Big Data approaches in social and behavioral science: four key trade-offs and a call for integration

Mahmoodi, Jasmin; Leckelt, Marius; Zalk, Maarten van; Geukes, Katharina; Back, Mitja D.

doi:10.1016/j.cobeha.2017.07.001

Cited by 41 publications

(32 citation statements)

References 32 publications

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“…conducting entrepreneurship studies utilizing Big Data and AI (e.g., computerized methods and specific software, smartphone methods, data mining, machine learning; Gosling and Mason 2015;Kosinski et al 2016;Zomaya and Sakr 2017). 5) Issues of prediction vs. explanation; inductive, data-driven approaches vs. deduction, theory-driven approaches; and bigness vs. representativeness (Mahmoodi et al 2017). Empirical work, in turn, could address the following and other issues: 1) New Big Data-based metrics of entrepreneurial activity and quality (Guzman and Stern 2016).…”

Section: ) a Practical Introduction Into Relevant Methods Technologmentioning

confidence: 99%

Artificial intelligence and big data in entrepreneurship: a new era has begun

Obschonka¹,

2019

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While the disruptive potential of artificial intelligence (AI) and Big Data has been receiving growing attention and concern in a variety of research and application fields over the last few years, it has not received much scrutiny in contemporary entrepreneurship research so far. Here we present some reflections and a collection of papers on the role of AI and Big Data for this emerging area in the study and application of entrepreneurship research. While being mindful of the potentially overwhelming nature of the rapid progress in machine intelligence and other Big Data technologies for contemporary structures in entrepreneurship research, we put an emphasis on the reciprocity of the co-evolving fields of entrepreneurship research and practice. How can AI and Big Data contribute to a productive transformation of the research field and the real-world phenomena (e.g., "smart entrepreneurship")? We also discuss, however, ethical issues as well as challenges around a potential contradiction between entrepreneurial uncertainty and rule-driven AI rationality. The editorial gives researchers and practitioners orientation and showcases avenues and examples for concrete research in this field. At the same time, however, it is not unlikely that we will encounter unforeseeable and currently inexplicable developments in the field soon. We call on entrepreneurship scholars, educators, and practitioners to proactively prepare for future scenarios.

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Section: ) a Practical Introduction Into Relevant Methods Technologmentioning

confidence: 99%

Artificial intelligence and big data in entrepreneurship: a new era has begun

Obschonka¹,

2019

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“…This may alleviate the need for expert interviews or the use of small, unrepresentative surveys to obtain a first understanding of the main factors influencing certain industries, jurisdictions, or the like. It may also reduce the risk that theorists fall victim to confirmation bias (Mahmoodi et al, 2017), which is especially a problem in closed research communities clustered around a narrow set of questions, where publication success depends on positive reviews by those peers who advanced their careers by studying the questions in a specific way. Dreaming ahead, the best applied theoretical researchers may regularly motivate their choice of key model variables by using the results of big data analyses.…”

Section: Resultsmentioning

confidence: 99%

Data Science for Institutional and Organizational Economics

Prüfer

2018

SSRN Journal

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To which extent can data science methods -such as machine learning, text analysis, or sentiment analysis -push the research frontier in the social sciences? This essay briefly describes the most prominent data science techniques that lend themselves to analyses of institutional and organizational governance structures. We elaborate on several examples applying data science to analyze legal, political, and social institutions and sketch how specific data science techniques can be used to study important research questions that could not (to the same extent) be studied without these techniques. We conclude by comparing the main strengths and limitations of computational social science with traditional empirical research methods and its relation to theory.

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“…The high dimensionality of new types of data and the fact that for many traceable behaviors no a-priori hypotheses exist, render a purely deductive scientific approach unsuitable in many cases or at least inefficient. A more iterative and alternating process between inductive and deductive scientific practices has been called for (Mahmoodi, Leckelt, Zalk, Geukes, & Back, 2017). Using ML algorithms together with methods of model interpretation could help to complete the circle between prediction and explanation in personality psychology (Shmueli, 2010…”

Section: Visualizations Of Predictor Effectsmentioning

confidence: 99%

Personality Research and Assessment in the Era of Machine Learning

Stachl¹,

Pargent²,

Hilbert³

et al. 2019

Preprint

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The increasing availability of high-dimensional, fine-grained data about human behavior, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyze the data and interpret the results appropriately. Machine learning models are well-suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modeling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modeling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.

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Big Data approaches in social and behavioral science: four key trade-offs and a call for integration

Cited by 41 publications

References 32 publications

Artificial intelligence and big data in entrepreneurship: a new era has begun

Artificial intelligence and big data in entrepreneurship: a new era has begun

Data Science for Institutional and Organizational Economics

Personality Research and Assessment in the Era of Machine Learning

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