Supervisory support for subordinates’ use of information and communication technologies: development and preliminary validation of a scale in a Chinese context

In this phase, we incorporated ICT control, perceived control of time, sense of learning, and sense of vitality as criterion variables. ICT control can be viewed as an ICT-specific construct, and perceived control of time was included to assess possible consequences of frequent ICT interruptions or constant availability requirements (Hu, Barber, et al., 2021; Hu, Park, et al., 2021). The sense of learning and vitality are two established subconstructs of a relatively general occupational health construct, namely, thriving at work (Porath et al., 2012; Spreitzer et al., 2005). As such, we examined constructs close to ICT-related consequences in experiencing control, and how certain aspects of thriving at work may be affected.

Supervisory ICT support and ICT Control

ICT control denotes the perception of the degree of control an individual has over how to use and/or choose technology at work. Low ICT control was found to be associated with certain individual outcomes, such as increased ICT stress, strain, and burnout (e.g., Day et al., 2010, 2012). As one of the important components for the perceived ICT demands (Day et al., 2012), the lack of ICT control might induce potential or even actual loss of an individual’s mastery over the way, the type, and where one uses ICT for work purposes. Given that mastery has long been operationalized as an example of personal characteristics which constitute resources (Hobfoll et al., 2018), low ICT control might trigger a loss spiral. Drawing on the conservation of resources (COR) theory (Hobfoll et al., 2018), the resource loss associated with low ICT control would hamper an individual from thriving in a stressful working environment. But similar to organizational ICT support (Day et al., 2012), supervisory ICT support can also serve as a job resource that assists employees in today’s digital working environment. Following the COR theory’s corollary 1, stronger supervisory ICT support may offer employees augmented job resources. Consequently, this increased resource availability may render employees less vulnerable to resource loss (Hobfoll et al., 2018), thereby fostering a strengthened mastery experience concerning the way, the type, and where one uses ICT for work purposes. Therefore, we expect that supervisory ICT support might help employees gain more robust ICT control. This leads to the following hypothesis:

Hypothesis 1: Supervisory ICT support is positively related to employees’ ICT control.

Supervisory ICT support and perceived control of time

Perceived control of time delineates an individual’s feeling of being in control of one’s time (Macan, 1994). This feeling related to mastery can be operationalized as a job resource according to COR theory (Hobfoll et al., 2018). We focus on this outcome given that individuals with greater resources are usually less vulnerable to resource loss and more capable of resource gain (Hobfoll et al., 2018). One’s perceived control of time may be impaired as a variety of synchronous (e.g., instant messengers) and asynchronous (e.g., e-mail) communication technologies have been used for work purposes. For example, employees tend to feel the need to respond quickly to each incoming e-mail even though it is usually not necessary to be dealt with immediately (Barber and Santuzzi, 2015). In addition, work-related ICT use on mobile devices is blurring the boundaries between work and life domains, which creates extended and/or constant availability requirements for employees (Day et al., 2012; Dettmers et al., 2016). Messages via ICT can come in any time, and employees who rely on it for work-related communications might experience a loss of control over their time. However, supervisory ICT support can equip employees with more resources and buffer the adverse effects mentioned above (Hobfoll et al., 2018). As such, we argue that supervisory ICT support might facilitate employees’ perceived control of time. This leads to the following hypothesis:

Hypothesis 2: Supervisory ICT support is positively related to employees’ perceived control of time.

Supervisory ICT support and sense of learning

Sense of learning is one of the essential components of thriving at work, capturing the sense of acquiring greater knowledge and skills (Spreitzer et al., 2005). We included it as a criterion because it is closely related to individual excellence (Spreitzer et al., 2005), and employees need to adapt to today’s rapidly changing ICT work context. Specifically, previous researches have shown that certain characteristics of ICT, namely techno-complexity and techno-uncertainty, can create heavy learning demands on employees to constant update their ICT-related knowledge and skills (e.g., Day et al., 2012; Tarafdar et al., 2007). Otherwise, employees may not be able to properly use ICT for performing their job duties. However, supervisory ICT support should help under this circumstance. Drawing on the COR theory, a stronger supervisory ICT support might offer employees with greater job resources, which in turn facilitate employees to cope with the ICT-related learning demands at work (Hobfoll et al., 2018). As such, employees would be more likely to have a desirable subjective experience in terms of learning that helps them navigate and change the ICT work context. Based on the arguments above, we contend that supervisory ICT support can be expected to promote the sense of learning experienced by employees in today’s digital working environment. This leads to the following hypothesis:

Hypothesis 3: Supervisory ICT support is positively related to employees’ sense of learning.

Supervisory ICT support and sense of vitality

Sense of vitality is also an essential component of thriving at work. It captures the positive feeling of being energetic and alive (Spreitzer et al., 2005). This positive feeling has a close connection with the more general construct well-being. Given that both scholars and practitioners used to call for designing possible ICT-related interventions to boosting employee well-being (e.g., Hu, Barber, et al., 2021; Stich et al., 2018), including sense of vitality as a criterion variable of the current study is straightforward. Comparing to the common ICT demands and/or technostress creators (e.g., techno-overload, constant availability requirements), supervisory ICT support can be categorized as a specific ICT intervention that helps to alleviate the negative effect of work-related ICT use on employees. By displaying stronger ICT-related support, supervisors might offer employees with greater job resources to cope with the ICT demands and/or technostress creators at work (Hobfoll et al., 2018). Similar evidence has been found for the contributor role that resources play to promote individual well-being (Hobfoll et al., 2018). For example, O’Driscoll et al. (2010) identified training and support for ICT users as two critical organizational-level factors that enhance employees’ well-being in the digital working environment. Day et al. (2012) found that organizational ICT support was associated with lower level of employee strain, exhaustion, and cynicism. Based on the arguments above, we expect that supervisory ICT support may help in promoting employees’ sense of vitality. This leads to the following hypothesis:

Hypothesis 4: Supervisory ICT support is positively related to employees’ sense of vitality.

Methods (Phase 4)

Participants and Procedure (Phase 4)

All participants were full-time white-collar workers in the information and technology service industry. They were recruited through Credamo, an online survey platform similar to Prolific, commonly used in China. Given its trustworthy data collection services, more and more research (e.g., Ma and Li, 2024; Zhou et al., 2024) has been using Credamo to conduct survey and experiment in recent years. To mitigate the problem of common method bias (CMB), we followed Podsakoff et al.’s (2024) recommendations and conducted a two-wave survey to introduce temporal separation between the measurement of the independent variables in Wave 1 and criterion variables in Wave 2, with an interval of ~2 weeks between the two waves.

Each participant received an incentive of 10 RMB (~1.38 USD) for providing complete and valid responses in our two-wave survey. And responses were retained only when participants completed both waves, and 400 matched responses were received. After dropping low-quality data by following DeSimone et al.’s (2015) procedure, 387 responses were retained for analysis. Amongst these participants, 54.26% were female, and 95.09% had completed college-level education or higher. The average age and total working years of these participants were 34.14 (SD = 3.54) and 10.13 years (SD = 3.94), respectively. The participants were predominantly from private enterprises (71.83%) and state-owned businesses (18.09%), with ~10.08% employed by foreign-invested or mixed-ownership companies.

Measures (Phase 4)

The measures used in this phase, originally published in English, were translated into Chinese following the same procedure as in Phase 3 before being presented to participants. To strengthen causal inference and reduce potential CMB, we employed a temporal separation strategy by measuring supervisory ICT support in Wave 1 and the criterion variables (i.e., ICT control, perceived control of time, sense of learning, and sense of vitality) in Wave 2.

Supervisory ICT support (Wave 1)

Supervisory ICT support was rated by participants on the eight-item scale we developed ranging from 1 (Strongly disagree) to 7 (Strongly agree). Sample items included “My supervisor helps me get the technology upgrades that I need” and “My supervisor cares about my feelings in the face of information demands at work”. Cronbach’s α for this measure was 0.86.

ICT control (Wave 2)

ICT control was assessed with the three-item subscale adapted from Day et al.’s (2012) perceived ICT demands measure. Participants were asked to indicate the frequency to which they experienced the decision authority over ICT using a six-point Likert scale ranging from 1 (never) to 6 (always). A sample item was “I have control over how I use technology at work”. Cronbach’s α for this measure was 0.71.

Perceived control of time (Wave 2)

Perceived control of time was assessed with four reversed coding items adapted from Macan’ (1994) work. Participants were asked to indicate the extent to which they can affect how their time is spent using a seven-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree). A sample items was “I find it difficult to keep to my schedule because others take me away from my work”. Cronbach’s α for this measure was 0.73.

Sense of learning and vitality (Wave 2)

Sense of learning and vitality were measured with the thriving at work scale developed by Porath et al. (2012) using a seven-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree). In this scale, five items assessed sense of learning, and five items assessed sense of vitality. The Chinese version of this scale has been validated in previous study (e.g., Zhu et al., 2024). Sample items for these two dimensions were “I find myself learning often” and “I feel alive and vital”, respectively. Cronbach’s α was 0.89 for the thriving at work scale as a whole. And the Cronbach’s α for the sense of learning and the sense of vitality subscales were 0.76 and 0.84, respectively.

Control variables (Wave 1)

Following recent recommendations regarding the selection of control variables(Sturman et al., 2022), we included control variables based on their theoretical relevance and potential to confound the effects of supervisory ICT support on the four criterion variables. This approach enabled us to avoid a purely mechanical inclusion of control variables and instead ground our choices in established conceptual arguments and prior empirical research.

First, we controlled for ICT familiarity, as individuals with greater ICT knowledge may better cope with ICT-related demands (e.g., Nouri et al., 2022). Four items adapted from Dong et al.’s (2024) AI familiarity scale were used to assess participants’ familiarity with ICT on a seven-point Likert scale, ranging from 1 (Strongly disagree) to 7 (Strongly agree). A sample item was “I know a lot about ICT”. Cronbach’s α for this measure was 0.75.

Second, we included transformational leadership to account for its conceptual overlap with supervisor behaviors (Bass, 1990). It has commonly been controlled for in previous research when establishing the validity of new leadership and/or supervision constructs and measures (e.g., Zhu et al., 2019). Participants were instructed to respond to Carless et al.’(2000) seven-item short measure of transformational leadership using a seven-point Likert scale, ranging from 1 (Strongly disagree) to 7 (Strongly agree). The Chinese version of this scale has been validated in previous study (e.g., Lin, 2023). A sample item was “My supervisor communicates a clear and positive vision of the future”. Cronbach’s α for this measure was 0.89.

Third, we controlled for social desirability to mitigate bias in self-reported responses (Arthur et al., 2021) and to facilitates the testing for potential CMB using the directly measured latent variable technique (Podsakoff et al., 2024). Drawing on Tan et al.’s (2022) research, we asked the participants to respond to five items adapted from Crowne and Marlowe (1960) using a seven-point Likert scale, ranging from 1 (Strongly disagree) to 7 (Strongly agree). A sample item was “No matter who I’m talking to, I’m always a good listener”. Cronbach’s α for this measure was 0.75.

Fourth, we also controlled for several individual characteristics usually found to have a close association with individual well-being (e.g., Ariño-Mateo et al., 2024; Day et al., 2012; Kleine et al., 2019). Specifically, gender, measured as a dichotomous variable coded as 0 for male and 1 for female; educational level, coded as 1 for some college education or lower, 2 for undergraduate level education, 3 for graduate-level education; and tenure, the number of years working.

Statistical strategies (Phase 4)

We used the R package “psych” (Revelle, 2024) to assess the internal consistency reliability for each measure, and to conduct the descriptive statistics and Pearson correlation analysis. Then, we conducted a series of CFAs using the R package “lavaan” (Rosseel, 2012) to assess the latent factor structure of our measurement model, as well as the potential CMB. To test our hypotheses, we conducted a series of hierarchical linear regressions using the R package “stats” developed by the R Core Team (2024) and contributors worldwide. All analyses were conducted using the freeware tool RStudio 2022.07.1 (Posit team, 2024) in the R (version 4.1.0) environment.

Results (Phase 4)

Confirmatory factor analysis for the measurement model

We conducted a series of CFAs to examine the goodness-of-fit for our measurement model, which comprised eight latent constructs: social desirability, ICT familiarity, supervisory ICT support, transformational leadership, ICT control, perceived control of time, sense of learning, and sense of vitality. Given the small sample size, we employed item parceling for each subdimension of supervisory ICT support before constructing the measurement model (Bandalos and Finney, 2001). The overall fit indices (χ²₍₅₆₆₎ = 879.62, CFI = 0.95, TLI = 0.94, RMSEA (90% CI) = 0.04 (0.03, 0.04), SRMR = 0.04) supported our measurement model well, outperforming all alternative models with fewer factors. Thus, our measurement model had a good fit to the data (Hair et al., 2018).

Although we separated independent and criterion variables over time, we cannot rule out CMB because all the measures were collected from one source. Drawing on Podsakoff et al.’s (2024) work, we applied the directly measured latent variable technique and tested for CMB, modeling all items loading onto social desirability. Even though this model exhibited a good fit to the data (χ²₍₅₄₃₎ = 866.78, CFI = 0.95, TLI = 0.94, RMSEA (90% CI) = 0.04 (0.03, 0.04), SRMR = 0.06) (Hair et al., 2018), its fit indices did not surpass those of the measurement model (Δχ² = 12.84, Δdf = 23, p = 0.96). Furthermore, the average variance explained by the directly measured latent variable (i.e., social desirability) for items measuring other constructs was 0.15, falling far below the commonly suggested 0.50 cutoff for the presence of a substantial common factor (Hair et al., 2018). Therefore, CMB was considered not to be a problem.

Descriptive statistics

Means, standard deviations, and correlations are presented in Table 4. Results show that supervisory ICT support was positively related to ICT control (r = 0.51, p < 0.001), perceived control of time (r = 0.49, p < 0.01), sense of learning (r = 0.58, p < 0.001), and sense of vitality (r = 0.66, p < 0.001), respectively. These correlations provide preliminary support for the criterion validity of supervisory ICT support. Table 4 further illustrates that individuals with higher social desirability or ICT familiarity tended to provide more positive responses overall. Additionally, the table reveals a strong effect of transformational leadership, suggesting that individuals responded more positively when supported by transformational leadership.

Hypotheses testing

Taking the four criterion variables (i.e., ICT control, perceived control of time, sense of learning, and sense of vitality) as outcomes, we conducted a series of two-step regression analyses to examine the criterion validity of supervisory ICT support.

As can be seen in Table 5, supervisory ICT support had a positive association with employees’ ICT control (β = 0.32, p < 0.01), perceived control of time (β = 0.15, p < 0.05), sense of learning (β = 0.14, p < 0.05), and sense of vitality (β = 0.17, p < 0.01), respectively. Taking in to account the control variables, supervisory ICT support accounted for a significant increase in explained variance in ICT control (ΔR² = 0.03, p < 0.001), perceived control of time (ΔR² = 0.01, p < 0.05), sense of learning (ΔR² = 0.01, p < 0.05), and sense of vitality (ΔR² = 0.01, p < 0.01). These results further support Hypothesis 1, 2, 3, and 4, which stated that Supervisory ICT support is positively related to employees’ ICT control, perceived control of time, sense of learning, and sense of vitality. Thus, we concluded that the criterion validity of supervisory ICT support is adequate.

Supplementary analysis

First, we conducted multicollinearity diagnostic protocol due to the high correlation between transformational leadership and supervisory ICT support was high (r = 0.80, p < 0.001; see Table 4). Variance inflation factors (VIFs) for all predictors (including controls) in our criterion-validity regressions were found to range from 1.10 to 3.16, falling below the conventional cutoff of 10 (Hair et al., 2018) and the more conservative threshold of 5 (O’Brien, 2007). Recent methodological critiques (e.g., Kalnins and Hill, 2025), however, have highlighted these widely used VIF cutoffs are overly permissive and may overlook problematic levels of multicollinearity. We therefore re-estimated each model after excluding transformational leadership that exhibited the highest VIF (3.16). And the direction and significance of supervisory ICT support effects remained stable (see Appendix A), indicating that multicollinearity did not severely bias our findings (Kalnins and Hill, 2025).

Second, we reran the regression analyses after excluding social desirability to assess whether the positive effects of supervisory ICT support on the criteria were confounded by potential CMB bias (Podsakoff et al., 2024). The results (see Appendix B) indicated that supervisory ICT support still had positive associations with employees’ ICT control (β = 0.33, p < 0.001), perceived control of time (β = 0.17, p < 0.05), sense of learning (β = 0.13, p < 0.10), and sense of vitality (β = 0.18, p < 0.01). These findings indicated that all our hypothesized positive relationships between supervisory ICT support and each of the four criteria were not confounded by the potential CMB concerns stemming from social desirability.

Third, we further examined the effects of the two dimensions of supervisory ICT support—ICT consideration and ICT updating—on each of our criterion variables. When these moderately correlated (r = 0.66, p < 0.001) dimensions entered simultaneously, ICT consideration uniquely had positive associations with employees’ ICT control (β = 0.25, p < 0.001), perceived control of time (β = 0.13, p < 0.05), and sense of vitality (β = 0.20, p < 0.001), while ICT updating uniquely had a positive association with employees’ sense of learning (β = 0.24, p < 0.001; see Appendix C). Since VIFs in these models peaked at 3.36 for transformational leadership, we re-estimated each model after excluding this variable to mitigate potential redundancy following the recommendations of Kalnins and Hill (2025). Without serious multicollinearity bias concerns—VIFs peaked at 2.02 for ICT updating exhibit—all statistically significant regression coefficients for both ICT consideration and ICT updating in these new models exhibit the expected positive signs (see Appendix D). Importantly, the pattern of the effects ICT consideration has on the four criterion variables held no matter transformational leadership was included or excluded (see Appendix C and Appendix D), suggesting that the people-centered dimension—ICT consideration—may serve a more important role in supervisory ICT support. Moreover, the effects of ICT consideration and ICT updating remained consistent even after excluding social desirability (see Appendix E).

Taken together, these supplementary analyses demonstrated that (1) multicollinearity did not severely bias our findings, (2) CMB did not confound the effects of supervisory ICT support, and (3) while both subdimensions of supervisory ICT support contributed uniquely to different outcomes, the people-centered dimension—ICT consideration—may play a more pivotal role in general.