V olume 25 Issue 4 Ar ticle 2 December 2023 Human Resour ce Management Systems and Firm Inno v ation: A Human Resour ce Management Systems and Firm Inno v ation: A Meta-Analytic Study Meta-Analytic Study Y ang Zhang Indiana Univ ersity Southeast, School of Business, New Albany , USA , yz152@ius.edu Matthew D . Griffith Univ ersity of T exas at El P aso, W oody L. Hunt College of Business Administr ation, El P aso, USA , mdgriffith@utep.edu F ollow this and additional works at: https:/ /www .ebrjournal.net/home P ar t of the Human Resour ces Management Commons Recommended Citation Recommended Citation Zhang, Y ., & Griffith, M. D . (2023). Human Resour ce Management Systems and Firm Inno v ation: A Meta- Analytic Study . E conomic and Business Re view , 25(4), 202-215. https:/ /doi.or g/10.15458/2335-4216.1327 This Original Ar ticle is br ought t o y ou for fr ee and open access b y E conomic and Business Re view . It has been accepted for inclusion in E conomic and Business Re view b y an authoriz ed edit or of E conomic and Business Re view . ORIGINAL ARTICLE Human Resource Management Systems and Firm Innovation: A Meta-Analytic Study Y ang Zhang a, * , Matthew D. Grifth b a Indiana University Southeast, School of Business, New Albany, USA b University of Texas at El Paso, Woody L. Hunt College of Business Administration, El Paso, USA Abstract Building on the resource-based view, this paper examines the meta-analytic relationships between Human Resource Management (HRM) systems and different types of rm innovation (innovation in products or services, innovation in processes, and innovation in people and organizations) and the moderating role of sampled industries and sampled cultural clusters in these relationships. With 119 records from 57 unique papers published between 2000 and 2020, this study found that HRM systems positively contribute to innovation in products or services, innovation in processes, and innovation in people and organizations. Sampled industries and cultural clusters signicantly moderate the relation- ships between HRM systems and innovation in products or services. These results may be biased because most empirical researchers focused on innovation in products or services instead of innovation in processes or innovation in people and organizations. Despite the dynamism of HRM systems, researchers are most like to include compensation, training, and performance appraisal while studying HRM systems and rm innovation. Keywords: Human Resource Management systems, Firm innovation, Country culture clusters, Industry, Meta-analysis JEL classication: L2, M10, M54 Introduction W hen explaining how Human Resource Manage- ment (HRM) systems contribute to rm perfor- mance, processes, strategies, and culture, researchers came up with two main explanations. Both explana- tions are driven by the resource-based view. In the systematic explanation, HRM systems demonstrate their effect through the conguration or aggregation of practices on rm human resources. In the strategic explanation, HRM systems adjust, inuence, and de- velop the needed human resources to achieve results (Bowen & Ostroff, 2004). Firm innovation has been studied at the rm performance level, the rm process level, the rm strategy level, and rm culture level. To explore relationships between HRM systems and rm innovation, we applied the resource-based view and integrated the rm innovation literature. There are different classications of rm innova- tion; we selected the results of Knight (1967). This classication of rm innovation aligned tightly with different types of rm resources in the resource-based view. Barney (1991) suggested that rm survival and success depended on three types of resources: physical capital resources, human capital resources, and organizational capital resources. Knight (1967) listed the following types of rm innovation: product or service innovation, production-process innova- tion, and organizational-structure and people inno- vation. Both physical capital resources and product or service innovation focus on rm outputs. Both human capital resources and production-process in- novation focus on rm processes. Both organiza- tional capital resources and organizational-structure and people innovation focus on rm culture and strategies. Empirical studies of HRM systems and rm innova- tion have inconsistent results. To resolve this problem, we claried different types of rm innovation, con- ducted a meta-analysis based on existing empirical Received 22 February 2023; accepted 31 May 2023. Available online 5 December 2023 * Corresponding author. E-mail addresses: yz152@ius.edu (Y. Zhang), mdgrifth@utep.edu (M. D. Grifth). https://doi.org/10.15458/2335-4216.1327 2335-4216/© 2023 School of Economics and Business University of Ljubljana. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). ECONOMIC AND BUSINESS REVIEW 2023;25:202–215 203 studies, and tested the moderating effect of sampled industries and sampled cultural clusters. Overall, this paper aims to make three theoreti- cal contributions: 1) it enhances the practicality and legitimacy of the resource-based view when examin- ing relationships between HRM systems and different types of rm innovation; 2) it proves the positive ef- fect that HRM systems have on different types of rm innovation; and 3) it examines the moderating effects of sampled industries and sampled cultural clusters on the relationship between HRM systems and differ- ent types of rm innovation. 1 Theoretical background and hypotheses According to the resource-based view, rms are resource bundles (Barney, 1991; Penrose & Penrose, 2009). HRM inuences rm performance through inuencing rm resources. This view provides fun- damental support to explain why and how HRM inuences rm performance. HRM includes all poli- cies and practices to acquire, train, appraise, and reward past, current, and future employees (Dessler, 2000). In the literature on HRM, researchers have de- veloped increasing interest in studying HRM systems instead of individual HRM practices (Monks et al., 2013; Nishii & Paluch, 2018; Velikorossov et al., 2020). One of the main reasons is that the effect of one HRM practice depends on other HRM practices (Boon et al., 2019). Similarly, Guest and Conway (2011) found that the effect of a combination of HRM practices (HRM systems) is stronger than the effect of individual HRM practices. Ferraris et al. (2019) explored the inuence of HRM systems on ambidextrous work in smart city projects. Chadwick et al. (2015) examined how HRM systems inuence rm performance. Lepak et al. (2006) dened HRM systems as collec- tions of individual HRM practices to achieve over- arching goals. Many researchers named their HRM systems based on intended goals. For example, Tang et al. (2015) dened the strategic HRM system as “a combination of strategy-oriented practices such as stafng, training and development” (p. 167). Zhang et al. (2016) dened the capability-based HRM system as “a set of people management strategies and activi- ties that enable employees to develop their skills and knowledge and ultimately contribute to competitive advantages” (p. 133). Soo et al. (2017) dened intellec- tual capital-enhancing HRM systems as “HRM prac- tices that not only develop human capital but build social relationships and interactions (and the associ- ated social capital that emerges from these exchanges) as well as the technology, systems, routines, and databases for knowledge capture and sharing—that organizations build the crucial learning capabilities necessary for innovation and performance” (p. 433). Ceylan (2013) dened commitment-based HRM sys- tems as practices that “provide career development and long-term growth opportunities, and to increase group motivation and social interactions” (p. 211). Zhou et al. (2013) dened collaborative HRM sys- tems as the HRM practices of “internal human capital to develop teamwork and cross-functional teamwork skills” (p. 267). Al-Tal and Emeagwali (2019) dened a knowledge-based HRM system as HRM practices “designed to improve organization’s knowledge pro- cess” (p. 8). Many researchers also refer to HRM sys- tems as high-performance work systems (Armstrong et al., 2010; Fu et al., 2015; Gürlek, 2021; Messersmith & Guthrie, 2010) and high-involvement work systems (Boxall & Macky, 2009; Gollan, 2005; Rehman et al., 2019). Following this trend, this paper only focuses on HRM systems instead of individual HRM practices. Firm innovation is a type of rm performance. Researchers have widely applied the resource-based view to explain the relationship between HRM sys- tems and rm innovation (Donate et al., 2016; Lopez- Cabrales et al., 2009; Messersmith & Guthrie, 2010; Oke et al., 2012). Many empirical studies have found positive relationships between HRM systems and rm innovation. However, other studies have found negative or non-signicant relationships between HRM systems and rm innovation (Beugelsdijk, 2008; Jimenez-Jimenez & Sanz-Valle, 2005; Liu et al., 2017). One explanation of the inconsistency is sampling bias or selection bias, which represents a deviation be- tween samples and the population. A meta-analytic study can reduce the bias effect and examine the “true” relationships between HRM systems and rm innovation at the population level. Unlike an em- pirical paper, a meta-analysis aims to nd construct relationships at the population level after correct- ing the inuence of sampling errors, measurement errors, range restrictions, etc. Meta-analysis collects data from existing (published and unpublished) stud- ies. When the number of independent studies is small and/or when the sample sizes of these studies are small, it is possible that the meta-analytic results are biased. According to the resource-based view, rms can achieve sustainable competitive advantages and long-term success through three types of resources: physical capital resources, human capital resources, and organizational capital resources. Physical cap- ital resources include a rm’s technology, plants, and equipment. Human capital resources include experience, judgement, intelligence, and insight of individual managers and workers in a rm. Orga- nizational capital resources include a rm’s formal and informal planning, controlling, and coordinating 204 ECONOMIC AND BUSINESS REVIEW 2023;25:202–215 system, as well as informal relations among groups within the rm (Barney, 1991). Although Barney’s ex- amples mainly applied to manufacturing elds rather than service elds, he suggested different levels of resources. The physical capital resources focus on the outcome or result aspect of rm performance. The human capital resources focus on the formalized procedures aspect of rm performance. The orga- nizational capital resources emphasize the dynamic and communicational aspect of rm performance. The three levels of resources align well with different types of rm innovation that Knight (1967) suggested: product or service innovation, production-process innovation, organizational-structure and people in- novation. This alignment provides support to test the relationships between HRM systems and different types of rm innovation. Therefore, we hypothesize that: H1. At the population level, HRM systems enhance rm (a) innovation in products or services, (b) innovation in processes, and (c) innovation in people and organizations. While examining relationships between HRM sys- tems and rm innovation, researchers generally be- lieve that sampled industries play a critical role in these relationships. Some researchers have suggested to conduct industry-specic studies (Bhatnagar, 2012; Li et al., 2015; Lu et al., 2015; Sung & Choi, 2018; Thai Hoang et al., 2006). For example, Fındıklı et al. (2015) found that strategic HRM systems enhanced rm knowledge management capacity and innovation (ex- ploration and exploitation). They believed that more context-specic future research was needed. Ceylan (2013) found that commitment-based HRM systems enhanced rm product, process, organizational, and marketing innovation activities. She suggested re- searchers collect data from specic industries. Many other researchers recommended conducting research in diverse industries (Leticia Santos-Vijande et al., 2013; Mahmood et al., 2017; Natalicchio et al., 2018). For example, Do et al. (2018) found that innovation-led HR policies enhanced rm innova- tion. They recommended future researchers to test this nding in various research settings and multiple industry contexts. Patel et al. (2013) found that high- performance work systems enhanced organizational ambidexterity (exploration and exploitation). They suggested that future researchers use large samples from a broad cross-section of industries to enhance generalizability. Perdomo-Ortiz et al. (2009) found that total quality management-based HRM systems enhanced both technological innovation and non- technological innovation in a rm. They believed that future research could benet from collecting data from multiple industries. Similarly, Lepak et al. (2007) encouraged future research to study the industry pressures on rm HRM systems and practices. Despite different approaches to study the inuence of industry or industries, it is clear that sampled in- dustries inuence the effect that HRM systems have on rm innovation. Therefore, we hypothesize that: H2. At the population level, sampled industries moderate relationships between HRM systems and rm (a) innova- tion in products or services, (b) innovation in processes, and (c) innovation in people and organizations. In addition to sampled industries, country, culture, and cultural clusters are broadly mentioned as po- tential moderators of the relationship between HRM systems and rm innovation. For example, de Araújo Burcharth et al. (2014), Fellnhofer (2017), and Wei and Atuahene-Gima (2009) recommended future research to examine how HRM systems inuence rm inno- vation in different countries. Cooke and Saini (2010), Hohenberg and Homburg (2016), and Soto-Acosta et al. (2014) encouraged future HRM researchers to fully consider the inuence of national culture. Ma et al. (2019) and Naqshbandi et al. (2019) thought that future research can benet from collecting data from different countries and cultural settings. Although used interchangeably, country and cul- ture are different constructs. Country refers to the land of a person’s birth, residence, or citizenship. It also refers to the political state, nation, or territory (Merriam-Webster, n.d.). Although country some- times represents culture, it is a poor proxy for culture (Taras et al., 2016). According to Taras et al. (2009), culture is relatively stable, includes values, beliefs, norms, and traditions, is shared within a population. For HRM researchers, it is more benetable to study the inuence of culture instead of the inuence of country. While exploring national culture, researchers found that cultural clusters are more relevant than culture per se in innovation studies, organizational studies, and international business studies (Beugelsdijk et al., 2018, 2017; Lorenzen & Frederiksen, 2008; Ronen & Shenkar, 2013). Similarly, Soto-Acosta et al. (2016) suggested future researchers to combine rms from different cultures while examining the relationships between HRM systems and rm innovation. Do et al. (2016), Lau and Ngo (2004), and Tang et al. (2015) recommend future researchers to draw samples from different nations and regions. Therefore, we hypothe- size that: H3. At the population level, sampled cultural clusters moderate relationships between HRM systems and rm (a) innovation in products or services, (b) innovation in processes, and (c) innovation in people and organizations. ECONOMIC AND BUSINESS REVIEW 2023;25:202–215 205 0 1 2 3 4 5 6 7 8 9 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Fig. 1. Trend of quantitative publications in rm innovation and HRM systems. 2 Scope of search and keywords Based on available data or individual studies, meta-analytic researchers aim to generate the best estimation of the true relationship between studied constructs. The best estimation is called the effect size, which should have the statistical errors and sam- pling bias corrected (Schmidt & Hunter, 2015). Unlike empirical studies, meta-analytic studies use archived data, which generally come from the correlation ma- trix of empirical studies. Thus, to be included in this meta-analytic study, a paper must have included cor- relation value(s) between variables of interest. In rare situations, a paper included empirical data without disclosing correlation values. In such a case, we con- tacted the paper authors for details and clarications. We conducted a thorough search in the follow- ing databases: CORE, Directory of Open Access Journals, EBSCO, Emerald Insight, Google Scholar, JSTOR, ProQuest, SAGE, Science Direct, Social Sci- ence and Research Network, Springer Link, Tay- lor & Francis Online, Wiley, and Web of Science. The search keywords included “innovation,” “inno- vativeness,” “innovate,” “human resource,” “HR,” “high performance work system,” and “high com- mitment work system.” To make this study more comprehensive, we also searched for keywords about individual HRM practices (“training,” “com- pensation,” “reward,” “performance management,” “appraisal,” “promotion,” “employee participation,” “teamwork,” “hiring,” “stafng,” “employee em- powerment,” “recruitment,” and “selection”), since researchers might not be consistent about naming their constructs empirically. The paper searching was nalized at the beginning of 2021. With the list of potential papers, we read and eval- uated how HRM systems were measured in these papers. After consulting HRM experts and scholars, we believed that an HRM system needed to include at least 3 individual HRM practices. This was done because the focus of this study was to quantify rela- tionships between HRM systems and different types of rm innovation. We are grateful for the support from University of Texas at El Paso faculty and re- search assistants. 3 Preliminary data analysis After searching in published papers, we looked at relevant professional reports, called for unpublished or in-progress studies, and considered governmental statistics, ultimately nding 57 unique papers pub- lished between 2000 and 2020: 51 journal publications, 3 dissertations, 2 theses, and 1 research report. Fig. 1 shows the publications of quantitative papers in the eld of rm innovation and HRM systems. Based on the current selection criteria, the rst paper was published in 2000. However, up until 2005, more and more empirical works were devoted to exploring the relationship between rm innovation and HRM sys- tems. Among these 57 papers, 26 papers included data from manufacturing industries; 7 papers in- cluded data from service industries; and 24 papers included data from both manufacturing and service industries. As shown in Table 1, most of the selected papers surveyed companies in the Anglo, Confucian Asia, and Western Europe cultural clusters (House et al., 2004). Some of these papers included more than one type of HRM system and/or rm innovation. In total, these Table 1. Sampled cultural clusters of selected papers. Cultural cluster list No. of selected papers Percentage Africa and Middle East 3 5.26% Anglo 15 26.32% Confucian Asia 18 31.58% Southern Asia 3 5.26% Western Europe 18 31.58% 206 ECONOMIC AND BUSINESS REVIEW 2023;25:202–215 Table 2. Selected papers by different types of rm innovation. Innovation in products or services Adebanjo et al. (2020); Armstrong et al. (2010); Boehm et al. (2014); Botelho (2020); Ceylan (2013); Chang et al. (2013); Chen et al. (2018, 2019); Collins (2000); Collins and Smith (2006); Do (2017); Donate and Guadamillas (2011, 2015); Donate et al. (2016); Fu et al. (2015); Gahan et al. (2021); Gürlek (2021); Jimenez-Jimenez and Sanz-Valle (2008); Kang (2015); Kianto et al. (2017); Lepak et al. (2007); Li et al. (2019); Liu (2011); Lopez-Cabrales et al. (2009); Mavondo et al. (2005); Messersmith (2008); Messersmith and Guthrie (2010); Nasution et al. (2011); Nieves and Osorio (2017); Nieves et al. (2016); Olander et al. (2015); Papa et al. (2018); Patel et al. (2013); Sheehan (2014); Smith et al. (2012); Soo et al. (2017); Soto-Acosta et al. (2016, 2017); Stock and Zacharias (2011); Tang et al. (2015); Wang and Chen (2013); Wei et al. (2011); Zhang et al. (2016); Zhou et al. (2019, 2013) Innovation in processes Al-Tal and Emeagwali (2019); Ceylan (2013); Chang et al. (2019); Jimenez-Jimenez and Sanz-Valle (2007, 2008); Mavondo et al. (2005); Messersmith (2008); Messersmith and Guthrie (2010); Nieves et al. (2016); Smith et al. (2012) Innovation in people and organizations Ceylan (2013); Chang and Huang (2005); Collins (2000); Fu et al. (2015); Jimenez-Jimenez and Sanz-Valle (2005); Kang (2015); Ko and Ma (2019); Liu et al. (2017); Messersmith and Guthrie (2010); Para-González et al. (2018); Patel et al. (2013); Rasheed et al. (2017); Razouk (2011); Song et al. (2019); Stock and Zacharias (2011); Zhang and Li (2009) Table 3. Components of HRM systems and different types of rm innovation. Innovation in products Innovation in Innovation in people or services processes and organizations Compensation and benets 60 11 20 Job and work design 57 10 17 Training and development 66 14 20 Recruiting and selection 50 12 17 Employee relations 31 8 9 Communication 45 10 8 Performance management and appraisal 53 11 19 Promotions 27 9 12 Turnover, retention, and exit management 4 2 0 Other 5 0 1 0 5 10 15 20 25 30 3 4 5 6 7 8 9 Fig. 2. The number of individual HRM practices within HRM systems. papers contributed 119 records to this meta-analytic study. For the 119 records, 80 records were in the cate- gory of innovation in products or services, 15 records were in the category of innovation in processes, and 24 records were in the category of innovation in peo- ple and organizations (see Table 2 for details). For the present study, an HRM system should include at least three individual HRM practices. Fig. 2 presents the number of individual HRM practices in the HRM systems of the selected records. About half of the se- lected HRM systems included three or four individual HRM practices. The most complicated HRM system included nine individual HRM practices. The average number of individual HRM practices within HRM systems was ve. Table 3 shows different types of HRM practices in the HRM systems among the se- lected 119 meta-analytic records. One can observe that when exploring overall rm innovation by HRM sys- tems, training and development and compensation and benets were the most frequent HRM practices within the HRM systems studied. The same pattern can be found for innovation in products or services. When examining innovation in processes, perfor- mance management and appraisal showed up more frequently in the HRM systems than when explaining other types of rm innovation. The same pattern of ECONOMIC AND BUSINESS REVIEW 2023;25:202–215 207 innovation in processes was also found in innovation in people and organizations. Studies were matched to the country from which the data were obtained. Then, a modied version of the prior categorizations of country clusters (Hof- stede, 2001; House et al., 2004; Ronen & Shenkar, 2013) was used to place each study into a par- simonious listing of country culture clusters. The culture clusters and the countries therein were as fol- lows: Middle East: Turkey, Jordan; Anglo: Australia, United States, Ireland, United Kingdom; Confucian Asia: China, Vietnam, South Korea; Southern Asia: India, Malaysia, Indonesia, Pakistan; and Western Eu- rope: Spain, Germany, Finland, Italy, Portugal, France. When looking at the different cultural clusters and industries, we realized that most of selected records came from Anglo, Confucian Asian, and Western Eu- ropean researchers. Manufacturing companies were more welcomed by HRM researchers in their studies of rm innovation than were service companies in most of the cultural clusters, except for Africa and the Middle East (see Table 4). The popularity of manufac- turing companies also showed in the different types of rm innovation. In the eld of human resource man- agement systems and rm innovation, researchers were less likely to carry out their empirical studies purely based on service companies. Innovation in products or services have received more researchers’ attention than innovation in processes or innova- tion in people and organizations have (see Table 5). Table 6 presents the HRM components within HRM systems that explained rm innovation in different in- dustries. If a study collected data from manufacturing companies, it was very likely to include compen- sation and benets, training and development, and performance management and appraisal in its HRM systems. However, if a study collected data from service companies, it was very likely to include com- pensation and benets, training and development, and recruitment and selection in its HRM systems. It is worth noting that turnover, retention, and exit management are the individual HRM practices least likely to be included within an HRM system in studies that explore rm innovation. When analyzing the different types of rm innova- tion by cultural clusters, we realized that innovation in products or services played a dominant role in the eld of HRM systems and rm innovation re- gardless of the sampled cultural cluster. We also found that researchers had no strong preference for Table 4. Sampled cultural clusters and sampled industries. Sampled industries Cultural clusters Manufacturing Service Manufacturing and service Africa and Middle East 0 1 6 Anglo 31 5 9 Confucian Asia 13 1 12 Southern Asia 2 1 0 Western Europe 19 3 16 Table 5. Different types of rm innovation and sampled industries. Sampled industries Types of rm innovation Manufacturing Service Manufacturing and service Innovation in products or services 47 7 26 Innovation in processes 3 3 9 Innovation in organizations and people 15 1 8 Table 6. Components of HRM systems and sampled industries. Sampled industries Components of HRM systems Manufacturing Service Manufacturing and service Compensation and benets 48 11 32 Job and work design 44 7 33 Training and development 50 9 41 Recruiting and selection 39 9 31 Employee relations 27 7 14 Communication 39 7 17 Performance management and appraisal 49 5 29 Promotions 24 3 21 Turnover, retention, and exit management 2 3 1 Other 4 1 1 208 ECONOMIC AND BUSINESS REVIEW 2023;25:202–215 Table 7. Different types of rm innovation and sampled cultural clusters. Sampled cultural clusters Types of innovation Africa and Middle East Anglo Confucian Asia Southern Asia Western Europe Innovation in products or services 3 28 18 2 29 Innovation in processes 2 8 1 0 4 Innovation in organizations and people 2 9 7 1 5 Table 8. Components of HRM systems and sampled cultural clusters. Sampled cultural clusters Components of HRM systems Africa and Middle East Anglo Confucian Asia Southern Asia Western Europe Compensation and benets 7 32 19 3 30 Job and work design 0 30 22 3 29 Training and development 7 41 22 3 27 Recruiting and selection 7 36 17 2 17 Employee relations 0 29 5 2 12 Communication 0 26 18 2 17 Performance management and appraisal 2 38 16 2 25 Promotions 0 25 7 1 15 Turnover, retention, and exit management 0 4 1 0 1 Other 0 3 3 0 0 cultural clusters when studying innovation in pro- cesses and innovation in people and organizations (see Table 7). Performance management and ap- praisal, job and work design, compensation and benets, and training and development were the most popular individual HRM practices within HRM systems that explained rm innovation in different cultural clusters (see Table 8). 4 Meta-analytic results and hypotheses testing In the literature on meta-analysis, researchers de- sign their statistical software based on either a xed- effects model or a random-effects model. Fixed-effects models assume that exactly the same value underlies all studies in the meta-analysis, while random-effects models allow the possibility that population param- eters vary from study to study. The random-effects model is generally used more often. Fixed-effects models are a specic case of random-effects models in which the standard deviation equals zero. Rosenthal- Rubin’s meta-analysis approach is based on a xed- effects model. Hedges and Olkin’s or Hedges and Ve- vea’s meta-analyses approaches with random-effects models are rarely used in the literature (Schmidt & Hunter, 2014). Schmidt and Hunter’s (2014) meta- analysis approach is widely used in the literature and is based on a random-effects model. Field (2001) found that the three approaches performed similarly when effect sizes were homogeneous, but the Schmidt & Hunter approach had the best performance when effect sizes were heterogeneous. Therefore, this meta- analysis study uses the approach and software from Schmidt and Hunter. Range restriction can be considered a selection bias; it reects the deviation between a sample and its pop- ulation. Researchers categorize range restriction into direct range restriction and indirect range restriction. They generally believe that indirect range restriction is commonly found in empirical studies (Hunter et al., 2006; Le et al., 2016; Schmidt et al., 2006). Given the intrinsic difculties of social science, Dahlke and Wiernik (2019) suggested that researchers should adjust range restriction, especially indirect range re- striction, by using a comprehensive meta-analysis as the population value and then calculating the range restriction ratio by the reliability differences. In the Schmidt & Hunter meta-analysis approach, range re- striction is on the independent variable side rather than the dependent variable side (Schmidt & Hunter, 2014). However, based on the intrinsic dynamism of HRM systems and their components, it is very challenging to identify a convincing population level parameter for range adjustment. Therefore, no range restriction is adjusted in this meta-analytic study. Before conducting any meta-analytic procedures, researchers need to assess the degree of publication bias in their data. Publication bias refers to the system- atically representative differences between published studies and unpublished studies (Rothstein et al., 2005). Kepes et al. (2012) provided an insightful review of the different approaches to assessing publi- cation bias: failsafe n, subgroup analyses, funnel plot, trim and ll, cumulative meta-analysis, correlation- and regression-based methods, and selection models. Among these seven approaches, Kepes et al. (2012) recommended the selection models and cumulative meta-analysis approaches to assess publication bias ECONOMIC AND BUSINESS REVIEW 2023;25:202–215 209 when researchers conduct meta-analyses with hetero- geneous assumptions. Selection models are widely used in the Hedges meta-analysis methods; the cumu- lative meta-analysis approach is broadly applied in the Schmidt & Hunter meta-analysis methods (Kepes et al., 2012). In this paper, we used the Schmidt & Hunter meta-analysis software and therefore applied the cumulative meta-analysis approach to detect the extent of any publication bias in our dataset. For a cumulative meta-analysis, if the mean corrected correlation increases in size as small sample studies are added, this indicates the possibility of publica- tion bias in the low-sample-size studies (Schmidt & Hunter, 2015). In this paper, the range of mean cor- rected correlation ranged from .288 to .424. The results suggest that concern for publication bias is low in our dataset. This meta-analysis included 57 unique papers that contributed 119 records to the topic of HRM sys- tems and different types of rm innovation. Ac- cording to Valentine et al. (2010), researchers only need two studies to conduct a meta-analysis. The bias of meta-analytic results mainly comes from the sample representativeness of included papers, not the number of included papers. Similarly, Schmidt and Hunter (2014) recommended conducting a meta- analysis with three independent studies. Following suggestions from Schmidt and Hunter (2014), we con- ducted a meta-analytic study for a group with at least three records. Hypothesis 1 suggested that, at the population level, HRM systems enhance rm (a) innovation in products or services, (b) innovation in processes, and (c) innovation in people and organizations. Table 9 shows the relationships between HRM sys- tems and types of innovation. The relationship be- tween HRM systems and innovation in products or services includes 80 independent studies with 14,429 rm-level responses. The mean true score correlation ( ˆ ¯ p) is .370, which is statistically signicant at 95% con- dence level. Therefore, H1(a) was supported. The relationship between HRM systems and innovation in processes includes 15 independent studies with 2834 rm-level responses. The mean true score correlation ( ˆ ¯ p) is .362, which is statistically signicant at 95% con- dence level. Therefore, H1(b) was supported. The relationship between HRM systems and innovation in people and organizations includes 24 independent studies with 3973 rm-level responses. The mean true score correlation ( ˆ ¯ p) is .358, which is statistically sig- nicant at 95% condence level. Therefore, H1(c) was supported. Hypothesis 2 proposed the moderating effect of sampled industries at the population level. Following the testing approaches in other meta-analysis studies (Cooke & Sheeran, 2004; Schepers & Wetzels, 2007), we rst calculated the relationship between HRM sys- tems and different categories of rm innovation in types of different industries. See Table 10 for relation- ships between HRM systems and types of innovation in industries. Then, we used Fisher’s Z method to compare the correlation parameters between HRM systems and different types of rm innovation. As shown in Table 11, the relationships between HRM systems and innovation in products or services are stronger in the service industries than in the man- ufacturing or mixed industries (p < .001). However, the moderating effect of sampled industries was not statistically signicant for the relationships between HRM systems and innovation in processes or between HRM systems and innovation in people and organi- zations. Therefore, Hypothesis 2(a) was supported. Hypotheses 2(b) and 2(c) were not supported. Hypothesis 3 proposed the moderating effect of sampled cultural clusters at the population level. Table 12 shows relationships between HRM systems and innovation in products or services in differ- ent cultural clusters. Based on Fisher’s Z values in Table 13, this relationship is the strongest in the African and Middle East cultural cluster, followed by the Confucian Asian cultural cluster. The relationship strengths are not statistically signicantly different in the Anglo cultural cluster and the Western Euro- pean cultural cluster. Hypothesis 3(a) was partially supported. Table 14 shows the relationship between HRM systems and innovation in processes in the Table 9. HRM systems and types of innovation. k N ¯ r SD r SD pre SD res ˆ ¯ p SD p CV LL CV UL CI LL CI UL %Var Innovation in products or services 80 14,429 .319 .167 .070 .152 .370 .173 .148 .592 .327 .412 17.410 Innovation in processes 15 2834 .311 .257 .069 .248 .362 .285 .002 .727 .211 .513 7.150 Innovation in people and organizations 24 3973 .301 .194 .074 .179 .358 .210 .078 .627 .266 .450 14.761 Note: kD number of independent samples; ND total sample size; ¯ rD sample-size-weighted mean observed correlation; SD r D sample-size-weighted standard deviation of observed correlations; SD pre D standard deviation of observed correlations predicted from all artifacts; SD res D standard deviation of observed correlations after removal of variances due to all artifacts; ˆ ¯ pD mean true score correlation (corrected for unreliability in both variables); SD p D true score standard deviation; CV LL and CV UL D lower and upper bounds, respectively, of the 80% credibility interval; CI LL and CI UL D lower and upper bounds, respectively, of the 95% condence interval around the mean true score correlation; %VarD percentage of variance attributable to statistical artifacts. 210 ECONOMIC AND BUSINESS REVIEW 2023;25:202–215 Table 10. HRM systems and types of innovation in industries. Types of innovation Industry k N ¯ r SD r SD pre SD res ˆ ¯ p SD p CV LL CV UL CI LL CI UL %Var Innovation in products or services Manufacturing 47 8432 .305 .131 .070 .110 .354 .126 .192 .515 .310 .397 28.850 Innovation in products or services Service 7 1307 .538 .217 .061 .209 .623 .239 .317 .929 .436 .809 7.776 Innovation in products or services Mixed 26 4690 .284 .163 .071 .147 .328 .169 .112 .544 .256 .401 18.668 Innovation in processes Service 3 549 .377 .140 .068 .122 .438 .141 .258 .619 .254 .623 23.507 Innovation in processes Mixed 9 1640 .361 .299 .068 .291 .420 .335 .009 .848 .193 .647 5.242 Innovation in people and organizations Manufacturing 15 2168 .309 .177 .079 .159 .367 .186 .129 .606 .261 .474 19.851 Innovation in people and organizations Mixed 8 1685 .297 .218 .067 .207 .353 .243 .042 .664 .174 .532 9.406 Note: kD number of independent samples; ND total sample size; ¯ rD sample-size-weighted mean observed correlation; SD r D sample-size-weighted standard deviation of observed correlations; SD pre D standard deviation of observed correlations predicted from all artifacts; SD res D standard deviation of observed correlations after removal of variances due to all artifacts; ˆ ¯ pD mean true score correlation (corrected for unreliability in both variables); SD p D true score standard deviation; CV LL and CV UL D lower and upper bounds, respectively, of the 80% credibility interval; CI LL and CI UL D lower and upper bounds, respectively, of the 95% condence interval around the mean true score correlation; %VarD percentage of variance attributable to statistical artifacts. Table 11. Moderation effects of industries (Fisher’s Z values) on HRM systems and innovation in products or services. ˆ ¯ p k N A B A. HRM systems and innovation in products or services in manufacturing industries .354 47 8432 B. HRM systems and innovation in products or services in service industries .623 7 1307 6.047 C. HRM systems and innovation in products or services in mixed industries .328 26 4690 .807 6.217 Note: |Z|> 1.960 is signicant at two-tailed .05 level; | Z|> 2.576 is signicant at two-tailed .01 level; | Z|> 3.291 is signicant at two-tailed .001 level. Table 12. HRM systems and innovation in products or services in cultural clusters. Cultural clusters k N ¯ r SD r SD pre SD res ˆ ¯ p SD p CV LL CV UL CI LL CI UL %Var African and Middle East 3 668 .507 .137 .058 .125 .587 .142 .405 .770 .407 .767 17.727 Anglo 28 6554 .276 .105 .063 .085 .319 .097 .195 .444 .274 .365 35.260 Confucian Asian 18 2970 .345 .148 .072 .130 .400 .149 .209 .590 .320 .479 23.288 Western European 29 3747 .314 .198 .082 .180 .363 .206 .099 .627 .280 .447 17.021 Note: kD number of independent samples; ND total sample size; ¯ rD sample-size-weighted mean observed correlation; SD r D sample-size-weighted standard deviation of observed correlations; SD pre D standard deviation of observed correlations predicted from all artifacts; SD res D standard deviation of observed correlations after removal of variances due to all artifacts; ˆ ¯ pD mean true score correlation (corrected for unreliability in both variables); SD p D true score standard deviation; CV LL and CV UL D lower and upper bounds, respectively, of the 80% credibility interval; CI LL and CI UL D lower and upper bounds, respectively, of the 95% condence interval around the mean true score correlation; %VarD percentage of variance attributable to statistical artifacts. Table 13. Moderation effects of cultural clusters (Fisher’s Z values) on HRM systems and innovation in products or services. ˆ ¯ p k N A B C A. HRM systems and innovation in products or services in the African and Middle East cultural cluster .587 3 668 B. HRM systems and innovation in products or services in the Anglo cultural cluster .319 28 6554 4.208 C. HRM systems and innovation in products or services in the Confucian Asian cultural cluster .400 18 2970 2.907 2.104 D. HRM systems and innovation in products or services in the Western European cultural cluster .363 29 3747 3.478 1.215 .881 Note: |Z|> 1.960 is signicant at two-tailed .05 level; | Z|> 2.576 is signicant at two-tailed .01 level; | Z|> 3.291 is signicant at two-tailed .001 level. African and Middle East cultural cluster and in the Western European cultural cluster. Although both groups have positive effect sizes, the moderating ef- fect of sampled cultural clusters was not statistically signicant for the relationships between HRM sys- tems and innovation in processes. Hypothesis 3(b) was not supported. Table 15 shows the relationship between HRM systems and innovation in people and ECONOMIC AND BUSINESS REVIEW 2023;25:202–215 211 Table 14. HRM systems and innovation in processes in cultural clusters. Cultural clusters k N ¯ r SD r SD pre SD res ˆ ¯ p SD p CV LL CV UL CI LL CI UL %Var Anglo 8 1487 .225 .112 .071 .087 .262 .100 .135 .390 .172 .353 40.295 Western European 4 828 .192 .162 .068 .147 .223 .169 .007 .440 .039 .408 17.716 Note: kD number of independent samples; ND total sample size; ¯ rD sample-size-weighted mean observed correlation; SD r D sample-size-weighted standard deviation of observed correlations; SD pre D standard deviation of observed correlations predicted from all artifacts; SD res D standard deviation of observed correlations after removal of variances due to all artifacts; ˆ ¯ pD mean true score correlation (corrected for unreliability in both variables); SD p D true score standard deviation; CV LL and CV UL D lower and upper bounds, respectively, of the 80% credibility interval; CI LL and CI UL D lower and upper bounds, respectively, of the 95% condence interval around the mean true score correlation; %VarD percentage of variance attributable to statistical artifacts. Table 15. HRM systems and innovation in people and organizations in cultural clusters. Cultural clusters k N ¯ r SD r SD pre SD res ˆ ¯ p SD p CV LL CV UL CI LL CI UL %Var Anglo 9 1317 .230 .057 .080 0 a .273 0 a .273 a .273 a .229 .317 100 b Confucian Asian 7 176 .239 .151 .075 .131 .284 .154 .088 .481 .151 .417 24.827 Western European 5 1035 .342 .237 .067 .228 .406 .268 .064 .749 .159 .653 7.855 Note: kD number of independent samples; ND total sample size; ¯ rD sample-size-weighted mean observed correlation; SD r D sample-size-weighted standard deviation of observed correlations; SD pre D standard deviation of observed correlations predicted from all artifacts; SD res D standard deviation of observed correlations after removal of variances due to all artifacts; ˆ ¯ pD mean true score correlation (corrected for unreliability in both variables); SD p D true score standard deviation; CV LL and CV UL D lower and upper bounds, respectively, of the 80% credibility interval; CI LL and CI UL D lower and upper bounds, respectively, of the 95% condence interval around the mean true score correlation; %VarD percentage of variance attributable to statistical artifacts. a Based on a simulation study from Brannick et al. (2019), researchers found the Hunter & Schmidt method tends to have a narrower range for credibility intervals and condence intervals compared to other meta-analysis methods. The main reason is that the Hunter & Schmidt method selects estimators with small sampling variances. “When the number of effect sizes is small (say 5 or 10), the difference can be large enough to be consequential” (Brannick et al., 2019: 494). b Percentage of variance attributable to statistical artifacts as calculated by the Hunter & Schmidt meta-analysis program was actually greater than 100%, since the Hunter & Schmidt method tends to overestimate the amount of variance due to sampling error when K and N are small (Brannick & Hall, 2001; Rabl et al., 2014). organizations in the Anglo cultural cluster, the Confu- cian Asian cultural cluster, and the Western European cultural cluster. Although all groups have positive effect sizes, the moderating effect of sampled cultural clusters was not statistically signicant for the rela- tionships between HRM systems and innovation in people and organizations. Hypothesis 3(c) was not supported. 5 Discussion and future research direction This paper has examined the meta-analytic rela- tionships between HRM systems and different types of innovation based on existing empirical studies. It has followed the research direction suggested by Bowen and Ostroff (2004) and studied the meta- features of HRM systems. Our ndings show that HRM systems enhance all three types of rm in- novation. Training and job design are high-frequent practices within HRM systems used to explore rm innovation. This paper has also followed the research directions suggested by Bhatnagar (2012), Do et al. (2016), and Natalicchio et al. (2018) and examined the effects of industry and cultural clusters. We nd that most empirical studies have been conducted in the Anglo, Confucian Asia, and Western Europe cul- tural clusters. Although researchers have realized the existence of different types of rm innovation, in- novation in products or services has received more study interest than innovation in processes or than innovation in people and organizations. Based on the current sample, we nd that sampled cultural clus- ters and industries moderate the relationship between HRM systems and innovation in products or services (p< .05). Cohen (1988) suggested that .1, .3, and .5 repre- sented small, medium, and large effect sizes. Some researchers suggested .2, .5, and .8 as the small, medium and large effect sizes (Gignac & Szodorai, 2016; Sawilowsky, 2009). Other researchers suggested that the effect size strength should vary based on sample size (Goulet-Pelletier & Cousineau, 2018). De- tailed discussion of effect sizes goes beyond the scope of this paper. We would like to remind readers that, if the meta-analytic effect size ( ˆ ¯ p) is small and/or in- signicant at the population level, it is still possible that the independent variable plays a signicant role in the dependent variable in a niche eld or sample. We would like to remind readers to hold criti- cal views on relations between HRM systems and rm innovation. Although this paper has examined different types of rm innovation, it is a construct mainly studied as an outcome variable. On the one hand, emphasizing innovation excessively may harm 212 ECONOMIC AND BUSINESS REVIEW 2023;25:202–215 rm short-term operational performance and nan- cial performance. On the other hand, rm innovation may have been included or studied in rm perfor- mance. Researchers found an inverted-U relationship between HRM systems and rm performance (Chi & Lin, 2011; Gu & Liu, 2022). Finally, we would like to bring up the limitations of this paper and hope to clarify some future research directions. First, compared to innovation in products or services, innovation in processes and innovation in people and organizations have fewer empirical records. Future researchers can benet from exploring the relationship between HRM systems and inno- vation in processes and innovation in people and organizations. Second, we have not found convinc- ing population-level parameters for range restriction adjustment. Our study has not addressed the range restriction inuence. Future researchers should make more effort to control or reduce the effect of range restriction in their empirical or meta-analytic stud- ies. Third, our study has listed different types of rm innovation. Future researchers can examine re- lationships among these types of rm innovation in addition to HRM systems. Funding details This project has not received any funding. Disclosure statement The authors report there are no competing interests to declare. References Adebanjo, D., Teh, P . L., Ahmed, P . 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