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1. Introduction and background

Malisetty et al (2017) defines “predictive analytics” as techniques that integrate machine learning and statistics to generate forecasts by analysing real-time information in conjunction with historical data. The aim of predictive analytics in HRM is to forecast outcomes like future sales performance, high-risk turnover candidates, and training needs (Fitz-Enz and John Mattox, 2014).

According to Fitz-Enz and John Mattox (2014) this method uses larger patterns in training, remuneration, and culture fit in conjunction with talent statistics including productivity indicators, attrition rates, and performance ratings. Today, an increasing number of vendors, such as Oracle and IBM, are offering HR departments dashboards and platforms for predictive analytics that are customised to meet their requirements, enabling companies to make calculated workforce planning decisions (Kakulapati et al., 2020).

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Predicting mental health crises, spotting engagement issues before they worsen, and scientifically mapping leadership pipelines are examples of emerging use cases.

Conversely, corporate prediction algorithms are riddled with difficulties like discrimination, unfairness, and violations of employee privacy; there is also a lack of transparency and accountability surrounding these algorithms (Ekawati, 2019).

Ekawati’s (2019) study showed that attributes including race, gender, and skill level could affect talent analytics models. Others question if predictive software exacerbates the structural inequalities already in place by making it much more difficult for marginalised individuals to access opportunities (Tambe et al., 2019).

According to Boakye and Lamptey (2020), non-Western countries have yet to sufficiently explore these moral quandaries. According to Oladipupo and Olubusayo. (2020), Nigeria is one nation that is rapidly developing technologically, as seen by the increase in the use of predictive HR analytics there especially among multinational companies.

However, efforts to guarantee transparency are falling behind. Although regulatory monitoring is sparse among these enterprises, Boakye and Lamptey (2020) argue that local organisations are customising global systems like SAP and Oracle for human management.

This research intends to investigate the viewpoints of human resource specialists working for African multinational corporations as a result. With regard to algorithmic accountability and data ethics in relation to workforce choices based on data, this campaign aims to draw attention to potential channels for solutions.

2. The statement of Problem

In non-Western cultures like Nigeria, predictive analytics poses questions concerning worker privacy, responsibility, and prejudice. These questions currently have a lack of clear solutions (Oladipupo and Olubusayo., 2020). Large multinational companies are customising these HR decision-making tools to suit their own requirements; hence, they need governance frameworks that consider the local environment and strike a balance between ethical considerations and analytical power (Boakye and Lamptey, 2020).

On the other hand, there is little study that highlights the viewpoints of emerging economies. Tambe et al (2017) point out that although data cultures vary widely across countries, most research focuses on American and European laws such as GDPR. According to Fitz-Enz and John Mattox (2014), contextual implementation dynamics are overlooked due to the dominance of global manufacturers in analytical tool venues.

Hence, the main objective of this research is to provide insight into the manner by which multinational corporations (MNCs) that operate in developing countries such as Nigeria need to evaluate the possible ethical risks associated with predictive HR analytics and devise plans to mitigate or eradicate them.

3. Aim and Objectives

This study aims to:

Analyze perspectives of HR professionals around the ethical implications of deploying predictive analytics for workforce decisions in a developing country like Nigeria.

The objectives are to:

1. Explore the use cases and attitudes related to predictive analytics in HR practice.

2. Investigate the transparency, accountability and regulatory measures organizations currently apply when adopting predictive workforce analytics.

3. Highlight contextual nuances around consent, privacy, bias and surveillance that underpin ethical perspectives on predictive HR analytics.

4. Propose a framework for ethical deployment of predictive analytics in diverse organizational contexts.

4. Research Methodology

The study will use a two-phase sequential mixed methods approach to gather perspectives on ethical issues concerning the application of predictive analytics in workforce selection from human resource experts employed by multinational corporations in Nigeria.

Phase 1: Survey

Sending an online survey to Nigerian human resources specialists employed by multinational corporations is the first step towards implementing it. According to Symonds and Gorard (2011), surveys might simplify the process of obtaining descriptive impressions for specific topics from larger respondent pools.

Using a five-point Likert scale, the survey will collect demographic data in addition to questions regarding predictive analytics ideas, existing organisational adoption tactics, application areas, perceived advantages, and opinions on ethical challenges. The most significant subjects that will be covered are transparency, accountability mechanisms, monitoring, privacy, prejudice, and consent.

Phase 2: Interview

Following that, a limited number of HR specialists from the selected organizations will be interviewed. Semi-structured interviews are an effective method for gathering detailed, exploratory data about people’s choices, experiences, and recommendations (Longhurst, 2009). These discussions may also effectively handle complex themes like ethics and culture. In this section, we’ll examine a few organisational procedures that support the ethical use of predictive analytics.

Policies and guidelines, algorithm audits, assessments of privacy threats, discriminatory tests, worker consent procedures, and transparency efforts are a few instances of these procedures. In order to resolve moral quandaries, we will confer with specialists. Verbatim transcriptions will be subjected to theme analysis techniques in order to identify noteworthy patterns pertaining to ethical dilemmas, cultural values, and structural barriers that affect the proper use of predictive HR analytics in both nations.


Boakye, A., and Lamptey, Y. A. (2020). The rise of HR analytics: Exploring its implications from a developing country perspective. Journal of Human Resource Management8(3), 181-189.

Ekawati, A. D. (2019). Predictive analytics in employee churn: A systematic literature review. Journal of Management Information and Decision Sciences22(4), 387-397.

Fitz-Enz, J., and John Mattox, I. I. (2014). Predictive analytics for human resources. John Wiley and Sons.

Kakulapati, V., Chaitanya, K. K., Chaitanya, K. V. G., and Akshay, P. (2020). Predictive analytics of HR-A machine learning approach. Journal of Statistics and Management Systems23(6), 959-969.

Longhurst, R (2009). “Interviews: In-depth, semi-structured.” International encyclopedia of human geography. Elsevier, 2009. 580-584.

Malisetty, S., Archana, R. V., and Kumari, K. V. (2017). Predictive Analytics in HR Management. Indian Journal of Public Health Research and Development8(3).

Oladipupo, O. O., and Olubusayo, F. H. (2020). Human resource analytics dimensions and employee engagement in manufacturing industry in Nigeria: a conceptual review. Journal of Management Information and Decision Sciences23(5), 629-637.

Symonds, J. E., and Gorard, S (2011). “Death of mixed methods?: Or the rebirth of research as a craft.” Evaluation and Research in Education 24.2 (2011): 121-136.

Tambe, P., Cappelli, P., and Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review61(4), 15-42.




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