By Authors:
Evelyn Y. Zhang – Nanyang Business School, NTU, Singapore
Brandy L. Aven – Tepper School of Business, Carnegie Mellon University
Adam M. Kleinbaum – Tuck School of Business, Dartmouth College
Interviewers:
Asya Karabayeva – IE Business School
Jingze Wang – UCL School of Management
Article link: https://journals.sagepub.com/doi/abs/10.1177/00018392231221070
- In this paper you discover a condition under which the gender gaps in network advantage disappears, and that is – mobility. What is the motivation behind the operationalization of mobility? How did you think of mobility as a critical factor, did this come from an observation of female workers doing better when they move positions in real life, or did this factor emerge among other factors that you may have been looking at as conditions that help females use brokerage more efficiently?
Thank you for this question. This work is definitely influenced by and speaks to the conversation about mobility and network dynamics.
Mobility is crucial because it introduces changes to the formal structures of organizations to which social networks of employees hew closely. When people switch jobs, it naturally leads to the creation of new social connections, enabling them to broaden and update their networks, a process that is often difficult to achieve, as suggested by network studies. We were motivated by the need for more insight into how mobility influences networks and how these changes are linked to the network characteristics employees had before they moved.
When we consider how these network changes vary by gender, the theoretical framework becomes even more interesting. We think it is fascinating to show that the network characteristics common to women that are conventionally considered disadvantageous may induce women to broker when they are faced with mobility.
The review process challenged us to integrate our findings coherently and weave them into a clear narrative. It took us a lot of iterations with the literature on gender, mobility, and networks to finally grasp that mobility grants women license to broker.
It is possible that just as mobility helps women to legitimize their networking behaviors and take advantage of brokerage positions in the setting of our study, other organizational situations might provide similar licensing opportunities for women or other stereotyped groups. We hope our paper clearly communicates this and encourages more explorations into how women can be supported in leveraging their organizational networks.
- In your results, you found that men are more likely than women to overlook the value of retaining old ties and quickly reforming their networks around the task requirements of their new roles. What are your speculations about why this happens? Could it be because males are less emotionally invested and attached to their professional ties?
Drawing on existing literature regarding networks and gender, we think that gender disparities likely arise for two primary reasons.
First, women tend to be embedded in dense networks with few structural holes, and they are more likely to be surrounded by strong ties that share common contacts. Such ties have been found to be more resistant to decay than are weaker ties.
Second, women and men differ in how they sustain their relationships in the face of mobility. When an employee leaves to join another, distant group and ties can be retained only through electronic communication, such as email instead of in-person interactions, women may be more likely than men to maintain such remote connections.
I personally don’t think men are inherently less emotionally invested in their professional relationships and our results do not speak directly to emotional investment at all. As I said, men may not be as effective in maintaining such connections over long distances, as indicated by prior research. Our findings are derived from a substantial dataset of objective, longitudinal network data based on electronic communication. While these findings are robust, they do not address individual perceptions or emotions. I think this is an interesting direction for future work.
- In your paper, you leverage an exceptionally comprehensive dataset from Big Bank, specifically consisting of anonymized email data, including sender and receiver IDs, message size, and timestamps, combined with employee demographics and performance metrics spanning two years. Could you elaborate on the process of acquiring such a robust dataset? Additionally, what advice would you offer to emerging researchers seeking to obtain data from organizations for their studies?
Thanks for noting the comprehensiveness of this dataset. Accessing such proprietary information was indeed a privilege. During my time at the Tepper School of Business, our professors formed a collaboration center with a financial institution aimed at gaining a deeper insight into consumer behavior. Although I can’t name those who aided us, their support in kick-starting this center was crucial.
At one of professional forums, my advisor Brandy, who co-authored this paper, and I had the good fortune to connect with an HR executive from this financial institution. We expressed our interest in their internal data, which aligned well with their goals to boost their sales team’s performance and keep their star performers. This shared interest led to their agreement to compile, clean, and send the data to the center’s secure server. The process, however, was anything but simple; it involved over a year of consultations and rigorously testing the data before we received the initial batch of observations. Brandy’s tremendous effort and dedication was pivotal in making this possible.
Since graduating, I’ve had the chance to work more extensively with other organizational data. I’ve learned that every organization is unique, making generalized advice difficult. The effort required for large organizations to share data is significant, involving many departments like legal, HR, operations, and IT, and entails high costs for both parties involved. Thus, it is important to gain support from top management of the organizations; setting shared expectations and fostering trust is also vital before any data exchange.
- The analytic approach is very impressive, making us interested in understanding how your team collaborates with reviewers to refine and deliver your analysis strategy. Could you please shed light on which aspects of the analysis were included into your original manuscript at the very beginning? Also, how would you tackle the model specification in such a scenario, especially given this type of data format is rather uncommon. How did you delegate tasks in analysis and writing the paper with your co-workers, and what advice would you give to Phd students embarking on their first team projects?
The modeling approach in this paper has undergone considerable development from start to finish. This is due to the importance of ensuring that the model accurately reflects our hypotheses, leading to several iterations to determine which models should be included in the main manuscript and which should be placed in the appendix.
To analyze the effects of mobility and gender differences, one can compare movers with their pre-move selves or with non-movers. In our initial submission, for the sake of simplicity, we reported on a within-person fixed effect model with a sample of only movers (currently in the appendix), comparing their situation post-mobility to their own pre-mobility, and then we examined the gender-based changes. We also implemented the Coarsened Exact Matching (CEM) approach initially, but chose to present these models in the appendix as robustness checks to avoid overwhelming the audience. Throughout each submission, we have strived for clarity in explaining our choice among different methodological approaches. For instance, while a fixed-person within-individual comparison model provides the benefit of controlling for unobservable individual characteristics by comparing the same individuals over time, it could introduce selection bias and potentially overlook other unobservable factors that may influence the decision to move. During the review process, a reviewer suggested moving the CEM analysis into the main manuscript. We agreed with this suggestion and made the appropriate revisions.
The structure of our data, an unbalanced panel of individual observations over several months, is not particularly unusual. We gained significant insight from the detailed methodological descriptions by Rogan and Sorenson (2014), which enhanced our understanding of CEM and triple differences analyses in the context of our hypotheses. We also benefited from the advice of method review panel experts, who provided guidance on clearly reporting results. In general, I believe it is beneficial to start with visualizing the raw data, then present simple models without controls, and gradually introduce more complex controls.
I think in our job it is a lifelong task to learn how to work with other people. I am still learning how to be a good coauthor. This project is central to my dissertation; I wrote the initial drafts and conducted the data analyses. As the project evolved, my co-authors Brandy, Adam, and I engaged in collective discussions about each change, meticulously going over every word of the paper and the responses to reviewers. Working with Brandy and Adam has been an enlightening and enjoyable learning experience.
For students, the first project is an excellent chance to learn the ropes of writing papers and engaging with reviewer feedback. This process is full of tacit knowledge that’s hard to transfer, even among coauthors. My advice would be to really dive into the learning process and make the most of the experience. A small tip that has worked well for me is to pause before automatically accepting all edits. For each change a co-author suggests, I find it useful to think about their reasons and how the revision improves the sentence. This reflection not only aids in understanding their perspective but also sharpens my own rhetorical skills.
- Building off of your research and findings, are there any particular directions at the intersection of gender and network literatures you would like to pursue or want others to pursue?
The research question that motivates our work is, very simply: Are there conditions under which the gender gap in both brokerage and in returns to brokerage is mitigated? We focus on one such condition: mobility. Future research, possibly including experimental examination of mechanisms, could more fully explore precisely why building brokerage positions through the combination of mobility and tie maintenance provokes less of a gender discount.
Provided that social networks are inherently relational, we believe that exploring other forms of licensing for women’s organizational networks might prove a promising avenue for future research.
While the licenses provided by situations like mobility appear beneficial, we must acknowledge that the underlying biases prompting their necessity should be questioned. It’s essential to continue building upon research that explores how different networking strategies could enable women to achieve equal or even superior outcomes compared to men.
Interviewer bios:
Asya Karabayeva (asya.karabayeva@ie.edu) is a PhD student in Organizational Behavior and Human Resource Management at IE Business School. Her research examines careers in the context of new forms of work, with an emphasis on gender inequalities.
Jingze Wang (jingze.wang.21@ucl.ac.uk) is a doctoral candidate at UCL School of Management in the Organizations and Innovation group. His current research focuses on social networks, workplace interpersonal relationships, as well as human-AI collaboration.