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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The author approaches the discussion using a theoretical and historical reconstruction based on published and unpublished materials.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 article approaches the possibilities of photo elicitation as a technique for social research in the landscape of technology-mediated instantaneous interpersonal communication.

This case study, which involved persons with prison experience in the process of returning to the community, demonstrates how participant-generated photographs made with mobile handheld electronic devices and the meanings participants have attached to them allowed the research to take a co-creative turn.

The data analyzed show the potential of photo elicitation to build a link between researcher and researched that empowers the latter with agency in designing the results and also throughout the research process as a whole, thus allowing the former to reach a deeper level of understanding of the research participants' social reality.

The research conducted showcases the possibilities of this technique to approach the field of emotions from the ethnography and how they can build knowledge – especially in the work with vulnerable populations in vulnerable contexts – and generate new categories of analysis.

Optimal application and commitment toward financial management practices enhance organization performance. This study aims to assess the influence of financial management practices on organizational performance of small- and medium-scale enterprises.

Data were collected from 45 small-sized and 72 medium-sized firms. Data supported the hypothesized relationships. Construct reliability and validity were established through confirmatory factor analysis. The conceptual model and hypotheses were evaluated by using structural equation modeling.

The results indicate that working capital significantly influenced organizational performance. Capital budget management significantly influenced organizational performance. A non-significant influence of asset management on organizational performance was observed.

The generalizability of the findings will be constrained due to the research’s SMEs focus and cross-sectional data.

The study’s findings will serve as valuable pointers for stakeholders and decision-makers of SMEs in the development of well-articulated and proactive financial management systems to ensure competitiveness, sustainability, viability and financial competences.

The study adds to the corpus of literature by evidencing empirically that financial management practices significantly influenced SMEs’ performance.

The purpose of this study is to discover what attributes of casino buffet restaurants are the most important for customers’ willingness to pay (WTP).

Choice-based conjoint analysis was used in this study to test seven attributes: food, price/value, real price, service, atmosphere, the number of reviews and user-generated star ratings. Sawtooth Software was used to do the conjoint analysis, and a series of significance t-tests were run to determine the significance of each attribute on WTP with Statistical Package for the Social Sciences (SPSS).

Based on a survey of 483 respondents who had visited a buffet at a casino within the last two years, this study found that food is ranked as the most significant attribute of a casino buffet restaurant, followed by real price and service quality.

Theoretically, this work is the first to the authors’ knowledge to apply the antecedents of behavioral intention to willingness-to-pay for niche restaurants. Practically, the results of this study will help casino buffet operators as they re-open after COVID-19. Future studies could collect data in the post-pandemic environment and examine WTP at casino buffets in different geographic locations.

This study aims to evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine.

This study made computational experiments using support vector machine (SVM) classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making.

On average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia–Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process.

Investors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendations, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy.

This paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, which is of potential value for researchers in quantitative finance and market professionals who operate in the financial segment.