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The aim of this paper is to define pracademia and conceptualise it in relation to educational contexts. This paper contributes to and stimulates a continuing and evolving conversation around pracademia and its relevance, role and possibilities.

This paper is a conceptual exploration. It draws upon existing and emerging pieces of literature, the use of metaphor as a meaning-making tool, and the positionalities of the authors, to develop the concept of pracademia.

The authors posit that pracademics who simultaneously straddle the worlds of practice, policy, and academia embody new possibilities as boundary spanners in the field of education for knowledge mobilization, networks, community membership, and responding to systemic challenges. However, being a pracademic requires the constant reconciling of the demands of multi-membership and ultimately, pracademics must establish sufficient legitimacy to be respected in two or more currently distinct worlds.

This paper has implications for knowledge mobilization, networks, boundary spanners, leadership, professional learning, and connecting practice, policy, and research. While the authors are in the field of education, this exploration of pracademia is relevant not only to the field of education but also to other fields in which there is a clear need to connect practice/policy with scholarship.

This paper provides a new definition of pracademia and argues that pracademia identifies an important yet relatively unknown space with many possibilities in the field of education.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

Research into entrepreneurial networking activities has ignored an aspect that is important to the entrepreneurs‐does it make sense to pay dues to an organization that promises networking opportunities to help build their business? This study looked at that aspect of networking by comparing revenue growth rates and average number of employees between those businesses whose owners belong to paid membership organizations and those who do not. No differences were found between the two groups of entrepreneurial firms. While there are still benefits to joining these organizations, entrepreneurs should not expect to grow their business because of membership.

Entrepreneurship education (EE) is critical for developing the skills of tomorrow's entrepreneurs and leaders. While significant research examines the content, student learning processes and outcomes, less studied are the entrepreneurship educators and their pedagogical preferences. Following a cognitive process model of decision-making, this study explores how self-efficacy, philosophy of teaching, entrepreneurship training and teaching experience influence entrepreneurship educator preferences to follow either a teacher-centric or a student-centric approach. This study also includes gender in a secondary analysis of the relationships.

The data were collected from 289 entrepreneurship educators in 2021, and fuzzy-set comparative qualitative analysis (fsQCA) was used to obtain configurations of conditions (causal recipes) that lead to teacher-centric or student-centric model. A secondary analysis explores whether there are different configurations of conditions when gender is added to the analysis.

The results of our fsQCA analysis reveal multiple configurations of conditions (causal recipes) that result in a preference for either a teacher-centric or student-centric approach to teaching entrepreneurship. The authors find that teaching experience is the main condition for the teacher-centric model, while self-efficacy and entrepreneurship training are the main conditions for the pathways leading to student-centric model. The fsQCA results also show that the configurations are affected when gender is taken into account in the analysis.

This study, one of the first of its kind, uses a configurational approach to examine pathways that contribute to the teaching preferences of entrepreneurship educators. This paper uses self-efficacy, teaching philosophy, teaching experience and entrepreneurship training as conditions to identify multiple unique pathways that result in either a teacher-centric or student-centric pedagogical model in EE. Notably, differences by gender are also found in this study.

The purpose of this study is to show that an important antecedent of perceived knowledge quality is an employee’s position in the organizational network due to their participation in different interest groups. In particular, this study theorizes that brokers establish a network of groups, which increases perceived knowledge quality vis-a-vis the social capital that employees draw on.

To test this study’s hypotheses on the influence of the structural position of knowledge brokers on the positive effects of social capital on perceived knowledge quality, this study combines data from an online survey with longitudinal archival data from a youth-care organization who used an enterprise social network (ESN) for knowledge sharing.

This study found a mitigating effect of being a broker on the relationship between trust and perceived knowledge quality, and also between inter-team interaction and perceived knowledge quality for lower levels of both trust and inter-team interaction on perceived knowledge quality.

Although the hypotheses are supported, in light of prior research, the authors would have expected stronger and positive effects.

This research is particularly interesting because it emphasizes the important role of social capital. For organizations that deal with trust issues, it might help to stimulate employees to broaden their activity on ESNs by becoming active in multiple groups.

While knowledge sharing on ESNs is generally conducive for creating organizational value, there is a lack of understanding of what drives employees’ perception of the quality of shared knowledge, and how this perception may depend on their position in the social network. To investigate this question, the authors turn to social capital theory.

This paper examines the possible cooperation options in terms of empty container repositioning across alliances for shipping lines based in the Greater China Region (China, Taiwan and Hong Kong SAR), after the three global shipping alliances reformed in April 2017.

This paper offers a comprehensive review to the latest shipping alliances and introduces a new cooperative dimension among shipping lines based in the Greater China Region which are member of different shipping alliances.

Cooperation among shipping lines in the Greater China Region in terms of empty container handling is possible in terms of resource sharing among shipping lines across alliances that fosters mega shipping line formation in the future.

Shipping lines should review their current empty container repositioning strategies and explore cooperation among non-alliance members having headquarters in proximity for quick responsiveness in empty container repositioning plan and execution.

This is a research directly analyzing the empty repositioning plan of the major shipping lines and their major service routes, fleet and containers.

Interruptions are prevalent in knowledge work, and their negative consequences have driven research to find ways for interruption management. However, these means almost always leave the responsibility and burden of interruptions with individual knowledge workers. System-level approaches for interruption management, on the other hand, have the potential to reduce the burden on employees. This paper’s objective is to pave way for system-level interruption management by showing that data about factual characteristics of work can be used to identify interrupting situations.

The authors provide a demonstration of using trace data from information and communications technology (ICT)-systems and machine learning to identify interrupting situations. They conduct a “simulation” of automated data collection by asking employees of two companies to provide information concerning situations and interruptions through weekly reports. They obtain information regarding four organizational elements: task, people, technology and structure, and employ classification trees to show that this data can be used to identify situations across which the level of interruptions differs.

The authors show that it is possible to identifying interrupting situations from trace data. During the eight-week observation period in Company A they identified seven and in Company B four different situations each having a different probability of occurrence of interruptions.

The authors extend employee-level interruption management to the system-level by using “task” as a bridging concept. Task is a core concept in both traditional interruption research and Leavitt's 1965 socio-technical model which allows us to connect other organizational elements (people, structure and technology) to interruptions.