Proactive Personality Scale--Short Version

Seibert, Scott E.; Grant, J. Michael; Kraimer, Maria L.

doi:10.1037/t09083-000

Cited by 2 publications

(2 citation statements)

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“…The proactiveness scale consisted of 6 items, adapted from the study conducted by Seibert et al (1999). These items evaluated participants' proactiveness about career decisions and actions (Seibert et al, 1999). A 6-item scale was used to gauge participants' levels of ambition.…”

Section: Methodsmentioning

confidence: 99%

Examining Career-Orientation Determinants of Managerial Roles in Nepal

Joshi,

Ghimire,

Subedi

et al. 2023

Management Dynamics

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This study examines the factors influencing career orientations among Nepalese professionals and students. The objective is to explore the correlations between proactive personality, ambition, career self-management, mentorship relationships, commitment, and career orientations. The research uses a sample of 275 participants involved in managerial roles in different corporate houses. The study adopted descriptive and causal-comparative approaches. The major findings reveal significant positive correlations between these factors and career orientations. Regression analysis underscores the substantial predictive role of ambition (β = 0.302), career self-management (β = 0.302), and commitment (β = 0.370), indicating their significant impacts on career orientations. These insights contribute to a deeper understanding of the interplay of individual attributes and external factors in shaping career trajectories. Future research could expand geographically and adopt longitudinal perspectives to enrich these findings and recommendations for enhancing career development practices.

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Section: Methodsmentioning

confidence: 99%

Examining Career-Orientation Determinants of Managerial Roles in Nepal

Joshi,

Ghimire,

Subedi

et al. 2023

Management Dynamics

View full text Add to dashboard Cite

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“…Among many other available computer models used for simulating rainfall-runoff processes, the Soil and Water Assessment Tool [4], the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) [5], the Variable Infiltration Capacity Model [6], the HBV-light model [7], and the J2000 model [8] are frequently used by hydrological modellers around the world [9][10][11]. However, time-series hydrological models inherit uncertainties in streamflow estimations due to unavailability of long-term data including meteorological and streamflow data [12].…”

Section: Introductionmentioning

confidence: 99%

Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka

Gunathilake

Karunanayake

Gunathilake³

et al. 2021

Applied Computational Intelligence and Soft Computing

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Accurate streamflow estimations are essential for planning and decision-making of many development activities related to water resources. Hydrological modelling is a frequently adopted and a matured technique to simulate streamflow compared to the data driven models such as artificial neural networks (ANNs). In addition, usage of ANNs is minimum to simulate streamflow in the context of Sri Lanka. Therefore, this study presents an intercomparison between streamflow estimations from conventional hydrological modelling and ANN analysis for Seethawaka River Basin located in the upstream part of the Kelani River Basin, Sri Lanka. The hydrological model was developed using the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS), while the data-driven ANN model was developed in MATLAB. The rainfall and streamflows’ data for 2003–2010 period have been used. The simulations by HEC-HMS were performed by four types of input rainfall data configurations, including observed rainfall data sets and three satellite-based precipitation products (SbPPs), namely, PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The ANN model was trained using three well-known training algorithms, namely, Levenberg–Marquadt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG). Results revealed that the simulated hydrological model based on observed rainfall outperformed those of based on remotely sensed SbPPs. BR algorithm-based ANN algorithm was found to be superior among the data-driven models in the context of ANN model simulations. However, none of the above developed models were able to capture several peak discharges recorded in the Seethawaka River. The results of this study indicate that ANN models can be used to simulate streamflow to an acceptable level, despite presence of intensive spatial and temporal data sets, which are often required for hydrologic software. Hence, the results of the current study provide valuable feedback for water resources’ planners in the developing region which lack multiple data sets for hydrologic software.

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Proactive Personality Scale--Short Version

Cited by 2 publications

References 0 publications

Examining Career-Orientation Determinants of Managerial Roles in Nepal

Examining Career-Orientation Determinants of Managerial Roles in Nepal

Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka

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