Data-Driven, Statistical Learning Method for Inductive Confirmation of Structural Models

Automatic extraction of structural models interferes with the deductive research method in information systems research. Nonetheless it is tempting to use a statistical learning method for assessing meaningful relations between structural variables given the underlying measurement model. In this paper, we discuss the epistemological background for this method and describe its general structure. Thereafter this method is applied in a mode of inductive confirmation to an existing data set that has been used for evaluating a deductively derived structural model. In this study, a range of machine learning model classes is used for statistical learning and results are compared with the original model

Anticipation and Risk – From the inverse problem to reverse computation

Abstract. Risk assessment is relevant only if it has predictive relevance. In this sense, the anticipatory perspective has yet to contribute to more adequate predictions. For purely physics-based phenomena, predictions are as good as the science describing such phenomena. For the dynamics of the living, the physics of the matter making up the living is only a partial description of their change over time. The space of possibilities is the missing component, complementary to physics and its associated predictions based on probabilistic methods. The inverse modeling problem, and moreover the reverse computation model guide anticipatory-based predictive methodologies. An experimental setting for the quantification of anticipation is advanced and structural measurement is suggested as a possible mathematics for anticipation-based risk assessment

A Framework for Pragmatic Reliability

We propose a framework for pragmatic reliability-in-the-limit criteria, extending the epistemic reliability framework (Kelly 1996). We identify some common scientific contexts which complicate the application or interpretation of epistemic reliability criteria, drawing heavily from economics for illustrative examples. We then propose an extension of the standard framework, where inquiry is constrained by both epistemic and non-epistemic factors. This provides analogous notions of pragmatic underdetermination and pragmatic reliability with respect to a particular goal, as well as a principled method for extracting solvable problems from unsolvable ones

Identification of Tumor Evolution Patterns by Means of Inductive Logic Programming

In considering key events of genomic disorders in the development and progression of cancer, the correlation between genomic instability and carcinogenesis is currently under investigation. In this work, we propose an inductive logic programming approach to the problem of modeling evolution patterns for breast cancer. Using this approach, it is possible to extract fingerprints of stages of the disease that can be used in order to develop and deliver the most adequate therapies to patients. Furthermore, such a model can help physicians and biologists in the elucidation of molecular dynamics underlying the aberrations-waterfall model behind carcinogenesis. By showing results obtained on a real-world dataset, we try to give some hints about further approach to the knowledge-driven validations of such hypotheses

Bayesian Probability and Statistics in Management Research: A New Horizon

This special issue is focused on how a Bayesian approach to estimation, inference, and reasoning in organizational research might supplement—and in some cases supplant—traditional frequentist approaches. Bayesian methods are well suited to address the increasingly complex phenomena and problems faced by 21st-century researchers and organizations, where very complex data abound and the validity of knowledge and methods are often seen as contextually driven and constructed. Traditional modeling techniques and a frequentist view of probability and method are challenged by this new reality

Methodological fit for empirical research in international business:A contingency framework

We seek to complement and extend the article by Welch, Piekkari, Plakoyiannaki, and Paavilainen-Mäntymäki (J Int Bus Stud 42:740–762, 2011), winner of the 2021 JIBS Decade Award, which advanced knowledge on case-based theory development in international business (IB). Similarly, we examine dimensions of scholarly inquiry across qualitative and quantitative research, using inductive and deductive approaches. Recent years have featured unprecedented growth in the volume and availability of data from diverse national contexts, offering novel opportunities for innovative research. Accordingly, we build on the logic of Welch et al. (2011) not only to elaborate on but also to call for a more pluralistic view of data and methodology. We advocate using a wider range of data and advanced methods in IB research, framed at the appropriate stage of theory development. We examine the interplay among theory, research design, data, and analytical technique, highlighting the role of data in methodological pluralism. While IB scholars have favored confirmatory approaches in deductive theory building, we argue for more exploratory research using both qualitative and quantitative data. We develop a contingency framework that highlights the stages of theory development, across the nexus of exploratory/confirmatory and qualitative/quantitative approaches, to guide empirical scholarship. We conclude by calling for triangulation and adopting the most appropriate combination of theory, research design, data, and analytical technique, to develop theory in IB research.</p