The effect of innovation performance on the adoption of human resources analytics in business organizations

ERA, 32 (2): 1126–1144. DOI: 10.3934/era.2024054 Received: 02 October 2023 Revised: 15 December 2023 Accepted: 08 January 2024 Published: 25 January 2024 http://www.aimspress.com/journal/ERA Research article The effect of innovation performance on the adoption of human resources analytics in business organizations Eithel F. Bonilla-Chaves1, Pedro R. Palos-Sánchez2,*, José A. Folgado-Fernández3 and Jorge A. Marino-Romero3 1 School of Business Administration, Technological Institute of Costa Rica, Cartago 30109, Costa Rica 2 Department of Financial Economy and Operation Management, University of Sevilla, Sevilla 41018, Spain 3 Department of Financial Economy and Accounting, Universidad de Extremadura, Cáceres 10071, Spain * Correspondence: Email: [email protected]. Abstract: Our study objective is to examine the determinants that influence the adoption of human resource (HR) analytics, along with the influence of the external variable called Innovation Performance. The research model was developed by adapting the theoretical model of the unified theory of the acceptance and use of technology (UTAUT) by adding the external variable, Innovation Performance. The data was collected using a survey at Amazon Mechanical Turk (MTurk) in the USA. Initially, a total of 602 responses were obtained. Finally, a total of 554 questionnaires were obtained after using information quality filters for debugging. This study reveals that the main influence on the adoption of HR analytics is exerted by performance expectancy, social influence, facilitating conditions, and innovation performance on behavioral intention. Likewise, facilitating conditions, innovative performance, and behavior intention are the major influences for Use Behavior. This was found from an empirical analysis using the generalized structured component analysis (GSCA) software package that shows, with tabled data, the major relationships of the research model. This research into the use of HR Analytics investigated the standard determinants of UTAUT and the Innovation Performance external variable, that influence the adoption of HR analytics in business organization. 1127 Electronic Research Archive Volume 32, Issue 2, 1126-1144. Keywords: business organization; companies; HR analytics; innovation performance; UTAUT; GSCA 1. Introduction Harnessing the potential of digitization for strategic purposes is particularly relevant to the digital transformation of organizations with digital strategy being a decisive factor [1,2]. The formulation and execution of HR strategies along with HR practices are also relevant for human resource management (HRM) as they establish how digitization potential can be exploited to create value for organizations using the digital strategies presented by Strohmeier [3], and Marino-Romero et al. [4]. This is an example of the way HR analytics can systematically create value for an organization by basing all human resources decisions on evidence. HR analytics is defined by Marler et al. [5] as an HR-enabled information technology practice that uses descriptive, visual, and statistical analysis of data related to HR processes, human capital, organizational performance, and external economic parameters to improve business impact and enable data-driven decision-making. The COVID-19 pandemic has allowed some companies to recognize the relevance of HR analytics for crisis management [6–8]. However, the research by Dahlbom et al. [9] found that the low levels of expectation for the success of HR analytics in companies are due to an inability of managers to see the benefits of using it to manage people in the business when one of the most important points for the adoption of HR analytics is the support of management. The adoption of HR analytics has become more relevant as Shet et al. [10] and Minbaeva [11] state that data quality, analytical skills, and strategic ability to act are critical elements for the effective and successful adoption of HR analytics in organizations. Other studies of technology adoption [12,13] propose that an empirical examination can be used to find the practical application of the adoption and use of HR analytics for evidence-based decisionmaking for the organization and staff and can broaden understanding [14,15]. In addition, it is known by using effective strategic HR practices companies can increase their ability to introduce new products or services, as well as influence the staff to achieve better results and give a higher value to innovation. The adoption and use of HR analytics can play a critical role in this due to the influence of Innovation Performance. Vargas et al. [16] state that HR analytics should be considered a complex innovation as it integrates technological tools and data analysis. The novelty of this study is the use of the generalized structured component analysis (GSCA) algorithm, which provides a global least squares optimization criterion with respect to PLS [17–19]. Moreover, GSCA can easily deal with both formative and reflective indicators and is also free from the problem of factor score indeterminacy [20]. The following research question has been formulated: What standard determinants, including Innovation Performance, can be identified as factors having a significant positive influence on the adoption of HR analytics? The main objective of this paper is to examine the determinants that influence the adoption of HR analytics with an empirical study and find the influence of the external variable, Innovation Performance. This article is organized as follows: in the first part, the subject, the purpose, the objective, and the research question are presented. This section includes the literature review identifying and 1128 Electronic Research Archive Volume 32, Issue 2, 1126-1144. consulting the scientific articles about the adoption of HR analytics resulting in a proposal for the adaptation of the theoretical model of the UTAUT for the process of innovative technology adoption by including the Innovation Performance external variable to understand the model of HR analytics adoption. Then, it includes the eight hypotheses proposed for this study. The following section explains the methodology used in this study, which was a survey with a questionnaire. The questionnaire (Appendix A) was published on the Amazon MTurk crowdsourcing platform with a sample of (n = 602) users to understand their opinions of how HR can use analytics to manage people at work with a data-driven approach. The analysis of the results is then made using GSCA and this is followed by a discussion and conclusions with suggestions for possible future lines of research. 1.1. Adoption of HR analytics in business organizations Fernandez et al. [21] identified the barriers to the adoption of HR analytics in organizations and grouped them into four categories Data and models, Software and technology, people and management, as shown in Figure 1. Figure 1. Barriers to the adoption of HR analytics. Adapted from Fernandez et al. [21]. To help companies overcome these barriers Fernandez et al. [21] also proposed a set of key factors, which are organized into four major categories, preparation, development, dissemination, and team, as seen in Figure 2. 1129 Electronic Research Archive Volume 32, Issue 2, 1126-1144. Figure 2. Key factors for HR analytics adoption. Adapted from Fernandez et al. [21]. Shet et al. [10] provide a framework for HR analytics adoption that identifies five factors that influence adoption. These are technological, environmental, data governance, and individual. The work by [22] complements such adoption by identifying three main areas of interest for HR analytics which are grouped as follows, (a) “HR analytics enablers”, which are factors that enable or support HR analytics initiatives, include technological factors and organizational enablers, (b) “HR analytics applications”, which refers to the different types of applications of HR analytics, from descriptive applications that are more traditional to predictive/prescriptive applications, and (c) “The value of HR analytics”, which identifies the value generated by HR analytics, with a dual focus on employee-related value and organizational or business value. Concerning the “HR analytics applications”, Figure 3 shows the stages of the evolution of HR analytics: Figure 3. Stages of the evolution of HR analytics. Adapted from Margherita [22]. 1.2. Previous studies on the adoption of HR analytics Research by Ramzi et al. [23] explains that the research aimed to investigate the perception of 1130 Electronic Research Archive Volume 32, Issue 2, 1126-1144. HR analytics by HR professionals and find the aspects that affect HR analytics adoption. The findings show that there is a negative relationship between the aspects of performance, effort expectation, and social influence in the adoption of HR analytics. The study by Arora [24] evaluated the aspects that determine an individual’s decision to adopt HR analytics. The results suggest that performance expectation influences the intention to adopt HR analytics. In contrast, effort expectation and social influence did not influence the behavioral intention to use it. In addition, the use behavior of HR analytics was determined by the behavioral intention to use it and the facilitating conditions. In the research by Ekka et al. [25], the goal was to explore the behavioral intention to use HR analytics by HR professionals. The results revealed a significant positive influence of performance expectation, effort expectation, social influence, and facilitating conditions on behavioral intention to use HR analytics. Similarly, it was noted that behavioral intention has a significant positive influence on use behavior for HR analytics. Peisl et al. [26] investigated the acceptance of technology and the use behavior of HR managers for the adoption of predictive HR analytics during hiring processes. However, the results of the study and the application of the hypotheses model are transferred to other researchers because they were developed or included by the authors. 2. Investigation model and research hypotheses The research model and hypotheses are formulated to examine the determinants of HR analytics adoption and measure the effect of innovation performance. They were developed by adapting the theoretical model of UTAUT and the external variable, innovation performance. UTAUT has been widely used in research to investigate the adoption process for innovative technologies along with the behavioral intention to use them, as it has a predictive efficiency of 70% [27–29]. UTAUT was formulated after reviewing and comparing eight empirical models of information technology adoption and finding that four constructs act as direct determinants of acceptance by users and use behavior. These are performance expectancy, effort expectancy, social influence, and facilitating conditions [25]. The determinants of HR analytics adoption are presented below. 2.1.1. Performance expectancy Performance expectancy is defined by Venkatesh [30] as the degree to which a person believes that using the system will help them improve work performance. Performance expectations have consistently proven to be one of the strongest predictors of behavioral intention to adopt and use information technology [31]. The study by Hmoud et al. [31] provides understanding and knowledge about the factors influencing the adoption of artificial intelligence (AI) in human resources information systems (HRIS) and the behavioral intention to use it by HR professionals. They found that Performance Expectation has an important influence on the behavioral intention to use AI by HR professionals in HRIS. The following hypothesis is thus proposed for this research: Hypothesis 1 (H1). Performance expectation has a significant positive influence on the behavioral intention to use HR analytics. 1131 Electronic Research Archive Volume 32, Issue 2, 1126-1144. 2.1.2. Effort expectation Effort Expectation is defined as how easy the user thinks the system will be to use. In the study by Ouirdi et al. [32], which focuses on the adoption of social networks in the recruitment and selection of employees, effort expectation had a positive influence on the behavioral intention to use social networks when hiring employees. Therefore, the following hypothesis was proposed: Hypothesis 2 (H2). Effort expectation has a significant positive influence on the behavioral intent to use HR analytics. 2.1.3. Social influence Social influence is defined by Venkatesh [30] as the degree to which an individual perceives that others believe it important to use the new system. Rahman et al. [33] identified the most important factors affecting HRIS adoption, and social influence was found to have a significant effect on behavioral intention to use the system in HRIS adoption. The following hypothesis was then proposed for this research: Hypothesis 3 (H3). Social influence has a significant positive influence on the behavioral intention to use HR analytics. 2.1.4. Facilitating conditions Facilitation conditions are defined by Venkatesh [30] as the extent to which a person believes that there is an organizational and technical infrastructure to support the use of the system. Hmoud et al. [31] found that facilitation conditions did not have a significant influence on the behavioral intention of HR professionals to use AI for HRIS. However, in the study by Ouirdi et al. [32] facilitation conditions had a positive impact on the use behavior for social media when hiring employees. The following hypotheses were proposed for this research: Hypothesis 4 (H4). Facilitating conditions have a significant positive influence on the behavioral intention to use HR analytics. Hypothesis 5 (H5). Facilitating conditions have a significant positive influence on the use behavior for HR analytics. 2.1.5. Behavioral intention to use and use behavior. The behavioral intention to use refers to the intention to use a particular system or technology. In the study by Rahman et al. [28], it was also found that the behavioral intention to use has a significant effect on the use behavior in the adoption of HRIS. Similarly, in the study by Ouirdi et al. [32], the behavioral intention to use had a positive influence on the use behavior of social networks when hiring employees. The following hypothesis is therefore proposed for testing: Hypothesis 6 (H6). Behavioral intention to use HR analytics has a significant positive influence on the use behavior of HR analytics. 1132 Electronic Research Archive Volume 32, Issue 2, 1126-1144. 2.1.6. Innovation performance Innovation performance refers to the degree to which a company is successful in achieving its goals in terms of new products or services [34]. The study by Vargas et al. [16] used innovation theory to inspect the adoption of analytics applied to human resources. In the research by Shet et al. [10], the diffusion of innovation model (DOI) was used as the theoretical basis to understand and contextualize HR analytics as an innovation in HRM. The research of HR analytics at an organizational level by McCartney et al. [35] shows that interest in HR analytics can be linked to organizational outcomes, including innovation. In this research, the relationship between the adoption of HR analytics and the external variable, innovation performance will be studied using the following hypotheses: Hypothesis 7 (H7). Innovation performance has a significant positive influence on the behavioral intention to use HR analytics. Figure 4. Suggested theoretical model. Source: Adapted from Venkatesh [30]. Hypothesis 8 (H8). Innovation performance has a significant positive influence on the use behavior for HR analytics. The existing research explained above was used to create the research hypotheses and the suggested theoretical model shown in Figure 4. The constructs that influence HR analytics adoption are found by analyzing the constructs and their relationships. The hexagons in Figure 4 represent the constructs, which are also known as latent variables, connected by arrows numbered 1 to 8, showing the proposed hypotheses. 3. Materials and methods The collection method and the measurement scale for the data of this study on the adoption of 1133 Electronic Research Archive Volume 32, Issue 2, 1126-1144. HR analytics and innovation performance are presented in this section of the research work. 3.1. Data collection The data for the suggested model was collected using a questionnaire. The questions were created after reviewing previous studies on technology adoption using the UTAUT technology acceptance model [30], and including the external variable of Innovation Performance to expand the model and find out if it is a determining factor for behavioral intention to use or use behavior for HR analytics. The questionnaire was tested on 25 people with experience in the field of analytics or human resources who were not included in the study samples [2,3]. The respondents completed the questionnaire and provided information about the clarity or difficulty of the questions. The results confirmed the validity and reliability of the scales used in the final questionnaire [36]. Of the 602 questionnaires collected from the participants, 554 were valid, all “Mturkers” [37] from the Amazon MTurk platform in the United States of America (USA), characterized by the use of HR analytics to manage people at work with a data-driven approach. The questionnaire applied the good practices criteria for information collection on crowdsourcing platforms, selection of the MTurk worker, the validity of the survey, and quality filters for cleaning the information obtained [38–41]. The data was collected between November and December 2022. Before the questionnaire could be answered, a link in Qualtrics was prepared for the Mturk platform. 3.2. Measurements A total of 6 questions were used to find general information about the respondent (industry sector, number of employees in the company, work experience, level of education, age group, and gender), and 2 questions were used as a quality control measure (What is 2 + 2? and When were you born?) and 27 questions were answered using a 5-point Likert scale where 1 = (“strongly agree”) and 5 = (“strongly disagree”). The questions were grouped for the 7 constructs used in the research (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral Intention, use behavior, and innovation performance). 3.3. Data analysis method The GSCA approach [20,42] was used to test the hypothetical model presented in Figure 4 - All analyses were made using the GSCA software [43]. Although GSCA has been criticized [44], it has been used in a variety of domains and studies [45–48]. Afthanorhan et al. [49] have found that only a few studies are using GSCA. This algorithm is versatile enough to capture complex relationships between variables, including higher-order components and multi-group comparisons [20]. 𝑍𝑉 = 𝑍𝑊𝐴 + 𝐸 , Ψ = Γ 𝐴 + 𝐸 , (1) Hwang et al. [20] explain Ψ = ZV, and Γ = ZW. In (1), Ψ is an N by T matrix of all endogenous observed and composite variables, Γ is an N by D matrix of all exogenous observed and composite 1134 Electronic Research Archive Volume 32, Issue 2, 1126-1144. variables, V is a J by T matrix of component weights associated with the endogenous variables, W is a J by D matrix of component weights for the exogenous variables, A is a D by T supermatrix consisting of a matrix of component loadings relating components to their observed variables, denoted by C, in addition to a matrix of path coefficients between components, denoted by B, that is, A = [ C, B ], and E is a matrix of residuals. 4. Results A total of 554 valid questionnaires (92.02% of the 602 responses) were obtained for the following analysis stage of the research on HR analytics adoption. The results of the general information section have the descriptive statistics shown in Table 1. Table 1. General information about the participants. Variable Range Frequency Gender Female 193 Male 360 Other 1 Age group 18–29 years 246 30–39 years 173 4049 years 91 50–59 years 28 60 years and above 16 Level of education High school 51 Technical Specialty 19 Undergraduate 377 Postgraduate 107 Work experience Less than 1 year 17 1 to less than 3 years 168 3 to less than 5 years 167 5 years or more 202 Number of employees in the company Less than 100 employees 158 100 to 499 employees 273 500 employees or more 123 Industry sector Construction 18 Energy and Utilities 7 Finance and Insurance 65 Health Care and Social Assistance 52 IT and Telecom 255 Logistics and Transport 8 Manufacturing 76 Public Sector and Education 10 Wholesale and Retail 39 Other 24 1141 Electronic Research Archive Volume 32, Issue 2, 1126-1144. 5. J. H. Marler, J. W. Boudreau, An evidence-based review of HR Analytics, Int. J. Human Resour. Manage., 28 (2017), 3–26. https://doi.org/10.1080/09585192.2016.1244699 6. D. Minbaeva, Disrupted HR?, Hum. Resour. Manage. 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Nagdev, A. Rajesh, Redefining HR using people analytics: The case of Google, Hum. Res. Manage. Int. Dig., 26 (2018), 3–6. https://doi.org/10.1108/HRMID-06-2017-0112 53. J. A. Folgado-Fernández, M. Rojas-Sánchez, P. Palos-Sánchez, A. G. Casablanca-Peña, Can virtual reality become an instrument in favor of territory economy and sustainability?, J. Tourism Serv., 14 (2023), 92–117. https://doi.org/10.29036/jots.v14i26.470 54. J. F. Arenas-Escaso, J. A. Folgado-Fernández, P. Palos-Sánchez, Internet interventions and therapies for addressing the negative impact of digital overuse: A focus on digital free tourism and economic sustainability, BMC Public Health, 24 (2024), 176. https://doi.org/10.1186/s12889- 023-17584-6 1144 Electronic Research Archive Volume 32, Issue 2, 1126-1144. Appendix A Table A1. Questionnaire and constructs. Items Description Performance expectancy. Adapted from Venkatesh et al. [30] (ED1) I would find HR analytics useful in my work. (ED2) Using HR analytics allows me to perform tasks more quickly. (ED3) Using HR analytics increases my work performance. (ED4) If I use HR analytics, I will increase my chances of getting a raise. Effort Expectancy. Adapted from Venkatesh et al. [30] (EE1) If I used HR analytics, I would find this clear and understandable. (EE2) It would be easy for me to become proficient in using HR analytics. (EE3) I would find HR analytics easy to use. (EE4) Learning how to use HR analytics is easy for me. Social Influence. Adapted from Venkatesh et al. [30] (IS1) The people who influence my behavior think I should use HR analytics. (IS2) The people who are important to me think I should use HR analytics. (IS3) The senior management of the company has been helpful in supporting the use of HR analytics. (IS4) In general, the organization has supported the use of HR analytics. Facilitating conditions. Adapted from Venkatesh et al. [30] (CF1) I have the resources required to use HR analytics. (CF2) I have the knowledge required to use HR analytics. (CF3) HR analytics is not compatible with other systems I use. (CF4) A person (or group) is available to help with any difficulties I have using HR analytics. Innovative performance. Adapted from [53–55] (DI1) The company's new products and services are often perceived as very novel by customers. (DI2) New products and services in the company often pit us against new competitors. (DI3) The company has introduced more innovative products and services than its competitors over the past 5 years. (DI4) The company is faster in bringing new products or services into the market than its competitors. Behavior Intention. Adapted from Venkatesh et al. [30] (IU1) I intend to use HR analytics in the coming months. (IU2) I can predict I will use HR analytics in the coming months. (IU3) I plan to use HR analytics in the coming months. Use behavior. Adapted from Venkatesh et al. [30] (AU1) Using HR analytics is a good idea. (AU2) HR analytics makes work more interesting. (AU3) Working with HR analytics is fun. (AU4) I like working with HR analytics. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)