This paper investigates how social media and digital technologies inuence Human Resource Management (HRM) from a sociotechnical systems (STS) perspective. Beyond social media, particular attention in the paper is given to the use of articial intelligence (AI), primarily due to the fact that this digital tool becomes increasingly integrated into HRM functions. Through a comprehensive literature review and empirical analysis based on a survey of 167 companies whose operations are located in the territory of the Republic of North Macedonia, this paper explores how the sociotechnical conguration of HRM information systems evolves in the context of social media, the other digital platforms and AI. For these purposes, multiple linear regression model was used with the OLS method of estimation of the coecients. The ndings demonstrate that organizations which align digital technologies with human-centric values and social processes report better HR function performance.
The digital technologies, such as social media, the other digital platforms and articial intelligence
(AI) are rapidly transforming the way organizations manage their people and resources. Human
Resource Management (HRM), which traditionally focused on administrative and support roles, is
now becoming a central part of digital innovation strategies. These technologies are used in many
HRM functions, such as the recruitment process, the creating the image of the company as a potential
employer, training and development, performance management, employees’ engagement with the
company, and employees’ satisfaction and retention. They can help reduce time, improve accuracy,
and support data-driven decisions [
Many researchers have emphasized that it is not enough to focus only on the technical performance
of social media, digital platforms and AI. It is equally important to consider the social context in
which these systems operate. This is the key idea of the sociotechnical systems (STS) approach [
The sociotechnical perspective also helps in understanding how digital platforms and AI adoption
aects job roles, decision-making processes, and employee trust. Kudina and van de Poel [
By focusing on the sociotechnical aspects of digital technologies adoption, this research aims to help organizations make better decisions when using them in HRM systems. It supports the view that technology should serve people, not replace or control them and that responsible innovation is key to the future of work. As organizations continue to invest in technologies such as social media and AI technologies, understanding the balance between automation and human values will be essential for achieving both eciency and fairness in HRM.
The purpose of this study is to explore how social media, the other digital platforms and digital tools including AI aects HRM from a sociotechnical perspective. It investigates how digital technologies and social systems interact, and how organizations can design HRM systems that are both eective and human-centered. The study uses both theory and survey data to understand how social media, the other digital platforms and AI inuence the overall performance of HR functions.
The central objective of this study is to explore the impact of social media and other digital technologies and AI, on Human Resource Management systems through a sociotechnical lens. Specically, the study aims to understand how these digital technologies interact with the human, organizational, and ethical dimensions of HRM, and how these interactions inuence overall system performance. The study also seeks to identify the conditions under which social media, other digital platforms and AI contribute positively to HRM outcomes, and the factors that may hinder its eective integration.
To achieve this, the study pursues the following sub-objectives:
To map the current landscape of the digital technologies in HRM, with a focus on the recruitment process, the image of the company as a potential employer, training and development, performance management, employees’ engagement with the company, and employees’ satisfaction and retention.
To analyse the sociotechnical conguration of digital HRM systems, identifying key social, technical, and organizational elements.
To evaluate the eects of sociotechnical alignment in HRM systems on the total performance of HR function.
To evaluate the eects of adoption of social media and the other digital platforms on the total performance of HR functions.
To examine the eects of AI adoption on the total performance of HR function.
To propose a set of best practices for integrating social media, digital platforms and AI into HRM in a manner that aligns with sociotechnical principles and ethical guidelines. This objective not only reects the current state of academic inquiry into social media and the other digital platforms and HRM, but also addresses pressing concerns from industry practitioners who seek to balance digital innovation with responsibility. The ndings are intended to guide HR profesionals and managers, system designers, and policymakers in craing strategies that leverage social media, dierent digital platforms and AI while preserving the human essence of human resources.
To structure the inquiry, the study is guided by following primary research questions:
RQ1: What are the social and technical implications of using social media, digital platforms and AI in HR functions?
RQ2: How does the sociotechnical alignment of Human Resource Management systems inuence the company ’s performance of HR functions? RQ3: How does the social media aect the company ’s performance of HR functions? RQ4: How do the digital platforms aect the company ’s performance of HR functions? RQ5: How does the AI aect the company ’s performance of HR functions? RQ6: How does the size of the company in terms of number of employee aect the company’s performance of HR functions? In alignment with these research questions, the following hypothesis are proposed:
Hypothesis (H1): The sociotechnical alignment of HRM systems positively inuences the performance of HR functions.
Hypothesis (H2): The social media usage positively inuences the performance of HR functions.
Hypothesis (H3): The digital platforms usage positively inuences the overall performance of HR functions.
Hypothesis (H4): The AI usage positively inuences the overall performance of HR functions.
These hypotheses rest on the assumption that technological benets alone are insucient for sustainable improvement in HR performance. Instead, true performance gains are realized when the implementation of technology is accompanied by parallel adjustments in organizational culture, workow, communication practices, and stakeholder engagement, which is tested with H1. The hypotheses are tested through empirical data gathered from a cross-sectoral survey of companies from the Republic of North Macedonia.
The integration of digital technologies such as social media, digital platforms and AI into HRM is
transforming how organizations attract, develop, evaluate, and retain employees. They are being
used across multiple HR functions and oer the promise of increased eciency, better
decisionmaking, and data-driven insights [
In recruitment, AI is automating tasks such as resume screening, chatbot-led pre-screening interviews, and candidate ranking. These tools can help HR departments reduce costs, speed up processes, and ensure standardized selection criteria. Nonetheless, studies have shown that many of these systems, when trained on historical data, replicate biases disadvantaging women, older applicants, or ethnic minorities [8, 9]. As a result, ethical recruitment requires the use of bias mitigation techniques, diverse data training sets, and human-in-the-loop validation [10]. In performance management, digital tools are employed to provide continuous feedback, track performance indicators, and recommend training or promotion paths. While this oers real-time insights and improves documentation, it can also lead to over-monitoring and dehumanized evaluations if not checked by HR practitioners [11]. Algorithms cannot always capture contextual factors such as emotional intelligence, creativity, or team collaboration, which are crucial to holistic performance assessment [12].
AI along with other digital platforms are increasingly central in corporate learning ecosystems. Learning Management Systems (LMS) powered by AI can create adaptive learning paths tailored to employees’ career goals, competencies, and performance history [13]. This supports lifelong learning and agile workforce planning. However, implementation challenges remain, such as unequal access to technology, dierences in digital skills, and the need for personalization without violating privacy.
The rise of digital technologies have also redened the competencies required of HR professionals [14, 15, 16]. Rather than simply managing HR operations, professionals must now understand digital platforms, AI systems, interpret algorithmic outputs, and assess their fairness and compliance. Budhwar et al. [17] emphasize the importance of cultivating new skills in digital ethics, data literacy, and human-AI collaboration. These competencies are increasingly critical for managing the human implications of automation.
From an organizational culture perspective, the introduction of social media and AI with the other
digital platforms may aect trust dynamics. Employees may perceive AI-led decisions as opaque or
impersonal, leading to resistance, stress, or disengagement [18, 10]. On the positive side, predictive
analytics driven by AI can be used to forecast attrition, map future skill needs, and align talent
strategies with long-term organizational goals. For instance, AI can identify potential leaders or
uncover retention risks before they manifest, giving HR teams a strategic edge [7]. Moreover, the
use of AI must be aligned with ethical and legal norms, especially in jurisdictions with evolving
regulations such as the EU AI Act. High-risk applications in recruitment or employee monitoring
will soon be subject to mandatory transparency and accountability requirements [
The sociotechnical integration of digital technologies such as social media, digital platforms and AI into HRM has become a key topic in both academic and professional debates. A growing body of research addresses the dual nature of digital platforms and AI, their technical capabilities and their social implications. This review synthesizes relevant literature to provide a foundation for understanding how digital technologies reshape HRM systems when examined through a sociotechnical lens.
The digitalization of HRM has introduced major changes in how HR tasks are performed, managed, and experienced. As social media, digital platforms and AI technologies become more common in HR departments, it is essential to understand not just how these systems function technically, but also how they aect people, work relationships, and organizational structures. This is where the sociotechnical systems (STS) perspective becomes important.
The STS approach focuses on the interaction between people (the social system) and technology (the
technical system). It argues that both must be designed together to achieve eective, fair, and
sustainable outcomes [
Additionally, STS thinking draws attention to the evolving nature of systems and the need for
exibility. As noted by Brocke et al. [
Several scholars have emphasized that HRM systems shaped purely by eciency concerns risk neglecting the broader socio-ethical dimensions of work [18, 11]. Digital technologies such as social media and AI-driven tools may enhance consistency and productivity, but they can also create barriers to communication and lead to feelings of surveillance or disempowerment among employees. Therefore, STS theory suggests that digital HRM must be designed in a participatory and inclusive manner.
When HR functions become digitalized, they oen change workows and employee roles. For
example, AI systems used for performance reviews may shi decision-making away from managers
to algorithmic systems. While this may improve consistency, it can also reduce the role of human
judgment and make employees feel monitored or judged by machines [11, 17]. Without careful
planning, digital HR systems may unintentionally weaken employee trust or reduce transparency
[
One important contribution to the sociotechnical view is the idea of “sociotechnical envelopment,” which shows how organizations can manage AI driven digital tools and systems that are dicult to fully understand. Asatiani et al. [5] explain that organizations can set clear boundaries for how AI systems are used, monitor their outputs, and involve human reviewers to ensure ethical outcomes. This is especially important in HRM, where decisions aect people ’s careers and well-being. In many cases, this also involves ensuring that the legal requirements for fairness and non-discrimination are met [19].
In addition, digital HR tools oen reect the assumptions and goals of their designers. If these tools are based mainly on eciency and cost reduction, they may overlook important social values like diversity, inclusion, and worker autonomy. According to Sartori and Theodorou [11], AI systems tend to reproduce existing inequalities if they are not carefully managed. The long-term eects of AI on organizational culture, inclusion, and employee development should be monitored continuously. In summary, the sociotechnical perspective helps us see that digital HRM is not just about using new tools. It is about reshaping how work is done, how decisions are made, and how people are treated. Successful digital transformation in HR requires aligning social media, digital platforms and AI with human needs, ethical values, and organizational culture. When done well, this can lead to systems that are both ecient and fair, supporting long-term trust and performance in the workplace. A sociotechnical approach ensures that technological advancement in HRM is achieved not at the expense of people, but in partnership with them.
Khan et al. [6] and Upadhyay and Khandelwal [
From a sociotechnical perspective, Guest et al. [
Dwivedi et al. [13] explore the application of digital platforms powered by AI in learning and
development, showing how personalized AI-driven tools can support skills development but may
also contribute to inequality if not properly managed. Budhwar et al. [17] echo this concern, calling
for upskilling of HR professionals in AI ethics and digital uency. Brynjolfsson and McAfee [
Overall, the reviewed literature strongly supports a sociotechnical approach to digital technologies in HRM. It underscores the importance of balancing eciency with fairness, innovation with ethics, and automation with human-centered values. These works provide the foundation for the empirical analysis that follows in this paper, highlighting both the promises and perils of social media, digital platforms and AI in shaping the future of HRM.
This study adopts a quantitative research methods aimed at exploring the inuence of social media,
digital platforms and AI on HRM performance, with a specic emphasis on sociotechnical alignment.
In this study, the inuence of social media as a commonly used tool in HRM is distinguished from
the inuence of other digital platforms. The research design follows a deductive approach, grounded
in existing sociotechnical systems theory (Trist and Bamforth [
The data were collected through a structured online questionnaire from 167 companies across various Macedonian industries, including nance, manufacturing, telecommunications, retailing, services and technology. The survey targeted HR managers, IT leads in HR departments, and senior executives responsible for digital transformation initiatives. Participants were selected through stratied sampling to ensure representation across company sizes and digital maturity levels. The multiple linear regression model with which we investigate the relationships between the variables, takes the following form: IndexOPHRi = β0 + β1 SocMediaUsFreqi + β2 DigPlatUsFreqi + β3 EmplReai + β4 AIi + β5 Sexi + β6 LnParAgei + The dependent variable is index of overall performance of HR functions (IndexOPHR), which is the average of the answers of six questions in the questionnaire. The six questions relate to the performances of the company with regard to its the recruitment process, the image of the company as a potential employer, training and development, performance management, employees’ engagement with the company, and employees’ satisfaction and retention. The participants answered each of the questions using the ve-point Likert scale, where a higher score indicates a better performance. An answer with a value of 1 means unsatisfactory or poor, 2 means satisfactory, 3 means good, 4 means very good, and 5 means excellent.
The main independent variable is Employee reaction (EmplRea) which measure the level of sociotechnical alignment in HRM systems. The variable takes a higher value if employees have a more positive reception towards the introduction of new digital tools and platforms in the company. An answer with a value of 1 means unsatisfactory or poor, 2 means satisfactory, 3 means good, 4 means very good, and 5 means excellent. The other two important independent variables are social media usage frequency (SocMediaUsFreq) and digital platforms usage frequency (DigPlatUsFreq), which measure the frequency with which companies use social media and the other digital platforms for various HR functions, respectively. An answer with a value of 1 means not using, 2 means very rarely, 3 means monthly, 4 means weekly, and 5 means daily. Higher values indicate that companies use social media and digital platforms more oen, respectively. AI (AI) is a binary independent variable that takes the value of 1 if the company applies AI for its HR functions and 0 otherwise. There are several control variables in the proposed model. Sex (Sex) is a binary variable that takes the value of 1 if the participant in the survey is female and 0 if the participant is male. We also control for the age of participants (ParAge) and for their highest education level, by including dummy variables (ParBSc – Batchelor of Science, ParMSc - Master of Science and ParPhD - PhD) for the three levels of higher education, whereas the reference group are participants whose highest level of education is high school. We also introduce control variables for the company age (CompAge) and for the number of employees (NoEmpl). Finally, we control for the company sectors, by introducing dummy variables for each of the industries (CompSecFin – Financial; CompSecIT – Technology; CompSecMan – Manufacturing; CompSecRet – Retailing; CompSecTelecom – Telecomunication; CompSecOthServ – Other Services;), whereas the reference group includes the rest of the sectors, not included in the list in the questionnaire. We use the natural logarithm (ln) instead of the original values in order to lower the volatility of participant’ s age, company’ s age and the number of employees.
Table 1 and Table 2 show the descriptive statistics and correlation matrix, respectively. Regarding the correlation coecients between the independent variables, the coecient between social media usage frequency and digital platforms usage frequency is around 0.6 (statistically signicant at 1% level), which may indicate signs of strong, positive correlation between these two variables. However, since the coecient is lower than 0.7, we believe there is no concern for multicollinearity, and therefore include both independent variables in the model. Furthermore, the variable overall performance of HR functions corresponds to a question that asks the participants how they rate the overall performance of the HR functions of the company they work in. The participants answer it with the ve-point Likert scale and this question is supposed to serve as a proxy for the dependent variable, which is in fact the average of the answers of six questions. The fact that the correlation coecient between these two variables is slightly larger than 0.7 (statistically signicant at 1% level) means that the two variables are generally highly correlated. For this study, we decide to use the index of overall performance of HR functions as the dependent variable, and since this is the average of six dierent scores, we estimate the coecients of the model with the OLS method. Table 3 shows the regression results. Employee reaction has a positive coecient and is statistically signicant (at 1% level), which means that companies where the employees respond more positively to the introduction of new digital tools, have a better performance in terms of the HR functions, thereby conrming Hypothesis H1. Hypothesis H2 is also supported, as the independent variable Social media usage frequency also has a positive coecient and is statistically signicant (at 10% level), meaning that companies which use social media for HR functions more frequently, on average have a higher index of overall performance of HR functions. In other words, using social media for HR functions on a more frequent basis has a positive impact on the company in terms of the HR functions. Conversely, Hypotheses H3 and H4 are rejected, indicating that the variables digital platform usage frequency and AI are not statistically signicant, meaning that the other digital platforms and AI do not have an impact on the performance of the company in terms of its HR functions. Concerning the control variables, sex has a negative coecient and is statistically signicant (at 5% level), meaning that, on average, the companies with female participants in the survey have worse performance in terms of the HR functions, compared to the companies with male participants in the survey. The number of employees of the company also has a negative coecient and is statistically signicant (at 5% level), meaning that, on average, companies with more employees rate their HR functions less positively. This indicates that in larger companies it becomes more dicult for the company to organize and manage its HR functions, relative to smaller companies, and therefore larger companies have worse performance with regard to the HR functions.
In order to check the properties of the model, we conduct the Breusch-Pagan-Godfrey test for heteroskedasticity and the Breusch-Godfrey test for serial correlation (up to 2 lags). The results indicate that the model does not have heteroskedasticity and it does not have serial correlation (up to 2 lags) as well.
This study has explored the inuence of digital technologies such as social media, digital platforms
and AI on HRM through a sociotechnical lens. The analysis of contemporary literature from the eld
shows that social media, digital platforms and AI can make HR processes faster, more accurate, and
more scalable. However, these gains are maximized when these technologies are embedded in an
organizational culture that promotes ethical use, inclusivity, and transparency, which is in line with
[
From a theoretical perspective, the study supports the foundational arguments of sociotechnical
systems theory (Trist and Bamforth [
The results of a comprehensive empirical analysis involving 167 surveyed companies indicate that the degree of sociotechnical alignment within HRM systems and the frequent use of social media have a positive inuence on the overall performance of HRM functions. Conversely, the other digital platforms and articial intelligence do not exhibit a signicant impact on HRM performance. Furthermore, the empirical research shows that larger companies have worse performance with regard to the HR functions.
Practically, this study highlights several key recommendations for HR leaders and policymakers. First, digital transformation strategies should go beyond adopting digital technologies and instead focus on redesigning work processes, training employees, and establishing ethical oversight mechanisms. Second, HR professionals must be equipped with new skills in digital literacy, data interpretation, and ethics to ensure that the use of digital technologies such as digital platforms and AI aligns with both business goals and employee well-being. Finally, the future of HRM will not be determined solely by how advanced the technology is, but by how well organizations are able to integrate these technologies into inclusive, transparent, and adaptive sociotechnical systems.
The author(s) have not employed any Generative AI tools.
[5] Asatiani, Aleksandre, Pekka Malo, Per Rådberg Nagbøl, Esko Penttinen, Tapani Rinta-Kahila,
and Antti Salovaara. "Sociotechnical envelopment of articial intelligence: An approach to
organizational deployment of inscrutable articial intelligence systems." Journal of the
association for information systems 22, no. 2 (2021): 325-352.
[6] Khan, Rizwan, Altaf Hussain, and Sharjeel Ahmad. "Revolutionizing human resource
management: The transformative impact of articial intelligence (AI) applications."
International Journal of Social Science & Entrepreneurship 3, no. 4 (2023): 306-326.
[7] Margherita, Alessandro. "Human resources analytics: A systematization of research topics and
directions for future research." Human Resource Management Review 32, no. 2 (2022): 100795.
[8] Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. "Dissecting racial
bias in an algorithm used to manage the health of populations." Science 366, no. 6464 (2019):
447-453.
[9] Raghavan, Manish, Solon Barocas, Jon Kleinberg, and Karen Levy. "Mitigating bias in
algorithmic hiring: Evaluating claims and practices." In Proceedings of the 2020 conference on
fairness, accountability, and transparency, pp. 469-481. 2020.
[10] Binns, Reuben, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao, and Nigel Shadbolt. "'It's
Reducing a Human Being to a Percentage' Perceptions of Justice in Algorithmic Decisions." In
Proceedings of the 2018 Chi conference on human factors in computing systems, pp. 1-14.
2018.
[11] Sartori, Laura, and Andreas Theodorou. "A sociotechnical perspective for the future of AI:
narratives, inequalities, and human control." Ethics and Information Technology 24, no. 1 (2022):
4.
[12] Budhwar, Pawan, Soumyadeb Chowdhury, Georey Wood, Herman Aguinis, Greg J. Bamber,
Jose R. Beltran, Paul Boselie et al. "Human resource management in the age of generative
articial intelligence: Perspectives and research directions on ChatGPT." Human Resource
Management Journal 33, no. 3 (2023): 606-659.
[13] R. Dwivedi, J. Edwards, A. Eirug, and V. Galanos, “Articial Intelligence (AI): Multidisciplinary
Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and
Policy,” International Journal of Information Management 57 (2021): 101994.
[14] HRM Handbook. “Dave Ulrich’s HR Model”, (2021).
[15] Mirjana Borota Popovska, Vesna Zabijakin Catleska, Marija Topuzovska Latkovikj and Blaze
Josifovski. Мапирање на потребите од вештини и знаење на менаџерите и
професионалците за човечки ресурси во ИТ индустријата. Institute for Sociological,
Political and Juridical Research, University “Ss. Cyril and Methodius” Skopje, (2024).
[16] Mirjana Borota Popovska and Marija Topuzovska Latkovikj. Competency Framework for
Human Resource Professionals. Skopje: Institute for Sociological, Political and Juridical
Research, University “Ss. Cyril and Methodius” Skopje, (2015).
[17] Budhwar, Pawan, Ashish Malik, MT Thedushika De Silva, and Praveena Thevisuthan. "Articial
intelligence– challenges and opportunities for international HRM: a review and research
agenda." The InTernaTIonal Journal of human resource managemenT 33, no. 6 (2022):
10651097.
[18] Dignum, Virginia. Responsible articial intelligence: how to develop and use AI in a responsible
way. Vol. 2156. Cham: Springer, 2019.
[19] European Commission. Proposal for a Regulation Laying Down Harmonised Rules on Articial
Intelligence (Articial Intelligence Act), 2021. (Articial Intelligence Act).
https://eurlex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206
[
Nature machine intelligence 1, no. 9 (2019): 389-399.
[
Survey of Companies on the Use of Social Media and Digital Tools in Human Resource Management Part 1: General Questions
a) Female b) Male 2. How old are you?
a) Secondary education b) Bachelor’s degree c) Master’s degree d) Doctoral degree What is your job position in the company? a) Human Resources Manager b) Employee responsible for Human Resources c) Owner responsible for Human Resources d) Other (please specify) 5. In which industry does your company operate? a) Technology – IT b) Finance c) Retail d) Manufacturing e) Other services f) Telecommunications g) Other (please specify) 6. How many employees does your company have?
Which social media platforms are used in your company to perform HR functions (employee recruitment and attraction, company promotion and events, employee referrals)? a) LinkedIn b) Facebook c) Instagram d) Twitter (X) e) TikTok f) YouTube g) Other (please specify) h) We do not use social media Answer questions 10 to 12 only if you use social media 10. How oen does your company use social media for HR functions? a) Daily b) Weekly c) Monthly d) Very rarely 11. For which HR activities do you use social media? a) Recruitment and talent attraction b) Employer/Company branding c) Company promotion d) Employee referrals e) Other (please specify) 12. What type of content do you most frequently share on social media/digital platforms? a) Job postings b) Employee testimonials c) Content related to company culture d) Industry news e) Employee branding campaigns f) Training and development opportunities g) Insights into management performance h) Compensation and salary comparisons i) Other (please specify) Questions related to other digital tools and HR soware 13. Besides social media, which digital tools/platforms are used in your company to perform HR functions? a) Company portal/website b) Recruitment and talent attraction tools c) Employee training and development tools d) Employee communication and collaboration platforms e) Payroll and compensation soware f) Employee performance measurement soware g) Other (please specify) h) We do not use digital tools or HR soware 14. If you do not use digital tools or HR soware (answer h from the previous question), please state the reasons: 15. If you do, how oen does your company use digital platforms for HR functions? a) Daily b) Weekly c) Monthly d) Very rarely 16. How do employees react when new digital tools and platforms for HR functions are introduced? a) Excellent b) Very good c) Good d) Fair e) Poor 17. Does your company use Articial Intelligence for HR functions? a) Yes b) No Part 3: Questions on Company Performance from the HR Perspective 18. How do you evaluate your company’s performance in terms of recruitment and talent attraction? a) Excellent b) Very good c) Good d) Fair e) Poor 19. How do you evaluate your organization’s image as a potential employer? a) Excellent b) Very good c) Good d) Fair e) Poor