Technology readiness and usability of office automation system in suburban areas

Received Jun 20, 2019 Revised Jul 30, 2019 Accepted Dec 25, 2019 Readiness and usability issues are important attributes of an information system (IS) success. Currently, IS studies that have been assessing the issues in suburban areas are still limited. The purpose of this study was to measure the above-mentioned phenomena by combining the readiness and usability models. The proposed model consisted out 10 constructs with 25 indicators. The four constructs were from the readiness model and the six ones were the usability model. The 89 samples were collected using an online survey based on purposive random sampling. Partial least squares structural equation modeling (PLS-SEM) method with SmartPLS 3.2.8 was used for analyzing the data. The findings may have contributed to the IS research field, in terms of the readiness and usability issues on the IS use in a suburban area in Indonesia.


INTRODUCTION
The IS use in an organization is a necessity that must be immediately fulfilled, let alone adapted to the needs of the Industrial Revolution 4.0 [1]. An organization needs very careful consideration regarding its use, considering that the use of IS requires not a small amount of money [2][3][4][5] In terms of readiness [6], the organization must measure all aspects of its use [3,[7][8][9]. The use of IS in organizations is usually needed to facilitate users [10][11][12][13] in helping their work with high-efficiency IS [2][3][4][5]. Consideration regarding the use of IS requires sustainability studies [14], remembering this is very important for the development of an organization [15][16][17].
Some studies have argued that what plays an important role in maintaining the sustainability of IS use [14] is a high-efficiency IS that is felt directly by the user [2,4,18,19] and supported by readiness factors [20][21][22]. In some suburban institutions, problems were found regarding the implementation of IS related to readiness issues, including problems in the availability of human resources and infrastructure [23][24][25][26][27]. On the other hand, it is less understood that the use of IS is mainly in terms of ease of use. The literature review by reviewing and understanding several studies regarding the use of IS, combining Readiness and Usability models [3] can be an alternative to be used in measuring the level of readiness and usefulness of the use of IS so that it can predict the use of IS for future organizations.
TELKOMNIKA Telecommun Comput El Control  Technology readiness and usability of office automation system in suburban areas (Dwi Yuniarto) 677 This research was conducted in several suburban area institutions with the aim to determine the level of readiness and usability of IS use and to determine what factors influence the readiness and usability of IS use. The readiness model is a model used to measure the level of readiness in the use of IS [6] through variable optimism, innovation, discomfort, and insecurity [6]. The variable in the usability model is used as the dependent variable that is used to measure in terms of learnability, efficiency, memorability, reliability, and satisfaction [3,28,29] in determining the high and sustainable use of IS [14]. Based on the research program mentioned above, two research questions were asked to guide implementation. RQ1: What are the characteristics of office automation system users in the suburban area? RQ2: What factors influence the readiness and usability of the use of office automation systems in the suburban area? The structure of this paper is at stake in five parts. The introduction contains background exposure to the problem, delivery of objectives, and questions of the Study. The second part is a literature review. The next stage is the delivery of research methods, including procedures, population and samples, data collection, sampling, tools, data analysis, and interpretation points. The results and analysis part include the results of the descriptive and inferential analysis, contributions, limitations, and research recommendations supported by other studies. And finally, the conclusions section, summarizes the paper, in relation to the points highlighted in all sections.

LITERATURE REVIEW
Office automation systems are part of an information system. Office automation systems are present to handle jobs whose scope is limited and are only intended to support individual work in the office [13]. Uneven developments regarding the application of information systems in several regions resulted in the use of information systems, in this case, office automation systems became preachers [10][11][12]. To avoid the illusion of the application of information systems it is necessary to measure readiness problems. Measurement of readiness using the Technology Readiness Index model has been introduced for a long time by Parasuraman [6]. TRI is used to determine the readiness to use information systems from four variables (optimism, innovation, discomfort, and insecurity) [6].
In addition to the measurement of readiness, the re are several other factors that greatly influence the implementation of the use of information systems, namely those relating to learnability, effectiveness, memorability, reliability, and satisfaction [3,28,29]. These factors are contained in the Usability model of Nielsen. The combination of the two models in Figure 1 is expected to provide a solution to the problems that arise in the background above. Some researchers develop a successful model of IS use by adopting, combining, and adopting technology readiness and IS success models, in terms of IS integration assessment. input-process-output logic and procession and causal models of the IS model are used [30,31]. This study also combines two models with IPO logic [3]. Basically, after combining two models consisting of variables, the indicators appear that are used as references in making questions in the questionnaire [32].

RESEARCH METHOD
This research was completed in eight steps described in the research procedure as shown in Figure 2. Literature review (1) is the first step taken as material in shaping the research program. The second stage is research design (2), which is a stage in designing research, continuing to the development model stage (3). At the stage of the development, the model produces a new model result combining two models (Readiness and Usability models) to produce research models and research instruments (4). After the models and instruments are created, a survey (5) is conducted to obtain data (data obtained by distributing questionnaires through google form) which are then analyzed (6) and interpreted (7) so that findings are finally reported (8) in written form [9]. The researcher distributed 89 copies of the questionnaire through google form to several institutions based on the experience of the respondent's profile. The question given is the result of the variables and indicators contained in the model.

RESULTS AND ANALYSIS 4.1. Demographics information
The characteristics of respondents presented in Table 1 are related to the problem of trust and validity of data sources [33]. It can be seen clearly that the demographic spread of this study is the trust and validity of the data used [33]. Readiness and Usability of the SI profile are presented in Table 2 useful for estimating the spread of data on research findings and the quality of findings referred to the tendency of data validity to be used in terms of the IPO logic of the research implementation [33].

The statistical analysis results
At the stage of the results of statistical analysis, there are several stages to process data from the questionnaire. This stage consists of evaluating the reflective measurement model and the assessment of structural models, namely evaluating reflective measurements by evaluating internal consistency reliability using composite reliability, reliability indicators, convergence validity, and discriminant validity. Evaluation of the structural model is a step to determine whether a hypothesis is based on the research model, and the value of R 2 from endogenous latent variables in the path model and the last step to assess the contribution of exogenous constructs to endogenous latent variables.
From Figure 3, composite reliability for all reflective constructions is higher than 0.708 and has a high level of internal consistency reliability. Outside loading OPT1, DCF3, ISC3, and ISC4 is more than 0.4 but below 0.7. These four indicators need to analyze the impact of eliminating indicators on AVE and composite reliability. The composite reliability for all reflective constructions is higher than 0.708 and the removal of indicators increases value. From the results above, it was found that the removal of the OPT1, DCF3, ISC3, and ISC4 indicators did not increase composite reliability, while the AVE increased. Then it needs to be analyzed when all three indicators cannot be removed from the model.
From Table 3, we can see that all external indicators loading on construction is higher than cross-loading with other constructs and the square root of AVE is higher than the highest correlation with other constructs. From Table 4 and 5, each construct predictor tolerance (VIF) of the DCF, EFC, INV, ISC, LRN, MMR, OPT, RLB, and STF predictors is lower than 5 and higher than 0.2. The critical value for the two-sided test is 1.65 (significance level = 10%), 1.96 (significance level = 5%), and 2.57 (significance level = 1%). To assess the significance of the path coefficient a significant level of 5% is used and a one-sided test. The significance level is 1.64. PLS-SEM aims to maximize the value of R 2 from endogenous latent variables in the path model. Thus, the goal is the value of high R 2 . While the correct interpretation of the R 2 value level depends on the particular model and research discipline. In general, the value of R 2 0.75, 0.50, or 0.25 for endogenous construction can be described as each being substantial, medium, and weak. The R 2 values of the endogenous SYU construct are medium. While the endogenous constructs of EFC, LRN, MMR, RLB, and STF are each weak.   Table 7, the resulting Q 2 value greater than 0 indicates that the exogenous construct has predictive relevance for the endogenous construct under consideration. As a relative measure of predictive relevance (Q 2 ), values of 0.02, 0.15, and 0.35 respectively indicate that exogenous constructs have small, medium, or large predictive relevance for certain endogenous constructs.   Figure 3. Results of the outer model assessment

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The analysis in Table 8 results that efficiency is influenced by innovation and insecurity. If it is associated with a profile of readiness and usability, it is clear that users assume that new things or innovations are considered to affect the security of users [34][35][36]. Insecurity factors themselves influence the factors of reliability and satisfaction, according to the results of the research that has been done [37]. On the other hand optimism factors significantly influence the factors of efficiency, learnability, satisfaction [38]. Regarding usability factors from the results of the analysis above are significantly influenced by reliability factors [38]. This research was conducted in suburban areas consisting of respondents with different backgrounds, which could affect at least the accuracy of the results of the analysis and also influence the number of not significant results [2,8,20,31,32,35].

CONCLUSION
Based on the results of filling in respondents' profiles, there were 52% stating that readiness had an effect on usability and 44% said it was very influential. The results of the statistical analysis state that in terms of readiness and usability of the use of IS there are significant results regarding the factors that influence. Innovation affects efficiency, insecurity affects efficiency, insecurity affects reliability, insecurity affects satisfaction, optimism influences efficiency, optimism affects learning ability, optimism affects satisfaction, and reliability affects the usefulness of the system. Regarding other factors that were stated to be insignificant, it would be noted for further research, given the profile of respondents that could influence the results. For those interested in using IS topics, the main attraction is to further develop and measure further, so that the best systems are formed. It should be a very big concern about the sample used in this study, given that the sample is only used in institutions that have solid activity in the use of IS, it is better for other researchers to try to implement the measurement model that has been built including questionnaires in various institutions in the regions different.