All employees benefit: arguments that help increase support for affirmative action in academic careers

Women remain underrepresented in higher academic ranks (e.g., Grade B and Grade A positions) despite near parity at the doctoral level. The study is grounded in role congruity theory, masculine defaults, and hegemonic masculinity, which jointly suggest systemic barriers and biases disadvantaging women in academic leadership. Affirmative action programs aim to counteract such discrimination, yet often face backlash, particularly from non-beneficiaries (men), frequently due to zero-sum beliefs and perceptions of unfairness. Building on the model of attitudes toward affirmative action programs, this study examines whether framing justifications for women-only career development programs (gain-focused vs. cost/zero-sum focused vs. neutral) influences perceived fairness and importance. The research tests four hypotheses: H1, programs for all employees will be rated more favorably than women-only programs; H2, women will be more supportive than men; H3, gain-message framing will increase favorable perceptions relative to neutral; H4, loss-message framing will decrease men’s support but increase women’s support relative to neutral.

Prior work documents persistent gender inequities in academia, including biased leadership stereotypes, masculine cultural defaults, reduced recognition for women’s contributions (e.g., authorship credit gaps, publication prestige disparities), and funding inequities. These systemic barriers harm women’s advancement, self-efficacy, and sense of belonging. Affirmative action in academia often includes career development programs (mentoring, courses, coaching, networking, grants, and prizes) targeted at women. However, affirmative action frequently faces backlash from non-target groups due to beliefs that discrimination has ended, fairness concerns, and zero-sum threat perceptions. The model of attitudes toward affirmative action suggests that information framing (highlighting gains vs. costs) can shape support. Earlier studies mostly examined quota policies; evidence shows framing can alter attitudes (e.g., positive framing increases support). Justifications emphasizing benefits to disadvantaged groups (compensation) or to all (instrumental) can differentially affect perceived benefits and fairness. There is limited work on women-only career development programs, motivating the present focus.

Design: Online experimental study with three between-subjects text conditions (gain-message, loss-message, neutral) and a within-subject manipulation of intervention target (programs offered only to women vs. to all employees). Participants were randomly assigned to one of the three text conditions. Participants and recruitment: 695 individuals accessed the survey (April–May 2023). After exclusions for attention checks (n=130), inattentive reading admissions (n=31), and small gender minority subsample (3 trans women, 2 non-binary, 1 intersex, 3 other), and missing sociodemographics in covariate analyses (n=15), the final analytic sample was N=510 (52.5% cisgender women; 47.5% cisgender men; mean age 29.5, SD 9.5). Recruitment was primarily via Prolific (n=452; compensation GBP 1.5; mean completion 11 minutes, SD 4.6), with additional participants via Facebook/Instagram (n=58; no compensation). Inclusion targeted residents of Austria, Germany, or Switzerland. Ethics: The institutional ethics committee confirmed no formal approval required under Austrian law; informed consent obtained; study conformed to the Declaration of Helsinki and APA standards. Materials and manipulations: Participants read one of three texts: (1) gain-message condition emphasized that diverse teams and interventions to increase diversity benefit all employees and argued that different groups need tailored interventions; (2) loss-message condition described existing discrimination against women and stated that implementing women-focused interventions would replace existing programs that mostly benefit men, evoking a zero-sum perspective; (3) neutral condition described linear academic career progression with no justification for women-only programs. Reading attention was self-reported and used to exclude inattentive readers. Stimuli for evaluation: Eight real-world advertisements/calls for women-only academic career development interventions (courses/workshops, prizes, or funding) from Austrian universities or the Austrian Science Fund (FWF) were selected and anonymized. For each, a modified version was created targeting all academic employees. Each participant rated four women-only (original) and four all-employee (modified) ads in counterbalanced order. Measures: After each ad, participants rated fairness (1=not at all fair to 7=very fair; midpoint 4 neutral) and importance (1=not at all important to 7=very important; midpoint 4). Mean fairness and importance scores were computed separately for women-only ads and all-employee ads. Internal consistencies were ≥ 0.66. Sociodemographics included age, gender identity, sexual orientation, relationship status, education, employment, and nationality. Randomization and counterbalancing: Participants were randomly assigned to text conditions and to one of two ad-order conditions (even vs. odd items as women-only vs. all-employee). Statistical analysis: Mixed-design ANCOVAs (SPSS v29) tested effects of Text condition (neutral [reference], gain, loss) and Gender (between-subjects) and Advertisement Target (within-subject: all employees vs. women-only) on fairness and importance, with covariates: age, relationship status, sexual orientation, nationality, education, and employment. Assumptions: variables approximately normal (skew −0.6 to 1.8; kurtosis −0.6 to 3.7). Significance threshold p ≤ 0.05. Power analysis (G*Power v3.1.9.7) assuming small effect (f=0.1) indicated N≈486 required; achieved N=510.

• Sample: N=510; 52.5% cisgender women, 47.5% cisgender men; mean age 29.5 (SD 9.5).
• Overall ratings: Interventions for all employees were rated more favorably than women-only interventions.
  • Fairness (means across conditions): all-employee M≈5.6; women-only M≈4.5.
  • Importance (means across conditions): all-employee M≈5.2; women-only M≈5.0.
• Gender differences: Women gave higher ratings than men across both targets; the gender gap was larger for women-only interventions.
  • Women: fairness for women-only M≈4.8; importance M≈5.4.
  • Men: fairness for women-only M≈4.1; importance M≈4.5.
• Text condition effects:
  • No significant effects of text condition on ratings of interventions offered to all employees.
  • For men, both gain- and loss-message increased fairness ratings of women-only interventions versus neutral; gain-message also increased importance ratings versus neutral. Women’s ratings were not significantly influenced by text condition.
• Representative descriptive by condition (women-only interventions):
  • Men fairness: neutral ≈3.8; loss ≈4.4; gain ≈4.3.
  • Men importance: neutral ≈4.4; loss ≈4.5; gain ≈4.7.
• Inferential statistics (mixed ANCOVAs; df as reported):
  • Main effect of Advertisement Target (affirmative action yes vs. no): Fairness F≈27.4, η²=0.05, p<0.001; Importance F≈5.7, η²=0.01, p<0.05.
  • Advertisement Target × Text: Fairness F≈4.6, η²=0.02, p<0.05 (driven by higher men’s fairness for women-only in gain/loss vs. neutral); Importance not significant.
  • Advertisement Target × Gender: Fairness F≈6.8, η²=0.01, p<0.01; Importance F≈9.6, η²=0.02, p<0.01 (men weighted all-employee > women-only more than women did).
  • Main effect of Gender: Fairness F≈37.3, η²=0.07, p<0.001; Importance F≈84.9, η²=0.15, p<0.001.
• Hypotheses: H1 and H2 supported; H3 supported for men (gain-message), not for women; H4 partially supported—contrary to expectation, loss-message increased men’s fairness ratings relative to neutral; women were not significantly affected.

Findings confirm that career development programs open to all employees are perceived as fairer and more important than women-only programs, indicating potential backlash to targeted affirmative action. Women consistently expressed more favorable attitudes than men, particularly toward women-only initiatives, aligning with prior literature. Critically, men’s perceptions shifted with messaging: emphasizing broad benefits of diversity (gain-message) increased both fairness and importance ratings for women-only programs; providing information about existing discrimination against women and describing replacement of male-benefiting programs (loss-message) increased men’s fairness ratings relative to neutral. This suggests that informing about discrimination can counteract zero-sum perceptions, and framing benefits to all can align with self-interest to boost support. Women’s ratings were unaffected by message framing; although they rated importance of women-only programs on par with all-employee programs, they still judged fairness lower for women-only offerings, potentially reflecting system justification, zero-sum concerns, or anticipation of stigma/retaliation. The results highlight the significance of communication strategies when implementing affirmative action: providing evidence of ongoing discrimination and clearly articulating organization-wide benefits can improve support among men, who often hold decision-making roles. Effects were small, indicating room for stronger or multiple justifications and supportive context cues.

Providing clear justifications—either by evidencing discrimination against women in academia or by highlighting team-wide benefits of diversity—can increase men’s support for women-only career development programs, while women’s perceptions were not significantly altered by message framing. Overall, women-only programs were rated less fair than all-employee programs. Future research should test richer benefit-framing strategies to enhance effect sizes, examine whether attitudinal shifts translate into more supportive organizational climates and behaviors, and assess whether supportive climates reduce retaliation and increase women’s engagement with targeted programs.

No pre-test of message comprehension; the loss-message combined both zero-sum and discrimination information, potentially yielding opposing effects. Stimuli varied by intervention type (courses/workshops, prizes, funding), which may have reduced internal consistency and introduced heterogeneity in responses. The design implied gender binarism and excluded gender minority participants from analysis due to small numbers, limiting inclusivity and generalizability. Reliance on self-report introduces potential biases (e.g., social desirability, inattentive responding despite controls). Observed effects were small; message content may require refinement to strengthen impact.