Banks must follow situation to suit market tastes and stay competitive with other competitors, and also there are urgencies to cope with digital era. However, the financial services sector must be ready to adapt to changes in business models and ensure the readiness of banking employees. As stated by Dasgupta and Gupta (2019), the development of the Internet and various technological applications has been widely applied and adopted by organizations or companies in their business operational processes, and it is also affecting banks as part of financial services. As a result, organizations are increasingly prioritizing technology in every aspect, allowing them to develop more digital products and services (Kahan, 2018; Wirtz, 2024).
Currently, banks in the world have quite tight competition in developing the digitalization of their products both for customers and for internal parties, in this case employees. Therefore, this digital development process is also felt down to the staff level, which is included in the development of the work methods that are carried out. Furthermore, it is done so that the process that occurs is also in line with the digitalization strategy for customers, and this is no exception to the banks in Indonesia.
As one of the front lines of a bank and a party that interacts directly using digital applications or websites, the staff at these banks must adjust and adapt to rapid developments and see how employees adjust to several digital programs such as applications and websites that are their work tools from previously being run manually using standard work tools such as Microsoft Office to a digital means that is automatically integrated. The success of technology adoption is not only interpreted as an organization succeeding in building technology but also about how ready the human resources (HR) working in the bank or called employees are to utilize technology to improve the work efficiency and service quality.
The readiness of human resources (HR) or employees in facing technological changes is influenced by employee motivation in utilizing digital tools in their workplace. This will be a challenge in itself because some of the staff employees at these banks already have quite extensive work experience working manually. However, it is pertinent to find whether there is any internal motivation from within themselves or external motivation when they have to be faced with being able to quickly adapt to the use of digital tools such as applications and websites or it could even be that employees without the need for motivation will actively use the application or website as a work aid.
In the meantime, employee readiness to accept technological change needs to be assessed, and one study found that the readiness might be closely related to intrinsic motivation (Trang, 2024). But, other study showed that motivation is divided into two types, namely, extrinsic motivation and intrinsic motivation. Extrinsic motivation is related to doing something to get external rewards or avoid punishment, while intrinsic motivation is doing something for the benefit of oneself such as happiness and pleasure (Feng, et al., 2016; Deci and Ryan 2000).
However, one of the well-known models to describe the extent to which employees are confident in the use of technology and how the technology makes their work processes easier is the TAM model with perceived usefulness and perceived ease of use (Davis, 1989) and the extent to which employees believe the use of certain digital work tools will be useful to make things easier (Venkatesh and David, 2000).
This study will examine the influence of intrinsic motivation and extrinsic motivation that may arise from employees so that they can influence employee readiness in the digital work process they undergo by using the mediation of perceived ease of use and perceived usefulness. This study is considered quite important because with the current changes in digital technology, there has been no research that specifically discusses the relationship between employee motivation and the digital adaptation process in Indonesian banking. Therefore, management can know what steps to take for employees in terms of implementing digital technology as a work tool such as applications and websites.
According to research conducted by Venkatesh, et al. (2000), they argued that employees’ intentional digital readiness is determined by employees’ perceived usefulness and perceived ease of use in carrying out digitalization. Efforts to prepare employees, in addition to being facilitated by the organization, also require the participation of employees themselves, which is then interpreted as intrinsic motivation that will determine employee intentions when using digital equipment (Trang, 2024), and this view is supported by Bastari, et al. (2020), who stated that both intrinsic motivation and extrinsic motivation can increase the perception of the ease of using digital equipment. Furthermore, in relation to PEU and PU, it was once conveyed by Höyng and Lau (2023), who in their study stated that PEU and PU have a positive relationship with employee readiness to adopt digital equipment. However, the opinion of Meng and Li (2023) shows a dissenting opinion compared to other studies, where in their study extrinsic motivation is considered to have an influence on PU in the use of digital equipment, while intrinsic motivation has no influence on PU. In addition, the study conducted by Lu, et al. (2023) stated that if someone has motivation, both intrinsic and extrinsic motivation, then employees will be more ready to use digital tools to complete their work.
Because there are still different views on the dynamics between factors that can influence employee intentions toward digitalization, this study intends to explore the dynamics between intrinsic motivation and extrinsic motivation toward employee digital readiness. This study explores the phenomena occurring among employees at the staff level as direct users through perceived ease of use (PEU) and perceived usefulness (PU) in relation to the use of applications or websites that support their work, through the following research questions:
- Q1:
Can intrinsic and extrinsic motivation have a direct or indirect impact on employee intentional digital readiness?
- Q2:
What are the direct and indirect impacts of extrinsic motivation on employees’ intentional digital readiness?
Intrinsic motivation plays a role in formulating a person’s behavior and actions (Sheng, Jue and Weiwei, 2008). Intrinsic motivation is associated with activities carried out for individual satisfaction that are separated from consequences, benefits, and objective activities (Ryan and Deci, 2000). Based on research of Wang, Zhu and Jin (2024), intrinsic motivation encourages employees to improve performance through a sense of achievement, challenges, and trust in the work process, while intrinsic motivation in employees is characterized by a high level of commitment to their work, which psychologically creates a pattern for determining a career through attention and increased interest in the work itself. Intrinsic motivation can generally be interpreted as the pleasure felt, referring to the extent to which the technological activity is considered enjoyable regardless of the consequences of the work (David, Bagozzi and Warshaw, 1992).
According to the self-determination theory, motivation is divided into two parts, namely, extrinsic motivation and intrinsic motivation. The definition of extrinsic motivation itself is doing something to get external rewards, while intrinsic motivation is doing something for internal rewards that arise from within oneself, such as happiness and pleasure (Ryan and Deci, 2000; Feng, et al., 2016). Basically, an employee who does a task always has self-motivation, whether to fulfill one’s obligations because the individual has been given a reward in the form of a salary or there is motivation from within to be able to actualize oneself; hence, he/she slowly enjoys doing one’s work. From an extrinsic motivation perspective, an employee’s behavior is influenced by the perception of the value and benefits of the actions one takes. The basic purpose of extrinsic motivation is to receive awards or reciprocity from the company (Kowal and Fortier, 1999; Ryan and Deci, 2000; Kankanhalli, et al., 2005). Strategic rewards are useful for motivating individuals to perform desired behavior (Pettinger, 1998). An appreciation or award given by a company for certain achievements to make an employee work much more diligently and quickly can be considered as motivation that arises from an external source because it is influenced by a reward. Organizational rewards can range from financial incentives such as salary increases and bonuses to non-financial rewards such as promotions and job security. Lin (2007) found that extrinsic motivation, such as expected rewards from the company and reciprocal benefits, can only partially predict attitudes and intentions to share knowledge. Salleh, et al. (2016) said that employees with low motivation will have poor performance at work. Therefore, several policies to encourage employees to implement their duties can be given in the form of additional rewards from what is carried out by employees with very satisfactory results.
The technology acceptance model (TAM) is one of the models that has been tested in previous studies and is powerful in testing the use of technology (Igbaria, Guimaraes and Davis, 1995; Chau, 1996; McFarland and Hamilton, 2006). TAM is a concept used to describe predictors of technology acceptance and the attitudes and behavior of end users in using information technology both individually and in groups (Davis, 1989). In addition, TAM also has a stable basis for explaining the acceptance of use in an organizational context (Park, et al., 2014; Nurqamarani, Sogiarto and Nurlaeli, 2024). The use of technology based on TAM is through two elements, namely, perceived ease of use and perceived usefulness. Perceived ease of use defines the perception of easy-to-understand use in using information technology systems, while perceived usefulness is the perception of the use of information technology systems in improving job performance (Venkatesh and Davis, 2000; Bastari, et al., 2020).
Employees’ intentional digital readiness is defined as how employees are willing to exert energy and effort to carry out digital processes and determine their subsequent behavior or attitudes (Becker and Huselid, 2006; Bouckenooghe, Devos and Van Den Broeck, 2009). Höyng and Lau (2023) in their research proposed a model to directly and indirectly observe and examine employees’ intentional digital readiness using the Employee Digital Acceptance Model (EDAM), which is an extension of TAM driven by technology (Davis, 1989). This study will be explored from the perspective of TAM, namely, through perceived usefulness and perceived ease of use.
Intrinsic motivation is a perception of user pleasure that provides a relationship with the TAM concept to be described in attitudes toward technology use (Rouibah, et al., 2016; Alalwan, et al., 2018; Teo, et al., 2019). According to Teo, Lim and Lai (1999), it was discovered that intrinsic motivation increases ease of use and positively influences attitudes toward the use of technology. Intrinsic motivation affects the perception of the use of digital work tools in the banking work process (Bastari, et al., 2020). Meanwhile, according to Trang (2024), digital intrinsic motivation is an important component in the acceptance and continuous use of technology.
H1: Intrinsic motivation positively influences perceived ease of use.
Based on Trang (2024), one of the important things in organizational behavior and technology acceptance models is the influence of digital intrinsic motivation on perceived usefulness as part of TAM by employees. Empirically, a study by Sheng, et al. (2008) found that intrinsic motivation is a significant predictor of perceived usefulness and technology adoption. The study emphasized that when employees use technology because of the interest or satisfaction they get from their tasks, they tend to find it useful. This was also supported by research conducted by Gagné, et al. (2014), who found that intrinsic motivation is related to the perception that digitalization is easier to use and more useful. Overall, these studies describe the importance of intrinsic motivation in making employees realize the benefits of digital technology.
H2: Intrinsic motivation positively influences perceived usefulness.
Extrinsic motivation in the form of financial motivation and employee rewards helps to generate creativity and innovation (Van Dijk and Ende, 2002). In addition, previous studies have also found that extrinsic motivation is positively related to perceived usefulness and perceived ease of use. For example, Meng and Li (2023) argued that most technology acceptance has been done from an extrinsic motivation perspective. This means that the focus is on how external rewards or outcomes affect individual acceptance and use of technology. Fagan, et al. (2008) found that extrinsic motivation is positively related to perceived ease of use when employees intend to use applications and websites in their work. In the use of application-based or web-based technology, the process of working on it may not always be fun and interesting for employees; thus, extrinsic motivation plays an important role in the continuation of the use of application-based and webbased technology; therefore, the process being carried out is perceived to be easier.
H3: Extrinsic motivation positively affects perceived ease of use.
Roca and Gagne (2008) examined the factors of intention to continue e-learning in the workplace and argued that both intrinsic and extrinsic motivation have a positive and direct role in the desire to continue it. From an extrinsic motivation perspective, an employee’s behavior is influenced by the perception of the value and benefits of the actions taken by him. The basic purpose of extrinsic motivation is to receive rewards or reciprocity from the company (Kowal and Fortier, 1999; Ryan and Deci, 2000; Kankanhalli, et al., 2005). Furthermore, previous studies found that extrinsic motivation is positively related to perceived usefulness (Meng and Li, 2023).
H4: Extrinsic motivation positively affects perceived usefulness.
Wang, Zhu and Jin (2024) stated that intrinsic motivation in employees is characterized by a high level of commitment to their work, which psychologically creates a pattern for determining a career through increasing attention and interest in the work itself. They also said that intrinsic motivation can provide employees with the drive to improve performance through a sense of achievement, challenges, and trust in the work process. Both intrinsic motivation and extrinsic motivation can influence the way a person views technology. This is in accordance with the findings of research conducted by Meng and Li (2023), who noticed that satisfaction is the main determinant of continued desire, followed by perceived usefulness, and both intrinsic motivation and extrinsic motivation positively predict perceived ease of use. In addition, Bastari, et al. (2020) observed that intrinsic motivation and extrinsic motivation increase ease of use and have a positive impact on attitudes toward technology use. Digital intrinsic motivation is an important component in the acceptance and continued use of technology (2024). However, interesting findings from Höyng and Lau (2023) showed that employees’ intentional digital readiness is very important in the acceptance of new technologies, including changes in the digitalization process.
H5a: Intrinsic motivation influences employees’ intentional digital readiness mediated by perceived ease of use.
H5b: Extrinsic motivation influences employees’ intentional digital readiness mediated by perceived ease of use.
Perceived usefulness describes the extent to which employees in using certain digital work tools can improve their employee performance (Davis, 1989). Based on previous research, the easier the use of these digital work tools will increase employee perceptions of the usefulness of these tools (Venkatesh and Davis, 2000; Gefen, et al., 2003). The influence of digital intrinsic motivation on perceived usefulness as part of TAM by employees is an important part of organizational behavior and technology acceptance models (Trang, 2024). An empirical study by Sheng Jue and Weiwei (2008) found that intrinsic motivation is a significant predictor of perceived usefulness and technology adoption. The results of the study by Lu and Pang (2023) revealed that if someone has intrinsic motivation and extrinsic motivation, the individual will be more ready to explore and use the available technology and resources. Meanwhile, according to Moon and Kim (2001), it is necessary to reflect on extrinsic motivation in designing user interfaces which are key to increasing the use of applications or websites. In this study, perceived usefulness will act as a mediator between intrinsic motivation and extrinsic motivation toward employees’ intentional digital readiness.
H6a: Intrinsic motivation influences employees’ intentional digital readiness, which is mediated by perceived usefulness.
H6b: Extrinsic motivation influences employees’ intentional digital readiness, which is mediated by perceived usefulness.
Figure 1 presents the study model.
Research Model
(Source: Authors’ interpretation based on the hypothesis development)
This study aimed to explain the relationship between intrinsic motivation, extrinsic motivation, perceived ease of use (PEU), perceived usefulness (PU), and employees’ intentional digital readiness. Thus, the research design used an explanatory method based on survey results through a questionnaire with minimal interference. The study setting utilized the minimal interference method, allowing for a normal flow of data collection without intervention from researchers (Saunders, et al., 2019). The unit of analysis in this study was individuals who worked in Central Bank of Indonesia classified as KBMI III with a staff position and used digital tools (web or application) while working. The data collection in this study applied a cross-sectional method, where data were only collected once in the research period (Saunders, et al., 2019) from September to October 2024. This was done with the aim of collecting relevant data in order to obtain appropriate answers to the research questions. The data collection was carried out using an online questionnaire with the Google Form application. According to Creswell (2014), the most appropriate research approach to test causal relationships is a quantitative approach.
The sampling method utilized the non-probability sampling method with convenience sampling. The unit of analysis of this study was individuals with the object of research from bank employees from 11 banks classified as KBMI III, including BANK SMBCI, OCBC, Maybank, BTN, BSI, Bank Permata, CIMB Niaga, Bank Panin, Bank UOB, and Bank Danamon. Banks with the KBMI III category were selected in the study because the number of banks classified as KBMI III was 11 banks with an unknown total employee population. The employee population used for sampling was employees with staff positions and using digital work tools such as applications or websites that supported their work. Therefore, filter questions were mandatory to be filled in. The sample size to be taken was based on the total number of questions from 5 variables with 21 statements. Thus, the minimum number of samples that had to be met was 21 x 10 = 210 samples. This is in accordance with Hair, et al. (2022), in which the sample size is determined using the 10 times rule.
The intrinsic motivation was measured using three statements developed by Gagné, et al. (2014) and Bastari, et al. (2020): for example, “I use digital work tools (applications/web) because I enjoy using them.” The extrinsic motivation was determined by adopting three statements from Fagan, Neill and Wooldridge (2008) and Meng and Li (2023) with an example question “Using digital work tools (applications/web) increases my effectiveness in my work.” While for the measurement of PEU and PU, this study used statements from Venkatesh, et al. (2000) and Davis (1989) with each: PEU 7 statements, one of which was “By using digital work tools (applications/web), I can easily complete tasks” and PU 5 questions, including “Using digital work tools (applications/web) helps me complete tasks faster.” To measure employees’ intentional digital readiness, three statements were adopted from Bouckenooghe, Devos and Van den Broeck (2009) with the question “I am ready to be part of the digitalization process.” In this survey, the respondents were asked to provide an assessment related to these variables with a 5-point Likert scale, where 1 means “strongly disagree” to 5 means “strongly agree.” Based on the opinion of Willits, et al. (2016), the Likert scale consists of a series of statements related to a particular attitude or opinion, in which the respondents indicate their level of agreement on a bipolar scale. The responses are usually scored and summed up to produce a composite score that reflects the respondents’ overall attitudes.
According to, Hair et al. (2022), SEM-PLS testing consists of two models, namely, the outer model and the inner model. The inner model measures the quality of the model and hypothesis testing, while the outer model measures the validity and reliability of the question items. Validity testing on the outer model is done by considering the convergent validity and discriminant validity. Convergent validity is measured through the outer loading and average variance extracted (AVE) values, which are said to be valid if the outer loading value is greater than 0.70 and the AVE is greater than 0.50. Then, discriminant validity testing uses conceptually similar constructs, where the Heterotrait-Monotrait Ratio of Correlations (HTMT) limit value is lower than 0.90 (Hair, et al., 2022). In addition, reliability testing represents composite reliability (CR) and Cronbach’s alpha (CA) values with CA and CR limit values greater than 0.70 indicating adequate reliability. Based on Hair, et al. (2022), in testing the inner model, a collinearity analysis, the significance of the relationships between variables, and an explanatory power assessment (R-square and f-square) were carried out. The collinearity analysis was tested based on the Variance Inflation Factor (VIF) value to evaluate the collinearity of the variables. The VIF value is not more than 5 and ideally based on Mason and Perreault (1991) and Becker, et al. (2014), and the VIF value is less than 3. The significance of the relationship between variables is tested by looking at the p-value, where a p-value < 0.05 indicates that there is an influence of the independent variable on the dependent variable. Furthermore, the explanatory power assessment is carried out by calculating the R-square, namely, endogenous variability explained by exogenous variables. Based on Chin (1998), the R-square criterion value is divided into three categories, namely, > 0.67 (substantial), > 0.33 (moderate), and < 0.19 (weak). In addition, to measure the effects of the exogenous latent variables on the endogenous latent variables, F-square is used. Based on Cohen (2013), the effect of the f-square size < 0.02 indicates a small influence; 0.02 < f2 < 0.15 is moderate, and > 0.35 has a large influence.
In the process of doing the analysis techniques and testing the hypotheses, this study used Smart PLS 4 Software and partial least squares (PLS). To obtain the respondent data that were in accordance with this study, data filters were applied on the respondents such as the respondents must be employees from a bank, have staff positions (not decision-making officials), and use digital work tools (applications/web). Of the 293 respondents, there were 237 respondents who fell into the criteria (referring to the filter questions) (Table 1.). According to Fincham (2009), ideally the questionnaire responses can approach 60% to be considered relevant to the research objectives. Furthermore, with the sample results of 237 respondents having reached 112.8% of the planned sample size of 210, it has met the requirements.
Descriptive Statistics
(Source: Respondents profile)
| Characteristics | Frequency | Percentage |
|---|---|---|
| Gender | ||
| Male | 140 | 59.07% |
| Female | 97 | 40.93% |
| Age (years) | ||
| 18 to < 25 | 63 | 26.58% |
| 25–34 | 101 | 42.62% |
| 35–44 | 57 | 24.05% |
| 45–54 | 16 | 6.75% |
| Job position | ||
| Back office | 112 | 47.26% |
| Frontliner | 125 | 52.74% |
| Work locations | ||
| Branches | 181 | 76.37% |
| Head offices | 56 | 23.63% |
| Length of services at work (years) | ||
| <2 | 41 | 17.30% |
| 2–5 | 101 | 42.62% |
| 6–10 | 51 | 21.52% |
| >10 | 44 | 18.57% |
The following (Table 2) are the results of the outer model program calculations after using Smart PLS 4 software.
Construct Validity and Reliability
(Source: PLS-algorithm result, authors’ interpretation (1))
| Construct | Items | Outer loading | Cronbach’s alpha | Composite reliability (rho_a) | Composite reliability (rho_c) | Average variance extracted (AVE) |
|---|---|---|---|---|---|---|
| Intrinsic motivation | MI1 | 0.897 | - | - | - | - |
| MI2 | 0.860 | 0.862 | 0.873 | 0.915 | 0.783 | |
| MI3 | 0.896 | - | - | - | - | |
| Extrinsic motivation | ME1 | 0.901 | - | - | - | - |
| ME2 | 0.900 | 0.887 | 0.888 | 0.930 | 0.815 | |
| ME3 | 0.908 | - | - | - | - | |
| Perceived ease of use | PEU1 | 0.817 | - | - | - | - |
| PEU2 | 0.776 | - | - | - | - | |
| PEU3 | 0.828 | - | - | - | - | |
| PEU4 | 0.846 | 0.929 | 0.931 | 0.942 | 0.701 | |
| PEU5 | 0.860 | - | - | - | - | |
| PEU6 | 0.863 | - | - | - | - | |
| PEU7 | 0.867 | - | - | - | - | |
| Perceived usefulness | PU1 | 0.812 | - | - | - | - |
| PU2 | 0.854 | - | - | - | - | |
| PU3 | 0.817 | 0.896 | 0.900 | 0.923 | 0.707 | |
| PU4 | 0.859 | - | - | - | - | |
| PU5 | 0.860 | - | - | - | - | |
| Employees’ intentional digital readiness | EID1 | 0.887 | 0.880 | |||
| EID2 | 0.898 | 0.883 | 0.926 | 0.806 | ||
| EID3 | 0.908 | - | - | - |
This study used the loading factor value to test the convergent validity. All the variable calculations have a loading factor value above 0.7. As shown in Table 2, the outer loading factor of all the items is greater than the minimum threshold of 0.5 proposed by Hair, et al. (2022). Based on Hair, et al. (2022), if the outer loading factor is greater than 0.5 but less than 0.7, then the researchers should consider the composite reliability (CR) and average variance extracted (AVE) to make a final decision in eliminating items. However, because the outer loading results of all the items are not below 0.7, all the items can be analyzed further. To assess the reliability scale used in this study, Cronbach’s alpha and composite reliability were used. Still according to Hair, et al. (2022), the Cronbach’s alpha and composite reliability must be greater than 0.7. As shown in Table 2, all the results of the Cronbach’s alpha and composite reliability are greater than 0.7, indicating sufficient reliability. To evaluate the convergence scale, this study used the AVE as suggested by Hair, et al. (2022), in that the AVE value must be greater than 0.5. Based on Table 2, all AVE calculation results are greater than 0.5, thus indicating sufficient convergence.
Table 3 shows that the correlation value of each construct has a value below 0.9; thus, the results of this analysis provide evidence that the correlation value of each construct has met the requirements of the minimum limit of discriminant validity. This study used the HTMT (Heterotrait-Monotrait) ratio to assess the discriminant scale. According to Hair, et al. (2022), HTMT must be less than 0.9. In Table 3, all ratios are less than 0.9, which indicates a satisfactory discriminant.
Discriminant Validity of the Heterotrait-Monotrait
(Source: PLS-algorithm result, authors’ interpretation (2))
| Construct | Employee’ intentional digital readiness | Extrinsic motivation | Intrinsic motivation | Perceived ease of use | Perceived usefulness |
|---|---|---|---|---|---|
| Employees’ intention digital readiness | - | - | - | - | - |
| Intrinsic motivation | 0.555 | 0.827 | - | - | - |
| Extrinsic motivation | 0.504 | - | - | - | - |
| Perceived ease of use | 0.735 | 0.714 | 0.740 | - | - |
| Perceived usefulness | 0.764 | 0.654 | 0.671 | 0.821 | - |
The results of the hypothesis testing are shown in Table 4. The relationship between intrinsic motivation (IM) and perceived ease of use (PEU) has a positive effect, with a path coefficient value of 0.417 and a VIF inner value of 2.131 from a standard value < 5, indicating no collinearity problems. The t-statistic value is 5.191, which means it is greater than the minimum limit of 1.96, indicating that this relationship is significant. In addition, the resulting p-value of 0.000 is below < 0.05, so hypothesis 1 is supported. Seeing the results of the R2 value of 0.505, it shows that 50.5% of the variation in PEU can be explained by intrinsic motivation, while the f2 value of 0.165 indicates a moderate effect of intrinsic motivation on PEU. In hypothesis 2, the relationship between intrinsic motivation (IM) and perceived usefulness (PU) has a path coefficient value of 0.364, a VIF inner value of 2.131, a t-statistic of 4.028, and a p-value of 0.000; therefore, H2 is supported. Then, the effect of intrinsic motivation (IM) on perceived usefulness (PU) has a moderate effect (R2: 0.408 and f2: 0.105). The relationship between extrinsic motivation (EM) and perceived ease of use (PEU) also has a positive effect, so H3 is supported. According to the test results in Table 4, it has a path coefficient value of 0.346, a VIF inner value of 2.131, a t-statistic of 3.959, and a p-value of 0.000. The R2 value of 0.505 provides a variation in the moderate category, and the f2 value of 0.114 provides a moderate effect. In hypothesis 4, the effect of extrinsic motivation (EM) on perceived usefulness (PU) is supported, with a path coefficient value of 0.322 and a VIF inner value of 2.131 meeting the requirements and not showing any collinearity problems. The resulting t-statistic value of 3.567 means it is greater than the minimum limit of 1.96, which indicates that there is a significant relationship. This is reinforced by the resulting p-value of 0.000 above 0.05, which affirms that hypothesis 4 is supported.
Hypothesis Testing
(Source: Bootstrapping result, authors’ interpretation)
| Relationship | Path Coef. | VIF-inner | T-statistics | P-value | R2 | f2 | Decision |
|---|---|---|---|---|---|---|---|
| IM > PEU | 0.417 | 2.131 | 5.191 | 0.000 | 0.505 | 0.165 | Supported |
| IM > PU | 0.364 | 2.131 | 4.028 | 0.000 | 0.408 | 0.105 | Supported |
| EM > PEU | 0.346 | 2.131 | 3.959 | 0.000 | 0.505 | 0.114 | Supported |
| EM > PU | 0.322 | 2.131 | 3.565 | 0.000 | 0.408 | 0.082 | Supported |
| IM > PEU > EID | 0.148 | 2.310 | 3.038 | 0.001 | 0.520 | - | Supported |
| EM > PEU > EID | 0.123 | 2.310 | 2.673 | 0.004 | 0.520 | - | Supported |
| IM > PU > EID | 0.151 | 2.310 | 3.193 | 0.001 | 0.520 | - | Supported |
| EM > PU > EID | 0.134 | 2.310 | 2.749 | 0.003 | 0.520 | - | Supported |
From the results of the bootstrapping test in Table 4 and visualize in Figure 2., it reveals the significant effect of intrinsic motivation (IM) on employees’ intentional digital readiness (EID) mediated by perceived ease of use (PEU). It has a path coefficient value of 0.148 from the effect of intrinsic motivation (IM) on employees’ intentional digital readiness (EID) mediated by perceived ease of use (PEU). The VIF inner value of 2.310 from the ideal value < 3 does not indicate any collinearity problems. The t-statistic value of 3.038, which means it is greater than the minimum limit of 1.96, indicates that this relationship is significant. In addition, the resulting p-value of 0.001 below < 0.05 strengthens the relationship between the variables; therefore, hypothesis 5a is supported. Meanwhile, the results of the extrinsic motivation (EM) test on employees’ intentional digital readiness (EID) mediated by perceived ease of use (PEU) in Table 4 with a path coefficient value of 0.123 also show a positive influence, but it has a small influence from extrinsic motivation on employees’ intentional digital readiness mediated by perceived ease of use (PEU). The VIF inner value of 2.310 from the ideal value < 3 does not indicate any collinearity problems. The t-statistic value is 2.673 which is greater than the minimum limit of 1.96, indicating that this relationship is statistically significant, and the resulting p-value is 0.004 below < 0.05, indicating that there is a relationship between the variables; therefore, hypothesis 5b is supported.
Research Model SEM-PLS
(Source: Bootstrapping result)
The test results show that the influence of intrinsic motivation (IM) on employees’ intentional digital readiness (EID) mediated by perceived usefulness (PU) with a path coefficient value of 0.151, a VIF inner value of 2.310, and a t-statistic value of 3.193 indicates a significant relationship. This is reinforced by a p-value of 0.001 below the minimum value < 0.05; therefore, hypothesis 6a is supported. Then for hypothesis 6b, namely, extrinsic motivation (EM) on employees’ intentional digital readiness (EID) mediated by perceived usefulness (PU) also has a significant relationship with a path coefficient value of 0.134, indicating a positive influence. The VIF inner value of 2.310 with the t-statistic value of 2.749 indicates that it is greater than the minimum limit of 1.96, indicating that there is a statistically significant relationship. The resulting p-value of 0.003 is exceeded by the minimum value of < 0.05, which affirms that H6b is supported.
In looking at the results of the R2 value of 0.520 from intrinsic motivation and extrinsic motivation on employees’ intentional digital readiness, both mediated by PEU or PU, it shows that there is a variation of 52.0% in employees’ intentional digital readiness, which can be explained by intrinsic motivation and extrinsic motivation. This shows the significance importance and contributions that both motivations influencing employees’ intentional digital readiness. In the initial trial using PLS SEM, all independent variables of intrinsic motivation, extrinsic motivation, PEU, and PU were tested directly against employees’ intentional digital readiness for an R2 value of 0.524. Thus, between the testing of intrinsic motivation and extrinsic motivation variables on employees’ intentional digital readiness, both mediated by PEU or PU with direct testing of all R-square value variables, there is not much difference.
The purpose of this study was to examine the impact of intrinsic motivation and extrinsic motivation on employees’ intentional digital readiness working in the Indonesian banking sector, where it is suspected that both types of motivation affect employees’ intentional digital readiness, especially in banks that are included in the KBMI III category through perceived ease of use (PEU) and perceived usefulness (PU). This study succeeded in collecting respondents who could represent the research objectives. The findings of this study reveal that perceived ease of use (PEU) and perceived usefulness (PU) are influenced by the intrinsic motivation of employees when using digital tools in their work. This intrinsic motivation is influenced by the pleasure of employees when completing their tasks with work aids such as applications and the Web. This finding confirms the research of Sheng, Jue and Weiwei (2008) and Trang (2024), which revealed that intrinsic motivation contributes to perceived ease of use (PEU), perceived usefulness (PU), and the adoption of digital technology. In a study, it was also found that intrinsic motivation is related to the perception that digitalization is easier to use and useful (Gagné, et al., 2014). Thus, this research can provide practical implications for KMBI III category banks in Indonesia emphasizing the importance of creating digital work aids with perceived ease of use (PEU) and perceived usefulness (PU) for employees.
The results of this study reveal that intrinsic motivation influences employees’ intentional digital readiness through perceived ease of use (PEU) and perceived usefulness (PU). This shows that those who are intrinsically motivated tend to be more enthusiastic about preparing themselves for digital transformation because employees see digital work aids as easy-to-use and useful tools. Thus, bank policies are needed to increase employee intrinsic motivation by providing recognition, autonomy, and significant work. This helps workers to be more prepared to face digital change.
In addition, this study also found that extrinsic motivation has an influence on perceived ease of use (PEU) and perceived usefulness (PU). Extrinsic motivation shows that it has an influence on employees’ perceived ease of use (PEU) when using digital aids in their work. This finding is in accordance with the research of Fagan, et al. (2008) and Lu and Pang (2023), which states that extrinsic motivation is positively related to the perception of ease of use in using applications and websites at work. In addition to having an influence on perceived ease of use (PEU), extrinsic motivation also has an influence on perceived usefulness (PU). The results of this finding are also the same as the findings of Meng and Li (2023), which revealed that extrinsic motivation affects perceived usefulness (PU) in the use of applications in work. However, the findings of this study are not in line with the research of Lu and Pang (2023) that extrinsic motivation is not enough to create perceived usefulness (PU).
So it can be concluded that this study found that extrinsic motivation affects employees’ intentional digital readiness through perceived ease of use (PEU) and perceived usefulness (PU). Extrinsic motivation influences employees’ digital readiness intentions through perceived ease of use (PEU), which means that extrinsic motivated employees tend to be more enthusiastic about preparing themselves for digital transformation because they see digital work tools as easy-to-use tools. This is also mediated through perceived usefulness (PU), which shows that extrinsic motivation also influences the use of digital work tools in helping employees complete their work.
This study was conducted to try to gain an understanding about employees’ intentional digital readiness in dealing with changes in the work process implemented by a company which is in line with the company’s strategy in the company’s digitalization process in all fields. Based on the results obtained, it can be seen that Indonesian banks’ employees have high readiness to use digital tools in their work, such as for applications and websites, and this arises from the desire and drive from within the employee, which is mediated by both perceived ease of use and perceived usefulness. However, what is interesting is that although support was found between extrinsic motivation mediated by perceived ease of use (PEU) and perceived usefulness (PU) on employees’ intentional digital readiness, it has a value that is not so significant when compared to intrinsic motivation. It can be said that the attitudes of the employees are mature enough to use digital work tools to help complete their work. However, employees are willing to use digital tools such as applications or websites that are considered to make it easier for them to complete their tasks. This motivation arises from an awareness within themselves. The managerial implication of this study is that banking organizations, especially those from banks that are going to implement full digitalization, should better prepare their employees to face the challenges of digitalization that continue to grow.
Although this study contributes to the literature and practice of digital adoption in the Indonesian banking sector, this study still has limitations and is designed only according to the level of staff positions in the use of digital work aids such as applications and websites. So there are still some limitations. First, this study had a sample size of 237 employees; hence, the results of this study still cannot represent the whole Indonesian banking sector. Second, the data from R2 of 0.520 from intrinsic motivation and extrinsic motivation on employees’ intentional digital readiness, both mediated by PEU or PU, show that there is a variation of 52.0% in employees’ intentional digital readiness, which can be explained by intrinsic motivation and extrinsic motivation; hence, there are still other variables that can affect employees’ intentional digital readiness that are not reflected in this study. Third, the current study only took samples from employees with staff levels who were considered direct users of digital aids such as applications or websites. However, the next work process after the staff level is to move to the managerial level; hence, the sample in the next study can examine the managerial position level.
Furthermore, this study recommends further research to investigate the digital adoption process at the managerial level in addition to a representative sample of all bank categories in Indonesian banks to provide a broader picture of how the acceptance of the digitalization process as a tool can help employees’ work in the company. In addition, there are still other variables that can support employees’ intentional digital readiness, one of which is employees’ skills.