Technology acceptance model of Tuberculosis Integrated Information System in Indonesian primary healthcare

Abstract

: This article investigates the acceptance model of the technology of tuberculosis integrated information system (TIIS) adopted and its effects on the actual use in the Indonesian healthcare facility. Some factors such as the perceived usefulness and ease of use are investigated to understand their relationship with the system’s actual use. Quantitative research has been done in this study using a structural equation model (SEM) and partial least square. This study shows that respondents’ perceived usefulness and the ease of use of TIIS positively affect the adopted system’s actual use. The evaluation of the influence of perceived usefulness and perceived ease of use and external variables such as subjective, image, job relevance, and facilitating condition on the intention to use using TIIS to support the implementation of the program of prevention of infectious diseases in a developing country such as Indonesia would help the government to treat the tuberculosis disease better.

Keywords:

• healthcare
• acceptance model
• usefulness
• the actual use

1. Introduction

The accelerated development of information technology and the growing population of internet e-service users are widely accepted, not only in high-tech industries and traditional industries but also in the medical industry. In the past, information flows were mostly paper-based and resulted in slow, unreliable, and error-prone information. An Internet system that has grown worldwide has become a powerful tool with the potential to significantly enhance the management of organizations, including the medical industry (Dutta‐Bergman, 2004; Health information technology in health is essential because it can support users and decision-makers, for example, in reducing clinical errors in diagnostics and treatment (Ammenwerth et al., 2003). Moreover, health services that do not adapt to their system information management will become inefficient and lose their patients’ confidence (Ammenwerth et al., 2003).

The application of a computerized health information system is a significant investment worldwide; evaluating the healthcare system needs to be done multi-dimensionally. Approximately three-quarters of its implementation is considered a failure, and no evidence can increase health professionals’ productivity. Those responsible for computerized systems’ performance need to pay attention to the lessons learned to avoid further wastage of scarce health resources (Littlejohns et al., 2003). Health information is a complex environment because of the rapid organizational structure changes, culture, and resources and management that affect information systems’ implementation and use (Jha et al., 2009). IT uses may be useful in improving performance, better performance, and further improving efficiency and lowering costs, thereby improving patient care quality and comfort (Holden & Karsh, 2010).

The implementation of recording and reporting of the application and results of health program activities using information technology through the Tuberculosis Integrated Information System (TIIS) in Indonesian primary healthcare facilities still found obstacles, especially in terms of delays in reporting. The reluctance of healthcare staff to use new technology in their work activities is influenced by several factors such as their use of technology, the ease of use of technology, the perceived benefits of using technology, and external factors such as social norms job relevancy. Thus, to see how technology, especially information technology, is accepted and used in supporting a healthcare service, the technology acceptance model (TAM) approach could be implemented. The TAM is a well-known model for explaining a person’s intentions to use technology, where perceived usefulness (PU) and perceived ease of use (PEOU) are fundamental in influencing personal attitudes and behaviors using technology (Davis, 1989).

With the above problems related to the use and utilization of the TIIS program through this research, it is essential to evaluate the influence of PU and PEOU and external variable such as subjective, image, job relevance, and facilitating condition on the intention to use (AU) using TIIS. This is to support the implementation of the program of prevention of infectious diseases (PPID). The structure of this article is written in five sections. Section 1 (Introduction) discusses the background of this study and identifies the gap of previous studies. Section 2 discusses the related studies that contributed to developing the framework. Subsequently, the research methodology is discussed in the next section, followed by Section 4, which discusses the results and managerial implications. The final section is the conclusion.

2. Review of related literature

External variables (VE), according to Davis (1989), are the factors that mediate PU and PEOU in assessing acceptance of a technology that can include subject norm, image, job relevance, output quality, and result in indemonstrability. External variables can be found in the form of a healthcare management system (HMS) availability. The use of external variables in the application of HMS for healthcare has been widely applied using TAM to determine its impact on user’s technology acceptance behaviour (Melas et al., 2011; Tsai et al., 2020). In health information system, external variables that could influence technology acceptance are gender, education, experience use, age, and the quality of the activity results (Holden & Karsh, 2010; Tung et al., 2008). A critical factor that seems to influence technology acceptance in health professionals is organizational perception, training, and support. The organizational context consists of subjective norms and facilitator perception. Facilitator perception is a predictable influential variable for nurses and doctors to use new technology. It is crucial to provide adequate training for health professionals to increase acceptance of telemonitoring. Healthcare providers receiving new systems are one of the main problems that must be addressed to ensure telemonitoring programs’ success (Gagnon et al., 2012). External variables in the form of experience using the learning management system (LMS) have a negative and insignificant effect on the behavior intention to use (BIU), perceived usefulness (PU), and perceived ease of use (PEOU). However, external variables in the form of job relevancy have a positive and significant influence on PU and PEOU (Alharbi & Drew, 2014). Concerning this and the existing field conditions through observations of researchers, finally, the external variables that are assessed can affect the problems that arise, namely subjective norm, image, job relevancy, and facilitating conditions.

2.1. Perception of ease of use

Davis (1989) defines perceptions of ease of use (PEOU) as a person’s level of confidence that in using technology will be free from difficulties; the use of technology will make it easier for someone to complete their work. The perception of ease of use (PEOU) has a positive and significant effect on the behavior intention to use the system (BIU) directly (Alharbi & Drew, 2014; Gagnon et al., 2012) and influence perceptions of usefulness (PU; Alharbi & Drew, 2014). The perception of ease of use has no significant effect on the intention to use the system at the reception of e-learning (Park, 2009; Yi et al., 2006).

2.2. Perceived usefulness

The health information system benefits include the dimensions that make work easier (makes the job more comfortable), useful, increase productivity, enhance effectiveness (effectiveness), develop job performance (improve job performance). Moreover, health information system benefits make the job easier, useful, increase productivity and effectiveness, enhance efficacy, and improve job performance (Giannini, 2015). Perception of usefulness has a positive and significant effect directly on the intention to use health information systems (Jun et al., 2010; Yun, 2013). Perceptions of usefulness on the acceptance of healthcare information system do not significantly influence the intention to use health information systems (Djamasbi et al., 2009; Vitari & Ologeanu-Taddei, 2018) and also have no significant effect on the actual use of information systems on internet users (Lin et al., 2011).

2.3. Behaviour intention to use

Social and interpersonal communication networks play an essential role in influencing adoption decisions; professionalization and medical practice specialization tend to hold opinions and suggestions from their highly valued colleagues. The direct path from the subjective norm to the mind is an internalization effect that can be the basis for one’s feelings about technology’s usefulness (Jackson et al., 2013). The most robust relationship is found in healthcare technology self-efficacy and behavioral intention, followed by attitude. Both benefits and perceived ease of use were found to affect user attitudes significantly (Rahman et al., 2016). Subjective norms identified as the greatest determinant of perceived benefits and efficacy of technology adoption have the greatest effect on perceptions of use (Calisir et al., 2009). System accessibility is not significant for all constructions except perceptions of ease of use. Of all the statements above, healthcare technology self-efficacy is an important variable, followed by subjective norms influencing behavioral intentions to use e-healthcare information system. Self-efficacy of information technology can be considered intrinsic motivation and subjective norms, which may be intrinsic motivation factors that can help users to regulate self-motivation in using technology (Venkatesh, 2000).

Intention to use the healthcare system (BIU) has a positive and significant influence on the real use (AU) in the acceptance of m-health service system in facilitating the users operating process (Venkatesh, 2000) and acceptance of second life for enhancing healthcare education on users perceptions (Chow et al., 2012).

2.4. Actual use

Indicators of health system actual use are that it is repeated and more frequent use on healthcare information system has been studied previously such as by Maillet et al. (2015) and Chiu and Ku (2015). Actual use is an external psychomotor response measured by someone with real use (Davis, 1989) and conceptualized on measuring the frequency and duration of time using technology (Wibowo, 2008). The actual use can be obtained from a significant relationship between perceived usefulness and perceived ease of use in technology acceptance for healthcare information system (Lu & Gustafson, 1994). On the other hand, the perception of ease of use does not significantly influence the real benefit of preventive mobile health services in China (Guo et al., 2013).

3. Methodology

This research was conducted in 2020 on primary healthcare facilities based on Directly Observed Treatment of the Tuberculosis System (DOTS) in the work area of District Health offices in East Java province, Indonesia. Purposively sampling respondents selected were officers of the prevention of tuberculosis program, either government or private agencies, with a total of 79 respondents.

A quantitative approach is used in this study, with instruments developed by Yi et al. (2006). Questions in this term are focused on factors affecting the acceptance of new technology, which include subjective norms, images, job relations, facilitation conditions, perceived usefulness, perceived ease of use, behavioral intention to use variables, and actual use.

This study’s research framework investigates the relationship between perceived usefulness, which includes perceived ease of use and external variables, and actual use. The dimensions and items of TAM are made based on prior studies (see Yi et al., 2006; Chismar & Wiley-Patton, 2002; Venkatesh & Bala, 2008; Holden & Karsh, 2010). In this study, 34 questions about the perceived use questioned to the respondents are developed. Seven hypotheses were developed based on the research model plan framework. The research framework and hypotheses are shown in the following Figure 1.

Figure 1. Research framework.

In this study, three dimensions of external variables such as subjective norms, image, and job relevancy were used to examine the relationship between external variables and perceptions of the usefulness of health system information. Yu and Tao (2009) and Alharbi and Drew (2014) believed that subjective norms, job level, and job relevancy have a positive impact on users' perceived usefulness on technology.

3.1. Hypotheses

Based on the research conducted (Yu et al., 2009), external variables, including subjective norm and image, significantly impact the perception of nurses’ use (perceived usefulness) in the health information system application. According to Alharbi & Drew (Alharbi & Drew, 2014), job relevancy as an external variable has a strong relationship in this study. It has a positive impact on the perceived usefulness (perceived usefulness) of a new system.

H1 = External Variable (VE) has a positive and significant effect on the perception of the usefulness (PU) of the TIIS program.

H2 = Perceived ease of use (PEOU) positively impacts the perceived usefulness (PU) of the TIIS program.

H3 = Perceived ease of use (PEOU) variable has a positive and significant effect on the intention to use (BIU) TIIS program.

H4 = Perception of Use (PU) of the Integrated Tuberculosis Information System (TIIS) has a positive and significant effect on the desire to use (BIU) TIIS program.

H5 = perception of ease of use (PEOU) has a positive and significant effect on the real use (AU) of the TIIS program.

H6 = perception of usefulness (PU) has a positive and significant effect on actual use (AU) of the TIIS program.

H7 = The desire to use (BIU) has a positive and significant effect on the actual use (AU) of the TIIS program.

4. Results

It is indicated by descriptive statistics analysis that out of 79 respondents, eight officers (10.13%) stated strongly agreeing to use TIIS software because other people also used TIIS. Moreover, 54 officers agreed (68.35%), eight officers expressed doubt (10.13%), nine officers stated disagree (11.39%). No officer said strongly disagree (0%). Most of the PPID Tuberculosis program officers agreed with TIIS software to implement reporting on job activities, considering that all health workers started the Ministry of Health, Provincial, District, and City Health Offices had been using this software since 2014. However, PPID Tuberculosis program officials also stated that they did not agree to use TIIS software because other people use this TIIS software even though it was necessary for them concerning current technological developments. Detailed statistics detailed results for each indicator are shown in Table 1.

Table 1. Descriptive statistics analysis

Variables	Item	Mean			Mode	Min	Max
Perception of respondents on actual use	X1.1	3.77	3.90	3.99	4	2	5
	X1.2	4.01			4	2	5
	X1.3	3.91			4	2	5
	X1.4	4.05	3.98		4	2	5
	X1.5	3.97			4	2	5
	X1.6	3.92			4	2	5
	X1.7	3.95	3.99		4	2	5
	X1.8	3.94			4	2	5
	X1.9	4.05			4	2	5
	X1.10	3.99			4	2	5
	X1.11	4.01			4	2	5
	X1.12	4.11	4.06		4	2	5
	X1.13	4.01			4	2	5
	X1.14	4.06			4	2	5
	X1.15	3.97			4	2	5
	X1.16	4.13			4	2	5
Perception of respondents on perceived ease of use	X2.1	3.95	3.97		4	2	5
	X2.2	3.97			4	2	5
	X2.3	4.05			4	2	5
	X2.4	3.96			4	2	5
	X2.5	3.99			4	2	5
	X2.6	3.90			4	2	5
Perception of respondents on perceived usefulness	Z1.1	4.03	4.00		4	2	5
	Z1.2	4.03			4	2	5
	Z1.3	4.03			4	2	5
	Z1.4	4.04			4	2	5
	Z1.5	3.90			4	1	5
Perception of respondents on perceived usefulness	Z2.1	4.03	3.97		4	2	5
	Z2.2	3.94			4	2	5
	Z2.3	3.95			4	2	5
Perception of respondents on actual use	Y1	3.97	3.90		4	2	5
	Y2	3.82			4	2	5

The results also indicate that all constructs in this study are more than 0.70 in both composite reliability and Cronbach’s Alpha value (see, Table 2). It shows that the constructs are reliable and able to proceed to the next step. Figure 2 show the final structural model Partial Least Squares.

Figure 2. Final structural model Partial Least Squares (PLS).

Table 2. Reliability test

Latent variables	Cronbach’s alpha	Remark
External variable (X1)	0.957	Reliable
Perceived ease of use (X2)	0.949	Reliable
Perceived usefulness (Z1)	0.989	Reliable
Behaviour intention to use (Z2)	0.960	Reliable
Actual use (Y)	0.940	Reliable

In this study, the standard values of composite reliability ≥0.60 standard Cronbach’s alpha ≥0.70 and average variance extracted (AVE) ≥0.50 (JR Hair et al., 2016) are used to examine the validity and reliability of the data constructs. The results of the outer loading test are shown in Table 3.

Table 3. Final outer loading test

Latent variables	AVE	Composite reliability	Cronbach’s alpha	Remark
External variable (X1)	0.5464	0.9502	0.9443	Valid and reliable
Perceived ease of use (X2)	0.7499	0.9473	0.9331	Valid and reliable
Perceived usefulness (Z1)	0.8392	0.963	0.9517	Valid and reliable
Behaviour intention to use (Z2)	0.9100	0.9681	0.9505	Valid and reliable
Actual use (Y)	0.8572	0.9231	0.8334	Valid and reliable

This study uses the determination coefficient analysis (R²; JR Hair et al., 2016; J. Hair et al., 2011). Table 4 shows the value of R square for dependent variables.

Table 4. R-square value

Latent variables	R-square
Perceived usefulness—PU (Z1)	0.6061
Behavior intention to use—BIU (Z2)	0.7048
Actual use—AU (Y)	0.6527

This study uses three dependent variables. The first dependent variable is the variable perceived usefulness (Z₁), which is influenced by the external variable (X₁) and perceived ease of use (X₂). The second dependent variable is the behavior intention (Z₂), which is influenced by the sensed usefulness variable (Z₁). The final dependent variable is the actual use variable (Y) controlled by the behavior intention to use (Z₂) variable.

The value of the R-square test for the perceived usefulness variable (Z₁), which is 0.6061, indicates that the perceived usefulness of the TIIS is correlated by external variable (X₁) and perceived ease of use variable (X₂) with the value of 60.61%. In contrast, the rest (39.39%) is not able to be described in this study. The most significant value of R-square for the variable of behavior intention to use (Z₂) (0.7048) is constructed by perceived usefulness (Z₁). Moreover, the value of R-square’s determination coefficient for the actual use variable (Z₂), which is 0.6527, indicates that the variable is influenced by the variable of behavior intention to use (Z₂) with the value of 65.27%. In contrast, the rest (34.73%) is affected by another variable outside of this study.

Another evaluation of the model has been done using the path coefficient to investigate the path coefficient’s significance between variables. The results that indicate the path coefficient between variables are shown in Table 5.

Hypotheses 1: External variables have a positive effect on perceived usefulness.

Table 5. Path coefficient

Variables	Original sample	tvalue	pvalue
External variable (VE) -> Perceived usefulness (PU)	0.411	2.2249	0.013
Perceived ease of use (PEOU) -> Perceived usefulness (PU)	0.4193	2.0404	0.021
Perceived ease of use (PEOU) -> Behaviour intention to use (BIU)	0.5776	4.5972	0.000
Perceived usefulness (PU) -> Behaviour intention to use (BIU)	0.319	2.6039	0.005
Perceived ease of use (PEOU) -> Actual use (AU)	0.292	1.5657	0.059
Perceived usefulness (PU) -> Actual use (AU)	0.1224	0.9245	0.178
Behaviour intention to use (BIU) -> Actual use (AU)	0.452	2.231	0.013

The hypothesis testing results indicate that the relationship of external variables with perceived usefulness shows a path coefficient value of 0.411 with a statistical t_value of 2.2249. This value shows that t_value> t_table (1.960) and is significant (p < 0.05%). This shows that H₀ is rejected and Ha is accepted so that external variables have a positive and direct influence on perceived usefulness. It shows that the first hypothesis is accepted.

Hypotheses 2: Perceived ease of use has a positive impact on perceived usefulness

The relationship of the perceived ease of use variable with perceived usefulness shows a path coefficient value of 0.4193 with a statistical t value of 2.0404. This value indicates that t_value is greater than t_table (1.960) and significant or p ≤ 0.05%. This shows that H0 is rejected and Ha is accepted. The perceived ease of use has a positive and direct influence on perceived usefulness, which means that the second hypothesis is accepted.

Hypotheses 3: Perceived ease of use has a positive impact on behavior intention to use.

Perceived ease of use positively influences the behavior intention to use (P_value = 0.000 < sig. (α = 0.05) and t_value = 4.5972 > t_table(1.96)). The perceived ease (perceived ease of use) positively influences on behavioral intention to use. It indicate that the ease of learning have a strong influence on the respondents’ intention to use the behavior intention to use TIIS software.

Hypotheses 4: Perceived usefulness has a positive impact on behavior intention to use.

The results of testing the hypothesis show that the relationship of perceived usefulness with behavior intention to use leads to a path coefficient value of 0.319 with a statistical t_value of 2.6039. This value indicates that t value is greater than t_table (1.960) and significant or p ≤ 0.05%. This result suggests that the hypothesis is accepted.

Hypotheses 5: Perceived ease of use has a positive impact on actual use

Perceived ease of use has an insignificant effect on actual use with a path coefficient of 0.292 and t_table of 1.5657. The test results show that the t_value < t_table (1.960) and not significant (p > 0.05%). The results above indicate that the hypothesis is rejected.

Hypotheses 6: Perceived usefulness has a significant positive impact on actual use.

The other result says that the perceived usefulness has an insignificant effect on the actual use of TIIS software with a path coefficient of 0.1224 and t statistics of 0.9245, which is indicated by t_value < t_table (1.960) and not significant (p > 0.05%). The results above suggest that the sixth hypothesis is rejected.

Hypotheses 7: Behavior intention to use has a positive impact on actual use

Behavior intention to use has a positive effect on actual use with a path coefficient of 0.452 and t-statistics of 2.231. The test results show that t_value > t_table (1.960) and significant (p > 0.05%), where the hypothesis is accepted.

5. Discussion and implications

The dimensions of the external variables in the form of subjective norm, image, job relevancy, and facilitating conditions positively and significantly affect perceptions of TIIS software users’ use. It is primarily on indicators using TIIS software tasks completed on time from job relevancy dimensions. The respondents argued that assignments that could be completed on time through TIIS software influenced respondents’ perceptions of TIIS software’s usefulness in supporting their tasks in recording reporting activities and results of the Tuberculosis program. This result is in line with a previous study by Chismar and Wiley-Patton (2002), who found that technology can improve the quality of productivity and service delivery effectiveness as a whole. Similar results by Rouibah et al. (2011) show that the dimensions of external variables such as subjective norms, images, and facilitating conditions have a direct effect of the intention to use information technology on e-shopping. In terms of healthcare information system, the results of this study is relevant with the findings of the study by Ketikidis et al. (2012). It found that subjective norms and job relevancy directly predicted the use intention of health information technology.

The influence perceived ease of use on perceived usefulness, where indicators are easy to learn, has the most considerable impact on perceived usefulness. With the ease of using TIIS software, the respondents felt the benefits of using TIIS software. Yusoff et al. (2009), in their study on the acceptance of e-library technology, found that perceived ease of use has a significant influence on perceptions of usefulness. The finding of this study also supports a previous study by Schnall et al. (2015) who indicated that perceived ease of use and perceived usefulness are the factors related to mobile health technology use.

TIIS software that is easy to use makes respondents intend to use TIIS software to record the reporting of the tuberculosis program activities they are working on. This result is relevant to a study by He et al. (2018) and Shahid Iqbal et al. (2018). They found that the perception of ease of use of a new system or technology significantly affects the user’s intention and efficacy to use the system. Wang et al. (2014) also revealed that smart health users had a high level of intention, self-efficacy, and innovativeness when using healthcare application.

The fourth hypothesis which evaluates the impact of perceived usefulness on behavior intention to use indicates that there is a positive impact on the two variables. The results of this study indicate the same finding. For instance, the previous investigation of the correlation between technology usage and user’s motivation to use technology in their tasks by Choy et al. (2015) found that there is a strong relationship between technology usage and the behavior intention to use technology. The respondents in this study felt that using TIIS software was more effective in completing their tasks, so they intended to continue using TIIS software. The effectiveness of healthcare information technology has also been studied by Kisekka and Giboney (2018) and Rho et al. (2014), who found that the effectiveness of information security increases the frequency of user access to health records and positive attitude toward healthcare information technology.

The rejection of the fifth hypothesis indicates that although TIIS software is provided for easy use, it does not always make respondents use this software significantly in their work. Similar results can also be found in a previous study by Yusoff et al. (2009), who found that perceptions of ease of use had no significant effect on the actual use of information technology. The respondents’ level of study could also cause a negative impact of perceived ease of use on the system use. The number of respondents with a higher education degree is 27.02%, while most of them (72.98%) have completed high school. As most workers who have completed their education are old workers, the interaction with new information technology in the workplace was much more limited than younger workers who have a higher education level (Morris & Venkatesh, 2000). Thus, although their perceived ease of use for the TIIS program is applied in their workplace, this new technology’s actual usage is not significant.

The sixth hypothesis which shows that there is an insignificant relationship between perceived ease of use and actual use might be triggered by the age of respondents. The respondent’s age might trigger the rejection of this hypothesis. The majority of respondents (62.02%) have an age below 40, which indicates that their attitude influences technology in their workplace. This is in line with a previous study by Morris and Venkatesh (2000), who found that younger workers were more strongly influenced by attitude toward using the technology than perceived usefulness. Bhattacherjee and Hikmet (2007) also illustrated that there are asymmetric effects of healthcare information system usage from user’s perceived ease of use.

The last hypothesis shows that the effect of behaviour intention to use TIIS software has the most significant value affecting the software’s actual use. The respondents’ intention to use TIIS software in the execution of their daily work is shown by using TIIS software that is more frequent or repetitive. This is relevant to the result of previous studies (Masudin et al., 2018) which investigated the drivers affecting green supply chain management (GSCM) and found that intention behaviour is the factor affecting GrSCM adoption. The significant relationship of behaviour intention to use healthcare information system found in this study supports the finding of Karahoca et al. (2018). They found that there is a positive effect on the behaviour intention to adopt IoT healthcare products.

5.1. Managerial implications

Though practitioners and academics have widely discussed the discussion related to the acceptance model of management personnel and its influence on an organization’s performance, discussing the acceptance model in healthcare is still rare, especially for tuberculosis. Therefore, the TIIS program in Indonesia is essential and significant to adopt by primary healthcare staff. The implication of this study’s findings is essential for management in terms of some results of this study, which are unfortunately found some counterproductive findings to implement TIIS software. For instance, the results show that workers’ level of education would significantly impact TIIS usage. Managers in primary healthcare should consider improving the requirements for future recruitment of workers at the skilled-education level. The level of workers’ level of education, the interaction with a new system or technology would be higher (Bresnahan et al., 2002; Morris & Venkatesh, 2000).

A manager of primary healthcare should also be aware that the workers’ attitude influences the acceptance of TIIS in the workplace. Venkatesh and Morris (2000) found that attitude using technology would be affected by three factors, such as age, social influence, and their role in technology. Specifically, to consider the worker’s social impact and role in technology, management could do some activities to arrange for workers to have their technology acceptance, such as training and workshop about the technology adopted. The more that a worker perceives that technology is easy to use, the more favourable that worker’s attitude toward the use of technology (Porter & Donthu, 2006).

5.2. Limitations

This study investigates the factors affecting the acceptance of healthcare information technology in the primary healthcare facility in Indonesia. The findings of this study depend on the choice of the previous constructs, which are based on prior literature (Alharbi & Drew, 2014; Venkatesh, 2000) and our objective observation of primary healthcare physicians behaviour. However, there might be other enablers or inhibitors of external variables that were not involved in this study. This would provide guidance for further research to examine additional enablers to get further findings in the application of TIIS program in healthcare facilities. Moreover, the validation of such other constructs in healthcare information system acceptance would help the advance of this study. The constructs would also be related to the number of respondents collected. Further study considers the complex construct in healthcare information technology acceptance requires more respondents that would also be advancing the results of this study.

The location of this study focuses on primary health facilities in Indonesia, which of course are located in small towns and even rural areas. In developing countries, the site selection of the study is crucial because it is related to the quality of administrative staff whose quality of education in rural areas is lower than that of the central health service facilities in big cities. According to Porter and Donthu (2006) the education level of workers will affect the acceptance of new technology. Future studies can compare the results of their research by taking samples of TIIS users in central hospitals with higher quality admin resources.

6. Conclusion

TAM’s influence on the organization’s performance is widely discussed previously; however, its implications on the healthcare sectors are rarely paid attention. This study investigated the effects of the applications of integrated information systems in Indonesian primary healthcare. The results indicated that the actual use of tuberculosis information technology in the Indonesian healthcare system is affected by staff perceptions, such as perceived usefulness, perceives ease of use, and staff behavior intention to use. Further study could be done by investigating other information systems applied in the higher healthcare system in which the staff have a higher level of education.

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Additional information

Funding

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