Social media as an information system: improving the technological agility

173

Although blockchain has attracted a great deal of attention from academia and industry there is a lack of studies on acceptance drivers. This study explores blockchain acceptance by mining the collective intelligence of users on Twitter. It maps blockchain user acceptance drivers to technology acceptance constructs. The analysis shows that users are attracted by security, privacy, transparency, trust and traceability aspects provided by blockchain. On Twitter more discussions on blockchain benefits than on drawbacks. Initial coin offering (ICO) is extensively discussed. The study provides guidelines for managers and concludes by presenting the limitations of the study along with future research directions.

Section: Collective Intelligence and Crowd Wisdommentioning

confidence: 99%

Perceived usefulness, ease of use and user acceptance of blockchain technology for digital transactions – insights from user-generated content on Twitter

Grover

Kar

Janssen

et al. 2019

173

“…Shan et al (2020) proposed a quantification method to assess the dynamic value of social media data. Senadheera, Warren, and Leitch (2017) provided dynamism to social media strategy formation to enhance business agility. Franke, Charoy, and Khoury (2013) proposed a framework based on social media data for coordination of activities in dynamic situations.…”

Section: Related Workmentioning

confidence: 99%

“…Adopting rationality is one of the key social factors for decision-making (Tan et al 2013). There is an increased focus on research involving social media (Senadheera, Warren, and Leitch 2017). Focus on the realistic need for social media data for business decision-making, machine learning algorithms can be categorised into unsupervised and supervised learning, depending on whether the data are labelled (Alpaydin 2020).…”

Section: Introductionmentioning

confidence: 99%

L-measure evaluation metric for fake information detection models with binary class imbalance

Wang

Hsu

et al. 2020

Fake information in social media frequently causes social issues. The amount of fake information is smaller than that of real information, this leads to class imbalance. Some improved classification methods and metrics to resolve the imbalance and evaluate model performance have been proposed, respectively. However, the existing metrics for classification methods have many limitations. This paper proposes the robust metric, L-measure, that can reasonably evaluate all models with binary class imbalance with different IRs. L-measure also require less computation than the Matthews correlation coefficient. Finally, this paper demonstrates the validity of the proposed metric under different IRs with examples from UCI and Kaggle.

“…Social media is an important technology for crisis response, primarily because of the tools that enable open exchange of timely information through conversation and interaction (Lachlan et al 2016;Senadheera, Warren, and Leitch 2017). Even in the relatively unexplored context of local government, the adoption and use of social media tools for crisis communication and social media's role in crisis management has been investigated by academics (Graham, Avery, and Park 2015).…”

Section: Social Media and Crisis Managementmentioning

confidence: 99%

“…Tesco suffered a lot during the scandal and was seeking a better method of interaction with customers to win back trust and lost market share. The reaction of customers towards Tesco during this crisis can provide important insights for crisis managers in affected companies when faced with similar crises in the future (Senadheera, Warren, and Leitch 2017;Tse et al 2016). Facebook was chosen as the social media platform in this analysis because Tesco used Facebook as one of its main platforms to communicate information to the customers during the scandal.…”

Section: Introductionmentioning

confidence: 99%

An investigation of social media data during a product recall scandal

Tse

Loh

Ding

et al. 2018

As social media has become an important part of modern daily life, users often share product opinions online and these tend to spike when large companies undergo crises. This paper investigates customer online responses to a large company crisis by uncovering hidden insights in social media comments and presents a framework for handling social media data and crisis management. Analysis of textual Facebook data from users responding to the 2013 horsemeat scandal is presented. In this study, we used a novel comprehensive data analysis framework alongside a text-mining framework to objectively classify and understand customer perceptions during this horsemeat scandal. This framework provides an effective approach for investigating customer perception during a company crisis and measures the effectiveness of crisis management practices which the company has adopted. Our analyses show that social media can provide important insights into customer behaviour during crisis communications.