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1 – 10 of 85Chetana Balakrishna Maddodi and Pallavi Upadhyaya
The purpose of this study is to review and synthesize the literature on in-app advertising, identify gaps and propose future research directions.
Abstract
Purpose
The purpose of this study is to review and synthesize the literature on in-app advertising, identify gaps and propose future research directions.
Design/methodology/approach
The authors use a systematic literature review (SLR) approach, following the PRISMA guidelines, to investigate the current state of research in in-app advertising. The study uses 44 shortlisted articles from the Scopus and Web of Science databases. Using the Theory-Context-Characteristics-Methodology (TCCM) framework, the authors analyze the gaps in theory, context, characteristics and methods.
Findings
Using thematic analysis, the authors identify five main themes in the in-app advertising literature, namely, ad platform optimization; mobile app user psychology and behavior; ad effectiveness; ad fraud; and security, privacy and other user concerns. The findings show the need for empirical research, with a strong theoretical foundation in emerging ad formats of in-app advertising, user behavior and buy-side of in-app advertising.
Originality/value
This is a maiden study to conduct a domain-based SLR in the emerging field of in-app advertising using the TCCM framework. The authors highlight the key differences between in-app advertising and mobile web advertising. The authors propose theories in the advertising field that could be used in future empirical studies of in-app advertising.
Propósito
El propósito de esta investigación es revisar y sintetizar la literatura sobre la publicidad en Apps, identificar lagunas y proponer futuras direcciones de investigación.
Diseño
Utilizamos un enfoque de revisión sistemática de la literatura, siguiendo las directrices PRISMA, para investigar el estado actual de la investigación en publicidad en aplicaciones. El estudio utiliza 44 artículos preseleccionados de las bases de datos Scopus y Web of Science (WoS). Utilizando el marco Teoría-Contexto-Características-Metodología (TCCM), analizamos las lagunas en teoría, contexto, características y métodos.
Conclusiones
Mediante un análisis temático, identificamos cinco temas principales en la literatura sobre publicidad en aplicaciones, a saber: optimización de plataformas publicitarias; psicología y comportamiento de los usuarios de aplicaciones móviles; eficacia publicitaria; fraude publicitario; seguridad, privacidad y otras preocupaciones de los usuarios. Nuestros hallazgos muestran la necesidad de investigación empírica, con una sólida base teórica en los formatos publicitarios emergentes de la publicidad en Apps, el comportamiento del usuario y el buy-side de la publicidad en Apps.
Originalidad
Se trata de un estudio pionero para realizar una revisión sistemática de la literatura basada en el dominio en el campo emergente de la publicidad en Apps utilizando el marco TCCM. Destacamos las principales diferencias entre la publicidad en aplicaciones y la publicidad en la web para móviles. Proponemos teorías en el campo de la publicidad que podrían utilizarse en futuros estudios empíricos sobre la publicidad en Apps.
目的
本研究旨在回顾和总结有关应用内广告的文献, 找出差距并提出未来的研究方向。
设计
我们采用系统性文献综述方法, 遵循 PRISMA 指南, 调查应用内广告的研究现状。研究使用了 Scopus 和 Web of Science (WoS) 数据库中的 44 篇入围文章。利用理论-背景-特征-方法(TCCM)框架, 我们分析了理论、背景、特征和方法方面的差距。
研究结果
通过主题分析, 我们确定了应用内广告文献的五大主题, 即广告平台优化; 移动应用用户心理和行为; 广告效果; 广告欺诈; 安全、隐私和其他用户关注点。我们的研究结果表明, 有必要在应用内广告的新兴广告形式、用户行为和应用内广告买方等方面开展实证研究, 并奠定坚实的理论基础。
独创性
这是一项首次使用 TCCM 框架对新兴的应用内广告领域进行基于领域的系统性文献综述的研究。我们强调了应用内广告与移动网络广告的主要区别。我们提出了广告领域的理论, 可用于未来的应用内广告实证研究。
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Hedonic value is commonly conceded as a determinant of behavioral intentions toward location-based advertising (LBA). However, the careful consideration of a mechanism behind…
Abstract
Purpose
Hedonic 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/approach
A sample of 486 mobile users was recruited to evaluate the research model using structural equation modeling (SEM).
Findings
Results 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 implications
The findings offer practitioners an understanding of how to improve hedonic value and continuance intention and develop effective LBA strategies in emerging markets.
Originality/value
This 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.
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Mobile location-based service (m-LBS) seems like a new class of personalized service due to location positioning technologies. This work aims to investigate consumer readiness…
Abstract
Purpose
Mobile location-based service (m-LBS) seems like a new class of personalized service due to location positioning technologies. This work aims to investigate consumer readiness (RED) toward m-LBS based on integrating pull effect- and push effect-related factors into the technology acceptance model (TAM).
Design/methodology/approach
An online survey collected data from 423 participants, and the research framework was analyzed using structural equation modeling (SEM).
Findings
The results divulge that consumer RED is determined by TAM antecedents, including usefulness (USE) and ease of use (EOU). EOU motivates USE in m-LBS. Regarding pull effect-related factors, absorptive capacity (ABC) is the strongest positive factor influencing consumer RED to use m-LBS, followed by technology willingness (TWI) and innovativeness (INN). Moreover, INN, trust (TRU) and perceived risk (RIS) significantly influence USE and EOU.
Originality/value
This work endeavors to explicate customer RED toward m-LBS by incorporating some meaningful pull effect-related dimensions (i.e. ABC, TWI and INN) and pushing effect-related dimensions (i.e. RIS) into crucial antecedents rooted in TAM. Thus, the findings assist practitioners in developing marketing strategies by boosting pull effects and controlling push effects on customer engagement in m-LBS.
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Robert Ciuchita, Johanna Katariina Gummerus, Maria Holmlund and Eva Larissa Linhart
Digital advertising enables retailers to rely on large volumes of data on consumers and even leverage artificial intelligence (AI) to target consumers online with personalised and…
Abstract
Purpose
Digital advertising enables retailers to rely on large volumes of data on consumers and even leverage artificial intelligence (AI) to target consumers online with personalised and context-aware advertisements. One recent example of such advertisements is programmatic advertising (PA), which is facilitated by automatic bidding systems. Given that retailers are expected to increase their use of PA in the future, further insights on the pros and cons of PA are required. This paper aims to enhance the understanding of the implications of PA use for retailers.
Design/methodology/approach
A theoretical overview is conducted that compares PA to traditional advertising, with an empirical investigation into consumer attitudes towards PA (an online survey of 189 consumers using an experimental design) and a research agenda.
Findings
Consumer attitudes towards PA are positively related to attitudes towards the retailer. Further, perceived ad relevance is positively related to attitudes towards PA, which is moderated by (1) consumer perceptions of risks related to sharing their data with retailers online and (2) consumer perceptions of AI's positive potential. Surprisingly, the disclosed use of AI for PA does not significantly influence consumer attitudes towards PA.
Originality/value
This paper contributes to the literature on technology-enabled services by empirically demonstrating that ad relevance drives consumer attitudes towards PA. This paper further examines two contingencies: risk beliefs related to data (i.e. the source of PA) and perceptions of AI (i.e. the somewhat nebulous technology associated with PA) as beneficial. A research agenda illuminates central topics to guide future research on PA in retailing.
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Abstract
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Nicola Bilstein, Alexander P.P. Henkel and Kristina Heinonen
Wei Xiong, Ziyi Xiong and Tina Tian
The performance of behavioral targeting (BT) mainly relies on the effectiveness of user classification since advertisers always want to target their advertisements to the most…
Abstract
Purpose
The performance of behavioral targeting (BT) mainly relies on the effectiveness of user classification since advertisers always want to target their advertisements to the most relevant users. In this paper, the authors frame the BT as a user classification problem and describe a machine learning–based approach for solving it.
Design/methodology/approach
To perform such a study, two major research questions are investigated: the first question is how to represent a user’s online behavior. A good representation strategy should be able to effectively classify users based on their online activities. The second question is how different representation strategies affect the targeting performance. The authors propose three user behavior representation methods and compare them empirically using the area under the receiver operating characteristic curve (AUC) as a performance measure.
Findings
The experimental results indicate that ad campaign effectiveness can be significantly improved by combining user search queries, clicked URLs and clicked ads as a user profile. In addition, the authors also explore the temporal aspect of user behavior history by investigating the effect of history length on targeting performance. The authors note that an improvement of approximately 6.5% in AUC is achieved when user history is extended from 1 day to 14 days, which is substantial in targeting performance.
Originality/value
This paper confirms the effectiveness of BT on user classification and provides a validation of BT for Internet advertising.
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Teresa Fernandes and Rodrigo Oliveira
Social media has become an inescapable part of our lives. However, recent research suggests that excessive use of social media may lead to fatigue and users’ disengagement. This…
Abstract
Purpose
Social media has become an inescapable part of our lives. However, recent research suggests that excessive use of social media may lead to fatigue and users’ disengagement. This study aims to examine which brand-related factors contribute to social media fatigue (SMF) and its subsequent role on driving lurking behaviors, particularly among young consumers.
Design/methodology/approach
Based on survey data from 282 young users of social media, a holistic model of brand-related drivers and outcomes of SMF was tested, emphasizing the contribution of brands’ social media presence to users’ disengagement.
Findings
Research shows that branded content overload and irrelevance, as well as branded ads intrusiveness significantly impact SMF, which in turn plays a mediating role between brand-related drivers and lurking behaviors. The authors further conclude that the impact of SMF on lurking is stronger for users who follow a larger set of brands.
Originality/value
The study contributes to social media research by addressing its “dark side” and empirically validating the role of brands’ social media presence in developing young users’ fatigue and disengagement. The study further adds to the scant literature on SMF, which was mostly developed outside the branding field. Research also provides valuable insights to brands on how to improve their social media performance.
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