IDEAS home Printed from https://ideas.repec.org/a/rbs/ijbrss/v13y2024i7p557-565.html

Effects of evaluation on employees' performance in Isiolo county government

Author

Listed:
  • Agnes Mungania

    (Meru University of Science and Technology)

  • Hussein Guyo

    (Meru University of Science and Technology)

  • Rael Mwirigi

    (Chuka University, Tharaka Nithi, Kenya)

Abstract

Management by Objectives (MBO) is a performance management approach where employees and managers collaborate to set individual goals aligned with broader organizational objectives. While MBO's potential benefits are recognized, its effectiveness particularly in the context of varying organizational cultures and appraisal purposes, remains unclear. The general objective of the study was to investigate the relationship between MBO and employee performance within Isiolo County Government. The study specifically examined the influence of evaluation on employees’ performance in Isiolo County. The study was grounded on image theory. The population of study was 260 employees across seven county ministries. Data was collected using a questionnaire subjected to pilot study. Quantitative data was analyzed using both descriptive and inferential statistics. The study showed that overall evaluation (with a p-value of 0.897) does not significantly contribute to employee performance within the County Government of Isiolo. Therefore, the null hypothesis for evaluation was accepted. Because the study found no significant influence of evaluation on employee performance, there is a critical need for organization to refine their evaluation systems. To gain a deeper understanding of the long-term impact of evaluation on employee performance in Kenyan county governments, longitudinal research is warranted. The study recommends need for Isiolo County Government to improve its evaluation processes. Evaluators should provide valuable input to aid in performance improvement in employees of Isiolo county government. Key Words:Evaluation, Employee Performance, Isiolo County

Suggested Citation

  • Agnes Mungania & Hussein Guyo & Rael Mwirigi, 2024. "Effects of evaluation on employees' performance in Isiolo county government," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 13(7), pages 557-565, October.
    • Handle: RePEc:rbs:ijbrss:v:13:y:2024:i:7:p:557-565
    DOI: 10.20525/ijrbs.v13i7.3879
    as

    Download full text from publisher

    File URL: https://ssbfnet.com/ojs/index.php/ijrbs/article/view/3879/2561
    Download Restriction: no
    File URL: https://doi.org/10.20525/ijrbs.v13i7.3879
    Download Restriction: no
    File URL: https://libkey.io/10.20525/ijrbs.v13i7.3879?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    Full references (including those not matched with items on IDEAS)

    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. Christoph Riedl & Eric Bogert, 2024. "Who Benefits from AI? Self-Selection, Skill Gap, and the Hidden Costs of AI Feedback," Papers 2409.18660, arXiv.org, revised Apr 2026.
    2. Olimpia Ban & Irina Maiorescu & Mihaela Bucur & Gabriel Cristian Sabou & Betty Cohen Tzedec, 2024. "AI between Threat and Benefactor for the Competences of the Human Working Force," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 762-762, August.
    3. Byung-Jik Kim & Julak Lee, 2024. "The mental health implications of artificial intelligence adoption: the crucial role of self-efficacy," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    4. Milan Miric & Nan Jia & Kenneth G. Huang, 2023. "Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents," Strategic Management Journal, Wiley Blackwell, vol. 44(2), pages 491-519, February.
    5. Standaert, Thomas & Andries, Petra, 2026. "Overcoming difficulties in knowledge transfer: Harnessing the power of AI to drive process innovation," Technovation, Elsevier, vol. 149(C).
    6. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    7. Heirati, Nima & Pitardi, Valentina & Wirtz, Jochen & Jayawardhena, Chanaka & Kunz, Werner & Paluch, Stefanie, 2025. "Unintended consequences of service robots – Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 194(C).
    8. Koehler, Maximilian & Sauermann, Henry, 2024. "Algorithmic management in scientific research," Research Policy, Elsevier, vol. 53(4).
    9. Li, Yunjian & Song, Yixiao & Sun, Yanming & Zeng, Mingzhuo, 2024. "When do employees learn from artificial intelligence? The moderating effects of perceived enjoyment and task-related complexity," Technology in Society, Elsevier, vol. 77(C).
    10. Mammadov Huseyn & Africa Ruiz-Gandara & Luis Gonzalez-Abril & Isidoro Romero, 2024. "Adoption of Artificial Intelligence in Small and Medium-Sized Enterprises in Spain: The Role of Competences and Skills," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 848-848, August.
    11. Anuj Kapoor & Madhav Kumar, 2025. "Frontiers: Generative AI and Personalized Video Advertisements," Marketing Science, INFORMS, vol. 44(4), pages 733-747, July.
    12. Zhou, Qiwei & Chen, Keyu & Cheng, Shuang, 2024. "Bringing employee learning to AI stress research: A moderated mediation model," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    13. Manav Raj & Justin Berg & Rob Seamans, 2023. "Art-ificial Intelligence: The Effect of AI Disclosure on Evaluations of Creative Content," Papers 2303.06217, arXiv.org, revised Jun 2024.
    14. Wang, Sai & Wen, Wen & Niu, Yuhao & Li, Xin, 2024. "Digital transformation and corporate labor investment efficiency," Emerging Markets Review, Elsevier, vol. 59(C).
    15. Shirish Sundaresan & Isin Guler, 2025. "Algorithmic Recommendation Tools and Experiential Learning in Clinical Care," Organization Science, INFORMS, vol. 36(5), pages 1786-1802, September.
    16. Shan Chen & Xiuming Wang & Yuanzhao Ding, 2025. "Novel Governance Mode in Employment Systems: A Literature-Based Bibliographic Analysis," International Journal of Global Business and Competitiveness, Springer, vol. 20(1), pages 107-117, December.
    17. Xuan, Hongzhou & He, Guibing, 2025. "Negative feedback from robots is received better than that from humans: The effect of feedback on human–robot trust and collaboration," Journal of Business Research, Elsevier, vol. 193(C).
    18. Jing Wang & Zeyu Xing & Rui Zhang, 2023. "AI technology application and employee responsibility," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    19. Jiaqi Xu & Xiaofei Tang & En-Chung Chang & Haoyu Peng, 2024. "Working with AI: the impact of organizational intelligent service strategy on employees’ perception of career achievement," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-20, December.
    20. Marc Eulerich & Aida Sanatizadeh & Hamid Vakilzadeh & David A. Wood, 2024. "Is it all hype? ChatGPT’s performance and disruptive potential in the accounting and auditing industries," Review of Accounting Studies, Springer, vol. 29(3), pages 2318-2349, September.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:rbs:ijbrss:v:13:y:2024:i:7:p:557-565. 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: Umit Hacioglu (email available below). General contact details of provider: https://edirc.repec.org/data/ssbffea.html .

    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.