TY - JOUR
T1 - Beyond AI-powered context-aware services
T2 - the role of human–AI collaboration
AU - Jiang, Na
AU - Liu, Xiaohui
AU - Liu, Hefu
AU - Lim, Eric Tze Kuan
AU - Tan, Chee-Wee
AU - Gu, Jibao
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Purpose - Artificial intelligence (AI) has gained significant momentum in recent years. Among AI-infused systems, one prominent application is context-aware systems. Although the fusion of AI and context awareness has given birth to personalized and timely AI-powered context-aware systems, several challenges still remain. Given the “black box” nature of AI, the authors propose that human–AI collaboration is essential for AI-powered context-aware services to eliminate uncertainty and evolve. To this end, this study aims to advance a research agenda for facilitators and outcomes of human–AI collaboration in AI-powered context-aware services. Design/methodology/approach - Synthesizing the extant literature on AI and context awareness, the authors advance a theoretical framework that not only differentiates among the three phases of AI-powered context-aware services (i.e. context acquisition, context interpretation and context application) but also outlines plausible research directions for each stage. Findings - The authors delve into the role of human–AI collaboration and derive future research questions from two directions, namely, the effects of AI-powered context-aware services design on human–AI collaboration and the impact of human–AI collaboration. Originality/value - This study contributes to the extant literature by identifying knowledge gaps in human–AI collaboration for AI-powered context-aware services and putting forth research directions accordingly. In turn, their proposed framework yields actionable guidance for AI-powered context-aware service designers and practitioners.
AB - Purpose - Artificial intelligence (AI) has gained significant momentum in recent years. Among AI-infused systems, one prominent application is context-aware systems. Although the fusion of AI and context awareness has given birth to personalized and timely AI-powered context-aware systems, several challenges still remain. Given the “black box” nature of AI, the authors propose that human–AI collaboration is essential for AI-powered context-aware services to eliminate uncertainty and evolve. To this end, this study aims to advance a research agenda for facilitators and outcomes of human–AI collaboration in AI-powered context-aware services. Design/methodology/approach - Synthesizing the extant literature on AI and context awareness, the authors advance a theoretical framework that not only differentiates among the three phases of AI-powered context-aware services (i.e. context acquisition, context interpretation and context application) but also outlines plausible research directions for each stage. Findings - The authors delve into the role of human–AI collaboration and derive future research questions from two directions, namely, the effects of AI-powered context-aware services design on human–AI collaboration and the impact of human–AI collaboration. Originality/value - This study contributes to the extant literature by identifying knowledge gaps in human–AI collaboration for AI-powered context-aware services and putting forth research directions accordingly. In turn, their proposed framework yields actionable guidance for AI-powered context-aware service designers and practitioners.
KW - Artificial intelligence
KW - Context-aware
KW - Human–AI collaboration
UR - http://www.scopus.com/inward/record.url?scp=85143334679&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85143334679&origin=recordpage
U2 - 10.1108/IMDS-03-2022-0152
DO - 10.1108/IMDS-03-2022-0152
M3 - RGC 21 - Publication in refereed journal
SN - 0263-5577
VL - 123
SP - 2771
EP - 2802
JO - Industrial Management and Data Systems
JF - Industrial Management and Data Systems
IS - 11
ER -