Work Outcomes of Job Crafting Among the Different Ranks of Project Teams

Abstract

This study examined the effects of job crafting on the work meaningfulness and work engagement of project participants of different ranks. Although previous research has shown that job crafting affects employees’ work outcomes, this topic is under-researched in project management settings. Our findings indicate that work meaningfulness partially mediates the relationship between job crafting and work engagement in the case of project team members and fully mediates it in the case of project managers. They suggest the necessity to apply different means to influence productive project behaviors of the two groups studied. These may include, in particular, changing the approach to job design of project team members and focusing on team job crafting work to build more opportunities to job craft.

Keywords

job crafting work meaningfulness work engagement project team rank project manager project team member structural equation modeling

Introduction

The human factor of project success is increasingly emphasized in the project management literature (Jetu et al., 2011; Suhonen & Paasivaara, 2011). This is connected with a recent shift in project management research from tools and techniques toward the interactions between people and projects, and thus, the social and behavioral aspects of the management of projects (Haffer & Haffer, 2015; Leybourne, 2007). This article strengthens this growing stream of research. Project environments create a unique space for this kind of academic research, as it encompasses a specific group of employees—project team members and project managers. As projects are time-limited structures that create unique products or services, successful execution requires all project participants to have cross-functional skills (Dwivedula & Bredillet, 2010). In addition, projects are characterized by environmental uncertainties and execution limitations (Turner & Simister, 2004) and, at times, function within ambiguous standards, resulting in uncertainty of outcomes (Dwivedula & Bredillet, 2010). Thus, a flexible and innovative approach, full of intellectual effort, is necessary for project team members to maneuver through ambiguity, constraints, and uncertainties that often result in project team members requesting autonomy (Dwivedula & Bredillet, 2010). Autonomy is an important factor for project managers that influences and shapes a project manager’s conduct, professional identity, and authority to make decisions (Hodgson & Paton, 2016).

This research study uses the definition of job crafting from the job demands–resources model (Tims & Bakker, 2010), which defines job crafting as a proactive action that employees invoke to change how they perceive or conduct their work by increasing their work-role fit. Overall, the increase in their work-role fit aligns the characteristics demands and resources of their job with their skills, abilities, and desires. Outcomes such as an increase in job satisfaction, work meaningfulness, and work engagement are possible (Rudolph et al., 2017). According to Walker and Lloyd-Walker (2019), by the 2030s, the management of projects will be characterized by an extended role of job crafting among individuals and teams. This extended role of job crafting is likely to increase work meaningfulness and have the potential to engage employees in team job crafting, which supports an increased level of project success (Walker & Lloyd-Walker, 2019). In addition to the extended role of job crafting within teams, rank within teams affects the level of job crafting (Berg et al., 2010; Lazazzara et al., 2020). Therefore, there is value in studying the effect of job crafting on work outcomes among different ranks of project teams by investigating whether different approaches should be used to influence behaviors of the project team that increase project work. Project managers and project team members understand and expect that there are different levels of authority and autonomy based on rank, which are preconditions (Niessen et al., 2016) and predictors (Grant & Ashford, 2008; Wrzesniewski & Dutton, 2001) of the level of job crafting employees undertake. Rank is one of the contextual variables that is central to the interpretation of the pattern that motivates employees through a variety of job crafting forms (Lazazzara et al., 2020). In addition, rank varies depending on the organizational environment and the components of job design and suggests which employees are more independent and better able to choose their own ways of doing things (Berg et al., 2010; Lazazzara et al., 2020). Therefore, our intention is to investigate how job crafting affects different work outcomes when the rank of project participants is considered in the analysis.

This work aims to study the effects of job crafting as a driver of person–job fit that leads to work meaningfulness and work engagement of employees of different ranks (i.e., project team members and project managers). The research attempts to answer the two following research questions:

Q1. To what extent does job crafting influence the work engagement of project managers versus project team members?

Q2. To what level does work meaningfulness attribute to an increased relationship between job crafting and work engagement for both project managers and project team members?

Building on the previous literature and findings, this study developed and verified a research model (Figure 1), where the effect of job crafting on work meaningfulness was investigated and the effect on work engagement was analyzed.

Figure 1

Research model.

While job crafting and its outcomes have recently received increased attention in organizational research (cf. Karatepe & Eslamlou, 2017; Shin et al., 2020), similar research within a project environment is a significant gap (Niessen et al., 2016). An exception is a study by Amble (2017) that reports job crafting as a new indirect form of collective learning applied in a service development project. We are aware of only a few recent articles in the project management literature that discuss the remaining constructs of this research (i.e., work meaningfulness and work engagement; cf. Iorio & Taylor, 2015; Matthews et al., 2018; Zhong et al., 2018). Nevertheless, we are not aware of any study that investigates job crafting outcomes of work meaningfulness and work engagement within project environments.

Regarding management literature in general, including industrial and organizational psychology literature, the relationships indicated in the research model presented in Figure 1 were examined fully (Peral & Geldenhuys, 2016) or partially (cf. Tims et al., 2016; Baik et al., 2018; Karatepe & Eslamlou, 2017; Matsuo, 2019; May et al., 2004, Shin et al., 2020) in several studies. However, again, we are unaware of any study that considered these relationships with respect to the ranks of examined employees. Previous studies (cf. Esteves et al., 2017; Karatepe & Eslamlou, 2017; Peral & Geldenhuys, 2016; Shin et al., 2020) discussed relationships among non-managers (cf. high school teachers, public health nurses and hospital nurses, frontline hotel employees, or flight attendants) or in multi-profession (cf. cross-sectional, single-industry, or cross-departmental) samples where employees of different ranks were not analyzed separately (cf. Baik et al., 2018; May et al., 2004; Tims et al., 2016). We are not aware of any study regarding job crafting and its work outcome where project managers and project teams were the focus.

Considering the existing research gaps, this article contributes to the research on job crafting and project environments in two ways. First, it expands the previous research on job crafting by examining how work meaningfulness affects the relationship between job crafting and work engagement. Second, we investigate the relationships among job crafting, work meaningfulness, and work engagement in terms of employees of different ranks, such as project managers and project team members. Thus, this study fills a research gap in project management theory related to job crafting outcomes for project participants of different ranks.

Theoretical Background and Hypotheses

Job Crafting

Job crafting can take two forms in the context of project work, namely individual job crafting or collaborative job crafting within teams (Leana et al., 2009). Individual job crafting is defined as a proactive action that employees invoke to change how they perceive or conduct their work by increasing their work-role fit (Tims & Bakker, 2010). In turn, team job crafting is defined as the level of effort team members use to shape their job demands and resources together (Tims et al., 2013). However, job crafting undertaken by individuals and teams is not mutually exclusive, and project team members can participate in both at the same time (Leana et al., 2009; Tims et al., 2013). Simultaneously, Mattarelli and Tagliaventi (2015) found that individual job crafting enables team job crafting. In this study, we focus on individual job crafting in project teams; in the Discussion section at the end of the article, we indicate implications of the achieved results for team job crafting.

Considered from the perspectives of both individuals and teams, job crafting has foundational underpinnings from two job design theories: the job characteristics theory and model (Hackman & Oldham, 1976) and the job demands–resources theory (Demerouti et al., 2001). Job demands–resources theory builds on job characteristics model (Demerouti et al., 2001). Whereas job characteristics model is a managerial-driven (top-down) approach to job design, job demands–resources theory and the theory of job crafting view job design as an employee-driven (bottom-up) approach (Demerouti et al., 2001; Wrzesniewski & Dutton, 2001). Job crafting complements job redesign as employees proactively modify a job toward more challenging and meaningful work (Demerouti & Bakker, 2014; Oldham & Hackman, 2010; Wrzesniewski & Dutton, 2001).

Job demands–resources theory (Demerouti et al., 2001) views employees as the driver of how to balance job resources and demands to reduce job strain, which causes distress when job demands exceed job resources (cf. Bakker & Demerouti, 2017; Demerouti et al., 2001; Tims & Bakker, 2010; Tims et al., 2012). Through job crafting, employees achieve job balance by increasing structural resources, social resources, and/or challenging demands, while decreasing hindering job demands (cf. Tims & Bakker, 2010; Tims et al., 2012). Overall, a balance between job demands and resources motivates employees to engage at work, achieve goals, and experience work meaningfulness (cf. Tims et al., 2016; Van Wingerden et al., 2017).

Job crafting in project work may include various examples for project managers and project team members, respectively. Project managers craft their jobs, for example, by altering the scope or nature of project tasks (to increase structural job resources), building trust and strengthening the initiative of project team members (to increase social job resources), starting new projects (to increase challenging job demands), or shaping the extent or nature of interpersonal relationships in projects (to decrease hindering job demands). In turn, project team members craft their jobs, for example, by requesting more autonomy in a project (to increase structural job resources); asking for feedback from other project members, including the project manager (to increase social job resources); undertaking new project tasks (to increase challenging job demands); or reducing the monotony of project tasks (to decrease hindering job demands).

In summary, because employees are closest to their own job tasks, employees have the discernment necessary to adjust how to achieve their goals by tapping into their talents, skills, and strengths (cf. Berg et al., 2013; Slemp et al., 2015; Wrzesniewski & Dutton, 2001). Employees balance resources and demands in a dynamic and flexible manner that does not prescribe a specific way to complete a task, but rather, allows employees to apply creative and innovative approaches (cf. Bakker & Demerouti, 2017; Demerouti et al., 2015; Wrzesniewski & Dutton, 2001), which further enable project teams to function successfully (Dwivedula & Bredillet, 2010). Employee-driven job modification through job crafting continues to show promise as a means for employees and employers to experience positive work outcomes, such as work meaningfulness and work engagement (Demerouti & Bakker, 2014). This research seeks to uncover more about job crafting within the different ranks of a project team and the outcomes of work meaningfulness and work engagement.

Work Meaningfulness

Work meaningfulness is a positive attribute from job crafting (Tims et al., 2016). As employees spend the majority of their waking lives at work, they prefer that their time spent working results in positive activities and outcomes (Bailey & Madden, 2016). The result of employees aligning their job to their perceptions creates a sense of purpose, significance, value, and belongingness, which enables their work to become more meaningful (Tims et al., 2016; Wrzesniewski, 2003). Researchers use a common definition to describe work meaningfulness, which is that “the work and/or its context are perceived by its practitioners to be, at minimum, purposeful and significant” (Pratt & Ashforth, 2003). This research uses the common definition of work meaningfulness, along with the terms of value and belongingness, which occur when employees experience work meaningfulness (cf. Martela et al., 2018; Michaelson et al., 2014; Pratt & Ashforth, 2003).

To elucidate the elements that work meaningfulness entails, Steger et al. (2012) found that work meaningfulness is a eudaimonic psychological state that is multidimensional. There are three subdimensions to this purpose-oriented psychological state, which include psychological meaningfulness in work, meaning making through work, and greater good motivation (Steger et al., 2012). The psychological meaningfulness in work is about an employee’s individual judgment about their experience of the significance with their work. The work transcends the basic components of executing job tasks, resulting in a specific internal and/or external purpose for the organization (Steger et al., 2012; Steger, 2017). The meaning making through work is about how work transcends to relate to an employees’ broader meaning in life. This is the degree to which work shapes more meaning for employees’ lives or is congruent with employees’ lives (Steger et al., 2012; Steger, 2017). The greater good motivation is about the positive and prosocial effect that work has on others. Work transcends the employee to the greater good of others and society (Steger et al., 2012; Steger, 2017).

Overall, employees indicate that work meaningfulness outweighs having positive outcomes from work, such as career progression, compensation, and rewards (Bailey & Madden, 2016; Giancola, 2014). This research adds to the conversation on how job crafting affects employees’ ability to have work meaningfulness, along with its positive outcomes, and considers the differing ranks of employees within project teams.

Work Engagement

Another positive attribute of job crafting is work engagement (cf. Chen, 2019; Demerouti & Peeters, 2018; Karatepe & Eslamlou, 2017; Matsuo, 2019; Tims et al., 2016). Researchers provide strong evidence that work engagement is essential for employees to produce positive outcomes, such as behavioral and organizational outcomes (Bakker, 2010). Positive behavioral and organizational work outcomes of work engagement encompass job performance, both in-role and extra-role, which results in positive emotions, creativity, and the ability to influence other employees to engage as a team (Bakker et al., 2004; Bakker, 2009). Various definitions of work engagement within the literature fall into one of the two categories, representing engagement as a state of motivation or as a form of achievement (Byrne, 2015). This research views engagement as a state of motivation from the seminal definition by Kahn (1990).

The definition by Kahn (1990) was the first attempt to define personal work 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” (p. 694). The physical aspect refers to the input of energy used by employees to complete their tasks in an organization. The cognitive aspect relates to what employees think about an organization, its managers, and the conditions of work. Finally, the emotional aspect indicates the presence of positive or negative emotions of the workers toward an organization and its leaders (Kahn, 1990; Kular et al., 2008). This research seeks to know more about the mechanism through which work engagement arises under the influence of job crafting within the different ranks of a project team and the implications job crafting has on the relationship between work meaningfulness and work engagement.

Hypothesis Development

Considering the literature discussed earlier, job crafting has an important role for project teams to experience higher energy and enthusiasm with their work, resulting in increases in work engagement and work meaningfulness. We assume that this logic remains true for both project managers and project team members, considering the results of previous research conducted among non-managers (e.g., Shin et al., 2020) and in multi-profession samples (e.g., Tims et al., 2016). Hence, relying on the theoretical framework of both the motivational mechanism of the job characteristics model and the job demands–resources model, as well as considering the findings of previous studies in the project setting (cf. Amble, 2017; Iorio & Taylor, 2015; Matthews et al., 2018; Zhong et al., 2018), we put forward the following hypotheses:

H_1a: There is a positive and direct relationship between job crafting and work meaningfulness of all project participants.

H_1b: There is no significant difference between project managers and project team members when it comes to the relationship between job crafting and work meaningfulness.

H_2a: There is a positive and direct relationship between job crafting and work engagement of all project participants.

H_2b: There is no significant difference between project managers and project team members when it comes to the relationship between job crafting and work engagement.

Considering that employees who experience work meaningfulness achieve a sense of job satisfaction, reduction in work-related stress, and enhanced performance (cf. Bailey & Madden, 2016; Steger et al., 2013), and that these positive outcomes further benefit organizations, as organizations experience an increase in employees’ work commitment, work engagement, and reduced absenteeism (cf. Bailey & Madden, 2016; Geldenhuys et al., 2014), we state the following hypotheses:

H _3a : There is a positive and direct relationship between work meaningfulness and work engagement of all project participants.

H _3b : There is no significant difference between project managers and project team members when it comes to the relationship between work meaningfulness and work engagement.

Because of the positive outcomes associated with work meaningfulness, researchers continue to seek answers concerning how, where, and why employees find work meaningfulness, along with understanding the effects of job crafting (Rosso et al., 2010). Therefore, following Peral and Geldenhuys (2016), who confirmed a mediating role of psychological meaningfulness on the effect of job crafting on work engagement among high school teachers, we intend to test this relationship in project environments. Thus, we further hypothesize:

H ₄ : Work meaningfulness mediates the relationship between job crafting and work engagement of all project participants.

Methodology

Data Collection

A quantitative research approach was adopted for this study using a questionnaire survey to empirically evaluate the theoretical model and hypotheses. The research survey was conducted in Poland in 2018. Project managers and project team members of small, medium, and large companies were invited to participate. Two research techniques were used—computer-assisted telephone interviewing (CATI) and computer-assisted web interviewing (CAWI). Each interview was preceded by telephone contact to verify the size of the enterprise and recruit a person who met the research criteria (i.e., project manager or project team member). Eligible respondents were asked for a telephone interview. In the cases of respondents who did not have such availability or preferred to complete the questionnaire independently, a link to the CAWI questionnaire was sent to the respondents’ email address. There were 114 respondents who completed the survey, which included 42 project managers and 72 project team members.

Sample Characteristics

The respondents represent different types of organizations and different sectors and answered the survey questions by considering the characteristics of their personal and most recent project experiences.

Analyzing the selected respondents’ characteristics, project managers constituted 36.8% of the sample, whereas project team members constituted 63.2%. Most respondents (48.2%) were representatives of medium-sized organizations employing between 50 and 249 people, followed by representatives of large organizations employing 250 or more people (42.1%). The industry demographics were also diverse. Most respondents represented manufacturing (38.6%); professional, scientific, and technical services (21.9%); and construction (11.4%). In each of the two groups of respondents, those with 4 and up to 10 years of experience with projects (46.5%) and those exceeding 10 years of experience with projects (41.2%) constituted almost one half of the sample. Almost one third (31.6%) of the respondents had a professional project management certification. The respondents’ tenure as members of the current project team ranged from a few months to over two years, and the team size varied. Most respondents (64%) indicated projects had a duration between 2 and 23 months. The majority of the respondents indicated that the number of people working on the project team was up to 10 (64%); however, a larger project team size had up to 100 people (8.8%).

Measurements

The current study controlled for position held within the project by asking the respondents to indicate their current role on the project as either a project manager or a project team member. The three constructs of job crafting, work meaningfulness, and work engagement were measured as reflective latent variables. Respondents answered questions regarding the three constructs based on their most recent project experience. From the relevant literature, three multiple-dimensional scales were collected to operationalize the research constructs, as shown in Figure 1.

All items of the scale were assessed with a 7-point Likert-like agreement scale that ranged from “1 = strongly disagree” to “7 = strongly agree.” Higher scores exhibit a higher level of agreement for the constructs. As the scales were not in the respondents’ native language, and to ensure the same meaning of the scales’ content, the survey instruments were converted from English to Polish using the form of reverse translation.

The job crafting construct was assessed using an adapted version of the Job Crafting Scale (JCS), which measures the subdimensions of job demands and job resources, including 21 items developed by Tims et al. (2012). The items in this scale measure four independent dimensions, which are as follows: structural resources (SR; five items), hindering demands (HD; six items), social resources (SoR; five items), and challenging demands (CD; five items). A sample is “I try to develop my capabilities” (SR), “I make sure that my work is mentally less intense” (HD), “I ask my supervisor to coach me” (SoR), and “When an interesting activity comes along, I offer myself proactively as project co worker” (CD).

The work meaningfulness construct was assessed using an adapted version of the Work as Meaning Inventory (WAMI) scale developed by Steger et al. (2012), which measures the three following independent subdimensions: psychological meaningfulness in work (PMW; four items), meaning making through work (MM; three items), and greater good motivation (GG; three items). A sample includes the following: “I have found a meaningful career” (PMW), “My work helps me better understand myself” (MM), and “The work I do serves a greater purpose” (GG).

The work engagement construct was assessed using an adapted version of the Psychological Engagement Scale (PES) developed by May et al. (2004), which was based on Kahn’s (1990) conceptualization of psychological engagement. The PES measures three independent subdimensions, which are as follows: cognitive (C; four items), emotional (E; four items), and physical (P) engagement (five items). Sample items are as follows: “Time passes quickly when I perform my job” (C), “I really put my heart into my job” (E), and “I stay until the job is done” (P).

To analyze the study data, structural equation modeling (SEM) was applied with a two step procedure consisting of first-order and second-order analyses. Partial least squares (PLS) regression was used to analyze the data. PLS was chosen for the study because it provides greater flexibility compared with covariance-based standard error of the mean techniques. In particular, PLS does not demand that the data be normally distributed (Fornell & Bookstein, 1982). In addition, PLS is best suited for analysis where the aim is to predict constructs measured by many indicators (Haenlein & Kaplan, 2004; Hair et al., 2011). In our study, all three constructs have at least 10 items. PLS is also appropriate for analyses with relatively small sample sizes (Chin & Newsted, 1999; Hair et al., 2014; Wold, 1985). Finally, this method was selected because the research constructs are both reflective and formative (Chin, 2010; Hair et al., 2017). We used WarpPLS^® version 6.0 to analyze the data (Kock, 2018).

Analysis and Results

In this study, we used a second-order hierarchical latent variable model, reflective-formative type, in which the first-order components measured by reflective factors form the second-order components measured by formative factors (Becker et al., 2012; Jarvis et al., 2003; Ringle et al., 2012). The standard two-stage modeling approach was applied, first analyzing the measurement model and then the structural model (Hair et al., 2011; Henseler et al., 2016a; Kock, 2018). First, we evaluated the measurement model, which defined the reliability and validity assessment of the constructs. Next, we assessed the structural model, which clarified the relationship among the examined constructs. The two-step approach was adopted to model higher order constructs. In the first step, estimation of the first-order detailed constructs for work engagement (WE), work meaningfulness (WM), and job crafting (JC) and their indicators were conducted, and the latent variable scores were saved. In the second step, the saved latent variable scores were used as formative indicators.

Measurement Model—Assessment of First-Order Reflective Constructs

For the measurement model, we first examined the appropriateness of the first-order constructs. In compliance with the recommendations by Hair et al. (2017), the assessment of a reflective measurement model includes evaluation of internal consistency reliability, convergent validity, and discriminant validity.

First, internal consistency reliability was evaluated using Cronbach’s alpha (CA) and composite reliability (CR) coefficients. For exploratory research, an acceptable CR and CA should be α > 0.60 (Hair et al., 2017; Kock, 2018; Nunnally & Bernstein, 1994). As shown in Table 1, the results showed that all except one of the coefficients exceeded 0.60. Although one of the constructs scored below the recommended minimum α > 0.60 for CA, all indicators have acceptable scores based on CR.

Table 1

Internal Consistency Reliability (CR and CA) and Convergent Validity (AVE and Combined Loadings)

Items	WM_PMW	WM_MM	WM_GG	JC_SR	JC_HD	JC_SoR	JC_CD	WE_C	WE_E	WE_P	p-value
CR	0.877	0.843	0.868	0.917	0.822	0.873	0.837	0.748	0.798	0.797	—
CA	0.813	0.719	0.772	0.877	0.713	0.806	0.739	0.495^a	0.662	0.616	—
AVE	0.641	0.641	0.688	0.737	0.542	0.634	0.565	0.499	0.497	0.568	—
WM_1_PMW	0.799	0.179	−0.191	0.037	−0.044	−0.122	−0.105	−0.129	0.114	−0.018	<0.001
WM_4_PMW	0.842	0.074	0.076	−0.021	0.066	0.070	0.028	0.109	−0.120	0.103	<0.001
WM_5_PMW	0.811	0.031	0.060	0.206	0.063	−0.015	0.022	0.152	−0.246	0.101	<0.001
WM_8_PMW	0.747	−0.308	0.054	−0.161	−0.096	0.069	0.057	−0.150	0.281	−0.207	<0.001
WM_2_MM	0.079	0.750	−0.372	0.252	−0.064	−0.165	−0.139	−0.085	0.195	0.005	<0.001
WM_7_MM	0.055	0.813	−0.147	−0.217	−0.057	0.121	0.095	0.047	−0.044	−0.037	<0.001
WM_9_MM	−0.124	0.837	0.476	−0.016	0.113	0.030	0.032	0.031	−0.131	0.031	<0.001
WM_3_GG	−0.061	−0.254	0.785	0.048	−0.069	−0.029	−0.127	0.251	−0.071	−0.116	<0.001
WM_6_GG	0.033	0.266	0.887	−0.055	0.032	−0.059	0.000	−0.071	−0.076	0.073	<0.001
WM_10_GG	0.022	−0.045	0.813	0.014	0.032	0.092	0.123	−0.165	0.151	0.033	<0.001
JC_1_SR	0.059	−0.047	−0.025	0.924	0.002	−0.086	0.018	−0.034	0.087	−0.020	<0.001
JC_2_SR	0.137	0.037	−0.040	0.910	−0.021	0.032	0.031	0.132	−0.191	0.015	<0.001
JC_3_SR	−0.115	0.370	−0.234	0.868	−0.020	0.024	−0.152	−0.126	−0.099	0.010	<0.001
JC_4_SR	−0.112	−0.437	0.367	0.714	0.048	0.040	0.122	0.030	0.251	−0.005	<0.001
JC_6_HD	0.025	0.291	−0.242	0.189	0.836	−0.204	0.034	−0.237	0.058	0.012	<0.001
JC_7_HD	0.209	0.044	−0.185	0.145	0.850	−0.150	0.084	−0.218	0.067	0.095	<0.001
JC_8_HD	−0.174	−0.184	0.335	−0.270	0.592	0.327	−0.072	0.258	0.001	−0.044	<0.001
JC_9_HD	−0.151	−0.272	0.255	−0.192	0.630	0.165	−0.090	0.366	−0.168	−0.102	<0.001
JC_12_SoR	0.194	−0.060	−0.135	0.023	−0.015	0.815	0.046	−0.037	0.047	−0.027	<0.001
JC_13_SoR	−0.190	−0.035	0.047	0.168	−0.030	0.849	−0.041	−0.048	0.033	0.076	<0.001
JC_14_SoR	0.381	0.034	−0.033	−0.209	0.033	0.787	−0.084	0.062	0.041	0.013	<0.001
JC_15_SoR	−0.407	0.072	0.132	0.004	0.016	0.728	0.087	0.031	−0.136	−0.073	<0.001
JC_18_CD	0.023	−0.518	0.137	−0.016	−0.007	0.079	0.764	−0.086	0.207	0.001	<0.001
JC_19_CD	−0.028	0.531	0.020	−0.143	0.209	−0.016	0.750	0.065	−0.340	0.140	<0.001
JC_20_CD	−0.066	0.338	−0.141	0.024	−0.202	−0.069	0.641	−0.162	0.142	0.101	<0.001
JC_21_CD	0.055	−0.262	−0.035	0.124	−0.026	−0.005	0.838	0.144	0.007	−0.203	<0.001
WE_1_C	−0.216	0.355	−0.197	0.010	0.090	0.080	0.126	0.740	−0.061	0.242	<0.001
WE_2_C	−0.089	−0.295	0.256	0.158	0.012	0.021	0.129	0.742	−0.220	−0.172	<0.001
WE_4_C	0.359	−0.070	−0.070	−0.198	−0.119	−0.119	−0.300	0.631	0.329	−0.082	<0.001
WE_5_E	0.220	−0.253	0.091	0.130	−0.023	−0.004	−0.135	0.296	0.721	−0.046	<0.001
WE_6_E	0.157	−0.373	−0.016	0.144	0.012	0.141	−0.038	0.226	0.723	−0.126	<0.001
WE_7_E	−0.147	0.121	0.018	−0.128	−0.050	−0.110	0.125	−0.188	0.698	−0.010	<0.001
WE_8_E	−0.251	0.544	−0.098	−0.161	0.064	−0.032	0.055	−0.363	0.676	0.195	<0.001
WE_9_P	−0.120	−0.314	0.411	0.130	0.042	0.016	0.102	0.071	0.159	0.662	<0.001
WE_11_P	0.195	0.290	−0.316	0.031	0.021	−0.008	−0.042	−0.193	−0.114	0.780	<0.001
WE_13_P	−0.090	−0.023	−0.032	−0.135	−0.055	−0.005	−0.043	0.128	−0.021	0.811	<0.001

^aWeakness in the research notated in research limitations. Construct kept for theoretical accuracy and completeness.

The convergent validity was tested with factor loadings. As a criterion for determining that a measurement model has acceptable convergent validity, two requirements are recommended: The p-values associated with the loadings should be equal to or lower than 0.05, and the loadings should be equal to or greater than 0.50 (Hair et al., 2009). According to Hair et al. (2017), the proposed recommendations for outer loadings are values between 0.40 and 0.70; researchers must examine the effect of item removal on the composite reliability and content validity of the construct. Thus, we removed eight indicators where their deletion led to an increase in the composite reliability while not decreasing the average variance extracted (AVE). As shown in Table 1, all retained items loaded were above the recommended minimum cut-off of 0.50 (Hair et al., 2009; Hulland, 1999). For convergent validity, researchers must examine the AVE. In agreement with Fornell and Larcker (1981), an AVE value of 0.50 and higher indicates a sufficient degree of convergent validity. As presented in Table 1, all AVE values rounded to the nearest hundredth, and thus, met this criterion.

The discriminant validity check was carried out using AVE. As stated by Fornell and Larcker (1981), for discriminant validity to occur, the square root of the AVE of each construct must be greater than other correlations regarding that construct. The results obtained were acceptable. Table 2 shows that the square root of AVE for each variable is greater than those of the off-diagonal elements.

Table 2

Discriminant Validity—Correlation of Latent Variables with Square Root of AVEs

Items	WW_PMW	WM_MM	WM_GG	JC_SR	JC_HD	JC_SoR	JC_CD	WE_C	WE_E	WE_P
WM_PMW	0.801	0.792	0.541	0.686	0.044	0.246	0.458	0.284	0.479	−0.135
WM_MM	0.792	0.801	0.607	0.651	0.048	0.320	0.516	0.339	0.544	−0.055
WM_GG	0.541	0.607	0.829	0.382	0.048	−0.035	0.195	0.078	0.435	−0.091
JC_SR	0.686	0.651	0.382	0.858	0.105	0.169	0.526	0.374	0.444	−0.098
JC_HD	0.044	0.048	0.048	0.105	0.736	0.104	0.140	0.113	0.087	−0.243
JC_SoR	0.246	0.320	−0.035	0.169	0.104	0.796	0.216	0.114	0.124	−0.004
JC_CD	0.458	0.516	0.195	0.526	0.140	0.216	0.751	0.376	0.441	−0.080
WE_C	0.284	0.339	0.078	0.374	0.113	0.114	0.376	0.706	0.536	0.181
WE_E	0.479	0.544	0.435	0.444	0.087	0.124	0.441	0.536	0.705	0.186
WE_P	−0.135	−0.055	−0.091	−0.098	−0.243	−0.004	−0.080	0.181	0.186	0.754

Note. Square roots of average variances extracted (AVEs) shown on diagonal.

Measurement Model—Assessment of Second-Order Formative Constructs

The assessment of the second-order formative constructs includes evaluation of a test for convergent validity to determine the significance and relevance, as well as the presence of collinearity among indicators (Hair et al., 2017). All indicators of the latent variables showed significance and relevance (p < 0.05 for indicator outer weights, t-statistic > critical ratio at 0.05 confidence level), as well as an acceptable effect size (ES > 0.02; Kock, 2018). Table 3 summarizes the validity and relevance of the formative indicators.

Table 3

Results Summary of Exploratory Formative Indicator Validity and Relevance Tests

Items	Weight	Loadings>0.50	p-value for Weight<0.05	t-statistic for Weight>1.96	VIF <3.3	ES >0.02
Work meaningfulness—three indicators
WM_PMW	0.390	0.897	<0.001	4.596	2.726	0.349
WM_MM	0.401	0.922	<0.001	4.739	3.054	0.369
WM_GG	0.350	0.804	<0.001	4.080	1.610	0.281
Job crafting—four indicators
JC_SR	0.469	0.797	<0.001	5.638	1.390	0.373
JC_HD	0.213	0.360^a	0.009	2.397	1.027	0.077
JC_SoR	0.296	0.502	<0.001	3.403	1.059	0.148
JC_CD	0.486	0.826	<0.001	5.874	1.426	0.402
Work engagement—three indicators
WE_C	0.511	0.839	<0.001	6.212	1.418	0.428
WE_E	0.512	0.841	<0.001	6.227	1.420	0.430
WE_P	0.293	0.481^b	<0.001	3.377	1.046	0.141

^a,bThere is empirical support to retain the indicators, as the indicators’ weight is significant (Hair et al., 2017).

To evaluate the reliability of the formative constructs further, tests for multicollinearity were performed by examining the variance inflation factor (VIF) of the items. Items with VIF scores of lower than 3.3 are deemed acceptable (Diamantopoulos & Siguaw, 2006). The VIF scores for all the items did not exceed 3.054, demonstrating adequate construct reliability (Diamantopoulos & Siguaw, 2006; see Table 3 for the summary of the VIF for the formative constructs).

Structural Model Assessment

To reveal the relationships between the constructs in the research model, the structural model path coefficient (β) and path significance (p-value) were examined. The results of the hypothesis testing, along with effect sizes (f2), are presented in Table 4. Values of 0.35, 0.15, and 0.02 suggest large, medium, and small effects, respectively (Cohen, 1988).

Table 4

Hypothesis Testing—All Project Participants

Hypothesis	Path	Path coefficient (β)	p-value	Effect size (f2)	Result
H_1a	JC→WM	0.609	<0.01	0.371	Supported
H_2a	JC→WE	0.280	<0.01	0.120	Supported
H_3a	WM→WE	0.256	<0.01	0.106	Supported
H₄	Mediation	0.156	<0.01	0.066	Supported

From Table 4, we can make the following conclusions for all project participants:

JC has a significant effect on WM at a p-value < 0.01 and β = 0.609. Thus, hypothesis H_1a is supported.

JC has a significant effect on WE at a p-value < 0.01 and β = 0.280. Thus, hypothesis H_2a is supported.

WM has a significant effect on WE at a p-value < 0.01 and β = 0.256. Thus, hypothesis H_3a is supported.

When checking the significance of the indirect effect, we notice that the indirect relationship between JC and WE is significant (β = 0.156, p < 0.01); therefore, WM partially mediates the relationship between JC and WE. Thus, hypothesis H₄ is supported.

Global model fit and quality indices (Kock, 2018) were also calculated for the whole model, meeting the following criteria:

Average path coefficient (APC) = 0.382, p < 0.001

Average R-squared (ARS) = 0.298, p < 0.001

Average adjusted R-squared (AARS) = 0.289, p < 0.001

Average block VIF (AVIF) = 1.482, acceptable if ≤ 5, ideally ≤ 3.3

Average full collinearity VIF (AFVIF) = 1.524, acceptable if ≤ 5, ideally ≤ 3.3 (indicating no multicollinearity issue; Kock, 2018)

Tenenhaus GoF (GoF) = 0.416, small ≥ 0.1, medium ≥ 0.25, large ≥ 0.36

Simpson’s paradox ratio (SPR) = 1, acceptable if ≥ 0.7, ideally = 1

R-squared contribution ratio (RSCR) = 1, acceptable if ≥ 0.9, ideally = 1

Statistical suppression ratio (SSR) = 1, acceptable if ≥ 0.7

Nonlinear bivariate causality direction ratio (NLBCDR) = 1, acceptable if ≥ 0.7

These fit and quality indices suggest a model–data fit that was acceptable.

In this study, the R ² (coefficient of determination) values were 0.371 for WM and 0.226 for WE. The values measured for Stone–Geisser (Q ²) in this analysis were 0.371 for WM and 0.240 for WE, which can be considered satisfactory (if greater than 0).

Multigroup Analysis

Multigroup analysis (MGA) in a PLS path modeling framework is a means of testing whether group-specific parameter estimates (mostly path coefficients) differ significantly between two groups (Hair et al., 2017; Henseler & Chin, 2010). Our database was split into two groups according to a grouping variable—the rank of a project participant. To carry on the MGA and ensure the validity of outcomes and conclusions of multigroup comparisons, measurement invariance was established (Chin et al., 2016; Henseler et al., 2016b). The Satterthwaite method, which is a classic method that is commonly used for MGAs and measurement invariance testing (Kock, 2014), was applied. According to the obtained data, all variables showed good invariance (p-values > 0.05).

The results of the MGAs are presented in Table 5. The model was executed twice, one for each database, and the beta parameters and p-values were estimated in groups. In MGA, path coefficients are compared. Here, p-values are assumed to be higher than 0.10 for the conclusion that no significant differences are present (Kock, 2014).

Table 5

Hypothesis Testing—Multigroup Analysis Results

Hypothesis	Path	Group 1 Project Managers (N = 42)		Group 2 Project Team Members (N = 72)		Group 1 vs. Group 2		Result
Hypothesis	Path	Pathcoefficient (β)	p-value	Pathcoefficient (β)	p-value	Absolute pathcoefficient differences	p-value(one-tailed)	Result
H_1b	JC→WM	0.612	<0.01	0.62	<0.01	0.007	0.481	Supported
H_2b	JC→WE	0.153	0.148	0.375	<0.01	0.221	0.087*	Not supported
H_3b	WM→WE	0.434	<0.01	0.224	0.023	0.21	0.092*	Not supported

As indicated in Table 5, the MGA tests showed significant differences between the two groups of project managers (N = 42) and project team members (N = 72) on two of the paths, JC→WE and WM→WE. From Table 5, we make the following conclusions:

Both project team members’ and project managers’ JC have a significant effect on WM, at a p-value of <0.01, with path coefficients of β = 0.620 and β = 0.612. Thus, hypothesis H_1b is supported.

Project team members’ JC has a significant effect on WE, at a p-value of <0.01 and β = 0.375, whereas for project managers, this effect is not significant, at a p-value of 0.148. Thus, hypothesis H_2b is not supported.

Both project team members’ and project managers’ WM have a significant effect on WE, at a p-value of <0.05 and β = 0.224 for the first group and at a p-value of <0.01 and β = 0.434 for the second group. At the same time, in this case, group-specific path coefficients differ significantly between the two groups, meaning that the effect of WM on WE is significantly stronger for project managers than it is for project team members. Thus, hypothesis H_3b is not supported.

Discussion

Conclusions

This study developed and verified a research model that investigated the effect of job crafting on work meaningfulness and work engagement among project participants. The results obtained from the analysis indicated that relationships did exist between job crafting and work meaningfulness, between job crafting and work engagement, and between work meaningfulness and work engagement among project participants. Thus, the results support acceptance for hypotheses H_1a, H_2a, and H_3a. In terms of hypothesis H₄, which stated that work meaningfulness mediated the relationship between job crafting and work engagement of all project participants, the analysis showed that work meaningfulness partially mediated this relationship and, as such, hypothesis H₄ was accepted.

The results obtained from the MGA within the PLS path modeling framework showed significant differences between the two groups—project managers and project team members—on two of the paths, namely between job crafting and work engagement and between work meaningfulness and work engagement. Thus, hypotheses H_2b and H_3b were not accepted, whereas hypothesis H_1b, which stated that there is no significant difference between project managers and project team members when it comes to the relationship between job crafting and work meaningfulness, was supported. Regarding the observed differences, the findings show that, first, the effect of work meaningfulness on work engagement is significantly stronger for project managers than it is for project team members, and second, the rank of project participants moderates the relationship between job crafting and work engagement.

The findings from this study provide support for the importance of job crafting in a project environment for both project team members and project managers. Nonetheless, the study discloses that there is a difference in how job crafting influences different work outcomes in both groups of project participants. Regarding project team members, job crafting affects both work engagement and work meaningfulness positively and directly. In the case of project managers, job crafting affects work engagement indirectly through work meaningfulness. Thus, work meaningfulness partially mediates the relationship between job crafting and work engagement in the case of project team members and fully mediates it in the case of project managers. At the same time, the relationship between work meaningfulness and work engagement is significantly stronger for project managers when compared with project team members. A detailed review of the research findings on the theoretical implications, practical implications, and limitations of the research follows in the sections below.

Theoretical Implications

Job crafting has not been broadly studied in the project management environment (Niessen et al., 2016). Therefore, this research will lead to a better understanding of the relationships among job crafting, work meaningfulness, and work engagement, extending empirical findings into the context of project participants at different ranks. Adding rank as a control variable of the study allowed the ability to capture the perspectives of both project managers and project team members.

This study’s results correspond with the outcomes of previous research. The findings align with those of Peral and Geldenhuys (2016) and confirm the relationships proposed in our research model for all project participants, namely a positive and direct relationship between job crafting and work engagement, as well as a mediating role of work meaningfulness within this relationship. This is also consistent with previous research findings regarding positive individual two-sided relationships (JC→WM, JC→WE, WM→WE) included in the research model obtained for either lower-rank employees or mixed samples encompassing both lower and higher rank employees (cf. Baik et al., 2018; Chen, 2019; Demerouti & Peeters, 2018; Karatepe & Eslamlou, 2017; Tims et al., 2015). To the best of the authors’ knowledge, the examined project-related relationships have not been investigated regarding employee rank, and this study is among the first where rank was taken into account. Particularly, this study demonstrates that when a project participant’s rank is considered, differences in the results occur. The abovementioned relationships confirmed for all project participants remained in effect only for project team members. The results are different for project managers, showing that there are differences in the mechanism through which work engagement arises under the influence of job crafting in these two groups of employees. Thus, this study expands the findings of previous work exploring the impact of job crafting on employees’ work outcomes (cf. Karatepe & Eslamlou, 2017; Tims et al., 2016) and contributes to the increasing knowledge base in this research stream. Moreover, this study’s results provide support for previous studies, which indicated that rank in organization relates to job crafting (Berg et al., 2010).

Specifically, this research’s results indicate that project managers’ job crafting affects their work engagement only indirectly through work meaningfulness. At the same time, the relationship between work meaningfulness and work engagement is significantly stronger in the case of project managers when compared with project team members. The research findings from Berg et al. (2010) may explain the variance between project managers and project team members, which showed that rank, serving as a proxy for degrees of formal power and autonomy, may affect how employees view challenges in job crafting and make the adaptive efforts (e.g., going outside work boundaries, trust building). Their findings indicate that lower rank employees hold roles where they find it comparatively simpler to adjust to their work environments by generating more opportunities to job craft, whereas employees at higher ranks feel more limited despite having greater formal autonomy and power in positions (Berg et al., 2010). These constraints, referring to project managers, may result from three factors, namely the nature of job responsibilities, interdependence, and visibility.

First, the nature of job responsibilities means that employees at a higher rank have job designs that require adherence to standard procedures and governance that is specific to their profession (AXELOS, 2017; Project Management Institute [PMI], 2017). Consequently, higher rank employees may encounter comparatively less freedom to combine job demands and resources through job crafting since they feel obliged to concentrate their energies on specific standards and actions to achieve end goals. Overall, this view dampens their efforts to proactively shift their limits of jobs (Berg et al., 2010). Second, there is the constraint due to interdependence, which restricts the ability for an individual to job craft (Wrzesniewski & Dutton, 2001). Employees at higher ranks typically perceive their interdependence with others more strongly than lower rank employees do when considering whether to job craft (Berg et al., 2010). Therefore, higher rank employees may respond to their perceived interdependence as a constraint based on the nature of their job design and responsibilities. As a result, higher rank employees may reduce their efforts to job craft more than lower rank employees (Berg et al., 2010). Third, there is the concept of visibility, implying that workplace conduct at higher ranks of organizations is often more noticeable than those at lower ranks (Ortega, 2003). Consequently, due to visibility, higher rank employees may be reluctant to conduct job crafting by modifying their tasks in ways that may draw unwarranted attention, which could place the fulfillment of their prescribed goals at risk (Berg et al., 2010).

We suppose that the constraints indicated by Berg et al. (2010) caused project managers to experience higher hindering job demands, resulting in a reluctance to job craft compared with project team members. As such, the inability to align job resources with job demands reduces work meaningfulness and the level of work engagement for project managers. Based on job demands–resources theory (Demerouti et al., 2001), hindering job demands are related to a deficit of energy resulting in job burnout rather than to positive outcomes (Hakanen et al., 2008). Project managers who experience an increase in hindering demands may be more likely to experience an increase in negative work outcomes, such as exhaustion or stress, instead of positive outcomes, such as work engagement. Further, for increased engagement to manifest, project managers need to use their job resources (cf. autonomy and power) in a way that decreases hindering job demands (Esteves et al., 2017; Wrzesniewski, 2003; van Wingerden et al., 2018) and increases work meaningfulness. As a result, when project managers craft their jobs and use resources that increase their job fit, their work engagement may increase.

To summarize, given the limited number of studies on job crafting effects in project environments (Niessen et al., 2016), this study helps address the research gap in two ways. First, it records a positive and significant relationship among job crafting, work meaningfulness, and work engagement in project participants. Second, it indicates different types of mediating effects of work meaningfulness in the relationship between job crafting and work engagement among project participants within different ranks (i.e., partial mediation in the case of project team members and full mediation in the case of project managers). In addition, this research adds to previous research by including a regional setting of Polish project environments and providing support for the validity of results within a different context of society and occupation.

Practical Implications

This study provides some important implications for practice. First, given the study results, managers should consider changing their approach to the job design of project team members, as the design of a job is fundamentally important for the psychological perceptions of the employees at work (Wrzesniewski et al., 2013). Job design guides the perception of employees’ work motivation (Dwivedula & Bredillet, 2010). While the traditional role of a manager was to design jobs in a top-down manner, today’s project team members’ proclivity for the empowering nature of work requires project managers to accept and stimulate a bottom-up, individualized, and proactive way to engage in job redesign (Esteves et al., 2017). Project team members do not want to be placed according to a one size-fits-all approach (Rudolph et al., 2017) and positioned as passive recipients of the jobs they hold. Therefore, project managers should positively affect project team members’ perceived work meaningfulness and work engagement by giving project team members an ability to utilize multiple skills, adequate autonomy, and opportunities to obtain job related feedback (Dwivedula & Bredillet, 2010). Project managers should address project team members’ needs for challenges, development, and occupational satisfaction within an environment of high collaboration (Walker & Lloyd-Walker, 2019).

Second, project managers may overcome the constraints they experience when tailoring their work environments to build more opportunities to job craft (Berg et al., 2010) by concentrating on team job crafting work. As mentioned in the Introduction section, the future of project management will require an extensive use of job crafting among individuals and teams (Walker & Lloyd-Walker, 2019). Team job crafting refers to the way the team communicates and functions as an interdependent and purpose-driven combination of people (Morgeson & Hofmann, 1999). Thus, team job crafting has a greater effect than the sum of individual team members’ job crafting (Walker & Lloyd-Walker, 2019). The approach of team job crafting has the potential to lead to a joint effect of increased work meaningfulness and engagement for both project managers and team members (Walker & Lloyd-Walker, 2019). Perhaps a project manager’s change in how they perceive constraints such as the nature of their job responsibilities, interdependence, and visibility (Berg et al., 2010) could increase their willingness to job craft. Should they embrace and participate in team job crafting, project managers, even with job designs that prescribe only end goals, might experience a more symbiotic relationship through the nature of team job crafting (Mäkikangas et al., 2017). This action, from the project manager, could drive the project manager and team members toward a common purpose and understanding of the value that the project provides, resulting in the achievement of the shared project goals (Leana et al., 2009; Mäkikangas et al., 2017). Having a common purpose and value has the potential to increase both the project managers’ and project team members’ work meaningfulness and engagement (Steger, 2017). Team job crafting means that decisions about what resources and demands to craft and how to do it are made collectively, not individually (Tims et al., 2013).

Limitations and Future Research

This research has limitations that can reduce the impact of the results. The first limitation is the sample size and characteristics. Due to the busy schedule of project management practitioners, the sample selected for the questionnaire was purposive. The sample was relatively small (N = 114) and consisted of project management practitioners employed in organizations operating in one European country, Poland. The next limitation, which resulted from the previous one, is that respondents of the survey were not asked to select their project roles in accordance with agile approaches to project management (Project Management Institute [PMI], 2017), where teams are self-organized, managed through servant leadership principles, and may incorporate rotating leadership or the project management role within the team (Lappi & Aaltonen, 2017). However, this was intended due to the issue being examined, and because, according to KPMG (2019), the majority of companies operating in Poland that manage projects apply waterfall approaches to project management with some degree of hierarchy (cf. an internal proprietary project management methodology—67%, an internal project management methodology based on global standards—22%; a certified project management methodology, e.g., PRINCE2^®, IPMA—37%), with only 7% declaring they use the agile approaches to project management. In this study, the percentage of certified respondents in the research sample was 31.6%; however, none of them declared that they use agile approaches to project management. Thus, the geographical context of the study determined the adopted research procedure, but it may have simultaneously supported our conclusion. However, it should be noted that, if the study had covered multiprojects (Canonico & Söderlund, 2010; Huemann et al., 2007), they could have influenced the results. Therefore, the implications of this article are indicative rather than conclusive, and the generalizability of the findings is limited. Despite the small sample size, the presented model parameters comply with best practice recommendations for PLS-SEM (Hair et al., 2011; Kock, 2018).

The next limitation is the use of adapted measurement scales, which provided mixed results among the different types of reliability tests. These results indicate that heterogeneity may exist within the data. Although the construct reliability for one indicator retained for the cognitive subdimensions of WE was lower with the confirmatory analysis, each dataset for all constructs analyzed shows consistent reliability with the composite reliability based on the SEM covariance algorithms. Because this study was exploratory in nature, with the main goal of testing the relationships among the three constructs (JC→WM→WE), the weak indicator was kept for theoretical accuracy and completeness. A unified rating format was used throughout the questionnaire because the dataset was gathered for a different research reason, and the researchers decided to optimize the questionnaire completion method by offering standardized rating scales for all the measures. At the same time, an analysis of validity and reliability was conducted, so this should minimize the concerns over unifying the rating scales.

Another limitation is that it is not possible to assign causality in the examined relationships, as the study was not longitudinal. In addition, the self-reporting character of the study design may be prone to response bias (Furnham, 1986). At the same time, the examined respondents represent different types of organizations and different sectors, which may minimize a single-industry bias (Chang & Teng, 2017), supporting the study’s external validity.

We offer the following recommendations for future research: The findings of the current study should be followed up with qualitative surveys, such as case studies, to provide a more detailed picture of both project team members’ and project managers’ perspectives. This is justified specifically in relation to seeking confirmation of the impact of Berg et al.’s (2010) findings on the nature of job responsibilities, interdependence, and visibility, which resulted in various work outcomes of job crafting when considering the rank of project participants. Another interesting issue to investigate is the way the project management methodologies (cf. AXELOS, 2017; Project Management Institute [PMI], 2017), which formalize project management governance and processes, affect project managers’ job crafting. As the causal order of the relationship between job crafting and work engagement has not yet been established (Tims et al., 2015), we recommend conducting research as a longitudinal study among project managers. Whereas Tims et al. (2015) conducted a longitudinal study that examined a heterogeneous group of participants employed at a Dutch chemical plant, we suggest analyzing the relationship among work engagement, job crafting intention, job crafting, and prospective work engagement with a homogenous sample of project managers. When it comes to the quantitative surveys, we recommend repeating them with larger samples encompassing several hundred observations and in another context (e.g., different geographical locations that span across countries and even continents, where agile project team participants could be included) to generate more generalizable findings.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Rafał Haffer has been affiliated with Nicolaus Copernicus University, Toruń, Poland. He is an Associate Professor in the Faculty of Economic Sciences and Management and head of the Logistics Department. He has completed several research projects ordered by national (e.g., Polish Ministry of Higher Education) and international (e.g., European Commission) organizations alone or as part of a team. He is the author or co-author of several dozen scientific publications and has attended, presented, and led sessions at several national and international conferences. In his personal research, Rafał deals with such issues as business improvement, performance measurement, supply chain management, project management and organizational behavior. He is also employed in a private company where he manages an attitude research business unit. He can be contacted at rafalh@umk.pl

Joanna Haffer has been affiliated with the WSB University in Toruń, Poland. She is an Assistant Professor at the Institute of Management and her doctoral thesis concerned project management. Joanna is the author or co-author of several scientific publications and she has attended and presented at several national and international conferences. In her personal research she deals with such issues as project management and organizational behavior. She is also employed in a private company, where she manages the diagnostic products business unit. She can be contacted at joanna.haffer@wsb.torun.pl

Donna Lynne Morrow is an Adjunct Instructor at the University of Dallas, Texas, USA, where she obtained her DBA degree. Donna has attended, presented, and led tracks at several international conferences, including the European Academy of Management (EURAM), Association of Information Systems (AIS), and the Academy of Management (AOM). In addition, Donna presented the constructs of work meaningfulness and job crafting at the 2nd and and 3rd International Symposia on Meaningful Work. She has two journal articles published, one has been accepted, and she is working on several others. Donna has over 30 years of leadership experience in the aviation and financial industries, spanning both business and technology areas. She can be contacted at dlmorrow@udallas.edu

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