Published online: 04 Dec 2014.

This article was downloaded by: [katja upadyaya] On: 09 December 2014, At: 09:12 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK The Journal of Positive Psychology: Dedicated to furthering research and promoting good practice Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/rpos20 Cross-lagged associations between study and work engagement dimensions during young adulthood Katja Upadyaya a & Katariina Salmela-Aro ab a Helsinki Collegium for Advanced Studies, University of Helsinki, P.O. Box 4, 00014 Helsinki, Finland b Department of Psychology, University of Jyväskylä, P.O. Box 35, 40014 Jyväskylä, Finland Published online: 04 Dec 2014. To cite this article: Katja Upadyaya & Katariina Salmela-Aro (2014): Cross-lagged associations between study and work engagement dimensions during young adulthood, The Journal of Positive Psychology: Dedicated to furthering research and promoting good practice, DOI: 10.1080/17439760.2014.983958 To link to this article: http://dx.doi.org/10.1080/17439760.2014.983958 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the Content ) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions

The Journal of Positive Psychology, 2014 http://dx.doi.org/10.1080/17439760.2014.983958 Cross-lagged associations between study and work engagement dimensions during young adulthood Katja Upadyaya a * and Katariina Salmela-Aro a,b a Helsinki Collegium for Advanced Studies, University of Helsinki, P.O. Box 4, 00014 Helsinki, Finland; b Department of Psychology, University of Jyväskylä, P.O. Box 35, 40014 Jyväskylä, Finland (Received 17 July 2013; accepted 21 October 2014) The present four-wave longitudinal study investigated the cross-lagged associations between three study and work engagement dimensions (e.g. energy, absorption, and dedication) over the transition from post-comprehensive studies to higher education or work. Various antecedents (e.g. gender, GPA) and consequences (e.g. satisfaction in life, education and work, well-being, and educational outcomes) of the three engagement dimensions were also examined. The study is part of the longitudinal Finnish Educational Transitions (FinEdu) study, and followed 851 participants from age 17 to 23. The developmental dynamics showed that, in particular, students study- and work-related energy predicted feelings of absorption and dedication during their post-comprehensive education and after the transition to higher education or work. Moreover, high dedication in one s studies or work resulted in high satisfaction in life, studies, and work and increased well-being. No differences related to academic track emerged in the developmental dynamics of study/work engagement. Keywords: study and work engagement; energy; absorption; dedication; life-satisfaction; study and work transition; Finnish young adults Recently, growing interest has been shown in engagement in studies and work owing to its overall positive effects on one s performance, motivation, and well-being. Engagement in studies and work has shown positive associations with students high academic performance (Ladd & Dinella, 2009; Li, Lerner, & Lerner, 2010; Sirin & Rogers-Sirin, 2004), self-esteem, life satisfaction (Salmela-Aro & Upadyaya, 2012), well-being (Li & Lerner, 2011), and employees learning and development (Salanova, Agut, & Peiro, 2005; Salmela-Aro, Tolvanen, & Nurmi, 2009). However, engagement in studies and work has seldom been investigated in the same study (Upadyaya & Salmela-Aro, 2013b), although it has been suggested that vocational development begins early in one s school years (Hartung, Porfeli, & Vondracek, 2005; Orthner, Jones-Sanpei, Akos, & Rose, 2013), and although studies on developmental cascades have suggested that the associations between academic and work competence peak during emerging adulthood when the young adults undergo the transition from studies to work (Masten, Desjardins, McCormick, Kuo, & Long, 2010). Thus, high engagement in studies may spill over to high engagement at work and therefore facilitate young adults career success. Moreover, longitudinal research on the developmental dynamics of the study and work engagement dimensions and their consequences is lacking. Consequently, the present four-wave longitudinal study addressed the multidimensional quality and development (Appleton, Christenson, Kim, & Reschly, 2006; Fredricks, Blumenfeld, & Paris, 2004; Jimerson, Campos, & Greif, 2003; Wang, Willet, & Eccles, 2011) of the study and work engagement dimensions by investigating the cross-lagged associations between energy, dedication, and absorption in studies and work. Young people were followed from the beginning of their postcomprehensive education at age 17 until four years after finishing their post-comprehensive education at age 23. Several antecedents and consequences of the separate engagement dimensions were subjected to further examination. Three dimensions of study and work engagement Study engagement has been previously defined as a multidimensional construct that unites behavioral, cognitive, and affective components which describe students academic participation, commitment, positive and negative emotions, investment, and willingness to exert effort in one s studies (Appleton, Christenson, & Furlong, 2008; Appleton et al., 2006; Finn & Voelkl, 1993; Fredricks et al., 2004). It has been further suggested that the underlying construct of engagement is the same for students and employees (Wefald & Downey, 2009). Thus, recent studies originating from the work engagement *Corresponding author. Email: katja.upadyaya@helsinki.fi 2014 Taylor & Francis

2 K. Upadyaya and K. Salmela-Aro literature (Hakanen, Bakker, & Schaufeli, 2006; Salanova et al., 2005; Schaufeli, Bakker, & Salanova, 2006) have examined adolescents and young adults engagement, as a phenomenon resembling flow, using the descriptive dimensions of energy, dedication, and absorption while studying or working (Salmela-Aro & Upadyaya, 2012, 2014; Schaufeli, Salanova, Gonzalez-Roma, & Bakker, 2002). Of these three dimensions, energy refers to high mental resilience and affects while studying/working, a willingness to invest effort in one s studies/work, and a positive approach. The dimension of energy is also similar to the affective component described in previous studies. Dedication, in turn, is characterized by a cognitive sense of significance, enthusiasm, pride, and inspiration regarding school/work, as well as perceiving studies/work as meaningful. Absorption resembles the behavioral component of previous research and is characterized by behavioral accomplishments, being fully concentrated and happily engrossed in one s studying/ work so that time passes quickly. These three dimensions are separate constructs of study and work engagement, although correlating highly with each other (Salmela-Aro & Upadyaya, 2014; Schaufeli, Salanova et al., 2002). One limitation of the previous engagement research has been the absence of studies investigating the crosslagged associations between the three separate engagement dimensions (Fredricks et al., 2004). It is possible that, even if the three engagement dimensions are highly correlated, variation exists in their associations during studying compared to working, and that some dimensions might be displayed more at different grade levels and ages (Fredricks et al., 2004). For example, students have been identified who experience a high level of behavioral or emotional engagement, but at the same time, score low in the two remaining aspects of engagement (Fredricks, 2011). On the same token, a study following students engagement across grades five to eight found that students emotional engagement decreased more than their behavioral engagement (Li & Lerner, 2011). Within the recent framework of study/work engagement, it has been found during post-comprehensive education that adolescents feelings of energy, absorption, and dedication with studies become increasingly differentiated and also increasingly come to resemble those associated with work engagement (Salmela-Aro & Upadyaya, 2012), which also argues for the need to study the interrelations of these engagement dimensions further. Some studies also suggest that high energy is a central element of engagement for university students (Schaufeli, Martinez, Pinto, Salanova, & Bakker, 2002) and it is possible that high energy at work is equally important in promoting the two remaining aspects of work engagement. Young adults engagement may also vary according to their academic track and current study/ work situation. For example, students following an academic track often feel more exhausted and less engaged in their studies than students following a vocational track (Salmela-Aro & Upadyaya, 2012), which may also show in the interrelations of the separate engagement dimensions. However, although stability exists in the developmental trajectories of students overall engagement in studies and higher education or work (Upadyaya & Salmela-Aro, 2013b), less is known about the interrelations between the engagement dimensions during this key life transition. This was the focus of the present study. Antecedents and consequences of engagement To clarify the possibility that variation exists in the antecedents (e.g. academic performance, gender, socioeconomical status, i.e. SES) and consequences (e.g. life satisfaction, well-being, educational outcomes) of the different engagement subtypes (Upadyaya & Salmela-Aro, 2013a) merits further research (National Center for School Engagement, 2006). At earlier grades, students academic performance is typically associated with all aspects of engagement (Dotterer & Lowe, 2012; Ladd & Dinella, 2009; Li et al., 2010; Sirin and Rogers-Sirin, 2004), while maintaining high levels of energy, which is important, particularly among university students (Schaufeli, Martinez et al., 2002). Moreover, differences related to gender and family background exist in engagement: females typically experience higher levels of overall school engagement than males (Marks, 2000; Salmela-Aro & Upadyaya, 2014), probably because female students tend to perform better at school (Pomerantz, Altermatt, & Saxon, 2002) and attribute greater importance to academic achievement than males (Berndt & Miller, 1990). Similarly, males and students from lower income families are more likely than females and students with higher socioeconomic status to experience rapid decreases in engagement and to follow unstable school engagement trajectories, often leading to school dropout (Archambault, Janosz, Morizot, & Pagani, 2009; Janosz, Archambault, Morizot, & Pagani, 2008; Li & Lerner, 2011). However, less is known about the extent to which students gender, SES, and academic performance predict their feelings of energy, absorption, and dedication. Previous research has shown that engagement with studies or work is also reflected in several aspects of personal well-being and educational outcomes. Students with a high level of engagement seldom suffer from depressive symptoms (Li & Lerner, 2011), often experience high life satisfaction (Lewis, Huebner, Malone, & Valois, 2011; Salmela-Aro & Upadyaya, 2014), and have high study-related personal resources (Ouweneel, Le Blane, & Schaufeli, 2011). Students who experience high study-related energy pass their exams more often and

The Journal of Positive Psychology 3 feel more efficacious than peers who report feeling less energetic (Schaufeli, Martinez et al., 2002). Moreover, according to the demands and resources model, high engagement in studies/work promotes one s subsequent well-being, life satisfaction, personal growth and learning, and decreases ill-being (e.g. depressive symptoms) (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001; Hakanen et al., 2006; Salmela-Aro & Upadyaya, 2014). Further, following the same framework, work-related energy, absorption, and dedication are typically positively associated with job satisfaction and organizational commitment (Hakanen et al., 2006; Schaufeli, Taris, & van Rhenen, 2008). Clarification of the possibility that variation exists in the antecedents and consequences of study/work engagement among young adults who are currently at the last phase of their education or at the beginning of their career merits further research. Thus, the present study aimed at examining this. Schooling in Finland Compulsory comprehensive education in Finland lasts for nine years until the students are 16 years old. After that, approximately 50% of adolescents enter senior high schools and approximately 41% go to vocational schools (School Statistics, Central Statistical Office of Finland, 2010). Average academic achievement in the ninth grade is the minimum requirement for admission to senior high school. Both senior high schools and vocational education take three to four years to complete, after which students may apply to institutes of higher education. Approximately 39% of high school graduates start studying, 44% begin working, and 25% studying and working one year after finishing high school, whereas 8% of young adults with a degree from a vocational school are studying, 69% working, and 10% both studying and working one year after their graduation. Aims and hypotheses The aim of the present four-wave longitudinal study was to examine the cross-lagged associations between the three dimensions of study and work engagement (i.e. energy, absorption, and dedication) during and after the transition from post-comprehensive studies to higher education or work. In addition, the role of several antecedents (e.g. academic performance grade point average [GPA], SES, gender) and consequences (e.g. life, work, and study satisfaction, well-being, educational outcomes) was examined. We hypothesized that the three engagement dimensions would be positively associated during postcomprehensive education and higher education or work (H1), but as a part of students adaptation to the new study/work environment the associations between the dimensions would momentarily decline during the transition to higher education or work (H2). We expected, in particular, that young adults energy in higher education or work would strongly predict the other two engagement dimensions (H3). Moreover, we hypothesized that females and students from higher SES backgrounds and with high academic performance at the beginning of the study would experience higher levels of energy, absorption, and dedication than males and students from lower SES families and with a lower level of performance (H4). In addition, we expected that a high level of energy, absorption, and dedication at Time 4 would increase overall feelings of satisfaction and well-being, and manifest as higher educational outcomes (H5). Method Sample and procedure The data were drawn from the Finnish Educational Transitions (FinEdu) study, which recruited all the ninth-grade students in a medium-sized town (population = 88,000) in Central Finland. The present study used the data from post-comprehensive education onwards. Students from 13 post-comprehensive schools (six high schools and seven vocational schools) participated in the study. The first two years of measurements were carried out during each year of the post-comprehensive education: the first was half a year after the transition to post-comprehensive school (Time 1, Mean age = 17, N = 818) and the second was one year later (Time 2, Mean age = 18, N = 749). After the post-comprehensive education, the time interval between the follow-ups was approximately two years. Thus, the third measurement was carried out two and a half years after Time 2 (Time 3, Mean age = 21, N = 611), when most of the students had already finished their post-comprehensive education. The fourth measurement was carried out two years after Time 3 (Time 4, Mean age = 23, N = 599). The participation rate across the 13 schools ranged from 65% to 100%, with an average of 84%. First, all the school principals and school boards were informed about the project and invited to participate. Having received their consent, the researchers then sought written permission from parents to gather data from the students. Consistent with the general population in Central Finland (Kuopion Lukiokoulutus, 2009), the majority of the participants (99%) were Finnish speaking. The students most often lived with both their parents (62%), or with their mother or father alone (25%), or either their mother or father living with her/his new spouse (11%), or with somebody else (1%). A total of 23% of the adolescents had siblings. The occupational distribution of the parents was as follows: 27% of the fathers and 20% of the mothers worked in higher white-collar occupations, 16.4 and 49% worked in lower white-collar occupations (e.g. doctors, teachers), 36 and 17% had

4 K. Upadyaya and K. Salmela-Aro blue-collar occupations (e.g. cooks, bus drivers), 11 and 4% were private entrepreneurs, 1 and 2% were students, 3 and 2% were retired, and 5 and 6% had some other status (e.g. unemployed). The questionnaires (Times 1 and 2) were group administered to the students in their classrooms during regular school hours, and at Times 3 and 4, they were mailed to the participants. Measures Study and Work Engagement was measured at Times 1 4 using the abbreviated student version of the Utrecht Work Engagement Scale, UWES-S (see Salmela-Aro & Upadyaya, 2012 for the questionnaire and factor structure; Schaufeli, Salanova et al., 2002, 2006; Seppälä et al., 2009). At Times 1 and 2, the inventory concerned engagement in studies, and at Times 3 and 4, it covered engagement with studies or work, depending on the participant s current situation. The scale consists of nine items measuring energy (e.g. When I study/work, I feel that I am bursting with energy), dedication (e.g. I am enthusiastic about my studies/work), and absorption (e.g. Time flies when I m studying/working) in relation to studies and work. The responses were rated on a 7-point scale (0 = not at all; 6 = daily). The correlations for energy at different measurement times varied between 0.30 and 0.57 for dedication between 0.22 and 0.55 and for absorption between 0.29 and 0.56. The Cronbach s alpha reliabilities for each subscale are presented in Table 1. Satisfaction with Studies and Work (Time 4) was measured with four questions (e.g. How satisfied are you with your current place of study/job?; How much do you enjoy your current studies/job?). The participants responded only to the study-related questions if they were currently mainly studying or only to the workrelated questions if they were currently mainly working. However, a small amount of the participants (N = 191) reported that they were both studying and working at Time 4 and rated both study and work satisfaction. The responses were rated on a 5-point scale (1 = not at all; 5 = very much). Two sum scores were formed separately for satisfaction with studies or work. Life Satisfaction (Time 4) was assessed using the five-item Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985). The items (e.g. I am satisfied with my life) were rated on a 5-point scale ranging from 1(I totally disagree) to5(i totally agree). A sum score was calculated from all five items. Cronbach s alpha was 0.89. Educational Outcomes (Time 4) was measured by asking the participants about their recent life situation, work, and education. The answers were coded as follows: 1 = has not finished high school, 2 = has finished high school but is not studying further, 3 = currently studying for a second post-comprehensive degree, 4 = currently studying at a polytechnic, 5 = has finished polytechnic, 6 = currently studying at a university, 7 = has finished university studies. Depressive Symptoms were measured at Time 4 using the Finnish Depression Scale (DEPS-10; Salokangas, Stengård, & Poutanen, 1994). The scale consists of 10 questions, drawn from the Hopkins checklist, on the participant s mood over the last month (e.g. I felt sad; I felt my future was hopeless). Student rated their responses on a 4-point scale ranging from 1 (not at all) to 4 (very much). A total score based on the mean of the ratings was computed. Academic Performance was measured in accordance with the GPA of the final comprehensive school report (Time 0) on a scale ranging from 4 (lowest) to 10 (highest). Demographics. Gender was coded 1 = female, 2 = male. School track (Time 1) was coded as 1 = high school student (N = 529), 0 = vocational school student (N = 226); Family SES was coded as 1 = blue collar, 2 = white collar. Work/study status (Time 4) was coded 1 = studying full time (N = 349), 2 = working full time (N = 170). 1 A small minority of the participants (N = 67) were neither working nor studying at Time 4, and 13 participants did not reply to the question concerning their work/study status at Time 4. Statistical analyses The research questions were analyzed by path models with regression coefficients. The statistical analyses were performed using the Mplus statistical package (Version 6; Muthén & Muthén, 1998 2014) with a missing data method. This missing data method uses all the data that are available in order to estimate the model without imputing the data. Because the distributions of the variables were skewed, the model parameters were estimated using the MLR estimator (Muthén & Muthén, 1998 2014). Goodness of fit was evaluated using five indicators: χ 2 test, Comparative Fit Index (CFI), Tucker Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). First, a path model was constructed to examine the cross-lagged associations between the young adults energy, dedication, and absorption in their studies and work. All the endogenous variables were allowed to covary. The tested model included stability coefficients for each engagement dimension, as well as cross-lagged paths from the previous engagement dimensions to subsequent energy, dedication, and absorption. Next, the participants gender, family SES, and academic performance were added in the model as antecedents of energy, dedication, and absorption; educational outcomes, depressive symptoms, and satisfaction with work,

The Journal of Positive Psychology 5 Table 1. Means, variances, and Pearson correlation coefficients. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 1. Energy a 2. Energy b 0.57 *** 3. Energy c 0.34 *** 0.39 *** 4. Energy d 0.34 *** 0.30 *** 0.46 *** 5. Dedication a 0.86 *** 0.53 *** 0.28 *** 0.31 *** 6. Dedication b 0.53 *** 0.87 *** 0.36 *** 0.25 *** 0.55 *** 7. Dedication c 0.26 *** 0.31 *** 0.85 *** 0.42 *** 0.22 *** 0.31 *** 8. Dedication d 0.31 *** 0.26 *** 0.44 *** 0.86 *** 0.30 *** 0.26 *** 0.45 *** 9. Absorption a 0.81 *** 0.50 *** 0.28 *** 0.30 *** 0.83 *** 0.50 *** 0.23 *** 0.28 *** 10. Absorption b 0.50 *** 0.81 *** 0.34 *** 0.29 *** 0.48 *** 0.82 *** 0.30 *** 0.26 *** 0.56 *** 11. Absorption c 0.26 *** 0.33 *** 0.78 *** 0.34 *** 0.22 *** 0.32 *** 0.78 *** 0.36 *** 0.29 *** 0.38 *** 12. Absorption d 0.28 *** 0.28 *** 0.42 *** 0.80 *** 0.27 *** 0.27 *** 0.39 *** 0.83 *** 0.30 *** 0.32 *** 0.41 *** 13. GPA 0.17 *** 0.11 * 0.03 0.05 0.22 *** 0.16 *** 0.04 0.10 0.16 *** 0.14 ** 0.02 0.06 14. Gender 0.07 * 0.10 * 0.05 0.05 0.04 0.10 ** 0.05 0.02 0.04 0.07 0.04 0.02 0.14 *** 15. Depression 0.18 *** 0.17 ** 0.23 *** 0.42 *** 0.17 ** 0.12 * 0.20 ** 0.38 *** 0.15 ** 0.16 ** 0.11 0.30 *** 0.08 0.11 ** 16. Life Satisfaction 0.20 *** 0.20 *** 0.25 *** 0.45 *** 0.17 *** 0.17 *** 0.26 *** 0.46 *** 0.15 ** 0.18 *** 0.19 *** 0.35 *** 0.19 *** 0.02 0.63 *** 17. Work Satisfaction 0.16 ** 0.16 * 0.23 *** 0.38 *** 0.15 * 0.08 0.20 ** 0.41 *** 0.13 * 0.12 * 0.16 * 0.34 *** 0.06 0.00 0.24 ** 0.25 *** 18. Study 0.13 * 0.10 0.28 *** 0.49 *** 0.11 0.07 0.26 *** 0.54 *** 0.14 * 0.09 0.23 ** 0.45 *** 0.09 0.02 0.35 *** 0.49 *** 0.27 ** Satisfaction 19. Educational Outcomes 0.14 ** 0.12 * 0.02 0.28 *** 0.12 ** 0.12 * 0.01 0.10 * 0.10 * 0.09 * 0.02 0.06 0.66 *** 0.06 0.08 0.21 *** 0.09 0.13 * M 3.52 3.42 4.39 4.35 3.81 3.65 4.41 4.31 3.34 3.20 4.06 4.00 7.91 1.59 4.80 3.69 3.82 3.39 SD 1.90 1.94 1.38 1.47 1.87 1.84 1.55 1.79 2.13 2.15 1.45 1.75 0.70 0.35 1.61 0.91 0.83 1.67 Cronbach s α 0.81 0.83 0.84 0.85 0.81 0.83 0.78 0.81 0.87 0.88 0.90 0.90 0.91 0.89 0.83 0.90 a Time 1. b Time 2. c Time 3. d Time 4. *p < 0.05; **p < 0.01; ***p < 0.001.

6 K. Upadyaya and K. Salmela-Aro studies, and life were added as outcomes in the model. To identify the final model, all of the statistically nonsignificant paths were set to zero. Next, in order to examine whether the same model (e.g. the final model) would show the same fit for students on the academic or vocational track, and young adults who were either studying or working at Time 4, all the analyses were also carried out using the Mplus multigroup procedure (Muthén & Muthén, 1998 2014). In these analyses, the data were divided into two samples: students following an academic or vocational track, and young adults who were working or studying at Time 4. In addition, to better test whether the cross-lagged regression parameters would differ across school track and study/work status, path models were carried out comparing three alternative models to the initial multigroup model, in which all the parameters were constrained to be equal for the two groups (e.g. restricted model). In the first alternative model, all the parameters were set free (e.g. unrestricted model); in the second alternative model, the basic path model, including the variables for energy, absorption, and dedication was constrained equally for the two groups, whereas the paths from/to their antecedents and consequences were set free (e.g. semi-restricted model 1); and in the third alternative model, the paths from the antecedents and to the consequences were constrained equally for the two groups and the basic model was set free (e.g. semi-restricted model 2). If the fit of the model was good and no significant modification indices were found, the model was assumed to fit the groups equally. Finally, alternative multigroup models were compared according to their χ 2 values. Results Means, variances, and correlations are presented in Table 1. The initial path model fitted the data moderately (χ 2 (114, N = 871) = 374.30, p = ns, CFI = 0.96, TLI = 0.94, RMSEA = 0.05, SRMR = 0.08). After setting all the non-significant paths to zero, the final model fitted the data well (χ 2 (137, N = 871) = 209.65, p = ns, CFI = 0.99, TLI = 0.99, RMSEA = 0.03, SRMR = 0.07; Figure 1). The results showed that, as expected, despite the high stability of the dependent variables, several cross-lagged paths were identified between the engagement dimensions. In particular, students feelings of energy (Times 1 and 3) positively predicted their subsequent dedication and absorption in post-comprehensive studies (Time 2) and higher education or work (Time 4). Moreover, dedication in studies (Time 1) positively predicted subsequent energy during the second year of postcomprehensive education (Time 2). Further, students absorption during the second year of post-comprehensive studies (Time 2) positively predicted their subsequent energy and dedication in higher studies or work (Time 3). The results showed further that females experienced a higher level of study-related energy than males, and students with high academic performance also reported high energy, dedication, and absorption in their studies (Figure 1). Moreover, dedication in higher education or work positively predicted the young adults educational outcomes and satisfaction with studies, work, and life. Feelings of energy positively predicted the young adults life satisfaction and negatively predicted their depressive symptoms and educational outcomes. No associations were found between family SES and students engagement. In order to examine whether the same model would show the same fit for students on the academic or vocational track, and for young adults who were working or studying at Time 4, the analyses were also carried out using the Mplus multigroup comparison (Muthén & Muthén, 1998 2014). In these analyses, the data were divided into two samples: males and females, students following an academic or vocational track, and young adults who were working or studying at Time 4. The initial multigroup was compared to three alternative models. If the fit of the model was good and no significant modification indices were found, the model was assumed to fit the groups equally. The fit indices for the various multigroup models are presented in Table 2. The results showed that the unrestricted model and semi-restricted model 1 better fit the data than the restricted model for young adults who were either studying or working at Time 4 (Table 3). The results of the semi-restricted model 1 showed further that gender predicted energy at Time 1 only among the young adults who were working at Time 4 compared to those who were studying at Time 4 (Figures 2 and 3). Moreover, academic performance predicted one s energy dedication and absorption at Time 1 only among those who were still studying at Time 4 (Figures 2 and 3). In addition, dedication predicted young adults satisfaction with their studies only among those participants who were studying at Time 4, and satisfaction with work only among those participants who were working at Time 4 (Figures 2 and 3). In all the multigroup models, the predictions from one s dedication and energy to educational outcomes diminished, probably due to the smaller number of participants in the model. Smaller sample sizes in the multigroup analyses may have also resulted in relatively high SRMR values. No differences in the parameters were found in the study track multigroup analyses. Discussion The present study examined cross-lagged associations between three dimensions of study and work engagement, i.e. energy, absorption, and dedication, following young adults from age 17 to 23 over their transition

The Journal of Positive Psychology 7 Figure 1. Cross-lagged associations between young adults energy, dedication, and absorption in studies or work. Standardized coefficients, unstandardized coefficients, and standard errors in parentheses. Notes: ***p < 0.001; **p < 0.01; *p < 0.05; gender 1 = female, 2 = male; GPA ranged between 4 (lowest) and 10 (highest). Table 2. Goodness-of-fit summary for the tested path models. Model N χ 2 df p CFI TLI RMSEA SRMR Track (N vocational track = 226; N academic track =529) Restricted 755 470.90 302 0.00 0.98 0.97 0.04 0.08 Unrestricted 755 440.30 274 0.00 0.98 0.97 0.04 0.08 Semi-restricted 1 755 459.95 290 0.00 0.98 0.97 0.04 0.08 Semi-restricted 2 755 451.18 285 0.00 0.98 0.97 0.04 0.09 Work/Study Status (N studying = 349; N working =170) Restricted 519 453.27 302 0.00 0.97 0.97 0.04 0.10 Unrestricted 519 397.74 274 0.00 0.98 0.97 0.04 0.09 Semi-restricted 1 519 410.71 291 0.00 0.98 0.97 0.04 0.09 Semi-restricted 2 519 441.37 286 0.00 0.97 0.96 0.05 0.10 Table 3. Results for the multigroup model comparisons. Model comparison χ 2 df p Track Restricted unrestricted 30.60 28 0.34 Restricted semi-restricted 1 10.95 12 0.53 Restricted semi-restricted 2 19.72 17 0.29 Work/study status Restricted unrestricted 55.52 28 0.01 Restricted semi-restricted 1 42.56 11 0.00 Restricted semi-restricted 2 11.89 16 0.75 from post-comprehensive education to higher education or work. The developmental dynamics between the engagement dimensions showed, in particular, that having high energy and positive affect in studies and work led to increased absorption and dedication. Feelings of absorption and behavioral commitment in their studies, in turn, predicted the young adults dedication and energy after the transition to higher education or work. Dedication and cognitive sense of significance in higher studies or work predicted high educational outcomes and satisfaction in life, work, and studies.

8 K. Upadyaya and K. Salmela-Aro Figure 2. Cross-lagged associations between young adults (Who were studying at Time 4) energy, dedication, and absorption in studies or work. Standardized coefficients, unstandardized coefficients, and standard errors in parentheses. Notes: ***p < 0.001; **p < 0.01; *p < 0.05; dashed lines represent nonsignificant associations between the variables. Figure 3. Cross-lagged associations between young adults (Who were working at Time 4) energy, dedication, and absorption in studies or work. Standardized coefficients, unstandardized coefficients, and standard errors in parentheses. Notes: ***p < 0.001; **p < 0.01; *p < 0.05; dashed lines represent nonsignificant associations between the variables.

The Journal of Positive Psychology 9 Cross-lagged associations between study- and work-related energy, absorption, and dedication The first aim of this study was to examine the development of study- and work-related energy, absorption, and dedication and their interrelations over time. As expected, the three dimensions of study/work engagement were positively associated over the study years (H1), and some of these associations momentarily declined during the transition to higher education or work (H2). Further, as expected (H3), young adults high level of energy in their studies or work positively predicted their subsequent absorption and dedication before and after the transition to higher education or work. These results resemble those of previous research s where high energy was a central element of engagement for university students, promoting their academic achievement and sense of efficacy (Schaufeli, Martinez et al., 2002). Our results add to the previous findings by showing that energy is a central element of engagement also prior to university education (e.g. during post-comprehensive studies). Similarly, previous research has shown that emotional engagement (which resembles the concept of study-related energy; Upadyaya & Salmela-Aro, 2013a) fuels behavioral engagement (e.g. similar to absorption) among elementary and middle school students (Skinner, Furrer, Marchand, & Kindermann, 2008). Dedication in studies was important in maintaining a high level of study-related energy at the beginning of post-comprehensive education. These results may reflect the fact that highly dedicated students typically feel inspired and invest effort in their schoolwork, which in turn may lead to increased academic success (Perdue, Manzeske, & Estell, 2009) and study-related energy and positive emotions. However, later on, high energy becomes more prominent in adolescents and young adults engagement, increasing one s dedication and absorption in studies/work. During the transition, feelings of absorption predicted subsequent high energy and dedication in higher studies or work. The concept of absorption resembles that of behavioral engagement s concept (Upadyaya & Salmela-Aro, 2013a), which in turn has been defined as the central element of school engagement (Skinner et al., 2008), and thus, easily facilitates experience of the other aspects of engagement. Our results concerning later phases of education showed that feelings of absorption and behavioral accomplishment were important especially during the transition to higher education/work. This may be due to the fact that of the three dimensions of engagement, absorption bears the closest resemblance to flow (Schaufeli, Salanova et al., 2002), and it is possible that positive flow-like experiences (see also Csikszentmihalyi, 1990) help young adults in adapting to their new study or work environment, which also manifests as an increase in their dedication and energy in their studies/work. However, prior and after the transition, absorption did not show any predictions to the other two aspects of engagement. This may reflect the fact that developmental changes occur in the associations between the different engagement dimensions, and more future studies would be needed to examine them further. These results also suggest that developmental cascades occur in the associations between study and work engagement: beneficial functioning in one aspect of engagement spills over to other aspects of engagement in a long-lasting way (see also Masten et al., 2005, 2010). Our results showed that high engagement in studies promotes young adults subsequent engagement in their work and higher studies. High work engagement, in turn, promotes success at work (Salanova et al., 2005), which is one of the most salient developmental tasks in adulthood (Masten et al., 2010). Antecedents and outcomes of study and work engagement Our results partly confirmed our hypotheses (H4) by showing that female students experienced higher study-related energy (Time 1) than their male counterparts. Previous studies have shown that females typically experience higher levels of overall engagement (Marks, 2000; Salmela-Aro & Upadyaya, 2014) and emotional engagement (Li & Lerner, 2011), similar to energy (Upadyaya & Salmela-Aro, 2013a), than males. Moreover, the multigroup comparisons showed that high energy at Time 1 was more typical for those females who were working at the end of the study (at Time 4). It is possible that, these results either reflect the motivational processes of engagement (Hakanen et al., 2006), or reflect the fact that students with high levels of energy simultaneously feel exhausted with their studies (Maslach, Schaufeli, & Leiter, 2001) and may transit to work earlier than their less-exhausted counterparts. Moreover, students with high academic performance reported higher energy, dedication, and absorption in their studies, and higher educational outcomes than those whose level of performance was lower. Further analyses showed that these young adults also continued their education later on (Time 4). Similarly, students academic performance is typically associated with their feelings of engagement at all grade levels (Dotterer & Lowe, 2012; Ladd & Dinella, 2009; Li et al., 2010; Sirin & Rogers-Sirin, 2004). In our study, familial socioeconomical status did not predict study/work engagement which may be due to cultural differences in the factors influencing engagement (see also Upadyaya & Salmela-Aro, 2013a). Concerning the consequences, the results partly confirmed our hypotheses (H5) by showing, in particular,

10 K. Upadyaya and K. Salmela-Aro that young adults dedication in their studies/work positively predicted their life/work/study satisfaction, and educational outcomes. Previous cross-sectional studies have reported that high dedication towards one s work reduces psychosomatic symptoms (Koyuncu, Burke, & Fiksenbaum, 2006). However, our results added to these findings by showing that high dedication in studies/work increased several aspects of well-being. In previous studies, it has been found that cognitive engagement, a concept resembling dedication (Salmela-Aro & Upadyaya, 2012), predicts students subsequent life satisfaction (Lewis et al., 2011). This may be due to the fact that students who experience high dedication/cognitive engagement also value education highly (Lewis et al., 2011). Moreover, study/work-related energy also resulted in high life satisfaction and in decreased depressive symptoms, probably because high energy at studies/work increases psychological well-being (see also Koyuncu et al., 2006). Dedication and energy have been considered the main elements of work engagement (Gonzalez-Roma, Schaufeli, Bakker, & Lloret, 2006), which may explain their stronger effects on well-being. Some previous studies among employees have shown that overall engagement (Schaufeli et al., 2008; Wefald & Downey, 2009) and dedication and absorption (Koyuncu et al., 2006) predict high job satisfaction. Our results, however, highlight the importance of dedication and add to the previous findings by showing that during higher education studies and at the beginning of the career, dedication plays an important role both in work and study satisfaction. Moreover, engaged students often experience positive emotions (Reschly, Huebner, Appleton, & Antaramian, 2008), while happy people have been reported to be more open to career-related opportunities (Cropanzano & Wright, 2001); these factors may have led to high life, work, and study satisfaction, and better educational outcomes among the highly dedicated young adults in our study. Similarly, high engagement in studies facilitates academic success (Annunziata, Hogue, Faw, & Liddle, 2006), which may explain the higher educational outcomes among the present highly dedicated young adults. A high level of energy, in turn, negatively predicted educational outcomes. It is possible that students who report high levels of energy simultaneously feel exhausted (Maslach et al., 2001), which further manifests as lower levels of educational outcomes. In a recent study, Tuominen-Soini and Salmela-Aro (2014) compared engaged students and engaged exhausted students and revealed that, despite their engagement and attribution of positive value to school, engaged exhausted students were more exhausted, more stressed by their educational aspirations, more preoccupied with possible failures in school and more willing to give up in the face of demanding school tasks. Strengths, limitations, and future directions One of the limitations of the present study is that the inventory that was used is a relatively new measure of study and work engagement. Thus, even though this measure has been used widely in work-related research (Hakanen et al., 2006; Salanova et al., 2005; Schaufeli et al., 2006), more studies would be needed to examine young adults energy, absorption, and dedication during their transition to higher education studies/work. In addition, measurement error might have affected the results to some extent as sum scores were formed to measure the engagement dimensions. Smaller sample sizes in the multigroup models may also have resulted in relatively high SRMR values. More studies would be needed to examine the associations between the separate engagement dimensions using latent variables and larger samples. However, the study results were promising, in the sense that they showed that similar engagement dimensions can be found among students and workers, providing also the possibility to examine the continuum between study and work engagement. The results also importantly contributed to the engagement research by investigating the longitudinal cross-lagged associations between various engagement dimensions (Fredricks et al., 2004). Moreover, the study participants were young adults facing the transition to higher education/ work, and consequently, the results can be generalized only to this age group. It is possible that among students and workers in other educational/work settings and cultural backgrounds, different results might have been obtained. Future studies should examine the associations between the separate engagement dimensions, as well as their antecedents and consequences, among different age groups (Glanville & Wildhagen, 2007; Ladd & Dinella, 2009). For example, it is possible that, in this study setting, dedication and absorption are more important among elementary students, whereas energy becomes a more central element among older students. It is also possible that during different stages of education and work, various other antecedents, such as support from others, contribute to one s energy, absorption, and dedication in studies/work. For example, peers and colleagues engagement and enthusiasm may show in one s own engagement (see also Kinderman, 2007). Future studies should examine these associations further. The results further revealed that the associations between study- and work-related energy, absorption, and dedication, and their antecedents and outcomes were relatively similar for different subgroups of young adults. However, it is possible that more differences exist among young adults according to other characteristics that were not examined in the present study. For example, availability of several study/work-related resources and amount of demands at studies or work may affect the

The Journal of Positive Psychology 11 ways one s engagement develops over the transition to higher education/work (Upadyaya & Salmela-Aro, 2013b). More future studies would be needed to examine these associations further. Overall, the results of the present study showed that energy was a central element in young adults engagement in studies/work. High energy increased overall well-being, whereas post-comprehensive study-related absorption supported the other elements of engagement after the transition to higher education or work. Developmental cascades (Masten et al., 2005, 2010) occurred in the longitudinal associations between the engagement dimensions, and positive spillover from engagement in studies to engagement in higher education or work was identified. Future interventions could be targeted to prevent negative spirals originating from low study engagement (see also Masten et al., 2005). Moreover, young adults dedication in their studies/work, in particular, was beneficial for their overall well-being and educational outcomes, suggesting that, even though cognitive engagement and dedication in studies (see also Upadyaya & Salmela-Aro, 2013a) has been less well investigated than the other two dimensions, a high level of dedication may help young adults to adapt to their new developmental tasks in emerging adulthood. Funding The research reported in this article is funded by a grant from Jacobs Foundation and supported by a grant from the Academy of Finland [grant numbers 134931, 139168, 273872, 273361]. Note 1. Some participants (N = 191) were both studying and working, however, their work/study status was coded according to what was their full-time status. For example, some participants who were working were also studying for the university entrance exams during their free time. References Annunziata, D., Hogue, A., Faw, L., & Liddle, H. A. (2006). Family functioning and school success in at-risk, inner-city adolescents. Journal of Youth and Adolescence, 35, 105 113. doi:10.1007/s10964-005-9016-3 Appleton, J. J., Christenson, S. L., & Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45, 369 386. doi:10.1016/j.jsp.2006.04.002 Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the student engagement instrument. Journal of School Psychology, 44, 427 445. doi:10.1016/ j.jsp.2006.04.002 Archambault, I., Janosz, M., Morizot, J., & Pagani, L. (2009). Adolescent behavioral, affective, and cognitive engagement in school: Relations to dropout. Journal of School Health, 79, 408 415. doi:10.1111/j.1746-1561.2009.00428.x Berndt, T. J., & Miller, K. E. (1990). Expectancies, values, and achievement in junior high school. Journal of Educational Psychology, 82, 319 326. doi:10.1037/0022-0663.82.4.664 Cropanzano, R., & Wright, T. A. (2001). When a happy worker is really a productive worker: A review and further refinement of the happy-productive worker thesis. Consulting Psychology Journal: Practice and Research, 53, 182 199. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York, NY: Harper and Row. Demerouti, E., Bakker, A., Nachreiner, F., & Schaufeli, W. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86, 499 512. doi:10.1037/0021-9010.86.3.499 Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satifaction with life scale. Journal of Personality Assessment, 49, 71 75. Dotterer, A. M., & Lowe, K. (2012). Classroom context, school engagement, and academic achievement in early adolescence. Journal of Youth and Adolescence, 40, 1649 1660. doi:10.1007/s10964-011-9647-5 Finn, J. D., & Voelkl, K. E. (1993). School characteristics related to student engagement. Journal of Negro Education, 62, 249 268. Fredricks, J. A. (2011). Engagement in school and out-ofschool contexts: A multidimensional view of engagement. Theory Into Practice, 50, 327 335. doi:10.1080/ 00405841.2011.607401 Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59 109. doi:10.3102/00346543074001059 Glanville, J. L., & Wildhagen, T. (2007). The measurement of school engagement: Assessing dimensionality and measurement invariance across race and ethnicity. Educational and Psychological Measurement, 67, 1019 1041. doi:10.1177/ 0013164406299126 Gonzalez-Roma, V., Schaufeli, W. B., Bakker, A. B., & Lloret, S. (2006). Burnout and work engagement: Independent factors or opposite poles? Journal of Vocational Behavior, 68, 165 174. doi:10.1016/j.jvb.2005.01.003 Hakanen, J., Bakker, A., & Schaufeli, W. (2006). Burnout and engagement among teachers. Journal of School Psychology, 43, 495 513. doi:10.1016/j.jsp.2005.11.001 Hartung, P., Porfeli, E., & Vondracek, F. (2005). Child vocational development: A review and reconsideration. Journal of Vocational Behavior, 66, 385 419. doi:10.1016/ j.jvb.2004.05.006 Janosz, M., Archambault, I., Morizot, J., & Pagani, L. S. (2008). School engagement trajectories and their differential predictive relations to dropout. Journal of Social Issues, 64, 21 40. doi:10.1111/j.1540-4560.2008.00546.x Jimerson, S. R., Campos, E., & Greif, J. L. (2003). Toward an understanding of definitions and measures of school engagement and related terms. The California School Psychologist, 8, 7 27. Kinderman, T. A. (2007). Effects of naturally existing peer groups on changes in academic engagement in a cohort of sixth graders. Child Development, 78, 1186 1203. doi:10.1111/j.1467-8624.2007.01060.x Koyuncu, M., Burke, R. J., & Fiksenbaum, L. (2006). Work engagement among women managers and professionals in a Turkish bank: Potential antecedents and consequences. Equal Opportunities International, 25, 299 310. doi:10.1108/02610150610706276