Search results

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The author approaches the discussion using a theoretical and historical reconstruction based on published and unpublished materials.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

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

This study aims to examine how the theory of planned behaviour and technology acceptance theory can be used to understand the adoption of non-pharmaceutical interventions (NPIs). The relationships between attitudes, behavioural intentions towards using NPIs, actual use of NPIs and word-of-mouth (WOM) were examined and compared between early and late adopters.

A survey was conducted to test the hypotheses with partial least squares structural equation modelling (n = 278).

The results indicate that relationships between attitudes, intentions and behavioural intentions were positive and significant in the whole data set – and that there were differences between the early and late adopters. WOM had no substantial relationship with actual usage and early adopters’ behavioural intentions.

This research gives a better sense of how WOM impacts attitudes, behavioural intentions and actual usage among early and late adopters of NPIs and highlights the effectiveness of WOM, especially among late adopters of NPIs. Furthermore, using the TAM allows us to make specific recommendations regarding encouraging the use of NPIs. A new three-stage communications model is introduced that uses early adopters as influencers to reduce the NPI adoption time by late adopters.

Religion could drive development. Although Ghana is touted as the most religious country in the world, notably, some Charismatic/Pentecostal churches operate at the expense of community development and members’ welfare. This study sought to achieve three objectives: to determine whether there is an opportunity for organizing the various churches for interfaith cooperative collective action; to assess the association between people’s religiosity and the propensity to join interfaith cooperative collective action and to assess people’s perceptions of the institutional framework that could facilitate the organization of the religious community in Ghana for interfaith collective action.

Descriptive statistics and an ordered probit model (OPM) were used to analyze cross-sectional data from a representative sample of households in the Greater Accra Region. Thematic analysis was also used to analyze the qualitative data.

The study found that generally, there is a positive response to a proposal to mobilize churches in an interfaith cooperative collective action, but distrust poses a great threat to interfaith cooperative collective action. The study also found that affiliation with the Seventh-Day Adventist Church and Pentecostal/Charismatic is negatively (positively) associated with the propensity to join a collective action, respectively. Finally, the results of the study found that accountability, proper management and fair distribution of the proceeds from a collective action will help in mobilizing churches in Ghana in an interfaith collective action.

This is the first major study to explore the possibility of interfaith collective action among religious denominations aimed at accelerating poverty reduction and wealth creation in any developing country.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2023-0670

This study aims to investigate the relationship between risks and the expected return of financial investment because the relationship between them is negative; if the investors agree to the higher level of risk, they have the greater the expected return; therefore, investors always require a degree of proportionality between the risks and returns.

This study applied the standard deviation, variance, coefficient of variation methods and matrix function to measure risks. Besides, the dataset is a return on equity ROE, which is collected in three companies at time series from 2005 to 2020.

When the variance or the standard deviation is higher, the return on the securities is higher, but the securities are a higher risk and vice versa. The results showed risk levels of stocks that are 2.509%, 0.367%, 3.666% and the corresponding return mean of 38.68%, 23.99% and 14.02%.

The results support the portfolio management policy appropriately. This study identifies issues for managers, investors and readers to consider: have a comprehensive solution among microcosmic policies, finance policy, investment policy and other policies to control and balance the relationship between risks and returns; have appropriate policies to regulate funds to stimulate investment in the long term.

The purpose of this meta-analysis is to encapsulate the outcomes and generate meaningful conclusions by examining the factors that influence consumers' purchase and non-purchase behaviour intention in a virtual reality retailing context.

This study integrates the outcomes from 52 studies, including 403 relationships involving 19,188 samples. The analysis was conducted using R-metafor and AMOS software.

The findings indicate that key factors that influence purchase and non-purchase behavioural intentions are virtual reality (VR)characteristics, virtual reality experience and consumer attitudes. VR experience is the strongest predictor for purchase decisions in virtual environment ,while consumer attitude towards VR most strongly influences the non-purchase behaviour of the consumers. Furthermore, the age of the respondents, cultural backgrounds (high vs low power distance) and gender moderate the relationship between consumers' attitudes and purchase and behaviour intentions.

Marketers can positively influence consumer attitudes and behavioural intentions by prioritizing the design of the virtual environment and facilitating unique experiences (by manipulating different sensory stimuli) in virtual retailing.

The current meta-analysis reconciles and reinforces the findings in the extant literature and provides a robust empirical generalization of the critical factors that influence consumers' purchase or behavioural intentions in a virtual retailing context.