A Knowledge Adoption Model Based Framework for Finding Helpful User-Generated Contents in Online Communities

Many online communities allow their members to provide information helpfulness judgments that can be used to guide other users to useful contents quickly. However, it is a serious challenge to solicit enough user participation in providing feedbacks in online communities. Existing studies on assessing the helpfulness of user-generated contents are mainly based on heuristics and lack of a unifying theoretical framework. In this article we propose a text classification framework for finding helpful user-generated contents in online knowledge-sharing communities. The objective of our framework is to help a knowledge seeker find helpful information that can be potentially adopted. The framework is built on the Knowledge Adoption Model that considers both content-based argument quality and information source credibility. We identify 6 argument quality dimensions and 3 source credibility dimensions based on information quality and psychological theories. Using data extracted from a popular online community, our empirical evaluations show that all the dimensions improve the performance over a traditional text classification technique that considers word-based lexical features only

Money Talks: A Predictive Model on Crowdfunding Success Using Project Description

Existing research of crowdfunding mainly focuses on the basic properties of the project such as category and goal, the information content of the project, however, is barely studied. By introducing Elaboration Likelihood Model into crowdfunding context and using a large dataset obtained from Kickstarter, a popular crowdfunding platform, we study the influence of project descriptions in terms of argument quality and source credibility, and investigate their impacts on funding success. We find information disclosed in project descriptions is associated with funding success. We also examine the practical impacts of project description by using a predictive model. Results show that our model can predict with an accuracy rate of 73% (71% in F-measure), which represents an improvement of 15 percentage points over the baseline model and 4 percentage points over the mainstream model. Overall, our results provide insights to researchers, project owners and backers to better study and use crowdfunding platforms

On the Drivers of Information Adoption in Online Communities

Online communities have become a prevalent means for information exchange among individuals with shared interests. While several studies exist on the individuals’ motivation to contribute information to online communities, less is known about what factors drive information adoption in these communities. This article proposes a theoretical framework of antecedents of individuals’ adoption of contributed information in online communities. Drawing on the Elaboration Likelihood Model, we develop hypotheses regarding both central and peripheral routes of information evaluation and contend that information quality, information source trustworthiness, and information recipient level of trust in the online community are the main factors that influence adoption of information in online communities. Furthermore, we identify the antecedents of information source trustworthiness and information recipient trust in the online community

Understanding the Emotional and Informational Influence on Customer Knowledge Contribution through Quantitative Content Analysis

Customer knowledge contribution is a vital source of business value. Existing studies paid limited attention to emotional influence on knowledge contribution. Drawing upon social support theory, this study attempts to elaborate the influence of emotional support and informational support on knowledge contribution of customers in a firm-hosted online community. Through quantitative content analysis including product feature extraction and sentiment analysis, we analyzed content data from 2318 users. A set of research hypotheses were tested via regression analysis of panel data. We found that informational support (information diagnosticity and source credibility) and emotional support (emotional consistency and emotional difference) significantly affect customer knowledge contribution. This study contributes to knowledge contribution literature by showing the emotional and informational influence, and provides insights for community managers

A taxonomy for deriving business insights from user-generated content

Deriving business insights from user-generated content (UGC) is a widely investigated phenomenon in information systems (IS) research. Due to its unstructured nature and technical constraints, UGC is still underutilized as a data source in research and practice. Using recent advancements in machine learning research, especially large language models (LLMs), IS researchers can possibly derive these insights more effectively. To guide and further understand the usage of these techniques, we develop a taxonomy that provides an overview of business insights derived from UGC. The taxonomy helps both practitioners and researchers identify, design, compare and evaluate the use of UGC in this IS context. Finally, we showcase an LLM-supported demo application that derives novel business insights and apply the taxonomy to it. In doing so, we show exemplary how LLMs can be used to develop new or extend existing NLP applications in the realm of IS

Feasibility investigation of crowdsourcing-based product design and development for manufacturing

In the era of Industry 4.0, to help manufacturers make quick response to rapidly changing market and customer needs, this research explores the feasibility of realizing benefits of crowdsourcing in product design and development from a lifecycle point of view through investigations on product design quality control and crowdsourcing technology theories, product design lifecycle information modelling, and simulation platform prototyping. It intends to help manufacturers create a product-service ecosystem to deliver values to all involved stakeholders of a PDD process. This study started with building up the theoretical foundation of product design quality control in crowdsourcing design environment. Then, key crowdsourcing technologies for realizing a lifecycle PDD process on a crowdsourcing platform while enabling the design quality were explored. Thirdly, a multi-layer product design lifecycle information model was developed to accommodate all design related information in a PDD process and the identified information at each design phase and the relationships and interactions among information entities were evaluated by case studies and ORM modelling method, respectively. Finally, two crowdsourcing platform prototypes based on the PDLIM were developed to test their effectiveness in communicating design information among stakeholders and delivering value to them. The proposed research made contributions to knowledge through the following improvements/advancements: (1) understanding of key factors affecting product design quality in crowdsourcing design environments, (2) a technical foundation of crowdsourcing technologies for PDD process, (3) a novel product design lifecycle information model accommodating design information in crowdsourcing environments, and (4) guidelines on developing intermediary and integrated crowdsourcing platforms for PDD

Influences on e-WOM adoption in two female online communities: the cases of Kuwait and Saudi Arabia

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonOnline communities (OCs) are an important source of electronic-word-of-mouth (e-WOM), but few studies have examined such messages in a Middle Eastern context. This research develops a conceptual framework that can be used as an instrument to guide empirical work in the field of e-WOM in female OCs. Researchers in similar areas may find this work useful as exemplifying a novel approach to the study of e-WOM adoption in different OCs. This study, of e-WOM adoption in two female-only Arabic-language online forums in Kuwait and Saudi Arabia, is grounded in three models: the Information Adoption Model, the Information Systems Continuance Model and the Knowledge Contribution Model, because no particular theory or set of theories currently dominates OC research. In particular, researchers are strongly recommended to start building their own theories of e-WOM phenomena, because this area is still young and has grown rapidly in recent years. The research design comprises two phases. The first is a content analysis, which was appropriately used to analyse the online textual conversations, since it offers a deep understanding of the phenomenon in its real context. The purpose of this phase was to identify the main determinants of e-WOM adoption in female OCs in Kuwait and Saudi Arabia, from which a conceptual model could be developed. It investigated the characteristics of the messages influencing the adoption of e-WOM. It began with a pilot study, where 90 threads were analyzed, followed by the content analysis of the two cases. A total of 765 threads were analysed in the Kuwaiti case, comprising 6200 messages broken down into 17,832 units of analysis. In the Saudi case, 1168 threads were analyzed, containing 17,320 messages and 31731 units of analysis. In both cases there was a prevalence of emotional features in messages, coded as ‘community bonding’. In the second phase, semi-structured interviews were conducted, with the aim of illuminating the similarities and differences in terms of e-WOM determinants between Kuwaiti and Saudi culture by refining the research model codes and sub-codes. This phase was also intended to answer research questions on the current situation in terms of online role behaviours in female-only online beauty forums in Kuwait and Saudi Arabia; on how online behavioural roles influence females to adopt e-WOM; and on the role in e-WOM adoption of the following demographic variables: nationality, age, marital status, employment, education level, hours spent in the community and length of community membership. Fifty women, comprising 25 members of each of the two communities, were interviewed online to gain more knowledge of the factors that impede or facilitate the adoption of e-WOM. The qualitative results show that argument quality, community bonding and information need fulfilment were all significant in affecting participants’ tendency to adopt e-WOM. This study concludes with specific implications for relevant theories and useful findings at the individual, organizational and societal levels