








































36 

A Study of the Consumer-Company Identification on 
Mobile Application’s Attributes and Apparel 

Purchase Intention   

Zui Chih Lee, Ph.D. 
New Jersey City University 

Mengsteab Tesfayohannes, Ph.D. 
Farmingdale State College 

Min Jon Kuo, Ph.D. 
National Dong Hwa University 

Purpose 
In this demanding and diversified social commerce market characterized by consumers’ quickly 
changing expectations, service quality for mobile-retailing is imperative. Consumer-Company 
Identification (CCI) is becoming an important tool for building a company’s brand. This 
research examines how mobile application (app) attributes, mobile-service quality and CCI lead 
to mobile consumers’ purchase intention for apparel products through app.  

Design/methodology/Approach 
An experimental survey was conducted with 300 respondents. Seven hypotheses were examined 
by a structural equation model to explore the relationships amongst mobile app attractiveness, 
mobile-service quality, CCI, perceived usefulness, and consumers’ intention to purchase 
products from the mobile app.  

Findings 
Our findings supported the relationships amongst consumers’ perceived usefulness of the 
mobile app, mobile-service quality attributes, and consumer purchase intention. Our findings 
also supported CCI’s impact on consumers’ intention to purchase through the application of the 
Technology Acceptance Model.  

Research implications 
Our research finding showed consumers’ psychological attachment toward apparel brands and 
its mobile app. The research further demonstrated the consumer-retailer relationships, and the 
findings support consumer purchase pattern through mobile app apparel purchase. Retailers’ 
attention to dimensions of mobile-service quality of app design will strengthen consumers’ 
perceived usefulness of their mobile app.  

Keywords: TAM model, online apparel shopping, identification, mobile-service quality 

Introduction 
Mobile commerce has, in recent years, gained prominence within the online and e-tailing 

business. The Department of Commerce indicated that retail e-commerce sales in 2019 grew to 

http://journals.sfu.ca/abr ADVANCES IN BUSINESS RESEARCH 
2020, Volume 10, pages 36-54 



37 
 

$365.2 billion, an increase of 15.7% since 2018. Apparel and accessories e-sales totaled $103 billion 
in 2019, up 22.6% year-over-year and is expected to be as high as $194 billion in 2024 (Clement, 
2020). Retailers’ investments in online technology (e.g., websites, mobile apps) were intended to 
meet the diversified demands of mobile commerce market. Distinct from general e-commerce, 
mobile commerce empowers customers to shop anytime and anywhere without location and time 
limits (Chen, 2018; Wang et al., 2015). A mobile application (app) is a computer program designed 
to run on a mobile always-on device such as a smartphone/tablet or smartwatch, with features 
such as a pocket or purse-size case, smaller screen, virtual keyboard, limited processor, and always-
connected Internet (Agrebi & Jallais, 2015; Wang et al., 2015). In this demanding and diversified 
social commerce market characterized by consumers’ quickly changing expectations, mobile 
application design and service quality for mobile retailing is imperative. Therefore, this study 
examines the correlation amongst consumers’ intention of apparel purchase, the consumer-
company identification through company’s mobile application attributes. We examine attributes 
based on dimensions of app attributes (attractiveness, informativeness), and service quality 
attributes (efficiency, system availability). Based the degree of correlation amongst mobile 
application attributes, we can determine how mobile search can lead to purchase intention.  
 

Literature Review 
App technology is gaining its popularity within mobile commerce market given its ability 

to provide timely information and a more interactive, immediate, and personal shopping 
experience (Wang et al., 2015). For marketers, apps enhance the effectiveness of transaction 
security for personal data more than website cookie that is tracked by the competitors (Sarkar, et 
al., 2019). In comparison to website shopping, mobile apps provide the advantage of accessibilities 
such as, search convenience, access convenience, and service recovery convenience (Almarashdeh, 
et al., 2019; Holmes, et al., 2014; Wang et al., 2015). Efficient service recovery procedure highlights 
the online interaction and procedures communication, which conveys efficiency, efficacy, and 
timely perceived procedural justice to customers (Almarashdeh, et al., 2019). Mobile innovations 
support consumers’ decision-making at diversified pre-purchase activities, such as product and 
price search and alternatives comparison, via apps click-only transactions. As compared to direct 
sales, retail apps are more suitable for promoting clear product information (Holmes et al., 2014). 
Furthermore, mobile apps also provide channels for customer retention, reinforcement of 
customers’ existing habits or behaviors, and strengthening customers’ loyalty. Mobile retailers can 
integrate other devices (e.g., desktop computer, notebook) or channels with new information, 
brands, or products (Wang, et al., 2015; Kim, 2017). Hence, this study applied the Technology 
Acceptance Model to investigate factors that lead to customers’ intention to purchase apparel and 
accessories solely through an app. 
 

Technology Acceptance Model 
The Technology Acceptance Model (TAM) discusses the interconnectedness amongst the 

characteristics of a particular technology, its users’ attitude, and his/her behavior (Davis, 1986). 
TAM theorizes consumers’ perceived ease-of-use (PEOU) and perceived usefulness (PU) of a 
particular technology. PEOU speaks to the degree of effort people believe that using a particular 
technology would require, and is presented as an antecedent to PU. PU refers to the extent 
potential users believe that using a particular technology can meet their objective. In the mobile 
environment, previous research indicated that PEOU is not significantly positively correlated to 
the intention to shop via smartphone. This is because users are usually already accustomed to 
access to unlimited information via Internet emails on their mobile devices. PU plays a more 
critical role as compared to PEOU during consumers’ mobile shopping (Agrebi & Jallais, 2015; 
Ooi & Tan, 2016). Therefore, PU is applied in this study to examine consumers’ intention to 
purchase apparel or accessories through a mobile app.  

As for the antecedents of PU, previous research confirmed that accessibility, convenience 
of usage (Kim, et al., 2009), perceived enjoyment (Agrebi & Jallais, 2015), mobile perceived 
compatibility (MPC), mobile perceived security risk (MPSR) (Ooi and Tan, 2016), and decision-



38 
 

making styles (i.e., quality, novelty-fashion, and price consciousness) are positively correlated to 
PU in the mobile environment (Sarkar et al., 2019). This study will further investigate the attributes 
of app design, the attributes of mobile-service quality during the retailer’s online transactions, and 
Consumer-Company Identification (CCI) as potential antecedents to PU of the mobile app.  

In this study, we examined users’ intention to purchase apparel and accessories through 
the mobile app. Users’ behavioral intention is the crucial dependent variable in TAM. As for its 
antecedents, except PU, previous research confirmed perceived enjoyment (Agrebi & Jallais, 2015), 
habitual purchase (i.e., previous product purchase). Mobile shoppers tend to use mobile devices 
to shop for habitual products that they already have a history of purchasing (Wang, et al., 2015). 
Mobile perceived compatibility (MPC) MPC, mobile perceived trust (MPT) were applied to assess 
a new mobile technology acceptance model (MTAM), which consists of mobile usefulness (MU) 
and mobile ease of use (MEU) to determine SCC adoption (Ooi & Tan, 2016). The platform 
identifies habit, price comparison preference, and shopping independence preference (Chen, 2018) 
to be positively correlated to users’ behavioral intention in the mobile environment. In this study, 
we focused on PU and CCI. 
 
Mobile Application Design Attributes 

Mobile application (app) is a type of software designed to run on a mobile device, such 
as smartphone or tablet. App design is a critical factor for developing technology that fits mobile 
customers’ needs and values related to online shopping and purchases. Hedonic value, spurred 
by positive e-store image, leads to consumer purchase (Chang & Tseng, 2013). By contrast, 
utilitarian value emphasizes complete information by users who are more task-oriented, safer 
transactions, and so on (Agrebi & Jallais, 2015; Holmes et al., 2014). An improved mobile 
design, which contains both hedonic and utilitarian values, enhances the browsing efficiency for 
consumers’ product search (Lee et al., 2015). App and website features are important for 
customer relationship management (e.g., affective commitment) and e-loyalty (Bilgihan & 
Bujisic, 2015). Visually appealing features, with cues (e.g., color and size) that facilitate 
navigation, result in impulsive online purchases (Liu et al., 2013).  

Consumers are attracted by web design such as layout, color, complexity, and processing 
speed (Chen, et al., 2002; Hausman & Siekpe, 2009). Online stores pique consumers’ interest with 
brand personality around browsing and buying (Brown et al., 2003). Fashion design characteristics 
significantly sustain consumers’ expectations and fulfillment based on how an app functions 
(Hausman & Siekpe, 2009; Parasuraman et al., 2005; Zhang et al., 2007). Järveläinen (2007) 
emphasized website’s importance in consumers’ online search and purchase. Positive Word of 
Mouth (WOM) occurs when consumers perceive a website or app to be innovative and has 
superior e-service quality. These attributes promote consumers’ loyalty and intentions. O’Cass and 
Carlson (2012) found that a sport team’s website operation success depends significantly on fans’ 
positive brand image and trust toward that sport team’s website, which further influences fans’ 
website loyalty. This study measures consumer reactions to app design attribute via two constructs: 
perceived attractiveness and perceived informativeness.  
 
Perceived Attractiveness and Informativeness  

The perceived attractiveness of an app depends on the aesthetics of its graphic design – 
including layout, color scheme, print size/type, as well as the number of videos, photographs, 
graphics and animation. These dimensions retain consumers’ attention for the product 
advertisements (Hoffman & Novak, 1996; Hoque & Lohse, 1999; Schlosser & Kanfer, 1999). 
Veryzer and Hutchinson (1998) found that visual style influences website usage frequency and 
attitudes towards purchase, which promotes users’ perceived usefulness of this technology. 
 

The quality of information is critical for online group buying (OGB) websites for high 
utility-oriented consumers (Wang, 2016). The quality, credibility, and quantity of consumers’ 
reviews are considered a form of electronic WOM, which leads to online repurchase intention 
(Matute et al., 2016). Website informativeness measures the magnitude of information for a 



39 
 

product/service to accomplish consumers’ shopping objectives. Ducoffe’s (1996) Extended Use 
& Gratification Theory indicated that consumers are motivated by psychological needs beyond 
those that are strictly tied to their functional objectives. Hence, consumers will continue to choose 
and use the technology that successfully gratifies their psychological and functional needs. Katz et 
al. (1973) indicated that consumers are more likely to continue using a form of technology when 
if that technology satisfies their needs.   
Hausman and Siekpe (2009) indicated that the usage of familiar language within a website 
strengthens consumers’ ability to quickly access desired information. Hardware requirement, such 
as mobile devices’ screen size, offers enhanced ease for navigating product information. Well-
organized app page and layout maximize consumers’ search efficiency to receive quality of 
information during shopping. Venkatesh, et al (2003), Brown and Venkatesh (2005), and 
Venkatesh (2006) reported that websites or related online/mobile that offer well organized 
information and well-developed service foster stronger consumer impression (Lee, et al., 2015). 
In the study of mobile augmented reality (MAR) app, Dacko (2016) verified that the completeness 
of information received from the app increases users’ conviction in what they are buying. Other 
benefits, not as apparent as purchase certainty, are increased shopping efficiency and better prices. 
The hypotheses regarding these two mobile app attributes are as follows: 
 

H1: A mobile app’s perceived attractiveness is positively related to consumer perceptions 
of app usefulness.  

 
H2: A mobile app’s perceived informativeness is positively related to consumer 

perceptions of app usefulness.  
 
Mobile-Service Quality Attributes   

Mobile-service quality is defined as the assessment of website/app usefulness related to 
consumer attitudes toward brand preference, choice of channel, and behaviors. Website quality 
influences repurchase intention through enhanced customer satisfaction, trust, and commitment 
in online transaction (Shin et al., 2013). Mobile-services should facilitate consumers in efficiently 
completing all phases of an online transaction: shopping, purchasing, receiving, and return 
product. Early research determined that the perception of service quality is based on the services 
that customers expect a company to offer versus the services that the company actually offers 
(Grönroos, 1982; Lehtinen & Jarmo, 1982; Lewis & Bernard, 1983; Parasuraman et al., 1985; 
Sasser et al., 1978). E-business can be conducted largely without human intervention but relies 
heavily on service quality. Globerson and Maggard (1991) found that well-developed service 
process was designed to meet consumers’ quality expectations toward the website (e.g., for an 
Appropriate greeting, for timely service). Parasuraman et al. (2005) further suggested that 
companies that focus on Mobile-service quality will sustain brand impression via customized 
service.  
 

Different dimensions were applied to improve the quality of a website/app. Cronin and Taylor 
(1992) found that the service quality dimensions of an intangible operation and service process 
impact attitudes towards a website. Online or mobile consumers can be easily discouraged by low 
mobile-service quality attributes, such as lack of customer service responsiveness, inefficient 
navigation, complexity in payment process, and perceived security risk during a transaction (Elliot 
& Fowell, 2000; Ooi & Tan, 2016).  
 

System efficiency refers to the ease and speed of accessing and using an app for purchase. 
System availability refers to the reliability of an app’s operations. System efficiency speaks to how 
well the app functions assist consumers’ shopping and purchasing, whereas system availability 
speaks to whether the app functions are working properly.  
 



40 
 

Weinberg (2000) found that visitors’ evaluation of e-service quality is positively correlated to 
the speed of a website/app. Parasuraman et al (2005) noted that mobile and online consumers rely 
more on the impressions of service quality (e.g., package tracking, prompt customer service 
response). Additionally, Yen and Lu (2008) and Chang et al. (2009) and Lee et al. (2015) reported 
that online consumers’ perception of e-service quality is positively correlated to their intentions to 
future purchase.  

Mobile app’s flexibility and the ease-of-use prompt consumers’ repeat purchase, word of 
mouth, retention, cross buying, brand loyalty and satisfaction (Wahab et al., 2011). As for business-
to-business market, Luo and Lee (2011) recognized business-to-business marketing improvements 
in airline industry’s e-services by reducing waiting time, which enhances consumers’ perceived 
usefulness of and trust in the company’s websites. O’ Cass and Carlson (2012) found that website 
e-service quality was a strong determinant of fans’ trust and loyalty toward the sports teams with 
frequent usage. Su et al. (2015) found that tourism companies, which focus on consumers’ positive 
quality feedbacks of e-service, maintain online satisfaction for future usage of the same website. 
There are numerous, valid measures of e-service quality (e.g., Barnes & Vidgen, 2002; 
Wolfinbarger & Gilly, 2003; Zhang et al., 2007). For this study, we will focus on two attributes of 
mobile-service quality: system efficiency and system availability. Hypotheses regarding the two 
relevant mobile-service quality attributes are as follows: 

 
H3: A mobile app’s mobile-service quality attribute of efficiency is positively related to 

consumer perceptions of app’s perceived usefulness.  
 
H4: A mobile app’s mobile-service quality attribute of system availability is positively 

related to consumer perceptions of app’s perceived usefulness.  
 
Company-Consumer Identification   

Ashforth and Mael (1989) investigated the role of organizations in individual’s social 
identities and coined the term “organization identity.” Organization identification happens when 
people believe an organization to be associated with the characteristics they consider to be self-
referential or self-defining (Pratt, 1998).  

Consumer-Company Identification (CCI) is derived from Social Identity Theory (Tajfel 
and Turner, 1979). Social Identity Theory addresses how individuals identify with social groups. 
Members of a social group (in-group) identify with that group and then make comparisons with 
out-groups in an effort to enhance the self-esteem status of the in-group, and thereby increase 
their own personal self-esteem. Tajfel and Turner (1985) found that when individuals perceive 
themselves to have membership in a group, their self-esteem rises by positively differentiating 
their in-group as compared with an out-group based on a value important to them. Walther and 
Tidwell (1995) reported that people in a group were more interested in other group members who 
displayed the same social cues they portray and have more positive perceptions of those who 
showed similar social cues (i.e., identities). Loyal members of a group theoretically try to improve 
their group’s standing (Riketta and Laderer, 2005).  

Extending Social Identity Theory into the realm of marketing, Kramer (1991) reported 
that organizational consumers often have self-categorization based on various social groups (e.g., 
gender, ethnicity, occupation, religion, and sport teams). Subsequently, the consumers establish 
strong ties with social groups’ self-categorization process, which helps an individual determine 
“who am I?” through the comparison of one’s own defining characteristics (e.g., personality traits, 
values, demographics) with those of others (Ashforth & Mael, 1989; Dutton et al., 1994). Solomon 
and Schopler (1982) and Kleine et al. (1993) found that consumers shop for and own identity-
related products to express their belongings with a specific brand or company.   
 

Bhattacharya and Sen (2003) applied Social Identity Theory to develop a framework of the 
antecedents and outcomes of CCI. This group identification is based on the customers’ 
perceptions that a company and its own identities are similar, distinctive or unique, and reflective 



41 
 

of a desired prestige or status. CCI is then theorized to predict several desired outcomes, including 
company loyalty, company promotion, consumer recruitment, and consumer resilience to negative 
information.  

Goffman (1959) noted the importance of company logos in such a consumer-company 
connection process. Later, Ahearne et al. (2005) found that CCI perceptions are derived both from 
an organization overall, as well as the company representative who interacted with the consumers. 
This was positively correlated to consumers’ related product purchase from the organization (e.g., 
promoting product, recruiting consumers).   
Bonabeau (2004) identified the online strategies that suggested social cues offered via a website 
could motivate consumers’ recognition and association with the website/company. Lee and 
Yurchisin (2011) found consumers’ positive perception of a website to be useful when identifying 
with the company and its owned brands, and in turn experience CCI. Lee et al. (2015) reported 
that the website attributes of visual attractiveness, perceptions of e-service quality, and CCI lead 
to significant perceived usefulness of the website.  

In the study of mobile augmented reality (MAR) shopping apps, Dacko (2016)’ 
categorization indicated that consumers with higher experiential orientations have higher intention 
to download and use retail apps. According to Mathwick et al (2001), Dacko (2016) categorized 
MAR shopping apps into four types: (1) extrinsic-active value: similar to economic value and give 
emphasis to customers’ return on investment (ROI) and shopping efficiency; (2) extrinsic-reactive 
value: highlighting the value of service excellence; (3) intrinsic-active value: pursuing the intrinsic 
enjoyment of shopping or the feeling of escapism by shopping; (4) intrinsic-reactive values: 
underscoring the visual, aesthetic, or entertainment appeal. The MAR shopping app users would 
select apps that provide one or several of these values and benefits, which would not be provided 
in other shopping.  

Agrebi and Jallais (2015) implied that consumers would go mobile shopping to fulfill their 
needs for hedonic or/and utilitarian value. Holmes et al. (2014) concluded a relatively ambivalent 
phenomenon: some consumers deemed hedonic value (e.g., enjoyment, fun, excitement) as 
important drivers to adopt mobile shopping, whereas others thought the hedonic value might 
hinder consumers’ willingness to mobile-shop and instead emphasized more utilitarian value (e.g., 
convenience and accessibility). Chen (2018) indicated customers may use mobile shopping apps 
to reflect their lifestyles, which are fabricated and affected by one’s experience, beliefs, values, 
culture, family, friends, social classes, and other reference groups. In light of aforementioned 
Social Identity Theory, consumers are orientated to categorize themselves into various social 
groups based on gender, ethnicity, occupation, etc. In the self-categorization process, individuals 
compare their own defining characteristics, e.g., values, personality traits, with those of others 
(Dutton et al., 1994; Kramer, 1991). Therefore, consumers prefer to choose the tools reflecting or 
fitting their own values or lifestyles in order to acquire their specific objectives or benefits. In other 
words, consumers would instinctively or deliberately adopt the shopping apps with a high level of 
CCI and perceive that using the apps can enhance delivery their objectives (i.e., perceived 
usefulness, PU).  Thus, the hypothesis regarding CCI is as follows:  
 

H5: Consumers’ consumer-company identification (CCI) is positively related to 
consumers’ perceptions of app’s usefulness.  

 
Apparel purchase aims to deliver consumers’ self-image in accordance with the personal 

characteristics of products, such as trendiness, sophistication, level of luxury and self-esteem. 
App/brand/company attempts to align its image to its consumer’s personalities. This emotional 
and trustful bond provides consumers a natural motive for online shopping (Lee and Yurchisin, 
2011). The more consumers perceive the website to be useful, the more likely they will conduct 
future purchase (Lee et al., 2015). Perceived usefulness of telebanking was proven as a significant 
driver of behavioral intention (Alalwan, 2016). Therefore, based on TAM, the following 
hypothesis about the mobile app is proposed: 
 



42 
 

H6: Consumers’ perceived usefulness of a mobile app is positively correlated to the  
       intention to purchase apparel and accessories from the mobile app. 

 
Store brand identification has significant social influence on consumers’ brand loyalty and their 

store purchase intention (Calvo-Porral & Levy-Mangin, 2016). Mobile app’s identification is 
theorized to act as a mediating factor that combines purchase search streams with consumer 
behavior. This new branding strategy is for online retailers to develop a relationship bond with 
the customer (So et al., 2016). We have inferred that consumers’ CCI is positively correlated to 
their perceptions of app’s usefulness and the perceived usefulness is positively correlated to 
consumers’ intention to purchase apparel and accessories from the mobile app. According to 
Elaboration Likelihood Model (ELM) of persuasion (Petty and Cacioppo, 1984), under the central 
route, persuasiveness is driven by consumer’s level of message consideration and elaboration on 
the true merits of a subject, for example, consumer consider it useful to purchase apparel and 
accessories from their mobile app. The CCI here arises from different cues and the CCI itself is 
also a cue. This study planned to examine the effect of the CCI to purchase apparel and accessories 
from the mobile app (i.e., the peripheral route) without considering its usefulness (i.e., the central 
route). Hence, we put forth the following hypothesis: 

 
H7: Consumers’ consumer-company identification (CCI) is positively related to their 

intention to purchase apparel and accessories from the mobile app.  
 

Figure 1 - Hypothesis model 
 

 
Measurement  
Questionnaire Development 

A survey was developed to test hypotheses based on participants’ previous experience with 
the mobile app (see Appendix). The questionnaire consisted questions for measuring conceptual 
constructs. The first section contained a four-item measure adapted from Lee et al. (2015). 
Participants were directed to think of their favorite mobile app while rating their level of agreement 

 

Mobile service 

quality attributes 
Attributes 

Perceived 

Informativeness 

 

Efficiency 

Perceived 

Usefulness 

Of app 

Intention  

To Purchase 

through app 

  

 

 

System  

Availability 

 

Consumer-Company 

Identification 
 

H2 

 

H4 

 

H5 

  

H1 

 

 

H3 

 

Perceived 
Attractiveness 

  

 

H7 

 

H6 

 

Mobile app attributes 



43 
 

with each of the items on a seven-point Likert-type scale (1 = strongly disagree, 7 = strongly 
agree).  
After the pilot study from summer 2016, the “majority” of 30 business major students selected a 
company’s app. The participants were asked to evaluate the clarity of instrument items. All aspects 
of the questionnaire were presented, including wording, question content, sequence, form and 
layout, question difficulty, and instructions. Relevant editorial changes were employed based on 
the feedback of participants.  
 
Description of Sample and Responses 

The experimental survey was completed by 300 participants, with a total of 250 usable 
questionnaires, and the demographic information is displayed in Table 1. 
 
Table 1 – Demographic Information 

 

 
 
Measurement Model Analysis 

Factor analysis consists of exploring the patterns of relationships among variables. These 
patterns are represented by what are called factors. Examination of the loadings of variables on 
each factor helps to identify the character of underlying dimensions. Confirmatory factor analysis 
(CFA) was conducted to assess the validation of scales for the measurement of specific constructs. 
Each construct is assessed by its own indicators. The model contains indicators and latent variables 
(labeled as constructs).  
 

   
Characteristics   Frequency / Percentage   

Number of  Respondent s   Total: 250   
Gender   Total   Percentage   
           Male    
           Female   
           Missing    

   66   
   183   

   1   

26.6 %   
73.4 %   
0.3%   

  
  
  

Age   (Mean), Standard Deviation   22   5.06     
Ethnicity         
          Caucasian      1 58   62.2 %     
          African - American      48   18.2 %     
          Hispanic - Latino(a)      7   3.1 %     
          Asian - American      3   1 .0 %     
          Other      34   12.4 %     
Year at school         
          Freshmen   57   22.8 %     
          Sophomore   50   20.0 %     
          Junior   64   25.5 %     
          Senior   56   22.4 %     
          Graduate level   23   8.6 %     
Major        
          M arketing and consumer  s tudies         1 04   4 1.6 %     
          Business Administration   and related major       89   35.4 %     
          Other Majors      57   23.0 %     
Monthly income         
           Under $300   75     28.9 %     
           $300 - $499   58     23.1 %     
           $500 - $749   38     15.2 %     
            $750 - $999   1 8     7.9 %     
           $1000 - $1299   29     10.7 %     
           $1300 or more   32     12.4 %     
        

  



44 
 

Table 2 - Descriptive statistics and correlations   
 

 
Reliability and Validity  

First, all standardized factor loadings were greater than 0.50 except the SA-1 and SA-4 of 
the mobile-system availability construct and the EF-3 of the mobile-service efficiency (see Table 
3), which indicates reasonable convergent validity (Nunnally and Bernstein, 1994). Measurement 
model analysis was used to assess the reliability and validity of measurement items. First, 
Cronbach’s α was used to assess reliability related to internal consistency between constructs and 
set an acceptable level that is more than 0.7 (Hair et al., 1998). Cronbach’s α values ranged from 
0.86 to 0.96 (excluding the mobile-service quality construct, which has the lowest Cronbach’s α, 
0.62), and thus indicate high internal consistency among items. 

Second, convergent validity refers to the degree to which the items of a specific construct 
share a proportion of variance in common (Hair et al., 1998). High convergent validity indicates 
that measurement scales meet the intended concept. Three standards to measure convergent 
validity were used: (1) a factor loading value larger than 0.5; (2) Composite reliability (CR) larger 
than 0.7; (3) Average variance extracted (AVE) larger than 0.5 (Bagozzi & Yi, 1988; Fornell & 
Larcker, 1981; Hair et al., 1998). CR also tests the internal consistency of the measured items 
representing a latent construct (Hair et al., 1998). As shown in Table 3, factor loading values 
ranged from 0.25 to 0.98, with most results above 0.7, therefore meeting acceptable levels. The 
CR ranged from 0.61 to 0.98, with most in the 0.9 range. AVE values exceeded the threshold of 
0.5, from 0.36 to 0.95, indicating the convergent validity of constructs is acceptable and explained 
a relatively high level of variance in common (Fornell & Larcker, 1981).  

Lastly, discriminant validity requires a construct to be distinctive from other constructs. 
The square root of the AVE must be greater than its correlations with other latent constructs 
(Fornell and Larcker, 1981). As shown in Table 2, the values of square root of the AVE ranged 
from 0.60 to 0.98, greater than its correlations with other latent constructs. These results indicate 
that discriminant validity exists between constructs. 

 
 
 
 
 
 
 

 
 
 
 
 

Model 

Variable 
Mean  

Std. 

Dev 

Correlations 

1 2 3 4 5 6 7 

1. PA 5.7 0.9 (0.85)       

2. IN 5.7 0.9 0.55** (0.85)      

3. EF 6.0 0.8 0.58** 0.57** (0.76)     

4. SA 5.3 1.6 0.38** 0.33** 0.47** (0.60)    

5. CCI 4.5 1.7 0.48** 0.34** 0.33** 0.41** (0.88)   

6. PU 5.7 1.2 0.58** 0.55** 0.59** 0.39** 0.48** (0.91)  

7. IP 4.0 2.0 0.32** 0.23** 0.28** 0.39** 0.72** 0.42** (0.98) 

Note: *, if p<0.05; **, if p<0.01. The bold diagonal values are the square root of the average 
variance extracted for each construct. 

1. PA = Perceived Attractiveness; IN =  Perceived Informativeness; EF = Mobile-Service 

Efficiency; SA = Mobile-Service System Availability; CCI = Consumer-Company 

Identification; PU = Perceived Usefulness; IP= Intention to Purchase through app. 

 



45 
 

Table 3 - Factor Loading, Reliability, CFR, and AVE 
 

 
 
Structural Model Analysis and Hypotheses Testing 
Model Testing  

Structural equation modeling (SEM) was conducted using the full information maximum-
likelihood estimation procedure through AMOS 26. The full model had a χ² test-statistic of 
849.45 (d.f. = 360; p < .000), and fit indexes were GFI=0.81, NFI=0.88, IFI=0.93, TLI=0.92, 
and CFI=0.93. The model’s RMSEA index is 0.074, with a 90 percent confidence interval 
between 0.067 and 0.080, indicating an acceptable model fit for the data (see Figure 2). 
 
 
 

Construct Standardized 

Factor 

Loading 

Construct  

Reliability 

(Cronbach’s á) 

Composite 

Factor 

Reliability 

(CFR) 

Average 

Variance 

extracted values 

(AVE) 

Perceived Attractiveness (PA) 

 

 

 

PA1 

PA2 

PA3 

PA4 

0.88 

0.84 

0.86 

0.82 

0.91 0.91 0.72 

Perceived Informativeness (IN) 

 

IN1 

IN2 

IN3 

0.89 

0.83 

0.82 

0.86 0.88 0.72 

Mobile-Service  

Efficiency (EF) 

 

EF1 

EF2 

EF3 

EF4 

EF5 

EF6 

EF7 

EF8 

0.76 

0.72 

0.25* 

0.76 

0.65 

0.84 

0.73 

0.83 

0.91 0.90 0.57 

Mobile-Service 

System Availability (SA) 

 

SA1 

SA2 

SA3 

SA4 

0.38* 

0.79 

0.50 

0.43** 

0.62 0.61 0.36 

Consumer-Company Identification 

(CCI) 

 

CCI1 

CCI2 

CCI3 

CCI4 

CCI5 

0.90 

0.85 

0.85 

0.92 

0.85 

0.93 0.95 0.82 

Perceived Usefulness of app (PU) 

 

PU1 

PU2 

PU3 

PU4 

0.77 

0.94 

0.96 

0.95 

0.89 0.98 0.95 

Intention to Purchase through app 

(IP) 

 

IP1 

IP2 

IP3 

0.96 

0.98 

0.98 

0.96 0.94 0.76 

Note:  

* The standardized factor loading of the item is less than .50. Cronbach’s á, CFR, and AVE are calculated after   

   deleting the item.  

** The item is not deleted for keeping enough indicators although the loading is less than .50. 

 



46 
 

 
 

Figure 2 - SEM results  

 

 
Discussion 

In Figure 2, H1 was supported. Specifically, the positive and direct relationship predicted 
in H1 between perceived attractiveness and perceived usefulness was supported by the data (β1= 
0.17, z = 2.16, p < .05). This result corresponds with the findings of Chen and Wells (1999), Chen 
et al. (2002), and Hausman and Siekpe (2009). The Use and Gratification Theory suggests that a 
high level of informativeness and engagement, along with low level of irritation, are factors that 
will likely generate a favorable impression of a website (Chen et al., 2002). Perceived usefulness 
was related to respondents’ attitudes toward the app or website, similar to a relationship found by 
Agarwal and Venkatesh (2002) and Hu et al. (2009). Results supported that users decide whether 
to use mobile app based on perceptions of its attractiveness (e.g., design, layout, colors). This 
finding is consistent with a study by Lee et al. (2015). Seock and Norton (2008), which indicated 
that attractiveness was important to perceptions of a website’s usefulness, as well as findings by 
Chen and Wells (1999), Moon (2004), and Song and Zinkhan (2003). In this study, it is possible 
that respondents’ perceived usefulness was affected by users’ familiarity with the store and its 
carrying product brands. 

H2 predicting a positive relationship between app informativeness and perceived 
usefulness, was supported (β2= 0.16, z = 2.13, p < 0.05). The information offered by the app (e.g., 
context informativeness) was found to be significant to users’ perceptions of app’s usefulness. 

 

Mobile service 

quality attributes 
           

Perceived 

Informativeness 

 

Efficiency 

Perceived 
Usefulness 

Of app 

Intention  

To Purchase 

Trough app 

 

 

χ2=849.45 (d.f. = 360) 

χ2/d.f =2.36 

GFI=0.81 
NFI=0.88 

IFI=0.93 

TFI=0.92 

CFI=0.93 
RMSEA=0.074 

System  
Availability 

 

Consumer-Company 

Identification 

Note: (z-value; two-tailed) *z-value=1.96 (p< .05), **z=2.58 (p<.01), ***z=3.45 

(p<.001). 

1. Indicator variables, correlations among exogenous variables, and disturbances have  

    been omitted for notational simplicity 

2. Coefficients from completely standardized solution  

3. Dotted lines indicate the hypothesis is not supported 

0.16* 

 

0.08 
 

0.22*** 

  

     0.17* 

 

 

0.24* 

 

Perceived 
Attractiveness 

  

 

0.08 

 

0.7*** 

 

Mobile app Attributes 



47 
 

These findings suggest that in order to increase consumer’s perceived usefulness of an app, an 
informative function and instruction which emphasis on visual design are important.  

The positive relationship between mobile-service quality-efficiency and perceived 
usefulness of mobile app (H3) was supported (β3= 0.24, z = 2.00, p < 0.05). The more consumers 
perceive efficiency from app usage, the more likely it is that consumers will use the mobile app. 
With an efficient app design (e.g., fast and filter search functions, price comparisons, three-
dimensional product presentation, and colorful layout), consumers can assess the specific qualities 
of the product.  

The positive relationship between mobile-service quality-system availability and perceived 
usefulness of mobile app (H4) was not supported (β4= 0.084, z = 0.73, p > 0.05). The download 
speed and availability may not be a significant consideration for app users because consumers may 
expect the mobile environment to have different signal strengths. The service should combine 
online and offline activities. Within the mobile environment, the typical mobile service 
communication tools such as discussion forums, virtual chats, emails, and FAQs may not be 
presented in a consistently stable Internet environment, e.g., at home or in the office 
(Almarachdeh et al., 2019). Customers usually do not distinguish the mobile system availability 
from the whole service image when they evaluate the mobile-service quality. The relatively 
unstable mobile connection exacerbates the image, leading to an assessment of decreased 
usefulness. 

Social Identity Theory showed individuals’ attempt to fit-in through identification with a 
group (Tajfel & Turner, 1979). Consumers have a greater tendency to purchase product/service 
from companies/brands relevant to their self-identity. They prefer to shop at stores whose 
organizational identities are similar to their own, also known as consumer-company identification 
(CCI). H5, predicting a positive relationship between CCI and perceived usefulness of mobile app, 
was also supported (β5= 0.22, z = 3.66, p < .001), similar to the results of Kleine et al. (1993), Lee 
et al. (2015), and Solomon and Schopler (1982). Consumers’ sense of belongingness facilitates CCI 
identification with an app and strengthens a positive attitude by enhancing their product or 
purchase experiences.  

Consumers collect information and knowledge through repeated online usage (Alba & 
Hutchinson, 2000; Koufaris, 2002; Raju et al., 1995). Analyzing consumers’ behavioral intentions, 
H6 predicted a positive relationship between perceived usefulness of the mobile app and 
consumers’ intention to purchase. This was supported by the data (β6= 0.70, z = 12.08, p < 0.001), 
confirming the theory that perceived usefulness is a predictor of behavioral intention (Venkatesh 
& Morris, 2000). 

Lastly, H7, which examined the positive correlation between CCI and users’ intention to 
purchase apparel within the mobile app, was not supported. According to Elaboration Likelihood 
Model (ELM) of persuasion (Petty and Cacioppo, 1986), a consumer can be persuaded through 
central route (i.e., via perceived usefulness) or peripheral route. The statistical result evidenced 
that the effect of persuasion through central route was stronger than the peripheral route. CCI 
may indirectly influence consumers’ intention to purchase apparel and accessories through the 
perceived usefulness of the app itself. Perceived usefulness of the app acts as a mediator between 
CCI and consumers’ intention for mobile shopping.  

Similar to Brown and Venkatesh (2005), this study found that a mobile app is perceived 
to be useful and increases consumers’ intention to purchase apparel and accessory. CCI is a 
relatively significant factor influencing perceived usefulness (H7 is not supported) of a mobile app, 
thus strengthening the gap in the TAM model.   
 

Conclusion and Implications 
This study examined the drivers leading to consumers’ intentions to use an app to search 

for information and purchase of apparel products. According to the findings, perceived 
attractiveness (e.g., color, layout) is a significant attribute that influences the perceived usefulness 
of an app.  
 



48 
 

Additionally, the research provided an understanding of how consumers evaluate the mobile app 
as useful. Online/Mobile advertisement may employ advisors and decision-supporting 
technologies that interact with their consumers via instant text message to communicate product 
shipping and payment confirmation through mobile devices.  

Thirdly, this study examined the causal relationship of consumers’ perceived usefulness of 
the company’s app toward their intention to purchase apparel products from the app. Results of 
this study support the positive relationship between consumers’ perceived usefulness and purchase 
intentions (Agrebi & Jallais, 2015; Kim et al., 2009; Mathieu & Zajac, 1990; Meyer et al., 2002; 
Riketta, 2002; Zhang et al., 2007). Our study provides CCI as another variable in Theory of 
Reasoned Action (TRA) and TAM to predict perceived usefulness and further intention to 
purchase. Mobile marketing strategies should address brand/image similarities between the 
company’s identity and consumers’ personalities, values, and lifestyles to significantly foster 
stronger CCI.  

Previous research has indicated that consumers tend to use apps to conduct habitual 
products/services purchase from the retailers that they have trading experience before. The 
products/services that mobile consumers purchase are often the ones with which they are most 
familiar, have low involvement, and with short consumption cycles (Wang et al., 2015). By contrast, 
the research also indicated that consumers tend to use mobile device to conduct pre-purchase 
activities, such as information search and alternatives review.  

Consumers require an extended period of time to gain additional information during their 
planning to purchase products or services, with high or medium-involvement, which experience 
longer consumption cycles (Holmes et al., 2014). Mobile app supports in-store shopping and post-
purchase. On the other hand, an app allows consumers to assess whether the products or services 
align to what they are seeking (Dacko, 2016). App purchases have the advantages of mobile devices, 
such as timeliness and convenience. App designers also need to deal with challenges such as 
smaller screen, unstable processing speed, and reduced security (Wang et al., 2015). In addition to 
the functionality, consumers also demand a more attractive layout to further satisfy their service 
needs (including fun, excitement, and enjoyment), as well as utilitarian values (including timely 
information updates, higher discount).  

Retailers and marketers should not view an app as a replication of the Internet, but as a 
mobile supplement for current Internet-based activities (Holmes et al., 2014). Companies should 
launch a collection of apps, instead of relying on one single app, to cater to users’ diversified 
expectation of needs and values (Kim et al., 2017). Consumers can comfortably browse apps to 
assess primary prices/product information and check-out though email or alternative internet 
platform for the final purchase decision. Apps with timely promotion updates can also facilitate 
consumers’ in-store shopping. It is critical for mobile’s seamless experience and more user-
efficient browsing environment by integrated Omni-channel stores, websites, and apps to better 
function and benefit from mobile shopping experience. 

Apparel and accessories retailers can consider designing apps for different product features 
and target segments. The app could focus more on the brand image advertisement, push 
notification service, and other promotion activities. Perceived attractiveness of an app may be 
more useful for better customer engagement. In contrast, for consumers who seek lower priced 
and generic items from apps, the apps should focus more on perceived informativeness and 
service efficiency to facilitate consumers’ comparison and search efficiency.  

Consumer-Company Identification (CCI) provides another route to enhance consumers’ 
mobile search and purchase. Apparel retailers and marketers should understand mobile customer’ 
expectation, including individual’s values, personality traits, and lifestyles. Mobile apps’ design and 
advertisement content should continually enhance the visual presentation and services quality 
attributes in accordance with the company’s target customers’ characteristics and brand attitudes. 
A well-designed mobile app with a high level of CCI, can exude the perception of appeal, 
trustworthiness, and a sense of belongingness. This will sustain the customer’s journey that leads 
to a strong sense of usefulness toward further intention of purchase, which is the key impact for 
this study.  



49 
 

Limitations and Recommendations for Further Research 
This study relied on respondents’ predisposition to an app. Therefore, limitations result 

from the individual preference of each respondent. For instance, individuals that were assigned to 
an app they liked had a more positive or different response than individuals who were assigned to 
an app of a product that he/she was not familiar. Secondly, the research was limited by the fact 
that it was a single time survey and carried out in a 15-20-minute period. It is likely that different 
consumers need varying time durations when browsing and searching through an app. Thus, 
results may differ if respondents did not have a time limit for completing the survey. Thirdly, 
respondents may not have been motivated to shop on the assigned app and may prefer an app 
with products or brands with which they identify more strongly.  
 
 

References 
Agarwal, R. & Venkatesh, V. (2002). Assessing a firm’s web presence: A heuristic evaluation 

procedure for the measurement of usability. Information Systems Research, 13(2), 168–186. 
Agrebi, S. & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping, 

Journal of Retailing and Consumer Services, 22, 16–23. 
Ahearne, M., Bhattacharya, C.B. & Gruen, T. (2005). Antecedents and consequences of customer-

company identification: Expanding the role of relationship marketing. Journal of Applied 
Psychology, 90(3), 574–585. 

Alalwan, A.A., Dwivedi, Y.K., Rana, N.P. & Simintiras, A.C. (2016). Jordanian consumers' 
adoption of telebanking. The International Journal of Bank Marketing, 34(5), 690–709. 

Alba, J.W. & Hutchinson, J.W. (2000). Knowledge calibration: What consumers know and what 
they think they know. Journal of Consumer Research, 27(2), 123–156. 

Almarashdeh, I., Jaradat, G., Abuhamdah, A., Alsmadi, M., Alazzam, M.B., Alkhasawneh, R. & 
Awawdeh I. (2019). The difference between shopping online using mobile apps and website 
shopping: A case study of service convenience. International Journal of Computer Information 
Systems and Industrial Management Applications, 11, 151–160. 

Ashforth, B.E. & Mael, F. (1989). Social identity theory and the organization. Academy of 
Management Review, 14(1), 20–39.  

Bagozzi, R.P. & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the 
Academy of Marketing Science, 16(1), 74–94. 

Barnes, S.J. & Vidgen, R.T. (2002). An integrative Approach to the assessment of e-commerce 
quality. Journal of Electronic Commerce Research, 3, 114–127. 

Bilgihan, A. & Bujisic, M., (2015). The effect of website features in online relationship marketing: 
A case of online hotel booking. Electronic Commerce Research and Applications, 14(4), 222–232. 

Bhattacharya, C.B. & Sen, S. (2003). Consumer-company identification: A framework for 
understanding consumers' relationship with companies. Journal of Marketing, 67(2), 76–88. 

Bonabeau, E. (2004) The perils of the imitation age. Harvard Business Review, 82(6), 45–54. 
Brown, M., Pope, N. & Voges, K. (2003). Buying or browsing? An exploration of shopping 

orientations and online purchase intention. European Journal of Marketing, 37(11/12), 1666–
1684. 

Brown, S.A. & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline 
model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399–426. 

Calvo-Porral C. & Levy-Mangin, J.-P. (2016). Food private label brands: the role of consumer trust 
on loyalty and purchase intention. British Food Journal, 118(3), 679–696 

Chang, E.C. & Tseng Y.F. (2013). Research note: E-store image, perceived value and perceived 
risk. Journal of Business Research, 66(7), 864–870.  

Chang, H.H., Wang, Y.H. & Yang, W.Y. (2009). The impact of e-service quality, customer satisfaction 
and loyalty on e-marketing: Moderating effect of perceived value. Total Quality Management & 
Business Excellence, 20(4), 423–443. 

Chen, H.J. (2018). What drives consumers’ mobile shopping? 4Ps or shopping preferences?. Asia 
Pacific Journal of Marketing and Logistics, 30(4), 797–815.  



50 
 

Chen, Q., Gillenson, M.L. & Sherrell, D.L. (2002). Enticing online consumers: An extended 
technology acceptance perspective. Information Management, 39(8), 705–719.  

Chen, Q. & Wells, W.D. (1999). Attitude toward the site. Journal of Advertising Research, 39(5) 27–
37. 

Clement, J. (2020). U.S. fashion and accessories e-retail revenue 2017-2024. Statista 2020, 
https://www.statista.com/statistics/278890/us-apparel-and-accessories-retail-e-commerce-
revenue/ 

Cronin, J.J. & Taylor, S.A. (1992). Measuring service quality-Reexamination and extension. Journal 
of Marketing, 56(3), 55–68. 

Dacko, S.G. (2016). Enabling smart retail settings via mobile augmented reality shopping apps. 
Technological Forecasting & Social Change, 124, 243–256. 

Davis, F.D. (1989). Perceived usefulness, perceived ease of use and user acceptance of      
      information technology. MIS Quarterly, 13(3), 319–340.  
Ducoffe, R.H. (1996). Advertising value and advertising on the web. Journal of Advertising Research, 

36, 21–35.  
Dutton, J.M., Dukerich, J.M. & Harquail, C.V. (1994). Organizational images and member 

identification. Administrative Science Quarterly, 39(34), 239–263. 
Elliot, S. and Fowell, S. (2000). Expectations versus reality: A snapshot of consumer experiences 

with Internet retailing. International Journal of Information Management, 20(5), 323–336.  
Fornell, C. & Larcker, D. (1981). Structural equation models with unobservable variables and 

measurement error. Journal of Marketing Research, 18(1), 39–50. 
Globerson, S. & Maggard, M.J. (1991). A conceptual model of self-service. International Journal of 

Operations & Production Management, 11(4), 33–43. 
Goffman, E. (1959). The Presentation of Self in Everyday Life, Doubleday, Garden City, NY. 
Grönroos, C. (1982). An Applied Service Marketing Theory. European Journal of Marketing, Vol. 16, 

No. 7, pp. 30–41.  
Hair, J.F., Anderson, R.E., Tatham, R.L. & Black, W.C. (1998). Multivariate data analysis, Prentice-

Hall International, Inc., New York, NY. 
Hausman, A. & Siekpe, J.S. (2009). The effect of web interface features on consumer online 

purchase intentions. Journal of Business Research, 62(1), 5–13. 
Hoffman D.L. & Novak, T.P. (1996). Marketing in hypermedia computer-mediated environments: 

Conceptual foundations. Journal of Marketing, 60(3), 50–68. 
Holmes, A., Byrne, A. & Rowley, J. (2014). Mobile shopping behaviour: insights into attitudes, 

shopping process involvement and location. International Journal of Retail & Distribution 
Management, 42(1), 25–39. 

Homburg, C., Wieseke, J. & Hoyer, W.D. (2009). Social identity and the service-profit chain. 
Journal of Marketing, 73(1), 38–54. 

Hoque, A.Y. & Lohse, G.L. (1999). An information search cost perspective for designing 
interfaces for electronic commerce. Journal of Marketing Research, 36(3), 387–394. 

Hu, P.J.H., Brown, S.A., Thong, J.Y.L., Chan, F.K.Y. & Tam, Y.K. (2009). Determinants of service 
quality and continuance intention of online services: The case of eTax. Journal of the American 
Society for Information Science and Technology, 60(2), 292–306.  

Järveläinen, J. (2007). Online purchase intentions: An empirical testing of a multiple-theory model. 
Journal of Organizational Computing & Electronic Commerce, 17(1), 53–74. 

Katz, E., Gurevitch M. & Haas, H. (1973). On the use of the mass media for important things. 
American Sociological Review, 38(2), 164–181. 

Kim, C., Mirusmonov, M. & Lee, I. (2009). An empirical examination of factors influencing the 
intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322. 

Kim, M., Kim. J., Choi, J. & Trivedi, M. (2017). Mobile shopping through applications: 
Understanding application possession and mobile purchase. Journal of Interactive Marketing, 39, 
55–68. 

Kleine, R., Kleine, S.S. & Kernman, J.B. (1993). Mundane consumption and the self: A social-
identity perspective. Journal of Consumer Psychology, 2(3), 209–235. 



51 
 

Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online 
consumer behavior. Information System Research, 13(2), 205–224. 

Kramer, R.M. (1991). Intergroup relations and organizational dilemmas: The role of categorization 
processes. Research in Organizational Behavior, 13, 191–207. 

Lee, H.H., Kim, J. & Fiore, A.M. (2010). Affective and cognitive online shopping experience: 
Effects of image interactivity technology and experimenting with Appearance. Clothing and 
Textiles Research Journal, 28(2), 140–154. 

Lee, Z.C., Hodges, N. & Watchravesringkan, K. (2015). ‘An investigation of antecedents and 
consequences of consumers’ attitudes toward an Apparel website. International Journal of 
Electronic Customer Relationship Management, 9(2/3), 138–157.  

Lee, Z.C. & Yurchisin, J. (2011). The impact of website attractiveness, consumer-website 
identification, and website trustworthiness on purchase intention. International Journal of 
Electronic Customer Relationship Management, 5(3/4), 272–287. 

Lehtinen, U. & Lehtinen, J.R. (1982). Service quality: A study of quality demensions. unpublished 
working paper, Service Management Institute, Helsinki, Finland. 

Lewis R.C. & Booms, B.H. (1983). The marketing aspects of service quality. in Berry L., Shostack, 
G. and Upah G. (Eds): Emerging Perspectives on Services Marketing, 99–107, American Marketing, 
Chicago, IL.  

Liu, Y, Li, H. & Hu, F. (2013). Website attributes in urging online impulse purchase: An empirical 
investigation on consumer perceptions. Decision Support Systems, 55(3), 829–837.  

Luo, S. & Lee, T. (2011). The influence of trust and usefulness on customer perception of e-
service quality. Social Behavior and Personality: An international journal, 39, 825–838. 

Mathieu, J.E. & Zajac, D.M. (1990). A review and meta-analysis of the antecedents, correlates, and 
consequences of organizational commitment. Psychological Bulletin, 108(2), 171–194. 

Mathwick, C., Malhotra, N.,& Rigdon, E. (2001). Experiential value: conceptualization, 
measurement and application in the catalog and Internet shopping environment. Journal of 
Retailing, 77(1), 39–56.  

Matute, J., Polo-Redondo, Y. & Utrillas, A. (2016). ‘The influence of EWOM characteristics on 
online repurchase intention. Online Information Review,  40(7), 1090–1110.  

Meyer, J.P., Stanley, D.J., Herscovitch, L. & Topolnytsky, L. (2002). Affective, continuance and 
normative commitment to the organization. Journal of Vocational Behavior, 61(1), 20–52. 

Moon, B. (2004). Consumer adoption of the Internet as an information search and product 
purchase channel: Some research hypotheses. International Journal of Internet Marketing and 
Advertising, 1(1), 104–118. 

Nunnally, J.C. & Bernstein, I.H. (1994) Psychometric theory (3rd Ed.), McGraw-Hill, New York, NY. 
O'Cass, A. & Carlson, J. (2012). An empirical assessment of consumers' evaluations of web site 

service quality: Conceptualizing and testing a formative model. Journal of Services Marketing, 
26(6), 419–434. 

Ooi, K.-B. & Tan, G.W.-H. (2016). Mobile technology acceptance model: An investigation using 
mobile users to explore smartphone credit card. Expert Systems with Application, 59, 33–46.   

Parasuraman, A., Zeithaml, V.A. & Berry, L.L. (1985). A conceptual model of service quality and 
its implications for future research. Journal of Marketing, 49(4), 41–50  

Parasuraman, A., Zeithaml, V.A. & Malhotra, A. (2005). E-S-QUAL: A multiple-item scale for 
assessing electronic service quality. Journal of Service Research, 7(3), 213–233. 

Petty, R.E. & Cacioppo, J.T. (1984). The elaboration likelihood model of persuasion. Advances in 
Experimental Social Psychology, 19, 123–205.  

Pratt, M.G. (1998). To be or not to be: Central questions in organizational identification. In 
Whetten, D.A. and Godfrey, P.C. (Eds.): Identity in Organizations: Building Theory Through 
Conversations, pp.171–207. Sage Publications, Thousand Oaks, CA. 

Raju, P.S., Lonial, S.C. and Mangold, W.G. (1995). Differential effects of subjective knowledge, 
objective knowledge, and usage experience on decision making: An exploratory investigation. 
Journal of Consumer Psychology, 4(2), 153–180. 



52 
 

Riketta, M. (2002). Attitudinal organizational commitment and job performance: A meta-analysis. 
Journal of Organizational Behavior, 23(3), 257–266. 

Riketta, M. & Landerer, A. (2005). Does perceived threat to organizational status moderate the 
relation between organizational commitment and work behavior?. International Journal of 
Management, 22(2), 193–200. 

Sarkar, S., Khare, A. & Sadachar, A. (2019). Influence of consumer decision-making styles on use 
of mobile shopping applications. Benchmarking: An International Journal, 27(1), 1–20. 

Sasser, W.E. Jr., Olsen, R.P. & Wyckoff, D.D. (1978) Management of Service Operations: Text and Cases, 
Allyn & Bacon, Boston, MA. 

Schlosser, A.E., Shavitt, S. & Kanfer, A. (1999). Survey of Internet users' attitudes toward Internet 
advertising. Journal of Interactive Marketing, 13(3), 34–54. 

Shin, J.I., Chung, K.H., Oh, J.S. & Lee, C.W. (2013). The effect of site quality on repurchase 
intention in Internet shopping through mediating variables: The case of university students in 
South Korea. International Journal of Information Management, 33(3), 453–463. 

Seock, Y.K. & Norton, M.J.T. (2008). College students’ perceived attributes of Internet website 
and online shopping. College Student Journal, 42(1), 186–198. 

So, K.K.F., King, C., Sparks, B.A. & Wang, Y. (2016). Enhancing customer relationships with 
retail service brands: The role of customer engagement. Journal of Service Management, 27(2), 
170–193 

Solomon, M. & Schopler, J. (1982). Self-consciousness and clothing. Personality and Social Psychology 
Bulletin, 8(3), 508–514. 

Song, J.H. & Zinkhan, G.M. (2003). Features of web site design, perceptions of web site quality, 
and patronage behavior. Proceedings of Annual Meeting of the Association of Collegiate Marketing 
Educators, Houston, TX. 

Su, L., Huang, S.S. & Chen, X. (2015). Effects of service fairness and service quality on tourists: 
Behavioral intentions and subjective well-being, Journal of Travel & Tourism Marketing, 32(3), 
290–307. 

Tajfel, H., & Turner, J. (1979). An integrative theory of intergroup conflict. In Austin, W. and 
Worchel, S. (Eds.): The Social Psychology of Intergroup Relations, 33–47, Brooks/Cole, Monterey, 
CA. 

Tajfel Tajfel, H. & Turner, J.C. (1985). The social identity theory of inter-group behavior. In 
Worchel, S. and William, G.A. (Eds.), Psychology of Intergroup Relations,6–24, Nelson-Hall, 
Chicago, IL. 

van der Heijden, H. (2003). Factors influencing the usage of websites: The case of a generic portal 
in the Netherlands. Information & Management, 40(6), 541–549. 

Venkatesh, V., & Morris, M. (2000). Why don't men ever stop to ask for directions? Gender, social 
influence, and their role in technology acceptance and usage behavior. MIS Quartly, 24(1),115-
140. 

Venkatesh, V. (2006). Where to go from here? Thoughts on future directions for research on 
individual-level technology adoption with a focus on decision making. Decision Sciences, 37(4), 
497–518. 

Venkatesh,V., Morris, M.G., Davis, G.B. & Davis, F.D. (2003). User acceptance of information 
technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. 

Veryzer, R.W., Jr. & Hutchinson, J.W. (1998). The influence of unity and prototypicality on 
aesthetic responses to new product designs. Journal of Consumer Research, 24(4), 374–394. 

Wahab, S., Zahari, A.S.M., Al Momani, K. & Nor, N.A.M. (2011). The influence of perceived 
privacy on customer loyalty in mobile phone services: An empirical research in Jordan. 
International Journal of Computer Science Issues, 8(2), 45–52. 

Walther, J.B. & Tidwell, L.C. (1995). Nonverbal cues in computer-mediated communication, and 
the effect of chromatics on relational communication. Journal of Organizational Computing & 
Electronic Commerce, 5(4), 355–379. 

Wang, R.J.-H., Malthouse, E.C. & Krishnamurthi, L. (2015). On the go: How mobile shopping 
affects customer purchase behavior. Journal of Retailing, 91(2), 217–234. 



53 
 

Wang, S.T. (2016). The moderating role of consume characteristics in the relationship between 
website quality and perceived usefulness. International Journal of Retailing and Distribution, 44(6), 
627–639. 

Weinberg, B.D. (2000). Don't keep your Internet customers waiting too long at the (virtual) front 
door. Journal of Interactive Marketing, 4(1), 30–39. 

Wolfinbarger, M. & Gilly, M.C. (2003). eTailQ: Dimensionalizing, measuring and predicting etail 
quality. Journal of Retailing, 79(3), 183–198. 

Yen, C.-H. & Lu, H.-P. (2008). Effects of e-service quality on loyalty intention: An empirical study 
in online auction. Managing Service Quality, 18(2), 127–146. 

Zhang, J., Fang, X. & Liu, S.O. (2007). Online consumer search depth: Theories and new findings. 
Journal of Management Information Systems, 23(3), 71–95. 

 
 

Appendix: Adopted measurement items definition and source  
 

Table 4: Sources of Scales  

Constructs Number 

of Items 

Examples Literature 

Source(s) 

Mobile app  

attributes 

4 Perceived attractiveness 

 

• The layout of this app is attractive.  

• The colors on the app are attractive.  

• The design of this app is eye-

catching. 

• Overall, I find this app looks very 

nice. 

 van der   

 Heijden 

(2003) 

Lee, et al 

(2015) 

 

 3 Perceived Informativeness 

• This app is a good source of product 

information. 

• This app supplies relevant 

information for my purchase decision. 

• This app function is informative about 

the company’s product. 

Hausman 

& Siekpe 

(2009) 

 

Mobile-

service quality 

attributes 

8 Efficiency 

 

• This app makes it easy to find what I 

need. 

• It is easy to browse anywhere on the 

app. 

• This app enables me to complete a 

transaction quickly. 

• Information at this app is well 

organized. 

• This app loads its pages fast. 

• This app is simple to use. 

• This app enables me to get on to it 

quickly. 

• This app is well organized. 

Parasuram

an et al. 

(2005) 

 4 System availability 

• This app is always available for 

business. 

• This app launches and runs right 

away. 

 



54 
 

• This app does not crash. 

• Pages at this app do not freeze after I 

enter my order information. 

Perceived 

usefulness of 

the app 

4 • I find this app is useful. 

• The app improves my shopping 

performance. 

• The app enhances my shopping 

effectiveness. 

• The app increases my productivity in 

searching and purchasing products. 

Hausman 

and 

Siekpe 

(2009)  

Consumer-

company 

identification 

 

5 • I strongly identify with this 

company/app. 

• I feel good about being a customer of 

this company/app. 

• I like to tell others that I am a 

consumer of this company/app. 

• This company/app image fits me well. 

• I feel attached to this company/app. 

Lee et al. 

(2015) 

Homburg 

et al 

(2009) 

Intention 

to purchase 

though the app  

3 • I intend to purchase through this app 

in the near future. 

• It is likely that I will purchase through 

this app. 

• I expect to purchase through this app 

in the near future. 

Hausman 

and 

Siekpe 

(2009) 

 

 

 

 

 

 

 

 

 

 

 


