Consolidating the theoretical foundations of digital human resource management acceptance and use research: a meta-analytic validation of UTAUT

Theres, Christian; Strohmeier, Stefan Article — Published Version Consolidating the theoretical foundations of digital human resource management acceptance and use research: a meta-analytic validation of UTAUT Management Review Quarterly Provided in Cooperation with: Springer Nature Suggested Citation: Theres, Christian; Strohmeier, Stefan (2023) : Consolidating the theoretical foundations of digital human resource management acceptance and use research: a meta-analytic validation of UTAUT, Management Review Quarterly, ISSN 2198-1639, Springer International Publishing, Cham, pp. 1-33, https://doi.org/10.1007/s11301-023-00367-z This Version is available at: https://hdl.handle.net/10419/313169 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Management Review Quarterly https://doi.org/10.1007/s11301-023-00367-z 1 3 Consolidating thetheoretical foundations ofdigital human resource management acceptance anduse research: ameta‑analytic validation ofUTAUT ChristianTheres1 · StefanStrohmeier1 Received: 7 November 2022 / Accepted: 31 July 2023 © The Author(s) 2023 Abstract With rapid technological progress, the adoption of digital technology in human resource management (HRM) has become a crucial step towards the vision of digital organizations. Over the last four decades, a substantial body of empirical research has been dedicated towards explaining the phenomenon of digital HRM. Moreover, research has applied a wide array of theories, constructs, and measures to explain the adoption of digital HRM in organizations. The results are fragmented theoretical foundations and inconsistent empirical evaluations. We provide a comprehensive overview of theories applied in digital HRM adoption research and propose an adjusted version of the unified theory of acceptance and use of technology as a consolidating theory to explain adoption across settings. We empirically validate this theory by combining evidence from 134 primary studies yielding 768 effect sizes via meta-analytic structural equation modelling. Moderator analyses assessing the influence of research setting and sample on effects show significant differences between private and public sector. Findings highlight research opportunities for future studies and implications for practitioners. Keywords Digital HRM· Meta-analysis· e-HRM· Meta-analytic structural equation modeling· UTAUT JEL Classification O150· C400· M120 * Stefan Strohmeier [email protected]land.de https://www.mis.uni-saarland.de Christian Theres [email protected] 1 Saarland University, Campus, 66123Saarbrücken, Germany C.Theres, S.Strohmeier 1 3 1 Introduction During recent decades, research on the adoption of digital technology in supporting HR activities has relied on various theoretical foundations that have evolved independently (Bondarouk and Ruël 2009; Larkin 2017; Strohmeier 2007, 2020). This plethora of theoretical tangents leaves theory in the field of digital Human Resource Management (HRM) in a fragmented state (Martin and Reddington 2010; Ruël etal. 2011; Strohmeier 2012) and may even lead to authors “cherrypicking” the model that best fits their data (Venkatesh etal. 2003). Merging these tangents under the umbrella of a unifying theory not only allows the integration of existing evidence but also offers common theoretical understanding, or baseline, that future research efforts can expand on. Our study, thus, aims to achieve this goal by presenting an integrative overview of theory applied in digital HRM adoption research. We show that a modified version of the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh etal. 2003) consolidates a large proportion of individual theories applied in digital HRM adoption research. We then proceed by systematically and meta-analytically synthesizing the field’s quantitative evidence to empirically validate UTAUT as a unifying theory while motivating, discussing, and testing potential mediators and moderators. The adoption of end-users has been frequently proposed as the crucial link between the technical implementation of digital HRM and its contribution to organizational effectiveness (Ruël and Bondarouk 2014). At the same time, we find a missing consensus on relevant key constructs to explain such adoption. This lack of a common theoretical basis severely limits the comparability, let alone the reproducibility of current results, and leads to inconsistent empirical evaluations (Bondarouk etal. 2017). Accordingly, reviews in the field have repeatedly called for a so far missing comprehensive examination of adoption factors for almost fifteen years now (Bondarouk etal. 2017; Ruël and Bondarouk 2014; Strohmeier 2007). Proposing a unifying, generalizable theory that includes a predefined set of constructs, provides recommended operationalizations, and offers specific testable hypotheses is a vital step to address this issue. Earlier works have made a case for UTAUT as a candidate for such a unifying concept in digital HRM by applying the theory in individual studies (e.g., Laumer etal. 2010; Obeidat 2016; Ruta 2005). However, to qualify for this consolidating purpose, it remains to show that UTAUT both integrates earlier research efforts on the phenomenon and that its validity can be confirmed across digital HRM settings. To this end, we take a theory-driven approach to show that UTAUT integrates earlier research efforts and to meta-analytically confirm the validity of the paradigm across digital HRM settings. Hereby, we complement the set of exclusively narrative and qualitative reviews in the field (e.g., Bondarouk etal. 2017; Strohmeier 2007; Tursunbayeva etal. 2017) to provide the much-needed common theoretical basis but may also offer guidance for more consistent and reproducible future research efforts.. In addition to this general objective, we offer two additional contributions to address the idiosyncrasies of UTAUT in digital HRM. First, UTAUT has been proposed in different versions, including a varying set of constructs when applied in various contexts. The discussions center around an 1 3 Consolidating thetheoretical foundations ofdigital human… appropriate set of predictors (e.g., Sykes etal. 2009; Venkatesh etal. 2012) as well as suitable mediators to explain usage (e.g., Chang etal. 2016; Rondan-Cataluña etal. 2015). For instance, concerning user attitudes, scholars have argued for (e.g., Dwivedi etal. 2019; Huseynov and Özkan Yıldırım 2019; Oshlyansky etal. 2007) as well as against (e.g., Chang etal. 2016; Venkatesh etal. 2012; Yi etal. 2006) the inclusion of this construct as a mediator. Moreover, different variants of the theory have been gradually introduced, including a version based on meta-analytic results (meta-UTAUT; Dwivedi etal. 2019, 2020). As the choice of constructs may significantly improve the explanatory power of the theory, it is of vital importance to motivate and test adjustments to the UTAUT framework to guide future evaluations. Hereby, we aim to confirm if the original set of predictors is suitable to explain adoption in digital HRM and whether common mediators, i.e., attitudes and intentions emerge as relevant in our field. Second, within the UTAUT literature, there has been an ongoing debate on a suitable set of moderators (Dwivedi et al. 2019; Venkatesh 2022). For example, both individual characteristics and environmental variables may moderate relationships, depending on context. This discussion is especially relevant in digital HRM because the field combines research in the preceding concepts of Human Resource Information Systems (HRIS) (e.g., DeSanctis 1986; Mathys and LaVan 1982; Mayer 1971; Tomeski and Lazarus 1974) and Electronic Human Resource Management (e-HRM) as well as its subsets such as e-recruiting or organizational e-learning (e.g., Lengnick-Hall and Moritz 2003; Lepak and Snell 1998; Ruël etal. 2004; Strohmeier 2007). Accordingly, scholars have studied technology adoption across various HR functions, organizational settings, and various groups of stakeholders (Strohmeier 2007). For this reason, research may greatly benefit from comparisons of adoption across contexts. Yet, the options for comparing associations in different settings and samples are severely limited within a single study. In contrast, summarizing evidence across numerous studies meta-analytically allows assessing the influence of various study characteristics on relationships between constructs. More specifically, such tests may reinforce the value of a unifying theory by attesting to its generalizability. In brief, our review summarizes theory in empirical research as a basis (1) to metaanalytically test the validity of UTAUT as an overarching and consolidating theory to explain digital HRM adoption across settings, (2) to propose field-specific adjustments to the theory, and (3) to examine if study-level characteristics moderate relationships between individual constructs. This allows testing existing field-specific hypotheses, explore the influence of other possible moderators, and offer a baseline theory to guide future research efforts towards a more unified view on the phenomenon. 2 Motivation—technology adoption research indigital human resource management 2.1 Overview oftheoretical foundations The field of digital HRM can be characterized as a confluence of two streams of scholarly work, research on Information Systems (IS) and research on Human C.Theres, S.Strohmeier 1 3 Resources (HR). Additionally, some theoretical perspectives in both streams track back their origins to psychology, e.g., the Theory of Reasoned Action (Fishbein and Ajzen 1975) or the Theory of Planned Behavior (Ajzen 1991). In empirical digital HRM studies, scholars have primarily made extensive use of dominant IS paradigms (Marler etal. 2009; Marler and Dulebohn 2005). While all these theories aim to explain the adoption and use of digital HRM in some capacity, they rely on varying sets of constructs and operationalizations. Yet, for our present goal of summarizing evidence across primary we should choose a theory that (1) allows us to include as much primary research as possible (2) without sacrificing the integrity of primary study constructs, i.e., primary study constructs should have conceptual equivalents in the unifying theory. An informed decision in this matter requires a more in-depth examination of theory applied in digital HRM research. To this end, we conducted a scoping review (cf. Pham etal. 2014). As frequently suggested, we use this scoping review as a preliminary analysis to a inform our systematic review (Arksey and O’Malley 2005) by summarizing earlier research findings and by mapping the heterogenous body of empirical literature on digital HRM (Mays etal. 2001). We collected empirical studies reporting some sort of effect size and studying digital HRM adoption. The studies were either cited in one of the earlier, narrative digital HRM reviews or resulted from an initial search in the Web of Science. We did not enforce any additional inclusion criteria for this scoping procedure. This yielded a sample of 171 studies. Note that this set should not be seen as an exhaustive list of digital HRM adoption research but rather a comprehensive and, for the purpose at hand, representative sample of empirical studies in our field. Out of the 171 studies, only 36 employed some sort of digital HRM-specific theory. The remaining 135 contributions adapted theoretical frameworks from IS research with some researchers even combining multiple frameworks. Table1 provides a summary with respect to the prevalence of individual theories in this 135-study sample. For each theory, the prevalence in empirical digital HRM literature, i.e., the number and percentage of applications for the respective paradigm, is listed in the rightmost column. As some studies utilized a combination of two theories, the total number of applications sums up to 162. Our scoping review allows three important insights. First, we see a clear dominance of IS theories over field-specific paradigms. Almost 80% of the studies in our scoping review (135 out of 171) relied on established paradigms from IS research. A simple and plausible explanation for this dominance is the relative maturity of IS theories because each model has been evaluated across various IS subfields. These theories also include a predefined set of constructs, provide recommended operationalizations, and come with various testable propositions. While this general trend of merely adapting existing theories limits theory building, it also makes constructs comparable across studies. A second observation is the dominance of the original version of TAM (Davis 1989) in quantitative digital HRM antecedents research. This potentially poses problems because TAM, in its original form, has been extensively criticized for its poor explanative and predictive power and for its disputed heuristic value (Bagozzi 2007; Chuttur 2009; Legris etal. 2003; Venkatesh 2000). Following this reasoning, we decided against testing TAM in our study, even though it emerges as the most prevalent theory. Last, this multitude of frameworks confirms 1 3 Consolidating thetheoretical foundations ofdigital human… Table 1 Application of general IS theory in empirical digital HRM research a UTAUT, Unified Theory of Acceptance and Use of Technology (Venkatesh etal. 2003); TAM, Technology Acceptance Model (Davis 1989), IDT, Innovation Diffusion Theory (Moore and Benbasat 1991; Rogers 1983) ; TAM 2, Technology Acceptance Model (extended) (Venkatesh and Davis 2000) ; TPB, Theory of Planned Behavior (Ajzen 1991) ; TRA, Theory of Reasoned Action (Fishbein and Ajzen 1975) ; TOE, Technology–organization–environment Framework (Tornatzky and Fleischer 1990) ; TTF, Task-technology Fit (Goodhue and Thompson 1995) b PE, Performance Expectancy; EE, Effort Expectance; SI, Social Influence; FC, Facilitating Conditions; ATT, Attitude; BI, Behavioral Intention; USE, Digital HRM Usage c Sample, 162 applications of a single theory in 135 empirical digital HRM adoption studies (some studies combined two theories) TheoryaConstructs with matching definitions in UTAUT Respective UTAUT constructsbExamples in digital HRM Prevalence in digital HRMc UTAUT All – Laumer etal. (2010), Obeidat (2016), Yoo etal. (2012) 28 (17.28%) TAM Perceived usefulness, perceived ease of use, behavioral intention, (usage) PE, EE, BI, (USE) Erdoğmuş and Esen (2011), Voermans and van Veldhoven (2007), Yusliza and Ramayah (2012) 68 (41.98%) IDT Relative advantage, complexity/ease of use, compatibility PE, EE, FC Florkowski and Olivas‐Luján, (2006), Kassim etal. (2012), Parry and Wilson (2009) 21 (12.96%) TAM 2 Perceived usefulness, perceived ease of use, behavioral intention, subjective norm/image, behavioral intention, usage PE, EE, SI, BI, USE Marler etal. (2009), Park etal. (2012), Rym etal. (2013) 15 (9.26%) TPB Subjective norm, perceived behavioral control, attitude, behavioral intention SI, FC, (ATT), INT Lin (2010), Roca etal. (2006), Yusliza and Ramayah (2011) 11 (6.79%) TRA Subjective norm, attitude, behavioral intention SI, (ATT), INT Bermúdez-Edo etal. (2010), Jan etal. (2012), Luor etal. (2009) 9 (5.56%) TOE Technology/organization characteristics, adoption (intention or usage) FC, INT/USE Masum etal. (2016), Warui (2016), Yeh (2014) 6 (3.70%) TTF Technology characteristics, utilization FC, USE Huang and Chuang (2016, 2017), Lippert and Forman (2006) 4 (2.47%) C.Theres, S.Strohmeier 1 3 the fragmented state of theory in digital HRM. Yet, our tabulation also suggests that efforts to consolidate theory may be fruitful. Table1 demonstrates that most theories applied in digital HRM are either (1) one of the eight theories that were unified to form the original UTAUT or (2) have at least some conceptually equivalent constructs in UTAUT. To illustrate this claim, our summary (Table1) also shows the primary study constructs and their respective UTAUT equivalent for each applied theory. Given these similarities, both conceptually and empirically, it seems appropriate to test the validity of UTAUT as on overarching and consolidating theory to explain digital HRM adoption. Additionally, by summarizing primary study constructs under the umbrella of meta-analytic constructs, i.e., UTAUT constructs, all theories prevalent in digital HRM are consolidated in a fashion similar to Venkatesh etal.’s original article. In addition and on a larger scale, we are interested in the explanatory value of UTAUT in the field of digital HRM. Thus, we not only examine the bivariate relationships between all UTAUT constructs but also use meta-analytic structural equation modeling to evaluate the model fit to the meta-analytically pooled results. 2.2 Field specific adjustments toUTAUT We made two adjustments to the original UTAUT for the present study, the introduction of user attitudes as a central construct and the inclusion of moderators specific to digital HRM research. In line with earlier contributions, we hypothesize that user attitudes mediate the relationships between all exogenous constructs and behavioral intention while having a merely indirect effect on usage. This inclusion of attitude has been proposed in general IS theories (Ajzen 1991; Fishbein and Ajzen 1975), in research studying technology adoption and use (Kim et al. 2009; Taylor and Todd 1995; Yang and Yoo 2004), as well as in UTAUT studies specifically (e.g., Dwivedi etal. 2019; Huseynov and Özkan Yıldırım 2019; Oshlyansky etal. 2007). In addition, it has been argued that an individual’s behavior towards using a new technology is shaped by attitude and that the construct must be considered to adequately explain intentions and use behavior (Chong 2013; Rana etal. 2017). Consequently, attitude is introduced as (1) a direct antecedent of behavioral intention (Dwivedi etal. 2017; Fishbein and Ajzen 1975) and (2) a direct consequence of performance expectancy, effort expectancy, social influence, and facilitating conditions (Dwivedi etal. 2020; Šumak etal. 2010). Conceptually, we hypothesize that digital HRM users form attitudes towards the innovation based on their perceptions, opinions of others, and the provided support. These attitudes are assumed to be linked to the users’ intentions to use the innovation and thus, in consequence, influence their actual use behavior (Guimaraes and Igbaria 1997). The inclusion of attitude is also consistent with a recently introduced approach to UTAUT based on meta-analytic results (metaUTAUT; Dwivedi etal. 2019, 2020). Our proposed model corresponds to metaUTAUT with one exception. Original UTAUT hypothesized no direct influence of facilitating conditions on behavioral intentions when effort expectancy is also 1 3 Consolidating thetheoretical foundations ofdigital human… included in the model (Venkatesh etal. 2003). We argue that this claim might still hold because the empirical evidence listed to justify the inclusion of this path is exclusively based on models with intentions as the only dependent variable (Eckhardt etal. 2009; Foon and Fah 2011; Yeow and Loo 2009). Thus, when trying to explain actual usage, as in the present study, we omit a hypothesized direct path between facilitating conditions and behavioral intentions. Our second adjustment aims at investigating the dynamic nature of technology adoption and use in more detail. To this end, we take a contingency approach to applying UTAUT by examining a wide range of potential moderators (c.f. Venkatesh etal. 2003). More specifically, our study considers three sets of characteristics. First, digital HRM research has introduced various studylevel characteristics that may exert an influence on relationships between digital HRM usage and its antecedents. As these potential moderators are specifically proposed in our field of research, we apply a confirmatory approach to validate their presence. In particular, authors have proposed the sector of the organization (Panayotopoulou etal. 2007; Shilpa and Gopal 2011; Strohmeier and Kabst 2009), the background of study respondents (Bondarouk etal. 2009; Bondarouk and Ruël 2009; Bos-Nehles and Meijerink 2018), and regional context (Florkowski and Olivas‐Luján 2006; Ramirez and Zapata-Cantú 2008; Strohmeier and Kabst 2009) as potentially influential characteristics. Sector characteristics can facilitate or inhibit digital HRM adoption because the use of technology and employees’ computer literacy is varying greatly across sectors (Strohmeier and Kabst 2009). Similarly, respondents with an HR background are expected to assess the usefulness of a prospective digital HRM system of service more accurately than employees without such a background (Bondarouk and Ruël 2009). Concerning study region, “Western” and “Eastern” economies with their respective cultural differences may perceive adoption factors differently (Bondarouk and Ruël 2009), e.g., collectivist cultures may be more affected by social influences regarding system use. Second, following the original UTAUT model, we also assessed the influence of gender and age of respondents on relationships. However, since the impact of relevant prior experience of users and their voluntariness of use are scarcely researched phenomena in the field of digital HRM, we could not include these characteristics in our moderator analysis. We complement this second, explorative set of moderators by adding publication year to capture the possible changes in outcomes over time. Finally, we examine the impact of methodological characteristics such as functional scope of the implementation (see below), peer-review status, and level of analysis on effect sizes. In sum and from a theory building perspective, our first adjustment will give us the opportunity to evaluate if the inclusion of attitudes helps to explain usage in our field and if future contributions will benefit from measuring this construct. In addition, our second adjustment may inform future theory building efforts if different models and links between relationships are needed to explain digital HRM usage in a particular context or with specific user backgrounds in mind. The adjusted unifying research model is visualized in Fig.1. C.Theres, S.Strohmeier 1 3 2.3 Measures—UTAUT constructs inthefield ofdigital HRM Next, we briefly discuss how the individual UTAUT constructs are operationalized in digital HRM research. Hereby, we aim to show that the aforementioned commonalities across theories also extend to the respective measurement items. Besides the direct application of UTAUT scales (e.g., Eckhardt etal. 2009; Laumer etal. 2010; Obeidat 2016; Wong and Huang 2011), digital HRM research uses various representative study constructs to capture the individual concepts. Following the classification of Venkatesh etal. (2003), we summarize these definitions under the umbrella of the corresponding UTAUT constructs. Construct definitions and representative constructs are presented with example measures in Table2. Given the prevalence of TAM in digital HRM research, it is not surprising that performance and effort expectancy are frequently measured in the form of perceived Fig. 1 Proposed research model 1 3 Consolidating thetheoretical foundations ofdigital human… that we performed, strictly speaking, path analysis. To evaluate model fit, we report χ2 test statistics and common fit indices, including CFI, RMSEA and SRMR, and consult the common associated thresholds. Following earlier MASEM studies in IS research, we then inspected critical ratios (CRs) to identify non-significant path estimates (Dwivedi etal. 2019, 2021; Otto etal. 2020; Sabherwal etal. 2006). 3.4 Moderator analyses We assessed moderator effects of study characteristics on effect sizes in two steps. First, we used mixed-effect (meta-regression) models for all bivariate relationships to explain at least part of the heterogeneity in true correlations (Raudenbush 2009). As for the meta-analyses, REML estimation was used to the amount of residual heterogeneity in the effect sizes. Additionally, to achieve more accurate control of Type I error rates we applied Knapp and Hartung adjustment for the test statistics (Knapp and Hartung 2003; Viechtbauer etal. 2014). Due to the restrictive studies to covariates ratio and resulting statistical power restrictions, we tested each moderator individually instead of including them all collectively in one model (Viechtbauer 2007). If multiple tests for a single moderator showed statistical significance, we obtained evidence against the hypothesis of homogeneity across all studies. In the second stage, we applied one-stage MASEM (OSMASEM, Jak and Cheung 2019) to assess the influence of each categorical study characteristics by regressing model parameters on moderator variables. OSMASEM can be seen as an extension to TSSEM that allows to examine between-study heterogeneity on the level of individual (direct) path coefficient in the path model. Model fit of our proposed model was assessed and moderation was tested for all hypothesized direct paths. For each moderator, differences were assessed via a likelihood ratio test, comparing the base models without and model with the moderator. We performed these OSMASEM analyses for all field-specific moderators, i.e., sector, respondent group, region, and functional scope. 3.5 Outlier andpublication bias To assess the possible presence of publication bias, we calculated a fail-safe k value (Orwin 1983; Rosenthal 1979), performed a regression test for funnel plot asymmetry (Egger etal. 1997; Sterne and Egger 2005), and inspected the associated funnel plot for each bivariate relationship. Due to the limitation of Rosenthal’s fail-safe k (Borenstein etal. 2009; Orwin 1983), we calculated each fail-safe k value based on Orwin’s approach with a target effect size of r = 0.1 (Orwin 1983). The value then represents the number of studies (samples) averaging null results that we would have to add to the given set of effect sizes to reduce the unweighted average correlation to a negligible unweighted average correlation. To identify potential outliers, we consulted several influence diagnostics based on leave-one-out analysis, i.e. repeatedly fit the model, leaving out one study at a time. We can assess the influence on the overall result and on heterogeneity if the corresponding study was omitted. Different diagnostic measures are tested against C.Theres, S.Strohmeier 1 3 common cut-offs to determine if a study is a an outlier candidate (Viechtbauer and Cheung 2010). We report results with and without outliers. Analyses of publication bias and outlier are done at the level of each univariate meta-analysis using the variance-stabilizing transformation (z-transformed and back-transformed) for the correlations. 3.6 Robustness checks To test the robustness of our decisions, we replicated our analysis in various scenarios. First, to validate our choice of MASEM approach, we performed a univariate approach in addition to our TSSEM approach and compared the results. Second, to validate our choice of using corrected correlations, we replicated the TSSEM analysis using uncorrected correlations. In addition, we calculated the covariance matrix from our stage 1 correlation matrix and used it as input for the stage 2 analysis. The covariance matrix was calculated from our pooled correlation matrix using the pooled standard deviations for each construct (Cheung and Chan 2009; Dwivedi etal. 2019). These pooled values were calculated by averaging all standard deviations reported in primary studies. In case scales deviated from the standard measurement scale (5-point Likert scale), the standard deviations were transformed accordingly. The resulting covariance matrix was then used as input for the analysis. Third, we replicated the analysis with outlier effect sizes removed. Finally, we replicated the analysis with grey literature excluded. Besides lending credibility to our chosen methods, these calculations also highlight opportunities of the MASEM toolset for future studies in IS literature. All analyses were conducted in R (version 4.2.2, R Core Team 2021). The TSSEM approach and the moderator analyses via OSMASEM were realized using metaSEM (version 1.3.0, Cheung 2015b). Meta-regressions, test for publications bias, and outlier analyses were performed using the metafor package (version 3.8-1, Viechtbauer 2010). The path analysis for the univariate approach during the robustness checks was conducted using lavaan (version 0.6-13, Rosseel 2012). 4 Results 4.1 Descriptive statistics The included studies were conducted during the period from 1997 to 2021. The mean age of respondents was 32.87. Samples had an average of 45.16% female respondents. Respondents had some sort of HR background in 25.71% of studies. 60.71% of the samples were collected in Eastern, 26.43% in Western, and 8.57% in African countries. More samples were collected from firms in the private sector (44.29%) than from firms in the public sector (30.71%). For the remaining studies, the sector was either not specified or data collection spanned over multiple sectors. The outlets were mostly journals (77.14%) followed by conference proceedings (11.43%). Two thirds (66.43%) of documents were peer-reviewed. The level of 1 3 Consolidating thetheoretical foundations ofdigital human… analysis was the individual in 80.60% and the organization in the remaining 19.40% of the studies. About half of the contributions investigated implementation affecting multiple HR functions (45.55%) with the other half focusing on a single HR function (54.45%). 4.2 Stage 1 results Stage 1 results from all 21 bivariate relationships between the 7 UTAUT constructs are summarized in Table3. Besides the pooled correlation estimate used to populate the pooled correlation matrix, we show the number of effect sizes and the cumulative sample size for each relationship. Moreover, we provide statistics to assess the presence of between-study heterogeneity and fail-safe K values for each metaanalysis. Pooled correlations estimates were generally moderate ranging from 0.354 Table 3 Pooled correlation matrix (TSSEM approach, reliability corrected correlations) The pooled correlations (i.e., the bold values) are the main results from this analysis and the basis for the following calculation in the stage 2 analysis Cells below the diagonal contain three values (k, N, r) with k being the number of effects included in the meta-analysis, N being the total sample size for the relationship, and r being the pooled effect size estimate. Cells above the diagonal contain three values (τ2, I2, FSNes=0.1) with τ2 representing an estimate for between-study heterogeneity, I2 describing the percentage of variation across studies that is due to heterogeneity, and FSNes=0.1 being Orwin’s failsafe N with a target effect size of r = 0.1 Construct PE EE SI FC ATT INT USE 0.033 0.033 0.034 0.048 0.029 0.039 Performance expectancy (PE) – 95.03 93.77 93.60 95.31 94.32 94.09 503 254 185 152 389 184 94 0.032 0.042 0.030 0.026 0.031 Effort expectancy (EE) 23,586 – 93.11 93.62 94.34 92.19 90.70 0.528 193 170 136 267 111 59 52 0.037 0.017 0.015 0.022 Social influence (SI) 16,819 13,502 – 94.59 87.68 87.34 89.18 0.458 0.412 111 76 169 52 47 43 29 0.031 0.034 0.041 Facilitating conditions (FC) 12,597 17,364 8146 – 93.89 92.98 94.20 0.434 0.424 0.428 48 109 88 27 25 20 12 0.032 0.010 Attitude towards DHRM (ATT) 7712 5616 6391 3724 – 95.37 81.96 0.531 0.536 0.424 0.431 128 20 67 63 38 31 18 0.021 Behavioural intention (INT) 16,164 16,226 8265 7898 4187 – 93.30 0.555 0.459 0.482 0.404 0.626 71 47 36 17 25 6 12 Digital HRM usage (USE) 9697 12,934 4812 12,408 1995 2277 – 0.422 0.362 0.354 0.395 0.411 0.562 C.Theres, S.Strohmeier 1 3 to 0.626. The largest pooled correlations were found between intention and attitude (r = 0.626), intention and usage (r = 0.562), and intention and performance expectancy (r = 0.555). The number of effect sizes used to calculate the pooled estimate varied greatly between cells, ranging from 6 to 94 effect sizes. Without exception, correlations for all relationships were very heterogenous as indicated by high τ2 estimates [0.010–0.048] and I2 statistics [81.96–95.37%]. This substantial variation in effect sizes across studies can be explained by the presence of moderators on study level. Fail-safe k values based on Orwin’s approach were high, ranging from 20 to 503, suggesting that we would need a substantial number of additional unpublished studies reporting null effects to render a hypothetical pooled correlation negligible (r < 0.1). Egger’s regression test indicated funnel plot asymmetry for merely three relationships leading to a detailed inspection of the associated funnel plots. Note that regression tests do not test for publication bias directly. They merely test for funnel plot asymmetry for which publication bias is only one of many possible reasons. We noticed a pattern of missing studies on the lower right side of almost all asymmetric funnel plots, i.e., missing studies with large reported effect sizes and higher standard errors. This pattern makes publication bias as the reason for the asymmetry very unlikely because it is improbable that studies finding large (“good”) effects would not have been published even if they had small sample sizes (Sterne etal. 2011). Influential case diagnostics indicated outliers for three relationships (one effect size in EE-SI and SI-ATT each, two effect sizes for ATT-USE). Removing these outliers effect sizes did not affect the conclusion in any way.2 Stage 2 results are reported with outliers. 4.3 Stage 2 results After transforming the pooled correlation matrix obtained in stage one to a pooled covariance matrix, we used this data as input for our path analysis. We tested both the original UTAUT model without the attitude construct and our proposed model with attitude as an additional mediator construct. The results are depicted in Table4. Model fit for the original UTAUT model was good (χ2(4) = 5.520, p = 0.24, CFI = 0.99, RMSEA = 0.003, SRMR = 0.021) while the fit for our proposed model was excellent (χ2(5) = 3.844, p = 0.57, CFI = 1.00, RMSEA = 0.000, SRMR = 0.016). Following our approach of model modification, we eliminate the non-significant path between effort expectancy and intention based on the CR (β = 0.052, p > 0.05). For this emergent model, all path estimates were significant and the model showed excellent fit (χ2(6) = 4.868, p = 0.56, CFI = 1.00, RMSEA = 0.000, SRMR = 0.017). Since the fit is equivalent to the hypothesized model, we prefer the model with more degrees of freedom that also explains more variance in intention. This emergent model is visualized in Fig.2 and explains about 40.8% of variance in attitude, 51.3% of variance in intention, and 40.9% of variance in usage. 2 All test statistics for publications bias and results with and without outliers can be found in the supplementary material (Appendix C). 1 3 Consolidating thetheoretical foundations ofdigital human… 4.4 Moderator analyses The results of the meta-regressions for all coded study characteristics across all bivariate relationships are visualized in Table5 as follows: For each individual significant test, we list the name of the associated study characteristic in the corresponding cell in the matrix. For example, since the meta-regression for study sector was significant at the 5%-level for the relationship between effort expectancy and intention, the identifier (sector) and the associated p-value of the F-statistic (p = 0.009) is listed in the corresponding cell in the matrix depicted in Table5. Significant tests for categorical moderators are listed in the cells below the diagonal while significant tests for continuous moderators are listed in the cells above the diagonal. We obtained the highest number of significant tests for study sector and gender (4), followed by year (3). In only few relationships had the peer-review status (1) or the level of analysis (2) any significant influence. Functional scope, respondent group, and mean age were not significant as a moderating characteristic in any relationship. Notably, we found no single bivariate relationship for which multiple tests for moderators were significant. Table 4 Path coefficients and fit-indices for the main models *p < 0.05 Original UTAUT UTAUT UTAUT With attitude Emergent model Relationship ATT ← PE – 0.257* 0.230* ATT ← EE – 0.284* 0.318* ATT ← SI – 0.126* 0.121* ATT ← FC – 0.146* 0.143* INT ← ATT – 0.388* 0.428* INT ← PE 0.359* 0.246* 0.260* INT ← EE 0.175* 0.052 - INT ← SI 0.251* 0.186* 0.187* USE ← FC 0.238* 0.224* 0.226* USE ← INT 0.515* 0.520* 0.518* Fit-indices χ25.520 3.844 4.868 df 456 CFI 0.99 1.00 1.00 RMSEA 0.003 [0.000, 0.008] 0.000 [0.000, 0.006] 0.000 [0.000, 0.006] SRMR 0.021 0.016 0.017 Variance Explained R2 (Attitude) – 0.405 0.408 R2 (Intention) 0.406 0.495 0.513 R2 (Usage) 0.408 0.409 0.409 C.Theres, S.Strohmeier 1 3 These results were reified by the OSMASEM analyses. Since we regressed all parameters corresponding to the nine direct effects in our emergent model on moderator variables, the change in degrees of freedom is nine for all likelihood ratio tests. Sector emerges as the only moderator variable that moderated single parameters in the model. In particular, the coefficient for the path between facilitating conditions and usage (β = 0.298, p = 0.019, stronger for public sector) as well as the coefficient for the path between intentions and usage (β = −0.176, p = 0.017, weaker for public sector) was moderated by sector. Yet, the likelihood ratio test, comparing the model with and without moderator, was not significant (ΔLL(Δdf = 9) = 16.61, p = 0.055). Neither respondent group (ΔLL(Δdf = 9) = 10.60, p = 0.303) nor study region (ΔLL(Δdf = 9) = 8.42, p = 0.492) or functional scope (ΔLL(Δdf = 9) = 9.11, p = 0. 427) showed a significant test statistic and did not moderate any individual parameters. All moderated paths are highlighted in Fig.2. Accordingly, we estimated separate pooled correlation matrices for effect sizes collected in private sector and public sector organizations. That is, we performed a Fig. 2 Path diagram-emergent model with moderating effects 1 3 Consolidating thetheoretical foundations ofdigital human… separate random-effects TSSEM analysis for both subgroups.3 In total, the private sector matrix was constructed using 312 effect sizes while the public sector matrix included only 237 effect sizes. The total sample sizes were 12,686 (private) and 11,050 (public). For the stage 2 analysis, we then fitted our emergent model (without the deleted path) to both pooled subgroup correlation matrices. For the private sector subgroup, the fit was excellent, even better than for our main model with all collected data (χ2(6) = 0.479, p = 0.99, CFI = 1.000, RMSEA = 0.000, SRMR = 0.007). However, the fit for the public sector model was not very good (χ2(6) = 12.90, p < 0.05, CFI = 0.985, RMSEA = 0.010, SRMR = 0.038). This also showed in terms of explained variance in the endogenous constructs, i.e., the private sector data explained about 3.1% more variance in attitude, about 11.5% more variance in intention, and about the same variance in usage. We refrained from testing model modifications beyond the UTAUT framework, e.g., eliminating entire constructs from the model, due to the lack of theoretical justification for such alterations. Table 5 Summary of significant moderator effects for all study characteristics Cells below the diagonal list significant tests for moderators for categorical characteristics (sector, region, level, peer-review) Cells above the diagonal list significant tests for moderators for continuous characteristics (year of publication, gender, i.e., percentage of female employees in the sample) Functional scope, respondent group, and mean age were not significant in any relationship (at the 0.05level) Construct PE EE SI FC ATT INT USE Performance expectancy (PE) – Gender Gender p = 0.028 p = 0.013 Effort expectancy (EE) Sector – Year Year p = 0.009 p = 0.028 p = 0.047 Social influence (SI) Sector Sector – p = 0.007 p = 0.018 Facilitating conditions (FC) – Year p = 0.194 Attitude towards DHRM (ATT) Peer – Gender p = 0.043 p = 0.025 Behavioural intention (INT) Level Sector – Gender p = 0.023 p = 0.021 p = 0.014 DHRM usage (USE) Level Region – p = 0.037 p = 0.007 3 The stage 1 correlation matrices for both subgroups are available in the supplementary material (Appendix C). C.Theres, S.Strohmeier 1 3 4.5 Robustness checks4 First, performing the MASEM using a univariate approach instead of TSSEM, i.e., synthesizing z-transformed correlations from primary studies via pairwise aggregation, yielded comparable back-transformed pooled correlations in the first stage analysis. The pooled correlations ranged from 0.375 to 0.663. Notably the betweenstudy heterogeneity is somewhat higher in this approach indicated by τ2 estimates [0.015–0.132] and I2 statistics [76.77–97.31%]. Fitting our emergent model in the second stage analysis, using the harmonic mean of cell sample sizes (Burke and Landis 2003; Colquitt etal. 2000, N = 6588) yielded acceptable fit (χ2(6) = 122.491, p < 0.05*, CFI = 0.990, RMSEA = 0.054, SRMR = 0.018). Notably, if we perform TSSEM with a covariance matrix instead of the correlation matrix and harmonic mean as sample size, we obtain very similar results (χ2(6) = 131.698, p < 0.05*, CFI = 0.988, RMSEA = 0.056, SRMR = 0.022). Second, recalculating TSSEM with uncorrected instead of reliability corrected correlations leads to lower pooled correlations but also slightly worse fit to our emergent model (χ2(6) = 11.5472, p = 0.07, CFI = 0.995, RMSEA = 0.005, SRMR = 0.024). Third, since only four out of 768 effect sizes emerged as potential outliers, stage 2 results are unaffected (to the second decimal place) by the removal of outliers. Finally, re-estimating the model without grey literature left 111 independent samples and showed almost identical fit (χ2(6) = 4.77, p = 0.57, CFI = 1.00, RMSEA = 0.000, SRMR = 0.018). Performing the analysis with grey literature only was not possible due to low number of independent samples (k = 29). 5 Discussion Our study aims to motivate the need of a unifying theoretical basis with three contributions in mind. That is, we aimed to test and discuss the validity, the benefits, and implications of using UTAUT as such a basis, to examine the need of field-specific adjustments to the theory, and to provide valuable insights regarding generalizability and differences between research contexts. Our contributions to each goal are summarized below. 5.1 Validating acommon baseline—implications Our study complements and extends the set of earlier, narrative reviews in that it allows insights regarding the strength of associations between digital HRM antecedents. Here, we offer empirical support for an adjusted version of UTAUT as a suitable theory to explain digital HRM adoption across settings. With that, we 4 All statistics described in this chapter are available in the supplementary material (Appendix D). This includes stage one and two results of the univariate analysis and the associated path diagram. Note that using the OSMASEM approach without any moderator and on the full data necessarily yields the exact same results as TSSEM. 1 3 Consolidating thetheoretical foundations ofdigital human… provide a unifying basis that future efforts can expand on. The implications of this find are intriguing, both from a theoretical and a practitioner’s point of view. From a theoretical perspective, we gain various important insights. Overall, the empirical validation of our proposed theory aims at advancing theory in digital HRM research as it yields (a) a predefined set of relevant constructs, (b) a summary of applicable operationalizations for these constructs, and (c) a collection of testable hypotheses. Accordingly, future digital HRM studies may use UTAUT as a foundation and common baseline that can possibly be extended for various settings. We explicitly do not suggest UTAUT as a one-size-fits-all solution for future evaluations. Rather, we expect upcoming research to adapt and extend this common baseline. Using such a unifying theory will not only contribute to the comparability of future evaluations but will also encourage authors to move away from using original TAM and all the explanatory and predictive limitations that come with it. In addition, it may prevent authors from “cherry-picking” their favorite model that best fits their data while simultaneously neglecting other important constructs and will help the field towards more consistent and reproducible research. Assuming the digital HRM practitioner’s lens, insights on relevant constructs linked to digital HRM usage can motivate adoption decisions and direct possible interventions before, during, and after implementation. Managing user intentions emerges as a necessary requirement for successful adoption. This may be accomplished by thoroughly educating and informing users while simultaneously involving them in all project stages. Here, firms may also direct their interventions towards managing user perceptions, social influences, and other facilitating preconditions. Raising high expectations of performance can be crucial for a successful adoption and continued usage of digital HRM, possibly even more so than for similar innovations in the area of information systems. Designing digital HRM services in a way that clearly conveys their potential accessibility and utility paves the way for a seamless implementation. Regarding expected effort, our suggestions are in line with earlier IS contributions, i.e., firms are encouraged to align system capabilities with user requirements by pursuing clean and easy-touse designs (Martín and Herrero 2012; Rana etal. 2015). Moreover, engaging users to share digital HRM recommendations and best-practices, e.g., via virtual communities (Šumak etal. 2010), may shape social influences by presenting digital HRM usage as a generally endorsed and rewarding behavior. In terms of facilitating conditions, a construct that was mainly operationalized using measures of compatibility and perceived resources, it is vital that organizations (1) provide sufficient resources for a successful implementation, (2) clearly convey what they try to achieve through digital HRM, and (3) align digital HRM to the current values and demands of potential users. In addition to following all these recommendations, practice is well advised to constantly adapt to possible changes in user perceptions and intentions during all stages of adoption. Firms should actively seek feedback from users, listen to their concerns, and convey the organizational vision of digital HRM across all hierarchical levels. C.Theres, S.Strohmeier 1 3 5.2 Necessity offield‑specific extensions Second, our analysis supports the notion that easier to use, useful, more compatible, and peer endorsed digital HRM systems and services are associated with more positive attitudes, intentions, and, in consequence, a greater extent of usage. In addition, we find interrelations between all predictors, e.g., easier to use systems are expected to perform better or vice versa. This confirms our proposed set of common predictors to guide future evaluations. In contrast, comparing our proposed model with the original UTAUT model does not conclusively answer the open question about the importance of attitudes in explaining digital HRM usage. Results suggest that model fit improves when including attitudes, especially for uncorrected correlations and covariance inputs, yet the differences are relatively small. Thus, we cannot argue for the critical necessity of the attitude construct. Nonetheless, UTAUT (in both the original and our emergent variant) emerges as excellent in explaining digital HRM adoption. 5.3 Generalizability acrosscontexts Last, our study contributes to the debate if digital HRM and its antecedents are subject to contextual influences. Concerning influential study characteristics, we obtain the most convincing evidence for variations across different sectors. This result is in line with earlier digital HRM contributions in that it supports the hypothesis of sectoral differences exerting an influence on effects (Panayotopoulou etal. 2007; Shilpa and Gopal 2011; Strohmeier and Kabst 2009). Bivariate relationships show that direct associations with digital HRM usage are to an extent comparable across sectors, yet the public sector data does not show good model fit. This may indicate that, while the measured antecedents are adequate, a different model, other than UTAUT, might be more appropriate to explain public sector digital HRM usage. Importantly, facilitating conditions emerge as a stronger predictor of usage in the public sector, while usage intentions are more relevant in the private sector. Intuitively, this is plausible because existing resources are usually the limiting factor in public sector organizations. Organizational and technical infrastructure needs to be compatible with the innovation. Infrastructure is rarely upgraded explicitly to facilitate such an innovation. In contrast, intentions in the private sector, especially those of decision makers and stakeholders are a critical requirement for business decisions, such as a digital HRM implementation. Future research efforts should continue our efforts to investigate possible differences between digital HRM adoption in the private and public sector. For other potential moderators, we do not confirm such an influence. No single study characteristic emerges as an overall influential moderator. This in itself is an important find as it leaves the possibility that perceptions towards digital HRM adoption are comparable for various stakeholders in different regions of the world. Thus, our unifying theory not only offers a common understanding in one particular context but instead can serve as an outset for theory building across various 1 3 Consolidating thetheoretical foundations ofdigital human… Park Y, Son H, Kim C (2012) Investigating the determinants of construction professionals’ acceptance of web-based training: an extension of the technology acceptance model. Autom Constr 22:377–386. https:// doi. org/ 10. 1016/j. autcon. 2011. 09. 016 Parry E, Wilson H (2009) Factors influencing the adoption of online recruitment. Pers Rev 38(6):655– 673. https:// doi. org/ 10. 1108/ 00483 48091 09922 65 Pham MT, Rajić A, Greig JD, Sargeant JM, Papadopoulos A, McEwen SA (2014) A scoping review of scoping reviews: advancing the approach and enhancing the consistency. Res Synth Methods 5(4):371–385. https:// doi. org/ 10. 1002/ jrsm. 1123 Purnomo SH, Lee Y-H (2013) E-learning adoption in the banking workplace in Indonesia: an empirical study. Inf Dev 29(2):138–153. https:// doi. org/ 10. 1177/ 02666 66912 448258 R Core Team (2021) R: a language and environment for statistical computing. R Foundation for Statistical Computing. https:// www.Rproje ct. org/ Ramirez J, Zapata-Cantú L (2008) E-HR adoption by firms in Mexico: an exploration study. Rio’s Int J Sci Ind Syst Eng Manag 2:44–73 Rana NP, Dwivedi YK, Williams MD, Weerakkody V (2015) Investigating success of an e-government initiative: validation of an integrated IS success model. Inf Syst Front 17(1):127–142. https:// doi. org/ 10. 1007/ s107960149504-7 Rana NP, Dwivedi YK, Lal B, Williams MD, Clement M (2017) Citizens’ adoption of an electronic government system: towards a unified view. Inf Syst Front 19(3):549–568. https:// doi. org/ 10. 1007/ s107960159613-y Raudenbush SW (2009) Analyzing effect sizes: random-effects models. Handb Res Synth Meta Anal 2:295–316 Riley RD (2009) Multivariate meta-analysis: the effect of ignoring within-study correlation. J R Stat Soc A Stat Soc 172(4):789–811 Roca JC, Chiu C-M, Martínez FJ (2006) Understanding e-learning continuance intention: an extension of the technology acceptance model. Int J Hum Comput Stud 64(8):683–696. https:// doi. org/ 10. 1016/j. ijhcs. 2006. 01. 003 Rogers EM (1983) Diffusion of innovations, 3rd edn. Collier Macmillan Rondan-Cataluña FJ, Arenas-Gaitán J, Ramírez-Correa PE (2015) A comparison of the different versions of popular technology acceptance models: a non-linear perspective. Kybernetes 44(5):788–805. https:// doi. org/ 10. 1108/K0920140184 Rosenthal R (1979) The file drawer problem and tolerance for null results. Psychol Bull 86(3):638–641. https:// doi. org/ 10. 1037/ 00332909. 86.3. 638 Rosseel Y (2012) Lavaan: an R package for structural equation modeling and more. Version 0.5-12 (BETA). J Stat Softw 48(2):1–36. https:// doi. org/ 10. 18637/ jss. v048. i02 Rothstein HR, Sutton AJ, Borenstein M (2005) Publication bias in meta-analysis. Prevention, assessment and adjustments. Publication Bias in Meta-Analysis, pp 1–7 Rozelle AL, Landis RS (2002) An examination of the relationship between use of the internet as a recruitment source and student attitudes. Comput Hum Behav 18(5):593–604. https:// doi. org/ 10. 1016/ S07475632(02) 00002-X Ruël H, Bondarouk T, Looise JK (2004) E-HRM: innovation or irritation an explorative empirical study in five large companies on web-based HRM. Manag Rev 15(3):364–380. https:// doi. org/ 10. 5771/ 093599152004-3364 Ruël H, Magalhães R, Chiemeke CC (2011) Human resource information systems: an integrated research agenda. In: Bondarouk T, Ruël H, Kees Looise J (eds) Advanced series in management. Emerald Group Publishing Limited, pp 21–39. https:// doi. org/ 10. 1108/ S18776361(2011) 00000 08006 Ruël H, Bondarouk T (2014) E-HRM research and practice: facing the challenges ahead. In: Handbook of strategic e-business management. Springer, pp 633–653 Ruta CD (2005) The application of change management theory to HR portal implementation in subsidiaries of multinational corporations. Hum Resour Manag 44(1):35–53. https:// doi. org/ 10. 1002/ hrm. 20039 Rym B, Olfa B, Mélika BM (2013) Determinants of E-learning acceptance: an empirical study in the Tunisian context. Am J Ind Bus Manag 3(3):307–321. https:// doi. org/ 10. 4236/ ajibm. 2013. 33036 Sabherwal R, Jeyaraj A, Chowa C (2006) Information system success: individual and organizational determinants. Manag Sci 52(12):1849–1864. https:// doi. org/ 10. 1287/ mnsc. 1060. 0583 Sabir F, Abrar M, Bashir M, Baig SA, Kamran R (2015) E-HRM impact towards company’s value creation: evidence from banking sector of Pakistan. Int J Inf Bus Manag 7(2):123 C.Theres, S.Strohmeier 1 3 Schrag M, Mueller C, Oyoyo U, Smith MA, Kirsch WM (2011) Iron, Zinc and Copper in the Alzheimer’s Disease Brain: A Quantitative Meta-Analysis. Some Insight on the Influence of Citation Bias on Scientific Opinion. Progress in Neurobiology 94(3):296–306. https:// doi. org/ 10. 1016/j. pneur obio. 2011. 05. 001 Sharpe D (1997) Of apples and oranges, file drawers and garbage: why validity issues in meta-analysis will not go away. Clin Psychol Rev 17(8):881–901. https:// doi. org/ 10. 1016/ S02727358(97) 00056-1 Shilpa V, Gopal R (2011) The implications of implementing electronic-human resource management (e-HRM) systems in companies. J Inf Syst Commun 2(1):10. https:// doi. org/ 10. 5296/ jmr. v7i3. 7462 Sterne JAC, Sutton AJ, Ioannidis JPA, Terrin N, Jones DR, Lau J, Carpenter J, Rucker G, Harbord RM, Schmid CH, Tetzlaff J, Deeks JJ, Peters J, Macaskill P, Schwarzer G, Duval S, Altman DG, Moher D, Higgins JPT (2011) Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ 343(1):d4002–d4002. https:// doi. org/ 10. 1136/ bmj. d4002 Sterne JA, Egger M (2005) Regression methods to detect publication and other bias in meta-analysis. In: Publication bias in meta-analysis: prevention, assessment and adjustments. Wiley, pp 99–110 Strohmeier S (2007) Research in e-HRM: Review and implications. Hum Resour Manag Rev 17(1):19– 37. https:// doi. org/ 10. 1016/j. hrmr. 2006. 11. 002 Strohmeier S (2012) Assembling a big mosaic—a review of recent books on electronic human resource management (e-HRM). Ger J Hum Resour Manag 26(3):282–294. https:// doi. org/ 10. 1177/ 23970 02212 02600 305 Strohmeier S (2020) Digital human resource management: a conceptual clarification. Ger J Hum Resour Manag 34(3):345–365. https:// doi. org/ 10. 1177/ 23970 02220 921131 Strohmeier S, Kabst R (2009) Organizational adoption of e-HRM in Europe: an empirical exploration of major adoption factors. J Manag Psychol 24(6):482–501. https:// doi. org/ 10. 1108/ 02683 94091 09740 99 Šumak B, Polancic G, Hericko M (2010) An empirical study of virtual learning environment adoption using UTAUT. In: 2010 Second international conference on mobile, hybrid, and on-line learning, pp 17–22. https:// doi. org/ 10. 1109/ eLmL. 2010. 11 Sykes TA, Venkatesh V, Gosain S (2009) Model of acceptance with peer support: a social network perspective to understand employees’ system use. MIS Q 33(2):371. https:// doi. org/ 10. 2307/ 20650 296 Tang RW, Cheung MW-L (2016) Testing IB theories with meta-analytic structural equation modeling: the TSSEM approach and the univariate-r approach. Rev Int Bus Strategy 26(4):472–492. https:// doi. org/ 10. 1108/ RIBS0420160022 Taylor S, Todd PA (1995) Understanding information technology usage: a test of competing models. Inf Syst Res 6(2):144–176 Tomeski EA, Lazarus H (1974) Computerized information systems in personnel—a comparative analysis of the state of the art in government and business. Acad Manag J 17(1):168–172. https:// doi. org/ 10. 2307/ 254782 Tornatzky LG, Fleischer M (1990) Processes of technological innovation. Lexington books Tursunbayeva A, Bunduchi R, Franco M, Pagliari C (2017) Human resource information systems in health care: a systematic evidence review. J Am Med Inform Assoc 24(3):633–654. https:// doi. org/ 10. 1093/ jamia/ ocw141 Ulrich D (1997) Human resource champions: the next agenda for adding value and delivering results. Harvard Business School Press van Birgelen MJH, Wetzels MGM, van Dolen WM (2008) Effectiveness of corporate employment web sites: how content and form influence intentions to apply. Int J Manpow 29(8):731–751. https:// doi. org/ 10. 1108/ 01437 72081 09193 23 Venkatesh V (2000) Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf Syst Res 11(4):342–365. https:// doi. org/ 10. 1287/ isre. 11.4. 342. 11872 Venkatesh V (2022) Adoption and use of AI tools: a research agenda grounded in UTAUT. Ann Oper Res 308(1–2):641–652. https:// doi. org/ 10. 1007/ s1047902003918-9 Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204. https:// doi. org/ 10. 1287/ mnsc. 46.2. 186. 11926 Venkatesh V, Bala H (2008) Technology acceptance model 3 and a research agenda on interventions. Decis Sci 39(2):273–315. https:// doi. org/ 10. 1111/j. 15405915. 2008. 00192.x 1 3 Consolidating thetheoretical foundations ofdigital human… Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478. https:// doi. org/ 10. 2307/ 30036 540 Venkatesh V, Thong JY, Xu X (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q 36(1):157–178. https:// doi. org/ 10. 2307/ 41410 412 Viechtbauer W (2007) Accounting for heterogeneity via random-effects models and moderator analyses in meta-analysis. J Psychol 215(2):104–121. https:// doi. org/ 10. 1027/ 00443409. 215.2. 104 Viechtbauer W (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw 36(3):1–48. https:// doi. org/ 10. 18637/ jss. v036. i03 Viechtbauer W, Cheung MW-L (2010) Outlier and influence diagnostics for meta-analysis. Res Synth Methods 1(2):112–125. https:// doi. org/ 10. 1002/ jrsm. 11 Viechtbauer W, López-López J, Sanchez-Meca J, Marín-Martínez F (2014) A comparison of procedures to test for moderators in mixed-effects meta-regression models. Psychol Methods 20(3):360–374. https:// doi. org/ 10. 1037/ met00 00023 Viswesvaran C, Ones DS (1995) Theory testing: combining psychometric meta-analysis and structural equations modeling. Pers Psychol 48(4):865–885. https:// doi. org/ 10. 1111/j. 17446570. 1995. tb017 84.x Voermans M, van Veldhoven M (2007) Attitude towards E-HRM: an empirical study at Philips. Pers Rev 36(6):887–902. https:// doi. org/ 10. 1108/ 00483 48071 08224 18 Wahyudi E, Park SM (2014) Unveiling the value creation process of electronic human resource management: an indonesian case. Public Person Manag 43(1):83–117. https:// doi. org/ 10. 1177/ 00910 26013 517555 Warui CM (2016) Determinants of human resource information systems usage in the Teachers Service Commission’s Operations in Kenya. Doctoral dissertation, Jomo Kenyatta University of Agriculture and Technology Wickramasinghe V (2010) Employee perceptions towards web-based human resource management systems in Sri Lanka. Int J Hum Resour Manag 21(10):1617–1630. https:// doi. org/ 10. 1080/ 09585 192. 2010. 500486 Williamson IO, Lepak DP, King J (2003) The effect of company recruitment web site orientation on individuals’ perceptions of organizational attractiveness. J Vocat Behav 63(2):242–263. https:// doi. org/ 10. 1016/ S00018791(03) 00043-5 Wong W-T, Huang N-TN (2011) The effects of E-learning system service quality and users’ acceptance on organizational learning. Int J Bus Inf 6(2):205–221. https:// doi. org/ 10. 6702/ ijbi. 2011.6. 2.4 Yang H-D, Yoo Y (2004) It’s all about attitude: revisiting the technology acceptance model. Decis Support Syst 38(1):19–31 Yeh CR (2014) E-HR adoption in Taiwan: an exploration of potential multilevel antecedents and consequences. In: Knowledge management in organizations—9th international conference, KMO 2014, proceedings, pp 126–135 Yeow PHP, Loo WH (2009) Acceptability of ATM and transit applications embedded in multipurpose smart identity card: an exploratory study in Malaysia. Int J Electron Govt Res 5(2):37–56. https:// doi. org/ 10. 4018/ jegr. 20090 40103 Yi MY, Jackson JD, Park JS, Probst JC (2006) Understanding information technology acceptance by individual professionals: toward an integrative view. Inf Manag 43(3):350–363. https:// doi. org/ 10. 1016/j. im. 2005. 08. 006 Yoo SJ, Han S, Huang W (2012) The roles of intrinsic motivators and extrinsic motivators in promoting e-learning in the workplace: a case from South Korea. Comput Hum Behav 28(3):942–950. https:// doi. org/ 10. 1016/j. chb. 2011. 12. 015 Yusliza MY, Ramayah T (2011) Explaining the intention to use electronic HRM among HR professionals: results from a pilot study. Aust J Basic Appl Sci 5(8):489–497 Yusliza MY, Ramayah T (2012) Determinants of attitude towards E-HRM: an empirical study among HR professionals. Proc Soc Behav Sci 57:312–319. https:// doi. org/ 10. 1016/j. sbspro. 2012. 09. 1191 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.