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When purchasing digital content (DC), consumers are typically influenced by various information sources on the website. Prior research has mostly focused on the individual effect of the information sources on the DC choice. To fill the gap in the previous studies, this research includes three main effects: information cascades, recommendations and word of mouth. In particular, the purpose of this paper is to focus on the interaction effect of information cascades and recommendations on the number of software downloads.

The authors use the panel generalized least squares estimation to test the hypotheses by using a panel data set of 2,000 pieces of software at download.cnet.com over a month-long period. Product ranking and recommendation status are used as key independent variables to capture the effects of information cascades and recommendations, respectively.

One of this study’s findings is that information cascades positively interact with recommendations to influence the number of software downloads. The authors also show that the impact of information cascades on the number of software downloads is greater than one of the recommendations from a distributor does.

Information cascades and recommendations have been considered as the primary effects for online product choices. However, these two effects typically are not considered together in one research. As previous studies have mainly focused on each effect, respectively, the authors believe that this study may fill the gap by examining how these effects are interacted to one other to influence customers’ choices. The authors also show that the impact of information cascades on the number of software downloads is greater than one of the recommendations from a system does.

An experiment is undertaken to assess how the level of information provided to survey groups impacts upon the decisions they make. In this experiment, a group of experts is surveyed first to determine both the forms and levels of information important to them regarding an obscure environmental resource (remote mountain lakes), as well as their ranking of particular examples of these resources in accordance with their own criteria. Then three different groups of respondents are given different levels of this information to assess how their WTP for the resources responds to varying levels of this information, and how their rankings of the different goods alters with the information provided. The study reports evidence that generally increased levels of information provide significant quantitative changes in aggregate WTP (the enhancement effect), as well as a credible impact on their ranking of the various goods. On closer examination, much of the enhancement effect appears to be attributable to the changes in ranking, and to changes in the WTP for a single lake at each level of information. In addition the ranking does not respond in any consistent or coherent fashion during the experiment until the information provided is complete, including a ranking of subjectively reported importance by the expert group, and then the survey group converges upon the expert's group rankings. In sum, the experiment generates evidence that is both consistent with the anticipated effects of increased levels of information but also consistent with the communication of information-embedded preferences of the expert group. It may not be possible to communicate expert-provided information to survey groups without simultaneously communicating their preferences.

The purpose of this paper is to introduce a novel framework for visual-aided ontology-based multidimensional ranking and to demonstrate a case study in the academic domain.

The paper presents a method for adapting semantic web technologies on multiple criteria decision-making algorithms to endow to them dynamic characteristics. It also showcases the enhancement of the decision-making process by visual analytics.

The semantic enhanced ranking method enables the reproducibility and transparency of ranking results, while the visual representation of this information further benefits decision makers into making well-informed and insightful deductions about the problem.

This approach is suitable for application domains that are ranked on the basis of multiple criteria.

The discussed approach provides a dynamic ranking methodology, instead of focusing only on one application field, or one multiple criteria decision-making method. It proposes a framework that allows integration of multidimensional, domain-specific information and produces complex ranking results in both textual and visual form.

Ranking relevant journals is very critical for researchers to choose their publication outlets, which can affect their research performance. In the management information systems (MIS) subject, many related studies conducted surveys as the subjective method for identifying MIS journal rankings. However, very few consider other objective methods, such as journals’ impact factors and h-indexes. The paper aims to discuss these issues.

In this paper, top 50 ranked journals identified by researchers’ perceptions are examined in terms of the correlation to the rankings by their impact factors and h-indexes. Moreover, a hybrid method to combine these different rankings based on Borda count is used to produce new MIS journal rankings.

The results show that there are low correlations between the subjective and objective based MIS journal rankings. In addition, the new MIS journal rankings by the Borda count approach can also be considered for future researches.

The contribution of this paper is to apply the Borda count approach to combine different MIS journal rankings produced by subjective and objective methods. The new MIS journal rankings and previous studies can be complementary to allow researchers to determine the top-ranked journals for their publication outlets.

This study aims to examine how information professionals select a social media channel for information discovery and delivery. This analysis was focused to provide usage based ranking of social media channels for information discovery and delivery. This study has also measured the preference of social media as compared with other information and communication channels such as radio, TV, newspapers, etc., for information discovery and delivery. This study compared the global social media rank with the study rank to record the variances in the light of uses and gratification theory.

For this quantitative research study a self-administered survey questionnaire was used to collect data from the participants of the study. Sample of this study was 700 information professionals necessarily user of social media.

Findings of this reveals that social media is the most preferred channel for information discovery and delivery among information professionals and study validates the assumption of uses and gratification theory with a view that information professionals are independent and active users of social media and global rank of social media is significantly different from the rank developed in this study.

The present study is limited to information professionals only and considers social media (only top 20 sites) as an information and communication channel among information professionals.

This study has determined the preference of social media as an information and communication channel as compared with other information and communication channels and present a ranking based on usage among information professional, which is significantly different from the existing global user based ranking.

Social media provides versatility of information in different forms and large numbers of information professionals are the users of social media around globe. This study shall help information professional to select appropriate channels for information discovery and delivery. Usage based ranking provided in this study shall stream line the social media practices at large.

This study has developed a usage based rank of top social media. This study elaborated the preference of social media as an information and communication channel.

Decision-making is always an issue that managers have to deal with. Keenly observing to different preferences of the targets provides useful information for decision-makers who do not require too much information to make decisions. The main purpose is to avoid decision-makers in a dilemma because of too much or opaque information. Based on problem-oriented, this research aims to help decision-makers to develop a macro-vision strategy that fits the needs of different clusters of customers in terms of their favorite restaurants. This research also focuses on providing the rules to rank data sets for decision-makers to make choices for their favorite restaurant.

When the decision-makers need to rethink a new strategic planning, they have to think about whether they want to retain or rebuild their relationship with the old consumers or continue to care for new customers. Furthermore, many of the lecturers show that the relative concept will be more effective than the absolute one. Therefore, based on rough set theory, this research proposes an algorithm of related concepts and sends questionnaires to verify the efficiency of the algorithm.

By feeding the relative order of calculating the ranking rules, we find that it will be more efficient to deal with the faced problems.

The algorithm proposed in this research is applied to the ranking data of food. This research proves that the algorithm is practical and has the potential to reveal important patterns in the data set.

Learning to rank algorithms inherently faces many challenges. The most important challenges could be listed as high-dimensionality of the training data, the dynamic nature of Web information resources and lack of click-through data. High dimensionality of the training data affects effectiveness and efficiency of learning algorithms. Besides, most of learning to rank benchmark datasets do not include click-through data as a very rich source of information about the search behavior of users while dealing with the ranked lists of search results. To deal with these limitations, this paper aims to introduce a novel learning to rank algorithm by using a set of complex click-through features in a reinforcement learning (RL) model. These features are calculated from the existing click-through information in the data set or even from data sets without any explicit click-through information.

The proposed ranking algorithm (QRC-Rank) applies RL techniques on a set of calculated click-through features. QRC-Rank is as a two-steps process. In the first step, Transformation phase, a compact benchmark data set is created which contains a set of click-through features. These feature are calculated from the original click-through information available in the data set and constitute a compact representation of click-through information. To find most effective click-through feature, a number of scenarios are investigated. The second phase is Model-Generation, in which a RL model is built to rank the documents. This model is created by applying temporal difference learning methods such as Q-Learning and SARSA.

The proposed learning to rank method, QRC-rank, is evaluated on WCL2R and LETOR4.0 data sets. Experimental results demonstrate that QRC-Rank outperforms the state-of-the-art learning to rank methods such as SVMRank, RankBoost, ListNet and AdaRank based on the precision and normalized discount cumulative gain evaluation criteria. The use of the click-through features calculated from the training data set is a major contributor to the performance of the system.

In this paper, we have demonstrated the viability of the proposed features that provide a compact representation for the click through data in a learning to rank application. These compact click-through features are calculated from the original features of the learning to rank benchmark data set. In addition, a Markov Decision Process model is proposed for the learning to rank problem using RL, including the sets of states, actions, rewarding strategy and the transition function.

The purpose of this paper is to examine information cascades in the context of users’ e-book reading behavior and differentiate it from alternative factors that lead to herd behavior, such as network externalities and word-of-mouth effects.

This paper constructed panel data using information concerning 226 e-books in 30 consecutive days from Sina.com’s reading channel (Book.Sina.com.cn) from October 2, 2013, to October 31, 2013 of the same year in China. A multinomial logit market-share model was employed.

E-books’ ranking has a significant impact on their market share, as predicted by informational cascades theory. Higher ranking e-books’ clicks will see a greater increase as a result of an increase in clicks ranking. Due to the information cascades effect, review volume had no impact on the market share of popular e-books. Total votes had a powerful impact on the market share of e-books, showing that once information cascade occurred, it could be enhanced by the increase in total votes. The total clicks of e-books had a significant impact on their market share, suggesting that online reading behavior would be influenced by network externalities.

As important information, the ranking or popularity of e-books should be carefully considered by online reading web sites, publishers, and authors. It is not enough for the authors and publishers of e-books to simply pay attention to the content. They should design their marketing strategies to allow network externalities and informational cascades to work for them, not against them. Online reading web sites should also focus on eliminating certain behavior, such as “brush clicks” and “brush votes,” in order to prevent an undesirable information cascade due to false information.

To the best of the knowledge, this is the first study to examine information cascades in the context of users’ e-book reading behavior. Moreover, this study can help other researchers by utilizing a large sample of daily data from one of the earliest online reading platforms in China.

The purpose of this paper is to provide a selective review of literature and presents a conceptual framework in journal and institution rankings. Several streams of ranking literature are analyzed.

The authors provide a conceptual framework to analyze the literature of journal and school ranking. Thus, several streams of ranking literature are analyzed to support the conceptual framework.

Through the lens of a context-driven framework, the authors point to originality, utility, and timeliness as aspects that contribute to the recent increase of the ranking literature. Finally, the authors discuss other issues that arise within ranking due to subjective biases, institutional preferences and difficulties establishing weighting measurements, as well as the future direction of ranking.

The authors propose a context-based ranking framework to analyze rankings as factors that influence the environment may ultimately affect the usefulness of these rankings. It also implies that ranking of a journal or institution is a relative measure, as the context in which rankings are derived may change over time. Ultimately, the relative benchmarks used in the ranking will change as newer, more relevant metrics are developed.

The conceptual framework is new and provides a useful benchmark to understand ranking of journals and school.

The purpose of this paper is to examine whether Washington State school district financial reporting and budget reporting meet the information needs of school board directors charged with governance.

Washington State school board directors were surveyed and asked to rank information items’ usefulness in carrying out their governance role. School district annual reports, budgets and websites were examined to determine whether the identified information was reported and easily transparent to those charged with governance and the public.

Directors rank information on strategic oversight, budget planning and student outcomes as more useful, consistent with the strategic role of new public management. Follow-on analysis of district annual financial reports, budgets and websites reveal that the availability of the information ranked useful by directors is limited. The findings suggest an information gap exists between directors’ information needs and school district reporting. Annual reports and budgets, when provided, often provide typical financial statements and variance data, respectively, rather than reporting on mission-aligned performance measures. The main consequence of the information gap may be compromised decision-making effectiveness.

By directly asking those charged with governance what information they identify as useful and then examining whether the information is reported in the annual report, budget or website, the study links user information needs to information transparency.