From Friends to Feedback: Effect of Social Influence on Mobile Shopping in the Post-COVID Era

Abstract

Although mobile shopping is a new norm after the pandemic, its proliferation is still not very mature in developing nations. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this research employs the SEM technique to examine the effect of mobile shopping frequency and repurchase intent among 198 young mobile shopping consumers in Pakistan. Our findings suggest that purchase frequency is a key variable in consumer behavior and plays a significant role in building mobile shopping repurchase intentions. Findings further suggest that effort expectancy, unlike the performance expectancy, also strongly affects the relationship between purchase frequency and repurchase intention as an intervening mechanism, while a strong interaction effect from the social influence is also noted. This research offers insightful implications for researchers and marketers in the realm of e-commerce and mobile shopping domains.

1. Introduction

The rise of mobile technology has revolutionized the way we shop, making it more accessible and convenient than ever before. Mobile shopping has become the preferred method of e-commerce, with global sales expected to reach USD 534 billion by 2024 [1]. Although developed countries have already embraced the trend of mobile shopping, it is worth noting that 83% of the global population lives in developing nations [2], where mobile shopping barriers are still needed to cross [3,4]. The population of Pakistan, which is classified as an emerging economy, is the fifth largest in the world. In recent years, the country has experienced a surge in the number of mobile broadband users to 122 million [5].

In particular, a collectivist country like Pakistan presents a unique case for studying mobile shopping behavior, especially among young consumers in the post-COVID-19 era. It is crucial to recognize that the pandemic acted as a catalyst for accelerated digital transformation, especially in the realm of e-commerce and mobile shopping. The surge in mobile shopping, a phenomenon experienced worldwide, has become particularly pertinent in Pakistan [6]. Studying technology adoption trends is becoming more and more important from the standpoint of the factors that deter individuals from engaging in frequent mobile shopping as well as the components that can overcome these factors.

There are certain related observations in the literature regarding the behavior of consumers in collectivist culture, due to which it was important to investigate the generalization of this model in collectivist culture. Ref. [7] states that collective societies are less likely to adopt new ideas and may not readily accept online buying [8], while an individualistic culture places more value on seeking convenience and variety [9]. Similarly, collectivist societies value social interaction and group consensus over individual opinions [10]. However, because mobile shopping is seen as a very private activity, those near the customers are unable to affect their attitudes and behaviors. Being a collectivist society, Pakistan therefore exhibits more risk, which significantly discourages the adoption and use of mobile shopping applications and we found it important to see the consumer behavior related to mobile shopping in this context.

Consumer decision-making is heavily influenced by friends and reference groups in a collectivist society, which has been acknowledged by consumer theorists [11,12]. Building on the normative social influence perspective, individuals are driven to make purchases that stem from the need to follow a group’s or society’s rules and norms. In the context of online shopping, customers may continuously consult their friends or reference groups to understand socially acceptable behavior and seek approval [13]. Specifically, we aim to explore how positive normative social influence affects the intention to repurchase in the mobile shopping context.

The need for tailoring traditional technology adoption theories to suit the specific social and cultural context, particularly in the context of developing nations, has been consistently emphasized in the existing literature. Some researchers have reinforced this by incorporating demographic variables, as seen in the work of [14]. Other studies have expanded upon traditional models by adding new elements such as perceived value, cultural variations and trust [15,16,17,18,19,20]. Additionally, some research has confirmed the need for customization by selectively including the most used constructs instead of the entire model. For example, ref. [21] studied the mobile shopping repurchase intention in Indonesia and chose a subset of constructs after thoroughly reviewing the recent literature. Following the same analogy, this research endeavors to study the habits of youthful consumers participating in mobile shopping in a growing economy. To enhance the relevance and applicability of our research to the developing nation’s context, we will draw upon technology adoption theories and selectively integrate relevant constructs from UTAUT and its variants, which have been previously used in recent studies conducted in Pakistan [15,16,17,18].

Ref. [14] developed UTAUT based on the eight generally employed theoretical models of TAM. Their model comprised four core aspects and four moderators influencing key relationships, where social influence was identified as one of the core aspects influencing both intention and usage. Now, with the creation and widespread usage of Web 2.0 and mobile apps, the importance of social influence as a concept of group and collective behavior has increased [17,22]. Moreover, two other core aspects, i.e., performance expectancy and effort expectancy, have been used as main variables in this study along with purchase frequency as an independent variable.

The present study examines the mobile buying habits of youth customers in Pakistan, particularly focusing on repurchase intention. We are interested in understanding the following:

• Whether shopping frequency enhances the relationship between repurchase intention and perceived benefits (performance expectancy) (RQ1).
• Whether shopping frequency enhances the relationship between repurchase intention and ease-of-use (effort expectancy) (RQ2).
• Lastly, taking into account Pakistan’s collectivist culture, this study explores the ways in which social influence from family and peers may affect the associations between performance expectancy, effort expectancy, frequency of shopping and repurchase intention (RQ3).

This study endeavors to fill the void by investigating the moderating role of social influence perspective and purchase frequency in shaping the repurchase behavior of young consumers in Pakistan. This research can offer insights into the underlying mechanisms that drive consumer behavior and provide marketers with effective strategies to promote mobile shopping in Pakistan.

After the opening section, this study delves into a comprehensive review of the relevant literature in Section 2, which outlines the research framework and associated hypotheses. The methodology and analysis are presented in Section 3 followed by a discussion and research implications in Section 4. The study concludes in Section 5.

2. Literature Review

The different aspects of purchase intentions, repurchase intentions and actual purchase behavior regarding technology-based services and applications have been covered by several technology adoption theories. Notable theories such as the theory of reasoned action (TRA), technology acceptance model (TAM), diffusion of innovation (DoI) model, task technology fit model (TTF), unified theory of acceptance and use of technology (UTAUT) and its extended version (UTAUT2) have gained recognition [15]. Ref. [23] conducted a comprehensive review of various established theoretical models commonly used to explain technology use and acceptance, including the theory of reasoned action, the technology acceptance model and the theory of planned behavior. As a result of their research, they introduced a cohesive integrated model called the unified theory of acceptance and use of technology (UTAUT). In the UTAUT, four primary constructs, namely social influence, effort expectancy, performance expectancy and facilitating conditions, are identified as significant factors influencing both behavioral intentions to use technology and actual usage. The UTAUT model, in particular, has seen widespread application in technology adoption and diffusion studies due to its applicability across various concepts [24]. UTAUT demonstrates a remarkable 70% explained variance in behaviors, surpassing other behavioral intention models [23]. Numerous studies have further confirmed the potency of the four constructs in UTAUT1, showing they account for 71%–75% of the total variances influencing consumers’ online purchase intentions [25,26].

The need to broaden the applicability of technology adoption theories is emphasized in recent research to better understand consumer behavior towards diverse technology-based services and applications. According to [27], evaluating the UTAUT just with the original components can lead to misleading results in certain circumstances. Furthermore, researchers have extensively expanded and customized the UTAUT in different disciplines by incorporating numerous additional constructs that align with the specific context [28], as several studies have shown that enhancing the UTAUT with extra constructs enhances its predictive ability [29]. Scholars have incorporated new constructs into traditional technology adoption models to explore the complexities of behavioral patterns among existing and potential customers [30,31,32,33,34].

Moreover, researchers have proposed a more generalized model by selecting only those constructs that consistently show significant results in the literature [35]. Following a similar approach, our study adopts a strategy of carefully selecting relevant constructs and prioritizing customer-centric elements. We have integrated selected constructs from the UTAUT model with shopping frequency to create a customized model. The objective is to test various constructs in the social settings of a developing country, specifically Pakistan.

2.1. Theoretical Framework

In this study, we integrated shopping frequency with selected components of the UTAUT model as shown in Figure 1. Customizing traditional technology adoption models to suit specific contexts is a well-established practice in the relevant literature. This approach enhances the models’ interpretive capabilities and offers valuable consumer-centric insights [15].

The primary aim is to identify factors that either promote or hinder the mobile shopping intentions of young aspiring customers in the social settings of Pakistan, a developing country. The selection of constructs is based on their consistent findings, as recommended in recent literature [27]. For instance, in a study by [36], personal innovativeness was integrated into the UTAUT to explore factors influencing usage intention of smartphone applications (apps), revealing that performance expectancy, effort expectancy, social influence and personal innovativeness significantly influenced re-usage intention. Additionally, ref. [37] protracted the UTAUT by incorporating the expectancy confirmation model and task-technology fit model, adding trust, perceived task-technology fit and satisfaction to the original UTAUT. Their study demonstrated that performance expectancy, social influence, trust and task-technology fit were significant predictors of continuous intention to use. Drawing from prior research, the current study aims to include shopping frequency as an additional construct in the three main factors from UTAUT (performance expectancy, effort expectancy and social influence) to enhance the prediction of determinants affecting repurchase intention in mobile shopping. Facilitating conditions were omitted from the original model due to previous studies that found their insignificant impact on technology usage behavior when adopting the UTAUT [36,37,38,39]. Below are detailed explanations of each concept used in the current study.

2.1.1. Shopping Frequency

The concept of shopping frequency refers to the frequency with which a customer makes purchases within a given period [40]. Organizations have a valuable opportunity to enhance customer experiences and cultivate loyalty when they have a better understanding of purchase frequency, making it a critical performance indicator to monitor [41]. Several research articles have explored customer groups based on their purchasing frequency [27,42]. It is crucial to comprehend market segmentation in this way, from a broad management perspective, because frequent shoppers generate much higher sales than occasional ones [43]. Additionally, marketers often propose focusing on heavy and frequent buyers rather than light and infrequent ones [44]. The needs of different customer groups based on shopping frequency are important for customer retention, especially in an online shopping context where customer churn is high [45]. Businesses should strive to convert infrequent buyers to regular ones and loyal heavy buyers [46,47]. Moreover, the differences between frequent and infrequent purchasing behaviors are also highlighted by research in different shopping contexts [48,49].

Refs. [27,50] suggested that shopping frequency may influence repurchase intentions. Even though shopping frequency plays a crucial role in consumer behavior, there is a scarcity of research work conducted on the influence of shopping frequency on repurchase intention [46,51,52].

Prior purchase experience is the most important predictor of intention, and more research is needed to investigate the implications of experience levels on m-shopping repurchase intention adoption [53]. We contend that to more accurately pinpoint customers’ wants; marketers need to concentrate more on consumer categories based on the frequency of their purchases. As a result, we postulate the following:

H1. 

Shopping frequency has a positive influence on m-shopping repurchase intention.

2.1.2. Performance Expectancy

Performance Expectancy (PE) represents “the degree to which consumers will consider getting some benefits while using a technology” [14], affecting their imminent intentions [23]. Performance expectancy is a crucial driver of technology adoption and the use of new technologies, as per the research by [54,55]. Additionally, because this variable is significant in many studies relating to the adoption and usage of different technologies, many academics have examined it [31,55,56]. When customers enjoy their online shopping experience, their satisfaction rises, and they are more likely to return [57,58].

Consumers’ online repurchase intentions have been observed to be influenced by performance expectancy; henceforth, a proportional relationship is witnessed between performance expectancy and repurchase intention in e-commerce [59,60,61]. According to [62], prolonged web and internet use lessens the impact of perceived usefulness on consumers’ intentions in the future. Furthermore, high shopping frequency is associated with higher performance expectancy when making purchases online [63]. Due to these inconsistent findings, further research should be conducted to determine how different levels of experience affect the link between performance expectancy and intention [57].

Regardless of the consumer’s country of origin, performance expectations are one of the most significant influences on mobile shopping intentions [15,64]. Consumers in developed as well as developing nations believe that performance expectations are the primary motivator for online and mobile purchasing intentions [65,66]. Therefore, we propose the following:

H2. 

Shopping frequency has a positive influence on performance expectancy.

H3. 

Performance expectancy has a positive effect on m-shopping repurchase intention.

2.1.3. Effort Expectancy

The term “effort expectancy” refers to a person’s expectations regarding how simple it will be to use a particular piece of technology [67,68] and, like performance expectancy, affects their future intentions and one of the crucial aspects influencing the acceptance of mobile payments is effort expectancy [23,69,70]. If convenience is guaranteed, the consumer would be more prone to use mobile shopping services [61,71,72,73,74]. Intent to make an online repurchase is positively correlated with effort expectancy according to the literature [60]. Online shoppers are more likely to make another purchase when the effort expectancy is low [61], and experience advantageously moderates the link between effort expectations and behavioral intentions [57]. Ref. [75] found no connection between performance expectancy and effort while several others reported a positive relationship [76,77]. Thus, based upon the justification above, it leads to the following hypotheses.

H4. 

Shopping frequency has a positive influence on effort expectancy.

H5. 

Effort expectancy has a positive influence on m-shopping repurchase intention.

H6. 

Effort expectancy has a positive influence on performance expectancy.

2.1.4. Social Influence

The level to which customers perceive that significant people in their lives, such as their friends and family, think they should adopt a certain technology is known as social influence [14]. Social influence is a process where individuals assess their social group’s success in adopting an innovation before deciding to do so. The phenomenon of information surplus and the steadily growing number of information sources are the reason for the increased scholarly interest in studying social influence among internet users [78]. In the past, a person’s close-knit group of friends and relatives exercised the majority of their social influence. Due to the development of the internet, individuals who are active online and have not previously interacted with online shoppers now have access to social influence [79].

Consumers are more likely to intend to shop online if they know that their family and friends frequently do it or think highly of it. An individual whose frequency of online purchases is on the lower side will be more affected by social influence [63,77]. The information from social groups serves only to supplement an individual’s existing attitudes and may not necessarily dictate their decision [22]. Individuals’ confidence in their attitude toward an object increases the likelihood that this attitude will guide subsequent behavior toward the object.

Social influence is cited in earlier research as a major factor in e-commerce activities in Vietnam [80], mobile commerce in Pakistan [15], and mobile banking in Albania [81]. However, few researchers found that social influence is context-related and does not always affect behavioral intention [33,34,71,82,83].

Multiple studies still focus on the direct impact of social influence on individuals’ behaviors [15,30,84], even though social influence modifies the relationship between people’s attitudes and behaviors [85,86,87]. Thus, to better understand how the relationship between attitudes and social influence influences people’s behavioral intentions, we need to better grasp the role of social influence as a moderator [78]. The link between shopping frequency, effort expectancy, performance expectancy and repurchase intention may therefore be moderated by social influence.

Therefore, we propose the following hypotheses:

H7a. 

Social influence moderates the influence of shopping frequency on m-shopping repurchase intention.

H7b. 

Social influence moderates the influence of performance expectancy on repurchase intention.

H7c. 

Social influence moderates the influence of effort expectancy on repurchase intention.

3. Methodology

The model of the current study is derived from the UTAUT model to assess how the shopping experience influences the repurchase intention for mobile shopping. We have utilized structural equation modelling SEM methodology for testing the relationships of independent and dependent variables using the WarpPLS-8 tool.

Pakistan comprises more than 60% adult youth population [15] and the technology proliferation is still not mature [88]. The barriers of digital literacy, cybersecurity concerns and lack of structural and regulatory assurance mechanisms make it difficult for even young people to engage in mobile shopping. Mobile purchase apps and mobile shopping platforms adoption is yet to win customers’ trust [88,89,90] and mobile apps acceptance is growing steadily. Hence, our focus rests on the generic use of mobile shopping through online apps and platforms available to the users. According to the 2023 Statista report [91], the top two sectors for mobile shopping in Pakistan by sales volume have been the electronics and mobile and fashion brands over the past several years. Hence, we also tailored our questions and included these two sectors to probe the online shopping frequency of the adult population in these two sectors. In particular, we intend to investigate the usage patterns and purchase frequency of the young adult population and its effects on various factors of effort expectancy, performance expectancy and repurchase intent for engaging in mobile shopping apps and online mobile platforms. The examination of the relation between repurchase frequency and repurchase intent will help online marketers and app developers to especially consider the effects of effort and performance expectancy and use it to lure customers into online shopping

3.1. Sample and Data Collection Exercise

This research was conducted from students who are enrolled in various business degree programs in the faculty of business administration at the Bahauddin Zakria University Multan—one of the premier institutions of education in the Southern Punjab province of Pakistan. The respondents were enrolled in different semesters and we ensured that the full-time students were only part of this study due to being easily approachable for data collection through the class instructors. The consideration of students as the target population for this study allowed us to test our hypothesis for that segment of the community which is well-versed in mobile shopping applications and the use of mobile and handheld devices. After doing the pilot testing of this study with the 10 senior faculty members and a class of 35 senior students, we incorporated changes suggested by the respondents of the pilot testing and went on with the major part of data collection using Google Forms. It is pertinent to mention that all the questionnaire items including the demographics and the items related to our major constructs were selected based on previously tested questionnaire items from the recent literature. The details are provided in Appendix A.

Moreover, the questionnaire items used seven-point anchors for data collection with the arrangement starting from strongly disagree = 1, while strongly agree was equal to 7. For a general study using SEM as the methodology for analysis, the rule of 5 or the rule of 10 is considered to be a universal acceptance for the sample size. It means that the sample size should be at least 10 times the number of independent variables in a particular model, whereas the WarpPLS guidelines suggested the minimum sample size should be 160 using the inverse square root method. We targeted a sample of over 300 respondents and a total of over 300 students were requested to participate in the study, out of which, 232 responses were received. Out of these 232, the usable responses were assessed to be 198. The descriptive statistics showed that about 55% of our respondents are males and the remaining are females. Around 53% of respondents are aged under 25 years of age; 29% fall in the 25–30 years age group, while the remaining are older than 30 years of age. We have around 45% of the respondents coming from bachelor’s degree programs and others are from master’s or doctoral programs. A little under 40% of our respondents are involved with mobile shopping every 3 months, while 26% of the respondents engage in mobile shopping activity every month while the remaining respondents engage in mobile shopping weekly or even less than that.

3.2. Analysis and Results

Following the recommended step-wise analysis procedure, the CFA was performed to assess the measurement model while the final structural model was further analyzed for the fitness of the model and requisite path-estimation for the relations using the WarpPLS-8 tool for SEM analysis [88,92].

3.3. Measurement Model

The factor loadings, AVE, CR and VIF values are shown in

Table 1. Item loading, Composite Reliability, Cronbach values, AVE, VIF measures.

	Items	Loading	Cronbach α	CR	AVE	VIF
Performance Expectancy	Per Ex1	0.773	0.847	0.898	0.688	2.22
	Pe Ex2	0.888
	Pe Ex3	0.846
	Pe Ex4	0.805
Effort Expectancy	Eff Ex1	0.798	0.738	0.838	0.572	2.548
	Eff Ex2	0.501
	Eff Ex3	0.822
	Eff Ex4	0.851
Repurchase Intention	Rep Int1	0.832	0.865	0.918	0.789	1.626
	Rep Int2	0.917
	Rep Int3	0.912
Social Influence	Soc Inf1	0.887	0.878	0.925	0.804	1.987
	Soc Inf2	0.921
	Soc Inf3	0.882

. The measurement model’s initial analysis was carried out to ensure that the outcomes met the recommended standards for item loadings, reliability and validity of constructs.

Table 2. Discriminant validity results.

	Perf Exp	Eff Exp	Rep Int	Soc Inf
Perf Exp	0.829
Eff Exp	0.682	0.756
Rep Int	0.325	0.38	0.888
Soc Inf	0.561	0.507	0.448	0.897

Diagonal values in bold show the square root of average variance extracted.

illustrates the outcomes for discriminant validity measures, according to the recommended criteria [93,94].

Following the literature recommendations, Table 1 illustrates the reliability measures and factor loadings, as well as the composite reliability and measurement of AVE to assess convergent validity [95,96].

All coefficients and p-values are provided in the measurement model in Figure 2. Table 2 below shows that all our research constructs reported the discriminant validity results as per thresholds recommended in the literature; for example, [97,98].

3.4. Structural Model

The structural model provides us with all results according to recommended thresholds. The provided goodness of fit and other measures are given in

Table 3. Model Fit Indices.

Model Fit Indices
Index	Values	Threshold Levels
Avg. Path Coeff. APC	0.245	p.val < 0.001
Avg. adj. R2. AARS	0.344	p.val < 0.001
Avg. adj. R2. AARS	0.334	p.val < 0.001
Avg. block AVIF	1.308	Ideal value ≤ 3.3
Avg. Full Coll. VIF = AFVIF	2.042	Ideal value ≤ 3.3
Tenenehaus Goodness of Fit	0.543	Large for values ≥ 0.36
Sympson’s Paradox Rat.	0.778	≥0.7 acceptable
Rsq. Contrib. Ratio = RSCR	0.986	≥0.9 acceptable
Stat Supr. Ratio = SSR	1.000	≥0.7 acceptable
Nonlinear bivar. Caus ratio = NLBCDR	0.72	>0.7 is acceptable

[99].

3.5. Analysis and Results

The results demonstrate agreement with most of our hypotheses. Notably, a total of 66% variance is explained using the conceptual model of this research for repurchase intentions while the variance explained for effort expectancy and performance expectancy are 14% and 47%, respectively. For shopping frequency, the most significant effect is observed on effort expectancy according to H4 where the B-value is 0.38 (p < 0.001) while a moderate influence is reported for repurchasing intentions with the B-value being 0.12 (p < 0.05). Hence, H1 finds support. However, no effect is observed for performance expectancy. Hence, H2 is rejected.

For effort expectancy, the strongest influence is observed for its relationship with performance expectancy with a B-value = 0.70 (p < 0.001), while its influence on repurchase intention is also strongly significant where the B-value is 0.35 (p < 0.001). Performance expectancy’s influence on repurchase intention is also significant with a B-value of 0.24 and p-value < 0.001. It indicates that shopping frequency has a stronger indirect influence on repurchase intentions as compared to its direct influence. We will discuss this more under the indirect/mediating effects section below. It will be interesting to see how these indirect effects compare the effort and performance expectancy on the repurchase intentions.

3.6. Mediation Effects

Three out of the four mediating relationships prove to be significant for this research. Two mediation paths are single mediation relations that are significant; effort expectancy can strongly mediate the relationship between shopping frequency and performance expectancy with an effect size of 0.052 while effort expectancy bears a strong mediation influence for the relationship between shop frequency and repurchase intentions. However, no mediation is observed for performance expectancy between shopping frequency and repurchase intentions. Incidentally, we also observe a strong serial mediation influence of effort expectancy and performance expectancy on the relation between shopping frequency and the repurchase intent as shown below in

Table 4. Mediation Effects.

Mediation Effects
Path	B-Value	p-Value	Effect Size	Significance
shop freq-->eff exp-->per exp	0.26	<0.001	0.052	***
shop freq-->eff exp-->rep int	0.131	<0.001	0.041	***
shop freq-->per exp-->rep int	−0.013	0.392	0.004	Not significant
shop freq-->eff exp-->per exp-->rep int	0.07	<0.05	0.021	**

p-value < 0.10 *; p-value < 0.05 **; p-value < 0.01 ***.

.

3.7. Moderation Effect

Table 5. Moderation Effects.

Path	B-Value	p-Value	Significance
shop freq-->rep int moderated by soc infl	0.149	<0.05	**
eff exp-->rep int moderated by soc infl	−0.03	0.34	Not significant
per exp-->rep int moderated by soc infl	−0.162	<0.05	**

p-value < 0.10 *; p-value < 0.05 **; p-value < 0.01 ***.

provides the results of the moderation of social influence for three relationships. The association between shopping frequency and repurchase intention is strongly influenced by the social influence factor, suggesting that a high social influence will strengthen the relationship between shopping frequency and repurchase intention, while the social influence appears to negatively affect the relationship between performance expectations and repurchase intent. It means that a strong social influence will dampen the relation between performance expectancy and repurchase intentions through mobile shopping. This negative effect is stronger in the case of the relation between performance expectancy vs. purchase intentions which means that influence from other important people around the respondents favors the use of mobile shopping strongly. It is noteworthy to mention that with the increasing effect of social influence, both the effect of performance and effort expectancy are negatively influenced indicating that the burden of effort and performance might now be shared by the respondent and the other person whose influence is important to the respondents, as also noted by [94].

4. Discussion

4.1. Main Results

Our research model has used three key constructs of the UTAUT model rather than the entire model, i.e., performance expectancy, effort expectancy and social influence, and included other constructs like shopping frequency and repurchase intention. This research investigated the model for the young and learned users from a developing country who are youngsters and active users of smartphones. Identifying how customers behave during the mobile shopping process could benefit online businesses in determining what customers require and translating this into a mobile shopping experience that meets their needs. The findings indicate that different determinants of repurchase intentions are affected by shopping frequency via mobile shopping. As a result, shopping frequency—a customer-based attribute—is important in determining mobile shopping repurchase intentions.

The results tabulated below in

Table 6. Hypotheses Summary.

S. No.	Hyp. No.	Statement	Status
1	H1	Shopping frequency has a positive influence on m-shopping repurchase intention.	Accepted
2	H2	Shopping frequency has a positive influence on performance expectancy.	Rejected
3	H3	Performance expectancy has a positive effect on m-shopping repurchase intention.	Accepted
4	H4	Shopping frequency has a positive influence on effort expectancy	Accepted
5	H5	Effort expectancy has a positive influence on m-shopping repurchase intention.	Accepted
6	H6	Effort expectancy has a positive influence on performance expectancy.	Accepted
7	H7a	Social influence moderates the influence of shopping frequency on m-shopping repurchase intention	Accepted
8	H7b	Social influence moderates the influence of performance expectancy on repurchase intention	Rejected
9	H7c	Social influence moderates the influence of effort expectancy on repurchase intention	Accepted

indicate that mobile shopping frequency positively influences mobile shopping repurchase intentions (H1), affirming the fact that the increase in shopping frequency further enhances mobile shopping repurchase intentions. As online businesses experience a high churn rate [100], focusing on strategies to increase shopping frequency can reduce the churn rate [101]. Our results for shopping frequency and repurchase intention are consistent with previous findings in similar studies [51,102,103,104].

Furthermore, it is observed that shopping frequency does not affect performance expectancy, hence rejecting H2. Since the sample size of this study comprises a relatively young consumer segment for which there is no marked difference in the expectation of an improved performance, it is not unusual to observe that shopping frequency does not influence the performance expectancy as also observed in the literature [27]. However, a full mediation is observed in effort expectancy and mobile shopping repurchase intention (H3). Similar findings were noted in earlier studies indicating that effort expectancy is more important than performance expectancy for technology acceptance [32,39,105,106]. It may further be inferred that in the case of mobile shopping in Pakistan, the perceived utility among customers can be significantly impacted by the user interface’s friendliness. Increased acceptance of technology in daily life is encouraged by its usability and the perception of the benefits of its users [107].

Similarly, H4 and H5 are also supported in our study, indicating that shopping frequency tends to have the most significant impact on effort expectancy along with a moderate influence on repurchase intention. Earlier studies also found that effort expectancy has a significant role in predicting mobile commerce behavior [108,109,110]. Some researchers also noted that effort expectancy is significant for those with less experience [14,57]. Our study found that effort expectancy becomes more positively sensitive with increased shopping frequency. The results of the study reveal that the utilization of mobile shopping platforms in Pakistan, such as applications and mobile-friendly online marketplaces is very user-friendly. The research findings confirm that in developing countries, effort expectancy has a direct positive association with repurchase intentions. This indicates that marketers and sellers strive to create a user-friendly experience for all customers, regardless of their level of expertise [15,111,112]. The key to preserving a positive relationship that motivates customers to continue their online buying activities via mobile and other gadgets is regular communication with the consumers about the most recent feature additions and improvements.

Furthermore, the study found no evidence of a relationship between shopping frequency and performance expectancy (H5). However, shopping frequency has a substantial indirect impact on repurchase intentions by way of performance expectancy (H3). These findings indicate that performance expectancy, through complete mediation, is a powerful and significant predictor of repurchase intentions in mobile shopping, which aligns with previous research in the field. For example, refs. [15,34,59] noted that customers showed appreciation for the technological benefits during the use of mobile shopping activities. Studies conducted in various countries including Pakistan, India, Malaysia, Spain, Hong Kong and Canada have shown that the use of mobile shopping platforms offers numerous benefits to customers in developing markets [113,114,115,116,117]. Therefore, a pretty significant selling point for mobile shopping would appear to be its potential to help customers complete purchasing tasks more quickly and effectively [118]. If customers are confident in the services’ utility, they choose mobile shopping apps. This suggests that when users find the services to be beneficial and simple to use, they are eager to include mobile shopping in their daily lives.

4.2. Moderation Effect of Social Influence

We investigated the moderating effect of social influence on the relationships between mobile shopping frequency, effort expectancy, performance expectancy and repurchase intention. This study aimed to close a research gap by examining the influence of this characteristic on repurchase intention in the setting of mobile shopping as prior studies had not focused here. Our results show that the connection between shopping frequency and repurchase intention (H7a) was significantly strengthened by social influence as depicted in Figure 3 below.

The younger generation actively engages in mobile shopping, and their social circle notices positive experiences, acting as a reinforcement to consider mobile shopping more frequently for online purchases. Customers are more likely to accept and practice new technologies (like m-commerce) in their daily lives if they receive recommendations and endorsements from influential people. Similarly, for Hypothesis 7c, it is observed that as the effect of social influence increases the perception of performance expectancy, mobile shopping is lowered as external support becomes available to a person intending to carry out a repurchase through mobile shopping apps, etc. This is also reflected in the slope graph in Figure 4 below which shows that the effect of social influence is negatively moderating the association between performance expectancy and repurchase intentions.

However, the findings fail to support H7b. We think that effort expectancy requires mental effort to bring intangible benefits that are not noticed by the peer group. Because our participants are tech-savvy university students, young consumers’ intent toward m-commerce acceptance and usage is heavily influenced by the viewpoint of their peers and seniors [34]. So, another interpretation could be that youngsters are more interested in having acceptance from their contemporaries for their mobile shopping activity and the process of using apps or websites on the mobile phone is the least of their priorities when it comes to social influence.

4.3. Theoretical Implications

The importance of periodically contextualizing technology frameworks has been emphasized by theorists, who have sought to enhance these frameworks by incorporating principles from other models. In line with this approach, our study focuses on a demographic of tech-savvy young people from a developing nation, who have a high level of familiarity and experience with mobile technology and apps. Our goal is to investigate the relative impact of various factors influencing repurchase intentions in the context of mobile shopping behavior. By doing so, we aim to provide valuable insights that extend beyond the immediate context of the COVID-19 pandemic and contribute to a deeper understanding of contemporary consumer behavior, with a specific focus on mobile shopping, within the unique socio-economic settings of Pakistan.

This study found that social influence, effort expectancy, performance expectancy and shopping frequency were the four most important elements in influencing repurchase behavior for mobile delivery. Firstly, this research contributes by extending the relationship with the moderating effect of social influence between independent, mediating and dependent variables. Numerous studies included this aspect of mobile buying as an independent variable, but none of them were able to notice or record the moderating impact of this construct on mobile shopping [15,64,84,119,120]. According to the results, consideration should be given to social influence on m-shopping repurchase intention. Second, social influence has a major role in the relationship between shopping frequency and repurchase intention, but it shows no discernible moderating effect on the association between effort and performance expectations and repurchase intention.

Secondly, this study also concluded that shopping frequency as an independent variable did play a salient role in influencing mobile shopping behavior which we could not find as an independent variable in any studies related to mobile shopping and that with the increase in shopping frequency, performance expectancy remains uninfluenced. These findings somewhat align with those of [107,121] who also found that performance expectancy has no significant effect on intentions. However, effort expectancy, a highly significant mediator, has a full effect on performance expectancy. Purchase frequency does not have a direct significant relationship with performance expectancy, it only works through effort expectancy. This is one of the main contributions of this study. Since effort expectancy plays the most important role in direct and mediating relationships, we theorize that in technologically convenient services, making the applications effortless is perhaps the most important. Consumers are avoiding the inconvenience of physical shopping and choosing to use mobile applications, thus being effort-sensitive. Although many studies have used effort expectancy and performance expectancy as mediators in their studies [122,123], according to our understanding, none have used effort expectancy as a mediator of performance expectancy.

Since mobile shopping is inherently a more favored choice for the youth segment, this research studied the young consumers’ actual buying behaviors and related factors. Peer pressure is always an established mechanism for opinion-building in a collectivistic society; hence, we tried to assess the magnitude of this mechanism by investigating how this social influence regulates the relationships of purchase frequency, effort and performance expectancy, respectively, with the repurchase intentions. The results indicate that the relationship between effort expectancy and repurchase intention remains unaffected, but the relationship receives some negative push in the case of performance expectancy. This makes an interesting case to further explore the relative magnitudes of this effect for the adult consumers who are more frequently engaged in mobile shopping activity. Apart from this, a separate stream of literature could also explore the non-intention mechanisms for mobile shopping under the influence of this social influence factor [124].

Lastly, another contribution to the literature is that most of the previous studies on mobile shopping have been conducted in Western countries, while this study took place in an emerging market context. The results obtained from this different setting hold potential for new avenues of research and further investigation.

To effectively manage and develop mobile shopping within the retail industry, a thorough understanding of customer behavior is crucial [125]. The current study provides a foundation for future research by highlighting innovative avenues for exploration. To comprehend the fundamental mechanisms more thoroughly, researchers should delve deeper by examining various dimensions of shopping frequency, effort expectancy, performance expectancy and social influence in the context of mobile shopping.

4.4. Managerial Implications

Consumers who wish to engage in mobile shopping expect user-friendliness and ease of use as crucial features of mobile shopping platforms. In general, social media engagement can help build a sense of community and leverage social influence by allowing consumers to interact and share information. Incorporating social media integration can increase the platform’s visibility and generate goodwill. Managers should also explain the advantages of mobile shopping, such as the time and money saved, as well as the improvement in accuracy and efficiency compared to traditional shopping methods.

In our study, we found that purchase frequency positively influences repurchase intentions. Online retailers should aim to build customer loyalty through excellent customer service, a strong brand identity, and fostering a sense of community. Marketing intelligence tools can be used to identify trends and patterns for personalized marketing campaigns. Furthermore, by implementing personalization features, such as giving customers quicker access to frequently used features and changing the platform’s features to make usage more joyful, marketers can also improve the user experience [126]. In the context of developing nations and the low purchase power of consumers, mobile shopping should provide sales promotions and price discounts to lure consumers.

The second important finding of our study is that effort expectancy plays a very important role in repurchase intentions. It is positively influenced by purchase frequency and influences purchase intentions, and also plays a full mediating role between purchase frequency and performance expectancy. To improve effort expectancy, a strong attempt should be made to create a user-friendly and intuitive mobile shopping interface [127]. Therefore, managers should focus on simplifying interfaces, streamlining checkout and providing clear instructions and product information. Technical support, helpdesk services and training resources are also necessary to ensure a smooth user experience. In the case of developing nations, there should be an option to use mobile shopping in the local language as well due to low levels of foreign language literacy [128]. These tasks will improve the effort expectancy of the consumers making them perceive that lesser effort is required in making mobile purchases. Similarly, the significance of mobile shopping’s aesthetic appeal cannot be ignored. To improve the consumer’s perception of the website’s values, the user interface must be beautifully designed. Particularly, m-shopping merchants should be selective when selecting the typefaces, colors and images for the m-shopping interface [129]. Additionally, they must hire a skilled photographer to take images of the products and enhance their appearance when shown on the m-shopping interface. This will improve the overall purchase intentions of the consumers and also improve the likelihood of purchase by consumers with low foreign language literacy in a developing nation.

Furthermore, this study highlights the importance of social influence in driving mobile commerce behavior. Businesses can leverage the power of social influence by encouraging users to share their experiences with friends and followers, providing incentives for referrals, and using influencers to promote the platform [130]. This can help increase the adoption and repurchase rate of the platform and create a sense of trust and authenticity among consumers.

New users are often apprehensive about online shopping and require initiatives that build trust and minimize barriers. Managers should emphasize user-friendliness by showcasing the app’s ease of use through designing carefully onboarding tutorial and easy-to-understand interfaces. Offering safe payment choices like cash on delivery might address security concerns, and highlighting guest checkout options can lower the barrier to registration for new users. To enhance performance expectancy and leverage social influence, partnering with relevant social media influencers can effectively promote the app and showcase its benefits through their channels. Offering welcome discounts and referral programs can further incentivize first-time purchases and user acquisition.

Lastly, it can also be concluded that when it comes to mobile shopping in Pakistan, users’ perceptions of the interface’s usefulness can have a big influence on how useful they think it is. The ease of use of technology and people’s perception of its advantages foster its increased acceptance in daily life. The influence of performance expectancy on repurchase intention is not moderated by social influence. We believe that performance expectancy necessitates mental work in order to provide intangible advantages that the peer group ignores. Since our participants are tech-savvy college students, the opinions of their seniors and peers have a significant impact on young consumers’ intents about adoption and usage of m-commerce. Consequently, we contend that young people prioritize social influence less than the experience of utilizing mobile applications or websites, preferring instead to be accepted by their peers for their mobile buying habits.

4.5. Limitations and Future Directions

The present study also has some limitations. One of the potential limitations of this study is that the data were collected from a relatively homogenous group of student participants belonging to a collectivistic culture, which can greatly influence the online shopping rates of a country [131]. Furthermore, individuals from such cultures tend to exhibit a more favorable attitude towards in-group members than those outside of their group. Customer endorsement can build trust in online purchases more efficiently in collectivistic countries, as compared to individualistic cultures in research by [132]. Moreover, business students are most advanced in the use of modern technologies in making purchases due to their quick learning abilities and being exposed to e-commerce platforms due to their educational background. On the other hand, these students may also have some monetary constraints due to their inability to work full time.

Secondly, further investigation into a range of social influence methods, such as compliance, conformism, persuading, social loafing, social facilitation, deindividuation, observer effect, bystander effect and peer pressure, should also be conducted in future research [133].

Thirdly, with the current study, we have only limited ourselves to a sample of business students. The study may be limited in its generalizability to other populations or settings. Youngsters from different cultures or developing countries may have different attitudes and behaviors, and the results may not apply to these groups. Furthermore, the findings may not apply to individuals who do not identify as young people. To improve the generalizability of this study’s findings, future research should undertake a study involving various age groups and cultural backgrounds.

Lastly, the most valuable technology for online commerce is considered to be augmented reality (AR), according to [134]. Mobile shopping is now more efficient because of the incorporation of AR technology, which gives users access to tactile and visual product information. A further benefit of AR is that it is an immersive and engaging technology that improves the overall purchasing experience [135]. Future research should investigate the potential of integrating AR applications into mobile shopping to enhance the customer experience. Such integration can enrich the customer’s mobile shopping experience and streamline the order fulfilment process, resulting in increased shopping efficiency.

5. Conclusions

This study evaluates key drivers of mobile shopping behavior that influence repurchase intentions in developing countries. The results emphasize the significance of shopping frequency, social influence, effort expectancy and performance expectancy in influencing customers’ inclinations to repurchase. Particularly, effort expectancy has the highest direct effect on repurchase intention, while performance expectancy and effort expectancy together have the strongest indirect effect. Further highlighting the importance of social variables in mobile shopping behavior is the moderating effect of social influence on the connection between shopping frequency and repurchase intention. These results imply that improving the usability and user interface of mobile shopping apps and ensuring that they operate in a trustworthy and dependable manner can have a beneficial impact on users’ intentions to make additional purchases in future. Additionally, social influence can play a significant role in shaping customers’ perception of mobile shopping, and companies can leverage this influence to promote the use of their apps among potential customers. Overall, these results provide valuable insights for businesses looking to improve their mobile shopping services and enhance customer loyalty through repurchase intentions.