85,703 research outputs found
Audience Responses to Gender Stereotypes in Advertising
Advertising has demonstrated linguistic, contextual, and sexual gender stereotypes since its inception; it seems poised to continue doing so as advertising’s presence in society proliferates. Upon analyzing these stereotypes, examples can be found throughout media, especially in television. All this begs the question: Are these stereotypes actually effective at selling products or services to their intended audience? Do men react positively to stereotypes of men or women; and vice versa, how do women react? If gender stereotypes are employed in advertising purely through force of habit and not evidenced prudence, then the advertising landscape stands to gain immensely from taking a more progressive view; otherwise, stereotypical advertising is defensible if only from a financial perspective
Sequential Selection of Correlated Ads by POMDPs
Online advertising has become a key source of revenue for both web search
engines and online publishers. For them, the ability of allocating right ads to
right webpages is critical because any mismatched ads would not only harm web
users' satisfactions but also lower the ad income. In this paper, we study how
online publishers could optimally select ads to maximize their ad incomes over
time. The conventional offline, content-based matching between webpages and ads
is a fine start but cannot solve the problem completely because good matching
does not necessarily lead to good payoff. Moreover, with the limited display
impressions, we need to balance the need of selecting ads to learn true ad
payoffs (exploration) with that of allocating ads to generate high immediate
payoffs based on the current belief (exploitation). In this paper, we address
the problem by employing Partially observable Markov decision processes
(POMDPs) and discuss how to utilize the correlation of ads to improve the
efficiency of the exploration and increase ad incomes in a long run. Our
mathematical derivation shows that the belief states of correlated ads can be
naturally updated using a formula similar to collaborative filtering. To test
our model, a real world ad dataset from a major search engine is collected and
categorized. Experimenting over the data, we provide an analyse of the effect
of the underlying parameters, and demonstrate that our algorithms significantly
outperform other strong baselines
Online advertising: analysis of privacy threats and protection approaches
Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft
An empirical assessment of factors affecting the brand-building effectiveness of sponsorship
Purpose: This study assesses, in two different live sponsorship environments, the contribution of sponsorship to consumer-based brand equity.
Design/methodology/approach: The study adopts a quantitative survey methodology, employing self-administered questionnaires at two UK sporting events (athletics and cricket). To isolate the impact of sponsorship, questionnaires were also distributed to comparison sample groups not exposed to the sponsorship activities. The elements of consumer-based brand equity are operationalised in line with Aaker‟s (1996) brand equity measurement tool.
Findings: Sponsorship can be an appropriate vehicle through which to build consumer-based brand equity; however brand building success is not guaranteed and is subject to a range of factors impacting upon particular sponsorships, including strength of the sponsor-event link, leverage activities and clutter. The most successful sponsorship displayed marked contributions to building brand associations, perceived quality and brand loyalty. However, the presence of sponsorship clutter in particular was found to impact negatively upon the perception of quality transferred to a brand through sponsorship.
Research limitations/implications: The use of live event settings limits the ability to tightly control all variables; therefore replication of this study using experimental methodologies is recommended. Nonetheless, findings indicate managers should consider the above mentioned contextual factors when selecting sponsorships in order to maximise sponsorship success.
Originality/value: This study explores the contribution of sports sponsorship to consumer-based brand equity in live sponsorship settings, addressing concerns over the generalizability of previous experimental studies. Equally, this study compares the brand equity-building effectiveness of sponsorship for two sponsors, which differ on a range of contextual factors that impact upon sponsorship success
Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements
Emotion evoked by an advertisement plays a key role in influencing brand
recall and eventual consumer choices. Automatic ad affect recognition has
several useful applications. However, the use of content-based feature
representations does not give insights into how affect is modulated by aspects
such as the ad scene setting, salient object attributes and their interactions.
Neither do such approaches inform us on how humans prioritize visual
information for ad understanding. Our work addresses these lacunae by
decomposing video content into detected objects, coarse scene structure, object
statistics and actively attended objects identified via eye-gaze. We measure
the importance of each of these information channels by systematically
incorporating related information into ad affect prediction models. Contrary to
the popular notion that ad affect hinges on the narrative and the clever use of
linguistic and social cues, we find that actively attended objects and the
coarse scene structure better encode affective information as compared to
individual scene objects or conspicuous background elements.Comment: Accepted for publication in the Proceedings of 20th ACM International
Conference on Multimodal Interaction, Boulder, CO, US
Randomised controlled trials of complex interventions and large-scale transformation of services
Complex interventions and large-scale transformations of services are necessary to meet the health-care challenges of the 21st century. However, the evaluation of these types of interventions is challenging and requires methodological development.
Innovations such as cluster randomised controlled trials, stepped-wedge designs, and non-randomised evaluations provide options to meet the needs of decision-makers. Adoption of theory and logic models can help clarify causal assumptions, and process evaluation can assist in understanding delivery in context. Issues of implementation must also be considered throughout intervention design and evaluation to ensure that results can be scaled for population benefit. Relevance requires evaluations conducted under real-world conditions, which in turn requires a pragmatic attitude to design. The increasing complexity of interventions and evaluations threatens the ability of researchers to meet the needs of decision-makers for rapid results. Improvements in efficiency are thus crucial, with electronic health records offering significant potential
Counterfactual Estimation and Optimization of Click Metrics for Search Engines
Optimizing an interactive system against a predefined online metric is
particularly challenging, when the metric is computed from user feedback such
as clicks and payments. The key challenge is the counterfactual nature: in the
case of Web search, any change to a component of the search engine may result
in a different search result page for the same query, but we normally cannot
infer reliably from search log how users would react to the new result page.
Consequently, it appears impossible to accurately estimate online metrics that
depend on user feedback, unless the new engine is run to serve users and
compared with a baseline in an A/B test. This approach, while valid and
successful, is unfortunately expensive and time-consuming. In this paper, we
propose to address this problem using causal inference techniques, under the
contextual-bandit framework. This approach effectively allows one to run
(potentially infinitely) many A/B tests offline from search log, making it
possible to estimate and optimize online metrics quickly and inexpensively.
Focusing on an important component in a commercial search engine, we show how
these ideas can be instantiated and applied, and obtain very promising results
that suggest the wide applicability of these techniques
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