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Testing the Relationship Between Information and Knowledge in Computer-Aided Decision-Making

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Abstract

Information and knowledge are two foundational concepts in Information Systems (IS) research, but their relationship remains vague, and researchers continue to use them interchangeably in studies. This limits the ability to clearly specify what information is in the systems and how it is used for operations and decisions. We address the issue in the context of computer-aided decision-making. In contrast to the traditional view that information is the input of knowledge, we argue, from the knowledge-based view of information, that information is produced from knowledge and functions as the basis of decisions. Further, cognitive factors such as one’s general knowledge in the task domain and guidance in decision aids influence knowledge and information for decisions. An information-centric framework is developed and tested based on a popular production-planning decision task. The result supports the direct impact of information on decisions and the indirect impact of knowledge via its influence on information. This study is the first to empirically test the relationship between knowledge and information in IS research. It calls for more attention to the information concept and how it is processed in IS.

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Notes

  1. A study on the applications of IBM Watson, a highly-celebrated AI system, in healthcare shows that AI overpromised but underdelivered its values (Strickland, 2019). Of 24 projects launched at different healthcare providers in 2011–2017, only five delivered systems that are currently in use.

  2. Knowledge Management (KM) and Big Data are among the latest developments in CADM (Pauleen & Wang, 2017), but they both raise some concerns. For instance, as Big Data emphasizes data-driven decision-making and treats data as the creator of value in organizations and societies, it seems “to render obsolete previous depictions of the ‘data-information-knowledge’ relationship and, in effect, spell the end of knowledge management (Tian, 2017).” Although KM researchers attempt to explore KM’s role in Big Data (Pauleen & Wang, 2017; Tian, 2017), the role may not be justified theoretically unless the relationship among data, information, and knowledge is (re-) examined.

  3. Other factors, such as one’s socioeconomic status (Shavers, 2007), price sensitivity (Wittink & Kaul, 1995) and relevance (Mano & Oliver, 1993), could also influence a person’s perception of price and the importance of certain features to the person. How these factors may interact with, or relate to, one’s information and knowledge within a decision context can be explored in future research. We address this issue as a limitation in Sect. 5.3.

  4. We use the conventional term “information search” to represent the cognitive activities in the design stage, avoiding the unnecessary addition of new nomenclature. However, strictly speaking, it is data and knowledge that are sought, rather than information. If any sort of information is produced to guide learning or the search for alternatives (i.e., heuristics), it is produced from preliminary data and knowledge and serves as the basis for a sub-decision in solving the larger problem (Li & Kettinger, 2006).

  5. An example of misinformation from Big Data is the failure of Google Flu Trends (GFT), a project aimed at using Big Data collected from Google’s search engine to predict influenza trends (Agarwal and Dhar 2014). Despite its early success, the prediction was so off-track that Google later terminated the service (Lazer et al., 2014).

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Acknowledgements

The authors would like to thank the review team, including the editors and two anonymous reviewers, for their comments and suggestions, which have substantially improved this paper.

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  1. Department of Accounting and Information Management, Haslam College of Business, University of Tennessee, 625 Stokely Management Center, Knoxville, TN, 37996, USA

    Yuan Li

  2. Department of Management, The Wilbur O. and Ann Powers College of Business, Clemson University, Clemson, SC, 29634, USA

    William J. Kettinger

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Li, Y., Kettinger, W.J. Testing the Relationship Between Information and Knowledge in Computer-Aided Decision-Making. Inf Syst Front 24, 1827–1843 (2022). https://doi.org/10.1007/s10796-021-10205-w

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