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In a world of mass technological advancement in our daily lives and in business, the HR function is facing an uphill battle. How can HR professionals ensure they are digitally transforming at the right pace for their business without losing the all-important human touch?

This paper outlines the thinking behind integrating artificial intelligence (AI) and automation technologies into HR, and it explores in depth each of the key ways in which we are beginning to see these technologies change HR as we know it. From operations to recruitment and interviewing, to on-boarding employees and maintaining performance, the opportunities are numerous – and they are right on the horizon.

AI and automation are already being integrated into HR in many organisations around the world. However, we can in the near future expect to see technology not only automating back-office functions, but increasingly taking on the more “human” elements of HR roles. There is a fine balance between man and machine, and while these technologies will increase efficiency, decrease bias and improve the value of HR in businesses, the human touch will always be the key to success.

This paper assesses not only how technology is impacting HR but also the interplay between man and machine, and it offers insights into how HR professionals can balance the need for digital transformation with the core human element of human resources. As such, it ties the human and the technology together inextricably, concluding that AI and humans work better together.

Project risk management (PRM) and human resource management (HRM) are the two critical success factors (CSFs) for international project management. This paper aims to correlate these two CSFs, identify the human resource (HR) barriers, develop a hybrid model for risk management and develop strategies to overcome the HR barriers to effective risk management in international projects.

In total, 20 key HR barriers have been identified through a literature survey and verified by project professionals. These HR barriers are ranked according to their ability to trigger other barriers by analysing their interactions using the decision-making trial and evaluation laboratory (DEMATEL) method. Based on Ulrich’s revised model for HR functions, a hybrid framework for international PRM has been proposed.

DEMATEL analysis categorized nine barriers as cause barriers and 11 as affected barriers. The “PROJECTS” model proposed for HR strategy development suggests eight strategies to overcome these nine cause barriers. The hybrid PRM framework developed includes the effect of the HR dimension.

This paper presents the generalized prioritization of HR barriers to international PRM. For a specific international project, the HR barriers and their prioritization may change slightly. The hybrid framework for PRM and the strategy development model suggested are yet to be validated.

Correlating two CSFs in international project management, i.e. HRM and PRM and ranking the HR barriers using the DEMATEL method is the uniqueness of this research paper. The hybrid framework developed for PRM based on HR functions in Ulrich’s revised model and the proposed new HR strategy development model “PROJECTS” are unique contributions of this paper.

Introduction: Many organisations nowadays use artificial intelligence (AI) in human resource (HR) activities like talent acquisition, onboarding of new employees, learning and development, succession planning, retention of employees, and automation of administrative tasks. When AI is integrated with HR practices, it helps HR personnel to focus more on the strategic aspects of the HR function and relieve them from routine HR activities.

Purpose: The readiness of employees to accept any change depends on organisational facilitation to change, employee willingness to accept the change, the requirement for change, situational factors, etc. This research studies the factors influencing employees’ change readiness towards acceptance of AI in HR practices. The researchers also strive to develop a conceptual technology adoption model for AI in HR practices by studying the earlier models. Finally, the research explores the acceptance of AI by various service sector employees and identifies whether there is any difference in their acceptance of AI based on demographic variables.

Methodology: A conceptual framework was derived using a combination of previous models, including the Technology Readiness Index (TRI), Change Readiness Scale, Technology Acceptance Model (TAM), Technology, Organization, and Environment (TOE) model, and change readiness scale. A structured questionnaire was designed and distributed to 228 respondents from the service sector based on the conceptual framework. An exploratory factor analysis (EFA) was used to determine the elements that influence employees’ level of change readiness.

Findings: The exploratory results on data collected from 228 respondents show that the model can be used for further research if a confirmatory factor analysis and validity and reliability test are performed. Employees are aware of AI and how it is used in HR practices, based on the study results. Moreover, while most respondents favour using AI in their company’s HR practices, they are wary of some aspects of AI.

The era of work 4.0 demands organizations to expedite their digital transformation to sustain their competitive advantage in the market. This paper aims to help the human resource (HR) department digitize and automate their analytical processes based on a big-data-analytics framework.

The methodology applied in this paper is based on a case study and experimental analysis. The research was conducted in a specific industry and focused on solving talent analysis problems.

This research conducts digital talent analysis using data mining tools with big data. The talent analysis based on the proposed framework for developing and transforming the HR department is readily implementable. The results obtained from this talent analysis using the big-data-analytics framework offer many opportunities in growing and advancing a company's talents that are not yet realized.

Big data allows HR to perform analysis and predictions, making more intelligent and accurate decisions. The application of big data analytics in an HR department has a significant impact on talent management.

This research contributes to the literature by proposing a formal big-data-analytics framework for HR and demonstrating its applicability with real-world case analysis. The findings help organizations develop a talent analytics function to solve future leaders' business challenges.