Prediction, hype, and fear: Advancing Information Systems research in “the AI age”
这篇观点文章呼吁信息系统学者更系统地研究技术未来，区分预测、可能性与炒作，并以人工智能为例提出四个分析视角（能力、采纳、价值、后果），指出恶意使用和过度依赖等盲点。
Information Systems (IS) research is well-positioned but under-equipped to study technological futures at a time when claims about artificial intelligence (AI) are reshaping investment, policy, and public discourse. This perspective advances three arguments. First, IS scholarship should engage more systematically with digital futures, drawing on approaches for reasoning under uncertainty, such as Bayesian methods and established Futures Studies techniques, to distinguish prediction, projection, possibility, and hype. Second, technology hype is itself a legitimate object of IS research, and widely used frameworks such as the Gartner Hype Cycle appear limited in their ability to inform practice. Third, AI serves as a critical test case, combining heavy supply-side investment with unproven demand-side impact and unresolved questions of value and consequence. We propose four analytically distinct lenses for studying AI, namely, capability, adoption, value, and consequence, and identify two underexamined blind spots: bad actors deploying AI at scale and structural over-dependence on imperfect AI. We invite contributions to the Journal of Information Technology that examine how claims about technological futures are produced, circulated, institutionalized, resisted, and realized.