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Linking enterprise information systems success to female employees’ work–family enrichment in China

Published online by Cambridge University Press:  21 February 2024

Wenhao Deng
Affiliation:
School of Management and Economics, Beijing Institute of Technology, Beijing, China Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, China Yangtze River Delta Research Institute, Beijing Institute of Technology, Jiaxing, China
Tianan Yang
Affiliation:
School of Management and Economics, Beijing Institute of Technology, Beijing, China Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, China Yangtze River Delta Research Institute, Beijing Institute of Technology, Jiaxing, China
Xuemei Ju
Affiliation:
School of Management and Economics, Beijing Institute of Technology, Beijing, China Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, China
Jianwei Deng*
Affiliation:
School of Management and Economics, Beijing Institute of Technology, Beijing, China Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, China Yangtze River Delta Research Institute, Beijing Institute of Technology, Jiaxing, China
*
Corresponding author: Jianwei Deng; Email: [email protected]
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Abstract

The spread and application of enterprise information systems (EISs) has provided scholars and managers with a new perspective to enhance the work–family enrichment. Based on the work–family enrichment theory, this research aims to examine the ability of female employees to enhance their work–family enrichment by applying the resources accumulated through the use of EISs by combining the technology acceptance model with the DeLone & McLean Information Systems Success Model. The findings based on a survey of 823 full-time female employees in China indicated that the information systems quality factors (including information quality, system quality, and service quality) were positively associated with female employees’ work–family enrichment. In addition, the chained mediating effect of perceived ease of use and perceived usefulness was examined. The results can help female employees to perform positively in both work and family spheres and provide positive support for the promotion of the social fertility policies.

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press in association with Australian and New Zealand Academy of Management.

Introduction

As women’s social status and participation increase in the 21st century, unlocking the potential of female employees’ productivity is crucial to the improvement of overall social productivity. According to the China Statistical Yearbook, women accounted for 49.41% and 48.95% of those with undergraduate and graduate higher education in 2020, respectively, and this proportion has gradually increased over the past 5 years to nearly 50%. However, the difficulty of reconciling the dual roles of work and family for female employees has become one of the core factors that confines their employment and productivity. In the context of three-child policy in China from 2021, there is a risk of further intensifying the core conflict between companies’ fundamental goal of maximizing economic benefits and the operational pressures brought by female employees’ pregnancy and childbirth (Shen & Jiang, Reference Shen and Jiang2020). Companies not only need to pay for the direct costs of maternity leave, such as wages and job vacancies, but also have to bear the indirect losses caused by the possible decline of job skills and lack of updated knowledge after returning to work after childbirth. Therefore, despite the increasing number of female employees entering the labor market, managing the relationship between work and family remains an important and complex challenge.

Compared to Western societies, Chinese employees are more willing to integrate work and family roles due to collectivist attitudes (Ashforth & Fugate, Reference Ashforth and Fugate2000) and therefore experience less work–family conflict and more work–family enrichment (Spector et al., Reference Spector, Allen, Poelmans, Lapierre, Cooper, O’Driscoll, Sanchez, Abarca, Alexandrova, Beham and Brough2007). Many scholars have suggested that positive work–family enrichment relationships are predominant among female employees (Aryee, Srinivas, & Tan, Reference Aryee, Srinivas and Tan2005; Mcnall, Masuda, & Nicklin, Reference Mcnall, Masuda and Nicklin2009). In 2006, Greenhaus and Powell (Reference Greenhaus and Powell2006) defined work–family enrichment as ‘occurring when resources from work area contribute to the more effective functioning of family area.’ Although existing scholarship has extensively explored how enterprises could lead to work–family enrichment for female employees, including work flexibility (Carlson, Grzywacz, & Kacmar, Reference Carlson, Grzywacz and Kacmar2010), positive leadership styles (Wu, Kuo, Lin, Hu, Wu, & Cheng, Reference Wu, Kuo, Lin, Hu, Wu and Cheng2020), employee identity (Wayne, Randel, & Stevens, Reference Wayne, Randel and Stevens2006), and workaholic personalities (Taheri, Asarian, & Shahhosseini, Reference Taheri, Asarian and Shahhosseini2020), it has overlooked the fact that the skills acquired in the work environment could also improve their family experiences. For example, the time management skills that female employees acquire in their work roles can directly improve their childcare roles (Nicklin & Mcnall, Reference Nicklin and Mcnall2013). Similarly, the European Working Conditions Survey (European Foundation for the Improvement of Living and Working Conditions, 2007) suggests that the availability and use of information technology may also affect the interactions between employees’ work and family. The latest study found through field experiments with employees in German public agencies that corporately provided mobile devices decrease work–life conflict (Lemmer, Jahn, Chen, & Niehaves, Reference Lemmer, Jahn, Chen and Niehaves2023). However, few studies to date have examined the impact of female employees’ use of information technology in the work domain on their family domain from a theoretical or empirical perspective.

The rapid development and widespread use of information systems as a core resource of enterprises marks the gradual informatization and intelligence of employees’ work style, which provides ideas for coordinating the work–family relationship of female employees. According to China’s Fourth National Economic Census, 83.9% of surveyed enterprises made investments in information technology, with a total amount of about 653.35 billion yuan in 2018. An enterprise information system (EIS) is a combination of hardware, software, and network services built to collect, process, organize, store, and disseminate information, such as supply chain management systems, customer relationship management systems, enterprise resource planning systems, and e-learning systems. From the theoretical perspective of work–family enrichment, the development of EIS provides a valuable resource for employee skill enhancement, which can directly help employees with family-related issues. For example, organizations use Information and Communication Technology and Internet technologies to develop online courses and training programs and supplement training opportunities missed by female employees due to maternity leave through e-learning systems (Cheng, Reference Cheng2012). In addition, the use of tele-conferencing systems provides communication and meeting capabilities for communicators from different locations, reducing the time and cost of business activities for female employees (Park, Rhoads, Hou, & Lee, Reference Park, Rhoads, Hou and Lee2014). From the perspective of information systems success, although most studies have examined the positive effects of information systems success on corporate profits, costs, or market share at the organizational level (Asmussen & Moller, Reference Asmussen and Moller2020; Pan, Nunes, & Peng, Reference Pan, Nunes and Peng2011; Tian & Xu, Reference Tian and Xu2015; Zhu, Li, Wang, & Chen, Reference Zhu, Li, Wang and Chen2010) and have also explored the relationship between information systems success and employee satisfaction with information systems use and intention to use it (Laumer, Maier, & Weitzel, Reference Laumer, Maier and Weitzel2017; Lee & Kim, Reference Lee and Kim2017), whether EIS could positively enrich the work–family relationship of female employees still needs to be further tested.

Over the past three decades, a large number of studies related to management information systems have assessed the success of building an EIS from the perspective of end users (i.e., employee perceptions). Scholars have commonly adopted the D&M information system success model proposed by Delone and Mclean (Reference Delone and Mclean2003), which proposes to assess the net benefits of information systems through three dimensions of information systems quality, including information quality, system quality, and service quality. Although a large body of research has explored the mechanisms by which perceived EISs quality affects employee satisfaction with information systems use, intention to use, and job performance using the D&M Information System Success Model (D&M ISSM), such as enterprise content management systems (Laumer, Maier, & Weitzel, Reference Laumer, Maier and Weitzel2017), social network services systems (Lee & Kim, Reference Lee and Kim2017), and cloud financial information systems (Li & Wang, Reference Li and Wang2021), it has consistently failed to link EISs success to employee performance in non-work domains or in work–home interactions. The technology acceptance model (TAM), proposed by Davis in 1986, provides a framework for assessing employees’ cognitive responses to information systems, including perceived usefulness and perceived ease of use. The model provides an important theoretical underpinning for this study to explore whether building corporate information systems improves employees’ skills or mindsets and thus their work–family relationships. Therefore, in order to link the construction of corporate information systems with employees’ positive work–family relationships, this study combines the TAM and D&M information system success models to comprehensively assess the success of corporate information systems in terms of two dimensions: employees’ perceived information systems quality and information systems cognitive responses, and to analyze in depth the specific mechanisms by which the successful construction of corporate information systems affects the process of female employees’ work–family enrichment.

Overall, this study aims to examine the contribution of applying EIS to female employees’ work–family enrichment in China and provide insight into the mechanism of this impact in terms of multidimensional success factors including information systems quality and users’ perceptions. This investigation makes two major contributions. First, we enriched and complemented the lack of positive effects of skill-enhancing resources in studies of the antecedents of work–family enrichment. Second, we bridged the gap between enterprises’ technology resources to employees’ psychosocial factors by linking EIS and employees’ work–family relationship, which could help companies and female employees cope with the challenges of dual work–family role adaptation brought by maternity policy.

Conceptual background and hypotheses

Evaluating framework of information systems success

From the perspective of socio-technical system theory, both technical and social factors need to be included in any model that indicates the success of an information system (Bostrom & Heinen, Reference Bostrom and Heinen1977). In the last three decades of information systems research, scholars have generally used two theoretical models to assess the success of an EIS at both the technical and user levels. On the one hand, Delone and Mclean (Reference Delone and Mclean2003) proposed the updated D&M ISSM and used three separate dimensions: information quality, system quality, and service quality to assess the success of information systems at the technical level. Information quality assesses whether the system ensures that the information is transmitted as intended; system quality assesses whether the system produces information accurately and efficiently; and service quality refers to the overall support provided by the service provider. On the other hand, the TAM proposed by Davis (Reference Davis1986) provides a framework for assessing users’ cognitive responses to information systems, including perceived usefulness and perceived ease of use. Perceived usefulness refers to ‘the extent to which a person believes that using a particular system will improve his/her performance,’ while perceived ease of use refers to ‘the extent to which a person believes that using a particular system requires no physical or mental effort’. Early research has demonstrated that perceived usefulness is a meaningful measure of the success of information systems in voluntary and compulsory use environments (Seddon & Kiew, Reference Seddon and Kiew1996).

This study combines the D&M ISSM and TAM to examine the success of information systems and their consequences by considering information quality, system quality, and service quality of information systems as technical dimensions and perceived information systems usefulness and ease of use as user dimensions. In a large number of previous studies, information systems quality factors have been shown to be related to user perceived usefulness and ease of use. First, for information quality, earlier studies have found a statistically significant relationship between information quality and perceived usefulness (Teo & Wong, Reference Teo and Wong1998), and Chen (Reference Chen2010) and Alsabawy, Cater-Steel, and Soar (Reference Alsabawy, Cater-Steel and Soar2016) also found a significant relationship between information quality and perceived usefulness towards e-learning systems. Similarly, existing studies have also found that information quality directly affects perceived ease of use in online shopping systems (Ahn, Ruy, & Han, Reference Ahn, Ruy and Han2004), hospital information systems (Chen & Hsiao, Reference Chen and Hsiao2012), and cloud enterprise resource planning (ERP) systems (Kuo, Kang, & Yang, Reference Kuo, Kang and Yang2023). Therefore, this study argues that the information quality of an EIS can positively predict employees’ perceived usefulness and ease of use of the information systems.

Hypothesis 1a: Information quality is positively related to perceived usefulness.

Hypothesis 1b: Information quality is positively related to perceived ease of use.

Second, a large number of studies have addressed system quality as a prerequisite for usefulness and/or ease of use, as system design characteristics are directly linked to enhanced perceived usefulness and perceived ease of use (Liaw, Reference Liaw2008; Sabherwal, Jeyaraj, & Chowa, Reference Sabherwal, Jeyaraj and Chowa2006; Wixom & Todd, Reference Wixom and Todd2005). Seddon and Kiew (Reference Seddon and Kiew1996) have consistently shown in their studies that system quality is an important determinant of perceived usefulness. Chiou and Fang’s (Reference Chiou and Fang2005) study found that system quality has a significant impact on the perceived ease of use of Internet users. Judicial workers, nurses, and university lecturers also intend to perceive usefulness and ease of use when the system qualities of e-justice systems (Oktal, Alpu, & Yazici, Reference Oktal, Alpu and Yazici2016), nursing process information systems (Ho, Ho, & Chung, Reference Ho, Ho and Chung2019), and learning management systems (Sulaiman, Mahomed, Rahman, & Hassan, Reference Sulaiman, Mahomed, Rahman and Hassan2023) are high, respectively. Indeed, high levels of system quality can produce a comfortable usage environment where users can effectively identify functional groups of information systems and efficiently navigate through the materials provided by the information systems, such as online retailing systems (Ahn, Ryu, & Han, Reference Ahn, Ryu and Han2007), hotel front office systems (Kim, Lee, & Law, Reference Kim, Lee and Law2008), and land registration information systems (Abab, Wakjira, & Negash, Reference Abab, Wakjira and Negash2021). Therefore, this study argues that the system quality of an EIS positively predicts employees’ perceived information systems usefulness and ease of use.

Hypothesis 2a: System quality is positively related to perceived usefulness.

Hypothesis 2b: System quality is positively related to perceived ease of use.

Finally, in response to service quality, Lin and Wu (Reference Lin and Wu2002) found that reliable and accurate service, willingness to help customers, knowledge, courtesy, and personalized attention to customers by the information systems department and IT support staff increased users’ perceived ease of use. In addition to this, Ngai, Poon, and Chan (Reference Ngai, Poon and Chan2007) empirically demonstrated a significant positive effect between service quality and perceived usefulness. Wang and Wang (Reference Wang and Wang2009) similarly reported a direct positive effect of service quality on system perceived usefulness, where system perceived usefulness is part of the user satisfaction construct. Recent studies have focused on several emerging information systems, such as e-procurement systems (Brandon-Jones & Kauppi, Reference Brandon-Jones and Kauppi2018), artificial intelligence-enabled clinical decision support systems (Ji et al., Reference Ji, Genchev, Huang, Xu, Lu and Yu2021), and behavioral electronic health records systems (Yoo et al., Reference Yoo, Lim, Jung, Lee, Lee, Kim and Hwang2022), all of which have verified the essential role of service quality in improving perceived ease of use and usefulness. Therefore, this study concluded that the service quality of an EIS can positively predict employees’ perceived information systems usefulness and ease of use.

Hypothesis 3a: Service quality is positively related to perceived usefulness.

Hypothesis 3b: Service quality is positively related to perceived ease of use.

Meanwhile, perceived usefulness and ease of use, the two main dimensions that reflect users’ cognitive responses to information systems, have been applied in more than 30 of the 39 high-quality articles that have applied the TAM as of 2005, indicating that users’ perceived ease of use has a positive impact on perceived usefulness (Hung, Liang, & Chang, Reference Hung, Liang and Chang2005). Recently, Mishra, Shukla, Rana, Currie, and Dwivedi (Reference Mishra, Shukla, Rana, Currie and Dwivedi2023) utilized a meta-analytic structural equation modelling (SEM) to further review 84,343 samples from 214 independent studies, confirming the direct impact of perceived ease of use on perceived usefulness. Therefore, this paper argues that perceived ease of use of information systems can positively predict perceived usefulness of information systems.

Hypothesis 4: Perceived ease of use is positively related to perceived usefulness.

Work–family enrichment theory and information systems success

Greenhaus and Powell (Reference Greenhaus and Powell2006) propose a theoretical model of work–family enrichment based on augmentation theory in terms of multi-role involvement and resource sharing and transfer. The work–family enrichment theory summarizes five work or family resources that contribute to work–family enrichment: skills and ways of thinking (referring to a range of task-related cognitive and interpersonal skills, coping skills, multi-tasking skills, and knowledge gained from role experiences), psychological and physical resources (e.g., self-efficacy, self-esteem, and optimism), social capital resources (e.g., online socialization), work flexibility (e.g., flexible work adjustments), and material resources (e.g., money and gifts).

While the impact of gains across domains is bidirectional (Carlson, Kacmar, & Williams, Reference Carlson, Kacmar and Williams2000), i.e., work-to-family and family-to-work, this study focuses on the direction of enrichments from work-to-family. First, recent literature suggests that the antecedents of work-to-family enrichment come from the work domain rather than the family domain, such as employees’ perceptions of family support organizations (Carlson, Ferguson, Kacmar, Grzywacz, & Whitten, Reference Carlson, Ferguson, Kacmar, Grzywacz and Whitten2011). And, work-to-family enrichments are more likely to be influenced by organizational practices (Halpern, Reference Halpern2006; Wang & Walumbwa, Reference Wang and Walumbwa2007). In contrast, family-to-work enrichments are more influenced by family structures, resources, and processes (Shockley & Singla, Reference Shockley and Singla2011). Furthermore, as high levels of work–family enrichment are associated with positive career attitudes (e.g., higher organizational commitment) and behaviors (e.g., lower turnover rates), work-to-family enrichments tend to realize potential gains for both the employee and the organization (Anderson, Coffey, & Byerly, Reference Anderson, Coffey and Byerly2002; Casper, Harris, Taylor-Bianco, & Wayne, Reference Casper, Harris, Taylor-Bianco and Wayne2011; Russo & Buonocore, Reference Russo and Buonocore2012). Therefore, this study explores the impact of variables originating in the work domain, i.e., the success of EISs, on work–family enrichment rather than family–work enrichment.

According to the definition of work–family enrichment, as a prerequisite for gaining from the work domain to the family domain, female employees must integrate their work into the family domain to some extent (Matthews & Barnes-Farrell, Reference Matthews and Barnes-Farrell2010). There is no doubt that the application of EIS has made it possible to significantly dilute the boundaries between work and family, and female employees can easily switch between work and family roles, thus managing both work and family roles at the same time (Wu, Hunter, & Sublett, Reference Wu, Hunter and Sublett2021). For example, research has shown the convenience and practicality of e-learning systems in balancing work and family for international female students (Kibelloh & Bao, Reference Kibelloh and Bao2014). In addition, the spread of EIS has placed greater demands on female employees’ problem-solving, communication and collaboration, and information processing skills (Kozanoglu & Abedin, Reference Kozanoglu and Abedin2020). For example, a few investigations have shown that the success of an organization’s supply chain management system is closely related to the employees’ skills and knowledge related to the use of information technology (Dehgani & Navimipour, Reference Dehgani and Navimipour2019; Jiang, Tian, Malayeri, & Balali, Reference Jiang, Tian, Malayeri and Balali2020).

As important predictors of work–family enrichment, the skills and ways of thinking gained in the work domain are resources that female employees can draw upon when they need to solve problems or cope with challenging events, such as childcare (Daniel & Sonnentag, Reference Daniel and Sonnentag2016). It has been shown that employees with higher skill diversity are more motivated and self-efficacious, able to demonstrate better performance and manage demand better (Fried & Ferris, Reference Fried and Ferris1987). Improving skills in fully operating and implementing information device functions can also stimulate employees’ knowledge transfer behavior (Yuan et al., Reference Yuan, Tsai, Dai, Chen, Chen, Wu and Deng2017), which may include purposeful knowledge sharing across individual boundaries, such as work and family. Powell et al. similarly verified that the skills employees learn on the job may lead to a happier and more fulfilling family life (Powell, Francesco, & Ling, Reference Powell, Francesco and Ling2009).

Therefore, we assume that the success of EIS enables female employees to develop new skills and perspectives, which enable them to perform their job duties more effectively and to be able to apply it to their families in a way that is consistent with family role expectations. Since perceived usefulness reflects the extent to which a person believes that using a particular system has improved his/her job performance (Seddon, Reference Seddon1997), we suggest the following hypothesis and the research framework was shown in Fig.1.

Figure 1. The theoretical model.

Hypothesis 5: Perceived usefulness is positively related to work–family enrichment.

Hypothesis 6: Perceived usefulness mediates the association between perceived ease of use and work–family enrichment.

Hypothesis 7a: Perceived usefulness mediates the association between information quality and work–family enrichment.

Hypothesis 7b: Perceived usefulness mediates the association between system quality and work–family enrichment.

Hypothesis 7c: Perceived usefulness mediates the association between service quality and work–family enrichment.

Hypothesis 8a: Perceived ease of use and perceived usefulness conduct as orderly chain structure, mediating the association between information quality and work–family enrichment.

Hypothesis 8b: Perceived ease of use and perceived usefulness conduct as orderly chain structure, mediating the association between system quality and work–family enrichment.

Hypothesis 8c: Perceived ease of use and perceived usefulness conduct as orderly chain structure, mediating the association between service quality and work–family enrichment.

Methods

Participants and procedure

We conducted our survey from several state-owned and multinational enterprises in mainland China, which were generally more advanced in terms of information technology construction. In addition, front-line employees accounted for a larger proportion of the total workforce in the research enterprises. The career development problems of female employees due to childbirth were more prominent within these labor-intensive enterprises. Therefore, we took full-time female employees in several departments of the research enterprises, such as the production department, quality department, R&D department, and administration department, as the whole sample, and randomly select them according to the employee numbers to ensure the integrity and objectivity of the data. We conducted an online survey and sent links of the questionnaire to all participants. Before participants formally answered the questions, the questionnaire would inform them that their participation was completely voluntary, anonymous, and confidential in order to avoid bias in the participants’ responses. In addition, the questionnaire was designed with the independent and dependent variables placed in separate sections to minimize common method bias (Podsakoff, Mackenzie, Lee, & Podsakoff, Reference Podsakoff, Mackenzie, Lee and Podsakoff2003). A total of 1,500 questionnaires were distributed and 1,266 questionnaires were received, with a return rate of 84.4%. Since the online survey required all questions to be answered before submission, the response rate of all returned questionnaires was 100%. After the questionnaires were collected, 823 valid questionnaires were finally obtained after screening and elimination, and the utilization rate of the questionnaires was 65%.

Table 1 shows the demographic characteristics of the surveyed participants. Of the final sample, 100% were women, 71.57% of whom were married with children. About 86.02% of the female employees were between the ages of 26 and 45 and faced challenges in maintaining their skills and competitiveness at work during childbirth or parenting. In terms of education, only 21.51% of female employees had a bachelor’s degree and 48.72% had a junior high school degree or less. About 94.41% of the respondents were front-line or general employees within their departments and 72.78% of female employees work more than 70 hr per week, which meant more than 10 hr per workday. In general, the majority of the participants were front-line female employees with lower education and longer working hours, who often had more difficulty balancing work and family and played the most basic role in safeguarding the operation and development of the enterprises, and therefore need enterprises to explore richer work resources to enhance their work–family enrichment level.

Table 1. Demographic characteristics

Measures

We applied a 5-point Likert-type measurement to measure the key variables (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree). All of the key measurements were in Chinese. Due to the different types and efforts of information systems construction in various surveyed enterprises, this study did not limit the specific types of EIS, such as supply chain management systems or enterprise resource planning systems, in order to explore the generally extended impact of enterprise technology resources on the work–family interactions of female employees, which is consistent with previous empirical studies in the field of information systems (Lee, Fogliasso, Jung, Shin, & Shum, Reference Lee, Fogliasso, Jung, Shin and Shum2023; Rahman, Hossain, Chowdhury, & Hoque, Reference Rahman, Hossain, Chowdhury and Hoque2022; Turetken, Ondracek, & IJsselsteijn, Reference Turetken, Ondracek and IJsselsteijn2019).

Information systems quality

Although current research has defined several factors that can reflect EIS information quality, system quality, and service quality, none of unified measurement index has been developed (Liu & Arnett, Reference Liu and Arnett2000; Nelson, Todd, & Wixom, Reference Nelson, Todd and Wixom2005; Sousa & Voss, Reference Sousa and Voss2006). Since this study does not focus on a specific EIS or information systems department, in order to obtain employees’ overall quality evaluation of EIS quality, a 6-item scale originally developed by Pai and Huang (Reference Pai and Huang2011) with Chinese samples was applied to measure information systems quality, including information quality, system quality, and service quality, with two items for each quality. A sample item for information quality was ‘The information systems can provide correct information.’ The Cronbach’s α for information quality scale was .85. A sample item for system quality was ‘The information systems can be linked or integrated with the information of other systems.’ The Cronbach’s α for system quality scale was .90. A sample item for service quality was ‘When I have a problem, the information center staff can help me solve it.’ The Cronbach’s α for system quality scale was .93.

Perceived usefulness and ease of use

Since this study is not specific to a particular information system and focuses on the changes in employee skills or work styles caused by EIS, a 4-item scale originally developed by Pai and Huang (Reference Pai and Huang2011) with Chinese samples was applied to measure perceived usefulness and ease of use, with two items for each variable. A sample item for perceived usefulness was ‘Information systems can improve my professional skills.’ The Cronbach’s α for perceived usefulness scale was .91. A sample item for perceived ease of use was ‘The interface of the enterprise information systems is clear.’ The Cronbach’s α for perceived ease of use scale was .94.

Work–family enrichment

A 3-item scale originally developed by Fisher, Bulger, and Smith (Reference Fisher, Bulger and Smith2009) was applied to measure work–family enrichment. Sample items include ‘After work, I have enough time to take care of my family affairs’ and ‘Outside of work, I still have the energy to do things with my family.’ The Cronbach’s α for work–family enrichment scale was .80.

Control variables

We controlled female employees’ demographic variables: age (1 = 25 years and below, 2 = 26–35 years, 3 = 36–45 years, 4 = 46 years and above), education (1 = middle school and below, 2 = high school, 3 = junior college, 4 = bachelor’s degree, 5 = master’s degree and above), work hours per week (1 = 40 hr and below, 2 = 41–50 hr, 3 = 51–60 hr, 4 = 61–70 hr, 5 = 71 hr and above), and marital status (1 = single, 2 = married without child, 3 = married with child) because of their potential effects on work–family enrichment (Cooklin et al., Reference Cooklin, Westrupp, Strazdins, Giallo, Martin and Nicholson2014; Grzywacz & Marks, Reference Grzywacz and Marks2000; Wayne, Musisca, & Fleeson, Reference Wayne, Musisca and Fleeson2004).

Analytical strategies

This study is based on covariance-based SEM (CB-SEM) – the preferred statistical tool for the assessing measurement and structural relationships in the social sciences over the past 40 years (Hair, Reference Hair2022; Sarstedt, Hair, & Ringle, Reference Sarstedt, Hair and Ringle2023) – to provide a measure of goodness of model fit, in order to achieve the purpose of hypothesis testing in theoretical models composed of reflective indicators. Compared to the ability of partial least squares SEM to make causal predictions for key variables in the exploration of complex models with relatively weak theoretical foundations and small sample sizes (Hair, Hult, Ringle, & Sarstedt, Reference Hair, Hult, Ringle and Sarstedt2021; Hair, Ringle, & Sarstedt, Reference Hair, Ringle and Sarstedt2011), the CB-SEM used in this study is based on a relatively mature theoretical framework and models, including D&M information system success model, TAM, and work–family enrichment theory, as well as a relatively large sample size.

Data preparation and all statistical analysis, including descriptive statistics and path analysis, were conducted using SPSS 26.0 (IBM Corp.: Armonk, NY, USA) and Amos 21.0 (IBM Corp.: Armonk, NY, USA). We performed descriptive statistical analysis of the research variables with the Pearson correlation test used to check the significance of the correlation between the study variables. Then, we conducted the confirmatory factor analyses to provide support for discriminant validity among the six constructs. The local fit of the model was assessed on the basis of the following criteria: factor reliability values of .60 or higher, p < .05 for all factor loadings; goodness normed fit index (GFI), comparative fit index (CFI), Tucker–Lewis index (TLI), and normed fit index (NFI) of .90 or higher; and root mean square error or approximation (RMSEA) less than .08.

After testing the model fit with the CB-SEM, a chained mediating model was established and tested the hypotheses using maximum likelihood estimation (Anderson & Gerbing, Reference Anderson and Gerbing1988; Hair et al., Reference Hair, Hult, Ringle and Sarstedt2021, Reference Hair, Ringle and Sarstedt2011) and bootstrapping technique (Edwards & Lambert, Reference Edwards and Lambert2007). At least 1,000 bootstrap samples were used to accurately adjust the upper and lower limits of a 95% confidence interval for indirect effects, which were significant when it did not include zero.

Results

Confirmatory factor analyses

Considering that three quality factors and two perception factors of information systems reflect the technical and user dimensions, respectively, we used nested confirmatory factor analysis to assess the differential validity of the model structure and ensure that information quality, system quality, service quality, perceived ease of use, perceived usefulness, and work–family enrichment were independent variables that are different from each other. We constructed five alternative models by combining three quality factors or two perception factors to compare with the six-factor model. Table 2 shows that the six-factor model fitted the data well: χ2(54) = 206.96, GFI = .96; CFI = .98; RMSEA = .06; TLI = .98; NFI = .98. Therefore, this study considers information quality, system quality, service quality, perceived ease of use, perceived usefulness, and work–family enrichment as six different factors.

Table 2. Results of confirmatory factor analyses for construct validity

Note: N = 823. PU = perceived usefulness; PE = perceived ease of use; RMSEA = the root-mean-square error of approximation; CFI = the comparative fit index; TLI = Tucker–Lewis index.

Descriptive statistics

Table 3 shows the means, standard deviations, and correlations of the studied variables. As expected, information systems quality was positively linked with perceived ease of use and usefulness (r = .40–.78, p < .01). In addition, perceived ease of use was positively linked with perceived usefulness (r = .79, p < .01). Finally, perceived usefulness was positively linked with work–family enrichment (r = .39, p < .01).

Table 3. Means, standard deviations, and correlations of variables

Note: N = 823.

* p < .05, **p < .01 (two-tailed).

Values in parentheses on the diagonal are the Cronbach’s α value of each scale.

Hypotheses testing

To test hypotheses 1, 2, 3, 4, and 5, we estimated a CB-SEM in which work–family enrichment was predicted by perceived usefulness, and four control variables, and in which perceived usefulness was predicted by perceived ease of use, information quality, system quality, and service quality. The results were summarized in Fig.2 and the structural model fitted the data well: χ2(102) = 371.48, GFI = .95; CFI = .97; RMSEA = .06; TLI = .96; NFI = .96. The results showed that the information quality was positively linked with both perceived usefulness (β = .08, SE = .04, p < .05) and perceived ease of use (β = .29, SE = .04, p < .01), and hypothesis 1a and hypothesis 1b are both tested. The system quality only showed a significant positive effect on perceived usefulness (β = .09, SE = .02, p < .01) and a small effect on perceived ease of use (β = .01, SE = .02, p > .05); therefore, hypothesis 2a was tested and hypothesis 2b was not tested. The service quality significantly increased perceived ease of use (β = .53, SE = .04, p < .01) and perceived usefulness (β = .40, SE = .04, p < .01), and hypothesis 3a and hypothesis 3b were tested. There was a significant positive association between perceived ease of use and perceived usefulness (β = .48, SE = .04, p < .01), and hypothesis 4 was tested; perceived usefulness in turn had a significant positive effect on work–family enrichment (β = .27, SE = .03, p < .01), and hypothesis 5 was tested.

Figure 2. Results of hypothesized model, with standardized maximum likelihood estimates (numbers not in bold are standardized regression coefficients and numbers in bold explain variability; χ2 = 371.48, p = .00; GFI = .95; CFI = .97; RMSEA = .06; TLI = .96; NFI = .96).

We used the multiplicative coefficient test and Bootstrap sampling method to further examine the chain mediating role played by perceived ease of use and usefulness between EIS information quality, system quality, service quality, and work–family enrichment, and the results are shown in Table 4. The total effect referred to the regression coefficient from the independent variable to the dependent variable when there are no chained mediating variables in the model (i.e., perceived ease of use and perceived usefulness); the direct effect referred to the regression coefficient from the independent variable to the dependent variable when mediating variables are added to the model; the indirect effect was obtained by multiplying the regression coefficients from the independent variable to the mediating variable and from the mediating variable to the dependent variable.

Table 4. The mediating role of perceived usefulness between information systems quality and work–family enrichment

Note: N = 823;

** p < .01, *p < .05; IQ = information quality; SQ = system quality; SEQ = service quality; PU = perceived usefulness; PE = perceived ease of use; WFE = work–family enrichment; 95% Boot CI denotes the 95% confidence interval calculated by Bootstrap sampling.

The results indicated that perceived usefulness played a significant mediating role between perceived ease of use (hypothesis 6; 95% CI = [.08, .27]), information quality (hypothesis 7a; 95% CI = [.11, .24]), system quality (hypothesis 7b; 95% CI = [.14, .23]), and service quality (hypothesis 7c; 95% CI = [.06, .22]) with work–family enrichment. Furthermore, perceived ease of use and usefulness played a significant chain mediating role between information quality (hypothesis 8a; 95% CI = [.07, .09]), system quality (hypothesis 8b; 95% CI = [.05, .08]), and service quality (hypothesis 8c; 95% CI = [.04, .06]) with work–family enrichment. Among them, perceived usefulness fully mediates the relationship between system quality and work–family enrichment (percentage of mediating effect = 100%). In the other paths, the mediating effect of perceived usefulness was partially mediated with effect of 44.15% (path PE→PU→WFE), 49.42% (path IQ→PE→PU→WFE), and 34.26% (path SEQ→PE→PU→WFE), respectively.

Discussion

This study investigates the mechanism of EIS success on the work–family enrichment in the Chinese context by using the work–family enrichment theory as a conceptual framework and combining the D&M ISSM and the TAM to evaluate the information systems success. The results show that the information quality, system quality, and service quality of EIS have a positive impact on the perceived ease of use and usefulness, among which the perceived ease of use has a positive impact on the perceived usefulness, and then the perceived usefulness has a positive impact on the work–family enrichment of female employees. By verifying how the success of EIS can enrich the work–family relationship of female employees, it not only helps to promote theoretical understanding of work–family domain research among management scholars but also is crucial for human resource managers to develop relevant policies to improve employee and corporate productivity.

First, the most important contribution of this study is to build a theoretical bridge between the success of EIS and the positive work–family relationship of female employees, to innovatively connect the technological resources provided by companies with the psychosocial factors of employees’ responses. In studies exploring the predictors of work–family enrichment, skills resources still have a large theoretical gap compared to other widely validated workplace resources. From the firm’s perspective, since the introduction of the work–family enrichment theory in 2006, most scholars have explored the role of firm-provided work–family support policies (Butts, Casper, & Yang, Reference Butts, Casper and Yang2013), flexible work environments (Wang, Wang, Yao, Hsu, & Lawler, Reference Wang, Wang, Yao, Hsu and Lawler2019), and positive leadership support (Wu et al., Reference Wu, Kuo, Lin, Hu, Wu and Cheng2020) in enhancing work–family enrichment for female employees. By contrast, this paper is the first to suggest that the skills resources such as enhancing employees’ information processing skills, knowledge, and experience transfer capabilities (Yu-Hsi et al., Reference Yu-Hsi, Sang-Bing, Chien-Yun, Hsiao-Ming, Wan-Fei and Chia-Huei2017) that female employees can harvest through the use of EIS can be applied to the family domain to enhance their family functions, such as becoming more proficient in use information or communication technology, think more sharply and address the needs of their family members. Similar studies have also found that cultivating employees’ cybersecurity awareness during the use of enterprise systems can enhance their security behavior at home (Goel, Zhang, & Williamson, Reference Goel, Zhang and Williamson2023) and the high quality of information technology support for physicians, such as introduction of electronic medical record systems would leads to work–life balance (Bardoel & Drago, Reference Bardoel and Drago2016). In the long run, positive EIS cognitive responses and higher levels of work–family enrichment are more likely to keep female employees engaged and committed to their organizations (Daniel & Sonnentag, Reference Daniel and Sonnentag2016; Shih, Reference Shih2003). Therefore, female employees can benefit from the process of building EIS, as long as they find them useful.

Second, this study combined the quality dimension of D&M ISSM and the user dimension of TAM to assess the impact of EIS success. The results showed that the quality factors of EIS had a significant positive impact on both perceived usefulness and ease of use of them. Research on EIS success has been of interest to research and practice in the field of information systems and technology for decades. Combining the quality of an EIS with the perceived response to it provides a comprehensive picture of employee evaluations and attitudes, which was first proposed by Seddon in 1997 when he extended the information system success model (Seddon, Reference Seddon1997). After that, this evaluating framework has been validated by scholars to evaluate e-learning systems (Chen, Reference Chen2010; Wang, Wei, Chen, & Wang, Reference Wang, Wei, Chen and Wang2023), business process management systems (Poelmans, Reijers, & Recker, Reference Poelmans, Reijers and Recker2013), enterprise resource planning systems (Chung, Skibniewski, & Kwak, Reference Chung, Skibniewski and Kwak2009), and electronic health records systems (Ebnehoseini, Tabesh, Deghatipour, & Tara, Reference Ebnehoseini, Tabesh, Deghatipour and Tara2022). We collected data from different types of enterprises, taking the EIS as a whole, and examined the technological factors of the EIS that could positively impact the user dimension, providing a theoretical basis for the subsequent EIS success as an important workplace resource that could be utilized. From the perspective of business managers, we suggest enterprises invest sufficient time and effort in the quality of their information systems construction programs and make it a key goal to stimulate user perceived usefulness of information systems, for example by increasing user involvement in the system development and implementation process (Petter, DeLone, & McLean, Reference Petter, DeLone and McLean2013).

Finally, the third contribution of this study is to highlight the importance of studying and practicing work–family related issues of female employees in the Chinese context. According to the most recent meta-analysis in 2020, despite the growing number of empirical studies on work–life research in the West, there is still limited attention to the Asian workforce in this area, especially the Chinese workforce (Le, Newman, Menzies, Zheng, & Fermelis, Reference Le, Newman, Menzies, Zheng and Fermelis2020). According to the scores of the 5-point Likert scale in this study, the overall work–family enrichment score for female employees is 3.17 and the score for employees under 30 years old is 3.11. Therefore, the level of work–family enrichment remains low in the Chinese female employees, especially for young women. Nowadays, the people’s sense of gain and harmonious work–family relationships are important for Chinese employees (Ashforth & Fugate, Reference Ashforth and Fugate2000). In this context, profit maximization is no longer the only indicator of corporate competence, and the inability to reconcile employees’ work and family relationships has become an important factor that seriously affects corporate competitiveness. This study expands the predictors of work–family enrichment for female employees in the context of the policy environment in China, which helps management scholars to better understand the work–family relationship issues of female employees in China from a theoretical perspective, and helps human resource and business managers to formulate policies to improve the work–family enrichment of them.

Practical implications

From the perspective of business managers, further attention should be paid to the functionality of EISs and the format and structure of the information they use, a process that is not only of high importance when changing a new system, although these factors are still important in systems that users are all very familiar with using (Leidner & Kayworth, Reference Leidner and Kayworth2006). The provision of adequate resources is important because the development of high-quality EISs is by nature quite time-consuming and dynamic, requiring frequent changes (Goodhue, Kirsch, Quillard, & Wybo, Reference Goodhue, Kirsch, Quillard and Wybo1992). Organizations should invest sufficient time and effort in the quality and complexity of their information systems building programs, including the allocation of sufficient human and financial resources, etc., as a way to enhance positive feedback from employees on the quality of information systems. In addition, although the ultimate goal of business managers in adopting EISs is to improve the competitiveness and productivity of the business, they must first focus on the end-user level, i.e., the cognitive response level of employees. Finally, in the family-centered Chinese society, profit maximization is no longer the only indicator of corporate competence, and the inability to reconcile employees’ work and family relationships has become an important factor that seriously affects corporate competitiveness. Companies should begin by breaking down the perception of work and family as separate or conflicting role areas and instead see the family sphere of working women as an integral part of the role of management policy. It is, therefore, the manager’s job not only to focus on whether employees are performing positively in the work area as a result of the interventions of management policy but also to help them establish long-term, mutually beneficial exchange relationships between the organization and the family.

Limitations and future directions

There are four main limitations in this study as follows. First, although this study avoided common method bias to the greatest extent possible, all questionnaires were self-assessed and single-sourced, and the variables investigated in this study were inherently correlated. Therefore, no causal conclusions can be drawn. A multi-point measurement approach may be more accurate, such as measuring variables related to the EIS success during work time and measuring work–family enrichment during family time. Another suggestion is to capture perceived work–family enrichment from multiple sources (e.g., spouse, supervisor, and coworker) to mitigate the problem of common method bias. Second, although this study bundled all EIS together to create a measure with greater variability and broader impact, it may implicitly assume that these information systems have equal impact on users. Future research could be specific to different EIS, such as supply chain management systems, customer relationship management systems, or enterprise resource planning systems, to provide more focused results and responses. Third, the samples in this study were all from China, but there was a lack of consideration of contextual variables specific to China, such as leadership and traditional Chinese culture. We suggest that future research further consider whether employees respond differently to EIS in different cultural contexts, which may provide promising results for management practitioners in managing cross-cultural differences. Finally, although this study used a heterogeneous sample, which contributes to statistical power, caution should be exercised in generalizing the results beyond the current study. We suggest that future studies be replicated for specific industries or organizations to test and extend the applicability of existing research models.

Conclusion

In the context of the implementation of the ‘three-child’ policy and the rapid development of enterprise information technology, this paper empirically investigates the influence mechanisms of female employees’ use of EIS in their work roles to accumulate resources for their family enrichment. Specifically, using the three dimensions (i.e., information quality, system quality, and service quality) of the D&M ISSM to evaluate the quality of EIS and the two dimensions (i.e., perceived usefulness and ease of use) of the TAM to evaluate the cognitive responses of EIS users, this study estimates that EIS success has a significant positive contribution to the level of work–family enrichment of female employees. In addition, perceived ease of use and perceived usefulness play a chain mediating role between quality factors and work–family enrichment. Both human resource managers and individual employees can take this as a lesson to intervene in the construction level of EIS and work–family enrichment level, so as to help female employees alleviate the stress of both work and family roles, encourage them to make full use of the resources of EIS to develop their knowledge and skills, and apply the skill enhancement in the workplace to enrich their family functions.

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grants 71974011, 72174022, 71804009, 71972012).

Conflicts of interest

The authors have no competing interests.

References

Abab, S. A., Wakjira, F. S., & Negash, T. T. (2021). Determinants of the land registration information system operational success: Empirical evidence from Ethiopia. Land, 10(12), .CrossRefGoogle Scholar
Ahn, T., Ruy, S., & Han, I. (2004). The impact of the online and offline features on the user acceptance of Internet shopping malls. Electronic Commerce Research and Applications, 3(4), 405420.CrossRefGoogle Scholar
Ahn, T., Ryu, S., & Han, I. (2007). The impact of web quality and playfulness on user acceptance of online retailing. Information & Management, 44(3), 263275.CrossRefGoogle Scholar
Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2016). Determinants of perceived usefulness of e-learning systems. Computers in Human Behavior, 64, 843858.CrossRefGoogle Scholar
Anderson, S. E., Coffey, B. S., & Byerly, R. T. (2002). Formal organizational initiatives and informal workplace practices: Links to work–family conflict and job-related outcomes. Journal of Management, 28, 787810.Google Scholar
Anderson, J. C., & Gerbing, W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 27(1), 524.Google Scholar
Aryee, S., Srinivas, E. S., & Tan, H. H. (2005). Rhythms of life: Antecedents and outcomes of work-family balance in employed parents. Journal of Applied Psychology, 90(1), 132146.CrossRefGoogle ScholarPubMed
Ashforth, B. E., & Fugate, K. M. (2000). All in a day’s work: Boundaries and micro role transitions. Academy of Management Review, 25(3), 472491.CrossRefGoogle Scholar
Asmussen, C. B., & Moller, C. (2020). Enabling supply chain analytics for enterprise information systems: A topic modelling literature review and future research agenda. Enterprise Information Systems, 14(5), 563610.CrossRefGoogle Scholar
Bardoel, E. A., & Drago, R. (2016). Does the quality of information technology support affect work–life balance? A study of Australian physicians. International Journal of Human Resource Management, 27(21), 26042620.CrossRefGoogle Scholar
Bostrom, R. P., & Heinen, J. S. (1977). MIS problems and failures: A socio-technical perspective. MIS Quarterly, 1(3), 1732.CrossRefGoogle Scholar
Brandon-Jones, A., & Kauppi, K. (2018). Examining the antecedents of the technology acceptance model within e-procurement. International Journal of Operations and Production Management, 38(1), 2242.CrossRefGoogle Scholar
Butts, M. M., Casper, W. J., & Yang, T. S. (2013). How important are work-family support policies? A meta-analytic investigation of their effects on employee outcomes. Journal of Applied Psychology, 98(1), 125.CrossRefGoogle ScholarPubMed
Carlson, D. S., Ferguson, M., Kacmar, K. M., Grzywacz, J. G., & Whitten, D. (2011). Pay it forward: The positive crossover effects of supervisor work-family enrichment. Journal of Management, 37(3), 770789.CrossRefGoogle Scholar
Carlson, D. S., Grzywacz, J. G., & Kacmar, K. M. (2010). The relationship of schedule flexibility and outcomes via the work-family interface. Journal of Managerial Psychology, 25(3-4), 330355.CrossRefGoogle Scholar
Carlson, D. S., Kacmar, K. M., & Williams, L. J. (2000). Construction and initial validation of a multidimensional measure of work–family conflict. Journal of Vocational Behavior, 56(2), 249276.CrossRefGoogle Scholar
Casper, W. J., Harris, C., Taylor-Bianco, A., & Wayne, J. H. (2011). Work–family conflict, perceived supervisor support and organizational commitment among Brazilian professionals. Journal of Vocational Behavior, 79(3), 640652.CrossRefGoogle Scholar
Chen, H. J. (2010). Linking employees’ e-learning system use to their overall job outcomes: An empirical study based on the IS success model. Computers and Education, 55(4), 16281639.CrossRefGoogle Scholar
Cheng, Y. M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361390.CrossRefGoogle Scholar
Chen, R. F., & Hsiao, J. L. (2012). An empirical study of physicians’ acceptance of hospital information systems in Taiwan. Telemedicine and e-Health, 18(2), 120125.CrossRefGoogle ScholarPubMed
Chiou, Y. W., & Fang, G. D. (2005). A study of web portal user behavior. Web Journal of Chinese Management Review, 8(1), 4360.Google Scholar
Chung, B., Skibniewski, M. J., & Kwak, Y. H. (2009). Developing ERP systems success model for the construction industry. Journal of Construction Engineering and Management, 135(3), 207216.CrossRefGoogle Scholar
Cooklin, A. R., Westrupp, E. M., Strazdins, L., Giallo, R., Martin, A., & Nicholson, J. M. (2014). Fathers at work: Work–family conflict, work–family enrichment and parenting in an Australian cohort. Journal of Family Issues, 37(11), 125.Google Scholar
Daniel, S., & Sonnentag, S. (2016). Crossing the borders: The relationship between boundary management, work-family enrichment and job satisfaction. International Journal of Human Resource Management, 27(4), 407426.CrossRefGoogle Scholar
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.Google Scholar
Dehgani, R., & Navimipour, N. J. (2019). The impact of information technology and communication systems on the agility of supply chain management systems. Kybernetes, 48(10), 22172236.CrossRefGoogle Scholar
Delone, W. H., & Mclean, E. R. (2003). The Delone and Mclean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 930.Google Scholar
Ebnehoseini, Z., Tabesh, H., Deghatipour, A., & Tara, M. (2022). Development an extended-information success system model (ISSM) based on nurses’ point of view for hospital EHRs: A combined framework and questionnaire. BMC Medical Informatics and Decision Making, 22(1), 117.CrossRefGoogle ScholarPubMed
Edwards, J. R., & Lambert, L. S. (2007). Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychological Methods, 12(1), 122.CrossRefGoogle ScholarPubMed
European Foundation for the Improvement of Living and Working Conditions. (2007). Fifteen years of working conditions in the EU: Charting the trends. Retrieved from www.eurofound.europa.eu/ewcoGoogle Scholar
Fisher, G. G., Bulger, C. A., & Smith, C. S. (2009). Beyond work and family: A measure of work/nonwork interference and enhancement. Journal of Occupational Health Psychology, 14(4), 441456.CrossRefGoogle Scholar
Fried, Y., & Ferris, G. R. (1987). The validity of the job characteristics model: A review and meta-analysis. Personnel Psychology, 40(2), 287322.CrossRefGoogle Scholar
Goel, L., Zhang, J. Z., & Williamson, S. (2023). Work-to-home cybersecurity spillover: Construct development and validation. Information Systems Management, 40(3), 290300.CrossRefGoogle Scholar
Goodhue, D. L., Kirsch, L. J., Quillard, J. A., & Wybo, M. D. (1992). Strategic data planning: Lessons from the field. MIS Quarterly, 16(1), 1134.CrossRefGoogle Scholar
Greenhaus, J., & Powell, G. (2006). When work and family are allies: A theory of work-family enrichment. Academy of Management Review, 31(1), 7292.CrossRefGoogle Scholar
Grzywacz, J. G., & Marks, N. F. (2000). Reconceptualizing the work–family interface: An ecological perspective on the correlates of positive and negative spillover between work and family. Journal of Occupational Health Psychology, 5, 111126.CrossRefGoogle ScholarPubMed
Hair, J. F. (2022). Reflections on SEM: An introspective, idiosyncratic journey to composite-based structural equation modeling. SIGMIS Database 52, SI (December 2021), 101113.Google Scholar
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) using R. Cham: Sage publications.CrossRefGoogle Scholar
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139151.CrossRefGoogle Scholar
Halpern, D. F. (2006). How organizations can alleviate the traffic jam at the intersection of work and family. American Behavioral Scientist, 49(9), 11471151.CrossRefGoogle Scholar
Ho, K. F., Ho, C. H., & Chung, M. H. (2019). Theoretical integration of user satisfaction and technology acceptance of the nursing process information system. PLoS One, 14(6), .CrossRefGoogle ScholarPubMed
Hung, S. Y., Liang, T. P., & Chang, C. M. (2005). A meta-analysis of empirical research using TAM. Journal of Information Management, 12(4), 211234.Google Scholar
Jiang, N., Tian, E. L., Malayeri, F. D., & Balali, A. (2020). A new model for investigating the impact of urban knowledge, urban intelligent transportation systems and IT infrastructures on the success of SCM systems in the distributed organizations. Kybernetes, 49(11), 27992818.CrossRefGoogle Scholar
Ji, M. T., Genchev, G. Z., Huang, H. Y., Xu, T., Lu, H., & Yu, G. J. (2021). Evaluation framework for successful artificial intelligence-enabled clinical decision support systems: Mixed methods study. Journal of Medical Internet Research, 23(6), .CrossRefGoogle ScholarPubMed
Kibelloh, M., & Bao, Y. K. (2014). Perceptions of international female students toward e-learning in resolving high education and family role strain. Journal of Educational Computing Research, 50(4), 467487.CrossRefGoogle Scholar
Kim, T. G., Lee, J. H., & Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29(3), 500513.CrossRefGoogle Scholar
Kozanoglu, D. C., & Abedin, B. (2020). Understanding the role of employees in digital transformation: Conceptualization of digital literacy of employees as a multi-dimensional organizational affordance. Journal of Enterprise Information Management, 34(6), 16491672.CrossRefGoogle Scholar
Kuo, C. S., Kang, Y. W., & Yang, H. L. (2023). Investigating the determinants of continuance intention on cloud ERP systems adoption. Advances in Mechanical Engineering, 15(4), .CrossRefGoogle Scholar
Laumer, S., Maier, C., & Weitzel, T. (2017). Information quality, user satisfaction, and the manifestation of workarounds: A qualitative and quantitative study of enterprise content management system users. European Journal of Information Systems, 26(4), 333360.CrossRefGoogle Scholar
Lee, S. H., Fogliasso, C. E., Jung, S. H. Y., Shin, M. M., & Shum, C. (2023). User psychological states and enterprise information systems adoption: Evidence from the United States, Zimbabwe, South Korea, and Paraguay. Service Business, 17(2), 477497.CrossRefGoogle Scholar
Lee, S., & Kim, B. G. (2017). The impact of qualities of social network service on the continuance usage intention. Management Decision, 55(4), 701729.CrossRefGoogle Scholar
Leidner, D. E., & Kayworth, T. (2006). Review: A review of culture in information systems research: Toward a theory of information technology culture conflict. MIS Quarterly, 30(2), 357399.CrossRefGoogle Scholar
Lemmer, K., Jahn, K., Chen, A. D. L., & Niehaves, B. (2023). One tool to rule? – A field experimental longitudinal study on the costs and benefits of mobile device usage in public agencies. Government Information Quarterly, 40(3), .CrossRefGoogle Scholar
Le, H., Newman, A., Menzies, J., Zheng, C., & Fermelis, J. (2020). Work–life balance in Asia: A systematic review. Human Resource Management Review, 30(4), .CrossRefGoogle Scholar
Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioural intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers and Education, 51(2), 864873.CrossRefGoogle Scholar
Lin, C. S., & Wu, S. (2002). Exploring the impact of online service quality on portal site usage. Proceedings of the 35th Hawaii International Conference on System Sciences, 7(206), 26542661.CrossRefGoogle Scholar
Liu, C., & Arnett, K. P. (2000). Exploring the factors associated with web site success in the context of electronic commerce. Information & Management, 38(1), 2333.CrossRefGoogle Scholar
Li, Y. X., & Wang, J. H. (2021). Evaluating the impact of information system quality on continuance intention toward cloud financial information system. Frontiers in Psychology, 12, .Google ScholarPubMed
Matthews, R. A., & Barnes-Farrell, J. L. (2010). Development and initial evaluation of an enhanced measure of boundary flexibility for the work and family domains. Journal of Occupational Health Psychology, 15(3), 330346.CrossRefGoogle ScholarPubMed
Mcnall, L. A., Masuda, A. D., & Nicklin, J. M. (2009). Flexible work arrangements, job satisfaction, and turnover intentions: The mediating role of work-to-family enrichment. Journal of Psychology, 144(1), 6181.CrossRefGoogle Scholar
Mishra, A., Shukla, A., Rana, N. P., Currie, W. L., & Dwivedi, Y. K. (2023). Re-examining post-acceptance model of information systems continuance: A revised theoretical model using MASEM approach. International Journal of Information Management, 68, .CrossRefGoogle Scholar
Nelson, R. R., Todd, P. A., & Wixom, B. (2005). Antecedents of information and system quality: An empirical examination within the context of data warehousing. Journal of Management Information Systems, 21(4), 199235.CrossRefGoogle Scholar
Ngai, E. W., Poon, J. K. L., & Chan, Y. H. (2007). Empirical examination of the adoption of WebCT using TAM. Computers and Education, 48(2), 250267.CrossRefGoogle Scholar
Nicklin, J. M., & Mcnall, L. A. (2013). Work–family enrichment, support, and satisfaction: A test of mediation. European Journal of Work & Organizational Psychology, 22(1), 6777.CrossRefGoogle Scholar
Oktal, O., Alpu, O., & Yazici, B. (2016). Measurement of internal user satisfaction and acceptance of the e-justice system in Turkey. Aslib Journal of Information Management, 68(6), 716735.CrossRefGoogle Scholar
Pai, F. Y., & Huang, P. (2011). Applying the technology acceptance model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650660.CrossRefGoogle Scholar
Pan, K., Nunes, M. B., & Peng, G. C. (2011). Risks affecting ERP post-implementation: Insights from a large Chinese manufacturing group. Journal of Manufacturing Technology Management, 22(1), 107130.CrossRefGoogle Scholar
Park, N., Rhoads, M., Hou, J., & Lee, K. M. (2014). Understanding the acceptance of teleconferencing systems among employees: An extension of the technology acceptance model. Computers in Human Behavior, 39, 118127.CrossRefGoogle Scholar
Petter, S., DeLone, W., & McLean, E. R. (2013). Information systems success: The quest for the independent variables. Journal of Management Information Systems, 29(4), 761.CrossRefGoogle Scholar
Podsakoff, P. M., Mackenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879903.CrossRefGoogle ScholarPubMed
Poelmans, S., Reijers, H. A., & Recker, J. (2013). Investigating the success of operational business process management systems. Information Technology & Management, 14(4), 295314.CrossRefGoogle Scholar
Powell, G. N., Francesco, A. M., & Ling, Y. (2009). Toward culture-sensitive theories of the work–family interface. Journal of Organizational Behavior, 30(5), 597616.CrossRefGoogle Scholar
Rahman, M. S., Hossain, M. A., Chowdhury, A. H., & Hoque, M. T. (2022). Role of enterprise information system management in enhancing firms competitive performance towards achieving SDGs during and after COVID-19 pandemic. Journal of Enterprise Information Management, 35(1), 214236.CrossRefGoogle Scholar
Russo, M., & Buonocore, F. (2012). The relationship between work–family enrichment and nurse turnover. Journal of Managerial Psychology, 27, 216236.CrossRefGoogle Scholar
Sabherwal, R., Jeyaraj, A., & Chowa, C. (2006). Information system success: Individual and organizational determinants. Management Science, 52(12), 18491864.CrossRefGoogle Scholar
Sarstedt, M., Hair, J. F., & Ringle, C. M. (2023). “PLS-SEM: Indeed a silver bullet” – Retrospective observations and recent advances. Journal of Marketing Theory and Practice, 31(3), 261275.CrossRefGoogle Scholar
Seddon, P. B. (1997). A re-specification and extension of the Delone and Mclean model of is success. Information Systems Research, 8(3), 240253.CrossRefGoogle Scholar
Seddon, P. B., & Kiew, M. (1996). A partial test and development of DeLone and McLean’s model of IS success. Australasian Journal of Information Systems, 4(1), 90109.CrossRefGoogle Scholar
Shen, Y., & Jiang, L. (2020). Labor market outcomes of professional women with two children after the one-child policy in China. Journal of Social Issues, 76(3), 632658.CrossRefGoogle Scholar
Shih, H. P. (2003). Extended technology acceptance model of internet utilization behavior. Information & Management, 41(6), 719729.CrossRefGoogle Scholar
Shockley, K. M., & Singla, N. (2011). Reconsidering work-family interactions and satisfaction: A meta-analysis. Journal of Management, 37(3), 861886.CrossRefGoogle Scholar
Sousa, R., & Voss, C. A. (2006). Service quality in multichannel services employing virtual channels. Journal of Service Research, 8(4), 356371.CrossRefGoogle Scholar
Spector, P. E., Allen, T. D., Poelmans, S. A. Y., Lapierre, L. M., Cooper, C. L., O’Driscoll, M., Sanchez, J. I., Abarca, N., Alexandrova, M., Beham, B., & Brough, P. (2007). Cross-national differences in relationships of work demands, job satisfaction and turnover intentions with work–family conflict. Personnel Psychology, 60(4), 805835.CrossRefGoogle Scholar
Sulaiman, T. T., Mahomed, A. S. B., Rahman, A. A., & Hassan, M. (2023). Understanding antecedents of learning management system usage among university lecturers using an integrated TAM-TOE model. Sustainability, 15(3), .CrossRefGoogle Scholar
Taheri, F., Asarian, M., & Shahhosseini, P. (2020). Workaholism and workplace incivility: The role of work–family enrichment. Management Decision, 59(2), 372389.CrossRefGoogle Scholar
Teo, T. S., & Wong, P. K. (1998). An empirical study of the performance impact of computerization in the retail industry. Omega, 26(5), 611621.CrossRefGoogle Scholar
Tian, F., & Xu, S. X. (2015). How do enterprise resource planning systems affect firm risk? Post-implementation impact. MIS Quarterly, 39(1), 3960.CrossRefGoogle Scholar
Turetken, O., Ondracek, J., & IJsselsteijn, W. (2019). Influential characteristics of enterprise information system user interfaces. Journal of Computer Information Systems, 59(3), 243255.CrossRefGoogle Scholar
Wang, P., & Walumbwa, F. O. (2007). Family-friendly programs, organizational commitment, and work withdrawal: The moderating role of transformational leadership. Personnel Psychology, 60(2), 397427.CrossRefGoogle Scholar
Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761774.CrossRefGoogle Scholar
Wang, P., Wang, S. H., Yao, X., Hsu, I. C., & Lawler, J. (2019). Idiosyncratic deals and work‐to‐family conflict and enrichment: The mediating roles of fit perceptions and efficacy beliefs. Human Resource Management Journal, 29(4), 600619.CrossRefGoogle Scholar
Wang, Y. M., Wei, C. L., Chen, W. J., & Wang, Y. S. (2023). Revisiting the e-learning systems success model in the post-COVID-19 age: The role of monitoring quality. International Journal of Human-computer Interaction, 1, 116.Google Scholar
Wayne, J. H., Musisca, N., & Fleeson, W. (2004). Considering the role of personality in the work–family experience: Relationships of the big five to work–family conflict and facilitation. Journal of Vocational Behavior, 64(1), 108130.CrossRefGoogle Scholar
Wayne, J. H., Randel, A. E., & Stevens, J. (2006). The role of identity and work–family support in work–family enrichment and its work-related consequences. Journal of Vocational Behavior, 69(3), 445461.CrossRefGoogle Scholar
Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85102.CrossRefGoogle Scholar
Wu, C., Hunter, E. M., & Sublett, L. W. (2021). Gaining affective resources for work-family enrichment: A multisource experience sampling study of micro-role transitions. Journal of Vocational Behavior, 125, .CrossRefGoogle Scholar
Wu, C. Y., Kuo, C. C., Lin, C.‐W., Hu, W. H., Wu, C. Y., & Cheng, S. (2020). How does benevolent leadership lead to work–family enrichment? The mediating role of positive group affective tone. Stress and Health, 36(4), 496506.CrossRefGoogle ScholarPubMed
Yoo, S., Lim, K., Jung, S. Y., Lee, K., Lee, D., Kim, S., … Hwang, H. (2022). Examining the adoption and implementation of behavioral electronic health records by healthcare professionals based on the clinical adoption framework. BMC Medical Informatics and Decision Making, 22(1), 19.CrossRefGoogle ScholarPubMed
Yuan, Y. H., Tsai, S. B., Dai, C. Y., Chen, H.-M., Chen, W.-F., Wu, C.-H., & Deng, Y. (2017). An empirical research on relationships between subjective judgement, technology acceptance tendency and knowledge transfer. PLoS One, 12(9), .CrossRefGoogle ScholarPubMed
Yu-Hsi, Y., Sang-Bing, T., Chien-Yun, D., Hsiao-Ming, C., Wan-Fei, C., & Chia-Huei, W. (2017). An empirical research on relationships between subjective judgement, technology acceptance tendency and knowledge transfer. PLoS One, 12(9), .Google Scholar
Zhu, Y., Li, Y., Wang, W., & Chen, J. (2010). What leads to post-implementation success of ERP? An empirical study of the Chinese retail industry. International Journal of Information Management, 30(3), 265276.CrossRefGoogle Scholar
Figure 0

Figure 1. The theoretical model.

Figure 1

Table 1. Demographic characteristics

Figure 2

Table 2. Results of confirmatory factor analyses for construct validity

Figure 3

Table 3. Means, standard deviations, and correlations of variables

Figure 4

Figure 2. Results of hypothesized model, with standardized maximum likelihood estimates (numbers not in bold are standardized regression coefficients and numbers in bold explain variability; χ2 = 371.48, p = .00; GFI = .95; CFI = .97; RMSEA = .06; TLI = .96; NFI = .96).

Figure 5

Table 4. The mediating role of perceived usefulness between information systems quality and work–family enrichment