Exploring public mood toward commodity markets: a comparative study of user behavior on Sina Weibo and Twitter

PurposeAlthough knowledge based on business intelligence (BI) is crucial, few studies have explored the core of BI knowledge; this study explores this topic.Design/methodology/approachThe authors collected 1,306 articles and 54,020 references from the Web of Science (WoS) database and performed co-citation analysis to explore the core knowledge of BI; 52 highly cited articles were identified. The authors also performed factor and cluster analyses to organize this core knowledge and compared the results of these analyses.FindingsThe factor analysis based on the co-citation matrix revealed seven key factors of the core knowledge of BI: big data analytics, BI benefits and success, organizational capabilities and performance, information technology (IT) acceptance and measurement, information and business analytics, social media text analytics, and the development of BI. The cluster analysis revealed six categories: IT acceptance and measurement, BI success and measurement, organizational capabilities and performance, big data-enabled business value, social media text analytics, and BI system (BIS) and analytics. These results suggest that numerous research topics related to big data are emerging.Research limitations/implicationsThe core knowledge of BI revealed in this study can help researchers understand BI, save time, and explore new problems. The study has three limitations that researchers should consider: the time lag of co-citation analysis, the difference between two analytical methods, and the changing nature of research over time. Researchers should consider these limitations in future studies.Originality/valueThis study systematically explores the extent to which scholars of business have researched and understand BI. To the best of the authors’ knowledge, this is one of the first studies to outline the core knowledge of BI and identify emerging opportunities for research in the field.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Data from social media and apps can create both challenges and opportunities for managers. For example, analyzing social media text can help managers understand public opinion (Chen et al, 2020). COVID-19 has demonstrated the value of modeling and predicting how the pandemic would spread.…”

Section: Implications For Practicementioning

confidence: 99%

Exploring core knowledge in business intelligence research

Shiau

Chen

Wang

et al. 2022

INTR

View full text Add to dashboard Cite

show abstract

“…Social media platforms are commonly used by people to exchange views, and discuss real-time news [ 35 ]. Sina Weibo is one of the most influential social media platforms [ 65 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, factors affecting garbage classification behavior can be summarized. Social media, represented by Sina Weibo, has billions of users in China [ 35 , 36 ]. The public can browse real-time news and even ask for help [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring the motivations and obstacles of the public's garbage classification participation: evidence from Sina Weibo

Zhang

2023

J Mater Cycles Waste Manag

View full text Add to dashboard Cite

China has been implementing garbage classification to improve resource recycling for many years. Since garbage classification is essentially a social activity, it needs the active participation of the public. However, the phenomenon of "high practice, low effect" is widespread in most cities. Therefore, this paper uses the data from Sina Weibo to analyze the reasons for the poor garbage classification effect. First, the key factors affecting residents' willingness to participate in garbage classification are identified based on the text-mining method. Further, this paper analyzes the reasons that promote or hinder the residents' intention of garbage classification. Finally, the resident's attitude towards garbage classification is explored by the score of the text's emotional orientation, and further the reasons for the positive and negative emotional orientation are analyzed, respectively. The main conclusions are as follows: (1) The proportion of residents holding negative sentiment towards garbage classification is as high as 55%. (2) Residents' positive emotions are mainly caused by the public's sense of environmental protection inspired by publicity and education, and the incentive measures taken by the government. (3) The main reasons for negative emotions are imperfect infrastructure and unreasonable garbage sorting arrangements.

show abstract

“…As regards the methodology, and as a core element of the study, we use the Twitter happiness index as a proxy of investors' online sentiment, in line with the work by Zhang et al (2016), Shen et al (2018) and Teti et al (2019), Shiva, andSingh (2020), andChen, Lai, andCai (2021), which can be considered as an indicator of global sentiment, and taking into account the role of social media as a tool for investing and predicting (Oh and Sheng 2011;Piñeiro-Chousa et al 2017). In addition, as it is a daily index, it will help to capture emotions at each moment more suitably, as opposed to most sentiment indicators considered in other studies such as those by Lemmon and Portniaguina (2006), Schmeling (2009) and Bathia and Bredin (2012), who use information with a monthly frequency but which is not able to reveal the momentum effect in the short term.…”

Section: Introductionmentioning

confidence: 99%

Global environmental equities and investor sentiment: the role of social media and Covid-19 pandemic crisis

et al. 2023

View full text Add to dashboard Cite

According to researchers, information generated from social media provides useful data for understanding the behaviour of various types of financial assets, using the sentiment expressed by these network users as an explanatory variable of asset prices. In a context in which investment based on sustainability and environmental preservation values is vital, there is no known scientific work that analyses the relationship between social networks and environmental investment, which is closely related to the 2030 Agenda for Sustainable Development. In this study, we aim to identify how investor sentiment, generated from social networks, influences environmental investment and whether this influence depends on the time variable, as well the role of the pandemic crisis and the Russia-Ukraine war. Our results show different forms of behaviour for the different periods considered, with the proximity between the two types of variables being time-varying. For shorter periods, proximity occurred mainly during the pandemic crisis, repeatedly revealing that sentiment is a risk factor in environmental investment and in particular how important the information generated from social networks can be in pricing environmental assets. For longer periods, no common stochastic trends were identified. The mechanisms generating the series are thus characterised by a certain autonomy.

show abstract

Exploring public mood toward commodity markets: a comparative study of user behavior on Sina Weibo and Twitter

Cited by 11 publications

References 20 publications

Exploring core knowledge in business intelligence research

Exploring core knowledge in business intelligence research

Exploring the motivations and obstacles of the public's garbage classification participation: evidence from Sina Weibo

Global environmental equities and investor sentiment: the role of social media and Covid-19 pandemic crisis

Contact Info

Product

Resources

About