journal of research in human resources management

Journal of Research in Human Resources Management

Online: 2645-5072

theories, models and systems related to human resources management

Persian; including English abstracts and bibliographies

 Quarterly

Editor-in-Chief

Ali Farahi, PhD.

Place of Publication

University of Imam Hussein

http://hrmj.ihu.ac.ir/

Type of Access

Open Access (OA)

Type of License

Type of Publication

Publication fee:  free for review process and publication.

Full text access : Open Access

Revenue Sources

Institutional support, Donation

Type of Material

Serial (Periodical)

Description

It is an open-access peer-reviewed journal in Persian (including English abstracts and references) devoted to the field of the Human Resources Management and serves as a forum for discussion and experimentation. It serves as a forum for new research in Management, Organizational Behavior Management and Human Resources Management disciplines.

Innovation and human resource management: a systematic literature review

European Journal of Innovation Management

ISSN : 1460-1060

Article publication date: 18 January 2022

Issue publication date: 19 December 2022

This study aims to map scientific publications, intellectual structure and research trends in the development of human resource management (HRM) by adopting innovative practices. Specifically, it aims to (1) identify the fundamental contributions of research and to (2) determine the lines of research that constitute the most prominent intellectual structure to contribute to defining a future research agenda.

Design/methodology/approach

This study employs bibliometric, bibliographic coupling and cluster analysis techniques. To evaluate any potential patterns among the articles, it is analyzed how those were jointly cited. Hierarchical cluster analysis was also applied to those subject to bibliographic coupling analysis within the scope of grouping the interrelated articles into distinct sets.

The results enabled the identification and classification of various theoretical perspectives on human resources development through the adoption of innovative practices into four main approaches: (1) organizational factors of success, (2) strategic HRM, (3) human behavior and (4) learning management.

Originality/value

This study identifies, explores, analyzes and summarizes the main themes contributing to deepening the literature by identifying the priority areas concerning HRM through the adoption of innovative practices that can guarantee international standards of excellence.

• Systematic literature review

Jotabá, M.N. , Fernandes, C.I. , Gunkel, M. and Kraus, S. (2022), "Innovation and human resource management: a systematic literature review", European Journal of Innovation Management , Vol. 25 No. 6, pp. 1-18. https://doi.org/10.1108/EJIM-07-2021-0330

Emerald Publishing Limited

Copyright © 2022, Mariana Namen Jotabá, Cristina I. Fernandes, Marjaana Gunkel and Sascha Kraus

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

1. Introduction

Potgieter and Mokomane (2020) argue that the strategic emphasis of a human resource management (HRM) department can be summarized as the effective management of teams and individuals in an organization aimed at competitive advantage and performance success. Thus, there is growing interest in investigating the role of HRM departments and practices in supporting companies' capacity for innovation ( Engelsberger et al. , 2021 ). Due to the recent transformation (such as digitization) of most organizations, HRM's role in strategic management has become more important ( Zhou et al. , 2020 ), as these practices can provide tools for change and innovation and support strategic decision-making in organizations ( Sheehan et al. , 2016 ).

The HR strategy is increasingly related to the prevailing organizational strategy, experiencing the direct impact of ongoing changes while supporting the organization's development in the emerging digital environment. Thus, HRM practices have increasingly considered the needs of Industrial Revolution 4.0, which inevitably points to huge changes in the established system and its contexts. Within this scope of change, high-performing organizations adopt radically different forms and become more digital and innovative ( Deloitte, 2017 ).

HRM plays a significant role in supporting changes. Thus, several studies have begun to examine the relationship between HRM and innovation, specifically, practices that contribute to innovation in organizations. Looise and van Riemsdijk (2004) suggest that four aspects of HR are important for innovation in an organization, namely, work design, people, performance management and rewards, as well as communication and participation. De Leede and Looise (2005) present a model relating the HRM strategy to organizational results, such as innovation and success, noting that HRM practices, resulting from the strategy, can lead to results such as creativity, commitment and competencies, resulting in the organizational results of innovation and success.

Although HR practices and employees seem essential for innovation, empirical research linking the areas of HRM and innovation is quite scarce ( De Leeds and Looise, 2005 ; Laursen and Foss, 2014 ; Seeck and Diehl, 2017 ). Given the strong and growing focus on innovation, the HRM of organizations also needs to review their employees' skills. According to Meskó et al. (2018) , 50% of all current jobs will be outdated in the next two decades. This leads to the imperative challenges faced by HRM in advancing at a faster pace, adapting practices and routines as well as facilitating organizational learning ( Muñoz-Pascual et al. , 2019 ). HR practices are innovative and support innovation within organizations ( Kossek, 1987 ; Looise and van Riemsdijk, 2004 ).

Innovation in HR is related to changes in the social systems of organizations and the adoption and diffusion of these innovations, due to environmental forces and social processes ( Koosek, 1987 ). As noted by Looise and van Riemsdijk (2004) , these HR innovations are fundamental to innovation within organizations. Supporting employees' careers and establishing a goal and objective systems with rewards for successfully undertaking and conducting innovation ( Cano and Cano, 2006 ) are important for innovation. Thus, reflecting on the impacts that innovation can enhance regarding the future of work and employment is important. Furthermore, the HRM's role in supporting these changes should be carefully analyzed. Hence, a more in-depth analysis of HRM structures, rethinking routine activities, reviewing policies, developing new knowledge and skills and enabling teams to work in work environments that are completely different from previous ones.

Seeck and Diehl (2017) were the first and so far only scholars to systematize the theme of innovation in HRM, identifying 35 empirical studies linking HRM and innovation over 25 years (1990–2015). The results indicate the importance of the relationship: HRM practices implemented by organizations have a positive effect on innovation. Given the growing importance, and the speed of innovation, examining the development of this strand of literature is of utmost importance. This is also observed by Natalicchio et al. (2018) who conclude that the direct effect of HRM is of interest to research and the moderating role of HR practices requires a broader debate in the literature.

Thus, inspired by the work of Seeck and Diehl (2017) and building upon as well as updating it, we aimed to stimulate academic improvement and provide a better sense of direction and offer a thorough and systematic review of this expanding literature. We focus on addressing the following questions: What constitutes innovation in HRM? What theories support research on innovation in HRM? Our study makes several important contributions to the literature. First, we present a systematic review of the literature on innovation in HRM using bibliometric techniques (e.g. Donthu et al. , 2021 ). This helps identify the previous literature's findings and sets the stage for new research, summarizing the main knowledge gaps and directions. Second, our review challenges several theoretical/conceptual assumptions prevalent in HRM innovation research and offers new perspectives that can shape future research. Third, we define a road map for an informed research agenda that proposes multiple improved directions.

Overall, our study aimed to conduct a mapping of scientific publications, intellectual structure and research trends in the area of innovation in human resources management. Specifically, we intend to (1) identify the fundamental contributions of research in this area and (2) determine the lines of research that constitute the most prominent intellectual structure to contribute to the definition of a future research agenda.

2. Methodology

This study aimed to critically analyze studies that examine HRM's role in innovative companies through a systematic literature review (SLR), bibliographic coupling and cluster analysis techniques. The SLR process starts with the definition, objectives and conceptual limitations ( Kraus et al. , 2020 ). This study concentrates on the macro-context of strategic HRM associated with innovation to broaden the conceptual understanding of the adoption of such practices. For this, the following goals were established: (1) identifying studies published in scientific journals on HRM practices related to adopting and conducting innovation in organizations; (2) proposing an integrated evaluation of the problems and discoveries of the leading individual studies to understand the scenario surrounding human resources and innovation; and (3) presenting implications for HRM practices ( Denyer and Tranfield, 2009 ).

Next, the software package VOSviewer was used to generate bibliometric maps and identify bibliographic coupling in the article references. Bibliographic coupling classifies two articles when they make recourse to the same reference item ( Kessler, 1963 ). Each cluster was determined by analyzing the content and keywords, and thus, the most pertinent information of the articles in the sample. The resulting clusters serve as a starting point for organizing the scientific outputs.

2.1 Selecting the review method

This work aimed to overcome the challenges associated with the increasing volume of scientific production (e.g. subjectivity), as evaluating and comprehending a topic's literature requires scientific analytical tools ( Kraus et al. , 2021 ). Therefore, it engages in a systematic process of identifying, analyzing and synthesizing discrete streams ( Snyder, 2019 ; Kraus et al. , 2020 ; Vrontis and Christofi, 2021 ) to establish the theoretical underpinnings of in–home service consumption. For this, we adopted a hybrid review methodology by combining a bibliometric and framework-based review ( Figure 1 ) ( Snyder, 2019 ). The bibliometric review enabled us to quantify the productivity of scientific research, identify thematic clusters and establish the foundations of in–home service consumption ( Mas-Tur et al. , 2020 ). The framework-based review set the foundations for the proposed innovation and HRM framework and a comprehensive understanding of innovation and HRM. A review based on bibliometric analysis provides a powerful set of methods and measures for studying the structure and process of scholarly communication. To study the available literature, we relied on three widely used techniques of bibliometric analysis: evaluative, relational and review techniques ( Echchakoui, 2020 ). The evaluation technique focuses on the academic impact and includes three types of measures: influence (e.g. number of citations per year and per author), productivity (e.g. number of publications per year and per author) and hybrid (a combination of influence and productivity) (e.g. the average number of citations per paper). The relational technique explores the relationship between units of analysis on a specific topic or research field, identifying patterns and networks among journals, publications and/or authors. Co-citation analysis, bibliographic coupling, co-authorship analysis and co-word analysis are examples of relational techniques (e.g. Kraus et al. , 2012 ). The review techniques refer to systematic literature reviews, meta-analyses or qualitative studies ( Echchakoui, 2020 ). The present study encompasses all three bibliometric techniques.

2.2 Data collection and processing methods

A literature search was conducted using the Web of Science database. The search terms used were “innovation” and “human resource management” (and possible abbreviations). A total of 532 articles were obtained.

To obtain the primary objective and specific goals, the search focused on articles from academic journals, narrowing them to 446 articles; followed by the filters “topic,” in the categories of “management” and “business,” in English language and in December 2020. In summary, 241 articles indexed in the database were identified in the Web of Science , which can be considered “the most prestigious database and leading academic institutions and the research world” ( Gasparyan et al. , 2013 , p. 1271). Figure 1 provides the details of the research protocol.

The data were processed using VOSviewer software (version 1.6.15), which sets the parameters for bibliographic coupling at a minimum cluster size of six articles. This procedure resulted in a final sample of 237 articles, which were grouped into four clusters. Among them, four articles excluded by the software were disregarded. Furthermore, based on the exclusion criteria, after reading the publications, 201 articles were excluded because they were not related to HR and innovation and the adoption of innovative practices in HRM, including theoretical/conceptual and empirical publications. Descriptive statistics were produced using SPSS Statistics software version 27.0.

Each scientific publication included in the sample was analyzed regarding (1) the performance, thus, the descriptive statistical data and (2) trends in clusters along with the cluster descriptions.

3.1 Performance

As demonstrated by the previous overview study of Seeck and Diehl (2017) , the number of publications relating HRM to innovation is relatively low. However, our study shows that there has been a rising interest in the topic, as presented in Figure 2 . From 2015, in which the overview study of Seeck and Diehl ended its analysis, there has been a sharp increase in the number of publications. There were only 18 studies on the topic from 1987 (the date of the first publication) to 2015, but another 18 from 2016 to 2020.

When examining the research methods of the publications, we found that the majority, namely 20 studies (55.6%), were quantitative by nature, followed by 11 (30.6%) qualitative studies. Among them, four (11%) were conceptual, and one (2.8%) was a mixed-method study that applied qualitative and quantitative methods.

A broad range of methods were employed across the articles. Regarding the quantitative articles, five publications utilized structural equation modeling, and four used regression analyses as methods, making them the most common methods. Case studies were the most popular method for qualitative studies with seven publications, followed by two studies using document analysis, and two using mixed methods design. Regarding conceptual studies, three were theory publications, and only one was a literature review. The only mixed-method study utilized linear regressions and telephone interviews as the quantitative qualitative methods, respectively.

3.2 Cluster trend s

To portray the trends in the literature regarding innovation and HRM, we approached the bibliographic confluences among the 36 studies in the sample. This resulted in the definition of the four clusters. This organization of the clusters and respective publications contained in each was designated by the software tool for the construction and visualization of bibliometric networks ( VOSviewer, 2021 ). Figure 3 presents a visual model of the cluster network.

Descriptive analyses were conducted to examine patterns in journals, groups of authors and publications related to the group and topic, as well as the number of citations related to the authors. Table 1 presents the journals in which the studies were published and the number of citations in the publications during data collection.

The identified articles can be grouped into four clusters ( Figure 4 ):

The following table provides an overview of the articles in the four clusters:

Although the overall number of publications in the area was low, a broad range of journals served as an outlet for the studies. Human Resources Management and the International Journal of Manpower published the largest number of publications (three publications each). In the former, two publications belong to Cluster 2, in 2020 and 2019, and one article to Cluster 1, which was published in 1987 and is the first publication in our study sample. In the latter, one article belongs to Cluster 3 (year of publication 2020), another to Cluster 1 (year of publication 2011) and one to Cluster 2 (year of publication 2005). International Journal of Project Management, Journal of Management, Journal of Organizational Change Management, Organization Science and Technovation have served as an outlet for two studies, whereas the remaining journals have published only one study in the area.

An examination of the citations revealed five author teams with over 100 citations: Seibert et al. (2001) with 637 citations under the auspices of Cluster 1, Lopez-Cabrales et al. (2009) with 175 citations belong to Cluster 2, Akgun et al. (2007) received 137 citations for their articles in Cluster 3, Chou (2014) with 108 citations in Cluster 2 and Kwak and Anbari (2009) gained 103 citations for an article in Cluster 4. Of the 36 published articles, four were not cited during data collection, which may be because they were all published in 2020.

4. Cluster descriptions

In the next step, all articles in each respective cluster were read and analyzed to determine whether they responded to the research objective of providing implications for HRM. The analysis enabled the identification of shared characteristics and points of divergence, which led to the establishment of the research categories for each cluster. The four research clusters are discussed below:

4.1 Cluster 1: Organizational factors of success

The cluster “organizational success factors” comprising 11 articles, focuses on understanding the relationship between proactivity and innovation and the appropriate role of the HR manager.

Proactiveness is a personality trait that is positively related to career growth and innovation ( Seibert et al. , 2001 ). HRM systems are mediators that influence the development of work and increase proactive behaviors and motivation, vital for the development of organizations ( Tummers et al. , 2015 ). According to Shaw et al. (2005) , the adoption of human resources compensation models is crucial for organizational innovation, regardless of the adopted compensation models.

Baruk (2017) clarifies that employer branding is important, and necessary for companies, such as employer brands, to establish strategies that allow them to achieve organizational innovation. From the viewpoint of Bayo-Moriones et al. (2020) , HR and their performance evaluation must be aligned with the company's innovation strategy.

In this cluster, a group of three authors who focused their publications on knowledge management as a success factor for innovation can be identified.

The creation, transformation and use of different types of knowledge must be considered fundamental assets in innovative performance ( Nielsen and Rasmussen, 2011 ). For these authors, knowledge management is strictly related to learning, organization and innovation, which have a direct impact on the performance of companies. According to Feldman et al. (2019) , regarding innovation, companies must adopt five practices: promote human resources based on their characteristics related to taking initiative and ability to lead, perform job rotation, pay attention to the remuneration system, provide job security and hire workers based on knowledge and experience. Ganz (2020) argues that companies with clear innovation goals should experiment with the best strategies to adopt, according to their human resources. For this, they must experiment in low-risk environments and then apply the definitive strategy in a real context.

Kossek (1987) clarified that business innovation is directly linked to the ability to form networks and HRM alliances with professors and consultants. Moreover, it clarifies that senior management's role is to present the HR department and its respective executives as crucial elements in strategic decisions, in the construction of a work environment in which workers believe that executives care about their welfare. According to Ottenbacher and Harrington (2010) , there are two global success factors for innovation: market attractiveness and strategic HRM. Thus, service advantage, empowerment, employee training and behavior-based assessment all influence the intended outcomes of innovation.

4.2 Cluster 2: Strategic HRM

This cluster consists of 10 articles that contribute to understanding the impact of strategic HRM on innovation.

For Natalicchio et al. (2018) , the success of innovation practices is not in the recruitment of highly qualified employees but in the ability to implement employee training activities. In other words, innovation occurs through teams, with a focus on learning and developing innovative minds. Thus, it is important to adopt collaborative and competitive mechanisms to manage innovative ideas that arise within a company ( Cano and Cano, 2006 ; Bergendahl and Magnusson, 2014 ). According to Wang et al. (2005) , HRM has a direct and positive impact on the entrepreneurship process and, consequently, on the success of innovation activities. Omta et al. (1994) add the importance of management control and human resource practices to innovation's success.

Companies should also adopt advanced technological systems in HRM to create a collaborative culture that establishes alliances and partnerships; they should promote relationship networks for the exchange of experiences and technological support. This stimulus to organizational learning, through the development of human capital and its absorption capacity, is a predictor of organizational innovation ( Perez et al. , 2002 ; Muñoz-Pascual et al. , 2019 ; Pradana et al. , 2020 ). Hence, Lopez-Cabrales et al. (2009) argue that the impact of innovation and organizational performance depends on the systematization of HR knowledge. They argue that knowledge-based HRM practices have a positive influence on innovation and profit. However, it is important to realize that these practices become more difficult in small-and medium-sized companies ( Muñoz-Pascual et al. , 2019 ). Della Torre et al. (2020) remind us that, despite the importance of technological systems for innovation activities to be successful, it is essential to implement motivational systems dedicated to raising workers' motivation.

4.3 Cluster 3: Human behavior

This cluster consists of nine articles that help us understand how human behavior contributes to innovation activities.

Along with physical and financial capital, human capital drives companies toward innovation activities. Several authors argue that organizational development is achieved through human capital, as it enables companies to obtain an innovative capacity that allows them the necessary resilience to face the obstacles and challenges arising from globalization, competitiveness and the knowledge-based economy ( Menéndez Blanco and Montes-Botella, 2017 ; Marjanski et al. , 2019 ). For Yazici et al. (2016) , innovation and proactivity are key factors for organizational growth. The organizational climate also promotes the well-being of employees and, therefore, if companies have more satisfied employees, they can implement innovative activities to achieve better results ( Chou, 2014 ; Kao et al. , 2020 ).

In innovation activities, the leader's behavior has a direct impact. In organizational environments, marked by high competitiveness and uncertainty, innovation is vital for survival and long-term success. In these circumstances, leaders with altruistic behavior can create business environments that facilitate innovation, through appropriate learning atmospheres ( Escrig et al. , 2016 ; Kiesnere and Baumgartner, 2019 ).

Another promoter of innovation and its success is the emotional capacity of companies and its impact on organizational learning. This learning ability is directly linked to product innovation and company performance ( Akgün et al. , 2007 ; Soomro and Shah, 2015 ).

4.4 Cluster 4: Learning management

This cluster is composed of six articles that relate learning management to innovation.

HR practices (recruiting and selecting activities, as well as training programs) must be effective and aligned with the knowledge management strategy and the business, regarding organizational strategy, for innovation activities ( GOPE, Elia and Passiante, 2018 ). Companies that adopt knowledge management practices can generate a competitive advantage as a result of the innovative process ( Gonzalez and de Melo, 2018 ). Gonzalez and de Melo (2018) show that the knowledge management process is impacted by five contextual factors: HRM, supportive leadership, learning culture, autonomy and information technology systems. Olander et al. (2015) argue that human capital and knowledge are the Allies of innovation. There are several practices related to commitment, trust, motivation and a sense of responsibility, which strengthen loyalty and improve the preservation of the company's intellectual capital.

For Calamel et al. (2012) , the solution of sustainable models lies in innovation practices and identifying increasing levels of cooperation as well as creating collaborative projects in HRM; through collective learning different skills can be developed. In sustainable models focused on industrial ecology, the optimization and better efficiency of resources are achieved through the integration and coordination of skills, innovations and new routines in functional areas, innovation and development of all technologies, waste control, human resource adjustments, management of environmental constraints and networking and marketing ( Kwak and Anbari, 2009 ; Kabongo and Boiral, 2017 ).

5. Discussion

To support future research on HRM practices on innovation, we established the conclusions from a review of the evidence derived from the peer-reviewed literature using the Web of Science database. This was aimed at developing a structure that illustrates the core considerations around this theme, enabling the identification of behaviors for the adoption of innovative practices in HRM, evaluating the problems and discoveries and providing indications for human resource strategic management and policy practices ( Aguinis et al. , 2021 ). For this, we used a framework that categorizes the clusters, specifically, organizational factors of success, strategic HRM, human behavior and learning management.

This duly highlights that this conceptual structure was developed by ascertaining the facts supporting the development of the knowledge base. This study identified, along with the four direct clusters around the core areas in strategic HRM, 15 themes/subareas of interest: proactivity, innovation in services, factors of influence in HRM, HR subsystems, knowledge management in HRM, organizational performance, HRM practices, learning capacity, impact on the organizational climate, impact on entrepreneurship, leadership, factors of organizational growth, impact on the organizational climate, project management and sustainable business models.

The subareas arise from the content analysis of the articles in each cluster. In Cluster 1, organizations with greater chances of obtaining superior organizational results present elements such as proactivity, the practice of innovation in services offered, knowledge management practices, the adoption of HR systems and innovation in HRM subsystems in their routines. These success factors are interconnected with Cluster 2, which complements the strategic management of the area and its practices as key elements for performance and competitiveness gains. In Cluster 3, the relevance of behavior and human capital emerges to capture and enjoy the benefits of innovation, contributing to the growth and learning capacity of the organization through people, promoting impact on the organizational climate and developing the entrepreneurial spirit within the company itself. Moreover, the importance of leadership was analyzed to stimulate the construction of environments that allow their employees to be open to radical and incremental innovations. Finally, in Cluster 4, high-performance HRM practices as well as their effective ability in the relationship with knowledge management convey reiterate the existence of HR practices aimed at enabling individual learning, motivation and staff retention. This may prove favorable for HR managers to encourage employees to engage in learning processes and, consequently, improve organizational results and innovation.

Hence, we detailed the main trends in the literature on the motivations and obstacles to the adoption of innovation in HRM, as shown in Figure 5 .

Based on the reviewed articles, we identified various limitations of the research and, consequently, representing some potential contributions for consideration by future research projects, as outlined in Table 2 .

6. Conclusion

This study sought to critically analyze the literature to drive the development of HR through the adoption of innovative practices. We may affirm that this research field has been ongoing since 1987. Despite the 33 years of research, the field remains in the construction phase, and a significant proportion of the studies only adopt exploratory qualitative approaches. The trends regarding the number of articles published in this timeframe, despite the relatively low total number (only 36 publications), reflect an increasing level of academic interest in studying innovation in association with HRM, whether at the conceptual understanding level or through empirical studies enabling the development of new policies and more modern HRM practices, bringing better results that can benefit the company–HR sector–teams triad. The results obtained demonstrate that 2019 may turn out to be a landmark in this scientific field regarding associating innovation in HR given the surge in publications.

There was also the scope for identifying how the authors' main interests focus on understanding and developing mathematical models that can assist in identifying the organizational success factors in knowledge management, proactivity and HR subsystems. This objective arises from providing greater recognition of the factors that favor innovation-friendly management, as well as helping HR managers plan where they can prioritize efforts for organizational growth.

Furthermore, the research, to a certain extent, advances conclusions on the debate about knowledge management in the majority of these studies and that permeates throughout the clusters. These emphasize the relevance of learning and stimulating the development of teams and, as such, standing out on the list of priority tasks for HRM. This also pointed out how, paradoxically, this interlinkage between knowledge management and the clusters in the current research – the “learning management” cluster registered the lowest number of publications regarding the other groups. The justification may reflect how this theme underlies all approaches and is, therefore, not an individual theme of lesser interest.

This also advanced with the need to reflect on the importance of the HRM role within the organizations deemed innovative as well as those seeking to develop their innovative environments and as a mediator in this process to assist companies facing competitive markets.

This also verified only a low level of research on approaches to the deployment of technologies, specifically, the adoption of systems versus innovation in the HR department. This raises questions about how HR might better accompany technological practices and means. Would it be a good innovation practice for HR to adopt systems that facilitate routine tasks and management? What image should HR convey in support of other sectors and the organization without bolstering its position, thus, without adopting innovative practices by deploying software and “tech practices” that facilitate and enable their tasks?

The research also corroborates an understanding of the future of work in approaching research that presents sustainable business models, acclaimed for applying more modern and longer-lasting organizational practices.

As every study, also ours has a number of limitations. First, our keywords, process and use of specific databases (Web of Science) may have resulted in the omission of potentially relevant other studies. Second, because we concentrated on analyzing and integrating existing research, we did not provide research propositions connecting the themes and elements of innovation and HRM. Third, this review only included studies published in peer-reviewed academic journals written in English; it excludes books, conference proceedings and other literature, as well as articles written in other languages that might have be relevant. Although we are aware of and confident in our results, we believe they are representative of the research conducted in this field. Hence, we believe that we provided a perspective of the intellectual structure of this field of study, along with the contribution of our conceptual model, for future investigation.

Research protocol

Number of publications per year

Cluster network

Clusters of innovative practice adoption

Framework for adopting innovation in HRM

Key journals with the most cited publications and authors

Future directions

Aguinis , H. , Jensen , S.H. and Kraus , S. ( 2021 ), “ Policy implications of organizational behavior and human resource management research ”, Academy of Management Perspectives , Vol. ahead-of-print , doi: 10.5465/amp.2020.0093 .

Akgün , A.E. , Keskin , H. , Byrne , J.C. and Aren , S. ( 2007 ), “ Emotional and learning capability and their impact on product innovativeness and firm performance ”, Technovation , Vol.  27 No.  9 , pp.  501 - 513 , doi: 10.1016/j.technovation.2007.03.001 .

Baruk , A.I. ( 2017 ), “ Contentment of employees vs their prosumeric activity in the scope of recommending an employer ”, Journal of Business and Industrial Marketing , Vol. 32 No. 5 , pp. 742 - 751 , doi: 10.1108/JBIM-10-2016-0240 .

Bayo-Moriones , A. , Galdon-Sanchez , J.E. and Martinez-de-Morentin , S. ( 2020 ), “ Business strategy, performance appraisal, and organizational results ”, Personnel Review , Vol. 50 No. 2 , pp. 515 - 534 , doi: 10.1108/PR-09-2019-0498 .

Bergendahl , M. and Magnusson , M. ( 2014 ), “ Combining collaboration and competition: a key to improved idea management? ”, European Journal of International Management , Vol.  8 No.  5 , pp.  528 - 547 , doi: 10.1504/EJIM.2014.064603 .

Calamel , L. , Defélix , C. , Picq , T. and Retour , D. ( 2012 ), “ Inter-organisational projects in French innovation clusters: the construction of collaboration ”, International Journal of Project Management , Vol.  30 No.  1 , pp.  48 - 59 , doi: 10.1016/j.ijproman.2011.03.001 .

Cano , C.P. and Cano , P.Q. ( 2006 ), “ Human resources management and its impact on innovation performance in companies ”, International Journal of Technology Management , Vol.  35 Nos 1-4 , pp.  11 - 28 , doi: 10.1504/IJTM.2006.009227 .

Chou , C.J. ( 2014 ), “ Hotels' environmental policies and employee personal environmental beliefs: interactions and outcomes ”, Tourism Management , Vol.  40 , pp.  436 - 446 , doi: 10.1016/j.tourman.2013.08.001 .

De Leede , J. and Looise , J.K. ( 2005 ), “ Innovation and HRM: towards an integrated framework ”, Creativity and Innovation Management , Vol.  14 No.  2 , pp.  108 - 117 .

Della Torre , E. , Salimi , M. and Giangreco , A. ( 2020 ), “ Crowding‐out or crowding‐in? Direct voice, performance‐related pay, and organizational innovation in European firms ”, Human Resource Management Journal , Vol.  59 No.  2 , pp.  185 - 199 , doi: 10.1002/hrm.21987 .

Delloite ( 2017 ), Deloitte Global Human Capital Trends - Rewriting the Rules for the Digital Age , Deloitte Univerity Press , available at: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/About-Deloitte/central-europe/ce-global-human-capital-trends.pdf ( accessed January 2020 ).

Denyer , D. and Tranfield , D ( 2009 ), “ Producing a systematic review ”, in Buchanan , D.A. and Bryman , A. (Eds), The Sage Handbook of Organizational Research Methods , Sage Publications , pp. 671 - 689 .

Dominguez , E.E. , Mallen , B.F.F. , Chiva , G.R. and Lapiedra , A.R. ( 2016 ), “ How does altruistic leader behavior foster radical innovation? The mediating effect of organizational learning capability ”, Leadership and Organization Development Journal , Vol. 37 No. 8 , pp. 1056 - 1082 .

Donthu , N. , Kumar , S. , Mukherjee , D. , Pandey , N. and Lim , W.M. ( 2021 ), “ How to conduct a bibliometric analysis: an overview and guidelines ”, Journal of Business Research , Vol.  133 , pp.  285 - 296 .

Echchakoui , S. ( 2020 ), “ Why and how to merge Scopus and Web of Science during bibliometric analysis: the case of sales force literature from 1912 to 2019 ”, Journal of Marketing Analytics , Vol.  8 No.  3 , pp.  165 - 184 .

Engelsberger , A. , Halvorsen , B. , Cavanagh , J. and Bartram , T. ( 2021 ), “ Human resources management and open innovation: the role of open innovation mindset ”, Asia Pacific Journal of Human Resources , Vol. ahead-of-print , doi: 10.1111/1744-7941.12281 .

Escrig , E.D. , Broch , F.F.M. , Gómez , R.C. and Alcamí , R.L. ( 2016 ), “ How does altruistic leader behavior foster radical innovation? The mediating effect of organizational learning capability ”, Leadership and Organization Development Journal , Vol.  37 No.  8 , pp.  1056 - 1082 , doi: 10.1108/LODJ-03-2015-0050 .

Feldman , M.P. , Ozcan , S. and Reichstein , T. ( 2019 ), “ Falling not far from the tree: entrepreneurs and organizational heritage ”, Organization Science , Vol.  30 No.  2 , pp.  337 - 360 , doi: 10.1287/orsc.2018.1222 .

Ganz , S.C. ( 2020 ), “ Hyperopic search: organizations learning about managers learning about strategies ”, Organization Science , Vol. 31 No. 4 , doi: 10.1287/orsc.2019.1330 .

Gasparyan , A.Y. , Ayvazyan , L. and Kitas , G.D. ( 2013 ), “ Multidisciplinary bibliographic databases ”, Journal of Korean Medical Science , Vol.  28 No.  9 , pp.  1270 - 1275 , doi: 10.3346/jkms.2013.28.9.1270 .

Gonzalez , R.V.D. and de Melo , T.M. ( 2018 ), “ The effects of organization context on knowledge exploration and exploitation ”, Journal of Business Research , Vol.  90 , pp.  215 - 225 , doi: 10.1016/j.jbusres.2018.05.025 .

Gope , S. , Elia , G. and Passiante , G. ( 2018 ), “ The effect of HRM practices on knowledge management capacity: a comparative study in Indian IT industry ”, Journal of Knowledge Management , Vol.  22 No.  3 , pp.  649 - 677 , doi: 10.1108/JKM-10-2017-0453 .

Kabongo , J.D. and Boiral , O. ( 2017 ), “ Doing more with less: building dynamic capabilities for eco‐efficiency ”, Business Strategy and the Environment , Vol.  26 No.  7 , pp.  956 - 971 , doi: 10.1002/bse.1958 .

Kao , C.Y. , Tsaur , S.H. and Huang , C.C. ( 2020 ), “ The scale development of organizational culture on customer delight ”, International Journal of Contemporary Hospitality Management , Vol.  32 No.  10 , pp.  3067 - 3090 , doi: 10.1108/IJCHM-02-2019-0128 .

Kessler , M.M. ( 1963 ), “ Bibliographic coupling between scientific papers ”, American Documentation , Vol. 14 No. 1 , pp. 10 - 25 , doi: 10.1002/asi.5090140103 .

Kiesnere , A.L. and Baumgartner , R.J. ( 2019 ), “ Sustainability management emergence and integration on different management levels in smaller large‐sized companies in Austria ”, Corporate Social Responsibility and Environmental Management , Vol.  26 No.  6 , pp.  1607 - 1626 , doi: 10.1002/csr.1854 .

Kossek , E.E. ( 1987 ), “ Human resources management innovation ”, Human Resource Management Journal , Vol.  26 No.  1 , pp.  71 - 92 , doi: 10.1002/hrm.3930260105 .

Kraus , S. , Filser , M. , Eggers , F. , Hills , G.E. and Hultman , C.M. ( 2012 ), “ The entrepreneurial marketing domain: a citation and co‐citation analysis ”, Journal of Research in Marketing and Entrepreneurship , Vol.  14 No.  1 , pp.  6 - 26 , doi: 10.1108/14715201211246698 .

Kraus , S. , Breier , M. and Dasí-Rodríguez , S. ( 2020 ), “ The art of crafting a systematic literature review in entrepreneurship research ”, International Entrepreneurship and Management Journal , Vol.  16 No.  3 , pp.  1023 - 1042 .

Kraus , S. , Mahto , R.V. and Walsh , S.T. ( 2021 ), “ The importance of literature reviews in small business and entrepreneurship research ”, Journal of Small Business Management , Vol. ahead-of-print , doi: 10.1080/00472778.2021.1955128 .

Kwak , Y.H. and Anbari , F.T. ( 2009 ), “ Analyzing project management research: perspectives from top management journals ”, International Journal of Project Management , Vol.  27 No.  5 , pp.  435 - 446 , doi: 10.1016/j.ijproman.2008.08.004 .

Laursen , K. and Foss , N.J. ( 2014 ), Human Resource Management Practices and Innovation , Oxford Handbooks Online , doi: 10.1093/oxfordhb/978019969 .

Lee , H.W. , Pak , J. , Kim , S. and Li , L.Z. ( 2019 ), “ Effects of HRM systems on employee proactivity and group innovation ”, Journal of Management , Vol.  45 No.  2 , pp.  819 - 846 , doi: 10.1177/0149206316680029 .

Looise , J.K. and van Riemsdijk , M. ( 2004 ), “ Innovating organisations and HRM: a conceptual framework ”, Management Revue , Vol. 15 No. 3 , pp. 277 - 287 .

Lopez‐Cabrales , A. , Pérez‐Luño , A. and Cabrera , R.V. ( 2009 ), “ Knowledge as a mediator between HRM practices and innovative activityin cooperation with the school of business administration ”, HRM: Published The University of Michigan and in Alliance with the Society of Human Resources Management , Vol.  48 No.  4 , pp.  485 - 503 , doi: 10.1002/hrm.20295 .

Marjański , A. , Sułkowski , Ł. , Marjańska-Potakowska , J. and Staniszewska , K. ( 2019 ), “ Social capital drives SME growth: a study of family firms in Poland ”, German Journal of HRM , Vol.  33 No.  3 , pp.  280 - 304 , doi: 10.1177/2397002219847668 .

Mas-Tur , A. , Kraus , S. , Brandtner , M. , Ewert , R. and Kürsten , W. ( 2020 ), “ Advances in management research: a bibliometric overview of the Review of Managerial Science ”, Review of Managerial Science , Vol.  14 No.  5 , pp.  933 - 958 .

Menéndez Blanco , J.M. and Montes-Botella , J.-L. ( 2017 ), “ Exploring nurtured company resilience through human capital and human resource development ”, International Journal of Manpower , Vol. 38 No. 5 , pp. 661 - 674 , doi: 10.1108/ijm-11-2015-0196 .

Meskó , B. , Hetényi , G. and Győrffy , Z. ( 2018 ), “ Will artificial intelligence solve the human resource crisis in healthcare? ”, BMC Health Services Research , Vol.  18 No.  1 , p. 545 .

Muñoz-Pascual , L. , Curado , C. and Galende , J. ( 2019 ), “ How does the use of information technologies affect the adoption of environmental practices in SMEs? A mixed-methods approach ”, Review of Managerial Science , Vol. 15 , pp. 75 - 102 , doi: 10.1007/s11846-019-00371-2 .

Natalicchio , A. , Petruzzelli , A.M. , Cardinali , S. and Savino , T. ( 2018 ), “ Open innovation and the human resource dimension ”, Management Decision , Vol. 56 No. 6 , pp. 1271 - 1284 , doi: 10.1108/MD-03-2017-0268 .

Nielsen , P. and Rasmussen , P. ( 2011 ), “ Knowledge management in the firm: concepts and issues ”, International Journal of Manpower , Vol.  5 No.  6 , pp.  479 - 493 , doi: 10.1108/01437721111158161 .

Olander , H. , Hurmelinna-Laukkanen , P. and Heilmann , P. ( 2015 ), “ Human resources–strength and weakness in protection of intellectual capital ”, Journal of Intellectual Capital , Vol.  16 No.  4 , pp.  742 - 762 , doi: 10.1108/JIC-03-2015-0027 .

Omta , S.W.F. , Bouter , L.M. and Vanengelen , J.M.L. ( 1994 ), “ Managing industrial pharmaceutical research-and-development - a comparative-study of management control and innovative effectiveness in european and anglo-american companies ”, R&D Management , Vol.  4 , pp.  303 - 315 , doi: 10.1111/j.1467-9310.1994.tb00886.x .

Ottenbacher , M.C. and Harrington , R.J. ( 2010 ), “ Strategies for achieving success for innovative versus incremental new services ”, Journal of Services Marketing , Vol.  24 , pp.  3 - 15 , doi: 10.1108/08876041011017853 .

Perez , C.C. and Quevado , C.P. ( 2006 ), “ Human resources management and its impact on innovation performance in companies ”, International Journal of Technology Management , Vol. 35 Nos 1-4 , p. 11 , doi: 10.1504/ijtm.2006.009227 .

Pérez , M.P. , Sánchez , A.M. and de Luis Carnicer , M.P. ( 2002 ), “ Benefits and barriers of telework: perception differences of human resources managers according to company's operations strategy ”, Technovation , Vol.  22 No.  12 , pp.  775 - 783 , doi: 10.1016/s0166-4972(01)00069-4 .

Potgieter , I.L. and Mokomane , S.E. ( 2020 ), “ Implementation of HRM functions in selected small manufacturing companies in Ga-Rankuwa industrial area, Gauteng, South Africa ”, SA Journal of HRM , Vol.  18 No.  1 , pp.  1 - 11 , doi: 10.4102/sajhrm.v18i0.1282 .

Pradana , M. , Pérez-Luño , A. and Fuentes-Blasco , M. ( 2020 ), “ Innovation as the key to gain performance from absorptive capacity and human capital ”, Technology Analysis and Strategic Management , Vol. 32 No. 7 , pp. 822 - 834 , doi: 10.1080/09537325.2020.1714578 .

Seeck , H. and Diehl , M.R. ( 2017 ), “ A literature review on HRM and innovation–taking stock and future directions ”, The International Journal of HRM , Vol.  28 No.  6 , pp.  913 - 944 .

Seibert , S.E. , Kraimer , M.L. and Crant , J.M. ( 2001 ), “ What do proactive people do? A longitudinal model linking proactive personality and career success ”, Personnel Psychology , Vol.  54 No.  4 , pp.  845 - 874 , doi: 10.1111/j.1744-6570.2001.tb00234.x .

Shaw , J.D. , Gupta , N. , Mitra , A. and Ledford , G.E. Jr ( 2005 ), “ Success and survival of skill-based pay plans ”, Journal of Management , Vol.  31 No.  1 , pp.  28 - 49 , doi: 10.1177/0149206304271376 .

Sheehan , C. , De Cieri , H. , Cooper , B. and Shea , T. ( 2016 ), “ Strategic implications of HR role management in a dynamic environment ”, Personnel Review , Vol.  45 No.  2 , pp.  353 - 373 , doi: 10.1108/PR-04-2014-0071 .

Snyder , H. ( 2019 ), “ Literature review as a research methodology: an overview and guidelines ”, Journal of Business Research , Vol.  104 , pp.  333 - 339 .

Soomro , B.A. and Shah , N. ( 2015 ), “ Developing attitudes and intentions among potential entrepreneurs ”, Journal of Enterprise Information Management , Vol.  28 No.  2 , pp.  304 - 322 , doi: 10.1108/JEIM-07-2014-0070 .

Tummers , L. , Kruyen , P.M. , Vijverberg , D.M. and Voesenek , T.J. ( 2015 ), “ Connecting HRM and change management: the importance of proactivity and vitality ”, Journal of Organizational Change Management , Vol. 28 No. 4 , pp. 627 - 640 , doi: 10.1108/JOCM-11-2013-0220 .

VOSviewer ( 2021 ), “ Visualing scientifing landscapes ”, available at: https://www.vosviewer.com/ ( accessed May 2021 ).

Vrontis , D. and Christofi , M. ( 2021 ), “ R&D internationalization and innovation: a systematic review, integrative framework and future research directions ”, Journal of Business Research , Vol.  128 , pp.  812 - 823 .

Wang , Z. , Chen , J. and Zang , Z. ( 2005 ), “ Strategic human resources, innovation and entrepreneurship fit ”, International Journal of Manpower , Vol. 26 No. 6 , pp. 544 - 559 , doi: 10.1108/01437720510625458 .

Yazici , S. , Köseoglu , M.A. and Okumus , F. ( 2016 ), “ Identification of growth factors for small firms: evidence from hotel companies on an island ”, Journal of Organizational Change Management , Vol.  29 No.  6 , pp.  994 - 1029 , doi: 10.1108/JOCM-12-2015-0231 .

Zhou , Y. , Liu , G. , Chang , X. and Wang , L. ( 2020 ), “ The impact of HRM digitalization on firm performance: investigating three-way interactions ”, Asia Pacific Journal of Human Resources , Vol.  59 No.  1 , pp.  20 - 43 , doi: 10.1111/1744-7941.12258 .

Corresponding author

Related articles, all feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

• All subject areas
• Agricultural and Biological Sciences
• Arts and Humanities
• Biochemistry, Genetics and Molecular Biology
• Business, Management and Accounting
• Chemical Engineering
• Computer Science
• Decision Sciences
• Earth and Planetary Sciences
• Economics, Econometrics and Finance
• Engineering
• Environmental Science
• Health Professions
• Immunology and Microbiology
• Materials Science
• Mathematics
• Multidisciplinary
• Neuroscience
• Pharmacology, Toxicology and Pharmaceutics
• Physics and Astronomy
• Social Sciences
• All subject categories
• Acoustics and Ultrasonics
• Advanced and Specialized Nursing
• Aerospace Engineering
• Agricultural and Biological Sciences (miscellaneous)
• Agronomy and Crop Science
• Algebra and Number Theory
• Analytical Chemistry
• Anesthesiology and Pain Medicine
• Animal Science and Zoology
• Anthropology
• Applied Mathematics
• Applied Microbiology and Biotechnology
• Applied Psychology
• Aquatic Science
• Archeology (arts and humanities)
• Architecture
• Artificial Intelligence
• Arts and Humanities (miscellaneous)
• Assessment and Diagnosis
• Astronomy and Astrophysics
• Atmospheric Science
• Atomic and Molecular Physics, and Optics
• Automotive Engineering
• Behavioral Neuroscience
• Biochemistry
• Biochemistry, Genetics and Molecular Biology (miscellaneous)
• Biochemistry (medical)
• Bioengineering
• Biological Psychiatry
• Biomaterials
• Biomedical Engineering
• Biotechnology
• Building and Construction
• Business and International Management
• Business, Management and Accounting (miscellaneous)
• Cancer Research
• Cardiology and Cardiovascular Medicine
• Care Planning
• Cell Biology
• Cellular and Molecular Neuroscience
• Ceramics and Composites
• Chemical Engineering (miscellaneous)
• Chemical Health and Safety
• Chemistry (miscellaneous)
• Chiropractics
• Civil and Structural Engineering
• Clinical Biochemistry
• Clinical Psychology
• Cognitive Neuroscience
• Colloid and Surface Chemistry
• Communication
• Community and Home Care
• Complementary and Alternative Medicine
• Complementary and Manual Therapy
• Computational Mathematics
• Computational Mechanics
• Computational Theory and Mathematics
• Computer Graphics and Computer-Aided Design
• Computer Networks and Communications
• Computer Science Applications
• Computer Science (miscellaneous)
• Computer Vision and Pattern Recognition
• Computers in Earth Sciences
• Condensed Matter Physics
• Conservation
• Control and Optimization
• Control and Systems Engineering
• Critical Care and Intensive Care Medicine
• Critical Care Nursing
• Cultural Studies
• Decision Sciences (miscellaneous)
• Dental Assisting
• Dental Hygiene
• Dentistry (miscellaneous)
• Dermatology
• Development
• Developmental and Educational Psychology
• Developmental Biology
• Developmental Neuroscience
• Discrete Mathematics and Combinatorics
• Drug Discovery
• Drug Guides
• Earth and Planetary Sciences (miscellaneous)
• Earth-Surface Processes
• Ecological Modeling
• Ecology, Evolution, Behavior and Systematics
• Economic Geology
• Economics and Econometrics
• Economics, Econometrics and Finance (miscellaneous)
• Electrical and Electronic Engineering
• Electrochemistry
• Electronic, Optical and Magnetic Materials
• Emergency Medical Services
• Emergency Medicine
• Emergency Nursing
• Endocrine and Autonomic Systems
• Endocrinology
• Endocrinology, Diabetes and Metabolism
• Energy Engineering and Power Technology
• Energy (miscellaneous)
• Engineering (miscellaneous)
• Environmental Chemistry
• Environmental Engineering
• Environmental Science (miscellaneous)
• Epidemiology
• Experimental and Cognitive Psychology
• Family Practice
• Filtration and Separation
• Fluid Flow and Transfer Processes
• Food Animals
• Food Science
• Fuel Technology
• Fundamentals and Skills
• Gastroenterology
• Gender Studies
• Genetics (clinical)
• Geochemistry and Petrology
• Geography, Planning and Development
• Geometry and Topology
• Geotechnical Engineering and Engineering Geology
• Geriatrics and Gerontology
• Gerontology
• Global and Planetary Change
• Hardware and Architecture
• Health Informatics
• Health Information Management
• Health Policy
• Health Professions (miscellaneous)
• Health (social science)
• Health, Toxicology and Mutagenesis
• History and Philosophy of Science
• Horticulture
• Human Factors and Ergonomics
• Human-Computer Interaction
• Immunology and Allergy
• Immunology and Microbiology (miscellaneous)
• Industrial and Manufacturing Engineering
• Industrial Relations
• Infectious Diseases
• Information Systems
• Information Systems and Management
• Inorganic Chemistry
• Insect Science
• Instrumentation
• Internal Medicine
• Issues, Ethics and Legal Aspects
• Leadership and Management
• Library and Information Sciences
• Life-span and Life-course Studies
• Linguistics and Language
• Literature and Literary Theory
• LPN and LVN
• Management Information Systems
• Management, Monitoring, Policy and Law
• Management of Technology and Innovation
• Management Science and Operations Research
• Materials Chemistry
• Materials Science (miscellaneous)
• Maternity and Midwifery
• Mathematical Physics
• Mathematics (miscellaneous)
• Mechanical Engineering
• Mechanics of Materials
• Media Technology
• Medical and Surgical Nursing
• Medical Assisting and Transcription
• Medical Laboratory Technology
• Medical Terminology
• Medicine (miscellaneous)
• Metals and Alloys
• Microbiology
• Microbiology (medical)
• Modeling and Simulation
• Molecular Biology
• Molecular Medicine
• Nanoscience and Nanotechnology
• Nature and Landscape Conservation
• Neurology (clinical)
• Neuropsychology and Physiological Psychology
• Neuroscience (miscellaneous)
• Nuclear and High Energy Physics
• Nuclear Energy and Engineering
• Numerical Analysis
• Nurse Assisting
• Nursing (miscellaneous)
• Nutrition and Dietetics
• Obstetrics and Gynecology
• Occupational Therapy
• Ocean Engineering
• Oceanography
• Oncology (nursing)
• Ophthalmology
• Oral Surgery
• Organic Chemistry
• Organizational Behavior and Human Resource Management
• Orthodontics
• Orthopedics and Sports Medicine
• Otorhinolaryngology
• Paleontology
• Parasitology
• Pathology and Forensic Medicine
• Pathophysiology
• Pediatrics, Perinatology and Child Health
• Periodontics
• Pharmaceutical Science
• Pharmacology
• Pharmacology (medical)
• Pharmacology (nursing)
• Pharmacology, Toxicology and Pharmaceutics (miscellaneous)
• Physical and Theoretical Chemistry
• Physical Therapy, Sports Therapy and Rehabilitation
• Physics and Astronomy (miscellaneous)
• Physiology (medical)
• Plant Science
• Political Science and International Relations
• Polymers and Plastics
• Process Chemistry and Technology
• Psychiatry and Mental Health
• Psychology (miscellaneous)
• Public Administration
• Public Health, Environmental and Occupational Health
• Pulmonary and Respiratory Medicine
• Radiological and Ultrasound Technology
• Radiology, Nuclear Medicine and Imaging
• Rehabilitation
• Religious Studies
• Renewable Energy, Sustainability and the Environment
• Reproductive Medicine
• Research and Theory
• Respiratory Care
• Review and Exam Preparation
• Reviews and References (medical)
• Rheumatology
• Safety Research
• Safety, Risk, Reliability and Quality
• Sensory Systems
• Signal Processing
• Small Animals
• Social Psychology
• Social Sciences (miscellaneous)
• Social Work
• Sociology and Political Science
• Soil Science
• Space and Planetary Science
• Spectroscopy
• Speech and Hearing
• Sports Science
• Statistical and Nonlinear Physics
• Statistics and Probability
• Statistics, Probability and Uncertainty
• Strategy and Management
• Stratigraphy
• Structural Biology
• Surfaces and Interfaces
• Surfaces, Coatings and Films
• Theoretical Computer Science
• Tourism, Leisure and Hospitality Management
• Transplantation
• Transportation
• Urban Studies
• Veterinary (miscellaneous)
• Visual Arts and Performing Arts
• Waste Management and Disposal
• Water Science and Technology
• All regions / countries
• Asiatic Region
• Eastern Europe
• Latin America
• Middle East
• Northern America
• Pacific Region
• Western Europe
• ARAB COUNTRIES
• IBEROAMERICA
• NORDIC COUNTRIES
• Afghanistan
• Bosnia and Herzegovina
• Brunei Darussalam
• Czech Republic
• Dominican Republic
• Netherlands
• New Caledonia
• New Zealand
• Papua New Guinea
• Philippines
• Puerto Rico
• Russian Federation
• Saudi Arabia
• South Africa
• South Korea
• Switzerland
• Syrian Arab Republic
• Trinidad and Tobago
• United Arab Emirates
• United Kingdom
• United States
• Vatican City State
• Book Series
• Conferences and Proceedings
• Trade Journals

• Citable Docs. (3years)
• Total Cites (3years)

Follow us on @ScimagoJR Scimago Lab , Copyright 2007-2024. Data Source: Scopus®

Cookie settings

Cookie Policy

Legal Notice

Privacy Policy

AI in Human Resource Management: Literature Review and Research Implications

• Published: 24 January 2024

Cite this article

• Yuming Zhai 1 ,
• Lixin Zhang 1 &
• Mingchuan Yu   ORCID: orcid.org/0000-0001-7576-509X 2  

1212 Accesses

Explore all metrics

This study sorted out the literature on the application of AI in HRM from 2012 to 2021 using CiteSpace to derive the history of research in this field. Further, the research emphasis has shifted from the AI algorithm level to the application level. We proposed a conceptual paradox model to explain the positive and negative effects of AI in workplaces. We also discussed theoretically the practical implications of this study. Finally, this study offers relevant information that can help support and expand future research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis

Impact of Artificial Intelligence on HR practices in the UAE

A Bibliometric Analysis of Artificial Intelligence and Human Resource Management

Availability of data and materials.

The datasets generated for this study are available on request to the corresponding author.

Aboytes-Ojeda, M., Castillo-Villar, K. K., & Eksioglu, S. D. (2019). Modeling and optimization of biomass quality variability for decision support systems in biomass supply chains. Annals of Operations ResEarch, 314 (2), 319–346.

Article   Google Scholar  

Aghion, P., Jones, B. F., & Jones, C. I. (2017). Artificial intelligence and economic growth . NBER Working Papers.

Book   Google Scholar  

Alavi, M., Marakas, G. M., & Yoo, Y. (2002). A comparative study of distributed learning environments on learning outcomes. Information Systems Research, 13 , 404–415.

Ang, S., & Cummings, L. L. (1994). Panel analysis of feedback-seeking patterns in face-to-face, computer-mediated, and computer-generated communication environments. Perceptual and Motor Skills, 79 , 67–73.

Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR andanalytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26 (1), 1–11.

Atsalakis, G. (2014). New technology product demand forecasting using a fuzzy inference system. Operational Research, 14 (2), 225–236.

Bader, V., & Kaiser, S. (2019). Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence. Organization, 26 (5), 655–672.

Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2021). An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial Marketing Management, 92 , 178–189.

Ballouki, I., Douimi, M., & Ouzizi, L. (2017). Decision support tool for supply chain configuration considering new product re-design: An agent-based approach. Journal of Advanced Manufacturing Systems, 16 (4), 291–315.

Bankins, S., & Formosa, P. (2020). When AI meets PC: Exploring the implications of workplace social robots and a human-robot psychological contract. European Journal of Work and Organizational Psychology, 29 (2), 215–229.

Beer, D. (2017). The social power of algorithms. Information, Communication & Society, 20 , 1–13.

Beer, M., Spector, B., & Lawrence, P. R. (1984). Managing human assets: The groundbreaking . Harvard Business Program.

Google Scholar  

Bell, M. P., Marquardt, D., & Berry, D. P. (2014). Diversity, immigration, and the new American multi-racial hierarchy. Journal of Managerial Psychology, 29 (3), 285–303.

Benbya, H., Davenport, T. H., & Pachidi, S. (2021). Artificial intelligence in organizations: Current state and future opportunities . Social Science Electronic Publishing.

Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019). Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology, 16 (11), 703–715.

Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57 , 102225.

Braganza, A., Chen, W., Canhoto, A., & Sap, S. (2021). Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust. Journal of Business Research, 131 , 485–494.

Brown, K. G. (2001). Using computers to deliver training: Which employees learn and why. Personnel Psychology, 54 , 271–296.

Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence . Harvard Business Review. https://starlab-alliance.com/wp-content/uploads/2017/09/AI-Article.pdf

Brynjolfsson, E., Rock, D., & Syverson, C. (2017). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics . National Bureau of Economic Research.

Cançado, L. V., Vendramine, F. M., Corrêa, A. D., Oliveira, J. E., Castro, P. S., Chapman, D. S., Uggerslev, K. L., & Webster, J. (2003). Applicant reactions to face-to-face and technology-mediated interviews: A field investigation. Journal of Applied Psychology, 88 , 944–953.

Cappelli, P., Tambe, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: challenges and a path forward . Social Science Electronic Publishing.

Cardy, R. L. and Miller, J. S. (2005). eHR and performance management: A consideration of positive potential and the dark side. In H. G. Gueutal, & D. L. Stone (Eds.) , 138–165.

Chapman, D. S., Uggerslev, K. L., & Webster, J. (2003). Applicant reactions to face-to-face and technology-mediated interviews: A field investigation. Journal of Applied Psychology, 88 , 944–953.

Charlwood, A., & Guenole, N. (2022). Can HR adapt to the paradoxes of artificial intelligence. Human Resource Management Journal, 20 , 545–566.

Chen, C. (2017). Science mapping: A systematic review of the literature. Journal of Data and Information Science, 2 (2), 1–40.

Cheng, M. (2017). Causal modeling in HR analytics: A practical guide to models, pitfalls, and suggestions . Academy of Management Proceedings.

Cheng, M. M., & Hackett, R. D. (2019). A critical review of algorithms in HRM: Definition, theory, and practice . Academy of Management Annual Meeting Proceedings.

Chowdhury, R., Rakova, B., Cramer, H., & Yang, J. (2020). Putting responsible AI into practice . MIT Sloan Management Review.

Cleveland Jeanette, N., Byrne Zinta, S., & Cavanagh, T. M. (2015). The future of HR is RH: Respect for humanity at work. Human Resource Management Review, 25 (2), 146–161.

Collings, D. G., Nyberg, A. J., Wright, P. M., & McMackin, J. (2021). Leading through paradox in a COVID-19 world: Human resources comes of age. Human Resource Management Journal, 31 (4), 819–833.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48 (1), 24–42.

der Aalst, Van, et al. (2018). Robotic Process Automation. Department of Informatics, Decision Sciences and Systems, Technical University of Munich (TUM),. Boltzmannstr, 3 , 85748. Munich, Germany.

Dineen, B. R., & Allen, D. G. (2013). Internet recruiting 2.0: Shifting paradigms. In K. Y. T. Yu & D. M. Cable (Eds.), The Oxford Handbook of Recruitment (pp. 382–401). Oxford University Publishers.

Dineen, B. R., & Soltis, S. M. (2011). Recruitment: A review of research and emerging directions. In S. Zedeck (Ed.), APA handbook of industrial and organizational psychology , 43–66.

Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30 (1), 114–132.

Earley, P. C. (1988). Computer-generated performance feedback in the magazine-subscription industry. Organizational Behavior and Human Decision Processes, 41 , 50–64.

European Commission. (2019). A definition of artificial intelligence: Main capabilities and disciplines . EC Report. https://ec.europa.eu/futurium/en/system/files/ged/ai_hleg_definition_of_ai_18_december_1.pdf

Fay, C. H., & Nardoni, R. E. (2009). In M. J. Kavanagh & M. Thite (Eds.), Performance management, compensation, benefits, payroll, and the human resource information system (pp. 338–360)

Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2016). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing and Service Operations Management, 18 (1), 69–88.

Flechsig, C., Lohmer, J., & Lasch, R. (2019). Realizing the full potential of robotic process automation through a combination with BPM. Logistics management: Strategies and instruments for digitalizing and decarbonizing supply chains-Proceedings of the German Academic Association for Business Research, Halle, 2019 (pp. 104–119). Springer International Publishing.

Chapter   Google Scholar  

Ford, M. (2015). The rise of the robots: Technology and the treat of a jobless future . Basic Books.

Gigerenzer, G., & Todd, P. M. (1999). Fast and frugal heuristics: The adaptive toolbox (pp. 3–34). Oxford University Press.

Gill, M. (2000). E-learning technology and strategy for organizations. In K. Fry (Ed.), The business of e-learning: Bringing your organization in the knowledge economy. Sydney, Austrailia: University of Technology.

Gillhan, J. (2018). The macroeconomic impact of artificial intelligence . PWC & Company Report.

Guerrero, L., & Posthuma, R. A. (2014). Perceptions and behaviors of Hispanic workers: A review. Journal of Managerial Psychology, 29 (6), 3–30.

Gupta, S., Modgil, S., & Bhattacharyya, S. (2022). Artificial intelligence for decision support systems in the field of operations research: Review and future scope of research. Annals of Operations Research, 308 , 215–274.

Guner, H. U., Chinnam, R. B., & Murat, A. (2016). Simulation platform for anticipative plant-level maintenance decision support system. International Journal of Production Research, 54 (6), 1785–1803.

Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61 (4), 5–14.

Herrmann, H. (2022). The arcanum of artificial intelligence in enterprise applications: Toward a unified framework. Journal of Engineering and Technology Management, 66 , 101716.

Hirschman, A. O. (1970). Exit, voice, and loyalty. Responses to decline in firms, organizations, and states . Cambridge, MA: Harvard University Press.

Hsiao, J. P., Jaw, C., Huan, T.-C., & Woodside, A. G. (2015). Applying complexity theory to solve hospitality contrarian case conundrums. International Journal of Contemporary Hospitality Management, 27 (4), 608–647.

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61 (4), 577–586.

Jatobá, M., Santos, J., Gutierriz, I., Moscon, D., Fernandes, P. O., & Teixeira, J. P. (2016). Evolution of artificial intelligence research in human resources. Procedia Computer Science, 164 , 137–142.

Jia, Q., Guo, Y., Li, Y., & Chen, Y. (2018). A conceptual artificial intelligence application framework in human resource management (pp. 106–114). Proceedings of the 18th International Conference on Electronic Business.

Jeske, D., & Santuzzi, A. M. (2015). Monitoring what and how: Psychological implications of electronic performance monitoring. New Technology, Work and Employment, 30 (1), 62–78.

Jin, X.-H., & Zhang, G. (2011). Modelling optimal risk allocation in PPP projects using artificial neural networks. International Journal of Project Management, 29 (5), 591–603.

Johnson, R. D., & Gueutal, H. G. (2011). Transforming HR through technology: The use of her and human resource information systems in organizations. SHRM effective practices guidelines series. Alexandria, Virginia: SHRM.

Johnson, R. D., Hornik, S., & Salas, E. (2008). An empirical examination of factors contributing to the creation of successful e-learning environments. International Journal of Human-Computer Studies, 66 , 356–369.

Kaplan, J. (2016). Humans Need not apply: A guide to wealth and work in the age of artificial intelligence . Yale University Press.

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62 (1), 15–25.

Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63 (1), 37–50.

Larson, L., & DeChurch, L. A. (2020). Leading teams in the digital age: Four perspectives on technology and what they mean for leading teams. The Leadship Quarterly, 31 (1), 101–377.

Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society, 5 (1), 1–15.

Lee, K. C., Lee, N., & Lee, H. (2012). Multi-agent knowledge integration mechanism using particle swarm optimization. Technological Forecasting and Social Change, 79 (3), 469–484.

Lee, M. K., Jain, A., Cha, H. J., Ojha, S., & Kusbit, D. (2019). Procedural justice in algorithmic fairness: Leveraging transparency and outcome control for fair algorithmic mediation. Proceedings of the ACM on Human-Computer Interaction, 3 (CSCW), 1–26.

Leonardi, P., & Contractor, N. (2018). Better people analytics measure who they know, not just who they are . Harvard Business Review.

Li, M. (2019). Are your algorithms upholding your standards of fairness? Harvard Business Review. https://hbr.org/2019/11/are-your-algorithms-upholding-your-standards-of-fairness

Li. (2021). The application of artificial intelligence in enterprise financial risk prevention and control—Based on big data environment. Finance and Accounting Communications, 20 , 62.

Lichtenthaler, U. (2019). Extremes of acceptance: Employee attitudes toward artificial intelligence. Journal of Business Strategy, 41 (5), 39–45.

Lin, Y., Lei, H., Addo, P. C., et al. (2016). Machine learned resume-job matching solution . arXiv preprint arXiv:1607.07657

Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24 (3), 149–157.

Mariana, B., & Drăguţ, L. D. (2016). Random forest in remote sensing: A review of applications and future directions. Isprs Journal of Photogrammetry and Remote Sensing, 114 , 24–31.

McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27 (4), 12–14.

McCorduck, P. (1979). Machines who think: A personal inquiry into the history and prospects of artificial intelligence . San Francisco, CA: W.H. Freeman and Company.

Mesquita, L. D. (2018). The “artificial” consumer: Approaches between artificial intelligence and marketing (pp. 3–6). Enanpad, Curitiba: ANPAD Meetings.

Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58 (3), 103–434.

Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151 , 264–269.

Nascimento, M. A., & Anna, Q. M. (2010). The quest for artificial intelligence: A history of ideas and achievements (pp. 1–4). Enanpad, São Paulo: ANPAD Meetings.

Nilsson, N. (1971). Problem-solving methods in artificial intelligence . New York: McGraw-Hill.

Nilsson, N. J. (2010). The Quest for Artificial Intelligence: A history of Idea sand Achievements. Cambridge University Press, Kybernetes, 40 (9/10), 1553–1553.

O’Connell, M. S., Doverspike, D., Gillikin, S., & Meloun, J. M. (2001). Computer anxiety: Effects on computerized testing and implications for e-recruiting. Journal of e-Commerce and Psychology, 1 , 25–39.

OECD (2019). Artificial intelligence in society. OECD Publishing. Organisation for Economic Co-operation and Development. https://www.oecd.org/publications/artificial-intelligence-in-society-eedfee77-en.htm

Oracle and Future Workplace. (2019). Global research highlights how AI is changing the relationship between people and technology at work . Oracle.

Orvis, K. A., Brusso, R. C., Wasserman, M. E., & Fisher, S. L. (2011). Enabled for e-learning? The moderating role of personality in determining the optimal degree of learner control in an e-learning environment. Human Performance, 24 , 60–78.

Pessach, D., Singer, G., Avrahami, D., Ben-Gal, H. C., Shmueli, E., & Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134 , 113–290.

Petriglieri, G., Ashford, S. J., & Wrzesniewski, A. (2019). Agony and ecstasy in the gig economy: Cultivating holding environments for precarious and personalized work identities. Administrative Science Quarterly, 64 , 124–170.

Phillips-Wren, G., Mora, M., Forgionne, G. A., & Gupta, J. N. (2009). An integrative evaluation framework for intelligent decision support systems. European Journal of Operational Research, 195 (3), 642–652.

Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25 , 401–426.

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46 (1), 192–210.

Rampersad, G. (2020). Robot will take your job: Innovation for an era of artificial intelligence. Journal of Business Research, 116 , 68–74.

Ransbotham, S., Candelon, F., Kiron, D., LaFountain, B., & Khodabandeh, S. (2021). The cultural benefits of artificial intelligence in the Enterprise . Cambridge, MA, USA: MIT Sloan Management Review and Boston Consulting Group.

Reiter-Palmon, R., Brown, M., Sandall, D. L., Buboltz, C., & Nimps, T. (2006). Development of an O*NET web-based job analysis and its implementation in the U.S. Navy: Lessons learned. Human Resource Management Review, 16 , 294–309.

Ritzer, G. (2011). The Mcdonaldization of Society . Sage.

Ruël, H., Bondarouk, T., & Looise, J. K. (2004). E-HRM: Innovation or irritation. An explorative empirical study in five large companies on web-based HRM. Management Revue, 15 , 364–380.

Salas, E., DeRouin, R., & Littrell, L. (2005). Research based guidelines for distance learning: What we know so far. In H. G. Gueutal & D. L. Stone (Eds.), The brave new world of eHR: Human resources management in the digital age (pp. 104–137). San Francisco: Jossey Bass.

Silverman, R. E., & Gellman, L. (2015). Women in the workplace (A special report) – Apps to battle job bias: Software takes on hiring and workplace practices. The Wall Street Journal Eastern edition.

Stein, V., & Scholz, T. M. (2020). Manufacturing revolution boosts people issues: The evolutionary need for “human automation resource management” in smart factories. European Management Review, 17 (2), 391–406.

Stoltzfus, J. C. (2011). Logistic regrssion: A brief primer. Academic Emergency Medicine, 10 , 1099–1104.

Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2015). The influence of technology on the future of human resource management. Human Resource Management Review, 25 (2), 216–231.

Stone, D. L., Lukaszewski, K., & Isenhour, L. C. (2005). E-Recruiting: Online strategies for attracting talent. In H. G. Gueutal & D. L. Stone (Eds.), The Brave New World of eHR: Human Resources Management in the Digital Age (pp. 22–53). Jossey Bass.

Sullivan, J. (2014). A walk through the HR department of 2020 (pp. 7–9). Workforce Solutions Review.

Tambe, P., Hitt, L. M., Rock, D., & Brynjolfsson, E. (2019). IT, AI and the growth of intangible capital . Available at SSRN 3416289.

Toniolo, K., Masiero, E., Massaro, M., & Bagnoli, C. (2020). Sustainable business models and artificial intelligence: Opportunities and challenges. Knowledge, people, and digital transformation (pp. 103–117). Springer.

Ulrich, D., Younger, J., Brockbank, W., & Ulrich, M. (2012). HR from the outside in: Six competencies for the future of human resources . McGraw Hill.

van den Broek, E., Sergeeva, A., Huysman, M., & Huysman Vrije, M. (2021). When the machine meets the expert: An ethnography of developing AI for hiring. Management Information Systems Quarterly, 45 (3), 1557–1580.

Vrontis, D., Christofi, M., Pereira, V. E., Tarba, S. Y., Makrides, A., & Trichina, E. (2021). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management, 33 , 1237–1266.

Wallace, P., & Clariana, R. B. (2005). Test mode familiarity and performance-Gender and race comparisons of test scores among computer literate students in advanced information systems. Journal of Information Systems Education, 16 , 177–183.

Wamba-Taguimdje, S.-L., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26 (7), 1893–1924.

Wang, X., Wang, L., Zhang, L., Xu, X., Zhang, W., & Xu, Y. (2017). Developing an employee turnover risk evaluation model using case-based reasoning. Information Systems Frontiers, 19 (3), 569–576.

Welsh, E. T., Wanberg, C. R., Brown, E. G., & Simmering, M. J. (2003). E-learning: Emerging uses, empircial results and future direction. International Journal of Training and Development, 7 , 245–258.

Wei, J. (2019). Applying machine learning to human resource management systems . Huazhong University of Science and Technology. Dissertation.

Winston, P. H. (1984). Artificial intelligence (2nd ed., pp. 75–80). Addison-Wesley Longman Publishing Co. Inc.

Winston, P. E. (2015). Model-based story summary . Atlanta: 6th International Workshop on Computational Models of Narrative.

Xu, Q., Liu, X., Jiang, C., & Yu, K. (2016). Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk. Applied Stochastic Models in Business and Industry, 32 (6), 882–908.

Zhou, Z. (2016). Machine learning . Tsinghua University Press.

Zhou, S., Yu, B., Sun, A., Long, C., Li, J., Yu, H., & Li, Y. (2022). A survey on neural open information extraction: Current status and future directions . arXiv preprint arXiv:2205.11725

Download references

This study was supported by the National Natural Science Foundation of China (Grant Nos. 71802134 and 72372079).

Author information

Authors and affiliations.

School of Economics & Management, Shanghai Institute of Technology, Shanghai, 201418, China

Yuming Zhai & Lixin Zhang

College of Business Administration, Ningbo University of Finance and Economics, Ningbo, 315175, China

Mingchuan Yu

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Mingchuan Yu .

Ethics declarations

Conflict of interest.

The authors declared no competing interests.

Additional information

Publisher's note.

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Zhai, Y., Zhang, L. & Yu, M. AI in Human Resource Management: Literature Review and Research Implications. J Knowl Econ (2024). https://doi.org/10.1007/s13132-023-01631-z

Download citation

Received : 03 May 2023

Accepted : 13 November 2023

Published : 24 January 2024

DOI : https://doi.org/10.1007/s13132-023-01631-z

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

• Artificial intelligence
• Human resource management
• Bibliometric review
• Paradox lens
• Find a journal
• Publish with us
• Track your research

• Printed Journal
• Indexed Journal
• Refereed Journal
• Peer Reviewed Journal

P-ISSN: 2663-3213, E-ISSN: 2663-3361, Impact Factor: RJIF 5.6

• P-ISSN: 2663-3213
• E-ISSN: 2663-3361
• Impact Factor: RJIF 5.6
• Toll Free: 1800-1234070
• Working hours 10:00 AM-06:00 PM
• Editorial Board
• Instructions
• Indexing and Abstracting
• Special Issue
• Copyright Form (DOC)
• Copyright Form (PDF)
• Sample Paper

Human Resource Management Journal | International Journal of Research in Human Resource Management

• Policy initiatives
• Worker rights, benefits and responsibilities
• Employee-management relations
• Strategic planning and allocation
• Education and training
• Recruitment and work practices
• Professional development and leadership
• Cultural, cross-cultural and gender issues
• Role of the unions and their impact on organisational productivity and worker satisfaction
• Worker employability and transferability of skills
• Wage and incentive aspects of employment
• Worker retirement issues
• Impact of technology on human resources practices
• Employees as a source of innovation and creation

Human Resource Management Journal is Indexed and Abstracted in Following Databases

Related Journals

• Management Journal

Related Journal Subscription

• Finance Magazine Subscription
• Management Journal Subscription
• Journal of Commerce Subscription
• Business Journal Subscription
• Human Resource Management Journal Subscription
• Tourism Journal Subscription

Related Links

• Important Links
• Economics Journal Subscription
• Finance Journal Subscription
• Marketing Journal Subscription

International Journal of Research in Human Resource Management

• Article Submission
• Helpline No.: +91-9711224068
• Fast Publication: +91-7048922346

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

Build a Corporate Culture That Works

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their culture in such a way that the words become an organizational reality that molds employee behavior as intended.

All too often a culture is described as a set of anodyne norms, principles, or values, which do not offer decision-makers guidance on how to make difficult choices when faced with conflicting but equally defensible courses of action.

The trick to making a desired culture come alive is to debate and articulate it using dilemmas. If you identify the tough dilemmas your employees routinely face and clearly state how they should be resolved—“In this company, when we come across this dilemma, we turn left”—then your desired culture will take root and influence the behavior of the team.

To develop a culture that works, follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value statement.

Start by thinking about the dilemmas your people will face.

Idea in Brief

The problem.

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their corporate culture in such a way that the words become an organizational reality that molds employee behavior as intended.

What Usually Happens

How to fix it.

Follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value.

At the beginning of my career, I worked for the health-care-software specialist HBOC. One day, a woman from human resources came into the cafeteria with a roll of tape and began sticking posters on the walls. They proclaimed in royal blue the company’s values: “Transparency, Respect, Integrity, Honesty.” The next day we received wallet-sized plastic cards with the same words and were asked to memorize them so that we could incorporate them into our actions. The following year, when management was indicted on 17 counts of conspiracy and fraud, we learned what the company’s values really were.

• EM Erin Meyer is a professor at INSEAD, where she directs the executive education program Leading Across Borders and Cultures. She is the author of The Culture Map: Breaking Through the Invisible Boundaries of Global Business (PublicAffairs, 2014) and coauthor (with Reed Hastings) of No Rules Rules: Netflix and the Culture of Reinvention (Penguin, 2020). ErinMeyerINSEAD

Partner Center

• Book a Speaker

Introduction

Keeping benefits packages competitive.

The SHRM Employee Benefits Survey returns with new insights for 2024, headlined by modern additions and updated definitions across a wide berth of potential benefits offerings. Evolving upon nearly 30 years of employee benefits research, this comprehensive annual survey of HR professionals captures the prevalence across the spectrum of various employee benefits and perks provided by organizations.

A competitive job market comes with a need for organizations to provide equally competitive benefits offerings. As organizations face labor shortages, those who adapt their total compensation and benefits packages with creative and modern offerings put themselves in better positions to attract and retain talent. The goal of the SHRM Employee Benefits Survey is to gain an accurate representation of benefits offerings throughout the United States. SHRM members can use the findings to discover and benchmark the benefits changes organizations have implemented. With the inclusion of even more items in 2024—as well as new research diving into the average vacation, sick, and PTO days granted by employers—SHRM hopes to provide an even more comprehensive picture of the employee benefits landscape than ever before.

See How Your Benefits Stack Up

To help you compare your organization’s benefits against those surveyed, we’ve provided an online, interactive benchmarking tool. The power is in your hands to explore results for the last five year and to filter results according to your organization’s industry, size and location.  Do you work in health care in California? Results are available specifically for an organization like yours. What about a medium-sized trucking company in the South? Yep, results are available for that as well.*

This tool not only equips you to see the overall results of the SHRM Employee Benefits Survey, but also allows you to call up custom-filtered results any time you need them. You can also export the results for later reference.

We’re excited to share these results and equip you with the information to help you build better workplaces. Select any of the benefits categories on the navigation bar to get started.

*For confidentiality purposes, a minimum of five responses is required to show filtered results. For filters resulting in 5-19 responses, results will display with an asterisk to denote a low response count.

License Agreement

By using the employee benefits survey results interactive online tool you agree to our license agreement. click to see full details..

LICENSE AGREEMENT FOR THE SHRM EMPLOYEE BENEFITS SURVEY RESULTS INTERACTIVE ONLINE TOOL, SURVEY RESULTS AND REPORT

By opening and using the SHRM Employee Benefits Survey Results interactive online tool (the "Interactive Online Tool"), the Benefits Survey Results (the “Results”), and generating a Report (the “Report”) (and together the “Results and Report”), you (“User”) hereby agree as follows:

(i) That the Society for Human Resource Management is the exclusive provider and owner of the Interactive Online Tool and exclusive copyright owner of the Results and Report.

(ii) User has the right, by this License, to use the Interactive Online Tool and Results and Report solely for the internal purposes of their employer (“Company”) or for the internal purposes of a single client of Company (“Single Client”), and to make or distribute copies of the Results and Report to other employees within the Company or to employees within the Single Client, provided that such other Company employees or Single Client employees may only use the Results and Report for the internal purposes of the Company or Single Client. The Results and Report may not be shared to external third parties by any Company employees, Single Client employees or User. Except as allowed above with respect to use by employees of Company for the internal purposes of Company or employees of Single Client for the internal purposes of Single Client, User, Company and Single Client are strictly prohibited from printing, making or distributing any copies of the Results and Report in any type of media.

(iii) All materials, reports, data, records, including all export files and reports generated from the Interactive Online Tool, regardless of format (e.g., PDF, CSV), and any other intellectual property created or compiled by SHRM for the Results or in generating the Report, contained in the Results and Report and all copies thereof, collectively the “SHRM Intellectual Property”, shall be the sole property of SHRM.  

(iv) Neither User, Company nor Single Client has any right to sell or sublicense, loan or otherwise convey or distribute the Interactive Online Tool, the Results or the Report or any copies thereof in any media to any third parties outside of the Company or Single Client.

© 2024 Society for Human Resource Management. All rights reserved.

SHRM is a member-driven catalyst for creating better workplaces where people and businesses thrive together. As the trusted authority on all things work, SHRM is the foremost expert, researcher, advocate and thought leader on issues and innovations impacting today’s evolving workplaces. With nearly 340,000 members in 180 countries, SHRM touches the lives of more than 362 million workers and their families globally. Discover more at SHRM.org.

This publication may not be reproduced, stored in a retrieval system or transmitted in whole or in part, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of the Society for Human Resource Management, 1800 Duke Street, Alexandria, VA 22314, USA.

This report is published by the Society for Human Resource Management (SHRM). SHRM cannot accept responsibility for any errors or omissions or any liability resulting from the use or misuse of any such information.

HR Daily Newsletter

New, trends and analysis, as well as breaking news alerts, to help HR professionals do their jobs better each business day.

Success title

Success caption

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Gout and Diet: A Comprehensive Review of Mechanisms and Management

Yingling zhang.

1 School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China

2 Engineering Research Center of Shanghai College for TCM New Drug Discovery, Shanghai 201203, China

3 Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China

Associated Data

Data available in a publicly accessible repository.

Gout is well known as an inflammatory rheumatic disease presenting with arthritis and abnormal metabolism of uric acid. The recognition of diet-induced systemic metabolic pathways have provided new mechanistic insights and potential interventions on gout progression. However, the dietary recommendations for gouty patients generally focus on food categories, with few simultaneous considerations of nutritional factors and systemic metabolism. It is worthwhile to comprehensively review the mechanistic findings and potential interventions of diet-related nutrients against the development of gout, including purine metabolism, urate deposition, and gouty inflammation. Although piecemeal modifications of various nutrients often provide incomplete dietary recommendations, understanding the role of nutritional factors in gouty development can help patients choose their healthy diet based on personal preference and disease course. The combination of dietary management and medication may potentially achieve enhanced treatment effects, especially for severe patients. Therefore, the role of dietary and nutritional factors in the development of gout is systematically reviewed to propose dietary modification strategies for gout management by: (1) reducing nutritional risk factors against metabolic syndrome; (2) supplementing with beneficial nutrients to affect uric acid metabolism and gouty inflammation; and (3) considering nutritional modification combined with medication supplementation to decrease the frequency of gout flares.

1. Introduction

Throughout history, gouty disease has always been strongly associated with abundant foods and immoderate alcohol intake. Gout is even known as the “king’s disease” and symbolized social status in ancient times, as only the upper class could afford to consume wine and meats [ 1 ]. Nevertheless, gout currently has been well established as a global health problem and has gained attention due to its increasing incidence rate, multiple metabolic comorbidities, and high premature mortality [ 2 ]. Gout is well known as a phlogistic arthritis that is associated with hyperuricemia and elevation of urate in tissues. The increased urate causes the generation of monosodium urate (MSU) crystals, and MSU crystal deposition in and around the first metatarsophalangeal joint, knee, and fingers represents a clinical sign of gout [ 3 ]. The clinical symptoms of gout develop in several stages, including asymptomatic hyperuricemia, MSU crystal formation, intermittent gout and chronic gout [ 4 ]. Effective gout management mainly relies on the use of therapeutic strategies to control uric acid levels or achieve crystal dissolution. While current clinical principles based on medicinal management for gout have been well implemented [ 5 ], dietary modification and lifestyle changes have also been recommended for gout patients, since a suboptimal diet and obesity/diabetes-diseases of affluence contribute significantly to the risk of developing gout [ 5 , 6 , 7 ], increasing the burden of medical expenses. At present, dietary recommendations have been updated worldwide, and nutritional science for the management of gout has advanced dramatically [ 8 , 9 ]. Historical dietary recommendations for gouty patients tend to emphasize the concepts of “high-” and “low-” levels of the same nutrient, but the role of nutritional elements in gout is difficult to classify by a single beneficent or harmful criterion because problems of systemic metabolism arise when the balance between nutrient intake and consumption is disturbed. This means that although dietary management is considered to be an essential aspect of gout therapeutic strategies [ 2 ], a potential dietary mechanism in gout development is out of date or incomprehensive, and a systemic overview of dietary and nutritional factors of gout is needed for well-designed dietary management based on research practice. Moreover, studies have shown that dietary factors mainly focus on food classification, while diet-induced systemic metabolism is rarely mentioned in the progression of gout. It is worthwhile to comprehensively review the mechanistic findings of diets aimed at purine metabolism, urate deposition and gouty inflammation. Our acknowledgment of gout-related nutritional factors can provide a theoretical basis for on-target and comprehensive dietary guidelines for gout patients with different complications or at different stages.

2. Role of Dietary Consumption in the Progression of Gouty Diseases

2.1. uric acid disturbance: emphasis on purines, 2.1.1. formation of uric acid.

Purines are the basic components of nucleotides needed for building DNA and RNA within mammalian cells, and purine nucleotides, such as guano-sine-5′-triphosphate and adenosine triphosphate (ATP), are essential for regulating energy metabolism and intracellular functions, respectively. Disorders of purine metabolism are associated with considerable variations in the concentration of serum uric acid (SUA) because uric acid is the ultimate product of purine catabolism in humans. Purine source analyses show that nearly two-thirds of purines in the body are endogenous, and the remaining purines that enter the body via foods are known as exogenous purines [ 10 ]. As a direct source of exogenous nucleotides and uric acid, dietary purines are vital for maintaining the balance of purine metabolism in mammalian cells by a coordinated process of de novo biosynthesis, the salvage pathway, and purine inter-conversion or degradation. As shown in Figure 1 , these exogenous purines can be dephosphorylated into nucleosides in the body as part of the digestive course, accompanied by the oxidative release of free bases [ 11 ]. Subsequently, the degraded bases can be recycled into nucleotides in the tissues via the purine salvage pathway or wholly degraded into uric acid mainly within the liver or the small intestine [ 11 , 12 ]. Adenosine monophosphate (AMP) and guanine monophosphate (GMP) are usually the predominant forms of purine nucleosides derived from foods. The enzymatic degradation process uses deaminase and GMP reductase to convert AMP and GMP, respectively, to inosine monophosphate (IMP) [ 13 ]. AMP/GMP can also be dephosphorylated to generate adenosine/guanosine in a process catalyzed by nucleosidase. Both IMP and adenosine are then processed into inosine. The transformation of inosine to hypoxanthine is catalyzed by purine nucleoside phosphorylase, and eventually hypoxanthine utilizes the dual oxidation of xanthine oxidase (XO) to produce uric acid. Additionally, the conversion of guanosine into uric acid can be catalyzed by guanine deaminase following guanine formation by nucleotidase [ 13 ]. When purine overload in the body empowers the body’s ability to manage it, excessive uric acid can accumulate in the bloodstream. This condition presenting with an elevated SUA concentration is known as hyperuricemia, and gout induced by hyperuricemia is deemed to be the metabolic disease linked to purines. All meats and edible plants contain purines, and some foods contain higher concentrations. Thus, overindulgent intake of a high-purine diet, including seafoods and animal offal, can trigger the excessive accumulation of purine metabolites, giving rise to the excessive accumulation of uric acid in the body [ 5 ]. In addition, some purine-free drinks can accelerate the promotion of purine degradation; for example, alcohol intake consumes large amounts of ATP to produce AMP in the liver, leading to the rapid occurrence of increased SUA levels [ 14 ]. The consumption of yeast-rich foods, such as bread and yeast drinks, can lead to a high colonization of Saccharomyces cerevisiae in the gut [ 15 ], which can gradually elevate the secretion of uric acid in the host.

Potential mechanisms of diet-induced gout progression in humans. Diets provide abundant raw materials of purine, which is mainly metabolized in the liver, promoting uric acid production. Meanwhile, it can interfere with the intestinal environment, homeostasis, and urate transport to induce high levels of uric acid, leading to hyperuricemia and ultimately to gout. Additionally, gouty inflammation is caused by IL-1β production after the activation of NLRP3 by macrophages that ingest MSU crystals, and a second signal is required in humans by stimulating the activation of TLR signaling pathways that can be induced by diets. Moreover, neutrophil infiltration and diet-induced low-grade inflammatory states will exacerbate gouty inflammation. AMP, adenosine monophosphate. ASC, apoptosis-associated speck-like protein containing a caspase recruitment domain. GMP, guanine monophosphate. IL, interleukin. IMP, inosine monophosphate. LPS, lipopolysaccharide. MSU, monosodium urate. MyD88, myeloid differentiation factor88. NF-κB, nuclear factor kappa B. NLRP3, pyrin domain-containing protein 3. TLR, toll-like receptor. TNF, tumor necrosis factor. XO, xanthine oxidase.

2.1.2. The Excretion of Uric Acid

Under normal conditions, nearly 90% of uric acid is reabsorbed into the human body, and the remaining uric acid is excreted in the feces and urine [ 16 ]. When purines exceed the limit value for normal production and catabolism, the synthesis and excretion of uric acid are out of balance, and the circulating uric acid level is elevated. The kidneys are responsible for eliminating approximately two-thirds of circulating uric acid, with the remaining one-third excreted by the intestine and gut microbiota [ 12 ]. Diets could affect uric acid excretion by regulating the excretory function of the kidney and intestine. Because foods can come into contact with the intestinal tract and regulate intestinal homeostasis [ 17 ], dietary factors are involved in intestinal urate handling mechanisms. Endogenous uric acid from the bloodstream or as a constituent of saliva, bile, or peptic juices transfer from the enterocyte cytoplasm into the intestine tissues. In this process, enterocyte urate transporters are critical for maintaining urate homeostasis in the intestine, and an enterocyte-specific deficiency of these transporters, including ATP-binding cassette transporter G2 (ABCG2/BCRP) and NPT5 (SLC17A4), can impair the enterocyte urate transport process, which is also affected by exogenous metabolism, such as dietary fat and sugar [ 18 ]. Similarly, a study of 8709 participants suggested that high simple sugar exposure was found to interfere with the ability of SLC2A9 (encoding GLUT9) to mediate renal uric acid excretion without additive genotype-specific interaction [ 19 ]. After entering the intestinal tissue, uric acid can be degraded into nitrogen or CO 2 by the uricase activity found in the gut microbiota. The associations between diet-induced gut microbiota reconstruction and the progression of hyperuricemia/gout have been highlighted in recent research, as evidenced by the fact that long-term adherence to the typical Western diet caused an obvious reduction in the diversity of the gut microbiota, particularly those that degrade uric acid and produce metabolites known to benefit uric acid excretion [ 20 ]. For example, as a microbiota-derived metabolite, the short-chain fatty acid butyric acid was thought to promote intestinal uric acid excretion [ 21 ]; however, a fat-rich diet reduces the abundance of beneficial bacteria that produce short-chain fatty acids [ 20 ]. In addition, the diet is mainly designed to provide calories for energy expenditure, and the related energy metabolism and metabolites produced as a result of dietary modification can affect uric acid excretion. By way of illustration, a ketogenic diet converts energy metabolism substrates from sugars to fats with the production of large ketone bodies such as acetyl acetate and β-hydroxybutyrate (BHB) [ 22 ], which induce fluid acidification and cause uric acid precipitation. The production of BHB inhibits uric acid excretion by competing for binding sites of uric acid transporters.

2.2. Uric Acid Disturbance: Emphasis on Purines

Monosodium urate crystals formation occurs when the urate concentrations continue to rise beyond the point required for spontaneous generation (nucleation). The increased volume of MSU deposition can aggravate the progression of symptomatic gout [ 23 ]. Diets that contribute to excessive SUA levels can induce the formation and deposition of MSU crystals, and other factor changes, such as diet-induced fluid acidification and salt deposition, also promote the growth of urate crystallization [ 24 ]. Alcohol consumption and fasting can induce elevated lactic acid levels to decrease the local pH to create an acidic condition, which might be a risk factor for MSU deposition, as the increased concentrations of calcium ions in an acidic environment aggravate the decrease in MSU crystal solubility [ 25 ].

2.3. Gouty Inflammation

Some observations show that in some circumstances, uric acid can show antioxidant properties in the form of urate. However, soluble urate acts as a proinflammatory stimulus to fuel the maturation and production of interleukin (IL)-1β [ 26 ], thus strongly driving acute gouty inflammation and giving rise to chronic long-term inflammatory consequences of the disease. It is known that the pathogenesis of gouty inflammation involves the cleavage of C5 and generation of C5a and C5b-9 on the surface of urate crystals [ 27 ]. Urate crystal deposition is also extensively recognized as a danger signal for the influx of innate immune cells [ 28 ]. Mechanistically, MSU crystal-induced inflammatory gouty flares are caused by the activation of pyrin domain-containing protein 3 inflammasome (NLRP3) with consequent IL-1β secretion from macrophages and neutrophils, resulting in acute inflammatory responses, intense pain and joint swelling [ 28 ]. The initiation of NLRP3-dependent IL-1β activation includes a priming signal associated with nuclear factor κB (NF-κB) and a secondary signal of caspase-1 assembly activation; thus, MSU crystals alone are insufficient to induce IL-1β secretion. Additionally, other endogenous costimulatory factors, such as myeloid-related protein-8/-14 in phagocytes, can increase MSU crystals-mediated IL-1β secretion in a TLR-4-dependent pathway [ 27 ].

Diets also have a significant impact on the systemic phenotype of the innate immune system. For example, a Western diet or meat-based patterns intersect with a low-grade inflammatory response, which permanently biases the immune system toward a proinflammatory phenotype [ 29 ], fueling gouty inflammation. In contrast, the Mediterranean diet or vegetable- and fruit-based patterns have been reported to prevent systemic inflammation and gouty flares [ 30 ]. A fiber-rich diet has been proven to rapidly resolve the urate crystal-mediated inflammatory response in a gout-like mouse model [ 31 ]. Diet intervention affecting the neutrophil inflammasome has been explored. For example, a ketogenic diet alleviates urate crystal-induced gouty flares by increasing BHB, which can block NLRP3/caspase-1-dependent IL-1β expression in neutrophils and urate crystal-activated macrophages, reducing inflammatory neutrophil recruitment [ 32 ].

3. Nutrient Element-Richness and Structure Determine the Role of Dietary Factors in Gout

A large amount of clinical evidence, shown in Table 1 , indicated the close connection between adherence to the described dietary patterns and the risk of gout-related metabolic disorders, hyperuricemia, and metabolic syndrome. Commonly described dietary patterns, such as a high-carbohydrate diet, a high-protein diet, and a high-unsaturated fat diet, actually represent the proportional and structural collocation of dietary nutrient elements [ 33 ]. It has been shown that the beneficial dietary patterns against hyperuricemia usually contain a higher intake of vitamins, fiber, and unsaturated fatty acids and are often supplemented with appropriate amounts of minerals and high-quality protein, promoting a health state in which systemic metabolism is prone to disease improvement [ 34 , 35 ]. These observations can be explained by the fact that important nutrients in foods can be considered determinants of dietary factors in gouty development. For example, the Western diet is characterized by a high level of sugar, while fiber richness in the Dietary Approaches to Stop Hypertension (DASH) diet can increase satiety to reduce sugar intake from other high-energy foods and play a vital role in decreasing gout incidence [ 34 ]. Therefore, the influence of dietary factors on gouty disease is the effect of nutrient element-richness and structures on systemic metabolism. Understanding the potential mechanisms of nutrients, as shown in Figure 2 , in gout development can facilitate an understanding of the overall nutritional balance needed for the prevention or treatment of gout.

Nutrition-induced systemic metabolism involved in gouty disease. Metabolites of fat, carbohydrate and protein and the resulting metabolic diseases promote the development of gout, including changing intestinal flora, accelerating purine metabolism, promoting MSU deposition, activating macrophages, and inhibiting uric acid excretion. ADP, adenosine diphosphate. AMP, adenosine monophosphate. ATP, adenosine triphosphate. FFAs, free fatty acids. F6P, fructose 6 phosphate. KHK, ketohexokinase. LPS, lipopolysaccharide. MSU, monosodium urate. NAFLD, nonalcoholic fatty liver disease. NAFPD, nonalcoholic fatty pancreas disease. TG, triglyceride. TLR, toll-like receptor. UA, uric acid. XO, xanthine oxidase.

Dietary intervention associated with the improvement of serum uric acid and indicators of metabolic syndrome.

Note: data are from clinical trials that have been included in PubMed since 2012. A direct search was used to search for the following terms: “diet and uric acid” or “diet and gout” or “food and uric acid” or “food and gout”. A total of 462 articles were obtained. After following these exclusion criteria—repetitive articles, acute trials, dietary supplements, combination of drugs and food, questionnaire survey, exercise interference, and mismatched intervention subjects—a total of 32 articles showed the effect of diet on uric acid and other indicators of metabolic syndrome. ↑—increase; ↓—decrease; AA—amino acid; AUC—lower area under the curves; BG—blood glucose; BW—body weight; CRP— C -reactive protein; DASH—Dietary Approaches to Stop Hypertension; Ereq—energy requirement; HDL—high density lipoprotein; ICAM-1—intercellular cell adhesion molecule-1; LDL—low density lipoprotein; MDA—malondialdehyde; MS—indicators of metabolic syndrome; OR—odds ratio; PUA—plasma uric acid; SUA—serum uric acid; TBARS—thiobarbituric acid reactive substance. TC—total cholesterol; TG—triglyceride; UAM—indicators of uric acid metabolism; UUA—urine uric acid.

3.1. Energy-Type Nutrition Overload Can Induce Hyperuricemia and Inflammation

3.1.1. high fat.

Dietary fat is primarily metabolized into triglycerides within intestines and packaged as chylomicrons for delivery to peripheral tissues, where adipocytes further transfer triglycerides into free fatty acids (FFAs) for energy uptake and storage [ 67 ]. A small amount of FFAs can be absorbed by the liver tissue, together with lipids remaining in the chylomicron [ 67 ]. High-fat foods can evoke the pleasure of eating and promote the individual’s desire to consume more energy-dense diets [ 68 ], which culminates in the overproduction of FFAs. Free fatty acids-mediated metabolic events initiate acute onset of gouty disease in the presence of MSU crystals deposited in the joint. The interaction of FFAs with TLR2 synergized with MSU crystals leads to the release of IL-1β induced by ASC/caspase 1 [ 69 ].

High fat consumption can cause excessive accumulation of triglycerides, inducing increased fat mass and obesity. It has been reported that overweight/obesity was connected with 60% of hyperuricemia cases in a clinical trial of 14,624 adults [ 70 ], possibly due to lipid metabolic disorder promoting purine metabolism by elevating XO activity [ 71 ]. Hypertrophy and hyperplasia of adipocytes are associated with higher oxygen consumption, which evokes hypoxic damage in other tissues, resulting in the chronic inflammation of obesity [ 72 ]. Nonalcoholic fatty liver disease and nonalcoholic fatty pancreatic disease subsequently occur when lipid overload occurs in the liver and pancreatic tissue, causing metabolic dysfunction in both and affecting acid-base balance. Metabolic acidosis further promotes hypercalciuria, low urine pH and hypocitraturia, predisposing patients to MSU crystal deposition and calcium renal stone formation [ 73 ]. In response to excessive FFAs circulation, insulin secreted from pancreatic β-cells upregulates the expression of renal urate transporters, including GLUT9 and URAT1, and decreases ABCG2 levels, promoting high SUA levels [ 18 ]. Uric acid conversely induces lipid accumulation and insulin resistance (IR), thereby forming a vicious cycle of uric acid and insulin [ 74 ].

Prior to the onset of IR and obesity, high fat intake has been found to upregulate the expression of reactive oxygen species (ROS) in adipose tissue and liver, along with the metabolic disturbance of adipocytes and the dysregulation of adipokine release [ 75 ], promoting or aggravating MSU-mediated NF-κB-dependent inflammation. For example, leptin levels secreted from adipose tissues were elevated in patients with gouty inflammation, and leptin can facilitate MSU-induced acute gout-related proinflammatory cytokine production in macrophages and synoviocytes [ 76 ]. The key adipocyte-derived chemokines McP-1 and LTB4 recruit proinflammatory macrophages to induce inflammation amplification, thus aggravating gouty inflammation [ 77 ]. Furthermore, a high-fat diet changes gut microbiota composition, leading to reduced microbiota diversity and an increased ratio of Firmicutes to Bacteroidetes and to the reduction of microbiota-derived beneficial metabolites such as butyric acid, which further aggravates gouty arthritis [ 20 ].

3.1.2. High Sugar

Sugars are the most abundant macromolecules in nature and can be classified according to their structure into monosaccharides, complex carbohydrates, and glycoconjugates [ 78 ]. They are the primary carbon source for ATP production and cellular biosynthesis. Sugars from diets can be absorbed as glucose, galactose or fructose in the liver portal circulation. The liver and gut normally process galactose and fructose into lactate, glucose and organic acids through gluconeogenesis, glycogenolysis, aerobic oxidation and other pathways [ 78 ]. High sugar consumption might initiate metabolic disease processes accompanied with hyperglycemia, IR, and fat accumulation. Moreover, high sugar intake in obese patients increases serum urate and decreases the percent of uric acid to creatinine clearance, indicating a close association between hyperuricemia and a high sugar diet [ 79 ]. Sugar-sweetened beverages containing high-fructose corn syrup and sucrose or almost equal amounts of fructose and glucose, which account for approximately one-third of added sugar consumption in the diets of American adults [ 80 ], have been thought to be closely connected with a high prevalence of hyperuricemia in Western countries [ 81 ]. Long-term high sugar consumption has been found to accelerate the accumulation of uric acid and promote MSU deposition in fly renal tubules, suggesting that a similar problem may occur in human excretory systems under dietary challenges [ 82 ]. In a follow-up study of 650 participants, the results confirmed that a high-sugar diet participates in kidney dysfunction and uric acid metabolism disorders [ 82 ].

The metabolic effects of sugar are distinct from those of starch principally because of the fructose component. Studies of dietary sugar intervention in animals and humans have demonstrated that overconsumption of fructose, but not glucose, can manifest multiple traits of metabolic syndrome [ 83 , 84 , 85 ], indicating that fructose might be responsible for high sugar-driven hyperuricemia and gout [ 86 ]. Fructose metabolism starts within the small intestinal tissue, where fructose can be absorbed by the facilitative hexose transporter GLUT5 (SLC2A5) and converted by ketohexokinase. Notably, exposure to high fructose increases the intestinal villus length to expand the surface area of intestinal cells that can absorb more nutrients from food [ 87 ], possibly aggravating high fructose-mediated metabolic disorders via overconsumption of nutrients. Excessive fructose consumption-induced gouty syndrome is related to the altered gut microbiota and its metabolites and induces inflammation and fatty acid disorders. Since high fructose intake induces the proliferation of mucus-degrading bacteria in the gut microbiota, decreased mucus glycoproteins can promote intestinal barrier damage and pathogen invasion [ 88 ].

Fructose-derived metabolites can be transferred from the intestinal tissues to the liver and systemic circulation, and the redundant fructose can also directly reach the hepatic tissue or enter the systemic circulation when the intestinal clearance capacity reaches an upper limit [ 89 ]. Fructose culminates the main rate-limiting step of glycolysis and is rapidly converted into ketohexokinase to generate fructose-1-phosphate, which is further metabolized into glyceraldehyde 3-phosphate and dihydroxyacetone phosphate. The acute fructose overload in the liver leads to ATP degradation and a decrease in ATP synthesis, both of which lead to an increase in AMP levels and stimulation of AMP deaminase activity, subsequently accelerating the formation of uric acid with consequent hyperuricemia. More notably, the costly fructose metabolism can also result in renal inflammation and fibrosis and form kidney stones due to calcium salt precipitation in high-fructose diet-fed mice [ 90 ]. Alarmingly, a normal physiological concentration of fructose in the kidney still causes a risk of defective elimination of uric acid and activation of renal inflammation by increasing the expression of intercellular adhesion molecule-1 in the serum and endothelial cells [ 91 ]. Therefore, carbohydrate restrictions, especially fructose intake, have been regarded as an efficient diet intervention for gouty patients to modulate the disease state. It has been shown that the intake of fructose-rich fruits could bring about a temporary upregulation of uric acid. However, the moderate consumption of these foods over a long time can facilitate the excretion of uric acid, which could be correlated with the alkalization of body fluids [ 92 ]. Therefore, it is always suggested that limiting fructose-rich soft drinks or reducing the consumption of high-fructose drinks rather than fruits is better for gout improvement.

3.1.3. High Protein

Proteins are biomacromolecules formed by folding long chains of amino acids, and dietary protein digestion in the gastrointestinal tract can provide amino acid dipeptides and tripeptides for cellular metabolism and protein synthesis in muscle or other tissues. High protein consumption is well known for its promotion of energy expenditure and urea synthesis. In addition, high dietary protein intake can also affect uric acid homeostasis, since protein digestion can generate several amino acids, such as glutamine, glycine and threonine, to induce purine synthesis, promoting the development of hyperuricemia [ 93 ]. A cohort study with 193,676 participants further revealed that higher nondairy animal protein consumption results in a disturbance of uric acid metabolism, including a reduced level of citrate and a higher level of uric acid and acidic urine, which subsequently promotes uric acid stone formation [ 94 ]. In addition, long-term high dietary protein intake has been shown to have adverse effects on uric acid elimination due to increased intraglomerular pressure and flow in kidney tissues [ 95 ]. The severity of glomerular damage has been significantly attributed to elevated SUA levels; hence, dietary recommendations for gouty patients often suggest that excessive protein intake should be restricted to avoid placing kidney tissue under undue stress.

Studies have also shown inconsistent effects of dietary proteins from different sources [ 96 ]. Overconsumption of animal proteins is linked to an elevated prevalence of gout, whereas overconsumption of plant protein (soybeans and soy products) or dairy product intake are associated with a reduced risk [ 8 , 96 ]. When compared with foods rich in plant proteins, an animal protein-rich diet promotes acidic urine production and uric acid stone formation [ 94 ]. Conversely, substitution of plant-based protein for a carbohydrate-rich diet can attenuate IR and compensatory hyperinsulinemia [ 97 ], thus improving the renal clearance of urate. Therefore, choosing appropriate dietary protein sources and controlling the amount of protein intake might represent an effective intervention for the improvement of gouty diseases.

3.2. Adequate Consumption of Essential Nutritional Elements Leads to Beneficial Effects against Gout

3.2.1. vitamins.

Vitamins are essential trace elements that act as regulators of physiological and pathological functions, such as participating in immune responses, antioxidant activities and redox reactions. It has been demonstrated that an adequate intake of vitamin supplements or consumption of vitamin-rich fruits and vegetables seems to be a valid approach for hyperuricemia and gout treatment. Vitamins such as vitamin A, vitamin E, and vitamin C show beneficial effects against oxidative stress and inflammation, as well as effectively decreasing SUA levels [ 98 , 99 , 100 ], and the same uric acid-lowering effect also appears in a vitamin D-rich diet [ 101 ]. Additionally, vitamin E is also considered a membrane stabilizer that inhibits MSU crystal-induced hemolysis [ 102 ]. Many studies performed in humans and animals have shown that vitamin C (l-ascorbic acid) consumption can affect uric acid reabsorption and excretion to reduce SUA levels [ 100 ]. Both uric acid and vitamin C can be reabsorbed in the proximal tubule via anion-exchange transport, and vitamin C overload can competitively suppress the reabsorption of uric acid [ 103 ] in the filtrate. Meanwhile, its downregulation of URAT1 activity and/or Na + -dependent anion cotransporter could promote uric acid excretion [ 100 ]. The uricosuric function of vitamin C also appears to directly act on the glomerulus by reducing glomerular microvascular ischemia and increasing afferent arteriole dilation, thus increasing the glomerular uric acid filtration rate [ 104 ]. Furthermore, vitamin C reduces the incidence of gout by alleviating the NF-κB/NLRP3-related inflammatory response to MSU deposition [ 105 ].

3.2.2. Minerals

Minerals, including potassium, zinc, calcium, copper, iron, and selenium are micronutrients that are essential for body metabolism [ 73 ], and deficiencies or excesses of these micronutrients are potentially hazardous occurrences that might be involved in the development of gout. It is well known that dietary potassium consumption has obvious diuretic and natriuretic effects, and even a minor potassium insufficiency triggers an impairment in the kidney’s capacity to secrete sodium chloride and retain sodium [ 106 ], resulting in renal dysfunction, while long-term routine potassium replenishment aggravates thiazide diuretic-mediated elevation of uric acid [ 107 ]. A similar facilitation effect was observed when iron accumulation triggers increased saturated transferrin-mediated XO activity [ 108 , 109 ]. Minerals also have a crucial role in maintaining acid-base balance. This has been attributed to keeping urine electrically neutral by regulating the secretion of anions such as chloride, sulfate, and phosphate in kidney tissues [ 73 ]. For instance, urinary calcium loss is a crucial risk factor that can trigger calcium stone formation and cause a uric acid excretion disorder [ 73 ]. Normal calcium intake can decrease the potential risk of kidney stone formation and is conducive to uric acid elimination in renal tissue [ 110 , 111 ].

3.2.3. Fibers

Dietary fibers refer to plant-derived carbohydrates that are resistant to hydrolyzation or assimilation in the upper gastrointestinal tract, and fibers trapped in the gut can increase intestinal viscosity and satiety, as well as reduce gastric emptying rate and regulate intestinal conduction [ 112 ]. Correspondingly, dietary fibers reduce the intake and absorption of high-energy food [ 113 ]. The consumption of dietary fibers can manage glucose and lipid metabolism to regulate energy balance [ 113 , 114 ]. More importantly, a lack of these fibers leads to a slower recovery of gut dysbiosis, and dietary fiber supplementation is able to improve the composition of the gut microbiota [ 115 ], suggesting a close connection between fiber and intestinal flora disorder in gout patients. The fiber fermentation process by gut microbiota accompanies the release of microbiota-driven metabolites (short-chain fatty acids, SCFAs) that show beneficial effects on the host’s health [ 116 ]. After dietary fiber intake, acetate is the most abundant microbiota-derived SCFAs in the blood and can quickly resolve MSU-induced inflammation by promoting caspase-dependent apoptosis of neutrophils and the excretion of the anti-inflammatory IL-10 against LPS-induced inflammation [ 31 ]. Butyrate is another SCFAs mainly produced by microbiotal fermentation of indigestible fibers, and has been shown to improve lipid accumulation in the liver and pancreas, thereby reducing uric acid metabolism abnormalities by XO activation [ 71 ]. Butyrate can also decrease the activation of NF-κB induced by LPS and the translation or transcription of IL-1β by inhibiting histone deacetylases in human peripheral blood mononuclear cells [ 117 ]. Therefore, the consumption of more fiber-rich whole grains, vegetables and fruits is beneficial for regulating gastrointestinal homeostasis, reducing the intake of unhealthy foods and reshaping the gut microbiota. Meanwhile, the salutary metabolites produced by the microbiota-induced digestion of dietary fiber can regulate the inflammatory state of gouty patients and reduce uric acid production, all of which are conducive to the management of gout.

4. Recommended Nutritional Management and Its Combination with Drug Therapy

The close association of specific foods with SUA levels, as shown in Table 1 , illustrates that the essential impact of food on health is ascribed to the synthesized effects of the nutrients from food, specifically the dominant or beneficial nutritional factors determining the final performance. Taking dairy products as an example, the increased intake of dairy products, especially those with low fat, can reduce the incidence of gout [ 8 ]. Late season skim milk, which contains higher levels of orotic acid than early season skim milk, has a preferential impact on the excretion of uric acid [ 118 ]. Moreover, glycomacropeptide and G600 milk fat addition in skim milk can greatly and effectively relieve joint pain and the frequency of gout flares [ 119 ].

The dietary management for gouty patients is commonly self-prescribed and centers around the control of purine sources, such as reduced consumption of purine-rich foods, which theoretically attenuates uric acid production; but patients often fail to adhere to recommendations in the long term due to the limited palatability of purine-free diets [ 34 , 37 ]. Although piecemeal modifications of the various yet limited numbers of nutrients often provide incomplete dietary recommendations [ 34 ], attention should be given to nutrient richness and structure to avoid the burden of inappropriate dosage. For example, the plasma concentrations of vitamin C saturation ranges daily from 200 to 400 mg, implying that exceeding the recommended supplemental dose has little effect on the consequences [ 120 ]. More importantly, taking high-dose and long-term supplements of vitamin C may be associated with adverse effects, and the resulting excessive uric acid excretion could elevate the risk of kidney stones in gouty patients [ 121 , 122 ]. As shown in Figure 3 , recommended nutritional interventions should emphasize the necessity for appropriate supplementation of plant-derived fibers and dairy-derived protein and persuade patients to avoid high-compensation consumption of refined saturated fats and carbohydrates [ 34 ]. The typical dietary patterns include a DASH and Mediterranean diet, both of which are comprised of fruits, vegetables, and low-fat dairy products with reductions in total and saturated fats. Increasing evidence supports that consuming a DASH diet can continuously attenuate SUA in hyperuricemia patients and reduce the incidence of gout in participants [ 34 , 45 ]. Similar SUA-lowering effects have been observed in a research investigations of the Mediterranean diet [ 58 ]. Moreover, intervention with the DASH diet combined with adequate sodium and plant-derived protein shows more beneficial functions in reducing SUA levels [ 33 , 37 ].

Recommended food-derived nutritional interventions with anti-gouty mechanisms. Dietary management recommendations for gout patients include appropriate intake of fiber, minerals, and vitamins, as well as the selection of high-quality sugars, fats, and proteins, which are usually of plant origin. In addition, the consumption of products containing probiotics helps regulate intestinal homeostasis in patients with gout. ↑—increase; ↓—decrease; →—maintain; UA—uric acid; HQ—high-quality.

Most cases of dietary management related to uric acid reduction are reported in the nongouty population (as shown in Table 1 ). Dietary modification against gouty disease is commonly selected as an adjunct therapeutic strategy with medicinal drug therapy [ 10 ]; however, clinical trial data on the effects of dietary modifications combined with pharmacological interventions on gout are mostly lacking, as shown in Table 2 . By way of illustration, colchicine and nonsteroidal anti-inflammatory agents (NSAIDs) have been used to treat acute gouty inflammation. Their combined use with dietary micronutrient supplementation can remedy micronutrient deficiency induced by NSAIDs and colchicine [ 123 , 124 ]. Allopurinol, a first-line drug used as a xanthine oxidase inhibitor against gout, requires high protein replenishment to facilitate renal clearance [ 125 ]. Therefore, our recommendation is for individuals to follow a healthy diet for prevention purposes, and for patients with mild gout, we recommend the DASH and Mediterranean diet, which focus on plant-based components. Additionally, we recommend a reduction in the consumption of high-fat foods (fast food and cream products), especially foods with trans fatty acids (such as margarine and butter), and for individuals to pay attention to the amount of nutrient supplements consumed. For patients with severe gout, dietary modification and medication should be combined, and health care providers should remind patients of food–drug interactions to achieve synergistic effects.

Combination of nutrition modification and drug supplementation.

Note: Data are from clinical trials that have been included in PubMed. A direct search was used to search for the following terms: “diet” or “food” with “drug” or “medicine” or “treatment” with “gout” or “hyperuricemia”; “diet” or “food” with commonly used clinical anti-gout drugs including “zurampic (lesinurad)”, “colchicine”, “febuxostat”, “allopurinol”, “probenecid”, “aspirin”, “pegloticase”, “benzbromarone” and “etoricoxib”. A total of 751 articles were obtained. After following these exclusion criteria—repetitive articles, dietary supplements, drug interactions, formulation improvement, exercise interference, nonmarket food and mismatched intervention subjects—a total of 7 articles showed the effect of the combination of diet and medication in the treatment of gout. ↑—increase; ↓—decrease; AUC—area under curve; C max —maximal plasma concentration; SUA—serum uric acid; T max —time to reach C max .

5. Conclusions and Future Perspective

Our study has shown the important role of diet in gout development and management and how dietary adjustments based on nutrient composition should be an important component of routine care for gout. Gout patients are commonly prone to choose health management by dietary modification because diet changes can instantly affect gout flares via multiple signaling pathways. Diet-induced systemic metabolic pathways, including purine, lipid, and glucose metabolism, as well as energy balance and gut microbiota changes, have provided new mechanistic insights and potential interventions for gout progression. Since foods are eventually metabolized into multiple nutrients for metabolic homeostasis in the body, dietary modification might represent an appropriate nutritional regulation for gout patients or for potential patients to effectively reduce the incidence of gout. The critical role of nutritional factors on gout development also supported the following recommended nutritional modification strategies: (1) reducing nutritional risk factors against metabolic syndrome; (2) supplementing with beneficial nutrients to affect uric acid metabolism and gouty inflammation; and (3) considering nutritional modification combined with medication supplementation to decrease the frequency of gout flares. Our consistent principle is that, in terms of diet, nutritional balance should be analyzed from the point of view of enrichments and structures of nutritional elements. Evidence supports that a low-fat, low-carb, plant-based dietary intervention is suitable for gouty patients; however, we need to pay specific attention to the golden rule of healthy dietary intake, that is, moderation. In addition, we advocate the combination of medications and dietary modification for gouty patients in therapy, and caution that they should note the impact of nutritional factors on drug pharmacokinetics and pharmacodynamics. However, many human studies focusing on the relationship of food and SUA levels often do not involve gouty patients. A limited number of clinical research studies explore food/nutrition and gout or anti-gout drugs, resulting in limited being drawn conclusions. In conclusion, the dietary mechanisms and nutritional basis provide scientific evidence for the prevention and improvement of gouty diseases, and dietary modifications based on effective regulatory mechanisms may be a promising strategy to reduce the high prevalence of gout.

Acknowledgments

We would like to thank Peili Rao, Yannan Zheng and Yijie Song, for their revision suggestions of figures.

Funding Statement

This research was funded by the Key-Area Research and Development Program of Guangdong Province (2020B1111110003).

Author Contributions

Y.X. and H.X. initiated the idea and performed the study. Y.Z. and S.C. wrote the manuscript. Y.Z., S.C., M.Y., Y.X. and H.X. discussed and revised the manuscript. H.X. has final responsibility for all parts of this manuscript. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Lewiston Sun Journal

Account Subscription: ACTIVE

Questions about your account? Our customer service team can be reached at [email protected] during business hours at (207) 791-6000 .

• Advertiser Democrat

Norway Savings names Vice President of Human Resources

You are able to gift 5 more articles this month.

Anyone can access the link you share with no account required. Learn more .

With a Lewiston Sun Journal subscription, you can gift 5 articles each month.

It looks like you do not have any active subscriptions. To get one, go to the subscriptions page .

Loading....

A Mechanic Falls native, Amy Fox has over 20 years of experience in the financial sector.

NORWAY —Norway Savings Bank is pleased to announce that Amy Fox has been named the new Vice President of Human Resources.

Fox possesses over 20 years of experience in the financial sector, including time in retail, deposit operations, lending, and branch management.

A native and resident of Mechanic Falls, Fox says she is thrilled to be joining an organization she has long admired in part for its dedication to giving back.

“I have always worked for organizations that have a positive impact on their community,” said Fox. “As someone who grew up here, I have always been aware of Norway Savings Bank and its community involvement. It makes me excited to be part of a team that puts community at the forefront of their business. Not only do they encourage their team members to get involved, but NSB gives employees paid time to get out in their communities and volunteer for organizations they believe in.”

Fox is a licensed HR professional (SHRM-SCP) and recently completed her bachelor’s degree in Business.

“Throughout my career, it’s been my experience that the best organizations can be identified by the way in which they prioritize their team members,” she said. “NSB believes that its team members are its greatest source of strength, which speaks volumes. I’m excited to be a part of that and to assist the bank in its continuing efforts to make this the best place to work in Maine.”

NSB has promoted a culture of caring, community, and connection since 1866. Learn more about career opportunities at the bank by visiting https://www.norwaysavings.bank/get-to-know-us/careers/.

Comments are not available on this story.

Send questions/comments to the editors.

Advertiser Democrat Headlines

• Enter your email
• Phone This field is for validation purposes and should be left unchanged.

Member Log In

Please enter your username and password below. Already a subscriber but don't have one? Click here .

Not a subscriber? Click here to see your options

Home / Resources / News and Trends / ISACA Now Blog / 2024 / Navigating the Challenges of Cybersecurity in the Modern Data Landscape

Navigating the challenges of cybersecurity in the modern data landscape.

In contemporary economies, data serves as the foundational element driving innovation, fostering corporate strength and shaping societal structures. An expert in cybersecurity is acutely aware of the profound implications of data privacy, security, risk management and operational intricacies within the global data ecosystem. In this blog post, I aim to elucidate solutions to these multifaceted challenges while presenting a compendium of best practices derived from empirical research and industry insights.

The Significance of Data in Modern Economies

The exponential growth of the global data sphere, projected to reach 175 zettabytes by 2025 , underscores the unparalleled importance of data in driving the Fourth Industrial Revolution. Consequently, the imperative to address privacy, security, risk and operational imperatives within the data ecosystem becomes increasingly paramount.

Cross-Border Data Transfer: Balancing Efficiency with Security

The phenomenon of cross-border data transfer is indispensable for facilitating seamless transnational operations. However, its execution presents formidable challenges in maintaining data privacy and security. To navigate this intricate landscape effectively, organizations must adhere to rigorous best practices:

• Data encryption: Implement robust encryption protocols to safeguard data integrity throughout transit, thereby ensuring confidentiality and authenticity.
• Standardized protocols: Adherence to standardized communication protocols, such as HTTPS, mitigates the risks associated with cross-border data transfer by establishing a common framework for secure information exchange.
• Data minimization: Prioritize the transmission of only essential data, thereby reducing the surface area for potential security breaches and enhancing regulatory compliance.

Data Localization: Compliance and Operational Considerations

The advent of data localization laws necessitates meticulous attention to compliance and operational strategies to navigate regulatory requirements effectively. Key considerations include:

• Compliance assessment: Maintain a comprehensive understanding of data localization regulations across relevant jurisdictions and institute mechanisms to ensure ongoing adherence.
• Hybrid cloud solutions: Embrace hybrid cloud architectures to strike a balance between local data storage mandates and the benefits of centralized security infrastructure.
• Data encryption and tokenization: Employ advanced encryption and tokenization techniques to fortify data protection mechanisms and facilitate compliance with stringent localization mandates.
• Data lifecycle management: Institute robust data lifecycle management practices to govern data usage from inception to disposal, thereby ensuring regulatory compliance and mitigating operational risks.

Globalization and Risk Management: Proactive Strategies for Resilience

Globalization engenders new opportunities and challenges, necessitating a proactive risk management approach to safeguard organizational interests. Essential components of this strategy include:

• Risk assessment: Conduct regular assessments of cybersecurity risks across all operational domains, enabling proactive risk mitigation measures and strategic resource allocation.
• Incident response plans: Develop comprehensive incident response protocols tailored to global operations, ensuring swift and effective mitigation of cybersecurity threats.
• Third-party due diligence: Implement stringent due diligence procedures to vet external service providers and partners, thereby mitigating third-party cybersecurity risks.
• Employee training: Cultivate a culture of cybersecurity awareness among employees through targeted training initiatives, empowering them to serve as frontline defenders against cyberthreats.

Operations in the Data Ecosystem: Ensuring Operational Excellence

Effective data operations are contingent upon robust governance frameworks and adherence to industry best practices. Critical considerations include:

• Data governance framework: Establish a comprehensive data governance framework delineating roles, responsibilities and protocols governing data collection, storage and usage.
• Data auditing and monitoring: Deploy sophisticated auditing and monitoring tools to maintain continuous surveillance of data assets, enabling timely detection and response to security incidents.
• Regular security audits: Conduct routine security audits to identify vulnerabilities and weaknesses within the data infrastructure, facilitating proactive risk mitigation measures.
• Data privacy by design: Integrate data privacy principles into product and system design processes, ensuring that privacy considerations are embedded within the foundational architecture.

Move Forward with Confidence

In conclusion, the effective management of data privacy, security, risk and operational imperatives is indispensable for organizational success in the digital era. By adhering to best practices and maintaining a proactive stance toward emerging threats, organizations can navigate the complexities of the modern data landscape with confidence and resilience.

Editor’s note:  For additional insights on this topic, read Mathura’s 2024 ISACA Journal article, volume 2, “Addressing the Privacy, Security, Risk, and Operations Aspects of the Data Ecosystem.”

Additional reesources

ISACA Journal Article

Addressing the privacy, security, risk, and operations aspects of the data ecosystem.

Data privacy, security, risk management and operations are complex aspects of the global data ecosystem, and enterprises should address them with solutions based on research and best practices.

Frameworks, Standards and Models

Use the right frameworks to add value to your role and enterprise.

Top Effective 10 Data Governance Tools

The rise of ChatGPT gives information security professionals even more to account for while also underscoring critical connection points between security and the overarching need for digital trust.

Data Management Proficiency: Practical Insights for Quality, Security and Trust

Evaluating quality, security and trust in the context of emerging technologies and current business practices is critical for organizations to have effective data management and governance.

ISACA Now By Year