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The purpose of this article is to show (1) how social innovations are created through co-production in social enterprises in Finland and (2) how enabling ecosystems for the creation of social innovations can be enhanced by the government.

This study is a descriptive case study. The data comprises focus group interviews that were conducted during a research project in Finland in 2022. The interviewees represented different social enterprises, other non-profit organisations and national funding institutions.

Social enterprises create social innovations in Finland through co-production, where service innovation processes, activism and networking are central. Also, to build an enabling ecosystem, government must base the system upon certain elements: enabling characteristics of the stakeholders, co-production methods and tools and initiatives by the government.

The authors address an important challenge that social enterprises struggle with: The position of social enterprises in Finland is weak and entrepreneurs experience prejudice from both the direction of “traditional” businesses and the government which often does not recognise social enterprise as a potential partner for public service delivery. Nonetheless, social enterprises create public value by contributing to the co-production of public services. They work in interorganisational networks by nature and can succeed where the traditional public organisations and private businesses fail.

This study aims to increase knowledge of the ability of nutrition labels to guide consumer choices in real-life environments.

Food consumption and plate waste data were collected from two self-service restaurants (SSR) with different customer groups over six observation days: three control and three intervention (with nutrition labelling) periods. Study Group 1 consisted of vocational school students, mostly late adolescents (N = 1,710), and Group 2 consisted of spa hotel customers, mostly elderly (N = 1,807). In the experimental restaurants, the same food was served to the buffets during the control and intervention periods.

The nutrition label in the lunch buffet guides customers to eat fewer main foods and salads and to select healthier choices. Increased consumption of taste enhancers (salt and ketchup) was observed in the study restaurants after nutritional labelling. Nutrition labelling was associated with a reduction in plate waste among the elderly, whereas the opposite was observed among adolescents.

The results provide public policymakers and marketers with a better understanding of the effects of nutrition labelling on consumer behaviour. Future studies should further evaluate the effects of nutrition labelling on the overall quality of customer diets and the complex environmental, social, and psychological factors affecting food choices and plate waste accumulation in various study groups.

The purpose of this study is to describe latent classes explaining the innovation logic in the Finnish construction companies. Innovativeness is a driver of competitive performance and vital to the long-term success of any organisation and company.

Using finite mixture structural equation modelling (FMSEM), the authors have classified innovation logic into latent classes. The method analyses and recognises classes for companies that have similar logic in innovation activities based on the collected data.

Through FMSEM analysis, the authors have identified three latent classes that explain the innovation logic in the Finnish construction companies – LC1: the internal innovators; LC2: the non-innovation-oriented introverts; and LC3: the innovation-oriented extroverts. These three latent classes clearly capture the perceptions within the industry as well as the different characteristics and variables.

The presented latent classes explain innovation logic but is limited to analysing Finnish companies. Also, the research is quantitative by nature and does not increase the understanding in the same manner as qualitative research might capture on more specific aspects.

This paper presents starting points for construction industry companies to intensify innovation activities. It may also indicate more fundamental changes for the structure of construction industry organisations, especially by enabling innovation friendly culture.

This study describes innovation logic in Finnish construction companies through three models (LC1–LC3) by using quantitative data analysed with the FMSEM method. The fundamental innovation challenges in the Finnish construction companies are clarified via the identified latent classes.

The purpose of this paper is to identify the areas and logic of integration of different stakeholders using different methods and to analyse their applicability and challenges in practical projects. The main aim is to describe how these different methods impact value creation.

Action design research was carried out in a large hospital construction project where the first author acted as an “involved researcher” and the second author acted as an “outside researcher”. Two workshops were organised to evaluate the direct and indirect challenges and benefits of the applied four methods and to explain how different methods enable value creation.

All the studied methods provide good results in terms of usability and commitment to the aims of the project, thus delivering the direct benefits expected. Process, people and tools logic works well in this case project when applying the methods properly. Significant evidence was provided on secondary deliverables of the methods, and all analysed methods had a significant impact in the area of leading people, clarifying what “focus on people” means and how it is enabled.

Focus on people can be achieved through different operative methods if applied in the right way. It is necessary to select the most suitable methods based on all the direct and indirect deliverables.

This case project offered a platform to analyse integration methods in a real-life project using the collaborative contract method. The authors were able to participate in the analysis by taking action from the very beginning of the project in terms of training, learning, continuous development and coaching of these methods and evaluating the applicability.

This paper aims to answer the questions of what clothing practices related to sustainable fashion can be observed in young consumers' daily lives in Finland’s capital region and what prevents their further proliferation.

This is qualitative research that draws from 22 semi-structured interviews with high school students in the capital area of Finland. The data were analyzed with the use of thematic analysis, a flexible method of data analysis that allows for the extraction of categories from both theoretical concepts and data.

This paper contributes to studies of young people’s consumption with the practice theory approach, putting forward the category of following sustainable fashion as an integrative practice. The three-element model of the practice theory allows answering the question of challenges that prevent the practice from shaping. The paper further advances this approach by identifying a list of context-specific dispersed practices incorporated into sustainable fashion.

The study suggests practical ways of improving clothing consumption based on the practice theory approach and findings from empirical research. Sustainable practices require competences, knowledge and skills that the school, as an institution working closely with high school students, could help develop.

The study contributes to the current studies of sustainability and youth culture of consumption with a practice theory approach and findings, related to a particular context of a country from Northern Europe.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.