Adapting to Frequent Changes: The Roles of Job Crafting and Personal Needs

Abstract

Drawing on the transactional theory of stress and self-regulation theory, we propose a conceptual framework to examine how change frequency relates to approach or avoidance adaptations. Multiwave, multisource data from a matched sample of 424 subordinates and their supervisors indicate that (a) approach and avoidance crafting mediates the negative relationship between change frequency and adaptivity, (b) the need for human connection weakens the relationship between change frequency and approach–avoidance crafting, (c) the need for control strengthens the relationship between change frequency and approach–avoidance crafting, and (d) change frequency has a weakened (strengthened) indirect effect on adaptivity via approach crafting and avoidance crafting when employees have a high need for human connection (control). This study expands the research on job crafting and adaptivity and provides practical implications for organizations undergoing or soon to undergo changes.

Keywords

adaptivity change frequency job crafting need for control need for human connection

In 2019, the global economy grew more slowly than it had since the 2008 financial crisis. Financing difficulties, industry recession, and market contraction caused Deutsche Bank, Tesla, Siemens, Tencent, Huawei, and other enterprises to begin tightening management practices. The coronavirus disease 2019 (COVID-19) pandemic not only suddenly accelerated worldwide slowdowns in economic growth and employment, but it also promoted rapid applications of digital office systems, leading to the brisk development of emerging industries such as 5G, cloud computing, and artificial intelligence. In reaction to economic recession and technological changes, 53% of companies in Asia and the Pacific report expecting to undergo restructuring in 2021 for short-term survival and long-term sustainability (Page, 2021).

Organizational change requires both internal and external innovation and risky actions (Zhang et al., 2019). Managers must recognize that employees are as essential as change initiatives (Oreg, 2011; Vakola et al., 2020), and the successful implementation of change depends on the degree to which employees cope with, respond to, and support changes that affect their roles as organization members, called adaptivity (Griffin et al., 2007). Adaptable employees are more likely to take initiatives to effect change (Strauss et al., 2015), cope with work challenges (Berg et al., 2010), and improve performance (Bodla & Ningyu, 2017; Solberg & Wong, 2016).

Adaptivity is influenced by leader–member exchange, meaning-making, organization-based self-esteem (van den Heuvel et al., 2014), perceived transformative HR practices (Bodla & Ningyu, 2017), job crafting intervention (Demerouti et al., 2020), and quality of change communication (Petrou et al., 2018), but few studies have examined whether adaptivity is affected by employee perceptions of how often their work environment has undergone changes: namely, change frequency (Rafferty & Griffin, 2006). Frequent changes can increase uncertainty perceptions (Nery et al., 2019), burnout (Zin & Talet, 2016), emotional exhaustion (Johnson, 2016), disruption of shared responsibilities (Carter et al., 2013), reduced support for change (Johnson, 2016), job dissatisfaction (Rafferty & Griffin, 2006), and turnover intentions (Rafferty & Griffin, 2006). Consequently, we devised three research questions: Does change frequency negatively impact adaptivity? What mechanism mediates this effect? What contextual factors amplify or buffer this effect?

Organizational changes evoke change responses among employees (Straatmann et al., 2016), whose cognitions regarding changes (Piderit, 2000) determine whether they support (Kim et al., 2010) or resist (Jones & Van de Ven, 2016; Oreg, 2017) these changes. The concept of job crafting, originating from job design, refers to the physical and cognitive changes that employees make to their job tasks and relational boundaries (Wrzesniewski & Dutton, 2001). In times of organizational change, employees may adapt by redesigning their work in a bottom-up approach to improve conditions (Petrou et al., 2015; Sekiguchi et al., 2017). Indeed, empirical research in the Netherlands and Greece (Petrou et al., 2016 , 2017) examined the antecedents and consequences of job crafting in the context of cutbacks and other changes to show that the types of change events (Petrou et al., 2015) and the quality of change communication (Petrou et al., 2018) encourage employees to use job-crafting strategies to seek challenges and adapt to changes. However, studies on job crafting have mostly focused on resources (Tims et al., 2012), without considering that employees are driven by a variety of motivations (Bipp & Demerouti, 2015; Bruning & Campion, 2018).

Bruning and Campion (2018) integrated role-based literature (Wrzesniewski & Dutton, 2001) and motivation-based taxonomy (Bipp & Demerouti, 2015) to propose a framework that suggests that job crafting includes approach-oriented and avoidance-oriented crafting strategies. Employees who understand and accept the challenges will form problem-focused and improvement-based goals leading to active, effortful, and motivated approach crafting—perhaps expanding their work and social roles. In contrast, employees who have avoidant, prevention orientations will use avoidance crafting to evade, reduce, or eliminate some of their work (Bipp & Demerouti, 2015; Petrou et al., 2015).

The transactional theory of stress (Lazarus & Folkman, 1984, 1987) suggests that as individuals interact with the environment, they form challenge appraisals or threat appraisals and react accordingly. Organizational change creates stressful workplaces (Smollan & Morrison, 2019) that evoke cognitive appraisals and corresponding behaviors. Infrequent changes are perceived as discrete events, with endpoints easily identified, but frequent changes indicate unpredictability and uncertainty, leading to more severe impacts (Babalola et al., 2016). That is, frequent changes will lower employees' challenge appraisals to change events, so that they are less willing to adapt by approach job crafting. Frequent changes may also enhance employees' threat appraisals to change events, so that they are likely to adopt avoidance job crafting. Therefore, based on the transactional theory of stress (Lazarus & Folkman, 1984, 1987), our first objective was to investigate whether change frequency negatively impacts adaptivity through approach and avoidance crafting as mediation mechanisms.

In addition, self-regulation theory (Bandura, 1991) posits that individuals monitor changes according to the nature of the events and respond according to self-regulation characteristics largely determined by basic psychological needs, either for human connection or situation control. Individuals with a strong need for human connections tend to find meaning in life by building relationships (Leary et al., 2013), and they usually pay special attention to information that can promote interpersonal relationships (Pickett et al., 2004). Frequent organizational changes have changed the pattern of interpersonal relationships in the workplace, making it easier for employees with a high need for human connection to find new opportunities to connect with others. Thus, these employees are more likely to make challenge appraisals of the change and perform approach crafting. Contrariwise, individuals who have a strong need for control manipulate events to meet their desires (Greenberger, 1982). Frequent changes increase uncertainty in the work environment (Carter et al., 2013; Johnson, 2016), which weakens employees' sense of control over existing work procedures, skills, and social relationships. Once employees high in control needs feel that it is difficult to maintain control over the dynamic work environment, they tend to form threat appraisals and adopt avoidance job-crafting strategies to cope with external changes. Therefore, based on self-regulation theory (Bandura, 1991), our second objective is to explore whether the need for connection or control will regulate cognitive appraisals of change frequency, crafting strategies, and adaptive consequences.

In summary, integrating the transactional theory of stress (Lazarus & Folkman, 1984, 1987) and self-regulation theory (Bandura, 1991), we establish a moderated mediation model to examine: (1) whether approach and avoidance crafting mediate the negative relationship between change frequency and adaptivity; and (2) whether the indirect effects are conditional on the need for human connection or control. Our examination of whether personal needs determine responses to change was conducted within the highly collectivist Chinese context (Feng et al., 2019), which emphasizes interpersonal relations (Ren & Chadee, 2017). This presents an interesting context to examine whether employees' responses to changes are conditional on their personal needs.

Theory and Hypotheses

Change Frequency, Job Crafting, and Adaptivity

Change frequency indicates individual perceptions of how frequently changes occur in their work environment (Rafferty & Griffin, 2006). Frequent organizational change leads to continuous changes in workflow, reduces work efficiency, adversely affects the ability of employees (Kiefer, 2005), and increases their worry about losing power, influence, and status (Boonstra & Gravenhorst, 1998). Thus, employees lack sufficient work resources and psychological resources to effectively cope with the uncertain and changeable demands brought about by frequent changes (Carter et al., 2013; Johnson, 2016). In addition, frequent changes in workflow, task, technology, and market are often accompanied by department restructuring and job rotation (Kiefer, 2005), which might break the existing interpersonal relationship pattern (Carter et al., 2013; Shaw et al., 2006). Not only can employees not obtain social support from their original work partners, but they also must spend time and effort in expanding new social connections to adapt to the dynamic and changing working environment (Bruning & Campion, 2018). Consequently, employees lack sufficient social resources to effectively cope with the high demands brought about by frequent changes (Carter et al., 2013; Johnson, 2016), and it is difficult to adapt to organizational changes. Thus, we expect that change in frequency will decrease employee adaptivity.

Through what mechanisms can change frequency reduce employee adaptability? The transactional theory of stress (Lazarus & Folkman, 1984, 1987) suggests that individual responses to stress can be divided into cognitive appraisal and coping efforts, and cognitive appraisal is a two-step process consisting of primary and secondary appraisals. Primary appraisal is an individual's cognitive evaluation of the challenge or threat of the situation or event, while secondary appraisal is their evaluation of their coping resources and options available to deal with the challenge or threat. Primary and secondary appraisals are highly dependent and interactive processes; specifically, if the stress situation appears to offer recognition, praise, or growth, individuals are more likely to make a challenge appraisal and use problem-focused or approach strategies to cope with pressure. In contrast, if the stress situation appears to threaten their goals and potentially hinder their development, they will make threat appraisals and apply emotion-focused or avoidance-coping responses. Organizational change is a macro-level source of stress and pressure (Smollan & Morrison, 2019; Vakola & Nikolaou, 2005), indicating both innovation and risk potential (Zhang et al., 2019). Highly innovative change brings challenges and opportunities for individual improvement and enhanced resources, which will evoke challenge appraisals; however, risky organizational change brings uncertainty and the loss of individual resources, which will evoke threat appraisals (Rafferty & Restubog, 2017).

Frequent change disrupts work routines, triggers uncertainty about the future, and leads to distrust and anger toward the management. Under frequent changes, employees are more likely to focus on risks, overlook potential resources and benefits, and form threat appraisals rather than challenge appraisals. Again, the transactional theory of stress (Lazarus & Folkman, 1984, 1987) explains that threat appraisals encourage negative avoidance-oriented strategies regarding job crafting, while challenge appraisals drive positive and approach-oriented strategies toward job crafting. Thus, change frequency will negatively impact adaptivity by inhibiting approach crafting and promoting avoidance crafting for the following reasons.

Employees who work in frequently changing organizations perceive that each change threatens self-set goals and development; thus, they form threat appraisals and avoidance job-crafting strategies. To reduce obstacles, they will strategically attenuate or eliminate parts of their work roles, job requirements, effort expenditures, or task accountabilities—perhaps avoiding meetings and time-consuming tasks. In terms of resource crafting, they may mentally or physically extricate themselves from their dynamic, complex environment by using withdrawal strategies to avoid tasks, interactions, and communications. Such avoidance-oriented crafting strategies are ineffective in coping with organizational change (Amiot et al., 2006; Petrou et al., 2012), which makes employees unable to actively adjust their behavior to adapt to work redesign and changes in organizational strategy. Therefore, work role reduction and withdrawal strategies fail to enhance adaptation. Thus, we propose the following hypothesis:

Hypothesis 1a: Avoidance crafting mediates the negative relationship between change frequency and adaptivity.

Moreover, frequent changes make it difficult for employees to gain recognition and grow in their work; thus, they form weak challenge appraisals and are less willing or likely to adopt approach crafting to reduce uncertainties. Specifically, in terms of role crafting, they avoid using social expansion strategies, developing professional networks, or contributing social resources to the collective organization. Similarly, in terms of resource crafting, they avoid actively trying to gain new knowledge or learn emerging technologies to improve workflow and efficiency. In brief, in the face of frequent organizational changes, employees are unwilling to meet the challenges and adopt approach-oriented job crafting behaviors. Consequently, they will not adjust their work roles and resources to adapt to changes in the environment. Hence, we propose the following hypothesis:

Hypothesis 1b: Approach crafting mediates the negative relationship between change frequency and adaptivity.

Personal Needs as Moderators

Self-regulation theory (Bandura, 1991) suggests that human behavior is influenced by external events and self-regulation. The self-regulative mechanism operates through three main principal subfunctions: self-monitoring of one's own behavior, judgment of one's own behavior, and affective self-reaction. Therefore, employees' response to the stress brought about by the change event depends not only on the event itself but also on their self-regulation mechanism. For employees, their cognitive appraisals of organizational change may be affected by their personal needs. In other words, they selectively appraise changes according to their needs for human connections or control. Specifically, those motivated to form human connections find the greatest meaning by building interpersonal relationships (Leary et al., 2013). They are likely to judge changes positively and show strong approach-oriented self-regulation. In contrast, those who need control are motivated to maintain it by manipulating events (Greenberger, 1982) and forming negative, defensive attitudes. Thus, we propose that the need for human connection or control will moderate the relationship between change frequency and approach–avoidance crafting from different directions.

The need for human connection is a powerful, fundamental, extremely pervasive motivation with multiple strong influences on emotional patterns and cognitive processes (Hoogervorst et al., 2013). Employees who need human connection deeply desire to be accepted, form relationships, and belong to social groups (Leary et al., 2013). They attend to information that enhances interpersonal connections (Pickett et al., 2004) and work to maintain relationships (Kelly, 2012). Frequent change makes it easier for them to find opportunities brought about by the change, which helps them build connections and interpersonal relationships. Consequently, they will have positive attitudes toward change, show enhanced challenge appraisals, and exhibit reduced threat appraisals, leading to approach crafting rather than avoidance crafting. For example, they will improve the quantity and quality of their social capital and obtain work-related information and resources by using social expansion strategies to develop and strengthen social interactions with coworkers, leaders, customers, professionals, and other stakeholders.

Conversely, employees who have a low need for human connection lack motivation for interpersonal connections. They tend to work independently and avoid devoting time, energy, or other resources to expanding their social networks. They will perceive frequent changes as threats rather than challenges and cope by applying conservative, defensive strategies and thus showing avoidance crafting rather than approach crafting behaviors, such as reducing their work roles, avoiding time-consuming tasks, and reducing meeting times. Accordingly, we propose the following hypotheses:

Hypothesis 2a: The need for human connection moderates the relationship between change frequency and approach crafting, such that the negative relationship is weaker under a high need for human connection.

Hypothesis 2b: The need for human connection moderates the relationship between change frequency and avoidance crafting, such that the positive relationship is weaker under a high need for human connection.

Employees who have a high need for control want to be in charge of various work factors (Greenberger, 1982), but frequent changes such as constant task reallocations, workflow alterations, new technology, and team reconstructions greatly increase uncertainties (Carter et al., 2013; Johnson, 2016), which affect their control over various factors. When employees constantly attempt but fail to regain control, they will have learned helplessness (Seligman, 1975) and eventually become convinced that outcomes cannot be controlled by their actions; as a result, employees experience strengthened threat appraisals and weakened challenge appraisals. Consequently, they will respond negatively to organizational change through avoidance crafting rather than approach crafting and use withdrawal strategies to avoid troublesome tasks and contact with new coworkers.

In contrast, employees with a low need for control do not have a strong motivation to control various factors in their work (Greenberger, 1982). Even if organizational change frequently changes their status quo, they will not try to regain control, and accordingly they will not produce negative emotions, such as learned helplessness (Seligman, 1975). Therefore, employees with a low need for control can respond flexibly to organizational change with an open and positive attitude. They will form challenge appraisals rather than threat appraisals and adopt approach crafting rather than avoidance crafting. Rather than adhering to tradition, they willingly learn and adopt new technologies and procedures. Moreover, low control needs can help these employees shift their attention from the external environment to their own psychological state. Thus, employees with low need for control are more likely to perceive and regulate their emotions and demands and to adopt metacognition strategies to cope with frequent organizational changes. For example, they can adjust their cognition of the changing work and interpersonal relationships and give new meaning and value to their work. In addition, they will use their thoughts to overcome bad moods caused by frequent changes and to focus and devote themselves to their work. Hence, we propose the following hypothesis:

Hypothesis 3a: The need for control moderates the relationship between change frequency and approach crafting, such that the negative relationship is stronger under a high need for control.

Hypothesis 3b: The need for control moderates the relationship between change frequency and avoidance crafting, such that the positive relationship is stronger under a high need for control.

We hypothesized that change frequency is negatively related to adaptivity via approach and avoidance crafting. In addition, the need for human connection (need for control) moderates the relationship between change frequency and approach (avoidance) crafting. Taken together, we propose that the need for human connection and the need for control moderate the indirect effect between change frequency and adaptivity via approach and avoidance crafting. Specifically, under frequent change, a higher need for human connection will generate approach crafting, which aligns with adaptivity. Conversely, a higher need for control will generate avoidance crafting, which conflicts with adaptivity. Accordingly, we propose the following hypotheses:

Hypothesis 4a: The need for human connection moderates the mediating effect of approach crafting on the relationship between change frequency and adaptivity, such that the indirect effect is weaker under a high need for human connection.

Hypothesis 4b: The need for human connection moderates the mediating effect of avoidance crafting on the relationship between change frequency and adaptivity, such that the indirect effect is weaker under a high need for human connection.

Hypothesis 5a: The need for control moderates the mediating effect of approach crafting on the relationship between change frequency and adaptivity, such that the indirect effect is stronger under a high need for control.

Hypothesis 5b: The need for control moderates the mediating effect of avoidance crafting on the relationship between change frequency and adaptivity, such that the indirect effect is stronger under a high need for control.

Figure 1 shows the overall conceptual model.

Figure 1.

Research model.

Method

Sample and Procedure

We conducted a multisource, three-wave online survey of six high-technology, finance, and service industry Chinese firms that had undergone major organizational changes during the previous 6 months. Through the part-time MBA program of a university in southwest China, we contacted the HR department managers of the six firms, explained the purpose and design of the survey, and asked them to agree to administer the survey within their own firms. With the assistance of these managers, we obtained supervisors' and employees' rosters for the firms. To ensure anonymity, we conducted the survey and made the payment for filling in the questionnaire through the survey platform of Questionnaire Star. Specifically, through the Questionnaire Star platform, we created a separate link for each questionnaire, set up the reward rules for the participants to fill in the questionnaire, and paid the corresponding amount of money to the platform in advance. Next, we sent the link of the questionnaire to each participant via email. On the front page of each questionnaire, we explained the anonymity of the survey and affirmed that the data would be used only for academic research. After each participant submitted the completed questionnaire, the platform automatically generated a QR code. Using WeChat (a social software widely used in China) installed on the mobile phone, the participant scanned the code and received a reward of ¥10 (approximately $1.5) paid to their account for filling in the questionnaire each time.

To reduce common method variance (Podsakoff et al., 2003), we obtained data from different sources (i.e., supervisors and subordinates) at three time points. In the first wave, subordinates provided information about their demographics and the frequency of change in their firms. In the second wave, they answered scales measuring their needs for human connection and control. In the final wave, they reported their adaptivity, while their corresponding supervisors provided demographics and evaluated subordinates' job crafting and adaptivity.

At time 1, we distributed questionnaires to 1,361 subordinates and received 890 responses (65% response rate). At time 2, we sent surveys to 890 subordinates, of whom 587 responded fully (66% retention rate). At time 3, we distributed surveys to 587 subordinates and 115 supervisors, and we obtained 434 responses from the former (74% retention rate) and 87 responses from the latter (76% response rate), yielding 424 matches. Thus, our final sample included 424 matches, with an overall response rate of 31%.

In the final sample, 63% of subordinates were male, with a mean age of 33.05 (SD = 8.17) years, and 87% had a bachelor's degree or above. Of the supervisors, 80% were male, with a mean age of 37.86 (SD = 8.18) years, and 87% had a bachelor's degree or above.

Measures

The survey was initially constructed in English. All items were translated into Chinese following Brislin's (1986) back-translation procedures. All variables were measured using a 6-point scale (1 = strongly disagree to 6 = strongly agree).

Change Frequency. We assessed change frequency using a 3-item scale (α = 0.92) developed by Rafferty and Griffin (2006); the items include “Change frequently occurs in our company” and “It is difficult to identify when changes start and end.”

Need for Human Connection. The need for human connection was assessed using a 10-item scale (α = 0.92) by Leary et al. (2013); example items include “I try hard not to do things that will make other people avoid or reject me” and “I need to feel that there are people I can turn to in times of need.”

Need for Control. The need for control was assessed using a 12-item scale (α = 0.98) by Greenberger (1982); example items include “I would like to have influence over variety of tasks” and “I would like to have influence in general over work factors.”

Job Crafting. Approach crafting and avoidance crafting were measured using a 30-item scale adapted from Bruning and Campion (2018). Approach job crafting consisted of five dimensions: work role expansion, social expansion, work organization, adoption, and metacognition; example items include “This employee expands their role by providing opinions on important issues” and “This employee actively initiates positive interactions with others at work.” Avoidance job crafting consisted of two dimensions: work role reduction and withdrawal; example items include “This employee finds ways to get others to take their place in meetings” and “This employee works in a way that allows them to avoid others at work.”

Adaptivity. Adaptivity was assessed using the 3-item scale developed by Griffin et al. (2007). We asked both subordinates and supervisors to report the subordinates' adaptivity to ongoing organizational change; example items include “I (This employee) adapted well to changes in core tasks” and “I (This employee) coped with changes to the way I (they) have to do my (their) core tasks.” Because the correlation between the subordinates' and supervisors' ratings was high (r = 0.825, p < .001), we used the subordinate's rating in subsequent analyses to alleviate the potential common method variance problem.

Control Variables. Following previous studies (Bruning & Campion, 2018; Rafferty & Griffin, 2006), we controlled for gender, age, and education level. Gender was coded as a dummy variable, with 0 = male and 1 = female. Age was self-reported in years. Education level was coded as 1 = high school or below, 2 = junior college, 3 = bachelor's degree, and 4 = master's degree or above.

Construct Validity

We conducted confirmatory factor analyses (CFAs) to examine the construct validity of key variables. Considering the multidimensional nature of the job crafting scale, we improved model fit by creating parcels to correspond to the five dimensions of approach crafting and the two dimensions of avoidance crafting (Little et al., 2002). We compared the hypothesized six-factor model with the five alternative models. As Table 1 shows, the six-factor model provided a reasonable fit with all fit indices within acceptable levels (χ² = 1795.04, df = 545, χ²/df = 3.29, root mean square error of approximation [RMSEA] = 0.07, standardized root mean squared residual [SRMR] = 0.05, Comparative Fit Index [CFI] = 0.92, Tucker–Lewis Index [TLI] = 0.91) and had a significantly better fit than the alternative models. The CFA results supported the distinctiveness of the six variables for further analyses.

Table 1.

Confirmatory Factor Analysis Result of Construct Validity.

Model test	χ²	d.f.	χ²/d.f.	RMSEA	SRMR	CFI	TLI
Model 1 Six-factor model: CF, NHC, NFC, APC + AVC, and AD	1795.04	545	3.29	0.07	0.05	0.92	0.91
Model 2 Five-factor model A: CF, NHC, NFC, APC + AVC, and AD (APC and AVC combined)	2380.90	550	4.33	0.09	0.12	0.88	0.88
Model 3 Five-factor model B: CF + AD, NHC, NFC, APC, and AVC (CF and AD combined)	3045.86	550	5.54	0.10	0.10	0.84	0.83
Model 4 Five-factor model C: CF, NHC + NFC, APC, AVC, and AD (NHC and NFC combined)	4,035.72	550	7.34	0.12	0.17	0.78	0.76
Model 5 Three-factor model: CF + AD, NHC + NFC, and APC + AVC (CF and AD combined, NHC and NFC combined, APC and AVC combined)	5,831.75	557	10.47	0.15	0.22	0.67	0.64
Model 6 Single-factor model: CF + NHC + NFC + APC + AVC + AD (All constructs are combined)	8,077.86	560	14.42	0.18	0.21	0.52	0.50

Note. n = 424; χ² = chi-squared value, df = degree of freedom; RMSEA = root mean square error of approximation; SRMR = standardized root mean squared residual; CFI = Comparative Fit Index; TLI = Tucker–Lewis Index; CF = ;change frequency; NHC = need for human connection; NFC = need for control; APC = approach crafting; AVC = avoidance crafting; AD = adaptivity.

Analytic Strategy

All hypotheses were tested using structural equation modeling (SEM) techniques with Mplus 7.4. To test the mediation hypotheses, we used bootstrapping analysis (Preacher & Hayes, 2008), generating 10,000 bootstrap samples and computing 95% bias-corrected confidence intervals. To test the moderation hypotheses, we centered all variables involved in the interaction terms and entered the two moderators simultaneously to obtain a conservative test that speaks to the relative impact of each interaction when controlling for the other (Aiken & West, 1991).

Results

Descriptive Statistics and Correlations

Table 2 shows means, standard deviations, intercorrelations, and reliabilities of all variables in our study.

Table 2.

Descriptive Statistics and Correlations.

	Mean	SD	1	2	3	4	5	6	7	8	9
1. Gender	0.37	0.48
2. Age	33.05	8.17	−0.22***
3. Education level	3.17	0.69	0.03	−0.26***
4. Change frequency	2.95	1.30	0.12*	0.10*	−0.32***	(0.92)
5. Need for human connection	2.68	0.95	0.14**	0.08	−0.25***	0.32***	(0.92)
6. Need for control	3.86	1.36	−0.13**	−0.16**	0.34***	−0.55***	0.27***	(0.98)
7. Approach crafting	4.61	0.73	−0.02	−0.03	0.16***	−0.23***	−0.48***	0.14**	(0.97)
8. Avoidance crafting	3.06	1.14	0.10*	0.16**	−0.33***	0.55***	0.44***	−0.54***	−0.18***	(0.94)
9. Adaptivity	4.55	1.11	−0.07	0.01	0.10*	−0.21***	−0.35***	0.16***	0.54***	−0.26***	(0.94)

Note. n = 424; Gender: 0 = female; 1 = male. Education level: 1 = high school or below; 2 = junior college; 3 = bachelor's degree; 4 = master's or above. Internal consistency reliabilities are in parentheses.

*p < .05; **p < .01; ***p < .001.

Hypothesis Testing

Table 3 shows the testing results for H1a and H1b. Change frequency was negatively related to approach crafting (b = −0.13, p < .01, 95% CI = [−0.20, −0.06]) but positively related to avoidance crafting (b = 0.47, p < .001, 95% CI = [0.36, 0.57]), and approach crafting was positively related to adaptivity (b = 0.87, p < .001, 95% CI = [0.72, 1.05]) but negatively related to adaptivity (b = −0.22, p < .01, 95% CI = [−0.36,−0.10]). Bootstrapping results indicated that change frequency had significant indirect effects on adaptivity via approach crafting (indirect effect = −0.11, p < .01, 95% CI = [−0.18, −0.05]) and avoidance crafting (indirect effect = −0.10, p < .01, 95% CI = [−0.18, −0.05]), supporting H1a and H1b.

Table 3.

Unstandardized Path Coefficients and Indirect Effects for Mediation Effects.

Path	Coeff.	SD	95% CI
Change frequency → Approach crafting	−0.13**	0.04	[−0.20, −0.06]
Approach crafting → Adaptivity	0.87***	0.08	[0.72, 1.05]
Change frequency → Approach crafting → Adaptivity	−0.11**	0.03	[−0.18, −0.05]
Change frequency → Avoidance crafting	0.47***	0.05	[0.36, 0.57]
Avoidance crafting → Adaptivity	−0.22**	0.07	[−0.36, −0.10]
Change frequency → Avoidance crafting → Adaptivity	−0.10**	0.04	[−0.18, −0.05]

Note. n = 424.

*p < .05; **p < .01; ***p < .001.

Table 4 presents the testing results for H2a, H2b, H3a, and H3b. The interaction between change frequency and need for human connection was positively related to approach crafting (b = 0.12, p < .001, 95% CI = [0.06, 0.18]) but negatively related to avoidance crafting (b = −0.09, p < .05, 95% CI = [−0.17, −0.01]), supporting H2a and H2b. Meanwhile, the interaction between change frequency and need for control was negatively related to approach crafting (b = −0.07, p < .05, 95% CI = [−0.13, −0.01]) but positively related to avoidance crafting (b = 0.13, p < .01, 95% CI = [0.04, 0.22]), supporting H3a and H3b.

Table 4.

Results of Regression Models for Testing the Moderation Effects.

Variable	Approach crafting	Avoidance crafting
Variable	I	II
Control
Gender	0.07	0.04
Age	0.00	0.01
Education level	0.04	−0.09
IV
Change frequency	−0.10**	0.35***
Moderator
Need for human connection	−0.29***	0.22***
Need for control	−0.07	−0.29***
Interaction
Change frequency * Need for human connection	0.12***	−0.09*
Change frequency * Need for control	−0.07*	0.13**

Note. n = 424.

*p < .05; **p < .01; ***p < .001.

We estimated the simple slopes and plotted the interactions at 1 SD above and below the mean for each moderator (Aiken & West, 1991). Figure 2 shows that under high need for human connection (1 SD above the mean), the simple slope was not significant (b = 0.03, p > .05), but under low need for human connection (1 SD below the mean), the simple slope was negative and significant for change frequency in approach crafting (b = −0.22, p < .001). Figure 3 demonstrates that under high need for human connection, a positive and significant simple slope occurred for change frequency on avoidance crafting (b = 0.25, p < .001); under low need for human connection, the positive relationship became stronger (b = 0.44, p < .001). Figure 4 indicates a negative and significant simple slope for change frequency on approach crafting under high need for control (b = −0.17, p < .01) but a nonsignificant simple slope under low need for control (b = −0.03, p > .05). Figure 5 illustrates that under high need for control, the simple slope was positive and significant for change frequency on avoidance crafting (b = 0.48, p < .001); under low need for control, the positive relationship became weaker (b = 0.22, p < .001). The results further supported Hypotheses 2a, 2b, 3a, and 3b.

Figure 2.

Interaction between change frequency and need for human connection in predicting approach crafting.

Figure 3.

Interaction between change frequency and need for human connection in predicting avoidance crafting.

Figure 4.

Interaction between change frequency and need for control in predicting approach crafting.

Figure 5.

Interaction between change frequency and need for control in predicting avoidance crafting.

Table 5 summarizes the testing results for H4a, H4b, H5a, and H5b. As Table 5 shows, change frequency had a nonsignificant indirect effect on adaptivity via approach crafting under high need for human connection (conditional indirect effect = 0.03, p > .05, 95% CI = [−0.06, 0.11]) but a significantly negative effect under low need for connection (conditional indirect effect = −0.19, p < .001, 95% CI = [−0.27, −0.11]). Overall, conditional indirect effects (high vs. low need for human connection) were significantly different (difference = 0.22, p < .001, 95% CI = [0.10, 0.33]), supporting H4a. In addition, change frequency had a significant negative indirect effect on adaptivity via avoidance crafting under high need for human connection (conditional indirect effect = −0.06, p < .01, 95% CI = [−0.11, −0.02]) but a stronger effect under low need (conditional indirect effect = −0.12, p < .01, 95% CI = [−0.19, −0.05]). Overall, the difference was significant (difference = 0.05, p < .05, 95% CI = [0.00, 0.10]), supporting H4b.

Table 5.

Results of Regression Models for Testing the Moderated Mediation Effects.

Path	Change frequency → Approach crafting → Adaptivity			Change frequency → Avoidance crafting → Adaptivity
Path	Coeff.	SE	95% CI	Coeff.	SE	95% CI
Moderator: Stage 1 Need for Human Connection
High (+1 SD)	0.03	0.04	[−0.06, 0.11]	−0.06**	0.02	[−0.11, −0.02]
Low (−1 SD)	−0.19***	0.04	[−0.27, −0.11]	−0.12**	0.04	[−0.19, −0.05]
High-low difference	0.22***	0.06	[0.10, 0.33]	0.05*	0.03	[0.00, 0.10]
Moderator: Stage 1 Need for Control
High (+1 SD)	−0.14**	0.05	[−0.24, −0.05]	−0.12**	0.04	[−0.20, −0.05]
Low (−1 SD)	−0.02	0.03	[−0.09, 0.04]	−0.06**	0.02	[−0.10, −0.01]
High-low difference	−0.12*	0.06	[−0.23, −0.01]	−0.07*	0.03	[−0.12, −0.01]

Note. n = 424.

*p < .05; **p < .01; ***p < .001.

Table 5 also indicates that change frequency had a significant negative indirect effect on adaptivity via approach crafting under high need for control (conditional indirect effect = −0.14, p < .01, 95% CI = [−0.24, −0.05]), but a nonsignificant effect when it was low (conditional indirect effect = −0.02, p > .05, 95% CI = [−0.09, 0.04]). Overall, the difference was significant (difference = −0.12, p < .05, 95% CI = [−0.23, −0.01]), supporting H5a. Meanwhile, change frequency had a significant negative indirect effect on adaptivity via avoidance crafting under high need for control (conditional indirect effect = −0.12, p < .01, 95% CI = [−0.20, −0.05]), but the effect became weaker under low need for control (conditional indirect effect = −0.06, p < .01, 95% CI = [−0.10, −0.01]). Overall, the difference was significant (difference = −0.07, p < .05, 95% CI = [−0.12, −0.01]), supporting H5b.

Discussion

Drawing on the transactional theory of stress (Lazarus & Folkman, 1984, 1987) and self-regulation theory (Bandura, 1991), we examine how the frequency of organizational changes relates to employee adaptions through job crafting. Our conceptual framework is based on an approach–avoidance job-crafting framework (Bruning & Campion, 2018). Using multiwave, multisource data from 424 subordinates and corresponding supervisors, we find that approach and avoidance job crafting mediate the relationship between change frequency and adaptivity. Change frequency has weaker effects on approach and avoidance crafting among employees with high need for human connection or low need for control. Change frequency also has weaker indirect effects on approach and avoidance crafting among employees who have high need for human connection but stronger effects among those who have a high need for control.

Theoretical Implications

Our study makes three contributions to the literature. First, we add to the research on the predictors of adaptivity by considering the new angle of change frequency. Although adaptivity is essential for organizational competitiveness (Chen et al., 2011), we lack research on the antecedents of adaptivity. Transformative HR practices (Bodla & Ningyu, 2017) and quality communications about changes (Petrou et al., 2018) are known to positively affect adaptivity, but studies have neglected negative predictors such as frequent changes. In the context of Chinese organizations, we show that frequent changes, a macrolevel stressor, negatively impact employees' cognition and coping strategies, which then reduce their ability to adapt. Thus, our findings expand our knowledge of the antecedents of adaptivity and provide a fresh perspective on how organizational change events negatively impact individuals.

Second, previous studies of job crafting in the context of organizational change were mainly based on the resource-demand framework (Petrou et al., 2015, 2016; Tims et al., 2012), ignoring the influence of regulatory focus on employees' choice of crafting strategies. Our study introduces Bruning and Campion’s (2018) role-resource, approach–avoidance model of job crafting into organizational change research and clarifies the definition and manifestation of approach–avoidance-oriented job crafting. Furthermore, based on the transactional theory of stress, we show that frequent changes may evoke efforts to adapt through approach or avoidance-oriented job crafting strategies depending on employee motivations, resulting in paradoxical effects on adaptivity to change (Bipp & Demerouti, 2015; Bruning & Campion, 2018). By examining the paradoxical effects of approach and avoidance job crafting on employee adaptivity, we advance empirical research on job crafting in the context of organizational change.

Finally, using the self-regulation perspective, we provide new insights into the boundary conditions between change frequency and approach–avoidance job crafting. The moderating roles of job crafting and motivations have been attributed to self-reflection (Matsuo, 2019), psychological capital (Sesen & Ertan, 2019), individual work orientations, and opportunities for job crafting (Walk & Handy, 2018), but few studies have examined personal needs as amplifying or buffering effects of change frequency on approach–-avoidance crafting. Consequently, we focus on personal needs for human connection and control as moderators. We find evidence that connection and control needs moderate the relationships among change frequency, cognitive appraisals, and behavioral reactions from different directions. These conclusions afford a renewed understanding of how individual psychological states interact with behavioral responses to cope with change.

Practical Implications

Our study has several implications for organizational management. First, managers should acknowledge that frequent changes have negative effects on employees and thus should be avoided whenever possible. Change decisions and policies should be carefully planned to allow employees to adapt. Moreover, managers should fully communicate with employees before implementing change policies and ensure the consistency of policies during the implementation period to avoid frequent changes that make it difficult for employees to adapt.

Second, managers should recognize that change will drive employees toward approach or avoidance job crafting according to their orientations. In the process of organizational change, managers may use job-crafting interventions (Schoberova, 2015) as a tool to regulate employees to craft their jobs so as to effectively adapt to the changing work environment. Specifically, managers can facilitate a dialog on the added value of job crafting in the workplace (van Wingerden & Poell 2017), allow freedom, and show trust in employees' job-crafting behaviors, which may increase their perceived opportunities to craft (Dubbelt et al., 2019; Tims & Derks, 2013). In addition, managers should maintain timely and open communication with employees when implementing change measures. For example, through regular communication meetings, managers can gain a timely understanding of employees' new problems or experiences and provide positive feedback and support.

Moreover, managers can collaborate with the works council or other participatory groups to offer job-crafting training to stimulate and support their employees (Dubbelt et al., 2019; van den Heuvel et al., 2015). Such training programs should include new professional knowledge training, new process training, new system operation skills training, the process of change, and future benefits to the company, the team, and the individual. Through training, the company can effectively improve employees' acceptance of change, promote positive job crafting behaviors, and facilitate adaptivity.

In addition to their supportive attitude to job crafting, managers should also model “good” crafting behaviors (i.e., the approach crafting that has positive effects for the employee and the organization; cf. Demerouti, 2014) and appreciate and share positive job-crafting experiences of the proactive crafters (Dubbelt et al., 2019). More importantly, the company can select high-performance employees as mentors to lead the entire team to craft their work. Mentors not only need to change their working relationship and cognition, but also lead the team to improve their professional knowledge and skills and try to form their own teamwork tactics.

Finally, managers should be aware that employees' personal needs affect their adaptivity to frequent changes. Managers can allocate employees according to their individual differences and adjust their work to special groups of employees (Demerouti, 2014). Those with high demand for human connection and an open mind can be assigned to new positions and businesses to encourage them to undertake the most challenging jobs, take the lead in job crafting, and expand their influence in the organization; however, those who are more conservative and have a high need for control can be given more time to adapt.

Limitations and Directions for Future Research

This study has several limitations. First, we found that change frequency affects adaptivity through the mediation of two different job crafting strategies, but the cognitive appraisal mechanism between change frequency and job crafting is not clear. Future research can further explore how challenge and threat cognitive appraisals (Kaltiainen et al., 2020; LePine et al., 2015) mediate the relationship between change events and job crafting.

Second, although we used a multisource, multiwave time-lagged design to collect data, potential common method variance still exists. Future studies should apply experimental designs or case studies to further establish causal orders among our proposed associations (Stensaker et al., 2021). In addition, our data came from collectivist China, but personal needs for human connection and control might have different implications across cultures (Hornsey et al., 2018; Zhang et al., 2020). Thus, we encourage future research to replicate our study in individualist countries to observe whether the findings can be generalized to other cultural settings.

Finally, our individual-level research disregards team-level variables that affect the relationship between change events and employee responses. Future research could investigate whether team-level contextual factors, such as team change climate (Rafferty & Griffin, 2006), transformational leadership (Peng et al., 2021), and leader support for change (Ford et al., 2021), play cross-level moderating roles in the relationship between change frequency and adaptivity.

Conclusion

Given the important role of employees' reactions in organizational changes (Vakola et al., 2020), we explore the antecedents of employee change adaptivity. Based on the transactional theory of stress (Lazarus & Folkman, 1984, 1987), we find that frequent change as a macro-level stressor has a positive (negative) impact on employees' avoidance crafting (approach crafting), which further leads to lower adaptivity. In addition, from the perspective of self-regulation (Bandura, 1991), we show that employees need human connection buffers, while the need for control amplifies the indirect relationship between change frequency and adaptivity via approach–avoidance crafting. In this study, we extend the theory and research on organizational change and job crafting and offer practical implications for organizational change management.

Footnotes

Acknowledgments

We thank Ning Chen for his advice on earlier drafts of this paper.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (Grant No. 72072019, 72132009 and 72174096) and the Fundamental Research Funds for the Central Universities of China (Grant No. ZYGX2020FRJH012).

ORCID iDs

Lu Chen

Kaixuan Tang

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