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Article

Relationship between Work-Life Balance and Job Performance Moderated by Knowledge Risks: Are Bank Employees Ready?

by
Michele Samuele Borgia
1,
Francesca Di Virgilio
2,
Maura La Torre
1,* and
Muhammad Adnan Khan
3
1
Department of Management and Business Administration, University “G. D’Annunzio” of Chieti-Pescara, 65127 Pescara, Italy
2
Department of Economics, University of Molise, 86100 Campobasso, Italy
3
Department of Business Administration, Barani Institute of Sciences, Sahiwal 57000, Pakistan
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(9), 5416; https://doi.org/10.3390/su14095416
Submission received: 15 March 2022 / Revised: 21 April 2022 / Accepted: 27 April 2022 / Published: 30 April 2022
(This article belongs to the Special Issue Rethinking Sustainability in Human Resource Management)
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Abstract

:
Despite the focus on knowledge risks in the literature, a limited number of studies have empirically examined technological knowledge risks in terms of digitalization, old technologies, and cybercrime as moderating variables in the relationship between work-life balance and job performance. To address this gap, this paper investigated the moderation effects of technological knowledge risks on the relationship between work-life balance and job performance during the pandemic period in employees of cooperative credit banks. A quantitative approach that involved gathering surveys was adopted. Applying PLS-SEM, the empirical findings revealed that technological knowledge risks have a significant impact on the relationship between work-life balance and job performance. Additionally, this research encourages managers to create and maintain a healthy work environment that promotes valuable employees’ job performance while also evaluating the use of new technological advances and their related risks.

1. Introduction

The COVID-19 pandemic, in addition to causing humanitarian and socio-economic emergencies, exposed companies of all sizes and sectors to different and complex challenges, which impacted various aspects of organizational design and behavior [1]. This is especially the case for small and medium-sized enterprises (SMEs) due to the difficulties that these organizations may encounter in finding the financial resources and human skills necessary to promptly and effectively manage the turmoil deriving from these crises [2,3]. Regarding work occupation, the COVID-19 pandemic caused career shocks that were differentiated in the short and long term [4], as well as significant changes in the status of some occupations and their relative value [5]. Therefore, organizations and employees had to face a work environment that was profoundly changed by the crisis [6], with the implementation of remote work practices influencing organizational performance [7], leading to financial and bureaucratic constraints and making it harder to monitor workers’ output [8]. Sudden changes following the COVID-19 pandemic could also have a positive or negative impact on employee work-life balance and job performance [9,10]. Technological advances, routine planning, and self-discipline become essential to maintaining good standards of work performance, especially when workers had to work from home (WFH) [11], in which case it was also important to ensure a home environment conducive to work, which could protect physical and mental health at the same time [12].
This changed situation after the COVID-19 pandemic also led organizations to be exposed to a greater number of risks, including risks related to knowledge management [13]. Considering the knowledge risks mapped and classified by Durst and Zieba [14] into human, technological, and operational domains, it can be noted that some of the threats associated with these risks became more serious during the COVID-19 pandemic. Human knowledge risks such as knowledge hiding, unlearning, and forgetting may be more likely to occur in the context of remote working. Employees can easily hide knowledge behind their private screens, while the lack of direct contact with workmates could favor the unlearning and forgetting of valuable knowledge due to communication being virtual and contacts filtered [13]. Knowledge risks most likely to affect employees who WFH are those related to the use of technology. Knowledge risks related to cybercrime, for example, could occur more easily in a less secure home internet network that could possibly be exposed to hacker attacks or through risky behaviors, such as sharing sensitive files via private devices. Knowledge risks related to old technologies could also affect remote workers, as employees may use outdated programs that do not allow IT security devices to be updated [13,14]. SMEs may find it more difficult to maintain their organizational balance—in particular, the balance between work and private life in the case of WFH, while also considering the effects of possible exposure to knowledge risks linked to the use of technology [15,16].
Considering the above, this paper aims to investigate the possible effects of technological knowledge risks (KRT) on the work-life balance (WLB) and job performance (JP) of employees of small banks. In particular, it investigates the moderating effects of KRT in terms of the risk of knowledge digitalization (KRTD), risks related to old technologies (KRTO), and risks related to cybercrime (KRTC) [17] on the relationship between WLB and JP in cooperative credit banks (CCBs). Haider, Jabeen, and Ahmad [18] stated that, although many scholars have investigated the WLB/JP relationship, the mechanisms that explain its functioning have still received little consideration. To fill this gap, it may be useful to introduce variables that mediate or moderate this relationship. A statistical analysis of mediation can be applied to check whether the effect of one variable on a second variable is transmitted through a third variable [19], while moderation occurs when the direction of the effect of a predictor variable on a result variable varies as a function of the value of another variable—that is, the moderator [20]. The extant literature has highlighted mediating and moderating effects of several variables on the WLB/JP relationship. Baral and Bhargava [21] examined the mediating role of work-family enrichment in the relationships between organizational interventions for WLB; in another study, optimism was tested as a mediator of the relationship between WLB, life satisfaction, and creative performance [22]. Furthermore, Sari and Seniati [23] investigated the effects of WLB on lecturers’ organizational commitment, using job satisfaction as a mediator; meanwhile, Rasheed, Mukhtar, Anwar, and Hayat [24] focused on the moderating role of obligation felt on employees’ desire for revenge.
Despite the wide range of studies available, to the best of our knowledge, in the literature so far, no study has investigated the WLB/JP relationship considering the role of KRT as a moderating variable. Against this background, in this paper, a quantitative approach that involved gathering surveys from a sample of 100 employees at CCBs in Italy was adopted. Data collection started on April 2021 and finished in June 2021. The chosen sample allowed us to fill another gap in the literature, as there is a noticeable lack of studies on this topic focused on organizations belonging to the financial and banking sectors. Using MACRO Process, a moderation analysis was performed in order to analyze the collected data and verify the possible moderating effects of KRT on the WLB/JP relationship.
Both theoretical and practical implications can be derived from this study. This research was based on the WLB/JP relationship, providing new insights into its functioning through the introduction of KRT as a moderating variable. Furthermore, encouraging attention to be paid to the technological knowledge risks of WFH and remote working and their possible effects on the WLB/JP relationship could help managers create and maintain a safe working environment for their employees.
The present paper is organized as follows: in Section 2, we discuss the findings on knowledge risks, WLB, and JP of past studies. Then, we develop a research model with hypotheses and methods. In the following section, empirical results are provided. The last section of the paper presents a discussion of our solutions and recommendations, possible future research directions, and conclusions.

2. Literature Review

2.1. Knowledge Risks: The State of the Art

Knowledge risk is an emerging field within the areas of knowledge management (KM) and intellectual capital (IC) [25] that aims to offer solutions to problems related to conventional risk management methods, applying KM tools and techniques for the management of organizational risks [26].
Knowledge risks have seldom been defined in the literature [14]. A more complete definition was proposed by Durst and Zieba [14] (p. 2), who describe knowledge risk as “a measure of the probability and severity of adverse effects of any activities engaging or related somehow to knowledge that can affect the functioning of an organization on any level”. This definition highlights the potential harmfulness of knowledge risks for organizations of all types and sizes, as well as the need to address these risks in relation to each other, preferably following an integrated approach [27].
The scientific literature on knowledge risks is still fragmented at present, addressing knowledge risks in isolation and often without comparing the organizational contexts involved [28]. Durst and Zieba [29] proposed the first taxonomy for knowledge risks to foster more rigorous research on this topic and, at the same time, support organizations in preventing or reducing these risks. Some types of knowledge risks were then identified and categorized according to their origin—internal or external to the organization. The risk of knowledge waste is another problem that occurs within organizations; it arises when valuable knowledge that is available in the organization is not used. An example of a knowledge risk originating outside an organization is knowledge spillover, which occurs when knowledge spills over to competitors, who gain a competitive advantage from it [29]. In order to further highlight the differences between types of knowledge risk, the same authors [14] divided knowledge risks into human, technological, and operational types. Human knowledge risks include knowledge hiding, forgetting, and unlearning—i.e., risks linked to the personal, social, psychological, and cultural factors of individuals. An organization is exposed to the risk of knowledge hiding, which is when people intentionally hide knowledge that has been requested by another person for reasons related to their fear of losing a competitive advantage or a position of power. The second category includes knowledge risks related to the use of technology by organizations, such as the risk of cybercrime (e.g., hacker attacks); the risk of digitalization—namely, the excessive dependence of organizations on technology rather than human factors; and risks associated with the obsolescence of organizational technological equipment [14]. The COVID-19 pandemic has heightened companies’ risk of exposure to KRT, as organizations during this period often resorted to WFH solutions due to the implementation of lockdown measures [13]. In this regard, it is important to ensure the adoption of cyber-safety behaviors based on digital safety skills in order to maintain a safe digital work environment [30] and encourage the development of employees’ digital skills to enable them to better cope with the new paradigm of Industry 4.0 [31]. These skills should also be adequately defined and measured [32,33] in order to avoid phenomena such as digital divides and inequality [34,35,36,37], especially in the era of the COVID-19 pandemic, which required considerable efforts from human resources departments to improve the digital skills of employees to allow them to maintain a satisfactory WLB even working remotely [38,39].
Operational knowledge risks that can affect organizations in their regular operations include knowledge outsourcing risks, the risk of using outdated/unreliable knowledge, and the risk of knowledge waste [14].
All organizations can be exposed to one or more knowledge risks, both private [17,40] and public [41]. SMEs are at particular risk, as they may experience greater difficulties in managing knowledge risks due to their lower capital endowments [42]. Organizational performance [43], as well as organizations’ ability to achieve economic, social, and environmental sustainability goals, can also be affected by knowledge risks [44,45].
Despite these valuable contributions, the research on knowledge risks still needs to be expanded, as this field is in an early stage of development [46], especially in relation to organizations belonging to the banking and financial sectors. Recent contributions have confirmed these findings, highlighting that very few studies clearly refer to one or more knowledge risks affecting banks or other financial companies [47], despite risk culture being strongly rooted in these organizations [48].

2.2. Latest Trends on Work-Life Balance

The extant literature on this topic has provided multiple definitions of WLB, focusing on the amount of time spent in work or on one’s private life, on the satisfaction derived from time spent in each domain, and on the importance attributed by individuals to each of these two roles [49]. Some authors have argued that the definitions of WLB adopted so far do not take into account developments in the personal sphere, in work contexts, and in relations between employees [50]. In a recent publication [51], the limits and prejudices related to the tendency to separate work from other spheres of life were highlighted, underlining the urgency of obtaining a broader view of this topic that is in step with the present times. Meanwhile, Lewis and Beauregard [52] provided an overview of definitions of WLB, integrating two literature streams—namely, work-family interface research and critical management and organizational studies. The modern concept of WLB is the result of a research evolution that has taken place in this field, attempting to respond over time to socio-economic and workplace developments [53,54,55,56]. Scientific research on the topic of WLB has intensified since the early 1990s; at present, there is a wide variety of approaches to this topic, with various definitions and different analysis perspectives having been proposed [57,58,59,60]. A significant proportion of the literature has focused on WLB perception, considering the perspectives of owners, employees, and managers [61,62] as well as gender issues [63,64,65,66].
From some of the most recent literature reviews in this area, interesting insights into how the WLB concept has evolved over time have emerged with respect to different contexts. Rashmi and Kataria [67] provided a systematic review of the literature focusing on WLB based on a bibliometric analysis; this shed light on emerging research themes such as flexible work arrangements, gender differences in WLB, the work-life interface and its related concepts, and WLB policies and practices, underlining the lack of a knowledge structure in the literature for this issue. Sirgy and Lee [68] pointed out the need for an integrative framework for WLB literature reviews, which would allow problems related to the holistic concept of WLB to be overcome, thus enabling its dimensions, antecedents, moderators, mediators, and consequences to be identified. The relationship between WLB and work engagement was considered in one literature review that explored its various antecedents, mediators, and moderators [69]. Another section of the literature has highlighted the correlation between WLB and other organizational variables, such as corporate performance and policies [70,71], while work-life concerns from the point of view of enrichment and depletion were addressed in [72]. The literature has not neglected to consider the “dark side” of WLB, in some cases associating the work-family backlash with WLB policies [73].
The COVID-19 pandemic forced many companies around the world to WFH. This affected employees’ WLB, leading to both positive outcomes by increasing organizational productivity [74] and negative impacts, given the numerous challenges experienced by workers [75]. The global pandemic also shed light on how men and women managed change, showing their different strategies for dividing their time between work and family in WFH situations [76,77] and trying to avoid gender inequalities in the distribution of duties and responsibilities [78]. Employees, even the youngest and most accustomed to technology, were forced to develop new skills to allow smart working, sometimes without strong support from e-training and e-leadership programs [79].
WLB has also been addressed with reference to the banking sector. Recent studies have dealt with WLB in commercial banks [80,81], some focusing on public sector banks [82] or gender issues [83,84,85]; others have considered the impacts of changes in working systems in the banking sector [86], the impacts of psychological well-being and satisfaction with coworkers on the WLB/JP relationship [18], and the role of employee assistance and leave programs [87].

2.3. Job Performance

Job performance (JP) can be defined as the “total expected value to the organization of the discrete behavioral episodes that an individual carries out over a standard period of time” [88] (p. 39). The state-of-the-art research on JP research highlights a proliferation of theoretical and empirical studies on JP predictors, including: employees’ ability, especially in entry-level jobs [89]; psychological well-being and job satisfaction [90]; employee engagement [91,92]; work stressors and coworker support [93]; ability and non-ability [94]; emotional and cognitive intelligence [95,96]; and cognitive reflection [97].
The relationship between JP and the age of employees has been widely discussed in the literature. Some authors have compared older employees’ performance with younger employees [98], while others have considered the relationship between employee age and JP, distinguishing professionals and non-professionals [99]. Scholars have also addressed the relationship between workers’ age and JP, including some of the ten JP dimensions, such as organizational citizenship behaviors, safety performance, general counterproductive work behaviors, and aggression in the workplace [100]. Another part of the JP literature has explored the mediating effect of resistance to change on the relationship between age and JP [101]. The relationship between JP and gender has also been analyzed in studies that found gender-based differences in performance [102] and differences in gender proportionality [103].
The COVID-19 pandemic affected the JP of employees in organizations from different sectors, particularly during WFH [104,105]. Team interactions during the pandemic period, both internal and external to the organization, were investigated in one study [106], as was the association between perceived stress, psychological distress, and job performance [107].
The JP of bank employees has also been addressed in the literature. The effects on JP of several variables, such as organizational commitment [108], training and work experience [109], and nonwork and personal resources [110], have been analyzed. Recently, the JP of bank employees has been linked with competitive intelligence [111], work-family conflicts [112], and emotional intelligence [113].

3. Theoretical Background and Hypotheses Development

3.1. Theoretical Foundation of the Study

According to other studies in the literature [114,115,116,117], the theoretical background of the present study comes from McClelland’s Theory of Motivational Needs [118] and the work-life spillover theory [119]. The Theory of Motivational Needs states that unmet needs can affect individuals’ behaviors, including their commitment to work and their JP; furthermore, the need for affiliation leads to building relationships between individuals, even outside the workplace: full satisfaction with life can only be achieved through the balanced satisfaction of the needs of both work and private life [114,115,116,117]. Spillover theory postulates that a domain exerts a spillover effect on other domains and that spillover can be horizontal, vertical, or ubiquitous based on the relationship between domains [114,117]. Regardless of its type, spillover can have positive or negative effects depending on the outcomes of individuals [120]. If employees can benefit from effective WLB policies, it may have a positive spillover effect on their JP. Improper knowledge management, especially of technological knowledge, can expose an organization to KRT [17]. If employees apply obsolete technologies when they WFH, they may have to extend their working hours by taking time away from their private lives while still not even maintaining high-quality JP (negative spillover effect). Considering need theory and spillover theory, we postulated that WLB affects JP and that technological knowledge risks would moderate this relationship.

3.2. Hypotheses Development

In recent years, scholars have dealt with WLB in an organizational context, underlining the crucial role played by WLB policies and practices in improving work environments through the reduction in work-family conflicts [121]. Several studies have demonstrated the multiple benefits brought about by a good WLB for both employees, in terms of JP improvement, and for organizations, in terms of a reduction in the costs related to excessive turnover, absenteeism, and loyalty issues [122,123,124,125]. This debate led us to the following hypothesis:
Hypothesis 1 (H1).
Work-life balance is positively related to employees’ job performance.
Despite the several studies that have focused on the WLB/JP relationship, the debate regarding the functioning of the mechanisms that explain this relationship is not yet sufficiently developed [18]. To this end, introducing moderation or mediation variables for the WLB/JP relationship into its analysis could be useful. Bae [126] considered the moderating effect of job insecurity on the WLB/JP relationship, while Mendis and Weerakkody [127] proposed a mediation model to explain the WLB/JP relationship within the telecommunication industry considering the mediating role of employees’ job satisfaction. Medina and Prieto [128] compared US and India WLB programs to determine the moderating effects of the value of such programs on the relationships between perceived organizational support and JP, affective commitment, deviant workplace behavior, turnover intention, and fatigue level. Another study related to the private sector of emerging markets examined the influence of WLB, person-job fit, and job satisfaction on organizational commitment, revealing the mediating role of job satisfaction on work conditions [129]. In another study, the effects of some stress factors, such as organizational structure, organizational leadership, and job security, on WLB were tested [130], while Alfanza [131] analyzed the relationship between telecommuting intensity and employees’ JP and WLB. The impacts of techno-stressors on WLB were also addressed, examining the role of job self-efficacy as a buffer for these negative impacts [132]. Still considering the topic of technology at work, some scholars have highlighted the importance of specific formal training and skills in innovative sectors in improving work security [133,134].
These findings have led us to the following hypothesis regarding the role of KRT in moderating the WLB/JP relationship:
Hypothesis 2a
(H2a). The positive impact of WLB on JP is higher when KRTD is high (positive moderation).
Hypothesis 2b
(H2b). The positive impact of WLB on JP is higher when KRTO is high (positive moderation).
Hypothesis 2c
(H2c). The positive impact of WLB on JP is higher when KRTC is high (positive moderation).
Figure 1 shows the research model of the present study, schematically representing the hypotheses formulated. According to the model, the independent variable is WLB, the dependent variable is JP, and moderating variable is KRT, respectively specified in KRTD, KRTO and KRTC.

4. Methodology

4.1. Questionnaire and Measurements

Based on the research model illustrated, a structured five-point scale questionnaire [135] was developed to collect data from the employees of Italian CCBs. The final form of the questionnaire included two main sections. The first one comprised questions on the demographic features of respondents, including their gender, education, contractual condition of work, organizational position, and their work from home status (smart working). The second section (Appendix A) included measures of five constructs, our choice of constructs and the items describing these constructs were made based on previous studies. For example, WLB was measured using 8 items adapted from [136,137,138,139]; some sample items were “Work together with family responsibilities make me a more complete person” and “Find time for hobbies or to keep friendships and family relationships is difficult”. JP was measured using 3 items adapted from [140], and some sample items were as follows: “Compared to other employees doing similar work, how productive are you?”. Moreover, KRT was measured using 8 items adapted from [17]—in particular, 3 items were used to measure KRTD, 3 were used to measure KRTO, and 2 were used to measure KRTC. Some sample items included “Working from home makes me feel more competent in technology management” and “Working from home made me think about the possibility of equipping my home with the most modern technology”.
The survey form was reviewed by four professors specializing in the Italian banking sector to check the validity of the content before it was administered to participants.

4.2. Sampling and Data Gathering

Employees of CCBs in Italy were the target population of this study. Consequently, convenience sampling was used, considering that members of the target population were homogeneous and characterized as being employees. In this regard, probabilistic sampling techniques were not conducted in this study because of the large population size and the fact that it is distributed across many geographical regions of Italy. This research employed an online survey to collect data from respondents. Invitations to fill in the survey were sent through emails to the selected CCBs likely to participate in this research. The online survey was distributed in April 2021, and data collection was finished in June 2021. In total, 100 valid responses were obtained. The total population was 131 (from the databases on which the questionnaire was based), with the sample size being 74 units after screening. There was an interval of 95% with a margin of error of 8%—this describes how much we would expect the survey results to reflect the views of the overall population (see Appendix B). To find out the respondents’ profiles, a descriptive analysis was conducted. Table 1 illustrates the demographic features of the respondents, while the descriptive statistics of the responses to the 18 items are reported in Table 2.
From the demographic profiles of the respondents, it was found that 55.4% of the respondents were men, and 44.6% were women; 92.4% of the respondents were working full time, while 7.6% were working part-time. In addition, 64.9% of the respondents worked as employees, and 35.1% worked as managers (executives, senior executives, and directors). Moreover, it was found that, with regard to the qualifications of the respondents, 31.1% were high school graduates, 50.0% had a Bachelor’s degree, and 17.6% had a post-graduate degree. It was also found that 75.7% of the respondents had worked from home for more than 3 days, and only 24.3% of the respondents had worked from home for less than 3 days during the pandemic period.
Table 2 shows the main descriptive statistics of the 18 items investigated. In particular, the mean, the median, the standard deviation, the minimum, and the maximum were reported. Normality and multicollinearity were essential for the data collected; the data were found to meet the standards of normality, and no issue with multicollinearity was found. After these basic preliminary assumptions, data analysis was conducted using two-step approaches—i.e., a measurement model and a structural model—as explained by the authors of [141,142].

4.3. Sampling Procedures of Data Analysis

Considering the sample size and the nature of the causal relationships between the latent variables, in this study, IBM SPSS Statistics v28 and its MACRO Process [143] were used to conduct the entire analysis. IBM SPSS Statistics v28 was used to perform the confirmative factor analysis and calculate Cronbach’s alpha and other indexes. In contrast, the MACRO Process was used to perform moderation analysis, as it allows one to generate indicators, graphs, and specific statistical indexes for use in moderation analyses. The Process template used was Template no. 1 [143].

5. Research Findings

Measurement Model Testing

For the WLB factor, items 1,2, 3, and 6 were eliminated from the analysis as they had very low loadings (<0.4). For the KRTD factor, item 2 was eliminated, as it had a very low loading (<0.4). Table 3 explains the factor loadings of the individual items, Cronbach’s alpha, composite reliability (CR), and the average variance extracted for the variables. As per the values shown in Table 3, it can be seen that the data were valid and reliable. It is recommended by Hair, Hult, Ringle, and Sarstedt [144] that loadings below 0.40 should not be considered for analysis. In Table 3, both Cronbach’s alpha and the composite reliability for the internal consistency of constructs are shown. In addition, when SEM is applied for data analysis, CR is preferred over Cronbach’s alpha. However, the alpha and CR values were above 0.70, indicating internal consistency. Additionally, a CR value above 0.95 is not desirable [144], but the CR values were satisfactory in our study. The alpha values should all be greater than 0.6 to indicate the scale’s reliability: only that of KRTD was lower, but, being 0.59, it was considered acceptable. Therefore, the discriminant validity was tested for further evaluation to ensure that one construct does not correlate with any other construct in the study [145].
The extracted mean-variance (AVE) measures the amount of variance captured by a construct in relation to the amount of variance due to the measurement error. If the AVE is less than 0.50, the variance due to the measurement error will be greater than the variance due to the construct, meaning that the convergent validity of the construct is questionable. In this case, the AVE indices were all greater than 0.5.
The methods widely used in this type of research are considered insufficient; thus, the Heterotrait–Monotrait ratio of correlations (HTMT) was introduced by Henseler, Ringle, and Sarstedt [146]. According to HTMT, these values should be lower than 0.85, and in our construct, which is shown in Table 4, all construct values were below 0.85, except for the HTMT between KRTC and KRTO.
After verifying the reliability and validity of the data collected for the validation of the hypothesis of the present study, the authors performed the normality test by observing asymmetry and kurtosis for each of the obtained factors: from Table 5. It is noted that the values are all between −2 and +2, so the factors can all be considered normally distributed.
Since the sample size was 74 and was thus sufficiently large, it is also possible to use these factors for data analysis using the moderation analysis technique.
In the obtained models, we found that neither the effect of WLB nor the single effects of the three moderators had a significant impact on JP (the p values were all higher than 0.05).
However, the effect of the interaction between WLB and KRTO had a positive (β = 0.247) and significant (p = 0.018) effect on JP. We also found a positive (β = 0.300) and significant (p = 0.010) effect of the interaction between WLB and KRTC on JP (representations of the two interactions are reported in Figure 2 and Figure 3). The effect of the interaction between WLB and KRTD on JP was not significant (p = 0.101). Table 6 elaborates these results. Therefore, the hypotheses H1 and H2a were not verified, while H2b and H2c were verified; the R2 of the model with interaction between WLB and KRTO was 0.105—i.e., 10.5% of JP was explained—while the R2 of the model with interaction between WLB and KRTC was 0.104—i.e., 10.4% of JP was explained.
The results of the hypothesis testing are also presented in Table 6.

6. Discussion

The present study aimed to investigate the influence of KRT in terms of digitalization, old technologies, and cybercrime on moderating the relationship between WLB and JP. To address this goal, a quantitative approach that involved using a survey to gather data from a sample of employees of CCBs in Italy was adopted. Moderation analysis was applied to analyze the collected data and test the research hypotheses. Based on the empirical findings of this paper, the hypotheses H2b and H2c were confirmed.
We found that the effect of WLB on JP was positive but not significant by itself, while its positive strength increased significantly when the respondent’s KRTD score was high or when the respondent’s KRTC score was high: this shows that KRTD and KRTC have a significant and positive moderating effect on the relationship between WLB and JP. We also found that, differently from KRTD and KRTC, KRTD did not have a statistically significant moderating effect on the relationship between WLB and JP.
This means that employees are comfortable with their work and family responsibilities and that their performance’s overall quality and quantity are good. Moreover, they are flexible enough to adapt to any changes, whether from job to job, task to task, or place to place. This study also found that employees have a positive attitude towards their self-assessment and skill development even if they are comfortable with a virtual organization. We also highlighted that employees are fond of technological tools, have knowledge of manufacturing technologies, and are eager to improve their well-being and satisfaction with their co-workers. This is in line with the findings of prior studies, such as that of Putra, Pratama, Linggautama, and Prasetyaningtyas [147], who found a significant relationship between the WLB and JP of bank employees and confirmed that individuals with high levels of JP would have a high level of WLB [18]. This implies that employees can perform negotiations and accomplish their work on time, and spend quality time with their family, even during the pandemic period, when resorting to WFH for more than three days due to lockdown measures [13]. In addition, our findings highlighted KRT as a moderator of the WLB/JP relationship, in terms of KRTO and KRTC, especially in correspondence with WFH, agreeing with studies that have highlighted the presence of a significant relationship between knowledge risks and organizations in their regular operations [14], as well as a significant positive association between JP, KRTO, and KRTC [17]. Hence, to encourage the adoption of WFH, organizations must ensure the maintenance of a productive WLB/JP relationship and, on the other hand, implement actions to prevent and mitigate knowledge risks that could affect employees struggling with technology—i.e., KRTD, KRTO, and KRTC.

7. Conclusions

For the first time, the present study attempts to examine the moderation mechanisms of KRT in terms of KRTD, KRTO, and KRTC on the WLB/JP relationship for small bank employees during the pandemic period. Apart from substantiating the streams of literature focusing on KRT, WLB, and JP, this analysis generally reflects the interaction between performance and work and family responsibilities in the banking sector, developing effective strategies for managing KRT, maintaining productivity, and achieving a WLB, which have emerged as problems during the COVID-19 crisis. In this scenario, the study has further important theoretical implications.

7.1. Theoretical and Practical Implications

The present paper analyzes the relationship between WLB and JP in CCBs, considering the moderating effect of technological knowledge risks—in particular, the risk of knowledge digitalization, the risk associated with old technologies, and the risk of cybercrimes. The potential harmfulness of technological knowledge risks makes their prevention and mitigation a critical issue. Against the underdeveloped state of research addressing the moderating effects of technological knowledge risks on the relationship between WLB and JP, this paper seeks to further develop the current body of knowledge by exploring the role of knowledge risks as a moderating variable. Furthermore, despite several scholars having investigated the WLB/JP relationship in banks, few studies have addressed some variables’ mediating and moderating effects on this relationship [18]. Practically no one has considered the effect of knowledge risks on these variables far.
This paper could also help guide professionals by emphasizing that paying attention to a potentially very dangerous type of risk—e.g., knowledge risk—can improve the performance of employees working from home and applying complex technologies.
This result was supported by previous literature on the JP of bank employees linked to risk dimensions [148] and work-family conflict [149]. Consequently, organizations should focus on investing more in managing knowledge risks, which would ultimately benefit the organization [43], even in the banking sector.
This study provides important guidelines for promoting remote working in organizations. It shows that employees should more frequently exhibit empowering behaviors in remote work than in the workplace, emphasizing the exchange of knowledge and information with their colleagues and managers.
This research shows how employees are inclined to collaborate with their colleagues and hence share their knowledge; participative decision making in this regard would also have far-reaching effects on individual and organizational performance. Amongst employees’ behaviors, in remote work, removing barriers leads to the best performance, showing how the lack of WLB in an organization can also be a barrier to better performance. Productivity relies on workers being able to focus at the appropriate time to recover from work-related stress and maintain a healthy work-life balance.
From a macro perspective, the banking sector should train managers on how to manage KRT—in particular, old technologies and cybercrime—which implies that managers have to pay adequate attention to IT infrastructure and integration with human resources management in response to these challenges in the future.

7.2. Limitations and Future Research Trends

There are some limitations to the present study. The first limitation is related to the methodology used. This paper examined the influence of KRT in terms of digitalization, old technologies, and cybercrime as a factor moderating the relationship between WLB and JP. Thus, future research should test this link among other organizations belonging to the banking and financial sectors (e.g., significant banks and other companies carrying out financial intermediation activities). Secondly, this research employed an online survey as a data collection tool to gather primary data from a relevant population. Therefore, other data collection instruments (e.g., self-administered questionnaires, semi-structured interviews, face-to-face interviews) can be used to achieve this purpose. Thirdly, the current paper focused on the employees of CCBs in Italy. A comparative study is also suggested to provide clear and solid findings and implications. Moreover, the research model can be investigated in another country with a similar culture and conditions (e.g., a Western European country) and/or a nation with a different culture and conditions (e.g., a country from the Middle East, North Africa, Asia, South America, etc.). This could help to provide rigorous results and outline the links between the studied constructs. Lastly, the present study focuses on the perspective of employees of CCBs. As a result, future research should concentrate on employees’ perspectives on other job positions, such as managers, and use different variables and items. Additionally, further studies could be conducted from the industry perspective (e.g., the service industry). One more option could be considering “ethical” behavior as a variable in the study.
Finally, the antecedents of KRT should be assessed in future research to provide solid findings on the critical issues and most crucial factors influencing JP in the banking and finance sectors within different contexts.

Author Contributions

Conceptualization, M.S.B., F.D.V. and M.L.T.; Formal analysis, M.S.B.; Investigation, M.L.T.; Methodology, M.S.B., F.D.V. and M.L.T.; Project administration, M.S.B.; Software, M.A.K.; Supervision, M.S.B. and F.D.V.; Writing—original draft, M.S.B., F.D.V. and M.L.T.; Writing—review & editing, F.D.V. and M.L.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study because it was not required. The data is not considered to be sensitive or confidential in nature. Vulnerable or dependent groups were not included. The subject matter was limited to topics that are strictly within the professional competence of the participants.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Constructs and sources.
Table A1. Constructs and sources.
ConstructsItemsReferences
Work-life balance (WLB)WLB1 Work together with family responsibilities make me a more complete person.[136,137,138,139]
WLB2 Manage the work and family responsibilities as I am doing it makes me feel competent.
WLB3 Work makes me feel good with myself and this is good for my private life.
WLB4 I don’t have a long time to socialize/relax with my partner/see the family during the week.
WLB5 Find time for hobbies or to keep friendships and family relationships is difficult.
WLB6 I often have to take home the job during the weekend.
WLB7 I worry about the effects of stress from work on my health.
WLB8 I would like to reduce working hours and stress levels, but I feel I have no control over the current situation.
Job Performance
(JP)		JP1 Compared to other employees doing similar work, the overall quality and quantity of my performance is good.[140]
JP2 Compared to other employees doing similar work, how productive are you?
JP3 Compared to other employees doing similar work, how well do you anticipate problems that may arise and try to prevent them or minimize their effects?
Technological Knowledge Risks
(KRT)		KRTD
Knowledge risks related to digitization		[17]
Working from home made me neglect relationships with partners /family because I work more hours.
Working from home makes me feel more competent in technology management.
Continuing to work from home 1 or 2 days a week, even after the pandemic, can pose a threat to interpersonal relationships in the company.
KRTO
Knowledge risks related to old technologies
Working from home made me think about the possibility of equipping my home with the most modern technology.
Working from home increases my curiosity about technology even outside of work.
Working from home increases the likelihood of making mistakes related to the use of technology.
KRTC
Knowledge risks related to cybercrime
Working from home exposes the company to risks related to technology.
Working from home does not make me feel safe from possible cyber attacks.

Appendix B

Marginal error calculation.
Margin   of   error = z × σ n
where:
  • z = z-score;
  • n = sample size;
  • σ = population standard deviation.
In this case, z is equal to 1.96 (=95% confident interval), n = 74, and σ = 0.57.

References

  1. Ratten, V. Coronavirus and international business: An entrepreneurial ecosystem perspective. Int. Bus. Rev. 2020, 62, 629–634. [Google Scholar] [CrossRef]
  2. Kuckertz, A.; Brändle, L.; Gaudig, A.; Hinderer, S.; Reyes, C.A.M.; Prochotta, A.; Berger, E.S. Startups in times of crisis: A rapid response to the COVID-19 pandemic. J. Bus. Ventur. Insights 2020, 13, e00169. [Google Scholar] [CrossRef]
  3. Durst, S.; Svensson, A.; Palacios Acuache, M.M.G. Peruvian Small and Medium-Sized Enterprises in Times of Crisis—Or What Is Happening over Time? Sustainability 2021, 13, 13560. [Google Scholar] [CrossRef]
  4. Akkermans, J.; Richardson, J.; Kraimer, M.L. The COVID-19 crisis as a career shock: Implications for careers and vocational behavior. J. Vocat. Behav. 2020, 119, 103434. [Google Scholar] [CrossRef] [PubMed]
  5. Kramer, A.; Kramer, K.Z. The potential impact of the COVID-19 pandemic on occupational status, work from home, and occupational mobility. J. Vocat. Behav. 2020, 119, 103442. [Google Scholar] [CrossRef]
  6. Diab-Bahman, R.; Al-Enzi, A. The impact of COVID-19 pandemic on conventional work settings. Int. J. Sociol. Soc. Policy 2020, 40, 909–927. [Google Scholar] [CrossRef]
  7. Zhang, T.; Gerlowski, D.; Acs, Z. Working from home: Small business performance and the COVID-19 pandemic. Small Bus. Econ. 2022, 58, 611–636. [Google Scholar] [CrossRef]
  8. Islam, M.A.; Igwe, P.A.; Rahman, M.; Saif, A.N.M. Remote working challenges and solutions: Insights from SMEs in Bangladesh during the COVID-19 pandemic. Int. J. Qual. Innov. 2021, 5, 119–140. [Google Scholar] [CrossRef]
  9. Putri, A.; Amran, A. Employees’ Work-Life Balance Reviewed from Work from Home Aspect During COVID-19 Pandemic. Int. J. Manag. Sci. Inf. Technol. 2021, 1, 30–34. [Google Scholar] [CrossRef]
  10. Tušl, M.; Brauchli, R.; Kerksieck, P.; Bauer, G.F. Impact of the COVID-19 crisis on work and private life, mental well-being and self-rated health in German and Swiss employees: A cross-sectional online survey. BMC Public Health 2021, 21, 74. [Google Scholar] [CrossRef]
  11. Kaushik, M.; Guleria, N. The impact of pandemic COVID-19 in workplace. Eur. J. Bus. Manag. 2020, 12, 1–10. [Google Scholar]
  12. Birimoglu Okuyan, C.; Begen, M.A. Working from home during the COVID-19 pandemic, its effects on health, and recommendations: The pandemic and beyond. Perspect. Psychiatr. Care 2022, 58, 173–179. [Google Scholar] [CrossRef] [PubMed]
  13. Zieba, M.; Durst, S.; Gonsiorowska, M. Knowledge Risks in the COVID-19 Pandemic. In Proceedings of the IFKAD 2021: Managing Knowledge in Uncertain Times, Rome, Italy, 1–3 September 2021. [Google Scholar]
  14. Durst, S.; Zieba, M. Mapping knowledge risks: Towards a better understanding of knowledge management. Knowl. Manag. Res. Pract. 2019, 17, 1–13. [Google Scholar] [CrossRef]
  15. Durst, S.; Ferenhof, H.A. Knowledge leakages and ways to reduce them in small and medium-sized enterprises (SMEs). Information 2014, 5, 440–450. [Google Scholar] [CrossRef] [Green Version]
  16. Temel, S.; Durst, S. Knowledge risk prevention strategies for handling new technological innovations in small businesses. VINE J. Inf. Knowl. Manag. Syst. 2020, 51, 655–673. [Google Scholar] [CrossRef]
  17. Zieba, M.; Durst, S.; Gonsiorowska, M.; Zralov, Z. Knowledge Risks in Organizations–Insights from Companies. In European Conference on Knowledge Management; Academic Conferences International Limited: Reading, UK, 2021; pp. 864–873. [Google Scholar]
  18. Haider, S.; Jabeen, S.; Ahmad, J. Moderated mediation between work life balance and employee job performance: The role of psychological wellbeing and satisfaction with coworkers. Rev. De Psicol. Del Trab. Y De Las Organ. 2018, 34, 29–37. [Google Scholar] [CrossRef] [Green Version]
  19. MacKinnon, D.P.; Cheong, J.; Pirlott, A.G. Statistical mediation analysis. In APA Handbook of Research Methods in Psychology, Vol. 2. Research Designs: Quantitative, Qualitative, Neuropsychological, and Biological; Cooper, H., Camic, P.M., Long, D.L., Panter, A.T., Rindskopf, D., Sher, K.J., Eds.; American Psychological Association: Washington, DC, USA, 2012; pp. 313–331. [Google Scholar]
  20. Marsh, H.W.; Hau, K.-T.; Wen, Z.; Nagengast, B.; Morin, A.J.S. Moderation. In The Oxford Handbook of Quantitative Methods: Statistical Analysis; Little, T.D., Ed.; Oxford University Press: Oxford, UK, 2013; pp. 361–386. [Google Scholar]
  21. Baral, R.; Bhargava, S. Work-family enrichment as a mediator between organizational interventions for work-life balance and job outcomes. J. Manag. Psychol. 2010, 25, 274–300. [Google Scholar] [CrossRef]
  22. Bouzari, M.; Karatepe, O.M. Does optimism mediate the influence of work-life balance on hotel salespeople’s life satisfaction and creative performance? J. Hum. Resour. Hosp. Tour. 2020, 19, 82–101. [Google Scholar] [CrossRef]
  23. Sari, R.L.; Seniati, A.N.L. The role of job satisfaction as mediator between work-life balance and organizational commitment among lecturers. Psychol. Educ. 2020, 57, 106–110. [Google Scholar]
  24. Rasheed, K.; Mukhtar, U.; Anwar, S.; Hayat, N. Workplace knowledge hiding among front line employees: Moderation of felt obligation. VINE J. Inf. Knowl. Manag. Syst. 2022, 52, 284–302. [Google Scholar] [CrossRef]
  25. Bratianu, C. A holistic approach to knowledge risk. Manag. Dyn. Knowl. Econ. 2018, 6, 593–607. [Google Scholar] [CrossRef]
  26. Massingham, P. Knowledge risk management: A framework. J. Knowl. Knowl. Manag. 2010, 14, 464–485. [Google Scholar] [CrossRef] [Green Version]
  27. Durst, S.; Henschel, T. Knowledge Risk Management: From Theory to Praxis; Springer: Cham, Switzerland, 2020; Volume 10, pp. 978–983. [Google Scholar]
  28. Durst, S.; Bruns, G.; Henschel, T. The management of knowledge risks: What do we really know? In Global business expansion: Concepts, Methodologies, Tools, and Applications; IGI Global: Hershey, PA, USA, 2018; pp. 258–269. [Google Scholar]
  29. Durst, S.; Zieba, M. Knowledge risks-towards a taxonomy. Int. J. Bus. Environ. 2017, 9, 51–63. [Google Scholar] [CrossRef]
  30. Dodel, M.; Mesch, G. An integrated model for assessing cyber-safety behaviors: How cognitive, socioeconomic and digital determinants affect diverse safety practices. Comput. Secur. 2019, 86, 75–91. [Google Scholar] [CrossRef]
  31. Garay-Rondero, C.L.; Martinez-Flores, J.L.; Smith, N.R.; Morales, S.O.C.; Aldrette-Malacara, A. Digital supply chain model in Industry 4.0. J. Manuf. Technol. Manag. 2020, 31, 887–933. [Google Scholar] [CrossRef]
  32. Allmann, K.; Blank, G. Rethinking digital skills in the era of compulsory computing: Methods, measurement, policy and theory. Inf. Commun. Soc. 2021, 24, 633–648. [Google Scholar] [CrossRef]
  33. Dorschel, R. Reconsidering digital labour: Bringing tech workers into the debate. In New Technology, Work and Employment; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2022. [Google Scholar]
  34. Jamil, S. Evolving Newsrooms and the Second Level of Digital Divide: Implications for Journalistic Practice in Pakistan. J. Pract. 2022, 1–18. [Google Scholar] [CrossRef]
  35. Van Kessel, R.; Wong, B.L.H.; Rubinić, I.; O’Nuallain, E.; Czabanowska, K. Is Europe prepared to go digital? Making the case for developing digital capacity: An exploratory analysis of Eurostat survey data. PLoS Digit. Health 2022, 1, e0000013. [Google Scholar] [CrossRef]
  36. Oyedemi, T.D.; Choung, M. Digital inequality and youth unemployment. Communication 2020, 46, 68–86. [Google Scholar] [CrossRef]
  37. Eichhorn, T.; Jürss, S.; Hoffmann, C.P. Dimensions of digital inequality in the sharing economy. Inf. Commun. Soc. 2022, 25, 395–412. [Google Scholar] [CrossRef]
  38. Carnevale, J.B.; Hatak, I. Employee adjustment and well-being in the era of COVID-19: Implications for human resource management. J. Bus. Res. 2020, 116, 183–187. [Google Scholar] [CrossRef]
  39. Jeske, D. Remote workers’ experiences with electronic monitoring during COVID-19: Implications and recommendations. Int. J. Workplace Health Manag. 2022. [Google Scholar] [CrossRef]
  40. Lee, R.W.; Yip, J.Y.; Shek, V.W. Knowledge Risk and Its Mitigation: Practices and Cases; Emerald Group Publishing: Bingley, UK, 2021. [Google Scholar]
  41. Durst, S.; Lindvall, B.; Bruns, G. Knowledge risk management in the public sector: Insights into a Swedish municipality. J. Knowl. Manag. 2018, 24, 717–735. [Google Scholar] [CrossRef]
  42. Durst, S.; Edvardsson, I.R. Knowledge management in SMEs: A literature review. J. Knowl. Manag. 2012, 16, 879–903. [Google Scholar] [CrossRef]
  43. Durst, S.; Hinteregger, C.; Zieba, M. The linkage between knowledge risk management and organizational performance. J. Bus. Res. 2019, 105, 1–10. [Google Scholar] [CrossRef]
  44. Bratianu, C.; Neştian, A.Ş.; Tiţã, S.M.; Vodã, A.I.; Guţã, A.L. The impact of knowledge risk on sustainability of firms. Amfiteatru Econ. 2020, 22, 639–652. [Google Scholar]
  45. Durst, S.; Zieba, M. Knowledge risks inherent in business sustainability. J. Clean. Prod. 2020, 251, 119670. [Google Scholar] [CrossRef]
  46. Durst, S. How far have we come with the study of knowledge risks? VINE J. Inf. Knowl. Manag. Syst. 2019, 49, 21–34. [Google Scholar] [CrossRef]
  47. La Torre, M. Risk in Banking: Developing a Knowledge Risk Management Framework for Cooperative Credit Banks; Springer: Berlin/Heidelberg, Germany, 2020. [Google Scholar]
  48. Carretta, A.; Fiordelisi, F.; Schwizer, P. Risk Culture in Banking; Springer: Berlin/Heidelberg, Germany, 2017. [Google Scholar]
  49. Brough, P.; Timms, C.; Chan, X.W.; Hawkes, A.; Rasmussen, L. Work–life balance: Definitions, causes, and consequences. In Handbook of Socioeconomic Determinants of Occupational Health: From Macro-Level to Micro-Level Evidence; Springer: Cham, Switzerland, 2020; pp. 473–487. [Google Scholar]
  50. Kelliher, C.; Richardson, J.; Boiarintseva, G. All of work? All of life? Reconceptualising work-life balance for the 21st century. Hum. Resour. Manag. J. 2019, 29, 97–112. [Google Scholar] [CrossRef] [Green Version]
  51. Bertolini, S.; Poggio, B. Introduction to the Research Handbook on Work-Life Balance. In Research Handbook on Work–Life Balance; Edward Elgar Publishing: Cheltenham, UK, 2022. [Google Scholar]
  52. Lewis, S.; Beauregard, T.A. The meanings of work-life balance: A cultural perspective. In The Cambridge Handbook of the Global Work-Family Interface; Cambridge Handbooks in, Psychology; Shockley, K., Shen, W., Johnson, R., Eds.; Cambridge University Press: Cambridge, UK, 2018; pp. 720–732. ISBN 9781108235556. [Google Scholar]
  53. Kalliath, T.; Brough, P. Work–life balance: A review of the meaning of the balance construct. J. Manag. Organ. 2008, 14, 323–327. [Google Scholar] [CrossRef]
  54. Gregory, A.; Milner, S.; Windebank, J. Work-life balance in times of economic crisis and austerity. Int. J. Sociol. Soc. Policy 2013, 33, 528–541. [Google Scholar] [CrossRef] [Green Version]
  55. Swathi, R.; Mohapatra, A. Work-life balance: Evolution and models—A study in the Indian context. Int. J. Sci. Res. 2015, 6, 1910–1914. [Google Scholar]
  56. Bertolini, S.; Musumeci, R. Doing research on work-life balance. In Research Handbook on Work–Life Balance; Edward Elgar Publishing: Cheltenham, UK, 2022. [Google Scholar]
  57. Powell, G.N.; Greenhaus, J.H.; Allen, T.D.; Johnson, R.E. Introduction to special topic forum: Advancing and expanding work-life theory from multiple perspectives. Acad. Manag. Rev. 2019, 44, 54–71. [Google Scholar] [CrossRef]
  58. Chatrakul Na Ayudhya, U.; Prouska, R.; Beauregard, T.A. The impact of global economic crisis and austerity on quality of working life and work-life balance: A capabilities perspective. Eur. Manag. Rev. 2019, 16, 847–862. [Google Scholar] [CrossRef] [Green Version]
  59. Khateeb, F.R. Work Life Balance—A Review of Theories, Definitions and Policies. Cross Cult. Manag. J. 2021, XXIII, 27–55. [Google Scholar]
  60. Muafi, M.; Siswanti, Y.; Anwar, M.Z. Work life balance in Islamic perspective (WLBIP) and its impact on organizational citizenship behavior in islamic perspective (OCBIP) and service performance. Int. J. Res. Bus. Soc. Sci. 2021, 10, 223–230. [Google Scholar] [CrossRef]
  61. Helmle, J.R.; Botero, I.C.; Seibold, D.R. Factors that influence perceptions of work-life balance in owners of copreneurial firms. J. Fam. Bus. Manag. 2014, 4, 110–132. [Google Scholar] [CrossRef]
  62. Braun, S.; Peus, C. Crossover of work–life balance perceptions: Does authentic leadership matter? J. Bus. Ethics 2018, 149, 875–893. [Google Scholar] [CrossRef] [Green Version]
  63. Fujimoto, Y.; Azmat, F.; Härtel, C.E. Gender perceptions of work-life balance: Management implications for full-time employees in Australia. Aust. J. Manag. 2013, 38, 147–170. [Google Scholar] [CrossRef] [Green Version]
  64. Naz, S.; Fazal, S.; Khan, M.I. Perceptions of women academics regarding work–life balance: A Pakistan case. Manag. Educ. 2017, 31, 88–93. [Google Scholar] [CrossRef]
  65. Matulevicius, S.A.; Kho, K.A.; Reisch, J.; Yin, H. Academic medicine faculty perceptions of work-life balance before and since the COVID-19 pandemic. JAMA Netw. Open 2021, 4, e2113539. [Google Scholar] [CrossRef] [PubMed]
  66. Lekchiri, S.; Eversole, B.A. Perceived work-life balance: Exploring the experiences of professional Moroccan women. Hum. Resour. Dev. Q. 2021, 32, 35–53. [Google Scholar] [CrossRef]
  67. Rashmi, K.; Kataria, A. Work–life balance: A systematic literature review and bibliometric analysis. Int. J. Sociol. Soc. Policy 2021. [Google Scholar] [CrossRef]
  68. Sirgy, M.J.; Lee, D.J. Work-life balance: An integrative review. Appl. Res. Qual. Life 2018, 13, 229–254. [Google Scholar] [CrossRef]
  69. Wood, J.; Oh, J.; Park, J.; Kim, W. The relationship between work engagement and work–life balance in organizations: A review of the empirical research. Hum. Resour. Dev. Rev. 2020, 19, 240–262. [Google Scholar] [CrossRef]
  70. Leitão, J.; Pereira, D.; Gonçalves, Â. Quality of work life and organizational performance: Workers’ feelings of contributing, or not, to the organization’s productivity. Int. J. Environ. Res. Public Health 2019, 16, 3803. [Google Scholar] [CrossRef] [Green Version]
  71. Chaudhuri, S.; Arora, R.; Roy, P. Work–Life balance policies and organisational outcomes–a review of literature from the Indian context. Ind. Commer. Train. 2020, 52, 155–170. [Google Scholar] [CrossRef]
  72. Rothbard, N.P.; Beetz, A.M.; Harari, D. Balancing the scales: A configurational approach to work-life balance. Annu. Rev. Organ. Psychol. Organ. Behav. 2021, 8, 73–103. [Google Scholar] [CrossRef]
  73. Perrigino, M.B.; Dunford, B.B.; Wilson, K.S. Work–family backlash: The “dark side” of work–life balance (WLB) policies. Acad. Manag. Ann. 2018, 12, 600–630. [Google Scholar] [CrossRef] [Green Version]
  74. Irawanto, D.W.; Novianti, K.R.; Roz, K. Work from home: Measuring satisfaction between work–life balance and work stress during the COVID-19 pandemic in Indonesia. Economies 2021, 9, 96. [Google Scholar] [CrossRef]
  75. Palumbo, R. Let me go to the office! An investigation into the side effects of working from home on work-life balance. Int. J. Public Sect. Manag. 2020, 33, 771–790. [Google Scholar] [CrossRef]
  76. Putranti, H.R.D.; Suparmi, S.; Susilo, A. Work Life Balance (WLB) Complexity and Performance of Employees during COVID-19 Pandemic. Arthatama 2020, 4, 56–68. [Google Scholar]
  77. Rodríguez-Rivero, R.; Yáñez, S.; Fernández-Aller, C.; Carrasco-Gallego, R. Is it time for a revolution in work–life balance? Reflections from Spain. Sustainability 2020, 12, 9563. [Google Scholar] [CrossRef]
  78. Hjálmsdóttir, A.; Bjarnadóttir, V.S. “I have turned into a foreman here at home”: Families and work–life balance in times of COVID-19 in a gender equality paradise. Gend. Work Organ. 2021, 28, 268–283. [Google Scholar] [CrossRef]
  79. Wiradendi Wolor, C.; Solikhah, S.; Fidhyallah, N.F.; Lestari, D.P. Effectiveness of e-training, e-leadership, and work life balance on employee performance during COVID-19. J. Asian Financ. Econ. Bus. 2020, 7, 443–450. [Google Scholar] [CrossRef]
  80. Oludayo, O.A.; Falola, H.O.; Obianuju, A.; Demilade, F. Work-life balance initiative as predictor of employees’ behavioural outcomes. Acad. Strateg. Manag. J. 2018, 17, 1–17. [Google Scholar]
  81. Pathak, R.R. Work life balance in Nepalese commercial banks. J. Bus. Soc. Sci. 2018, 2, 116–125. [Google Scholar] [CrossRef]
  82. Lenka, S.; Subudhi, R. Work Life Balance, Job Satisfaction and Organizational Commitment: A Study on Bank Employees Bhubaneswar, Odisha. Int. J. Res. Anal. Rev. 2019, 6. Available online: https://ssrn.com/abstract=3352463 (accessed on 11 March 2022).
  83. Ratnesh, M.; Sinha, A.R.; Ali, A. Antecedents and consequences of work life balance among women bank employees: A study on selected individual level factors. JIMS8M J. Indian Manag. Strategy 2019, 24, 12–20. [Google Scholar] [CrossRef]
  84. Uddin, M.; Ali, K.B.; Khan, M.A.; Ahmad, A. Supervisory and co-worker support on the work-life balance of working women in the banking sector: A developing country perspective. J. Fam. Stud. 2021, 1–21. [Google Scholar] [CrossRef]
  85. Fuadiputra, I.R.; Novianti, K.R. The Effect of Work Autonomy and Workload on Job Satisfaction of Female Workers in the Banking Sector: Mediating the Role of Work Life Balance. Winners 2020, 21, 85–91. [Google Scholar] [CrossRef]
  86. Samson, G.N.; Waiganjo, M.; Koima, J. Effect of workplace environment on the performance of commercial banks employees in Nakuru town. Int. J. Managerial Stud. Res. 2015, 3, 76–89. [Google Scholar]
  87. Ogar, C.A.; Amanze, D. Work-Life-Balance: The Nigerian Organizational Experience (A Study of Selected Banks in Ebonyi State). Int. J. Res. Innov. Soc. Sci. 2019, 3, 145–157. [Google Scholar]
  88. Motowidlo, S.J. Job performance. Handb. Psychol. Ind. Organ. Psychol. 2003, 12, 39–53. [Google Scholar]
  89. Hunter, J.E.; Hunter, R.F. Validity and utility of alternative predictors of job performance. Psychol. Bull. 1984, 96, 72. [Google Scholar] [CrossRef]
  90. Wright, T.A.; Cropanzano, R. Psychological well-being and job satisfaction as predictors of job performance. J. Occup. Health Psychol. 2000, 5, 84. [Google Scholar] [CrossRef]
  91. Keller, R.T. Job involvement and organizational commitment as longitudinal predictors of job performance: A study of scientists and engineers. J. Appl. Psychol. 1997, 82, 539. [Google Scholar] [CrossRef]
  92. Dalal, R.S.; Baysinger, M.; Brummel, B.J.; LeBreton, J.M. The relative importance of employee engagement, other job attitudes, and trait affect as predictors of job performance. J. Appl. Soc. Psychol. 2012, 42, E295–E325. [Google Scholar] [CrossRef]
  93. Beehr, T.A.; Jex, S.M.; Stacy, B.A.; Murray, M.A. Work stressors and coworker support as predictors of individual strain and job performance. J. Organ. Behav. 2000, 21, 391–405. [Google Scholar] [CrossRef]
  94. Kanfer, R.; Kantrowitz, T.M. Ability and non-ability predictors of job performance. Psychol. Manag. Individ. Perform. 2002, 2, 27. [Google Scholar]
  95. Dhani, P.; Sharma, T. Emotional intelligence and personality traits as predictors of job performance of it employees. Int. J. Hum. Cap. Inf. Technol. Prof. 2018, 9, 70–83. [Google Scholar] [CrossRef]
  96. Dhliwayo, P.; Coetzee, M. Cognitive intelligence, emotional intelligence and personality types as predictors of job performance: Exploring a model for personnel selection. SA J. Hum. Resour. Manag. 2020, 18, 13. [Google Scholar] [CrossRef]
  97. Salgado, J.F.; Otero, I.; Moscoso, S. Cognitive reflection and general mental ability as predictors of job performance. Sustainability 2019, 11, 6498. [Google Scholar] [CrossRef] [Green Version]
  98. Man, G.M.; Man, M. Competence of older employees. Sci. Bull. 2018, 23, 89–93. [Google Scholar] [CrossRef] [Green Version]
  99. Waldman, D.A.; Avolio, B.J. A meta-analysis of age differences in job performance. J. Appl. Psychol. 1986, 71, 33. [Google Scholar] [CrossRef]
  100. Ng, T.W.; Feldman, D.C. The relationship of age to ten dimensions of job performance. J. Appl. Psychol. 2008, 93, 392. [Google Scholar] [CrossRef] [PubMed]
  101. Kunze, F.; Boehm, S.; Bruch, H. Age, resistance to change, and job performance. J. Manag. Psychol. 2013, 28, 741–760. [Google Scholar] [CrossRef]
  102. Green, C.; Jegadeesh, N.; Tang, Y. Gender and job performance: Evidence from Wall Street. Financ. Anal. J. 2009, 65, 65–78. [Google Scholar] [CrossRef] [Green Version]
  103. Mackey, J.D.; Roth, P.L.; Van Iddekinge, C.H.; McFarland, L.A. A meta-analysis of gender proportionality effects on job performance. Group Organ. Manag. 2019, 44, 578–610. [Google Scholar] [CrossRef]
  104. Susilo, D. Revealing the effect of work-from-home on job performance during the COVID-19 crisis: Empirical evidence from Indonesia. J. Contemp. Issues Bus. Gov. 2020, 26, 23–40. [Google Scholar]
  105. Troll, E.S.; Venz, L.; Weitzenegger, F.; Loschelder, D.D. Working from home during the COVID-19 crisis: How self-control strategies elucidate employees’ job performance. Appl. Psychol. 2021. [Google Scholar] [CrossRef] [PubMed]
  106. Wu, Y.J.; Antone, B.; Srinivas, A.; DeChurch, L.; Contractor, N. Teamwork in the time of COVID-19: Creating, dissolving, and reactivating network ties in response to a crisis. J. Appl. Psychol. 2021, 106, 1483–1492. [Google Scholar] [CrossRef] [PubMed]
  107. Meunier, S.; Bouchard, L.; Coulombe, S.; Doucerain, M.; Pacheco, T.; Auger, E. The Association between Perceived Stress, Psychological Distress, and Job Performance During the COVID-19 Pandemic: The Buffering Role of Health-Promoting Management Practices. Trends Psychol. 2022, 1–21. [Google Scholar] [CrossRef]
  108. Lovely, S.N.; Afzal, M.K.; Alam, Z. Impact of job satisfaction and organizational commitment on job performance of private bank employees in Bangladesh. USV Ann. Econ. Public Adm. 2019, 19, 112–123. [Google Scholar]
  109. Muntazeri, S.; Indrayanto, A. The impact of education, training and work experience on job satisfaction and job performance. J. Akunt. Manaj. Dan Ekon. 2018, 20, 50–69. [Google Scholar]
  110. Karatepe, O.M.; Ozturk, A.; Kim, T.T. The effects of nonwork and personal resources on frontline bank employees’ work engagement and critical job outcomes. Int. J. Bank Mark. 2019, 37, 858–879. [Google Scholar] [CrossRef]
  111. Hanif, N.; Arshed, N.; Farid, H. Competitive intelligence process and strategic performance of banking sector in Pakistan. Int. J. Bus. Inf. Syst. 2022, 39, 52–75. [Google Scholar] [CrossRef]
  112. Riaz, M.K.; Jamal, W.; Latif, K.F. Moderating role of intrinsic motivation on the relationship of work family conflict and job performance in frontline officers of Islamic banks. Glob. Bus. Econ. Rev. 2019, 11, 161–179. [Google Scholar] [CrossRef]
  113. Kumari, P.; Priya, B. Impact of emotional intelligence on job performance and organizational commitment among bank managers. Int. J. Interdiscip. Multidiscip. Stud. 2017, 4, 300–311. [Google Scholar]
  114. Aruldoss, A.; Kowalski, K.; Parayitam, S. The relationship between quality of work life and work-life-balance mediating role of job stress, job satisfaction and job commitment: Evidence from India. J. Adv. Manag. Res. 2021, 18, 36–62. [Google Scholar] [CrossRef]
  115. Smith, K.T. Work-life balance perspectives of marketing professionals in generation Y. Serv. Mark. Q. 2010, 31, 434–447. [Google Scholar] [CrossRef]
  116. Arnolds, C.A.; Boshoff, C. The influence of McClelland’s need satisfaction theory on employee job performance: A causal study. J. Afr. Bus. 2003, 4, 55–81. [Google Scholar] [CrossRef]
  117. Bell, A.S.; Rajendran, D.; Theiler, S. Job stress, wellbeing, work-life balance and work-life conflict among Australian academics. E-J. Appl. Psychol. 2012, 8, 25–37. [Google Scholar] [CrossRef]
  118. McClelland, D.C. The Achieving Society; The Free Press: New York, NY, USA, 1961. [Google Scholar]
  119. Zedeck, S. Exploring the domain of work and family concerns. In Work, Families and Organizations; Zedeck, S., Ed.; Jossey-Bass Publishers: San Francisco, CA, USA, 1992. [Google Scholar]
  120. Dén-Nagy, I. A double-edged sword?: A critical evaluation of the mobile phone in creating work–life balance. New Technol. Work Employ. 2014, 29, 193–211. [Google Scholar] [CrossRef]
  121. Dousin, O.; Collins, N.; Kler, B.K. Work-life balance, employee job performance and satisfaction among doctors and nurses in Malaysia. Int. J. Hum. Resour. Stud. 2019, 9, 306–319. [Google Scholar] [CrossRef] [Green Version]
  122. Chimote, N.K.; Srivastava, V.N. Work-life balance benefits: From the perspective of organizations and employees. IUP J. Manag. Res. 2013, 12, 62. [Google Scholar]
  123. Amin, M.; Malik, M.S. Impact of work-life balance on employees’ performance in Pakistani context. Int. J. Manag. Organ. Stud. 2017, 6, 1–13. [Google Scholar]
  124. Johari, J.; Tan, F.Y.; Zulkarnain, Z.I.T. Autonomy, workload, work-life balance and job performance among teachers. Int. J. Educ. Manag. 2018, 32, 107–120. [Google Scholar] [CrossRef]
  125. Adnan Bataineh, K. Impact of work-life balance, happiness at work, on employee performance. Int. Bus. Res. 2019, 12, 99–112. [Google Scholar] [CrossRef]
  126. Bae, H.Y. The Effects of Work-Life Balance on Job Performance: Moderating Effects of Job Insecurity. J. Bus. Educ. 2015, 29, 329–349. [Google Scholar]
  127. Mendis, M.D.V.S.; Weerakkody, W.A.S. The impact of work life balance on employee performance with reference to telecommunication industry in Sri Lanka: A mediation model. Kelaniya J. Hum. Resour. Manag. 2017, 12, 72–100. [Google Scholar] [CrossRef] [Green Version]
  128. Medina, E.; Prieto, L. Moderating Effects of Work-Life Balance Programs’ Perceived Value on Relationships between Organizational Support and Employee Outcomes. J. Manag. Issues 2022, 34, 61–81. [Google Scholar]
  129. Hasan, T.; Jawaad, M.; Butt, I. The Influence of Person–Job Fit, Work–Life Balance, and Work Conditions on Organizational Commitment: Investigating the Mediation of Job Satisfaction in the Private Sector of the Emerging Market. Sustainability 2021, 13, 6622. [Google Scholar] [CrossRef]
  130. Mughal, F.; Ahmad, M.; Gondal, A.M.; Awan, T.M.; Chaudhry, I.G. Repercussions of stressors on employee work-life balance: Analysing the banking industry of Pakistan. J. Glob. Bus. Adv. 2010, 3, 325–347. [Google Scholar] [CrossRef]
  131. Alfanza, M.T. Telecommuting Intensity in the Context of COVID-19 Pandemic: Job Performance and Work-Life Balance. Econ. Bus. 2021, 35, 107–116. [Google Scholar] [CrossRef]
  132. Ma, J.; Ollier-Malaterre, A.; Lu, C.Q. The impact of techno-stressors on work–life balance: The moderation of job self-efficacy and the mediation of emotional exhaustion. Comput. Hum. Behav. 2021, 122, 106811. [Google Scholar] [CrossRef]
  133. Lewandowska, A.; Stopa, M. The impact of innovative professional qualifications on the sense of employment security: Evidence from Poland. Econ. Sociol. 2020, 13, 72–83. [Google Scholar] [CrossRef]
  134. Ratna, R.; Tanpreet, K. The impact of information technology on job related factors like health and safety, job satisfaction, performance, productivity and work life balance. Int. J. Manag. Prudence 2016, 7, 45–57. [Google Scholar]
  135. Saunders, M.; Lewis, P.; Thornhill, A. Research Methods for Business Students; Pearson Education: London, UK, 2009. [Google Scholar]
  136. Dex, S.; Bond, S. Measuring work-life balance and its covariates. Work Employ. Soc. 2005, 19, 627–637. [Google Scholar] [CrossRef]
  137. Daniels, L.; McCarraher, L. The Work–Life Manual; Industrial Society: London, UK, 2000. [Google Scholar]
  138. Marshall, N.L.; Barnett, R.C. Work-family strains and gains among two-earner couples. J. Community Psychol. 1993, 21, 64–78. [Google Scholar] [CrossRef]
  139. Losoncz, I.; Bortolotto, N. Work-life balance: The experiences of Australian working mothers. J. Fam. Stud. 2009, 15, 122–138. [Google Scholar] [CrossRef]
  140. Ahmad, H.; Ahmad, K.; Shah, I.A. Relationship between job satisfaction, job performance attitude towards work and organizational commitment. Eur. J. Soc. Sci. 2010, 18, 257–267. [Google Scholar]
  141. Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The use of partial least squares path modeling in international marketing. In New Challenges to International Marketing; Advances in International Marketing; Sinkovics, R.R., Ghauri, P.N., Eds.; Emerald Group Publishing Limited: Bingley, UK, 2009; Volume 20, pp. 277–319. [Google Scholar] [CrossRef]
  142. Arshad, I.; Ibrahim, Y. Uncertainty avoidance, risk avoidance and perceived risk: A cultural perspective of individual investors. Hasanuddin Econ. Bus. Rev. 2019, 3, 21–33. [Google Scholar] [CrossRef]
  143. Hayes, A.F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, 2nd ed.; The Guilford Press: New York, NY, USA, 2017. [Google Scholar]
  144. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M.; Thiele, K.O. Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. J. Acad. Mark. Sci. 2017, 45, 616–632. [Google Scholar] [CrossRef]
  145. Ringle, C.M.; Sarstedt, M.; Mooi, E.A. Response-Based Segmentation Using Finite Mixture Partial Least Squares. In Data Mining. Annals of Information Systems; Stahlbock, R., Crone, S., Lessmann, S., Eds.; Springer: Boston, MA, USA, 2010; Volume 8. [Google Scholar] [CrossRef]
  146. Henseler, J.; Ringle, C.M.; Sarstedt, M. Testing measurement invariance of composites using partial least squares. Int. Mark. Rev. 2016, 33, 115–135. [Google Scholar] [CrossRef]
  147. Putra, K.C.; Pratama, T.A.; Linggautama, R.A.; Prasetyaningtyas, S.W. The Impact of Flexible Working Hours, Remote Working, and Work Life Balance to Employee Satisfaction in Banking Industry during COVID-19 Pandemic Period. J. Bus. Manag. Rev. 2020, 1, 341–353. [Google Scholar] [CrossRef]
  148. Barakchi, A.; Beheshtifar, M.; Moghadam, M.N. Investigating the relationship between competitive intelligence and risk dimensions and job performance of employees in Kerman Saderat bank branches. J. Contemp. Issues Bus. Gov. 2021, 27, 6. [Google Scholar]
  149. Tewal, B.; Lengkong, V.P.; Pandowo, M.; Nelwan, O.S. The Effect of Work-Family Conflict on Job Satisfactionand Job Performance of Banking Employee in North Sulawesi. Am. J. Humanit. Soc. Sci. Res. 2021, 5, 24–32. [Google Scholar]
Sustainability 14 05416 g001 550
Figure 1. The research model.
Figure 1. The research model.
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Figure 2. KRTO moderating effect.
Figure 2. KRTO moderating effect.
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Figure 3. KRTC moderating effect.
Figure 3. KRTC moderating effect.
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Table
Table 1. Profiles of the respondents.
Table 1. Profiles of the respondents.
CharacteristicsCategoryFrequencies(%)
GenderMale4155.4
Female3344.6
EducationHigh school graduate2331.1
Bachelor’s degree3750.0
Post-graduate degree1317.6
Missing11.4
Smart working
During pandemic period		More than 3 days5675.7
Less than 3 days1824.3
Work statusFull time6993.2
Part time56.8
Work positionEmployee4864.9
Manager2635.1
Total 74100
Table
Table 2. Item statistics.
Table 2. Item statistics.
ItemsMeanMedianStandard DeviationMin.Max.
WLB13.8841.00615
WLB23.5041.13815
WLB34.2240.86415
WLB43.5141.18515
WLB53.7041.21315
WLB62.4721.29515
WLB72.9231.37215
JP13.6040.68225
JP23.6440.76925
JP33.7640.71825
KRTD12.772.501.34025
KRTD22.8431.06015
KRTD32.0921.24115
KRTO13.3941.31215
KRTO22.8631.21715
KRTO32.1121.20015
KRTC12.5521.29115
KRTC22.2821.22215
Table
Table 3. Measures of constructs reliability and convergent validity.
Table 3. Measures of constructs reliability and convergent validity.
Construct/ItemsLoadingsCRαAVEs
Work-Life Balance (WLB)0.860.750.68
WLB40.87
WLB50.88
WLB70.71
Job Performance (JP)0.860.76068
JP10.87
JP20.88
JP30.72
Knowledge Risks Related to Digitalization (KRTD)0.830.590.58
KRTD10.84
KRTD30.84
Knowledge Risks Related to Old Technologies (KRTO)0.810.620.65
KRTO10.73
KRTO20.80
KRTO30.76
Knowledge Risks Related to Cybercrime (KRTC)0.910.810.84
KRTC10.92
KRTC20.92
CR = Composite reliability; α = Cronbach’s alpha.
Table
Table 4. Heterotrait–Monotrait ratio (HTMT).
Table 4. Heterotrait–Monotrait ratio (HTMT).
Constructs1. JP2. KRTC3. KRTO4. KRTD5. WLB
1. JP
2. KRTC0.13
3. KRTO0.210.98
4. KRTD−0.030.640.57
5. WLB0.130.18−0.30−0.54
Table
Table 5. Test of normality.
Table 5. Test of normality.
FactorAsymmetryKurtosis
WLB−0.340−0.806
JP0.5720.750
KRTD0.736−0.147
KRTO−0.016−0.028
KRTC0.382−0.715
Table
Table 6. The assessment of the structural models.
Table 6. The assessment of the structural models.
VariableβSEtp Value95% CI
WLB0.0210.1270.1620.872[−0.232; 0.273]
KRTD−0.0480.127−0.0380.705[−0.302; 0.205]
WLB * KRTD0.1890.1131.6630.101[−0.038; 0.415]
WLB0.0710.1200.5920.556[−0.169; 0.312]
KRTO0.1590.1211.3180.192[−0.082; 0.400]
WLB * KRTO0.2470.1022.4200.018 *[0.043; 0.450]
WLB0.0550.1180.4630.645[−0.181; 0.290]
KRTC0.0730.1180.6200.537[−0.163; 0.309]
WLB * KRTC0.3000.1142.6400.010 *[0.073; 0.527]
Dependent variable: JP; * p < 0.05.
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Borgia, M.S.; Di Virgilio, F.; La Torre, M.; Khan, M.A. Relationship between Work-Life Balance and Job Performance Moderated by Knowledge Risks: Are Bank Employees Ready? Sustainability 2022, 14, 5416. https://doi.org/10.3390/su14095416

AMA Style

Borgia MS, Di Virgilio F, La Torre M, Khan MA. Relationship between Work-Life Balance and Job Performance Moderated by Knowledge Risks: Are Bank Employees Ready? Sustainability. 2022; 14(9):5416. https://doi.org/10.3390/su14095416

Chicago/Turabian Style

Borgia, Michele Samuele, Francesca Di Virgilio, Maura La Torre, and Muhammad Adnan Khan. 2022. "Relationship between Work-Life Balance and Job Performance Moderated by Knowledge Risks: Are Bank Employees Ready?" Sustainability 14, no. 9: 5416. https://doi.org/10.3390/su14095416

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