Earticle

This research is situated at the intersection between industry 4.0, circular economy and tourism, in an attempt to observe the fourth industrial revolution at the service of the application of circular economy principles in the tourism industry. This approach has gained importance due to the COVID-19 pandemic, which has accelerated fundamental dynamics of change linked to business digitization and environmental sustainability. Within the theoretical framework delimited by the aforementioned intersection, the ‘goCircular Radar’ project, launched by ‘TheCircularLab’, from Ecoembes (Spain), has been taken as an empirical reference. Among the 165 startups in the circular economy sector, special attention has been paid to those that are oriented, or have a potential application, to tourism. The activities they carry out are described, with particular attention to the technologies they use and their contribution to circularity.

The purpose of this study is to explore the factors that influence the purchase of AI speakers in China. We integrate the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-technology fit (TTF) model into one model and put forward assumptions. According to the characteristics of AI speakers, we selected 6 independent variables, such as Performance Expectation, Effort Expectation, Social Influence, Facilitating Conditions, Task and Technology-characteristics. The final impact on purchase behavior is evaluated through Task-technology fit and purchase intention. After counting 478 samples, through SPSS22.0 and AMOS analysis, hypotheses have been proved by strong experimental data, except facilitating conditions. These results also imply that improving the technical level of AI speakers and enhancing consumers’ purchasing intention are the central line of marketing. Based on this, we put forward several suggestions to marketers, including strengthening the research and development of AI speaker technology, and building a circle of friends of AI speakers.

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.