The Future of Work (FoW) has garnered significant attention among scholars and practitioners, with the advent of Artificial Intelligence (AI) playing an important role in shaping this discourse. ...Despite the common perception that intelligent machines pose a threat to workers in routine roles, AI technologies are increasingly being utilized for advanced tasks carried out by knowledge workers. Drawing on state-of-the-art research and real-life examples we develop an integrated framework to explore the future of academic work. Our focus is on academics, an essential yet under-researched group of knowledge workers, and we discuss their work in relation to AI across space, time, and task dimensions. Our analysis reveals that the usage of AI technologies can have implications for the research, teaching, and service activities of academics and thereby also for the creation, acquisition, dissemination, and application of knowledge. Based on our framework we develop scenarios and propose a future research roadmap.
•The increasing possibilities of Artificial Intelligence (AI) are likely to influence academics’ future of work.•A framework is presented that should be used to study if and how AI usage by academics can shape research, teaching, and academic services.•Potential changes in where, when, and what academic activities are performed and how AI shapes the acquisition, creation, dissemination, and application of knowledge are identified.•The impact of AI usage on academics is dependent on key drivers: AI technology-development, individual-level characteristics, and higher-level factors.•The state-of-the-art is presented in a future research roadmap as a basis for scenarios.
Purpose
Although we know that HRM practices can have a huge impact on employees’ innovative work behaviour (IWB), we do not know exactly which practices make the difference and how they affect IWB. ...Thus, the purpose of this paper is to determine the best HRM practices for boosting IWB, to understand the theoretical reasons for this, and to discover mediators and moderators in the relationship between HRM practices and IWB.
Design/methodology/approach
Based on a systematic review of the literature, the authors carried out a content analysis on 27 peer-reviewed journal articles.
Findings
Working with the definitions and items provided in the articles, the authors were able to cluster HRM practices according to the ability-motivation-opportunity framework. The best HRM practices for enhancing IWB are training and development, reward, job security, autonomy, task composition, job demand, and feedback.
Practical implications
The results of this study provide practical information for HRM professionals aiming to develop an HRM system that generates innovative employee behaviours that might help build an innovative climate.
Originality/value
A framework is presented that aggregates the findings and clarifies which HRM practices influence IWB and how these relationships can be explained.
This volume explores and presents challenges that "traditional" organisations experience once they take off towards self-managing organisations - what Laloux (2014) called Teal Organisations. It ...offers a new roadmap for leaders who are responsible for the implementation of self-managing teams in organisations.
Although research has shown that HRM can contribute to innovation, only limited scholarly attention has been given to the bottom-up emergence of innovation based on an employees' ideas. Furthermore, ...while scholars have accumulated knowledge about innovative behavior in business contexts where innovation is nurtured, to fully understand 'innovation by employees' we need to also address business contexts where innovation by employees is not overtly expected. For these reasons, the aim of this paper is to explore the bottom-up emergence of so-called employee-driven innovation (EDI) in a formalized business context and study how HRM support mechanisms contribute to this. We conducted an explorative single case study in a highly formalized company - a medical laboratory in the Netherlands. The data analysis was based on 40 interviews, documents, and observations. We found that a highly formalized business context does support innovation by employees, but that this has specific employee-driven innovation routes through which employees innovate. Furthermore, the findings suggest that the content and process of EDI emergence should be studied using a multilevel perspective. We offer recommendations on how EDI routes should be modelled and reflect on what this contributes to the HRM - innovation literature.
Supplemental data for this article is available online at https://doi.org/10.1080/09585192.2021.1913625 .
Strategic Human Resource Management (SHRM) researchers have recently turned their attention to using various levels of analysis in examining the relationship between HRM and performance. Despite ...several calls for research that integrates multiple levels of analysis, HRM research has yet to apply a multilevel approach to its full advantage. In our view, the paucity of multilevel research is rooted in the lack of what we label multilevel thinking: the application of multilevel principles. In this conceptual paper, we develop 9 guidelines based on tailored multilevel HRM principles that offer a course of action for scholars who are interested in conducting multilevel HRM research. Following Kozlowski and Klein (2000), we build these principles around the what, how, where, when, and why questions in multilevel HRM research. Based on an analysis of 88 empirical multilevel HRM studies, we identify the approaches commonly applied when using multilevel principles, explain the weaknesses in current multilevel HRM studies, and offer what we consider good examples of a rigorous approach.
•A three-dimensional conceptualization of multilevel HRM is proposed.•Multilevel thinking has been limitedly applied in the HRM literature.•A systematic approach of multilevel principles provides new insights for HRM.•Nine guidelines offer a course of action for conducting multilevel HRM research.
Aims
The purpose of this paper was to investigate the relationship between high‐involvement human resource management, autonomy, affective organisational commitment and innovative behaviours of ...nursing staff who care for elderly clients.
Background
Nursing teams are increasingly required to demonstrate innovative behaviours that enhance care quality. Nursing leaders need to create environments where nursing staff have sufficient autonomy and feel a sense of commitment to support these behaviours. The appropriate implementation of these processes and practices may lead to greater involvement.
Methods
A cross‐sectional survey‐based research design was employed to explore the experiences of involvement practices, autonomy, affective organisational commitment and innovative behaviours of 567 nursing staff workers from four elderly care organisations in the Netherlands.
Results
The results demonstrate that a bundle of high‐involvement practices positively influences innovative behaviour and that affective commitment and autonomy fully mediate this relationship.
Conclusions
The study highlights the role of autonomy and commitment as routes towards translating involvement practices into nurses’ innovativeness.
Implications for Nursing Management
To create an innovative environment, leaders need to create a positive climate by providing nurses with opportunities to enhance their competence, relatedness and autonomy through active involvement. Leaders should, therefore, encourage involvement as a mechanism to promote innovation.
Abstract
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.
Although work‐floor employees are important sources of creative ideas, having creative ideas is not enough to implement bottom‐up innovations. This is particularly evident in formalized contexts. ...Research shows that innovations require the provision of resources because they are crucial for each phase of the innovation process, from the generation of ideas to their implementation. Nevertheless, a better understanding is needed of how these resources are acquired and used by work‐floor employees to pursue their innovative ideas. Therefore, in this study, we adopt a resource‐mobilization perspective and examine the types of resources and resource access mechanisms that are needed in a bottom‐up innovation process. Adopting an exploratory case study based on 43 interviews and document analysis, we show that specific actions are deployed as resource access mechanisms to acquire five types of resources in the bottom‐up innovation process. Different resources are important in the three phases, and the mechanisms by which these resources are acquired play a key role in explaining the effect of these resources. In sum, our research adds to the understanding of the actions and factors that shape bottom‐up innovation endeavours.
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