key: cord-0949316-n8wt3ewt
authors: Ali, Mohammad Afshar; Alam, Khorshed; Taylor, Brad; Rafiq, Shuddhasattwa
title: Does ICT maturity catalyse economic development? Evidence from a panel data estimation approach in OECD countries
date: 2020-09-11
journal: Econ Anal Policy
DOI: 10.1016/j.eap.2020.09.003
sha: f3d2b869ca0298dff892f9954544b2569cc8516c
doc_id: 949316
cord_uid: n8wt3ewt

To date, definitions of information and communication technology (ICT) development used in quantitative studies on the relationship between economic development and ICT are incomplete and often based on single indicators. Thus, this study investigates the link between ICT maturity and economic development in the Organisation of Economic Cooperation and Development (OECD) countries. A novel composite index of ICT maturity that includes previously neglected dimensions of ICT maturity, such as affordability and quality of internet connectivity, is utilised. The baseline estimations using the feasible generalised least squares indicate that ICT maturity is associated with an increase in economic development by 1%–3.8% in OECD countries. These findings have been cross-validated by applying the generalised method of moments estimation. Results imply that the holistic development of ICT, including infrastructure, skills, and affordability, can augment economic development.

Many empirical studies have confirmed that information and communication technology (ICT) can play a significant role in the socio-economic development of a nation (Asongu & Le Roux, 2017; Ferrigno-Stack et al., 2003; Obijiofor, 2009) . Consequently, the governments of developed and developing countries have greatly invested in the development and diffusion of ICTs. Undoubtedly, ICT is a major catalyst for economic development. However, the nexus between ICT and economic development has been the subject of much debate. Some researchers are optimistic about the role of ICT in development (Palvia et al., 2018) , whereas others suggest that ICT alone will not lead to economic development unless accompanied by social changes and other complementary factors (Morales-Gómez & Melesse, 1998) . Thus, the literature is inconclusive on whether ICT is a significant driver of economic development.

Importantly, some scholars have argued that the definitions used to measure ICT maturity in the literature are not comprehensive (Baller et al., 2016; Sridhar & Sridhar, 2008) . Therefore, the assessment of ICT's contribution to economic development might be flawed.

Most of the empirical studies considering the relationship between ICT and economic growth have concluded that a positive and significant relationship exists (Salahuddin & Alam, 2015; Salahuddin & Alam, 2016) . However, the relationship between ICT and economic development is less clear. The concept of 'economic development' is broader than that of 'economic growth'. The latter is concerned with the quantitative expansion of an economy's output, whereas the former includes the qualitative aspects which tend to accompany growth in the narrower sense (Ranis, 2004) . Economic development includes distributive issues of economic growth, that is, income inequality, the composition of social expenditure and measures of political well-being (Jingfeng & Zhao'an, 2018; Ranis, 2004; Srinivasan, 1994) . Considering that ICT affects every area of life rather than simply the productive capacity of an economy, the relationship between ICT and economic development is an important area of study.

Existing studies explaining the nexus between economic development and ICT have used incomplete, partial or single indictor-based definitions of ICT development (Kundu & Sarangi, 2004; Lam & Shiu, 2010; Sridhar & Sridhar, 2008) . Hence, this study aims to investigate the relationship between economic development and ICT maturity levels in the Organisation of Economic Cooperation and Development (OECD) countries using a holistic approach. Unlike the previous studies (Kundu & Sarangi, 2004; Lam & Shiu, 2010; Sridhar & Sridhar, 2008) , the current study follows a comprehensive definition of the overall level of ICT maturity (Ali et al., 2020) to measure the effect of ICT on economic development. Ali et al. (2020) pointed out that the ICT maturity level is comprised of six dimensions of a country's ICT development, namely, access, use, skills, affordability, efficiency and quality. Such dimensions significantly explain the socioeconomic outcomes of a nation. The weightings of these subcomponents are systematically determined through structural equation modelling rather than being arbitrarily defined (for details, see the technical note in Appendix A).

The primary research question of this study is whether any significant relationship exists between economic development and the level of overall development of the ICT sector. To the best of the authors' knowledge, this study is the first attempt to explain the nexus between economic development and ICT maturity using a longitudinal dataset of OECD countries applying a standard panel data estimation framework. Therefore, this study provides several novel contributions to the existing literature. Firstly, this study employs a comprehensive composite index to measure the level of ICT maturity. Then, this index is used to capture the effects of ICT maturity on economic development by employing an advanced panel data estimation framework. In this regard, the study incorporates three new dimensions in measuring the overall ICT maturity level (viz. affordability, efficiency and quality). These dimensions are shown to significantly explain the overall maturity level of ICT alongside conventional considerations of ICT development including access, use and skills (Ali et al., J o u r n a l P r e -p r o o f Journal Pre-proof 2020). Single indicator-and other partial definition-based ICT development indices have several limitations including the following: subjective estimation; bias arising from the estimation of the weights of individual indicators and sub-indices; use of inappropriate quantitative models and faulty estimation arising from the exclusion of important dimensions including affordability, quality and efficiency aspects of ICT from the estimation models (Ali et al., 2020; Hair et al., 1995) . Secondly, the panel data estimation techniques used in this study yield a reliable and accurate estimate than previous studies of the association between economic development, ICT maturity and other macro-economic and governance variables.

The nexus between economic performance and technological advancement is deeply rooted in established theories of economic growth and development. For example, Solow's growth model postulates that economic growth can be generated in an economy by accelerating technological change (Solow, 1957) . Subsequently, the endogenous growth theory developed by Romer (1990) and the capability theory of Sen (1985) indicated that technological progress can significantly affect the economic development process. On this view, the well-being of a nation is ultimately determined by its capabilities. ICTs, specifically internet access, enhance human capabilities by assisting in communication and information acquisition. Following this approach, an extensive body of empirical study has investigated the effect of ICT on economic development (Asongu & Le Roux, 2017; Bankole et al., 2013; Palvia et al., 2018) . In these studies, proxy variables, such as human development (Ashraf et al., 2015; Asongu & Le Roux, 2017; Budd & Ziegler, 2017; Walsham, 2017) , quality of life (Chiao & Chiu, 2018; Kadijevich et al., 2016) and well-being (Ganju et al., 2015; Palvia et al., 2018) , have been used to define economic development.

Focusing on different target populations, some studies have reported a positive correlation between ICT use and quality of life (Chiao & Chiu, 2018; Kadijevich et al., 2016) . Studies J o u r n a l P r e -p r o o f Journal Pre-proof have also shown that at the national level, the economic well-being of a nation is dependent on its ICT infrastructure (Czernich et al., 2011; James, 2014; Nouinou et al., 2015) . At the micro level, studies have examined the impact of ICT use on the quality of work and personal lives (De Wet et al., 2016; Gopinathan & Raman, 2016) . These studies indicate that ICT has played a significant role in enhancing the quality of life by maintaining a balance between working life and the personal one.

Other studies exploring the nexus between ICT and human development have shown that ICT diffusion promotes inclusive human development (Asongu & Le Roux, 2017; Brown & Brown, 2008) . Moreover, empirical research has found that ICT positively contributes to the development of indigenous communities (Ashraf et al., 2015; Madden et al., 2012) .

Community-based studies also reported that ICT can act as a catalyst to promote development at the community level by mitigating social constraints (Ashraf et al., 2017) . A few studies have claimed that ICT can augment human development by enhancing health-related outcomes (Bankole et al., 2013; UN, 2010) . However, Samoilenko and Osei-Bryson (2016) counterintuitively revealed that the annual revenue in the telecommunications sector had no significant influence on human development.

Another group of studies attempt to relate ICT use with socio-economic and mental well-being.

For example, Ganju et al. (2015) and Palvia et al. (2018) indicated that ICT use significantly improves a country's economic well-being by alleviating poverty, whereas Sims et al. (2017) noted a positive association between ICT use and mental well-being. Moreover, some studies demonstrated that ICT-based programmes improved the mental well-being of young people by not only facilitating entertainment and socialising activities but also providing access to mental health information and support (Ellis et al., 2012; Stephens-Reicher et al., 2011) . Studies have also suggested that social media can be used as a potential source of data on health to implement J o u r n a l P r e -p r o o f Journal Pre-proof policies to enhance well-being. Expressed differently, social media can be used to disseminate public messaging and model population sentiment (Yeung, 2018) .

In contrast to the findings of the aforementioned studies, a few studies have highlighted ICT's negative effects. For instance, Nimrod (2018) found that technostress has a negative influence on life satisfaction. In addition, Bekaroo et al. (2016) argued that the widespread adoption of ICT drained a substantial amount of energy and power, thereby leading to complex environmental problems with severe repercussions on people's quality of life. Morawczynski and Ngwenyama (2007) noted that ICT maturity alone cannot promote economic development;

other factors are also important. For example, income growth has an indirect effect on human development as it promotes literacy and health outcomes by mobilising private and public resources (Ranis, 2004) . Moreover, social expenditure on health and education are significant predictors of economic development (Ranis et al., 2000) . Research has shown that the quality of the environment is a significant predictor of economic development (Jingfeng & Zhao'an, 2018; Shahiduzzaman & Alam, 2014; Shahiduzzaman & Alam, 2017) . Existing research has also shown that the ideological stance of a country's ruling political party affects its overall economic development by inducing partisan cycles in society's savings, investment and capital stocks (Aidt et al., 2018; Potrafke, 2017) . More specifically, accelerated investment attributed to economic conservatism initiated by right-wing parties seems to drive developed countries to the path of economic development (Aidt et al., 2018; Potrafke, 2017) . Nevertheless, Srinivasan (1994) pointed out that measures of political well-being and income inequality are the major determinants of human development, which have been ignored in the theorisation of UNDP's Human Development Index (HDI). In this regard, income inequality addresses the distributional aspects of economic growth and also indicates whether economic development is inclusive. Moreover, several studies indicate that globalisation is positively correlated with economic development (Dreher, 2006; Pleninger & Sturm, 2020; Ulucak et al., 2020) . Although the existing literature convincingly demonstrates the association between particular dimensions of ICT maturity and economic development, there is a lack of empirical investigation of this relationship using a comprehensive measure of the ICT maturity level at a cross-country setting. In particular, these studies have used either access to ICT infrastructure or use of ICT devices at the expense of other factors that we consider important (Asongu & Le Roux, 2017; Ganju et al., 2015; Palvia et al., 2018) . To this end, the current study employs the modified ICT maturity level index (MIMLI) to investigate the ICT-development nexus based on the novel and comprehensive ICT development measurement index computation method formulated by Ali et al. (2020) . MIMLI incorporates new dimensions of ICT development including affordability, efficiency and quality alongside conventional considerations of access, use and skills. Recent empirical evidence has shown that the exiting ICT development indices including ICT development index (IDI) are not a comprehensive measurement of ICT maturity as IDI ignores significant factors, such as the quality, affordability and institutional efficiency of the telecommunication services (Baller et al., 2016; Raghupathi & Wu, 2011; Sridhar & Sridhar, 2008) . This study fills the gap in the literature by applying a highly comprehensive composite index of ICT maturity to the question of whether ICT maturity promotes economic development. To do so, the study deploys a standard panel data estimation approach using a balanced longitudinal dataset of OECD countries.

This study incorporated several control variables to explore the association between economic development and ICT maturity level. The selection of the variables was based on the existing literature (see Section 2 and Table 1 for details). The data were collected from several international databases, including Cornell University (2017) [ Table 1 about here] Table 1 provides the definitions of the variables included in the models along with the corresponding summary statistics. In this study, the dependent variable is economic development, the explanatory variable is ICT maturity level and remaining variables are control variables (for details, see Section 3.2 on model specification). The main variable of interestthe MIMLI is computed following the study of Ali et al. (2020) . This study uses the MIMLI score estimated through the formative measurement model for using the PLS-SEM for the period 2006-2015. The index building mechanism is thoroughly discussed in details in Appendix A as an Online Supplement to this article. This index is an extension of the IDI (ITU, 2009) and modified IDI (Gerpott & Ahmadi, 2015) . The index consists of six sub-indices: access, use, skills, affordability, efficiency and quality (a detailed description of the variables used in constructing the index is in Appendix Tables A1 and A2) .

Here, Scales are defined as follows: HDI, 0 to 1; Gini index, 0 to 1; MIMLI, 1 to 10;

OverGovtIdeol, 1 to 10; and Globalisation, 1 to 100. Except the Gini index, a higher score indicates better performance in all indices. GDPG, PE, Forest and VoteShare are expressed in percentage form so as to exhibit less volatility. GovtEff is a multinominal categorical variable ranging from -2.5 to +2.5. Appendix Table B provides the summary statistics of the variables in the panel dimension. The descriptive statistics demonstrate that there are disparities in terms of the level of economic development and ICT maturity among OECD countries.

[ Table 2 about here] Table 2 reports the correlation matrix amongst the dependant, explanatory and control variables. Moreover, Table 2 shows that the correlation coefficient between economic J o u r n a l P r e -p r o o f Journal Pre-proof development and ICT maturity is very high (0.7747) and statistically significant. The correlation of economic development is positive and statistically significant with other explanatory variables including GDPG (0.0939), government expenditure (0.6686), government effectiveness (0.7855) and globalisation (0.6224). As expected, economic development is negatively correlated with income inequality (0.5561).

This study deploys a set of panel data estimation models to investigate the association between economic development and the ICT maturity level. The selection of variables is determined by the following factors: (i) the theoretical foundations of the study rooted in endogenous growth theory (Romer, 1990) and capability theory (Sen, 1985) ; and (ii) the extensive body of literature outlined in Section 2. The estimation specification is as follows:

where i stands for a given country and t represents the year. HDIit represents the value of the 

Two estimation techniques have been used to estimate the hypothesised models: (i) Panel feasible generalised least squares (FGLS) method is used to estimate the baseline model, and

(ii) generalised method of moments (GMM) is applied to estimate the robustness of the baseline estimations.

Generalised least squares (GLS) is commonly used to estimate the unknown parameters in a linear regression model where a certain degree of potential correlation exists amongst the residuals. In these cases, ordinary least squares (OLS)-based estimations will be statistically inefficient, which can lead to flawed inferences (Wooldridge, 2010) . The FGLS method is prescribed where heteroscedasticity problem can potentially arise (Wooldridge, 2010) . The procedure to run FGLS to correct for heteroscedasticity is as follows:

(i) Run the regression of the dependent variable (y) on explanatory variables (x1, x2, …, xk) and obtain the regression residuals, ̂.

(ii) Estimate log(̂2) by firstly squaring the OLS regression residuals and then taking the natural log.

log(̂2) = 0 + 1 1 + 2 2 + … … . + + … .

(3)

(iii) Run the regression of log(̂2) on 1 , 2 , … … . and obtain fitted values, ̂.

(iv) Exponentiate the fitted values from the previous step and estimate the following:

Finally, estimate the following equation

GMM estimation is used to check the robustness of the baseline estimations. This method necessitates that a particular number of moment conditions are to be specified for the regression model (Wooldridge, 2010) . (Bond et al., 2001) . The specification used in this study is based on Roodman (2009a Roodman ( , 2009b which is an extension of Arellano and Bover (1995) . This extension checks for cross-sectional dependency and restricts instrument proliferation (Asongu & Nwachukwu, 2018; Tchamyou & Asongu, 2017; Tchamyou et al., 2019) . 

The conventional OLS or panel data-based regression (e.g. fixed effect or random effect estimation) is not appropriate in this case as the regression coefficients would yield biased and inconsistent estimates due to heteroscedasticity and autocorrelation issues. To cope with this problem, FGLS is used to conduct baseline estimations (Romano & Wolf, 2017; Wooldridge, 2010) . [ Table 3 about here]

A series of further robustness checks was conducted to ascertain the stability of our results.

Only the regression estimates using Eq. 2.3 have been reported in each case due to space constraints and to keep the paper simple.

A GMM estimation of the proposed regression model was conducted to examine the robustness checks one step further. GMM, developed by Arellano (1991) and Arellano and Bover (1995) , is widely used to check for potential endogeneity in a dynamic panel model. Difference and system GMM have been applied to estimate the impact of ICT maturity on economic development as outlined in Eq. 6.

In Eq. 6, GDPG, Gini index and PE are assumed to be endogenous. Based on existing literature, Table 4 report the GMM estimations using difference and system GMM, respectively. In both cases, the coefficient of ICT maturity is positive and statistically significant. This finding implies that the the Sargan and Hansen OIR shows that the null hypothesis of the overall endogeneity of the instruments used in the estimation cannot be rejected. In sum, these results suggest that the instruments used in the model are valid and reliable.

Any [ Table 4 about here]

In addition to above-mentioned robustness estimations, baseline estimations have been supplemented by corresponding OLS-based estimations (Table 4 ) incorporating fixed period (column 5) and fixed country effects (column 6). As expected, the inferences remain the same compared with the baseline and other robust estimations.

Whether the assumptions of the OLS are violated before conducting the baseline estimations following Eqs. 2.1, 2.2 and 2.3 needs further research. Thus, a series of diagnostic tests was conducted after running the OLS regression using the variables of those aforementioned equations. Table 5 shows a summary of the diagnostic tests. Moreover, Table 5 shows that all models suffer from the presence of group-wise heteroskedasticity and autocorrelation problems, but they are free from the problem of multicollinearity. The variance inflation factor for the explanatory variables of Eq. 2.1 ranges from 1.16 to 3.37; for Eq. 2.2, between 1.11 and 2.33; for Eq. 2.3, between 1.07 and 2.33.

[ Table 5 about here]

As mentioned in the literature review, a significant association between ICT maturity and economic development has been found. However, those studies relied on partial definitions (Asongu & Le Roux, 2017; Bankole et al., 2013; Ganju et al., 2015; Nouinou et al., 2015; Palvia et al., 2018; UNDP, 2017) . The current study explains the relationship between ICT maturity and economic development using a composite index of ICT maturity consisting of access, use, skills, affordability, efficiency and the quality dimensions of ICT. Using cluster J o u r n a l P r e -p r o o f analysis based on a novel index, this study demonstrates that ICT maturity significantly catalyses economic development. The current research also differs from previous empirical work in the methodological approaches undertaken. This study employs a panel data estimation framework to investigate the influence of ICT maturity on economic development at the country level during the post-treatment period coupled with innovation efficiency. Although evidence demonstrates a causal nexus between innovation and ICT maturity, evidence on how ICT leads to economic development is limited (Arendt & Grabowski, 2017; Billon et al., 2016; Pradhan et al., 2017) . The result of the regression estimations shows that ICT maturity has a significant positive effect on economic development.

A detailed picture of the association between economic development and ICT maturity would, of course, require analysis of the concrete relationship between ICT and development in particular countries. Although a full analysis of this sort is beyond the scope of this study, some descriptive statistics for particular countries are instructive insofar as they suggest areas for further research. ITU (2015) reported that the top three countries that have dynamically improved their position in terms of ICT development in recent times are Denmark, Iceland and the Republic of Korea. Our findings also suggest that throughout 2006-2015, the MIMLI in these three countries surged from 7.05 to 9.32, 7.01 to 9.07 and 7.12 to 9.39, respectively (Appendix Table B ). Following the trend of their respective advancement in the ICT maturity level, during that period, the economic development (measured by HDI) in those three economies also rose from 0.90 to 0.93, 0.89 to 0.92 and 0.87 to 0.90, respectively (Appendix Table B ). The correlation between economic development and ICT maturity for Denmark is the highest (0.9254) followed by Iceland ( The current study has also shown that inequality is another factor that slows economic development by impeding the distributional efficiency. This finding is consistent with that of earlier empirical studies (Chiao & Chiu, 2018; Ganju et al., 2015; Ranis et al., 2000) . In addition, the findings reported in earlier empirical investigations, government effectiveness, the vote share and ideology of the ruling political parties were also found to be significant predictors of economic development. This result implies that economically right-wing parties tend to promote economic development. In sum, these results imply that good governance and sound policy regulations arising from a strong government contribute positively towards economic development. These results must be treated with caution because they are primarily included in this study as control variables. However, they are broadly consistent with focussed works of political economists (Aidt et al., 2018; Karimi & Heshmati Daiari, 2018; Potrafke, 2017; Srinivasan, 1994) . The current study also found that globalisation positively affects economic development. This finding is congruent with the findings of a couple of relevant studies which reported that globalisation has a positive relationship with human development in developed countries (Atif et al., 2012; Borjas & Ramey, 1994) . As suggested by endogenous growth theory, this positive association between globalisation and economic development might be mediated through technological advancements. Theses advancements can be attributed to relaxation or removal of trade barriers as a part of pro-globalisation measures (Ulucak et al., 2020) . However, several studies reported that globalisation has no significant positive effect on economic development (Haseeb et al., 2020; Ulucak et al., 2020) . These J o u r n a l P r e -p r o o f results can be explained by the low level of globalisation associated with trade barriers that prevail in developing economies. In line with these findings, several scholars argued that the direction of the association between economic development and globalisation varies between developed and emerging economies due to the differentiated economic condition (Atif et al., 2012; Haseeb et al., 2020) .

This empirical study investigates whether economic development responds to ICT maturity in OECD countries. To do so, a series of baseline estimations is enumerated. The study reveals that ICT maturity enhances economic development by approximately 1%-3.8% in those countries. Among the control variables, GDP growth, government expenditure, environmental quality and governance and policy variables are found as significant predictors of economic development. These findings were supported by a series of robustness checks including GMM estimations.

The findings of this empirical work have practical implications. The results imply that the holistic development of ICT can augment economic development. In this regard, ICT laggards should seek to draw lessons from the successes of recent ICT success stories, such as Denmark,

Iceland and the Republic of Korea. The success of these countries suggests that easing ICT use and enhancing ICT skills are crucial dimensions to ICT development. More generally, at the country level, policymakers should devise policies to enhance the affordability of ICT services.

In this study, fiscal measures and regulatory reform are possible pathways to reduce barriers to entry and prevent anticompetitive behaviour.

On a cautionary note, the present investigation has some limitations. Firstly, the findings show a general association between ICT maturity and economic development in OECD countries.

However, this finding does not provide the details of this relationship or the specific policies J o u r n a l P r e -p r o o f which can be formulated to promote ICT maturity. A detailed country-level case study or qualitative comparative analysis designed to reveal the mechanisms through which ICT maturity promotes economic development would be helpful in this regard. Secondly, we have been unable to consider the effect of ICT diffusion on inclusive development in this study, although income inequality has been included as a control variable. This limitation is because the UNDP reports inequality-adjusted HDI only every 5 years rather than annually. The connection between ICT and socioeconomic disadvantage is of great importance. Further research at the micro level or targeted for particularly disadvantaged groups would be worthwhile insofar the effect of uneven ICT development and socioeconomic inequality could be revealed. Finally, this study was conducted before the COVID-19 pandemic and does not speak directly to the effects thereof. ICT maturity is unlikely to have a significant effect on the extent to which countries are able to withstand the severe disruptions to work practices and brick-and-mortar sales. Further study in this area will be valuable once post-outbreak economic data become available.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Note:*, ** and *** denote statistically significant at 1%, 5% and 10%, respectively.

J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f

Shades of red and blue: government ideology and sustainable development

Incorporating affordability, efficiency, and quality in the ICT development index: Implications for index building and ICT policymaking

Trade Openness, Factor Productivity, And Economic Growth: Recent Evidence From Oecd Countries

Econometric Modelling of Tax Impact on Economic Growth: Panel Evidence from OECD Countries

Some tests of specification for panel data:monte carlo evidence and an application to employment equations

Another look at the instrumental variable estimation of error-components models

Innovations, ICT and ICT-driven labour productivity in Poland: A firm level approach

An extended conceptual framework to understand information and communication technology-enabled socioeconomic development at community level in Bangladesh

Impact of ICT usage on indigenous peoples' quality of life: Evidence from an Asian developing country

The murder epidemic: A global comparative study

Enhancing ICT for inclusive human development in Sub-Saharan Africa

Openness, ICT and entrepreneurship in sub-Saharan Africa

Globalization and income inequality: a panel data analysis of 68 countries

Global information technology report

The impact of ICT investments on human development: A regression splines analysis

Impacts of ICT on the natural ecosystem: A grassroot analysis for promoting socio-environmental sustainability

ICT use by households and firms in the EU: links and determinants from a multivariate perspective

GMM estimation of empirical growth models

Time-series evidence on the sources of trends in wage inequality

Next generation ICT policy in South Africa: Towards a human development-based ICT policy

Social Innovation and the Capability Approach-Introduction to the Special Issue AU -Chiappero-Martinetti

The Mediating Effect of ICT Usage on the Relationship Between Students' Socioeconomic Status and Achievement

Parliaments and governments database (ParlGov): Information on parties, elections and cabinets in modern democracies. Development version

Does globalization affect growth? Evidence from a new index of globalization

Young men, mental health, and technology: Implications for service design and delivery in the digital age

Internet and society: A summary of research reported at WebShop

Does information and communication technology lead to the well-being of nations? A country-level empirical investigation

Composite indices for the evaluation of a country's information technology development level: Extensions of the IDI of the ITU

Foreign Direct Investment, Female Education, Capital Formation, and Economic Growth in Japan and South Korea

Information system quality in work-life balance. Knowledge Management and E-Learning

The KOF Globalisation Indexrevisited

Multivariate Data Analysis

Nexus Between Globalization, Income Inequality and Human Development in Indonesian Economy: Evidence from Application of Partial and Multiple Wavelet Coherence

Measuring the information society-The ICT development index

Yearbook of Statistics: Telecommunication/ICT Indicators

Internet Use, Welfare, and Well-Being: Evidence From Africa

Research on coupling relationship between environmental quality and regional economic growth based on VAR model

Healing through ICT: enhancing wellbeing in an Aboriginal community

Utilising information and communication technologies for development: The social dimensions

Unraveling the impact of investments in ICT, education and health on development: an analysis of archival data of five West African countries using regression splines. The Electronic Journal of Information Systems in Developing Countries

Technostress: measuring a new threat to well-being in later life

Big data: Measuring how information technology can improve the economic growth and better life. Paper presented at the Information and Communication Technologies (WICT)

Mapping theoretical and practical issues in the relationship between ICTs and Africa's socioeconomic development

OECD science, technology and industry outlook

Digital development in Korea: Building an information society

ICT for socio-economic development: A citizens' perspective. Information and Management

The effects of economic globalisation and ethnic fractionalisation on redistribution

Partisan politics: The empirical evidence from OECD panel studies

The Relationship Between Information and Communication Technologies and Country Governance: An Exploratory Study

Human development and economic growth

Economic growth and human development

Resurrecting weighted least squares

Endogenous technological change

How to do xtabond2: An introduction to difference and system GMM in Stata

A note on the theme of too many instruments

Internet usage, electricity consumption and economic growth in Australia: A time series evidence

Information and Communication Technology, electricity consumption and economic growth in OECD countries: A panel data analysis

Human Development and Macroeconomic Returns within the Context of Investments in Telecoms: An Exploration of Transition Economies

Well-being, agency and freedom: The Dewey lectures 1984

A reassessment of energy and GDP relationship: The case of Australia

Trade-off between CO2 emissions and income: is there any evidence of an Environmental Kuznets Curve in Australia?

Information and communication technology use is related to higher well-being among the oldest-old

Technical change and the aggregate production function

Telecommunications infrastructure and economic growth: Evidence from developing countries

Human development: a new paradigm or reinvention of the wheel?

Reaching the hard-to-reach: How information communication technologies can reach young people at greater risk of mental health difficulties

Information sharing and financial sector development in Africa

Inequality, ICT and financial access in Africa

The nexus between economic globalization and human development in Asian countries: an empirical investigation

Financing Mechanisms for Information and Communication Technology for Development

World Income Inequality Database -WIID3

Information Technology, Innovation and Human Development: Hospital Information Systems in an Indian State AU -Sahay

Econometric Analysis of Cross Section and Panel Data

World Development Indicators

Social media as a catalyst for policy action and social change for health and well-being: Viewpoint

The paper is a part of the PhD study of the first author. The PhD program was funded by the