research paper of human resource policy

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

We're Hiring!
Help Center

Human Resources Management Theories, Policies and Practices: A Review of Literature

Related Papers

Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration

Sudhir Saha

International Journal of Management Research and Emerging Sciences

Asian Social Science

Ahmed Alkali

Human Resource Management Review

Dianna Stone

Felipe Alejandro Torres Castro

480) matrix, we empirically explore the state of the art in human resource management (HRM) research. The data were obtained through a questionnaire directed to HRM scholars all over the world, in which they were asked about their particular theoretical and methodological approaches. The evidence obtained shows clearly that HRM scholars are progressively abandoning the universalistic perspective and completing their models with contingent and contextual variables. Trying to classify the different contributions proposed and discuss their integration, HRM is described as a field of research with three dimensions: subfunctional, strategic and international. The paper concludes that to provide reliable explanations and valid responses to professional problems, HRM research must advance simultaneously in these three dimensions. As follows from our analysis, there are certain HR issues that still need to be addressed: (1) the strategic use of HR practices, (2) their international applicability, (3) global HR strategies and (4) the synergic integration of HR activities. Nevertheless, to advance our knowledge in these issues, it seems necessary to integrate previous research in subfunctional, strategic and international aspects of HRM.

(F2G-HN) Nguyen Tien Minh Quan

Dian Damayanti

International Journal of Training and Development

Niki Kyriakidou

British Journal of Management

Fernando HR

Drawing on Snow and Thomas's (Journal of Management Studies, 31 (1994), pp. 457–480) matrix, we empirically explore the state of the art in human resource management (HRM) research. The data were obtained through a questionnaire directed to HRM scholars all over the world, in which they were asked about their particular theoretical and methodological approaches. The evidence obtained shows clearly that HRM scholars are progressively abandoning the universalistic perspective and completing their models with contingent and contextual variables. Trying to classify the different contributions proposed and discuss their integration, HRM is described as a field of research with three dimensions: subfunctional, strategic and international. The paper concludes that to provide reliable explanations and valid responses to professional problems, HRM research must advance simultaneously in these three dimensions. As follows from our analysis, there are certain HR issues that still need to be addressed: (1) the strategic use of HR practices, (2) their international applicability, (3) global HR strategies and (4) the synergic integration of HR activities. Nevertheless, to advance our knowledge in these issues, it seems necessary to integrate previous research in subfunctional, strategic and international aspects of HRM.

Human Resource Management Theories: Evaluation of their Significance on People Management Practices in Competitive Organisations

Makara Arthur

Abstract Human resources are at the centre of management of contemporary organisations in order for them to be both resilient to change and competitive. Managing people requires to be grounded in the attendant theories of Human Resources Management (HRM), hence the concept of Strategic Human Resources Management (SHRM). Whereas theories exist, there has been limited linkage of people management strategies and these theories. The purpose of this paper was to evaluate HRM theories and their significance in HRM in competitive organisation. Eleven (11) HRM theories were evaluated, and their strategic role in HRM documented. In addition, their strengths and weaknesses were also developed. It was concluded that HRM theories are very relevant and essential in SHRM in competitive organisations and all HRM ought to understand their relevance and applicability in people management for their organisations to be competitive. Keywords: human resources, theories, competitive organisations, human resources management

HRM practices and innovation: an empirical systematic review

International Journal of Disruptive Innovation in Government

ISSN : 2516-4392

Article publication date: 22 April 2020

Issue publication date: 28 January 2021

The relationship between human resource management practices (HRMP) and innovation has been described as a black box, where a lot still needs to be investigated. Thus, the aim of this paper is to investigate the nature of the link that exists between HRMP and innovation in both public and private organizations. To do so, theoretical underpinnings and existence of a mediating or a moderating mechanism is inspected.

Design/methodology/approach

Based on an empirical systematic review of research conducted between 2010 and 2018, content analysis has been conducted for 31 peer-reviewed articles in the English language.

Inspecting the nature of relations existed in the chosen articles, interesting findings are addressed relative to the nature of the human resource management systems (HRMS) used, practices encompassed and their different utility. HRMS has been shown to be associated with product innovation yet more evidence is needed for supporting process innovation.

Practical implications

The HRMS/HRMP and innovation relationship is inspected, important practices that would guide managers to induce innovation are highlighted. Usage of multiple HRMS and contingency in constructing such systems is indicated.

Originality/value

Contribution to comprehend the black box and areas for future research has been offered.

Systematic review
HRM practices
HRM systems

Easa, N.F. and Orra, H.E. (2021), "HRM practices and innovation: an empirical systematic review", International Journal of Disruptive Innovation in Government , Vol. 1 No. 1, pp. 15-35. https://doi.org/10.1108/IJDIG-11-2019-0005

Emerald Publishing Limited

Published in International Journal of Disruptive Innovation in Government . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. 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 license may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Human resource management practices (HRMP) have been gaining an increased attention especially in the fields of economics of the organization, strategic management and human resource management (HRM) ( Laursen and Foss, 2003 ). Moreover, the past two decades were characterized by noticeable progress in researching human resource management systems (HRMS) ( Wei and Lau, 2010 ). HRMS and innovation relationship in firms is growing as many researchers inspected this area (Vogus and Willbourne, 2003; Beugelsdijk, 2008 ; De Winne and Sels, 2010 ; Ma Prieto and Pilar Pérez-Santana, 2014 ; Chen et al. , 2018 ). This growing interest is because of the continuous search for having a competitive advantage in a highly turbulent environment ( Jimenez-Jimenez and Sanz-Valle, 2008 ; Shipton et al. , 2005 ).

Innovation can be promoted through proper management of people ( Shipton et al. , 2005 ). Moreover, firms intending to innovate consider HRMP as a precious resource ( Beugelsdijk, 2008 ). Furthermore, human capital when leveraged organizational expertizes are developed, thus innovation would emerge as new products and services ( Chen and Huang, 2009 ). Several ways can be adopted to inspect the HRMP and outcomes linkage. However, the current approach is the following: complementarities or bundle of practices or individual practice in isolation ( Wright and Boswell, 2002 ).

This study seeks to contribute for the comprehension of the HRM and innovation relationship. It has been identified as a black box by several researchers including ( Beugelsdijk, 2008 ; Laursen and Foss, 2003 ; Messersmith and Guthrie, 2011). Thus, this study tries to inspect the way by which HRM and innovation are linked. Moreover, if there is a need for a mediating or moderating mechanism to understand such a relation.

In what follows the paper is arranged accordingly, first the methodology of the papers selection is explained. Next, the papers are summarized according to the way that HRMP or human resources systems affect innovation. Then, the existence of mediators and moderators as an explaining mechanism is examined. Eventually, practical implication, directions for future research and conclusion of the study are presented.

Methodology of the review

The 31 studies analyzed were published from January 2003 to December 2018 in 18 Journals ( Table I ). The list is mainly based on high ranking journals with a proven history and impact in the HRM research. The database used includes the following: Academy of Management, Sage Journals, Wiley online library, Taylor and Francis online, science direct, Oxford Academic and Emerald insight.

As a start, the research objective is defined and the conceptual boundaries are set. HRMP and innovation are conceptualized according to the following dimensions: HRMP (bundle/single); characteristics of HRMP; definitions of innovation; dimensions of innovation; the existence of a moderator–mediator; outcomes of HRMP in an indication for innovation in all its forms. Moreover, the focus was on the firm level.

Data collection method

The database on HRMP and innovation in firms was built through specific inclusion criteria. Figure 1 resembles the selection process adopted; as a start, the AJG Academic journal guide for journal ranking was examined to select, which journals to include in the study. Second, the main concentration was on HRM and employment journals. Moreover, the secondary and supportive source of data were, namely, general management, organization studies, innovation, psychology, economics, international business and hospitality. Third, titles, abstracts and keywords are inspected within the selected journals using the following key terms: “HRMP;” innovation and firm.

Studies identified counted 3,118, however, those that were not listed in AJG (2018) academic guide for journal ranking was dropped. Moreover, books, reviews, case studies, introductions, editorials, proceedings and abstracts were also excluded; only empirical articles were taken into consideration. Studies that had zero citations, except those published in 2018 was dropped. Next, all articles published before 2010 and included in the study had at least 60 citations. Also, research papers having the workplace and the organization as their unit of study was dropped, leaving us with 29 articles. However, studies that used companies and firms interchangeably were adopted, which gave us an addition of 2 articles, leaving us with 31 articles.

Human resource management practices and innovation in firm research

The HRMP and innovation relationship in firms is tested in a variety of contexts in this systematic review. This review declares that HRMP and innovation in firms are being empirically explored and has an international appeal as different countries are encompassed.

Distribution of studies

Laursen and Foss (2003) declared that the attention to HRMP and innovation in firms goes back to the late nineties. Their paper is considered to be essential in inspecting the relationship between HRMP and innovation in firms. Thus, the current study took the year 2003 as a starting point to inspect the previously mentioned relationship. The variance of interest in such a relationship is quite noticed since 2010 ( Figure 1 ). The years 2010-2018 accounts for the most empirical output in the field of study ( n = 22). Moreover, the main journals in the study are the following: Human Resource Management (6 articles), The International Journal of Human Resource Management (6 articles), International Journal of Manpower (2 articles), Human Resource Management Journal (2 articles) and Journal of Management (2 articles). Two third of the articles were published in human resource management journals ( n = 20).

Furthermore, the quality of the journals used was distributed accordingly. Approximately 10 per cent of the studies used were published in Grade 4* journals; 41 per cent were published in Grade 4 journals; 31 per cent were published in Grade 3 journals and the remaining 18 per cent were published in Grade 2 journals.

In addition, articles revealed a spread over 15 countries, namely, China and Spain dominated the articles count, eight articles for China and seven for Spain, the USA, the UK and Korea counted for two articles each. The rest of the articles were distributed along 10 countries mainly located in Europe. Thus, suggesting an opportunity for a globalized research, if supported with more samples from different countries. Moreover, what has been noticed supports the claim that China is heading to be the world`s innovator ( Casey and Koleski, 2011 ).

Theoretical perspective

To identify the theories used, Nolan and Garavan (2016) approach is adopted, thus, relying on “what theory is not by” ( Sutton and Staw, 1995 ). Human resources theories were spotted such as, namely, human capital theory is used to explain the relationship between innovations and organizational culture; social context theory to explain the organizational culture and employee behavior relationship ( Lau and Ngo, 2004 ). Moreover, learning theories is noticed, for example: organizational learning theory used to explain the impact of knowledge enhanced on innovation ( Chang et al. , 2013 ; Shipton et al. , 2005 ); Upper echelon theory was used to stress the importance of managers’ knowledge in evoking innovation ( De Winne and Sels, 2010 ) ( Figure 2 ).

Furthermore, the resource-based view (RBV) usage is prominent either in isolation or in complementarities. As for the first, RBV has been deployed to explain, namely, the influence of competitive advantage, the support of the knowledge, skill and abilities and intellectual capital on innovation, respectively ( Jimenez-Jimenez and Sanz-Valle, 2008 ; Lopez‐Cabrales et al. , 2009 ; Donate et al. , 2016 ). While for the later, RBV has been combined with creativity theory as an antecedent for creativity, thus leading to innovation ( Beugelsdijk, 2008 ); institutional theory to grab a better understanding of the context as RBV alone fails to do so ( Cooke and Saini, 2010 ); and dynamic capabilities (DC) to enhance innovative performance ( Messersmith and Guthrie, 2010 ).

In addition, the social exchange theory was used in combination with equity theory. Both theories support the claim that employees value the relationship with organization relative to incentives and rewards received ( Jiang et al. , 2012 ). Thus, when employees are valued, they reciprocate the organization with an extra effort and novelty in doing things. Also, the job characteristics theory is used in combination with social cognitive theory to the support the impact of change-oriented HRMS ( Lee et al. , 2016 ). Job characteristics theory increases self-responsibility toward the change and social cognitive theory enhances self-efficacy. Also, organizational support theory was used to explain how managerial support and HRMP would enhance R&D activities, and thus innovation ( Stock et al. , 2014 ). Besides, the presence of knowledge-based view not to be ignored in explaining the importance of knowledge management's impact on innovation ( Andreeva et al. , 2017 ; Chen and Huang, 2009 ).

Finally, the usage of the ability, motivation, opportunity (AMO) framework developed by Bailey (1993) is noticed to be prominent after the year 2014. HRMP are declared to be channeled through, the ability enhancement, motivation and opportunity given for employees (Ma Prieto and Pilar Pérez-Santana, 2014 ; Fu et al. , 2015 ; Lee et al. , 2016 ; Diaz-Fernandez et al. , 2017 ).

Methodology

To analyze the methodology characteristics three aspects have been examined, namely, the industry, the unit of analysis and methods adopted.

The main industry that has been noticed in the chosen articles is the manufacturing sector as it is present in 11 articles. The information and communication technology, is present in 6 papers. The food and beverage, automotive and service industry is present in four research studies. The wholesale trade, computer software industry, electronics, chemical industry, construction and hotel industry was noticed to be covered in 3 articles. The catering, transportation, financial service and textile industry is allocated in two papers. The health and personal service, retail trade, internet and added values services, biotechnology and pharmaceutics and metallurgy industry were inspected in one article each. What is noticed of what been mentioned above that the focus is on the manufacturing industry and there are still some industries to be covered such as oil, education and advertising industries. However, what is interesting that one of the articles excluded the agriculture sector. This may raise some questions and would constitute an opportunity for future research.

Unit of analysis

The individual is the essential unit of investigation of HRMP and innovation in firm research. The human resource director (HRD) was exclusively the unit of analysis in five articles, the Chief Executive Officer (CEO) in one article and the manager. Top executives including (CEO, general manager) were the unit of analysis in three papers, the CEO and the HRD in two papers, the CEO, production manager and HRD in one paper. Moreover, the CEO, middle-level managers and local stake holders was the unit of analysis in one paper, the CEO, HRD and financial controller in one article. Furthermore, The HRD and owner/manager (entrepreneur), was the unit of analysis in two research studies, the HRD and technology manager in one paper, the HRD, operational manager and employee in one article, the HRD, strategic director, production manager and the employee in one paper. Also, the senior, middle and junior managers were the unit of analysis in one paper, the senior executives in one article and the marketing manager and R&D manager in one research. As noticed, almost all of papers have focused on either top or middle management to represent the firm without giving an attention to the lower level of employees. Thus, supporting the claim that employeès opinion and reaction to HR practices is usually not addressed in HRM literature ( Nolan and Garavan, 2016 ).

Methods used

The empirical systematic literature review revealed some aspects about the methodological trends used. In total, 27 studies used questioners or surveys (interchangeably) for data collection, only two of them were longitudinal, while the rest were cross-sectional. Moreover, two studies used a mixed approach of a questioner and an interview. Furthermore, the rest two articles have adopted an interview approach with a longitudinal nature, thus a total of four articles having a longitudinal approach.

Content analysis

The content analysis of HRMP and innovation in firms focused on the following aspects: HRMP (bundle/single); existence of a moderating or a mediating variable, namely, characteristics of HRMS; definitions of innovation; outcomes of HRMP in an indication for innovation in all its forms.

Human resource management systems or human resource management practices

Lado and Wilson (1994) defined an HRMS as “a set of distinct but interrelated activities, functions and processes that are directed at attracting, developing and maintaining or disposing of a firm’s human resources.” Thus, indicating for the complementary and interrelated nature of the practices formulating an HRMS that imposes a competitive advantage for the firm. Moreover, high-performance work systems (HPWS) in accordance with what have been mentioned earlier is defined as “a system of HRMP designed to enhance employees’ skills, commitment and productivity in such a way that employees become a source of sustainable competitive advantage” ( Pfeffer and Jeffrey, 1998 ).

Moreover, the majority of researchers have adopted HMR practices in isolation to inspect its impact on performance ( Wright and Boswell, 2002 ). However, there is a call for adopting sophisticated HRMS to induce product and technological innovation ( Shipton et al. , 2005 ). HRMP when adopted as a system, is expected to evoke innovation as noticed in many research studies, for example: De Winne and Sels (2010) , Lopez-Cabrales et al. (2009) and many others.

The notion of complementarities is essential for HRMP to induce innovation ( Laursen and Foss, 2003 ). However, it has been found that isolated HRMP induce innovation to a certain extent. However, their interactive impact will be more significant ( Beugelsdijk, 2008 ; Shipton et al. , 2006 ). Furthermore, the impact of a single practice of HRM on a firm`s performance is not beneficial ( Lau and Ngo, 2004 ). Additionally, Jimenez-Jemenez and Sanz-Valle (2005) in their study announced a lack of support for the claim that HRMP in isolation would induce innovation.

Moreover, the aspect of integration and fit is highlighted as; HRM system alone might not induce innovation unless accompanied by an organizational culture that supports innovation. Furthermore, the existence of an innovative strategy accompanied by the HRMP is essential for firm innovation (Jimenez-Jemenez and Sanz-Valle, 2005). On the other hand, the alignment of HRMP toward the same goal may have a negative effect ( Andreeva et al. , 2017 ).

In summary, papers that used HRMP as a bundle was ( n = 26); in isolation ( n = 4); a mixture of a bundle and isolation ( n = 1). It is noticed that most researchers agree on the notion of the bundle, however, lack of agreement is noticed relative to the type of practices to integrate in the system (Jimenez-Jemenez and Sanz-Valle, 2005).

Human resource management systems characteristics

A variety of HRMS is used in literature with different HRMP and purposes. HRMS are categorized according to their purpose, namely, innovation-oriented encompassing practices that help build an innovative culture ( Lau and Ngo, 2004 ); a learning supportive ( De Saa-Perez and Díaz-Díaz, 2010 ; Laursen and Foss, 2003 ; Shipton et al. , 2005 ; Shipton et al. , 2006 ); an exploration and behavior fit to strategy ( Cooke and Saini, 2010 ); flexibility and adaptive capability-oriented system to face the rapid environmental changes ( Chang et al. , 2013 ; Jimenez-Jimenez and Sanz-Valle, 2008 ; Martínez-Sánchez et al. , 2011 ; Wei and Lau, 2010 ); a system that allow firms to evoke knowledge and build expertize ( Andreeva et al. , 2017 ; Chen and Huang, 2009 ; De Winne and Sels, 2010 ; Lopez-Cabaralez et al. , 2009; Sung and Choi, 2018 ); high performance work systems used to motivate and build human and social capital ( Fu et al. , 2015 ; Donate et al. , 2016 ; Messersmith and Guthrie, 2010 ); commitment oriented that establish social relations and evokes employee commitment toward the organization and risk taking ( Ceylan, 2013 ; Chen et al. , 2018 ; Neives and Osorio, 2017; Zhou et al. , 2013 ); a collaboration HRMS that helps in the development of equality relationship ( Zhou et al. , 2013 ); high involvement work practices that induce management coworkers support ( Ma Prieto and Pérez-Santana, 2014 ); a change oriented that impact employee psychological status such as self-efficacy and responsibility to change ( Lee et al. , 2016 ); and a creativity inducing system ( Liu et al. , 2017 ).

In summary, HRMS that builds knowledge capabilities evokes flexibility and learning is highly used in research. Moreover, commitment systems are quite noticed, however, the concepts of fit, culture and collaboration need to be more research as few studies have been encountered. Additionally, the same systems encompassing different HRMP were used for different purposes. Furthermore, different systems have been used for the same purpose.

Systems used for different purposes are high performance work system, high commitment human resource system. The first was used to; motivate, build human and social capital ( Messersmith and Guthrie, 2010 ); to enhance adaptive capability ( Wei and Lau, 2010 ); and induce innovative work behavior ( Fu et al. , 2015 ). The latter, was used to support learning ( De Saa-Perez and Díaz-Díaz, 2010 ); enhance innovative capability ( Zhou et al. , 2013 ) and innovative behavior, evoke organizational commitment and employee risk-taking Chen et al. (2018) and alignment of strategy ( Cooke and Saini, 2010 ). This supports the notion that HRMS are used interchangeably especially HPWS, high involvement work system (HIWS) and high commitment work systems (HCWS) ( Chen et al. , 2018 ).

Human resource management practices in isolation

Utilization of HRMP in isolation is quite noticed and adopted in recent research studies. The practices used can be categorized according to their purpose of usage. Lau and Ngo (2004) used three practices directed toward mindfulness; Jiang et al. (2012) adopted eight practices to evoke employee creativity; Stock et al. (2014) used four innovation-oriented practices; and Diaz-Fernandez et al. (2017) incorporated four practices aiming at enhancing employee abilities, motivation and opportunity to innovate.

Innovation by definition

Different definitions of innovation have been encountered, thus a trial has been conducted to set a certain trend for the definitions adopted. The definition by West and Far, used by Jiang et al. (2012) , Shipton et al. (2005) and Shipton et al. (2006) . It captures the deliberate behavior directed toward new (products, ideas and processes), that is new to the adopting unit and beneficial for the organization and society. Moreover, its usage has been noticed to be mainly for the technological products and processes.

Next, the prominent author relied upon in defining innovation was Damanpour, as there has been three definitions established during the following years 1989, 1991 and 1998. The articles are developed by: Diaz-Fernandez et al. (2017) , Ceylan (2013) , Chang et al. (2011) , Chen and Huang (2009) , Fu et al. (2015) , Jemenez-Jemenez and Sanz Valle (2008), Wei and Lau (2010) and Zhou et al. (2013) . Such definitions consider innovation as a performance outcome. Moreover, it captures the innovative strategy, product, project, process and organizational innovation. Furthermore, the measuring scale of patents and the classification of radical and incremental innovation was realized.

Additionally, innovation as newness in products, services, work and practices is addressed relying on ( Rogers, 1983 ). In addition, innovation has been considered to be embedded in knowledge according to kogut and Zander (1992) , Nonaka (1994) and Smith et al. (2005) .

In summary, the definition of innovation adopted is mainly that of Damanpour, which states that, namely, “the adoption of an idea or behavior, whether a system, policy, program, device, process, product or service, that is new to the adopting organization” ( Damanpour et al. , 1989 ).

Mediator or moderator

Almost half the studies ( n = 17) have used a mediator or a moderator as an explaining tool for the indirect linkage between HRMP and innovation in firms ( Lau and Ngo, 2004 also Wei and Lau, 2010 ). The mediators used are as follows: Organizational culture, knowledge management capacity, unique knowledge, valuable knowledge, adaptive capability, innovation-oriented strategy, employee creativity, cross-functional research and development, absorptive capacity, innovative work behavior, human and social capital, firm ownership and middle managers innovative behavior. On the other hand, the moderators incorporated are, namely, environmental dynamism, strategic activities, compensation and benefits, employee creativity, work-family conflict and work climate.

In the following section, the outcomes of the articles included in the review are presented accordingly; and the HRMP and innovation relationship (direct/indirect). Moreover, the direct relationship is categorized into bundles, isolation and utilization of both approaches.

Human resource management systems

First, trying to find the best bundle of practices for product innovation in firms, Laursen and Foss (2003) adopted two systems, namely, the first composed of nine practices and the second composed of two; however, both having a learning objective. Their sample was 913 Danish firms with at least 100 employees. Results indicated that the complementarities effect between practices enhances their impact on innovation, however, only seven of the first system had a positive significant impact. Moreover, Shipton et al. (2005) examined the British context by sampling 32 firms having at least 70 employees. The system adopted is learning-oriented composed of six practices. Results indicated a significant impact on product production and technology innovation, however, no impact on the process. This notion was supported by Jiménez-Jiménez and Sanz-Valle (2008), when exploring the Spanish context, with a sample of 173 firms having more than 50 employees.

Also, De Winne and Sels (2010) , with a sample of 294 startup firms in Belgium inspected the impact of HRMP as a bundle on product, process and service innovation. The systems composed of five practices directed toward knowledge creation and retention. Results indicated high positive significance between the bundle of practices and the mentioned types of innovation. In addition, De Saa-Perez and Díaz-Díaz (2010) , while investigating the Canary Islands by sampling 157 firms having more than 10 employees. High commitment HRMP was used such as internal promotion, group-based performance appraisal among six practices. It was noticed the existence of a positive influence on product and process innovation, yet this influence varies relative to sectors.

Furthermore, Messermith and Gutherie (2010) handled a sample of 2018 firm in the USA having 20 to 100 employees. HPWS was adopted, it supported the emergence of product, organizational but not process innovation. Besides, Zhou et al. (2013) inspected two systems of HRMP, commitment and collaboration in the Chinese context of 125 firms having 50 employees and above. Both systems indicated a positive impact on organizational innovation, however, when implemented together, a negative interactions emerges this hindering innovation. The commitment-based system was used by Ceylan (2013) , which enhanced various forms of innovation This positive impact on innovation is also reflected when studying 109 firms with 50 employees or more in Spain ( Nieves and Osorio, 2017 ).

In summary, different usage of HRMP systems shown a positive association with product innovation, however, little evidence is provided to support the emergence of process innovation. Moreover, innovation level varies among sectors as some are influenced by specific types of system of practices. Thus, according to the sector, careful selection of practices should be adopted. Furthermore, it was noticed that when implementing two different types of systems, the impact of both systems on innovation is diminished. This is explained according to ambidexterity as there should be a balance if more than one system is adopted.

Next, Vogus and Wellborne (2003) examined the USA by a sample of 184 firms having an average of 238 employees. HRMP was used in isolation, results indicated that innovation output is strongly increased by these practices. Moreover, Beugelsdijk (2008) examined the Dutch context with a sample of 988 firms having a minimum of 5 employees. Outcomes highlighted the importance of adopting practices that stress training and incentives to induce incremental innovation such as follows: training, performance-based pay. While, for radical innovation the adopted practices should induce autonomy.

Combination

Then, Shipton et al. (2006) inspected the UK context through 22 firms having an average of 236 employees. They adopted a set of practices that evoke exploratory learning; results indicated that induction, appraisal, training and teamwork had a significant impact on product innovation yet; appraisal had no impact on technical system innovation. Moreover, contingent reward had no impact on both types of innovation, however, when combined with other practices as a system its impact becomes obvious. In addition, the combined influence had a stronger impact on technical innovation.

Moreover, Chang et al. (2011) when adopting selection and training practices in isolation both had a positive impact on incremental and radical innovation. However, the joint adoption had a negative impact on incremental innovation. Thus, a proper identification of practices so that, they won` t impact each other negatively. Besides, Andreeva et al. (2017) adopted 3 knowledge-oriented practices to inspect jointly and separately in 259 companies with at least 100 employees in Finland. The separate impact of rewards and appraisals was positive on incremental innovation, however, no interaction impact. While, for radical innovation rewards had a positive impact while the interactive impact was negative. This supports the notion of careful selection when combing practices.

In summary, various HRMP have been examined if being used would enhance innovation, surprisingly most studies revealed that single practices would evoke innovation. However, when combined with each other innovation will be hindered. Thus, contradicting what has been mentioned above relative to the impact of bundles of HRMP on innovation.

Mediators and moderators

Finally, the existence of a mediating or moderation mechanism to explain the HRMP and innovation linkage is noticed. Lau and Ngo (2004) used innovation-oriented HRMP as a bundle in 332 firms having more than 50 employees in Hong Kong. The system used to create cross-functional teams that support change. It had a positive impact on innovation through the organizational culture. Moreover, knowledge management capacity as a moderator was adopted by Chen and Huang (2009) while examining Taiwanese firms. Results supported the mediating impact between HRMP as a bundle and innovation (administrative and technical). Furthermore, Lopez-Cabrales et al. (2009) examined the Spanish context with a sample of 86 firms having more than 50 employees. Two types of bundles was adopted; knowledge-based and Collaborative HRMP mediated by valuable knowledge and unique knowledge respectively. Hence, both systems had no direct effect, while only collaborative HRMP has an impact on innovation mediated by unique knowledge.

In addition, partial support has been recognized when examining the HPWS and product innovation relationship mediated by adaptive capability ( Wei and Lau, 2010 ). Also, Cooke et al. (2010) inspected the impact of high commitment work practices on product, process and customer service innovation through alignment of strategy. Strong influence has been noticed, which was explained by the adoption of practices supporting each other. Also, Jiang et al. (2012) tested the impact of HRMP in isolation on technological and organizational innovation mediated by employee creativity. All practices indicated a positive mediation, however, training and performance appraisal were not.

Next, cross-functional R&D was inspected as a mediator between HRMP in isolation and product program innovativeness. The test conducted in the German context with a sample of 125 firms having 50 employees and above ( Stock et al. , 2014 ). Training and rewards had a strong influence on product program innovativeness, however, recruitment had no impact. Besides, the mediating role of absorptive capacity between flexibility-oriented HRMS and incremental innovation was inspected in China. Both systems indicated a significant association with firm innovativeness, however, when implemented together the positive impact fades ( Chang et al. , 2013 ).

Then, Ma Prieto and Pilar Pérez-Santana (2014) adopted a supportive work environment as a mediator between high involvement HRMP and innovative work behavior. The study was conducted in Spain handling sample of 198 firms. Outcomes indicated that direct and the mediated relationship between HRMP targeting employee’s abilities, skills and opportunities and innovative work behavior is significant. As well, Fu et al. (2015) when examining the Irish context adopted HWPS and organizational innovation relationship mediated by innovative work behavior. The sample included 120 firms and results supported the direct and the mediated relationship.

Subsequently, Donate et al. (2016) sampled 72 firms in Spain, where two systems are adopted. High profile performance systems composed of five practices and a collaborative system composed of seven practices. The relation with product and process innovation was examined through human and social capital. Results indicated that both systems positively impacted product and process innovation when mediated through human and social capital respectively. In addition, Lee et al. (2016) investigated the Korean context sampling 11 firms while adopting a change-oriented HRM system. The suggested relationship between HRM system and group innovation is through employee proactively. Primary results indicated a channeling effect of employee proactive behavior, however, no mediating effect.

As for the moderated relationship between HRMP and innovation, environmental dynamism was used by Martínez-Sánchez et al. (2011) in the Spanish context. The study encompassed two flexibility-oriented systems; internal and external numerical. Moreover, the internal system is composed by its turn from functional and numerical. Results indicated that for both direct and moderated relationship the following. The internal system with both its subsystems indicated a positive relationship with innovativeness, however, only consulting contracting firms in the external system is in positive relation.

Furthermore, Diaz-Fernandez et al. (2017) conducted a longitudinal study in the Spanish context encompassing a sample of 1,363 firms. He used four HRMP in isolation to be moderated by compensation and benefits. Results indicated that only employment security and investment in new training technologies had a significant impact on innovation as long as this relationship is moderated by high salaries. However, employment security, compensation when implemented in isolation had no impact on innovation. Moreover, the language training and training in new technologies had not impact.

Additionally, what is interesting is the existence of a mediator and a moderator in three studies encompassed in the review. First, Liu et al. (2017) investigated the Chinese context by sampling 57 firms. Two systems are adopted, the employee experienced performance HRM and employee experienced maintenance-oriented HRM. The two systems implemented with employee creativity as moderator and firm ownership as a mediator. The multilevel relationship indicated a positive impact on firm innovation. Next, Sung and Choi (2018) examined the Korean contest with a two-set of knowledge stock and flow-oriented practices. The mediators used firm knowledge flow and stock, while the moderator is the strategy. Flow and stock facilitating HRMP indicated a positive impact on firm innovation through firm knowledge flow. Moreover, the moderating effect is partial as innovation is impacted through knowledge stock. Thus there is a need for a proper implementation of high levels of firm knowledge flow if to make use of firm knowledge stock in inducing innovation.

Finally, Chen et al. (2018) inspected 113 firms in the Chinese context where a high commitment work system is used. The system impact on innovative behavior is studied through middle managers innovative behavior; this relation is moderated by work-family conflict and work climate. The managers’ innovative behavior successfully mediates the relationship between HCWS and firm innovative performance. However, the direct relationship was not significant, moreover work-family conflict had a negative impact on innovative behavior. Furthermore, the combined effect of HCWS with both moderating variables indicated a positive impact on innovative behavior.

In summary, the research is rich with trials to explain the relationship between HRMP and innovation through a mechanism. However, the mediating mechanism is more popular among research, thus, what would be beneficial is search for further moderators to explain the above-mentioned relationship. In what follows managerial implications for practice are presented.

Important practical implications are uncovered for managers that need to acquire human resources skills and competencies, which would enhance the firm`s survival rate. First, it has been noticed that the existence of training in most of the HRMS is present and plays a vital role in inducing innovation. Lack of training might be reflected in the absence of innovation, however, presence of training would prevent employees from being square minded. Thus, managers are required to focus on human capital development and adopt practices that foster knowledge and enrich employees` skills. Fostering knowledge includes the process of acquiring and sharing information among employees. Sharing information can be motivated through a bonus system that reward combined effort rather than individual ones. Moreover, managers can promote a learning environment by having the proper infrastructure needed and through nurturing social ties. On the other hand, it was noticed that training had no impact on innovation; this case needs to be investigated closely.

Second, managers have to be aware to what practices to use in the HRMS, as some practices when combined together would negatively impact the learning process in the organization. Just as the presence of individual appraisal and pay for performance. Such a case will result in conflict, which can be resolved by careful selection and proper fit among HRMP to be included in the system. Moreover, the fit is not restricted to the practices only, as the fit should take into consideration the company strategy. Third, managers who provide a secure working environment for their employees as replacing contracts with full-time schedules, tolerate and encourage risk-taking, will lead provoke innovation. Forth, cultural aspects should be treated carefully, as when ignored will have negative impact on innovation, as cultural changes require the adjustment of management approach.

Fifth, the importance of selecting and hiring employees with unique knowledge and high education and take the proper measures to retain talents and key persons that are considered vital. This can be done through career development, promotions, flexibility, autonomy, motivation and investment in leadership practices in a dynamic environment. Finally, managers would implement more than one HRM system, however, these systems should be implemented in synergy.

Future research

As noticed in the review the theoretical underpinning of the HRMP, innovation relationship is quite noticed. However, there is still a space to examine more theories to explain this relationship, for example. Trait theory can be adopted as it explains the individual-level factors, which might impact HRMS positively or negatively ( Tett and Burnett, 2003 ).

Moreover, regarding the methodology, sampling size in most studies was limited, thus, it would be beneficial to in large it. Furthermore, the impact of the context in which the practices were implemented should have been closely inspected ( Vogus and Welbourne, 2003 ). In addition, the sector was controlled for; however, it would of interest to inspect the type of practices that would impact each sector. Also, the longitudinal approach is scarce as noticed only four articles adopted it ( Diaz-Fernandez et al. , 2017 ; Shipton et al. , 2005 ; Shipton et al. , 2006 ; Sung and Choi, 2018 ). Hence, longitudinal studies could grab the impact of the HRMP on innovation in different time intervals. Moreover, the field lacks studies that examined the sample of investigation before and after implementing the HRMP. Finally, face to face interviews when conducted would yield more in-depth information about the field of study.

Furthermore, tow contradicting perspectives have been encountered regarding the parsimony of practices. As for the first, a call is noticed for a limited number of practices, thus inducing flexibility (Jimenez-Jemenez and Sanz-Valle, 2005). While, the latter the inclusion of enormous sets of practices is noticed ( Donate et al. , 2016 ; Martínez-Sánchez et al. , 2011 ; Zhou et al. , 2013 ). Moreover, substitution of practices or using alternative practices would be an area of interest to be inspected. Additionally, agreement on the type of practices that are aligned and fit is missing. Finally, the inclusion of more variables to portray the linkage between HRMP and innovation is appealing such as organizational structure, psychological contract and organizational capital.

The 31 empirical articles reviewed suggest some improvement toward understanding the HRMP and innovation relationship in firms. The context diversity in which the studies have been conducted reveals that the HRMP and innovation relationship is a rich field yet a lot to be discovered. Practical implication are indicated, which would act as guidance for what of practices would induce innovation if implemented. However, as noticed there no specific system to apply as firms and cultural has to be dealt with according to contingency. Moreover, it suggests some additional theories to be used for inspecting the HRMP and innovation relationship.

In addition, the study encompasses areas of strength and weaknesses, as for the first the types of journals selected are high ranking, which reflects reliability of review. While the latter, the study included only empirical articles, which can be considered a weakness, as many conceptual articles was dropped. Moreover, the studies interpreted the HRMP as a bundle in different ways, with different inclusion of practices for the same system. Furthermore, all unpublished studies, Grade 1 journals, books and abstracts were excluded.

Chart of articles selection method

Distribution of empirical HRMP and innovation publications

List of journals and ranking

Summary of HRMP and innovation publications

*The presence of a Moderator; **the presence of Mediator

Andreeva , T. , Vanhala , M. , Sergeeva , A. , Ritala , P. and Kianto , A. ( 2017 ), “ When the fit between HR practices backfires: exploring the interaction effects between rewards for and appraisal of knowledge behaviours on innovation ”, Human Resource Management Journal , Vol. 27 No. 2 , pp. 209 - 227 .

Bailey , T. ( 1993 ), “ Organizational innovation in the apparel industry ”, Industrial Relations , Vol. 32 No. 2 , pp. 30 - 48 .

Beugelsdijk , S. ( 2008 ), “ Strategic human resource practices and product innovation ”, Organization Studies , Vol. 29 No. 6 , pp. 821 - 847 .

Casey , J. and Koleski , K. ( 2011 ), Backgrounder: China’s 12th Five-Year Plan , US-China Economic and Security Review Commission .

Ceylan , C. ( 2013 ), “ Commitment-based HR practices, different types of innovation activities and firm innovation performance ”, The International Journal of Human Resource Management , Vol. 24 No. 1 , pp. 208 - 226 .

Chang , S. , Gong , Y. and Shum , C. ( 2011 ), “ Promoting innovation in hospitality companies through human resource management practices ”, International Journal of Hospitality Management , Vol. 30 No. 4 , pp. 812 - 818 .

Chang , S. , Gong , Y. , Way , S.A. and Jia , L. ( 2013 ), “ Flexibility-oriented HRM systems, absorptive capacity, and market responsiveness and firm innovativeness ”, Journal of Management , Vol. 39 No. 7 , pp. 1924 - 1951 .

Chen , C.J. and Huang , J.W. ( 2009 ), “ Strategic human resource practices and innovation performance – the mediating role of knowledge management capacity ”, Journal of Business Research , Vol. 62 No. 1 , pp. 104 - 114 .

Chen , Y. , Jiang , Y.J. , Tang , G. and Cooke , F.L. ( 2018 ), “ High‐commitment work systems and Middle managers’ innovative behavior in the Chinese context: the moderating role of work‐life conflicts and work climate ”, Human Resource Management , Vol. 57 No. 5 , pp. 1317 - 1334 .

Cooke , F.L. and Saini , D.S. ( 2010 ), “ (how) does the HR strategy support an innovation oriented business strategy? an investigation of institutional context and organizational practices in Indian firms ”, Human Resource Management: Published in Cooperation with the School of Business Administration, the University of MI and in Alliance with the Society of Human Resources Management , Vol. 49 No. 3 , pp. 377 - 400 .

Damanpour , F. , Szabat , K.A. and Evan , W.M. ( 1989 ), “ The relationship between types of innovation and organizational performance ”, Journal of Management Studies , Vol. 26 No. 6 , pp. 587 - 602 .

De Winne , S. and Sels , L. ( 2010 ), “ Interrelationships between human capital, HRM and innovation in Belgian start-ups aiming at an innovation strategy ”, The International Journal of Human Resource Management , Vol. 21 No. 11 , pp. 1863 - 1883 .

Diaz-Fernandez , M. , Bornay-Barrachina , M. and Lopez-Cabrales , A. ( 2017 ), “ HRM practices and innovation performance: a panel-data approach ”, International Journal of Manpower , Vol. 38 No. 3 , pp. 354 - 372 .

De Saa-Perez , P. and Díaz-Díaz , N.L. ( 2010 ), “ Human resource management and innovation in the canary islands: an ultra-peripheral region of the European Union ”, The International Journal of Human Resource Management , Vol. 21 No. 10 , pp. 1649 - 1666 .

Donate , M.J. , Peña , I. and Sanchez de Pablo , J.D. ( 2016 ), “ HRM practices for human and social capital development: effects on innovation capabilities ”, The International Journal of Human Resource Management , Vol. 27 No. 9 , pp. 928 - 953 .

Fu , N. , Flood , P.C. , Bosak , J. , Morris , T. and O’Regan , P. ( 2015 ), “ How do high performance work systems influence organizational innovation in professional service firms? ”, Employee Relations , Vol. 37 No. 2 , pp. 209 - 231 .

Jiang , J. , Wang , S. and Zhao , S. ( 2012 ), “ Does HRM facilitate employee creativity and organizational innovation? A study of Chinese firms ”, The International Journal of Human Resource Management , Vol. 23 No. 19 , pp. 4025 - 4047 .

Jimenez-Jimenez , D. and Sanz-Valle , R. ( 2005 ), “ Innovation and human resource management fit: an empirical study ”, International Journal of Manpower , Vol. 26 No. 4 , pp. 364 - 381 .

Jimenez-Jimenez , D. and Sanz-Valle , R. ( 2008 ), “ Could HRM support organizational innovation? ”, The International Journal of Human Resource Management , Vol. 19 No. 7 , pp. 1208 - 1221 .

Kogut , B. and Zander , U. ( 1992 ), “ Knowledge of the firm, combinative capabilities, and the replication of technology ”, Organization Science , Vol. 3 No. 3 , pp. 383 - 397 .

Lau , C.M. and Ngo , H.Y. ( 2004 ), “ The HR system, organizational culture, and product innovation ”, International Business Review , Vol. 13 No. 6 , pp. 685 - 703 .

Lado , A.A. and Wilson , M.C. ( 1994 ), “ Human resource systems and sustained competitive advantage: a competency-based perspective ”, The Academy of Management Review , Vol. 19 No. 4 , pp. 699 - 727 .

Laursen , K. and Foss , N.J. ( 2003 ), “ New human resource management practices, complementarities and the impact on innovation performance ”, Cambridge Journal of Economics , Vol. 27 No. 2 , pp. 243 - 263 .

Lee , H.W. , Pak , J. , Kim , S. and Li , L.Z. ( 2016 ), “ Effects of human resource management systems on employee proactivity and group innovation ”, Journal of Management , p. 149206316680029 .

Liu , D. , Gong , Y. , Zhou , J. and Huang , J.C. ( 2017 ), “ Human resource systems, employee creativity, and firm innovation: the moderating role of firm ownership ”, Academy of Management Journal , Vol. 60 No. 3 , pp. 1164 - 1188 .

Lopez‐Cabrales , A. , Pérez‐Luño , A. and Cabrera , R.V. ( 2009 ), “ Knowledge as a mediator between HRM practices and innovative activity ”, Human Resource Management , Vol. 48 No. 4 , pp. 485 - 503 .

Ma Prieto , I. and Pérez-Santana , M.P. ( 2014 ), “ Managing innovative work behavior: the role of human resource practices ”, Personnel Review , Vol. 43 No. 2 , pp. 184 - 208 .

Martínez-Sánchez , A. , Vela-Jiménez , M.J. , Pérez-Pérez , M. and de-Luis-Carnicer , P. ( 2011 ), “ The dynamics of labour flexibility: relationships between employment type and innovativeness ”, Journal of Management Studies , Vol. 48 No. 4 , pp. 715 - 736 .

Messersmith , J.G. and Guthrie , J.P. ( 2010 ), “ High performance work systems in emergent organizations: implications for firm performance ”, Human Resource Management , Vol. 49 No. 2 , pp. 241 - 264 .

Nieves , J. and Osorio , J. ( 2017 ), “ Commitment-based HR systems and organizational outcomes in services ”, International Journal of Manpower , Vol. 38 No. 3 , pp. 432 - 448 .

Nolan , C.T. and Garavan , T.N. ( 2016 ), “ Human resource development in SMEs: a systematic review of the literature ”, International Journal of Management Reviews , Vol. 18 No. 1 , pp. 85 - 107 .

Nonaka , I. ( 1994 ), “ A dynamic theory of organizational knowledge creation ”, Organization Science , Vol. 5 No. 1 , pp. 14 - 37 .

OECD/Eurostat ( 2005 ), “ Guidelines for collecting and interpreting innovation data ”, available at: www.keepeek.com/Digital-Asset-Management/oecd/science-and-technology/oslomanual_9789264013100-en (accessed 8 August 2015 ).

Pfeffer , J. and Jeffrey , P. ( 1998 ), The Human Equation: Building Profits by Putting People First , Harvard Business Press .

Rogers , M.E. ( 1983 ), Diffusion of Innovations , The Free Press .

Shipton , H. , Fay , D. , West , M. , Patterson , M. and Birdi , K. ( 2005 ), “ Managing people to promote innovation ”, Creativity and Innovation Management , Vol. 14 No. 2 , pp. 118 - 128 .

Shipton , H. , West , M.A. , Dawson , J. , Birdi , K. and Patterson , M. ( 2006 ), “ HRM as a predictor of innovation ”, Human Resource Management Journal , Vol. 16 No. 1 , pp. 3 - 27 .

Smith , K.G. , Collins , C.J. and Clark , K.D. ( 2005 ), “ Existing knowledge, knowledge creation capability, and the rate of new product introduction in high-technology firms ”, Academy of Management Journal , Vol. 48 No. 2 , pp. 346 - 357 .

Stock , R.M. , Totzauer , F. and Zacharias , N.A. ( 2014 ), “ A closer look at cross‐functional R&D cooperation for innovativeness: innovation‐oriented leadership and human resource practices as driving forces ”, Journal of Product Innovation Management , Vol. 31 No. 5 , pp. 924 - 938 .

Sutton , R.I. and Staw , B.M. ( 1995 ), “ What theory is not ”, Administrative Science Quarterly , Vol. 40 No. 3 , pp. 371 - 384 .

Sung , S.Y. and Choi , J.N. ( 2018 ), “ Building knowledge stock and facilitating knowledge flow through human resource management practices toward firm innovation ”, Human Resource Management , Vol. 57 No. 6 , pp. 1429 - 1442 .

Tett , R.P. and Burnett , D.D. ( 2003 ), “ A personality trait-based interactionist model of job performance ”, Journal of Applied Psychology , Vol. 88 No. 3 , p. 500 .

Vogus , T.J. and Welbourne , T.M. ( 2003 ), “ Structuring for high reliability: HR practices and mindful processes in reliability‐seeking organizations ”, Journal of Organizational Behavior , Vol. 24 No. 7 , pp. 877 - 903 .

Wright , P.M. and Boswell , W.R. ( 2002 ), “ Desegregating HRM: a review and synthesis of micro and macro human resource management research ”, Journal of Management , Vol. 28 No. 3 , pp. 247 - 276 .

Zhou , Y. , Hong , Y. and Liu , J. ( 2013 ), “ Internal commitment or external collaboration? The impact of human resource management systems on firm innovation and performance ”, Human Resource Management , Vol. 52 No. 2 , pp. 263 - 288 .

Corresponding author

Related articles, we’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Browse All Articles
Newsletter Sign-Up

HumanResources →

No results found in working knowledge.

Were any results found in one of the other content buckets on the left?
Try removing some search filters.
Use different search filters.

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

Perspective
Open access
Published: 13 May 2024
Volume 4 , article number 34 , ( 2024 )

Cite this article

You have full access to this open access article

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

66 Accesses

Explore all metrics

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.

Avoid common mistakes on your manuscript.

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.

Article MathSciNet Google Scholar

Download references

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and affiliations.

Hult International Business School, Dubai, United Arab Emirates

Ali Fenwick

Wittenborg University of Applied Sciences, Apeldoorn, The Netherlands

Ali Fenwick & Gabor Molnar

Hult International Business School, Ashridge, UK

Piper Frangos

You can also search for this author in PubMed Google Scholar

Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. The significance of contributions made to the paper align with the author’s position as contributing author being first, second, and third author.

Corresponding author

Correspondence to Ali Fenwick .

Ethics declarations

Competing interests.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

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

Download citation

Received : 07 February 2024

Accepted : 22 April 2024

Published : 13 May 2024

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

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Humanizing AI
AI leadership
AI knowledge
AI policies
Organizational culture
Behavioral science
Find a journal
Publish with us
Track your research

Evaluating Local Government Policy Innovations

A case study of surabaya's efforts in combating stunting and enhancing public health services quality.

Deasy Arieffiani Universitas Hang Tuah
Mas Roro Lilik Ekowanti Public Administration Department, Hang Tuah University, Surabaya, Indonesia

This research aims to evaluate regional innovations in implementing Surabaya City government policies to reduce stunting rates and improve the quality of public health services. A qualitative descriptive method was used with a case study approach involving field observations and structured interviews. The research results show the success of Posyandu Prima in reducing stunting rates significantly in the last two years. The Surabaya City Government has proven effective in managing this program's human resources and budget. The active involvement of Great Surabaya Cadres (KSH) and non-governmental organizations also contributed greatly to the program's success. Cross-sector collaboration plays an important role in supporting implementation. Institutional characteristics, such as commitment to public health and ability to collaborate, also matter. Theoretically, this research shows that synergy between the parties involved and government commitment can achieve significant results in handling the stunting problem. In conclusion, the Prima Posyandu Program has proven successful in reducing stunting rates and improving the quality of public health services in Surabaya. Additionally, the collaborative efforts between community stakeholders, healthcare providers, and governmental bodies underscore the crucial role of multi-sectoral partnerships in addressing complex public health issues like stunting. This synergy fosters comprehensive approaches that combine local knowledge, resources, and policy support to effectively combat stunting and enhance the well-being of communities. Thus, the Prima Posyandu Program's success is a compelling example of how concerted action and sustained commitment can yield tangible improvements in population health outcomes.

Adair, L. S., Carba, D. B., Lee, N. R., & Borja, J. B. (2021). Stunting, IQ, and Final School Attainment in the Cebu Longitudinal Health and Nutrition Survey Birth Cohort. Economics & Human Biology, 42, 100999. https://doi.org/10.1016/j.ehb.2021.100999

Aditri, F., Sufyan, D. L., & Puspareni, L. D. (2022). Policy Implementation Strategy of West Bandung District Health Office in Stunting Intervention During COVID-19 Pandemic. Journal of Global Nutrition, 1(2), 75–92. https://doi.org/10.53823/jgn.v1i2.24

Adnyana, S. (2014). Perbedaan Status Gizi Balita Berdasarkan Frekuensi Kunjungan ke Posyandu dan Tingkat Pengetahuan Ibu di Desa Bungaya Kecamatan Bebandem Kabupaten Karangasem Provinsi Bali. Jurnal Bina Praja, 6(3), 221–226. https://doi.org/10.21787/jbp.06.2014.221-226

Anggraini, T., & Melin Wula, H. V. (2021). Governmental Performance in Integrated Stunting Countermeasures in Border Regions: Evidence from Timur Tengah Utara Regency. Jurnal Studi Sosial dan Politik, 5(2), 252–263. https://doi.org/10.19109/jssp.v5i2.9561

Ansell, C., & Gash, A. (2007). Collaborative Governance in Theory and Practice. Journal of Public Administration Research and Theory, 18(4), 543–571. https://doi.org/10.1093/jopart/mum032

Bhutta, Z. A., Akseer, N., Keats, E. C., Vaivada, T., Baker, S., Horton, S. E., Katz, J., Menon, P., Piwoz, E., Shekar, M., Victora, C., & Black, R. (2020). How Countries Can Reduce Child Stunting at Scale: Lessons From Exemplar Countries. The American Journal of Clinical Nutrition, 112, 894S-904S. https://doi.org/10.1093/ajcn/nqaa153

Bryson, J. M., Crosby, B. C., & Stone, M. M. (2015). Designing and Implementing Cross-Sector Collaborations: Needed and Challenging. Public Administration Review, 75(5), 647–663. https://doi.org/10.1111/puar.12432

Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE Publications.

Daniel, D., Qaimamunazzala, H., Prawira, J., Siantoro, A., Sirait, M., Tanaboleng, Y. B., & Padmawati, R. S. (2023). Interactions of Factors Related to the Stunting Reduction Program in Indonesia: A Case Study in Ende District. International Journal of Social Determinants of Health and Health Services, 53(3), 354–362. https://doi.org/10.1177/27551938231156024

Elmighrabi, N. F., Fleming, C. A. K., & Agho, K. E. (2024). Factors Associated with Childhood Stunting in Four North African Countries: Evidence from Multiple Indicator Cluster Surveys, 2014–2019. Nutrients, 16(4), 473. https://doi.org/10.3390/nu16040473

Erlyn, P., Hidayat, B. A., Fatoni, A., & Saksono, H. (2021). Nutritional Interventions by Local Governments as an Effort to Accelerate Stunting Reduction. Jurnal Bina Praja, 13(3), 543–553. https://doi.org/10.21787/jbp.13.2021.543-553

Essa, W. Y., Nurfindarti, E., & Ruhyana, N. F. (2021). Strategies for Handling Stunting in Bandung City. Jurnal Bina Praja, 13(1), 15–28. https://doi.org/10.21787/jbp.13.2021.15-28

Fatahillah, R. E. P., & Noviyanti. (2023). Analisis Survei Kepuasan Masyarakat pada Pelayanan Kesehatan Ibu dan Anak (KIA) di Puskesmas Gayungan Kota Surabaya. Jurnal Inovasi Administrasi Negara Terapan, 1(1), 178–190. https://journal.unesa.ac.id/index.php/innovant/article/view/25898

Ferguson, L. C., & Clark, T. N. (1979). The Policy Predicament: Making and Implementing Public Policy by George C. Edwards and Ira Sharkansky. Administrative Science Quarterly, 24(1), 149. https://doi.org/10.2307/2989886

Habimana, J. de D., Uwase, A., Korukire, N., Jewett, S., Umugwaneza, M., Rugema, L., & Munyanshongore, C. (2023). Prevalence and Correlates of Stunting among Children Aged 6–23 Months from Poor Households in Rwanda. International Journal of Environmental Research and Public Health, 20(5), 4068. https://doi.org/10.3390/ijerph20054068

Halik, A. (2015). Implementasi Kebijakan Pelimpahan Urusan Pemerintahan Lingkup Kementerian Dalam Negeri. Jurnal Bina Praja, 7(2), 131–148. https://doi.org/10.21787/jbp.07.2015.131-148

Iryani, R. Y., Maulidiah, S., Rahman, K., Prihatin, P. S., & Febrian, R. A. (2022). Capacity of Community Government in Convergence Stunting Prevention in Sinaboi Countries Sinaboika District, Rokan Hilir District. International Journal of Health Sciences, 619–638. https://doi.org/10.53730/ijhs.v6nS4.5595

Jeyakumar, A., Godbharle, S., & Giri, B. R. (2021). Determinants of Anthropometric Failure Among Tribal Children Younger than 5 Years of Age in Palghar, Maharashtra, India. Food and Nutrition Bulletin, 42(1), 55–64. https://doi.org/10.1177/0379572120970836

Kwami, C. S., Godfrey, S., Gavilan, H., Lakhanpaul, M., & Parikh, P. (2019). Water, Sanitation, and Hygiene: Linkages with Stunting in Rural Ethiopia. International Journal of Environmental Research and Public Health, 16(20), 3793. https://doi.org/10.3390/ijerph16203793

Lacey, A., & Luff, D. (2009). Qualitative Data Analysis. The NIHR RDS for the East Midlands/Yorkshire & the Humber.

Macella, A. D. R., Mardhiah, N., & Handayani, S. W. (2022). A Study of Leadership Innovation in Stunting Prevention and Handling in Simeulue, Aceh Province, Indonesia. International Journal of Advances in Social Sciences and Humanities, 1(1), 50–57. https://doi.org/10.56225/ijassh.v1i1.39

Media, Y. (2014). Kualitas Pelayanan Kesehatan Ibu Hamil dan Bersalin di Daerah Terpencil (Studi Kasus di Nagari Batu Bajanjang, Kabupaten Solok, Provinsi Sumatera Barat). Jurnal Bina Praja, 6(1), 43–52. https://doi.org/10.21787/jbp.06.2014.21-30

Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative Data Analysis: A Methods Sourcebook. SAGE.

Mwita, F. C., PrayGod, G., Sanga, E., Setebe, T., Joseph, G., Kunzi, H., Webster, J., Gladstone, M., Searle, R., Ahmed, M., Hokororo, A., Filteau, S., Friis, H., Briend, A., & Olsen, M. F. (2024). Developmental and Nutritional Changes in Children with Severe Acute Malnutrition Provided with n-3 Fatty Acids Improved Ready-to-Use Therapeutic Food and Psychosocial Support: A Pilot Study in Tanzania. Nutrients, 16(5), 692. https://doi.org/10.3390/nu16050692

Nadeak, H. (2014). Implementasi Peraturan Pemerintah Nomor 19 Tahun 2008 tentang Kecamatan. Jurnal Bina Praja, 6(3), 183–196. https://doi.org/10.21787/jbp.06.2014.183-195

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods. SAGE Publications.

Pemerintah Kota Surabaya. (2023, February 16). Program Pemkot Surabaya “Posyandu Prima” Dijadikan Percontohan Nasional. Pemerintah Kota Surabaya. https://surabaya.go.id/id/berita/72605/program-pemkot-surabaya-posyandu-prima-dijadikan-percontohan-nasional

Prasetyo, A., Noviana, N., Rosdiana, W., Anwar, M. A., Hartiningsih, Hendrixon, Harwijayanti, B. P., & Fahlevi, M. (2023). Stunting Convergence Management Framework through System Integration Based on Regional Service Governance. Sustainability, 15(3), 1821. https://doi.org/10.3390/su15031821

Rahman, S. A., Amran, A., Ahmad, N. H., & Khadijeh Taghizadeh, S. (2019). The Contrasting Role of Government and NGO Support Towards the Entrepreneurs at Base of Pyramid and Effect on Subjective Wellbeing. Journal of Small Business & Entrepreneurship, 31(4), 269–295. https://doi.org/10.1080/08276331.2018.1498261

Rustikawati, K., Setyowati, D., & Herawati, N. (2019). Sistem Informasi Geografis Status Gizi Buruk Balita di Dinas Kesehatan Kota Yogyakarta Berbasis Mobile Android. Jurnal Teknologi, 12(2), 153–158. https://doi.org/10.3415/JURTEK.V12I2.2703

Sekretariat Percepatan Pencegahan Stunting. (2019). Strategi Nasional Percepatan Pencegahan Anak Kerdil (Stunting). Sekretariat Percepatan Pencegahan Stunting.

Setyawan, D., Priantono, A., & Firdausi, F. (2021). George Edward III Model. Publicio: Jurnal Ilmiah Politik, Kebijakan dan Sosial, 3(2), 9–19. https://doi.org/10.51747/publicio.v3i2.774

Sirait, F. E. T. (2021). Policy Communication and the Solidity of the Jokowi’s Second Term Coalition in Handling Covid-19. Jurnal Bina Praja, 13(2), 257–268. https://doi.org/10.21787/jbp.13.2021.257-268

Siswati, T., Iskandar, S., Pramestuti, N., Raharjo, J., Rubaya, A. K., & Wiratama, B. S. (2022). Impact of an Integrative Nutrition Package through Home Visit on Maternal and Children Outcome: Finding from Locus Stunting in Yogyakarta, Indonesia. Nutrients, 14(16), 3448. https://doi.org/10.3390/nu14163448

Suratri, M. A. L., Putro, G., Rachmat, B., Nurhayati, Ristrini, Pracoyo, N. E., Yulianto, A., Suryatma, A., Samsudin, M., & Raharni. (2023). Risk Factors for Stunting among Children under Five Years in the Province of East Nusa Tenggara (NTT), Indonesia. International Journal of Environmental Research and Public Health, 20(2), 1640. https://doi.org/10.3390/ijerph20021640

swaranews.com. (2023, January 26). Prevalensi Stunting Surabaya Terendah se-Indonesia. Swaranews.com. https://swaranews.com/baca-4764-prevalensi-stunting-surabaya-terendah-se-indonesia

Tamrin, M. H. (2017). Interaksi Aktor Kebijakan dalam Pengelolaan Wilayah Jembatan Suramadu dalam Perspektif Advocacy Coalition Framework (ACF). JKMP (Jurnal Kebijakan dan Manajemen Publik), 5(2), 141–158. https://doi.org/10.21070/jkmp.v5i2.1312

Tamrin, M. H., & Lubis, L. (2023). Pengelolaan KEE Ujung Pangkah Melalui Kolaborasi Stakeholders. Literasi Nusantara Abadi Grup.

Tarmizi, S. N. (2023, January 25). Prevalensi Stunting di Indonesia Turun ke 21,6% dari 24,4%. Sehat Negeriku. https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20230125/3142280/prevalensi-stunting-di-indonesia-turun-ke-216-dari-244/

Taufiqurokhman, T. (2023). Equality Strategy for Reducing Stunting Prevalence Rate: Case Study of DKI Jakarta Province. Jurnal Bina Praja, 15(3), 495–506. https://doi.org/10.21787/jbp.15.2023.495-506

Titaley, C. R., Ariawan, I., Hapsari, D., Muasyaroh, A., & Dibley, M. J. (2019). Determinants of the Stunting of Children Under Two Years Old in Indonesia: A Multilevel Analysis of the 2013 Indonesia Basic Health Survey. Nutrients, 11(5), 1106. https://doi.org/10.3390/nu11051106

Umiyati, S., & Tamrin, M. H. (2021). Penta Helix Synergy in Halal Tourism Development. Proceedings of the 4th International Conference on Sustainable Innovation 2020–Social, Humanity, and Education (ICoSIHESS 2020). https://doi.org/10.2991/assehr.k.210120.108

Utami, T., Kosasih, K., & Sayidin, R. (2023). Analysis of Policy Formulation and Implementation of Stunting Reduction in Penajam Paser Utara District in 2021. Journal on Education, 5(4), 13218–13227. https://doi.org/10.31004/joe.v5i4.2322

Utami, W. A., Rikza, A., Anggresta, P., & Nuryananda, P. F. (2022). The Role of Institutional Collaboration Between Actors in Protecting the Economic Security of Indonesian Migrant Workers With Financial Literacy. Jurnal Bina Praja, 14(2), 373–383. https://doi.org/10.21787/jbp.14.2022.373-383

Van Meter, D. S., & Van Horn, C. E. (1975). The Policy Implementation Process. Administration & Society, 6(4), 445–488. https://doi.org/10.1177/009539977500600404

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods. SAGE Publications.

How to Cite

Endnote/Zotero/Mendeley (RIS)

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License .

Make a Submission

Manuscript Template

Accreditation Certificate

Download Certificate

Journal Policies

Focus and Scope
Section Policies
Peer Review Process
Publication Frequency
Open Access Statement
Article Processing Charges
Plagiarism Check
References Management
Author Guidelines
Copyright Notice and Licensing
Publication Ethics and Malpractice Statement
Crossmark Policy Page

Abstracting & Indexing

See complete lists

Citation Analysis

Collaboration

Domestic Policy Strategy Agency Ministry of Home Affairs

Jalan Kramat Raya Nomor 132 Jakarta Pusat DKI Jakarta - 10430

p-ISSN: 2085-4323 e-ISSN Elektronik: 2503-3360

DOI: 10.21787/jbp

Jurnal Bina Praja has been accredited by the Ministry of Research and Technology/Head of the National Research and Innovation Agency of the Republic of Indonesia in SINTA 2 based on Decree Number 200/M/KPT/2020

More information about the publishing system, Platform and Workflow by OJS/PKP.

Program Finder
Admissions Services
Course Directory
Academic Calendar
Hybrid Campus
Lecture Series
Convocation
Strategy and Development
Implementation and Impact
Integrity and Oversight
In the School
In the Field
In Baltimore
Resources for Practitioners
Articles & News Releases
In The News
Statements & Announcements
At a Glance
Student Life
Strategic Priorities
Inclusion, Diversity, Anti-Racism, and Equity (IDARE)
What is Public Health?

research@BSPH

The School’s research endeavors aim to improve the public’s health in the U.S. and throughout the world.

Funding Opportunities and Support
Faculty Innovation Award Winners

Conducting Research That Addresses Public Health Issues Worldwide

Systematic and rigorous inquiry allows us to discover the fundamental mechanisms and causes of disease and disparities. At our Office of Research ( research@BSPH), we translate that knowledge to develop, evaluate, and disseminate treatment and prevention strategies and inform public health practice. Research along this entire spectrum represents a fundamental mission of the Johns Hopkins Bloomberg School of Public Health.

From laboratories at Baltimore’s Wolfe Street building, to Bangladesh maternity wards in densely packed neighborhoods, to field studies in rural Botswana, Bloomberg School faculty lead research that directly addresses the most critical public health issues worldwide. Research spans from molecules to societies and relies on methodologies as diverse as bench science and epidemiology. That research is translated into impact, from discovering ways to eliminate malaria, increase healthy behavior, reduce the toll of chronic disease, improve the health of mothers and infants, or change the biology of aging.

120+ countries

engaged in research activity by BSPH faculty and teams.

of all federal grants and contracts awarded to schools of public health are awarded to BSPH.

citations on publications where BSPH was listed in the authors' affiliation in 2019-2023.

publications where BSPH was listed in the authors' affiliation in 2019-2023.

Departments

Our 10 departments offer faculty and students the flexibility to focus on a variety of public health disciplines

Centers and Institutes Directory

Our 80+ Centers and Institutes provide a unique combination of breadth and depth, and rich opportunities for collaboration

Institutional Review Board (IRB)

The Institutional Review Board (IRB) oversees two IRBs registered with the U.S. Office of Human Research Protections, IRB X and IRB FC, which meet weekly to review human subjects research applications for Bloomberg School faculty and students

Generosity helps our community think outside the traditional boundaries of public health, working across disciplines and industries, to translate research into innovative health interventions and practices

Introducing the research@BSPH Ecosystem

The research@BSPH ecosystem aims to foster an interdependent sense of community among faculty researchers, their research teams, administration, and staff that leverages knowledge and develops shared responses to challenges. The ultimate goal is to work collectively to reduce administrative and bureaucratic barriers related to conducting experiments, recruiting participants, analyzing data, hiring staff, and more, so that faculty can focus on their core academic pursuits.

Research at the Bloomberg School is a team sport.

In order to provide extensive guidance, infrastructure, and support in pursuit of its research mission, research@BSPH employs three core areas: strategy and development, implementation and impact, and integrity and oversight. Our exceptional research teams comprised of faculty, postdoctoral fellows, students, and committed staff are united in our collaborative, collegial, and entrepreneurial approach to problem solving. T he Bloomberg School ensures that our research is accomplished according to the highest ethical standards and complies with all regulatory requirements. In addition to our institutional review board (IRB) which provides oversight for human subjects research, basic science studies employee techniques to ensure the reproducibility of research.

Research@BSPH in the News

Four bloomberg school faculty elected to national academy of medicine.

Considered one of the highest honors in the fields of health and medicine, NAM membership recognizes outstanding professional achievements and commitment to service.

The Maryland Maternal Health Innovation Program Grant Renewed with Johns Hopkins

Lerner center for public health advocacy announces inaugural sommer klag advocacy impact award winners.

Bloomberg School faculty Nadia Akseer and Cass Crifasi selected winners at Advocacy Impact Awards Pitch Competition

Department of Agricultural, Food, and Resource Economics Innovation Lab for Food Security Policy, Research, Capacity and Influence

Adoption of Sustainable Agricultural Intensification Practices and their Welfare Impacts: Comparative Evidence from Malawi, Uganda and Ethiopia

May 14, 2024 - Anderson Gondwe, Lemekezani K. Chilora, Dinah Salonga, Aleksandr Michuda and Kristin Davis

Sustainable intensification practices are popular interventions for enhancing soil fertility and crop yield, and eventually improving household income and food security. Using the Living Standards Measurement Study - Integrated Surveys on Agriculture panel data from Ethiopia, Malawi, and Uganda, we conduct a multi-country comparative analysis of the adoption of sustainable intensification practices and their impacts on food and nutritional security. While most studies use the sex of the household head to define gender, we base our gender variable on decision-making: male, female, and joint households' decision-making at a farm level. We use multinomial logit, multinomial endogenous switching regression and multinomial endogenous treatment effects models to account for selection bias and endogeneity originating from both observed and unobserved heterogeneity. Our analysis shows that adoption of sustainable intensification practices is impacted household size, wealth, livestock ownership, agroecological zones, and gender decision-making at a farm level. Our econometric analysis reveals that the relationship between the adoption of sustainable intensification practices and households' food and nutritional security varies by country, confirming the importance of considering country-specific contexts and practices when designing agricultural interventions. Policymakers should consider promoting the adoption of sustainable intensification practices as they have shown to have a positive impact on food and nutritional security. Sustainable intensification practices s, along with training programs for farmers, are crucial for enhancing knowledge and resource availability to implement sustainable intensification practices and improve food and nutrition security effectively. There is a need to increase investments in agricultural research, extension services, and climate-smart agriculture.

Sustainable intensification practices, welfare, multinomial logit, multinomial endogenous switching regression and multinomial endogenous treatment effects

DOWNLOAD FILE

Tags: prci research paper

new - method size: 1 - Random key: 0, method: personalized - key: 0

STAAARS+ RFP webinar Sept 14 2022

Published on September 15, 2022

PRCI STAAARS+ Teams Presentation Video 2022

Published on July 26, 2022

Scoping Study of Agriculture Development Strategy of Nepal (ADS) (Five-year achievements)

Published on February 1, 2023

Sugarcane Production and Food Security in Uganda

Published on September 1, 2023

Institutional Arrangements Between Sugarcane Growers and Millers in Uganda and Implications for Grower Productivity and Profitability

Rwanda Natural Forest Cover Dynamics between 2015 and 2020

Published on June 19, 2023

Accessibility Questions:

For questions about accessibility and/or if you need additional accommodations for a specific document, please send an email to ANR Communications & Marketing at [email protected] .

prci research paper,
innovation lab for food security policy research capacity & influence

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Welcome to the Purdue Online Writing Lab

Welcome to the Purdue OWL

This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.

Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

The Online Writing Lab at Purdue University houses writing resources and instructional material, and we provide these as a free service of the Writing Lab at Purdue. Students, members of the community, and users worldwide will find information to assist with many writing projects. Teachers and trainers may use this material for in-class and out-of-class instruction.

The Purdue On-Campus Writing Lab and Purdue Online Writing Lab assist clients in their development as writers—no matter what their skill level—with on-campus consultations, online participation, and community engagement. The Purdue Writing Lab serves the Purdue, West Lafayette, campus and coordinates with local literacy initiatives. The Purdue OWL offers global support through online reference materials and services.

A Message From the Assistant Director of Content Development

The Purdue OWL® is committed to supporting students, instructors, and writers by offering a wide range of resources that are developed and revised with them in mind. To do this, the OWL team is always exploring possibilties for a better design, allowing accessibility and user experience to guide our process. As the OWL undergoes some changes, we welcome your feedback and suggestions by email at any time.

Please don't hesitate to contact us via our contact page if you have any questions or comments.

All the best,

Social Media

Facebook twitter.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
09 May 2024

Cubic millimetre of brain mapped in spectacular detail

Carissa Wong

You can also search for this author in PubMed Google Scholar

Rendering based on electron-microscope data, showing the positions of neurons in a fragment of the brain cortex. Neurons are coloured according to size. Credit: Google Research & Lichtman Lab (Harvard University). Renderings by D. Berger (Harvard University)

Researchers have mapped a tiny piece of the human brain in astonishing detail. The resulting cell atlas, which was described today in Science 1 and is available online , reveals new patterns of connections between brain cells called neurons, as well as cells that wrap around themselves to form knots, and pairs of neurons that are almost mirror images of each other.

The 3D map covers a volume of about one cubic millimetre, one-millionth of a whole brain, and contains roughly 57,000 cells and 150 million synapses — the connections between neurons. It incorporates a colossal 1.4 petabytes of data. “It’s a little bit humbling,” says Viren Jain, a neuroscientist at Google in Mountain View, California, and a co-author of the paper. “How are we ever going to really come to terms with all this complexity?”

Slivers of brain

The brain fragment was taken from a 45-year-old woman when she underwent surgery to treat her epilepsy. It came from the cortex, a part of the brain involved in learning, problem-solving and processing sensory signals. The sample was immersed in preservatives and stained with heavy metals to make the cells easier to see. Neuroscientist Jeff Lichtman at Harvard University in Cambridge, Massachusetts, and his colleagues then cut the sample into around 5,000 slices — each just 34 nanometres thick — that could be imaged using electron microscopes.

Jain’s team then built artificial-intelligence models that were able to stitch the microscope images together to reconstruct the whole sample in 3D. “I remember this moment, going into the map and looking at one individual synapse from this woman’s brain, and then zooming out into these other millions of pixels,” says Jain. “It felt sort of spiritual.”

Rendering of a neuron with a round base and many branches, on a black background.

A single neuron (white) shown with 5,600 of the axons (blue) that connect to it. The synapses that make these connections are shown in green. Credit: Google Research & Lichtman Lab (Harvard University). Renderings by D. Berger (Harvard University)

When examining the model in detail, the researchers discovered unconventional neurons, including some that made up to 50 connections with each other. “In general, you would find a couple of connections at most between two neurons,” says Jain. Elsewhere, the model showed neurons with tendrils that formed knots around themselves. “Nobody had seen anything like this before,” Jain adds.

The team also found pairs of neurons that were near-perfect mirror images of each other. “We found two groups that would send their dendrites in two different directions, and sometimes there was a kind of mirror symmetry,” Jain says. It is unclear what role these features have in the brain.

Proofreaders needed

The map is so large that most of it has yet to be manually checked, and it could still contain errors created by the process of stitching so many images together. “Hundreds of cells have been ‘proofread’, but that’s obviously a few per cent of the 50,000 cells in there,” says Jain. He hopes that others will help to proofread parts of the map they are interested in. The team plans to produce similar maps of brain samples from other people — but a map of the entire brain is unlikely in the next few decades, he says.

“This paper is really the tour de force creation of a human cortex data set,” says Hongkui Zeng, director of the Allen Institute for Brain Science in Seattle. The vast amount of data that has been made freely accessible will “allow the community to look deeper into the micro-circuitry in the human cortex”, she adds.

Gaining a deeper understanding of how the cortex works could offer clues about how to treat some psychiatric and neurodegenerative diseases. “This map provides unprecedented details that can unveil new rules of neural connections and help to decipher the inner working of the human brain,” says Yongsoo Kim, a neuroscientist at Pennsylvania State University in Hershey.

doi: https://doi.org/10.1038/d41586-024-01387-9

Shapson-Coe, A. et al. Science 384 , eadk4858 (2024).

Article Google Scholar

Download references

Reprints and permissions

Neuroscience

Temporal multiplexing of perception and memory codes in IT cortex

Article 15 MAY 24

Volatile working memory representations crystallize with practice

Evolution of a novel adrenal cell type that promotes parental care

Organoids merge to model the blood–brain barrier

Research Highlight 15 MAY 24

How does ChatGPT ‘think’? Psychology and neuroscience crack open AI large language models

News Feature 14 MAY 24

Brain-reading device is best yet at decoding ‘internal speech’

News 13 MAY 24

Senior Research Assistant in Human Immunology (wet lab)

Senior Research Scientist in Human Immunology, high-dimensional (40+) cytometry, ICS and automated robotic platforms.

Boston, Massachusetts (US)

Boston University Atomic Lab

Postdoctoral Fellow in Systems Immunology (dry lab)

Postdoc in systems immunology with expertise in AI and data-driven approaches for deciphering human immune responses to vaccines and diseases.

Global Talent Recruitment of Xinjiang University in 2024

Recruitment involves disciplines that can contact the person in charge by phone.

Wulumuqi city, Ürümqi, Xinjiang Province, China

Xinjiang University

Tenure-Track Assistant Professor, Associate Professor, and Professor

Westlake Center for Genome Editing seeks exceptional scholars in the many areas.

Westlake Center for Genome Editing, Westlake University

Faculty Positions at SUSTech School of Medicine

SUSTech School of Medicine offers equal opportunities and welcome applicants from the world with all ethnic backgrounds.

Shenzhen, Guangdong, China

Southern University of Science and Technology, School of Medicine

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Transforming the understanding and treatment of mental illnesses.

Información en español

Celebrating 75 Years! Learn More >>

About the Director
Advisory Boards and Groups
Strategic Plan
Offices and Divisions
Careers at NIMH
Staff Directories
Getting to NIMH

Excellent to the “Core”: World Class Neuroimaging at NIMH

Peter Bandettini, Ph.D., Chief of the Section on Functional Imaging Methods and Director of the Functional Magnetic Resonance Imaging Core Facility, on behalf of the NIMH Intramural Research Program

May 15, 2024 • 75th Anniversary

Follow the NIMH Director on

For 75 years, NIMH has transformed the understanding and treatment of mental illnesses through basic and clinical research—bringing hope to millions of people. This Director’s Message, guest-written by NIMH’s Intramural Research Program , is part of an anniversary series celebrating this momentous milestone.

Our sensations, thoughts, and emotions are grounded in internal physical processes that are difficult to see and measure, much less understand. For well over a century, the links between what is happening in our brains and how we experience the world have started to emerge as brilliant innovations that allow more insightful questions.

As far back as the late 1800s, links between brain lesion locations and specific functional deficits were starting to be identified. Other means for measuring physiologic changes with brain activity were also being developed, ranging from precise balances that detected subtle increases in blood volume in the brain associated with increased mental activity to scientifically rigorous detection in animal models of blood volume changes, showing signal changes that were stunningly like functional magnetic resonance imaging (fMRI) signals now commonly seen 100 years later.

In the past 50 years, the National Institute of Mental Health (NIMH)'s Division of Intramural Research Programs (IRP) has been a world leader in developing and implementing advanced brain imaging technology. The division has fostered world-class facilities and seminal scientific advances that have allowed us to delve into the complex circuitry of the human brain. We’ve started shedding light on mechanisms and principles of brain function and are uncovering disease signatures and potential inroads for treatment.

Early innovations in brain imaging

A powerful imaging approach known as positron emission tomography (PET) is used today in research and clinical practice to create a map of metabolic processes or neurotransmitter activity in the brain. This technology was pioneered in the 1970s by NIMH IRP researcher Louis Sokoloff, M.D., Ph.D. He developed a method by which a radioactive tracer accumulated in specific brain regions in proportion to more active areas. He collaborated with computer scientists at NIMH to create a computer program that could measure this radioactivity and translate it into color-coded images representing brain activity. By the end of the 1970s, this technology had been adapted for use with humans, and PET scanners became available for use in medical and research settings.

The tools developed by Dr. Sokoloff have been refined over the years and are still in use today. For example, NIMH researcher Robert Innis, M.D., Ph.D., in collaboration with radiochemist Victor Pike, Ph.D., and colleagues are using a state-of-the-art PET facility at the National Institutes of Health (NIH) to develop a new way to deliver radioactive markers to brain cells, improving the mapping of neuroinflammation in the brain. This advance will allow researchers to better understand the role of neuroinflammation in a range of brain-based disorders from depression to Alzheimer’s disease.

The birth of core facilities at NIMH

Since the 1980s, NIH has been at the forefront of developing and nurturing resources to push the boundaries of brain science. Neuroimaging instrumentation can be expensive, and true expertise is rare. Having dedicated, shared groups, or “core facilities,” that develop sophisticated methods and collaborate with other scientists has proven to be an effective way to incubate breakthrough experiments and produce key findings.

An early shared resource, the NIH-wide Nuclear Magnetic Resonance Center, was created in 1987. It was here that seminal early work pioneering MRI was performed. Robert Turner, Ph.D., pioneered blood-oxygen-level-dependent contrast used in the most common noninvasive brain imaging methods today. Peter Basser, Ph.D., along with Denis Le Bihan, M.D., Ph.D., invented diffusion tensor imaging , which was fundamental to the creation of the spectacular fiber track images used today to reveal the connectivity in the human brain.

Decades of discovery, collaboration, and growth

Examples of the many types of structural, physiological, and functional contrast available from MRI. Courtesy of NIMH.

The NIMH IRP has led the way in creating and supporting core facilities, helping push the boundaries of neuroscience research.

One example is the proliferation of fMRI within NIH. The discovery of fMRI in the early 1990s revolutionized brain imaging and neuroscience. NIMH and the National Institute of Neurological Disorders and Stroke (NINDS) initiated an MRI facility to advance this exciting technology and to accommodate adventurous neuroscience research groups wanting to perform MRI and fMRI—peering into the living human brain not only to understand its structural details but now, thanks to fMRI, to observe functional activation with unprecedented speed, resolution, and fidelity. In 1999, Peter Bandettini, Ph.D., was recruited by NIMH to direct the new Functional MRI Facility (FMRIF) and lead his own research group in developing fMRI methodology, interpretation, and applications.

An image showing activation on the somatosensory cortex associated with the movement of different fingers. Activation caused by this movement in layer IV of the cortex (shown by the blue line in the graph) and the rest of the cortex (shown by the red line in the graph) is related to the brain making sense of these signals. Credit: Yu et al., 2016, Science Advances. © The Authors, some rights reserved; exclusive licensee AAAS. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-

Over the years, the FMRIF has developed and adopted the latest capabilities and services for a rapidly growing number of investigators who want to use fMRI or MRI in their brain research. For example, the image to the right shows exquisite resolution fMRI of detailed cortical circuitry activation with individual finger activation. Not only is fMRI able to delineate the somatosensory activation with each finger, but it can show, from the peaks in the graphs, that the activation is in layer IV in the somatosensory cortex, which has only been known from research with animals to be selectively active with stimulation.

Two cortical layers show brain activation when tapping a finger (image on the left), but only one cortical layer is activated when imagining tapping a finger (image on the right). This comparison helps reveal the precise area in the brain where the finger movement signal originates and where mental imagery activates the motor cortex. Courtesy of NIMH.

In another example, the image to the left shows two cortical layers becoming active for finger tapping and only one layer active for imagining of finger tapping. This higher resolution has tremendous potential to allow the creation of more detailed and informative maps of brain function—delving ever more into the complex circuitry and potentially revealing subtle differences associated with disorders.

Additional NIMH core facilities that develop and disseminate the latest neuroimaging technology to IRP scientists have rapidly emerged following the establishment of the fMRI core facility.

In 2000, NIMH created a Neurophysiology Imaging Facility for nonhuman primate imaging, led by David Leopold, Ph.D.
In 2001, Robert Cox, Ph.D., established the Scientific and Statistical Computing Core to help develop and maintain a brain imaging software platform called Analysis of Functional NeuroImages (AFNI), widely used by researchers to analyze MRI and fMRI data. The core is now led by Paul Taylor, Ph.D.
Also in 2001, the Magnetoencephalography Core Facility was created to focus on research using magnetoencephalography and electroencephalography. This core facility was initially led by Rich Coppola, DSc., and is now led by Allison Nugent, Ph.D. Dr. Coppola was also instrumental in working with Dr. Sokoloff in the early days, converting his metabolism maps to color images.
In 2002, the Magnetic Resonance Spectroscopy Core Facility , led by Jun Shen, Ph.D., was added to provide support for researchers using magnetic resonance spectroscopy, a method for identifying specific metabolite concentrations in the brain.
In 2016, the Machine Learning Team , led by Francisco Pereira, Ph.D., and the Data Science and Sharing Team , led by Adam Thomas, Ph.D., began helping IRP investigators analyze and share their data more broadly.
Research groups focusing on imaging methods, including the Noninvasive Neuromodulation Unit headed by Sarah Hollingsworth “Holly” Lisanby, M.D., and the Section on PET Neuroimaging Sciences led by Robert Innis, Ph.D., round out the NIMH’s vast neuroimaging resources. Neuromodulation is a method by which the brain is stimulated with high spatial and temporal accuracy—either for treatment or research.
In 2016, a Center for Multimodal Neuroimaging was created to link all the imaging-related cores together. This center serves as a nexus where the neuroimaging groups can come together to find points of fruitful intersection.

NIMH core facilities today

An image displaying the acronyms for NIMH core facilities. Courtesy of NIMH.

Today, the brain imaging core facilities remain at the forefront of science, helping NIH researchers undertake cutting-edge work. For example, the fMRI core facility has five MRI scanners, which are managed by five physicist staff scientists and used by more than 30 principal investigators performing both clinical and basic human neuroimaging research. Such a concentration of scanning technology and expertise cannot be found anywhere else in the world. Of note are the two 7T scanners in the core facility. These are the most powerful scanners approved for human use; only about 120 are available worldwide. Such powerful scanners enable NIH researchers to address entirely new questions about human functional brain organization, connectivity, and hierarchy and measure subtle differences and similarities between individuals.

Embraced by NIMH, the concept of core facilities and teams marked a paradigm shift for the institute. Cores scale with demand and foster expertise and strong collaborations with and between principal investigators. The resulting science and productivity demonstrate their success. Since 2000, NIH IRP investigators published more than 1,000 papers using the fMRI core facility. NIH and NIMH IRP investigators have just begun exploring the possible questions that may be asked using this unprecedented collection of world-leading tools.

NIMH has some of the most sophisticated tools for peering into the human brain. It is also home to world-class scientists, engineers, programmers, and clinicians who develop and wield these powerful instruments. The goal is to bridge that elusive connection between our physical brains and our sensations, thoughts, and emotions. The ultimate goal is that, with a deep and precise understanding of these connections, we can make more rapid headway toward improving mental health.

IMAGES

Human Resource Thesis Sample
Human Resource Policy Research Paper Example
One Page Human Resource Policy Brief Presentation Report
HR Policy Template
Human Resources Management Research Paper Topics
(PDF) A qualitative exploration of the human resource policy

VIDEO

Human Resource Planning
HR policy
Human resource management 1st year previous year paper 2021 Question paper Bcom Important questions
ALTAI CASHMERE
PRINCIPLES AND PRACTICE OF MANAGEMENT JUNE 2018
ORIGAMI HUMAN EASY TUTORIAL STEP BY STEP

COMMENTS

The employee perspective on HR practices: A systematic literature
Introduction. Using the SHRM process model (Nishii & Wright, Citation 2008), researchers within the field of SHRM describe the process of the development, implementation, and perceptions of HR policies and practices, from different stakeholders, and how these work towards organizational performance.A key feature in this model is the particular attention devoted to the potential differences ...
A Systematic Review of Human Resource Management Systems and Their
Strategic human resource management (SHRM) research increasingly focuses on the performance effects of human resource (HR) systems rather than individual HR practices (Combs, Liu, Hall, & Ketchen, 2006).Researchers tend to agree that the focus should be on systems because employees are simultaneously exposed to an interrelated set of HR practices rather than single practices one at a time, and ...
(PDF) Human Resource (HR) Practices
Human Resource (HR) practices are an integral part of an organization's management strategy that focuses on. effectively managing the organization's workforce. HR practices enco mpass a wide range ...
Research Topics and Collaboration in Human Resource Development Review
This paper presents the findings on the research themes, structural coherence, and semantic relevance based on clusters formed by normalized distance measures. ... Policy and process: Work and employee engagement ... and human resource technologies: Implications and applications for research. Human Resource Development Quarterly, 32(3), 243-350 ...
Human Resource Management Review
Conceptual Development for Future Research The Human Resource Management Review (HRMR) is a quarterly academic journal devoted to the publication of scholarly conceptual/theoretical articles pertaining to human resource management and allied fields (e.g. industrial/organizational psychology, human capital, labor relations, organizational behavior). ). HRMR welcomes manuscripts that focus on ...
Human Resources Management Theories, Policies and Practices: A Review
The findings from this study also indicate that human resources management policies and practices must be hinged on carefully selected HRM philosophies that are objectively selected and applied within the right contexts. Human resources management policies and practices were also identified as key drivers of employee performance and engagement.
Human Resource Management Journal
The Human Resource Management Journal has published several research papers exploring various aspects of HR in contexts of change and turmoil from a number of perspectives. This virtual special issue on HRM in times of turmoil brings together a collection of papers which, when viewed together can help shed light on some of the challenges and ...
Full article: Imprinting in HR process research: a systematic review
Introduction. Strategic human resource management (HRM) research has traditionally adopted a firm-level, employer-focused approach to examine the relationship between (one or a set of) HR practices and employees and organisational outcomes (Wright & Ulrich, Citation 2017).Despite the body of valuable knowledge gleaned from this body of work (known as HR content research; Sanders et al ...
HRM practices and innovation: an empirical systematic review
Also, research papers having the workplace and the organization as their unit of study was dropped, leaving us with 29 articles. However, studies that used companies and firms interchangeably were adopted, which gave us an addition of 2 articles, leaving us with 31 articles. Human resource management practices and innovation in firm research
(PDF) The role and impact of HRM policy
Paper type Research paper. 1. Literature review. ... A.A. and Budhwar, P.S. (2007), "The effect of human resource management policies on. organizational performance in Greek manufacturing ﬁrms
(PDF) The impact of Human resource management practice on
Having HRM practices. in place, would positively improve the firm's performance; such as, revenue r eturns, benefit, competitiveness an d. market share (Katou, 2008). According to Anwar, (2017 ...
A Review Of The Literature On Human Resource Development: Leveraging Hr
The Human Resources (HR) concept has undergone significant changes in how it is viewed as a capability in modern industry. The study of HR is fraught with disagreement regarding its origin as well as laden with discourse on the implications for contemporary management. Drucker (1954) created the term "human resources" in his seminal work . The
Human Resource Practices and Policies: A Literature Review
In this paper, the research questions asked are as follows: ... practice policies", "human resource management practice policies", and "hrm practices policies". A total of 114 papers were obtained. Screening was carried out with inclusion criteria including journal articles, reviews
Human Resource Practices and Policies: A Literature Review
Age-Related Human Resource Management Policies and Practices: Antecedents, Outcomes, and Conceptualizations. Work, Aging and Retirement, 7(4), 257-272. Crimmins, G. (2017). Feedback from the coal-face: how the lived experience of women casual academics can inform human resources and academic development policy and practice.
Green human resource management in the context of organizational
The paper's structure is as follows: the subsequent section outlines the methodology, followed by performance and science mapping analyses. ... Green human resource management: policies and practices. Cogent Business & Management, Informa UK Limited, 2 (1) ... Green human resource management research in emergence: a review and future directions ...
The research-practice gap in the field of HRM: a ...
In recent studies, researchers agree that there is a substantial gap between research and practice in the field of human resource management (HRM). The literature exploring the causes and consequences of this gap does not represent a finely structured discourse; it has focused on analysing the gap from the practitioner side, and it is based on opinions and theoretical discussions rather than ...
Human Resource Articles, Research, & Case Studies
by Anna Lamb, Harvard Gazette. When COVID pushed service-based businesses to the brink, tipping became a way for customers to show their appreciation. Now that the pandemic is over, new technologies have enabled companies to maintain and expand the use of digital payment nudges, says Jill Avery. 02 Jan 2024.
The critical role of HRM in AI-driven digital transformation: a
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.
PDF Human Resource Policies
IJRTI1706021 International Journal for Research Trends and Innovation (www.ijrti.org) 99 HUMAN RESOURCE POLICIES 1GOWSALYA R S , 2SOUNDARYA R 1Assistant Professor, 2PG scholar Dept of Management Studies ... Rajendhiran (2007) concluded in one of his papers that, the human resource is a very special kind of resource. If it is
PDF A Research Paper on Human Resources Planning, Process and Developing
A Research Paper on Human Resources Planning, Process and Developing Atyeh Mohammed Alzhrani Abstract The present work addresses in a clear and simple way, the management of human resources in service organizations where staff is relevant to the achievement of policies, goals and objectives. they provide the creative and productive spark and ...
Evaluating Local Government Policy Innovations
The research results show the success of Posyandu Prima in reducing stunting rates significantly in the last two years. The Surabaya City Government has proven effective in managing this program's human resources and budget. The active involvement of Great Surabaya Cadres (KSH) and non-governmental organizations also contributed greatly to the ...
Role of HRM Practices in Organization Performance: A Survey Approach
Human resource management is essential for all. types of enterprises. The bulk of research indicates that HRM and organizational performance ha ve. a positive link. Eac h organization' s main ...
research@BSPH
Research at the Bloomberg School is a team sport. In order to provide extensive guidance, infrastructure, and support in pursuit of its research mission, research@BSPH employs three core areas: strategy and development, implementation and impact, and integrity and oversight. Our exceptional research teams comprised of faculty, postdoctoral ...
Adoption of Sustainable Agricultural Intensification Practices and
There is a need to increase investments in agricultural research, extension services, and climate-smart agriculture. Keywords. Sustainable intensification practices, welfare, multinomial logit, multinomial endogenous switching regression and multinomial endogenous treatment effects
Electronics
Network virtualization (NV) technology is the cornerstone of modern network architectures, offering significant advantages in resource utilization, flexibility, security, and streamlined management. By enabling the deployment of multiple virtual network requests (VNRs) within a single base network through virtual network embedding (VNE), NV technology can substantially reduce the operational ...
Welcome to the Purdue Online Writing Lab
Mission. The Purdue On-Campus Writing Lab and Purdue Online Writing Lab assist clients in their development as writers—no matter what their skill level—with on-campus consultations, online participation, and community engagement. The Purdue Writing Lab serves the Purdue, West Lafayette, campus and coordinates with local literacy initiatives.
Cubic millimetre of brain mapped in spectacular detail
Credit: Google Research & Lichtman Lab (Harvard University). Renderings by D. Berger (Harvard University) Researchers have mapped a tiny piece of the human brain in astonishing detail.
A Systematic Review of Human Resource Management Systems and Their
In sum, we present a systematic review of existing empirical studies on HR systems and analyze the development of the field over time. We take a comprehensive approach and focus on all choices researchers make when designing a study on HR systems, explicitly linking conceptualization and measurement of the HR system.
Excellent to the "Core": World Class Neuroimaging at NIMH
The NIMH Intramural Research program has been a world leader in developing and implementing advanced brain imaging technology that has allowed us to delve into the complex circuitry of the human brain. Learn more about their role in developing, implementing, and supporting the use of imaging technology in this guest-written Director's Message.

Human Resources Management Theories, Policies and Practices: A Review of Literature

Related Papers

RELATED PAPERS

HRM practices and innovation: an empirical systematic review

Design/methodology/approach

Practical implications

Originality/value

Introduction

Methodology of the review

Data collection method

Human resource management practices and innovation in firm research

Distribution of studies

Theoretical perspective

Methodology

Unit of analysis

Methods used

Content analysis

Human resource management systems or human resource management practices

Human resource management systems characteristics

Human resource management practices in isolation

Innovation by definition

Mediator or moderator

Human resource management systems

Combination

Mediators and moderators

Future research

Further reading

Corresponding author

All feedback is valuable

Join us on our journey

Questions & More Information

HumanResources →

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

Cite this article

1 Introduction

1.1 Definitions

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

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

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

3.1.1 Culture: key challenges

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

3.2 AI Leadership: evolving leadership requirements

3.2.1 Leadership: key challenges

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

3.3 AI knowledge

3.3.1 Knowledge: key challenges

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

3.4 AI policies

3.4.1 Policies: key challenges

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

3.5 AI tools

3.5.1 AI tools: key challenges

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

4 HRM—strategic facilitation of human-centric AI

5 Conclusion

Data availability

Code availability

Author information

Contributions

Corresponding author

Ethics declarations

Additional information

Rights and permissions

About this article

Share this article

Evaluating Local Government Policy Innovations

How to Cite

Make a Submission

research@BSPH

Conducting Research That Addresses Public Health Issues Worldwide

120+ countries

Departments

Centers and Institutes Directory

Institutional Review Board (IRB)

Introducing the research@BSPH Ecosystem

Research at the Bloomberg School is a team sport.

Research@BSPH in the News

The Maryland Maternal Health Innovation Program Grant Renewed with Johns Hopkins

Department of Agricultural, Food, and Resource Economics Innovation Lab for Food Security Policy, Research, Capacity and Influence

Adoption of Sustainable Agricultural Intensification Practices and their Welfare Impacts: Comparative Evidence from Malawi, Uganda and Ethiopia