Opening the black box: Uncovering the leader trait paradigm through machine learning

Doornenbal, Brian M.; Spisak, Brian R.; Laken, P.A. van der

doi:10.1016/j.leaqua.2021.101515

Cited by 32 publications

(18 citation statements)

References 47 publications

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“…As a final step, we use AML to attribute weights to the resultant factors and generate the consolidated indexes (overall and per sector). AML provides a hierarchy of predictors by checking the significance of each variable (Doornenbal et al , 2021; He et al , 2021).…”

Section: Methodsmentioning

confidence: 99%

“…It finds the optimal solutions between a set of variables (predictors) and a target (in this case, FDI). AML enables big data exploration and validates theoretical patterns derived from the data based on abductive reasoning, contributing to phenomenon-based theorizing (Doornenbal et al, 2021;von Krogh, 2018). A target variable (dependent variable) is selected, and suitable models are suggested through machine learning based on algorithms for accurate predictions (Larsen and Becker, 2021).…”

Section: Machine Learning Approachmentioning

confidence: 99%

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Greenfield FDI attractiveness index: a machine learning approach

Alon

Bretas

Sclip

et al. 2022

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Purpose This study aims to propose a comprehensive greenfield foreign direct investment (FDI) attractiveness index using exploratory factor analysis and automated machine learning (AML). We offer offer a robust empirical measurement of location-choice factors identified in the FDI literature through a novel method and provide a tool for assessing the countries' investment potential. Design/methodology/approach Based on five conceptual key sub-domains of FDI, We collected quantitative indicators in several databases with annual data ranging from 2006 to 2019. This study first run a factor analysis to identify the most important features. It then uses AML to assess the relative importance of each resultant factor and generate a calibrated index. AML computational algorithms minimize predictive errors, explore patterns in the data and make predictions in an empirically robust way. Findings Openness conditions and economic growth are the most relevant factors to attract FDI identified in the study. Luxembourg, Hong Kong, Singapore, Malta and Ireland are the top five countries with the highest overall greenfield attractiveness index. This study also presents specific indices for the three sectors: energy, financial services, information and communication technology (ICT) and electronics. Originality/value Existent indexes present deficiencies in conceptualization and measurement, lacking theoretical foundation, arbitrary selection of factors and use of limited linear models. This study’s index is developed in a robust three-stage process. The use of AML configures an advantage compared to traditional linear and additive models, as it selects the best model considering the predictive capacity of many models simultaneously.

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Section: Methodsmentioning

confidence: 99%

Section: Machine Learning Approachmentioning

confidence: 99%

Greenfield FDI attractiveness index: a machine learning approach

Alon

Bretas

Sclip

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Hence, in keeping with the perspectives of positivism and interpretivism and in adopting methodological pluralism for OB research programs, this essay argues that while conventional quantitative and qualitative methods continue to be utilized to explain and understand behaviors (Buchanan, 1998;Bryman & Bell, 2003), applying ML-based algorithms is recommended when the goals are exploration of patterns, investigation of complex relationships among variables, and precision (in research models and in answers to research questions). Using AI in research methodologies adheres to abductive inquiry, wherein unexpected observations made by ML add value for inductive theorizing and subsequent hypothesis generation (Doornenbal et al, 2021). In acknowledging concerns relating to the lack of transparency in decision-making in ML (Lindebaum & Ashraf, 2021), the conditional applications of grey box techniques would enrich and complement existing core methodologies rather than competing with them (Leavitt, Schabram, Hariharan, & Barnes, 2020).…”

Section: Moving Forward With a Boundary Conditionmentioning

confidence: 99%

“…AI and related technologies can aid research methods by offering nuance and improving predictability. Using ML in data analysis in behavioral studies would help build better predictive models and enable scholars to conduct more predictive style research alongside engaging in conventional hypothesisdriven research(Doornenbal et al, 2021). AI would also allow us to answer research questions quantitively with high precision (e.g.…”

mentioning

confidence: 99%

“…1. The usage of the term black box in this essay is intended to reflect the common understanding of the black box as it is used in computer programming, data science, neuroscience, and more recently in management research (Castelvecchi, 2016;Becker, Cropanzano, & Sanfey, 2011;Doornenbal, Spisak, & van der Laken, 2021). While consideration was given to developing alternate terminology to data "boxes," the goal of this essay was not intended to rename, but to draw attention to these technologies.…”

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confidence: 99%

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A perspective on embracing emerging technologies research for organizational behavior

Philip

2021

OMJ

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Purpose Emerging technologies are capable of enhancing organizational- and individual-level outcomes. The organizational behavior (OB) field is beginning to pursue opportunities for researching emerging technologies. This study aims to describe a framework consisting of white, black and grey boxes to demonstrate the tight coupling of phenomena and paradigms in the field and discusses deconstructing OB’s white box to encourage data-driven phenomena to coexist in the spatial framework. Design/methodology/approach A scoping literature review was conducted to offer a preliminary assessment of technology-oriented research currently occurring in OB. Findings The literature search revealed two findings. First, the number of published papers on emerging technologies in top management journals has been increasing at a steady pace. Second, various theoretical perspectives at the micro- and macro- organizational level have been used so far for conducting technology-oriented research. Originality/value By conducting a scoping review of emerging technologies research in OB literature, this paper reveals a conceptual black box relating to technology-oriented research. The essay advocates for loosening OB’s tightly coupled white box to incorporate emerging technologies both as a phenomenon and as data analytical techniques.

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Sick of Leading? Supervisory Responsibility and Its Consequences for Sickness Absenteeism and Sickness Presenteeism

Funk

2024

J Bus Psychol

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This research examines the impact of leadership positions with supervisory responsibility on two labor-market related health behaviors—sickness absenteeism and sickness presenteeism, i.e., working while being sick. Drawing on the job demands-resources (JD-R) model, this study posits that supervisory responsibility, serving as both a job resource and a demand, reduces sickness absenteeism while concurrently increasing sickness presenteeism behavior. The study identifies permanent availability and time pressure as two key aspects of leadership positions with supervisory responsibility that mediate these relationships. Using German-linked employer-employee data, the empirical results suggested that having supervisory responsibility reduces sickness absenteeism while at the same time elevating the engagement in sickness presenteeism. Furthermore, these relationships are partially mediated by leaders’ need for permanent availability and the time pressure inherent in positions with supervisory responsibility. This research advances our understanding of job characteristics of leadership positions by illustrating that job demands can yield favorable outcomes, offering valuable insights into the complex interplay between leadership positions and leaders’ health behaviors.

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Opening the black box: Uncovering the leader trait paradigm through machine learning

Cited by 32 publications

References 47 publications

Greenfield FDI attractiveness index: a machine learning approach

Greenfield FDI attractiveness index: a machine learning approach

A perspective on embracing emerging technologies research for organizational behavior

Sick of Leading? Supervisory Responsibility and Its Consequences for Sickness Absenteeism and Sickness Presenteeism

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