Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research

By Bongsug Chae. 2015

454 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/c5073d765e8843de49cfe00e3046446ecea5bc26

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Citations in the corpus

Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic (2021, unknown): “…of an analytical framework (Twitter Analytics) for analyzing tweets with the ambition to generate insights on SCDs and reporting them to SC partners Chae (2015) x x x x Application of variance-based structural equation modelling to analyze the relationship between data analytics and SCRES…”
Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives (2016, methodology): “This system is an interactive dashboard for studying communication processes on Twitter microblogging platform, which has the ability to produce high-level statistical and visual overview of vast communication networks.”
Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice (2017, background): “, 2015) and supply chain planning satisfaction (Chae et al., 2014a). Tan et al. (2015) described how text analytics capability could be combined with other analytics capabilities to develop innovative products.”
Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India (2020, background): “Even attempts are being taken to explore the potentiality of this platform for improving Supply Chain Management (Chae, 2015; Rathore, Ilavarasan, & Dwivedi, 2016; Shareef, Mukerji, Alryalat, Wright, & Dwivedi, 2018).”
The impact of big data on world-class sustainable manufacturing (2015, background): “Chae [28] has argued that in the present situation, social media and big data are complementary to each other.”
COVID-19 Public Sentiment Insights and MachineLearning for Tweets Classification (2020, unknown): “Novel analytical frameworks have also been used to analyze supply chain management (SCM) related twitter data about, providing important insights to improve SCM practices and research [33].”
Big data and predictive analytics for supply chain and organizational performance (2017, background): “Scholars highlight the importance of BDPA for transforming supply chains (Chae, 2015; Hazen et al., 2014; Waller & Fawcett, 2013).”