1,721,091 research outputs found
Predicting Online Invitation Responses with a Competing Risk Model Using Privacy-Friendly Social Event Data
Full text linkPredicting people's responses to invitations is an important issue for social event management, as the decision-making process behind member responses to invitations is complicated. The purpose of this paper is to suggest a privacy-friendly method to predict whether and when people will respond to open invitations. We apply the competing risk model to predict member responses. The predictive model uses past social event participation data to infer a network structure among people who accept or reject invitations. The inferred networks collectively show the extent to which people are likely to accept or reject invitations. Validated using real datasets including 31,230 people and 8,885 events, the proposed method not only presents the variables that predict attendance (such as past attendance and social network), but also those that predict faster responses. This approach is privacy friendly, as it requires no personal information regarding people and social events (such as name, age and gender or event content). This work contributes to the predictive modeling literature as the first study of a competing risk model developed for replies to a social invitation. Our findings will help event organizers predict how many people will attend events, allowing them to organize effectively
Agent, catalyst, or apocalypse? Engaging analytical challenges using Large language models
No full textLeveraging natural language processing for effective knowledge management in data science product innovation
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There is always someone to learn from: A text analytics approach to understand solo and teaming in data science innovation
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Boosting performance in data science competition using topic-driven analytics: evidence from recommendation system design on Kaggle
No full textResearch developments in the recommendation system and electronic commerce literature present more accurate and comprehensive recommendation system solutions. However, while these developments add new features to the recommendation systems, the question of whether a novel solution would excel in practice remains. Open innovation and crowdsourcing platforms are becoming an arena for designers to test their solutions in business competitions. We show how structural topical modeling identifies topical themes that improve contestant performance using forum message data during the competition period. Our topic modeling analysis identifies technological and business issues that emerge in recommendation system development. An econometric framework further investigates the link between topic distribution and performance. The multiperiod difference-in-differences estimator reports no significant statistical relation when linking all message communications to the performance. However, topic-dominant and topic-dispersed messages are both found to positively and significantly impact performance. Our result shows that structural topical modeling has an essential role to critically examine the most valuable message links to boost performance. Stakeholders may prioritize the messages with specific topics and&#x002F;or a mixture of topics. We provide research and practical implications for researchers, business analysts, developers, and managers to improve their experiences when engaging in recommendation system design on platforms.</p
The impact of forum content on data science open innovation performance: a system dynamics-based causal machine learning approach
No full textOpen innovation in data science generally takes the form of public competitions where teams exchange messages and solutions by competing and collaborating simultaneously. Team behaviours are widely heterogeneous in terms of the performance of their solutions and the participation in knowledge creation. We present a novel research framework for open innovation by integrating system dynamics and structural topic modelling to extract open factors and adopting a machine learning-based difference-in-differences estimator to understand the impact of team behaviour on their performance using data from Kaggle's competition. Our results identify four team behaviour categories—active, learner, lurker, and passive— in data science open innovation competitions which depend on the performance of their solutions and actions related to posting and reading messages in the forum. Furthermore, the activities of model evaluation, community support, and business understanding are the top three most positive and significant factors affecting team performance. Our research contributes to the literature by highlighting the value of forum feedback and exploring the data science activities in the forum discussion, in relation to innovation performance, to enrich the empirical understanding of open innovation. Research implications for researchers and practitioners participating in, organising, and supporting data science open innovation activities are provided
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