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Traditional Korean buildings do not differ significantly in form or structural style according to era or building type. The authors interpret this from a generative rather than a typological perspective. The generation perspective considers factors forming the buildings and is connected to the prevailing thoughts of the era.

This study analyzes the generation method of seowon facilities in the Joseon Dynasty (1392–1897), focusing on the Dosan Seowon. Based on Koreans' long-term thinking, the authors applied the extracted architectural space generation layers for analysis, and present an integrated method of generation layers when the Dosan Seowon was built.

The immanent, physical and body perceptual layers presented for seowon formation analysis are represented by thought, form and territory. Specific aspects of these layers in the Dosan Seowon are analyzed, including the architectural arrangement that connects the land conditions with neo-Confucian courtesy and order, the collective architectural form considering the energy of yin and yang, and the elements of objects that affect the human body perception. This form of architecture was closely linked with and strongly influenced by monistic philosophy.

After the Korean War, architects judged traditional buildings only by shapes, and quickly accepted Western architecture's forms. Presenting a generative perspective of traditional Korean architecture expands the theoretical research direction of modern succession.

This is the first attempt to examine the generation method based on the Dosan Seowon's generation layers.

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.”

This paper aims to examine the interaction effect among the subjective social class, service level and recovery type on post-failure service evaluations (recovery satisfaction and willingness to spread positive word-of-mouth).

A total of 270 US consumers were recruited via Amazon MTurk. This study adopted a 2 (Subjective social class: high vs low) × 2 (Service level: luxury vs mid-scale) × 2 (Recovery type: customer self-recovery vs joint recovery) between subjects’ factorial design using a scenario-based survey method.

The results from the three-way multivariate analysis of covariance confirmed that a joint recovery is ineffective for high subjective social class individuals in a mid-scale hotel setting. Moreover, the moderated mediation analysis revealed that this tendency can be explained by high subjective social class individuals’ tendency to attribute blame externally to self-service technologies (SSTs).

The results of this study suggest that mid-scale hotels should deploy employees in the SST service area based on the profile of their main customers. If a mid-scale hotel is positioning itself to appeal to high subjective social class customers, then employees should be aware of the fact that customers may not be highly satisfied if they receive assistance.

This study expands the current knowledge on customers’ psychological differences based on subjective social class. Furthermore, the findings of this study contribute to academia by providing evidence of external attribution among high subjective social class individuals.

This paper aims to present a machine learning framework for using big data analytics to predict the reliability of solder joints. The purpose of this study is to accurately predict the reliability of solder joints by using big data analytics.

A machine learning framework for using big data analytics is proposed to predict the reliability of solder joints accurately.

A machine learning framework for predicting the life of solder joints accurately has been developed in this study. To validate its accuracy and efficiency, it is applied to predict the long-term reliability of lead-free Sn96.5Ag3.0Cu0.5 (SAC305) for three commonly used surface finishes such OSP, ENIG and IAg. The obtained results show that the predicted failure based on the machine learning method is much more accurate than the Weibull method. In addition, solder ball/bump joint failure modes are identified based on various solder joint failures reported in the literature.

The ability to predict thermal fatigue life accurately is extremely valuable to the industry because it saves time and cost for product development and optimization.

Weather is one of the main factors affecting labour productivity. Existing weather-productivity models focussed on hot and cold climates paying less attention to the tropics. Many tropical countries are expected to be the most areas affected by accelerated climate change and global warming, which may have a severe impact on labour health and productivity. The purpose of this paper is to assess whether the existing models can be used to predict labour productivity based on weather conditions in the tropics.

Five models are identified from the literature for evaluation. Using real labour productivity data of a high-rise building project in Malaysia, the actual productivity rate was compared with predicted productivity rates generated using the five models. The predicted productivity rates were generated using weather variables collected from an adjusting weather station to the project.

Compared with other models evaluated in this paper, the United States Army Corps of Engineers (USACE) was found to be the best model to predict productivity based on the case study data. However, the result shows only a 57% accuracy level of the USACE model indicating the need to develop a new model for the tropics for more accurate prediction.

The result of this study is perhaps the first to apply meteorological variables to predict productivity rates and validate them using actual productivity data in the tropics. This study is the first step to developing a more accurate productivity model, which will be useful for project planning and more accurate productivity rate estimation.

The theoretical findings serve as a foundation for further research into understanding sulfide-based solid-state electrolytes, ultimately advancing the progress of all-solid-state batteries.

The electronic properties of Li₇P₃S₁₁ are thoroughly explored through first-principles calculations.

This investigation encompasses the intricate atom-dominated valence and conduction bands, spatial charge density distribution and the breakdown of atom and orbital contributions to van Hove singularities. Additionally, the compound’s wide and discrete energy spectra reflect the substantial variations in bond lengths and its highly anisotropic geometric structure. The complex and nonuniform chemical environment indicates the presence of intricate hopping integrals.

This study provides valuable insights into the critical multiorbital hybridizations occurring in the Li-S and P-S chemical bonds. To validate the theoretical predictions, experimental techniques can be employed. By combining theoretical predictions with experimental data, a comprehensive understanding of the geometric and electronic characteristics of Li₇P₃S₁₁ can be achieved.

This study aims to determine the effects of e-service quality (e-SQ) in fashion e-storescapes (online retail store environments) on e-customer citizenship behaviour (e-CCB) and e-repurchase intention (e-RI), as well as to investigate whether e-CCB plays a mediating role in these relations.

In line with the aim of this study, the relationships between the variables were examined by using the mediation effect analysis within the framework of a Stimulus-Organism-Response (S-O-R) model. An online survey was developed based on the scales with proven reliability and validity. The convenience sampling method on social media sites was used to collect data for analysis from 500 volunteer participants who had previously shopped online for fashion brands between 1st and 30th September 2020. The partial least squares (PLS) path analysis method was used to analyse the research model.

This study revealed that the dimensions of e-SQ, except e-efficiency and e-privacy, have positive effects on all dimensions of e-CCB. Concurrently, this study identified that the dimensions of e-CCB have positive effects on e-RI. Subsequently, the study determined that the dimensions of e-CCB played a full mediator role between the dimensions of e-SQ and e-RI.

The primary limitation of this study is that the study is based on the perceptions of fashion e-customers about the customers' e-tailing (online retailing) experiences, which is only reached via the convenience sampling method.

In this study, the positive relationships between e-CCB and e-RI were accepted as the acquirements of e-SQ in fashion e-storescapes. In this context, the reasons for fashion customers to repurchase from an e-storescape are not only low prices and product variety, but also the exhibition of extra-role behaviours in the form of e-SQ acquirements. These implications, which will attract the attention of fashion e-tailers, may enable the e-tailers to modify the e-SQ of e-tailers' e-storescapes based on customer feedback.

This study is unique as the study draws on the S-O-R theory to provide insight into the stimulus effect of e-storescape e-SQ on the mediating role of e-CCB. The current study is expected to contribute to the literature by highlighting the reasons for changes in consumption attitudes, intentions and behaviours amongst customers that prefer e-tailing for fashion apparel shopping.

This paper develops a plausible software availability model for operational use. In particular, we consider two kinds of restoration scenarios; one involves the debugging activity and the other does not involve the debugging activity. The time‐dependent behavior of the software system during the operation phase is described by a Markov process. Several stochastic quantities for software availability measurement are derived from this model. Finally, numerical examples are shown to illustrate software availability analyses.

This study aims to explore customer personality-related antecedents of customer citizenship behaviors (CCBs) that benefit service providers. It also investigates two-step consequences of CCBs: customer satisfaction and intention to continue the relationship.

US consumers (n = 665) participated in online surveys regarding three types of service businesses with different levels of customization and customer contact. Data were analyzed using structural equation modeling.

Results show a significant, positive impact of the two dimensions of prosocial personality (i.e. other-oriented empathy and helpfulness) and proactive personality on CCBs. Additionally, CCBs increase customer satisfaction and, in turn, intention to continue the relationship.

This study suggests the importance of customer prosocial and proactive personality as antecedents of CCBs. Beyond intention to participate in CCBs, the present study shows that customers perceived satisfaction from CCBs, resulting in intention to continue the relationship with their service provider. Further research should investigate other types of customer personalities such as conscientiousness and agreeableness.

Service providers should understand customer personalities that lead to voluntary behaviors that benefit their organizations. This understanding allows the service providers to better communicate with their customers and to receive more assists from customers.

Previous research has shown that customers’ attitudinal perceptions impact CCBs. In contrast, this study highlights the strong and positive impact of customer personalities, prosocial and proactive personality, on CCBs. Another significant contribution of this study is that it incorporates the potential consequences of CCBs.

Introduction The application of statistics to the study of economic relationships is often characterized by unique problems. Although prediction may be an important part of a study's objectives, generally the econometrician is more concerned with the form of the relationship between economic variables for such information enables him to define the transmission mechanism between economic variables. Knowledge of the functional relationship between variables makes it possible to trace the impact of a change in a control or policy variable on and through other variables in the system under study.