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Associated Press
19-05-2025
- Business
- Associated Press
Implications of buy-online-and-assemble-in-store approach for firms, consumers and environment
FAYETTEVILLE, GA, UNITED STATES, May 19, 2025 / / -- By building a game-theoretic model, researchers have found that a larger proportion of professional consumers can incentivize firms to adopt the buy-online-and-assemble-in-store (i.e., BOAS), while higher handling or traveling costs may lead firms to avoid its use. This study not only contributes to the existing literature, but also provides actionable insights for practitioners. With the rapid development of digital technology and diversification of consumer needs, companies have adopted the buy-online-and-assemble-in-store (i.e., BOAS) approach to better meet the individual needs of consumers. Through BOAS, consumers can place orders online, pick up the products and enjoy services such as assembly at a physical shop. However, due to uncertain post-processing costs, it remains unclear whether companies would benefit from the use of BOAS. Furthermore, the impacts of BOAS on consumers and the environment are of research interest. In a study published in the KeAi journal Sustainable Operations and Computers, a team of researchers at South China University of Technology considered a monopolistic firm selling products that require consumers' post-processing assembly for effective use. 'There are two consumer types with heterogeneity in handling products and consumption preferences,' explains lead author Guanxiang Zhang. 'Specifically, professional consumers excel at product post-processing and prioritize product price and quality more highly, whereas amateur consumers demonstrate weaker post-processing skills and are more concerned with convenience and service quality.' Notably, the distribution of consumer types and product post-processing costs affect the firms' introduction strategies of the BOAS. Companies may be more willing to introduce the BOAS when facing a high proportion of professional-type consumers. Conversely, with higher handling costs, traveling costs and value perception proportions, firms may be more inclined to forego introducing the BOAS. 'Generally, the use of BOAS is favorable for consumers because it offers a better consumer experience,' says Zhang. 'However, we found BOAS may put consumers at risk, as its may idecrease consumer surplus when the share of professional consumers is high. This also explains why companies like Uniqlo went downhill after introducing the BOAS while TUHU gained a strong competitive advantage in the car service industry.' Furthermore, from the perspective of environmental performance, the introduction of the BOAS channel can reduce waste and energy consumption by centralizing the final assembly or customization of products. 'Implementing the use of BOAS may raise the retail price of products in the online channel and ultimately reduce total demand, says Lipan Feng, corresponding author when discussing the effects of BOPS use on firms' pricing decisions and demand. 'Our study contributes to the existing body of knowledge by elucidating the nuanced effects of BOAS on firm strategies and sustainability, providing actionable insights for practitioners navigating the complex omni-channel landscape.' References DOI 10.1016/ Original Source URL Funding Information This work was supported by the National Natural Science Foundation of China (72372044; 72002024), the Guangzhou Basic and Applied Basic Research Foundation (2023A04j1071), the Guangdong Soft Science Research Project (2024A1010060001), and the Project of Guangzhou Philosophy and Social Science Planning (2023GZYB20). Lucy Wang BioDesign Research email us here Legal Disclaimer: EIN Presswire provides this news content 'as is' without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.
Associated Press
21-04-2025
- Science
- Associated Press
Elucidating Earth's Interior Through Advanced Teleseismic Phase Picking
GA, UNITED STATES, April 21, 2025 / / -- A deep learning-based scheme is proposed for automated and efficient processing of teleseismic phases. 1. The validation with two teleseismic phases, PcP and PKiKP, demonstrate high detection accuracies and low picking errors. 2. Deep-learning picking of first peaks is more accurate than picking first breaks of teleseismic phases. 3. This proposed scheme would enhance mining of teleseismic phases and probing of Earth's interior structures and their dynamics. How do scientists explore Earth's hidden interior — its crust, mantle, and core? The answer lies in earthquake waves. Like X-rays, these seismic waves travel through and reflect off internal structures, allowing scientists to visualize the planet's interior. By analyzing these waveforms, seismologists can create 'B-scans' of structure or 'CT scans' of physical properties. Identifying these reflections in seismic data, however, is complex and time-consuming. Reflected phases can be distorted by structural discontinuities and local heterogeneities, making it easy to mistake noise for real signals. This challenge underscores the need for efficient and accurate automatic phase pickers to handle the vast volume of teleseismic earthquake data, whose phases often reflect off deep structures within the Earth. A recent study published in KeAi's Artificial Intelligence in Geosciences (KeAi) introduces a new deep learning-based workflow to automatically detect and pick teleseismic phases with high efficiency and accuracy. 'To improve the workflow's performance, we divide it into three parts: phase preparation, detection, and picking,' explains leadingco-author Dr. Congcong Yuan, a postdoctoral researcher at Cornell University. 'We apply physical constraints during preparation to highlight potential signals. The detection step filters out low-quality data, so that the final picking step can determine arrival times more accurately and without bias.' 'This approach allows fast, reliable and robust teleseismic phase processing. 'It enables us to extract more meaningful data, helping us better understand the physics and dynamics deep inside the Earth,' adds Yuan. 'Yuan and colleagues are collaborating with another research team in teleseismic imaging to apply this method to specific tectonic regions. 'The more high-quality phase data we have, the more we can uncover about Earth's inner workings,' he says. 'For decades, seismologists have faced the tedious task of processing seismic data,' notes co-author Prof. Jie Zhang from the University of Science and Technology of China. 'With the rise of deep learning, seismology is reaching a turning point from semi-automatic workflows to truly autonomous systems.' References DOI 10.1016/ Original Source URL Lucy Wang BioDesign Research email us here Legal Disclaimer: EIN Presswire provides this news content 'as is' without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.
