Abstract
Purpose
Involving employees in making management decisions is a powerful way to enhance organizational performance. However, employee involvement (EI) might exacerbate psychosocial stress at work. This paper aims to investigate this issue, illuminating the implications of EI on work-related stress.
Design/methodology/approach
Secondary data were collected from the third wave of the European Survey of Enterprises on New and Emerging Risks. A conditional process analysis based on ordinary least square regression and bootstrap sampling was accomplished to obtain evidence of the implications of EI on psychosocial risks (PSR) at work, taking into consideration the mediating role of organizational health promotion initiatives (HPI).
Findings
EI increased the sources of psychosocial stress at work, adding to individual job demands. Involving employees was positively related to a greater organizational concern for HPI, which, in turn, lessened psychosocial strain.
Practical implications
Although it contributes to organizational performance, EI propels work-related stress, which undermines individual and collective wellbeing. Involvement practices should be coupled with tailored HPI to address the PSR at work triggered by involvement, empowering people to cope with strain.
Originality/value
Scientific literature emphasizes the positive implications of EI on organizational performance, but little is known about its side effects on work-related stress. The paper provides original insights into this topic, arguing that HPI are necessitated to address the drawback of involvement on work-related stress.
Keywords
Citation
Palumbo, R., Hinna, A. and Decastri, M. (2023), "The imperfections of employee involvement: harnessing the consequences of involvement practices on psychosocial risks at work", International Journal of Organizational Analysis, Vol. 31 No. 8, pp. 60-74. https://doi.org/10.1108/IJOA-10-2022-3451
Publisher
:Emerald Publishing Limited
Copyright © 2023, Rocco Palumbo, Alessandro Hinna and Maurizio Decastri.
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
Democracy (Warner, 2019) and pluralism (Dundon et al., 2022) are important ingredients of the recipe for organizational resilience. This is especially true in an increasingly turbulent environment, which calls for organizational openness and flexibility to cope with unprecedented management challenges (Adobor, 2020). Establishing a democratic and pluralist workplace relies on empowering people, making them able to achieve control over their job, develop positive work attitudes and internalize goals that are relevant for organizational success (Menon, 2001). For this to happen, organizations should involve employees (Riordan et al., 2005), enabling them to actively shape organizational processes and dynamics (Carmeli et al., 2010).
Involvement is a human resource management practice that seeks the employees’ participation in addressing management decisions (Busch-Casler et al., 2021). Previous research emphasized that involvement nurtures organizational justice, realizing democracy in the workplace (Frega, 2021). Moreover, it enhances collaboration (Naqshbandi et al., 2019) and energizes the employees’ work dedication (Flocco et al., 2022). However, the positive implications of involvement are not immediate, since they depend on how employees’ increased participation affects the individual work experience (Cavallone and Palumbo, 2022). Recently, literature started quarrelling over this topic (Tian and Zhai, 2019), highlighting that involvement has ambiguous effects on psychosocial stress at work (Palumbo and Cavallone, 2022). On the one hand, involvement enhances the employees’ awareness of organizational processes (Tian and Gamble, 2018), making them capable of tackling job demands and exploiting job resources (Gallie and Zhou, 2020). On the other hand, it generates management challenges (Boxall et al., 2019; Oppenauer and Van De Voorde, 2018), enacting role overload and work intensification (Ebrahimi and Rad, 2017; Teo and Waters, 2002). These considerations call into question the effects of employee involvement (EI) on work conditions (Philip and Arroswsmith, 2021).
Involving employees in addressing management decisions has been generally understood as a valuable tool to overcome work-related stress (Butts et al., 2009) and achieve safety in the workplace (Adler et al., 1997) through participation. However, to the best of the authors’ knowledge, only limited attention has been paid to the side effects of involvement on work strain (Frank et al., 2022; Palumbo, 2021). The article attempts to push forward what we know about this issue, providing an answer to the following research question:
What are the implications of EI on psychosocial stress at work?
An empirical study design was arranged to answer this question. A particular form of EI was investigated, which was targeted at facilitating employees’ participation in crafting solutions to overcome sources of stress at work. In doing so, we paid attention to the digitalization of the work environment (Palumbo, 2021). In fact, the pervasiveness of digital technologies makes it difficult to align the workplace’s technical features with the soft factors associated with human resource management practices (Khuntia et al., 2015). The technocentricity ushered in by digitalization generates sensations of time pressures, job insecurity and reduced control over the job (Palumbo and Cavallone, 2022), which expand the sources of psychosocial stress at work (Jensen et al., 2022; Trusson et al., 2018).
To achieve a comprehensive account of EI’s implications on psychosocial stress, we included a mediating variable in our empirical analysis, consisting of the design of health promotion initiatives (HPI) intended to enhance well-being at work (Parry et al., 2022). This approach permitted us to investigate both the direct and indirect implications of EI on psychosocial stress at work, delivering interesting insights to scholars and practitioners. The article is organized as follows. The next section develops the theoretical background against which this study was established. The third section depicts the study design and presents the statistical approach used to get evidence of involvement’s implication on work-related stress. The report of the findings is included in the fourth section. The study results are discussed in the fifth section, which inspires the conceptual and practical implications, as argued in the concluding section.
Conceptual background
EI consists of a set of human resource management practices intended to increase self-determination at work (Wallace et al., 2016). EI embraces a holistic approach (Potnuru et al., 2021), giving people voice to partake in organizational decisions (Olison and Roloff, 2008) and address management challenges (Cotton, 1993). Involving employees enhances the meaningfulness of work (Frega, 2021). It empowers people to unravel management issues (Lasrado et al., 2016), design actions intended to improve individual and collective performance (Pasmore and Friedlander, 1982), and assess the implications of organizational dynamics on work-related wellbeing (Meirinhos et al., 2022).
EI is especially fitting to cope with concerns that affect individual work conditions (Adler et al., 1997). Literature emphasized the advantages of letting people participate within organizational initiatives intended to address psychosocial risks (PSR) at work (Walters, 2011). Employees’ voices enable organizations to spot sources of stress in the workplace (Underhill, 2013), facilitating the identification of areas for intervention to advance the work climate and improve organizational performance (Ogbonnaya et al., 2013). Embracing a social cognitive perspective (Shea and Howell, 1998), it is assumed that involved employees benefit from greater awareness of factors, behaviors and dynamics influencing their ability to thrive in the workplace, which empowers them to address sources of work-related stress (Butts et al., 2009). Hence, it is assumed that:
EI reduces psychosocial strains at work.
Previous studies reported that EI is conducive to an organization-wide effort to enhance wellbeing at work (Sorribes et al., 2021), facilitating a person–environment fit (Noblet and LaMontagne, 2006). Alongside promoting a sense of coherence in the workplace, which is essential to reduce perceptions of psychosocial stress (Mackie et al., 2001), EI determines greater organizational focus on sustaining workplace health (Riaz and Townsend, 2022), with positive contributions to psychophysical wellbeing (Nöhammer et al., 2010). Drawing on social network theory, EI enacts an organizational setting that is receptive to HPI (Bell et al., 2022). In turn, this has positive implications for individual and collective wellbeing (DeJoy et al., 2018). Involving employees sets the conditions for a greater managerial concern for the workforce’s conditions, corroborating the organizational commitment toward establishing healthy work environments (Grawitch et al., 2009). Therefore, it is hypothesized that:
EI determines a greater focus on HPI at work.
HPI mostly stick to a preventive approach to curb psychosocial stress at work (Tetrick and Winslow, 2015). They encourage people to take positive actions to reduce fall in their mental and physical health (Cantonnet et al., 2022). By curtailing the factors that determine psychosocial strain and soliciting a greater awareness of preventive actions that can be taken to escape work-related stress (Ramaci et al., 2017), HPI foster the establishment of a healthy organizational climate (Mohamed et al., 2022). This augments employees’ wellbeing and protects them against the risks of stressful organizational dynamics (Cook et al., 2007). From this standpoint, it is assumed that:
HPI reduce psychosocial stress at work.
These arguments lead us to investigate the indirect effects of EI on addressing the sources of psychosocial stress at work. Employees’ participation in organizational decision-making and problem-solving targeted to overcome work-related stress entails increased organizational readiness to establish a healthy work environment (Day et al., 2014). Therefore, involvement practices might have an indirect effect on curbing psychosocial stressors (Giga et al., 2003). Such an indirect effect is activated via the mediating role of HPI, which concur in tackling sources of stress in the workplace (Roy et al., 2019). In sum, it is hypothesized that:
HPI mediate the relationship between EI and psychosocial stress, so that people will perceive less strain in the workplace.
Figure 1 depicts the conceptual background against which this study was conceived, visualizing the research hypotheses. As detailed below, an empirical study design was arranged to collect evidence of the implications of employees’ involvement on sources of PSR at work.
Research design and methodology
A conditional process analysis was designed to meet the study’s aims. This methodology permitted us to investigate the direct and indirect effects of EI on PSR at work, providing us with evidence to test the research hypotheses. Acknowledging the distinguishing impact of digital technologies’ pervasiveness on work-related stress, we decided to look at diverse work settings, contrasting highly digitalized and poorly digitalized environments (Palumbo, 2021). We used the approach proposed by Hayes (2018), which is based on ordinary least squares regressions and bootstrap sampling. This study design delivers identical results to more articulated techniques for conditional process analysis, such as structural equation modeling (Hayes and Rockwood, 2020), minimizing errors in computation processes and achieving parsimony in data analysis (Hayes et al., 2017). The PROCESS macro (vers. 3.4) embedded in the IBM Statistical Package for Social Science (SPSS, vers. 26) was used to expedite statistical elaborations. Model no. 4, which is tailored to simple mediation analysis, has been run. This methodological approach enabled us to investigate the direct and indirect implications of EI on PSR at work as mediated by the design of health promotion interventions.
Secondary data were collected from the third European Survey of Enterprises on New and Emerging Risks, a pan-European survey conducted by the Occupational Safety and Health Agency of the European Union that conveys a comprehensive representation of workplace conditions across different occupations. The survey targeted all establishments having five or more employees. For each unit of analysis, the questionnaire was delivered to the person who knew best about health and safety issues. A stratified sampling technique intended to ensure comparability of data across participating countries was implemented.
This study was focused on a subsample of two countries, i.e. Norway and Slovenia. The decision to analyze these countries was motivated by two reasons. On the one hand, both self-financed a boost of the sample coverage, which was relatively larger in comparison to the residing population. On the other hand, the two countries expanded the master questionnaire, including items about peculiar sources of stress at work, such as violence (threats, abuse or assaults), bullying and sexual harassment, which were not gauged elsewhere. The study sample consisted of 3,018 companies, two thirds of which were established in Norway (64.6%), whilst the remaining part was located in Slovenia (35.4%). Table 1 reports a brief profile of the study sample.
Service industries accounted for half the sample (48%), followed by the human health sector (13.3%). About one in five companies were either manufacturing firms (11.7%) or entities operating in the fields of mining, construction and provision of water and energy (11.5%). Most organizations were either small- or medium-sized companies employing fewer than 50 employees (76%). About one in four organizations were owned by public sector entities (25.1%). Two thirds reported a good economic situation (66.2%), whilst a small portion suffered from financial shortcomings (6.9%). A large group of companies disclosed a noticeable digitalization (89.5%), which involved the automation of organizational processes and/or the implementation of digital solutions to enhance performance. People aged 55 years and older represented at least a quarter of the workforce in a third of the sample (30.1%).
A formative approach was used to operationalize the study variables (Coltman et al., 2008). EI was measured as an interval scale variable deriving from the aggregation of three dichotomous variables:
the employees’ participation in crafting measures intended to address psychosocial stress at work;
the employees’ engagement in open discussions to assess the implications of technologies on wellbeing; and
the arrangement of team meetings to discuss issues related to organizational health and safety.
Similarly, HPI were gauged as an interval scale variable. Four different factors were taken into consideration, including:
interventions intended to raise awareness about healthy nutrition;
addiction prevention measures (e.g., smoking cessation or alcohol reduction);
encouragement of sports activities outside working hours; and
promotion of back exercises, stretching or other physical exercises at work.
Lastly, PSR at work were assessed as an interval scale variable obtained from the aggregation of eight items, which concerned the various sources of psychological and social stress in the workplace, such as:
time pressure;
poor communication or cooperation;
job insecurity;
interaction with difficult interlocutors;
long or irregular working hours;
threats, abuse or assaults;
bullying; and
sexual harassment.
All variables were mean-centered to minimize micro sources of multicollinearity. As previously anticipated, two different models were elaborated to discriminate digitalized companies from organizations with limited digitalization. This enabled us to account for the work-related challenges that are typical of workplaces undergoing a digital transformation. Alongside the variables reported above, the economic sector and organizational size were included as covariates to check the results’ consistency.
Findings
The study results are depicted in Tables 2 and 3, which refer to the two models implemented in our empirical research. Table 2 focuses on companies with limited digitalization (model 1), whilst Table 3 addresses highly digitalized organizations (model 2). EI was positively related to PSR at work, both among companies that underwent a digitalization process (coeff: 0.07, significant at the 0.001 level) and among organizations with limited digitalization (coeff: 0.13, significant at the 0.01 level). Hence, contrary to H1, EI was found to increase sources of psychosocial stress at work.
Making employees involved was found to trigger the organizational propensity to advance workplace healthiness. EI was positively associated with the implementation of HPI when digitalized companies were contemplated (coeff: 0.24, significant at the 0.001 level), as well as when the focus was put on institutions with limited digitalization (coeff: 0.27, significant at the 0.001 level). This finding led us to confirm H2. HPI were negatively related to the occurrence of PSR at work. More specifically, HPI had a negative effect on PSR, with limited statistical significance across nondigitized firms (coeff: −0.06, significant at the 0.10 level) and with statistical significance among digitalized companies (coeff: −0.03, significant at the 0.01 level). From this standpoint, H3 was partially supported.
The indirect effect of EI on PSR, as mediated by the implementation of HPI, was negative for both digitalized and nondigitalized companies. However, it was statistically significant only for the former (effect: −0.01, significant at the 0.01 level), whilst it did not yield statistical significance among the latter. The total effect of EI on PSR was lower than the direct effect in model 1 (0.11 Vs 0.13) and in model 2 (0.06 Vs 0.07). Therefore, H4 was partially supported.
Discussion
Involving employees was found to have drawback on the psychosocial climate at work, leading us to disconfirm H1. On the one hand, enabling people to have a voice in shaping management decisions related to well-being in the workplace augments the awareness of extant sources of psychosocial strain at work (Mellor et al., 2011). On the other hand, EI nurtures challenge-related stress, intensifying individual work efforts (Tian and Gamble, 2018) and determining time pressures (Palumbo and Cavallone, 2022). Increased involvement generates work intensification and extensification (Allan and Lovell, 2003), which undermine the individual’s capability to address psychosocial stress (Boxall and Macky, 2016) and aggravate strain (Zuzanek, 2004). These arguments explain the counterintuitive positive relationship between EI and PSR, which is not consistent with previous evidence reported by scholars (Kalleberg et al., 2009). However, this finding echoes previous research emphasizing that EI is not good nor bad in absolute terms (Wilkinson et al., 1997), since its implications should be assessed contemplating the distinguishing organizational and cultural attributes of the setting within which it is implemented (Haber, 2016).
Interestingly, EI indirectly contributed to curbing sources of stress at work. The more employees are engaged in HPI, the greater their capability to benefit from involvement, escaping sources of psychosocial stress at work (Boxall and Macky, 2014). As forecasted by H2, EI fosters the organizational readiness to design and implement HPI, which, in line with H3, counterbalance the sources of psychosocial stress at work (Grawitch et al., 2007). From this point of view, EI indirectly curbs psychosocial stress, stimulating the individual and collective feeling of organizational ownership and supporting their commitment toward healthy work environments (Chandwani and Varkkey, 2015). Engaging people in positive actions aimed at improving the individual and collective wellbeing has cascading implications for managing stress in the workplace, thus corroborating our H4 (Grawitch et al., 2015). This is especially true in work settings that are affected by digitalization. In fact, the pervasiveness of digital technologies nurtures particular sources of PSR at work since it increases time pressures, generates job insecurity and rearticulates interpersonal relationships (Palumbo, 2022). Coping with these sources of stress through HPI is imperative to address the imperfections of EI and support people in achieving wellbeing (Day et al., 2016).
Theoretical and practical implications can be drawn from the study findings discussed above. From a conceptual perspective, the study results highlight that involving people in addressing management decisions related to psychosocial sources of strain has ambiguous effects on work-related stress. Involvement practices increase job demands, enacting an intensification of work that exacerbates the individual’s exposure to the determinants of work stress. Therefore, involvement’s positive effects on work-related stress are primarily indirect. Involving people in tackling organizational decisions nurtures a greater organizational concern for promoting healthiness at work. The focus on initiatives aimed at setting a healthy workplace enables people to take precautionary actions and preventive measures to overcome work-related stress, thus reducing psychosocial strain. The attention goes beyond fix-it interventions and corrective actions, which are not sufficient to overcome psychosocial stress in the workplace. EI makes people aware of the special challenges they face at work, stimulating them to take positive actions to cope with such challenges and enhance individual and collective well-being.
Embracing a management perspective, the findings emphasize that organizational interventions intended to promote the employees’ participation in addressing sources of psychosocial stress at work should be merged with healthy workplace initiatives. The latter augment the employees’ consciousness of sources of strain and enable them to take preventive actions to avoid psychosocial stress at work. If health promotion interventions are missing, giving voice to employees turns into an imperfect approach to address work-related stress. In these circumstances, people who are involved do not get control over the triggers of psychosocial strain. Rather, work intensification and increased job demands heralded by involvement engender an exacerbation of work stress. Implementing a healthy workplace policy substantiates the employees’ confidence in the organization’s focus on improving work conditions, thus gearing management actions intended to fix psychosocial stress.
Conclusions
Giving people the voice to shape management decisions and empowering them to fix organizational issues has been considered an effective strategy to address work-related stress. However, EI has ambiguous effects on psychosocial strain at work. By intensifying commitments and augmenting job demands, it is likely to exacerbate pressures and nurture work-related stress. To avoid such shortcomings, initiatives intended to involve employees should be coupled with HPI, enabling the establishment of a psychologically safe and healthy workplace. HPI provide people with an antidote to work-related strain and permit them to overcome psychosocial stress.
Further research is necessitated to advance what we currently know about the implications of EI on psychosocial stress. A longitudinal study will enable us to obtain dependable evidence of the causal link between EI, HPI and work-related stress. Besides, qualitative research is needed to get a fine-grained understanding of how involvement paves the way for a greater employees’ propensity to participate in organizational health promotion actions that enact a healthy workplace and overcome sources of stress at work. Lastly, in-depth comparative studies are recommended to push forward our understanding of the organizational contingencies that affect the perception of psychosocial stress at work. This is expected to shed some light on the conditions under which EI and health promotion practices alleviate psychosocial stress at work.
Figures
The study sample (n = 3,018)
| Variable | Total | |
|---|---|---|
| No. | % | |
| Country | ||
| Norway | 1,951 | 64.6 |
| Slovenia | 1,067 | 35.4 |
| Economic sector | ||
| Manufacturing | 354 | 11.7 |
| Mining, construction and electricity, gas and water provision | 347 | 11.5 |
| Service industries | 1,447 | 48 |
| Education | 203 | 6.7 |
| Human health services | 400 | 13.3 |
| Public administration | 148 | 4.9 |
| Other | 119 | 3.9 |
| Size (number of employees) | ||
| Between 5 and 9 employees | 1,008 | 33.4 |
| Between 10 and 49 employees | 1,288 | 42.6 |
| Between 50 and 250 employees | 524 | 17.4 |
| More than 250 employees | 198 | 6.6 |
| Ownership | ||
| Publicly owned | 758 | 25.1 |
| Privately owned | 2,249 | 74.5 |
| Do not know/do not answer | 11 | 0.4 |
| Economic situation | ||
| Good | 1,998 | 66.2 |
| Fair | 768 | 25.4 |
| Dangerous | 208 | 6.9 |
| Do not know/do not answer | 44 | 1.5 |
| Ratio of elderly employees (aged 55 years or older) | ||
| 0% | 523 | 17.3 |
| 25% or less | 1,589 | 52.6 |
| Between 26% and 50% | 732 | 24.3 |
| More than 50% | 118 | 3.9 |
| Do not know/do not answer | 56 | 1.9 |
| Workplace digitalization | ||
| Highly digitalized | 304 | 10.1 |
| Poorly digitalized | 2,703 | 89.5 |
| Do not know/do not answer | 11 | 0.4 |
The output of the simple mediation analysis: companies with limited digitalization (N = 304)
| Outcome variable: HPI | ||||||
| Model summary | ||||||
| R | R2 | MSE | F | df1 | df2 | p |
| 0.3679 | 0.1353 | 0.0876 | 5.7707 | 8 | 295 | 0.0000 |
| Model | ||||||
| Coeff. | se | t | P | LLCI | ULCI | |
| Const. | 0.1516 | 0.0627 | 2.4187 | 0.0162 | −0.0109 | 0.3141 |
| EI*** | 0.2747 | 0.0707 | 3.8833 | 0.0001 | 0.0913 | 0.4581 |
| Sector: manufacturing | −0.0478 | 0.0673 | −0.7100 | 0.4783 | −0.2224 | 0.1268 |
| Sector: service industry | −0.0613 | 0.0397 | −1.5446 | 0.1235 | −0.1642 | 0.0416 |
| Organizational size*** | 0.1056 | 0.0284 | 3.7246 | 0.0002 | 0.0321 | 0.1792 |
| Ownership: public sector | 0.0279 | 0.0575 | 0.4847 | 0.6283 | −0.1211 | 0.1768 |
| Workforce age | 0.0393 | 0.0399 | 0.9850 | 0.3254 | −0.0642 | 0.1428 |
| Economic situation: fair | −0.0222 | 0.0363 | −0.6100 | 0.5423 | −0.1163 | 0.0720 |
| Teleworking arrangements | −0.0675 | 0.0603 | −1.1178 | 0.2646 | −0.2239 | 0.0890 |
| Outcome variable: PSR | ||||||
| Model summary | ||||||
| R | R2 | MSE | F | df1 | df2 | p |
| 0.4101 | 0.1682 | 0.0265 | 6.6052 | 9 | 294 | 0.0000 |
| Model | ||||||
| Coeff. | se | t | P | LLCI | ULCI | |
| Const. | 0.1062 | 0.0348 | 3.0512 | 0.0025 | 0.0160 | 0.1964 |
| EI** | 0.1273 | 0.0399 | 3.1936 | 0.0016 | 0.0240 | 0.2307 |
| HPI† | −0.0567 | 0.0320 | −1.7704 | 0.0777 | −0.1397 | 0.0263 |
| Sector: manufacturing* | −0.0921 | 0.0371 | −2.4863 | 0.0135 | −0.1882 | 0.0039 |
| Sector: service industry† | −0.0414 | 0.0219 | −1.8884 | 0.0600 | −0.0982 | 0.0154 |
| Organizational size** | 0.0768 | 0.0160 | 4.8109 | 0.0000 | 0.0354 | 0.1181 |
| Ownership: public sector | 0.0104 | 0.0316 | 0.3305 | 0.7413 | −0.0715 | 0.0924 |
| Workforce age | 0.0002 | 0.0220 | 0.0084 | 0.9933 | −0.0568 | 0.0572 |
| Economic situation: fair* | −0.0403 | 0.0200 | −2.0161 | 0.0447 | −0.0921 | 0.0115 |
| Teleworking arrangements | 0.0001 | 0.0332 | 0.0035 | 0.9972 | −0.0861 | 0.0863 |
| Outcome variable: PSR (total effect model) | ||||||
| Model summary | ||||||
| R | R2 | MSE | F | df1 | df2 | p |
| 0.3992 | 0.1593 | 0.0267 | 6.9884 | 8 | 295 | 0.0000 |
| Model | ||||||
| Coeff | se | t | p | LLCI | ULCI | |
| Const. | 0.0976 | 0.0346 | 2.8218 | 0.0051 | 0.0079 | 0.1872 |
| EI** | 0.1118 | 0.0390 | 2.8636 | 0.0045 | 0.0106 | 0.2129 |
| Sector: manufacturing* | −0.0894 | 0.0372 | −2.4066 | 0.0167 | −0.1858 | 0.0069 |
| Sector: service industry† | −0.0379 | 0.0219 | −1.7306 | 0.0846 | −0.0947 | 0.0189 |
| Organizational size*** | 0.0708 | 0.0156 | 4.5225 | 0.0000 | 0.0302 | 0.1113 |
| Ownership: public sector | 0.0089 | 0.0317 | 0.2796 | 0.7799 | −0.0733 | 0.0911 |
| Workforce age | −0.0020 | 0.0220 | −0.0928 | 0.9262 | −0.0592 | 0.0551 |
| Economic situation: fair† | −0.0390 | 0.0200 | −1.9475 | 0.0524 | −0.0910 | 0.0129 |
| Teleworking arrangements | 0.0039 | 0.0333 | 0.1183 | 0.9059 | −0.0824 | 0.0903 |
| Direct and indirect effects of Dig on PS-W | ||||||
| Effect of EI on PSR | ||||||
| Effect | se | t | p | LLCI | ULCI | |
| Direct** | 0.1273 | 0.0399 | 3.1936 | 0.0016 | 0.0240 | 0.2307 |
| Total** | 0.1118 | 0.039 | 2.8636 | 0.0045 | 0.0106 | 0.2129 |
| Indirect effect of EI on PSR | ||||||
| Effect | BootSE | BootLLCI | BootULCI | |||
| EI → HPI → PSR | −0.0156 | 0.0099 | −0.0474 | 0.0069 | ||
***Significant at the 0.001 level; **significant at the 0.01 level; *significant at the 0.05 level; †significant at the 0.10 level
The output of the simple mediation analysis: digitalized companies (N = 2,703)
| Outcome variable: HPI | ||||||
| Model summary | ||||||
| R | R2 | MSE | F | df1 | df2 | p |
| 0.4229 | 0.1789 | 0.0935 | 73.3491 | 8 | 2,694 | 0.0000 |
| Model | ||||||
| Coeff. | se | t | p | LLCI | ULCI | |
| Const. | 0.1012 | 0.0220 | 4.6114 | 0.0000 | 0.0446 | 0.1578 |
| EI*** | 0.2411 | 0.0214 | 11.2498 | 0.0000 | 0.1858 | 0.2963 |
| Sector: manufacturing | 0.0242 | 0.0197 | 1.2273 | 0.2198 | −0.0267 | 0.0751 |
| Sector: service industry† | −0.0250 | 0.0141 | −1.7676 | 0.0772 | −0.0614 | 0.0114 |
| Organizational size*** | 0.1163 | 0.0071 | 16.289 | 0.0000 | 0.0979 | 0.1347 |
| Ownership: public sector*** | 0.0590 | 0.0156 | 3.7817 | 0.0002 | 0.0188 | 0.0991 |
| Workforce age | −0.0048 | 0.0135 | −0.3552 | 0.7225 | −0.0396 | 0.0300 |
| Economic situation: fair*** | 0.0534 | 0.0130 | 4.1175 | 0.0000 | 0.0200 | 0.0869 |
| Teleworking arrangements*** | −0.0571 | 0.0149 | −3.8366 | 0.0001 | −0.0955 | −0.0187 |
| Outcome variable: PSR | ||||||
| Model summary | ||||||
| R | R2 | MSE | F | df1 | df2 | P |
| 0.3936 | 0.1550 | 0.0327 | 54.8681 | 9 | 2,693 | 0.000 |
| Model | ||||||
| Coeff. | se | t | p | LLCI | ULCI | |
| Const. | 0.1708 | 0.0130 | 13.1042 | 0.0000 | 0.1372 | 0.2044 |
| EI*** | 0.0661 | 0.0130 | 5.0983 | 0.0000 | 0.0327 | 0.0996 |
| HPI** | −0.0304 | 0.0114 | −2.6715 | 0.0076 | −0.0598 | −0.0011 |
| Sector: manufacturing*** | −0.0477 | 0.0117 | −4.0813 | 0.0000 | −0.0778 | −0.0176 |
| Sector: service industry† | −0.0160 | 0.0084 | −1.9183 | 0.0552 | −0.0376 | 0.0055 |
| Organizational size*** | 0.0616 | 0.0044 | 13.9124 | 0.0000 | 0.0502 | 0.0730 |
| Ownership: public sector* | 0.0198 | 0.0092 | 2.1385 | 0.0326 | −0.0041 | 0.0436 |
| Workforce age | 0.0103 | 0.0080 | 1.2892 | 0.1974 | −0.0103 | 0.0309 |
| Economic situation: fair*** | −0.0490 | 0.0077 | −6.3662 | 0.0000 | −0.0689 | −0.0292 |
| Teleworking arrangements*** | 0.0650 | 0.0088 | 7.3635 | 0.0000 | 0.0423 | 0.0878 |
| Outcome variable: PSR (total effect model) | ||||||
| Model summary | ||||||
| R | R2 | MSE | F | df1 | df2 | p |
| 0.3908 | 0.1527 | 0.0328 | 60.6962 | 8 | 2,694 | 0.0000 |
| Model | ||||||
| Coeff | se | t | p | LLCI | ULCI | |
| Const. | 0.1678 | 0.0130 | 12.9038 | 0.0000 | 0.1342 | 0.2013 |
| EI*** | 0.0588 | 0.0127 | 4.6323 | 0.0000 | 0.0261 | 0.0915 |
| Sector: manufacturing*** | −0.0484 | 0.0117 | −4.1409 | 0.0000 | −0.0786 | −0.0183 |
| Sector: service industry† | −0.0153 | 0.0084 | −1.8263 | 0.0679 | −0.0368 | 0.0063 |
| Organizational size*** | 0.0580 | 0.0042 | 13.7274 | 0.0000 | 0.0471 | 0.0689 |
| Ownership: public sector† | 0.0180 | 0.0092 | 1.9473 | 0.0516 | −0.0058 | 0.0418 |
| Workforce age | 0.0104 | 0.0080 | 1.3060 | 0.1917 | −0.0102 | 0.0310 |
| Economic situation: fair*** | −0.0507 | 0.0077 | −6.5906 | 0.0000 | −0.0705 | −0.0309 |
| Teleworking arrangements*** | 0.0668 | 0.0088 | 7.5724 | 0.0000 | 0.0440 | 0.0895 |
| Direct and indirect effects of Dig on PS-W | ||||||
| Effect of EI on PSR | ||||||
| Effect | se | t | p | LLCI | ULCI | |
| Direct*** | 0.0661 | 0.0130 | 5.0983 | 0.0000 | 0.0327 | 0.0996 |
| Total*** | 0.0588 | 0.0127 | 4.6323 | 0.0000 | 0.0261 | 0.0915 |
| Indirect effect of EI on PSR | ||||||
| Effect | BootSE | BootLLCI | BootULCI | |||
| EI → HPI → PSR** | −0.0073 | 0.0029 | −0.0151 | −0.0002 | ||
***Significant at the 0.001 level; **significant at the 0.01 level; *significant at the 0.05 level; †significant at the 0.10 level
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Corresponding author
About the authors
Rocco Palumbo, PhD, is Associate Professor of Organization Studies at the University “Tor Vergata” of Rome, where he teaches Introduction to Organizational Change and Organisational Communication. His main research interests include, but are not limited to, work digitalization and discrimination in the workplace.
Alessandro Hinna, PhD, is Full Professor of Organization Studies at the University “Tor Vergata” of Rome, where he teaches business organization. His research engages, among others, with organizational change management in the public sector, behavioral dimensions of public governance, and human resource management.
Maurizio Decastri, PhD, is Full Professor of Organization Studies and Human Resource Management at the University “Tor Vergata” of Rome, where he teaches business organization. His research areas include, but are not limited to, human resource management in the public sector, organizational design and organizational change.