Factors Affecting Customer Engagement in Shopping Malls through ...

ISSN 2277- 5846 The International Journal Of Management

THE INTERNATIONAL JOURNAL OF MANAGEMENT

Factors Affecting Customer Engagement in Shopping Malls through SoLoMo (Social, Local, Mobile) Applications Dr.Anna Tarabasz Assistant Professor, S P Jain School of Global Management, Dubai Nitin Patwa Assistant Professor, S P Jain School of Global Management, Dubai Dr.Kirti Khanzode Assistant Professor, S P Jain School of Global Management, Dubai Ankit Chaudhary Post Graduate Scholar, S P Jain School of Global Management, Dubai Neha Jain Post Graduate Scholar, S P Jain School of Global Management, Dubai Sandipan Basu Post Graduate Scholar, S P Jain School of Global Management, Dubai Soumyadeepa Dhar Post Graduate Scholar, S P Jain School of Global Management, Dubai

Abstract: The domain of SoLoMo ( Social, Local, Mobile) no longer remains un-researched. Through a systematic approach using interviews, observations, and literature review, we had identified the drivers of customer engagement in shopping mall using SoLoMo applications. This paper contributes to the research on SoLoMo by drawing attention to the importance of customer engagement in deciding on the features of an ideal SoLoMo shopping application. It delineates the relationships among privacy, social media, location, incentives and ease of use of SoLoMo shopping application thus providing better insight for marketers, brands and retailers to devise the right strategy for mobile engagement of customers. Keywords : SoLoMo applications, privacy, social media, location, incentives, ease of use 1.Introduction With the omnipresence of smart-phones, there has been an increased interest in understanding the changing face of mobile marketing in academic literature. A Microsoft (Microsoft Tag, 2012) report mentions that out of 4 billion mobile phones in use all over the globe, 1.08 billion of them are smart-phones. The report also underscores that by 2014 the total number of mobile phone internet users will take over the number of desktop internet users in the world. With the increasing smart phone penetration, brands are gradually competing to reach the target customers using the mobile medium (Chandon, 2009). Academic literature, especially in the field of mobile marketing has mainly focused on content sharing (Sultan et al., 2008), personalization (Chung et al., 2009 and Murthi et al.,2003), social media (Ghose et al., 2011) and local search (Sultan et al., 2008) as disparate entities. Early academic work defined mobile marketing as selling goods and services using mobile technology (Hosbond et al., 2007). While more recent research has shown that mobile marketing is more than just buying and selling over mobile medium, it is indiscriminately dependent on allowing customers to create and share content based on

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The International Journal Of Management physical location and engaging customers in a meaningful way with locally relevant content and local search options (Sultan et al., 2008) along with understanding customer persona using social media (Ghose et al., 2011). In recent years, the extensive development in the field of social media, location based services and ubiquitous mobile technology has given rise to the concept of SoLoMo. Pakela (2012) explains SoLoMo as the convergence of Social, Local and Mobile platforms that can be used for effective brand communication. The same is defined by (Heng-Li & Shiang-Lin (2017) as emerging mobile services, combining different software and hardware techniques, enabling users to obtain location-based information at any time and place as well as exchange, interact, and communicate messages with other people in real time. Our extensive literature review indicates that, so far, wide-ranging scholarly research on SoLoMo has not been done, yet this topic is becoming a trending for digital marketing according to non-academic sources of information Moreover, little research has been carried out on the various contexts where better brand-customer relations can be built using SoLoMo. Prior academic journals (Balasubramanium 1998, Choudhary 2008) have talked about the growing competition between shopping malls and online retailers. However, a few academic literatures (Forman 2009, Rigby 2011) propose that a lot of customers will prefer traditional brick-and-mortar shopping if the engagement quotient of the shopping malls is enhanced by adoption of digital technology. Thus, our objective is to focus on understanding the implications to customer engagement in a shopping mall through SoLoMo applications. Our study will emphasize where retailers and brands should expend resources for seamless mobile engagement of customers to positively impact the bottom line of their business. The research has been conducted in three phases. First, a secondary research was conducted on the web and literature from reputed journals was reviewed. The qualitative research was conducted to identify the factors affecting customer engagement in a shopping mall through SoLoMo applications. Second, a primary research was conducted through interviewing and surveying mobile marketing experts, retailers and shoppers in shopping malls. Third, the data analysis was done using SmartPLS2.0M3 software. 2. Materials and Methods 2.1. Evolution of SoLoMo Mobile marketing is a channel that offers direct communication with customers, anytime and anywhere (Scharl et al. 2005). The first instance of Mobile marketing can be traced back to the days when SMS was used as an advertising medium. Merisavo et al. (2007) indicated that by 2004, more than half of direct marketers and marketing agencies in Europe’s most matured markets such as UK and Finland had adopted SMS as advertising medium. By 2010, we saw that besides the growth of mobile telephony, there was an extensive use of SMS leading to an exponential growth of wireless data services opening up a whole new medium of SMS marketing (Kim et al., 2010). The continuous advances in technology like miniaturization of computer, higher processing power, increased bandwidth of communication etc have made ubiquitous computing a reality in the form of smart phones (Yoo, 2010). Thus, marketers have increasingly used new applications and services linked to mobile phones for brand communications, such as multimedia messaging (MMS), games, music, and digital photography (Merisavo et al.2007). Balasubramanian (2002) observes that business gurus have predicted a seamless mobile world where commerce happens anywhere and anytime. The advent of e-commerce did not change the purchasing behavior of customers as much as the advent of m-commerce has changed with the spurt in smart devices such as smart phones and tablets. Pura's (2005) survey on location-based SMS services mentions that behavioral intentions to use mobile services is strongly influenced by context and monetary value of the content delivered. Kaasinen (2003) defines context aware system as one that provides relevant information and services to the user where relevancy is determined by the task the user performs. Moreover, Merisavo et al.(2007) and Kaasinen (2003) mentioned that with location-based mobile services, the location of a single customer at a given time can be identified and mobile advertising can be made contextually valid (e.g., a dinner offer when passing by a favorite restaurant in the evening), which in turn can provide more value to the customer. Nichols (2009) observed that data from mobile device users in an area can be aggregated by location based services, maintaining anonymity of users. Nichols (2009) has also underscored the point that mobile location-based social networking will become important in years to come. Beach et al. (2008) observed that people are increasingly interested in finding out products and services around them. This finding makes it more pertinent for marketers to develop repository of location specific information and make it available to the customers on demand. Beach et al. (2008) also focused on finding out ways in which online social networks can be harnessed using mobile devices in local contexts that involve social interaction – a context which is currently being referred to as “SoLoMo”. Pakela (2012) observed that customers are increasingly pre-occupied with their smart devices. So, it becomes paramount for brands to understand how to capture the attention of customers using smart devices and new technologies which customers are increasingly using to make purchase decisions. Hence, it is imperative to understand how social, local and mobile factors influence customers’ purchase decisions. As shown in Figure 1, SoLoMo is represented as the convergence of Social, Local and Mobile media.

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The International Journal Of Management

Location

SoLoMo

Figure 1: SoLoMo SoLoMo is likely to set new standards for personalization of services. Personalization is identified by Chung et al. (2009) as essential to track customer’s behavior and build effective brand communication. Literature on internet marketing proposes that personalization process consists of three stages: (1) understanding customer preferences, (2) matching offerings to customers, and (3) evaluating the process of understanding and matching (Murthi et al., 2003). Empirical studies have shown that personalization can reduce customer search costs and enhance customer loyalty, which translates to enhanced profitability (Liu et al., 2010). Sheth et al. (2000) defined customer centric marketing as understanding and satisfying the needs and wants of individual customers rather than those of market or market segments. We believe that marketing based on SoLoMo applications is highly customer centric. With personalized deals related to restaurants, theme parks, super markets and shopping malls, SoLoMo has great potential to transform the face of mobile marketing. 2.2. Why SoLoMo applications for Shopping Malls? Dommermuth (1967) regarded that the term shop refers to looking for goods and services from various retailers. Over the past few years, the way customers shop has changed dramatically. Nowadays customers, bombarded with tons of information and plethora of options, often struggle to choose the products and services that best suit their needs (Davenport, 2011). Mertes (1949) stated that changes in buying habits of customers have led to evolution of retailing. A century and a half ago, the growth of big cities and advent of railroad networks made it possible for customers to shop in department stores. Fifty years later, with the advent of automobiles, shopping malls dotted the cities and the suburbs posing a challenge to city-based department stores (Rigby, 2011). A shopping mall consists of a large bunch of retail stores closely located under one roof often consisting of highly substitutable competitors located in close proximity to each other (Vitorino, 2012). Davenport (2011) mentions that customers often face difficulties while shopping in a mall such as in finding the location of stores, information of stores providing deals or discounts. It is also a very daunting task for shoppers to locate stores that have issued latest products and to conduct price comparisons. Customers often find it difficult to spot products and services that will best meet their requirements. Sometimes customers have no option but to choose from the alternatives available. While other times, it is possible that they choose none of the available alternatives (Parker, 2011). Academic literature by Balasubramanium (1998), Choudhary (2008) and Rigby (2011) has highlighted that besides local competition, retailers are directly competing against remotely located direct marketers like deal sites, e-tailers etc. such as Amazon. This is because, unlike traditional retail outlets, the online retailers gather large amounts of information about their visitors and use the information to enhance a visitor’s experience by providing personalized information or recommendations (Liu, 2010). However, prior academic literature has highlighted that some features of traditional retailers can be suitably leveraged to compete with the growing online retailers. Kim (2003) discusses that in brick-and-mortar businesses, trust is based on personal relationships and one to one interactions between buyer and seller whereas in online shopping the issue of trust building becomes more critical. Moreover, Watson (2004) says that many online initiatives have failed as customers are unwilling to shop online as it does not allow users to touch and feel the products. Rigby (2011) has further substantiated the point that customers aspire for the best of both physical and digital worlds. On one hand, they want the benefits of shopping in a store such as the ability to touch products and experience personal service. On the other hand, they want the facility to search instantaneous information, compare prices, and browse reviews and recommendations from social media. Rigby (2011) further observed that retailers must now resort to digital media intelligence services to gauge the effect of marketing campaigns via digital channels such as mobile applications, websites, e-mail and social networks. Digital technology can essentially enable delivery of customized recommendations using inbuilt recommendation algorithms, eliminate checkout lines and give access to customer’s purchase history.

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The International Journal Of Management We believe that effective use of SoLoMo applications in shopping malls can bridge the present gap between customers and retailers. These applications will endow the customers with a particular shopping mall specific deals and promotions based on their needs and preferences. SoLoMo will power retail brands with ample scope to reach customers through innovative mobile marketing campaigns by creating high value propositions, enabling greater personalization, and providing easier information management. Mangelsdorf et al (2011) mentions that customers can now access the web while on the go and turn their commuting time or waiting time to searching time or shopping time. Customers choose channels by considering the relative advantage of channels at two stages of the purchase process: information gathering and transaction execution (Choudhury et al., 2008). Tailoring information for customers such that it matches their current location and context is paramount not only to beat competition but also to build long term relationships with customers. In customer marketing, customizing marketing activities have been regarded highly as a valuable marketing strategy (Rossi et al., 1996). Sultan (2005) very aptly referred to this whole new array of marketing applications on smart devices as “brand in hand” indicating smart devices as a branding tool for delivering marketing information directly in the hands of people while they are shopping, watching a sporting event, commuting, working or doing household chores. According to Huber (2015) geolocation technologies, have become mass-efficient in the course of the success story of the smartphone, have further developed local commerce, allowing, for example to locally and individually adapt advertising. They have impacted positively the digitalization of stationary commerce. Studies on personalization have considered it vital to serve personalized content to customers in a timely manner. Ho et al. (2011) observed that by and large customers prefer to be presented with content as early as possible so that the process of selection is eased. Watson et al. (2002) predicted that in the future customers can stay connected using a universal smart device irrespective of their location. As products become highly commoditized, firms should bring in service innovations to gain competitive advantage (Chesbrough, 2011). Mobile marketing activities pay maximum dividends when introducing services rather than customer or industrial goods (Dickinger, 2004). Impulse purchase behavior among customers is seen in low value and low involvement product categories. (Kannan, 2001) proposed that impulse purchase behavior is shaped by availability and accessibility of products. The frequency of low value, low involvement impulse purchases are likely to be high in wireless environment such as that offered by smart devices. However, while significant body of research have examined the importance of social media, location based services and mobile marketing in silos, none of the academic literature has woven together the convergence of all the three platforms in the context of engagement in shopping mall. “Engagement” is being seen as the new effectiveness parameter for innovative brand communication (Gambetti, 2010). Our paper is among the first in the emerging literature to research on the factors influencing customer engagement through SoLoMo applications. The study will help retailers in a mall to increase the footfall in their stores and enrich customer’s shopping experience by using SoLoMo applications. 2.3. Research Framework and Hypothesis Development SoLoMo has the potential to set new paradigm in customer centric marketing. Accordingly, we applied the delta model to devise a customer focused approach to recommend features for SoLoMo shopping applications that strategically fit business needs. Delta model positions customers at the center of strategizing unlike other conventional models which are more focused on competitor analysis (Mangelsdorf, 2009). Michael Porter appreciated Delta model as the most influential strategic framework to device best product strategy (Hax, 1999). The model explains that a SoLoMo based shopping application can be aimed at achieving high level of customer engagement by providing personalized offers and easy to use interface. It suggests that the proposed SoLoMo application is highly differentiated as it is specially focused towards enhancing buying experience in a shopping mall. Delta model explains how incentives can help businesses in improving customer bonding. Value proposition can further be enhanced by focusing more on customer engagement and feedback which will drive profitability. The model examines four execution strategies: business strategic agenda, innovation, operational effectiveness and customer targeting. It explains the significance of identifying shoppers’ preferences to implement our strategic objective. Figure 2 graphically explains the application of delta model in our research. The independent variables that affect customer engagement in shopping malls using SoLoMo applications are social media integration, location based services, privacy, incentives, ease of use and content. These variables have been derived after going through a number of high quality academic journals that are directly or indirectly related to SoLoMo. These variables were further validated by discussion and interview with several industry experts in the field of mobile application development and mobile marketing. 2.4. Social Media Integration The emergence of global and interactive social networking websites is creating immense impact on customers’ buying behavior (Dutta, 2010, David 2004). Social media enables unobstructed interaction among friends and peer group. Comprehending the online activities of customers facilitate businesses to devise an effective marketing strategy (Katona et al.,

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The International Journal Of Management 2010). Though the phenomenon of expanding one’s social sphere is impacted by many cultural and social aspects (Humphereys, 2010), prior research shows that members of social media platforms have a tendency to reach out to extend their social group. These social media groups have allowed marketers to get a competitive edge (Wang, 2007; Bampo et al., 2008) as it helps in targeting numerous customers (Ansari 2011, Hanaki et al. 2007) using customized advertisements (Bucklin et al. 2010, Skiera 2011). Zhu (2010) pointed that customer reviews on social media act as a great stimulant for product awareness. As more and more customers are relying on the information provided by such forums these social networks can have a strong positive effect on the user buying behavior (Moe 2011, Bucklin et al. 2010 and Ghose 2011). Another insight is that customers rely more on the opinion of the people who have a similar profile as theirs (Xie, 2011). Our research will assess the level of customer acceptance of social media as a platform to connect to SoLoMo applications. Further, it will posit whether marketers should be guided by the preferences shown by customers on social media websites. We will also study whether customers are willing to share their purchases on social media. Hypothesis 1 (H1): Social media integration positively affects the customer engagement in shopping malls through SoLoMo application. 2.5. Location Based Services Kaasinen (2003) defines location based services as class of services that deliver content related to a specific location. Typically location based services comprise of offerings like maps, localized discount coupons, shop locator services, yellow pages etc. Awareness about the user’s location can be used to deliver appropriate, attractive and timely content like deals and promotions. Prior research shows that customers are more interested in the products which are located nearby (Beach A. et al., 2008). For retailers, location based deals can increase the conversion rate of buyers. Customers, distracted and bombarded with information and options, often struggle to find the products or services that will best meet their needs (Davenport et al. 2010). Thus, location based services can help decrease confusion and improve shopping experience. Marketers can enrich customer’s shopping experiences by providing localized deals and offers based on their geographical location (Rao, B., et al., 2003). Though customer’s location can be determined using global positioning system devices GPS (Shugan, 2004 and Gazzard, 2011), identifying their exact location in closed spaces can be challenging. Besides, it is very difficult for the providers to figure out the context of visiting a particular location (Rao B.,et al., 2003). Location based service providers must alleviate privacy concerns of users to curb the misuse of information revealed by customers (Nichols 2009, Sinderen et al. 2006). Although location based marketing is developing, more research remains to be done on the usage of mobile applications in a shopping mall. In the research, an attempt was made to understand the customers’ willingness to accept location based deals and promotions. Moreover, the research also tried to seek the customers’ views on the integration of localized maps for smooth navigation inside a mall. Hypothesis 2 (H2): Location based services boost customer engagement in shopping malls through SoLoMo application. Privacy Privacy can be defined as the ability of an individual to control the terms and conditions under which their personal information is acquired and used (Westin 1967, Belanger 2011). Information privacy is becoming a concern for business owners, scholars, privacy activists, government regulators, and customers alike (Smith, 2011). Social media activities are creating large repositories of information about customers’ demographics and purchase habits (Rust, 2009). Marketers are increasingly using social media websites to collect information about customers and personalize offerings. Use of such personal information may raise privacy concerns among customers (Culnan, 1993), since digital content is easily transmitted and copied (Malhotra, 2004). Moreover, with multiple challenges on cyber security araising nowadays as i.e.: identity theft, phishing, cyber stalking and data leakage (Tarabasz, 2017), researches underline multiple risk aligned with the use of mobile technology (Taeksoo, Won Sang, 2014) or not even being aware of threats awaiting (Kim, Park, 2013). Customers may reveal information voluntarily or businesses can collect customer information by tracking their online behavior using cookies and click-stream analysis (Rust, 2009). Customers who are less willing to share their personal information may pose dilemma for marketers between maintaining information transparency and personalization (Awad, 2006). Prior research suggests that customers will be more willing to continue their relationship with a firm if it follows fair practices to collect information (Culnan, 1999). Besides, monetary incentives rewarded to customers can positively influence them to disclose personal information (Hui, 2007). Mobile application providers must ease user’s privacy concerns by providing secure network and encryption technologies to limit online illegal activity. Providers need to strike a balance between user’s privacy concerns and overall benefits that can be derived from location based services (Rao B. , 2003). This research will provide insights of the degree of customers’ willingness to disclose information related to their real time location, usual purchases, interests and preferences and their acceptability of deals and offers directly pushed to their mobile phones. Hypothesis 3(H3): Willingness to let go privacy concerns enhances customer engagement in shopping malls through SoLoMo application. Hypothesis 4(H4): Integration of social media with SoLoMo application increases the privacy concerns of customers. Hypothesis 5(H5): Location based services through SoLoMo application increases the privacy concerns of customers.

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The International Journal Of Management 2.6. Incentives Incentives and rewards can be a motivating factor for the customers to participate in a marketing activity. Traditionally, retailers have been exercising incentives in the form of discounts to entice customers (Zhu, 2009).Customers often get delighted by incentives such as unexpected discounts which lead to increase in their overall spend of owing to unplanned purchases (Valenzuela, 2010) . Fuller (2010) identifies monetary rewards as one of the motivators that drive customers to engage with a brand. In some cases, customers may even postpone their purchases in anticipation of future discounts (Gonul, 1996). In the past, due to absence of detailed information of target customers, discount coupons were mass distributed through traditional advertising mediums. Customized couponing can help firms in getting improved response (Rossi, 1996). Marketers must not overwhelm customers with discount offers and must educate them on how to use discount vouchers. Incentives and discount offers should be easily redeemable which can encourage shoppers to shop more (Dickinger, 2008). Customers redeem coupons only if they believe that they will get high returns (Musalem et al., 2008). Discount coupons and monetary incentives are emerging as an effective marketing tool for acquiring new customers. Coupons are gaining significant prominence on online mediums through the opening of new channels such as email and mobile applications (Kumar, 2011). Effective use of discount coupons can help businesses in getting an edge over competition (Shin et al., 2010). Moreover, discount offers can be very useful for relatively new retailers in the market. This research studies the impact of monetary rewards on the customer engagement through a SoLoMo based application in a shopping mall. Further this paper will examine the customers’ level of acceptance of incentives aligned to their interests and preferences. Hypothesis 6 (H6): Incentives have a positive impact on customer engagement in shopping malls through SoLoMo application. Hypothesis 7 (H7): Incentivizing customers for using SoLoMo application reduces their privacy concerns. 2.7. Ease of Use ‘Ease of use’ is a vital factor governing the quick adoption of a mobile application by customers (Taylor, 2011). A mobile application which requires minimum efforts to navigate and browse content is easy to use. A good user interface is paramount for ease of use. Although, a mobile application may have high utility, low perceived ease of use may hamper its adoption (Davis, 1999). Mobile applications should be customizable and content developers must consider short attention span of users while designing them (Rao S., 2007). Experts like Steve Jobs have always favored simple and easy to use products. Jobs always aimed at creating products that are user friendly and have great design (Isaacson, 2012). Application developers should limit the number of features in an application to prevent complexity. Many brands today are endangering customer relationship by overloading the application with new features, thus enhancing the application’s capability but at the expense of usability (Rust, 2006). With the emergence of new technologies, mobile phones are used as a medium to transfer money. Keeping the findings such as mobile payment (Watson, 2002) in mind, this research will focus on examining customers’ acceptability for the use of mobile phones as a payment device. Further, this research will assess the significance of SoLoMo application’s smooth interface as a factor boosting customer engagement. Hypothesis 8 (H8): Ease of use positively influences customer engagement in shopping malls through SoLoMo application. Hypothesis 9 (H9): Social media integration positively affects the ease of use of SoLoMo application. 2.8. Content Mobile phone advertisements can be delivered in the form of short messaging service (SMS) and multi-media messaging (MMS). SMS can only support text while MMS can support not only text but also image, video and audio. Literary evidence shows that video messages can affect multiple senses at once and make advertising more effective (Siau & Shen 2003, Teixeira 2010). The form of message delivery can strongly influence customers’ perception towards advertised product (Pieters 2004, Hinz 2012). With increasing fragmentation of media markets and recent advances in technology, loss of advertising effectiveness has been a great concern for marketers. Content must be informative (Ghosh, 2004) and contextually relevant (Tam, 2006). This research will examine the degree of acceptance of different kinds of messages in engaging customers in a shopping mall. Hypothesis 10 (H10): Mode of content delivery has a positive effect on customer engagement in shopping malls through SoLoMo application. 2.9. Data Collection We conducted an experimental survey on a group of 30 respondents. Subsequently, we interviewed about 15 respondents to take their feedback on the quality of the questionnaire. We attached a note at the beginning of the survey explaining the concept of SoLoMo and the purpose of our research. We covered the quantitative as well as the qualitative aspects of our research in the questionnaire. An Exploratory Factor Analysis (EFA) was then conducted on the first set of responses. We then modified the survey based on the feedback and EFA results. We used a 5-point Likert scale to design the survey questionnaire in order to measure the customers’ level of agreement (Albaum, 1997). The scale varied from “strongly disagree” to “strongly agree” which was designed on the basis of the research done by Ajzen (1991).

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The International Journal Of Management The second half of the survey captured the demographic details of the respondents. The survey was open only for smart phone users who use mobile applications. An email invitation to complete the online survey was sent to around 400 people, of which 318 responded. However, only 243 responses qualified for the survey. Another 36 responses were partially filled and hence rejected for analysis leaving 207 valid responses. A summary of the demographic characteristics of respondents is provided in Table 1. Measure

Items

Frequency

Percent

Gender

Male Female

150 57

72.5% 27.5%

Age

Less than 18 18 to 22 22 to 26 Above26

4 22 73 108

1.9% 10.6% 35.3% 52.2%

Occupation

Student Employed self-employed Home maker Others

74 110 12 10 1

35.7% 53.1% 5.8% 4.8% 0.5%

Country

SE Asia India USA & Canada Australia Middle-East Europe Others

40 56 37 20 23 27 4

19.3% 27.1% 17.9% 9.7% 11.1% 13.0% 1.9%

Apps Used

Maps News Shopping Social Networking Education Health Gaming Others

179 154 78 180 72 39 124 67

86.5% 74.4% 37.7% 87.0% 34.8% 18.8% 59.9% 32.4%

Time spent on mobile apps

Less than 30 mins 30 to 60 mins Greater than 60 mins

38 89 80

18.4% 43.0% 38.6%

Time spent on mobile gaming

Nil 71 Less than 30 mins 82 30 to 60 mins 36 Greater than 60 mins 18 Table 1: Demographic Breakdown of Respondents (n=207)

34.3% 39.6% 17.4% 8.7%

2.10. Data Analysis We used PLS (partial least squares) technique to validate the responses and to test the hypotheses. PLS modeling allows reflective and formative computations of the latent variables (Gudergan et. al, 2008). Reflective computations were done for the data set. SmartPLS 2.0M3 software was used to carry out the tests (Ringle et al. 2005).

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The International Journal Of Management A confirmatory factor analysis (CFA) was performed on the data set. Subsequently, a burnout process was performed to remove those items whose outer loadings were less than 0.7 (Chin et al. 2003). This was followed by bootstrapping process (Chin 1998) with 200 re-samples to determine the t-values. 2.11.Measurement Validation and Reliability We evaluated the reliability and consistency of our measurement with the communality values and the composite reliability scores. The composite reliability (CR) scores exceeded the recommended cut-off of 0.7 and the communality values also met the required cut-off of 0.6 (Nunally 1978, Gefen et al. 2000). AVE Customer Engagement Social Media Integration Location Privacy Incentives Ease Of Use Content

0.724563

Composite Reliability 0.84022

0.697856

0.902018

0.700471 0.73653 0.674678 0.694359 0.703755

R Square 0.345228

Cronbachs Alpha 0.620956

Communality

Redundancy

0.724563

0.010045

0.85432

0.697856

0.823038 0.583506 0.700471 0.848206 0.374736 0.643642 0.73653 0.861413 0.764052 0.674678 0.819219 0.046821 0.564767 0.694359 0.826088 0.579517 0.703755 Table 2: Reliability Validation of Latent Constructs

0.057694 0.032224

Besides communality, we used Cronbach’s alpha as another measure of reliability. Cronbach’s alpha is an important and pervasive statistics in research involving test construction and use (Cronbach, 1951). Various research papers recommend varied cut-off levels for the Cronbach’s alpha values. Robinson, et al (1991) mentions a minimum recommended value of 0.6 while Cortina (1993) recommends a minimum value of 0.70 and a maximum value of 0.95. However, Schmitt (1996) states that alpha is not an approximate index of unidimentionality to access homogeneity. Schmitt (1996) suggests that there is no sacred level of acceptance of the alpha values and in many cases alpha values above 0.5 give meaningful results. All the alpha values in our analysis are above 0.56 with the communality, reliability and average variance expected (AVE) values well above the recommended cut-off levels. Also, the AVE values for each construct were above the recommended score of 0.5 (Bagozzi and Yi 1988, Dillon and Goldstein 1984). These values prove that the model is reliable. We then proceeded to test the discriminant validity. The items under a construct should have higher loadings than other constructs (Compeau, Higgins & Huff, 1999). Each loading should be above 0.7. The following table shows the cross-loadings.

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Ease of Use 0.290

Incentives

Location

Privacy

DV5

Customer Engagement 0.859

Content

0.268

Social Media Integration 0.107

0.511

0.302

DV6

0.844

0.371

0.352

0.402

0.317

0.092

0.234

Ease1

0.356

0.854

0.322

0.272

0.219

0.158

0.159

Ease2

0.287

0.814

0.232

0.326

0.200

0.200

0.046

Inc1

0.466

0.305

0.854

0.308

0.339

0.224

0.295

Inc2

0.339

0.241

0.792

0.183

0.125

0.105

0.183

Inc3

0.425

0.267

0.817

0.213

0.291

0.128

0.264

Loc1

0.272

0.276

0.137

0.772

0.383

0.310

0.276

Loc2

0.402

0.318

0.327

0.897

0.545

0.289

0.372

Priv2

0.274

0.222

0.294

0.440

0.838

0.295

0.179

Priv3

0.313

0.210

0.269

0.527

0.878

0.331

0.285

Soc1

0.027

0.193

0.088

0.202

0.274

0.773

0.172

Soc2

0.148

0.211

0.172

0.325

0.306

0.875

0.198

Soc3

0.160

0.140

0.219

0.339

0.324

0.891

0.242


0.209

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The International Journal Of Management Ease of Use 0.166

Incentives

Location

Privacy

Soc4

Customer Engagement 0.040

Content

0.317

Social Media Integration 0.796

0.161

0.298

Con2

0.226

0.122

0.214

0.393

0.241

0.181

0.852

Con3

0.209

0.089

0.311

0.262

0.219

0.257

0.825

0.253

Table 3: Cross Loadings Next, as shown in Table 5, the square root of the AVE of each construct was calculated. The correlation between each construct is more than 0.7 and greater than the correlation with other constructs (Fornell and Larcker 1981, Chin 1998). This ensures that the discriminant validity of the model is satisfactory. Content

Customer Engagement

Ease Of Use

Incentives

Location

Privacy

Content

0.839

Customer Engagement

0.260

0.851

Ease Of Use

0.127

0.387

0.834

Incentives

0.311

0.509

0.334

0.821

Location

0.393

0.412

0.356

0.295

0.837

Privacy

0.275

0.343

0.251

0.327

0.566

0.858

Social Media Integration

0.259

0.117

0.212

0.194

0.352

0.366

Social Media Integration

0.835

Table 4: Discriminant validity of constructs Note: value on the diagonal is the square root of AVE 2.11. Structural Model We performed the hypothesis testing using SmartPLS. The path coefficients were determined using the PLS path weighing algorithm. The path coefficients (also called beta values) indicate the strength of the relationships between different variables. The following screenshot shows model’s beta values.

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Figure 2: Results of PLS factor analysis (SmartPLS snapshot) A bootstrapping re-sampling procedure of 200 samples was then performed to determine the t-values (Chatelinet et al. 2002). The t-values indicate the level of significance between the constructs. The following screenshot shows the model’s tvalues.

Figure 3: Results of PLS bootstrapping algorithm (SmartPLS screenshot) The t-values were used as deciding point to test the hypothesis. A 5% significance level (p customer engagement -0.111 location--> customer engagement 0.211 privacy--> customer engagement 0.086 social media integration--> privacy 0.174 location--> privacy 0.459 incentives--> customer engagement 0.364 incentives--> privacy 0.158 ease of use--> customer engagement 0.187 social media integration--> ease of use 0.212 content--> customer engagement 0.048 Table 5: Path coefficients and hypothesis testing Note: t-values > 1.96* (p< 0.05); t-values > 2.58** (p< 0.01)

t-value 2.025 2.206* 1.443 2.588** 5.969** 5.381** 2.486* 2.148* 3.387** 0.674

Supported NO YES NO YES YES YES YES YES YES NO

3.Results This section will discuss the result of all the hypotheses, their implications and other findings from the survey. Hypothesis (H1) is not supported since the path from social media integration to customer engagement (b=-0.111, p