. Liu, X.Y. Zhang, L.Y. Zhang, Y. Zhao, The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: An empirical investigation, Electronic Commerce Research 19 (3) (2019) 521–547.

[45]

J. Lu, D. Wu, M. Mao, W. Wang, G. Zhang, Recommender system application developments: A survey, Decision Support Systems 74 (2015) 12–32.

Digital Library

[46]

E. Majd, M. Hobson, An incorporated RUU model for multi-agent systems in e-commerce, Journal of Enterprise Information Management 33 (5) (2020) 905–921.

[47]

K. Mitra, M.C. Reiss, L.M. Capella, An examination of perceived risk, information search and behavioral intentions in search, experience and credence services, Journal of Services Marketing 13 (3) (1999) 208–228.

[48]

P. Nelson, Information and consumer behavior, Journal of Political Economy 78 (2) (1970) 311–329.

[49]

M. Newman, Networks: An introduction, Oxford University Press, London, 2010.

[50]

G. Oestreicher-Singer, B. Libai, L. Sivan, E. Carmi, C. Yassin, The network value of products, Journal of Marketing 77 (3) (2013) 1–14.

[51]

G. Oestreicher-Singer, A. Sundararajan, Recommendation networks and the long tail of electronic commerce, MIS Quarterly 36 (1) (2012) 65–83.

[52]

G. Oestreicher-Singer, A. Sundararajan, The visible hand? Demand effects of recommendation networks in electronic markets, Management Science 58 (11) (2012) 1963–1981.

[53]

E. Otte, R. Rousseau, Social network analysis: A powerful strategy, also for the information sciences, Journal of Information Science 28 (6) (2002) 441–453.

[54]

S.H. Park, S.P. Han, From accuracy to diversity in product recommendations: Relationship between diversity and customer retention, International Journal of Electronic Commerce 18 (2) (2013) 51–71.

[55]

B. Pathak, R. Garfinkel, R.D. Gopal, R. Venkatesan, F. Yin, Empirical analysis of the impact of recommender systems on sales, Journal of Management Information Systems 27 (2) (2010) 159–188.

Digital Library

[56]

M.J. Pazzani, D. Billsus, Content-based recommendation systems, in: P. Brusilovsky, A. Kobsa, W. Nejdl (Eds.), The Adaptive Web: Methods and Strategies of Web Personalization, Springer, Berlin, Heidelberg, 2007, pp. 325–341.

[57]

F. Ren, S. Dewan, Industry-level analysis of information technology return and risk: What explains the variation?, Journal of Management Information Systems 32 (2) (2015) 71–103.

[58]

J.B. Schafer, F. Dan, J. Herlocker, S. Sen, Collaborative filtering recommender systems, Springer, Berlin Heidelberg, 2007.

Digital Library

[59]

B. Shao, X. Li, G. Bian, A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph, Expert Systems with Applications 165 (2021).

[60]

J. Son, S.B. Kim, Content-based filtering for recommendation systems using multiattribute networks, Expert Systems with Applications 89 (2017) 404–412.

Digital Library

[61]

A.T. Stephen, O. Toubia, Deriving value from social commerce networks, Journal of Marketing Research 47 (2) (2010) 215–228.

[62]

A. Susarla, J.H. Oh, Y. Tan, Social networks and the diffusion of user-generated content: Evidence from youtube, Information Systems Research 23 (1) (2012) 23–41.

[63]

Q. Wang, J.J. Yu, W.W. Deng, An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations, Electronic Commerce Research 19 (1) (2019) 59–79.

Digital Library

[64]

W. Wang, J. Xu, M. Wang, Effects of recommendation neutrality and sponsorship disclosure on trust vs. Distrust in online recommendation agents: Moderating role of explanations for organic recommendations, Management Science 64 (11) (2018) 5198–5219.

Digital Library

[65]

J.M. Wooldridge, Econometric analysis of cross section and panel data (2nd edition), MIT Press, Cambridge, MA, 2010.

[66]

J.H. Xiao, Y. Wu, K. Xie, Q. Hu, Managing the e-commerce disruption with IT-based innovations: Insights from strategic renewal perspectives, Information & Management 56 (1) (2019) 122–139.

[67]

J.J. Xu, I. Benbasat, R.T. Cenfetelli, The nature and consequences of trade-off transparency in the context of recommendation agents, MIS Quarterly 38 (2) (2014) 379–406.

[68]

Y. Xu, J. Ma, Y.H. Sun, J. Hao, Y. Sun, Y. Zhao, Using social network analysis as a strategy for e-commerce recommendation, Proceeding of Pacific Asia Conference on Information Systems , 2009.

[69]

D. Yang, X. Gao, Online retailer recommender systems: A competitive analysis, International Journal of Production Research 55 (14) (2017) 4089–4109.

[70]

N. Yang, J. Jo, M. Jeon, W. Kim, J. Kang, Semantic and explainable research-related recommendation system based on semi-supervised methodology using BERT and IDA models, Expert Systems with Applications 190 (2022).

[71]

H. Zhang, L. Zhao, S. Gupta, The role of online product recommendations on customer decision making and loyalty in social shopping communities, International Journal of Information Management 38 (1) (2018) 150–166.

Digital Library

[72]

M.Y. Zhang, J. Bockstedt, Complements and substitutes in online product recommendations: The differential effects on consumers’ willingness to pay, Information & Management 57 (6) (2020).

[73]

Z. Zhang, H. Lin, K. Liu, D. Wu, G. Zhang, J. Lu, A hybrid fuzzy-based personalized recommender system for telecom products/services, Information Sciences 235 (6) (2013) 117–129.

[74]

Z. Zhu, S. Lin, Y. Jiang, Q. Liu, The coordination effect of B2B digital process capabilities on competitive performance: balancing or complementing, Journal of Enterprise Information Management 35 (3) (2022) 918–946.

[75]

Z. Zhu, J. Zhao, A.A. Bush, The effects of e-business processes in supply chain operations: Process component and value creation mechanisms, International Journal of Information Management 50 (2020) 273–285.

Index Terms

Recommendation networks of homogeneous products on an E-commerce platform: Measurement and competition effects

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 201, Issue C

Sep 2022

1333 pages

ISSN:0957-4174

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 September 2022

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