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Building Usage Attitude for Mobile Shopping Applications

Empirical study

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International Journal of Management Science and Business Administration
Volume 5, Issue 6, September 2019, Pages 21-28

Building Usage Attitude for Mobile Shopping Applications: an Emerging Market Perspective

DOI: 10.18775/ijmsba.1849-5664-5419.2014.56.1003

1 Hammad Mushtaq, 2 Yan Jingdong, 3 Mansoora Ahmed, 4 Muhammad Ali

1,2,3,4 School of Management, Wuhan University of Technology, Wuhan, P.R. China
1 School of Business & Economics, University of Management & Technology, Lahore, Pakistan

Abstract: Mobile commerce has gained pace in recent years, providingan extended interaction channel between consumers and online retailers. Regardless of a significant increase in mobile internet usage, the adoption of mobile e-commerce (MC) services has not been as far-reached in the growing economies of the world. Recently, research in the e-commerce applied Technology Acceptance Model (TAM) indicated contradicting results.Some research studies found perceived usefulness has a significant effect on online purchase attitude, whereas in another study it was insignificant. We understand this, among many other factors, to be a consequence of the different levels of internet usage and its penetration in the region. We performed this study in Pakistan origin, a growing economy, where internet usage has significantly increased in the last decade or so. The e-commerce industry reviews pointed out that low consumer trust and poor logistics might be the key constraints in B2C mobile e-commerce adoption in Pakistan. Based on these grounds we adapted TAM by including consumer trust belief in mobile e-commerce and excluding perceived ease of use, as many research studies reported it insignificant for online shopping context. The proposition developed that trust in online shopping has an impact on improved perceptions about the usefulness of this interaction channel between online buyers and sellers. This positive usage attitude facilitates favorable online purchase intentions. The research conducted based on empirical evidence from Pakistan, and the model was developed on the bases of TAM, consumer trust in MC, and perceived usefulness and trust in mobile e-commerce. An online questionnaire survey was distributed to gather responses. Data analysis was performed with the partial least square technique or structured equation modeling. The research findings show that belief in trustworthiness of e-commerce has a more significant impact on usage attitude for mobile e-commerce as compared to PU. This implicates that online retailers in Pakistan should focus more on developing trust of their prospects by providing trust cues and other trust-building mechanisms. The research model empirically studied on evidence from Pakistan was first of its kind . In the future, the research can be conducted on specific categories of items (such as apparel fashion, technology base products) or services (such as traveling, hotels) developing effective strategies for retailers in B2C mobile e-commerce.

Keywords: Mobile e-commerce, Mobile trust, Online Shopping, TAM, Pakistan

Building Usage Attitude for Mobile Shopping Applications: An Emerging Market Perspective


The widespread adoption of mobile phones and its related technologies have enabled the business world to extend interaction channels with consumers. Mobile e-commerce (MC), which isessentially an extension of electronic commerce, has emerged along with many other useful services enabled via mobile devices and networks. This additional service scape, especially in the business-to-consumer (B2C) context, has gained appreciation from consumers around the world. The mobile technologies embedded in MC enable consumers to do shopping in an even more convenient and timely manner than traditional E-commerce, as the mobile handset is accessible by the consumers at almost all times and all locations they travel. According to reports from Internet World Stats, mobile services recently amounted to 4.2% of overall world GDP, and are expected to contribute close to 5% in 2020. In the coming years, the number of smartphones that will be used will exceed 6 billion, coming to be preferred internet access devices. Shopping conducted via internet-enabled mobile phones in Turkey and China accounts for the highest proportion of their entire monthly consumption via smartphones or tablets (44% and 42%, respectively). Similarly, the online purchasing trend has been appreciated and followed by consumers in emerging economies, such as Pakistan. As per Internet World Stats (2017) Pakistan has 44.6 million Internet users with 22.2% penetration. Recently, Mobile E-Commerce is picking up its pace in Pakistan though it still significantly lags from its true potential (Tago, Chandio, Chandio, Sharif, and Abbasi, 2017). The internet access via smartphones is becoming more and more accepted as it is becoming less costly and easy to use. Despite staggering online business growth patterns in Pakistan, the consumer usage of MC has been somehow constrained, thereby prompting researchers to study this area further. Lately, the Express Tribune (respectable Pakistan newspaper) article titled “Pakistan lags behind in online sales”(Hanif, 2019) stressed trust and logistics as key concerns for the online consumers. Moreover, the absence of an online payment mechanism causes the majority of online commerce transactions having to be paid by cash upon the delivery. This news article called for a research study, forming a problem statement for the online consumer behavior research to be conducted, especially in the domain of mobile-based shopping in the region. We take into the account the technology adoption, considering that MC purchases effected consumer perceptions and usage attitude. At a broader spectrum, recently researchers (Thakur and Srivastava, 2013) noted that the acceptance of newer technologies was not the same in developing countries as it was in the developed ones. Similarly, our examination of the literature repository using the criteria “Mobile E-Commerce in Pakistan” resulted in very few studies that focus on explaining the customer online purchase attitudes towards Mobile E-Commerce. This enticed further research of deficiency in online purchases in the MC context. The Technology Acceptance Model (TAM) originally developed by (Davis, 1989) has been confirmed and extended in the past two decades by investigations in the domain of information systems and related research field,s such as online consumer behavior (Gefen, Karahanna, and Straub, 2003). According to TAM Perceived Usefulness (PU) of the technology leads to development of an attitude towards usage, resulting finallyin the actual usage of the system. Marketing researchers such as (Chang, Sun, Pan, and Wang, 2015; Gefen et al., 2003) extended TAM with the inclusion of the consumers’ trust in an online shopping environment.
In the next section, we briefly discuss the theoretical underpinnings leading to the research hypotheses development. After that, section 3 describes the research methods, whilesection 4 provides results of two-staged data analysis. The last section combines discussion (based on data analysis), implications (for academics and marketing managers) with conclusive statements of the research.

2. Literature Review and Research Model

2.1 Purchase Behavior in B2C Mobile E-Commerce

Mobile commerce comprises three main pillars, namely online customers, mobile shopping applications or websites and online retailers. From a consumer perspective, mobile e-commerce is consumed essentially through the use of mobile applications or mobile-based web (mobile service-scape to interact with the sellers) intended to shop for items on one’s mobile device and/or tablets. In the very beggining of e-commerce, consumer trust perceptions and familiarity with the interaction platform were established as significant (Gefen, 2000) contributors to drive the online purchase intention. Mobile e-commerce B2C platforms are generally utilized by retailers who may or may not have an established brand reputation. The significant issue for online retailers is to comprehend the consumer behavior of their target market and to gain knowledge that will help them develop rewarding relationships, thus ensuringa sustainable online consumer base.

2.1.1 Perceived Usefulness

Perceived usefulness (PU) is defined as a person’s perception that “using a system would improve a task performance”, and as such represents a key concept in TAM (Davis, 1989). While PU was originally proposed to study the technology adoption, it has been accepted more and more in recent marketing research aimed at studying the online consumer behavior within the B2C market (Chang et al., 2015; Chhonker, Verma, and Kar, 2017). According to MC, the customers should feel in control and safe enough to perform transactions using a mobile application. The PU of mobile website or application generally depends on the efficiency of features provided to consumers, such as browse and compare products with suggested alternatives (E. Kim and Hong, 2010). Hence, in terms of MC adoption, PU is essentially the consumers’perception that using MC brings to consumers not only convenience, but also savings both in terms of time and money. Many developing economies are in the early stage of adopting and implementing technologies as compared to the technologically advanced states. Perceived usefulness has been commonly used in the technology adoption research and found significant for developing purchase intention over MC (Lim, Osman, Salahuddin, Romle, and Abdullah, 2016). There is a contrast in findings among developed and developing countries. For example (Hernández, Jiménez, and José Martín, 2011) study done in Spain revealed that PU has a substantial impact in developing online buying conduct, whereas according to another study performed in Iran by Aghdaie (Aghdaie, Piraman, and Fathi, 2011) found that PU does not substantially affect online buying attitude. This can be a result of varied viewpoints about the respondents belonging to different stages of technology diffusion. Based on this argument, we opine that further study regarding PU’s influence on their internet shopping attitude and intention is a useful contribution in online consumer behavior research.

2.1.2 Trust in MC

Despite the unique benefits of mobile services and mobile e-commerce B2C, overcoming consumers’ trust issues (Hillman and Neustaedter, 2017) continues to be a major obstacle in its adoption in both developed and developing economies. (Mayer, Davis, and Schoorman, 1995) define trust as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action which is important to the trustor, irrespective of the ability to monitor or control that other party”. According to (Beldad, de Jong, and Steehouder, 2010) the challenge and difficulty with the trust concept are that it has not yet been defined with any universal explanation,as different disciplines treat it in significantly different ways. Generally, the definitions of trust are classified into two categories. One group treats the trust as perception about the conduct of a collaborating partner (Morgan and Hunt, 1994), while the other cogitate it to be a psychological state containing reception and experience of susceptibility (Rousseau, Sitkin, Burt, and Camerer, 1998). The online trust was elaborated by Bart (Bart, Shankar, Sultan, and Urban, 2005) to further Rousseau’s explanation as “online trust includes customer perceptions on how the site would deliver on expectations, how believable the site information is and how much confidence the site commands”. The importance of online trust is stressed mainly due to the issues such as “information asymmetries” and “consumer insecurity” inherient to online phenomenon (Stewart, 2003). Giovannini (Giovannini, Ferreira, Silva, and Ferreira, 2015) posits an explanation of MC trust as “one’s willingness to accept vulnerability while interacting with another through a mobile device given extant expectations regarding intentions and behavior of the other party”. We agree with this definition of mobile e-commerce, and use it as the foundation to build the conceptual and theoretical model of this research. Recently scholars (Hillman and Neustaedter, 2017; Oliveira, Alhinho, Rita, and Dhillon, 2017; Tago et al., 2017) have pointed out gaps in describing trust in the dynamic “e-servicescape” (Teng, Ni, and Chen, 2018) and mobile e-commerce contexts.

2.1.3 Usage Attitude Towards Mobile Shopping Application and Purchase Intension

The usage attitude towards mobile-based online shopping is the degree to which consumers consider this platform to provide a positive experience (Crespo and Del Bosque, 2010). Recently, Huseynov (Huseynov and Yıldırım, 2016) noted attitude towards online buying as key among other factors to affect mobile-based online shopping intentions. Similarly, (Chang et al., 2015) found that individual consumer attitudes towards mobile shopping technologies are positively related to their positive experience that enables favorable intentions. Kim (J. B. Kim, 2012) extended TAM and established that the early stage trust belief in online shopping has a stronger effect on attitude to use this channel compared to PU, however, PU cannot be ignored. A unified model for e-commerce relational exchange as described by (Palvia, 2009) extended TAM so as to includethe trustworthiness of online vendor, reported significant positive impact of trust belief on usage attitude towards vendor website.

2.2 Research Hypothesis and Model

As discussed earlier, the stronger PU of the mobile commerce platform may impact the consumer intention to use mobile services in order to perform shopping task. The more favorable consumer’s perceptions of the mobile shopping application, the more PU will contribute to developing a shopping attitude. Based on this, we hypothesize the following relationships:
H1: Perceived usefulness of mobile e-commerce applications has a direct and positive effect on attitude to use it.
From the existing literature review, we notice that there are contradictory findings in terms of the relationship between PU and trust. Taking trust as a psychological state, there is a higher possibility of its impact on consumer perceptions. The favorable trust perceptions about the platform will positively influence the perceived usefulness.H2: The trust in mobile e-commerce has a direct and positive effect on perceived usefulness.
The trust belief in mobile e-commerce develops a favorable attitude towards the usage of mobile shopping services, impacting further the purchase considerations using this platform. Furthermore, we hypothesize H3 and H4 such that.
H3: Trust in mobile e-commerce has a direct positive effect on attitude towards the use of mobile shopping application
H4: Mobile shopping application usage attitude has a direct positive effect on consumer purchase intention.
Amin (Amin, Rezaei, and Abolghasemi, 2014) stated that PU is a comparatively more influential predictor as compared to the perceived ease of use when it comes to user attitudes about using MC. In recent research (Al-Maghrabi, Dennis, and Vaux Halliday, 2011; Gong, Stump, and Maddox, 2013; Lim et al., 2016) on consumer behavior in online shopping, studies reportthat PU was significant for the intention of online shopping platform usage but perceived ease of use had an insignificant influence on usage attitude. Marketing researchers such as (Chang et al., 2015; Gefen et al., 2003) extended TAM so as to include consumers’ thrust in an online shopping context. Building on these grounds, we performed empirical data collection and analysis to study the impact of PU and Trust in developing an purchasing attitude towards mobile shopping.
Figure 1.0, represents the model of the current study, established on the basis of extended TAM model, with the modification of exclusion of “perceived ease of use” and and inclusion of trust beliefs as an originator of PU and usage attitude of mobile e-commerce.

3. Research Methods

A positivist survey methodology was adopted. The online questionnaire was developed using google forms. All questionnaire items were adopted from existing research studies as discussed below. However, necessary modifications were made to adjust the scope to mobile e-commerce context. To collect data about PU, questionnaire items were adapted from preceding studies of Amin, Davis, Zhou (Amin et al., 2014; Davis, 1989; Zhou, 2012) to assess the model and examine the hypotheses. Similarly, trust (TR) was adapted from Lin (Lin and Wang, 2006), purchase intention (PI) was adapted from WU (Wu, Hu, and Wu, 2010), and finally, the usage attitude towards online shopping was adapted from (Oliveira et al., 2017).
The questionnaire was distributed via online social network of closely linked groups regarded as having some online shopping experience. The usable sample size was 235. The demographic characteristics show that most of the respondents who participated in the survey with regards to mobile shopping experience, were males within the age group of 18 to 49, with education level from undergraduate to graduate level, income level 1000 to 150000 and preferred as their choice of mobile shopping application.

4. Data Analysis and Results

The partial least square (PLS) method was adopted to perform data analysis as it is an established technique used in the structured equation model (SEM) for testing causal relations based on empirical data (Henseler, Hubona, and Ray, 2017). We considered the PLS technique as it has established its reputation to statistically investigate and assess the underlying associations using combinations of statistical figures and qualitative causative assumptions. The software used was Smart PLS 3.0 (Ringle, Wende, and Becker, 2015). As per guidelines (Joseph F Hair, Risher, Sarstedt, and Ringle, 2019), a two-step method was applied to examine the model, stage one was the measurement model test while the second stage was for structure model testing.

4.1 Outer Measurement Model Evaluations

Outer measurement model evaluations are listed in Table 1, generated using Smart PLS 3.0 consistent PLS algorithm. The results in Table 1.0 comprise indicator loadings, composite reliability (CR), Cronbach’s alpha (CA) and the average variance extracted (AVE) for all items in the measurement model. Indicator reliability is established by accessing the indicator loadings which should be above 0.7, but any value above 0.4 can be accepted if it does not damage the values of CR and AVE and the individual indicator holds theoretical importance in the model (Joe F Hair, Ringle, and Sarstedt, 2011). All indicator loadings are above 0.4 acceptable values with lowest was ATOS1, i.e. 0.532. However, ATOS1 item was retained in the model as reliability and validity test conforms to the standard cutoff values taking a liberal approach as per guidelines by (Brown, 2014). It was essential to determine the internal consistency of all indicators by verifying whether all constructs have CA above 0.7 (Nunnally, Bernstein, and Berge, 1967). All constructs in the model comply with the standard test value of 0.7 with the PI found 0.771 as the lowest CA values in the model. While CA has been widely used as a test for construct reliability, however, it has also been criticized for the underestimation problem. Werts (Werts, Linn, and Jöreskog, 1974) solved this problem by developing composite reliability (CR) test. Here all constructs CR values conform to 0.7 cutoff standards as shown in table 1 with the lowest 0.771 for purchase intention. The AVE should be above 0.5 for latent variables to explain more than half of the items. All AVE values in Table 1.0 listed more than 0.5 thus satisfy the validity of the measurement of constructs. Table 2 provides cross-loading for the indicators of items that show there is no issue of indicator collinearity on other items of the measurement model. Cross loadings of the indicators with constructs are listed in Table 2.
In the next step, we check discriminant validity using Fornell and Larcker method (Fornell and Larcker, 1981). This method uses the squared correlation of a construct with all other constructs such that its own AVE’s square value. Table 3 shows the square value of AVE for discriminant validity.

Table 1: Construct reliability and validity

Main Constructs Items Loadings Cronbach’s Alpha CR AVE
Attitude: Mobile Shopping ATOS1 0.532 0.827 0.85 0.593
ATOS2 0.874
ATOS3 0.803
ATOS4 0.826
Purchase Intention PI1 0.807 0.771 0.778 0.55
PI2 0.793
PI3 0.814
PI4 0.85
Perceived Usefulness PU1 0.946 0.889 0.889 0.666
PU2 0.614
PU3 0.614
Trust TR1 0.941 0.892 0.893 0.631
TR2  0.914
TR3 0.633
TR4 0.657
TR5 0.774


Table 2: Cross-loadings

ATOS1 0.532 0.454 0.376 0.254
ATOS2 0.874 0.487 0.374 0.587
ATOS3 0.803 0.394 0.279 0.576
ATOS4 0.826 0.421 0.249 0.593
PI1 0.428 0.807 0.588 0.345
PI2 0.446 0.793 0.479 0.423
PI3 0.49 0.814 0.465 0.431
PI4 0.466 0.85 0.525 0.455
PU1 0.373 0.602 0.946 0.27
PU2 0.258 0.379 0.614 0.227
PU3 0.25 0.386 0.614 0.168
TR1 0.612 0.512 0.272 0.941
TR2 0.591 0.5 0.269 0.914
TR3 0.419 0.342 0.172 0.633
TR4 0.473 0.293 0.201 0.657
TR5 0.558 0.325 0.27 0.774


Table 3: Fornell-Larcker criterion test

ATOS 0.770
PU 0.403 0.741
PI 0.561 0.630 0.816
Trust 0.674 0.302 0.507 0.794

4.2 Evaluation of the Inner Structural Model

Stage one of the measurement model reliability and validity tests were approved, the next step was to measure the inner structural model. The known standards to approach the evaluations of the structure model are namely coefficient of determination (R2), Path coefficient (b value) and T-statistic value. A consistent bootstrapping procedure with 5000 resamples with no significant changes was used to estimate the path significance levels. The results generated from the bootstrapping procedure are shown in Table 4, having values estimated for both Path Coefficients (β) and T-statistics. Table 4 provides the significance of the hypothesis 4. Thus, all hypotheses are supported. The path coefficients are graphically shown in Figure 2 the highest is 0.608 between PU and usage attitude, followed by 0.561 for the path usage attitude and PI. The lowest path coefficient was 0.219 for PU and usage attitude. As per the results of the inner model evaluation (Table 4 and Figure 2) PU and trust latent variables explain 49.8% (0.498) of the variance in consumer usage attitude of mobile e-commerce shopping website/application. All three latent variables (PU, Trust and Usage Attitude) explain 31.4% of the variance of consumer purchase intention. As shown in Table 4, all hypotheses, i.e. H1, H2, H3, and H4 were found as supported by the statistics with significant t-statistics values at p < 0.05.

Table 4: Path coefficient and T-statistics

Hypothesized Path Path Coefficient β T-Statistics p Values Findings
Attitude: Mobile Shopping -> Purchase Intention 0.561 8.490 0.000 Supported
PU -> Attitude: Mobile Shopping 0.219 3.071 0.002 Supported
Trust -> Attitude: Mobile Shopping 0.608 8.722 0.000 Supported
Trust -> PU 0.302 3.506 0.000 Supported

Figure 2: PLS-SEM measurement model

5. Discussion, Implications, and Conclusion

The findings of the research show that all path coefficients were significant, with the lowest being the one between PU and usage attitude (β=0.219, p<0.025) while the strongest coefficient was between trust and usage attitude (β=0.608, p<0.025). Overall trust beliefs were found to be well integrated with parsimonious TAM (excluded PEOU) in the mobile e-commerce context. The study found trust to be a strong contributor in developing positive consumer perceptions that the mobile e-commerce website or application is useful and more importantly, it strongly impacts positive mobile shopping application usage attitude among consumers. These findings were found consistent with various prior studies (Al-Maghrabi et al., 2011; Chang et al., 2015; Gefen et al., 2003).

Interestingly the path coefficient between trust and attitude towards shopping was more than the path between PU and usage attitude. Although both PU and trust were found to have a significant effect on the usage attitude, trust was found to be a more important contributor. The path analysis between trust beliefs, usage attitude, and purchase intention is strongest, implyingthe importance of trust-building in gaining consumer favorable purchase intentions. Thus, it can be concluded that PU and Trust are strong predictors of usage attitude and usage attitude is a good predictor of purchase intention. The results also indicate that developing the initial trust is a significant contributor to online shopping system adoption.

The research findings indicate that mobile e-commerce vendors should focus on devising customer relationship strategies with an emphasis on trust-building. Trust building cues such as celebrity endorsement on social network sites and corporate blogs, trust-marks or third party certification (e.g., SecureTrust), online chat option with a customer representative or phone contact information for direct contact, existing customer reviews and rankings.  These trust signals should be an integral part of devising all sorts of communications carried out with the prospect consumers.

In conclusion, the research was conducted with the aim to study consumer purchase intentions for mobile technology. The study established and empirically tested the trust integrated technology adoption model. Alongside this, it highlights the importance of forming trust-building communications and strategies to increase mobile e-commerce adoption in emerging markets. This study was one of very few attempts based on responses from emerging markets such as Pakistan. We propose that further research on specified areas in mobile e-commerce, such as those based upon the type of products (apparel fashion, technology base products) or services (such as traveling, hotels) elaborating on how to devise specific strategies for mobile e-commerce vendors.


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