Influence of mobile application service quality and convenience on young customer retention

Abstract

Purpose

Given the rising popularity of mobile commerce among young consumers, this study aims to examine the effect of mobile applications service quality (MASQ), service convenience (SERCON) and satisfaction contributing to the retention of young consumers towards mobile applications.

Design/methodology/approach

Primary data were collected from 213 active online young smartphone users who have used mobile apps for shopping through a structured questionnaire. Structural equation modelling is used to analyse the data.

Findings

The results of this study reveal that both MASQ and SERCON strongly support satisfaction, which leads to the retention of young customers.

Originality/value

This study is one of the few relevant pieces of research that would benefit mretailers encompassing mobile commerce applications to improve their MASQ and SERCON with cutthroat competition in gaining and retaining young customers for shopping through smartphone applications.

Citation

Huma, S., Ahmed, W., Ikram, M. and Najmi, A. (2024), "Influence of mobile application service quality and convenience on young customer retention", Spanish Journal of Marketing - ESIC, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SJME-11-2023-0310

1. Introduction

Due to the broad adoption of smartphones and their ability to remain constantly connected to the internet, young consumers are able to make purchases while on their phones without being limited by time or location (Bera and Bhattacharya, 2024; Ahmed et al., 2023). New app releases via the Apple Store or Google Store, as well as faster cellular networks, have an impact on the retail industry by bringing businesses’ value chains closer together (Grewal et al., 2017; Verhoef et al., 2015). Retailers are additionally providing specialized mobile apps for shopping to their clients as a result of Mobile commerce application’s (MCA) growing development of mobile apps and provision of mobile services (m-services) (Gupta and Arora, 2017; Lu et al., 2017). According to globenewswire.com, the projected market size of mobile application shops is anticipated to increase from US$165.9bn in 2022. Mobile applications have the ability to disseminate specific service features that are not available on other web platforms. In comparison to offline and online shopping, Mobile app-based shopping offers several distinct advantages. These include ubiquity – which allows customers to browse or complete transactions whenever and wherever they choose – localization – which allows customers to use global positioning system on their mobile devices to find stores or locations closest to them – and personalization – which allows customers to receive in-app customized push notifications based on their preferences (Zhang et al., 2023; Marriott and Williams, 2018).

Traditional retailers that had only operated brick-and-mortar stores impulsively recognized the growing significance of electronic and mobile sales and distribution channels as drivers for overall company performance and future development and the declining role of the traditional brick-and-mortar business, m-retailers constantly working on how to retain their customers with functional aspects of mobile applications (Thakur, 2018; Bera and Bhattacharya, 2024). The implementation of the retailers’ own mobile app service quality (MASQ), which enables the specific improvement of their MCA services, is essential to those retailers, as more and more brick-and-mortar retailers are also competing in the m-service environment with mobile apps and young customers demanding higher applications quality services (Huang et al., 2015; Ahmed et al., 2023).

Using mobile technology, MCA retailers provide an extra mobile channel to support customers’ in-store and outdoor purchasing, extending the traditional and e-services offered by these businesses. The overall objective of these mobile apps is to improve users’ shopping experience. Unfortunately, it has been stated that there isn’t much research done on service provisioning on mobile devices using shopping apps. Since the majority of previous research did not distinguish between smartphone applications and other online platforms (such as websites and social media) (Kumar et al., 2017; Wulfert, 2019)., Hence, the study of MASQ is relatively new. To date, several researchers have studied the factors that influence consumer satisfaction and repurchase intention (Chowdhury, 2023; Cai and Leung, 2020; Troise et al., 2020; Wen et al., 2021; Zhang et al., 2023). However, little attention has been paid to the young consumer experience with MASQ (Yeo et al., 2017; Kumar, 2017). Prior research has revealed that several factors, such as convenience, benefits, ease of use, timing and quality and security, are positively linked to consumer preferences for application services but how these factors of MASQ affect customer satisfaction is underdeveloped (Özer et al., 2013). To create a conceptual model of the quality of smartphone application services, research that measures mobile application service directly or even the quality of mobile application services is still lacking. Therefore, it is more difficult to measure the service quality of mobile apps provided through MCA than it is to measure the traditional service quality provided to physical establishments (Kumar et al., 2017). Moreover, there is still a considerable research gap in understanding consumers’ satisfaction and behavioural intentions towards MASQ. Therefore, this study attempts to investigate the underlying relationship between MASQ, consumers’ satisfaction and the behavioural intention of young consumers.

Despite the MCAs, m-retailers should proceed with placing an online order without any inconvenience. For customers to avoid comparing the issues that come while making purchases online – such as the checkout procedure, payment options, shipping and after-sales support – with the experience of visiting an actual store, service convenience (SERCON) is essential. It is well established that prior pleasant experiences with internet purchasing have a beneficial impact on future purchase intentions. Due to their lack of familiarity with online applications, many customers choose not to make purchases online (Huang and Jin, 2020; Cao et al., 2018). As a result, giving new customers a convenient initial interaction may influence their future purchases. Knowing how to improve SERCON appears to be essential to the success of an online business since it enhances consumers’ online experiences, which in turn boosts satisfaction and ultimately encourages repurchase. According to marketing research, one of the main factors motivating consumers to buy products and services online is SERCON (Duarte et al., 2018; Jiang et al., 2013). Furthermore, the impact of SERCON on customer satisfaction and, ultimately, customer intention to repurchase has received less attention from earlier academics. Despite the strategic significance of SERCON, comparatively few empirical studies have been conducted to investigate the aspects of mobile app SERCON (Mahapatra, 2017). Young consumers use mobile phones and smart devices such as tablet computers, smartwatches and voice assistant hubs for convenience like shopping online, paying bills, smart travelling services, transferring money online and many more to save time (Li et al., 2020; Ahmed et al., 2023). Unlike desktop websites, shopping with mobile phone apps is gaining popularity quickly among Generation Z (Hanif et al., 2018). The importance of shopping convenience necessitates an urgent need for MCA retailers to understand and focus on those convenience attributes that are considered most important for shopping on a mobile phone. Therefore, it has been observed that very few studies have incorporated SERCON and MASQ to determine whether they affect consumers’ satisfaction and lead to continuous usage of intentions.

Therefore, this study uses MASQ and SERCON as significant factors in the relationship between satisfaction and future purchase intentions for young consumers. In this study, researchers contribute to the growing body of literature on MASQ, SERCON and satisfaction through MCA.

2. Theoretical background and hypotheses development

2.1 The means-end chain theory

MEC theory stands for Mean End Chain. The means-end chain (MEC) model developed by Zeithaml (1988) is a cognitive model of consumer behaviour widely applied in marketing research. The MEC theory posits: “consumers choose to perform behaviours that they believe will lead to desired outcomes” (Phan et al., 2019). This has been widely studied by researchers, including Sreelakshmi and Prathap (2023), Li and Shang (2020), Phan et al. (2019), Sun et al. (2009) and Kaciak and Cullen (2006). Moreover, MEC theory is based on chain linkages of attributes, consequences and values that posit that Customers’ beliefs regarding the quality of products and services shape their perceptions of value, which in turn affect their inclinations to buy or repurchase (Lai et al., 2009; Pura 2005; Ruiz et al., 2008). According to Sankaran and Chakraborty (2021), the means “M” refers to the attribution of product and service, the end “E” refers to the desired values of the customer, whereas the chain “C” refers to the linkages of the product and services attributions which contribute in achieving desired values of the customer. It has been determined that “the attribute-consequence value relationship” is the essential component of consumer behaviour (Kaciak, 2011; Kaciak and Cullen, 2006). The idea that value perception plays a significant role in customers’ decision-making process – in which they select products and services based on how well they serve their values – is supported by the theory. The model supports the same idea of the relationship between perceived quality and purchasing intention (Li and Shang, 2020). It examines how customers’ value perceptions are influenced by cost-benefit analyses of products and services, which in turn influences their intentions to buy or repurchase (Grunert and Grunert, 1995; Sun et al., 2009).

MEC has been widely used to assess the customer motivation to take action in a digital environment, such as studies investigating the underlying motivations of customers to opt for mobile payments (Sankaran and Chakraborty, 2021). Ku et al. (2021) attempted to map the values perception of gamers using augmented reality using MEC. Li and Shang (2020) applied MEC to investigate the low continuous intention to use e-government websites among citizens of China. The prior studies suggest the significance of MEC theory for accessing the behaviours of customers on digital platforms; however, a comprehensive framework detailing the relationships among consumer satisfaction, perceived SERCON, perceived service quality and the intention to continue using mobile applications has yet to be established in the literature on MASQ continuance (Wulfert, 2019).

This study proposes an extended means-end model incorporating customer satisfaction to elucidate these interrelationships more thoroughly. The model suggests that MASQ and convenience act as the means through which perceived value is derived. Furthermore, perceived value and satisfaction are identified as key constructs that influence the intention to continue using MASQ.

2.2 Relationship between mobile app service quality and customer’s satisfaction and retention

Previous research on service quality metrics has been used to evaluate factors that influence website success (Liu and Arnett, 2000), satisfaction with e-commerce channels (Devaraj et al., 2002) and the quality of virtual community websites. The measurement is an evolution of an idea that Parasuraman on service quality (SERVQUAL) suggested in 1998. The SERVQUAL model is an established method used to measure service quality for both products and services. It focuses on the discrepancy between customers’ expectations and perceptions of their experiences in five dimensions: tangibles, responsiveness, assurance, reliability and empathy (Parasuraman et al., 1988). To satisfy the customers they serve, company units are encouraged by online communication technology to construct and develop online service platforms. This draws researchers to focus on developing the quality component of services for the online environment. Collier and Bienstock (2006) argued that models like SERVQUAL can be used to gauge the quality of offline services. As opposed to electronic service quality, E-service quality is characterized by its individuality, which might affect how customers perceive the quality of the service. Santos (2003) explained that e-service quality is the overall customer assessment and judgement of the perfection and quality of e-services in the digital market. Efficiency, fulfilment, system availability, privacy, responsiveness, compensation and contact are the seven dimensions of E-service quality. However, Li et al. (2001) proposed that the measuring scale has nine dimensions, each derived from the viewpoints of the company and the consumer that connect via mobile phones. Efficiency, website design, responsiveness, system availability, empathy, security, privacy and trust are the nine dimensions to consider for mobile service quality. The aforementioned method might be useful for gauging the quality of e-services provided by different businesses; however, it might not be the greatest method for assessing the quality of services provided by smartphone apps. Afterwards, Wulfert (2019) effectively demonstrated how E-service quality and mobile service quality are related. Since mobile service quality does not address every facet of the MCA, the MASQ model was introduced.

2.2.1 Mobile application service quality model.

The definition of MASQ used in this study is taken from Santos (2003) and Wulfert (2019), who defined it as the entirety of customer assessments and judgments about the ease and quality of e-service delivery through mobile applications. As a result, the quality of mobile applications differs from that of e-services. MASQ is more concerned with the mobile apps executed on smartphones and tablets that provide m-services to users. There is some detail overlap among the constructions that earlier researchers proposed. Furthermore, as already noted, developing a conceptual model of the service quality of smartphone applications requires more than just a scale for measuring mobile service quality. Therefore, this study modified the MASQ measurement that Wulfert (2019) conducted, depending on the instruments and discussions of previous literature. The disconfirmation paradigm is used to measure the MASQ by analysing customers’ expectations and perceptions of the quality of service provided by the mobile shopping app. While the remaining three dimensions – efficiency, information quality and security and privacy – are directly tied to the interaction quality of m-shopping apps, the other two – visual appeal and system availability – are principally related to environment service quality. Since, Wulfert (2019) asserted that MASQ attributes might differ depending on the suitability of the study; therefore, based on the literature review, Table 1 provides the operational definition of each attribute tested in this study that may create value in the digital environment.

Accordingly, based on the literature review, the current study incorporates five attributes of MASQ as antecedents influencing customer satisfaction and the intention to continue using a mobile application. According to Hu et al. (2015), efficiency is crucial when it comes to online buying because it influences how satisfied customers are with their purchases. Found that efficiency and customer satisfaction are positively correlated in his earlier study. The finding is in accordance with research by Ting et al. (2016), which found that efficiency had the most influence on online shoppers’ e-satisfaction. In the context of e-service quality, earlier research has shown a positive and significant association between efficiency and customer satisfaction (Alarifi and Husain, 2021; Egala et al., 2021). In a similar vein, if a customer perceives system availability as meeting or exceeding projected availability, a high maximum availability quality (MASQ) is attained (Wulfert, 2019). In terms of the information quality dimension, a company’s ability to offer correct information about its goods or services can both persuade a consumer to buy it and increase their level of satisfaction. According to a recent study of mobile commerce users, loyalty intention behaviour and customer satisfaction are influenced by the quality of the information both directly and indirectly. Similarly, according to Stiakakis and Petridis (2014), security and privacy have “a very strong impact on m-service quality”. Concerns about privacy and security are pertinent to every stage of the business process, from information to post-purchase. Previous studies have suggested that security may have an impact on how satisfied customers are with the quality of e-services (Egala et al., 2021). Likewise, The user interface of the mobile app and features like the internal search engine and filters that facilitate faster information retrieval are under the purview of the design dimension. Many authors have demonstrated how an e-service’s or m-service’s visual design significantly affects the customer’s perception of quality. (e.g. Brady and Cronin, 2001; Lu et al., 2009; Stiakakis and Petridis, 2014; Vlachos and Vrechopoulos, 2008; Wolfinbarger and Gilly, 2002). In addition to simple SERVQUAL, the design dimension affects the consumer’s assessment of the retailer as well as their inclination to reuse and return (Rosen and Purinton, 2004; Sohn and Tadisina, 2008). Albadarneh and Qusef (2017) state that to attain a high degree of customer satisfaction, the design must be expertly executed and feature MCA interfaces that are well-organized. Apart from that, a well-organized interface design would facilitate users’ rapid familiarisation with the application. Thus, based on the aforementioned discussion, the following hypothesis is derived:

H1.

Application quality significantly impacts the satisfaction.

H2.

Application quality significantly impacts the customer’s retention.

2.3 Relationship between service convenience and customer’s satisfaction and retention

In marketing theory, convenience is first defined in terms of product classification. According to Berry et al. (2002), convenience is the measure of how consumers perceive the time and effort required to use a mobile device. Convenience is becoming crucial for online businesses to remain competitive. (Jeng, 2016). A simple and effective shopping experience is what is referred to as convenience (Wolfinbarger and Gilly, 2001). The most important factors in retail convenience, according to the literature, are time and effort convenience (Seiders et al., 2007). It comes as no surprise that e-commerce retailers have committed substantial amounts of money to improving the online convenience features of their service offerings. (Saha et al., 2022). Moeller et al. (2009) examined the effects of five shopping convenience dimensions – decision, access, search, transaction and after-sales convenience – on consumer loyalty and retention in the context of retailing. Beauchamp and Ponder (2010) distinguished between customers who made purchases online and those who did not use four criteria: search, evaluation, possession convenience and post-purchase convenience. They discovered that for online buyers, search convenience was more significant than transaction convenience. Jiang et al. (2013), however, contended that Beauchamp and Ponder’s (2010) dimensions and variables did not adequately capture the distinctive features of internet buying. Jiang et al. (2013) therefore, identified key elements of online buying convenience based on research by Berry et al. (2002) and Seiders et al. (2007) and created a reliable tool for measuring online shopping convenience, such as search, evaluation, possession and post-purchase convenience. Therefore, based on the literature review, search, evaluation, possession and post-buy convenience for various stages of a purchase cycle are included in the current study as antecedents influencing customer satisfaction and continuation usage intention of shopping on a mobile application.

According to Aagja et al. (2011), behavioural intentions are positively influenced by SERCON. Consequently, the following definitions of the four dimensions of online mobile app convenience apply: Consumer perception of the time and effort required to search for a product is known as search convenience. Research has shown that customers’ online shopping experiences will be faster and easier for them to navigate if shops help customers with their product searches (Kollmann et al., 2012). Search convenience positively affects customer satisfaction and attitude (Beauchamp and Ponder, 2010a; Roy et al., 2018; Seiders et al., 2000). Consumer perception of the time and effort required to evaluate a product is known as evaluation convenience. Due to their user-friendly interfaces and quick response codes, smartphones equipped with cutting-edge technology, including mobile phone apps, enable customers to quickly access, discover, compare and order products as well as make shopping lists (Mahapatra, 2017). Because of their convenience and perceived value in terms of the consumer experience, these mobile phone applications encourage consumers to continue shopping on their phones and have a favourable impact on the experience of using them (Kang et al., 2015). In a similar vein, possession convenience refers to how easy it is for customers to feel like they can own what they want and enjoy those benefits. Channel preference is influenced by convenience and support in possession (Johnson et al., 2006; Laukkanen, 2007). Consumers frequently seek validation in the form of feedback before making a final purchasing choice. Smartphones provide the ability to effectively communicate information to gain confidence in the decision (Carlson and Zmud, 1999). Consumers’ perceived time and effort expenditures to contact the provider after using the service is known as post-purchase convenience (Forsythe and Shi, 2003). The ease with which assurance and endorsement on purchase decisions can be achieved impact channel preferences. According to Ko et al. (2005), when it comes to “human message interaction” and “human-human interaction”, smartphones are the most interactive platforms. The benefit of service delivery, as promised, is offered by the real-time tracking system (Srinivasan et al., 2002). Customers can quickly report service failures by mailing or calling the hotline. Through a helpline number or human assistance on a cell phone, customers can get immediate service assistance (Chiang and Liao, 2012). Only two of the five convenience dimensions, according to Chang and Polonsky (2012), have a favourable impact on behavioural intention. They claimed that depending on the type of service under consideration, the impact of various convenience factors on behavioural intentions varies. Both Jiang et al. (2013) and Mpinganjira (2016a, 2016b) concurred that SERCON positively influences repurchase intention when it comes to mobile shopping. In light of this discussion and supporting evidence, we put forth the following hypotheses. Therefore, the research hypotheses were proposed as follows:

H3.

SERCON significantly impacts on satisfaction.

H4.

SERCON significantly impacts on customer’s retention.

2.4 Relationship between customer’s satisfaction and retention

Customer satisfaction plays a very important role because it is a good forecaster of customer retention (Muylle et al., 2004). Additionally, customer satisfaction is not only a significant behavioural element for service quality but also encourages continuous intention, which is supported by (Heiller et al., 2003). To continue using the same online service provider, overall customer satisfaction intensity is associated. Studies focusing on mobile shopping usage have found that customer satisfaction plays a significant role in repeat purchase intention (Hung et al., 2012; Lin and Wang, 2006). Moreover, many studies indicated a very positive, dominant and significant relationship between customer satisfaction and continuous intention under the platform of the online era. They highlighted that customer satisfaction is the highly strong antecedent of continuance usage of online sites as when customers perceive to acquire a positive efficacy in the future, then satisfaction facilitates customer’s repurchase intention (Duarte et al., 2018; Chen and Fu, 2018). Therefore, we led to the hypothesis that:

H5.

Satisfaction significantly impacts on customer’s retention.

The proposed framework of the study is shown in Figure 1.

3. Methodology

The present study used a quantitative research approach, using a survey research design to meet the study’s objectives. Therefore, a self-administered survey questionnaire was developed to collect data from young respondents aged 30 and under based on the scales adapted from the past literature measured on a 5-point Likert scale ranging from 1 for strongly disagree to agree (refer Table 2). A sample is representative of young consumers in Pakistan as the questionnaires were filled with young buyers who have experience of at least one-time mobile shopping. The questionnaire was designed to gather data from age groups less than 30, possessing a smartphone and having experience with any mobile app in the recent past. The data is gathered by using a purposive sampling technique.

While designing the questionnaire, procedural remedies for countering common method bias were considered, as discussed by Podsakoff et al. (2012), including placing temporal delay and reverse coded items. Before addressing it to potential respondents, experts validated the questionnaire, whereas the results of the pilot study support the final data collection. The methodology of the present study is summarized in Figure 2. Moreover, for this study, the sample comprised only young smartphone users who have used mobile apps for shopping. Gen Y, also known as millennials, are those who were born between 1980 and 1994. They fall into the young consumer sector, but it is contended that consumers between the ages of 26 and 40 are considered mature consumers (Ligaraba et al., 2023). Therefore, we ultimately chose to focus just on people born between 1995 and 2002 (ages 18–24), categorized as Generation Z (Reeves and Oh, 2007) and (Kitchen and Proctor, 2015), which is the primary categorization used in this study. Moreover, the data collection for the current study was conducted in September–December 2022 through an online questionnaire created on Google Forms, whereby the potential respondents were approached through social media platforms, including Facebook, Instagram, Linkedin and Twitter. A similar data collection technique is also found in the study by Ligaraba et al. (2023), examining the context of South Africa, investigating the context of Jakarta, Indonesia. Nevertheless, purposive sampling was used, where respondents were briefed that their participation was voluntary, without any particular incentive or reward for participation, and was compliant with all required ethical considerations. Keeping in mind the recent advances suggested by Sarstedt et al. (2023), Kock and Hadaya’s (2018) inverse square root method was considered for the minimum sample size, which appears to be at least 160 for the complex models. Hence, the final data comprised 213 respondents. The demographic profiles of the respondents are shown in Table 3.

4. Data analysis

The gathered data were initially screened for missing values, out-of-range data and outliers before applying structural equation (SEM) modelling using partial least square (PLS) method. PLS-SEM is more appropriate when the model is relatively complex, formative higher-order constructs are used, a new theoretical model is proposed and the sample size is relatively small (Henseler et al., 2016). SmartPLS 3.4.4 software is used for the data analysis.

4.1 Content validity

Content validity is considered to be strong if the factor loadings of items within a construct are higher than the rest of the constructs in the model (Hair et al., 2013). Items with higher loading on other constructs than their construct loadings were eliminated. Moreover, the majority of the factor loadings are greater than 0.7, which shows the properties of items for measuring related concepts. Results are tabulated in Table 4, where all items were significantly loaded on their respective constructs higher than others.

4.2 Convergent validity

Convergent validity is the extent to which a group of items converge to measure the same concept (Hair et al., 2013). Three methods examine it. Firstly, highly loaded factor loadings with at least more than 0.7 of factor loadings and are statistically significant. Secondly, the value of average variance extracted (AVE) above 0.5 is considered an acceptable threshold for convergent validity (Fornell and Larcker, 1981). Thirdly, it is validated through composite reliability, which should be greater than 0.7 (Hair et al., 2016). Table 4 illustrates all the values above the limits, confirming the convergent validity assumption.

4.3 Discriminant validity

Discriminant validity is the extent to which a set of items can distinguish a variable from another variable in the model. In this research, discriminant validity was analysed using two criteria. First, all items within the construct were checked to be strongly loaded on their respective constructs than the other constructs, and differences between loading on the respective construct and the cross-loading were higher than 0.1 (Gefen and Straub, 2005). The second discriminant validity approach was suggested by Fornell and Larcker (1981). The correlation matrix in Table 5, has a diagonal line of elements representing AVE’s square roots with the absolute value of their correlation of the constructs in rows and columns. The values in the diagonal line are greater than the others in the rows and columns values, confirming the discriminant validity. Moreover, the heterotrait-monotrait ratio (a statistical method) criterion is also used for the discriminant validity; most of the values are below 0.85 except the four values, which are relatively similar concepts and are significantly lesser than 1 (see Table 6). These criteria are aligned with the recent guidelines of PLS SEM-based analysis (Sarstedt et al., 2023).

4.4 Structural model (inner model) and hypotheses testing

PLS analysis uses bootstrapping to estimate both the measurement model and the structural model. Results have been reported below in Tables 7, 8, 9, Figures 2 and 3 using a bootstrap resampling procedure of 5,000 subsamples (Hair et al., 2016).

There are five hypotheses investigated through PLS-SEM. Table 9 shows the results of hypotheses tested and there are five significant indicators for the formative construct MASQ i.e. system efficiency, A, information quality, visual appeal and privacy and security, while there are four significant factors contributing to SERCON that is search convenience, evaluation convenience, TC and post-purchase convenience (PPC) as illustrated in Table 7, 8 and Figure 3.

4.5 Predictive accuracy of the model

The predictive accuracy of the model is examined through R square. R square values greater than 26% reflect substantial predictive power. Table 10 shows that 41.4% of satisfaction (SAT) is explained by MASQ and SERCON, whereas 49.0% of customer intelligence (CI) is explained by SAT, MASQ and SERCON, confirming that constructs used in this study are highly predictive to estimate the outcome. This signifies acceptable model fit and good prediction quality.

The above-illustrated results support the main notion of this study, which is that MASQ and SERCON play a significant role in enhancing customer satisfaction and retaining customers.

5. Conclusion and discussion

Through the use of mobile applications, mobile commerce apps expand these retailers’ traditional and online offerings by providing an additional mobile channel for customers to shop both inside and outside of physical stores. All things considered, the goal of these mobile apps is to provide a variety of unique features that will increase the outstanding value they provide to customers. Mobile apps are becoming the main topic of academic papers due to the intensifying rivalry in the app market, changing customer expectations, improvements in interactive technology and consumers’ quick adoption of new technology (Bera and Bhattacharya, 2024). Therefore, the purpose of this study is to determine the MASQ and SERCON aspects that influence the consumption experience and intention to continue using a mobile application for future purchasing. Since the MCA is still in its infancy worldwide, the study’s addition to the literature – which focuses on m-shopping via the application’s continuous usage intention – is therefore noteworthy. This study looked at what motivates young users to use a mobile application for a greater amount of time. Young consumers are interested in adopting mobile app-based shopping and retailing for convenience and ease of purchasing. According to (Wen et al., 2014), retailers got a new opportunity through MCA to fulfil their potential customer’s demands. Young consumers can easily buy their desired goods with the help of their smartphones. Generation Z has greater access to knowledge than prior generations because of their enormous purchasing power; this young sample becomes a desirable target market as their disposable income increases, so they will continue to engage in more e-commerce (Ahmed et al., 2023). Therefore, this study focused on the most neglected area as younger consumers are more acclimated to the practices of online buying from mobile apps and ease of convenience, which ultimately affects their decision to continue usage. Therefore, this research is very timely in understanding this MCA better and prioritizing the focal points that may reap maximum benefits from this less explored research. Improved customer experience is quickly taking over all mediums. Insights on the implementation of MASQ and SERCON across different purchase cycles for a great mobile-based purchasing experience appear to be most efficiently addressed by this study. Our study’s analyses provide insightful information for creating young consumer experiences that are both highly valuable and engaging. This can, therefore, result in a rise in the adoption of MASQ, meeting the expectations of value held by young customers. The study’s analysis indicates that the most important characteristics are availability, security and privacy, app visuals, information quality and efficiency. These essential attributes of MASQ satisfy customers (β = 0.413, p < 0.001) and lead to retain the customers (β = 0.335, p < 0.001). Thus, H1 and H2 are supported; the finding is consistent with the study conducted by Zariman et al. (2023). Similar to this, it is determined that implementation of MASQ is important in producing user value, such as workflow, battery drain, expectation fulfilment, features, design, not freezing, screen size and messaging capability. Therefore, the result of this study highlighted that m-retailers should, in particular, offer up-to-date, clear and consistent information about the products and services they sell via MCA, return guidelines, shipping charges, methods of payment and physical store locations. Moreover, m-retailers are advised to use augmented and virtual reality technologies to properly showcase their goods and services to improve the aesthetics of mobile app design. For instance, the augmented reality furniture application from IKEA enables customers to use the camera on their mobile device to place furniture pieces virtually in their homes. Furthermore, m-retailers should implement mobile threat defence solutions to mitigate the perceived danger of m-shoppers; such solutions successfully identify and manage risk by safeguarding apps, devices and networks against developing threats.

Second, the conceptual framework proposed extends former contributions on SERCON (Thuy, 2011; Jiang et al., 2013; Roy et al., 2018; Duarte et al., 2018; Kumar, 2017) by verifying the significant consequence of online shopping convenience subdimensions on customer satisfaction and behavioural intentions and future customer retention (β = 0.287, p < 0.001) thus supporting H3 and H4 (β = 0.190, p < 0.05). Furthermore, this study’s findings confirmed that customer satisfaction positively affects retention of customers (β = 0.282, p < 0.001), supporting H5. The current result is consistent with (Cao et al., 2018; Ashraf et al., 2020; Anand et al., 2023), who found that satisfaction strongly influences future purchase intentions. The findings of the research demonstrated that customer satisfaction was influenced by convenience. This is because long lines are not an inconvenience during the online checkout process. Customers can be served with timely, accurate and personalized service help with interactive videos as a retention tactic. Additionally, the findings imply that SERCON affects continued usage of m-shopping. Thus, mobile applications should be appealing because of their convenient features, such as simple cancellation procedures and tracking of product movement. Customers would be drawn to the apps by an inventive way to trace their transaction history and link to location-based rewards and promotions. The ability to keep an eye on service quality and on-time delivery can improve convenience after the sale. An app specifically for handling incomplete orders or broken products should be created and made available.

5.1 Theoretical implication

The present research adds to our understanding of consumer behaviour regarding MASQ and SERCON attributes, making a theoretical and practical contribution and the direct impact of these dimensions on customer satisfaction with the service provided when they use mobile applications. Therefore, the study believed that the research findings would be of interest to future researchers and MCA providers. This study’s major theoretical contribution is to highlight the role of MASQ and SERCON as a key moderator in the relationship between satisfaction and future purchase intentions. Previous studies have already confirmed the impact of satisfaction on future purchases. Still, the current study adds to the existing knowledge by applying a framework based on MASQ and online SERCON and disclosing its sub-dimensions’ role in generating satisfaction. The findings of the study will also help business owners develop and enhance online business strategies in the future. The findings of this study provide several important theoretical implications. First, through an in-depth, systematic investigation into the construct of MASQ and its constituting dimensions, this study has identified five key dimensions of MASQ, namely efficiency, system availability, information quality, visual appeal and security and privacy; these dimensions are critical determinants of customer satisfaction and continuation usage. First, this study has identified five key dimensions of MASQ: efficiency, system availability, information quality, visual appeal and security and privacy. These dimensions are critical determinants of customer satisfaction and continuation usage. The investigation was thorough and methodical. While the remaining three dimensions – efficiency, information quality and security and privacy – are directly tied to the interaction quality of m-shopping apps, the other two – visual appeal and system availability – are principally related to environment service quality. The two major research studies in the m-shopping literature (Huang et al., 2015) have identified several mobile app service quality dimensions, but they do not conduct an empirical investigation into how MASQ affects other significant categories, such as customer satisfaction and customer continuous usage, in the context of young consumers.

5.2 Managerial implications

Our findings have practical implications for managers who want to strengthen their online customers’ future purchase intentions. The findings of this study have significant ramifications for m-retailing practitioners. To increase the m-shopping experience of younger customers, m-retailers would be well advised to excel in all the mobile app service quality aspects revealed in this study. The key mobile app service quality measures used in this study are designed to provide an effective tool to assess overall m-shopping service quality encompassing MCA. Thus, m-retailers are recommended to use the MASQ measurement instrument validated in this study to identify the strengths and weaknesses of their service quality as perceived by mobile app-based users. To provide m-shoppers with superior service, m-retailers should not only enhance the levels of responsiveness, personalization, ease of use and aesthetics but also attenuate customers’ perceived risk. In enhancing customer satisfaction and continuous usage, it is important to note that m-retailers should always focus on developing user-friendly, risk-free and visually appealing mobile app designs. The findings revealed that the consumption experience and intention to continue using m-shopping via applications are influenced by SERCON attributes such as search, evaluation, possession and post-purchase convenience. M-retailers should thus be aware of the importance of reinforcing personalized and understandable product descriptions on smartphone apps to facilitate evaluation. A mobile application that presents features through text, pictures and video improves the user’s assessment of the product. An MCA should include walk-through tours with avatars and dynamic drop-down menus with several levels of information to help guide users through mobile application navigation. The mobile apps should have a straightforward layout with a visualized map that allows for rapid, user-command-driven sorting and classification to maximize efficiency while minimizing effort. An application that is easy to use reduces the cognitive load and time required for navigation. Instant peer and consumer reviews and comments offer the benefit of quick and easy assessment prior to ordering and making a final decision. Based on their surfing habits, shopping lists, locations and purchasing preferences, MCA systems can create value for customers. For quickly grasp the contribution and implications of our paper, we have included a Table 11 summarizing the main conclusions and theoretical or managerial implications of our study.

5.3 Limitations and future research directions

This research focuses on limited factors, so there should be more detailed research on customers’ continuous usage intention, such as purchase uncertainty. Additionally, the current research has tested the hierarchal order relationships of service convenience and MASQ on SAT and customer retention (CR). However, the precise exploration of the individual dimensions of the mentioned higher-order variables with SAT and CR is a potential avenue that will also enhance the literature. Besides, a limited age group is the focus of this research, so in future research, there should be a focus on other age groups as well. Furthermore, there should be research on factors that directly impact males and females and influence them to purchase online. Finally, with the ongoing methodological advances related to the PLS-SEM and its application, revisiting the studied framework and its implications will be a noteworthy future contribution.

Figures

Figure 1.

Proposed framework

Figure 2.

Methodology plan

Figure 3.

Measurement model

Table 1.

Operational definitions

MASQ attributes	Description	Source
System efficiency	The app’s ability to respond quickly and is easy to use	Santos (2003)
Availability	The retailer’s ability to promptly and politely respond to customer requirements related when interacting with the mobile shopping app
Information quality	The provision of accurate and precise information by the retailer	Wulfert (2019)
Visual appeal	The aesthetic features and layout of the user interface design	Wen et al. (2014)
Privacy and security	The protection of system and network resources from any external or internal attack and the protection of the customers’ personal data	Zeithaml et al. (2002)

Table 2.

Source of measuring instrument

Variables	Sources
System efficiency	Wolfinbarger and Gilly (2003)
Availability
Information quality	Wolfinbarger and Gilly (2003); Zhou et al. (2011)
Visual appeal
Privacy/security	Wolfinbarger and Gilly (2003)
Search convenience:	Mahapatra (2017)
Evaluation convenience	Mahapatra (2017)
Transaction convenience	Mahapatra (2017)
Post purchase convenience	Mahapatra (2017)
Satisfaction
Customer retention	Mahapatra (2017); Ahmed et al. (2023)

Table 3.

Demographics

Variable	Cases (%)	Variable	Cases (%)
Gender		Personal income level (monthly)
Male	121 (56.8)	Less than 10,000	24 (11.26)
Female	92 (43.2)	10,000–50,000	187 (87.79)
		50,000 or more	2 (0.93)
Age		Last product bought online
Less than 20	10 (4.69)	Before 6 months	142 (66.6)
18–24	96 (45)	More than a year	63 (29.57)
25–30	117 (54.9)	One time	8 (3.75)

Note:

Descriptive statistics (n = 213)

Table 4.

Factors loading and convergent validity

Constructs	Items	Loading	T value	P value	CR	AVE
A	Mobile commerce app is always available for business	0.649	10.736	0.000	0.818	0.602
	Mobile commerce app never crash	0.843	30.321	0.000
	Mobile commerce app never stop while I doing transactions	0.821	22.563	0.000
	I like to continue using my preferred mobile commerce app	0.699	11.781	0.000	0.769	0.526
CR	I intend to increase my use of mobile commerce app	0.727	15.711	0.000
	I would like to use mobile commerce app again for shopping	0.748	16.904	0.000
EC	I am able to find various goods through my preferred mobile commerce app	0.851	28.73	0.000	0.793	0.658
	For better understanding mobile commerce app provide information in text and graphics	0.769	17.843	0.000
	Mobile commerce app provide accurate information	0.686	11.895	0.000	0.756	0.509
IQ	Mobile commerce app provides easy to understand information	0.666	10.949	0.000
	Mobile commerce app accurately provides the information I need	0.783	21.422	0.000
	Mobile commerce app allows easy return of purchases	0.676	12.411	0.000	0.774	0.534
PPC	Mobile commerce app takes little time to cancel my orders if needed	0.775	21.608	0.000
	It is very simple to provide feedback after consumption of products on mobile commerce app	0.737	9.478	0.000
	Mobile commerce app does not share my personal information to other websites	0.777	22.482	0.000	0.818	0.6
PS	Mobile commerce app has good security features	0.722	12.996	0.000
	Mobile commerce app is reputable	0.823	25.46	0.000
	I am satisfied with mobile commerce app experience	0.726	14.012	0.000	0.797	0.567
SAT	My feeling with using mobile commerce app was good	0.766	20.98	0.000
	Mobile commerce app fulfils my expectations	0.767	20.342	0.000
	My preferred mobile commerce app is helpful for making purchases	0.698	10.651	0.000	0.787	0.553
SC	My preferred mobile commerce app provide full information related to desired product with same category	0.762	19.408	0.000
	It is easy to follow product classification through My preferred mobile commerce app	0.769	16.819	0.000
SE	Mobile commerce app is easy to use	0.807	21.767	0.000	0.797	0.663
	Mobile commerce app is well organized	0.821	22.851	0.000
	It is convenient to make payment through mobile commerce app	0.822	25.859	0.000	0.86	0.672
TC	Mobile commerce app provides multiple payment options	0.827	20.176	0.000
	I feel safe while making transaction through mobile commerce app	0.811	19.041	0.000
	Mobile commerce app colours, layout and animations is attractive	0.792	23.414	0.000	0.817	0.598
VP	Mobile commerce app looks professionally designed	0.712	16.216	0.000
	Mobile commerce app overall look visually appealing	0.812	28.723	0.000

Table 5.

Discriminant validity (HTMT)

Construct	A	CI	EC	IQ	PPC	PS	SAT	SC	SE	TC	VP
A
CI	0.788
EC	0.758	0.823
IQ	0.567	0.796	0.615
PPC	0.599	0.673	0.544	0.665
PS	0.633	0.835	0.659	0.919	0.582
SAT	0.769	0.988	0.691	0.769	0.675	0.668
SC	0.599	0.801	0.976	0.552	0.418	0.647	0.698
SE	0.870	0.970	0.635	0.620	0.588	0.664	0.828	0.566
TC	0.589	0.496	0.583	0.430	0.565	0.371	0.531	0.511	0.525
VP	0.648	0.676	0.503	0.831	0.648	0.879	0.629	0.613	0.660	0.396

Note:

HTMT = Heterotrait-monotrait ratio

Table 6.

Discriminant validity (Fornell and Larcker criteria)

Construct	A	CR	EC	IQ	PPC	PS	SAT	SC	SE	TC	VP
A	0.776
CR	0.479	0.725
EC	0.433	0.437	0.811
IQ	0.334	0.424	0.302	0.713
PPC	0.361	0.386	0.290	0.358	0.73
PS	0.416	0.512	0.38	0.544	0.370	0.775
SAT	0.492	0.587	0.383	0.441	0.410	0.434	0.753
SC	0.378	0.475	0.576	0.319	0.261	0.418	0.432	0.744
SE	0.491	0.509	0.312	0.308	0.326	0.383	0.457	0.315	0.814
TC	0.415	0.330	0.355	0.265	0.371	0.266	0.372	0.346	0.321	0.82
VP	0.430	0.410	0.295	0.485	0.405	0.582	0.406	0.393	0.377	0.285	0.773

Table 7.

Formative construct for MASQ

Construct	Loading	SD	T statistics	P values
A → MASQ	0.292	0.032	9.252	0.000
IQ → MASQ	0.236	0.027	8.787	0.000
PS → MASQ	0.312	0.025	12.724	0.000
SE → MASQ	0.207	0.024	8.770	0.000
VP → MASQ	0.288	0.021	13.479	0.000

Table 8.

Formative construct for SERCON

Construct	Loading	SD	T statistics	P values
EC → SERCON	0.28	0.032	8.897	0.000
PPC → SERCON	0.310	0.035	8.830	0.000
SC → SERCON	0.375	0.039	9.741	0.000
TC → SERCON	0.407	0.041	9.904	0.000

Table 9.

Hypotheses testing results

No.	Hypotheses	Estimates	SD	T statistics	P values	Decision
H1	MASQ → SAT	0.413	0.088	4.696	0.000	Accepted
H2	MASQ → CR	0.335	0.076	4.404	0.000	Accepted
H3	SERCON →SAT	0.287	0.088	3.275	0.001	Accepted
H4	SERCON → CR	0.190	0.088	2.161	0.031	Accepted
H5	SAT → CR	0.282	0.085	3.310	0.001	Accepted

Table 10.

Predictive power of construct

Construct	R square
SAT	0.414
CR	0.49

Table 11.

Summary

Main conclusions	Theoretical/managerial implications
Mobile apps enhance in-store and online shopping experiences	Invest in mobile app development to offer unique features and increase customer value
Key MASQ dimensions: efficiency, availability, security, visuals and information quality	Focus on improving these MASQ dimensions to boost customer satisfaction and retention
Young consumers value convenience in mobile shopping	Target Generation Z with mobile-friendly shopping experiences
MASQ influences customer satisfaction and future usage intentions	Create user-friendly, visually appealing and secure mobile apps to meet customer expectations
Service convenience (SERCON) affects customer retention and satisfaction	Enhance app features like easy navigation, personalized descriptions and instant reviews
Innovative technologies like augmented reality improve user experience	Integrate AR and VR to showcase products effectively
Addressing security and privacy concerns is essential	Implement mobile threat defence solutions to enhance consumer trust