Academic literature on the topic 'Polychronic'

System response delay has been cited as the single most frustrating aspect of using the Internet and the most worrisomeaspect of Web application design. System response time (SRT) research generally concludes that delay should be eliminated where possible to as little as a few seconds, even though delay reduction is costly. Unfortunately, it is not clear if these conclusions are appropriate outside of the developed world where nearly all of the SRT research has taken place. Cultural effects have been, hence, generally missing from SRT research. The one SRT study to date outside of the developed world did report differences using the theoretical construct of cultural chronism, and this finding could limit the generalizability of SRT research findings from developed countries to many economically developing nations. However, limitations and potential confounds in this single study render those findings tentative. The end of Apartheid in South Africa allowed an opportunity to conduct a longitudinal free simulation experiment that overcomes the critical limitations of this previous research. Subjects were members of historically polychronic and monochronic groups who had been segregated by Apartheid and now live in an integrated society with shared infrastructure and computer access. Results find that members of the historically polychronic group are more accepting of longer delays and are more willing to trade longer delays for improved functionality than are their historically monochronic counterparts. Furthermore, tests find that members of the historically monochromic population that came of age in a desegregated, majority-polychronic culture appear to be polychronic themselves and to differ significantly from the older monochronic generation. Results from this study can be applied to design culturally sensitive applications for users in the developing economies of the world.

The present study intended to investigate the effect of polychronicity and time management on work-related quality of life among bank employees. It also attempted to examine the moderating role of time management in predicting work-related quality of life from polychronic tendencies. Gender differences were also investigated along the study variables. Purposive sample consisted of 300 bank employees including men and women with age range of 27 to 52 years (M = 32.5, SD = 4.26) was acquired. Measures of Polychronic Attitude Index (Kaufman, Lane, & Lindquist, 1991), Time Management Behavior Scale (Poposki, Oswald, & Chen, 2008), and Work-related Quality of Life Scale (Zeng et al., 2011) were used to assess study variables. Results showed that polychronicity was negatively associated with time management and work-related quality of life, while time management and work-related quality of life were positively aligned with each other. Findings also revealed that time management buffers the relationship between polychronicity and work-related quality of life and offered a cushioning effect against the repercussions of ploychronicity. Gender differences indicated that men reflected less polychronic tendencies, better time management, and elevated work-related quality of life as compared to their female counterparts. Findings of the present study would bear practical implications for job design and managing various tasks at workplace.

Context. This commentary is based on an innovative approach to the development of predictive analytics. It is centered on the development of predictive models for varying stages of chronic disease through integrating all types of datasets, adds various new features to a theoretically driven data warehousing, creates purpose-specific prediction models, and integrates multi-criteria predictions of chronic disease progression based on a biomedical evolutionary learning platform. After merging across-center databases based on the risk factors identified from modeling the predictors of chronic disease progression, the collaborative investigators could conduct multi-center verification of the predictive model and further develop a clinical decision support system coupled with visualization of a shared decision-making feature for patient care. The Study Problem. The success of health services management research is dependent upon the stability of pattern detection and the usefulness of nosological classification formulated from big-data-to-knowledge research on chronic conditions. However, longitudinal observations with multiple waves of predictors and outcomes are needed to capture the evolution of polychronic conditions. Motivation. The transitional probabilities could be estimated from big-data analysis with further verification. Simulation or predictive models could then generate a useful explanatory pathogenesis of the end-stage-disorder or outcomes. Hence, the clinical decision support system for patient-centered interventions could be systematically designed and executed. Methodology. A customized algorithm for polychronic conditions coupled with constraints-oriented reasoning approaches is suggested. Based on theoretical specifications of causal inquiries, we could mitigate the effects of multiple confounding factors in conducting evaluation research on the determinants of patient care outcomes. This is what we consider as the mechanism for avoiding the black-box expression in the formulation of predictive analytics. The remaining task is to gather new data to verify the practical utility of the proposed and validated predictive equation(s). More specifically, this includes two approaches guiding future research on chronic disease and care management: (1) To develop a biomedical evolutionary learning platform to predict the risk of polychronic conditions at various stages, especially for predicting the micro- and macro-cardiovascular complications experienced by patients with Type 2 diabetes for multidisciplinary care; and (2) to formulate appropriate prescriptive intervention services, such as patient-centered care management interventions for a high-risk group of patients with polychronic conditions. Conclusions. The commentary has identified trends, challenges, and solutions in conducting innovative AI-based healthcare research that can improve understandings of disease-state transitions from diabetes to other chronic polychronic conditions. Hence, better predictive models could be further formulated to expand from inductive (problem solving) to deductive (theory based and hypothesis testing) inquiries in care management research.