Sponsorship congruence refers to the extent to which associations held for a property are perceived as sharing content and meaning with those held for a brand. Congruence has been shown to enhance the benefits derived from sponsorship. Consequently, it is important for an incongruent sponsor to articulate how it is related to a property. Analogical articulation represents a creative way of resolving perceptions of incongruity. A contribution of the current research is that it is the first to distinguish sponsorship effects on the basis of articulation type. Across four studies in which analogical articulations of incongruent sponsorships are compared to unarticulated and nonanalogical articulations (for both congruent and incongruent sponsorships), we find that analogical articulations improve perceptions of an incongruent sponsor's fit with a property and also perceptions of the sponsorship alignment's creativity. Analogical articulations also elicited a more pronounced “aha” moment of insight, which was found to mediate the effect of analogical articulation on perceptions of fit and creativity. Finally, analogical articulations yielded greater recall of the sponsorship.

The use of multiple screens, also known as multiscreening, is assumed to have detrimental consequences for advertising outcomes. However, many people are engaging in this form of media multitasking on a daily basis. Therefore, it is important to focus on how to improve the effectiveness of advertisements when multiscreening. The aim of this study is to examine a key facilitator of advertising effects when multiscreening, namely task relevance. In two separate experiments—an online study (n = 280) and a laboratory (n = 185) study—we showed that people who engage in related multiscreening have better brand memory and more positive brand attitudes than people who engage in unrelated multiscreening via attention and subsequent program involvement. The results of the current study contribute to our understanding of multiscreening and advertising effects by showing that multiscreening does not always have to be detrimental to advertising effects. Furthermore, this study is unique because it combines two methodological approaches of multiscreening research.

Pre-roll advertising is a novel form of online video advertising that provides consumers with an option to skip after viewing a brief forced segment. Pre-roll ads are unique from other forms of online advertising because pre-roll ads play in exactly the space in which consumers are expecting to view their intended content. Such interruption and obstruction not only heightens consumer irritation but also causes attention-getting tactics, which are redundant in a highly attentive state, to backfire. For these reasons, existing ad avoidance literature may not adequately address this unique format. Employing a large industry data set of pre-roll ads representing multiple countries and product categories, we empirically examine skipping behavior using a broader range of ad characteristics than previous research. We propose that less complex affective ad characteristics increase skipping by failing to engage cognitive resources and therefore leaving cognitive resources available to experience irritation. We further posit that, in a pre-roll context, attention-getting ad characteristics are not only superfluous but actually increase the likelihood consumers recognize pre-roll content as advertising and skip. Our empirical findings support these assertions, identifying new factors driving ad avoidance, and calling into question the applicability of attention-getting advertising approaches in the novel pre-roll environment.

Though research literature addresses a broad range of advertising impact models, studies on the channel preferences of online purchasers have received little attention, regarding both multichannel settings and channel interplay in click sequences. To provide advertisers a method for better evaluating customer channel preference, this study investigates the path to purchase by building on four multichannel clickstream data sets from three industries, recorded with cookie-tracking technologies. Applying a Cox model and clustering techniques supports delineation of empirical generalizations and industry-specific findings on channel exposure, including their antecedents and distinct channel click sequences. Across data sets, online users show idiosyncratic channel preferences for a limited set of one or two channels rather than multiple online vehicles. Both channel homogeneous click sequences and combinations of two channels (including branded contacts) are effective as purchase predictors. Our study also presents industry-specific results regarding the influence of click sequences on purchase intent, thereby providing insights for advertising research, particularly as are suited to optimization of online advertising activities.

Trust is an important factor, particularly in viral/social advertising, and computing trust scores for individual users of a social network is crucial for several applications in the advertising research and practice. However, research on trust in the advertising field has been limited, and the application of computational trust to advertising research using big data is rare. Addressing the gap in the research literature, this study proposed and empirically tested a new social media analytics method, the Trust Scores in Social Media (TSM) algorithm, for measuring individual users' trust levels in a social network. TSM proposes the concept of negatively reinforced trust scores and introduces two complementary measures of trust, trustingness, and trustworthiness. Another unique and important element in the TSM algorithm is the incorporation of trust-decision involvement to adjust trust scores depending on the level of trust-decision involvement of different networks. Using small survey data and big data from social networks, this study demonstrated the effectiveness of the TSM algorithm. Various applications of the TSM algorithm to viral/social advertising research and practice are also discussed.

We propose and test an integrated process explanation for why narrative ads in general are more persuasive than non-narrative ads. A study involving a random sample of 25 narrative and 25 non-narrative TV commercials and 484 nonstudent research participants confirmed that a set of four process variables—emotive response, ad hedonic value, ad credibility, and perceived goal facilitation—collectively mediate the positive effects of narrative (versus non-narrative) ads on attitude toward the ad (Aad) and brand attitudes. The results also supported moderated mediation processes, with type of featured product and product involvement as important moderators. Implications for theory as well as practice are discussed.

Data collection using Internet-based samples has become increasingly popular in many social science disciplines, including advertising. This research examines whether one popular Internet data source, Amazon's Mechanical Turk (MTurk), is an appropriate substitute for other popular samples utilized in advertising research. Specifically, a five-sample between-subjects experiment was conducted to help researchers who utilize MTurk in advertising experiments understand the strengths and weaknesses of MTurk relative to student samples and professional panels. In comparisons across five samples, results show that the MTurk data outperformed panel data procured from two separate professional marketing research companies across various measures of data quality. The MTurk data were also compared to two different student samples, and results show the data were at least comparable in quality. While researchers may consider MTurk samples as a viable alternative to student samples when testing theory-driven outcomes, precautions should be taken to ensure the quality of data regardless of the source. Best practices for ensuring data quality are offered for advertising researchers who utilize MTurk for data collection.

Advertising research is a scientific discipline that studies artifacts (e.g., various forms of marketing communication) as well as natural phenomena (e.g., consumer behavior). Empirical advertising research therefore requires methods that can model design constructs as well as behavioral constructs, which typically require different measurement models. This article presents variance-based structural equation modeling (SEM) as a family of techniques that can handle different types of measurement models: composites, common factors, and causal–formative measurement. It explains the differences between these types of measurement models and clears up possible ambiguity regarding formative endogenous constructs. The article proposes confirmatory composite analysis to assess the nomological validity of composites, confirmatory factor analysis (CFA) and the heterotrait-monotrait ratio of correlations (HTMT) to assess the construct validity of common factors, and the multiple indicator, multiple causes (MIMIC) model to assess the external validity of causal–formative measurement.

TEST 02 - Elsevier's Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.

In this opinion piece, the author speculates on the future of advertising. Arguing that the challenge of predicting the future lies mainly in the lack of an acceptable definition of the field, the author solves that problem by developing a set of postulates to create boundaries for the theory and practice of the discipline. The postulates of the present discipline are used as the base from which the future might evolve. Three scenarios are proposed for the future of advertising: (1) creeping incrementalism, (2) reversal of buyer/seller roles, and (3) reinvention of the field. The author suggests that those scenarios will develop and play out based on the developmental speed and acceptance of the various technologies identified.