Introduction
Theory related to behavioral practice, including rational behavior theory, explains that an individual's level of intention to act has an important effect on behavioral practice (Bagozzi, 1992; Rhodes and Yao, 2015). It is also said that a long time difference between intention and practice reduces the influence of that intention (Biddle and Mutrie, 2008); however, the influence of the intention to practice or continue to act on behavior remains important. Therefore, in health research, the intention to sustain is considered an important variable in many areas. However, in Korea, research on the intention to continue sports is sparse. To date, most studies on Korean athletes have focused on factors that affect performance rather than the intention to continue exercising (Kim, 2015; Lee and Lee, 2023; Yoon, 2022). However, recently, the importance of encouraging young athletes to continue exercising has been recognized, as only few young athletes are discovered and nurtured into professional players, which is a lengthy process (Chamera et al., 2023; Kong, 2022; Oh and Yang, 2017; Pires and Ugrinowitsch, 2021). Athletes are more likely to stop exercising during their participation if they do not find joy in the sport itself or if their motivation to participate decreases (Gonzalez-Hernandez et al., 2023; Weiss and Amorose, 2008). This will also affect their performance, result in potential withdrawal, and exacerbate exhaustion (Park et al., 2017). Therefore, for young athletes, the intention to continue exercising is as important as their performance. Thus, it is crucial to identify ways to encourage them and increase their intention to continue exercising (Diaz-Garcia et al., 2021).
The effort-reward imbalance (ERI) model described by Siegrist (1996) suggests that the results obtained in relation to what one is doing are evaluated by a reciprocal relationship between effort and reward. In other words, when you feel that the reward is less than the effort you make, an imbalance in reciprocity occurs, which causes stress related to your actions. Stress generated in this manner negatively affects physical and mental health. The ERI model, which has been mainly applied in measuring job stress, has recently been suggested for college student school life (Wege et al., 2017), jockeys (Kathleen et al., 2017), and athletes (Hong et al., 2023). The ERI model has been reported to be useful for explaining many social phenomena, although not all situations. Siegrist (1996) explained that in the current ERI model, when there is no alternative or when there is a strategy for future gains and high motivation, such as immersion or fun, the situation continues to work. Moreover, several scholars state that the quality of time and effort required to learn a skill are both important to become an expert in the field (Kuhlmann and Ardichvili, 2015; Persky and Robinson, 2017). For athletes, this means investing time and effort to reach the highest performance level. However, it is not easy to get a satisfactory reward because the top positions are limited. As such, athletes may be aware of this ERI; however, it is unclear whether this results in psychological issues.
Previous studies have reported that a prolonged imbalance between effort and reward affects absenteeism and subsequent mental symptoms such as stress, depression, burnout, and job dissatisfaction, as well as physical symptoms, such as cardiovascular and musculoskeletal disorders (Devreux et al., 2012; Rugulies et al., 2017; Willis et al., 2008). Considering athletes, significant effort is expended over an extended period. Therefore, they may be exposed to an imbalance between effort and reward over a long time as well. However, there is limited empirical research on whether the ERI model is applicable to athletes.
Therefore, this study aimed to identify the existing differences between the ERI, weekly training time and various mental variables of athletes depending on individual characteristics to confirm whether the ERI is important to athletes. We also intended to confirm the difference in the athletes' effort, reward, grit, exhaustion, intention to continue exercise and weekly training time between the high and low ERI groups. The research results obtained in this way can also determine whether the effort-reward imbalance model is applicable to athletes. Furthermore, our results may provide the basis to identify ways to alleviate the ERI perceived by athletes.
Methods
Participants
For our study, we recruited a total of 795 college student athletes from universities in Seoul, Gyeonggi-do, Gangwon-do, Chungcheong-do, and Jeolla-do, Korea registered by 2022. To identify the sample size, we used analysis of variance, an anticipated statistical power of 0.90 and α-error probability of 0.05 with an effect size of 0.30. A minimum sample size of 382 was identified (G*Power 3.1.9.7, Heinrich-Heine-University, Düsseldorf, Germany). However, because we intended to compare groups in top 30% and bottom 30% of the ERI scale, we selected a sample size of 800, considering the probability of omission; thus, twice the 382 derived from the G*Power results. Five individuals were excluded from our analysis owing to insufficient or inconsistent responses. We used convenience sampling to solicit athletes and a self-reported questionnaire for the evaluation.
The frequency was confirmed by measuring the effort-reward ratio of the study participants from the collected data. We then established two groups, one with results in bottom 30% of the ERI scale and he second one with the results in top 30% of the ERI scale. There were 227 athletes in the low ERI group (men: 198, women: 29), with an average age of 20.23 ± 1.18 years. The frequency of weekly exercise in that group was 5.67 ± 0.71 days, the average daily training time was 4.25 ± 1.56 hours, and the weekly exercise time was 24.27 ± 10.05 hours. In the high ERI group, there were 230 athletes (men: 208, women: 22) with an average age of 20.10 ± 1.22 years. The frequency of weekly exercise in this group was 5.87 ± 0.69 days, the average daily training time was 4.10 ± 1.47 hours, and the weekly exercise time was 24.32 ± 9.66 hours. Only responses selected from the top and bottom 30% groups of the ERI were used for ANOVA, and those not within this range were not considered. Moreover, athletes practiced 23 different sports disciplines (professional leagues: basketball, soccer, volleyball, baseball; non-professional leagues: rugby, wrestling, badminton, boxing, ice skating, softball, swimming, skiing, Korean wrestling, archery, weightlifting, judo, cycling, rowing, gymnastics, taekwondo, tennis, and handball).
Instruments
The applied questionnaire consisted of 64 questions: five on general characteristics, 14 on effort, 14 on reward, 12 on grit, four on exercise continuous intention, and 15 on exhaustion. However, based on item, reliability, and construct validity analyses, we looked at all 14 effort items and all 4 exercise continuous intention items, but only 13 items related to reward, 9 to grit, and 9 items of exhaustion. The reliability and validity analyses are shown in Table 1.
Table 1
Content, validity, and reliability of the survey.
[i] A: training reinforcement efforts, B: interpersonal relationship efforts, C: nutrition management efforts, D: cognitive and psychological efforts, E: future stability, F: social support, G: positive growth, SRMR: standardized root mean square residual, CFI: comparative fit index, TLI: Turker-Lewis index, RMSEA: root mean square error of approximation assessed through confirmatory factor analysis H: Consistency of Interests, I: Perseverance of Effort J: Reduced sense of accomplishment, K: Physical/emotional exhaustion, L: Devaluation of sports practice. SRMR: standardized root mean square residual, CFI: comparative fit index, TLI: Turker-Lewis index, RMSEA: root mean square error of approximation (a) Items were reverse scored; (b) Items were excluded from the analysis assessed through confirmatory factor analysis
General Characteristics
We asked basic questions covering age, gender, the type of sport, frequency of weekly exercise, and daily training time of athletes. Athletes self-reported all the answers in the questionnaire. Among participants, 291 players were in professional leagues while 504 players were not. For the frequency of weekly training, 254 athletes trained two to five days, 443 athletes trained six days, and 98 athletes trained seven days; 304 athletes trained one to three hours, 322 athletes trained four to five hours, and 169 athletes trained for six hours or more. By multiplying the frequency of weekly training by the daily training time, we confirmed that the average training time was 24 hours per week.
Effort-Reward Imbalance
We used the Korean effort and reward scale developed by Park and Kim (2021) to measure the degree of the ERI in athletes. We measured effort and reward as perceived by athletes based on 14 questions on effort under four factors (training reinforcement, interpersonal relationship, nutrition management, and cognitive and psychological efforts) and 13 questions on reward under three factors (future stability, social support, and positive growth). Responses to these questions were scored on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). As a result of verifying the construction validity, we found that the model suitability of questions for effort (4 factors, 14 questions) and reward (3 factors, 13 questions) was appropriate (effort: χ2 = 208.44; df = 71; p < 0.001; Q = 2.936; standardized root mean square residual [SRMR] = 0.049; Turker-Lewis index [TLI] = 0.941; comparative fit index [CFI] = 0.954; root mean square error of approximation [RMSEA] = 0.065; reward: χ2 = 158.59; df = 59; p < 0.001; Q = 2.688; SRMR = 0.037; CFI = 0.978; TLI = 0.971; RMSEA = 0.061). Following the methods of Siegrist (2012) and Wege et al. (2017), we determined the individual degree of the ERI, and the effort-reward ratio, by dividing the total score for effort by the total score for reward. The suitability of this method was also suggested by Siegrist et al. (2009).
Grit
We used a scale that verified validity by translating the grit questionnaire developed by Duckworth et al. (2007), consisting of 12 questions and 2 factors: maintaining interest and continuing efforts. In the scale, questions under the “maintenance of interest” factor were analyzed through reverse scoring, where the higher the score, the higher the level of grit. Responses were scored on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). As a result of verifying the grit questions through confirmatory factor analysis, we used the two factors and nine questions (χ2 = 52.694; df = 26; p = 0.001; Q = 2.027; SRMR = 0.029; CFI = 0.984; TLI = 0.978; RMSEA = 0.047).
Continuous Exercise Intention
To evaluate athletes’ intentions to continue exercising, adapted from the continuous intention questionnaires used by Ajzen (2006) and Hoffman and Novak (1996), we followed the questions used by Park et al. (2021). Those comprised four questions on continuing to exercise, with a higher score indicating a higher intention to continue exercising. A five-point Likert scale (1 = not like me at all to 5 = very much like me) was used to measure the responses. By verifying the construct validity of the continuous intention question, model suitability was found to be appropriate (χ2 = 6.873; df = 2; p = 0.032; Q = 3.436; RMR = 0.017; CFI = 0.996; TLI = 0.987; RMSEA = 0.073).
Burnout
To confirm burnout in athletes in Korea, previously, Han and Kim (2011) translated the Athlete Burnout Questionnaire developed by Raedeke and Smith (2001) and used a scale with high suitability after validation. The scale is intended to assess athletes’ exhaustion and consists of three factors (physical and emotional exhaustion, a reduced sense of achievement, and devaluation of sports practice). Questions 1 and 14 (deleted in the analysis process) were scored as reverse questions and used for analysis under the “reduced sense of achievement” factor. Responses were scored on a six-point Likert scale (1 = very strongly disagree to 6 = very strongly agree). After verifying the composition validity of the burnout question, three factors and nine questions were used (χ2 = 87.853; df = 23; p < 0.001; Q = 3.820; SRMR = 0.047; CFI = 0.965; TLI = 0.946; RMSEA = 0.079).
Procedures
Athletes who expressed their willingness to participate in the study signed a consent form which indicated the purpose of the study and the contents of the survey. The form also stated that the survey could be stopped at any time. For those willing to participate, a small gift was provided. The collected surveys were checked and converted into data suitable for analyses, excluding unusable data and those who did not respond truthfully to the survey (e.g. excluding unusable data and the data that showed the same response to most surveys). The study was conducted in accordance with the guidelines of the Declaration of Helsinki and the protocol was approved by the Institutional Review Board of the Seoul National University of Science and Technology (IRB approval number: 2021-0005-01; approval date: 3 June 2021).
Statistical Analyses
The data were analyzed using Jamovi 1.6.15 and SPSS 23.0/AMOS 23.0 (IBM Corp., Armonk, NY, USA). First, suitability was confirmed through item reliability and exploratory and confirmatory factor analyses of the measurement tool. Second, to confirm differences according to the ERI, the lower and upper 30% groups were established. Responses not included in both groups were not used for the independent t-test. An independent sample t-test was conducted to confirm differences in psychological variables between the two groups. All statistical significance levels were set at p < 0.05.
Results
Difference by Variables
Differences in effort, reward, ERI, grit, exhaustion, and intention to continue exercising according to gender, professional league sports, weekly training frequency, and weekly training time are presented in Table 2. Significant difference in psychological variables according to gender was only reflected in the sub-variables under effort: training reinforcement (t = 13.379; p < 0.001) and overall effort (t = 6.208; p = 0.013). Variables such as “training reinforcement” showed higher scores for male than for female athletes, but in reality, female athletes had more weekly training time than male athletes (t = 7.086; p = 0.008). There were no gender differences in terms of the other variables.
Table 2
Differences in psychological variables according to characteristics.
[i] A: training reinforcement efforts, B: interpersonal relationship efforts, C: nutrition management efforts, D: cognitive and psychological efforts, E: future stability, F: social support, G: positive growth, ERI: effort-reward imbalance* p < 0.05, ** p < 0.01, *** p < 0.001, assessed through one-way analysis of variance
Table 2
(cont.) Differences in psychological variables according to characteristics.
There were significant differences between groups in various psychological variables between players in sports with professional leagues (basketball, soccer, volleyball, baseball) and those in sports without professional leagues (effort: t = 39.358, p < 0.001; reward: t = 12.560, p < 0.001; grit: t = 5.355, p = 0.001; burnout: t = 5.832, p = 0.016; and exercise continuous intention: t = 5.205, p = 0.023). However, there was no difference in weekly training time between athletes competing in professional leagues and their counterparts (rugby, wrestling, badminton, boxing, ice skating, softball, swimming, skiing, Korean wrestling, archery, weightlifting, judo, cycling, rowing, gymnastics, taekwondo, tennis, and handball) (t = 0.239, p = 0.625).
Regarding differences in psychological variables according to the frequency of weekly training, those with higher training frequency had significantly higher effort factors than those with lower training frequency (F = 7.346, p = 0.010).
In addition, those with higher training frequency felt a greater ERI than those with lower frequency in weekly training (F = 3.668, p = 0.026). In addition, grit and exercise continuity intention were statistically higher in the group with high training frequency compared to the one with low training frequency (grit: F = 3.209, p = 0.041; exercise continuity intention: F = 4.430, p = 0.012). Depending on the daily training time, those with more training per day showed higher effort, reward factors, and intention to continue exercising than those with less training time per day (effort: F = 5.309, p = 0.005; reward: F = 11.229, p < 0.001; continuous exercise intention: F = 3.163, p = 0.043).
Differences in Psychological Variables according to the ERI
In our study, we investigated how psychological variables were perceived by the high and low ERI groups (Table 3). As a result, it was found that the group with a high ERI reached significantly higher values in effort factors than the group with a low ERI (t = 36.526, p < 0.001) and the group with a low ERI received more rewards than the group with a high ERI (t = 364.307, p < 0.001). Moreover, among the sub-factors of grit, factors of continuing effort (t = 6.865, p = 0.009) and intention to continue exercising (t = 24.851, p < 0.001) presented higher values in the lower ERI group than in the higher ERI group. However, exhaustion was higher in the higher ERI group (t = 36.864, p < 0.001).
Table 3
Differences in psychological variables according to the effort-reward imbalance level.
Table 3
(cont.) Differences in psychological variables according to the effort-reward imbalance level.
Discussion
The relatively recently organized ERI model for job stress research (Siegrist, 2001) explains that the principle of reciprocity is important in the role that humans play in social life in the work environment. Among various social lives, reciprocity in the job field is understood as the effort that individuals expend on job activities with the expectation of an adequate reward. If an imbalance between individual efforts and reward recognition persists, negative emotional reactions occur, eventually affecting health (Bosma et al., 1998). This captures the usefulness of the ERI model in various job environments. However, its usefulness for athletes has not yet been confirmed. Therefore, our study evaluated whether the ERI model affects the mental state of athletes and ultimately, their intention to continue to exercise. Based on our analysis, we found that male athletes stated that they expended more effort in strengthening their training than female athletes. Kim et al. (2020) indicate that male athletes are better at self-management related to training than female athletes. However, Heo’s (2003) study shows no significant gender differences. In our study, the size of the classified groups varied by gender, which is worth considering when discussing gender-based outcomes. However, one interesting finding was that even though male athletes mentioned that they put extensive effort into training, female athletes actually spent more time training than male athletes did during the week.
According to the results of professional league sports players and non-professional players, there was no difference in exercise time between the two, although professional league athletes recorded higher effort factors. Female athletes who trained for more hours did not rate their efforts as high, neither did athletes in sports who trained for a similar number of hours and were not part of professional leagues. These results highlight the need for a follow-up study on why participants evaluated their efforts so low despite considerable time spent on practice. In Korea, sports without professional leagues are considered unpopular with the public or unfamiliar to the public. Moreover, in general, the annual salary and compensation systems are poor compared with those of professional leagues. In fact, annual salaries vary greatly, not only in Korea but also globally, depending on the sports and the league. For example, the average annual salary of an NBA player is more than $8 million, while in the East Coast Hockey League, a rookie earns less than $24,000 (Gough, 2023). Hong and Fraser (2021) explain that athletes are also stressed by noncompetitive factors, such as job insecurity, along with stress linked to competition. Athletes who play in environments that emphasize sportsmanship, such as amateurism and fair play, may be unable to achieve financial reward. In fact, rewards, such as salary and future stability, are important for professional athletes. In addition, future stability of the sport plays a significant role for college athletes who have not yet entered the professional status.
Our results showed that players in professional leagues had higher grit as well as a greater intention of continuing to exercise and lower burnout than those who were not in these leagues. This result is partially supported by Hong et al.’s study (2023). Our results also showed that mental variables may vary greatly depending on the sport. Although a slightly different issue, recently, there have been opinions that question the inequality of the compensation system according to gender in sports (Cavil and Jenkins, 2023). Moreover, we should also consider how reward systems differ depending on the popularity of the sport. In our study, those with high weekly training frequency or daily training time recognized higher effort, reward, grit, and exercise continuity intention than those with less training frequency; and the group with a high training frequency also recognized a large ERI. There are only few similar previous studies, however, Granz et al. (2019) reported that higher training was linked to burnout and training intention. Training intensity has also been linked to a correlation with emotional/physical exhaustion (Vetter and Symonds, 2010). Nevertheless, in our study, burnout did not show significant differences between the groups.
In the analysis of differences according to the degree of the ERI, the group with a low ERI recounted a higher level of effort and reward than the group with a high ERI. In addition, we found that athletes with a low ERI responded highly to the grit sub-factors, “perseverance of effort”, and the intention to continue exercising. In contrast, the group with a high ERI showed higher burnout. In short, athletes who were highly aware of the ERI scored low on positive mental factors related to exercise behavior and high on negative mental factors. Although not the same, it was reported that high ERI recognition on the job results in low scores on variables such as job satisfaction and well-being risk (Cho et al., 2021), suggesting that low ERI recognition is related to positive psychology in the job environment. However, a study on Chinese university students reports that a high ERI is closely related to stress (Kequn et al., 2023), while studies on female nurses (Rastjoo and Zandvanian, 2021) and information technology experts (Raju et al., 2022) report high correlations. In our study, the high ERI group had a greater level of burnout than the low ERI group, which is consistent with previous studies. The fact that the athletes’ ERI relates to positive and negative variables which affect their sports situation confirms the value of the application of the ERI model in the sports field, similarly to that in other job areas. However, unlike other jobs, the sports sector requires considerable time and effort to reach a top position and differs from other jobs as the participant may experience a long-term ERI. Therefore, follow-up studies are warranted to evaluate what variables endure during the ERI over the long term.
Conclusions
Our study measured college athletes’ effort, reward, ERI, grit, burnout, and intention to continue exercising and identified differences in mental variables in groups with a high and a low ERI. We contribute to literature in two meaningful aspects. First, we confirmed that the ERI model can be applied to athletes, similarly to its application in various occupational areas. Therefore, continuous research should be conducted to identify ways to lower the ERI for continuous exercise performance and the mental health of athletes. Second, we confirm that the ERI model is applicable to athletes, but further longitudinal research is required to examine the extent of the ERI for athletes. Moreover, we found a relationship between the effort perceived by athletes and actual exercise time. Female athletes indicated greater actual exercise time, while male athletes indicated less exercise time but evaluated their efforts higher than their female counterparts. Of course, the quality of effort cannot be evaluated only by time factors, but this is an interesting aspect and should be explored in future research. Although the results of this study are interesting, the number of male and female athletes was significantly different; thus, the results should be interpreted with caution. In addition, we believe the results would be more interesting if we evaluated the stress presented in previous studies of the ERI model and the perceived performance presented in the present study in athletes. Therefore, follow-up studies that consider these aspects are necessary.
