The advent of Industry 5.0 has brought a wealth of digital information to mobile app stores. With the help of emerging technologies such as machine learning and explainable artificial intelligence (XAI), these large amounts of user-generated data can be efficiently captured and analyzed. In this study, we propose an app store analysis framework and demonstrate the utility of the framework by mining and prioritizing user requirements in three popular video conferencing apps. We used the Sentistrength sentiment analysis tool, structural topic modeling, the Gephi web analysis tool, machine learning, and XAI techniques to conduct an in-depth analysis of user requirements in Microsoft Teams, ZOOM Cloud Meetings, and Google Meet. The findings indicated that Steal data, Audio and video quality, Customer service, Hacker issues, Meeting and account passwords, Mute and unmute, Features, and Office platform were the web conferencing system's key areas for improvement. The study demonstrated the usability of app store analysis frameworks and the great potential of XAI to provide insights about requirements prioritization by interpreting machine learning models. Additionally, it offered valuable suggestions for app developers on using the massive data in app stores to improve their apps.

The widespread development of digital technology facilitates the emergence of new entrepreneurial modes, of which crowdfunding digital platforms are one. In the digital environment of crowdfunding platforms, digital entrepreneurs can obtain the essential resources necessary for their startups' rapid and cost-effcient development. However, the information asymmetry derived from the digital nature of crowdfunding platforms leads to a lower chance of success for entrepreneurial ventures in this market, especially those in digital technology, limiting the important role that crowdfunding platforms can play in digital entrepreneurship. To this end, we focus on the risk disclosure section introduced by crowdfunding platforms to alleviate information asymmetry and explore the influence mechanism of the content of risk disclosure on entrepreneurial resource acquisition in crowdfunding digital platforms. By employing a novel text mining technique structural topic modelling, we analyse the risk disclosure texts of 4,284 digital technology crowdfunding projects and successfully identify various factors that constrain the development of digital technology ventures in crowdfunding platforms. Furthermore, we find that the risk topics digital entrepreneurs disclose negatively affect entrepreneurial resource acquisition. However, this relationship is moderated by the reward structure setting in the context of reward-based crowdfunding. The findings of this study not only enrich the literature on crowdfunding and digital entrepreneurship but also provide valuable practical implications on how crowdfunding digital platforms can be used to promote the development of digital entrepreneurship.

This article introduces a new metric for evaluating digital innovation in enterprise transformation using textual analysis of annual reports from U.S.-listed companies. Through network analysis and topic modeling, we identified 12 topics categorized into three main areas: digital technology innovation, customer-oriented digital strategy, and digital transformation in traditional business operations. Our research indicates that digital innovation strategies are critical for maintaining competitiveness and have shifted to a more innovation management-oriented approach. We also found differences in digital innovation strategies between companies and industries. Our study contributes to the theoretical significance of enterprise management and sustainable development.

The sharing economy represented by Airbnb has evolved rapidly. It is particularly important to identify and understand how consumer concerns change over time. As a result, this study employs structural topic modelling using room type and time as covariates to extract topics from 896,658 Airbnb reviews in London and to observe the variation in the prevalence of topics over time. The findings show that the topic proportion changed relatively sharply in the early years of Airbnb (2010-2013) and during the COVID-19 pandemic (2020-2022), but relatively smoothly in the middle period (2014-2019). This research also discovered that the proportion of topics on customers' special experiences has been decreasing while the proportion of topics on their overall experience has been increasing. This shift could be attributed to an increase in the number of professional hosts, which has accelerated the standardisation of the Airbnb service.

Drawing from extant retailing and supply chain research, this paper studies the dual channel supply chain decision-making of member channel, and obtains the optimal price strategy, maximum demand and maximum total revenue of the supply chain of network channel and retailing channel under the centralized decision-making and decentralized decision-making respectively. The contributions of this study identify that investing in big data within a certain threshold can improve the channel service level, reduce the channel price and improve the income of the supply chain. Supply chain members improve the channel service level and increase the corresponding channel price. The supply chain can get the most advantages when manufacturers and retailers make centralized decisions. This paper provides a starting point for new retailing academic and practical research in a domain that is deficient in empirical research, provides the theoretical framework to new retailing enterprises and decision-making model for their sustainable competitive advantage.