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Following the standard practice of using nutrition claims to denote food functionality, this study empirically explores Chinese consumers’ willingness-to-pay for functional processed meat products by using three nutrition claims (namely “increased calcium,” “containing omega-3”, and “reduced salt”) made on pork sausages. It also aims to outline the typical characteristics of Chinese consumer segments based on preferences.

A choice-based choice experiment is utilized to investigate Chinese consumers’ valuation on attributes of interest regarding functional sausage products. First-hand data was collected in the two cities of Xi'an and Beijing.

There are market potentials for domestic and/or imported functional processed meat products among Chinese consumers. Nutrition claims made on pork sausages are appealing to Chinese consumers, and therefore, monetarily rewarded by them. Being imported from a more developed country of origin could both positively and negatively impact consumers’ WTP for nutrition claims made on pork sausages. Furthermore, specific functional modification strategies should be taken into account when addressing different segments of the Chinese market. In addition, regional impacts between Xi'an and Beijing are implied in terms of consumers’ valuation for functional pork sausages.

Limitations in the current study are mainly two folds. First, the WTP estimation magnitudes are subject to a hypothetical bias by using a stated preference approach. Second, this study only focuses on pork sausages to explore consumers’ perceptions and selects three nutrition claims among many other relevant options.

Implications are provided for meat marketers and for Chinese official food policymakers, such that promoting meat products with a nutrition claim is an attractive marketing strategy for foreign food manufacturers in China, and more reformulated meat products with better nutritional compositions should be allowed in the Chinese market.

To the best of the author’s knowledge, this research is the first to fill in the literature blank on investigating the consumers’ valuation for functional meat in the emerging market of China. Because when taking Chinese consumers as a target market and evaluating their perceptions of food quality-related labeling and certifications, the existing literature is mainly limited to topics of product safety, organic/green products, and geographical origins. However, nutrition claims, as marketable credence attributes that associate closely to the main characteristics of the functional food products, have been explored to a much lesser extent among Chinese consumers.

People often face constraints such as a lack of time or information in taking decisions, which leads them to use heuristics. In these situations, fast and frugal rules may be useful for making adaptive decisions with fewer resources, even if it leads to suboptimal choices. When applied to financial markets, the recognition heuristic predicts that investors acquire the stocks that they are aware of, thereby inflating the price of the most recognized stocks. This paper aims to study the profitability against the market of the most recognized stocks in Europe.

In this paper, the authors perform a survey and use Google Trends to study the profitability against the market of the most recognized stocks in Europe.

The authors conclude that a recognition heuristic portfolio yields poorer returns than a market portfolio. In contrast, from the data collected on Google Trends, weak evidence was found that strong increases in companies monthly search volumes may lead to abnormal returns in the following month.

The applied investment strategy does not account for transaction costs, which may jeopardize its profitability given the fact that it is necessary to revise the portfolio on a monthly basis. Despite the results obtained, they are useful to understanding the performance of recognition heuristic strategies over a comprehensive time horizon, and it would be interesting to depict its viability during different market conditions. This analysis could provide additional information about a preferable scenario for employing our strategies and, ultimately, enhance the profitability of recognition heuristic strategies.

Through the exhaustive analysis performed here on the recognition heuristic in the European stock market, it is possible to conclude that no evidence was found for the viability of exploring this type of strategy. In fact, the investors would always gain better returns when adopting a passive investment strategy. Therefore, it would be wise to assume that the European market presents at least a degree of efficiency where no investment would yield abnormal returns following the recognition heuristic.

The main objective of this paper is to study the performance of the recognition heuristic in the financial markets and to contribute to the knowledge in this field. Although many authors have already studied this heuristic when applied to financial markets, there is a lack of consensus in the literature.

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.

The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.

The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.

There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.