Impact of the Introduction of E-Payslips to Civil Servants in Developing Countries Based on the Utaut Model ()
1. Introduction
The e-payslip system started with Non-Government organizations and recently the government of the Republic of Zambia started implementing the e-payslips system (Mainza, 2017) . Smart Zambia is an initiative established under Government Gazette No. 836 of 2016 as an E-government division in the office of the President. E-pay slips were officially introduced in the civil service in 2017. The system is aimed at complementing the Payroll Management and Establishment Control (PMEC) System which remain the Government’s payroll management and processing system.
There are a number of benefits regarding the introduction of e-pay slip system by the government of Zambia. Employees are able to access vital and detailed information about their pay anywhere and anytime. They are able to download, send electronically and print historical payslips to help them meet evidence requirements for loans and pension benefits. Payslips can now be stored in a single and secure place. Before e-pay slips, workers had to endure an anxious wait before printed pay slips are distributed and add to their filing piles every other month. The Government, through printing and distribution of payslips was using a lot of money to print and distribute payslips to public service workers. The introduction of e-pay slips has seen the Government save K72 million on pay slip printing and K68 million on annual cost of paper (SMART Zambia, n.d.) . The e-pay slip system has also helped the Government curb fraud and to get rid of ghost civil servants. This has helped the Government to save up money from salaries from those who were either dead or out of employment. The e-pay slip system has also complemented Government to achieve its green environment initiatives. This is because the phasing out of paper slips entails reduction in cutting down of trees and low energy usage.
A country’s capacity to attain development demands the introduction of modern facilities that reduces on the use of paper and concentrates on electronic measures (Jensen & Meckling, 2013) . E-payslips is one form of modern service aimed at preventing the tedious process of printing pay slips each month while using tons of paper which is against the modern rule of reducing on the use of paper. Just like any other newly introduced service, the impact of the service on users may be negative or positive. Various factors are usually at play in the outcome of the adoption, i.e. whether the technology is deemed as being positive or negative depends on factors that need to be explored. The adoption of most of this technological solution can be influenced by the user’s education background, access to devices, internet connectivity, and the ability to use this device. Currently, there is little known of how this initiative has been embraced by civil servants. The level of adoption and the challenges limiting adoption requires investigation and as such it was the purpose of this study to assess the level of adoption of e-pay slips and the challenges users may encounter.
2. Key Literature Review
E-pay slips or Electronic pay slips, sometimes referred to as “epayslips” are pay slips available online to employees through secure web portals (Mallin, 2012b) . The online web portal is called Employee self-service (Ibid). E-payslips in ESS is a self-service payslip facility that enables employees to access their payslips directly from a secure website using their desktops, laptops, iPad or smart phones. Printing and issuing hard-copy payslips is time-consuming and costly (Donaldson & Davies, 2009) . With e-Payslips, employees have the liberty of downloading and printing their payslips online. It is an easy experience for them, and saves time for the accounting and HR departments in both public and private companies.
Using the employee self-service online portal, each payslip is only accessible by each individual member of staff with their own nominate username and unique password (Mainza, 2017) . During set up the employee is given a username and password for login to ESS. They can then access their epayslips through this. Employees can change their password to ESS login as often as they would like.
Employees can log on to the e-payslip website and will be able to view any payslips of current and previous months, giving a secure portal to keep their payslips for their own records (Centona, 2010) . Employers can choose to setup a secure domain for employee self-service by publishing ESS using https protocol and this ensures all data is encrypted and meaningless to hackers. Switching to epayslips not only marks out as a modern employer as there are tangible benefits for employees and organizations alike. Electronic payslips are part of employee self-service in the modern HR systems. As well as making life easier for HR and payroll teams, employees also appreciate the benefits of having instant access to their pay records online.
While the requirement to print certain documents remains, there is little doubt that this approach is hardly environmentally friendly. But times are changing and there are some documents traditionally delivered in hard copy that have made the successful move to digital. With the government of Zambia and other governments around the world increasing pressure on organizations to reduce their negative environmental impact and to operate more sustainably, the need to tackle one of the biggest paper generators payslips has been recognized.
According to independent research, a single paper payslip contributes 2.8 grams of CO₂ to the UK’s total emissions (Bradbury, 1999) . This shows that e-pay slips have been a main offender for decades, creating the seemingly endless need for paper production up to 52 times a year per employee (Alchian & Damtez, 2017) . E-payslips remove the need for the production and circulation of paper-based payslips, allowing employees to view this information online.
Making the shift from paper to electronic payslips a good idea for both the reputation and balance of the business, not to mention one less thing for Mother Nature to worry about. E-pay slips automatically reduce the environmental impact of the business, cut the carbon emissions involved with printing and posting, as well as significantly minimize the company’s spending on paper, ink and stamps (Jensen & Meckling, 2013) . There are also the benefits e-payslips bring to employees, such as the ability to quickly rectify a mistake, and the avoidance of details of pay falling into the wrong hand.
Although data loss incidences are rare, there are a handful of risks involved with traditional payslip distribution. For example, with independent distributers, there is that chance that payslips may be lost during transit, leaving confidential information, such as wages, bonuses and overtime, open to unauthorized eyes (Mallin, 2012a) .
In this technological age, where most households are connected to the internet via several devices, it seems natural that such a common process would catch up with the rest of the modern world. Alchian & Damtez (2017) recommend that any business consider moving from a paper-based to electronic approach, and embrace the benefits of HR software. Proponents of the paperless generation such as Jensen & Meckling (2013: 12) have identified a number of merits for the use of e-pay slips as opposed to the use of paper based pay slips including:
1) The cost of stationery sees a significant reduction.
2) Less time is required from payroll to create and distribute payslips, giving them time to add value in other areas.
3) The paper consumption of your business significantly decreases, helping to reduce your carbon footprint and save more trees.
4) Security concerns are diminished, thanks to the ability to view details securely within an online, password protected environment.
5) An automated and streamlined payroll system.
6) Fewer instances of incorrect pay, because e-payslips can be accessed before money hits employees bank accounts.
7) Information is accessible anywhere, at any time as long as you have an internet connection.
Modern companies are working hand in glove with electronic media companies in creating e-pay slips as the world reduces on use of paper. At the time when the world has gone “Green” for the purposes of reducing human activities impact on climate change.
2.1. Conceptual Framework
The UTAUT model consists of six main constructs, namely performance expectancy (“PE” hereafter), effort expectancy (“EE” hereafter), social influence (SI), facilitating conditions (FC), behavioral intention (“BI” hereafter) to use e-pay slips, and usage behavior as shown in Figure 1 below. The four determining components of BI and usage behavior are PE, EE, SI, and FC (Venkatesh et al., 2003) while Gender, age, experience, and voluntariness of use are the moderators that affect usage of technology.
2.2. Theoretical Framework
Several theories and models have been developed, over the years, to establish factors that affect individuals’ decision to adopt and use information systems to enhance performance and boost user satisfaction (Imran & Grego, 2007) . Earlier researches that have been carried out can describe how application of existing theories in technological context can assist in the development of specific technology adoption approaches. These dimensions are applicable since a user’s decision to select a specific electronic service delivery system over more traditional one can be considered as an issue regarding technology adoption (Gilbert, Balestrini, & Littleboy, 2004) .
UTAUT’s theoretical model suggests that the actual use of technology is determined by behavioral intentions. It is hypothesized that perceived potential for technology adoption depends on the direct effects of four major components: expectations of achievement, expectations of effort, social impact, and conditions of support. The effects of predictors are moderated by age, gender, experience, and whether they are used voluntarily (Venkatesh et al., 2003) .
Performance expectations are defined as “the extent to which an individual believes that using a system will help them achieve better job performance” (Venkatesh et al., 2003) . Performance expectations are components of the Technology Acceptance Model (TAM), TAM2, Combined TAM and Planned Behavior Theory (CTAMTPB), Motivation Model (MM), PC Use Model (MPCU), and Innovation Diffusion Theory (IDT). Based on and social cognitive theory (SCT) (i.e., perceived usefulness, extrinsic motivation, job suitability, relative dominance, and outcome expectations). It is the strongest predictor of usage intent and is important in both discretionary and forced settings (Zhou, Lu & Wang, 2010a; Venkatesh, Thong, & Xu, 2016) .
Effort expected is defined as “the level of usability associated with using the system” (Venkatesh et al., 2003) . Effort expectations build from perceived ease of use and complexity, as definitions and measures of TAM, MPCU, and IDT are similar. Structural effects become negligible after long-term use of this technique (Gupta, Dasgupta, & Gupta, 2008a; Chauhan & Jaiswal, 2016b) .
Social clout is defined as “the extent to which an individual perceives that a significant other believes the individual should use the new system” (Venkatesh et al., 2003) . Social influences are similar to the subjective norms, social factors, and images used in TRA, TAM2, TPB, CTAMTPB, MPCU, and IDT, and demonstrate that people’s behavior is adapted to the perceptions of others. The impact of societal influence is significant when technology use is mandated (Venkatesh et al., 2003) . In mandatory contexts, individuals can use technology based on compliance requirements, but not personal preference (Venkatesh & Davis, 2000) . This may explain the inconsistent effects that structures showed in further studies validating the model (Zhou, Lu & Wang, 2010b; Chauhan & Jaiswal, 2016a) .
Facilitating conditions are defined as “the extent to which an individual believes that the organizational and technical infrastructure exists to support the use of the system” (Venkatesh et al., 2003) . Facilitative conditioning components are formed from compatibilities, perceived behavioral control, and facilitating conditioning of components drawn from the TPB, CTAMTPB, MPCU, and IDT. Facilitating conditions have a direct positive effect on intent to use, but the effect is not significant after the first use. This model therefore suggests that facilitative conditions have a direct and significant impact on consumption behavior (Venkatesh et al., 2003) .
2.3. Objectives
This study aims to understand the key factors influencing the adoption of e-payslips among health sector civil servants and intends to devise a framework for enhancing their adoption. The objectives include identifying the primary determinants impacting e-payslip adoption and offering a structured plan to elevate the acceptance and utilization of electronic payslips within the healthcare workforce.
Research Hypothesis
Based on the aforementioned objectives, the following hypotheses were tested:
1) H1: The intention of civil servants to use e-pay slips will have an positive impact on their assertiveness (H11), apparent practicality (H12), professed ease of use (H13), individual innovativeness (H14), subjective norm (H15), e-pay slip accessibility (H16), computer and high-speed internet connectivity.
2) H2: Civil servants’ assertiveness of the e-pay slip will have an affirmative bearing on their supposed efficacy (H21), supposed ease of use (H22), personal innovativeness (H23), subjective norm (H24), e-pay slip accessibility (H25).
3) H3: Civil Servants’ supposed efficacy of e-pay slip will have an affirmative bearing on the supposed ease of use (H31), personal innovativeness (H32), subjective norm (H33), e-pay slip accessibility (H34).
4) H4: Civil Servants’ supposed ease of use of e-pay slip will have an affirmative bearing on personal innovativeness (H41), subjective norm (H42), e-pay slip accessibility (H43).
3. Research Methodology
3.1. Introduction
This section presented the research methods such as the research design, sources of data, sample size, sampling methods as well as methods of data collection, reliability of data, validity of data and ethics that would be used in this study.
The study was conducted by surveying government of civil servants who draw their salaries through PMEIC from Livingstone District Health Office.
3.2. Research Design
The methods used where both qualitative and quantitative data. The qualitative method was used to obtain in-depth information, while the quantitative was used to generalize the results. The use of both qualitative and quantitative methods complemented each other in order to validate the methods that were used to realize the objectives of the research study.
3.3. Source of Data
Primary and secondary data sources were used. The primary data was collected from the target population from which the sample size was obtained. The target population was all civil servants at Livingstone District Health Office. Secondary data was obtained from relevant literature, including publications and reports on the effectiveness and efficiency of e-payslips.
3.4. Sample Size
The Simple Random sampling technique was considered more representative of the total population as opposed to other sampling techniques because it was focused on maximizing the number of eligible respondents. The case study was carried out at Livingstone District Health Office where questionnaires were distributed and collected.
The study employed stratified random sampling which is a method of sampling that involves the division of a population into smaller groups known as strata. In stratified random sampling, the strata are formed based on members' shared attributes or characteristics (Centona, 2010) . A random sample from each stratum is taken in a number proportional to the stratum’s size when compared to the population. These subsets of the strata were then pooled to form a random sample. The study employed this sampling technique to select one hospital and the participants. About 100 participants made it into the sample and the response rate was 100%. The sample size was determined using Yamane’s (1967) :
n = N/(1 + N(e2)),
where:
· n is the sample size;
· N is the population size;
· e is the desired level of precision;
In this case, we have:
· N = 484;
· e = 0.1.
Plugging these values into the formula gives:
n = 484/(1 + 484 × 0.12)
n ≈ 82.88
Therefore, the minimum sample size is approximately 83. However, in order to enhance reliability and increase generalizability, a larger sample size of 100 was chosen given that it was feasible.
3.5. Methods of Data Collection
The study required the collection of primary data. In carrying out this study, the researcher collected primary data using the questionnaire method. The advantages of using this method include: not expensive, gives respondents’ time to complete the instrument and reduces Hawthorne effects given its anonymity. However, some drawbacks in using this method that could be experienced include: some respondents may not be literate, lack of opportunity to probe respondents, low reply rate and ambiguity of questions. In mitigation, the researcher used the following guidelines to maximize response rate: Careful design of individual questionnaire; Clear layout of the questionnaire form; lucid explanation of the purpose of the questionnaire (Gable, 1994) .
3.6. Data Analysis
Primary data was processed and analyzed using the Statistical Package for Social Scientists. This package was used due to its excellent way of quickly analyzing large volumes of data gathered through various methods of research. Its ability to simplify difficult analytical tasks and to keep one from making mistakes or forgetting important details. A category is a set of criteria that are integrated around a theme. The first step in content analysis was to identify the categories and then coded the data. The coded data would offer some evidence about the dominant categories, attributes and trends. Some data were presented in narrative form. The results were analyzed using the Pearson’s Correlation to determine the relationship between the predictors and the adoption and use of the e-Payslip services.
3.7. Reliability of Data
To ensure reliability and validity, the study collected data using methods that have been used before and were successful as well as instruments that have been used in different setting and at different time points and have shown reliability and validity. These included the inter-rater method (whereby the results of the researcher were compared with the results of the research assistant that was employed) as well as the use of the internal consistency method (where the questions and the responses were logically related and tallied).
3.8. Validity of the Data
The determined data validity is transferred to the research instrument using the content validity. Content validity ensured that the research instrument was representative of the complete content of the phenomenon studied. In addition, content validity ensured that all aspects of the research subject were covered.
4. Ethical Consideration
Introductory letter from UNZA was obtained, followed by consent of hospital director was obtained. Brief explanations of the purpose and the procedures of the study were revealed to prospective participants before their consent. This was in line with the ethical demand that a research must ensure that relevant persons and authorities are consulted and informed, and the necessary permission obtained and applied (Gable, 1994) . The matter of confidentiality was explained to the informants so as to build a bond of trust between the interviewer and them. No force was used on the informants to take part in the study.
5. Demographics and Results Discussion
5.1. Demographic Profile of Respondents
Demographic data covers a set of features of respondents including: gender, marital status, age group, highest level of education, and type of employment. The demographic data is represented in Table 1. From the 219 respondents in this study, 65% are male and 35% are females.
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Table 1. Demographic profile of respondents.
5.2. Reliability and Factor Analysis
To enable internal validity, the research instrument used adopted a proportional number of questions on studies that were in tandem to this study in relation to both the variables that were independent and those that were dependable. The dependable variable included adoption and use of the e-Payslip which complements the Payroll Management and Establishment Control (PMEC) System of which remains the Government’s payroll management and processing system. When it comes to the independent variables, the underlisted were administered in the research tool:
1) Performance Expectancy;
2) Effort Expectancy;
3) Social Influence;
4) Facilitating Conditions;
5) Behavioural Intention.
Of the aforementioned independent variables, the respondents needed to rate as either 1 = “strongly disagree” to 5 = “strongly Agree” (on a five-point Likert scale). As per requirement and in order to ensure that the questionnaire was effective enough, a sample of three was used to pilot it and necessary corrections were worked on.
When it comes to reliability analysis, all the variables that were adopted in the study ensured that they represented the mandate of the government’s payroll management and processing system. In ensuring that reliability was further attained, using Cronbach’s Alpha coefficient, the coefficients were all above 0.7, suggesting that used in the questionnaire were quite reliable. And to ensure that no outliers were attained, all items showing coefficients less than 0.7 were excluded from the analysis.
5.3. Hypothesis Results, Analysis and Interpretation
Hypothesis 1: EE has a significant influence on the BIs of health workers to use e-pay slips. In this hypothesis, the correlation coefficient between Employee Engagement (EE) and Behavioral Intentions (BIs) is 0.29. This positive correlation indicates a moderate positive relationship between EE and BIs. Therefore, there is some evidence to suggest that higher levels of employee engagement might lead to increased behavioral intentions to use e-pay slips among health workers.
Hypothesis 2: PE has a significant influence on the BIs of health workers to use e-pay slips. The correlation coefficient between Perceived Ease of Use (PE) and Behavioral Intentions (BIs) is 0.42. This positive correlation indicates a moderately positive relationship between PE and BIs. This implies that health workers who perceive e-pay slips as easier to use are more likely to express stronger behavioral intentions to use them.
Hypothesis 3: Satisfaction has a significant influence on the BIs of health workers to use e-pay slips. The correlation coefficient between Satisfaction and Behavioral Intentions (BIs) is 0.18. This positive correlation suggests a weak positive relationship between Satisfaction and BIs. It indicates that higher levels of satisfaction among health workers might be associated with slightly increased behavioral intentions to use e-pay slips.
Hypothesis 4: Trust has a significant influence on the BIs of health workers to use e-pay slips. The correlation coefficient between Trust and Behavioral Intentions (BIs) is 0.15. This positive correlation indicates a weak positive relationship between Trust and BIs. It suggests that higher levels of trust in the e-pay slip system might be associated with slightly increased behavioral intentions among health workers to use it.
The regression analysis conducted in this study showcases a robust model illustrating the relationship between predictors—Facilitating Conditions (FC), Employee Engagement (EE), Social Influence (SI), and Performance Expectancy (PE)—and their influence on Behavioral Intention (BI) towards e-payslip adoption within the health sector.
The model exhibits a commendable fit, as denoted by the high coefficient of determination (R Square = 0.912) and adjusted R Square at .908, indicating that approximately 91.2% of the variability in the Behavioral Intention (BI) can be explained by the four predictor variables—FC, EE, SI, and PE.
Moreover, the regression equation is statistically significant (F = 245.557, p < 0.001), suggesting that the model as a whole significantly predicts the Behavioral Intention (BI) of health workers towards e-payslip adoption.
Breaking down the individual coefficients, each predictor variable—FC, EE, SI, and PE—illustrates a significant positive relationship with BI, as evidenced by their respective coefficients (p < 0.001). Notably, Performance Expectancy (PE) demonstrates the strongest influence (β = 0.532), followed closely by Social Influence (SI) (β = 0.566), Facilitating Conditions (FC) (β = 0.388), and Employee Engagement (EE) (β = 0.129).
The findings suggest that Health Workers’ Behavioral Intention (BI) toward e-payslip adoption is notably influenced by their perceptions regarding Performance Expectancy (PE), Social Influence (SI), Facilitating Conditions (FC), and to a lesser extent, Employee Engagement (EE). These outcomes underpin the importance of perceived utility, social encouragement, available resources, and employee involvement in steering the adoption of e-payslips within the healthcare sector.
6. Discussion Linking the UTAUT Model to the Study Results
The primary objective of this research study was to identify the factors from the UTAUT model that influence the adoption and use of electronic payroll (e-payslip) services on public sector e-government platforms. The results show that UTAUT can account for public sector customer behavior related to the adoption and use of electronic payroll services, and that all but one of the assumed relationships in UTAUT are supported (Abbad, 2021) . Societal impacts did not have a noticeable impact on system adoption and use. This is consistent with other findings on the adoption of “unsupported” technologies (Jambulingam, 2013) and in the e-government context (Abbad, 2021) .
Hypothesis 1: EE has a significant influence on the BIs of health workers to use e-pay slips. In this hypothesis, the correlation coefficient between Employee Engagement (EE) and Behavioral Intentions (BIs) is 0.29. This positive correlation indicates a moderate positive relationship between EE and BIs. Therefore, there is some evidence to suggest that higher levels of employee engagement might lead to increased behavioral intentions to use e-pay slips among health workers.
Hypothesis 2: PE has a significant influence on the BIs of health workers to use e-pay slips. The correlation coefficient between Perceived Ease of Use (PE) and Behavioral Intentions (BIs) is 0.42. This positive correlation indicates a moderately positive relationship between PE and BIs. This implies that health workers who perceive e-pay slips as easier to use are more likely to express stronger behavioral intentions to use them.
Hypothesis 3: Satisfaction has a significant influence on the BIs of health workers to use e-pay slips. The correlation coefficient between Satisfaction and Behavioral Intentions (BIs) is 0.18. This positive correlation suggests a weak positive relationship between Satisfaction and BIs. It indicates that higher levels of satisfaction among health workers might be associated with slightly increased behavioral intentions to use e-pay slips.
Hypothesis 4: Trust has a significant influence on the BIs of health workers to use e-pay slips. The correlation coefficient between Trust and Behavioral Intentions (BIs) is 0.15. This positive correlation indicates a weak positive relationship between Trust and BIs. It suggests that higher levels of trust in the e-pay slip system might be associated with slightly increased behavioral intentions among health workers to use it.
7. Conclusion
Concluding this study, the in-depth investigation into e-payslip adoption at the Livingstone District Health Office has yielded valuable insights, harmonizing empirical findings with established scholarly perspectives. The amalgamation of quantitative data and existing research has paved the way for strategic recommendations that aim to enhance the adoption process.
The exploration of factors influencing e-payslip adoption has unearthed diverse viewpoints, with the actual percentages serving as pivotal guideposts throughout the analysis. These quantitative indicators, when synthesized with the existing scholarly discourse, have shed light on critical dimensions of adoption. Notably, the salient role of performance expectancy, the significance of effort expectancy, the influence of social factors, the necessity of facilitating conditions, and the embodiment of behavioral intention have emerged from the empirical landscape. These empirical insights have seamlessly aligned with established theoretical frameworks, culminating in the formulation of a pragmatic framework attuned to the unique context of the Livingstone District Health Office.
8. Limitations
The study was limited to Livingstone. The other limitation was a lack of money to fully research in the other areas in order to diversify regions and places. From the entire country of 10 provinces, only one was researched on.
9. Recommendations
· The Government must create a loan scheme for smart phones and laptops which health workers can obtain on loan. This will increase access to e-pay slips as the technology requires access to internet through smart technology.
· Promotion and easing of user friendliness of e-pay slips.
· Adoption of a wide range of measures such computer rooms in health centres where workers can check their pay slips.
· Raise the ethical profile of the authority and strengthen health worker belief in the value of using smart technology as opposed to paper which is environmentally unfriendly.
· Smart Zambia should intensify the use of e-payslip to not only government workers but also others.
· Safety should be guaranteed to the users of e-payslip.
· A study could further be undertaken, widening the scope of moderators to include age, gender, experience, and voluntariness of use as variables to determine views of the use of e-payslips across these characteristics.
Acknowledgements
Special thanks go to all the participants in this research, both at developmental and data collection points. Special thanks also go to my mother, entire family, my supervisor as well as my course mates and staff in my department.