Collaborations between media organisations are becoming an increasingly common practice in the field of journalism. Academic research, so far, mostly focused on large-scale investigations and communities of digital outlets, such as fact-checkers networks. However, new types of inter-firm partnerships are emerging, namely between legacy media and tech startups, towards media innovation and digital transformation. The paper to which this database is connected aims to advance the theoretical understanding of the relationship between collaborations and media innovation. We formulate an original analytical model to assess the influence of organisational factors of collaborations on knowledge sharing, a key condition for explorative innovation. Based on the experience of the Stars4Media programme, we present an empirical application of the analytical model (based on this data-set) to a case study of thirty collaborative projects involving seventy-six European media companies.

How was this dataset collected?

The empirical analysis draws on a structured survey filled in by the 76 team leaders of the companies involved. Circa 50% of the companies represented in the survey were micro-size companies (<10 employees), the remaining 50% was composed of a mix of small- (10–50 employees), medium- (50–250 employees) and large-sized companies (>250 employees). This cross-sectional study, conducted through an online survey designed in Google Forms, was issued at the end of the implementation phase of the Second Edition of the Stars4Media programme, making this study a cross-sectional, correlational research (Field, 2018). The 76 team leaders were asked to provide answers on behalf of their team, after consulting with them. To avoid multiple interpretations of the main technical vocabulary and concepts, a glossary was provided to all respondents. Filling in the survey was part of the mandatory project deliverables for the companies involved, but the companies were made aware that their responses to the survey would be used exclusively for research purposes.

How has it been processed?

Once the responses to the survey were collected, the data were imported in SPSS Statistics. Ordinal independent and dependent variables were created and were given a label for each of the 3 organizational factors (independent variables) and the knowledge sharing (dependent variable).

The first organizational factor is 'centrality of multidisciplinarity of activities' and corresponds to question 18 (Independent variable 1)

The second organizational factor is 'intensity of collaboration' and corresponds to question 17 (Independent variable 2)

The third organizational factor is 'agency of innovation design' and corresponds to question 11a (Independent variable 3)

The two dependent variables were knowledge sharing (Q22) and capacity to design innovations (Q23).

Descriptive statistics, particularly cross-tabulation, were chosen as the method of data analysis (Momeni et al., 2018). The observed data were analysed to assess whether there was an association between two variables (an independent one and a dependent one) observed in each test of the four tests conducted, applying the Pearson Chi-Squared test, and to assess the strength of this association, looking at Cramer’s V (Field, 2018; Momeni et al, 2018).

Four distinct tests were conducted. The first three tests were designed to observe, separately, the association of each of the three structural/organisational factors (independent variables) with knowledge-sharing (the dependent variable) of the teams involved. A fourth test was conducted to observe if a certain level of knowledge-sharing (independent variable) would correspond to a certain level of innovation capacity (dependent variable) gained as a result of the collaboration.

Microsoft Excel, SPSS Statistics