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. 2023 Nov 3;36(6):732–740. doi: 10.1080/08995605.2023.2265286

Peer effects on organizational commitment: Evidence from military cadets

Seungju Hyun a,✉, Xyle Ku a,b, Joonyoung Hu c, Byeonghyeon Kim d, Hoyoun Ki a, Jaewon Ko a
PMCID: PMC11622643  PMID: 37921631

ABSTRACT

The commitment of soldiers to the military is essential because it could lead to increased morale, motivation and retention. Despite the accumulation of knowledge about predictors of organizational commitment (OC), efforts to investigate environmental factors influencing OC are in their infancy. We note that individuals shape their attitudes toward the environment based on information obtained from their surroundings, and we investigate peer effects on OC using data from a natural experiment of randomly-assigned military academy roommates. A total of 400 cadets (Sex ratio: 93.5% male, Age: 21.13 ± 1.43 years) from 136 living quarters participated in this quantitative study. In both self- and roommate-reports, we found that the average affective commitment (AC), continuance commitment (CC), and normative commitment (NC) of roommates in a living quarter can still predict AC, CC, and NC of the remaining individual in that same living quarter, respectively, even after controlling for the personal predictors of that remaining individual. Additionally, in self-report, we discovered that when there is a high heterogeneity in AC among roommates within a living quarter, the AC of the remaining individual in that living quarter tends to be higher, even after controlling for the personal predictors of that remaining individual. These findings provide initial evidence that attempting to assign soldiers with low OC to the same living quarters as those with high OC may be worthwhile.

KEYWORDS: Organizational commitment, peer effect, soldier, cadet

What is the public significance of this article?—Military organizations desire soldiers who have a strong commitment to military service and to their units. Considering that roommates are an important aspect of the social network within the military, we investigated the peer effects of OC. As a result, we found that when roommates in a living quarter have high levels of OC, the remaining individual in that living quarter also tend to have high levels of OC. Similarly, when roommates in a living quarter have differing levels of AC, the remaining individual in that living quarter exhibit higher levels of AC. These peer effects of OC among roommates provide valuable evidence to assist in the assignment of soldiers to living quarters.

Introduction

Nowadays, since Allen (2003) and Gade (2003) made notable efforts to bring military organizational commitment (OC) research into the scientific mainstream, research on OC in the military has continued to advance based on the comprehensively established model of Meyer and Allen (1997). According to this model, the commitment an individual feels toward the military organization can be considered in terms of three psychological ties: emotional attachment to the military (affective commitment; AC), perceived costs associated with leaving the military (continuance commitment; CC), and feelings of moral obligation to remain in the military (normative commitment; NC).

As in the civilian sector, OC is predictive of key outcomes among soldiers, including job performance, perceived readiness, morale, and reenlistment intentions (Gade et al., 2003; Karrasch, 2003). These effects have a greater impact in the military, where they may make the difference between life and death more than in most civilian occupations (Booth-Kewley et al., 2017). In addition, soldiers must risk their lives for their country and endure unpredictable work tasks, longer absences from home, frequent moves, and irregular working hours (Alvinius et al., 2017). Maintaining manpower in such a challenging organization is an important human resource issue (Allen, 2003), and therefore, detailed studies on the antecedent variables that influence military members’ OC are necessary.

Theoretically and empirically, OC is considered to result from two broad types of factors, namely, personal and organizational factors (Demir et al., 2009). Among them, investigations on personal factors have been somewhat conducted. For example, individuals’ job tenure (Santos & Norland, 1994), stress (Cicei, 2012), meaning of life (Markow & Klenke, 2005), life satisfaction (Yilmaz, 2008), and depression (Booth-Kewley et al., 2017) have been found to affect their OC. On the other hand, there has been relatively little research on environmental factors, particularly in the context of the military, apart from the work of Langkamer and Ervin (2008), who considered psychological climate as a predictor. Based on several findings in military samples (Huntington-Klein & Rose, 2018; Lyle, 2007), we can infer that the OC of roommates can be an environmental factor that affects the OC of individual soldiers.

Roommates play a significant role within peer networks in a group, and their influence can be substantial within the unit (Li & Guo, 2016). This is because individuals develop attitudes toward the organization based on the information they acquire within their immediate environment, and these attitudes are often resistant to change (Salancik & Pfeffer, 1978). In particular, the mere observation of someone else’s behavior can increase the likelihood of engaging in the same behavior (Cutler & Glaeser, 2008). Considering this, the attitudes of roommates toward the organization can significantly influence the level of organizational commitment of the remaining individual. Interestingly, previous studies have found peer effects among college students’ roommates in terms of binge drinking, aggressive behavior, smoking, and risky sexual behavior (Eisenberg et al., 2014; Li & Guo, 2016). Military researchers have also discovered peer effects in cadets’ academic achievement (Carrell et al., 2009), decisions regarding staying in the military academy (Huntington-Klein & Rose, 2018; Lyle, 2007), and financial decision-making (Lieber & Skimmyhorn, 2018). However, their investigations have focused on the effects of entire company members or a number of colleagues within the company, possibly overlooking subtle interactions within smaller subgroups (Carrell et al., 2011). Therefore, in this study, we aim to measure the peer effects on OC among roommates who spend a significant amount of time together, leading to enforced interactions (Stinebrickner & Stinebrickner, 2006).

Notably, we leverage the distinctive setting of the military academy to precisely estimate the peer effects of OC within the military context. The assignment of cadets to rooms in the military academy is strictly randomized, presenting a rare opportunity to mitigate potential biases such as selection bias (Carrell et al., 2009). Furthermore, cadets who share the same room engage in various activities together, including dining, studying, training, and sleeping, with limited opportunities for interaction with external family or friends (Lyle, 2007). These factors contribute to a significant level of social interaction among roommates (Li & Guo, 2016). Taking all of this into account, we hypothesize that roommates’ OC will predict the OC of the other individual even after controlling for personal predictors such as grade, perceived stress, meaning of life, life satisfaction, and depression. Specifically, we calculate peer effects by examining the mean of each OC subscale among roommates (MAC, MCC, MNC), or the standard deviation of each OC subscale between roommates (SDAC, SDCC, and SDNC), and determine their significance (Booij et al., 2017). Based on previous studies estimating the peer effect (e.g., Jaccard et al., 2005), we assume that the higher the mean of roommates’ OC, the higher the OC of the remaining individual. However, we conducted an exploratory analysis of the relationship between the standard deviation of OC between roommates and the OC of the other individual. We considered that whether the peer effect is caused by homogeneity or heterogeneity depends on the variables being targeted (e.g., Wilkinson & Fung, 2002). Furthermore, the individual OC subscales, which are dependent variables, measure both self-report (ACself, CCself, and NCself) and roommate-report (ACroommate, CCroommate, and NCroommate) to ensure the reliability of the results.

Method

Participants

We determined the sample size using G*Power 3.1.9.7 software, which recommended a minimum sample size of n = 160 for detecting a medium effect size (f 2 = 0.15) using hierarchical regression, with a power of 0.95 and eight predictors (Faul et al., 2009). Based on the results of the power analysis, we recruited 400 cadets (93.5% male, mean age = 21.13 years; SD = 1.43 years) from the Korea Military Academy (KMA). This group consisted of 116 freshmen, 100 sophomores, 112 juniors, and 72 seniors. Out of the 136 living quarters, 6 rooms accommodated 2 cadets, 38 rooms accommodated 3 cadets, and 92 rooms accommodated 4 cadets. The members of each living quarter are all of the same sex and class.

Procedure

The research protocol received approval from the Public Institutional Review Board of the Korean Ministry of Health and Welfare (Protocol number: P01-202208-01-002). Considering the need for adequate interaction among room members, we posted the recruitment notice at a point in time when roommates had been living together for approximately 5 months. All room members checked the recruitment notice posted on the campus bulletin board and expressed their intention to participate by joining our created anonymous online chat room. After confirming that all room members had entered the chat room, we presented a comprehensive overview of the research, emphasized the voluntary nature of participation, ensured confidentiality, and explained the data collection process. Once we confirmed that all occupants of the room expressed their willingness to participate, we provided them with the online survey link through the chat room. All room members were strictly randomly assigned at the beginning of the semester, so participants were not able to choose or exchange their roommates, whether during the room assignment process or the survey participation process.

Measures

Before responding to OC items, participants were requested to indicate their position by listing the names of all room members in alphabetical order, including themselves. Following this, participants were given instructions to assign the label “A” to the first member, “B” to the second member, “C” to the third member, “D” to the fourth member, and so on, while listing the names of room members in alphabetical order, including themselves. Subsequently, participants were asked to sequentially evaluate OC of members A, B, C, D, and so on. The remaining measures were assessed based on their applicability to each participant individually, and no evaluations were carried out for roommates. The questionnaire was administered in the Korean language and encompassed inquiries about demographic characteristics, housing information, as well as the following scales:

Organizational commitment

We utilized eighteen items from measures developed by Meyer et al. (1993). These measures consist of three subscales associated with AC (e.g., “The KMA has a great deal of personal meaning for me”), CC (e.g., “I feel that I have too few options to consider leaving the KMA”), and NC (e.g., “I would feel guilty if I left the KMA right now”). Participants were requested to rate themselves and their respective roommates on a scale ranging from 1 (completely disagree) to 5 (completely agree). Based on these ratings, we were able to generate two types of independent variables. The first type is the average of AC, CC, and NC among roommates (MAC, MCC, and MNC). The second type is the heterogeneity of AC, CC, and NC among roommates, represented by the standard deviation (SDAC, SDCC, and SDNC). For example, MAC represents the average AC among roommates, while SDAC represents the standard deviation of AC among roommates. A higher value of MAC indicates a higher average AC among roommates, whereas a higher SDAC signifies greater heterogeneity in AC among roommates. In addition, Additionally, we were able to generate two types of dependent variables. The first type is the self-evaluation of one’s own AC, CC, and NC (ACself, CCself, and NCself), and the second type is the evaluation by roommates of the remaining individual’s AC, CC, and NC (ACroommate, CCroommate, and NCroommate). The Cronbach’s alphas for AC, CC, and NC were .93, .89, and .93, respectively.

Perceived stress

We assessed perceived stress using the 10-item Perceived Stress Scale (Cohen et al., 1983; α = .81). Respondents rated the frequency of their stress in the past month on a 5-point scale ranging from 1 (never) to 5 (very often). An example item included: “How often have you been upset because of something that happened unexpectedly?”

Meaning in life

Meaning in life was assessed using the Meaning in Life Questionnaire (Steger et al., 2006; α = .81). This scale consists of ten items that are rated on a 7-point scale ranging from 1 (absolutely untrue) to 7 (absolutely true). Example items include “I understand my life’s meaning” and “I am always looking to find my life’s purpose.”

Life satisfaction

We measured life satisfaction using the Satisfaction with Life Scale (Diener et al., 1985; α = .86). Five items were rated on a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). An example item is “In most ways my life is close to the ideal.”

Depression

We measured depression using the Emotional Stress Inventory (Chon et al., 2020; α = .90). This scale consists of 21 items and assesses the constructs of depression, anxiety, and anger symptoms specifically in the Korean population. For our study, we utilized only seven items that are directly related to depression (e.g., “I don’t find anything interesting”), and participants rated their responses on a 6-point scale ranging from 1 (completely disagree) to 6 (completely agree).

Data analysis

All statistical analyses were conducted using SPSS version 27. Initially, bivariate correlations were applied to measure the relationships among five personal variables (grade, perceived stress, meaning in life, life satisfaction, and depression), six peer effect variables (MAC, MCC, MNC, SDAC, SDCC, and SDNC), and six dependent variables (ACself, CCself, NCself, ACroommate, CCroommate, and NCroommate). Additionally, we were interested in determining whether there was a significant relationship between environmental predictors and criterion variables while holding the personal predictors constant. We also aimed to assess the unique and incremental predictiveness of environmental predictors. To achieve this, we used hierarchical regression analysis. In step one, personal variables were entered, followed by the entry of peer effect variables in step two. All assumptions of hierarchical regression were met.

Results

The bivariate correlations are presented in Table 1. Among the personal variables, grade, perceived stress, meaning in life, and life satisfaction were all significantly associated with ACself, ACroommate, NCself, and NCroommate. However, depression was only significantly associated with ACself. Furthermore, only meaning in life was significantly associated with CCself, and only life satisfaction was significantly associated with CCroommate. Regarding the peer effect variables, MAC, MCC, and MNC were all significantly correlated with their corresponding dependent variables. Additionally, SDAC was only significantly correlated with ACroommate. However, both SDCC and SDNC were not significantly correlated with their corresponding dependent variables.

Table 1.

Descriptive statistics and correlations among research variables.

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1. MAC –                              
2. MCC .30*** –                            
3. MNC .66*** .36*** –                          
4. SDAC –.23*** –.12* –.08 –                        
5. SDCC –.06 –.08 .06 .32*** –                      
6. SDNC –.09 –.07 –.03 .54*** .21*** –                    
7. ACself .33*** .09 .23*** .07 .01 .02 –                  
8. CCself .07 .21*** .06 –.05 –.03 –.07 .29*** –                
9. NCself .24*** .10* .34*** .09 .06 .04 .63*** .38*** –              
10. ACroommate .60*** .09 .39*** .18*** –.03 –.06 .38*** .12* .27*** –            
11. CCroommate .22*** .57*** .21*** .13* –.08 –.06 .11* .16*** .08 .25*** –          
12. NCroommate .51*** .19*** .65*** .13* .02 –.09 .33*** .10* .30*** .62*** .33*** –        
13. PS –.09 .02 –.09 .01 –.05 –.01 –.34*** –.02 –.20*** –.14** .01 –.13* –      
14. MIL .15** .08 .13*** –.04 –.02 –.09 .19*** .11* .17** .10* .04 .10* –.08 –    
15. LS .12* .07 .11* –.02 .04 –.03 .27*** .09 .20*** .18*** .10* .18*** –.21*** .47*** –  
16. DEP –.06 .01 –.06 –.02 –.03 –.04 –.11* .03 –.09 –.08 .01 –.08 .23*** –.40*** –.53*** –
M 3.61 3.70 3.10 0.56 0.49 0.57 3.61 3.72 3.11 3.58 3.60 3.08 2.87 4.99 4.20 3.23
SD 0.62 0.48 0.63 0.38 0.37 0.41 0.81 0.68 0.83 0.63 0.49 0.62 0.55 0.83 1.27 1.13
Range 1–5 1–5 1–5 0–1.89 0–1.89 0–1.89 1–5 1–5 1–5 1–5 1–5 1–5 1–5 1–7 1–7 1–6
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PS: perceived stress; MIL: meaning in life; LS: life satisfaction; DEP: depression. *p < .05. **p < .01. ***p < .001.

Table 2 presents the results of a hierarchical regression analysis examining the prediction of ACself, CCself, and NCself by peer effect variables. The table indicates that none of the personal variables included in step 1 consistently influenced ACself, CCself, and NCself. However, MAC, MCC, and MNC remained significant predictors of ACself, CCself, and NCself, respectively, even after controlling for the effects of personal variables. Regarding the standard deviations of roommates’ OC, only SDAC was found to explain a significant amount of additional variance in ACself, even after accounting for the variance attributed to personal variables.

Table 2.

Results of hierarchical regression analyses examining self-reported OC.

Variable R2 ΔR2 F B SE β p
ACself
Step 1 .214 .214 20.45        
 Grade       −.155 .034 −.209 <.001
 PS       −.472 .068 −.328 <.001
 MIL       .069 .051 .073 .174
 LS       .121 .036 .196 <.001
 DEP       .051 .039 .072 .197
Step 2 .262 .048 18.94        
 MAC       .301 .066 .222 <.001
 SDAC       .276 .095 .133 .004
CCself
Step 1 .024 .024 1.825        
 Grade       .012 .033 .019 .717
 PS       −.017 .065 −.014 .794
 MIL       .098 .049 .119 .046
 LS       .043 .034 .081 .211
 DEP       .084 .037 .140 .025
Step 2 .066 .043 3.788        
 MAC       .299 .073 .207 <.001
 SDAC       −.015 .093 −.008 .875
NCself
Step 1 .103 .103 8.612        
 Grade       −.133 .038 −.172 .001
 PS       −.271 .076 −.181 <.001
 MIL       .073 .057 .073 .198
 LS       .092 .040 .144 .02
 DEP       .030 .044 .042 .487
Step 2 .174 .071 11.219        
 MAC       .370 .066 .273 <.001
 SDAC       .111 .096 .055 .249
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PS: perceived stress; MIL: meaning in life; LS: life satisfaction; DEP: depression.

Table 3 displays the results of a hierarchical regression analysis examining the prediction of ACroommate, CCroommate, and NCroommate by peer effect variables. In step 1, only the effects of life satisfaction on ACroommate, CCroommate, and NCroommate were found to be significant, albeit small. In step 2, the inclusion of MAC, MCC, and MNC as predictors resulted in a significant incremental contribution to the prediction of ACroommate, CCroommate, and NCroommate, respectively, after controlling for the effects of personal predictors. However, the inclusion of SDAC, SDCC, and SDNC as predictors did not yield significant predictions for the corresponding dependent variables, even after controlling for personal variables.

Table 3.

Results of hierarchical regression analyses examining roommate-reported OC.

Variable R2 ΔR2 F B SE β p
ACroommate
Step 1 .126 .126 10.843        
 Grade       −.165 .027 −.300 <.001
 PS       −.140 .053 −.131 .009
 MIL       −.001 .040 −.001 .984
 LS       .056 .028 .122 .046
 DEP       .012 .031 .022 .703
Step 2 .340 .214 27.485        
 MAC       .471 .046 .468 <.001
 SDAC       −.108 .067 −.070 .105
CCroommate
Step 1 .015 .015 1.178        
 Grade       .014 .023 .030 .560
 PS       .009 .047 .010 .853
 MIL       .008 .035 .013 .825
 LS       .053 .024 .141 .030
 DEP       .034 .027 .079 .205
Step 2 .343 .327 27.766        
 MAC       .589 .044 .571 <.001
 SDAC       −.047 .056 −.036 .395
NCroommate
Step 1 .060 .060 4.793        
 Grade       −.078 .028 −.140 .006
 PS       −.130 .056 −.121 .020
 MIL       .009 .042 .012 .838
 LS       .063 .029 .137 .031
 DEP       .007 .032 .014 .820
Step 2 .413 .353 37.460        
 MAC       .596 .040 .611 <.001
 SDAC       −.107 .058 −.073 .068
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PS: perceived stress; MIL: meaning in life; LS: life satisfaction; DEP: depression.

Taken together, these results support our hypothesis that roommates’ OC is a significant predictor for the other individual’s OC, even after controlling for their personal predictors such as grade, perceived stress, meaning of life, life satisfaction, and depression.

Discussion

OC is of vital concern to military organizations because it enables soldiers to perform their work more effectively, enhances their likelihood of remaining in the military, and fosters their loyalty to the organization (Gade et al., 2003). Furthermore, considering the benefits of retaining experienced soldiers and the substantial expenses associated with recruiting and training new personnel, the military has a vested interest in cultivating OC as a means of sustaining its human resources (Allen, 2003; Booth-Kewley et al., 2017). The need for factors that predict OC has become more critical, but so far, research efforts have mainly focused on linking only personal characteristics of soldiers to OC. However, given that factors influencing OC are divided into personal and environmental variables (Demir et al., 2009), and social groups affect individual behavior (Lyle, 2007), we verified that a person’s OC can be predicted based on their roommates’ OC.

Overall, the average of roommates’ OC predicted the other individual’s OC in both self- and roommate-report. According to the social learning theory (Akers, 2009), roommates’ behaviors may increase the other individual’s behavior if positive definitions attached to the behavior are learned, whereas roommates’ behaviors may reduce the other individual’s behavior if negative definitions are learned. Considering that the military encourages organizational orientation (Soeters et al., 2006), cadets living with a majority of roommates with high OC are likely to perceive OC in a positive or neutral manner, and they are more likely to engage in dedicated actions with their peers. In contrast, cadets living with a majority of roommates with low OC are more likely to exhibit the opposite pattern (Li & Guo, 2016). Our findings provide evidence that supports social learning theory while extending it to an organizational context.

In addition, these results provide the initial evidence of the peer effect among roommates, indicating deep interactions that go beyond the organizational-level analyses attempted in previous military studies (e.g., Huntington-Klein & Rose, 2018; Langkamer & Ervin, 2008; Lieber & Skimmyhorn, 2018). From a perspective of manpower maintenance and management, these findings suggest that it can be beneficial to consider assigning a soldier with low OC to the same room as colleagues who exhibit high OC. In particular, it is noteworthy that peer effects on OC remained significant even after controlling for all personal variables. To foster the personal variables recommended in prior studies for soldiers, the military should design and implement staff development programs (Salami, 2008). However, our findings demonstrate that significant effects are likely to occur even if the military focuses on how soldiers are assigned to rooms without implementing development programs.

On the other hand, the predictive power of the standard deviation of OC between roommates was only observed in AC. The greater the heterogeneity in AC between roommates, the higher the AC reported by the other individual. In rooms with high AC heterogeneity, there are roommates with both high AC and low AC coexisting. Since the military promotes organizational orientation (Soeters et al., 2006), it is probable that the remaining individuals will observe that roommates with high AC receive positive evaluations, whereas roommates with low AC receive negative evaluations within the military context (Guerra & Sepúlveda, 2014). Consequently, it can be inferred that the remaining individuals are more likely to adopt thoughts and behaviors associated with high AC, as suggested by social learning theory (Akers, 2009). Nevertheless, since no significant effect was observed when examining the AC of the remaining individual as reported by the roommate, further measurements are necessary in the future to ascertain whether these results were influenced by sample characteristics.

Finally, we noted the peer effect of roommates on NC. Numerous commitment researchers focus their attention on AC, assuming it to be the optimal form of commitment (Gade, 2003). However, in contrast to the civilian sector, NC could be particularly significant in the military, where dedication and loyalty are highly esteemed (Karrasch, 2003), and it holds potential utility in predicting and explaining military performance and readiness (Gade, 2003). For example, if a soldier’s AC is not accompanied by strong NC, he or she may rapidly lose AC when faced with the possibility of deployment to an undesirable and potentially dangerous location (Karrasch, 2003). Interestingly, Meyer et al. (2013) also confirmed that the AC/NC-dominant group exhibited higher intention to stay and demonstrated greater organizational citizenship behavior than the AC-dominant group, utilizing latent profile analysis. This indicates that in the military, NC complements AC, leading to a synergy effect (Johnson et al., 2009). Promoting NC alongside AC can thus yield more benefits for the organization than focusing on AC alone. While further research is required to establish causality, our findings offer an early quantitative insight into the effective approach to increasing NC, which holds particular significance for soldiers.

The random assignment of roommates in the military academy is advantageous as it helps avoid selection bias resulting from peer group preferences. However, there are certain limitations that should be acknowledged. First, caution must be exercised when generalizing the findings to the broader military organization, as the study focused specifically on cadets. Cadets may possess different characteristics, such as family background and personality, that could vary from the general military population to some extent. Moreover, the culture within a military academy might differ from that experienced by other military personnel. Second, the available data has limited information about the nature of interactions between roommates, which could potentially influence the outcomes of the study. Third, it is important to recognize that the cross-sectional design employed in this study does not allow for the determination of a causal relationship between roommates’ OC and the OC of the other individuals. Fourth, since we recruited participants anonymously, we were unable to determine the specific squads or platoons to which they belonged. However, in future research, it would be intriguing to collect this data and then conduct multilevel modeling to explore the extent to which it contributes to explaining the variance in outcome variables. For instance, Thomas et al. (2005) examined that the relationship between soldiers’ interpersonal conflict and OC could be moderated by the support from supervisors at two organizational levels. Furthermore, Heffner and Gade (2017) attempted to measure OC for different dimensions within the military and conducted path analysis. Given these considerations, we can proceed with our future research by having cadets’ OC assessed by their discipline officers and conducting multilevel modeling. Alternatively, we can assess cadets’ OC separately in small unit and large unit, followed by conducting multilevel modeling.

Despite these limitations, the study makes a valuable contribution to our comprehension of how peers’ attitudes toward the organization can influence one another. By shedding light on the peer effect in the military setting, this research enhances our understanding of the dynamics of organizational commitment within military contexts.

Funding Statement

This study was supported by the ROK Army Soldiers’ Value and Culture Research Center in 2023. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contribution

Seungju Hyun served as lead for study conceptualization, study design, funding acquisition, formal analysis, and writing-review and editing. Xyle Ku, Joonyoung Hu and Byeonghyeon Kim served in a supporting role for recruitment of participants and formal analysis. Hoyoun Ki and Jaewon Ko served in a supporting role for formal analysis and writing.

Data availability statement

The data that support the findings of this study are available from the corresponding author, Seungju Hyun, upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Seungju Hyun, upon reasonable request.

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