Latent Class Analysis Method Research Articles

A latent class analysis of hopelessness in relation to depression and trauma during the COVID-19 pandemic in China

BackgroundHopelessness is closely related to depression, trauma, and some organic diseases. Yet our understanding of the heterogeneity of hopelessness is limited. This study aimed to explore the heterogeneity of hopelessness, how it corresponds to the severity of depression, and the effect of traumatic experiences on it during COVID-19. MethodThe current study measures 28,360 Chinese college students (67.4 % of women) with the Beck Hopelessness Scale (BHS), Trauma Experience Questionnaire, and Patient Health Questionnaire-9 (PHQ-9). The method of latent class analysis (LCA) was used to identify different sub-groups of hopelessness with differences in emotion, motivation, and cognition. Logistic regression and analysis of variance were used to determine the characteristics of different sub-groups. ResultsThree latent classes were identified: Negative affective cognition class (C1, N = 5940, 20.9 %), Negative thought cognition class (C2, N = 1358, 4.8 %), and Low hopelessness class (C3, N = 21,062, 74.3 %). Gender, only child or not, birthplace, family economic status, and grade are predictors of the latent category of hopelessness. There are significant differences in depression among different latent classes, and C1 > C2 > C3 (p < 0.001). Traumatic experience is a risk factor for the hopeless latent classes (OR > 1, P < 0.001). LimitationsResearch findings may not be applied to other populations and rely on subjective reports. Ignore the effects of other protective and risk factors. Lack of longitudinal research, unable to explore causality. ConclusionsThe current study provides evidence for the heterogeneity of hopelessness and informs targeted interventions for mental health problems (hopelessness) in college students.

Bayesian analysis of diagnostic sensitivity and specificity for detecting infectious salmon anaemia virus (ISAV) using IFAT and real-time RT-PCR testing from laboratories in Atlantic Canada

Infectious Salmon Anaemia virus (ISAV) is the causative agent of Infectious Salmon Anaemia (ISA), an economically important disease in farmed Atlantic salmon (Salmo salar L.). Immunofluorescence antibody test (IFAT) and real-time reverse transcription polymerase chain reaction (qRT-PCR) are commonly used diagnostic tests for ISAV active surveillance in Atlantic Canada; however, their diagnostic accuracy is not well reported. In the absence of a reference test, we used Bayesian latent class analysis methods to estimate diagnostic sensitivity (DSe) and specificity (DSp) of IFAT and qRT-PCR. We compared diagnostic performance of qRT-PCR, performed at two laboratories, and IFAT (performed at another laboratory) using field samples obtained from two separate salmon populations (total n = 130) in province of Newfoundland and Labrador, Canada. Conditional independence models for two tests (IFAT and qRT-PCR) and two populations (low and high prevalence) were run separately on datasets from each laboratory. Informative priors were used for DSe and DSp of qRT-PCR and IFAT based on prior knowledge, while flat priors (beta 1,1) were used for prevalence. qRT-PCRs had similarly high DSe (>91%). IFAT was the least sensitive method (DSe range, 56.9%–60.4%). Both IFAT (DSp > 98.8%) and qRT-PCRs (DSp > 97.9%) showed high DSp. Real-time RT-PCR provided the greatest DSe for routine surveillance of Atlantic salmon farms while not sacrificing DSp. Sample size for the study was not likely adequate to estimate all parameters with very high confidence, which could have influenced the posterior probability interval calculated for DSe, resulting in less diagnostic confidence in the results (posterior probability for Sensitivities for the tests within 10% of the true population value instead of 5%). Although IFAT provided a high DSp, the qRT-PCR performed similarly in this regard. The findings indicate that the qRT-PCR is fit for ISAV surveillance programs.

Error Propensities Amongst Finance and Accounting Professionals: Can We Quantitatively Measure Illusion of Control or Chaos?

Most financial and accounting tasks and analytics, whether associated with the past or future, assume knowledge of process, variation, and statistics. Yet, finance and accounting personnel averaging 13 years of experience could not distinguish non-random from random time-series strings in an assessment using statistical process control charts. Respondents scored no better than guessing compared to a series of true-false questions. Latent class analysis methods within partial least square structural equation modeling successfully uncovered segments of respondents with large explained variance and significant paths to explicate tendencies toward type I or type II error rates, i.e., an illusion of control or illusion of chaos. Relationships between the desirability of control, personal fear of invalidity, and error rates were more varied than expected.

PCR42 Applying Principal Component Analysis (PCA) and Latent Class Analysis (LCA) Methods across Patient Centered Outcomes to Characterize Patient-Centered Acromegaly Control

Currently, there is no specific characterization of acromegaly control from the patient’s perspective. This analysis utilizes principal component analysis and latent class analysis to define a dichotomous patient-centered acromegaly control (PCAC: well controlled versus poorly controlled).

A Latent Class Analysis of Student Eye Care Behavior: Evidence From a Sample of 6-17 Years Old in China.

PurposeTo understand the latent classes and distribution of an adolescent eye care behavior, and to provide a basis for the formulation of appropriate adolescent vision health management interventions.MethodsInformation on eye behavior and eye health of primary and secondary school students in Wuhan was collected by multistage stratified cluster sampling. The latent class analysis (LCA) method was used to analyze the students' eye care behavior, and the latent class model (LCM) was built.ResultsA total of 6,130 students were enrolled in this study, of which 53.56% were males, aged from 6 to 17 years old, with an average age of 10.33 ± 2.60. The latent class results classified the adolescents' eye care behaviors into bad behaviors, moderate behaviors, and healthy behaviors. The model fitting results were as follows: Akaike Information Criterion (AIC) was 36,698.216, Bayesian Information Criterion (BIC) was 36,906.565, Adjusted Bayesian Information Criterion (aBIC) was 36,808.056, and entropy was 0.838.Compared with the healthy behaviors class, the bad behaviors class was more prevalent in high schools (p = 0.003), non-demonstration schools (p = 0.001), and most of this group had astigmatism (p = 0.002). The moderate behaviors class predominately consisted of females (p = 0.001), 15–17 years old (p = 0.005, 6~8 years old as the reference), from non-demonstration schools (p < 0.001), and most had myopia (p = 0.009).ConclusionThere were differences in basic demographic characteristics, visual acuity development level, and family visual environment among different classes. In the management and intervention of an adolescent vision health, we should continue to promote the visual health management of adolescents based on visual monitoring and realize the early intervention and guidance of individuals in bad behaviors class.

Latent Class Profiles of Police Violence Exposure in 4 US Cities and Their Associations with Anticipation of Police Violence and Mental Health Outcomes

While studying polyvictimization is well established within the broader violence literature and applied to other types of violence, it has yet to be documented whether polyvictimization also presents in patterns of police violence exposure (i.e., neglectful, psychological, physical, and sexual police violence). Our objective was to analyze latent patterns of co-occurring police contact and their associations with mental health. By applying latent class analysis (LCA) methods to the 2016 and 2017 Surveys of Police-Public Encounters (N = 2615), conducted in 4 Northeastern US cities, we identified classes of direct and vicarious police violence and compared sociodemographic characteristics among classes using multinomial regression. Classes were regressed on mental health outcomes. LCA identified four classes of police contact. Compared to Positive Police Contact (33.0%) class members, members of the (a) Extreme Police Violence (4.0%) class reported higher anticipation of future police victimization, psychological distress, and suicide ideations and attempts; they were more likely to be Black, cisgender men, and Latinx; (b) members of the High Police Violence (23.6%) class reported higher anticipation of future police victimization and psychological distress; they were more likely to be Black, Native American, and multiracial; members of the (c) Low Police Contact (39.5%) class had comparable mental health outcomes; they were more likely to report a household income < $19,999. Notably, no participants were unexposed to police contact. Polyvictimization presents in experiences of police violence and disproportionately impacts structurally marginalized people.

Study on the associations of physical activity types and cardiovascular diseases among Chinese population using latent class analysis method

Previous studies reported on the association between physical activity (PA) and cardiovascular diseases (CVDS) among the Western population. However, evidence on the association between different patterns of PA and the risk of CVDS among Chinese population are limited. This study aims to evaluate the association of different PA types and the risk of CVDS in a Chinese adult population. A total of 3568 community residents were recruited from Jiangsu Province of China using a stratified multistage cluster sampling method. The latent class analysis method was employed to identify the types of PA, and the Framingham risk score (FRS) was used to estimate the risk of CVDS within 10 years. Three types of PA were identified: CLASS1 represented participants with high occupational PA and low sedentary PA (32.1% of male, 26.5% of female), ClASS2 represented those engaging in low occupational PA and high leisure-time PA (27.0% of male, 14.2% of female), and CLASS3 represented low leisure-time and high sedentary PA (40.9% of male, 59.3% of female). The average of FRS in males was higher than that in females across PA types. CLASS1 (OR = 0.694, 95%CI 0.553–0.869) and CLASS2 (OR = 0.748, 95%CI 0.573–0.976) were both found to be protective against CVDS in males; however, such associations were not statistically significant among females. Therefore, higher occupational or leisure-time PA appear to be associated with decreased risk of CVDS, while more sedentary behaviors may increase the risk of CVDS, particularly for male Chinese adults.

Patterns of treatment in older patients with newly diagnosed advanced bladder cancer: A SEER dataset analysis.

Bladder cancer is one of the top 10 cancers diagnosed in Americans with a median age of 73. This is the patient population that tends to be older with multiple medical conditions, and previously described variability in treatment in the earlier stages of the disease. This study aimed to evaluate the first-line therapeutic choices for older adults newly diagnosed with advanced bladder cancer. In addition, this work evaluated predictors of response as well as the role of events of functional importance in relation to treatment assignment. A population-based cohort study was conducted using the SEER-Medicare database of patients with advanced stage bladder cancer not eligible for curative intent therapy between 2010 and 2013. Patient groups of interest were compared via univariate and multivariate associations. Additionally, a latent class analysis was applied to identify classes with similar features in reference to events of functional importance-events linked to the maintenance or improvement of physical function status. Within the sample, we noted that a minority of patients received a standard cisplatin-containing regimen (14.77%) and a majority did not receive any chemotherapy (59.69%). Most patients were over age 75. The adjusted odds ratio of no chemo versus cisplatin in patients aged 76 and older compared to patients 66-75 was 6.61 (4.79-9.13; p < 0.0001). We applied latent class analysis methods to the dataset, and three classes demonstrated very low and moderate levels of functional events in the 12 months prior to a person's first outpatient visit for advanced bladder cancer care. Patients with new diagnosis of advanced bladder cancer largely do not receive the recommended first-line systemic therapy of cisplatin chemotherapy, and a significant majority does not receive any treatment. When evaluating the association between class assignment and predictors of chemotherapy use, such as comorbidity and age, patients with "low usage overall" were more likely to receive chemotherapy. Yet even patients who received chemotherapy had some events of functional importance.

Identification of customers’ purchasing behaviour profiles in the context of corporate social responsibility

Purpose: The aim of the article is to identify customers' purchasing behaviour profiles on the basis of characterizing the process of making a decision to purchase a product from food industry companies’ indicators (observable variables) in the context of corporate social responsibility (CSR). Design/methodology/approach: The data for the research were collected from a survey concerning a group of 801 customers from the Świętokrzyskie Voivodeship. The resources were pre-explored and pre-processed to enable further studies. In order to obtain customers profiles, the latent class analysis (LCA) method was used. It enables identification of homogeneous groups (latent classes) of customers based on selected indicators. Findings: The impact on customers’ purchasing behaviour of 15 CSR activities undertaken by enterprises from several different groups (in relation to: environment, society, employees, contractors, and customers) was examined. Six profiles of customer purchasing behaviour were identified. They were labelled and subjected to descriptive characteristics. Research limitations/implications: The results point out the need to continue the research based on a broader countrywide data set. Practical implications: The research findings can contribute to improving the effectiveness of food industry companies in the range of CSR activities. Due to this, these companies will be able to take more effective steps to retain existing customers and acquire new ones. Social implications: Taking corporate social responsibility actions contributes to solve social and environmental problems. It can also affect the quality of life in a society. Nowadays, it is an important and developmental research area. Originality/value: The conducted study showed that latent class analysis is proper tool for analysing the qualitative data obtained in the questionnaire surveys. The work provides a vital information on the impact of corporate social responsibility activities by food industry companies on customers' purchasing behaviour. Keywords: corporate social responsibility, customer profiles, purchasing behaviour, food industry, latent class analysis. Category of the paper: Research paper.

Latent class analysis for identification of occupational accident casualty profiles in the selected Polish manufacturing sector

The objective of the analysis is identifying profiles of occupational accident casualties as regards production companies to provide the necessary knowledge to facilitate the preparation and management of a safe work environment. Qualitative data characterizing employees injured in accidents registered in Polish wood processing plants over a period of 10 years were the subject of the research. The latent class analysis (LCA) method was employed in the investigation. This statistical modelling technique, based on the values of selected indicators (observed variables) divides the data set into separate groups, called latent classes, which enable the definition of patterns. A procedure which supports the decision as regards the number of classes was presented. The procedure considers the quality of the LCA model and the distinguishability of the classes. Moreover, a method of assessing the importance of indicators in the patterns description was proposed. Seven latent classes were obtained and illustrated by the heat map, which enabled the profiles identification. They were labelled as follows: very serious, serious, moderate, minor (three latent classes), slight. Some recommendations were made regarding the circumstances of occupational accidents with the most severe consequences for the casualties.

Association Between Dietary Patterns and Plasma Lipid Biomarker and Female Breast Cancer Risk: Comparison of Latent Class Analysis (LCA) and Factor Analysis (FA).

Background: Diet research focuses on the characteristics of “dietary patterns” regardless of the statistical methods used to derive them. However, the solutions to these methods are both conceptually and statistically different.Methods: We compared factor analysis (FA) and latent class analysis (LCA) methods to identify the dietary patterns of participants in the Chinese Wuxi Exposure and Breast Cancer Study, a population-based case-control study that included 818 patients and 935 healthy controls. We examined the association between dietary patterns and plasma lipid markers and the breast cancer risk.Results: Factor analysis grouped correlated food items into five factors, while LCA classified the subjects into four mutually exclusive classes. For FA, we found that the Prudent-factor was associated with a lower risk of breast cancer [4th vs. 1st quartile: odds ratio (OR) for 0.70, 95% CI = 0.52, 0.95], whereas the Picky-factor was associated with a higher risk (4th vs. 1st quartile: OR for 1.35, 95% CI = 1.00, 1.81). For LCA, using the Prudent-class as the reference, the Picky-class has a positive association with the risk of breast cancer (OR for 1.42, 95% CI = 1.06, 1.90). The multivariate-adjusted model containing all of the factors was better than that containing all of the classes in predicting HDL cholesterol (p = 0.04), triacylglycerols (p = 0.03), blood glucose (p = 0.04), apolipoprotein A1 (p = 0.02), and high-sensitivity C-reactive protein (p = 0.02), but was weaker than that in predicting the breast cancer risk (p = 0.03).Conclusion: Factor analysis is useful for understanding which foods are consumed in combination and for studying the associations with biomarkers, while LCA is useful for classifying individuals into mutually exclusive subgroups and compares the disease risk between the groups.

Operating Room Time Prediction: An Application of Latent Class Analysis and Machine Learning

Objective: The objective of this work is to build a prediction model for Operating Room Time (ORT) to be used in an intelligent scheduling system. This prediction is a complex exercise due to its high variability and multiple influential variables. Materials and methods: We assessed a new strategy using Latent Class Analysis (LCA) and clustering methods to identify subgroups of procedures and surgeries that are combined with prediction models to improve ORT estimates. Three tree-based models are assessed, Classification and Regression Trees (CART), Conditional Random Forest (CFOREST) and Gradient Boosting Machine (GBM), under two scenarios: (i) basic dataset of predictors and (ii) complete dataset with binary procedures. To evaluate the model, we use a test dataset and a training dataset to tune parameters. Results and discussion: The best results are obtained with GBM model using the complete dataset and the grouping variables, with an operational accuracy of 57.3% in the test set. Conclusion: The results indicate the GBM model outperforms other models and it improves with the inclusion of the procedures as binary variables and the addition of the grouping variables obtained with LCA and hierarchical clustering that perform the identification of homogeneous groups of procedures and surgeries.

The Impact of Residents' Leisure Time Allocation Mode on Individual Subjective Well-being: The Case of China

As the main tension in Chinese society has become that between people's growing need for a better life and unbalanced and inadequate development, leisure activities have become a main factor affecting the subjective well-being of Chinese residents. This study uses micro data from the 2019–2020 Chinese Residents’ Economic Life Survey as the sample, applies the latent class analysis (LCA) method to conduct a joint analysis of all leisure activities of individuals, and then divides individual leisure time modes into four types: general leisure, stationary leisure, family-friendly leisure and sports/health care-related leisure. To overcome endogeneity problems from missing variables, sample selection bias, and two-way causality, this paper uses the Heckman two-step and propensity score matching methods to empirically analyse the impacts of residents' leisure time allocation patterns on individual subjective well-being. The study finds that sports/health care-type leisure is most helpful in improving individual subjective well-being, followed by general leisure, family-friendly leisure and stationary leisure, which have relatively weaker impacts on subjective well-being. The estimation results remain robust and reliable after we introduce the idea of ​​misclassification probability to test the robustness of the findings. On this basis, a group heterogeneity analysis by region, income level, education level, marital status and age is carried out. The research conclusions of this article can help guide residents in rationally allocating their leisure time and provide a policy reference for the construction of leisure facilities in China’s cities.

Women's consumption of men's professional sport in Canada: Evidence of the ‘feminization’ of sports fandom and women as omnivorous sports consumers?

Women sports fans have been substantially understudied compared to their male counterparts. While a growing number of studies seek to redress this, there remains a stark absence of quantitative approaches that would allow investigations regarding patterns of women’s sporting consumption and historical trends in the potential growth of this fandom. Using large-scale survey data from Canada from 1990 through to 2015, and employing quantitative methods of latent class and regression analysis, this study seeks to redress these issues by testing the ‘feminization’ thesis of increased women’s sporting fandom over the past three decades. In addition, we consider whether women’s fandom has become increasingly ‘omnivorous’ over this time period and the nature of this consumption today. Results show support for the feminization thesis. These findings are significant as through the use of quantitative methodologies we evidence the narrowing gender gaps in professional sports following between men and women, and women’s increasingly omnivorous consumption of sports. However, we find substantial gender gaps and inequalities in omnivorism by which the evidence suggests increased socio-economic and cultural barriers to omnivorous consumption of sport for women. We suggest that these women omnivores may be able to utilize their sporting knowledge in an instrumental way for benefits in various social settings, especially workplaces. It is hoped that this article will pave the way for further quantitative studies on women sports fans across different contexts.

Military and nonmilitary stressors associated with mental health outcomes among female military spouses

AbstractObjectiveThis study uses a stress process framework and person‐centered methods to describe patterns of concurrent stressors across multiple domains and to associate patterns with female military spouse mental health.BackgroundMost military families are resilient. However, a subset of military spouses experiences adverse outcomes in the context of war‐related stress. To date, a focus on military‐specific stressors has largely obscured the effects of stress unrelated to military service on the well‐being of military spouses.MethodsData were drawn from a 2012 survey of 343 U.S. Army spouses, measuring intrapersonal (e.g., adverse childhood experiences), family (e.g., work–family conflict), and military stressors (e.g., cumulative deployments). Outcomes included moderate or more severe depression, anxiety, and posttraumatic stress disorder (PTSD).ResultsThe three‐step method of latent class analysis identified three classes: low (58.86% of participants), moderate (21.62%), and high (19.52%) stress. Prevalence of mental health problems was significantly elevated in the high‐stress class. In this group, 35.3%, 36.3%, and 39.5% of spouses' screenings indicated at least moderate depression, anxiety, and PTSD symptomatology, compared with 3.0%, 3.9%, and 2.7% in the low‐stress group.ConclusionsResults suggest many military spouses have low stress exposure across domains and low rates of mental health symptoms. However, a subset of spouses may experience both intrapersonal and family‐level risk associated with elevated rates of mental health problems.ImplicationsFindings highlight the critical role of nonmilitary stressors in the lives of military spouses and the importance of assessing for and providing support to spouses around these issues.

The Etiology of Pneumonia in HIV-uninfected Children in Kilifi, Kenya: Findings From the Pneumonia Etiology Research for Child Health (PERCH) Study.

In the 1980s, Streptococcus pneumoniae and Haemophilus influenzae were identified as the principal causes of severe pneumonia in children. We investigated the etiology of severe childhood pneumonia in Kenya after introduction of conjugate vaccines against H. influenzae type b, in 2001, and S. pneumoniae, in 2011. We conducted a case-control study between August 2011 and November 2013 among residents of the Kilifi Health and Demographic Surveillance System 28 days to 59 months of age. Cases were hospitalized at Kilifi County Hospital with severe or very severe pneumonia according to the 2005 World Health Organization definition. Controls were randomly selected from the community and frequency matched to cases on age and season. We tested nasal and oropharyngeal samples, sputum, pleural fluid, and blood specimens and used the Pneumonia Etiology Research for Child Health Integrated Analysis, combining latent class analysis and Bayesian methods, to attribute etiology. We enrolled 630 and 863 HIV-uninfected cases and controls, respectively. Among the cases, 282 (44%) had abnormal chest radiographs (CXR positive), 33 (5%) died in hospital, and 177 (28%) had diagnoses other than pneumonia at discharge. Among CXR-positive pneumonia cases, viruses and bacteria accounted for 77% (95% CrI: 67%-85%) and 16% (95% CrI: 10%-26%) of pneumonia attribution, respectively. Respiratory syncytial virus, S. pneumoniae and H. influenza, accounted for 37% (95% CrI: 31%-44%), 5% (95% CrI: 3%-9%), and 6% (95% CrI: 2%-11%), respectively. Respiratory syncytial virus was the main cause of CXR-positive pneumonia. The small contribution of H. influenzae type b and pneumococcus to pneumonia may reflect the impact of vaccine introductions in this population.

An integrated clustering and copula-based model to assess the impact of intersection characteristics on violation-related collisions

The main goal of this study is to investigate the impact of a variety of factors on the frequency and the severity of pedestrian-vehicle collisions that involve pedestrian violations. To that end, the collision dataset of the City of Hamilton between 2010 and 2017 was reviewed to filter out pedestrian collisions that involved pedestrian violations. A Latent Class Analysis (LCA) method was applied to divide the dataset into a set of homogeneous clusters, based on traffic and intersection characteristics. A copula-based multivariate model was then developed for each cluster in order to study the impact of the different factors on collisions under the prevailing conditions of each cluster. The results showed that the number of bus stops within the intersection area is directly associated with the frequency and the severity of collisions involving pedestrian violations. A reduction in collisions was observed with the increase in the frequency of buses at intersections that are located along main transit routes. Moreover, the presence of schools near the intersection tends to increase the frequency of collisions involving pedestrian violations, especially at large intersections. The results also revealed that the presence of central refuge islands, despite their overall safety benefits, increases the likelihood of collisions involving pedestrian violations in large intersections. The results of this study provide valuable insights for a better understanding of the safety consequences of pedestrian violations. Such understanding assists engineers and planners to design intersections that reduce the frequency of pedestrian violations and mitigate their negative safety consequences.

Identification and spatiotemporal evolution analysis of high-risk crash spots in urban roads at the microzone-level: Using the space-time cube method

The problem of urban crashes brings huge challenges and threats to local police and governments, especially in many cities in developing countries such as China. To reduce the frequency and severity of urban crashes, the local government in China has gradually taken interest in conducting detailed actions of traffic safety improvement at the microzone-level. Therefore, the primary goal of this study is to try a new method in spatiotemporal data mining techniques, the space-time cube method, to find high-risk crash spots at the spatiotemporal level and to obtain their spatiotemporal evolution patterns. The cumulative frequency curve method was performed to identify high-risk crash spots, and the contributory factors of forming these spots were analyzed by the latent class analysis method. The results showed that: (1) key parameters’ selection is crucial in the space-time cube construction; (2) the exit ramp gore point in interchanges, intersections, and entrances of neighborhoods were prone to have many high-risk crash spots at the spatiotemporal scale; and (3) locations with consecutive, persistent, and sporadic hotspots patterns need different risk monitoring strategies and traffic safety improvement. The feasibility and advantages of the space-time cube method in hotspots identification at the microzone-level were confirmed.

소셜미디어 브랜드 메시지의 언어적 특성이 고객인게이지먼트에 미치는 영향

Social media is regarded as a means to increase customer engagement, and the use of social media to induce customer engagement is expanding as a major marketing trend of companies in recent years. According to the speech act theory, it is highly likely that not only the content is trying to convey, but also the communication method affects customer engagement. In this context, this study classified the combinations of various linguistic styles contained in the Twitter messages of global brands into classes, and analyzed the relationship between these classes and customer engagement. For empirical analysis, 17,621 tweet messages from six representative global brands were collected. And after analyzing linguistic characteristics using LIWC 2015, an automated text analysis program, we classified the linguistic style combinations of global brand tweet messages through the Latent Class Analysis method. Using Zero-Inflated Negative Binomial regression analysis, the relationship between the class and customer engagement classified through linguistic style combination was analyzed. As results, the linguistic style combinations of global brand tweet messages were divided into 4 classes, and it was found that there was a difference in customer engagement performance according to each class.

Classification of individual managers' systems thinking skills based on different organizational ownership structures

AbstractThe current body of literature lacks studies related to organizational managers' classification of systems thinking (ST) skills based on both their overall systemic tendency and the organizational ownership structure. The purpose of this study is to assess and classify the ST skills of senior managers who currently work in a complex business environment. Initially, we clustered managers' overall systemic thinking (OST) using the Bayesian latent class analysis (BLCA) method into two distinct clusters: managers with upper OST (holistic thinker) and managers with lower OST (reductionist thinker). Further, we classified managers' ST skills into two predefined classes to understand the characteristics of each group better. A total of 51 senior managers from two different organizational structures participated in this study. Results show that the ST skills of managers in public are more towards the upper OST/holistic cluster, whereas managers from the private sector have an inclination towards lower OST/reductionist cluster.