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This research applied Homer and Kahle's (1988) theoretical framework, which describes the hierarchical relations of personal values, attitude and behavior to test the moderated mediation model. The major focus of this study was to evaluate how individual characteristics such as fashion involvement and materialism influenced the formation of socially responsible attitudes and ethical fashion purchasing intentions. By focusing on fashion aficionados and materialists, this research examines each step of the hierarchical model by exploring the relation between values and attitude and evaluating the attitude–behavior gap. The main finding highlights the critical role that attitude plays in ethical fashion consumption.

Different sets of latent models with each ethical quality such as ethical concern and ethical obligation were tested to explore if those consumer characteristics had any moderating effects on both front-end (IV-M) and back-end (M-DV) relations of Homer and Kahle's (1988) hierarchical model.

Both ethical values failed to instill a socially responsible mindset in individuals who were heavily immersed in fashion or materialism. Once such attitudes were formed, however, those who were fashion-conscious or materialistic were more likely to purchase ethical apparel than those who were less interested in fashion or materialism.

Previous research has mainly identified external or situational factors that create the attitude or intention and behavior gap in ethical consumption. Given the lack of understanding about psychological factors in understanding the gap, this study added to the literature by identifying fashion involvement and materialism as critical factors positively influencing attitude and behavioral intention associations.

The study aims to examine whether and how second-hand clothing shoppers differ from non-shoppers on various psychographic variables, including environmentalism, perception of contamination, price sensitivity and perception of vintage clothing. Additionally, this study hopes to uncover whether and how the aforementioned psychographic variables help predict second-hand clothing shopping behaviour, specifically shopping frequency at second-hand clothing stores.

Data were collected through a survey method from 152 college students.

Results showed that college students who shopped at second-hand clothing stores were more likely to be environmentally conscious, more sensitive to higher prices and more likely to wear used clothing to express a vintage look and to be “green”, and to perceive used clothing to be less contaminated, as compared to those who did not shop at second-hand clothing stores. This study concluded that, among college students, second-hand clothing shoppers may do so not only for economic reasons but also for creation of style and feeling special about themselves.

This study suggests that college students who shop at second-hand clothing stores are different from those who do not shop at second-hand stores in terms of their environmental attitudes, perceptions of contamination from used clothing, sensitivity to prices and how they feel about vintage clothing. Further, financial concern (i.e. price sensitivity) is no longer the only reason for second-hand clothing shopping.

Little research has been conducted to understand second-hand clothing shopping behaviour among college students. This study examined multiple psychographic variables and provided insights into college students’ second-hand shopping behaviour.

The purpose of this paper is to determine the influences of fat content and storage temperature on the quality of frozen pork patties during storage to evaluate the shelf life set by the manufacturer.

Changes in moisture content, acid value, peroxide value, thiobarbituric acid value, total volatile basic nitrogen content, and the sensory score of frozen pork patties with 10 and 15 percent fat contents during storage at −5, −15, and −23°C for six months.

Although microbiological quality remained unchanged, moisture content decreased, and lipid rancidity and protein putrefaction increased significantly during storage. More rapid deterioration in quality was observed in patties stored at −5°C than those stored at lower temperatures. Lipid rancidity and protein putrefaction increased more rapidly (but not significantly) in patties with 15 percent fat than those with 10 percent fat. Overall acceptance of the sensory properties was closely correlated with the above quality indicators, except thiobarbituric acid value. Acceptable qualities of the samples were maintained for the first two, four, and six months under storage at −5, −15, and −23°C, respectively.

The current shelf life of frozen pork patties set by manufacturers, assuming a storage temperature of −18°C, needs to be readjusted considering practical storage temperatures.

No studies have comprehensively explored the effects of fat content and storage temperatures on the quality of ground meat products during frozen storage. These approaches to determine quality changes may be useful for manufacturers to predict and control the quality of their products.

Although the study of credit ratings has focused on traditional credit bureau resources, scholars have recently emphasized the importance of big data. The purpose of this paper is to examine both how these data affect the credit evaluations of small businesses and how financial managers use them to stabilize their risks.

Using data from 97,889 data points for normal guarantees and 1,678 data points for accidents in public funds, the authors explore the effects of trade area grades as well as the superiority of the use of big data when evaluating credit ratings for small businesses.

The results indicate that the grade information of trade areas is useful in predicting accident rates, particularly for small businesses with high credit scores (AAA-A). On the other hand, the accident rates of small businesses with low credit scores increased from 3.15-16.67 to 3.20-33.3 percent. These findings demonstrate that accident rates for the businesses with high credit scores decrease, but accident rates for businesses with low credit scores increase when using the grades of trade areas.

The authors contribute to the literature in two ways. First, this study provides one of the first investigations on information on trade areas through public financial perspectives, thereby extending the financial risk and retail literature. Second, the current study extends the research on the credit evaluation of small businesses through the big data application of real transaction-based trade areas, answering the call of Park et al. (2012), who recommended an exploration of the relationship between business start-ups and financial risk.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

Chinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market.

To facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio.

The authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases.

The results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis.

From 1953 to 1961, the South Korean economy grew slowly; the average per capita GNP growth was a mere percent, amounting to less than $100 in 1961. Few people, therefore, look for the sources of later dynamism in this period. As Kyung Cho Chung (1956:225) wrote in the mid‐1950s: “[South Korea] faces grave economic difficulties. The limitations imposed by the Japanese have been succeeded by the division of the country, the general destruction incurred by the Korean War, and the attendant dislocation of the population, which has further disorganized the economy” (see also McCune 1956:191–192). T.R. Fehrenbach (1963:37), in his widely read book on the Korean War, prognosticated: “By themselves, the two halves [of Korea] might possibly build a viable economy by the year 2000, certainly not sooner.”

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

The aim of the present paper was the study of mechanical and microstructural behaviour of 6061/Al2O3/20p metal matrix composite sheets joined by Friction Stir Welding. The material was welded into the form of sheets of 7 mm thickness after T6 treatment and was tested in tension and fatigue at room temperature. The mechanical behaviour of the material was found to depend strongly on the particles fracture across the weld. The tensile properties in longitudinal direction resulted higher respect to the transversal ones. The fatigue endurance (S‐N) curve of the welds was obtained by using a resonant electro‐mechanical testing machine under constant loading control up to 250 Hz sine wave loading. The cyclic fatigue tests were conducted in the axial total stress‐amplitude control mode with min max R = σ / σ The microstructure resulting from the FSW process was studied by employing optical and scanning electron microscopy.