Agent-based modeling and simulation of the decision behaviors of e-retailers

Publication Date11 Jun 2018
AuthorGuoyin Jiang,Shan Liu,Wenping Liu,Yan Xu
SubjectInformation & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems
Agent-based modeling and
simulation of the decision
behaviors of e-retailers
Guoyin Jiang
School of Public Administration,
University of Electronic Science and Technology of China, Chengdu, China
Shan Liu
School of Management, Xian Jiaotong University, Xian, China
Wenping Liu
School of Information Management and Statistics,
Hubei University of Economics, Wuhan, China, and
Yan Xu
School of Management, Shandong Technology and Business University,
Yantai, China
Purpose Social media facilitates consumer exchanges on product opinions and provides comprehensive
knowledgeof online products. The interactionbetween consumers and e-retailersevolves into a collectiveset of
dynamics within a complex system. Agent-basedmodeling is well suited to stimulate such complex systems.
The purpose of thispaper is to integrate agent-based modeland technique for order performanceby similarity
to ideal solution (TOPSIS) to simulate decisionbehaviors of e-retailers in competitive online markets.
Design/methodology/approach An agent-based network model using the TOPSIS driven by actual
price data is developed. The authors ran an experimental model to simulate interactions between online
consumers and e-retailers and to record simulation data. A nonparametric test is used to conduct data
analysis and evaluate the sensibility of parameters.
Findings Simulation results showed that different profits could be obtained for various brands
under different social network structures. E-retailers could achieve more profits through cross-selling than
single-selling; however, the highest profits can be achieved when some adoptcross-selling, whereas others use
single-selling. From a game perspective, the equilibrium for price-adjustment frequency can be determined
from the simulation data. Thus, price adjustment differences significantly affect e-retailer profit.
Originality/value This study provides new insights into the evolutionary dynamics of online markets.
This work also indicates how to build an integrated simulation model with an agent-based model and TOPSIS
and how to use an integrated simulation model and interpret its results.
Keywords TOPSIS, E-commerce, Cross-selling, Agent-based modelling, Collective dynamics
Paper type Research paper
1. Introduction
Inquiring aboutand sharing product orservice information onlinethrough reviews and social
media have become popular online shopping practices. Participants can easily exchange
relevantinformation concerningproducts and servicesusing social mediatools (Kim and Park,
2013). Stelzner(2013) found that 86 percent of e-retailers in the USA regardedsocial media as
vital for their businesses. ChinaInternet Network Information Centre (CNNIC)(2014) reported
that 32.5 percent of consumers who purchased products online were driven by social factors.
Industrial Management & Data
Vol. 118 No. 5, 2018
pp. 1094-1113
© Emerald PublishingLimited
DOI 10.1108/IMDS-07-2017-0321
Received 22 July 2017
Revised 13 December 2017
Accepted 28 January 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
This work was partially supported by a grant from the National Natural Science Foundation of China
(Nos 71671060, 61672213, 71501080, 71501113), the Fundamental Research Funds for the Central
Universities of China with Grant No. ZYGX2017KYQD185, the Excellent Youth ScientificInnovative
Teams Foundation of the Higher Education Institutions of Hubei Province, China (No. T201516).
The price dispersion of homogeneous products in electronic markets presents an
interesting research area (Pan et al., 2004). Electronic markets have significantly decreased
menu costs. E-retailers can easily plan and coordinate their pricing activities. Consumers
can proactively determine price adjustments using comparison tools. Researchers can
explore efficient market disciplines to disperse the prices of homogeneous goods using this
interesting interaction between expanded consumer price awareness and marketerspricing
adjustments. The literature review shows that most studies have focused solely on price
strategy without considering other purchasing behaviors or social norm factors. Oh and
Lucas (2006) investigated the price strategies of the computer market from frequency and
timing perspectives. Li et al. (2013) analyzed loss-leader pricing strategies by considering
demographic characteristics and focusing on the product features of books, such as
coverage, category and format (trade and paperback).
E-commerce that involves social media has given rise to electronic word-of-mouth (WOM)
marketing, which considerably influences online purchasing decisions. As online market
matures, the price is no longer the sole or even the most important factor that significantly
influences the decisions of vendors and clients (Liu, 2015; Zhang et al., 2017; Liu et al., 2017;
Gao et al., 2018). CNNICreported that seven factors significantlyaffect a consumerspurchase
decision: WOM, purchasing experience, delivery service, user rating, brand, promotion and
price. Brand,WOM and price are the three mostimportant factors. Many studieshave focused
on price dispersion (i.e. heterogeneity in price levels), but none of them considered
heterogeneity strategies in competing for factors such as product features, brand and WOM.
The present study deals directly with pricing strategy and selling with regard to
homogeneous online products under social networks. E-retailers should understand how
social networksaffect profits and identifywhat strategies best matchnetwork structures with
consumer behavior.
Some e-retailers reduce prices to attract consumers to their websites and hope those
consumers also purchase other highly profitable products. Other e-retailers simply post high
prices and cater to shoppers who are not inclined to perform price searches or who pursue
brand loyalty. Price adjustment is influenced by comprehensive strength (brand image,
WOM, price advantage, promotions), consumersdemographic characteristics and market
capacity. Existing literature has emphasized the frequency and timing of price adjustments
and investigatedwhether retailers change prices simultaneously or throughprice staggering.
Single-selling has been traditionally adopted by e-retailers in competitive environments.
However, as e-commerce becomes increasingly tied to social media, cross-selling turns into a
popular sales strategy that is now widely applied in e-retail. Well-known websites, such as, and, use cross-selling to promote highly profitable
products. The present paper examines the features of price adjustment under single- and
cross-selling strategies and analyzes the strategies that e-retailers adopt when the price is
the primary description. We address the following questions:
(1) How do social network structures affect the various brand profits of e-retailers in an
online market?
(2) What is the optimal price-adjustment frequency for e-retailers when using single- or
cross-selling strategies?
(3) How does price-adjustment frequency affect profits under single- and cross-selling
Agent-based modeling (ABM) can simulate interactions and emergent behaviors in online
marketplaces. We used ABM driven by actual data to explore the above-mentioned questions.
An experimental methodology driven by actual events or data allows for various characteristics
of consumers and products to be incorporated into the agent-based model (Schramm et al., 2010).
of e-retailers

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