Exploring the Usefulness of User-Generated Content for Business Intelligence in Innovation

Abstract

This study presents a systematic approach that integrates the information adoption model (IAM) with topic modeling to analyze the digital voice of users in online open innovation communities (OOICs) and empirically examines the usefulness of UGC with large amounts of redundant information and varying content quality across two dimensions: information quality and information source credibility. A total of 61,227 bug comments were collected from the OOIC of Huawei EMUI and analyzed using binary logistic regression. The results show that information timeliness and completeness have a positive effect on the usefulness of UGC in OOICs; conversely, information semantics have a negative effect on the usefulness of UGC. Prior user experience has no influence on the usefulness of UGC in OOICs, while active user contribution has a positive effect on the usefulness of UGC. The results of this study offer several implications to researchers and practitioners, and thus could serve as a pivotal reference source for further investigation of potential determinants of UGC usefulness in OOICs.

Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media

“…To reveal the main topics voiced by the community regarding COVID-19 vaccine misinformation, we used Latent Dirichlet Allocation (LDA) (Blei et al, 2003), a widely used method in the data analytics literature (Daradkeh, 2019d(Daradkeh, , 2021bGarcia & Berton, 2021;Maier et al, 2018). LDA is a three-layer Bayesian probability model that groups frequently co-occurring words into topics.…”

“…The word-by-topic matrix indicates how a group of words can be used to form a topic. Conversely, the topic-by-document matrix reveals how people perceive important topics and in turn can indicate people' sentiment tendencies toward topics (Daradkeh, 2021b). Because the goal of this study is to analyze community sentiment toward COVID-19 vaccine misinformation topics, only relevant sentiment and most relevant COVID-19 vaccine misinformation topics were examined.…”

“…The perplexity score is a means of measuring the goodness of fit of a topic model (Blei et al, 2003). In general, the smaller the perplexity value of the model, the better it fits the topics occurring in a corpus and their relevance to different documents (Daradkeh, 2019a(Daradkeh, , 2019b(Daradkeh, , 2019c(Daradkeh, , 2019d(Daradkeh, , 2020(Daradkeh, , 2021a(Daradkeh, , 2021b(Daradkeh, , 2021c(Daradkeh, , 2022Daradkeh & Al-Dwairi, 2018;Daradkeh & Sabbahein, 2019;Maier et al, 2018).…”

Knowledge graph-based method for solutions detection and evaluation in an online problem-solving community

“…Information usefulness as a topic is addressed in the literature from different perspectives. Generally, most studies discussed information usefulness in the context of business intelligence [12] and more specifically in business internet services [13]. Evaluating information usefulness shared in online communities is a critical issue specially in this area of big data and knowledge sharing in social media.…”

“…In his method, the author used a text analytic framework to extract important features from online forums in order to evaluate the information usefulness. In another work, Daradkeh [12] examines the usefulness of user generated content with large amounts of redundant information in order to analyze the digital voice of users in online open innovation communities. Generally, used approaches for information usefulness evaluation are based on extracting many features from online discussions and classify the usefulness of either threads or posts [14].…”