Qualitative researchers across disciplines, including information systems (IS), face new pressures to ensure the transparency of their studies and their accountability for knowledge claims. As qualitative research becomes more scrutinized, researchers need to demonstrate transparency in their methods. However, the methods sections in published articles may not provide enough details to meet the changing expectations and policies of journals. This raises the issue of how to judge a qualitative study without imposing inappropriate criteria, such as quantitative metrics (e.g., volume of data) or standard templates that may not match the diversity of qualitative approaches. Based on these concerns, we clarify the status of data and their adequacy for achieving research objectives. We show how data adequacy can support theoretical reasoning in three modes of inference: induction, deduction, and abduction. We include illustrative practices for researchers wishing to adopt more transparent practices for judging and reporting data adequacy.

中文翻译：

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什么时候才足够？对 IS 定性研究中数据充分性的批判性评估


包括信息系统 （IS） 在内的跨学科定性研究人员面临着新的压力，需要确保其研究的透明度和对知识声明的责任。随着定性研究受到越来越多的审查，研究人员需要证明其方法的透明度。但是，已发表文章中的方法部分可能无法提供足够的详细信息来满足期刊不断变化的期望和政策。这就提出了一个问题，即如何在不施加不适当的标准的情况下判断定性研究，例如定量指标（例如，数据量）或可能与定性方法的多样性不匹配的标准模板。基于这些问题，我们阐明了数据的状态及其实现研究目标的充分性。我们展示了数据充分性如何以三种推理模式支持理论推理：归纳、演绎和绑架。我们为希望采用更透明做法来判断和报告数据充分性的研究人员提供了说明性做法。