Effectiveness of online behavioral targeting: A psychological perspective

Özçelik, Feriştah; Varnalı, Kaan

doi:10.1016/j.elerap.2018.11.006

Cited by 39 publications

(41 citation statements)

References 70 publications

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“…Also, the influence of irritation decreases with increasing user experience and is no longer significant after the fifth trip. The finding that irritation has a negative effect on mobile LBS' K value-in-use is principally consistent with earlier findings, which found a negative influence of irritation on attitudes toward an advertising message (e.g., Ozcelik and Varnali 2019), on the perceived value of LBA (e.g., Lin and Bautista 2020;Xu et al 2009b), on advertising value (e.g., Martins et al 2019), on the attitudes toward LBS (Lin et al 2013) and on the intention to use LBA (e.g., Richard and Meuli 2013). However, we illustrate here that this connection also exists in the context of value-in-use.…”

Section: Discussionsupporting

confidence: 90%

“…If a customer finds advertising messages to be irritating or annoying, this leads to negative feelings toward the advertisement (Yang et al 2013) and reduces perceived advertising value (Ducoffe 1995(Ducoffe , 1996. Several studies have investigated irritation's negative effects on advertising value and attitudes toward mobile ads (e.g., Ozcelik and Varnali 2019;Lin and Bautista 2020;Martins et al 2019;Lin et al 2013;Richard and Meuli 2013;Xu et al 2009b). They found that using mobile LBS during inner-city visits can hinder the achievement of customers' individual goals.…”

Section: Epistemic Benefits According Tomentioning

confidence: 99%

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Mobile Location-Based Services’ Value-in-Use in Inner Cities: Do a Customer’s Shopping Patterns, Prior User Experience, and Sales Promotions Matter?

Schwipper

Peche

Schmitz

2020

Schmalenbach Bus Rev

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Mobile location-based services (LBS) represent a promising opportunity for inner-city retailers and service providers to react to changes in customer behavior due to digitalization. To gain competitive advantages, mobile LBS must offer customers high value-in-use and help them reach their shopping goals during their inner-city visits. Shopping goals differ depending on shopping patterns; thus, these patterns may influence customers’ evaluation of mobile LBS during inner-city visits. Since value-in-use is not only a context-specific but also a temporally dynamic construct, customers’ user experience must also be considered. Therefore, this study investigates the influence of customers’ shopping patterns and current user experience on their evaluation of mobile LBS’ value-in-use during inner-city visits. Moreover, the impacts of the offers transmitted through mobile LBS on the value-in-use are examined. Using field test data, we empirically verify a conceptualization of mobile LBS and determine a comprehensive view of mobile LBS’ value-in-use during shopping trips with different shopping patterns and user experience within a mixed-method analysis. Our results identify both utilitarian and hedonic value-in-use components as being empirically relevant for high value-in-use evaluations regarding mobile LBS in inner cities. Furthermore, the relevance of monetary benefits, fun benefits, and irritation on value-in-use vary according to customers’ user experience. A customer’s shopping pattern affects the value-in-use of mobile LBS; however, this effect is not as differentiated as expected. Moreover, the number of relevant monetary and non-monetary offers transmitted during an inner-city visit are shown to represent a potential, albeit limited, management instrument for affecting mobile LBS’ value-in-use.

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Section: Discussionsupporting

confidence: 90%

Section: Epistemic Benefits According Tomentioning

confidence: 99%

Mobile Location-Based Services’ Value-in-Use in Inner Cities: Do a Customer’s Shopping Patterns, Prior User Experience, and Sales Promotions Matter?

Schwipper

Peche

Schmitz

2020

Schmalenbach Bus Rev

View full text Add to dashboard Cite

show abstract

“…As far as the benefits are concerned, the theory of uses and gratifications was mainly used to identify OBA's potential benefits (Sutanto et al, 2013;Ozcelik and Varnali, 2019). Specifically, the OBA makes advertisements more relevant to consumers who, seeing a relevant and personalized ad for them, are less likely to avoid the advertising (McDonald and Data-driven digital advertising Cranor, 2010;Ur et al, 2012).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Data-driven digital advertising: benefits and risks of online behavioral advertising

Aiolfi

Bellini

Pellegrini

2021

IJRDM

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PurposeThe research aims to investigate how individuals can be persuaded to make purchases through repeated and personalized messages. Specifically, the study proposes a framework of the potential benefits and risks of the online behavioral and data-driven digital advertising (OBA), which can help researchers and practitioners to better understand shopping behavior in the online retailing setting. In addition, the research focuses on the role of privacy concerns in affecting avoidance or adoption of OBA.Design/methodology/approachThe authors apply a structural equation modeling (SEM) approach with partial least square (PLS) regression method to test the research hypotheses through data coming from a structured questionnaire.FindingsOBA is a controversial type of advertising that activates opposing reactions on consumers' perspective. Specifically, acceptance of the OBA is positively related to relevance, usefulness and credibility of the personalized advertisements, while the intention to avoid personalized ads is strictly related to the privacy concerns. Consequently, OBA acceptance and avoidance affected the click intention on the ad and the behavioral intention that are decisive for the success of data-driven digital advertising.Originality/valuePrior research came up with complex theoretical frameworks that explain antecedents of OBA focusing only on ethical issues in marketing, on the effectiveness of a single OBA campaign or on how to create a successful advertising campaign. However, no study focuses on the intended or actual behavior of shoppers. Specifically, filling the gap in the existing literature, our research applies an SEM approach to identify both benefits and risks and the antecedents of the actual behavior of individuals in terms of actual purchases promoted by OBA.

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“…Concerning marketing strategy, behavioral targeting customizes messages to individual consumers based on their specific shopping interests and characteristics like gender, age, and ethnicity (Ozcelik & Varnali, 2018). Behavioral targeting links identifiers within consumers' browsers to track browsing behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Demography, Psychograph and Behavioral Aspects of Telecom Customers Using Predictive Analytics to Increase Voice Package Sales

Goenandar

Ariyanti

2021

JCS

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In 2018, Telkomsel's core business shifted its main services from Telephone and SMS services to Data and Digital services, since a declining trend of revenue starting 2014. However, telephone service still contributed 28.4% to the revenue and was the second largest, while SMS gave 4.1%. This research predicts voice package buyers using predictive analytics to identify customer profiles and significant variables to form appropriate target customer segmentation. Logistic regression was used to predict customers who would buy voice packages using 15 input variables. Next, analytics was done by dividing the data into 70% training data sets and 30% testing data obtained from customer voice package user data. The model accuracy gained 97.2%, and the top seven significant variables were formed. Then five clusters of customer segmentation were formed based on top significant variables using the K-Means clustering technique. Based on the results of the prediction model and clustering, behavioral targeting was conducted to provide targeted gimmick products based on five segmentations formed, and then it was divided into two main target customers by considering the similarity of behaviors based on revenue voice, minutes of voice usage, voice transactions, day of voice usage and data payload, thus it was more targeted.

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Effectiveness of online behavioral targeting: A psychological perspective

Cited by 39 publications

References 70 publications

Mobile Location-Based Services’ Value-in-Use in Inner Cities: Do a Customer’s Shopping Patterns, Prior User Experience, and Sales Promotions Matter?

Mobile Location-Based Services’ Value-in-Use in Inner Cities: Do a Customer’s Shopping Patterns, Prior User Experience, and Sales Promotions Matter?

Data-driven digital advertising: benefits and risks of online behavioral advertising

Analysis of Demography, Psychograph and Behavioral Aspects of Telecom Customers Using Predictive Analytics to Increase Voice Package Sales

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