Whether, How and Why Do Consumers Differ in terms of their
Usage of Search Oriented Mobile Shopping Applications?
Master Thesis
Author:
Maria Katerelou (10604111)
MSc Business Studies – Marketing track
Amsterdam Business School, Universiteit van Amsterdam Academic Year: 2013-2014
Under supervision of Dr. Umut Konuş, assistant professor of Marketing at University of Amsterdam
Second assessor: Ms. E. Korkmaz
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Contents
Abstract ... 2 Introduction ... 3 1. Literature Review ... 6 1.1. Information Search ... 8 1.2. Demographics of m-shoppers ... 101.3. Psychological attitudes and values ... 11
1.4. Benefits ... 13
2. Conceptual framework and hypotheses ... 18
3. Research Methodology ... 24
4. Data Analysis ... 26
4.1. Reliability and validity test ... 26
4.2. Descriptives ... 27
4.3. Hypotheses testing ... 30
4.4. Structural equation modeling ... 32
4.5. Regression ... 34
5. Discussion ... 37
6. Managerial Implications ... 41
7. Limitations and further research ... 42
Appendix A. Survey Questionnaire ... 44
2 Abstract
The rapid growth of mobile technology has an impact on the way consumers collect information before they conduct purchases. In order to customize their services and increase sales, companies are now focusing on consumers’ search patterns. This study investigates whether consumers use mobile shopping applications to search information before they engage in m-shopping and whether this use is influenced by demographics (age, gender and profession), psychological attitudes and values (innovativeness, hedonism, and price sensitivity), and benefits (convenience and security). Data was collected from 291 subjects. Independent-samples t-tests, chi-square tests, SEM and multiple regression analysis were applied to test the suggested conceptual model. The results of this study provide a better understanding of how consumers use search oriented mobile shopping applications in order to choose products and services online. The study showed that consumers who use mobile shopping apps to search information are innovative, seek for pleasure, enjoyment, convenience, and low prices, and are concerned with security issues. Demographics were found not to be associated with the use of search oriented mobile shopping apps. The results of this study will be useful for companies in segmenting the market according to consumers’ preferences, optimizing their marketing and communication strategies and tactics and making their mobile shopping applications more efficient.
3 Introduction
During the last decades, developments in the IT field have brought tremendous changes in the way transactions are conducted. Internet technology advancements have redefined the role of key market players, and have changed consumer response to online purchasing via electronic means. The advent of Web 2.0 and social media, and the explosive growth of mobile devices, have provided users with access to numerous products and services worldwide.
The rapid growth of mobile commerce is associated with an increasing level of consumer experience with mobile devices (Zhou et al, 2007). Many industry sources indicate that shopping through mobile phones is increasing significantly and that the continuously improved functionality of mobile devices will help this trend continue (Holmes et al., 2014). However, the widespread use of wireless technologies for transactional and business-related communications among individuals and companies does not necessarily involve financial transactions (Meso et al., 2005). In fact, 68% of smartphone owners use their mobile devices to conduct a retail search, while just 35% of them complete a retail purchase through their smartphone (JiWire, 2013). This indicates that the growth of mobile technology has an impact on the way consumers collect information before they conduct purchases.
Many studies have focused on consumer information acquisition in the pre-purchase phase (e.g. Bettman and Kakkar, 1986; Bettman and Jacoby, 1976; Capon and Burke, 1980; Schaninger and Sciglimpaglia, 1981). The acquisition, usage and transfer of knowledge are probably the most important acts across the consumption process (Hirschman and Wallendorf, 1982). Consumers seek for new information about products in order to solve present consumption problems, or to create a base of potentially useful product knowledge (Hirschman and Wallendorf, 1982). Srinivasan (1990) suggests that information search behavior is vital for researchers, because it
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affects the efficiency of the market economy. It is, therefore, interesting to investigate how this behavior is expressed through mobile devices.
This paper examines how consumers should differ in collecting information before they buy products or services, by using mobile shopping applications. Although three types of mobile shopping applications are identified, i.e. search, purchase and after sales services applications, this paper focuses on search oriented applications for several reasons. First, it has been found that there is behavioral variability in consumer information seeking (Moorthy et al., 1997; Kiel and Layton, 1981; Brandt and Day, 1972; Katona and Mueller, 1955; Newman and Staelin, 1972). Second, the manner in which consumers search for, process, and use information is a complex phenomenon that is still not completely understood (Peterson and Merino, 2003), thus it sets forth questions about the factors that influence the information search process. Third, research has shown that observing consumer information search may reveal consumer preferences (Chorus and Timmermans, 2008).
Based on literature, this paper assumes that variables such as demographic characteristics (e.g. Kumar & Lim, 2008; Hernandez et al., 2011; Mort & Drennan, 2005; Bigne et al., 2005; Kumar & Mukherjee, 2013), innovativeness (e.g. Hirunyawipada and Paswan, 2006; Donthu and Garcia, 1999; Yang, 2005; Goldsmith, 2001), hedonism (e.g. Kaul, 2007; Overby and Lee, 2006; Deli-Gray et al., 2011; Holbrokk and Hirschman, 1982; Scarpi, 2012), price sensitivity (Reibstein, 2002; Zheng et al., 2011; Degeratu et al., 2000), convenience (e.g. Childers, 2001; Ozok and Wei, 2010; Gillett, 1976) and security (Park and Kim, 2003; Gefen, 2000; Salisbury, 2001) may affect consumers’ e-shopping behavior. Although scholars have extensively studied these variables in
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of mobile shopping applications. In addition, current literature on mobile shopping applications and motivators of mobile shopping adoption proves to be limited, if not non-existent.
This gap in literature leaves space for the exploration and investigation of factors on which consumers differentiate in terms of their usage of search oriented mobile shopping applications, which is the main objective of this study. More specifically, this paper examines whether consumers use mobile shopping applications to search information and whether this use is influenced by demographics (age, gender and profession), psychological attitudes and values (innovativeness, hedonism, and price sensitivity), and benefits (convenience and security).
In pursuing this objective, this paper contributes to the marketing literature for two reasons. First, this research is the first to examine the factors that may influence the use of a particular type of mobile shopping applications. Second, the outcomes of this study may allow managers to derive preferences from revealed information-seeking behaviors, and thus to improve their marketing and communication strategies. Third, companies and developers may use the results of this study to adjust the environment of their mobile applications according to consumers’ preferences and needs.
This paper is structured as follows: the first section includes the review of relevant literature. In the second section the conceptual framework and hypotheses of the research are analyzed. The third section details the sampling, methodology and variables, while the fourth section discusses the data analysis and results. The fifth section includes the discussion on managerial implications. Finally, in the last section the limitations of this study and suggestions for future research are presented.
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1. Literature review
Mobile commerce (or m-commerce) has attracted a lot of scholar attention in the last few years, because of its exponential growth. The Mobile Marketing Association (2010) defines mobile commerce as “the one or two-way exchange of value facilitated by mobile consumer electronic
devices (e.g. mobile handset) which is enabled by wireless technologies and communication networks”. Another definition of mobile commerce includes any transaction with a direct or
indirect monetary value that is performed via a wireless telecommunication network (Barnes, 2002). Balasubramanian et al. (2002) criticize this definition by pointing that wireless communication is not necessarily conducted via mobile devices. Incorporating this criticism in their own definition, Benou and Vassilakis (2010) describe m-commerce as an activity that is a) related to a (potential) commercial transaction, b) conducted via wireless and mobile communication networks and c) uses wireless and mobile devices as user interfaces. On the other hand, Sharma (2009) widens the scope of m-commerce by defining it as the subset of e-commerce that includes all e-commerce transactions carried out using a mobile device. In his study, Chong (2013) defines m-commerce as “any transaction, involving the transfer of ownership or rights to use goods and services, which is initiated and/or completed by using mobile access to computer-mediated networks with the help of mobile devices”.
According to Tiwari et al. (2008), mobile commerce comprises individual mobile services, which include -amongst others- mobile banking, mobile information services, and mobile shopping. Obviously Tiwari et al. (2008) separate mobile shopping, which is defined as mobile purchasing of goods and services, from m-commerce. Similarly, Ozok and Wei (2010) refer to
mobile shopping as a branch of m-commerce that includes purchasing of consumer goods (retail
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to purchases of goods and services implemented with the use of internet via mobile devices such as mobile phones (smartphones) and/or tablets.
A purchase in the context of mobile shopping can be implemented via the use of internet browsers (by visiting websites) or via applications. These applications, named mobile applications or mobile apps, are developed with software designed to run on mobile devices such as smartphones and tablets (Techopedia 2010), and enable users to implement a complete financial transaction through these devices. Mobile shopping apps aim by default to the realization of sales and target a wide range of consumers, that is, an audience with great differences in terms of personal characteristics, preferences, needs and desires, and experience with technology (Benou & Vassilakis, 2010).
The use of mobile apps has shown a tremendous growth over the last decade; over 1.9 billion apps are present in the marketplace of the three dominant mobile platforms (iOS, Android and Windows) (Infocom Analysis 2013). The available applications fall under many categories, such as games, social networking, entertainment and shopping apps. It is interesting that despite the large expansion of mobile devices and mobile apps, shoppers make less than 3% of annual purchases on their mobile devices (RIS Cognizant, 2013). This may suggest that mobile shopping has still great opportunities for growth.
This paper identifies three basic types of mobile shopping apps, based on their utility and facilitation of the shopping process: applications for information search, purchase, and after sales
services. All types are considered important to test in the context of factors that differentiate their
users, however this would be beyond the scope of this study. Considering research that has shown that 82% of consumers who own a smartphone browse online for product information before buying while in-store (Google and MARC, 2013), and studies that showed that seeking for
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information on products and retailers is the most important task for consumers in the pre-purchase phase (Detlor et al., 2003), this paper will focus on search oriented mobile shopping applications. This type of mobile shopping apps allows consumers to make comparisons of products, prices, and/or read reviews for stores and products from other customers. These applications do not process purchases, but instead they facilitate the pre-purchase procedure by providing guidance to consumers. For example, the ‘BuyVia’ application prompts users to select the product categories they prefer, in order to create their personal profile. After completion, users receive personalized offers according to their preferences. All offers mention availability of products at online retailers and percentage of discount the user receives by choosing each of the options. Based on geo-location services, the ‘RedLaser’ application provides information about current deals at local
stores and allows users to save their favorite product or service categories and to compare prices between numerous online and offline retailers. Another information-search oriented mobile shopping application is ‘ShopAdvisor’. This application allows users to find products or brands
they would consider buying or would like to remember later, to add products to a personal ‘WatchList’, and to get real-time alerts when prices decline. Moreover, ‘Skyscanner’ is a renowned
mobile application that allows users to compare numerous flights from over 1000 airlines globally and to get the best offer.
1.1. Information Search
Consumer information search has been extensively studied in marketing literature. Dewey (1910) was the first to introduce the five stages of consumer’s buying process, which is depicted
in Figure 1. Information search is defined as the consumer’s effort to seek, identify and assess information sources in internal (internal search) and external business environments (external
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search), in order to make a buying decision (Dewey, 1910). Internal search concerns the buyer’s previous exposure to and experience with a product, service or a brand. By definition, internal information search includes memory and occurs prior to external information search (Peterson and Merino, 2003). External search regards the acquisition of new information from a variety of marketer controlled and uncontrollable sources, such as commercials, magazines, friends and relatives, independent testing reports and the like (Brown, 1988).
Consumers’ information search behavior precedes all purchasing and choice behaviors (Peterson and Merino, 2003), thus its understanding is critical for firms’ strategic decision making (Moorthy et al., 1997). Scholars have focused on various aspects, such as the effects of consumers’ previous beliefs on search strategies (Moorthy et al., 1997); the interrelated behaviors of information seeking (Kiel and Layton, 1981); the association of types of information about products and services with the purchase behavior (Nelson, 1970); and the role of the Internet in the formation of consumers’ information search patterns (Peterson and Merino, 2003). Teo and Yeong (2003) suggest that while consumers move online to make purchase decisions, the cognitive and social context of decision-making changes in such a way that is yet only partially understood. With regard to information search through online media, Ratchford et al. (2001) found that a) the increased access to the Internet and the skill at using it increase the amount of information acquired by consumers, and b) consumers exhibit different behaviors of information search, based on their skills of using the Internet and their access to it (Ratchford et al., 2001). In addition, Chorus and Timmermans (2008) showed that observed information search concerning an uncertain attribute
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of a good can sufficiently estimate consumer preferences regarding this good. This study aims to explore the application of these findings in the mobile shopping context, and more specifically regarding the use of search oriented mobile shopping applications.
1.2. Demographics of m-shoppers
Mobile shopping apps may be numerous, but do we know who uses mobile shopping? According to Pew Research Center (2012), the use of applications is more popular among people aged between 18 and 29 (65%) and 30-49 (53%), and with a high level of educational background in the US. A research by uSamp (2012) shows that men engage in mobile shopping more (45%) than women (34%). Another study about mobile shopper’s profile has shown that 60% of male
respondents spent more than an average of $249 compared to 475 of female participants (Adobe, 2011). In a more narrow scope, Bigne et al. (2005) describe the profile of the average Spanish M-shopper (i.e. the customer who engages in purchases via m-commerce) as men and women aged between 14 and 24, that belong to middle class and are mainly residents of rural areas. Another study exhibits greater mobile usage diffusion among older and educated men in Rwanda (Blumenstock et al., 2010). Towards the same direction, Wu and Wang (2005) show that online consumers tend to be older, well-educated and with higher income.
The current literature on mobile shopping focuses mainly on either technical details (Cyr et al., 2006; Barnes, 2002;) or consumer behavior-wised issues (Lu & Su, 2009; Mahatanankoon et al., 2005; Karaatli et al., 2010; Wu & Wang, 2005; Yang, 2012; Chen et al., 2011; Childers et al., 2001), missing the examination of the way consumers use mobile shopping applications as part of their m-shopping behavior. Hernandez et al. (2011) examine e-shoppers’ age, gender and income as moderators of online shopping behavior, concluding that they do not condition the
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behavior of the experienced e-shopper. It is interesting to investigate whether there is a similar effect of sociodemographics of m-shoppers. Serenko et al. (2006) notice that age, gender and income moderate perceptions and behavioral outcomes regarding mobile phone services. Hence, age and gender are indicated to affect consumer behavior. Kumar and Lim (2008) find that different generations have different perceptions of and expectations from mobile services, but their study is limited to the examination of mobile service loyalty decisions. Karaatli et al. (2010) found that mobile services may affect consumers’ decision-making at different stages, thus leaving space
for further exploration of mobile shopping patterns.
1.3. Psychological attitudes and values
Innovativeness. Innovativeness is defined as the “degree to which an individual is relatively
earlier in adopting new ideas than the average member of his/her social system” (Roger &
Shoemaker, 1971). Similarly, Hirunyawipada and Paswan (2006) define consumer innovativeness as the tendency to willingly embrace change, try new things and buy new products more often and more quickly than others. An innovative shopper is one who feels more comfortable and is more accepting of new technologies, without asking for proof of their performance (Kumar and Mukherjee, 2013). Therefore, an innovative consumer is more likely to have a positive attitude towards adopting innovation than a non-innovative consumer (Yang, 2005). Many scholars have examined the role of innovativeness on consumers’ perception and adoption of new technologies and online services. Donthu and Garcia (1999) found that the Internet shopper is a convenience seeker who is innovative and more impulsive and variety seeker than the non-Internet shopper. Goldsmith (2001) and Citrin et al (2000) found that internet shopping innovativeness is positively correlated with more frequent online shopping. Moreover, Limayem et al (2000) found that
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personal innovativeness has both direct and indirect effects, mediated by attitude, on intentions of online shopping. Also Blake et al. (2003) found that consumer – and more specifically, internet shopping- innovativeness influences online purchasing, and is related to the variety of product classes bought online, both in regard to purchasing and seeking information online. Therefore, the indication that innovativeness plays an important role in consumers’ engagement in online
shopping leaves space for this paper to hypothesize that innovative consumers may also adopt a positive attitude towards mobile shopping.
Hedonism. Hedonism comes from the Greek word hedone, which means pleasure,
enjoyment or delight (O’Shaughnessy and O’Shaughnessy, 2002). Overby and Lee (2006) define hedonic value as ‘an overall assessment (i.e. judgment) of experiential benefits and sacrifices, such as entertainment and escapism’. Towards the same direction, Kaul (2007) links hedonic value or ‘hedonism’ to the aesthetic and experience-based subjective aspects of consumption, in terms of
regarding mundane products as rich symbols. In a consumption context, hedonism or hedonic value refers to the ‘sense of pleasure’ associated with shopping (Kaul, 2007). Holbrook and Hirschman (1982) describe consumers’ hedonic values as ones deriving from enjoyment and
pleasure with regard to the shopping process. Many scholars highlight the importance of hedonism in the online shopping behavior. For example, Wolfinbarger and Gilly (2001) examined differences in motivation between goal-oriented and experiential shopping. The authors found that experiential online shoppers prefer to buy online for fun, excitement, and bargain hunting. Other scholars see that hedonic consumers may enjoy buying online because of the unique features of this channel and because of the fun and curiosity that e-shopping offers (Scarpi, 2012; Dall’Olmo-Riley et al., 2005). Moreover, Ono et al. (2012) suggest that intention towards browsing through mobile devices in online stores is positively affected by hedonic motivations such as adventure. In
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contrast, Kim and Han’s (2011) study findings revealed that utilitarian value, instead of hedonic
value, is the only factor that influences adoption intention of mobile data services. Considering these studies, it is interesting to examine whether and how hedonism affects consumers’ behavior in a mobile shopping environment.
Price sensitivity. Price is considered one of the most important factors that influence
consumers’ choices (Reibstein, 2002). Price sensitivity refers to the change of the consumer demand resulting from the rise or fall of price (Low et al., 2013). Another definition of price sensitivity from Zheng et al. (2011) regards the degree to which a customer tolerates price increases for a specific product in terms of economic and psychological traits. It is a fact that the Internet has rendered consumers able to search thoroughly and detect the lowest prices among products and services. Beldona et al. (2005) assert that low prices are considered one of the reasons as to why people buy online. Brynjolfsson and Smith (1999) found that online prices are lower than those in physical stores. Regarding the profile of online shoppers, Degeratu et al. (2000) found that people who tend to buy online may not be as price sensitive as the general population. The authors conclude that price sensitivity is higher online, but this is due to online promotions being stronger signals of price discounts. Moreover, Pagani (2004) found that money savings deriving from multimedia mobile services seem very important for young people aged 18-24. Although scholars have investigated the importance of price sensitivity in the context of e-commerce, there is no relevant literature in the context of mobile shopping, from an application perspective.
1.4. Benefits
Convenience. Convenience is manifested by the opportunity to shop at home 24/7 days a
week (Childers, 2001). It represents the device being continuously at the user’s disposal and easy to use (Ozok and Wei, 2010). Another definition of shopping convenience includes the time, space
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and effort saved, in terms of placing and cancelling orders, returns and refunds, and timely delivery of orders (Gerht et al., 1996). Gillett (1976) suggests that convenience is the primary motivating factor in consumers’ decision to buy at home, and the major strength of modern in-home retailing.
Reynolds (1974) also supports this view, by stating that convenience is an evident benefit that consumers may anticipate when engaging in in-home buying. Convenience has been reported as the primary reason for shoppers to shop on the Internet (Forsythe and Shi, 2003). Likewise, Rohm and Swaminathan (2004) suggest that the online shopper may be motivated by the convenience of placing orders online regardless of the location and time. Li et al. (1999) conclude that consumers who value convenience are more likely to buy on the Web, while those preferring experiencing products are less likely to buy online. From a mobile shopping point of view, Chen et al (2011) suggest that communication facilities within m-commerce are key applications for the delivery of convenience to consumers. Since convenience shoppers usually select a shopping channel because of the time or effort savings it offers (Heitz-Spahn, 2013; Clarke and Flaherty, 2003), it is interesting to investigate whether convenience is actually a motivator of m-shoppers.
Security. Despite advances in online security mechanisms, consumers have still concerns
about disclosing their private and financial information (Park and Kim, 2003) and using an impersonal transaction medium such as the Web for secure transactions (Swaminathan et al., 1999; Gefen, 2000). Users might have more concerns on privacy and security issues using m-commerce given that data is transferred wirelessly, thus making interception of data much easier (Chong and Chan, 2012). Lack of consumer-perceived security and trust in vendors and payment systems is considered to inhibit the adoption of electronic and mobile commerce transactions (Siau et al., 2004). Flavián and Guinalíu (2006) define perceived security as “the subjective probability with which consumers believe that their personal information (private and monetary) will not be
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viewed, stored, and manipulated during transit and storage by inappropriate parties in a manner consistent with their confident expectations”. Many studies have investigated the role of perceived security in the conduction of electronic commerce transactions. Salisbury et al. (2001) discuss about perceived web security, defined as the degree to which a consumer believes that the World Wide Web is secure for the transmission of sensitive information. Moreover, Chellapa and Pavlou (2002) found that perceived information security influences consumers’ decision in engaging in online purchases. Furthermore, McCole et al. (2010) assert that online privacy and security concerns are intertwined and when combined, may actually prevent online purchasing from taking place. This study adopts the notion that security concerns about the m-purchase phase may also have an indirect effect on users’ intention to seek for information through their mobile devices. This effect is expected to be stronger for users that engage in all phases of a purchase through the same channel (i.e. mobile shopping).
Table 1. Overview of literature.
Variable Source Description Expected outcome
Innovativeness
Kumar &
Mukherjee (2013)
Innovative consumers feel more comfortable and are more accepting of new technologies.
Innovativeness is expected to be positively associated with the use of search oriented mobile shopping applications Citrin et al. (2000) Innovativeness only partially influences consumers’ adoption of online
shopping.
Limayem et al (2000)
Personal innovativeness has both direct and indirect effects, mediated by attitude, on intentions of online shopping.
Hedonism
Deli-Gray et al. (2011)
Although consumers make buying decisions based on functional reasons, hedonic, emotional feelings also influence their choices and purchase decisions.
Hedonism is expected to be positively associated with the use of search oriented mobile shopping applications Scarpi (2012) Consumers can shop either because they seek cognitively-oriented
benefits and rationally need to involve in purchases, or because they want to be immersed in the shopping experience and pursue sensory gratification and fun, rather than efficiency.
Babin et al. (1994) Hedonism can influence unplanned, impulsive purchases. Kim and Han
(2011)
Utilitarian value, instead of hedonic value, is the only factor that influences adoption intention of mobile data services
Price sensitivity
Beldona et al. (2005)
Low prices are considered one of the reasons as to why people buy online.
Price sensitivity is expected to be positively associated with the use of search oriented mobile shopping applications Degeratu et al.
(2000)
People who tend to buy online may not be as price sensitive as the general population.
Pagani (2004) Money savings deriving from multimedia mobile services seem very important for young people (aged 18-24).
Convenience
Gillett (1976) Convenience is the primary motivating factor in consumers’ decision
to buy at home, and the major strength of modern in-home retailing. Convenience is expected be positively associated with the use of search oriented mobile shopping applications Reynolds (1974) Convenience is an evident benefit that consumers may anticipate when
engaging in in-home buying. Forsythe & Shi
(2003)
Convenience has been reported as the primary reason for shoppers to shop on the Internet.
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Rohm & Swaminathan (2004)
The online shopper may be motivated by the convenience of placing orders online regardless of the location and time
Heitz-Spahn (2013); Clarke & Flaherty (2003)
Convenience shoppers usually select a shopping channel because it offers time or effort savings.
Chen et al (2011) Communication facilities within m-commerce are key applications for the delivery of convenience to consumers.
Li et al. (1999) Consumers who value convenience are more likely to buy on the Web.
Security
Chong and Chan (2012)
Users might have more concern on privacy and security issues using m-commerce given that data is transferred wirelessly.
Security is expected to be positively associated with the use of information-search mobile shopping applications Siau et al. (2004) Lack of consumer-perceived security and trust in vendors and payment
systems is considered to inhibit the adoption of electronic and mobile commerce transactions.
Salisbury et al. (2001)
Perceived web security concerns the degree to which a consumer believes that the World Wide Web is secure for the transmission of sensitive information.
Chellapa &Pavlou (2002)
Perceived information security influences consumers’ decision in engaging in online purchases.
Demographics
Hernandez et al. (2010)
E-shoppers’ age, gender and income do not condition the behavior of
the experienced e-shopper. Age is expected to be associated with the use of search oriented mobile shopping applications.
Gender is expected not to be associated with the use of search oriented mobile shopping applications.
Different types of profession are expected to be associated with the use of search oriented mobile shopping applications. Serenko et al.
(2006)
Age, gender and income moderate perceptions and behavioral outcomes regarding mobile phone services.
Kumar & Lim (2008)
Different generations have different perceptions of and expectations from mobile services.
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2. Conceptual framework and hypotheses
Age
According to Hernandez et al. (2011), socioeconomic variables do not moderate the influence of previous use of internet, nor the perception of e-commerce. Another study has shown that the age of user has apparent effect on mobile service perceptions and loyalty decisions (Kumar & Lim, 2008). Age is also shown to moderate the relationships of prior expectations-perceived quality and customer satisfaction – price tolerance with regard to mobile services (Serenko et al., 2006). Moreover, age is suggested to be one of the variables that best predict m-commerce behavior, according to Bigne et al. (2005). Younger users generally engage in m-commerce more frequently than their older counterparts (Chong, 2013). Apparently, these findings suggest that there is a negative relationship between consumers’ age and use of electronic services. Therefore,
this paper adopts the following hypothesis:
H1a. Age of consumers is negatively associated with the use of search oriented mobile shopping applications.
Gender
Regarding gender, Okazaki and Mendez (2013) found that it moderates the relationship between interface design and ease of use regarding m-commerce. However, Bigne et al. (2005) found that users’ gender does not play an important role in the m-purchase decision. Towards the same direction, Serenko et al. (2006) examined the effect of consumers’ gender on forming
perceptions and behavioral outcomes with regard to mobile phone services, showing that this effect was very limited. Moreover, Chong (2013) showed that there is no difference between men and
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women in their m-commerce activities. Therefore, considering these findings, the following hypothesis will be tested:
H1b. Gender is not associated with the use of search oriented mobile shopping applications.
Profession
Profession is a variable that has not been sufficiently tested in previous studies with regard to potential effects on mobile commerce or mobile shopping. Kim et al. (2012) suggest that an analysis comparing students and workforce may improve our ability to understand the purchasing behavior of e-shoppers. Bhattacherjee and Sandford (2006) examined job relevance (defined as ‘the message recipient’s perceived relevance of an IT system to their work’) in relation to ELM
and found that it has a strongly positive effect on the association between argument quality and perceived usefulness. Kim (2008) showed that individuals who work exhibit greater intention to use mobile wireless technologies that are useful in the workplace. Moreover, a study of the London School of Economics and the Carephone Warehouse showed that young people between 18 and 24 years old are addicted to their mobile phone in the UK (The Carephone Warehouse, 2006). In addition, many studies have found that mobile phone ownership and frequency of use are very high among students (e.g. Mezei et al., 2007; Söderqvist et al., 2007; Punamaki et al., 2007). Therefore, this paper proposes the following hypotheses:
H1c. Being an employee is positively associated with the use of search oriented mobile shopping applications.
H1d. Being a student is positively associated with the use of search oriented mobile shopping applications.
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Innovativeness
Continuing with the psychological variables, innovativeness was found to have a positive relation with actual adoption and acquisition of novel information about new products (Hirunyawipada and Paswan, 2006). Kumar and Mukherjee (2013) also found that high personal innovativeness is positively related to perceived ease of use and enjoyment of mobile shopping. Similar findings derived from Yang’s (2005) study, which showed that innovation-adoption
variables were powerful and consistent predictors of consumers’ perceived usefulness and ease of use of mobile commerce. Moreover, Donthu and Garcia (1999) showed that innovativeness is a motivational characteristic of consumers who shop online. Citrin et al. (2000) found that domain-specific innovativeness has a direct positive influence in consumer adoption of Internet shopping. Therefore, this study proposes the following:
H2a. Consumer innovativeness is positively associated with the use of search oriented mobile shopping applications.
Hedonism
As previously stated, hedonism was found positively related to shopping values and everyday product purchases by Deli-Gray et al. (2011). Moreover, Scarpi et al. (2014) compared utilitarian and hedonic intentions of shopping online and offline and found that when consumers shop for fun (hedonic value), they enjoy seeking price bargains and spending more time shopping online. Scarpi (2012) also found that hedonism is strongly and positively associated with purchase frequency and purchased amounts in the context of online shopping. Another study that measured hedonic and utilitarian shopping values, showed that there was a strong positive correlation between subjects’ experiential shopping motivations and hedonic values (Babin et al., 1994).
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However, Kim and Han (2011) found that potential adopters of mobile data services are more likely to focus on utilitarian rather than on hedonic values to decide whether to adopt these services. Considering these findings, and in order to investigate whether hedonism indeed influences mobile shopping adoption, this paper proposes:
H2b. Consumer hedonism is positively associated with the use of search oriented mobile shopping applications.
Price sensitivity
Pagani (2004) found that price is among the most important determinants of adoption of multimedia mobile services. Brynjolfsson and Smith (2000) found that online prices are lower than those offline, thus becoming an important reason for consumers to engage in online shopping. In contrast, Chu et al. (2008) found that households are less price sensitive when shopping online than when shopping offline. However, Shankar et al. (1999) suggest that the online medium has a main effect on price sensitivity by facilitating consumers’ price search, which increases price sensitivity relative to offline shopping. Therefore, there is a need to examine if price sensitivity is related to purchases through online media, and specifically via mobile shopping applications. Consequently, this paper hypothesizes that:
H2c. Consumer price sensitivity is positively associated with the use of search oriented mobile shopping applications.
Convenience
Rohm and Swaminathan (2004) examined the typology of online shoppers and found that convenience shoppers are motivated by online shopping convenience and are less likely to exhibit
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physical store orientation. Forsythe and Shi (2003) found that time/convenience risk could predict frequency of searching online, but not the intention to buy online, since some internet shoppers do not proceed with online purchases due to hesitance towards delays or inconvenience. However, Reynolds (1974) analyzed the catalog buying behavior and concluded that for a number of catalog buyers (offline purchase) and for certain types of goods, convenience is not the primary benefit sought. On the contrary, Li et al. (1999) found that non-Web buyers, occasional Web buyers, and frequent Web buyers valued convenience in shopping more highly as their Web shopping frequency increased. On the basis of these arguments, this study suggests the following hypothesis:
H3a. Expected convenience from mobile shopping is positively associated with the use of search oriented mobile shopping applications.
Security
At their study, Park and Kim (2003) found that security perception had a positive effect on information satisfaction and relational benefit, in the context of online shopping. Salisbury et al. (2001) suggest that security is an important belief about web-based shopping, and that increased levels of perceived Web security may lead to greater intent to purchase products on the Web. Moreover, Chong and Chan’s (2012) findings show that Malaysian and Chinese users are generally
concerned about the security and privacy offered by m-commerce and do not trust transactions that lack physical contact. In addition, McCole et al. (2010) found that trust in the Internet is decreased when consumers have higher privacy and security concerns. Chellapa and Pavlou (2002) also found that perceived information security is positively related to trust in electronic commerce transactions. Therefore, by extending previous research about security in a mobile commerce context, this paper assumes the following:
23 H3b. Perceived security is positively associated with the use of search oriented mobile shopping applications.
The current literature examines many other factors that may influence the m-commerce adoption or the use of online media in general. For example, Bigne et al. (2005) found that social class, experience of e-shopping and Internet exposure play an important role in m-commerce adoption. Chong and Chan (2012) showed that trust and variety of services have a significant and positive impact with consumer decisions to adopt m-commerce in Malaysia, while cost and social influence have a negative effect. In addition, Degeratu et al. (2000), found that brand name and visual cues such as product design have a higher impact on online choices. Moreover, it was found that visual design aesthetics significantly impacted consumers’ loyalty intentions towards a mobile service (Cyr et al., 2006). Despite the fact that these factors are also important, their examination is beyond the scope of this study. Therefore, these indicative factors, namely ‘other factors’, are presented in red, at the right of the following conceptual model, because they are not examined in the context of this research. The focal variables that are examined in this paper (i.e. demographics, psychological values and expected benefits) are shown in green at the left of Figure 2.
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3. Research Methodology
Sample. This survey was conducted in Greece and the Netherlands. With a total population
of 27.68 million (i.e. 11.28 million in Greece and 16.4 million in the Netherlands), a 5% margin of error, and a confidence level of 90%, the suggested sample size is estimated around 270 respondents, by using the following formula:
n = N*X / (X + N - 1)
where X = Zα/22 -*p*(1-p) / MOE2, and Zα/2 is the critical value of the normal distribution at a/2,
MOE is the margin of error, p is the sample proportion, and N is the population size1.
1Retrieved from http://www.select-statistics.co.uk/sample-size-calculator-proportion. Figure 2. Conceptual framework.
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This paper adopted a self-selection sampling as the most appropriate method, provided the limited time available and the nature of the research question. The self-selection sampling reduced the time necessary to seek appropriate subjects.
Data collection. Α web-based English survey questionnaire was developed to test the
hypotheses of this study. Subjects from Greece and the Netherlands were invited to participate in the survey by email and through social media (Facebook and LinkedIn). During a period of 10 days, 3.439 consumers were invited to fill in the survey. From a total of 343 responses, 52 responses were deleted because the respondents did not fully complete the questionnaire. This yields a final sample of 291 respondents, or an 8.4% response rate.
Pre-test. The questionnaire was pre-tested through interviews with 5 subjects, and the
identified errors were corrected and incorporated in the questionnaire. All subjects completed the survey in less than 10 minutes.
Independent Variables. The independent variables of this study are demographics,
psychological attitudes and values, and expected benefits from the use of information-search oriented mobile shopping applications. The demographic variables are measured in terms of age, gender and types of profession. Drawing on the work of Manning et al (1995), 4 items from the Consumer Novelty Seeking (CNS) scale were adopted for the measurement of consumer innovativeness (a=0.92). For hedonism, 4 items from Kim and Han (2011) were used (a=0.946). Moreover, for the measurement of price sensitivity, 4 items (a= 0.774-0.860) from Heitz-Spahn (2013) were used. Convenience was measured based on 4 items (a=0.862) adapted from Childers et al. (2001). The scale for security was based on 4 items adopted from Lallmahamood (2007) (a=0.858).
26 Dependent variable. The usage of search oriented mobile shopping applications is
measured with 19 items. The first and second item receive a Yes/No answer, and the third item is a list of possible uses of mobile shopping applications for information seeking. The rest of the items are divided across different industries, i.e. travel & entertainment, clothing and shoes, and music and books. For every industry there is a pair of items that consist of 1 item receiving a Yes/No answer, and one item scoring on a 5-point Likert scale with anchors ranging from “Never” to “Always” (see Appendix A for the survey questionnaire).
4. Data
Analysis
Statistical software SPSS 22 was used for the data analysis of this study. Less than 10% missing values were found across 9 questions of the survey, therefore HOTDECK imputation was used as the most appropriate method for values replacement. Moreover, Question 33, which comprises of 4 items from Lallmahamood (2007), was measured with a 5-point Likert scale from the author, whereas a 7-point Likert scale was used in this paper. For the conversion of the measurement scale, standardization (Z-score) was applied to the items of this question.
4.1. Reliability and validity test
Reliability test. Reliability is determined by Cronbach’s alpha (Mukhrerjee and Nath, 2003,
and Kim et al., 2010). Cronbach’s alpha coefficient was above .70, thus significant (Pallant, 2005) for all psychological values, i.e. innovativeness (a=.906), hedonism (a=.916), and price sensitivity (a=.907). Among benefits, Cronbach alpha coefficient was found significant for convenience (a=.931), and marginally significant for security (a=.711). However, by investigating the item-total correlations, one item regarding security was found very low (.056). By checking square
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multiple correlations, the item still received a very low value (.028). Therefore the item was deleted, and Cronbach alpha was improved for security (a=.881). All results are shown in Table 2.
Validity test. In order to test the validity of the constructs, a factor analysis is necessary to
be applied (Kim et al., 2010; Miyazaki and Fernandez, 2000). The first step when performing a factor analysis is to assess the suitability of the data for factor analysis (Pallant, 2005). KMO (0.897) and Bartlett’s test (p=0.000) were both significant, therefore a factor analysis was
appropriate (Pallant, 2005). Moreover, it was found that four components recorded Eigenvalues above 1, explaining 73.60% of the variance. Therefore, 4 factors were selected for rotation. Since there were no previous expectations about which variables will be associated with each other (Stangor, 2011), an exploratory factor analysis was conducted. Orthogonal rotation (varimax) was specified because it was hypothesized that the constructs are not associated. As shown in Table 3, all four components show mainly loadings greater than 0.40 (Nunnally and Bernstein, 1994), thus the questionnaire achieves the required construct validity.
4.2. Descriptives
Male respondents in this study (38.8%) were less than female respondents (61.2%). People aged between 15 and 34 account for 88.6% of the respondents. In terms of education and profession, 48.1% of the respondents hold a bachelor and 37.8% a master degree, whereas 68.4%
Table 2. Reliability analysis
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of respondents were students and 20.3% employees. In terms of country of residence, 24.1% of the respondents live in Greece, 71.1% in the Netherlands, and 4.8% in other countries2.
As for the mobile devices, 91.1% of the subjects have a smartphone, of which 36.4% are iPhone and 24.7% Samsung devices. In addition, 62.5% of the respondents do not own a tablet, indicating that tablets have yet possibilities of penetrating the market. Among tablet owners, 23% own an iPad and 13.1% an Android-based device, while only 0.7% hold Windows tablets.
2 Because the questionnaire of this study was published on LinkedIn, a very limited number of subjects outside Greece and the Netherlands also responded. Nevertheless, these responses did not affect the results significantly.
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Moreover, 90% of the respondents use applications on their smartphone or tablet in general, and 92.8% use internet connection on their mobile device(s). Additionally, among respondents who do own neither a smartphone, nor a tablet, i.e. 22 out of 291 subjects (7.5%), 52.8% would consider using mobile shopping applications to search information about products and services; 45.5% consider mobile shopping apps appropriate for information-search oriented activities; 60% see apps as innovative technology; 45% believe that mobile apps are secure for purchases; 45% would use mobile apps as a more convenient way of shopping; 55% would search via mobile apps for the
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best price; and just 35% of these respondents would use mobile shopping applications for fun. Table 4 shows the descriptive statistics of the respondent profile.
4.3. Hypotheses testing
Hypotheses H1a, H1b, H1c and H1d address whether age, gender and profession associate with the use of mobile shopping applications. The variables measured by these hypotheses, along with the use of mobile shopping apps, are categorical, meaning they receive answers such as yes/no, male/female etc. In order to test whether there is a statistically significant association between the means in two unrelated groups of categorical variables (e.g. age or gender or profession vs. use of mobile shopping apps), the most appropriate method is deemed the application of independent t-tests (Pallant, 2005). Considering that the use of search oriented mobile shopping applications was measured with multiple items (questions 12, 14, 6, 18, 20, and 22, regarding different industries, see Appendix A), and in line with Cyr et al. (2006), a set of independent-samples t-tests was applied to test hypothesis H1b. As shown in Table 5, no significant difference was found between males and females regarding the use of search oriented mobile shopping applications for tickets for concerts, theatres or other events (Q12: t(270)=-0.960,
Table 5. Independent sample t-test analysis comparing gender and use of information-search oriented mobile shopping applications.
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p=.338), travel tickets (Q14: t(270)=-0.736, p=.462), clothes and shoes (Q18: t(270)=1.076, p=.283), (e-) books (Q20: t(270)=-1.05, p=.295), and (online) music and movies (Q22: t(270)=0.589, p=.557). However, a significant difference was found between males (M=1.67, SD=0.47) and females (M=1.79, SD=0.41) who use mobile shopping applications to search information for hotels or vacation packages (Q16: t(270)=-2.102, p=.037).
Likewise, a set of t-tests was applied to test whether profession is associated with the use of search oriented mobile shopping apps. Being student was found to have a significant relationship with information search about concerts or theatres (Q12: t(270)=3.084, p=.002), travel tickets (Q14: t(270)=2.909, p=.004), hotels and vacation (Q16: t(270)=4.320, p<.001), and books and e-books (Q20: t(270)=3.476, p=.001), but no significant relationship with information search about clothes and shoes (Q18: t(270)=1.157, p=.248) and (online) music and movies (Q22: t(270)=0.506, p=.614) through mobile shopping applications. Similar results were found about employees who search for information using mobile apps. More specifically, being employee was found to have a significant association with the use of search oriented mobile apps concerning concerts or theatres (Q12: t(270)=-3.466, p=.001), travel tickets (Q14: t(270)=-3.022, p=.003), hotels and vacation (Q16: t(270)=-3.912, p<.001), and books and e-books (Q20: t(270)= -2.383, p=.018), but not concerning clothes and shoes (Q18: t(270)=-1.904, p=.058), and (online) music and movies (Q22: t(270)= -0.625, p=.533).
A Pearson chi-square test is appropriate to compare two categorical variables, each of which can have more than two categories (Pallant, 2005). In this paper, age is a categorical variable which is measured with five categories (i.e. 15-24, 25-34, 35-44, 45-55, and over 55). Therefore, a set of Pearson chi-square tests was applied as the most appropriate method to examine whether there is a relationship between age and the use of search oriented mobile shopping apps (yes/no
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answer) across the focal industries (see above). At least 3 cells had a frequency less than the expected value of 0.5 for all questions, which means that one of the basic assumption of the chi-square test was violated (Pallant, 2005). In this case, relying on the asymptotic result of the p value would be problematic, therefore the usage of Exact tests was deemed appropriate (Mehta and Patel, 2010). The exact p value based on Pearson’s statistic was not significant for Q12 (p=.482), Q18 (p=.766), Q20 (p=.214), and Q22 (p=.852). However, a significant relationship was found between age and the use of mobile shopping apps concerning search of information about travel tickets (Q14: p=.025) and vacation (Q16: p=.005).
4.3.1. Structural equation modeling
This paper replicates the method of To and Liao (2007), Kim et al. (2010), and Lu and Su (2009), by applying a structural equation modeling (SEM) analysis to compare the suggested model to the data. The SEM analysis is used here also to test the causal relationships among the focal variables (StatSoft 2013). SPSS AMOS 22 was used for the examination of the correctness of the suggested research model. The model fit is assessed on the basis of RMSEA, TLI, NFI, RFI, and IFI indices (Kim et al., 2010). As shown in Table 5, all indicators have acceptable values, therefore it is suggested that the model overall provides a valid framework for the measurement of the focal variables. Moreover, chi-square was 1006.165 with 294 degrees of freedom, significant at p=.000, thus indicating a sufficient fit with the model.
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The path coefficients from the SEM analysis are shown in Figure 2. Age (β=-.08, t(291)=2.927, p=.003), gender (β=.008, t(291)=.159, p=.874), being a student (β= .043, t(291)= -1.168, p=.243), and being an employee (β= -.017, t(291)= 2.701, p=.007) were found to have no significant relationship with the use of search oriented mobile shopping apps in total. Innovativeness (β=.19, t(291)=2.823, p=.005), hedonism (β=.252, t(291)= 3.596, p=.005) , convenience (β=.353, t(291)=4.864, p<.001) and security (β=.255, t(291)=3.566, p<.001) exhibited a significant positive effect on usage of mobile shopping apps. The results also showed that price sensitivity has a negative but weak effect (β= -.108, t(291)=1.603, p=.109) on the usage of information-search oriented mobile shopping applications.
34 4.3.2. Regression
Nachtigall et al. (2003) suggest that a well-fitting model shown by SEM analysis is not sufficient to represent a causal model. Moreover, many scholars have used structural equation modeling merely as a confirmatory analysis (e.g. Kim et al., 2012; Hartono et al., 2014; Hernandez et al., 2010; Crespo and del Bosque, 2008). For these reasons, a standard multiple regression was applied as an additional analysis. This type of regression was chosen as the most appropriate method that allows the examination of how much variance in a dependent variable can be explained by a set of independent variables as a group (Pallant, 2005). In this study, the standard multiple regression indicated the contribution of each of the independent variables in the use of search oriented mobile shopping applications. Moreover, since t and chi-square tests examined only the existence of statistically significant relationships between demographics and use of mobile shopping apps, the regression allowed the investigation of the causality suggested by hypotheses H1a-H1d.
The psychological values and attributes (innovativeness, hedonism, and price sensitivity), and the benefits (convenience and security), in addition to demographics (age, gender, and profession) were used as the independent variables. The use of search oriented mobile shopping applications was the dependent variable of the model. This variable was calculated on the basis of the scores of the items used for the measurement of the use of search oriented mobile shopping applications (questions 12 to 22).
Multicollinearity. All tolerance values were greater than .10 and VIF less than 10, thus indicating absence of multicollinearity (Pallant, 2005). As shown in Table 7, all independent variables are not highly correlated (r<.90), thus confirming that no multicollinearity issues have occurred (Pallant, 2005).
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Innovativeness (r=.388), hedonism (r=.511), convenience (r=.525), and security (r=.392) appear to have a significant positive effect on the use of search oriented mobile shopping apps, while age (r=-.178), gender (r=.048), being student (r=.224), being employee (r=.234) were found to have no significant effect (r< .3, according to Pallant, 2005). In contrast, price sensitivity (r= -.321) was found to have a negative relationship with the use of mobile shopping apps.
R2 was .369, which indicates that the model explains 36.90% of the variance in usage of
information-search oriented mobile shopping applications (Pallant, 2005). The model reaches statistical significance (df= 9, p<.001). Convenience made the strongest unique contribution to explaining the model (beta= -.291, t= -3.936, p<.001), while hedonism (beta=-.161, t=-1.829, p=.043), security (beta= -.155, t= -2.579, p=.010) and innovativeness (beta= -.136, t= -2.291, p=.023) followed. Price sensitivity (beta= .100, t=1.829, p=.068), gender (beta= -.017, t= -.315, p=.753), age (beta= -.094, t= -1.444, p= .150) and profession in terms of being student (beta= .044, t= .527 p= -.076) and being employee (beta= .113, t= 1.581, p= .115) did not contribute significantly. The results of the regression analysis are shown in Table 8.
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Considering the results from hypothesis testing in total, we can infer the following conclusions:
Table 9. Results from hypotheses testing. Table 8. Standard multiple regression analysis.
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5. Discussion
This study investigates the association of demographics, psychological values and expected benefits with the use of search oriented mobile shopping applications. Gender was found to have no association with information search, thus supporting hypothesis H1b. This finding is consistent with Bigne et al. (2005) and Chong (2013). The great diffusion, availability and accessibility of mobile devices to both sexes are probably the reasons for the occurrence of this finding. This result is not surprising, because the market is abundant with smartphones, tablets or mobile applications “especially designed for women”, meaning female consumers are a significant
target group that can help companies increase their market shares.
Although research has shown that older users of smartphones and tablets are less likely to download and use applications because of their overwhelming design and environment (Deloitte 2014), this study found that regarding information search through mobile apps, consumers do not differ in terms of age. This finding is consistent with Hernandez et al. (2011) who showed that age (among other socioeconomic variables) does not affect the perception of e-commerce. However, the result contradicts with the suggestion that age can predict m-commerce behavior (Bigne et al., 2005), and that young users engage in m-commerce more frequently than older users (Chong, 2013). This is a surprising finding, given that since most mobile shopping applications are designed by young people for young people (Deloitte 2014), a negative association between age and use of mobile shopping apps was expected. One possible explanation could be that as featured phones are getting hard to find over time, seniors are obliged to use smartphones which come with applications. In addition, many mobile shopping applications offer features and functions that facilitate daily routine tasks, thus they may be deemed as useful to older consumers as they do to young ones.
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The profession-related variables, expressed as “student” and “employee”, were initially expected to be positively associated with the use of search oriented mobile shopping applications. However, hypotheses H1c and H1d were rejected. This finding challenges perceptions that the use of mobile shopping applications is mainly linked to professional purposes. Moreover, it contradicts with Kim’s (2008) suggestion that people who work are inclined to use mobile technologies more often than unemployed users. One possible explanation could be that the information-search related items used in this paper were not profession-related, but rather of general interest (e.g. vacation, books, movies, and clothes), thus they might not be sufficient to prove any relevant association. Another explanation could be that employees consisted only 20.3% of the respondents, thus possibly affecting the results of this study. Future research should investigate the effect of different types of profession on the use of mobile apps more thoroughly.
At this point it is important to clarify that the above results regard the overall use of search oriented mobile shopping apps. Looking more specifically at the results of t and chi-square tests, one can conclude that demographics show some relationship with information search about travel tickets and vacation. This could mean that men look more frequently for information about vacation (e.g. hotels, tickets etc.), possibly because they are usually in charge of arranging holidays. Moreover, younger users of mobile shopping applications appear to look for such information more often than their older counterparts, probably because they are able to travel more often. In addition, students and employees appear to be partially associated with a higher frequency of information search about travel services (possibly because of their obligations, e.g. students travel from their home town or country to the city where their university is, and employees travel because of work), holidays (probably because both groups have to schedule their vacation in advance, due to lack of free time, compared to unemployed subjects), books or e-books (for study
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or professional purposes), and concerts and theaters (which possibly attract more these two categories of respondents, compared to e.g. older consumers or consumers who may not afford such types of entertainment).
Innovativeness is positively related with the use of mobile shopping applications, thus supporting hypothesis H2a. This finding is consistent with Kumar and Mukherjee (2013), who found that innovative users are more positive towards mobile shopping. While Citrin et al. (2000) suggest that innovativeness influences the adoption of internet shopping positively, this study extends this claim to the context of mobile shopping. This finding is not surprising, because information search is generally a simple task that does not engage the user any further, compared to the purchase process itself. Therefore, consumers were expected to exhibit a more favorable behavior towards search oriented mobile shopping applications. This could mean that different results might have occurred if this study examined purchases through mobile shopping apps. Another explanation could be that the use of applications for information search could be currently deemed an important market trend that satisfies innovative users’ need to feel different ant unique. Likewise, hedonism was found to be positively associated with the usage of mobile shopping applications, thus supporting hypothesis H2b. Consumers who value enjoyment, pleasure and fun, appear to be more open and positive towards using mobile shopping apps to search information about products and services. This result confirms Scarpi et al.’s (2014) findings
that consumers who engage in shopping for fun, enjoy seeking products and spend more time online. In line with Childers et al. (2001), this finding could also be interpreted as an evidence that hedonic aspects of new media are very important to consumers. A mere technology-oriented perspective that establishes shopping media as ‘cold information systems’ is not sufficient
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consumers’ attitudes. This is very evident in the app marketplace. Most applications are
commercially attractive, with a strong focus on design and aesthetics. Therefore, consumers probably download and use applications because they offer a more pleasant environment, and a more exciting experience.
Contrary to the expectations of this study and unlike other studies which have shown that consumers are less price sensitive when shopping online than when shopping offline (Chu et al., 2008), the results showed that price sensitivity is negatively associated with information search through mobile shopping applications. This is consistent with Shankar et al. (1999), who suggest that consumers become more price sensitive when shopping online, because they are able to look for bargains more extensively. One possible explanation, in line with Degeratu et al. (2000), could be that price sensitivity is higher online because more offers and discounts are available through online media, thus consumers are able to detect and choose products or services with the lowest price. Alternatively, the global economic may have urged consumers to be currently interested in lower prices and/or bargains more intensely than a few years ago.
As suggested by hypothesis H3a, and in line with Li et al. (1999), convenience was found to be positively related to the use of mobile shopping applications. Consumers deem mobile apps as a convenient, facilitating medium through which they are able to reach the desired information about products and services. This finding was expected, because search oriented mobile apps offer to users the opportunity to search information online whenever and wherever they wish, even if this does not necessarily predict their intention to engage in mobile shopping (Forsythe and Shi, 2003). It is interesting that convenience was the variable that contributed the most to the suggested regression model, thus indicating the importance and role of this benefit to consumers’ choices.
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Finally, this study showed that security influences the use of mobile apps positively, thus supporting hypothesis H3b. Security is still an important matter that concerns consumers who use mobile shopping applications. This finding is consistent with Park and Kim (2003), and Chellapa and Pavlou (2002), who showed that perceived security of online media leads to more trust of electronic commerce. It is true that many search oriented applications require personal data, such as email, gender, and address, in order to customize their services and provide a more personal experience to consumers. Users are generally expected to be reluctant with such requirements in the context of online environments, therefore this finding is not surprising. Consumers feel more comfortable when using mobile shopping apps which they perceive as secure and trustworthy, even if these applications facilitate merely information search.
6. Managerial implications
The results of this study provide a deeper understanding of the profile of consumers who use search oriented mobile shopping applications. The finding that demographics do not necessarily generate any information search patterns implies that mobile shopping application ads targeting at different demographic groups might not be very efficient. Instead, marketers should focus on consumers’ search habits in order to promote ads that tie with these habits. This paper showed that a consumer who uses search oriented mobile shopping apps is innovative and seeks pleasure, convenience and low prices. These dimensions provide managers and marketers with a more comprehensive understanding of antecedents that may have an impact on consumers’ willingness to use mobile shopping applications.
Another important implication for managers is that new strategies are needed to change consumers’ perception of the security of mobile shopping applications. The finding of this study,
