Although attention has been given to the importance of positivity in the workplace, it has only recently been proposed as a new way in which to focus on organisational behaviour. The psychological resources which meet the criteria for positive organisational behaviour best are hope, selfefficacy, optimism and resilience.
The purpose of this study was to investigate the construct validity of the Psychological Capital Questionnaire (PCQ), with specific reference to its psychometric properties.
The sample included a total of 1749 respondents, 60 each from 30 organisations in South Africa.
A multifactorial model was statistically explored and confirmed (with exploratory factor analysis and confirmatory factor analysis, respectively).
The results support the original conceptualisation and empiricallyconfirmed factorial composition of Psychological Capital (PsyCap) by four elements, namely
Based on the results obtained, it seems that the PCQ is a suitable (valid and reliable) instrument for measuring PsyCap. This study could thus serve as a reference for the accurate measurement of PsyCap.
Human resources play a crucial role in the success of organisations and help them to achieve a sustainable competitive advantage. A number of studies have been conducted seeking to find effective ways to attract and manage talented employees (or human capital) and the part played by compensation, job design, worklife balance and growth opportunities, among others, have been examined (Barnett & Hall
The purpose of this study is to build on work previously done by Luthans et al. (
This study is intended to contribute by establishing a valid measure of PsyCap. Such a measure has been set by Luthans (
The objectives of this study were, first, to determine the construct (factorial) validity of the original factorial composition of the PsyCap (Luthans et al.
Although attention has been given to the importance of positivity in the workplace, it has only recently been proposed as a new way in which to focus on organisational behaviour (Cameron, Dutton & Quinn
The psychological resources which meet the criteria for positive organisational behaviour best are hope, selfefficacy, optimism and resilience (Luthans
Authors such as Luthans and Youssef (
The individual PsyCap components.
Luthans et al. (
‘If I should find myself in a jam at work, I could think of many ways to get out of it’ [hope]; ‘I usually take stressful things at work in stride’ [resilience]; and ‘When things are uncertain for me at work I usually expect the best’ [optimism]. (p 1)
The PCQ follows a sixpoint Likert scale ranging from 1 to 6, (1 = strongly disagree and 6 = strongly agree). Respondents have to provide responses based on how they think about themselves right now. Dawkins et al. (
Du Plessis and Barkhuizen (
An important scientific concept to evaluate the validity of a measure is construct validity. Construct validity is the extent to which a test measures the concept or construct that it is intended to measure. Construct validity is usually tested by measuring the correlation in assessments obtained from several scales purported to measure the same construct. There is no cutoff that defines construct validity. It is important to recognise that two measures may share more than construct similarity. Specifically, similarities in the way that constructs are measured may account for some covariation in scores, independent of construct similarity (DeVellis
Benson (
The first objective of this study was to examine the structural component, construct validity, that involves the inspection of the internal relationships among items or subscales representing a particular measure, using such statistical analyses as correlations, exploratory and confirmatory factor analyses, and reliability analyses. The external component entails establishing a nomological net, or examining the relationships between the construct of interest and related constructs.
This external investigation of construct validity entails both convergent and discriminant validity. Researchers emphasise that this third step is particularly critical in establishing necessary validity evidence for a scale (Benson
This study employed a typical empirical paradigm using a crosssectional design and quantitative analysis. Surveys were used as a data generation technique. Leedy and Ormrod (
The population (N) of the study is the employees of 30 organisations, with the sample being 60 employees per organisation selected randomly by the participating coresearchers.
The characteristics of the participants in terms of the three relevant demographical variables, namely sector, race and gender, are reported in
Sample characteristics – Frequencies Of demographical variables, race, gender and the sector in which employed.
Variable  Category  %  

Race  African  1067  61.0 
Mixed race  170  9.7  
Indian  134  7.7  
White  363  20.8  
Gender  Female  816  46.7 
Male  924  52.8  
Sector  Private  984  56.3 
Public  765  43.7 
The total sample consisted of 1749 participants. In terms of the racial distribution, the majority of the participants were African (61%), followed by white (20.8%), mixed race (9.7%) and Indian (7.7%). The representation of the gender groups was slightly higher for men at 52.8% compared to 46.7% for women. The racial and gender distribution of the sample seems to be relatively representative of the South African workforce in general, taking into consideration that the distribution of the workforce as indicated in Statistics South Africa (
The characteristics of the participants in terms of the mean age as well as mean tenure, both expressed in years, are reported in
Age and tenure statistics of the sample (
Category  

Age  38.44  9.53  1622 
Tenure  8.83  7.67  1674 
M, Mean; SD, Standard deviation.
The mean age of the respondents was 38.44 years (
The statistical analysis was conducted with the use of Statistical Package for the Social Sciences (SPSS), version 23. The statistical analysis was performed by using SPSS 23, supported by SPSS Amos (Analysis of Moment Structures).
The dataset was first cleaned up by means of case screening, followed by variable screening in order to explain why there was variation in the data. It was deemed necessary to follow this process to ensure that there were no missing values in the dataset and also to get a feel for the dataset. The dataset was further inspected for unengaged responses by running a standard deviation on inspected cases with
The first step of the factor analysis was to evaluate the appropriateness of the sample size. The item to respondent ratio is ±1:73, which is acceptable according to Meyers, Gamst and Guarino (
To aid in the interpretation of the initial results, oblique rotation and specifically the Promax rotation was used, as it is assumed (based on the relevant literature) that the factors are correlated (Tabachnick & Fiddell
To operationalise this construct definition, a higher order, multidimensional model of the PsyCap construct was conducted by means of a confirmatory factor analysis (CFA). CFA is generally intended to examine whether a secondorder ethical risk factor exists and whether it explains the relationships among the five lowerorder factors (as identified by the exploratory factor analysis) with Analysis of Moment Structures maximum likelihood procedure (Byrne
The first model was a onefactor solution (unidimensional) in which all the items identified through the exploratory factor analysis were indicative of one larger PsyCap factor. The second was a firstorder factor model in which items were allowed to load onto their respective factors. The third was a secondorder factor model in which items were loaded onto their respective factors and the factors loaded on a secondorder latent PsyCap factor.
The validity of the PCQ was also established, according to the various definitions and types of validity provided. Cohen, Swerdlik and Sturman (
The strategy adopted for model crossvalidation was to use a combination of the Likelihood Ratio Test (differences in
Information on convergent validity was created by calculating the correlation between the PCQ (and its components/factors) and several other measures. It was hypothesised, supported by previous studies and literature, that Psychological Capital would correlate significantly with (developers indicated, with the Cronbach alpha coefficients (
A correlation of 0.4 is an indication of convergence with 0.50 and higher – a clear sign of convergence (Cohen et al.
Multiple regression was used to assess the discriminant validity of the factors. The PCQ factors will be used as independent (or predictor) variables in a multiple regression, with the hypothesised related constructs mentioned above as dependant variables. The rationale is to inspect the beta values, and to determine whether discriminant validity exists through the unique contribution of the PCQ factors when the beta values are inspected.
Ethical clearance was obtained from the Unisa Graduate School of Business Leadership’s research ethical committee before the field work was conducted. The ethical clearance application included all the standard items such as: consent of participants (with an explanation of the study); permission to conduct the study in the respective organisations; inclusion criteria and the methodology to be used (pencil and paper). The research ethics clearance certificate is dated 16 February 2016, with reference number 2016_SBL_002_CA.
An initial analysis of the PCQ was done through the assessment of the Cronbach alpha coefficient of the original factors and the results, together with the descriptive statistics of the four factors, are reported in
Descriptive statistics, Cronbach’s alpha coefficient of the original PCQ factors.
Variable  Items  Mean  Skewness  Kurtosis  

Selfefficacy  1–6  4.67  0.90  −0.95  1.06  0.90 
Hope  7–12  4.63  0.78  −0.83  1.74  0.86 
Resilience  13–18  4.50  0.66  −0.30  0.48  0.67 
Optimism  19–24  4.24  0.64  −0.04  0.37  0.55 
Total PsyCap  1–24  4.51  0.61  −0.51  0.83  0.90 
The Cronbach alpha coefficients (
The structural validity of the original PsyCap factor structure was further analysed by means of a CFA. Missing values in the dataset, related to the PCQ constructs, were deleted casewise as the total dataset consisted of enough cases to accommodate this measure (the deletion was less than 5% which is considered to be the limit). A test for normality was performed.
The results of the three models tested are reported in
Comparison of
Structure  Δ 
CFI  RMSEA  

Onefactor model 
5549  252  22.02    0.72  0.110 
Firstorder factor model  2041  224  9.11  3508 
0.90  0.068 
Secondorder factor model  2104  226  9.31  3445 
0.90  0.069 
Note: All chisquare values are significant at
CFI, comparative fit index; RMSEA, root mean square error of approximation.
, all 24 items as determined by the exploratory factor analysis;
,
, Onefactor model;
, Firstorder factor model;
, Secondorder factor model.
The firstorder factor model, with
It was further decided to assess the goodness of fit of the factor structure as determined by Du Plessis and Barkhuizen (
Due to the relatively poor psychometric properties reported in
The K1 rule was used in conjunction with the scree plot to determine the number of factors. The Kaiser’s criterion focusing on eigenvalues >1 was performed and is reported in
Eigenvalues >1 and explanation of variance.
Component  Initial Eigenvalues 


Total  % of Variance  Cumulative %  
1  8.67  36.12  36.12 
2  1.87  7.77  43.89 
3  1.70  7.08  50.99 
4  1.34  5.58  56.55 
5  1.08  4.49  61.03 
Five factors reported eigenvalues >1, with the first factor explaining 36.12% of the variance in the construct
Cattell’s scree plot.
Due to the fact that the interpretation of the scree plot does not yield a clear answer in terms of the number of factors to retain, the Monte Carlo parallel analysis simulation technique was utilised. The eigenvalues obtained from the actual data are compared to the eigenvalues obtained from the random data. If the actual eigenvalues from the principal component analysis from the actual data are greater than the eigenvalues from the random data, then the factor is retained. The results are reported in
Results of the Monte Carlo parallel analysis.
Component  Actual eigenvalues from principal component analysis  Criterion value from parallel analysis  Decision 

1  8.67  1.21  Accept 
2  1.87  1.19  Accept 
3  1.70  1.16  Accept 
4  1.34  1.14  Accept 
5  1.08  1.12  Reject 
The results of the Monte Carlo parallel analysis yielded a fourfactor model. The four factors accounted for 56.55% of the total variance (see
Pearson correlations between extracted factors (
Extracted factors  

F^{1}  1.00       
F^{2}  0.61 
1.00     
F^{3}  0.59 
0.65 
1.00   
F^{4}  −0.12 
−0.09 
−0.08 
1.00 
, Correlation is significant at the
, Correlation is significant at the
F^{14}, represents the extracted factors (unnamed at this stage).
The correlations between the F^{1}, F^{2} and F^{3} factors were relatively high, ranging between 0.59 and 0.65. F^{4}, however, reported low (although statistically significant) correlations with the other three factors. The fact that factors are strongly related overall suggests the appropriateness of an oblique factor rotation method and, consequently, Promax rotation was used. The results of each of the four factors are summarised in
Factor 1 – Selfefficacy.
Number  Item  Original factor 
Factor loading  Mean (/6)  Skewness  Kurtosis  Cronbach alpha  

PsC1  ‘I feel confident analysing a longterm problem to find a solution.’  Selfefficacy  0.65  0.54  4.81  0.90         
PsC2  ‘I feel confident in representing my work area in meetings with management.’  Selfefficacy  0.72  0.63  4.78  0.94         
PsC3  ‘I feel confident contributing to discussions about the company’s strategy.’  Selfefficacy  0.93  0.72  4.40  1.17         
PsC4  ‘I feel confident helping to set targets/goals in my work area.’  Selfefficacy  0.89  0.76  4.63  1.15         
PsC5  ‘I feel confident contacting people outside the company (e.g., suppliers, customers) to discuss problems.’  Selfefficacy  0.90  0.67  4.62  1.26         
PsC6  ‘I feel confident presenting information to a group of colleagues.’  Selfefficacy  0.89  0.72  4.75  1.14         
PsC7  ‘If I should find myself in a jam at work, I could think of many ways to get out of it.’  Selfefficacy  0.51  0.49  4.62  1.02         
, Luthans, F., Avolio, B.J., Avey, J.B. & Norman, S.M., 2007, ‘Positive psychological capital: Measurement and relationship with performance and satisfaction’,
Factor 2 – Hope & Optimism.
Number  Item  Original factor 
Factor loading  Mean (/6)  Skewness  Kurtosis  Cronbach alpha  

PsC8  ‘At the present time, I am energetically pursuing my work goals.’  Hope  0.61  0.65  4.60  1.04         
PsC10  ‘Right now I see myself as being pretty successful at work.’  Hope  0.63  0.56  4.53  1.05         
PsC11  ‘I can think of many ways to reach my current work goals.’  Hope  0.71  0.63  4.67  0.97         
PsC12  ‘At this time, I am meeting the work goals that I have set for myself.’  Hope  0.51  0.47  4.56  1.01         
PsC19  ‘When things are uncertain for me at work, I usually expect the best.’  Optimism  0.45  0.43  4.32  1.02         
PsC21  ‘I always look on the bright side of things regarding my job.  Optimism  0.86  0.57  4.60  1.05         
PsC22  ‘I’m optimistic about what will happen to me in the future as it pertains to work.’  Optimism  0.88  0.56  4.47  1.16         
, Luthans, F., Avolio, B.J., Avey, J.B. & Norman, S.M., 2007, ‘Positive psychological capital: Measurement and relationship with performance and satisfaction’,
Factor 3 – Resilience.
Number  Item  Original factor 
Factor loading  Mean (/6)  Skewness  Kurtosis  Cronbach alpha  

PsC9  ‘There are lots of ways around any problem.’  Hope  0.44  0.47  4.80  1.06         
PsC14  ‘I usually manage difficulties one way or another at work.’  Resilience  0.70  0.54  4.66  0.89         
PsC15  ‘I can be ‘on my own’, so to speak, at work if I have to.’  Resilience  0.80  0.54  4.69  1.06         
PsC16  ‘I usually take stressful things at work in stride.’  Resilience  0.45  0.34  4.14  1.19         
PsC17  ‘I can get through difficult times at work because I’ve experienced difficulty before.’  Resilience  0.87  0.67  4.76  1.01         
PsC18  ‘I feel I can handle many things at a time at this job.’  Resilience  0.43  0.54  4.67  0.92         
, Luthans, F., Avolio, B.J., Avey, J.B. & Norman, S.M., 2007, ‘Positive psychological capital: Measurement and relationship with performance and satisfaction’,
Factor 4 – Buoyancy.
Number  Item  Original factor 
Factor loading  Mean (/6)  Skewness  Kurtosis  Cronbach alpha  

PsC13  ‘When I have a setback at work, I have trouble recovering from it, moving on.’  Resilience  0.68  0.47  2.91  1.33         
PsC20  ‘If something can go wrong for me workwise, it will.’  Optimism  0.80  0.63  3.29  1.22         
PsC23  ‘In this job, things never work out the way I want them to.’  Optimism  0.71  0.57  3.02  1.34         
, Luthans, F., Avolio, B.J., Avey, J.B. & Norman, S.M., 2007, ‘Positive psychological capital: Measurement and relationship with performance and satisfaction’,
The factor loadings also ranged between 0.43 and 0.93 for the four factors. The criteria of a factor loading cutoff point of 0.40 for inclusion in the interpretation of a factor (Hair et al.
F
The items as well as the factors were tested for multivariate normality. All the items, as well as the factors reporting skewness and kurtosis values for both factors, do not exceed the critical values of 2 and 7, respectively (West, Finch & Curran
The Cronbach alpha coefficients (α) of
A similar process, as described in
Comparison of
Structure  df  Δ 
CFI  RMSEA  

Onefactor model 
5549  252  22.02    0.72  0.110 
Firstorder factor model  1806  222  8.14  3 743 
0.92  0.064 
Secondorder factor model  1808  224  8.07  3 741 
0.92  0.063 
Note: All chisquare values are significant at
CFI, comparative fit index; RMSEA, root mean square error of approximation.
, all 24 items as determined by the exploratory factor analysis;
,
, Onefactor model;
, Firstorder factor model;
, Secondorder factor model.
Assessment of the bestfitting model within the three models was conducted through the application of CFA. The onefactor model (all 24 items) was identified as the worstfitting model (CFI = 0.72, RMSEA = 0.110). By analysing the chisquare test values, it further appears that the firstorder factor model is slightly better than the secondorder factor model. The difference in chisquare between the secondorder factor and firstorder factor models is 2 (i.e., 3743–3741), which is distributed as chisquare with 222–224 = 2 degrees of freedom. The bestfitting model is thus the firstorder model (model_{b}) in which all 24 items loaded directly on their respective factors (i.e.
The convergent validity of the PCQ was investigated by comparing it to a range of instruments which were also used in the broader study. These instruments and constructs are within the domain of positive organisational behaviour and were selected because of their hypothesised relationship with the PsyCap construct. The instruments/constructs used are:
Convergent validity of the adapted (reconfigured) Psychological Capital Questionnaire factors by means of correlations (Pearson) with other related measures.
Variable  Selfefficacy  Hope & Optimism  Resilience  Buoyancy  Psycap total 

Harmonious passion  0.43 
0.49 
0.29 
−0.05  0.47 
Obsessive passion  0.21 
0.31 
0.15 
0.21 
0.21 
Supplementary fit  0.39 
0.34 
0.24 
0.01  0.37 
Complementary fit  0.35 
0.43 
0.24 
−0.05 
0.40 
Organisational energy: Affective  0.35 
0.34 
0.08 
0.00  0.31 
Organisational energy: Cognitive  0.50 
0.36 
0.22 
−0.02  0.42 
Organisational energy: Behavioural  0.47 
0.38 
0.27 
−0.03  0.43 
, Correlation is significant at the
, Correlation is significant at the
From
The correlation coefficients reported for
In order to determine discriminant validity, multiple regressions were performed with
Discriminant validity of the adapted (reconfigured) Psychological Capital Questionnaire factors.
Variable  Selfefficacy 
Hope & Optimism 
Resilience 
Buoyancy 


Harmonious passion  0.21 
0.02  0.44 
0.03  −0.13 
0.03  0.02  0.02  0.28 
Obsessive passion  0.01 
0.03  0.40 
0.04  −0.13 
0.04  0.22 
0.02  0.16 
0.15 
0.02  0.41 
0.03  −0.13 
0.03  0.11 
0.02  0.25 

Supplementary fit  0.33 
0.03  0.26 
0.04  −0.09 
0.04  −0.05  0.02  0.17 
Complementary fit  0.17 
0.03  0.44 
0.03  −0.15 
0.03  −0.01  0.02  0.21 
0.25 
0.02  0.35 
0.03  −0.12 
0.02  0.02  0.02  0.24 

Organisational energy: Affective  0.36 
0.03  0.46 
0.04  −0.46 
0.04  0.05  0.02  0.21 
Organisational energy: Cognitive  0.44 
0.02  0.18 
0.03  −0.19 
0.03  0.03  0.02  0.27 
Organisational energy: Behavioural  0.41 
0.03  0.23 
0.03  −0.11 
0.04  0.02  0.02  0.24 
0.40 
0.02  0.28 
0.03  −0.25 
0.03  0.03  0.02  0.30 
, Significant at the
Note: The large differences in betas (ß) are marked in bold to indicate discriminant validity.
All the multiple regression results, as reported in
Due to the relatively poor psychometric properties, and convergent as well as discriminant validity results, a CFA was conducted with the exclusion of the
Comparison of
Structure  df  Δχ^{2}  CFI  RMSEA  

Onefactor model 
3694  228  16.02  0.63  0.093  
Firstorder factor model  1343  162  8.30  4206 
0.93  0.065 
Secondorder factor model  978  141  6.93  4571 
0.95  0.058 
Note: All chisquare values are significant at
CFI, comparative fit index; RMSEA, root mean square error of approximation.
, all 24 items as determined by the exploratory factor analysis;
,
, Onefactor model;
, Firstorder factor model;
, Secondorder factor model.
The bestfitting model, after the assessment of the three CFA models, is thus the secondorder model (model_{c}) in which the reduced number of items (21 of the original 24) loading on
The purpose of this study was not to determine invariance between demographic groups, but it was deemed necessary to conduct an elementary crossvalidation assessment of the preferred, secondorder factor structure as reported in
The purpose of this study is to examine the instrument properties of the PCQ which (unlike other positive organisational constructs) have not been studied intensively, especially in the South African/African context. Validity of any measurement is regarded as paramount and is even included in the criteria for any construct to be regarded a positive organisational construct. The objectives of this study are twofold: first, to determine the construct (factorial), and second, to determine the discriminant and convergent validity of the PCQ. The substantive component of construct validity, although not directly an objective of this study, would be addressed where deviations from the original constructs (and items) of Luthans et al. (
Construct validity – also referred to as factorial validity and based on the results of both an EFA and a CFA – was conducted with the primary purpose of defining the underlying structure among the 24 items of the PCQ.
The first step was to examine the psychometric properties of the original factors of PsyCap as proposed by Luthans et al. (
Based on these relatively poor psychometric results, it was decided to conduct an EFA, a decision supported by the results of the Bartlett’s test of sphericity and the KMO. The EFA with Promax rotation, as well as the Monte Carlo parallel analysis simulation, yielded a fourfactor solution, explaining close to 57% of the variance.
The four factors extracted by means of the EFA reported reasonable psychometric properties, with Cronbach alpha coefficients of 0.90 and 0.60 (the lowest for F^{4} which is a factor with only three items, all of which are negativelyworded). The factors were named in accordance with their original theoretical and PCQ names, with the
In order to satisfy the substantive element of construct validity (although not the aim of this study), and with full acceptance of the Luthans et al. (
The second factor is a composite factor which includes items of the original
having the explanatory style that attributes positive events to internal and pervasive causes, and further having the willpower to succeed now and in the future, even if this requires a change of paths in order to succeed. (p. 1)
The third factor,
All the elements of the original conceptualisation of PsyCap have been included so far, with the only deviation being that of the composite factor
CFA was further conducted on this factor structure, as determined by the EFA. The results explain that the bestfitting model is the secondorder factor model, which is a confirmation of the EFA results. This secondorder factor model consists of PsyCap as a super factor and equal contributions of the four factors (
The adjusted (reconfigured) factor structure was exposed to a rigorous investigation for construct validity, which also included convergent validity. This was based on the hypothesised relationship that PsyCap (specifically the newlyconfigured factors) has related work attitudinal and positive organisational behaviour constructs. The constructs chosen are
The third construct validity measure performed was that of discriminant validity. This was done through a basic multiple regression with all the work attitudinal and positive organisational behaviour constructs as dependent variables, and the newlyconfigured factors of the PCQ as independent or predictor variables. The results are consistent with the findings in the convergent validity analysis with
Due to the fact that
The bestfitting model (also when compared to the CFA results discussed earlier) is the secondorder model, with
The instrument in its adapted (reconfigured) structural configuration was thus found to be valid on the substantive (theoretical / conceptual), structural (factorial) and external (discriminant / convergent) levels. It has also been found to be reliable, all adding up to overall evidence that it is a suitable instrument to accurately measure PsyCap. The results of the crossvalidation assessment support the notion of configural invariance; that is, participants belonging to different groups (in this instance, the gender groups) conceptualise the constructs in the same way. It is thus an indication that data collected from each group decompose into the same number of factors, with the same items associated with each factor.
PsyCap is considered to be an important positive organisational behaviour construct in developing individuals in the workplace to become the best that they can become. As clearly indicated in the criteria of categorisation as a positive organisational behaviour construct, there must be an accurate measurement to enable the individual (as well as the organisation) to design and implement directed developmental interventions to enhance it.
The value of this study lies in the fact that the original conceptualisation of PsyCap – consisting of
This research does have certain limitations, however, mainly in terms of the methodology. The PCQ is based on selfreporting – a method which may lead to method bias, and this may still be a reality, even with the assurance provided to participants during the briefing regarding anonymity as well as confidentiality. Social desirability and subsequent response bias will always remain a concern and a limitation in studies such as this one, while selfreporting may be seen as a onesided report from the respondents’ side. An additional possible limitation is that the wording of the initial scale was used ‘as is’, without adapting it to the South African (multilingual) context.
A further limitation of this study is the drawback of a crosssectional design which might have increased the relationship between the four components artificially.
A recommendation for further studies is to investigate the relationship between the four components (and related measures) over a period of time through a longitudinal study. Another recommendation is to analyse results further with the possible addition of the effect of membership of specific demographic groups (e.g., generational differences) and the determination of the antecedents and consequences of PsyCap on work attitudes and organisational behaviour.
PsyCap profiling could also be considered in future research to determine how different organisational cultures, climates and leadership styles impact on the employees’ PsyCap.
The authors declare that they have no financial or personal relationship(s) that may have inappropriately influenced them in writing this article.
Both authors, A.G. and Y.T.J., contributed equally to the writing of this article.