Factors Affecting Customer Engagement in Shopping Malls

The International Journal Of Management

23 Vol 6 Issue 4 (October, 2017) www.theijm.com

Factors Affecting Customer Engagement in Shopping Malls

through SoLoMo (Social, Local, Mobile) Applications

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

ISSN 2277- 5846

THE INTERNATIONAL JOURNAL OF MANAGEMENT

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



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.



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.



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.,



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.



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).



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 150 72.5% Female 57 27.5% Age Less than 18 4 1.9% 18 to 22 22 10.6% 22 to 26 73 35.3% Above26 108 52.2%

Occupation Student 74 35.7% Employed 110 53.1% self-employed 12 5.8% Home maker 10 4.8% Others 1 0.5% Country SE Asia 40 19.3% India 56 27.1% USA & Canada 37 17.9% Australia 20 9.7% Middle-East 23 11.1% Europe 27 13.0% Others 4 1.9% Apps Used Maps 179 86.5% News 154 74.4% Shopping 78 37.7% Social Networking 180 87.0% Education 72 34.8% Health 39 18.8% Gaming 124 59.9% Others 67 32.4% Time spent on mobile apps Less than 30 mins 38 18.4% 30 to 60 mins 89 43.0% Greater than 60 mins 80 38.6% Time spent on mobile gaming Nil 71 34.3% Less than 30 mins 82 39.6% 30 to 60 mins 36 17.4% Greater than 60 mins 18 8.7%

Table 1: Demographic Breakdown of Respondents (n=207) 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).



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 Composite

Reliability R Square Cronbachs

Alpha Communality Redundancy

Customer Engagement

0.724563 0.84022 0.345228 0.620956 0.724563 0.010045

Social Media Integration

0.697856 0.902018 0.85432 0.697856

Location 0.700471 0.823038 0.583506 0.700471 Privacy 0.73653 0.848206 0.374736 0.643642 0.73653 0.057694

Incentives 0.674678 0.861413 0.764052 0.674678 Ease Of Use 0.694359 0.819219 0.046821 0.564767 0.694359 0.032224

Content 0.703755 0.826088 0.579517 0.703755 Table 2: Reliability Validation of Latent Constructs

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.

Customer

Engagement Ease of

Use Incentives Location Privacy Social Media

Integration Content

DV5 0.859 0.290 0.511 0.302 0.268 0.107 0.209 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



Customer Engagement

Ease of Use

Incentives Location Privacy Social Media Integration

Content

Soc4 0.040 0.166 0.161 0.298 0.317 0.796 0.253 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

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 Social Media Integration

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 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.



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 t-values.

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<0.05) is used as a decision

criterion (Cowles 1982, Fisher 1925). The following table summarizes the results of the structural model.



Hypothesis Relationship Coefficient t-value Supported

H1 social media integration--> customer engagement -0.111 2.025 NO H2 location--> customer engagement 0.211 2.206* YES H3 privacy--> customer engagement 0.086 1.443 NO H4 social media integration--> privacy 0.174 2.588** YES H5 location--> privacy 0.459 5.969** YES H6 incentives--> customer engagement 0.364 5.381** YES H7 incentives--> privacy 0.158 2.486* YES H8 ease of use--> customer engagement 0.187 2.148* YES H9 social media integration--> ease of use 0.212 3.387** YES

H10 content--> customer engagement 0.048 0.674 NO

Table 5: Path coefficients and hypothesis testing Note: t-values > 1.96* (p< 0.05); t-values > 2.58** (p< 0.01)

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<0.01) is insignificant. This indicates that integration with social media does not positively affect the customer engagement. A look at the survey responses indicate that only 54% of the respondents were willing to receive deals that they have liked in social media. 45% of the respondents were willing to receive deals liked by their friends in social media. Only 27% of the respondents were willing to share their purchases on social media. Thus, the above values further substantiate that people are not keen of social media integration with SoLoMo applications. Moreover the path coefficient of Hypothesis (H1) is -0.111 which indicates that social media integration negatively affects the customer engagement. This shows that though social media usage is on a rise but people are not keen to receive deals based on their behavior in social media.

Hypothesis (H2) is supported with 95% significance level since the path from location to customer engagement (b=0.211, p<0.05) has a significant positive coefficient. This indicates that the location based services positively impact the customer engagement. Moreover, 62% of the respondents were willing to receive deals and promos directly pushed to their mobile phone and 90% of the respondents were willing to use maps in the mobile application to navigate to the shops of their interest.

The path coefficient from privacy to customer engagement (b=0.086, p<0.01) is not significant enough, thus failing to support the Hypothesis (H3). It implies that willingness to let go privacy concerns does not necessarily enhance customer engagement. The ‘privacy’ construct mainly dealt with questions to find out the ease with which customers are ready to divulge their location, purchasing behavior and preferences. As seen in the survey results, 67% of the respondents were willing to have their location tracked. 70% of the respondents did not mind if their usual purchases were recorded.

Both the Hypothesis (H4) and (H5) are supported with 99% significance level each as the path from social media integration to privacy (b=0.174, p<0.01) and the path from location to privacy (b=0.459, p<0.01) have significant positive coefficients. This states that integration with social media and location based services increase the privacy concerns of customers using the SoLoMo application. Hypothesis (H6) is supported with a 99% significance level because the path from incentives to customer engagement (b=0.364, p<0.01) is significant. This indicates that incentives positively impact customer engagement. Further, 72% of the respondents acceded that they will increase their shopping expense if the application rewards them in the form of discount coupons, mobile talk-time, free samples etc.

‘Incentives’ as a latent variable has a significant path to privacy (b=0.158, p<0.05), supporting Hypothesis (H7) with 95% significance level. This suggests that customers are willing to share their personal information if rewarded effectively. Hypothesis (H8) is supported with 95% significance level because the path from ease of use to customer engagement (b=0.187, p<0.05) has a significant positive coefficient. ‘Ease of use’ features positively influence customer engagement. 95% of the respondents were willing to browse for deals from any location. 73% of the respondents were also open to try card-less and cashless payment options. The path from social media integration to ease of use (b=0.212, p<0.01) is significant, thus Hypothesis (H9) is supported with 99% significance level thus indicating that social media integration positively affects the ease of use of SoLoMo applications. Hypothesis (H10) is not supported since the path coefficient from content to customer engagement (b=0.048, p<0.01) is not significant. This states that ‘content’ construct does not positively impact the customer engagement. The ‘content’ variable had questions to determine if the customers prefer deals in the form of videos, text updates or digital posters. The insignificance of the Hypothesis (H10) indicates that the form of the messages does not impact customer engagement through SoLoMo



applications. Moreover, the survey results show that most of the customers do not prefer deals and promos in the form of videos since only 25% of the respondents were in favor of video messages.

The survey responses also indicate that 39% of the respondents find it tough to select products and services from a wide range of options in a shopping mall. Moreover, 69% of the respondents feel that a SoLoMo application that guides them while shopping will enhance their shopping experience. This makes a strong case to develop SoLoMo-based mobile application for shopping malls. Figure 4 illustrates the PLS Hypothesis model.

Location

Content

Ease of use

Incentives

Privacy

Social integration Customer

engagement

H4 (0.174) H3 (0.086)

H7 (0.158)

H2 (0.211)

H1 (-0.111)

H10 (0.048)

H9 (0.212)H8 (0.187)

H6 (0.364)

Figure 4: Result of PLS structural model analysis

Note: Significant relation (), Insignificant relation (- - >) 3.1. Assessment of Fit

We also conducted a Goodness of Fit (GoF) assessment for our PLS path modeling results (Amato et al. 2004). GoF is defined as the geometric mean and average R-square values (for endogenous constructs; Tenenhaus et al. 2005).

The GoF value was calculated to be 0.42 (the average AVE value was .70 and average R-square value was .25), this is more than the cut-off value of 0.36. The base line values to validate the PLS model are GoF(small)=0.1, GoF(medium)=0.25 and GoF(large)=0.36 (Akter et al. 2011).Thus, on the basis of the calculated GoF value, our PLS calculations are valid (Wetzels et al. 2009). 4.Discussion

We used Delta model to illustrate the strategic implications of launching a customer oriented SoLoMo shopping application. The model gives a comprehensive framework to sustain and upgrade the SoLoMo shopping application. The model emphasizes the fact the customers should be at the centre of strategic decision making, further strengthening the basis of our research.

The factors examined in this model have come out of several contemporary theories and perspectives that have been considered paramount in influencing customers' decision to purchase. The results for the individuals who responded to our survey show that certain prior beliefs as established by contemporary scholarly literature were secondarily related to the dependent variable. Overall, we came to identify three factors that strongly sustain customer engagement using SoLoMo applications. They are privacy, incentives and location. This reinforced our claim that the antecedent beliefs were indeed competing.

While prior research (e.g., Pakela, 2012) underscored the importance of engaging customers using SoLoMo, our research identified contrary results indicating that integration with social media does not significantly influence customer engagement. Our results emphasize the contemporary belief presented in literature that location positively influences customer engagement highlighting the need to engage customers with context based on their current location.

Prior research highlights the importance of privacy in content sharing over social media. By the same token, our research identifies that privacy concerns negatively impact customer engagement. However, social media integration and location significantly impact privacy concerns, indicating that customers exercise caution while revealing their identity over social media or over location-aware services. Furthermore, our results show that customers acknowledge that integration with social media contributes positively to the ease of use of SoLoMo application. This underscores the belief that customers value privacy over ease of use of SoLoMo application.



While antecedent research identified the positive impact incentivizing has had on customer engagement, our research gave new insights to identify that, by the same token, and incentivizing positively impacts relinquishing privacy of identity over social media and location-aware services. Our results also indicate that customers are not concerned about the form in which content is delivered.

4.1.Implication to Industry

SoLoMo applications have opened doors for brands to engage customers on the basis of their preferences and location. In our study, we have primarily focused on understanding SoLoMo applications for shopping malls. Our study shows that people are ready to use SoLoMo-based mobile applications to guide them while shopping in a mall. Also, people are ready to use such applications for local search and for making mobile based payments. However, not many are willing to receive product and service recommendations based on what they or their friends have liked in social media. Hence, in a shopping mall, brands and retailers have to understand what people are looking for while shopping. The SoLoMo shopping application can prompt customers to answer what they are looking once they enter a mall. Based on the options customer select, relevant deals and promotions can be served. We propose that customers be asked for their choice from time to time to help guide them in the path to purchase in the most non-intrusive manner. Also, customers can be recommended to purchase products on the basis of their usual purchases. The results highlight that customers find it confusing to locate stores they are interested in shopping from in a mall. Hence, we propose that a SoLoMo based mobile application should include maps of floor plans with directions between stores. This can be done for instance by using Google Map Floor Plans (Krumm et al. 2005, Loffler et al. 2008)

Our analysis indicates that people are willing to let their purchase history be recorded. Also, people are willing to increase their shopping spend if incentivized for using the SoLoMo application. We propose that the rewards be aligned to match customers’ interests. Customers can also be prompted to answer their preferences of rewards. Our survey results show that 60% of the respondents use gaming applications suggesting that gamification of SoLoMo application may result in enhanced customer engagement.

Therefore, by taking care of the above findings in designing SoLoMo based shopping applications, shopping malls can ensure more footfalls, higher repeat rates and greater loyalty thus resulting in positively impacting their profitability. Customers will also have a delightful shopping experience thus resulting in a win-win situation for both businesses and customers. 4.2.Limitations and future research

We identify quite a few limitations of this study. First, our survey results are limited to the views of 207 respondents. The respondents were from South-East Asia, Middle-East, India, Australia, US, Canada and Europe. Despite our earnest efforts, few respondents from China, Africa and Russia filled our survey. Also, the number of responses from Japan is less than expected. Thus, we identify that the survey would have given a more holistic picture if the spread of respondents was uniform across the globe.

Second, our research was limited to six latent (independent) variables which we identified by literature research. However, identifying more sub-variables which may affect the customer engagement through a SoLoMo application in a shopping mall remains to be researched.

Third, we were constrained by the limited research on SoLoMo in high quality academic journals. This limited the scope of the hypotheses tested in the research. However, we recognize that our attempt to gather insights from disparate scholarly literature makes this paper serve as a foundation for future research on SoLoMo. Future research should endeavor to understand why customers are not willing to receive recommendations based on their persona in social networking systems.

It also remains to be researched how to have customers willingly allow authentication using social networking systems. Furthermore, research needs to unravel the reasons why customers are unwilling to share their purchases on social media. Also, future research needs to identify the best ways to incentivize customers and the role of gamification to engage customers. To excel in high technology markets, businesses need to keep innovating constantly (Dutta et al., 1999). It would be interesting to see if further research can explain how to commercialize these innovations to address the wants of customers coming to shopping malls. 4.3.Conclusion

The goal of this research was to understand the drivers of customer engagement in a shopping mall through SoLoMo applications. Investigating the subject allowed us to discuss whether businesses can leverage SoLoMo applications to enrich customers’ shopping experience and enhance value proposition. Our results offer new insights into customers’ perception towards privacy concerns and social media integration. We found that incentivizing customers eases their privacy concerns. Customers are willing to increase their shopping spend if rewarded effectively. Further, we established that contrary to common belief, customers are unwilling to share their purchases on social media websites. Our results indicate that customers are unlikely to accept offers guided by their social media activities. From a theoretical standpoint, the research applies the Delta Model to the SoLoMo framework. The study significantly contributes to the otherwise scantily researched topic of



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