Search results

This paper aims to study the pricing strategies of an online trading platform with indirect network externalities by considering heterogeneous trading behavior in the downstream market and the long tail.

The game theory, optimization and comparative static are used in this research. The equilibria are derived from the game theory, and with them, the authors optimize the platform’s profit function. Comparative static is used to study pricing strategies.

It is found that with heterogeneous trading behavior, the transaction-based model is more profitable than the subscription-based model by reason of the feasibility of “price discrimination”. However, with certain advantages of subscription fees such as avoiding offline transactions, the subscription-based model is better with a concentrated distribution of sellers’ revenues (the Gini coefficient is small). With a lucrative long tail, the platform should set a low price to attract small sellers in the long tail. Besides, if the Gini coefficient is large, the effects of the market entry barrier of sellers on the optimal price in each model may be opposite.

It implies that the choice of revenue models and pricing strategies are influenced by the Gini coefficient or the long tail. The exogenous setting in which buyers can use the platform for free needs further extension.

The authors provide insights on how to choose revenue models and how to price the sellers with the long tail phenomenon.

This paper emphasizes the role of the long tail on pricing strategies and the effect of heterogeneous trading behavior on model selection.

This paper is aimed primarily at academic library managers and acquisition librarians. By analogy to Pareto studying the relationship between clients and turnover, the paper will study subscriptions to e‐journals and usage statistics. The aim is to evaluate the long tail of usage statistics and to compare it with subscription lists of individually selected titles and packages (big deals).

The paper exploits usage statistics and subscription data from a national usage study of an academic publisher. Data are from 2010.

Usage statistics are partly shaped by the long tail effect. Individual subscriptions of journals are more selective than big deals, and trend towards a traditional retail curve. Unlike subscriptions through packages, usage and individual subscriptions can be related by a similar inclination. But both types of subscriptions fail to predict the popularity of a title in its usage.

The paper uses data from a national usage study and tries to identify global trends. Thus, it does not distinguish between customer categories, disciplines or activity domains.

The paper considers the opportunity provided by big deal for acquisition policy. Ready‐made big deals sometimes appear as an unbounded and excessive supply, not suited to true and sufficient users' needs, but on the other hand, selective acquisition policy cannot completely anticipate online usage behaviour.

Only a few studies distinguish Pareto from long tail distributions in usage statistics, and there is little empirical evidence on the impact of selected subscriptions versus big deals on these statistics.

The purpose of this paper is to elaborate the long tail of topical search queries, including the influence of current events, posed to a large, urban public library discovery system.

Search queries from the months of June, July, August and September 2014 (1,488,339 total queries) were collected from the Edmonton Public Library’s BiblioCommons database using Google Analytics and exported to Excel. The data were then analyzed using descriptive statistics, frequency counts and textual analysis to explicate the long tail of search, (including the most popular searches) and to explore the relationship between topical search queries and current events.

The findings support the long tail theory, as the aggregate tail of topical search queries comprised the vast majority of the total searches and current events exert some influence on the nature and frequency of topical searches.

Data collection was limited to four months of the year; thus, comparisons across the year cannot be made. There are practical implications for public libraries in terms of marketing and collections, as well as for improving catalogue functionality, to support user search behaviour.

Not much research attention has been focused on the nature of topical search queries in public libraries compared to academic libraries and the Web. The findings contribute to developing insight into the divergent interests of intergenerational public library users and the topics of materials they are searching for.

The purpose of this paper is to examine the demand curve for information on tourism destinations and accommodation. The current study compares the demand curves for this information to trends described by Chris Anderson as the “long tail”.

The current study examines the demand for information about accommodation establishments and destinations in Australia through the Australian Tourism Data Warehouse (ATDW). The study examines the demand for information received through the ATDW in 2009 for 5,600 Australian destinations and over 33,200 accommodation listings. Demand for information was measured by page impressions (PIs). Over 10 million PIs were received for destinations and more than 17 million PIs were received for accommodation listings, all of which were examined.

The current research shows that both accommodation and destination demand curves display the extended demand curve typical of the long tail phenomenon. The analysis also shows that demand curves within the aggregate demand curve also follow “long tail” demand curves. The study contributes to understanding of the demand curve for tourism information for Australian product using the ATDW.

The paper provides analysis of tourism information demand in the context of the “long tail” phenomenon.

One challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data.

Using Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs.

The comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation.

TRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors’ work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors’ work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.

The personal learning environment driven approach to learning suggests a shift in emphasis from a teacher‐driven knowledge‐push to a learner‐driven knowledge‐pull learning model. One concern with knowledge‐pull approaches is knowledge overload. The concepts of collective intelligence and the Long Tail provide a potential solution to help learners cope with the problem of knowledge overload. The paper aims to address these issues.

Based on these concepts, the paper proposes a filtering mechanism that taps the collective intelligence to help learners find quality in the Long Tail, thus overcoming the problem of knowledge overload.

The paper presents theoretical, design, and implementation details of PLEM, a Web 2.0 driven service for personal learning management, which acts as a Long Tail aggregator and filter for learning.

The primary aim of PLEM is to harness the collective intelligence and leverage social filtering methods to rank and recommend learning entities.

Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these problems. Factors affecting excess zeros and/or long tails are discussed, as well as how they can bias the results when traditional distributions or models are used. Recently introduced multi-parameter distributions and models developed specifically for such datasets are described. The chapter is intended to guide readers on how to properly analyse crash datasets with excess zeros and long or heavy tails.

Methodology – Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings – In the event that the characteristics of the crash dataset cannot be changed or modified, recently introduced multi-parameter distributions and models can be used efficiently to analyse datasets characterised by excess zero responses and/or long tails. They offer a simpler way to interpret the relationship between crashes and explanatory variables, while providing better statistical performance in terms of goodness-of-fit and predictive capabilities.

Research implications – Multi-parameter models are expected to become the next series of traditional distributions and models. The research on these models is still ongoing.

Practical implications – With the advancement of computing power and Bayesian simulation methods, multi-parameter models can now be easily coded and applied to analyse crash datasets characterised by excess zero responses and/or long tails.

The aim of this paper is to study if a link can be established between the role of new public libraries in contemporary society and the application of the long tail paradigm to the physical world (according to Chris Anderson's analysis in his 2006 book entitled The Long Tail: Why the Future of Business is Selling Less of More), and in particular if these new libraries can learn new, useful lessons for their collection and service planning starting from this point.

The analysis will be carried out through theoretical means and the proposal of some case studies of newly built public libraries in Europe. In particular, an Italian public library, the Sala Borsa Library in Bologna, an English one, the Whitechapel Idea Store, and one in Spain, the Jaume Fuster Library. Each of these libraries will offer a different point of view and a different answer for strengthening the relationship between public libraries and citizens' needs.

The proposed theoretical analysis and the case studies raise the need to evaluate the existing public libraries and to plan the new ones in relation to the following issues: the long tail paradigm, together with other trends characterising the contemporary urban lifestyles, put the survival of public libraries under threat, because a generic offer with limited choice finds no place in today's cultural and economic landscape anymore; central public libraries need to rely upon large and functional buildings, comprehensive collections encompassing hits and niches, extensive opening hours, a broad variety of services and edutainment activities and an aptitude to embody a social role; local/branch libraries cannot survive below certain sizes, unless they bet on very specific niches of the public, for example either people who are less willing or unable to move around the city very much (elderly people, children, disabled people and so on) or topics and subjects which are only superficially covered by other libraries and suppliers on the territory; and in general, public libraries should emphasise their role as service desks functioning as a go‐between for other – more specialised – bibliographic services and for other learning, informative, entertaining and cultural opportunities inside and outside the metropolitan area.

An analysis of the consequences the long tail has on the future development of libraries has already been started in library science. However, no specific considerations have been taken on how the application of this paradigm could (and should) change the relations in the urban library networks and help big and small public libraries in finding new balance and complementary roles in satisfying citizens' needs.

The purpose of this paper is to discover the economic effects of open innovation investigated the following research questions: do economic effects of open innovation – a certain economic phenomenon or economic paradigm that surpasses the level of the management strategies of individual enterprises – exist? If so, what are the economic effects?

The authors analyse the change of classical economic characteristics, such as diminishing marginal products, economy of scale, and X-inefficiency, which are selected by literature review to find out the effects of open innovation. The authors select long-tailed phenomena and App Store phenomena, which are a direct result of open innovation. From these, the authors find out the effects of open innovation.

Through exploratory-level studies, the economic characteristics of open innovation have been identified: gradual increases of marginal products, the economy of diversity, and X-efficiency improvement.

These three economic characteristics of open innovation have been verified through secondary analysis methods based on the long-tailed phenomenon and App Store phenomenon. Open innovation triggers new economic effects. Thus, the authors should create new strategies and policies to treat open innovation that are based on additional deep research.

This paper introduces new ideas about open innovation in economics.

According to the findings, open innovation will give the authors new ways to develop continuously in a knowledge-based economy.

For the first time, the authors understand the economic value of open innovation and its implications.

This paper is aimed at describing how companies can find new opportunities for customer retention and lifetime value by applying the concepts of dialogue marketing, network‐building and relevant rewards.

The paper cites the work of Chris Anderson, Editor‐in‐Chief of Wired Magazine, and the work of Italian economist Vilfredo Pareto. The paper explains how the works of these two men, and how programs put into place at two companies (Hewlett‐Packard and Rain Bird), have opened new vistas in customer retention.

The study found that by applying specific marketing principles, companies can do a better job of retaining all customers, specifically those customers who are not in the top 20 percent of revenue‐producers.

Most companies believe that 80 percent of their business comes from 20 percent of their customers. However, by applying specific marketing principles, companies can do a better job of retaining all customers, specifically those customers who are not in the top 20 percent of revenue‐producers.

The paper takes a new look at an old principle (the 80‐20 Pareto Principle).