Location-based Advertising Research Articles

Effect of location-based advertising filtering mechanism in smart service

Location-based advertising (LBA) refers to the delivery of marketing messages to mobile devices based on consumer location. The literature finds that LBA improves marketing effectiveness; however, distrust threatens consumers’ acceptance of LBA. This research suggests that an LBA filtering mechanism, which allows consumers to select advertising categories according to their preferences autonomously, may overcome the distrust. Its effect was empirically investigated in a smart retailing service platform. Two field experiments were conducted in a large shopping center with a total of 360 consumers. The results reveal that filtered (vs. unfiltered) LBA leads to more favorable behavioral intention through serial mediation of reduced psychological distance and enhanced trust. Moreover, the effect of LBA filtering in reducing psychological distance is more prominent for consumers with a prevention (vs. promotion) focus and with (vs. without) a purchase plan. The findings have implications for the system design and marketing of LBA.

A Deep Learning-Based Hybrid CNN-LSTM Model for Location-Aware Web Service Recommendation

Advertising is the most crucial part of all social networking sites. The phenomenal rise of social media has resulted in a general increase in the availability of customer tastes and preferences, which is a positive development. This information may be used to improve the service that is offered to users as well as target advertisements for customers who already utilize the service. It is essential while delivering relevant advertisements to consumers, to take into account the geographic location of the consumers. Customers will be ecstatic if the offerings displayed to them are merely available in their immediate vicinity. As the user’s requirements will vary from place to place, location-based services are necessary for gathering this essential data. To get users to stop thinking about where they are and instead focus on an ad, location-based advertising (LBA) uses their mobile device’s GPS to pinpoint nearby businesses and provide useful information. Due to the increased two-way communication between the marketer and the user, mobile consumers’ privacy concerns and personalization issues are becoming more of a barrier. In this research, we developed a collaborative filtering-based hybrid CNN-LSTM model for recommending geographically relevant online services using deep neural networks. The proposed hybrid model is made using two neural networks, i.e., CNN and LSTM. Geographical information systems (GIS) are used to acquire initial location data to collect precise locational details. The proposed LBA for GIS is built in a Python simulation environment for evaluation. Hybrid CNN-LSTM recommendation performance beats existing location-aware service recommender systems in large simulations based on the WS dream dataset.

Mobile Phone Location-Based Advertising (MLBA): Emergence, premises and future research agenda

The exponential rise in smartphone usage globally has spurred marketers to explore novel advertising strategies. One such innovative approach is mobile phone location-based advertising (MLBA), which allows for precise consumer targeting based on geographical proximity. This study delves into the intricacies of MLBA as a cutting-edge advertising tool, examines its origins, and principles, and outlines future research directions. Positioned at the intersection of information technology and marketing, this interdisciplinary topic warrants comprehensive analysis to advance the field. Through a meticulous review of the literature, this paper offers valuable insights into the efficacy of MLBA and its implications for both theory and practice in promotional efforts. Identifying key research gaps, lays the groundwork for future studies, facilitating a deeper understanding of MLBA's potential as a marketing tool. The findings underscore the importance of systematically evaluating the concept and its components, providing marketers and researchers with a concise yet comprehensive framework for guiding future research endeavors.

Pokémon GO as an Advertising Platform: The Case for Locative Advertising in Location-based Games

Traditional location-based advertising (LBA), such as billboards and signage, has long been a staple of direct-to-consumer advertising. In recent years, however, the prominence and popularity of location-based games have made digital LBA even more appealing. This article draws on an original research project devised to explore a notable gap in the literature on locative media: the impacts of LBA on small businesses in the location-based game Pokémon GO . The project was conducted between August and October 2021, employing semi-structured interviews with 35 businesses leveraging Niantic’s sponsored location LBA. Our findings indicate that (1) participant businesses found location-based game advertising to be rewarding, (2) LGA can act as an amenity offered by the business, and (3) local community is an essential factor for success in location-based game advertising. In sum, this research demonstrates that local businesses could successfully utilize LGBs like Pokémon GO to advertise themselves.

The role of privacy-related factors in consumer perceptions of smart advertising

PurposeAs a smart service, location-based advertising (LBA) integrates advanced technologies to deliver personalized messages based on a user’s real-time geographic location and needs. However, research has shown that privacy concerns threaten the diffusion of LBA. This research investigates how privacy-related factors (i.e. LBA type, privacy self-efficacy (PSE) and consumer generation) impact consumers’ value-in-use and their intention to use LBA.Design/methodology/approachThis study developed and examined an LBA value-in-use framework that integrates the role of LBA type, consumers’ PSE and consumer generation into the technology acceptance model (TAM). Data were collected through two experiments in the field with a total of 374 consumers. The proposed relationships were tested using PROCESS modeling.FindingsThe results reveal that pull (vs push) LBA causes higher value-in-use in terms of perceived usefulness and perceived ease of use, leading to greater usage intention. Further, the differences in the mediated relationship between pull- and push-LBA are larger among consumers of low PSE (vs high PSE) and Generation Z (vs other generations). The findings suggest that the consumer value-in-use brought about by LBA diminishes when using push-LBA for low PSE and Generation Z consumers.Originality/valueThis research is the first to integrate the privacy-related interactions of LBA type and consumer characteristics into TAM to develop a TAM-based LBA value-in-use framework. This study contributes to the literature on service value-in-use, smart services and LBA by clarifying the boundary conditions that determine the effectiveness of LBA in enhancing consumers’ value-in-use.

The Evolution of Location-Based Advertising: Challenges and Opportunities

This paper explores the historical development of location-based advertising (LBA), examines current technologies in use, and assesses the future potential of LBA in the ad tech industry. It addresses the challenges associated with privacy concerns, data accuracy, and technological limitations while highlighting the opportunities for personalized and location-specific marketing. Through a detailed analysis, the paper aims to provide insights into the effectiveness of LBA and its impact on consumer engagement and business growth.

A framework for consumers' assessments of location-based advertising (LBA) and selective exposure to privacy-related online information

PurposeThe purpose of the current research is to present an explanatory framework for how people selectively attend to privacy-related news information about LBA depending on the extent to which they know about LBA already as well as their appraisals of privacy threats and efficacy.Design/methodology/approachThe proposed model was tested using structural equation modeling based on a total of 522 useable responses obtained from an online survey.FindingsThe results revealed two different approaches to information exposure: (1) people choose to seek out privacy-related news articles when their persuasion knowledge and perceived threat level are high, whereas (2) they tend to avoid such information when perceived threats accompany fear as well as psychological discomfort, or when they believe that they are knowledgeable about LBA practices and highly capable of protecting their privacy.Originality/valueWith the development of real-time location-tracking technologies, the practice of LBA is becoming increasingly popular. As such, however, concerns about data collection and privacy are also on the rise, garnering a great deal of media attention. Despite the importance and constant stream of news reports on the subject, a comprehensive understanding of consumers' privacy assessments and information consumption remains underexamined. By incorporating the persuasion knowledge model and extended parallel process model, the current research presents an explanatory framework for consumers' privacy perceptions and information choice.

We are just 10 feet away! How does location-based advertising affect consumer-brand engagement?

Location-based advertising is continuing to grow in popularity among brands and retailers to deliver marketing messages and promotions. However, little is known about how and why these ads generate brand engagement. This study fills this gap. This paper indicates that two important features of location-based advertising (LBA)—location congruency and personalization—influence brand engagement and that the underlying mechanism behind this relationship is consumer brand identification. Further, it shows it is not only the personalization feature of these ads that increases its effectiveness, but the ad should also be location-congruent to make it effective. The conceptual model is tested by manipulating location-congruency and personalization features of LBA in two different contexts. This study is among the first to provide empirical evidence that LBA increases brand engagement and to uncover the theoretical mechanism of this relationship.

Implementasi Location Based Advertising Sebagai Media Promosi Pada Rumah Jamur Takalar

This community service activity involves MSME Rumah Jamur Takalar as the partner. The partner's problem is the lack of knowledge and skills in marketing their products via online platforms and the business location/business name is not yet known by the people of Takalar. The purpose is to help the partner expand their market reach. The specific target to be achieved is the implementation of Location Based Advertising (LBA) as a means of partner’s promotion. The methods that used are mediation and training methods. The result of this activity is that Rumah Jamur Takalar has successfully registered and activated their business account on My Ads Telkomsel as a platform that provides LBA (Location Based Advertising). The results of LBA's performance as a promotional media show that LBA activities are 100% successful in delivering advertising content according to specific target locations (Takalar Regency and its surrounding areas).

What reduces the privacy concerns of the customers towards the use of location-based advertising? An empirical investigation

ABSTRACT Location-based advertising (LBA) allows marketers to send personalised communications to customers in real-time based on their real-time location. However, customers must divulge their personal information in order to enjoy the benefits of LBA. This raises serious privacy concerns on the part of customers. Very few research studies have looked into the factors that might alleviate customers’ concerns about their privacy when utilising LBA. Therefore, this research attempts to address this research gap and examines the factors that might reduce the privacy concerns of customers towards using LBA. The research model for the study is developed using privacy calculus theory to predict the behavioural intention of the customers towards using LBA. The data of 383 respondents who have experience using LBA was evaluated using Structural Equation Modelling (SEM). The findings indicate that advertising value has a favourable influence and privacy concerns have an unfavourable influence on customers’ intentions to use LBA where advertising value plays a much more important role. Privacy self-efficacy and brand trust play an influential role in reducing privacy concerns. By contrast, privacy policy stimulates even more privacy concerns, thereby impacting the intention of the customers towards using LBA.

The effects of brand trustworthiness and credibility on location-based advertising: moderating effects of privacy concern and social influence

Location-based advertising (LBA) allows consumers to access customized advertising messages on their mobile devices, regardless of their geographic location. This study examines how brand trustworthiness and credibility drive LBA function acceptance and the relationship between LBA function and advertisement acceptance, and behavioral intentions for promotion engagements. Privacy concerns and social influence are employed as the moderating variables in the brand-technology-promotion acceptance model (BTPAM). The results from an online survey of 277 respondents in Vietnam, which were analyzed by CB-SEM and multiple group analysis, indicate that the proposed relationships in the BTPAM are all significant. Moreover, social influence and privacy concerns moderate the relationship between customers’ mobile function acceptance, advertisement acceptance, and engagement behaviors. The implications for retailers are discussed for effective location-based advertising strategies.

Supporting secure dynamic alert zones using searchable encryption and graph embedding

Location-based alerts have gained increasing popularity in recent years, whether in the context of healthcare (e.g., COVID-19 contact tracing), marketing (e.g., location-based advertising), or public safety. However, serious privacy concerns arise when location data are used in clear in the process. Several solutions employ searchable encryption (SE) to achieve secure alerts directly on encrypted locations. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the public-key setting–hidden vector encryption, and propose a graph embedding technique to encode location data in a way that significantly boosts the performance of processing on ciphertexts. We show that the optimal encoding is NP-hard, and we provide three heuristics that obtain significant performance gains: gray optimizer, multi-seed gray optimizer and scaled gray optimizer. Furthermore, we investigate the more challenging case of dynamic alert zones, where the area of interest changes over time. Our extensive experimental evaluation shows that our solutions can significantly improve computational overhead compared to existing baselines.

Coupon or my privacy: How consumers choose to disclose their personal information and accept mobile location‐based advertising (LBA) through privacy calculus

AbstractWith their capacity to track geo‐specific location information rapidly expanding, the practice of location‐based advertising (LBA) is becoming increasingly prevalent. While LBA can provide relevant and financial benefits, concerns about data collection and privacy are also on the rise. In the following two experiments, based on the privacy calculus theory and parallel mediation analyses, an explanatory framework is presented to lay out how consumers choose to disclose their personal information and accept LBA depending on location congruent levels of a mobile ad message by assessing the benefits as well as costs of disclosing personal information. In Study 1 (N = 186), it was found that both perceived benefits and costs fully mediated the experimental effects of location congruence (congruent vs. incongruent) on intentions to disclose personal information as well as accept LBA practice. In Study 2 (N = 230), a 2 (location congruence: congruent vs. incongruent) × 2 (sales promotion: yes vs. no) factorial experiment was conducted. The results from moderated mediation analysis showed that privacy concerns due to location tracking disappeared when sales promotion was offered. These results indicate that consumers' information disclosure and LBA acceptance can be understood as a trade‐off between incentives and privacy issues in which privacy concerns can be alleviated when financial benefits are offered.

Acceptance of Location-Based Advertising by Young Consumers: A Stimulus-Organism-Response (S-O-R) Model Perspective

ABSTRACT This study examines the influence of Permission, Trust, Relevance, and Context factors on the acceptance of location-based advertising in India using the Stimuli-Organism-Response (S-O-R) model. The survey responses (N=459) are collected in India using simulated situations. The analysis indicates that newer concepts relating to Permission and Trust are of low priority, but Relevance and Context play a significant role in the acceptance. The implications of the findings for retailers and other professionals are discussed.

A hedonic value-based consumer continuance intention model toward location-based advertising

PurposeHedonic value is commonly conceded as a determinant of behavioral intentions toward location-based advertising (LBA). However, the careful consideration of a mechanism behind hedonic motivation and its subsequent impact on continuance intention is inadequate. This study aims to explore the formation of hedonic value and its motivation for prolonged usage toward LBA.Design/methodology/approachA sample of 486 mobile users was recruited to evaluate the research model using structural equation modeling (SEM).FindingsResults reveal that perceived utility and promotional offers are the strongest indicators of hedonic value. Moreover, social support and contextual convenience play an essential role in heightening hedonic value. Furthermore, the research lenses attempt to clarify the direct, indirect influences of hedonic value, irritation and perceived credibility on continuance intention.Practical implicationsThe findings offer practitioners an understanding of how to improve hedonic value and continuance intention and develop effective LBA strategies in emerging markets.Originality/valueThis study narrows the gap of current literature by formulating a hedonic value-based continuance intention model based on uses and gratifications theory (UGT). Additionally, this work illuminates the insights into hedonic value toward LBA by identifying its motivations, including perceived utility, promotional offers, social support and contextual convenience.

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge- or cloud-only architectures.

The Consideration Factors of Adopting Location-Based Advertising Push Platform

The Consideration Factors of Adopting Location-Based Advertising Push Platform

Artificial Intelligence and Metaheuristic-Based Location-Based Advertising

Location-based services (LBS) are necessary for obtaining the important details since the user needs vary based on the location. Location-based advertising (LBA) are utilized for abandoning the user location and to offer assistance by using the obtained information. Therefore, an efficient machine learning and metaheuristic-based model referred as GANM is designed for LBS. Initially, the potential location information is evaluated utilizing the geographic information system (GIS). Thereafter, significant features are computed using the location data. Obtained features are then segmented to improve the process of LBS. Adaptive network-based fuzzy inference system (ANFIS) is then utilized to efficiently classify the user data. Finally, the optimization of classified documents is achieved using the nondominated sorting genetic algorithm-III (NSGA-III). Extensive experiments are performed to validate GANM for LBS. Comparative analyses reveal that GANM outperforms the competitive LBS models in terms of F-score, accuracy, sensitivity, specificity, and area under curve by 2.2734%, 2.3981%, 2.3947%, 2.4271%, and 2.1638%, respectively.

Understanding mediators in location-based mobile marketing and why commuting hubs are so effective—a mediation analysis of a randomized field experiment

ABSTRACT Due to its inexpensiveness, speed of implementation and adaptation, push location-based mobile advertising (LBMA) can constitute a powerful, cost-effective marketing tool. Thus, in theory, LBMA bears considerable potential for conducting effective marketing campaigns. However, in reality, to tap into this potential, practitioners, and academics must first understand how targeting strategies, and in particular spatial targeting – determine LBMA’s success rates. While the role of location for the effectiveness of LBMA is well understood, the underlying mechanisms through which location can influence LBMA redemption rates remain widely unexplored. Based on a field experiment with same-day LBMA coupons, this study extends the existing research and shows that location determines LBMA redemption rates via a cognitive and affective component of consumers’ ad perception. Further, this study shows that surprisingly, and contrary to the prevailing mantra that location congruency yields the best LBMA redemption rates, commuting hubs are an appealing alternative to location congruent LBMA.

Increasing Retail Patronage Intention and Retail Store Image Through Location-Based Advertising: The Moderating Role of Trust in Retailer

Increasing Retail Patronage Intention and Retail Store Image Through Location-Based Advertising: The Moderating Role of Trust in Retailer