Data Quality in Social Media Analytics for Operations and Supply Chain Performance Management

Fabian Siekmann*, Aseem Kinra, Herbert Kotzab

*Corresponding author af dette arbejde

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

Abstract

Social media analytics (SMA) is claimed to be an opportunity for practical inquiry to create new knowledge and possibilities but is only slowly finding its way into practice due to uncertain information quality. Good Operations and Supply Chain Management (OSCM) decisions are just as good as the data they are based upon. A more detailed consideration of data quality is needed, especially when natural language data is processed for decision-making. Motivated by recent calls in the domain, the purpose of this study is to investigate how big data quality is considered in SMA for operations and supply chain performance. The study employs a directed qualitative content analysis of 56 research contributions based on the re-analysis of a previous systematic literature review. The results reveal that within performance-oriented SMA literature, intrinsic and contextual data quality are not comprehensively addressed by OSCM-research to date. More particularly it is shown, that contextual data quality assessment remains a challenge for the analysis of textual social media data. The study contributes by reporting how data quality is considered for SMA in operations and supply chain performance management (OSCPM) literature from an intrinsic and contextual perspective. Based on the results of this analysis, data relevancy and data believability are identified as levers to reduce information uncertainty in SMA-aided decision-making, paving the way for future research on contextual social media data quality in OSCM.
OriginalsprogEngelsk
TitelDynamics in Logistics : Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany
RedaktørerMichael Freitag, Aseem Kinra, Herbert Kotzab, Nicole Megow
Antal sider13
UdgivelsesstedCham
ForlagSpringer
Publikationsdato2022
Sider104-116
ISBN (Trykt)9783031053580
ISBN (Elektronisk)9783031053597
DOI
StatusUdgivet - 2022
Udgivet eksterntJa
BegivenhedThe 8th International Conference on Dynamics in Logistics. LDIC 2022 - WWW
Varighed: 23 feb. 202225 mar. 2022
Konferencens nummer: 8
https://www.ldic-conference.org/registration/virtual-platform

Konference

KonferenceThe 8th International Conference on Dynamics in Logistics. LDIC 2022
Nummer8
LokationWWW
Periode23/02/202225/03/2022
Internetadresse
NavnLecture Notes in Logistics
ISSN2194-8917

Emneord

  • Operations and Supply Chain Management
  • Social media analytics
  • Data quality

Citationsformater