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Explores one of the many exciting advertising research possibilities spawned by the Web, namely the efficacy of banner advertisements designed to lure the browser to an external Web page. Traditional advertising research usually relies on self‐report or memory. With Web advertisement banners, on the other hand, we can track actual behavior. In our pilot study, we demonstrate conclusively that click‐through rate, the percentage of visitors to a Web page clicking on an advertisement banner, can vary according to the advertisement copy. We also find that the imperative call for behavior, “Click here”, has a positive effect. These findings, using a new research method with a new medium, open the door to further advertising and communication research on Web advertisement banners.

Endorsement marketing has been widely used to generate consumer attention, interest and purchase decisions among targeted audiences. Internet celebrities who become famous on the Internet are dependent on strategic intimacy to appeal to their followers. Our study aims to examine how multiple exposures to Internet celebrity endorsements influence consumers’ click and purchase decisions in the context of influencer marketing.

Based on a unique and representative dataset, the authors first model consumers’ choices for clicks and purchases with two panel fixed-effect logit models linking clicks and purchases with the frequency of exposure to Internet celebrity endorsement. To further control the endogeneity produced by the intercorrelation between the click and purchase models, the authors also adopt the two-stage Heckman probit structure to jointly estimate the two models using Maximum Likelihood Estimation. Robustness checks confirm the effectiveness of the models.

The results suggest that Internet celebrity endorsement plays a significant role in bringing referral traffic to e-commerce sites but is less helpful in affecting conversion to sales. The impact of repetitive Internet celebrity endorsements on consumers’ click decisions is U-shaped, but the role of Internet celebrities as online retailers will “shape-flip” this relationship to a negative linear relation.

Our study is the first to investigate the repetitive exposure effect of Internet celebrity endorsement. The results show a contradictory pattern with a wear-out effect of repetition in the advertising literature. This is the first study to show how the endorsing self, which is a common business model in influencer marketing, moderates the effectiveness of influencer marketing.

This study aims to analyze whether average video watch time or click-through rates (CTR) on YouTube videos are more closely associated with high numbers of views per subscriber using linear regressions.

In 2018, YouTube began releasing CTR data to its video creators. Since 2012, YouTube has emphasized how it favors watch time over clicks in its recommendations to viewers. To the best of the author’s knowledge, this is the first academic study looking at that CTR data to test what matters more for views on YouTube. Is watch time or CTR more important to getting views on YouTube?

The author analyzed new video releases on YouTube. This paper finds almost no or limited evidence that higher percent audience retention or total average watch time per view, respectively, are associated with more views on YouTube. Instead, videos with higher CTR got significantly more views.

The author knows no other study that tests the relative importance of CTR or watch time per view in predicting views for new videos on YouTube.

The aim of this paper is to examine the impact of three main technologies on converting browsers into customers: impact of review rating (location rating and service rating), recommendation and search listings.

This paper estimates conversion rate model parameters using a quasi-likelihood method with the Bernoulli log-likelihood function and parametric regression model based on the beta distribution.

The results show that a high rank in search listings, a high number of recommendations and location rating have a significant and positive impact on conversion rates. However, service rating and star rating do not have a significant effect on conversion rate. Furthermore, room price and hotel size are negatively associated with conversion rate. It was also found that a high rank in search listings, a high number of recommendations and location rating increase online hotel bookings. Furthermore, it was found that a high number of recommendations increase the conversion rate of hotels with low ranks.

The findings show that hotels’ location ratings are more important than both star and service ratings for the conversion of visitors into customers. Thus, hotels that are located in convenient locations can charge higher prices. The results may also help entrepreneurs who are planning to open new hotels to forecast the conversion rates and demand for specific locations. It was found that a high number of recommendations help to increase the conversion rate of hotels with low ranks. This result suggests that a high numbers of recommendations mitigate the adverse effect of a low rank in search listings on the conversion rate.

This paper contributes to the understanding of the drivers of conversion rates in online channels for the successful implementation of hotel marketing.

The purpose of this paper is to explore the effects of online advertising spending on automobile sales through both search and non-search advertising.

Sales data of the top 52 vehicle models were collected in two consecutive years in China. The advertising spending data of both formats were collected from a leading consulting company and a major search engine company. Then several empirical models were proposed to evaluate the effects of online advertising on automobile sales. Two extended models were further investigated for search advertising.

The results revealed that both formats of online advertising have significantly positive effects on automobile sales. However, excessive spending on non-search advertising does not help sales and a moderate budget is preferred. On the other hand, spending on search advertising has no such constraint to improve the vehicle sales.

The empirical findings have proved the importance of online advertising to the automobile companies and thus can help companies improve their decision making in online advertising allocation strategies.

This study provides a better understanding of the relationship between online advertising spending and automobile sales, and helps business to define sophisticated online advertising strategies to improve sales performance.

To describe how a personal librarian program at a large university developed, has evolved, and continues to function. This paper aims to assist other librarians in developing their own personal librarian programs. It will also assist librarians who are working to connect to students.

To produce this paper, internal documents were reviewed, existing data were investigated, those who assisted in the development of the program were consulted and literature on personal librarian programs was reviewed.

Personal librarian programs can be an efficient way to connect to students and can create awareness about library services, especially without a formal orientation for new students. The personal librarian program discussed here connects the library to a large number of students with little time and effort. Planning is important in developing a working program.

Librarians can use this article to understand how a personal librarians program functions and how it can benefit their libraries. The paper emphasizes revising an existing program to work more effectively and using planning documents and assessment to help an outreach program run smoothly.

This paper details how a personal librarian program was developed and has evolved as well as how the program functions. The value is in the ways in which the program has been revised and has evolved and in the role that planning has taken in creating an effective program.

Many recommender systems are generally unable to provide accurate recommendations to users with limited interaction history, which is known as the cold-start problem. This issue can be resolved by trivial approaches that select random items or the most popular one to recommend to the new users. However, these methods perform poorly in many cases. This paper aims to explore the problem that how to make accurate recommendations for the new users in cold-start scenarios.

In this paper, the authors propose embedded-bandit method, inspired by Word2Vec technique and contextual bandit algorithm. The authors describe user contextual information with item embedding features constructed by Word2Vec. In addition, based on the intelligence measurement model in Crowd Science, the authors propose a new evaluation method to measure the utility of recommendations.

The authors introduce Word2Vec technique for constructing user contextual features, which improved the accuracy of recommendations compared to traditional multi-armed bandit problem. Apart from this, using this study’s intelligence measurement model, the utility also outperforms.

Improving the accuracy of recommendations during the cold-start phase can greatly raise user stickiness and increase user favorability, which in turn contributes to the commercialization of the app.

The algorithm proposed in this paper reflects that user contextual features can be represented by clicked items embedding vector.

Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected.

Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study.

The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices.

Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.

Summarizes the papers presented at The Second Marketing Science and the Internet Conference entitled: “Understanding Consumer Behaviour on the Internet”, held in Los Angeles, 28‐30 April 2000. Identifies key topical issues and future research agendas. Starts from the premise that research into consumer behaviour in the e‐marketplace is in its infancy, and that a variety of different types of contributions will be necessary to achieve a more informed understanding of consumer behaviour in this new context. Groups current work under four headings: cognition – concerned with the consumer response to specific features embedded in the interface between the consumer and the organisation; customisation – which reviews the various options for personalisation in the marketing exchange, and their effectiveness and acceptability to the consumer; cumulation – which explores the cumulative effect of consumer behaviour on the marketplace; and context – concerned with the relativities between online and traditional retailing and business environments.

The main purpose of this study was to determine whether users of the online social network site, Facebook, actually look at the ads displayed (briefly, to test the existence of the phenomenon known as “banner blindness” in this website), thus ascertaining the effectiveness of paid advertising, and comparing it with the number of friends' recommendations seen.

In order to achieve this goal, an experiment using eye‐tracking technology was administered to a total of 20 participants from a major university in the USA, followed by a questionnaire.

Findings show that online ads attract less attention levels than friends' recommendations. A possible explanation for this phenomenon may be related to the fact that ads on Facebook are outside of the F‐shaped visual pattern range, causing a state of “banner blindness”. Results also show that statistically there is no difference in ads seen and clicked between women and men.

The sample type (undergraduate and graduate students) and the sample size (20 participants) inhibit the generalization of the findings to other populations.

The paper includes implications for the development of an effective online advertising campaign, as well as some proposed conceptualizations of the terms social network site and advertising, which can be used as platforms for discussion or as standards for future definitions.

This study fulfils some identified needs to study advertising effectiveness based on empirical data and to assess banner blindness in other contexts, representative of current internet users' habits.