Introduction

The information and communication technologies (ICTs) have changed the world in which we live tremendously in the past few decades. Someone referred to the reality that ICT plays a key function in social change. ICT not only connects people but also has the potential to maximize commitment, transparency, responsibility and human development (Iqbal et al. 2020; Latif et al. 2018). ICT is able to grow firms’ economic prosperity and earnings and to create jobs that have a big impact, which would lead countries to increase expenditure on research and development activities and to the introduction of a new ICT system. This deposit has created new ICT innovations and fostered further global economic growth (Nair et al. 2020). Advances in ICT have also brought together many parts of the world that encouraged developing countries to adopt the technology used by developed nations, thus enhancing production and faster economic growth in former countries. In the recent decades, the new information structure has improved and promoted ICT in a better way (Sahoo and Mathiyazhagan 2003; Anitha 2012; Temiz and Gökmen, 2014a, b; Sinha and Sengupta 2019).

Information and telecommunication technology has always played a significant part in economic and social growth. The principle explanation for the social, economic and cultural stagnation in developed countries has now been widely accepted as being that they cannot create, grow and use the best technology in the production operations. The extent and effect of this technology have been one of today’s most critical problems in the world and has captured the world’s attention (Badri et al. 2019; Lu 2018; Batool et al. 2019). In the foreign development network, the rapid developments in knowledge and communication technologies and growing economic globalization provide new possibilities for developed countries to take part in global output, thereby allowing these countries to enhance their competitiveness. Competitiveness internationally covers not only trade liberalization, but also the growing economic value of the ICT market. Developing economies are also experiencing several obstacles to develop and optimize the advantages of the ICT industries associated with these economies. The ICT sector’s characteristics differ from those of the other sectors in the economy, especially as the prices of goods begin to decrease (Fakher 2016; Anwar et al. 2016; Asteriou et al. 2014; Haseeb et al. 2019).

Several studies have emphasised the connection amid economic progress, FDI, urbanization, income inequality, international tourism and human capital decrease (John 2016; Gönel and Aksoy 2016; Tee et al. 2017; Duarte et al. 2017; Sultanuzzaman and Shakij 2018; Masipa 2018; Hammami and Dal Zotto 2020; Samir and Mefteh 2020; Rehman et al. 2021; Alvarado et al., 2021a, b; Rehman et al. 2021; Alvarado et al., 2021a, b), but the primary motive of the present analysis was to demonstrate the association amid gross domestic product growth, ICTE, foreign direct investment, trade and renewable energy in Pakistan. Two unit root test including ADF and the Phillips-Perron (P-P) was utilized to check the variable stationary and series stability, while an autoregressive distributed lag bounds testing approach was employed to verify the variable linkages with long- and short-run causal dynamics. Furthermore, cointegrating regression analysis with FMOLS, DOLS and CCR techniques was also applied to check the variable’s association.

In addition to the introduction, the remaining part of the paper is organized as follows: ‘Existing literature’deals with the previous literature on the topic, while ‘Methods and data’ provides data, an econometric approach and unit root tests as well as the ARDL model. The results of various tests were seen in ‘Empirical results’. The study conclusion and policy proposals are illustrated in the ‘Conclusions and recommendations’.

Existing literature

The production of information and communication technology gives developed countries unique prospects for change in many prospects. It eliminates historical and geographical constraints and makes it possible, as industrialized economies, and in manufacturing and foreign exchange, to perform more effectively. A well-developed information technology infrastructure can contribute to drastically evolving environment for foreign affairs and can improve strategic advantages and enhance socio-economic growth opportunities. The information technology resources may shape an interconnected network between individuals, enterprises and administrations in various economies all over the world. Advanced telecommunication networks need to be established in emerging countries to compete in the dynamic international market and to gain new foreign investment (Avgerou 1998; Bon et al. 2016; Jorgenson and Vu 2016; Niebel 2018). Increased ICT facilities and logistical assistance may influence internal markets and reduce the adverse externalities of ICT instruments. This is one of the reasons which draw FDI directed to exports. Advancement in the technology has also improved accountability and has become the main factor of foreign investment, the path to good governance and the elimination of corruption. In democracies, power sharing is more open than defending property owners’ rights and reducing the danger of foreign direct investment, thus enhancing the capacity to fascinate the investment especially in emerging economies (Bankole et al. 2011; Soper et al. 2012; Lindo 2017; Lee et al. 2018).

The exponential rise of ICT in global products and services development impacts all economies and impregnates civilization as a whole. The key motive factors for economic growth by improved production and human advancement are both related to knowledge and communication technologies. Obviously, developments in ICTs have altered the forms of jobs, qualifications, roles and work activities in organizations, agencies and community as well as impacted their efficiency (Badri et al. 2016; Hassan et al. 2019; Kurniawati 2020). Investment now seems to be a crucial element in every country’s production and growth. Specifically, a few years back, emerging countries claimed that the weapon used by established countries to regulate their economy was a foreign direct investment. The emergence of globalization and its resulting systemic reforms and foreign law, however, would facilitate economic cooperation and remove the gap between countries that had once crippled trade (Abdouli and Hammami 2017; Sothan 2017; Bermejo Carbonell and Werner 2018).

The standard and effect on financial sector production are thus prerequisites for successful economic progress along with recognition of other economic characteristics. Consequently, the economic growth influence and FDI will rely on the financial sector’s development in each region. Indeed, funds from foreign investors cross the financial framework of a nation until it is assigned to the respective economic sectors. In the banking market, direct international investment and economic development are intermediate (Acquah and Ibrahim, 2020; Hossain 2016; Williams 2017; Tahir et al. 2019). Foreign direct investment will improve technology transfer and thereby enhance factors’ output performance. It is reasonable to assume that technical developments are converted into an improvement in labour efficiency, culminating in an increase in capital production. Given the value of technology and expertise in growing output levels, if economic growth is powered by innovation, it is appropriate for foreign direct investment to boost progress (Azman-Saini et al. 2010; Barro and Lee 2013; Szkorupová 2014).

Appropriate alternatives to different issues are sought of foreign direct investment. FDI offers important resources, technologies and expertise, to support economic growth and development. Moreover, new opportunities across the globe can be more readily obtained by foreign direct investment. In fact, several factors, such as skilled and qualified workforce and productivity, have a significant effect on the recruiting and retention of foreign direct investment (Shaari et al. 2014; Monaghan 2012; Almfraji and Almsafir 2014). The ICTs are one of the key industries that foster economic development in the present age of globalization. It fosters technical advances in digital connectivity and industrial technology and stimulates economic growth and development. Once globalization has started, countries need to strengthen their networks of expertise and exchange in order to provide good stewards for the transfer of information inside and through countries. Globalization is necessary and encouraging the universal and rapid use of ICT systems (Kaware and Sain 2015; Olusanya 2013; Jinadu et al. 2016; Bhujabal and Sethi 2020).

Significantly larger utilization of IT (Information Technology) has lowered communication costs and consequently improved the flow and performance of information. ICT is a sign of technical progress and a central element in developed countries’ economic progress (Farhadi et al. 2012; Bertschek et al. 2015; Aghaei and Rezagholizadeh 2017). Trade and investment usually has a beneficial influence on economic development, as it tends to construct physical resources, to generate jobs, to improve potential in production and to strengthen local labour skills. The study finds that increased influxes of FDI in South Asia were connected to the multiple rises in national investors’ spending, which indicates that there is a correlation impact between foreign investment and gross domestic product (Agrawal 2000; Li and Liu 2005; Anwar and Nguyen 2010).

Methods and data

An autoregressive distributed lag (ARDL) bounds testing technique with cointegrating regression analysis was used to verify the variable association. All variable time series data were taken from the WDI (https://data.worldbank.org/country/pakistan), and the data range is 1985–2017. Study variables are ICTE, foreign direct investment, trade and renewable energy. The trends of the variables are plotted in Fig. 1.

Fig. 1
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Plot of variables trends

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Econometric model specification and unit root tests

In order to demonstrate the association amid variables, we can specify the following model as:

$$ {\mathrm{GDPG}}_{\mathrm{t}}=\mathrm{f}\left({\mathrm{FDIN}}_{\mathrm{t}},{\mathrm{ICTE}}_{\mathrm{t}},{\mathrm{TRA}}_{\mathrm{t}},{\mathrm{RECO}}_{\mathrm{t}}\right) $$
(1)

Equation (1) explains that GDPGt denotes GDP growth, FDINt is showing foreign direct investment, variable ICTEt specifies the information and communication technology, variable TRAt illustrates trade in Pakistan and RECOt denotes renewable energy consumption. Furthermore, we can also write Eq. (1) as:

$$ {\mathrm{GDPG}}_{\mathrm{t}}={\upzeta}_0+{\upzeta}_1{\mathrm{FDIN}}_{\mathrm{t}}+{\upzeta}_2{\mathrm{ICTE}}_{\mathrm{t}}+{\upzeta}_3{\mathrm{TRA}}_{\mathrm{t}}+{\upzeta}_4{\mathrm{RECO}}_{\mathrm{t}}+{\upvarepsilon}_{\mathrm{t}} $$
(2)

Log-linear model can be demonstrated by using logarithmic form of all variables and specified as:

$$ \mathrm{Ln}\left({\mathrm{GDPG}}_{\mathrm{t}}\right)={\upzeta}_0+{\upzeta}_1\mathrm{Ln}\left({\mathrm{FDIN}}_{\mathrm{t}}\right)+{\upzeta}_2\mathrm{Ln}\left({\mathrm{ICTE}}_{\mathrm{t}}\right)+{\upzeta}_3\mathrm{Ln}\left({\mathrm{TRA}}_{\mathrm{t}}\right)+{\upzeta}_4\mathrm{Ln}\left({\mathrm{RECO}}_{\mathrm{t}}\right)+{\upvarepsilon}_{\mathrm{t}} $$
(3)

Equation (3) verifies the logarithmic form all variables including ICTE, FDI, trade and renewable energy. Time measurement is denoted by t, and error term is denoted by εt, where ζ1 to ζ4 are illustrating the model coefficients.

In addition, all variables with an ARDL model have been tested for the unit root in a stationary manner which can be specified as:

$$ \Delta {\mathrm{E}}_{\mathrm{t}}={\upalpha}_{{}^{\circ}}+{\updelta}_{{}^{\circ}}\mathrm{K}+{\updelta}_1{\mathrm{L}}_{\mathrm{t}-1}+\sum \limits_{\mathrm{i}=1}^{\mathrm{m}}{\upbeta}_1\Delta {\mathrm{L}}_{\mathrm{t}-1}+{\varepsilon}_t $$
(4)

Equation (4) illustrates the unit root test, where Δ is differential operator.

ARDL model specification to cointegration test

Present analysis followed a research method developed by Pesaran and Shin (1998) for ARDL and continued by Pesaran et al. (2001) further to define the relation entreaty and independent variable with long- and short-run assessments. The cointegration testing method takes place regardless of the order in which the variables I(0) and I(1), rather than I(2), are merged. The F-statistical calculation is used for evaluating the co-movement of the long-term regression between the research variables of interest. As in uncontrolled with UECM, ARDL model can generally be specified as:

$$ \Delta {\mathrm{LnGDPG}}_{\mathrm{t}}={\upvartheta}_0+\sum \limits_{\mathrm{i}=1}^{\mathrm{u}}{\upvartheta}_{1\mathrm{j}}\Delta {\mathrm{LnGDPG}}_{\mathrm{t}-\mathrm{l}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{u}}{\upvartheta}_{2\mathrm{j}}\Delta {\mathrm{LnFDIN}}_{\mathrm{t}-\mathrm{l}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{u}}{\upvartheta}_{3\mathrm{j}}\Delta {\mathrm{LnICTE}}_{\mathrm{t}-\mathrm{l}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{u}}{\upvartheta}_{4\mathrm{j}}\Delta {\mathrm{LnTRA}}_{\mathrm{t}-\mathrm{l}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{u}}{\upvartheta}_{5\mathrm{j}}\Delta {\mathrm{LnRECO}}_{\mathrm{t}-\mathrm{l}}+{\upvartheta}_6{\mathrm{LnGDPG}}_{\mathrm{t}-1}+{\upvartheta}_7{\mathrm{LnFDIN}}_{\mathrm{t}-1}+{\upvartheta}_8{\mathrm{LnICTE}}_{\mathrm{t}-1}+{\upvartheta}_9{\mathrm{LnTRA}}_{\mathrm{t}-1}+{\upvartheta}_{10}{\mathrm{LnRECO}}_{\mathrm{t}-1}+{\upvarepsilon}_{\mathrm{t}} $$
(5)

where Δ displays a differential operator, u displays the lag order and εt is the error expression. Long-run analyses between variables can be defined as:

$$ \Delta {\mathrm{LnGDPG}}_{\mathrm{t}}={\upbeta}_0+\sum \limits_{\mathrm{i}=1}^{\mathrm{m}}{\upbeta}_{1\mathrm{i}}\Delta {\mathrm{LnGDPG}}_{\mathrm{t}-\mathrm{i}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{m}}{\upbeta}_{2\mathrm{i}}\Delta {\mathrm{LnFDIN}}_{\mathrm{t}-\mathrm{i}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{m}}{\upbeta}_{3\mathrm{i}}\Delta {\mathrm{LnICTE}}_{\mathrm{t}-\mathrm{i}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{m}}{\upbeta}_{4\mathrm{i}}\Delta {\mathrm{LnTRA}}_{\mathrm{t}-\mathrm{i}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{m}}{\upbeta}_{5\mathrm{i}}\Delta {\mathrm{LnRECO}}_{\mathrm{t}-\mathrm{i}}+{\upvarepsilon}_{\mathrm{t}} $$
(6)

m presents the lag order in the Eq. (6). In an ECM (error correction model), the short-run dynamics between variables are shown in the ARDL technique and specified as:

$$ \Delta {\mathrm{LnGDPG}}_{\mathrm{t}}={\mathrm{a}}_0+\sum \limits_{\mathrm{i}=1}^{\mathrm{f}}{\mathrm{a}}_{1\mathrm{i}}\Delta {\mathrm{LnGDPG}}_{\mathrm{t}-\mathrm{i}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{f}}{\mathrm{a}}_{2\mathrm{i}}\Delta {\mathrm{LnFDIN}}_{\mathrm{t}-\mathrm{i}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{f}}{\mathrm{a}}_{3\mathrm{i}}\Delta {\mathrm{LnICTE}}_{\mathrm{t}-\mathrm{i}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{f}}{\mathrm{a}}_{4\mathrm{i}}\Delta {\mathrm{LnTRA}}_{\mathrm{t}-\mathrm{i}}+\sum \limits_{\mathrm{i}=1}^{\mathrm{f}}{\mathrm{a}}_{5\mathrm{i}}\Delta {\mathrm{LnRECO}}_{\mathrm{t}-\mathrm{i}}+{\upalpha \mathrm{ECM}}_{\mathrm{t}-1}+{\upvarepsilon}_{\mathrm{t}} $$
(7)

Equation (7) shows the short-run dynamic where f denotes the lag order. ARDL approach has been used to monitor the diverse ties between GDP growth, ICTE, FDI, trade and renewable energy use. In respect for acceptance and denial of a null hypothesis, in the upper case, the F values measured are smaller than the prescribed limits. The non cointegration-based theories are dismissed and suggest the cointegrated reliance of interaction and the invaders between variables.

Empirical results

Statistical analysis of variables

Table 1 displays the statistical values of all variables and the statistics from Jarque-Bera, and the probability values prove that the variables are frequently distributed.

Table 1 Statistical analysis of the variables
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Correlation amid variables

Table 2 reports the result of the correlation analysis amid GDP growth, ICTE, FDI, trade and renewable energy; outcomes show that all variables are normally correlated.

Table 2 Correlation amid variables
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Unit root test

Table 3 indicates the unit root test outcomes for Augmented Dickey-Fuller (Dickey and Fuller 1981) and Phillips-Perron (Phillips and Perron 1988). Each of the test results revealed that the significance of 1%, 5% and 10% of the variables was not protected by the spectrum of I(2), therefore that the autoregressive distributed lag (ARDL) model was used. Several approaches for cointegration regarding data analysis are presented in the different studies. However, the ARDL model is recommended, because of its numerous advantages, to be stronger and favoured, for instance because not all variables are in the same order in inclusion in the classification. Therefore, because of its simplicity and suitability for models of mixed variables, we chose the ARDL approach for cointegration.

Table 3 Unit root test results
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Optimal lag selection

Table 4 illustrates the results of the lag order selection in order to demonstrate the association amid variables.

Table 4 VAR lag order selection criteria
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Bounds test to cointegration

The results of the ARDL limit test are depicted in Table 5. Calculated F-statistics surpassed the maximum vital bond, which were 7.415524. The cointegration results show the connection between GDP growth, ICTE, FDI, trade and renewable energy.

Table 5 Bounds test to cointegration outcomes
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The variable robustness was confirmed through long-run association by applying Johansen and Juselius cointegration technique (Johansen and Juselius 1990). Trace test and maxeign statistics indicate the rejection of null hypothesis with having nil cointegration.

Johansen cointegration test

Table 6 reports the outcomes of the cointegration test which indicates the hypothesis rejection at 0.05% level.

Table 6 Cointegration test results
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Long- and short-run dynamics

Table 7 reports the outcomes of the long- and short-run analysis amid variables with their coefficients and probability values.

Table 7 Long- and short-run estimation
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Table 7 explains the variables correlation by long- and short-run dynamics. The ECM entails the cointegration amid prescribed variables and presence of short-run analysis and association which used to detain the adjusted measurement. The outcomes via long-run analysis which is illustrated in the Table 7 show that the variables ICTE, trade and renewable energy have constructive coefficients of 0.545437, 0.745343 and 2.072191 with probability values of 0.0769, 0.2280 and 0.0473 correspondingly. During analysis, foreign direct investment exposes an adverse association with GDP growth. The advancement of innovations linked to ICTE has been recognized as the key path to generate further jobs and economic growth. The advancement of information and technology sector has been described as the principal transformer of economic operation and foreign exchange. The expanded usage of information and communication technology lowered correspondence costs and eventually enhanced dissemination and construction of evidence. Information technology is a sign of the improvements in innovations and is a significant economic force in developed countries (World Bank 2017; Xing 2018; Stanley et al. 2018; Rath and Hermawan 2019). Information and communication technology influences economic development, including transport, transport, schooling, utilities, entertainment and science information. Information and communication technology promotes international transfers and allocations of ideas and influences all facets of contemporary life. Moreover, as a consequence of the transformation in the information technology market, human growth is experiencing a change in mindset, the phase in widening the multiple options such as schooling, safe living and living standards (Yakunina and Bychkov 2015).

The development of information technology improves the living conditions and standards of the population, shortens distances, shortens travel and trade, creates investment opportunities and provides new workers. The liberalization has quickly expanded wireless carriers, built up information technology networks and allowed major investments to improve the economy. Secondly, Asian countries are presently undergoing highly precarious economic and human development status (Nath and Liu 2017; Zhang 2019). Another macroeconomic factor shaping economic growth is the influx of foreign direct investment. It directly impacts the economy by providing valuable tangible and intangible properties such as technologies and its related strands, the formation of resources and the creative capacity. It indirectly boosts global production by promoting investment finance for multiple host markets and creates favourable externalities, integrating new management capabilities in a number of sectors, boosting economic performance by creating high quantities of jobs and the government revenues and financing the current account deficit. This stimulates economic development. Dynamic linkages between industrial growth and foreign direct investment influx are therefore critical for consistent political economic policies (Iamsiraroj and Ulubaşoğlu 2015; Temiz and Gökmen, 2014a, b; Sokhanvar 2019). Plots of CUSUM and CUSUM of squares are showed in the Figs. 2 and 3. Furthermore, Figs. 4 and 5 demonstrate one-step forecast test and N-step forecast test.

Fig. 2
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CUSUM plot

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Fig. 3
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CUSUM of squares plot

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Fig. 4
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Plot of one-step forecast test

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Fig. 5
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Plot of N-step forecast test

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Cointegrating regression analysis

The cointegrating regression analysis outcomes are reported in the Table 8 with FMOLS, DOLS, and CCR. The outcomes of Panel A revealed that variables ICTE, FDI and trade have constructive linkage with GDP growth. Similarly Panel B results exposed that ICTE, foreign direct investment and trade have significant association with GDP growth. Furthermore, Panel C results of canonical cointegrating regression also exposed that the coefficients of foreign direct investment, information and communication technology have constructive association with GDP growth, while the variable renewable energy consumption has adverse influence to GDP growth in Pakistan during all panel analyses. Figure 6 clearly demonstrates the outcomes of Panel A (FMOLS), Panel B (DOLS) and Panel C (CCR) amid prescribed variables linkages.

Table 8 Results of FMOLS, DOLS and CCR
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Fig. 6
figure 6

Dynamic association of variables through FMOLS, DOLS and CCR

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Conclusions and recommendations

The major aim of this analysis was to check the dynamic association of ICTE, FDI, trade and renewable energy usage with GDP growth in Pakistan by utilizing data series ranging from 1985 to 2017. Data stationarity was verified by applying unit root tests including ADF and P-P, while an ARDL approach was used to check the dynamic association amid study variables with long- and short-run analysis. Furthermore, cointegrating regression analysis with FMOLS, DOLS and CCR was applied to demonstrate the variable causality. The outcomes during long-run analysis show that ICTE, trade and renewable energy have constructive linkage to GDP growth, while foreign direct investment has adverse influence to GDP growth in Pakistan. Similarly, the outcomes of cointegrating regression analysis exposed that all variables including FDI, information and communication technology and trade have positive and constructive association with GDP growth, while in this analysis, renewable energy consumption causes the adverse association to GDP growth in Pakistan.

Foreign direct investment plays a crucial role in emerging economies. FDI frequently contributes to the transition and raise of new technology to the host country. Economists believe that emerging technologies and high-quality management provide for pressures on domestic foreign investment while keeping the sector competitive. Further, external direct intervention, such as labour control and training opportunities, in developing economies, has introduced significant and positive external factors to improve commodity production. On the basis of the results, the Pakistani government needs potential measures to upgrade data technologies to boost economic development. Several policy proposals for the Pakistani government are presented here. In order to draw national and foreign investors to invest in Pakistan, it is necessary to improve the security and insurance schemes. With a view to minimizing domestic production expense, Pakistan can increase final exports by expanding infrastructure, developing new roads, extending transport facilities, and installing advanced machines.