AN0191107270;[mmhi]01jan.26;2026Jan28.04:06;v2.2.500
Analysis of Work Flexibility Policies Among AAU Members
Workplace flexibility has become increasingly important in industries across the world. The COVID-19 quarantines expedited the adoption of remote work among other flexible workplace programs, but their uniformity, efficacy, and sustainability are still being scrutinized. The purpose of this study is to review work flexibility policies in American higher education and attempt to identify coherent themes that may be beneficial to creating a shared sense of meaning and pertinent variables in data-driven decision-making. We employed a qualitative content analysis to better understand the approaches to work flexibility across higher education institutions (HEIs) within the Association of American Universities (AAU) as of 2022. Using the literature, we clarify and suggest definitions of various flexible work programs based on data revealed during the content analysis and provide findings regarding the influence of relationships between multiple institutional and community characteristics that may be related to the adoption of these programs.
Keywords: higher education; work flexibility; flexible work arrangements; FWAs; AAU
Employee retention in higher education is critical to the health and success of institutions because it provides stability for students, consistency of policy implementation, and reduces institutional cost ([7]; [26]). Given that 60–70% of all institutional expenses are directed towards personnel ([20]) and that the cost to replace an employee after they leave is between 16% and 20% of the employee's salary for midlevel personnel and 213% of the annual salary of executive leaders ([7]), research is needed that focuses on cost-effective ways to retain talent. This matter also has a level of urgency in the field of higher education as it has experienced increasingly high staff turnover (14.3% 2022–2023, 12% 2021–2022) ([4]). Surveys are showing that employees are placing greater importance on flexibility in the workplace ([2]; [3]; [4]; [12]); however research on the topic of work flexibility has yielded inconsistent results ([33]; [36]) and, in terms of higher education, has been incredibly sparse. Two significant problems in understanding the impact of work flexibility are the heterogenous definitions of the term work flexibility being used across organizations and uneven application of practices across units and departments ([5]; [33]; [36]).
In this study, we focused on using the literature available to develop more homogenous definitions of work flexibility based on evidence and common practice in hopes of establishing a more uniform vocabulary throughout the industry and adding value to continued scholarly research on the matter. Using the United States (US) [10] terminology, we define work flexibility as an employee's ability to control when, where, and how much they work. Programs such as remote and hybrid work, which reflect the where aspect of employee work, have become more normalized since the nationwide quarantine during the COVID-19 pandemic ([2]; [29]). However, other practices such as flextime and compressed workweeks (when employees work) as well as job sharing, temporarily reduced hours, and phased retirement (how much employees work) are also seeing an increase in various workplaces ([10]; [35]; [37]). This study focused on understanding work flexibility within the context of administrative and managerial professionals (i.e., non-teaching staff) at HEIs. We employed a qualitative content analysis of organizational policies and guidelines surrounding work flexibility to better understand the various approaches across institutions within the 63 members of the AAU within the United States as of 2022, in an effort to either discover or create consistency in operational planning and policy throughout the field. Additionally, we explored internal demographic and institutional data from the Integrated Postsecondary Education Data System (IPEDS) as well as external community data from the US Census Bureau to analyze potential patterns that may be indicative of variables considered during the decision-making process of whether, and which, flexible workplace policies to adopt. To create a more comprehensive understanding of the evolution of working conditions in America, the literature review begins with a brief historical review of labor relations in the US before moving into a more contemporary discussion of the subject. Specifically, this study was guided by the following research questions:
RQ1: What forms of workplace flexibility are codified in institutional policy? RQ2: How is the adoption of workplace flexibility associated with institutional or community characteristics?Our findings indicate that there is a left skew to the distribution of work flexibility programs offered within the field of higher education with varying levels of formality, centralization, and accessibility. In relation to question two, we found few statistically significant variables related to either the institution or the local community that likely influenced the adoption or rejection of work flexibility programs. While we do want to clarify that this study is limited to reviewing the policy outcomes of HEIs and does not explore what factors influenced each institution's decision-making process, the lack of statistically significant findings did raise further questions about how policies were being decided and what types of data points HEIs are considering.
Literature Review
We frame this study in the literature based on work flexibility from two angles. First, we begin with a focus on the historical context of workers' rights and the evolution of the American workplace. Second, we discuss the growth of worker flexibility in more modern terms. We conclude with a review of the scant existing literature focused on workplace flexibility in higher education.
History of Workplace Flexibility
The history of work flexibility in the United States (US) is a story of continued, incremental evolution through concerted effort. Employee standards for workplace norms have frequently been the result of demanding an improved quality of life through unionization and market changes resulting from crises such as the Great Depression or COVID-19 ([11]; [14]; [24]; [27]; [29]; [34]; [36]). Prior to industrialization and the Market Revolution of the late eighteenth century, the United States was predominantly agrarian, which meant a considerably different lifestyle than a typical employee in the modern era ([11]; [14]; [24]; [27]). Once industrialization began, the US saw drastic increases in transportation, urbanization, manufacturing, and technology, and by 1850 almost half of all Americans had transitioned into non-agriculture-related work (i.e., manufacturing, mining, banking, etc.) ([27]). However, the legal rights and protection of workers was not an inherent part of the transition to a new economy of wage-based employees and dangerous working conditions, long hours, exploitation, and employee abuses were well documented occurrences ([11]; [27]). The first trade unions were established in the 1830s with bargaining goals such as a regulated 10-hour workday, safe work environments, and legal recourse for employees who were hurt or died on the job ([27]). The majority of employee rights considered commonplace today including minimum wage, overtime pay, unemployment insurance, and workplace safety regulations were implemented in the 1930s in an effort to decrease layoffs during the Great Depression ([24]; Morris, 1967). Similarly, despite increasing technological improvements over time, experimentation with remote and hybrid work programs were largely motivated by the gas crisis of the 1980s that restricted the mobility of many Americans ([34]).
Between 1985 and 1997, Americans with access to a flexible work schedule grew from 12.4% to 27.6%; however that increase was not evenly distributed across all workplaces or demographics ([14]). [14] work at the time revealed that in order to receive flexible work arrangements, workers typically had to either be self-employed, work part-time, work nightshift, have a college degree, or already be regularly working over 50 hours per week. These factors meant that those receiving flexible work arrangements often did so in exchange for regularly being overworked (e.g., working 50 or more hours per week) or by working in roles with increased volatility in scheduling and/or compensation (e.g., working part-time or becoming self-employed). [14] work also shows how US work flexibility has continued to evolve, as at the time of the study, work flexibility was more narrowly defined as control over the start and end times of their workday (or what we would define as flextime).
By 2010, that definition would be expanded by the US Council of Economic Advisors to include programs like remote and hybrid work as well ([9]). The [9] also reported findings that, while 79% of employers reported offering some level of flexible work schedule, only 28% of employees reported having access to said flexibility. The report discusses how a majority of these flexible work options were either only available to part-time workers or at special request (ergo, employees may not be aware of the policy). Remote and hybrid work was significantly less common with only 15% of workers reported being able to work from home at least once a week.
More Recent Understandings of Work Flexibility
As was witnessed during the COVID-19 quarantines of 2020–2021, the greatest shift towards increased work flexibility was the result of a global pandemic that created external pressure for governments and businesses to adapt more innovation technologies and flexible practices or face critical structure failure ([2]; [29]; [41]). [2] study on work-from-home practices found that COVID-19 accelerated the adoption of remote and hybrid work programs "equivalent to almost 40 years of pre-pandemic growth" (para. 6) with the largest increases being seen in the business services, finance, and technology industries. The [13], which collects data on 6,500 companies employing more than 100 million workers, conducted surveys between October 2022 and July 2023 to analyze trends in location-based work flexibility across industries. Their findings revealed that fully on-site work was generally declining across US companies (from 49% to 39% in Q3), and while tech companies typically lead the way in work flexibility, with 97% offering some form of FWAs, the data revealed that 85% of non-tech companies also offer some form of FWA. Of their sample, education was ranked in the top five least flexible industries, alongside restaurants and food service, hospitality, retail and apparel, and manufacturing and logistics. However, the data did not delineate between primary, secondary, or postsecondary education. Additionally, while these insights are valuable, they are limited to location-based FWAs (i.e., remote and hybrid), suggesting more research needs to be done to understand the other types of FWAs being utilized across industries.
[12] study with McKinsey and Company using the American Opportunity Survey explored the feelings of 25,062 adults aged 18 and older in the United States between March and April (2022) about work flexibility. By and large, findings indicated that more Americans were adopting flexible work arrangements (FWAs) in both white- and blue-collar fields, and work flexibility was becoming an increasingly significant priority for workers. When asked the rationale for job hunting, a desire for more FWAs appeared as the number three motivation behind higher pay and better career opportunities. Survey findings also revealed that 58% of participants reported being able to work from home at least one day a week, and 35% were able to work from home five days a week. When offered flexible work arrangements, 87% of participants stated they would take them. Of the 13% of participants who stated they would decline FWAs, this segment predominantly consisted of older workers (55–64 years old) with lower incomes ($25,000–$74,999). As of May 2023, [2] also found that employees between 30–39 were the most likely to work hybrid, and employees between 50–64 were least likely to work hybrid, working more fully on-site or fully remote than any other demographic surveyed.
Despite the apparent desirability of FWAs, scholars have had somewhat conflicting results on the impacts and efficacy. [36] systematic literature review on articles related to flexible work practices between 2011–2021 (n = 113) found inconsistent results. To start, attempting to define flexible work proved challenging as articles used contradicting verbiage or used different terms interchangeably (e.g., telework and remote). Overall, their literature review found articles stating that FWAs are both good for work-life balance and may also lead to work-life conflict. These findings, as [36] discussed, are likely the result of unclear definitions and applications of flexible work policies. Especially considering the exacerbating effects of the COVID-19 pandemic in forcing many unprepared institutions to adopt policies without the necessary resources or time to prepare, studies done during this time period need to be considered within their context. [36] also highlighted that some studies on the work flexibility focused on the negative impacts of technologies associated with FWAs but failed to discuss the use of technological acceptance models, the process of digital transformation, or organizational change. [33] study of work-flexibility and work-related well-being utilizing the 2002–2018 General Social Survey—Quality of Worklife also found the seemingly contradictory findings that increased working from home increased employees' stress (22%) and job satisfaction (65%). Yet they also found strong associations between an employee's ability to change their schedule and take time off correlated with decreased likelihood of job stress and increased personal health and job satisfaction. [5] study on formal and informal FWAs in the workplace also illustrated an important challenge for researchers as some employees may be receiving the benefits of FWAs through informal or undocumented work agreements, making the equity and efficacy of these programs more difficult to track.
Work Flexibility in Higher Education
[41] described the changes that occurred as a result of the COVID-19 quarantines as revolutionary in regard to their impacts on the higher education landscape. The sudden demand to shift all teaching and learning online impacted every possible stakeholder, and while students and faculty have largely been the focus of scholarly research, the work modalities and role boundaries of staff and administrators has also been fundamentally altered ([41]). While research is still emergent on the impacts and efficacy of online versus face-to-face learning, [41] describe how pre-pandemic assumptions and antiquated practices need to be challenged to create HEIs that are more agile and resilient in the face of change.
As with the country at large, HEIs definition and application of FWAs has been extremely heterogeneous. This presents problems outside of research, as inconsistent understanding and application may lead to employee dissatisfaction (Smyth et al., 2022). In Smyth et al.'s (2022) qualitative study with higher education staff members (n = 60) they found that the predominantly decentralized processes for FWAs resulted in questions of bias, fairness, equity, and favoritism. Participants in the study reported that they believed it unfair that faculty and senior staff were not subject to the same authorization processes and scrutiny when requesting FWAs. Additionally, because these arrangements are often at the discretion of direct supervisors, many reported feeling that luck was the biggest deciding factor in whether your requests were approved. Other factors that participants reported as impacting their access to FWAs included concerns for job security (especially from junior staff or women in predominantly male fields), whether your supervisor considered a request valid (e.g., need for dependent care versus desire for personal space), and levels of authorization required, which participants explained could require multiple stressful meetings that may potentially result in increased micromanagement.
In [19] publication Managing for Competency with Innovation Change in Higher Education, they point out that if higher education plans to remain current and culturally relevant, as a field, they will need to address their historic disinclination to embrace change. While the piece specifically discusses organizational agility within the context of digital transformation, [19] provides a compelling case that higher education is a business, and their previous monopoly on knowledge gatekeeping has fundamentally collapsed with the advent of the internet. When programs are adopted, they are typically centered around students' learning experience, more so than on holistic organizational development ([19]). [21] outlined a common assumption of HEIs that they are adequately innovative as long as their e-learning platforms are tenable. However, just as the Market Revolution of the eighteenth century moved American workers from agrarian to industrialized labor, fundamentally changing the way people conceptualize work, the digital age may again be deconstructing our paradigms of acceptable work conditions. This leads us into our next section, our theoretical framework, and the diffusion of innovations.
Theoretical Framework
[32] diffusion of innovations theory was selected as the framing for this study given the consistent evolution of workplace flexibility over time. In response to COVID-19, institutional leaders were forced to develop workplace flexibility policies and practices in response to public health needs and increasing employee demands ([25]). There are four main phases of innovation diffusion identified by [32] and five potential factors associated with innovation adoption. While this study will not analyze how and why workplace flexibility occurs, the concepts associated with this theory will be used to analyze the data in this study to provide insight for higher education leaders.
The four phases of innovation include innovation awareness, decision, implementation, and sustainability ([22]). In the first phase of awareness, individuals or organizations begin to see signs of innovation. This phase is often filled with early adopters of innovations which allows for others to assess the need to innovate. During the second phase of innovation, decision, individuals, and organizations typically weigh the benefits and risks of an innovation to make a decision to adopt or forego a given innovation. If individuals or organizations choose to adopt an innovation, they enter the implementation phase. The length of this phase varies widely, but the general idea is that during this time, stakeholders can assess the innovation and make small adjustments as needed. Sustainability of the innovation is the final phase.
Five factors have been found to relate to whether an innovation is adopted or not ([17]; [25]). Relative advantage is the first factor associated with innovation adopted and is defined as the degree to which an innovation is seen as better than the previous practice. In this study, the relative advantage is the adoption of formalized workplace flexibility practices. Compatibility is the second factor, and it refers to the alignment between the values, experiences, and needs of the organization and the innovation. The third factor is complexity of the innovation. Complexity is important because as complexity increases, adoption decreases for most innovations. Triability is the fourth factor and refers to the ability for individuals or organizations to actually experiment with an innovation. The last factor is the observability of the given innovation. This factor highlights the importance of assessment and evaluation of innovation. The final three factors related to how the innovation might be applied and how it will be received. In this study, we do not seek to understand motivation but are primarily concerned with the final three factors and how they may be impacting various HEIs in whether they move toward or away from the sustainability phase. We hope that by highlighting the workplace flexibility policies and variance between institutions, we will be able to inform institutional leaders as they seek to develop policy.
Data and Methods
This study employed a multiple case study design which focused on reviewing multiple data points ([15]). To create a more cohesive understanding of work flexibility policies in higher education, researchers focused on reviewing policies and guidelines in place for the 63 members of the Association of American Universities (AAU) based in the US as of 2022. We selected this group for three reasons. First, these institutions tend to be more resourced than other higher education institutions. The AAU was founded in 1900 and is composed of the leading research HEIs in the country that, as a group, continue to receive the majority of federal funding ([1]). This is important as more resourced institutions are less likely to be inhibited from experimenting with FWAs (the triability factor) due to a lack of resources or similar infrastructure-related barriers. Second, these institutions typically have a larger employee base than other HEIs. In addition, the variance in job functions across teaching, research, and beyond make the opportunities for FWAs more likely at these institutions. Finally, AAU institutions are spread across the United States in states with differing employment laws which we felt created a natural national sample. Given these differences, we felt it possible that their institutional policies may be more advanced and informative than other institutional samples.
We collected workplace flexibility policy and guidelines data between November 2022 and January 2023. During our data collection, six institutions were still in the process of finalizing their work flexibility policies, but we treated their proposed policies as complete for this study. In alignment with the [10] description mentioned earlier, we identified programs that addressed the when, where, and how much of work flexibility and selected the following programs: remote, hybrid, flextime, compressed workweeks, job sharing, temporarily reduced hours, and phased retirement.
Content Analysis
In alignment with the multiple case study approach ([15]), researchers reviewed numerous web pages and institutional documentation to understand each organization's approach to FWAs. Specifically, we used public domain information and search engine tools citing keywords such as the name of the HEI and "work flexibility policy," "work flexibility guidelines," "remote work policy," "remote work guidelines," and searching programs within the institutional documentation individually if they were not included in related work flexibility or remote work policies or guidelines. Throughout the study, we reviewed the institutional policies and guidelines and quickly discovered some confusion around inconsistent verbiage and terminology between institutions (reinforcing prior research).
As part of our findings Table 1 outlines the operational definitions developed to categorize each program. The definitions we created were based on the most widely used characteristics of each program, and the terminology used was also chosen because it was the most commonly applied among subjects. For example, [23] described a remote worker as one who is on-site no more than one full day per month and no more than eight days per year. [28] described a remote worker as one who works off-site 80% or more of the time, has no dedicated workspace, but they may still be expected to maintain an occasional presence on campus. [40] described a remote worker as one who works entirely off-site, and although they may have access to hotel or UCSC on-site resources, they do not have a personal dedicated workspace. By reviewing all 63 institutions, we were able to identify and refine the common characteristics of a remote work (and other programs) per policy and develop a more uniform template.
Table 1: Variables Reviewed for Statistical Significance and Sources
| Source | Variable |
|---|---|
| IPEDS: | |
| Average Instructional Staff Salary Percentage of Pell Eligible Students Total Instructional Staff by FTE Student Total Number of Instructional Staff Total Student Enrollment | |
| HEI Web Page: | |
| # of Fully Online Bachelor's and Master's Programs Presently Available Compressed Workweek Definition FWA Defined as a Privilege?Flextime DefinitionHybrid Work DefinitionJob Sharing DefinitionLevels of Authorization for FWAsPerformance Requirements for FWA approvalPhased Retirement DefinitionPolicy or GuidelineRemote Work DefinitionTemporarily Reduced Hours DefinitionTitling Verbiage | |
| US Census Bureau (by county): | |
| Average Commute TimeAverage Manager SalaryAverage Office/Administrator SalaryEducational Attainment of Bachelor's or HigherMedian AgeMedian Gross RentMedian Household IncomeMinimum WagePopulationPoverty Rate | |
This did lead to the re-categorizing of certain terms that did not meet the normative definition. For example, we chose to describe employees who work predominantly off-site as remote instead of teleworkers or telecommuting because, of the 63 institutions reviewed, 60 had policies that allowed workers to work entirely or predominantly off-site and of that group only 11 (18%) deviated from the term "remote." Out of all 63 subjects, the term telecommuting or telework was used by 20 different institutions: 11 institutions used the term to describe remote programs, six used the terms interchangeably with the word remote (i.e., telework/remote), and three used it to describe what we would characterize as a hybrid policy. [39], for example, titles their program as telecommuting (which could easily be misinterpreted as a remote program); however, it only allows off-site work for a maximum five days per pay period, and full-time off-site work is strictly prohibited. Under a more normative understanding of program characteristics, this would more accurately be titled as a hybrid program (and for the purpose of our study was categorized as such).
Once information was collected related to participant FWAs, researchers utilized [18] data to review and analyze whether there were statistically significant relationships between work flexibility program adoption patterns and institutional characteristics and US Census Bureau data to see if there were any potential patterns of adoption related to community characteristics (e.g., commute times, minimum wage). See Table 1 for details regarding variables analyzed and their sources. To understand external variables potentially related to the acceptance or rejection of FWAs, researchers also explored demographic data related to the county of the main campus including minimum wage, population, median household income, median gross rent, average commute time, poverty rates, educational attainment of a bachelor's or higher, and median age as provided by the [38].
Findings
In this section, we will first focus on the forms of workplace flexibility evident through our coding process and the existent literature. Second, we will discuss how the adoption of workplace flexibility is associated with various organizational characteristics. Throughout our discussion of the findings, we will provide examples of the content found within institutional policy.
Forms of Workplace Flexibility
We utilized existing literature to define the various FWAs in a more uniform way, which allowed us to create a more standardized vocabulary and increase clarity for future scholars and HEI leadership alike. To provide an example of the vocabulary related heterogeneity across HEIs: Brandeis University titles their FWAs as Telecommuting and Alternative Work Scheduling ([8]); Northwestern University titles their FWAs as Workplace Strategies ([30]); Yale University titles their FWAs as Work Models, which includes separate FlexPlace and FlexTime options ([42]); and Harvard University titles their FWAs FlexWork and includes both time and place flexibility options under one policy ([16]). While this may seem like a benign problem, this type of subtle variation may be a contributing factor to misunderstandings in both research and recruitment as potential scholars and employees have the barrier of inconsistent language to overcome when exploring and comparing FWAs across organizations. As an example of our amalgamations based on existing literature, we chose to use the term flexible work arrangements because 36% of AAU members in the study titled their flexible work policies or guidelines as such (the greatest majority ahead of telecommuting at 11% and alternative work arrangement at 6%). Table 2 presents the forms of flexible work our study explored and our definitions based on the literature.
Table 2: Forms of Flexible Work Arrangement and Definitions
| Program Name | Definition |
|---|---|
| Compressed Workweek | Working a full-time allotment of hours in four days or less.Common example: four, 10-hour workdays or 4/10. |
| Flextime | Adjusting starting, break, or end times during the week. This arrangement does not reduce overall hours worked nor the number of days worked.Common example: working from 7:00 a.m.–3:00 p.m. instead of 9:00 a.m.–5:00 p.m. |
| Hybrid | Participants work out of office (or have the opportunity to work out of the office) part-time. This arrangement typically requires workers to be on-site, or available to be on-site, at least part-time.Common example: working from home two days out of a five-day work week. |
| Job Sharing | An agreement where two part-time workers share the responsibilities of one full-time job.Common example: one person works the position from Monday–Wednesday and another person works the same position from Thursday–Friday. |
| Phased Retirement | Allows workers approaching retirement to begin reducing their hours to part-time work and make necessary workload adjustments as they begin transitioning into retirement.Common example: one to two years prior to retirement, participants would move to a part-time schedule, begin training their replacement, and focus on finalizing and delegating projects. |
| Reduced Hours | Allows workers to voluntarily reduce their schedule to less than full time for a certain amount of time.Common example: when business is slow, a worker may elect to reduce their hours and would then return to full-time hours once business picks up again. |
| Remote | Participants work out of the office full-time. If on-site attendance is required at all, it is rare (quarterly or less) and would require advance travel coordination and planning as remote workers do not have on-site workstations.Common example: working full-time from home. |
The diversity of variation within work flexibility programs was found to be a left skew of distribution with 2 institutions offering 1 or fewer programs, 7 offering 2–3 programs, 27 offering 4–5 programs, and 27 offering 6 or more programs. More restrictive HEIs (those offering 3 or fewer programs) included the following: Boston University, Stony Brook University, University of Buffalo, University of California Santa Cruz, University of Wisconsin Madison, New York University, and University of Utah. To illustrate how a more restrictive institutional policy is written, [6] guidelines for remote staff stated that "eligible staff members may request to work remotely up to two days per week" (para. 4); that "staff may not be the primary care provider for any dependent during remote work hours" (para. 10); and
as a general rule, schools, colleges, and departments will not provide staff who work remotely at their own request with any additional computers or accessories (e.g., mouse, keyboard, external monitors) for on campus offices or remote work locations. The University will also not provide other home office equipment (e.g., furniture) to staff who work remotely at their own request. (para. 13)
In contrast, the least restrictive HEIs (within the 17% offering ≥ 7 programs) included University of Texas, Austin; University of Michigan; University of Minnesota Twin Cities; University of Iowa; University of Colorado Boulder; Michigan State University; Harvard University; Emory University; Dartmouth University; Cornell University; and Case Western Reserve University. For an example of how these differences in ideology manifest in policy language, [16] policy states "no staff shall be excluded from proposing flexwork regarding the times and places where their essential duties are performed, that all staff shall have access to an equitable process by which flexwork proposals are considered, and that proposals shall not be unreasonably denied" (para. 3), and that "successful flexwork requires individuals and groups to address such basics as technology and equipment, information security, ergonomic considerations, employee wellbeing, non-Harvard work—including dependent-care" (para. 8).
Institutional Characteristics and Adoption of Workplace Flexibility Options
In this study we also analyzed institutional characteristics that may be associated with adoption of FWAs. Table 3 focuses on staff and student size in relation to the adoption of said FWAs. We found that many forms of flexibility are adopted at similar rates, and while phased retirement and reduced work hours are found more at large institutions whereas job sharing is more likely to be found at smaller institutions, the correlation was not statistically significant.
Table 3: FWAs in Comparison to Staff Size and Student Enrollment
| Staff Size | Student Enrollment | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Total | <6,000 | 6001 – 12,000 | 12,001 –18,000 | >18,000 | <14,500 | 14,501–25,500 | 25,501–40,000 | >40,000 | |
| Remote Work | 95% | 86% | 95% | 100% | 100% | 100% | 94% | 85% | 100% |
| Hybrid Work | 100% | 100% | 100% | 100% | 100% | 100% | 100% | 100% | 100% |
| Flex Time | 86% | 80% | 85% | 90% | 87% | 93% | 88% | 69% | 89% |
| Compressed Work | 86% | 80% | 85% | 90% | 87% | 93% | 88% | 69% | 89% |
| Phased Retirement | 57% | 47% | 55% | 65% | 63% | 67% | 41% | 61% | 61% |
| Job Sharing | 44% | 47% | 55% | 35% | 37% | 67% | 47% | 23% | 39% |
| Reduced Hours | 44% | 40% | 35% | 50% | 63% | 40% | 47% | 46% | 44% |
| Avg. People to Approve | |||||||||
| Flexible Work | 2.5 | 2.87 | 2.10 | 2.60 | 2.62 | 2.87 | 2.29 | 2.53 | 2.38 |
| Standard Deviation | 1.2 | 1.55 | 0.91 | 1.18 | 1.06 | 1.35 | 1.1 | 1.27 | 1.14 |
| N | 63 | 15 | 20 | 20 | 8 | 15 | 17 | 13 | 18 |
Note. Percentage of institutions allowing select flexible work arrangements by number of staff and student enrollment.
Table 4 focuses on whether or not the institution considered FWAs a privilege or a business decision as dictated by the needs of the role. Privilege, or the accessibility of an FWA, was determined by reviewing the process(es) outlined for FWA acquisition including variables such as how many levels of authorization were required for FWA approval (more levels of authorization required indicated a privileged status, see Table 2 for average number of levels for authorization), whether individual performance was a deciding factor in allowing FWAs, whether the policy explicitly described FWAs as a "privilege," and whether or not FWAs were granted due to dependent care needs. Policies that explicitly stated FWAs could not be used to address dependent care needs were classified as a privilege. Policies that either stated that FWAs could be used to address these needs, or policies where dependent care was not mentioned, were classified as business decisions. Based on these parameters we identified that 31% of AAU members consider FWAs a privilege. We note that results vary in this table. Generally, we found that smaller institutions tend to look at FWAs as a privilege while larger institutions perceive it as business decision, although again, results were not statistically significant likely due to the low levels of variance. We also provide a proportion of institutions that provide training and manuals, as this also signifies the level of support provided for institutional adoption.
Table 4: FWAs as a Privilege or Business Decision via Staff Size and Student Enrollment
| Staff Size | Student Enrollment | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Total | <6,000 | 6001–12,000 | 12,001–18,000 | >18,000 | <14,500 | 14,501–25,500 | 25,501–40,000 | >40,000 | |
| Performance Requirements | 24% | 27% | 20% | 25% | 25% | 40% | 12% | 39% | 11% |
| Work Flex is Privilege | 90% | 47% | 30% | 25% | 25% | 53% | 24% | 38% | 17% |
| Work Flex is a Business Decision | 68% | 53% | 70% | 75% | 75% | 47% | 76% | 62% | 83% |
| Have a work Flexibility Policy | 51% | 53% | 55% | 40% | 63% | 67% | 65% | 31% | 39% |
| Remote Training Resources | 90% | 80% | 95% | 95% | 87% | 80% | 88% | 92% | 100% |
| Remote Training Manual | 81% | 67% | 85% | 80% | 100% | 67% | 71% | 100% | 89% |
| N | 63 | 15 | 20 | 20 | 8 | 15 | 17 | 13 | 18 |
Community Characteristics and Adoption of Workplace Flexibility Options
We now provide the results of our logit regressions, which accounted for select institutional characteristics and community characteristics. We conducted multiple regressions with many different variables but settled on three potential community indicators that could be used by institutional leaders when deciding to offer flexible work, and four institutional characteristics. Our results are presented in odds ratios, and we find that there is a positive relationship between the total number of instructional staff and the adoption of workplace flexibility policies, though the relationship is not substantive. We also found that large student enrollment when compared to small institutions has a negative relationship with the adoption of workplace flexibility policies.
Discussion
[32] diffusion of innovation theory states that social systems communicate ideas, and the perceived value of the ideas—as determined by the five factors of relative advantage, compatibility, complexity, triability, and observability—affect the rate of adoption. The literature has shown that, in terms of work flexibility, industries across the United States are slowly seeking to adopt more FWAs and are moving away from the fully on-site model ([13]; [12]; [4]). As [27] noted, the regulated eight-hour workday was not adopted until the 1930s—one hundred years after the first trade unions were formed. Similarly, [2] findings that the work-from-home related policy adjustments forced by COVID-19 quarantines expedited remote and hybrid work adoption by 40 years based on prior patterns of industry. However, because the COVID-19 quarantines rapidly shifted the entire economy through the questions of relative advantage or compatibility, it appears some HEIs are stepping back to reconsider the compatibility of these innovations while many more appear to be navigating into the triability and complexity facets, and a small group of early adopters are moving forward to the final stage of sustainability. However, our findings were not able to identify any specific institutional or community characteristics statistically significant enough to predict which types of institutions are more or less likely to move forward with innovative adoption. It is our hope that some of the data we have compiled in relation to both vocabulary and potentially influential characteristics related to data-driven decision-making will be valuable to both AAUs and other HEIs who may emulate these organizations.
Table 5: Logit Regression Results for the Relationship Between Select Contextual Factors and an Institution's Adoption of Work Flexibility Policies (N = 63)
| Odds Ratio | Sid. Err. | z | P>z | |
|---|---|---|---|---|
| County Characteristics (2021) | ||||
| Population | 1.000 | 0.000 | 1.370 | 0.171 |
| Median Gross Rent | 0.999 | 0.002 | −0.780 | 0.437 |
| Median Household Income | 1.000 | 0.000 | −0.190 | 0.851 |
| Institutional Characteristics (2021) | ||||
| Percent of Students Pell Eligible | 1.013 | 0.056 | 0.240 | 0.808 |
| Average Instructional Staff Salary | 1.000 | 0.000 | 0.520 | 0.606 |
| Total Instructional Staff | 1.000 | 0.000 | 2.490 | 0.013* |
| Total Students (Ref. <14.500) | ||||
| 14,501–25,500 | 0.191 | 0.197 | −1.610 | 0.108 |
| 25,501–40,000 | 0.588 | 0.687 | −0.450 | 0.649 |
| >40,000 | 0.023 | 0.034 | −2.570 | 0.01* |
| Constant | 1.032 | 2.328 | 0.010 | 0.989 |
Note. The
1 * denotes results statistically significant at the P