DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, Dahae | ko |
dc.contributor.author | Shin, Donghyuk | ko |
dc.contributor.author | Auh, Seigyoung | ko |
dc.contributor.author | Han, Sang Pil | ko |
dc.date.accessioned | 2024-08-13T01:00:08Z | - |
dc.date.available | 2024-08-13T01:00:08Z | - |
dc.date.created | 2024-07-22 | - |
dc.date.issued | 2022-12-13 | - |
dc.identifier.citation | 43rd International Conference on Information Systems: Digitization for the Next Generation, ICIS 2022 | - |
dc.identifier.uri | http://hdl.handle.net/10203/322287 | - |
dc.description.abstract | Can companies use artificial intelligence to attain the Double Bottom Line (simultaneous pursuit of financial performance and social impact) by enhancing equity? Drawing on equity theory, we develop a conceptual model whereby perceived AI quality positively affects firm performance that is mediated by equity in the educational technology sector. Using observational data collected from a global AI-powered learning app, we find support for educational equity as a full mediator between perceived AI quality and firm performance. Moreover, we also find support for conditional indirect effects. The mediating role of educational equity is moderated by political, economic, socio-cultural, and technological factors. Our research contributes to the growing popularity of transforming a business model from a bottom line to a double bottom line approach. We discuss how our study extends the IS literature on the integration between artificial intelligence and equity and the managerial implications for an inclusive information system. | - |
dc.language | English | - |
dc.publisher | Association for Information Systems | - |
dc.title | Achieving the Double Bottom Line with Artificial Intelligence by Addressing Inequity: A Global Comparative Analysis of an Educational Technology Firm | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 43rd International Conference on Information Systems: Digitization for the Next Generation, ICIS 2022 | - |
dc.identifier.conferencecountry | DK | - |
dc.contributor.localauthor | Shin, Donghyuk | - |
dc.contributor.nonIdAuthor | Jeong, Dahae | - |
dc.contributor.nonIdAuthor | Auh, Seigyoung | - |
dc.contributor.nonIdAuthor | Han, Sang Pil | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.