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Food service establishments and online food delivery companies use a revenue share model based on a commission rate. Because of the asymmetry of bargaining power, many food service establishments are vulnerable to a high commission rate. What is missing in the ongoing discussion about the revenue share model is the perspective of food consumers, who are the third party in the multi-sided market.

Within a willingness-to-pay (WTP) framework, we study if food consumers have preferences for the commission rate charged by food delivery companies to food service establishments. With 456 random consumers in the United States, we conduct a controlled experiment in which information is used as treatment in two groups. In the first group, the provided information only relates to the revenue share model (i.e. economic). In the second group, participants also received information about price control initiatives (i.e. economic and political).

Based on WTP-space mixed logit model results, there is a significant effect of information on preferences for the commission rate. While participants in the control group exhibited no aversion to the commission rate, participants who received treatment had a significant and negative WTP. The magnitude of the effect is estimated at -$1.08 for participants in the first treatment and -$2.28 for participants in the second treatment.

To date there is no applied research on the preferences of consumers in the online food order and delivery industry with respect to upstream conditions (i.e. commission rates).

Smart farming technologies (SFTs) can increase yields and reduce the environmental impacts of farming by improving the efficient use of inputs. This paper is to estimate farmers' preference and willingness to pay (WTP) for a well-defined SFT, smart drip irrigation (SDI) technology.

This study conducted a discrete choice experiment (DCE) among 1,300 maize farmers in North China to understand their WTP for various functions of SDI using mixed logit (MIXL) models.

The results show that farmers have a strong preference for SDI in general and its specific functions of smart sensing and smart control. However, farmers do not have a preference for the function of region-level agronomic planning. Farmers' preferences for different functions of SDI are heterogeneous. Their preference was significantly associated with their education, experience of being village cadres and using computers, household income and holding of land and machines. Further analysis show that farmers' WTP for functions facilitated by hardware is close to the estimated prices, whereas their WTP for functions wholly or partially facilitated by software is substantially lower than the estimated prices.

Findings from the empirical study lead to policy implications for enhancing the design of SFTs by integrating software and hardware and optimizing agricultural extension strategies for SFTs with digital techniques such as videos.

This study provides initial insights into understanding farmers' preferences and WTP for specific functions of SFTs with a DCE.

This study aims to explore the extent to which Chinese multinational enterprises (MNEs) rely on supranational institution – the Belt and Road Initiative (BRI) – versus host country institutional quality to navigate their foreign location choice.

This study uses a conditional logit regression model using a sample of 1,302 greenfield investments by Chinese MNEs in 54 BRI participating countries during the period 2011–2018.

The results indicate that as a supranational institution, the BRI serves as a substitution mechanism to address the deficiencies in institutional quality in BRI participating countries, thereby attracting Chinese MNEs to invest in those countries. In addition, the BRI’s substitution effect on host country institutional quality is more pronounced for large MNEs, MNEs in the manufacturing industry and MNEs in inland regions.

This study expands the understanding of the BRI as a supranational institution for MNEs from emerging markets and reveals its substitution effect on the host country institutional quality. Furthermore, it highlights that MNEs with diverse characteristics gain varying degrees of benefits from the BRI.

Ticket sales are an essential source of income for football clubs and federations. Analyzing the determinants of fans' willingness-to-pay for tickets is therefore an important exercise. By knowing the match- and fan-related characteristics that influence how much a fan wants to pay for a ticket, as well as to what extent, football clubs and federations can modify their ticket offering and targeting in order to optimize this revenue stream.

Using a detailed discrete choice experiment, based on McFadden's random utility theory, this paper formulates a Bayesian hierarchical multinomial logit model. Such models are very common in the discrete choice modeling literature. The analysis identifies to what extent match and personal attributes influence fans' willingness-to-pay for games of the Belgian men's and women's football national teams.

The results show that the strength of the opponent, the type of competition, the location of the seats in the stadium, the day and kick-off time of the match and the ticket price exert an influence on the choice of the respondent. Fans are attracted most by competitive games against strong opponents. They prefer to sit along the sideline, and they have clear preferences for specific kick-off days and times. The authors also find substantial variation between socio-demographic groups, defined in terms of factors such as age, gender and family composition.

The authors use the results to estimate the willingness-to-pay for match tickets for different socio-demographic groups. Their findings are useful for football clubs and federations interested in optimizing the prices of their match tickets.

To the best of the authors' knowledge, no stated preference methods, such as discrete choice analysis, have been used to analyze the willingness-to-pay of sports fans. The advantage of discrete choice analysis is that options and variations in tickets that are not yet available in practice can be studied, allowing football organizations to increase revenues from new ticketing instruments.

Storytelling through virtual reality (VR) combines the strengths of cutting-edge technology with traditional informational campaigns. As a tool for climate change mitigation, VR has been shown to educate individuals and stimulate both emotional and cognitive responses that promote pro-environmental behavior. This paper aims to investigate whether these benefits extend to the field of green investing through an experiment conducted with a sample of small business entrepreneurs.

The experimental design involved making choices between bonds varying in maturity dates, annual interest and environmental classification (regular versus green). To identify potential impacts of the immersive experience on investment decisions, these choices were made both before and after exposure to VR videos illustrating the devastating effects of climate change. A multiple price list was employed to elicit subjects' risk preferences, enabling the joint estimation of the treatment effect and the risk and time preference parameters.

The findings indicate that, when risk and time preference parameters are controlled for, a VR experience can nudge toward green investment choices. This effect is more profound among those who already exhibit a greater propensity to opt for green investments.

Previous research shows that negative emotions, such as guilt, affect pro-environmental intentions, as well as actions, while message vividness through immersive experiences is effective in nudging greener behavior. Since analogous results in the framework of financial investments are not currently available, this paper seeks to test whether VR videos depicting the adverse effects of climate change can generate negative emotions associated with experiencing these effects and make them salient in subsequent investment decisions made by small business entrepreneurs.

The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.

This study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.

With sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.

Future research could use the same method to study other types of sponsors, such as title sponsors or other product categories.

Sponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.

Prior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.

This study aims to examine the determinants of Foreign Corrupt Practices Act (FCPA) violations and consequences of FCPA enforcements.

This paper uses publicly available data from Compustat, I/B/E/S and Thomson Reuters databases, combined with Securities and Exchange Commission (SEC) and Department of Justice (DOJ) cases, to extract insights on FCPA violations and enforcements using econometric approaches.

The main determinants of FCPA violations appear to be firm size, multinational structure, country corruption and Sarbanes-Oxley Act control weaknesses. Traditional misreporting risks (F-score and M-score) do not predict FCPA violations. This study discovers significant differences between FCPA violations by motivation, as in, sale generation, rent extraction or cost evasion. Bribes motivated by sale generation or rent extraction are partially driven by the extent of the firm’s global operations, whereas bribes motivated by cost evasion relate to internal influences. This study also finds that enforcement is more salient for criminal violations (DOJ enforcement), compared to civil violations (SEC enforcement).

This research provides new insights into the determinants of FCPA violations while underscoring the need for effective measures to combat bribery and promote ethical business practices. This research contributes to the ongoing efforts to curtail bribery, offering valuable insights into the characteristics of firms more likely to engage in bribery and contexts in which these activities occur. It provides critical implications for regulatory bodies, highlighting the differential responses of firms to varying types of enforcement, namely, criminal versus civil, as the authors observe greater decreases in internal control weaknesses following DOJ enforcement compared to SEC enforcement.

For enforcement agencies, the findings underscore the importance of rigorous criminal enforcement against FCPA violations, highlighting the improved control environments prompted by DOJ actions. Managers will find this research relevant, as it demonstrates that a firm’s entry into international markets substantially elevates the risk of its representatives engaging in bribery with foreign officials. In addition, the results are of interest to regulators, revealing that the underlying motivations driving a firm’s activities can significantly alter the factors to consider that might lead to an FCPA violation.

This paper is the original work of the authors and explores the determinants and consequences of FCPA violations and enforcement actions since 2002. To the best of the authors’ knowledge, it is the first to explore bribe determinants by their motive and documents industry-wide benefits arising from criminal enforcement.

While some countries have used debt to drive economic growth, the asymmetric effect on sub-Saharan African (SSA) countries has received little attention in the empirical literature. This paper therefore examines the asymmetric effect of external debts on economic growth.

The panel nonlinear autoregressive distributed lag (NARDL) approach was employed in the study for 29 sub-Saharan African countries from 1990 to 2021. The cross-sectional dependence test was used to determine the presence of cross-sectional dependence, while the second-generation panel unit root tests was used to examine the unit-root properties.

The empirical results show that external debt has an asymmetric effect on economic growth in both the short and long run. In the long run, a positive shock in external debts of 1% triggers an upturn in economic growth by 0.216% while a negative shock triggers 0.354% decline in economic growth. This implies that the negative shock of external debts has a much stronger impact on economic growth than the positive shock. In the short run, a positive shock in external debts by 1% triggers a decline in economic growth by 0.641%, while a negative shock of 1% triggers a fall in economic growth of 0.170%.

The paper used the NARDL model to examine the asymmetric impact of external debt on the economic growth of SSA countries, which has not been extensively studied. It is recommended that governments in the selected countries in sub-Saharan Africa should drive economic growth by promoting domestic revenue mobilization since external debts impede economic growth.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

The purpose of this paper is to critically examine the multinational oil companies’ (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on mainstreaming gender sensitivity in cash crop market supply chains in the Niger Delta region of Nigeria.

This paper adopts an explanatory research design with a mixed method to answer the research questions and test the hypotheses. A total of 1,200 rural women respondents were sampled across the Niger Delta region.

Results from the use of a combined logit model and propensity score matching indicate a significant relationship between the GMoU model and mainstreaming gender sensitivity in cash crop market supply chains in the Niger Delta.

This study implies that MOCs’ CSR interventions that improve women’s access to land and encourage better integration of food markets through improved roads and increased mobile networks would enable women to engage in cash crop production.

This implies that improving access to credit through GMoU cluster farming targeted at female farmers would improve access to finance and extension services for women in cash crop production in the Niger Delta.

This research contributes to the gender debate in the agricultural value chain from a CSR perspective in developing countries and is rational for demands for social projects by host communities. It concludes that businesses have an obligation to help solve problems of public concern.