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Air transport accounts for nearly 40% worth of the global trade cargo volume, where more than 50% of the air cargo is carried on passenger flights. Therefore, this paper aims to focus on identifying the influencing factors for both passenger and cargo demand-driven networks to smoothen the global supply chain.

The data for the study was collected through literature reviews and interviews with industry experts. The analytical hierarchy process was used to analyze the expert's opinions on the critical factors affecting air cargo demand growth. Regression analysis was conducted using the selected variables to develop a model to calculate air cargo demand growth.

According to the expert opinion, it was identified that facilities under airport capacities and facilities are mainly affected by the air cargo carried by combi carriers. The model was developed considering the air connectivity index and air cargo demand at destination variables.

The factors identified here are mainly related to the current situation in Sri Lanka. Applying this methodology to other economic zones will add new factors related to their economic contexts and could be generalized as the influencing factors for the growth of air cargo demand by finding more results.

Previous studies have been conducted using different factors and models to forecast air cargo demand, and those did not consider demand from combi and all-cargo carriers together. More than 98% of air cargo trades in Sri Lanka are happening through combi carriers. Hence, Sri Lanka will be a best case study to analyze the behavior of combi carriers.

This study aims to revisit the empirical debate about the asymmetric relationship between oil prices, energy consumption, CO₂ emissions and economic growth in a panel of 184 countries from 1981 to 2020.

A relatively new research method, the PVAR system GMM, is applied.

The outcome of the PVAR system GMM model at the group level in the study suggests that oil prices exert a positive but statistically insignificant effect on economic growth. Energy consumption is inversely related to economic growth but statistically significant, and the correlation between CO₂ emissions and economic growth is negative but statistically insignificant. The Granger causality test indicates that oil prices, CO₂ emissions, oil rents, energy consumption and savings jointly Granger-cause economic growth. A unidirectional causality runs from energy consumption, savings and economic growth to oil prices. At countries’ income grouping levels, oil prices, oil rent, CO₂ emissions, energy consumption and savings jointly Granger-cause economic growth for the high-income and upper-middle-income countries groups only, while those variables did not jointly Granger-cause economic growth for the low-income and lower-middle-income countries groups. The modulus emanating from the eigenvalue stability condition with the roots of the companion matrix indicates that the model is stable. The results support the asymmetric impacts of oil prices on economic growth and aid policy formulation, particularly the cross-country disparities regarding the nexus between oil prices and growth.

From a methodological perspective, to the best of the author’s knowledge, the study is the first attempt to use the PVAR system GMM and such a large sample group of 184 economies in the post-COVID-19 era to examine the impacts of oil prices on countries’ growth while controlling for other crucial variables, which is noteworthy. Two, using the World Bank categorisation of countries according to income groups, the study adds another layer of contribution to the literature by decomposing the 184 sample economies into four income groups: high-income, low-income, upper-middle-income and lower-middle-income groups to investigate the potential for asymmetric effects of oil prices on growth, the first of its kind in the post-COVID-19 period.

The main purpose of this paper is to explore whether the nature of innovation is accumulative or radical and to what extent past year accumulation of technology stock can predict future innovation. More importantly, the authors are concerned with whether a change of policy regime or a variance in the quality of technology will moderate the nature of innovation.

The authors examined a dataset of 3.6 million Chinese patents during 1985–2015 and constructed more than 5 million citation pairs across 8 sections and 128 classes to track knowledge spillover across technology fields. The authors used this citation dataset to calculate the technology innovation network. The authors constructed a measure of upstream invention, interacting the pre-existing technology innovation network with historical patent growth in each technology field, and estimated measure's impact on future innovation since 2005. The authors also constructed three sets of metrics – technology dependence, centrality and scientific value – to identify innovation quality and a policy dummy to consider the impact of policy on innovation.

Innovation growth is built upon past year accumulation and technology spillover. Innovation grows faster for technologies that are more central and grows more slowly for more valuable technologies. A pro-innovation and pro-intellectual property right (IPR) policy plays a positive and significant role in driving technical progress. The authors also found that for technologies that have faster access to new information or larger power to control knowledge flow, the upstream and downstream innovation linkage is stronger. However, this linkage is weaker for technologies that are more novel or general. On most occasions, the nature of innovation was less responsive to policy shock.

This paper contributes to the debate on the nature of innovation by determining whether upstream innovation has strong predictive power on future innovation. The authors develop the assumption used in the technology spillover literature by considering a time-variant, directional and asymmetric matrix to model technology diffusion. For the first time, the authors answer how the nature of innovation will vary depending on the technology network configurations and policy environment. In addition to contributing to the academic debate, the authors' study has important implications for economic growth and industrial or innovation management policies.

Introduction: In the context of Sri Lanka, this study compares how institutions and financial development (FD) affect economic growth (EG) and inclusive growth (IG).

Purpose: The well-structured administration and judicial system at the provincial level have been established against the socioeconomic vulnerabilities in the country for an extended period. Still, the country as a whole and provincial level is experiencing huge income and social inequality, though there are required provisions for enhancing the well-being of the people.

Methodology: The study consists of data from the nine provinces from 2013 to 2019. The analysis used the Dynamic Spatial Durbin Model (D-SDM) to explore the spatial dependencies between the provinces. Two models were developed: the interaction of the financial service activities (FSA) and insurance, reinsurance, and pension (INPEN), representing the FD with the EG and IG with and without. The IG index was estimated by principal component analysis (PCA) using indicators of the four dimensions. The results indicated spatial dependency among FD’s interaction with EG when provincial tax (PROTAX) and provincial expenses (PROEXP) are the provincial institutions.

Findings: The IG model results showed the IG’s spatial dependency moderated by the FD and only the IG model between the provinces. PROEXP showed a significant positive spillover impact among provinces towards the IG.

Practical Implications: The finding inform economic policy making while identifying weaknesses in existing local governments. Attention must be given to how poverty can be reduced, enhancing the well-being of the people with the proper channelling of finance and government institutional mechanisms.

The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.

We investigate the role of fiscal policy, through several measures of government revenues and expenditures and redistribution, on disposable and market income inequality and economic growth as well as the interaction between inequality and growth for 31 European countries from 1995 to 2019.

We use a simultaneous equations model to assess the linkage between economic growth, inequalities and fiscal policy variables.

(1) While disposable income inequality has a negative effect on all fiscal policy variables, market income inequality has a mixed effects; (2) for Eastern European countries, public consumption and direct taxation positively influence economic growth; conversely, for Western European countries, the effects are negative; (3) disposable and market income inequality have a positive effect on growth for Eastern European countries, and a negative influence on growth for Western European countries; (4) growth contributes to the increase of disposable and market income inequality for Eastern European countries; for Western European countries, the effects are opposite; and (5) fiscal policy allows for the attenuation of disposable income inequality.

The different results between the role of market and disposable income inequality levels lead us to suggest tax progressivity as an important feature to consider when analyse the trivariate relationship between inequalities, fiscal policy and growth. Furthermore, there are different dynamics between inequality and growth, and the role of fiscal policy, on both Eastern and Western European countries.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.

This paper aims to propose the mechanism of cross-network effect embedded, which can help cross-border e-commerce (CBEC) platforms strengthen cooperative relationships with sellers more equitably and effectively by using the network structural characteristics of the platforms themselves.

A two-stage evolutionary game model has been used to confirm the influence factors. The mathematical derivation of evolutionary game analysis is combined with the simulation method to examine the role of cross-network effect in cooperation. The evolutionary game model based on the cross-network effect is proposed to achieve better adaptability to the study of cooperation strategy from the two-sided market perspective.

The evolutionary game model captures the interactions of cross-network effect and the influence factors from a dynamic perspective. The cross-network effect has a certain substitution on the revenue-sharing rate of SMEs. CBEC platforms can enhance the connection between consumers and the website by improving the level of construction, which is a good way to attract sellers more cost-effectively and efficiently.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specificCBEC platforms.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specific CBEC platforms.

Investigations that study cooperation strategy from the cross-network effect perspective in CBEC are limited. The research figured out which influence factors are affected by the cross-network effect in cooperation. A two-stage evolutionary game model was proposed to explain the interaction of the factors. The evolutionary game analysis with a simulation method was combined to highlight the role of cross-network effect on cooperation strategy to give a deeper investigation into the sustainable cooperation ofCBEC.

This chapter examines the influence of external public borrowing resources on economic progress in Tunisia. The study focuses on two stages: First, the influence is studied in a direct sense and then in an indirect sense, i.e., through a transmission channel of this influence. By applying the autoregressive distributed technique with staggered lags (ARDL), over a period ranging from 1986 to 2019, the results showed that the influence of external borrowing resources on growth seems to be unfavorable in the short term but positive in the long term, hence the importance of the empirical technique chosen. Second, three interaction variables were tested, namely total government expenditure, government investment expenditure, and the real effective exchange rate. The results obtained call for better attention to the channels identified to maximize the positive influence of external public debt on the country's economic progress.

This study aims to determine the contributing factors to technical knowledge valuation at the related quadruple levels of commercialisation, including the idea, benchtop technical knowledge, prototype technical knowledge and commercialised technical knowledge, and then classify the factors by the valuation objectives.

The study method is descriptive-causal, and documentation tools of published scientific research articles in authentic local and international journals were used to extract the contributing factors to technical knowledge valuation. Moreover, the Likert spectrum-based questionnaire is used to determine the weight of each determined component. On the other hand, hierarchical analysis is used based on the extracted results from the distributed classification questionnaire among scholars to determine the allocable weight of each component.

The results indicate that at the idea step, the highest ranks among the contributing factors to technical knowledge valuation are for the indicators of innovation rate enhancement, novelty, creation of new products, profitability growth and dependence decline. In the benchtop technical knowledge step, the indicators of profitability growth, product quality enhancement, novelty, production risk drop, innovation rate enhancement, production costs drop, product price competitiveness and independence from rare machinery have the highest impact coefficients on valuation. Moreover, the prioritisation of factors in prototype technical knowledge shows that the indicators of productive risk decline, infrastructure, decrease in product delivery time, productivity growth and profitability growth are the most critical factors in technical knowledge valuation. Finally, profitability growth factors, production cost drop, productive risk drop, creating a new product, product price competitiveness and dependence decline determine the most valuable technical knowledge in the commercialisation phase.

The most salient innovation of the study involves the development levels of technical knowledge in the commercialisation cycle for determining the contributing factors to technical knowledge valuation and using multivariate decision-making methods to classify the so-called factors. The major limitation can be the context of the study because the paper was carried out by Iranian assessors and specialists using the experiences, opinions and approaches of opinion leaders based on the dominant social, cultural and accounting background of a developing country, not a developed one.

This paper is applicable because it elucidates the technical knowledge valuation factors for managers and owners of technological and knowledge-based companies to facilitate value determination and register the technical knowledge of innovative products in financial statements for the logical presentation of available intangible assets in the economic unit. Besides, in the high-tech area, collecting information from the contributing factors to technical knowledge valuation provides an opportunity to support intellectual property rights and facilitate transaction processes. Finally, in legal areas, in cases of breaching intellectual property rights relative to technical knowledge, the determination of technical knowledge value provides a solid basis for estimating the damage rate.