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This paper aims to explore the relationship between prudence in risk attitudes and patience of time preference of Chinese apple growers regarding off-farm cold storage of production and marketing in non-harvest seasons. The authors also consider the effect of farmer participation in cooperative-like organizations known as Farm Bases (FBs).

The authors use multiple list methods and elicitation strategies to measure Chinese apple farmers' risk attitudes and time preferences. Because these farmers can either sell their apples immediately to supermarkets or intermediaries or place them in storage, the authors assess correlations between their storage decisions and their preferences regarding risk and time. The authors also differentiate risks involving gains and losses and empirically examine individual risk attitudes in different scenarios.

Marketing decisions are moderately associated with risk attitudes but not time preference. Farmers with memberships in local farmer cooperatives are likely to speculate more in cold storage. Thus, risk aversion behavioral and psychological motives affect farmers' decision-making of cold storage and intertemporal marketing activities. However, membership in cooperatives does not always result in improved income and welfare for farmers.

The research confirms that behavioral factors may strongly drive vulnerable smallholder farmers to speculate into storage even under seasonal and uncertain marketing volatility. There is the need to think deeper about the rationale of promoting cooperatives and other agricultural forms, because imposing these without careful consideration can have negative impacts.

Do risk and time preferences affect the decision of farmers to utilize storage facilities? This question is important because it is not clear if and how risk preferences affect the tradeoff between consuming today and saving for tomorrow, especially for farmers in developing countries.

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.

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

This study aims to examine the effects of prior small-scale changes to wealth on subsequent risky choices.

The paper opted for a laboratory experiment in which subjects perform two sequences of risky tasks. In between these two sets, the author transfers money for real for a randomly selected half of the subjects. Data on choices before and after the transfer of money are used to estimate risk attitudes and analyze whether the transfer of money affected attitudes to risk.

The author finds that the money gain does not change subjects' risk preferences – neither in a within- nor in a between-subject design. This suggests that individuals' risky choices are consistent with their constant absolute (CARA) risk aversion preferences, a result that supports a key assumption in recent literature on the calibration critique of decision theories and the view that individuals engage in narrow framing.

Because of the relatively small transfer of money, the research results may lack generalizability.

The paper includes implications for the reference-dependent and other theories that explain how prior outcomes affect risk-taking behavior in sequential problems.

The results are relevant to the research community studying risk-taking behavior as the results shed new light on a well-known result put forward by a seminal paper by Thaler.

This paper fills in an identified gap in the literature which is the need to test the house-money effect in a more realistic setting (over repeated risk-elicitation tasks, with money given outside the lotteries and in a within-subject design).

Expanding on the real-world financial market framework and considering the current market turmoil, with cryptocurrencies (where contracts for difference (CFDs) are extremely common) (Hasso et al., 2019) displaying unprecedented volatility, the authors aim to test in an online laboratory setting whether displaying a risk warning message is truly effective in reducing the level of risk taken and whether the placement of this method makes a difference.

To explore the impact of risk disclosure framing on risk-taking behavior, the authors conducted an online pair-wise lottery choice experiment. In addition to manipulating risk awareness through the presence or absence of risk warning messages of varying intensity, the authors also considered dynamic inconsistency, cognitive ability and questionnaire-based financial risk tolerance (FRT) scores. The authors aimed to identify potential relationships between these variables and experimentally elicited risk aversion. The authors' study offers valuable insights into the complex nature of risky decision-making and sheds light on the importance of considering dynamic inconsistency in addition to risk awareness and aversion.

The authors' results provide statistical evidence for the efficacy of informative and very salient messages in mitigating risky decision, hinting at several policy implications. The authors also provide some statistical evidence in support of the relationship between cognitive abilities and risk preferences. The authors detect that individual with low cognitive abilities scores display great risk aversion.

This study investigates the impact of risk warning messages on investment decisions in an online laboratory setting – a unique approach. However, the authors go beyond this and also examine the potential influence of dynamic inconsistency on decision-making, adding further value to the literature on this topic. To ensure a comprehensive understanding of the participants, the authors collect data on cognitive ability and FRT using questionnaires. This study provides a simple and cost-effective framework that can be easily replicated in future research – a valuable contribution to the field.

The scenario of clean wines is rather articulated, and many consumers perceive diverse types of wines as a homogeneous category, not actually related with the true characteristics of the products. Additionally, most often, individuals turning to these wines are driven either by health concerns or environmental consciousness or by curiosity. The purpose of this study is to understand whether there are differences in monetary preferences for four distinct clean labels and to analyze the level of interest of diverse market segments of regular wine consumers for this specific category of wines.

This study applied a computer-assisted Web interviewing technique. A survey was administrated in mid-September 2021 by a professional panel provider to a quota-based sample (N = 1,113) of Italian regular wine consumers. Individual willingness-to-pay (WTP) for red wines carrying different claims (organic, natural, low-sulfites and no-additives) and a conventional counterpart were collected. Clean wines’ WTP were subsequently used for hierarchical clustering.

Among the clean labels presented, respondents reported a higher WTP for organic wine. Cluster analysis yielded three actionable segments: “Easygoing wine enjoyers” (63.7%), “Convenience drinkers” (13.4%) and “Clean wine passionate” (23%). The latter reveals high preferences for all the investigated clean wines.

Sociodemographics and wine-related characteristics of regular wine consumers particularly interested in clean wines are depicted in this study; further analysis should delve on the core drivers of individual preferences.

Wineries should consider the heterogeneous interest of regular wine consumers for clean wines, developing tailored strategies for specific market segments. Stakeholders interested in safeguarding consumers should carefully monitor the landscape of different clean claims entering the wine market.

To the best of the authors’ knowledge, no previous study has simultaneously analyzed regular wine consumers’ preferences for the four types of clean labels.

Electricity savings from energy-efficient appliances (EEAs) may have a significant impact on reducing global warming. There are several barriers confronted by EEAs, which have lowered their acceptance rate. The current study aims to identify and highlight key barriers to strengthening domestic sector adoption of EEAs in developing countries.

In the current study, 13 barriers were discovered by an in-depth literature review and the judgement of experts as well. Further, integrated “interpretive structural modeling” (ISM) and “decision-making trial and evaluation laboratory” (DEMATEL) approaches are used to evaluate barriers. The ISM technique is implemented to categorize barriers into distinct hierarchy levels and “cross-impact matrix multiplication applied to classification” (MICMAC) analysis to divide barriers among four clusters “independent, linkage, dependent and autonomous.” Moreover, the DEMATEL methodology is applied to classify the barriers among cause and effect clusters.

The integrated ISM and DEMATEL approach suggests that the topmost influencing barriers to the acceptance of EEAs are the lack of Government policies and initiatives, lack of attractive loan financing and subsidized energy prices.

This study would help researchers, regulators, producers, policymakers and consumers to comprehend the need for additional developments and understand that the adoption of EEAs is a current need. Overall, the results of this study expedite stakeholders with the key barriers that may assist to enhance the acceptance of EEAs within the domestic sector.

An extensive literature survey showed a dearth of studies for the identification, modeling and analysis of barriers collectively. Therefore, the current work used the ISM and DEMATEL approaches to fill the gap and to provide more comprehensive knowledge on barriers related to the acceptance of EEA.

Given the importance of credit rating agencies’ (CRAs) assessment in affecting international financial markets, it is useful for policymakers and investors to be able to forecast it properly. Therefore, this study aims to forecast sovereign risk perception of the main agencies related to Brazilian bonds through the application of different machine learning (ML) techniques and evaluate their predictive accuracy in order to find out which one is best for this task.

Based on monthly data from January 1996 to November 2018, we perform different forecast analyses using the K-Nearest Neighbors, the Gradient Boosted Random Trees and the Multilayer Perceptron methods.

The results of this study suggest the Multilayer Perceptron technique is the most reliable one. Its predictive accuracy is relatively high if compared to the other two methods. Its forecast errors are the lowest in both the out-of-sample and in-sample forecasts’ exercises. These results hold if we consider the CRAs classification structure as linear or logarithmic. Moreover, its forecast errors are not statistically associated with periods of changes in CRAs’ opinion of any sort.

To the best of the authors’ knowledge, this study is the first to evaluate the performance of ML methods in the task of predicting sovereign credit news, including not only the sovereign ratings but also the outlook and credit watch status. In addition, the authors investigate whether the forecasts errors are statistically associated with periods of changes in sovereign risk perception.

This study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.

The study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.

Following the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.

This study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.