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This paper aims to focus on the re-presentation of the cultural phenomena hygge in Denmark and fika in Sweden in destination branding and address the inevitability of their essentialization through the branding process.

Three relevant semi-structured interviews with destination marketing organisation’s employees were conducted, as well as a content-based analysis of three social media channels (Facebook, Twitter, Instagram). A total of 465 posts in total were analysed (140 Facebook posts, 109 Twitter posts, 216 Instagram posts).

This study demonstrates how, when communicated through social media, intangible cultural assets are transformed into tangible elements. It explains why the re-presentation and place branding processes necessarily simplify and essentialize the destination.

Destination branding scholars have traditionally criticised the flattening and essentialization of culture in destination branding and have called for a more nuanced approach to presenting a destination. This paper situates destination branding as a process that necessitates the manipulation of the presentation of the destination, which inevitably essentializes the place; this is intended. Critical destination branding researchers need to rethink their criticisms and acknowledge the inherent essentialization goal of destination branding.

The aim of the study is to examine the effects of opportunity creation and discovery on the performance of family firms. Specifically, from the tenets of dynamic capabilities and organizational contingency perspectives, this study proposes and tests a framework of how family firms' creation and discovery behavior impact venture growth and the conditions under which such impact can vary.

The study uses moderated-hierarchical regression to analyze survey data from 156 family-owned small and medium-sized enterprises (SMEs) operating within a sub-Saharan African economy.

The findings indicate that creation behavior has a curvilinear U-shaped relationship with venture growth, while discovery behavior has a direct positive relationship with venture growth. Further analysis reveals that the curvilinearity of the U-shaped relationship between creation and venture growth will be stronger for older family firms than for younger ones.

The study findings may be limited by the cross-sectional nature of the data and the specific focus on family firms only.

The results highlight the significance of pursuing both opportunities among family firms. In fact, both creation and discovery opportunities are significant drivers of family firm growth, albeit in different capacities. Relatedly, managers of older family firms (compared to younger firms) can invest more in exploiting creative opportunities.

From these findings, governments and other stakeholders should create enabling environment and institutional frameworks conducive to exploiting opportunities by entrepreneurial firms.

The study is novel – as it provides unique findings on the performance implications of creation and discovery behavior of entrepreneurial family firms within developing economies.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.