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The purpose of this paper is to contribute to the existing literature by providing insights about how employees make attributions about the reasons why management adopts HRM practices based on a case study of a company in Turkey.

The case study was carried out through semi-structured interviews with multiple employee groups aimed at capturing a broad range of perspectives. The data were processed by way of categorical and frequency analyses, which are forms of content analysis.

Non-managerial employees make similar attributions regarding recruitment and selection, training, performance as well as career development and benefits.

Acknowledging different views regarding the changing nature of employees' attributions, qualitative longitudinal research would provide more rigorous analysis in terms of ascertaining whether there are any alterations in employees' attitudes towards HRM practices over time.

Management and practitioners can draw valuable insights from the fact that when the meanings attached to HR practices by different employee groups are agreed upon, this may lead to their more enthusiastic involvement with these practices.

This study examines employees' attributions regarding HR practices through a case study. It provides evidence that when employees ascribe positive meanings to such practices, they are more committed to them and this may promote more favourable outcomes regarding HR efforts.

This study aims to explore the emergence of the human resource (HR) analyst role. The job posts on LinkedIn display the industry demand and skills required by the organizations. This study identifies the different knowledge, skills and abilities (KSA) required for an HR analyst role in different stages of professional growth (i.e. entry-level, middle-senior level and top-level) across different industries/sectors as applicable to the crisis.

A total of 80 job posts were extracted from LinkedIn. Details such as industry, job levels, qualifications, job experience, job functions, job descriptions (JDs) and job skills (JS) were collected. Further, 30 videos were extracted from YouTube and converted into text. Text analysis was conducted using NVivo software to analyze JDs, JS and job functions. Using NVivo, word frequency, word cloud, word tree and treemap were created to visualize the data. Finally, ten in-depth interviews were conducted with senior HRA managers based in India to understand the essential competencies required for the HR analyst role and the strategies to develop them.

The findings indicate that not only technical skills are needed, but business and communication skills are particularly important for all job levels during a crisis. The JD word cloud showed words, such as data, business, support and management, and the word tree depicted HR data and change agents as important words with many related sentences as branches. General JS included analytical, communication, problem-solving and management. Technical JS were the most widely used and included structure query language, system applications & products in data processing, human capital management, TABLEAU, management information system and PYTHON. Strategies to develop these competencies included case studies, live projects, internships on HR analytics (HRAs) assignments and mentoring by senior HRA professionals.

The sample used was small, as the study included 80 job posts available on LinkedIn restricted to India. The study was restricted to qualitative approach and text analytics was used. Survey methods and a quantitative approach can be used to collect data from HR recruiters, job holders and senior leaders to understand the role of HRAs in the job market and then these variables can be tested empirically.

Based on the McCartney et al.’s (2020) competency model for the HR Analyst role, this study has explored the KSA framework using data visualization techniques and used text analytics to analyze LinkedIn job posts for different levels, videos from YouTube and in-depth interviews. It also mapped the KSA for the HR analyst role to the various stages of crisis system management given by Mitroff (2005). The use of social media analytics, such as analyzing LinkedIn data and YouTube videos, are highlighted.