article review of human resource management

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HR’s New Role

• Peter Cappelli
• Ranya Nehmeh

Though the human resources function was once a strong advocate for employees, in the 1980s things changed. As labor markets became slack, HR shifted its focus to relentless cost cutting. Because it was hard for employees to quit, pay and every kind of benefit got squeezed. But now the pendulum has swung the other way. The U.S. unemployment rate has been below 4% for five years (except during the Covid shutdown), and the job market is likely to remain tight. So today the priorities are keeping positions filled and preventing employees from burning out. Toward that end HR needs to focus again on taking care of workers and persuade management to change outdated policies on compensation, training and development, layoffs, vacancies, outsourcing, and restructuring.

One way to do that is to show leaders what the true costs of current practices are, creating dashboards with metrics on turnover, absenteeism, reasons for quitting, illness rates, and engagement. It’s also critical to prevent employee stress, especially by addressing fears about AI and restructuring. And when firms do restructure, they should take a less-painful, decentralized approach. To increase organizational flexibility and employees’ opportunities, HR can establish internal labor markets, and to promote a sense of belonging and win employees’ loyalty, it should ramp up DEI efforts.

In this tight labor market, cost cutting is out. Championing employee concerns is in.

Idea in Brief

The pendulum swing.

For decades, when U.S. labor markets were slack, HR focused on cost cutting, which meant squeezing employees’ pay, benefits, and training. But now that labor markets are tight, the challenge is to retain workers.

The New Priorities

HR must focus on keeping positions filled and preventing employees from burning out or becoming dissatisfied.

The HR function must educate leaders about the true costs of turnover, address employee anxiety about AI and restructuring, lobby for investments in training, rethink how contract workers and vendors are used, and strengthen diversity, equity, and inclusion efforts.

From World War II through 1980 the focus of the human resources function was advocating for workers—first as a way to keep unions out of companies and later to manage employees’ development in the era when all talent was grown from within. Then things changed. Driven by the stagflation of the 1970s, the recession of the early 1980s, and more recently the Great Recession, HR’s focus increasingly shifted to relentless cost cutting. Decades of slack labor markets made slashing HR expenses easy because it was hard for people to quit. Pay and every kind of benefit, including training and development, got squeezed. Work demands went up, and job security fell.

• Peter Cappelli is the George W. Taylor Professor of Management at the Wharton School and the director of its Center for Human Resources. He is the author of several books, including Our Least Important Asset: Why the Relentless Focus on Finance and Accounting Is Bad for Business and Employees (Oxford University Press, 2023).
• Ranya Nehmeh is an HR specialist working on topics related to people strategy, human capital, leadership development, and talent management and is the author of The Chameleon Leader: Connecting with Millennials (2019).

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Human resource management (HRM) in the performance measurement and management (PMM) domain: a bibliometric review

International Journal of Productivity and Performance Management

ISSN : 1741-0401

Article publication date: 11 May 2021

Issue publication date: 16 August 2022

The literature highlights the key role of human resource management in developing effective organizational performance measurement and management. To understand the state of the art of this role, the paper reviews the literature on human resource management in the performance measurement and management domain.

Design/methodology/approach

The paper conducts a bibliometric literature review on 1,252 articles to identify the prevailing research trends and the conceptual structure of human resource management in the performance measurement and management domain.

The study highlights a growing number of publications and four themes related to human resource management in performance measurement and management. It also underlines the shift from static to the dynamic performance measurement and management systems within organization which is expected to be more suited to current and future contexts.

Practical implications

The paper highlights the need to manage the identified themes as strategic organizational assets and further develop the strategic dimension of human resource management practices leveraging on project management and information systems.

Originality/value

The paper goes beyond the traditional focus on performance appraisal of human resource management studies and assumes the challenge of connecting two research fields: human resource management and performance measurement and management.

• Performance measurement
• Performance management
• Human resource management
• Bibliometric literature review
• Science mapping
• Organizational performance

Garengo, P. , Sardi, A. and Nudurupati, S.S. (2022), "Human resource management (HRM) in the performance measurement and management (PMM) domain: a bibliometric review", International Journal of Productivity and Performance Management , Vol. 71 No. 7, pp. 3056-3077. https://doi.org/10.1108/IJPPM-04-2020-0177

Emerald Publishing Limited

Copyright © 2021, Patrizia Garengo, Alberto Sardi and Sai Sudhakar Nudurupati

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Organizational performance measurement and management system (PMMS) is described as an integrated system for supporting the decision-making process through a set of performance measures on tangible and intangible assets ( Smith and Bititci, 2017 ). This system gives feedback to employees on the outcome of actions reflecting the procedures used to implement business strategy. Since the introduction of the first performance measurement and management (PMM) models, human resource management (HRM) has been considered a relevant intangible asset for creating a competitive advantage ( Kaplan and Norton, 1996 ; Neely et al. , 2001 ). Human resources represent the employees under direct control of the company; their management is the process or processes focused on maximizing employee performance to achieve the employer's strategic objectives ( Wood, 1999 ). Despite the recognized relevance of HRM, the available studies on its role in developing organizational PMM models remain embryonic and at an exploratory stage ( Bourne et al. , 2018 ; Sardi et al. , 2019 ). On one hand, the PMM literature outlines the importance of having qualified leaders at all levels; for instance, to move organizations towards continuous process improvement and knowledge sharing ( Bititci, 2015 ; Bourne et al. , 2013 ; Garengo et al. , 2005 ). This literature creates the condition for integrating HRM in organizational PMMS; however, it rarely happens ( Sardi et al. , 2020b ). On the other hand, the HRM literature primarily focuses on HRM issues; for instance, human resource performance management practices and employee performance appraisal, i.e. “ the process by which we evaluate the individual performance of an employee over some time ” ( DeNisi and Smith, 2014 ). However, this literature rarely proposes approaches for developing effective organizational performance measurement and management systems ( Sardi et al. , 2020b ). Although several researchers have underlined the need to further investigate the role of HRM in the PMM domain ( Bourne et al. , 2013 ; Smith and Bititci, 2017 ), to date no scholar or practitioner have developed an effectively integrated view that applies a dual approach equally based on HRM in PMM research conventions.

To address this research gap, this paper aims to map HRM studies in the PMM domain and identify the most relevant themes and their role in PMM. A bibliometric literature is conducted to answer three specific research questions: (1) What is the trend of HRM publications in the PMM domain? (2) What is the conceptual structure of the HRM research in the PMM domain? (3) What are the thematic evolutions in the HRM research in the PMM domain? To answer these questions, a systematic literature review is performed using two different approaches: performance bibliometric analysis and science mapping technique. Both approaches are suitable to answer the research questions at hand ( Cobo et al. , 2011 ; Taticchi et al. , 2015 ).

This paper is organized into five sections as follows. The following section details the methodology chosen to conduct the literature review using a bibliometric approach. This section also synthesizes the useful findings for answering the three specific research questions. The findings section subsequently discusses the main themes that emerge and synthesizes key evidence. Finally, the conclusion summarizes the main contributions of this paper to the PMM literature and practice.

2. Methodology

The paper explores the literature over three time periods (i.e. 1976–1996; 1997–2007 AND 2008–2019) consistent with the evolution of PMM research ( Bititci et al. , 2012 ). The three periods were identified are all about ten years long, and they refer to the evolution of PMM field. The first period is characterized by the introduction of BSC ( Kaplan and Norton, 1996 ; Neely et al. , 1995 ), then several papers describe its evolution ( Franco-Santos et al. , 2007 ; Kaplan and Norton, 2005 ; Neely, 2005 ) and finally, new trends of PMM are described according to a holistic and systematic view ( Bititci et al. , 2012 ; Bourne et al. , 2018 ). Table 1 describes the research protocol for the data collection, and Table 2 presents the methods applied to analyse the data. The performance bibliometric analysis and the science mapping technique were chosen as they are effective in objectively examining the evolutionary trend of research studies and have been effectively applied in prior business literature reviews ( Dabic et al. , 2014 ; Garengo and Sardi, 2020 ; Hassini et al. , 2012 ; Neely, 2005 ; Sardi et al. , 2020a ; Taticchi et al. , 2015 ). These analyses provide a means for the objective, systematic and quantitative consideration of the published articles ( Furrer and Sollberger, 2007 ). The advantage of employing performance bibliometric analysis is the ability to demonstrate the relevance of a research topic based on the number of published papers and their citation numbers ( Culnan and Swanson, 1986 ). The science mapping technique is a useful tool for assessing and analysing academic research output by contributing to the progress of knowledge based on an objective analysis ( Martínez et al. , 2015 ). It highlights the evolution of a given theme over a fixed period and supports a better understanding of each research theme within the literature. Furthermore, it highlights the relationships between the themes and the evolution of these relationships over time ( Furrer et al. , 2008 ; Furrer and Sollberger, 2007 ).

Data collection: as this paper seeks to review the literature on HRM in the PMM domain, it selected research that addresses issues related to HRM and PMM together. Using the terminology of the two research fields, the authors identified useful keyword strings to investigate this research domain adopting the process suggested by Tranfield et al. (2003) . The authors of this study investigated the field through a preliminary qualitative literature review and interviews with academics, practitioners and consultants to identify the useful main issues to investigate this field. The collected information supported the identification of the main keyword strings necessary to investigate the literature on HRM in the PMM domain ( Tranfield et al. , 2003 ). Figure 1 presents and defines each keyword according to the most recognized definitions. The data collection process gathered 1,252 documents.

2.1 Data analysis

The authors applied a bibliometric analysis to the selected studies ( Cobo et al ., 2011 , 2015 ; Neely, 2005 ; Taticchi et al. , 2015 ) in the three periods. The data analysis was performed using two dimensions of analysis: performance bibliometric analysis to answer the first research question ( Table 2 – Group 1) and a science mapping technique to answer the second and third research questions ( Table 2 – Group 2).

Performance bibliometric analysis (group 1) allows for investigating a certain body of knowledge under different perspectives, such as the publication number and most prolific journals. The authors adopted this research methodology to provide a complete representation of the research areas in terms of quantity and quality of scientific enquiry and gaps in the literature ( Taticchi et al. , 2015 ). In conducting the performance bibliometric analysis, the authors analysed the distribution of the number of publications by periods to draw a “frame” of the state of the art of HRM studies in the PMM domain.

Science mapping analysis (group 2) comprises co-word analysis to identify the main themes in HRM in the PMM domain along with their evolution ( Cobo et al. , 2011 ). Themes (or clusters) are main groups of similar and closely linked keywords. Each theme includes a sub-group of keywords (or sub-themes) that are strongly linked to each other ( Callon et al. , 1991 ). The name of each theme was extracted from the densest sub-themes belonging to the same theme. The methodological foundation of co-word analysis is the idea that the co-occurrence of keywords should describe the contents of the documents. As such, the more keywords that two papers share in common, the more similar the two publications are and, thereby, the more likely they are to derive from the same research field ( Van Eck and Waltman, 2009 ). The themes are visualized by strategic diagrams, which are graphical representations of the most important research themes investigated in HRM within the PMM domain. In these graphical representations, themes are represented by spheres whose volume is proportional to the number of papers associated with each theme ( Thomé et al. , 2016 ).

The role of each theme is visualized in the strategic diagram through two dimensions: centrality and density of the themes in the research domain ( Callon et al. , 1991 ; Cobo et al. , 2011 ). Centrality refers to the links of a theme with other themes. The stronger and numerous these links are, the more this theme represents a set of research problems that are considered crucial by the community. Meanwhile, density determines the strength of the links that tie the keywords in a cluster. The stronger these links are, the more the research problems corresponding to the cluster constitute a coherent and integrated role. Density provides an effective representation of the cluster's capacity to maintain itself and to develop over time in the field under consideration ( Callon et al. , 1991 ). Figure 2 details the content of the four quadrants of the strategic diagram ( Callon et al. , 1991 ; Cobo et al. , 2015 ).

To understand the conceptual structure of the HRM research in the PMM domain, the most predominant centrality clusters and density clusters were selected for investigation in each period. First, the themes with the highest centrality were analysed using a cluster network analysis to understand their constituent keywords (or sub-themes) and their relationship. The most central cluster identified the main keyword of a theme as the centre of the network. Two keywords were considered connected if they appear in the same documents. The thickness of the line connecting the keywords represents the depth of their mutual relationships. Second, to better understand the conceptual structure of the HRM research in the PMM domain, the authors read the papers belonging to the four themes and sub-themes to synthesize the main evidence.

U  = each detected theme in the sub-period t

V  = each detected theme in the next sub-period t  + 1

t  = sub-period

T t  = the set of detected themes of the sub-period t

The inclusion index reveals a thematic evolution from theme U (period t ) to theme V (period T t +1 ) if there are keywords that appear in both associated thematic networks. Thus, V is a theme that evolved from U . Moreover, keywords k  ∈  U ∩ V are considered a “conceptual nexus”( Cobo et al. , 2011 ), graphically represented by a line (see Figure 5 ). In this way, if there is a “conceptual nexus” between U and V (i.e. if they share some elements in common), a line links themes in sub-period t to themes in sub-period t  + 1. The thickness of the lines is proportional to the strength of the links among themes from one period to another. Concerning the type of lines, a solid line indicates that the theme maintains the same name in the next period, or that the theme is incorporated within a theme of the following period. Meanwhile, a dotted line indicates that a theme does not maintain the same name and is not incorporated within a theme of the following period (non-conceptual nexus). However, in this case, the theme shares important elements with clusters of the following period.

3. Findings

3.1 the trend in hrm studies in the pmm domain.

The performance bibliometric analysis of the 1,252 papers revealed a growing relevance of HRM studies in PMM domain. The number of papers substantially increased, particularly in the last ten years ( Figure 3 ).

The analysis of the number of papers and citations by author's country indicates that the US is the country with the highest number of publications (258 papers). The investigated research area developed its roots in the US, and a large gap remains between the number of publications from the US and other countries. However, authors from additional countries recently started to investigate this research area, including scholars from the United Kingdom, Australia, India, Malaysia and Canada ( Figure 3 ).

The analysis of the journals' published papers affirmed that the investigated topic is configured as a cross-disciplinary research area, even if the most prolific journals are largely related to the HRM area ( Table 3 ). The International Journal of Human Resource Management publishes the highest number of papers and devotes significant attention to strategic HRM in a global environment, international business and organizational behaviour. In the last period, the second most prolific journal is International Journal of Productivity and Performance Management . It publishes innovative developments in performance measurement and management oriented to improve individual, group and organizational performance ( Abbaspour and Dabirian, 2019 ; Ensslin et al. , 2013 ; Rompho, 2017 ; Zigan et al. , 2008 ).

The analysis of the most prolific authors in the literature suggests that there are five main scholars ( Table 4 ). Wickramasinghe, Stanton and Long were the most productive authors in the last decade. Wickramasinghe, based out of the University of Moratuwa, Sri Lanka, is the most prolific author in this area. He has published seven documents on different topics such as performance management in SMEs, total quality management and HRM practice ( Wickramasinghe, 2012 ; Wickramasinghe and Liyanage, 2013 ). Stanton (Melbourne University), who examines job performance evaluation using empirical approaches, has served as co-author for six relevant papers and published studies in six different journals – see, for instance – ( Nankervis and Stanton, 2010 ; Nankervis et al. , 2012 ; Stanton and Nankervis, 2011 ; Vo and Stanton, 2011 ). Besides, he has written empirical papers on HRM practices focussing on different countries (Australia, Vietnam, the US, Japan and Singapore). Meanwhile, Long (Universiti Teknologi, Malaysia) published five papers in seven years. Three of the papers explored HRM practices that are associated with performance measurement and management practices, whereas the others examined the skills and competencies of HRM specialists ( Long and Ismail, 2008 ; Long and Perumal, 2014 ; Shahnaei and Long, 2014 ).

As depicted in Table 5 , analysis of the citations of the 1,252 selected papers revealed the most important papers in the existing literature that are considered reference points in HRM study in PMM research ( Castilla, 2008 ; Igbaria and Baroudi, 1995 ; Meyer and Smith, 2000 ; Mithas et al. , 2011 ; Pulakos and Wexley, 1983 ; Sturman, 2003 ).

The findings related to the first research question show the relevance of HRM research in the PMM domain; there has been an increase in the number of journals, papers and citations interested in HRM within PMM in recent years, as well as a broadening of authors' countries of origin. Although most of the papers are published by authors from the United States, authors from other countries have recently started to investigate issues related to HRM in PMM domain. The number of papers published by authors in the United Kingdom and Australia as well as authors from developing countries (e.g. India and Malaysia) is also beginning to increase. Thus, HRM research in PMM domain is becoming a global phenomenon.

3.2 Conceptual structure of HRM research in the PMM domain

Analysis of the conceptual structure of HRM research in the PMM domain confirms its growing relevance. The conceptual structure also reflects the growing number of themes related to HRM and PMM, as visualized in Figure 4 . Within HRM research in the PMM domain, four main themes currently prevail HRM practices, employee performance appraisal, project management and information systems. These themes are central in the existing literature. As depicted in Figure 4 , they exhibit the highest density and highest strength of links with sub-themes. As such, the four main themes are classified as motor clusters, which mean that they are considered crucial themes by the scientific community and are researched in relationship with numerous sub-themes.

3.3 Thematic evolution of the HRM research in the PMM domain

The analysis of the thematic evolution of the HRM research in the PMM domain highlights several changes in the key themes across the investigated periods. Some themes grew in relevance over the investigated periods, while others appeared as distinctly new themes, as reflected in the thematic evolution map ( Figure 5 ).

The thematic evolution of the HRM research in the PMM domain suggests an accelerating change in the most relevant themes, particularly in recent years. Only the employee performance appraisal theme was present and maintained high importance over all three periods of study. As Figure 5 demonstrates, some new themes emerged in this time (such as human resource management practices and project management), while others were absorbed into more relevant themes (such as management practices and training).

The authors synthesized an overall strategic diagram with the main themes (i.e. the themes that appear in more than five papers) appearing in the three investigated periods ( Figure 6 ). To better understand the conceptual structure of the HRM research in the PMM domain and its future trend, the authors reviewed the papers associated with the four motor clusters (i.e. HRM practices, employee performance appraisal, project management and information systems) to provide useful insights for understanding the role of HRM in the PMM domain.

3.3.1 HRM practices

Since the early 1970s, several HRM studies have highlighted the key connection between HRM practices and business strategy ( Sparrow et al. , 1994 ; Wright and Mcmahan, 1992 ), and several papers have investigated the human resource practices associated with various business strategies ( Golden and Ramanujam, 1985 ; Lengnick-Hall et al. , 2009 ). On one hand, the HRM literature has investigated strategic performance measurement as a key HRM practice, and particular attention has been devoted to the key role of the strategic approach in fostering coordination and congruence among HRM practices ( Guest, 1997 ; Wright et al. , 2005 ). On the other hand, the PMM literature has highlighted the key role of PMMSs in aligning HRM practices to achieve organizational strategic objectives and effectively manage organizational performance ( Bititci, 2015 ; Kaplan and Norton, 1996 ; Neely and Adams, 2001 ).

Recent research has emphasized the growing relevance of PMMS in creating organizational alignment ( Hanson et al. , 2011 ; Micheli and Manzoni, 2010 ), investigating the balanced scorecard as a strategical communication and management-control device ( Malina and Selto, 2015 ). Burney and Widener (2013) , for example, underlined the increasing use of performance measurement and management systems that “translate a firm's strategy to its employees” to facilitate internalized motivated behaviours ( Burney and Widener, 2013 ). Malina and Selto (2015) demonstrated the need for using PMMS to drive employees' attention towards the company's strategic objectives ( Malina and Selto, 2015 ). Melnyk et al. (2014) further defined PMMS as “ultimately responsible for maintaining alignment and coordination” between all organizational resources ( Melnyk et al. , 2014 ). Moreover, Shahsavari-Pour et al. (2017) underlined the need to use the strategy maps introduced by Kaplan and Norton (2000) to communicate simply and effectively with employees “about how to achieve the companies' strategic goals and will not miss any value drivers in the management process” ( Shahsavari-Pour et al. , 2017 ). According to this literature, the alignment of HRM practices and organizational strategic objectives is increasingly essential for the effective design of organizational PMMS.

3.3.2 Employee performance appraisal

Employee performance appraisal is defined as the system through which an organization provides employees with feedback about their performance, and it is essential in improving individual performance ( Chattopadhayay and Ghosh, 2012 ; DeNisi and Murphy, 2017 ; DeNisi and Smith, 2014 ). Employee performance appraisal studies focused on employee measurement issues, with attention paid to issues such as the impact on working engagement ( Bartram et al. , 2015 ; Lappalainen et al. , 2019 ; Poovathingal and Kumar, 2018 ) and employees' turnover intentions ( Appelbaum et al. , 2011 ; Iqbal et al. , 2015 ; Poon, 2004 ). However, the literature shows that the implementation of the performance appraisal needs the understanding of the social context within which it operates ( Levy and Williams, 2004 ) and that global uncertainty to be wielding a significant influence on performance management ( Maley and Kramar, 2014 ). Furthermore, the literature underlines that the relationship between performance appraisal satisfaction and work performance is both mediated and moderated by employees' intrinsic work motivation; it is a negative relationship for employees with low intrinsic motivation, positive relationship for those with high intrinsic motivation ( Kuvaas, 2006 ).

Employees have to experience positive appraisal reactions for performance appraisal to positively influence employee behaviour ( Kuvaas, 2007 ). The relationship between perceptions of developmental performance appraisal and self-reported work performance is mediated by employees' intrinsic motivation and strongly moderated by their autonomy orientation. The relationship was positive for employees with a weak autonomy orientation, but the relationship was negative for those with a strong autonomy orientation ( Kuvaas, 2007 ).

To manage employees as effective strategic assets, an organization should use PMMS to align HRM to company values and strategic goals ( Crain, 2009 ). According to Caruth and Humphreys (2008) , if performance appraisal is not included in PMMS and thereby in the systematic strategy implementation process, its definition “becomes an exercise in futility instead of a vital control measurement” ( Caruth and Humphreys, 2008 ). However, despite the relevance recognized in the literature concerning employee performance appraisal and management in the last 20-years ( Maley et al. , 2020 ), its poor employee acceptability ( Maley et al. , 2020 ) and effective integration in organizational performance measurement and management system remain poorly understood in many organizations ( Smith, 2018 ; Sardi et al. , 2020b ).

The existing literature underlines the need for adopting a multidisciplinary approach that integrates employee motivation, leadership, fairness, behaviour, emotional aspects ( Dewettinck and van Dijk, 2013 ; Ding et al. , 2015 ; Kampkötter, 2017 ; Lakshman, 2014 ) and the need for creating a strong link between employee performance appraisal and company strategy ( Smith and Bititci, 2017 ). In the last few years, several scholars have emphasized the growing relevance of strategic management of employee performance appraisal and its impact on performance measurement and management system effectiveness. If employee performance appraisal is linked to the overall PMMS, managers are pushed to be more proactive in communicating any relevant issues related to strategy to the top management ( Butterfield et al. , 2004 ; Dewettinck and van Dijk, 2013 ; Hooi, 2019 ; Jääskeläinen and Laihonen, 2013 ; Mondal and Ghosh, 2012 ). Finally, the analysis of theme shows that employee performance appraisal is increasingly necessary for favouring the alignment of individual employees' endeavours with the organizational strategic objectives and in the developing of effective PMMS.

3.3.3 Information system

To date, the literature has devoted particular attention to the role of information systems in supporting strategy implementation and PMMS adoption through fostering a connective relationship between employees, customers and suppliers ( Dewettinck and van Dijk, 2013 ; Nudurupati et al. , 2016 ). Several scholars have also underlined the strong impact of information systems (ISs) on key HRM practices such as job design, recruitment, retention, performance management and training, along with the growing relevance of human resource information systems ( Blount, 2011 ; Garengo and Bititci, 2007 ; Igbaria and Greenhaus, 1992 ).

The rapid development of information technology over the last decade has further affirmed the key role of information system in leveraging an organization's human resources to achieve its strategic objectives and support the development of a PMMS. A human resource information system is an essential decision support tool in achieving strategic and operational objectives ( Kavanagh et al. , 2007 ). Several studies have recently denoted the use of technology as a medium of connection and integration to supplement task fulfilment in organizations and support the effective adoption of PMMS. These studies examined issues such as Internet-based resource management ( Marler and Parry, 2016 ), business-to-employee (“B2E”) ( Huang et al. , 2004 ) and electronic human resource management (e-HRM) ( Stanton and Coovert, 2004 ; Strohmeier, 2007 ). As described to this literature, the information system is largely supporting the alignment of HRM with organizational strategic objectives and the development of effective PMMS.

3.3.4 Project management

The literature review reflects the decreasing relevance of PMMS models (such as balanced scorecard and Performance Prism), which are often as inflexible, and the increasing attention paid to project management, which is identified as an important emerging theme in the PMM literature ( Taticchi et al. , 2015 ). Some researchers have highlighted the growing need for project management activities such as planning, executing and closing the work of a team to achieve specific strategic objectives and create the bases for a PMMS ( Kim et al. , 2018 ; Yun et al. , 2016 ). Scholars also studied the effectiveness of project management in improving the integration and development of employees' competencies and its role in fostering integration with performance evaluation ( Chen and Lee, 2007 ).

In the last few years, the research on PMMS models has shifted from the design of organizational PMMS to the development of quantitative methodologies to solve specific issues related to PMM and HRM. Gemünden et al. (2018) investigated the creation of strategic measurement systems as a priority to ensure that any system is aligned with the goals and objectives of the organization ( Gemünden et al. , 2018 ). Also, Chen and Lee (2007) investigate the performance indicators of people who manage projects ( Chen and Lee, 2007 ). They proposed a performance evaluation method for project managers based on managerial practices that incorporate leadership and positive behaviours. Chen (2014) further highlighted that project human factors are essential stimulants in innovation performance, which in turn affects the performance of capital projects ( Chen, 2014 ). Moreover, Wickramasinghe and Liyanage (2013) underlined the need to include projects measures related to teamwork, communication, performance evaluation, empowerment, rewards and recognition, and skill-development practices in PMMS ( Wickramasinghe and Liyanage, 2013 ). It is clear that in the current business environment, the most relevant issue is not the choice of an effective PMMS model, but the definition of project management activities related to measuring HRM practices ( Zhang and Li, 2009 ) and their effective integration with strategy management, human resources practices and employee performance ( Crain, 2009 ).

The shifting attention from rigid performance measurement models to flexible project management tools may be the added value of this study. This shifting attention may favour the alignment of HRM practices with specific strategic project objectives.

4. Discussion

The findings highlight a high relevance of HRM research in the PMM domain. As indicated by the bibliometric analysis, there has been an increasing trend of all information analysed. The conceptual structure of this research area point out themes such as HRM practices, employee performance appraisal, project management and information systems. Below, the authors discuss the main evidence to represent research findings.

Since the 1950s, the literature has highlighted the need to face the rapid change in the environmental condition with the adoption of an organic form of organization. Burns and Stalker (1969) addressed companies towards an organic organizational structure to quickly adapt to frequent and fast environmental changes ( Burns and Stalker, 1969 ). In this scenario, organizational performance measurement and management and HRPM have evolved over the decades. On the one hand, organizational PMM highlights the need to manage multicultural collaboration, open innovation, sustainability, etc. ( Bititci et al. , 2012 ; Bourne et al. , 2018 ). On the other hand, HR performance management highlights the need to keep employees happy and groom them for progress, to have organizational agility, regular checks with employees and promotes teamwork ( Cappelli and Tavis, 2016 ). Some companies worry that align individual and organizational goals, award merit raises and identify poor performers are becoming a hard challenge. Managing and developing organisation and people became a greater concern. Companies had to find new ways of meeting that need ( Bititci, 2015 ; Cappelli and Tavis, 2016 ). However, until now, not enough attention has been given to the development of organic systems supporting the high adaptability and flexibility required by companies ( Bititci et al. , 2012 ; Bourne et al. , 2018 ).

The strategic management of the four identified themes and their inclusion in an integrated PMMS should, thus, favour a new way of managing organizational control. Integrated PMMS should be based on the integrated conception of the organization where overall firm optimization requires managing interdependent organizational assets and its interaction. This interaction could also become the basis of the learning process, i.e. the process of gaining, sharing and utilizing the knowledge accumulated by individuals and transferring it through the organization to meet its strategic goals and trigger a process of systematic revision ( Franco-Santos et al. , 2007 ).

Integrated PMMS becomes essential to communicate strategic objectives and activate the double-loop learning process using performance information by feedback ( Kaplan and Norton, 2005 ; Nudurupati et al. , 2021 ). This means that integrated PMMS should not simply be a control mechanism but also an organic and innovative learning system ( Molleman and Timmerman, 2003 ) able to drive managers' actions in effectively structuring, bundling and leveraging firm resources with particular attention to HRM practices, employee performance appraisal, project management and information systems. The analysed literature describes that producing performance increments may be best achieved by orienting the performance measurement and management system to promote employee engagement ( Bititci, 2015 ; Gruman and Saks, 2011 ; Smith and Bititci, 2017 ). In particular, organizations should innovate HR performance management practices to move companies away from heavy to simpler process ( Pulakos et al. , 2019 ); it should be moved from formal system to focussing on the performance management behaviours that matter every day ( Pulakos et al. , 2015 ).

The findings of this literature review can be represented by the definition of a conceptual framework which describes a need to translate strategic objectives into effective managerial practices and favour the alignment and interaction of the four identified themes ( Figure 7 ). The strategy is the glue that binds these themes together, favouring the development of integrated PMMS and, as a consequence, the growth of firm sustainable performance.

According to this conceptual framework, PMMS should be configured as an organic system able to evolve and adapt itself to the changing business environment through adaptation and alignment processes ( Garengo et al. , 2005 ; Smith and Bititci, 2017 ). These processes should be favoured by the intrinsic capability of PMMS in supporting the translation of business strategy in action and the integration of the key organisational themes. The integrated PMMS becomes an effective strategic system as it captures, in a non-occasional fashion, the strategy at the level of management choices and actions leveraging on the key themes. The main evidence of this study seems to be the shifting attention from rigid performance measurement models to flexible project management tools favours the alignment of HRM management practices with specific strategic project objectives. It highlights the shift from static to the dynamic and integrated organizational performance measurement and management systems with HRM within organization which is expected to be more suited to current and future contexts ( Bianchi et al. , 2017 ). Furthermore, it allows to engage employees in conversation about people and organizational performance every day ( Bititci, 2015 ; Pulakos et al. , 2015 ) and also by online chats integrated into performance measurement and management systems ( Sardi et al. , 2020c ).

5. Conclusion

The paper confirms that the high relevance of HRM in the PMM domain is undeniable for scholars and practitioners. As described in the previous sections, in the last ten years, there is a growing relevance of the HRM research in the PMM domain along with an increasing number and rapid evolution of the main investigated themes.

The research gives useful theoretical and practical insights for developing an integrated PMMS. First, it provides a conceptual framework that supports the translation of strategic objectives into effective managerial practices and favours the strategic alignment and integration of the main themes related to PMM and HRM to foster firms' influential performance. Second, it suggests that the development of PMMS integrated with strategic HRM leveraging project management and information systems; however, it has to shift from a static to the dynamic performance systems for being more suited to current business contexts. The authors encourage case studies to explore, test and validate the conceptual framework and further detail the relationship between the four identified themes.

The authors recognized two main limitations. First, the search process could have been influenced by the different meanings assigned to the keyword strings by the PMM and HRM studies. Second, as a result of the use of broad criteria and keywords in selecting papers, some of the identified papers were not closely related to the PMM fields and therefore could not effectively contribute to the findings. Although these limitations may represent potential weaknesses of this study, the authors believe that these limitations are also strength of this research. These limitations fostered the inclusion of many contributions from different research streams, which supported the objective identification of a wide range of themes useful to define the conceptual structure of HRM in the PMM domain.

Definition of the keywords

Strategic diagram

Distribution of papers by period for most productive authors' countries

Strategic diagrams from 1976 to 2019

Thematic evolution (1976–2019)

Main themes of the HRM in PMM domain

Conceptual framework supporting the development of integrated PMMS

Data collection: research protocol

Data analysis: methods and dimensions of analysis

Most prolific journals

Most prolific authors

Most cited papers

Abbaspour , S. and Dabirian , S. ( 2019 ), “ Evaluation of labor hiring policies in construction projects performance using system dynamics ”, International Journal of Productivity and Performance Management , Vol. 69 No. 1 , pp. 22 - 43 .

Appelbaum , S.H. , Roy , M. and Gilliland , T. ( 2011 ), “ Globalization of performance appraisals: theory and applications ”, Management Decision , Vol. 49 No. 4 , pp. 570 - 585 .

Bartram , T. , Boyle , B. , Stanton , P. , Sablok , G. and Burgess , J. ( 2015 ), “ Performance and reward practices of multinational corporations operating in Australia ”, Journal of Industrial Relations , Vol. 57 No. 2 , pp. 210 - 231 .

Bianchi , C. , Bovaird , T. and Loeffler , E. ( 2017 ), “ Applying a dynamic performance management framework to wicked issues: how coproduction helps to transform young people's services in Surrey county Council, UK ”, International Journal of Public Administration , Vol. 40 No. 10 , pp. 833 - 846 , Routledge .

Bititci , U.S. ( 2015 ), Managing Business Performance: The Science and the Art , Wiley Blackwell , Edinburgh .

Bititci , U.S. , Garengo , P. , Dörfler , V. and Nudurupati , S. ( 2012 ), “ Performance measurement: challenges for tomorrow ”, International Journal of Management Reviews , Vol. 14 No. 3 , pp. 305 - 327 .

Blount , Y. ( 2011 ), “ Employee management and service provision: a conceptual framework ”, Information Technology and People , Vol. 24 No. 2 , pp. 134 - 157 .

Bourne , M. , Franco-Santos , M. , Micheli , P. and Pavlov , A. ( 2018 ), “ Performance measurement and management: a system of systems perspective ”, International Journal of Production Research , Vol. 56 No. 8 , pp. 1 - 12 .

Bourne , M. , Pavlov , A. , Franco-Santos , M. , Lucianetti , L. and Mura , M. ( 2013 ), “ Generating organisational performance: the contributing effects of performance measurement and human resource management practices ”, International Journal of Operations and Production Management , Vol. 33 No. 11 , pp. 1599 - 1622 .

Burney , L.L. and Widener , S.K. ( 2013 ), “ Behavioral work outcomes of a strategic performance measurement system-based incentive plan ”, Behavioral Research in Accounting , Vol. 25 No. 2 , pp. 115 - 143 .

Burns , T. and Stalker , G.M. ( 1969 ), “ The management of innovation ”, The Economic Journal , Vol. 79 No. 314 , pp. 403 - 405 .

Butterfield , R. , Edwards , C. and Woodall , J. ( 2004 ), “ The new public management and the UK police service: the role of the police sergeant in the implementation of performance management ”, Public Management Review , Vol. 6 No. 3 , pp. 395 - 415 .

Callon , M. , Courtial , J.P. and Laville , F. ( 1991 ), “ Co-word analysis as a tool for describing the network of interactions between basic and technological research: the case of polymer chemsitry ”, Scientometrics , Vol. 22 No. 1 , pp. 155 - 205 .

Cappelli , P. and Tavis , A. ( 2016 ), “ The performance management revolution 10, 58–67 ”, Harvard Business Review , No. 10 , pp. 58 - 67 .

Caruth , D.L. and Humphreys , J.H. ( 2008 ), “ Performance appraisal: essential characteristics for strategic control ”, Measuring Business Excellence , Vol. 12 No. 3 , pp. 24 - 32 .

Castilla , E.J. ( 2008 ), “ Gender, race, and meritocracy in organizational careers ”, American Journal of Sociology , Vol. 113 No. 6 , pp. 1479 - 1526 .

Chattopadhayay , R. and Ghosh , A.K. ( 2012 ), “ Performance appraisal based on a forced distribution system: its drawbacks and remedies ”, International Journal of Productivity and Performance Management , Vol. 61 No. 8 , pp. 881 - 896 .

Chen , H.L. ( 2014 ), “ Innovation stimulants, innovation capacity, and the performance of capital projects ”, Journal of Business Economics and Management , Vol. 15 No. 2 , pp. 212 - 231 .

Chen , S.H. and Lee , H.T. ( 2007 ), “ Performance evaluation model for project managers using managerial practices ”, International Journal of Project Management , Vol. 25 No. 6 , pp. 543 - 551 .

Cobo , M.J. , López-Herrera , A.G. , Herrera-Viedma , E. and Herrera , F. ( 2011 ), “ An approach for detecting, quantifying, and visualizing the evolution of a research field: a practical application to the Fuzzy Sets Theory field ”, Journal of Informetrics , Vol. 5 No. 1 , pp. 146 - 166 .

Cobo , M.J. , Martínez , M.A. , Gutiérrez-Salcedo , M. , Fujita , H. and Herrera-Viedma , E. ( 2015 ), “ 25 years at knowledge-based systems: a bibliometric analysis ”, Knowledge-Based Systems , Vol. 80 , pp. 3 - 13 .

Crain , D.W. ( 2009 ), “ Only the right people are strategic assets of the firm ”, Strategy and Leadership , Vol. 37 No. 6 , pp. 33 - 38 .

Cronin , J.J. Jr , Smith , J.S. , Gleim , M.R. , Ramirez , E. and Martinez , J.D. ( 2011 ), “ Green marketing strategies: an examination of stakeholders and the opportunities they present ”, Journal of the Academy of Marketing Science , Vol. 39 , pp. 158 - 174 .

Culnan , M.J. and Swanson , E.B. ( 1986 ), “ Research in management information systems, 1980–1984: points of work and reference ”, MIS Quarterly: Management Information Systems , Vol. 10 No. 3 , pp. 289 - 301 .

Dabic , M. , González-Loureiro , M. and Furrer , O. ( 2014 ), “ Research on the strategy of multinational enterprises: key approaches and new avenues ”, BRQ Business Research Quarterly , Vol. 17 No. 2 , pp. 129 - 148 .

DeNisi , A.S. and Murphy , K.R. ( 2017 ), “ Performance appraisal and performance management: 100 years of progress? ”, Journal of Applied Psychology , Vol. 102 No. 3 , pp. 421 - 433 .

DeNisi , A.S. and Smith , C.E. ( 2014 ), “ Performance appraisal, performance management and firm-level performance: a review, a proposed model and new directions for future research ”, The Academy of Management Annals , Vol. 8 No. 1 , pp. 127 - 179 .

Dewettinck , K. and van Dijk , H. ( 2013 ), “ Linking Belgian employee performance management system characteristics with performance management system effectiveness: exploring the mediating role of fairness ”, International Journal of Human Resource Management , Vol. 24 No. 4 , pp. 806 - 825 .

Ding , M.J. , Kam , B.H. , Zhang , J.Y. and Jie , F. ( 2015 ), “ Effects of human resource management practices on logistics and supply chain competencies – evidence from China logistics service market ”, International Journal of Production Research , Vol. 53 No. 10 , pp. 2885 - 2903 .

Ensslin , S.R. , Ensslin , L. , Back , F. and Lacerda , R.T.D.O. ( 2013 ), “ Improved decision aiding in human resource management: a case using constructivist multi-criteria decision aiding ”, International Journal of Productivity and Performance Management , Vol. 62 No. 7 , pp. 735 - 757 .

Franco-Santos , M. , Kennerley , M. , Micheli , P. , Martinez , V. , Mason , S. , Marr , B. , Gray , D. and Neely , A. ( 2007 ), “ Towards a definition of a business performance measurement system ”, International Journal of Operations and Production Management , Vol. 27 No. 8 , pp. 784 - 801 .

Furrer , O. and Sollberger , P. ( 2007 ), “ The dynamics and evolution of the service marketing literature: 1993–2003 ”, Service Business , Vol. 1 No. 2 , pp. 93 - 117 .

Furrer , O. , Thomas , H. and Goussevskaia , A. ( 2008 ), “ The structure and evolution of the strategic management field: a content analysis of 26 years of strategic management research ”, International Journal of Management Reviews , Vol. 10 No. 1 , pp. 1 - 23 .

Garengo , P. and Bititci , U.S. ( 2007 ), “ Towards a contingency approach to performance measurement: an empirical study in Scottish SMEs ”, International Journal of Operations and Production Management , Vol. 27 No. 8 , pp. 802 - 825 .

Garengo , P. and Sardi , A. ( 2020 ), “ Performance measurement and management in the public sector: state of the art and research opportunities ”, International Journal of Productivity and Performance Management , in press .

Garengo , P. , Biazzo , S. and Bititci , U.S. ( 2005 ), “ Performance measurement systems in SMEs : a review for a research agenda ”, International Journal of Management Reviews , Vol. 7 No. 1 , pp. 25 - 47 .

Garengo , P. , Biazzo , S. , Simonetti , A. and Bernardi , G. ( 2005 ), “ Benchmarking on managerial practices: a tool for SMEs ”, TQM Magazine , Vol. 17 No. 5 , pp. 440 - 455 .

Gemünden , H.G. , Lehner , P. and Kock , A. ( 2018 ), “ The project-oriented organization and its contribution to innovation ”, International Journal of Project Management , Vol. 36 No. 1 , pp. 147 - 160 .

Golden , K.A. and Ramanujam , V. ( 1985 ), “ Between a dream and a nightmare: on the integration of the human resource management and strategic business planning processes ”, Human Resource Management , Vol. 24 No. 4 , pp. 429 - 452 .

Gruman , J.A. and Saks , A.M. ( 2011 ), “ Performance management and employee engagement ”, Human Resource Management Review , Vol. 21 No. 2 , pp. 123 - 136 .

Guest , D.E. ( 1997 ), “ Human resource management and performance: a review and research agenda ”, International Journal of Human Resource Management , Vol. 8 No. 3 , pp. X1 - 276 .

Hanson , J.D. , Melnyk , S.A. and Calantone , R.A. ( 2011 ), “ Defining and measuring alignment in performance management ”, International Journal of Operations and Production Management , Vol. 31 No. 10 , pp. 1089 - 1114 .

Hassini , E. , Surti , C. and Searcy , C. ( 2012 ), “ A literature review and a case study of sustainable supply chains with a focus on metrics ”, International Journal of Production Economics , Vol. 140 No. 1 , pp. 69 - 82 .

Hooi , L.W. ( 2019 ), “ Firm performance: is organizational learning capability the magic wand? ”, International Journal of Productivity and Performance Management , Vol. 68 No. 8 , pp. 1411 - 1433 .

Huang , J.-H. , Jin , B.-H. and Yang , C. ( 2004 ), “ Satisfaction with business-to-employee benefit systems and organizational citizenship behavior: an examination of gender differences ”, International Journal of Manpower , Vol. 25 No. 2 , pp. 195 - 236 .

Igbaria , M. and Baroudi , J.J. ( 1995 ), “ The impact of job performance evaluations on career advancement prospects: an examination of gender differences in the IS workplace ”, MIS Quarterly: Management Information Systems , Vol. 19 No. 1 , pp. 107 - 122 .

Igbaria , M. and Greenhaus , J.H. ( 1992 ), “ The career advancement prospects of managers and professionals: are MIS employees unique? ”, Decision Sciences , Vol. 23 No. 2 , pp. 478 - 499 .

Iqbal , M.Z. , Akbar , S. and Budhwar , P. ( 2015 ), “ Effectiveness of performance appraisal: an integrated framework ”, International Journal of Management Reviews , Vol. 17 No. 4 , pp. 510 - 533 .

Ittner , C.D. and Larcker , D.F. ( 2003 ), “ Coming up short on nonfinancial performance measurement ”, Harvard Business Review , November , pp. 88 - 95 .

Jääskeläinen , A. and Laihonen , H. ( 2013 ), “ Overcoming the specific performance measurement challenges of knowledge-intensive organizations ”, International Journal of Productivity and Performance Management , Vol. 62 No. 4 , pp. 350 - 363 .

Kampkötter , P. ( 2017 ), “ Performance appraisals and job satisfaction ”, International Journal of Human Resource Management , Vol. 28 No. 5 , pp. 750 - 774 .

Kaplan , R.S. and Norton , D.P. ( 1996 ), “ Linking the balanced scorecard to strategy ”, California Management Review , Vol. 39 No. 1 , pp. 53 - 79 .

Kaplan , R.S. and Norton , D.P. ( 2000 ), The Strategy-Focused Organization: How Balanced Scorecard Companies Thrive in the New Business Environment , Harvard Business School Press , Boston, MA .

Kaplan , R.S. and Norton , D.P. ( 2005 ), “ The balanced scorecard: measures that drive performance ”, Harvard Business Review , Vol. 83 Nos 7–8 , pp. 172 - 180+194 .

Kavanagh , P. , Benson , J. and Brown , M. ( 2007 ), “ Understanding performance appraisal fairness ”, Asia Pacific Journal of Human Resources , Vol. 45 No. 2 , pp. 132 - 150 .

Kim , C.W. , Yoo , W.S. , Lim , H. , Yu , I. , Cho , H. and Kang , K.I. ( 2018 ), “ Early-warning performance monitoring system (EPMS) using the business information of a project ”, International Journal of Project Management , Vol. 36 No. 5 , pp. 730 - 743 .

Kuvaas , B. ( 2006 ), “ Performance appraisal satisfaction and employee outcomes: mediating and moderating roles of work motivation ”, International Journal of Human Resource Management , Vol. 17 No. 3 , pp. 504 - 522 .

Kuvaas , B. ( 2007 ), “ Different relationships between perceptions of developmental performance appraisal and work performance ”, Personnel Review , Vol. 36 No. 3 , pp. 378 - 397 .

Lakshman , C. ( 2014 ), “ Leveraging human capital through performance management process: the role of leadership in the USA, France and India† ”, International Journal of Human Resource Management , Vol. 25 No. 10 , pp. 1351 - 1372 .

Lappalainen , P. , Saunila , M. , Ukko , J. , Rantala , T. and Rantanen , H. ( 2019 ), “ Managing performance through employee attributes: implications for employee engagement ”, International Journal of Productivity and Performance Management , Vol. 69 No. 9 , pp. 2119 - 2137 .

Lengnick-Hall , M.L. , Lengnick-Hall , C.A. , Andrade , L.S. and Drake , B. ( 2009 ), “ Strategic human resource management: the evolution of the field ”, Human Resource Management Review , Vol. 19 No. 2 , pp. 64 - 85 .

Levy , P.E. and Williams , J.R. ( 2004 ), “ The social context of performance appraisal: a review and framework for the future ”, Journal of Management , Vol. 30 No. 6 , pp. 881 - 905 .

Long , C.S. and Ismail , W.K.W. ( 2008 ), “ Understanding the relationship of HR competencies and roles of Malaysian human resource professionals ”, European Journal of Social Sciences , Vol. 7 No. 1 , pp. 88 - 103 .

Long , C.S. and Perumal , P. ( 2014 ), “ Examining the impact of human resource management practices on employees' turnover intention ”, International Journal of Business and Society , Vol. 15 No. 1 , pp. 111 - 126 .

Maley , J. and Kramar , R. ( 2014 ), “ The influence of global uncertainty on the cross-border performance appraisal: a real options approach ”, Personnel Review , Vol. 43 No. 1 , pp. 19 - 40 .

Maley , J.F. , Dabic , M. and Moeller , M. ( 2020 ), “ Employee performance management: charting the field from 1998 to 2018 ”, International Journal of Manpower , Vol. 42 No. 1 , pp. 131 - 149 , doi: 10.1108/IJM-10-2019-0483 .

Malina , M.A. and Selto , F.H. ( 2015 ), “ Behavioral-economic nudges and performance measurement models ”, Journal of Management Accounting Research , Vol. 27 No. 1 , pp. 27 - 45 .

Marler , J.H. and Parry , E. ( 2016 ), “ Human resource management, strategic involvement and e-HRM technology ”, International Journal of Human Resource Management , Vol. 27 No. 19 , pp. 2233 - 2253 .

Martínez , M.A. , Cobo , M.J. , Herrera , M. and Herrera-Viedma , E. ( 2015 ), “ Analyzing the scientific evolution of social work using science mapping ”, Research on Social Work Practice , Vol. 25 No. 2 , pp. 257 - 277 .

Melnyk , S.A. , Bititci , U.S. , Platts , K. , Tobias , J. and Andersen , B. ( 2014 ), “ Is performance measurement and management fit for the future? ”, Management Accounting Research , Vol. 25 No. 2 , pp. 173 - 186 .

Meyer , J.P. and Smith , C.A. ( 2000 ), “ HRM practices and organizational commitment: test of a mediation model ”, Canadian Journal of Administrative Sciences , Vol. 17 No. 4 , pp. 319 - 331 .

Micheli , P. and Manzoni , J.-F. ( 2010 ), “ Strategic performance measurement: benefits, limitations and paradoxes ”, Long Range Planning , Vol. 43 No. 4 , pp. 465 - 476 .

Mithas , S. , Ramasubbu , N. and Sambamurthy , V. ( 2011 ), “ How information management capability influences firm performance ”, MIS Quarterly: Management Information Systems , Vol. 35 No. 1 , pp. 237 - 256 .

Molleman , E. and Timmerman , H. ( 2003 ), “ Performance management when innovation and learning become critical performance indicators ”, Personnel Review , Vol. 32 No. 1 , pp. 93 - 113 .

Mondal , A. and Ghosh , S.K. ( 2012 ), “ Intellectual capital and financial performance of Indian banks ”, Journal of Intellectual Capital , Vol. 13 No. 4 , pp. 515 - 530 .

Nankervis , A. and Stanton , P. ( 2010 ), “ Managing employee performance in small organisations: challenges and opportunities ”, International Journal of Human Resources Development and Management , Vol. 10 No. 2 , pp. 136 - 151 .

Nankervis , A.R. , Stanton , P. and Foley , P. ( 2012 ), “ Exploring the rhetoric and reality of performance management systems and organisational effectiveness – evidence from Australia ”, Research and Practice in Human Resource Management , Vol. 20 No. 1 , pp. 40 - 56 .

Neely , A. ( 2005 ), “ The evolution of performance measurement research: developments in the last decade and a research agenda for the next ”, International Journal of Operations and Production Management , Vol. 25 No. 12 , pp. 1264 - 1277 .

Neely , A. and Adams , C. ( 2001 ), “ The performance prism perspective ”, Journal of Cost Management , Vol. 15 No. 1 , pp. 7 - 15 .

Neely , A. , Gregory , M. , Platts , K. , Neely , A. , Gregory , M. and Platts , K. ( 1995 ), “ A literature review and research agenda ”, International Journal of Operations and Production Management , Vol. 15 No. 4 , pp. 80 - 116 .

Neely , A. , Adams , C. and Crowe , P. ( 2001 ), “ The performance prism in practice ”, Measuring Business Excellence , Vol. 5 No. 2 , pp. 6 - 13 .

Nudurupati , S.S. , Garengo , P. and Bititci , U.S. ( 2021 ), “ Impact of the changing business environment on performance measurement and management practices ”, International Journal of Production Economics , Vol. 232 , 107942 .

Nudurupati , S.S. , Tebboune , S. and Hardman , J. ( 2016 ), “ Contemporary performance measurement and management (PMM) in digital economies ”, Production Planning and Control , Vol. 27 No. 3 , pp. 226 - 235 .

Poon , J.M.L. ( 2004 ), “ Effects of performance appraisal politics on job satisfaction and turnover intention ”, Personnel Review , Emerald Group Publishing , Vol. 33 No. 3 , pp. 322 - 334 .

Poovathingal , J.S. and Kumar , D.V. ( 2018 ), “ Quantifying the contribution of competencies toward performance ”, International Journal of Productivity and Performance Management , Vol. 67 No. 4 , pp. 693 - 716 .

Pulakos , E.D. , Hanson , R.M. , Arad , S. and Moye , N. ( 2015 ), “ Performance management can be fixed: an on-the-job experiential learning approach for complex behavior change ”, Industrial and Organizational Psychology: Perspectives on Science and Practice , Vol. 8 No. 1 , pp. 51 - 76 .

Pulakos , E.D. , Mueller-Hanson , R. and Arad , S. ( 2019 ), “ The evolution of performance management: searching for value ”, Annual Review of Organizational Psychology and Organizational Behavior , Annual Reviews , Vol. 6 No. 1 , pp. 249 - 271 .

Pulakos , E.D. and Wexley , K.N. ( 1983 ), “ The relationship among perceptual similarity, sex, and performance ratings in manager-subordinate dyads ”, Academy of Management Journal. Academy of Management , Vol. 26 No. 1 , pp. 129 - 139 .

Rompho , N. ( 2017 ), “ HC and financial performance with two HRM strategies ”, International Journal of Productivity and Performance Management , Vol. 66 No. 4 , pp. 459 - 478 .

Sardi , A. , Garengo , P. and Bititci , U.S. ( 2019 ), “ Measurement and management of competences by enterprise social networking ”, International Journal of Productivity and Performance Management , Vol. 68 No. 1 , pp. 109 - 126 .

Sardi , A. , Sorano , E. , Garengo , P. and Cantino , V. ( 2020a ), “ Big data and performance measurement research: trends, evolution and future opportunities ”, Measuring Business Excellence , in press .

Sardi , A. , Sorano , E. , Garengo , P. and Ferraris , A. ( 2020b ), “ The role of HRM in the innovation of performance measurement and management systems: a multiple case study in SMEs ”, Employee Relations . doi: 10.1108/ER-03-2020-0101 .

Sardi , A. , Sorano , E. , Ferraris , A. and Garengo , P. ( 2020c ), “ Evolutionary paths of performance measurement and management system: the longitudinal case study of a leading SME ”, Measuring Business Excellence , Vol. 24 No. 4 , pp. 495 - 510 .

Shahnaei , S. and Long , C.S. ( 2014 ), “ An examination of strategic human resource management practices in Iranian international sector performance ”, Research Journal of Applied Sciences, Engineering and Technology , Vol. 8 No. 23 , pp. 2363 - 2368 .

Shahsavari-Pour , N. , Asadi , H. , Asadi-Pasoojani , M. , Kazemi , M. , Mirzaie , A. and Karami , M. ( 2017 ), “ A new approach for building a strategy map based on digraph theory ”, International Journal of Applied Management Science , Vol. 9 No. 1 , pp. 1 - 18 .

Smith , M.K. ( 2018 ), “ High-involvement innovation: views from frontline service workers and managers ”, Employee Relations , Vol. 40 No. 2 , pp. 208 - 226 .

Smith , M. and Bititci , U.S. ( 2017 ), “ Interplay between performance measurement and management, employee engagement and performance ”, International Journal of Operations and Production Management , Vol. 37 No. 9 , pp. 1207 - 1228 .

Sparrow , P. , Schuler , R.S. and Jackson , S.E. ( 1994 ), “ Convergence or divergence: human resource practices and policies for competitive advantage worldwide ”, International Journal of Human Resource Management , Vol. 5 No. 2 , pp. 267 - 299 .

Stanton , J.M. and Coovert , M.D. ( 2004 ), “ Turbulent waters: the intersection of information technology and human resources ”, Human Resource Management , Vol. 43 No. 2 , pp. 121 - 125 .

Stanton , P. and Nankervis , A. ( 2011 ), “ Linking strategic HRM, performance management and organizational effectiveness: perceptions of managers in Singapore ”, Asia Pacific Business Review , Vol. 17 No. 1 , pp. 67 - 84 .

Strohmeier , S. ( 2007 ), “ Research in e-HRM: review and implications ”, Human Resource Management Review , Vol. 17 No. 1 , pp. 19 - 37 .

Sturman , M.C. ( 2003 ), “ Searching for the inverted U-shaped relationship between time and performance: meta-analyses of the experience/performance, tenure/performance, and age/performance relationships ”, Journal of Management , Vol. 29 No. 5 , pp. 609 - 640 .

Taticchi , P. , Garengo , P. , Nudurupati , S.S. , Tonelli , F. and Pasqualino , R. ( 2015 ), “ A review of decision-support tools and performance measurement and sustainable supply chain management ”, International Journal of Production Research , Vol. 53 No. 21 , pp. 6473 - 6494 .

Thomé , A.M.T. , Scavarda , L.F. , Scavarda , A. and Thomé , F.E.S.D.S. ( 2016 ), “ Similarities and contrasts of complexity, uncertainty, risks, and resilience in supply chains and temporary multi-organization projects ”, International Journal of Project Management , Vol. 34 No. 7 , pp. 1328 - 1346 .

Tranfield , D. , Denyer , D. and Smart , P. ( 2003 ), “ Towards a methodology for developing evidence-informed management knowledge by means of systematic review ”, British Journal of Management , Vol. 14 No. 3 , pp. 207 - 222 .

Van Eck , N.J. and Waltman , L. ( 2009 ), “ How to normalize cooccurrence data? An analysis of some well-known similarity measures ”, Journal of the American Society for Information Science and Technology , Vol. 60 No. 8 , pp. 1635 - 1651 .

Vo , A. and Stanton , P. ( 2011 ), “ The transfer of HRM policies and practices to a transitional business system: the case of performance management practices in the US and Japanese MNEs operating in Vietnam ”, International Journal of Human Resource Management , Vol. 22 No. 17 , pp. 3513 - 3527 .

Wickramasinghe , V. ( 2012 ), “ Influence of total quality management on human resource management practices: an exploratory study ”, International Journal of Quality and Reliability Management , Vol. 29 No. 8 , pp. 836 - 850 .

Wickramasinghe , V. and Liyanage , S. ( 2013 ), “ Effects of high performance work practices on job performance in project-based organizations ”, Project Management Journal , Vol. 44 No. 3 , pp. 64 - 77 .

Wood , S. ( 1999 ), “ Human resource management and performance ”, International Journal of Management Reviews , Vol. 1 No. 4 , pp. 367 - 413 .

Wright , P.M. and Mcmahan , G.C. ( 1992 ), “ Theoretical perspectives for strategic human resource management ”, Journal of Management , Vol. 18 No. 2 , pp. 295 - 320 .

Wright , P.M. , Snell , S.A. and Dyer , L. ( 2005 ), “ New models of strategic HRM in a global context ”, International Journal of Human Resource Management , Vol. 16 No. 6 , pp. 875 - 881 .

Yun , S. , Choi , J. , de Oliveira , D.P. and Mulva , S.P. ( 2016 ), “ Development of performance metrics for phase-based capital project benchmarking ”, International Journal of Project Management , Vol. 34 No. 3 , pp. 389 - 402 .

Zhang , Y.-C. and Li , S.-L. ( 2009 ), “ High performance work practices and firm performance: evidence from the pharmaceutical industry in China ”, International Journal of Human Resource Management , Vol. 20 No. 11 , pp. 2331 - 2348 .

Zigan , K. , Macfarlane , F. and Desombre , T. ( 2008 ), “ Intangible resources as performance drivers in European hospitals ”, International Journal of Productivity and Performance Management , Vol. 57 No. 1 , pp. 57 - 71 .

Corresponding author

About the authors.

Patrizia Garengo is Associate Professor of Performance Management and Business Management at the University of Padua. She holds a PhD in Business Management and Industrial Engineering, University of Padua (Italy) and she is a research fellow at the Centre for Strategic Manufacturing (DMEM), Strathclyde University. Her research interests include organizational development and performance measurement systems, with particular attention to SMEs. To date she has published over 100 papers in international journals and conferences on performance measurement and management.

Alberto Sardi holds a management degree at the University of Milan and a PhD in Management Engineering at the University of Padua (Italy). He worked for about 10 years in private organizations covering different roles. Currently, he is Assistant professor at the University of Turin (Italy). His research topic focuses on Innovative Performance Management in organizations with particular attention to performance measurement systems implementation. Moreover, he looks towards new social media technologies in the performance measurement area.

Sai Sudhakar Nudurupati gained his MSc and PhD from the University of Strathclyde, UK. He received an Outstanding Doctoral Award from the European Foundation for Management Development. Prior to joining GITAM, Sai has worked for 11 years at Manchester Metropolitan University, Exeter University and Strathclyde University in various teaching and research roles. He has published over 25 papers in reputed international journals and magazines (listed on Australian Business Deans Council Journal list and Chartered Association of Business Schools Academic Journal Guide) and received two best paper awards from Emerald and Institute of Engineering Technology respectively. Sai spent 5 years in SGB, UK implementing continuous improvement projects.

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The critical role of HRM in AI-driven digital transformation: a paradigm shift to enable firms to move from AI implementation to human-centric adoption

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• Published: 13 May 2024
• Volume 4 , article number  34 , ( 2024 )

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• Ali Fenwick   ORCID: orcid.org/0000-0002-5412-9745 1 , 2 ,
• Gabor Molnar   ORCID: orcid.org/0000-0002-3536-8599 2 &
• Piper Frangos   ORCID: orcid.org/0000-0002-3560-7473 3  

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The rapid advancement of Artificial Intelligence (AI) in the business sector has led to a new era of digital transformation. AI is transforming processes, functions, and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. However, the implementation and adoption of AI systems in the organization is not without challenges, ranging from technical issues to human-related barriers, leading to failed AI transformation efforts or lower than expected gains. We argue that while engineers and data scientists excel in handling AI and data-related tasks, they often lack insights into the nuanced human aspects critical for organizational AI success. Thus, Human Resource Management (HRM) emerges as a crucial facilitator, ensuring AI implementation and adoption are aligned with human values and organizational goals. This paper explores the critical role of HRM in harmonizing AI's technological capabilities with human-centric needs within organizations while achieving business objectives. Our positioning paper delves into HRM's multifaceted potential to contribute toward AI organizational success, including enabling digital transformation, humanizing AI usage decisions, providing strategic foresight regarding AI, and facilitating AI adoption by addressing concerns related to fears, ethics, and employee well-being. It reviews key considerations and best practices for operationalizing human-centric AI through culture, leadership, knowledge, policies, and tools. By focusing on what HRM can realistically achieve today, we emphasize its role in reshaping roles, advancing skill sets, and curating workplace dynamics to accommodate human-centric AI implementation. This repositioning involves an active HRM role in ensuring that the aspirations, rights, and needs of individuals are integral to the economic, social, and environmental policies within the organization. This study not only fills a critical gap in existing research but also provides a roadmap for organizations seeking to improve AI implementation and adoption and humanizing their digital transformation journey.

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1 Introduction

AI is set to revolutionize the global economy, with projections estimating its contribution to be around $15.7 trillion by 2030. Nevertheless, today's reality differs from the potential: approximately 70–85% of AI initiatives fail, often due to launch issues or lack of business value creation [ 1 , 2 ]. This suggests that the operationalization of AI is complex and can be challenging for organizations making investments in AI-fueled transformation. The journey from AI implementation to effective adoption is fraught with challenges, including technical and human-centric barriers, often leading to disappointing results or non-adoption.

Integrating AI into business operations can reshape how companies function and compete [ 3 , 4 ]. As firms increasingly implement advanced digital AI tools, human resource management (HRM) becomes more complex [ 5 , 6 ]. While AI technologies such as machine learning, natural language processing, and robotics are enhancing workplace efficiency and productivity [ 7 , 8 ], the need for HRM to manage this transition often remains underexplored (e.g., [ 9 ]). Existing literature is abundant in discussing the use of AI within HRM, yet it overlooks how HRM can significantly influence the successful implementation and adoption of AI systems (e.g., [ 10 , 11 ]). Also, the strategic involvement of HRM in influencing adoption and aligning AI initiatives with overall business objectives is scarcely explored or emphasized. Böhmer and Schinnenburg [ 12 ] discuss the potential of AI-driven HRM to contribute to organizational capabilities and the application of AI in strategic HR, respectively, but do not delve into the specific role of HRM in shaping AI initiatives.

Our paper explores the role of HRM in enhancing the efficacy of AI applications within organizational settings. It explores HRM's role in giving strategic advice on AI use, making AI at the workplace more human-centric, and helping people in the organization adapt to and accept AI. The literature on AI-driven HRM is still in its infancy. While some researchers (e.g., [ 12 ]) acknowledge the potential contributions AI-driven HRM departments can make, they do not explore the role of HRM shaping AI digital transformation or how HRM can influence the next generation of AI-HRM technology [ 13 ]. Our paper aims to fill this gap by providing a framework on how and where HRM exert their influence in human-centric decision-making within the organization (e.g., [ 11 ]). We propose a conceptual framework of how HRM can support AI-based digital transformation and facilitate a paradigm shift to help organizations succeed in their AI efforts by outlining and highlighting the implications of culture, leadership, knowledge, policy, and tools on AI adoption. Our perspective is novel because, traditionally, the emphasis on digital transformation has been rather technocratic, focusing primarily on the technical aspects of development and implementation (e.g., [ 14 ]). Our framework shifts this narrative by placing the human component at the forefront, arguing that the success of AI implementation and adoption in organizations is contingent upon the employment of a human-centric approach. Successful AI implementation and adoption will need to be defined by respective internal stakeholder groups and align with overall organizational goals. Success can be co-defined and achieved across various stakeholder groups in collaboration with HRM.

1.1 Definitions

Before explaining how HRM can support AI implementation and adoption in the workplace through a humanizing AI lens, definitions need to be provided to articulate our ideas and discuss how they relate in the context of this research. In this paper, we adopt Boselie's [ 15 ] definition of HRM, which views it as a combination of policies and practices shaping employment relationships to achieve specific objectives, including both organizational and employee/societal outcomes.

The function of HRM traditionally covers HR planning, selection and recruitment, talent progression, learning and development, reward, employee relations, and the management of HR systems (e.g., [ 16 , 17 , 18 ]). Beyond an administration function, HRM has positioned itself in current times as a business partner to the organization (e.g. [ 19 ]). Depending on the size of the organization and the type of industry HRM’s function and responsibilities can differ significantly, which affects how far reaching HRM can be within human-centric AI-driven digital transformation.

Definitions of AI, like those from Afiouni [ 20 ] and Lee et al. [ 21 ], generally describe it as either mimicking human thinking or solving problems like humans. AI combines ‘‘artificial,’’ referring to human-made objects [ 22 ], with ‘‘intelligence,’’ meaning a computer’s ability to learn and reason [ 23 ]. However, intelligence in AI is still debated, with concepts like weak and strong AI [ 24 ] used to differentiate levels of machine intelligence. For this paper, AI is defined following Duan et al. [ 25 ] as machines’ ability to learn from experience and perform human-like tasks. In the paper, our primary focus is weak (or Narrow AI) tools, especially as they relate to workplace usage, but the findings are also relevant to the early appearance of strong AI (or Artificial General Intelligence) tools which aim to reproduce human intelligence capabilities (e.g. [ 26 ]). That is, when talking about AI in this paper, we refer primarily to current generation deep learning models such as Artificial Neural Networks (ANN) and Generative AI (GenAI), unless indicated otherwise.

Implementing AI involves the practical steps of integrating AI technologies into existing processes and systems, including technical setup, data integration, and staff training. It focuses on the operational aspects, ensuring AI tools work effectively within an organization's existing infrastructure [ 27 , 28 , 29 ]. Adoption of AI, in contrast, is about the ‘acceptance’ and ‘usage’ of something new rather than the detailed steps of making it operational [ 30 ]. We argue that adoption should be more deliberate and planned integration of AI, aligning its use with the organization's strategic goals to optimize outcomes. It involves assessing how AI impacts various business areas, planning resources, and managing risks. It considers the long-term role of AI in enhancing competitive advantage and aligns it with ethical and societal values. While implementation deals with the ‘how’ of AI integration, adoption addresses the ‘why’ and ‘what’, ensuring AI contributes to the organization's success and is ‘‘part of the business DNA’’ of the firm [ 1 ]. Both AI implementation and adoption should be guided through a human-centric lens (hereafter also referred to as humanizing AI) to ensure success in the short-term and in the long-term. In this context, human-centric AI describes the outcome or objective of creating AI systems that prioritize human needs, values, and ethical considerations, ensuring that the technology supports and enhances human well-being and decision-making. That is, human-centric AI emphasizes the integration of AI into frameworks in a way that positively impacts human lives.

It is also important to define what humanizing AI means. If the concept of humanizing AI is not adequately defined, it creates ambiguity and uncertainty regarding its implementation and purpose. Humanizing AI, in a narrow definition, (i) involves developing AI that not only comprehends human emotions and subconscious dynamics but also interacts with humans naturally, (ii) supports and augments human characteristics and skills, (iii) is deployed in a trustworthy manner [ 31 ]. Trustworthiness in AI reflects how confident one feels in the decisions that AI makes (e.g., [ 32 , 33 ]). Trustworthiness is enhanced when employees know that AI is used to enhance their skills and experience at work and that it is used in a responsible manner (e.g., [ 34 , 35 ]). We acknowledge that different internal stakeholders (e.g., managers, leaders) can view trustworthiness differently. However, addressing each difference in perspective goes beyond the scope of this paper.

The goal is not to make AI human, but to enhance AI’s ability to relate to and assist humans in a more personalized and context-aware manner. In this context, AI is an augmentative tool, as opposed to solely focusing on automation. AI’s role in complementing and enhancing human skills and decision-making processes, rather than replacing them. Humanizing AI prioritizes enhancing the human experience, making AI more intuitive and empathetic, and aligning with human values and potential [ 36 ]. Humanizing AI by itself does not guarantee a harmonious or symbiotic human-AI relationship, but it is essential for building trust with machines. Humanizing AI should occur at various interconnected levels (within the organization) and act as a conduit to addressing many of the ethical and people challenges between humans and machines [ 31 ]. As AI matures, it moves toward more advanced cognitive architectures [ 13 ], necessitating context-specific interpretations of its use and human-centricity [ 37 ]. However, focusing only on creating AI systems that mimic human characteristics is not sufficient. Humanizing AI also needs to address the behavioral concerns and societal consequences (e.g., [ 38 ]); therefore, our paper defines humanizing AI in the workplace from a behavioral perspective. The behavioral view of humanizing AI blueprints how to develop and apply AI in the workplace from a multidimensional approach. An approach that promotes not only human performance and well-being but also highlights possible solutions on how to address issues concerning AI explainability, AI ethics, and responsible use of AI. Human-centric AI describes the outcome or objective of creating AI systems that prioritize human needs, values, and ethical considerations.

The paper is structured as follows: this first section sets the stage by exploring the human-centric perspective of AI, and defining key terms. The next section delves into the human-centric, integrated approach necessary for implementing and adopting AI in the workplace, emphasizing the role of HRM in fostering a harmonious relationship between humans and AI. Finally, the paper concludes with discussing HRM’s strategic facilitation of AI from implementation to adoption.

2 The critical role of HRM in enabling a more human-centric approach to AI adoption

Despite rapid developments in AI within organizations, its adoption remains challenging due to factors like AI-related fears (e.g., [ 39 ]), trust issues [ 40 , 41 ], knowledge gaps (e.g., [ 27 , 42 ]), and integration difficulties (e.g., [ 43 ]). These barriers are primarily human related, underscoring the importance of a humanizing AI approach in AI implementation and adoption. Many organizations mainly focus on the efficiency and productivity gains of AI, but do not sufficiently address the human factor (e.g., [ 44 ]). HRM's commitment to human-centric approaches to AI is not just about ethical responsibility or a moral imperative; it is also a business and strategic priority for retaining a talented workforce. The failure to prioritize human-centric AI could make it difficult for businesses to attract and retain skilled professionals, undermining their competitive edge. And, similar to diversity and inclusion initiatives today, could make customers less willing to buy from you if your company’s AI policies and practices are perceived to be not human-centric. As HRM inherently concerns itself with the human elements within organizations, it would seem logical and a natural evolution of HRM's function to facilitate the move from AI implementation to a more human-centric adoption. Doing so ensures that technological advancements, like AI, are leveraged to complement and enhance the human workforce rather than marginalize it.

Traditionally, HRM in organizations was considered an administrative function, focusing on compliance and workforce management using rudimentary tools [ 45 ]. In the mid twentieth century, HRM evolved into Personnel Management, adopting technology to manage people as a resource, thus enhancing skills and productivity through behavioral understanding [ 46 , 47 ]. The advent of strategic HRM marked a shift towards a partnership role within organizations, leveraging data through human resources information systems (HRIS) to improve decision-making [ 48 ]. Currently HRM is often considered a business partner in organizations, integrating digital strategies which value employees as competitive assets, prioritizing diversity, and aligning technology with human values [ 49 , 50 ]. With AI's emergence, HRM confronts the challenge of harmonizing technological efficiency with a human-centric approach, addressing AI ethics and value enhancement [ 51 , 52 ]. This forward-focused AI-driven phase represents a critical inflection point, where human centricity plays a more prominent role in the value creation process of the organization.

Besides humanizing AI, to facilitate the symbiotic relationship between humans and machines, it is also important to ‘‘digitize’’ the human. What we mean by digitizing the human in the organizational context is that HR (i) trains employees to understand what AI is and how it works, (ii) enhances employee skills and capabilities to work with AI, and (iii) creates an environment which is conducive to embracing new ways of doing things. By humanizing AI and digitizing humans, HRM takes an active approach to create a more symbiotic relationship between humans and machines in the workplace.

We argue that successful AI-driven digital transformation in organizations depends on five key elements: culture, leadership, knowledge, policies, and tools. In the next section, we explore these five elements that, if addressed in an integrated and human-centric way, can enable firms to move successfully from AI implementation to adoption. Culture drives innovation and adaptability, and it is often cited as critical for AI integration success [ 53 ]. Leadership is important as it drives the strategic vision, ensures alignment of AI initiatives with business goals, and fosters an environment conducive to new technology uptake and experimentation (e.g., [ 4 ]). This is underscored in the literature on transformational leadership in the digital age [ 54 ]. The knowledge element emphasizes the importance of skill development in the workplace to address the gap between current workforce skills and the requirements for effectively implementing and adopting AI systems [ 55 ]. Organizational AI principles, or policies, provide a necessary ethical and governance framework, guiding responsible and sustainable AI use; this aspect is increasingly being highlighted in contemporary research on AI ethics (e.g., [ 56 ]). AI tools, including hardware and software, are also essential for the practical implementation and operationalization of AI, enabling businesses to harness AI capabilities for enhanced decision-making and efficiency. As tools continuously evolve, they need to be more adapted and more integrated. HRM plays a critical role in each of these five elements (see Fig.  1 ). Also indicates that the relationship between these five elements is not of a linear nature.

The critical role of HRM in culture, leadership, knowledge, policies, and tools

3 How HRM can address current AI implementation and adoption challenges using a humanizing AI approach

As AI applicability and outcomes evolve in commercial business environments, so do the associated implementation and adoption challenges. We emphasize the need for more human-centric approaches to help address the key barriers currently affecting AI implementation and adoption. We acknowledge the fact that every organization is unique in terms of structure and stage of AI implementation and outline general overarching challenges and recommendations assuming they will be applied according to each individual organization's circumstances. We address each of these challenges in our conceptual framework (Fig.  2 ), highlighting the critical role HRM plays in facilitating effective AI-driven digital transformation through the support of culture, leadership, knowledge, policy, and tools. Our research and recommendations focus on HRM influencing internal stakeholders throughout organizations yet acknowledge an anticipated flow-on effect beyond organizational boundaries to industry and society.

HRM facilitating human-centric AI implementation and adoption enabled leadership, tools, and policy guided through an organizational cultural framework

3.1 Culture: bringing and binding humans and machines together in the workplace

Culture plays an important role in adopting new technologies, such as AI (e.g., [ 57 ]). Organizational culture has been defined in many ways but converges to the invisible glue that keeps the people together and provides a shared understanding of norms, rituals, and unspoken assumptions about how things function in the organization (e.g., [ 58 ]). The culture of the organization is mainly shaped by the leaders of the organization (e.g., [ 59 ]), and impacts how the operational strategy is executed and the policies are designed. For example, efficiency-based leadership approaches versus transformational leadership approaches will affect the choices made on how to run the organization and which emphasis it places on resource management and optimization differently using AI (e.g., [ 60 , 61 ]).

3.1.1 Culture: key challenges

Organizational culture is necessary to innovate, compete, and thrive in the long-term (e.g., [ 62 ]). In recent years, culture has been cited as a key enabler of AI adoption (e.g., [ 63 , 64 , 65 ]). Various attributes of organizational culture such as innovation drive, trust, learning orientation, risk appetite, and decision-making transparency (e.g., [ 66 , 67 , 68 , 69 ]) amongst others can affect AI implementation and adoption. When talking about AI transparency it’s important to differentiate between transparent AI and transparency in AI use. Transparent AI (or explainable AI as it is often referred to) refers to explainability of AI models. Employees need to know that AI models are explainable when deemed important to understand how AI-tools have made decisions (such as during hiring or firing decisions). Transparency in AI usage is also vital to the organization as it needs to be clear how AI is being used in the organization. Employees will be less willing to use AI or even work for an organization if it is not clear how AI is being used in the workplace (such as for surveillance purposes). The issue arises because higher explainability often results in reduced accuracy. As AI tools become more proficient, it becomes harder to understand how they reach their decisions, making it challenging to trust, debug, or fully leverage in sensitive or critical applications.

3.1.2 Culture: HRM’s active role in creating an AI friendly environment

HRM plays an integral role in developing and guiding organizational culture (e.g., [ 70 , 71 ]). Not only in ensuring that the organization is willing to work with AI, but also to ensure that AI is implemented and deployed in a human-centric manner. This role involves building an environment where employees trust AI systems and are motivated to incorporate AI into their workflows. To achieve this, HRM has to advocate for a culture of transparency and open communication regarding the use of AI tools. HRM must encourage leaders to set examples by using AI tools transparently in their decision-making processes, demonstrating trust in these systems. HRM should facilitate regular feedback loops (e.g., [ 72 ]) where employees can share their experiences and concerns with AI, ensuring their voices are acknowledged, considered, and acted upon appropriately. Additionally, it is important to challenge and reshape inappropriate AI initiatives. Actionable behaviors that promote AI adoption should be embedded into the organization's culture. This can be achieved through recognition and reward systems that incentivize innovative uses of AI and performance metrics that reflect the effective integration of AI in work processes [ 73 ]. By aligning AI adoption with personal and team objectives, employees are more likely to embrace AI as a tool for success rather than a threat to their job security [ 74 ]. By shaping the culture this way, HRM can create a psychologically safe environment where experimentation and risk-taking are encouraged, and employees feel excited to work with AI tools without fear of repercussions or losing one’s job.

Key to adopting AI is the culture's ability to foster a willingness to work with new technologies. Often the behavioral literature is considered when trying to identify reasons why professionals don’t trust working with AI. Interestingly, the automation-augmentation literature provides pathways to increase both trust in, and willingness to adopt AI. For example, Henkel et al. [ 75 ] explain that automation of tasks can help free up needed time and other resources performed on mundane jobs. This free time can be spent on more important and engaging tasks such as creativity and customer interaction. The augmentation literature (e.g., [ 75 , 76 , 77 ] shows that when AI is used to augment people’s skills, professionals are more likely to use AI at work.

Conversely, AI deployment also affects organizational culture. Algorithms and AI tools can change employee behaviors, decision-making processes, and collaboration dynamics [ 78 ]. For instance, AI can influence what information employees receive, shaping beliefs and interactions [ 78 ]. Generative AI, with its programming, can also affect attitudes and behaviors, particularly when it's designed to understand language and emotions (e.g., [ 79 ]). In this context, culture development is reinforced through technical output and engagement with AI. It is therefore important that HRM monitor the effect AI has on cultural formation in the organization. As AI becomes more integrated, organizational culture evolves to include both humans and machines. Strategically leveraging culture through leadership, knowledge, policy, and AI tools is key for successful AI implementation and adoption. If the current culture hinders AI adoption, a cultural shift may be necessary to foster a more technology-friendly environment.

3.2 AI Leadership: evolving leadership requirements

Leadership plays an influential role in how open employees are to change, effectively implementing new technologies, and successfully accepting these technologies (e.g., [ 80 , 81 ]). Organizational leaders increasingly integrate AI tools into the workplace, promoting a data-driven culture, encouraging experimentation, and providing resources and expertise [ 82 ]. Their role is crucial in deploying AI effectively and fostering human-centered AI usage across all employee groups [ 83 ]. By setting a clear AI vision, focusing on innovation, addressing ethical concerns, and prioritizing AI training and upskilling, leaders enable organizations to harness AI's potential fully [ 4 , 84 , 85 ]. They also cultivate an environment open to new technology, which is essential for AI's long term optimization success [ 86 ].

3.2.1 Leadership: key challenges

The literature highlights the vital role of leadership in new technology acceptance and adoption by assessing organizational readiness (e.g., [ 30 , 60 ]) and reducing employee resistance toward new technology, including AI (e.g., [ 60 , 77 ]). However, there is limited evidence on how leaders can effectively adapt and lead in an AI-driven environment (e.g., 60, 88]). This lack of understanding is further perpetuated by literature focusing only on suggesting AI implementation frameworks and strategies on the technical aspects of this exercise and less on the human element [ 88 ]. Common challenges for leaders when dealing with AI implementation and adoption include a lack of digital skills (e.g., [ 87 ]), which leads to a lack of understanding and awareness, lack of AI regulatory and governance experience e.g., [ 89 ], and not being able to deal effectively with lowering employee resistance to change and motivating AI adoption (e.g., [ 90 ]).

3.2.2 Leadership: HRM aligns and facilitates technocratic and human-centric needs for AI success

The strategic facilitation of human-centric AI by HRM in organizations begins at the highest level, working collaboratively with leadership teams to set clear implementation and adoption criteria. This work involves HRM professionals liaising between the domain experts and the executive leadership to map complex AI concepts to strategic business objectives. In this process, HRM must assist leadership in identifying key areas where AI can have the most significant impact, thereby prioritizing AI initiatives that promise high returns and long-term benefits to the organization. To facilitate this, HRM must play an active role in educating the leadership team to understand the potential of AI to enhance productivity, decision-making, and overall business outcomes. This goes beyond the technical aspects of AI, encompassing its ethical implications, risks, and potential biases. By equipping leaders with this knowledge, HRM enables leadership to make informed decisions about AI implementation and required skills and competencies within the organization. A critical aspect of HRM's role is to ensure that leadership approaches AI adoption with a human-centric perspective. HRM must advocate for AI solutions that augment human capabilities and emphasize the importance of employee well-being and ethical considerations in AI deployment. HRM should encourage leaders to communicate transparently with employees about AI initiatives, addressing fears or misconceptions and highlighting the benefits of AI in improving work processes and personal development.

From the behavioral perspective, we focus on the engagement aspects of leadership in lowering resistance to change and AI adoption [ 90 ], as well as the psychological aspect of resistance, such as the threat AI posed on one’s job identity (e.g., [ 91 ]). Leadership engagement as a pathway to lower employee resistance to AI emphasizes the importance of interpersonal qualities of leader–follower engagement, such as the involvement of employees in the decision-making and implementation process [ 92 ], addressing employee concerns about AI through transparent and empathetic dialogue [ 93 ], and collaborating with various stakeholders across the organization to build a culture for AI acceptance (e.g., [ 4 , 94 ]). HRM can play a key part in facilitating this engagement through town hall meetings and organizing regular meetings to better understand how people believe AI will affect their jobs and how the organization can support in alleviating fears. The active role of leadership in creating the vision, creating the right environment, and engaging employees in the AI implementation and adoption process is vital, and HRM plays a critical role in enabling leaders to win the hearts and minds of its followers.

3.3 AI knowledge

The rapid advancement of AI has created a significant demand for specialized AI knowledge and skills in the workforce [ 95 ]. This demand spans various sectors and industries, impacting technology-focused roles and extending to other areas such as healthcare, finance, marketing, and more [ 96 ]. The complexity and novelty of AI technologies equate to a growing gap between the skills available in the current workforce and the skills required to implement and manage AI systems effectively [ 55 ]. The role of HRM is to facilitate human-centric AI digital transformation within organizations. Therefore, its focus is primarily internal. Though HRM doesn’t have a direct impact on society, if more organizations take a similar approach to implementing AI within organizations, then this could generate more trust in AI by the general public” Not taking a human-centric approach to AI usage within HRM not only prevents transformation efforts within organizations and more data-driven decision-making, but also jeopardizes advancements toward safe artificial general intelligence (e.g., [ 97 ]).

3.3.1 Knowledge: key challenges

A key challenge in bridging the knowledge and skills gap is the need for comprehensive AI education and training. Traditional educational systems have been slow to integrate AI and machine learning curricula, leading to a shortage of qualified AI training and development professionals [ 98 ]. Even in technology-forward companies, employees often lack the necessary skills to work alongside AI systems effectively [ 99 ]. This shortage of AI talent can slow down the adoption of AI technologies, limit innovation, and increase reliance on a small pool of experts, which often includes costly external advisors. Moreover, the evolving nature of AI technology means that continuous learning and skill development are essential. Machine learning and AI-embedded technical solutions are fast-paced fields where new advancements and techniques emerge regularly. Professionals in the field must continually update their knowledge to stay relevant and valued. As AI advances, this necessity will flow on throughout the organization to all employee populations. This requires a commitment to lifelong learning and adaptability, which can be a significant challenge for individuals and organizations. In addition to technical skills, there's a growing need for interdisciplinary knowledge that combines AI expertise with domain-specific insights [ 100 ]. For instance, in healthcare, professionals need to understand both AI algorithms and medical practices to develop effective AI solutions [ 101 ]. The requirement for interdisciplinary knowledge further complicates the skill gap issue, as it necessitates a blend of diverse expertise that is rare in the current job market [ 102 ]. Another dimension of this challenge is ethical considerations and AI literacy. As AI systems become more integrated into everyday life, there's a need for a broader understanding of AI among the general public, including ethical implications, privacy concerns, and the potential for bias in AI systems. This understanding is crucial for informed decision-making and responsible use of AI technologies. The role of HRM in organizations in upskilling workforces is critical. This investment is not only technical training but also fostering an AI-ready culture that encourages experimentation, innovation, human-centricity, and continuous learning.

3.3.2 Organizational knowledge and upskilling: HRM advances AI knowledge and skills

When it comes to AI knowledge and skill development, HRM is best positioned to manage this responsibility. HRM is the custodian of the organization’s data and plays an important part in overseeing the correct usage of data within AI-driven applications. This is important to ensure data quality and to minimize the impact of bias in AI decision-making. Not doing so would undermine the success of AI implementation in the workplace for all stakeholders. HRM also takes an active role in upskilling and reskilling initiatives, preparing the workforce for the AI-enabled future [ 103 ]. This task involves anticipating and identifying skill gaps and developing training programs that are tailored to the needs of different employee segments based on the AI solutioned deployed [ 55 ]. By fostering a culture of continuous learning, HRM can ensure that employees are equipped to work with and alongside AI and are empowered to leverage AI tools to enhance their work. One of the biggest causes of resistance to AI in organizations is the lack of awareness and skills [ 104 ]. Addressing this issue will not only improve organizational capabilities, but also address some of the psychological barriers employees have about AI and consequently improve AI adoption [ 105 ]. Understanding how people respond to AI learning opportunities provides HRM insights to improve future training initiatives and inform talent management strategies, policy and AI tool design (e.g., [ 106 ]). Though upskilling and reskilling of the workforce is second nature to HRM, a more integrated perspective to knowledge management and skills development is required in AI environments which can help the organization learn faster and hire more effectively as the organization transitions toward an AI-ready environment. HRM plays an important role in balancing between the technical needs of the organization and the human talent required for AI implementation and adoption (e.g., [ 107 ]).

3.4 AI policies

AI policies play an important role in shaping a productive AI environment in organizations. Company policies are needed to ensure that AI is developed and used ethically, equitably, and transparently in the workplace and to help employees feel safe and more willing to adopt AI tools at work (e.g., [ 108 , 109 ]). In recent years, various ethical concerns have emerged related to AI development and usage such as lack of explainability in AI decision-making e.g., [ 110 ], bias and discrimination (e.g., [ 111 ]), online manipulation by AI e.g., [ 112 ], data privacy scandals (e.g., [ 113 ]), amongst others. Moreover, employees don’t fully trust AI yet and need to feel safe knowing that AI systems won’t be used in a way which will harm them (e.g., [ 114 ]). It is naive to continue to think that human beings are aware of how algorithms affect decision-making and have the abilities to control themselves in the face of increasingly sophisticated manipulation techniques [ 31 ]. The EU AI Act [ 115 ], is the world’s first comprehensive set of rules to protect humans from harm by AI, which will come into effect in 2025, considers AI systems which affect how employees are treated ‘high risk’ AI systems—alongside those used in border control and law enforcement. Having human-centric and ethical AI policies in place at an institutional level which respect and enhance human properties is becoming increasingly important which consequently foster trust and support AI adoption in the workplace.

3.4.1 Policies: key challenges

To implement and adopt AI, firms need to deal with many challenges, foremost being the translation of broad, high-level ethical guidelines into concrete corporate policies. These abstract principles lack specificity, leaving companies to navigate a patchwork of legal frameworks without a prescriptive regulatory approach [ 116 ]. The disparity between the rapid innovation in AI and the sluggish development of legal structures creates a regulatory void, making consistent policy application difficult. Complicating this landscape is the absence of common aims and fiduciary duties in AI, often leading firms to prioritize efficiency and profitability over ethical considerations and public interest [ 90 ]. It is also a problem that AI is used in many different areas and domains, each needing its own rules. Firms also face a challenge in aligning AI policies with the divergent regulatory landscapes across the globe (e.g., [ 117 ]). The interplay of national, international, and professional policy guidelines is outside of the scope of this paper. However, we can determine that the absence of international consensus amplifies non-compliance risk, as companies must interpret and apply a spectrum of high-level guidelines to their specific operations [ 118 ]. As global companies work to implement AI, they must navigate a labyrinth of international regulations that lack a cohesive framework, leading to conflicting approaches in different jurisdictions [ 119 ]. This dissonance creates a significant hurdle for global firms aiming to maintain ethical standards while ensuring legal compliance in various markets. The result is often a fragmented strategy that can hinder the coherent adoption and scaling of AI technologies. Data protection and privacy regulations, varying significantly across jurisdictions, also add complexity for multinational entities [ 120 ].

3.4.2 AI Policies: HRM shapes and monitors human-centric AI implementation and usage

It is important to acknowledge that the ethical framework guiding AI use varies significantly across organizations, often influenced by strategic interests or marketing purposes rather than a genuine commitment to ethical development. This disparity can be amplified by the absence of stringent AI regulations in various jurisdictions, leading to ethical declarations that serve more as corporate virtue signaling than substantive ethical engagement [ 121 ]. To mitigate these risks, it is essential for organizations to advocate for and adhere to robust regulatory standards that ensure AI ethics are deeply integrated into every aspect of technology development and deployment, moving beyond mere compliance to genuinely ethical practices. HRM plays an important role in developing and enforcing AI policy. Taking a human-centric approach to AI policy design, company policies should, from implementation to enforcement, prioritize the protection and well-being of employees while ensuring responsible use of AI. During the initial stages of AI deployment, human-centric AI policies can provide guidelines and mechanisms that safeguard employees' rights, privacy, and job security throughout the AI implementation process. This includes transparent communication about the purpose and effect of AI tools, clear policies regarding data collection and usage, and mechanisms to address any potential biases related to how AI makes decisions in mission critical operations. By actively engaging employees in the initial implementation process, and addressing employee fears and concerns, companies can foster a supportive and inclusive work environment that values employee contributions and ensures fair treatment while adapting to AI work processes and tools. In addition, corporate policies should outline stringent measures to prevent the misuse of technology. Companies should be committed to responsible AI practices, ensuring that the technology is not employed in ways that violate ethical principles or infringe upon individuals' rights. Responsible AI should start during the design process [ 122 ] and continue throughout the implementation and solution/system adoption phases. Regular audits and assessments should be conducted to evaluate the effect of AI on employees and the wider society, identifying and addressing any unintended consequences or risks. By implementing comprehensive AI policies that prioritize employee protection, well-being, and responsible usage, organizations can strike a balance between leveraging the (financial) benefits of AI and ensuring the technology is utilized in a manner that aligns with ethical standards and societal values. HRM plays a crucial role in advocating policies that protect employee privacy and data security, addressing concerns around AI and automation potentially leading to job displacement or unfair treatment. These policies should be crafted to promote ethical AI usage, ensuring transparency, fairness, and accountability in AI systems.

3.5 AI tools

AI tools and solutions are constantly evolving. HRM must be at the forefront of understanding and disseminating the value of company-specific AI applications and employee implications (e.g., [ 11 ]). Most AI development for organizational use focuses on automation, smart solutions, and helping employees make better decisions with the aim to work faster, more efficiently, and gain a competitive advantage (e.g., [ 123 , 124 , 125 ]). With the recent rise of generative AI (e.g., advanced language models and cognitive tools), AI usage in knowledge-based white-collar professions (e.g., accounting, doctors, lawyers) has grown significantly. More recently, application development, graphic, and video AI-powered design tools are now also available, making it possible for employees with limited to no graphic design or coding experience to create digital content and mobile platforms. As AI tools continue to become more accessible and understandable to organizations, HRM will continue to bridge technical specifics and human acceptance at firm-level.

3.5.1 AI tools: key challenges

To humanize AI from an application perspective, HRM needs to focus on asserting human agency through its usage. If cognitive tools support decision-making, then this is considered a human-centered approach. However, if AI tools limit human beings' ability to use their brains effectively (e.g., creative and critical thinking), these tools are not considered human empowering. When people work together, synergies are created through dynamic interactions that cannot be achieved by oneself and that benefit work processes and outputs [ 126 , 127 ]. When knowledge and practice are integrated for automation purposes, it makes work easier and faster to do. However, what gets lost in the automation of workflows and practices are the synergies that naturally occur in collaboration and the benefits that arise from group dynamics [ 128 ]. There is a risk that the drive for productivity based on efficiency and speed alone actually diminishes the benefits of collaborative work done by humans and can harm human potential in the long term. Another concern with AI tools is the fear many workers have when working with AI and the effect AI tools have on one’s professional identity. Not addressing these concerns will prevent the adoption of AI systems in the workplace. Finally, humans need to understand how AI tools make decisions (especially when there is a human in the loop). Feeling confident that (integrated) AI systems are ‘competent’ co-pilots is still a major concern many employees have, especially today.

3.5.2 AI tools: HRM enabling tools to augment human values and capabilities

HRM plays a critical role in driving human-centric AI adoption. It does this by guiding tool selection and formulating organizational policies for AI use (e.g., [ 13 ]). HRM must be actively involved in the selection process of AI tools to ensure they align with the organization's values, culture, and workforce skills. This role thoroughly assesses various AI tools to determine their suitability for ease of use, integration with existing systems, and their potential to enhance employee performance and engagement. Moreover, with the ongoing integration of AI in the workplace and human to machine interaction, future AI applications will become more integrated (e.g., [ 36 ]), assisting workers in their job as co-pilots and augmenting existing skills in co-decision-making and the emergence of collaborative human–machine teams (e.g., [ 129 ]). Being able to translate policies and human needs to AI developers will aid in the development of more human-centric AI tools and systems. HRM plays a pivotal role in how AI tools should be implemented, used, and adapted to ensure uptake and responsible usage.

4 HRM—strategic facilitation of human-centric AI

HRM can effectively navigate the complexities of AI human-centric adoption and engage in multidimensional activities, from collaborating with leadership to setting clear adoption criteria to developing policies and practices prioritizing ethical AI usage and employee well-being (Table  1 ).

5 Conclusion

This paper highlights the multifaceted contributions of HRM in enabling digital transformation, emphasizing the importance of aligning AI initiatives with organizational goals and human values. Through a comprehensive review of organizational culture, leadership, knowledge, policies, and tools, we identified critical strategies for operationalizing human-centric AI, underscoring the need for a holistic approach encompassing technological proficiency and ethical sensitivity. We found that a human-centric paradigm shift is essential for firms to transition from mere AI implementation to strategic adoption.

Our research fills a gap in the existing literature by focusing on the critical role of HRM in AI strategic adoption rather than its application to HR tasks. Our findings suggest that HRM must take an active role in facilitating AI integration, ensuring that the technology enhances rather than replaces human capabilities. This involves prioritizing employee well-being, advocating for ethical AI usage, and fostering a culture of trust and transparency.

While this paper provides a conceptual framework for the role of HRM in AI strategic adoption, empirical studies are needed to validate and refine the framework. Future research could involve case studies or longitudinal research in diverse organizational contexts to observe how the framework operates in real-world settings. In addition, quantitative research could be conducted to statistically analyze the effect of various HRM strategies on the successful strategic adoption of AI in organizations. This could include surveys and data analysis to understand the correlation between HRM practices and AI implementation success rates.

The future of AI in the workplace is not just about technological advancement but also about reshaping organizational culture and leadership approaches. HRM's role in this transformation is critical, requiring a balance between technical expertise and a deep understanding of human psychology and organizational behavior. It can facilitate a more harmonious and productive relationship between humans and machines by advocating for AI solutions that augment human potential and addressing concerns related to fears, ethics, and employee well-being.

Data availability

Data sharing is not applicable to this article.

Code availability

Not applicable.

Gartner. The CIO's guide to artificial intelligence. 2019. https://www.gartner.com/smarterwithgartner/the-cios-guide-to-artificial-intelligence

Weiner J. Why AI/data science projects fail: how to avoid project pitfalls. Berlin: Springer Nature; 2022.

Google Scholar  

Loureiro SMC, Guerreiro J, Tussyadiah I. Artificial intelligence in business: state of the art and future research agenda. J Bus Res. 2021;129:911–26.

Article   Google Scholar  

Fountaine T, McCarthy B, Tamim S. Building the AI-powered organization. Harvard Business Rev. 2019;97(4):62.

Chowdhury S, Budhwar P, Dey PK, Joel-Edgar S, Abadie A. AI-employee collaboration and business performance: integrating knowledge-based view, socio-technical systems and organisational socialisation framework. J Bus Res. 2022;144:31–49. https://doi.org/10.1016/j.jbusres.2022.01.069 .

Makarius EE, Mukherjee D, Fox JD, Fox AK. Rising with the machines: a sociotechnical framework for bringing artificial intelligence into the organization. J Bus Res. 2020;120:262–73.

Alsheibani, S., Messom, C., Cheung, Y., & Alhosni, M. Artificial Intelligence Beyond the Hype Exploring the Organisation Adoption Factors. ACIS 2020 Proceedings. 33. 2020.

Ambati, L. S., Narukonda, K., Bojja, G. R., & Bishop, D. Factors influencing the adoption of artificial intelligence in organizations–from an employee’s perspective. 2020.

Pan Y, Froese FJ. An interdisciplinary review of AI and HRM: challenges and future directions. Hum Resour Manag Rev. 2023;33(1): 100924.

Pereira V, Hadjielias E, Christofi M, Vrontis D. A systematic literature review on the impact of artificial intelligence on workplace outcomes: a multi-process perspective. Hum Resour Manag Rev. 2023;33(1): 100857.

Prikshat V, Islam M, Patel P, Malik A, Budhwar P, Gupta S. AI-Augmented HRM: literature review and a proposed multilevel framework for future research. Technol Forecast Soc Chang. 2023;193: 122645.

Böhmer N, Schinnenburg H. Critical exploration of AI-driven HRM to build up organizational capabilities. Empl Relat Int J. 2023;45(5):1057.

Fenwick A, Molnar G, Frangos P. Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace. Front Artif Intell. 2024;6:1272823.

Vial G. Understanding digital transformation: a review and a research agenda. Manag Digit Transform. 2021. https://doi.org/10.4324/9781003008637-4 .

Boselie P. Strategic human resource management: a balanced approach. New York: McGraw Hill; 2014.

Karatop B, Kubat C, Uygun Ö. Talent management in manufacturing system using fuzzy logic approach. Comput Ind Eng. 2015;86:127–36.

Sitzmann T, Weinhardt JM. Approaching evaluation from a multilevel perspective: a comprehensive analysis of the indicators of training effectiveness. Hum Resour Manag Rev. 2019;29(2):253–69.

Torres EN, Mejia C. Asynchronous video interviews in the hospitality industry: considerations for virtual employee selection. Int J Hosp Manag. 2017;61:4–13.

Sakka F, El Maknouzi MEH, Sadok H. Human resource management in the era of artificial intelligence: future HR work practices, anticipated skill set, financial and legal implications. Acad Strateg Manag J. 2022;21:1–14.

Afiouni, R. Organizational learning in the rise of machine learning (2019). ICIS 2019 Proceedings, Munich. 2019. https://aisel.aisnet.org/icis2019/business_models/business_models/2

Lee J, Suh T, Roy D, Baucus M. Emerging technology and business model innovation: the case of artificial intelligence. J Open Innov. 2019;5(3):1–13.

Simon HA. The sciences of the artificial. Cambridge: MIT press; 1996.

Russel S, Norvig P. Artificial intelligence: a modern approach. London: Pearson; 2016.

Searle JR. Minds, brains and programs. Behav Brain Sci. 1980;3:417–57.

Duan Y, Edwards JS, Dwivedi YK. Artificial intelligence for decision making in the era of big data—evolution, challenges, and research agenda. Int J Inf Manage. 2019;48:63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021 .

Goertzel B. Human-level artificial general intelligence and the possibility of a technological singularity: a reaction to ray kurzweil’s the singularity is near, and McDermott’s critique of kurzweil. Artif Intell. 2007;171(18):1161–73. https://doi.org/10.1016/j.artint.2007.10.011 .

Bérubé M, Giannelia T, Vial G. Barriers to the implementation of AI in organizations: findings from a Delphi Study. Hawaii Int Conf Syst Sci. 2021. https://doi.org/10.2251/hicss.2021.805 .

Merhi MI. An evaluation of the critical success factors impacting artificial intelligence implementation. Int J Inf Manage. 2023;69: 102545.

Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. Reshaping business with artificial intelligence: closing the gap between ambition and action. MIT Sloan Management Review. 2017. 59(1).

Alsheibani, S., Cheung, Y., & Messom, C. Artificial Intelligence Adoption: AI-Readiness at Firm-Level. In PACIS (p. 37). 2018.

Fenwick A, Molnar G. The importance of humanizing AI: using a behavioral lens to bridge the gaps between humans and machines. Disc Artif Intell. 2022. https://doi.org/10.1007/s44163-022-00030-8 .

Dlugatch R, Georgieva A, Kerasidou A. Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care. BMC Med Ethics. 2023;24(1):42.

Ribeiro, M. T., Singh, S., & Guestrin, C. Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016. (pp. 1135–1144).

Zhou L, Paul S, Demirkan H, Yuan L, Spohrer J, Zhou M, Basu J. Intelligence augmentation: towards building human-machine symbiotic relationship. AIS Trans Human-Computer Interact. 2021;13(2):243–64.

Schoenherr JR, Abbas R, Michael K, Rivas P, Anderson TD. Designing AI using a human-centered approach: explainability and accuracy toward trustworthiness. IEEE TransTechnol Soc. 2023;4(1):9–23.

Del Giudice M, Scuotto V, Orlando B, Mustilli M. Toward the human–centered approach human resource management review a revised model of individual acceptance of AI. Human Resourc Manag Rev. 2023. https://doi.org/10.1016/j.hrmr.2021.100856 .

Wilkens U, Langholf V, Ontrup G, Kluge A. Towards a maturity model of human-centered AI—A reference for AI implementation at the workplace. In: Sihn W, Schlund S, editors. Competence development and learning assistance systems for the data-driven future. Gito Verlag; 2021. p. 179–98.

Chapter   Google Scholar  

Ozmen Garibay O, Winslow B, Andolina S, Antona M, Bodenschatz A, Coursaris C, Falco G, Fiore SM, Garibay I, Grieman K, Havens JC. Six human-centered artificial intelligence grand challenges. Int J Human-Computer Interact. 2023;39(3):391–437.

Zhan ES, Molina MD, Rheu M, Peng W. What is there to fear? Understanding multi dimensional fear of AI from a technological affordance perspective. Int J Human Computer Interact. 2023. https://doi.org/10.1080/10447318.2023.2261731 .

Gillespie N, Lockey S, Curtis C. Trust in yartificial intelligence: a five country stud. Univ Queensland KPMG Austr. 2021. https://doi.org/10.14264/e34bfa3 .

Choung H, David P, Ross A. Trust in AI and its role in the acceptance of AI technologies. Int J Human-Computer Interact. 2023;39(9):1727–39.

Alsheiabni, S., Cheung, Y., & Messom, C Factors inhibiting the adoption of artificial intelligence at organizational-level: A preliminary investigation. In Americas Conference on Information Systems 2019 (p. 2). Association for Information Systems. 2019

Gallivan MJ. Organizational adoption and assimilation of complex technological innovations. SIGMIS Database. 2001;32:51.

Jarrahi MH. Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Bus Horiz. 2018;61(4):577–86.

Mahoney TA, Deckop JR. Evolution of concept and practice in personnel administration/human resource management (PA/HRM). J Manag. 1986;12(2):223–41.

Kaufman BE. The Development of HRM in Historical and International Perspective’. In: Boxall P, Purcell J, Wright PM, editors. The Oxford Handbook of Human Resource Management. Oxford University Press; 2007. p. 19–47.

Kim S, Wang Y, Boon C. Sixty years of research on technology and human resource management: looking back and looking forward. Hum Resour Manage. 2021;60(1):229–47.

Hendrickson AR. Human resource information systems: backbone technology of contemporary human resources. J Lab Res. 2003;24(3):381.

Wright C. Reinventing human resource management: business partners, internal consultants and the limits to professionalization. Human Relat. 2008;61(8):1063–86. https://doi.org/10.1177/0018726708094860 .

Malik A, Srikanth NR, Budhwar P. Digitisation, artificial intelligence (AI) and HRM. In: Crawshaw J, Budhwar P, Davis A, editors. Human Resource Management: Strategic and International Perspectives. Thousand Oaks: SAGE Publications; 2020. p. 88–111.

Chui M, Yee L, Hall B, Singla A. The state of AI in 2023: generative AI’s breakout year. Atlanta: McKinsey Global Publishing; 2023.

Budhwar P, Chowdhury S, Wood G, Aguinis H, Bamber GJ, Beltran JR, Boselie P, Lee Cooke F, Decker S, DeNisi A, Dey PK. Human resource management in the age of generative artificial intelligence: perspectives and research directions on ChatGPT. Hum Resour Manag J. 2023;33(3):606–59.

Latif, S. T. M. Study of the effect of choice of organizational culture on artificial intelligence (AI) resources adoption (Master's thesis, NTNU). 2020. https://ntnuopen.ntnu.no/ntnu-xmlui/bitstream/handle/11250/2777698/no.ntnu%3Ainspera%3A57320302%3A36177752.pdf?sequence=1 (Accessed 20 Nov, 2023).

Mandagi DW, Rantung DI, Rasuh D, Kowaas R. Leading through disruption: The role of transformational leadership in the digital age. J Mantik. 2023;7(3):1597–1161.

Sofia M, Fraboni F, De Angelis M, Puzzo G, Giusino D, Pietrantoni L. The impact of artificial intelligence on workers’ skills: upskilling and reskilling in organisations. Inform Sci Int J Emerg Transdiscipl. 2023;26:39–68.

Canca C. Operationalizing AI ethics principles. Commun ACM. 2020;63(12):18–21.

Hoffman N, Klepper R. Assimilating new technologies: The role of organizational culture Global Information Systems. Milton Park: Routledge; 2008. p. 225–37.

Sun S. Organizational culture and its themes. Int J Business Manag. 2008;3(12):137–41.

Nadkarni S, Prügl R. Digital transformation: a review, synthesis and opportunities for future research. Manag Rev Q. 2021;71:233–341.

Frick NR, Mirbabaie M, Stieglitz S, Salomon J. Maneuvering through the stormy seas of digital transformation: the impact of empowering leadership on the AI readiness of enterprises. J Decis Syst. 2021;30(2–3):235–58. https://doi.org/10.1080/12460125.2020.1870065 .

El Toufaili B. The effects of transformational leadership on organizational performance-A theoretical approach. Proc Int Manag Conf. 2017;11(1):153–63.

Hazem SM, Zehou S. Organizational culture and innovation: a literature review In 2019 3rd International on education, culture and social development (ICECSD 2019). Amsterdam: Atlantis Press; 2019.

Dora M, Kumar A, Mangla SK, Pant A, Kamal MM. Critical success factors influencing artificial intelligence adoption in food supply chains. Int J Prod Res. 2022;60(14):4621–40.

Merhi MI. A process model of artificial intelligence implementation leading to proper decision making. In: Conference on e-Business, e-Services and e-Society. Cham: Springer; 2021. p. 40–6.

Merhi MI. Evaluating the critical success factors of data intelligence implementation in the public sector using analytical hierarchy process. Technol Forecast Soc Chang. 2021;173: 121180. https://doi.org/10.1016/j.techfore.2021.121180 .

Bedué P, Fritzsche A. Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption. J Enterp Inf Manag. 2022;35(2):530–49. https://doi.org/10.1108/JEIM-06-2020-0233 .

Currie, Neil. Risk based approaches to artificial intelligence. Crowe Data Management 2019.

Jackson D, Allen C. Enablers, barriers and strategies for adopting new technology in accounting. Int J Account Inf Syst. 2024;52: 100666.

Yu L, Li Y. Artificial intelligence decision-making transparency and employees’ trust: The parallel multiple mediating effect of effectiveness and discomfort. Behav Sci. 2022;12(5):127.

Den Hartog N, Verburg RM. High performance work systems, organisational culture and HRM effectiveness. Hum Resour Manag J. 2004;14(1):55–78.

Carroll WR, Dye K, Wagar TH. The role of organizational culture in strategic human resource management. In: Ashkanasy NM, Wilderom CPM, Peterson MF, editors. The Handbook of organizational culture and climate. California: Sage; 2011. p. 423–40.

Rydén P, El Sawy O. Real-time management: When AI goes fast and flow. In: platforms and artificial intelligence: the next generation of competences. Cham: Springer International Publishing; 2022. p. 225–43.

Flynn, M., Smitherman, H. M., Weger, K., Mesmer, B., Semmens, R., Van Bossuyt, D., & Tenhundfeld, N. L. Incentive mechanisms for acceptance and adoption of automated systems. In 2021 Systems and Information Engineering Design Symposium (SIEDS) (pp. 1–6). IEEE. 2021.

Lichtenthaler U. Extremes of acceptance: employee attitudes toward artificial intelligence. J Bus Strateg. 2020;41(5):39–45.

Henkel AP, Bromuri S, Iren D, Urovi V. Half human, half machine–augmenting service employees with AI for interpersonal emotion regulation. J Serv Manag. 2020;31(2):247–65.

Davenport T, Guha A, Grewal D, Bressgott T. How artificial intelligence will change the future of marketing. J Acad Mark Sci. 2019;48(1):24–42.

Fei-Fei, L., "How to Make A.I. That’s good for people". 2018. https://www.nytimes.com/2018/03/07/opinion/artificial-intelligence-human.html (Accessed 15 Apr, 2024)

Akmeikina, E., Eilers, K., Li, M. M., & Peters, C. (2022). Empowerment effects in human-machine collaboration-a systematic literature review and directions on hybrid intelligence behavior patterns.

Hajarolasvadi N, Ramirez MA, Beccaro W, Demirel H. Generative adversarial networks in human emotion synthesis: a review. IEEE Access. 2020;8:218499–529.

Nilsen P. Overview of theories, models and frameworks in implementation science. In: Nilsen P, Birken SA, editors. Handbook on Implementation Science. Cheltenham: Edward Elgar Publishing Limited; 2020. p. 8–31.

Paschen J, Paschen U, Pala E, Kietzmann J. Artificial intelligence (AI) and value co-creation in B2B sales: activities, actors and resources. Australas Mark J. 2021;29(3):243–51.

Deloitte. State of AI in the Enterprise—5th edition. 2023). https://www2.deloitte.com/uk/en/pages/deloitte-analytics/articles/state-of-ai-in-the-enterprise-edition-5.html (Accessed 1 Aug 2023).

Canbek M. Artificial intelligence leadership: imitating Mintzberg’s managerial roles in business management and communication perspectives in industry. IGI Global. 2020. https://doi.org/10.4018/978-1-5225-9416-1.ch010 .

Frangos P. An integrative literature review on leadership and organizational readiness for AI. Eur Conf Impact Artif Intell Robot. 2022;4(1):145–52.

Xu W, Dainoff MJ, Ge L, Gao Z. Transitioning to human interaction with ai systems: new challenges and opportunities for HCI professionals to enable human-centered AI. Int J Human-Computer Interact. 2023;39(3):494–518. https://doi.org/10.1080/10447318.2022.2041900 .

Wijayati D, Rahman Z, Fahrullah A, Rahman M, Arifah I, Kautsar A. A study of artificial intelligence on employee performance and work engagement: the moderating role of change leadership. IJM. 2022;2(43):486–512. https://doi.org/10.1108/ijm-07-2021-0423 .

Watson GJ, Desouza KC, Ribier VM, Lindič J. Will AI ever sit at the C-suite table? The future of senior leadership. Bus Horiz. 2021;64(4):465–74. https://doi.org/10.1016/j.bushor.2021.02.011 .

Popa C. Adoption of artificial intelligence in agriculture bulletin of the university of agricultural sciences & veterinary medicine Cluj-Napoca. Agriculture. 2011. https://doi.org/10.15835/buasvmcn-agr:6454 .

Mittelstadt B. Principles alone cannot guarantee ethical AI. Nature Machine Intell. 2019;1(11):501–7.

De Cremer D. Leadership by algorithm: who leads and who follows in the AI era? Petersfield: Harriman House Limited; 2020.

Jussupow E, Spohrer K, Heinzl A. Identity threats as a reason for resistance to artificial intelligence: survey study with medical students and professionals. JMIR formative research. 2022;6(3): e28750.

Iannotta M, Meret C, Marchetti G. Defining leadership in smart working contexts: a concept synthesis. Front Psychol. 2020;11: 556933.

Neubauer, R., Tarling, A., & Wade, M. Redefining leadership for a digital age. global centre for digital business transformation. 2017. 1–15.

Jöhnk J, Weibert M, Wyrtki K. Ready or not, AI comes— an interview study of organizational ai readiness factors. Business Inform Syst Eng. 2021;63(1):5–20. https://doi.org/10.1007/s12599-020-00676-7 .

Alekseeva L, Azar J, Gine M, Samila S, Taska B. The demand for AI skills in the labor market. Labour Econ. 2021;71: 102002.

Dwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T, Duan Y, Dwivedi R, Edwards J, Eirug A, Galanos V. Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manage. 2021;57: 101994.

Everitt T. Towards safe artificial general intelligence (Doctoral dissertation. Canberra: The Australian National University, Australia; 2019.

Chrisinger D. The solution lies in education: artificial intelligence & the skills gap. On Horizon. 2019;27(1):1–4.

Hancock B, Lazaroff-Puck K, Rutherford S. Getting practical about the future of work. McKinsey Quarterly. 2020;1:65–73.

Ceccaroni, L., Bibby, J., Roger, E., Flemons, P., Michael, K., Fagan, L., & Oliver, J. L. Opportunities and risks for citizen science in the age of artificial intelligence. Citizen Science: Theory and Practice. 2019. 4(1).

Oerther DB, Glasgow ME. The nurse+ engineer as the prototype V-shaped professional. Nurs Outlook. 2022;70(2):280–91.

Bansiya M, Patidar H. The impact of artificial intelligence on labor markets. EPRA Int J Res Develop (IJRD). 2023;8(6):254–9.

Chowdhury S, Dey P, Joel-Edgar S, Bhattacharya S, Rodriguez-Espindola O, Abadie A, Truong L. Unlocking the value of artificial intelligence in human resource management through AI capability framework. Hum Resour Manag Rev. 2023;33(1): 100899.

Kar, S., Kar, A. K., & Gupta, M. P. Talent scarcity, skill distance and reskilling resistance in emerging digital Technologies-Understanding employee behaviour. 2020.

Mirbabaie M, Brünker F, Möllmann NR, Stieglitz S. The rise of artificial intelligence–understanding the AI identity threat at the workplace. Electron Markets. 2022;32:1–27.

Kimseng T, Javed A, Jeenanunta C, Kohda Y. Applications of fuzzy logic to reconfigure human resource management practices for promoting product innovation in formal and non-formal R&D firms. J Open Innov Technol Market Complexity. 2020;6(2):38.

Khatri, S., Pandey, D. K., Penkar, D., & Ramani, J. Impact of artificial intelligence on human resources. In Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019. Springer Singapore. 2020.

Siebecker MR. Making corporations more humane through artificial intelligence. J Corp L. 2019;45:95.

Torre F, Teigland R, Engstam L. 7 AI leadership and the future of corporate governance. Digit Trans of Labor. 2019. https://doi.org/10.4324/9780429317866-7 .

Suresh, H., & Guttag, J. V. A framework for understanding unintended consequences of machine learning. arXiv preprint arXiv:1901.10002 , 2(8). 2019.

Fabi, S., Xu, X., & de Sa, V. Exploring the racial bias in pain detection with a computer vision model. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 44, No. 44). 2022.

Susser D, Roessler B, Nissenbaum H. Online manipulation: hidden influences in a digital world. Georgetown Law Technol Rev. 2019;4:1.

Dembrow B. Investing in human futures: how big tech and social media giants abuse privacy and manipulate consumerism. U Miami Bus L Rev. 2021;30:324.

Smuha NA. Beyond the individual: governing AI’s societal harm. Int Policy Rev. 2021. https://doi.org/10.14763/2021.3.1574 .

European Commission. Regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain union legislative acts. 2021. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52021PC0206

Fukuda-Parr S, Gibbons E. Emerging consensus on ‘ethical AI’: human rights critique of stakeholder guidelines. Global Pol. 2021;12:32–44.

Wu, W., & Liu, S. A Comprehensive Review and systematic analysis of artificial intelligence regulation policies. arXiv preprint arXiv:2307.12218 . 2023.

Park, B. The world wants to regulate AI, but does not quite know how. The Economist. (2023a). https://www.economist.com/business/2023/10/24/the-world-wants-to-regulate-ai-but-does-not-quite-know-how

Park, S. Bridging the Global Divide in AI Regulation: A proposal for contextual, coherent, and commensurable framework. Washington International Law Journal, 33(2), 2023b.

Cortez EK. Data protection around the world: privacy laws in action. Berlin: Springer Nature; 2020.

Hagendorff T. The ethics of AI ethics: an evaluation of guidelines. Mind Mach. 2020;30(1):99–120.

Bundy A. Preparing for the future of artificial intelligence. AI Soc. 2016;32:285–7. https://doi.org/10.1007/s00146-016-0685-0 .

Wamba-Taguimdje S, Wamba SF, Kamdjoug JRK, Wanko CET. Influence of artificial intelligence (Ai) on firm performance: the business value of Ai-based transformation projects. BPMJ. 2020;7(26):1893–924.

Einola K, Khoreva V. Best friend or broken tool? Exploring the co-existence of humans and artificial intelligence in the workplace ecosystem. Hum Resour Manage. 2023;62(1):117–35.

García-Buades ME, Peiró JM, Montañez-Juan MI, Kozusznik MW, Ortiz-Bonnín S. Happy-productive teams and work units: a systematic review of the ‘happy-productive worker thesis.’ Int J Environ Res Public Health. 2020;17(1):69.

Van Den Hout JJ, Davis OC. Promoting the emergence of team flow in organizations. Int J Appl Posit Psychol. 2022;7(2):143–89.

Schultze T, Drewes S, Schulz-Hardt S. A test of synergy in dynamic system control tasks. J Exp Psychol Gen. 2021;150(5):890–914. https://doi.org/10.1037/xge0000975 .

Li, Q., Peng, Z., & Zhou, B. (2022). Efficient learning of safe driving policy via human-ai copilot optimization. arXiv preprint arXiv:2202.10341 .

Siemon D. Elaborating team roles for artificial intelligence-based teammates in human-AI collaboration. Group Decis Negot. 2022;31(5):871–912.

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Fenwick, A., Molnar, G. & Frangos, P. The critical role of HRM in AI-driven digital transformation: a paradigm shift to enable firms to move from AI implementation to human-centric adoption. Discov Artif Intell 4 , 34 (2024). https://doi.org/10.1007/s44163-024-00125-4

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DOI : https://doi.org/10.1007/s44163-024-00125-4

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ADVANCING PARKINSON’S DISEASE RESEARCH IN CANADA: THE CANADIAN OPEN PARKINSON NETWORK (C-OPN) COHORT

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Background Enhancing the interactions between study participants, clinicians, and investigators is imperative for advancing Parkinson’s disease (PD) research. The Canadian Open Parkinson Network (C-OPN) stands as a nationwide endeavor, connecting the PD community with ten accredited universities and movement disorders research centers spanning –at the time of this analysis– British Columbia, Alberta, Ontario, and Quebec.

Objective Our aim is to showcase C-OPN as a paradigm for bolstering national collaboration to accelerate PD research and to provide an initial overview of already collected data sets.

Methods The C-OPN database comprises de-identified data concerning demographics, symptoms and signs, treatment approaches, and standardized assessments. Additionally, it collects venous blood-derived biomaterials, such as for analyses of DNA, peripheral blood mononuclear cells (PBMC), and serum. Accessible to researchers, C-OPN resources are available through web-based data management systems for multi-center studies, including REDCap.

Results As of November 2023, the C-OPN had enrolled 1,505 PD participants. The male-to-female ratio was 1.77:1, with 83% (n = 1098) residing in urban areas and 82% (n = 1084) having pursued post-secondary education. The average age at diagnosis was 60.2 ± 10.3 years. Herein, our analysis of the C-OPN PD cohort encompasses environmental factors, motor and non-motor symptoms, disease management, and regional differences among provinces. As of April 2024, 32 researchers have utilized C-OPN resources.

Conclusions C-OPN represents a national platform promoting multidisciplinary and multisite research that focuses on PD to promote innovation, exploration of care models, and collaboration among Canadian scientists.

PLAIN LANGUAGE SUMMARY Teamwork and communication between people living with Parkinson’s disease (PD), doctors, and research scientists is important for improving the lives of those living with this condition. The Canadian Open Parkinson Network (C-OPN) is a Canada-wide initiative, connecting the PD community with ten accredited universities and movement disorders research centers located in –at the time of this analysis– British Columbia, Alberta, Ontario, and Quebec. The aim of this paper is to showcase C-OPN as a useful resource for physician and research scientists studying PD in Canada and around the world, and to provide snapshot of already collected data. The C-OPN database comprises de-identified (meaning removal of any identifying information, such as name or date of birth) data concerning lifestyle, disease symptoms, treatments, and results from standardized tests. It also collects blood samples for further analysis.

As of November 2023, C-OPN had enrolled 1,505 PD participants across Canada. Most of the participants were male (64%), living in urban areas (83%), and completed post-secondary education (82%). The average age at diagnosis was 60.2 ± 10.3 years. In this paper, we look at environmental factors, motor and non-motor symptoms, different disease management strategies, and regional differences between provinces. In conclusion, C-OPN represents a national platform that encourages multidisciplinary and multisite research focusing on PD to promote innovation and collaboration among Canadian scientists.

Competing Interest Statement

JMM has grants from Patient Centered Outcomes Research Institute (2021 to 2023), Parkinson Foundation: PD GENEration (2023 to present), the Canadian Consortium on Neurodegeneration in Aging (2018-present), and Brain Canada (2018 to present). JMM serves as a US delegate of Oxford University Press (2022 to 2026). JMM serves as Vice President of the American Academy of Neurology and is on the Board of Directors of Parkinson Foundation. APS was a past consultant for Hoffman La Roche; received honoraria from GE Health Care Canada LTD, Hoffman La Roche. APS serves on the Board Directors of Parkinson Canada and Canadian Academy Health Sciences. APS is supported by Canadian Institutes of Health Research (CIHR) (PJT173540) and Krembil Rossy Chair program. DAG has received honorariums for speaking from Ipsen and for consulting from Abbvie. DAG is involved in clinical trials via CIHR, Cerevel Therapeutics, Hoffman La Roche, UCB Biopharma, and Bial R&D Investments. DAG has also received grants from CIHR, Parkinson Canada, Brain Canada, Parkinson Research Consortium, EU Joint Programme Neurodegenerative Disease Research, uOBMRI, and NIH. ZGO, LVK, and APS are Editorial Board Members of this journal but were not involved in the peer review process of this article nor had access to any information regarding its peer review. MC, GPM, MB, CPN, CD, IK, SB, AB, RC, ND, PAM, MJM, DM, MGS, AJS, EAF, and OM have no conflicts of interests to report pertaining to this study.

Funding Statement

This study was funded by Parkinson Canada and Brain Canada through the Canada Brain Research Fund, with the financial support of Health Canada.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee/IRB of the University of Calgary gave ethical approval for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

All data produced in the present study are available upon reasonable request to the authors.

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