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BUTCHERS, BAKERS, AND BARCHARTS: HOW DIGITIZED INFORMATION AFFECTS GENDER DIFFERENCES IN PERFORMANCE

Alexandra C. Feldberg PhD Candidate in Organizational Behavior

Harvard University

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ABSTRACT

This study asks: does increased access to digitized information affect the performance of men and

women workers differently? I find that the availability of information in digital platforms

disproportionately improves women’s performance in a male-dominated organization. I theorize

that digitized information helps women by serving as a relationship substitute, an alternative

channel to traditional relationship networks through which peripheral group members can gain

access to performance-enhancing information. Using interviews, observations, and archival data,

I take advantage of an intervention occurring within a 100-store grocery chain—when it introduced

a weekly online report providing managers with a high-level summary of their departments’

performance along key metrics. Comparing sales across 152 departments twelve weeks prior to

and following the report’s implementation shows that women managers benefit disproportionately

from the report’s introduction but stronger network ties with peers and supervisors attenuate its

benefits. Findings offer new directions for research on gender inequality and knowledge transfer

by suggesting that digital channels of knowledge distribution can offset disparities arising from

relationship networks in organizations.

Keywords: Gender, Knowledge-transfer, Technology, Intra-organizational, Field research

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INTRODUCTION

A long line of research suggests relationships, and information flowing through them,

contribute to gender disparities in the workplace. Relationships are important conduits of

information, and thus differences in relationships can lead to differences in men and women’s

access to information. Research has shown that women often lack sponsors (Ibarra, Carter and

Silva 2010) and mentors (Noe 1988, Srivastava 2015), take longer to locate experts (Singh, Hansen

and Podolny 2010), and are excluded from social interactions open to male members of their

organizations (Roth 2006, Turco 2010). These network disadvantages translate into unequal access

to the knowledge and skills critical to individual performance and the fulfillment of core business

goals.

But, traditional information networks in organizations are changing. Increasingly,

organizations rely upon digitized information—i.e., information that is “codified” (Hansen 1999)

and made “explicit” (Polanyi 1958) through media (text, video, etc.) so it is separated from

people—and digital platforms to distribute information to disparate workplaces and diverse

workers. This proliferation of digital platforms has profoundly shifted the ways that individuals

develop knowledge and skills and transfer them within organizations (Van Knippenberg et al.

2015). And while information digitization has become fundamental to businesses built on global

and asynchronous operations, virtually no research has considered how the storage and

dissemination of information through digital channels might be changing traditional gender

dynamics in the workplace. Because it does not require relationships for access, the availability of

information in digital form might be shifting interaction patterns and democratizing access to

information within organizations. Thus, I ask, does the availability of information codified and

stored in digital platforms benefit women more than men?

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In this paper, I integrate research on gender and knowledge transfer in organizations to address

this question. I posit that women will benefit disproportionately from the increasing availability of

digitized information. The mechanism I theorize is that digital platforms—and the information

they can store—will operate as a relationship substitute, offering peripheral groups (here, women),

the opportunity to bypass traditional relationship networks to gain access to performance-

enhancing information. As a test of this mechanism, I hypothesize that women will benefit less

from the introduction of digitized information when they have stronger relationships with

colleagues in their organization.

The specific research setting I examine is “FOODCO,” a brick-and-mortar grocery chain of

approximately 100 stores undergoing significant changes as a result of the digital age and

information economy. Employing a full-cycle research design (Chatman and Flynn 2005), I collect

qualitative data to formulate hypotheses and quantitative data to assess the effect of the availability

of digitized information on men and women’s performance. Interviews with managers and

observations within stores inform my theory and hypotheses for why information digitization

could disproportionately affect women. I test my hypotheses by leveraging a change in the

company’s dissemination of reports, which increased managers’ access to digitized information.

Analyzing the change in department managers’ performance in periods preceding and following

the report’s implementation, I compare the performance of men and women working in conditions

with high and low access to digitized information. Both qualitative and quantitative results suggest

that information digitization is democratizing, offering a relationship substitute for workers who

otherwise may not get information through other people within their organizations. With this

finding, I contribute to theory and research on gender, relationships, and information transfer in

organizations operating in the digital age.

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THEORY

Relationships are conduits of information. Through their relationships, workers commonly

seek “strategic information”—like gossip, or the “goings on” of an organization, social support, or

friendship (Podolny and Baron 1997). Likewise, through relationships individuals might learn

information quickly (Burt 1992), such as when a job opening will become available (Fernandez

and Sosa 2005, Rubineau and Fernandez 2013) or what benchmarks are appropriate in salary

negotiations (Belliveau 2005, Leibbrandt and List 2014). Within organizations, differences in

relationships contribute to differences in information about the execution of work, its social milieu,

and opportunities to advance.

Research has long shown that because of differences in their relationships, knowledge does

not diffuse evenly across men and women. Within organizations, there are two reasons for this

uneven diffusion. First, based simply on the formal, hierarchical arrangements of organizations,

men and women are often privy to different sorts of information and less likely to come into

contact through shared work activities. Jobs and positions are segregated along lines of gender in

most modern organizations (Baron and Bielby 1985). Segregation occurs as individuals sort into

different roles (Padavic and Reskin 2002, Reskin and Roos 1990) with different trajectories

(Barnett, Baron and Stuart 2000, Collins 1993, Collins 1997) and even different tasks (Chan and

Anteby 2016, Fletcher 2001). While men are disproportionately represented in high-paying, high-

status, and senior positions, in most organizations women are concentrated in low-paying, low-

status occupations (Kalev 2009, Stainback and Tomaskovic-Devey 2012) and underrepresented at

leadership levels (Meyerson and Fletcher 2000, Morrison, White and Van Velsor 1992, Powell

and Butterfield 1994). Second, the informal structure within organizations—where interactions

occur in settings not prescribed by roles or tasks—is often divided by gender (Brass 1985, Ibarra

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1992, Kleinbaum, Stuart and Tushman 2013). Individuals tend to establish relationships with those

who share ascriptive categories (McPherson, Smith-Lovin and Cook 2001). When people are alike,

they might believe that others are more trustworthy (Lincoln and Miller 1979), accepting (Ely

1995, Ibarra 1995), and likeminded (Allen and Wilder 1979, Wilder 1978); or they might exchange

more information and social support (Chattopadhyay, George and Shulman 2008, Ibarra 1992,

Ibarra 1995) and be more likely to provide sponsorship in the form of “valued support, feedback,

and assignments” (McGinn and Milkman 2013:1055).

Because people gravitate toward same-gender relationships and are sorted into gendered roles,

these ascriptive categories largely shape their information channels. For instance, women have

been shown to have longer paths for locating experts than men (Singh, Hansen and Podolny 2010),

which potentially slows down their execution of certain tasks. Women are often excluded from

social settings (Antilla 2002, Kanter 1977, Roth 2006), which in turn may limit their knowledge

about peers and clients (Groysberg 2010). And even though evidence suggests that well-connected

mentors are critical for their advancement (Burt 1998), women often lack sponsors and mentors

(Cox and Nkomo 1991, Ibarra, Carter and Silva 2010, Noe 1988), which may close off important

channels for strategic information, career advice, and social support (Padavic and Reskin 2002,

Srivastava 2015). To the extent that their interactions are separate from men (who tend to occupy

roles high in status and power), women are likely to miss out on information relevant to their

performance and advancement.

Yet, in the information economy, where technology makes information ubiquitous, this may

be changing: barriers to accessing information are breaking down (Benkler 2006). An explosion

of content stored online, in databases, or in other digital platforms—“explicit” (Nonaka 1994,

Polanyi 1958) or “codified” (Hansen 1999) information—has fundamentally transformed how

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people access information to complete tasks within their jobs (Barley 2015, Leonardi and Vaast

2017). Information flows freely through text, video, and other sources that do not require face-to-

face contact for access (Hwang, Singh and Argote 2015). Workers now have the option to utilize

digital platforms for information in lieu of face-to-face relationships and real-time interactions. An

abundance of information online and in databases makes this possible. Workers can acquire the

information they need to do their jobs at different times from multiple sources and not just people

within their organizations.

Despite changes in the ways that information moves, research has not considered the

implications of these changes for individuals who have historically missed out on information as

they have been excluded from relationship networks in their organizations. Because digitization

increasingly separates information from people, however, I argue that inequality in access to

information could be changing. Specifically, in this study I consider how and why digitized

information could affect the performance of women in a male-dominated organization. With this

focus, I advance scholarship on gender and knowledge transfer in organizations.

SETTING

In choosing the industry to study to answer my research question, I sought to meet two criteria.

First, since I wanted to test its performance implications, information needed to be available both

through digital platforms and relationships and have a measurable influence on performance.

Second, as understanding differences between men and women was central to my question, the

setting needed to contain a sufficient number of women and men in similar positions to enable

comparisons between them. The US grocery industry fulfilled these criteria and offered a setting

well-suited to the research purposes of this study.

Information in the Grocery Industry

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In the grocery business, the ability to wield and manipulate information effectively has proven

critical to the success of brick-and-mortar stores. Broad product assortments, perhaps the defining

feature of the modern supermarket, require operators to pay meticulous attention to inventory

management—particularly as stores are vulnerable to product loss due to theft, expiration, damage,

etc. and notoriously low margins (between 1% and 3%). Understanding and adapting to trends in

data allow retailers to keep track of rates-of-sales of thousands of products, forecast demand at the

item and category levels, and be consistently in-stock to maximize sales of best-selling items.

Facing fierce competitive pressures, stores’ viability may even hinge on how they manage

these data. In recent years, online retailers have begun to develop scale and technology that speeds

distribution. Such efforts seem to be paying off. As ecommerce grocery companies (e.g., Peapod,

Amazon) gain market share in shelf-stable products, they have created competitive pricing

pressures on stores (PG 2017). In response, traditional formats have focused attention on

perishable products—the most profitable categories if managed properly—to balance declining

sales of shelf-stable products. Given these trends, perishable product offerings are critical to the

success of brick-and-mortar grocery retailers. But perishable products risk spoilage. Therefore,

these retailers rely increasingly on data to anticipate and meet demand.

For managers, the influx of available data presents the challenge of how to allocate time and

attention. In an age of ecommerce, an essential differentiator for brick-and-mortar stores is the

guarantee that products will be on the shelves, available to customers when they arrive in stores

(PG 2017). Being in-stock requires managers to make accurate predictions as they order products

so inventory is fresh to meet both promotional and seasonal needs. How managers harness and

make sense of data stored in digital platforms—tracking past performance and future projections—

thus has a direct bearing on their performance.

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Gender in the Grocery Industry

The grocery industry comprises a sizable share of the US workforce and employs an even split

of men and women. Accounting for roughly 15 percent of the entire US retail industry’s workforce,

the industry employed more than 2.5 million individuals in 2017 (BLS 2017) across nearly 40,000

establishments (FMI 2017). About 48 percent of these workers were women (BLS 2016).

Despite overall gender balance, within stores departments and levels are highly segregated.

Men tend to dominate high-skilled, specialized, high-mobility departments (e.g., meat / seafood);

women tend to dominate low-skilled, clerical, low-mobility departments (e.g., checkout counters)

(Skaggs 2008). While there is relative parity in gender representation at middle-management

ranks, this parity quickly vanishes as individuals move into leadership positions in stores or

corporate offices. For instance, in 2016, women accounted for nearly two thirds of frontline

managers but only 20 percent of general managers (CPD 2016). Even though men are

overrepresented in general management positions—making gender a salient feature of daily work-

life (Ridgeway 1991, Ridgeway 1997)—relative parity at middle manager levels means that it is

possible to compare the performance of men and women in similar positions.

FOODCO

For this study, I focused on FOODCO, a traditional brick-and-mortar retailer located in the

United States. FOODCO is a chain of approximately 100 stores, containing five product-category

departments and employing nearly 8,000 people at any point in time. Like the rest of the grocery

industry, the representation of women within FOODCO declined sharply at the level of the store

leaders (i.e., Store Managers and Assistant Store Managers). Roughly 40 percent of department

managers and fewer than 15 percent of store leaders were female. In effect, the majority of senior

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workers (manager and above) within stores were male. Figure 1 depicts the store hierarchy and

gender representation across levels.

--------------- Insert Figure 1 about here ---------------

Managers at FOODCO were responsible for tactical planning, supervising employees, and

merchandising. While tasks varied across departments, department managers’ core metrics were

relatively standardized. Staff from the corporate headquarters aggregated and circulated

information to managers in stores through reports tracking sales and shrinkage (i.e., loss of

inventory value), databases with information concerning recalls, display planograms, daily

promotions, end-cap placements, and platforms predicting quantities of products needed to stock

shelves. Managers then used this information to make decisions about their departments—from

where to place products on store shelves to when to order inventory to how to schedule

subordinates.

DATA AND METHODS

My research at FOODCO integrated “complementary” qualitative and quantitative data (Small

2011) in a full-cycle research design (Chatman and Flynn 2005). Drawing upon a variety of data

sources collected sequentially enabled me to identify inductively an intervention that occurred

within the company (the introduction of a new weekly report that codified information required

for critical tasks), explore specific reasons for why it could affect performance, and then assess its

performance implications overall and by gender (Creswell et al. 2003).

I undertook data collection in two main phases. First, I collected qualitative data, which

consisted of interviews and observations. I undertook two rounds of interviews in 25 stores. I

interviewed store leadership (Store Managers and Assistant Managers) and then Department

Managers. From my qualitative data I developed hypotheses grounded in site-specific data.

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Furthermore, these interviews first alerted me to a naturally-occurring intervention: a company-

wide change in reporting. Second, I collected quantitative data, which consisted of archival data

from multiple company databases. Examining quantitative data before and after the intervention

allowed me to test the effects of an increase of digitized information on men and women’s

performance.

Qualitative Methods

Initially I interviewed individuals at store leadership levels. Between November 2016 and

January 2017, I contacted a stratified random sample (based on managers’ gender, store size, and

store newness) of managers and interviewed 26 Store Managers (SMs) and Assistant Store

Managers (ASMs). It was from these conversations that I first learned about a company-wide

increase in digitized information—in the form of a weekly report (the “Weekly Data Summary

Report”) for managers—that the company had recently implemented.

Then, between August 2017 and January 2018, I went back into the field to conduct additional

interviews and observations, this time of 27 department managers (DMs) in stores. For this round

of data collection, I contacted employees based on what I had learned from initial interviews about

the WDSR. This sample was roughly balanced to reflect store size (headcount), DMs’ gender, and

DMs’ tenure. I also conducted more than fifty hours of observations in stores and company

trainings. These data allowed me to understand the reasons why the report’s implementation could

have affected men and women’s performance in different ways. Table 1 summarizes the complete

interview sample.

--------------- Insert Table 1 about here ---------------

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All interviews were semi-structured and ranged between 45 and 120 minutes. While I adapted my

interviews as new themes emerged, in order to compare themes across interviews I covered central

questions of the interview guide with each participant (see supplement for sample questions).

To understand how managers thought about the report, I undertook in-depth analysis of

interview transcripts and field notes. Interviews were recorded and transcribed. Once transcription

had been completed, I read and coded transcripts for prominent themes (Charmaz 2014, Emerson

2001). I then used the qualitative software package NVivo to assign codes systematically across

transcripts. As I coded, I wrote memos to track emerging themes, and catalogue major points of

each interview in “contact summary forms” (Miles and Huberman 1994).

QUALITATIVE FINDINGS AND HYPOTHESIS DEVELOPMENT

Intervention: The Weekly Data Summary Report

Through my interviews with SMs and ASMs, I learned that FOODCO had recently increased

the digitized information available to managers with the introduction of a report, officially called

the “Weekly Data Summary Report” (WDSR). At a high-level, the WDSR was an online report

that aggregated key performance indicators to provide a weekly snapshot of each department’s

performance across the company. The report was delivered to managers’ inboxes as a Microsoft

Excel workbook that displayed departments’ actual numbers against projections, highlighting

numbers far below projections in red and numbers moderately below projections in yellow. The

workbook contained information for all stores but was organized by department—each department

had its own worksheet-tab—making it easy to view and compare one’s performance in relation to

other similar departments across the company. Figure 2 contains an illustration of the report.

--------------- Insert Figure 2 about here ---------------

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Most of the information contained within the WDSR was not new. Proactive managers could

peruse company portals, databases, and reports to learn much of its information. But, by

summarizing sales, purchases, shrink, and labor information in one place, the WDSR obviated the

need to sift through company systems to obtain metrics that could aid a department’s performance.

The WDSR’s consolidation function was especially critical as the physical intensity of much

of their work meant that managers had to make ongoing decisions about where to spend their

time—on “the floor” supervising subordinates and managing inventory or in “the office” using the

store computer to view various reports. A familiar refrain in interviews was: “I can’t sell products

that aren’t on the shelves.” Feeling a constant need to check for and fill “holes” on the floor,

department managers spent much of their time inspecting the floor and stocking inventory. But

managers needed to balance this time on the floor with analytical tasks mostly done on computers

in store offices. In the words of one store manager: “There is so much information on [the

computer]…I mean I wish there was more time to dive into all of it. There’s just so much out there

but not enough time in the day to get through all of it” (SM-17, Male). As a result, especially when

their attention was divided among other activities and time was scarce, by consolidating key

performance data the report helped them gain efficiency. One manager told me: “What [the

WDSR] does is it puts all the information I need to know as a department head on one sheet. I love

that. It puts everything into one” (DM-65, Male). Managers perceived the report to be so valuable

that they affectionately referred to it as “The Weekly Bible.”1

Interviews also suggested that the report could affect performance. Clarifying areas of need—

like missing sales or excessive shrink—allowed managers to redirect their activities. One DM told

me that the WDSR focused her attention on her inventory levels:

1 This name has been disguised to maintain anonymity of the site.

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“For the period they really want you at 55 percent purchases. Then you can go by and say well I spent this much this week and this much last week and these are my sales. So, if I'm getting a little bit heavy on purchases, I know I can cut that back.” (DM-50, Female)

Accurate ordering would keep shelves filled and reduce shrink from spoilage, helping the DM to

maximize sales. With performance feedback, others devised creative strategies to reduce their

shrink numbers and hit their targets. In one bakery DM’s words, seeing the WDSR made her want

to “grab extra sales.” She would put this to practice by coming up with new ways to repurpose

products:

“we started making cake pops out of cake scraps that we would [otherwise] throw in the trash. So we just take icing and crumble up cakes, roll them in a little icing, and we were letting them freeze. And now we're making money off of something that was gonna go in the trash” (DM-62, Female)

Provided DMs planned carefully, all departments could operate this way. Meat could be smoked;

cheese could be sliced and put into sandwiches; fruit could be chopped and packed into containers.

While interview participants generally described the report as a valuable source of information,

they also provided suggestive evidence that the value of the report was different for men and

women. Across my conversations, not a single male department manager—but one third of female

department managers—mentioned the WDSR unprompted.

Research on information transfer and networks has largely neglected how digitized information

could differently affect men and women’s performance. In fact, most research linking gender and

information transfer conceives of information transferred only through relationship networks (e.g.,

Ibarra 1992, Kanter 1977, Singh, Hansen and Podolny 2010). But, qualitative findings from my

interview data indicated that women may have found the information flowing through digital

platforms (hereafter I refer to this simply as “digitized information”) to be more valuable than men

did. To the extent that digitized information was more valuable for women than it was for men, it

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is also conceivable that this could have boosted their performance more than it did for men.

Therefore:

Hypothesis 1 (H1): women’s performance will benefit more than men’s performance from an increase of digitized information

Next, I looked to the qualitative data to try to glean why women may have perceived the report to

be of greater value than men did.

Explaining Women’s Performance Gains: Digitized information as a Relationship Substitute

Further analysis of qualitative data revealed that women may have found the report to be

valuable because it substituted as a conduit for information that men were already receiving

through their relationships. Interviews suggested that the report armed women with new

information about how they were performing relative to key metrics so they could then adjust

activities to improve performance. Interviews also suggested that men had been gaining

information contained within the report from other sources.

The value of relationships. In managers’ accounts, the highest quality and most relevant

information flowed through ties with peers (other department managers) and supervisors (store

leadership). Consistent with prior research (e.g., Chiaburu and Harrison 2008, Kram and Isabella

1985, Morrison 1993), managers believed peers to be valuable sources of tacit knowledge about

how to do a job or improve performance. Likewise, and also consistent with past research (e.g.,

Kram 1985, Ragins and McFarlin 1990, Seibert, Kraimer and Liden 2001), managers recalled

learning tips from supervisors about how to order, stock products (for instance, what was high-

margin or a fast-seller) or respond to the preferences of local clientele.

The strength of these ties mattered for information-transfer, too. Individuals who had worked

with colleagues in the same stores for longer periods of time described being more comfortable

reaching out with questions or for advice. Consistent with prior research on propinquity showing

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that people develop relationships with others in close physical proximity (e.g., Allen 1970, Liu

and Srivastava 2015, Reagans 2011), managers were more likely to describe strong relationships

with people who worked in the same stores. One meat manager explained of her peers:

“I interact with the deli because I have their product, and I share a cooler with them. I get stuff from produce all the time. [DM name]’s been here as long as I’ve been with the company and he’s been at this same store the whole time. I interact with all of them on a daily basis” (DM-43, Female)

Increased contact enhanced trust and mutual understanding between managers. They even

described bonds with coworkers as familial. One DM stated: “[y]ou know we spend more time

here with each other than we do with our actual families most of the time…So you know we try to

keep like one big family” (DM-53, Male). Having more contact with peers and supervisors made

managers more likely to “reach out to them and ask them” questions (DM-50, Female). In effect,

through longer-term, in-store relationships with peers and supervisors, managers were able to

access information relevant to performance.

How digitized information substituted for relationships. While strong ties with peers and

supervisors served as conduits of information, men and women recalled markedly different

experiences of such relationships and, in turn, the value of the report. Counts of coded interview

transcripts revealed that men devoted substantially more of their interviews to describing

mentoring relationships than women did. Having strong relationships—through which critical

information was already flowing—meant the introduction of the report provided less of an

information-boost for men. Men described the report as useful, but the utility of the information it

provided overlapped with that which they had already gained through their relationships—they

often recounted mentors and peers who had taught them to sift through and make sense of the

numerous reports the company disseminated. In contrast, women described the report as critical.

As women rarely recalled guidance from mentors or peers, the report might therefore have

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substituted for information they were not already receiving from relationships. For women,

therefore, the report focused their attention on the most important metrics and areas of need.

Interviews suggested that relationships with other men, particularly those in store leadership

positions, helped to guide male DMs in identifying and interpreting key data. The relationships

men described with supervisors (almost exclusively male due to the demographic composition of

store leadership) and peers were multiplex: they were generated through formal reporting ties and

then reinforced through out-of-work social activities like video games, golf, hunting, and fishing.

For instance, one meat DM told me about his relationship with the produce DM in his store (they

even had lunch together every day). Before managing the meat department, he had worked for the

produce DM. Shadowing this DM, he had learned how to sort through various information

sources—the intranet, weekly tracking sheets, etc. In his words:

“When I was in produce, I worked with [current produce DM’s name]…And I just gradually learned. He showed me how to do it and then I learned from him…we have reports [and] a summary sheet. We have a weekly tracker, and we've got our portal, which is where we get our sales from, [and] then we have our operation sheet, which has got our shrink numbers, and all these that we pulled in go in certain places. Sometimes it has to be done in a specific order…[I learned from] watching and doing it” (DM-60, Male)

Other male DMs recounted being in constant contact with their superiors. One manager said: “[The

Store Manager will] text me usually 8 o'clock at night whenever I'm here, ‘Hey, how's business?’”

(DM-53, Male). Then the SM would drill into how the department had done against key metrics.

Through these exchanges DMs vicariously experienced information-gathering. Observing a

mentor or checking in with a supervisor provided managers with insight into how more senior

workers acquired information and which metrics they prioritized. As department managers were

flooded with information from a variety of digital sources, having a supervisor or peer point out

the important metrics meant the relationship steered the managers’ attention to what was

important. Because this was, in large measure, the value of the WDSR, its utility overlapped with

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the utility men were getting from their relationships. As one manager explained, he had learned

that the WDSR was just “one of the ways” he could “see financially where [he was] at” (DM-42,

Male). Another remarked that it was possible to acquire the information store within the WDSR

“in a lot of ways” (DM-54, Male). Indeed, men were more likely than women to describe the

WDSR as one of many tools they used to assess performance and make decisions on a weekly

basis.

In women’s accounts, the kinds of relationships men described were mostly absent. By-and-

large, women did not recall the same opportunities to learn through peers and supervisors. In fact,

such relationships seemed more the exception than the rule, as illustrated by one woman’s

comment that a supervisor “actually did have the patience to teach me stuff” (DM-50, Female).

Especially in male-skewed departments like grocery, produce, and meat, women described feeling

alienated from (mostly) male colleagues and that “men still talk down to us” (DM-66, Female). As

one DM explained of dynamics between her SM and another DM:

“I can tell you for a fact I get treated differently because I’m a woman. It’s the three of us—the other manager is at the same level—I’m the only woman. We will come in at the start of the day. I will get my list of stuff and he will send me on. [Name of SM] will walk with [name of DM] all day long, and they’re talking about business and they’re talking about this that and the other thing. And they’re making plans and they’re staying up to date on stuff. I find out everything after the fact. … [Name of SM] tells [Name of DM] everything. They text constantly even when one of them is not at work, constantly meeting in the office together. [DM] knows everything that is going on because [SM] is always talking about it. I find out everything after the fact.” (DM-24)

Even though department managers were roughly balanced (approximately 40 percent were

female), women were typically the demographic minority within their stores at the level of

department or store management. Across eight managerial positions (refer back to Figure 1), stores

generally only employed two or three women; it would therefore not be uncommon for a female

department manager to have only one other woman in a peer or supervisory role.

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In this context, women talked about WDSR as if it were a sieve: the report consolidated and

filtered reams of information to highlight areas of need when relationships did not serve this

function. According to one female manager, the WDSR “gives you a good idea on what you need

to work on” (DM-57, Female). Guiding their attention to “problem-areas,” the report cut down the

time required to search for key information. Another believed the report saved her time: “you don't

have to look everywhere for the numbers. You just read the lines straight across and it's got it right

there” (DM-51, Female). By indicating where departments sat relative to company-assigned

targets, the WDSR directed women managers’ attention when relationships did not. Comparing

her knowledge of her department’s performance before and after the report, one woman explained:

“I'm trying to think back [to before the WDSR]. I just feel like I didn't know as much of the expectations that were set for me. Like, this way I know. OK. Our shrink is supposed to be this percent, our purchases should be this percent. And I know like back then it was like OK, keep this stuff down, don't over order. I wasn't as aware of what was expected of me...the exact numbers which I feel is helpful, to me, because I like to know everything exactly and keep stuff as organized as I can.” (DM-58, Female)

Making explicit the expectations that FOODCO held for managers and then where they sat relative

to these expectations, the report may have filled a gap in women’s knowledge that men had been

accessing all along through their relationships.

Based on insights from interviews, I come to my second hypothesis and the mechanism

explaining why women should benefit more from the introduction of digitized information.

Specifically, I argue that digitized information will be a relationship substitute—providing

information that flows through men’s relationships but, due to gender-segregated relationship

networks within the organization, will be largely absent for women. If this were the mechanism,

we might expect women with the strongest ties to benefit less from digitization than women with

the weakest ties. Formally:

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Hypothesis 2 (H2): the benefits of digitized information for women’s performance will be inversely related to the strength of their ties with supervisors and peers, such that women who have the weakest ties will see the greatest performance gains after an increase of digitized information

QUANTITATIVE METHODS & DATA

Next, I turned to quantitative data to test my hypotheses. Mapping dates of the WDSR’s

introduction (February 2016) to FOODCO’s administrative records allowed me to assess how the

availability of the report (or, increasing access to digitized information) affected managers’

performance. To measure gender differences in performance, I merged personnel records from two

databases. The first database contained demographic information for employees of all departments

and stores and included a unique employee identifying code tracking individuals’ employment—

e.g., lateral transfers, promotions, terminations—within the company over time (n=1,374,960

employee-days). The second database contained weekly sales records from all store-departments

(n=75,088 sales-weeks).

To construct my sample for analysis I matched sales records to demographic characteristics of

individuals occupying managerial positions at each time-point. My main analyses constrained the

sample to managers who had been in their roles for 15 months prior to and three months after the

report’s implementation (this allowed me to assess year-over-year performance changes for up to

three months pre- and post-implementation).2 I chose to constrain the sample in this way so as to

compare managers’ performance before and after the WDSR implementation with their own

performance—as opposed to the performance of another manager in the same department—in the

prior year. Applying exclusion criteria yielded data for 152 DMs and departments, or 3,648

2 In robustness checks, I expand this window of time.

21

department-week observations. Figure 3 depicts the report’s implementation in FY2016 and

comparison (control) weeks in FY2015.

--------------- Insert Figure 3 about here ---------------

Dependent Variable

The dependent variable, the year-over-year Change in department sales, was captured by the

percent change between 2016 and 2015 for department j in store k at time t (measured in weeks)

as follows:

𝐶ℎ𝑎𝑛𝑔𝑒𝑖𝑛𝑑𝑒𝑝𝑎𝑟𝑡𝑚𝑒𝑛𝑡𝑠𝑎𝑙𝑒𝑠012 =𝑠𝑎𝑙𝑒𝑠𝑖𝑛2016012 −𝑠𝑎𝑙𝑒𝑠𝑖𝑛2015012

𝑠𝑎𝑙𝑒𝑠𝑖𝑛2015012

For FOODCO, and other retailers, sales represent the most critical performance metric; sales

dictate everything from who can work when to what inventory can be on the shelves. Furthermore,

sales represented an appropriate performance measure for my purposes since, as interviews

revealed, information from the report could help managers improve their sales.

As illustrated in Figure 4, grocery retail is highly seasonal. Sales peak at major holidays like

Thanksgiving, Christmas, Easter, and July 4. The predictability of seasonal sales trends meant that

the setting was well-suited to comparing within-department changes over time.

--------------- Insert Figure 4 about here ---------------

Independent and Moderator Variables

Because I sought to understand differences in performance from before and after the

implementation of the report, the main independent and moderator variables captured the time of

the report’s implementation, self-reported gender (from HR records) of the department manager,

and the strength of the manager’s relationships within FOODCO. Post-implementation was a

dummy variable representing the 12 weeks before and after the implementation week of the WDSR

22

(1=post-implementation period). Female was a time-invariant indicator variable from HR records

measuring the DM’s self-reported gender (1=female) in each department.

Average tie strength was a time-invariant continuous variable from HR records measuring the

average same-store overlap in tenure between the focal manager and other managers or store

leaders since 2012—that is, the average duration of time that the focal manager worked in the same

store with other individuals at the level of DM or SM over the past five years. I calculated average

tie strength as follows:

𝑎𝑣𝑒𝑟𝑎𝑔𝑒𝑡𝑖𝑒𝑠𝑡𝑟𝑒𝑛𝑔𝑡ℎ =𝑠𝑡𝑟𝑒𝑛𝑔𝑡ℎ𝑜𝑓𝑎𝑙𝑙𝑡𝑖𝑒𝑠𝑡𝑜𝑡𝑎𝑙𝑛𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝑡𝑖𝑒𝑠

Strength of all ties referred to the total time overlap (in years) between the focal manager and all

other DMs, ASMs, and SMs working within the same store since 2012. Higher values would

indicate that the focal manager had worked alongside (i.e., in the same store as) individuals

occupying these levels at the time of the report’s implementation for longer periods of time. Total

number of ties referred to the number of department or store managers with whom the focal

manager had worked in the same store since 2012. Higher values would indicate that the focal

manager had worked alongside more different individuals in this period. Higher values of the

average tie strength variable represented greater average overlap in a department manager’s tenure

with peers or supervisors in FOODCO, providing a rough measure of how much interpersonal

contact the department manager had had with colleagues at peer and supervisory levels of the

organization (this included all colleagues for the five years preceding time t). Figure 5 illustrates

employees’ overlap with same-store colleagues.

--------------- Insert Figure 5 about here ---------------

Control Variables

23

Department-level controls were change variables that accounted for characteristics of the

manager’s department that could have been correlated with sales. Because the change in sales was

likely to be correlated with the number of people employed within a given department, Change in

total subordinates was a continuous variable measuring the change between the same weeks of

2016 and 2015 of the number of full- and part-time employees in department j of store k at time t.

To control for performance differences that may have been the result of changes in a department’s

demographic characteristics (e.g., Jehn, Northcraft and Neale 1999), I included variables

measuring the change in the gender and race of managers’ subordinates. Change in proportion of

female subordinates and Change in proportion of nonwhite subordinates measured the change in

the proportion of department j of store k at time t that self-identified as either female or nonwhite

(racial categories aggregated to be either “white” or “nonwhite”). Higher values indicated an

increase in the female or nonwhite employees reporting to the DM in department j of store k at

time t for the same weeks in 2016 and 2015. Since departments’ sales were likely to be correlated

with employees’ skills, I also included a change variable to account for age, tenure, and grade of

employees. Change in age of subordinates, Change in tenure of subordinates, and Change in grade

of subordinates3 were continuous variables approximating a change in the average experience level

of employees in department j of store k at time t between the same weeks of 2016 and 2015.

Store-level controls accounted for characteristics of the manager’s store that could have been

correlated with the department’s sales. Change in store sales was a continuous variable calculated

analogously to the change in department sales for store k at time t, as follows:

𝐶ℎ𝑎𝑛𝑔𝑒𝑖𝑛𝑠𝑡𝑜𝑟𝑒𝑠𝑎𝑙𝑒𝑠12 =(𝑠𝑎𝑙𝑒𝑠𝑖𝑛201612−𝑠𝑎𝑙𝑒𝑠𝑖𝑛2016@12) − (𝑠𝑎𝑙𝑒𝑠𝑖𝑛201512−𝑠𝑎𝑙𝑒𝑠𝑖𝑛2015@12)

(𝑠𝑎𝑙𝑒𝑠𝑖𝑛201512−𝑠𝑎𝑙𝑒𝑠𝑖𝑛2015@12)

3 Grade is a company-assigned variable ranging from 1 to 12 and designating a role’s complexity and corresponding roughly with compensation.

24

This variable measured year-over-year change in sales at the store level for all departments in store

k minus those of department j. Including this variable helped to account for variation in sales that

may have been due to factors outside of a DM’s control (e.g., extreme weather, competitive

pressures) and that would have affected all departments within a store on a given week.

I also calculated characteristics of a manager’s peers and supervisors. Mirroring those of the

department-level were control variables capturing changes in peers’ and supervisors’ demographic

characteristics. Change in proportion female, Change in proportion nonwhite, Change in age,

Change in tenure, and Change in grade were continuous variables that captured changes between

the same weeks of 2016 and 2015 in characteristics of DMs, ASMs, and SMs employed in store k

at time t. Table 2 contains descriptive statistics of and correlations between all key variables.

--------------- Insert Table 2 about here ---------------

To test the effect of the report’s implementation on department performance by gender, I used

linear regression with fixed effects for each department-store (e.g., the bakery department in store

#2, the meat department in store #37). Including department-store fixed effects controlled for time-

invariant characteristics of the department that could have been associated with its performance.

The regression model took the following form, where subscript j represents departments, k

represents stores, and t represents week:

𝑂𝑢𝑡𝑐𝑜𝑚𝑒 = 𝛽E +𝛽G𝑃𝑜𝑠𝑡𝐼𝑚𝑝𝑙𝑒𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛012 +𝛽J(𝑃𝑜𝑠𝑡𝐼𝑚𝑝𝑙𝑒𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛012 × 𝐹𝑒𝑚𝑎𝑙𝑒𝐷𝑀01)+ 𝛽OP𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠012Q + 𝛽RP𝐷𝑒𝑝𝑎𝑟𝑡𝑚𝑒𝑛𝑡 − 𝑆𝑡𝑜𝑟𝑒𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡𝑠@1Q

Because there were multiple observations over time for each department-store, I clustered robust

standard errors at the department-store level. Significant, positive values on the interaction term

would indicate that, relative to male managers, female managers benefited more from an increase

of digitized information.

25

In addition to the main models, I used hybrid models (Allison 2005) to estimate the difference

in the effects of the report on men and women. Hybrid models, also known as “between-within”

models, combine fixed effects and random effects to allow the estimation of both the within-unit

and between-unit change of covariates. To calculate the hybrid model, I decomposed time-varying

predictors into two components: deviations from group means (capturing within-department

variation) and group means (capturing between-department variation). In reporting the models, I

include deviations from group-means of time-varying predictor variables (representing within-

department variation, which are nearly identical to estimates from fixed effects models) as well as

group means for the time-invariant predictor variables of interest (managers’ gender and tie

strength); I do not report group means for time-varying predictor variables. Since gender and tie

strength are time-invariant and therefore drop out of the fixed effects models described above,

including hybrid models allows me to interpret gender and tie strength differences in performance

resulting from the report’s implementation.

QUANTITATIVE RESULTS

The majority of managers in the sample were white, in their late-40s, had worked at FOODCO

for about 15 years, and supervised departments that employed, on average, 9 full- and part-time

workers. Managers had worked alongside—i.e., in the same store as—about 40 other DMs, SMs,

and ASMs since 2012 at FOODCO, for an average duration of approximately 2 years (see Table

3 for descriptive statistics).

--------------- Insert Table 3 about here --------------

Table 4 contains regression results from both fixed effects and hybrid models. Models 1

through 4 are control models. Models 3 and 4 show that, controlling for time-varying department-

26

and store-characteristics, the effect of the report on the change in department sales4 is positive.

Overall DMs’ sales improved in 2016 relative to sales in 2015 following the introduction of the

report. Including an interaction term between manager gender (1=female) and the dummy for the

report’s implementation, Models 5 and 6 test H1. The positive and significant (p<.001)

relationship between manager gender and the weeks following the report’s implementation provide

support for main predictions in H1: an increase of digitized information improves the year-over-

year sales performance for women more than it does for men. Calculating predicted margins from

the hybrid models (Model 6) shows that, on average, women’s year-over-year sales increased from

10.3 percent (pre-implementation) to 14.8 percent (post-implementation). Men’s year-over-year

sales stayed roughly constant. Comparing the difference in men and women’s year-over-year sales

before and after the report is illuminating: while women’s year-over-year sales were greater than

those of men prior to the report, this difference increased by nearly 60 percent following the

report’s implementation. Figure 6 depicts the change in sales graphically.

--------------- Insert Table 4 about here --------------

--------------- Insert Figure 6 about here --------------

Models 7 through 10 incorporate the covariate measuring managers’ tie strength. Models 7 and

8 include an interaction term to test whether a DM’s tie strength influences the report’s effects on

year-over-year department sales. A negative but weakly significant effect (p=.09) of the

interaction between tie strength and the report’s implementation shows that managers’ average tie

strength attenuates the effect of the report’s implementation on year-over-year department sales.

Interacting DM’s gender, tie strength, and the report’s implementation, Models 9 and 10 suggest

that this effect is driven by women. Indeed, the negative and significant effect (p<.05) of the three-

4 For concision, I refer to this as “year-over-year sales.”

27

way interaction between the manager’s average tie strength, gender, and the report’s

implementation on year-over-year sales provide support for H2: the introduction of digitized

information is less helpful for year-over-year sales when a manager has strong ties across the

organization and is female. Put another way, tie strength with peers and supervisors attenuates the

positive effects of digitized information on women’s year-over-year performance, while it does

not do the same for men’s year-over-year performance. Again, calculating predicted margins from

hybrid models (see Model 10) is revealing. The report’s implementation increased year-over-year

sales most for women with low tie strength (i.e., at the bottom quartile). Women with low tie

strength benefited most from the report, exhibiting a 6 percent (from 11 percent pre-

implementation to 17 percent post-implementation) increase in year-over-year sales, compared to

a 0.5 percent (2.5 percent pre-implementation to 2 percent post implementation) decrease in year-

over-year sales among men with low tie strength. Figure 7 depicts these changes.

--------------- Insert Figure 7 about here --------------

Last, I test whether the report had similarly positive effects on sales when interacted with the

DM characteristics of race, age, grade, and tenure (see Table 5). The interaction effect between

gender and the report’s implementation (treatment) on department sales is positive and significant

and remains so even with the inclusion of all other individual characteristics. Aside from gender,

none of the other individual DM’s characteristics has a significant relationship with department

sales after the report’s implementation. The absence of a significant interaction effect between race

and the treatment is perhaps surprising; I return to this point in the discussion.

--------------- Insert Table 5 about here --------------

To check the robustness of the findings, I conducted several additional analyses. First, since

departments themselves are highly gender-segregated, it is conceivable that the relationship

28

between gender and the introduction of digitized information could have indicated that some

departments were naturally more responsive to the report. To assess this possibility, I included an

interaction term in the regression models between the treatment and a dummy variable for female-

dominated departments (1=female-dominated). Even with the inclusion of the interaction term,

interacting manager gender with the report’s introduction predicted a significant increase in sales

(see Table A3). This suggests that departmental differences did not drive the main effect.

Second, it is also possible that the effects of the report may have dissipated over time. Could

women have been responsive to the report only immediately following its introduction? Expanding

the window of time measured—from 12 to 18 weeks before and after the report’s

implementation—reveals that the digitized information had persistent effects for all key

independent variables (see Table A4). Comparing departments with the same managers before and

after the intervention was central to my analysis. But, due to managerial turnover within these

departments, it was not possible to compare a longer duration of time and retain the same sample

of managers.

Third, while the inclusion of store year-over-year sales helped to control for factors occurring

at the store level that could have influenced department-sales (e.g., competitive pressures, weather

events), store year-over-year sales were highly correlated with department year-over-year sales.

To determine whether effects in the main models were robust to the exclusion of store year-over-

year sales, I re-ran all models without this predictor variable (see Table A5). Results remained

consistent in sign and significance with those presented in the main text.

Last, in my main models, control variables accounted only for the change in department-

characteristics, and not absolute values. To the extent that department managers’ gender correlated

with department characteristics—for example, if women’s departments systematically had more

29

subordinates than those of men—it was possible that measuring control variables in terms of

change may have obscured effects of these characteristics on year-over-year sales. To determine

whether specific department characteristics drove the effects of the report on year-over-year sales,

I calculated the mean for each control variable over the entire treatment window and interacted

each of these variables with the treatment variable (see Table A6). While interactions between the

treatment and department tenure and proportion of the department that was female had significant

and positive effects on year-over-year sales, the main effects (gender*treatment) held. These

results suggested that characteristics of different departments did not drive the results.

DISCUSSION

This study investigates the effects of digitized information on men and women’s performance.

Employing a full-cycle research design (Chatman and Flynn 2005), I conduct interviews and

exploit the introduction of an online report—the intervention—that occurred across 152

departments of a large grocery chain. Interviews with department managers helped me develop

specific hypotheses. While all managers believed that the report enabled them to consolidate key

performance indicators, men and women’s accounts indicated that they derived different benefits

from this function. Men’s accounts suggested that the report was redundant with information

already flowing through their relationships with peers and mentors in the organization. Women’s

accounts suggested that information from the report substituted for information gaps stemming

from an absence of relationships. In particular, they could take action after the report had helped

them focus on the most important company targets, filtering an abundance of information when

mentorship and close friendships with bosses were lacking. My quantitative analyses support the

hypotheses developed from my qualitative data by showing how, after the increase of digitized

information, women’s sales improved more than those of men. Moreover, having worked

30

alongside the same colleagues in the same store for longer periods of time attenuated the benefits

of the report more for women than it did for men.

A key contribution of this paper is the theory that digitized information can operate as a

relationship substitute within organizations. I show that, by digitizing information, organizations

can replace information flowing through relationships—potentially to benefit those on the

periphery of organizational networks. As technologies advance and organizations amass

unprecedented amounts of data (Schwab 2016), the opportunities to digitize different kinds of

information extend far beyond reports. Consider how technology could be transforming

knowledge access through a variety of platforms: information for how to perform a task may come

from talking to or observing a more experienced colleague or through a YouTube video showing

someone thousands of miles away performing the very same task. Information about an existing

client could come from contacting colleagues who have served this client or searching IT systems

that store the client’s transaction history. My findings suggest that the digitization of information

should lessen the need to access information through relationships—potentially opening up new

avenues for individuals who have historically been on the margins of their organizations or

professions to acquire knowledge and skills.

With this contribution, I advance literature on gender inequality and knowledge-transfer. Prior

studies on gender inequality in organizations have shown that women often miss out on

information flowing through male-dominated relationship networks (Ibarra 1992, Kanter 1977,

Singh, Hansen and Podolny 2010). The implicit assumption of this research is that relationship

networks pose a nearly intractable barrier to information access for groups occupying the periphery

of organizational networks. I broaden this lens, by suggesting that traditional patterns of network

exclusion might be changing as information flows through other channels—here, as it resides in

31

digital platforms. As women now comprise nearly half of the overall US workforce but remain

underrepresented at organizations’ leadership levels, digital technologies may hold significant

promise for tapping into an enormous but underutilized reservoir of talent.

Additionally, my findings make two contributions to existing research on gender in

organizations. First, I propose a novel way that relationships may make a difference for women,

and perhaps individuals in peripheral positions more broadly. The vast majority of existing

research in this domain has focused on the value of relationships for women and minorities insofar

as they serve as either “pipes” or “prisms” (Podolny 2001): conduits of information or signals

about quality (e.g., see Briscoe and Kellogg 2011, Padavic and Reskin 2002). For instance, studies

of mentoring have commented extensively on how these functions of relationships can help (or

harm) women. From the perspective of a junior woman, interacting with senior women might

provide opportunities for the transfer of tacit knowledge (Kram 1985), or gaining exposure to

senior women in their organization can instill the belief that they belong and that “people like me”

can succeed (McGinn and Milkman 2013:1044). From the perspective of other organizational

actors, seeing junior women being mentored can signal quality or ability, buoying their careers

with “borrowed” social capital (Burt 1998) or evidence that they have undertaken and been

successful in challenging assignments (Briscoe and Kellogg 2011). I add to this existing research

by suggesting that relationships could serve a third important role, as a sieve that filters

information—a crucial function when workers today are at risk of information overload (O'Reilly

1980, Van Knippenberg et al. 2015). In the context I study here, digitized information helped to

offset the disadvantage that individuals on the outside faced when they lacked relationships with

colleagues who could steer their attention to the most important metrics.

32

Second, this research contributes to studies of transparency and inequality. Scholars have

shown that reducing ambiguity and increasing transparency in evaluation contexts—e.g., hiring,

promotions, compensation—can attenuate disparities (e.g., Botelho and Abraham 2017, Castilla

2015, Dobbin, Schrage and Kalev 2015). The mechanism at play in this stream of research is

largely understood from the perspective of evaluators: individuals on hiring or promotion

committees experience greater accountability and thus their biases are held in check as their

decision-making is made transparent. Here, I consider transparency not from the perspective of a

gatekeeper or evaluator, but rather from the perspective of individual men and women whose

performance is on the line. In this respect, my study suggests digitizing information that is pertinent

to task execution can provide a sort of work-process transparency, as codifying information helped

guide women toward activities men may have known to do based on direction from mentors and

friends. Perhaps most similar to this idea is from research on transparency in pay, which has shown

that when the negotiability of compensation is included in a job description women are more likely

to negotiate for higher starting salaries (Leibbrandt and List 2014), as decreased ambiguity in turn

reduces the tendency to conform to stereotypes (Bowles, Babcock and McGinn 2005). Here, with

the finding that digitized information helped to level the information-playing field and, as a result,

disproportionately benefited women, my study also illustrates how transparency in individuals’

work processes can be democratizing.

Directions for Future Research

While a core strength of this study was a naturally-occurring intervention, future work might

expand it in a few ways. Conducting a field study with observational data enhanced the external

validity of my findings, but it was not without limitations. First, because I restricted my sample

only to managers who had remained within the same positions and departments of the company

33

for the entire duration of time that I measured (18 months), I draw upon a relatively small sample

of managers. Second, since the report was rolled out to all departments at once, it was not randomly

assigned to a treatment and control group. Third, because of turnover, not everyone included in

the interview sample had been employed at FOODCO at the time of the report’s implementation.

Thus, future studies might expand their sample size, rely on a randomized experiment, and

interview individuals participating in the experiment before and after it occurs.

An important boundary condition of this study, and one that future research should continue to

explore, is the demographic composition of managers in the setting. Within the chain I studied,

similar to many organizations and industries (e.g., Stainback and Tomaskovic-Devey 2012),

gender was highly salient as men tended to occupy the majority of managerial and leadership

positions within stores. Consequently, women often described being excluded from male-

dominated networks. It is in this context that I argue that access to information through digital

platforms was particularly potent because it helped offset disparities arising from sex-segregated

relationship networks. But, does digitized information disproportionately improve women’s

performance when there is gender balance across company ranks? To the extent that it substitutes

for information flowing through relationships, digitized information may not benefit women in a

gender-integrated setting. Further research should continue to investigate how an organization’s

demographic composition could shape effects of digitized information on women’s performance.

Though the specific case of digitization that I studied had clear benefits, it is possible that it

could be harmful, as well. In enacting workplace policies, organizational leaders may be tempted

to believe technology will be a panacea for ameliorating workplace inequality. After all, digital

connectivity has facilitated dispersed and remote work. Benefits are often cited: flexibility in—or

control over—their work schedules offer parents the opportunity to work from home (Kelly, Moen

34

and Tranby 2011); virtual environments attenuate destructive stereotypes that often arise from

face-to-face contact (Hwang, Singh and Argote 2015) and disadvantage members of visible

minority groups; technology enables individuals who may be “outsiders” in their professional

communities to contribute their work products unencumbered by dynamics that may induce

anxiety (Jeppesen and Lakhani 2010). Indeed, my study adds to the evidence that there may be

ways in which digital platforms could benefit women. But, it is also possible to imagine that

women might be more reticent to seek out help from others if they have information readily

available to them, perhaps over time inadvertently weakening their relationships (Merton 1936).

And even as we move into an increasingly digital world, relationships will likely still matter for

individuals’ mobility. As such, future research should continue to investigate not only the potential

benefits, but also potential drawbacks of information digitization for women and minorities.

A natural question arising from this study is about other marginalized groups. We might expect

to see other minorities, such as those of different races or sexual orientations, also benefit from

digitized information for similar reasons to women. In this study, I did not find any differential

effects of the report by race. This was surprising. One possibility for the absence of a difference

could have been that I had relatively few nonwhite employees in the sample. Another possibility

is that the relationship dynamics—and, resulting effects of digital platforms—I observed may have

been specific to gender. Irrespective of their race, the men I interviewed often described social

relationships with colleagues built on shared common interests like video games or sports. The

strength of men’s relationships with other men across race is reminiscent of what Turco (2010)

observed in her study of the leveraged buyout industry: black men in this industry were not isolated

from men’s networks in the same way that white women were, primarily because of common

35

interests in sports. Additional research is needed to explore the relationship between digitized

information, race, and performance.

Another boundary condition of this study is that I focus solely on the digitization of information

that was redundant with the information that some individuals (like mentors or peers) had

possessed already. My findings might have been different had I studied individuals’ access to

information that was not already in circulation and therefore that was novel. Today, the rate of

production of novel information is accelerating as businesses harness insights from unprecedented

volumes of data. With increased computing power, companies are now able to see new patterns in

everything from how their employees interact to how their customers shop. Likewise, with

smartphones, information about virtually every topic imaginable is now at workers’ fingertips.

Though not the focus of my study, future research should consider whether digitizing more novel

information—or, other kinds of information more generally—would differentially affect men and

women’s performance.

Understanding how different forms of information affect day-to-day practices of workers is

only going to increase in importance as digital technologies are today ubiquitous and rapidly

advancing in their sophistication. Findings of this study should apply broadly to any organization

where employees are inundated with information in digital form. More broadly my findings show

how even through incremental changes in technological practices and systems that appear

unrelated to gender, there may be different consequences for men and women within organizations.

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41

FIGURES

Figure 1. Illustrative organizational chart depicting Department Manager (DM) position in formal hierarchy relative to peers, supervisors, and subordinates.

Note. Organizational chart depicts sample reporting structure of single store. Percentage female reflects proportion of females across stores at those bands: general management (Store Managers or Assistant Store Managers) and department management (Bakery, Deli, Produce, Meat/Seafood, or Grocery Department Managers). Excluded from organizational chart are employees staffing frontend of store.

14% female

40% female

Store Manager

Asst. Store Manager

Grocery DM

Asst. Store Manager

Meat/Sea. DMProduce DMDeli DMBakery DM

Bakery subordinate

Bakery subordinate

Bakery subordinate

Deli subordinate

Deli subordinate

Deli subordinate

Deli subordinate

Deli subordinate

Deli subordinate

Produce subordinate

Produce subordinate

Produce subordinate

Produce subordinate

Meat/Sea. subordinate

Meat/Sea. subordinate

Meat/Sea. subordinate

Grocery subordinate

Grocery subordinate

Grocery subordinate

Grocery subordinate

Grocery subordinate

supervisor peer subordinate

42

Figure 2. Partial snapshot of Weekly Data Summary Report (WDSR) for Meat Departments in Stores #28-34.

Store Sales Actual

Sales Target

Sales Actual vs.

Budget

Purchases - Cost

Purchases % of Sales Shrink

Shrink as % of

Sales Labor Labor

Budget

Labor Actual vs.

Budget Shrink

Shrink % of Sales

28 14889 15800 (911) 8018 54% 2192 15% 933 1,141 208 1,287 4.85%

29 11928 19222 (7294) 9019 76% 3499 29% 1,651 1,749 98 1,942 18.81%

30 10029 8075 1954 5702 57% 2902 29% 1,978 1,138 (840) 883 3.53%

31 14029 13991 38 10283 73% 1399 10% 1,658 1,368 (290) 1,387 4.10%

32 19388 20021 (633) 9330 48% 2383 12% 3,442 3,964 522 1,626 7.93%

33 8052 9288 (1236) 5003 62% 2919 36% 2,391 2,262 (129) 1,551 6.66%

34 22034 23008 (974) 10028 46% 3084 14% 1,298 1,810 512 1,084 3.58%

Note. Figure provides fictitious sample of meat department’s weekly numbers. Dark grey cells reflect numbers far below company projections, light grey cells reflect numbers moderately below (or, with shrinkage, above) company projections. Shrink refers to product loss due to spoilage, theft, or damage. Figure 3. Timeline of weeks compared before and after WDSR implementation in Feb. 2016.

11/08/15

11/15/15

11/22/15

11/29/15

12/06/15

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12/27/15

01/03/16

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02/21/16

02/28/16

03/06/16

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03/20/16

03/27/16

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11/09/14

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12/28/14

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04/19/15

WDSR implemented February 2016

Pre-implementation Post-implementation

43

Figure 4. Log of mean weekly sales across all department across major holidays for FY2015 versus FY2016.

Figure 5. Illustrative network graph of employee mobility and relationships across Stores #5, #7,

#21, and #81

Note. Figure depicts network graph of department managers in FOODCO stores over time. Clusters broadly map to stores, vertices represent individuals who reached managerial ranks in the company since 2012. Edge weight reflects the overlap of managers by store—thicker edges mean that managers spent more time working alongside one another in a given store.

10.1

10.2

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10.5

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Sale

s (lo

gged

)

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2015 2016

Thanksgiving

Christmas

President’s Day

EasterMemorial Day

July 4

Labor Day

44

Figure 6. Effects of WDSR implementation on weekly department sales by male and female department managers, error bars represent 95% confidence intervals

Note. Figure depicts marginal effects of the report’s implementation (the intervention) on weekly department sales—estimates produced from hybrid models.

Figure 7. Effects of WDSR implementation on weekly department sales by high and low tie strength and male and female department managers, error bars represent 95% confidence

intervals

Note. Figure depicts marginal effects of the report’s implementation (the intervention) on weekly department sales—estimates produced from hybrid models. Low tie strength refers to managers’ tie strength at the bottom quartile; high tie strength refers to managers’ tie strength at the top quartile.

0.00

0.02

0.04

0.06

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Cha

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us 2

015)

Effects of WDSR Implementation on Weekly Department Sales by Male and Female Department Managers

Male Female

-0.05

0.00

0.05

0.10

0.15

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0.25

Pre-Implementation Post-Implementation

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Effects of WDSR Implementation on Weekly Department Sales by Tie Strength and Male and Female Department Managers, 95% CIs

Male Department Manager-Low Male Department Manager-High

Female Department Manager-Low Female Department Manager-High

45

TABLES

Table 1. Interview sample Position Male Female Total

Store Management 20 6 26 Store Manager 13 2 15 Assistant Store Manager 7 4 11

Department Managers 15 12 27 Bakery Manager 1 4 5 Deli Manager 2 3 5 Grocery Manager 4 2 6 Meat Manager 4 2 6 Produce Manager 4 1 5

Total 35 18 53 Note: Interviews conducted between November 2016 and January 2018.

46

Table 2. Correlation matrix and descriptive statistics for variables included in main regression analyses, as well as department manager characteristics.

Variable mean SD min max (1) (2) (3) (4) (5) (1) ∆ in department sales 0.07 0.26 -0.47 2.67 1.00 (2) ∆ in store sales 0.04 0.20 -0.48 1.93 0.78 1.00 (3) Post-Implementation (1=post) 0.50 0.50 0.00 1.00 0.00 -0.04 1.00 (4) Average tie strength 1.77 0.78 0.68 4.27 -0.08 -0.10 0.00 1.00 (5) Female DM 0.40 0.49 0.00 1.00 0.22 0.04 0.00 0.08 1.00 (6) Nonwhite DM 0.13 0.34 0.00 1.00 0.05 -0.01 0.00 -0.01 0.12 (7) Age of DM 48.06 10.31 24.00 70.00 -0.03 0.01 0.00 -0.03 0.07 (8) Grade of DM 9.17 0.83 6.00 10.00 0.18 0.19 0.00 -0.03 0.06 (9) Tenure of DM 15.35 7.66 1.58 43.38 -0.08 -0.09 0.00 0.27 -0.10 (10) ∆ in total subordinates 0.17 0.47 -0.67 4.00 0.41 0.39 0.01 0.00 0.06 (11) ∆ in % full-time subordinates 0.25 0.60 -1.00 5.67 0.12 0.05 -0.01 -0.14 -0.02 (12) ∆ in age of subordinates -0.01 0.25 -0.66 1.90 -0.07 -0.06 0.02 0.03 -0.04 (13) ∆ in tenure of subordinates 1.55 1.00 -0.99 17.62 -0.10 -0.08 -0.37 0.03 0.03 (14) ∆ in grade of subordinates 0.01 0.07 -0.33 0.67 -0.04 -0.08 -0.02 -0.01 0.03 (15) ∆ in % female subordinates 0.05 0.45 -1.00 2.20 0.04 0.04 -0.04 0.03 -0.06 (16) ∆ in % nonwhite subordinates -0.02 0.50 -1.00 3.00 0.12 0.06 0.01 -0.01 -0.02 (17) ∆ in age of peers and supervisors -0.02 0.07 -0.25 0.31 -0.08 -0.08 -0.05 0.05 -0.02 (18) ∆ in tenure of peers and supervisors 0.06 0.26 -0.61 1.45 -0.07 -0.08 -0.03 0.14 -0.02 (19) ∆ in grade of peers and supervisors 0.00 0.03 -0.13 0.13 -0.17 -0.21 -0.01 0.05 -0.02 (20) ∆ in % female peers and supervisors 0.05 0.41 -1.00 2.43 -0.04 -0.01 0.06 -0.08 -0.10 (21) ∆ in % nonwhite peers and supervisors 0.14 0.56 -1.00 4.00 -0.08 -0.08 0.00 -0.08 -0.07

Variable (6) (7) (8) (9) (10) (11) (12) (13) (14) (6) Nonwhite DM 1.00 (7) Age of DM -0.03 1.00 (8) Grade of DM -0.01 0.18 1.00 (9) Tenure of DM -0.04 0.26 -0.04 1.00 (10) ∆ in total subordinates 0.14 -0.02 0.02 0.00 1.00 (11) ∆ in % full-time subordinates -0.07 0.02 0.11 -0.11 -0.26 1.00 (12) ∆ in age of subordinates -0.01 0.03 -0.18 0.04 -0.20 0.09 1.00 (13) ∆ in tenure of subordinates -0.09 0.13 0.10 -0.05 -0.12 0.13 0.09 1.00 (14) ∆ in grade of subordinates -0.04 -0.12 -0.08 -0.10 -0.20 0.30 0.17 0.12 1.00 (15) ∆ in % female subordinates 0.05 -0.10 0.03 -0.01 0.08 0.00 -0.04 0.08 0.08 (16) ∆ in % nonwhite subordinates -0.01 -0.04 0.02 0.00 0.03 0.04 -0.07 0.03 0.02 (17) ∆ in age of peers and supervisors 0.06 0.03 -0.07 0.10 -0.04 -0.03 -0.09 0.03 -0.01 (18) ∆ in tenure of peers and supervisors -0.06 0.02 0.11 0.00 -0.06 0.00 0.00 0.02 0.03 (19) ∆ in grade of peers and supervisors 0.06 -0.02 0.04 -0.02 -0.16 0.03 0.00 0.11 0.04 (20) ∆ in % female peers and supervisors -0.05 -0.02 -0.04 0.02 -0.08 0.07 0.01 -0.02 0.03 (21) ∆ in % nonwhite peers and supervisors -0.02 -0.11 -0.20 -0.04 0.03 -0.06 -0.02 -0.09 0.12

Variable (15) (16) (17) (18) (19) (20) (21) (15) ∆ in % female subordinates 1.00 (16) ∆ in % nonwhite subordinates -0.01 1.00 (17) ∆ in age of peers and supervisors -0.02 -0.02 1.00 (18) ∆ in tenure of peers and supervisors -0.08 -0.06 0.15 1.00 (19) ∆ in grade of peers and supervisors 0.01 -0.10 0.18 0.24 1.00 (20) ∆ in % female peers and supervisors -0.05 0.02 0.03 -0.07 0.12 1.00 (21) ∆ in % nonwhite peers and supervisors 0.02 -0.04 -0.20 -0.03 -0.11 0.00 1.00 Note: ∆ indicates percent change between comparable weeks in 2015 and 2016. For example, mean ∆ in department sales of .07 indicates an average of 7 percent increase between same weeks in 2015 and 2016.

47

Table 3. Raw descriptive statistics of Department Manager (DM) characteristics

Variable All Department Managers Male DMs Female DMs Mean SD Min Max Mean SD Min Max Mean SD Min Max

Manager is nonwhite 0.13 0.34 0.00 1.00 0.10 0.30 0.00 1.00 0.18 0.38 0.00 1.00 Manager age (in years) 48.06 10.24 24.00 70.00 47.47 10.86 24.00 70.00 48.97 9.13 29.00 65.00 Manager tenure (in years) 15.37 7.76 1.58 43.38 16.14 8.80 1.58 43.38 14.20 5.61 3.35 30.52 Total subordinates 8.46 5.29 0.00 37.00 8.42 4.84 0.00 28.00 8.51 5.92 1.00 37.00 SM, ASM, DM coworkers (#) 39.82 16.64 6.00 93.00 39.45 14.48 6.00 82.00 40.38 19.46 6.00 93.00 Average tie strength 1.77 0.78 0.68 4.27 1.73 0.74 0.82 4.27 1.83 0.84 0.68 4.27 Note: SM refers to Store Manager, ASM refers to Assistant Store Manager, and DM refers to Department Manager.

48

Table 4. Ordinary least squares regression models with department-store (N=152) fixed effects predicting effect of Weekly Data Summary Report (WDSR) implementation on the year-over-year change in department sales and interaction of implementation with (a) manager gender and (b) average tie strength on change in department sales. Odd-numbered models are hybrid/between-within models; even-numbered models are fixed effects mdoels.

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Hybrid Hybrid Hybrid Hybrid Hybrid

Female DM X Post-Implementation

0.048*** 0.048*** 0.049*** 0.049*** 0.096*** 0.096*** (0.011) (0.010) (0.011) (0.010) (0.027) (0.027)

Average tie strength 0.003 0.007 (0.007) (0.010)

Average tie strength X Post-Implementation

-0.010+ -0.010+ 0.001 0.001 (0.006) (0.006) (0.008) (0.007)

Average tie strength X Female

-0.009 (0.016)

Average tie strength X Fem. DM X Post-Impl.

-0.026* -0.026* (0.013) (0.013)

Post-Implementation (dummy)

0.017** 0.017** -0.003 -0.003 0.014 0.014 -0.006 -0.006 (0.006) (0.006) (0.005) (0.005) (0.011) (0.011) (0.014) (0.013)

Female DM 0.099*** 0.099*** 0.075*** 0.074*** 0.090* (0.019) (0.019) (0.018) (0.018) (0.042)

Store sales (∆) 0.889*** 0.889*** 0.896*** 0.896*** 0.893*** 0.893*** 0.890*** 0.890*** 0.889*** 0.889*** (0.060) (0.059) (0.062) (0.060) (0.060) (0.058) (0.059) (0.058) (0.058) (0.057)

Total subordinates (∆) -0.001 -0.001 -0.004 -0.004 -0.005 -0.005 -0.005 -0.005 -0.006 -0.006 (0.014) (0.014) (0.013) (0.013) (0.012) (0.011) (0.011) (0.011) (0.011) (0.011)

% full-time subordinates (∆)

0.001 0.001 0.000 0.000 0.001 0.001 0.001 0.001 0.001 0.001 (0.009) (0.008) (0.009) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008)

Age of subordinates (∆)

-0.005 -0.005 -0.014 -0.014 -0.019 -0.019 -0.012 -0.012 -0.007 -0.007 (0.019) (0.019) (0.019) (0.019) (0.022) (0.021) (0.020) (0.019) (0.017) (0.017)

Tenure of subordinates (∆)

-0.002 -0.002 0.003+ 0.003+ 0.002 0.002 0.002 0.002 0.002 0.002 (0.003) (0.003) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Grade of subordinates (∆)

-0.094+ -0.094+ -0.092+ -0.092+ -0.086 -0.086+ -0.090+ -0.090+ -0.104+ -0.104* (0.053) (0.052) (0.054) (0.053) (0.052) (0.051) (0.053) (0.052) (0.053) (0.052)

% female subordinates (∆)

-0.007 -0.007 -0.006 -0.006 -0.007 -0.007 -0.007 -0.007 -0.006 -0.006 (0.007) (0.007) (0.007) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006)

% nonwhite subordinates (∆)

-0.001 -0.001 -0.003 -0.003 0.001 0.001 0.001 0.001 0.000 0.000 (0.006) (0.006) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

Age of peers and supervisors (∆)

-0.016 -0.016 -0.001 -0.001 -0.027 -0.027 -0.020 -0.020 -0.026 -0.026 (0.065) (0.064) (0.064) (0.063) (0.061) (0.060) (0.064) (0.063) (0.064) (0.063)

Tenure of peers and supervisors (∆)

-0.021 -0.021 -0.019 -0.019 -0.025 -0.025 -0.029 -0.029 -0.032 -0.032 (0.021) (0.020) (0.021) (0.020) (0.020) (0.020) (0.021) (0.020) (0.021) (0.020)

Grade of peers and supervisors (∆)

0.144 0.144 0.143 0.143 0.159 0.159 0.168 0.168 0.173 0.173 (0.100) (0.098) (0.102) (0.100) (0.113) (0.111) (0.115) (0.113) (0.123) (0.121)

% female peers and supervisors (∆)

0.010 0.010 0.011 0.011 0.012 0.012 0.014 0.014+ 0.015+ 0.015+ (0.008) (0.007) (0.008) (0.007) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008)

% nonwhite peers and supervisors (∆)

0.008 0.008 0.002 0.002 0.001 0.001 0.001 0.001 0.001 0.001 (0.012) (0.012) (0.012) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011)

Constant 0.034*** -0.006 0.018** -0.015 0.020** -0.005 0.022*** -0.009 0.022*** -0.016 (0.006) (0.021) (0.007) (0.020) (0.006) (0.020) (0.006) (0.024) (0.006) (0.025)

Store-Department FE Yes No Yes No Yes No Yes No Yes No R2 0.91 0.70 0.91 0.70 0.91 0.70 0.91 0.70 0.91 0.71 N 3,648 3,648 3,648 3,648 3,648 3,648 3,648 3,648 3,648 3,648 Note: Results refer to 24-week window—12 weeks before and after WDSR implementation. +p<.10, *p<.05, **p<.01, ***p<.001. Robust Standard Errors clustered at the store-department level in parentheses.

49

Table 5. Ordinary least squares regression models with department-store (N=152) fixed effects predicting effect of WDSR implementation on year-over-year change in department sales and effects of the interaction of WDSR implementation with department manager characteristics, including: (a) gender, (b) race, (c) age, (d) grade, (e) tenure.

Variable (1) (2) (3) (4) (5) (6) Female DM X Post-Implementation

0.048*** 0.050*** (0.011) (0.010)

Nonwhite DM X Post-Implementation

-0.001 -0.009 (0.019) (0.019)

Age of DM X Post-Implementation

0.000 -0.000 (0.000) (0.000)

Grade of DM X Post-Implementation

0.005 0.003 (0.005) (0.005)

Tenure of DM X Post-Implementation

0.000 0.001 (0.001) (0.001)

Post-Implementation (dummy) -0.003 0.017** 0.016 -0.027 0.013 -0.028 (0.005) (0.006) (0.020) (0.046) (0.012) (0.049)

Store sales (∆) 0.893*** 0.896*** 0.896*** 0.896*** 0.896*** 0.894*** (0.060) (0.062) (0.062) (0.061) (0.061) (0.060)

Total subordinates (∆) -0.005 -0.004 -0.004 -0.003 -0.004 -0.004 (0.012) (0.013) (0.013) (0.012) (0.013) (0.011)

% full-time subordinates (∆) 0.001 0.000 0.000 0.000 0.001 0.001 (0.008) (0.009) (0.008) (0.009) (0.008) (0.008)

Age of subordinates (∆) -0.019 -0.014 -0.014 -0.012 -0.014 -0.019 (0.022) (0.019) (0.019) (0.019) (0.019) (0.021)

Tenure of subordinates (∆) 0.002 0.003+ 0.003+ 0.003 0.004+ 0.003 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Grade of subordinates (∆) -0.086 -0.091+ -0.092+ -0.091+ -0.092+ -0.083 (0.052) (0.054) (0.054) (0.054) (0.054) (0.052)

% female subordinates (∆) -0.007 -0.006 -0.006 -0.006 -0.006 -0.007 (0.006) (0.007) (0.007) (0.007) (0.007) (0.006)

% nonwhite subordinates (∆) 0.001 -0.003 -0.003 -0.003 -0.003 0.001 (0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

Age of peers and supervisors (∆)

-0.027 -0.000 -0.000 0.000 0.000 -0.022 (0.061) (0.063) (0.064) (0.064) (0.064) (0.060)

Tenure of peers and supervisors (∆)

-0.025 -0.019 -0.019 -0.019 -0.020 -0.025 (0.020) (0.021) (0.021) (0.021) (0.021) (0.020)

Grade of peers and supervisors (∆)

0.159 0.142 0.143 0.155 0.140 0.145 (0.113) (0.106) (0.102) (0.104) (0.101) (0.121)

% female peers and supervisors (∆)

0.012 0.011 0.011 0.012 0.011 0.012 (0.008) (0.007) (0.008) (0.008) (0.008) (0.008)

% nonwhite peers and supervisors (∆)

0.001 0.002 0.002 0.002 0.002 0.001 (0.011) (0.012) (0.012) (0.012) (0.012) (0.011)

Constant 0.020** 0.018** 0.018** 0.018** 0.017** 0.020** (0.006) (0.007) (0.007) (0.007) (0.007) (0.006)

Store-Department FE Yes Yes Yes Yes Yes Yes R2 0.91 0.91 0.91 0.91 0.91 0.91 N 3,648 3,648 3,648 3,648 3,648 3,648 Note: Results refer to 24-week window—12 weeks before and after WDSR implementation. +p<.10, *p<.05, **p<.01, ***p<.001. Robust Standard Errors clustered at the store-department level in parentheses.

50

APPENDIX

Table A1. Correlation matrix and descriptive statistics for variables included in main regression analyses, as well as department manager characteristics. Restricted to female department managers.

Variable mean SD min max (1) (2) (3) (4) (1) ∆ in department sales 0.14 0.35 -0.39 2.67 1.00 (2) ∆ in store sales 0.05 0.20 -0.48 1.44 0.85 1.00 (3) Post-Implementation (1=post) 0.50 0.50 0.00 1.00 0.05 -0.03 1.00 (4) Average tie strength 1.84 0.84 0.68 4.27 -0.14 -0.15 0.00 1.00 (5) Nonwhite DM 0.18 0.39 0.00 1.00 0.01 -0.05 0.00 -0.02 (6) Age of DM 49.00 9.21 29.00 65.00 -0.04 0.06 0.00 -0.04 (7) Grade of DM 9.23 0.90 6.00 10.00 0.20 0.20 0.00 -0.10 (8) Tenure of DM 14.37 5.50 3.35 30.52 0.01 0.09 0.00 0.12 (9) ∆ in total subordinates 0.21 0.56 -0.55 4.00 0.48 0.42 0.02 -0.09 (10) ∆ in % full-time subordinates 0.23 0.64 -0.70 5.67 0.20 0.15 -0.02 -0.15 (11) ∆ in age of subordinates -0.02 0.22 -0.46 1.90 -0.13 -0.03 0.05 0.10 (12) ∆ in tenure of subordinates 1.58 1.19 -0.54 17.62 -0.18 -0.14 -0.27 0.03 (13) ∆ in grade of subordinates 0.01 0.06 -0.17 0.33 -0.08 -0.10 -0.04 0.07 (14) ∆ in % female subordinates 0.01 0.41 -1.00 2.20 0.07 0.05 -0.03 -0.02 (15) ∆ in % nonwhite subordinates -0.03 0.46 -1.00 3.00 0.20 0.13 -0.06 0.08 (16) ∆ in age of peers and supervisors -0.02 0.07 -0.22 0.23 -0.08 -0.11 0.01 0.01 (17) ∆ in tenure of peers and supervisors 0.06 0.29 -0.61 1.45 -0.09 -0.07 0.00 0.18 (18) ∆ in grade of peers and supervisors 0.00 0.03 -0.13 0.08 -0.21 -0.17 0.01 0.01 (19) ∆ in % female peers, supervisors -0.01 0.35 -1.00 1.29 -0.07 -0.04 0.07 -0.23 (20) ∆ in % nonwhite peers, supervisors 0.09 0.49 -1.00 3.00 -0.04 -0.03 -0.01 -0.06

Variable (5) (6) (7) (8) (9) (10) (11) (12) (5) Nonwhite DM 1.00 (6) Age of DM -0.11 1.00 (7) Grade of DM -0.03 0.21 1.00 (8) Tenure of DM -0.02 0.30 0.14 1.00 (9) ∆ in total subordinates 0.11 0.00 -0.06 -0.03 1.00 (10) ∆ in % full-time subordinates -0.07 0.00 0.16 0.07 -0.23 1.00 (11) ∆ in age of subordinates -0.12 0.05 -0.10 -0.08 -0.15 0.12 1.00 (12) ∆ in tenure of subordinates -0.08 0.13 0.05 0.01 0.03 0.06 0.24 1.00 (13) ∆ in grade of subordinates -0.02 -0.10 -0.10 -0.03 -0.08 0.20 0.12 0.08 (14) ∆ in % female subordinates 0.09 -0.08 0.02 0.21 0.14 0.02 -0.14 0.02 (15) ∆ in % nonwhite subordinates -0.05 -0.08 0.19 -0.08 0.10 -0.05 -0.05 0.04 (16) ∆ in age of peers and supervisors 0.08 -0.12 -0.06 0.17 -0.10 0.05 -0.09 0.07 (17) ∆ in tenure of peers and supervisors -0.17 0.15 0.17 0.00 -0.07 -0.01 -0.05 0.06 (18) ∆ in grade of peers and supervisors 0.02 -0.11 0.04 0.03 -0.12 0.03 -0.01 0.11 (19) ∆ in % female peers, supervisors -0.09 -0.17 -0.07 0.02 -0.01 0.00 -0.11 -0.11 (20) ∆ in % nonwhite peers, supervisors -0.04 0.18 -0.12 -0.11 0.06 -0.11 0.03 -0.06

Variable (13) (14) (15) (16) (17) (18) (19) (20) (13) ∆ in grade of subordinates 1.00 (14) ∆ in % female subordinates -0.11 1.00 (15) ∆ in % nonwhite subordinates -0.03 0.21 1.00 (16) ∆ in age of peers and supervisors -0.02 0.30 0.14 1.00 (17) ∆ in tenure of peers and supervisors 0.11 0.00 -0.06 -0.03 1.00 (18) ∆ in grade of peers and supervisors -0.07 0.00 0.16 0.07 -0.23 1.00 (19) ∆ in % female peers, supervisors -0.12 0.05 -0.10 -0.08 -0.15 0.12 1.00 (20) ∆ in % nonwhite peers, supervisors -0.08 0.13 0.05 0.01 0.03 0.06 0.24 1.00 Note: ∆ indicates % change between comparable weeks in 2015 and 2016. For example, mean ∆ in department sales of .07 indicates an average of 7% increase between same weeks in 2015 and 2016.

51

Table A2. Correlation matrix and descriptive statistics for variables included in main regression analyses, as well as department manager characteristics. Restricted to male department managers.

Variable mean SD min max (1) (2) (3) (4) (1) ∆ in department sales 0.02 0.17 -0.47 1.17 1.00 (2) ∆ in store sales 0.03 0.20 -0.46 1.93 0.83 1.00 (3) Post-Implementation (1=post) 0.50 0.50 0.00 1.00 -0.07 -0.06 1.00 (4) Average tie strength 1.72 0.74 0.82 4.27 -0.06 -0.06 0.00 1.00 (5) Nonwhite DM 0.10 0.30 0.00 1.00 0.06 0.02 0.00 -0.03 (6) Age of DM 47.45 10.92 24.00 70.00 -0.06 -0.02 0.00 -0.03 (7) Grade of DM 9.13 0.78 7.00 10.00 0.16 0.18 0.00 0.01 (8) Tenure of DM 15.99 8.73 1.58 43.38 -0.15 -0.16 0.00 0.37 (9) ∆ in total subordinates 0.15 0.40 -0.67 2.00 0.31 0.37 -0.01 0.08 (10) ∆ in % full-time subordinates 0.25 0.58 -1.00 3.00 0.03 -0.02 0.00 -0.14 (11) ∆ in age of subordinates 0.00 0.26 -0.66 1.75 0.01 -0.07 0.00 -0.01 (12) ∆ in tenure of subordinates 1.53 0.85 -0.99 4.57 0.00 -0.03 -0.48 0.03 (13) ∆ in grade of subordinates 0.01 0.07 -0.33 0.67 -0.03 -0.08 -0.01 -0.06 (14) ∆ in % female subordinates 0.07 0.47 -1.00 2.14 0.04 0.03 -0.05 0.07 (15) ∆ in % nonwhite subordinates -0.01 0.52 -1.00 3.00 0.06 0.02 0.06 -0.07 (16) ∆ in age of peers and supervisors -0.02 0.07 -0.25 0.31 -0.09 -0.07 -0.09 0.08 (17) ∆ in tenure of peers, supervisors 0.07 0.23 -0.51 1.30 -0.04 -0.08 -0.05 0.11 (18) ∆ in grade of peers, supervisors 0.00 0.03 -0.13 0.13 -0.16 -0.23 -0.03 0.09 (19) ∆ in % female peers, supervisors 0.08 0.44 -1.00 2.43 0.04 0.00 0.06 0.02 (20) ∆ in % nonwhite peers, super. 0.17 0.59 -0.58 4.00 -0.10 -0.10 0.01 -0.08

Variable (5) (6) (7) (8) (9) (10) (11) (12) (5) Nonwhite DM 1.00 (6) Age of DM 0.01 1.00 (7) Grade of DM -0.01 0.15 1.00 (8) Tenure of DM -0.04 0.26 -0.12 1.00 (9) ∆ in total subordinates 0.17 -0.05 0.11 0.03 1.00 (10) ∆ in % full-time subordinates -0.06 0.03 0.06 -0.19 -0.29 1.00 (11) ∆ in age of subordinates 0.08 0.03 -0.24 0.08 -0.24 0.07 1.00 (12) ∆ in tenure of subordinates -0.11 0.13 0.16 -0.09 -0.33 0.20 -0.03 1.00 (13) ∆ in grade of subordinates -0.06 -0.13 -0.07 -0.13 -0.31 0.37 0.20 0.16 (14) ∆ in % female subordinates 0.03 -0.10 0.05 -0.10 0.04 -0.01 0.01 0.12 (15) ∆ in % nonwhite subordinates 0.02 -0.02 -0.09 0.03 -0.02 0.11 -0.09 0.03 (16) ∆ in age of peers and supervisors 0.05 0.11 -0.08 0.07 0.01 -0.08 -0.08 -0.02 (17) ∆ in tenure of peers, supervisors 0.05 -0.08 0.05 -0.01 -0.04 0.01 0.03 -0.02 (18) ∆ in grade of peers, supervisors 0.11 0.03 0.05 -0.04 -0.21 0.03 0.01 0.12 (19) ∆ in % female peers, supervisors -0.01 0.05 -0.01 0.01 -0.12 0.10 0.06 0.04 (20) ∆ in % nonwhite peers, super. 0.01 -0.23 -0.25 -0.02 0.01 -0.03 -0.04 -0.11

Variable (13) (14) (15) (16) (17) (18) (19) (20) (13) ∆ in grade of subordinates 1.00 (14) ∆ in % female subordinates -0.01 1.00 (15) ∆ in % nonwhite subordinates 0.04 -0.05 1.00 (16) ∆ in age of peers and supervisors -0.07 -0.08 -0.08 1.00 (17) ∆ in tenure of peers, supervisors -0.03 -0.11 -0.04 0.28 1.00 (18) ∆ in grade of peers, supervisors 0.00 0.00 -0.09 0.15 0.20 1.00 (19) ∆ in % female peers, supervisors 0.06 -0.07 0.09 0.08 0.03 0.10 1.00 (20) ∆ in % nonwhite peers, super. 0.13 0.05 -0.01 -0.24 -0.10 -0.13 0.03 1.00 Note: ∆ indicates % change between comparable weeks in 2015 and 2016. For example, mean ∆ in department sales of .07 indicates an average of 7% increase between same weeks in 2015 and 2016.

52

Table A3. Ordinary least squares regression models with dept-store (N=152) fixed effects and gender-segregated department dummy predicting effect of WDSR implementation on year-over-year change in department sales and effects of the interaction of WDSR implementation with (a) department manager gender on change in department sales and (b) average tie strength on change in department sales.

Variable (1) (2) (3) (4) (5) Female DM X Post-Implementation

0.035* 0.037* 0.086* (0.017) (0.017) (0.033)

Average tie strength X Post-Implementation

-0.010 0.001 (0.006) (0.008)

Average tie strength X Female DM X Post-Implementation

-0.025+ (0.013)

Gender segregated department X Post-Implementation

0.016 0.014 0.010 (0.017) (0.017) (0.018)

Post-Implementation 0.017** -0.004 0.013 -0.006 (0.006) (0.005) (0.011) (0.014)

Store sales (∆) 0.889*** 0.896*** 0.893*** 0.890*** 0.889*** (0.060) (0.062) (0.060) (0.059) (0.058)

Total subordinates (∆) -0.001 -0.004 -0.005 -0.005 -0.006 (0.014) (0.013) (0.012) (0.011) (0.011)

% full-time subordinates (∆) 0.001 0.000 0.001 0.000 0.001 (0.009) (0.009) (0.008) (0.008) (0.008)

Age of subordinates (∆) -0.005 -0.014 -0.016 -0.010 -0.006 (0.019) (0.019) (0.020) (0.019) (0.017)

Tenure of subordinates (∆) -0.002 0.003+ 0.002 0.002 0.001 (0.003) (0.002) (0.002) (0.002) (0.002)

Grade of subordinates (∆) -0.094+ -0.092+ -0.088+ -0.091+ -0.104+ (0.053) (0.054) (0.052) (0.052) (0.053)

% female subordinates (∆) -0.007 -0.006 -0.007 -0.007 -0.007 (0.007) (0.007) (0.006) (0.006) (0.006)

% nonwhite subordinates (∆) -0.001 -0.003 0.000 0.000 0.000 (0.006) (0.005) (0.005) (0.005) (0.005)

Age of peers and supervisors (∆) -0.016 -0.001 -0.021 -0.015 -0.022 (0.065) (0.064) (0.062) (0.064) (0.064)

Tenure of peers and supervisors (∆)

-0.021 -0.019 -0.026 -0.030 -0.032 (0.021) (0.021) (0.020) (0.020) (0.021)

Grade of peers and supervisors (∆)

0.144 0.143 0.159 0.167 0.172 (0.100) (0.102) (0.115) (0.117) (0.124)

% female peers and supervisors (∆)

0.010 0.011 0.012 0.013 0.015+ (0.008) (0.008) (0.008) (0.008) (0.008)

% nonwhite peers and supervisors (∆)

0.008 0.002 0.001 0.001 0.001 (0.012) (0.012) (0.011) (0.011) (0.011)

Constant 0.034*** 0.018** 0.020** 0.022*** 0.023*** (0.006) (0.007) (0.006) (0.006) (0.006)

Store-Department FE Yes Yes Yes Yes Yes R2 0.91 0.91 0.91 0.91 0.91 N 3,648 3,648 3,648 3,648 3,648 Note: Results refer to 24-week window—12 weeks before and after WDSR implementation. +p<.10, *p<.05, **p<.01, ***p<.001. Robust Standard Errors clustered at the store-department level in parentheses.

53

Table A4. Ordinary least squares regression models with department-store (N=152) fixed effects predicting effect of Weekly Data Summary Report (WDSR) implementation on year-over-year change in department sales and interaction of implementation with (a) manager gender on change in department sales and (b) average tie strength on change in department sales.

Variable (1) (2) (3) (4) (5) Female DM X Post-Implementation

0.036** 0.037** 0.081* (0.012) (0.012) (0.032)

Average tie strength X Post-Implementation

-0.010+ 0.001 (0.005) (0.007)

Average tie strength X Female DM X Post-Implementation

-0.025+ (0.014)

Post-Implementation 0.013+ -0.002 0.014 -0.005 (0.007) (0.006) (0.012) (0.016)

Store sales (∆) 0.891*** 0.894*** 0.892*** 0.889*** 0.888*** (0.102) (0.103) (0.101) (0.101) (0.100)

Total subordinates (∆) 0.007 0.008 0.008 0.008 0.007 (0.013) (0.013) (0.012) (0.012) (0.012)

% full-time subordinates (∆) 0.006 0.006 0.006 0.006 0.006 (0.010) (0.009) (0.009) (0.009) (0.009)

Age of subordinates (∆) -0.003 -0.007 -0.010 -0.005 -0.000 (0.018) (0.018) (0.020) (0.018) (0.016)

Tenure of subordinates (∆) -0.001 0.002 0.002 0.001 0.001 (0.002) (0.001) (0.001) (0.001) (0.001)

Grade of subordinates (∆) -0.071 -0.068 -0.064 -0.069 -0.081 (0.054) (0.053) (0.054) (0.053) (0.051)

% female subordinates (∆) -0.006 -0.006 -0.005 -0.005 -0.005 (0.005) (0.005) (0.005) (0.005) (0.005)

% nonwhite subordinates (∆) 0.004 0.003 0.005 0.004 0.004 (0.005) (0.005) (0.005) (0.005) (0.005)

Age of peers and supervisors (∆) -0.026 -0.019 -0.037 -0.029 -0.036 (0.048) (0.048) (0.048) (0.049) (0.049)

Tenure of peers and supervisors (∆)

-0.007 -0.006 -0.008 -0.011 -0.013 (0.016) (0.016) (0.017) (0.017) (0.017)

Grade of peers and supervisors (∆)

0.120 0.111 0.117 0.122 0.126 (0.114) (0.117) (0.122) (0.124) (0.129)

% female peers and supervisors (∆)

0.018* 0.018* 0.018* 0.019* 0.019* (0.008) (0.008) (0.008) (0.008) (0.008)

% nonwhite peers and supervisors (∆)

0.007 0.006 0.005 0.005 0.006 (0.008) (0.008) (0.008) (0.008) (0.008)

Constant 0.027*** 0.015 0.016+ 0.017+ 0.018+ (0.006) (0.010) (0.009) (0.009) (0.009)

Store-Department FE Yes Yes Yes Yes Yes R2 0.91 0.91 0.91 0.91 0.91 N 5,472 5,472 5,472 5,472 5,472 Note: Results refer to 36-week window—18 weeks before and after WDSR implementation. +p<.10, *p<.05, **p<.01, ***p<.001. Robust Standard Errors clustered at the store-department level in parentheses.

54

Table A5. Ordinary least squares regression models with dept-store (N=152) fixed effects and gender-segregated department dummy predicting effect of WDSR implementation on year-over-year change in department sales and effects of the interaction of WDSR implementation with (a) department manager gender on change in department sales and (b) average tie strength on change in department sales.

Variable (1) (2) (3) (4) (5) Female DM X Post-Implementation

0.059** 0.063*** 0.143** (0.019) (0.019) (0.046)

Average tie strength X Post-Implementation

-0.034** -0.014 (0.010) (0.011)

Average tie strength X Female DM X Post-Implementation

-0.045* (0.020)

Post-Implementation -0.007 -0.032*** 0.025 -0.010 (0.009) (0.008) (0.018) (0.019)

Total subordinates (∆) 0.039 0.040 0.038 0.038 0.036 (0.030) (0.031) (0.030) (0.029) (0.029)

% full-time subordinates (∆) 0.021 0.021 0.022 0.021 0.021 (0.021) (0.021) (0.020) (0.019) (0.019)

Age of subordinates (∆) -0.003 0.001 -0.005 0.017 0.025 (0.035) (0.036) (0.039) (0.034) (0.030)

Tenure of subordinates (∆) -0.006 -0.009+ -0.010* -0.012* -0.012** (0.005) (0.005) (0.005) (0.005) (0.005)

Grade of subordinates (∆) -0.164+ -0.165+ -0.159+ -0.171* -0.193* (0.088) (0.087) (0.086) (0.086) (0.088)

% female subordinates (∆) 0.016 0.015 0.015 0.015 0.015 (0.014) (0.014) (0.014) (0.014) (0.014)

% nonwhite subordinates (∆) 0.002 0.003 0.008 0.006 0.006 (0.009) (0.009) (0.009) (0.009) (0.008)

Age of peers and supervisors (∆) 0.148 0.141 0.108 0.130 0.120 (0.137) (0.137) (0.131) (0.135) (0.133)

Tenure of peers and supervisors (∆)

-0.079+ -0.079+ -0.086+ -0.099* -0.104* (0.047) (0.047) (0.046) (0.048) (0.048)

Grade of peers and supervisors (∆)

-0.226 -0.225 -0.203 -0.171 -0.162 (0.244) (0.243) (0.248) (0.246) (0.252)

% female peers and supervisors (∆)

0.013 0.013 0.014 0.018 0.021 (0.015) (0.014) (0.015) (0.015) (0.016)

% nonwhite peers and supervisors (∆)

0.008 0.010 0.008 0.008 0.009 (0.022) (0.021) (0.021) (0.021) (0.021)

Constant 0.070*** 0.076*** 0.079*** 0.084*** 0.085*** (0.006) (0.008) (0.008) (0.008) (0.007)

Store-Department FE Yes Yes Yes Yes Yes R2 0.76 0.76 0.76 0.76 0.76 N 3,648 3,648 3,648 3,648 3,648 Note: Results refer to 24-week window—12 weeks before and after WDSR implementation. +p<.10, *p<.05, **p<.01, ***p<.001. Robust Standard Errors clustered at the store-department level in parentheses.

55

Table A6. Ordinary least squares regression models with dept.-store (N=152) fixed effects predicting effect of interaction between mean of department- & store-level controls and report implementation on change in dept. sales.

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Female DM X Post-Implementation

0.095*** 0.097*** 0.096*** 0.095*** 0.095*** 0.064* 0.096*** 0.093*** 0.097*** 0.096*** 0.095** 0.095***

(0.027) (0.027) (0.027) (0.027) (0.027) (0.030) (0.028) (0.027) (0.028) (0.028) (0.028) (0.027)

Post-implementation X total subordinates

-0.002

(0.001)

Post-implementation X % full-time subordinates

0.020

(0.028)

Post-implementation X Age of subordinates

-0.000

(0.001)

Post-Implementation X Tenure of subordinates

-0.055+

(0.032)

Post-implementation X Grade of subordinates

0.025

(0.018)

Post-Implementation X % female subordinates

0.057**

(0.019)

Post-Implementation X % nonwhite subordinates

-0.016

(0.029)

Post-Implementation X Age of peers, supervisors

-0.001

(0.001)

Post-Implementation X Tenure of peers, supervisors

0.000

(0.001)

Post-Implementation X Grade of peers, supervisors

0.002

(0.008)

Post-Implementation X % female peers, supervisors

-0.021

(0.044)

Post-Implementation X % nonwhite peers, supervisors

-0.056 (0.042)

Average tie strength X Post-Implementation

0.001 0.002 0.002 0.002 0.001 -0.000 0.001 0.002 0.000 0.001 0.002 -0.000

(0.008) (0.008) (0.008) (0.008) (0.008) (0.007) (0.008) (0.008) (0.008) (0.008) (0.008) (0.007)

Average tie strength X Female DM X Post-Implementation

-0.026* -0.026* -0.026* -0.026* -0.025+ -0.023+ -0.026* -0.025+ -0.026* -0.026* -0.027* -0.026*

(0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013)

Post-Implementation 0.010 -0.014 -0.002 0.028 -0.119 -0.019 -0.002 0.041 -0.009 -0.028 0.000 0.002 (0.018) (0.017) (0.028) (0.025) (0.079) (0.013) (0.017) (0.039) (0.017) (0.073) (0.018) (0.014)

Constant 0.022*** 0.023*** 0.022*** 0.025*** 0.023*** 0.023*** 0.022*** 0.022*** 0.022*** 0.023*** 0.022*** 0.022*** (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006)

Store-Dept. FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes R2 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 N 3,648 3,648 3,648 3,648 3,648 3,648 3,648 3,648 3,648 3,648 3,648 3,648 Note: Results refer to 24-week window—18 weeks before and after WDSR implementation. +p<.10, *p<.05, **p<.01, ***p<.001. Robust Standard Errors clustered at the store-department level in parentheses. Controls included in model but not depicted here are: year-over-year Change in store sales, Total subordinates (∆), % full-time subordinates (∆), Age of subordinates (∆), Tenure of subordinates (∆), Grade of subordinates (∆), % female subordinates (∆), % nonwhite subordinates (∆), Age of peers and supervisors (∆), Tenure of peers and supervisors (∆), Grade of peers and supervisors (∆), % female peers and supervisors (∆), % nonwhite peers and supervisors (∆)