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The purpose of this study is to first examine the factors affecting the intra-household perception of climate change. Second, the study investigates the impact of the perception of climatic stress on the operators’ and spouses’ intra-household adaptation strategies (farm and household financial strategies).

The study uses household survey data from Vietnam’s Mekong Delta. The study uses probit and negative binomial count data approaches to evaluate the empirical model.

Results confirm the existence of intra-household gender differences in the adaptation strategies. The authors found that although spouses perceive climatic stress, they are less likely to adapt to such stresses when it comes farming enterprise, but more likely to adapt to household financial strategies. In contrast, farm operators, in the presence of climatic stresses, undertake both farm and household finance adaptation strategies.

Investment in climate smart agriculture can help households in managing climatic stresses.

A farmer in Asia, and Vietnam in particular, faces significant risks from climatic changes. In Vietnam, agriculture is easily affected by natural disasters and climatic changes. This study provides insights into the perception of climatic changes by operators and spouses in Vietnam’s Mekong Delta. Perceived changes in the climate have a greater impact on women because they typically lack the necessary tools to adapt to climate change. The current findings could be useful in managing climatic risk in Vietnam’s Mekong Delta and be helpful to policymakers in designing risk management strategies in response to climatic changes.

The purpose of this paper is to investigate the endogeneity of asset values and how it relates to farm financial stress in US agriculture. The authors conceptualize an implied measure of farm financial stress as a function of debt position. The authors posit that there are variations in the asset values that are beyond the farmer’s control and therefore have implications on farm debt.

The framework recognizes the endogeneity of return on assets (ROA). It uses a non-parametric technique to approximate the variance of expected ROA (VEROA). The authors model the rate of return on agricultural assets and interest rate with a formulation that focuses on macroeconomic policy. Further, the authors use a dynamic balanced panel data set from 1960 to 2011 for 15 US agricultural states from the Agricultural Resource Management Survey, and information from traditional state-level financial statements.

Estimation of linear dynamic debt panel data models accounting for the endogeneity of ROA and VEROA is a challenging task. Estimated variances are unstable. Hence, the authors focus on variance specification that uses the residuals squared from the ARIMA specification and non-parametric estimators. Arellano-Bover/Blundell-Bond generalized method of moments estimation procedures, although may be biased, show that VEROA has a negative and significant effect on the total amount of debt in the agricultural sector.

The instruments used in this analysis are lagged regressors which may be weakly correlated with the relevant first-order condition, hence not properly identifying the parameters of interest. Future research could include the identification of better instruments, potentially use of sequential moment conditions.

Unlike previous study, the authors use non-parametric approximation of VEROA. The authors model the rate of return on agricultural assets and interest rate with a formulation that focuses on macroeconomic policy. Second, the authors make use of a large dynamic balanced panel data set from 1960 to 2011 for 15 agricultural states in the USA. To the best of the authors’ knowledge, this study is one of the few that provides evidence on risk-balancing behavior at the agricultural sector level, of the USA.

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

This paper aims at studying numerically the entropy generation of nanofluid flowing over an inclined sheet in the presence of external magnetic field, heat source/sink, chemical reaction along with slip boundary conditions imposed on an impermeable wall.

A suitable similarity transformation technique has been used to convert the coupled nonlinear partial differential equations to ordinary differential equations (ODEs). The ODEs are then solved simultaneously using the finite difference method implemented through an in-house computer program. The effects of different controlling parameters such as magnetic parameter, radiation parameter, Brownian motion parameter, thermophoresis parameter, chemical reaction parameter, Reynolds number, Brinkmann number, Prandtl number, velocity slip parameter, temperature slip parameter and the concentration slip parameter on the entropy generation and Bejan number have been discussed comprehensively through the relevant physical insights for the first time.

The relative strengths of the irreversibilities due to heat transfer, fluid friction and the mass diffusion arising due to the change in each of the controlling variables have been delineated both in the near-wall and far-away-wall regions, which may be helpful for a better understanding of the thermo-fluid dynamics of nanofluid in boundary layer flows. The numerical results obtained from the present study have also been validated with results published in open literature.

The effects of different controlling parameters such as magnetic parameter, radiation parameter, Brownian motion parameter, thermophoresis parameter, chemical reaction parameter, Reynolds number, Brinkmann number, Prandtl number, velocity slip parameter, temperature slip parameter and the concentration slip parameter on the entropy generation and Bejan number have been discussed comprehensively through the relevant physical insights for the first time.

The purpose of this paper is to examine the rice consumption by rice grain types under the rising income scenario in Bangladesh. Generally, with an increase in income, households tend to consume more food items that are high-value, enriched foods and protein, such as meat and fish, by substituting for cereals. However, consumers also substitute when it comes to grain quality. For example, cereals, such as rice, are available in a range of qualities from the ordinary type (coarse-grain) to the premium type (fine grain). The authors postulate that as household incomes increase, households may consume more premium-type rice (or fine-grain rice), while overall consuming less rice or fewer carbohydrates.

Using the Bangladesh Household Income and Expenditure Survey 2000, 2005, and 2010, and applying multivariate probit and seemingly unrelated regression estimation procedures, this study quantifies the impact of income, household demographics, and urbanization on rice consumption by rice grain types (coarse-grain, medium-grain, and fine-grain types).

The results show that urban, wealthy households and, households headed by educated heads and spouses, are more likely to consume fine-grain rice than their counterparts.

After yield, grain type is the second most important factor for farmers when considering the adoption of a new variety. The price of rice and other cereals is highly associated with the grain type. This study concludes that plant breeding programs of major cereals, such as rice and wheat, should take into account the consumer grain-type preferences when developing new varieties.

This study aims to understand how free-play credits affect risk-seeking behavior in slot players. Extant results suggest they encourage risk aversion, counter to the primary aim of increasing spend per visit. The results inform operators as to the effectiveness of what has become the primary play incentive for casino marketers within many of the world’s markets.

Within a quasi-experimental grouped design, 365 days of player-level performance data from four different casinos were analyzed to determine whether player losses (casino revenues) and time played differed on visits that included free-play redemptions from those that did not. Hypotheses were tested via paired-samples t-tests and Mann–Whitney U tests.

On balance, neither player losses nor time played were significantly different on the free-play visits. Neither the house money effect nor the endowment effect was supported. The results were most consistent with the prospect-theory-with-memory editing rule. No findings indicated increased risk-seeking behavior associated with the free-play offers.

Casino operators are afforded insight related to how costly free-play campaigns affect gaming spend and playtime. Both are critical to understanding the impact of free-play on the gambler’s experience.

The 365-day samples extended existing research by analyzing the impact of free-play offers on risk-taking behaviors within the scope of a perpetual/ongoing campaign. Comparisons of observed daily behavior/outcomes were made between separate tiers of like-kind gamblers from each of four different casinos. Quasi-hedonic editing rules were applied to a multistage decision framework.

The purpose of this paper is to investigate the impact of internet usage on financial performance of small farm business households in the USA. In particular, the authors want to assess the impact of internet usage on small farm businesses, where the owner’s main occupation is farming. Using a nationwide farm-level data in the USA and a non-parametric matching estimator, the study finds a significant positive impact of internet usage on gross cash income, total household income, off-farm income. The study further suggests that small farm businesses receive benefits from internet usage as it facilitates reduction in income risk through off-farm income sources, as well as a reduction in marketing and storage costs; households’ non-farm transportation and vehicle leasing expenses.

In this study, the authors use the “nearest neighbors” matching method in treatment evaluation, developed by Abadie and Imbens (2002). In this method, a weighting index is applied to all observations and “nearest neighbors” are identified (Abadie et al., 2004). Although matching estimation through the nearest neighbor method does not require probit or logit model estimation per se, the authors have estimated a probit model because it allows the authors to check the balancing property and to analyze the association of included variables with the likelihood of internet use.

The study suggests that small farm business households using the internet are better off in terms of total household income and off-farm income. As compared to the control group (which is counterfactual, representation of small farm businesses not using the internet), small farm businesses using the internet earn about $24,000-$26,000 more in total household income and about $27,000-$28,000 more in off-farm income. Also, small farm businesses using the internet earn about $4,100-$4,900 more in gross cash farm income compared to their counterpart. The estimate of ATT for NFI is not different from zero. However, gross cash farm revenue increased significantly.

To this end internet can provide an important role in information gathering. Internet is one of the convenient means to access and exchange information. Information and communication facilitation through internet have opened up new areas of commerce, social networking, information gathering, and recreational activities beyond a geographical bound. Producers and consumers can take advantages of internet in both collaborative and competitive aspects in economic activities as it can reduce the information asymmetries among economic agents.

Farmers will seek assistance in interpreting data and applying information to their farming operations, via the internet. Therefore, it is essential that land grant universities continue to improve the delivery of electronic extension and provide information in a clear and concise manner.

Studies in farm households have mainly investigated factors influencing internet adoption, purchasing patterns through internet, internet use, and applications. In most cases, impact analyses of communication and information technologies such as internet in agricultural businesses are discussed with references to large scale farm businesses. Thus, the authors know very little about access to the internet when it comes to small farm businesses and small farm households and about how it impacts well-being of small farm households.

The purpose of this paper is to develop an understanding of factors and their associated attributes that largely influence achievement of manufacturing flexibility.

Using two sequential phases consisting of literature review, plant visits and focus group interviews, the study identifies key factors that influence manufacturing flexibility and develop and validate these factors through postal survey. In total, 211 responses from multiple industries were collected to analyze the data.

The study identifies and develops eight factors and their associated 39 attributes that largely influence achievement of manufacturing flexibility. Out of eight underlying constructs, operational improvement practices construct reported highest level of variance followed by advanced manufacturing technology, human resource practices, supplier flexibility, supplier integration, customer integration, product-process technology integration and marketing and manufacturing integration.

The scope of the study is limited to the plant level. Therefore, other strategic-level factors, such as business strategy, the amount of investment, leadership quality have not been addressed in this research.

The findings can assist managers in improving the level of manufacturing flexibility by specifying key factors essential for achievement of manufacturing flexibility. An important implication for managers is that identification of factors should be followed by proper assessment and implementation so as to remain competitive in the market.

The findings provide insight into the factors that facilitate in achievement of manufacturing flexibility.

The purpose of this paper is to proffer an alternative conceptualization of food integrity and a market-based food integrity intelligence system.

Food fraud is interpreted as a symptom of asymmetric knowledge. Consumer collaboration for knowledge exchange and diffusion of innovation (KEDI) safeguards food markets. The concept of communicative action is applied to conceptualize and analyze key elements for designing a collaborative food integrity intelligence system.

The model of market-based KEDI consists of three dimensions: intelligence flows, organization memory, and social sensitivities. Decentralized control is crucial to effect system innovation.

The paper integrates managerial, marketing, and economic approaches and develops a model for managing food integrity intelligence.