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The purpose of this paper is to contribute to the tourism management literature by: first, developing a hybrid neural network that will be able to predict tourists' overall satisfaction of their travel experience; and second, prioritizing the travel attributes based on their proportional impact on tourists' overall satisfaction of their travel experience in Iran.

A total of 1,870 questionnaires were distributed amongst foreign tourists in the departure lounge of “Imam Khomeini International Airport” over a period of three months. The data were used to develop a hybrid neural network in which the “rough set” is used to reduce travel attributes and the neural network to predict tourists' overall satisfaction of travel experience. After the model proved its predictive accuracy, using the sensitivity analysis of the neural network travel attributes were prioritized based on their impact on tourists' overall satisfaction.

The results were quite promising in that the proposed hybrid neural network was able to predict tourists' overall satisfaction with a relatively low amount of error (RMSE=0.05246). Furthermore, it was demonstrated that rough sets theory is capable to be applied effectively to feature selection of large datasets in the tourism context. Finally, it was found that “improving tourism infrastructures of the country” in addition to “globally promoting the image of Iran” (as a secure and pleasant destination) are of the highest priority for Iran's tourism industry to reach to its full potential.

Besides developing a data mining tool which is an efficient means for predicting tourists' overall satisfaction, the paper's findings provide precious information for tourism policy makers in Iran by prioritizing those travel attributes that have the greatest impact on foreign tourists' overall satisfaction of their travel experience.

The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.

The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.

Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

This study aims to address how the social structure of the hospitality management field has evolved from 1960 to 2016.

The informal social structure of the hospitality management literature was analyzed by collecting authorship data from seven hospitality management journals. Co-authorship analyses via network analysis were conducted.

According to the findings, throughout the history of hospitality management, international collaboration levels are relatively low. Based on social network analysis, the research community is only loosely connected, and the network of the community does not fit with the small-world network theory. Additional findings indicate that researchers in the hospitality management literature are ranked via degree centrality, closeness centrality and betweenness centrality. Cliques, which contain at least five researchers, and core researchers are identified.

This study helps both scholars and practitioners improve the informal structure of the field. Scholars must generate strong ties to strengthen cross-fertilization in the field; hence, they collaborate with authors who have strong positions in the field. Specifically, this provides a useful performance analysis. To the extent that institutions and individuals are rewarded for publications, this study demonstrates the performance and connectivity of several key researchers in the field. This finding could be interesting to (post)graduate students. Hospitality managers looking for advisors and consultants could benefit from the findings. Additionally, these are beneficial for journal editors, junior researchers and agencies/institutions.

As one of the first study in the field, this research examines the informal social structure of hospitality management literature in seven journals.

In the last decade, sustainable sourcing decision has gained tremendous attention due to the increasing governmental restrictions and public attentiveness. This decision involves diverse sets of classical and environmental parameters, which are originated from a complex, ambiguous and inconsistent decision-making environment. Arguably, supply chain management is fronting the next industrial revolution, which is named industry 4.0, due to the fast advance of digitalization. Considering the latter's rapid growth, current supplier selection models are, or it will, inefficient to assign the level of priority of each supplier among a set of suppliers, and therefore, more advanced models merging “recipes” of sustainability and industry 4.0 ingenuities are required. Yet, no research work found towards a digitalized, along with sustainability's target, sourcing.

A new framework for green and digitalized sourcing is developed. Thereafter, a hybrid decision-making approach is developed that utilizes (1) fuzzy preference programming (FPP) to decide the importance of one supplier attribute over another and (2) multi-objective optimization on the basis of ratio analysis (MOORA) to prioritize suppliers based on fuzzy performance rating. The proposed approach is implemented in consultation with the procurement department of a food processing company willing to develop a greener supply chain in the era of industry 4.0.

The proposed approach is capable to recognize the most important evaluation criteria, explain the ambiguity of experts' expressions and having better discrimination power to assess suppliers on operational efficiency and environmental and digitalization criteria, and henceforth enhances the quality of the sourcing process. Sensitivity analysis is performed to help managers for model approval. Moreover, this work presents the first attempt towards green and digitalized supplier selection. It paves the way towards further development in the modelling and optimization of sourcing in the era of industry 4.0.

Competitive supply chain management needs efficient purchasing and production activities since they represent its core, and this arises the necessity for a strategic adaptation and alignment with the requirement of industry 4.0. The latter implies alterations in the avenue firms operate and shape their activities and processes. In the context of supplier selection, this would involve the way supplier assessed and selected. This work is originally initiated based on a joint collaboration with a food company. A hybrid decision-making approach is proposed to evaluate and select suppliers considering operational efficiency, environmental criteria and digitalization initiatives towards digitalized and green supplier selection (DG-SS). To this end, supply chain management in the era of sustainability and digitalization are discussed.

The purpose of this paper is to develop a combined intuitionistic fuzzy analytic hierarchy process (IFAHP) and fuzzy multi-objective optimization approach to select suppliers and allocate the orders to them in the bottled water production context.

First, the primary weights of criteria associated with the supplier selection problem are calculated using the IFAHP technique. Then a fuzzy multi-objective optimization model is developed to allocate the appropriate amount of orders to each supplier.

The proposed methodology has been successfully implemented in the case of an Iranian food company in its bottled water factory. Results demonstrate our model is capable of practically handling the uncertainty in DMs’ preference that leads to effective and efficient supplier selection and order allocation decisions.

The authors develop a novel hybrid decision-making tool to tackle the uncertainty in decision-makers’ opinions with a demonstrated applicability and some promising outcomes in efficiently allocating the order quantity to suppliers in the area of bottled water production.

The purpose of this paper is to provide an alternative framework that will assist in understanding the adoption of digital food shopping. The coronavirus disease 2019 (COVID-19) pandemic has exacerbated the demand for digital shopping, but the adoption of digital shopping for food has not accelerated as fast as in other product categories. This study considered the role of socio-cultural factors to understand the reason for slow adoption of digital technology to access food. A cultural framework that can be used to investigate socio-cultural factors in this context was lacking, however, this paper provides a discussion of social and cultural factors and developed measurement scales to assist in understanding cultural change acceptance in consumers' adoption of digital technology to purchase food.

Using Hayes' process analysis, this paper investigated how cultural acceptance – mediated by consumer affection and appeal and measuring the moderated effects of digital trust (DT) – determined the eventual impact on consumer intention to adopt digital food retailing. This paper also considered moderated mediation with parallel mediations (consumer affection and appeal, digital convenience (DC) and consumer digital readiness) interacting with DT and consumer learning.

The authors found that cultural acceptance of digital technology (CADT) is an antecedent to the adoption of digital shopping for food, but this is also mediated by consumers' appeal and affection for digital technology and consumers' digital readiness.

This study also indicates that DT influences consumer appeal and affection (CAA), especially amongst female consumers.

The paper represents an empirical investigation of a new conceptual framework that considers socio-cultural factors to understand consumers' use of digital technology in food shopping which has been an existing knowledge gap in current literature.

The space fractional PDEs (SFPDEs) play an important role in the fractional calculus field. Proposing a high-order, stable and flexible numerical procedure for solving SFPDEs is the main aim of most researchers. This paper devotes to developing a novel spectral algorithm to solve the FitzHugh–Nagumo models with space fractional derivatives.

The fractional derivative is defined based upon the Riesz derivative. First, a second-order finite difference formulation is used to approximate the time derivative. Then, the Jacobi spectral collocation method is employed to discrete the spatial variables. On the other hand, authors assume that the approximate solution is a linear combination of special polynomials which are obtained from the Jacobi polynomials, and also there exists Riesz fractional derivative based on the Jacobi polynomials. Also, a reduced order plan, such as proper orthogonal decomposition (POD) method, has been utilized.

A fast high-order numerical method to decrease the elapsed CPU time has been constructed for solving systems of space fractional PDEs.

The spectral collocation method is combined with the POD idea to solve the system of space-fractional PDEs. The numerical results are acceptable and efficient for the main mathematical model.