Organizational climates represent one of the primary constructs for understanding workplaces, since worker perceptions of climates have a strong impact on nearly all aspects of organizational life including employee attitudes and behaviors, team processes, and productivity (Ehrhart & Kuenzi, Reference Ehrhart, Kuenzi and Wright2015). In accordance with such importance, the quantity of research that has been carried out on this concept is huge and started already in Reference Lewin, Lippitt and White1939 with the study on “social climates” by Lewin and colleagues. Since then, a relevant number of theoretical and empirical developments took place, which yielded an increased understanding of these constructs (Schneider et al., Reference Schneider, González-Romá, Ostroff and West2017). Notwithstanding, with the COVID–19 health crisis the great majority of workplaces and workers experienced in their daily life the introduction of a strong element of novelty, namely the practice of teleworking. Indeed, if in 2015, only the 17% of European workers was used to resort to telework practices, in 2020 such percentage rose to 37%, with peaks of 50–60% in the Northern European countries (The European Foundation for the Improvement of Living and Working Conditions [Eurofound], 2020; Eurofound & International Labour Office [ILO], 2017) or even higher for knowledge workers (Maitland & Thomson, Reference Maitland and Thomson2014). In regards it is worth pointing out that, due the increased autonomy that this relevant change is introducing into employees life, the job characteristic model (JCM; Hackman & Oldham, Reference Hackman and Oldham1975, Reference Hackman and Oldham1976, Reference Hackman and Oldham1980; Morgeson & Humphrey, Reference Morgeson and Humphrey2006) provides theoretical support for its link with productivity, a link that is empirically and qualitatively further corroborated also through additional studies (Anakpo et al., Reference Anakpo, Nqwayibana and Mishi2023; Gibson et al., Reference Gibson, Gilson, Griffith and O’Neill2023). Notwithstanding, as made clear in their reviews by Charalampous and colleagues (Reference Charalampous, Grant, Tramontano and Michailidis2019) and Lunde and colleagues (Reference Lunde, Fløvik, Christensen, Johannessen, Finne, Jørgensen, Mohr and Vleeshouwers2022), the mediating mechanisms between the variables of teleworking and productivity need to be urgently identified and pointed out. In regards, a possible mechanism explaining such relationship could be grounded on the happy-productive hypothesis by Cropanzano and Wright (Reference Cropanzano and Wright2001), leading to the expectation that aspects making workers happier, make them indirectly also more productive. In this sense, when flexible work arrangements are considered, they could be linked to improved work environments, thus organizational climate(s), because of the strong impact that HR practices have in shaping workplaces (Bowen & Ostroff, Reference Bowen and Ostroff2004, Reference Bowen and Ostroff2016). In turn, improved workplaces are renowned to positively impact employees’ well-being (Warr, Reference Warr1987, Reference Warr2007), which can then be finally linked to increased productivity (Cropanzano & Wright, Reference Cropanzano and Wright2001).
On a second note, when the climate literature is specifically considered, it is worth pointing out that insights are missing on the simultaneous role that molar and focused climates may play in the explanation of organizational phenomena (Ehrhart & Kuenzi, Reference Ehrhart, Kuenzi and Wright2015). Concretely, workplaces are characterized by the presence of multiple climates (Kozlowasky & Klein, Reference Kozlowasky, Klein, Klein and Kozlowsky2000) that have been linked to similar outcomes, such as well-being and productivity. Nevertheless, not much is known about if and how much each of them contributes to the prediction of such outcomes (Ehrhart & Kuenzi, Reference Ehrhart, Kuenzi and Wright2015). Hence, despite the relevant volume of studies on these constructs, empirical evidence is still missing on this very aspect, which makes it important to be covered (Ehrhart & Kuenzi, Reference Ehrhart, Kuenzi and Wright2015).
As reported above, the current scientific literature presents some relevant gaps that need to be urgently filled for increasing the understanding of the mediating mechanisms explaining the relationship between teleworking and productivity. Therefore, the present study was set out to understand whether the molar climate for well-being dimensions, the focused climates for excellence and for innovation, and eudaemonic well-being represent a relevant mediating mechanism to explain the relationship between telework flexibility and scientific productivity. In line with the multilevel nature of the considered constructs, the research question was explored both at an individual and team level to have a complete understanding of the investigated relationships.
Molar and Focused Climates: A distinction
As mentioned above, the climate construct has received much attention over time, both by scientists and practitioners, producing a series of relevant developments. Schneider and colleagues (Reference Schneider, Barbera, Schneider and Barbera2017) summarize and categorize such achievements in four main categories. Specifically, two of them (i.e., the 1971–1985 era and the 1986–1999 era) are core to the definition and distinction between molar and focused climates and are explored in the lines that follow.
The Molar Climate or Climate for Well-Being
The molar climate, as recently relabeled into climate for well-being (viz., molar climate for well-being) by Schneider and colleagues (Reference Schneider, Ehrhart, Macey, Ashkanasy, Wilderom and Peterson2011), aims at capturing the extent to which workers perceive their workplaces as “warm and friendly” (Schneider et al., Reference Schneider, González-Romá, Ostroff and West2017); a positive place where to work. As it is possible to notice, the climate variable is conceptualized as an attribute of the workplace but is perceived by the employees in the workplace (Schneider et al., Reference Schneider, González-Romá, Ostroff and West2017). It is in this regard that James and Jones (Reference James and Jones1974) made a fundamental specification that allowed to clarify how to best handle this peculiarity of the climate construct, hence to overcome the level-of-analysis issue (Schneider et al., Reference Schneider, Ehrhart and Macey2013). To do so, the authors proposed a differentiation between psychological and organizational climate (James & Jones, Reference James and Jones1974). As to the first one, it needs to be considered as to a construct that merely refers to the individual perceptions of the workplace, which are limited to the individual experience of it and, thus, cannot be approximated to any kind of objectified description of the workplace itself (James & Jones, Reference James and Jones1974). On the other hand, the construct of organizational climate, which grounds on the individual scores aggregated at the relevant unit level after checking the relevant aggregation indexes (LeBreton & Senter, Reference LeBreton and Senter2008), refers to a shared perception of the workplace. It is exactly for this sharedness of the perceptions that the organizational climate, on the contrary of the psychological one, can then be considered as an approximation workplace feature and not merely as a subjective perception of it. In light of all what reported above, it can then also be highlighted the importance of hypothesizing and running statistical models at such different levels.
In terms of measurements, especially in the 1971–1985 era pointed out by Schneider and colleagues (Reference Schneider, González-Romá, Ostroff and West2017), the proliferation of climate assessment tools was maximal generating a situation in which no two articles used the same measurement scale. Nevertheless, with a further definition of the concept and with the design of climates scales on relevant taxonomies and theoretical frameworks some assessment tools became a reference point. Concretely, the taxonomy by Ostroff (Reference Ostroff1993) and the competing values framework (CVF) by Quinn and Rohrbaugh (Reference Quinn and Rohrbaugh1983) became a foundation for several climate scales as for example the one by Patterson and colleagues (Reference Patterson, West, Shackleton, Dawson, Lawthom, Maitlis, Robinson and Wallace2005). In the same vein, when Schneider and colleagues’ (Reference Schneider, Ehrhart, Macey, Ashkanasy, Wilderom and Peterson2011) recent relabeling of the molar climate as the climate for well-being is considered, also the Vitamin Model by Warr (Reference Warr1987, Reference Warr2007) has been considered as a theoretical framework of relevance for the design of climate scales. Indeed, this model identifies on theoretical groundings multiple workplace factors that have a relevant impact on employees’ well-being and that need to be considered jointly for having a thorough understanding of the related phenomena (Warr, Reference Warr1994).
Focused Climates
The molar climate is usually regarded as a foundation for the focused ones (Ehrhart & Kuenzi, Reference Ehrhart, Kuenzi and Wright2015; Ehrhart & Raver, Reference Ehrhart, Raver, Schneider and Barbera2014). The rationale behind this functional relationship lays in the empirically supported view according to which the climate for well-being sets the adequate conditions in the workplace for carrying out strategic goals that are more strongly connected to the focused climate (Ehrhart & Kuenzi, Reference Ehrhart, Kuenzi and Wright2015). The author who first pointed out the necessity of differentiating between climate types was Schneider (Reference Schneider1975), who proposed the so called band-with argument. According to the author, the bandwidth of the molar climate measures was too broad for having relevant relationships with the narrower bandwidth of the constructs that were expected to be predicted. Accordingly, the concept of climate for something, or focused climate, was introduced with the aim of specifying the very aspect that such specific climate was supposed to capture (Schneider, Reference Schneider1975). The relevance of such theoretical differentiation was subsequently empirically supported by consistent evidence, showing a strong improvement in terms of criterion, and especially predictive, validity (Schneider & Barbera, Reference Schneider, Barbera, Schneider and Barbera2014).
Consistently, the molar climate was then differentiated from the focused climates, which are usually categorized into two different types; process climates and strategic climates (Schneider et al., Reference Schneider, Ehrhart, Macey, Ashkanasy, Wilderom and Peterson2011; Schneider & Barbera, Reference Schneider, Barbera, Schneider and Barbera2014). As to the former, they focus on capturing aspects of the workplace that are related to organizations’ internal processes and try to capture the essence of how practices and processes are carried out in an organization. Examples of process climates are, for example, justice climate, ethical climate, and climate for excellence. As to the latter, they focus on the outcomes or strategic goals that an organization may have and their achievement. In this regard, the relevant literature has proliferated and multiple constructs and measurement tools assessing strategic climates have been developed lately. For example, the climates for customer service, for innovation, and for safety, represent some of the most typically researched ones (Schneider et al., Reference Schneider, Ehrhart and Macey2013), while the climate for sustainable commuting (Martinolli et al., Reference Martinolli, de Angelis, Tordera and Pietrantoni2021) may represent a recent application of the construct.
The Climates for Excellence and for Innovation in the Context of the Present Study
In the context of the present study, which was carried out with excellence research teams granted by the European Research Council (ERC), the climates for excellence and for innovation were considered particularly suitable. As to the former, which can be addressed as a process climate, it is here conceptualized, basing on Ehrhart and colleagues (Reference Ehrhart, Schneider and Macey2013), as the shared perceptions and meanings attached to the policies, practices, and procedures that workers experience about the achievement of the highest standards of performance and the behaviors they observe getting rewarded, supported, and expected in regards. As can be intuitively understood, in research teams receiving a conspicuous amount of European funds and that are expected to produce high-quality results, excellence is expected to play a crucial role in these workplaces. Hence, the climate for excellence was regarded as particularly suitable for capturing workers’ perceptions about the processes and procedure of excellence that are in place. As to the latter, it clearly represents a strategic climate since innovation can be regarded as an outcome or strategic goal for top research teams; producing innovation is, indeed, one of the ultimate outcomes for a research team. Basing on Ehrhart and colleagues (Reference Ehrhart, Schneider and Macey2013), the climate for innovation can be defined as the shared perceptions and meanings attached to the policies, practices, and procedures that workers experience about the production of innovative outcomes and the behaviors they observe getting rewarded, supported, and expected in regards. Being innovation a relevant goal for research teams, the climate for innovation was then also regarded as particularly suitable for capturing workers’ perceptions about the processes and procedures related to innovation that are in place.
Climates and Eudaemonic Well-Being as Mediating Mechanisms of the Relationship between Flexible Teleworking and Scientific Productivity
Along with the recent and rapid increase in the usage of the practice of teleworking, the number of empirical studies investigating the relationship between teleworking and productivity has also surged (Hackney et al., Reference Hackney, Yung, Somasundram, Nowrouzi-Kia, Oakman and Yazdani2022). Notwithstanding, the results about such relationship are still unclear (Hackney et al., Reference Hackney, Yung, Somasundram, Nowrouzi-Kia, Oakman and Yazdani2022) and its explaining mechanisms need yet to be fully explored and understood (Charalampous et al., Reference Charalampous, Grant, Tramontano and Michailidis2019; Lunde et al., Reference Lunde, Fløvik, Christensen, Johannessen, Finne, Jørgensen, Mohr and Vleeshouwers2022). To fill this gap, the present study proposes a mediational model that takes as theoretical framework of reference the happy-productive hypothesis (Cropanzano & Wright, Reference Cropanzano and Wright2001) and is explained further as follows. As mentioned above, grounding on the happy-productive hypothesis by Cropanzano and Wright (Reference Cropanzano and Wright2001), happy workers are more productive. Consistently, aspects that foster well-being may then be expected to indirectly promote productivity. In this sense, work environments, which can be assessed by the means of the molar climate for well-being, represent a crucial source of well-being, as reported in the multiple theoretical and empirical works by Warr (Reference Warr1987, Reference Warr2007). In addition to the molar climate, also focused climates can play a joint role in the promotion of well-being. For example, when eudaemonic well-being, which focuses on growth and development (Ryff & Keyes, Reference Ryff and Keyes1995), is considered, climate constructs such as the ones for excellence or for innovation can play a relevant role in enhancing the levels of this type of well-being. Indeed, working in environments that promote excellence and innovation can be expected to foster professional growth, thus eudaemonic well-being. On a final turn, then also aspects that have an impact on the work environment could potentially and indirectly be related to an increase in well-being, thus productivity. In this regard, Bowen and Ostroff (Reference Bowen and Ostroff2004, Reference Bowen and Ostroff2016) point out how HR practices have a significant impact on the perception of the work environment, thus climate(s), as it may be the case for the practice of flexible teleworking that fosters flexible work arrangements. More details on the proposed rationale are reported in the sections that follow.
Flexible Teleworking and Climates
The practice of teleworking can be designed and implemented in the workplace by leveraging on and manipulating multiple of its components, such as its frequency, voluntariness, flexibility, quantity, and need for justification, with different effects on workers (Martinolli et al., Reference Martinolli, Sanín Posada, Belli and Tordera2023). Among these, flexibility (a.k.a., flextime), namely the possibility to decide when to telework, represents the one component that mostly captures the essence of teleworking and most strongly impacts employees’ everyday work experience. Consistently, Beckel and Fisher (Reference Beckel and Fisher2022) pointed out to expect this component of teleworking to positively relate, despite the lack of empirical evidence, with variables capturing the social context of work environments; as it may then be the case for organizational climate(s). This expected relationship can be further explained when the rationale provided by Bowen and Ostroff (Reference Bowen and Ostroff2004, Reference Bowen and Ostroff2016) is considered. Indeed, according to the authors, HR practices, policies, and procedures can be regarded as communications from employers to employees and shape workers’ perceptions about their workplace. Such rationale has already found empirical support for multiple HR practices (e.g., Veld et al., Reference Veld, Paauwe and Boselie2010), but has not yet been applied and explored within the context of the practice of teleworking. On these grounds, the practice of flexible teleworking is then expected to have a direct and positive relationship with variables capturing workplace context, such as the molar climate for well-being. This construct is comprehensive and embraces, through its multidimensionality, a relevant number of workplace aspects that can differently relate to the considered HR practice. Consistently, with reference to the framework of the JCM (Hackman & Oldham, Reference Hackman and Oldham1975, Reference Hackman and Oldham1976, Reference Hackman and Oldham1980; Morgeson & Humphrey, Reference Morgeson and Humphrey2006), when it comes to the relationships between the HR practice of flexible teleworking and the various climate dimensions some differentiations can be expected. In an exploratory way, telework flexibility could, for example, be regarded as an allocation of resources that favors enhanced perceptions of autonomy and work-life balance due to the increased freedom of choice it provides employees with to best schedule and manage their workday. Similarly, telework flexibility could be perceived as an additional benefit that contributes to positive perceptions in terms of compensation. Furthermore, telework flexibility could result to become a powerful resource to overcome the workspace inconveniences that have been pointed out to characterize the work environments where research is conducted (Mazzi, Reference Mazzi1996).
On the other hand, with reference to the climates for excellence and innovation, also in this case telework flexibility could be expected to have a positive link with such climate constructs. Indeed, it is not a novelty that providing employees with sufficient autonomy and freedom in the management of their jobs tends to promote the generation, validation, and implementation of ideas, thus new and high-quality work results (de Jong & Den Hartog, Reference de Jong and Den Hartog2007; Krause, Reference Krause2004; Newman et al., Reference Newman, Round, Wang and Mount2020). In this sense, telework flexibility can then be regarded as an HR practice fostering flexible work arrangements that may contribute to the perception of working in a work environment that strives for excellence and innovation.
Climates, Eudaemonic Well-Being, and Scientific Productivity
Well-being has been usually understood and explored under two main and complementary perspectives, namely the hedonic and the eudaemonic one; with the first that has attracted most of research efforts (Bartels et al., Reference Bartels, Peterson and Reina2019). Specifically, the former perspective refers to the happiness and an individual’s cognitive and affective evaluation of life or work life (Diener, Reference Diener2000). In contrast, the latter focuses more on the optimal functioning and the growth of a person (Ryff & Keyes, Reference Ryff and Keyes1995). Grounding on the above reported definition of well-being, the link between the considered climates and eudaemonic well-being appears to be clear. Indeed, as to the molar climate for well-being (Schneider et al., Reference Schneider, Ehrhart, Macey, Ashkanasy, Wilderom and Peterson2011), which captures most workplace features relevant to workers well-being, it can be expected, on definitional and nomonological grounds, to have a direct and positive relationship with well-being outcomes. On the other hand, enhanced climates for excellence and for innovation can assumingly contribute to making feel employees as working in workplaces striving for standars of excellence and innovation, with consequent positive impacts on their professional growth. Some empirical hints that go in this direction are reported in the systematic review by Newman and colleagues (Reference Newman, Round, Wang and Mount2020), which highlights the link between the climate for innovation and both physiological well-being and job satisfaction (viz., hedonic well-being).
Finally, moving to the relationship between eudaimonic well-being and scientific productivity, it is renown that, basing on the happy-productive hypothesis (Cropanzano & Wright, Reference Cropanzano and Wright2001), workers feeling psychologically healthy are expected to also be more productive. The reason why this would occur is explained through the social exchange theory (Blau, Reference Blau and Sills1968; Emerson, Reference Emerson1976), according to which employees would attribute their feelings of well-being also partly to the organization they work for and pay the company back by being more productive (Zelenski et al., Reference Zelenski, Murphy and Jenkins2008).
Eudaimonic Well-Being as a Team Level Variable
Well-being and more in general affect variables have been mainly investigated at an individual level (Gamero et al., Reference Gamero, González-Romá and Peiró2008). Nothwistanding, relevant theoretical and impirical developments have shown its added value also when considering it as a team level variable. Precisely, multiple authors have pointed out that team members can develop a shared affect that can play a relevant role for understanding team behaviours (Barsade & Gibson, Reference Barsade and Gibson2007; Valls et al., Reference Valls, Tomás, González-Romá and Rico2021). Accordingly, George (Reference George1990) suggested the group affective tone as a new concept to consider as “consistent or homogeneous affective reactions within a group” (p. 77). On these groundings, the variable of eudaimonic well-being was regarded as suitable to be considered also as a team level construct, after the relevant aggregation checks.
Individual and Team Level Modeling: An Exploratory Approach
As stressed by Kozlowasky and Klein (Reference Kozlowasky, Klein, Klein and Kozlowsky2000), when testing models at different levels of analysis it is relevant to explain the relationships among the considered variables at the various levels due to the differences that can arise from the multilevel structure itself. Precisely, as also highlighted by Barsade and Gibson (Reference Barsade and Gibson2007) and George (Reference George1990), when same constructs are considered at different levels of analysis (e.g., individual and team level) different outcomes can be expected because of their recognizable and measurable differences (Barsade, Reference Barsade2002). With exploratory purposes, in the present study, the proposed model is considered as a homologous model (Kozlowasky & Klein, Reference Kozlowasky, Klein, Klein and Kozlowsky2000), thus as a model that at both levels conceives similar relationships. In terms of rationale, it is indeed expected that, grounding on the theories reported above, teams that use the practice of teleworking flexibly have enhanced perceptions in terms of climate for well-being dimensions, climate for excellence and climate for innovation (Bowen & Ostroff, Reference Bowen and Ostroff2004, Reference Bowen and Ostroff2016). In turn, such increased climate perceptions at the team level are expected to support the growth of the team (Warr, Reference Warr1987, Reference Warr2007), thus eudaemonic well-being. Finally, teams that feel better are expected to produce more (Cropanzano & Wright, Reference Cropanzano and Wright2001).
On the theoretical and empirical groundings exposed until here, the hypotheses that follow were formulated (see Figure 1) and are tested both at the individual and team level of analysis:
H 1 – The relationship between telework flexibility and scientific productivity is fully mediated by the climate for well-being dimensions and eudaemonic well-being both at the individual and team level. Concretely, higher scores on telework flexibility are related to higher scores of climate for well-being, and its dimensions, which in turn improve employees’ eudaemonic well-being, and finally lead to an increased scientific productivity.
H 2 – The relationship between telework flexibility and scientific productivity is fully mediated by the climate for excellence and eudaemonic well-being both at the individual and team level. Concretely, higher scores on telework flexibility are related to higher scores of climate for excellence, which in turn improve employees’ eudaemonic well-being, and finally lead to an increased scientific productivity.
H 3 – The relationship between telework flexibility and scientific productivity is fully mediated by the climate for innovation and eudaemonic well-being both at the individual and team level. Concretely, higher scores on telework flexibility are related to higher scores of climate for innovation, which in turn improve employees’ eudaemonic well-being, and finally lead to an increased scientific productivity.
Figure 1. Graphic Representation of Hypothesized Statistical Model.
In addition, considering the outcome variable of scientific productivity, at the individual level the variables of gender and age were included as control variables. Whereas, at the team level, age, budget, gender percentage, grant type, research field, and team size were included as control variables.
Teleworking Flexibility, Climate for Work-Life Balance, and Gender
The practice of teleworking has been found to have positive effects on a wide array of work related aspects (Charalampous et al., Reference Charalampous, Grant, Tramontano and Michailidis2019; Lunde et al., Reference Lunde, Fløvik, Christensen, Johannessen, Finne, Jørgensen, Mohr and Vleeshouwers2022), yet, in terms of gender, some relevant differences have been pointed out, especially when considering work-life balance (Rodríguez-Modroño & López-Igual, Reference Rodríguez-Modroño and López-Igual2021). Indeed, unequal housework distribution across gender is unfortunately still a reality and has historically characterized more the southern than the Nordic European countries (Suero, Reference Suero2023). For this very reason then, the relationship flexible teleworking and the climate dimension of work-life balance is controlled for gender, expecting this relationship to be stronger for men than for women, who probably lose the benefits of flexible teleworking due to increased housework.
Method
Transparency and Openness
All data, analysis code, and research materials are made publicly available at the Open Science Framework and can be accessed at https://doi.org/10.17605/OSF.IO/FKZUA. To analyze data the statistical software that follow were used: IBM SPSS version 23, the jamovi statistical software (version 2.3; Jamovi, 2022) was used to run reliability analyses, the statistical software R (version 3.6.3; R Core Team, 2020), and Mplus (Muthén & Muthén, Reference Muthén and Muthén2017). The study design, hypotheses, and analysis plans were not pre-registered.
Participants
At the individual level, the sample was composed of 358 members working in teams based in Spain and granted by the ERC. As to age, 58.4% of the sample was less than 35 years old, 37.1% had age between 35 and 50, 4.2% between 50 and 65, and the remaining 0.3% was older than 65 years. As for gender, 51%% of the participants identified themselves as male, 46.4% as female, 0.3% of the participants did not identify themselves with any provided option, and 2.3% did not want to express themselves in regards. In terms of positions, 4.2% of the sample was composed of full professors, 3.7% of “Profesor/a Titular de Universidad (TIP)”, 1.4% of “Profesor/a Contratado/a Doctor/a”, 0.9% of doctoral assistant professors, 0.5% of teaching assistants, 0.9% of collaborators, 4.2% of associate professors, 32.2% of post-doctoral researchers, 29.4% of pre-doctoral researchers, 2.3% of undergraduate and post graduate students, 3.3% of doctoral technicians, 9.3% of technicians, and the remaining 7.5% of other types of professionals.
At the team level, the sample was composed of 48 ERC-granted teams operating in multiple sectors (see Table 1) and distributed all over Spain but with a relevant concentration in the cities of Barcelona (i.e., 25%) and Madrid (i.e., 18.8%). Teams were composed on average of 8.6 members (SD = 3.39), with a minimum of 3 and a maximum of 19. As to the budget, on average teams were supported with € 2,061,107.1 (SD = € 1,400,375.7), with a minimum of € 1,064,712.00 and a maximum of € 9,057,250.00. As to gender, teams were composed on average by 45.95% of females (SD = 25%), with a minimum of 0.00% and a maximum of 100%. Leaders’ gender, 70.40% of the teams was leaded by males and 29.60% by females. In conclusion, it is worth mentioning that minimum 55% of the team members needed to have replied to the survey to be included in the dataset for running the analyses at the team level.
Table 1. ERC-Teams Fields of Research
Procedure
Data were collected following the approval obtained from the Ethical Committee of the Spanish institution in charge of the present project. All the 206 Principal Investigators (P.I.) leading an ERC-granted project in Spain, at the moment of the data collection (i.e., March-May 2022), were contacted via email asking for participation and distribution of the relevant survey among the components of their teams. In total, 48 teams (i.e., 23.3%) decided to participate in the data collection process in exchange for a descriptive team report summarizing the main statistics about the assessed constructs. Considering the increasing internationality of research teams, the survey was made available both in Spanish and English after a thorough back-translation process (Brislin, Reference Brislin1970; World Health Organization, 2023), which involved four experts with high proficiency both in English and Spanish. The survey remained active for 68 days and was closed on the 13th of March 2022.
Measures
Telework Flexibility
As to the flexibility with which the practice of teleworking can be used by team members, respondents were asked to assess the four statements that follow through a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The items were worded as follows: “From 1 to 5, to what extent can you decide how many days to telecommute?”, “From 1 to 5, to what extent can you decide how to distribute your hours/days of telecommuting throughout the week?”, “From 1 to 5, to what extent can you decide how to distribute your days of telecommuting throughout the month?”, and “From 1 to 5, to what extent can you decide to telecommute “at the last minute”?”. Cronbach’s (α) and McDonald’s (ω) coefficients were found to be. 92, suggesting an adequate internal consistency. In terms of model fit, the relevant indexes for telework flexibility resulted to be adequate (CFI =. 99; TLI =. 99; RMSEA =. 05; SRMR =. 01) and the factor loadings to significantly (i.e., p <. 001) overcome the minimum required threshold (i.e.,. 40).
Climate for Well-Being
As to the climate for well-being, or molar climate (Schneider et al., Reference Schneider, Ehrhart, Macey, Ashkanasy, Wilderom and Peterson2011), it was measured by means of the ECO VI scale (Martinolli et al., Reference Martinolli, Tordera and Sanín Posada2024). The scale, initially developed by Toro (Reference Toro1992, Reference Toro1996, Reference Toro2008), is theoretically framed into the Vitamin Model by Warr (Reference Warr1987, Reference Warr2007), which is particularly suitable since it theoretically identifies the relevant workplace features that affect employees’ well-being. The scale is composed of 13 dimensions, with 3 items each, and a 5-point Likert response scale ranging from 1 (strongly disagree) to 5 (strongly agree). Examples of items and the relevant reliability per dimension are reported in Table 2. In terms of overall reliability, the ECO VI scale resulted to have Cronbach’s (α) and McDonald’s (ω) coefficient of. 93. In terms of model fit, the relevant indexes for climate for well-being resulted to be adequate (CFI =. 94; TLI =. 93; RMSEA =. 04; SRMR =. 05), confirming its structure composed of 13 dimensions, and the factor loadings to significantly (i.e., p <. 001) overcome the minimum required threshold (i.e.,. 40).
Table 2. Climate Dimensions, Items, and Reliability
Climate for Innovation
As to the climate for innovation, it was assessed with a four items scale, based on Anderson and West (Reference Anderson and West1998), and a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree); an example of item is “In my team, people are always looking for new ways of looking at problems”. The scale was found to have a good internal consistency, indeed Cronbach’s (α) and McDonald’s (ω) coefficient were found to be. 88. In terms of model fit, the relevant indexes for the tool resulted to be adequate (CFI =. 98; TLI =. 95; RMSEA =. 12; SRMR =. 02) and the factor loadings to significantly (i.e., p <. 001) overcome the minimum required threshold (i.e.,. 40).
Climate for Excellence
As to the climate for innovation, it was assessed with a four items scale, based on Anderson and West (Reference Anderson and West1998), and a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree); an example of item is “In my team, there is a real concern to achieve the highest standards of performance”. The scale was found to have a good internal consistency, indeed Cronbach’s (α) was equal to. 77 and McDonald’s (ω) coefficient to. 78. In terms of model fit, the relevant indexes for the tool resulted to be adequate (CFI =. 99; TLI =. 97; RMSEA =. 07; SRMR =. 02) and the factor loadings to significantly (i.e., p <. 001) overcome the minimum required threshold (i.e.,. 40).
Eudaemonic Well-Being
Eudaemonic well-being was assessed by the means of the Eudaimonic Workplace Well-being scale (EWWS) by Bartels and colleagues (Reference Bartels, Peterson and Reina2019), which is composed of eight items equally distributed between two dimensions: The interpersonal one and the intrapersonal one. The former dimension was composed of items such as “Among the people I work with, I feel there is a sense of fellowship”, while the latter of items such as “I feel I am able to continually develop as a person in my job”. Respondents could assess items through a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The scale was found to have generally a good internal consistency, Cronbach’s (α) McDonald’s (ω) coefficients were found equal to. 85. Specifically, with reference to the specific sub-dimensions, for the interpersonal one Cronbach’s (α) was found equal to. 86 and McDonald’s (ω) to. 87, while for the intrapersonal dimension both Cronbach’s (α) and McDonald’s (ω) were found equal to. 80. In terms of model fit, the relevant indexes for EWWS resulted to be adequate (CFI =. 94; TLI =. 91; RMSEA =. 11; SRMR =. 07), confirming its bi-dimensional structure, and the factor loadings to significantly overcome the minimum required threshold (i.e.,. 40). In the context of the present study, the variable in question has been used as a second order variable, as theorized by the authors of the scale, namely Bartels and colleagues (Reference Bartels, Peterson and Reina2019), and used by other research works that have confirmed the suitability to proceed with so (Mahomed et al., Reference Mahomed, Oba and Sony2022).
Scientific Productivity
At the individual level, respondents were asked to report the number of published articles, both as corresponding authors and co-authors, and the number of presentations that they have written and presented in 2021. On the other hand, at the team level, Principal Investigators (P.I.) were asked to report the number of articles that were published since the start of the project, which was subsequently cross-checked online on the official web pages of the ERC project. The total number of team publications was then divided by the number of months from the start of the ERC project, so to have a comparable index across teams.
Control Variables
In terms of control variables, age, gender, and team size were asked to be reported by the respondents. On the contrary, budget, type of grant (i.e., Starting Grant, Consolidator Grant, Advanced Grant, and Synergy Grant), and field of research were directly retrieved from the official webpage of the European Research CouncilFootnote 1. Percentage of gender at the team level was computed as the percentage of females present in the research team.
Analyses
First, the dataset was checked to identify missing data, which amounted to be less than the limit of 5% for which data imputation is required (Fichman & Cummings, Reference Fichman and Cummings2003).
Using IBM SPSS software version 23, the relevant consistency analyses were performed to check the reliability of the used measures and benchmarked against the threshold pointed out in the literature (Cortina, Reference Cortina1993; Nunally, Reference Nunally1978). To confirm the factorial structure of the used measurement tools in the considered sample, multiple confirmatory factor analyses (CFA) were conducted using the statistical software Mplus (Muthén & Muthén, Reference Muthén and Muthén2017). Maximum Likelihood estimation was used since data distribution was normal. The model fit was assessed using multiple indices. The comparative fit index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). For CFI and TLI, values greater than. 90 are usually considered as a reasonable model fit, whereas stringent recommendations suggest values close to. 95 (Hu & Bentler, Reference Hu and Bentler1999). For the RMSEA and the SRMR, values below. 08 are traditionally considered a reasonable model fit, whereas stringent recommendations suggest values close to. 06 (Hu & Bentler, Reference Hu and Bentler1999).
Before running the analyses at the team level of analysis, additional tests were performed to assess aggregation. Aggregation allows assessing that each member’s score was similar enough to those within their team and that each member’s score was significantly different to those among the other considered teams. In doing so, the average deviation index (ADI; Burke et al., Reference Burke, Finkelstein and Dusig1999) and the agreement index for multi-item scales rWG(J) (James et al., Reference James, Demaree and Wolf1984) were computed and analyzed for scales so as to ensure within-team agreement. Since the response scale to each item was composed of 5 points, the cut-off value for ADI is. 83, more precisely ADI must be smaller than. 83 to indicate acceptable agreement (Burke & Dunlap, Reference Burke and Dunlap2002). On the other hand, rWG(J), values above. 70 are considered to provide evidence of agreement (Bliese, Reference Bliese2022). As suggested by the scientific literature, also the intraclass correlation coefficients (viz., ICC1 and ICC2) were computed (Bliese, Reference Bliese1998). ICC(1) was considered for evaluating the level of consistency of responses among team members, while ICC(2) was considered for estimating the reliability of the team means (Bliese, Reference Bliese, Klein and Kozlowski2000). The commonly observed cut-off values for ICC(1) typically range between. 05 and. 20 (Bliese, Reference Bliese, Klein and Kozlowski2000), although LeBreton and Senter (Reference LeBreton and Senter2008) have suggested that an ICC(1) of 0.05 represents a small-to-medium effect. Bliese (Reference Bliese, Klein and Kozlowski2000) also suggests that values of ICC(2) above. 70 should be considered acceptable, while Fleiss (Reference Fleiss1999) states that ICC(2) levels lower than. 40 are poor, those from. 40 to. 75 are fair to good, and those greater than. 75 are excellent. Finally, also a one-way analysis of variance (ANOVA) was carried out to determine whether there was statistically significant difference in between-teams discrimination in the considered team level constructs. All aggregation analyses were conducted with the statistical software R (version 3.6.3) (R Core Team, 2020) by using the R package “multilevel” in its version 2.6 (Bliese et al., Reference Bliese, Chen, Downes, Schepker and Lang2022).
For testing the formulated hypotheses both at the individual and team level of analysis, the statistical software Mplus (Muthén & Muthén, Reference Muthén and Muthén2017) was used to perform a structural equation modeling on the relevant dataset. In this case, the weighted least squares mean and variance adjusted (WLSMV) estimation model was used since the tested statistical model contained some nominal variables (e.g., gender). As to the inclusion of the whole set of control variables in the statistical model, it is worth pointing out that they were simultaneously included during the computation of the two (i.e., individual and team level) whole models.
Results
Satisfying results were found both at the individual and team level of analysis and are reported in the lines that follow.
Results at the Individual Level of Analysis
In the correlation matrix that follows can be found the correlations (i.e., Pearson’s r) among the relevant variables at the individual level of analysis (see Table 3).
Table 3. Correlation Matrix at the Individual Level
Note. * p <. 05. ** p <. 01
At the individual level (see Figure 2), with reference to H 1, testing the mediating role of the dimensions of climate for well-being and eudaemonic well-being between teleworking flexibility and scientific productivity, partial support was found. Indeed, as it is possible to see in Figure 2, teleworking flexibility was found to have a direct and positive relationship with a number of climate dimensions, specifically team support, organizational clarity, compensation, autonomy, professional development, acoustic comfort, furniture, and finally work-life balance. As to this last climate dimension, it was found that gender played a relevant role, meaning that, as expected, the relationship was stronger for men than for women. In turn, the climate dimension of team support was found to have a direct relationship with eudaemonic well-being, as well as the climate dimension of resources availability, of teamwork, and of interpersonal relations. Finally, eudemonic well-being was found to have a direct and positive relationship with scientific productivity. On these groundings, the climate dimension of team support, together with eudaimonic well-being, appeared to have the structural potentiality to represent a mediating mechanism between telework flexibility and scientific productivity, yet the indirect effect was found to be not significant (i.e., p =. 13). Notwithstanding, it is relevant to point out that the level of significance for the here considered indirect effect, was relevantly lower than for the paths with the other climate dimensions. In addition, the simple indirect effect mediated by eudaemonic well-being between team support and scientific productivity resulted to be significant (β =. 31, p <. 02). This represents an argument for assuming that with a greater sample size this indirect effect would result significant, given the complexity of the considered statistical model.
Figure 2. Results at the Individual Level of Analysis.
Note. n.s. = Non-significant.
* p <. 05. ** p <. 01. *** p <. 001.
As to H 2, testing the mediating role the climate for excellence and eudaemonic well-being between teleworking flexibility and scientific productivity, partial support was found. Indeed, no direct relationship was found between telework flexibility and the climate for excellence, yet the focused climate in question was positively liked to eudaemonic well-being, thus, as above reported, to scientific productivity.
As to H 3, testing the mediating role of the climate for innovation and eudaemonic well-being between teleworking flexibility and scientific productivity, partial support was found. Indeed, telework flexibility was found to have a direct and positive relationship with the climate for innovation, which in turn was positively linked to eudaemonic well-being, thus, as above reported to scientific productivity. On these groundings, the climate for innovation, together with eudaimonic well-being, appeared to have the structural potentiality to represent a mediating mechanism between telework flexibility and scientific productivity, yet the indirect effect was found to be not significant (i.e., p =. 13). Notwithstanding, it is relevant to point out that the level of significance for the here considered indirect effect, was relevantly lower than for the paths with the other climate dimensions. In addition, the simple indirect effect mediated by eudaemonic well-being between the climate for innovation and scientific productivity resulted to be significant (β =. 18, p <. 03). Also in this case, this represents an argument for assuming that with a greater sample size this indirect effect would result significant, given the complexity of the considered statistical model.
As to the results of the control variables on scientific productivity, age was understandably found to play a relevant role (β =. 40, p <. 001), on the contrary of gender.
Results at the Team Level of Analysis
Before running the statistical analyses at the team level, the due aggregation indexes were checked as reported in the scientific literature (LeBreton & Senter, Reference LeBreton and Senter2008). The outcomes of such analyses are reported in Table 4 and provide justification for the performance of statistical analyses at the team level.
Table 4. Aggregation Indexes for Team-Level Analysis
In the correlation matrix that follows can be found the correlations (i.e., Pearson’s r) among the relevant variables at the team level of analysis (see Table 5).
Table 5. Correlation Matrix at the Team Level
Note. * p <. 05. ** p <. 01.
At the team level (see Figure 3), with reference to H 1, testing the mediating role of the dimensions of climate for well-being and eudaemonic well-being between teleworking flexibility and scientific productivity, partial support was found. Indeed, teleworking flexibility was found to have a direct and positive relationship with a few climate dimensions, such as organizational clarity, compensation, autonomy, professional development, and finally the climate dimension of acoustic comfort. In turn, the climate dimension of team support was found to have a direct relationship with eudaemonic well-being, as well as the climate dimension of Principal Investigator’s support. Finally, contrarily to the individual level, eudemonic well-being was not found to have a direct and positive relationship with scientific productivity.
Figure 3. Results at the Team Level of Analysis.
Note. n.s. = Non-significant.
* p <. 05. ** p <. 01. *** p <. 001.
As to H 2, testing the mediating role the climate for excellence and eudaemonic well-being between teleworking flexibility and scientific productivity, partial support was found. Indeed, no direct relationship was found between telework flexibility and the climate for excellence, yet the climate was positively liked to eudaemonic well-being.
As to H 3, testing the mediating role the climate for innovation and eudaemonic well-being between teleworking flexibility and scientific productivity, partial support was found. Indeed, telework flexibility was found to have a direct and positive relationship with the climate for innovation, while it was not significantly related to eudaemonic well-being.
As to the results of the control variables on scientific productivity, only the type of grant (β =. 60, p <. 001) was found to play a relevant role, showing that scientific productivity increases with the level of relevance of the grant itself. On the other hand, the model resulted to be stable after controlling for gender percentage team size, budget, research field, and age, whose relationship with scientific productivity resulted to be not significant.
Discussion
The present study was set out to explore whether the molar climate for well-being dimensions, jointly with the climate for excellence and for innovation and eudaemonic well-being represent a relevant mediating mechanism in the relationship between telework flexibility and scientific productivity. Indeed, in spite of the numerous studies investigating such relationship, results are unclear (Hackney et al., Reference Hackney, Yung, Somasundram, Nowrouzi-Kia, Oakman and Yazdani2022) and its linking mechanisms not fully covered (Charalampous et al., Reference Charalampous, Grant, Tramontano and Michailidis2019; Lunde et al., Reference Lunde, Fløvik, Christensen, Johannessen, Finne, Jørgensen, Mohr and Vleeshouwers2022). In addition, despite the climate construct represents one of the most investigated variables (Schneider et al., Reference Schneider, González-Romá, Ostroff and West2017), not much is known about how it is affected by the HR practice of teleworking, thus how this practice relates to relevant workers’ perceptions of the workplace. On a final note, with specific reference to the climate literature, it is worth pointing out that insights were missing on the simultaneous role that molar and focused climates may play in the explanation of organizational phenomena (Ehrhart & Kuenzi, Reference Ehrhart, Kuenzi and Wright2015). Specifically, not much was known about whether and to what extent they contributed one another to the prediction of organizational outcomes (Ehrhart & Kuenzi, Reference Ehrhart, Kuenzi and Wright2015).
Basing on the results of the performed analyses, telework flexibility represents a relevant climate foci for molar and focused climates, both at the individual and team level of analysis. Indeed, flextime appeared to positively relate to most of the climate for well-being dimensions and with the climate for innovation. As to the former, it is interesting to notice that telework flexibility may then represent a relevant strategy for overcoming the inconveniences that usually characterize workplaces in research team (e.g., acoustic comfort and adequate furniture; Mazzi, Reference Mazzi1996) and to improve the possibilities of professional development, the perceptions about compensation, and of team support. As to the climate for innovation, it resulted that providing the opportunity to flexibly choose when to work and from where favors the perception of working in a work environment that supports the formation of creative and innovative ideas. Thus, this finding is completely in line with the workplace innovation literature that highlights the relevant role that HR practices can play when promoting innovation is the goal (Prus et al., Reference Prus, Nacamulli and Lazazzara2017).
Moving to the relationships between climates and eudaemonic well-being, both climate for well-being dimensions, the climate for excellence, and the climate for innovation were found to play a relevant role in the promotion of eudemonic well-being, especially at the individual level. Hence, both molar and the considered focused climates jointly play a relevant role in the prediction and explanation of relevant outcomes (Ehrhart & Kuenzi, Reference Ehrhart, Kuenzi and Wright2015). With specific reference to the climate for well-being dimensions, team support, teamwork, interpersonal relations, and resources availability had a particular strong relationship with well-being. As to this last one, it is worth noticing the negative sign of such relationship, meaning that a greater availability of resources was related to a lower level of eudaemonic well-being. A possible explanation can lay in the fact that an increased possibility to resort to resources can lead to isolation, preventing interactions with colleagues and feeling well from a eudemonic perspective. This explanation finds support in the study by Stoian and colleagues (Reference Stoian, Caraiani, Anica-Popa, Dascălu and Lungu2022), where resources availability is put into relationship with professional isolation. Also, differently from the individual level, it was found that shared perceptions about the Principal Investigator’s (P.I.) support were negatively related to eudaemonic well-being at the team level. A possible explanation can lay in the fact that P.I.s’ excessive presence and willingness to support their team can make become the team excessively dependent on the leader, thus obstacle the natural development of relationships among team members and growth opportunities within the team. In line with this, Deci and colleagues (Reference Deci, Connell and Ryan1989) highlight how relevant it is to promote autonomy within the team for activating beneficial processes among team members. Finally, as to the climate for excellence and for innovation, as expected they were found to represent a relevant source of both professional and personal growth, thus of eudaemonic well-being.
In addition, moving to the relationship between eudaemonic well-being and scientific productivity, a possible explanation behind the fact that it was found to be significant only at the individual level of analysis, could be found in the operationalization of the team-level KPI itself. Indeed, at the team level, only the studies published under the relevant ERC grant were considered, hence only a subset of works was included, with weakening effects on the relationship itself.
In conclusion, with specific reference to the climate dimension of work-life balance, it is important to notice that, at the individual level, gender played a relevant role. More precisely, telework flexibility resulted to be a stronger promoter of the perceptions of work-life balance for men than women, as expected basing on previous empirical findings (Rodríguez-Modroño & López-Igual, Reference Rodríguez-Modroño and López-Igual2021; Suero, Reference Suero2023).
Theoretical Implications
In terms of theoretical implications, as a first outcome of this research work the practice of flexible telework represents a relevant climate source as other HR practices (Veld et al., Reference Veld, Paauwe and Boselie2010). Hence, scientists and practitioners are suggested to start considering this practices when investigating team and organizational phenomena that involve climate variables, especially now that the practice of telework has become so widespread (Eurofound, 2020; Eurofound & ILO, 2017).
Secondly, the present study contributed to start shedding light on a relevant gap within the climate literature. As pointed out by Ehrhart and Kuenzi (Reference Ehrhart, Kuenzi and Wright2015), empirical insights were missing about the joint contribution of molar and focused climates in the explanation of organizational outcomes. In regards, the evidence produced through the present study show that, in addition to the molar climate for well-being, also focused climates play a relevant role in the explanation of employees’ well-being.
Finally, as to the identification of mediating mechanisms explaining the relationship between teleworking and scientific productivity that was pointed out by Charalampous and colleagues (Reference Charalampous, Grant, Tramontano and Michailidis2019) and Lunde and colleagues (Reference Lunde, Fløvik, Christensen, Johannessen, Finne, Jørgensen, Mohr and Vleeshouwers2022), it can be stated that climates and eudaemonic well-being seem to play a relevant role in this sense. Specifically, they represent a joint mechanism explaining how telework flexibility leads to an increase in scientific production, yet more light needs to be shed in regards given the limitations of the present study.
Practical Implications
In terms of practical implications, implementing the practice of teleworking so that workers can resort to it flexibly has been shown to produce strong and positive repercussions on relevant climate dimensions. Hence, team leaders, such as Principal Investigators of ERC granted teams, should take into serious consideration providing their team members with the opportunity to decide when to telework both on a weekly and monthly basis. Secondly, flexible teleworking can be strategically used for increasing the perceptions of work-life balance, yet gender differences should be taken into account, and relevant adjustments should be implemented. Indeed, it would result to be particularly valuable for men, while for women additional countermeasures should be taken to prevent negative effects. Finally, as may be the case for most of the ERC-granted teams, which are granted on average with a € 2mln subsidy each, its members can be provided with a wide array of resources. In this case, the Principal Investigator should put much attention on creating adequate opportunities for stimulating discussions aiming fostering personal and professional growth.
Despite the valuable contribution of this study, some limitations need to be pointed out. Firstly, it needs to be highlighted that the present study has a cross-sectional design, thus cannot be used for drawing causal conclusions about the considered relationships. Secondly, the sample sizes both for the individual (i.e., 358) and for the team (i.e., 48) level analysis were, considering the complexity of the statistical model, relatively small, reducing the power of the statistical tests carried out. In turn, the reduced statistical power limited, to a certain extent, the detection of significant relationships. On the one hand, grounding on the results at the individual level, the indirect effects of the hypothesized mediating mechanisms would have assumingly resulted to be strongly significant based on the detected trends in terms of significance. On the other hand, grounding on the results at the team level, it becomes clear that a bigger sample would have led to more significant relationships, such as the relationship of telework flexibility, P.I.’s support, autonomy, climate for innovation or climate for excellence with scientific productivity, this based on the relevant outputs in terms of bivariate correlations. Thirdly, having reached out to all 206 currently active Spanish ERC-granted teams, this geographic specificity may hinder the generalizability of the findings to other research teams. However, considering the internationality of the contexts in which excellence teams use to operate (e.g., international collaborations, attendance and presentation at international conferences), the main features that characterize research teams of excellence, at least in Europe, can, to a certain extent, be similar across countries. Moreover, the multiplicity of areas in which the considered research team operated can be regarded as an additional factor that diminishes the effects of such regionality.
In terms of future research, the authors recommend carrying out studies on the topic but with a longitudinal research design for exploring the causal links among the considered variables. Secondly, it would be advisable for similar future studies to have a more comprehensive reach-out including teams that are based in other European countries. This would allow having a more comprehensive understanding of the investigated phenomena and could allow detecting possible diversities across nations. Thirdly, it would be relevant to carry out the tested statistical model with a larger sample in that it would allow having a more solid testing of the investigated relationships. In addition, it would allow researchers to run a cross-level statistical model and, possibly, consider gender as a model moderator. As to the former and given the specificity of the considered variables, it would be interesting to run a “1–2–1–1” cross level model that considers only team climates as aggregated variables. This would allow increasing the understanding on how the considered individual and team level variables relate to one another and result in increased scientific productivity. As to the latter, given the implicit and explicit gender inequalities that are still characterizing a good number of workplaces, considering gender as a model moderator would allow detecting to more thoroughly possible changes in the considered relationships when considering one gender or the other. Fourthly, considering the complexity and diversity that characterize international research teams, as for example in terms of multiculturality, age, background, it would be interesting to investigate the possible effects of fault-lines along with the models tested in the present study. Indeed, as highlighted in the work by Valls and colleagues (Reference Valls, Tomás, González-Romá and Rico2021), demographic fault-lines can have negative effects on team performance. Finally, the outcomes of the present study can be used as a starting point for future studies aiming at filling the relevant gap pointed out by Ehrhart and Kuenzi (Reference Ehrhart, Kuenzi and Wright2015). The authors precisely point out that empirical insights are missing about the interactions among the molar climate for well-being and focused climates, both in terms of process and strategic climates. Consistently, the present study can then be regarded as a first empirical step in such direction, suggesting that when eudaemonic well-being is considered as outcome the considered climates seem to tend to add their effects one upon the other. Clearly, this represents a first speculation that needs to be addressed with an empirical study that directly addresses and hypothesizes on the interactions among the considered climate constructs.
To conclude, despite the limitations, the authors believe that the present work provides the community of scientists, practitioners, and Principal Investigators with useful findings that improve the understanding on the management of excellence research teams in view of their well-being and productivity.
Funding statement
This work was supported by the Comunidad de Madrid (Grant Number 2018–T1/SOC–10409).
Authorship credit
Conceptualization (GM, NT); Data curation (GM); Formal Analysis (GM, IT); Funding acquisition (SB); Investigation (GM, NT); Methodology (GM, NT, IT); Project administration (GM); Resources (GM); Software (GM, IT); Supervision (NT, IT); Validation (n.a.); Visualization (GM); Writing – original draft (GM); Writing – review & editing (ASP, NT, IT, SB, GM).
Data sharing
The authors state that all the relevant datasets and syntaxis are available at Open Science Framework and can be accessed at https://doi.org/10.17605/OSF.IO/FKZUA.
Conflicts of Interest
None.
Open access
