1 Introduction

Western society emphasizes academic achievement and excellence as the cornerstones of satisfactory social and vocational integration (Solberg-Nes et al. 2009). Universities play a crucial part in achieving this goal. Their students, however, coming from different cultural and socio-economic backgrounds, vary often markedly in basic potential, prior education, motivation and social/emotional needs (McKenzie and Schweitzer 2001). Some enter university equipped with cognitive and psychological resources that sustain them in effectively coping with the challenges they encounter; others struggle to adjust to the peculiarities and requirements of the academic environment, hence are frequently prone to stress and anxiety (Gerdes and Mallinckrodt 1994; Morrison and Cosden 1997).

Academic adjustment is multifaceted, reflecting in addition to students’ learning capacity their motivation, how they conceptualize their academic goals, the strategies they apply to achieve them, their satisfaction with the academic environment, and so on (Baker and Siryk 1984a, b). Academic adjustment has been suggested to consist of and be measured by the student’s functioning in four distinct domains (Baker and Siryk 1984a, b, 1986, 1989; Gerdes and Mallinckrodt 1994). The first domain, “academic achievement” is grounded in students’ learning-motivation, the appropriateness of their study skills to particular study requirements and their ability to earn satisfactory grades. The second domain, “social adjustment” stands for students’ involvement in their study environment, including their ability to establish social networks. The third domain, “personal emotional adjustment” reflects students’ psychological and physical conditions. It is indicative of their self-perception and represents their copying with study-related challenges that lead to the arousal of stress and anxiety. The fourth and last domain, “institutional adjustment” is revealing of how students feel about their relation to academy, in general, and to their academic environment, in particular.

This study examined the relation of psychological capital (PsyCap) to academic adjustment in university students as a potential factor underpinning academic success. PsyCap is a core construct reflecting individuals’ positive psychological state of development (Luthans 2002a; Luthans et al. 2004; Luthans and Youssef 2004). It has been conceptualized within positive psychology (Seligman 1998, 2002) in general, and in the examination of specific positive organizational behavior in particular (Luthans 2002a, b). It is built of four capacities: (1) self-efficacy: the confidence to take on and put in the necessary effort to succeed in performing challenging tasks; (2) optimism: a positive attribution about succeeding now and in the future; (3) hope: preserving toward goals, and when necessary, redirecting paths to goals in order to succeed; (4) resiliency: the ability to bounce back and even beyond when confronted with problems and adversity in order to attain success (Luthans et al. 2006b, 2007b).

PsyCap is considered relatively malleable and open to development (state-like), namely it is not static and is more open to change in comprising positive resources and to development through short-term intervention (Luthans et al. 2006a, 2007a, 2008a). PsyCap’s impact has mainly been tested in relation to satisfaction, production and well-being within management, and at the organizational level among employees. Evidence from such research indeed shows a positive link across these three domains (Avey et al. 2010; Culbertson et al. 2010; Luthans et al. 2007a, b, 2008b). Surprisingly however, studies on the contribution of PsyCap to the well-being and academic achievements of university students are so far rather rare. Among them, some investigated the relation of PsyCap to students’ grade point average (GPA) (Luthans et al. 2012; Vanno et al. 2014). Findings suggest that PsyCap serves as a positive predictor of students’ GPA, where students’ with higher levels of PsyCap show higher GPA, and vice versa. Another endeavor (Siu et al. 2014) focused on the relation between PsyCap and students’ study engagement (how dedicated students are to their studies) and how this is mediated by their intrinsic motivation (how interested students are in the study challenges and how much they enjoy them). As with GPA, students manifesting higher levels of PsyCap showed more study engagement. Moreover, intrinsic motivation turned out to mediate the relation of these two study variables, with higher level of PsyCap being associated with higher levels of intrinsic motivation and enhanced study engagement.

A different aspect of PsyCap as a potential mediator has been probed for its role in the relation of academic stress to psychological symptoms (e.g., depression, somatization), physical symptoms (e.g., headache, stomachache) and life satisfaction (Riolli et al. 2012). This research pinpoints PsyCap’s ability to moderate the influence of academic stress in the development of psychological and physical symptoms. It also suggests that PsyCap accompanies students’ life satisfaction. Similar conclusions have been drawn from a longitudinal study (Avey et al. 2011) with college students on the essence of the relationship between PsyCap and well-being and how it is mediated by positive emotions (e.g., happiness, love, joy) and anxiety and stress. Overall, as the researchers expected, PsyCap proved a positive predictor of students’ well-being. Interestingly, on a more specific level, higher levels of PsyCap were associated with enhanced levels of positive emotions that reduced anxiety and stress, consequently increasing well-being. The logical conclusion from these findings is that PsyCap underlies creation of positive emotions, and on the other hand helps students to cope with academic stress and anxiety. As a result, students will elevate their achievements and experience higher levels of well-being.

Evidence concerning the relation between self-efficacy, optimism, hope and resiliency—PsyCap’s positive capacities—and academic adjustment strongly suggests that each of these components plays a central role in the way students adjust to the requirements of their academic environment (Aspinwall and Taylor 1992; Brissette et al. 2002; Heiman and Kariv 2004; Phinney and Haas 2003; Raskind et al. 1999; Reiff et al. 1994). Examination of each of these components, in conjunction with findings from the investigation of PsyCap as a holistic resource, lead to the conclusion that the whole is more than the sum of its parts. More specifically, students’ trust in their own skills in dealing with academic requirements, their ability to recruit these skills to increase chance of success with regard to well-defined goals and to work for their realization with a positive attitude even when they encounter significant difficulties, is essential for successful academic adjustment.

So far, research on PsyCap and its contribution to academic life is sparse, focusing primarily on academic adjustment as reflected in GPA and students’ more general well-being. The aim of the present study was to broaden understanding of PsyCap as a resource with the potential to underpin academic adjustment in four distinct domains: academic achievement, social adjustment, personal emotional adjustment and institutional adjustment. We were particularly interested in revealing whether and to what extent PsyCap was indicative of students’ functioning in these domains. We predicted that PsyCap as a core construct would be positively associated with academic adjustment overall, and with students’ functioning in each of the above academic domains in particular. We specifically expected that out of the PsyCap and academic adjustment subscales, ‘hope’—representing will power and agency—would correlate with academic achievement, and ‘resilience’—indicating the ability to bounce back—would correlate with personal–emotional adjustment.

2 Methods

2.1 Participants

The study examined 250 BA students (mean age = 25, SD = 2.52) at the University of Haifa; 60.4% of them were in their second year and 39.6% were in later years. First-year students were not chosen because that level may not be suitable for assessing academic adjustment: those students are actually in the course of familiarizing themselves with academic requirements and academic life.

For a representative sample, participants were selected from a wide range of study domains (see Table 1) and were about equally divided by gender (44% males, 56% females). All had normal or corrected-to-normal vision and intact hearing. They were not diagnosed as having a learning disability nor did they have any recorded history of mental, emotional or behavioral disorders. All were recruited through flyers distributed on the Haifa University campus; they were paid for their participation.

Table 1 Distribution of participants by faculties and departments
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2.2 Instruments

To test our research hypothesis we used three questionnaires, one to gather demographic information and the two others to assess relevant information on the participants’ PsyCap and academic adjustment.

2.2.1 Demographic Information Questionnaire

This questionnaire was developed specifically for the present study to collect relevant information regarding the participants’ demographic and academic characteristics, as well as details of their diagnostic and treatment history (see “Appendix 1”).

2.2.2 Psychological Capital Questionnaire (PCQ)

The PsyCap questionnaire used in this study is a modified Hebrew version of the questionnaire of Luthans et al. (2007a, b). Its items, generally formulated in a positive tone, reflect PsyCap’s four psychological capacities with respect to strengths and academic outcomes. Originally developed as tool for assessing PsyCap as a general positive resource of state-like nature, our questionnaire was modified to measure its sustaining potential in relation to a specific academic context. In the course of this modification we decided to reduce the six-point Likert scale used in the original questionnaire in order to enhance the distinction between the different levels.

Our PCQ comprises 24 items the appropriateness of which is evaluated on a five-point Likert scale from “Strongly disagree” (1) to “Strongly agree” (5) (see “Appendix 2” for an English translation of the modified version). It comprises four six-item subscales, each designed to assess one of the four target psychological capacities: self-efficacy (item numbers: 1, 6, 10, 16, 21, 24; e.g., “I feel confident analyzing a study-related long-term problem to find a solution.”), hope (item numbers: 2, 7, 12, 15, 20, 23; e.g., “There are lots of ways around any study-related problem”), optimism (item numbers: 5, 9, 11, 14, 17, 19; e.g., “When things are uncertain for me as a student, I usually expect the best”), resilience (item numbers: 3, 4, 8, 13, 18, 22; e.g., “I can deal with study-related difficulties because I’ve experienced difficulty before”). Of the total 24 items, three were reversed (9, 17, 22).

The score range of the PCQ is 24–120, with higher score indicating higher levels of PsyCap. Reliability (Cronbach’s alpha) of the original questionnaire for all items was .93; for the four subscales it was as follows: self-efficacy .87, hope .87, optimism .78, resilience .72. Similarly, reliability for the PCQ used in the present study was overall high .89; for the four subscales it was self-efficacy .76, hope .77, optimism .71, resilience .63.

2.2.3 Academic Adjustment Questionnaire (AAQ)

The AAQ used in this study is a shortened Hebrew modified version of the Students Adaptation to College Questionnaire (SACQ) developed by Baker and Siryk (1989) to assess four specific domains of academic adjustment. For this study, the original items of the SACQ were partly modified in order to adapt their content to the reality of Israeli university students.

The AAQ has 28 items and comprises four subscales. The appropriateness of each item is evaluated on a nine-point Likert scale from “Suits me very much” (1) to “Doesn’t suit me at all” (9) (see “Appendix 3” for the English translation of the Hebrew version). The first subscale has six items that assess students’ academic achievements and their managing academic requirements (item numbers: 2, 8, 15, 19, 21, 22; e.g., “I find academic studies difficult”); the second subscale has eight items that measure students’ social skills with reference to their inter-connectedness with other students (item numbers: 3, 5, 7, 11, 16, 20, 24, 25; e.g., “I have difficulty feeling comfortable in connecting with other students”); the third subscale has seven items on students’ personal and emotional well-being (item numbers: 1, 4, 9, 12, 14, 17, 26; e.g., “Lately I’ve been feeling downcast and moody”); the last subscale has seven items that assess students’ satisfaction with their academic institution (item numbers: 6, 10, 13, 18, 23, 27, 28; e.g., “ I am happy with my decision to study at my university”). Of the total 28 items half were reversed (1, 4, 9, 10, 12, 13, 14, 15, 16, 17, 20, 21, 22, 26).

The score range of AAQ is 28–252, where a higher score indicates higher levels of academic adjustment. Reliability (Cronbach’s alpha) of the original SACQ was .92–.95, overall, and for the four subscales it was as follows: academic .81–.90, social .83–.90, personal–emotional .77–.86, and institutional .85–.91. Reliability scores for the AAQ used here was overall high .86; and for the four subscales: academic .61, social .78, personal–emotional .85, and institutional .77.

In addition to the AAQ, academic adjustment was assessed by the students’ GPA taken at two time points: the beginning and the end of the second semester of the academic year, the latter after completion of all requirements of the current year. This measure was added to avoid the one one-source problem, in this case using data from questionnaires only. This is suspected of creating bias reflecting inaccuracies due to subjective interpretation, particularly when individuals complete more than one questionnaire

2.3 Study Procedure

Participants were tested individually in the authors’ lab after the end of the first semester. It was after they took exams, that is, before starting the second semester, a stage often accompanied by new challenges. They were informed that the study aimed at a better understanding of the experiences, attitudes and feelings of university students working for the BA degree. Data collection was computerized, participants having first completed the demographic information questionnaire which included their consenting to participate in the study. Next they were asked to show the experimenter the official score form with their most recent GPA, calculated from the final course scores of their studies so far. The experimenter coded their GPAs as the basis for the evaluation of their academic adjustment. The PCQ and AAQ were administered immediately thereafter, the PCQ always given first. At the end of the second semester participants mailed their official score forms showing their updated GPA.

3 Results

Table 2 presents questionnaire averages and standard deviations of the two research variables.

Table 2 Questionnaire averages and standard deviations of the two research variables
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The statistical model used for examining the relation between PsyCap and academic adjustment was based on a Structural Equation Modeling (SEM) procedure. First we ran a Confirmatory Factor Analysis (CFA), a method we used to validate the relationship among the latent (PsyCap and academic adjustment) and the observed variables (item ratings on a particular questionnaire). More specifically, CFA estimates whether the collected data are compatible with a hypothesized model consisting of a latent variable and a series of observed variables assumed to be its indicators, to reveal the strength of correlations between them; acceptable indicator load is λ > .25 (Schreiber et al. 2006; Suhr 2006). Table 3 shows loads and explained variances for PsyCap and academic adjustment.

Table 3 CFA of the latent variable PsyCap and academic adjustment
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We calculated Goodness-of-Model Fit for each of the two latent variables to reveal the degree to which the hypothesized models matched the collected data. We conducted this analysis with three different indices. The first two, Comparative Fit Index (CFI) and Tucker–Lewis Index (TLI), use a range of 0–1, with rise in value indicating better goodness-of-model fit. The third index, Root Mean Square Error of Approximation (RMSEA), uses a range of 0.0–0.1, with decrease in value indicates better goodness-of-model fit. This is considered satisfactory if CFI and TLI values are ≥ .90 and RMSEA value is ≤ .06 (Suhr 2006). Goodness-of-model fit for the hypothesized PsyCap model was CFI = 1.00, TLI = 1.00 and RMSEA = .00. For the hypothesized academic adjustment model it was CFI = .99, TLI = .99 and RMSEA = .04.

A series of intra- and inter-Pearson correlations between the two latent variables PsyCap and academic adjustment and their subscales pinpointed a strong positive correlation between PsyCap and academic adjustment, r = .62, p < .001. Averages of the two GPA collections were close to identical (M1 = 84.67, M2 = 85.16). Their correlation was high (r = .85, p < .001) and their correlation with the variables of the PsyCap and Academic Adjustment questionnaires was uniform. We therefore decided to merge them into one single GPA variable representing their average. Table 4 shows Pearson correlations between the subscales of PsyCap and academic adjustment.

Table 4 Intra and inter Pearson correlation between PsyCap and academic adjustment subscales
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These analyses yielded several correlations. First, the four subscales indicating the individuals’ PsyCap were all strongly and positively associated, r ≥ .55, p < .001. Secondly, like PsyCap, the subscales of academic adjustment were significantly and positively correlated with each other, r ≥ .34, p < .001, except institutional adjustment and GPA where correlations with the other variables were weak, r ≤ .28, p < .001, or non-significant. Thirdly, the PsyCap subscales hope and optimism were strongly and positively correlated with academic adjustment, r = .50; r = .61 respectively, p < .001. Moreover, the association of PsyCap’s four subscales with GPA were generally rather weak, r = .13, p < .05, or non-significant; the exception was hope, which correlated moderately and positively with this academic measure, r = .38, p < .01.

Correlation analyses provide insights into the association of specific variables, but they fail to reflect a system of multiple associations. We therefore decided to conduct a SEM analysis—a statistical procedure based upon two models: a structural model that uses multiple regression and a measurement model in the form of CFA. Combined, these two models produce a broad statistical model that considers multiple regressions and multicollinearity and, by accounting for each variable’s unique contribution to the overall explained variance, minimizes the impact of random errors (Schreiber et al. 2006; Suhr 2006).

Several findings revealed by SEM (see Fig. 1) deserve attention, in the first place the high and positive association between the two target variables, β = .86, p < .001. PsyCap explains 74% of variance in academic adjustment. Further, loads of PsyCap’s observed variables are high and uniform. The same is generally true also for academic adjustment’s observed variables. However, as can be seen from the figure, load from the institutional subscale and GPA to academic adjustment was weaker than loads from the other observed variables. In all, indices of goodness-of-model fit were all uniformly high, indicating that the hypothesized model matches the collected data.

Fig. 1
figure 1

SEM analysis of the relationship between PsyCap and academic adjustment

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4 Discussion

This study set out to broaden understanding of PsyCap as a resource for enhancing successful academic adjustment. We considered this endeavor important because academic success, intrinsically linked to academic adjustment, is agreed to be the basis of satisfactory occupational and social integration into modern society. In particular, because students encounter a challenging academic reality in many ways, investigating their ability to recruit positive resources that might lead to better academic adjustment is warranted—all the more as research on PsyCap’s potential to enhance academic adjustment to tertiary education is surprisingly rare; in the case of Israel there is none at all.

We considered how much PsyCap, as a multifaceted positive core construct, is associated with students’ academic adjustment overall, and with their self-perception in four distinct domains (academic achievement, social adjustment, personal emotional adjustment and institutional adjustment) in particular. We hypothesized that PsyCap would prove positively correlated with university students’ academic adjustment.

Findings yielded by correlational as well as SEM analyses fully substantiated this hypothesis, suggesting PsyCap as holistic resource playing a central part in students’ academic adjustment. The same also seems true for PsyCap’s four positive capacities, bearing in mind that data were collected through questionnaires only.

Our evidence seems to accord with findings of other researchers into PsyCap’s enhancing potential in relation to variables such as study engagement and intrinsic motivation (Siu et al. 2014)—both inherent ingredients of academic adjustment. On the other hand, it does not seem to lend clear-cut support to previous findings on the relation between PsyCap and GPA (e.g., Luthans et al. 2012; Vanno et al. 2014). So although the academic achievement subscale and GPA within the academic adjustment construct are treated as complementary components, they actually may not be two sides of the same coin. As our findings indicate, they seem to be essentially different in nature—one subjective and the other objective. This conclusion appears warranted by the finding that PsyCap’s subscales were all significantly and positively correlated with the four questionnaire-collected academic adjustment subscales. Note however that students’ GPA correlated significantly with only two of PsyCap’s components: hope and resilience. There was apparently no such relationship with respect to self-efficacy and optimism.

Why do PsyCap’s four components correlate so diversely with GPA? For a satisfactory answer to this central question we must take a closer look at each component’s essence. Self-efficacy stands for a person’s inner belief, and optimism represents a person’s explanatory style. Thus, both are primarily subjective (e.g., Brady-Amoon and Fuertes 2011) hence may not necessarily be backed up by real factual achievements. Hope and resilience, on the other hand, are different: the former reflects a person’s ability to direct him/herself to goals in order to succeed; the latter is the ability to bounce back when confronted with adversity (Luthans et al. 2007a, b). In other words, they both reflect active maximization of opportunities and quality of life. GPA logically is a product directly related to taking action and recovery from previous failure. This may reasonably explain why in this study hope and resilience, but not self-efficacy and optimism, correlated with student’s GPA. This line of interpretation also supports our specific predictions regarding the way hope and resilience correlate with academic achievement and personal–emotional adjustment, respectively. Findings from the present study indeed highlight that hope and resilience seem to play a central role in relation to academic outcomes, with resilience suggested also to have particular relevance for a person’s psycho-emotional well-being.

As stated, beyond PsyCap’s relation to academic achievement and GPA, some researchers consider its potential contribution to students’ social and emotional adjustment to academic life (Avey et al. 2011; Riolli et al. 2012). Such endeavors pinpoint PsyCap’s potential to enhance students’ satisfaction and well-being while coping with requirements related to academic reality. This possible relation between PsyCap and social–emotional aspects in dealing with academic life is further corroborated by our present findings. They indeed confirm that students with higher levels of PsyCap also evince enhanced willingness to engage into social interactions, thereby getting greater satisfaction from social activities that are a part of academic life. They further elucidate the potential of students’ PsyCap to magnify personal and emotional well-being, which becomes reflected in a more adaptive coping style.

In sum, evidence from the present study implies that conclusions from research in positive organizational behavior (Avey et al. 2010; Culbertson et al. 2010; Luthans et al. 2007a, b, 2008a, b) on PsyCap being a positive resource in enhancing individuals’ satisfaction, well-being and productivity are generalizable to the academic context as well. It in fact shows that PsyCap, as a positive core construct, explains a fairly substantial part of variance (74%) in students’ academic adjustment. It highlights PsyCap as a cornerstone able markedly to enhance students’ academic adjustment in its four distinct domains. PsyCap is deemed relatively malleable and open to development (Luthans et al. 2006a, b; 2007a, b, 2008a, b), so university policy makers should give high priority to fully exploiting its potential to facilitate students’ effective adjustment to academic life. Such efforts should include the development of intervention programs for students who struggle in adjusting themselves to academic life.

4.1 Limitations and Future Research

Except GPA, data on the different study variables were collected at one particular time, so a causal interpretation of the findings deriving from them is not possible; this circumstance should be taken into account in expanding this line of research. This limitation should also be kept in mind in interpreting the proposed model that indicates the direction of the PsyCap– academic adjustment relationship to go from the former to the latter, although in principle it could be the reverse. This issue might be determined in a future longitudinal study.

The study sample did not include first-year university students as their study scores were too limited for building a reliable GPA. Moreover, our questionnaires were designed to access a wide range of academic skills, a significant number of which were not part of the experience of first-year students in Israel, for example, active presentations before the class, debates, writing academic papers, etc. The inclusion of such students should however be considered in future research, using a longitudinal design that would allow a follow-up of their academic outcomes after a fuller experience of academic life.

Finally, except GPA, we collected data via questionnaires that represent participants’ attitudes and feelings in a primarily subjective manner. Future research therefore should develop tools for collecting such data also by a more objective procedure. This is recommended despite evidence implying that people perceive and evaluate their life situation with notable accuracy (Alper et al. 1998; Bogler and Somech 2002).