What attracts vehicle consumers’ buying. A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?
| Published date | 20 November 2019 |
| Date | 20 November 2019 |
| Pages | 57-78 |
| DOI | https://doi.org/10.1108/IMDS-01-2019-0034 |
| Author | Fuli Zhou,Ming K. Lim,Yandong He,Saurabh Pratap |
| Subject Matter | Information & 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 |
What attracts vehicle
consumers’buying
A Saaty scale-based VIKOR (SSC-VIKOR)
approach from after-sales textual perspective?
Fuli Zhou
School of Economics and Management,
Zhengzhou University of Light Industry, Zhengzhou, China
Ming K. Lim
College of Mechanical Engineering,
Chongqing University, Chongqing, China and
Centre for Business in Society,
Coventry University, Coventry, UK
Yandong He
Research Center on Modern Logistics,
Graduate School at Shenzhen,
Tsinghua University, Beijing, China and
School of Industrial Engineering and Innovation Sciences,
Eindhoven University of Technology, Eindhoven, The Netherlands, and
Saurabh Pratap
Department of Mechanical Engineering,
Indian Institute of Information Technology,
Design and Manufacturing, Jabalpur, India
Abstract
Purpose –The increasingly boomi ng e-commerce developm ent has stimulated vehi cle consumers to
express individual rev iews through online for um. The purpose of this pape r is to probe into the vehicle
consumer consumptio n behavior and make rec ommendations for pote ntial consumers from t extual
comments viewpoint.
Design/methodology/approach –A big data analytic-based a pproach is designed to di scover vehicle
consumer consumptio n behavior from online perspective. To redu ce subjectivity of expe rt-based
approaches, a paralle l Naïve Bayes approach is designed to an alyze the sentiment analysis, and the Saaty
scale-based (SSC) scori ng rule is employed to obta in specific sentimental va lue of attribute class,
contributing to the mult i-grade sentiment cla ssification. To achi eve the intelligent reco mmendation for
potential vehicle cu stomers, a novel SSC-VIK OR approach is develop ed to prioritize vehicle brand
candidates from a big da ta analytical viewpoint.
Findings –The big data analytics argue that “cost-effectiveness”characteristic is the most important factor
that vehicle consumers care, and the data mining results enable automakers to better understand consumer
consumption behavior.
Research limitations/implications –The case study illustrates the effectiveness of the integrated
method, contributing to much more precise operations management on marketing strategy, quality
improvement and intelligent recommendation.
Industrial Management & Data
Systems
Vol. 120 No. 1, 2020
pp. 57-78
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-01-2019-0034
Received 21 January 2019
Revised 26 June 2019
Accepted 9 October 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
The authors would like to thank anonymous referees for the remarkable comments and enormous
endeavor on the paper improvement. This study is financially supported by following programs: the
Soft Science Research Project in Henan Province from Henan Science and Technology Department
(Grant No. 192400410016); and the Scientific Research Starting Fund for Doctors from Zhengzhou
University of Light Industry (Grant No. 0140/13501050042).
57
What attracts
vehicle
consumers’
buying
Originality/value –Researches of consumer consumption behavior are usually based on survey-based
methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid
SSC-VIKOR approach is developed to fill the gap from the big data perspective.
Keywords Sentiment classification, Intelligent recommendation, Parallel Naïve Bayes method,
Sentiment value, Saaty scale-based VIKOR (SSC-VIKOR)
Paper type Research paper
1. Introduction
With rapid development of the automobile industry, China has become the nation with the
most annual vehicle production amount from all over the world since 2009 (Zhou, Lim, He,
Lin and Chen, 2019). To improve vehicle product performance and narrow the gap with
developed automobile organizations, the quality improvement management practice has
been performed by Chinese domestic auto industries in the light of the warranty data and
global quality research system (Kim et al., 2007; Zhou, Lin, Wang, Zhou and He, 2016).
However, with increasing vehicle consumers and soaring open environment, the explosively
growing quality feedback and massive data produced by vehicle users are flowing into the
industrial organizations.
The mass textual comments of vehicle consumers play an increasing significant role on
the product marketing and brand reputation, also providing valuable guidance on the
development of the industrial organizations (Tiwari et al., 2018). Compared with traditional
continuous improvement on the basis of maintenance and warranty data, there are
increasing automobile industries focusing on the voice of consumers like textual comments,
customer complaints and spreading rumors (Zhou, Wang and Samvedi, 2018). This
situation leads to the innovative quality improvement practices performed and employed by
auto factories concerning qualitative information of vehicle users (Donauer et al., 2015; Shah
et al., 2016). It is the consumers’feedback that drives the continuous improvement and
unconformity recorrection (Shah et al., 2016; Zhou, Wang, Goh, Zhou and He, 2019; Zhou,
Wang and Samvedi, 2018). Those organizations which can take full advantage of the mass
data to provide guidance on production and marketing operations will be regarded as a
triumph (Guajardo et al., 2016).
Furthermore, the booming development of internet and e-commerce has motivated the
online interactive communication, and product consumers show an increasing tendency to
share the consumption experience and product use cognition through the internet (Su et al.,
2019; Wamba et al., 2018). With rapid development of information technology and electronic
commerce, users and consumers prefer to express their requirements, product using
experiences and individual evaluation using more understandable comments, such as texts,
pictures and expressions, rather than the traditional parameters and structured information
(He et al., 2019; Wang et al., 2016; Pappas, 2016). The individual-level data and interactively
textual comments have been accumulated in an increasingly extraordinary speed (Tiwari
et al., 2018). The soaring availability of consumer-oriented mass data provides both
automobile organizations and vehicle users with unprecedented chances to tailor decisions
to the requirements and behavior preferences of vehicle consumers (Wei and Zhang, 2018).
In addition, potential users tend to focus on the consumers’experience information who has
bought the product, instead of specific product parameters provided by the manufacturer.
Therefore, it is of great significance to identify the useful evidence from mass consumers’
consumption information (Ziegele et al., 2017).
The data mining management practice on fast selling goods based on textual comments
of consumers has been performed and analyzed from online mass data, which contributes to
more precise operations management on consumers’requirement identification, marketing
policies establishment, strategic improvements and brand promotion (Hardesty and
Bearden, 2009; Quoquab et al., 2017). With an increasing popularity of online shopping and
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