Driving innovation: Public policy and human capital

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Driving innovation: Public policy and human capital Helena Lenihana, , Helen McGuirkb, Kevin R. Murphyc ⁎

a

Department of Economics, Kemmy Business School, University of Limerick, Ireland Department of Economics, Kemmy Business School, University of Limerick and Hincks Centre for Entrepreneurship Excellence, School of Business, Cork Institute of Technology, Ireland c Department of Work and Employment Studies, Kemmy Business School, University of Limerick, Ireland b

ARTICLE INFO

ABSTRACT

Keywords: Innovation Human capital Human resource systems Innovation policy Policy program

Human capital, the set of skills, knowledge, capabilities and attributes embodied in people, is crucial to firms’ capacity to absorb and organize knowledge and to innovate. Research on human capital has traditionally focused on education and training. A concern with the motivationally-relevant elements of human capital such as employees’ job satisfaction, organizational commitment, and willingness to change in the workplace (all of which have been shown to drive innovation), has often been overlooked in economic research and by public policy interventions to date. The paper addresses this gap in two ways: First, by studying firms’ human resource systems that can enhance these elements of human capital, and second, using the results of this research as a springboard for a public policy program targeted at elements of human capital that have been ignored by traditional education and training interventions. Using a sample of 1070 employee-managers in Ireland, we apply a series of probit regressions to understand how different human resources systems influence the probability of employeemanagers reporting the motivationally-relevant elements of human capital. The research: (1) Finds that respondents in organizations with certain human resource systems are more likely to report motivationally-relevant elements of human capital. Specifically, employee-managers in organizations with proactive work practices and that consult with their employee-managers increase the predicted probability of reporting that they are satisfied with their job, willing to change, and are committed to the organization; (2) Highlights the need to consider the role of policy interventions to support the motivationally-relevant elements of human capital; (3) Proposes a new policy program offer to support the motivationally-relevant elements of human capital in order to increase firms’ innovation activity.

1. Introduction Innovation is a well-recognized determinant of growth, and it is a challenge for both academics and practitioners to understand why and how firms innovate (Montalvo et al., 2006). Human capital, the set of skills, knowledge, capabilities, and other attributes embodied in people that can be translated into productivity (Abel and Gabe, 2011; Fulmer and Ployhart, 2014), is crucial to firms’ capacity to absorb and organize knowledge and to innovate (Protogerou et al., 2017; Teixeira and Tavares-Lehmann, 2014; Subramaniam and Youndt, 2005). Traditionally, economists have defined human capital largely in terms of knowledge and intellectual capital. It is now widely recognized that this focus on knowledge does not fully capture the domain of human capital (Arvanitis and Stucki, 2012; Bell, 2009). In the last 20 years, the human capital concept has evolved significantly, and current conceptions of human capital include a wide range of human attributes

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that are relevant to job performance and productivity, ranging from personality traits, work attitudes and values (Ployhart and Moliterno, 2011) to characteristics such as creativity, wellbeing, self-efficacy and resilience (Grimaldi et al., 2012, 2013; Madrid et al., 2017; Newman et al., 2014; OECD, 2007; Tan, 2014). The expansion of the domain of human attributes that define human capital can be usefully understood with a taxonomy highlighting the distinction between can do and will do attributes (Ployhart and Moliterno, 2011; see also Chiaburu and Lindsay, 2008; Gibbons and Weingart, 2001; Zhao and Chadwick, 2014). According to this taxonomy, some attributes contribute to employees’ ability to execute essential job tasks. Classic exemplars of can do attributes include cognitive ability, general knowledge, job knowledge and problem-solving skills. Other human attributes influence willingness to exert effort, to contribute ideas and to assist fellow colleagues. Classic exemplars of will do attributes include job-related personality traits, work attitudes and

Corresponding author. E-mail addresses: [email protected] (H. Lenihan), [email protected] (H. McGuirk), [email protected] (K.R. Murphy).

https://doi.org/10.1016/j.respol.2019.04.015 Received 3 October 2017; Received in revised form 23 March 2019; Accepted 5 April 2019 0048-7333/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

Please cite this article as: Helena Lenihan, Helen McGuirk and Kevin R. Murphy, Research Policy, https://doi.org/10.1016/j.respol.2019.04.015

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beliefs. This can do/will do taxonomy is highly consistent with almost a century of research on the determinants of human performance, research that recognizes both ability and motivation as independent determinants of job performance; for the most recent meta-analytic review of the roles of motivation and ability, see Van Iddekinge et al. (2018). There is considerable evidence that innovation and the success of organizations, require behaviors that go beyond the usual role requirements of jobs and depend substantially on employees’ motivation and willingness to engage in these behaviors (Chiu, 2018; McGuirk et al., 2015; Shalley, 1995; Menold et al., 2014). In particular, employees’ attitudes regarding both their jobs and their organizations appear to be important determinants of their willingness to engage in the work behaviors needed to support innovation (Allen et al., 2011; Bateman and Organ, 1983; Cetin et al., 2015; Moorman, 1993; Zhao and Chadwick, 2014; Coad et al., 2014; Kato et al., 2015). These perceptions and attitudes about jobs and organizations comprise a critically important component of human capital that can be brought to bear in fostering innovation in organizations. Knowledge and job-related skills represent can do attributes; tangible proxies for these attributes (e.g., level of education, amount of job training) have been the traditional focus of public policy aimed at enhancing human capital (Becker, 1964; Cohen and Soto, 2007; Marshall et al., 1993; Nistor, 2007). Despite growing evidence regarding the importance of will do human capital attributes in business, there has been an almost complete absence of public policy initiatives to address these aspects of human capital. This is in large part because the targets for public policy are less obvious when attempting to build will do attributes. Policy interventions addressing the will do aspects of human capital are a prime focus of the current paper. In this study, we aim to address the following key questions: (1) What human resource systems, policies, and practices of firms are linked to motivationally-relevant (will do) human capital attributes, such as employee-managers’ job satisfaction, commitment to their organization, and willingness to change? (2) What are the implications for public policy in terms of policy instruments that can effectively promote the development and support of these human capital attributes? As we describe below, both of these represent distinct contributions to the empirical and policy-oriented literatures. This is achieved by demonstrating the empirical links between several organizational policies and practices and will do elements of human capital that are relevant to innovation. We then use this information as a springboard for a public policy program intervention designed to help organizations assess and tailor their policies and practices in ways that can facilitate the growth of human capital to support the firm’s innovative capacity. We focus on employee-managers, a cohort used in several innovation studies (e.g., Leiva et al. (2011) and seen as key to innovation (Fitjar et al., 2013). We argue for the importance of creating a firmlevel culture that hones human resource systems, thus promoting innovation. In this context, managers are key. Following Becker’s (1964) and Oketch’s (2006) studies of the determinants of human capital as measured by education, we seek to examine the determinants of motivationally-relevant elements of human capital. Understanding the factors that underpin these human capital attributes is significant for innovation theory development and is of practical value to policy makers and firms seeking to increase innovation activity. The remainder of this paper is organized as follows: In section 2, we set out the theoretical context of the research. In section 3, we explain the data and methodology. In section 4, we present the empirical results of the regression analyses. In section 5, we discuss policy supports and implications for policy regarding the development of the motivationally-relevant elements of human capital. We propose a new policy program offer, with the ultimate aim of driving firm-level innovation. Section 6 concludes and explores both the implications and the limitations of our research.

2. Theoretical context of human capital and human resource systems Interest is growing in measuring human capital beyond education and training (e.g., Perdreau et al., 2015; Arvanitis and Stucki, 2012). However, there are challenges to measuring human capital’s motivationally-relevant elements, such as work attitudes or motivation (Coronado et al., 2008); measuring these elements is an attempt to make visible what is invisible (Kramer, 2008). These challenges may explain why, in economic research and public policy, researchers frequently overlook these elements of human capital. Our analysis focuses on three elements of human capital that appear to be the most directly relevant to understanding employee-managers’ willingness and motivation to contribute to innovation in work organizations. These elements are employee-managers’ job satisfaction, commitment to their organization, and willingness to change in the workplace. 2.1. How motivationally-relevant elements of human capital provide a foundation for innovation The first element of human capital we focus on, job satisfaction, is defined as individuals’ wellbeing or level of contentment in relation to their job (Judge and Kammeyer-Mueller, 2012). Job satisfaction supports a number of firm-level functions, including formulation of knowledge and problem-solving strategies (Judge and KammeyerMueller, 2012; Whitman et al., 2010). Individuals who are highly satisfied with their jobs are more likely to engage in behaviors necessary for successful motivation, for example, they are motivated to exert extra effort, take risks, learn new skills, and contribute unique ideas to their organization (Bowling, 2010; Organ and Ryan, 1995; Weikamp and Göritz, 2016). In contrast, individuals who are less satisfied by their jobs (e.g., because they find their job stressful) are less likely to engage in behaviors necessary for successful innovation (Eatough et al., 2011; LePine et al., 2002). The second element of human capital we focus on is employeemanagers’ identification with and commitment to their organization (Mowday et al., 1981; Williams and Anderson, 1991). A wide range of work attitudes can contribute to firms’ performance (Melesse, 2016). Constructs such as organizational identification and commitment are particularly relevant to understanding innovation because innovative behavior is often risky; these risks are more readily undertaken by individuals who both trust and care for the success of their organization (Dalal, 2005; George and Bettenhausen, 1990; LePine et al., 2002; O’Reilly and Chatman, 1986; Organ, 1988; Organ and Ryan, 1995). Finally, the third element of human capital we focus on is willingness to change. A number of studies examine the role of employees’ willingness to change (e.g., to change the level of technology, skills and responsibility required to improve how work is done) in determining organizational success (Pulakos et al., 2000, 2002; van den Berg and van der Velde, 2006) and employees’ orientation toward innovation (Montalvo et al., 2006). Willingness to change is found to influence the adoption or rejection of innovations (Agarwal and Prasad, 1998). 2.2. Human resource systems connected to the motivationally-relevant elements of human capital Although organizations cannot directly control the perceptions and attitudes of workers (Colarelli and Arvey, 2015), they can decisively influence these perceptions and attitudes by how they interact with their workforce. In particular, there is clear evidence (summarized below) that well-managed human resource systems have a strong effect on the probability of employees being satisfied, committed, and willing to make the changes, take the risks, and exert the extra effort that innovation requires. 2

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Human resource systems in organizations deal with recruiting, hiring, training, evaluating, rewarding, and sometimes sanctioning workers (e.g., through redundancies, disciplinary processes, and terminations). These systems provide important information to employees, ranging from orientation and organizational socialization to performance feedback (Cascio, 2012). This information, together with other outcomes of these human resource processes (e.g., rewards), influence the perceptions and attitudes of employees. A substantial body of research links the quality of human resource systems with employee attitudes, perceptions, and beliefs. For example, there is evidence that human resource systems that provide timely performance feedback enhance employees’ a) success at adapting to changing conditions and b) their willingness to adapt and change their workplace behavior to create new products and processes (Pulakos et al., 2000, 2002). Piening et al. (2013) note that when organizations provide incentives to employees (e.g., training, opportunities for salary increase and advancement), they are likely to respond with favourable perceptions and behaviors. If implemented effectively, well-constructed human resource programs and practices are likely to cause employees to view themselves as operating a social exchange relationship characterized by mutual trust, respect, and support (Evans and Davis, 2005; Kehoe and Wright, 2013). In turn, this positive relationship is likely to motivate employees to engage in a range of behaviors that encourage and support innovation. Human resource practices that provide information and support to employees appear to contribute especially to the encouragement of innovation. Cohen and Levinthal (1990) refer to the importance of absorptive capacity, which includes the contributions made by individuals and also an organization’s capacity to exploit these contributions. Such high-involvement practice is of growing interest in the organizational performance and human resource management literatures (Böckerman et al., 2012). There is evidence linking aspects of high-quality human resource systems to specific work attitudes, including job satisfaction (Gould-Williams, 2003), organizational commitment (Allen et al., 2003; Meyer and Smith, 2000; Whitener, 2001), and willingness to change (Pulakos et al., 2000, 2002). However, to date, there are few relevant studies in the context emphasized in this paper – studies that span numbers of organizations and that examine data in a specific national context (in this paper, Ireland). Most studies cover firms in the North American region. As a result, the current study makes unique and valuable contributions to the literature in two distinct ways. First, there is abundant evidence that work attitudes and reactions to organizational practices vary across cultures (Aycan et al., 2000; Erez, 1997; Hofstede, 2001). We show that the proposed relationships between organizational practices and work attitudes hold in the specific culture for which a set of policy interventions is designed. Second, we propose firm-level policy interventions (i.e., helping organizations identify and implement appropriate human resource practices). It is important to demonstrate these links between human resource practices and work attitudes in samples that include multiple firms. To summarize, our analysis of literature linking human resource policies to job attitudes leads to the hypothesis:

employee-managers who show higher levels of the motivationally-relevant elements of human capital (e.g., job satisfaction, organizational commitment, willingness to change) to be more likely found in firms with human resource systems focused on developing these attributes. Given the importance of innovation in the Irish economy (DJEI, 2017), a strong case can be made for developing public policy interventions designed to assist organizations in identifying, developing, and implementing the types of human resource systems that support innovation. In the context of the current research, we examine human resource systems under the headings of proactive work practices, consultation, frequency of information delivery from management, work arrangements, and alternative pay and conditions. This is discussed in more detail in Section 3. 3. Testing the proposed relationships – data and methodology This section discusses the source of the dataset and methodology for testing the proposed relationships between firm factors (i.e., effective human resource systems) and the will do, motivationally-relevant elements of human capital. The context of our empirical analysis is Ireland. Ireland is a fitting case study: it is a small open economy (population 4.7 m; workforce c. 2 m.) with a clear enterprise policy commitment to boost job creation and innovation (DJEI, 2017). However, we believe a thorough understanding of motivationally- relevant human capital attributes can apply to different country contexts, meriting further research. It can apply on the understanding that innovation systems can vary significantly, as Fagerberg (2016) has demonstrated. It should also be borne in mind that human resource systems (and associated will do human capital elements) can also vary significantly by economy. 3.1. Source of the data employed Ireland’s National Centre for Partnership and Performance (NCPP) Workplace Survey 2009, (which includes employees, not self-employed entrepreneurs) provided the information used in our analysis. The NCPP dataset provides information on public and private organizations. However, the current research analyses only information on private organizations (firms). This focus is because private firms are the main source of innovation (Sørensen and Torfing, 2012). The dataset includes information on individual employee-managers working for private firms in Ireland in 2007–2008 (62% of NCPP observations). It includes employees’ attitudes and experiences and firm-specific details. Furthermore, we acknowledge NCPP (2009) data is limited in the absence of multiple responses from firms. We conducted a descriptive analysis on firm, and employees’ characteristic, comparing the refined NCPP (2009) dataset and the original dataset. For example, in the original dataset, 57% of private firms employ less than 50 people (small firms); in our dataset, 53% are small firms. 3.2. Description of data used in analysis

H1. The adoption of efficient and effective human resource systems is positively related to will do, motivationally-relevant elements of employee-managers' human capital

We used 1070 observations gathered during 2007–2008. Among employee-managers, 43.5% had third-level degrees or higher and the average age was 41 years. As expected, the highest number of firms (38% of observations) are in the Dublin (capital city) region. Tables 1 and 2 and Appendices A and B provide further details.

With regards to a role for public policy, many organizations, especially small ones, lack the knowledge and expertise to either a) reliably assess work attitudes or b) identify human resource practices relevant to these attitudes and apply the most current research (Cassell et al., 2002; de Kok and Uhlaner, 2001; Kroon et al., 2013; Matlay, 1999). Public policy interventions can help by making the required resources, expertise, and knowledge widely available to organizations, providing direct support for their innovation efforts. Based on the literature reviewed above, one would expect

3.2.1. Dependent variables To capture employee-managers’ will do, motivationally-relevant human capital attributes, we used 21 separate measures under three broad headings: job satisfaction, organizational commitment, and willingness to change in the workplace. Table 1 presents the 21 variables as dependent variables. We measure job satisfaction (eight 3

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Table 1 Summary of questions used for the dependent variables from National Centre for Partnership and Performance Workplace Survey (2009).

Table 2 Summary of variables used to estimate factors promoting the motivationallyrelevant attributes of human capital.

Agree with the statement Job Satisfaction In general, I am satisfied with my present job I am satisfied with my physical working conditions I am satisfied with my hours of work I am satisfied with my earnings from my current job My job is secure I work under a great deal of pressure (R) I never seem to have enough time to get everything done in my job (R) I often have to work extra time, over and above the formal hours of my job to get through the job or help out (R) Organisational Commitment I am willing to work harder than I have to in order to help this organisation succeed My values and the organisation’s values are very similar I am proud to be working for this organisation I would turn down another job with more pay in order to stay with this organisation I feel very little loyalty to the organisation I work for (R) I would take almost any job to keep working for this organisation Willingness to accept change in workplace over next 2 years – -increase in the responsibilities you have -increase in the pressure you work under -increase in the level of technology or computers involved in your work -being more closely supervised or managed at work -increase in the level of skills necessary to carry out your job -having to work unsocial hours -increased responsibility for improving how your work is done

Description of independent variables Human Resource Systems Proactive Work Practices

94% 95% 86% 74% 68% 70% 53%

Frequency of receiving information from management Consultation Forms part of Pay and Conditions at work 1. Regular increment 2. Employee share options, profit sharing or gain sharing 3. Bonus schemes 4. Merit/performance related pay 5. Non-monetary performance incentives Firm’s Work Arrangement 1. Is working from home in normal working hours used in your workplace? 2. Are flexible hours/flexitime used in your workplace? 3. Are job sharing/week on-week off etc., used in your workplace? 4. Are part-time hours used in your workplace? 5. Are regular performance reviews or appraisals used in your workplace? Control Variables Mean Std. Dev Respondent’sb Age 41.07 10.207 b Respondent’s : - Education (third level) Firm Size (Small - < 50 employees) Firm’s regional location (cost of living)

61% 91% 86% 93% 53% 88% 40%

87% 59% 92% 52% 93% 46% 93%

Note: (R) indicates reverse-scored questions. In the original NCPP (2009) survey, the variables above were presented as Likert Scales. In the current study, they have all been transformed to binary variables. The binary dependent variables take the following values: one if the employee-manager agrees with the statement (values 4 and 5 in the Likert scale); otherwise, zero.

Summary statistics Cronbach’s alpha - 0.79 (8 items)a Cronbach’s alpha - 0.84 (7 items)a Cronbach’s alpha - 0.78 (4 items)a Binary (Yes) 44% 28% 51% 35% 16% 34% 49% 25% 61% 70% Min 17 Binary (Yes) 43% 53%

Max 66

38% (Dublin)

Source: NCPP (2009). a Further details of these variables, including means and standard deviations, are presented in Appendix A. b Refers to employee-managers.

variables), organizational commitment (six variables), and willingness to change in the workplace (seven variables). Each variable is theoretically linked to the respective will do elements of human capital and discussed below. The NCPP dataset provides these 21 questions in the form of Likert scale. However, the distribution of responses to items measuring satisfaction, commitment, and willingness to change tended to cluster in such a way that they create distributions that were essentially binary. For example, in our item measuring satisfaction with hours worked, employee-managers tended to be either agree/strongly agree or disagree with the statement of I am satisfied with my hours of work. As such, we recoded satisfaction with hours of work in binary form, where “1″ indicates satisfaction and ‘0″ indicates dissatisfaction. We applied this binary transformation to all 21 dependent variables. The binary dependent variables take the following values: one if the employeemanager agrees with the statement (values 4 and 5 in the Likert scale); otherwise, zero. Our use of multiple dependent variables is similar to the methodology employed by Fitjar and Rodriguez-Pose (2011).

questions from the NCPP dataset to measure job satisfaction. Intuitively, some of the measures used are positively related to job satisfaction whilst others are negatively related. For example, if an employee-manager indicates they are satisfied with their earnings or are satisfied in their job, then we can expect a positive relationship with job satisfaction. On the other hand, if a respondent indicates that they agree with the statements that they never seem to have enough time to complete their job, work under a great deal of pressure and often work extra time, we reverse-score the responses. The reverse-scoring of these three questions ensures that all high scores reflect higher levels of job satisfaction. That is, if the respondent marked “5″ in NCPP, we marked it in our study as “1″, and marked “4″ to “2″. We then applied the binary transformation, as described earlier. 3.2.3. Organizational commitment Dating back to the work of Pinder (1984) and Porter et al. (1974), organizational commitment is considered as a strong belief in the organization, acceptance of its goals and values, willingness to exert effort on behalf of the organization, desire to stay with the organization, and contribute to the organization’s improvements, growth, and productivity. The current study captures such commitment using six questions related to employee-managers’ commitment to the organization; these are presented in Table 1. Similar to Jiao and Zhao (2014) and Dayan et al. (2016), we reverse-scored the only negatively phrased question from the NCPP dataset. This variable is linked with

3.2.2. Job satisfaction Job satisfaction has been extensively studied and is considered a multidimensional construct: it measures employees’ attitudes to overall job satisfaction, physical working conditions, working hours, rewards (earnings), job security, and attitudes to the challenges and stress caused by job activities (Abelha et al., 2018; Moon and Jung, 2018; González et al., 2016; Banerjee-Batist and Reio, 2016; Hrnjic et al., 2018). To capture such multidimensional attitudes, we employ eight 4

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organizational commitment: I feel very little loyalty to the organisation I work for. If the respondent marked “5″ in NCPP, we marked it in our study as “1″, and marked “4″ to “2″. We then applied the binary transformation, as described earlier.

3.4. Controls Other variables that may impact on employee-managers’ motivationally-relevant human capital are controlled for in the models. We include the following control variables:

• Firm size: Idson (1990) observed lower levels of job satisfaction in

3.2.4. Willingness to change Employee-managers’ willingness to accept change in the workplace has long been related to broad mindedness and openness (Mignonac, 2008); hence, as discussed in Section 2, such managers are more open to dealing with change, and will do type attributes. Dayan et al. (2016) employ questions about managers’ openness to trying new things and their willingness to be flexible, pursue new opportunities, and take new challenges. In our research, we capture willingness to change using seven variables such as, willingness to increase; responsibility, pressure, level of skill, technology usability, working unsocial hours.

• • •

3.3. Independent variables

With respect to the last variable, the cost of living: due to data limitations, we employ the regional location of the firm as a proxy for the cost of living experienced by employee-managers. Given that the cost of living in the Dublin region is the highest in the country (CSO, 2018), we use the Dublin region as a reference point in the model. Table 2 presents summary statistics for the independent variables including details of the control variables. To test the goodness-of-fit of the control variables in our models, we conducted a Bayesian information criterion (BIC) and Akaike information criterion (AIC) test. We found the addition of the four control variables resulted in a marginal change to the test results. Therefore, it is appropriate to include the four variables (Kass and Raftery, 1995).

Effective human resource systems are most often described in terms of bundles of related policies and practices that help to assess, develop, retain, reward and communicate with employees (Brewster et al., 2004; Cascio, 2012; DeNisi and Smith, 2014; Huselid, 1995; Murphy et al., 2018; Noe et al., 2011). In this study, we were able to measure several aspects of effective human resource management systems described in this literature. We employed a total of 13 variables:

• Three

•

larger establishments and suggests this can be largely explained by the inflexibility of larger work environments. Employee-managers’ age: Finegold et al. (2002) found employees’ age had a significant effect on their willingness to change and commitment to the organization. Employee-managers’ level of formal education: Formal education is commonly included in the human capital literature (e.g. Becker, 1964; Protogerou et al., 2017). The cost of living: Some authors found that the cost of living may influence job satisfaction and was a reason for older employees to make changes in their job (e.g., Idson, 1990; Finegold et al., 2002).

scaled variables: proactive work practices, frequency of information, and consultation. Each has a 0.8 Cronbach’s Alpha score of reliability. Appendix A details each individual question related to these independent variables; it also includes the mean and standard deviation. Appendix B reports the factor analysis, confirming the unidimensionalty of our scales. Two classes of variables under two headings, work arrangements and pay and conditions.

3.5. Estimating drivers of motivationally-relevant elements of human capital Our estimations use a series of probit regressions1 . A total of 21 models are estimated to capture the effect of human resource systems on a range of specific measures of these will do elements of human capital constructs. The equation is as follows:

The first variable, proactive work practices, can measure the strength of the firm’s willingness to incorporate information from external sources, work in teams and respond to changes in the external environment (Johnson, 2001). Some of the eight items on this scale could be taken as direct indicators of innovation (e.g., People in my organisation are always searching for new ways of looking at problems; This organisation is prepared to take risks in order to be innovative; New ideas are readily accepted in my workplace) but the scale is not solely or even primarily oriented to innovation per se, but rather to a suite of human resource practices that include collaboration, working in teams, scanning the environment for new opportunities and responding to changes in the environment. The second variable, frequency of receiving information from management (7 items), captures an organization’s communication with employees on a range of topics – from new product development to staff reductions. It is assumed that the workplace has a reporting structure because the respondents are employee-managers (NCPP, 2009). The third scaled variable, consultation, captures four items related to consultation with employee-managers. For example, it captures consultation with employee-managers before decisions are taken, communication of reasons why changes occur, and whether attention is paid to their views or opinions. The final two classes of variables are work arrangements and pay and conditions. Work arrangements include five binary variables capturing the firm’s use of arrangements such as working from home, flexible hours, and part-time job sharing. The final class of variables, pay and conditions at work (5 binary variables), include firms’ offerings of regular increments, bonus schemes, and merit/performance-related pay.

Motivationally-relevant elements of human capitali = α0 + α1Inti + α2Contij + εi Where firm i’s motivationally-relevant elements of human capitali refers to employee-managers’ job satisfaction, commitment to their organization, and willingness to accept change (the 21 variables described in Table 1). The independent variable Inti refers to the firm’s human resource systems. Contij controls for the individual, firm size, and regional location. In order to test for multicollinearity, we carried out two tests common in the literature: inter- predictor correlation test and variance inflation factors (VIF) (Thompson et al., 2017). The inter-predictor correlation test found one instance of high correlation (between respondent’s tenure at the organization and respondent’s age), resulting in the tenure variable being omitted from the analysis. The data was also subjected to variance inflation factors (VIF); the results show all variables were less than 10, indicating multicollinearity was not an issue (Hair et al., 1995). Based on this result and on relevant theory, we believe our variables represent distinct constructs. We limited potential endogeneity issues by including only predetermined independent variables (Naz et al., 2015). To verify that our results were robust with respect to endogeneity, we applied the 1 The choice of a probit model over a logit model is an issue of concern to researchers (Childers, 2011, p.51), although Childers considers it a “personal choice”. Following her (2011) suggestion, we experimented with the two models and found similar results.

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estimator proposed and applied by Lewbel (2012). This estimator is an increasingly used robustness test when it is possible that some variables are endogenous (e.g. Heim et al., 2017; Liu et al., 2016 and Camagni et al., 2016). It generates instrumental variables (IV) from heteroscedasity; it controls for potential endogeneity of our variables (i.e. the human resource systems) that, in turn, allows us to cross check our results. As sufficient instruments are not available, we estimate each equation with the generated instrumental variable technique. Overall, the use of Lewbel’s generated instruments allows us to conclude that the sign and magnitude of the coefficients estimated for our firm-level variables are retained2 . As we note in the results section (4), statistical controls can help in minimizing endogeneity, but they do not fully resolve questions of causality. The interpretation of our findings in terms of the likely causal direction of the flow from human resource policies to will do attributes and potentially to subsequent increases in innovation depends on a mix of empirical finding, past literature and logical argument.

4.1. Job satisfaction The positive coefficients of firms’ proactive work practices indicate that these practices are likely to influence employee-managers’ job satisfaction. For example, we find that greater use of human resource systems is linked to an increased probability (at the 1% level of significance) that employee-managers are satisfied with their present job, hours of work, level of earnings, and they are secure in their job ceteris paribus. Furthermore, proactive work practices are associated with an increased probability of having employee-managers who are satisfied with their physical working conditions (at the 5% level of significance). A greater frequency of information is negatively associated with an increased probability of employee-managers’ job satisfaction with working hours and job security. However, greater levels of consultation by firms is linked to an increased probability of employee-managers being satisfied with their present job, physical working conditions, hours of work, earnings, indicating their job is secure and that they do not have to work under pressure and they have enough time to complete their work. All of these indicate an increase in job satisfaction. As expected, employee-managers working in organizations offering pay and conditions such as pay increments and bonus schemes are associated with an increased probability that they are satisfied with their earnings. Similarly, firms offering increments show increased probability that employee-managers are satisfied with their job security at the 1% level of significance. Our results also reveal that firms’ use of increments (as part of pay and conditions) is associated with an increased probability that employee-managers feel they do not work under a great deal of pressure and seem to have enough time to complete their work (indicating an increase in job satisfaction). Our results also show that the use of particular human resource systems such as working from home (part of work arrangements) is associated with an increased probability that employee-managers are satisfied with their earnings, but not satisfied with their job security. We find working from home type arrangements is associated with an increased probability of employee-managers indicating they work under pressure, never have enough time and often work extra time. Firms using flexitime arrangements is associated with an increased probability that employee-managers are satisfied with their physical working conditions and hours of work, but is linked to an increased probability that such managers feel they do not have to work under pressure and they have enough time to complete their work (indicating an increase in job satisfaction). Part-time type work arrangements is associated with an increased probability that employee-managers feel their job is secure. In the case of firms that appraise performance, our results find a link to a decreased probability that their employeemanagers are satisfied with their earnings, but feel secure in their job at a 10% level of significance. However, firms that use appraisals are associated with a decreased probability that employee-managers feel they do not work under pressure, have enough time to complete their work, and do not have to work extra time (indicating a decrease in job satisfaction). With respect to the control variables, small firms (less than 50 employees), are linked to a decreased probability of employee-managers being satisfied with their earnings. Older employee-managers are associated with an increased probability of indicating that they are satisfied with their present job and hours of work. Finally, employeemanagers in organizations located in Dublin (the capital city region), a proxy for cost of living, are linked to a decreased probability that they have enough time to complete their work.

4. Results Based on the theory presented in Section 2, our empirical analysis attempts to provide evidence that links the effect of human resource systems to the probability of supporting the will do, motivationally-relevant elements of human capital. The results reveal that the predicted probability of employee-managers reporting will do elements of human capital is higher in firms with effective human resources systems measured by proactive work practices and consultation.3 However, in the case of frequency of information, pay and conditions, and work arrangements, the results are mixed and many of the variables reveal no significant relationship to the will do elements of human capital. Taking each of the three will do elements of human capital analyzed here (job satisfaction, organizational commitment, and willingness to change) we provide commentary and present detailed results in Table 3a and b. Before moving to detailed analyses of the three will do constructs studied here, it is useful to comment broadly on the hypothesis tested in this study. Every measure of job satisfaction and organizational commitment used in this study is related (after taking into account control variables included in our probit analyses) to one or more measures of proactive work practices, frequency of information sharing, consultation, pay and conditions and/or work practices. Similarly, all of the measures of willingness to change, with the exception of willingness to be more closely supervised are related to one or more measures of proactive work practices, frequency of information sharing, consultation, pay and conditions and/or work practices. However, it is important to note that there is considerable variability in the relationships between specific human resource policies and measures of satisfaction, commitment and willingness to change. In most cases, there are some policies that are linked with these will do constructs and others that are not. Thus, while our general proposition that the human resource practices studied here are related to satisfaction, commitment and willingness to change received support, there are a number of relationships detailed in Table 3a and b and described below that are nonsignificant and some that are in the opposite direction than expected.

2 Due to limited space, full details of results are available from the authors on request. 3 It should also be noted that a variety of analytic methods can be applied to these data. For example, at an earlier stage of this research the dependent variables were grouped into scales. Application of this method yielded very similar results, but presented our findings in a form that made their concrete interpretation more difficult, for example, by obscuring which practices affect which will do measures. Full details of results are available from the authors on request.

4.2. Organizational commitment Organizational commitment as a measure of will do human capital includes employee-managers’ willingness to help the organization to succeed, to have values very similar to those of the organization, to be proud to work for the organization, and to stay with, and be loyal to the 6

7

Wald chi2(17) Pseudo R2 Log pseudo likelihood

Constant

Respondent's 3rd level Education Firm Location (Dublin)

Respondent's age

Controls Firm Size (small)

Appraisals used

Part-time hours

Job share

Flexitime

Work Arrangements Home working

Non-Monetary incentives

Merit/performance

Bonus schemes

Share options

Pay & Conditions Increments

Frequency of information Consultation

Proactive work practices

(a)

0.040 (0.084) 0.005 (0.006) −0.115 (0.144) 0.016 (0.029) 1.253*** (0.346)

0.016 (0.086) 0.013* (0.007) −0.090 (0.142) 0.011 (0.028) 0.996*** (0.355) 60.49 0.154 −185.856

0.085 (0.169) 0.315* (0.162) −0.087 (0.179) −0.159 (0.150) 0.237 (0.161)

−0.077 (0.171) 0.068 (0.152) 0.017 (0.166) 0.093 (0.143) 0.082 (0.156)

54.04 0.148 −182.277

0.027 (0.141) −0.145 (0.184) 0.132 (0.182) 0.187 (0.207) −0.174 (0.204)

56.12 0.067 −409.575

−0.078 (0.058) 0.019*** (0.005) 0.069 (0.106) −0.002 (0.020) 0.432 (0.270)

−0.028 (0.114) 0.182* (0.106) 0.106 (0.128) −0.168 (0.105) 0.011 (0.123)

0.152 (0.102) 0.071 (0.122) −0.029 (0.121) −0.108 (0.123) 0.063 (0.141)

I am satisfied with my… physical working hours of work conditions 0.184** 0.191*** (0.074) (0.056) −0.030 −0.059* (0.043) (0.032) 0.188*** 0.094*** (0.043) (0.034)

0.137 (0.130) −0.068 (0.161) 0.315* (0.176) 0.030 (0.186) −0.172 (0.200)

0.375*** (0.086) −0.055 (0.042) 0.111*** (0.042)

present job

Job Satisfaction

96.78 0.088 −552.548

0.131** (0.052) 0.006 (0.004) 0.026 (0.093) 0.016 (0.018) −0.159 (0.236)

0.242** (0.103) 0.025 (0.096) 0.101 (0.113) 0.004 (0.093) −0.180* (0.107)

0.294*** (0.092) −0.059 (0.113) 0.306*** (0.106) 0.156 (0.110) −0.080 (0.127)

0.174*** (0.052) −0.038 (0.028) 0.108*** (0.031)

earnings

94.39 0.075 −625.612

−0.068 (0.049) −0.005 (0.004) −0.137 (0.089) 0.009 (0.017) 0.355 (0.222)

−0.180* (0.095) 0.041 (0.089) 0.002 (0.103) 0.240*** (0.087) 0.191* (0.103)

0.405*** (0.086) 0.003 (0.105) 0.131 (0.100) −0.001 (0.104) 0.028 (0.119)

My job is secure 0.172*** (0.048) −0.067** (0.027) 0.126*** (0.030)

63.39 0.049 −628.542

−0.024 (0.049) 0.005 (0.004) 0.001 (0.088) 0.009 (0.016) −0.629*** (0.234)

−0.229** (0.096) 0.311*** (0.089) −0.102 (0.103) 0.024 (0.089) −0.322*** (0.101)

0.275*** (0.085) −0.084 (0.105) −0.037 (0.100) −0.026 (0.102) 0.051 (0.117)

Work under pressure (R) −0.068 (0.048) −0.046* (0.027) 0.108*** (0.032)

42.22 0.028 −719.203

0.067 (0.047) −0.002 (0.004) 0.032 (0.084) −0.056*** (0.016) 0.208 (0.218)

−0.235*** (0.090) 0.161* (0.085) −0.104 (0.097) 0.010 (0.083) −0.261*** (0.098)

0.138* (0.081) 0.111 (0.099) −0.047 (0.097) −0.021 (0.097) −0.082 (0.109)

Never have enough time (R) −0.036 (0.045) −0.033 (0.025) 0.085*** (0.029)

Table 3 (a) Results from multiple probit regressions (Job Satisfaction). (b) Results from multiple probit regressions (Organizational commitment and Willingness to change in the workplace).

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61.88 0.046 −677.385

−0.026 (0.048) −0.000 (0.004) −0.177** (0.085) −0.026 (0.016) 0.124 (0.224)

−0.482*** (0.093) 0.117 (0.087) −0.006 (0.100) 0.094 (0.085) −0.187* (0.099)

0.065 (0.083) −0.087 (0.102) −0.026 (0.097) −0.068 (0.099) −0.030 (0.115)

Often work extra time (R) −0.031 (0.046) −0.012 (0.026) 0.040 (0.030)

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8 −0.074 (0.076) 0.011* (0.006) −0.081 (0.134) −0.012 (0.026) 1.548*** (0.345)

−0.029 (0.066) 0.019*** (0.006) −0.427*** (0.111) −0.024 (0.021) 0.876*** (0.296)

144.16 0.210 −344.152

Wald chi2(17) Pseudo R2 Log pseudo likelihood

Little Loyalty to the Org. (R)

127.41 0.096 −669.049

−0.123** (0.048) 0.008* (0.004) −0.295*** (0.086) −0.019 (0.016) −0.023 (0.226)

0.106 (0.092) 0.074 (0.087) 0.011 (0.102) 0.100 (0.085) 0.056 (0.100)

0.105 (0.083) 0.010 (0.102) 0.005 (0.099) 0.037 (0.100) −0.199* (0.112)

0.297*** (0.048) −0.012 (0.026) 0.141*** (0.030)

85.41 0.116 −332.289

−0.058 (0.061) 0.011** (0.005) −0.099 (0.114) −0.018 (0.020) 0.892*** (0.304)

0.059 (0.123) −0.020 (0.114) 0.008 (0.137) 0.225** (0.113) −0.051 (0.133)

−0.038 (0.112) −0.257** (0.129) 0.160 (0.132) 0.263* (0.144) 0.049 (0.162)

0.167*** (0.063) −0.012 (0.034) 0.199*** (0.036)

Note: (R) indicates reverse-scored questions. ***, ** and * indicate significance at the 99%, 95% and 90% significance level respectively.

84.42 0.205 −216.061

−0.067 (0.146) 0.190 (0.147) −0.219 (0.157) −0.048 (0.138) 0.131 (0.148)

−0.005 (0.121) 0.053 (0.121) −0.015 (0.137) −0.048 (0.115) −0.007 (0.128)

Non-Monetary incentives Work Arrangements Home working 0.225 (0.142) Flexitime −0.047 (0.125) Job share −0.007 (0.145) Part-time hours 0.143 (0.120) Appraisals used −0.061 (0.137) Controls Firm Size (small) −0.014 (0.065) Respondent's age −0.003 (0.006) Respondent's 3rd level 0.026 education (0.121) Firm Location 0.023 (Dublin) (0.022) Constant 1.320*** (0.295)

Bonus schemes Merit/performance

Share options

57.33 0.090 −285.467

0.076 (0.146) −0.319* (0.171) −0.059 (0.165) −0.118 (0.164) 0.289 (0.217)

0.231** (0.116) −0.138 (0.134) −0.124 (0.131) 0.019 (0.133) −0.052 (0.150)

−0.105 (0.117) 0.241 (0.156) 0.077 (0.143) 0.147 (0.155) −0.008 (0.181)

0.470*** (0.074) 0.013 (0.039) 0.136*** (0.041)

0.497*** (0.067) −0.021 (0.035) 0.179*** (0.036)

Pay & Conditions Increments

Stay with the Org.

Keep working for Org.

86.43 0.066 −673.250

−0.110** (0.048) −0.003 (0.004) −0.364*** (0.087) −0.030* (0.016) 0.423* (0.223)

−0.109 (0.092) 0.007 (0.087) 0.009 (0.101) −0.144* (0.085) 0.069 (0.100)

0.136 (0.084) −0.044 (0.102) −0.036 (0.098) −0.250** (0.100) 0.228** (0.115)

0.281*** (0.048) −0.037 (0.026) 0.027 (0.030)

74.27 0.101 −370.094

0.198*** (0.062) −0.02*** (0.005) 0.020 (0.112) 0.000 (0.020) 1.481*** (0.281)

0.051 (0.120) 0.105 (0.107) 0.083 (0.131) −0.050 (0.106) −0.025 (0.122)

0.020 (0.105) −0.273** (0.133) 0.370*** (0.132) 0.014 (0.133) 0.236 (0.167)

Increase responsib ility 0.128** (0.057) −0.039 (0.033) 0.119*** (0.035)

Proud to work for Org.

Help the org. to succeed

Values are similar to Org.

Willingness to change in the workplace

Organizational (Org.) commitment

0.183*** (0.065) 0.059* (0.036) 0.081** (0.038)

Proactive work pract. Frequency of information Consultation

(b)

Table 3 (continued)

71.73 0.052 −685.151

0.127*** (0.048) −0.009** (0.004) −0.084 (0.085) 0.002 (0.016) 0.391* (0.223)

−0.031 (0.091) 0.004 (0.086) −0.020 (0.098) −0.118 (0.085) 0.004 (0.099)

−0.083 (0.083) 0.065 (0.101) 0.303*** (0.098) −0.107 (0.099) 0.110 (0.114)

0.129*** (0.046) −0.050* (0.026) 0.133*** (0.030)

Increase work pressure

72.57 0.143 −241.616

0.122 (0.075) −0.015** (0.006) 0.083 (0.128) 0.036 (0.024) 1.312*** (0.331)

0.103 (0.151) 0.046 (0.136) −0.132 (0.158) 0.304** (0.129) 0.221 (0.139)

−0.266** (0.126) 0.203 (0.189) 0.356** (0.157) 0.469** (0.194) −0.383** (0.184)

Increase Tech/ computer −0.068 (0.062) 0.084** (0.039) 0.010 (0.044)

34.60 0.025 −722.702

−0.018 (0.046) −0.015*** (0.004) −0.162* (0.083) 0.026* (0.016) 0.781*** (0.222)

−0.037 (0.089) 0.010 (0.084) −0.104 (0.097) 0.008 (0.083) −0.053 (0.098)

0.008 (0.081) −0.151 (0.098) 0.084 (0.096) −0.122 (0.097) −0.012 (0.110)

0.043 (0.044) −0.002 (0.025) 0.035 (0.029)

Closely supervised

39.47 0.086 −238.829

0.049 (0.079) −0.021*** (0.006) 0.038 (0.128) 0.045* (0.026) 2.132*** (0.362)

−0.096 (0.140) 0.238* (0.129) 0.045 (0.157) −0.032 (0.126) 0.046 (0.140)

−0.188 (0.127) −0.118 (0.165) −0.007 (0.155) 0.310* (0.162) −0.058 (0.178)

0.131* (0.068) 0.079** (0.036) −0.046 (0.041)

Increase level of skills

39.19 0.027 −719.719

0.064 (0.046) 0.007* (0.004) −0.087 (0.084) 0.006 (0.016) −0.467** (0.218)

0.165* (0.089) 0.084 (0.084) −0.034 (0.097) −0.017 (0.083) −0.042 (0.099)

−0.036 (0.081) −0.142 (0.098) −0.021 (0.096) −0.054 (0.097) −0.009 (0.110)

Work unsocial hours 0.102** (0.045) 0.013 (0.025) 0.079*** (0.030)

51.13 0.097 −235.921

0.035 (0.073) −0.012* (0.007) 0.143 (0.134) 0.056** (0.025) 1.462*** (0.357)

0.135 (0.155) −0.120 (0.136) 0.384** (0.190) 0.231* (0.126) 0.117 (0.143)

−0.150 (0.125) −0.169 (0.161) 0.367** (0.162) −0.017 (0.172) −0.027 (0.192)

0.081 (0.066) 0.067* (0.038) 0.035 (0.040)

Improve how work is done

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organization. In our analysis, greater use of proactive work practices is associated with an increased probability that employee-managers are committed to the organization; all measures of organizational commitment are positively related with the use of proactive work practices. However, greater frequency of receiving information from management is not linked to most measures of organizational commitment, with the possible exception of a willingness to help the organization succeed (significant at the 10% level). On the other hand, there are strong and positively significant links between levels of consultation and organizational commitment. In particular, a greater level of consultation is associated with an increased predicted probability (at the 1% level of significance) of the following: employee-managers help the organization succeed; their values are very similar to the values demonstrated by the organization; they are proud to work for the organization; they will stay with the organization and they are loyal. Our results also show organizations’ use of share options to be associated with a decreased probability that employee-managers are proud to work for the organization and are loyal. However, our results reveal that, if a firm offers merit/performance related pay, this is linked to an increased probability of employee-managers indicating their loyalty to the organization; but a decreased probability that they remain working for the organization. We find that the use of non-monetary incentives has mixed effects, increased commitment to staying with the organization but decreased interest in working for this organization. With respect to an organization’s work arrangements, providing parttime hours also has mixed effects, and is associated with an increased probability of employee-managers loyalty (at the 5% level of significance) but a decreased probability of their commitment to continue working for the organization (at the 10% level of significance). For the control variables, employee-managers working in small firms (less than 50 employees) are associated with a decreased probability of turning down another job with more pay in order to stay with the organization. Similarly, in small firms, employee-managers are linked to an increased probability of taking almost any job in the organization to keep working there (at the 5% level of significance). Older employee-managers are associated with an increased probability of having values similar to their organization, to be proud to work for their organization, to stay with the organization, and indicate loyalty to the organization. Employee-managers having a third level education is associated with a decreased probability of their commitment to the organization. Firms located in the Dublin region are linked to a decreased probability of employee-managers taking almost any job to keep working for the organization (at the 10% level of significance).

the 1% level of significance). In the case of pay and conditions, firms offering increments is associated with a decreased probability that employee-managers are willing to increase their use of technology in the workplace. Firms offering share options is associated with a decreased probability that employee-managers are willing to increase responsibility in the workplace (at the 5% level of significance). Our results also show that firms offering bonus schemes is linked to an increased probability that employee-managers are willing to increase their levels of responsibility, increase the work pressure, increase the use of technology, and improve how work is done. Firms that offer merit/performance-based pay and conditions show an increased probability that employee-managers are willing to increase technology and the level of skills required to carry out their work. If a firm offers non-monetary incentives as part of pay and conditions, then this is linked to a decreased probability that employeemanagers are willing to increase the use of technology in the workplace (at the 5% level of significance). We find that ‘working from home’ type arrangements are associated with an increased probability that employee-managers are willing to work unsocial hours (at the 10% level of significance). Similarly, firms offering flexitime work arrangements is associated with an increased probability that employee-managers are willing to increase their level of skills required in the workplace, at the 10% level of significance. This is also the case when firms offer job share arrangements. In firms that offer these arrangements, this is associated with an increased probability that employee-managers are willing to improve how their work is done. Also, in the case of firms offering parttime hours, employee-managers are more willing to increase the level of technology in the workplace (at the 5% level of significance). Firms that offer part-time hours are associated with an increased probability of their employee-managers’ willingness to increase the use of technology in the workplace and improve how work is done. For the control variables, there is an increased probability, at the 1% level of significance, that small firms have employee-managers who are willing to increase their responsibility and the pressure they work under. Similar to the other elements of motivationally-relevant human capital, age and education are linked to an increased willingness to change. We find that, if a firm increases the number of older employeemanagers in the workplace, then there is a decrease in the probability of increased levels of responsibility and levels of work pressure. There is also a decrease in the probability of the use of technology, levels of supervision, and levels of required skills. In addition, there is a decreased probability that older employee-managers will improve how work is done (at the 10% level of significance). Employee-managers having a third level education decreases the probability of their willingness to be closely supervised or managed at work, (at the 10% level of significance). Finally, the location of the firm, a proxy for cost of living, is associated with employee-managers’ willingness to change. Firms located in Dublin are associated with an increased probability that employee-managers are willing to increase how closely they are supervised, their level of skills, and improve how work is done. However, this location decreases the probability of employees-managers willing to work unsocial hours.

4.3. Willingness to accept change The third and final element of will do motivationally relevant human capital captured by this research reveals that organizations providing greater proactive work practices are associated with an increased probability that employee-managers are willing to accept change in the workplace. Such change comes in the form of increased responsibility (at the 5% level of significance), work pressure (at the 1% level of significance), level of required skills (at the 10% level), and a willingness to work unsocial hours (i.e., to change their work hours from ‘normal’ working hours (at the 5% level)). In cases where firms provide greater frequency of information to employee-managers, this is associated with a decreased probability that such managers are willing to increase the pressure they work under (at the 10% level of significance). However, such firms are associated with an increased probability of their employee-managers willingness to use technology, to increase their level of skills, and to improve how their work is done (at the 10% level of significance). Furthermore, a greater level of consultation is linked to an increased probability that employee-managers are willing to increase their level of responsibility at work, to increase their work pressure, and to work unsocial hours (all at

4.4. Summary of empirical findings Our results find that firms providing human resource systems, such as greater use of proactive work practices and greater levels of consultation with employee-managers are associated with an increased probability of job satisfaction, organizational commitment, and willingness to change of such managers. Our results also find that bonus schemes (as part of pay and conditions) are linked to an increased probability of motivationally-relevant human capital, as measured by job satisfaction and willingness to change. It should however, be acknowledged that some of the human resource systems variables reveal 9

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mixed results. For example, we find frequency of information, job share and flexitime (part of work arrangements) to have no significant relationship to the majority of the will do elements of human capital. In some cases human resource systems variables (e.g. the receipt of share option as part of pay and conditions) even have a negative impact. The general proposition that more effective human resource practices are likely to lead to more positive work attitudes is well established in the literature, but the results presented here are nevertheless highly informative. One of the dominant themes in the last 10 years of research on human resource management is the importance of contextualization (Cheng, 1994; Cooke, 2018; Jackson and Schuler, 1995; Johns, 2017, 2018; Larsen and Brewster, 2000; Ployhart et al., 2006; Tsui, 2006; Von Glinow et al., 2002). Contextualization is particularly important in research on human resource management systems because of persistent differences in the way these systems are developed and used across nations and regions of the world (Lazarova et al., 2008). General knowledge about principles of human resource management is always valuable, but this search for generalizable knowledge can sometimes lead to a fruitless search for universal best practices. It is very possible that the human resource practices that are most effective for supporting will do elements of human capital in the particular context studied here (companies operating in Ireland) will be different in other contexts. Thus, there is considerable value in teasing out the specific mixes of human resource policies that might be most useful in specific settings. Our results suggest that some of the human resource practices studied here are related to satisfaction, commitment and willingness to change and others are not. In particular, proactive work practices, information sharing and consultation have wide-ranging effects. Policies aimed at increasing rewards sometimes have beneficial effects on satisfaction, but in some instances can lead to decreased commitment. Similarly, policies that are often put forward as employee benefits (e.g., offering opportunities for flexitime and working from home) can sometimes lead to perceptions of job insecurity and increased feelings of pressure. It will be important in future analyses to determine whether the relationships shown in Table 3a and b hold up in replications. It is likely that future studies will provide guidance for narrowing the list of human resource policies that are consistently linked with the job attitudes studied here. Our detailed empirical analysis of the variables that support motivationally-relevant elements of human capital highlights the importance of human resource systems (detailed in Sections 2 and 3). In this regard, our policy recommendations are based on an assumption that organizational policies have a causal impact on the motivationally-relevant elements of human capital. Yet, it is often prohibitively difficult to demonstrate causality on the basis of the type of survey data analyzed here (Cook and Campbell, 1979). This is in large part because these data often fail to satisfy the requirement that proposed causes must be assessed prior to supposed effects. Wunsch et al. (2010) and Cox (1992) argue that knowledge of the content area allows one to make inferences based on the plausibility of causal arguments in various directions. The assumption that organizational practices are the cause of rather than the effect of employee attitudes and perceptions is widely supported in research in human resource management (Cascio, 2012; Murphy et al., 2018). Thus, we justify the assumption that the causal direction flows from workplace policies to work attitudes, rather than from attitudes to work policies (e.g., that increases in job satisfaction cause organizations to adopt work policies such as increased information sharing) on the basis of both logic and existing theory. That is, our results are consistent with our interpretation that the human resource systems of the organization are likely to influence workplace attitudes and beliefs. Finally, one more potential confound in the causal interpretation of our results should be noted. As noted earlier in this section, several items on the proactive work practices scale refer to activities that could be thought of as innovation per se (e.g., People in my organisation are

always searching for new ways of looking at problems) whereas others refer to personnel practices that are not directly linked to innovation (e.g., My employer encourages employees to work in teams in order to improve performance). The factor-analytic results shown in Appendix B give us reason for confidence in interpreting this scale in terms of a suite of related human resource practices rather than in terms of a mix of innovation and proactive human resource management, but nevertheless, the presence of innovation items in this scale should be considered when drawing causal inferences. Given this background, the following discussion of the policy implications focuses on the role of policy in influencing firm-level factors to support motivationally-relevant elements of human capital. This is on the understanding that the policy’s overall objective is to drive firmlevel innovation. In particular, the empirical findings linking organizational policies and practices with will do elements of human capital (known drivers of innovation) provides a basis and justification for proposing public policy interventions to assist organizations build capacity to diagnose their own policies and practices and to implement policies and practices that provide the strongest support for innovation. 5. Can policy support the development of the will do, motivationally-relevant elements of human capital? The results presented in this study provide general support for the hypothesis that human resource policies and practices of an organization are likely to influence motivationally-relevant elements of human capital, elements that are critical to driving firm-level innovation. In particular, they provide information in an Irish context that proactive work practices and consultation, have clear potential for supporting important will do aspects of human capital. In discussing policy implications, and based on our earlier findings that some of the human resources systems variables analysed, support motivationally-relevant human capital, our aim in this section is to provoke conversation and debate among the academic and policy making communities alike regarding the factors that support these elements of human capital. In moving from empirical analysis to avenues of exploration for policy makers, we wish to highlight that developments in policy, prescriptions, and implications need to be based on sound empirical analyses. In this section, we explore policy’s potential role with respect to the promotion and development of the will do, motivationally-relevant elements of human capital with a particular focus on Ireland. In the context of innovation policy, Bell (2009, p.37) suggests that “hardly any attention is given to measures for fostering the creation of non-R&D innovation capabilities in and by industrial firms”. Montresor and Vezzani (2016) describe investing in intangibles for innovation as an example of change in policy focus. It should be acknowledged from the outset that, when making policy suggestions, we do not assume the “…unproblematic and straightforward translation of these into the formulation of innovation policies” (Flanagan et al., 2011, p.704). Nevertheless, there is clear potential for public policy to make a meaningful contribution to the development of innovation in organizations and to the conditions that support innovation. It is important to note that the cost of public funds is high and that any public policy intervention that involves the allocation of scarce resources to private firms needs to demonstrate that its (potential) social value outweighs its opportunity costs (Leibowicz, 2018; Lenihan, 2011). The policy intervention described below provides organizations with assistance in kickstarting their own processes for building aspects of human capital that have traditionally been ignored, taking advantage of the public sector’s unique ability to offer organizations assistance and expertise in analysing and improving their own policies and practices, with an aim toward building aspects of human capital needed to support innovation. Turning to the specific case of Ireland, a review of Irish innovation policy documents shows that there was a complete absence of any reference to the will do elements of human capital before 2008. For 10

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example, the 2004 report from the Irish Enterprise Strategy Group refers solely to can do type attributes in the form of education and training (e.g., p. 26 of the report). Similarly, the Irish government’s Strategy for Science, Technology and Innovation (DETE, 2006) only highlights the importance of the can do type attributes of human capital where it outlines the need to “develop coherent programmes for placing S&E graduates…The Teaching Company Scheme…assists interaction between firms and higher education institutes, based on the placement of recent graduates in firms to develop innovation processes” (p. 42). A 2008 policy document Building Ireland’s Smart Economy is still very much concerned with the can do attributes of human capital. However, it begins to shift its thinking somewhat in the direction of acknowledging the will do attributes of human capital. The document refers to "building the innovation or ‘ideas’ component of the economy through the utilisation of human capital-the knowledge, skills and creativity of people-and its ability and effectiveness in translating ideas into valuable processes, products and services" (Department of the Taoiseach, 2008, p.7). In a Forfás (2009) report Skills in Creativity, Design and Innovation, there is also this marginal shift. The report refers to “The complementary skills needed by people with specialist skills to enable them to be creative and to perform effectively as innovators” (p.3) and also makes reference to “…measures required to develop skills required for innovation in the workplace” (p.3). This theme continues in subsequent reports, for example, a 2010 policy report of the Irish Innovation Taskforce refers to “…engendering cultures and attitudes which are supportive to innovation…” (p. 24) and also makes reference to “… creative minds into the centre of innovative businesses” (p.77). The 2015 Innovation 2020 report from the Irish government places a clear upfront emphasis on education as highlighted by the following quotation “We will support the full continuum of talent development from primary through to Postdoctoral research…” (p.7). However, it is not until page 35 that the document engages in a discussion of soft skills, what we term in the current paper will do elements of human capital; it outlines a “focus on complementary skills, such as critical thinking, creativity, entrepreneurship and these will be essential to Ireland’s continued success” (p. 35). The Department of Business, Enterprise and Innovation (DBEI, 2018, p. x) has recently re-emphasised this point, noting that investments by the Irish government to “upskill and reskill” employees in the workplace should focus on “delivering the skills, competences and abilities for the 21st Century where collaboration, problem solving and creativity is prized”. However, neither of these policy reports refer to how such complementary skills will be achieved, for example, through policy interventions. To summarize the Irish case based on a thorough review of policy documents, one could reasonably conclude that a prime focus on the can do elements of human capital as a driver of firm-level innovation still prevails. However, there is a small though increasing acknowledgement in policy documents of the importance of developing will do elements of human capital in workers. What is also evident in the Irish case is that there is a clear absence of discussion on how policy initiatives or instruments might be employed by policy makers to promote or support the development of such will do human capital attributes. There is this absence despite the rhetoric of an increasing policy focus with respect to the will do elements of human capital.

changed (e.g., Laranja et al., 2008; Gustafsson and Autio, 2011). Approaches like Systems Innovation (SI) and National Systems of Innovation (NSI) involve complex, non-linear information exchange mechanisms that determine the success of innovation (Woolthuis et al., 2005; Edquist and Hommen, 2006). Woolthuis et al. (2005) argue that, in the case of SI, systems failures justify government intervention. These failures include infrastructural, institutional, and capabilities failures (the latter being particularly relevant to the will do attributes of human capital); actors include demand entities, firms, knowledge institutes, and third parties. Warwick (2013) explains systems failures as arising from interactions between institutions in firms’ learning and operating environments. Institutional failure relates to problems with formal written laws governing institutions; soft institutional failure (most relevant to the will do elements of human capital) relates to problems inherent in firms’ culture and values. However, as outlined by Haapanen et al. (2014), government failures can undermine policy instruments aimed at overcoming market/ systemic failures. The authors argue that sufficient rationale for policy intervention should exist at all times (taking account not only of market/systemic failures but also government failures). Public policy is frequently operationalized through the use of policy instruments. According to Smits and Kuhlmann (2004), policy instruments are increasingly systemic. This development is natural, given the move away from overcoming market failures towards overcoming systemic failures. We employ the term ‘policy instrument’ to mean “techniques of governance, which… involve the utilization of state resources, or their conscious limitation, in order to achieve policy goals” (Howlett and Rayner, 2007, p.2). 5.2. Justifying public support for the will do elements of human capital To ensure effective policy support for the will do elements of human capital, it is necessary to examine the sources of problems that policy proposes to address. According to the European Entrepreneurial Region report (EU, 2015), policy and innovation initiatives should aim to enhance human capital. In relation to human resource systems (such as work arrangements/practices) that support the will do elements of human capital’s contribution to innovation, our findings indicate potential firm-level systems failure. We reframe Woolthuis et al.’s (2005) framework to include firmlevel systems failure. Soft institutional and capabilities failures best explain the combined failures of actors (e.g., knowledge institutions, firms and employees) and rules/system (e.g., organizational culture, laws and values). Such firm-level systems failures highlight a potential role for public policy supporting the will do elements of human capital (again, with the ultimate aim of driving firm-level innovation and always bearing in mind that there is an opportunity cost when using public funds). Public policy can address systems failures in two ways: strengthen/preserve existing systems or create new systems (Carlsson and Jacobsson, 1997). Based on the above framework, we assert the need for policy support for the will do elements of human capital to target both the firm and the individual. It should promote culture change across the workforce and in firms that do not provide human resource systems outlined in Sections 2 and 3. According to the systems failure approach, policy support will enhance opportunities and capabilities for innovation (Metcalfe, 2005). This thinking ties in well with a classic contribution to the literature on science, technology and innovation policy whereby, Lundvall and Borras (2005) advocate for a mode of policy intervention to support firm-level innovation which “takes into account that competence is unequally distributed among firms” (p. 611), thus justifying a role for policy intervention. As highlighted earlier, a focus on the will do elements of human capital in Ireland, although increasing somewhat of late, has not been a consistent or indeed prime policy focus of innovation policy in Ireland. A review of policy and related documents in a number of developed

5.1. Justification of public policy support for innovation Promotion of innovation is regarded as a driver of economic growth (Dolfsma and Seo, 2013) and competitive advantage at the national, industry, and firm level. What is good for innovation tends to be good for growth, thus justifying public policy intervention in the presence of market failures. Over time, policy focus has shifted from market failure (grounded in neo-classical theory) to systemic failure (grounded in evolutionary/ systemic approaches). As a result, the rationale for intervention has 11

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economies (e.g. Australia, Sweden, UK, Singapore, New Zealand, Denmark - de Rassenfosse et al. (2011); OECD (2015, 2017, 2018); European Commission (2017,2018a,2018b); BIS (2011,2016); Australian Government (2015,2017); NPCEC (2012); New Zealand Government (2012, 2018); Christensen (2014), Alex and Petrina (2017); Costa et al. (2018) suggest that Singapore and the UK place greater weight on the will do elements of human capital (with the ultimate aim of driving firm-level innovation) than other developed countries (BIS, 2011, 2016). Interestingly, Singapore promotes productivity and an innovative mindset while the UK aims to develop complementary non-technical innovations, including intangible human capital assets (Singapore Government, 2014; BIS, 2011). The case of Singapore is of particular interest from a will do human capital perspective because, even as far back as 2003, policy documents in Singapore highlighted the need for employees to have a mix of hard (can do) and soft skills (will do) coupled with a clear focus on having underlying supportive human resource systems and practices (e.g., the Economic Review Committee (MTI, 2003)). The MTI report makes reference to setting up a “HR Centre of Excellence within the Singapore Business Federation to drive excellence in HR practices in the private sector” (p. 166). The report also makes explicit reference to the enhancement of human capital management: “We need our organisations to consciously enable workers to be at their best, and at the same time, build and expand their capabilities to become more productive, more resilient, able to tap their creativity and initiative, and bring forth new innovation” (p. 174). Recent reports such as those on the future of the Singaporean economy (MTI, 2017) also emphasize that workers need to develop deep skills to stay relevant and that firms must be able to organize people and ideas to create value. Specifically, referring to human resource systems and practices, the 2017 report refers to getting companies “to take a bigger role in developing workers. The Government should help build up companies’ leadership and human resource (HR) management capabilities so that more companies will recognise the importance and have the knowhow to develop their employees” (p. 24). It also refers to encouraging and enabling firms to hire and advance workers based on skills and competencies beyond academic grades or qualifications. Finally, the Singaporean 2017 report highlights the greater emphasis that will be placed on soft skills beyond those of a technical variety, specifically making reference to the fact that “employers will look increasingly to essential generic skills, such as social and collaborative skills” (p. 102). In particular, despite Singapore placing a continued emphasis on the will do elements of human capital in policy documents over many years, to our knowledge no specific policy instrument focuses on promoting or supporting the development of the will do elements of human capital as a driver of firm-level innovation in Singapore.

There are reasons to argue, however, that public policy not only plays a role in this task (i.e. promoting the will do elements of human capital), but that it plays a unique role, offering organizations resources they would be unlikely to be able to bring to bear on their own. We would argue that at the very least the initial push for organizations to get involved in new ways of promoting the will do elements of human capital may need to come from government. Organizations can tend to be slow to get involved in what they perceive as new, risky activities where private returns on investment are not immediately obvious to them4 . At all times the arguments we put forward here are against a backdrop that public policy interventions should occur where there is clear a-priori evidence that likely benefits of intervention will outweigh the likely social costs (from both direct and opportunity cost perspectives). As noted in Section 5.4 that follows, our proposal is that organizations themselves would bear 50 per cent of the costs associated with the policy program intervention. The public policy program described below is focussed on short consultation with organizations to help them do things they are not well situated to do on their own, particularly to make top-level and unbiased expertise available to organizations to help them diagnose and solve their own shortcomings with regard to developing human resource strategies that will enhance the will do elements that are fundamental to innovation. The intervention we propose is directed toward helping organizations make the best use of available research and theory to assess and improve their human resource policies, with a focus on creating conditions favourable to the development of the will do elements of human capital described in this study (with the ultimate aim of driving firm-level innovation). One of the challenges organizations face, particularly small and medium-sized organizations, is that they are unlikely to have the in-house expertise to adequately diagnose their human resource policies and bring the most current research to bear in helping to structure these policies to optimally enhance innovation (Hewitt-Dundas, 2006; Madrid-Guijarro et al., 2009). As a result, many organizations turn to consulting firms that may have the requisite expertise, but that also have a vested interest in promoting and selling the particular products and services their firm offers (Block, 2013; Pritchett, 2002). What public policy can do is to offer an honest broker – i.e., a source of expertise and information that is not biased by a need to promote particular products or approaches and that can assist organizations in making the best choices, informed by the best and most current research. This is a unique service that is unlikely to be duplicated in the private sector and that takes advantage of the public nature of policy to offer solutions that are tailored to the needs of the organization, not to the needs of the consultants who hope to promote their particular programs or products. Overtime and once organizations begin to see the benefits that derive from a focus on the will do elements of human capital to drive innovation and once such uncertainty is reduced, they themselves will in all likelihood start to invest directly (and without government stimulus) thus removing the need for such a public policy intervention. Indeed, as organizations themselves witness the actual or potential benefits of creating conditions favourable to the development of the will do elements of human capital, we anticipate a likely transition from public to private efforts would work. We view the public policy interventions we have outlined as a way of helping organizations to put themselves on a path that will most effectively encourage the will do elements of human capital and in turn innovation. However, as we argue below, in the short term at least, a policy scheme (such as we detail below) is likely to be necessary to encourage firms to actively promote what they may initially regard as new and somewhat risky elements of human capital (when compared to investing in tried

5.3. The unique role of public policy in supporting will do elements of human capital The previous sections made a general case that public policy has a role in supporting innovation and that this support can and should go beyond simply supporting can do elements of innovation-related human capital, through education, training and the like. Showing that public policy can support these aspects of innovation does not, however, always mean that public policy is the optimal or even an appropriate vehicle for accomplishing this task. Public policy intervention should take place whenever a lack of mechanisms impede organizations themselves to develop these human resource practices for innovation. As discussed earlier, policy intervention is justified where it can be anticipated a-priori that there would be a sub-optimal level of positive social benefit (in this case will do human capital that promotes innovation) in the absence of such intervention, whilst also being cognizant of the costs associated with such intervention (Leibowicz, 2018).

4 Czarnitzki and Toole (2007,2011) make a related point when they discuss the effects of patents and subsidies on reducing uncertainty for firms so that they increase R&D investment with a higher social return relative to private return.

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and tested traditional human capital elements such as those of education and training). Organizations have been very successful in adapting to the need to develop can do facets of human capital, ranging from training in specific job tasks to education schemes designed to enhance general knowledge and skills. There is every reason to believe that organizations will follow a similar trajectory in developing will do aspects of human capital.

good example is Enterprise Ireland’s Lean Business Offer (comprising LeanStart, LeanPlus, and LeanTransform); it aims to increase performance and competitiveness at individual-level and firm-level (Enterprise Ireland, 2018a,b). InnovativePeople4Growth has similarities to the Lean Business Offer. It mirrors Lean Business Offer with respect to financial commitment by firms and government, as well as level of involvement, participants, and time allocation. It should also be noted that Lean Business Offer recently received a positive program evaluation (DJEI, 2015) for its efficient and effective design; adopting it as a template in designing InnovativePeople4Growth could save time and finances. InnovativePeople4Growth incentivizes firms to promote the will do elements of human capital as a competitive resource. The suite of instruments, taking a firm-level systems failure approach, increases innovation by improving employees’ opportunities, capabilities, and efficiencies. The offer is detailed as follows:

5.4. InnovativePeople4Growth This section suggests a proposal for a new policy program offer to develop/support the will do elements of human capital within firms. To our knowledge, the offer is novel.5 Policy has a role to play in terms of helping firms to identify and improve the capacity they already have. Given the relative newness of a focus by firms on the will do elements of human capital as a driver of firm-level innovation, there is a role for government in terms of minimizing the risks and uncertainty (perceived or real) to individual organizations. Whereas personality traits of individuals are largely unchangeable, many of the will do elements of human capital along with the human resource systems to support such human capital elements as specified in the current research can be supported by policy interventions. This research, like Lenihan (2004), does not recommend a complete reconstruction of current innovation support instruments. Instead, it suggests the need to:

1 InnovativePeople4Growth Start involves a one-day consultation with a facilitator appointed by a public agency (e.g., Enterprise Ireland) to benchmark the firm’s focus on the will do elements of human capital. 2 InnovativePeople4Growth Lean Start Plus assists firms in understanding Lean tools and techniques as well as the value of the employees’ attitudes and perceptions with respect to innovation activities. 3 InnovativePeople4Growth Change helps create conditions that develop the will do elements of human capital, resulting in lasting support for human capital. 4 InnovativePeople4Growth Review monitors developments and outlines plans for continued promotion of the will do elements of human capital.

• Recognize the will do elements of human capital as a competitive •

resource, and the significance of firms’ human resource systems in developing such will do elements of human capital. Explore the development of a will do human capital-centered pilot program offer targeting individuals, and firms’ human resource systems, to help firms encourage an innovative mindset among employees. Firms should focus on creating a culture that fosters employees’ job satisfaction, organizational commitment, and willingness to change where innovation is everyone’s concern.

Ideally, firms undertake all four InnovativePeople4Growth programs to ensure maximum benefit. In addition, the programs are not restricted to any particular firm size, sector, or ownership type (this can be evaluated post program evaluations). Developing the will do elements of human capital potentially impacts the general workforce, thus strengthening system-wide capabilities. Movement of people between employers, and other mechanisms such as spin-off dynamics and collaborative networks, helps develop a national innovation mindset. Like any new policy intervention, InnovativePeople4Growth requires ex-ante, interim, and ex-post evaluation to ensure value for money/accountability and support future improvements (Lenihan, 2011). Key evaluation criteria need to be met, including cost-effectiveness, distributional equity, and political feasibility. Most programs offered by Irish development agencies require financial commitment by firms. With the Lean Business Offer, firms generally supply 50% of overall program cost; the Irish government agency pays the balance. We propose a similar approach for InnovativePeople4Growth. There is a role for government in putting the will do elements of human capital on the agenda, in stimulating the activities of firms, and in helping to coordinate those activities. There is also a potential role for government to provide firms with access to the knowledge, resources, and expertise needed to develop, implement, and monitor the human resource systems that provide a basis for the will do elements of human capital. In sum, our proposed public policy program intervention has two key features that distinguish it from several other potential approaches to enhancing innovation-relevant human capital. First, it is focussed on an aspect of human capital that has been almost completely ignored by public policy to date (Singapore and a lesser extent the UK being partial exceptions) – the will do elements of human capital. We present analyses that will help organizations and policy makers focus their efforts on human resource policies and practices that are most strongly linked to these aspects of human capital, and we present a lean and efficient

In a similar vein to discussions by Flanagan et al. (2011) about ‘policy entrepreneurship’ and ‘windows of opportunity’ for policy, we argue that the instrument proposed here goes quite some way towards answering the call in recent literature for systemic innovation policy instruments (e.g. calls by Wieczorek and Hekkert (2012) and OECD (2015)). It could reasonably be argued that the current paper represents the first step on the road of policy entrepreneurship regarding the introduction of a systemic policy instrument focused on the will do elements of human capital. A pilot program offer6 would benefit policy makers. Innovation is by its nature experimental; so too should policy interventions that support it. Support for the will do elements of human capital may emerge directly through targeted policy interventions or indirectly through other interventions. Our proposed pilot offer, which we title InnovativePeople4Growth, is based on our findings that the will do elements of human capital are supported by certain firm factors. InnovativePeople4Growth (detailed in Table 4) syncs with current Irish policy promoting competitiveness, innovation, and improved productivity in pursuit of economic growth (DJEI, 2017). We argue that effective support necessitates an offer of complementary programs. A 5 This conclusion results from a comprehensive review of international academic and policy making literature, which uncovered no similar offers. 6 Conducting a pilot program is common practice. ‘Innovation Vouchers’ were piloted in the Netherlands in 2004 before a full roll out in 2006 (Cornet et al., 2006). An example of this in the Irish case is the ‘Business Partners Pilot’-introduced in 2009 on a small pilot basis resulting in a full roll out subsequently on a much larger scale.

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Table 4 Overview of InnovativePeople4Growth policy program offer. InnovativePeople4Growth Programs/Activities 1. InnovativePeople4Growth Start Short in-firm consultancy

Objectives/Intended Outcomes

Existing/New program

» Assess level of existing supports at firm- level for the will do, motivationally-relevant elements of human capital 2. InnovativePeople4Growth Lean Start Plus 7 days input from expert consultant on » Reduce costs and refine process principles of Lean Business Offer » Introduce Lean skills » Introduce mindset among all personnel that is focused on the will do elements of human capital 3. InnovativePeople4Growth Change Program introducing changes to » Introduce Lean principles; promote merits/value of focusing on the will organizational structures do elements of human capital through supports for employees’ job satisfaction, commitment to organization and willingness to change in the workplace. » Develop innovative mindset/culture where innovation is everyone’s concern 4. InnovativePeople4Growth Review Short in-firm consultancy » Review/assess changes/benefits of a focus on the will do elements of human capital; identify possible future adjustments

New program Existing program plus: introducing the will do elements of human capital to management; developing strategy to implement supports for the will do elements of human capital New program

New program

organizational science literatures, our research provides valuable contributions to theory, practice, and policy. From a theoretical perspective, our research makes two key contributions. First, our research extends the understanding of human capital and its supports, with the ultimate objective being that of driving firm-level innovation. Our findings concur with Cowling (2016) on the importance of building firm-level capabilities in support of innovation activity. Our findings help to bring some specificity to this literature by highlighting specific human resource management policies and practices that can be empirically linked to the motivational components of innovation. Second, our research highlights the need to consider the role of public investment in supporting the will do, motivationally-relevant elements of human capital as a driver of firm-level innovation. In particular, we outline a program for developing and implementing interventions that give organizations the tools and knowledge needed to support their employees’ motivation to innovate. We affirm Bell’s (2009, p.50) call for greater focus on “broad magnitudes and trends of the more important non-R&D components of innovative activity”, and policy discussion “about the kind of innovation capability that is created and accumulated”. From the perspective of practice at the level of the organization, our research suggests that firms’ innovation activity may benefit from human resource systems such as proactive work practices, consultation and bonus schemes (part of pay and conditions). These systems motivate employees and support positive work attitudes such as job satisfaction, organizational commitment and willingness to change in the workplace. Interestingly, one of the human resource systems we measure, frequency of information, does not appear to have much impact on the probability of will do traits. This may suggest that among potentially useful human resource systems, some appear to be more closely linked to will do traits than others. From the perspective of policy implication, our research suggests that public policy can support the development of elements of human capital that have heretofore been largely ignored in debates about how to support innovation in organizations. Literature examining the role of public policy in human capital development has focused almost exclusively on can do elements of human capital – specifically, knowledge and skills (Becker, 1964; de Rassenfosse et al., 2011). Our results suggest that public policies can aid firms as they identify, implement, and monitor particular human resource management policies. These are the policies that are empirically shown to enhance and develop both the attitudes and beliefs necessary to support firm-level innovation. A total overhaul of current programs is unnecessary. Instead, policy should recognize the value of the will do elements of human capital and

set of policy interventions to make maximum use of this research. Second, our proposed interventions are designed to have a time-limited footprint. That is, these interventions consist mainly of time-limited consultations designed to aid organizations in diagnosing their human resource systems and making improvements in those systems in ways most likely to foster innovation. Finally, these interventions represent a set of efforts to help organizations help themselves. That is, they focus on marshalling expertise and providing organizations with the tools and information they need to build human resource systems that enhance innovation. The proposed interventions do not require the public sector to impose systems on organizations or to manage these systems. Rather the proposed strategy represents a way to most effectively provide organizations with access to the information and expertise they require to adequately diagnose and to design responses to shortcomings in their own human resource systems. 6. Conclusions In this paper, we examined empirically-supported public policy interventions that can help firms develop and enhance motivationallyrelevant (will do) elements of human capital, elements that are required to support firm-level innovation. Public policy targeted at increasing human capital traditionally concerns itself with the can do attributes of human capital (usually knowledge and skills), resulting in interventions that involve education and training. The development of will do attributes, such as attitudes and perceptions influencing employees’ willingness to innovate, require different public policy interventions. Our analysis, based on information retrieved from the Irish National Centre for Partnership and Performance Workplace Survey (NCPP, 2009), reports that firms providing human resource systems, such as greater use of proactive work practices and greater levels of consultation with employee-managers are associated with an increased probability of job satisfaction, organizational commitment, and willingness to change of such managers. We also report that bonus schemes (as part of pay and conditions) are linked to motivationally-relevant human capital, as measured by job satisfaction and willingness to change. It would be remiss however, not to acknowledge that some of the human resource systems variables reveal mixed results. For example, we report that greater frequency of information, job share and flexitime (part of work arrangements) have no significant relationship to the majority of the will do elements of human capital. In some cases, human resource systems variables such as the receipt of share options as part of pay and conditions have a negative impact. By boundary-spanning the economics, innovation, and 14

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the importance of human resource systems in their development. Market and systemic failures may also justify public support for the will do elements of human capital. Public policy interventions can help by making the necessary resources, expertise, and knowledge available to organizations, so that the organizations can focus on the will do elements of human capital. A role for government exists in terms of minimizing the risks (whether real or perceived) associated with firms investing in the will do elements of human capital in their organizations. The ultimate goal is to drive firm-level innovation. We propose a new policy program offer (InnovativePeople4Growth) to support the will do, motivationally-relevant elements of human capital in order to increase firms’ innovation activity. This offer incentivizes firms to promote the will do elements of human capital as a competitive resource. Regarding avenues for future research, such research might usefully consider a broader range of variables that are likely to influence work attitudes. For example, job satisfaction and related work attitudes are strongly influenced by factors such as the design of jobs (e.g., opportunities for autonomy and meaningful work) and the quality of supervisor-subordinate relationships (Hackman and Oldham, 1975; Judge and Kammeyer-Mueller, 2012). Of course, satisfaction, commitment, and the like are not the only work attitudes that are likely to influence willingness to innovate. Perceptions of justice have the potential to influence will do components of human capital. A substantial literature deals with perceptions of fairness and justice in procedural allocation of rewards and interpersonal treatment (Colquitt et al., 2001; Cropanzano and Kackmar, 1995). More comprehensive datasets providing further information on the impact of firm-level variation in these factors could allow future research to extend and improve our work. In this paper we have presented each program in our InnovativePeople4Growth policy offer as a ‘stand-alone’ program, so that firms can adopt an á la carte approach. However, the interaction between programs has potential as an effective policy instrument mix. Further investigation and future research is merited to study the effective interactions between programs supporting the will do elements of human capital. This could be based on Flanagan et al.’s (2011) multi-

level, multi-actor analysis, and/or Lanahan and Feldman’s (2015) examination of multi-level innovation policy in US small business innovation research programs. This study relied on data from the NCPP (2009) survey. While this survey provides reliable data that is national in scope, there are several potential drawbacks in relying on archival data. First, the NCPP (2009) survey includes only a single response per organization. It is possible that different members of the same organization might have quite different perceptions and understandings of organizational policies and that a multi-respondent survey might have provided higher levels of external validity. Also, this survey was not specifically designed to measure the key constructs in our study, that is, the determinants of will do aspects of human capital relevant to innovation. The mapping of survey items to these constructs is necessarily a matter of judgment and therefore fallible. Despite these limitations, our research represents an important step forward for academics, policymakers, and firms in how they consider and analyze the roles that the motivationally-relevant (will do) elements of human capital, firms’ human resource systems, and public policy interventions might play (with the ultimate aim of driving firm-level innovation). Conflict of interest None. Acknowledgements This article has greatly benefited from the guidance of the Editor, Maryann Feldman, and the valuable comments and suggestions of three anonymous reviewers of this journal. Moreover, the authors would like to thank Pablo Garrido-Prada, Kevin Mulligan, Mauricio Perez-Alaniz and Justin Doran for helpful comments and discussions. Helen McGuirk acknowledges Postdoctoral funding from the Irish Research Council (GoIPD/2015/764). We are grateful also for comments on earlier versions of this paper from participants at the Lundvall Symposium, Aalborg, Denmark and EU-SPRI Conference. The views expressed in this paper, and any remaining errors or omissions, are our own.

Appendix A. Questions from NCPP (2009) survey used for the independent variables Description of independent variables Proactive work practices New ideas are readily accepted in my workplace People in my organisation are always searching for new ways of looking at problems Customer needs are considered top priority in my organisation This organisation is prepared to take risks in order to be innovative This organisation is quick to respond when changes need to be made My employer encourages employees to collaborate with people in other organisations This organisation is continually looking for new opportunities in a changing environment My employer encourages employees to work in teams in order to improve performance Frequency of receiving information from management on The level of competition faced by your employer Plans to develop new products or services Plans to introduce new technology Plans to re-organise the company Plans to change work practices e.g. work in teams Information on sales, profits, market share Plans for staff reductions Consultation How often are you and your colleagues consulted before decisions are taken that affect your work? If changes in your work occur, how often are you given the reason why? If you have an opinion different from your supervisor/manager can you say so? If you are consulted before decisions are made, is any attention paid to your views or opinions?

15

Mean

Std. Dev.

Min

Max

1.981498 2.044053 1.65815 2.208811 2.057269 2.369163 1.886344 1.92511

0.660428 0.687375 0.719692 0.816233 0.725741 0.841138 0.676083 0.727777

1 1 1 1 1 1 1 1

5 6 5 6 5 6 5 6

3.406162 3.322129 3.141923 2.981326 3.070962 3.263305 2.861811

0.790315 0.899567 0.966943 0.960737 0.942559 0.854755 1.030175

2 1 1 1 1 2 1

4 4 4 4 4 4 4

4.670485

1.271989

1

6

4.993833 5.562115

1.26371 0.941579

1 1

6 6

4.929515

1.249466

1

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Appendix B. Factor Analysis for three independent variables Variable Proactive work practices New ideas are readily accepted in my workplace People in my organisation are always searching for new ways of looking at problems Customer needs are considered top priority in my organisation This organisation is prepared to take risks in order to be innovative This organisation is quick to respond when changes need to be made My employer encourages employees to collaborate with people in other organisations This organisation is continually looking for new opportunities in a changing environment My employer encourages employees to work in teams in order to improve performance Frequency of information The level of competition faced by your employer Plans to develop new products or services Plans to introduce new technology Plans to re-organise the company Plans to change work practices e.g. work in teams Information on sales, profits, market share Plans for staff reductions Consultation How often are you and your colleagues consulted before decisions are taken that affect your work? If changes in your work occur, how often are you given the reason why? If you have an opinion different from your supervisor/manager can you say so? If you are consulted before decisions are made, is any attention paid to your views or opinions?

Factor 1

Uniqueness

0.6981 0.7250 0.5682 0.5980 0.7236 0.4913 0.7351 0.6392

0.5127 0.4743 0.6772 0.6424 0.4764 0.7587 0.4597 0.5914

0.6912 0.7596 0.7365 0.7475 0.7314 0.6713 0.5486

0.5223 0.4230 0.4575 0.4412 0.4650 0.5493 0.6990

0.8176 0.8259 0.6510 0.8097

0.3315 0.3179 0.5762 0.3444

Note: Factor Analysis is used to evaluate scales and reduce large numbers of related variables to manageable numbers. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) value was 0.86 (a value of > 0.05 indicates suitability for factor analysis) (Long and Freese, 2006)

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