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The purpose of this paper is to empirically test the predictions in Titman (1984) and Berk et al. (2010) which indicate that firms with higher leverage will pay chief executive officer (CEO) and employee more. In addition, this paper examines whether financial distressed firms utilize leverage as a bargaining tool to reduce labor costs.

This paper conducts ordinary least squares regression analysis to investigate: CEO compensation which represents critical employees and lower-level employee compensation which represents less critical employees. Empirical data consist of US publicly held companies during the period between 2006 and 2013.

This paper finds that firms with higher levels of leverage tend to compensate employees for their human capital risk and that financially distressed firms consider leverage a bargaining tool by which to depress labor costs, which leads to lower employee compensation as compared to that of financially healthy firms.

This paper highlights the importance of keeping balance between human capital and labor costs. In the case that human capital risk might not be fully compensated by firms facing financial distress, vicious cycle could occur because a failure of considering human capital might invite unrecoverable consequence. This could be done in future research.

This paper has three contributions. First, this paper supports the Titman (1984) and Berk et al. (2010) by empirically documenting that high-leveraged firms compensate their employees for potential human capital risk. Second, this paper adds to the literature by empirically providing that human capital risk might not be fully compensated if the firms are facing financial distress. Finally, this paper contributes to the authorities by showing that employees’ interests may be sacrificed if the firm is under financial distress.

Cognition, conflict and cohesion constitute an inseparable body of group dynamics in entrepreneurial teams. There have been few studies of how entrepreneurial team members interact with each other to enhance venture performance. The purpose of this paper is to develop and test a model that explains the trinity of cognition, conflict and cohesion in terms of social interaction between entrepreneurial team members.

Drawing upon the existing literature concerning entrepreneurial teams, the hypothesized model posits that shared cognition influences team cohesion through the mediating effects of intra-team conflicts. The model also postulates that team cohesion is positively associated with new venture performance and entrepreneurial satisfaction. Structural equation modeling is used to test the hypothesized model, using data that were collected from 203 entrepreneurial teams from technology-based companies in Taiwan.

The results show that shared cognition in entrepreneurial team members maintains team cohesion by restraining conflict and that team cohesion has a positive influence on entrepreneurial members’ satisfaction and new venture profitability.

The leader of a new venture team must endeavor to improve shared cognition between entrepreneurial members. To strengthen shared cognition, the leader can hold formal workshops to build consensus, informal meetings to share views, or use social media to enhance common understanding.

This paper verifies the connections between shared cognition, conflicts and cohesion in entrepreneurial teams in predicting new venture success and highlights the importance of cultivating a shared cognition in an entrepreneurial team to manage conflicts.

The paper proposes customer involvement can be considered an organization-level construct of knowledge creation in the new process development. Specifically, the paper evaluates three distinct organizational practices as knowledge antecedents – competitor orientation, social network and internal coordination – that can facilitate the adoption of customer involvement in the process innovation development.

The paper empirically tests this theory for 2,000 firms that are stratification sampled from a population of 33,844 Taiwanese firms, and a data set of 170 valid questionnaires is collected. The questionnaire was mainly modified from a Kim and Kim (2010) measure which was designed based on the 3rd edition of the Oslo Manual OECD/Eurostat 2005. The concept of customer involvement in new service development proposed by Alam (2002) was also applied to the questionnaire.

(1) The antecedents of customer involvement, which include competitor orientation, external social networks and internal coordination, function as a determinant to nourish customer involvement. (2) Customer involvement significantly positively mediates the relationship between knowledge antecedents and new process performance. (3) Customer involvement is a crucial knowledge creation for improving the new process innovation performance in manufacturing firms.

Two basic tenets of theory building serve as the foundation of the model in this paper. First, research on customer involvement is augmented by showing that customer involvement can emerge as a shared perception among organizational members that is distinct from individual-level involvement. Moreover, customer involvement in process innovation can help firms manage their knowledge and further enhance firm performance. Second, the knowledge management model provides a key lens through which researchers can take a process-oriented view that focuses on customer involvement as a unique capability that firms can develop in process innovation.

The purpose of this research is to describe an efficient search methodology to help improve the search results in the top portion of a lengthy search list. When facing a lengthy search list, people often limit themselves to the top ten items on the list, even though there may be more useful information after the top ten items.

This study proposes an ontology‐based adaptive semantic search to significantly improve the search experience. To capture the semantic difference of search terms, naïve ontology is used to store the relationship among terms. Before a search term is processed by the search engine Lucene, the related words of the search term are selected from ontology structures to form new query phrases in the process of query expansion. The weighting of the expanded query phrases is dynamically learned by observing the users' clicking behaviors.

Research results show that with the aid of ontology the average precision rate of all cases is dramatically higher than the precision rate for the default search result. Even in the worst cases, in some situations, this ontology is still close to the precision rate for the default search result.

This paper shows how it is possible to improve the precision rate of items retrieved after a search and thus avoid information overload.