LQ Call for Papers: Leadership in the Digital Era: Social Media, Big Data, Virtual Reality, Computational Methods, and Deep Learning

Leadership in the Digital Era: Social Media, Big Data, Virtual Reality, Computational Methods, and Deep Learning

The study of leadership has always evolved, but this is truer than ever before as we transition into the 21st century (Dinh et al., 2014). The rapid evolution in the study of leadership reflects real world realities in geopolitical and institutional change and in particular, the advancement of technology (Kremer, 1993). Hence, we have now entered into a digital era in which leadership itself has changed as well as the means through which we study it.

To date, studies from various disciplines have contributed to our understanding of leadership in the digital era. These contributions have varied from theoretical and practical contributions to methodological advances in the means through which leadership is studied, including close versus distant leadership (Shamir, 1995). In management and applied psychology, research has explored how various forms of leadership facilitate group performance across different types of electronic media (Raghuram et al., 2018). Such research has quite a history in various domains, even leadership. For example, experimental work has investigated the effectiveness of leadership in face-to-face, desktop videoconferencing, or text-based chatting (Hambley, O’Neill, & Kline, 2007; Sosik, 1997). However, this research has not been leveraged sufficiently to better understand the various facets of leadership as a phenomenon.

Advancements have also happened beyond the management and applied psychology fields. As early as the mid-1990s, computer simulations illustrated how bias in performance evaluations adversely affected females as they advanced in leadership pipelines (Martell, Lane, & Emrich, 1996). In political science, novel research has shed light on how politicians have been able to influence their constituents and vice versa via conversations on social media (Barbera et al., 2014). Powered by machine learning and deep learning in particular (LeCun, Bengio, & Hinton, 2015), advancements in computer science, such as social sensing (e.g., analysis of emotional and behavioral cues) and natural language processing, have grown in popularity. For instance, we have gained an understanding of how algorithms can give bosses insight into how their employees are feeling (Waddell, 2016) and research has explored how automatically sensed behavior can predict job performance (Schmid Mast, Gatica-Perez, Frauendorfer, Nguyen, & Choudhury, 2015).

Other fields, such as biology (Gavrilets, Auerbach, & Van Vugt, 2016; Powers & Lehmann, 2014) have also simulated models to study topics such as leader emergence (see also Edelson et al., 2018). In addition, gene-culture coevolution has long relied on models and experiments to examine the hypothesis that humans have evolved both to defer to and to learn from successful, prestigious individuals (Henrich and Gil-White, 2001; Henrich, 2015). The significance of this idea in contemporary leadership studies awaits analysis via computational methods. Economics uses computer mediated experiments to allow for strategic interactions, and clean experimental tests, among players in various social interactions relevant to leadership (for a review see Zehnder, Herz, & Bonardi, 2017). Moreover, the era of big data is upon us, which brings many opportunities and challenges (Wenzel & Van Quaquebeke 2018). By using big data and insights from genetics, pretty soon we will be able to have “as-if” randomized designs harnessing the exogeneity of gene single-nucleotide polymorphism to estimate the causal effect of a phenotype (e.g., intelligence, personality) on outcomes like leadership effectiveness (e.g., see DiPrete, Burik, & Koellinger, 2018; von Hinke, Smith, Lawlor, Propper, & Windmeijer, 2016). The pace of developments in machine learning is such that today, computer algorithms can quickly achieve super-human skills in complex-decision making domains on the basis of first principles and using only self-reinforced learning (Sliver et al., 2018). How can such insights be used to better understand leadership?

Despite the advancements listed above, several questions remain unanswered that provide exciting challenges to be addressed with future research. For instance, how has the introduction of social media provided new opportunities to study leader-follower exchange relationships? In what ways can deep learning be applied to advance our approaches to leadership evaluation? How can artificial intelligence be used to develop leadership skills?

Topics that we will consider for the special issue, include, but are not limited to the following:1. Formal and informal leadership via various digital means, such as social media.

  1. Formal and informal leadership via various digital means, such as social media.
  2. Use of computational social sensing to provide greater insight into responses of leadership behaviors.
  3. Application of technology (e.g., deep learning) for leader evaluation (non-verbal, verbal, appearance), psychometric testing, and leader development.
  4. The generalization of face-to-face (i.e., close) models of leadership generalize to virtual contexts.
  5. Harnessing big data from demographic, psychological, behavioral, or genetic levels, to apply to models of leadership.
  6. Application of nature- or theory-inspired computational models of leadership (e.g., agent-based simulations) to better the leadership process or how leadership evolves over time.
  7. Use of technological innovations (e.g., virtual reality, automated coaching, etc.) to advance leadership training.
  8. Exploiting technology and big data in the context of “natural experiments” (e.g., exogenous shocks, legal interventions) in the study of leadership.
  9. Examining whether evolved social cognition privileges the transmission of information available to leaders, and how this link may drive social dynamics and cultural evolution
  10. Methodological how-to guides concerning the study of leadership in the digital era.

Summary: In this special issue, we would like to see the application of rigorous scientific standards to the study of leadership in the digital era. We intend to publish theoretical and empirical manuscripts, as well as reviews or critiques that will advance our understanding of leadership in the digital era. Specifically, we are interested in research that will help inform basic or applied research and consequently impact policy. In addition to management and psychology research on leadership, we welcome scholarship from other fields including biology, anthropology, computer science, economics, political science, and sociology, among others. Key to success in the submission process is to ensure clear theorizing and operationalization, and, where relevant, well-justified causal claims for empirical papers; creative measurement and designs will be especially welcomed (see Antonakis 2017, pp. 11-16).

Submission Process: Authors can submit their manuscripts starting from 4 September 2019 but no later than the submission deadline of 18 October 2019 (by 15h00 European Central Time), online via The Leadership Quarterly’s EVISE submission system at https:// www.evise.com/profile/#/LEAQUA/login

To ensure that all manuscripts are correctly identified for consideration for this Special Issue, it is important that authors select “SI: Digital” when they reach the “Article Type” step in the submission process. Manuscripts should be prepared in accordance with The Leadership Quarterly’s Guide for Authors available on the journal web page. All submitted manuscripts will be subject to The Leadership Quarterly’s double blind review process.

Research Data: Research data forms the backbone of research articles and provides the foundation on which knowledge is built. Researchers are increasingly encouraged, or even mandated, to make research data available, accessible, discoverable and usable. Although not mandatory, the journal encourages authors to submit their data at the same time as their manuscript. Further information can be found at: https://www.elsevier.com/authors/author-services/research-data

References

Antonakis, J. (2017). On doing better science: From thrill of discovery to policy implications. The Leadership Quarterly, 28(1), 5-21.

Allen, D. G., & Vardaman, J. M. (2017). Recruitment and retention across cultures. Annual Review of Organizational Psychology and Organizational Behavior, 4(1), 153–181.

Barbera, P., Bonneau, R., Egan, P. Jost, J. T., Nagler, J., Tucker, J. (2014). Leaders or followers? Measuring political responsiveness in the U.S. Congress using social media data.

DiPrete, T. A., Burik, C. A., & Koellinger, P. D. (2018). Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data. Proceedings of the National Academy of Sciences, 201707388.

Dinh, J. E., Lord, R. G., Gardner, W. L., Meuser, J. D., Liden, R. C., & Hu, J. (2014). Leadership theory and research in the new millennium: Current theoretical trends and changing perspectives. The Leadership Quarterly25(1), 36–62.

Edelson, M. G., Polania, R., Ruff, C. C., Fehr, E., & Hare, T. A. (2018). Computational and neurobiological foundations of leadership decisions. Science, 361(6401), 1-8.

Gavrilets, S., Auerbach, J., & Van Vugt, M. (2016). Convergence to consensus in heterogeneous groups and the emergence of informal leadership. Scientific Reports, 6.

Hambley, L. A., O’Neill, T. A., & Kline, T. J. B. (2007). Virtual team leadership: The effects of leadership style and communication medium on team interaction styles and outcomes. Organizational Behavior and Human Decision Processes103(1), 1–20.

Henrich, J. (2015). The secret of our success. Princeton: Princeton UP.

Henrich, J. and Gil-White, F. J. (2001). The evolution of prestige: freely conferred deference as a mechanism for enhancing the benefits of cultural transmission. Evolution and Human Behavior22, 165-196.

Kremer, M. (1993). Population growth and technological change: One million B.C. to 1990. The Quarterly Journal of Economics, 108, 681-716.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436-444.

Martell, R. F., Lane, D. M., Emrich, C. (1996). Male-female differences: A computer simulation. The American Psychologist. 157-158.

Raghuram, S., Hill, N. S., Gibbs, J. L., & Maruping, L. M. (2018). Virtual work: bridging research clusters. Academy of Management Annals.

Powers, S. T., & Lehmann, L. (2014). An evolutionary model explaining the Neolithic transition from egalitarianism to leadership and despotism. Proceedings of the Royal Society B: Biological Sciences, 281(1791), 20141349.

Schmid Mast, M., Gatica-Perez, D., Frauendorfer, D., Nguyen, L., & Choudhury, T. (2015). Social sensing for psychology: Automated interpersonal behavior assessment. Current Directions in Psychological Science24(2), 154-160.

Shamir, B. (1995). Social Distance and Charisma – Theoretical Notes and an Exploratory-Study. The Leadership Quarterly, 6(1), 19-47.

Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140-1144.

Sosik, J. J. (1997). Effects of transformational leadership and anonymity on idea generation in computer-mediated groups. Group & Organization Management, 22(4), 460-487.

von Hinke, S., Smith, G. D., Lawlor, D. A., Propper, C., & Windmeijer, F. (2016). Genetic markers as instrumental variables. Journal of health economics, 45, 131-148.

Waddell, K. (2016). The algorithms that tell bosses how employees are feeling. The Atlantic.

Wenzel, R., & Van Quaquebeke, N. (2018). The Double-Edged Sword of Big Data in Organizational and Management Research:A Review of Opportunities and Risks. Organizational Research Methods, 21(3), 548-591.

Zehnder, C., Herz, H., Bondardi, J.-P. (2017). A productive clash of cultures: Injecting economics into leadership research. The Leadership Quarterly, 28, 65-85.

Special issue editors

George C. Banks*, University of North Carolina, Charlotte, USA

Shelley D Dionne, Binghamton University, Binghamton, USA

Hiroki Sayama, Binghamton University, Binghamton, USA

Marianne Schmid Mast, University of Lausanne, Switzerland

*address correspondence to: George Banks gbanks3@uncc.edu

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