1. Central Business School, Central University Accra Ghana, Ghana; baah_lydia201@gmail.com

*             Correspondence:  masudlydia984@gmail.com

Keywords: system quality, service quality, continuous customer intention, trust, satisfaction

1. INTRODUCTION

The utilization of mobile communication has resulted in a significant transformation in the usage of mobile devices in the Philippines. The proliferation and integration of emerging communication technologies have facilitated and introduced novel applications for mobile devices.  Currently, there is a growing trend in the sale and purchase of products or services through mobile devices, which has captured the attention of researchers [1].  Based on information from sources within the e-commerce market, it is projected that there will be a substantial increase in the number of smartphone users in the coming years, reaching millions.  The widespread adoption of mobile devices is anticipated to generate a substantial increase in demand for mobile usage, consequently driving growth within the telecommunications industry.  The global mobile purchase has witnessed a significant surge, aligning with the projected growth.  Acquiring new customers is a significant objective for mobile vendors; however, it is even more crucial to maintain the loyalty and satisfaction of their existing customer base [2].  Recent findings have indicated that the expenditure associated with acquiring a new user is fivefold higher in comparison to the cost of re-engaging with an existing user. When examining the disparities between online and offline commerce, it becomes apparent that mobile commerce is now characterized by increased levels of risk due to the susceptibility of mobile networks.  There is a prevalent sense of mistrust surrounding mobile vendors and mobile payment systems [3].  In the Philippine context, the adoption of mobile technology has not garnered significant traction among consumers, and the industry’s growth has fallen short of initial expectations.  In contrast to entertainment applications such as television, the usage of mobile music applications has been significantly higher, as indicated by the adoption rate of mobile purchases, which primarily involves transactional applications [4].  The mobile purchase has been deemed highly unsatisfactory in relation to the expenditure incurred for its development and management.  Despite the potential for mobile vendors to provide a wide range of advanced services through handheld devices, there is a lack of consumer willingness to utilize mobile devices for shopping purposes. As a result, the growth in mobile purchases has been relatively slow [5].  The initial phase for mobile vendors involves the promotion of mobile purchases, with the primary objective being to facilitate and retain existing customers. This is achieved by providing assistance throughout the purchasing process and encouraging customers to make continuous purchases [6].  Research has revealed that the expenditure associated with acquiring new customers is fivefold greater than the investment allocated towards retaining existing users [7].  The Mobile Purchase Service has played a significant role in investment and has served as a valuable resource for mobile service providers. However, the inability to recover costs and generate profits may lead to the discontinuation of this service. This decision is influenced by the presence of intense competition among various industry participants. The user did not provide any text to rewrite.  There is a limited number of existing studies regarding the behavior of mobile shoppers, resulting in a gap in knowledge regarding the factors that influence and impact the persistence of such behavior.  The investigation of initial adoption and the technology associated with its purchase has not yet been a central area of focus for researchers.  The main aim of this study is to assess the system quality of mobile devices in order to establish a sense of trust between mobile vendors and consumers.  The impact of mobile service quality on consumer trust and its influence on consumer satisfaction levels, which in turn drive purchase or repurchase behavior, is a topic of interest.  The study has also assessed the impact of trust on examining the enduring intention of consumers.

2. LITERATURE REVIEW

Recent studies have shown the relationship between system quality and service quality with continuous customer intention, and with reference to this, the researchers have found few relationships that have significant effects [9].

2.1 Mobile Purchase

The commerce of mobile is growing all around the world very rapidly  Many researchers have defined “Mobile Commerce” in different ways and stated that mobile commerce is being used as wireless technology, assisting technological transactions, used as hand hold device, also used for the search of information [10], the performance of tasks for consumer B2B, Inter and Intra-enterprise connection and communication  In this study, the term Mobile Commence is said to be any transaction that is done for Paid purpose held over wireless channels  According to previous research in mobile purchase  This research elaborated that smart phone purchase is somehow related to mobile purchase [11]  Purchase of mobile has now become easy because of internet, customers use mobile to plan the pre-shopping activities (finding store hours and directions), and they are used for composing, modifying and placing orders to conduct purchase transactions online [12]  With the help of mobile devices without looking at time or location, comparing general purchase on internet, the purchase on mobile allows one to serve on internet without constraint of time, location, mobile network and devices  Based on location based services mobile service providers can get information about location  The information once taken could be related to information or service and could be used to provide relevant information or practical services to users [13]  Hence the internet in mobile could be utilized as retailers provided customized service pin pointing the location to interact with customers or consumer buying handsets  This has provided convenience and intern value to customers thus facilitated the adoption of mobile purchase [14]. This too contains uncertainty and risk due to lack of control and virtual potential opportunism. Mobile Encryption systems are not as intact as online encryption systems. This help to draw about consumer interest in concern about security of mobile while purchasing  They are concerned if mobile vendors could easily and effectively protect their personal information i-e privacy Security of payment [15].

2.2 Trust

Trust reflects a willingness to be vulnerable based on positive expectations toward another party’s behavior [16]. There are usually three types of beliefs that are included in the trust.

1. Ability
2. Integrity
3. Benevolence

Here ability means that the vendors of mobile have enough skills and have gathered enough knowledge for the fulfillment of tasks [17]. Integrity means that those vendors know that their prime responsibility is not to deceive the customer, and they must keep the promise [18]. Benevolence means that while making mobiles, the vendors keep the interest of the consumer, not only their own benefits [19]. Because of the vulnerability of perception like distrust and payment systems of online commerce or mobile commerce have been found to be in greater risks or uncertainties compared to offline commerce [20]. Mobiles, due to their technological inputs, may sometime also get infected by trogon horses or viruses. Mobile purchase indeed is a subject of security and privacy concerns, thus including risk and mistrust as has been encountered through studies. Thus mobiles do not have tangible indicators for testing product quality   The buyers of mobile may also have to oversee if their accounts of credit cards and passwords can be transmitted or stored [21]  Mobile shoppers may have concern with privacy of location without consulting the users themselves  Due to the problem of privacy and security concerns, there is greater risk and perceived uncertainty and risk  This resultantly will have to build trust so as to facilitate or mitigate perceived risk; hence mobile could continually be used  Behavioral intention could then become an important determinant of “trust” [22]  Researchers suggested “privacy- Trust- behavioral intention model” for understanding electronic commerce in context of consumer behavior  The main and basic reason for most of the people for not conducting online shopping is that people mistrust transactions performed electronically  Researchers demonstrate that trust has always been a positive association with shopping of mobile [23].

2.3 Satisfaction

Through multiple interactions with mobile vendors, they developed a feeling of satisfaction. The usage of mobile is usually disconnected if the consumer of the direct user is not satisfied with the mobile vendor or disconnects its usage. Earlier studies have shown that satisfaction is positively associated with fewer behavior [24]

2.4 System Quality

The quality of the system is associated with connection, speed, visual appeal, ease of use and navigation when it comes to mobile system mobile networks and terminals have freed users from terminal and spectral constraints and enabled them to conduct ubiquitous purchase [25]  The appearing connection and communication has been affecting the trust of users while operating transaction in mobile  There are different mobile sites that users usually find difficult to use and due to this consumers feel that service providers are not capable enough to incorporate their service that led them to harm their quality [26]  A study in 2009 revealed that system quality had been affecting the trust of users in mobile banking  Mobile websites have been affected by quality of system  Consumers may feel discontent because of lack of involvement while operating the sites, this leads to the feeling of lack of control due to service might be taken abruptly [27]  This results in a decrease in perceived control and increased consumer frustration that would undetermined their experience with the mobile vendor  A poor quality of system can never satisfy users as they always are ready to accept the adoption of commerce system that satisfies them  Early researches had notified the effects of system quality on the satisfaction of the user of sites of mobile internet, the work of mobile in healthcare service   [28.

2.5 Service Quality

Reliability, responsiveness, personalization and assurance reflect the term “service quality”  Consumers however will never develop trust in them if the mobile vendors give unreliable services and provide slow response which fails to develop trust [29]  For example, if the consumer doesn’t get a timely answer about the queries of customers this tells them that the vendors of mobile are not capable enough to deal with certain information  So the quality of service has impact on consumer trust  The service quality has affected the trust of consumers and virtually traveling communities, which indicates the relation of service quality being positively related to service payment of mobile [30]  Moreover, online shopping satisfaction is positively associated with service quality  “Virtual Travel community” and SMS [31]  Consumer could present services as to develop the trust and this helps in attracting the customers so that they feel satisfaction and this leads to “repurchase” of product [32]  The following figure shows the framework of the present research model where system quality, service quality and satisfaction has an impact on trust where the trust has an impact on consumer continuous intention, which is being dependent variable  Following hypothesis has been tested in the study.

H1: System Quality has a direct relation with trust.

H2: Service quality has a direct relation with trust.

H3: Satisfaction has a direct relation with trust.

H4: Trust has a direct relation with continuous consumer intention.

 

 

Figure 01: Theoretical Framework

3. MATERIALS AND METHODS

The data was collected by liker scale-based questionnaire consisting generic questions taken from different sources  The questions of system quality and service quality were adapted from, the questions for satisfaction were adapted from, the questions for trust were taken from and questionnaire for consumer continuous intention were adapted from  After selecting the questionnaire, we decided the sample size for our study  According to the Steven [33] the sample size for social science researches should be greater than 15 times from its predictor  In our research model  we have 5 predictors so according to this our sample size should be greater than 80  So we selected simple random sampling technique and collected the data from N=450 respondents who were student of university of engineering and technology  Partial least square based on structure equation modeling  method is used to analyzed and measure the research hypothesis  Variant of multiple regression can easily solved with PLS-SEM technique  PLS- SEM is a multivariate technique which can consider much comparison between many dependent and independent variables  Smart PLS version 3.0 is used to collect and analyzed data it was first time used by Harman Wold and Joreskog [34]  Cronbach’s alpha as well as composite reliability in PLS- SEM technique was used to check the reliability of constructs in region of Pakistan  The appropriate factor loading value for valid constructs should be greater than 0.700  Cross-factor loading value in PLS-SEM technique was used to check the discriminant validity.

4. RESULTS AND DISCUSSION

The table below shows the reliability being greater than 0.6 hence showing that the questionnaire is reliable and could be used to study the prescribed variables. In this type of analysis, firstly measurement model is tested through testing the convergent and discriminant validity.

Table 01: Loading and Reliability Analysis

Variables	Items	Outer Loading	Cronbach’s Alpha	Composite Reliability	Average Variance Extracted (AVE)
Consumer Continues Intention	INT1	0.926	0.868	0.919	0.791
	INT2	0.903
	INT3	0.837
Satisfaction	SAT1	0.958	0.917	0.948	0.858
	SAT2	0.916
	SAT3	0.905
Service Quality	SER1	0.823	0.847	0.908	0.767
	SER2	0.911
	SER3	0.891
System Quality	SYS1	0.843	0.925	0.944	0.77
	SYS2	0.898
	SYS3	0.888
	SYS4	0.862
	SYS5	0.895
Trust	TRU1	0.886	0.839	0.903	0.756
	TRU2	0.881
	TRU3	0.840

4.1 Outer loading

Table no. 1 shows the outer loadings of the indicators. According to the threshold value i-e >0.6, all the values show significant results. This sown the validity of the questionnaire.

4.2 Average Variance Extracted

Table no. 1 shows the AVE values for indicators. As this is an average value hence the threshold value tells that it has to be greater than 0.5. As all the values show significant results hence is the convergent validity.

Table 02: Fornell-Larcker Method

	Consumer Continues Intention	Satisfaction	Service Quality	System Quality	Trust
Consumer Continues Intention	0.889
Satisfaction	0.915	0.926
Service Quality	0.835	0.841	0.876
System Quality	0.680	0.764	0.791	0.877
Trust	0.882	0.868	0.817	0.760	0.869

Table no. 2 shows the values of average value extracted, which according to the threshold, has to be greater than 0.6. Here, the positive value shows the positive relationships, and the negative shows weaker or sometimes no result. The diagonal shows the maximum results. The above table has shown the positive and direct relation of variables with other variables and themselves. It, however, shows the highest positive relationship with themselves.

Table 03: Cross Loading

	Consumer Continues Intention	Satisfaction	Service Quality	System Quality	Trust
INT1	0.926	0.880	0.763	0.640	0.882
INT2	0.903	0.821	0.748	0.681	0.788
INT3	0.837	0.728	0.718	0.478	0.662
SAT1	0.857	0.958	0.790	0.682	0.821
SAT2	0.835	0.916	0.765	0.667	0.777
SAT3	0.851	0.905	0.780	0.772	0.813
SER1	0.717	0.677	0.823	0.774	0.703
SER2	0.751	0.775	0.911	0.647	0.738
SER3	0.723	0.754	0.891	0.658	0.702
SYS1	0.569	0.622	0.630	0.843	0.672
SYS2	0.630	0.718	0.704	0.898	0.697
SYS3	0.572	0.670	0.666	0.888	0.642
SYS4	0.571	0.639	0.747	0.862	0.629
SYS5	0.639	0.698	0.722	0.895	0.689
TRU1	0.745	0.750	0.723	0.647	0.886
TRU2	0.716	0.660	0.642	0.628	0.881
TRU3	0.827	0.834	0.752	0.697	0.840

Table no. 3 has shown the results of cross-loadings which represent the loading of one construct with its own indicators and indicators of another. It tells that the value of the construct with itself should be maximum and should be lesser than that with others. According to this logic, satisfaction with satisfaction, service quality with service quality, system quality with system quality, and trust with trust show the highest loadings as compared to others  Although, the loadings show values higher than 0.4, which tells that kept result is validly Measuring the Structural model using PLS-SEM. The figure shown below contains the model run of PLS-SEM.

Figure 02: Structural Model PLS-SEM

In the above table, the causal relationships among different models are shown. In the inner model, the relationship between different proposed models is shown. Here, one unit increase in system quality will increase 0.219 units in the trust, one unit increase in service quality will increase 0.156 units increase in the trust, one unit increase in satisfaction will increase 0.565 units in the trust, and one unit increase in the trust will increase 0.882 units increase in continuous consumer intention.

Table 04: PLS-SEM Path Analysis

Sr. No.	Hypothesis	Path Coefficients	T Statistics (|O/STDEV|)	P Values	Supported
H1	System Quality has a direct relation with trust	0.158	7.594	0.000	Yes
H2	Service quality has a direct relation with trust	0.219	2.593	0.010	Yes
H3	Satisfaction has a direct relation with trust	0.565	2.019	0.044	Yes
H4	Trust has a direct relation with continuous consumer intention.	0.882	43.583	0.000	Yes

4.3 T-Statistics

According to Joseph F. [34], the value of T- statistics for all the variables have to be greater than 1.96, and according to that, all the values give significant results. Hence it shows the goodness of fit of the model. The value for satisfaction -> Trust is 7.594; for Service Quality -> Trust, it’s 2.593; for System Quality -> Trust, it’s 2.019; for Trust -> Consumer Continues Intention, it’s 43.583, which according to threshold values, shows the maximum output.

4.4 P-value

The P-value tells that if the hypothesis we were to test turns out to be true or false. According to the results, all the hypothesis has been accepted as the threshold tells if the value of P for any combination is lesser than 0.05, we accept that very hypothesis [35]. Now the value of P Satisfaction -> Trust is 0.000, for Service Quality -> Trust is 0.010, System Quality -> Trust is 0.044, and for Trust -> Consumer Continues Intention 0.000. As all of them are lesser than 0.05, we accept all hypotheses. The results of PLS-SEM show a P-value of 0.000, which tells the significant positive relationship between system quality to trust, and 0.000, which shows the acceptance of the hypothesis. H1: Accepted H10: Rejected. Now, the system quality and trust show significant results, i-e 0.00, and has shown a significant direct result as well which is too 0.010. This proves the acceptance of another hypothesis. H2: Accepted H20: Rejected. Satisfaction with trust shows a P-value of 0.044 which again proves the acceptance of H3. H3: Accepted H30: Rejected. Lastly, trust, when measured with continuous consumer intention, shows a significant value of 0.000. This resultantly lets the acceptance of H4. The above statement shows that all the hypothesis were accepted, which means that if the system quality, service quality, and satisfaction gets better, the customer would intend to purchase the mobiles. Also, the service quality, system quality, and satisfaction combine to build trust that will then intended to create continuous consumer intention.

5. CONCLUSIONS

Upon conducting an extensive literature review, it was discovered by the researchers that previous studies had only sporadically examined the continuous intention towards mobile purchase, specifically in the context of the Philippines.  Consequently, it is of utmost significance to secure a highly influential mobile vendor and facilitate their operations.  The objective of this research endeavor was to examine the primary factor that exerts the greatest influence on a customer’s inclination to engage in recurring purchases of mobile devices.  A model was formulated to examine the impact of satisfaction, service quality, and system quality on consumers’ continuous intention to purchase mobile devices, with due consideration to the significance of trust.  The results indicated that trust plays a significant role in service quality, system quality, and customer satisfaction. Furthermore, once trust is established, it becomes the primary motivator for a consumer’s continued intention to engage in mobile purchases.  Based on the research results, it is imperative for the vendor to consider both the system and service quality of mobile devices. Additionally, the level of customer satisfaction plays a crucial role in fostering a continuous intention to engage in subsequent purchases.  Although this study was conducted with meticulous oversight and the results were interpreted cautiously, it is important to acknowledge that every research endeavor is subject to certain limitations.  The study was conducted exclusively in the Philippines, a nation where mobile commerce is experiencing rapid growth but is still in its early stages. We are optimistic that our research will establish a standard for various contexts and that its discoveries will be utilized as a supplementary resource by numerous domestic and international mobile corporations in their sales operations and further investigations.  Our survey focused exclusively on evaluating the service and system quality. However, it is important to acknowledge that there exist numerous other factors that warrant investigation. These factors include, but are not limited to, the quality of information provided, concerns pertaining to privacy and security, as well as individual user characteristics.  The vendors’ implementation of this strategy will significantly enhance customer retention.  The research conducted in this study is founded upon a cross-sectional model, thereby limiting the ability to conduct longitudinal analysis.  If this study were conducted without any demographic limitations, it is plausible that our research would yield divergent and more all-encompassing outcomes.

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