دانلود مقاله بررسی تاثیر استرس های فنی بر رضایت شغلی و خانوادگی
خلاصه
1. معرفی
2 - پیشینه تحقیق
3 - مدل و فرضیه های تحقیق
4 - روش تحقیق
5 - نتایج و تجزیه و تحلیل
6 - بحث و پیامدها
7 - محدودیت ها و مسیرهای تحقیقات آینده
8 - نتیجه گیری
سپاسگزاریها
منابع
Abstract
1 - INTRODUCTION
2 - RESEARCH BACKGROUND
3 - RESEARCH MODEL AND HYPOTHESES
4 - RESEARCH METHOD
5 - RESULTS AND ANALYSIS
6 - DISCUSSION AND IMPLICATIONS
7 - LIMITATIONS AND FUTURE RESEARCH DIRECTIONS
8 - CONCLUSION
ACKNOWLEDGEMENTS
REFERENCES
چکیده
تحولات اخیر در فناوری اطلاعات و ارتباطات مرز بین محل کار و خانه را از بین برده است. این می تواند تأثیر منفی بر رفاه کارکنان داشته باشد و بنابراین توجه روزافزون دانشگاهیان و متخصصان را به خود جلب کرده است. در این مطالعه، ما یک مدل تحقیقاتی مبتنی بر دیدگاه معاملاتی استرس و چارچوب استرس‌زای چالش-موانع ایجاد کردیم. ما دو بعد تعارض کار و خانواده را به عنوان استرس ادراکی ناشی از یک چالش مزمن و موانع تکنواسترس‌ها تعریف کردیم که در نهایت بر رضایت کارکنان در هر دو حوزه کار و خانواده تأثیر می‌گذارد. ما مدل خود را با استفاده از یک مطالعه نظرسنجی سه موجی با تاخیر زمانی با داده های جمع آوری شده از 268 کارمند آزمایش کردیم. چالش‌ها و موانع تکنواسترس‌ها تأثیرات متفاوتی بر این دو شکل اصلی تعارض کار-خانواده (مبتنی بر زمان و مبتنی بر فشار) داشتند، اما اثرات منفی بیشتری بر رضایت شغلی و خانوادگی ایجاد کردند. به طور کلی، ما هم مشارکت علمی و هم عملی در زمینه‌های استفاده از فناوری مرتبط با کار و تعارض کار و خانواده داریم.
Abstract
Recent developments in information and communication technology have blurred the line between the workplace and the home. This can have a negative influence on employees' well-being and thus has gained increasing attention from academics and practitioners. In this study, we developed a research model based on the transactional perspective of stress and the challenge–hindrance stressor framework. We defined the two dimensions of work–family conflict as the perceptual stress resulting from a chronic challenge and hindrance technostressors, which ultimately affect employees' satisfaction in both the work and family domains. We tested our model using a three-wave time-lagged survey study with data collected from 268 employees. Challenge and hindrance technostressors had different effects on these two main forms of work–family conflict (time-based and strain-based) but further induced negative effects on both job and family satisfaction. Overall, we make both scientific and practical contributions to the fields of work-related technology use and work–family conflict.
Introduction
Managing employee health and productivity is an essential aspect of modern organisational strategies. A study by Aviva (Wedgwood, 2022) showed that, nowadays, more employees were attracted to their current job for the work-life balance (41%) than the salary (36%). Other industry reports have shown that work–life conflicts are the main reason for job resignations (Joblist, 2021). Employees are currently experiencing increasing work–life conflict due to technology-induced stress (Li et al., 2021; Sarker et al., 2018). Previous studies have suggested that technology-related work stress often bleeds over into employees' home domains with the potential to bring harm to both domains (Harris et al., 2021). Clearly understanding technology-related work–family conflict is therefore important, as a failure to intervene in the resulting stress can lead to major personal and family problems for employees (e.g., depression and divorce), which can cause productivity losses worth billions of dollars (Butts et al., 2015).
The term technostress has been coined for the stress-creating effects of technology use, in terms of the stressful experiences resulting from individuals' inability to cope with information systems (IS) in a healthy manner (e.g., Ayyagari et al., 2011; Ragu-Nathan et al., 2008). Previous studies have investigated how technology use at the workplace induces technostress and blurs the boundary between work and family (Ayyagari et al., 2011; Butts et al., 2015). Among them, a significant number of studies examined episodic technostressors which are acute and result from transitory and periodic events (e.g., system breakdown) (Weinert et al., 2020). For example, technology-mediated interruption, which is considered as an episodic technostressor, is found to significantly hinder employees' work and nonwork performance, by increasing errors in tasks and their execution time (Chen & Karahanna, 2018; Galluch et al., 2015). Similarly, Benlian (2020) investigated how daily and transient (i.e., episodic) technostressors trigger short-term work–family spillover and affect employees' home lives. While, it is worth noting that, the chronic technostressors (e.g., “techno-overload” and “techno-invasion”) employees experience over the long-term can also significantly impact work–family conflict, as they result in ongoing resource depletion (Harris et al., 2021). In fact, studies have suggested that work–family conflict is associated more closely with chronic stressors because addressing such a source of stress requires a change in work or family life over a long period of time (Galluch et al., 2015). In addition, work–family conflict is often considered to be a type of role stress, which manifests in a prolonged response to chronic job-related stressors (e.g., work overload) (Ahuja et al., 2007; Grzywacz et al., 2006). Accordingly, as information systems (IS) researchers have mainly assumed that technostress is relatively static, investigations of how aggregated and retrospective technostress experiences contribute to employees' work–family conflict and ultimately influence their performance in both domains are needed.
RESULTS AND ANALYSIS
Although our proposed model operates at the individual level, our data structure is nested, with 268 employees within 64 teams. Single-level analyses (e.g., ordinary regression) violate the assumption of observation independence when hierarchically clustered data are used and lead to downwardly biased standard errors (Preacher et al., 2010). Multilevel structural equation modelling (MSEM) can address our mediation hypotheses when the data are hierarchically organised (see Appendix C for a review; Preacher et al., 2010). As such, we followed Preacher et al. (2010) and conducted two-level path analyses using multilevel SEM with Mplus 7.4 (Muthén & Muthén, 1998). In this approach, we first conducted a psychometric assessment of the measurement model and then evaluated the hypothesised model. This approach ensured that the conclusions drawn from the hypothesised model were based on a set of measures with desirable psychometric properties (Hair et al., 2010). To avoid identifying any spurious cross-level moderation effects, we centered all of the predictors at the individual level (i.e., grand-mean centering) to reduce multicollinearity (Hofmann, 1997; Hofmann & Gavin, 1998). To confirm the robustness of our hypothesis testing, we also tested the conditional indirect effects using Monte Carlo bootstrap simulations in R (Preacher & Selig, 2012).