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Abstract

There has been a stream of research that explores how the present generation of workers (i.e., Millennials) may be different from previous generations (e.g., Baby Boomers and Gen Xers). This line of research often considers Millennials as homogeneous and concludes any differences to be “generational effects.” However, it is unlikely for a generation, which spans almost 20 years, to be uniformly homogeneous with respect to their work values and attitudes. Findings on generational differences conducted in the United States are also often generalized to other countries, ignoring the potential for national influences. In this regard, we apply a multi-method approach using three samples to demonstrate that there are differences within the Millennial generation that affect work values, preferences for work/life balance, and attraction to employer attributes. Specifically, we focus on the heterogeneity resulting from differences in age, gender, relationship status, and nationality. Our results suggest that Millennials are not as homogeneous as we assumed, and this can limit the effectiveness of managerial policies designed to improve individual and work outcomes for an entire generation of workers. Our study demonstrates that it is important for us to understand how individual, relational, and contextual factors may contribute to the heterogeneity within a generation.

JEL CLASSIFICATION M12, M14, M54

Keywords

heterogeneity work values organizational attraction employer attributes

“Research on generational differences, as a popular topic of media attention, is susceptible to exaggeration and reductionism.”

–Lyons and Kuron (2014, p. 153)

Introduction

Generation Y (Gen Y), also variously known in popular media as “Generation Me,” “nGen,” “iGen,” or “Millenials,” includes a cohort that spans almost 20 years, from 1982 to 1999 (Twenge et al., 2010). Some research on generational differences suggests that Millennials differ from previous generations on several work-related dimensions such as personality traits, values, attitudes, and behaviors (Twenge & Campbell, 2008; Twenge et al., 2004; Wells & Twenge, 2005). However, there has been considerable debate on whether true generational differences really exist (Magni & Manzoni, 2020; Rudolph et al., 2021; Rudolph & Zacher, 2020). Some find Millennials to be different from previous generations, notably with Gen Xers (those born between 1965 and 1981) (Sessa et al., 2007; Sirias et al., 2007; Twenge et al., 2008), while others simply were unable to detect any meaningful generational differences (Costanza & Finkelstein, 2015; Cucina et al., 2018; Wong et al., 2008). What has frequently been overlooked is the potential for heterogeneity within a generation. This is an important concern as generalizations about a generation must consider potential differences that may be attributed to dimensions that cut across generations. As Rudolph et al. (2021) caution, generational differences are averages, and there is often substantial variation within each generational cohort. We surmise that some of the generational differences reported between Millennials and other generations may be less pronounced than represented in popular literature and media once additional factors are considered.

Many characteristics ascribed to Millennials have been difficult to establish; they are often the result of personal observations which are susceptible to confirmatory bias. Furthermore, most research on Millennials has been conducted in the United States and the findings are frequently generalized to other countries to describe Millennials and their influence in the workplace, without considering contextual differences (Covri et al., 2007). In our review of the literature on Millennials, it becomes apparent that most characteristics ascribed to this generation either by the media or by popular literature are not strongly supported by empirical findings, leading some scholars (Rudolph et al., 2021; Rudolph & Zacher, 2020) to outright reject them. The present article seeks to demonstrate the heterogeneity within the Millennial generation that arises out of age, gender, relationship status, and national differences, using three independent studies, each focusing on different facets of work values and preferences. We deliberately use the term “Millennial” (a US-centric term) in this paper to demonstrate its inaccurate depiction in existing studies; a more accurate term would be “an age delimited cohort¹” to avoid stereotyping a heterogeneous group with fluid boundaries.

Conceptual background and theory

Mannheim (1928, 1952), who conceived generational research, frames generations as cohorts of individuals who were born in the same historical and social region, which he describes as sharing “the same generational location [. . .and, with it, being] able passively to undergo or actively to use the handicaps and privileges inherent in a generational location” (p. 394). He exemplifies the absence of such a common generational location with the case of Prussian and Chinese youth around the year 1800, suggesting that it is improbably for all members of one generation to share such a generational location. However, for Mannheim, the co-presence in a given time and place would not be sufficient for constituting an actual generation. Drawing on Heidegger’s (1927) concept of generational Geschick [fate] he sees “generations as an actuality only where a concrete bond is created between members of a generation by their being exposed to the social and intellectual symptoms of a dynamic destabilization” (p. 395), “while those [connected] groups within the same actual generation which work up the material of their common experiences in different specific ways, constitute separate generation units” (p. 396). Thus, we argue that from Mannheim’s perspective, Millennials do not meet the test of an actual generation, since Millennials in different geographical regions, on account of sharing a common birth period, do not perforce have to possess the same set of values, preferences, or attitudes. Furthermore, it can be argued that geography aside, the differing bonds connecting the diverse members of the Millennial generation (i.e., the diverse generation units within this generation) might also have diverse values, preferences, and attitudes. Following Mannheim, we advance that an individual’s age, gender, and relationship status might generate different bonds among the Millennials, which in turn shape diverse “generation units” with differing values, preferences, and attitudes. We approach our research question through three independent studies that focus on different facets that constitute uniting bonds within the Millennial generation, using different commonalities that are associated with these bonds. Each study follows a different methodological approach, and we develop separate hypotheses for each study.

Homogeneity within cohorts sharing, more or less, the same age, can be explained by maturation effects (Rhodes, 1983). Relatedly, some attitudes and behaviors that specific age groups share might be better attributed to life stages, such as attending university, establishing a career, and starting a family regardless of their generation (Parry & Urwin, 2011). However, these effects, in combination, also contribute to the heterogeneity among generations which comprises individuals being born within a time span of almost 20 years for the Millennials.

Research on the generational heterogeneity among the Millennials remains scarce (see De Cooman & Dries, 2012; Ng et al., 2010; Twenge et al., 2010). Ng et al. (2010) have shown that Millennials’ career expectations vary by gender. Likewise, De Cooman and Dries (2012) argue that individual differences (e.g., gender) explain Millennials’ work values. We also know from past research that gender differences exist because of sex-specific developmental pathways (see Arden & Plomin, 2006). Exploring differences within Millennials may enhance our understanding of this generation, challenge findings of comparative studies, and provide helpful insights for organizations and their recruitment and retention strategies (Gibson, 2015). Hence, to fill this gap, our study focuses on work-related aspects (i.e., work values, job characteristics, and employer attributes). This article employs three studies to demonstrate that Millennials are a less homogeneous group than they have often been represented. Drawing on Mannheim’s generation theory, we propose that individual factors (e.g., age and gender), relational factors (e.g., relationship status), and contextual factors (nationality) may establish within-generational bonds in the formation of generation units. In this regard, we anticipate that Millennials possess different work values, and consequently are attracted to different job characteristics (e.g., work/life balance) and employer attributes.

In Study 1, we analyze whether and how age, gender, and relationship status are associated with different work values espoused by the Millennials. In Study 2, we apply conjoint analysis to demonstrate how employer attraction may be related to work/life balance for Millennials arising from age, gender, and relationship status differences. In Study 3, we investigate whether and how employer attraction may be related to nationalities among Millennials. We conclude with a discussion of our findings and suggest avenues for future research.

Study 1—work values

One criticism when making comparisons across generations is the inability to parcel out age or life cycle effects. Kooij et al. (2011) in a meta-analysis on age- and work-related motives find positive relationships between age and intrinsic motives, and negative relationships between age and growth needs strength and extrinsic motivations. This important finding suggests that age affects attitudes about work and work values more than affiliation with a generation. Levenson (2010, p. 258) describes this as “measurement problem” because they experience a common life stage. For example, younger Millennials who are entering the workforce would encounter many opportunities and choices about work and focus on extrinsic work values, while older Millennials who are more concerned with career development would prioritize other work values. Considering life cycle evolution, we surmise that the work values among Millennials are likely to differ. On this basis, we hypothesize that

Hypothesis 1a: Work values among Millennials vary based on age differences.

Generational research also considers men and women homogeneously as a singular cohort. This is problematic because research on gender differences in the context of work values indicates that work values differ between men and women (Beutell & Brenner, 1986; Duffy & Sedlacek, 2007; Kaifi et al., 2012; Terjesen et al., 2007). For example, Pollmann-Schult (2009) found that men are more extrinsically oriented on pay and status than women. This finding is supported by Dolan et al. (2004), who highlight the importance of gender in the context of work and life domains. Whereas women find intrinsic and family-related work values important, men focus on socioeconomic status. However, after controlling for education, Mottazl (1986) shows that attitudes about work do not differ between men and women in upper-level occupations. Workers in lower-level occupations show stronger gender differences. While men emphasize extrinsic factors, women are more concerned with the social aspects of work. Because research on work values has identified differences between men and women, gender differences are likely to be present within the Millennial generation. Hence, we hypothesize that

Hypothesis 1b: Work values among Millennials vary based on gender differences.

Relationship status has also been found to affect work values. People who are in relationships emphasize work/life balance more strongly than singles (Abele et al., 1999). Likewise, Millennials in a relationship may emphasize different work values than singles because of a desire to spend time with their partners. However, research has also shown that married individuals place more importance on pay and financial rewards than singles (Kirkpatrick Johnson, 2005). The emphasis on pay may stem from lifestyle changes and financial pressures associated with raising a family (Gorman, 2000). For example, Rowe and Snizek (1995) showed that people who were married are more likely to express a preference for higher income than are those who were not married. Hence, we hypothesize the following:

Hypothesis 1c: Work values among Millennials vary based on differences in relationship status.

Methodology

Sample

Data for Study 1 were collected via an online survey. Participants were recruited through several Facebook groups and on the Swiss internet platform Ronorp. All incoming business students at a Swiss university were also invited to participate in the study via email. The survey was administered in German, and the sample is best considered as convenient, representative of the Swiss student population. A total of 445 complete responses were received. We excluded 27 respondents who were born prior to 1982, leaving a final sample of 418 Millennials. The average age of the sample was 26 years and there were more female (64%) than male respondents. Over half of the respondents (51.2%) reported being in a relationship. We test for multicollinearity using the variance inflation factor (VIF) and the condition index. Correlations between the variables were modest. All VIFs (<1.043) were well below the acceptable limit of 10 (Hair et al., 2010). The condition indices (<17.33) were also below the critical values suggested by Hair et al. (2010). Thus, multicollinearity is not deemed to be a concern.

Measures

Work values

The work values inventory originally developed by Super (1970) contains 15 scales with three items each: achievement, aesthetic, altruism, associates, creativity, economic return, independence, intellectual stimulation, management, prestige, (job) security, (good) supervisory relationships, surroundings, variety, and way of life. We used the German work values inventory (Seifert & Bergmann, 1983), which includes Super’s (1970) 15 values plus an added career scale. All the items are listed in Appendix 1. Both factor loadings and the values for Cronbach’s alpha suggest good measurement properties.

Age

We measured age by asking respondents for their birth year.

Gender

Respondents were asked to indicate their gender: male or female (1, 0).

Relationship status

Respondents were also asked to indicate if they are in a relationship or single (1, 0).

Results

The descriptive statistics for all the variables are reported in Table 1. To test the hypotheses, multiple regression analysis was used (Cohen et al., 2002). Table 2 presents the results of the analysis. In total, we run 16 regression analyses, one for each of the 16 work values.

Table 1.

Study 1—Descriptive statistics.

	M	SD	01.	02.	03.	04.	05.	06.	07.	08.	09.	10.	11.	12.	13.	14.	15.	16.	17.	18.
1. Altruism	3.85	0.78	.24**
2. Aesthetic	2.58	1.09	.16**
3. Creativity	3.43	0.87	.08	.41**
4. Intellectual stimulation	3.69	0.66	.12*	.10*	.47**
5. Independence	3.72	0.68	.23**	.17**	.39**	.38**
6. Achievement	4.09	0.64	−.03	.18**	.43**	.39**	.29**
7. Prestige	3.34	0.86	−.06	−.11*	.10	.24**	.29**	.24**
8. Management	2.85	0.87	−.17**	−.06	.14**	.20**	.36**	.21**	.59**
9. Economic return	3.28	0.80	.16**	−.15**	−.02	.11*	.27**	.09	.57**	.52**
10. Security	4.03	0.77	.24**	−.14**	−.14**	−.06	−.03	.17**	.25**	.16**	.31**
11. Surroundings	4.14	0.61	.35**	.07	.07	.01	.20**	.25**	.17**	.13*	.23**	.47**
12. Supervisor relationship	4.35	0.56	.33**	.22**	.16**	.02	.19**	.30**	−.01	−.04	.02	.27**	.46**
13. Associates	4.32	0.60	.17**	.09	.05	.03	.07	.17**	.01	−.06	.07	.26**	.39**	.49**
14. Variety	4.12	0.63	.23**	.21**	.44**	.38**	.39**	.37**	.08	.07	.08	.06	.19**	.27**	.20**
15. Way of life	4.03	0.72	−.03	.18**	.07	−.03	.19**	.08	.03	−.03	.17**	.06	.36**	.29**	.18**	.14**
16. Career	3.60	0.82	.06	−.08	.20**	.24**	.40**	.32**	.60**	.63**	.65**	.31**	.28**	.15**	.12*	.25**	.07
17. Age	26.04	3.77	−.20**	.12*	.20**	.12*	.21**	.02	−.15**	−.15**	−.16**	−.23**	−.03	.08	−.06	.12*	.10*	−.14**
18. Gender	.36	0.48	−.04	−.16**	.12*	.15**	.03	−.08	.12*	.09	.07	−.13**	−.09	−.28**	−.10*	−.11*	−.04	.06	−.00
19. Relationship status	.51	0.50	.24**	−.02	−.07	−.04	.02	.02	−.11*	−.11*	−.01	.08	.04	.03	−.04	.06	.07	−.03	.19**	−.08

SD: standard deviation.

N = 418; *p < .05; **p < .01; ***p < .001.

Table 2.

Study 1—Regression analysis.

Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12	Model 13	Model 14	Model 15	Model 16
	Work values
	Altruism	Aesthetic	Creativity	Intellectual stimulation	Independence	Achievement	Prestige	Management	Economic return	Security	Surroundings	Supervisor relationship	Associates	Variety	Way of life	Career
Age	.07	.13**	.22***	.12*	.21***	.01	−.13**	−.13**	−.16***	−.25***	−.04	.08	−.05	.11*	.09^†	−.14**
Gender	−.20***	−.16***	.11*	.14**	.03	−.08	.12*	.09^†	.07	−.12*	−.08^†	−.28***	−.10*	−.11*	−.03	.06
Relationship status	−.07	−.06	−.10*	−.05	−.02	.02	−.07	−.08	.03	.12*	.04	−.01	−.04	.03	.05	.01
R ²	.05	.04	.06	.04	.04	.01	.04	.04	.03	.08	.01	.08	.02	.03	.01	.02
Adjusted R²	.04	.04	.06	.03	.04	.00	.04	.03	.02	.08	.00	.08	.01	.02	.01	.02
∆R²	.05	.04	.06	.04	.04	.01	.04	.04	.03	.08	.01	.08	.02	.03	.01	.02
F	6.668***	6.039***	9.413***	5.319**	6.328***	0.907	5.894***	5.043**	4.346**	12.629***	1.386	12.737***	2.074	3.897**	1.872	3.225*

Regression coefficients are reported as standardized β values. The p values for the dependent variables altruism, aesthetic, surroundings, and supervisor relationship are calculated based on heteroscedasticity-robust standard errors.

N = 418; ^†p < .10; *p < .05; **p < .01; ***p < .001.

Table 2 presents the results of the regression analyses used to test Hypotheses 1a, b, and c. We obtain clear results for work value differences for age and gender. In both instances, we find significant effects for eleven work values. For age, we find significant and positive associations for creativity (β = .22, p < .001) and independence (β = .21, p < .001). Conversely, age is negatively associated with security (β = −.25, p < .001). For gender, women place a significantly higher value on altruism (β = −.20, p < .001), aesthetic (β = −.16 p < .001), and supervisor relationship (β = −.28, p < .001) than men. For relationship status, we find only two significant differences. Millennials in a relationship place a higher value on security (β = .12, p < .05) and a lower value on creativity (β = −.10, p < .05).

We also calculated effect sizes to derive a more general and robust description of the size of the effects obtained in our regression analyses (Fritz et al., 2012). We report Cohen’s d, Cohen’s U3, and Tilton Overlap in Table 3. Given that age is a continuous variable, we dichotomized age before calculating the effect sizes for age. Based on a median split, we distinguish two age groups (i.e., 26 or younger and 27 or older).

Table 3.

Study 1—Means and SDs (in brackets), Cohen’s d, Cohen’s U3, and Tilton Overlap.

Variables	Work values
	Altruism	Aesthetic	Creativity	Intellectual stimulation	Independence	Achievement	Prestige	Management	Economic return	Security	Surroundings	Supervisor relationship	Associates	Variety	Way of life	Career
Age
26 or younger	3.83 (0.83)*	2.51 (1.10)	3.29 (0.84)	3.64 (0.64)	3.61 (0.66)	4.09 (0.65)	3.44 (0.82)	2.92 (0.87)	3.35 (0.79)	4.20 (0.74)	4.16 (0.65)	4.32 (0.59)^†	4.35 (0.62)	4.07 (0.66)	3.96 (0.73)	3.69 (0.77)
27 or older	3.87 (0.72)*	2.65 (1.08)	3.57 (0.87)	3.74 (0.68)	3.83 (0.69)	4.08 (0.64)	3.24 (0.89)	2.77 (0.86)	3.20 (0.81)	3.85 (0.76)	4.11 (0.57)	4.38 (0.52)^†	4.28 (0.58)	4.16 (0.59)	4.10 (0.70)	3.50 (0.86)
Cohen’s d	0.06	0.13	0.33	0.15	0.33	0.02	0.24	0.17	0.18	0.47	0.08	0.10	0.13	0.14	0.20	0.24
Cohen’s U3 (%)	52.30	55.10	62.90	56.00	63.00	50.80	59.30	56.90	57.00	68.00	53.10	54.10	55.00	55.50	58.00	59.40
Tilton Overlap (%)	97.70	98.20	86.90	94.00	86.80	99.20	90.60	93.10	93.00	81.50	96.90	95.90	95.00	94.50	92.00	90.50
Gender
Female	3.97 (0.75)	2.71 (1.12)*	3.35 (0.86)	3.62 (0.66)	3.70 (0.70)	4.13 (0.62)	3.26 (0.84)	2.79 (0.84)	3.24 (0.77)	4.10 (0.77)	4.18 (0.64)	4.46 (0.50)*	4.36 (0.59)	4.16 (0.60)	4.05 (0.69)	3.56 (0.83)
Male	3.65 (0.78)	2.36 (0.99)*	3.56 (0.86)	3.82 (0.65)	3.74 (0.65)	4.02 (0.68)	3.48 (0.88)	2.95 (0.91)	3.35 (0.85)	3.90 (0.76)	4.07 (0.57)	4.14 (0.60)*	4.24 (0.62)	4.02 (0.67)	3.99 (0.78)	3.66 (0.79)
Cohen’s d	0.41	0.34	0.24	0.31	0.06	0.16	0.26	0.19	0.14	0.27	0.18	0.60	0.20	0.23	0.08	0.13
Cohen’s U3 (%)	65.90	63.30	59.60	62.10	52.20	56.40	60.10	57.50	55.60	60.50	57.20	72.70	58.00	59.10	53.10	55.00
Tilton Overlap (%)	83.80	86.60	90.30	87.70	97.80	93.50	89.80	92.40	94.40	89.40	92.80	76.30	91.90	90.80	96.90	95.00
Relationship status
Single	3.89 (0.72)*	2.61 (1.12)	3.49 (0.84)	3.71 (0.66)	3.70 (0.67)	4.07 (0.62)	3.43 (0.86)	2.94 (0.85)	3.28 (0.86)*	3.96 (0.78)	4.11 (0.65)	4.33 (0.56)	4.34 (0.60)	4.07 (0.65)	3.98 (0.73)	3.62 (0.83)
In a relationship	3.82 (0.83)*	2.56 (0.99)	3.36 (0.89)	3.67 (0.66)	3.73 (0.69)	4.10 (0.66)	3.25 (0.85)	2.75 (0.88)	3.27 (0.74)*	4.09 (0.76)	4.16 (0.58)	4.36 (0.55)	4.29 (0.61)	4.15 (0.60)	4.08 (0.71)	3.58 (0.81)
Cohen’s d	0.09	0.04	0.14	0.07	0.04	0.05	0.21	0.22	0.01	0.17	0.08	0.06	0.09	0.12	0.14	0.05
Cohen’s U3 (%)	53.60	51.60	55.70	52.80	51.60	51.90	58.40	58.70	50.40	56.60	53.20	52.50	53.40	54.90	55.50	52.00
Tilton Overlap (5)	96.60	98.20	94.30	97.20	98.40	98.10	91.50	91.20	99.60	93.40	96.80	97.50	96.60	95.10	94.50	98.00

N = 418. This table shows the effect sizes for differences in work values by age, gender, and relationship status. The standard deviations are reported in brackets next to the means of the respective groups. We also report whether the standard deviations across groups are significantly different from each other. The p values are based on the Levene test to assess the equality of variances.

†

p < .10; *p < .05; **p < .01; ***p < .001.

The patterns obtained from Cohen’s d, Cohen’s U3, and Tilton Overlap (Table 3) are consistent with the conclusions derived from the regression analyses. Smaller regression coefficients (and hence correspondingly higher p values) co-occur with smaller values for Cohen’s d, Cohen’s U3 (close to 50%), and Tilton Overlap (close to 100%).² Conversely, larger regression coefficients co-occur with larger values for Cohen’s d, Cohen’s U3 (larger than 50%), and smaller values for Tilton Overlap.

As most values for Cohen’s d are below 0.2, the effect sizes are small (Cohen, 1988). For gender, the effect sizes for altruism, aesthetic, and supervisor relationship stand out. In the case of the supervisor relationship, Cohen’s d equals 0.60. According to Cohen (1988), this suggests a medium effect size and implies that 72.6% of the women in our sample score higher on valuing the supervisor relationship than for men. The distribution for female and male respondents shows an overlap of 76.3%. Finally, for relationship status, we do not find any large effect sizes for the work values investigated.

If there is homogeneity among the Millennial generation, we would expect to observe values for Cohen’s d to be 0% or close to 0%, values for Cohen’s U3 to be 50% or close to 50%, and values for Tilton Overlap to be 100% or close to 100%. Table 3, however, indicates that this is not the case for the groups created based on age, gender, and relationship status. The divergence from the values indicative of homogeneity as described above provides further support for the heterogeneity among Millennials.

Following Mannheim (1952, 1928), it is worth noting that the gender differences on the assessments of supervisor relationships, altruism, and aesthetics at work may point to differing generational bonds that create differing generational units. Although speculative, the sample in this study is Swiss, and compulsory military service tradition among male Swiss nationals may highly influence gender differences in the desired work values between Swiss men and women. Likewise, for age, Cohen’s d for creativity, independence, and security is above 0.20, indicating that Millennials value security more, and creativity and independence less as they grow older. The uniting bonds creating these generation units may simply be school/university attendance (creativity, independence) vis-à-vis building their career (stability) (also see Kuron et al., 2015).

In sum, we find strong support for Hypotheses 1a and 1b, and weaker support for work-value differences based on relationship status. Our results provide some support for the notion that work values espoused by Millennials are at least partially explained by their age and gender.

Discussion

Study 1 reveals some differences in work values reported by Millennials based on age, gender, and relationship status. Age and gender are most strongly associated with work values, suggesting the possibility of maturation as well as gender effects. The results confirm that work values among Millennials are not homogeneous, which makes generational comparisons problematic. It also raises questions on conclusions about shifting work values in previous studies. For example, Cennamo and Gardner (2008), found Millennials valued autonomy and work–life balance more than previous generations (Gen Xers and Baby Boomers), but they did not control for age, gender, and relationship status. Likewise, Wong et al. (2008) as well as Lyons et al. (2007), found differences in work values across generations, but they also did not consider age, gender, and relationship status. Hence, we need to interpret findings from previous studies with caution. We discuss these results in more detail, and within a broader Mannheim perspective, in the concluding discussion.

Study 2—work/life balance

In Study 2, we focus on work/life balance as a job characteristic which is described as a central work value (Darcy et al., 2012). According to comparative generational studies, Millennials attach more importance to work/life balance and value leisure time more than previous generations (Cennamo & Gardner, 2008; Ng et al., 2010; Twenge et al., 2010). This might be a generational effect or a reflection of a common shift in society where employees have become increasingly concerned with balancing work demands and personal life. Research suggests that work/life balance has a positive effect on organizational attraction and long-term employment (Bretz & Judge, 1994; Casper & Buffardi, 2004; Honeycutt & Rosen, 1997; Rau & Hyland, 2002). Given the rising number of Millennials in the workforce, organizations have prioritized work/life balance as a key job characteristic in attracting new employees.

Research also uncovered different job characteristics that increase work flexibility such as part-time work and flexible workplaces, and the results show positive effects for both individuals and organizations (Forsyth & Polzer-Debruyne, 2007; Hill et al., 2001; Rau & Hyland, 2002). Work flexibility improves job satisfaction, productivity, and tenure for employees (Forsyth & Polzer-Debruyne, 2007; Rau & Hyland, 2002) and strengthens organizational commitment, job involvement, and organizational attraction for employers (Caillier, 2013; Rau & Hyland, 2002). Job seekers are likely to value work flexibility when evaluating prospective employers (see Onken-Menke et al., 2018).

In Study 2, we focus on part-time work, sabbaticals, and a flexible workplace as three practices that increase work flexibility. Part-time work allows employees to spend more time with their families and friends and to pursue interests outside of work. This is important for Millennials given that they prioritize leisure over work (Twenge et al., 2010). Employees benefit from this freedom and, on average, are more productive compared with those employed full-time. However, employees would receive lower salaries, fringe benefits, and diminished opportunities for promotion (Kalleberg, 2000; Tilly, 1996). Given that the negative effects may be less salient for Millennial job seekers (Twenge & Kasser, 2013), we surmise that job characteristics that promote work/life balance would be positively related to organizational attraction. Sabbaticals are another policy that may signal flexibility to prospective job applicants. A sabbatical is a paid long-term leave that employees can use for pursuing further education, personal development, or for traveling, which benefits both individuals and organizations (e.g., invigorated employees and enhanced human capital) (Carr & Tang, 2005). For job seekers, a sabbatical may increase organizational attraction because it offers employees an opportunity for a break after years of employment to pursue their interests or take time for themselves. Finally, a flexible workplace offers employees the ability to choose the place where they work. Working from home is the most common form of workplace flexibility, which has gained popularity following the COVID19 pandemic. The influence of working from home on job retention, career progression, work/life balance, and personal/family life satisfaction has been found to be more positive than conventional working and commuting to the office (Hill et al., 2003). The possibility to work from home is enticing because employees have the freedom to manage work (e.g., when and how they work), and also benefit from commute time and costs. The flexibility and freedom to work from home are therefore likely to enhance organizational attraction for Millennials (see Smith, 2010). Accordingly, we hypothesize that

Hypothesis 2a: There is a positive relationship between work/life balance practices and a job seeker’s willingness to enter into a long-term employment relationship with an organization. Specifically, (a) part-time work, (b) sabbaticals, and (c) a flexible workplace are positively associated with a job seeker’s willingness to enter into a long-term employment relationship with an organization.

Similar to Study 1, we surmise that there are differences within Millennials based on age, gender, and relationship status. For example, work/life balance may be more important for women and individuals who are in a relationship, because of a desire to spend time with their partner or family. Thus, we anticipate that Millennial’s willingness to enter into long-term employment, on the basis of attraction to work/life balance practices, may be affected by age, gender, and relationship status. In other words, attraction to work/life balance practices will likely not be the same for all Millennials, making it difficult to make assumptions for Millennials concerning the relationship between work/life balance practices and organizational attraction. Hence, we use age, gender, and relationship status as moderators to identify differences within Millennials. Accordingly, we hypothesize the following:

Hypothesis 2b: Age moderates the positive relationship between work/life balance practices (part-time work, sabbaticals, and a flexible workplace) and job seekers’ willingness to enter into a long-term employment relationship with an organization.

Hypothesis 2c: Gender moderates the positive relationship between work/life balance practices (part-time work, sabbaticals, and a flexible workplace) and job seekers’ willingness to enter into a long-term employment relationship with an organization.

Hypothesis 2d: Relationship status moderates the positive relationship between work/life balance practices (part-time work, sabbaticals, and a flexible workplace) and job seekers’ willingness to enter into a long-term employment relationship with an organization.

Methodology

Sample

For this study, we recruited 150 participants who are graduate students at a Swiss university. The survey was conducted during class. A total of 110 students participated in the study, yielding a response rate of 73.3%. We excluded one participant who did not pass the test–retest reliability (see analytical procedure). Our final sample consisted of 109 participants. The average age was 24.8 years, there were slightly more men (51%), and 45% of the respondents reported being in a relationship.

Analytical procedure

For this study, we undertook a conjoint experiment, which is a decomposition method for studying human decision-making processes (Devendorf & Highhouse, 2008; Graves & Powell, 1995; Strauss et al., 2001). This enables researchers to examine respondents’ underlying preference structure with respect to identified attributes, and thus uncovers their decision rules (Green & Srinivasan, 1990; Louviere, 1988). Because conjoint analysis is a real-time method, it overcomes many of the potential biases associated with post hoc research designs. Post hoc methods collect data after the fact, typically asking decision-makers to self-report their decision rules and choices with respect to past events. This is problematic because of respondents’ limited ability to recall past events and the potential for social desirability bias. Conjoint analysis transcends these concerns because it uncovers decision policies based on actual and mostly simplified decisions.

Experimental design

Hypothetical organization profiles were constructed using four work attributes—part-time work, sabbaticals, and flexible workplace (which inform work/life balance practices), and skill variety (which is used as a control condition to differentiate from work/life balance practices). A full factorial design would yield 16 (2⁴) possible combinations of attribute levels, and a test–retest design would require 32 decision scenarios, which is overwhelming for participants (Bruns et al., 2008; Choi & Shepherd, 2004; Monsen et al., 2010; Shepherd, 1999). Following Hahn and Shapiro (1966), all main effects of the work attributes on job seekers’ decisions are tested with a reduced number of profiles, using an orthogonal fractional factorial which reduces the profiles to 8, enabling us to test all of the hypotheses (Box et al., 1978; Montgomery, 2009). Replicating the 8 unique organization profiles resulted in a total of 16 profiles to be evaluated by each participant. Duplicating the original profiles allows estimating decision consistency and the use of an additional measure of reliability within the final statistical analysis (Louviere, 1988). The 16 profiles³ were randomly assigned to each participant to avoid order effects. A practice case was included at the beginning of the conjoint experiment to familiarize participants with the format and the evaluation task.

Hierarchical linear modeling (HLM) examines the variance within and between individuals and allows for the decomposition of the foundational structure of decision policies (Bruns et al., 2008). This procedure is appropriate for experimental decision-making studies because it controls for autocorrelation and heteroscedasticity (Choi & Shepherd, 2005; Hofmann, 1997; Holland & Shepherd, 2013). Multicollinearity is tested by calculating the VIFs. It is not an issue since it is below the cutoff value of 10 (Kutner et al., 2004; Robinson & Schumacker, 2009).

Measures

Dependent variable. Participants were asked to respond to the following question: “What is the likelihood that you will enter into a long-term employment relationship with a company as described above?” The decision outcome was measured on a 7-point Likert-type scale (1 = very unlikely; 7 = very likely).

Job characteristics (Independent variables)

We included four attributes that represented the independent variables. Whereas three attributes (i.e., part-time work, sabbaticals, and a flexible workplace) describe work/life balance job characteristics, skill variety describes a general job characteristic that has been found to be important when assessing organizational attraction.

Age

We measured age by asking respondents for their birth year.

Gender

Respondents were asked to indicate their gender: male or female (1, 0).

Relationship status

Respondents were also asked to indicate if they are in a relationship or single (1, 0).

Results

Table 4 shows the means, SDs, and correlations. Correlations between the variables are .00 and SDs are 0.50, due to the orthogonal fractional factorial design (Patzelt & Shepherd, 2008).

Table 4.

Study 2—Descriptive statistics.

	M	SD	01.	02.	03.	04.	05.	06.	07.
01. Job seekers’ willingness to enter into long-term employment relationships with an organization	4.03	1.92
02. Part-time work	.00	.50	.167**
03. Sabbatical	.00	.50	.121**	.000
04. Flexible workplace	.00	.50	.091**	.000	.000
05. Skill variety	.00	.50	.103**	.000	.000	.000
06. Age	24.813	1.64	.380**	.000	.000	.000	.000
07. Gender	.53	.50	.345**	.000	.000	.000	.000	.250**
08. Relationship status	.45	.50	.265**	.000	.000	.000	.000	.005	−.400

SD: standard deviation.

N = 1,744 decisions, **p < .01.

Table 5 presents the HLM results. All values are reported as standardized coefficients. Level 1 effects of the decision on the dependent variable appear in the first row. A job seeker’s willingness to enter into a long-term employment relationship is positively associated with organizations offering part-time work (coefficient: .950, p < .001), sabbaticals (coefficient: .851, p < .001), and workplace flexibility (coefficient: .897, p < .001). Hypothesis 2a is thus supported for all three work/life balance practices. Skill variety included to test for robustness is also significantly positive (coefficient: 2.761, p < .001).

Table 5.

Study 2—Hierarchical linear regression analyses.

Evaluation criteria		Work/life balance job characteristics			General job characteristic
	Intercept	Part-time work	Sabbatical	Workplace flexibility	Monitoring board
	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient
	(SE)	(SE)	(SE)	(SE)	(SE)
Level 1 effects on DV
Intercept	4.032***	.950***	.851***	.897***	2.761***
	(.047)	(.068)	(.054)	(.052)	(.090)
Interaction effects between Level 2 and Level 1 variables on DV
Age	−.042(.032)	−.007(.043)	−.051(.031)	.050^† (.029)	−.020(.063)
Gender	.156(.099)	−.423** (.148)	.061(.111)	−.038(.111)	.157(.190)
Relationship status	−.068(.093)	−.059(.139)	−.120(.110)	−.144(.105)	.222(.180)

Dependent variable: Job seekers’ willingness to enter into long-term employment relationship with the organization.

†

p < .1;*p < .05; **p < .01; ***p < .001; N = 1,744 decisions nested within 109 job seekers. Values in parentheses are SEs.

The moderating effects on Level 2 appear in the following rows. Hypothesis 2b, which proposes that age moderates the relationship between work/life balance decision attributes and the dependent variable, is supported for workplace flexibility (coefficient: .050, p < .10), but not supported for the other work/life balance practices. Hypothesis 2c, which proposes that gender has a moderating effect on the relationship between work/life balance decision attributes and a job seeker’s willingness to enter into a long-term relationship, is supported for part-time work (coefficient: −.423, p < .01), but it is not supported for the other work/life balance practices. Given that relationship status does not moderate the association between work/life balance decision attributes and willingness to enter into a long-term employment relationship with an organization, Hypothesis 2d is not supported.

To facilitate an interpretation of the significant interaction effects, we plot the graphs in Figures 1 and 2 (Aiken & West, 1991; Cohen et al., 2002). Figure 1 demonstrates that the opportunity to work part-time shows a stronger positive association with willingness to enter into long-term employment for women compared to men. Figure 2 shows that workplace flexibility displays a stronger positive association with a job seeker’s willingness to enter into a long-term employment when job seekers are older.

Figure 1.

Study 2—Interaction of part-time work with job seekers’ willingness to enter into long-term employment.

Figure 2.

Study 2—Interaction of workplace flexibility with job seekers’ willingness to enter into long-term employment.

Discussion

Study 2 study yields partial support for the notion that a desire for work/life balance among Millennials may differ by gender and age. Specifically, gender moderates the relationship between part-time work and job seekers’ willingness to enter into long-term employment. In addition, age moderates the association between workplace flexibility and job seekers’ willingness to enter into long-term employment. Women may desire part-time work with a view toward balancing family with work (Hill et al., 2004), while younger respondents may prefer workplace flexibility given a trend toward working from home and the ability to combine work with leisure activities. Although employers are developing work/life balance policies to attract and retain Millennial workers, the reaction to these employer initiatives varies by age and gender. Study 2 also suggests the heterogeneity among Millennials highlighting the difficulty to make comparisons across different generations. We will discuss these results in greater detail in the concluding discussion.

Study 3—employer attributes⁴

Previous studies on Millennials have usually been focused on the United States (Parry & Urwin, 2011), and these findings are generalized to other countries. Different historical events in different countries shape each generation differently, each with different start and end years (e.g., consolidation, cultural revolution, social reform in China, see Egri & Ralston, 2004; prereform, reform, postreform in China, see Tang et al., 2017). Thus, when research reports on what characterizes a generation, several concerns arise, particularly when considering nonwestern countries, since historical and social contexts shape a generation in a specific location in time.

While we focused on heterogeneity within Millennials arising from age, gender, and relationship status in Studies 1 and 2, we now consider national differences in Mannheim’s generational location in Study 3 as another contextual factor in the heterogeneity within the Millennial generation. Specifically, we analyze how national location influences employer attributes for Millennial job seekers.

Employer branding often differentiates a firm as a desirable employer (Buil et al., 2016). The purpose of an employer branding strategy is to attract and recruit the right potential employees (Mosley, 2007). Thus, it is advantageous for employers to know which employer attributes are attractive to job seekers; however, it is questionable whether specific employer attributes are valued by Millennials irrespective of national location, age, and gender. To our knowledge, there has been no research on whether and how nationality might explain Millennial preferences in shaping their initial employment expectations. Exploring national differences in employer attributes will therefore shed light on possible heterogeneity among the Millennial generation in different national contexts. Costanza and Finkelstein (2015) note that international, globalized organizations become the best employers in select countries by understanding workforce trends in certain locations.

In this study, we focus on Swiss and Russian nationals as proxies for two different sources of generational location. Switzerland is characterized as a highly industrialized nation, while Russia exemplifies an emerging economy. Millennials in Switzerland had been raised during periods of economic growth and prosperity, while Millennials in Russia grew up in times of political, economic, and social change and instability. Following Hofstede (2001), Russia and Switzerland differ markedly on power distance, individualism-collectivism, masculinity/femininity, and uncertainty avoidance. Russians scored higher on uncertainty avoidance than Swiss, which might present an aberration from the period of economic and political stagnation in the 1980s in Russia when Russian citizens were guaranteed a job and a modest standard of living. However, they scored lower than the Swiss on individualism, which can be explained by the suppression of individualism, when official egalitarian values were promoted at the expense of personal freedom and freedom of expression. Hence, drawing on Mannheim’s (1952, 1928) conceptualization of generational locations [German: Lagerung] as the specific historical and social location of a generation in a given place, we hypothesize that

Hypothesis 3: The perception of employer attributes among Millennials varies based on differences in generational locations.

Methodology

Sample

We collected data from graduate business students at five universities in the German-speaking part of Switzerland and two universities in Russia. A total of 600 questionnaires were distributed to students during class time, and 555 were completed yielding a response rate of 92.5%. Given our interest in the attitudes of Millennial job seekers from Switzerland and Russia, we limited our sample to respondents who were either of Swiss or Russian nationalities. The final dataset consisted of 448 participants (216 Swiss, 232 Russians). In the Swiss sample, the mean age is 25.1 years and half (50%) are men. In the Russian sample, the mean age is 21.6 years and one-third (34%) are men.

Measures

Employer attributes

To become an employer of choice, employer practices should be aimed at matching job seekers’ expectations with work attributes (Branham, 2005). The authors worked with Universum, a consulting firm in employer branding, to develop a holistic bundle of work attributes that were derived from a literature review. A total of 19 work attributes were measured using a 7-point Likert-type (1 = very unimportant, 7 = very important): prestige (Cennamo & Gardner, 2008), good reference for future career (Knox & Freeman, 2006), corporate social responsibility (Twenge et al., 2010), culture that respects one’s individuality (Twenge & Campbell, 2008), culture that values diversity among personnel (Twenge, 2010), culture that supports equality between sexes (Twenge, 2010), culture that accepts minorities (Twenge, 2010), challenging work, opportunities for relocation abroad and international travel (Knox & Freeman, 2006), secure employment (Bristow et al., 2011), flexible working conditions (Hershatter & Epstein, 2010), good work/life balance (Deal et al., 2010), attractive geographic location, friendly work environment (Martin et al., 2005), performance-related bonus (Ng et al., 2010), professional training and education (Bristow et al., 2011), personal mentoring/coaching (Branham, 2005), leadership opportunities (Wong et al., 2008), and good possibilities for rapid promotion (Ng et al., 2010).

Generational location

We used nationality as a proxy for generational locations (1 = Russian, 0 = Swiss).

Post hoc test variables

To test the effect of age and gender that was hypothesized in Study 1, we included the two variables in the regression analyses. The mean age for the combined sample is 23.3 years and 42% were men.

Results

Descriptive statistics and correlations for all of the variables are reported in Table 6. We test Hypothesis 3 through multiple regression. Table 7 presents the results of the analysis. In total, we run 19 regression analyses (Cohen et al., 2002), one for each of the 19 employer attributes.

Table 6.

Study 3—Descriptive statistics.

	M	SD	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.	13.	14.	15.	16.	17.	18.	19.	20.	21.
1. Prestige	5.55	1.17
2. Good reference for future career	6.13	0.90	.44**
3. High level of corporate social responsibility (CSR)	5.69	1.19	.10*	.14**
4. Culture that respects one’s individuality	6.21	0.86	.10*	.15**	.27**
5. Culture that values diversity among personnel	5.56	1.18	.03	.08	.36**	.43**
6. Culture that supports equality between sexes	5.52	1.55	.00	.10*	.34**	.22**	.39**
7. Culture that accepts minorities	4.99	1.64	−.08	.06	.35**	.20**	.43**	.65**
8. Challenging work	5.98	0.94	−.03	.12*	.19**	.23**	.28**	.19**	.30**
9. Opportunities for relocation abroad and international travel	5.40	1.42	.18**	.18**	.06	.13**	.14**	−.01	.01	.17**
10. Secure employment	5.79	1.10	.31**	.23**	.21**	.19**	.08	.21**	.16**	.03	.09
11. Flexible working conditions	5.93	1.09	.07	.05	.23**	.19**	.23**	.19**	.16**	.14**	−.02	.17**
12. Good work–life balance	6.30	0.92	.04	.04	.22**	.18**	.21**	.22**	.22**	.08	−.06	.27**	.40**
13. Attractive geographic location	5.59	1.17	.26**	.15**	.07	.05	.04	.11*	.03	−.04	.03	.28**	.31**	.37**
14. Friendly work environment	6.35	0.86	.11*	.10*	.40**	.13**	.23**	.21**	.20**	.16**	.01	.26**	.32**	.34**	.33**
15. Performance-related bonus	5.65	1.20	.31**	.17**	.10*	.13**	.05	−.08	−.09*	.10*	.25**	.07	.03	−.10	.17**	.09
16. Professional training and education	6.06	0.94	.19**	.24**	.24**	.18**	.14**	.13**	.12*	.28**	.20**	.16**	.12**	.09*	.12*	.17**	.31**
17. Personal mentoring/coaching	5.75	1.05	.11*	.21**	.38**	.22**	.24**	.21**	.26**	.27**	.08	.21**	.31**	.17**	.14**	.29**	.20**	.40**
18. Leadership opportunities	6.00	1.02	.28**	.24**	.20**	.17**	.23**	.11*	.07	.20**	.26**	.11*	.19**	.02	.23**	.20**	.36**	.36**	.37**
19. Good possibilities for rapid promotion	5.87	1.11	.45**	.34**	.11*	.15**	.02	.01	−.10*	−.02	.29**	.22**	.02	−.11*	.22**	.05	.51**	.25**	.10*	.46**
20. Nationality	0.52	0.50	.34**	.07	−.04	−.08	−.23**	−.23**	−.38**	−.32**	.17**	.09	−.21**	−.15**	.11*	−.09	.42**	.06	−.15**	.10*	.49**
21. Age	23.28	2.33	−.31**	−.12**	−.04	.03	.13**	.10*	.24**	.24**	−.17**	−.11*	.11*	.06	−.16**	.04	−.38**	−.08	.10*	−.11*	−.44**	−.73**
22. Gender	0.42	0.49	−.02	−.06	−.11*	−.03	.03	−.36**	−.18**	.05	−0.03	−.21**	.02	−.08	−.12**	−.13**	.05	−.05	−.07	.00	−.09	−.17**	.19**

SD: standard deviation.

N = 418; ^†p < .10; *p < .05; **p < .01.

Table 7.

Study 3—Regression analysis.

Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12	Model 13	Model 14	Model 15	Model 16	Model 17	Model 18	Model 19
	Prestige	Good reference for future career	High level of CSR	Culture that respects one’s individuality	Culture that values diversity among personnel	Culture that supports equality between sexes	Culture that accepts minorities	Challenging work	Opportunities for relocation abroad and international travel	Secure employment	Flexible working conditions	Good work–life balance	Attractive geographic location	Friendly work environment	Performance-related bonus	Professional training and education	Personal mentoring/coaching	Leadership opportunities	Good possibilities for rapid promotion
Nationality	.24***	−.05	−.15*	−.12^†	−.29***	−.37***	−.45***	−.31***	.09	.02	−.28***	−.24**	−.02	−.13*	.32***	.01	−.19**	.04	.35***
Age	−.15*	−.15*	−.13^†	−.05	−.08	−.10	−.05	.01	−.11	−.06	−.09	−.09	−.16*	−.03	−.17*	−.07	−.02	−.08	−.19**
Gender	.04	−.04	−.12*	−.04	−.01	−.40***	−.24***	−.01	.01	−.19***	−.01	−.10*	−.10*	−.15**	.14**	−.04	−.10*	.03	.00
R ²	.13	.02	.02	.01	.06	.22	.20	.10	.03	.05	.05	.04	.03	.03	.20	.01	.03	.01	.25
Adjusted R²	.12	.01	.02	.00	.05	.22	.20	.10	.03	.04	.04	.03	.03	.02	.20	.00	.03	.01	.25
F	21.35***	2.56^†	3.61*	1.43	8.59***	41.96***	37.21***	16.49***	5.25**	7.53***	7.35***	5.98**	5.28**	4.66**	37.80***	1.15	5.18**	1.94	49.70***

Regression coefficients are reported as standardized β values. With the exception of the employer attributes prestige, opportunities for relocation abroad and international travel, and attractive geographic location all p values for the remaining employer attributes are calculated based on hetereoskedasticity-robust standard errors.

N = 448; ^†p < .10; *p < .05; **p < .01; ***p < .001.

In each model, we entered nationality, age, and gender as independent variables. Hypothesis 3, which hypothesizes that nationality affects employer attributes, is supported for 13 of the 19 attributes. The strongest nationality-based differences are found for prestige (β = .24, p < .001), culture that accepts minorities (β = −.45, p < .001), challenging work (β = −.31, p < .001), performance-related bonus (β = .32, p < .001), and good possibilities for rapid promotion (β = .35, p < .001). While prestige, performance-related bonus, and good possibilities for rapid promotion are significantly more important for Russians, culture that accepts minorities and challenging work are significantly more important for Swiss.

We also find some evidence that the perception of employer attributes is associated with gender and age. Older study participants place less importance on prestige (β = −.15, p < .05), performance-related bonus (β = −.17, p < .05), and good possibilities for rapid promotion (β = −.19, p < .01). For gender, we find that male respondents place significantly less emphasis on a culture that supports equality between sexes (β = −.40, p < .001).

Consistent with Study 1, we also calculated effect sizes to derive a more general and robust description of the size of the effects obtained in our regression analyses (Fritz et al., 2012). We report Cohen’s d, Cohen’s U3, and Tilton Overlap in Table 8. Given that age is a continuous variable, we dichotomized age before calculating the effect sizes related to age. Based on a median split, we distinguish two age groups (i.e., 23 or younger and 24 or older).

Table 8.

Study 3—Means and SDs (in brackets), Cohen’s d, Cohen’s U3, and Tilton Overlap.

Variables	Prestige	Good reference for future career	High level of CSR	Culture that respects ones individuality	Culture that values diversity among personnel	Culture that supports equality between sexes	Culture that accepts minorities	Challenging work	Opportunities for relocation abroad and international travel	Secure employment	Flexible working conditions	Good work–life balance	Attractive geographic location	Friendly work environment	Performance-related bonus	Professional training and education	Personal mentoring/coaching	Leadership opportunities	Good possibilities for rapid promotion
Nationality
Swiss	5.14(1.15)	6.07(0.85)*	5.74(1.11)^†	6.28 (0.75)	5.83(1.00)***	5.88(1.32)***	5.63(1.32)***	6.29(0.68)***	5.15(1.41)	5.69(1.14)	6.17(0.91)**	6.45(0.80)*	5.45(1.10)	6.43(0.65)***	5.13(1.26)***	6.00(0.87)*	5.91(0.92)***	5.90(0.96)^†	5.31(1.09)***
Russian	5.93(1.06)	6.19(0.93)*	5.65(1.27)^†	6.14(0.95)	5.30(1.27)***	5.18(1.66)***	4.40(1.68)***	5.69(1.05)***	5.63(1.39)	5.89(1.05)	5.72(1.19)**	6.17(0.99)*	5.71(1.22)	6.28(1.01)***	6.13(0.91)***	6.12(0.99)*	5.59(1.14)***	6.09(1.07)^†	6.38(0.84)***
Cohen’s d	0.72	0.14	0.08	0.16	0.47	0.47	0.81	0.67	0.34	0.19	0.43	0.31	0.22	0.18	0.92	0.12	0.31	0.19	1.11
Cohen’s U3 (%)	76.40	55.50	53.10	56.50	68.00	68.10	79.10	74.70	63.50	57.30	66.50	62.20	58.80	56.90	82.10	54.90	62.10	57.70	86.60
Tilton Overlap (%)	71.90	94.50	96.80	93.50	81.50	81.40	68.60	73.90	86.30	92.60	83.20	87.70	91.20	93.00	64.60	95.10	87.70	92.30	58.00
Age
23 or younger	5.88(1.04)	6.22(0.84)	5.74(1.20)	6.21(0.88)	5.44(1.23)**	5.40(1.63)*	4.69(1.71)*	5.80(1.03)*	5.57(1.39)	5.87(1.04)	5.85(1.17)**	6.24(0.97)	5.71(1.21)	6.31(0.97)*	6.01(1.00)***	6.15(0.98)*	5.69(1.15)**	6.10(1.08)^†	6.29(0.85)***
24 or older	5.11(1.20)	6.02(0.95)	5.63(1.18)	6.20(0.84)	5.72(1.08)**	5.68(1.41)*	5.40(1.44)*	6.23(0.73)*	5.16(1.43)	5.68(1.17)	6.05(0.95)**	6.38(0.84)	5.42(1.09)	6.41(0.68)*	5.16(1.27)***	5.94(0.86)*	5.82(0.90)**	5.86(0.92)^†	5.29(1.16)***
Cohen’s d	0.70	0.23	0.09	0.01	0.24	0.19	0.45	0.47	0.29	0.18	0.18	0.15	0.25	0.11	0.76	0.22	0.12	0.24	1.00
Cohen’s U3 (%)	75.70	59.10	53.50	50.40	59.40	57.40	67.20	68.00	61.40	57.0	57.30	56.10	59.80	54.20	77.60	58.70	54.60	59.60	84.10
Tilton Overlap (%)	72.70	90.80	96.50	99.60	90.50	92.60	82.40	81.50	88.50	93.00	92.70	93.90	90.10	95.80	70.40	91.20	95.40	90.30	61.70
Gender
Female	5.57(1.09)*	6.18(0.86)	5.81(1.20)	6.23(0.90)	5.53(1.22)^†	5.99(1.28)***	5.23(1.52)^†	5.94(0.95)	5.43(1.47)	5.98(1.03)*	5.92(1.06)	6.37(0.83)^†	5.71(1.13)	6.45(0.83)	5.60(1.27)^†	6.10(0.93)	5.81(1.04)	6.00(1.04)	5.95(1.11)
Male	5.52(1.27)*	6.07(0.95)	5.53(1.17)	6.18(0.81)	5.59(1.12)^†	4.86(1.65)***	4.65(1.73)^†	6.03(0.92)	5.36(1.35)	5.52(1.14)*	5.96(1.13)	6.22(1.02)^†	5.42(1.20)	6.22(0.88)	5.72(1.10)^†	6.01(0.95)	5.65(1.07)	6.01(1.00)	5.75(1.09)
Cohen’s d	0.05	0.12	0.23	0.06	0.05	0.78	0.26	0.10	0.05	0.43	0.04	0.16	0.25	0.27	0.11	0.10	0.15	0.01	0.18
Cohen’s U3 (%)	52.00	54.60	59.20	52.40	52.00	78.30	64.20	53.80	52.00	66.60	51.60	56.40	59.80	60.70	54.20	54.00	56.00	50.40	57.20
Tilton Overlap (%)	98.00	95.40	90.80	97.60	98.00	69.50	85.60	96.20	98.00	83.00	98.40	93.60	90.10	89.20	95.80	9.00	94.00	99.60	92.80

N = 448. This table shows the effect sizes for differences in employer attributes by age, gender, and relationship status.

The SDs are reported in brackets next to the means of the respective groups. We also report whether the SDs across groups are significantly different from each other. The p values are based on the Levene test to assess the equality of variances.

†

p < .10; *p < .05; **p < .01; ***p < .001

The inferences drawn from the effect-size analysis underscore the notion that Russians are markedly different from Swiss on many employer attributes. Especially large effect sizes are obtained for the following employer attributes: culture that accepts minorities (Cohen’s d = 0.81), performance-related bonus (Cohen’s d = 0.92), and good possibilities for rapid promotion (Cohen’s d = 1.11). Russians appear to emphasize less on a culture that accepts minorities compared to Swiss, a difference that might be explained by less collectivist upbringing among Swiss Millennials. However, Russians, rate performance-related bonus and good possibilities for promotion as markedly higher than the Swiss, likely because of a drive toward market capitalism and (and a turn against communal paternalism). Medium effect sizes for nationality are observed for prestige and challenging work. While Russians attach more importance to prestige, they place less emphasis on challenging work compared to the Swiss.

Respondents aged 23 or younger consider prestige (Cohen’s d = 0.70), performance-related bonus (Cohen’s d = 0.76), and good possibilities for rapid promotion (Cohen’s d = 1.00) as more important than respondents aged 24 or older. This may be explained by the fact that older Millennials have entered the workforce and have developed more realistic job expectations. For gender, the largest Cohen’s d amounts to 0.78 indicating that women attach markedly more importance to a culture that supports equality between sexes compared to men. This reflects a strong desire by women to overcoming more patriarchal structures in place.

As the values for Cohen’s d, Cohen’s U3, and Tilton Overlap (Table 8) markedly deviate from the reference values that are indicative of homogeneity (i.e., Cohen’s d = 0, Cohen’s U3 = 50%, Tilton Overlap = 100%), we again find support for heterogeneity among Millennials based on differences in generational location, age, and gender.

Overall, our analyses suggest the influence of nationality in the perception of preferred employer attributes among Millennials and hence provide some support for Hypothesis 3.

Discussion

Study 3 examines the importance of different employer attributes of job seekers based on whether they are from Switzerland or Russia. The study highlights differences between Russian and Swiss Millennial job seekers. We conclude that the generational location, on the basis of nationality, is another potentially important factor that contributes to the heterogeneity among Millennials. In addition, our results also point to potential issues when generalizing generational findings from one national context to another. We will discuss these results in concert with those from Studies 1 and 2 in our concluding discussion.

Concluding discussion

Challenging research that makes comparisons across generations, this article investigates generational heterogeneity by exploring whether and how differences in work values, work/life balance practices, and employer attributes are associated with age, gender, relationship status, and nationality among Millennials. We draw from Mannheim’s (1952) generation theory and propose that individual factors (e.g., age and gender), relational factors (e.g., relationship status), and contextual factors (e.g., nationality) may lead to within-generational bonds that constitute different generation units with different work values and organizational attraction arising from job characteristics (e.g., work/life balance), and employer attributes (e.g., branding). Our results from three studies support our general premise; these factors influence Millennial preferences on a broad range of workplace characteristics, raising questions on the appropriateness when making comparisons across generations that span 15–20 years, or when generalizing a generation from one generational location with different development, historical, and social contexts to another. Past research measuring generational differences on work-related variables adopted a cohort-based perspective which focuses on mean differences among generations (Foster, 2013). This neglects differences within cohort variances which biases/skews research findings. As a result, findings from comparative studies must be interpreted with caution.

To demonstrate how individual variables are related to work-related variables within a single generation, we conducted three studies. In Study 1, we analyze how age, gender, and relationship status among Millennials are associated with work values. The results provide some support for our hypotheses indicating that Millennials are not homogeneous across 16 work values. As Millennials advance in age, aesthetic, creativity, intellectual stimulation, independence, variety, and way of life tend to become more important, while prestige, management, economic return, security, and career become, on average, less important. These trends suggest that age-related differences are (more) influential in predicting work values for Millennials. Study 2 provides further support for the relevance of age for work/life balance and workplace flexibility practices. Millennials’ willingness to enter into a long-term employment relationship with an organization is associated with age; older Millennials display a tendency to consider this more important than younger Millennials. As a control variable in Study 3, age is also associated with some employer attributes for Millennials.

With respect to gender, Study 1 finds that women on average emphasize certain work values (e.g., altruism, aesthetic, and supervisor relationship) more than men, and the reverse holds true for men on other work values (e.g., intellectual stimulation, prestige, and management). Thus, our findings suggest that women and men espouse different work values despite sharing a common generation (Beutell & Brenner, 1986; Duffy & Sedlacek, 2007; Kaifi et al., 2012; Terjesen et al., 2007). Study 2 further shows that gender moderates the relationship between part-time work and Millennials’ willingness to enter into long-term employment. It is not surprising for women to express less interest when part-time work is not offered. Study 3, which included gender as a control variable, similarly finds gender differences in some employer attributes among Millennials.

We also find differences on the basis of relationship status on a smaller number of work attributes. In Study 1, Millennials who are single value creativity, while those in a relationship value security. However, we did not find any effect for relationship status as a moderating variable between work/life balance and organizational attraction in Study 2. Although relationship status appears to have less influence on differences in most of the work values, compared to age and gender, we believe that being in a relationship affects perceptions of work values likely because decisions and plans are often made together with the partner. Care and responsibility for someone in private life are likely to affect work life. The literature on work-family conflict strongly supports this assumption (Carlson & Kacmar, 2000).

When we explore differences in employer attributes in Study 3, we find differences between Swiss and Russian Millennials. Although the Swiss and Russian respondents belong to the same generation, national location influences their perceptions of employer attributes and attractiveness. This finding is noteworthy because most research studies on Millenials are conducted in the U.S. and are generalized to other countries. Although differences in terms of the generation’s location between Western Europe and the United States may be lower compared to differences between the United States and Russia, there are likely to still be differences that must be considered when making conclusions about a generation.

Taken together, our three studies demonstrate differences among Millennials arising from age, gender, relationship status, and nationality. Thus, when comparing mean differences in generational studies, these differences are neglected because data are aggregated to a generational value.

Limitations and future research

This study has a number of limitations, some of which suggest promising avenues for future research. First, the data of all three studies were self-reported, which may give rise to social desirability and response-set biases. However, there was good variance and normal distribution in the responses, giving us no indication of these concerns. Second, the samples in Studies 2 and 3 are Millennial graduate students and are well placed for assessing job-seeker preferences and commitment (Brown et al., 2006). However, it would be informative to investigate whether these results hold for Millennials who have work experience and might interpret work and organizational attributes differently (Cable et al., 2000; Kuron et al., 2015). Third, we investigate the heterogeneity among Millennials on the basis of age, gender, relationship status, and nationality, and although significant differences were found, little variance was explained (see Ng et al., 2010). We encourage future research to identify additional factors. For example, socioeconomic status, education, sexual orientation, and gender identity may offer additional insights (see Lyons et al., 2014). Fourth, the samples were collected in Switzerland (for all studies) and Russia (Study 3), and in following our own advice, care must be taken when generalizing the results to other countries. Finally, this study focuses on differences within the Millennial generation but made no comparison with other generations. We also suggest a more elaborate design on examining differences between generations along with the moderating effects of individual variables such as age, gender, origin, social location, and relationship status.

Practical implications

Our paper highlights heterogeneity within the Millennial generation on the basis of age, gender, relationship status, and nationality, which is common across all generations. With this knowledge in mind, we suggest that a “one size fits all” human resource management (HRM) policy aimed at attracting and retaining the Millennial generation is inadequate as aging (maturation), lifestyle choices, and national contexts play a role in affecting what Millennials prioritize and desire in their work and from their employers (Claus, 2019; King & Vaiman, 2019). This finding is not surprising given that Kuron et al. (2015) have previously identified shifts in work values when Millennials transition from school to work.

With this in mind, we suggest that employers consider diverse work and life experiences may require different responses with respect to HRM practices. Younger Millennials lacking in experience may be drawn to more extrinsic aspects of work (e.g., prestige, management, economic return, and career), while those who have had several years of work experience—and have had these needs fulfilled—value more intrinsic aspects (e.g., intellectual stimulation, independence, supervision relationship, and variety). Recruitment and employer initiatives for college students should be aimed at early career attraction, while job design, work responsibilities, and supervision should be crafted with career progression in mind.

Likewise, we know from research that gender roles and life choices remain dominant in our work lives (Hakim, 2018), thus the provision of work/life balance and flexible workplace arrangements will continue to be more popular with women than men, at least with the Millennial generation. However, as society becomes more egalitarian over time, gender differences in future generations may weaken.

Finally, we draw attention to the need for caution when transferring HRM policies and practices from one country or region to another. Despite sharing the same location in time, not all generations share the same work values and attitudes or desire the same organizational attributes. Employers operating across national boundaries should therefore consider historical and socioeconomic development stages when designing appropriate HRM policies to attract Millennials and future generations of employees.

Footnotes

The data for this paper were collected by Sara Ferroni (Study 1),Annette Vogt (Study 2),and Elena Hubschmid (Study 3). We thank them for allowing us to use their data. Preparation of this manuscript is supported in part by the Smith Professorship in Equity and Inclusion in Business at Queen’s University.

Authors’ note

An earlier version of this paper was presented at the EAWOP Small Group Meeting on Young People’s Work,Employment and Careers,29 June–3 July 2020.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

ORCID iDs

Eddy S Ng

Thomas Köllen

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Do “one-size” employment policies fit all young workers? Heterogeneity in work attribute preferences among the Millennial generation

Abstract

Keywords

Introduction

Conceptual background and theory

Study 1—work values

Methodology

Sample

Measures

Work values

Age

Gender

Relationship status

Results

Discussion

Study 2—work/life balance

Methodology

Sample

Analytical procedure

Experimental design

Measures

Job characteristics (Independent variables)

Age

Gender

Relationship status

Results

Discussion

Study 3—employer attributes 4

Methodology

Sample

Measures

Employer attributes

Generational location

Post hoc test variables

Results

Discussion

Concluding discussion

Limitations and future research

Practical implications

Footnotes

Authors’ note

Declaration of conflicting interests

ORCID iDs

References

Study 3—employer attributes⁴