The differential value of resources in predicting employee engagement

Helena D. Cooper-Thomas (Department of Management, School of Business, Auckland University of Technology, Auckland, New Zealand)
Jessica Xu (International Business Machines (IBM), Auckland, New Zealand)
Alan M. Saks (Centre for Industrial Relations and Human Resources, University of Toronto, Toronto, Canada)

Journal of Managerial Psychology

ISSN: 0268-3946

Publication date: 2 July 2018



The purpose of this paper is to apply and test a theory specifying which resources are most important for employee engagement. Specifically, this paper draws on resource theory to outline six resources (love, status, services, information, goods, money) provided by the organization that employees will exchange for engagement.


The paper’s main focus is theoretical, outlining how resource theory provides a more nuanced classification and understanding of the workplace antecedents of engagement. Specifically, engagement is proposed to represent love as a resource, since engagement represents the whole-hearted investment of oneself. Thus, employees will exchange engagement for employer resources that similarly denote individual warmth and caring. The resource classification is assessed using engagement data from IBM NZ (n=13,929).


The theoretical analysis identifies eight workplace resources, five of which are proposed to be exchanged for engagement: mission, vision and values; opportunities for development; supportive leadership; job resources; and teamwork. Subsequent empirical analysis of IBM NZ data identified three similar constructs, with two being stronger predictors of employee engagement: learning and development; and vision and purpose. This provides some initial support for the application of resource theory to engagement.

Practical implications

Resource theory enables the identification of specific resources that will more strongly facilitate engagement: those which demonstrate warmth and caring for the employee.


Resource theory adds specificity in identifying which workplace resources will be exchanged for engagement, and therefore extends existing models of engagement, and is valuable for future employee engagement research and practice.



Cooper-Thomas, H., Xu, J. and M. Saks, A. (2018), "The differential value of resources in predicting employee engagement", Journal of Managerial Psychology, Vol. 33 No. 4/5, pp. 326-344.

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Copyright © 2018, Emerald Publishing Limited

Individuals differ in the degree to which they feel able to bring their full selves to work, reflecting their work engagement (Kahn, 1990). From a humanistic perspective, employee engagement supports organizations in providing working conditions that enable employee productivity and well-being (Shantz et al., 2016; Shuck and Wollard, 2010). Arguably, organizations are equally interested in engagement due to its economic benefits (Harter et al., 2002). Given these advantages, it is unsurprising that many practitioner organizations specialize in measuring employee engagement (CEB Global, 2018; Gallup, 2018; Willis Towers Watson, 2018; see also Shuck and Wollard, 2010). We address a key issue for employers, and arguably of interest to employees too, namely which workplace resources provided by organizations to employees are the best predictors of employee engagement.

Theoretical background

Kahn (1990, p. 694) defines engagement as “the harnessing of organization members” selves to their work roles; in engagement, people employ and express themselves physically, cognitively and emotionally during role performances’. When a person is engaged, they are fully psychologically present and immerse themselves totally in their work role. Engagement has been proposed to have both dispositional (Byrne et al., 2017; Wefald et al., 2011) and organizational antecedents (Shantz et al., 2016; Tuckey et al., 2012). Our focus here is on organizational antecedents for the practical reason that, with an existing workforce, features of the context are easier to alter than dispositional factors. For example, organizations may aim to increase engagement through optimizing work design (Christian et al., 2011), providing feedback or offering learning and development opportunities (Christian et al., 2011; Eldor and Harpaz, 2016; Shantz et al., 2016).

Two theoretical models predominate employee engagement research, namely job demands-resources (JD-R) (Bakker and Demerouti, 2007, 2017) and conservation of resources (COR) (Hobfoll, 2002, 2011), with a third approach, social exchange theory, also occasionally being used (Chughtai and Buckley, 2013; Saks, 2006). These theories share a common view that more employer-provided resources enable greater employee engagement.

However, these theories do not specify which resources are more or less important. Bakker and Demerouti (2017, p. 278) acknowledge this, stating “JD-R theory is a heuristic and flexible model. However, this flexibility could be also the Achilles’ heel of the model, as this comes at the cost of specificity and the quality of its predictions.” Similarly, the resource caravans of Hobfoll’s (2011) model aggregate resources together, thereby ignoring issues of relative importance. Finally, in their recent review of social exchange theory, Cropanzano et al. (2017) identify the lack of theoretical precision within the family of social exchange models as problematic. Thus, studies typically measure a handful of job resources and simply predict that all job resources will be related to engagement. Other studies aggregate heterogeneous job resources which “conceals the effects and relative contribution of specific resources and limits the capacity for research to inform practice” (Biggs et al., 2014, p. 304). Thus, the current state of research on employee engagement makes little to no distinction between the value of different job resources. Rather, a resource is a resource is a resource and all job resources are equal.

Our first contribution then is theoretical. We draw on resource theory of social exchange (“resource theory”; Foa, 1971; Foa and Foa, 1980) and notions of reciprocity (Blau, 1964; Gouldner, 1960) to explicate how resources provided by the organization may be differentially exchanged for engagement on the part of the employee. This approach is consistent with JDR, COR and social exchange approaches in focusing on resources, but has the additional benefit of greater specificity. Thus, we outline the six resources proposed in resources theory, then review resources antecedent to engagement to identify links, and in turn predict the strength of association of the resources with engagement.

Our second contribution is empirically testing this model based on resource theory using a large, nationally representative data set of New Zealand employees, with data from IBM NZ. Factor analysis indicated eight resources provided by the organization. We classified these in line with our initial theoretical analysis, and then investigated our predictions using relative weights analysis to provide a rigorous initial test of resource theory in understanding engagement (Gray and Cooper, 2010).

A third contribution is practical, and echoes Lewin’s (1952) adage that there is nothing as practical as good theory. Given the time, energy, and money many organizations spend trying to raise staff engagement levels, resource theory provides a valuable and efficient means of identifying antecedent factors likely to foster engagement. Specifically, organizations can review their resources to identify those most likely to be exchanged by employees for engagement, based on resource theory, namely those that show warmth and caring for each employee. Organizations can then decide which resources to modify, improve or introduce to enable the exchange of engagement.

Social exchange as a framework for understanding engagement

Social exchange theory provides a family of conceptual models that facilitate understanding of ongoing reciprocal exchanges underpinning organizational behavior (Cropanzano et al., 2017). Social exchange concepts explain why employees respond to work-related resources with varying levels of engagement (Saks, 2006). For example, Saks (2006) argues that employees can decide how much of themselves to give to their work, that is engagement, in exchange for resources provided. Social exchange differs, therefore, from JDR and COR in focusing on the two-way exchange between employees and their organization.

Modern social exchange theory is developed from a range of theories (Cropanzano and Mitchell, 2005) including the original works of Gouldner (1960), Blau (1964) and the Foas (Foa, 1971; Foa and Foa, 1980, 2012; Turner et al., 1971). Blau (1964, p. 93) proposes that the key, basic distinction of social from economic exchange is that “social exchange entails unspecified obligations [italics in original]” although some future return is expected. While theoretically there is a large body of evidence supporting such a social exchange, for example that HRM practices from the employer are exchanged by employees for innovative work behavior (Alfes et al., 2013), there is a lack of a priori specificity as to which resources will be exchanged (Cropanzano et al., 2017). Specifically, Cropanzano et al. (2017, p. 1) state “an important criticism of social exchange theory, however, is that it lacks sufficient theoretical precision, and thus has limited utility.” We propose resource theory as providing this additional finesse.

Resource theory arose out of the need to extend social exchange theory to identify which resources would be exchanged between parties (Foa, 1971). Specifically, Foa’s starting point was the problem that, whereas economic exchanges involve the finite transfer of a resource from one person to another, such as a piece of merchandise, social exchanges may not entail a resource loss by the giver. Foa provides the example of love, which a person can give without any loss. Foa’s aim was to classify all types of resources to better understand and predict what would be exchanged a priori, ranging from social through economic resources.

Resources are anything that can be transmitted from one person to another, which includes physical objects, verbal communication and nonverbal actions (Foa and Foa, 2012). Foa (1971) outlines six resources: love, status, information, goods, services and money (see Figure 1). Love involves affectionate regard, warmth, and caring, and notably may be platonic; status is the provision of esteem and prestige; information is the giving of advice, instructions, or opinions in a neutral way that does not engender love or status; goods are tangible products or materials; services are activities or labor provided to another; and finally, at the most economic end of the spectrum, money refers to any currency or token that has a standardized value.

It may be challenging to consider love as a resource to be exchanged in a work setting. We provide two arguments to support our perspective. First, in practice many consulting organizations promote drivers of engagement as enabling employers to win employees’ hearts and minds (AonHewitt, 2012; Hay Group, 2015). Winning employees’ hearts and minds can be conceptualized more simply as winning employees’ affection or love. Effectively, practitioners are already referring to love, although being coy about using that term.

Second, academic research has developed related constructs connoting this full connection of the employee to their work or their organization. Rich et al. (2010) state that “engagement involves investing the hands, head, and heart in active, full work performance” (p. 619). Similarly, in discussing organizational identity formation, Ashforth and Schinoff (2016, p. 128) note, “Individuals may follow their heart (affect), hands (behavior), or head (cognition) into internalizing the identity.” Research has also investigated work passion, which includes love for one’s job (Ho and Astakhova, 2017).

Thus, both practice and science already feature strong connections of employees with their work and organization, and some researchers have used the term love. To draw accurately on resource theory, and remain conceptually consistent, we retain the term love, although emphasizing its platonic nature.

Empirical evidence for the role of love in predicting engagement comes from recent research by Barsade and O’Neill (2014) with staff at a healthcare facility. They found that a culture of companionate love predicted employees having higher engagement and lower withdrawal. To investigate the generalizability of companionate love across industries, Barsade and O’Neill conducted two additional studies. First, they identified the words caring and love as occurring in the values and management principles of many successful organizations (e.g. Pepsi Co, Southwest Airlines). Second, they conducted an additional study with over 3,000 employees across seven industries, finding that companionate love predicted employee engagement. Overall, while conceiving of love as a resource may be challenging, either the term love or synonyms are already being used in practice and research.

Returning to resource theory, the six resources differ along two dimensions, concreteness vs symbolism and particularism vs universalism (Foa, 1971; Foa and Foa, 1980). Concreteness refers to how tangible the resource is, with services and goods being the most real, verifiable resources that can be provided in an exchange. Contrasting these are status and information, which may exist purely as verbal behaviors, and can be more symbolic. Love and money vary in how tangible they are: Love may be exchanged verbally or nonverbally; money can exist as hard currency but is also symbolized in other ways, for example a credit card or healthcare insurance.

The second dimension, of particularism through universalism, refers to how specific the resource is to the agent providing it. Love is the most particularistic, since its value derives directly from the donor, contrasting with money as the least particularistic – or most universal – since the value remains stable regardless of the giver. Status and services are less particularistic than love, with some degree of importance derived from the source of these resources. For example, it matters more if you receive acknowledgment from your boss for the quality of your work compared with a junior team member. Goods and information are more universal still, as their value is approximately equivalent regardless of the source, yet they remain slightly more particularistic than money. For example, the trustworthiness of information varies depending on the source. Thus, the more particularistic resources of love, status and services are intrinsically valuable for what they signify or provide, whereas the more universal resources of goods, information and money have value either or both for the pleasure of acquiring them and for what they enable (Foa and Foa, 1980). These differences in concreteness and particularism are depicted in Figure 1 by the placement of the six resources on two axes (see bold font).

Resource theory, like social exchange theory, proposes that people prefer to exchange a resource similar to the one they received (Blau, 1964; Cropanzano and Mitchell, 2005; Foa, 1971; Foa and Foa, 1980). In their recent review of social exchange theory, Cropanzano et al. (2017) draw on Lyons and Scott’s (2012) concept of homeomorphic reciprocity to explain this preference for exchanging similar items. When equivalent resources cannot be exchanged, neighboring resources will be exchanged (Foa, 1971); this is represented by the proximity of the six resources around the circle in Figure 1. For example, love and money are the most distant and are unlikely to be exchanged, whereas love and status are closely related and may be exchanged. As a tangible workplace example of neighboring resources being exchanged, a request from a junior to a senior colleague for help with a spreadsheet offers recognition to the senior colleague (status); in return, the senior colleague may exchange ideas on how to proceed (information) and perhaps also a friendly, supportive comment (love). This example also illustrates another idea from resource theory – that an item given in an exchange may represent multiple resources. Foa and Foa (1980) provide the analogy that resources are like chemical elements that are usually found in compound forms, made up of proximally situated resources.

Thus, drawing on resource theory and the underpinning notion of reciprocal exchange, we argue that engagement is a resource that employees may provide in exchange for equivalent types of resources from the organization. This allows a more fine-grained analysis and a priori prediction of the specific organizational resources that predict engagement.

Classifying resources to be exchanged

Resource theory may only be applied to engagement if relevant constructs can be mapped onto the six resources. Starting with engagement, Kahn’s (1990) research reveals that engaged employees give their whole selves in their work. There is a range of evidence demonstrating the extent to which employees give of themselves to their work, in terms of their physical and temporal exertion at work (Brown and Leigh, 1996; Jansen and Kristof-Brown, 2005), emotional attachment (Den Hartog and Belschak, 2007; Ho and Astakhova, 2017) and cognitive effort (Wu et al., 2014). This investment of oneself in engagement is congruent with the resource of love because it is whole-hearted and highly particularistic to the exchange partner – their employing organization.

According to resource theory, individuals will only exchange love for an equivalent resource from the organization. Optimally then, engagement will be exchanged for organizational resources that similarly represent caring and warmth. If that is not possible, proximal resources representing status or services may be exchanged for engagement (Turner et al., 1971). We reviewed the literature to identify organizational resources theorized as antecedent to engagement, and identified the following: Mission, vision, and values; opportunities for development; supportive leadership; communication; training; teamwork; job resources; and rewards and recognition.

The vision, mission and values of an organization represent fundamental elements that employees may align with. Biggs et al. (2014) investigated strategic alignment, which is the employee’s perception both that their job aligns with the organization’s strategic priorities, and that those priorities are important. Strategic alignment predicted engagement longitudinally (Biggs et al., 2014). While vision has been proposed to predict engagement (Fleck and Inceoglu, 2010), it has more commonly been investigated as part of transformational leadership in predicting engagement (Breevaart, Bakker, Demerouti and van den Heuvel, 2015). The vision and mission of the organization also relate to values, with employee-organization value congruence predicting engagement (Rich et al., 2010). In summary, the mission, vision and values of an organization predict engagement among employees.

As resources, the mission, vision, and values are symbolic, providing an aspirational purpose. Additionally, organizational objectives that contribute to society provide status to the employee (Schneider et al., 2003), for example working for Greenpeace if the employee emphasizes the environment, or for a health technology company that improves quality of life. Based on our analyses, we associate the resource of mission, vision and values with both love and status (see Figure 1).

Development is theorized to predict engagement (Fleck and Inceoglu, 2010), and this is borne out in Crawford et al.’s (2010) meta-analysis, in which opportunities for development was a strong predictor of engagement (ρ=0.47). Opportunities for development, as well as training as a separate factor, predicted engagement among both nurses and healthcare office staff in the UK’s National Health Service (Shantz et al., 2016), and learning climate predicted engagement in a diverse Israeli sample (Eldor and Harpaz, 2016). In a study on caregivers in long-term care facilities in Italy, Sarti (2014) found that greater learning opportunities was the strongest predictor of work engagement among six job resources investigated. When training and development are grouped with other organizational resources, these also predict engagement (Alfes et al., 2013; Boon and Kalshoven, 2014).

Tailored opportunities for development reflect a concern for the individual’s needs and career path, such as a project role to develop specific skills; this individualized caring fits with the resource of love. Simultaneously, the actual providing of the opportunity represents a service, in terms of activities or labor provided to another, for example a senior colleague acting as a mentor. In contrast, training is likely to be more generic, aimed at upskilling a cohort of employees, and this fits better across two resources – services, which are activities provided to another, and information, which is giving instructions in a neutral way. While opportunities for development and training are often combined in research as training and development, for clarity we represent them separately, as depicted in Figure 1.

Leadership, as well as managerial and supervisor behavior, predict engagement (Breevaart, Bakker, Demerouti, and Derks, 2015; Tims et al., 2011). Empowering leadership, a follower-centered construct in which leaders encourage followers to take responsibility (Tuckey et al., 2012), predicts firefighters’ engagement. Relationship-oriented leadership, including showing genuine interest and providing encouragement, predicts employee engagement more strongly than task-oriented leadership, and personal honesty (Xu and Cooper-Thomas, 2011). Similarly, transformational leadership predicts daily employee engagement (Tims et al., 2011), and leader-member exchange predicts engagement via increased resources (Breevaart, Bakker, Demerouti and van den Heuvel, 2015). Overall, thoughtful and supportive leader and manager behavior predicts engagement.

Considering resource theory, such leader behavior has a love component, since it shows caring and warmth. Supportive leadership may also have a status component, because a leader who listens to the ideas of her direct reports is valuing their input, providing them with respect (Tuckey et al., 2012). Finally, supportive leadership requires the manager to provide labor or activities on behalf of the employee, for example designing appropriate tasks. This suggests the resource of services. Therefore, we situate supportive leadership as relating to all three of these resources, of love, status and services, as depicted in Figure 1.

Theoretical models propose that communication from the organization will predict engagement (Fleck and Inceoglu, 2010), yet two qualitative studies provide inconclusive evidence. In a newly merged services company, Reissner and Pagan (2013) found that communication fostered “tentative engagement” (p. 2755), with employees remaining cautious following a major change. Townsend et al. (2014) examined employment relations strategies across three hotels, and similarly found genuine efforts by general managers to improve communication yet without clear impacts on engagement. Thus, while theoretically a link has been made for communication fostering engagement, the empirical evidence is unclear.

In terms of resource theory, communication may be categorized across two resources. First, communication clearly represents information – the giving of advice, instructions or opinions; however, Foa and Foa (1980, 2012) propose that information is given neutrally, whereas communication, when used as an HR practice to raise engagement, may be intended to show respect, which relates to the resource of status. Thus, we categorize communication as representing both information and status resources, as illustrated in Figure 1.

Teamwork predicts engagement across diverse settings. Among Chinese airport freight workers, where a positive teamwork environment was emphasized, this facilitated psychological safety, and in turn engagement (Liao et al., 2013). In a sample of Spanish teams, three team social resources were combined (supportive team climate, coordination, and teamwork), and predicted aggregated team-level engagement (Torrente et al., 2012). Chughtai and Buckley (2013) investigated Irish research centers and found that trust in team members predicted team psychological safety, which in turn predicted engagement.

Team factors that predict engagement represent a mix of resources. When employees feel supported and safe taking risks, these are akin to the warmth and caring of love (Chughtai and Buckley, 2013; Liao et al., 2013; Torrente et al., 2012). Alongside these are task-oriented team elements, such as being clear about work goals (Torrente et al., 2012), representing activities provided to another, that is, the resource of services. Thus, teamwork can include elements of both love and services, and we depict teamwork accordingly in Figure 1.

Job resources have been among the most widely studied antecedents of engagement. Employees with greater resources manage work demands better, and experience higher engagement (Bakker and Demerouti, 2017; Hobfoll, 2011). A wide range of job resources have been studied (Christian et al., 2011; Crawford et al., 2010; Halbesleben, 2010). Autonomy, task variety, and task significance may be provided only to more skilled employees, or those at higher levels of seniority, and therefore may represent status. Feedback provided by a colleague represents their labor or action toward an individual, representing services. Social support comes from colleagues who care about the individual, representing love. Therefore, we categorize job resources broadly as representing love, status and services (see Figure 1).

A final type of resource that has also been mentioned in the engagement literature and is salient to employees is rewards and recognition (Crawford et al., 2010; Maslach and Leiter, 1997). Rewards may be pecuniary, such as a bonus, raise or movie tickets, yet may also provide social status where the award is public, such as announcing a success or promotion. In their meta-analysis, Crawford et al. found rewards and recognition was weakly related to engagement, but the 80 percent credibility interval included zero. Based on resources theory, the financial aspect of rewards would fit within money, whereas recognition fits within status; we depict this in Figure 1.

The present study

Drawing on resources theory (Foa and Foa, 2012) which proposes the exchange of proximal resources, and our own suggestion that engagement represents the whole investment of oneself as in love, our overall proposition is that resources that are highly particular and show caring and warmth, that is love, will be exchanged for engagement (see Figure 1). Having focused on theoretical arguments and past research for the majority of this paper, we next present an initial empirical test of the model using a large cross-sectional sample of data from IBM NZ.

The analysis comprised two stages. First, we factor analyzed the resources provided to employees and, drawing on our initial theoretical work, categorized these against the six resources. Second, we investigated the relative strength of these resources in predicting employee engagement to assess whether resource theory can be applied to engagement. As noted above, we expect that resources most proximal to love will have stronger associations with engagement due to the greater likelihood of exchange, while those most distal to love will have weaker associations with engagement as they are less likely to be exchanged (see Figure 1).


Sample and procedure

We used an archival data set from IBM NZ, who are the largest employee engagement survey provider in NZ in terms of number of employees surveyed. This data set comprises 13,929 respondents from 167 organizations who had participated in IBM NZ’s 2008 Best Workplaces survey (IBM NZ, n.d.). The sample includes a wide range of industry sectors and various sizes of organizations. Of the respondents providing demographic information, slightly more were men (57 percent; women 43 percent), with nearly half of the respondents between 30 and 59 years (47 percent), 19 percent were managers/team leaders and 43 percent were non-managerial staff, and 44 percent had 0–2 years’ tenure. Most respondents completed surveys online, although some used pen-and-paper questionnaires, such as staff in restaurants, supermarkets and in manual roles.


Responses were given on a Likert scale ranging from 1 (strongly disagree), through 3 (neutral), to 5 (strongly agree).


The IBM engagement items came from a pre-existing scale belonging to JRA, an organization acquired by IBM NZ. Three items measure the cognitive, emotional and behavioral aspects of engagement (Kahn, 1990): “I look for ways to do my job more effectively” (cognitive), “I take an active interest in what happens in this organization” (emotional), and “I feel inspired to go the extra mile to help this organization succeed” (behavioral). This approach of using one item for each of these three dimensions appears in applied research (Ruck et al., 2017; Shantz et al., 2016), as well as a shortened version of the Utrecht Work Engagement Scale (UWES) (Schaufeli et al., 2017). We conducted a validation study of the IBM engagement scale against the UWES (Schaufeli et al., 2006) and the job engagement scale (Rich et al., 2010; see also Byrne et al., 2016). Strong, positive and significant correlations between all three scales, and with fit perceptions, job satisfaction, and intent to quit, supported the validity of the IBM engagement measure (details available from first author).

Workplace resources

IBM NZ’s Best Workplaces survey measures a range of workplace resources. These ranged from the employee’s role and personal development, through relationships with others, and the broader organizational mission. Example items are given below.

The survey included items on gender, position, age and tenure.

Results – factor analysis

Factor analysis of the 32 workplace resources items resulted in seven factors with eigenvalues exceeding 1, and Cronbach’s α ranging from 0.76 to 0.91 (see Table I). We discuss each emergent factor within the context of the theoretical model, noting that we have separated our theoretical and empirical analyses by using different names.

Contrasting the factors we identified from past research with those extracted here, four are similar in content and therefore are situated similarly in relation to the six resources (see Figure 2 and compare with Figure 1). Opportunities for development was identified from past research and classified as representing resources of love and services. A similar factor emerged from the IBM NZ data, which we named learning and development, corresponding to the opportunities and encouragement provided by the organization to develop and use knowledge and skills. This factor included six items; an example being “There are career and personal development opportunities for me in this organization.” Given the conceptual similarity, we categorized learning and development as representing love and services resources, which the employee will be more willing to exchange for engagement.

Compared with the concept of mission, vision, and values that emerged from our analysis of past research, we identified a factor of vision and purpose which refers to a clear understanding and belief in the vision of the organization. This factor comprised four items, with an example item being “I believe in what this organization is trying to accomplish.” We classified this similarly as representing resources of love and status, and therefore anticipated employees would readily exchange this for engagement.

For supportive leadership, based on our analysis of past research, we proposed this represented resources of love, status and services. A factor of supervisor quality emerged from the IBM NZ data, which refers to one’s immediate manager treating direct reports respectfully, being open to ideas, and providing effective direction and encouragement. There were five items in this factor; an example item is “The person I report to treats people with respect.” Conceptually, supervisor quality is similar to supportive leadership, and we correspondingly categorized it as representing the three resources of love, status, and services. Because supervisor quality includes love as a resource, we anticipated it would be exchanged for engagement.

Rewards and recognition demonstrates weak and unreliable prediction of engagement (Crawford et al., 2010). A similar factor was extracted from the IBM NZ’s workplace resources items, which we named acknowledging success. This factor includes acknowledging and celebrating employees’ contributions (recognition), and providing fair pay and benefits (reward). There were three items in this factor, with an example being “This organization rewards outstanding performance.” This corresponds with rewards and recognition, and we similarly classified acknowledging success as representing status and money, and expected only a weak relationship with engagement.

The remaining three factors extracted from IBM NZ’s workplace resources items bore less resemblance to the conceptual factors identified from past research. The teamwork factor emerging from the IBM NZ data had no elements of warmth and caring corresponding to love, but rather reflected an operational view of teamwork: Being able to rely on others, and working effectively together. Therefore, we named this four-item factor effective teamwork, with an example item being “I have confidence in the ability of the people in my team.” This factor only represents the resource of services, and not love, and therefore we expected a weak relationship with engagement, at most.

As we noted above, a wide range of job resources have been studied (Bakker and Demerouti, 2017), and these may represent aspects of love, status and services. A mixed factor of job resources emerged from the IBM NZ data, which we named job balance; this comprises work characteristics that facilitate the completion of work, including the balance of personal and work life, autonomy, and having appropriate tools and resources (see James et al., 2011, for a similar factor relating to managing work and non-work responsibilities). This factor contained four items, with an example item being “I am able to maintain a balance between my personal and working life.” Primarily the items refer to work conditions that enable effectiveness, suggesting the resource of services, but the inclusion of tools relates to the resource of goods also; therefore, we depict job balance as relating to both resources of services and goods. The resource of love is not relevant to job balance, and hence we anticipate only a weak relationship with engagement.

Finally, in our conceptual model we identified communication as representing resources of status and information. A construct of multi-way communication emerged from the IBM NZ data, which refers to the open and honest sharing of knowledge and ideas across the organization, such that employees feel listened to and informed. This factor comprised six items; an example item is “Communication in this organization is open and honest.” Although communication has been proposed as a tool to raise engagement (Fleck and Inceoglu, 2010), the IBM items suggest neither caring for employees (love) nor status. Therefore, we classified multi-way communication as representing the resource of information only. As a resource, information is distal from love and therefore multi-way communication is expected to have a weak relationship with engagement at most.

In summary, of the seven antecedent workplace resource factors extracted from the IBM NZ data, four of these matched well with previous conceptual work, and of these, we argue three include the resource of love (Foa and Foa, 1980). On this basis we expect that these three – learning and development, vision and purpose, and supervisor quality – will be more strongly related to employees’ engagement, whereas relationships for the other workplace resource factors will be weaker.

Results – predicting engagement

The survey included items on gender, position, age and tenure. We had no theoretical reason to include control factors, their inclusion does not provide a more conservative test of hypotheses (Becker et al., 2016), and made minimal difference to the results. Therefore, we did not include demographic variables in our final analyses. The means, standard deviations, Cronbach’s α, and correlations of the study variables are shown in Table I. Given the large sample size, even small correlations are significant. All seven workplace factors correlate strongly with engagement (r’s 0.50 to 0.69) and show mostly moderate intercorrelations (r’s 0.38 to 0.62).

We used multiple regression analyses to investigate the contribution of workplace antecedents in predicting engagement (see Table II). The overall model was significant, explaining 64 percent of the variance in employee engagement (R2=0.64, F (7, 13921)=3,495.17, p<0.001). The strongest predictors of engagement were learning and development (β=0.28, p<0.001), and vision and purpose (β=0.23, p<0.001). Supervisor quality was also significant (β=0.08, p<0.001), although the weak β coefficient suggests low practical significance (Ferguson, 2009).

We conducted relative weights analysis to investigate whether the differential relationships of the workplace resources with engagement corresponded with our propositions (Tonidandel and LeBreton, 2015). An additional benefit of relative weights analysis for these data is that the correlations of workplace resources with engagement are all strong and positive, and therefore differentiating their unique effects adds valuable information. Relative weights analysis transforms the predictor variables into orthogonal variables which are maximally related to their original predictor variables, and minimally related to the other predictor variables, indicating which predictor variables are relatively more important.

The results in Table II show all relative weights are significant as the BCa CI does not include zero. Of the seven resources, learning and development explains the most variance in engagement (RW =0.138) followed by vision and purpose (RW = 0.119). The remaining five workplace antecedents show similar but lower relative weights (RWs of 0.056–0.089). These results provide preliminary support for resource theory in that the resources that are more proximal to love have stronger associations with engagement.


Theoretical implications

The present study is unique in being the first to use resource theory (Foa, 1971; Foa and Foa, 1980, 2012) to conceptualize engagement as a love resource, to classify resources antecedent to engagement, and to investigate these propositions empirically. Taking the first of these, considering engagement as akin to love as a resource may at first seem far-fetched. To substantiate our point of view, we outlined similar terminology occurs in both practice (AonHewitt, 2012; Hay Group, 2015) and academic research (Ashforth and Schinoff, 2016; Rich et al., 2010). Moreover, we are not the first to propose this link, with Barsade and O’Neill (2014) having established the important role of companionate love for employee engagement.

Our findings with a large sample of NZ employees support the proposition that resources more closely aligned with love – in this case learning and development, and vision and purpose – are stronger predictors of engagement relative to other resources. We had proposed that supportive leadership would also be exchanged for engagement, aligning with past research (Tuckey et al., 2012; Xu and Cooper-Thomas, 2011). However, supervisor quality, while showing a strong correlation with engagement, exhibited a weak relative weight compared with other resources. It may be that the supervisor’s role in identifying opportunities for direct reports (Breevaart et al., 2014; Breevaart, Bakker, Demerouti and van den Heuvel, 2015), and providing a motivating vision (Tims et al., 2011), were accounted for in the factors of learning and development, and vision and purpose, respectively, accounting for the weaker relative weight for supervisor quality (see also Biggs et al., 2014).

Conceptually, resource theory complements other theoretical approaches investigating workplace resources antecedent to engagement, for example, aligning well with Kahn’s (1990) notions of meaningfulness and availability as antecedents to engagement. Employees who perceive a vision and purpose in their work are likely to experience meaning, because they understand and value what they are working toward (Rich et al., 2010; see also Crawford et al., 2010). For availability, employees may accrue resources through learning and development which enable them to be available, and thus more fully engage in role performances.

Our results also complement JDR (Bakker and Demerouti, 2017) and COR theories (Hobfoll, 1989, 2011), in which job resources can be either intrinsically or extrinsically motivating. With a wide range of workplace resources investigated (Christian et al., 2011; Crawford et al., 2010), our theoretical analysis and results using resource theory add greater sophistication and precision by identifying a priori relatively more important resources antecedent to engagement, namely those that denote warmth and caring. This precision has been absent in the engagement literature which has failed distinguish the importance of different resources.

Strengths and limitations

A strength of our research is developing a theoretical approach that allows workplace resources to be meaningfully differentiated, identifying those which are more vs less likely to be exchanged for engagement, and providing an initial investigation with a large data set. The results may generalize to countries with a similar immigrant history (e.g. Australia, Canada, USA) or a European culture (e.g. Eire, Germany).

We intend that our examination of resource theory will enable future research investigating whether resources that include elements of warmth and caring – that is, love – better predict engagement. Our decision not to investigate dispositional features of engagement (Byrne et al., 2017) leaves open the possibility that dispositional and contextual factors will interact to determine employee engagement, providing further research opportunities.

Of the seven potential antecedents of engagement investigated empirically, their correlations with engagement were all strong and positive (r’s 0.50 to 0.69) such that all would predict engagement if they were considered individually. Relative weights analysis enabled evaluation of whether workplace resources that align with love are more uniquely predictive of engagement, and this was largely supported.

However, the data are cross-sectional and self-report, which may result in common method variance (CMV). There is considerable argument over whether CMV results in biases, and whether these increase or decrease relationships (Podsakoff et al., 2003). Our findings of theoretically meaningful differences in the relationships between the resources and engagement suggest that the results are not simply due to CMV. Furthermore, Lance et al. (2010) concluded that attenuation from measurement error largely offsets inflationary bias from a common method.

Practical implications and future research

Resource theory is practically useful (Lewin, 1952), enabling identification of those resources most likely to bring about higher levels of employee engagement. This aligns with a long tradition of research that illustrates the value of showing individual concern (Roethlisberger and Dickson, 1939; Townsend et al., 2014), more recent research demonstrating that an organizational culture of companionate love fosters engagement (Barsade and O’Neill, 2014), as well as practitioner emphasis on winning employees’ hearts and minds (AonHewitt, 2012; Hay Group, 2015). Thus, even in the fast-paced twenty-first century work environment, this personalized concern is relevant.

Focusing on the two key resources predicting engagement, employees who perceive their organization invests in developing their knowledge and skills, represented as learning and development, report higher engagement. Importantly, learning and development budgets are often the quickest to be axed in tough economic times (Aguinis and Kraiger, 2009), yet this may backfire if employees then withdraw their willingness to invest their whole selves into their work. The other key predictor of engagement was vision and purpose. This is in line with recent research showing that strategic alignment and high commitment HRM practices contribute to engagement (Biggs et al., 2014; Boon and Kalshoven, 2014), and aligns with work role fit being an important resource for engagement (Crawford et al., 2010; Rich et al., 2010).

Beyond the resources we identified, there may be other ways that organizations can demonstrate warmth and caring to increase engagement, and these may emerge as ways of working evolve. Further analysis of HR practices would be valuable, to evaluate whether those which most closely resemble love more strongly predict engagement. Love resources might include specific developmental practices such as mentoring or coaching that provide learning and development opportunities, or corporate sustainability initiatives that align to the organization’s vision and purpose (De Roeck et al., 2016).

Additionally, variations in employment relationships may provide useful insights. For example, non-standard work arrangements (George and Chattopadhyay, 2017) focused on more universal resources of information, money, and goods, would be expected to have weak links with engagement. The issue of reciprocity is also intriguing: although both parties may be able to change, it is likely that the organization provides relatively stable levels of resources, whereas the employee offers an initial level of engagement at entry, which is likely to align subsequently with social norms (Salancik and Pfeffer, 1978) and the provisions of the organization (Delobbe et al., 2015). Longitudinal studies would provide insights on patterns of resource provision and engagement.

Research should continue to leverage practitioner data sets to investigate employee engagement (Harter et al., 2010), especially because these data are being used to make decisions that affect many thousands of employees, as compared with data sets collected for academic purposes. Due to their wide impact, there is an ethical imperative to investigate practitioner measures even though they may not align precisely with academic constructs.


In conclusion, we used resource theory to propose that engagement is a love resource, referring to an employee’s investment of their whole self in their role. We argued that engagement is exchanged primarily for workplace love resources that similarly reflect warmth and caring. We provided conceptual and empirical data to support our arguments, finding that learning and development, and vision and purpose, were most predictive of engagement. Resource theory provides a coherent framework for understanding and differentiating workplace antecedents of engagement. Practically, we suggest that organizations seeking ways to improve employee engagement provide employees with resources that demonstrate warmth and caring.


A theoretical classification of resources for exchange

Figure 1

A theoretical classification of resources for exchange

An empirical classification of resources exchanged in the IBM NZ data

Figure 2

An empirical classification of resources exchanged in the IBM NZ data

Means, standard deviations, Cronbach’s α and correlation coefficients

Mean SD 1 2 3 4 5 6 7 8 9 10 11
 1. Vision and purpose 4.25 0.56 0.78
 2. Learning and development 3.92 0.72 0.54** 0.88
 3. Supervisor quality 4.25 0.70 0.43** 0.54** 0.91
 4. Effective teamwork 4.30 0.64 0.40** 0.46** 0.51** 0.91
 5. Job balance 4.01 0.67 0.44** 0.54** 0.48** 0.45** 0.76
 6. Acknowledging success 3.76 0.82 0.51** 0.56** 0.44** 0.38** 0.49** 0.78
 7. Multi-way communication 3.81 0.73 0.62** 0.61** 0.53** 0.48** 0.54** 0.61** 0.89
 8. Engagement 4.21 0.57 0.63** 0.69** 0.54** 0.50** 0.58** 0.60** 0.64** 0.88
 9. Position 1.40 0.62 0.05** 0.12** 0.05** 0.03** −0.03** 0.07** 0.08** 0.18**
10. Tenure 2.82 1.55 −0.04** 0.00 −0.06** −0.02* −0.05* −0.07** −0.11** 0.02* 0.27**
11. Age 4.25 1.39 −0.01 −0.01 −0.03** −0.01 0.00 −0.01 −0.04** 0.08** 0.22** 0.44**
12. Gender 0.57 0.50 −0.07** −0.04** −0.04** −0.03** −0.07** −0.11** −0.02* −0.04** 0.18** 0.10** 0.12**

Notes: n=13,929. *p<0.05; **p<0.01

Results of the regression and relative weights analyses predicting engagement

β RW % RW Lower BCa CI Higher BCa CI
Learning and development 0.281*** 0.138 22 0.133 0.144
Vision and purpose 0.231*** 0.119 18 0.113 0.124
Supervisor quality 0.076*** 0.064 10 0.060 0.069
Effective teamwork 0.081*** 0.056 9 0.051 0.060
Job balance 0.143*** 0.085 13 0.080 0.090
Acknowledging success 0.140*** 0.089 14 0.084 0.094
Multi-way communication 0.079*** 0.086 13 0.082 0.090

Notes: n=13,929. BCa CI, bias corrected and accelerated confidence interval. *p⩽0.05; **p⩽0.01; ***p⩽0.001


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Further reading

Bakker, A.B. and Xanthopoulou, D. (2009), “The crossover of daily work engagement: test of an actor-partner interdependence model”, Journal of Applied Psychology, Vol. 94 No. 6, pp. 1562-1571, doi: 10.1037/a0017525.

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Schaufeli, W.B., Salanova, M., Gonzalez-Roma, V. and Bakker, A.B. (2002), “The measurement of engagement and burnout: a two sample confirmatory factor analytic approach”, Journal of Happiness Studies, Vol. 3 No. 1, pp. 71-92, doi: 10.1023/A:1015630930326.

Supplementary materials


The authors thank Scott Tonidandel for assistance with the relative weights analysis.

Corresponding author

Helena D. Cooper-Thomas can be contacted at: