Explaining Change with Digital Trace Data: A Framework for Temporal Bracketing

Digital trace data, along with computational techniques to analyze them, provide novel means to study how organizational phenomena change over time. Yet, as digital traces typically lack context, it is challenging to explain why and how such changes take place. In this paper, we discuss temporal bracketing as an approach to integrate context into digital trace data-based research. We conceptualize a framework to apply temporal bracketing in the analysis of digital trace data. We showcase our framework on the grounds of data from an onboarding process of a financial institution in Central Europe. We point to several implications for computationally intensive theory development around change with digital trace data

Bayesian astrostatistics: a backward look to the future

This perspective chapter briefly surveys: (1) past growth in the use of Bayesian methods in astrophysics; (2) current misconceptions about both frequentist and Bayesian statistical inference that hinder wider adoption of Bayesian methods by astronomers; and (3) multilevel (hierarchical) Bayesian modeling as a major future direction for research in Bayesian astrostatistics, exemplified in part by presentations at the first ISI invited session on astrostatistics, commemorated in this volume. It closes with an intentionally provocative recommendation for astronomical survey data reporting, motivated by the multilevel Bayesian perspective on modeling cosmic populations: that astronomers cease producing catalogs of estimated fluxes and other source properties from surveys. Instead, summaries of likelihood functions (or marginal likelihood functions) for source properties should be reported (not posterior probability density functions), including nontrivial summaries (not simply upper limits) for candidate objects that do not pass traditional detection thresholds.Comment: 27 pp, 4 figures. A lightly revised version of a chapter in "Astrostatistical Challenges for the New Astronomy" (Joseph M. Hilbe, ed., Springer, New York, forthcoming in 2012), the inaugural volume for the Springer Series in Astrostatistics. Version 2 has minor clarifications and an additional referenc

Unravelling Collective Social Media Affordance Dynamics During Crises: An Analysis of Online Mental Health Discourse

Social media can serve as a platform for collective engagement with diverse affordances during crises. We explore how social media served this role by focusing on how online mental health discourse evolved during the COVID-19 pandemic. Specifically, we examine shifts in collective affordance dynamics within the online mental health community using Twitter. A comprehensive dataset of mental health-related tweets from 2018 to 2022 was collected (N = 3,953,836) and analysed using Computationally Intensive Theory Discovery as a guiding methodology. A subset of 757 representative tweets were categorised into a cascading set of actor groups. Analysis uncovers that collective engagement transitioned from decentralised actor utilisation (pre-crisis) to centralised organisational utilisation (early-crisis), culminating in centralised actor utilisation (late-crisis). The study contributes theoretically to collective affordance knowledge by integrating dynamics in an online setting and practically by revealing key actors\u27 evolution in shaping online discourse across crisis phases

Data Science for Social Good

Data science has been described as the fourth paradigm of scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges—our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI has sparked debates about the sociotechnical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for “data science for social good” (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of sociotechnical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the JAIS special issue on data science for social good. We hope that this editorial and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are attracting proportionately less attention with each passing day

Discovery of high-entropy ceramics via machine learning

AbstractAlthough high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. Machine learning (ML) applied to materials science can accelerate development and reduce costs. In this study, we propose an ML method, leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability (i.e., entropy-forming ability) of disordered metal carbides. The relative importance of the thermodynamic and compositional features for the predictions are then explored. The approach’s suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of 70 new compositions; several predictions are validated by additional density functional theory calculations and experimental synthesis, corroborating the effectiveness in exploring vast compositional spaces in a high-throughput manner. Importantly, seven compositions are selected specifically, because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. Incorporating the Group VI elements into the rock-salt structure provides further opportunity for tuning the electronic structure and potentially material performance

Building Theory using Methodological Pluralism in Computational Theory Construction

We propose some guidelines to triangulate qualitative data analysis by extending the computational theory construction approach. Our approach addresses the challenge of methodological pluralism, which combines disparate computational techniques, methods, and lexicons (Miranda et al., 2022). Although previous literature has identified the use of mixed methods in information systems (IS) research (Reis et al., 2022), support of complementary inferences and their validation can be improved. Thus, we propose an iterative process to discover frames (Miranda et al., 2022) in qualitative data, such as social media, transcripts, and articles. We will elucidate the following aspects of computational theory construction research with methodological pluralism: a) Method design and fit, b) Data sampling and wrangling strategy, c) Pattern recognition and convergence, d) Theoretical inferences. First, we examine the distinctions in employing a methodologically plural approach with qualitative methods. The methodologically plural approach leverages multiple techniques and methods to build theory and provides a multi-level picture for thorough and richer insights. The method design explores the appropriate methodological approaches and lexicons from literature support that suits the data. We provide some guidelines on how researchers can adapt multiple methods in the subsequent analysis phases. Research with temporal analysis needs to construct a timeline or episodic periods, such as, for specific events in a climate movement (e.g., Lee and Bharati, 2022; Vaast et al., 2017). The episodic periods are important to understand the research context, data nuances, and overall focal phenomenon. The qualitative data is analyzed in an automated and systematic approach, as adapted from previous IS research methodologies, such as natural language processing (NLP). Wrangling and cleaning the data appropriately for each method is an important preprocessing phase and a major challenge. In qualitative data analysis, handling noise reduction involves some automated methods. We plan to illustrate how multiple qualitative analysis techniques, such as NLP, semantic network analysis (SNA), and non-negative matrix factorization (NMF), can be leveraged for diachronic analysis in this process. The analyses assist in the discovery of key topics with topic modeling or word to word co-occurrences graphs with SNA. Using an iterative approach, a comprehensive convergence of the categorization needs to be developed during the iterative analysis phases that involve multiple scholars as well as literature support. Researchers can perform model calculations or apply further statistical analysis on their data. The approach includes robustness and validation checks when analyzing new or existing constructs for contextual understanding of patterns within the data. Lastly, theoretical inferences can be discerned from the data analysis phases

Employing Machine Learning to Advance Agent-based Modeling in Information Systems Research

In recent years, computationally intensive theory construction, leveraging big data and machine learning (ML), has gained significant interest in the information systems (IS) community. The integration of computational methods can generate novel methodological paradigms or enhance existing methods. Agent-based modeling (ABM) is one of the computational methods that has recently proliferated in IS research to generate computationally intensive theories. However, ABM is still in nascent state of adoption in IS research and entails some pathological challenges that limit its applicability and robustness. With the goal of advancing ABM in IS research, this article proposes a methodological framework that integrates ML within relevant steps of ABM. The framework is demonstrated in an exemplary IS study, showing its potential for addressing the pathological challenges of ABM. We finally discuss the implications of applying the proposed methodological framework in IS research