Examining the Influence of Human Capital on Employees’ Innovative Work Behaviour: A Moderated Serial Mediation Model

Abstract

Organisations invest in human capital to achieve favourable organisational performance. The purpose of this research is to explain how organisational human capital investments influence an individual’s human capital and innovative work behaviour (IWB). Drawing on Social Exchange Theory and its subset Affect Theory of Social Exchange, this study empirically examines how the human resource management activity of human capital investments manifests at the individual level by developing and testing a moderated serial mediation model. A total of 115 employees working in a diverse set of industries, such as service, manufacturing, information technology, consultancy and education, who had received at least one training from their current employer, participated in the survey. The participants completed five standardized, valid and reliable instruments. SPSS was employed for data analysis. Hypotheses were tested using regression analysis. Results show that both gratitude and knowledge management mediate the relationship between human capital and IWB and the moderating effects of job characteristics. This study extends current literature and integrates macro–micro human capital by exploring how and when human capital leads to the generation of micro social orders. The concept of micro social orders refers to repeated interactions (exchange frequency), emotional reactions, perceptions of cohesion and affective sentiments of a group/organisation due to social structures. This research also highlights how managers can establish positive reciprocity obligations and enhance employees’ gratitude that helps to achieve IWB.

Keywords

Human capital human capital investment innovative work behaviour macro–micro human capital gratitude knowledge management Social Exchange Theory Affect Theory of Social Exchange micro social orders

Introduction

Organisations make significant investments in human capital to achieve organisational growth and performance. Human capital here refers to the knowledge, skills, abilities and other characteristics (KSAOs) possessed by organisation’s employees such as technical know-how, critical thinking skills and ability to plan and act, essential for achieving organisational goals (Crook et al., 2011; Youndt & Snell, 2004). The rise of increasingly dynamic, uncertain and fast-paced business environment demands employees to continuously display their ability to adapt to the changing business scenarios and to offer creative solutions to complex problems (Leal-Rodríguez & Albort-Morant, 2019). Consequentially, researchers have aimed to understand and explain the quality of human capital necessary to survive, increasing global competition (Chatterjee et al., 2014). An established method of improving this quality or any requisite KSAOs is by investing in organisational activities that enhance human capital, such as training and development (Kucharčíková et al., 2018). These investments in human capital are known as human capital investments.

Owing to its importance in organisational life, human capital and human capital investments continue to attract scholarly attention across a plethora of disciplines such as strategic management, human resource management (HRM), organisational behaviour (OB) and strategic HRM. This literature has collectively established that human capital investments positively impact firm performance (e.g. Ballot et al., 2001; Crook et al. 2011) and are necessary for achieving sustained competitive advantage (e.g. Lepak & Snell, 1999). This literature has discussed: (1) investments in generic versus firm-specific human capital (Lazear, 2009; Neal, 1995) and the link between generic and firm-specific human capital (Morris et al., 2017), (2) differences in individual- and firm-level human capital (Crocker & Eckardt, 2014) and (3) the processes of emergence of human capital (Ployhart et al., 2006).

Most scholarly work in human capital has adopted either a macro-level perspective or a micro-level perspective and has paid scant attention towards understanding aggregate-level human capital. According to Ployhart and Moliterno (2011), macro scholars explore firm-level phenomena and, therefore, study the aggregate impact of organisational level KSAOs on organisational outcomes such as competitive advantage (e.g. Mahsud et al., 2011). On the other hand, micro scholars investigate individual-level phenomena, i.e. how individual-level human capital impacts outcomes such as individual performance (e.g. Harris et al., 2015). Due to this segregation, current scholarly work has failed to explain cross-level influences (such as the impact of individual human capital on group-level activities such as knowledge management [KM]) and has not accounted for how firms influence KSAOs that emerge as an essential resource at the unit/organisational level (Nyberg et al., 2018). It has also overlooked the causes and boundary conditions of aggregate-level human capital (Wright et al., 2018). Examining cross-level influences is important as it can explain how individuals across various forms, such as single entities or in groups, develop an understanding of situational factors (e.g. human capital investments) (Mathieu et al., 2012). Furthermore, since past researchers have focused only on one macro or micro perspective, we know very little about how the aggregate human capital emerges from individual KSAOs. Scholars (e.g. Nyberg & Wright, 2015; Nyberg et al., 2018) have therefore called for integration across macro and micro perspectives to study how individual (micro-level) human capital leads to organisational (macro-level) outcomes. One of the possible ways of achieving this integration is by examining how does the macro-level activity of human capital investments generate affective, cognitive and behavioural dimensions (micro social orders) at the collective level via changes in individual-level behaviours (Lawler et al., 2014). Therefore, in this study, we address the literature gap of macro–micro integration and explain how the HRM activity of human capital investments manifests at the individual level (in the form of human capital) and generates micro social orders. Micro social orders refer to repeated interactions among the individuals of a group/organisation because of various social structures, which makes them perceive themselves as members of that unit (group/organisation) (Lawler, 2002). These recurrent patterns of interaction lead to the formation of emotional reactions, perceptions of cohesion and affective sentiments among the individuals of a group/organisation (Lawler et al., 2008). Research has concluded that any activity that manifests by an increase in any desirable OB at an individual level would lead to an aggregate increase in organisational performance (Motowidlo, 2000). Hence, such manifestations would lead to the integration of macro- and micro-level outcomes. Thus, this study aims to explain the process through which human capital investments operate at the individual level.

This research study hypothesises that an increment in the perception of human capital leads to gratitude at the emotional level, which in turn leads to positive cross-level impact of KM. KM then finally leads to innovative work behaviour (IWB). Since employees’ ability to innovate or the tendency of individuals to engage in IWB is one of the most critical skills demanded by employers (Bos-Nehles et al., 2017), it is adopted in this study as the main outcome of interest. Janssen and Van Yperen (2004) defined IWB as an intentional generation, promotion and realisation of new ideas and procedures. Also, the study postulates that job characteristics moderate the impact of KM on IWB. We draw from Social Exchange Theory (SET) (Blau, 1964) and its subset Affect Theory of Social Exchange (Lawler, 2001) to explore how and when human capital and IWB exhibit a positive relationship. SET and Affect Theory of Social Exchange are used because of their ability to explain the conditions essential at the structural and cognitive level for various social unit attributions (i.e. affective attachments to the organisation) (Cook et al., 2013). These attributions during exchange processes influence how people feel, think and behave, creating micro social orders.

In developing and testing this moderated serial mediation model, this study makes the following contributions. First, studying the impact of individuals’ organisational level activity/event/practice enhances our understanding of how the activity is manifested at the individual level. Examining the individual-level impact of human capital investments is crucial because perceptions of an individual’s own KSAOs shape their behaviours (Daly & Wilson, 2017). If KSAOs follow the valuable, rare, non-imitable and non-substitutable (VRIN) typology, they may serve as a source of sustained competitive advantage for the firm (Sujchaphong, 2013). Second, by studying the impact of human capital on extra-role behaviour (a behavioural outcome beyond what is expected in jobs) of innovation, the present study extends the range of human capital outcomes. Third, we also develop and empirically test a model that illustrates the process by which human capital influences employees’ behaviours and boundary conditions, which has also been long missing from the literature. Lastly, testing this process model also helps to understand how and when human capital investments generate micro social orders, which demonstrates the link between individual emotion and behaviour. Also, it adds to the gap of integrating macro–micro human capital. From the practitioner’s perspective, companies might benefit from our research by gaining an understanding of the process by which investments in human capital leads to IWB. In terms of theory, this research advances knowledge about individuals’ psychological and behavioural outcomes via affective-level reciprocity. Using job characteristics as a moderator between KM and IWB, this research study helps identify conditions essential for micro social orders.

This study is structured as follows. In the following section, we present a review of the current literature on human capital. This is followed by a discussion of the development of the conceptual framework and hypothesis. This section explains how SET and Affect Theory of Social Exchange contribute to hypothesis development. The following section proceeds to the statement of the hypothesis which talks about the interrelationships among variables hypothesised. We then discuss the research methodology and measures adopted in the study and present our data analysis. The results section then examines the main findings. We conclude by presenting the theoretical and practical implications of this study.

Literature Review

Human capital research has come a long way since the inception of the concept by Becker (1964) and Schultz (1961) in economics to understand how individuals choose to invest in themselves to attain more wages. Human capital research in management started with a macro perspective, i.e. how firm-level human capital influenced various firm-level outputs such as organisational performance and organisational learning. As this research progressed, and the construct was adopted in multiple disciplines such as strategic management, and HRM, various studies explored how various HR practices led to positive organisational outcomes with human capital as a mediating mechanism (e.g. Combs et al., 2006; Judge et al., 2010). With an increase in such studies, an acknowledgement was made that human capital has been sufficiently explored at the organisational level (Harris et al., 2015). Attention now should be shifted to the investigation of human capital at the individual level (Coff & Kryscynski, 2011). This recognition brought in a need “to unpack collective concepts to understand how individual-level factors impact organisations, how the interaction of individuals leads to emergent, collective, and organisation-level outcomes and performance, and how relations between macro variables are mediated by micro actions and interactions” (Felin et al., 2015, p. 576). The primary focus of literature to study micro foundations of human capital was to explicate mechanisms that generate positive outcomes at the organisational level.

This attention to micro foundations led to two significant developments. First, to distinguish the macro-level human capital from individual-level human capital, a new terminology, human capital resource was introduced. It refers to KSAOs that are available for unit relevant purposes (Ployhart et al., 2014). The use of the new terminology helped avoid communication problems relating to individual- and collective-level human capital. Differentiation in human capital resource and human capital are based on three elements: (1) structure (human capital resource can consist of both individual- or unit-level capacities, but the capacities are relevant for serving the unit-level purpose whereas human capital consists only of individual capacities); (2) function (human capital resource research aims to achieve competitive advantage whereas human capital research aims to understand mechanisms that influence individuals to achieve competitive advantage) and (3) level (human capital resource aims to capture multiple levels whereas human capital exists only at the individual level) (Ployhart et al., 2014). Second, with more research on micro foundations, human capital research entered the domain of OB (Ployhart, 2015). This OB perspective led to an essential diversion of the focus of human capital research towards individual-level outcomes and behaviours; most of the prior work focused exclusively on organisational competitive advantage, thus examining only the end outcome at the macro level. However, this move away from macro and only towards micro failed to capture psychological and behavioural interactions happening at the intermediate and intersectional levels, thus creating a black box between how macro and micro human capital can be linked.

Over the last decade, micro-level studies on human capital resource have gained momentum (e.g. Aguinis & O’Boyle, 2014; Söllner, 2010) to understand how individual-level human capital impacted various individual-level outcomes that led to a greater human capital resource. Recent research studies suggest that research in micro foundations has reached a saturation state, and more research in the same is just reconstructing the present findings (Felin et al., 2015; Nyberg et al., 2018). Consequently, the authors have suggested the need for integration of various disciplines across which human capital has been studied (Boon et al., 2018) and integration across macro- and micro-level human capital (Nyberg et al., 2018). While integration across disciplines would yield interesting opportunities to seek research questions that lie at intersections of these disciplines (e.g. Boon et al., 2018), integration across macro and micro human capital would help unveil processes linking organisation and individual-level outcomes.

The literature review concludes that the integration of macro–micro human capital is a significant research gap. Recent work in strategic HRM and HRM has focused on how various HR practices or employee trends/patterns impact the content of the human capital resource (e.g. Bidwell et al., 2015; Dineen & Allen, 2016; Eckardt et al., 2018; Oh et al., 2018). For example, Call et al. (2015) explored how changes in turnover rates over time have unit-level consequences. More recently, Wang and Zatzick (2019) examined how decisions around hiring such as hiring patterns, hiring rate and hiring rate dispersion impact human capital resource in terms of their ability for innovation. Literature within OB has explored plausible mechanisms that might lead to good organisational performance or enhance human capital resource (e.g. Mahoney & Kor (2015) in their study showed how useful is firm-specific human capital and how it can be stimulated and protected, and Murnane (2016), in her study, talked about how organisational commitment behaviours can be weaved in various leadership programmes). Although collectively these works tackle the important question of how to enhance the KSAOs of existing or future employees, thus generating a competent human capital resource, they have not addressed the gap of integrating macro and micro human capital.

Development of Conceptual Framework and Hypothesis

To extend research in human capital literature and address the preceding gap of lack of macro–micro integration of human capital, the present study develops and tests a model of how and when human capital generates micro social orders. Micro social orders represent the joint consequences of how structure impacts the interaction and vice versa (Cook et al., 2013). It helps explain the development of person–unit ties and consequent person–person ties in an organisation. Our model captures how the HRM activity of human capital investments leads to the generation of person–unit ties in the form of gratitude, which subsequently leads to person–person ties that leads to KM behaviour. From previous research, it can be extrapolated that the investigation of micro social orders generating behavioural consequences at the individual level leads to an anticipation of impact at the organisational level (Motowidlo, 2000). Keeping this in mind, we draw on SET and Affect Theory of Social Exchange to produce a comprehensive understanding of psychological (emotional) pathways underpinning human capital that thereupon turns into behavioural outcomes at the individual level.

SET is based on the premise that employees in an organisation undergo a “series of interactions that generate obligations” (Cropanzanon & Mitchell, 2005, p. 874). An important tenet of SET is reciprocity (Gouldner, 1960). It emphasises that an action by one party leads to a response by another. For example, if a person provides a benefit, the receiving party should respond in kind (Gergen, 1969).

Affect Theory of Social Exchange is a subset of SET. According to Lawler (2001), Affect Theory of Social Exchange posits that the degree of jointness in a task governs the perception of actors regarding whether they belong to a social unit or not (Lawler, 2001). It describes how and when emotions generated by a social exchange process produce stronger or weaker ties about various social units (Lawler, 2001). Social exchange literature (Emerson, 1981) classifies exchanges into four categories: (1) productive exchange (when actors interweave contributions made by individuals to make the final product, e.g. coauthoring scholars), (2) negotiated exchange (when individuals give directly to one another over a period of time based on previously agreed terms, e.g. salary for work), (3) reciprocal exchange (actors make their receiving without an explicit expectation of reciprocity, e.g. advising to a colleague) and (4) generalised exchange (an indirect form of exchange, where givers and receivers are not matched in pairs, e.g. a PhD student receiving review of his/her work from a faculty who is not his/her supervisor). The Affect Theory of Social Exchange elucidates on when and how various forms of exchanges generate (1) global feelings (involuntarily feelings that are immediately felt as a result of exchange such as feelings of good/bad, up/down, pleasure/displeasure), (2) perceptions of shared responsibility and (3) an attribution process generating affective attachments to social units (Lawler, 2001). Affect Theory of Social Exchange specifies conditions under “which social unit attributions of emotion will overcome or mitigate self-serving biases to produce a person to unit attachments” (Lawler et al., 2008, p. 524).

This research adopts SET and Affect Theory of Social Exchange to understand both intrapersonal and interpersonal behaviours underlying organisational human capital investments, thus explicating the micro social orders mechanisms generating because of these investments. SET and Affect Theory of Social Exchange combined are the most appropriate theoretical lens as they reside on the principle of analysing how social structures shape individual emotions and their consequences for relations, groups and networks (Cook et al., 2013). We investigate first by understanding how the HRM activities of human capital investments generate positive reciprocal obligations among employees towards their organisation. Second, how these resources in the form of KSAOs become a source of exchanges across coworkers in an organisation.

We employ a moderated serial mediation model to delineate the process of how human capital investments lead to IWB. A serial mediation is a chain of mediators having high causality flowing in a particular direction of causal flow. A moderated serial mediation is one in which one or more than one relationship is/are moderated, such that the serial mediation (indirect effect) would yield different results at different values of the moderator (Hayes, 2013). The model depicts that increment in human capital at the individual level generates psychological (gratitude) and behavioural (KM) outcomes (see Figure 1). The logic behind the generation of the psychological mechanism of gratitude is that when organisations invest in employees’ human capital, employees feel empowered in terms of resources (newly acquired KSAOs). They experience this empowerment in the form of pleasantness (global feeling), and this leads to a feeling of reciprocity which manifests as gratitude. These newly acquired KSAOs lead to the exchange of information among other members in the organisation leading to knowledge-donating and knowledge-collecting behaviours—collectively known as KM behaviours. The foundation of knowledge generated by these KM behaviours is the firm-specified KSAOs, thus leading to unique, valuable and context-specific new knowledge-opening opportunities to engage in IWB. Various organisations and jobs differ in terms of their job characteristics, depending on the industry, culture and kind of work expected out of them. These job characteristics might influence our serial mediated relationship of human capital leading to gratitude, gratitude leading to KM, KM leading to IWB depending on various dimensions of the job characteristics, thus acting as a moderator to our serial mediation.

Figure 1.

Source: The authors.

Statement of Hypothesis

Human Capital and IWB

Employees’ level of human capital is an essential source of empowerment in terms of resources for employees (Cabello-Medina et al., 2011). Generation of these resources leads to a feeling of giving or “norm of reciprocity” (Gouldner, 1960), as suggested by SET. The theory advocates that transactions between parties of various kinds and interdependent relationships generate the norm of reciprocity and quid pro quo. For example, if one party offers a benefit, the receiving party is obligated similarly. The reverse is also true, i.e. if a party receives unfavourable treatment, they would respond to the offering party in a similar manner (Huang et al., 2016). A stream of research suggests that social exchange norms guide various intra-organisational relationships (Oparaocha, 2016). Arguing on similar lines, we posit that HR practices enhance an employee’s KSAOs shape organisational transactions and resource exchanges in a manner that it causes reciprocal obligations of positive quality. This reciprocity may get manifested in the form of behaviours that are beyond their job descriptions, i.e. extra-role behaviours like IWB. In light of the above reasoning and support from previous researches (Michael et al., 2011; Wojtczuk-Turek & Turek, 2015), we hypothesise that:

Hypothesis 1: Human capital correlates positively with IWB.

Mediating Role of Gratitude Between Human Capital and KM Relationship

Organisations by engaging in investments of human capital enter in an exchange with employees. They create an environment that enables employees to experience an increment in the level of KSAOs possessed by them. Employees perceive this kind of exchange as a reciprocal exchange (Flynn, 2005). Reciprocal exchange is a type of exchange where the giving party provides rewards without an explicit expectation of reciprocity, i.e. there is a time lapse between giving and receiving (Cook et al., 2013). Affect Theory of Social Exchange posits that this reciprocity in the above case generates in employees’ global feelings of pleasantness directed towards the organisation. These feelings then lead to reciprocation in the form of gratitude (Lawler et al., 2008). Gratitude is a “feeling of appreciation in response to an experience that is beneficial to, but not attributable to, the self” (Fehr et al., 2017, p. 364). Also, previous research studies in OB have established that human resource practices that result in reciprocal exchange give rise to gratitude (Fazal et al., 2017). Drawing from these arguments, the current research study expects a positive relationship between human capital and gratitude.

The SET literature establishes that exchanges at work can happen in terms of seven resources: goods, services, money, love/affect, status, information and work itself (Cox, 1999). In the case of increment in human capital, the exchange most likely to happen is through information. So, the entity of exchange in the above-mentioned reciprocal relationship is “information”. The new information in terms of KSAOs and a feeling of gratification for the organisation leads to knowledge-donating and knowledge-collecting behaviours collectively known as KM. KM is defined as the process by which individuals create/collect, organise, transfer and apply knowledge for improving organisational performance (Bassi, 1997). Knowledge is a vital resource necessary for providing a sustained competitive advantage to firms (Wang & Noe, 2010). The relation between human capital and KM is well entrenched in the literature (Wiig, 1997), and our model explains the “how” of this relationship via the emotion of gratitude. Considering the reasoning presented earlier, this study hypothesises that:

Hypothesis 2: Gratitude mediates the relationship between human capital and KM.

Mediating Role of KM Between Human Capital and Innovative

Workplace Behaviour Further Moderated by Job Characteristic

A unique feature of human capital is that firm-specific KSAOs can lead to the generation of knowledge that could lead to sustained competitive advantage because of its VRIN characteristic (Halawi et al., 2005). Studies have called for exploration of the social exchange perspective to examine the link of KM with innovation (Wang et al., 2010). Consequently, we argue that human capital can lead to KM via various social and technical processes. Drawing from SET, when an employee feels empowered in terms of KSAOs as developed via various HR practices, individuals feel a need to give back to the organisation, and they do so by engaging in various KM practices (Hsu & Sabherwal, 2012). As per Barley et al. (2018), “Individuals can share and produce knowledge through interaction and embody knowledge through virtual tools” (p. 279). This knowledge hence created can be a source of innovation for the organisation as firm-specific human capital generates knowledge that is valuable and unique (Lopez-Cabrales et al., 2009). This unique knowledge possessed by employees may help them to engage in IWB.

We have so far conceptualised that human capital leads to IWB via KM. Despite these predictions, various contextual factors might influence these relationships. This study takes into consideration one of the most dominant contextual factors—job characteristics. According to job characteristics model (Hackman & Oldham, 1976), work quality can be enhanced by articulating jobs around five dimensions: (1) variety (the degree to which a job requires the use of several different skills and talents), (2) identity (the degree to which the job requires completion of a “whole” piece of work, or doing a task from beginning to end with a visible outcome), (3) significance (the degree to which the job has a substantial impact on the lives of other people), (4) autonomy (the degree to which the job provides substantial freedom) and (5) feedback (the degree to which the job provides clear information about performance levels). Literature has established that enriching jobs along the dimensions mentioned earlier lead to enhanced performance (De Spiegelaere et al., 2014; Demerouti et al., 2015).

In the existing literature, implications of job characteristics on KM are also well studied (Foss et al., 2009; Janz & Prasarnphanich, 2003). However, despite the prevalence of importance that inherent characteristics of jobs can play on IWB and KM, interaction effect has not been studied of job characteristics between KM and IWB (Cai et al., 2019). Our reasoning suggests that favourable job characteristics may lead employees to perceive job characteristics as resources that might help to disseminate KSAOs. Thus, strengthening the positive relationship with IWB. Based on the above reasoning, we expect the following hypothesis to hold:

Hypothesis 3: Job characteristics moderate the relationship between KM and IWB such that the positive indirect effect of human capital on IWB through KM will be more pronounced at favourable levels of the moderator.

A Moderated Serial Mediation Model of Human Capital Leading to Innovative

Workplace Behaviour

The hypothesis proposes a serial mediation model linking human capital and IWB. It suggests that increment in human capital may relate to an increased feeling of gratitude towards the organisation, which in turn enhances the tendency to share information through KM behaviours, and ultimately promotes IWB. Moreover, job characteristics show an interaction effect between KM and IWB. This mediation chain is in line with our aim of examining the process by which human capital investments operate at the individual level. It explains how and when human capital generates micro social orders. We therefore hypothesise as follows:

Hypothesis 4: Job characteristics moderate the relationship between KM and IWB such that the positive indirect effect of human capital to IWB through KM and gratitude in serial will be more pronounced at favourable levels of the moderator.

Methods

Data Collection

Participants and Procedure

We drew the sample from Indian employees working in the diverse nature of organisations such as service, manufacturing, information technology, consultancy and education, who had received at least one training from their current employer. We used a survey instrument to collect data. We used self-report measures to collect data on various variables. The validated scales used in the questionnaire have been used earlier in the South Asian context by eminent Indian research in their works (e.g. Agarwal & Gupta, 2018; Chatterji & Kiran, 2017; Fongtanakit, 2013; Sharma & Garg, 2016; Sinha et al., 2016). Since English is one of the official languages in India, the questionnaire was drafted in English, and the respondents were communicated about the author’s availability for clarification in case of doubt and queries. Throughout the data collection process, there were no concerns raised regarding the respondents’ inability to comprehend the questions in English. We contacted 392 people via a web-based survey instrument using Questionpro. The online questionnaire was divided into three parts, with an option to complete three questionnaires within a month’s time. This was to reduce the possibility of common method variance (CMV) associated with a single source and one time (Podsakoff et al., 2003). We also contacted 135 people in Jaipur (India) via a hard copy of our survey. The offline survey questionnaire was allowed to be kept by respondents for 2 weeks to reduce the chances of CMV.

Of note, 115 usable questionnaires out of the 527 questionnaires distributed were returned for a 21.82 per cent response rate. Of the 115 employees who participated in the study, 76 (66.08%) were males and 39 (33.91%) were females. In terms of age, 86 (74.78%) were below 30 years, 15 (13.04%) were between 31 and 35 years, 6 (0.05%) were between 36 and 40 years, 4 (0.03%) were between 41 and 45 years and 4 (0.03%) were above 45 years. In terms of academic qualification, 45 (39.13%) were graduates, 52 (45.22%) had a masters’ degree and the rest were PhDs or without any degree. Once all the responses were recorded and received, they were made suitable for further analysis by eliminating outliers and questionnaires with missing data.

Measures

All measures used in the study were adopted from established scales written in English. The questionnaire was administered in the English language; thus, there were no issues concerning translation/retranslation.

Human Capital

Human capital construct consists of human capital perception and human capital uniqueness. Human capital perception and human capital uniqueness have been designed according to the scale developed by Subramaniam and Youndt (2005) and Lepak and Snell (2002), respectively. With Likert scale responses ranging from 1 (strongly disagree) to 5 (strongly agree), the respondents were asked to indicate their perception of human capital incorporated by organisation in them (e.g. “I possess excellent skills because of my organisation’s investments in my training, learning, and development”).

Gratitude

GQ-6 questionnaire was used to assess gratitude. It was adopted and changed to organisation settings following previous studies (Andersson et al., 2007; Waters, 2012). With Likert scale responses ranging from 1 (strongly disagree) to 5 (strongly agree), the respondents were asked to indicate how grateful they feel towards the organisation (e.g. “If I had to list everything that I felt grateful for at my workplace, it would be a very long list”).

Knowledge Management

KM was measured by a scale developed by Van den Hooff and Hendrix (2004). It has two subscales, knowledge donating and knowledge collecting. Knowledge donating was measured using three items (e.g. “When I have learned something new, I tell my colleagues about it”), which assess the degree of employee willingness to contribute knowledge to colleagues. Knowledge collecting was measured using four items (e.g. “When I need certain knowledge, I ask my colleagues about it”), which refers to collective beliefs or behavioural routines related to the spread of learning among colleagues. Respondents were asked to indicate how they feel at work using a five-point scale, ranging from 1 (strongly disagree) to 5 (strongly agree).

Innovative Work Behaviour

A six-item employee-rated scale measuring IWB was adopted from the studies by De Jong and Den Hartog (2010). The employees were asked to rate the frequency with which they displayed different behaviours (e.g. “In your job, how often do you apply new knowledge”) on a five-point scale ranging from 1 (always) to 5 (never).

Job Characteristics

The 10-Likert items from the revised form of the Job Diagnostic Survey (see Hackman & Oldham, 1974; Idaszak & Drasgow, 1987) were used. On a seven-point scale (1, “very inaccurate”, to 7, “very accurate”), participants indicated the accuracy of statements such as “There is autonomy in my job” (autonomy); “The job requires me to use many complex high-level skills” (variety); “My job involve doing a ‘whole’ and identifiable piece of work” (identity); “My job is significant or important that is the results of my work is likely to significantly affect the lives or well-being of other people” (significance); “Doing the job itself provides me with information about my work performance that is, the actual work itself provide clues about how well I am doing—aside from any ‘feedback’ coworkers or supervisors may provide” (feedback).

Control Variables

To demonstrate the robustness of the results, we controlled for demographic variables such as age, experience and industry type.

Analysis

Descriptive Statistics

The descriptive statistics and correlations between the four constructs are present in Table 1. We tested for zero-order correlations between constructs and found the strength of correlations as expected, thus supporting the hypotheses.

The data analysis preceded by a series of steps. Missing values in the data set were imputed using Expected Maximization on SPSS. We tested the data for dimensionality and conducted a dimension reduction test using maximum likelihood. As per the results of factor analysis, two items were dropped from Human Capital scale (items 4 and 5 of Human Capital Uniqueness subscale), three items were dropped from Gratitude scale (items 3, 4 and 6), two items were deleted from KM scale (items 3 and 4 of Knowledge Donating subscale). The construct validity of the model was tested to confirm that constructs are theoretically related (convergent validity), but at the same time the constructs are dissimilar and non-overlapping in nature (discriminant validity) (Hair et al., 2016). To test the reliability of the scales, Cronbach’s alpha was calculated. Table 2 shows the results of construct validity (convergent and discriminant validity) and reliability. The criterion for assessing construct validity is by observing the average variance extracted (AVE) and composite reliability (CR). The AVE was calculated by applying the ratio of squares of the sum of factor loadings divided by the square of the sum of factor loadings and the error variances of the variables. Hair et al.’s (2016) guidelines were followed to analyse construct validity that sets a threshold of 0.5 and above for AVE and CR, and Diamantopoulos and Siguaw’s (2000) guidelines were followed who set the threshold for 0.60 for CR. The findings of AVE and CR are presented in Table 2. Both the values for all five factors are found to be above the threshold, thus confirming construct validity and reliability.

Table 1.

Descriptive Statistics and Correlations

	Mean	Standard Deviation	Age	No. of Training	Total Work Experience (in Years)	Human Capital	Gratitude	KM	IWB	Job Characteristics
Age	29.23	7.67	1
No. of training	1	6.21	0.91**	1
Total work experience (in years)	6.5	2.12	0.81	0.76	1
Human capital	2.71	0.71	0.11	0.57	0.55	(0.770)
Gratitude	3.65	0.52	0.41	0.62	0.55	0.61**	(0.841)
KM	3.12	0.61	0.12	0.64	0.61	0.81**	0.71**	(0.921)
IWB	4.01	0.93	0.22	0.79	0.52	0.56**	0.51**	0.65**	(0.844)
Job characteristics	3.77	0.51	0.04	0.59	0.62	0.41	0.36	0.61	0.25	(0.813)

Source: The authors.

Note: ** refers to p < 0.01.

Table 2.

Convergent and Discriminant Validity of Constructs

Variable	AVE	CR
Human capital	0.54	0.77
Gratitude	0.52	0.67
KM	0.61	0.80
IWB	0.73	0.89
Job characteristics	0.82	0.97

Source: The authors.

To test the hypotheses, a composite scale variable was generated in SPSS 23 and analysed in SPSS macro, PROCESS version v3.4, as suggested by Hayes (2013). The advantage of using PROCESS Macro is it generates an index of moderated mediation with simple slopes results (standard error, t-value, p-value), providing a better understanding of the relationship between variables. The four hypotheses under the study, namely direct effect, the mediation (indirect effect), moderated mediation and the serial mediation, were tested at 95 per cent confidence interval and bootstrapped at 5,000 samples.

Testing the Direct Effect

The total and direct effect of human capital on IWB is reported in Table 3. The total effect is statistically significant at B = 0.211 (95% confidence interval, bootstrapped, 0.197; 0.287). The direct effect found to be statistically significant B = 0.012 (95% confidence interval, bootstrapped, 0.006; 0.061). The hypothesis 1 is accepted and confirms an existing significant relationship between human capital and IWB. The total effects prove the significance of mediating variables (gratitude and KM) since the B-value of total effect increases in the presence of mediators. The results are mentioned in Table 3.

Testing of Indirect Effect

Table 3.

Total Effect, Direct Effect and Indirect Effect

Hypotheses	Effect	B	BootLLCI	BootULCI
	Total effect(Human capital → IWB)	0.211	0.197	0.287
1	Direct effect(Human capital → IWB)	0.012	0.006	0.061
2	Indirect effect(Human capital → G → KM)	0.197	0.032	0.234

Source: The authors.

The second hypothesis tested the indirect relationship between human capital and KM mediated by gratitude. Statistical analysis was conducted on the hypothesis, and the results were found to be statistically significant at B = 0.197 (95% confidence interval, 5,000 bootstrapping 0.032; 0.234). The result supports the mediation of gratitude between human capital and KM. The results of indirect mediation are reported in Table 3.

Testing the Moderated Mediation

The third hypothesis tested the moderating effect of job characteristics on the relationship between KM and IWB. Statistical analysis conducted on the hypothesis confirmed that job characteristics moderated the relationship between KM and IWB (the interaction effect was found to be statistically significant, B = 0.071, p = 0.002). Using the composite variable score of the construct, simple slopes were generated and analyses revealed that when the job characteristics in employees are high (1 SD above the mean or +1 SD), the influence of KM on IWB is 0.630 (p = 0.000). When the value reached mean, the influence decreases to 0.582 (p = 0.000). Then as the value of moderator further decreased to low (1 SD below mean or −1 SD), the influence of KM on IWB decreased to 0.512 (p = 0.000). Hence, it can be concluded that employees with higher levels of job characteristics positively moderate the relationship between KM and IWB.

The index of moderated mediation indicating the conditional indirect effect of human capital on IWB through KM for employees with improved job characteristics was statistically significant (0.021, 95% confidence interval, bootstrapped, 0.034; 0.003). The results of hypothesis 3 testing the conditional indirect effect are reported in Table 4. The results show that as the strength of moderator (job characteristics) increases, the conditional indirect effect (of human capital on IWB through KM) increases as the value of moderator increases. Thus, hypothesis 3 is supported.

Testing the Moderated Serial Mediation

We tested the fourth hypothesis, which represents the conceptual framework of the research study. To quantify the effect of job characteristics on the indirect effect of human capital on IWB via Gratitude and KM, an index of moderated mediation was generated. An index of moderated mediation provides the most direct evidence of moderated mediation (Hayes, 2013). The results were found to be statistically significant (B = 0.019, 95% confidence interval, bootstrapped, 0.021; 0.001). The conditional indirect effect in Table 5 shows that as the moderator’s value increases, the conditional direct effect increases, thus accepting the hypothesis 4.

Table 4.

Moderated Mediation Results of Human Capital and Innovative Work Behaviour for All Levels of Job Characteristics

Hypothesis	Moderator (Job Characteristics)	Effect	BootLLCI	BootULCI
3	Low (−1 SD)	0.302	0.223	0.365
	Medium (mean)	0.321	0.201	0.381
	High (+1 SD)	0.360	0.054	0.401

Source: The authors.

Table 5.

Moderated Serial Mediation

Hypothesis	Moderator (Job Characteristics)	Effect	BootLLCI	BootULCI
4	Low (−1 SD)	0.231	0.109	0.365
	Medium (mean)	0.292	0.171	0.381
	High (+1 SD)	0.350	0.251	0.401

Source: The authors.

Discussion

The main objective of this research was to delineate a process of how human capital investments manifest at the individual level to create an understanding of how and when micro social orders get generated. The results of this study made an attempt to enhance our understanding of various affective (gratitude) and behavioural (KM and IWB) level and individual-level factors (reflecting micro social orders) which get demonstrated as a result of investments in employees’ human capital. We investigated a relationship between human capital and IWB with gratitude, KM as underlying mediating mechanisms. This study also identified job characteristics as an important moderating variable that might enhance or decrease the serial mediation effect depending on their content. The use of moderated serial mediation model on employees from various industries yielding significant results for all our hypothesis strengthens our findings’ validity and generalisability.

The central thesis of this study is that when organisations invest in their employees, they feel empowered in terms of resources in the form of newly acquired KSAOs, which leads to a feeling of gratitude towards the organisation. These newly attained resources are exchanged in the form of information with peers and colleagues, leading to KM behaviours. This involvement in firm-specific KM behaviours leads to generation, promotion and realisation of new ideas and procedures, thus promoting IWB. The results of the present study are consistent with studies in the past investigating the relationships among variables considered here (Michael et al., 2011; Wiig, 1997; Wojtczuk-Turek & Turek, 2015). However, this research addresses the most prominent gap in integrating macro–micro human capital by investigating how and when human capital generates micro social orders. Micro social orders uncover interaction patterns and emotional reactions of employees because of various activities directed by the organisation towards the employees.

Using the perspective of SET and Affect Theory of Social Exchange, the results emphasise that management practices of human capital investments lead to an increase in an individual’s IWB. An important implication of this finding is that the underlying mechanisms for IWB that our study finds support for: gratitude and KM behaviours (hypothesis 2). These are individual-level factors but are highly contagious to other immediate colleagues and peers. KM behaviour cannot exist in isolation; by definition, it involves knowledge donation and knowledge sharing. They are thus leading to actions that create reciprocity and influence the macro-level structures such as organisational-level outcomes (e.g. performance and learning) or more abstract concepts such as culture or overall well-being.

The results also provide a novel perspective on how job characteristics as boundary condition affect the relationship between human capital and IWB (hypothesis 3). Consistent with our proposition, the results found that favourable levels of various components (autonomy, variety, identity, significance and feedback) of job characteristics enhanced the effect of gratitude and behaviour of KM and IWB, resulting from an increment of human capital. This result signifies both the kind of job characteristics responsible for IWB and what organisations can do if in case they want to increase gratitude and KM behaviours.

Theoretical Implications

From a theoretical perspective, this study is important for several reasons. First, it helps to understand how organisational level HRM activity of human capital investment manifests at the individual level. For example, organisations strategically frame policies around employee development and invest in training and development programmes. However, it is equally important to see the translation of this activity in the minds of employees, both emotionally and behaviourally. Examining the individual-level impact of human capital investments helps gain insights about how perceptions of individuals own KSAOs because of human capital shape their behaviours. Second, by empirically developing and testing a model that illustrates the process by which human capital influences employees’ IWB as well as the boundary conditions, this study contributes to existing human capital literature. Third, it is the first study that uses SET and Affect Theory of Social Exchange to investigate how and when human capital investment generates micro social orders (repeated interactions, emotional reactions, perceptions of cohesion and affective sentiments of a group/organisation due to social structures). Lastly, testing this process model also attempts to answer calls to integrate macro–micro human capital (Nyberg et al., 2018; Wright et al., 2018).

Organisations invest lots of financial resources as well as efforts to create a competent pool of employees. Considering this argument, our research by understanding the process by which investments in human capital leads to IWB adds to the knowledge of consequences at the psychological and behavioural levels. This study provides empirical evidence that gratitude and KM behaviours are important underlying variables that mediate the positive relationship between human capital and IWB. This finding is essential from the view of organisational success and survival. As organisations continue to look for ideas and procedures to foster innovativeness, thus the insight that gratitude leading to KM behaviours can lead to IWB can help them devise their policies and programmes accordingly.

Practical Implications

The present study has some significant implications for practitioners. First, the organisation’s aim to have a positive relationship with employees in terms of an increased sense of belongingness, trust and pleasant feelings such as happiness, satisfaction and gratitude by implementing various policies and cultural aspects that employees find valuable and appreciate. Our study empirically examines that investments in human capital lead to the positive feeling of gratitude. Thus, helping organisations realise one more strategy to enhance these positive reciprocation norms.

Second, this study has implications for managers seeking to advance their employees’ IWB. This study provides evidence that managers should invest in and develop their employee’s human capital by increasing their KSAOs through education, training and experience. Thus, managers/supervisors should take time to explore the kinds of KSAOs highly valued by the employee and are consistent with the requirements of the employee’s job. By identifying the human capital required by an employee and by investing in that kind of requirement, an organisation can establish positive reciprocity obligations and enhance employees’ motivation that would be manifested in the form of extra-role behaviour like IWB by an employee.

Third, the results of this study indicate that organisations that have adopted the practice of designing jobs with favourable job characteristics (i.e. jobs containing more skill variety, task identity, task significance, autonomy and generating feedback provides opportunities for employees) lead to a higher quality of IWB in employees. The presence of jobs with favourable job characteristics signals employees that their organisation cares for them. Hence, a feeling of gratitude emerges that enhances favourable reciprocity and manifests IWB. Hence, managers should try and implement jobs high in various job characteristics.

Limitations and Future Research Directions

Although this article makes valuable theoretical and managerial contributions, this research is not without limitations. In this section, we acknowledge these limitations. Few limitations emerge from its methodological design. First, the data are cross-sectional, which does not permit inferences to be drawn regarding causal relationships between human capital, gratitude, KM and IWB. Future research could employ a longitudinal design even though this would not completely resolve the difficulty of substantiating causality, but it would make the findings more robust. Second, all constructs were measured using self-report questionnaires from a single source. Although we took measures like dividing the questionnaire into different sections and allowing sufficient time to respond, eliminating CMV (or common method bias) is very difficult. A CMV can be attributed to the numerous aspects of data measurement which are not under the researcher’s control (Podsakoff et al., 2003). Future research could further eliminate the chances of CMV by collecting data from multiple sources (e.g. having supervisors/peers rate employees’ KM and IWB). Third, there are chances of social desirability bias as employees might have wanted to report their cognitive (gratitude) and behavioural reactions (KM and IWB) in a way that presents a favourable image about themselves or their organisation (Restubog et al., 2013). Although we attached an online form while employing surveys online and briefed them verbally while conducting surveys offline that complete confidentiality of information will be maintained, but still we cannot completely rule out this possibility (Podsakoff et al., 2003). To enhance generalisability, this study should be replicated in similar and different contexts.

Another limitation that this study suffered is that we have examined only IWB as extra-role behaviour. When an employee goes beyond the job description or responsibilities and works for the overall betterment of the organisation, the employee is said to show extra-role behaviour (Somech & Drach-Zahavy, 1999). Outcomes at the individual level are a multidimensional construct, and future studies may benefit from the incorporation of other descriptors of work outcome. It is plausible that with increment in human capital employees not only engage in more IWB but also engage in other positive behaviours such as organisational citizenship behaviour. It could also be helpful to see what happens to negative behaviours such as counterproductive work behaviours. Future research might also explore how these behaviours vary across age, work experience, gender, qualification level and designation of the employee.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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