scholarly journals Constructing an Adoption Model of Proactive Environmental Strategy: A Novel Quantitative Method of the Multi-Level Growth Curve Model

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1962 ◽  
Author(s):  
Stanley Y. B. Huang ◽  
Shih-Chin Lee ◽  
Yue-Shi Lee
Keyword(s):  
Growth Curve ◽  
Past Research ◽  
Growth Curve Model ◽  
Environmental Strategy ◽  
Cross Sectional ◽  
Proactive Environmental Strategy ◽  
Curve Model ◽  
Multi Level ◽  
High Level ◽  
Corporate Social

To fill in the literature flaws that have not been detected in previous studies, this research, therefore, examines the driving factors of proactive environmental strategy (PES). First, this research proposes how corporate social responsibility (CSR) predicts the agricultural company’s PES through the intermediary mechanism of green organization identification (GOI) of the top management team (TMT) according to symbolic context and theory of high-level echelon, to solve the first gap in exploring what factors can drive the PES. Second, this research proposes a multi-level growth curve model (MGCM) to solve how individuals adjust their behavioral intentions over time according to their translation and understanding of their use environment, because past studies consist of almost cross-sectional properties. Third, past research has also neglected the multi-level framework, leading to hierarchical reasoning bias. Therefore, this research believes that the MGCM can fill in the multi-level gap. Finally, this research collected 400 TMT employees from 100 different agricultural companies in Taiwan in three-stage time for six months. The results show that CSR will significantly lead to more growth in GOI, and more growth in GOI will lead to more growth in PES adoption. The research results can not only advance the agricultural sustainability literature but also serve as a guide for agricultural companies to implement PES.

scholarly journals Can Primary Medical Institutions Lead to Worse Health Status for Patients with Noncommunicable Diseases Compared with High-Level Hospitals? A Follow-Up Observation Study in China

2019 ◽  
Vol 16 (8) ◽  
pp. 1336 ◽  
Author(s):  
Yadong Niu ◽  
Ting Ye ◽  
Yan Zhang ◽  
Liang Zhang
Keyword(s):  
Health Status ◽  
Healthcare System ◽  
Growth Curve ◽  
Primary Healthcare ◽  
Utility Score ◽  
Growth Curve Model ◽  
Observation Study ◽  
Medical Institutions ◽  
Curve Model ◽  
High Level

The weak primary healthcare system in China brings challenges to the national strategy of primary medical institutions providing general health needs for patients with non-communicable diseases (NCDs). It is necessary to explore the potential discrepancies in health status for patients with NCDs if they go to primary medical institutions rather than high-level hospitals. Data was obtained from Surveillance of Health-seeking Behavior in Hubei Province. Respondents were investigated six times to collect information on health service utilization and health-related quality of life (HRQoL). Ninety-two hypertension patients who went to medical institutions of the same level were included. HRQoL was measured by the Chinese version of EQ-5D-3L. A multilevel growth curve model was applied to analyze whether provider level could influence HRQoL. The utility score and visual analogue scale (VAS) of patients varied insignificantly over six months (p > 0.05). A growth curve model showed that comorbidity was the only factor significantly influencing utility score (p = 0.019). Time and comorbidity were the only influencing factors of VAS (p < 0.05). Our findings indicated that the level of healthcare provider had no significant impact on the health status of patients with NCDs. As such, this study concludes that the primary healthcare system in China is qualified to be the health gatekeeper for NCDs patients.

Problem gambling symptomatology and alcohol misuse among adolescents: A parallel-process latent growth curve model.

2017 ◽  
Vol 31 (4) ◽  
pp. 447-456 ◽  
Author(s):  
Seema Mutti-Packer ◽  
David C. Hodgins ◽  
Nady el-Guebaly ◽  
David M. Casey ◽  
Shawn R. Currie ◽  
...  
Keyword(s):  
Problem Gambling ◽  
Growth Curve ◽  
Alcohol Misuse ◽  
Parallel Process ◽  
Latent Growth Curve ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

Mental health outcomes in military veterans: A latent growth curve model.

2018 ◽  
Vol 24 (3) ◽  
pp. 228-235 ◽  
Author(s):  
Justin T. McDaniel ◽  
Kate H. Thomas ◽  
David L. Albright ◽  
Kari L. Fletcher ◽  
Margaret M. Shields
Keyword(s):  
Mental Health ◽  
Health Outcomes ◽  
Growth Curve ◽  
Military Veterans ◽  
Latent Growth Curve ◽  
Mental Health Outcomes ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

Decreased COVID-related adaptive behavior and increased negative affect: A multivariate latent growth curve model

2021 ◽  
pp. 135910532110216
Author(s):  
Hai-Ping Liao ◽  
Xiao-Fu Pan ◽  
Xue-Qin Yin ◽  
Ya-Fei Liu ◽  
Jie-Yang Li ◽  
...  
Keyword(s):  
Negative Affect ◽  
Adaptive Behavior ◽  
Growth Curve ◽  
Initial Level ◽  
Behavioral Changes ◽  
Latent Growth Curve ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

Data from a longitudinal questionnaire investigation of three time waves were used to investigate affective and behavioral changes and their covariant relationship among Chinese general population during the COVID-19 pandemic from March to May 2020. 145 participants aging from 15 to 63 completed three waves of survey. Latent growth curve analyses found that negative affect gradually increased as the pandemic continued. A faster increase in negative affect was related to a greater decrease in adaptive behavior and faster increase in non-adaptive behavior. A higher initial level of negative affect was related to a slower increase in non-adaptive behavior.

scholarly journals Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups

2009 ◽  
Vol 33 (6) ◽  
pp. 565-576 ◽  
Author(s):  
Nilam Ram ◽  
Kevin J. Grimm
Keyword(s):  
Stress Response ◽  
Growth Curve ◽  
Growth Modeling ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Longitudinal Change ◽  
Growth Curve Model ◽  
Growth Mixture ◽  
Multiple Group ◽  
Curve Model

Growth mixture modeling (GMM) is a method for identifying multiple unobserved sub-populations, describing longitudinal change within each unobserved sub-population, and examining differences in change among unobserved sub-populations. We provide a practical primer that may be useful for researchers beginning to incorporate GMM analysis into their research. We briefly review basic elements of the standard latent basis growth curve model, introduce GMM as an extension of multiple-group growth modeling, and describe a four-step approach to conducting a GMM analysis. Example data from a cortisol stress-response paradigm are used to illustrate the suggested procedures.

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