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The paper provides a detailed historical account of Douglass C. North's early intellectual contributions and analytical developments in pursuing a Grand Theory for why some countries are rich and others poor.

The author approaches the discussion using a theoretical and historical reconstruction based on published and unpublished materials.

The systematic, continuous and profound attempt to answer the Smithian social coordination problem shaped North's journey from being a young serious Marxist to becoming one of the founders of New Institutional Economics. In the process, he was converted in the early 1950s into a rigid neoclassical economist, being one of the leaders in promoting New Economic History. The success of the cliometric revolution exposed the frailties of the movement itself, namely, the limitations of neoclassical economic theory to explain economic growth and social change. Incorporating transaction costs, the institutional framework in which property rights and contracts are measured, defined and enforced assumes a prominent role in explaining economic performance.

In the early 1970s, North adopted a naive theory of institutions and property rights still grounded in neoclassical assumptions. Institutional and organizational analysis is modeled as a social maximizing efficient equilibrium outcome. However, the increasing tension between the neoclassical theoretical apparatus and its failure to account for contrasting political and institutional structures, diverging economic paths and social change propelled the modification of its assumptions and progressive conceptual innovation. In the later 1970s and early 1980s, North abandoned the efficiency view and gradually became more critical of the objective rationality postulate. In this intellectual movement, North's avant-garde research program contributed significantly to the creation of New Institutional Economics.

This study examines how the introduction of mobile money transfers, while making it efficient and convenient to access funds, has affected rural households’ savings behavior and the banking sector.

This study utilizes Fiji’s most recent agricultural census data to model the agricultural household’s saving decision. The study estimates an probit model to examine rural households' savings behavior. Furthermore, it utilizes time series secondary data to examine how funds transfer has been channeled to rural households in Fiji.

Firstly, the results demonstrate that with the mobile money transfer platform launch, the banking sector has lost substantial money previously used to pass through its system, thus losing service fees and interest income. Furthermore, the findings demonstrate that those using mobile wallet platforms to receive money are more likely not to have a savings account with the bank. Noting the cultural systems and social settings of the native households and the ease of payments via the mobile platform, they tend to spend more on consumption rather than saving, thus making these households more vulnerable during shocks such as natural disasters.

While mobile money transfer is hailed as a revolution, no research has yet picked up the downside to it, that of undermining the very effort by policymakers to get low-income rural households to save. Secondly, this study also highlights how mobile money transfer deprives the banking system of a significant transfer fee income and a source of funds to pool and lend to earn interest income. Furthermore, this study brings to the forefront a dichotomy about how a rural indigenous community sees the welfare and prosperity of their community much differently than what economics textbooks portray.

This present study uses political, economic, social, technological, legal and environmental (PESTLE) analysis and the strategic management theory to examine how external factors, namely the coronavirus (COVID-19) pandemic, the industrial revolution (IR) 4.0 technologies, the fuel price crisis and Sultanate of Oman Logistics Strategy (SOLS) 2040, affect the performance of container terminals in Oman.

A hybrid decision-making method that combined the analytical hierarchy process technique and Bayesian network model was used to achieve the objective of the present study.

The COVID-19 pandemic (54.60%) most significantly affected the performance of container terminals in Oman, followed by IR 4.0 technologies (19.66%), SOLS (17.00%) and fuel price crisis (8.74%). Container terminals in Oman were also found to perform “moderately,” considering the uncertainty of external factors.

This study enriches existing literature by using PESTLE analysis to assess the impact of the external business environment on firm performance in the context of the maritime industry as well as highlights how challenging external environmental factors affect the performance of container terminals in Oman.

This study contributes to developing novel study models and determining the performance level of container terminals in Oman considering integrated uncertainties and external factors such as the COVID-19 pandemic, IR 4.0 technologies, the SOLS 2040 and the fuel price crisis.

With revolutions in the telecommunication sector having led to wide unprecedented consequences in all facets of human life, this paper aims to examine the relationship between cell phone tower base stations (CPTBSs) and residential property prices within the City of Johannesburg (CoJ), South Africa.

The authors align their work with global literature and assess how the impact of CPTBSs influences residential property values in South Africa. The authors use a semi-log hedonic pricing model to test the hypothesis that proximity of CPTBSs to residential properties does not account for any variation in residential property prices.

The results show a significant impact that proximity of CPTBS has on residential property sale prices. However, the impact of CTPBSs’ proximity on residential property prices depends on their distance from the residential properties. The closer a residential property is to the CTPBS, the greater the impact that the CTPBS will have on the selling price of the residential property.

With international studies offering mixed findings on the impact of CPTBSs on residential property values, there is limited research on their impact in South Africa. The findings of this study offer crucial insights for the real estate practitioners, property owners, telecommunications companies and the public, providing a nuanced understanding of the relationship between CPTBSs and property values. This research helps property owners understand the effects of CPTBSs on their properties, and it assists property valuers in gauging the impact of CPTBSs on property values.

The purpose of this study is to investigate the impact of terrorist attacks on the volatility and returns of the stock market in Tunisia.

The employed sample comprises 1250 trading day from the Tunisian stock index (Tunindex) and stock closing prices of 64 firms listed on the Tunisian stock market (TSM) from January 2011 to October 2015. The research opts for the general autoregressive conditional heteroscedasticity (GARCH) and exponential generalized conditional heteroscedasticity (EGARCH) models framework in addition to the event study method to further assess the effect of terrorism on the Tunisian equity market.

The baseline results document a substantive impact of terrorism on the returns and volatility of the TSM index. In more details, the findings of the event study method show negative significant effects on mean abnormal returns with different magnitudes over the events dates. The outcomes propose that terrorism profoundly altered the behavior of the stock market and must receive sufficient attention in order to protect the financial market in Tunisia.

Very few evidence is found on the financial effects of terrorism over transition to democracy cases. This paper determines the salient reaction of the stock market to terrorism during democratic transition. The findings of this study shall have relevant implications for stock market participants and policymakers.

The purpose of this study is to explore the causal relationship between smart transportation technology innovation and green transportation efficiency.

A comprehensive framework is used in this paper to assess the level of green transportation efficiency in China based on the instrumental variable – generalized method of moments model, followed by an examination of the impact of innovation in smart transportation technology on green transportation efficiency. Additionally, their non-linear relationship is explored, as are their important moderating and mediating effects.

The findings indicate that, first, the efficiency of green transportation is significantly enhanced by innovation in smart transportation technology, which means that investing in such technologies contributes to improving green transportation efficiency. Second, in areas where green transportation efficiency is initially low, smart transportation technology innovation exerts a particularly potent influence in driving green transportation efficiency, which underscores the pivotal role of such innovation in bolstering efficiency when it is lacking. Third, the relationship between smart transportation technology innovation and green transportation efficiency is moderated by information and communication technology, and the influence of smart transportation technology innovation on green transportation efficiency is realized through an increase in energy efficiency and carbon emissions efficiency.

Advancing green transportation is essential in establishing a low-carbon trajectory within the transportation sector.

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

Young consumers are increasingly using online reviews and celebrity influence to make purchase decisions. The purpose of this study is to ascertain the influence of online customer reviews, celebrity influencer’s attractiveness, celebrity influencer’s credibility on female millennials’ purchase intention of beauty products.

To validate the research questions and hypotheses, data were obtained from young female consumers using an electronic self-administered survey questionnaire that was close ended. A total of 203 valid responses were obtained from which data were analysed by making use of structural equation modelling Mplus and the Statistical Package for the Social Sciences version 28.

The obtained results showed that the seven hypotheses of the study were positive. However, two hypotheses were negative, namely, celebrity influencer attractiveness did not have a significant influence on the attitude of consumers; and brand loyalty was not significantly correlating with young female consumers’ purchase intention of beauty products.

Given that millennials are known to be active users of social media and often consult online peer product reviews, marketers and practitioners of beauty industry should improve the effectiveness and usability of beauty influencers and online reviews to attract female millennial consumers.

This research contributes to understanding young female consumers’ attitudes towards purchasing beauty products, especially the combined influence of group influence (online reviews) and media influence (celebrity beauty influencers) on such attitudes.

The purpose of this study is to systematically review available state-of-the-art literature on comparative studies on Quick Commerce (Q-commerce) and E-commerce and their greenhouse gas (GHG) emissions.

The literature survey methodology is based on the funneling approach of Kitchenham (2004), where results are obtained according to inclusion and exclusion criteria. The literature review methodology used for this study covers the period from 2016 to 2022. The areas considered for the survey are operations, logistics and supply chain network design for the distribution of goods in e-business. After deciding on the criteria, a total of 140 articles were extracted from 9 journal articles that study e-commerce and environmental emissions.

The result of this study reveals that GHG emissions from both modes of shopping depend on various parameters such as speed of delivery, last-mile depot locations, logistics and vehicle efficiency, customers’ order patterns and average basket size. Furthermore, the findings also highlight the difference between Q-commerce and E-commerce supply chain networks.

This study only accounts for GHG emissions from logistics activities, but there are other sources of GHG emissions in the overall supply chain that are not taken into consideration. Supply chain/business analysts in Q-commerce companies might refer the findings from this study to measure GHG emissions from their operations.

This is the first study in the Q-commerce field that uses a structured approach to find relevant literature from the years 2016 to 2022 and focuses on GHG emission measurement.

Technological innovations provide huge opportunities to expand and revolutionize the scope of products and services offered. This is particularly true for tourism, which is undergoing significant changes due to the development of new technologies. The level of technology diffusion depends on several factors like the exchange of information among peers, and the attitude and shared perception among the contributors. The aim of the study is to explore the diffusion of technology in tourism with a specific focus on the social media discourse around new technologies. Thus, the paper investigates the level of interest in these new technologies analysing the information exchange occurring between individuals on Twitter in order to explore the influence of reciprocal networking.

To capture the attitudes expressed in the industry, the study analyses the ongoing discourse on Twitter as a proxy for the participants “interest in new technologies. Through a social network analysis of the tweets and retweets conducted over a period of nine months, the research maps the level of information exchange about the diffusion of new technologies. Moreover, the sentiment analysis provides an interesting overview of the individuals” attitudes towards the awareness or the adoption of new technologies.

Our analysis has provided several insights: (1) the information network on blockchain in tourism consists of participants who change very quickly over time (high turnover of accounts); (2) some contributors have an extremely important role in influencing the flow of information in the system (information centralization), they can have a generalist (discussing several topics) or a specialist (focusing on a specific topic) behaviour and this strategic choice influences their network's structure; (3) these central nodes also have an impact on the definition of positive and negative sentiment towards a topic (sentiment influencer).

The paper contributes to the literature on technology diffusion, by focusing on one of the preconditions of diffusion that is the shared positive attitude towards technological innovation. More specifically, we adopt a network-based approach, which is useful to explain the level of information exchange and the public discourse that can impact the shared perception and attitude towards technological innovation. The study also highlights the role of knowledge brokers in influencing this public discourse. Future studies can deepen the association between positive perception, higher levels of information exchange and increasing usage of specific technologies. Our results also suggest further exploring the opportunity to combine social media data and other sources of information to shed more light on the technological innovation diffusion processes.

This paper shows how practitioners can benefit from the analysis of information exchange about new technologies in tourism adopting a network perspective with the aim of understanding the level of influence among contributors. Moreover, the increasing interest in blockchain technology and the potential combination between social media data and other sources of information can offer promising insights.

The present study explores the level of technology diffusion through the analysis of information exchange on social media (Twitter). Furthermore, the dynamics of individual user behaviour offers a better understanding about media effects.

While previous research is focused on the users' perception towards the development of new technologies in tourism, the aim of this study is to investigate the dynamics behind the level of diffusion of information and awareness about these new technologies, which still represents an unexplored area of research.