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This paper aims to discuss how collaborative classification works in online music information retrieval systems and its impacts on the construction, fixation and orientation of the social uses of popular music on the internet.

Using a comparative method, the paper examines the logic behind music classification in Recommender Systems by studying the case of Last.fm, one of the most popular web sites of this type on the web. Data collected about users' ritual classifications are compared with the classification used by the music industry, represented by the AllMusic web site.

The paper identifies the differences between the criteria used for the collaborative classification of popular music, which is defined by users, and the traditional standards of commercial classification, used by the cultural industries, and discusses why commercial and non‐commercial classification methods vary.

Collaborative ritual classification reveals a shift in the demand for cultural information that may affect the way in which this demand is organized, as well as the classification criteria for works on the digital music market.

Collective creation of a music classification in recommender systems represents a new model of cultural mediation that might change the way of building new uses, tastes and patterns of musical consumption in online environments.

The paper highlights the way in which the classification process might influence the behavior of the users of music information retrieval systems, and vice versa.

Merger and acquisitions (M&A) is a process of restructuring two or more companies into one, a process that occurs frequently in many companies. Previous studies on M&A mainly paid attention to the potential gains from a merger, while ignored the problem of how to select the partners to merge. This paper aims to select the best partner from different candidates for a given company to merge.

Each company's historical data are used to identify each company's own production technology. With resources change, each company's new operation is restricted by its own production technology. Then, a 0–1 integer programming is proposed to select the best partner for M&A.

The banking industry involving 27 China's commercial banks is given to verify the applicability of our proposed model. The study shows the best partner selection for each bank company.

On the theoretical side, the study uses each company's own historical data to construct its own production technology to compressively reflect the production change after M&A. On the practical side, the study uses the proposed model to help the 27 commercial banks in China to select their best merger partner.

The purpose of this research is to investigate whether inclusion of risk assessment variables in the multiple discriminant analysis (MDA) model improved the banks ability in making correct customer classification, predict firm's performance and credit risk assessment.

The paper reviews literature on the application of financial distress and credit scoring methods, and the use of risk assessment variables in classification models. The study used a sample of 56 performing and non‐performing assets (NPA) of a privatized commercial bank in Tanzania. Financial ratios were used as independent variables for building the MDA model with a variation of five MDA models. Different statistical tests for normality, equality of covariance, goodness of fit and multi‐colinearity were performed. Using the estimation and validation samples, test results showed that the MDA base model had a higher level of predictability hence classifying correctly the performing and NPA with a correctness of 92.9 and 96.4 percent, respectively. Lagging the classification two years, the results showed that the model could predict correctly two years in advance. When MDA was used as a risk assessment model, it showed improved correct customer classification and credit risk assessment.

The findings confirmed financial ratios as good classification and predictor variables of firm's performance. If the bank had used the MDA for classifying and evaluating its customers, the probability of failure could have been known two years before actual failure, and the misclassification costs could have been calculated objectively. In this way, the bank could have reduced its non‐performing loans and its credit risk exposure.

The valiadation sample used in the study was smaller compared to the estimation sample. MDA works better as a credit scoring method in the banking environment two years before and after failure. The study was done on the current financial crisis of 2009.

Use of MDA helps banks to determine objectively the misclassification costs and its expected misclassification errors plus determining the provisions for bad debts. Banks could have reduced the non‐performing loans and their credit risks exposure if they had used the MDA method in the loan‐evaluation and classification process. The study has proved that quantitative credit scoring models improve management decision making as compared to subjective assessment methods. For improved credit and risk assessment, a combination of both qualitative and quantitave methods should be considered.

The findings have shown that using the MDA, commercial banks could have improved their objective decision making by correctly classifying the credit worthiness of a customer, predicting firm's future performance as well as assessing their credit risk. It has also shown that other than financial variables, inclusion of stability measures improves management decision making and objective provisioning of bad debts. The recent financial crisis emphasizes the need for developing objective credit scoring methods and instituting prudent risk assessment culture to limit the extent and potential of failure.

The purpose of this paper is to explore the evolution of commercial aerospace supply chains.

The approach is presented as evolutionary steps by introduction of key supply chain practices. These steps are brought together by applying cladistics, a classification approach from the biological sciences, to classify the evolutionary relationships between supply chain forms. This is presented in two stages.

Earlier research produced a “conceptual cladogram” from secondary data that describes the evolution of aerospace supply chains. This paper expands on this through empirical validation and develops a “factual cladogram”, revealing a newly emerging supply chain form. Key practices define the change of supply chain forms in the evolutionary adaptation to market realities and to proactive responses to increased competition.

The factual cladogram as such does not necessarily prescribe world‐class performance but may serve as a platform for discussing and monitoring the properties of the emergent supply chain in light of strategies for the future. As such it becomes a strategic bench‐marking tool for the change of practices, technologies and products.

In practical terms, the approach may be developed as a strategic tool for policy development, and a decision support tool through the creation and exploration of future supply chain form scenarios.

Theoretically, this paper elaborates on a framework for how and why evolution produces diversity of supply chain form.

This paper aims to address the advantage of considering an evolutionary classification scheme for commercial aerospace supply chains. It is an industry wide approach. By going beyond the performance of the single firm and considering the whole supply chain for a product a better understanding of present states and performances of the firms within the chain can be achieved.

The approach is presented as evolutionary steps by introduction of key supply chain characters. These steps are brought together by applying cladistics to classify the evolutionary relationships between supply chain forms.

Key character states define the change of supply chain forms in the evolutionary adaptation to market realities and to proactive responses to increased competition.

The potential benefits of this approach include a benchmark of best practice, a strategic tool for policy development, and the creation of future scenarios.

Describes an approach automatically to classify and evaluate publicly accessible World Wide Web sites. The suggested methodology is equally valuable for analyzing content and hypertext structures of commercial, educational and non‐profit organizations. Outlines a research methodology for model building and validation and defines the most relevant attributes of such a process. A set of operational criteria for classifying Web sites is developed. The introduced software tool supports the automated gathering of these parameters, and thereby assures the necessary “critical mass” of empirical data. Based on the preprocessed information, a multi‐methodological approach is chosen that comprises statistical clustering, textual analysis, supervised and non‐supervised neural networks and manual classification for validation purposes.

When decision makers encounter new assurance services that can be customized for individual clients, they must include them in their pre-existing categorization of assurance, a cognitive task known as postclassification. This paper draws upon three literatures (classification research in accounting, theory of assurance, and cognitive psychology) in order to suggest how this task might be modeled and studied empirically, using the example of SysTrust™. The role of a necessary condition for successful postclassification called the category use effect (Ross, 2000), in which decision makers are reminded of pre-existing categories when they learn to use new categories, is explained.

This study employs creative strategies to contentanalyse Super Bowl commercials from 2001 to 2009, focusing specifically on message strategies. The findings aim to answer four research questions. What are the message strategies commonly employed in Super Bowl commercials? What are the trends of the message strategies employed in Super Bowl commercials over time? What are the relationships between the message strategies and the commercial likeability? What are the specific roles played by the high- vs. low-involvement product categories?

This paper studies the impact of ownership structure on performance in European banking both prior and during the recent crisis. We use a panel of European banks during the period 1996–2011 and utilize random effects estimations in order to identify differences in bank performance (profitability, loan quality, and cost efficiency) due to differences in ownership structure. Both stakeholder and shareholder banks have distinct advantages, shareholder banks showing better profitability before the crisis but stakeholder banks having higher loan quality before and during the crisis. Differences in profitability and loan quality between stakeholder and shareholder banks before the crisis are especially pronounced in countries that experienced a banking crisis after 2007. There is strong a heterogeneity in performance between different stakeholder ownership groups. With the exception of private savings banks, profitability and loan quality of stakeholder banks has improved relative to that of general shareholder banks during the crisis years. The paper contributes to the previous literature by comparing pre-crisis and crisis performance and includes more refined ownership classifications. The results indicate that the survival of the stakeholder model is due to its competitive advantages. Our findings provide support for those arguing that the diversity of organizational structures is worth preserving. Ownership pluralism should become a policy objective in the banking industry.

The purpose of this study is to empirically investigate the extent to which two types of commercial partnerships (business partner and non-business partner) affect the collaborative innovation of firms in emerging economies. Specifically, the roles of two commercial partnerships are investigated. Additionally, the study explores the moderating effect of external technological uncertainty and internal dynamic capabilities on the relationship between two commercial partnerships and on collaborative innovation.

Using a sample of 370 high-tech firms in China, the authors applied the partial least squares structural equation modeling approach to model these relationships.

The findings reveal opportunities and challenges for companies according to two intensities of commercial partnership for collaborative innovation. The partnership contribution to innovation and competiveness is different within the two routes and ranges. The findings indicate that (1) intense commercial relationships with business partners have a stronger positive significant impact on collaborative innovation than those with non-business partners and (2) non-business partners have a weaker positive impact on collaborative innovation at high external technological uncertainty. It was also found that (3) the positive impact of business partners on collaborative innovation is weakened when a firm has high dynamic capabilities, whereas the positive impact of non-business partners is strengthened.

Insight into the roles of two commercial partnerships in achieving collaborative innovation facilitates the advancement of the theoretical understanding of the circumstances under which cooperative innovation can be more effective under different partnerships.

A key strategic question is whether comprehensiveness enables firms to make better strategic decisions in various environments. In the process of innovation, companies must choose different types and quantities of partners, and they must regulate their partners’ innovative behavior by establishing a corresponding network structure and relationship rules. The current study focuses on analysis of how different intensities of commercial partnerships affect collaborative innovation. This research provides a theoretical framework that creates a new classification of commercial relations with regard to collaborative innovation, and it highlights the difference between the two types of partnerships. This study finds that there are many problems in the selection of innovative partners in China’s high-tech companies. Therefore, companies should strengthen their understanding of cooperative innovation, and they should build and manage highly efficient innovation networks. This study helps companies, high-tech industry associations, academia and government to take enhanced, informed actions.