Technology Affordances for Enhancing Job Performance in Digital Work

ABSTRACT

The increasing use of digital technologies leads to more contingent, flexible, and distributed work arrangements between individuals and organizations, resulting in wide adoption of digital work. This makes understanding the impact of digital technologies on job performance critical. This study investigates the affordance of digital technologies for enhancing job performance in digital work. A technology affordance theory-based model is tested and validated through structural equation modeling of the collected survey data. The study shows that using digital technologies can enhance job performance through improved coordination, communication, knowledge sharing, and decision-making. This study is the first in applying the technology affordance theory for exploring the role of digital technologies in job performance in digital work. The findings can lead to better formulated strategies and policies to facilitate digital technology use.

KEYWORDS:

• Digital work
• technology affordance
• job performance
• knowledge sharing
• communication
• decision-making
• coordination

Introduction

Digital work is about performing job-related tasks using digital technologies based on the arrangement between individuals and organizations from remote locations.^1,2 It is evolved from various practices including teleworking, telecommuting,³ e-working, digital workplace, remote working,⁴ and agile working.⁵ Digital work is becoming increasingly popular due to its benefits including higher job satisfaction, increased autonomy, improved productivity, reduced work-family conflict, and reduced commuting time and costs¹ and the changing organizational strategy for pursuing partial or full remote work in the new “normal” working environment after COVID-19.^6,7

There are many studies for understanding the impact of using specific digital technologies on job performance under various circumstances.^2,8 Chung et al.,⁹ for example, reveal that the adoption of mobile enterprise systems improves job performance. Yu et al.¹⁰ find that excessive social media use can lead to information, communication and social overloading that significantly reduces job performance. Lee and Lee¹¹ show that the use of Facebook and KakaoTalk has a positive impact on job performance through better information sharing. Wijayati et al.¹² state that the use of artificial intelligence technologies has a significant positive effect on job performance. Such studies have provided mixed findings on the impact of digital technology use on job performance.

Digital work is carried out in a unique environment in which digital technologies have become the infrastructure for delivering working commitments in remote locations.¹³ Such environments different from traditional working settings are often characterized by (a) increasing isolation of individuals and lack of interaction in between, (b) reduced supervision, (c) growing demand of engagement online. The use of digital technologies both affords and constrains the delivery of working commitments^1,14 and increases the complexity of the interaction between digital technology use and individuals.^15,16 It blurs the boundary between work and nonwork and exacerbates work-life conflict. This may negatively impact job performance.^1,14 This shows that the unique characteristics of digital work imply the need for further investigating the impact of digital technology use on job performance.

Existing studies have explored the use of specific digital technologies and its impact on job performance with inconclusive findings as discussed above. There is, however, little research that has directly investigated the impact of digital technology use on job performance in digital work. With the increasing adoption of digital work, the need for better understanding how digital technologies can be used for enhanced job performance is becoming critical. This study addresses this issue with the research question as follows: How can digital technologies be leveraged to enhance job performance in digital work?

This study investigates the affordance of digital technologies for enhancing job performance in digital work. Drawing on the technology affordance theory, a conceptual model is developed. Such a model is then tested and validated using structural equation modeling of the survey data in Australia. The study shows that using digital technologies can significantly improve coordination, communication, knowledge sharing, and decision-making, leading to better job performance. The study is the first in applying the technology affordance theory for exploring digital technology affordances at digital work and their impact on job performance whose findings can lead to better strategies and policies to facilitate digital technology use.

The remaining paper is organized as follows. Section 2 analyses existing literature in digital work, technology affordance, and job performance. Section 3 proposes a conceptual model. Section 4 describes the research method. Section 5 gives the data analysis results. Section 6 articulates the research findings. Section 7 concludes the study.

Related work

Digital work covers technology-centric working practices through the adoption of digital technologies.^6,16 This leads to the identification of several characteristics of digital work including flexibility, autonomy, digital technology use, and remote location.^2,13 The flexibility of digital work is reflected in terms of where, how, and when work is done. The autonomy is related to the freedom that individuals have in carrying out their work.¹⁷ Digital work involves using digital technologies to deliver working commitment from remote locations. Such characteristics provide individuals with promise for better job performance.¹⁶

Digital technologies can afford individuals better job performance due to their capability in improving communication and coordination^18,19 and in enhancing knowledge sharing and decision-making.^20,21 They are knowledge sharing enablers with significant influence on job performance.²² The use of digital technologies can improve communication and coordination.^23,24 It can provide individuals with timely and relevant information, thus facilitating the making of informed decisions.

The use of digital technologies sometimes constraints the completion of working activities.¹⁶ It can increase stress and burnout and make it hard for individuals to separate their lives from work.¹⁴ Using digital technologies puts extra pressure on individuals in delivering working commitment.²¹ Furthermore, the use of digital technologies exposes individuals to almost anytime and anywhere.² This makes individuals under increasing technocratic and peer control.²⁵ Such undesirable controls negatively affect the delivery of digital work and result in an adverse impact on job performance.²¹

Affordance is constituted in the relationship between the characteristics of artifacts, the goal and the capability of individuals and the environment.^16,26 It offers specific possibilities under circumstances. The use of the affordance concept helps explore the characteristics of the environment and investigate the interaction between the environment and individuals.²⁷ This is because technological artifacts provide specific possibilities from the view of different people for actions.^16,20

Technology affordance is about the possibility of specific technologies that facilitate the achievement of the goal through their use.^15,28 It is related to technological possibilities for goal-oriented actions of individuals. Technology affordance is grounded in each organizational context for appreciating the socio-dynamics of technology use under various circumstances.²⁷ It considers how individuals interact with technologies with respect to the characteristics of the environment that individuals are in.^16,24

Adopting the technology affordance theory provides opportunities for better understanding the use of digital technologies in digital work and its impact on job performance.^15,28 It helps focus on the affordance of digital technologies for specific possibilities in relation to the goal of individuals. The use of the technology affordance theory facilitates the conceptualization of perceived technology use based on the relationship between the characteristics of technologies and the goal of individuals. It helps appreciate the potential of specific technologies and understand how individuals can realize the potential through technology use to achieve their goals, leading to better job performance.^20,27

Job performance is a complex construct that can be approached from the perspective of in-role job performance and innovative job performance.^2,29 In-role job performance reflects the task within the duty of individuals.²⁰ This requires individuals to demonstrate formal behaviors for achieving their performance objectives based on the job description. Innovative job performance focuses on activities beyond routine job requirements for achieving novel outcomes.³⁰ It requires generating and adopting novel ideas and acquiring the power necessary to implement such ideas in pursuing new goals.

Much research has been done in understanding what affects job performance from different perspectives. Cronin et al.,³¹ for example, show that job satisfaction significantly affects job performance. Kundu et al.³² reveal that leadership is positively associated with job performance. Kock and Moqbel³³ state that positive emotions about the use of technologies play a critical role in determining job performance. Bui et al.¹⁴ find that work–life balance is positively linked to job performance. All these studies have shown that there are various critical factors that affect job performance in organizations.

The use of digital technologies both affords and constrains the delivery of digital work.^2,29 As a result, understanding the impact of digital technology use on job performance is becoming critically important. This leads to numerous studies in exploring the relationship between the use of specific digital technologies including (a) social media, (b) digital platforms, and (c) collaborative technologies and job performance under various circumstances.^20,34,35

Social media-based studies recognize the potential of social media in communication and knowledge sharing and explore the impact of social media use on job performance. Ali-Hassan et al.,³⁰ for example, show that social media use can enhance mutual understanding of shared goals and improve knowledge sharing, leading to better job performance. Chen et al.²⁰ state that visibility, association, editability and persistence of social media affect job performance. Liang et al.³⁵ reveal that the use of social media can enhance sharing and transferring of knowledge for improving job performance. Naeem and Ozuem³⁶ prove that social media use can improve collaboration, coordination, and cooperation for better job performance. Zhang et al.³⁷ find that social media use can decrease individuals’ collaboration and increase information overload, therefore negatively affecting job performance.

Digital platform-oriented studies explore the characteristics of such platforms and their impact on job performance. Chung et al.,⁹ for example, show that habitual use and task-technology fit of digital platforms positively affect job performance. Sun et al.³⁸ state that association, visibility, persistence, and editability of digital platforms positively affect job performance. Zhang and Shao³⁹ reveal that visibility, editability, association, and interactivity exhibit significant influence on job performance through perceived task autonomy and task feedback.

Collaborative technology-aligned studies focus on investigating the capacity of collaborative technologies and their impact on job performance. Kang et al.⁴⁰ find out that the use of collaborative technologies positively affects job performance. Cordes³⁴ states that collaboration and information sharing can improve the performance of virtual teams through better communication and decision-making. Duan et al.¹⁶ reveal that collaborative technology use provides an innovative alternative for individuals to collaborate, leading to better job performance. Table 1 presents a summary of the discussion above.

Table 1. Studies on the use of digital technologies and job performance.

Digital technologies	References	Theories	Critical factors
Social media	Ali-Hassan et al. (2015)	Social capital theory	Communication and knowledge sharing
	Yu et al. (2018)	stressor-strain-outcome model	Information, communication, and social overloading
	Chen et al. (2019)	Technology affordance theory, social network theory	Visibility, association, editability, persistence
	Liang et al. (2020)	Use and gratification theory	Knowledge sharing, social interaction, relationship development
	Naeem and Ozuem (2021)	Social cognitive theory, technology acceptance model, technology-organization-environment framework, diffusion of innovation theory	Collaboration, coordination, and cooperation
Digital platforms	Chung et al. (2015)	Task-technology fit	Habitual use, task-technology fit
	Sun et al. (2020)	Technology affordance theory	Association, visibility, persistence, and editability
	Zhang and Shao (2021)	Job characteristics theory	visibility, editability, association and interactivity
Collaboration technologies	Kang et al. (2012)	Social comparison theory	Perceived usefulness, Perceived ease of use, Collaborative technology use
	Cordes (2016)	Collaboration process framework	Collaboration, information sharing, communication and decision making
	Waizenegger et al. (2020)	Technology affordance theory	Collaboration and knowledge sharing

The discussion above has demonstrated the impact of specific digital technologies on job performance from different perspectives. There is, however, lack of research that explores how digital technology affordances affect job performance. There is no agreeable understanding of the impact of digital technologies on job performance in a digitalized environment. It is unclear how digital work is enabled by digital technology use for the goal of individuals from the perspective of technology affordance.¹⁵ This demonstrates the need for further exploring the role of digital technology affordance in digital work.

A conceptual model

This study applies the technology affordance theory²⁶ for investigating the impact of digital technology use on job performance. The use of this theory provides a suitable lens for exploring the affordance of digital technologies in digital work for enhancing job performance. This can lead to better understanding of how the integration of technological and organizational features creates possibilities for better job performance.^20,29

Drawing from the technology affordance theory, a conceptual model for examining the affordance of digital technologies and their impact on job performance in digital work is developed as shown in Figure 1. The affordance of digital technologies for digital work is reflected in coordination, communication, knowledge sharing, and decision-making.

Figure 1. The conceptual model.

Coordination affordance is related to the use of digital technologies for improving the ability of individuals to coordinate their efforts for completing work activities.⁴¹ It is measured by how work activities are harmonized, coordinated, and supported for achieving organizational goals using digital technologies. The affordance of digital technologies for coordination has been explored.²⁰ Wu et al.⁴² find that digital technologies provide affordance for coordinating work activities, managing financial resources, developing human capital, and making workspace flexible in organizations.

Communication affordance is concerned about the use of digital technologies for enhancing communication between and among individuals.⁴¹ It is reflected by the frequency, formalization, and openness of information exchange using digital technologies. Frequency is related to how often communication occurs. Formalization is about the level of spontaneity of communication. Openness is concerned about the easiness of communication and the degree of understanding between and among individuals.

Digital technologies are enablers of communication between individuals and between individuals and organizations.^16,24 Duan et al.¹⁶ find that collaborative technologies substantially improve communication by addressing the needs and concerns of individuals. Wu et al.⁴² highlight the use of digital technologies to improve internal communication and facilitate teamwork for achieving organizational competitiveness.

Knowledge sharing affordance is related to transferring information and expertise to help others carry out their work.³⁸ It is measured by the extent that individuals want to share their knowledge using digital technologies.²¹ Digital technologies have four affordances for facilitating knowledge sharing, including (a) removing temporal and spatial barriers, (b) providing information access, (c) enhancing knowledge sharing processes, and (d) assessing knowledge sharing outcomes. Digital work involves processes in which participants are engaged in facilitating organizational knowledge creation and sharing using digital technologies for better outcomes.⁴² Digital technologies such as discussion boards and collaborative tools often include features that support dialogic practices for knowledge sharing.

Decision-making affordance is about using digital technologies for facilitating decision-making in digital work.⁴³ It is measured by how digital technologies are used to facilitate decision-making through communication between stakeholders and provision of timely information. Decision-making affordance of digital technologies has been investigated. Strong et al.⁴³ find that collaboration technologies facilitate rapid decision-making by improving the decision-making process. Wang et al.⁸ demonstrate that digital technologies provide decision-making affordance for better job performance. Deng et al.²¹ reveal that digital technologies enable improved communication and enhanced decision-making via instant team-based interaction in a digitalized working environment.

The above affordance of digital technologies for coordination, communication, knowledge sharing, and decision-making facilitates the adoption of digital work, leading to better job performance. Job performance is reflected by in-role and innovative job performance.³⁰ In-role job performance concerns activities specified in the job description. Innovative job performance is related to the creative and novel solutions generated for improving the performance of organizations. The affordance of digital technologies for digital work can facilitate the improvement of in-role and innovative job performance.

The influence of digital technologies on in-role job performance has been explored in existing research. Chen et al.²⁰ find that digital technologies assist organizations in management and facilitate the coordination of schedules and activities, leading to better in-role performance. Duan et al.¹⁶ point out that digital technologies enable seamless communication between employees in digital work for conducting work duties, resulting in better in-role job performance. Chopra et al.⁴⁴ claim that digital technologies have an impact on team performance, revealing that digital technologies are critical in leveraging knowledge for better in-role job performance. With the adoption of digital technologies for digital work, individuals can conduct in-role work activities effectively, leading to better in-role job performance. Along this line, the following hypothesis is proposed:

H1.

Technology affordance positively influences in-role job performance in digital work

Using digital technologies at digital work improves innovative job performance.²⁹ Innovative job performance is associated with the development of novel ideas and solutions for solving specific problems. It is positively linked with effective communication, close collaboration, and adequate knowledge sharing.²⁰ Digital technologies provide affordances in facilitating collaboration, communication, knowledge sharing, and decision-making, thus leading to improved innovative job performance. Chen et al.²⁰ suggest that communication and knowledge sharing affordances from enterprise social technologies are significant enablers of innovative job performance. Marchiori et al.⁴⁵ state that digital technologies are effective in supporting improved communication, information and knowledge sharing, inter-organizational exchanges, and organizational learning processes, which positively contribute to innovative job performance. The discussion above leads to the proposition of the hypothesis as follows:

H2.

Technology affordance positively influences innovative job performance in digital work

The relationship between age and job performance is explored in the literature. Bertolino et al.⁴⁶ show that younger employees are associated with better innovative job performance. Kooij et al.,⁴⁷ however, reveal that age is largely unrelated to job performance as older employees are able to adjust their job to their personal preferences and develop motives out of their own initiative. Such mixed results show the need for further exploring the relationship between age and job performance in digital work.

Qualification is a predictor of job performance. Subramanian et al.⁴⁸ show that individuals with higher education have better learning ability, leading to enhanced innovative job performance. This is further supported by Shahzad⁴⁹ who claims that qualification does not have any significant influence on in-role and innovative job performance. This shows the need for exploring the relationship between qualification and job performance in digital work.

The number of children that individuals have affects job performance.⁵ The flexibility of digital work allows individuals to balance work and family commitments like looking after children, leading to better job performance. Ollo-López et al.⁵ find that employees who have caring responsibilities better respond to the autonomy and control promised by digital work with a more positive attitude toward work, leading to improved job performance. Weinert et al.⁵⁰ and Chu et al.,⁵¹ however, show that children serve as a source of distraction and stress to individuals in digital work, therefore negatively affecting job performance. The mixed results in existing research show the need for exploring the relationship between the number of children and job performance in digital work.

Research method

This study explores the affordance of digital technologies for enhancing job performance in digital work. A survey-based quantitative method is adopted. Survey is an effective technique for exploring the attitudes and behaviors of individuals with empirical evidence.^52,53 The adoption of such a method facilitates the examination and validation of specific relationships among constructs in the research model in a real situation.^53,54

This study follows the paradigm proposed by Creswell and Creswell⁵³ 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 then used for testing and refining the constructs.⁵³

The questionnaire consists of the demographic profile of respondents and the measurement items. Table 2 presents the constructs, items, and their origins. Digital technologies in the digital work context include artificial intelligence, big data, enterprise social media, and digital platforms like Microsoft Teams and Zoom. Technology affordance is conceptualized as a second-order construct measured by coordination, communication, knowledge sharing, and decision-making. All first-order constructs are measured using a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5).

Table 2. Measurement items and sources.

Construct	Item	Source
Coordination (CR)	CR1 work activities are harmonized	Lindsjorn et al. (2016)
	CR2 work activities are coordinated
	CR3 work activities are supported
	CR4 help express concerns and issues
Communication (CM)	CM1 frequent communication	Lindsjorn et al. (2016)
	CM2 communicate in spontaneous meetings, phone conversations
	CM3 communicate directly and personally
	CM4 ideas and information shared openly
	CM5 little concern with the openness of the information flow
Knowledge sharing (KS)	KS1 routinely share knowledge	Pangil and Chan (2014); Cordes (2016)
	KS2 routinely seek out knowledge
	KS3 routinely share ideas openly
	KS4 the team is good at using team members’ knowledge
	KS5 colleagues are willing to help others
	KS6 colleagues keep their best ideas
Decision-making (DM)	DM1 provide relevant information	Duan et al. (2023)
	DM2 provide timely information
	DM3 provide accurate information
	DM4 improve communication
	DM5 provide better interactions between stakeholders
	DM6 deal with the complexity
Job performance in-role (JP)	JP1 complete the specified duties JP2 meet formal performance requirements	Chen et al. (2019)
	JP3 fulfill all required responsibilities
	JP4 never neglect the obligated aspects of the job
	JP5 always perform essential duties
Innovative job performance (JI)	JI1 creating new ideas for improvements
	JI2 mobilizing support for innovative ideas
	JI3 searching out new working methods
	JI4 acquiring approval for innovative ideas
	JI5 transforming innovative ideas into useful applications
	JI6 generating original solutions to problems
	JI7 introducing innovative ideas
	JI8 making members enthusiastic for innovative idea
	JI9 evaluating the application of innovative ideas

Australia is selected as the sample population in this study. This is due to the wide use of digital technologies for work in the country and the growing adoption of digital work in the increasingly digitalized working environment resulted from the longest lockdown from the surge of COVID-19 pandemic.¹⁶ A purposive survey is used for collecting the data in January 2021, leading to the collection of 237 responses from respondents aged 18 years or older who are working on a full- or part-time basis in Australia.

A data screening process is conducted to tackle missing values, outliers, normality, and multicollinearity.⁵⁵ This leads to the deletion of 38 cases. One hundred and ninety-nine responses are therefore retained for analysis. The dataset is further examined for the common method bias using Harman’s one-factor test.⁵⁶ The result shows that the common method variance is 37.07%. This is less than the threshold of 50%,⁵⁶ showing that the common method bias does not impact the validity of the findings in the study.

Table 3 presents the descriptive statistics of the respondents above. There is not much difference in the gender distribution with 51.8% male and 47.6% female respondents, respectively. 31.7% of respondents are aged 35–44, followed by 24.1% from the 45–54 age group, 16.1% from the 65–64 age group, and 15.6% from the 25–34 age group. Regarding the weekly income, 32.2% of respondents have a weekly income of $1,000 to $1,999 with all the wages and salaries, government benefits, pensions, allowances, and other income, followed by 27.1% with a weekly income of $2,000 to $2,999, and 21.6% with a weekly income of $3,000 or more. Most respondents (62.3%) hold a postgraduate degree. Most respondents (61.8%) work for large organizations. 80.9% of respondents hold the role of Managers and Administrators, Professionals, Associate Professionals. Education and Training (39.7%) and Information Media and Telecommunications (25.1%) are the top two industry sectors that most respondents belong to, followed by Financial and Insurance Services (5.5%).

Table 3. An overview of the descriptive statistics of respondents.

Measure	Item	Percentage
Gender	Male	51.8%
	Female	47.6%
Age	18–24	7.0%
	25–34	15.6%
	35–44	31.7%
	45–54	24.1%
	55–64	16.1%
	65+	5.5%
Education	Primary education	0
	Secondary education	1.0%
	Certificate/Diploma	8.5%
	Bachelor degree	28.1%
	Postgraduate degree	62.3%
Weekly income	$3,000 or more	21.6%
	$2,000–$2,999	27.1%
	$1,000–$1,999	32.2%
	$1–$999	15.1%
	Nil income and Negative income	4.0%
Organization size	1–4	5.0%
	5–19	14.1%
	20–199	19.1%
	200+	61.8%
Role	Managers and Administrators, Professionals, Associate Professionals	80.9%
	Tradespersons and Related Workers, Advanced Clerical and Service Workers	7.0%
	Intermediate Clerical, Sales and Service Workers, Production and Transport Workers	7.0%
	Elementary Clerical, Sales and Service Workers, Labourers and Related Workers	5.0%
Industry	Mining	0.5%
	Manufacturing	3.0%
	Construction	0
	Trade	3.5%
	Transport, Postal and Warehousing	2.5%
	Information Media and Telecommunications	25.1%
	Financial and Insurance Services	5.5%
	Rental, Hiring and Real Estate Services	3.0%
	Professional, Scientific and Technical Services	4.0%
	Education and Training	39.7%
	Health Care and Social Assistance	2.5%
	Accommodation and Food Services	1.5%
	Arts and Recreation Services	1.0%
	Administrative and Support Services	2.5
	Electricity, Gas, Water and Waste Services	2.5%
	Other Services	3.2%

Data analysis

This study uses SEM for analyzing the collected data to better understand the impact of digital technology affordances on job performance. It follows a two-step approach including measurement model analysis and structural model examination.⁵⁴

Measurement model analysis

CFA is adopted in measurement model analysis for assessing the contribution of each indicator variable while evaluating the adequacy of the measurement model for further analysis.¹⁶ A three-step CFA analysis is processed using AMOS version 26 based on the collected data, including model specification, model modification, and model estimation.

Model specification assesses the multivariate normality of the data set to facilitate the use of the maximum likelihood method for estimation. Model modification involves developing 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. It uses 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). Table 4 presents the results of measurement model analysis.

Table 4. An overview of measurement model analysis indices.

Construct	Item	FL	IR	CR	χ^2/df	p	CFI	RMSEA	AVE	α
CM	CM2	0.82***	0.67	0.84	1.58	0.17	0.95	0.05	0.64	0.84
	CM3	0.78***	0.61
	CM4	0.80***	0.64
CR	CR1	0.75***	0.56	0.88					0.65	0.88
	CR2	0.86***	0.74
	CR3	0.85***	0.72
	CR4	0.75***	0.56
KS	KS1	0.90***	0.81	0.88					0.71	0.88
	KS2	0.85***	0.72
	KS3	0.77***	0.59
DM	DM1	0.77***	0.59	0.89					0.58	0.89
	DM2	0.81***	0.66
	DM3	0.77***	0.59
	DM4	0.81***	0.66
	DM5	0.71***	0.50
	DM6	0.71***	0.50
TA	CM	0.71***	0.50	0.86					0.60	0.86
	CR	0.74***	0.55
	KS	0.77***	0.59
	DM	0.88***	0.77
JP	JP1	0.85***	0.72	0.91					0.73	0.91
	JP2	0.91***	0.83
	JP3	0.90***	0.81
	JP5	0.74***	0.55
JI	JI2	0.74***	0.55	0.94					0.68	0.91
	JI3	0.72***	0.52
	JI5	0.85***	0.72
	JI6	0.82***	0.67
	JI7	0.88***	0.77
	JI8	0.86***	0.74
	JI9	0.88***	0.77
Recommended value		≥0.70	≥0.50	≥0.50	≤3.00	≥0.05	≥0.90	≤0.08		≥0.70

***p ≤ .001.

In the model modification process, convergent validity and discriminant validity are assessed.⁵⁴ Convergent validity is the degree to which the measurement items within a construct are related.⁵⁷ 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 items. 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.⁵⁷ This leads to 8 items being dropped from 35 items in the original model. The remaining items with FLs ranged from 0.71 to 0.91 and the constructs with CRs above 0.84 as shown in Table 4 indicate a high convergent validity.

Discriminant validity measures whether measurement items that are not theorized to be related are unrelated.⁵⁷ It is assessed through the comparison of the AVE of a construct and the squared correlation of this construct to other constructs.⁵⁵ The discriminant validity of both first-order constructs and second-order construct are assessed. Table 5 shows the comparison results.

Table 5. The squared correlation matrix and AVE.

	CM	CR	KS	DM	TA	JP	JI
CM	0.64
CR	0.52	0.65
KS	0.32	0.25	0.71
DM	0.38	0.48	0.44	0.58
TA	N/A	N/A	N/A	N/A	0.60
JP	0.25	0.27	0.26	0.27	0.41	0.73
JI	0.04	0.04	0.19	0.14	0.17	0.03	0.68

An assessment of the results in Table 4 reveals that all constructs show high discriminant validity. This is demonstrated by the value of AVE ranging from 0.60 to 0.73. The AVE of 0.68 for innovative job performance, for example, is higher than the squared correlation between technology affordance (0.17) and in-role job performance (0.03). This means that innovative job performance is of high discriminant validity.^16,21

The reliability test evaluates the internal consistency of measurement items.⁵⁵ It includes assessing item reliability (IR) and construct reliability. IR measures the extent to which a measurement item contributes to its theoretical construct. It is examined using the squared correlation value. An item is reliable if the IR value is greater than 0.50.⁵⁷ The IR values for all the items are ranged between 0.50 and 0.83. This shows that these items are sufficient for measuring their theorized constructs. Construct reliability measures the consistency between items of a construct. Cronbach’s alpha (α) coefficient is commonly adopted for assessing construct reliability with a threshold at 0.70. All constructs have α coefficients above 0.84. This shows that these constructs have construct reliability.

The final measurement model is estimated using the relevant GOF statistics.⁵⁷ Table 4 shows the results. The insignificance of the chi-square (χ²) value normalized by the degree of freedom (χ²/df) (1.58) within the cutoff value of 3.00 shows that the model is not significantly different from the data. The RMSEA value of 0.05 and the CFI value of 0.95 show a good match between the data and the model. This means that the final measurement model is sufficient to proceed with further analysis.

Structural model examination

The overall fitness of the structure model is examined using the GOF indices discussed above including χ²/df, RMSEA, GFI, AGFI, TLI, and CFI. This is to assess the hypothesis in the proposed model as shown in Figure 1. Table 6 shows an overview of the overall fitness assessment indices. The chi-square (χ²) value normalized by the degree of freedom (χ²/df) is 1.70, less than the recommended threshold of 3. The GFI (0.84) and the AGFI (0.80) exceed the cutoff value of 0.8. The TLI (0.94) and the CFI (0.94) 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 results above suggests that the measurement model fits well with the data.⁵⁷

Table 6. Overview of the GOF indices.

Model fit indices	Recommended value	Actual value
χ^2/df	1<NC < 3	1.70 (p ≤. 001)
RMSEA	<0.08	0.06
GFI	>0.80	0.84
AGFI	>0.80	0.80
TLI	>0.90	0.94
CFI	>0.90	0.94

Table 7 shows a summary of the hypothesis testing result in the structural model. The path coefficients (β) along with their significant levels (p-value) confirm the statistical support of both hypotheses.

Table 7. Hypothesis testing results.

Hypothesis	Path	Coefficient (β)	S.E.	p-Value	Support for model
H1	TA → JP	0.63	0.123	0.000	Supported
H2	TA → JI	0.37	0.131	0.000	Supported

Figure 2 presents the structural model with the relevant statistics. Specifically, technology affordance is reflected by the affordance for coordination (β = 0.81, p < .001), communication (β = 0.77, p < .001), knowledge sharing (β = 0.72, p < .001), and decision-making (β = 0.86, p < .001). Technology affordance has significant influence on the improvement of both in-role job performance (β = 0.63, p < .001) and innovative job performance (β = 0.37, p < .01). Age (β = 0.18, p < .01) has a significant impact on in-role job performance. Qualification (β = 0.37, p < .001) significantly influences innovative job performance. The number of school-age children, however, does not have a significant impact on either in-role job performance (β = 0.03, p > .05) or innovative job performance (β = 0.02, p > .05).

Figure 2. The structural model.

Discussion

This study investigates the affordance of digital technologies for enhancing job performance in digital work. Technology affordance is conceptualized as a second-order construct with four dimensions including coordination, communication, knowledge sharing, and decision-making. Job performance is reflected by in-role job performance and innovative job performance. The results show that the use of digital technologies for digital work significantly contributes to the improvement of both in-role and innovative job performance through improved coordination, communication, knowledge sharing, and decision-making.

The control variables used in this study are age, qualification, and number of children. Age has a significant impact on in-role job performance. Qualification significantly influences innovative job performance. The number of school-age children, however, does not have a significant impact on either in-role job performance or innovative job performance.

The significant impact of using digital technologies in digital work for improving in-role performance is in line with the findings of previous studies.^20,29,34 This study echoes those studies by showing that individuals can effectively conduct in-role work activities with the adoption of digital technologies, leading to better in-role job performance.^20,35 Compared with innovative job performance, technology affordance has a stronger effect on in-role job performance. Respondents indicate that digital technologies can facilitate their working activities through better coordination, communication, knowledge sharing, and decision-making. They can therefore effectively perform essential duties, meet all the performance requirements, and fulfill all the responsibilities.

The positive influence of digital technologies on innovative job performance has been explored in the existing research.^20,30 Innovative job performance is related to the production of activities and ideas that exceed routine requirements. The findings from this study reinforce the finding that digital technology use can facilitate the improvement of innovative job performance.⁹ This is because digital technology use helps individuals create new ideas, propose new methods, gain support, generate innovative solutions, and implement innovative ideas. Furthermore, digital work improves work–life balance, making individuals more enthusiastic about innovative ideas¹⁴ that can lead to better job performance.

Age has a significant impact on in-role job performance. The increase in age is associated with enhanced in-role job performance in digital work. One possible explanation is that compared with younger employees, older employees are more likely to have caring responsibilities at home.^5,46 Digital work improves the balance between work and family commitments. As a result, individuals respond to autonomy and control with a positive attitude toward work, leading to better in-role job performance.

Qualification is significantly associated with innovative job performance. Employees with higher educational levels are found to have better critical thinking abilities which enhance the quality and the novelty of ideas at work.⁴⁸ In addition, essential knowledge, education and training, skills, and experience assure that ideas can be turned into innovations.³⁰ Individuals who have higher educational levels have better cross-functional learning ability due to the level of formal training and the knowledge obtained. This increases the chances for the application of new knowledge, leading to increased innovative job performance.⁴⁸

This study makes several contributions to existing research. Theoretically, this study provides a validated research model for exploring the impact of digital work on job performance from the perspective of technology affordance. Much research has been conducted for exploring what affects job performance from different perspectives including leadership,³² motivation, personality traits,³³ job satisfaction,³¹ and work-life balance.^1,14,48 There is, however, lack of research on how specific digital technology affordances affect job performance and no agreeable understanding of the impact of digital technologies on job performance in a digitalized environment is present. This study advances the knowledge by providing a foundation for capturing the role of digital technologies in facilitating the delivery of digital work from the perspective of technology affordance.

Furthermore, this study comprehensively investigates the digital technology affordance in digital work by formulating the technology affordance as a second-order construct, reflected by coordination, communication, knowledge sharing, and decision-making. The technology affordance perspective has been applied for investigating how the combination of technology and organizational features create possibilities that affect innovation in organizations.¹⁵ Limited studies, however, have explored the affordance of digital technologies for digital work.¹⁶ This study provides empirical evidence to show that using digital technologies can enhance job performance through using digital technologies for improving coordination, communication, knowledge sharing, and decision-making. The findings can be used as a foundation for future technology affordance research in digital work.

Practically, this research provides a timely study of the impact of digital work on job performance. This leads to better understanding of digital work and its role in designing and transforming existing working practices using digital technologies. The COVID-19 pandemic has created a unique context in which individuals are involuntarily required to adopt digital work, thus questioning the applicability of existing knowledge on digital work.⁸ Better understanding digital work and its role in designing and transforming existing working practices using digital technologies is thus timely needed. Such understanding is significant for organizations in their pursuit of better organizational performance using digital technologies, in particular in the period of post COVID-19 when digital work is becoming a new form of work practices.^6,16

Specifically, this study provides practical insights in understanding the role of digital technology affordance in enhancing job performance in digital work. To improve both in-role and innovative job performance, individuals should become familiar with the digital technology affordance of coordination, communication, knowledge sharing, and decision-making. This may involve explicit training and discussion to facilitate understanding the functionalities of specific digital technologies and how these technology affordances can assist employees in their work.

Conclusion

This study investigates the affordance of digital technologies for enhancing job performance in digital work. A research model is developed based on the technology affordance theory. Such a model is tested and validated with the use of structural equation modeling on the survey data in Australia. The results show that the use of digital technologies for digital work significantly contributes to the improvement of both in-role and innovative job performance through improved coordination, communication, knowledge sharing, and decision-making.

There are three limitations in this study that suggest future research. First, this study collects data in Australia. The findings of this study may be highly relevant to countries with a similar culture. Considering national culture as one of the main factors that affect digital work use,⁵⁸ more empirical evidence in other countries or cross-culture comparisons are necessary to generalize the research findings. Second, this study has not considered the impact of job characteristics on job performance. Different jobs have unique characteristics which may influence the digital technology affordance in digital work. It is therefore important to explore the effect of job characteristics on job performance in future studies for the development of appropriate strategies and policies in promoting digital work in organizations. Third, this study has not explored the actualization of technology affordances for enhancing job performance in digital work. Understanding the actualization process in future studies is important for better leveraging technology affordances to improve job performance in the increasingly digitalized working environment.

Disclosure statement

No potential conflict of interest was reported by the authors.