Influence of Psychological Capital on Innovation Capacity: An Empirical Study on Banking Institutions

Abstract

The capacity for innovation is a key component required for companies to generate added value and to expand into new markets. In this regard, it is important to find factors that improve the capacity for innovation. One such credible factor is psychological capital. This article aims to examine the influence of psychological capital on innovation capacity. An empirical study, based on data collected from 520 employees of banking institutions in Peru, was carried out to this end. Our results revealed the positive and significant influence of psychological capital on innovation capacity. Based on dimensions of psychological capital, it was found that self-efficacy and optimism positively influenced the capacity for radical innovation. Likewise, optimism and hope were found to positively influence the capacity for incremental innovation, but resilience had no positive influence on either radical innovation or incremental innovation. This work provides business leaders with a deeper understanding of psychological factors that are deemed necessary to promote and improve innovation capacity in companies.

Keywords

Radical innovation incremental innovation psychological capital innovation capacity banking innovation

Introduction

Innovation is considered to be one of the most important factors that improve the competitive advantage of firms (Lei et al., 2020; Villaluz & Hechanova, 2019). However, in many countries, there are firms with few resources for innovation. Therefore, researchers are seeking to identify factors that help boost innovation in such firms (Geldes et al., 2017; Lei et al., 2019; Shyu & Chiu, 2002).

Innovation capacity (IC) may be defined as that which enables firms to identify and implement new ideas, products, services and technologies to enhance their efficiency, and thereby gain a competitive edge (Drucker, 2014; Rujirawanich et al., 2011). Radical innovation (RAI) and incremental innovation (INI) are two kinds of IC. Incremental IC aims to augment the current knowledge to add greater value to existing products, whereas radical IC seeks to develop new features and benefits unique to existing products in the market (Sheng & Chien, 2016). Several researchers have tried to understand the antecedents or key factors that shape and promote the capacity for innovation in companies, psychological capital (PSC) being one of the most significant ones (Le, 2020; Lei et al., 2020; Khalili, 2016).

PSC comprises people’s positive psychological resources. It encompasses self-efficacy, hope, optimism and resilience. These factors are recognized as those that facilitate innovation among employees in a company (Luthans et al., 2007; Sweetman et al., 2011). Therefore, improving the PSC of collaborators could lead to improvements in ICs, especially in companies where there are insufficient economic resources for these purposes (Lei et al., 2019; Ratnaningsih et al., 2016).

Existing research indicates that the higher the positive PSC of an employee, the higher their level of innovative behaviour at work (Hsu & Chen, 2017; Tho & Duc, 2020; Ziyae et al., 2015). However, empirical research on the relationship between the dimensions of PSC and IC has not been comprehensively addressed in the literature, and the few existing results are also contradictory (Le, 2020; Schuckert et al., 2018). Certain recent studies suggest that there is a significant influence of the dimensions of PSC on innovation (Abbas & Raja, 2015; Kim et al., 2018; Lei et al., 2020; Mishra et al., 2019; Rego et al., 2012; Schuckert et al., 2018), while other studies indicate that there is no such influence (Le, 2020; Ziyae et al., 2015). For instance, Le (2020) found that, with the exception of self-efficacy, hope and resilience, optimism was not related to the radical IC of employees; similarly, apart from self-efficacy, optimism and resilience, hope was not related to incremental IC. For their part, Lei et al. (2020) found that, when compared to optimism, self-efficacy had a greater impact on individual and organizational ICs. Likewise, Ziyae et al. (2015) found that, except for resilience, PSC dimensions such as self-efficacy, optimism and hope did not contribute to improving information technology innovation. To fill this research gap, the present study sought to explore the role of PSC and its dimensions on IC. To this end, we investigated whether PSC and its dimensions have a positive influence on radical and incremental ICs. This work intends to provide theoretical initiatives as well as practical implications of how PSC affects the IC of companies.

Literature Review and Hypotheses

Psychological Capital

PSC is described as the positive psychological state of a person, characterized by self-efficacy, resilience, optimism and hope (Luthans et al., 2015). Snyder (2002) defined hope as a goal-oriented, positive, motivational state. In his theory, Snyder indicated that hope was key to achieving well-being and strengthening goals. Likewise, Stajkovic and Luthans (1998) defined self-efficacy as the conviction or confidence of an employee to successfully carry out a specific assigned task. Luthans (2002) defined optimism as a futuristic positive perspective and resilience as the ability of an individual to adapt to adverse situations and achieve positive results. In his Broaden-and-Build Theory, Fredrickson (2001) indicated that positive emotions had the ability to encourage novel thought and action, which increase the potential for demonstrating innovative behaviours, such as the sharing of creative ideas along with providing suggestions for improvement at work (Avey et al., 2010, 2011). Contextually, individuals with higher levels of PSC were rated by their supervisors as those who exhibited better innovativeness than individuals with lower levels of PSC (Abbas & Raja, 2015). Therefore, the higher the positive PSC of an employee, the higher their level of innovative behaviour at work (Hsu & Chen, 2017; Tho & Duc, 2020; Ziyae et al., 2015).

Innovation Capacity

IC is defined as the ability to understand and identify future needs and expectations of current and potential clients in order to respond promptly and appropriately. This is possible with an organization’s internal knowledge, including that of employees, and external knowledge based on an organizational culture that promotes the creation of new ideas and transforms them into successful innovations (Rajapathirana & Hui, 2018). Incremental and radical ICs are recognized as two key aspects of IC. Incremental IC aims to improve on current knowledge to add greater value to existing products, whereas radical IC seeks to develop new features and benefits unique to existing products in the market (Sheng & Chien, 2016). In fact, ‘radical innovation can be considered key to economic development’ (Souto, 2015, p. 143). Along with ensuring compatibility with existing business processes, identifying and exploiting radical and incremental innovation opportunities will perhaps be the greatest challenge facing business owners and managers. Strengthening IC is imperative for the success of companies (Heikkinen et al., 2018; Johansson et al., 2019; Le, 2020).

Psychological Capital and Innovation Capacity

Hsu and Chen (2017) examined the mediating role of PSC in the relationship between an organization’s innovation climate and the innovative behaviour of its employees. To this end, researchers analysed responses from 781 employees from 16 organizations in Taiwan and found that their PSC positively influenced their innovation behaviour. Likewise, Li et al. (2020) analysed the influence of an entrepreneur’s PSC on the innovation behaviour of employees within the context of the leader–member exchange relationship. Researchers analysed 106 entrepreneurs and 249 employees of large companies and found that the PSC of entrepreneurs had a positive influence on the innovation behaviour of employees. Along similar lines, Yuan and Chai (2020) found a positive relationship between employees’ PSC and innovation behaviour when analysing 100 employees from five high-tech companies. Similarly, Tho and Duc (2020) sought to determine the mediating role of exploratory and exploitative learning in the relationship between PSC and innovation by analysing 272 team leaders from Vietnamese companies. Their PSC was found to have a positive effect on innovation teams. Finally, Yan et al. (2019) attempted to study the relationship between psychological capital and the innovation behaviour of Chinese nurses. Researchers analysed 4,677 nurses from 18 hospitals in China and found that a high PSC could enhance the innovation behaviour of nurses.

Based on the above, the following hypothesis was raised:

H₁: PSC positively influences IC.

Previous research has indicated that dimensions of PSC significantly influence IC. Specifically, Abbas and Raja (2015) studied the influence of PSC on innovative performance. To this end, the researchers analysed 237 employees from different service sector companies in Pakistan and affirmed that employees with self-efficacy tend to be more creative and persevering in the face of difficulties and seek to define and apply innovative solutions in their companies. However, Ziyae et al. (2015) studied the effect of PSC dimensions on information technology innovation after analysing 132 employees including managers from branches of an agricultural bank in Tehran, Iran. They implemented descriptive correlational research and found that self-efficacy did not enhance innovation in information technology. For their part, Kim et al. (2018) studied the mediating effect of PSC in the relationship between the breach of psychological contract and innovation behaviour in services, by analysing 390 employees from 15 five-star hotels in South Korea. It was found that self-efficacy positively influenced the innovative behaviour of employees. However, Lei et al. (2020) sought to determine the effects of transformational leadership on IC of employees and the organization through the mediating role of self-efficacy and optimism. To this end, researchers analysed 330 employees from 90 companies in Vietnam and found that self- efficacy, when compared to optimism, had a greater impact on the IC of employees and the organization. Given the contradictions found in the literature, the following hypotheses were raised:

H_2.1: Self-efficacy positively influences radical IC.

H_2.2: Self-efficacy positively influences incremental IC.

Rego et al. (2012) analysed 201 employees of the commerce sector in Portugal and found that the ones who had a higher level of optimism were predisposed to having positive expectations about the results, with the result that they become more creative and willing to give new and effective solutions. Similarly, Kim et al. (2018) indicated that optimism positively influenced the innovation behaviour of employees. However, Le (2020) studied the mediating effect of PSC between transformational leadership and radical and incremental ICs by investigating 379 employees from 89 Vietnamese companies. The researcher found that optimism was not related to the radical IC of employees.

Given the contradictions found in the literature, the following hypotheses were raised:

H_3.1: Optimism positively influences radical IC.

H_3.2: Optimism positively influences incremental IC.

For their part, Luthans et al. (2007) indicated that collaborators who possess hope take risks and explore new paths to achieve their goals. They also recall positive individual and collective emotions. This allows them to establish an environment that supports innovative attitudes (Abbas & Raja, 2015; Fredrickson, 2004). Schuckert et al. (2018) studied the mediating effect of PSC in the relationship between authentic/transformational leadership and innovative behaviour in services by analysing 336 full-time front-line employees from 15 five-star hotels in South Korea over a period of 1 month. They found that hope positively influenced the innovation behaviour of employees. In contrast, Le (2020) found that hope was not related to incremental IC.

Given the contradictions found in the literature, the following hypotheses were raised:

H_4.1: Hope positively influences radical IC.

H_4.2: Hope positively influences incremental IC.

Highly resilient employees tend to be more adaptable and flexible when placed in confusing and complex situations (Coutu, 2002). In this sense, Mishra et al. (2019) sought to determine the mediating effect of PSC and supervisors’ support in the relationship between innovative work behaviour and work–family enrichment by analysing 398 employees from companies in the service sector. They found that resilience positively influenced the innovation behaviour of employees at work. Likewise, Ziyae et al. (2015) indicated that resilience enhanced innovation in the domain of information technology. Along the same lines, Le (2020) found that resilience positively influenced radical and incremental ICs.

Therefore, the following hypotheses were raised:

H_5.1: Resilience positively influences radical IC.

H_5.2: Resilience positively influences incremental IC.

Figure 1 presents the hypothesized model and the hypotheses raised in the study.

Figure 1.

Hypothesized Model.

Methodology

Sample and Data Collection

Banking institutions are maximizing their technological ICs as valuable resources to provide high-quality innovative products that drive efficiency, increase employee satisfaction and productivity, and generate higher financial performance (Yaw Obeng & Boachie, 2018). In this sense, the present study used surveys to collect empirical data from employees of banking institutions located in Peru. Convenience sampling was used to collect data during the period from July to November 2021.

Relevant questionnaires were used to check hypothetical relationships among the variables of the present investigation. The items of the questionnaires were measured based on a 6-point Likert-type scale that ranged from ‘strongly disagree = 1’ to ‘strongly agree = 6’. In this study, reverse translation was used to ensure consistency between the English and Spanish questionnaires. Likewise, a pilot test was carried out with 73 participants to determine their effectiveness. A total of 4,152 employees of banking institutions located in the city of Lima were contacted via e-mail and LinkedIn. Of the 541 responses received, 520 were valid, indicating a validity rate of 96%. The sample included 72% men (374 employees) and 28% women (146 employees).

Table 1 shows the demographic profile of the participants of this study.

Table 1.

Demographic Profiles of Participants.

Demographic	Frequency	Percentage
Gender
Male	374	72
Female	146	28
Educational level
Associate degree	58	11.1
Bachelor’s degree	277	53.3
Master’s degree/MBA	167	32.1
PhD	18	3.5
Job position
Junior staff workers	95	18.3
Senior staff workers	114	21.9
Lower-level managers	121	23.3
Middle-level managers	101	19.4
Senior-level managers	89	17.1

Measures

For the development of the proposed investigation, instruments from previous studies reported in the literature were considered.

Psychological Capital

PSC (a second-order construct), consisting of first-order constructs such as self-efficacy, optimism, hope and resilience, was measured using the 24-item questionnaire by Luthans et al. (2007) available in Spanish.

Innovation Capacity

IC (a second-order construct), consisting of first-order constructs such as radical and incremental ICs, was measured using the questionnaire of 10 items made by Sheng and Chien (2016). This questionnaire has been used successfully by Le (2020) and Lei et al. (2019) to measure the IC of employees from different organizations. The translation of the questionnaire from English to Spanish was carried out by an expert committee, which did the reverse translation as well. A pilot test was carried out to analyse its validity and reliability and subsequent validation (Tsang et al., 2017).

Data Analysis and Results

In the present study, confirmatory factor analysis (CFA) was used to examine the validity and reliability of the constructs. The composite reliability (CR) index, the average variance extracted (AVE) and the discriminant validity were calculated. CR > 0.7 shows an adequate reliability level, and AVE > 0.5 indicates adequate convergent validity (Hair et al., 2014; Schumacker & Lomax, 2004). If the square root of AVE is higher than the observed correlations, this construct is considered to have adequate discriminant validity (Fornell & Larcker, 1981; Fornell & Yi, 1992).

To test hypothetical relationships between study variables, structural equation modelling (SEM) was used in the AMOS SPSS software version 26. Similarly, considering multivariate non-normality, the indicators provided by the AMOS software were used for the unweighted least squares (ULS) procedure, namely, Adjusted Goodness-of-fit Index (AGFI), Bentler–Bonett’s Normed Fit Index (NFI) and Relative Fit Index (RFI), whose values must be higher than 0.90, in addition to the standardized root mean and squared residual (SRMR), whose value must be lower than 0.08 (Batista & Coenders, 2000; Bentler & Bonett, 1980; Bollen, 1989; Brown, 2006; Hoyle, 2012; Schumacker & Lomax, 2004). In addition, the bootstrapping procedure was performed in significant tests of the study hypotheses using 1,000 samples with a replacement to obtain the estimator and p-value distributions.

Reliability and Validity of the Measurement Scales

Measurement Model

SEM requires calculating the CR index, AVE, Cronbach’s α and discriminant validity. Table 2 presents descriptive statistics for the constructs considered in the first-order measurement model, and Table 3 shows the factor loadings of their respective items. The minimum CR was found in OPT (0.715) and the maximum in SEF (0.870), while the convergent validity was fulfilled in all constructs because all AVE values were higher than 0.5. Discriminant validity was also fulfilled in all constructs; that is, the items of a factor were related to its latent variable to a greater extent than to those of other constructs.

Table 2.

Descriptive Statistics.

Construct/Indicator	Mean	Standard Deviation
Self-efficacy (SEF)
SEF1	5.48	0.614
SEF2	5.27	0.731
SEF3	5.18	0.779
SEF4	5.41	0.665
SEF5	5.43	0.731
SEF6	5.52	0.630
Hope (HOP)
HOP1	5.06	0.769
HOP2	4.93	0.867
HOP3	5.16	0.751
HOP4	5.04	0.889
HOP5^a	5.33	0.812
HOP6^a	5.58	0.625
Resilience (RES)
RES1	4.81	0.934
RES2	5.17	0.757
RES3	5.17	0.768
RES4^a	4.20	1.500
RES5^a	5.09	0.950
RES6^a	4.96	0.919
Optimism (OPT)
OPT1	4.83	0.920
OPT2	5.11	0.805
OPT3	5.30	0.780
OPT4^a	4.02	1.283
OPT5^a	4.56	1.360
OPT6^a	4.49	1.277
Radical innovation capacity (RAI)
RAI1	4.62	1.164
RAI2	4.99	0.915
RAI3	4.87	1.015
RAI4	4.74	1.067
RAI5	4.86	0.985
Incremental innovation capacity (INI)
INI1	5.16	0.807
INI2	5.06	0.841
INI3	5.03	0.804
INI4	5.18	0.722
INI5	4.89	0.873

Note: ^aItems deleted in the purification process for the first-order measurement model.

Table 3.

Model Validity Measures.

Construct	Item	Standardize Loading	CR	AVE	Cα	Discriminant Validity
Self-efficacy (SEF)	–	–	0.870	0.529	0.868	0.727
	SEF1	0.669***
	SEF2	0.787***
	SEF3	0.795***
	SEF4	0.756***
	SEF5	0.652***
	SEF6	0.691***
Optimism (OPT)	–	–	0.715	0.558	0.701	0.747
	OPT1	0.772***
	OPT2	0.543***
	OPT3	0.693***
Hope (HOP)		–	0.816	0.528	0.807	0.727
	HOP1	0.604***
	HOP2	0.815***
	HOP3	0.732***
	HOP4	0.741***
Resilience (RES)	–	–	0.752	0.603	0.710	0.776
	RES1	0.536***
	RES2	0.756***
	RES3	0.760***
Radical innovation capacity (RAI)	–	–	0.838	0.525	0.823	0.724
	RAI1	0.349***
	RAI2	0.786***
	RAI3	0.828***
	RAI4	0.815***
	RAI5	0.730***
Incremental innovation capacity (INI)	–	–	0.847	0.526	0.846	0.725
	INI1	0.774***
	INI2	0.613***
	INI3	0.735***
	INI4	0.770***
	INI5	0.723***

Notes: Cα ≥ 0.7; CR ≥ 0.7; AVE ≥ 0.5; ***Significant at p < .001.

Table 4 shows that the discriminant validity of the first-order constructs was confirmed using the Heterotrait–Monotrait (HTMT) technique because the values on the main diagonal are lower than 0.85 (Hair et al., 2017; Henseler et al., 2015).

Table 4.

Heterotrait–Monotrait Analysis.

	RAI	INI	SEF	HOP	RES	OPT
RAI
INI	0.743
SEF	0.513	0.518
HOP	0.501	0.603	0.737
RES	0.379	0.437	0.637	0.663
OPT	0.546	0.621	0.657	0.742	0.611

Note: Diagonal elements are the square root of the AVE; ***p < .001.

Table 5 presents the CR index, AVE and discriminant validity of constructs considered in the second-order measurement model. The minimum CR was found in IC (0.849) and maximum in PSC (0.909), and the convergent validity was fulfilled in all constructs because all AVE values were higher than 0.5. Discriminant validity was also fulfilled in all constructs (values on the main diagonal); that is, items on one factor are not highly correlated with items on other factors. Discriminant validity was confirmed using the HTMT technique, indicating a value of 0.647, which is lower than 0.85 (Hair et al., 2017; Henseler et al., 2015).

Table 5.

Model Validity Measures.

	CR	AVE	PSC	IC
Psychological capital (PSC)	0.909	0.714	0.845
Innovation capacity (IC)	0.849	0.739	0.731***	0.860

Note: Diagonal elements are the square root of the AVE; ***p < .001.

Structural Model

Mardia’s test showed multivariate non-normality with a kurtosis value > 70 (kurtosis coefficient = 284.82; critical region = 65.63). Therefore, to calculate the goodness of fit, the ULS method was used, for which the observed variables are not required to follow a certain distribution (Brown, 2006; Schumacker & Lomax, 2004).

The second-order structural model is presented in Figure 2. The goodness-of-fit indices of the second-order structural model are shown in Table 6. To validate the hypotheses, the model must meet some assumptions that are verified by means of the goodness-of-fit indices. The AGFI, NFI and RFI present values > 0.90, and the SRMR presents a value < 0.08. Likewise, the CMIN/DF value ranges between 1 and 3, indicating that it is a perfect fit model.

Figure 2.

Path Coefficients of the Structural Model.

Table 6.

Fit Indices of the Model.

CMIN/DF	AGFI	NFI	RFI	SRMR
1.775	0.949	0.929	0.923	0.047

Hypothesis Testing

After satisfying the goodness-of-fit indicators in the second-order structural model, the bootstrapping technique was carried out, taking 1,000 samples to obtain the p-value and the coefficient of determination (R²). The results obtained most certainly state that PSC positively influences IC. In contrast, after working with the first-order structural model, it can be confirmed that there is a positive and statistically significant relationship between SEF and RAI (p = .002), OPT and RAI (p = ***), OPT and INI (***) and HOP and INI (p = ***). No statistically significant relationship was found between HOP and RAI and between SEF and INI. Although there was a statistically significant relationship at the 95% confidence level between RES and INI (p = .031), the relationship was not positive. Likewise, at the 90% confidence level, no positive relationship was found between RES and RAI (p = .072).

In Table 7 and Figure 3, results that allow us to validate the hypotheses of the study can be appreciated. The estimate column shows the estimators, while the sign indicates whether the relationship is direct or inverse.

Table 7.

Hypothesis-testing Results.

Hypotheses	Proposal Effect	Estimate	p-Value	Results
H1: PSC → IC	+	0.715	***	Accepted
H2.1: SEF → RAI	+	0.182	0.002	Accepted
H2.2: SEF → INI	+	0.080	0.315	Rejected
H3.1: OPT → RAI	+	0.381	***	Accepted
H3.2: OPT → INI	+	0.689	***	Accepted
H4.1: HOP → RAI	+	0.068	0.278	Rejected
H4.2: HOP → INI	+	0.279	***	Accepted
H5.1: RES → RAI	+	−0.074	0.072	Rejected
H5.2: RES → INI	+	−0.119	0.031	Rejected

Note: ***Significant at the .001 level.

Figure 3.

Notes: ***p < .001; **p < .05; *p < .1.

Regarding the R² determination coefficient, it was found that PSC explains 51.10% of the changes that occur in the IC. According to Hair et al. (2017), this value is acceptable.

Discussion

To fill the theoretical gaps regarding the relationship between PSC and innovativeness, this study investigated the effects of PSC and its dimensions on radical and incremental innovativeness. Previous studies have examined the effects of PSC on innovation in general but have not examined the influence of each of the specific dimensions of PSC on specific types of innovation (Le, 2020; Ziyae et al., 2015).

The present study reveals that PSC has a positive influence on employees’ innovativeness. These results are like those reported by Lei et al. (2020) and Le (2020). In addition, empirical results have shown that each dimension of PSC has an effect on each type of innovativeness.

The research results indicate that self-efficacy has a positive influence on radical innovativeness but has no influence on incremental innovativeness. These results are different from those reported by previous studies. For example, Lei et al. (2020) reported that self-efficacy had a positive influence on innovativeness, while Le (2020) reported that self-efficacy had a positive influence on both radical and incremental innovativeness. Similarly, in the present research, optimism was found to have a positive influence on both radical and incremental innovativeness. These results are similar to those reported by Lei et al. (2020), but partially different from those reported by Le (2020), who indicated that optimism had no influence on radical innovativeness. Another important finding of the present research is that hope had no positive influence on radical innovativeness; however, it did have a positive influence on incremental innovativeness. These results are partially different from those reported by Le (2020), who showed that hope had a positive influence on radical innovativeness but not on incremental innovativeness. Finally, the present research revealed that resilience had no positive influence on either radical innovativeness or incremental innovativeness. These results are contradictory to those reported by Le (2020), who indicated that resilience had a positive influence on radical and incremental innovativeness.

The empirical results show that optimism appears to be the most successful element in linking PSC with innovativeness. This finding will help managers and directors understand more deeply the psychological processes that lead to innovation and to better define the corresponding strategies.

Practical Implications

This work highlights the important role that the PSC of employees plays in improving their IC. In other words, results suggest that company directors and managers should focus on developing the PSC of their employees to foster IC. Consequently, the results of this study have provided an empirical basis and specific guidelines for Peruvian banking institutions to understand the effect PSC and its dimensions have on radical and incremental ICs. In this sense, companies need to promote internal mental health sessions and develop training programmes aimed at providing knowledge and skills to positively manage PSC at work (Luthans et al., 2007; Schuckert et al., 2018).

Limitations and Future Line of Research

This research has some limitations that are important to highlight. First, a cross-sectional design was used to investigate the correlation between the constructs. This implies that causal relationships could change over time. A longitudinal study would help overcome this limitation and consolidate results. Second, future research should explore other psychological factors that positively influence IC. Third, reasons why resilience does not have an influence on radical and incremental ICs should be investigated. Lastly, factors that could mediate or moderate the relationship between PSC and IC should be investigated.

Conclusions

The present research provides empirical evidence that supports the hypothesized model of the positive influence of PSC on IC. The study results also provide valuable information to help understand the influence of PSC dimensions (self-efficacy, hope, optimism and resilience) on specific ICs, such as radical and incremental innovativeness. The results suggest that the managers of organizations should pay attention to all dimensions of PSC as a whole. Therefore, a systematic approach could be used to improve the PSC of employees. Overall, this study is unique in its attempt to enable a deeper and more integrated understanding of new pathways that lead to enhancing specific ICs.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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