Many two-sided platforms categorize consumers into distinct groups according to their levels of activity. This study investigates a platform's pricing strategy when consumers are categorized into two distinct groups. By modeling a per-transaction fee in the platform's profit-maximizing objective, equilibrium results are derived for scenarios with and without consumer categorization. Then, the two scenarios are compared to explore the impact of categorizing consumers on the fees charged to users on both sides and the platform's profit. It is shown that, under consumer categorization, although sellers are charged a higher per-transaction fee, the expected profit is enlarged, and both the market scale and platform profit increase. The incremental profit of the platform first increases and then decreases in relation to the proportion of active consumers, and the benefit of categorizing consumers is maximized when active consumers are more than a half of the total.

Along with the worldwide trend of rapidly aging populations, diabetes mellitus and its comprehensive complications have become major public health issues. Considerable evidence suggests patients with diabetes mellitus have a higher risk of breast cancer. However, the relationships between the complications of diabetes mellitus and occurrence of breast cancer have not been well characterized. Despite the higher risk of breast cancer among patients with diabetes mellitus, patients with breast cancer constitute only a relatively small proportion of the diabetes mellitus data, leading to an imbalanced data set. This study proposes a hybrid machine learning scheme to cope with imbalanced data in the analysis of risk factors of breast cancer in patients with diabetes mellitus. The scheme combines the undersampling based on the clustering algorithm, the k-means algorithm, and the extreme gradient boosting algorithm. The results identify that occlusion stroke, diabetes with peripheral circulatory disorders, peripheral angiopathy in diseases classified elsewhere, and other forms of chronic ischemic heart disease are risk factors. This study provides an application of advanced methods in health care and shows the epidemiologic and informatics value of the proposed hybrid machine learning scheme.

Purpose - The purpose of this paper is to generate an understanding of the value-added to students enrolled in selected undergraduate business programs from an academic and market perspectives. Although there are numerous studies that rank undergraduate colleges and universities, the selection of the "best value" undergraduate business program is a formidable task for prospective students. This study uses data envelopment analysis (DEA), a linear programming-based tool, to evaluate undergraduate business administration programs. The DEA model connects costs (inputs) with benefits (outputs) to evaluate the value-added to students by undergraduate business programs from a market as well as academic perspectives. The study's findings should assist prospective students in selecting business programs that provide the best value from their individual perspectives. The results can also help schools to identify their corresponding market niche and allocate their recourses more effectively.Design/methodology/approach - Use DEA method. DEA was developed by Charnes et al. (1979) to evaluate the performance of multi-input and - output production operations. The analytical and computational capacities of DEA are firmly based on mathematical theory.Findings - This study takes a different approach toward the ranking of college programs. Most studies rank-order programs (universities) based on arbitrary weightings of attributes of quality and provide a general ranking of programs that is said meet the needs of many different constituencies including students, parents, donors, administrators' faculty and alumni.Originality/value - This is an original research using DEA and The Bloomberg/Businessweek online data for business school ranking.

ABSTRACTInformation cues about products influence consumer purchase decisions. Online review can enhance communication among consumers while affecting consumer perception by increasing awareness and reducing uncertainty. However, little is known on how Word-of-Mouth (WOM) and information cues interact, especially on experience goods like hotel rooms. To bridge this gap, we analyze data collected from Expeida.com and use actual booking numbers to measure sales performance instead of proxy approaches employed by previous researches. This research investigates the negative effect of WOM on information cues, namely room price and hotel star rating, to hotel online sales performance. Moreover, we confirm that room price and hotel star rating can have negative and positive impacts on hotel online booking respectively. If hotels receive positive WOM, their online sales performance is less likely to be influenced by room price and star rating. However, for hotels receiving negative WOM, their oMine sales performance is more likely to be influenced by room price and star rating. These findings will help researchers and industry practitioners better understand the impact of online WOM in an electronic commerce context.Keywords: Word-of-Mouth (WOM), price, Hotel star rating, IMonnation cue, Electronic coimnerce(ProQuest: ... denotes formulae omitted.)1. IntroductionConsumer reviews can be a good proxy for Word-of-Mouth (WOM) and can influence consumers' purchase decision [Zhu 2010]. The emergence of oMine review platforms provides useful references for potential consumers before consumption [Chevalier & Mayzlin 2006; Hu et al. 2009; Liu 2006; Senecal & Nantel 2004]. Breaking out of the limited geographic boundaries and time span of conventional WOM, oMine WOM can be searched and read anywhere at any time [Liu 2006]. In addition, under some circumstances, oMine WOM is measurable since consumers can score the products on these platforms [Park & Kim 2008], which provides an ideal environment for research.Prior studies show that consumers tend to rely more on the recommendations of others when purchasing experience goods versus search goods [Park & Lee 2009; Senecal & Nantel 2004; Weathers et al. 2007], which makes WOM management especially important for hotels [Ye et al., 2009; Ye et al., 2011]. First, like other experience products, the quality of hotels cannot be determined until consumption, which makes consumers incline to seek information from WOM. Second, during the consumption process, consumers take a lot of involvement, which makes them more likely to generate WOM after consumption [Stokes & Lomax 2002]. Third, the unique features of hotel product may be affected by WOM differentiated from other experience products such as books or movies. Comparing to books and movies, even though hotel rooms is more like a perishable product, the lifeline of hotels is much longer, so reviews may be posted in less density.Since consumers seek information to increase the awareness of product and reduce risk of purchase, they may need other intrinsic and extrinsic cues as well. Whereas extant studies have shown information cues such as product price and star rating affect consumer cognition towards product quality, empirical evidence provides little on how they may be affected by WOM and influence the sales performance in an online context, especially on experience goods like hotel rooms. This study mainly focuses on the moderating effects of online WOM on room price and hotel star rating in hotel online sales.One challenge for most empirical research on WOM is that real-time sales data is often hard to collect. Prior studies use proxy approaches to represent sales performance [Chevalier & Mayzlin 2006; Duan, et al. 2008; Ye, et al. 2009], for example, using the number of online reviews as a proxy for the number of online booking. Using a newly introduced special function provided by Expedia website, we are able to collect actual hotel booking data from Expedia, and demonstrate the interesting effect of WOM on hotel online sales performance. …

There has been much written on the individual topics of bankruptcy prediction, corporate performance, and forward/reverse stock splits. However, there is little research into the relationship between reverse stock splits and subsequent corporate performance and the potential for bankruptcy. Previous research suggested there is a negative drift in stock prices following reverse splits. The purpose of this study is to provide and empirically support rationales for reverse splits by classifying reverse splitting firms into two groups. The presumed rationales for engaging in reverse splits would differ between the two groups, so do the subsequent stock performance. Our results show that both neural networks and Z-scores can successfully distinguish the two groups of firms while neural networks outperforms Z-scores in finding the firms with best performing stocks.

For a business school, the selection of its peer schools is an important component of its International Association for Management Education (AACSB) (re)accreditation process. A school typically compares itself with other institutions having similar structural and identity-based attributes. The identification of peer schools is critical and can have a significant impact on a business school's accreditation efforts. For many schools the selection of comparable peer schools is a judgmental process. This study offers an alternative means for selection; a quantitative technique called Kohonen's Self-Organizing Map (SOM) network for clustering. In this research, we first demonstrate the capability of SOM as a clustering tool to visually uncover the relationships among AACSB-accredited schools. The results suggest that SOM is an effective and robust clustering method. Then, we compare the results of SOM with that of other clustering methods, such as K-means, Factor/K-means analysis, and kth nearest neighbor procedure. The objective of this study is to demonstrate that a two-dimensional SOM map can be used to integrate the results of various clustering methods and, thus, act as a visual decision support tool.

For a business school, the selection of its peer schools is an important component of its International Association for Management Education (AACSB) (re)accreditation process. A school typically compares itself with other institutions having similar structural and identity-based attributes. The identification of peer schools is critical and can have a significant impact on a business school's accreditation efforts. For many schools the selection of comparable peer schools is a judgmental process. This study offers an alternative means for selection; a quantitative technique called Kohonen's self-organizing map (SOM) network for clustering. SOM as a software agent uses visualization to present information to the school in choosing its peer schools.

Generally Accepted Accounting Principles (GAAP) provide the basis for measuring, valuing, and presenting financial information to investors and creditors. The Financial Accounting Standards Board (FASB) promulgates GAAP through its Statements of Financial Accounting Standards (SFAS). To guide its thinking in the promulgation of SFAS, the FASB employs as conceptual framework, called Qualitative Characteristics of Accounting Information. This research effort uses content analysis to analyze the decision specific qualities of relevance and reliability captured from 120 SFAS issued by the FASB. The results provide us with an understanding of the relative importance of relevance and reliability in the promulgation of SFAS.

There has been much written on the individual topics of bankruptcy prediction, corporate performance, and reverse stock splits. However, there is little research into the relationship between reverse stock splits and corporate performance as well as bankruptcies. The purpose of this study is to provide and empirically support rationales for reverse splits by classifying reverse splitting firms into two groups, those declaring bankruptcy within 2 years and those remaining solvent. The apparent rationales for engaging in reverse splits differ between the two groups, i.e., weak firms attempting to increase their stock price while solid firms seeking to reposition their stock in the market. Two alternative approaches, Altman's Z-scores and artificial neural networks, are used for classifying reverse splitting firms into the two groups. A comparison is then made of the relative success of Z-scores and neural networks in the classification. This study should generate an understanding of corporate rationale for engaging in reverse splits and the relative success of Z-scores and artificial neural networks in forecasting the two groups.

The tremendous growth of the Internet has created opportunities for consumers and firms to participate in an online global marketplace. It is conceivable that in the future every person with access to a computer will interact with firms marketing on the Internet. We foresee that advances in electronic commerce will dramatically alter the structure of businesses, especially in the marketing area.In this study, we extend the literature on marketing channel functions to include the Internet as a new option for selling products/services directly to customers. Important factors that inference the behaviors of online shoppers are identified. A classification scheme is used to categorize products/services selling on the Internet based on characteristics such as tangibility and price from the buyer's perspective. The classification scheme helps management to understand the difference in user preference over different product categories. A survey of experienced online shoppers was conducted to validate the scheme.

To meet mid-level market demand for Enterprise Resource Planning (ERP) packages, major ERP software vendors have streamlined their products to provide features tailored to the needs of mid-sized organizations. With numerous alternatives available, the selection of the "best" ERP software package is frequently a difficult decision. Oftentimes, mid-sized organizations lack the necessary technical expertise to make such a decision. This study uses Data Envelopment Analysis (DEA) to analyze and compare the performance of several leading mid-level ER-P packages. The DEA model connects costs (inputs) to capabilities/services (outputs) to evaluate the relative performance of individual software packages. Unlike most approaches, DEA does not require a set of pre-assigned weights for inputs and outputs and, thus, overcomes the deficiency introduced by using arbitrary weights. The findings of this study provide information technology managers and consultants a toot for non-subjective assessments of mid-level EPR packages.

After the "dot com" bubble burst, many e-commerce business models and applications failed to survive in the current business environment. In this research, we focus on some reasons of the failure of e-commerce business models and applications. The factors for e-commerce failure were classified as products characteristics and marketing channel characteristics. In this paper, we propose a framework, which-describe the relationships between the products characteristics and the marketing channels that are used to distribute their products. A utility formula is used to evaluate the possibility to succeed if a certain combination of products/channel is used in the market place.

Since the Nasdaq bubble burst in April 2000, the dot-com shakeout has seen scores of high-flying companies go under, thousands of people laid off and millions of dollars of venture capital tossed to the wind. Although there are many reasons that may cause a dot-com failure, in this research we look at the problem from the relationships between type of product and corresponding marketing strategies. We focused on the use of the Internet as a virtual storefront where products are offered directly to customers with the contention that product characteristics play a major role in the successfulness of its marketing on the Internet. We highlighted benefits of online marketing along three channel functions and identified factors that impact the use of online marketing approaches. We propose a framework to evaluate the marketing effectiveness in e-commerce with Internet marketing. A multidimensional modeling approach is presented which provides an integrated approach to link products to marketing channels.

The tremendous growth of the Internet has created opportunities for consumers and firms to participate in an online global marketplace. It is conceivable that in the future every person with access to a computer will interact with firms marketing on the Internet. The potential of the Internet as a commercial medium and market has been widely documented in a variety of media. In this research, we focus on the use of the Internet as a virtual storefront where products are offered directly to customers. Our contention is that both product characteristics and consumer purchase behaviors play major roles in the successfulness of its marketing on the Internet. If we can identify the factors that impact the use of on-line marketing approach, we can build a framework to help evaluate the chance for a company to succeed in e-commerce.

The Internet has provided a rare opportunity especially for small to medium sized enterprises. It moves organizations beyond the physical constraints of their traditional distribution channels and creates a world wide virtual community in which small and medium sized companies can compete with large enterprises. In this research, we focus on the use of the Internet as a virtual storefront where products are offered directly to customers. Our contention is that product characteristics play a major role in the successfulness of its marketing on the Internet. We reviewed benefits of online marketing along three channel functions and identified factors that impact the use of online marketing approach. A framework is proposed to help evaluate the chance for a company to succeed in ecommerce. Data of failed e-tailers in the last two years were collected and analyzed using the proposed framework.

From the Publisher: This handbook provides an up-to-date compendium of current technologies and applications, including strategic and operational planning, information resource management, project management, quality control, database management systems, data communications and end-user computing. New and emerging trends and technologies such as artificial intelligence, expert systems, and electronic commerce are also considered.

From the Publisher:Instant answers for every conceivable information systems question related to communications technologies, artificial intelligence, databases, online information services, financial and accounting software, financial modeling and "what-if" analysis, computer conferencing, executive information systems, decision support systems, computerized security systems, management information systems, company intranet systems, computer networking, manufacturing information systems, interactive financial planning systems, and marketing information systems.

Kohonen's self organizing map (SOM) network is one of the most important network architectures developed during the 1980's. The main function of SOM networks is to map the input data from an n dimensional space to a lower dimensional (usually one or two dimensional) plot while maintaining the original topological relations. A well known limitation of the Kohonen network is the “boundary effect” of nodes on or near the edge of the network. The boundary effect is responsible for retaining the undue influence of initial random weights assigned to the nodes of the network leading to ineffective topological representations. To overcome this limitation, we introduce and evaluate a modified, “circular” weight adjustment procedure. This procedure is applicable to a class of problems where the actual coordinates of the output map do not need to correspond to the original input topology. We tested the circular method with an example problem from the domain of group technology, typical of such a class of problems

From the Publisher:Instant answers for every conceivable information systems question related to communications technologies, artificial intelligence, databases, online information services, financial and accounting software, financial modeling and "what-if" analysis, computer conferencing, executive information systems, decision support systems, computerized security systems, management information systems, company intranet systems, computer networking, manufacturing information systems, interactive financial planning systems, and marketing information systems.