The Use of an Explicitly Theory-Driven Data Coding Method for High-Level Theory Testing in IOIS
The Use of an Explicitly Theory-Driven Data Coding Method for High-Level Theory Testing in IOIS
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发表时间：
2008-12
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通讯作者：
Kai Reimers;R. Johnston
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作者：
Kai Reimers;R. Johnston
作为国际实证研究的一部分，我们已经开发了一个高层次的组织间信息系统（IOIS）的结构和演变的理论，但在测试这一理论面临两个问题：非常大的因素可能会影响IOIS，需要绑定一个复杂的研究对象。我们描述了一种新的解决方案，涉及一个明确的理论驱动的编码，我们的原始经验数据产生一个新的解释“中观层次”的基础上，推导和测试预测。为了证明这种方法，我们首先给出了一个务实的分析，追溯到传统的选择自上而下（演绎）和自下而上（归纳）的方法连接理论和数据之间的问题。然后，我们评估我们的方法对标准的哲学立场的有效性。我们发现，在信息系统中普遍支持的硬经验主义观点不会承认我们的方法，但批判现实主义确实并建议对我们的中观层次进行概念性解释。
As part of an international empirical study we have developed a high-level theory of the structure and evolution of inter-organizational information systems (IOIS), but face two issues in testing this theory; the very large set of factors possibly influencing IOIS and need to bound a complex research object. We describe a novel solution involving an explicitly theory-driven coding of our raw empirical data to produce a new interpreted “meso-level” ground upon which to derive and test predictions. To justify this approach, we first give a pragmatic analysis which traces the problem to the traditional choice between top-down (deductive) and bottom-up (inductive) methods for linking theory and data. We then assess the validity of our approach against standard philosophical positions. We find that a hard Empiricist view commonly espoused in information systems would not admit our approach but that Critical Realism does and suggests a conceptual interpretation of our meso-level.