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With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

This paper aims to examine the time it would take to provide medical prophylaxis for a large urban population in the wake of an airborne anthrax attack and the effect that various parameters have on the total logistical time.

A mathematical model that evaluates key parameters and suggests alternatives for improvement is formulated. The objective of the model is to minimize the total logistical time required for prophylaxis by balancing three cycles as follows: the loading cycle, the shipping cycle and the service cycle.

Applying the model to two representative cases reveals the effect of various parameters on the process. For example, the number of distribution centers and the number of servers in each center are key parameters, whereas the number of central depots and the local shipping method is less important.

Various psychological factors such as mass panic are not included in the model.

There are few papers analyzing the logistical response to an anthrax attack, and most focus mainly on the strategic level. The study deals with the tactical logistical level. The authors focus on the distribution process of prophylaxis and other medical supplies during the crisis, analyze it and identify the parameters that influence the time between the detection of the attack and the provision of effective medical treatment to the exposed population.

This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.

This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.

This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.

This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.

This paper aims to use a data-driven approach to identify the factors and metrics that provide the best indicators of academic attrition in the Korean language program at the Defense Language Institute Foreign Language Center.

This research develops logistic regression models to aid in the identification of at-risk students in the Defense Language Institute’s Korean language school.

The results from this research demonstrates that this methodology can detect significant factors and metrics that identify students at-risk. Additionally, this research shows that school policy changes can be detected using logistic regression models and stepwise regression.

This research represents a real-world application of logistic regression modeling methods applied to the problem of identifying at-risk students for the purpose of academic intervention or other negative outcomes. By using logistic regression, the authors are able to gain a greater understanding of the problem and identify statistically significant predictors of student attrition that they believe can be converted into meaningful policy change.

This paper examines whether the successful bid rate of the OnBid public auction, published by Korea Asset Management Corporation, can identify and forecast the Korea business-cycle expansion and contraction regimes characterized by the OECD reference turning points. We use logistic regression and support vector machine in performing the OECD regime classification and predicting three-month-ahead regime. We find that the OnBid auction rate conveys important information for detecting the coincident and future regimes because this information might be closely related to deleveraging regarding default on debt obligations. This finding suggests that corporate managers and investors could use the auction information to gauge the regime position in their decision-making. This research has an academic significance that reveals the relationship between the auction market and the business-cycle regimes.

The recent promotion of prefabricated housing (PH) in China has resulted in a prosperous period for its implementation. However, transaction costs (TCs) cause low economic efficiency to stakeholders and hinder the further promotion of PH. No relevant study has yet been made to investigate the TCs and their causes in the PH field. This paper identifies critical TCs and explores the influencing factors from the developers' perspective.

Semi-structured interviews and a questionnaire survey were used to collect data about TCs and influencing factors. The most influential factors are identified with their impacts on particular TCs, yielded from correlation analysis and logistic regression.

From the developers' perspective in China's PH market, this study identified that the most concerning sources of TCs are: hidden costs arising from disputes, extra workloads from design changes, learning costs, intensive communication and coordination in assembly and unexpected information costs in decision-making. The use of an ordered logistic regression approach indicates that the four most influential factors are: qualification of the general contractor, mandatory local policies, owner type and competitiveness of the developer.

To reduce the TCs, experiencing learning and ensuring the design scheme's complicity are recommended to save information searching and exchanging costs. The implications for the PH developers are for them to: (1) professionalize their own organization and (2) procure high-qualified general contractors. For the policymakers, this means they should improve the clarity of the mandatory local policies for PH step-by-step.

By applying the TCs economic theory, this study explores factors that influence TCs in the PH industry. It sheds light on the influencing mechanism behind the TCs in the context of prefabricated housing.

There appears to be no empirical-based method in the literature for estimating if an engineering change proposal (ECP) will occur or the dollar amount incurred. This paper aims to present an empirically based approach to address this shortfall.

Using the cost assessment data enterprise database, 533 contracts were randomly selected via a stratified sampling plan to build two regression models: one to predict the likelihood of a contract experiencing an ECP and the other to determine the expected median per cent increase in baseline contract cost if an ECP was likely. Both models adopted a stepwise approach. A validation set was placed aside prior to any model building.

Not every contract incurs an ECP; approximately 80 per cent of the contracts in the database did not have an ECP. The likelihood of an ECP and the additional amount incurred appears to be statistically independent of acquisition phase, branch of service, commodity, contract type or any other factor except for the basic contract amount and the number of contract line item numbers; both of these later variables equally affected the contract percentage increase because of an ECP. The combined model overall bested current anecdotal approaches to ECP withhold.

This paper both serves as a published reference point for ECP withholds in the archival forum and presents an empirically based method for determining per cent ECP withhold to use.

The purpose is to examine the relationship between entrepreneurs’ exit strategies and modes of entry. The topic of exit strategies in the context of approaching retirement warrants further attention.

We apply logistic regression to analyse 1,192 responses to an online survey of firms with entrepreneurs aged over 55.

Family successors are more likely to choose family succession and buyers to choose to sell, but the association between founding and exit mode cannot be confirmed. Firm size is also significant. Our findings suggest that entry and exit via a business transfer are linked. Entrepreneurs might be influenced by their form of entry when choosing their exit strategy.

The data were collected from a single European country, limiting generalisation. Future research should incorporate intervening variables not controlled for here, such as, entrepreneurial experience. Future studies should also seek to test the existence of imprinting directly, as it is implied rather than verified here.

If the entry mode has a lasting effect on the entrepreneur as our results suggest, thus influencing the exit strategy selected, entrepreneurs could benefit from greater awareness of the imprinting mechanism. Increasing awareness of imprinted biases could unlock the benefits of exit strategies previously overlooked.

The study is the first to consider sale, family succession and liquidation as exit strategies in relation to the original entry mode of ageing owners. It contributes to the understanding of exit strategies of ageing entrepreneurs and proposes using entrepreneurial learning and imprinting as lenses to clarify the phenomenon.

Corruption is a major social problem, and scholars have devoted considerable attention to this phenomenon. However, less attention has been paid to how corruption spreads among organizations and what factors can make its spread more likely. This study aims to fill the gap by modelling corruption as an interorganizational contagion.

The authors used social contagion theory to model corruption as an interorganizational contagion, influenced by the susceptibility of organizations and the strength of contagion sources. The study analysed 736 medium and large Italian municipalities over a five-year period, with 3,146 observations (excluding missing data). The authors conducted a longitudinal analysis using panel logistic regression techniques and performed robustness and endogeneity checks through a dynamic panel data model.

The authors found that municipalities with a higher percentage of corrupt neighbouring municipalities were more likely to experience corruption. The probability of experiencing corruption was also significantly higher for municipalities with weaker organizational resistance to corruption contagion.

Previous studies have not clearly explained the organizational mechanisms behind the spread of corruption at the interorganizational level. The study suggests that corruption contagion at the municipal level occurs via reduced uncertainty in decision-makers and is influenced by the prevalence of corruption locally. The spread can be driven by conscious or unconscious mechanisms. This study challenges the idea that corruption contagion is immediate and inevitable. Organizational resistance to corruption can affect the risk of contagion, highlighting the importance of anti-corruption controls and ethical systems in preventing it.

Integration, that is, the coordination and alignment of tasks, is widely promoted as a means to improve hospital performance. A previous study examined integration and differentiation, that is, the extent to which tasks are segmented into subsystems, in a hospital's social network. The current study carries this research further, aiming to explain integration and differentiation by studying the rules and coordination mechanisms that agents in a hospital network use.

The current case study deepens the analysis of the social network in a hospital. All planning tasks and tasks for surgery performance were studied, using a naturalistic inquiry approach and a mixed method.

Of the 314 rules found, 85% predominantly exist in people's minds, 31% are in documents and 7% are in the information system. In the early planning stages for a surgery procedure, mutual adjustment based on hospital-wide rules is dominant. Closer to the day of surgery, local rules are used and open loops are closed through mutual adjustment, thus achieving integration. On the day of surgery, there is mainly standardization of work and output, based on hospital-wide rules. The authors propose topics for future research, focusing on increasing the hospital's robustness and stability.

This exploratory case study provides an overview of the rules and coordination mechanisms that are used for organizing hospital-wide logistics for surgery patients. The findings are important for future research on how integration and differentiation are effectively achieved in hospitals.