IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2016-46-3.html
   My bibliography  Save this article

Effort, Satisfaction and Outcomes in Organisations

Author

Listed:

Abstract

In this paper, an agent-based model of bounded-rational agents, who adapt both their effort intensity (by the interaction with other employees) and their stay-on-the-job-intention (by the alignment of their personal values with the Human-Resource Management (HRM) practices implemented by the organisation), is proposed. Our aim is to analyse: (i) the emergence of an organisational culture and its relationship with both formal organisational structures and employees' effort-behaviours; (ii) the increase of organisational performance by retaining valuable-performance employees whereas poor-performance employees are dismissed. We have obtained that: (i) Some possible combinations of both employees-effort behaviours and formal organisational structures can favour the emergence of organisational cultures more than others; (ii) The interaction between employees within matrix structures (balanced or strong) with a democratic team leadership favour the emergence of organisational cultures; (iii) High-effort managers are relevant for the emergence of high-performance organisational cultures; (iv) Turnover (voluntary or involuntary) affects to the emergence of organisational culture negatively. We conclude that the main challenge is to retain high effort managers by adapting the set of HRM practices to them.

Suggested Citation

  • Marta Posada & Celia Martín-Sierra & Elena Perez, 2017. "Effort, Satisfaction and Outcomes in Organisations," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(2), pages 1-9.
    • Handle: RePEc:jas:jasssj:2016-46-3
    as

    Download full text from publisher

    File URL: https://www.jasss.org/20/2/9/9.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. G. Fagiolo & C. Birchenhall & P. Windrum, 2007. "Empirical Validation in Agent-based Models: Introduction to the Special Issue," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 189-194, October.
    2. Arianna Dal Forno & Ugo Merlone, 2002. "A Multi-Agent Simulation Platform for Modeling Perfectly Rational and Bounded-Rational Agents in a Firm," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(2), pages 1-3.
    3. Paul Windrum & Giorgio Fagiolo & Alessio Moneta, 2007. "Empirical Validation of Agent-Based Models: Alternatives and Prospects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-8.
    4. Patrick M. Wright & John J. Haggerty, 2005. "Missing Variables in Theories of Strategic Human Resource Management: Time, Cause, and Individuals," management revue - Socio-Economic Studies, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 16(2), pages 164-173.
    5. Patrick M. Wright & John J. Haggerty, 2005. "Missing Variables in Theories of Strategic Human Resource Management: Time, Cause, and Individuals," management revue. Socio-economic Studies, Rainer Hampp Verlag, vol. 16(2), pages 164-173.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dehua Gao & Flaminio Squazzoni & Xiuquan Deng, 2018. "The role of cognitive artifacts in organizational routine dynamics: an agent-based model," Computational and Mathematical Organization Theory, Springer, vol. 24(4), pages 473-499, December.
    2. Catalina Radu & Alecxandrina Deaconu & Sorina Ioana Misu & Monica Triculescu, 2020. "The Impact of Work Investment on Performance," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 22(Special 1), pages 1103-1103, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lamperti, Francesco & Bosetti, Valentina & Roventini, Andrea & Tavoni, Massimo & Treibich, Tania, 2021. "Three green financial policies to address climate risks," Journal of Financial Stability, Elsevier, vol. 54(C).
    2. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.
    3. Kukacka, Jiri & Jang, Tae-Seok & Sacht, Stephen, 2018. "On the estimation of behavioral macroeconomic models via simulated maximum likelihood," Economics Working Papers 2018-11, Christian-Albrechts-University of Kiel, Department of Economics.
    4. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    5. LeBaron Blake & Winker Peter, 2008. "Introduction to the Special Issue on Agent-Based Models for Economic Policy Advice," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 141-148, April.
    6. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    7. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    8. Stephen Frenkel & Karin Sanders & Tim Bednall, 2013. "Employee perceptions of management relations as influences on job satisfaction and quit intentions," Asia Pacific Journal of Management, Springer, vol. 30(1), pages 7-29, March.
    9. Gianluca Capone & Franco Malerba & Richard R. Nelson & Luigi Orsenigo & Sidney G. Winter, 2019. "History friendly models: retrospective and future perspectives," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 9(1), pages 1-23, March.
    10. Gomes, Sharlene L. & Hermans, Leon M. & Thissen, Wil A.H., 2018. "Extending community operational research to address institutional aspects of societal problems: Experiences from peri-urban Bangladesh," European Journal of Operational Research, Elsevier, vol. 268(3), pages 904-917.
    11. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    12. Fatouh, Mahmoud & Markose, Sheri & Giansante, Simone, 2021. "The impact of quantitative easing on UK bank lending: Why banks do not lend to businesses?," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 928-953.
    13. Hossein Sabzian & Mohammad Ali Shafia & Ali Maleki & Seyeed Mostapha Seyeed Hashemi & Ali Baghaei & Hossein Gharib, 2019. "Theories and Practice of Agent based Modeling: Some practical Implications for Economic Planners," Papers 1901.08932, arXiv.org.
    14. Colasante, Annarita, 2016. "Evolution of Cooperation in Public Good Game," MPRA Paper 72577, University Library of Munich, Germany.
    15. García-Carbonell, Natalia & Martín-Alcázar, Fernando & Sánchez-Gardey, Gonzalo, 2016. "The views of Spanish HR managers on the role of internal communication in translating HR strategies into HRM systems," European Management Journal, Elsevier, vol. 34(3), pages 269-281.
    16. Fontana, Magda, 2010. "Can neoclassical economics handle complexity? The fallacy of the oil spot dynamic," Journal of Economic Behavior & Organization, Elsevier, vol. 76(3), pages 584-596, December.
    17. Marc Deissenroth & Martin Klein & Kristina Nienhaus & Matthias Reeg, 2017. "Assessing the Plurality of Actors and Policy Interactions: Agent-Based Modelling of Renewable Energy Market Integration," Complexity, Hindawi, vol. 2017, pages 1-24, December.
    18. Sylvain Barde & Sander van der Hoog, 2017. "An empirical validation protocol for large-scale agent-based models," Studies in Economics 1712, School of Economics, University of Kent.
    19. Colasante, Annarita, 2017. "Selection of the distributional rule as an alternative tool to foster cooperation in a Public Good Game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 482-492.
    20. Bert, Federico E. & Rovere, Santiago L. & Macal, Charles M. & North, Michael J. & Podestá, Guillermo P., 2014. "Lessons from a comprehensive validation of an agent based-model: The experience of the Pampas Model of Argentinean agricultural systems," Ecological Modelling, Elsevier, vol. 273(C), pages 284-298.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jas:jasssj:2016-46-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Francesco Renzini (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.