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10.3390/su11123443
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Open AccessArticle

The Role of Relationships at Work and Happiness: A Moderated Moderated Mediation Study of New Zealand Managers

by
Jarrod Haar
1,
Anja Schmitz
2,
Annamaria Di Fabio
3,* and
Urs Daellenbach
4
1
Department of Management, Auckland University of Technology, Auckland 1142, New Zealand
2
Business School, Pforzheim University of Applied Science, 75175 Pforzheim, Germany
3
Department of Education, Languages, Intercultures, Literatures and Psychology (Psychology Section), University of Florence, 50135 Florence, Italy
4
School of Management, Victoria University of Wellington, Wellington 6140, New Zealand
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(12), 3443; https://doi.org/10.3390/su11123443
Received: 13 June 2019 / Revised: 18 June 2019 / Accepted: 19 June 2019 / Published: 22 June 2019
(This article belongs to the Special Issue Well-Being and Happiness for Harmonization of Natural and Cultural Resources: Cross-Cultural Pillars of Sustainability)
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Abstract

Interpersonal relationships at work are important especially for the well-being of employees. The present study tests Positive Relational Management (PRM) and its influence on employee happiness, and we include two firm-level moderators and an individual-level mediator to better understand the potential complexity of effects. Importantly, we test this in the context of New Zealand, which has been under-represented in employee studies of happiness and is important due to a growing national interest in wellbeing. We test whether positive relationships at work shape greater meaningful work (MFW) and this then influences happiness and mediates the effects of PRM. We also include Human Capital (the quality of people inside the firm) and firm size as moderators and combine these all to test a moderated moderated mediation model in PROCESS. We test this on a sample of 302 New Zealand managers with time-separated data. We confirm the dimensionality and reliability of the PRM scale and find it is positively related to MFW and happiness, while MFW fully mediates the direct effect of PRM. We find interaction effects including a moderated moderated mediation effect, with the indirect effect of PRM differing depending on firm size and the strength of human capital. The implications for understanding the importance of relationships on employee happiness is discussed.
Keywords: Positive Relational Management; happiness; meaningful work; human capital; firm size; moderated moderated mediation Positive Relational Management; happiness; meaningful work; human capital; firm size; moderated moderated mediation

1. Introduction

Over the past few years, there has been a renewed focus on healthy organizations [1] and thus happier workers. This is due to the links between work and the well-being of workers [2] including happiness [3]. The present study focuses on the happiness of leaders due to the growing pressures they face [4] and the workplace challenges that might ultimately confront their happiness. It is important to examine leaders because research shows that leaders can influence others in their workplace—especially subordinates [5]. Hence, a happy leader may well lead to a happy organization. We use a positive psychology approach to underpin our focus on a positive outcome [6,7] like happiness, and examine the influence that positive interpersonal relationships play because they are a vital personal resource that managers can draw on to enhance their well-being [7].
Our study makes four contributions. First, it builds on recent research examining positive relationships at work and its influence on well-being [7,8,9] and extend this towards manager happiness. Second, we include MFW as a potential mediator due to its important role on employee outcomes including well-being [9] and suggest as a mediator it allows us to better understand the process of influence regarding positive relationships on happiness. Next, we test two firm-level moderators—human capital and firm size—to capture the context that relationships exist within (higher quality workers and larger-sized firms with greater resources) and then combine these to test for moderated moderated mediation [10]. Here, we examine firm-level factors as boundary conditions to better understand complex effects. Finally, our methodology includes time-lagged data from New Zealand managers to provide a distinct leadership angle to understanding happiness at work. Our focus on New Zealand is important because 2019 marked the beginning of the New Zealand Government’s wellbeing budget [11]. Our study model is shown in Figure 1.

2. Happiness

Research on happiness has drawn increasing attention since the 1980s and its positive effects on organizational measures such as economic performance, productivity and decreases in turnover, have been repeatedly demonstrated in research [12,13]. Happiness has been shown to be related to several positive outcomes in all major life domains such as work, relationships, and health [14]. Not only are successful people happier but happiness also increases the likelihood for success [14]. Happy workers show, for example, higher levels of activity, approach orientation, interest in work, and persistence when facing difficulties [15]. Despite the endless potential benefits of happiness, it has been poorly defined at times, with analyses showing considerable overlap with other constructs, such as job satisfaction or engagement [3,14].
These constructs contain elements of pleasant judgements or pleasant experiences but differ in terms of the level at which they are deemed to exist, the stability or variability over time and their content (see Ref. [3] for a detailed discussion). Most constructs in the happiness domain are commonly “defined and measured as transient states” [3] (p. 386). For the purpose of our paper, we thus define happiness as the propensity to experience frequent positive emotions, e.g., joy, interest, pride, pleasure, and infrequent negative emotions, e.g., sadness, anxiety, anger [3,14]. The frequency of the experience has been established as being more relevant for the happiness construct than the intensity of emotions [16]. Thus, being consistently positive and happy is more important than being deeply happy one day or weakly happy the next. Fisher (2010) [3] criticizes the measurement of happiness for its lack of the “holistic appreciation” of the construct (p. 391), and we, therefore, follow previous research using only a single-item measure, asking participants to rate their happiness at work [14,17,18].
In their review, Ref. [14] showed that cross-sectional, longitudinal, and experimental research, consistently demonstrated positive effects of happiness. Cross-sectionally, happy individuals report higher levels of mental and physical health, as well as higher levels of success in work and relationships [14]. Longitudinal studies also show positive effects of happiness across the different life domains: happiness precedes fulfilling and productive work, satisfying relationships, better mental and physical health, as well as longevity [14]. Furthermore, Ref. [14] highlights that a substantial body of experimental data demonstrated that short-term positive affect causes behaviors that parallel success (e.g., positive perceptions of self and others, self-efficacy, enjoyment of tasks). Transient happy moods have been shown to increase the likelihood for social interactions and lead to positive social consequences, such as seeking out others and initiating conversation [19], higher energy and greater interest in leisure activities [20,21], and increased openness and sensitivity to others [22].
Despite the consequences of happiness, there is little consensus concerning how happiness at work can be increased [3,13]. In her review, Ref. [3] concluded that “it appears that happiness is a function of environmental events and circumstances, stable tendencies in the person, and the fit between the two, with the possibility of limited modification by carefully chosen and intentionally varied volitional acts” (p. 394). Fisher (2010) [3] does allude to the importance of pleasant relationships at work towards shaping happiness and notes that workplace social connections have not undergone enough scrutiny by researchers, despite the known value of interpersonal relationships on well-being [23]. We now proceed to look at a new construct that examines the roles of positive relationships at work and hypothesize how this might influence happiness.

3. Positive Relational Management

The Positive Self and Relational Management (PS&RM) model [24] is a newly created positive psychology framework, which examines positive relationships in the workplace. Within the PS&RM model, Ref. [25] highlights the pivotal role that positive relational management (PRM) plays, because it includes a focus on relational management including the self [24,25]. Through PRM, Ref. [25] suggests that employees can build their individual competencies that enable them to accomplish and succeed when facing workplace challenges. Specifically, these competencies are built around skills relating to socialization, relationships and social support. The PRM scale includes interactions—not just with co-workers in the workplace—but also with the self—to capture relationships more completely [26].
The PRM scale [7,16] focuses on three dimensions: (1) respect, (2) caring, and (3) connectedness. The first dimension is defined by Ref. [7] as Respect, which relates to an employee’s respect for others, their own personal respect for themselves, as well as the perception of others’ respect for them. It is this multiple approach to PRM that enables a thorough encompassment around relationships [16,27]. The second dimension is Caring, which relates to an employee’s perception of the care they have for others, themselves, as well as the perception of how much others care for them. Thus, a high score in caring reflects someone who not only cares for others and themselves but feels other co-workers and work colleagues provide care for them too. The final dimension is Connectedness, which reflects the quality of connections with family, friends, workmates and significant others like a partner. Overall, the factors of respect, caring and connectedness form the PRM which provides positive psychological resources that employees should be able to draw on [25,26], to enhance their well-being and ultimately happiness. This resource has alignment with the Conservation of Resources (COR) framework [28,29], whereby employees seek to retain, acquire and minimize the loss of psychological resources [29]. Thus, PRM becomes a source of resources that enables a manager to draw on to facilitate their happiness. Here, we might expect managers struggling with a work challenge to engage and receive respect, care and connections, and this will reaffirm their value and benefit their happiness.
Studies of PRM have found positive links to well-being [30], including moods (positive affect), life satisfaction, flourishing, and meaningfulness [7,25]. Indeed, social connections that are of a higher quality can be important factors in shaping employee happiness [31,32]. For example, Ref. [33] reports that employees with strong relationships at work—specifically having a best friend—reported much higher engagement levels. Consequently, we expect managers who report higher levels of PRM (respect, caring and connectedness) will represent those managers with an abundance of psychological resources [28,29], which will enable them to draw on and ultimately experience greater happiness. We posit the following.
Hypothesis 1 (H1).
PRM will be positively related to happiness.

4. Meaningful Work

Spreitzer (1995) [34] defined meaningful work (MFW) as “the value of a work goal or purpose, judged in relation to an individual’s own ideals or standards. Meaning involves a fit between the requirements of a wok role and beliefs, values, and behaviors” (p. 1443). We focus on MFW because we argue that in a workplace context, PRM might shape manager happiness through the meaning found in work. Indeed, the links between MFW and job and non-work outcomes are supported [35]. For example, Ref. [36] found in a sample of employees including managers that MFW was a strong predictor of work satisfaction. Steger, Dik, and Duffy (2012) [37] found that MFW was positively related to multiple well-being outcomes including positively to life satisfaction and negatively to mental health issues. Thus, MFW can help shape the well-being of employees and we suggest this will include manager happiness. We suggest that quality relationships at work will influence MFW as workers with stronger experiences of respect, caring and connectedness, will feel more inspired and focused on their work leading to higher MFW. Di Fabio and Kenny (2019b) [9] found that PRM was positively related to meaning in life and well-being and similarly, meaning was beneficially related to well-being outcomes. This suggests that PRM will be positively related to MFW and MFW will be related to happiness. Thus, we test MFW as a mediator of the effect of PRM on happiness as under a CoR theory approach; PRM is a resource base that ultimately makes work more meaningful, which in turn, leads MFW to influence happiness. This aligns with studies finding MFW acts as a mediator to work factors [35,37].
We posit the following.
Hypothesis 2 (H2).
PRM will be positively related to MWF.
Hypothesis 3 (H3).
MFW will be positively related to happiness.
Hypothesis 4 (H4).
MFW will mediate the influence of PRM on happiness.

5. Firm Level Moderators

Due to our focus on managers, we include two firm-level moderators to test for other factors that might influence our existing relationships.

5.1. Human Capital

Singh and Rao (2016) [38] defined intellectual capital as “the sum of all organizational knowledge resources, which resides in aspects within as well as outside the organization” (p. 132), and these resources are viewed as critical for firm success [39]. These distinctive firm resources [40] include human capital, which refers to a firm’s workforce in terms of the skills, knowledge, and expertise they have [41]. Thus, a firm with a stronger human capital is likely to have more educated and skilled employees who can craft superior solutions to enhance the performance of their firm. Youndt and Snell (2004) [42] argued that a firm’s workforce is an essential component of every firm, although clearly workforces vary. In the present study, we suggest that a workforce that is of higher quality (high human capital) will provide relationships that are superior over those with less skilled workforces (low human capital). Thus, working with higher quality co-workers and subordinates will enhance (intensify) the benefits of PRM on outcomes. Ultimately, under CoR theory, this should provide better quality relationships and enhance the effects of PRM on MFW and happiness because the sources of interactions are of superior quality. We thus posit the following.
Hypothesis 5 (H5).
Human capital will moderate the influence of PRM on (a) MFW and (b) happiness.

5.2. Firm Size

Our second firm-level factor relates to firm size. There is evidence that firm size might play a role in effects, and this may be especially relevant in a New Zealand context, whereby the vast majority of firms are small in size with under 50 employees [43]. There is evidence that smaller-sized firms sometimes outperform larger sized firms [44,45], although this is not always the case [46], and in moderating studies, larger sized firms do appear to be more beneficial. For example, Ref. [47] argued that large-sized firms would have superior resources (e.g., financial) that produce better behaviors, while Ref. [48] found that large-sized firms adopted programs more readily than smaller sized firms. Under CoR theory, we expect large-sized firms to have superior resources—greater numbers of people, better quality of people and financial resources, that will ultimately provide stronger effects [44,47] from PRM towards MFW and manager happiness. This is because managers will have a broad range of superior resources to draw on to shape and enhance the positive work relationships. We also combine the potential moderating effects of human capital and firm size to suggest a three-way interaction whereby PRM’s beneficial influence will be enhanced by both firm size and human capita. We posit the following.
Hypothesis 6 (H6).
Large-sized firms will moderate the influence of PRM on (a) MFW and (b) happiness.
Hypothesis 7 (H7).
Large-sized firms and high human capital will moderate the influence of PRM on (a) MFW and (b) happiness.
Beyond these two-way and three-way moderating effects from firm-level factors, we also combine these and then explore firm size as a moderator of the moderating effect of human capital on the PRM–MFW–happiness relationship. Hence, firm size and human capital might potentially both act as boundary conditions, with firm size attenuating the influence and effectiveness of human capital on the indirect effect of PRM on happiness through MFW. This reflects a moderated moderated mediation effect, which Ref. [10] refers to as conditional process modelling. He states this approach is “an analytical strategy focused on quantifying the boundary conditions of mechanisms and testing hypotheses about the contingent nature of processes…whether an indirect effect (mediation) is dependent on another variable (moderation)” (p. 5). In the present study, we can examine two boundary conditions—first firm size and then human capital—to test their combined boundary condition influence on the relationships between PRM, MFW, and manager happiness. We expect managers in larger firms will report stronger indirect effects from PRM on happiness when the quality of their workforces (human capital) are strong, and reduced effects when they are weak. We also expect these relationships will be inherently weaker in small-sized firms where there are insufficient resources to promote and enhance the effectiveness of PRM on manager happiness. We posit the last hypothesis.
Hypothesis 8 (H8).
The indirect relationship between PRM and happiness via MFW will be moderated by firm size and human capital, such that the indirect effect of PRM becomes stronger as firm size gets larger and human capital gets stronger (moderated moderated mediation).

6. Methods

6.1. Participants and Sample

Data were collected from 302 private sector New Zealand managers recruited via a Qualtrics survey panel. The Qualtrics’ system is quality focused and automatically removes potential respondents who complete surveys outside the speed estimation (too fast or slow) and ensures only one survey response per participant. The Qualtrics’ system is proprietary and thus there is no information on the number of respondents approached or removed. This methodological approach has produced robust employee samples [49]. In the present study, respondents completed the survey in two waves, separated by a 1-month time gap. Survey 1 had demographic variables, PRM and the moderators, while survey 2 included happiness and MFW.
Overall, respondents worked in large-sized firms (523 employees, SD = 2921), and across a wide range of industries: retail (14%), professional services (13.3%), construction (7%), manufacturing (6.6%), and financial services (6.3%). Manager respondents were on average aged 42.7 years (SD = 12.8), had average job tenure of 8.7 years (SD = 6.2) and were highly educated: 20.2% postgraduate qualifications, 40.4% university degree, 26.2% technical qualification, and 13.2% high school qualification. Managers had 7.7 years job tenure (SD = 5.8) and were well spread across managerial hierarchy: 41.4% senior managers/executives, 40.7% middle-level managers, and 17.9% low-level managers.

6.2. Measures

PRM was measured using nine items of the PRMS by Ref. [7], coded 1 = strongly disagree, 5 = strongly agree. The construct has three dimensions: (1) respect, sample item “Others have respect for my value and my uniqueness”, (2) caring, sample item “I often take care of myself”, and (3) connectedness, sample item “I keep a balance in my relationships between family, friends and significant others”. We used three items per scale to minimize cross-loading across items. Following Ref. [7], we combine the items into a single construct (PRMS) and this had very good reliability (α = 0.86). This construct has been validated [8,9].
We measured Happiness by a single item frequently used in happiness research [14,17,18]. Participants were asked to rate their happiness using a 10-point scale (1 = extremely unhappy, 5 = neutral, 10 = extremely happy; [17]).
MFW was measured using the three-item scale by Ref. [34], coded 1 = strongly disagree, 5 = strongly agree. A sample item is “The work I do on this job is very important to me”. The measure had very good reliability (α = 0.89) and our reliability aligns well with other studies using the MFW construct (α = 0.87 [34]; α = 0.87 [50]).
Human Capital was measured using the three-item scale from Ref. [51], coded 1 = strongly disagree, 5 = strongly agree. A sample item is “The qualifications of our employees (human resources) are the best among all our competitors” (α = 0.80).
Firm Size was coded as the total number of full-time employees. This data was log transformed to induce normality.
Control Variables. We control for Manager demographics of Age (in years), Education (1 = high school, 2 = technical college, 3 = university degree and 4 = postgraduate qualifications), and Job Tenure (in years). We control for these demographics given their potential effects towards positive outcomes [5,52]. We also control for firm industries: Retail, Professional Services, Construction, Manufacturing, and Financial Services. These were all dummy variables where firm sector was coded 0 = not, or 1 = retail or construction etc.

6.3. Measurement Models.

We conducted a confirmatory factor analysis in SEM with AMOS version 25 and followed recommendations by Ref. [53] regarding goodness-of-fit indexes and thresholds: (1) the comparative fit index (CFI ≥ 0.90), (2) the root-mean-square error of approximation (RMSEA ≤ 0.08), and (3) the standardized root mean residual (SRMR ≤ 0.10). We test alternative CFA models combining the various PRMS constructs to determine if the theoretically derived constructs fit the data best. Our results are shown in Table 1.
Overall, the hypothesized measurement model was the best fit for the data: χ2(df) = 143.1 (90), CFI = 0.97, RMSEA = 0.04, and SRMR = 0.04. The alternative measurement models, whereby variations of the PRM dimensions were combined, all resulted in poorer fitting models (all p < 0.001) [54].

6.4. Analysis

Relationships were tested using PROCESS 3.1 (in SPSS v. 25), specifically model 12 (moderated moderated mediation). Control variables (age, education, job tenure, and industries: retail, professional services, construction, manufacturing and financial services) were entered in Step 1 with PRMS entered as the independent variable, MFW as the mediator variable and happiness as the dependent variable. Human capital and firm size were entered as the moderator variables, the products were mean-centered, and bootstrapping (5000 times) was used to provide confidence intervals. The skewness and kurtosis statistics indicated that each of these was within acceptable limits [54].

7. Results

Descriptive statistics for the study variables are shown in Table 2.
Table 2 shows that PRMS is significantly correlated with MFW (r = 0.34, p < 0.01) and human capital (r = 0.46, p < 0.01), as well as happiness (r = 0.21, p < 0.01). MFW is significantly correlated with human capital (r = 0.40, p < 0.01), firm size (r = –0.17, p < 0.01), and happiness (r = 0.37, p < 0.01). Happiness is significantly correlated with human capital age (r = 0.24, p < 0.01), firm size (r = –0.15, p < 0.01), and age (r = 0.18, p < 0.01).
The results of the moderated moderated mediation regression are presented in Figure 2.
The results show that PRMS is significantly related both to happiness (β = 0.69(0.21), p = 0.0013 [LL = 0.27, UL = 1.1]) and MFW (β = 0.22(0.08), p = 0.0088 [LL = 0.06, UL = 0.38]), supporting Hypotheses 1 and 2. Furthermore, MFW is significantly related to happiness (β = 0.77(0.17), p = 0.0000 [LL = 0.43, UL = 1.1]), and when included in the model, it fully mediates the effect of PRMS on happiness: (β= drops to 0.18(0.24), p = 0.4489 [LL = -0.29, UL = 0.66]). These findings support Hypotheses 3 and 4.
Human capital did not interact significantly with PRMS towards either MFW or happiness, failing to support Hypotheses 5. Firm size did interact with PRM towards MFW (β = 0.11(0.03), p = 0.0008 [LL = 0.05, UL = 0.18]) and similar so between PRM, human capital and firm size towards MFW (β = 0.11(0.04), p = 0.0108 [LL = 0.02, UL = 0.19]) for significant two-way and three-way interactions, supporting Hypothesis 6a and 7a. Finally, the results of the index of moderated moderated mediation was found to be significant (Index = 0.08(0.04), p = 0.0220 [LL = 0.01, UL = 0.17]). Regarding interpreting this effect, Ref. [10] indicates it means the indirect effect of PRMS on happiness (mediated through MFW) differs between managers in different-sized firms and workplaces with different human capital. To illustrate effects, we present the graphed interactions (two-way and three-way) in Figure 2 and Figure 3 and the moderated moderated mediation effects in Figure 4, Figure 5 and Figure 6.
The significant two-way interaction in Figure 2 shows that at low levels of PRMS, the influence on MFW is greater for manager respondents in small-sized firms than large-sized firms. When we compare respondents at high levels of PRMS, both groups of respondents report higher levels of MFW, but the group with the highest MFW is reported by respondents in large-sized firms, supporting the expected effects. The three-way interaction (Figure 3) shows that at low levels of PRMS, the influence on MFW is distinct, with respondents in high human capital firms reporting significantly higher MFW, with modest differences across firm size. When we compare respondents reporting high levels of PRMS, this group of respondents reports higher levels of MFW, although again there are clear distinctions (and benefits) in high human capital firms over low human capital. Here, respondents report the highest MFW when they work in large-sized firms, supporting our expected effects.
Regarding the moderated moderated mediation effects, we follow Ref. [55] to probe the conditional indirect effect. Specifically, we examine the magnitude and significance of the indirect effect of PRMS on happiness through MFW, conditional on human capital (at −2SD, mean, and +2SD) for firms across the sizes of small, average and large (Figure 3, Figure 4, Figure 5 and Figure 6). We find that for respondents in small-sized firms (Figure 3), there is no significant indirect effect of PRMS on happiness, conditional on human capital as all values cross zero. In average-sized firms (Figure 4), we find a significant indirect effect of PRM on happiness vis-à-vis MFW which was significant and strongly positive at low levels of human capital (estimate = 0.25(0.14), p = 0.0344; LLCI = 0.03; ULCI = 0.56). On the other hand, for those respondents in average-sized firms and with average human capital, the effect of PRMS on happiness vis-à-vis MFW was significant, positive and modest (estimate = 0.17(0.09), p = 0.0000; LLCI = 0.02; ULCI = 0.38). Finally, for those respondents in average-sized firms and with high human capital, the effect of PRMS on happiness vis-à-vis MFW was non-significant (estimate = 0.09(0.09), p = 0.1717; LLCI = -0.07; ULCI = 0.29).
Finally, for those respondents in large-sized firms with low human capital, the effect of PRMS on happiness vis-à-vis MFW was significant, positive and strong (estimate = 0.31(0.15), p = 0.0229; LLCI = 0.05; ULCI = 0.65). For those respondents in large-sized firms and with average human capital, the effect of PRMS on happiness vis-à-vis MFW was significant, positive and stronger (estimate = 0.37(0.14), p = 0.0054; LLCI = 0.12; ULCI = 0.68), and at high levels of human capital, this was stronger again (estimate = 0.42(0.16), p = 0.0035; LLCI = 0.15; ULCI = 0.76). Ultimately, these effects show that the indirect effect of PRM on happiness is stronger in weaker human capital firms when they are average sized, but as expected, in larger-sized firms, greater human capital leads to stronger indirect effects from PRM, supporting our hypothesis. Ultimately, the models account for a robust amount of variance towards MFW (31%) and more modest for happiness (21%).

8. Discussion

The present study examined the role of positive work relationships on the happiness of managers due to Ref. [2] highlighting the importance of work on the well-being of workers. This study extended the work of Ref. [9] around the importance of positive interpersonal relationships at work by examining manager happiness. Our focus on managers aligns with the importance of leaders in organizations [4], their ability to directly influence subordinates [5], and acknowledges the high pressures that leaders face [4]. Our positive psychology approach of PRM aligns with arguments that a healthy organization [1,2] should facilitate positive relationships and positive outcomes like happiness. In support, we found that healthy and positive interpersonal relationships (relating to higher respect, caring and connectedness) between managers and subordinates (in both directions)—as well as having respect, care and connections with themselves—were an essential factor in building manager happiness. Our findings align well with those of other PRM studies [7,8,9], indicating effect sizes that are similar and thus providing external validity to the potential benefits of positive relationships at work.
Further, we understand that positive work relationships and happiness align with greater well-being and work engagement and performance [7,56,57,58,59,60]. For example, Ref. [57] found that happiness is positively related to work engagement and negatively related to job burnout, highlighting beneficial well-being and work relationships for happier workers. Work engagement is a key organizational factor that plays a vital role including its positive links to individual and organizational performance [59]. Hence, happiness, engagement and performance are likely intertwined, making a happier employee more likely to be a productive one. In a similar vein, Ref. [61] reported that happier employees were less likely to be absent, highlighting the notion that a happier employee is a healthy employee. Thus, this ‘moto’ might be expanded to say happy employees are healthier and more productive.
Overall, our analysis showed support for the PRM scale in a sample of New Zealand managers, which extends the reach of this construct beyond Italy [7,25]. However, our analysis showed that while PRM was positively related to happiness, it was also a significant predictor of MFW, and this factor fully mediated the influence of PRM on happiness. This is an important contribution because it highlights the nature of relationships [62,63,64] and indicates a potential process pathway to understanding the effects. In consideration though, this might reflect the sample of managers. Our findings suggest that positive relationships build the importance and meaning of work, which in turn influences happiness. Thus, we find that a happy manager is one that finds strong meaning in their work, and this might be understood as being built on positive relationships at work. Further replication of these relationships on non-manager samples might confirm whether this is an effect unique to managers, or generalizable across other workers.
Our findings also help shape and build our understanding of MFW, especially in the context of managers. There is little exploration of the links between positive work relationships and MFW and similarly so towards happiness. Given the central role that work plays in most employees’ lives [65], we can only assume this centrality is even stronger for managers who face specific pressures from their roles [4]. We find that a manager with strong meaning in their work is significantly more likely to report happiness compared to other managers with low MFW. The three-way interaction effects also highlighted that high human capital is key for the MFW of managers, reflecting additional positive benefits at high levels of PRM. Our findings build on the most recent meta-analysis on MFW and its consequences found strong links to well-being but excluding happiness [66]. The present study shows that happiness is indeed another well-being outcomes influenced by work. Further studies examining the links between MFW and happiness are encouraged.
Finally, it was through examining boundary conditions that the present study makes its most important contributions. We found that the indirect influence of positive relationships at work varied markedly across firm size and in relationship to whether the organization worked for had stronger employee talent (high human capital). We found that in small firms, positive relationships had no significant indirect effect although this reversed in large sized firms. In firms with large size and thus extensive resources, we found the indirect influence of PRM on happiness strengthens as the quality of a firm’s workforce grows (human capital increases from low to high). Interestingly, in average-sized firms, there is a significant indirect effect from positive relationships and they are strongest in firms with weak human capital and reduce in strength when there is average human capital and are not significant at high levels of human capital.
These effects highlight the value in conducting moderated moderated mediation analysis, because such differences are not normally tested, typically being outside the scope of analytical tests [10]. Hayes (2018) [10] argues this test “quantifies how quickly the relationship between a moderator and an indirect effect is changing as a second moderator changes” (p. 4), and we find empirical evidence that highlights the dual nature, and conflicting effects, of firm size and human capital in our study. Ultimately, these findings highlight that the greater manager happiness from positive relationships at work (PRM) occurs in larger-sized firms populated with high-quality staff. The analysis also shows that as a boundary condition, firm size is important but especially so in combination with the firm-level construct of human capital. We argued that larger firms with better resourcing and ore quality workforces—with whom managers engage in interpersonal relationships more positively—would report the highest happiness, and this was effectively supported. Overall, these insights encourage further exploration of such factors in combination (moderated moderated mediation).

8.1. Implications and Future Research

These findings have implications for researchers and organizations who might be interested in the happiness of workers, especially managers. Future research might examine additional workplace factors towards happiness or extend the well-being outcomes to include mental health factors, like anxiety and depression. Further, researchers need to embrace the potential moderated moderated mediation tests and explore more complicated moderation tests to gain deeper insights into potentially complex relationships. In addition, other firm-level moderators might provide unique insights and we encourage further analysis of these, especially in tandem with boundary condition tests. For organizations, we suggest a happy leader means a happy workforce and organization, and the foundation might start with positive relationships (i.e., PRM). The links between happiness and absenteeism [61], satisfaction with performance [60], as well as happiness with engagement [57] and engagement with productivity [59] provide a strong business case for organizations focusing on employee happiness. Hence, happiness should not be considered a ‘nice’ organizational focus but an important and potentially imperative employee factor to focus on, due to the consequences of being a healthy and productive business. Thus, HR Managers might seek to facilitate greater engagement and interpersonal interactions between leaders and their subordinates, because greater development here is likely to have better effects throughout the organization—potentially beyond the managers own happiness. This might involve organizational practices seeking to implement shared activities and greater time for managers and subordinates to interact and socialize, although this should not include more meeting times [67]. In addition, practices around organizational training for inclusion and acknowledging the values of others [68], as well as training around the importance of social support with subordinates and others, is encouraged.

8.2. Limitations

Overall, while our sample size is adequate (n = 302), our separation of data across time (PRM and the moderators) at time 1 and MFW and happiness (a month later at time 2), follows suggestions by Ref. [69], and thus provides greater certainty and robustness in our analysis. Our analysis of CFA in SEM, the use of PROCESS and finding significant moderation effects also points to less potential for common method variance [70,71,72]. Despite these factors, we did test happiness using a single-item construct. While potentially leading to issues around unknown error variance and reliability, previous research shows that convergent validity between single-item and multi-item measures for satisfaction constructs are strong [73,74]. In addition, research of this construct showed good reliability as well as concurrent, convergent, and divergent validity [75], and it is well used [17] and shown to be very useful. Finally, it is worth noting that while our models are robust and informative, there is always the potential issue around constructs that were not included. For example, the Big 5 personality constructs have been found to link with happiness [76], and thus future studies might seek to include or control for Big 5 factors. Similarly, future studies might include other demographic factors as potential moderators such as gender, as this might also have important effects on the relationships examined here. Overall, we used a good sample of managers and time-lagged data and sophisticated analyses and suggest these findings are reliable and generalizable.

9. Conclusions

In conclusion, we find that positive interpersonal relationships at work help shape the happiness of managers, and this process seems to be best understood as working through enhancing perceptions around the meaning of work. However, these relationships are complex. In large-sized firms, we find distinct indirect effects whereby PRM is important and grows in value when the quality of staff is high. However, this indirect effect is not significant in small-sized firms and only partially influenced by human capital in average-sized firms. Hence, it appears that firm-level factors may put previously unknown important boundary conditions on these relationships, encouraging further exploration of these effects. Overall, a healthy organization has been promoted as one-way organizations can signal a positive place for employees to grow and thrive [1,2], and we find here that encouraging and supporting positive relationships is likely to shape happiness, specifically through MFW work managers.

Author Contributions

Conceptualization, J.H. and A.D.F.; Methodology, J.H. and U.D.; Formal Analysis, J.H.; Data Curation, J.H. and U.D.; Writing-Original Draft Preparation, J.H., A.S. and A.D.F.; Writing-Review & Editing, J.H., A.S., A.D.F. and U.D.; Supervision, A.D.F. & J.H.; Project Administration, U.D.; Funding Acquisition, U.D. and J.H.

Funding

This research was funded by the New Zealand Government for The National Science Challenge: Science for Technological Innovation.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 11 03443 g001 550
Figure 1. Study Model.
Figure 1. Study Model.
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Figure 2. PRMS x Firm Size with MFW as the Dependent Variable.
Figure 2. PRMS x Firm Size with MFW as the Dependent Variable.
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Figure 3. PRMS x Human Capital x Firm Size with MFW as the Dependent Variable.
Figure 3. PRMS x Human Capital x Firm Size with MFW as the Dependent Variable.
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Figure 4. Indirect Effects of PRMS on Happiness through MFW conditional on Human Capital for Small-Sized Firms.
Figure 4. Indirect Effects of PRMS on Happiness through MFW conditional on Human Capital for Small-Sized Firms.
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Figure 5. Indirect Effects of PRMS on Happiness through MFW conditional on Human Capital for Average-Sized Firms.
Figure 5. Indirect Effects of PRMS on Happiness through MFW conditional on Human Capital for Average-Sized Firms.
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Figure 6. Indirect Effects of PRMS on Happiness through MFW conditional on Human Capital for Large-Sized Firms.
Figure 6. Indirect Effects of PRMS on Happiness through MFW conditional on Human Capital for Large-Sized Firms.
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Table
Table 1. Results of Confirmatory Factor Analysis.
Table 1. Results of Confirmatory Factor Analysis.
Model Fit IndicesModel Differences
Modelχ2dfCFIRMSEASRMRΔχ2ΔdfpDetails
Model 1 143.1900.970.040.04
Model 2194.2950.950.060.0451.150.001Model 2 to 1
Model 3183.0950.960.070.0439.940.001Model 3 to 1
Model 4194.1950.950.060.0451.040.001Model 4 to 1
Model 5230.0990.930.070.0586.990.001Model 5 to 1
Model 1 = Hypothesized six-factor model: PRMS Respect, PRMS Caring, PRMS Connectedness, MFW, Happiness, and Human Capital. Model 2 = Alternative five-factor model: As per model 1, with PRMS Respect and PRMS Caring combined. Model 3 = Alternative five-factor model: As per model 1, with PRMS Respect and PRMS Connectedness combined. Model 4 = Alternative five-factor model: As per model 1, with PRMS Caring and PRMS Connectedness combined. Model 5 = Alternative four-factor model: As per model 1, with PRMS Respect, PRMS Caring and PRMS Connectedness combined.
Table
Table 2. Correlations and Descriptive Statistics of Study Variables.
Table 2. Correlations and Descriptive Statistics of Study Variables.
VariablesMSD12345678
1. Age42.712.8--
2. Education2.70.94−0.18 **--
3. Job Tenure8.76.20.50 **−0.07--
4. PRMS3.90.600.04–0.080.05--
5. MFW3.80.820.19 **–0.060.010.34 **--
6. Human Capital3.60.75–0.030.06–0.000.46 **0.40 **--
7. Firm Size3.42.3–0.38 **0.18**–0.23 **–0.10–0.17 **–0.08--
8. Happiness7.02.20.18 **–0.100.010.21 **0.37 **0.24 **–0.15 **--
N = 302. * p < 0.05, ** p < 0.01.
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