Myoung-jae Lee and Sanghyeok Lee
Standard stratified sampling (SSS) is a popular non-random sampling scheme. Maximum likelihood estimator (MLE) is inconsistent if some sampled strata depend on the response…
Abstract
Standard stratified sampling (SSS) is a popular non-random sampling scheme. Maximum likelihood estimator (MLE) is inconsistent if some sampled strata depend on the response variable Y (‘endogenous samples’) or if some Y-dependent strata are not sampled at all (‘truncated sample’ – a missing data problem). Various versions of MLE have appeared in the literature, and this paper reviews practical likelihood-based estimators for endogenous or truncated samples in SSS. Also a new estimator ‘Estimated-EX MLE’ is introduced using an extra random sample on X (not on Y) to estimate the distribution EX of X. As information on Y may be hard to get, this estimator's data demand is weaker than an extra random sample on Y in some other estimators. The estimator can greatly improve the efficiency of ‘Fixed-X MLE’ which conditions on X, even if the extra sample size is small. In fact, Estimated-EX MLE does not estimate the full FX as it needs only a sample average using the extra sample. Estimated-EX MLE can be almost as efficient as the ‘Known-FX MLE’. A small-scale simulation study is provided to illustrate these points.
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Sangeun Oh, Soram Park and Hyejin Jung
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Social implications
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.
Originality/value
This is the first attempt to examine the generation method based on the Dosan Seowon's generation layers.
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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…
Abstract
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.”
Heewon Kim and SooCheong (Shawn) Jang
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…
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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).
Practical implications
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.
Originality/value
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.
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Houtian Ge, Jing Yi, Stephan J. Goetz, Rebecca Cleary and Miguel I. Gómez
Using recent US regional data associated with food system operations, this study aims at building optimization and econometric models to incorporate varying influential factors on…
Abstract
Purpose
Using recent US regional data associated with food system operations, this study aims at building optimization and econometric models to incorporate varying influential factors on food hub location decisions and generate effective facility location solutions.
Design/methodology/approach
Mathematical optimization and econometric models have been commonly used to identify hub location decisions, and each is associated with specific strengths to handle uncertainty. This paper develops an optimization model and a hurdle model of the US fresh produce sector to compare the hub location solutions between these two modeling approaches.
Findings
Econometric modeling and mathematical optimization are complementary approaches. While there is a divergence between the results of the optimization model and the econometric model, the optimization solution is largely confirmed by the econometric solution. A combination of the results of the two models might lead to improved decision-making.
Practical implications
This study suggests a future direction in which model development can move forward, for example, to explore and expose how to make the existing modeling techniques easier to use and more accessible to decision-makers.
Social implications
The models and results provide information that is currently limited and is useful to help inform sustainable decisions of various stakeholders interested in the development of regional food systems, regional infrastructure investment and operational strategies for food hubs.
Originality/value
This study sheds light on how the application of complementary modeling approaches improves the effectiveness of facility location solutions. This study offers new perspectives on elaborating key features to encompass facility location issues by applying interdisciplinary approaches.
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Sung Yi and Robert Jones
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…
Abstract
Purpose
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.
Design/methodology/approach
A machine learning framework for using big data analytics is proposed to predict the reliability of solder joints accurately.
Findings
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.
Originality/value
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.
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Introduction – Markowitz (1952) argues that individuals act rationally in their financial decisions. In contrast, Kahneman and Tversky (1979) claim that the psychological…
Abstract
Introduction – Markowitz (1952) argues that individuals act rationally in their financial decisions. In contrast, Kahneman and Tversky (1979) claim that the psychological characteristics of people significantly affect financial decisions. In making these decisions, factors such as age, gender, and educational status may have an impact.
Purpose – The purpose of this study is to determine whether financial literacy has an impact on individuals’ cognitive biases related to financial investments.
Methodology – A sample of 444 individuals were surveyed.
Findings – In the results of study (1) it was determined that financial literacy leads to differences in cognitive biases; and (2) cognitive biases of individuals who do not receive finance education are different from individuals who receive finance education and professionals in the business world. The findings indicate that the increase in the level of financial literacy of individuals will reduce the cognitive biases and heuristics, and therefore will have a positive effect on the investor behavior in financial markets.
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Anas Al-Refaie, Ali Alashwal, Zulkiflee Abdul-Samad, Hafez Salleh and Ahmed Elshafie
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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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…
Abstract
Purpose
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.
Design/methodology/approach
The electronic properties of Li7P3S11 are thoroughly explored through first-principles calculations.
Findings
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.
Originality/value
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.