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This study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to provide manufacturers and other practitioners with a more accurate and objective way to assess potential channel members and mitigate information asymmetry in channel selection and negotiation.

The authors developed an open-source approach using publicly available Google satellite imagery and ML algorithms. A computer vision algorithm was used to count cars in store parking lots, and the data were processed with a CNN. Linear regression and various ML algorithms were used to estimate the relationship between parked cars and sales.

The relationship between parked cars and sales was nonlinear and dependent on the type of channel member. The best model, a Stacked Ensemble, showed that parking lot occupancy could accurately predict channel member performance.

The proposed approach offers manufacturers a low-cost and scalable solution to improve their channel member selection and performance assessment process. Using satellite imagery data can help balance the marketing channel planning process by reducing information asymmetry and providing a more objective way to assess potential partners.

This research is unique in proposing a method based on publicly available satellite imagery data to assess and predict channel member performance instead of forward-looking sales at the firm and industry levels like previous studies.

This study aims to assess the robustness and accuracy of the face-centred finite volume (FCFV) method for the simulation of compressible laminar flows in different regimes, using numerical benchmarks.

The work presents a detailed comparison with reference solutions published in the literature –when available– and numerical results computed using a commercial cell-centred finite volume software.

The FCFV scheme provides first-order accurate approximations of the viscous stress tensor and the heat flux, insensitively to cell distortion or stretching. The strategy demonstrates its efficiency in inviscid and viscous flows, for a wide range of Mach numbers, also in the incompressible limit. In purely inviscid flows, non-oscillatory approximations are obtained in the presence of shock waves. In the incompressible limit, accurate solutions are computed without pressure correction algorithms. The method shows its superior performance for viscous high Mach number flows, achieving physically admissible solutions without carbuncle effect and predictions of quantities of interest with errors below 5%.

The FCFV method accurately evaluates, for a wide range of compressible laminar flows, quantities of engineering interest, such as drag, lift and heat transfer coefficients, on unstructured meshes featuring distorted and highly stretched cells, with an aspect ratio up to ten thousand. The method is suitable to simulate industrial flows on complex geometries, relaxing the requirements on mesh quality introduced by existing finite volume solvers and alleviating the need for time-consuming manual procedures for mesh generation to be performed by specialised technicians.

This study aims to investigate the effect of risk tolerance on the individual choice of adopting Bitcoin in the form of making and receiving payment and receiving compensation.

The study uses data collected from an anonymous survey of 225 undergraduate and graduate students to measure their risk attitude using the general risk-taking propensity scale proposed by Zhang et al. (2018) and the risk-taking index, proposed by Nicholson et al. (2018). After controlling for a variety of personal traits, the study uses logistic regression to identify the predicted probabilities and marginal effects on individual choice of adopting Bitcoin.

The findings of this study suggest that individuals with a higher risk-seeking attitude are more likely to choose to receive payment for goods they sell in Bitcoin and more likely to choose to receive a portion of their compensation in cryptocurrency. Individuals in the higher-income groups are more likely to adopt Bitcoin 46% and 65% than their lower 14% and 45% and middle income 4% and 18% counterparts. While there was no statistically significant difference between males and females in adopting Bitcoin, respondents between the age of 26 and 29 were more likely to adopt Bitcoin. The effect on receiving gold was slightly smaller but highly comparable to that of receiving Bitcoin, which highlights a similar perception of risk toward the Bitcoin and gold.

The study uses a new data set collected by surveying 225 individuals and two different risk measurements to identify the relationship between perceived risk and Bitcoin adoption.

The purpose of this paper is to design a simple and accurate a-posteriori Lagrangian-based error estimator is developed for the class of backward differentiation formula (BDF) algorithms with variable time step size, and the adaptive time-stepping in BDF algorithms is demonstrated for efficient time-dependent simulations in fluid flow and heat transfer.

The Lagrange interpolation polynomial is used to predict the time derivative, and then the accurate primary result is obtained by the Gauss integral, which is applied to evaluate the local error. Not only the generalized formula of the proposed error estimator is presented but also the specific expression for the widely applied BDF1/2/3 is illustrated. Two essential executable MATLAB functions to implement the proposed error estimator are appended for practical applications. Then, the adaptive time-stepping is demonstrated based on the newly proposed error estimator for BDF algorithms.

The validation tests show that the newly proposed error estimator is accurate such that the effectivity index is always close to unity for both linear and nonlinear problems, and it avoids under/overestimation of the exact local error. The applications for fluid dynamics and coupled fluid flow and heat transfer problems depict the advantage of adaptive time-stepping based on the proposed error estimator for time-dependent simulations.

In contrast to existing error estimators for BDF algorithms, the present work is more accurate for the local error estimation, and it can be readily extended to practical applications in engineering with a few changes to existing codes, contributing to efficient time-dependent simulations in fluid flow and heat transfer.

This paper aims to examine sites of dissonance or consensus between global investor responses to the draft standards, International Financial Reporting Standards S1 (IFRS) (General Requirements for Disclosure of Sustainability-related Financial Information) and IFRS S2 (Climate-related Disclosures), issued by the International Sustainability Standards Board (ISSB).

A thematic content analysis was used to capture investor views expressed in their comment letters submitted in the consultation period (March to July 2022) in comparison to the ex ante position (issue of draft standards, March 2022) and ex post summary feedback (ISSB staff papers, September 2022) of the ISSB.

There was investor consensus in support of the ISSB and the development of the draft standards. However, there were sites of dissonance between investors and the ISSB, notably regarding the basis and focus of reporting (double or single/financial materiality and enterprise value); definitional clarity; emissions reporting; and assurance. Incrementally, the research further highlights that investors display heterogeneity of opinion.

The ISSB standards will provide a framework for future sustainability reporting. This research highlights the significance of such reporting to investors through their responses to the draft standards. The findings reveal sites of dissonance in the development and alignment of draft standards to user needs. The views of investors, as primary users, should help inform the development of sustainability-related standards by a global standard-setting body apposite to current policy and future reporting requirements, and their usefulness to users in practice.

To the best of the authors’ knowledge, this paper makes an original contribution to the comment letter literature, hitherto focused on financial reporting with a relative lack of investor engagement. Using thematic analysis, sites of dissonance are examined between the views of investors and the ISSB on their development of sustainability reporting standards.

The paper deals with the child benefits system in the Czech Republic, Slovak Republic and Sweden.

The authors describe the systems as the key baseline for subsequent qualitative and quantitative comparison. An essential element is the quantitative comparison of child benefits using their statistically stationarised values.

The Czech and Slovak systems provide a comparable rate of coverage as the Swedish system regarding the payment of both types of benefits, i.e. child benefits and tax allowances, for the first and second child; however, from the third child, the individual differences are considerable. Albeit the concepts of Czech and Slovak systems are framed by the same historical origins and conceptual approach, they differ significantly, with Slovakia providing the lowest aggregate level of child benefits.

The paper provides insight into the child benefit systems in the respective countries. These systems are at the centre of attention of policymakers who are attempting to maintain birth rates and reduce child poverty. The Czech Republic has the lowest level of at-risk-of-poverty rates for persons under 16 years of age, while natality rates are comparable.

Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

France is the third country in Europe after Italy and Spain for the number of employee-owned firms, with some 2,600 worker cooperatives (SCOPs). The authors propose a comprehensive review of SCOPs and any barriers to their expansion.

The authors analyse relevant legislation; review the rich empirical economic literature on SCOPs; and offer new descriptive empirical evidence comparing SCOPs and other French firms.

SCOPs benefit from a consistent legal framework and a well-structured and supportive cooperative movement. Cooperative laws allow attracting external capital, provide barriers against degeneration and encourage profit allocations that favour investment and labour. SCOPs are distributed across a wide range of industries; are larger than conventional firms, as capital intensive, more productive and survive better. Despite this good performance their number remains modest, perhaps because of information barriers.

An examination of the Italian and Spanish experiences and the relationship between SCOPs and the French labour movement might contribute to explaining the modest number of SCOPs.

The first comprehensive review of French worker cooperatives in four decades and the first with extensive comparative data on SCOPs and conventional French firms. With some of the best data on worker cooperatives in the world, findings have international relevance.

This research aims to investigate the adoption of future technologies in earthmoving applications. The increased development in automated driving systems (ADS) has opened up significant opportunities to revolutionize mobility and to set the path for technologies, such as electrification. The research also aims to explore the impact of automation on electromobility in earthmoving applications.

This paper adopts a multi-objective simulation-based optimization approach using machine learning in earthmoving applications.

This study concludes that ADS is “conditionally” an enabler for electrification. The study highlights and explains how local and global factors affect this conclusion. In addition to that, the research explores the impact of the equipment size on the integration of future mobility technologies. The shift from “elephant to ants” in the fleet selection resulted in improved feasibility from the integration of ADS in electrification.

This research provides fundamental considerations in the assessment of the impact of autonomous driving solutions on electromobility in the construction industry.

Financial constraints limit firms' ability to invest in working capital, which results in opportunity costs from lost sales or stockouts. The author examines initial public offering (IPO) firms' working capital management and build on the idea that newly listed firms experience a liquidity shock that allows them to invest more in working capital.

The empirical results are based on a sample of European IPO firms matched with comparable non-IPO firms; the author uses the generalized method of moments panel-data regressions to test the hypotheses.

The author observes that IPO firms increase their inventories-on-sales ratio, accounts receivable-on-sales ratio and operating working capital after the IPOs, which is consistent with the idea that going public relaxes financial constraints and allows firms to adopt more conservative working capital management practices. The observed results are stronger for smaller firms and zero-debt firms, which are the most financially constrained firms.

The study shows that working capital requirements can be financed via equity and not only via debt, and can even motivate the decision to go public for financially constrained firms.