JDBIM
(Journal of Digital Business and Innovation Managemen.
Volume 3.
Issue 2.
December 2024, 172-185 ISSN (Onlin.
: 2962-3898 DOI: 10.
26740/jdbim.
https://ejournal.
id/index.
php/jdbim Analyzing the Influence of Change Readiness on ChangeSupportive Behavior in Health Information Systems Ratna Wardani1.
Laili Wulandari2.
Titin Susanti3.
Endah Wulan Safitri4.
Rifka Sari Pratiwi5 12345 Faculty of Economics and Business.
Universitas Strada Indonesia.
Kediri.
Indonesia *Email:
ratnawardani61278@gmail.
Abstract The main objective of this study was to assess the effect of communication quality and trust on behavior change, which supports the information system of health workers in public health centers through the mediating variable of readiness for Data were collected from 341 health workers working in public health centers in Indonesia through enumerators as data collectors.
The statistical procedure is divided into descriptive analysis using SPSS 25 and structural equation model using bootstraps using Smart PLS 3.
Structural Equation Model analysis found several results.
The results positively affect the quality of communication, trust, willingness to change, and support for behavior change.
addition, the study readiness to mediate changes in communication quality and trust in supporting behavior change.
Therefore, this study shows that the quality of communication and trust is significantly associated with behavioral changes that support the mediation readiness for change in health workers in East Java.
Indonesia.
The main question of this study is to assess the effect of communication quality and trust on behavioral change that supports the information system of health workers at public health centers through the variable of mediation readiness for change.
Research has been conducted to answer this research question.
It has been empirically proven that the quality of communication and trust affects changes in supportive behavior with and without mediation.
We believe that the quality of communication and trust due to the combined effect of readiness for change produces behavioral changes that favor more than the quality of communication and trust itself.
Keywords: Communication Quality.
Trust.
Change Readiness.
ChangesSupportive Behavior.
Health Workers.
To cite this document:
Wardani.
Wulandari.
Susanti.
Safitri.
Pratiwi.
Analyzing the Influence of Change Readiness on Change-Supportive Behavior in Health Information Systems.
JDBIM (Journal of Digital Business and Innovation Managemen.
JDBIM (Journal of Digital Business and Innovation Managemen.
Volume 3 Issue 2.
December 2024
E-ISSN: 2962-3898
Page: 171-185 Received: 1 October 2024.
Accepted: 31 December 2024.
Published: 31 December 2024 *Corresponding author Email: ratnawardani61278@gmail.
INTRODUCTION
Change has become a natural new (Sharma et al.
, 2.
The world of health care has undergone drastic and fundamental changes through policies, programs, and reforms to provide efficient, effective, and equitable care services (Maruthappu et al.
, 2.
In addition, the implementation of changes in the provision of health services is often caused by policies aimed at improving patient safety and quality of care (Carvalho et al.
, 2.
The main challenge in implementing change is felt when some individuals see the changes shift from a positive or negative side.
Positive changes will provide benefits so that individuals are ready to accept change, while negative views about change tend to develop resistance (Holt.
Armenakis.
Feild, et al.
, 2.
In organizational change, employee behavior has an important role in managing organizational change effectively (Faupel & Syy, 2018.
Islam et al.
, 2020, 2.
Attitude is the main driver of behavior that rejects and supports individuals (Vaishnavi & Suresh, 2.
Jin et al.
states that a positive attitude towards change is focused on change, persistent, and trying hard to support and facilitate the implementation of change.
Researchers and practitioners underscore the importance of supportive behavior change for managing organizational change successfully (Adil.
Ahmad et al.
, 2.
There is still limited research investigating supportive behavior change, especially in the health sector and developing Existing research studies on behavioral change support more studies in developed countries and Europe (Faupel, 2020.
Fugate & Soenen, 2.
, which may not be appropriate to the context of Asian and developing countries due to cultural, economic, social, and contextual Recently, many researchers from Asia have used the concept of change in supporting behavior (Herscovitch & Meyer, 2.
, such as Adil .
who studied the concept of change in supporting behavior in manufacturing companies in Pakistan.
Ahmad et al.
who conducted https://ejournal.
id/index.
php/jdbim Wardani.
Wulandari.
Susanti.
Safitri.
Pratiwi.
Analyzing the Influence of Change Readiness on Change-Supportive Behavior in Health Information Systems.
JDBIM (Journal of Digital Business and Innovation Managemen.
research in Iraq on teacher behavior in public schools.
Islam et al.
who studied changes in supporting behavior in Bangladesh in the banking world, and also (Bayraktar & Jimynez, 2.
who conducted research in Taiwan.
This study found the research population and a gap in the research Therefore, this study discusses this and explores behavioral changes that support Public Health CentersAo health workers related to reporting on Public Health CentersAo Information Systems, especially in Indonesia.
The development of digital technology is strongly influencing changes in healthcare.
Most health facilities need to be able to use the technology and applications needed to provide optimal health services.
One of the steps taken by the Indonesian government is the One Data Indonesia One Data Indonesia is a government initiative to improve the interoperability and use of government data.
One Data Indonesia is a government data management policy to produce accurate, up-to-date, integrated, and accountable data.
As well as easily accessible and shared between central agencies and regional agencies through compliance with data standards, metadata, data interoperability, and the use of reference and data codes Parent (Government Regulation of the Republic of Indonesia.
No.
39, 2.
as the strategy and priority of strengthening the health information system for One Data Indonesia is the arrangement of transaction data in Health Service Facilities.
The information system in health service facilities is one of the data providers for One Health Data.
Based on the data released by the East Java Provincial Health Office in 2019, there are only 14 cities/districts whose information systems in Health Service Facilities are already interoperable.
Therefore, the changesupporting behavior possessed by the health workers in the Public Health Centers will be the key to overcome the complex process of change.
Therefore, the formulation of the problem in this research is to find out the important factors in the context and process of change that affect supportive behavior during changes made by the organization.
In general, readiness for change is a cognitive precursor to ChangeSupportive behavior (Rafferty & Minbashian, 2.
Although readiness for change is a predictor of change-supporting behavior, there has not been much research (Bakari et al.
, 2017.
Rafferty & Minbashian, 2.
Previous research has highlighted the change readiness approach as an important factor for increasing Change-Supportive behavior (Rafferty & Minbashian.
Several factors have been identified that can affect change However, only two factors are key components of change https://ejournal.
id/index.
php/jdbim JDBIM (Journal of Digital Business and Innovation Managemen.
Volume 3 Issue 2.
December 2024
E-ISSN: 2962-3898
Page: 171-185 readiness, namely the changing context and the change process (Holt.
Armenakis.
Harris, et al.
, 2.
In this study, the context of the change studied was trust, and the change process factor was the quality of The communication quality approach is considered a relevant factor for maintaining employee attitudes during changes (Men et , 2020.
Neill et al.
, 2.
Researchers also consider that trust also affects changing supportive behavior (Islam et al.
, 2021.
Men et al.
, 2.
The relationship between readiness for change and supportive behavior is a unique concept that has been largely ignored and requires further investigation.
We, therefore, found a conceptual research gap.
This research is based on the perspective of health workers who work in Public Health Centers which was not found in previous studies.
As mentioned above, previous researchers only focused on fields outside of health, especially in health centers.
Of course, the results of previous studies could not be used in health institutions, especially health centers.
So we find a research implication gap.
To fill all research gaps, we conducted this study.
METHODS
In this study, the research analysis unit was health workers who held the program in 37 Public Health Centers.
The target population is a clearly defined group of cases where a researcher takes a sample and generalizes the results from that sample.
The sampling technique used in this research is proportional random sampling.
G-power analysis was used to determine the number of samples.
The minimum number of samples in this study is The number of samples follows the number of samples obtained using G- Power with the following conditions: 1.
Test family = F-test, 2.
Effect size f2 = 0.
15, 3.
error problem = 0.
05,4.
Power .
- error proble.
= 0.
95, with the number of independent variables is 3.
In this study, data were collected using a questionnaire given directly to respondents in 37 health centers.
Researchers gave 1 to 2 weeks for respondents to fill out the questionnaire.
Three hundred and seventy-eight respondents returned the questionnaire from a total sample of 390.
Incomplete questionnaires were removed from the sample, and 341 questionnaires were completed, which were completed and used for https://ejournal.
id/index.
php/jdbim Wardani.
Wulandari.
Susanti.
Safitri.
Pratiwi.
Analyzing the Influence of Change Readiness on Change-Supportive Behavior in Health Information Systems.
JDBIM (Journal of Digital Business and Innovation Managemen.
analysis with a response rate of 90.
2%, which is appropriate for social science research (Neuman, 2.
To analyze the measurement and structural models using Partial Least Square (PLS) with smart-pls edition 3.
The use of PLS-SEM provides the benefit of higher statistical method strength than CB-SEM (Covariance Based SEM) (Joseph F.
Hair et al.
, 2.
This benefit holds when estimating the common factor model as assumed in CB-SEM.
(Sarstedt et al.
, 2.
Greater statistical power indicates that PLS-SEM can be used to identify influential relationships (Sarstedt & Mooi, 2.
The most important thing is that PLS- SEM is not only suitable for research with the Exploratory Factor Analysis (EFA) approach but also suitable for research with the Confirmatory Factor Analysis (CFA) approach (Joseph F.
Hair et al.
, 2.
A questionnaire was used to collect data by administering a survey.
All items were measured on a 7-point scale ranging from 1 .
trongly disagre.
to 7 .
trongly agre.
In this study, the quality of communication for change uses a concept developed by Miller et al.
, with 3 indicators, i.
, information provided on time, complete, and accurate, with 4 items.
Cronbach alpha was calculated to determine the reliability of the quality of communication.
Trust uses measurements by Schoorman et al.
, which can be used at all levels in the organization, namely individual, group, and organizational levels, with 3 indicators, namely:
ability, benevolence, and integrity as many as 7 items.
Schoorman et al .
said that ability, benevolence, and integrity can be further used to build trust.
If it can be developed and maintained, trust is a competitive advantage that impacts the organization.
Change readiness uses a measure originally developed by (Hanpachern, 1.
It has been adapted by (Thakur & Srivastava, 2.
, which has been used in developing countries and has eight statement items with indicators for promoting change and participating in change in the health sector.
However, only six items were used in this study due to the issue of external loading.
This study uses the change-supporting behavior measure by (Herscovitch & Meyer, 2.
with 3 dimensions, namely compliance, which consists of 3 items.
cooperation, which consists of 8 items.
and championing, which consists of 6 items.
Compliance, cooperation, and championing are successfully RESULTS AND DISCUSSION https://ejournal.
id/index.
php/jdbim JDBIM (Journal of Digital Business and Innovation Managemen.
Volume 3 Issue 2.
December 2024
E-ISSN: 2962-3898
Page: 171-185 The demographic characteristics of the respondents can be observed in Table 1.
Table 1 RespondentAos Demographic .
= .
No.
Variabel Demografi Classification Male Gender Female Total Senior High School Diploma Education Bachelor Postgraduate Total Administration Midwifery Nurse Doctor Profession Farmasi Nutrition Sanitarian Others Total 21 to 30 31 to 40 Age 41 to 50 51 to above Total Below 10 10 to 19 Working Period 20 to 29 30 above Total Frequency Source: Survey https://ejournal.
id/index.
php/jdbim Wardani.
Wulandari.
Susanti.
Safitri.
Pratiwi.
Analyzing the Influence of Change Readiness on Change-Supportive Behavior in Health Information Systems.
JDBIM (Journal of Digital Business and Innovation Managemen.
The first step in examining the common method bias problem is the collinearity problem (Latan & Hair, 2.
Multicollinearity analysis must be carried out to avoid bias in the regression analysis results.
The VIF value should be close to 3 or lower (Hair et al.
, 2.
The data in this study has a VIF value of less than 3, so it can be said that there is no collinearity problem in the data of this study.
Table 2 collinearity test
Construct
ROC
CSB
ROC
An evaluation of convergent validity was carried out to test the measurement model, including the value of outer loading, composite reliability.
Cronbach alpha, average variance extracted (AVE), and discriminant validity.
According to (Latan & Hair, 2.
, construct validity assesses how a measure tests the desired variable correctly.
In measuring convergent validity, the measurements that must be carried out are outer loading.
AVE.
Cronbach alpha, and composite reliability.
The results of the convergent validity measurement are presented in Table 3.
Table 3 Convergent Validity Construct
Quality of Communication Trust
Readiness for Change Item Code
QC1
QC2
QC3
QC4
TR1
TR2
TR3
TR4
TR5
TR6
TR7
ROC1
ROC2
ROC3
ROC4
ROC5
Outer Loading https://ejournal.
id/index.
php/jdbim AVE JDBIM (Journal of Digital Business and Innovation Managemen.
Volume 3 Issue 2.
December 2024
E-ISSN: 2962-3898
Page: 171-185
ChangeSupportive Behavior
ROC6
Comp Coop Champ Discriminant validity shows the uniqueness of one construct from another in the structural model.
HTMT is the average of the correlation of items across constructs relative to the average .
of the average correlation of items measuring the same construct.
From the HTMT matrix in Table 5.
23, a < ratio value of 0.
85 was obtained, so the assumption of discriminant validity was met (Latan & Hair, 2.
Table 4 Discriminant Validity: HTMT Ratio Statistic Construct
CSB
ROC
CSB
ROC
Structural Model Assessment: Hypothesis Testing Once the measurement model is evaluated and the requirements are met, the next step is to test the hypothesis on the structural model using smart-pls with bootstrapping resampling with 5000 subsamples to ensure that the results are not sample specific (Hair Jr.
et al.
, 2.
Table 5 shows that the quality of communication, trust, and readiness for change in health center information system reporting have a significant impact on changesupportive behavior.
When O 0.
05 and using a one-way test, the t-value must be more than 1.
> 1.
for the hypothesis to be accepted (Hair et al.
, 2.
Table 5 shows the results of the hypothesis testing and shows that all hypotheses are accepted.
Table 5 Direct Effect Testing the SEM-PLS Hypothesis Hypothesis Std.
Error QC -> ROC 0.
TR -> ROC 0.
Path Std.
Beta Confident Interval Result 00% 95.
420 Accepted 419 Accepted PValues https://ejournal.
id/index.
php/jdbim Wardani.
Wulandari.
Susanti.
Safitri.
Pratiwi.
Analyzing the Influence of Change Readiness on Change-Supportive Behavior in Health Information Systems.
JDBIM (Journal of Digital Business and Innovation Managemen.
QC -> CSB 0.
TR -> CSB 0.
ROC -> CSB 0.
Accepted Accepted Accepted Among all factors, readiness for change is the strongest predictor of Change-Supportive behavior with an estimate of 0.
684, then the trust of 116 and communication quality of 0.
Readiness for change is also influenced by communication quality by 0.
336 and trust by 0.
Table 6 Indirect Effect Testing the SEM-PLS Hypothesis Hypothesis Path
Std
Beta
Std
Dev
PCR
Values Confident Interval 00% 95.
Result
Accepted Accepted QC -> ROC
-> CSB
TR -> ROC 0.
> CSB
Table 6 is used to evaluate the indirect relationship.
The mediating variable of readiness for change between communication quality and change in supportive behavior has a significant relationship with a p- value 000 and an estimated level of 0.
The mediating variable of readiness for change between trust and supportive behavior change has a significant relationship with a p-value of 0.
000 with an estimate of 0.
From the analysis results, it can be seen that the quality of communication can improve Change- Supportive behavior through readiness for change.
The variance (VAF) calculation is carried out to assess whether the mediation is partial or total (Hayes, 2.
VAF is the value of the indirect effect on the total effect value.
VAF for the mediating effect of readiness for change is 67.
65% on the relationship between communication quality and Change-Supportive behavior and 65.
68% on trust and Change-Supportive Therefore, both mediations are partial.
The results of hypothesis testing are presented in Figure 1.
https://ejournal.
id/index.
php/jdbim JDBIM (Journal of Digital Business and Innovation Managemen.
Volume 3 Issue 2.
December 2024
E-ISSN: 2962-3898
Page: 171-185 Figure 1 Hypothesis Testing Result Unlike CB-SEM.
PLS-SEM evaluates the quality of the model from its predictive ability, that is, the coefficient of determination (R.
and the predictive ability (Q.
(Hair et al.
, 2.
The coefficient of determination is an endogenous construct that is explained by all the exogenous variables associated with it.
(Sarstedt & Mooi, 2.
The R2 value of 0.
67 indicates strong, 0.
33 indicates moderate, and 0.
19 indicates weak (Latan & Hair.
The R2 value for change supportive behavior is 0.
676, and the readiness for change has an R2 value of 0.
353, indicating reasonable The next step is to test the quality of the structural model with predictive relevance or Q2 using the Stone-Geisser test .
ee Table .
value of Q2 greater than zero for the endogenous construct indicates that the explanatory variable has predictive relevance.
Table 7 R2 and Q2 values Endogen
CSB
ROC
DISCUSSION