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A fundamental assumption underlying latent class analysis (LCA) is that class indicators are conditionally independent of each other, given latent class membership. Bayesian LCA enables researchers to detect and accommodate violations of this assumption by estimating any number of correlations among indicators with proper prior distributions. However, little is known about how the choice of prior may affect the performance of Bayesian LCA. This article presents a Monte Carlo simulation study that investigates (1) the utility of priors in a range of prior variances (i.e., strongly non-informative to strongly informative priors) in terms of Type I error and power for detecting conditional dependence and (2) the influence of imposing approximate independence on model fit of Bayesian LCA. Simulation results favored the use of a weakly informative prior with large variance–model fit (posterior predictive p–value) was always satisfactory when the class indicators were either independent or dependent. Based on the current findings and the additional literature, this article offers methodological guidelines and suggestions for applied researchers.
Jaehoon Lee; Kwanghee Jung; Jungkyu Park. Detecting Conditional Dependence Using Flexible Bayesian Latent Class Analysis. Frontiers in Psychology 2020, 11, 1987 .
AMA StyleJaehoon Lee, Kwanghee Jung, Jungkyu Park. Detecting Conditional Dependence Using Flexible Bayesian Latent Class Analysis. Frontiers in Psychology. 2020; 11 ():1987.
Chicago/Turabian StyleJaehoon Lee; Kwanghee Jung; Jungkyu Park. 2020. "Detecting Conditional Dependence Using Flexible Bayesian Latent Class Analysis." Frontiers in Psychology 11, no. : 1987.
Long He; Ruodan Shao; Youngho Song; Jungkyu Park. An Examination of the Antecedents and Consequences of Customer Mistreatment. Academy of Management Proceedings 2020, 2020, 1 .
AMA StyleLong He, Ruodan Shao, Youngho Song, Jungkyu Park. An Examination of the Antecedents and Consequences of Customer Mistreatment. Academy of Management Proceedings. 2020; 2020 (1):1.
Chicago/Turabian StyleLong He; Ruodan Shao; Youngho Song; Jungkyu Park. 2020. "An Examination of the Antecedents and Consequences of Customer Mistreatment." Academy of Management Proceedings 2020, no. 1: 1.
Linear Mixed Effect Models (LMEM) have become a popular method for analyzing nested experimental data, which are often encountered in psycholinguistics and other fields. This approach allows experimental results to be generalized to the greater population of both subjects and experimental stimuli. In an influential paper Bar and his colleagues (2013; https://doi.org/10.1016/j.jml.2012.11.001) recommend specifying the maximal random effect structure allowed by the experimental design, which includes random intercepts and random slopes for all within-subjects and within-items experimental factors, as well as correlations between the random effects components. The goal of this paper is to formally investigate whether their recommendations can be generalized to wider variety of experimental conditions. The simulation results revealed that complex models (i.e., with more parameters) lead to a dramatic increase in the non-convergence rate. Furthermore, AIC and BIC were found to select the true model in the majority of cases, although selection accuracy varied by LMEM random effect structure.
Jungkyu Park; Ramsey Cardwell; Hsiu-Ting Yu. Specifying the random effect structure in linear mixed effect models for analyzing psycholinguistic data. Methodology 2020, 16, 92 -111.
AMA StyleJungkyu Park, Ramsey Cardwell, Hsiu-Ting Yu. Specifying the random effect structure in linear mixed effect models for analyzing psycholinguistic data. Methodology. 2020; 16 (2):92-111.
Chicago/Turabian StyleJungkyu Park; Ramsey Cardwell; Hsiu-Ting Yu. 2020. "Specifying the random effect structure in linear mixed effect models for analyzing psycholinguistic data." Methodology 16, no. 2: 92-111.
Conservation of resources (COR) theory proposes that mistreatment by customers (termed “customer mistreatment”) can deplete employees’ resources, lessen their ability to regulate their behaviors, and result in them engaging in customer-directed deviant behavior. However, COR has been criticized for its lack of precision regarding how this process unfolds. Integrating the person-situation interactionist perspective with COR theory, the present paper aims to provide a deeper understanding of COR theory by explicating how individual characteristics and work context—namely, psychological detachment and supervisory unfairness—can combine to attenuate/exacerbate the relationship between customer mistreatment and employees’ customer-directed deviant behavior. Using a multilevel field study with 1,092 daily-based surveys among 157 Korean call-center representatives, our results show that frontline employees’ emotional exhaustion mediates the relationship between customer mistreatment and customer-directed deviant behavior that occurs on the next working day. When faced with customer mistreatment, employees with lower (vs. higher) psychological detachment were more likely to be emotionally exhausted and engage in customer-directed deviant behavior on the next working day. Moreover, their emotional exhaustion predicted customer-directed deviant behavior more so when their supervisors treated them unfairly (vs. fairly). Taken together, the results show that the mediating effect of emotional exhaustion was strongest among employees with low (vs. high) psychological detachment and who reported more (vs. less) supervisory unfairness. Theoretical, methodological, and practical implications as well as directions for future research are discussed.
Young Ho Song; Daniel P. Skarlicki; Ruodan Shao; Jungkyu Park. Reducing Customer-Directed Deviant Behavior: The Roles of Psychological Detachment and Supervisory Unfairness. Journal of Management 2020, 1 .
AMA StyleYoung Ho Song, Daniel P. Skarlicki, Ruodan Shao, Jungkyu Park. Reducing Customer-Directed Deviant Behavior: The Roles of Psychological Detachment and Supervisory Unfairness. Journal of Management. 2020; ():1.
Chicago/Turabian StyleYoung Ho Song; Daniel P. Skarlicki; Ruodan Shao; Jungkyu Park. 2020. "Reducing Customer-Directed Deviant Behavior: The Roles of Psychological Detachment and Supervisory Unfairness." Journal of Management , no. : 1.
Several methods of factor extraction have recently gained popularity as a procedure for dealing with estimation problems associated with small sample sizes, which can be found in the various behavioral science disciplines, such as comparative psychology and behavior genetics. Two popular approaches for particularly small samples (below 50) include unweighted least squares factor analysis (ULS-FA) and regularized exploratory factor analysis (REFA). However, it is unclear how well each of the approaches performs with small samples in the context of exploratory bifactor modeling. In the current study, a comprehensive simulation study was conducted to evaluate the small sample behavior of the two approaches in terms of bifactor structure recovery under different sample size, factor loading, number of variables per factor, number of factors, and factor correlation experimental conditions. The results show that REFA is recommended for use over ULS-FA, particularly in the conditions involving low factor loadings, few group factors, or a small number of variables per factor.
Sunho Jung; Dong Gi Seo; Jungkyu Park. Regularized Exploratory Bifactor Analysis With Small Sample Sizes. Frontiers in Psychology 2020, 11, 1 .
AMA StyleSunho Jung, Dong Gi Seo, Jungkyu Park. Regularized Exploratory Bifactor Analysis With Small Sample Sizes. Frontiers in Psychology. 2020; 11 ():1.
Chicago/Turabian StyleSunho Jung; Dong Gi Seo; Jungkyu Park. 2020. "Regularized Exploratory Bifactor Analysis With Small Sample Sizes." Frontiers in Psychology 11, no. : 1.
Efforts have been made to improve the performance of social enterprises through many studies on social entrepreneurs and social entrepreneurship. However, previous studies have conceptualized social entrepreneurship based on researches on commercial entrepreneurs. In addition, the scale used in the analysis of social entrepreneurship focuses primarily on behavioral aspects. Although the social and economic values pursued by social enterprises are important virtues for social entrepreneurs, research on the value orientation of social entrepreneurship is insufficient. The essence of a social enterprise is creating social value based on financial sustainability, so the concept of blended value has been recently emphasized. This study analyzed the relationships among blended value orientation, social entrepreneurship, and the performance of social enterprises. The results indicate that the blended value orientation of social entrepreneurs influenced social entrepreneurship and performance; social entrepreneurship fully mediated blended value orientation and performance. These findings suggest that it is important to focus on the blended value orientation of social entrepreneurs and social entrepreneurship in the promotion and policies of social enterprises.
Changhwan Shin; Jungkyu Park. How Social Entrepreneurs’ Value Orientation Affects the Performance of Social Enterprises in Korea: The Mediating Effect of Social Entrepreneurship. Sustainability 2019, 11, 5341 .
AMA StyleChanghwan Shin, Jungkyu Park. How Social Entrepreneurs’ Value Orientation Affects the Performance of Social Enterprises in Korea: The Mediating Effect of Social Entrepreneurship. Sustainability. 2019; 11 (19):5341.
Chicago/Turabian StyleChanghwan Shin; Jungkyu Park. 2019. "How Social Entrepreneurs’ Value Orientation Affects the Performance of Social Enterprises in Korea: The Mediating Effect of Social Entrepreneurship." Sustainability 11, no. 19: 5341.
Rajiv Amarnani; Ruodan Shao; Sandy Hershcovis; Stephen J. Frenkel; Markus Groth; Anya Madeleine Johnson; Jaclyn Koopmann; Feng Liu; Helena Nguyen; Jungkyu Park; Daniel Skarlicki; Yifan Song; Youngho Song; David Douglas Walker; Haibo Wu; Yumeng Yue. Aggression in Service Interactions: New Directions in Customer Mistreatment. Academy of Management Proceedings 2019, 2019, 1 .
AMA StyleRajiv Amarnani, Ruodan Shao, Sandy Hershcovis, Stephen J. Frenkel, Markus Groth, Anya Madeleine Johnson, Jaclyn Koopmann, Feng Liu, Helena Nguyen, Jungkyu Park, Daniel Skarlicki, Yifan Song, Youngho Song, David Douglas Walker, Haibo Wu, Yumeng Yue. Aggression in Service Interactions: New Directions in Customer Mistreatment. Academy of Management Proceedings. 2019; 2019 (1):1.
Chicago/Turabian StyleRajiv Amarnani; Ruodan Shao; Sandy Hershcovis; Stephen J. Frenkel; Markus Groth; Anya Madeleine Johnson; Jaclyn Koopmann; Feng Liu; Helena Nguyen; Jungkyu Park; Daniel Skarlicki; Yifan Song; Youngho Song; David Douglas Walker; Haibo Wu; Yumeng Yue. 2019. "Aggression in Service Interactions: New Directions in Customer Mistreatment." Academy of Management Proceedings 2019, no. 1: 1.
Culture is a key driving force in enhancing organizational performance. The results of recent studies indicate the importance of managers having the capacity to understand organizational culture and link it to organizational performance improvement. This study aims to examine the relationship between organizational culture and performance improvement in social enterprises. In the past, organizational culture was described in terms of a single dimension, but it is now understood that different cultures reflect different values and beliefs, in a seemingly contradictory manner, and can coexist within any given organization. We analyze the relationships among social enterprise networking, performance, and organizational culture, using the four organizational culture classifications of the competing values framework, which reflects recent perspectives. A survey was conducted among 100 social entrepreneurs, and latent profile analysis was applied to the data. The analytical results identify four latent profiles—namely, strong-balanced, weak-balanced, hierarchical, and group-dominant—and show that a balanced culture fosters high-level socioeconomic performance.
Changhwan Shin; Jungkyu Park. Classifying Social Enterprises with Organizational Culture, Network and Socioeconomic Performance: Latent Profile Analysis Approach. Journal of Open Innovation: Technology, Market, and Complexity 2019, 5, 17 .
AMA StyleChanghwan Shin, Jungkyu Park. Classifying Social Enterprises with Organizational Culture, Network and Socioeconomic Performance: Latent Profile Analysis Approach. Journal of Open Innovation: Technology, Market, and Complexity. 2019; 5 (1):17.
Chicago/Turabian StyleChanghwan Shin; Jungkyu Park. 2019. "Classifying Social Enterprises with Organizational Culture, Network and Socioeconomic Performance: Latent Profile Analysis Approach." Journal of Open Innovation: Technology, Market, and Complexity 5, no. 1: 17.
The inclusion of covariates improves the prediction of class memberships in latent class analysis (LCA). Several methods for examining covariate effects have been developed over the past decade; however, researchers have limited to the comparisons of the performance among these methods in cases of the single-level LCA. The present study investigated the performance of three different methods for examining covariate effects in a multilevel setting. We conducted a simulation to compare the performance of the three methods when level-1 and level-2 covariates were simultaneously incorporated into the nonparametric multilevel latent class model to predict latent class membership at each level. The simulation results revealed that the bias-adjusted three-step maximum likelihood method performed equally well as the one-step method when the sample sizes were sufficiently large and the latent classes were distinct from each other. However, the unadjusted three-step method significantly underestimated the level-1 covariate effect in most conditions. Keywords: covariate effects, latent class models, multilevel modeling
Jungkyu Park; Hsiu-Ting Yu. A Comparison of Approaches for Estimating Covariate Effects in Nonparametric Multilevel Latent Class Models. Structural Equation Modeling: A Multidisciplinary Journal 2018, 25, 778 -790.
AMA StyleJungkyu Park, Hsiu-Ting Yu. A Comparison of Approaches for Estimating Covariate Effects in Nonparametric Multilevel Latent Class Models. Structural Equation Modeling: A Multidisciplinary Journal. 2018; 25 (5):778-790.
Chicago/Turabian StyleJungkyu Park; Hsiu-Ting Yu. 2018. "A Comparison of Approaches for Estimating Covariate Effects in Nonparametric Multilevel Latent Class Models." Structural Equation Modeling: A Multidisciplinary Journal 25, no. 5: 778-790.
A multilevel latent class model (MLCM) is a useful tool for analyzing data arising from hierarchically nested structures. One important issue for MLCMs is determining the minimum sample sizes needed to obtain reliable and unbiased results. In this simulation study, the sample sizes required for MLCMs were investigated under various conditions. A series of design factors, including sample sizes at two levels, the distinctness and the complexity of the latent structure, and the number of indicators were manipulated. The results revealed that larger samples are required when the latent classes are less distinct and more complex with fewer indicators. This study also provides recommendations about the minimum required sample sizes that satisfied all four criteria—model selection accuracy, parameter estimation bias, standard error bias, and coverage rate—as well as rules of thumb for sample size requirements when applying MLCMs in data analysis.
Jungkyu Park; Hsiu-Ting Yu. Recommendations on the Sample Sizes for Multilevel Latent Class Models. Educational and Psychological Measurement 2017, 78, 737 -761.
AMA StyleJungkyu Park, Hsiu-Ting Yu. Recommendations on the Sample Sizes for Multilevel Latent Class Models. Educational and Psychological Measurement. 2017; 78 (5):737-761.
Chicago/Turabian StyleJungkyu Park; Hsiu-Ting Yu. 2017. "Recommendations on the Sample Sizes for Multilevel Latent Class Models." Educational and Psychological Measurement 78, no. 5: 737-761.
Growing research has found that when employees experience customer mistreatment, they can engage in sabotage toward the customer who mistreated them. The present study explored whether two factors, namely, employee conscientiousness and leader-member exchange (LMX), attenuates the association between customer mistreatment and employee sabotage. Daily surveys were administered to 236 call-center representatives in South Korea over a period of 10 days. The results show a cross-level moderation effect of conscientiousness in the relationship between daily customer mistreatment and employees’ daily service sabotage such that employees with a higher level of conscientiousness were less likely to conduct sabotage despite being mistreated by customers. In addition, a three-way interaction of mistreatment, conscientiousness and LMX was observed. Employees who were high on conscientiousness and had high level of LMX were least likely to commit sabotage in face of customer mistreatment. Theoretical and practical implications as well as directions for future research are discussed.
Young Ho Song; Ruodan Shao; Daniel Skarlicki; Jungkyu Park. The role of conscientiousness and LMX in the customer mistreatment and employee sabotage linkage. Academy of Management Proceedings 2016, 2016, 13337 .
AMA StyleYoung Ho Song, Ruodan Shao, Daniel Skarlicki, Jungkyu Park. The role of conscientiousness and LMX in the customer mistreatment and employee sabotage linkage. Academy of Management Proceedings. 2016; 2016 (1):13337.
Chicago/Turabian StyleYoung Ho Song; Ruodan Shao; Daniel Skarlicki; Jungkyu Park. 2016. "The role of conscientiousness and LMX in the customer mistreatment and employee sabotage linkage." Academy of Management Proceedings 2016, no. 1: 13337.
The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In the present study, a simulation study was conducted to investigate the impact of ignoring the higher-level nesting structure. Three criteria—the model selection accuracy, the classification quality, and the parameter estimation accuracy—were used to evaluate the impact of ignoring the nested data structure. The results of the simulation study showed that ignoring higher-level nesting structure in an MLCM resulted in the poor performance of the Bayesian information criterion to recover the true latent structure, the inaccurate classification of individuals into latent classes, and the inflation of standard errors for parameter estimates, while the parameter estimates were not biased. This article concludes with remarks on ignoring the nested structure in nonparametric MLCMs, as well as recommendations for applied researchers when LCM is used for data collected from a multilevel nested structure.
Jungkyu Park; Hsiu-Ting Yu. The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models. Educational and Psychological Measurement 2015, 76, 824 -847.
AMA StyleJungkyu Park, Hsiu-Ting Yu. The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models. Educational and Psychological Measurement. 2015; 76 (5):824-847.
Chicago/Turabian StyleJungkyu Park; Hsiu-Ting Yu. 2015. "The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models." Educational and Psychological Measurement 76, no. 5: 824-847.
The Multilevel Latent Class Model (MLCM) proposed by Vermunt (2003) has been shown to be an excellent framework for analyzing nested data with assumed discrete latent constructs. The nonparametric version of MLCM assumes 2 levels of discrete latent components to describe the dependency observed in data. Model selection is an important step in any statistical modeling. The task of model selection for MLCM amounts to the decision on the number of discrete latent components at both higher and lower levels and is more challenging than standard Latent Class Models. In this article, simulation studies were conducted to systematically examine the effects of sample sizes, clusters/classes distinctness, and the number of latent clusters and classes on the performance of various information criteria in recovering the true latent structure. Results of the simulation studies are summarized and presented. The final section presents the remarks and recommendations about the simultaneous decision regarding the number of latent classes and clusters when applying MLCMs to analyze empirical data.
Hsiu-Ting Yu; Jungkyu Park. Simultaneous Decision on the Number of Latent Clusters and Classes for Multilevel Latent Class Models. Multivariate Behavioral Research 2014, 49, 232 -244.
AMA StyleHsiu-Ting Yu, Jungkyu Park. Simultaneous Decision on the Number of Latent Clusters and Classes for Multilevel Latent Class Models. Multivariate Behavioral Research. 2014; 49 (3):232-244.
Chicago/Turabian StyleHsiu-Ting Yu; Jungkyu Park. 2014. "Simultaneous Decision on the Number of Latent Clusters and Classes for Multilevel Latent Class Models." Multivariate Behavioral Research 49, no. 3: 232-244.