Social media analytics and business intelligence research: A systematic review

Choi, Jin Sik; Yoon, Janghyeok; Chung, Jae-Min; Coh, Byoung-Youl; Lee, Jae-Min

doi:10.1016/j.ipm.2020.102279

Cited by 97 publications

(44 citation statements)

References 84 publications

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“…In terms of techniques used to conduct sentiment analysis, lexicon-based methods based on a pre-defined list of lexical features have been widely applied in social media research (Choi et al, 2020). Considering that implementing lexicon-based methods are less time-consuming and convenient to implements, and many lexicons developed by researchers in the text mining field can be directly applied to the analysis, hence we decided to use a lexicon-based method to conduct sentiment analysis.…”

Section: Sentiment Analysis In Consumer Researchmentioning

confidence: 99%

Exploring Sources of Satisfaction and Dissatisfaction in Airbnb Accommodation Using Unsupervised and Supervised Topic Modeling

Ding

Choo

et al. 2021

Front. Psychol.

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This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation (LDA) and supervised LDA (sLDA) approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the topic “guest conflicts” in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners.

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Section: Sentiment Analysis In Consumer Researchmentioning

confidence: 99%

Exploring Sources of Satisfaction and Dissatisfaction in Airbnb Accommodation Using Unsupervised and Supervised Topic Modeling

Ding

Choo

et al. 2021

Front. Psychol.

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“…As social media becomes popular, enterprises also attempt to figure out how to adopt social media to attract and engage their consumers. Enterprises apply technical characteristics of social media as business tactics including online collaboration, social support and information sharing (Choi et al, 2020;Chung et al, 2020). Sivarajah et al (2020) noted that enterprises can employ social media analytics for better understanding consumer needs and improving productivity and efficiency in order to achieve business sustainability.…”

Section: Social Commercementioning

confidence: 99%

Understanding Customer Motivation to Share Information in Social Commerce

Hsieh

2021

Journal of Organizational and End User Computing

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The gradual emergence of social commerce is enhancing the development of businesses. The opinions exchanged and information shared by each consumer in the community affects consumers’ purchasing decisions and participation behaviors. This study established a consumer information sharing model in social commerce based on the theories of social exchange and communication ecology. Data were collected from 204 respondents and PLS technique was used to analyze the data. The results indicated that reciprocity and community participation exhibited a significantly positive effect on information sharing. Reciprocity and community participation also had partial mediation effects on the consumer–community and consumer–platform relationships.

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“…Social media analytics is concerned with developing and evaluating the information processing tools to collect, analyze, monitor, and summarize the data gathered from a social media platform to facilitate the organization by providing them with the desired data. (Choi et al, 2020;Kordzadeh & Young, 2020) Social media analytics helps organizations extract useful information relevant to brand promotion and help the organization target a specific audience through social media sites. All of it is combined to make a social media landscape that has become an evident part of the business (Brooker et al, 2016).…”

Section: Social Media Analytics and Ethical Perceptionmentioning

confidence: 99%

Social Media Analytics Lens: A Study on Consumer Perceived Risk to Share Information on Social Media Networks

Majeed

Mahmood

Molnár

et al. 2021

HSSR

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Purpose of the study: This study focuses on understanding the potential unfavourable social media analytics practices (SMA) and how they may lead to the negative perception of consumer self-disclosure on social media sites and causing market discomfort for people. We also examined a framework regarding the SMA practices, and their impact on the customer’s trust to use social media networks. Methodology: Data were collected from 527 social media users with the help of an online questionnaire. Partial Least Squares Structural equation modeling—PLS-SEM was used using SmartPLS to validate the questionnaire and measure the hypothesized relationships between the constructs. Main Findings: The results show that perceived risk mediates the relationship between the perceived ethicality of social media analytics and the marketing discomfort of the consumers. Moreover, we also found that the negatively perceived ethicality of social media analytics leads to marketing discomfort of the consumers. This negative attitude impacts the consumer’s intention to purchase, interest in the product, and intention to be vulnerable toward the company. Applications of this study: These findings have practical utility for all types of organizations that use social media. This study is general for all social media sites. This study also provides direction for future research, such as managing the personal information of the social media users from being used by the organizations without consumer permission and developing ethical standards to use the consumer information for the marketing strategies. Novelty/Originality of this study: Social media analytics (SMA) gathered data from the social media platforms such as Twitter, Instagram, and blogs, representing a massive room to utilize public data, and these lead towards the ethical concerns of the consumers. For this problem, we studied a complete framework to identify the major outcomes of the PESMA on the consumers' self-disclosure and marketing comfort.

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Social media analytics and business intelligence research: A systematic review

Cited by 97 publications

References 84 publications

Exploring Sources of Satisfaction and Dissatisfaction in Airbnb Accommodation Using Unsupervised and Supervised Topic Modeling

Exploring Sources of Satisfaction and Dissatisfaction in Airbnb Accommodation Using Unsupervised and Supervised Topic Modeling

Understanding Customer Motivation to Share Information in Social Commerce

Social Media Analytics Lens: A Study on Consumer Perceived Risk to Share Information on Social Media Networks

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