Perceived Effectiveness
of Push vs. Pull Mobile Location-Based Advertising
Ramaprasad Unni, Robert Harmon
Portland State University
Abstract
The emergence of mobile phones as the leading personal communications device portends their attractiveness as a potentially lucrative media platform for marketers. This article presents initial consumer evaluations of mobile location-based advertising (LBA). LBA is a new form of marketing communication that uses location-tracking technology in mobile networks to target consumers with location-specific advertising on their cell phones. We use an experimental setting to test the effects of LBA characteristics on privacy concerns about location tracking, perceived benefits, value, and intentions to try LBA. LBA was described as a free, opt-in service from cell phone service providers. Results indicate that privacy concerns are high, and perceived benefits and value of LBA are low. LBA was relatively more effective when it becomes available upon explicit request by the consumer than when consumers are alerted to location-specific advertising or promotions for preferred product categories relevant to a specific location. Implications for marketers are discussed.
Introduction
Judged by the number of users worldwide, the cell phone is by far the most popular personal communications device for consumers and, therefore, is emerging as a coveted media platform for marketers (Yuan and Steinberg 2006). The use of mobile coupons and advertising is rapidly growing (Jakobson 2005) and marketers are developing innovative strategies to exploit this medium (Sultan and Rohm 2005). For the past several years, mobile operators (also referred to as wireless carriers) have been rolling out an infrastructure that would allow them to detect the location of individual mobile devices, such as cell phones and PDAs. The impetus for this infrastructure investment in the United States is the FCC's E911 Mandate that requires mobile operators to provide location information for mobile devices in order to facilitate emergency services (Angelides 2005; Jagoe 2003). In the coming years, location tracking technologies are expected to facilitate a wide-range of mobile services.
Location-based services (LBS) are broadly defined as services that are enhanced by and depend on information about a mobile device's position (Jagoe 2003; Mitchell and Whitmore 2003; Unni and Harmon 2003). These services include emergency and safety-related services, entertainment, navigation, directory and city guides, traffic updates, location-specific advertising and promotion, and site-based purchasing with e-wallet enabled mobile devices. They have the potential to add significant value by placing information, transactions, and entertainment in a location-specific context (Jagoe 2003).
In this paper, we examine consumer evaluations of location-based advertising (LBA). We define LBA as targeted advertising initiatives delivered to a mobile device from an identified sponsor that is specific to the location of the consumer. It is defined as a subset of location-based marketing (LBM). LBM activities would include all aspects of the marketing mix in the mobile location-based setting, whereas LBA is a narrower concept focused on the advertising strategy and communications elements of LBM. In turn, these concepts fall under the umbrella domain of mobile marketing and its derivative mobile advertising (Tahtinen 2005).
LBA is a new tool for marketers and it is therefore important to understand how consumers are likely to evaluate it. On one hand, consumers may see great benefits in receiving location-specific advertising, while on the other hand privacy concerns and the intrusiveness of such marketing messages may turn consumers away. We specifically explore the following two questions: (1) would consumers see greater value in LBA that is "pushed" to them than LBA that consumers receive only upon request ("pull"), and (2) would consumers perceive greater value in LBA with brand advertising than LBA with promotional offers.
Examination of these questions is relevant because mobile advertising and particularly LBA represent potentially powerful ways to enable marketers to reach and interact individually with consumers in new and innovative ways whenever and wherever they are ready to buy (Kenney and Marshall 2000). Traditional product-centric marketing would be replaced by customer-centric marketing approaches that would allow marketers to anticipate and address the needs of individual customers on a one-to-one basis (Sheth, Sisodia, and Sharma 2000; Watson et al. 2002). It is therefore important to understand how LBA characteristics affect consumer perceptions of LBA. For instance, marketers may prefer to push LBA to consumers after consumers sign-up for this service. However, despite prior consent, it is quite likely that consumers would find this form of LBA more intrusive than a pull LBA and may see less value in this form of communication. While there is a growing body of literature on mobile service adoption (Okazaki 2005a) and mobile advertising effectiveness (Barwise and Strong 2002; Tsang, Ho, and Liang 2004), research on LBA specifically is sc.
The remainder of this paper provides a brief background into LBA, LBA characteristics, types of LBA and message content, key independent variables, hypotheses, method, results, and study implications.
Background
Marketers have used knowledge of spatial and geographic information for store location and merchandizing decisions since the advent of modern retailing in the early twentieth century (Christensen and Tedlow 2000). Direct marketers have used location information of their prospects and customers since the beginning of direct-mail marketing (Petrison, Blattberg, and Wang 1997). Although the growth of the Internet and e-commerce may have made location information of consumers seemingly irrelevant, new technologies of location-enablement make location information mission critical in ways that were simply not possible earlier (Watson et al. 2002). These technologies enable marketers to offer timely personalized services and products that are location-specific. For the first time, marketers would be able to potentially link existing knowledge of the consumer's identity, financial status, and buying history with the LBS parameters of a purchase including its exact time, place, purchasing behavior, and situational context, as it happened in real time.
Location services have evolved rapidly over the last few years. An important aspect of next-generation LBS lies in the generation of location data. Previous location-based services required manual input of location data, such as street intersection or zip code. Relying on customer input of location data has the advantages of being inexpensive, and requiring no investment in special location equipment. These would also give more control to consumers and therefore raise less concern over privacy issues. However, the weaknesses of such systems are apparent. Consumers may not know their location, and consumer-supplied location information limits the ability of marketers and network operators to proactively offer a range of personalized services (Robinson 2000). Current technologies enable automatic generation and update of location data of individual mobile consumers in the mobile network thereby facilitating specific services such as location-dependent advertising and promotion to be triggered when the mobile device (consumer) is in a specific pre-defined area. Although the technologies differ in terms of how they calculate location coordinates, they are far more precise in locating a mobile customer and their performance matches requirements of the E911 Mandate (Jagoe 2003; Unni and Harmon 2003).
As defined earlier, LBA is location-specific mobile advertising. In keeping with the rapid adoption of text messaging (Shermach 2005), early applications of LBA are in the form of text messages (SMS). However, with the growing numbers of cell phones and other mobile devices possessing multimedia capabilities as well as advances in mobile technology, LBA in the form of multi-media messages (MMS) is foreseen in the near future (Stone 2005; Yuan and Steinberg 2006).
Marketing to consumers on their mobile devices such as cell phones has been examined in some recent studies (Barwise and Strong 2002; Haskin 2001). Marketers are experimenting with mobile advertising and promotions to consumers who have opted-in to receive promotional messages on their cell phones (Shermach 2005). However, these were not location-specific. One of the first examples of LBA was a service launched by ZagMe in the United Kingdom. It was launched as an opt-in advertiser-funded shopper-alert SMS-based service in late 2000 (Buckley 2004; Jagoe 2003). Registered consumers would receive advertising and promotional text messages on their cell phones when they were in a designated mall. Consumers could either purchase the item or have it held for them. Because the technology was not in place, location tracking was emulated by having consumers actually activate the service by calling a number or sending a text message when they were in the mall. The service was operational for about a year and attracted nearly 85,000 consumers. Sixty-eight percent of consumers activated the service on their first visit to the mall. However, the number of consumers who actually reactivated the service on subsequent visits was very small. Lack of funding subsequently led to suspension of this service (Buckley 2004). As LBA-oriented business models demonstrate their value and with the mobile data infrastructure increasingly in place, future generations of LBA services should face lower startup barriers than those experienced by pioneers such as ZagMe (Unni and Harmon 2003). However, understanding the effects of LBA characteristics on likely adoption of this service is critical. In this paper, we focus on two characteristics: type and content of LBA.
Given the negative perceptions and reactions to spam as well as regulatory pressures on mobile advertising, there is general agreement that LBA would be permission-based (Barnes and Scornavacca 2004). Empirical findings suggest that consumers would evaluate mobile advertising negatively unless they had previously consented to receive such advertising (Tsang, Ho, and Liang 2004). Consumers would sign up or opt-in to receive relevant advertising and promotions (Barwise and Strong 2002; Gratton 2002). Such an approach would also be necessary to ensure precision in targeting by LBA marketers (Godin 1999).
There are two broad types or approaches in delivering LBA - pull and push (Paavilainen 2002). Wireless pull advertising is any advertising message sent to the wireless subscriber upon request shortly thereafter on a one time basis. Pull LBA is advertising specific to the location of the consumer delivered to the mobile device only when it is explicitly requested for. In this type of LBA, the consumer initiates the request for advertising or promotions for preferred product categories close to his/her location. For example, consider a consumer who is heading to the mall. As she approaches the mall, she could use her cell phone to check for promotions in preferred categories from retailers in and around that area.
Wireless push advertising is any content sent by or on behalf of advertisers and marketers to a wireless mobile device at a time other than when the subscriber requests it. In push LBA, advertising messages are sent to a consumer's cell phone (or mobile device) based on that consumer's location and previously stated product preferences (Paavilainen 2002). This resembles the push technologies on the Internet pioneered by PointCast in the mid-1990's (Junnarkar 2000). PointCast delivered information to personal computers based on programmed preferences, eliminating the need to browse multiple web sites to gather news and other relevant information. With push LBA, consumers have less control and marketers have more control over the flow of advertising and promotions. This option may appeal to marketers because it overcomes consumer inertia in activating (and reactivating in some instances) LBA that is likely to be associated with pull LBA (Buckley 2004). Push LBA also provides a potentially effective way to trigger impulse buying. However, it would likely be more intrus.
The terms pull and push are different in meaning from their use in the context of traditional marketing. The pull approach in the traditional context refers to the use of mass advertising and consumer promotions to generate a demand or "pull" for a brand, while the push approach refers to the use of trade promotions to ensure stocking of a brand at the retail level (Shimp 1997). On the other hand, in the context of LBA and other non-traditional marketing, push is outbound communication originating from the marketer, while pull is inbound communication that is initiated by the consumer.
LBA Message Content: Advertising and Promotion
Several options for text-based mobile marketing exist. These include brand-building ads, special offers, and different types of promotions (Barwise and Strong 2002). These would translate to LBA when they take into account the location-specific information of a consumer. For example, Reebok used the service to send an alert for a novel promotion. They offered a free pair of athletic shoes to the first person to arrive at a nearby store and display the ZagMe message. More than fifty out of breath bargain hunters arrived at the store within four minutes (Buckley 2004).
In this research, we examine two broad categories of LBA message content: promotion and brand advertising. The Reebok example is a type of promotional LBA with an explicit incentive. LBA need not always be linked to such incentives. Mobile advertising can be effectively used for brand building to raise brand awareness and brand salience (Barwise and Strong 2002; Okazaki 2005b). For example, a text message informing consumers about the availability of a recently launched brand of shoes in a nearby store would be an example of brand awareness advertising. Given that LBA is designed to be highly relevant to the location as well as consumer preferences, both types of content could serve as triggers for action. The key difference is that promotional offers would have an explicit promotional offer. It is relevant to examine if consumers value promotions and brand advertising differently. For marketers, consumer promotions may be expensive and potentially difficult to manage.
There is considerable interest in understanding factors that influence adoption of mobile services (Okazaki 2005a). Researchers have used Davis's (1989) Technology Adoption Model (TAM) and multi-attribute models such as the Theory of Reasoned Action (Fishbein and Ajzen 1975) to examine intentions to use mobile services (e.g., Hung, Ku and Chang 2003; Lu et al. 2003). This work has highlighted the key role of perceived usefulness, user-friendliness, and social influence in predicting adoption of mobile services.
Another approach is based on the uses and gratifications research from the communications literature (Ducoffe 1996). Okazaki (2004) found perceived infotainment (combination of informativeness and entertainment) and irritation as the principal motives in pull-type wireless advertising acceptance. Perceived information content and entertainment had a positive effect, while perceived irritation had a negative effect on attitudes toward mobile pull-advertising. More recently, Nysveen, Pedersen, and Thorbjornsen (2005) tested an integrated model of mobile service usage for four different mobile services. Their findings showed a strong impact of perceived usefulness, enjoyment, perceived expressiveness ("use of mobile service to express image and personality of the consumer"), social influences, and perceived control on intentions to use mobile services.
We focus on the perceived value of LBA as a key antecedent of behavioral intentions towards LBA. This approach has been used to examine value perceptions in a number of service settings (e.g., Cronin, Brady, and Hult 2000).Value is broadly conceptualized as a trade-off between costs and benefits incurred in consuming a service. It is defined as the consumers' overall assessment of the utility of a service based on perceptions of costs incurred and benefits received (Zeithaml 1988). This assessment is based on what is received for what is given. In a service setting, perceived value is influenced by benefits that typically include perceived service quality and sacrifice that includes both monetary and non-monetary costs. Non-monetary costs refer to time, effort, and psychological costs involved in using a service (Baker et al. 2002). Psychological costs such as privacy concerns are particularly relevant in examining value perceptions of LBA. Direct marketing provides a context that is similar to LBA to examine perceived value. Social or economic gains from direct marketing such as better targeted marketing and relevant coupons are compared to attendant reduction in privacy to assess its perceived value and behavioral intentions (Goodwin 1991; Milne and Gordon 1993).
IPrivacy Concerns. We focus on privacy concerns relating to the tracking of location as the primary cost to the consumer. We also assume that consumers would be unwilling to pay to receive LBA, although consumers are willing to pay for LBS such as emergency services and navigation help services (Angelides 2005).
At a basic level, privacy is the degree to which personal information is not known to others (Rust, Kannan, and Peng 2002). Consumers are sensitive to the type of personal information collected, their control over use of this information, and their perceptions of marketers' knowledge about them (Phelps, Nowak, and Ferrell 2000). Concerns over privacy manifest when individuals believe their ability to control access others have to them and to information about them has been affected (Sieber 1998; Westin 1967). Therefore, there is general agreement that the ability of mobile carriers and marketers to track consumers and potentially combine this data with other behavioral and demographic data would raise serious privacy concerns among mobile consumers (Gratton 2002; Smith 2005; Sultan and Rohm 2005).
Industry bodies like the Mobile Marketing Association and Direct Marketing Association favor a permission-based opt-in approach for mobile marketing (Mobile Marketing Association 2003; Rodgers 2004). In such an approach, consumers would explicitly state their consent to receive marketing messages on their mobile devices (Gratton 2002; Saunders 2002). With the help of software such as the "privacy-conscious personalization" software from Bell Labs, consumers would be able to specify what location information is shared, when, with whom, how, and under what circumstances (Selingo 2004). Despite assurances from marketers and mobile service providers, location tracking is likely to be viewed as an intrusion into a consumer's personal space and make privacy concerns relating to location tracking salient.
Benefits. Benefits of LBA are personalized marketing messages and promotions for preferred products and services that are relevant to the consumer's location (Kalakota and Robinson 2002). Kenny and Marshall (2000) link increased perceptions of value to the marketer's ability to reach customers when and where they are ready to buy. The proper message in the right context can provide greater intimacy and a timelier, and thus more valuable, service. Personalized marketing messages would save consumers time because consumers would not have to sift through marketing messages to identify and select relevant messages (Ho and Kwok 2003). Properly targeted LBA would deliver only relevant marketing ads and promotions. Marketers have argued that consumers do not want more choice, but want products and services that are tailored to their needs (Pine, Peppers, and Rogers 1995). Consumers may also feel that offers and advertising would not be available if they had not opted-in for LBA. Such feelings of exclusivity may be seen as a benefit (Simonson 2005).
When perceived benefits associated with disclosure of personal information are salient and exceed the costs associated with information disclosure, consumers may be willing to overcome their privacy concerns (Goodwin 1991; Olivero and Lunt 2004). In the face of assurances that address privacy concerns, consumers are known to opt-in to receive marketing communications such as emails about special sales and announcements from preferred retailers and marketers (Krishnamurthy 2001). Such pragmatism among the majority of consumers is reported in both online and traditional marketplaces (Chellappa and Sin 2005; Sheehan 2002).
Hypotheses
Effects of Type: Push vs. Pull
It is assumed that LBA services would require consumers to opt-in. In pull LBA, privacy concerns are likely to be less salient because consumers would initiate the request to receive relevant LBA. Consumers would have greater control over receiving LBA. On the other hand, in the push format, even though the consumer has given prior consent for the use of location data, LBA would be intrusive and tend to interrupt the consumer. This would amplify privacy concerns because consumers would become more aware of being tracked. Therefore, we predict the following:
H1a: Privacy concerns for push LBA would be greater than those for pull LBA.
In pull LBA, it is likely that a need or a desire triggers the request for LBA. This is not the case with the push model. In the latter instance, the marketer uses contextual knowledge of a consumer's location and preferences to interrupt the consumers with a marketing message or offer. Lack of control over push LBA may result in negative evaluations of these interruptions (Xia and Sudarshan 2002). The intrusive nature of push LBA would diminish its value and may produce negative responses including avoidance (Edwards, Li, and Lee 2002). Pull LBA would have greater perceived value than push LBA. However, it is possible that a relevant push LBA may have high perceived value and favorable attitudes toward the marketer if the advertising or promotion matches the needs of the consumer. For example, a consumer shopping for an appliance is likely to perceive high value in a mobile coupon for a brand that she is interested in from a nearby retailer. The value of push LBA would depend on how well a marketer anticipates the needs of the consumer. For instance, even if the advertising or promotion matches the location, it may not meet the needs of a consumer (Rao and Minakakis 2003). For purposes of this study, we assume that LBA matches consumer needs. However, given the intrusive nature of push LBA, we predict reduced benefits and value for push LBA.
H1b: Perceived benefits for pull LBA would be greater than those for push LBA.
H1c: Perceived value for pull LBA would be greater than that for push LBA.
Prior research indicates a direct effect of perceived value on behavioral intentions towards a service (e.g., Baker et al. 2002; Cronin, Brady, and Hult 2000). If the above hypotheses are supported, we predict that consumers would be more likely to try pull LBA than push LBA.
H1d: Intentions to sign up for pull LBA would be greater than for push LBA.
Effects of Content: Promotional vs. Brand advertising
As discussed previously, privacy concerns are likely to arise when consumers perceive diminished control over marketers' access to them and to information about them (Sieber 1998; Westin 1967). The saliency of these concerns is unlikely to be affected by the content of LBA. Therefore, we hypothesize:
H2a: Privacy concerns for promotional and brand advertising LBA will not be significantly different.
One of the early commercial studies on mobile advertising found that traditional product-focused ads were of little interest to mobile consumers (Haskin 2001). While these ads were not location-specific, the results of the survey suggest that consumers desire tangible benefits in exchange for the trouble in receiving them. Kalakota and Robinson (2002) suggest that mobile advertising works best when consumers have a tangible offer or a marketing message on which they can act upon. The ZagMe experience provides some empirical support to this notion (Buckley 2004). Therefore, we propose the following hypotheses.
H2b: Perceived benefits would be greater for promotional LBA than those for brand advertising LBA.
H2c: Perceived value for promotional LBA would be greater than that for brand advertising LBA.
H2d: Intentions to sign up for promotional LBA would be greater than for brand advertising LBA.
Method
A 2 x 2 between-subjects experimental design was used to test the hypotheses. The factors were: (1) type of LBA (pull vs. push) and (2) message content of LBA (brand advertising vs. promotional). Subjects were undergraduate business students from an urban university who received extra credit for participating in the study. Using college students for this study was appropriate because this group is an important target market for cell phone products (Shermach 2005; Totten et al. 2005). Furthermore, most students at this urban university work either full- or part-time, are on average 24 years old, and make their own consumption decisions. In a recent survey of cell phone usage, consumers in the 18-24 year age group used cell phones 71% more than the average for all age groups, while the 25-36 age group used cell phones about 27% more than the overall average (Telephia 2006).
Upon arrival at a designated room, subjects were randomly assigned to one of the four conditions. After reading a brief description of a LBA that was described as a new service, consumers indicated their responses to the dependent variables, other relevant variables, and demographic information.
Brief descriptions of four types of LBA were created. The LBA service was described as a free, permission-based service from the consumer's mobile (cell phone) service provider. In this service, consumers would have the choice to indicate their preference to receive advertising (or promotions) for preferred product categories. However, description of the service concept was in general terms and not product-specific. The four LBA descriptions are included in the Appendix.
Stimuli were pre-tested with 22 undergraduate business students for prior knowledge about LBA, comprehension, and content equivalence. The information provided was considered equivalent and there were no problems in comprehending the content in the scenarios. Subjects also revealed no prior knowledge of LBA.
Privacy concerns about location tracking were measured with four items. Benefits were measured with three items. These measures were developed based on the authors' experience with the industry, vetted by our colleagues in the industry, and subsequently subjected to CFA procedures to verify the psychometric properties of these measures.
Measures for perceived value of LBA and intention to try LBA were adapted from Cronin, Brady, and Hult (2000). Likert-type scales were used for privacy concerns, perceived benefits, perceived value ("1 = Strongly Disagree" and "7 = Strongly Agree"). A seven-point semantic differential scale was used for behavioral intention towards LBA. The items and their psychometric properties are shown in Table 1.
TABLE 1
Scale Items, Composite
Reliability, and Average Variance Extracted (AVE)
We also assessed the perceived intrusiveness of LBA, level of trust for subject's mobile service provider, relevant information relating to cell phone usage, and demographic information. Perceived intrusiveness (Cronbach α = .76) was measured using three items on a seven-point scale ("This type of ad/promotion is interfering," "This type of ad/promotion is distracting," and "This type of ad/promotion is intrusive"). These were adapted from Edwards, Li, and Lee (2002). Trust for cell phone service provider (Cronbach α = .91) was assessed with four items (very undependable/very dependable, very incompetent/very competent, of very low integrity/of very high integrity, very unresponsive to customers/very responsive to customers). This scale was adapted from Sirdeshmukh, Singh, and Sabol (2002).
Results
Sixty-eight male (44.4%) and eighty-five female (55.6%) subjects participated in the study. The average age was 25 years old and their average cell phone monthly expenditures were about $53 (SD = 22.5). Subjects had no prior knowledge of LBA.
Confirmatory factor analysis (CFA) procedures with AMOS 5.0 were used to assess validity and reliability. There were 11 observed variables (see Table 1). All item loadings were significant (p < .0001) and standardized weights ranged from .62 to .97. However the squared multiple correlations of two indicators were below .50 (see Table 1 for these items), indicating problems of reliability with these items (Bollen 1989). The model chi-square was 109.93 (df = 38, p < .0001). The fit indices (comparative fit index (CFI) = .93, goodness-of-fit index (GFI) = .88, Tucker-Lewis index (TLI) = .89) were below recommended norms (Bagozzi and Yi 1988; Hair et al. 1998). The root mean square error of approximation (RMSEA) of .11 was also an indication of poor fit. RMSEA values ranging from .05 to .08 are indicators of good fit of the model (Hair et al. 1998).
The two indicators with problems of poor reliability were dropped and the revised model produced a chi-square of 56.76 (df = 21, p < .0001). The fit indices showed a marked improvement and indication of adequate fit (CFI = .96, GFI = .92, TLI = .93, and RMSEA = .07). Using procedures outlined by Fornell and Larcker (1981), we then calculated composite reliability and average variance extracted (AVE). Composite reliability of the constructs ranged from .82 to .93, indicating high internal consistency for each construct. AVE of the constructs ranged from .60 to .86, suggesting acceptable construct validity (Fornell and Larcker 1981). Reliability and AVE are reported in Table 1.
The two indicators with problems of poor reliability were dropped and the revised model produced a chi-square of 56.76 (df = 21, p < .0001). The fit indices showed a marked improvement and indication of adequate fit (CFI = .96, GFI = .92, TLI = .93, and RMSEA = .07). Using procedures outlined by Fornell and Larcker (1981), we then calculated composite reliability and average variance extracted (AVE). Composite reliability of the constructs ranged from .82 to .93, indicating high internal consistency for each construct. AVE of the constructs ranged from .60 to .86, suggesting acceptable construct validity (Fornell and Larcker 1981). Reliability and AVE are reported in Table 1.
TABLE 2.
Discriminant Validity
of Constructs
We used a 2 x 2 MANCOVA with type of LBA (push vs. pull) and message content (brand advertising vs. promotional) as the two factors, and trust as a covariate. Of the four cells, two cells had forty subjects each, one had thirty-six, and the fourth cell had thirty-seven subjects. Multivariate and univariate effects are reported in (Table 3).
Effect
of Trust as a Covariate
Trust was included as a covariate for hypothesis testing
to control for its effects on the dependent variables. Trust
is conceptualized as an expectation of the mobile carrier's
dependability, reliability, and integrity to deliver on its
promises (e.g., Sirdeshmukh, Singh, and Sabol 2002). Therefore,
greater levels of trust in the cell phone service provider
should lead to fewer concerns about privacy arising from location
tracking, and greater perceptions of benefits and value.
Trust was reasonably high (M = 4.99, SD = 1.13) and there were no significant differences in trust perceptions among the different service providers. As shown in Table 3, trust had a significant multivariate effect (Wilks' λ = .89, F (4,145) = 4.50, p < .01). Examination of univariate effects showed that trust had no significant effects on privacy concerns, value, or intentions. However, it did have a significant effect on perceived benefits. This suggests that trusted mobile carriers are more likely to succeed in communicating credible claims about LBA.
We then examined the significant two-way type x content interaction (Wilks' λ = .94, F (4,145) = 2.40, p < .05). Univariate between-subjects effects revealed a significant two-way interaction effect for perceived value only (Table 3). The interaction (Figure 1) shows that effects are magnified for push LBA. For push LBA, perceived value was significantly greater for promotions than for advertising (t (75)= 2.57, p < .01, two-tailed). In pull LBA, perceived value between advertising and promotional LBA was not significantly different (t (74)= 1.22, p >.05). Although perceived value for push LBA was low, promotions were valued more than advertising. Also, pull LBA was more valued than push LBA. The nature of this interaction allowed us to proceed with an examination of the main effects of LBA type (Hair et al. 1998).
FIGURE 1.
LBA Type
x Content Interaction Effect on Perceived Value
The significant multivariate main effect of LBA type (Wilks' λ = .71, F (4,145) = 14.56, p < .001) is accompanied by significant univariate between-subjects effects of LBA type for all the dependent variables (Table 3). Planned comparisons showed that privacy concerns were significantly greater for push than pull LBA (MPush = 5.78 vs. MPull = 4.47; t (151)= 6.37, p < .001, one-tailed). Therefore, H1a was supported.
We had hypothesized that perceived benefits and value for push LBA would be lower than pull LBA because of the intrusive nature of push advertising. Push LBA was significantly more intrusive than pull LBA (MPush = 5.20 vs. MPull = 4.51; t (151)= 3.22, p < .001, one-tailed). Perceived benefits were significantly greater for pull than push LBA (MPull = 3.84 vs. MPush = 2.82; t (151)= 4.67, p < .001, one-tailed). Perceived value was greater for pull than push LBA (MPull = 3.70 vs. MPush = 2.93; t (151) =3.22, p < .001, one-tailed). Intention to sign up for pull LBA was also greater than that for push LBA (MPull = 3.18 vs. MPush = 2.60; t (151) = 2.00, p < .05, one-tailed). These results support H1b, H1c, and H1d.
There was a weak main effect of LBA message content (Wilks'λ = .94, F (4,145) = 2.15, p < .10). Also, there was a significant univariate effect of LBA message content on privacy concerns (Table 3). Comparison of means showed that privacy concerns were significantly greater for promotional LBA than for advertising LBA (MPromo = 5.33 vs. MAd = 4.92; t (151) = 2.00, p < .05). No differences were hypothesized. There was no significant difference between levels of intrusiveness between advertising and promotion (MPromo = 4.75 vs. MAd = 4.97; t (151) = 1.03, p > .05). It is likely that the greater level of privacy concern for promotional LBA may be related to beliefs about giving up more personal information upon using a promotion. Therefore, H2a was not supported.
There were no significant differences between advertising and promotional LBA for perceived benefits (MPromo = 3.49 vs. MAd = 3.17; t (151) = 1.47, p > .05). As discussed earlier, the significant interaction effect (Figure 1) shows that perceived value was significantly greater for promotions in push LBA. But there were no significant differences between promotions and advertising in pull LBA. There were also no significant differences in intention to sign up between promotional and advertising LBA (MPromo = 2.89 vs. MAd = 2.89). Therefore, there was no support for H2b, H2c, and H2d.
Discussion
This study is an initial examination of issues relating to evaluation of types and message content of LBA. Although a very basic concept description was used, the results of this study are relevant to marketers. Pull LBA fared better than push LBA. However, value perceptions of LBA and intentions to try this service appear to be quite low (below 3.5 on a seven-point scale). Also, privacy concerns relating to location data were high, and perceived benefits were low. These results are in line with a recent study in the Japanese market that revealed very low mean values for pull-type wireless advertising (Okazaki 2004).
Mobile service providers and marketers are likely to face a huge challenge in getting consumers interested in signing up for LBA. Interestingly, initial surveys by market research agencies such as Driscoll and In-Stat showed a high level of interest and willingness to pay for location-based services such as navigation (driving directions), maps and guides, and traffic updates (Jagoe 2003; Smith 2005). Unlike LBA, these services are perceived to be more utilitarian and hence benefits and perceived value are easier to communicate. Results of our study show that the perceived benefits from LBA are low. Therefore, it is critical for marketers to convince consumers of the benefits of LBA. In this study, LBA service was portrayed as being offered by the cell phone operator. Trust in the operator had a positive effect on perceived benefits of LBA. This suggests that claims about LBA benefits are likely to be credible from trusted service providers (and marketers). It is plausible that consumers may be skeptical of the ability of mobile carriers to deliver benefits of LBA such as context-specific, timely advertising.
The results also highlight the importance of privacy concerns with respect to tracking of location information. It will be critical for marketers to use location data prudently and safeguard the privacy of its customers. The very idea that consumers can be tracked creates discomfort and a sense of loss of privacy. Privacy concerns become salient with the more intrusive push LBA, where consumers are likely to experience some loss of control, even though they would have opted to receive LBA. The consequent perception of reduced value of LBA can be countered through relevant need-specific tailored marketing. The positive impact of such personalized marketing may overcome the negative impact of privacy concerns while evaluating LBA (Ho and Kwok 2003; Milne and Gordon 1993). Prior research (e.g., Goodwin 1991; Olivero and Lunt 2004) has shown evidence of pragmatism among consumers in their willingness to trade personal information for rewards. Furthermore, although consumers may express privacy concerns, they frequently do not consult privacy policies (Jensen, Potts, and Jensen 2005). Therefore, marketers should focus on allaying concerns about location tracking. One option is to have LBA available only in the vicinity of specific retail locations or malls. However, privacy concerns for promotional LBA were greater. We speculate that this difference may be attributed to consumer beliefs that promotions are more easily tracked while responses to advertising LBA may remain anonymous. Another option is to offer incentives such as other mobile services at reduced rates or no charge.
Our study results clearly favor a pull approach. The main advantage in pursuing a push LBA approach is the opportunity to trigger impulse buying among consumers who have already opted-in and expressed their preferences. However, delivering personalized marketing offers based on customer preferences is a significant challenge (Simonson 2005). This implies that marketers have to rise to the challenge by providing tangible benefits of LBA and get people to request or "pull" this type of marketing. Marketers should also explore innovative incentives such as a reward program linked to number of requests for pull LBA and creative bundling of other services with LBA to encourage such inbound marketing. The role of incentives to adopt LBA was not examined in this research.
Interestingly, our research did not reveal significant differences in benefits and trial intentions between promotional and advertising LBA. However, promotions were perceived to have greater value than advertising when LBA was pushed. There was no significant difference in perceived value for promotions between pull and push LBA. On the other hand, perceived value of advertising dropped significantly in push LBA. This finding is consistent with industry studies on mobile advertising that point to the need to provide tangible incentives to create value in mobile marketing initiatives (Buckley 2004).
Limitations and Directions for Future Research
Research on LBA effectiveness is in the early stages and there is a dearth of empirical research on this topic. Our study is an initial examination of some issues relating to LBA in a scenario-based laboratory setting. A number of issues not addressed in our study are worthy of future research. First, we only examined a subset of benefits, namely time savings and better selection of merchandise. These benefits are utilitarian in nature. A more comprehensive examination of benefits of LBA that includes hedonic benefits is needed. For instance, based on prior research (Nysveen, Pedersen, and Thorbjornsen 2005), perceived enjoyment of using LBA is a potentially important variable that needs to be investigated for LBA.
Similarly, a more comprehensive examination of costs that affect value perceptions is needed. We restricted our examination of costs to privacy concerns arising only from location tracking. The privacy construct is multi-faceted and future research should examine specific concerns regarding dissemination and use of location information (Phelps, Nowak, and Ferrell 2000). For instance, it would be important to assess consumer beliefs about the consequences of giving up location information. Advertisers could then address these issues through better privacy policies and educate consumers about the use of location information. Other costs such as the irritation and intrusiveness of LBA also warrant further investigation. Further, a better conceptualization of the value of LBA has significant implications for the design and development of LBA messages.
Another important question that warrants investigation is the effectiveness of LBA for different types of products. There is evidence to suggest that mobile advertising would be more effective for frequently bought, low-priced products than for more expensive products (Barwise and Strong 2002). Related to this area of future research is the potential for LBA to trigger impulse buying.
Finally, the use of a convenience sample is a limitation of this study. The subjects in this study may fall in the target market for mobile services. However, generalizability of these results to the general population is likely to be affected. Future research should examine value perceptions of LBA with a more diverse sample that includes both younger and older individuals in different geographic regions.
Concluding Comments
LBA is uncharted territory for marketers. Developments in technology will provide marketers with tools that enable them to adopt contextual marketing practices that are truly focused on serving customers individually (Kenny and Marshall 2000; Sheth, Sisodia, and Sharma 2000). LBA represents one such technology application. However, for LBA to gain acceptance among consumers, marketers have to allay privacy concerns and effectively convey the value proposition of timely, personalized, location-specific marketing delivered to cell phones.
References
Angelides, Jason (2005), "U.S. Poised to Capitalize on Location Services," Directions Magazine, January 26, <www.directionsmag.com> (accessed on 8/17/2005).
Bagozzi, Richard P. and Youjae Yi (1988), "On the Evaluation of Structural Equation Models," Journal of the Academy of Marketing Science, 16 (1), 74-94.
Baker, Julie, A. Parasuraman, Dhruv Grewal, and Glenn B. Voss (2002), "The Influence of Multiple Store Environment Cues on Perceived Merchandise Value and Patronage Intentions," Journal of Marketing, 66 (April), 120-141.
Barnes, Stuart J. and Eusebio Scornavacca (2004), "Mobile Marketing: The Role of Permission and Acceptance," International Journal of Mobile Communications, 2 (2), 128-139.
Barwise, Patrick and Colin Strong (2002), "Permission-Based Mobile Advertising," Journal of Interactive Marketing, 16 (Winter), 14-24.
Bollen, Kenneth A. (1989), Structural Equations with Latent Variables, New York, NY: Wiley.
Buckley, Russell (2004), Location Based Advertising - Theory and Practice, White Paper, April 27, <www.unstatic.co.uk> (accessed on 11/7/2004).
Chellappa, Ramnath K. and Raymond G. Sin (2005), "Personalization versus Privacy: An Empirical Examination of the Online Consumer's Dilemma," Information Technology and Management, 6, 181-202.
Christensen, Clayton M. and Richard S. Tedlow (2000), "Patterns of Disruption in Retailing," Harvard Business Review, 78 (January/February), 42-45.
Cronin, J. Joseph, Michael K. Brady, and G. Thomas M. Hult (2000), "Assessing the Effects of Quality, Value, and Customer Satisfaction on Consumer Behavioral Intentions in Service Environments," Journal of Retailing, 76 (2), 193-218.
Davis, Fred D. (1989), "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology," MIS Quarterly, 13, 319-340.
Ducoffe, Robert H. (1996), "Advertising Value and Advertising on the Web," Journal of Advertising Research, 36 (September/October), 21-35.
Edwards, Steven M., Hairong Li, and Joo-Hyun Lee (2002), "Forced Exposure and Psychological Reactance: Antecedents and Consequences of the Perceived Intrusiveness of Pop-up Ads," Journal of Advertising, 31 (Fall), 83-95.
Fishbein, Martin and Icek Ajzen (1975), Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley.
Fornell, Claes and David F. Larcker (1981), "Evaluating Structural Equation Models with Unobservable Variables and Measurement Error," Journal of Marketing Research, 18 (February), 39-50.
Godin, Seth (1999), Permission Marketing: Turning Strangers into Friends, and Friends into Customers, New York, NY: Simon and Schuster.
Goodwin, Cathy (1991), "Privacy: Recognition of a Consumer Right," Journal of Public Policy and Marketing, 10 (Spring), 149-166.
Gratton, Eloise (2002), "M-Commerce: The Notion of Consumer Consent in Receiving Location-based Advertising," Canadian Journal of Law and Technology, 1 (2), 59-77.
Hair, Joseph. F., Jr., Rolph E. Anderson, Ronald L. Tatham, and William C. Black (1998), Multivariate Data Analysis, Fifth Edition, Upper Saddle River, NJ: Prentice Hall.
Haskin, David (2001), "Mobile Advertising: Will Anybody Ever Care?" MobileAdWatch, May 8, <www.mobileadwatch.com/insight/2001/insight20010508.shtml> (accessed on 8/17/2005).
Ho, Shuk Ying and Sai Ho Kwok (2003), "The Attraction of Personalized Service for Users in Mobile Commerce: An Empirical Study," ACM SIGecom Exchanges, 3 (4), 10-18.
Hung, Shin-Yuan, Cheng-Yuan Ku, and Chia-Ming Chang (2003), "Critical Factors of WAP Services Adoption: An Empirical Study," Electronic Commerce Research and Applications, 2, 42-60.
Jagoe, Andrew (2003), Mobile Location Services: The Definitive Guide, Upper Saddle River, NJ: Prentice Hall.
Jakobson, Leo (2005), "Coupons on the Go," Incentive, 179 (February), 16.
Jensen, Carlos, Colin Potts, and Christian Jensen (2005), "Privacy Practices of Internet Users: Self-reports versus Observed Behavior," International Journal of Human-Computer Studies, 63 (July), 203-227.
Junnarkar, Sandeep (2000), "PointCast Sends its Final Push Broadcast," C/Net News.com, February 17, <http://news.com.com/PointCast+sends+its+final+push+broadcast/2100-1023_3-237059.html> (accessed on 12/20/2005).
Kalakota, Ravi and Marcia Robinson (2002), M-Business: The Race to Mobility, New York, NY: McGraw-Hill.
Kenny, David and John F. Marshall (2000), "Contextual Marketing: The Real Business of the Internet," Harvard Business Review, 78 (November-December), 119-125.
Krishnamurthy, Sandeep (2001), "A Comprehensive Analysis of Permission Marketing," Journal of Computer-Mediated Communications, 6 (2) <http://jcmc.indiana.edu/vol6/issue2/krishnamurthy.html> (accessed on 11/21/2004).
Lu, June, Chun-Sheng Yu, Chang Liu, and James E. Yao (2003), "Technology Acceptance Model for Wireless Internet," Internet Research: Electronic Networking Applications and Policy, 13 (3), 206-222.
Milne, George R. and Mary Ellen Gordon (1993), "Direct Mail Privacy-Efficiency Trade-Offs within an Implied Social Contract Framework," Journal of Public Policy and Marketing, 12 (Fall), 206-215.
Mitchell, Kirk and Mark Whitmore (2003), "Location Based Services: Locating the Money," in Mobile Commerce: Technology, Theory, and Applications, Brian E. Mennecke and Troy J. Strader, eds., Hershey, PA: Idea Group, 51-66.
Mobile Marketing Association (2003), MMA Code for Mobile Marketing, November 3, <mmaglobal.com/modules/content/index.php?id=5> (accessed on 11/7/2003).
Nysveen, Herbjorn, Per E. Pedersen, and Helge Thorbjornsen (2005), "Intentions to Use Mobile Services: Antecedents and Cross-Service Comparisons," Journal of the Academy of Marketing Science, 33 (3), 330-346.
Okazaki, Shintaro (2004), "How do Japanese Consumers Perceive Wireless Ads? A Multivariate Analysis," International Journal of Advertising, 23 (4), 429-454.
--- (2005a), "New Perspectives of M-Commerce Research", Journal of Electronic Commerce Research, 6 (3), 160-164.
--- (2005b), "Mobile Advertising Adoption by Multinationals: Senior Executives' Initial Responses," Internet Research, 15 (2), 160-180.
Olivero, Nadia and Peter Lunt (2004), "Privacy Versus Willingness to Disclose in E-Commerce Exchanges: The Effect of Risk Awareness on the Relative Role of Trust and Control," Journal of Economic Psychology, 25 (April), 243-262.
Paavilainen, Jouni (2002), Mobile Business Strategies, London, Great Britain, IT Press.
Petrison, Lisa A., Robert C. Blattberg, and Paul Wang (1997), "Database Marketing: Past, Present, and Future," Journal of Direct Marketing, 11 (Fall), 109-125.
Phelps, Joseph, Glen Nowak, and Elizabeth Ferrell (2000), "Privacy Concerns and Consumer Willingness to Provide Personal Information," Journal of Public Policy and Marketing, 19 (Spring), 27-41.
Pine, B. Joseph, II, Don Peppers, and Martha Rogers (1995), "Do you want to keep your Customers Forever?" Harvard Business Review, 73 (March-April), 103-114.
Rao, Bharat and Louis Minakakis (2003), "Evolution of Mobile Location-based Services," Communications of the ACM, 46 (December), 61-65.
Robinson, Teri (2000), "Wireless Marketing is about Location, Location, Location," Internetweek, September 18, <www.internetweek.com/indepth/indepth091900.htm> (accessed on 8/17/2005).
Rodgers, Zachary (2004), "TRUSTe Launches Wireless privacy Guidelines," ClickZNews, February 20, <www.clickz.com/news/article.php/3315811> (accessed on 3/2/2006).
Rust, Roland T., P. K. Kannan, and Na Peng (2002), "The Customer Economics of Internet Privacy," Journal of the Academy of Marketing Science, 30 (4), 455-464.
Saunders, Christopher (2002), "MMA Pushes Privacy," ClickZNews, March 19, <www.clickz.com/news/article.php/994451> (accessed on 8/17/2005).
Selingo, Jeffrey (2004), "Protecting the Cellphone User's Right to Hide," The New York Times, 153 (52750), G7.
Sheehan, Kim B. (2002), "Toward a Typology of Internet Users and Online Privacy Concerns," The Information Society, 18 (January), 21-32.
Shermach, Kelly (2005), "Mobile Marketing Comes to North America," Technews World, February 28, <www.technewsworld.com/story/mobile/40925.html > (accessed on 11/9/2005).
Sheth, Jagdish N., Rajendra S. Sisodia, and Arun Sharma (2000), "The Antecedents and Consequences of Customer-Centric Marketing," Journal of the Academy of Marketing Science, 28 (1), 55-66.
Shimp, Terence A. (1997), Advertising, Promotion, and Supplemental Aspects of Integrated Marketing Communications, Fourth Edition, Fort Worth, TX: Dryden Press.
Sieber, Joan E. (1998), "Planning Ethically Responsible Research," in L. Bickman and D.J. Rog, eds., Handbook of Applied Social Research Methods, Thousand Oaks, CA: Sage, 127-156.
Simonson, Itamar (2005), "Determinants of Customers' Responses to Customized Offers: Conceptual Framework and Research Propositions," Journal of Marketing, 69 (January), 32-45.
Sirdeshmukh, Deepak, Jagdip Singh, and Barry Sabol (2002), "Consumer Trust, Value, and Loyalty in Relational Exchanges," Journal of Marketing, 66 (January), 15-37.
Smith, Brad (2005), "Carriers Find Their Way to LBS," Wireless Week, May 15, <www.wirelessweek.com> (accessed on 2/10/2006).
Stone, Brad (2005), "More Fun for Your Mobile," Newsweek, November 14, E32.
Sultan, Fareena and Andrew J. Rohm (2005), "The Coming Era of "Brand in Hand" Marketing," MIT Sloan Management Review, 47 (Fall), 83-90.
Tahtinen, Jaana (2005), "Mobile Advertising or Mobile Marketing: A Need for a New Concept," in Frontiers of E-Business Research, Marko Seppä, Mika Hannula, Anne-Mari Järvelin, Johanna Kujala, Mikko Ruohonen, and Tarja Tiainen, eds., Tampere, Finland: Tampere University of Technology and University of Tampere, 152-164.
Telephia (2006), Telephia Customer Value Metrics, Q3, January 17 <www.telephia.com> (accessed on 2/10/2006).
Totten, Jeff W., Thomas J. Lipscomb, Roy A. Cook, and William Lesch (2005), "General Patterns of Cell Phone Usage Among College Students: A Four State Study," Services Marketing Quarterly, 26 (3), 13-39.
Tsang, Melody, Shu-Chun Ho, and Ting-Peng Liang (2004), "Consumer Attitudes Toward Mobile Advertising," International Journal of Electronic Commerce, 8 (Spring), 65-78.
Unni, Ramaprasad and Robert Harmon (2003), "Location-Based Services: Models for Strategy Development in M-Commerce," in D. F. Kocaglu and T. R. Anderson, eds., Technology Management for Reshaping the World, Piscataway, NJ: IEEE, 416-424.
Watson, Richard T., Leyland F. Pitt, Pierre Berthon, and George M. Zinkhan (2002), "U-Commerce: Expanding the Universe of Marketing," Journal of the Academy of Marketing Science, 30 (4), 333-347.
Westin, Alan F. (1967), Privacy and Freedom, New York, NY: Atheneum.
Xia, Lan and D. Sudarshan (2002), "Effects of Interruptions on Consumer Online Decision Processes," Journal of Consumer Psychology, 12 (3), 265-280.
Yuan, Li, and Brian Steinberg (2006), "Sales Call: More Ads Hit Cellphone Screens," Wall Street Journal, Eastern Edition, 247 (27), B3.
Zeithaml, Valarie A. (1988), "Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence," Journal of Marketing, 52 (July), 2-22.
Appendix: Stimuli Used for Experimental Cells
The mobile carrier that provides your cell phone coverage has introduced a new service at no charge to you. The key aspects of this service are.
Push - Promotion Condition
You will receive exciting mobile coupons (sales offers) on your cell phone for brands when you are close to a retail store that carries these brands. All the marketing offers you receive will be for brands from product categories that you have pre-selected, and are available in nearby store(s). You will not receive offers for a preferred brand available at a store that is far away from your current location.
You will have full control over the maximum number of offers you wish to receive from your preferred product categories. You may discontinue this service at any time. Your information will not be shared with third party retailers.
Pull - Promotion Condition
You can check to see if you wish to receive on your cell phone exciting mobile coupons (sales offers) for your favorite brands when you are close to a retail store that carries these brands. All the marketing offers you receive will be for brands from product categories that you have pre-selected, and are available in nearby store(s). You will not receive offers for a preferred brand available at a store that is far away from your current location.
You will have full control over the maximum number of offers you wish to receive from your preferred product categories. You may discontinue this service at any time. Your information will not be shared with third party retailers.
Push - Advertisement Condition
You
will receive ads on your cell phone for your favorite brands
only when you are close to a retail store that carries these
brands. All the ads you receive will be for brands from product
categories that you have pre-selected, and are available in
nearby store(s). You will not receive ads for a brand available
at a store that is far away from your current location.
You will have full control over the maximum number of ads
you wish to receive from your preferred product categories. You
may discontinue this service at any time. Your information will
not be shared with third party retailers.
Pull - Advertisement Condition
You can check to see if you wish to receive ads on your cell phone for your favorite brands when you are close to a retail store that carries these brands. All the ads you receive will be for brands from product categories that you have pre-selected, and are available in nearby store(s). You will not receive ads for a brand available at a store that is far away from your current location.
You will have full control over the maximum number of ads you wish to receive from your preferred product categories. You may discontinue this service at any time. Your information will not be shared with third party retailers.
About the Authors
Ramaprasad Unni (Ph.D., Indiana University) is an Assistant Professor of Marketing at Portland State University.His research interests include consumer behavior in interactive environments and location-based services marketing.
Robert Harmon (Ph.D., Arizona State University) is Professor of Marketing and Technology Management with a joint appointment in the School of Business and the Maseeh College of Engineering and Computer Science at Portland State University. His research interests include technology marketing, location-based services, and ecological value engineering for high technology firms.
