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Three decades of academic and professional discourse on HR technologies (HRTs) have produced continued disagreement over construct definitions and research streams that are highly fragmented. These realities suggest that greater consistency in meanings is sorely needed if we are to integrate and upgrade knowledge in this area. This chapter draws on the findings of a systematic research review to properly define the content domains of human resource information systems (HRIS), virtual human resources (virtual HR), electronic human resource management (e-HRM), and business-to-employee (B2E) systems. An integrative synthesis was performed on 242 system-level writings that appeared in the literature from 1983 to 2017. The weight of the evidence strongly supports treating HRIS, virtual HR, e-HRM, and B2E systems as independent, complimentary constructs. While the first three comprise a firm’s HRT system, the fourth construct is more appropriately positioned in the business-collaborative system. The sample was further evaluated with an analytic framework to detect patterns of practice in research designs. This revealed that much more attention has been focused on system actions and outcomes than on attitudes and system characteristics. Different units of analysis were well represented aside from trans-organizational studies. Finally, a case is made for better contextualizing HRT research by recognizing differences in assimilation stage, functional penetration, and collective proficiency. These factors are rarely mentioned, let alone studied, raising additional concerns about measurement error. Detailed suggestions are offered on ways to incorporate them. Together, these materials should promote more sophisticated and generalizable assessments of technology, improving our ability to understand its impacts.

The purpose of this study was to develop and empirically examine a model of cloud-based human resource information systems (HRIS) adoption by small businesses based on the technology–organization–environment model (Tornatzky & Fleischer, 1990).

This study utilized a survey of 41 small- to medium-sized enterprises in the northeastern United States to examine what HR functions were being supported by cloud-based HRIS and the relationship between three technology factors, three organizational factors, and three environmental factors, and their relationship with the adoption of cloud-based HRIS.

Findings indicated that small businesses are most likely to implement cloud-based HRIS to support day-to-day HR operations. In addition, the findings indicated that top management support (positive), vendor support (positive), and anticipated growth (negative) were each related to organizational adoption of cloud-based HRIS.

The study illustrates how the adoption of a cloud-based HRIS is motivated by different factors than those underlying the adoption of other types of information systems. Executives and small business owners will need to adapt their strategy when considering cloud-based HRIS compared to other types of systems.

Given that small- to medium-sized organizations are the backbone of most global economies, findings from this study can help support society by helping these businesses better understand how to best consider the factors that will support the implementation of cloud-based HRIS.

This chapter represents one of the first to empirically validate a model of the factors affecting adoption of cloud-based HRIS by small businesses.

As e-HRM systems move into the ‘smart’ technology realm, expectations and capabilities for both the automational and informational features of e-HRM systems are increasing. This chapter uses the well-established DeLone and McLean (D&M) model from the information systems literature to analyze how a smart workforce management system can create value for an organization.

The chapter is based on an exploratory case study conducted with a North American industrial products firm. We review three systems-level predictors of success from the D&M model (system quality, information quality, and service quality) and evaluate the company’s systems on these attributes.

The company’s e-HRM systems fall short on the information quality dimension, which limits potential for overall system success related to smart workforce management.

The e-HRM literature focuses on individual-level factors of system success, while the D&M model uses more macro factors. Blending these may help researchers and practitioners develop a more complete view of e-HRM systems. Conclusions from this chapter are limited due to the use of a single, exploratory case study.

Companies must pay attention to all three predictors of system quality when developing smart workforce management systems. In particular, implementation of a data governance program could help companies improve information quality of their systems.

This chapter adds to the literature on smart workforce management by using a model from the information systems literature and a practical example to explore how such a system could add value.

Current global business challenges and circumstances are responsible for the need for global talent management (GTM) within multinational corporations (MNCs). Social media and networks are becoming key channels for global communication and collaboration. For GTM in MNCs, an effective usage of social media can potentially result in a competitive edge and create value. The purpose of this study is to answer the question of how.

By means of the Delphi method, three groups of experts were interviewed with questionnaires in two rounds: HR managers, HR researchers, and students following a Master of Science program in Business Administration.

The findings show that all groups assessed the necessity of GTM in MNCs. The HR managers and HR researchers agreed on the areas of focus and instruments that are essential for a successful GTM system. But concerning the application of social networks, the groups have different views. The graduate students are especially open-minded about social networks, and therefore they advocate the use of this tool. The HR managers, however, are skeptical toward this new media and even now have not integrated social networks into GTM to a great extent.

This study presents a GTM model for MNCs based on the combined findings from the literature review and the Delphi study. To our knowledge, this is a new approach. The model helps researchers and practitioners to align GTM in MNCs with the support of social media.

The pace of transformation in the business landscape has made it mandatory for the human resource function within the organisation to evolve, adapt and adjust to the demands of the marketplace. This chapter focuses on HRM 4.0 and the change in employer branding strategies due to rapid increase in digitalisation, for example, through analytics and big data. A conceptual framework is provided that links HRM 4.0 with employer branding strategies.

This paper investigates effective human-robot collaboration (HRC) and presents implications for Human Resource Management (HRM). A brief review of current literature on HRM in the smart industry context showed that there is limited research on HRC in hybrid teams and even less on effective management of these teams. This book chapter addresses this issue by investigating factors affecting intention to collaborate with a robot by conducting a vignette study. We hypothesized that six technology acceptance factors, performance expectancy, trust, effort expectancy, social support, organizational support and computer anxiety would significantly affect a users' intention to collaborate with a robot. Furthermore, we hypothesized a moderating effect of a particular HR system, either productivity-based or collaborative. Using a sample of 96 participants, this study tested the effect of the aforementioned factors on a users' intention to collaborate with the robot. Findings show that performance expectancy, organizational support and computer anxiety significantly affect the intention to collaborate with a robot. A significant moderating effect of a particular HR system was not found. Our findings expand the current technology acceptance models in the context of HRC. HRM can support effective HRC by a combination of comprehensive training and education, empowerment and incentives supported by an appropriate HR system.

Despite the vast talk about digitalization and its ability to transform how organizations manage their workforce and their talent, our understanding of how the implementation and use of Talent Management Information Technology (TM IT) changes various organizational processes is limited. In this chapter, we use a qualitative case study of a multi-business unit professional services firm to highlight the complexity of the human – technology interface. Contributing to our understanding of the role of human resource management in talent management, we show how perceptions and attitudes toward information technology, in combination with existing social systems influence the role HR managers play in high potential talent identification. The chapter provides a more nuanced and context-based account of how eHRM, HRM and talent management materialize in organizations as HR professionals struggle to remain relevant in an increasingly digital world.

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.