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This study investigated whether family−school conflict, directly and indirectly, predicts behavioral regulatory strategies (via emotion regulation) among women in higher education. The authors aim to hypothesize that the direct and indirect effects would vary by race/ethnicity.

Participants were 1,872 incoming first-year female students from a large, racially/ethnically diverse urban public university. They were classified into four racial/ethnic groups: 22% Asian American (n = 403), 11% Black (n = 209), 46% Latina (n = 865), and 21% White (n = 395). Data were collected from institutional records and an online student pre-matriculation survey. Multigroup structural equation modeling (SEM) was performed to explore the structural relations among key variables.

Family−school conflict was negatively associated with help-seeking in all racial/ethnic groups, whereas family−school conflict was negatively associated with time management only for Latina students. In addition, family−school conflict indirectly predicted time management and help-seeking through increased emotion regulation, particularly among Latina students.

In considering racial heterogeneity, the results showed the importance of analyzing racial/ethnic groups separately to obtain more accurate information on self-regulation mechanisms in family−school conflict contexts.

Understanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of this study is to investigate customers' reviews on an online hotel reservation platform, and explores their postbehaviors from their reviews.

The authors employ two different approaches and compare the accuracy of predicting customers' post behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit the hotel again (referred to as Prediction), while the second subsection examines whether participants want to visit one of the particular hotels when they read other customers' reviews (dubbed as Decision).

The accuracy of the machine learning approaches (73.23%) is higher than that of the experimental approach (Prediction: 58.96% and Decision: 64.79%). The key reasons of users' predictions and decisions are identified through qualitative analyses.

The findings reveal that using machine learning approaches show the higher accuracy of predicting customers' repeat visits only based on employed sentimental features. With the novel approach of integrating customers' decision processes and machine learning classifiers, the authors provide valuable insights for researchers and providers of hospitality services.

This paper aims to clarify how organizations manage their participation in networks to share and jointly create knowledge but also risk unwanted knowledge spillovers at the same time. As formal governance, trust and observation are less applicable in informal networks, the authors need to understand how members address the need to protect knowledge by informal practices. The study aims to investigate how the application of knowledge protection practices affects knowledge sharing in networks. The insights are relevant for organizational and network management to control knowledge risks but harvest the benefits of network engagement.

The authors opted for an exploratory study based on 60 semi-structured interviews with members of 10 networks. In two rounds, network managers, representatives and members of the networks were interviewed. The second round of interviews was used to validate the intermediate findings. The data were complemented by documentary analysis, including network descriptions.

Through analyzing and building on the theory of psychological contracts, two informal practices of knowledge protection were found in networks of organizations: exclude crucial topics and share on selected topics and exclude details and share a selected level of detail. The authors explored how these two practices are enacted in networks of organizations with psychological contracts.

Counter to intuition that the protection of knowledge can be strengthened only at the expense of knowledge sharing and vice versa, networks benefitted from more focused and increased knowledge sharing while reducing the risk of losing competitive knowledge by performing these knowledge protection practices.