Knowing What We Know: Theory, Meta-analysis, and Review
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ItemAn Observer-Relative Systems Approach to Information( 2019-01-08)By returning to the foundational principles of second-order cybernetics and resting on the central role of the observer, this essay explores how the distinction between data/information can be conceptualized. Using systems theory, we derive a series of systemic principles for the distinction between data/information and we illustrate them with a case study from Anti-Money Laundering.
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ItemHow the Future is Done( 2019-01-08)As technologies and human systems become increasingly impactful and pervasive, unexpected outcomes often leave researchers to perform ‘research autopsies’ to determine what went wrong. Despite concern around disruptive technologies and the growing complexity, interdependence and volatility of business environments, academics remained oriented to researching the here-and-now and assuming an extrapolation of the present into the future. This research offers “doing future(s)” as a critical research orientation to create discourses of alternative future(s) which our research bring forth. We argue that by engaging in doing future(s), academics provide a critical voice and participate in reframing and recalibrating the futures which we make through collective action. We provide an overview of future-studies approaches categorized by epistemic stance and illustrate the distinctions with a case example. We then describe broad implications for Information Systems research, as well as business practice.
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ItemReconsidering the Role of Research Method Guidelines for Qualitative, Mixed-methods, and Design Science Research( 2019-01-08)Guidelines for different qualitative research genres have been proposed in information systems (IS). As these guidelines are outlined for conducting and evaluating good research, studies may be denied publication simply because they do not follow a prescribed methodology. This can result in “checkbox” compliance, where the guidelines become more important than the study. We argue that guidelines can only be used to evaluate what good research is if there is evidence that they lead to certain good research outcomes. Currently, the guidelines do not present such evidence. Instead, when it is presented, the evidence is often an authority argument or evidence of popularity with usability examples. We further postulate that such evidence linking guidelines and outcomes cannot be presented. Therefore, it may be time for the IS research community to acknowledge that many research method principles we regard as authoritative may ultimately be based on speculation and opinion, and thus, they should be taken less seriously as absolute guidelines in the review process.
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ItemNavigating Through the Maze of Business Process Change Methods( 2019-01-08)Business Process Management (BPM) is an approach adopted by many organizations for improving their business processes in order to serve their customers more efficiently and effectively. Literature on BPM offers a plethora of methods used as a guide when improving business processes. Some are promoted as methods for process reengineering, while others as methods for improvement, redesign, or innovation. The number of BPM methods is overwhelming, such that organizations are faced with the challenge to select one that best fits their needs. In this paper, we follow a systematic literature review approach to investigate the characteristics of existing BPM methods. We find that the ambition, nature and perspective of the methods are important to determine whether they can be used for radical or incremental process change. Our findings point to the lack of research done on methods for radical process change.
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ItemNarrowing the Theory’s or Study’s Scope May Increase Practical Relevance( 2019-01-08)Numerous articles in top IS journals note as a limitation and lack of generalizability that their findings are specific to a certain type of technology, culture, and so on. We argue that this generalizability concern is about limited scope (e.g., explanatory breadth). The IS literature notes this preference for generalizability as a characteristic of good science and it is sometimes confused with statistical generalizability. We argue that such generalizability can be in conflict with explanation or prediction accuracy. An increase in scope (e.g., increasing explanatory breadth) can decrease explanation or prediction accuracy. Thus, in sciences such as cancer research, where explanation and prediction accuracy are highly valued, the cancer accounts (generally speaking) have become increasingly narrower (and less generalizable). IS thinking has not yet benefitted from these considerations. Whether generalizability is valued should be linked with the research aims. If the aim is practical applicability through explanation or prediction accuracy, then “limited” generalizability could be a strength rather than a weakness.
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ItemDeepCause: Hypothesis Extraction from Information Systems Papers with Deep Learning for Theory Ontology Learning( 2019-01-08)This paper applies different deep learning architectures for sequence labelling to extract causes, effects, moderators, and mediators from hypotheses of information systems papers for theory ontology learning. We compared a variety of recurrent neural networks (RNN) architectures, like long short-term memory (LSTM), bidirectional LSTM (BiLSTM), simple RNNs, and gated recurrent units (GRU). We analyzed GloVe word embedding, character level vector representation of words, and part-of-speech (POS) tags. Furthermore, we evaluated various hyperparameters and architectures to achieve the highest performance scores. The prototype was evaluated on hypotheses from the AIS basket of eight. The F1 result for the sequence labelling task of causal variables on a chunk level was 80%, with a precision of 80% and a recall of 80%.
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ItemA Goal-based Framework Integrating Disparate Media Choice Theories( 2019-01-08)Media choice and selection theories are numerous and highly fragmented. While much of this theorizing has helped IS researchers better understand what influences people’s media choices and selections, the proliferation of theories also leads to redundancies, and decreased clarity and impact. Here, we develop and apply an approach to "better know what we know" about a set of related theories. We present a unifying framework of media choice that (1) builds on prior work, (2) streamlines disparate lines of research, and (3) links media choices to goals. In addition to advancing media choice theorizing, the framework is a useful template for relating future research contributions to previous theories, an effective teaching aid, and a tool for practitioners applying media choice theories.
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