77 Organizacija, V olume 55 Issue 1, February 2022 Research Papers 1 Received: 20th October 2021; revised: 14th January 2022; accepted: 7th February 2022 Predicting HR Professionals’ Adoption of HR Analytics: An Extension of UTAUT Model Susmita EKKA, Punam SINGH University of Hyderabad, School of Management Studies, Hyderabad, India, 17mbph10@uohyd.ac.in (corresponding author), punamsingh@uohyd.ac.in Background and Purpose: To scale up HR innovation with HR technology, organizations worldwide are putting effort into adopting HR Analytics (HRA) among HR professionals and the actual use of HRA for organizational deci- sion-making. This study aims to explore the behavioral intention to use HRA from the perspective of HR profession- als by using UTAUT. Methodology: Partial least squares structural equation modeling (PLS-SEM) was employed to validate the model based on data collected via a survey from 270 HR professionals in India. Results: The result revealed a significant positive impact of performance expectancy, effort expectancy, social influ- ence, and facilitating condition on behavioral intention to use HRA. However, organization culture negatively mod- erates the relationship between HRA adoption intention and adoption behavior. The establishment of organizational culture as a moderator in Indian organizations is unique. Conclusion: The study extends the explanatory context of UTAUT and provides feasibility for the organizations to guide HR professionals to adopt HRA from multiple paths of intention and usage behavior. Managers, business lead- ers, and policymakers can use this finding to assist HRA adoption in their organizations. Keywords: Human resource analytics, Adoption intention, Adoption behaviour, Organization culture, UTAUT DOI: 10.2478/orga-2022-0006 1 Introduction Companies worldwide are experiencing the digital transformation of all their business functions, and HR or human resources has no exception. Digitalization of HR, amongst others, includes the adoption of HR analytics, a software tool to garner real-time and metrics-based in- sights for improved decision-making. The adoption of HR analytics has proved to be a game-changer, enabling or- ganizations to enhance employee skills, improve retention and gain a competitive edge (Van der Togt & Rasmussen, 2017). HR analytics is today a huge instrument for making progress; it exploits present information to expect future ROI and is viewed as a wellspring of vital benefit (Ben- Gal, 2019; Bindu, 2016). Several studies have testified its role in improving decision-making and managing, among other functions (Wandhe, 2020; Mohammed & Quddus, 2019). Despite the perceived benefits, the adoption of HRA among HR professionals remains sluggish (Vargas et al., 2018; Marler & Boudreau, 2017), primarily due to the adoption barriers of technology. Understanding the adoption behaviour is necessary for the adoption of technology. Various adoption model is used to study the intention to use technology and its ac- ceptance, i.e., actual adoption (behaviour/actual usage) of technology (Wang et al., 2020). Studies explain how tech- nology adoption impacts behavioural intention (Senaratne et al., 2019; Kabra , 2017). Ajzen (1985) states that “be- havioural intention is an individual’s subjective possibility of performing a specified behaviour, which is the major contributing factor to actual usage behaviour.” Although research has been extensively conducted and many theories proposed to explain it in different contexts of adoption, some critical issues remain to be addressed. 78 Organizacija, V olume 55 Issue 1, February 2022 Research Papers Extant literature (eg. Fernandez & Gallardo-Galardo, 2020; Kabra et al., 2017) on technology adoption is more focused on individual factors; however, it ignores the ma- jor barriers to adoption, most specifically the organization- al factor. However, the organizational factor plays a key role in any technology adoption, so there is a gap in the literature on this aspect, which the current study aims to fill using the UTAUT model. Several studies have evaluated the adoption of HRA giving more importance to individual factors (Fernandez & Gallardo, 2020; Vargas et al., 2018; Marler & Bourder, 2017). However, there is a need to extend our understand- ing of the influence of the organizational factor on HRA adoption. Organizational culture “has been identified as a critical factor in the success or failure of technology adop- tion” in an organization (Masoumeh et al., 2018; Wang & Chang, 2016). Limited efforts have been made to under- stand the role of the organizational factor, represented by organizational culture, in understanding and analyzing the adoption behaviour. Organizational culture influences the value and beliefs of an individual (Mohtaramzadeh et al., 2018; Eskiler et al., 2016). Organizational culture plays a unique role in technology adoption. Studies have explored the cultural impact on technology acceptance (Sunny, Pat- rick & Rob, 2019; Dwivedi, et al., 2016). Previous studies have considered the technology model to evaluate Inten- tion towards adoption (Ahmad, 2020; Singh et al., 2020). Intention is considered a “good preceder of actual behav- iour” (Ajzen, 2002). It is seen a majority of studies have not considered adoption barriers from the organizational level, which is clearly represented by organizational cul- ture. Studies (Akhtar et al., 2019) indicate that organiza- tional culture significantly impacts intention toward tech- nology acceptance. And also has a significant impact on actual technology adoption behaviour (Baptista & Olivei- ra, 2015). This indicates that organizational culture is vital in strengthening the relationship between Intention toward HR analytics adoption and behaviour. Thus, this study pro- poses organizational culture as a moderating factor, mod- erating the relationship between HRA adoption intention and usage behaviour. To the best of our knowledge, this is the first study of its kind to study the impact of organiza- tional culture on HRA adoption. In summary, after reviewing existing literature, we find the research lacking in several key aspects impacting technology adoption, specifically in the context of HRA adoption. While research has focused on individual-level factors, for instance, the response of HR professionals as an adoption barrier, little focus has been given to the or- ganizational factor, that is, organizational culture and how it impacts the adoption. This study aims to address this gap in the literature and provide new and crucial insights into HRA adoption by organizations. Accordingly, a conceptu- al model is then proposed regarding adopting HRA using UTAUT model. Organizational culture is incorporated into the Model as a moderator. This study adds to the exist- ing literature by representing that organizational culture (weak/strong) plays a crucial role in adopting HRA. Data is collected from HR professionals in India, and the proposed model is evaluated. The examination of the outcomes is shown and clarified briefly in the paper’s re- sults and discussions areas. The discoveries are advised in the last segment, and the hypothetical and useful ramifica- tions of the discoveries are from that point discussed. 2 Literature review 2.1 Logical framework for Human Resource Analytic Adoption Behaviour To examine user expectation and user adoption of the technology, specialists have utilized different innovation appropriation models, for example, the innovation diffu- sion theory (IDT), technology-organization-environment framework (TOE), institutional theory (IT), theory of planned behaviour (TPB), technology acceptance model (TAM), and unified theory of acceptance and use of tech- nology (UTAUT). Hosseini et al. (2016) and Cao et al. (2017) indicated that these models have been utilized to clarify technology adoption conduct in the management research field. HRA or human research analytics is software used to analyze data to improve employee performance and reten- tion (Vargas et al., 2018; Marler & Bourder, 2017). Suc- cessful adoption of HRA depends on both the organization and the individual behaviour of employees (Grayson et al., 2018). It is seen in some organizations that did not take individual employee intention and behaviour into account while implementing HRA, leading to adverse impact. The UTAUT model has been used to study user behaviour and Intention to accept or resist HRA implementation, there- by predicting its success or failure, as the case may be. According to Venkatesh & David (2003), user behaviour is determined by their intention to perform the behaviour. Various researchers have adopted UTAUT to analyze the adoption of new technology (Altalhi, 2021; Ammenwerth, 2019). The adoption behaviour of the employee depends to a large extent on the organizational culture, amongst other factors. Existing literature shows that organizational cul- ture can be a barrier to successful HRA implementation apart from end-users. UTAUT has been abundantly used in literature to predict user intention and behaviour towards technology adoption and is considered as amongst the best to study technology adoption in various contexts (Altalhi, 2021; Ammenwerth, 2019). While existing literature has thrown some light on organizational culture, the focus has been on individual factors, which is a gap that this study aims to fill. 79 Organizacija, V olume 55 Issue 1, February 2022 Research Papers Organizational culture influences the value and beliefs of employees, thereby impacting their behaviour (Eskiler et al., 2016). Several studies suggested that organization- al culture plays an important role in advancing technolo- gy adoption decisions (Liu et al., 2010; Khazanchi et al., 2007). They likewise feature the significance of thinking about culture while assessing technology acknowledgment (Borkovich et al., 2015; Srite, 2006). Accordingly, while thinking about technology acknowledgment and adoption, it is imperative to remember that culture impacts a per- son’s reasoning and behaviour. Considering a particularly immense impact culture has in transit individuals figure, it would be a consistent presumption that it would affect how they see, think, and carry on comparable to technology (Srite, 2006; Hofstede, 2001). Previous literature throws light on how organizational culture impacts individual In- tention to adopt technology (Akhtar et al., 2019) and im- pacts their adoption and usage (Gu et al., 2014). HRA is one of the more complex technologies in the context of HR (Vargas et al., 2018; Marler & Bourder, 2017). According to Jac Fitz-Enz (2010), “Analytics is a mental framework, first a logical progression and second a set of statistical tools.” The relationship between organiza- tional culture and information technology is complex and confrontational. According to Gu et al. (2014), technology adoption affects organizational culture and brings a genu- ine issue into the standard action inside the organization. This, in turn, leads to a redefining of the existing culture to encompass the new norms. Ribiere and Sitar (2003) showed that “organizational culture (OC) represents the character of an organization, which directs its employees’ day-to-day working relationships and guides them on how to behave and communicate.” 2.2 Hypothesis Development 2.2.1 Performance Expectancy Performance expectancy is “the degree to which an individual believes that using the system will help him or her to attain gains in job performance (Venkatesh et al., 2003, p.447)”. In this study, performance expectan- cy relates to the individual’s perception, i.e., HR profes- sionals using HRA will enhance their work performance with ease, influencing the behavioural intention to adopt HRA. Research has proven performance expectancy as a “strong predictor of behavioural intention” for acceptance of new technology (Kabra et al., 2017; Venkatesh et al., 2012). Studies show that using new technology enhances an individual’s job performance, the use of HRA improves the performance of an individual. To this extent, HRA has proved to be a game-changer, to enhance employee skills, improve decision-making, and managing other functions (Wandhe, 2020; Mohammed & Quddus, 2019; Van der Togt & Rasmussen, 2017). Previous research found that performance expectancy impacts behavioural intention to adopt new technology (Venkatesh et al., 2012). Based on previous research, we hypothesize the following: Hypothesis 1: Performance expectancy positively af- fects HR professional behavioural intention to adopt HRA. 2.2.2 Effort expectancy Effort expectancy is “the degree of ease associated with the use of the system (Venkatesh et al., 2003, p.450)”. In this study, effort expectancy relates to the belief that us- ing HRA is easy for HR professionals. Previous research found the relationship between effort expectancy and be- havioural intention while adopting a technology (Akhtar et al., 2017b; Jennings et al., 2015). Studies show how the system complexity influences an individual’s intention, the convenience of using the technology and the system’s compatibility with the individual experience and skill af- fect their intent to use the technology (Kabra et al.,2017; Akhtar et al., 2012). The ease or effort associated with uti- lizing the technology makes individual believes that sys- tem is easy to use. An individual’s belief towards using the technology, i.e., HRA is easy to use, higher will be the intention to adopt HRA. The direct impact of effort expec- tancy on behavioural intention on users to adopt technol- ogy has been seen in various studies (Kabra et al., 2017; Venkatesh et al., 2012). Based on previous research, we hypothesize the following; Hypothesis 2: Effort expectancy of HR professional positively affect behavioural intention to use/adopt HRA. 2.2.3 Social influence Social influence is “degree to which an individual per- ceives the importance of others’ belief of using the new system (Venkatesh et al., 2003, p. 451)”. In this study, so- cial influence is termed as HR professional belief about how other organizations’ HR believe about HRA usage. As social influence reflect the “effect of environmental factors such as opinions of a user’s friends, relatives, and superi- ors on behaviour” (Venkatesh et al., 2003), when they are positive, it may encourage the HR to adopt HRA. Prior research in adoption found that an individual behvaiour would incline to adopt the technology if colleagues and coworkers impact behvaioural intention to adopt the tech- nology (Kabra et al., 2017 ). Furthermore, the intention also depends on the support and commitment from top management and the peer group within the organization. This belief depends on subjective norms, image, and social factors. Previous studies show that social influence signif- icantly impacts HRA adoption (Vargas et al., 2018; Kabra et al., 2017). Thus we hypothesize: Hypothesis 3: Social influence positively impacts HR professional behavioural intention to adopt HRA. 80 Organizacija, V olume 55 Issue 1, February 2022 Research Papers 2.2.4 Facilitating condition Facilitating conditions are “the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system (Ven- katesh et al., 2003, p. 453)”. Using technology requires some specific skills, infrastructure, resources, etc. The user would be motivated to adopt the technology for the or- ganization’s benefit. Studies have theoretically supported the role of facilitating conditions (e.g., Kabra et al.,2017; Akhtar et al., 2012). In this study, facilitating condition is termed as the belief of HR professionals working in an organization about the existence of all the necessary sup- port to use HRA. Previous studies have shown the impact of facilitating conditions to adopt the technology (Kabra et al., 2017; Akhtar et al., 2012; Venkatesh et al., 2003). Therefore we hypothesize: Hypothesis 4: Facilitating conditions positively in- fluence the behavioural Intention of HR professionals to adopt HRA. 2.2.5 Behavioural Intention According to Venkatesh & Davis (2000), behavioural intention can be interpreted as individual willingness to- wards any aspect, reflecting their behaviour. Therefore, it is the predictor of behaviour (Ajzen, 1991), i.e., “a person’s readiness to perform a given behaviour.” Earli- er studies have documented the relationship between in- tention and behaviour (Venkatesh et al., 2003; Venkatesh & Davis, 2000). Research gives evidence that individual willingness, i.e., intention to perform a behaviour pre- dicts the actual behaviour (Wang et al., 2020; Taherdoost, 2020). Previous research has also confirmed a strong re- lationship between intention and behaviour (Bankole & Bankole, 2017; Attuquayefio & Addo, 2014). Furthermore, behavioural intention also explains why people behave in a certain way in certain situations (Osbourne and Clarke 2006). Previous literature shows that a person’s readiness to use a technology depends on their acceptance and inten- tion to use it (Fisk et al., 2011; Lin & Hsieh, 2007). Social sciences literature proves that behavioural intention (BI) directly impacts actual use (Bankole & Bankole, 2017; Attuquayefio & Addo, 2014). Various other studies also show a direct relationship between intention to use and the actual use of technology (Wang et al., 2020; Attuquayefio & Addo, 2014; Venkatesh et al., 2003). This study sug- gests that individuals with intention to use HRA will be more amenable to adopting HRA. Finally, since this study explores the intention to use HRA, the condition of actual usage behaviour is also of interest. Thus, in the context of this study, intention to adopt HRA is assumed to have a positive effect on HRA adoption. Therefore, the study hypothesis that: Hypothesis 5: Intention to adopt HRA significantly in- fluence the adoption behaviour of HRA. 2.2.6 Organizational Culture Organizational culture influences the value and beliefs of individual behaviour (Eskiler et al., 2016). According to Liu et al., 2010 organizational culture is a collection of shared assumptions, values, and beliefs reflected in its practices and goals while also enabling the members to un- derstand the organizational functions.” Various dimensions have been used in literature to measure organizational cul- ture, such as flexibility, control orientation (Khazanchi et al., 2007), and relational and transactional orientation (McAfee, 2002). But many researchers prefer to evaluate organizational culture using cultural traits, attributes, and dimensions that measure values, beliefs, and assumptions of an individual (Gordon & DiTomaso, 1992; O’Reilly et al., 1991). Based on the organizational culture definition by O’Reilly et al. (1991), Tsui et al. (2006) conceptualized a framework to identify organizational culture in different firms in China. They identified five dimensions: manage - ment control, customer orientation, employee orientation, innovativeness, and social responsibility. A study was con- ducted by Mohtaramzadeh & Cheah (2018), in which they implemented all these five dimensions of organization cul- ture to measure the “B2B e-commerce adoption in manu- facturing companies in Iran”. Tsui et al. (2006) explored the cultural impact on technology adoption using these five dimensions (Mohtaramzadeh et al., 2018; Kariyapperuma, 2016). Accordingly, we use the five cultural dimensions proposed by Tsui et al. (2006) to measure the impact of organizational culture on the adoption of HRA. Studies show that organizational culture contributes a major role in adopting technology (Khanzanchi et al., 2017; Liu et al., 2010). Culture has been widely studied in different contexts (Srite, 2006); however, limited attention has been given to study its role in the adoption of technol- ogy (Teo & Huang, 2018). A few exceptions show that or- ganizational culture plays a significant role in technology adoption (Bankole et al., 2017; Liu et al., 2010). Organiza- tional culture influences individual behaviour in adopting technology (Bankole & Bankole, 2017; Tseng, 2017). It is seen as a critical factor for technology adoption (Mohtara- mzadeh et al., 2018; Borkovich et al., 2015) and either strengthens or weakens it. Researchers claim that organiza- tional culture influences “individual behaviour in adopting technology” (Mohtaramzadeh et al., 2018). Understanding the importance of organizational culture in the adoption of technology is important as it impacts the thinking and behaviour of the employees (Teo & Huang, 2018) acts as a moderator between adoption and behavioural intention of an individual (Mohtaramzadeh et al.,2018; Zhao & Zhou, 2018). So far, only a few moderating variables, like age, 81 Organizacija, V olume 55 Issue 1, February 2022 Research Papers gender, educational qualification, have been explored in the context of HRA adoption (Vargas et al., 2018). Culture has been extensively cited in the literature, showing an im- portant role to play in this context (Halper, 2014). Based on previous research, we hypothesize the following. Hypothesis 6: Organizational culture significantly moderates the relationship between HRA adoption inten- tion and usage behaviour (HRA adoption Behaviour). Figure 1: Proposed Model 3 Research Methodology The Partial Least Squares (PLS) based Structural Equation Modeling (SEM) technique examines the afore- mentioned relationships in Figure 1. PLS is a “regres- sion-based path modeling technique that estimates path coefficients and partials out variance for the model” (Hall et al., 2012). PLS is highly recommended when the model consists of latent variables or composite-based models or used latent variables scores to estimate the inner model or for a small sample size (Hair et al., 2022). And also, this technique is suitable for exploratory testing and predictive applications. Our study is an initial attempt to empirically examine the behavioural intention to adopt HR analytics. Consequently, PLS is appropriate to test the inter-relation- ship we developed based on the literature review 3.1 Questionnaire design and variable measurement To test the proposed model in Figure 1, the question- naire survey method was chosen for data collection. Each construct is measured using multiple items, developed us- ing the procedure suggested by Churchill (1979). A five- point Likert scale ranging from 1 = “strongly disagree” to 7” strongly agree” is used to measure the items. The ques- tionnaire’s reliability and items are ensured by the exhaus- tive literature review, incorporating the experts’ opinions, and observing Cronbach’s Alpha values. Additionally, the questionnaire has been pilot tested on 27 respondents to avoid any ambiguity if present in the contest of HR analyt- ics. We used Venkatesh and Davis (2003) items to measure independent variables, and intention to adopt HRA was es- timated using a three-item scale (Appendix). A five-scale item used by Rogers (2003) was used to measure HRA adoption. Organizational culture is a multi-dimension construct (Cooke & Rousseau, 1988); therefore, it is important to evaluate each dimension separately. We adapted scales from Tsui et al., (2006) and Tsui et al.,(2002) to measure organization culture using twenty four (24) items under five dimensions :(i) innovativeness employee orientation (INN) (4); (ii) customer focus (CF) (5); (iii) employee ori- entation (EO) (8); (iv) social responsibility (SR) (3); and (v) systematic management control (SMC) (4). A clear 82 Organizacija, V olume 55 Issue 1, February 2022 Research Papers definition of each construct was also provided to avoid confusion among respondents. 3.2 Sample selection and data collection An online questionnaire survey using Google Forms was used to collect data from HR professionals working in organizations that had adopted HRA in India. A pilot study was done consisting of 27 samples to check the construct reliability and validity of the survey instrument. These samples were not included in the final sample of the study. Few changes were made after evaluating the pilot study. A purposive snowball sampling method was used for the pur- pose of data collection. The target population for this study is HR, who have experience using analytics. A total of 286 responses was received from 350 targeted respondents, 270 responses were taken into consideration for analysis, and 16 were eliminated due to errors. They yielded more than 80% response rate and were acceptable for the survey (Jennings et al., 2015). To enhance the response rate, tele- phonic reminder, personally contacted and visited them. Based on previous studies, the sample size was suitable for further analyses as (Hair et al., 1998) suggested that a sample size ranging between 5 and 10 times the number of items used in the scale is considered adequate. A total of 270 usable responses were used for this study. Among these, 150 were from females (55.56%) and 120 from males (44.44%), currently working in organiza- tions that had adopted HR analytics. A detailed review of respondents’ demographics is provided in Table 1. Accord- ing to markets research reports by Sierra-Cedar, Linkedin, and Deloitte, the respondents were taken, which found that the adoption of HR analytics greatly influenced the indus- try. Information technology, Financial Services, and retails show India’s highest adoption rate. However, the variance difference within the industries was found insignificant. Table 1: Respondents’ Demographics Category Items No of Respondents Percentage Gender Female 150 56% Male 120 44% Grand Total 270 100% Age 21-30 85 31% 31-40 108 40% 41-50 64 24% Above 50 13 5% Grand Total 270 100% Experience 6-10 Year 124 46% 11-15 Year 58 21% 1-5 Year 65 24% More than 15 Year 23 9% Grand Total 270 100% Job Position Manager 119 44% HRIS 77 29% Generalist 47 17% Specialist 27 10% Grand Total 270 100% Industry Information Technology 120 44% Financial Services 81 30% Retail 51 19% Health 18 7% Grand Total 270 100% 83 Organizacija, V olume 55 Issue 1, February 2022 Research Papers 3.3 Common method bias It is important to address bias in data as it can impact the accuracy of results (Podsakoff et al., 2003). Accord- ingly, Harman’s single factor technique was used to test the common method bias based on exploratory factor anal- ysis. The results reveal that the total variance for a single factor is 32.15%, which is less than the threshold value of 50%. Second, the full collinearity appraisal approach was utilized to distinguish Common method bias (Kock, 2015). The worth of the Variance inflation factor (VIF) was under- neath the limit worth of 3.3 (Hair et al., 2017; Kock, 2015); the highest VIF was 3.1 for innovation, which means that this study does not have a common bias problem. 3.4 Data Analysis A multivariate analysis approach, that is, partial least squares path modelling was chosen to test the proposed model (Figure 1.) This technique is widely used in social science research (Hair et al., 2013) and is recommended as the most suitable method for a small sample size with no multivariate homogeneity and normality requirements on data (Hair et al., 2017). Kaiser-Meyer-Olkin (KMO) test of sampling adequacy and Bartlett’s test of sphericity were employed to check whether the data set was suitable for factor analysis. The resulting KMO statistic value was 0.873, and Bartlett’s test result was significant at p < .05, suggesting that the data was appropriate for analysis and indicated an acceptable correlation among the items. Thus, the results show that factor analysis was suitable for the data used in this study. The analysis was conducted us- ing SmartPLS 3.0 with bootstrapping, and 2000 resamples were used to measure the path coefficient and significance level. 4 Result 4.1 Measurement Model Several tests were conducted to measure reliability, convergent, and discriminant validity, such as composite reliability and Cronbach α score for reliability, A VE for convergence and Fornell-Larcker criterion, and Hetero- trait-Monotrait Ratio (HTMT) for validating discriminate validity. Factor loading for each variable was tested to en- sure they loaded to their respective constructs and did not cross-load with other constructs. The loading of each item exceeded 0.7 (Hair et al., 2013). An appendix shows all the item loadings. From the table, we can see that in some cas- es, the item loading was lower than the suggested thresh- old of .070 (Chin, 2010). Literature indicates that item loading between 0.6 and 0.7 is acceptable if the loading of an item in the same construct is high. Table 2 presents the reliability of the measurement items verified at the item and construct levels using Cronbach’s (α), composite reli- ability (CR), and average variance extracted. The result in- dicates that Cronbach’s (α) values and CR score are larger than the suggested 0.70, and A VE values are greater than the threshold of 0.50, indicating acceptable convergent va- lidity for the first order construct. Construct Cronbach’s Alpha (α) Average Variance Extracted (AVE) Behaviour (AB) 0.822 0.650 Effort Expectancy (EE) 0.704 0.521 Facilitating Condition(FC) 0.714 0.529 Intention (IN) 0.873 0.797 Performance Expectancy (PE) 0.683 0.524 Social Influence (SI) 0.711 0.626 Organisation Culture (OC) 0.885 0.686 Employee Orientation (EO) 0.771 0.526 Customer Focus (CF) 0.862 0.647 Innovativeness (INN) 0.738 0.579 Systematic Management Control SMC 0.858 0.642 Social Responsibility (SR) 0.787 0.703 Table 2: Convergent validity 84 Organizacija, V olume 55 Issue 1, February 2022 Research Papers Five latent variables were used to form the second-or- der construct: organizational culture. Table 2 presents the convergent validity of the five variables: innovativeness, customer focus, employee orientation, social responsi- bility, and systematic management control. Appendix 1 indicates the loading. Some items’ loading was less than 0.70. According to Hair et al. (2012), these items can be deleted to increase the validity and reliability of the data. Therefore, we excluded one item from innovation and two items from employee orientation to achieve acceptable CR and A VE. Table 2 indicates that CR score and A VE of the five constructs are larger than the threshold of 0.70 and 0.50, respectively. Table 3 shows the convergent validity using A VE square roots larger than the correlation among construct diagonally. This indicates that the measurement model for the five constructs has good convergent and dis- criminate validity to form the second-order construct. Table 3: Second-Order Construct Correlation and Square Root of AVE CF EO INN SMC SR CF 0.804 EO 0.546 0.725 INN 0.668 0.645 0.761 SMC 0.547 0.443 0.581 0.801 SR 0.587 0.597 0.462 0.57 0.838 Table 4: Fornell–Larcker criteria Table 5: Heterotrait-monotrait ratio of correlations (HTMT) 1 2 3 4 5 6 7 1. Behaviour 0.81 2. Effort Expectancy 0.17 0.72 3. Facilitating Condition 0.28 0.25 0.73 4. Intention 0.53 0.47 0.49 0.89 5. Organisation Culture 0.61 0.37 0.39 0.54 0.83 6. Performance Expectancy 0.31 0.41 0.18 0.55 0.35 0.70 7. Social Influence 0.18 0.54 0.16 0.43 0.24 0.30 0.79 1 2 3 4 5 6 7 1. Behaviour 2. Effort Expectancy(EE) 0.21 3. Facilitating Condition(FC) 0.40 0.43 4. Intention 0.62 0.56 0.69 5. Organisation Culture(OC) 0.73 0.44 0.54 0.73 6. Performance Expectancy(PE) 0.38 0.59 0.34 0.67 0.42 7. Social Influence(SI) 0.23 0.77 0.26 0.52 0.30 0.45 85 Organizacija, V olume 55 Issue 1, February 2022 Research Papers The convergent validity of the second-order constructs was calculated manually using the formula suggested by Sarstedt et al. (2019). CR, and average variance extracted (A VE) of the higher-order construct, organizational cul- ture, were above the recommended limits of 0.70 and 0.50, respectively. Table 2 shows that all the constructs, includ- ing first and second-order constructs, have good conver- gent validity. Discriminant validity was examined using two meth- ods; a variance comparison was extracted from the con- struct with joint variance. We found that the square root of A VE is significantly higher than its correlations with different constructs for each construct. Table 4 shows For- nell–Larcker criteria diagonally, confirming that the dis- criminant validity is higher than its maximum correlation with any other construct. Heterotrait-Monotrait ratio of correlations (HTMT) was employed in the second test. Table 5 indicates the HTMT value between each construct, less than the sug- gested critical value of 0.85 (Kline, 2011). Therefore, our constructs establish adequate discriminant validity. Path Coefficient (β) T Statistics P Values Significance Intention -> Behaviour 0.166 2.538 0.010 ** OC*Intention -> Behaviour -0.116 2.594 0.010 ** OC -> Behaviour 0.523 9.856 0.000 *** EE -> Intention 0.130 2.375 0.018 * FC -> Intention 0.356 7.543 0.000 *** PE -> Intention 0.373 8.326 0.000 *** SI -> Intention 0.186 2.759 0.004 *** Table 6: Direct path coefficients with significance (Note= OC- Organization Culture; EE- Effort Expectancy; FC- Facilitating Condition; PE-Performance Expectancy; SI-Social Influence; “***” Significant at p<0.01; “**” Significant at p< 0.05; “*” P<0.1) Figure 2: Structural Model with path coefficient, factor loading with significance T>1.96 and R 2 86 Organizacija, V olume 55 Issue 1, February 2022 Research Papers 4.2 Structural Model To test the proposed model, we examined the overall explaining power of the structural Model, with variance explicated basis the independent variables and the degree and robustness of its paths, where all our hypotheses were parallel to a specified structural model path. The measure- ment model result indicates that the reliability and validity of the second-order construct, thus, qualifies for structural model estimates. Figure 2 presents the structural Model’s parameters: the loading factor of each construct, standard- ized path coefficient (β), and variance of the endogenous variable (R2) obtained using PLS-SEM graphs. The signif- icance of estimations is calculated by running a bootstrap analysis with 2000 resamples. Results of each hypothesis were obtained by examining the path significance provid- ed in Table 6, with the total path coefficients, t-statics, and p-values. Table 6 and Figure 2 present the estimated structur- al Model. R2 was used to measure the explaining power, which is interpreted similarly as regression (Chin, 2010). The explained variance of more than 10 % is considered suitable explanatory power (Falk & Miller, 1992). The R2 value for behaviour and intention to use HRA was 47.6% and 51.6%, respectively, indicating acceptable explanatory power of the Model. All paths estimated as per the pro- posed hypothesis were significant. Note: FC- Facilitating Condition; SI-Social Influence; EE- Effort Expectancy; PE-Performance Expectancy; IN-Intention; OC-Organizational Culture; BA-Behaviour- al Adoption; CM- Customer Focus; EO-Employee Orien- tation; Innovation; SMC-Social Management and control; SR-Social responsibility) 4.3 Moderating test In testing the interaction effect between HR analytics adoption intention and behaviour, the result indicates that organizational culture’s negative moderating effect is sig- nificant (β = -0.116, p< 0.010), thus supporting H2. The direct link between intention to adopt HRA and adoption behaviour of HRA is provided in Table 6 and shows that it is positive and significant. However, the interaction link between intention to adopt HRA and organizational culture toward the HR analytics adoption behaviour (OC*Inten- tion → HRA adoption behaviour) is negative (-0.116) and significant. The negative moderating effect between organ- izational culture means that if the value of organizational culture increases, the direct link between intention to adopt HRA and HRA adoption behaviour decreases. Therefore hypothesis H2 is supported. Figure 3 demonstrates the moderating interaction pat- tern using Aiken (1991), which is the process of finding slopes above and below the mean within one standard deviation of organizational culture. The finding implies that organizations with low organizational culture exhib- it a stronger effect between HRA adoption intention and HRA adoption behaviour than high organizational culture, as indicated in Figure 3. Even in the case of high organi- zation culture, the effect between HRA adoption intention and HRA adoption behaviour is linear, indicating the role of both high and low organizational culture in predicting HRA adoption behaviour. However, high organizational culture is less predictive than low organizational culture. Figure 3: Moderating Interaction Effect 87 Organizacija, V olume 55 Issue 1, February 2022 Research Papers 5 Discussion and implication 5.1 Discussion The findings of this research suggest that all the hypoth- esis are supported (Fig 2). All the factors like EE, PE, SI, and FC have a significant positive impact on the intention of HR professionals to adopt HRA. Also, HRA adoption intention has a significant positive influence on HRA adop- tion behaviour. This finding validates the original idea of the UTAUT theory (Venkatesh & Davis, 2003). However, organization culture (OC) is a moderating variable govern- ing the relationship between intention to adopt HRA and HRA adoption behaviour. The significant negative result of organizational culture is found in the relationship between intention to adopt HRA and HRA adoption. In other words, organization culture “weakens” the relationship between intention and behaviour to adopt HRA by influencing the HRA adoption behaviour of the HR professionals. These discoveries are clarified by how associations with “strong culture” are better situated to embrace HRA. This is on the grounds that associations with “strong culture” are bound to be imaginative; ready to send information, abilities, data sharing along the worth chain; embrace technology boldly; accentuate group assembling; and have more champions when contrasted with organizations with “weak culture” (Liu et al., 2010; Khazanchi et al., 2007). Accordingly, the way toward embracing new technology is worked within organizations with “strong culture” when contrasted with those with “weak culture.” Halper (2014) suggests that or- ganizations that are using analytics “analytics culture” is important for adoption of it. Vargas et al. (2018) state that “organizations must redefine their culture to analytics cul- ture to gain benefits of HRA. Different countries have dif- ferent cultures, i.e., a national culture. Due to the cultural differences, technology adoption also differs from country to country and organization to organization. Various stud- ies have shown how national culture impacts technology adoption (Brown et al., 1998; Suite & Karahanna, 2006; Merchant, 2007). Therefore, the adoption of technology also varies from country to country and organization to organization. Wang et al. (2020) conducted a study in the context of China and showed a positive moderating role of organizational culture in information technology adoption (ICT). In contrast, a study conducted by Mohataramzadeh et al. (2018) in Iran shows a negative moderating role of organization culture on B2BE adoption. Therefore, the findings of this study convey a very important message for Indian organizations to adopt innovative culture to imple- ment HRA successfully. 5.2 Implication for research The study contributes noteworthy research insights into HRA adoption. Findings of the study offer insights into HR professionals’ perception towards HRA adoption. There is dearth of scientific evidence aiding to decision-making concerning HRA adoption (Marler & Boudreau, 2017). Evidence from existing research suggests that HR Analyt- ics has positive effects, yet adoption rate is slow (Vargas et al., 2018; Marler & Boudreau, 2017). This study attempts to fill this gap in literature concerning the empirical evi- dence for HRA adoption. The study attempts to understand the low adoption of HRA through the lens of HR profes- sionals in the Indian context. A major part of existing re- search has only focused on the individual intention as an adoption barrier in successful HRA implementation. This study focuses on intention and usage behaviour using the UTAUT theory to adopt HRA. Secondly, it is probably the first study in HRA adoption that integrates organization culture as a moderator using UTAUT theory to study HR professionals’ adoption intention and behaviour in adopt- ing HRA. Thus, integrating organizational culture as a moderator in UTAUT theory for HRA adoption is a new perspective that will enhance the literature on the subject. The study strengthens previous literature highlighting the effect of social influence on an individual’s adoption of innovation (Frambach & Schillewaert, 2002; Talukder, 2012). 5.3 Implication for practice The study also has practical implications. The study explores the moderating effect of organizational culture, which reduces the adoption behaviour of HRA by HR professionals. Therefore, it provides broad insights which organizations can use to create an innovative analytic cul- ture, which serves as fertile ground for HRA adoption as organization culture plays an important role in technology adoption. Employees may be willing to adopt new tech- nologies but are restricted by the organizational culture. The study can assist managers in understanding the facil- itators and barriers of HRA adoption. The study supports the fact that HR professionals may be more likely to use HRA if systems are easy to use and training is provided. Thus, Managers can remove barriers to HRA adoption by introducing additional support and training programs (role plays, demonstration, innovation champions, and support groups). The value of HRA adoption needs to be promot- ed to increase the positive behavioural intention towards HRA as it directly influences the HRA adoption. Open and greater communication can increase the probability of adoption among potential employees. Providing the tools, resources, adequate, timely support, and training will result in developing positive intention, which has been shown to positively influence HRA adoption behaviour. 88 Organizacija, V olume 55 Issue 1, February 2022 Research Papers 6 Limitation and suggestions for future work This study has certain limitations that can be the sub- ject of future research. First, it is only limited to organiza- tions in India. However, more research needs to be con- ducted for other countries to enhance the generalizability of findings. This is more true as cultural ethos and values vary from one country to another. A study on cross-cultur- al national differences on HRA adoption is also needed. Second, HRA adoption data is collected by cross-sectional method i.e., at one point of time. Therefore, a longitudi- nal survey method research would be preferable for more casual inference between variables. Third, the study focus- es only on organizational culture as a moderating variable between the adoption intention of HRA and HRA adoption behaviour. There is a need to understand whether other moderating factors can effect or influence the Intention to adopt HRA for transformation of adoption intention HRA and HRA adoption behaviour. Future studies can be con- ducted to understand other moderating variables that can affect the intensity of behavioural Intention or promote the transformation of HRA adoption intention to adoption be- haviour. Future work can also be focussed on testing the model in different culture which will provide better and deeper insights on the role of culture in promoting HRA adoption. 7 Conclusion This study investigates the relationship between HR professionals’ intention to use and usage behaviour in adopting HRA. It investigates the predictor (Intention to adopt HRA) and formation mechanism on the usage be- haviour (HRA adoption behaviour). Existing literature shows that studies have mainly focused on individual barriers in adopting HRA (Vargas et al., 2018). This study extends the literature by adding organization culture as a moderating variable to understand this relationship. This is because organizational culture plays an important role in technology adoption (Mohtaramzadeh et al.,2018; Bork- ovich et al.,2015). Accordingly, we conducted an empiri- cal study to investigate the HRA adoption behaviour. Our results point to a significant positive relationship between adoption Intention of HRA and HRA adoption behaviour. However, the moderating role of organizational culture has a negative significant influence on the the adoption inten- tion of HRA and HRA adoption behaviour. This implies that organizational culture should be carefully managed for the successful adoption of HRA and other technolo- gies. It is seen that organizations have failed to adapt their culture to make it more innovative and analytical. Organi- zations urgently need to redefine their culture in tune with the evolving times and thus provide fertile ground for technology to take roots, grow, and thrive. Employees in a technology-ready company will be more amenable to ac- cepting new technology and reaping its various benefits. Literature Ahmad, A. B., Butt, A. S., Chen, D., & Liu, B (2020). 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Production Plan- ning & Control, 28(11-12), 891-905. https://doi.org/10 .1080/09537287.2017.1336802 Susmita Ekka is a Research Scholar at School of Management Studies, University of Hyderabad, a management graduate from Utkal University, Bhubaneswar, India. Her area of research interest is Human Resource Management, HR analytics, Technology Adoption, AI. Punam Singh is an Assistant Professor at School of Management Studies, University of Hyderabad, a Management graduate from IIT(ISM), Dhanbad, India. She holds a PhD from JNTU, Hyderabad, India. She has published papers and books in the areas of HRM, Variable Pay and CSR. She has also carried a number of consultancy assignments in the areas of Training Needs Assessment, Succession planning, Recruitment, and Promotion, framing of CSR policy, Conducting Baseline and Impact assessment Studies. Her research interest includes Compensation and Performance management, Corporate Social Responsibility and HR Analytics. 91 Organizacija, V olume 55 Issue 1, February 2022 Research Papers Sprejemanje kadrovske analitike s strani kadrovskih strokovnjakov: razširitev modela UTAUT Ozadje in namen: Da bi spodbudili inovacije pri upravljanju s kadrovskimi viri (HR) s tehnologijo kadrovske analitike, si organizacije po vsem svetu prizadevajo za uvedbo analitike človeških virov (HRA) med kadrovske strokovnjake in dejansko uporabo HRA za organizacijsko odločanje. Namen te študije je raziskati vedenjski namen uporabe HRA z vidika kadrovskih strokovnjakov z uporabo UTAUT. Metodologija: Izbrali smo modeliranje z uporabo strukturnih enačb z delnimi najmanjšimi kvadrati (PLS-SEM) za potrditev modela na podlagi podatkov, zbranih z raziskavo med 270 kadrovskimi strokovnjaki v Indiji. Rezultati: Pokazal se je pomemben pozitiven vpliv pričakovane učinkovitosti, pričakovanega napora, družbenega vpliva in organizacijske podpore na vedenjsko namero za uporabo HRA. Vendar organizacijska kultura negativno vpliva na razmerje med namero po uvedbi HRA in vedenjem pri uvajanju. Analiza organizacijske kulture kot mode- ratorja v indijskih organizacijah je originalen prispevek raziskave. Zaključek: Študija razširja pojasnjevalni kontekst UTAUT in osvetljuje izvedljivost za organizacije. Podaja smernice kadrovskim strokovnjakom pri uvajanju HRA in osvetli pomen namere in vedenja pri uporabiHRA. Vodje, managerji v podjetjih in oblikovalci politik lahko ugotovitve raziskave uporabijo za pomoč pri sprejemanju HRA v svojih orga- nizacijah. Ključne besede: Kadrovska analitika, Namen posvojitve, Vedenje pri posvojitvi, Organizacijska kultura, UTAUT 92 Organizacija, V olume 55 Issue 1, February 2022 Research Papers Appendix Adopted Scale Loading “Performance Expectancy Using HRA improves my working result 0.801 Using HRA enables me to accomplish my job/work quicker 0.663 Using HRA will increase my productivity 0.725 Using HRA improves my job performance 0.571 Effort Expectancy It will be easy for me to become skillful at using HRA 0.744 Learning to use HRA will be easy for me 0.778 I clearly understand how to use HRA 0.607 I do not have difficulty in explaining why using HRA may be beneficial 0.744 Social Influence People who influence my behaviour think that I should use HRA 0.771 People who are important to me think that I should use HRA 0.744 In general, I have been supported in the use of HRA 0.854 Facilitating Condition I have the necessary resources to use HRA 0.670 HRA is compatible with other systems that I use 0.785 A specific person or group is available for assistance with difficulties concerning the use of HRA. 0.722 HRA Adoption Intention to Use I intend to use HRA as often as needed 0.900 Whenever possible, I intend not to use the HRA 0.867 To the extent possible, I would use the HRA frequently 0.908 HRA Adoption Behaviour I am beginning to explore using HRA 0.814 I am interested in using HRA 0.712 I use HRA for some specific task 0.814 Using HRA improve the quality of work I do 0.847 Using HRA gives me greater control over my work 0.826 Employee Orientation (EO) Promoting feeling–sharing among employees 0.749 Emphasizing team building 0.617 Encouraging cooperation 0.673 Trusting in employees 0.705 Fertilizing cooperative spirit 0.701 Concerning the individual development of employees 0.724 Consideration among employees 0.691 Caring about opinions from employees 0.641 Customer Focus (CF) 93 Organizacija, V olume 55 Issue 1, February 2022 Research Papers Satisfying the need of customers at the largest scale 0.792 Sincere customer service 0.856 Customer is number 0.860 Providing first-class service to customers 0.739 The profit of customer is emphasized extremely 0.767 Innovativeness (Inn) Developing new products and services continuously 0.807 Ready to accept new changes 0.718 Adopting high–tech bravely 0.758 Encouraging innovation 0.765 Systematic management and control (SMC) Keeping strictly working disciplines 0.854 Formal procedures generally govern what people do 0.735 Having a clear standard on praise and punishment 0.720 Possessing a comprehensive system and regulations 0.885 Setting a clarity goals for employees 0.798 Social Responsibility (SR) Showing social responsibility 0.857 The mission of the firm is to serve 0.882 Emphasizing economic as well as social profits.” 0.773