The role of mobile shopping service quality in enhancing customers M-satisfaction, M-loyalty, and E-word of mouth

Abstract

With the increasing prevalence of mobile shopping (m-shopping) apps, service quality (SQ) has been recognized as a prominent factor in distinguishing the best apps available online. As limited studies have explored how to estimate mobile SQ, this study attempts to expand our understanding of the primary dimensions that shape customer judgments of the overall mobile SQ related to m-shopping activity by validating the mobile SQ (M-S-QUAL) scale in a new cultural setting, e.g., Egypt. It also examines the interrelationships between mobile SQ and three outcome variables: mobile satisfaction (m-satisfaction), mobile loyalty (m-loyalty), and electronic word of mouth (e-WOM). The results support the validity of using the M-S-QUAL scale to assess the mobile SQ of m-shopping platforms. Moreover, the findings emphasize the role of m-shopping SQ in enhancing consumers’ m-satisfaction, thereby improving m-loyalty and increasing favorable e-WOM. The results also show that responsiveness and efficiency are the primary driving forces underlying the SQ dimensions of the investigated outcome variables.

Graphical abstract

Introduction

The retail business has substantially evolved over the last two decades due to the emergence of online shopping via websites, followed by the expansion of mobile channels and social networking sites [81]. Because of the accelerated growth in the use of mobile devices globally, customers have become highly dependent on their smartphones for shopping activities [33]. Indeed, global mobile retail sales increased to nearly three-quarters (i.e., 72.9%) of all retail e-commerce sales in 2021 [70]. The uptake of mobile shopping, commonly referred to as “m-shopping,” has risen more rapidly than other types of mobile commerce [51]. The number of mobile platforms enabling consumers to shop continues to rise every year [36]. Several electronic vendors (e-vendors) have expanded their current business to mobile applications to take advantage of this market expansion and gain new clients [16, 23, 33, 78].

Due to the high cost of establishing and executing apps, businesses must ensure customers’ satisfactory experience with their shopping apps to encourage continuous usage and avoid shoppers losing interest in their apps and switching to competitors [12]. The level of service quality (SQ) associated with mobile platforms has been acknowledged as among the primary forces of client satisfaction [14, 41, 66, 80]. Therefore, companies are becoming more aware of the importance of concentrating on SQ to strengthen their competitive position and gain customer loyalty in this intensely competitive market [17, 60]. Hence, it has become increasingly imperative to recognize digital consumers’ perceptions and expectations of SQ toward new purchasing channels, such as m-shopping.

Limited research exists on how to estimate mobile SQ, despite its significance for businesses in detecting and boosting their apps’ SQ [45]. Although appraising e-SQ for a conventional e-commerce environment has been emphasized, current studies on mobile SQ are characterized by fragmentation and single-study observational approaches [35]. Some attempts were made to introduce a measure for assessing different SQ-related characteristics regarding m-commerce in diverse settings, such as mobile network operators [15] and mobile brokerage services [45]. As these measures have little in common, additional research is required to establish a scale that can estimate overall mobile SQ efficiently. This motivated us to test a scale recently developed specifically for assessing m-shopping, namely the mobile service quality (M-S-QUAL) scale introduced by Huang et al. [31], which proposed four items to estimate mobile SQ for physical goods shopping: contact, responsiveness, fulfillment, and efficiency. Responding to Huang et al. [31], who seek to apply the scale across various countries and cultures to validate the M-S-QUAL scale, this study applied the model in an emerging market setting to create a realistic impression of mobile customers and marketplaces in these regions, which constitute a large fraction of the globe’s population and economic growth [65]. Customers’ behavioral patterns regarding their m-commerce activities, including m-shopping, are impacted by cultural variances [47]. National cultural characteristics substantially influence consumers’ perceptions and preferences regarding m-commerce [28],hence, consumers in developed and emerging markets have different expectations from and views on the perceived quality of m-commerce activities [49]. Due to varying levels of advancement of e-commerce markets and in customer behavior across contexts, applying and generalizing the results of previous studies conducted primarily in Asian and Western countries to the contexts of emerging countries would be dubious without first attempting to understand these issues in their contexts [46]. Therefore, exploring mobile SQ dimensions fostering favorable customer behaviors is essential in emerging markets that are still poorly understood. This is particularly crucial in the context of m-shopping, as promoting the long-term success of this shopping technology requires an awareness of the factors that explain post-adoption behavior, such as mobile SQ [46]. In this regard, this paper aims to capture the elements used for evaluating the SQ of m-shopping platforms and investigate how they can improve mobile customers’ satisfaction and consequently enhance their mobile loyalty (m-loyalty) and increase positive electronic word of mouth (e-WOM) among shoppers from the perspective of emerging-market consumers. A qualitative approach was used to collect data via a paper-based survey to achieve these goals. Next, the proposed model was tested using structural equation modeling (SEM) on a convenience sample of actual mobile shoppers (m-shoppers).

This research contributes to the literature in three main ways. First, it extends our understanding of the dimensions that shape customer judgments of the overall mobile SQ related to m-shopping activity. Second, it explores how the investigated factors may contribute to increasing shoppers’ m-satisfaction, thereby gaining their m-loyalty and motivating them to generate positive e-WOM. Third, it extends the existing literature to establish a comprehensive framework for mobile SQ assessment by validating the M-S-QUAL scale [31] and applying it to another market with different attributes, like the Egyptian marketplace, which is regarded as the largest market in the Middle East and North Africa, to check its validity in diverse cultures and states. The Egyptian market was chosen specifically because it is regarded as one of the most promising markets for m-commerce in developing countries. In 2021–2022, the proportion of consumers using the Internet to search for goods and services reached approximately 72.7%, whereas the percentage of consumers who use the Internet to buy goods and services reached around 50.1% [19]. Despite the high prevalence of online shopping in the Egyptian market, Egyptian customers are known for their unpredictable purchasing habits and lack of loyalty to digital commerce, which present substantial challenges for digital vendors [2]. Therefore, identifying factors that can increase Egyptian consumers’ satisfaction and loyalty to m-shopping app providers is essential.

The remainder of this article is organized as follows. Section "Literature review" presents a literature overview of m-shopping and mobile SQ. Section "Research model and hypothesis development" outlines the research framework and hypotheses, and Sect "Methods" explains the current study methods. Section "Data analysis and results" provides the research analysis results, and Sect. "Discussion" reviews the major conclusions and limitations of this research.

Literature review

Mobile shopping

M-shopping refers to “the purchase of goods or services from mobile devices such as smartphones and tablets via a wireless network” [32], p. 165). This is a potent medium for linking consumers to retailers, thereby increasing sales and improving merchant revenue. It empowers corporations to have closer communications with their clients [38] and contributes to increasing customers’ purchase frequency as it allows them to obtain information and execute transactions without limitations to time or location [33]. It offers retailers and companies an opportunity to fulfill the needs of specific clients, recognize seasonal purchases, make tailored recommendations, promote novel goods, send promotions, and start new purchases. Therefore, various businesses have released m-shopping apps to reap these benefits [51].

M-shopping has gone mainstream among digital consumers [32] because it affords interaction, is easily accessible at any time and place, saves cost and time, and enables quick access to information [68]. Digital consumers mainly find that shopping through mobile apps rather than conventional websites requires less effort in terms of searching for information, placing orders, and executing transactions. They favor using mobile platforms rather than mobile websites because they are more convenient, quicker, and simpler to navigate [51]. Clients can utilize m-shopping stores to perform various shopping activities such as looking for items and prices, comparing goods, making purchases, preparing shopping lists, and executing various post-purchase tasks [69, 73]. Additionally, customers living in areas with limited accessibility to offline retailers can purchase their essentials using m-shopping apps to offset the high costs of offline shopping [16].

M-shopping platforms enrich customers’ shopping experiences by promoting discussion about purchased items, which allows clients to recommend items to others through social media, empowers consumers to obtain immediate and continuous notifications about the latest offers, as well as provide individualized product information [51]. Customers may even receive emotional benefits via the mobile channel that are not provided by conventional electronic channels, such as feelings of psychological comfort and reassurance. Such emotional benefits related to mobile phone consumption make shoppers more comfortable and assured about their purchase choices [81]. M-shopping can also offer consumers hedonic (e.g., joy and immersion) and social benefits (e.g., recognition, self-esteem, and social identification) [23].

Despite these benefits, the pace of m-shopping penetration varies between markets because customers are motivated by diverse values, desires, and experiences [26]. For example, evidence reveals that a large number of customers in African countries do not shop with their mobile phones compared with other parts of the world [46]. In this setting, it is imperative that we examine post-adoption client behavior toward m-shopping, including clients’ views and judgments of SQ across various marketplaces, to enhance its future diffusion.

M-shopping in the Egyptian market

Today, Egypt is among the pioneering states in the Middle East that possess a well-developed e-commerce setting and a sophisticated information technology infrastructure [2]. Indeed, it is regarded as one of the most appealing markets for m-shopping due to the significant proliferation level of mobile device usage in the Egyptian market, which represents around 101.02 million mobile users (out of a total population of 109.3 million) and the increasing number of mobile Internet subscribers, which was over 62 million (out of 101.02 million mobile users) in September 2021 [18].

Egypt’s population features several young people who are more inclined to embrace and participate in m-shopping activities than older customers because today’s younger generation relies heavily on the Internet and mobile devices for most of their activities [62].

For these reasons, the penetration rate of m-shopping activities in the Egyptian marketplace rises every year. A total of 40.1% of Internet consumers bought items online using their mobile phones during the third quarter of 2020 [71]. In fact, the e-SQ of m-commerce apps is a critical factor in shaping Egyptian consumer satisfaction [50], and potential m-commerce adopters in Egypt are extremely sensitive to SQ issues [20]. Accordingly, businesses and digital retailers must consider mobile SQ factors when developing mobile shopping apps to ensure success in the Egyptian market. In the following sections, we will discuss SQ in both e-commerce and m-commerce contexts.

SQ in an e-commerce environment

SQ represents a customer’s opinions and perspectives about the relative excellence or weakness of a corporation and its services [7]. Customers mainly determine the SQ level by comparing their former expectations to current service performance [25]. SQ includes various service elements and features desirable to customers. These elements vary among business environments and individuals and cannot be assessed or detected directly [82]. Therefore, scholars have proposed approaches for estimating SQ. The SERVQUAL scale pioneered by Parasuraman et al. [56, 58] is the most popular method in this regard, which proposes five dimensions for evaluating consumers’ expectations of SQ in service and retail companies: tangibles, reliability, responsiveness, assurance, and empathy.

With emerging online business channels, traditional SQ measures such as the SERVQUAL model had to be reformulated to reflect the features of SQ specific to the digital environment [1]. In an online context, e-SQ is characterized as a customer’s assessments and impressions of the quality and performance of the e-service acquired through a digital marketplace [63]. E-SQ evaluates an entire transaction conducted online, including website functionality, purchasing procedures, privacy rules, client support communications, returns handling, shipping, and order fulfillment [8].

The business success of an e-firm relies heavily on e-SQ. Most consumer complaints regarding e-retailers are attributed to poor e-SQ, so e-organizations should provide clients with an outstanding service experience to protect their reputation and acquire customer loyalty [24]. In this respect, many researchers have endeavored to establish novel methods for estimating e-SQ that specifically reflect the characteristics of e-commerce. Some examples of these scales include SITEQUAL, introduced by Yoo and Donthu [79], who developed four SQ determinants for online shopping sites: ease of use, aesthetic design, processing speed, and security. Meanwhile, Barnes and Vidgen [4] proposed WebQual, wherein they identified five factors for measuring the SQ of e-commerce websites: tangibles, reliability, responsiveness, assurance, and empathy. Moreover, Wolfinbarger and Gilly [77] developed ETailQ and suggested four factors to predict the quality of online retailers: website design, fulfillment/reliability, privacy/security, and customer service. E-S-QUAL was presented by Parasuraman et al. [57], who suggested four constructs to estimate the SQ offered by online consumer shopping websites: efficiency, fulfillment, system availability, and privacy. Bauer et al. [5] introduced ETransQual and identified five dimensions for measuring SQ online: functionality/design, enjoyment, process, reliability, and responsiveness. The following section discusses additional models for estimating SQ in an m-commerce environment.

Mobile SQ in m-commerce

With the subsequent advent of m-commerce, it became obvious that it has its own features and characteristics that distinguish it from e-commerce [54]. M-commerce platforms can afford services based on location, individualization, client, and context [76]. Mobile channels provide more portability due to the modest size and mobility of devices. This provides suitability and temporal and spatial flexibility advantages over conventional electronic channels [81]. In the context of m-commerce, specifically m-shopping, mobile SQ can be defined as “the extent to which a mobile channel facilitates efficient and effective shopping, purchasing, and delivery of products and services” [54], p. 3).

Because of the unique features of m-commerce, e-SQ measures are insufficient to explain the overall mobile SQ associated with m-commerce platforms. In fact, some aspects of these measures are incompatible with the m-commerce context. For example, dimensions related to m-commerce apps should replace the website design dimension, which refers to all elements of the consumer experience on the website in the eTailQ model [77], and the system availability dimension, which refers to the site’s proper technical functioning in the E-S-QUAL model [57]. Moreover, some new dimensions have emerged to evaluate SQ in the m-commerce environment, such as Kaatz’s [35] dimension of service ubiquity, which describes the retailer’s ability to make offers based on location and time. Therefore, mobile SQ measures should include elements and dimensions that are specific to m-commerce.

As mentioned earlier, investigations into mobile SQ are not extensive. The literature has recently been striving to develop a comprehensive framework to estimate mobile SQ in an m-commerce setting. For example, depending on fuzzy set theory, Choi et al. [15] proposed a method for assessing mobile SQ among customers of mobile network operators comprising six aspects: network, device, security, convenience, content, and customer support. Another example is Lu et al. [45] who suggested a measurement scale for mobile SQ regarding mobile brokerage services that includes three key elements, namely interaction, environment, and outcome quality. The same approach was adopted by Wang et al. [76] for a study on mobile communication platforms that emphasized the significant indirect impact of two factors—interaction and environmental quality—on a client’s intention to continue the service.

Although these studies aimed to develop an instrument to evaluate mobile SQ, the specifics of their setting and methodology limit their generalizability to evaluate mobile SQ in general [31]. Accordingly, it is critical to develop a comprehensive and robust framework for measuring the overall mobile SQ associated with m-commerce apps [78]. The M-S-QUAL scale proposed by Huang et al. [31], to the authors’ knowledge, is the first and only comprehensive and solid model to assess the mobile SQ of m-shopping platforms. Compared to other models, this scale incorporates SQ aspects unique to the m-shopping context from the perspective of actual customers. This approach suggests four major constructs for physical goods shopping: contact, responsiveness, fulfillment, and efficiency; privacy was added as a fifth construct when measuring virtual product shopping. This scale has been empirically validated by Omar et al. [54] in the UK. This study attempts to validate the scale in another country with a different culture and economic conditions.

Research model and hypothesis development

The current study attempts to provide empirical support for the M-S-QUAL method in an emerging country context, like the Egyptian market, to respond to Huang et al. [31] request for the scale to be applied to diverse marketplaces with distinct cultures to enhance its generality and reliability. It examines the potential impact of suggested aspects of M-S-QUAL on the outcome variables, which include m-satisfaction, m-loyalty, and e-WOM, as demonstrated in the hypothesized model depicted in Fig. 1.

Fig. 1

The research framework

This study relies on the M-S-QUAL scale for physical goods shopping, which involves four variables: contact, responsiveness, fulfillment, and efficiency. According to Huang et al. [31], contact describes the extent to which telephone and online agents are available to provide support. Responsiveness relates to the efficacy of the m-shopping app’s problem-solving procedure and return policy. Fulfillment refers to the degree to which the m-shopping app keeps its promises concerning order delivery and product availability. Efficiency describes the extent to which the m-shopping app is fast and easy to use.

Relation between mobile SQ and m-satisfaction

In the new era of m-commerce, client satisfaction is a critical component of corporate success. It plays an important role in gaining market share and revenue and preserving existing clients for m-commerce companies [62]. Mobile satisfaction (m-satisfaction) refers to the total response to the consistency between the anticipated and actual functioning of m-commerce [47]. A client’s m-satisfaction with an m-shopping app can be described as “the post-utilization stage that results from a cognitive evaluation process, by which individual consumer expectations regarding m-shopping experiences serve as the baseline to gauge their level of satisfaction” [26], p. 151).

Many studies in the e-business area have revealed that e-SQ is a primary motivation for e-satisfaction [5, 8, 9, 24, 37, 43, 60, 61, 74]. Mobile SQ is proven to have a considerable impact on a customer’s m-satisfaction with m-commerce in the domains of mobile payment [80], mobile banking [66], m-shopping [41], and augmented reality retail apps [14]. Therefore, we hypothesize the following:

H1

M-shopping SQ positively influences shopper m-satisfaction.

Furthermore, to examine the impact of the individual aspects of m-shopping SQ (contact, responsiveness, fulfillment, and efficiency) on m-satisfaction, we hypothesize the following:

H2

M-shopping SQ aspects (H2a) contact, (H2b) responsiveness, (H2c) fulfillment, and (H2d) efficiency positively influence shopper m-satisfaction.

Relation between m-satisfaction and m-loyalty

Establishing consumer loyalty is a key strategic target for any organization [64] as it represents an organization’s most valuable asset, through which a corporation may create a long-term mutually beneficial and profitable connection with its clients [59]. Loyalty is described as a profound commitment to repurchase an item continuously, regardless of situational effects or promotional activities that may modify a customer’s behavior [53]. It reflects the urge to rebuy, tolerance for a higher price, and willingness to acquire additional items from the same corporation [11]. This stems from a client’s belief that the value gained from a certain vendor or brand surpasses that given by a rival firm [73]. In this study, mobile loyalty (m-loyalty) can be characterized as an m-shopper’s obligation to buy items from the same m-shopping app.

A review of prior studies reveals the beneficial and primary role of e-satisfaction in creating and retaining online consumer loyalty [37, 39, 61]. This suggests that once a firm’s clients are satisfied, they are more likely to have a favorable impression and perception of the firm, show loyalty to its brand and products, and even engage in positive e-WOM [64]. In the same way, m-satisfaction is observed to be a crucial predictor of a customer’s m-loyalty with mobile payment apps [80], mobile telecommunications services [21], m-commerce [42], mobile advertising [44], and m-shopping [26, 41, 73]. Based on these findings, we suggest the following hypothesis:

H3

M-satisfaction positively influences shopper m-loyalty.

Relation between m-satisfaction and e-WOM

E-WOM represents any dynamic and continuous evaluation by a former, present, or potential customer about the goods, services, or brands that are accessible to various people, businesses, and communities online [55]. Customers often pursue e-WOM for several reasons, such as minimizing search and assessment efforts, mitigating risk regarding their purchase decisions, and better identifying the sellers and products that perfectly match their requirements and desires [72].

In a virtual environment, businesses are unable to control the generation and transmission of e-WOM because dissatisfied consumers can simply share their unwelcoming experiences, unfavorable sentiments, and negative opinions about goods or services online. They can propagate negative e-WOM through various digital channels such as online review sites, corporation websites, and social networks [40]. Satisfied clients tend to recommend the company to others [21]. For companies, favorable e-WOM gives them an opportunity to engage new customers and expand their market share [64]. Therefore, companies are concerned with identifying the factors leading to favorable e-WOM.

In an online business context, customer e-satisfaction has been identified as a major driver of e-WOM by the previous literature (e.g., [34, 60]). With respect to m-commerce, the link between m-satisfaction and e-WOM has been confirmed in various settings such as m-shopping [62], mobile apps [75], mobile healthcare [6], mobile telecommunication services [21], mobile social media services [55], mobile location-based “check-in” services [30], and social commerce platforms [48]. Accordingly, we propose the following hypothesis:

H4

Shopper m-satisfaction positively influences e-WOM.

Methods

Questionnaire design

The survey employed to test the proposed model relies on measurement scales derived from the existing literature. As shown in Table 1, the four factors comprising the M-S-QUAL scale (contact, responsiveness, fulfillment, and efficiency) were derived from Huang et al. [31], Omar et al. [54], and Parasuraman et al. [57]. M-satisfaction was adapted from Rodríguez et al. [61] and Wang et al. [76], whereas m-loyalty was derived from Groß [26] and Omar et al. [54], and E-WOM was adapted from Meilatinova [48] and San-Martín, et al. [62]. As this research targets Egyptian consumers who speak Arabic, the survey was translated into Arabic by the author, revised by two bilingual Egyptian researchers, and re-translated into English to verify the consistency of the instrument items throughout the two versions of the questionnaire, as recommended by Brislin [10].

Table 1 Survey items

As a preparatory step before gathering data, a pilot study was conducted to assess the questionnaire’s validity and ensure the clarity of its items. It involved 23 participants who were college students at a public university in Egypt, as well as a focus group of four academics in the marketing field. Their recommendations and opinions were considered, and the questionnaire was improved according to their feedback. The final survey used for this study features three sets of questions. The first set contained items pertaining to former purchasing experiences with m-shopping apps. Consistent with the research model, the second set contained 32 statements assessed using a 5-point Likert-type scale that measured the seven endogenous and exogenous variables used in the research framework. The last set involved questions referring to a participant’s sociodemographic attributes (e.g., age, gender, and educational level).

Sample design and selection

The present study’s target population includes consumers aged 18 years or older who have purchased clothing via an m-shopping platform at least once in the last six months. Clothing purchases were specifically targeted because apparel is one of the most common types of merchandise purchased online in most countries [67]. As no sample frame was available, a convenience sampling approach was employed to gather data from visitors to seven different shopping malls in Cairo, the capital of Egypt. The subjects were initially asked if they had ever utilized an m-shopping app on their mobile phones. Only those who confirmed that they had previously engaged in m-shopping activities were invited to participate in the survey. The entire questionnaire was disseminated to participants in a paper-based form. The survey was performed over an eight-week period from December 15, 2021, to February 13, 2022. After eliminating 14 invalid questionnaires that had missing values from the 275 responses obtained, our analysis included 261 valid responses.

As shown in Table 2, most of the respondents were aged 18–30 years (85.8%), whereas those between 31 and 40 accounted for 11.9% of participants, and only 2.3% were aged 41–50 years. The age distribution reveals that most participants are younger individuals under the age of 30. This gap exists be because the younger generation is more likely to adopt novel technologies like m-shopping apps than the older age groups, who may be unfamiliar with or uninterested in such apps and still favor conventional shopping. A large portion of the sample comprised women (69%), whereas men constituted 31%. Most of our respondents (81.6%) held a bachelor’s degree or equivalent. Additionally, the participants had varying levels of experience with m-shopping apps. Of the respondents, 37.2% had less than one year of experience, 26.1% had 1–2 years, 19.5% had 2–3 years, and 17.2% had more than 3 years of experience with m-shopping apps.

Table 2 Respondent profile

Data analysis and results

This research employed SEM using Amos v. 24 software to verify the validity and reliability of the measurement model and examine the causal links postulated.

Assessing the measurement model

The dataset was screened for normality. All skewness and kurtosis test values for the variables (contact, responsiveness, fulfillment, efficiency, satisfaction, loyalty, and WOM) were less than + 3 and − 3, indicating that all the variables were normally distributed. A first-order confirmatory factor analysis (CFA) was conducted for the four factors that constituted the M-S-QUAL scale to assess its validity. Then, a second-order CFA in which all the first-order factors were loaded onto one higher-order factor (MSSQ). An additional second-order CFA was conducted, including all the model constructs (MSSQ, satisfaction, loyalty, and WOM). The overall goodness of fit indices for the CFA analyses indicated an adequate fit of the models (see Table 3).

Table 3 Model fit indices of the CFA

As shown in Table 4, the standardized factor loading values for all the indicators were above the acceptable level of 0.5 [3]. Cronbach’s alpha (α) and composite reliability (CR) were also above 0.7, as suggested by Hair et al. [27] and Nunnally and Bernstein [52]. The average variance extracted reached or exceeded the recommended level of 0.5, except for contact and fulfillment, which were 0.480 and 0.490, respectively. The convergent validity for both constructs was considered adequate as their CR exceeded the threshold value as reported by Fornell and Larcker [22]. Thus, convergent validity and reliability were confirmed for the measurement models.

Table 4 Convergent validity and reliability of the measurement models

Discriminant validity was examined using the Heterotrait–Monotrait ratio (HTMT) method. All HTMT values for both the first-order MSSQ measurement model and second-order measurement model were less than the threshold of 0.9 (see Table 5), demonstrating that discriminant validity was established [29].

Table 5 Heterotrait–Monotrait ratio (HTMT) values for discriminant validity testing

Assessing the structural model

The structural model was assessed to examine the causal links postulated in the study framework. The structural model showed an acceptable fit (CMIN/DF = 2.019, P = 0.000, TLI = 0.906, CFI = 0.914, SRMR = 0.067, RMSEA = 0.063). As seen in Table 6, MSSQ had a significant influence on m-satisfaction (β = 0.1.266, P = 0.001), which supports H1. The results also revealed that m-satisfaction has a significantly positive effect on m-loyalty and e-WOM (β = 0.844, P = 0.001 and β = 0.854, P = 0.001, respectively), thereby supporting H3 and H4.

Table 6 Overview of the results of hypothesis testing

To examine the effect of the individual aspects of m-shopping SQ on m-satisfaction, m-loyalty, and e-WOM, we ran a second structural model without the MSSQ factor as a higher-order factor. The model indices indicated an acceptable fit (CMIN/DF = 1.894, P = 0.000, TLI = 0.917, CFI = 0.925, SRMR = 0.059, RMSEA = 0.059). The results show that both responsiveness (β = 0.795, P = 0.001) and efficiency (β = 0.552, P = 0.005) have a significant impact on customer satisfaction, which supports H2b and H2d. Unexpectedly, contact and fulfillment did not have a significant influence on satisfaction, with values of (β =  − 0.007, P = 0.964) and (β =  − 0.336, P = 0.264), respectively, thereby rejecting H2a and H2c. These results provide partial support for H2. Satisfaction also shows a significant influence on mobile loyalty (β =  − 0.849, P = 0.001) and e-WOM (β = 0.873, P = 0.001). Table 6 presents an overview of the results of the hypothesis tests.

Using a bootstrapping method, we conducted an additional mediation analysis to verify the influence of MSSQ on m-loyalty and e-WOM via m-satisfaction (resampling 2000 and confidence interval 0.95). Table 7 shows that MSSQ had a significant indirect effect on m-loyalty and e-WOM via m-satisfaction, indicating that m-satisfaction mediates the links between MSSQ and both m-loyalty and e-WOM. Further mediation tests were performed throughout the model to examine the indirect links between the individual aspects of the MSSQ (contact, responsiveness, fulfillment, and efficiency) and m-loyalty and e-WOM via m-satisfaction. Table 7 shows the indirect effects of responsiveness on m-loyalty and e-WOM through m-satisfaction were significant. The indirect effects of efficiency on m-loyalty and e-WOM via m-satisfaction were also significant. This demonstrated that m-satisfaction mediated the links between responsiveness and the endogenous variables, as well as between efficiency and the endogenous variables. However, the indirect effects of contact on m-loyalty and e-WOM through m-satisfaction were not significant. The indirect effects of fulfillment on loyalty and e-WOM via satisfaction were also not significant. This indicates that the link between neither fulfillment and the endogenous variables nor contact and the endogenous variables is mediated by m-satisfaction.

Table 7 Mediating model results

Discussion

As m-shopping is still in its infancy in emerging markets, including Egypt, and consumers’ perceptions of its SQ are not yet fully understood, mobile merchants should comprehend the perspectives and preferences of the first adopters to create a strategy that fulfills mobile buyers’ needs and achieves success in this business. This study offers m-shopping merchants and digital marketers guiding principles for successfully running and developing m-shopping apps in today’s highly competitive market. The M-S-QUAL scale gives stakeholders deeper insight into what aspects of SQ to emphasize on to improve the overall level of m-shopping app SQ. It will also help them identify the specific factors of SQ valued by m-shoppers the most and results in the most satisfaction, thereby inspiring loyalty and prompting favorable e-WOM.

Our findings demonstrate that four aspects—contact, responsiveness, fulfillment, and efficiency—establish m-shopping SQ, indicating that the M-S-QUAL scale for physical goods is suitable for measuring what is proposed. This supports and extends the findings of Huang et al. [31] and Omar et al. [54].

This study’s findings illustrate that m-shopping SQ directly enhances a shopper’s m-satisfaction. This is supported by other researchers who concluded that the SQ related to m-commerce apps is a key determinant of mobile customer satisfaction with m-shopping [41], mobile payment [80], mobile banking [66], and augmented reality retail apps [14]. Therefore, administrators and developers of shopping apps must think creatively about how they can maximize the SQ of their platforms to boost client satisfaction.

The results indicate that both responsiveness and efficiency influence a consumer’s m-satisfaction with an m-shopping app. Omar et al. [54] found that efficiency is the most significant driving force of all SQ dimensions in establishing customer satisfaction with an m-shopping platform. Chen et al. [13] emphasized the role of efficiency as a component of SQ in creating satisfaction with an AI chatbot. Relevant research has also indicated that responsiveness represents a key element of SQ for determining a consumer’s satisfaction with an online shopping site [5].

An unexpected finding of our analysis is that contact and fulfillment do not increase a consumer’s m-satisfaction. Consumers anticipate that m-shopping apps will fulfill their orders and provide appropriate contact as basic functions, so they do not contribute to increasing their overall satisfaction. However, improving the efficiency and responsiveness of such apps are critical issues to satisfy consumers.

The results support the positive association between consumer m-satisfaction and m-loyalty, which is consistent with studies on m-shopping [26, 41, 73], mobile payment apps [80], mobile telecommunication services [21], m-commerce [42], and mobile advertising [44].

These findings also affirm the relation between a customer’s m-satisfaction and e-WOM. Research has shown that satisfaction plays a crucial role in boosting positive e-WOM in various m-commerce settings, including m-shopping [62], mobile health [6], mobile social media services [55], mobile location-based “check-in” services [30], and social commerce platforms [48]. This implies that keeping m-shoppers satisfied will cause them to recommend and advocate for m-vendors’ apps in virtual communities. Their recommendations may encourage additional buyers to visit these applications because they are considered a very reliable source of information for other consumers [24].

Finally, the study’s findings support the role of m-shopping SQ in improving consumer m-loyalty and e-WOM via m-satisfaction. This suggests that focusing mobile merchants’ efforts on providing high levels of SQ will provide a pleasant experience for m-shoppers and keep them satisfied, leading to repeat purchases from the same platforms, sharing positive e-WOM, and recommending purchasing from these apps to others in the online environment.

Conclusions

Theoretical and practical implications

The present study offers various theoretical and practical implications for scholars and mobile retailers evaluating the SQ of m-shopping apps. Theoretically, it first expands on the existing literature on mobile SQ, which is still limited, by investigating the main determinants of m-shopping SQ using the M-S-QUAL model. Second, it validates the newly proposed M-S-QUAL scale for measuring m-shopping SQ in the emerging economics context. Third, it provides additional support for the positive associations between m-satisfaction, m-loyalty, and e-WOM in the context of m-commerce.

Practically, this study presents suggestions for managing and developing the SQ of m-shopping apps. First, m-shopping managers and application developers should consider improving their responsiveness and efficiency as these are the most prominent aspects of MSSQ that explain m-satisfaction. To improve the responsiveness of m-shopping platforms, managers should provide customers with a credible guarantee, respond rapidly to client issues, establish a clear procedure for handling returns, provide appropriate options for returning items, and present clear information about what they should do when they encounter a problem. To increase efficiency, managers and developers should work on accelerating client order processing, facilitating app navigation, and improving the functionality of app features. Second, this study encourages retailers and businesses to focus on improving customer satisfaction with their m-shopping platforms and make it a priority as it plays a significant role in improving consumer loyalty to such apps and motivating them to spread positive e-WOM, which is important because many consumers today rely on the opinions and recommendations of other consumers to make purchases.

Conclusion

This study builds on the existing literature to establish a comprehensive framework for assessing the mobile SQ of m-shopping platforms by validating the novel M-S-QUAL scale for physical goods in an emerging market setting. It also provides guiding principles for m-shopping managers and application developers to successfully operate and develop such apps.

The study confirms the role of mobile SQ associated with m-shopping apps in improving consumer m-satisfaction, resulting in improved m-loyalty and positive e-WOM. The study also shows that responsiveness and efficiency are the primary driving forces behind the SQ dimensions of the investigated outcome variables. Thus, executives and developers of m-commerce apps should focus their efforts on improving their platforms’ responsiveness and efficiency.

Limitations and suggestions for future research

This research features some limitations. The younger age group was overrepresented in the research sample compared to the other age groups. So, future studies should use a more age-diverse population in which all the age categories are well represented. Although this study depends on actual m-shopping consumers, it relies on a convenient sampling approach due to the difficulty of obtaining a sample of the target population. Future research should utilize other random sampling approaches to improve the generalizability of the results. Future research should investigate the moderating impact of gender, age, and income variables while studying present relationships. More studies are needed to validate the M-S-QUAL scale using other samples from various cultures.