1 Introduction

There is a social function of journalism that, in some cases, is incompatible with traditional business models, especially as a result of changes derived from the development of digital technology. Journalists are looking for ways to satisfy the information needs of citizens in a sustainable way. Globally, digital media initiatives that offer alternative models are emerging, such as informational and financial ones. These need to be studied in greater depth (Achtenhagen, 2017; Benson, 2018; Pickard, 2016; Scott et al., 2017). Digital media are gaining presence, in the sector and in society, in how they positively influence participation and sustainable social change (Fortunati & O'Sullivan, 2019).

Digital transformation contributes to journalistic information being considered a public good—just like healthcare, education or health—that has a significant impact on democratic societies (Cagé, 2016; Caro-González et al., 2019; Konieczna, 2014; Painter, 2019; Steiner et al., 2019). Journalistic information has a direct impact on improving value creation (Galindo-Martín et al., 2019), changing citizens' expectations (Mergel et al., 2019) and shaping public perceptions (Sheng & Lan, 2019). Accordingly, Zhang et al. (2019) demand intelligent public information and the alertness of social media.

As the advertising revenue that financed traditional journalism declines, journalistic information should be a service that benefits all citizens, whether paid or unpaid (Keneddy, 2018). Peters (2010) points out that if we accept the fact that the media play a key role in a democratic society, it is logical for this common good to receive public or civic support. In this context, Hermans and Drok (2018) define constructive journalism as the movement that reconsiders the values of professional journalism and welcomes a journalism that is oriented to the public, resolute, action-oriented and channeled into the future.

At this point, the non-profit business model analyzed in this article gains prominence. Non-profit media can end the dependence on advertisers' revenue and audiences by resorting to third party payment.Footnote 1 Many of these journalism companies rely on digital technology to offer news as a public service and under a non-advertising business model (Girija, 2019). If the market is unable to meet information needs, non-profit news organizations can fill that gap (Boehmer et al., 2018). This is the case in the United States,Footnote 2 where civil society is being strengthened by the emergence of new media aimed at serving its community, over and above the pursuit of economic value (Mitchell et al., 2013; Knight, 2015; Cervera, 2017; Creech & Nadler, 2018). Generally, enterprising non-profit journalists see their work as a form of public service (Benson, 2018; Ferrucci, 2015c). This strategic guideline has an impact on the choice of user segments regarding content and the way journalism is done. These organizations are free to focus on specific topics and geographic areas (Mitchell et al., 2013) and innovate by taking advantage of the possibilities offered by digital technology (García-Avilés et al., 2018). Non-profit media encourage diversity and plurality in the media space.

The purpose of this research is to find out whether the non-profit nature of these emerging media based on digital technology entails differences in the informational approach—and whether this approach is consistent with the public function that seems to be these businesses’ DNA. A comparative analysis examines whether non-profit digital media offer public service news services in contrast to for-profit digital media. This research fills a gap in the literature by analyzing both the organizational and informational characteristics of digital media (Seo & Vu, 2020).

Undoubtedly, this research is of practical use in helping to make these non-profit business models visible and advance them as complements and/or as a regeneration of traditional media systems, fostering a collective process that aims for the progress of daily life through information. The aim is to investigate the origin, type of content, journalistic genre or journalistic techniques, since the informative contents are posed in terms of a service to the society, one that helps citizens to solve problems.

The structure of the article is as follows: it begins with the first section, where a review of the literature on the subject is carried out; the second section presents the objective and hypotheses (Hs); the third section describes the population of digital media to be examined and the variables to be studied, as well as the method of analysis used; the fourth section sets out the results; and, finally, the conclusions and their theoretical and practical implications are offered, as well as the future lines of research.

2 Literature review

Robinson et al. (2019, p.368) grant digital journalism a personality of its own, the emerging characteristics of which "bring on transformations that must be theorized holistically, contextually, and relationally". However, the empirical studies that address the prolific journalistic and social environment are limited (Barranquero & Sánchez, 2018). That journalistic social enterprises would become a critical part of the mainstream media, institutionalising journalism as a social enterprise. This prediction has been supported by several studies (Konieczna & Powers, 2017; Scott et al., 2017), and social entrepreneurial journalism has become a crucial research topic (Huei-Ching et al., 2019). Media that function as social enterprises will be an important part of the future media landscape. These organisations have a significant impact on the community, and so they could mark the beginning of a new journalistic theory of democracy and provide an opportunity for academics to discuss the evolving role of journalism (Ferrucci, 2015c, 2017; Hermans & Drok, 2018; Juneström, 2019; Konieczna & Powers, 2017; Mitchell et al., 2013).

Non-profit business models have been attracting the interest of researchers in recent years and are becoming a significant part of the news ecosystem (Benson, 2018; Boehmer et al., 2019; Scott et al., 2017). There is recent literature on non-profit media (Birnbauer, 2018; Ferrucci, 2019; Konieczna, 2018). Although not-for-profit journalism organizations have been studied applying different conceptual nuances: the so-called non-profit journalism companies (Gray & Hopkins, 2019; Massey, 2018; Tenor, 2018), the non-profit news organizations (Benson, 2018; Konieczna, 2014; Konieczna & Powers, 2017; Konieczna & Robinson, 2014; Konieczna et al., 2018; Wright et al., 2019), digital nonprofits (Coates Nee, 2014; Ferrucci, 2019) or journalistic social enterprises (Caro-González et al., 2019) have been analyzed. From this point forward and to economise on language, except for texts cited, the acronym NP will be used generally for non-profit organizations or companies. For for-profit organizations or companies, the acronym FP is used.

NP journalism companies have a social and community focus and serve as an inescapable complement to FP news companies. Benson (2018) points out that these media boost investigative journalism and improve the sector by seeking to compensate and restore the civic mission of journalism. Konieczna et al. (2018) suggest that they are structures that work in collaboration with communities and strive to serve groups at risk of social exclusion. Likewise, non-profit media are perceived as a place where the results of investigative journalistic work are published, and they are a source of relevant information for communities (Żbikowska, 2016).

The discussion on how exactly the profit motive affects content is an experienced one. Differences in content exist depending on the media model (Beam, 1998, 2003). This relationship is the focus of this study. The approach conditions the content of these media, as there is evidence of a direct and positive local transmission, as well as increasing levels of satisfaction among the public (Syrdal & Briggs, 2016). In addition, Young et al. (2018) found that NP companies focus more on local journalism and investigative journalism. Konieczna (2014) states that NP organizations are changing the information available in the local media, as they are producing an increasing amount of public affairs news. However, studies of content differentiation are on the rise: the style of health news (Stroobant et al., 2018), quality of content (Wilson et al., 2017), and contextual reporting genres (McIntyre et al., 2018). There are differences in content between non-profit and for-profit organizations (Ferrucci et al., 2019; Ferrucci, 2015a, b).

NP organizations balance the information provided by traditional media (Tenor, 2018). These companies reflect community events as a "positive counter-image" and consider information to be a contribution to the common good. Along the same lines, Barranquero and Sánchez (2018, p.39), when talking about journalism cooperatives, define the service they offer as "a variant of traditional media … related to the ideas of alternation, citizenship and social change". Leckner et al. (2019) analyse a hyperlocal business model in Sweden, whose mission is to promote citizenship, strengthen democracy and reflect the local community. They point out that these media, with few resources and a lower growth rate than traditional media, cannot be considered as a replacement for consolidated media, but they do play a substantial role as complementary alternatives and contribute to media plurality in the local community (Graves & Konieczna, 2015). Public service by NP journalism organizations offers better and more critical information than that of traditional media (Requejo-Alemán & Lugo-Ocando, 2014).

It is therefore necessary to introduce the local context variable in order to understand the configuration of media systems and the emergence of NP organizations (Mellado et al., 2017). Thus, in the case of Ibero-America, external factorsFootnote 3 can explain the emergence of this type of organization, as it is a pioneer in data journalism practices and has enormous potential for social change (Borges-Rey et al., 2018; Segura et al., 2018). Boehmer et al. (2019) explore the number and diversity of sources used by FP organizations, finding them to be more in number and less in diversity. They also indicate that NP organizations tend to have very specific goals, and that the journalists working in them rely more on original, expert and unofficial sources than on traditional media.

Consequently, this research aims to provide new empirical evidence regarding the functioning and characterization of NP digital journalism companies, identifying those variables unique to them and which differentiate them from FP companies. In this way, this empirical study aims to verify the following hypotheses (Hs):

  • H1: NP digital media are characterized by content of a social and critical nature.

  • H2: NP digital media are characterized by using journalistic techniques linked to the deepening of information

  • H3: NP digital media are characterized by using their own sources to generate information

  • H4: NP digital media are characterized by using journalistic genres that allow treating the information in a more thoughtful way

3 Methodology

To determine the characteristics of non-profit digital media (Alberti & Varon, 2017; Boehmer et al., 2019; Leckner et al., 2019; Tenor, 2018), an analysis was conducted using statistical analysis (Oliveira et al., 2021): inference and binomial logistic regression (Rodríguez & Gutiérrez, 2007). This analysis was of the for-profit/non-profit digital media listedFootnote 4 in the SembraMedia directoryFootnote 5 (https://www.sembramedia.org/). From this directory, a sample of 509 Ibero-American digital media was selected (as of 20 February 2018). These media had relevant information on several variables: founders, mission, journalistic genre, owners, type of content, origin of content, journalistic techniques, etc. The sample covered is Spanish-speaking digital media, with the 509 digital media having the most information of the total media present in the public access directory. Data from the directory was extracted by using a Phyton programming language program that has a beautiful soup which is a proprietary library of the program to locate and extract the necessary information from the SembraMedia website, which was then exported to Excel and IBM SPSS (24v).

The variables analyzed (Table 1) were those proposed by SembraMedia. These variables were defined by consensus of the institution's team, including some of the most relevant researchers in this field, such as Salaverría (Salaverría, 2017; Salaverría & Cores, 2005), and professional consultants, such as Janine Warner or James Breiner. The categories used in the database are commonly used in the training of journalists and in the profession. On the other hand, it was also decided to keep these variables in order to be able to carry out comparative and longitudinal studies. The variables were used in previous research (Caro-González et al., 2021; Rojas-Torrijos et al., 2020; Salaverría et al., 2019; Bianchi, 2018).

Table 1 Analysis variables
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The binomial logistic regression technique was used to achieve the proposed objective and test the hypotheses. This choice was based on the characteristics of the available data when derived from dichotomous questions. Different authors (Ferrán, 2001, p.232; Visauta, 2003, p.52) agree that logistic regression enables classifying the belonging groups of the agents analyzed according to the estimated coefficients for each independent variable. Thus, it is a technique used in different fields—specifically in the area of economics and social sciences, as shown by Holienka et al. (2016); Irimia-Diéguez et al. (2016); Woo-Yung and Hm-Hak (2016); Zekić-Sušac et al. (2016); Aryal et al. (2018); Kail et al. (2018); and Saxena (2018). Specifically, the "forward (likelihood ratio)" method was selected for the binomial logistic regression, as it compares the input based on the significance of the scoring statistic and verifies elimination based on the probability of the statistic from the likelihood ratio, which is founded on estimates of the maximum partial likelihood.

The mathematical model of logistic binomial regression has the following expression (Hosmer & Lemeshow, 2000; Pampel, 2000):

$$\mathrm{p}=\frac{{e}^{z}}{1+{e}^{-(z)}} \mathrm{where} \mathrm{Z}={\beta }_{0}+{\beta }_{1}{X}_{1}+{\beta }_{2}{X}_{2}+\dots +{\beta }_{k}{X}_{k}$$

p is the probability that the event will occur. In this case, the dependent variable is the type of organization (for-profit organizations or non-profit organizations).

\({\beta }_{k}\) are the coefficients of the independent variables.

\({\beta }_{0}\) expresses the value of the probability of Z when the independent variables are zero.

\({X}_{k}\) are the values that independent variables can adopt.

Before conducting this analysis and in order to detect independent variables that might have some link with the dependent variable (type of organization, NP or FP), the population proportions were compared by means of the Z-test for independent samples, which permits verifying hypotheses referring to whether there are differences between two proportions coming from independent samples, which are those examined individually. In this case, it corresponds to the sample comparison between the for-profit journalism company (FP) and the non-profit journalism company (NP).

4 Results

This section first shows the variables analyzed in journalism companies (FP digital media and NP digital media). Then, after the necessary adjustments, the resulting model is drawn and, finally, the proposed model is validated.

4.1 Variables under study

The dependent variable and the independent variables considered in this research are shown in Table 1. Thus, the dependent variable in the model proposed is the type of organization (FP and NP), and the independent variables introduced into the statistical program for measurement are: journalistic genre, technique used, origin of content, coverage, and type of content. Not all these variables became part of the final model (Table 2), as several of them did not have a link to the dependent variable. Initially, the five variables show a significantly higher percentage in non-profit digital media (NP): Coverage (national and local); Type of content (environment, events, corruption, gender, LGBT, indigenous and refugees); Journalistic genre (critical, non-fiction column and essay); Technique employed (data coverage and journalism); and Origin of content (professional journalists, citizen journalism, content referring to materials cited from other sites and founding person).

Table 2 The most significant variables in non-profit digital media. He puesto puntos decimales
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4.2 Model design

After introducing all the variables mentioned in the previous section into the statistical program and performing the calculations defined in the methodology, the model detected those variables presented in Table 2 as useful variables to characterize an NP digital medium. This figure shows the coefficients of the independent variables selected in the logistic regression model and indicates their level of significance. In addition, the results of the Chi-squared test have been annexed, in order to determine the degree of explanation and the tests to determine the goodness of fit of the model.

Within type of content, those dedicated to covering environmental and journalism news stand out, as they are more likely to be found in NP media; while sport and science act in a negative way, subtracting the probability of being covered by NP media.

In terms of the journalistic technique used, data journalism is shown as a feature of NP digital media, as it has a positive coefficient; while breaking news plays a negative role, meaning that this technique is more present in FP digital media. This evidence includes learning about breaking news and monitoring current news (Arkaitz, 2019).

Regarding the origin of the content, it can be observed that NP media are not characterized by employing professional journalists (coefficient with a negative sign). However, it is characterized by being oriented toward the journalistic genre of the essay (coefficient with a positive sign).

Thus, the final binary logistic regression model is expressed as follows:

$$\mathrm{p}=\frac{{x}^{1}}{1+{e}^{-(z)}}$$

\(\begin{array}{l}\mathrm{where}:\;\mathrm Z=0.084+1.736X_{\mathrm{journalistic}\;\mathrm{genre}:\;\mathrm{essay}}-1.005X_{\mathrm{content}\;\mathrm{source}:\;\mathrm{professional}\;\mathrm{journalists}}+\\1.720X_{\mathrm{journalistic}\;\mathrm{technique}:\mathrm{data}\;\mathrm{journalism}}-0.831X_{\mathrm{journalistic}\;\mathrm{technique}:\mathrm{current}\;\mathrm{events}\;\mathrm{or}\;\mathrm{breaking}\;\mathrm{news}}-\\1,121X_{\mathrm{type}\;\mathrm{of}\;\mathrm{content}:\;\mathrm{sport}}+1,128X_{\mathrm{content}\;\mathrm{type}:\;\mathrm{environment}}+2,866X_{\mathrm{journalism}\;\mathrm{content}}-\\1,429X_{\mathrm{typeofcontent}:\mathrm{science}}\end{array}\)  

An omnibus test based on a Chi-squared test was used to validate the model, which shows that the significance is less than 0.05% and indicates that the independent variables explain the dependent variable (Table 2). To analyze the goodness of fit of the model, Cox and Snell’s R2 and Nagelkerke’s R2 have been used. These reached acceptable values (Table 2).

Table 3 shows that the prediction made by the model (predicted results) is correct in 82.9% of cases, which is a high percentage.

Table 3 Classification of observed and predicted results
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The tests carried out validated the final model, so that the derivations arising from it optimally predict the characteristics of digital media.

5 Conclusions

This research responds to the current digital media and whether they differ according to the business model (for-profit or non-profit). Non-profit digital media create environmental content. Non-profit digital media are characterized by journalistic work linked to social function, and this is a relevant difference with respect to the media that claim to be for-profit. It was verified that there are certain variables characteristic of one type of digital media or another: coverage, type of content, journalistic genre, technique used, and source of the content. In this way, the differentiating characteristics of these journalism business models can be obtained (Chart 1).

Chart 1
figure 1

Source: Author development

Variables that characterize FP and NP digital media.

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These findings are consistent with Boehmer et al. (2019) and Caro-González et al. (2021) regarding the source of the content, along with Young et al. (2018) on the type of content of non-profit journalism organizations. It is true that five key variables are analyzed in favor of addressing four of the hypotheses formulated about non-profit digital media. However, there are some similarities between non-profit and for-profit digital media that should be considered, such as the techniques used (data journalism) and the origin of the content (professional and citizen journalists).

After applying the binomial logistic regression model (with a high level of fit and goodness), the 4 hypotheses raised at the beginning of the investigation can be sustained (Chart 2):

  • H1: NP digital media are characterized by content of social and critical nature The inferential analysis shows, in NP digital media, content on environment, events, corruption, gender, LGBT, indigenous peoples and refugees. This unknown is reinforced by the binomial logistic regression analysis, in the sense that it marks the Environment and Data Journalism as characteristic content types in NP media. In the case of FP digital media, journalistic content corresponds to science and sport.

  • H2: NP digital media are characterized by using journalistic techniques linked to the deepening of information Binomial logistic regression analysis points to specialization in data journalism. NP digital media aims to investigate and deepen information and is less concerned with covering breaking news.

  • H3: NP digital media are characterized by using their own sources to generate information In the inferential analysis, it can be observed that the source of the content comes from citizen journalism, and the content refers to materials cited from other sites and the founder or founders themselves. With regression analysis, it can only be stated that, with respect to FP digital media, NP media are not characterized by employing professional journalists. This, combined with the previous results, leads to the conclusion that information is obtained through citizen journalism, content from other sites and content from the founders. The support for this response is therefore partial.

  • H4: NP digital media are characterized by using journalistic genres that allow treating the information in a more thoughtful way The binomial logistic regression analysis confirms that the majority of NP digital media opt for journalistic genres that allow them not only to transmit the information (as done by FP media), but also to provide reflection and criticism (essay).

Chart 2
figure 2

Source:Prepared by the authors using SembraMedia data

The most significant variables in non-profit digital media.

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Therefore, the characteristics associated with non-profit digital media (NP) are confirmed. These characteristics reflect a business model focused on the provision of a quality public service (Ferrucci, 2015c). However, the public information service is not only provided by the non-profit media, but also comes from the traditional media that are based on the public trust to inform citizens. Perhaps, today, the digital media are defending the classic service of informing. Boehmer et al. (2019) showcase the growing interest of academia in understanding the role of non-profit journalism organizations in the changing media marketplace. This research provides new empirical evidence that enables the understanding of these organizations and answers Wright et al. (2019) demand for more research to examine non-profit media. Non-profit digital media address information issues that are not covered by for-profit media and they thus contribute substantially to media plurality and diversity. In the same way, they deal with information niches abandoned by other media because they are not considered economically profitable. There are content differences between for-profit and non-profit digital Ibero-American media- This finding is seen in other works (Ferrucci et al., 2019; Ferrucci, 2015a, b).

The main limitation of this research concerns the data used. It would be advisable to have more companies and enriched data, which would allow adding other variables, such as the weight of citizen journalism, the social impact or corporate social responsibility. It should be noted that this is a sample limited to member organizations and available in the SembraMedia directory. While it is significant to analyze the content of non-profit digital media, it is recognized that the issue of content quality needs to be addressed in order to draw more robust conclusions concerning the four hypotheses of the article. Future lines of research are oriented toward the study of journalistic innovation derived from non-profit digital companies, in order to determine in more detail their funding channels and their effect on production routes. As demonstrated, the differences between not-for-profit and for-profit digital media should continue to be observed. Also, the similarities between not-for-profit and for-profit digital media should be deepened. Therefore, the similarities in the variables studied could be further investigated, as the variables require more depth. For example, we can now ask whether or not non-profit and for-profit digital media focus on the same goal, but with different funding models.

In short, the aim is to determine the impact of digital media in the community and in the journalistic media ecosystem through the development of specific research instruments (surveys) and the deepening of information through the development of case studies. In particular, research into non-profit digital media should be continued and the analysis of digital media content in relation to organizational models is promising.