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Managing academic performance by optimal resource allocation. / Grigoriev, Alexander; Mondrus, Olga.

In: Scientometrics, Vol. 127, No. 5, 05.2022, p. 2433-2453.

Research output: Contribution to journal › Article › peer-review

Harvard

Grigoriev, A & Mondrus, O 2022, 'Managing academic performance by optimal resource allocation', Scientometrics, vol. 127, no. 5, pp. 2433-2453. https://doi.org/10.1007/s11192-022-04342-5

APA

Grigoriev, A., & Mondrus, O. (2022). Managing academic performance by optimal resource allocation. Scientometrics, 127(5), 2433-2453. https://doi.org/10.1007/s11192-022-04342-5

Vancouver

Grigoriev A, Mondrus O. Managing academic performance by optimal resource allocation. Scientometrics. 2022 May;127(5):2433-2453. doi: 10.1007/s11192-022-04342-5

Author

Grigoriev, Alexander ; Mondrus, Olga. / Managing academic performance by optimal resource allocation. In: Scientometrics. 2022 ; Vol. 127, No. 5. pp. 2433-2453.

BibTeX

@article{25d47c240aae4e92b5530c7a80c0381a,
title = "Managing academic performance by optimal resource allocation",
abstract = "In this paper, we develop and study a complex data-driven framework for human resource management enabling (i) academic talent recognition, (ii) researcher performance measurement, and (iii) renewable resource allocation maximizing the total output of a research unit. Suggested resource allocation guarantees the optimal output under strong economic assumptions: the agents are rational, collaborative and have no incentives to behave selfishly. In reality, however, agents often play strategically maximizing their own utilities, e.g., maximizing the resources assigned to them. This strategic behavior is typically mitigated by implementation of performance-driven or uniform resource allocation schemes. Next to the framework presentation, we address the cost of such mitigation.",
keywords = "Incentives, Performance monitoring, Resource allocation, Strategic behavior, Talent performance, Talent recognition",
author = "Alexander Grigoriev and Olga Mondrus",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",
year = "2022",
month = may,
doi = "10.1007/s11192-022-04342-5",
language = "English",
volume = "127",
pages = "2433--2453",
journal = "Scientometrics",
issn = "0138-9130",
publisher = "Elsevier Science Publishing Company, Inc.",
number = "5",

}

RIS

TY - JOUR

T1 - Managing academic performance by optimal resource allocation

AU - Grigoriev, Alexander

AU - Mondrus, Olga

N1 - Publisher Copyright: © 2022, The Author(s).

PY - 2022/5

Y1 - 2022/5

N2 - In this paper, we develop and study a complex data-driven framework for human resource management enabling (i) academic talent recognition, (ii) researcher performance measurement, and (iii) renewable resource allocation maximizing the total output of a research unit. Suggested resource allocation guarantees the optimal output under strong economic assumptions: the agents are rational, collaborative and have no incentives to behave selfishly. In reality, however, agents often play strategically maximizing their own utilities, e.g., maximizing the resources assigned to them. This strategic behavior is typically mitigated by implementation of performance-driven or uniform resource allocation schemes. Next to the framework presentation, we address the cost of such mitigation.

AB - In this paper, we develop and study a complex data-driven framework for human resource management enabling (i) academic talent recognition, (ii) researcher performance measurement, and (iii) renewable resource allocation maximizing the total output of a research unit. Suggested resource allocation guarantees the optimal output under strong economic assumptions: the agents are rational, collaborative and have no incentives to behave selfishly. In reality, however, agents often play strategically maximizing their own utilities, e.g., maximizing the resources assigned to them. This strategic behavior is typically mitigated by implementation of performance-driven or uniform resource allocation schemes. Next to the framework presentation, we address the cost of such mitigation.

KW - Incentives

KW - Performance monitoring

KW - Resource allocation

KW - Strategic behavior

KW - Talent performance

KW - Talent recognition

UR - http://www.scopus.com/inward/record.url?scp=85126467810&partnerID=8YFLogxK

U2 - 10.1007/s11192-022-04342-5

DO - 10.1007/s11192-022-04342-5

M3 - Article

AN - SCOPUS:85126467810

VL - 127

SP - 2433

EP - 2453

JO - Scientometrics

JF - Scientometrics

SN - 0138-9130

IS - 5

ER -

ID: 35727217