Stimulating innovation: managing peer interaction for idea generation on digital innovation platforms

This study investigates user behaviours in online innovation communities which are enabled by digital technologies, to obtain an understanding of the relationship between user’s social interaction and their innovation contribution. The new type of innovation communities enable firms to crowdsource ideas from their users for developing new products and improving existing ones, and to facilitate the interactions among users. From an empirical study which collects a large-scale, quantitative data set from Microsoft’s Idea platform of Business Intelligent products, this paper focuses on the amount and diversity of users’ social interaction particularly their commenting behaviours on the platform, and uses the number of posted ideas and the number of implemented ideas to capture users’ contribution to the firm’s innovation development. The findings indicate that the amount of user interaction is positively related to the number of implemented ideas, but has an inverted U-shaped relationship with idea number. Moreover, diverse user interaction encourages idea posting, but is negatively associated with the number of implemented ideas. The findings should provide managerial guidance to firms on incentivizing and managing user interaction in online communities in order to improve firms’ innovation development

Revealing industry challenge and business response to Covid-19: a text mining approach

Purpose This study aims to conduct a ‘real-time’ investigation with user-generated content on Twitter to reveal industry challenges and business responses to the Covid-19 pandemic. Specifically, using the hospitality industry as an example, the study analyses how Covid-19 has impacted the industry, what are the challenges and how the industry has responded. Design/methodology/approach With 94, 340 tweets collected between October 2019 and May 2020 by a programmed web scraper, unsupervised machine learning approaches such as structural topic modelling are applied. Findings The results show that: (1) despite the adverse consequences from the pandemic, the hospitality industry has shown increasing interests in finding ways to survive, such as looking into novel technologies and adopting new business strategies; (2) the pandemic has created an opportunity for organisations to jump out from their daily business operations and rethink about the future development of the industry; (3) the Covid-19 impact is not only shown on the reduction in the job demand but also a change in the demand structure of the job market; (4) the use of novel text mining approaches on unstructured social media data is effective in identifying industry-level challenge and response to public emergencies. Originality This study contributes to the literature on business response during crises providing for the first time a study of utilising unstructured content on social media for industry- level analysis in the hospitality context

Stimulating innovation on social product development: an analysis of social behaviors in online innovation communities

With firms' increasing adoption of social product development strategies, such as mass collaboration and crowdsourcing, online users are actively participating in the development of new products and services via social media platforms. Online innovation communities (OICs), one representative of such social media platforms, have been used by large firms to collect ideas from their users and facilitate the product development process. While it is extensively studied that product ideas with high popularity on OICs are of great importance to the product development, research on what social behaviors of online users lead to the high popularity is largely unclear. This paper conducts an empirical study by collecting a large-scale, quantitative dataset from an OIC between 2014 and 2018. With the analysis of users' online idea posting and commenting behaviors, our results reveal that the idea contribution experience, together with comment diversity positively in uence the overall popularity of an individual's ideas, while the motivation of providing comments is negatively related. Moreover, user's innovation capability poses a positive effect on both overall and average popularity of ideas. These findings can help firms better incentivise their users on OICs to improve the effectiveness and efficiency of social product development

Analytical behaviour of concrete-encased CFST box stub columns under axial compression

[EN] Concrete-encased CFST (concrete-filled steel tube) members have been widely used in high-rise buildings and bridge structures. In this paper, the axial performance of a typical concrete-encased CFST box member with inner CFST and outer reinforced concrete (RC) is investigated. A finite element analysis (FEA) model is established to analyze the compressive behavior of the composite member. The material nonlinearity and the interaction between concrete and steel tube are considered. A good agreement is achieved between the measured and predicted results in terms of the failure mode and the load-deformation relation. The verified FEA model is then used to conduct the full range analysis on the load versus deformation relations. The loading distributions of different components inclouding concrete, steel tube and longitudinal bar during four stages are discussed. Typical failure modes, internal force distribution, stress development and the contact stress between concrete and steel tube are also presented. The parametric study on the compressive behavior is conducted to investigate the effects of various parameters, e.g. the strength of concrete and steel, longitudinal bar ratio and stirrup space on the sectional capacity and the ductility of the concrete-encased CSFT box member.Chen, J.; Han, L.; Wang, F.; Mu, T. (2018). Analytical behaviour of concrete-encased CFST box stub columns under axial compression. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 401-408. https://doi.org/10.4995/ASCCS2018.2018.6966OCS40140

Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities

With the rise of Large Language Models (LLMs), AI assistants' ability to utilize tools, especially through API calls, has advanced notably. This progress has necessitated more accurate evaluation methods. Many existing studies adopt static evaluation, where they assess AI assistants' API call based on pre-defined dialogue histories. However, such evaluation method can be misleading, as an AI assistant might fail in generating API calls from preceding human interaction in real cases. Instead of the resource-intensive method of direct human-machine interactions, we propose Automated Dynamic Evaluation (AutoDE) to assess an assistant's API call capability without human involvement. In our framework, we endeavor to closely mirror genuine human conversation patterns in human-machine interactions, using a LLM-based user agent, equipped with a user script to ensure human alignment. Experimental results highlight that AutoDE uncovers errors overlooked by static evaluations, aligning more closely with human assessment. Testing four AI assistants using our crafted benchmark, our method further mirrored human evaluation compared to conventional static evaluations.Comment: Accepted at LREC-COLING 202

Big data, big challenges: risk management of financial market in the digital economy

Purpose– The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It’s a big challenge for the government to carry out financial market risk management in the big data era. Design/methodology/approach– In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value. Findings– Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike Information Criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored. Originality/value– Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy