Unravelling the potential of social media data analysis to improve the warranty service operation
| DOI | https://doi.org/10.1108/IMDS-07-2022-0427 |
| Published date | 15 February 2023 |
| Date | 15 February 2023 |
| Pages | 1281-1309 |
| 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 |
| Author | Zahra Sarmast,Sajjad Shokouhyar,Seyed Hamed Ghanadpour,Sina Shokoohyar |
Unravelling the potential of social
media data analysis to improve
the warranty service operation
Zahra Sarmast and Sajjad Shokouhyar
Department of Management and Accounting, Shahid Beheshti University,
Tehran, Islamic Republic of Iran
Seyed Hamed Ghanadpour
Department of Industrial and Information Management, Shahid Beheshti University,
Tehran, Islamic Republic of Iran, and
Sina Shokoohyar
Department of Computing and Decision Sciences, Seton Hall University,
South Orange, New Jersey, USA
Abstract
Purpose –Warranty service plays a critical role in sustainability and service continuity and influences
customer satisfaction. Considering the role of social networks in customer feedback channels, one of the
essential sources to examine the reflection of a product/service is social media mining. This paper aims to
identify the frequent product failures through social network mining. Focusing on social media data as a
comprehensive and online source to detect warranty issues reveals opportunities for improvement, such as
user problems and necessities. This model will detect the causes of defects and prioritize improving
components in a product-service system based on FMEA results.
Design/methodology/approach –Ontology-based methods , text mining and senti ment analysis with
machine learning meth ods are performed on social med ia data to investigate produ ct defects, symptoms and
the relationship betwe en warranty plans and cus tomer behaviour. Also, th e authors have incorpora ted
multi-source data co llection to cover all the po ssibilities. Then th ea uthors promote a decisi on support system
to help the decision-ma kers using the FMEA process have a more comprehensive ins ight through customer
feedback. Finally, to validatet heaccuracy and reliability of the results, the authors used the operational data
of a LENOVO laptop from a warranty service centre and classifier performance metrics to compare the
authors’results.
Findings –This study confirms the validity of social media data in detecting customer sentiments and
discovering the most defective components and failures of the products/services. In other words, the
informative threads are derived through a data preparation process and then are based on analyzing the
different features of a failure (issues, symptoms, causes, components, solutions). Using social media data helps
gain more accurate online information due to the limitation of warranty periods. In other words, using social
media data broadens the scope of data gathering and lets in all feedback from different sources to recognize
improvement opportunities.
Originality/value –This work contributes a DSS model using multi-channel social media mining through
supervised machine learning for warranty-service improvement based on defect-related discovery to unravel
the potential aspects of social networks analysis to predict the most vulnerable components of a product and
the main causes of failures that lead to the inputs for the FMEA process and then, a cost optimization. The
authors have used social media channels like Twitter, Facebook, Reddit, LENOVO Forums, GitHub, Quora and
XDA-Developers to gather data about the LENOVO laptop failures as a case study.
Keywords Warrantyservice, Decision support system, Social networks,Data mining, Machinelearning,
FMEA
Paper type Research paper
1. Introduction
Customer feedback will lead the companies to the r eal users’needs and expectations.
Customers’requirements are m et using their comments and feedback, w hich results in
Improving
warranty by
data analysis
1281
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 18 July 2022
Revised 27 October 2022
12 December 2022
Accepted 22 December 2022
Industrial Management & Data
Systems
Vol. 123 No. 5, 2023
pp. 1281-1309
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-07-2022-0427
upgrading products and boosti ng services (Cui et al.,2017).Product d efects and failures data
are usually gathered through survey ques tionnaires, warranty claims, customer complaints
and after-sales service investi gations. Nonetheless, these met hods are restricted and not
comprehensive. Because the clas sical methods face constraints during the warranty peri od
and statistical population c an affect the data gathering com prehensiveness. Social media
channels play a vital role in today’s re putation of organizations, pr oducts and services
(Alkahtani et al.,2018), (Zheng et al.,2020), (Zheng et al., 2021)and(Yujuan et al.,2020). That
is why manufacturers are increas ingly engaged in social networks and eagerly use them to
introduce and develop their prod ucts (Farhadloo et al.,2016).
Besides, adopting an optimised approach for warranty services using comprehensive
and well-analysed data not only leads to resolving the problems but also can increase
customer satisfaction and decrease expenses. The selection through warranty solutions
(repair or replacement) depends on the relative severity of the failure and the reliability of
each choice (Zheng et al.,2021), (Shokouhyar et al., 2021)and(Fang and Hsu, 2019).
Therefore, there should be a complete analysis of failures to detect the most vulnerable
components, the related symptoms and the main causes of the issues. It is necessary to be
conscious of the severity associated with failures and the consequence of product failure on
customer satisfaction (Alkahtani et al., 2018), (Grambau and Hitzges, 2019), (Shokouhyar
et al., 2021)and(Fang and Hsu, 2019).
Previous literature reveals the power of social media data in discovering product
weaknesses. However, they mainly focus on the structure and classification of
defect-related texts and ignore the other properties that can enhance the investigation of
failures (Zheng et al., 2020)and(Zheng et al., 2021). Furthermore, the main focus in the
preceding literature is on using product defects gathered from social media mining to
support the manufacturers’considerations about improving the product’s quality.
Although it is valuable to gain previous information, there is a lack of failure analysis
using decision support models to make more reasonable decisions in warrantyservices. In
other words, investigating the root causes of failuresandmakingtherightdecisionto
repair or replace a component which are the main concerns of warranty service providers,
are not sufficiently considered. The novelty and creation of the current research are where
the online data is used to predict the most defective components, the most frequent
symptoms and the main causes to increase the awareness and agility for warranty service
and solution selection based on FMEA results and solution costs in products’failure before
it turns into a warranty claim.
This research addresses its objectives through warranty service providers’main
concerns: (1) Analysing the most frequent failures from different aspects that affect the
warranty services and prioritizing them, (2) Being prepared enough for the most vulnerable
components to keep the customers satisfied, (3) Determining the optimised solution for
warranty services during the warranty period. The article innovations are determined as four
aspects concurrently. First, the paper examines different public and specialised social
networks to ensure the data is comprehensive and pervasive enough. Second, it maintains a
supervised machine learning method based on product ontology to analyse the data and
promote a decision support system. Third, it reveals the most defective components of the
product with related aspects such as symptoms and causes. Fourth, it improves the warranty
services by using the warranty solutions with a product-service approach based on FMEA
results.
The current research questions debate the main concerns of the warranty service
providers and are answered through a systematic and methodological procedure; its
summary is shown in Figure 1 (Shokouhyar et al., 2021).
IMDS
123,5
1282
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