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This paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.

First, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.

Folksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.

The proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

We investigate the dynamics of competitive repositioning of firms in the deregulated U.S. airline industry (1979–1995) in terms of a firm's target market, strategic posture, and resource endowment relative to other firms in the industry. We suggest that, despite strong inertia in competitive positions, the direction of repositioning responds to external and internal alignment considerations. For external alignment, we examined how firms changed their competitive positioning to mimic the positions of similar, successful firms, and to differentiate themselves when experiencing intense rivalry. For internal alignment, we examined how firms changed their position in each dimension to align with the other dimensions of positioning. This internal alignment led to convergent positioning moves for firms with similar resource endowments and strategic postures, and divergent moves for firms with similar target markets and strategic postures. The evidence suggests that repositioning moves in terms of target markets and resource endowments are more sensitive to external and internal alignment considerations, but that changes in strategic posture are subject to very high inertia and do not appear to respond well to alignment considerations.

The purpose of this paper is to provide adaptive access to learning resources in the digital library.

A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.

This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.

The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.

The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.

This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.

This study sought to test two competing sets of hypotheses derived from two different theoretical perspectives regarding (1) the effects of leader–follower similarity and dissimilarity in psychological resilience on the follower's absenteeism in times of organizational crisis and (2) the moderating effect of relational demography (gender and age similarity) in these relationships.

Polynomial regression and response surface analysis were performed using data from 510 followers and 149 supervisors in a financial firm in Canada.

The results overall support the similarity–attraction perspective, but not the resource complementarity perspective. Dissimilarity in resilience was predictive of followers' absenteeism, and similarity in surface-level conditions (gender and age) attenuates the relational burdens triggered by resilience discrepancy.

The findings reiterate the importance of developing employees' resilience, while shedding light on the importance for managers of being aware of their potential misalignment with subordinates resilience.

The results (1) suggest that it is the actual (di)similarity with the leader, rather than leader's degree of resilience, that shapes followers' absenteeism and (2) add nuance to the resilience literature.

This paper aims to examine how integration modes impact the acquirer knowledge diffusion capacity of overseas mergers and acquisitions (M&As) effected by emerging market firms and the role played by the global innovation network position of the acquiring firms in affecting this relationship.

Through the use of structural equation modelling and bootstrap testing, the hypotheses are tested by drawing upon a sample of 102 overseas M&As effected by listed Chinese manufacturing companies.

The results show that acquirers from emerging countries are unable to increase the knowledge diffusion capacity unless they choose the right post-merger integration mode. This paper also finds that the relationship between integration mode and knowledge diffusion is channelled through the centrality and structural holes of acquirers in the global innovation networks. When considering the combinations of different resource similarities and complementarities of the acquired firms, differences emerge in the integration model and network embedded path of acquirers in emerging countries.

Emerging market multinational enterprises should consider post-merger integration as a crucial facilitator to the crafting of global innovation network positions that promote knowledge diffusion. The choices of integration mode and brand management autonomy should be matched with the resource similarities and complementarities that exist between the acquirer and target firms.

Based on the resource orchestration theory and by focussing on network centrality and structural hole as the crucial links, this study provides a nuanced understanding of the relationship between post-merger integration and knowledge diffusion and sheds light on latecomer firms from emerging countries.

The aim of this paper is to analyze the influence of intangible assets and similarity of resources on the choice between acquisitions and joint ventures and whether it is different in domestic and European operations. In order to test these relations, a sample of domestic and European growth deals was selected (563 deals, of which 449 are acquisitions and 114 are joint ventures). Results demonstrate that it is more probable that firms will choose acquisitions if there is a close similarity between the resources of the firms. Also, if the operation is domestic, companies with higher proportions of intangible resources prefer acquisitions.

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.

Measuring the similarity between two resources is considered difficult due to a lack of reliable information and a wide variety of available information regarding the resources. Many approaches have been devised to tackle such difficulty. Although content-based approaches, which adopted resource-related data in comparing resources, played a major role in similarity measurement methodology, the lack of semantic insight on the data may leave these approaches imperfect. The purpose of this paper is to incorporate data semantics into the measuring process.

The emerged linked open data (LOD) provide a practical solution to tackle such difficulty. Common methodologies consuming LOD mainly focused on using link attributes that provide some sort of semantic relations between data. In this work, methods for measuring semantic distances between resources using information gathered from LOD were proposed. Such distances were then applied to music recommendation, focusing on the effect of various weight and level settings.

This work conducted experiments using the MusicBrainz dataset and evaluated the proposed schemes for the plausibility of LOD on music recommendation. The experimental result shows that the proposed methods electively improved classic approaches for both linked data semantic distance (LDSD) and PathSim methods by 47 and 9.7%, respectively.

The main contribution of this work is to develop novel schemes for incorporating knowledge from LOD. Two types of knowledge, namely attribute and path, were derived and incorporated into similarity measurements. Such knowledge may reflect the relationships between resources in a semantic manner since the links in LOD carry much semantic information regarding connecting resources.

The study explores how a market-like organizational system realizes efficient and/or effective development by investigating the efficiency/effectiveness trade-off in micro-level exchanges.

The study is motivated by two principles: reciprocity and similarity. Reciprocal benefits drive exchanges. Accordingly, two agents for a potential exchange should have different resources. However, differences in resources usually cause lack of trust, which hinders the efficient occurrence of exchanges. Alternatively, if two parties have similar resource positions, they can conduct an exchange efficiently. Nevertheless, the similarity makes the exchange less effective. Therefore, an efficiency/effectiveness trade-off exists in micro-level exchanges. To understand how different focuses on the efficiency/effectiveness trade-off shape the macro-level performance, the author develops a complex adaptive systems model for computer simulations.

The author finds that an efficiency-focus institution facilitates a market-like organizational system's rapid emergence but hinders the system's effective development.

The study develops a model of how a dyadic exchange happens (or not) between any two parties in a competitive and uncertain environment and how the micro-level exchanges aggregate, suggesting one specific way to understand the micro-to-macro process of a market-like organizational system's economic dynamism. Future research is expected to improve the model with different contingencies.

The study's findings suggest that the efficiency-focus institution and the effectiveness-focus institution should be used at different times in a market-like organizational system's development process.

The study investigates the macro-level consequences building upon the micro-level efficiency/effectiveness trade-off.

The purpose of the paper is to examine the coevolutionary dynamics between multistage overseas mergers and acquisitions (M&A) integration and knowledge network reconfiguration and the impact of this coevolution on industrial technology innovation.

This paper builds a coevolution analysis framework in stages and constructs structural equation models for empirical tests using the Chinese technology-sourcing overseas M&A events that occurred from 2001 to 2012.

Overseas M&A integration and knowledge network reconfiguration are in a coevolutionary relationship, driving industrial technology innovation. The acquirer adopts initial integration degree that matches the resource relatedness between the acquiring and acquired parties, promoting initial industrial technology innovation through initial knowledge network reconfiguration. Initial knowledge network reconfiguration will feed back to the M&A integration decision in the mid-to-late stage through increasing knowledge similarity and narrowing network position difference. The higher the improvement of mid-to-late integration degree, the more it can drive mid-to-late industrial technology innovation through mid-to-late knowledge network reconfiguration.

Future research can accurately classify overseas M&A integration stages through case tracking and explore other network attributes.

Practical guidelines are provided for managers on how to implement a multistage overseas M&A integration strategy, optimize knowledge network reconfiguration and promote industrial technology innovation. Significant practical implications are presented, especially in academia, society and quality of life.

Different from the previous research considering M&A integration as a single-stage decision, this paper emphasizes the dynamics of the M&A integration process and explores the coevolution mechanism of multistage overseas M&A integration and knowledge network reconfiguration.