48
ICT Res.
Appl.
Vol.
No.
1, 2024, 48-68
Investigating the Effect of m-Commerce ApplicationAos Functional and Non-Functional Attributes on Usability and Continuance Intention Hotma Antoni Hutahaean1,2*.
Iman Sudirman1,3 & Rajesri Govindaraju1 Industrial Engineering Department.
Faculty of Industrial Technology.
Bandung Institute of Technology.
Jalan Ganesa No.
Bandung 40132.
Indonesia Industrial Engineering Department.
Atma Jaya Catholic University of Indonesia.
Jalan Sudirman No.
50 Jakarta, 12930.
Indonesia Industrial Engineering Department.
Pasundan University.
Jalan Dr.
Setiabudi No.
Bandung 40153.
Indonesia *E-mail: 33417002@mahasiswa.
Abstract.
The development of mobile commerce .
-commerc.
applications has indicated a shift in goals from ease-of-use to sustainable use in the future.
This shift has prompted changes in the combination of attributes that constitute the usability of m-commerce applications.
This study developed an m-commerce usability model that combines functional and non-functional attributes.
The research data was collected using questionnaires distributed to users living in Jakarta and was processed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.
The results demonstrate that efficiency, satisfaction, and effectiveness can explain the usability of m-commerce This study proved that three non-functional usability attributes Ae productivity, navigation, and memorability Ae have a positive effect on efficiency, while two functional attributes of usability Ae content and security Ae positively influence effectiveness.
The study further proved that beside non-functional attributes, functional attributes also play an important role in increasing the user experience in the m-commerce context and thereby contribute to improving the usability of m-commerce systems and the continuance intention.
The overall results of the study can be used in developing strategies for m-commerce application developers to design applications that can satisfy users and help them complete their tasks correctly and efficiently.
Keywords: continuance intention.
functional attributes.
m-commerce.
non-functional Introduction Mobile commerce, a common form of e-commerce, has been a growing trend during recent years.
Mobile commerce .
-commerc.
can be defined as selling and purchasing goods and services on online networks with the support of mobile devices .
These days many people use m-commerce to search information, make comparisons, and purchase products or services.
The data published by Received November 26th, 2023.
Revised April 2nd, 2024.
Accepted for publication May 7th, 2024.
Copyright A 2024 Published by IRCS-ITB.
ISSN: 2337-5787.
DOI: 10.
5614/itbj.
Investigating the Effect of m-Commerce ApplicationAos com shows that around 20 million people in Indonesia carried out online shopping activities in 2017, a number which continued to increase up to 85 million people by 2022 .
The development of m-commerce applications often results in a complex system with many functional features in the user interface .
The rich features do not always bring positive impacts from the perspective of the user experience because similar to e-commerce, m-commerce is different from face-to-face shopping experiences.
It has lower cognitive load and is considered anonymous and impersonal .
Thus, it is necessary for companies and designers to better understand the functional and non-functional attributes of m-commerce applications that are perceived to be important for mcommerce users in order to gain a high level of user experience.
As the significance of mobile application usage has increased, the usability of mobile applications is becoming a fundamental factor in determining the success of m-commerce.
Usability is defined by the International Standards Organization (ISO) as the extent to which a product can be used by specific users to achieve specific goals with effectiveness, efficiency, and satisfaction in a specific context of use .
By creating a seamless, user-friendly experience, a higher level of usability can increase customer satisfaction .
Further, according to Venkatesh, et al.
, achieving excellent m-commerce usability is one of the biggest Venkatesh, et al.
identified the user experience as an important prerequisite for the success of m-commerce.
Continuance intention of m-commerce applications is a significant measure of mcommerce success.
Continuance intention in mobile commerce refers to the constant and ongoing use of an application by its users.
It is necessary for developers and businesses to create m-commerce platforms that provide features and experiences that could drive userAos continuance intention.
Considering the importance of achieving e-commerce user loyalty in different contexts, continuance intention has become an interesting research topic.
The novelty of this research is that it developed a usability model that incorporates functional and non-functional attributes of m-commerce applications as antecedents as well as continuance intention as the dependent In this study, the influence of these functional and non-functional attributes on the usability of m-commerce applications was analyzed.
Combining functional and non-functional attributes of m-commerce applications in the analysis facilitated a thorough evaluation of the mobile commerce user This study also analyzed the relationship between usability and continuance intention.
This holistic approach provided a comprehensive view of how well m-commerce applications align with user needs and expectations, and can be expected to increase continuance intention.
Hotma Antoni Hutahaean, et al.
Model Development A model was developed to analyze the most important factors that can explain the usability of m-commerce applications and the influence of the m-commerce usability on continuance intention.
The model was developed based on the usability concepts of ISO 9241-11 .
This model was chosen because in it usability relates to the outcome of interacting with a system, product, or service, where usability is not defined as an attribute of a product.
According to ISO 924111 .
usability is how individual users can accomplish specific goals effectively, efficiently, and satisfactorily when utilizing a product within a given application The model developed in this study is presented in Figure 1.
The model incorporates functional and non-functional characteristics of m-commerce applications as antecedents.
Functional usability attributes pertain to internal system characteristics that can impact the systemAos usability .
A systemAos internal attributes .
can significantly impact its overall usability .
Guler .
examined the relationship between functional characteristics and usability in the context of mobile applications.
The present study highlighted the importance of considering functional attributes when developing usability Two functional usability attributes, namely security and content, were Meanwhile, non-functional attributes are external system attributes that emphasize usability factors, defined as the degree to which a website enables users to use its functions effortlessly and precisely.
The non-functional usability attributes examined in this study were navigation, productivity, memorability, which were correlated with the investigated constructs.
In the next section, a more detailed explanation of the model is presented.
Relationship between Usability and Continuance Intention Continuance intention refers to the desire of the user to continue utilizing the mobile commerce services offered by an application .
This commitment materializes in the form of engagement with the m-commerce system in various transactional activities.
Usability is a factor that can have a positive influence on continuance intention .
This underscores the idea that when m-commerce applications are user-friendly and provide significant utility, consumers are more inclined to remain loyal to them and continue using them in the future.
When users have a positive experience with a mobile commerce system, they are more likely to keep using it and recommend it to others, which ultimately benefits the m-commerce application long-term success and user retention .
Based on this argument, the following hypothesis is proposed:
Investigating the Effect of m-Commerce ApplicationAos H1: Usability of m-commerce positively affects the continuance intention Figure 1 Conceptual Research Model Usability ISO 9241-11 .
states that usability measures are determined by three attributes:
efficiency, satisfaction and effectiveness.
Efficiency is a crucial aspect that defines the ability of a system to provide high-level functionality and productivity by effectively utilizing resources and ensuring prompt task completion .
encompasses factors such as the speed at which tasks are accomplished and the softwareAos capacity to maximize available resources to achieve specific goals .
Efficiency is a key attribute considered in the development of usability models .
Further.
Nelson & Stagger .
and Matraf & Hussain .
have stated that there is a significant correlation between efficiency and customer satisfaction in the context of mobile e-book applications.
Efficient applications tend to provide a more comfortable, effective, and productive experience for users, which can naturally increase user satisfaction.
Conversely, inefficiencies in an application can led to user dissatisfaction, reduced trust in the application, and even may lead to customer churn.
Based on these arguments, the following hypotheses will be H2: Efficiency positively affects m-commerceAos user satisfaction H3: Efficiency positively affects the usability of m-commerce applications Effectiveness of m-commerce systems is the capacity of the systems to enable users to perform certain activities accurately in specific situations.
Effectiveness is measured based on the ability of users to accurately complete their task .
Hotma Antoni Hutahaean, et al.
Effective use of a systems will lead to increased user satisfaction.
Further, according to Anthony et al.
and Gupta et al.
effectiveness is a crucial factor that determines the overall usability value of a system .
Matraf & Hussain .
argue that usability of m-commerce application can be assessed by its effectiveness.
Based on these arguments, the following hypotheses are H4: Effectiveness positively affects the satisfaction of m-commerce users H5: Effectiveness positively affects the usability of m-commerce applications Satisfaction refers to the comfort and attitudes of users towards using a mcommerce application.
Several studies have considered satisfaction as an important factor in developing usability models .
It has been consistently proven that user satisfaction significantly influences the usability of mobile applications .
,19,.
Based on these arguments, the following hypothesis is H6: Satisfaction positively affects the usability of m-commerce applications Impact of Content and Security on Effectiveness Content and security are the m-commerce functional attributes that were included in the research model.
Content is defined as the capability of an application to present information and perform transactions .
Content plays a crucial role in the usability evaluation .
The presentation of textual information is a significant aspect considered in usability models .
Further, a successful mobile commerce application demonstrates clarity and focus in its content, ensuring that the information provided is comprehensive and relevant .
In the context of mobile applications, content is considered a critical issue for users because users strongly rely on the application to access information that may not be available through traditional channels .
Therefore, m-commerce applications need to provide different types of content, such as textual, visual, and pictorial, that is relevant for the systemAos goals .
Further.
Dawood, et al.
states the importance of resilience in software systems, which refers to their ability to perform operations accurately, to prevent failures and to ensure information and data security.
This can be achieved by implementing various levels of system protection, such as access permissions for authorized users or systems and other security related procedures aimed at preventing access from unauthorized users or other software.
Meanwhile.
Dawood, et al.
has reported that safety is a determining factor for Based on the arguments above the following hypotheses are Investigating the Effect of m-Commerce ApplicationAos H7: Content positively affects the effectiveness of m-commerce usage H8: Security positively affects the effectiveness of m-commerce usage Impact of Productivity.
Learnability.
Navigation and Memorability on Efficiency Productivity, learnability, navigation, and memorability are non-functional attributes that were incorporated in the research model.
Non-functional attributes are argued to affect the efficiency of using m-commerce applications.
With this model, the influence of these four attributes on the efficiency of m-commerce applications was analyzed.
Productivity is defined as the measure of the amount of output that users can get from using the software .
,24,.
Abushark, et al.
states that productivity is an important attribute contributing to the overall usability value.
Nelson & Staggers .
state that productivity can assess the value of efficiency.
Productivity measures the userAos ability to complete tasks quickly and efficiently .
Therefore, m-commerce applications that facilitate productivity will help users to complete purchases or transactions more quickly, which will increase user efficiency in using the application.
Learnability is a crucial factor in assessing usability.
It refers to the ease with which users learn and become proficient in using the functions of an m-commerce application .
,25,.
, with minimal effort and time .
,12,.
Learnability is an important attribute in the development of software applications .
that is closely linked to efficiency in mobile applications.
Nelson et al.
states that learnability significantly influences efficiency.
Users can navigate to carry out the tasks they have to carry out in a system through menus, graphical components, page sequences, and page layout.
Effective navigation ensures that users can easily and efficiently find the information they are looking for related to tasks they have to do, which ultimately contributes to a positive user experience .
Navigation can prevent confusion and irrelevant searches, which may lead to poor sales .
Matraf & Hussain .
examined the impact of navigation on user satisfaction with mobile e-book applications.
Similarly.
De Marsico & Levialdi in .
concluded that navigation significantly impacts website efficiency.
Nielsen .
states that a well-designed navigation system helps users to reach their desired destination quickly, thereby improving shopping efficiency in mobile applications.
Memorability plays a crucial role in m-commerce applications, encompassing several important factors.
It is defined as the capacity of a system to activate user intuition, enabling them to use diverse functions while minimizing reliance on memory .
Memorability involves the ability of the user to learn and remember various elements within the system, ensuring efficient usage over time .
Hotma Antoni Hutahaean, et al.
software development, memorability is a significant factor for usability assessment .
Based on the arguments mentioned above, the following hypotheses are proposed:
H9: Productivity positively affects the efficiency of m-commerce usage H10: Learnability positively affects the efficiency of m-commerce usage H11: Navigation positively affects the efficiency of m-commerce usage H12: Memorability positively affects the efficiency of m-commerce usage Data Collection and Processing Data Collection Data collection was conducted during 4 weeks in November 2022 through an online questionnaire survey.
The questionnaire was designed based on operational definitions of the research variables.
Each question used a sevenpoint Likert-like scale ranging from strongly disagree .
oded as .
to strongly agree .
oded as .
, with neutral .
oded as .
serving as the midpoint.
It was assumed that the distances between neutral and agree as well as strongly disagree and disagree were equal.
Before being distributed, the draft questionnaire underwent a pre-testing phase with 11 users of m-commerce applications.
The purpose was to ensure its clarity, legibility and content validity.
A panel of experts who were also users revised the questionnaire to ensure that the respondents would understand the statement An expert is a person who has experience in survey research and has a good understanding of m-commerce applications.
Subsequently, the questionnaire underwent a second round of testing with 40 users to establish its validity, reliability, and applicability.
The test demonstrated that the updated questionnaire met the criteria.
The final version of the questionnaire included 59 The questionnaire used in this study is provided in Appendix A.
The survey respondents were m-commerce users living in Jakarta.
Indonesia.
Purposive sampling was used with the purpose of getting respondents who were older than 20 years and had experience in purchasing online through mcommerce platforms at least 3 times.
In total, 138 questionnaires were collected and used.
This sample size was considered sufficient referring to a minimum sample size of 100 or 200 .
Demographic profile of the respondents is shown in Table 1.
Investigating the Effect of m-Commerce ApplicationAos Table 1 Profile of respondents.
Characteristic Gender Male Female Age range 21- 25 Number Percentage Tokopedia Last Education High School or Bachelor Master/Ph.
Characteristic Number Percentage Mobile commerce transaction activities Bill payment Mobile commerce transaction activities Voucher refill Fashion shopping Travel ticket Financial Household and other needs Types of m-commerce applications that are often used Data Processing and Analysis Measurement Model Validation The measurement model was assessed based on four main criteria, namely indicator reliability, internal consistency, convergent validity, and discriminant validity .
The first indicator, reliability, was tested by analyzing dependency among indicators.
This criterion tests the reliability of indicators and determines the relationship between latent variables and indicators, which is indicated by the outer loading value of each indicator and is considered feasible if the minimum value is 0.
Reliability testing in Smart PLS 3.
0 can use two methods, namely CronbachAos alpha and composite reliability.
According to Hair, et al.
, the CronbachAos alpha coefficient and composite reliability must be greater than 0.
7, although a value of 0.
6 is still acceptable and a value Ou0.
8 means the reliability is considered Table 2 shows that the CronbachAos alpha value ranged from 0.
778 to 0.
for the 11 latent variables and the composite reliability ranged from 0.
857 to These results indicate that the measurement model already had good composite reliability.
Hotma Antoni Hutahaean, et al.
Table 2
Code
USA1
USA2
USA3
USA4
USA5
MEM1
Memorability (MEM) Learnability (LRN) MEM2
MEM3
LRN1
LRN2
LRN3
SEC1
SEC2
Security (SEC) SEC3
SEC4
Content
(CON)
Loading Factor TStatistics Descriptive Statistics Mean St.
Dev Safety Error tolerance Robustness to an internal error Robustness to improper use Informative Item Indicator Construct Usability (USA) Indicator assessment and descriptive statistics.
CON1
Table 3 Construct Content Effectiveness Efficiency Learnability Memorability Navigation Productivity Satisfaction Security Usability Item name Easy to understand Simple to use Easy to find Easy to navigate Ability of user to Easy recollection after a substantial time lapse Logical steps to achieve tasks.
Comprehensibility Time to learn Self-descriptiveness Minimum memory Construct reliability and validity assessment.
CronbachAos Alpha Rho A Composite Reliability Average Variance Extracted (AVE) The second criterion was the internal consistency reliability of the measurement model, which was evaluated using the composite reliability value criterion.
The Investigating the Effect of m-Commerce ApplicationAos composite reliability values for all constructs ranged from 0.
857 to 0.
930 (Table With a value exceeding 0.
5, it can be concluded that a measurement model met the internal consistency criteria .
The third measurement model assessment criterion was the construct convergent validity, which was determined by the Average Variance Extracted (AVE) value with a value greater than 0.
5, i.
, explaining more than 50 percent of the variation in the indicators .
Table 3 shows the AVE values for all constructs .
anging 599 to 0.
, which means that the measurement model met the criteria for convergent validity.
The fourth criterion for evaluating the measurement model was discriminant validity, which verified the distinctiveness of each construct.
This assessment involves two criteria.
The first criterion examines the relationship between the indicators and the constructs, where the outer loading value of each indicator on its respective construct should surpass the cross-loading value of all other constructs .
With respect to the comparison between the outer loading and the cross-loading values, the outer loading value was higher for all indicators in the measurement model.
The second criterion was the Fornell Larcker criterion, which guarantees that the AVE root value for each construct is greater than the one with the highest correlation .
The Fornell Larcker criterion compares the square root of the AVE values in Table 3 with the latent variable correlations in Table 4.
Table 4
Construct
Content
Effectiveness Efficiency Learnability Memorability Navigation Productivity Satisfaction Security Usability CON
CUI
EFE
Latent variable correlation.
EFC
LRN
MEM
NAV
PRO
SAT
SEC
USA
Based on the two criteria for examining discriminant validity, it could be concluded that each construct in this structure was completely distinct from the Hotma Antoni Hutahaean, et al.
2 Structural Model Evaluation 1 Hypothesis Testing The model structure was assessed based on four criteria .
, namely the path coefficient, the coefficient of determination (R.
, the effect sizes .
, and the predictive relevance (QA).
To test the hypotheses, the statistical significance of the path coefficient was evaluated.
Smart PLS3 utilizes bootstrapping to generate t-statistics and p-values .
For the significance testing of the structural pathway, a replacement bootstrap subsample of 5,000 was drawn from the original sample.
The results of the hypotheses testing were analyzed using a oneway t-test with a significance threshold of 5% .
The f2 .
ffect siz.
value of the research model indicates the magnitude of the influence of each exogenous construct on the endogenous construct.
Various criteria can be used to set limits for collinearity values.
For instance, a value greater than 5 or greater than 10 suggests a potential for collinearity .
Table 5 displays the inner Variance Inflation Factor (VIF) values.
it is noteworthy that all values were less than 10.
This indicates that the results were not biased and there was no collinearity among the predictors in the preliminary study Furthermore, the correlation values of the latent variables, as shown in Table 5, support this conclusion.
The correlations indicate no strong correlations (>0.
9 or <-0.
among the latent variables, which further confirms the absence of Table 5
Construct
Content
Effectiveness Efficiency Learnability Memorability Navigation Productivity Satisfaction Security Usability CON
CUI
EFE
Variance Inflation Factor (VIF).
EFC
LRB
MEM
NAV
PRO
SAT
SEC
USA
To assess the hypotheses, the statistical significance of the path coefficient was Smart PLS3 employs bootstrapping to generate t-statistics and pvalues .
For the significance testing of the structural pathway, a replacement bootstrap subsample of 5,000 was drawn from the original sample.
The outcomes of the hypothesis testing, conducted using a one-way t-test and a significance threshold of 5% .
, are presented in Table 6.
Investigating the Effect of m-Commerce ApplicationAos According to Table 7, it can be concluded that the results could be considered favorable, since overall the independent variables could explain the high variability in the dependent variable: usability 84.
7%, satisfaction 79.
3%, effectiveness 69.
5%, and continuance intention 66.
Table 6 Structural model testing: direct effect/ Hypothesis Path Path Values .
USA ->CI EFC ->SAT EFC ->USA EFE ->SAT EFE ->USA SAT ->USA SEC ->EFE CON ->EFE PRO ->EFC H10
LRN ->EFC
H11
NAV ->EFC
H12
MEM >EFC
Notes: * p < 0.
** p < 0.
*** p < 0.
Table 7 t-Sat.
p-value .
Results supported*** not supported supported*** supported*** supported*** supported*** supported*** not supported Summary of R2 values.
Continuance Intention Effectiveness Efficiency Satisfaction Usability R-square R-square Adjusted The f2 effect size of the research model indicates the magnitude of the influence of each exogenous construct on the endogenous construct.
Table 8 shows that the influence of satisfaction on the usability, of content on effectiveness, and of usability on continuance intention were substantial, because the f2 value was >0.
The influence of effectiveness on satisfaction, of effectiveness on usability, and of productivity on efficiency could be considered moderate, because the f2 value was >0.
On the other hand, the influence of security on effectiveness, of memorability on efficiency, of navigation on efficiency, of efficiency on satisfaction, and of effectiveness on usability could be considered However, the learnability construct was shown to have no impact on Hotma Antoni Hutahaean, et al.
Table 8
Construct
Content
Effectiveness Efficiency Learnability Memorability Navigation Productivity Satisfaction Security Usability CON
CUI
EFE
Summary of f2 values.
EFC
LRN
MEM
NAV
PRO
SAT
SEC
USA
The results of the blindfolding process to assess the level of relevance of the predictions of the proposed model are shown in Table 9.
Based on Table 9, all Qsquare values for continuance intention, efficiency, effectiveness, satisfaction, and usability were more than 0.
It could be concluded that the predictions for the efficiency, satisfaction, effectiveness, usability, and continuance intention constructs were appropriate or relevant.
Table 9 also shows the predicted relevance value (QA) of the research variables.
The results showed that non-functional factors .
roductivity, learnability, navigation, and memorabilit.
could explain efficiency as much as 54.
Likewise functional factors .
ontent and securit.
could explain effectiveness as much as 46.
Further, efficiency and effectiveness could explain satisfaction as much as 52.
Efficiency, satisfaction, and effectiveness could explain usability as much as 56.
Finally, continuance intention could be explained by usability as much as 50.
Table 9 Construct Content Effectiveness Efficiency Learnability Memorability Navigation Productivity Satisfaction Security Usability Predictive Relevance (QA) values.
SSO
552,000
414,000
828,000
552,000
414,000
414,000
828,000
414,000
690,000
552,000
690,000
SSE
552,000
206,114
444,084
250,270
414,000
414,000
828,000
414,000
329,518
552,000
300,618
QA (= 1-SSE/SSO)
2 Model Fit The model fit testing was carried out using several indices, including standardized root mean square residual (SRMR).
NFI.
Root Mean Square Theta (RMS Thet.
Investigating the Effect of m-Commerce ApplicationAos and Goodness of Fit (GoF).
With SRMR criteria <0.
RMS Theta <0.
NFI
>0.
and GoF >0.
The results, as presented in Table 10, indicate that the RMS Theta or Root Mean Square Theta value of 0.
143 was around 0.
12, and the NFI value of 0.
680 could be considered moderately fit.
Based on these two model assessments, it was close to meeting the requirements of the model fit criteria.
The model was fit based on the SRMR value 0.
062 < 0.
Thus, it could be concluded that the model fit the Finally the GoF value of the research model, which was 0.
729, showed that this model had a high level of feasibility .
Table 10 Model Fit summary.
Assessment
D_ULS
D_G
SRMR
NFI
Rms Theta GoF Values Criteria >0.
>0.
<0.
Close to 1 <0.
>0.
Discussion This research showed that productivity, navigation, and memorability are three significant non-functional aspects of m-commerce systems that are necessary for efficient use of m-commerce applications.
The non-functional aspects generally mean that the application allows users to use its functions easily and precisely .
These results are in line with the findings from Nelson & Staggers .
, who studied the influence of productivity on efficiency in the health sector.
Learnability does not have a positive direct effect on efficiency.
This is in contrast with earlier studyAos findings .
This could be related to the fact that many users may be already so much familiar with using different mobile applications that they have become proficient in using those applications and are not so sensitive anymore when judging the learning process for using new applications.
This study also highlighted the importance of two functional factors, namely content and security.
When an application provides good quality content and security, it also means that the application operates according to its structure and makes users more effective in using the applications .
The functionalities of m-commerce applications that support user needs significantly determine user The studyAos results also showed that usability of m-commerce applications can be determined by satisfaction, effectiveness, and efficiency.
Further, the study highlighted that satisfaction is the factor that has the strongest influence on usability, followed by effectiveness and efficiency.
The usability Hotma Antoni Hutahaean, et al.
value (R.
847 shows that 84.
7% of usability could be explained by the predictor variables, which are efficiency, satisfaction, and effectiveness.
Continuance intention was found to be closely related to usability.
This is in line with the results of previous research on mobile phone .
mobile applications .
, online learning .
and mobile applications .
Further, continuance intention had an R-square of 0.
664, which shows that usability of an m-commerce application is a dominant factor in making users continue using the application.
The key strength of our proposed model is that it enhances the usability concept by separately analyzing the functional attributes and non-functional attributes as antecedents of the efficiency and effectiveness of m-commerce systemsAo usage.
Further, the model developed also helps to analyze the contribution of each construct Ae efficiency, satisfaction, and effectiveness Ae on satisfaction and the continuance intention in the context of m-commerce usage.
By combining all the aforementioned constructs in one comprehensive model, we developed a usability model that not only predicts customer needs effectively but also provides actionable insights that can be readily understood and acted upon by m-commerce system owners and developers.
Implications for Practice When considering the importance of different usability factors, usability is an important aspect that can be evaluated during the software development process, to ensure continued product usage .
M-commerce application developers must ensure that productivity is considered appropriate when using an mcommerce application.
Productivity can be expressed in terms of response time, loading time, and useful output.
Developers should create simple and adaptive applications, resulting in fast loading times and response times with output that meets user expectations.
Besides, navigation Ae which is expressed in terms of among others easy and efficient navigation, accessibility, and location Ae also should be addressed well.
Good navigation can improve the userAos ability to change the environmental structure and control content in real time.
For this reason, developers should create applications with straightforward navigation to make them easier for users to use.
Memorability indicates the applicationAos capacity to support users to understand, obtain, and remember the operation of the m-commerce application system.
It is also very important to improve the memorability of the systems by considering easy recall, logical steps, and comprehensibility when developing m-commerce applications.
M-commerce application developers must also pay attention to the effectiveness of system usage by enhancing content and security.
This can be realized by providing informative, accurate, and complete content as well as media-choice Investigating the Effect of m-Commerce ApplicationAos content for each transaction process from the time the user enters the application until the time when a transaction is completed.
Complete content and security will help users easily make transactions, select product items, and choose payment method.
integration with popular payment platforms will make it easier for users to carry out transactions comfortably.
Conclusion and Future Direction The main contribution of this research is the reconceptualization of the usability concept by more comprehensively compiling factors that constitute usability, i.
efficiency, satisfaction, and effectiveness.
The study also contributed by proving that beside non-functional attributes of m-commerce applications Ae such as productivity, navigation, and memorability Ae functional attributes also play an important role in increasing user experience and thereby contribute to the improvement of m-commerce systems usability and continuance intention.
This study also showed that non-functional attributes of these systems are strongly related to efficiency while the functional attributes are significantly correlated with the effective use of m-commerce systems.
These results can provide strategies for m-commerce application developers to design their applications to satisfy the users and help users to complete their tasks correctly.
When an application has high usability according to the user wishes, users will tend to continue using it.
Overall, this study showed that to get loyal users, mcommerce application developers must ensure that user usability requirements are met.
A seamless experience in conducting m-commerce transactions through the application encourages continued usage.
This research had several limitations.
First, it is better to use a probabilistic sampling technique so that the results are more representative and can be better Future studies should be done with a more comprehensive sampling method and expand the data collection to a wider population, to obtain more generalizable results.
Further, future research should also incorporate more predictive factors from various theories and models to identify other important functional aspects of usability, such as social influence and simplicity, which could provide deeper insight into the complex dynamics of user behavior and intention to continue using mobile commerce applications.
Acknowledgments The authors would like to express the deepest gratitude to ITB for the research grant under PPMI (Research.
Community Service, and Innovatio.
Program for the year 2020.
Hotma Antoni Hutahaean, et al.
References