Marion Inigo
email: marion.inigo@univ-tlse2.fr

Éric Raufaste

Academics' motivations explain time-allocation and well-being at work

Keywords: well-being, well-being at work, PERMA, higher education, motivation, Résumé bien-être, bien-être au travail, PERMA, Enseignement supérieur, motivation, Self-esteem through Teaching (SelfsT), d F4: Autonomy in Research Activities (AutoR), e F5: Positive Relationships with Students (RelStuds), f

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

time-allocation and well-being at work

Well-being at work is a major issue [START_REF] Clot | Occupational health: Models, measures and actions[END_REF] in education [START_REF] Laugaa | Stress and burnout among French elementary school teachers: A transactional approach[END_REF] and health care [START_REF] Gilibert | Disorders associated with burnout and causal attributions of stress among health care professionals in psychiatry[END_REF][START_REF] Roland-Lévy | Health and well-being at work: The hospital context[END_REF]. In Academia, well-being at work also tends to become a problem due to organizational changes.

The structure of research funding has also been changing rapidly in Europe.

Such organizational and legal changes can redefine academics' work [START_REF] Annoot | Le métier d'enseignant chercheur[END_REF] and affect satisfaction at work. More generally, academics may feel difficulties at work. Just like anybody, academics can be frustrated and express feelings of envy, jealousy and seeking of recognition [START_REF] Viry | Le monde vécu des universitaires: ou La république des égos [The lived world of academics: or The republic of egos[END_REF].

In specialised newspapers, survey after survey, French academics express feelings of discomfort, not to say anxiety (e.g., [START_REF] Blitman | Le moral des universitaires à la loupe [the morale of academics in the magnifying glass[END_REF][START_REF] Lecherbonnier | À l'université, le malaise devient politique[END_REF][START_REF] Stromboni | Le malaise s'enracine à l'université [discontent takes root at the university[END_REF]. Such discomfort also exists outside Europe (e.g. [START_REF] Bexley | The motivations, values and future plans of Australian academics[END_REF][START_REF] Blackmore | Strategic leadership in academic development[END_REF] and deserves psychological investigation.

In this paper, motivational determinants of academics' well-being at work were investigated. We start by studying the structure of academics' motivations (i.e., for research and teaching) and their relationships to well-being at work. Motivation can also affect time-allocation. All around the world, academics accomplish research, teaching, and collective tasks. Presumably, spending more time in an activity than in another depends on motivation. Most studies focussed on teaching [START_REF] Visser-Wijnveen | Development and validation of a questionnaire measuring teachers' motivations for teaching in higher education[END_REF] or educational development [START_REF] Blackmore | Strategic leadership in academic development[END_REF], but few studies investigated motives that are specific to higher education professionals. What about research and collective tasks? Over the last forty years, the plurality of administrative tasks has increased [START_REF] Annoot | Le métier d'enseignant chercheur[END_REF]. It may cause frustration, particularly in those who feel they carry an unjust load of collective tasks [START_REF] Misra | The ivory ceiling of collective work[END_REF]. A perceived lack of recognition and rewards might also promote demotivation in academics [START_REF] Winter | The academic work environment in Australian universities: a motivating place to work? Higher[END_REF].

In this study, various motives of academics were identified. The capacity of these motives to explain profiles of time-allocation, wellbeing, and the links between profiles and well-being were investigated.

Motivations in the academic workplace

Intrinsic vs. extrinsic motives. Our first objective is to understand well-being at academics' work. Because intrinsic motivation goes along with enjoyment and satisfaction (Vallerand et al., 2012), we first examine whether specific activities are associated with intrinsic motivations in academics. [START_REF] Visser-Wijnveen | Development and validation of a questionnaire measuring teachers' motivations for teaching in higher education[END_REF] studied motivation in higher education teachers, but only the motivation to teach, not the motivation for research activities or for collective tasks.

Teaching and research activities motivate academics more than collective tasks, with variations depending on specialty or status (see [START_REF] Musselin | Academic work and academic identities: A comparison between four disciplines[END_REF]. In an Australian study [START_REF] Bexley | The motivations, values and future plans of Australian academics[END_REF] [START_REF] Cucu-Ciuhan | Organizational culture versus work motivation for the academic staff in a public university[END_REF] suggested that self-fulfilment motivation was the main source of interest, higher that economic or social motivation.

Goal conflicts. Although academics enjoy some degree of latitude in timeallocation between job activities, time remains a limited resource.

Academics can succeed with regard to research or teaching, but more rarely in both domains [START_REF] Bucheit | Intra-institutional factors that influence accounting research productivity[END_REF][START_REF] Chow | Factors contributing to success in research and publications: insights of influential accounting authors[END_REF]. The amount of time devoted to research and teaching constrains the time left for collective tasks [START_REF] Lawrence | Faculty organizational commitment and citizenship[END_REF]. Research and teaching motives may also conflict because research productivity benefits from research efforts but is hindered by the teaching load [START_REF] Hardré | Faculty motivation to do research: across disciplines in researchextensive universities[END_REF]. Such conflict would generate, according to [START_REF] Esdar | The role of basic need satisfaction for junior academics' goal conflicts and teaching motivation[END_REF], psychological strain in 62% of German junior academics. [START_REF] Hellemans | Des risques psychosociaux à la charge psychosociale: le rôle des facteurs motivationnels chez les enseignants-chercheurs [From psychosocial risks to psychosocial load: the role of motivational factors in academic staff[END_REF] reported a study of work motivations and psychosocial risk in 185 tenured teachers from a University in Brussels, Belgium. The two most cited factors were difficulty to find enough time for research (88.6%) and workload associated with service (collective tasks) (83.8%).

Thus, optimising the working-time trade-off between research, teaching and collective activities seems inevitable [START_REF] Clark | The academic life. Small worlds, different worlds[END_REF]. In [START_REF] Becquet | Variations autour du travail des universitaires [variations around academics[END_REF], French tenured academics exhibited strong individual differences in the management of these activities, depending on practice areas. Academics in Sciences seemed to be more engaged in research activity than academics in the field of Arts and Literature, who in turn were more engaged in teaching. Academics can develop various timemanagement strategies. They can limit the weight of teaching or administration, partly delegate or even abandon some activities [START_REF] Becquet | Variations autour du travail des universitaires [variations around academics[END_REF]. A reasonable assumption is that motivational factors govern time allocation strategies.

Let us turn to the question of what is understood by 'well-being' and how to measure it.

Choosing a well-being framework

Well-being at work is multidimensional [START_REF] Fisher | Happiness at work[END_REF] and in the perspective of eventually applying diagnostic investigations to real work situations, a multidimensional approach can be preferred. Yet several multidimensional frameworks exist.

According to PERMA [START_REF] Seligman | Flourish[END_REF], five elements contribute to wellbeing. (P) Positive emotions refers to hedonic well-being, having a pleasant time with such feelings as satisfaction or pleasure. In the context of work, this element relates to 'job satisfaction' even though job situations may trigger both negative and positive affects [START_REF] Payne | Emotions at work: theory, research, and application for management[END_REF])-a feature also promoted by [START_REF] Kern | A multidimensional approach to measuring well-being in students: application of the PERMA framework[END_REF].

(E)

Engagement corresponds to the involvement in an activity or a project and refers to the concept of 'Flow' [START_REF] Csikszentmihalyi | Flow: the psychology of optimal experience[END_REF]. (R)

[Positive] Relationship regards experiencing positive, warm and trusting interpersonal relationships. A potentially crucial feature in many work domains (e.g., [START_REF] Coissard | Relationships at work and psychosocial risk: The feeling of belonging as indicator and mediator[END_REF], R is also relevant to the University context, with teaching and research colleagues, and with students. (M) Meaning corresponds to how people relate themselves to something higher than the self [START_REF] Seligman | Flourish[END_REF]. In academics, 'something higher than the self' might include various entities, from your local department to Science as a whole.... Finally, (A) Achievement is a feeling of self-fulfilment through achieving selfdefined objectives.

The Psychological Well-Being approach [START_REF] Ryff | Happiness is everything, or is it? Explorations on the meaning of psychological well-being[END_REF][START_REF] Ryff | The structure of psychological well-being revisited[END_REF] considers six dimensions. 'Positive relations with others' roughly corresponds to R. 'Purpose in life' resembles M (e.g., a low Purpose-inlife scorer 'lacks a sense of meaning in life', as Ryff formulates it, 1989, p. 1072) albeit more remotely because it also emphasizes goals.

The other dimensions seem very different, 'Self-acceptance', 'Autonomy', 'Personal growth', and 'environmental mastery'. More recently, [START_REF] Keyes | The Mental Health Continuum: From Languishing to Flourishing in Life[END_REF] proposed a mental-Health Continuum that combines the six dimensions of Ryff's

Psychological well-being to the three dimensions of subjective well-being (Positive and negative emotions, and life satisfaction, according to [START_REF] Diener | Subjective well-being[END_REF], and five dimensions of social well-being. Despite its 14-dimensions, this model does not embed the E and A elements of PERMA. Yet, the concept of Engagement seems well fitted to work situations that include discovery and invention [START_REF] Csikszentmihalyi | Creativity: flow and the psychology of discovery and invention[END_REF], two highly valued features for academic carriers. Achievement also sounds particularly relevant to academic carriers.

Hence, PERMA seems to be a promising framework for the study of wellbeing at work in academics, with the following general hypothesis: What intrinsically motivates academics should explain their well-being at work, as measured by PERMA elements.

Method

Testing strategy

The testing strategy involved three steps. First, a scale of job motives was designed, specifically dedicated to higher education academics and their activities (i.e., research, teaching, collective tasks). Second, from measures of relative time-allocation on research, teaching, and collective tasks, Time-Allocation Profiles were inducted. Because academics enjoy some freedom in allocating their time to the various job activities, Time-Allocation Profiles should be associated with measurable differences in some of the PERMA elements. Finally, it was tested whether the motives would explain (1) membership to Time-Allocation Profiles, and

(2) measures on the PERMA elements of well-being.

Material

Motives questionnaire. An exploratory set of 156 questions was built in a pilot study of French academics' motives [START_REF] Inigo | Élaboration d'une échelle des facteurs motivationnels du bien-être dans l'enseignement et la recherche en France[END_REF]. The set was designed to cover the eight metamotivational states of reversal theory [START_REF] Apter | The experience of motivation: the theory of psychological reversals[END_REF]. Ninety-three academics ranging from post-doc to full professor provided answers. Using PCA runs, we selected the 35questions subset used in the present study. Items were randomized and introduced by the sentence In the last four years, I appreciated my teacher-researcher job because... Responses were provided on a 7-point Likert type scale ranging from absolutely disagree to strongly agree.

Activity Involvement Measures. Participants rated their subjective involvement: During the last four years, the part of my time actually spent on this activity was . They responded using a tool consisting of three scales separately displaying relative-time allocation for teaching, research, and collective tasks (Figure 1). Validation required that the three values summed up to 100%.

Well-being questionnaires. The five elements of PERMA were assessed (items in Table 6). The French version of the SPANE scales [START_REF] Diener | New well-being measures: short scales to assess flourishing and positive and negative feelings[END_REF] provided Positive emotions, but also negative emotions. In PERMA, Engagement primarily relates to the concept of Flow [START_REF] Csikszentmihalyi | Flow: the psychology of optimal experience[END_REF]. We used the Educational Flow questionnaire (EduFlow, [START_REF] Heutte | Optimal learning experience in digital environments: theoretical concepts, measure and modelisation[END_REF], with four subscales, cognitive absorption, time transformation, autotelic experience and loss of selfconsciousness. In a professional context, however, the concept of Engagement may also relate to 'job involvement' [START_REF] Brown | A meta-analysis and review of organizational research on job involvement[END_REF] which "… is thought to depend on both need saliency and the potential of a job to satisfy these needs" (May et al., 2004, p. 12). Hence, participants were asked to answer a series of nine questions selected from the 'Emotional'

and 'Physical' subscales of the 'engagement' questionnaire (May et al., 2004, p. 36). Relationships were assessed using ten items from the 'Rewarding co-worker relations' dimension in May et al. (2004, p. 37),

and eight new items. Meaning was assessed using six items from the 'meaningfulness' dimension in May et al. (2004, p. 36). Achievement was assessed using three items from the PERMA scale (Positive Psychology Center, 2013), and two adapted from [START_REF] Kern | A multidimensional approach to measuring well-being in students: application of the PERMA framework[END_REF]. For all the items of the various parts of the questionnaire, the focus was placed on 'the past four years'. Other items were part of a larger research program and are not presented here.

Population and sampling procedure

The study URL was emailed to 1200 tenured academic members from three French Universities-Toulouse, Poitiers and Montpellier (initial response rate: 36%, N=431). We only retained data from participants who fully completed the motives questionnaire (N=222). They were aged 27 to 65 (M age =43.6), and 58% were women. Most had no habilitation (85%, N=189).

Most were from the humanities and social sciences (45%, N =100; Table 1).

Universities from the region of Toulouse represented 66% of all respondents, followed by Poitiers (17%) and Montpellier (15%). These universities were selected for geographical proximity. Respondents could not respond twice.

Procedure

The study was conducted online using Qualtrics © and began by participants' consent and personal data (i.e., sex, age, date of first registration into a doctoral program, date of first recruitment and professional field). The different parts of the questionnaire were randomised. All data were anonymous.

Data analyses

Structure of the motive scale.

A series of PCA was conducted with equamax rotation using SPSS © . On each run, only items with the main loading >.59 and no cross-loading >.33 were kept. On the last run, the cross-loading exclusion criterion was strengthened to .29 to keep only three items per subscale. Finally, a CFA using MPLUS © (MLR method) was carried out on the remaining items.

Time allocation.

A hierarchical analysis on activity-involvement data (Ward's method, Euclidean distances) suggested six clusters. Then a kmeans classification (k=6) was used to constitute six Time-Allocation Profiles.

Multinomial logistic regressions were conducted to investigate whether scores on the motive subscales could predict membership to Time-Allocation Profiles. Predictors were scores on the motivation subscales, Age and experience. The reference profile was Teaching-Research because these two activities are the baseline of job investment for an academic.

The generic name for academics in France is 'Enseignant-Chercheur' ('Teacher-Researcher'). Unsurprisingly, it was the modal category (N=56).

Explaining PERMA elements. Analyses were conducted separately on each PERMA element. PCA followed CFA analyses were used to obtain a structural model. Thereafter, path analyses were conducted with aggregated scores on the motivation subscales as predictors and PERMA elements as DV.

Aggregated scores on the PERMA elements were also introduced as dependent variables in a series of five one-way ANOVAs with Time-Allocation Profile as independent variable.

Results

Structure of the motives inventory

The final structure included 21 items (Table 2,Figure 2 order model, so the simpler was kept (Figure 2).

Time-Allocation Profiles

Of the 222 participants who completed the motivation inventory, 215 also provided Activity Involvement measures. Classification analyses provided six profiles (Figure 3) with a straightforward interpretation. The Up to this point, the motivational scales were established and timeallocation profiles were extracted. Now how the former could be a predictor of the latter will be examined.

Motivational scales predict Time-Allocation Profiles

Multinomial logistic regression confirmed that motivation subscales can partially explain membership to the six clusters. The resulting model had a good fit. Deviance was not significant and the likelihood Ratio test was significant χ 2 (20)=86.7, p=.000, Cox and Snell Pseudo-R 2 =.332.

Neither age, sex, nor experience significantly predicted time-allocation.

Belonging to a profile other than the modal Teaching-Research Profile was predicted by four motivation subscales (Table 5)

Relationships between Motivations and well-being

Positive emotions. Path analyses were run with motives as predictors.

DVs were the SPANE-P for positive emotions and SPANE-N for negative emotions. A three-motive solution (Figure 4 8).

Up to this point, results established a link between academic motives and well-being elements. How about TAP and well-being?

Relationships between Time-Allocation Profiles and well-being

To substantiate the link between time-allocation profiles and well-being, 1way ANOVAs with TAP as predictor and well-beings elements as DV were conducted.

Positive Emotions. The profiles did not significantly differ on positive or negative emotions.

General discussion

This exploratory study was designed so as to identify specific sources of In this discussion, it is argued that the IAPM really addresses motivations.

Then reasons of the links that were observed between motives and well-being elements are discussed and the limits of the study are presented. Finally, some perspectives are sketched.

On the motivational status of IAPM

Linking IAPM to basic psychological needs. The IAPM was developed using reversal theory [START_REF] Apter | The experience of motivation: the theory of psychological reversals[END_REF] The two Autonomy-related motives were involved in the explanation of all profiles. Only two other motives contributed to Time-Allocation Profiles.

This apparent prominence of autonomy is in agreement with SDT: 'even optimal challenges will not engender intrinsic motivation or flow unless people experience themselves as autonomous in carrying them out' (Deci & Ryan, 2000, p. 261).

Taken together, the link between IAPM motives and well-known psychological needs, and the capacity of these motives to explain relative time-allocation confirm IAPM motivational status. Nevertheless, we acknowledge that one might still question the direction of causality. Our data are only correlational. Motivation was not manipulated. For example, when obliged to spend time doing something unwanted, one could feel frustration but could also discover some unexpected features and become progressively interested in the task. Manipulating intrinsic motives is not an easy task, especially when such motives relate to characteristics developed after years of practice, as it is the case when academics' job motivations is studied.

From motives to well-being

In agreement with our hypothesis, IAPM motives explained 40 to 52% of the variance of the five PERMA elements of well-being. Four motives contributed to both time-allocation membership and well-being elements. All motives positively influenced PERMA elements with two notable exceptions. The items used to measure positive relations loaded two factors, positive relations but also a "linking factor". Surprisingly, Self-esteem through Teaching negatively loaded "linking". It seems that the more academics got job appreciation from the self-esteem provided by teaching, the less they felt positively linked to others. This is a point that deserves further investigation. The other exception is an inhibition of linking: People whose job appreciation stems from their Contribution to Progress by Research tended to report less linking to their peers. Remarkably, this motive positively loaded the engagement dimensions of cognitive absorption and job involvement, two subscales that could be associated to a lesser degree of relationship with colleagues. Job involvement, for example, directly amounts to the quantity of energy one puts in tasks (see items in Table 6).

Limitations

Confounding variables. This study focused on psychological determinants but many other variables can modulate well-being and time-allocation and it is difficult to avoid confounding. For example, "organizational characteristic" such as institutional type, resources and unit size could have an impact on the quality of work life [START_REF] Clot | Occupational health: Models, measures and actions[END_REF].

Further studies could take into account the fact that some activities are more stressful than others, which might impact the level of wellbeing at work [START_REF] Cerclé | Typological and factor analysis of the perceived stress measure by using the PSS scale[END_REF], particularly for some patterns of time-allocation.

Extrinsic motivations. [START_REF] Seligman | Flourish[END_REF] posited PERMA as a 'theory of uncoerced choice', thereby associating well-being elements to intrinsic motivation. However, motivations may also be shaped by situational interactions, and particularly by constraints on rewards. Arguably, academics may also be extrinsically motivated, even though Lawrence, Ott and Bell (2012) found no significant effect of salary on "organizational commitment" nor on time-allocation between the three types of activities. To investigate this question one might use general scales of motivation at work. Such scales have recently been developed under the SDT framework, and predict well-being or ill-being to some extent (e.g., [START_REF] Gagne | The multidimensional work motivation scale: validation evidence in seven languages and nine countries[END_REF][START_REF] Longo | Measuring need satisfaction and frustration in educational and work contexts: the need satisfaction and frustration scale (NSFS)[END_REF]. The Multidimensional Work

Motivation Scale focuses on the intrinsic/extrinsic motivation continuum [START_REF] Gagne | The multidimensional work motivation scale: validation evidence in seven languages and nine countries[END_REF]. Clearly, a thorough theoretical and empirical examination of the relationships between IAPM and more general motives such as those studied by SDT will be needed.

Causality direction. At a methodological level, all the models presented here were directional, with motivation being the cause and well-being the effect. The fact that autotelic activities foster intrinsic motivation [START_REF] Csikszentmihalyi | On the psychological selection of bio-cultural information[END_REF] suggests that well-being enters into feedback loops and should be studied accordingly (e.g., [START_REF] Fredrickson | Positive emotions trigger upward spirals toward emotional well-being[END_REF] ), the modal one (Figure 3).

Deviations from this modal pattern are accounted for by the other profiles, which in turn are explained by our motive inventory, IAPM.

Perspectives

The inventory of motives developed here could be useful to predict which activities academics are likely to engage in but it will require the use of methods that enable to prove causality. Recording absolute times will also be needed because more time spent on an activity can leave room for more deliberate practice that can improve performance and, subsequently, symbolic rewards from this activity. It may lead to positive feedback loops, with potential productivity gains. Other studies are needed to confirm this assumption. With regard to time-allocation, we also started another study in order to understand the relationship between "absolute time allocation" (i.e., the number of hours allocated during a standard week to research, teaching and collective tasks) and work-life balance.

Because nonprofessional life may compete with professional activities, this approach might help us to understand academics' career evolution [START_REF] Campbell | Faculty agency: Departmental contexts that matter in faculty careers[END_REF].

Another potential line of research relates to the initial process by which students become Faculty members. A preliminary study suggested several differences in the levels of motivation and well-being between tenured vs. non-tenured French academics [START_REF] Inigo | Élaboration d'une échelle des facteurs motivationnels du bien-être dans l'enseignement et la recherche en France[END_REF].

Recent researches determined that temporary contracts negatively influence Faculty staff enjoyment (Waaijer et al., 2016). Thus, the codevelopment of professional motivations and time-allocation to specific subtasks during career trajectories is an important topic that probably deserves more attention.

Conclusion

In this article, the IAPM, an inventory of motives specific to Faculty members was designed. At an applied level, the IAPM provides a new tool to evaluate academic staff' motivation at work. The present study also provides support for the use of PERMA elements of well-being with adults in a professional context. We also proposed an original tool for timeallocation measurement. The results provided here suggest that IAPM might be useful to predict relative time-allocation and well-being. Elucidating the diversity of time allocation profiles is also relevant to account for how a wide range of academics can find well-being and motivation in their profession. The study provides three contributions: an original inventory of the structure of Faculty members' motives; a first use of the PERMA model for the measurement of academics well-being; and a new tool for measuring differential involvement in academic activities.

References to figures

Figure 1.

ENGLISH: A screenshot of the Activity-Involvement Measurement tool.

All cursors were initially set to 0%, validation required that the three cursors summed up to 100%. Absorp: Cognitive absorption (Flow, [START_REF] Heutte | Optimal learning experience in digital environments: theoretical concepts, measure and modelisation[END_REF]. Autotel:

Autotelic experience (Flow, [START_REF] Heutte | Optimal learning experience in digital environments: theoretical concepts, measure and modelisation[END_REF]. Involv: Involvement [START_REF] May | The psychological conditions of meaningfulness, safety and availability and the engagement of the human spirit at work[END_REF].

Loadings and errors are standardized. Only significant relationships are represented (ps<.05) All ps<.01. All loadings and errors are standardized.

Correlations between predictors are not represented.

Tables Table 1 Descriptive Origin and wording of the items: see Table 6. PosRel: Positive professional relationships. Linking corresponds to 'not feeling alone' items.

Loadings and errors are standardized. Only significant relationships are represented (all ps<.01).

Tables Table 1 Descriptive statistics of respondents Origin and wording of the items: see Table 6.

  , and for the French version supplementary content). Sampling adequacy was good (KMO=.819). Alphas ranged between .796 and .924 for individual factors, .883 for the entire questionnaire. Seven factors explained 81.7% of the variance: (1) Contribution to Progress by Research (ProgresR) groups items presenting the overarching goal of research activity (e.g., 'My research works contribute to the development of knowledge'); (2) Teamwork in Teaching (TeamT) refers to team working dedicated to building courses (e.g., 'The educational teams that I lead bring me elements to improve my teaching'); (3) Self-esteem through Teaching (SelfsT) refers to the boost to self-esteem provided by team work around teaching (e.g., 'Teaching helps me build my self-confidence'); (4) Autonomy in Research Activities (AutoR) represents subjective autonomy and a sense of control in research activities (e.g., 'I feel myself autonomous in driving my researches'); (5) Positive Relationships with Students (RelStuds) refers to the pleasure provided by interactions dedicated to helping students (e.g., 'I appreciate interactions with students'); this factor provided the highest ratings (M=6.13 on a 7-point scale), leading to a slight loss of discrimination power due to a distribution cut on the right tail. (6) Autonomy in Teaching (AutoT) (e.g., 'I am autonomous in my teaching missions'); (7) Collaboration (RelPeers) refers to the positive relationships with colleagues (e.g., 'I can work on a collaborative basis with my research colleagues.') Interestingly, this factor combines items that relate to research and to teaching.

  Teaching-Research Profile corresponds to participants who declared spending approximately the same time in teaching and research activities, while keeping collective investment at a low level. This profile is modal, as could be expected given that research and teaching are the two main activities in the job: Teaching and research are mandatory. The Balanced Profile (N=39) gathers academics who weighted the three activities similarly. The Teaching Profile (N=51), the Research Profile (N=25), and the Collective tasks Profile, (N=7) correspond to academics who indicated precedence of one activity over the two others. Participants of the Teaching-collective Profile were Faculty members giving little weight to research. We could observe no Research-Collective profile.

  : The main contributing predictor was Autonomy in Research Activities (AutoR), χ 2 (5)=36.57, p=.000. It significantly favoured the reference profile over the Balanced, Teaching, Collective tasks, and Teaching-Collective profiles. This is to consider in the light of Figure 3, which shows that only participants in the Research Profile allocated more relative time to research than participants in the reference profile (i.e., Teaching-Research). Unsurprisingly, Autonomy in Research Activities was positively linked to the Research Profile. Nevertheless, despite its intuitive appeal, the latter contribution was only a trend (two-tailed), which might correspond to a slight lack of statistical power. The second motive that contributed to Time-Allocation Profile membership was Autonomy in Teaching (AutoT), χ 2 (5)=20.71, p=.001. AutoT significantly drove participants towards the Teaching Profile and away from the Research Profile. The third most important factor was Contribution to Progress by Research (ProgresR), χ 2 (5)=18.51, p=.002, which drove participants towards the Research Profile or, less intuitively, towards the Balanced Profile. Fourth and last, Teamwork in Teaching (TeamT) significantly contributed to Time-Allocation Profile membership, χ 2 (5)=11.57, p=.041.

  academic job appreciation and check whether they could explain (1) timeallocation between research, teaching and collective tasks, and (2) professional well-being a s measured with the PERMA framework. An Inventory of Academics' Positive Motives (IAPM) was developed and linked to measures of professional well-being based on the PERMA framework. IAPM ratings could explain ratings on the five PERMA elements as well as profiles of time-allocation between teaching, research and collective activities. In addition, participants in the various time-allocation profiles were related to significant differences in terms of the PERMA elements of well-being. The IAPM could help understanding what positively motivates academics' investment at work in their various activities. Previous works like Visser-Wijnveen et al. (2012) were devoted to the study of motivation in higher education with regard to teaching but not taking research or collective activities into account.

  Academics are often alone to process research subtasks such as reading, writing, or thinking. They might also have dispositions towards isolation (see[START_REF] Baron-Cohen | The autism-spectrum quotient (AQ): evidence from asperger syndrome/high-functioning autism, males and females, scientists and mathematicians[END_REF], for results suggesting increased autism trends in scientists).The previous empirical tests of the PERMA framework in the context of positive education by[START_REF] Kern | A multidimensional approach to measuring well-being in students: application of the PERMA framework[END_REF] andCoffey et al. (2016, Study 1) did not retain meaning as a final component of well-being. Possibly, their participants were too young. The 'meaning' component could be found in adult participants(Coffey et al., 2016, Study 2). The present study nevertheless offers a new contribution to the study of work activities related to meaning. The theoretical link from job meaning to well-being is not straightforward, though. Those who attach high meaning to their work can experience opposite effects depending on whether they are happy at work or not[START_REF] Bassi | The contribution of job happiness and job meaning to the well-being of workers from thriving and failing companies[END_REF]. If meaning depends on connecting to something 'bigger than the self' (e.g.,[START_REF] Seligman | Flourish[END_REF], Contribution to Progress by Research provides meaning because the scientific progress is arguably bigger than the Self. Even bigger than any individual's lifetime. Unsurprisingly, Contribution to Progress by Research was one of the four predictors of the Meaning element of PERMA. Positive Relationships with Students and Collaboration also enhanced Meaning, probably because relationships and collaboration can provide a feeling of connectedness with something external to the self.

Figure 2 .

 2 Figure 2.

Figure 3 .

 3 Figure 3.

Figure 4 .

 4 Figure 4.

Figure 5 ENGAGEMENT

 5 Figure 5

Figure 6 RELATIONSHIPS

 6 Figure 6

Figure 7 .

 7 Figure 7.

Figure 8 .

 8 Figure 8.

  Contribution to Progress by Research (ProgresR); b F2 : Teamwork in Teaching (TeamT); c F3 : Self-esteem through Teaching (SelfsT); d F4 : Autonomy in Research Activities (AutoR); e F5 : Positive Relationships with Students (RelStuds); f F6 : Autonomy in Teaching (AutoT); g F7 : Collaboration (RelPeers);

FiguresFigure 1 .

 1 Figures

  Figure 1. A screenshot of the Activity-Involvement Measurement tool.

Figure 2 .

 2 Figure 2. Structure of the motivation inventory (Standardized Loadings)

Figure 3 .

 3 Figure 3. Time-Allocation Profiles in a sample of French lecturers. Profiles inducted by k-means analysis (k=6) on the relative allocation time data.

Figure 4 .

 4 Figure 4. POSITIVE EMOTIONS (P in PERMA) explained by 3 IAPM motives.

Figure 5 .

 5 Figure 5. ENGAGEMENT (E in PERMA) by 5 IAPM motives. Autonomy in Research Activities (AutoR), Collaboration (RelPeers), Contribution to Progress by Research (ProgsR), Teamwork in Teaching (TeamT), and Positive Relationships with Students (RelStuds, N=214). Absorp: Cognitive absorption (Flow,[START_REF] Heutte | Optimal learning experience in digital environments: theoretical concepts, measure and modelisation[END_REF]. Autotel: Autotelic experience (Flow,[START_REF] Heutte | Optimal learning experience in digital environments: theoretical concepts, measure and modelisation[END_REF]. Involv: Involvement[START_REF] May | The psychological conditions of meaningfulness, safety and availability and the engagement of the human spirit at work[END_REF]. Loadings and errors are standardized. Only significant relationships are represented (ps<.05)

Figure 6 .

 6 Figure 6. RELATIONSHIPS (R in PERMA) explained by 3 IAPM motives. Collaboration (RelPeers), Self-esteem through Teaching (SelfsT), and Contribution to Progress by Research (ProgrsR, N=220).

  Contribution to Progress by Research (ProgresR); b F2 : Teamwork in Teaching (TeamT); c F3 : Self-esteem through Teaching (SelfsT); d F4 : Autonomy in Research Activities (AutoR); e F5 : Positive Relationships with Students (RelStuds); f F6 : Autonomy in Teaching (AutoT); g F7 : Collaboration (RelPeers);

  am able to meet the high demands of the situation fl1 I feel that what I do is under my control fl5 I know what I have to do at every step of the task fl9 Autotelic experience b I have the feeling of living a moment of excitement fl4 This activity makes me happy fl8 When I talk about these activities, I feel a strong emotion and I want to share it fl12 Involvement c I really put my heart into my job w1 I exert a lot of energy performing my job w5 I stay until the job is done w6 RELATIONSHIPS Positive relationships I have good relationship with my colleagues f r2 My colleagues stand by me f r6 I believe that my co-workers appreciate who I am d r13 My co-workers and I have mutual respect for one another d r15 Linking f I often feel alone because I have few professional relations to share my interests and my concerns with r1 It seems to me that most of my colleagues have more friends than me r5 At work I didn't live a lot of warm and confident relationships with others r7 MEANING c The work I do on this job is very important to me m1 My job activities are personally meaningful to me m2 My job activities are significant to me m3 The work I do on this job is worthwhile m4 The work I do on this job is meaningful to me m5 I feel that the work I do on my job is valuable m6 ACHIEVEMENT How much of the time do you feel you are making progress towards accomplishing your professional goals? d a1 How often do you achieve the important professional goals you have set for yourself? d a2 How often do you feel a sense of accomplishment from what you do professionally e a4 How often have you been pleased about completing something that was hard to do professionally e a5 Origin of the items: a Diener et al. (2010); b EduFlow (Heutte et al., 2014), Item fl12, "this activity" replaced by "these activities"; c May et al. (2004). d Adapted from the PERMA scale (Positive Psychology Center, 2013); e Adapted from Kern et al (2014). f Items specifically designed for the study;

  , 5479 academics indicated which aspect of work drew them to the profession.

	Intrinsic	intellectual	motives	had	attracted	most	people:
	opportunities	for	intellectually	stimulating	work	(95.9%	of
	respondents); Genuine passion for a field study (93.8%) and Opportunity to

contribute to new knowledge (91.1%). On the contrary, extrinsic motivation items received far less agreement: From 57.5% agreement given to Good or satisfactory income or 57.2% to Job security, down to 33.7% given to Status of the profession in the public eye. In Romania a study of 102 teachers from a state university (Cucu-Ciuhan & Guită-

  The seven motivational factors were correlated. Table4provides the matrix SRMR=.092. By adding two correlations -between TeamT and AutoR on one hand, and TeamT and RelPeers on the other hand-the fit could

	of correlations between corresponding subscales. A second-order factor was
	tested, χ 2 (182)=314.1, p<.001; AIC=13257.6; CFI=.940;	RMSEA=.057
	[.046,.068]; be improved to χ 2 (180)=280.0, p<.001;	AIC=13224.4;	CFI=.955;

The factorial structure was satisfyingly confirmed: χ 2 (168)=326.

1, p<.001; AIC=13196.6; CFI=.969; RMSEA=.043[.029,.055

]; SRMR=.056. RMSEA=.050 [.038,.061]; SRMR=.079. But even with these adjustments, the simpler model with just seven correlated factors outperformed the second-

  Four motives explained 49.3% of the variance in Meaning, χ 2 (29)=40.30, p=.01; CFI =.970; RMSEA=.057 [.028,.084]; SRMR=.035.

	Correlating residual variances of items m2 and m5 improved the fit with
	50.5%	of	explained	variance,	χ 2 (28)=36.4,	p=.133,	CFI	=.987,
	RMSEA=.037 [.000,.069], SRMR=.030 (Figure 7).
	) explained 39.6% of the Achievement. Three motives explained 52.2% of the variance of the
	variance in positive affect and 11.4% of the variance in negative Achievement factor, χ 2 (11)=23.57, p=.015, CFI =.965, RMSEA=.073
	affect. The fit was good, χ 2 (1)=1.10, p=.29, CFI =.999, RMSEA=.022 [.031,.113], SRMR=.028 (Figure

[.000,.183], SRMR=.013, although the upper value of the RMSEA CI was high. Autonomy in Research Activities and Collaboration positively predicted positive emotions and negatively negative emotions. Relations with students only predicted positive affect. Engagement. The structure of Engagement combined six items for flowcorresponding to the absorption and autotelic subscales-and three items for the dimension of Job Involvement (I really put my heart into my job; I exert a lot of energy performing my job; I stay until the job is done). Five motivational factors explained 48.8% of the variance in the Autotelic experience factor, 38.5% in the Cognitive Absorption factor, and 35.1% of Job Involvement. The fit was good, χ 2 (62)=85.29, p=.026, CFI =.965, RMSEA=.042 [.015,.062], SRMR=.055 (Figure 5). Relationships. After reversal, three items relating to a lack of connection with other people in the workplace constituted a dimension called linking. A Positive relationships dimension combined four other items. Three motives could explain 45.7% of the variance in the Linking factor, 38.5% in the Positive relationships factor. The fit was good, χ 2 (30)=36.47, p=.19, CFI=.990, RMSEA=.031 [.000,.063], SRMR=.042 (Figure 6).

Meaning.

  but basic needs proposed by other theories can also help understanding the explanatory power of IAPM items. Self-

	determination	theory	(SDT,	Deci	&	Ryan,	2000)	proposed	that
	intrinsically determined behaviours would satisfy three psychological
	(1943).	Finally,	Contribution	to	Progress	by	Research	relates
	straightforwardly to Meaning, a basic psychological need for Frankl (1963).
	Relative time-allocation.
	According to Latham and Pinder, motivation at work initiates work-related
	behaviour and determines 'its form, direction, intensity and duration'
	(2005, p. 486). Based on this premise, it was assumed that motivation could

needs: Autonomy, Competence, and Relatedness. Relatedness might underpin Teamwork in Teaching, Collaboration with peers, and Positive Relationships with Students, the most contributory factor to job appreciation. The need for Autonomy could clearly be the basis of Autonomy in Research Activities and Autonomy in Teaching, both tied in second rank as far as job appreciation is concerned. Besides SDT needs, Self-esteem through Teaching might relate to the desire for Self-esteem, a basic need according to Maslow predict involvement in academic activities. Our results confirmed that IAPM motives could partially explain belonging to time-allocation profiles. Motives of Teamwork in Teaching seemed to drive participants away from the Teaching Profile, which might appear paradoxical. However, team working hopefully saves time and enables redirecting efforts towards research or collective tasks.

  statistics of respondents

		Total (%)
	Literature	41 (18%)
	Political
	Studies
	Economics	32 (14%)
	Humanities	100 (45%)
	Social sciences
	Sciences	45 (20%)
	Medicines
	Biology
	Females	128 (58%)
	Males	94 (42%)
	With HDR	33 (15%)
	Without HDR	189 (85%)
	Total	222 (100%)

Table 2

 2 Motivational factors (PCA with equamax rotation, N=222)

		F1 a	F2 b		F3 c	F4 d	F5 e	F6 f	F7 g
	Item	ProgresR TeamT SelfsT AutoR RelStuds	AutoT	RelPeer
	y1	.866	.090		.033	.244	.113	.075	s .142
	y2	.881	.022		.008	.174	.077	.124	.183
	y5	.824	.076		.028	.194	.075	.139	.145
	y6	.127	.882		.079	-.012	.151	.032	.114
	y8	.053	.881		.054	-.105	.044	.000	.238
	y10	-.007	.904		.124	-.039	.013	.038	.141
	y11	-.036	.122		.895	.047	.151	.133	-.019
	y12	-.002	.123		.929	.066	.117	.067	.019
	y13	.098	.014		.878	.050	.137	.132	.063
	y16	.192	-.084	.110	.883	-.044	.171	.061
	y17	.210	-.031	.090	.874	.021	.217	.088
	y34	.244	-.066	-.033	.699	.247	.184	.200
	y20	.094	.068		.146	-.028	.833	.208	.096
	y21	.094	.066		.127	.051	.885	.162	.047
	y24	.062	.081		.152	.137	.860	.073	.142
	y27	.057	-.023	.142	.254	.128	.845	.105
	y28	.109	.161		.066	.115	.174	.810	.147
	y29	.177	-.067	.156	.194	.157	.819	.103
	y30	.048	.187		.087	-.084	.141	.219	.733
	y31	.163	.228		-.026	.161	.095	.009	.842
	y32	.272	.092		-.004	.279	.035	.123	.780
	M	5.19	3.76		4.72	5.48	6.13	5.65	4.99
	(SD)	(1.13)	(1.46 (1.33) (1.26	(0.94)	(1.10)	(1.30)
	Eigenvalue	2.62	3.05		6.20	1.44	1.72	1.11	1.02
	Variance (%)	12.45	14.54	29.54	6.85	8.19	5.26	4.83
	Teamwork in Teaching (TeamT); c F3: Self-esteem through
	Teaching (SelfsT); d F4:	Autonomy in Research Activities
	(AutoR);	e F5:	Positive		Relationships	with	Students
	(RelStuds); f F6: Autonomy in Teaching (AutoT); g F7:
	Collaboration (RelPeers);

a F1: Contribution to Progress by Research (ProgresR); b F2:

Table 3

 3 Items of the positive scale of Academics' motives

	Factor Text

Table 4

 4 Correlations between motivation subscales (N=222)

	Factors	TeamT	SelfsT AutoR RelStuds	AutoT	RelPeers
	ProgresR	.155 *	.096	.492 **	.242**	.324**	.415**
	TeamT		.198**	-.061	.190**	.098	.380**
	SelfsT			.154*	.326**	.291**	.105
	AutoR				.213**	.459**	.327**
	RelStuds					.377**	.273**
	AutoT						.334**
	* p < .05; ** p < .01

Table 5

 5 Time-Allocation Profiles regressed on motivational scales

	Profile	Motivation	B	(SE)	P	Exp(B)
	Balanced	AutoR	-.536	(.26) .043	.585
		ProgresR	.417	(.25) .099	1.518
	Teaching	AutoR	-.956	(.25) .000	.384
		AutoT	.496	(.25) .045	1.642
		TeamT	-.324	(.15) .029	.723
	Collective tasks AutoR	-1.551 (.43) .000	.212
	Research	AutoR	.583	(.35) .094	1.791
		ProgresR	.732	(.32) .023	2.08
		AutoT	-.816	(.29) .005	.442
	Teaching-	AutoR	-.652	(.26) .013	.521
	Collective
		Only predictors with p<.10 (two-tailed) are
		reported. Reference profile: Teaching-
		Research Profile

Table 7

 7 Psychometric properties of motivational and well-being scales

	Constru	Scale	Items	n	M	SE	α	Min	Max	Skew	Kurto
	ct										sis
	MOTIVAT	Contribu	3	222	5.19	0.08	.90	1.00	7.00	-0.54	0.80
	IONS	tion to
		Progress
		by
		Research
		Teamwork	3	222	3.76	0.10	.90	1.00	7.00	-0.16	-0.69
		in
		Teaching
		Self-	3	222	4.72	0.09	.92	1.00	7.00	-0.59	0.17
		esteem
		through
		Teaching
		Autonomy	3	222	5.48	0.08	.86	1.00	7.00	-0.91	0.45
		in
		Research
		Activiti
		es
		Positive	3	222	6.13	0.06	.88	2.33	7.00	-1.34	2.17
		Relation
		ships
		with
		Students
		Autonomy	3	222	5.65	0.07	.86	2.00	7.00	-0.98	0.94
		in
		Teaching
		Collabor	3	222	4.99	0.09	.80	1.00	7.00	-0.79	0.66
		ation
	PERMA
	Positiv	SPANE-P	3	216	5.05	0.06	.94	2.00	7.00	-0.98	0.94
	e
	emotion
	s	SPANE-N a	3	216	3.80	0.08	.87	1.33	7.00	-0.79	0.66
	Engagem	Cognitiv	3	214	4.68	0.08	.78	1.33	7.00	-0.24	0.18
	ent	e
		Absorpti
		on
		Autoteli	3	214	4.68	0.08	.80	1.33	7.00	-0.78	0.68
		c
		experien
		ce
		Job	3	214	6.06	0.06	.77	2.00	7.00	-1.37	2.53
		Involvem
		ent
	Relatio	Positive	4	220	4.91	0.09	.90	1.38	7.00	-0.85	0.03
	nships	relation
		s
		Linking	3	220	4.86	0.09	.69	1.00	7.00	-0;37	-0.05
	Meaning	Meaningf	6	214	5.92	0.07	.92	1.50	7.00	-1.52	2.80
		ulness
	Achieve	Willing	4	216	5.22	0.06	.86	2.00	7.00	-0.74	0.22
	ment

Table 2

 2 Motivational factors (PCA with equamax rotation, N=222)

		F1 a	F2 b	F3 c	F4 d	F5 e	F6 f	F7 g
	Item	ProgresR	TeamT	SelfsT	AutoR	RelStuds	AutoT	RelPeers
	y1	.866	.090	.033	.244	.113	.075	.142
	y2	.881	.022	.008	.174	.077	.124	.183
	y5	.824	.076	.028	.194	.075	.139	.145
	y6	.127	.882	.079	-.012	.151	.032	.114
	y8	.053	.881	.054	-.105	.044	.000	.238
	y10	-.007	.904	.124	-.039	.013	.038	.141
	y11	-.036	.122	.895	.047	.151	.133	-.019
	y12	-.002	.123	.929	.066	.117	.067	.019
	y13	.098	.014	.878	.050	.137	.132	.063
	y16	.192	-.084	.110	.883	-.044	.171	.061
	y17	.210	-.031	.090	.874	.021	.217	.088
	y34	.244	-.066	-.033	.699	.247	.184	.200
	y20	.094	.068	.146	-.028	.833	.208	.096
	y21	.094	.066	.127	.051	.885	.162	.047
	y24	.062	.081	.152	.137	.860	.073	.142
	y27	.057	-.023	.142	.254	.128	.845	.105
	y28	.109	.161	.066	.115	.174	.810	.147
	y29	.177	-.067	.156	.194	.157	.819	.103
	y30	.048	.187	.087	-.084	.141	.219	.733
	y31	.163	.228	-.026	.161	.095	.009	.842
	y32	.272	.092	-.004	.279	.035	.123	.780
	M	5.19	3.76	4.72	5.48	6.13	5.65	4.99
	(SD)	(1.13)	(1.46)	(1.33)	(1.26)	(0.94)	(1.10)	(1.30)
	Eigenvalue	2.62	3.05	6.20	1.44	1.72	1.11	1.02
	Variance (%) 12.45	14.54	29.54	6.85	8.19	5.26	4.83
	a F1: Contribution to Progress by Research (ProgresR); b F2: Teamwork in Teaching (TeamT); c F3:

Table 4

 4 Correlations between motivation subscales (N=222)

	Factors	TeamT	SelfsT	AutoR RelStuds	AutoT	RelPeers
	ProgresR .155 *	.096	.492 ** .242**	.324**	.415**
	TeamT		.198**	-.061	.190**	.098	.380**
	SelfsT			.154* .326**	.291**	.105
	AutoR				.213**	.459**	.327**
	RelStuds					.377**	.273**
	AutoT						.334**

Table 5

 5 Time-Allocation Profiles regressed on motivational scales

	Profile	Motivation	B	(SE)	p	Exp(B)
	Balanced	AutoR	-.536	(.26) .043	.585
		ProgresR	.417	(.25) .099 1.518
	Teaching	AutoR	-.956	(.25) .000	.384
		AutoT	.496	(.25) .045 1.642
		TeamT	-.324	(.15) .029	.723
	Service	AutoR	-1.551	(.43) .000	.212
	Research	AutoR	.583	(.35) .094 1.791
		ProgresR	.732	(.32) .023 2.08
		AutoT	-.816	(.29) .005	.442
	Teaching-Service AutoR	-.652	(.26) .013	.521
	Only predictors with p<.10 (two-tailed) are reported.
	Reference profile: Teaching-Research Profile

Table 6

 6 Items to measure PERMA elements

Table 7

 7 Psychometric properties of motivational and well-being scales

	Construc	Scale	Items	N	M	SE	α	Min	Max	Skew	Kurtos
	t										is
	MOTIVATI	Contributi	3	222	5.19	0.08	.90	1.00	7.00	-0.54	0.80
	ONS	on to
		Progress
		by
		Research
		Teamwork	3	222	3.76	0.10	.90	1.00	7.00	-0.16	-0.69
		in
		Teaching
		Self-	3	222	4.72	0.09	.92	1.00	7.00	-0.59	0.17
		esteem
		through
		Teaching
		Autonomy	3	222	5.48	0.08	.86	1.00	7.00	-0.91	0.45
		in
		Research
		Activities
		Positive	3	222	6.13	0.06	.88	2.33	7.00	-1.34	2.17
		Relationsh
		ips with
		Students
		Autonomy	3	222		0.07	.86	2.00	7.00	-0.98	0.94
		in
		Teaching
		Collaborat	3	222	4.99	0.09	.80	1.00	7.00	-0.79	0.66
		ion
	PERMA
	Positive	SPANE-P	3	216	5.05	0.06	.94	2.00	7.00	-0.98	0.94
	emotions
		SPANE-N a	3	216	3.80	0.08	.87	1.33	7.00	-0.79	0.66
	Engageme	Cognitive	3	214	4.68	0.08	.78	1.33	7.00	-0.24	0.18
	nt	Absorption
		Autotelic	3	214	4.68	0.08	.80	1.33	7.00	-0.78	0.68
		experience
		Job	3	214	6.06	0.06	.77	2.00	7.00	-1.37	2.53
		Involvemen
		t
	Relation	Positive	4	220	4.91	0.09	.90	1.38	7.00	-0.85	0.03
	ships	relations
		Linking	3	220	4.86	0.09	.69	1.00	7.00	-0.37	-0.05
	Meaning	Meaningful	6	214	5.92	0.07	.92	1.50	7.00	-1.52	2.80
		ness
	Achievem	Willing	4	216	5.22	0.06	.86	2.00	7.00	-0.74	0.22
	ent

Figure 7. MEANING (M in PERMA) explained by 4 IAPM motives. Contribution to Progress by Research (ProgrsR), Autonomy in Research Activities (AutoR), Positive Relationships with Students (RelStuds), and Collaboration (RelPeers, N=210). * p<.05, * * p<.01. All loadings and errors are standardized.

Figure 8. ACHIEVEMENT (A in PERMA) explained by 3 IAPM motives. Contribution to Progress by Research (ProgrsR), Autonomy in Research Activities (AutoR), and Collaboration (RelPeers, N= 216). All ps<.01. All loadings and errors are standardized. Correlations between predictors are not represented.