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Consumers are increasingly using search-based advertising in e-Business platforms to seek their desirable products. Platforms will choose a centralized advertising mechanism (CAM) or decentralized advertising mechanism (DAM) to offer a search advertising service to lower consumer search cost, as represented by using search time length. It is important for the platform to decide how to choose advertising mechanisms, and how to determine the optimal advertising price and search time length. To address these issues, this study aims to develop a theoretical approach under each mechanism to examine the platform’s optimal search-based advertising strategy by considering search cost.

In this study, two models are developed to examine the optimal search-based advertising strategy by considering consumer search cost (i.e. search time length). By comparing the platform’s profits under two models, the optimal advertising strategy, search time length and price are explored.

It is found that when the seller’s reserve benefit is sufficiently large, the platform benefits from choosing the DAM; otherwise, the CAM is a better choice. The advertising service is usually offered with a shorter search time length accompanied by a higher charge, and a longer search time length accompanied by a lower charge. Specifically, when the seller’s reserve benefit is substantially high, a DAM that benefits both the platform and seller is a better choice. This can explain why many platforms offer advertising services with a DAM.

This paper is the first theoretical study on addressing the search-based advertising strategy, especially the choice of advertising mechanisms, in the online advertising context. It is also the first piece of analytical research that considers the effect of consumer search cost on product demand, and then examines the optimal advertising price and search cost (i.e. search time length) for online platforms.

This study aims to investigate whether digital advertising can be effective despite consumer inattention and how certain common combinations of ad characteristics increase or decrease ad effectiveness under conditions of low attention.

Using two online experiments in naturalistic environment, the authors compare ad effects under focussed, divided and incidental attention, for certain ad characteristics, namely, appeal type and (mis)matching between appeal and brand type. The results are analysed using logistic regression.

Ad exposure under low attention does increase brand consideration and choice. The greatest uplift in impact occurs when moving from non-exposure to incidental attention. Under incidental attention, emotive advertising was more effective than rational advertising, as was matching rather than mismatching an emotional appeal to a hedonic brand. Conversely, under divided attention, rational advertising and mismatching a rational appeal to a hedonic brand were more effective.

This research explores the effectiveness of Twitter ads with an emotional or a rational appeal and the (mis)matching between appeal and utilitarian or hedonic brand type. Future research can examine other formats and creative elements of digital advertising that can affect the low-attention processing and the effects that occur.

Intrusive, attention-getting advertising strategies may not be necessary. Certain common creative devices can increase advertising effectiveness despite low attention, so marketers can ensure consumer-centric marketing communication.

There has previously been limited understanding of low-attention mechanisms in advertising and little evidence of ad effectiveness under conditions of low attention. The research also demonstrates that certain ad characteristics, linked to common creative devices, enhance the impact of advertising despite low attention.

This paper aims to uncover humor mechanisms. Humor mechanisms influencing consumer behaviors seem relatively under-researched. In consequence, the effectiveness of humorous appeals is often questioned and research has yet to provide clear guidelines regarding why, for whom and when these appeals work. After uncovering ads that contain the two main types of humor mechanisms, the distraction and combined-influence hypotheses are tested in combination with dispositional and situation involvement.

Using a focus group to define the ways consumers perceive humor, two pre-tests established a measure to identify arousal-safety (A-S) and incongruity resolution humor mechanisms. Two main studies (n = 486) test these mechanisms for two types of consumer groups (low and high NFC) in studies meant to replicate content-free and content-based media contexts.

The results show that consumers are likely to have higher attitudes towards the humor ads that contain A-S. When considering the type of ad mechanism used, the results support the distraction hypotheses even for consumers with high NFC and even when in high situational involvement. No support for the combined-influence hypothesis is uncovered.

It is shown that humorous mechanism is an important consideration when creating humor ads. The results also add more detailed support for the distraction hypothesis. From these results, marketers have a better understanding of humor mechanisms and practitioners of how to position their humorous advertising depending on the outcome behaviors they wish to encourage. Marketers are also advised to create humorous advertising that is simple rather than complex.

The purpose of this paper is to investigate the differences in consumer pleasure, arousal and purchase intention when consumers encounter food advertising on Instagram using different endorsers and visual complexity levels.

An experimental design was conducted involving 180 undergraduate students from several universities in Surabaya, Indonesia. The participants had actively used Instagram for at least one year.

Food ads endorsed by a celebrity generate more pleasure and arousal than those endorsed by food experts. Food advertising using high levels of visual complexity cues generates more pleasure and arousal than less complex advertising. However, less complex food ads using food experts create greater pleasure than those endorsed by celebrities. Consumer pleasure and arousal were significant mediators of the impact of endorser type and visual complexity on consumer purchase intentions.

As celebrities and higher levels of visual complexity result in more favorable responses to Instagram ads, food marketers need to consider increasing visual complexity when using celebrities in advertising by adding more objects, using more colors, objects, or textures and incorporating asymmetric elements in the advertisements.

This is one of the few studies comparing the effectiveness of celebrity and expert endorsers in Instagram advertising. Also, this research extends the existing knowledge about visual complexity in the context of social media advertising.

The pervasive spread of digital technologies brought an incredible boost in data availability. Companies are dealing with massive amount of data that wait to be exploited. At the same time, scholars are providing different strategies and methods to help companies capture the value embedded in their data to foster innovation and improve the efficiency of existing processes. In these research studies, data are the by-product of something else, and they are a silent asset that needs to be exploited. What if data might be considered the final goal? The paper aims to discuss these issues.

The research is based on an exploratory multiple case study analysis, on the basis of three cases used as an illustration for new ideas. In particular, the gathered data are analyzed according to models previously presented in the literature review, building on and expanding them.

The research proposes a data-driven approach to innovation, offering a peculiar view of the innovation process. The trigger point is the need of data that let begin the entire development process of a complex system. In this perspective, the application that data are a by-product of the entire innovation process and not the primary output is peculiar since the vast majority of the literature consider data as the by-product of the primary product.

Future research is needed to assess the replicability of the model outside the mobile app industry and to measure its performances. Nevertheless, this paper provides insights both for scholars and managers, enlarging the discussion on digital innovation and digital business models.

The results provide a development process to foster innovation relying on the need of data as the trigger point, guiding entrepreneurs and managers in the building process of the entire digital system.

Previous research studies often considered Big Data (BD) in innovation as a way to enlarge the current product offer or to make the innovation process more effective or efficient; this paper changes the perspective by considering BD as the trigger and the enabler of the entire digital innovation process.

The purpose of this paper is to model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are competing in the same auction or vertical industry, and examine resulting market outcomes, via a proposed simulation framework named Experimental Platform for Search Engine Advertising (EXP-SEA) supporting experimental studies of collective behaviors in the context of search engine advertising.

The authors implement the EXP-SEA to validate the proposed simulation framework, also conduct three experimental studies on the aggregate impact of electronic word-of-mouth (eWOM), the competition level and strategic bidding behaviors. EXP-SEA supports heterogeneous participants, various auction mechanisms and also ranking and pricing algorithms.

Findings from the three experiments show that both the market profit and advertising indexes such as number of impressions and number of clicks are larger when the eWOM effect is present, meaning social media certainly has some effect on search engine advertising outcomes, the competition level has a monotonic increasing effect on the market performance, thus search engines have an incentive to encourage both the eWOM among search users and competition among advertisers, and given the market-level effect of the percentage of advertisers employing a dynamic greedy bidding strategy, there is a cut-off point for strategic bidding behaviors.

This is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.

This paper aims to examine the B2B advertising effect on firm’s market value and whether/how its effectiveness can be enhanced with corporate social responsibility (CSR) strengths.

The authors propose that CSR can be a strategic complement to advertising and reinforce the latter’s positive effect on a firm’s performance in two logics: signaling mechanism and defensive mechanism. Using the Kinder, Lydenberg, and Domini database and final data obtained from Compustat, the authors applied fixed effect regression analysis to test the interaction effect of advertising expense and CSR strengths on firms’ market performance as operationalized in Tobin’s Q.

The result confirms that CSR moderates the B2B advertising effect on a firm’s market value. More importantly, the authors find that internal CSR activities that are closely related to a firm’s core business, compared to external CSR activities, more significantly enhance the advertising effectiveness on a firm’s market value.

This research provides guidelines for B2B firms to better prioritize resource allocation to CSR practices for achieving a better financial outcome.

The current study on the joint effect of advertising and CSR has important theoretical and managerial implications, given both tools are commonly used by most B2B firms but not necessarily integrated into one corporate marketing strategy.

This paper explores the nonlinear interactions of research and development (R&D) and advertising and their synergistic effect on firm performance using Tobin's Q. This study also aims to investigate differential synergy patterns under varying levels of exports with a precision impact on performance.

Unlike a conventional statistical approach, this study uniquely presents a neural network approach to explore the dynamic interplay of strategic factors. A multilayer perceptron neural network (MPNN) is designed to capture complex interaction patterns through a predictive analytic process.

This study finds that the impact of R&D and advertising is positive, with a greater effect on high-export firms. Moreover, the experiment results show that the synergy of R&D and advertising goes beyond the formatted positive/negative frame and actually has a reinforcing effect.

This study not only conveys the significant nexus of R&D and advertising for firm performance but also provides industry managers' practical means to assess the joint effect of R&D and advertising on firm performance. The proposed analytic mechanism in particular provides pragmatic decision support to managers in harmonizing their R&D and advertising efforts for a foreseeable impact.

This paper presents an innovative analytic process using the MPNN to explore the synergy between R&D and advertising. In addition to offering new perspectives on R&D and advertising, this study presents pragmatic implications for managing those strategic resources to meet performance targets.