Location-based Advertising Research Articles

How location-based advertising elicits in-store purchase

PurposeThis paper aims to investigate how location-based advertising (LBA) elicits in-store purchase intention. To deepen the understanding of LBA’s effect on consumers’ purchase decision, the research examines the role of consumers’ time consciousness in click intention in pull or opt-out LBA approaches. The study also explores how consumers react to LBA with an asymmetric dominance decoy versus a compromise decoy message.Design/methodology/approachTwo field experiments were conducted, and a total of 363 volunteers within 3 km of a shopping mall participated. The participants were asked to turn on their global positioning system and then informed that a convenience store was planning to launch a mobile coupon subscription service. Data collected were analysed using analysis of variance, regression analysis, bootstrapping and spotlight tests.FindingsThe results demonstrate that consumers had a higher intention to click pull LBA than to click opt-out push LBA. Consumers with high time-consciousness had greater click intentions for pull LBA than for opt-out push LBA. Consumers with low time-consciousness, however, showed no difference in click intention for either LBA approach. Further, click intention mediates the effect of LBA on in-store purchase intention, and the asymmetric dominance decoy message is a more powerful strategy for LBA to increase the likelihood of in-store purchase.Originality/valueThis research provides insight into location-based services marketing by revealing how time-consciousness and decoy promotional messages affect consumers’ reaction to LBA and in-store purchase intentions. The findings offer practical suggestions for retailers on how to reach and engage with consumers more effectively through the use of LBA.

Analyzing Location-Based Advertising for Vehicle Service Providers Using Effective Resistances

Vehicle service providers can display commercial ads in their vehicles based on passengers' origins and destinations to create a new revenue stream. In this work, we study a vehicle service provider who can generate different ad revenues when displaying ads on different arcs (i.e., origin-destination pairs). The provider needs to ensure the vehicle flow balance at each location, which makes it challenging to analyze the provider's vehicle assignment and pricing decisions for different arcs. For example, the provider's price for its service on an arc depends on the ad revenues on other arcs as well as on the arc in question. To tackle the problem, we show that the traffic network corresponds to an electrical network. When the effective resistance between two locations is small, there are many paths between the two locations and the provider can easily route vehicles between them. We characterize the dependence of an arc's optimal price on any other arc's ad revenue using the effective resistances between these two arcs' origins and destinations. Furthermore, we study the provider's optimal selection of advertisers when it can only display ads for a limited number of advertisers. If each advertiser has one target arc for advertising, the provider should display ads for the advertiser whose target arc has a small effective resistance. We investigate the performance of our advertiser selection strategy based on a real-world dataset.

Wi-Fi Signals Database Construction using Chebyshev Wavelets for Indoor Positioning Systems

Nowadays fast and accurate positioning of assets and people is as a crucial part of many businesses, such as, warehousing, manufacturing and logistics. Applications that offer different services based on mobile user location gaining more and more attention. Some of the most common applications include location-based advertising, directory assistance, point-to-point navigation, asset tracking, emergency and fleet management. While outdoors mostly covered by the Global Positioning System, there is no one versatile solution for indoor positioning. For the past decade Wi-Fi fingerprinting based indoor positioning systems gained a lot of attention by enterprises as an affordable and flexible solution to track their assets and resources more effectively. The concept behind Wi-Fi fingerprinting is to create signal strength database of the area prior to the actual positioning. This process is known as a calibration carried out manually and the indoor positioning system accuracy highly depends on a calibration intensity. Unfortunately, this procedure requires huge amount of time, manpower and effort, which makes extensive deployment of indoor positioning system a challenging task. approach of constructing signal strength database from a minimal number of measurements using Chebyshev wavelets approximation. The main objective of the research is to minimize the calibration workload while providing high positioning accuracy. The field tests as well as computer simulation results showed significant improvement in signal strength prediction accuracy compared to existing approximation algorithms. Furhtermore, the proposed algorithm can recover missing signal values with much smaller number of on-site measurements compared to conventional calibration algorithm.

Fine-grained Geolocation of Tweets in Temporal Proximity

In fine-grained tweet geolocation, tweets are linked to the specific venues (e.g., restaurants, shops) from which they were posted. This explicitly recovers the venue context that is essential for applications such as location-based advertising or user profiling. For this geolocation task, we focus on geolocating tweets that are contained in tweet sequences. In a tweet sequence, tweets are posted from some latent venue(s) by the same user and within a short time interval. This scenario arises from two observations: (1) It is quite common that users post multiple tweets in a short time and (2) most tweets are not geocoded. To more accurately geolocate a tweet, we propose a model that performs query expansion on the tweet (query) using two novel approaches. The first approach temporal query expansion considers users’ staying behavior around venues. The second approach visitation query expansion leverages on user revisiting the same or similar venues in the past. We combine both query expansion approaches via a novel fusion framework and overlay them on a Hidden Markov Model to account for sequential information. In our comprehensive experiments across multiple datasets and metrics, we show our proposed model to be more robust and accurate than other baselines.

Consumers’ attitude and adoption of location-based coupons: The case of the retail fast food sector

This study builds on the theory of parallel distributed processing, Thaler's exchange theory, and the trust theory to extend our existing knowledge on the location-based coupons literature in the retail fast food sector. Based on several location-based coupons provided by fast food chains, a mock coupon was designed and an online survey was conducted on a sample of 228 Canadians. Attitude toward location-based advertising is found to be significantly determined by consumers’ trust and perception of control. Attitude toward location-based coupons, however, is driven by monetary benefits as well as convenience and hedonic motives. Attitude toward location-based coupons is significantly predicted by attitude toward location-based advertising. Both of them have a significant impact on consumers’ intention of using location-based coupons. The study provides strong evidence supporting Thaler's exchange theory according to which customers are motivated by monetary benefits as well as convenience and hedonic aspects.

Increasing consumers’ intention to use location-based advertising

PurposeThis study aims to investigate the influence of advertising value and privacy concerns on consumers’ intention to use location-based advertising. It also explores if brand trust toward location-based advertising providers and consumers’ privacy self-efficacy reduce privacy concerns.Design/methodology/approachBased on the privacy calculus and expectancy theory, a conceptual model is developed and empirically tested through structural equation modeling using cross-sectional data of 1,121 actual smartphone users from Germany.FindingsAdvertising value positively and privacy concerns negatively affect consumers’ intention to use location-based advertising. As expected, brand trust and consumers’ privacy self-efficacy can reduce consumers’ privacy concerns.Research limitations/implicationsFurther research should test and validate the proposed framework in other cultures to gain insights into the culturally specific relevance of privacy concerns and their antecedents. The current study includes sociodemographics as potential moderators; additional studies could investigate other potential moderators (e.g. personality, values).Practical implicationsTo reduce consumers’ privacy concerns, location-based advertising providers should make their offers transparent and give consumers control, to increase their privacy self-efficacy. They also should work to strengthen their brand, monitor brand trust trends and avoid any trust-damaging behavior.Originality/valueThis study introduces brand trust toward location-based advertising providers and privacy self-efficacy as factors to reduce consumers’ privacy concerns. It also encompasses a broader, general sample of consumers, which increases the generalizability and practical relevance of the results and supports an initial investigation of sociodemographic factors as potential moderators in this context.

Using privacy calculus theory to explore entrepreneurial directions in mobile location-based advertising: Identifying intrusiveness as the critical risk factor

Location-based advertising is an entrepreneurial and innovative means for advertisers to reach out through personalised messages sent directly to mobile phones using their geographic location. The mobile phone users' willingness to disclose their location and other personal information is essential for the successful implementation of mobile location-based advertising (MLBA). Despite the potential enhancement of the user experience through such personalisation and the improved interaction with the marketer, there is an increasing tension between that personalisation and mobile users' concerns about privacy. While the privacy calculus theory (PCT) suggests that consumers make privacy-based decisions by evaluating the benefits any information may bring against the risk of its disclosure, this study examines the specific risks and benefits that influence consumers' acceptance of MLBA. A conceptual model is proposed based on the existing literature and a standardised survey was developed and targeted at individuals with known interests in the subject matter. From these requests, 252 valid responses were received and used to evaluate the key benefits and risks of MLBA from the users' perspectives. While the results confirmed the importance of internet privacy concerns (IPC) as an important determinant, they also indicate that monetary rewards and intrusiveness have a notably stronger impact on acceptance intentions towards MLBA. Intrusiveness is the most important risk factor in determining mobile users' intentions to accept MLBA and therefore establishing effective means of minimising the perceived intrusiveness of MLBA can be expected to have the greatest impact on achieving effective communications with mobile phone users.

Personalized Trajectory Privacy Protection Method Based on User-Requirement

Trajectory data often provides useful information that can be utilized in real-life applications, such as traffic planning and location-based advertising. Because people’s trajectory information can result in serious personal privacy leakage, trajectory privacy protection methods are employed. However, existing methods assume and use the same privacy requirements for all trajectories, which affect privacy protection efficiency and data utilization. This paper proposes a trajectory privacy protection method based on user requirement. By dividing different time intervals, it sets different privacy protection parameters for different trajectories to provide more detailed privacy protection. The proposed method utilizes the divided time intervals and privacy protection requirements to form a privacy requirement matrix, to construct an anonymous trajectory equivalence class and undirected graph. Then, trajectories are processed to form anonymous sets. Euclidean distance is also replaced with Manhattan distance in calculating the distance of the trajectories, which would improve the privacy protection and data utility and narrow the gap between the theoretical privacy protection and the actual protective effects. Comparative experiments demonstrate that the proposed method outperforms other similar methods in regards to both privacy protection and data utilization.

The Effect of Digital Marketing Implementation through Location Based Advertising on Customer’s Purchase Intention

There were two objectives of the research, those were to find out the effects of Location Based Advertising (LBA) on customer’s attitude and purchase intention. Furthermore, this research sought the enhancement effect of timing in several features on the customer’s attitude toward LBA and purchase intention. The features used were the content appeal, interactivity, control, attitude toward advertising in general, customization, and intrusiveness. This research was a conclusive study with descriptive design. Data collection was done by offline survey with 160 respondents who had ever received LBA in two different conditions. Meanwhile, data analysis was conducted using Structural Equation Modelling (SEM). The research shows several results. First, content appeal, control, and customization have significant and positive effects on attitude toward LBA. Second, attitude toward LBA has significant and positive effect on customer’s purchase intention. Third, timing enhances the positive effect of customization on attitude toward LBA. Last, timing also enhances the positive effect of attitude toward LBA oncustomer’s purchase intention.

Trajectory privacy protection method based on the time interval divided

Trajectory data often provides information that is well applicable to real-world scenarios such as traffic planning and location-based advertising. Individual trajectory information may disclose sensitive personal data, thus necessitating privacy protection methods. Current methods assume and utilize the same privacy requirements for all trajectories, which can impact their protection and data utilization efficiency. This paper proposes a privacy protection method based on divided time intervals which satisfy different privacy requirements. The method works by constructing a privacy requirement matrix and running trajectory pre-processing based on the different privacy requirements for trajectories in different time points and locations. It uses trajectories that satisfy the (l, δ) -constraint to construct an undirected trajectory graph. By finding the trajectory corresponding to the edge and vertex with the minimum weight, ki-anonymous sets are then constructed with trajectories which share the same or similar privacy requirements. The Manhattan distance can then be applied to calculate the space between trajectories distance, which narrows the gap between the theoretical privacy protection and the actual protective effects. Comparative experiments demonstrate that the proposed method outperforms other similar methods in regards to both privacy protection and data utilization.

Geographic Differential Privacy for Mobile Crowd Coverage Maximization

For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In this paper, we propose a method to maximize mobile crowd's future location coverage under a guaranteed location privacy protection scheme. In our approach, users only need to upload one of their frequently visited locations, and more importantly, the uploaded location is obfuscated using a geographic differential privacy policy. We propose both analytic and practical solutions to this problem. Experiments on real user mobility datasets show that our method significantly outperforms the state-of-the-art geographic differential privacy methods by achieving a higher coverage under the same level of privacy protection.

An interval type 2 hesitant fuzzy MCDM approach and a fuzzy c means clustering for retailer clustering

Owing to the advancements in information and telecommunication technologies, mobile location-based services are able to use previously collected mobile check-in data. This multi-dimensional data can provide new opportunities for research problems such as establishing new platforms for location-based advertising and location-based personalized recommendations. In other words, location data of potential customers indicate personal interests and visiting preferences. In some cases, customers’ preferences could not be easily determined or predicted while considering visiting patterns of mobile users. In this respect, this study provides a novel retailer segmentation approach based on multi-criteria decision-making (MCDM) combined fuzzy data clustering. The proposed model consists of two phases: (1) an interval type 2 hesitant MCDM approach for the determination of location perceived value and (2) retailer (store) clustering via different product sale prices with fuzzy data-based fuzzy c means (FcM) clustering. Proposed approach enables the simplification of FcM clustering adaptation to non-symmetric fuzzy data using dissimilarity measure. Using this integrated approach, advertisers and recommender system suppliers will be able to manage their product-special offerings to customers considering retailer segments and shopping mall characteristics. Additionally, the proposed approach constitutes the infrastructure of location-based recommender systems under imprecise environment.

Factors Influencing Intention to Use Location-Based Mobile Advertising among Young Mobile User Segments: A Social Exchange Perspective

By adopting the notions of social exchange and the unified theory of acceptance and use of technology, this study discusses how young mobile users weigh between benefit and cost (e.g., perceived risk) when determining to accept location-based advertising (LBA). It also examines the role of trust in the process of the information exchange. In addition, this study explores whether young mobile users can be segmented into heterogeneous user groups and the different groups require a unique application of the theoretically-driven factors. An online survey of college students from US universities reveals that benefits of LBA outweigh the perceived risk and that trust plays a key role in increasing LBA acceptance. Results suggest, however, that the determinants may differently influence different mobile user segments: innovative believers and conventional sceptics. The result highlights the importance of crafting marketing messages uniquely for each of the segments. Implications for theories and practitioners using LBA are discussed.

Factors influencing intention to accept location-based mobile advertising among young mobile user segments: a social exchange perspective

By adopting the notions of social exchange and the unified theory of acceptance and use of technology, this study discusses how young mobile users weigh between benefit and cost (e.g., perceived risk) when determining to accept location-based advertising (LBA). It also examines the role of trust in the process of the information exchange. In addition, this study explores whether young mobile users can be segmented into heterogeneous user groups and the different groups require a unique application of the theoretically-driven factors. An online survey of college students from US universities reveals that benefits of LBA outweigh the perceived risk and that trust plays a key role in increasing LBA acceptance. Results suggest, however, that the determinants may differently influence different mobile user segments: innovative believers and conventional sceptics. The result highlights the importance of crafting marketing messages uniquely for each of the segments. Implications for theories and practitioners using LBA are discussed.

Attitude, intention, and consumer behaviour of millennial generation toward location-based SMS advertising

The aim of this study is to ascertain the attitude, intention and consumer behaviour of millennial generation toward the implementation of marketing communication by using Short Message Service (SMS) technology according to consumer location. In current technological developments, the promotion strategy, particularly advertising, has adapted through various digital marketing media. Mobile marketing, one of them is SMS Advertising still used by various companies in order to increase potential consumer awareness as the first step to trial product and encourage the purchases. Several previous researches discovered that most consumer attitude toward the SMS advertising tend to be negative. Moreover, SMS with Location-based advertising service disturb consumer privacy. This study tries to see the indicators which can be optimized to develop attitudes, intention and positive behaviours and the affordable effort as suggestion that the advertisements can be delivered by SMS with location-based advertising services and more effective to get attention and accepted by consumers of millennial generation.

Comparing factors affecting attitudes toward LBA and SoLoMo advertising

This study compares the effectiveness of location-based advertising (LBA) and social-local-mobile (SoLoMo) advertising. The results show that SoLoMo advertising is more effective than LBA. Consumers perceive a higher level of brand interaction, entertainment, informativeness, perceived location awareness, and sociability in SoLoMo advertising than in LBA. This study also discovers that there are differences in consumers’ reaction to the ads on different situational contexts and platforms. The academic contribution of this paper lies in the direct comparison of the effectiveness of LBA and SoLoMo advertising, which was lacking in the literature. To our knowledge, this is the first study in which comparison between LBA and SoLoMo advertising is made. This paper fills the gap by investigating how factors associated with advances of modern smartphone functionalities, brand interaction, perceived location awareness, and sociability, affect attitudes towards ads. It also contributes to the theory of effectiveness of telecommunication promotion by examining how consumers react to ads in different situational contexts on different platforms. Finally, this study provides practical suggestions.

Efficient mining of regional movement patterns in semantic trajectories

Semantic trajectory pattern mining is becoming more and more important with the rapidly growing volumes of semantically rich trajectory data. Extracting sequential patterns in semantic trajectories plays a key role in understanding semantic behaviour of human movement, which can widely be used in many applications such as location-based advertising, road capacity optimisation, and urban planning. However, most of existing works on semantic trajectory pattern mining focus on the entire spatial area, leading to missing some locally significant patterns within a region. Based on this motivation, this paper studies a regional semantic trajectory pattern mining problem, aiming at identifying all the regional sequential patterns in semantic trajectories. Specifically, we propose a new density scheme to quantify the frequency of a particular pattern in space, and thereby formulate a new mining problem of finding all the regions in which such a pattern densely occurs. For the proposed problem, we develop an efficient mining algorithm, called RegMiner (<u>Reg</u>ional Semantic Trajectory Pattern <u>Miner</u>), which effectively reveals movement patterns that are locally frequent in such a region but not necessarily dominant in the entire space. Our empirical study using real trajectory data shows that RegMiner finds many interesting local patterns that are hard to find by a state-of-the-art global pattern mining scheme, and it also runs several orders of magnitude faster than the global pattern mining algorithm.

Big Data Driven Hidden Markov Model Based Individual Mobility Prediction at Points of Interest

With the emergence of smartphones and location-based services, user mobility prediction has become a critical enabler for a wide range of applications, like location-based advertising, early warning systems, and citywide traffic planning. A number of techniques have been proposed to either conduct spatio-temporal mobility prediction or forecast the next-place. However, both produce diverse prediction performance for different users and display poor performance for some users. This paper focuses on investigating the effect of living habits on the models of spatio-temporal prediction and next-place prediction, and selects one from these two models for an individual to achieve effective mobility prediction at users’ points of interest. Based on the hidden Markov model (HMM), a spatio-temporal predictor and a next-place predictor are proposed. Living habits are analyzed in terms of entropy, upon which users are clustered into distinct groups. With large-scale factual mobile data captured from a big city, we compare the proposed HMM-based predictors with existing state-of-the-art predictors and apply them to different user groups. The results demonstrate the robust performance of the two proposed mobility predictors, which outperform the state of the art for various user groups.

Does location congruence matter? A field study on the effects of location-based advertising on perceived ad intrusiveness, relevance & value

We investigate the effect of location-congruent mobile messages on perceived intrusiveness, value, and relevance through a field experiment using the Experience Sampling Method (ESM). We developed a mobile application for undergraduate students, featuring campus news and information concerning class schedules. This application also included daily ads for the University restaurant, which were either location-(semi)congruent or location-incongruent. Immediately after viewing the ads the app presented a short questionnaire to the participants for a period of four weeks, thereby measuring their perceived intrusiveness, relevance and value of these ads. During these four weeks daily ads were sent to 40 students, resulting in 107 responses from 23 participants. The results show that our participants perceived location-(semi)congruent ads as significantly more valuable and relevant, whereas no significant results were found for perceived intrusiveness. By investigating LBA in a field-study based on ESM utilizing participants’ own smartphone devices this study corroborates the presumed effects of location-(semi)congruency on marketing relevant ad perceptions.

An empirical examination of location-based mobile advertising effectiveness from value perspective

With the development of mobile technology, location-based mobile advertising has become worldwide. The development of effective advertising strategies is critical. Extant researches mainly focus on attitude and behaviour intention as the measures of advertising effectiveness, overlooking the important role of individual perceived value which is related to trade-off between benefits and sacrifices. So this study proposes a model to measure LBA effectiveness from the perspective of value and the relationship with user responses. This research model is empirically tested using survey data collected from 301 receivers of LBA in China. The results show that perceived LBA value is positively related to purchasing intention on advertised brands. In addition, perceived benefits including entertainment, convenience, and personalisation have direct positive effects on consumers' perceived LBA value. Privacy risk and irritation, as perceived sacrifices, have negative effects on consumers' perceived LBA value.