Achieving publications in high-impact journals is a major cornerstone for academic careers in the US and elsewhere in the world. However, apart from novel insights and relevant contributions to the field, there are expectations of editors and reviewers regarding the structure and language of manuscripts that prospective contributors have to adhere to. As these expectations are mostly communicated using best-practice examples, especially international researchers might often wonder how to implement them in their manuscripts. Applying an applied linguistics model to 60 papers that were published in US-based and Indian management journals we derive evidence-based advice for the writing of introductions.

Complex problem solving (CPS) is an up-and-coming twenty-first century skill that requires testtakers to solve dynamically changing problems, often assessed using computer-based tests. The log data that users produce when interacting with a computer-based test provide valuable information about each individual behavioral action they undertake, but such data are rather difficult to handle from a statistical point of view. This paper addresses this issue by building upon recent research focused on decoding log data and aims to identify homogeneous student profiles with regard to their ability to solve CPS tasks. Therefore, we estimated a discrete two-tier item response theory model, which allowed us to profile units (i.e., students) while taking into account the multidimensionality of the data and the explanatory effect of individual characteristics. The results indicate that: (1) CPS can be thought of as a three-dimensional latent variable; (2) there are ten latent classes of students with homogenous profiles regarding the CPS dimensions; (3) students in the higher latent classes generally demonstrate higher cognitive and noncognitive performances; (4) some of the latent classes seem to profit from learning-by-doing within tasks, whereas others seem to exhibit the reverse behavior; (5) cognitive and non-cognitive skills, as well as gender and to some extent age, contribute to distinguishing among the latent classes.

The article "Evidence-based understanding of introductions of research articles", written by "Nida ul Habib Bajwa, Cornelius J. König and Thiemo Kunze", was originally published Online First without Open Access. After publication in volume 124, issue 1, page 195-217, the author decided to opt for Open Choice and to make the article an Open Access publication.