favorable outcome hypothesis in developing countries

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Government Health Expenditure and Public Health Outcomes: A Comparative Study among EU Developing Countries

Mihaela onofrei.

1 Faculty of Economics and Business Administration, Alexandru Ioan Cuza University, 700505 Iasi, Romania; or.ciau@ierfono (M.O.); [email protected] (E.C.)

Anca-Florentina Vatamanu

Georgeta vintilă.

2 Department of Finance, The Bucharest University of Economic Studies, 010374 Bucharest, Romania; rf.oohay@ategroegalitniv

Associated Data

Data of the retrieved studies are shown in Appendix A .

The aim of this paper was to empirically analyze the relationship between public health expenditure and health outcomes among EU developing countries. Using regression analysis and factor analysis, we documented that public health expenditure and health outcomes are in a long-run equilibrium relationship and the status of health expenditure can improve life expectancy and reduce infant mortality. Secondarily, we studied how the status of good governance, health care system performance, and socioeconomic vulnerabilities affect the public health’s outcomes in the selected countries. We found that the effectiveness of health and the way to reduce infant mortality or to improve life quality is directed conditioned by good governance status. Moreover, the consolidation of health care system performance directly improves the quality of life among EU developing countries, which indicates that public policymakers should intervene and provide political and financial support through policy mixes.

1. Introduction

The run-up of the global financial crisis deepened economic shocks, poses a threat to health system performance, and caused distortions in the allocation of public resources. The people’s needs for health increased and public health outcomes indicators are influenced by the dimension of public reforms and the related governance framework. Typically, in crisis periods, governments’ expenditures tend to increase and citizen’s expectations are overly optimistic. When the process is reversed by both endogenous and exogenous factors, public health reforms are designed and planned considering the diversity of the healthcare system that is directly reflected in the degree of public health expenditure. As a consequence, the impact on health outcomes can vary between countries. As the quality of public health expenditure is reflected in health outcomes among countries, the co-movement and causal linkages between public health outcomes and healthcare expenditure depend on governments implication in providing a quality life for its citizens through a good health system, meaning that in times of vulnerabilities, the pressure is higher and require solid strategies in terms of revenue and expenditure [ 1 ]. The unprecedented financial stress related to the battle against the COVID-19 pandemic has led to a sharpening of spending conditions and most of the studies reveal that healthcare capacity faces major challenges and vulnerabilities [ 2 , 3 , 4 ]. However, Noura [ 5 ] noted that the gaps in health care systems already existed prior to the COVID-19 pandemic and the inefficiency in resource allocation was just easier to explain in such a time of vulnerabilities. In other words, ex-ante financial disturbances are a key determinant of health care system risk and an understanding of cyclical movements of financial indicators is a sine qua non for a correct design of public health care policies.

In their paper, Makina and Laytonb [ 6 ] revealed that the governments around the world responded to the COVID-19 crisis by aggressively deploying fiscal policy to boost health expenditure and the related public debt levels will put higher pressure on the governments around the world and will require concrete measures for fiscal consolidation. The capacity to generate new public reforms capable to improve public health performance and positively impact the overall well-being depends, of course, on the level of economic growth, because it is quite difficult to increase the money spent on health finance if there is not enough fiscal space to maneuver [ 7 , 8 ].

In line with the last point of view, the economic literature recognizes the benefits of good health and establishes a direct relationship between the status of health and the development of countries [ 9 , 10 , 11 , 12 ]. It is imperative for all countries to appropriately invest in their health sector to realize the linkage between longevity and benefits of the economy, but as Mohammad et al., 2018 revealed in their paper, the amount of spending should be managed by proper governance and adequate policies capable of streamlining public sector health funds.

It is recognized that research reveals mixed results related to government spending on health but leans toward positive outcomes from increased public spending [ 13 , 14 , 15 ]. There are two common approaches used to determine the implication of government spending on public health outcomes. The first one relies on Grossman’s product (which reveals the aggregate health production function) and considers health as a capital good that can be affected over time and that depends on several endogenous and exogenous variables [ 16 , 17 ]. The second rationale was developed by Zweifel and Breyer [ 18 ] and considers health as an output of the entire health care system, which is influenced by the related inputs and investigates, for instance, the relationship between health care expenditure (considered as inputs) and health outcomes (considered as outputs).

While it is widely acknowledged that the theoretical insights document a range of effects, from no impacts, to limited, and to the significant impact of public health expenditure on health outcomes, due to lack of studies performed on the profile of EU countries, we empirically analyzed the relationship between public health expenditure and health outcomes among EU developing countries. The study provides new evidence on a panel of EU developing countries and based on regression analysis and factor analysis, empirically analyzed the relationship between public health expenditure and health outcomes. The study has broader coverage and represents an important contribution to the literature by the fact that, in order to explain the variations in death rates across countries, it included three categories of factors: health, demographic, and socio-economic vulnerabilities indicators. Additionally, the effects of health expenditure on these categories of three factors were investigated, and based on the methodological approach, the endogeneity issues were addressed. We documented that public health expenditure and health outcomes are in a long-run equilibrium relationship and the status of health expenditure can improve life expectancy and reduce infant mortality. The remainder of the paper is structured as follows: In Section 2 we detail the methodology we employ, in Section 3 we present the empirical findings and discussion, and in Section 4 we conclude the study.

2. Empirical Framework and Methodology

The retrospective of theoretical insights documents a range of effects, from no impacts, to limited, and to the significant impact of public health expenditure on health outcomes. For example, some authors bring into focus the significant relationship between health expenditures and health indicators outcomes among countries with different health care systems [ 19 , 20 , 21 ], and others explore the effects of health expenditure on health outcomes in Sub-Saharan Africa and reveal the implication of health expenditure on reducing mortality rates and improving life expectancy at birth [ 22 ]. On the other hand, Kulkarni [ 23 ] validated the profile of the BRICS countries and found that alone, simply increasing health expenditure cannot positively impact health outcomes and a better quality of the financial system and related mechanisms is necessary, this being also supported by Kim et al. [ 24 ] in their paper entitled “Income, financial barriers to health care and public health expenditure: a multilevel analysis of 28 countries”, which also highlighted that health system financing should be better planned and capable of managing inequalities in access to health. Contrary to the above-mentioned literature insights, Yaqub et al. [ 25 ] brought into discussion the implication of corruption status, contending that public health expenditure has a negative effect on infant mortality and revealed, on the profile of Nigeria, that the success in reducing mortality rates and lowering the infant mortality depends on the implication in reduced considerably the level of corruption. Regarding the profile of EU countries, there is a lack of studies that explore the relationship between government health spending and public health outcomes, though we found some literature that reveals the relationship between health spending and foreign direct investment [ 26 ], and others that analyze the state of health spending in times of crisis [ 27 , 28 ]. Therefore, the main rationale for conducting current research on the profile of EU developing countries was based on the existing gap in the literature.

Following the literature insights mentioned in previous section, the ordinary least squares (OLS) and the two-stage least squares (2SLS) estimators were employed for analyzing the relationship between government health expenditure and public health outcomes, and the studies were both cross-sectional and panel data types. However, by retrospective analysis and above-mentioned literature insights, we realize that the approaches are blurred, and a lot of the variables employed in the two approaches are similar. Therefore, to avoid the methodological problems and to develop the study in line with literature validation, we employed panel data analysis and factor analysis, and we empirically analyzed the relationship between public health expenditure and health outcomes among EU developing countries over the period of 2000–2019. As a first step, the objective to analyze the implication of government health expenditure on public health outcomes requires the establishment of public health outcomes indicators. We followed the literature insights [ 29 , 30 , 31 , 32 , 33 ] and we used two public health outcomes indicators, namely life expectancy at birth and infant mortality. Secondarily, in order to explain the variations in death rates across countries, we used three categories of factors: health, demographic, and socio-economic vulnerabilities indicators (see Appendix A for a detailed description of each variable according to the related code and original source).

The first factor ( F 1) is related to the quality of life and dimension of public governance and includes eleven sub-indicators: Real GDP growth rate (RGGR), age dependency ratio (ADR), domestic general government health expenditure (DGGE), human capital index (HCI), nurses and midwives (NM), government effectiveness (GE), control of corruption (CCOR), political stability and absence of violence/terrorism (PS), regulatory quality (RQ), rule of law (RL), and people using safely managed sanitation services (PUSS). Concerning the variables used, we found validation in light of the approaches commonly applied in previous research, it being widely recognized that the improvements in socioeconomic performance, healthcare, employment, and politics are positively correlated with the longevity and direct impact the status of infant mortality [ 34 , 35 ]. Moreover, the results provided by Helliwell et al. [ 36 ] suggested that changes in governance quality within a policy-relevant time horizon can lead to significant changes in the quality of life and other previous research even confirm that people are more satisfied with their lives in countries with high-quality level of governance [ 37 , 38 , 39 ]. In an extensive documentation of the studies on the impact of the enhancement of quality of life and good administration on infant mortality, it is revealed that both well-being and good governance frameworks influence the trend of infant mortality [ 40 , 41 , 42 , 43 ].

Following the previous literature insights on the international comparison of health performance, we found that health outcomes and wellbeing are critical drivers of sustainable development, and the status of health care services depends on the system performance [ 44 , 45 , 46 , 47 ]. Thus, we included in our analysis the second factor ( F 2) named health care system performance, which incorporates five sub-indicators: the number of practicing physicians per 1000 Population (PH), Available hospital beds for the care of admitted patients (ABH), Age dependency ratio, young (AD-% of working-age population), Premature deaths, % total premature deaths ambient particulate matter (PDAP), and death rate, crude (DR per 1000 people). According to Kroneman and Siegers [ 48 ] at the EU level, the reduction of care hospitals represents a measure implemented to limit expenditure and David et al. [ 49 ] even confirmed that hospital bed reduction and multiple-system reform affect patient mortality. Leiyu Shi [ 50 ] examined the relationship between the availability of primary care and longevity, suggesting a significant implication of the number of specialty physicians and total mortality.

According to the literature insights, when we talk about growth in human capital, we refer to two important dimensions, education, and health, meaning that those variables positively affect per-capita income in the long run [ 51 , 52 , 53 ]. The healthcare system is an important determinant of sustainable development and should always be the core of the development of a nation, which is why some authors place a special emphasis on the effects of health expenditure on life expectancy and conclude that the expenditure growth can increase longevity. Jakovljevic et al. [ 13 ] and others such as Rahman et al. [ 54 ], using the World Bank data set for 15 countries over 20 years (1995–2014), revealed that health expenditure, including public and private, significantly reduced infant mortality rates. Therefore, the last factor included in the analysis ( F 3) captured socio-economic vulnerabilities, and included four sub-indicators: Standardized Gini variable, which measures the income inequality ( GINI ), unemployment, total ( UL % of the total labor force), population ages 65 and above (% of total population-POP), and labor force with intermediate education ( LFIE ). The human life span is relatively fixed, but improving the quality of life through health care quality could reduce the need for medical care. Nevertheless, House et al., 1990 argued that there is a direct relationship between age, socioeconomic status, and health outcomes, this point of view being also validated by other contemporary research conducted in the context of COVID-19 pandemic risk, which reveals the implication of the societal risk factors and economic vulnerability on mortality rate [ 55 , 56 , 57 ].

To assess the implication of government health expenditure on public health outcomes, we used ordinary least-squares regression model (OLS) analysis and factor analysis methods. The model includes relevant explanatory variables that influence the level of health outcomes, several categories of public expenses as proxies for the government actions towards health protection and in order to eliminate the problems of skewed distribution, to exclude the orthogonal relationship between components and generate independent components, Factors 1, 2, and 3 were computed based on exploratory factor analysis methodology and we tested the implication of three categories of factors: health, demographic, and socio-economic vulnerabilities indicators. We used as dependent variable two public health outcomes indicators, namely life expectancy at birth and infant mortality and, in order to explain the variations in death rates across countries, we used three categories of factors: health, demographic, and socio-economic vulnerabilities indicators. We used the OLS and factor analysis approach with the following specification:

where i and t indicate the country and year for each variable. The dependent variable L E i t represents a key metric for assessing population health and indicates life expectancy at birth, total (years). The independent variables are displayed in Appendix A , and include public health expenditure % of total health expenditure ( CHE ), real GDP per capita ( GDPCAP ), Gini coefficient ( GINI ), an indicator which measures the degree of inequality in countries’ health and the deviation of income distribution from totally equal distribution, unemployment, total % of total labor force ( UL ), the quality of life and dimension of governance ( F 1), Health care system performance ( F 2), and Socioeconomic vulnerabilities ( F 3). The last three factors, F 1, F 2, and F 3 were computed based on factor analysis.

where i and t indicate the country and year for each variable. The dependent variable M R I i t represents a key metric for infant mortality and indicates the number of children dying before reaching one year of age, expressed as a rate per 1000 live births in a given year. The independent variables are analogous to those indicated in Equation (1). The list of the examined countries includes Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia. Factors 1, 2, and 3 were computed based on exploratory factor analysis methodology, a method that avoids the problems of skewed distribution and excludes the orthogonal relationship between components and generate independent components. Factor analysis finds a few common factors (say, q of them) that linearly reconstruct the p original variables:

In which case, y i j represents the value of the i th observation of the j th variable, Z i k represents the i th observation on the kth common factor, b q j represents the set of linear coefficients named the factor loading, and finally, e i j represents the j th variables unique factor. The independent variables are displayed in Appendix A . The fixed-effects model has the following form:

Y i , t represents the dependent variable for country i at time t , α i represents an unknown country-specific constant, X i , t indicates the time-variant regressor matrix, and ε i , t , is the error term; in order to validate the appropriateness of the fixed-effects model, the Hausman test was performed

3. Empirical Findings and Discussion

Table 1 summarizes the results of estimating Equations (1) and (2) for the influence of public health expenditure on health outcomes among EU developing countries. The methodological approach includes two separate models with two dependent variables named life expectancy (see model 1) and infant mortality (see model 2). We checked the appropriateness of fixed-effects estimation by running the Hausman test, and the results provided in Table 2 reveal that the fixed effect model is to be used. The variables are validated by previous research [ 29 , 30 , 31 , 32 , 33 ] and represent the most common public health outcomes indicators. The results of the mixed-effect model show a positive relationship between government health expenditure and longevity, measured by life expectancy at birth. According to the fixed-effects model, an increase in health expenditures is associated with increase in life expectancy, and this effect is statistically significant at the 0.5% level. In other words, an increase in the overall public health spending reduces the number of the overall mortality level of a population. The results suggest that a one percent increase in public health expenditure decreases the infant mortality rate by 0.64 %. Thus, the results satisfy the viewpoint of Rahman et al. [ 54 ], who sees the dimension of public health expenditure as an opportunity to improve the health status of the population.

The results of the mixed-effect model.

Source: research results. Notes: the results include the coefficient of variable and t statistic results in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

The results of factorial analysis of the main components for estimating the three main factors.

Factor Analysis/Correlation, Method: Principal Factors, Rotation: (Unrotated).

Regarding the implication of economic output of countries, we found that the economic performance positively affects the well-being of EU developing countries, and a higher real GDP growth rate is related to higher life expectancy. Additionally, when we test the implication of income on infant mortality rate, the results showed a negative relationship, meaning that the higher the economic performance of countries, the lower the number of children dying before reaching one year of age (expressed as a rate per 1000 live births in a given year). Our results confirm, on the one hand, the study performed by Zaman et al. [ 58 ] who revealed that at the individual level, income had a direct influence on health spending, and on the other hand, the viewpoint of Blazquez-Fernández et al. [ 59 ] who considered that per-capita income can improve health outcomes. The results of the fixed-effects panel model suggest a strong positive relationship between the economic growth and the level of life expectancy.

The results obtained for the other variables presented in Table 1 . reveal an expected sign for the status of country inequality measured by the GINI index and suggest a negative relationship between country inequality and life expectancy. It seems that the higher the deviation of income distribution from a totally equal distribution, the lower the life expectancy rate. Similar results are provided by authors [ 60 , 61 ], who indicated that researchers must focus on examining inequality in life expectancy for judging the performance of communities regarding the length of life. The unemployment rate ( UL ) is significant and positively correlated with life expectancy and has a negative impact on infant mortality.

The results for life expectancy are in line with those provided by Granados and Diez Roux [ 62 ], who pointed out that the improvements in health are positively correlated with increases in the unemployment rate and contrary to those provided by Singh and Siahpush [ 63 ], who tested the relationship between unemployment and life expectancy in the United States and found that life expectancy was lower in areas with higher unemployment rates. Regarding the status of results for infant mortality (MRI), the literature suggests that an increase in the local unemployment rate is correlated with a statistically significant increase in the possibility of having a low birthweight baby, weighing less than 2500 g (see the study performed by Kaplan et al. [ 64 ] on the profile of American Community).

Further, following the literature, insights which suggest that the process of analyzing the evolution of public expenditure involves the necessity to study the influencing factors, economic, social, political, and military [ 65 ], we tested the implication of three categories of factors: health, demographic, and socio-economic vulnerabilities. Factor 1, 2, and 3 were computed based on exploratory factor analysis methodology and, as can be seen in Table 2 and Table 3 , each variable was given a ‘uniqueness’ score and the first three factors (factor 1, factor 2, and factor 3) explained 85% of the total variance.

Factor loadings (pattern matrix) and unique variances.

The results showed that the quality of life and dimension of governance ( F 1) was a significant statistic and has a negative impact on infant mortality. Undoubtedly, one possible explanation for this result is that many governments through improving the dimension of governance directly affect health outcomes and consolidate the status of wellbeing, thus decreasing infant mortality. These results agree with previous findings, showing that quality of government matters in public health and it is a gap in the literature, argued by the fact that most of the papers deal with economic, social, and political factors, but avoid the study of governmental factors [ 66 ]. In terms of exposure, we also found that the effectiveness of health and the way to reduce infant mortality or to improve life quality is conditioned by good governance status and the consolidation of health care system performance directly improves the quality of life among EU developing countries. Overall, the results enlarge our knowledge of the implications of government health expenditure on public health outcomes and indicate that in order to consolidate the status of public health, public policymakers should intervene and provide political and financial support through policy mixes.

4. Concluding Remarks

The availability of public financial resources represents an important condition for the performance of the health system. The run-up of the global financial crisis deepening the economic shocks increases people’s needs for health, poses a threat to health system performance, and caused distortions in the allocation of public resources. Using regression analysis and factor analysis, we investigated the relationship between public health expenditure and health outcomes among EU developing countries. The paper contributes to related literature through the expansion of the research concerning the evolution of health outcomes and the status of public health expenditure in EU developing countries. The study has a broader coverage and represents an important contribution to the literature by explaining the variations in death rates across countries and including three categories of factors: health, demographic, and socio-economic vulnerabilities indicators. Additionally, the effects of health expenditure on these categories of three factors were investigated and based on the methodological approach, the endogeneity issues were addressed. We studied how the status of good governance, health care system performance, and socioeconomic vulnerabilities affect the public health’s outcomes in the selected countries, and we found that public health outcomes indicators are influenced by the dimension of public reforms and related governance framework. In this particular sample, we also found a strong positive relationship between government health expenditure and longevity, measured by life expectancy at birth. An increase in the overall public health spending reduces the overall mortality level of a population. The results suggest that one percent increase in public health expenditure is associated with a decrease in infant mortality rate by 0.64%. Economic performance positively affects the well-being of EU developing countries, and a higher real GDP growth rate is related to higher life expectancy. Additionally, our results confirmed that income had a direct influence on health spending or could improve health outcomes and, as expected, country inequality measured by the GINI index was negatively correlated with life expectancy. Our study suggests the need for health policymakers in EU developing countries to implement active strategies that reduce the death rate and consolidate the wellbeing of communities, to intervene and provide political and financial support through policy mixes.

Appendix A. Variables Employed in the Analysis

The results of mixed-effect model.

Author Contributions

Conceptualization, M.O., G.V., A.-F.V. and E.C.; methodology, M.O., G.V., A.-F.V. and E.C.; software, M.O., G.V., A.-F.V. and E.C.; validation, M.O., G.V., A.-F.V. and E.C.; formal analysis, M.O., G.V., A.-F.V. and E.C.; investigation M.O., G.V., A.-F.V. and E.C.; data curation, M.O., G.V., A.-F.V. and E.C.; writing—original draft preparation, M.O., G.V., A.-F.V. and E.C.; writing—review and editing, M.O., G.V., A.-F.V. and E.C.; visualization, M.O., G.V., A.-F.V. and E.C.; supervision, M.O., G.V., A.-F.V. and E.C.; project administration, M.O., G.V., A.-F.V. and E.C.; funding acquisition, M.O., G.V., A.-F.V. and E.C. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Published: 24 February 2024

Modeling the link between tourism and economic development: evidence from homogeneous panels of countries

Pablo Juan Cárdenas-García ORCID: orcid.org/0000-0002-1779-392X 1 ,
Juan Gabriel Brida 2 &
Verónica Segarra 2

Humanities and Social Sciences Communications volume 11 , Article number: 308 ( 2024 ) Cite this article

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Metrics details

Development studies

Having previously analyzed the relationship between tourism and economic growth from distinct perspectives, this paper attempts to fill the void existing in scientific research on the relationship between tourism and economic development, by analyzing the relationship between these variables using a sample of 123 countries between 1995 and 2019. The Dumistrescu and Hurlin adaptation of the Granger causality test was used. This study takes a critical look at causal analysis with heterogeneous panels, given the substantial differences found between the results of the causal analysis with the complete panel as compared to the analysis of homogeneous country groups, in terms of their dynamics of tourism specialization and economic development. On the one hand, a one-way causal relationship exists from tourism to development in countries having low levels of tourism specialization and development. On the other hand, a one-way causal relationship exists by which development contributes to tourism in countries with high levels of development and tourism specialization.

A meta-analysis on global change drivers and the risk of infectious disease

favorable outcome hypothesis in developing countries

The economic commitment of climate change

Frequent disturbances enhanced the resilience of past human populations

Introduction.

Across the world, tourism is one of the most important sectors. It has undergone exponential growth since the mid-1900s and is currently experiencing growth rates that exceed those of other economic sectors (Yazdi, 2019 ).

Today, tourism is a major source of income for countries that specialize in this sector, generating 5.8% of the global GDP (5.8 billion US$) in 2021 (UNWTO, 2022 ) and providing 5.4% of all jobs (289 million) worldwide. Although its relevance is clear, tourism data have declined dramatically due to the recent impact of the Covid-19 health crisis. In 2019, prior to the pandemic (UNWTO, 2020 ), tourism represented 10.3% of the worldwide GDP (9.6 billion US$), with the number of tourism-related jobs reaching 10.2% of the global total (333 million). With the evolution of the pandemic and the regained trust of tourists across the globe, it is estimated that by 2022, approximately 80% of the pre-pandemic figures will be attained, with a full recovery being expected by 2024 (UNWTO, 2022 ).

Given the importance of this economic activity, many countries consider tourism to be a tool enabling economic growth (Corbet et al., 2019 ; Ohlan, 2017 ; Xia et al., 2021 ). Numerous works have analyzed the relationship between increased tourism and economic growth; and some systematic reviews have been carried out on this relationship (Brida et al., 2016 ; Ahmad et al., 2020 ), examining the main contributions over the first two decades of this century. These reviews have revealed evidence in this area: in some cases, it has been found that tourism contributes to economic growth while, in other cases, the economic cycle influences tourism expansion. Moreover, other works offer evidence of a bi-directional relationship between these variables.

Distinct international organizations (OECD, 2010 ; UNCTAD, 2011 ) have suggested that not only does tourism promote economic growth, it also contributes to socio-economic advances in the host regions. This may be the real importance of tourism, since the ultimate objective of any government is to improve a country’s socio-economic development (UNDP, 1990 ).

The development of economic and other policies related to the economic scope of tourism, in addition to promoting economic growth, are also intended to improve other non-economic factors such as education, safety, and health. Improvements in these factors lead to a better life for the host population (Lee, 2017 ; Todaro and Smith, 2020 ).

Given tourism’s capacity as an instrument of economic development (Cárdenas-García et al., 2015 ), distinct institutions such as the United Nations Conference on Trade and Development, the United Nations Economic Commission for Africa, the United Nations World Tourism Organization and the World Bank, have begun funding projects that consider tourism to be a tool for improved socio-economic development, especially in less advanced countries (Carrillo and Pulido, 2019 ).

This new trend within the scientific literature establishes, firstly, that tourism drives economic growth and, secondly, that thanks to this economic growth, the population’s economic conditions may be improved (Croes et al., 2021 ; Kubickova et al., 2017 ). However, to take advantage of the economic growth generated by tourism activity to boost economic development, specific policies should be developed. These policies should determine the initial conditions to be met by host countries committed to tourism as an instrument of economic development. These conditions include regulation, tax system, and infrastructure provision (Cárdenas-García and Pulido-Fernández, 2019 ; Lejárraga and Walkenhorst, 2013 ; Meyer and Meyer, 2016 ).

Therefore, it is necessary to differentiate between the analysis of the relationship between tourism and economic growth, whereby tourism boosts the economy of countries committed to tourism, traditionally measured through an increase in the Gross Domestic Product (Alcalá-Ordóñez et al., 2023 ; Brida et al., 2016 ), and the analysis of the relationship between tourism and economic development, which measures the effect of tourism on other factors (not only economic content but also inequality, education, and health) which, together with economic criteria, serve as the foundation to measure a population’s development (Todaro and Smith, 2020 ).

However, unlike the analysis of the relationship between tourism and economic growth, few empirical studies have examined tourism’s capacity as a tool for development (Bojanic and Lo, 2016 ; Cárdenas-García and Pulido-Fernández, 2019 ; Croes, 2012 ).

To help fill this gap in the literature analyzing the relationship between tourism and economic development, this work examines the contribution of tourism to economic development, given that the relationship between tourism and economic growth has been widely analyzed by the scientific literature. Moreover, given that the literature has demonstrated that tourism contributes to economic growth, this work aims to analyze whether it also contributes to economic development, considering development in the broadest possible sense by including economic and socioeconomic variables in the multi-dimensional concept (Wahyuningsih et al., 2020 ).

Therefore, based on the results of this work, it is possible to determine whether the commitment made by many international organizations and institutions in financing tourism projects designed to improve the host population’s socioeconomic conditions, especially in countries with lower development levels, has, in fact, resulted in improved development levels.

It also presents a critical view of causal analyses that rely on heterogeneous panels, examining whether the conclusions reached for a complete panel differ from those obtained when analyzing homogeneous groups within the panel. As seen in the literature review analyzing the relationship between tourism and economic development, empirical works using panel data from several countries tend to generalize the results obtained to the entire panel, without verifying whether, in fact, they are relevant for all of the analyzed countries or only some of the same. Therefore, this study takes an innovative approach by examining the panel countries separately, analyzing the homogeneous groups distinctly.

Therefore, this article presents an empirical analysis examining whether a causal relationship exists between tourism and economic development, with development being considered to be a multi-dimensional variable including a variety of factors, distinct from economic ones. Panel data from 123 countries during the 1995–2019 period was considered to examine the causal relationship between tourism and economic development. For this, the Granger causality test was performed, applying the adaptation of this test made by Dumistrescu and Hurlin. First, a causal analysis was performed collectively for all of the countries of the panel. Then, a specific analysis was performed for each of the homogeneous groups of countries identified within the panel, formed according to levels of tourism specialization and development.

This article provides information on tourism’s capacity to serve as an instrument of development, helping to fill the gap in scientific research in this area. It critically examines the use of causal analyses based on heterogeneous samples of countries. This work offers the following main novelties as compared to prior works on the same topic: firstly, it examines the relationship between tourism and economic development, while the majority of the existing works only analyze the relationship between tourism and economic growth; secondly, it analyzes a large sample of countries, representing all of the global geographic areas, whereas the literature has only considered works from specific countries or a limited number of nations linked to a specific country in a specific geographical area, and; thirdly, it analyzes the panel both individually and collectively, for each of the homogenous groups of countries identified, permitting the adoption of specific policies for each group of countries according to the identified relationship, as compared to the majority of works that only analyze the complete panel, generalizing these results for all countries in the sample.

Overall, the results suggest that a relationship exists between tourism and development in all of the analyzed countries from the sample. A specific analysis was performed for homogeneous country groups, only finding a causal relationship between tourism and development in certain country groups. This suggests that the use of heterogeneous country samples in causal analyses may give rise to inappropriate conclusions. This may be the case, for example, when finding causality for a broad panel of countries, although, in fact, only a limited number of panel units actually explain this causal relationship.

The remainder of the document is organized as follows: the next section offers a review of the few existing scientific works on the relationship between tourism and economic development; section three describes the data used and briefly explains the methodology carried out; section four details the results obtained from the empirical analysis; and finally, the conclusions section discusses the main implications of the work, also providing some recommendations for economic policy.

Tourism and economic development

Numerous organizations currently recognize the importance of tourism as an instrument of economic development. It was not until the late 20th century, however, when the United Nations World Tourism Organization (UNWTO), in its Manila Declaration, established that the development of international tourism may “help to eliminate the widening economic gap between developed and developing countries and ensure the steady acceleration of economic and social development and progress, in particular of the developing countries” (UNWTO, 1980 ).

From a theoretical point of view, tourism may be considered an effective activity for economic development. In fact, the theoretical foundations of many works are based on the relationship between tourism and development (Ashley et al., 2007 ; Bolwell and Weinz, 2011 ; Dieke, 2000 ; Sharpley and Telfer, 2015 ; Sindiga, 1999 ).

The link between tourism and economic development may arise from the increase in tourist activity, which promotes economic growth. As a result of this economic growth, policies may be developed to improve the resident population’s level of development (Alcalá-Ordóñez and Segarra, 2023 ).

Therefore, it is essential to identify the key variables permitting the measurement of the level of economic development and, therefore, those variables that serve as a basis for analyzing whether tourism results in improved the socioeconomic conditions of the host population (Croes et al., 2021 ). Since economic development refers not only to economic-based variables, but also to others such as inequality, education, or health (Todaro and Smith, 2020 ), when analyzing the economic development concept, it has been frequently linked to human development (Pulido-Fernández and Cárdenas-García, 2021 ). Thus, we wish to highlight the major advances resulting from the publication of the Human Development Index (HDI) when measuring economic development, since it defines development as a multidimensional variable that combines three dimensions: health, education, and income level (UNDP, 2023 ).

However, despite the importance that many organizations have given to tourism as an instrument of economic development, basing their work on the relationship between these variables, a wide gap continues to exist in the scientific literature for empirical studies that examine the existence of a relationship between tourism and economic development, with very few empirical analyses analyzing this relationship.

First, a group of studies has examined the causal relationship between tourism and economic development, using heterogeneous samples, and without previously grouping the subjects based on homogeneous characteristics. Croes ( 2012 ) analyzed the relationship between tourism and economic development, measured through the HDI, finding that a bidirectional relationship exists for the cases of Nicaragua and Costa Rica. Using annual data from 2001 to 2014, Meyer and Meyer ( 2016 ) performed a collective analysis of South African regions, determining that tourism contributes to economic development. For a panel of 63 countries worldwide, and once again relying on the HDI to define economic development, it was determined that tourism contributes to economic development. Kubickova et al. ( 2017 ), using annual data for the 1995–2007 period, analyzed Central America and Caribbean nations, determining the existence of this relationship by which tourism influences the level of economic development and that the level of development conditions the expansion of tourism. Another work examined nine micro-states of America, Europe, and Africa (Fahimi et al., 2018 ); and 21 European countries in which human capital was measured, as well as population density and tourism income, analyzing panel data and determining that tourism results in improved economic development. Finally, within this first group of works, Chattopadhyay et al. ( 2022 ), using a broad panel of destinations, (133 countries from all geographic areas of the globe) determined that there is no relationship between tourism and economic development.

Studies performed with large country samples that attempt to determine the causal relationship between tourism and economic development by analyzing countries that do not necessarily share homogeneous characteristics, may lead to erroneous conclusions, establishing causality (or not) for panel sets even when this situation is actually explained by a small number of panel units.

Second, another group of studies have analyzed the causal relationship between tourism and economic development, considering the previous limitation, and has grouped the subjects based on their homogeneous characteristics. Cárdenas-García et al. ( 2015 ) used annual data from 1990–2010, in a collective analysis of 144 countries, making a joint panel analysis and then examining two homogeneous groups of countries based on their level of economic development. They determined that tourism contributes to economic development, but only in the most developed group of countries. They determined that tourism contributes to economic development, both for the total sample and for the homogeneous groups analyzed. Pulido-Fernández and Cárdenas-García ( 2021 ), using annual data for the 1993–2017 period, performed a joint analysis of 143 countries, followed by a specific analysis for three groups of countries sharing homogeneous characteristics in terms of tourism growth and development level. They determined that tourism contributes to economic development and that development level conditions tourism growth in the most developed countries.

Finally, another group of studies has analyzed the causal relationship between tourism and economic development in specific cases examined on an individual basis. In a specific analysis by Aruba et al. ( 2016 ), it was determined that tourism contributes to human development. Analyzing Malaysia, Tan et al. ( 2019 ) determined that tourism contributes to development, but only over the short term, and that level of development does not influence tourism growth. Similar results were obtained by Boonyasana and Chinnakum ( 2020 ) in an analysis carried out in Thailand. In this case of Thailand (Boonyasana and Chinnakum, 2020 ), which relied on the HDI, the relationship with economic growth was also analyzed, finding that an increase in tourism resulted in improved economic development. Finally, Croes et al. ( 2021 ), in a specific analysis of Poland, determined that tourism does not contribute to development.

As seen from the analysis of the most relevant publications detailed in Table 1 , few empirical works have considered the relationship between tourism and economic development, in contrast to the numerous works from the scientific literature that have examined the relationship between tourism and economic growth. Most of the works that have empirically analyzed the relationship between tourism and economic development have determined that tourism positively influences the improved economic development in host destinations. To a lesser extent, some studies have found a bidirectional relationship between these variables (Croes, 2012 ; Kubickova et al., 2017 ; Pulido-Fernández and Cárdenas-García, 2021 ) while others have found no relationship between tourism and economic development (Chattopadhyay et al., 2022 ; Croes et al., 2021 ).

Furthermore, in empirical works relying on panel data, the results have tended to be generalized to the entire panel, suggesting that tourism improves economic development in all countries that are part of the panel. This has been the case in all of the examined works, with the exception of two studies that analyzed the panel separately (Cárdenas-García et al., 2015 ; Pulido-Fernández and Cárdenas-García, 2021 ).

Thus, it may be suggested that the use of very large country panels and, therefore, including very heterogeneous destinations, as was the case in the works of Biagi et al. ( 2017 ) using a panel of 63 countries, as well as that of Chattopadhyay et al. ( 2022 ) working with a panel of 133 countries, may lead to error, given that this relationship may only arise in certain destinations of the panel, although it is generalized to the entire panel.

This work serves to fill this gap in the literature by analyzing the panel both collectively and separately, for each of the homogenous groups of countries that have been previously identified.

The lack of relevant works on the relationship between tourism and development, and of studies using causal analyses to examine these variables based on heterogeneous panels, may lead to the creation of rash generalizations regarding the entirety of the analyzed countries. Thus, conclusions may be reached that are actually based on only specific panel units. Therefore, we believe that this study is justified.

Methodological approach

Given the objective of this study, to determine whether a causal relationship exists between tourism and socio-economic development, it is first necessary to identify the variables necessary to measure tourism activity and development level. Thus, the indicators are highly relevant, given that the choice of indicator may result in distinct results (Rosselló-Nadal and He, 2020 ; Song and Wu, 2021 ).

Table 2 details the measurement variables used in this work. Specifically, the following indicators have been used in this paper to measure tourism and economic development:

Measurement of tourist activity. In this work, we decided to consider tourism specialization, examining the number of international tourists received by a country with regard to its population size as the measurement variable.

This information on international tourists at a national level has been provided annually by the United Nations World Tourism Organization since 1995 (UNWTO, 2023 ). This variable has been relativized based on the country’s population, according to information provided by the World Bank on the residents of each country (WB, 2023 ).

Tourism specialization is considered to be the level of tourism activity, specifically, the arrival of tourists, relativized based on the resident population, which allows for comparisons to be made between countries. It accurately measures whether or not a country is specialized in this economic activity. If the variable is used in absolute values, for example, the United States receives more tourists than Malta, so based on this variable it may be that the first country is more touristic than the second. However, in reality, just the opposite happens, Malta is a country in which tourist activity is more important for its economy than it is in the United States, so the use of tourist specialization as a measurement variable classifies, correctly, both Malta as a country with high tourism specialization and to the United States as a country with low tourism specialization.

Therefore, most of the scientific literature establishes the need to use the total number of tourists relativized per capita, given that this allows for the determination of the level of tourism specialization of a tourism destination (Dritsakis, 2012 ; Tang and Abosedra, 2016 ); furthermore, this indicator has been used in works analyzing the relationship between tourism and economic development (for example, Biagi et al., 2017 ; Boonyasana and Chinnakum; 2020 ; Croes et al., 2021 ; Fahimi et al., 2018 ).

Although some works have used other variables to measure tourism, such as tourism income, exports, or tourist spending, these variables are not available for all of the countries making up the panel, so the sample would have been significantly reduced. Furthermore, the data available for these alternative variables do not come from homogeneous databases, and therefore cannot be compared.

Measurement of economic development. In this work, the Human Development Index has been used to measure development.

This information is provided by the United Nations Development Program, which has been publishing it annually at the country level since 1990 (UNDP, 2023 ).

The selection of this indicator to measure economic development is in line with other works that have defended its use to measure the impact on development level (for example, Jalil and Kamaruddin, 2018 ; Sajith and Malathi, 2020 ); this indicator has also been used in works analyzing the relationship between tourism and economic development (for example, Meyer and Meyer, 2016 ; Kubickova et al., 2017 ; Pulido-Fernández and Cárdenas-García, 2021 ).

Although some works have used other variables, such as poverty or inequality, to measure development, these variables are not available for all of the countries forming the panel. Therefore the sample would have been considerably reduced and the data available for these alternative variables do not come from homogenous databases, and therefore comparisons cannot be made.

These indicators are available for a total of 123 countries, across the globe. Thus, these countries form part of the sample analyzed in this study.

As for the time frame considered in this work, two main issues were relevant when determining this period: on the one hand, there is an initial time restriction for the analyzed series, given that information on the arrival of international tourists is only available as of 1995, the first year when this information was provided by the UNWTO. On the other hand, it was necessary to consider the effect of the Covid-19 pandemic and the resulting tourism sector crisis, which also affected the global economy as a whole. Therefore, our time series ended as of 2019, with the overall time frame including data from 1995 to 2019, a 25-year period.

Previous considerations

Caution should be taken when considering causality tests to determine the relationships between two variables, especially in cases in which large heterogeneous samples are used. This is due to the fact that generalized conclusions may be reached when, in fact, the causality is only produced by some of the subjects of the analyzed sample. This study is based on this premise. While heterogeneity in a sample is clearly a very relevant aspect, in some cases, it may lead to conclusions that are less than appropriate.

In this work, a collective causal analysis has been performed on all of the countries of the panel, which consists of 123 countries. However, given that it is a broad sample including countries having major differences in terms of size, region, development level, or tourism performance, the conclusions obtained from this analysis may lead to the generalization of certain conclusions for the entire sample set, when in fact, these relationships may only be the case for a very small portion of the sample. This has been the case in other works that have made generalized conclusions from relatively large samples in which the sample’s homogeneity regarding certain patterns was not previously verified (Badulescu et al., 2021 ; Ömer et al., 2018 ; Gedikli et al., 2022 ; Meyer and Meyer, 2016 ; Xia et al., 2021 ).

Therefore, after performing a collective analysis of the entire panel, the causal relationship between tourism and development was then determined for homogeneous groups of countries that share common patterns of tourism performance and economic development level, to analyze whether the generalized conclusions obtained in the previous section differ from those made for the individual groups. This was in line with strategies that have been used in other works that have grouped countries based on tourism performance (Min et al., 2016 ) or economic development level (Cárdenas-García et al., 2015 ), prior to engaging in causal analyses. To classify the countries into homogeneous groups based on tourism performance and development level, a previous work was used (Brida et al., 2023 ) which considered the same sample of 123 countries, relying on the same data to measure tourism and development level and the same time frame. This guarantees the coherence of the results obtained in this work.

From the entire panel of 123 countries, a total of six country groups were identified as having a similar dynamic of tourism and development, based on qualitative dynamic behavior. In addition, an “outlier” group of countries was found. These outlier countries do not fit into any of the groups (Brida et al., 2023 ). The three main groups of countries were considered, discarding three other groups due to their small size. Table 3 presents the group of countries sharing similar dynamics in terms of tourism performance and economic development level.

Applied methodology

As indicated above, this work uses the Tourist Specialization Rate (TIR) and the Human Development Index (HDI) to measure tourism and economic development, respectively. In both cases, we work with the natural logarithm (l.TIR and l.HDI) as well as the first differences between the variables (d.l.TIR and d.l.HDI), which measure the growth of these variables.

A complete panel of countries is used, consisting of 123 countries. The three main groups indicated in the previous section are also considered (the first of the groups contains 36 countries, the second contains 29 and the last group contains 43).

The Granger causality test ( 1969 ) is used to analyze the relationships between tourism specialization and development level; this test shows if one variable predicts the other, but this should not be confused with a cause-effect relationship.

In the context of panel data, different tests may be used to analyze causality. Most of these tests differ with regard to the assumptions of homogeneity of the panel unit coefficients. While the standard form of the Granger causality test for panels assumes that all of the coefficients are equal between the countries forming part of the panel, the Dumitrescu and Hurlin test (2012) considers that the coefficients are different between the countries forming part of the panel. Therefore, in this work, Granger’s causality is analyzed using the Dumitrescu and Hurlin test (2012). In this test, the null hypothesis is of no homogeneous causality; in other words, according to the null hypothesis, causality does not exist for any of the countries of the analyzed sample whereas, according to the alternative hypothesis, in which the regression model may be different in the distinct countries, causality is verified for at least some countries. The approach used by Dumitrescu and Hurlin ( 2012 ) is more flexible in its assumptions since although the coefficients of the regressions proposed in the tests are constant over time, the possibility that they may differ for each of the panel elements is accepted. This approach has more realistic assumptions, given that countries exhibit different behaviors. One relevant aspect of this type of tests is that they offer no information on which countries lead to the rejection of the lack of causality.

Given the specific characteristics of this type of tests, the presence of very heterogeneous samples may lead to inappropriate conclusions. For example, causality may be assumed for a panel of countries, when only a few of the panel’s units actually explain this relationship. Therefore, this analysis attempts to offer novel information on this issue, revealing that the conclusions obtained for the complete set of 123 countries are not necessarily the same as those obtained for each homogeneous group of countries when analyzed individually.

Given the nature of the variables considered in this work, specifically, regarding tourism, it is expected that a shock taking place in one country may be transmitted to other countries. Therefore, we first analyze the dependency between countries, since this may lead to biases (Pesaran, 2006 ). The Pesaran cross-sectional dependence test (2004) is used for the total sample and for each of the three groups individually.

First, a dependence analysis is performed for the countries of the sample, verifying the existence of dependence between the panel subjects. A cross-sectional dependence test (Pesaran, 2004 ) is used, first for the overall set of countries in the sample and second, for each of the groups of countries sharing homogeneous characteristics.

The results are presented in Table 4 , indicating that the test is statistically significant for the two variables, both for all of the countries in the sample and for each of the homogeneous country clusters, for the variables taken in logarithms as well as their first differences.

Upon rejecting the null hypothesis of non-cross-sectional dependence, it is assumed that a shock occurs in a country that may be transmitted to other countries in the sample. In fact, the lack of dependence between the variables, both tourism and development, is natural in this type of variables, given the economic cycle through the globalization of the economic activity, common regions visited by tourists, the spillover effect, etc.

Second, the stationary nature of the series is tested, given that cross-sectional dependence has been detected between the variables. First-generation tests may present certain biases in the rejection of the null hypothesis since first-generation unit root tests do not permit the inclusion of dependence between countries (Pesaran, 2007 ). On the other hand, second-generation tests permit the inclusion of dependence and heterogeneity. Therefore, for this analysis, the augmented IPS test (CIPS) proposed by Pesaran ( 2007 ) is used. This second-generation unit root test is the most appropriate for this case, given the cross-sectional dependence.

The results are presented in Table 5 , showing the statistics of the CIPS test for both the overall set of countries in the sample and in each of the homogeneous clusters of countries. The results are presented for models with 1, 2, and 3 delays, considering both the variables in the logarithm and their first differences.

As observed, the null hypothesis of unit root is not rejected for the variables in levels, but it is rejected for the first differences. This result is found in all of the cases, for both the total sample and for each of the homogeneous groups, with a significance of 1%. Therefore, the variables are stationary in their first differences, that is, the variables are integrated at order 1. Given that the causality test requires stationary variables, in this work it is used with the variation or growth rate of the variables, that is, the variable at t minus the variable at t−1.

Finally, to analyze Granger’s causality, the test by Dumitrescu and Hurlin ( 2012 ) is used. This test is used to analyze the causal relationship in both directions; that is, whether tourism contributes to economic development and whether the economic development level conditions tourism specialization. Statistics are calculated considering models with 1, 2, and 3 delays. Considering that cross-sectional dependence exists, the p-values are corrected using bootstrap techniques (making 500 replications). Given that the test requires stationary variables, primary differences of both variables were considered.

Table 6 presents the result of the Granger causality analysis using the Dumitrescu and Hurlin test (2012), considering the null hypothesis that tourism does not condition development level, either for all of the countries or for each homogeneous country cluster.

For the entire sample of countries, the results suggest that the null hypothesis of no causality from tourism to development was rejected when considering 3 delays (in other works analyzing the relationship between tourism and development, the null hypothesis was rejected with a similar level of delay: Rivera ( 2017 ) when considering 3–4 delays or Ulrich et al. ( 2018 ) when considering 3 delays). This suggests that for the entire panel, one-way causality exists whereby tourism influences economic development, demonstrating that tourism specialization contributes positively to improving the economic development of countries opting for tourism development. This is in line with the results of Meyer and Meyer ( 2016 ), Ridderstaat et al. ( 2016 ); Biagi et al. ( 2017 ); Fahimi et al. ( 2018 ); Tan et al. ( 2019 ), or Boonyasana and Chinnakum ( 2020 ).

However, the previous conclusion is very general, given that it is based on a very large sample of countries. Therefore, it may be erroneous to generalize that tourism is a tool for development. In fact, the results indicate that, when analyzing causality by homogeneous groups of countries, sharing similar dynamics in both tourism and development, the null hypothesis of no causality from tourism to development is only rejected for the group C countries, when considering three delays. Therefore, the development of generalized policies to expand tourism in order to improve the socioeconomic conditions of any destination type should consider that this relationship between tourism and economic development does not occur in all cases. Thus, it should first be determined if the countries opting for this activity have certain characteristics that will permit a positive relationship between said variables.

In other words, it may be a mistake to generalize that tourism contributes to economic development for all countries, even though a causal relationship exists for the entire panel. Instead, it should be understood that tourism permits an improvement in the level of development only in certain countries, in line with the results of Cárdenas-García et al. ( 2015 ) or Pulido-Fernández and Cárdenas-García ( 2021 ). In this specific work, this positive relationship between tourism and development only occurs in countries from group C, which are characterized by a low level of tourism specialization and a low level of development. Some works have found similar results for countries from group C. For example, Sharma et al. ( 2020 ) found the same relationship for India, while Nonthapot ( 2014 ) had similar findings for certain countries in Asia and the Pacific, which also made up group C. Some recent works have analyzed the relationship between tourism specialization and economic growth, finding similar results. This has been the case with Albaladejo et al. ( 2023 ), who found a relationship from tourism to economic growth only for countries where income is low, and the tourism sector is not yet developed.

These countries have certain limitations since even when tourism contributes to improved economic development, their low levels of tourism specialization do not allow them to reach adequate host population socioeconomic conditions. Therefore, investments in tourism are necessary there in order to increase tourism specialization levels. This increase in tourism may allow these countries to achieve development levels that are similar to other countries having better population conditions.

Therefore, in this group, consisting of 43 countries, a causal relationship exists, given that these countries are characterized by a low level of tourism specialization. However, the weakness of this activity, due to its low relevance in the country, prevents it from increasing the level of economic development. In these countries (details of these countries can be found in Table 3 , specifically, the countries included in Group C), policymakers have to develop policies to improve tourism infrastructure as a prior step to improving their levels of development.

On the other hand, in Table 7 , the results of Granger’s causal analysis based on the Dumitrescu and Hurlin test (2012) are presented, considering the null hypothesis that development level does not condition an increase in tourism, both in the overall sample set and in each of the homogeneous country clusters.

The results indicate that, for the entire country sample, the null hypothesis of no causality from development to tourism is not rejected, for any type of delay. This suggests that, for the entire panel, one-way causality does not exist, with level of development influencing the level of tourism specialization. This is in line with the results of Croes et al. ( 2021 ) in a specific analysis in Poland.

Once again, this conclusion is quite general, given that it has been based on a very broad sample of countries. Therefore, it may be erroneous to generalize that the development level does not condition tourism specialization. Past studies using a large panel of countries, such as the work of Chattopadhyay et al. ( 2022 ) analyzing panel data from 133 countries, have been generalized to all of the analyzed countries, suggesting that economic development level does not condition the arrival of tourists to the destination, although, in fact, this relationship may only exist in specific countries within the analyzed panel.

In fact, the results indicate that, when analyzing causality by homogeneous country groups sharing a similar dynamic, for both tourism and development, the null hypothesis of no causality from development to tourism is only rejected for country group A when considering 2–3 delays. Although the statistics of the test differ, when the sample’s time frame is small, as in this case, the Z-bar tilde statistic is more appropriate.

Thus, development level influences tourism growth in Group A countries, which are characterized by a high level of development and tourism specialization, in accordance with the prior results of Pulido-Fernández and Cárdenas-García ( 2021 ).

These results, suggesting that tourism is affected by economic development level, but only in the most developed countries, imply that the existence of better socioeconomic conditions in these countries, which tend to have better healthcare systems, infrastructures, levels of human resource training, and security, results in an increase in tourist arrivals to these countries. In fact, when traveling to a specific tourist destination, if this destination offers attractive factors and a higher level of economic development, an increase in tourist flows was fully expected.

In this group, consisting of 36 countries, the high development level, that is, the proper provision of socio-economic factors in their economic foundations (training, infrastructures, safety, health, etc.) has led to the attraction of a large number of tourists to their region, making their countries having high tourism specialization.

Although international organizations have recognized the importance of tourism as an instrument of economic development, based on the theoretical relationship between these two variables, few empirical studies have considered the consequences of the relationship between tourism and development.

Furthermore, some hasty generalizations have been made regarding the analysis of this relationship and the analysis of the relationship of tourism with other economic variables. Oftentimes, conclusions have been based on heterogeneous panels containing large numbers of subjects. This may lead to erroneous results interpretation, basing these results on the entire panel when, in fact, they only result from specific panel units.

Given this gap in the scientific literature, this work attempts to analyze the relationship between tourism and economic development, considering the panel data in a complete and separate manner for each of the previously identified country groups.

The results highlight the need to adopt economic policies that consider the uniqueness of each of the countries that use tourism as an instrument to improve their socioeconomic conditions, given that the results differ according to the specific characteristics of the analyzed country groups.

This work provides precise results regarding the need for policymakers to develop public policies to ensure that tourism contributes to the improvement of economic development, based on the category of the country using this economic activity to achieve greater levels of economic development.

Specifically, this work has determined that tourism contributes to economic development, but only in countries that previously had a lower level of tourism specialization and were less developed. This highlights the need to invest in tourism to attract more tourists to these countries to increase their economic development levels. Countries having major natural attraction resources or factors, such as the Dominican Republic, Egypt, India, Morocco, and Vietnam, need to improve their positioning in the international markets in order to attain a higher level of tourism specialization, which will lead to improved development levels.

Furthermore, the results of this study suggest that a greater past economic development level of a country will help attract more tourists to these countries, highlighting the need to invest in security, infrastructures, and health in order for these destinations to be considered attractive and increase tourist arrival. In fact, given their increased levels of development, countries such as Spain, Greece, Italy, Qatar, and Uruguay have become attractive to tourists, with soaring numbers of visitors and high levels of tourism specialization.

Therefore, the analysis of the relationship between tourism and economic development should focus on the differentiated treatment of countries in terms of their specific characteristics, since working with panel data with large samples and heterogenous characteristics may lead to incorrect results generalizations to all of the analyzed destinations, even though the obtained relationship in fact only takes place in certain countries of the sample.

Conclusions and policy implications

Within this context, the objective of this study is twofold: on the one hand, it aims to contribute to the lack of empirical works analyzing the causal relationship between tourism and economic development using Granger’s causality analysis for a broad sample of countries from across the globe. On the other hand, it critically examines the use of causality analysis in heterogeneous samples, by verifying that the results for the panel set differ from the results obtained when analyzing homogeneous groups in terms of tourism specialization and development level.

In fact, upon analyzing the causal relationship from tourism to development, and the causal relationship from development to tourism, the results from the entire panel, consisting of 123 countries, differ from those obtained when analyzing causality by homogeneous country groups, in terms of tourism specialization and economic development dynamics of these countries.

On the one hand, a one-way causality relationship is found to exist, whereby tourism influences economic development for the entire sample of countries, although this conclusion cannot be generalized, since this relationship is only explained by countries belonging to Group C (countries with low levels of tourism specialization and low development levels). This indicates that, although a causal relationship exists by which tourism contributes to economic development in these countries, the low level of tourism specialization does not permit growth to appropriate development levels.

The existence of a causal relationship whereby the increase in tourism precedes the improvement of economic development in this group of countries having a low level of tourism specialization and economic development, suggests the appropriateness of the focus by distinct international organizations, such as the United Nations Conference on Trade and Development or the United Nations Economic Commission for Africa, on funding tourism projects (through the provision of tourism infrastructure, the stimulation of tourism supply, or positioning in international markets) in countries with low economic development levels. This work has demonstrated that investment in tourism results in the attracting of a greater flow of tourists, which will contribute to improved economic development levels.

Therefore, both international organizations financing projects and public administrations in these countries should increase the funding of projects linked to tourism development, in order to increase the flow of tourism to these destinations. This, given that an increase in tourism specialization suggests an increased level of development due to the demonstrated existence of a one-way causal relationship from tourism to development in these countries, many of which form part of the group of so-called “least developed” countries. However, according to the results obtained in this work, this relationship is not instantaneous, but rather, a certain delay exists in order for economic development to improve as a result of the increase in tourism. Therefore, public managers must adopt a medium and long-term vision of tourism activity as an instrument of development, moving away from short-term policies seeking immediate results, since this link only occurs over a broad time horizon.

On the other hand, this study reveals that a one-way causal relationship does not exist, by which the level of development influences tourism specialization level for the entire sample of countries. However, this conclusion, once again, cannot be generalized given that in countries belonging to Group A (countries with a high development level and a high tourism specialization level), a high level of economic development determines a higher level of tourism specialization. This is because the socio-economic structure of these countries (infrastructures, training or education, health, safety, etc.) permits their shaping as attractive tourist destinations, thereby increasing the number of tourists visiting them.

Therefore, investments made by public administrations to improve these factors in other countries that currently do not display this causal relationship implies the creation of the necessary foundations to increase their tourism specialization and, therefore, as shown in other works, tourism growth will permit economic growth, with all of the associated benefits for these countries.

Therefore, to attract tourist flows, it is not only important for a country to have attractive factors or resources, but also to have an adequate level of prior development. In other words, the tourists should perceive an adequate level of security in the destination; they should be able to use different infrastructures such as roads, airports, or the Internet; and they should receive suitable services at the destination from personnel having an appropriate level of training. The most developed countries, which are the destinations having the greatest endowment of these resources, are the ones that currently receive the most tourist flows thanks to the existence of these factors.

Therefore, less developed countries that are committed to tourism as an instrument to improve economic development should first commit to the provision of these resources if they hope to increase tourist flows. If this increase in tourism takes place in these countries, their economic development levels have been demonstrated to improve. However, since these countries are characterized by low levels of resources, cooperation by organizations financing the necessary investments is key to providing them with these resources.

Thus, a critical perspective is necessary when considering the relationship between tourism and economic development based on global causal analysis using heterogeneous samples with numerous subjects. As in this case, carrying out analyses on homogeneous groups may offer interesting results for policymakers attempting to suitably manage population development improvements due to tourism growth and tourism increases resulting from higher development levels.

One limitation of this work is its national scope since evidence suggests that tourism is a regional and local activity. Therefore, it may be interesting to apply this same approach on a regional level, using previously identified homogeneous groups.

And given that the existence of a causal relationship (in either direction) between tourism and development has only been determined for a specific set of countries, future works could consider other country-specific factors that may determine this causal relationship, in addition to the dynamics of tourism specialization and development level.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Cárdenas-García, P.J., Brida, J.G. & Segarra, V. Modeling the link between tourism and economic development: evidence from homogeneous panels of countries. Humanit Soc Sci Commun 11 , 308 (2024). https://doi.org/10.1057/s41599-024-02826-8

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Is the course and outcome of schizophrenia better in the 'developing' world?

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1 Department of Psychiatry, Postgraduate Institute of Medical Education & Research, Chandigarh 160012, India.
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Historically, poor outcome has often been considered to be an integral part of the concept of schizophrenia, though in recent times this has been challenged by many cross-cultural studies. In this article, we review various studies pertaining to course and outcome of schizophrenia to have an understanding about variations in course and outcome of schizophrenia across cultures and nations. For better appraisal, the research studies have been divided into studies prior to cross-cultural World Health Organization (WHO) sponsored studies (Pre-WHO studies), WHO sponsored cross-cultural studies, and studies on course and outcome of schizophrenia not sponsored by WHO. We believe that the evidence arising from various studies across the globe largely supports the 'favorable outcome hypothesis in developing countries', i.e. developing countries have a larger proportion of patients with a good outcome and lesser percentage with a worst outcome as compared to developed countries, albeit amidst the controversies discussed by us. We suggest that in course and outcome studies, culture should not be used as a synonym for unexplained variance and research designs focusing at other potential factors impacting course and outcome of schizophrenia are much needed.

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Original research article, how a successful implementation and sustainable growth of e-commerce can be achieved in developing countries; a pathway towards green economy.

1 College of Economics and Management, Beijing University of Technology, Beijing, China
2 Asia-Europe Institute, University of Malaya, Kuala Lumpur, Malaysia
3 Faculty of Education, University of Malaya, Kuala Lumpur, Malaysia
4 Beijing Academy of Science and Technology, Beijing, China

Apart from the goal of the digital world and other benefits of e-commerce, it becomes the need of time during this COVID-19 pandemic. Successful implementation and sustainable growth of e-commerce in developing countries is a challenge. The goal of the digital world without the implementation and sustainable growth of e-commerce in developing countries is incomplete. Based on UTAUT theory, we have developed an integrated model to study the developing countries’ consumers’ adoption intentions towards e-commerce. We collected a valid useable sample of 796 respondents from a developing country, applied the SEM-ANN two-step hybrid approach to testing the proposed hypothesis, and ranked the antecedents according to their importance. Results revealed that Trust in e-commerce, Perceived risk of using e-commerce, Ease of use in e-commerce, Curiosity about e-commerce, Facilitating Conditions, and Awareness of e-commerce benefits influence the adoption intentions of developing countries’ consumers. Sensitivity analysis results revealed that Ease of use in e-commerce platforms and awareness of e-commerce benefits are the two most crucial factors behind the adoption intentions in developing countries. The study’s findings help authorities adopt sustainable e-commerce, multinational companies effectively market their goods online, and academics better understand how inhabitants of developing nations perceive e-commerce.

Introduction

We are on the verge of a technological revolution due to COVID-19, which has the potential to reshape the way people connect and go about organizing their daily activities. Increased globalization and commercialization are occurring as people and machines become more interconnected through the internet.

The COVID-19 pandemic changed the course of human lives; the term social distancing emerged in human lives to secure healthy lives and avoid infection and spread of COVID-19. Apart from many other benefits of e-commerce, E-commerce is an alternative to traditional means of shopping and a blessing in avoiding the spread of COVID-19. Traditional buying channels require personal interaction, and the chances of COVID-19 spread can be increased. Hence there is essential to implement e-commerce globally to facilitate a common consumer and open new horizons for small and big businesses.

E-commerce has several benefits over traditional shopping channels, such as 1) everything in one place, 2) saving time and money, 3) simplicity and comfort, 4) a wide range of selection and facilities to compare, 5) maintain a healthy lifestyle (COVID-19 context) ( Khurana, 2019 ).

Online shopping is more popular among millennials than among baby boomers. Millennials, defined as those between the ages of 25 and 34, are the largest demographic of online shoppers. 38.4% of US online buyers are under 35 years old. Only 14.4% of internet shoppers in the United States are 65 years old or older ( Law, 2021 ).

The majority of the studies on e-commerce adoption emphasize companies’ adoption of e-commerce and facilitate the e-commerce models of B2B (full), B2C (partial), and C2B (partial) and ignore the potential end-user who is an integral part of the successful model of e-commerce. Specifically, C2C (in full capacity), B2C (partial), and C2B (partial), because if end users will not adopt e-commerce, the full goal of a digital word is unachievable. Hence, motivating the end user to adopt e-commerce is the most important factor behind the goal of the digital world. But to motivate end-users to adopt e-commerce, first, we need to learn how various factors influence consumers’ intention to adopt e-commerce.

Recent studies focus on the adoption of e-commerce in logistics sector companies ( Juliet Orji et al, 2022 ), economic outcomes of e-commerce adoption ( Li et al, 2021 ), retail adoption of e-commerce ( Fuller et al., 2022 ).

Researchers claim that e-commerce is a rapidly developing market that draws a large number of entrepreneurs; it has a lower survival rate than other industries ( Cuellar-Fernández et al, 2021 ). User agreements for e-commerce are seldom reviewed but are virtually approved by users ( Chakraborty et al, 2022 ). Unfriendly provisions adversely impact customer satisfaction in terms of service (ToS), but the company’s survival is increased. Second, companies with a larger market share select more consumer-friendly phrases. Third, the severity of ToS plays a role in the relationship between market share and business performance ( Chakraborty et al., 2022 ). It can create mistrust between consumers and sellers, and end-users may hesitate to adopt e-commerce or quit it.

Researchers also examine the interaction of pandemic response plans with other facilitators of e-commerce adoption in the logistics industry and their influence on business performance. They found facilitating strategies and grouped them into technical-related, pandemic response-related, firm-level-related, and institution-related ( Juliet Orji et al., 2022 ). Still ignored the end-users of e-commerce.

Researchers also revealed that people’s demands and values might drive e-commerce adoption; e-compatibility with people’s digital abilities and infrastructure is crucial; exposure to dangerous material has been experimentally established as an impediment ( Ariansyah et al, 2021 ). This study is based on the net gain maximization framework/standard utility. They did not study the factors such as social influence, trust factor between seller and buyer, ease of use, or awareness of e-commerce because 72% of their sample were non-adopter of e-commerce (never experienced or had no knowledge about e-commerce). Researchers have also examined the differences between products in people’s acceptance of E-commerce. Still, they have overlooked the motivational factors for end-users that lead to the adoption of e-commerce ( Liu and Wei, 2003 ).

Researchers also studied the outcomes of e-commerce use and its impact on family earnings in china but ignored to study the factors that motivate an individual to adopt it. The possible reason might be an established infrastructure of e-commerce in China. They found e-commerce adoption has a considerable impact on family income, with e-commerce adopters earning much more money than non-adopters. While e-commerce adoption has had a big and detrimental influence on wages, it has had little effect on transfers. Using additional data, researchers found that the income consequences of e-commerce adoption vary depending on geographic region and household-level factors ( Li et al., 2021 ). Researchers also found that the success of online merchants is impacted by the deployment of e-commerce capabilities at the appropriate time ( Fuller et al., 2022 ). Lastly, we have explored through a literature review that the effect of gender as a moderator is rarely studied in e-commerce adoption. We believe it should be considered for a sustainable implementation of e-commerce as researchers claim that consumer psyche differs with gender. Furthermore, limitations of previous studies, such as “Individuals’ m-commerce activities were black-boxed. Their perspectives may alter” ( Ashraf et al., 2021 ), also motivates us to study the subject.

After a careful investigation of past literature, we have figured out that the researchers have rarely studied the e-commerce adoption factors in developing countries, which have a major portion of the world population. A developing country is one in which the quality of life, industrial development, and economic as well as other aspects of the country’s development stays at or below average. Around 6.62 billion people live in 152 nations classified as “developing” by the International Monetary Fund (IMF), 85.22% of the world’s population; this is a significant number ( Worlddata, 2021 ).

As e-commerce expands globally, economic giants are looking towards the digital world and bigger markets for their products. So it has become necessary to study how different factors influence developing countries’ users to adopt e-commerce. It will help policymakers and global firms to make plans to implement e-commerce globally and make a dream come true of a sustainable digital world. We have proposed the following study questions based on the above-mentioned research gap.

RQ1. How do different e-commerce related factors influence the adoption intentions of e-commerce of end-users in developing countries?

RQ2. What are the most significant predictors (importance-wise) behind the e-commerce adoption in developing countries?

Based on UTAUT, we have proposed an integrated model to answer these research questions. The study is one of the first to study users’ intention to adopt e-commerce in developing countries. We have selected Pakistan as our study area (selection detail is in the section research context). We have obtained data from five cities in Pakistan and analyzed the dataset using a hybrid SEM-ANN model to test the proposed hypothesis and ranked the understudy factors based on their normalized importance (SEM = RQ1, ANN = RQ2). Study results revealed that Trust in e-commerce, Curiosity about e-commerce, Ease of use in e-commerce platforms, Facilitating conditions to operate e-commerce and awareness of e-commerce benefits positively influence users’ e-commerce adoption intention, whereas perceived risk negatively influences the intention. Social factors behind adopting e-commerce are the only factor that was found insignificant in our study. Sensitivity analysis based on ANN revealed that Ease of use and Awareness of e-commerce benefits are the most influential factors behind the adoption of e-commerce in developing countries. Social influence, perceived risk, and trust factors are the least influential factors behind the adoption intentions of users in developing countries. Study results are beneficial for policymakers to make plans to achieve a sustainable e-commerce implementation, for multinational firms to introduce their products through e-commerce successfully, and for academics to understand the behaviour of developing countries’ residents towards e-commerce.

Theoretical Background

Using the Unified Theory of Acceptance and Use of Technology (UTAUT), we developed an extended model to explain better user behaviour and the influences on their e-commerce adoption intentions. After studying and comparing existing theories, a theory of adoption (UTAUT) was created and empirically tested ( Venkatesh et al, 2003 ) to explain users’ behaviour toward technology adoption. Researchers have used UTAUT to evaluate consumers’ behaviour to adopt 5G ( Mustafa et al., 2022e ), psyche to buy smartwatches ( Mustafa et al., 2022b ), e-commerce system adoption ( Hwang, 2010 ), consumers purchase intentions ( Chen et al., 2021 ; Javed et al, 2021 ), social e-commerce adoption ( Mamonov and Benbunan-Fich, 2017 ), consumer satisfaction in m-commerce ( Kalinić et al, 2021 ), Predicting m-commerce adoption determinants ( Chong, 2013a ), wearable payment ( Lee et al, 2020 ), word of mouth and satisfaction in mobile commerce services ( Kalinić et al, 2020 ), mobile financial service ( Yan et al, 2021 ) etc. We have integrated factors such as Trust, Perceived risk, Curiosity, and Awareness of e-commerce benefits to exclusively study the adoption intentions of e-commerce in developing countries (Detail literature is discussed in the following section). We have also studied the moderation effect of gender, which was originally incorporated in UTAUT2, an extended version of UTAUT ( Venkatesh et al, 2012 ). The reason behind not exclusively considering the UTAUT2 is that factors such as hedonic motivation, satisfaction, and habit are concerned about the post-adoption behaviour of technology that leads to its continuous use. After considering the relvant literature and theories we have incorporated new factors in UTAUT and proposed the following model to study e-commerce adoption intention in developing countries ( Figure 1 ).

FIGURE 1 . Conceptual framework.

Literature Review and Hypothesis Development

Social influence.

Individuals’ perceptions of others’ ideas about the appropriateness of utilizing a specific technology or service or doing an action are examples of subjective norms. An individual’s societal influence to accept or reject a technology is social influence ( Mustafa and Wen, 2022 ). Studies have found it a significant predictor; it does not influence an individual’s decision in some circumstances. In the acceptability of mobile payments, perceived simplicity of use and perceived utility are essential factors to consider ( Işık et al., 2021 ; Liébana-Cabanillas et al, 2018 ). In a recent study on 5G adoption, researchers discovered that Social factors play a critical role in 5G adoption ( Mustafa and Wen, 2022 ). Subjective Norms significantly influence customer satisfaction in the context of mobile purchasing, as has been shown (Isik et al., 2021; San-Martín et al., 2016 ). Researchers have also revealed that consumers’ willingness to use m-commerce is strongly influenced by social factors ( Yadav et al, 2016 ) and willingness to adopt the IoT ( Yan et al., 2022 ).

On the contrary, researchers argue that, In mobile commerce, consumer happiness is not favourably influenced by Social Influence (SI) ( Kalinić et al., 2021 ). Studies also found that SI does not significantly affect customers’ contentment with mobile commerce and their readiness to promote it to others ( Kalinić et al., 2020 ). The following hypothesis is proposed in light of the fact that customers’ success in adopting new technology or m-commerce relies heavily on social influence.

H1. Social influence positively influences the adoption intention of e-commerce in developing countries users.

Marketing studies in the last several decades have focused on the importance of trust in a corporate relationship and how it affects the longevity of that collaboration. Trustworthiness and security are two of the most common ways in which writers have tackled this issue, despite the fact that it is difficult to define trust ( Kalinić et al., 2021 ). Trust in online purchases is based on the assurance that firms will keep their commitments and responsibilities without manipulating or deceiving the buyer party ( Kalinić et al., 2021 ). Trust may also be described as a behavioural trait as “the predisposition of one party to be vulnerable to the actions of the other party based on the expectation that the other party will perform a particular action important to him or her, regardless of the ability to monitor or control the other” ( Mayer et al, 1995 ) that is a predisposition to follow a certain course of action. When customers trust service providers, they often expect their satisfaction level to rise, leading to increased loyalty over time ( Marinao-Artigas and Barajas-Portas, 2020 ). Attitude, user pleasure, behavioural intention, and loyalty were shown to be strongly associated with trust in ( Sarkar et al, 2020 ) meta-analysis of the antecedents and outcomes of trust in m-commerce. Recent studies revealed that customer happiness in mobile commerce is positively influenced by their trust in the merchant ( Kalinić et al., 2021 ) and brand trust positively influence purchase intention ( Tian et al, 2022 ). A considerable antecedent of contentment in mobile banking services and apps ( Poromatikul et al, 2020 ), as well as smartphone apps in fashion sales, has also been documented ( Aguilar-Illescas et al, 2020 ; Awan et al., 2022 ). We believe that people from developing countries have low income and seriously concerned about the online payment, they mighty hesitate to adopt e-commerce because they do not have a habit to pay online. Hence, if consumers have mistrust in e-payment and online purchase they may hesitate to use e-commerce. Based on the importance of e-commerce and other factors involved in online transactions we hapothise the following

H2. Trust of a consumer in e-commerce will positively influence e-commerce adoption in developing countries.

Perceived Risk

A study has revealed that the risk barrier in mobile social commerce is the second most critical factor preventing consumers from using social commerce ( Hew et al, 2019 ). Researchers define risk as to the uncertainty and possible negative effects of a business transaction ( Işık, 2013 ; Kalinic et al, 2019 ; Ali et al., 2021 ). Customer perception of risk is defined as consumers’ belief that they may be exposed to personal information leaks and money losses while using mobile payments ( Kalinic et al., 2019 ). ( Park and John, 2010 ) identified two primary forms of perceived risk. 1) Consumers’ risky behaviour results from internet businesses’ efforts to profit from the convenience of online buying. Customers’ consideration of online time, a pleasant mood, and a perception of the value of products/services are often factors in product risks. It is hard to regulate the transaction; thus, financial and security risks are associated with online buying. 2) Environmental risk results from emotional and impulsive considerations when participating in purchase activity. A negative correlation between user behaviour and perceived risk has been shown in recent research on mobile payments ( Kalinic et al., 2019 ). Another study in Indonesia figured out that e-commerce platform usage and perceived risk have no relation among generation Z. They claim that generation Z does not think risk factors can negatively affect or be a barrier to e-commerce adoption ( Lestari, 2019 ). Researchers have also related the perceived risk with income level. They argue that Malaysian people are less likely to use e-commerce platforms because they see internet purchasing as riskier ( Man Hong et al., 2017 ). Because online transactions are fraught with danger, it is critical that those making decisions take consumer behaviour into account when making a decision ( Lestari, 2019 ). With this dissussion on prior literature we hypothesie that

H3. Perceived risk negatively influences the adoption of e-commerce in developing countries.

Curiosity is defined as “a desire for information in the absence of extrinsic reward” ( Pekrun and Linnenbrink-Garcia, 2014 ). In simple words, it refers to the state of human desire where they search for the unknown and try to find the hidden and unexplored things that fascinate them. According to recent research, curiosity fuels our activities, and our lack of knowledge about these factors contributes significantly to our want to learn more ( Dahabiyeh et al, 2021 ). According to another research, “mystery” also plays a role in people’s desire to learn and their feeling of engagement in doing so ( Hill et al, 2016 ). It has also been shown that Curiosity plays an important role in shaping our behavioural intentions ( Dahabiyeh et al., 2021 ). Researchers have also revealed that Curiosity significantly invokes consumers to adopt 5G technology ( Mustafa and Wen, 2022 ). ( Hill et al, 2016 ) claims that actively interested customers are more likely to report greater buy motivation levels than those in a neutral or post-curious mindset. With this discussion and previous findings, we conclude that e-commerce is new to the consumers of developing countries, and Curiosity may play a significant role in its adoption. Hence we hypothesize that

H4. Curiosity about e-commerce will influence consumers from developing countries to adopt e-commerce.

Ease of Use

Ease of use is the degree to which a user feels that e-commerce involves little effort and has no or little complexities. A recent study has explored that as much as technology is easy to use, people tend to adopt it and use it ( Mustafa et al, 2021 ). Even though many people in developing countries are comfortable utilizing mobile devices like smartphones, still e-commerce apps may be a novel concept. That’s particularly true now when the number of mobile commerce apps and functionalities is growing fast. New and inexperienced mobile users may find it challenging to utilize features such as making financial transactions. App developers confront making their e-commerce apps as easy to use as possible, yet this may need sacrificing features and functionality to do so ( Chong, 2013b ). Ease of use has been studied in several studies and found to be a significant factor behind technology adoption such as mobile banking ( Malaquias and Hwang, 2019 ; Sharma et al, 2020 ), wearable payments ( Lee et al., 2020 ), mobile payment acceptance ( Liébana-Cabanillas et al., 2018 ), mobile commerce services ( Kalinić et al., 2020 ). But some studies have also shown that ease of use has no impact on the m-commerce use of Serbian people ( Liébana-Cabanillas et al, 2017 ) and m-commerce adoption in India ( Yadav et al., 2016 ). We believe that consumers in developing countries may have difficulties adopting e-commerce because of low literacy rates, insecure banking systems, and unawareness of online payment. Their engagement and use of platforms such as e-commerce will highly depend on their ease of use. Hence we hypothesize

H5. Ease of use in e-commerce platforms will influence consumers to adopt it in developing countries.

Facilitating Conditions

Facilitating conditions refer to the technical infrastructure and associated resources/devices to complete online shopping ( Venkatesh et al., 2003 ). Describe facilitating conditions as “the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system.” It is helpful to mould consumers’ behaviour to adopt or reject a new technology ( Mustafa and Wen, 2022 ). However, researchers in the past claim that facilitating conditions are the least important for Chinese consumers to adopt m-commerce ( Chong, 2013a ). Facilitating conditions found significant to adopting mobile banking ( Malaquias and Hwang, 2019 ; Sharma et al., 2020 ), wearable payments ( Lee et al., 2020 ), and mobile payment ( Liébana-Cabanillas et al., 2018 ; Rehman et al, 2022 ). Let’s particularly talk about the developing countries. There are still many localities where the basic required infrastructure is not well established, and consumers may lack facilitating conditions to carry on e-transactions. Hence we believe that facilitating conditions is one of the basic requirements for e-commerce. It will play a vital role in successfully accepting or rejecting e-commerce in developing countries. We hypothesis that

H6. Facilitating conditions required to use e-commerce will influence consumers’ e-commerce adoption in developing countries.

Awareness of e-Commerce Benefits

Awareness refers to the knowledge/recognition of benefits and costs/sacrifices made by an individual who adopts or rejects technology. Researchers have rarely investigated this factor in consumers’ e-commerce adoption intention. In the past, researchers found that many people have never heard of online banking, which is an excellent example of e-commerce. This lack of information is a major deterrent to clients using online banking. According to his findings from a survey of 500 Australian clients, most of them were unaware of the advantages of internet banking ( Sathye, 1999 ). This can also be true in the current scenario of developing countries, as e-banking or the use of digital currency is quite a new thing for developing nations. According to research by ( Howcroft et al, 2002 ), the difficulty in implementing e-commerce due to a lack of familiarity with online banking services was confirmed. We believe that if consumers are aware of the benefits that e-commerce can render, their intentions to adopt e-commerce will be much higher than those who know nothing or little about the benefits of e-commerce. Hence awareness of e-commerce benefits can play a vital role in adopting e-commerce, as explored in the 5G adoption ( Mustafa and Wen, 2022 ) and farmers’ awareness about climate change ( Ahmad et al., 2021 ; Sohail MT et al., 2022 ; Sohail et al, 2022 ). Based on this, we hypothesize that

H7. Awareness of e-commerce benefits will influence users of developing countries to adopt e-commerce.

Gender as Moderator

Male and female perception of certain factors is different from each other. The human psyche towards purchasing is different for both genders. A recent study in Indonesia revealed that men are more likely to make online purchases than women to be young, married, educated, and self-employed. They are also more likely to do so if they can readily get logistical assistance and financial support, and they are better at utilizing technology and have not been exposed to dangerous material ( Ariansyah et al., 2021 ). The use of technology can boost individual productivity, but the outcome depends on how people react to accepting a particular technology ( Lestari, 2019 ; Işık et al, 2020 ). Female students’ interest and desire to assess items and services are stimulated by personal innovation. It is common for women to thoroughly study a product before making a choice, saying that they have no intention of adopting it. If they are unsatisfied with the product, such as e-commerce, they will stop using it ( Lestari, 2019 ). Male students’ evaluation of e-commerce platforms is motivated by their conviction in their ability to perform well, a concept known as “self-efficacy.” Higher levels of self-efficacy and good feelings about an online shopping platform increase male students’ desire to use it. Whereas confidence in completing a task does not drive female students to assess items and services, a greater degree of confidence motivates them to use an e-commerce platform ( Lestari, 2019 ). Researchers also found that the online contribution pattern of male and female users is also different for both genders ( Mustafa et al., 2022d ; Mustafa and Wen, 2022 ). Another study revealed that the female group is more influenced by social conventions, whereas the male group is more influenced by pleasure to adopt e-commerce ( Hwang, 2010 ). Another study measures the moderation effect of gender in mobile payment adoption. Results revealed that trust is a strong predictor of customer satisfaction in mobile payments for female consumers but not male consumers ( Hossain, 2019 ). With this discussion, we believe there will be a difference in the influence of e-commerce adoption factors for both genders. Hence we hypothesize that

H8. Gender will moderate the relationship between Social influence (8a), Trust (8b), Perceived risk (8c), Curiosity (8d), Ease of use (8e), Facilitation conditions (8f), and awareness of e-commerce benefits (8g) and Behavioural intention to adopt e-commerce.

Methodology

Pakistan and e-commerce/research context.

Pakistan is one of the developing countries and one of the greatest untapped marketplaces for e-commerce globally; Pakistan has a population of around 220 million and a variety of financial inclusion options. E-commerce in Pakistan is growing at a faster rate than any other sector and has the capacity to become its economy’s driving force ( RLTSquare, 2020 ). E-Commerce revenue in Pakistan reached US $6 billion in 2021, making it the 37th biggest market in the world ( ecommerceDB, 2022 ). Pakistan has long sought to turn its economic and commercial operations into a lively and technologically sophisticated country, thanks to the emergence of an IT industry, an expanding population, and an increasing number of smartphones and internet users. It is possible that e-commerce may help a less developed nation, such as Pakistan, go a long way toward socioeconomic and technical advancement in a short time ( RLTSquare, 2020 ). Olx.com.pk, Daraz.pk, PakWheels.com, Zameen.com, Kaymu.pk, and Shophive.com are popular e-commerce platforms in Pakistan. According to the recently published dataset by the Pakistan telecommunication authority (PTA), there are 193 million cellular subscribers, and 113 million has 3g/4g subscription ( PTA, 2022 ). These incredible statistics make Pakistan one of the best places to test our model. Hence we have picked Pakistan as a study location to check the e-commerce adoption intention in the developing countries.

Data Collection

We have used a validated construct from previous studies to collect a dataset. Detailed measurement items of the construct used to capture the sample response are presented in Supplementary Material We have slightly changed the wording of measurement items to fit our study best and collect the response accurately. Two academics approved the modified version of the construct to carry on the study. We have conducted a pilot study before finally going into an in-depth survey. For this purpose, thirty household and twenty master’s level students were selected to test the finalized questionnaire’s readability and response time. Participants of the pilot study and the initial results of the pilot study provided favourable indications to carry on further investigation ( Kost and de Rosa, 2018 ). The pilot study respondents were not included in the final sample to avoid biases.

We have picked an online survey method to collect data and avoid human mismanagement in data handling. We have divided our population into five clusters (Lahore, Karachi, Islamabad/Rawalpindi, Peshawar, and Faisalabad) based on the literacy level, population condensation, and other facilities required for e-commerce. In these clusters, we have applied a Systematic sampling technique and picked every 10th consumer who visited the supermarkets to shop. This is one of the best ways to collect the response from a heterogeneous population ( Sekaran, 2019 ). We have used google forms to administrate the survey and collection of responses. Respondents were requested to provide their cell numbers to avoid multiple attempts, data cleaning purposes, and to collect follow-up responses. The survey lasted for 2 weeks, between the second and third week of March 2022.

Each respondent has explained the purpose of the study, and their consent was obtained before collecting their information and response. We have used a seven-point Likert scale to measure the response, with “1 representing strongly disagree and 7 as strongly agree.” Researchers explained that seven points Likert scale is more precise and easy to administer and is considered better than higher-order alternative scales ( Finstad, 2010 ). A total of 1,200 questionnaires were distributed in 5 clusters, with 240 in each cluster. 796 valid responses were collected, with an overall response rate of 66.33%. The sample size is much more than the threshold level of ten times per construct item to carry on statistical analysis ( Hair et al, 2020 ).

Demographics of Respondents

We have collected the age, gender, education, occupation, residential status, etc., of each respondent so that we can have a better outlook of our study sample and its characteristics. The detail of the demographic characteristics of our total sample (796) is presented in Table 1 .

TABLE 1 . Demographic characteristics.

Common Method Bias

A common method bias (CMB) may lead to the incorrect relationship among variables, as most data was collected at a specific time and from a single source CMB can be a potential threat to the robustness of findings ( Podsakoff et al, 2012 ). We performed several procedural checks and statistical tests to avoid CMB throughout the design and analysis phases in response to recommendations ( Podsakoff et al., 2012 ). Throughout the design process of our survey building, we paid significant attention. Scales that were not only simple and clear but also recognizable were used. We made it obvious to respondents that there were no right or wrong answers and that they may answer questions as genuinely as possible as part of the data collection process. During the analysis phase, we began with an exploratory factor analysis based on Harman’s single-factor test ( Podsakoff et al., 2012 ). Harman’s single-factor analysis showed that only 32.01% of the total variation could be explained by a single component, far less than the 50% threshold value. We also tested for CMB by conducting a follow-up survey 3 weeks later after doing the first survey. We choose one proxy item for each component to shorten the second survey’s original questionnaire ( Ashraf et al., 2021 ; Işık et al., 2022 ). Both the first and subsequent items were strongly associated with supportive results. Researchers have also suggested that if the value of VIF or full collinearity is less than 3.3, the data does not pose CMB problems ( Kock, 2015 ) Table 2 . We can claim that CMB is not a serious threat in our study based on these results.

TABLE 2 . Reliability and validity analysis.

We used PLS-SEM since it is highly recommended for studies that aim to predict and explore the dependent variables in order to explain the utmost amount of variation possible. As a result, the optimum method for making predictions is PLS-SEM ( Roldán and Sánchez-Franco, 2012 ; Zhongjun et al, 2022 ). It can also concurrently handle the structural (inner) and measurement (outer) models. It is possible to get more precise findings with a small sample size using the PLS-SEM. As a result, PLS-SEM appears to be the best method for this study. Recent studies have shown a surge in interest in using the PLS-SEM approach because of its potential advantages in Management science ( Mustafa et al., 2021 ; Mustafa and Wen, 2022 ).

As part of the PLS path modelling process, the constructs’ measurements are tested in two ways to guarantee that they are accurate and reliable: 1) “The measurement model evaluation shows the reliability and validity of the outer mode,” and 2) the structural model assessment identifies the inner model or connection among the latent components ( Hair et al., 2020 ).

Multivariate Assumptions

Mustafa and Wen (2022) state that prior to doing any multivariate tests, it is necessary to assess the multivariate assumptions of multicollinearity, linearity and homoscedasticity. During the survey’s data collection, we assured respondents’ privacy and made it clear that there was no right or wrong response. We followed earlier researchers and assessed whether the data distribution was normal using the Kolmogorov-Smirnov (K-S) test; however, it was found not ( Mustafa and Wen, 2022 ). Supplementary Material confirms the non-linear and linear interactions between independent and dependent constructs in terms of linearity. Finally, the variance inflation factor (VIF) scores were examined to determine whether the model had collinearity problems. According to ( Hair et al., 2020 ), VIF values less than five indicate that the gathered data does not include any issues about collinearity. According to the findings of this research, all indicators have VIF scores that are less than five in magnitude. Hence, no collinearity problem with the study dataset confirms the model’s robustness.

Finally, by following earlier studies, we construct a scatter plot of the regression standardized predicted value, and the residual value shows that the data supports this assumption ( Mustafa et al., 2021 ; Mustafa and Wen, 2022 ). Supplementary material contains the loadings and cross-loadings of the indicators.

Measurement Model

According to ( Hair Jr et al, 2016 ), a measuring model’s reliability is dependent on its discriminant and convergent validity. The instrument’s reliability was evaluated using indicator loadings and Cronbach’s Alpha (α). Using convergent validity, the constructs’ indicators were assessed for their ability to measure the research variables correctly. AVE is used to express the overall variation in the indicators, whereas CR indicates the variables’ reliability Table 2 . Elements with factor loadings of at least 0.6 have been included in the model ( Hair Jr et al., 2016 ) ( Figure 2 ). Assessed values of “α” are considerably higher than the threshold value of 0.7, composite reliability (CR) for all variables is above 0.7, and average variance extracted (AVE) is also found to be significantly higher than 0.50, a recommended value by experts ( Table 2 ). These results indicate the reliability of the construct used in the study ( Hair Jr et al., 2016 ).

FIGURE 2 . Measurement model.

Finally, before moving to the next step, we have applied the Fornell-Larcker criterion to determine the discriminant validity of the instrument. A robust discriminant validity has been established. Table 3 represents the results of the Fornell-Larcker criterion.

TABLE 3 . Discriminant validity (Fornell-Larcker Criterion).

Structural Model Assessment

Structural model evaluation is the next step in the PLS-SEM evaluation process. Evaluating predictive relevance, multicollinearity, the empirical significance of path coefficients and confidence level are all part of evaluating the structural path model ( Hair et al., 2020 ). The outcomes of this research were analyzed and interpreted in accordance with a set of basic guidelines ( Hair Jr et al., 2016 ). The R 2 value of the first model ( Table 4 ) for direct effect analysis on e-commerce adoption intention is 0.76 ( Q 2 = 0.61), whereas the same for Model 2 ( Table 5 ), examining the moderation effect of gender, is 0.88 ( Q 2 = 0.607). It indicates that 76% and 88% variance is explained in both the models by implied constructs, respectively. Q 2 score greater than 0.01 means that the model has a high degree of predictive accuracy ( Mustafa and Wen, 2022 ).

TABLE 4 . Model 1 (Direct path analysis).

TABLE 5 . Model 2 (Moderation analysis).

To examine the proposed hypothesis, we have run two models. Model 1 ( Figure 3 ) assesses the direct effect proposed in our model (H1-H7), and Model 2 examines the moderation effect of gender (H8). We run a bootstrapping of resampling 5,000 for each model. Results of Model 1 indicate that AEC ( β = 0.321; p < 0.001), CUR ( β = 0.137; p < 0.001), EOU ( β = 0.392; p < 0.001), FC ( β = 0.181; p < 0.001), TR ( β = 0.096; p < 0.001) positively influence e-commerce adoption intentions, whereas PR ( β = −0.065; p < 0.015) negatively influence ( Table 4 ). Social influence is the only predictor that does not affect users’ e-commerce adoption intentions. It confirms the H2-H9, but H1 is not supported.

FIGURE 3 . Path model (M1).

Moderating Effect Analysis

In the second model, we have added the interaction terms and checked the moderation effect of gender on direct relationships. The second model’s direct effect results hold out and serve as the robustness of model 1 results. Results in Table 5 indicate that gender positively moderates the relationship between AEC, EOU, TR, and BI, but it negatively moderates the relationship between PR and BI. These findings supports the H8 (b, c, g, and e) but rule out H8 (a, d, and f).

The control variable of education level and age in both models was insignificant.

Artificial Neural Networks Analysis

We have carried out a two-step analysis in this study to rank the antecedents according to their normalized importance and confirm our results’ robustness. Researchers claim that ANN has the upper hand over other variance-based techniques such as SEM, Multiple Linear Regression, Multiple Discriminant Analysis, and Binary Logistics Regression in prediction because of its deep learning and predictive accuracy ( Kalinić et al., 2021 ; Mustafa et al., 2021 ). Multivariate assumptions like linearity and normality are not required in ANN. Furthermore, no particular data type is required, and it may be expanded to new datasets without disrupting its balance; it can also address the challenges originating from insufficient data ( Kalinić et al., 2021 ; Mustafa et al., 2021 ), and it is robust for noise and outliers ( Kalinić et al., 2021 ; Mustafa et al., 2021 ). Researchers suggest that when the dataset has a non-linear relationship between endogenous and exogenous variables and data is not normally distributed, it is better to carry on two-step SEM-ANN modelling for robust results ( Kalinić et al., 2021 ; Mustafa et al., 2021 ).

Complex interactions between inputs and outputs may be modelled using ANNs, a frequently used artificial intelligence technology. ANNs are intelligent, resilient, and particularly efficient at modelling these relationships ( Kalinić et al., 2021 ; Mustafa et al., 2022a ). Numerous ANN models can be broadly divided into four categories, i.e., multilayer perceptron networks, feedforward neural networks, recurrent networks, and radial basis function networks. A multilayer perceptron (MLP) is a popular choice for technology adoption studies because of its many benefits ( Kalinić et al., 2021 ).

1. The robustness of MLP neural networks in the face of imbalanced datasets is shown.

2. It can change weight coefficients and build an input-output mapping to learn.

3. Non-linear connections may be modelled with MLP neural networks since it is also non-linear.

4. Without the user’s involvement, MLP is able to adapt to any situation.

In light of this aspect, we decided to use a feedforward back-propagation multilayer perceptron as a foundation ANN model for the investigation. We have adopted sigmoid as an activation function; two hidden layers and a number of hidden neurons were allowed to be selected by software following earlier research automatically ( Figure 4 ). We have used 90% of the data for training and 10% for testing to obtain comprehensive results ( Kalinić et al., 2021 ; Mustafa et al., 2021 ). 10-fold cross-validation process has been used to prevent over-fitting concerns.

FIGURE 4 . ANN model for BI.

Root means square error (RMSE) values predict the predictive power of ANN; in this study, these values are marginal and far below the threshold level of 1. It means our model has high predictive power and is accurate in prediction ( Kalinić et al., 2021 ; Mustafa et al., 2021 ). Furthermore, to assess the efficacy of the ANN models, we calculated a goodness-of-fit coefficient equivalent to the R 2 in the regression models based on a given approach ( Figure 5 ) ( Kalinić et al., 2021 ; Mustafa et al., 2022c ). Table 6 shows the accuracy of the ANN model’s predictions.

FIGURE 5 . Regression standard residuals.

TABLE 6 . RMSE values for training and testing.

Sensitivity Analysis

Finally, a sensitivity analysis was conducted using the ANN model, and the results are shown in Table 7 . The hidden layer of the neural model’s non-zero synaptic weights verified the relevance of the inputs. The root means square error (RMSE) results for the training and testing sets are shown for all 10 runs ( Table 6 ). The model output varies significantly with changes in each input’s value, which is used to calculate its “relative importance.” With the help of these results, we have calculated the normalized importance of each variable by calculating the ratio with respect to the highest average value. Sensitivity analysis results are presented in Table 7 .

TABLE 7 . Sensititvity analysis.

Ease of use influences e-commerce adoption, followed by an awareness of e-commerce benefits, facilitation conditions, and Curiosity. Trust, perceived risk and social influence are the least influential factors behind e-commerce adoption intentions.

This study aims to explore the potentially influential factors behind e-commerce adoption in developing countries so that a sustainable e-commerce implementation goal can be achieved and a dream of the digital world come true. For this reason, based on UTAUT, we have proposed an integrated model and selected a developing country to test our proposed model. We have used a dual-stage SEM-ANN model to test the hypothesis and rank the understudy factors according to their normalized importance.

H1 to H7 unveils a direct effect of variables, while H8 presents the moderating effect of gender on e-commerce adoption (RQ1). We have observed that social influence (H1) does not affect the developing countries’ consumers to adopt e-commerce, and they look for other reasons to consider e-commerce platforms. A possible reason behind this can be that e-commerce is new, and people have a habit of using traditional means of shopping; furthermore, the courier/delivery infrastructure is not well established, so people are not using it frequently and less talk about the use of e-commerce to shop when the people do not publicly consider about e-commerce it is hard to influence by society. Our findings are consistent with the previous studies ( Kalinić et al., 2021 ) but contradict ( Kao and André L’Huillier, 2022 ). Trust (H2) positively influence consumers toward e-commerce adoption. Trust evolve over time. Consumers interact with the sellers and value the after-sale services, which eventually build trust in the system, seller, and consumer.

Consumers in developing countries value the relationship between seller and buyer; hence if online businesses successfully win the trust of developing countries’ consumers, it is easy to influence them to use e-commerce platforms. As researchers have explored that in developing countries, consumers have low income, and they are much concerned about losing their money in online transactions ( Man Hong et al., 2017 ), so if consumers have trust in e-commerce, they will use it; otherwise, they will stop using it. Our findings are consistent with ( Sarkar et al., 2020 ), who conducted a meta-analysis on mobile commerce. Perceived risk (H3) negatively affects the adoption intention in developing countries. Consumers in developing countries are risk-averse and prefer a clean transaction and fair business when money is involved. A risk factor in e-commerce is considered one of the significant barriers. Our findings align with ( Kalinic et al., 2019 ) but contradict ( Lestari, 2019 ), who claim risk factor does not affect gen-z users. Curiosity (H4) also positively influences e-commerce adoption. Consumers in developing countries found curious about e-commerce and its operations. Our findings revealed that Curiosity about e-commerce could lead to the test use of e-commerce; after this, if consumers have a good experience, they may use it again and, over time, become permanent users of e-commerce. We suggest e-commerce platforms and online sellers pay exclusive attention to trust-building with consumers so that they rely on it and be permanent users. Our findings are consistent ( Hill et al., 2016 ; Mustafa and Wen, 2022 ). Ease of use (H5) put a positive insight into e-commerce adoption. People use technologies that are easy to use and easy to handle. Our findings suggest e-commerce app developers make it user friendly so new users easily cope with it and feel convenient using e-commerce applications. Our findings are consistent with ( Kalinić et al., 2020 ; Mustafa and Wen, 2022 ) but contradict ( Yadav et al., 2016 ; Liébana-Cabanillas et al., 2017 ). Facilitating conditions (H6) also positively influence user intention toward e-commerce adoption. Our findings suggest developing countries need to develop a good infrastructure to support e-commerce. Provide internet facilities and e-banking or other digital modes of payments; they also need to encourage people to use digital payments and safeguard their concerns. It contradicts the earlier research findings in china ( Chong, 2013a ) and supports ( Sharma et al., 2020 ) findings in Fiji. Lastly, awareness of e-commerce benefits (H7) is positively associated with consumers’ adoption intentions in developing countries. Consumers who are aware of the e-commerce benefits are more inclined to use e-commerce than those who have less knowledge. Our study findings suggest informing general consumers about e-commerce use benefits and motivating them to start using e-commerce platforms.

Gender’s moderating effect (H8) is also established in four relationships, i.e., gender moderates the effect of trust (H8b), perceived risk (H8c), ease of use (H8e), and awareness of e-commerce benefits (H8g) on e-commerce adoption intentions in developing countries. Findings revealed that females’ perception of the trust, perceived risk, ease of use, and awareness of e-commerce strongly moderate the e-commerce adoption intention in developing countries. It explains the adoption psyche of females that they value these factors more than their opposite gender. At the same time, social influence (H8a), Curiosity (H8d), and facilitating conditions (H8f) are not significantly moderated by gender in our study. Male was found to be less influential compared to females in the scenarios mentioned above. These findings contradict ( Ariansyah et al., 2021 ), where males are more influential toward e-commerce adoption or ( Lestari, 2019 ), who said males are more influential by self-efficacy in e-commerce adoption and support ( Hossain, 2019 ) who claim that females moderates the relationship between trust and m-payment or ( Lestari, 2019 ) who argue that females do more through comparison of product and technologies before adopting. Findings also contradict earlier studies when authors claim that females are more socially influenced in technology adoption and males look for enjoyment in e-commerce adoption ( Hwang, 2010 ).

Finally, to answer RQ2, we used ANN. We ranked the predictors according to their normalized importance. The study revealed that ease of use with normalized importance of 100% is the most influential factor behind adoption that straight contradicts the earlier findings ( Yadav et al., 2016 ), followed by an awareness of e-commerce benefits newly integrated variables in the study of e-commerce adoption intentions with normalized importance of 77%. These are followed by facilitating conditions (49.7%) and Curiosity (46%). Whereas trust (27.9%), perceived risk (19.7%), and social influence (13.1%) are found to be the least influential for developing countries’ consumers. We suggest e-commerce platform developers and service providers keep these factors in mind when implementing e-commerce in developing countries to achieve sustainable growth in the e-commerce business.

Theoretical Implications

Our study findings render some valuable implications in the available literature on e-commerce, especially in the context of developing countries. Firstly our findings improve the understanding of e-commerce adopters in developing countries and add a valuable contribution to the findings of prior studies ( Hwang, 2010 ; Yadav et al., 2016 ; Liébana-Cabanillas et al., 2017 ; Mamonov and Benbunan-Fich, 2017 ; Lestari, 2019 ; Lin et al, 2019 ; Kalinić et al., 2020 ; Ashraf et al., 2021 ; Cuellar-Fernández et al., 2021 ) that was conducted in different countries and measure different variables.

Secondly, we have integrated Curiosity and Awareness of e-commerce benefits as antecedents of e-commerce adoption and empirically tested them in the proposed model of e-commerce adoption that can be used in future studies as a base model.

Thirdly, we have added the moderation effect of gender in the available literature on e-commerce adoption so that a better understanding of gender differences can be attained, which will eventually be helpful in the sustainable growth of e-commerce.

Fourthly, we have ranked e-commerce adoption factors in developing countries using sensitivity analysis and figured out that awareness of e-commerce benefits is the second most important predictor behind e-commerce adoption in developing countries. Furthermore, we also suggest using ANN analysis to understand human psychological related factors better as it can dig down deep into the data and provide a better understanding of the phenomenon.

Lastly, we have provided a new insight to UTAUT by adding new variables to understand technology adoption behaviour. We have empirically proved that technology adoption models must add curiosity and awareness factors to assess human behaviour towards its adoption and use.

Practical Implications

This study’s results present immense practical implications for managers of e-commerce sites, policymakers, and multinational organizations. Based on the study findings, we have also presented some valuable suggestions to attain a sustainable implementation and growth of e-commerce in developing countries.

Starting with customer Trust building on e-commerce and perceived risk, as e-commerce involves online money transactions. In developing countries, the income level is low, and people are not advanced in using e-payments. They are also afraid of online scammers. We suggest developing countries’ Governments pass strict rules against cyber crimes and secure e-payment and online transactions. They can also initiate an insurance system for online transactions to minimize the risk factor. Governments and policymakers need to ensure that people feel secure in spending online. Once the Trust in e-payments is established, consumers will frequently use e-commerce platforms because they will not fear online scams or money loss ( Chakraborty et al., 2022 ).

In addition, e-commerce implementation and sustainable growth require a good infrastructure of facilitating conditions. Generally speaking, internet, courier service to make timely delivery, strong and secure banking system, particularly e-banking system or other moods of online payments needs to improve. This will add value to the use of e-commerce sites as we have found that ease of use and facilitating conditions influence the consumers’ intentions to adopt e-commerce.

We also suggest e-commerce application/web developers introduce interfaces that are more user-friendly. Complex interface and lengthy buying process may lead to less use of e-commerce platforms ( Fuller et al., 2022 ).

Lastly, we recommend conducting workshops and e-commerce awareness seminars to let people know more about e-commerce because we have figured out that Curiosity and Awareness of e-commerce positively influence adoption intention.

Financial institutions need to educate people about the e-payment methods and their use and educate people about the security of digital payments. We also recommend that advertising agencies produce commercials that can motivate a common person to use e-commerce and educate them about the benefits of e-commerce.

Limitations

Apart from several theoretical and practical implications, this study has some limitations that need to address here and can be used in future research directions. Firstly, we measured the adoption intention of e-commerce in general and overlooked the particular e-commerce platform characteristics such as Amazon, Olx, Daraz.pk, Taobao, Alibaba, etc., These site features can also influence the customers. Secondly, we consider the age and education as two demographic factors as a control variable and found them insignificant. Still, we believe that these demographic characteristics can influence the adoption intention as younger and old consumers perceive and behave differently, and so do the educated and illiterate. Thirdly we took our sample from Pakistan, and the e-commerce infrastructure is comparatively better than many neighbouring countries such as Yemen and Afghanistan or several African countries. Hence we believe that infrastructure facilities can play a great role and have the potential to change the perception of residents.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://docs.google.com/spreadsheets/d/1fayivac_U_AtJZnqbZ0RgDYecqTY-q2q/edit?usp=sharing&ouid=104056982942615139504&rtpof=true&sd=true .

Ethics Statement

Ethics review and approval/written informed consent was not required as per local legislation and institutional requirements.

Author Contributions

SM: conceptualization, methodology, software, writing—original draft. TH and YQ: review the final draft, edit, visualization, and handle the data flow and manage it. SK and RS: investigation, and data collection.

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.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2022.940659/full#supplementary-material

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Keywords: e-commerce, developing countries, sustainable growth, adoption intention, curiosity, awareness of e-commerce, SEM-ANN, Pakistan

Citation: Mustafa S, Hao T, Qiao Y, Kifayat Shah S and Sun R (2022) How a Successful Implementation and Sustainable Growth of e-Commerce can be Achieved in Developing Countries; a Pathway Towards Green Economy. Front. Environ. Sci. 10:940659. doi: 10.3389/fenvs.2022.940659

Received: 10 May 2022; Accepted: 22 June 2022; Published: 10 August 2022.

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Copyright © 2022 Mustafa, Hao, Qiao, Kifayat Shah and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tengyue Hao, [email protected] ; Yu Qiao, [email protected]

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Eco-Innovation and Green Productivity for Sustainable Production and Consumption

Impact of tropical cyclones on sustainable development through loops and cycles: evidence from select developing countries of Asia

Published: 05 May 2023
Volume 65 , pages 2467–2498, ( 2023 )

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Sweta Sen ORCID: orcid.org/0000-0002-3066-2385 1 ,
Narayan Chandra Nayak 2 &
William Kumar Mohanty 3

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Asian developing countries are frequently devastated by tropical cyclones. While the literature is replete with their impacts on economic growth, the impacts on critical sustainable development indicators, namely income inequality, health, and human capital accumulation, have seldom been explored. In this study, we measure the direct, indirect, and spillover impacts of tropical cyclones on the sustainable development of eight developing countries in Asia. This study uses the dynamic generalized method of moments model to estimate the impact of occurrences and casualties in these countries over 28 years. Our results indicate that recurrent tropical cyclones increase income inequality, reaching the threshold at 0.4 cyclone. The mortality rate tends to rise, which decreases after 2.5 cyclonic occurrences. Similarly, cyclones seem to initially reduce the expected years of schooling, which starts increasing after one cyclonic occurrence. These weakening impacts provide evidence of negative feedback loops. We also find evidence of domino effects and gender effects. The resilience factors are controlled for, as it helps the developing countries recover from the vicious cycles. The feedback loops can be broken by taking timely interventions, mitigation and adaptation.

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Henceforth, the notation ${\mathrm{TC}}_{n}$ is used to represent tropical cyclonic occurrences and TC_deaths is used for casualties from tropical cyclones, unless otherwise defined.

Specifications using higher-order cubic terms of tropical cyclonic occurrences were also tested, which were insignificant. This implies that the cubic specification does not yield any statistically meaningful explanatory power over the squared specification.

The ‘xtdpdgmm’ command of STATA 16 is used to estimate the dynamic panel data regression models (Kripfganz 2019 ).

We have taken the antilog of the estimated threshold and subtracted 1 from the estimated value. Accordingly, the formula becomes $\left(-\frac{{\delta }_{1}+{\delta }_{2}}{{2\delta }_{3}}\right)=\mathrm{ln}(1+\widehat{{\mathrm{TC}}_{n}})$ . Therefore, $\widehat{{\mathrm{TC}}_{n}}=\mathrm{exp}\left(-\frac{{\delta }_{1}+{\delta }_{2}}{{2\delta }_{3}}\right)-1$ . Note that ${\delta }_{1}\mathrm{ and }{\delta }_{2}$ are the coefficients of ${{\mathrm{TC}}_{n}}_{i,t} and {\mathrm{TC}}_{{n}_{i, t-1}}$ , respectively. We have estimated the threshold values $\widehat{{\mathrm{TC}}_{n}}$ considering both ${\delta }_{1}\mathrm{and }{\delta }_{2}$ or anyone based on their statistical sifgnificance.

Accordingly, the estimation of the threshold value of ${\mathrm{TC}}_{n}$ for income inequality $\left(-\frac{0.7}{2*(-1.117)}\right)=\mathrm{ln}\left(1+\widehat{{\mathrm{TC}}_{n}}\right) \mathrm{gives} (1+\widehat{{\mathrm{TC}}_{n}})=1.4.$ Hence, the threshold of ${\mathrm{TC}}_{n}$ ( $\widehat{{\mathrm{TC}}_{n}}$ ) for income inequality is 0.4 ( $=1.4-1$ ).

The estimation of the threshold value of ${\mathrm{TC}}_{n}$ for mortality rate $\left(-\frac{3.983}{2*(-1.609)}\right)=\mathrm{ln}\left(1+\widehat{{\mathrm{TC}}_{n}}\right) \mathrm{gives} (1+\widehat{{\mathrm{TC}}_{n}})=3.448\sim 3.5.$ Hence, the threshold of ${\mathrm{TC}}_{n}$ ( $\widehat{{\mathrm{TC}}_{n}}$ ) for mortality rate is 2.5 ( $=3.5-1$ ).

The estimation of the threshold value of ${\mathrm{TC}}_{n}$ for expected years of schooling $\left(-\frac{(-4.002)}{2*(3.335)}\right)=\mathrm{ln}\left(1+\widehat{{\mathrm{TC}}_{n}}\right)\mathrm{gives} (1+\widehat{{\mathrm{TC}}_{n}})=1.83\sim 2.$ Hence, the threshold of ${\mathrm{TC}}_{n}$ ( $\widehat{{\mathrm{TC}}_{n}}$ ) for expected years of schooling is 1 ( $=2-1$ ).

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Sen, S., Nayak, N.C. & Mohanty, W.K. Impact of tropical cyclones on sustainable development through loops and cycles: evidence from select developing countries of Asia. Empir Econ 65 , 2467–2498 (2023). https://doi.org/10.1007/s00181-023-02431-9

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Inside the 'favorable outcome hypothesis in developing countries'
The article "Is the course and outcome of schizophrenia better in the 'developing' world?" (Kulhara et al., 2009) presents exciting epidemiological perspectives on schizophrenia's course and outcome, which confirm the 'favorable outcome hypothesis in developing countries'.From that start point, the purpose of this letter is to discuss the most feasible explanations for this to happen.
Inside the 'favorable outcome hypothesis in developing countries'
Inside the 'favorable outcome hypothesis in developing countries' Asian J Psychiatr. 2010 Mar;3(1):33. doi: 10.1016/j.ajp.2009.12.003. Epub 2010 Mar 4. Author ... My conclusion is that this phenomenon relates to both protective and non-protective features of the developing countries.
Inside the 'favorable outcome hypothesis in developing countries'
Inside the 'favorable outcome hypothesis in developing countries' Inside the 'favorable outcome hypothesis in developing countries' Asian J Psychiatr. 2010 Mar;3(1):34. doi: 10.1016/j.ajp.2010.01.006. Epub 2010 Mar 4. Author Param Kulhara 1 Affiliation ...
Inside the 'favorable outcome hypothesis in developing countries'
Inside the 'favorable outcome hypothesis in developing countries' ... in the article "Is the course and outcome of schizophrenia better in the 'developing' world?". My conclusion is that this phenomenon relates to both protective and non-protective features of the developing countries. Recommended articles. References (7)
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Inside the 'favorable outcome hypothesis in developing countries' (Q87422054) From Wikidata. Jump to navigation Jump to search. scientific article published on 04 March 2010 ... Description Also known as; English: Inside the 'favorable outcome hypothesis in developing countries' scientific article published on 04 March 2010. Statements ...
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