TY - JOUR
T1 - Visual analytics for identifying product disruptions and effects via social media
AU - Zavala, Araceli
AU - Ramirez-Marquez, Jose Emmanuel
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/2
Y1 - 2019/2
N2 - In the last decade, there have been high profile product safety events that captured public attention on social networks. Researchers have attempted various studies on consumers' reaction to product recalls but hardly any studies were conducted to find out a way to identify recalls by using users' comments specifically on social media. The earlier a company can detect a product disruption, the more a company can do in preparation to reduce its impact. In this paper, we propose a visualization framework capable of identifying a possible product recall via social networks, like Facebook or Twitter. Customers' comments found in data that express a negative sentiment are considered as non-conforming observations and plotted on a p-chart, which helps to identify when the proportion of negative comments get out of control and, as a result, a company can diminish the response time. To check its viability, we conducted three event studies of well-known companies that have experienced product recalls. The results show that customers’ negative sentiments could be monitored with the aim of predicting when a product might necessitate a recall as well as reducing decision-makers response time.
AB - In the last decade, there have been high profile product safety events that captured public attention on social networks. Researchers have attempted various studies on consumers' reaction to product recalls but hardly any studies were conducted to find out a way to identify recalls by using users' comments specifically on social media. The earlier a company can detect a product disruption, the more a company can do in preparation to reduce its impact. In this paper, we propose a visualization framework capable of identifying a possible product recall via social networks, like Facebook or Twitter. Customers' comments found in data that express a negative sentiment are considered as non-conforming observations and plotted on a p-chart, which helps to identify when the proportion of negative comments get out of control and, as a result, a company can diminish the response time. To check its viability, we conducted three event studies of well-known companies that have experienced product recalls. The results show that customers’ negative sentiments could be monitored with the aim of predicting when a product might necessitate a recall as well as reducing decision-makers response time.
KW - Product recall
KW - Social media
KW - Statistical Process Control
KW - Visual analytics
UR - http://www.scopus.com/inward/record.url?scp=85059449647&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059449647&partnerID=8YFLogxK
U2 - 10.1016/j.ijpe.2018.12.020
DO - 10.1016/j.ijpe.2018.12.020
M3 - Article
AN - SCOPUS:85059449647
SN - 0925-5273
VL - 208
SP - 544
EP - 559
JO - International Journal of Production Economics
JF - International Journal of Production Economics
ER -