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What type of abstracts does the following abstract belong to? Purpose – The aim of this paper was to examine the incremental validity of positive psychological capital (PsyCap) in predicting several important employee outcomes while controlling for a full range of personality traits.Design/methodology/approach – Using a cross-sectional field study design, the authors collected data from 373 employees in South Korea. Findings – Hierarchical multiple regression analyses revealed that employees’ PsyCap is related to their perceived performance, turnover intention, work happiness, and subjective well-being, even after controlling for the Big Five personality traits.Research limitations/implications – By controlling for the Big Five traits, this study was able to evaluate the role of PsyCap more precisely. However, as the data were collected from the same source at one point in time, common method variance is a potential issue. Practical implications – The findings suggest that managers need to focus on developing employees’ PsyCap, given its unique effect on the outcome variables.Social implications – By developing employees’ PsyCap, organizations could turn work into a significant source of happiness and life satisfaction for their employees.Originality/value – This study identified the broad and unique effect of PsyCap on work and life outcomes beyond personality traits.Keywords Performance, Turnover intention, Big Five traits, Psychological capital, Subjective well-being, Work happinessPaper type Research paperReference: Yongduk Choi and Dongseop Lee. Psychological capital, Big Five traits, and employee outcomes. www.emeraldinsight.com/0268-3946.htm.
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In the Introduction, you should give information about your methodology or finding as detailed as possible so that the reader can understand your present study clearly.
Most research articles begin by indicating that the research field or topic is very useful or significant. They may focus on____________.
What type of abstracts does the following abstract belong to? This paper presents a consensus-based robust cooperative control framework for a wide class of linear time-invariant (LTI) systems, namely Negative-Imaginary (NI) systems. Output feedback, dynamic, Strictly Negative-Imaginary (SNI) controllers are applied in positive feedback to heterogeneous multiinput–multi-output (MIMO) plants through the network topology to achieve robust output feedback consensus. Robustness to external disturbances and model uncertainty is guaranteed via NI system theory. Cooperative tracking control of networked NI systems is presented as a corollary of the derived results by adapting the proposed consensus algorithm. Numerical examples are also given to demonstrate the effectiveness of proposed robust cooperative control framework. (Wang et al. 2015:64) Reference: Jianan Wang, Alexander Lanzon, Ian R. Petersen Robust cooperative control of multiple heterogeneous Negative-Imaginary systems Automatica 61 (2015) 64–72.
What type of abstracts does the following abstract belong to? We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-the art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE benchmark to 80.4% (7.6% absolute improvement), MultiNLI accuracy to 86.7% (5.6% absolute improvement) and the SQuAD v1.1 question answering Test F1 to 93.2 (1.5 absolute improvement), outperforming human performance by 2.0. (Ming et al. 2018)Reference: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT (1) 2019: 4171-4186.
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