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Article

A Person-Environment Fit Model to Explain Information and Communication Technologies-Enabled After-Hours Work-Related Interruptions in China

by
Shanshan Zhang
,
Fengchun Huang
,
Yuting Zhang
* and
Qiwen Li
School of Management, Zhejiang University of Technology, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(4), 3456; https://doi.org/10.3390/ijerph20043456
Submission received: 4 December 2022 / Revised: 11 February 2023 / Accepted: 13 February 2023 / Published: 16 February 2023
(This article belongs to the Section Digital Health)
Browse Figures
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Abstract

:
Given the ubiquitous nature of mobile devices and information and communication technologies (ICT), after-hours work-related interruptions (AHWI) occur anywhere and anytime in China. In the current study, an alternative person–environment (P-E) fit model of ICT-enabled AHWI, hereafter referred to as IAWI, that treats polychronic variables as moderated solutions are presented. A cross-sectional survey among 277 Chinese employees (average age: 32.04 years) was conducted in September 2022 and tested by PLS-structural equation modeling to validate our hypotheses. The results indicated that IAWI had a positive influence on employees’ innovative job performance and in-role job performance (β = 0.139, p < 0.05; β = 0.200, p < 0.01; β = 0.298, p < 0.001). Moreover, among employees with higher levels of polychronicity, the heightened effects of IAWI on innovative job performance were increased (β = 0.112, p < 0.05). This study offers implications for employees: under IAWI situations, they could search for a person–environment (P-E) that is fit to buffer the negative aspects of IAWI, consequently increasing their innovative job performance and in-role job performance. Future research could extend beyond this framework to explore employees’ IAWI and job performance balance.

1. Introduction

Information and communication technologies (ICT) have become a widespread media enabling people to interact with each other anywhere and at any time. ICT can break time and space constraints, specifically during a pandemic such as the COVID-19 outbreak. During a pandemic, people constantly switch back and forth between personal and work roles at home and at work. According to the Work Trend Index released by Microsoft in March 2021, more than 70% of employees want their respective companies to continue providing the option of working remotely from home [1]. Moreover, according to CNNIC 2022 [2], ICT-enabled work is increasingly rising, and ICT users in China have reached 1.027 billion. As a new driving force for economic growth, ICT-enabled telecommuting provides more future possibilities for employees, allowing companies to better adapt to the uncertainties and challenges brought about by sudden crises and digital transformations [3,4]. Nevertheless, flexible working hours also represent the beginning of an unlimited extension of working hours and the disruption of life. That is, given the pervasiveness of ICT, the boundary between work and nonwork/life becomes blurry. Technology-enabled channels have consequences, such as after-hours work-related interruptions (AHWI), which have received much attention from scholars [5].
ICT-enabled AHWI (hereafter referred to as IAWI) refers to work-related occurrences caused by technology during employees’ after-work hours. It breaks individuals’ ongoing nonwork activities and has increasingly become a prevalent work–life interaction form [5]. In general, prior research has used stress–strain-outcome frameworks and indicated the negative consequences of IAWI [6,7,8] (as shown in Table A1). They suggest that ICT invades employees’ personal life and drains personal resources for job demands. For instance, Cheng et al. (2020), who adopted the conservation of resources theory, found that technology-mediated work interruption leads to information overload [9]. Van et al. (2020) and Lanaj et al. (2014) postulated that IAWI results in emotional exhaustion and triggers lower work engagement and less proactive work behavior [10,11].
Nevertheless, given the growing reliance on ICT and the ubiquitous nature of IAWI, people routinely negotiate the boundaries between work and nonwork activities on a daily basis. Work and nonwork domains have become more integrated [12,13,14,15], especially during the COVID-19 period [3,4]. Notably, in China and some Asian countries (e.g., Japan and Korea), the cultural norm of collectivism promotes working after hours [16,17,18]. The Chinese give priority to work in order to benefit their family [19]. From the perspective of individual adaptability, individuals in China tend to adaptively utilize ICT as a means of promoting the integration of work and life. Despite this evidence, previous studies on Chinese employees have generally separately considered work and life, pinpointing that IAWI induces work–life conflict [20]. In particular, less research has explored the IAWI phenomenon from the individual adaptability perspective and examined how IAWI influences Chinese employees’ other behavioral outcomes, e.g., innovative job performance and in-role job performance. Further studies are needed to examine the work and nonwork domains enabled by ICT in China and the relationship between IAWI and its consequences, i.e., other aspects of employees’ behavioral outcomes.
As a key pillar in the organizational behavior field, job performance is defined as employees’ contribution to organizational goals [21]. In the current study, we explore the two types of employees’ behavioral outcomes, i.e., innovative job performance and in-role job performance, for the following reasons. First, in-role job performance and innovative jobs are the main goals sought by organizations, and innovative job performance is specifically not attached to supervisor ratings of performance. Janssen [22] assessed in-role performance in three subdimensions (job description, employee responsibilities, and performance requirements) and evaluated innovative performance by applying three subdimensions (idea generation, idea promotion, and idea realization). Meanwhile, Hagedoorn and Cloodt [23] used three measurements of innovative performance: research and development, patents, and announcements of new products. Second, only a few pieces of research have examined the relationships between IAWI and in-role job performance and innovative job performance, whereas the relationships between IAWI and traditional behavioral outcomes, such as task performance and nonwork performance, have been well established.
According to person–environment (P-E) fit theory, people have innate needs to seek a fit between the external environment and personal demands. In this sense, under IAWI situations, employees may search for a P-E fit that can meet their psychological or practical demands and is adaptable to the imposed ICT-enabled requirements [5,24]. This shift alters employees’ perceptions of IAWI. For instance, IAWI may be deemed positive, necessary, and constructive, providing a flexible and cross-domain environment for employees, consequently increasing their behavioral outcomes [5,25,26]. The P-E fit theory has been used in several digital technology studies and proven positive organizational outcomes, such as organizational commitment and improved task performance [5,24]. In this study, we believe that IAWI may positively influence job performance in line with the P-E fit logic.
Further, individuals routinely negotiate IAWI to accomplish daily work activities. This process of negotiation can be characterized by individual differences. Some employees may be better than others at processing information or dealing with requirements [27]. For instance, prior research has related individuals’ cultural preference differences, such as polychronicity, to ICT-related overload [24]. If an individual prefers to conduct multiple tasks in parallel, then he or she will perceive less information overload. However, how polychronicity matches/fits with IAWI and consequently influences in-role job performance and innovative job performance has not been explored. Above all, the present study is an attempt to bridge the abovementioned research gaps and focus on conceptualization and testing the positive effects of IAWI in China. We adopted a P-E fit lens to explore how polychronicity influences the relationships between IAWI and in-role job performance and innovative job performance. Such an examination may help explore the task-technology fit mechanisms among Chinese employees and cast a new light on the understanding of IAWI, polychronicity, and organizational behavioral outcomes.
The current study aims to (1) investigate IAWI from the P-E fit perspective and (2) incorporate the moderating effect of polychronicity to analyze how it affects the influence of IAWI on employees’ innovative job performance and in-role job performance. We address the deficiency of extant research regarding IAWI by employing a new theoretical perspective, namely, the P-E fit perspective. The time/temporal factor polychronicity is adequately incorporated into the IAWI model to better understand the P-E fit process. This approach is helpful in designing ICT applications that are better fitted to users’ temporal behaviors.
The outline of this study is briefly described as follows. Section 2 introduces the concepts of the P-E fit theory and polychronicity and presents the relationships between IAWI, innovative job performance, in-role job performance, and polychronicity. Section 3 and Section 4 describe the data collection process and concludes the findings. The implications of the findings and future research directions are also illustrated.

2. Theoretical Background

2.1. Person-Environment Fit Theory

According to P-E fit theory, the person and the environment not only directly affect individual outcomes but also interact with one another to affect these outcomes [28]. Individuals tend to seek out and create “fitting” environments that allow them to manifest their personalities [29]. Past research has established the existence of several types of fits, including the fit between the demands of the environment and personal abilities [30,31], the fit between individual needs and environmental supply [32], and the fit between organizational and personal values [33,34]. Previous studies have also proven a variety of positive outcomes that are linked to P-E fit, such as job satisfaction, organizational commitment, psychological well-being, turnover, job performance, and citizenship behaviors [35,36,37]. Under IAWI situations, employees may search for a P-E fit that can meet their psychological or practical demands and is adaptable to imposed ICT-enabled requirements [5,24]. In this study, we use the P-E fit perspective to explain how individuals can view technology-mediated work interruptions similar to a “fitting” environment, allowing them to experience greater job performance (i.e., in-role job performance and innovative job performance).

2.2. Polychronicity

Hall, an anthropologist, proposed the concept of cultural values and discovered polychronic cultures among major Native Americans [38]. In his work, polychronicity refers to an individual’s cultural preference for engaging in two or more tasks or activities simultaneously. Individuals who prefer to engage in multiple tasks simultaneously are polychronic individuals [39]. By contrast, individuals who prefer monochronic time are people who, for example, engage in one task at a time. Furthermore, individuals shape their own individual culture [40], and culture determines their thought processes [41]. Prior experiences are stored in an individual’s mental representations. If the new input matches with memories of past experiences, then it reflects a positive emotion and is said to be congruent [42]. If a mismatch exists between input and stored memory (e.g., individual’s cultural preference), then it is said to be incongruent, and the event does not proceed efficiently.

2.3. Hypothesis Development

A number of studies concerning the relationship between IAWI and job performance are limited to examinations of negative influences. However, P-E fit theory suggests that people have innate needs to seek a fit between a person and the environment. Under IAWI circumstances, employees feel urgent and hustled to engage in job tasks. They engage in jobs that are prescribed by specific roles. In contrast to extra-role behaviors that are outside one’s job description and would not be rewarded formally, in-role behaviors are monitored and rewarded [43,44]. Moreover, employees have an achievement-striving motivation to seek and complete in-role tasks even though they are exhausted [45,46]. Specifically, under the higher collectivist culture in China, the availability of IAWI makes employees spend more time, even “24-h”, at work [47]. Following Hofstede (1980), collectivist cultures focus more on achieving group goals [48]. Hu et al. (2022) posit that employees in China, one of the collectivistic countries, consider the threat of IAWI to be low and are dedicated to fulfilling individual tasks [49]. As such, IAWI is expected to lead to higher in-role job performance.
Otherwise, ICT can also be constructive and flexible. For instance, people can use ICT for a variety of purposes, such as making calls, chatting with others, playing games, and surfing the Internet. In the context of IAWI, Chinese employees believe after working hours, that work-related technology use is natural [49], and most of them receive work instructions during nonworking hours [50]. They also tend to creatively apply ICT applications under IAWI situations, allowing them to become more capable of completing tasks. In effect, individuals have an intrinsic motivation to learn and subsequently be rewarded [51]. Resource investments can serve as coping mechanisms to gain potential returns [52]. When employees experience IAWI, they may discover new approaches to innovatively use information technology as a means of increasing productivity [26]. In other words, employees invest resources to cope with IAWI, view IAWI from the positive side [52,53,54], and consequently achieve innovative job performance.
On the basis of the discussion above, we hypothesize the following:
H1: 
IAWI is positively associated with in-role job performance in China.
H2: 
IAWI is positively associated with innovative job performance in China.
Prior research has related polychronicity to ICT-related overload [24]. If an individual prefers to conduct multiple tasks in parallel, then he or she perceives less information overload. In this sense, if employees’ polychronicity scores increase, then they may be more adaptive to IAWI circumstances, which may facilitate the work to be accomplished. König and Waller [55] and Bluedorn [56] also argue that polychronicity shares a common meaning with multitasking. Although the explosion of ICT has increased the incidence of multitasking [57], polychronics believe that ICT also helps them to efficiently handle multiple tasks [57]. Hence, a match exists between IAWI and the individual’s cultural preference, that is, polychronicity. For Chinese employees needing to deal with ICT-enabled demands, if they are polychronic, then the event or task will proceed efficiently [27], resulting in increased job performance. We expect polychronicity to strengthen the relationship between Chinese employees’ perceptions of IAWI and their job performance (i.e., in-role job performance and innovative job performance).
Thus, we hypothesize the following:
H3: 
Polychronicity positively moderates the positive effect of IAWI on in-role innovative job performance in China.
H4: 
Polychronicity positively moderates the positive effect of IAWI on innovative performance in China.
In summary, our study focuses on exploring the positive impacts of IAWI on in-role job performance and innovative job performance among individuals with different polychronicity values. Polychronicity is adequate for use as a moderator in the IAWI model. Several control variables, such as age, gender, educational background, income, industry, company size, working years, and occupation ranking, are included to account for in-role job performance and innovative job performance [5,58]. The proposed model is shown in Figure 1.

3. Methodology

A cross-sectional survey design and employees across China were both considered in the empirical testing of the proposed research model. Before sending out the survey, the was piloted among ten employees and reviewed by three researchers. The questionnaire was distributed to them to check if the measurements of IAWI, polychronicity, in-role job performance, innovative job performance, and control variables were readable. Some modifications were made according to their feedback. Then, the questionnaire links were distributed to participants via a professional data collection agency in China: Sojump. The links lasted for one week, and the participants were offered a monetary incentive of 15 RMB for completing the survey. The agency offers a service that filters out non-working people. In particular, 311 people participated in the survey. There was one question about the subjects’ current status (What is your current status? 1. Students; 2. Part-time work; 3. Full-time work; 4. Unemployed). A total of 34 people who were not employees were excluded. Finally, a total of 277 employees were used for the subsequent data analysis.
Measurements of the independent and dependent variables were adapted from the literature (see Table A2). In particular, the measurement items for IAWI were adapted from Chen and Karahanna [5]. For instance, employees who have IAWI are those who are interrupted about work-related matters through technology, such as Wechat, Dingding, and so on, during their after-work time. The measurements for in-role job performance and innovative job performance were based on Williams and Anderson [59] and Janssen and Van Yperen [60], respectively. If employees completed the duties specified in their job descriptions and often searched out new working methods, these people had higher levels of in-role job performance and innovative job performance. Polychronicity was measured using the scales developed by Slocombe and Bluedorn [61]. Polychronic people prefer multitasking. All items, as detailed in Table A2, were measured on seven-point Likert-type scales, ranging from “strongly disagree” to “strongly agree.”
As our study was conducted among Chinese employees, we used the back-translation method to translate the original English survey instruments into Chinese [62]. In particular, four bilingual researchers initially translated the questionnaire into Chinese, and then another bilingual researcher translated the version back into English. After obtaining their feedback, some changes were incorporated, and then the links were sent out for pretest and formal data collection.

4. Analyses and Results

4.1. Statistical Data Analysis

Descriptive statistics are presented to show how the participants were distributed across all the control categories. Factors analysis is used to validate the reliability and validity of the constructs for the present study. In the correlation analyses, we examined the relationships between IAWI, polychronicity, in-role job performance, and innovative job performance.
The structural equation modeling method has been well established for assessing measurement models and structural models, and it is advantageous for theory development [63,64,65,66]. In view of the exploratory nature of the study, PLS-structural equation modeling was used to test the research model in SmartPLS 2.0 M3 (SmartPLS GmbH, Oststeinbek, Germany) [67]. Specifically, constructs’ reliability and validity analyses were conducted by performing the PLS algorithm program. The estimation of the path coefficients was computed by performing a standard mediation bootstrap resampling program via SmartPLS 2.0 M3 (SmartPLS GmbH, Oststeinbek, Germany).

4.2. Profile of Respondents

Table 1 shows the respondents’ demographics. Among the 277 participants, 141 (50.9%) were males, and 136 respondents (49.1%) were females, with an average age of 32.04 years (SD = 14.82). Most of them had a bachelor’s educational background (mean = 5.36; 1 = less than high school/secondary school; 2 = high school/secondary school; 3 = associate degree/higher diploma; 4 = bachelor’s degree; 5 = master’s degree; 6 = doctorate/PhD). On average, 225 (81.2%) respondents earned between 5001 and 20,000 RMB (mean = 3.46; SD = 0.88). The respondents mainly worked in the fields of internet/information systems, construction/manufacturing, and retail trade (mean = 4.67; SD = 3.09); those people are assumed to be reasonably representative of ICT users in China [2]. The vast majority of the respondents (49.1%) were employees (mean = 1.81; SD = 0.92), and 50.9% were basic-level managers, middle-level managers, senior leaders, and others (mean = 4.67; SD = 3.09). At least half of the respondents (47.7%) had worked for their company for more than 5 years and no more than 10 years (mean = 3.75; SD = 0.96). At least half of the companies (49.5%) employed 100–500 individuals.

4.3. Results of Assessing the Measurement Model

While common method variance is considered a concern in behavioral research [68], we addressed the potential issue of common method bias by performing three tests. First, the Harmon single-factor test was executed [69]. The results revealed that four factors were selected among the 16 components, and the first factor could explain only 26.48% of the total variance (as shown in Table 2). This number is below the threshold (i.e., 50%), which indicates that the study does not have a substantial common method bias problem. Second, following the work of Liang and Saraf [70], a common method factor analysis was included to estimate variances of all the principal constructs’ indicators. The average method-based variance was 0.003 (Table 3), which is much smaller than the average substantively explained variance of indicators (i.e., 0.621). Moreover, the substantive factor loadings of each indicator are statistically significant and larger than the method factor loadings. Third, according to Lowry and Gaskin [64] and Pavlou, Liang [71], if any correlation of constructs reached 0.9, then the survey had a common method bias problem. None of the correlations exceeded 0.9 (see Table 4); hence, our data did not suffer from common method bias. Thus, a common method bias is not a concern for this study.
Then, we tested the internal consistency of the sales. The values of composite reliability and Cronbach’s α all exceeded 0.7 (see Table 4), demonstrating high levels of reliability on the scales [72]. In addition, the convergent and discriminant validity of the constructs were examined via confirmatory factor analysis [73,74]. As shown in Table 4, the square root of the average variance extracted (AVE) was greater than its correlation with other variables, all AVE values exceeded 0.5, and all loadings exceeded 0.6 (see Table 3), indicating the acceptable discriminant validity and convergent validity [75,76,77] of the present study. The general mean and standard deviation characteristics of the constructs are also presented in Table 4.

4.4. Results of the Analysis of the Path Model

We performed a standard bootstrap resampling procedure in SmartPLS 2.0 M3 to test the proposed model [63]. The results indicate the positive significant influence of IAWI on in-role job performance and innovative job performance (β = 0.139, p < 0.05; β = 0.200, p < 0.01; β = 0.298, p < 0.001), supporting H1 and H2. Age, gender, education, industry, company size, working years, and occupation ranking had no influence on in-role job performance and innovative job performance. However, income is significantly positively related to in-role job performance and innovative job performance (β = 0.152, p < 0.05; β = 0.170, p < 0.05). Polychronicity has a positive moderating effect on the relationship between IAWI and innovative job performance (β = 0.112, p < 0.05), but the enjoyment has no moderation effect on the relationship between IAWI and innovative job performance (β = −0.093, p > 0.05). Therefore, H4 is supported, whereas H3 is not supported.
In summary, H1, H2, and H4 are significant at the 0.05, 0.01, and 0.001 levels, respectively (Figure 2). Table 5 presents the path coefficients (β) and significance (t values) of the research model.

5. Discussion of the Findings

The aim of this study was to examine how IAWI and polychronicity influence employees’ in-role job performance and innovative performance. The results confirm the proposed positive effects of IAWI on in-role job performance and innovative job performance and the positive moderating effect of polychronicity on the relationship between IAWI and innovative job performance. As shown in Figure 3, the relationship between IAWI and innovative job performance strengthens as polychronicity increases. In particular, when polychronicity is high, IAWI and polychronicity can be complementary when performing innovative job activities. When polychronicity is low, users have low intentions or habits to perform multitasking activities, and the path coefficient from IAWI to innovative job performance is weakened. Thus, H4 is supported.
Notably, polychronicity has no interaction effect on the relationship between IAWI and in-role job performance. This phenomenon may be attributed to employees adapting to IAWI situations in most cases. This P-E fit enables individuals to better deal with their moment-to-moment in-role tasks, which nurtures routines among Chinese employees. Polychronicity is less likely to affect the correlation from IAWI to in-role job performance. By contrast, polychronic individuals have greater latitude to leverage available resources; thus, people may choose to deal with IAWI situations through multitasking, which may lead to higher innovative activity handling. Future research may analyze the IAWI and in-role job performance phenomenon under varying personal circumstances. Another interesting finding is that income is positively correlated with in-role job performance and innovative job performance. This finding may be explained by income, which is a major concern for most people. Financial stress largely pushes individuals to work harder and be innovative when performing job tasks.
In summary, the findings prove the viability of the proposed hypotheses except for H3. This study may provide empirical support for examining the consequences of IAWI.

6. Theoretical Contribution and Practical Implications

First, this study helps to examine the relationships between IAWI and its consequences, i.e., in-role job performance and innovative job performance. In essence, interruption management is a common challenge faced by present-day employees. However, previous studies have demarcated work and life as two distinct spheres [13,78], focusing largely on employees’ work performance and nonwork performance. This study enriches the existing literature by revealing that IAWI has positive influences on other aspects of Chinese employees’ behavioral outcomes, i.e., in-role job performance and innovative job performance. We identified that IAWI could be deemed necessary and constructive, providing a flexible and cross-domain environment for Chinese employees, consequently increasing their behavioral outcomes.
Second, this study develops an alternative model for investigating IAWI through the P-E fit lens. Previous studies have generally addressed the negative outcomes of IAWI [79,80]. In particular, they focused on exploring the negative connotation of interruptions, i.e., increased emotional exhaustion and decreased task performance correlating with interruptions from a negative perspective [10,11]. However, interruptions may not always be counterproductive, as they may even yield desirable gains among Chinese employees from a positive perspective [26]. IAWI can be viewed as a mere activity or behavior that helps employees achieve organizational tasks and self-fulfillment [5,53,54,81]. The results of our study validate that, under IAWI situations, Chinese employees may search for a P-E fit that could meet their psychological or practical demands and is adaptable to the imposed ICT-enabled requirements. This may provide new insights for understanding employees’ perceptions of IAWI and provide new directions for future IAWI research.
Third, the results show that polychronicity and IAWI can work together to improve innovative job performance. Previous research has recognized the importance of polychronicity in dealing with information overload in the ICT context [24]. Polychronicity also plays an important moderating role in enhancing employees’ innovative job performance. Therefore, individuals and organizations must understand the role of individual differences as a means of effectively leveraging differences, thus promoting the positive effects of IAWI on innovative job performance. In summary, our study contributes an overall comprehensive understanding of IAWI and innovative job performance by combining P-E and individual difference/preference perspectives.
Finally, the results of this study have implications for practice. In particular, drawing on P-E theory, organizations and enterprises should provide more supporting regulations/policies, ICT facilities, and equipment to improve the matching and balance of people and the environment. Websites can provide a favorable and innovative ICT interface for users to help them cope with after-hours work-related tasks. For instance, preset text messages (e.g., “I will deal with it”), as an autonomous tool, can be set up for users to manage interruptions. In using this tool, technological solutions allow individuals with IAWI experiences to manage interruptions and be productive in their work.

7. Limitations and Future Research Directions

We acknowledge that this study has limitations. First, this study used a cross-sectional survey and self-reported data. As a result, people may overestimate their job performance. Future research can address this issue by conducting longitudinal field studies to track individuals’ actual behaviors over time. Second, the effects of IAWI may differ across countries; future cross-cultural studies can help assess the effects of cultural differences on IAWI. Third, we focused on the direct effect of IAWI on in-role job performance and innovative job performance. Future research may investigate other relevant mediators and moderators to further contextualize people’s P-E fit process under IAWI situations. Finally, the sample size of our study is relatively small, and a larger sample in larger areas could be selected for modeling tests in the future.

8. Conclusions

Cross-domain interruptions are unavoidable because of the increasing ubiquity of present-day mobile technologies. Prior studies have focused on the detrimental effects of IAWI. In this study, we highlighted the difference by concentrating on individuals’ P-E fit capabilities and needs. As hypothesized, Chinese employees tended to venture beyond the negative aspects of IAWI and were more inclined to complete in-role tasks and innovative job performance. Polychronicity and IAWI complement one another in improving innovative job performance. In conclusion, our study is insightful for future ICT-related research and has implications for individuals, organizations, and ICT developers.

Author Contributions

Conceptualization, S.Z. and Q.L.; Methodology, S.Z. and Y.Z.; Software, S.Z.; Validation, F.H., S.Z. and Y.Z.; Formal Analysis, S.Z.; Investigation, F.H. and Q.L.; Resources, S.Z.; Data Curation, Y.Z. and S.Z.; Writing—Original Draft Preparation, S.Z., F.H. and Q.L.; Writing—Review and Editing, F.H. and Y.Z.; Visualization, S.Z.; Supervision, Y.Z.; Project Administration, S.Z.; Funding Acquisition, S.Z. and Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by The Ministry of Education of Humanities and Social Science Youth Project in China (Grant Number 22YJC630206), Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ23G020008, and the Ministry of Education of the People’s Republic of China [Humanities and Social Sciences Research Fund 22YJAZH143].

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data of this study will be available from the first and corresponding authors upon reasonable request.

Acknowledgments

We thank all the participants and supporters for helping us complete the paper.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Summary of Technology-Mediated Work Interruptions Related Research.
Table A1. Summary of Technology-Mediated Work Interruptions Related Research.
StudyMethodContextTheories UsedConstructs in the Model
[24]SurveyUse of mobile phonesN/APolychronicity, age, memories of past cognitive overload, memories of past emotional overload, information and communication technology-related overload
[82]SurveyWork-home segmentation
or integration		P-E fit theoryWork-family conflict, work-home conflict stress, segmentation preferences, supplies, job satisfaction
[58]SurveySocial media usageUses and gratification theory, affordances theorySocial use, hedonic use, cognitive use, social capital (number of expressive ties, number of instrumental ties, relational dimension, cognitive dimension), job performance
[5]SurveyLife interruptedN/AInterruption overload, after-hours work interruptions, work performance, work exhaustion
[6]SurveyUse of a mobile device for work during family time (mWork) Conservation of resources (COR) theory and family systems theory mWork, job incumbent time-based work–life conflict (WFC), job incumbent strain-based WFC, job incumbent behavior-based WFC, incumbent, spousal resentment towards job incumbent’s organization, job incumbent organizational commitment, spousal commitment to job incumbent’s organization, job incumbent turnover intentions
[7]Quantitative and qualitative research methodologiesE-mail as a stress in people’s lifeN/ATotal hours worked, overload, coping, number of e-mails
[8]SurveyUse of communication technologies beyond normal work hours Boundary theory and related research on role integration Communication technologies use after hours, affective commitment, job involvement, ambition, employee work-to-life conflict
[9]SurveyInformation technology-mediated interruptionsConservation of resources theoryCongruent IT-mediated information interruption, incongruent IT-mediated information interruption, sequential processing, sequential processing, interruption overload, emotional exhaustion
[83]SurveyTechnology-mediated cross-domain interruptionsN/ATechnology mediated work-to-nonwork interruptions, work-to-nonwork conflict, nonwork performance, technology mediated nonwork-to-work Interruptions, nonwork-to-work conflict, work performance
[84]SurveyMobile usageRational actor theoryLevel of uncertainty, cost/benefit evaluation, predicted interruption value, interruption response decision
[85]SurveyWork–life BalanceConservation of resources theorySupervisors’ technology-mediated interruption behavior, information overload, sense of control, work/non-work exhaustion, work/non-work performance, supervisors’ work–life balance
[86]Qualitative research methodologyTechnology mediated work–life
Utilization of sociomaterial theoryWork–life balance, boundary management, technology, sociomateriality, power distance, collectivism
[87]SurveyConflict and quality of life in the digital ageConservation of resources theoryFrequency of interruptions outside of work, frequency of interruptions at work mediated by technology, conflicts outside of work, conflicts at work, performance at work, performance outside of work
[88]SurveyOnline interruptions on task performanceInformation richness theoryPerceived interruption, task performance, the rate’s types of interruptions, richness of interruption, the interruption rate on task performance
[89]SurveyTeleworkN/AFixed site telework, mobile telework, flexiwork, individual characteristics, organizational and technological contexts, the impacts on their work
[90]SurveyMobile devices in older usersInhibitory deficit theoryAge, demands from technology-mediated
interruptions, role-based stress, use of mobile technology for work
Table
Table A2. Measurement Scales (All scales were measured as 7-point Likert-type scales ranging from strongly disagree to strongly agree).
Table A2. Measurement Scales (All scales were measured as 7-point Likert-type scales ranging from strongly disagree to strongly agree).
Construct SourceItems
Information and communication technologies enabled after-hours work-related interruptionsAdapted from Chen and Karahanna [5]To what extent do you agree or disagree with the following?
AHWI1: During my cyber-life, I frequently get interrupted about work-related matters through technology (by phone, e-mail, messaging, Dingding, WeChat).
AHWI2. I frequently stop what I am doing during my cyber-life to initiate work-related activities through technology (by phone, e-mail, messaging, Dingding, WeChat).
AHWI3. During my cyber-life, dealing with work-related interruptions initiated by others (by phone, e-mail, messaging, Dingding, WeChat) is time-consuming.
AHWI4. Dealing with work interruptions I initiate during my cyber-life (by phone, e-mail, messaging, Dingding, WeChat) is time-consuming.
In-role job performanceAdapted from Williams and Anderson [59]To what extent do you agree or disagree with the following?
RJP1. I adequately complete assigned duties during my in-role job.
RJP2. I fulfill responsibilities specified in the job description during my in-role job.
IJP3. I always complete the duties specified in my job description.
IJP4. I meet all the formal performance requirements of the job.
IJP5. I fulfill all responsibilities required by this job.
IJP6. I successfully perform essential duties.
Innovative job performanceAdapted from Janssen and Van Yperen [60]How often do you perform the following work activities?
IJP1.I often create new ideas for improvements.
IJP2. I often mobilize support for innovative ideas.
IJP3. I often search out novel working methods.
IJP4. I often transform innovative ideas into useful applications.
PolychronicityAdapted from Slocombe and Bluedorn [61]To what extent do you agree or disagree with the following?
PLO1.I like to juggle several activities at the same time.
PLO2.I like to multi-task.

References

  1. Microsoft. World Trend Index Report. 2021. Available online: https://www.microsoft.com/en-us/worklab/work-trend-index/ (accessed on 22 September 2022).
  2. CNNIC. 50th Statistical Report on Internet Development in China. 2022. Available online: https://baijiahao.baidu.com/s?id=1742639780557516009&wfr=spider&for=pc (accessed on 31 August 2022).
  3. Comin, D.A.; Cruz, M.; Cirera, X.; Lee, K.M.; Torres, J. Technology and Resilience; National Bureau of Economic Research: Cambridge, MA, USA, 2022. [Google Scholar]
  4. Zhilian Recruiting; National School of Development of Peking University. Remote Stay-at-Home Reporting under the Impact of the Pandemic. 2022. Available online: http://www.100ec.cn/detail--6613405.html (accessed on 20 June 2022).
  5. Chen, A.; Karahanna, E. Life Interrupted: The Effects of Technology-Mediated Work Interruptions on Work and Nonwork Outcomes. MIS Q. 2018, 42, 1023–1042. [Google Scholar]
  6. Ferguson, M.; Carlson, D.; Boswell, W.; Whitten, D.; Butts, M.M.; Kacmar, K.M. Tethered to work: A family systems approach linking mobile device use to turnover intentions. J. Appl. Psychol. 2016, 101, 520–534. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Barley, S.R.; Meyerson, D.E.; Grodal, S. E-mail as a source and symbol of stress. Organ. Sci. 2011, 22, 887–906. [Google Scholar] [CrossRef] [Green Version]
  8. Boswell, W.R.; Olson-Buchanan, J.B. The use of communication technologies after hours: The role of work attitudes and work-life conflict. J. Manag. 2007, 33, 592–610. [Google Scholar] [CrossRef]
  9. Cheng, X.; Bao, Y.; Zarifis, A. Investigating the impact of IT-mediated information interruption on emotional exhaustion in the workplace. Inf. Process. Manag. 2020, 57, 1–16. [Google Scholar] [CrossRef]
  10. Van Der Lippe, T.; Lippényi, Z. Co-workers working from home and individual and team performance. New Technol. Work Employ. 2020, 35, 60–79. [Google Scholar] [CrossRef] [Green Version]
  11. Lanaj, K.; Johnson, R.E.; Barnes, C.M. Beginning the workday yet already depleted? Consequences of late-night smartphone use and sleep. Organ. Behav. Hum. Decis. Process. 2014, 124, 11–23. [Google Scholar] [CrossRef]
  12. Fleming, P.; Spicer, A. ‘You can checkout anytime, but you can never leave’: Spatial boundaries in a high commitment organization. Hum. Relat. 2004, 57, 75–94. [Google Scholar] [CrossRef]
  13. Duxbury, L.; Smart, R. The “myth of separate worlds”: An exploration of how mobile technology has redefined work-life balance. In Creating Balance? Springer: Berlin/Heidelberg, Germany, 2011; pp. 269–284. [Google Scholar]
  14. Duxbury, L.; Higgins, C.; Smart, R.; Stevenson, M. Mobile technology and boundary permeability. Br. J. Manag. 2014, 25, 570–588. [Google Scholar] [CrossRef]
  15. Bittman, M.; Brown, J.E.; Wajcman, J. The mobile phone, perpetual contact and time pressure. Work Employ. Soc. 2009, 23, 673–691. [Google Scholar] [CrossRef]
  16. Aryee, S.; Fields, D.; Luk, V. A cross-cultural test of a model of the work-family interface. J. Manag. 1999, 25, 491–511. [Google Scholar] [CrossRef]
  17. Park, J.-C.; Kim, S.; Lee, H. Effect of work-related smartphone use after work on job burnout: Moderating effect of social support and organizational politics. Comput. Hum. Behav. 2020, 105, 106194. [Google Scholar] [CrossRef]
  18. Fujimoto, Y.; Ferdous, A.S.; Sekiguchi, T.; Sugianto, L.-F. The effect of mobile technology usage on work engagement and emotional exhaustion in Japan. J. Bus. Res. 2016, 69, 3315–3323. [Google Scholar] [CrossRef]
  19. Yang, N.; Chen, C.C.; Choi, J.; Zou, Y. Sources of work-family conflict: A Sino-US comparison of the effects of work and family demands. Acad. Manag. J. 2000, 43, 113–123. [Google Scholar] [CrossRef]
  20. Wang, Z.; Chen, X.; Duan, Y. Communication technology use for work at home during off-job time and work–family conflict: The roles of family support and psychological detachment. Ann. Psychol. 2017, 33, 93–101. [Google Scholar] [CrossRef] [Green Version]
  21. Rich, B.L.; Lepine, J.A.; Crawford, E.R. Job engagement: Antecedents and effects on job performance. Acad. Manag. J. 2010, 53, 617–635. [Google Scholar] [CrossRef]
  22. Janssen, O. Fairness perceptions as a moderator in the curvilinear relationships between job demands, and job performance and job satisfaction. Acad. Manag. J. 2001, 44, 1039–1050. [Google Scholar] [CrossRef]
  23. Hagedoorn, J.; Cloodt, M. Measuring innovative performance: Is there an advantage in using multiple indicators? Res. Policy 2003, 32, 1365–1379. [Google Scholar] [CrossRef] [Green Version]
  24. Saunders, C.; Wiener, M.; Klett, S.; Sprenger, S. The impact of mental representations on ICT-related overload in the use of mobile phones. J. Manag. Inf. Syst. 2017, 34, 803–825. [Google Scholar] [CrossRef]
  25. Ragsdale, J.M.; Hoover, C.S. Cell phones during nonwork time: A source of job demands and resources. Comput. Hum. Behav. 2016, 57, 54–60. [Google Scholar] [CrossRef]
  26. Huo, W.; Xu, X.; Li, X.; Xie, J.; Sun, L. Work-related use of information and communication technologies after-hours (W_ICTs) and employee innovation behavior: A dual-path model. Inf. Technol. People, 2022; ahead-of-print. [Google Scholar]
  27. Badke, W. Information overload? Maybe not. Online 2010, 34, 52–54. [Google Scholar]
  28. Edwards, J.R.; Caplan, R.D.; Van Harrison, R. Person-environment fit theory. Theor. Organ. Stress 1998, 28, 67–94. [Google Scholar]
  29. Su, M.; Tan, Y.-y.; Liu, Q.-m.; Ren, Y.-j.; Kawachi, I.; Li, L.-m.; Lv, J. Association between perceived urban built environment attributes and leisure-time physical activity among adults in Hangzhou, China. Prev. Med. 2014, 66, 60–64. [Google Scholar]
  30. Dawis, R.V. The individual differences tradition in counseling psychology. J. Couns. Psychol. 1992, 39, 7. [Google Scholar] [CrossRef]
  31. Edwards, J.R. An examination of competing versions of the person-environment fit approach to stress. Acad. Manag. J. 1996, 39, 292–339. [Google Scholar] [CrossRef] [Green Version]
  32. Edwards, J.R.; Van Harrison, R. Job demands and worker health: Three-dimensional reexamination of the relationship between person-environment fit and strain. J. Appl. Psychol. 1993, 78, 628. [Google Scholar] [CrossRef]
  33. Cable, D.M.; Judge, T.A. Person–organization fit, job choice decisions, and organizational entry. Organ. Behav. Hum. Decis. Process. 1996, 67, 294–311. [Google Scholar] [CrossRef] [Green Version]
  34. O’Reilly III, C.A.; Chatman, J.; Caldwell, D.F. People and organizational culture: A profile comparison approach to assessing person-organization fit. Acad. Manag. J. 1991, 34, 487–516. [Google Scholar] [CrossRef] [Green Version]
  35. Cable, D.M.; DeRue, D.S. The convergent and discriminant validity of subjective fit perceptions. J. Appl. Psychol. 2002, 87, 875. [Google Scholar] [CrossRef] [Green Version]
  36. Kristof, A.L. Person-organization fit: An integrative review of its conceptualizations, measurement, and implications. Pers. Psychol. 1996, 49, 1–49. [Google Scholar] [CrossRef]
  37. Kristof-Brown, A.L.; Zimmerman, R.D.; Johnson, E.C. Consequences of Individuals’fit at work: A meta-analysis OF person–job, person–organization, person–group, and person–supervisor fit. Pers. Psychol. 2005, 58, 281–342. [Google Scholar] [CrossRef]
  38. Hall, E.T. The Dance of Life: The Other Dimension of Time; Anchor: Albany, NY, USA, 1984. [Google Scholar]
  39. Bluedorn, A.C.; Kaufman, C.F.; Lane, P.M. How many things do you like to do at once? An introduction to monochronic and polychronic time. Acad. Manag. Perspect. 1992, 6, 17–26. [Google Scholar] [CrossRef]
  40. Srite, M.; Karahanna, E.; Evaristo, J. Levels of culture and individual behaviour: An integrative perspective. Adv. Top. Glob. Inf. Manag. 2006, 5, 30–50. [Google Scholar]
  41. Ragu-Nathan, T.; Tarafdar, M.; Ragu-Nathan, B.S.; Tu, Q. The consequences of technostress for end users in organizations: Conceptual development and empirical validation. Inf. Syst. Res. 2008, 19, 417–433. [Google Scholar] [CrossRef] [Green Version]
  42. Bower, G.H. Mood and memory. Am. Psychol. 1981, 36, 129–148. [Google Scholar] [CrossRef] [PubMed]
  43. Organ, D.W. A restatement of the satisfaction-performance hypothesis. J. Manag. 1988, 14, 547–557. [Google Scholar] [CrossRef]
  44. Schnake, M. Organizational citizenship: A review, proposed model, and research agenda. Hum. Relat. 1991, 44, 735–759. [Google Scholar] [CrossRef]
  45. Halbesleben, J.R.; Bowler, W. Emotional exhaustion and job performance: The mediating role of motivation. J. Appl. Psychol. 2007, 92, 93. [Google Scholar] [CrossRef]
  46. Abelsen, S.N.; Vatne, S.-H.; Mikalef, P.; Choudrie, J. Digital working during the COVID-19 pandemic: How task–technology fit improves work performance and lessens feelings of loneliness. Inf. Technol. People, 2021; ahead-of-print. [Google Scholar]
  47. Xie, J.; Ma, H.; Zhou, Z.E.; Tang, H. Work-related use of information and communication technologies after hours (W_ICTs) and emotional exhaustion: A mediated moderation model. Comput. Hum. Behav. 2018, 79, 94–104. [Google Scholar] [CrossRef]
  48. Hofstede, G. Culture and organizations. Int. Stud. Manag. Organ. 1980, 10, 15–41. [Google Scholar] [CrossRef]
  49. Hu, W.; Ye, Z.; Zhang, Z. Off-time work-related smartphone use and bedtime procrastination of public employees: A cross-cultural study. Front. Psychol. 2022, 13, 814. [Google Scholar] [CrossRef] [PubMed]
  50. Chu, J. With WeChat Work Groups, “It’s Like Having Meetings All the Time”! What Do a Thousand Respondents Think of This Phenomenon? Available online: https://www.jfdaily.com/news/detail?id=113900 (accessed on 2 November 2018).
  51. Deci, E.L.; Ryan, R.M. The general causality orientations scale: Self-determination in personality. J. Res. Personal. 1985, 19, 109–134. [Google Scholar] [CrossRef]
  52. Hobfoll, S.E. Conservation of resource caravans and engaged settings. J. Occup. Organ. Psychol. 2011, 84, 116–122. [Google Scholar] [CrossRef]
  53. Adisa, T.A.; Ogbonnaya, C.; Adekoya, O.D. Remote working and employee engagement: A qualitative study of British workers during the pandemic. Inf. Technol. People, 2021; ahead-of-print. [Google Scholar] [CrossRef]
  54. Meske, C.; Junglas, I. Investigating the elicitation of employees’ support towards digital workplace transformation. Behav. Inf. Technol. 2020, 40, 1120–1136. [Google Scholar] [CrossRef]
  55. König, C.J.; Waller, M.J. Time for reflection: A critical examination of polychronicity. Hum. Perform. 2010, 23, 173–190. [Google Scholar] [CrossRef] [Green Version]
  56. Bluedorn, A.C. A unified model of turnover from organizations. Hum. Relat. 1982, 35, 135–153. [Google Scholar] [CrossRef]
  57. Tarafdar, M.; Tu, Q.; Ragu-Nathan, B.S.; Ragu-Nathan, T. The impact of technostress on role stress and productivity. J. Manag. Inf. Syst. 2007, 24, 301–328. [Google Scholar] [CrossRef] [Green Version]
  58. Ali-Hassan, H.; Nevo, D.; Wade, M. Linking dimensions of social media use to job performance: The role of social capital. J. Strateg. Inf. Syst. 2015, 24, 65–89. [Google Scholar] [CrossRef]
  59. Williams, L.J.; Anderson, S.E. Job Satisfaction and Organizational Commitment as Predictors of Organizational Citizenship and In-Role Behaviors. J. Manag. 1991, 17, 601–617. [Google Scholar] [CrossRef]
  60. Janssen, O.; Van Yperen, N.W. Employees’ goal orientations, the quality of leader-member exchange, and the outcomes of job performance and job satisfaction. Acad. Manag. J. 2004, 47, 368–384. [Google Scholar] [CrossRef] [Green Version]
  61. Slocombe, T.E.; Bluedorn, A.C. Organizational behavior implications of the congruence between preferred polychronicity and experienced work-unit polychronicity. J. Organ. Behav. Int. J. Ind. Occup. Organ. Psychol. Behav. 1999, 20, 75–99. [Google Scholar] [CrossRef]
  62. Brislin, R. Understanding Culture’s Influence on Behavior; Harcourt Brace Jovanovich: San Diego, CA, USA, 1993. [Google Scholar]
  63. Ringle, C.M.; Wende, S.; Will, A. SmartPLS 2.0 (Beta); SmartPLS GmbH: Hamburg, Germany, 2005. [Google Scholar]
  64. Lowry, P.B.; Gaskin, J. Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Trans. Prof. Commun. 2014, 57, 123–146. [Google Scholar] [CrossRef]
  65. Hair, J.F., Jr.; Sarstedt, M.; Ringle, C.M.; Gudergan, S.P. Advanced Issues in Partial Least Squares Structural Equation Modeling; SAGE: Los Angeles, CA, USA, 2018. [Google Scholar]
  66. Gudergan, S.P.; Ringle, C.M.; Wende, S.; Will, A. Confirmatory tetrad analysis in PLS path modeling. J. Bus. Res. 2008, 61, 1238–1249. [Google Scholar] [CrossRef]
  67. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 2011, 19, 139–152. [Google Scholar] [CrossRef]
  68. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
  69. Podsakoff, P.M.; Organ, D.W. Self-Reports in Organizational Research: Problems and Prospects. J. Manag. 1986, 12, 531–544. [Google Scholar] [CrossRef]
  70. Liang, H.; Saraf, N.; Hu, Q.; Xue, Y. Assimilation of enterprise systems: The effect of institutional pressures and the mediating role of top management. MIS Q. 2007, 31, 59–87. [Google Scholar] [CrossRef]
  71. Pavlou, P.A.; Liang, H.; Xue, Y. Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Q. 2007, 31, 105–136. [Google Scholar] [CrossRef] [Green Version]
  72. Straub, D.W. Validating instruments in MIS research. MIS Q. 1989, 13, 147–169. [Google Scholar] [CrossRef] [Green Version]
  73. Cenfetelli, R.T.; Bassellier, G. Interpretation of formative measurement in information systems research. MIS Q. 2009, 33, 689–707. [Google Scholar] [CrossRef]
  74. Gefen, D.; Rigdon, E.E.; Straub, D. Editor’s comments: An update and extension to SEM guidelines for administrative and social science research. MIS Q. 2011, 35, iii–xiv. [Google Scholar] [CrossRef]
  75. Chin, W.W.; Marcolin, B.L.; Newsted, P.R. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inf. Syst. Res. 2003, 14, 189–217. [Google Scholar] [CrossRef] [Green Version]
  76. Lowry, P.B.; Vance, A.; Moody, G.; Beckman, B.; Read, A. Explaining and predicting the impact of branding alliances and web site quality on initial consumer trust of e-commerce web sites. J. Manag. Inf. Syst. 2008, 24, 199–224. [Google Scholar] [CrossRef]
  77. Lowry, P.B.; Romano, N.C.; Jenkins, J.L.; Guthrie, R.W. The CMC interactivity model: How interactivity enhances communication quality and process satisfaction in lean-media groups. J. Manag. Inf. Syst. 2009, 26, 155–196. [Google Scholar] [CrossRef]
  78. Ollier-Malaterre, A.; Rothbard, N.P. Social media or social minefield? Surviving in the new cyberspace era. Organ. Dyn. 2015, 44, 26–34. [Google Scholar] [CrossRef]
  79. Beaudoin, C.E. Explaining the relationship between internet use and interpersonal trust: Taking into account motivation and information overload. J. Comput. Mediat. Commun. 2008, 13, 550–568. [Google Scholar] [CrossRef] [Green Version]
  80. Barclay, D.; Higgins, C.; Thompson, R. The partial least squares (PLS) approach to casual modeling: Personal computer adoption ans use as an Illustration. Technol. Stud. 1995, 2, 285–309. [Google Scholar]
  81. Sarker, S.; Ahuja, M.; Sarker, S. Work–life conflict of globally distributed software development personnel: An empirical investigation using border theory. Inf. Syst. Res. 2018, 29, 103–126. [Google Scholar] [CrossRef]
  82. Kreiner, G.E. Consequences of work-home segmentation or integration: A person-environment fit perspective. J. Organ. Behav. Int. J. Ind. Occup. Organ. Psychol. Behav. 2006, 27, 485–507. [Google Scholar] [CrossRef]
  83. Chen, A.; Karahanna, E. Boundaryless technology: Understanding the effects of technology-mediated interruptions across the boundaries between work and personal life. AIS Trans. Hum.-Comput. Interact. 2014, 6, 16–36. [Google Scholar] [CrossRef] [Green Version]
  84. Grandhi, S.; Jones, Q. Technology-mediated interruption management. Int. J. Hum.-Comput. Stud. 2010, 68, 288–306. [Google Scholar] [CrossRef]
  85. Cao, J.; Wang, Z.; Ye, X. Effects of Supervisors’ Technology-Mediated Interruption Behavior on Their Work-Life Balance. In Proceedings of the Twentieth Annual Pre-ICIS Workshop on HCI Research in MIS, Austin, TX, USA, 12 December 2021; pp. 1–5. [Google Scholar]
  86. De Alwis, S.; Hernwall, P.; Adikaram, A.S. “It is ok to be interrupted; it is my job”–perceptions on technology-mediated work-life boundary experiences; a sociomaterial analysis. Qual. Res. Organ. Manag. Int. J. 2022, 17, 108–134. [Google Scholar] [CrossRef]
  87. Maçada, A.; Freitas Junior, J.C.d.S.; Brinkhues, R.A.; Vasconcellos, S.D. Life Interrupted, but Performance Improved-Rethinking the Influence of Technology-Mediated Interruptions at Work and Personal Life. Int. J. Prof. Bus. Rev. 2022, 7, e0279. [Google Scholar] [CrossRef]
  88. Levy, E.C.; Ariel, Y. The Effects of Fragmented and Continuous Interruptions on Online Task Performance. Online J. Commun. Media Technol. 2022, 12, e202229. [Google Scholar]
  89. Garrett, R.K.; Danziger, J.N. Which telework? Defining and testing a taxonomy of technology-mediated work at a distance. Soc. Sci. Comput. Rev. 2007, 25, 27–47. [Google Scholar] [CrossRef] [Green Version]
  90. Tams, S.; Grover, V.; Thatcher, J.; Ahuja, M. Grappling with modern technology: Interruptions mediated by mobile devices impact older workers disproportionately. Inf. Syst. E-Bus. Manag. 2021, 20, 635–655. [Google Scholar] [CrossRef]
Ijerph 20 03456 g001 550
Figure 1. Proposed research model.
Figure 1. Proposed research model.
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Ijerph 20 03456 g002 550
Figure 2. Results of path-model analysis. * p < 0.05, ** p < 0.01.
Figure 2. Results of path-model analysis. * p < 0.05, ** p < 0.01.
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Figure 3. The moderating effect of polychronicity on the relationship between IAWI and innovative job performance.
Figure 3. The moderating effect of polychronicity on the relationship between IAWI and innovative job performance.
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Table
Table 1. Demographic characteristics of respondents (n = 277).
Table 1. Demographic characteristics of respondents (n = 277).
VariablesFrequency (Percent)Mean (Standard Deviation)
Age (enter age): 32.04 (14.82)
Gender:   1.49 (0.50)
  Male14150.9% 
  Female13649.1% 
Education: 4.05 (0.69)
  Less than high school/secondary school20.7% 
  High school/secondary school51.8% 
Associate degree/Higher Diploma238.3% 
  Bachelor’s degree20473.6% 
  Master’s degree3412.3% 
  Doctorate/Ph.D.93.3% 
Income  3.46 (0.88)
  Below 350041.4% 
  3501–5000227.9% 
  5001–10,00012545.1% 
  10,001–20,00010036.1% 
  20,001–50,000207.2% 
  Above 50,00162.2% 
Industry  4.67 (3.09)
  Agriculture, forestry and fishing62.2% 
  Internet/information systems9333.6% 
  Construction/manufacturing4917.7% 
  Transportation, storage, postal and courier services 207.2% 
  Import/export and wholesale155.4% 
  Retail trades 114% 
  Financial, insurance and real estate activities2910.5% 
  Accommodation and food service activities145.1% 
  Public administration114% 
  Education124.3% 
  Health41.4% 
  Others134.7% 
Company size: 2.29 (0.92)
  Below 1005018.1% 
  100–50013749.5% 
  501–10005118.4% 
  Over 100013914.1% 
Working years  3.75 (0.96)
  Below 1 year51.8% 
  1–3 years2910.5% 
  3–5 years (not include 3 years)5319.1% 
  5–10 years (not include 5 years)13247.7% 
  Above 10 years5820.9% 
Occupation-ranking  1.81 (0.92)
  Employee13649.1% 
  Basic-level manager6623.8% 
  Middle-level manager6824.5% 
  Senior leadership 51.8% 
  Other20.7% 
Table
Table 2. Total variance explained.
Table 2. Total variance explained.
ComponentInitial Eigenvalues
TotalVariance (%)Cumulative (%)
14.23726.48226.482
22.29214.32640.808
31.83511.4752.277
41.75210.95363.23
50.8945.58668.816
60.8325.19974.015
70.6684.17578.19
80.5733.58381.773
90.5453.40885.181
100.4752.96988.151
110.4262.6690.811
120.3952.46793.278
130.3742.3495.618
140.2961.85197.47
150.2171.35598.825
160.1881.175100
 Extracted sums of squared loadings
14.23726.48226.482
Table
Table 3. Common method bias analysis and factor loadings of measurement items.
Table 3. Common method bias analysis and factor loadings of measurement items.
ConstructItemFactor LoadingSubstantive Factor Loading (R1)R12Method Factor Loading (R2)R22
Information and communication technologies enabled after-hours work-related interruptions (IAWI)IAWI10.8840.76780.590 0.02900.001
IAWI20.8590.70180.493 0.06570.004
IAWI30.6170.82870.687 −0.05820.003
IAWI40.6020.79160.627 −0.02950.001
In-role job performance (RJP)RJP10.7690.72210.521 −0.01210.000
RJP20.6770.74030.548 −0.02840.001
RJP30.6600.65150.424 −0.00260.000
RJP40.6970.82470.680 −0.09540.009
RJP50.7690.68730.472 −0.00020.000
RJP60.7080.68820.474 0.12740.016
Innovative job performance (IJP)IJP10.8370.86620.750 −0.04700.002
IJP20.8000.84290.710 −0.08180.007
IJP30.8190.77650.603 0.06800.005
IJP40.7870.76060.579 0.05750.003
Polychronicity (POL)POL10.9090.94660.896 0.02470.001
POL20.9690.93880.881 −0.02470.001
Average   0.621  0.003
Table
Table 4. Means, standard deviations, Cronbach’s α, CR, AVE, and construct correlations.
Table 4. Means, standard deviations, Cronbach’s α, CR, AVE, and construct correlations.
 MeanStandard Deviation Cronbach’s αCRAVE1234
1. IAWI5.071.140.7760.8400.5660.752   
2. RJP5.920.700.8120.8590.5110.1250.715  
3. IJP5.590.890.8230.8830.6570.1900.4000.811 
4. POL4.371.570.8750.9370.883−0.0200.125−0.0750.940
CR = composite reliability, IAWI = information and communication technologies enabled after-hours work-related interruptions, RJP = in-role job performance, IJP = innovative job performance, POL = polychronicity. The diagonal elements (in bold and underlined) are the square roots of the average variance extracted (AVE) values.
Table
Table 5. Summary of path coefficients and significant levels.
Table 5. Summary of path coefficients and significant levels.
Tested PathPath Coefficient (β) t-Value
(df = 277) 		Hypothesis Supported?
Hypotheses    
H1. IAWI → In-role job performance 0.1392.020 *Yes
H2. IAWI → Innovative job performance0.2003.013 **Yes
H3. IAWI * Polychronicity → In-role job performance−0.0931.762Not supported
H4. IAWI * Polychronicity → Innovative job performance0.1122.143 *Yes
Covariates   
Age → In-role job performance 0.0410.874 
Gender → In-role job performance0.0890.523 
Education → In-role job performance−0.0180.401 
Income → In-role job performance0.152 *1.971 
Industry → In-role job performance−0.0521.062 
Company size → In-role job performance−0.0551.101 
Working years → In-role job performance0.1271.545 
Occupation-ranking → In-role job performance−0.0891.421 
Age → Innovative job performance −0.0310.303 
Gender → Innovative job performance0.0350.842 
Education → Innovative job performance−0.0921.509 
Income → Innovative job performance0.170 *2.284 
Industry → Innovative job performance−0.0561.139 
Company size → Innovative job performance0.0681.322 
Working years → Innovative job performance−0.0090.135 
Occupation-ranking → Innovative job performance0.1231.802 
* p < 0.05, ** p < 0.01.
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MDPI and ACS Style

Zhang, S.; Huang, F.; Zhang, Y.; Li, Q. A Person-Environment Fit Model to Explain Information and Communication Technologies-Enabled After-Hours Work-Related Interruptions in China. Int. J. Environ. Res. Public Health 2023, 20, 3456. https://doi.org/10.3390/ijerph20043456

AMA Style

Zhang S, Huang F, Zhang Y, Li Q. A Person-Environment Fit Model to Explain Information and Communication Technologies-Enabled After-Hours Work-Related Interruptions in China. International Journal of Environmental Research and Public Health. 2023; 20(4):3456. https://doi.org/10.3390/ijerph20043456

Chicago/Turabian Style

Zhang, Shanshan, Fengchun Huang, Yuting Zhang, and Qiwen Li. 2023. "A Person-Environment Fit Model to Explain Information and Communication Technologies-Enabled After-Hours Work-Related Interruptions in China" International Journal of Environmental Research and Public Health 20, no. 4: 3456. https://doi.org/10.3390/ijerph20043456

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