Job Performance in Digital Work: Do Personality Traits Matter?

ABSTRACT

This paper investigates whether personality traits matter in job performance in digital work. A conceptual model is developed within the background of the big five personality traits theory and the boundary theory. This model is then tested and validated using structural equation modeling of the survey data in Australia. The study shows that agreeableness, conscientiousness, and extraversion significantly influence job performance while neuroticism and conscientiousness have significant influence on work-life balance. It finds that individuals’ attitude toward digital work negatively moderates the influence of agreeableness on work-life balance and the impact of conscientiousness on job performance. The study reveals that work-life balance has a significant and direct influence on job performance. This study extends existing research on the relationship between job performance, work-life balance, and personality traits and enhances the knowledge of the interplay between digital technologies and individuals in digital work.

KEYWORDS:

• Personality traits
• work-life balance
• job performance
• digital work
• boundary theory

Introduction

The rapid advancement of digital technologies¹ and the evolving nature of the modern workforce² have created a digitalized working environment in which individuals utilize technologies to deliver working commitments in remote locations.^3–5 The changing organizational strategy for rearranging working activities in the new “normal” working environment further accelerates the adoption of digital work.⁶ As a result, individuals must navigate through specific digital technologies with energized or frustrated feelings to perform their tasks. In this process, the personality of individuals and its interplay with digital technologies are critical in determining their job performance.^7,8

Personality traits are the tendencies of individuals to behave in similar ways across various settings.⁹ There are five major personality traits including extroversion, agreeableness, conscientiousness, neuroticism, and openness that reflect individuals’ emotions, cognition, and behavior patterns in a working environment. Individuals with different personality traits behave differently that directly affects their job performance.^10,11 Better understanding personality traits and their impact, therefore, can help organizations address the negative influence of personality traits such as increased absenteeism, low productivity, drug abuse, alcoholism, hypertension and many more^10,12 and improve individuals’ job performance.¹¹

The increasing use of digital technologies in organizations has a fundamental impact on work-life balance and job performance.^13–15 It has dramatically transformed the traditional workplace. The use of digital technologies provides individuals with the flexibility to decide where, how, and when their work is done. Individuals are becoming increasingly isolated as working commitments are delivered remotely more in an independent manner.¹⁶ The boundary between working and nonworking is becoming increasingly blurred.^6,8 Furthermore, the affordance of digital technologies,² the constraint of such technologies on the behavior of individuals,¹⁵ and the complexity of the interaction between individuals and digital technologies^8,17 are inter-connected that directly affect individuals’ job performance. Better understanding the relationship between job performance, work-life balance, and personality traits in digital work, therefore, is becoming significant.

There are some studies on the relationship between personality traits, work-life balance, and job performance from different perspectives. Rusbadrol et al.,¹⁸ for example, reveal that there is a positive association between openness, agreeableness, and job performance. Johari et al.¹⁹ state that work-life balance is crucial for enhancing job performance. Hung²⁰ shows that personality influences job performance. Zell and Lesick⁷ point out that personality traits are strongly associated with job performance. Duan et al.⁶ find that flexible work practices can enhance work-life balance, leading to better job performance. These studies above have demonstrated that personality traits, work-life balance, and job performance are inter-related.

The adoption of digital work has transformed the traditional way of delivering their specific working commitments by individuals.^2,4 As a result, the use of digital technologies, and the attitude of individuals toward digital technology use which is often affected by their personality traits,^10,11 and the interplay between digital technology use and personality traits are becoming critical for job performance. Despite numerous studies on the relationship between personality traits, work-life balance, and job performance under various circumstances,^6,7,18,19 such studies have not explored such a relationship simultaneously in a comprehensive manner. Furthermore, it is unclear how the interplay between personality traits and digital technology use affects job performance in a digitalized working environment. With the increasing use of digital work, better understanding the interplay between personality traits, work-life balance, and job performance is becoming critical. This study addresses this issue with the research question as follows: how do personality traits influence work-life balance and job performance in digital work?

This study investigates whether personality traits matter in job performance in digital work. A comprehensive review of the related literature leads to the development of a conceptual model using the big five personality traits theory and the boundary theory. Such a model is tested and validated using structural equation modeling (SEM) of the survey data. The study shows that agreeableness, conscientiousness, and extraversion significantly influence job performance while neuroticism and conscientiousness have significant influence on work-life balance. It finds that individuals’ attitude toward digital work negatively moderates the influence of agreeableness on work-life balance and the impact of conscientiousness on job performance. Furthermore, the study reveals that work-life balance has a significant influence on job performance. This study extends existing research on the relationship between job performance, work-life balance, and personality traits and enhances the knowledge of how digital technologies impact individuals in digital work.

This study contributes to existing information systems literature in better understanding the interplay between digital technology use and personality traits for achieving better job performance in digital work. The unprecedented shift to digital work due to the COVID-19 pandemic has questioned the applicability of findings in previous studies on digital work and its impact on job performance. This study advances the knowledge by establishing a validated research model with empirical evidence for understanding how individuals with diverse personality traits can be better supported in the digital work environment. In addition, this study extends the applicability of big five personality traits theory and boundary theory in the context of digital work. Moreover, while recent studies have explored various aspects of digital work, such as work exhaustion, technology affordances,² and cyber-defense behaviors, the impact of personality traits of individuals on job performance is not investigated. This study complements the existing research by shedding light on the intricate relationship between job performance, work-life balance, and personality traits and enrich the understanding of the dynamic interplay between digital technologies and individuals in digital work.

In what follows, a review of related literature is presented, leading to the development of a conceptual model. The methodology is then discussed, followed by the presentation of data analysis results, discussion, and contribution. Finally, the conclusion, the limitation and future research are articulated.

Theoretical background

Digital work and work-life balance

Digital work originates from telework in the 1970s in response to the oil crisis.² It has evolved over three generations. The first generation is the Home Office relied on the use of personal computers and fixed telephones to replace long commuting hours.⁶ The second generation is the Mobile Office using laptop computers and mobile phone enabled wireless and portable work from locations other than home or office, accompanied by fast-growing dispersion of the Internet. The third generation is the Virtual Office based on the adoption of digital technologies using mobile and virtual connections between individuals and organizations from anywhere and at any time, leading to the increasing use of digital work.⁴

The use of digital work has created a digitalized working environment which is distinct from the traditional work practices.^2,4 Such working environments are often characterized by several common themes including the use of digital technologies, remote locations, contractual arrangements between individuals and organizations, and flexible working time.^1,17 Individuals who are mostly isolated must use various digital technologies to fulfill their working commitments without much support available traditionally.⁵ As a result, the ability of individuals to cope with such digital demands for better job performance is often affected by their personality traits and the interplay between personality traits and digital technology use.

Digital work offers several benefits, including increased productivity, improved autonomy, and reduced commuting time and costs.¹⁶ It can reduce traffic congestion and air pollution.² The flexibility in work arrangement enables individuals to have greater control over their time, leading to lower stress levels, improved job satisfaction, and increased employment opportunities.²¹ As a result, the adoption of digital work promotes better work-life balance, resulting in enhanced job performance.⁶

The use of digital technologies in digital work presents challenges.¹³ It can lead to increased burnout and blurred boundary between work and personal life.²² Extra pressures are added on individuals to meet work commitments.⁶ The constant accessibility provided by digital technologies exposes individuals to technocratic and peer control.²² As a result, individuals’ attitudes toward technology use which is usually determined by personality traits¹⁰ are affected which negatively influences job performance.

Work-life balance is about a healthy compromise between working life and personal life for individuals.^14,19 Enhancing work-life balance can improve job satisfaction.³ It can create a supportive work environment that allows individuals to manage the boundary between work and personal responsibilities, therefore reducing work-life conflicts²² and leading to better job performance.

The increasing adoption of digital work leads to several studies on the impact of digital technology use on work and life of individuals. Chong et al.²³ show that work-home interference negatively affects job performance. Asbari et al.²⁴ reveal that work-family conflict negatively influences job performance. Medina-Garrido et al.²⁵ find that neither the existence nor the accessibility of work-family policies has direct and positive impact on job performance. These studies focus on specific aspects of work and life and their impact on job performance without agreement. Further exploring the relationship between work-life balance and job performance can enhance the knowledge of how job performance is affected at digital work.

Personal traits and job performance

Personality traits are the characteristics that individuals have.²⁶ They affect how individuals behave that has a direct impact on job performance.²⁷ The use of digital technologies has transformed traditional workplaces,²⁸ leading to the formation of digitalized working environments in which the interaction between individuals and digital technologies is related to personality traits. Individuals with different personality traits often form different attitudes toward digital technology use. As a result, they would behave differently. This directly affects work-life balance and job performance.

Job performance reflects the effectiveness of individual behaviors in contributing to organizational goals.^2,8 It is becoming increasingly important in digital work due to the delivery of working commitments remotely⁴ and the lack of direct supervision traditionally available.¹⁶ Job performance can be approached from two perspectives including in-role job performance and innovative job performance.² In‐role job performance reflects on the task within the duty of individuals. Innovative job performance focuses on activities beyond routine job requirements for achieving novel outcomes. Depending on specific situations, both in-role job performance and innovative job performance can be used for assessing the contribution of individuals to organizational objectives.

There are some studies on the relationship between personality traits and job performance. Yang and Hwang²⁹ show that all big five personality traits significantly influence job performance. Judge and Zapata¹² state that personality traits can be used for predicting job performance. Aldulaimi and Abdeldayem³⁰ find that there is a significant and positive correlation between extroversion, openness, and conscientiousness and job performance.

Personality traits, work-life balance, and job performance at digital work

The characteristics of digital work promise better job performance.⁴ This is due to the affordance of digital technologies² and the potential for improving work-life balance.^1,6 There are, however, various constraints on the use of digital technologies¹⁵ and the complex interaction between individuals and digital technology use.¹⁶ This can increase work-stress and result in technology burnout, leading to different attitudes of individuals toward digital technology use.¹⁵ As a consequence, job performance can be negatively affected.

Personality traits affect the attitude and behavior of individuals.²⁶ The use of digital technologies at digital work has created new working environments in which individuals work away from traditional workplaces and the interaction between individuals are often facilitated and constrained using digital technologies.²⁸ Such environments would add pressures on individuals, leading to various attitudes toward digital technology use.³¹ This can affect work-life balance and job performance.

Personality traits, work-life balance, and job performance are inter-connected in digital work.¹⁹ Existing studies have explored the relationship between these three variables on a pairwise basis.^30,32 There is, however, lack of studies that have comprehensively investigated such relationships simultaneously. With the rapid transformed working environment, better understanding such relationships in an integrated manner is critical.

Research model and hypotheses

Individuals with different personality traits often demonstrate different attitudes toward using digital technologies and managing work-life balance, leading to different job performance.³¹ While the impact of personality traits on work-life balance and job performance have been well explored in traditional work settings,^18,20 such relationships remain unexplored in digital work due to the uniqueness of the digitalized working environment as discussed above.¹⁶ This study applies the big five personality traits theory and the boundary theory for investigating the effect of personality traits on work-life balance and job performance in digital work.

The big five personality traits theory captures the characteristics of individuals that can influence their attitudes on digital technology use.^9,26 It can explain individual differences in conducting work activities using digital technologies.³¹ As a result, the use of the big five personality traits theory in this study is appropriate.

The boundary theory is related to how individuals seek to create and maintain physical, cognitive, and behavioral boundaries between work and personal life.^32,33 It is often used for exploring how individuals manage work-life balance for better job performance. The use of digital work increasingly blurs the boundary between work and life.¹⁶ This presents unique challenges not existent in traditional work settings.^2,4 How individuals manage such challenge can affect work-life balance and job performance. This demonstrates that the use of the boundary theory in this study is desirable.

Drawing from the big five personality traits theory and the boundary theory, a conceptual model is developed as shown in Figure 1. The big five personality traits theory is used for exploring how personality traits influence work-life balance and job performance. The boundary theory is used to explain how individuals manage the boundary between work and personal life.

Figure 1. The conceptual model.

Note: The dotted line represents the moderation effect.

Agreeableness is related to cooperative, trustworthy, tolerant, and helpful.²⁷ It has a positive impact on work-life balance.³⁴ Individuals high in agreeableness experience less negative work-nonwork spillover.³⁵ Using digital technologies, individuals with agreeableness are better equipped for addressing isolation and loneliness,³⁵ leading to enhanced work-life balance. The discussion leads to the hypothesis as follows:

H1a. Agreeableness positively influences work-life balance

Agreeableness facilitates collaboration, communication, and knowledge sharing.^9,36 It has a positive effect on the intention of individuals to use collaborative technologies.²⁶ Individuals with agreeableness are easy to adopt new technologies. The effective use of digital technologies can lead to better job performance. This leads to the following hypothesis:

H1b. Agreeableness positively influences job performance

Openness is about curiosity, originality, and imagination.²⁷ Individuals with openness are creative, broad-minded, and willing to try new things. They are more capable of finding creative solutions for work-life problems.³⁵ Individuals with openness exhibit a greater ability to strike a balance between work and life.³⁴ Digital work requires individuals to adapt to the new form of work arrangements. Individuals with openness are more likely to adopt digital work and balance their work and non-work activities.³⁵ This leads to the hypothesis as follows:

H2a. Openness positively influences work-life balance

Openness affects job performance.³⁷ It positively influences the intention of individuals to use collaborative technologies.^26,37 Using digital technologies, open-minded individuals tend to have more favorable attitudes toward learning, which makes them more productive.²⁷ The discussion leads to the hypothesis as follows:

H2b. Openness positively influences job performance

Neuroticism is associated with lack of emotional stability.⁹ Individuals with neuroticism tend to experience negative emotions such as anxiousness, insecurity, and depression.³¹ They view the use of digital technologies as threatening and stressful.²⁶ Individuals high on neuroticism experience greater work-life spillover as they easily get upset with new working environments.³⁵ The complexity in the use of digital technologies increases anxiety and depression, leading to poor work-life balance.³¹ This leads to the following hypothesis:

H3a. Neuroticism negatively influences work-life balance

Neuroticism has negative influence on job performance.²⁷ Individuals with neuroticism have negative attitudes toward digital work.²⁷ They are emotional unstable in the use of digital technologies with negative impact on job performance.²⁹ Individuals high on neuroticism often are negative on adopting digital technologies, leading to poor job performance. The discussion leads to the following hypothesis:

H3b. Neuroticism negatively influences job performance

Conscientiousness is associated with organization, diligence, and perseverance.²⁹ It is positively related to work-life balance as individuals high in conscientiousness are better in time management and effective in minimizing spillover.³⁵ Individuals high on conscientiousness can better plan and organize their work and non-work activities, leading to better work-life balance. They are more capable of balancing work and life responsibilities.³⁴ This discussion leads to the following hypothesis:

H4a. Conscientiousness positively influences work-life balance

Conscientiousness positively affects job performance as the achievement of individuals is influenced by diligence and persistence.^9,29 This is because conscientiousness is consistent with the characteristics required in digital work including well-organized, self-disciplined, and self-motivated, leading to enhanced job performance.²⁷ Individuals high on conscientiousness are more motivated to perform better.³⁸ This leads to the following hypothesis:

H4b. Conscientiousness positively influences job performance

Extraversion is related to sociability, assertiveness, being outgoing and seeking interactions with others.⁹ Individuals high on extraversion are more capable in balancing their work and life.³ They are more likely to seek resources and solutions to minimize spillovers, resulting in improved work-life balance.³⁵ This shows that extraversion has positive influence on work-life balance.³ This discussion results in the following hypothesis:

H5a. Extraversion positively influences work-life balance

Extraversion has a negative association with job performance.²⁹ Individuals high on extraversion prefer environments abundant with stimulation, social interactions, and activities.³⁷ They are less likely to adopt digital work.²⁷ The nature of digital work means that there is lack of face-to-face communications and interactions.² As a result, job performance may be negatively affected. The discussion leads to the following hypothesis:

H5b. Extraversion negatively influences job performance

Work-life balance is about the management of working activities and family commitments for individuals.³³ It is positively related to job performance.¹⁴ Individuals with lower levels of work-life conflict have better job performance. This is because individuals with improved work-life balance have better job satisfaction. Overall, work-life balance has a significant positive relationship with the attitude and engagement of individuals, therefore positively affecting job performance.¹⁴ The following hypothesis is proposed:

H6. Work-life balance positively influences job performance

Digital work creates a new working environment in which individuals have the flexibility to decide where, how, and when their work can be done.¹⁵ Such an environment would affect work-life balance. Individuals with different personality traits often demonstrate different attitude toward digital technology use, leading to different work-life balance.³¹ The discussion leads to the following hypotheses:

H7a–H7e Attitude of individuals toward digital work moderates the influence of personality traits on work-life balance

The increasing use of digital technologies has a fundamental impact on job performance.² Individuals with different personality traits demonstrate different attitudes toward digital technology use.³⁹ O’Neill et al.³⁶ find that employees with organization, diligence, and sociability traits perform better in digital work than in traditional work. Aldulaimi and Abdeldayem³⁰ show that individuals with extraversion show less interest in digital work. They are more likely to perform well in traditional work in which social interactions are enabled. The discussion leads to the following hypotheses:

H7f–H7j. Attitude of individuals toward digital work moderates the influence of personality traits on job performance

Research method

This study adopts a survey-based quantitative method for exploring the influence of personality traits on job performance in digital work. Survey is an effective technique for investigating the attitudes and behaviors of individuals with empirical evidence.⁴⁰ The use of such a method facilitates the validation of specific theoretical relationships between the constructs in the research model in a real situation.⁴¹

This study follows the paradigm of Creswell and Creswell⁴² for validating the measurement model. The process includes formulating the theoretical constructs before testing and refining the measurement model. The measurement items within the theoretical constructs are derived from a comprehensive review of the digital work literature, followed by the pilot test for ensuring content validity. Confirmatory factor analysis (CFA) is used for testing and refining the constructs.

The questionnaire consists of the demographic profile of the respondents and the measurement items. Table 1 presents the constructs, items, and their origins. All constructs are measured using a five-point Likert scale ranging from strongly disagree (1) to strongly agree (5).

Table 1. Measurement items and sources.

Construct	Item	Source
Agreeableness (AGR)	AGR1 I feel much concern for others	Clark et al.^Citation27
	AGR2 I am interested in people
	AGR3 I don’t insult people
	AGR4 I sympathize with others’ feelings
Openness (OPE)	OPE1 I have a rich vocabulary
	OPE2 I don’t have difficulty understanding abstract ideas
	OPE3 I have a vivid imagination
	OPE4 I am interested in abstract ideas
Neuroticism (NEU)	NEU1 I get stressed out easily
	NEU2 (R) I am relaxed most of the time
	NEU3 I worry about things
	NEU4 (R) I seldom feel blue
Conscientiousness (CON)	CON1 I am always prepared
	CON2 I don’t leave my belongings around
	CON3 I pay attention to details
	CON4 I don’t make a mess of things
Extraversion (EXT)	EXT1 I am the life of the party
	EXT2 I talk a lot
	EXT3 I feel comfortable around people EXT4 (R) I keep in the background
Work life balance (WLB)	WLB1 a good balance between the time spent for work and non-work activities	Duan and Deng^Citation14
	WLB2 (R) have difficulty balancing my work and non-work activities
	WLB3 the balance between my work demands and non-work activities is currently about right
	WLB4 Overall, I believe that my work and non-work life are balanced
Job performance (JP)	JP1 complete the specified duties	Duan et al.^Citation6
	JP2 meet formal performance requirements
	JP3 fulfil all required responsibilities
	JP4 never neglect the obligated aspects of the job
	JP5 always perform essential duties
Attitude toward digital work (ATT)	ATT1 would like to use digital work	Aboelmaged and El Subbaugh^Citation49
	ATT2 wise idea to use digital work
	ATT3 pleasant to use digital work
	ATT4 desirable to use digital work

(R) means the item is reverse-coded.

To ensure the content validity of the questionnaire, pretests and pilot-tests are conducted.⁴² Five faculty members with expertise in digital work are involved in the pretest. The improved version is then pilot tested for assessing its clarity, readability, and understandability. The response to the pilot-tested survey is followed up with interviews with each respondent for gaining better understanding of the comprehensiveness of the instrument. A minor revision is made for rephrasing some statements in the survey. These pre and pilot tests suggest a fair degree of the content validity of the survey.

Australia is selected as the sample population in this study. This is due to the wide use of digital technologies in the country and the growing adoption of digital work resulting from the longest lockdown in the world from the surge of the COVID-19 pandemic.⁴³ A random sampling method⁴² is used with the assistance of the research service company Qualtrics for data collection. Data is collected from 324 survey respondents aged 18 years or older who are working on a full or part-time basis through an online survey. The data screening process is conducted for addressing the missing values, outliers, normality, and multicollinearity.⁴⁴ These processes lead to the deletion of 15 cases. As a result, 309 responses are retained for statistical analysis.

The minimum sample size required for generating consistent and reliable findings from SEM must be at least greater than the number of correlations in the input data matrix, with a ratio of 5 to 10 respondents per item.⁴⁴ There are 33 items in the conceptual model. This means that the sample size for the appropriate use of SEM is between 165 and 330. The sample size of 309 in this study shows that reliable results can be generated through data analysis.

The dataset is examined for the common method bias using Harman’s one-factor test.⁴⁵ The result shows that the common method variance is 26.4%. This is less than the threshold of 50%, showing that the common method bias does not impact the validity of the research findings in the study.

Table 2 presents an overview of the descriptive statistics of the respondents. The gender and age characteristics of the respondents are in line with the Australian population distribution. This shows that the sample can represent the population. 30.4% of respondents hold a bachelor’s degree. 30.4% have a certificate/diploma. 40.5% of respondents have a weekly income of $1,000 to $1,999, followed by 33.0% with a weekly income of $1 to $999, and 16.8% with a weekly income of $2,000 to $2,999. 57.3% of the respondents have full-time jobs.

Table 2. An overview of the descriptive statistics of respondents.

Measure	Item	Percentage
Gender	Male	48.9
	Female	51.1
Age	18–24	13.9
	25–34	19.7
	35–44	17.8
	45–54	15.2
	55–64	15.2
	65+	18.1
Education	Primary education	0.6
	Secondary education	21.0
	Certificate/Diploma	30.4
	Bachelor degree	30.4
	Postgraduate degree	17.5
Weekly income	$3,000 or more	9.1
	$2,000–$2,999	16.8
	$1,000–$1,999	40.5
	$1–$999	33.0
	Nil income and Negative income	0.6
Working status	Full time	57.3
	Part time	42.7

Data analysis

SEM is commonly adopted for conducting multivariate data analysis.⁴² This study uses SEM for analyzing the collected data to better understand the impact of personality traits on work-life balance and job performance. A two-step approach is followed including measurement model analysis and structural model examination.

CFA is adopted in measurement model analysis for assessing the contribution of each variable while evaluating the adequacy of the measurement model for further analysis.⁴¹ A three-step CFA analysis is processed using AMOS 26 based on the collected data, including model specification, model modification, and model estimation.⁴² Model specification assesses the multivariate normality of the data to facilitate the use of the maximum likelihood method for estimation. Model modification develops the best set of measurement items to represent a construct through iterative model refining and testing. Model estimation examines how much the data support the model. There are various goodness-of-fit (GOF) indices including the likelihood ratio chi-square (χ²), the ratio of χ² to degrees of freedom (χ²/df), the root mean-square error of approximation (RMSEA), and the comparative fit index (CFI) that can be used. Table 3 presents an overview of the measurement model analysis indices.

Table 3. An overview of the measurement model analysis indices.

Construct	Item	FL	IR	CR	χ^2/df	p	CFI	RMSEA	α
AGR	AGR1	0.78	0.59	0.83	1.95	.05	0.95	0.06	0.83
	AGR2	0.84	0.71
	AGR4	0.75	0.58
OPE	OPE3	0.83	0.69	0.79					0.79
	OPE4	0.79	0.62
NEU	NEU1	0.80	0.64	0.82					0.81
	NEU3	0.87	0.76
CON	CON2	0.71	0.50	0.77					0.76
	CON3	0.71	0.50
	CON4	0.77	0.59
EXT	EXT1	0.74	0.55	0.78					0.77
	EXT2	0.72	0.52
	EXT3	0.74	0.55
WLB	WLB1	0.75	0.56	0.88					0.88
	WLB3	0.88	0.77
	WLB4	0.89	0.79
JP	JP1	0.89	0.79	0.94					0.94
	JP2	0.85	0.72
	JP3	0.90	0.81
	JP4	0.85	0.72
	JP5	0.82	0.67
ATT	ATT1	0.81	0.66	0.91					0.91
	ATT2	0.72	0.52
	ATT3	0.93	0.86
	ATT4	0.92	0.85
Recommended value		≥0.70	≥0.50	≥0.50	≤3.00	≥.05	≥0.90	≤0.08	≥0.70

In the model modification process, convergent validity and discriminant validity are assessed.⁴¹ Convergent validity is about the degree to which the measurement items within a construct are related.⁴⁶ Three steps are involved in assessing convergent validity. Step one is to calculate the χ² value for each construct. If the χ² value rejects a construct at p < .05, modification indices should be used for identifying the common construct for the item. Step two is to drop any items that do not fit into the construct. Step three is to calculate the factor loading (FL) value and the composite reliability (CR) value. The commonly accepted threshold is that all FLs are statistically significant, with the FL value and the CR value at least 0.50, ideally 0.70 or higher.⁴⁶ This leads to 8 items being dropped from 33 items in the original model. The remaining items with FLs ranged from 0.71 to 0.93 and the constructs with CRs above 0.77 as shown in Table 3 indicate good convergent validity.

Construct reliability testing includes assessing item reliability and construct reliability.⁴⁴ The item reliability (IR) indicates the amount of variance in an item due to the underlying construct rather than errors. It is assessed using the squared multiple correlation value. An item is reliable if IR is greater than 0.50.⁴⁴ The IR values for all the items assessed ranging from 0.50 to 0.86 are higher than the threshold. These items are therefore deemed to be sufficient in measuring the construct. The construct reliability is examined by calculating the Cronbach’s alpha (α) coefficient with an acceptable value of 0.70. All eight constructs with high Cronbach’s alpha (α) coefficients above 0.76 demonstrate high construct reliability.

The discriminant validity of the construct is examined by comparing the squared root of AVE for each construct with the absolute correlation of this construct to other constructs.⁴⁶ Table 4 shows the results. The squared root of AVEs in eight constructs with a range of 0.73 to 0.86 are higher than the correlation of the construct with other constructs ranging from 0.02 to 0.66. This shows high discriminant validity in all eight constructs.

Table 4. Construct validity and correlations.

	AVE	AGR	OPE	NEU	CON	EXT	WLB	JP	ATT
AGR	0.63	0.79
OPE	0.66	0.49	0.81
NEU	0.70	0.16	0.03	0.84
CON	0.53	0.42	0.24	0.02	0.73
EXT	0.54	0.54	0.43	0.07	0.12	0.73
WLB	0.71	0.26	0.25	0.25	0.25	0.33	0.84
JP	0.74	0.48	0.28	0.11	0.66	0.08	0.33	0.86
ATT	0.72	0.31	0.21	0.21	0.08	0.14	0.05	0.15	0.85

*The square roots of AVEs are on the diagonal.

The final measurement model is estimated.⁴⁶ The fitness statistics used are the likelihood ratio chi-square (χ²), the ratio of χ² to degrees of freedom (χ²/df), the root mean square error of approximation (RMSEA), and comparative fit index (CFI). The insignificance of χ²/df with the value of (1.95) within the cutoff value 3.00 as shown in Table 3 demonstrates a good match between the data and the model. This means that the final measurement model is sufficient to proceed with further analysis.⁴¹

To test the moderation effects of ATT in the proposed conceptual model as shown in Figure 1, a two-step approach⁴⁷ is followed. Step one involves transforming all constructs that engage in latent interactions into single-indicator estimations using the principal component analysis, followed by the standardization and formation of product interactions. Step two is to include these product interactions in the structure model for hypothesis testing.

The overall fitness of the structure model is examined using various goodness-of-fit statistics, including χ²/df, RMSEA, GFI, RFI, TLI, and CFI. Table 5 presents a summary of the overall fitness assessment results. The chi-square (χ²) value normalized by the degree of freedom (χ²/df) is 2.01, less than the recommended threshold of 3. The GFI (0.90) and the RFI (0.88) exceed the cutoff value of 0.8. The TLI (0.93) and the CFI (0.93) are greater than the recommended threshold of 0.90. The RMSEA (0.06) is less than the cutoff value of 0.08. A combination of the above results suggests that the measurement model fits well with the data.

Table 5. Fit statistics of the research model.

Model Fit Indices	Recommended Value	Actual Value
χ^2/df	1<NC < 3	2.01 (p ≤ .001)
RMSEA	<0.08	0.06
GFI	>0.80	0.90
RFI	>0.80	0.88
TLI	>0.90	0.93
CFI	>0.90	0.93

Table 6 presents the results of the hypothesis testing in the structural model. The path coefficients (β) along with their significant levels (p-value) confirm the statistical support of H1b, H3a, H4a, H4b, H5b, H6, H7a, and H7i. There is, however, insufficient evidence to support other hypotheses.

Table 6. Hypothesis testing results.

Hypothesis	Path	Coefficient (β)	p-value	Support for model
H1a	AGR → WLB	0.04	.695	Rejected
H1b	AGR → JP	0.28	.001	Supported
H2a	OPE → WLB	0.14	.103	Rejected
H2b	OPE → JP	0.02	.798	Rejected
H3a	NEU → WLB	−0.26	.000	Supported
H3b	NEU → JP	0.05	.386	Rejected
H4a	CON → WLB	0.23	.004	Supported
H4b	CON → JP	0.48	.000	Supported
H5a	EXT → WLB	0.11	.208	Rejected
H5b	EXT → JP	−0.17	.018	Supported
H6	WLB → JP	0.19	.001	Supported
H7a	ATT x AGR → WLB	−0.15	.041	Supported
H7b	ATT x OPE → WLB	0.08	.241	Rejected
H7c	ATT x NEU → WLB	0.03	.587	Rejected
H7d	ATT x CON → WLB	0.10	.159	Rejected
H7e	ATT x EXT → WLB	0.04	.494	Rejected
H7f	ATT x AGR → JP	−0.04	.541	Rejected
H7g	ATT x OPE → JP	0.02	.742	Rejected
H7h	ATT x NEU → JP	−0.01	.907	Rejected
H7i	ATT x CON → JP	−0.17	.003	Supported
H7j	ATT x EXT → JP	0.02	.651	Rejected

Discussion

This study investigates the relationship between job performance, work-life balance, and personality traits in digital work. The results show that agreeableness (β = 0.30, p < .001), conscientiousness (β = 0.49, p < .001), and extraversion (β = −0.20, p < .01) significantly influence job performance. It reveals that neuroticism (β = −0.29, p < .001) and conscientiousness (β = 0.20, p < .01) have significant influences on work-life balance. In addition, individuals’ attitude toward digital work negatively moderates the influence of agreeableness on work-life balance (β = −0.15, p < .05), and the impact of conscientiousness on job performance (β = −0.17, p < .01). Furthermore, the study shows that work-life balance (β = 0.19, p < .001) has a significant and direct influence on job performance.

Agreeableness has a significant positive impact on job performance in digital work. This finding extends the significant relationship between agreeableness and job performance from traditional work.^34,39 It shows that individuals high in agreeableness are likely to achieve better job performance. Individuals with such a trait are more likely to build trust and maintain long-term relationships with others³⁶ for facilitating collaboration, communication, and knowledge sharing.⁹ This is important in digital work with the absence of face-to-face communication.²

Openness has no influence on job performance. One explanation is that the impact of openness on digital technology use for improving job performance is much limited when such technologies become mainstream.³⁹ Chorley et al.⁴⁸ find that openness has no impact on the adoption of social networking technologies because the majority are familiar with the technology. Xu et al.³⁹ show that openness is not associated with the adoption of mobile apps due to the widely adoption of such apps. Individuals with openness are creative and open to new experience. They are more likely to be the early adopter of digital technologies.⁴⁸ As digital work has been increasingly accepted,¹⁴ the impact of openness on the use of digital technologies for better job performance is weakened.

Neuroticism has significant influence on work-life balance in digital work. This demonstrates that individuals with strong neuroticism are more likely to experience poor work-life balance, leading to poor job performance. The finding of the negative influence of neuroticism on work-life balance is well supported.^31,35 A possible explanation is that digital work involves the use of digital technologies for conducting work activities. This adds complexities to individuals and makes those high in neuroticism feel stressful and threatening.^22,26 As a result, they find it hard to manage work-life balance, leading to poor job performance.

Conscientiousness has a significant positive impact on job performance in digital work. The positive relationship between conscientiousness and job performance is well reported in traditional work settings.^29,38 This research extends such a finding in digital work to show that individuals with strong conscientiousness are more likely to perform well as they are responsible, reliable, careful, and hardworking.⁹ In digital work with a considerable level of social isolation, individuals high in conscientiousness are more likely to be goal-oriented, self-disciplined, and self-motivated²⁷ for better job performance.

Extraversion has a negative influence on job performance in digital work. It is critical for positively influencing job performance in traditional work settings^30,32 in which social interactions with many people are enabled. The unique nature of digital work means that there is lack of face-to-face interactions. Such social isolation limits the opportunities of individual with extraversion to interact with coworkers. The delayed feedback which is different from the immediate response through face-to-face contact further adds frustrations. This reduces the effectiveness of communication, collaboration, and knowledge sharing⁹ in digital work, leading to poor job performance.

Attitude of individuals toward digital work is found to negatively moderate the influence of agreeableness on work-life balance and the impact of conscientiousness on job performance. Such findings suggest that individuals with agreeableness and conscientiousness demonstrate different attitudes toward digital work, leading to different work-life balance and job performance. Specifically, both the impact of agreeableness on work-life balance and the influence of conscientiousness on job performance are weakened in digital work compared with that in the traditional working setting.

Work-life balance positively influences job performance in digital work. This finding can be understood through the lens of the boundary theory. One important aspect of work-life balance is the frequency of work-life conflict.¹⁹ Digital work allows individuals to better manage their boundaries through the increased flexibility and autonomy,¹⁴ leading to a reduction of work-life conflict. This is particularly beneficial for individuals with caregiving responsibilities, as digital work allows them to plan, schedule and reconcile their professional and private lives more effectively.¹⁶ In addition, digital work can reduce commuting time and enable individuals to better attend to family commitments.⁴ The effective management of boundaries between work and non-work has a positive impact on work-life balance, therefore contributing to improved job performance.

This study contributes to existing research from both theoretical and practical perspectives. The theoretical contribution comes from four folds. First, this study provides a validated research model for exploring the impact of personality traits on work-life balance and job performance. Such a model addresses an existing research gap in current studies on exploring the critical factors influencing job performance from different perspectives such as technology affordance,² leadership, and motivation.⁵ Through investigating how personality traits affect work-life balance and job performance, this study advances the knowledge with empirical evidence by providing a foundation for capturing how individuals with different personality traits can be better supported in digital work.

Second, this study demonstrates the applicability of the big five personality traits theory in investigating individual differences for digital work. As individuals are being forced to adopt digital work due to the COVID-19 pandemic,^5,15 studies that investigate the impact of the characteristics of individuals on work-life balance and job performance add value. This study contributes to existing research by providing a timely perspective to explore how personality traits can be better understood for supporting individuals to achieve enhanced work-life balance and better job performance at digital work.

Third, this study extends the usefulness of the boundary theory into the digital work context. The increasing blurring of boundaries between work and non-work activities in digital work highlights the need for effective boundary management.² This study contributes to the extension of the boundary theory into the digital work context which is particularly important given the widespread adoption of digital work^2,5 and the need for effective boundary management strategies in such a new digital work environment.

Fourth, this study provides a holistic view of how work-life balance influences job performance in digital work. Existing research focuses on specific aspects of work and life and their impact on job performance, such as work-stress,¹⁵ work-home interference,²³ work-family conflict,²⁴ and work-family policies.²⁵ Given the affordance of digital technologies for improving work-life balance, an investigation on the better management of work and life boundaries for improved job performance in this study entails a broader and comprehensive understanding of the impact of work-life balance on job performance in digital work.

Practically, this research provides better understanding of the impact of personality traits on work-life balance and job performance. This helps organizations to better support individuals with different personality traits for better job performance in digital work. The COVID-19 pandemic has created a unique context in which individuals are involuntarily required to adopt digital work, thus questioning the applicability of the findings of existing studies on digital work and its impact on job performance.¹⁵ Better understanding the impacts of personality traits on work-life balance and job performance is not only timely but also necessary. Such understanding is significant for organizations in pursuing better organizational performance through offering digital work to individuals.⁵

Specifically, organizations can cultivate an empowering environment that leverages the personality strengths of individuals for better job performance in digital work through personality-based task allocation and better work-life balance support. Since agreeableness and conscientiousness have a significant positive influence on job performance, managers can consider assigning tasks that require collaboration and attention to details to individuals with such traits in digital work. On the other hand, cautious are needed when assigning tasks that require extraversion in digital work as it negatively influences job performance. In addition, this study finds that individuals with neuroticism and extraversion find it difficult to balance work and non-work activities in digital work. Organizations should thus implement support mechanisms to help employees with such traits manage stress and maintain a health work-life balance in digital work to improve job performance. This can be achieved by promoting the benefits of digital work and providing flexible work arrangements that allow individuals to better manage their work and non-work activities more effectively.¹⁵ Furthermore, individuals can establish clear boundaries between their work and non-work activities, such as setting specific work hours, turning off work notifications outside of these hours, and creating a designated workspace at home.² These boundaries can help individuals better manage their time and reduce work-life conflict, leading to improved job performance.

Conclusion

This study investigates whether personality traits matter in job performance at digital work. It shows that agreeableness, conscientiousness, and extraversion significantly influence job performance. The study reveals that neuroticism and conscientiousness have significant impact on work-life balance. Furthermore, the study states that individuals’ attitude toward digital work negatively moderates the influence of agreeableness on work-life balance and the impact of conscientiousness on job performance. It finds that work-life balance has significant and direct influence on job performance. This study is the first attempt to explore the intricate interplay between personality traits, work-life balance, and job performance in digital work.

There are limitations in this study that suggest future research. First, this study collects data in Australia. The findings may be highly relevant to countries with similar cultures. Considering national culture as a main factor that affects the digital work environment, more empirical evidence in other cultures is necessary to generalize the research findings. Furthermore, this study has not considered the nature of work.¹ Different jobs have their unique characteristics. It is important to explore the impact of job characteristics on job performance in future studies to complement and enrich the findings of this study for the development of appropriate strategies and policies in the increasing use of digital work.

Disclosure statement

No potential conflict of interest was reported by the author(s).