Introduction
The increased presence of information and communication technologies (ICT), which enabled employees to connect to work remotely, has led to many workers experiencing fatigue and work overload due to the technologies' overuse. This was fueled even further by the coronavirus disease 2019 (Covid-19) pandemic when employees compelled to work from home experienced techno-overload (Ingusci et al., Reference Ingusci, Signore, Giancaspro, Manuti, Molino, Russo and Cortese2021) and elevated technostress levels (stress associated with ICT (over)usage) (Hamouche, Reference Hamouche2021; Molino et al., Reference Molino, Ingusci, Signore, Manuti, Giancaspro, Russo and Cortese2020). Employees initiating efforts to alter aspects of their job tasks and work relationships to meet such new demands were seen as a response behavior (Laker, Patel, Budhwar, & Malik, Reference Laker, Patel, Budhwar and Malik2020). In particular, job crafting – an individual behavior displayed to decrease work demands and increase one's job-related resources – has been critical in helping employees build resilience (Berg, Dutton, & Wrzesniewski, Reference Berg, Dutton and Wrzesniewski2008), improve well-being and performance (Rudolph, Katz, Lavigne, & Zacher, Reference Rudolph, Katz, Lavigne and Zacher2017), and reduce stress when ICT-overload led to fatigue (Laker et al., Reference Laker, Patel, Budhwar and Malik2020).
Common examples of employees displaying a minor form of job crafting behavior during ICT over-exposure is when they proactively take regular breaks away from their work desk or computer (Baker & Slemp, Reference Baker and Slemp2018) or when they engage in task crafting by learning to use a new project management software for online document management, thereby using one technology to manage another (Wrzesniewski, LoBuglio, Dutton, & Berg, Reference Wrzesniewski, LoBuglio, Dutton and Berg2013). Thus, it would appear that a variety of ICT demands put on employees drive them to engage in such and other crafting response behaviors.
However, despite the recognized importance of response behaviors in stressful work situations, a holistic understanding of the combination of factors that influences employees to job craft remains understudied as prior research has mostly analyzed its predictors separately. Whereas personality researchers have found that individual differences like increased age or traits like proactive personality are positively related to job crafting (Bakker, Tims, & Derks, Reference Bakker, Tims and Derks2012; Lichtenthaler & Fischbach, Reference Lichtenthaler and Fischbach2016; Wong & Tetrick, Reference Wong and Tetrick2017), others have studied how various situational factors like job autonomy, workload, or technostress creators influence this behavior (Ingusci et al., Reference Ingusci, Signore, Giancaspro, Manuti, Molino, Russo and Cortese2021; Rudolph et al., Reference Rudolph, Katz, Lavigne and Zacher2017).
Hence, the purpose of this research is to examine a collection of drivers of job crafting pertaining to ICT usage. In doing so, we consider the notion that a combination of interlinked factors could be associated with driving this behavior rather than assuming that each predictor would have a linear, independent, or moderating effect on the outcome (Pittino, Visintin, & Lauto, Reference Pittino, Visintin and Lauto2016). Such a varied empirical investigation allows us to reveal how theoretically congruent factors like ICT-enabled technostress creators and proactive personality combine to drive job crafting behavior.
Because traditional linear and symmetric analytical approaches like regression analysis and structural equation modeling assume independence among all variables in a statistical model (Greckhamer, Misangyi, Elms, & Lacey, Reference Greckhamer, Misangyi, Elms and Lacey2008), extant literature has not adequately considered the combined effects and intricately complex conditions under which individuals would (or would not) engage in job crafting. Furthermore, in acknowledging the body of research that has found low to moderate differences in workplace values, attitudes, and intentions among individuals belonging to different generational groups worldwide (e.g., Bresman & Rao, Reference Bresman and Rao2017; Cennamo & Gardner, Reference Cennamo and Gardner2008; Cucina, Byle, Martin, Peyton, & Gast, Reference Cucina, Byle, Martin, Peyton and Gast2018), this study extends related literature by offering comparisons of a work behavior across generations (Gen Z, Gen Y, Gen X, and Baby Boomers). Lastly, the present study also attempts to narrow the research gap in behavioral literature which has examined job crafting for each generation, like millennials, separately (e.g., Gong, Greenwood, Hoyte, Ramkissoon, & He, Reference Gong, Greenwood, Hoyte, Ramkissoon and He2018).
The current examination of the drivers of job crafting is conducted using fuzzy set qualitative comparative analysis (fsQCA), which is a non-linear configuration-based analytical approach that recognizes synergistic effects (Supplementary Appendix 1). Following Gabriel, Campbell, Djurdjevic, Johnson, and Rosen's (Reference Gabriel, Campbell, Djurdjevic, Johnson and Rosen2018) recommendations for using fsQCA for person-centered research, the methodology was deemed appropriate for two reasons. First, we are interested in discovering theoretically aligned antecedent combinations that lead to not just the presence but also the absence of an outcome. The constructs in this research – proactive personality trait and technostress creators – can theoretically combine to form configurations that reveal occurrences of job crafting. Using Job Demands-Resources (JD-R) theory and relying on prior literature, this research integrates proactive personality as a key personal resource (Bakker & de Vries, Reference Bakker and de Vries2021) that employees utilize and ICT-enabled technostress creators as job demands they face. Unlike correlation-based quantitative methods, fsQCA allows the examination of logical connections between trait and technostress creating conditions that are necessary and/or sufficient (Dul, Reference Dul2016) for job crafting to occur. Subsequently, two research questions pertaining to the necessary condition are derived in a following section. Moreover, fsQCA facilitates the interpretation of the simultaneous effects of multiple predictor variables, which is often considered challenging in inferential statistics (Harms, Kraus, & Schwarz, Reference Harms, Kraus and Schwarz2009; Pittino, Visintin, & Lauto, Reference Pittino, Visintin and Lauto2016).
Second, a review of qualitative job crafting research found that the literature stream theorizing job crafting through a demands-resources lens has largely focused on quantitative research designs to examine and interpret its antecedents (Lazazzara, Tims, & De Gennaro, Reference Lazazzara, Tims and De Gennaro2020). Hence, as a novel qualitative methodological tool, fsQCA, would aid in extracting nuances for this literature stream that have not been achieved using conventional quantitative approaches.
Overall, the present research contributes to several bodies of literature, theory, methodology, and management practice. By analyzing a positive behavior (i.e., job crafting) displayed by individuals to reduce ICT-enabled technostress, we build on and extend the fsQCA-based examination of technostress creators and job burnout (a negative behavior) conducted by Khedhaouria and Cucchi (Reference Khedhaouria and Cucchi2019). As noted in the following section, the current research aids configurational theorizing of JD-R theory by applying the fsQCA methodology. Additionally, utilizing a configurational approach to theorize the role of proactive personality in job crafting behavior is a new contribution to extant literature because the proactive trait is mainly known to share correlational statistical significance with job crafting (Bakker, Tims, & Derks, Reference Bakker, Tims and Derks2012). The findings of this study are also useful for organizations that rely on ICT-based devices to operate and manage their workforce as well as for employees who use technology at their work every day. Moreover, this research distinguishes how proactive personality and ICT-enabled technostress creators configure as drivers of job crafting for Gen Z, Gen Y, and Gen X+ workers, thereby offering unique findings and implications for each generation in the workplace.
Theoretical development and literature review
Job demands-resources and configurational theorizing
The Job Demands-Resources (JD-R) model (Bakker, Demerouti, Taris, Schaufeli, & Schreurs, Reference Bakker, Demerouti, Taris, Schaufeli and Schreurs2003) is a theoretical framework of workplace stress which helps explain the consequences of an imbalance among the various demands and resources at one's job. Such a well-grounded perspective of work stress is appropriate as the current research pertains to ICT-enabled technostress creators. Within job crafting literature, the framework has been largely employed to understand crafting behavior in light of the demands employees face at work and the resources that help reduce those demands. The JD-R model posits that when hindering job demands increase, employees job craft by increasing (utilizing) their job, social, or self-resources (Demerouti, Bakker, Nachreiner, & Schaufeli, Reference Demerouti, Bakker, Nachreiner and Schaufeli2001; Tims & Bakker, Reference Tims and Bakker2010). In this regard, the job crafting construct consists of four factors that relate to demands and resources, namely, decreasing hindering job demands (e.g., organizing tasks to reduce cognitive overload), increasing challenging job demands (e.g., taking on new projects), increasing structural job resources (e.g., seeking learning and developmental opportunities), and increasing social job resources (e.g., improving work relationships, seeking feedback etc.) (Tims, Bakker, & Derks, Reference Tims, Bakker and Derks2012). Proactive personality trait is anchored as a personal resource that employees would tap into to job craft when facing job demands because proactive individuals are known to take initiatives to create a conducive working environment for themselves (Tims, Bakker, & Derks, Reference Tims, Bakker and Derks2012). On the other hand, this research theorizes factors of technostress creators – like invasion of privacy resulting from using employer ICT to work during one's personal time (Ayyagari, Grover, & Purvis, Reference Ayyagari, Grover and Purvis2011) – as job demands. Hence, the duality of the JD-R model lends an appropriate theoretical lens for the current research by facilitating the categorization of the study antecedents as job demands (i.e., technostress creators) and resources (i.e., proactive personality).
Moreover, by employing fsQCA to analyze the JD-R framework, this research also adheres to configurational theorizing (Cornelissen, Höllerer, & Seidl, Reference Cornelissen, Höllerer and Seidl2021), which allows the integration of various combinations of attributes to explain an outcome phenomenon. Moreover, grounding the exploratory fsQCA methodology in JD-R theory provides strong epistemological basis and ensures rigor because JD-R theory organically lends itself to a configuration-based evaluation as it entails understanding job crafting behaviors through a demands-resources categorization. Furthermore, as with qualitative methodologies that have limited generalizability, the application of fsQCA allows us to propose a midrange theory of the JD-R framework that is contextualized for workplace ICT. In other words, the theoretical contribution of the current fsQCA-based research is specific to job crafting behaviors that are enabled by the presence of ICT demands.
ICT-enabled technostress creators as job demands
Highly demanding workplace situations (i.e., job demands) can result in negative outcomes like psychological strain and burnout (Bakker & de Vries, Reference Bakker and de Vries2021). When viewed through the JD-R perspective, technostress creators are seen as a form of ICT-related job demand which negatively impacts employee well-being (Pfaffinger, Reif, & Spieß, Reference Pfaffinger, Reif and Spieß2022). Knowing that job crafting is a behavior displayed to decrease job demands, such technostress-inducing situations can combine to act as drivers of job crafting. Therefore, ICT-enabled technostress creators are positioned as job demands in the current research because the technostress creators construct consists of the following five factors that are associated with elevated stress experienced by employee (over)usage of ICT (Ayyagari, Grover, & Purvis, Reference Ayyagari, Grover and Purvis2011).
Each of the five technostress-creating factors has been theorized as job demands in extant literature. Work-home conflict relates to the tension between fulfilling work and home responsibilities due to work ICT use at home. A study of Australian workers examined the job demands of work-home conflict and work pressure (i.e., having an overload of work to complete in little available time) using the JD-R model to investigate their workplace stress (Pignata, Winefield, Provis, & Boyd, Reference Pignata, Winefield, Provis and Boyd2016). Work (techno) overload creates demanding situations of excessive work for employees due to ICT. In their research on job crafting, Ingusci et al. (Reference Ingusci, Signore, Giancaspro, Manuti, Molino, Russo and Cortese2021) employed techno overload as a modern job demand that workers are experiencing from the need to work faster, respond to queries quicker, and multi-task because technology facilitates such actions. Invasion of privacy describes situations wherein work ICT usage may violate employee privacy through tracking and surveillance. Such an invasion is seen as a job demand as prior research has emphasized the psychological strain that ICT invasion has on employees and recommends managers to alleviate these concerns by creating and following ICT work norms (Ayyagari, Grover, & Purvis, Reference Ayyagari, Grover and Purvis2011). Role ambiguity results from being unable to prioritize and allocate appropriate time for job tasks due to ICT problems, which can be a stressful situation for workers particularly during work overload. When analyzing job demands that nurses face, Boudrias, Trépanier, Foucreault, Peterson, and Fernet (Reference Boudrias, Trépanier, Foucreault, Peterson and Fernet2020) found that role ambiguity and role conflict contributed to negative outcomes like turnover intensions. As one of the most common job stressors, job insecurity – which relates to fear of job loss from new and advanced ICT – qualifies as a job demand that leads to anxiety and depression (Aguiar-Quintana, Nguyen, Araujo-Cabrera, & Sanabria-Díaz, Reference Aguiar-Quintana, Nguyen, Araujo-Cabrera and Sanabria-Díaz2021).
Proactive personality as a resource
The proactive personality trait has a positive association with employees increasing social and structural job resources (Bakker, Tims, & Derks, Reference Bakker, Tims and Derks2012) within the JD-R framework. Loi, Liu, Lam, and Xu (Reference Loi, Liu, Lam and Xu2016) studied proactive personality as a personal resource and proposed that employees become more proactive about their turnover intentions in a highly demanding work environment. JD-R theory recognizes the role of both organizational/job-related resources like managerial support as well as an employee's personal resources like proactive personality trait in mitigating job demands and reducing job strain (Bakker & de Vries, Reference Bakker and de Vries2021). Proactive personality is considered a personal resource that facilitates the effective utilization of organizational resources while job crafting because proactive employees are more likely to reach out and seek support to complete difficult tasks (Hardré, Reference Hardré2003). Individuals using their proactive trait as a resource to confront demanding situations were shown to mitigate the negative effects of ICT-enabled technostress like communication overload (Sumiyana & Sriwidharmanely, Reference Sumiyana and Sriwidharmanely2020). Although the application of fsQCA in micro-focused proactive personality literature is limited, studies have found that configurations containing the presence of this trait in CEO's and entrepreneurs influence strategic outcomes (e.g., Denoo & Soh, Reference Denoo and Soh2018; Yang, Bai, & Yang, Reference Yang, Bai and Yang2021).
Age range/generation
For the identified research gap, generational age range is incorporated into the present comparative analysis so as to detect configurational drivers of job crafting behaviors among Generations X+, Y, and Z. Although this research does not incorporate age range itself as a driver of job crafting, a comparison across generations is useful for the present ICT-related research because Gen Z, or digital natives, tend to be more comfortable with and immersed in ICT-device usage than their older counterparts like Gen X (Vigo, Reference Vigo2019). An assumption that younger employees may experience less technostress warrants an investigation of whether different generations are affected differently by technostress creators and the role that proactive personality plays in this context.
Research questions
Like qualitative studies that were designed to be exploratory (e.g., Palmer, Phadke, Nair, & Flanagan, Reference Palmer, Phadke, Nair and Flanagan2019), this research also does not involve the development of formal hypotheses regarding expected relationships due to fsQCA's exploratory and qualitative nature (Greckhamer et al., Reference Greckhamer, Misangyi, Elms and Lacey2008; Khedhaouria & Cucchi, Reference Khedhaouria and Cucchi2019). Hence, the present study is not exploratory in terms of its variables (as established measurement scales for job crafting, technostress creators, and proactive personality are used). The study is, however, exploratory for the configurations of variables we attempt to find because there lacks clear literature on how to hypothesize the combinatorial effects of these attributes (see Supplementary Appendix 1 for details on exploratory nature and net effects). Hence, in line with prior fsQCA literature in behavioral management (e.g., Gabriel et al., Reference Gabriel, Campbell, Djurdjevic, Johnson and Rosen2018), we offer two research questions pertaining to necessary conditions that can be adequately answered through this analysis. Specifically, with the purpose of this research being to examine a collection of job crafting drivers and having identified technostress creators and proactive personality as two theoretically congruent antecedents, the current research questions pertain to identifying fsQCA-based configurations and necessary conditions for the three generations.
Research Question 1a: Are there configurations of proactive personality trait and ICT-enabled technostress creators that are necessary for job crafting to occur?
Research Question 1b: Do the necessary conditions differ for each generation?
Methodology
Data sample
Data were collected through Prolific which is an online crowdsourced recruitment platform like the popular Mechanical Turk.Footnote 1 Utilizing the several pre-screening filters that Prolific allows, we targeted fully employed US-based workers who worked under a direct supervisor, used technology/software applications more than once a day at their job, and listed their occupation as senior manager, middle manager, supervisor or team leader, or non-managerial employee/worker/non-supervisory staff. These filters were applied to ensure that the sample consisted of individuals who worked with ICT and potentially engaged in job crafting behaviors. We launched separate surveys with the same questions for three-generational age groups – under 25 years (Gen Z; born 1997–2012), 25–40 years (Gen Y – millennials; born 1981–1996), and over 40 years (Gen X & Baby Boomers; born 1946–1980).Footnote 2 Consistent with Prolific's recommendation, participants were paid at a rate of least US $6.50 per hour. All participants were over 18 years of age, voluntarily agreed to participate in this study, and completed the entire survey in one sitting.
Pre-test sample
Prior to collecting the main sample, we pre-tested the survey questionnaire to ensure measurement scale reliability and assess questionnaire clarity (Hair, Celsi, Money, Samouel, & Page, Reference Hair, Celsi, Money, Samouel and Page2015). With 12 participants per age group, the final pre-test sample size was 35, after removing one participant who failed the two attention checks. The pretest sample was similar in demographic diversity to the main sample and consisted of non-managerial workers (72% in Gen Z; 50% in Gen Y; 75% in Gen X+) employed in software/IT (36% in Gen Z; 25% in Gen Y; 0% in Gen X+), finance (10% in Gen Z; 16% in Gen Y; 8% in Gen X+), retail (10% in Gen Z; 0% in Gen Y; 17% in Gen X+), and educational (10% in Gen Z; 42% in Gen Y; 17% in Gen X+) industries among miscellaneous others. As the reliability coefficients (provided in the Measures section) were satisfactory, the main sample was collected with no changes to the questionnaire
Main sample
Prior to launching the survey for the three age groups for the main sample, the ‘previous study’ pre-screener in Prolific was applied to exclude pre-test respondents. With 120Footnote 3 responses collected per age group (360 in total), the final main sample size was 335 (Gen Z = 97; Gen Y = 118; Gen X+ =120) after data cleaning and removing attention checks fails. The sample was evenly split at 50.15% (49.85%) male (female) with an average age of 35.76 years (18–69 years). The racial composition was 73.78% Caucasian, 5.67% African American, 3.51% Hispanic, 13.78% Asian American, and other. The majority of the overall sample (67.83%) had over 5 years of work experience. The occupational and industry composition of the main sample consisted of largely non-managerial workers (80% in Gen Z; 53% in Gen Y; 48% in Gen X+) employed in software/IT (13% in Gen Z; 20% in Gen Y; 22% in Gen X+), finance (7% in Gen Z; 7% in Gen Y; 11% in Gen X+), retail (23% in Gen Z; 7% in Gen Y; 4% in Gen X+), and educational (15% in Gen Z; 13% in Gen Y; 17% in Gen X+) industries among miscellaneous others.
Measures
Job crafting
Job crafting was measured using the scale developed by Tims, Bakker, and Derks (Reference Tims, Bakker and Derks2012). All 21 items (in Supplementary Appendix 2) were based on a 5-point Likert scale (1 = never, 5 = very often) and had a Cronbach's alpha of .87 for the pilot sample and .89 for the main study sample.
Proactive personality
We measured proactive personality using the scale developed by Bateman and Crant (Reference Bateman and Crant1993). All 17 items (in Supplementary Appendix 2) were based on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). The items had a Cronbach's alpha of .91 for the pilot sample and .91 for the main study sample.
ICT-enabled technostress creators
The technostress creators scale by Ayyagari, Grover, and Purvis (Reference Ayyagari, Grover and Purvis2011) consisted of 17 items (in Supplementary Appendix 2) based on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree), where each item relates to one of the five sub-factors. The scale included an instruction for the survey-takers that the term ICT referred to ‘Information and Communication Technologies including smartphones, laptop computers, and video conferencing tools like Zoom.’ The Cronbach's alpha for the scale was .94 for the pilot sample and .93 for the main study sample.
Data analysis and calibration
A factor analysis was conducted to confirm the structure of the three main study constructs – job crafting, technostress creators, and proactive personality – and to assess the convergent and discriminant validities of the scales. Most of the factor loadings (presented in Supplementary Appendix 2) were either very close or above the recommended .70 threshold (Hair, Black, Babin, & Anderson, Reference Hair, Black, Babin and Anderson2010), indicating good convergent validity for these constructs. Additionally, as shown in Table 1, the square roots of the average variance extracted (AVE) values calculated using the factor loadings were all greater than the correlations between constructs, thereby displaying good discriminant validity (Hair et al., Reference Hair, Black, Babin and Anderson2010).
Table 1. Correlations of study variables
Note: **(*) indicates that correlations are significant at the .01(.05) level. Diagonal elements (in bold) are the square roots of the average variance extracted (AVE).
All scale items were subjected to a confirmatory factor analysis (CFA) using the open-source structural equation modeling software, Ωnyx.Footnote 4 The fit indices for the three-factor structure (χ2 = 596.18; df = 190, p < .001, CFI = .88, RMSEA = .08, SRMR = .08) were all acceptable as they were very close to the recommended thresholds (CFI > .90, RMSEA < .05, SRMR < .09) (Hu & Bentler, Reference Bakker, Demerouti, Taris, Schaufeli and Schreurs1999). A common latent factor (CLF) test was also run in Ωnyx by following prior research and comparing the standardized regression weights of all items for two distinct models – one model with and one model without a common latent factor (Serrano Archimi, Reynaud, Yasin, & Bhatti, Reference Serrano Archimi, Reynaud, Yasin and Bhatti2018). The observed differences in the majority of the obtained regression weights were smaller than the recommended threshold (of less than .20), confirming that common method variance was not a major issue in the dataset (Gaskin, Reference Gaskin2018).
The analysis was conducted using the fsQCA 3.0 softwareFootnote 5 (Ragin & Davey, Reference Ragin and Davey2016). The first step involved data calibration, which involved translating all conditions into sets, and whereby each variable value was calibrated to range from 0 to 1 (Ragin, Reference Ragin2000). Given that all study variables were measured via 5-point Likert scales and to avoid the crossover point or point of ‘maximum ambiguity’ (Ragin, Reference Ragin2008, p. 30; Verkuilen, Reference Verkuilen2005), we used 0 to indicate full non-membership, 1 to indicate full membership, and input .33 and .66 for any in-between anchors. In other words, we avoided the midpoint value (i.e., .5) in the calibration to prevent risking the fsQCA software dropping it from the analysis as the software deems .5 as uninterpretable (neither in nor out of the set). Hence, during the data calibration process, the Likert scale values of 1.0, 2.0, 3.0, 4.0, and 5.0 were transformed to 0, .33, .66, 1, and 1, respectively. Any in-between values were calibrated to the lower ends. For instance, 1.5, 2.5, and 3.5 were coded as 0, .33, and .66, respectively, whereas 4.5 was coded as 1. This way, full membership was only assigned to those cases that exhibited high values (i.e., >4.0) in the respective variables.
The second step involved constructing a data matrix called the ‘truth table’, which includes all possible combinations of the antecedents examined (Ragin & Davey, Reference Ragin and Davey2016). As this study involved 5 technostress creator indicators and proactive personality (6 in total), the initial truth table consisted of 64 rows (26). In a subsequent step, all the rows that included combinations with zero observations were removed and only those configurations that were empirically observed were retained. To further refine the truth table, we relied on the frequency metric, which reflects the extent to which the combinations of attributes have sufficient empirical presence (Ragin, Reference Ragin2000). As the study variables were all continuous, the case threshold was set to 3, following expert guidelines, thereby disregarding configurations with fewer than 3 cases from the analysis (Cheng, Chang, & Li, Reference Cheng, Chang and Li2013). In the final step, configurations exhibiting high values in job crafting were determined based on the consistency metric at a threshold cutoff of .80 (Cheng, Chang, & Li, Reference Cheng, Chang and Li2013; Covin, Eggers, Kraus, Cheng, & Chang, Reference Covin, Eggers, Kraus, Cheng and Chang2016; Pittino, Visintin, & Lauto, Reference Pittino, Visintin and Lauto2018).
Following the above steps, we obtained the three solutions (named complex, parsimonious, and intermediate) that the fsQCA Quine-McCluskey algorithm derives (Fiss, Reference Fiss2011). These solutions differ based on how they treat ‘logical remainders’ (i.e., the discrepancy between logically possible combinations of conditions and empirically observed cases) (Mello, Reference Bakker, Tims and Derks2014). Specifically, the complex solution does not make any assumptions regarding cases that are not empirically observed in a dataset even though they could logically be observed, and is therefore, considered a conservative solution. The parsimonious solution, on the other hand, includes the logical remainders, but without evaluating their plausibility. The intermediate solution – which falls between the complex and parsimonious solutions – allows researchers to determine how the logical remainders should be treated on a theoretical basis. In that regard, the intermediate solution is considered superior to the other two (Ragin, Reference Ragin2000), as it neither ignores the logical remainders like complex solution does nor integrates them without a valid theoretical reason like the parsimonious solution. For these reasons, and consistent with previous fsQCA studies (e.g., Covin et al., Reference Covin, Eggers, Kraus, Cheng and Chang2016; Kosmidou & Ahuja, Reference Kosmidou and Ahuja2019), we only report the intermediate solution in the results.
Moreover, a robustness check was performed, wherein we repeated the previous analytical steps (that had applied a 3-case threshold and .80 consistency cutoff) using 4 cases as the threshold and .85Footnote 6 as the consistency cutoff point. We observed no significant differences among the findings across multiple analyses.
Results and findings
Table 2 summarizes the descriptive statistics for each generation including means, standard deviations, and minimum/maximum values observed for study variables. Descriptive statistics are reported for both uncalibrated (raw) and calibrated data. Table 1 presents the correlations between study variables for each generation and for the entire sample. Although the study was designed to collect and analyze equal (120) responses per generational group, the final sample sizes for the three groups were unequal from respondents being dropped (see Main sample section) and we, therefore, acknowledge the possibility of unequal variances among samples when interpreting the results. A preliminary reading of the correlations table reveals that proactive personality had a statistically significant relationship with job crafting for all generations and for the entire sample.
Table 2. Descriptive statistics for uncalibrated and calibrated data
Note: N = 335.
Table 3 shows results of the necessary conditions analysis and reveals whether any factor's presence or absence is necessary for job crafting to be observed. In other words, an individual factor is considered necessary when its absence cannot be compensated by the presence of any other factor in the model (Dul, Reference Dul2016). Necessary conditions are assessed through their consistency scores of at least .90 (Schneider & Wagemann, Reference Schneider and Wagemann2010). These scores reflect how consistently such conditions predict an outcome variable like job crafting. As seen in Table 3, the presence of proactive personality had the highest consistency score across the three generations (Gen Z = .96, Gen Y = .96, and Gen X+ = .97) among all observed conditions in their presence as well as in their absence. All other variable scores being lower than .90 indicates that neither the presence nor absence of any factor in technostress creators was necessary for job crafting to occur. Hence, we answer Research Question 1a by stating that proactive personality is a necessary attribute found in all obtained configurations of ICT-enabled technostress creators that show the presence of job crafting. In other words, proactive personality configures with technostress creators to drive job crafting because employees must possess this trait for them to display job crafting behavior when they are faced with technostress creating demands at work. Furthermore, as proactive personality was found to be a necessary condition for job crafting consistently for Gen Z, Gen Y, and Gen X+ workers, this necessary condition did not differ for each generation, thus answering Research Question 1b.
Table 3. Analysis of necessary conditions for presence of job crafting
Note: ~ indicates absence of the variable.
Beyond discovering the necessary conditions, fsQCA also displays results for sufficient conditions. Tables 4 and 5 include all the sufficient configurations for the presence and absence of job crafting, respectively, that bear relevance for job crafting literature. Sufficient conditions are those that produce the outcome of interest by themselves (Fainshmidt, Witt, Aguilera, & Verbeke, Reference Fainshmidt, Witt, Aguilera and Verbeke2020). The strength of these configurations is assessed based on their consistency and coverage metrics (Ragin, Reference Ragin2008). Whereas the consistency metric captures the extent to which the configurations can produce (i.e., are consistent with) the examined outcome (Fainshmidt et al., Reference Fainshmidt, Witt, Aguilera and Verbeke2020) and is the equivalent of statistical significance in a regression model, the coverage metric reflects the extent to which the configurations can sufficiently explain job crafting and is similar to the coefficient of determination (R2). As seen in Table 4, all consistency scores were above the recommended .75 threshold (Fainshmidt et al., Reference Fainshmidt, Witt, Aguilera and Verbeke2020; Ragin, Reference Ragin2008) suggesting that these configurations were sufficient for the presence and absence of job crafting.
Table 4. Causal configurations leading to presence of job crafting
Notes: QCA output of all three solutions. ‘C’ stands for (sufficient) ‘condition’. Black circles (‘•’) indicate the presence of causal antecedents and white circles (‘o’) indicate the negation or absence of causal antecedents. Large circles indicate core conditions or conditions that are part of both parsimonious and intermediate solutions. Small circles refer to peripheral conditions or conditions that occur only in the intermediate solution. The cells that are left blank indicate the ‘irrelevant’ conditions (e.g., Neither the presence nor absence of privacy invasion in Gen Z, C1 changes the configurational make-up of C1 for the presence of job crafting).
Table 5. Causal configurations leading to the absence of job crafting
Notes: QCA output of all three solutions. Black circles (‘•’), white circles (‘o’), large circles, small circles, and blank cells are represented the same way as described in Table 4 notes.
Following prior research by Fiss (Reference Fiss2011) and Pittino, Visintin, and Lauto (Reference Pittino, Visintin and Lauto2018), core and peripheral conditions are also reported in Tables 4 and 5. Because core conditions have the strongest empirical evidence linking them to job crafting, they appear in both the intermediate and parsimonious solutions. In contrast, peripheral conditions are non-essential (Pittino, Visintin, & Lauto, Reference Pittino, Visintin and Lauto2018) for high levels in job crafting and are, therefore, eliminated from the parsimonious (i.e., reduced) solution. The configurations obtained for each generation are described and interpreted ahead. Finding multiple configurations of proactive personality and ICT-enabled technostress creators to detect the presence (absence) of job crafting is a contribution of the current fsQCA-based research in showing that many different paths, and not just one optimal path in a model, drive job crafting behaviors (equifinality), which contrasts with linear methodologies (Supplementary Appendix 1). Beyond answering the research questions posed in this study, we deem it appropriate to present and discuss the following sufficient conditions adhering to fsQCA protocol and because they bear theoretical and practical implications for job crafting research.
Gen Z configurations (under 25 years)
As shown in Tables 4 and 5, for Gen Z workers, there were four (five) configurations (sufficient conditions) leading to the presence (absence) of job crafting, respectively. Proactive personality being a necessary condition for Gen Z means that when this trait combines with the five technostress creators, there will no observable combinations that reveal the presence of job crafting if proactive personality were absent (Note that in C1 proactive personality is irrelevant,Footnote 7 not absent). An interesting finding regarding proactive Gen Z workers is that when they experience a combination of work home conflict, privacy invasion, and work overload, they job craft (C4). Relating this finding to job crafting's factors, Gen Z – as the newest generation in the workforce – may be mitigating their ICT-enabled stress by increasing their social job resources through networking and getting coached by coworkers. Such social support from colleagues has shown to help when struggling with family-work conflicts (van Daalen, Willemsen, Sanders, & van Veldhoven, Reference van Daalen, Willemsen, Sanders and van Veldhoven2009). Moreover, Gen Z may be proactively seeking feedback and strategies from their supervisors on dealing with communication overload and invasion of their privacy. Also, role ambiguity and ICT-related job insecurity were not a concern for Gen Z (no configuration revealed their presence in Tables 4 or 5) perhaps due to their comfort with ICT devices and general perception of embracing technology. The configurations that showed the absence of job crafting (Table 5) for Gen Z contained peripheral conditions of work home conflict, privacy invasion, and work overload (C4 & C5). As peripheral conditions, these can be interpreted as Gen Z not engaging in craft behaviors when such ICT demands are weak.
Gen Y configurations (25–40 years)
For Gen Y workers, there were five (five) configurations leading to the presence (absence) of job crafting, respectively. Proactive Gen Y seemed to craft their job characteristics when faced with ICT work overload, role ambiguity, and job insecurity (C4). Increasing one's workload strategically is considered synonymous with crafting by increasing challenging job demands like taking on extra tasks and new projects (Tims, Bakker, & Derks, Reference Tims, Bakker and Derks2012). Even though these challenging job demands require more effort, employees who engage in such crafting behavior view this workload as beneficial for their career and personal growth. We contend that Gen Y, or millennials, who possess considerable work experience, are somewhat good with ICT (Vogels, Reference Bateman and Crant2019), and seek job advancement and enrichment from their work (Magni & Manzoni, Reference Magni and Manzoni2020), could be taking advantage of their ICT-enabled work overload by strategically increasing their challenging job demands. Additionally, when faced with role ambiguity, proactive Gen Y may be job crafting by actively decreasing their hindering job demands like finding solutions to their ICT issues to prioritize work tasks. This finding is consistent with experimental research that found that proactive individuals utilized the ‘confront’ aspect of this trait to handle difficult ICT-enabled technostress-creating situations (Sumiyana & Sriwidharmanely, Reference Sumiyana and Sriwidharmanely2020). Finally, Gen Y revealed a configuration for the presence of job crafting (Table 4, C3) that was similar to one found in Gen Z for absence (Table 5, C4).
Gen X+ configurations (over 40 years)
Lastly, there were four (seven) configurations leading to the presence (absence) of job crafting, respectively for Gen X+ workers. Interestingly, Gen X+ (C3) shared an exact same presence configuration with Gen Y (C4). Unlike Gen Z, for whom job insecurity was not a technostress creator, Gen X+ group contained two configurations that showed high levels of this ICT demand (C3 & C4). The demands-resources perspective maintains that feelings of job insecurity can be reduced by increasing one's structural resources like learning new skills and engaging in professional development activities (Jiang, Xu, & Wang, Reference Jiang, Xu and Wang2021). Being less technologically adept than younger generations, Gen X+ are likely to be concerned about ICT replacing their jobs and could be engaging in work pressure crafting (Kooij, Nijssen, Bal, & van der Kruijssen, Reference Kooij, Nijssen, Bal and van der Kruijssen2020) to reduce job insecurity. Our findings are useful for organizations to create developmental opportunities for concerned Gen X+ workers so that they could improve their structural job resources and thereby, engage in a positive form of job crafting.
Discussion
The highlight of this research involved using a novel methodology to present the various ways in which ICT-related drivers of job crafting act in concert for Gen Z, Gen Y, and Gen X+ workers. The robustness of the sample size and study design that followed fsQCA guidelines ensured rigor in the current qualitative research. In understanding the synergistic drivers of job crafting through the lens of Job Demands-Resources theory, ICT-enabled technostress creators were contextualized as job demands. The discovery of proactive personality as a necessary attribute for job crafting complements and reinforces the prior JD-R theoretical notion and regression-based findings wherein this trait shares a statistically significant relationship with job crafting (Bakker, Tims, & Derks, Reference Bakker, Tims and Derks2012; Plomp, Tims, Akkermans, Khapova, Jansen, & Bakker, Reference Plomp, Tims, Akkermans, Khapova, Jansen and Bakker2016; Tims, Bakker, & Derks, Reference Tims, Bakker and Derks2012). Hence, a contribution of this research lies in recognizing that while proactive personality should be present when job crafting is observed, this trait is not the only reason that an individual displays craft behavior when experiencing ICT overuse.
Regarding the research gap of comparing job crafting – a specific work behavior – across generations, the study had some interesting takeaways. While Gen Y and Gen X+ shared one exact same set of drivers that led them to job craft, we also discovered that for a different set of drivers that led Gen Y to job craft, the absence of those same drivers showed the absence of job crafting in Gen Z. The emergence of such a variety of configurations informs us that both the presence and absence of certain technostress creators could result in job crafting behaviors. Such combinatorial nuances found in these generations are consistent with extant literature that has found small differences in workplace attitudes and intentions among generations. The current study validates the existence of such slight differences even for a work behavior.
For an overarching discussion, it appears that Gen Y and Gen X+ share more behavioral similarities when faced with ICT demands than Gen Z. For instance, even the absence of work home conflict could make Gen Y and Gen X+ workers job craft when job insecurity is present, which was not the case for Gen Z. These dissimilarities observed for Gen Z are somewhat consistent with prior evidence that Gen Z see technology as an ‘extension of themselves’ (and are less threatened by it) as opposed to the older generations who hold a negative perception of the prevalence of ICT, viewing it as an invasion and addiction (Vigo, Reference Vigo2019). ICT-enabled role ambiguity was also not problematic for Gen Z as they seem comfortable with resolving ICT problems that hinder their work tasks. Interestingly, Gen Z – who are more engaged on social media and ICT devices – share a concern for strong privacy invading ICT demands just like the other two generations. In this regard, our study lends a perspective concerning the stereotypical notion about the younger generation's comfortability with technology. We show that even though certain technostress creators like job insecurity may not be a concern for Gen Z, they are certainly stressed about their work ICT devices tracing or monitoring their private activities. Gen Z also seem to have some dissimilar concerns than millennials specific to ICT demands. Hence, such findings carry practical implications for managers to exercise caution in not hastily assuming that the newest generation in the workforce shares all the same concerns as its predecessor.
Theoretical implications
As a case-oriented qualitative methodology that is based on a set-theoretic approach, fsQCA enriches job crafting research by enabling a mid-range theoretical advancement of the JD-R framework (Fiss, Reference Fiss2007; Supplementary Appendix 1). Whereas high-level general theories can be applied to various contexts, mid-range theories provide generalizable explanations of phenomena as they occur within a given context or for a specific sample (Boer, Holweg, Kilduff, Pagell, Schmenner, & Voss, Reference Boer, Holweg, Kilduff, Pagell, Schmenner and Voss2015). Hence, we build on JD-R's framework to offer a midrange theory that is detailed enough to explain job crafting within the ICT setting for the specific set of cases discovered in the current study results, thereby allowing researchers to interpret these unique and mixed findings. Our explanatory study contributes to JD-R theory by contextualizing proactive personality as a necessary resource for job crafting when Gen Z, Y, or X+ individuals attempt to mitigate their ICT demands. By utilizing fsQCA for theory advancement, this study joins prior micro-focused management research that have proposed mid-range theory (e.g., Kroon & Paauwe, Reference Kroon and Paauwe2021).
The present research contemporizes and elaborates JD-R theory by incorporating ICT as a modern-day job demand. Additionally, an understanding of how different ICT demands drive individuals to job craft makes JD-R theory relevant in the Covid-19 era as organizations increased their ICT usage for teleworking. For instance, among the five technostress factors, invasion of privacy and work home conflict were the two highest number of configurations (8 and 5, respectively) that led to job crafting across generations. This finding is consistent with related research on the effects of the pandemic regarding how increased work overload and privacy invasion led to more work-home conflict for employees (Vaziri, Casper, Wayne, & Matthews, Reference Vaziri, Casper, Wayne and Matthews2020). By offering comparisons of three generations, the study found that each ICT demand impacts each generation differently, thereby allowing for the varied appropriation of JD-R theory. An additional contribution of this research is in the configurational theorizing of JD-R framework by showing that job crafting is a complex, configurational outcome phenomenon that results from demands- and resources-related variables configuring in a variety of ways to create paths that cannot be adequately explained by linear relationships and singular theory.
Implications for practice and policy
The findings of this study can offer useful insights for organizational managers and policymakers toward updating management practices in today's digital age by understanding their organization's technostress-inducing situations. Managers must understand that configurations of proactive personality and ICT-enabled technostress creators could act as collective drivers of job crafting and utilize this understanding to minimize technostress-creating situations for their employees. Managers should assess the potential of extensive ICT usage to increase technostress as well as understand which of their employees are engaging in crafting behaviors to reduce privacy invasion, techno overload, and work-home conflict and then attempt to reduce technostress by implementing on-demand trainings and interventions. Employees improved their wellbeing when they participated in organization-led job crafting interventions like additional training days and being allowed more hands-on time at developmental activities designed to help them become self-managers (Van den Heuvel, Demerouti, & Peeters, Reference Van den Heuvel, Demerouti and Peeters2015). Managers could customize such interventions knowing that not all their employees proactively engage in job crafting.
While proactive personality is a necessary condition for job crafting to occur, there may be workers in all generations who possess a proactive personality but do not engage in job crafting (Table 5). To help reduce technostress, managers should offer proactive employees who already job craft promotion-focused job crafting tips (like leveraging ICT-based tools more to work with virtual teams) and provide guidance on prevention-focused job crafting to proactive employees who do not job craft (like reducing hindering ICT device demands and allowing them to maintain strict work and personal hours). During the Covid-19 pandemic's work-from-home situation and going forward, it has become imperative that organizational policies allow employees to access and utilize work resources with ease. In the wake of many remote employees running into technical issues with their official ICT devices or systems at home and finding it difficult to connect with the company's IT teams, experts offered suggestions like reaching out to tech-savvy colleagues and friends or even crowdsourcing solutions online (Forbes, 2021). Policymakers of multinational companies can educate themselves of such additional technostress-inducing situations for employees working from various locations and relax organizational integration norms that pressure employees to stay connected and respond to work matters outside of their working hours (Derks, van Mierlo, & Schmitz, Reference Derks, van Mierlo and Schmitz2014).
Knowing now that such multiple combinations of demands-resource attributes can lead to crafting behaviors differently in workers of different generations, organizations should create customized stress-reduction strategies. For instance, managers could help Gen Z employees' concerns about privacy invasion and work overload simultaneously (as they tend to occur hand-in-hand; see C4 in Table 4) and Gen X+ employees' ICT-related work home conflict, while also helping millennials address other concerning aspects of their job like finding meaning at work and obtaining more feedback (Magni & Manzoni, Reference Magni and Manzoni2020).
Limitations and future directions
Despite the contributions of this research, the cross-sectional single-source design and US-only sample are noted as limitations that should be considered when interpreting the results of this study. Although traits like proactive personality remain relatively stable over a person's lifetime (McCrae & Costa, Reference McCrae and Costa1985; Staw, Bell, & Clausen, Reference Staw, Bell and Clausen1986), incorporating longitudinal data in fsQCA would offer improved appraisals of ICT-related job crafting behaviors during an employee's job tenure. Even though ICT-enabled technostress creators and job crafting behaviors may be independently relevant for organizations and workers of any geographic location, the current fsQCA results obtained for a US sample are not generalizable for non-US entities (Misangyi, Greckhamer, Furnari, Fiss, Crilly, & Aguilera, Reference Misangyi, Greckhamer, Furnari, Fiss, Crilly and Aguilera2017). Hence, future research should investigate whether ICT (over)use in various national and cultural contexts lead to consistent crafting endeavors by employees. Moreover, ICT-enabled technostress factors were examined as drivers of overall job crafting in the current study. Future research should examine configurations generated from each job crafting (resource and demand) factor to understand how various technostress creators manifest crafting behaviors (e.g., whether the presence of ICT-enabled work overload makes employees increase their challenging job demands). With the current research presenting comparisons and contrasts across three generational groups, future studies could incorporate employee age as an antecedent. Lastly, a limitation regarding configurational theorizing is noted, in that this study did not utilize Furnari, Crilly, Misangyi, Greckhamer, Fiss, and Aguilera's (Reference Furnari, Crilly, Misangyi, Greckhamer, Fiss and Aguilera2020) suggestion of ‘naming’ configurations so as to further interpret emerging themes.
Conclusion
Through the demands-resources theoretical perspective and by using fsQCA, the present research configured proactive personality and technostress creators as drivers of job crafting behavior. Among the various configurations that emerged, we consistently found that employees with proactive personality in all three generations engaged in job crafting when faced with different forms of ICT demands. This finding suggests that proactive personality would dictate potential job crafting behaviors under stressful conditions like those caused by excessive ICT usage. The results of this study are particularly relevant for organizations – that employ workers of different ages – to understand which combinational factors drives each generation to job craft differently to reduce their ICT demand.
Using a novel methodology – that captures combinatorial effects as opposed to net effects – to analyze job crafting, we conducted rigorous case-oriented qualitative research for variable-oriented constructs that have, thus far, been largely analyzed through conventional quantitative linear techniques. fsQCA brought out those intricate intermingling among the resource and demand factors contextualized in this research. By offering proactive personality as a necessary condition within the various sufficient configurations of ICT demands, the present research offers new (and complementary) meaning to the prior regression-based findings regarding this trait's relationship with job crafting.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/jmo.2022.56.
Conflict of interest
The author(s) declare none.
