TY - GEN
T1 - Understanding smart locker user behavior through twitter
AU - Malyack, Colette
AU - Egbelu, Pius
N1 - Publisher Copyright:
© 14th International Conference on ICT, Society, and Human Beings, ICT 2021, 18th International Conference on Web Based Communities and Social Media, WBC 2021 and 13th International Conference on e-Health, EH 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Understanding smart locker sentiment and use is an area of increasing interest for package delivery organizations. Applications of this data could result in cost savings through route optimization and increased placement of smart locker technology. However, there has been little effort applied to gathering information related to public sentiment on this topic. Therefore, we gather and analyze Twitter data related to smart lockers to determine if there is change in sentiment over time and if socialization is greater in certain regions or communities. This analysis is performed through multiple statistical analyses, linear regression, time series decomposition, and logistic regression. Some significant findings indicate that socialization of tweets related to smart lockers increased over time and socialization is greatest in the more densely population continent of Asia. Future studies are encouraged to continue analysis of data related to smart lockers based on population density, as this could provide marketing and delivery optimization improvements to decrease cost without decreasing customer sentiment or service.
AB - Understanding smart locker sentiment and use is an area of increasing interest for package delivery organizations. Applications of this data could result in cost savings through route optimization and increased placement of smart locker technology. However, there has been little effort applied to gathering information related to public sentiment on this topic. Therefore, we gather and analyze Twitter data related to smart lockers to determine if there is change in sentiment over time and if socialization is greater in certain regions or communities. This analysis is performed through multiple statistical analyses, linear regression, time series decomposition, and logistic regression. Some significant findings indicate that socialization of tweets related to smart lockers increased over time and socialization is greatest in the more densely population continent of Asia. Future studies are encouraged to continue analysis of data related to smart lockers based on population density, as this could provide marketing and delivery optimization improvements to decrease cost without decreasing customer sentiment or service.
KW - Logistic Regression
KW - Microblogging Sites (MS)
KW - Smart Locker
KW - Statistical Analysis
KW - Time Series Decomposition
UR - http://www.scopus.com/inward/record.url?scp=85117445314&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85117445314&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85117445314
T3 - 14th International Conference on ICT, Society, and Human Beings, ICT 2021, 18th International Conference on Web Based Communities and Social Media, WBC 2021 and 13th International Conference on e-Health, EH 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021
SP - 110
EP - 117
BT - 14th International Conference on ICT, Society, and Human Beings, ICT 2021, 18th International Conference on Web Based Communities and Social Media, WBC 2021 and 13th International Conference on e-Health, EH 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021
PB - IADIS
T2 - 14th International Conference on ICT, Society, and Human Beings, ICT 2021, 18th International Conference on Web Based Communities and Social Media, WBC 2021 and 13th International Conference on e-Health, EH 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021
Y2 - 20 July 2021 through 23 July 2021
ER -