Page 16 - Azerbaijan State University of Economics

THE JOURNAL OF ECONOMIC SCIENCES: THEORY AND PRACTICE, V.82, # 2, 2025, pp. 4-31

focus shifts from merely evaluating and controlling employees to using data to empower
and support them in achieving their career goals.

Employee Engagement and Retention
Employee engagement and retention have positioned themselves as an important area for
academic and organisational exploration within HR analytics [Ravesangar K, Narayanan
S. 2024; Gaur B, Shukla VK, Verma A. 2019; Zebua NDK, Santosa NTA, Putra NFD.
2024;]. Several studies within the reviewed literature highlight that by leveraging
advanced analytical techniques, organisations can gain key insights that may not be
immediately accessible through conventional methods [Bakhru KM, Sharma A. 2019; Bu
W, Zhao M. 2021; Rasheed MH, Khalid J, Ali A, Rasheed M, Ali K. 2024; Silva A.
2023]. The insights gained from these techniques can assist in identifying targeted
interventions, such as personalised development plans, promotion initiatives, or
adjustments in workplace culture, guidelines and policies, thereby fostering a more
engaged workforce and minimising attrition rates [Roberts DR. 2013].

The existing literature highlights that researchers primarily utilised employee surveys and
sentiment analysis of employee feedback [Rombaut E, Guerry MA. 2017]. These studies
emphasise that organisations may get valuable insights into factors influencing employee
engagement, including job satisfaction, work-life balance, and prospects for growth and
development. Moreover, the emergence of AI-driven solutions has augmented this
process by offering dynamic, data-driven insights and personalised recommendations,
which aim to boost employee satisfaction and reduce turnover [Hughes C, Robert L,
Frady K, Arroyos A. 2019; Rožman M, Oreški D, Tominc P. 2022]. Network analysis is
predominantly used to understand employee relationships and identify key persons within
the organisation [Chamorro-Premuzic T, Akhtar R, Winsborough D, Sherman RA. 2017;
Yuan J. 2019]. This can aid employers in comprehending the social dynamics that shape
employee engagement and modifying their engagement strategies accordingly.
Furthermore, predictive models are being developed to identify employees at an increased
risk of turnover, enabling pre-emptive actions to retain the key employees [Roberts DR.
2013; Pratt M, Boudhane M, Cakula S. 2021].

Despite these advancements and their benefits, researchers have also mentioned the
challenges and limitations in this area. Analysing employee sentiment and ensuring
the precision of predictive models can be quite challenging, requiring a nuanced
understanding of the complex and interrelated factors that affect employee
engagement, making it difficult to fully capture the subtleties of employee attitudes
and behaviours [Khan SA, Tang J. 2016]. Moreover, organisations must maintain
transparency regarding their utilisation of employee data and ensure the timely
redressal of privacy and data security concerns because if employees perceive that

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