Journal of Entrepreneurship, Management and Innovation (2026)

Volume 22 Issue 2: 5-51

DOI: https://doi.org/10.7341/20262221

JEL Codes: M10; M20; O30; D91

Katarzyna Czernek-Marszałek, Ph.D, University of Economics in Katowice, 1 Maja 50, 40-287, Poland, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Patrycja Klimas, Professor, Wroclaw University of Economics and Business, Komandorska 118/120; 53-345 Wroclaw, Poland, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Dagmara Wójcik, Ph.D, University of Economics in Katowice, 1 Maja 50, 40-287, Poland, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Patrycja Juszczyk, Ph.D, University of Economics in Katowice, 1 Maja 50, 40-287, Poland, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Aleksandra Szpulak, Ph.D, Wroclaw University of Economics and Business, Komandorska 118/120; 53-345 Wroclaw, Poland, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

PURPOSE: This study aims to investigate whether and how managers’ social relationships (SR) are associated with organizational innovativeness (OI). It addresses gaps in prior research by adopting a multidimensional perspective on both constructs and focusing on creative industries, where relationships and innovation are particularly intertwined. METHODOLOGY: A mixed-methods research approach with a convergent design was employed, combining parallel quantitative and qualitative investigations. Quantitative data were collected via a survey of 690 managers from creative industries in Poland, with a final cleaned sample of 302 observations. The developed research model was tested using a covariance-based structural equation modeling (CB-SEM) approach. Qualitative insights were obtained from five focus group interviews (FGIs) involving 30 participants, which were analyzed using a deductive-inductive coding approach to enrich and triangulate the findings. FINDINGS: The quantitative results indicate that SR – comprising five valid building blocks (emotional intensity, community of interest, shared identity, private contacts, and professional community interactions) – are significantly associated with OI, although the strength and direction of these associations vary across building blocks and OI’s dimensions. Simultaneously, qualitative research emphasizes the strong ambivalent nature of SR, which typically stimulate innovation through trust and knowledge exchange, but may also constrain it under conditions of excessive emotional intensity, overly strong shared identity leading to closure, private contacts that become nepotistic or conflict-ridden, and professional community meetings that damage future cooperation. IMPLICATIONS: The study contributes to theory by integrating fragmented insights into the relationship between SR and OI and by proposing empirically derived, context-specific measurement models for both constructs. For practice, it suggests that managers may benefit from a better understanding of how social relationships are associated with organizational innovativeness and how they can be leveraged, while being mindful of potential downsides, such as relational lock-in. ORIGINALITY & VALUE: This research offers a novel, multidimensional examination of the SR–OI relationship within creative industries, introduces a two-dimensional model of OI, and illustrates the application of a rigorous multi-stage data cleaning approach rarely applied in management research.

Keywords: social relationships, organizational innovativeness, creative industries, structural equation modeling, focus group interviews, research triangulation, careless responding

INTRODUCTION

“Good management is essentially about

harnessing and optimising interpersonal relationships” (Nuttall, 2004: 16).

Interpersonal relationships, which are essentially social relationships (SR) (Sousa, 2005), established and maintained by managers of organizations, their owners or employees, are of significant importance for business activities in various, often completely different sectors of the economy (e.g., Durach & Machuca, 2018; Ekanayake et al., 2017; Ganguly et al., 2019; Granovetter, 2005; Grebski & Mazur, 2022; Kraft & Bausch, 2018; Leenders & Dolfsma, 2016; Parlar et al., 2020). It is acknowledged that SR constitute a multidimensional and complex construct, composed of specific features (e.g., emotionality), sources (e.g., joint membership in organizations, previous jobs), and components (e.g., trust, engagement, reciprocity), which we refer to as SR building blocks (Klimas et al., 2025).

One interesting perspective on SR held by managers is their meaning for the organizational innovativeness of the entities they represent. In the field of organizational innovativeness (OI), one of the most acknowledged approaches is the one proposed by Wang and Ahmed (2004), who stated that innovativeness is “an organization’s overall innovative capability of introducing new products to the market, or opening up new markets, through combining strategic orientation with innovative behavior and process” (Wang & Ahmed, 2004: 304). In this context, OI is conceptualized as a dynamic capability embedded not only in strategy and processes, but crucially in the quality of SR shaping organizational action (Koc & Bozdag, 2025). Accordingly, the alignment of strategic orientation with innovative behavior depends on relational foundations such as trust, engagement, and reciprocity, which enable knowledge transfer, coordination, and collective learning. OI thus emerges not merely as the outcome of innovation activities, but as an organizational property co-shaped by the multidimensional architecture of SR and the mechanisms of their institutionalization (Koc & Bozdag, 2025).

However, the link between managers’ social relationships and OI is intriguing, as current knowledge is quite ambiguous.

On the one hand, previous research indicates that SR constitute a critical source of novel information for OI (Durach & Machuca, 2018; Ganguly et al., 2019; Kraft & Bausch, 2018; Leenders & Dolfsma, 2016; Uzzi, 1996). They facilitate knowledge sharing, expertise locating, coordination, and stakeholder management (Fulk & Yuan, 2013; Leonardi, 2014), enabling the recombination of existing knowledge into new ideas (Fu, 2022). They also enhance the level and quality of knowledge exchanged between actors (Fulk & Yuan, 2013; García-Villaverde et al., 2021; Leonardi, 2014; Nahapiet & Ghoshal, 1998; Parlar et al., 2020). Through joint problem-solving, actors acquire tacit knowledge that is difficult to codify and transfer via market mechanisms (Davidsson & Honig, 2003) and that is essential for generating and accelerating innovative solutions (Fu, 2022; García-Villaverde et al., 2021; Granovetter, 2005). Beyond firm-level effects, SR also support the diffusion of innovativeness across and within sectors (Pittaway et al., 2004) by stimulating knowledge growth (Cimenler et al., 2016), idea generation and implementation (Chassagnon & Audran, 2011; Kijkuit & van den Ende, 2010), and the development of cooperation (Steinicke et al., 2012).

On the other hand, research has also shown that SR can limit OI, as partners may close themselves to external entities, which reduces the inflow and use of new ideas, becoming closed to innovative, alternative ways of acting, as well as a lack of diversity and “fresh” look, so-called “collective blindness” (Gargiulo & Benassi, 2000; Mitręga & Zolkiewski, 2012). They may also be characterized by lower adaptability and flexibility due to the adaptation to the (well-) known partners (Mizruchi & Stearns, 2001).

Notably, the literature suggests that SR matter for OI, particularly through the facilitated transfer of tacit knowledge; however, as Pittaway and colleagues (2004: 35) emphasize, “the processes through which informal networking relationships develop and subsequently influence innovativeness need to be investigated further”. Although some time has passed since this statement was formulated, research on the links between SR and OI remains limited. Indeed, a significant amount of research focused not on SR but on social capital, and not on OI but on innovations, although these constructs are not the same (e.g., Kraft & Bausch, 2018; Leenders & Dolfsma, 2016).

Second, although SR themselves have already been extensively researched in various sectors (e.g., the clothing industry – Uzzi, 1996; farming – Cush & Macken-Walsh, 2016; biotech – Rank, 2014; engineering – Grebski & Mazur, 2022), so far SR have not been considered widely in the context of creative industries (Alacovska & Bissonnette, 2021; Boyd et al., 2015; Cnossen et al., 2019; Lingo & Tepper, 2013), in particular regarding OI of entities operating in these industries. At the same time, the literature shows that SR play a particularly important role in creative industries, because the activities of entities operating in these industries are mainly based on creativity (Gabby & Zuckerman, 1998; Johansson, 2012; Parlar et al., 2020; Ruef, 2002; Tsai & Ghoshal, 1998), interactions and networks (Potts et al., 2008) facilitated by SR. What is more, creative industries are strongly based on OI – including the human factor – therefore establishing and developing SR plays an essential role for them (García-Villaverde et al., 2021; Gohoungodji & Amara, 2023; Koch et al., 2023; Parlar et al., 2020; Parmentier & Mangematin, 2014).

Third, it should be emphasized that, so far, no studies have analyzed the importance of SR for OI in such a multidimensional approach to both constructs. Existing research focuses on either one of the dimensions of OI, such as product, or one of the dimensions of SR, including private contacts or trust (Ellonen et al., 2008). The adoption of multidimensional measurement models is attributed to the complexity of the dependent and independent variables. Indeed, SR are seen as highly complex, consisting of various elements – in this paper, we refer to them as building blocks – that are interconnected, and their importance for business, as demonstrated by previous research, is significant (Klimas et al., 2025). Similarly, OI is acknowledged as a multidimensional construct (Ellonen et al., 2008; Ruvio et al., 2014; Ghosh & Srivastava, 2022; Wang & Ahmed, 2004) characterizing an organization’s capability to innovate (Wang & Ahmed, 2004). Although the multidimensional conceptualization of OI is acknowledged, empirical research in this field primarily focuses on product- and process-related aspects, while other dimensions, i.e., market, strategy, and behaviors, remain on the margins of researchers’ interest (Camison & Villar-López, 2011; Lee & Trimi, 2021).

In sum, we argue that identifying the meaning of SR in relation to OI is relevant, as it provides insight into how SR may be understood in the context of supporting firms’ OI. Considering the important role of SR in business, especially in creative industries and at the same time the importance of the creative industries and innovativeness themselves in the economy (Bilan et al., 2019; Gohoungodji & Amara, 2023; Kasprzak, 2017), this research is also valuable from an application point of view. Thus, we aim to investigate the role of SR in OI, using a multidimensional measurement approach for both variables.

To achieve our aim, we employed a quantitative survey and qualitative focus group interviews (FGIs) following a mixed-methods research approach with a convergent parallel design (Creswell & Plano Clark, 2018). On the one hand, we used a quantitative survey to test the hypothesis that SR are positively associated with OI. On the other hand, qualitative research in the form of FGIs was intended to diagnose the role of SR in OI, thereby exploring the existence of an association and better understanding its nature. We believe that combining these two methods in a form of convergent parallel application allowed us to achieve common triangulation-based benefits (Bhana, 2024), such as greater accuracy and relevance in the conclusions drawn (Jack & Raturi, 2006).

Data were collected from owners, directors or managers responsible for the organization’s relations with other entities on the market. In total, we collected 690 full questionnaires (after removing careless responses, the dataset comprised 302 records) and gathered qualitative data from 30 participants across 5 FGI. Our convergent parallel research design enabled us to quantitatively test hypotheses using covariance-based structural equation modeling (CB-SEM) to examine the positive association between SR and OI, and to qualitatively deepen the assumed relationship through FGI.

This study offers four noteworthy contributions to management literature: (1) integration of fragmentary states regarding the meaning of SR for OI; (2) presentation of a three-step process of detecting careless responding which although recommended in methodological literature (Johnson, 2005; DeSimone et al., 2015; Meade & Craig, 2012), is still rarely implemented in such a comprehensive manner in management research practice; (3) offering two-dimensional measurement model of OI applicable within creative industries; (4) testing the positive association between SR (i.e. their five building blocks: emotional intensity, community of interest, shared identity, private contacts, and meetings in the professional community) and OI comprising human and non-human innovativeness. As a contribution to managerial practice, this study demonstrates to managers that their SR may enhance as well as in some aspects constrain OI, the cornerstone of success in creative industries (Plum & Hassink, 2014; Wohl, 2022).

The paper is organized as follows. The next section presents the theoretical framework and is structured into three subsections of the literature review. These sections address SR and OI, concluding with the formulation of a hypothesis and a conceptual model. The third section describes the research methodology, including the application of a mixed-methods approach and convergent parallel design (i.e., survey and FGI), a description of the three-stage procedure for detecting careless responding, and the development of the measurement models. Section four reports the effects of the data analysis, including both quantitative results and qualitative findings. Next, we discuss the findings, and in the last section, we present conclusions.

THEORETICAL BACKGROUND

Social relationships 

Although social relationships are defined differently in the literature, in general, authors stress that they are embedded in individuals’ emotions and personal values and are largely spontaneous and intuitive (Johannisson & Mønsted, 1997). Following this view, in this paper, we define SR as “informal relations between individuals, characterized by a certain degree of emotionality” (Czernek-Marszałek et al., 2023a, p. 63).

The literature identifies diverse features and components of SR, which we call SR building blocks. These are: emotional intensity, community of interest, shared identity, private contacts, repeatability of relations, and meetings in the professional community (Klimas et al., 2025). These SR building blocks are indeed crucial in the day-to-day operations of entities whose products and services require creativity and innovativeness, the very source of which are interpersonal contacts. Such contacts are based on (Klimas et al., 2025):

1. Emotional intensity understood as an existing emotional bond between partners based on trust, kindness and mutual sympathy; it also refers to maintaining and repeating contact or engaging in relationship-building over time. These relationships are based on camaraderie, the desire to support the other party, and reciprocity in relationships (Bapna et al., 2017; Chassagnon & Audran, 2011; Raggio et al., 2014).
2. Community of interest understood as a relationship with people who share professional interests and a similar understanding of their work (sometimes treated as a way of life or hobby) (Bottero, 2005; Piselli, 2007; Sakalaki & Fousiani, 2012; Schulte-Holthaus, 2018). Such relationships are further characterized by familiarity with each other’s competencies and experiences, a comparable commitment to the relationship and a sense of fairness. They often involve maintaining informal contacts (e.g., via phone or email).
3. Shared identity, referring to a relationship with an individual with whom one shares a sense of belonging, for example, to the same local community. The parties may be united by shared knowledge of history, traditions, local culture, norms, values, and customs, often accompanied by a sense of local patriotism (Turner, 2007; Wang et al., 2010). Belonging to a specific community is typically associated with networks in the local environment and a sense of local identity.
4. Private contacts, i.e., those maintained outside of work hours and within family or close social circles, are based on intimacy or friendship. Sometimes these personal and professional contacts intertwine to such an extent that distinguishing between them becomes difficult (Heath, 2020; Milana & Maldaon, 2015).
5. Repeatability of relations understood as systematic (regular) and frequent interactions between parties (Bapna et al., 2017; Santoro et al., 2020).
6. Meetings in the professional community (conferences, trade fairs, workshops, festivals, team-building events, etc.) (Maurer & Ebers, 2006; Roberts, 2006; Heath, 2020), both formal and informal, constitute an important source of gaining new contacts and maintaining existing ones (Cillo et al., 2019; Santoro et al., 2020).

It needs to be highlighted that the importance of SR building blocks, such as trust or community of interest, may be different when these relations are built between the representatives of various types of entities, for example, suppliers, competitors, consultants, and in different economic sectors.

In strategic management research, SR has so far been analyzed mainly through its effects on inter-organizational relations (e.g. cooperation, competition or coopetition) (Cush & Varley, 2013; Darbi & Knott, 2023; Li et al., 2008), including both positive (Anderson & Jap, 2005; Czernek-Marszałek, 2020a; Roberts, 2006) and negative outcomes (Czernek-Marszałek, 2020b; Gulati et al., 2000; Kim, 2014; Mizruchi & Stearns, 2001) in specific sectors (e.g. video game industry – Czernek-Marszałek et al., 2023b). However, the literature still lacks a comprehensive analysis of SR building blocks in relation to broader aspects of business activity, including OI (Ellonen et al., 2008).

Organizational innovativeness 

Organizational innovativeness refers to the propensity (Wang & Ahmed, 2004) or organizational ability (Ghosh & Srivastava, 2022; Golipour et al., 2011) to innovate. To be more specific, it is the ability to generate and implement ideas effectively (Quandt & Castilho, 2017) and is determined by whether members of an organization are willing to adopt innovation or are resistant to it. The currently dominant approach views OI as a complex characteristic of the organization, reflected in its capability and ability to create, implement (Salavou, 2004), and commercialize innovations (Glabiszewski et al., 2019).

When it comes to a commonly acknowledged approach to OI, one can point to the work of Wang and Ahmed (2004), who propose a multidimensional approach towards innovativeness. They identify five interlinked dimensions of OI: product, market, process, behavioral, and strategic innovativeness. Product and market innovativeness are externally focused, whereas process and behavioral innovativeness are internally oriented. Strategic innovativeness refers to the ability to recognize external opportunities and align them with internal capabilities to deliver innovative products and enter new markets.

Notably, this perspective frames innovativeness beyond new products (Zirena-Bejarano et al., 2024) and improved processes, including the ability to open new markets through the alignment of strategic orientation and employees’ innovative behaviors (Wang & Ahmed, 2004). Although innovativeness is often difficult to observe directly (Martínez-Román & Romero, 2017), this approach provides measurable organizational-level proxies widely used in research (Ellonen et al., 2008; Pallas et al., 2013; Shoham et al., 2012), yet still rarely applied in studies of creative industries (Plum & Hassink, 2014; Wohl, 2022).

Social relationships and organizational innovativeness 

The literature suggests that OI may be a positive outcome of maintaining SR (Al-Twal et al., 2024; García-Villaverde et al., 2021; Grebski & Mazur, 2022; Powell, 1990; Singh et al., 2021). This seems to be particularly important in creative industries (Suhaimi et al., 2024), where creativity and innovation are strongly grounded in SR across organizations (Parmentier & Mangematin, 2014). Bastian and Tucci (2017) show that more innovative firms rely more on SR, especially because small and new companies – common in creative industries – often lack resources and compensate by drawing on knowledge embedded in their networks. Moreover, SR facilitate not only OI within and across firms but also the diffusion of innovations across sectors (Al Breiki et al., 2023; Kumar & Sinha, 2021; Pittaway et al., 2004).

The positive implications of SR for OI stem from relationships involving multiple actors with diverse experiences and resources. SR support knowledge development and the generation and implementation of new ideas (Bailey et al., 2022; Bhatti et al., 2021; Nahapiet & Ghoshal, 1998; Gilson, 2024). They also facilitate the exchange of tacit knowledge, which is difficult to codify (Granovetter, 1973; Reagans & McEvily, 2003; Uzzi, 1997; Gubbins & Dooley, 2021). Moreover, SR help in demanding situations, reduce business risks, and enable rapid feedback, fostering learning and proactive action (Murphy, 2002; Pesämaa et al., 2015; Al-Omoush et al., 2020; Balasubramanian et al., 2022). Finally, SR strengthen cooperation and trust, which supports tacit knowledge transfer, lowers transaction costs, and stimulates creativity - key mechanisms underlying OI (Tsai & Ghoshal, 1998; Granovetter, 1985; Czernek-Marszałek, 2020a; Uzzi & Spiro, 2005).

Based on the analysis of the existing literature, it can be assumed that SR are conducive to innovativeness across all five dimensions, i.e., product, process, market, strategic, and behavioral (Wang & Ahmed, 2004). 

SR and product innovativeness

In the context of product innovativeness, Gemünden, Ritter, and Heydebreck’s (1996) studied the importance of SR for innovation in six high-tech industries. Their research showed that companies that used SR were likely to have nearly 20% more product improvements than those that did not. They were also characterized by 7-10% higher product development. Moreover, other empirical studies (Perez & Sanchez, 2002; Harryson, 1997) have identified open and direct communication as a key success factor for engaging suppliers in the development of new products. Moreover, as noted by Chassagnon and Audran (2011), recurrent interactions with others in inventors’ interpersonal networks are very important, as inventors acquire experience, confidence, and a reputation. These generate new and successful collaborations, which strengthen an inventor’s innovativeness – i.e., its durable innovative capacity (Chassagnon and Audran, 2011). Moreover, Chenhall, Kallunki, and Silvola (2011) claim that, thanks to SR, a company can obtain resources – in this case, a component for a new product – earlier and faster than is typically the case on the market. This enables product innovation, making the company more competitive (Hendrayanti et al., 2021). 

SR and market innovativeness

As for market innovativeness, SR provide access to new markets or technologies and speed products to market (Selomon et al., 2016; Pittaway et al., 2004). Galbraith (2005), writing about the benefits of close relations between the company and customers, points to personalized, non-standard products and services, as well as support, education, and consulting to make customers more effective. Moreover, SR enable a firm to act proactively (Al-Omoush et al., 2020; Walter et al., 2006; Roxas et al., 2017). Thanks to SR, the so-called ‘multiple-lens’ benefits, referring to receiving diverse “criticisms that allow an actor to anticipate a variety of contingencies” occur (Mizruchi & Stearns 2001, p. 31). Moreover, SR play an important role in facilitating the sense-making process in which all parties involved in generating ideas and implementing them analyze the environment looking for solutions to specific problems (Czakon & Czernek, 2025; Kijkuit & van den Ende, 2007, 2010; Tüten & Ascigil, 2014). Also, other authors (e.g., Al-Omoush et al., 2020; Murphy, 2002) refer to the role of SR in solving problems, claiming that in times of crisis when the company needs to adapt to new market conditions or when it needs advice on markets, products, or technology, SR with trustworthy people are very helpful. All this favors market innovativeness.

SR and process innovativeness

As Golipour et al. (2011) emphasize, SR favor process innovativeness because they provide greater access to resources, including diverse sources of information. Research shows that relationships with suppliers, customers, research agencies, and other entities (Ramcharran, 2001; Laursen & Salter, 2006), and even competitors (Corbo et al., 2023; Baum et al., 2000), are conducive to process innovativeness. As Pittaway et al. (2004, p. 19) state: “Formal and informal communication between people with different information, skills and values increases the chance of unforeseen novel combinations of knowledge, which can lead to radical discoveries”. SR also affect the quality of information provided, enhancing the level of knowledge (Al-Twal et al., 2024; Nahapiet & Ghoshal, 1998; Greve & Salaff, 2003), which is crucial for process innovativeness. According to some studies (e.g., Pittaway et al., 2004), informal ties based on friendship and communication foster information exchange and process innovativeness more than market mechanisms do. This is possible mainly due to trust (Al-Twal et al., 2024; Golipour et al., 2011; Murphy, 2002), a key component of SR (Czernek-Marszałek et al., 2023b). Its importance for technological development and process innovativeness stems from the tacit nature of much knowledge. The transfer of tacit knowledge through market relations is often difficult or impossible due to its limited codifiability (Davidsson & Honig, 2003). It is more feasible through SR (Bhatti et al., 2021; Czernek-Marszałek et al., 2023a; Granovetter, 1973; Gubbins & Dooley, 2021; Reagans & McEvily, 2003; Santos, 2023; Uzzi, 1997), especially when trust is present. As Murphy (2002: 609) notes, “micro-level trust appears most important in facilitating the development and diffusion of the less tangible or tacit forms of knowledge since it emerges through face-to-face contact, shared experiences, and when there is a bond between two individuals”. Many studies show the positive role of trust not only in knowledge sharing but also in its generation and absorption (e.g., Chowdhury, 2005; Levin & Cross, 2004; Mooradian et al., 2005; Sánchez-García et al., 2023), all of which are important for process innovativeness (Cortese et al., 2024).

SR and behavioral innovativeness

Ellonen, Blomqvist, and Puumalainen (2008) claim that in the case of behavioral innovativeness, the key is benevolence as a component of vertical interpersonal trust. Ellonen et al. (2008) identified a significant relation between trust in the leaders’ reliability and behavioral innovativeness. They claim that a leader’s reliability supports employees’ activity towards behavioral innovativeness. This is because it promotes idea exchanges and increases the chance of accepting new ideas (Kijkuit & van den Ende, 2010). According to Cimenler et al. (2016, p. 587), SR allow for “incorporating new inputs from others and implementing new ideas from these inputs”. Ellonen et al. (2008) emphasize that trust, which boils down to the expectation that the organization will take innovative ideas seriously, is positively related to the implementation of these ideas. Therefore, SR, especially trust, foster creativity (Chassagnon & Audran, 2011; Kim et al., 2025; Liu et al., 2018; Tsai & Ghoshal, 1998; Uzzi & Spiro, 2005), which, to some extent, is considered behavioral innovativeness at the individual, personal level. 

SR and strategic innovativeness

Finally, in strategic innovativeness involving a complete and radical change in how the company is run, SR can be very useful for countering obstacles to innovativeness, which, according to Wang and Ahmed (2004), can be numerous. Innovativeness, especially the strategic kind, demands risk-taking rather than risk-avoidance (see, e.g., Adegbite & Govender, 2022; Tan & Tan, 2000) from leaders to the greatest extent. Thus, one of its obstacles may be the lack of managers’ willingness to change – especially when the organization is successfully operating in the market – or the lack of courage to take the risk of profound change when the need for it is already noticeable (Wang & Ahmed, 2004). SR, which allow for the reduction of risk (Czernek-Marszałek et al., 2023a), may be useful especially in the latter case, as it has been shown that in situations of uncertainty or complexity, managers must make risky decisions (Crawford & Jabbour, 2024; Pesämaa et al., 2015; Pittaway et al., 2004). Also, other authors (e.g., Pesämaa et al., 2015; Pittaway et al., 2004) stress that SR serve as emotional support for entrepreneurs undertaking risky activities. Murphy (2002: 609) argues that “without such strong links to competent individuals, innovation may be stagnated and risk-taking discouraged.”

Conceptual model

Given the above reasoning (e.g., inspirations taken, for instance, from Murphy, 2002; Luk et al., 2008; Chassagnon & Audran, 2011; Tüten & Ascigil, 2014; Leenders & Dolfsma, 2016; Roxas et al., 2017; Kraft & Bausch, 2018; Granovetter, 2018; Grebski & Mazur, 2022), but also considering the lack of prior research, we aim at testing the following hypothesis: 

H1: Social relationships are positively related to organizational innovativeness, such that higher levels of social relationships are associated with higher levels of organizational innovativeness.

Wang and Ahmed (2004) point out that all five dimensions of innovativeness are interrelated. Interestingly, while this scale is among the most used to measure innovativeness (Bamel et al., 2024), past applications across different empirical contexts – both across countries and industries – have consistently shown the need for adaptation. The final measurement models have varied in terms of the number of indicators and the dimensions of OI they capture (e.g., Çağlıyan et al., 2022; Ellonen et al., 2008; Ghosh & Srivastava, 2018; 2022; Riivari et al., 2012; Semerciöz et al., 2011). Therefore, given the lack of knowledge about the multidimensional structure of OI in the creative sectors adopted as the empirical context for our study, we formulate the conceptual model presented in Figure 1 at this stage.

Figure 1. Conceptual model

METHODOLOGY

This study focused on exploring the relationship between SR and OI and adopts a rigorous, multi-stage methodological design to ensure the robustness and validity of its empirical findings. The methodology section is structured to reflect the full research process, from empirical context and data collection to advanced analytical procedures. First, we describe the empirical setting and the data collection process (i.e., creative industries), followed by a detailed account of the three-stage data cleaning procedure applied to the raw dataset (i.e., reducing the initial sample of 690 to 302 observations). Next, we employ an exploratory approach to the adoption of measurement models for both dependent and independent variables (i.e., the six-dimensional SR model from Klimas et al. (2025) and the five-dimensional OI model from Wang and Ahmed (2004)). For measurement model validation, we employ a mixed approach: we anchor in confirmatory CB-SEM and, when justified, adopt PLSc-SEM and PLS-SEM. Finally, we present the data analysis procedures used to test the proposed research model (see Figure 1). At this stage, we employ the CB-SEM approach.

Empirical context

We purposefully limited the scope of our research to creative industries operating in Poland. Our decision was due to several reasons. Firstly, in creative industries, the importance of OI and SR is crucial. This is because entities from creative industries have an innovation-focused orientation. It is a certain type of openness to new ideas, new ways of acting, and creativity in operational methods (Menguc & Auh, 2006; Suhaimi et al., 2024). Thus, these entities create products of original or even unique character (Pratt, 2004). Moreover, as they strongly rely on OI, establishing SR also plays a significant role in this context (Parmentier & Mangematin, 2014). This is because OI often results from the transfer of knowledge, including tacit knowledge, between various entities maintaining SR. Thus, the choice of creative industries enabled us to ensure that the phenomena of interest would occur in the sample (Denzin & Lincoln, 1994). Secondly, the importance of creative industries in the global (Bilan et al., 2019) and the Polish economy (Kasprzak, 2017) is constantly growing. According to the report of The Economy of Culture in Europe, the competitive leadership of Europe’s economies depends on the creative and innovative potential, created especially in the creative industries (Bilan et al., 2019). The creative industries in Poland are also considered among the world’s leading exporters of goods and services.

The research was conducted in the following four creative industries: 1) performative arts (theatres); 2) local government cultural institutions (museums); 3) computer and video game industry (game development studios), and 4) cultural tourism (culinary routes). Our research team members had previously conducted research in these four industries and were familiar with their specifics and context. This research experience was important for reaching respondents across both research types and for the possibility of in-depth interpretation of the research findings. Moreover, given the specificities of the selected creative industries, the team’s in-depth knowledge enabled it to adopt a broader cognitive perspective.

Data collection

In both research methods, the key informants were defined as owners and/or members of top management. The focus on top management stemmed from the significance of managerial social networks for business phenomena and from the fact that strategic decisions related to innovation management are made at the top management level. Indeed, as shown in meta-analysis research, the social capital of top managers matters for innovation management (Kraft & Bausch, 2018). Importantly, the invitation to participate in the study (whether quantitative or qualitative) explicitly stated the research objectives and their scope, thereby clearly indicating the intended relevant recipients.

While the general profile of key informants remained consistent throughout the research process, it is important to emphasize that for qualitative research, the participant selection criteria were considerably more detailed (Bouncken et al., 2025) and are listed below.

The quantitative data gathering process was commissioned to a professional research company and lasted from July to October 2020. The data were collected using the Computer-Assisted Web Interview (CAWI) method. The sampling frame comprised 1686 entities representing the creative industries in Poland. Out of 1,686 questionnaires, we received 769 responses, and 917 were refusals. Of the surveys received, 690 could be further analyzed. After the three-step data cleaning process (see subsection below for more details), the final sample was reduced to 302 observations. On average, each CAWI lasted 20 minutes. The characteristics of the cleared sample are presented in Table A1 in Appendix A.

Our quantitative data were collected using a survey questionnaire prepared by the research team. In substance, it covered sections focused on measuring SR using 60 items (Klimas et al., 2025) and OI using 20 items (Wang & Ahmed, 2004), as well as sections characterizing key informants and the organizations they represent. Notably, both scales assume a reflective measurement approach, which dominates in social science research (Jarvis et al., 2003), including existing OI scales, not only Wang and Ahmed’s (2004) but also others (see Table 2 in Pallas et al., 2013).

Qualitative data collection was conducted through FGIs in June 2021, implemented by a professional research company. We decided to conduct group interviews, rather than individual ones, to create conditions for an intra-group discussion enabling participants to show different perspectives on the same problems (Siggelkow, 2007) as well as pay attention to the nuances of the issues raised, and thus obtain more in-depth opinions for analysis (Krueger & Casey, 2015; Morgan, 1997). 5 FGIs were conducted – each with 6 representatives from 4 creative industries, and one mixed group with representatives from 4 industries. The characteristics of the FGI participants are presented in Table A2 in the Appendix A.

Interviewees were selected based on the following criteria ensuring the diversity of interviewees, important in qualitative research: 1) a company significance in a given sector – “important players” on the market (e.g. recognizable in a given industry due to the level of innovativeness, but also less-distinguished entities); 2) type of activity – e.g. in the gaming sector – creators, distributors, publishers, producers of gaming equipment, etc. in the case of theatres – dramatic, puppet, musical and dance theatres and movement, etc.; 3) location of the entity – entities from different parts of Poland; 4) entity’s activity (selection of entities with high and low activity in this field); 5) form of ownership – public/private; 6) entity size (regarding the number of employees) – micro (up to 9), small (10-49), medium (50-249) and large ones (more than 249).

Our qualitative study was conducted using the FGI guide, which included instructions for the moderator and open questions for the FGI participants. The participants were asked about the importance of SR for the functioning of the industry and their companies; sources of social relationships; SR building blocks; and OI – how it manifests in their businesses and what role SR play for OI. Each FGI lasted an average of 137 minutes. All of them were conducted online due to the ongoing COVID-19 pandemic using the Zoom application. All interviews were recorded and transcribed after obtaining the participants’ consent.

Data cleaning

Before conducting the quantitative analysis, we screened our sample of 690 responses for signs of careless responding. As noted by Johnson (2005), response biases can arise from linguistic incompetence, carelessness and inattentiveness, and deliberate attempts to present oneself in a more favorable or unfavorable light. This issue is particularly pronounced in computer-based data collection compared to traditional paper-and-pencil methods. Following the framework of (Curran, 2016), we assessed the responses based on the following criteria: (i) response invariability, which included a long-string analysis based on frequency and the Cattell scree test (Cattell, 1966) as outlined by (Johnson, 2005), straightlining, and intra-individual response variability (IRV) as discussed by (Dunn, 2018); (ii) outliers, identified using Mahalanobis distances as described by Goldammer et al., (2020); and (iii) internal consistency, evaluated through even-odd consistency and the concept of psychometric synonyms (DeSimone et al., 2015). The cut-off points for these metrics followed the recommendations of Goldammer et al. (2020), Huang et al. (2012), and DeSimone et al. (2015). In the first step, we excluded 104 observations (15%) that displayed unusually long strings of identical answers in the SR section of the questionnaire (60 questions in total; see Table A3 in Appendix A for the long-string frequency table). We also removed all instances of straightlining and responses with very low IRV (more than 2 standard deviations below the mean) in the OI section of the questionnaire (20 questions in total). In the next step, we were left with 586 observations, which we then analyzed for outliers using multiple regression separately for the SR and OI parts of the sample. We regressed the relevant items on a metric unrelated to the study’s variables, i.e., the year of the company’s inception. Next, we removed all observations with D2/df> 2.5 (Hair et al., 2019), which accounted for an additional 25 observations (4%). In the final step, we assessed internal consistency for the OI section using even-odd correlations and the Spearman-Brown split-half formula for each individual response. We compared the corrected correlations against a recommended threshold of 0.26 (Goldammer et al., 2020) and identified 232 responders who likely provided biased answers. We also used the psychometric synonyms metric on the SR section. First, we calculated the item correlation matrices and identified 14 pairs with correlations of at least 0.6. Next, we computed corrected correlation coefficients between two sets of items (14 items in each set) for each respondent, excluding those with coefficients below 0.22 (Meade & Craig, 2012). This led to the exclusion of an additional 27 observations. Ultimately, after applying the procedures outlined above to clean the data, we were left with a small sample of N = 302, representing 43% of the initial sample size. Although the number of observations removed was substantial, it aligns with the dropout rates observed in other studies (Meade & Craig, 2012).

Measurement models of SR and OI

The conceptual model (Figure 1) underlying the quantitative stage of our research process comprises two variables: SR, the multidimensional independent variable, and OI, the multidimensional dependent variable. In both cases, the variables are considered complex and cover latent dimensions as claimed by the authors of the original measurement models (Wang & Ahmed, 2004; Klimas et al., 2025).

Recognizing the need for empirical adjustments of the applied measurement scales to the new research context (Ambuehl & Inauen, 2022), the complex and heterogenous nature of creative industries (Cnossen et al., 2019; Snowball et al., 2022 Gohoungodji & Amara, 2023), and the fact that our study spans four distinct creative sectors rather than focusing on just one, we took an adaptive approach to both measurement models. Therefore, as methodologically recommended (Boateng et al., 2018; Ambuehl & Inauen, 2022) for the replication of existing scales, we first screened the correlation matrices, then conducted an exploratory factor analysis (EFA), and finally a confirmatory factor analysis (CFA) on the raw, hence cleaned, primary data. It means that, in our study, before we tested the hypothesis, we explored whether the items theoretically and empirically validated in the original measurement scales were suitable for our research context and subsequently adopted the measurement approaches for both of our focal variables. Although the measurement scale for SR is quite recent, it should be noted that in the case of the commonly acknowledged measurement scale for OI, the same adaptive approach has been used regarding OI for instance by Ellonen et al. (2008) and Semerciöz et al. (2011) acknowledging that measurement models may (and do) show specific structure of OI in different empirical contexts (compare for instance: Çağlıyan et al., 2022; Ellonen et al., 2008; Ghosh & Srivastava, 2018; 2022; Riivari et al., 2012; Semerciöz et al., 2011).

The adoption and validation procedure performed separately for SR and OI measurement models includes the following steps: (i) determining the number of factors, based on Principal Component Analysis (PCA) and parallel analysis of simulated eigenvalues (Horn, 1965; O’connor, 2000) compared with RMSEA and RMSEA change based criterion of competing common factors models estimated with ML (Fabrigar & Wegener, 2012); (ii) merging EFA and CFA results aimed in achieving reliable measurement model (Patil et al., 2008), adopting the following rule of thumb: EFA loadings cut-off of 0.6 (Shevlin & Miles, 1998), and CFA loadings cut-off of 0.7 (Hair et al., 2011); (iii) CFA measurement model GOF assessment in terms of fit indices mix (Hu & Bentler, 1999): Standardized Root Mean Square Residual (SRMR) lower than 0.08, Nonnormed Fit Index (NFI) and Comparative Fit Index (CFI) above 0.9, Root Mean Square Error of Approximation (RMSEA) with the 0.1 maximal acceptable level (Schermelleh-Engel et al., 2003). We also report χ2 and df, the CMIN/df index with a threshold of 5 (Marsh & Hocevar, 1985). We next assessed measurement model reliability, convergent and discriminant validity relying on the following measures and relevant cut-offs: composite reliability CR above 0.7 (Hair et al., 2011), average variance extracted AVE above 0.5 (Cheung & Wang, 2017), heterotrait-monotrait ratios below 0.85  (Henseler et al., 2015).

We also tested our measurement models for Common Method Bias. First, by estimating the AVE of one common factor (Podsakoff et al., 2003), and second, by analyzing the latent factors’ Variance Inflation Factors VIFs, following the procedure of (Kock, 2014) with the maximal level of 3.3. To conduct both tests, we specified a second-order reflective-formative PLS-SEM model (i.e., type II model; Crocetta et al., 2021) with the repeated indicators approach of Lohmöller (2013) and applied the PLSc-SEM algorithm for estimation.

In the next stage, we checked how well the measurement model acts on “not seen” data. For this purpose, we employed a PLS-SEM predictive approach, following Hair et al. (2017a). To perform the analysis, we used the above PLS-SEM model to perform k-fold cross-validation in SmartPLS 4 (Ringle et al., 2015). We measured predictive relevance of latent factors by Stone-Geisser’s Q2, as well as Root Mean Square Error and Mean Absolute Error.

Finally, for our measurement models, we conducted weak and strong measurement invariance analyses across 4 sectors, following the recommendations of Vandenberg & Lance (2020) and applying noninvariance thresholds of ΔCFI ≤ -0.01 and ΔRMSEA ≥ 0.015, as proposed by Chen (2007).

The independent variable was measured using a valid measurement scale (Klimas et al., 2025) initially covering 39 indicators grouped into six SR building blocks: emotional intensity (EMI), community of interest (COMM), shared identity (SHI), private contacts (PRI), repeatability (REL), and meetings in a professional community (MET). The iteratively developed and ultimately adopted measurement model for SR generally aligns with the original solution; however, it is more parsimonious, both in the number of building blocks (exclusion of Repeatability in our study) and the number of items retained within them (the final measurement model includes 22 measurement indicators). Our final measurement model for SR comprises five building blocks (constructs) and two sub-constructs of COMM: Mutuality (MUT) and Passion (PAS). The validity statistics for the adopted model are presented in Table 1, and the items are listed in Table A4 in Appendix A.

Table 1. EFA and CFA results for SR final measurement model

Item	EFA factors’ indicators (pattern matrix)						CFA results
	EMI	SHI	MUT	PAS	MET	PRI	Standardized loading	Convergent validity statistics
RS58	0.939	-0.019	-0.015	0.013	-0.044	0.008	0.934	Factor EMI AVE=0.454 CR = 0.801
RS57	0.891	-0.004	0.006	0.022	0.036	-0.029	0.893
RS60	0.86	0.07	-0.039	-0.03	-0.064	0.022	0.850
RS59	0.809	-0.044	0.009	0.032	0.053	-0.029	0.809
RS12	0.625	-0.014	0.069	-0.061	0.033	0.047	0.642
RS2	0.071	0.826	-0.025	-0.047	-0.026	0.019	0.789	Factor SHI AVE = 0.692 CR = 0.913
RS1	-0.012	0.795	-0.052	-0.062	0.01	-0.061	0.724
RS10	-0.037	0.576	0.037	-0.028	-0.012	0.066	0.588
RS8	-0.052	0.565	0.153	0.081	0.021	-0.021	0.666
RS20	0.003	0.554	-0.113	0.113	0.034	0.065	0.575
RS48	0.008	-0.078	0.948	-0.018	0.003	0.029	0.915	Factor MUT AVE = 0.731 CR = 0.883
RS47	0.009	-0.044	0.943	-0.031	0.004	0.058	0.922
RS46	0.009	0.157	0.612	0.121	-0.026	-0.111	0.710
RS53	-0.027	-0.034	-0.039	0.922	-0.015	0.021	0.857	Factor PAS AVE = 0.728 CR = 0.883
RS54	0.016	0.009	0.055	0.893	-0.015	-0.054	0.925
RS55	-0.007	0.039	0.021	0.704	0.037	0.09	0.772
RS28	0.006	-0.054	-0.031	-0.041	0.906	0.1	0.868	Factor MET AVE = 0.635 CR = 0.834
RS27	-0.027	0.077	0.003	-0.067	0.826	-0.035	0.806
RS51	0.044	-0.012	0.029	0.183	0.628	-0.092	0.708
RS26	0.008	-0.091	-0.098	0.073	-0.033	0.849	0.777	Factor PRI AVE = 0.580 CR = 0.796
RS25	0.002	0.069	0.081	-0.042	-0.017	0.815	0.854
RS22	0.011	0.099	0.059	0.007	0.068	0.582	0.638
Discriminant validity HTMT ratios matrix factor MUT PAS EMI MET PRIV MUT           PAS 0.642         EMI 0.442 0.456       MET 0.416 0.541 0.418     PRIV 0.247 0.294 0.208 0.359   SHI 0.288 0.297 0.151 0.125 0.335
Goodness of fit	χ2= 214.430; df = 114; p-value = 0.000 RMSEA = 0.054						χ2 = 421.284; df = 194 CMIN/df = 2.172 RMSEA = 0.062; SRMR = 0.060 NFI = 0.896; TLI = 0.929; CFI = 0.940

Considering the above-referenced cut-offs for a variety of measurement models’ validity statistics, we conclude, based on the values reported in Table 1, that the solution is sufficiently acceptable for further structural analysis, although some indicators fall outside stringent thresholds. A few standardized CFA loadings are below 0.7, and their EFA counterparts are below 0.6 (i.e., items RS12, RS10, RS8, RS20, RS22), and the latent factor EMI has an AVE slightly below 0.5. We decided to keep all these items and the EMI factor, as any additional reduction would limit the construct’s AVE and the convergent validity of the whole scale. Apart from NFI, which is below 0.9, the remaining model’s fit measures meet requirements, even when certain items are not perfectly reliable. Generally, small absolute fit measures like SRMR and RMSEA (both below 0.07) and reasonably high CFI and NFI indices support the model. In the next stage, we, following the above-described procedure, assessed Common Method Bias, performed k-fold cross-validation, and measurement invariance analysis. Results are in Table 2. The Common Latent Factor AVE is 0.266, indicating low common variance among indicators. Compared to the OI measurement scale, the characteristics of the sample differ significantly – within SR measurement model, between-item correlations are definitely lower. All latent factors’ VIFs are definitely below the threshold of 3.3. Based on the above tests, we conclude that although CMB is visible, it is not a severe issue for this sample. The predictive power of the estimated model is quite weak, particularly given Q2 and RMSE – both measures indicate that some items produce quite large errors, resulting in a type of punishment reflected in the unfavorable levels of these metrics. When we look, however, at the MAE, we see that, on average, the estimated model typically generates errors not exceeding 0.71 on the 7-point Likert scale. Finally, we performed a weak and strong measurement invariance analysis across 4 sectors. For the measurement scale, we did not find statistically significant differences in the items’ loadings of our measurement model (Δχ2=48.344, Δdf = 48, p=0.459, ΔCFI = -0.000, ΔRMSEA = -0.001), and we conclude that the loading estimates do not differ across sectors. We also do not find significant differences in factor loadings and intercepts across sectors in our measurement model (Δχ2 = 60.451, Δdf = 48, p = 0.107, ΔCFI = -0.003, ΔRMSEA = -0.001).

Within the estimated measurement model, two latent factors - Mutuality and Passion - jointly constitute a single latent construct in the theoretically driven SR measurement model, namely Community of interest. On the one hand, this is consistent with the qualitative research underlying the originally developed scale, in which items initially identified qualitatively as separate constructs, i.e., Shared passion and Reciprocity (see Table 3 in Klimas et al., 2025), were ultimately captured within a single factor labelled as Community of interest in the subsequent quantitative study (see Table 4 in Klimas et al., 2025). On the other hand, this solution is also justified within our model, as the correlation between the two latent factors is relatively high (r = 0.611). We therefore combine the two latent factors into a single construct and adopt a higher-order mixed-level measurement model, as shown in Figure 2. To validate a higher order construct (HOC) indicating Community of Interest we follow the recommendations of (Sarstedt et al., 2019) to apply less restricted PLS-SEM approach, and received the following statistics: AVE = 0.565, CR = 0.886, loadings: 0.779 for Passion and 0.731 for Mutuality, HTMT ratios between Community of Interest and remaining latent constructs of SR scale equal 0.506, 0.541, 0.306, 0.330. We conclude that Community of Interest is well measured by two lower-order constructs: Mutuality and Passion.

Table 2. CMB and predictive relevance for SR final measurement model

Latent factor	latent factor’s VIF	Q²predict	RMSE	MAE
Emotional Intensity (EMI)	1.398	0.391	0.787	0.606
Meetings in professional community (MET)	1.625	0.411	0.774	0.534
Mutuality (MUT)	1.827	0.556	0.672	0.504
Passion (PAS)	2.127	0.623	0.619	0.484
Private Contacts (PRI)	1.279	0.238	0.881	0.710
Shared Identity (SHI)	1.217	0.286	0.851	0.688

The detailed results of adopting the measurement model for SR are summarized in Appendix B.

When it comes to measuring dependent variable, the scale developed and validated by Wang and Ahmed (2004) was chosen as it is one of the most widely used in research within management – a review of innovation management articles published in the European Journal of Innovation Management found that Wang and Ahmed’s paper (2004) is the second most cited in the entire field (Bamel et al., 2024). Originally, the measurement model of OI covered 20 indicators grouped in five dimensions: market, product, process, behavioral, and strategic. In this study, the measurement instrument was initially designed to include 20 previously validated indicators.

Figure 2. Estimated mixed-level higher-order measurement model of Social Relationship
Note: Displayed values are standardized path coefficients and items loadings.

Source: Authors’ elaboration using SmartPLS.

During the instrument preparation stage, one indicator included in the questionnaire did not correspond to the validated indicator specified in the original scale; instead, IN21 from Wang and Ahmed’s tool was included, rather than IN03. Consequently, this indicator (coded as OI13 in our study) was excluded from further analyses, and the final specification of the OI measurement model was based on 19 indicators validated by the original scale developers (four indicators each capturing the product, process, and behavioral dimensions, and three indicators - rather than four - capturing the market dimension).

The outcome of the iterative analyses is a mixed-level higher-order measurement model (see Table 3 and Figure 3). At the first level, the model comprises two dimensions of innovativeness: human (items originally reflecting strategic and behavioral innovativeness and directly focused on people) and non-human (items originally reflecting market, product, and process innovativeness and focused on organizational improvements). At the second level, within the non-human dimension, two subdimensions are distinguished: product management innovativeness and functional management innovativeness. Following standard recommendations for exploratory factor analysis (e.g., Hair et al., 2019, Chapter 3), the research team heuristically labeled the factors based on the pattern of factor loadings and the substantive content of the items. The final labels reflect the shared conceptual core of the items loading onto each factor and their alignment with the theoretical understanding of OI discussed earlier. The detailed results of adopting the OI measurement model are summarized in Appendix C.

Apart from RMSEA, which slightly exceeds the 0.1 maximal acceptable level, the remaining validity measures meet the measurement model’s requirements (see Table 3 and the list of items in Table A5 in Appendix A). CFA goodness-of-fit measures are acceptable: SRMR is below 0.08, NFI and CFI are above 0.9. Finally, the CMIN/df index is below 5. Convergent validity measures are all above recommended values: composite reliability CR is above 0.7, average variance extracted AVE is above 0.5, EFA loadings are above the cut-off of 0.6, and CFA loadings are above the cut-off of 0.7. All mentioned measures also confirm scale reliability. Discriminant validity heterotrait-monotrait ratios are below 0.85. EFA and CFA solutions coincide. AVE for one common factor equals 0.461, while latent factors’ VIFs are: 2.496, 1.321, and 2.215. All indicators share a large common variance of about 42%, which is expected in a reflective measurement model due to the interchangeability of indicators. As VIFs are less than 3.3 this common variance does not lead to high collinearity among latent factors, and CMB is not a serious problem for this estimated measurement model. Predictive relevance of latent factors measured by Stone-Geisser’s Q2, as well as Root Mean Square Error and Mean Absolute Error are acceptable (i.e., for PRD Q2 = 0.565, RMSE = 0.663, and MAE = 0.506; for FUN Q2 = 0.704, RMSE = 0.547, and MAE = 0.435; for HUM Q2 = 0.675, RMSE = 0.576, and MAE = 0.422). Q2 is above 0.5, indicating high predictive power, and both mean errors are around 0.5 on a 7-point Likert scale, which is low.

We performed a weak and strong measurement invariance analysis across 4 sectors. Although we found statistically significant differences between items’ loadings of our measurement model (Δχ2=58.284, Δdf = 33, p=0.004, ΔCFI = -0.007, ΔRMSEA = -0.002) and both loadings and intercepts (Δχ2=71.442, Δdf = 33, p< .01, ΔCFI = -0.010, ΔRMSEA = -0.000), but applying less strict rules based on ΔCFI and ΔRMSEA of Chen (2007), we conclude weak and strong OI measurement invariance is present in our sample.

Table 3. EFA and CFA results for the final measurement model of Organizational Innovativeness

Item	EFA factors’ indicators (pattern matrix)			CFA results
	Human OI (HUM)	Functional OI (FUN)	Product OI (PRD)	standardized loadings	Convergent validity metrics
OI1	-0.020	-0.013	0.922	0.904	Factor PRD AVE = 0.778 CR = 0.909
OI2	0.022	-0.027	0.942	0.923
OI3	-0.012	0.287	0.618	0.816
OI5	0.004	0.770	0.127	0.861	Factor FUN AVE = 0.712 CR = 0.907
OI6	-0.05	0.945	-0.050	0.879
OI7	0.019	0.880	-0.059	0.848
OI10	0.076	0.672	0.110	0.785
OI12	0.631	0.122	0.037	0.705	Factor HUM AVE = 0.668 CR = 0.930
OI14	0.696	0.192	-0.062	0.765
OI15	0.796	0.016	0.017	0.815
OI16	0.913	-0.065	-0.013	0.879
OI17	0.955	-0.105	0.021	0.908
OI18	0.921	-0.156	0.041	0.861
OI19	0.712	0.180	-0.060	0.770
Discriminant validity metrics				HTMT(HUM;FUN) = 0.491 HTMT(PRD;FUN) = 0.738 HTMT(PRD;HUM) = 0.381
Model goodness of fit measures	χ2= 209.953 df = 52; p-value = 0.000 RMSEA = 0.101			χ2 = 336.067; df = 74 CMIN/df = 4.541 RMSEA = 0.108; SRMR = 0.065 NFI = 0.906; TLI = 0.907; CFI = 0.925

Validity statistics for HOC (i.e., non-human innovativeness) are AVE = 0.585, CR = 0.941, loadings: 0.877 for functional OI and 0.744 for product OI, and an HTMT ratio between the human and non-human latent constructs of 0.504. All these statistics meet requirements indicating non-human innovativeness as a reliable and valid construct of OI.

Figure 3. An estimated mixed-level higher-order measurement model of Organizational Innovativeness

Note: Displayed values are standardized path coefficients and items loadings.

Source: Authors’ elaboration using SmartPLS.

Based on the literature review, we hypothesized that SR is positively associated with OI and developed a conceptual model, as presented in Figure 1. However, given the adjusted measurement models for both our key variables (Figures 2 and 3), which incorporate the empirically revealed structures, we can structure our research model as shown in Figure 4.

Figure 4. Adopted conceptual model – research model

Data analysis

In our quantitative investigation, to test the research hypothesis, we used composite CB-SEM (McDonald & Ho, 2002), which combines a measurement and path model. In the measurement model, observed variables (i.e., items) load on latent (common) factors, while the path model specifies the correlation structure among the common factors. We followed the confirmatory modeling strategy outlined by Jöreskog & Sörbom (1996), specifying a single CB-SEM model based on our research model (see Figure 4). Next, we evaluated the model’s fit to the data, following the guidelines of Hair et al. (2017). An acceptable model fit will support our research hypotheses.

In our qualitative investigation, after full transcription, the research analysis followed the Miles & Huberman (1994) approach, consisting of three iterative steps: data reduction, display, and verification.

Data reduction involved coding the transcription text based on the categorization resulting from the coding and, on this basis, selecting text fragments for further analysis. We used a deductive-inductive approach for coding. The coding unit was fragments of participants’ statements relating to the relationship between the building blocks of SR and OI. First, we used deductive codes in the form of – declared by our research participants – relations between OI and six SR building blocks, i.e., 1) emotional intensity, 2) community of interest, 3) shared identity, 4) private contacts, 5) meetings in the professional community, and 6) repeatability. For each building block, we created codes that reflected its possible meaning for OI: positive, negative, or neutral. Secondly, within these deductive codes, we identified – this time inductively, based on data from the research material – codes that detailed the meaning of a given building block of SR for OI, i.e., how a given building block in the participants’ opinion, positively or negatively associated with OI, or why a building block has no significance for OI. Inductive codes were developed iteratively by comparing fragments of data and grouping similar meanings. These steps allowed us to develop the final code structure applied to all five FGIs.

The next step – data display – enabled data organization, which in turn enabled the identification of certain patterns in the research material.

Finally, data verification involved interpreting the data using the literature and drawing conclusions. To ensure a thick description that supported the credibility of the qualitative research, quotes from the FGI (accompanied by an interview code) were presented in the results section.

RESULTS

Quantitative investigation

To test our main hypothesis regarding the positive association between SR and OI, we used a CB-SEM approach and estimated a composite structural model with one structural path linking SR and OI and two reflective-reflective measurement models for SR and OI. The estimated model is depicted in Figure 5. Estimated path coefficient SR → OI is b = 1.537, standardized value equals β=0.545, and is significant at the level of 0.01 (SE = 0.356; T = 4.319, p<.01). Goodness of fit indices are CFI = 0.897, TLI = 0.888, NFI = 0.834, SRMR = 0.085, RMSEA = 0.067.

Figure 5. Composite SEM linking Social Relationships and Organizational Innovativeness

Note: Displayed values are standardized path coefficients and item loadings. Social Relationships -> Organizational Innovativeness
b = 1.537, β=0.545, SE = 0.356; T = 4.319, p<.01. Goodness of fit indices are CFI = 0.897, TLI = 0.888, NFI = 0.834, SRMR = 0.085, RMSEA = 0.067.

Source: Authors’ elaboration using SmartPLS.

The estimated composite SEM goodness-of-fit measures are below the set threshold (i.e., below 0.9), which is partially a function of model complexity and sample size (Bentler, 1990; Kenny et al., 2003). Due to detailed SR and OI measurement models validity assessment, and robustness checks, we easily identify the possible sources: i.e., quite unstable estimates for items assigned to Private Contacts and Shared Identity (predictive power was very low, around 0.2). To observe the effect, we estimated an alternative model without the above-mentioned items and factors. As expected, correlation between SR and OI improves (b = 1.593, β=0.630, SE = 0.345; T = 4.610, p<.01), incremental fit indices increase (CFI = 0.913, TLI = 0.902, NFI = 0.867), but absolute fit measures are almost not affected (SRMR = 0.084, RMSEA = 0.074). The focal relationship between SR and OI remains robust in the improved model: b and β increase, and SE decrease, providing strong empirical support for the hypothesis. Although certain fit measures (notably SRMR > 0.08 and NFI < 0.90) suggest that some covariances remain unaccounted for in the conceptual model, the estimated model is good enough to provide a meaningful framework for understanding the association between SR and OI. We can conclude that within 4 surveyed creative industries, the three building blocks of SR have a strong relationship with OI – namely: Emotional intensity, Community of interest, and Meetings in professional community, remaining two constructs: Shared identity and Private contacts are related; however, the relationship seems to be ambiguous, suggesting that these dimensions warrant further investigation in future research.

Qualitative investigation

The analysis of the survey data demonstrated that SR are positively associated with the OI of the entities under study. Supportively, given our FGI, SR (e.g., those maintained within industry organizations such as associations) foster OI manifested in the undertaking of new initiatives, including the introduction of new and innovative products to the market:

Through my acquaintance with members of various associations, primarily culinary ones, I joined a program developed by the Polish Chamber of Regional and Traditional Products, Świętokrzyskie branch. My farm enrolled in this program precisely in order to seek out other, even more interesting initiatives […] namely regional products, handmade goods, and products originating from small-scale artisanal manufactures. [R1, FG3]

Moreover, SR enable the more rapid initiation, including financing and testing, of new ideas. At the same time, trust – being a key component of SR – facilitates persuading a partner to undertake innovative activities without the need to devote excessive effort to formal procedures or to building credibility from scratch.

If we have known one another for a long time and to a sufficient extent, when that person – or the company commissioning us, or conversely we ourselves – says, “Listen, let us do this differently; let us approach the issue from an entirely different perspective,” without such relationships, I believe it would be more difficult to initiate such an idea and move it forward; considerably more effort would need to be invested. By contrast, strong relationships shorten the path to launching the initiative and testing whether “perhaps it might succeed (…).” [R5, FGI2]

Additionally, SR grounded in openness foster communication and enable team members to propose ideas that may initially appear unrealistic. As the interviewees emphasized, the absence of fear of evaluation was conducive to the emergence of novel solutions, while the freedom to express thoughts translated directly into higher levels of employee creativity. SR thus functioned both as a catalyst for creativity and as a channel through which that creativity could be materialized as innovation.

If objectives are communicated clearly – what it is that we intend to achieve – even when someone devises a game that initially appears outlandish and unplayable, the team’s openness and its capacity to discuss it as a potential product constitute something we have consciously developed. (…) More generally, it is precisely because relationships within the company are strong and we are allowed to make mistakes – indeed, to say something foolish in everyday conversation – that we sometimes create entirely new things. [R6, FGI2]

It should, however, be emphasized that in certain cases SR also constituted a factor constraining OI, and at times even a barrier to innovation:

When an external entity with which we maintain [relationships], and within which we operate, imposes excessive constraints on us and limits our creativity, then – in my view – innovativeness declines. The greater the degree of autonomy an individual is granted in undertaking activities within the sphere of innovation and within the organization, the greater the likelihood that the individual’s talent will be effectively utilized. [R4, FGI2]

In other cases, close SR grounded in loyalty and commitment led organizations, out of concern about losing a partner, to refrain from opening themselves to new initiatives and ideas, or from seeking inspiration for their activities through collaboration with other entities:

We have, or at least it happens to everyone from time to time, that we start repeating ourselves and then we need some fresh blood and I know that if I take the same [person], then I know more or less what they will do for me, if the last three times were the same and that is sometimes a problem.

Similarly, affection and personal closeness raised dilemmas about continuing cooperation with individuals who, in the interviewees’ view, had ceased to develop and were diminishing the organization’s innovative potential. However, this decision was not straightforward due to a lack of courage to implement personnel changes:

This certainly happened to me […]; it concerned the employment of an individual who, in fact, was no longer developing. We held one another in high regard and were faced with a genuine dilemma, particularly given the strong collegial ties between us: should we prioritize innovativeness, or should we prioritize the relationship? [R4, FGI4]

The conducted FGIs also enabled identification of whether, and in what ways, the individual building blocks of SR (Figure 1) and OI are related. In this respect, the findings indicate that their relationships is frequently ambiguous. For most building blocks (with the sole exception of community of interests), the meaning for OI was both positive and negative.

Community of interests and organizational innovativeness

According to our interviewees, community of interests is the factor that fosters OI. Such communities play a pivotal role in supporting such innovativeness by cultivating a shared understanding and emphasizing common goals, passions, and areas of interest among their members. The collective motivation of individuals within such a community not only enhances engagement but also strengthens their determination to realize innovative ideas. Shared motivation and reciprocity thus create a solid foundation for a coherent approach to problem-solving and addressing challenges, paving the way for creative thinking and the pursuit of inventive and breakthrough solutions:

Mutual promotion, as well as the joint search for market outlets, is also essential. In order to create something new – be it a fair or another initiative – there must, of course, be a shared objective. [R6, FGI3]

People also get to know one another in places where they pursue their passions; through the realization of these passions, they discover shared areas of interest […] which serve, first, as a basis for establishing acquaintances and, subsequently, for initiating future cooperation. [R6, FGI1]

A shared passion thus functions as a social catalyst for cooperation, directly supporting creative processes. Passion operates as a mechanism that reduces social distance: individuals who share common interests move more rapidly from formal interactions to informal, more open, partnership-based relationships. This is of particular importance for innovativeness, the generation of new ideas, mutual understanding, and the development of a “common language” as innovations rarely emerge within highly formalized and hierarchical relationships:

I consider this to be very important, which is why I very often – in fact, always – ask during recruitment interviews whether video games constitute a genuine passion for the candidate. I find it far more effective to collaborate with such employees. I know they are better able to understand the ideas I seek to communicate to them. [R2, FGI2]

Private contacts and organizational innovativeness

Regarding private contacts, some interlocutors considered them conducive to OI, pointing to several reasons. First, private contacts foster trust, which in turn encourages individuals to share their ideas and observations more willingly and flexibly, thereby promoting open communication – previously identified as crucial to innovation processes.

Second, in such circumstances, individuals are more inclined to collaborate, initiating cooperation becomes easier, and its course and outcomes are often more favorable. Within this form of cooperation, developing in a more personal context, parties can express their thoughts more freely and experiment with creative concepts, which facilitates the emergence of innovative ideas:

I have such an experience of a private relationship with a colleague […] we have known each other since our university studies, and the fact that we know each other so well has only facilitated our cooperation. Indeed, when she became a director, the cooperation with the institution she managed finally took tangible form […], at last, we are materializing this cooperation and generating numerous ideas. [R1, FGI1]

Third, private contacts frequently translate into an expanded network of relationships – through them, an organization may gain access to diverse perspectives and resources (including knowledge), which in turn fosters innovativeness.

Additionally, the interviewees emphasized that private contacts, if misused, may constitute a significant barrier to OI. For example, it was noted that private relationships can give rise to informal pressures, unequal treatment, and decision-making that is not based on competence or substantive merit, but rather on interpersonal arrangements. Consequently, innovativeness may be constrained, since innovation requires the fair evaluation of ideas, broad access to resources, and efficient mechanisms of cooperation. One of the interviewees indicated that private contacts tend to be beneficial primarily in stable circumstances, whereas they may become problematic in times of crisis or conflict – particularly regarding the enforcement of agreements, the making of difficult managerial decisions, or the termination of cooperation. This, in turn, may prolong ineffective relationships and obstruct essential changes for innovative development. Private groups and strong interpersonal bonds within teams of cooperating entities may also generate organizational challenges, such as difficulties in work planning, a decline in time discipline, or reduced employee availability. Furthermore, private conflicts that transfer into the professional sphere may destabilize team functioning and necessitate organizational adjustments (e.g., reorganizing office space). Ultimately, private relationships may diminish organizational effectiveness and operational efficiency, thereby limiting the scope for innovative activities:

We have three or four individuals at work who maintain very close private relationships. A frequent issue is that they wish to take leave at the same time, which somewhat complicates our operations. Their lunch breaks can at times become excessively prolonged, and yet we are bound by specific deadlines. Moreover, when such private relationships deteriorate for personal reasons – for instance, due to a conflict – it has occurred that we had to reorganize the entire office layout and relocate these individuals to different workspaces. [R2, FGI4]

One of the interviewees also pointed to another issue – particularly significant in the context of public institutions – namely, the fact that close private ties may be perceived as nepotism, which is not only undesirable but also formally prohibited (especially within public entities). Consequently, this may impede OI, since innovation requires stable rules, trust in the decision-making process, and a sense of a supportive and fair organizational climate.

Emotional intensity and organizational innovativeness

Considering emotional intensity, the interviewees indicated that it may both strongly foster and significantly constrain OI. In terms of its positive association, it was noted that informal relationships “saturated with emotions” enhance openness, trust, and the freedom to exchange ideas, thereby facilitating the generation of innovative projects:

I may simply feel uncomfortable in highly formal relationships, and I always try – perhaps instinctively, as I am an artist and my work is closely connected with emotions – to infuse these contacts with positive emotions. For instance, with Krzysztof […] and Agnieszka […] we play badminton every week. They used to be excellent players, and I try to keep up with them; yet during successive sets on the court we exchange numerous observations, and various ideas for joint initiatives and potential collaboration emerge. For me, therefore, positive informal relationships are a fundamental condition for effective cooperation. [R4, FGI5]

Nevertheless, the study also revealed the “dark side” of emotional intensity in relationships. The interviewees indicated that excessive emotional intensity between partners may disrupt the “healthy” balance between strictly business-oriented and private interactions, leading to various difficulties, for example, in deciding whether to continue or terminate cooperation. This, in turn, may have adverse consequences for OI.

Participants in the FGIs indicated that excessive emotional intensity limited innovativeness, for example, in situations where an entity remained committed to a relationship with a business partner or employee to whom it was emotionally attached, at the expense of establishing new relationships that could potentially generate greater innovativeness:

It looks entirely different than if we had to invest time and energy in introducing ourselves to someone and searching for the best offer […]. It may well be that another provider could deliver the port faster or better, yet personal considerations tend to determine the choice rather than a detached calculation; perhaps this is where the drawback lies. [R4, FGI2]

The interlocutors also indicated that overly intense emotions may complicate professional relationships and destabilize decision-making processes. An overabundance of emotions makes it difficult to separate personal sympathy from substantive evaluation, which may hinder constructive criticism and the enforcement of quality standards in ongoing activities. This, in turn, may constrain innovativeness and impede the development of both the team and the organization:

It may become problematic when the other person with whom we maintain such a relationship does something incorrectly – fails to fulfill their responsibilities. In such situations, it is necessary to separate personal feelings from substantive considerations, which is not always easy. As mentioned earlier, a close acquaintance or friendship does not necessarily function well when problems arise. One must learn to operate under such conditions and be capable of saying, for example: “Listen, I care about you deeply, we have known each other for ten years and share a strong bond, but I cannot continue working with you on this because you are not performing it properly”. These are exceptionally difficult matters to articulate. [R5, FGI2]

Interestingly, the interviewees indicated that emotional intensity does not always correspond to the quality of the outcomes achieved. Strong emotions may lead to success (and even “reset” conflicts), yet they may also create an illusory sense of effective cooperation despite weak results, rendering their relationship with innovativeness inherently ambiguous:

An example that comes to mind – quite common in the theatre – illustrates this well. A successful premiere, that is, a positive outcome of collaboration, often “resets” the problems that arose along the way. At times, a performance is created in a less than favorable atmosphere […], charged with strong emotions, and the director may not be particularly kind or friendly in their manner of working. However, if the premiere ultimately proves outstanding, we are able to forget rather quickly the negative, emotionally intense experiences that accompanied the process. On the other hand, there are instances in which the work unfolds in an exceptionally positive, friendly, almost familial atmosphere, yet the resulting performance is weak and in retrospect, we remember the entire experience unfavorably. [R4, FGI5]

Shared identity and organizational innovativeness

As evidenced by the analysis of the collected research material, a shared identity may entail both stimulating and constraining potential for OI. As indicated by the interviewees, in many cases, a shared identity rooted in local culture and history served as a direct impetus for implementing product innovations, such as rediscovering forgotten products or their production methods (e.g., rose processing, traditional cuisine). It is also worth noting that a shared identity grounded in tradition and local heritage fostered the passion necessary to undertake new initiatives (for instance, in agriculture) and facilitated collaboration that provided access to new knowledge and development opportunities:

In my case, innovativeness essentially consists in rediscovering what was once well known. We are engaged in the production and processing of rose-based products. In the past, rose was a highly popular product; it was used to address various ailments, such as colds, respiratory issues, or circulatory problems. Over time, however, it was displaced by a trend favoring what was perceived as Western and non-Polish. For my part, I strive to revive what has been forgotten. Thus, innovativeness in this context fundamentally involves reaching back into tradition and this approach has proven effective. [R6, FGI3]

However, in certain cases, a shared identity also led to the limitation or even the complete exclusion of OI. As indicated by the interviewees, a hermetic industry environment – for example, one confined to a specific circle of video game developers – may inhibit the inflow of new ideas and concepts. Moreover, some interviewees emphasized that although a shared identity (e.g., the notion of a “large family of Polish gamedev”) is valuable, it does not necessarily determine a company’s success on a global scale. Ultimately, what remains decisive is the ability to deliver innovative products to the international market:

A community may also be exclusionary. If an overly hermetic environment emerges for instance, theatres likewise display a tendency to confine themselves to a particular artistic current or a fixed circle of creators – this can restrict openness to new influences. We attempt to counteract this tendency. For example, the Open the Door festival that we organize includes artists with various disabilities, and no other public theatre hosts a festival of this kind. At times, therefore, we are confronted with the realization that, although we have built a certain community, we may simultaneously be excluding others. It becomes necessary to “open the door” to different perspectives and participants, since a community, while fostering cohesion, can also generate exclusion. [R1, FGI4]

Meetings in professional community and organizational innovativeness

Meetings in professional community were likewise described in ways that reflected both their positive and negative significance for OI.

The interviewees emphasized that participation in industry gatherings – where entire professional communities often convene – creates opportunities to establish new contacts and develop relationships. From the perspective of innovativeness, such meetings were highlighted as important venues for the exchange of crucial information, sharing experiences, mutual learning, and pursuing marketing objectives, thereby contributing, in the longer term, to the development and innovativeness of the entire sector:

However, these meetings are generally very positive, as we attend and convene precisely to engage in what might be termed brainstorming sessions and to derive as much value from them as possible. [R2, FGI3]

Some of the interviewees cited examples of innovative products that they were able to develop in collaboration with partners because of industry meetings, which facilitated and enabled subsequent cooperation:

I realized that I did not personally know most people from the Warsaw theatres. The workshops and meetings held in various locations in Powsin, at the city hall, in one theatre and then another, as well as informal evening encounters over dinner, made it possible to establish interpersonal relationships and to get to know these individuals personally. These personal contacts had a tremendous impact on subsequent cooperation. For example, once I had met a colleague from Sinfonia Varsovia, it became much easier to cooperate on promotion and communicate when we later organized a joint open-air performance in Skaryszewski Park – the “Park Opera” composed by Wojtek Blacharz. The project involved actors from our theatre as well as musicians from Sinfonia Varsovia – trombonists, a violinist, and others. [R3, FGI5]

As mentioned, FGIs allowed us to conclude that meetings in the professional community can also negatively affect OI. Such meetings are often associated with the consumption of alcohol or other stimulants. As a result, the entities participating in such a meeting behave in ways that discredit them in the eyes of other business partners or create conflicts. This, in turn, prevents or hinders further business cooperation that promotes innovativeness:

With us, of course, yes [laugh], I mean, sometimes it happens that, you know, after these official parts, we still have a closed part of the party for ourselves, and of course there is alcohol in it, so you know that sometimes, some people shouldn’t drink alcohol or should know how to drink it in moderation, and there were various situations and various conflicts, where later it turned into purely business relations, and sometimes it happened that something ultimately didn’t work, didn’t work, wasn’t completed or someone did not cooperate due to the events that took place at such an industry meeting [R2, FGI4].

Repeatability and organizational innovativeness

Another building block relevant for OI was the repeatability of contacts. The interlocutors pointed to both its positive and negative associations with innovativeness. They emphasized that repeated interactions facilitate better mutual understanding between entities and contribute to the development of trust. In turn, trust fosters cooperation and supports OI. On the other hand, when a relationship is not perceived as emotionally positive (for example, when there is no personal affinity toward a partner), yet remains necessary – such as in the case of ongoing contact with a public official supervising a project – it may negatively affect the willingness to continue cooperation, as well as the organization’s potential for innovativeness:

It is similar to listening to songs: after some time, we may grow tired of a particular piece and no longer wish to hear it, or, conversely, the more frequently we listen to it, the more we come to appreciate it until it becomes one of our favorites. The same applies to relationships. There are situations in which we must remain in contact with someone who irritates us or whom we simply do not like […]. Certain relationships are repetitive within the framework of a given project, and my employees sometimes cannot wait for them to end – for the project to be completed and for the constant calls and visits from a particular individual to cease. On the other hand, we have also had experiences in recent years where the opposite proved true: the more we shared experiences, the more we talked, implemented projects together, and repeated our interactions, the more we came to value these relationships and to desire their continuation, along with further joint initiatives. It can operate in both directions. [R1, FGI1]

In summary, the qualitative research conducted through FGIs demonstrated that, overall, the interviewees perceived SR as directly or indirectly favoring OI. At the same time, they emphasized that such relations may also constitute a barrier or a constraining factor, hindering or delaying innovativeness. A closer examination of the SR building blocks provides a rationale for this ambivalent perception. The FGIs revealed that, for five of the six identified building blocks, their significance for OI was both positive and negative. Consequently, depending on the specific circumstances – such as excessive emotional intensity or the formation of hermetic groups closed to external actors – SR may fail to foster OI and may even impede it.

DISCUSSION

The aim of the research was to investigate the association between SR and OI, using a multidimensional measurement approach for both constructs and two complementary research methods. The quantitative analysis shows that SR and their three individual building blocks are significantly and positively associated with OI across two dimensions: human and non-human. The qualitative findings support claims about the positive associations of SR in general but also its three individual building blocks. However, interestingly, they also show that individual building blocks (five out of six) may be both positively and negatively related to OI. Conversations with our FGI interlocutors helped us understand why this may be happening.

As our research has shown, SR and OI are multidimensional concepts (Czernek-Marszałek et al., 2023b; Wang & Ahmed, 2004), difficult to define unambiguously and thus, also difficult to analyze. This is in line with previous literature where, regarding SR, authors provide several concepts that refer to the wider domain of relationship quality (Panayides, 2007) starting from Granovetter’s description of tie (Autry & Griffis, 2008; Granovetter, 1973, 1983; Levin & Cross, 2004) or relationship strength (Cavusgil et al., 2003) and ending with relational capital (Kale et al., 2000). In terms of innovativeness, the literature also lists its many dimensions or types; for example, the division used in our research is based on Wang and Ahmed (2004). Wang and Ahmed (2004) have shown that different dimensions of one phenomenon (for instance, different types of organizational culture or organizational resistance) (Naveed et al., 2022) may have different strengths and connections to innovativeness.

This also complicates the analysis of the relationship between these two categories, which is crucial for this paper. For example, in Granovetter’s (1973) approach, the acquisition of knowledge and innovation by individuals and organizations depends on the strength of ties, strong ties (based on emotional intensity) foster innovativeness. However, these strong ties can also be counterproductive in many respects (Czernek-Marszałek, 2020b; Granovetter, 1973; Gulati, 1995; Mitręga & Zolkiewski, 2012; Uzzi, 1996, 1997). Indeed, prior research indicates that the same elements of SR can be both beneficial and detrimental to organizations. This means that the same factor that enables a firm to develop innovativeness may also constrain it (Danneels, 2003; Fredberg & Piller, 2011). To date, however, there has been no clear picture of how different SR elements support and constrain innovativeness (Soosay & Hyland, 2005; Grawe et al., 2011; Panayides, 2006), so our aim was to fill this gap. We believe that our research findings, particularly the triangulation of quantitative and qualitative research achieved through a convergent parallel mixed design, enabled us to demonstrate the complexity of the phenomenon under analysis (i.e., the lack of a fully conclusive role for SR in OI).

The in-depth qualitative research supports the prior claims that SR can favor OI (e.g., Bailey et al., 2022; Bhatti et al, 2021; Czernek-Marszałek et al., 2023b; Gomez-del Rio & Rodriguez, 2022; Granovetter, 1973; Murphy, 2002; Pesämaa et al., 2015; Tsai & Ghoshal, 1998; Zheng et al., 2022). We found that relationships based on emotional intensity may significantly enhance innovativeness, particularly in creative environments, as informal, emotionally rich interactions foster trust, openness, and a sense of closeness. These conditions facilitate the free exchange of ideas, faster mutual understanding, and the joint development of new concepts (Bhatti et al, 2021; Gomez-del Rio & Rodriguez, 2022; Greve & Salaff, 2003). Moreover, such relationships strengthen motivation for collaboration (Chassagnon & Audran, 2011; Czernek-Marszałek et al., 2023b; Steinicke et al., 2012) and increase the willingness to take creative risks, thereby supporting the emergence of innovative initiatives (Czernek-Marszałek, 2020a; Ritala et al., 2009b; Steinicke et al., 2012) and increasing their chances of being transformed into viable projects. An extensive private network can also provide organizations with access to unique resources and information (Zhao et al., 2015; Jianyu et al., 2018). Emotional intensity is widely recognized as an important indicator of relationship quality (Kale et al., 2000; Petersen et al., 2008), and the literature suggests that relationship strength comprises both affective components – such as emotional intensity – and behavioral components (Granovetter, 1973).

At the same time, our interviewees indicated that excessive emotional intensity may disrupt the balance required in professional relationships and limit or inhibit collaboration with other entities, thereby restricting access to new ideas. Strong emotions can generate several barriers and constraints that inhibit processes conducive to OI. Strong emotions, especially negative ones, can limit rational thought and lead to impulsive decision-making, constraining OI. Intense emotions can lead to increased conflict within teams, having some negative implications for collaboration and openness to new ideas (Czernek-Marszałek, 2020b), thereby inhibiting innovativeness. Emotional intensity may also constrain effective communication (Uzzi, 1997; Anderson & Jap, 2005) and limit the exchange of ideas and information (Granovetter, 1973; Uzzi, 1996, 1997; Mitręga & Zolkiewski, 2012), both of which are crucial to the creative sector. Our findings further suggest that excessive emotional intensity may discourage organizations from seeking new partners, leading them instead to rely on existing relationships that, while emotionally strong, may offer lower innovative potential (but with whom they maintain strong emotional ties) (Czernek-Marszałek, 2020b; Nahapiet & Ghoshal, 1998). Moreover, researchers have identified different levels and dimensions of trust (e.g., interpersonal and institutional – Semerciöz et al., 2011; or lateral, vertical, and institutional – Ellonen et al., 2008), showing how it affects these dimensions differently. Wang and Ahmed (2004) noted that trust is based on emotional bonds. In this context, our qualitative research showed that emotional intensity (i.e., affective trust, or emotional bonds) may also be negatively associated with OI.

The next building block of SR, i.e., the community of interests, according to our interlocutors, is important for achieving common goals, motivation, and passion among representatives of creative sectors, thereby potentially enhancing individual engagement in developing innovative ideas and projects. Shared goals and interests also may act as social catalysts for collaboration, fostering partnerships – often informal – that enable innovations unlikely to emerge within rigid hierarchical structures. SR thus play a fundamental role in creative thinking, experimentation, and the search for breakthrough solutions (Bhatti et al, 2021; Chassagnon & Audran, 2011) by enabling the unrestricted flow of information and knowledge and acting as a catalyst for OI. These findings are consistent with studies showing that innovation processes are embedded in interactions among members of communities of interest (Brinks & Ibert, 2015). Such communities generate knowledge as an emergent outcome of their internal social dynamics and initiate innovation processes (Baldwin et al., 2006; Presutti et al., 2021). Our interviewees emphasized that communities of interest, indeed, play a crucial role in fostering OI by strengthening collective understanding and highlighting shared goals, passions, and interests. This dynamic supports creative thinking and the search for innovative solutions. Numerous examples of enthusiast-driven innovation further underline the importance of sharing knowledge within communities of interest, ranging from open-source projects and co-created video games (Jeppesen & Molin, 2003) to more tangible examples such as sports equipment or furniture hacking, as well as creative industries like photography (Grabher & Ibert, 2014).

Our interlocutors indicated that a shared identity – the third building block of SR, referring to a relationship with an individual with whom one shares a sense of belonging, for example, to the same local community (Turner, 2007; Wang et al., 2010) helps maintain a rich network of contacts and acquaintances, and thus favors innovativeness. This aligns with the literature, as sharing traditions, local culture, norms, values, and customs with individuals from other organizations is a key element of OI (Khorshid & Mehdiabadi, 2021). Nevertheless, beyond a moderate level, such shared identity among team members negatively affects innovativeness (Czernek-Marszałek, 2020b). Our FGI participants also argued that some communities with a shared identity are so hermetic that belonging to them can lead to isolation from other entities, excluding them from the opportunity to co-create new, innovative ideas. This is consistent with previous research findings indicating that when ties with business partners become overly closed and insular, partners have limited opportunities to encounter divergent perspectives (Czernek-Marszałek, 2020b; Nahapiet & Ghoshal, 1998). As a result, they may lack the capabilities and competencies needed to build effective relationships with partners outside their own community (Maurer & Ebers, 2006, p. 277).

It’s also important to note that SR may be negatively associated with an organization’s innovativeness when they begin to undermine professionalism and transparency. Overly strong ties can foster informal pressure, unequal treatment, and decision-making based on personal relationships rather than merit-based criteria, which limits the fair evaluation of ideas and the efficient allocation of resources. Moreover, tightly knit private groups within organizations may disrupt work organization, weaken time management, and transfer personal conflicts into professional settings, destabilizing collaboration (Uzzi, 1997; Anderson & Jap, 2005). As researchers indicate, when entrepreneurs work together for an extended period, they may become fatigued with the business relationship. As they become more familiar with each other’s weaknesses, interpersonal conflicts may emerge (Czernek-Marszałek, 2020b; Mitręga & Zolkiewski, 2012). Such conflicts can hinder further collaboration and may even lead to its dissolution (Mitręga & Zolkiewski, 2012), while simultaneously limiting or undermining opportunities for OI development. In public institutions, family ties can further increase the risk of nepotism, undermining trust and perceptions of fairness – both of which are essential for fostering an innovation-friendly organizational climate.

Our research also revealed that SR established and maintained during meetings in professional communities (e.g., conferences or seminars) is an important factor in industry development, as interviewees argued that the exchange of experiences during such meetings fosters innovation across the industry. Researchers emphasize that new expertise and knowledge (Dahlander & Frederiksen, 2012; Franke & Shah, 2003) can result from voluntary collaboration and the free sharing of knowledge. People with similar or the same professions are therefore valued for their significant contributions to firm-based innovation processes (Grabher et al., 2008; Jeppesen & Frederiksen, 2006; Poetz & Schreier, 2012). Interestingly, creative communities have been described as highly reflective (Amin & Roberts, 2008). These practices are embedded in an institutional environment that systematically stimulates and encourages reflective behavior. For participants in creative communities, reflective moments occur rather unsystematically when practitioners face challenging problems and encounter difficult or novel issues in everyday situations (Müller & Ibert, 2015; Al-Omoush et al., 2020; Vainauskienė & Vaitkienė, 2022). The SR developed during professional meetings may be a fundamental condition for collaboration (McGrath, 1991). Here, our interviewees also noted that industry meetings do not always foster innovativeness. This is especially true when participants overstep certain boundaries of good taste, for example, by drinking alcohol, which is detrimental to future collaboration, even in the context of innovation.

When it comes to private contacts, they proved crucial for building trust among representatives of creative industries and for establishing an open, flexible communication style – important from the OI point of view. Moreover, private contacts are often expressed through sympathy, knowledge of the other party, and trust, which, according to the interlocutors, are important for effective cooperation and the emergence of innovative ideas, solutions, and processes.

Scholars emphasized that private contacts enable the acquisition of knowledge about one another and on various issues important to their businesses (Czernek-Marszałek, 2020a). In turn, increasing the level of innovative solutions takes place thanks to joint problem solving, during which entities receive feedback and gain tacit knowledge (Arribas et al., 2013; Gubbins & Dooley, 2021; McTiernan et al., 2023; Reinhardt & Gurtner, 2018; Santos, 2023; Scuotto et al., 2017). From this perspective, our interlocutors indicated that private contacts are positively associated with OI. For instance, the case of the director of a cultural institution and her friend shows that solid private contacts (playing tennis together) significantly facilitated cooperation and generated many ideas. 

On the other hand, private contacts may constrain OI when they begin to undermine professionalism and transparency. Excessively strong ties can foster informal pressure, unequal treatment, and decisions based on personal relationships rather than merit-based criteria, which limits the fair evaluation of ideas and the effective allocation of resources. In addition, close-knit private groups within a team may hinder work organization, reduce time discipline, and transfer personal conflicts into the professional environment, destabilizing collaboration. In public institutions, family relationships may also create a risk of nepotism, weakening trust and the sense of fairness, both of which are crucial for building an innovation-supportive climate (Czernek-Marszałek, 2020b).

Finally, it’s worth emphasizing that, in quantitative studies, the repeatability of contact was excluded from the initial SR measurement model. Supportively, our FGI showed that it can be both irrelevant and negatively associated with innovativeness. Regarding the first issue, our interviewees indicated that maintaining the relationship between repeatability and OI is irrelevant to the dynamics of the relationship itself. They indicated that they „return” to relationships with a given entity as needed; the relationship doesn’t necessarily have to be regular or irregular; it can be ad hoc, even infrequent. Referring to the negative association between repeatability and OI, some indicated that, for example, the need to maintain relationships with certain individuals (e.g., repeated contact with an official overseeing a project) irritates employees, especially when the relationship is not perceived positively (is emotionally negative) but is, in a sense, forced by the situation (project implementation). The literature, though more focused on team relationships (van Offenbeek & Koopman, 1996) than on interorganizational relationships, suggests the opposite. Researchers have demonstrated a strong relationship between team meeting frequency (Jackson, 1996) and team performance, particularly team success (Brewer & Kramer, 1986). More recent research has examined meeting frequency to assess the extent of formal and informal interactions within a team, showing that meeting frequency significantly predicted team innovation (Drach-Zahavy & Somech, 2001). The difference in the results may be due to the level of analysis – intraorganizational versus interorganizational relationships – a matter that certainly requires further investigation.

To conclude, our research showed that, in order to improve OI most effectively, the focus should be on strengthening SR, as they are generally positively associated with OI. However, this association is often ambivalent. Thus, the key is to achieve an optimal balance, for example, in terms of the intensity of emotions or the frequency of contact across different components of SR.

CONCLUSIONS

Contributions

Our research allows us to make a theoretical contribution in several respects, which, from the perspective of knowledge development and cognitive value, can be conceptualized as a conceptual contribution based on model research design (Jaakkola, 2020), as it involves both the development and empirical testing of a model capturing relationships between key concepts.

Firstly, it combines the issues of SR and OI, which are multidimensional concepts (see: Czernek-Marszałek et al., 2023b; Wang & Ahmed, 2004) – not only difficult to define clearly but also to measure, and hence critically important nowadays. The conducted research enabled us to analyze the associations between SR building blocks and OI. So far, the literature has not presented this phenomenon from this perspective, because previous research on the role of SR for OI was fragmentary and highly context-limited (Ellonen et al., 2008). Such studies are concerned, for example, either with one of the dimensions of OI (e.g., product or behavioral) or with one of the dimensions (building blocks) of SR (e.g., trust). Former empirical works mostly focused on product innovativeness (Griffin, 2002; Tüten & Ascigil, 2014) “neglecting the importance of other dimensions such as market and process that contribute to overall innovativeness of organizations” (Tüten & Ascigil, 2014, p. 83). Meanwhile, the other aspects of OI are also very important.

Secondly, following emphasized methodological and empirical gaps in research on innovations and innovativeness within cultural and creative industries (Gohoungodji & Amara, 2023), we explored our sample in search of a new approach to measure OI by introducing its two-dimensional conceptualization, i.e. human and non-human innovativeness, which after nomological and predictive validation performed in a separate study, would be appropriate for research in the context of creative industries.

Thirdly, it may be especially suitable for organizations operating in creative industries, which were selected for our research and in the literature are mentioned as important entities regarding the topic of OI (Bilan et al., 2019; Koch et al., 2023; Snowball et al., 2022) and SR (Gu, 2010; Parmentier & Mangematin, 2014). Such human creativity is crucial because it drives innovation and most often results from the transfer of knowledge (especially tacit knowledge) between various entities where SR are relevant. At the same time, such studies are lacking in literature.

Fourthly, thanks to the approach used, the study allows us to better and more comprehensively understand the connections of different SR building blocks with OI. Thanks to the mixed-method approach used in our research inquiry, we can better understand the very complex, both positive and negative, interlinks.

This mixed study also makes methodological contributions in at least two ways. First, regarding the measurement of OI, it is recognized as a different approach to its structure compared to existing frameworks (characterized by 5- and 4-dimensional frameworks – e.g., Ellonen et al., 2008; Wang & Ahmed, 2004) that cover a two-dimensional conceptualization. This departure is particularly salient when contextualizing OI within industries distinct from the mainstream (as those investigated so far using the same conceptualization: ICT – Ellonen et al., 2008; oil refineries – Golipour et al., 2011; beverages – Semerciöz et al., 2011; marble and natural stone – Ozeren et al., 2013 public sector organizations and private industrial services – Riivari et al., 2012; petrochemical – Heyns & Jearey, 2013, aviation – Klimas, 2015; industrial SMEs – Çağlıyan et al., 2022; formal business organizations – Ghosh & Srivastava, 2022), where prevailing structural configurations might not adequately capture the nuances of innovation dynamics under creative industries. Our proposed framework is particularly relevant to creative industries, underscoring the vital importance of human resource creativity and individual innovativeness (Bilan et al., 2019; Cnossen et al., 2019; Gohoungodji & Amara, 2023; Koch et al., 2023; Plum & Hassink, 2014; Snowball et al., 2022). For instance, industries in the creative domain – by definition – prioritize artistic and inventive qualities intrinsic to human assets, rendering the conventional dimensions, often rooted in organizational factors (e.g., product innovation, process streamlining, market diversification), less pronounced or even tangential.

Second, we used a rigorous three-step procedure to remove responses from our initial sample that were likely influenced by careless answering (Johnson, 2005; DeSimone et al., 2015; Meade & Craig, 2012). While these procedures are well established in psychology, they are relatively new in business research (Curran, 2016). We want to emphasize their importance for ensuring the reliability of data analysis results. Careless responses can either inflate or deflate estimated model parameters and introduce variability that obscures underlying patterns in the dataset. When these conditions exist, the results obtained can be flawed. To observe the impact of this issue in our dataset, we analyzed the characteristics of the between-items correlation coefficients at various stages of the procedure. Our findings showed that the underlying patterns in the cleaned data were more apparent. These observations align with conclusions from other studies examining the outcomes of data cleaning procedures (Ward & Meade, 2023). In contrast, the excluded samples lacked discernible patterns, leading to reduced noise and bias in the cleaned sample, which was used for further analysis. Research results (Oppenheimer et al., 2009) and subsequent studies (Maniaci et al., 2014) confirm that improving data quality by removing careless responding increases statistical power. Therefore, by applying this procedure, we are more confident that we are not presenting spurious results in the paper. We see this as a contribution, as such a detailed approach to data cleaning, although recommended in psychometric and marketing, remains deficient in management research.

Managerial implications

SR, considered an emerging phenomenon among managers of individual organizations, may be an important resource for the organization’s innovativeness; therefore, the ability to properly manage SR and to diversify the portfolio of existing SR is crucial. It is necessary to make managers aware of the inseparable connection between managers’ SR and organizations’ activities. The importance of SR should be recognized by managers and intentionally used to increase innovativeness, which is critical in the creative industries. This applies to every creative industry, not only to entities within it in Poland, where the research was conducted. As such, based on the research findings described, we recommend that managers do not rely too heavily on the repeatability of existing SR, as it is not a strong component of SR in the creative industry context. In addition, given the quantitatively confirmed positive associations of emotional intensity, community of interest, and meetings in professional communities with OI, we suggest strengthening these aspects of SR. Given the qualitative findings, it is particularly recommended to involve individuals responsible for interorganizational relationships (especially those oriented toward co-innovation) in various communities of interest. This component was the only one that did not display mixed effects, showing exclusively positive associations with OI in the focus group interviews. Moreover, it seems crucial to inform managers of the need to adequately monitor all components of SR, as qualitative research has shown that the nature of their relationship with OI can be ambivalent and context-dependent (except for the community of interest). Particular attention should be paid to shared identity and private contacts, which were identified as ambiguous not only in the qualitative analysis but also in the quantitative study, due to unstable estimates in the statistical model.

Limitations

Our research has several limitations. The first group of them refers to the contextuality of our research investigation. The research focused on four deliberately chosen creative industries. Although we did not find differences in results by industry type, this does not mean the results can be generalized to all creative industries, particularly those not covered by this study. Given the limited transferability of the findings to the broader creative industries landscape, we recommend replicating the research in other industry settings. Furthermore, the research was conducted in Poland, which further limits the possibility of drawing general conclusions; research carried out in other countries, especially those with different socio-cultural specificities, could yield different results. This shows that, when considering replication, different national or international contexts should be considered. Therefore, we recommend quasi-replication (Bettis et al., 2016) as a possible direction for future research.

Secondly, our research process reflects limitations in the research design, particularly regarding the selection of key informants. Notably, our data collection processes adopted a single-informant approach, which, although dominant and common, has significant disadvantages in recognizing non-individual phenomena (Bou‐Llusar et al., 2016), such as OI in our case. Moreover, considering the target key informants (Homburg, 2012), it is worth noting that operational and functional employees, whose creative activities may have even greater implications for innovativeness than those of directors and management staff, were also included in the research. Next, the research process was designed as a cross-sectional study, and thus, the data were collected at a single time point, which is considered a limitation for the stability of the results (Strobl et al., 2025). Additionally, all FGIs were conducted online due to the COVID-19 pandemic, which may have influenced the findings by temporarily elevating the importance of digital SR. Both these shortcomings point out the need for a longitudinal research design.

Finally, our study has some limitations related to the specific types and methods employed. Regarding qualitative research, the study involved purposefully selected organizations and interviewees representing specific types of creative activity in Poland. Perhaps, in the case of other entities (as mentioned, also operating in other contexts, e.g., countries), the importance of SR and their individual components for OI will differ.

Regarding the quantitative research, three key issues should be outlined. In the context of OI, the data collection process assumed the use of a validated measurement scale developed by Wang and Ahmed (2004). In practice, however, two departures from the original scale occurred. First, in line with recommendations to simplify and standardize survey instruments, the research tool consistently employed exclusively positively worded items (Chyung et al., 2018; Dodeen, 2023; Zeng, Jeon, & Wen, 2024). It is therefore possible that rewording the originally reverse-keyed items into non-reverse items may have affected the obtained results. Second, due to an inadvertent inclusion at the questionnaire design stage, one item that had been excluded during the validation of the original scale was incorporated into the survey instrument. As this item had not been validated in the original measurement model, it was excluded from further analyses aimed at adaptation of the measurement model. Consequently, OI was measured using 19 rather than 20 items. It should be noted that although omitting a single indicator may have affected the results, we believe the impact is unlikely to be substantial. The adopted measurement approach retained a multi-item structure across all five original dimensions of OI, with a minimum of three indicators per latent construct (Danneels, 2016). Moreover, the structural models used for hypothesis testing were based on a comprehensively validated measurement model of OI, estimated using the collected - and importantly, thoroughly cleaned - raw data. Importantly, general recommendations for scale development and adaptation emphasize structural simplification of measured constructs (Worthington & Whittaker, 2006), particularly when instruments are applied in new empirical contexts (Ambuehl & Inauen, 2022). Consistent with these recommendations, the measurement model adaptation process conducted in this study further refined the OI scale, reducing the number of indicators from 19 to 12, a valid number. Last but not least in the context of limitations of quantitative part of our research process, as far as the quantitative part of our study is concerned, the extensive research tool used in the data gathering process (approx. 120 questions in total) can be indicated as a limitation, which could have caused tiredness and weariness of the respondents during the research process, potentially affecting the reliability of the answers provided. Nonetheless, we claim that the application of multi-stage analysis, focused on eliminating careless observation, minimized the risk of unreliable raw data.

Given all those aspects, we argue they warrant further research.

Future research directions

Based on the findings of your study, several potential directions for further research could deepen understanding of the links between SR and OI. Those include for instance: (1) investigation of SR’ building blocks significantly and negatively associated with OI (e.g. are there certain conditions under which they might be more beneficial?); (2) given contextual limitations of our study (one country and creative industries), the study replication in other contexts allowing for more general conclusions – e.g. to analyze whether, what building blocks of SR and how they are linked to innovativeness in creative industries in other countries (e.g. in Asia, where the role of SR is business is undoubtedly crucial) in order to compare the findings; (3) considering the dynamic nature of both SR and OI, conduct longitudinal studies to capture the evolution and changes undergo the link between these two and identify factors that affect these dynamics.

Acknowledgements

The project was financed from sources of the National Science Centre, Poland, according to decision UMO-2017/27/B/HS4/01051 and UMO-2021/43/B/HS4/01823.

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Appendix A

Table A1. Sample characteristics N=302 (frequency and relative frequency [%])

Size (no of employees)			Sector
self-employed (1)	24	7.9	tourism and gastronomy	103	34.1
micro (2-9)	89	29.5	theatres	81	26.8
small (10-49)	134	44.4	computer games	66	21.9
medium (50 - 249)	46	15.2	museums	52	17.2
large (250-999)	8	2.6
very large (min 1000)	1	0.3
Territorial range			Informant’s experience (years)
local	64	21.2	max 1	2	0.7
regional	82	27.2	<1-3)	12	4.0
national	76	25.2	<3-5)	34	11.3
European	44	14.6	<5-10)	87	28.8
global	36	11.9	<10-20)	116	38.4
			min 20	51	16.9
Informant’s gender
female	141	46.7	male	161	53.3
Informant’s age (years)			Informant’s education
18-25	10	3.31	vocational
26-35	53	17.55	secondary	16	5.29
36-45	122	40.40	higher
46-55	86	28.48		44	14.56
56-65	29	9.60
above 65	2	0.65		242	80.13

Table A2. Characteristics of FGI research participants

FGI No.	Research participant code	Position in the organization	Year of establishment of the entity	Location of the entity (registered office) – city, voivodeship	Size of the entity (by number of employees)	Form of ownership (Public- PUB, Private – PR, Nonprofit - NP)	The scope of the activity
Museums
FGI 1	R1	manager	1945	Gorzów Wlkp. (Lubuskie Voivodeship)	small	PUB Self-government of the Lubuskie Voivodeship	regional
FGI 1	R2	manager	1968	Wejherowo (Pomeranian Voivodeship)	small	PUB Wejherowo County, Pomeranian Voivodeship	global
FGI 1	R3	manager	1963	Kazimierz Dolny (Lublin Voivodeship)	medium	PUB Lublin Voivodeship	nationwide
FGI 1	R4	deputy director	1958	Łódź (Łódź Voivodeship)	small	PUB Lodz City Hall	nationwide
FGI 1	R5	manager	1953	Biecz (Subcarpathian Voivodeship)	small	PUB Biecz Municipality	regional
FGI 1	R6	manager	1929	Katowice (Silesian Voivodeship)	medium	PUB Local government of the Silesian Voivodeship	global
Computer and video games
FGI 2	R1	owner	2012	Katowice (Silesian Voivodeship)	micro	PR private sole proprietorship	global
FGI 2	R2	COO, project manager	2020	Katowice (Silesian Voivodeship)	small	PR limited liability company	global
FGI 2	R3	owner	2019	Kraków (Lesser Poland Voivodeship)	micro	PR limited liability company	global
FGI 2	R4	producer	2004	Kraków (Lesser Poland Voivodeship)	micro	PR private sole proprietorship	global
FGI 2	R5	member of the management board and managing director	2001	Wrocław (Lower Silesian)	small	PR limited liability company	global
FGI2	R6	owner	2016	Toruń (Kuyavian-Pomeranian Voivodeship)	micro	PR Spin Off	global
Culinary routes
FGI 3	R1	owner	1926	Pawłów (Świętokrzyskie Voivodeship)	micro	PR private sole proprietorship	European
FGI 3	R2	owner	2017	Wrocław (Lower Silesian Voivodeship)	micro	PR private sole proprietorship	regional
FGI 3	R3	owner	2009	Dolistowo Stare (Podlaskie Voivodeship)	micro	PR Farm	nationwide
FGI 3	R4	manager	2003	Radlin (Silesian Voivodeship)	micro	PR limited liability company	regional
FGI 3	R5	owner	1996	Lipka (Greater Poland Voivodeship)	micro	PR private sole proprietorship	nationwide
FGI 3	R6	employee	2013	Stara Wieś, Końskowola (Lubelskie voivodeship)	Micro	PR agricultural retail trade	regional
Theatres
FGI 4	R1	spokesperson, manager of the Programming Department	1907	Katowice (Silesian Voivodeship)	medium	PUB public institution	nationwide
FGI 4	R2	administration and HR manager	2002	Warsaw (Masovian Voivodeship)	small	PR joint stock company	global
FGI 4	R3	deputy director for organizational affairs	1948	Gdańsk (Pomeranian Voivodeship)	medium	PUB public institution	nationwide
FGI 4	R4	CEO	1958	Warsaw (Masovian Voivodeship)	medium	NP non-governmental organization	global
FGI 4	R5	owner	2016	Wrocław (Lower Silesian Voivodeship)	micro	PR private sole proprietorship	local
FGI 4	R6	secretary of the board, sales and marketing specialist	2009	Glinik Średni (Subcarpathian Voivodeship)	micro	PR private sole proprietorship	nationwide
Mixed industries
FGI 5	R1	communications manager	2008	Warsaw (Masovian Voivodeship)	medium	PUB Local Government Theatre	global
FGI 5	R2	deputy director	1958	Gdynia (Pomeranian Voivodeship)	medium	PUB Local Government Theatre	European
FGI 5	R3	spokesman	1944	Warsaw (Masovian Voivodeship)	medium	PUB Local Government Theatre	European
FGI 5	R4	chief executive or chief artistic officer	2004	Wrocław (Lower Silesian)	medium	PUB Local Government Theatre	nationwide
FGI 5	R5	spokesman	2008	Gdańsk (Pomeranian Voivodeship)	small	PUB Local Government Theatre	global
FGI 5	R6	producer	1893	Kraków (Lesser Poland Voivodeship)	medium	PUB Local Government Theatre	nationwide

Table A3. Frequency table of string length in 60 items measuring Social Relationships

String length	Response value							Total
	1	2	3	4	5	6	7
2	0	3	9	10	6	7	0	35
3	0	1	10	19	45	22	5	102
4	3	5	1	19	34	15	9	86
5	7	1	4	12	20	13	11	68
6	6	0	4	11	18	9	10	58
7	6	0	5	11	17	11	10	60
8	7	0	2	12	13	8	12	54
9	3	1	3	3	9	5	9	33
10	4	0	0	1	8	2	7	22
11	10	1	0	6	9	4	2	32
12	4	0	3	1	3	5	10	26
13	6	0	0	1	4	2	4	17
14	2	0	0	1	1	0	2	6
15	5	0	1	2	0	0	2	10
16	2	0	0	1	0	1	0	4
17	2	0	0	0	0	1	3	6
18	1	0	0	1	1	0	0	3
19	0	0	1	0	0	0	3	4
20	45	0	0	4	1	5	3	58
21	1	0	0	0	0	1	0	2
24	0	0	0	0	0	0	2	2
42	0	0	0	0	0	1	0	1
58	0	0	0	0	1	0	0	1
Total	114	12	43	115	190	112	104	690
Deleted	47	3	5	17	8	11	13	104

Note: highlighted are frequencies of unusually long-string of identical answers in a sequence of 60 items. Deleted are all responses with strings of marked length, and longer. Analysis performed in Python and Visual Studio Code, following the procedure described in (Curran, 2016) and (Johnson, 2005).

Table A4. Social Relationships – multidimensional measurement model adopted in the research

Extracted factors		Question	Original codes by the Klimas et al. (2025)	Codes
Emotional intensity		An inherent component of interpersonal relationships is trust.	EI_1	RS_2
		Maintaining interpersonal relationships requires repeated contact.	EI_2	RS_1
		Interpersonal relationships require maintaining contact.	EI_4	RS_10
		My interpersonal relationships are characterized by cordiality in dealing with the other party.	EI_5	RS_20
		An inherent component of interpersonal relationships is commitment.	EI_6	RS_8
		An inherent component of interpersonal relationships is sympathy.	EI_10	RS_7
Community of interest	Passion	In interpersonal relationships, I pay attention to the comparability of the inputs provided by the parties.	CI_1	RS_47
		What matters in interpersonal relationships is the comparative contribution of the parties to the outcomes of the relationship.	CI_2	RS_48
		What matters in interpersonal relationships is a sense of fairness.	CI_3	RS_46
		In interpersonal relationships, the commitment of the parties varies over time, but in the long term it should be comparable.	CI_4	RS_45
	Passion	Interpersonal relationships link me to people who have a similar understanding of the meaning of their work.	CI_6	RS_54
		Interpersonal relationships link me to people with whom I share professional interests.	CI_7	RS_53
		Interpersonal relationships link me to people for whom professional work is a hobby.	CI_11	RS_55
Shared identity		My interpersonal relationships involve a sense of belonging to the local community.	SI_1	RS_58
		My interpersonal relationships involve knowledge of local culture, traditions, norms, values, customs, etc.	SI_2	RS_60
		My interpersonal relationships involve active local patriotism (e.g., promoting local culture, traditions, people, etc.).	SI_3	RS_57
		My interpersonal relationships involve acquaintances in the local community.	SI_4	RS_59
		An inherent component of my interpersonal relationships is a sense of local identity.	SI_5	RS_12
Private contacts		I maintain direct informal contact in my interpersonal relationships in my private time, including with family.	PC_1	RS_26
		I maintain direct informal contact in my interpersonal relationships outside of work hours.	PC_3	RS_25
		My sustained interpersonal relationships combine professional & personal relationships.	PC_5	RS_22
Meetings in professional community		I maintain direct informal contact in my interpersonal relationships at popular community gathering places.	MC_1	RS_28
		I maintain direct informal contact in my interpersonal relationships at industry meetings, including team building trips.	MC_2	RS_27
		My interpersonal relationships are refreshed at formal & informal industry meetings.	MC_3	RS_51

Source: Based on scale validated in Klimas et al. (2025).

Table A5. Organizational Innovativeness – multidimensional measurement adopted in our research

Factor		Question	Original codes by Wang & Ahmed (dimension)	Code
Non-Human Innovativeness	Functional management	Our company changes production methods at a great speed in comparison with our competitors.	IN17 (Process)	OI_10
		In comparison with our competitors, our company has a higher success rate in new products and services launch.*	IN07 (Product)	OI_5
		In comparison with our competitors, our products’ most recent marketing program is revolutionary in the market.	IN08 (Market)	OI_6
		In new product and service introductions, our company is often at the cutting edge of technology.	IN08 (Market)	OI_7
	Product management	In new product and service introductions, our company is often first-to-market	IN01 (Product)	OI_1
		Our new products and services are often perceived as very novel by customers	IN02 (Product)	OI_2
		New products and services in our company often take us up against new competitors	IN04 (Market)	OI_3
Human Innovativeness		We encourage people to think and behave in original and novel ways.	IN27 (Behavior)	OI_18
		In our company we get a lot of support from managers if we want to try new ways of doing things.	IN20 (Behavior)	OI_12
		Key executives of the firm are willing to take risks to seize and explore ‘chancy’ growth opportunities.	IN22 (Strategic)	OI_14
		When we see new ways of doing things, we are first at adopting them.*	IN28 (Strategic)	OI_19
		We are willing to try new ways of doing things and seek unusual, novel solutions	IN26 (Behavior)	OI_17
		In our company, we tolerate individuals who do things in a different way.	IN25 (Behavior)	OI_16
		Senior executives constantly seek unusual, novel solutions to problems via the use of “idea men”.	IN24 (Strategic)	OI_15

Note: * reverse-coded in the original scale.

Source: Based on a scale validated in Wang and Ahmed (2004).

Appendix B

A step-by-step procedure for adopting SR measurement scale

At first, we conducted an initial correlation coefficient analysis and, based on it, we eliminated RS16 because it had negative correlations with almost all remaining items, including RS14 and RS15. These 3 items built one sub-construct of the SR scale, named Repeatability. Next, we applied parallel analysis, originally described by Horn (1965). To perform the analysis, we estimated a common factor model with Principal Component Analysis (PCA), and we compared the extracted raw data eigenvalues with the simulated 95th percentile of the distribution of random data eigenvalues generated by the SPSS syntax program for determining the number of factors using parallel analysis (O’connor, 2000). Tables B1 and B2 depict the results of parallel analysis and ML common factor competing models fit.

Table B1. Principal component analysis and simulated eigenvalues

No. of factors	PCA eigenvalue	Mean of distribution	95th percentile of distribution
1	11.447	1.744	1.825
2	3.748	1.653	1.726
3	2.532	1.583	1.633
4	2.181	1.526	1.567
5	1.644	1.477	1.517
6	1.581	1.429	1.468
7	1.469	1.385	1.426
8	1.099	1.341	1.384
9	1.039	1.302	1.330
10	0.887	1.268	1.300
11	0.878	1.229	1.261
12	0.772	1.193	1.228
13	0.693	1.159	1.189
14	0.585	1.124	1.155

Note – highlighted are eigenvalues greater than 95th percentile of distribution of simulated eigenvalues.

Table B2. Common factor models and their fit statistics

No. of factors	χ2	df	p-value	RMSEA	RMSEA change
1	4105.337	665	0.000	0.131	N/A
2	3027.533	628	0.000	0.113	0.018
3	2506.677	592	0.000	0.104	0.009
4	2030.41	557	0.000	0.094	0.010
5	1682.792	523	0.000	0.086	0.008
6	1397.497	490	0.000	0.078	0.007
7	1081.803	458	0.000	0.067	0.011
8	871.382	427	0.000	0.059	0.008

Note: Extraction method – Maximum Likelihood; rotation method – Promax (Kappa = 4).

Parallel analysis and RMSEA of competing common-factor models indicate a 6- to 7-factor solution. We compared both solutions using the SR scale and decided to begin with the 7-factor solution, as it clearly extracts components overlapping with the SR measurement model. Table B3 presents EFA and CFA loadings and goodness-of-fit indices.

Table B3. Initial EFA and CFA results for a 7-factor solution

Item	EFA factors’ loadings (pattern matrix)							CFA results
	EMI	SHI	COMM1	PRI	COMM2	MET	COMM3	Standardized loadings	Convergent validity statistics
RS2	0.799	0.101	-0.048	-0.133	-0.140	0.061	-0.014	0.626	Factor EMI AVE = 0.400 CR = 0.886
RS1	0.745	0.035	-0.061	-0.252	-0.119	0.112	-0.033	0.511
RS20	0.740	0.005	-0.190	-0.017	-0.012	0.047	0.017	0.604
RS10	0.715	-0.050	-0.057	-0.035	-0.190	0.011	0.167	0.572
RS8	0.690	-0.066	0.074	-0.111	-0.075	0.037	0.155	0.668
RS38	0.612	-0.044	0.037	0.211	0.099	0.056	-0.220	0.718
RS41	0.601	-0.001	0.053	0.085	0.183	-0.072	-0.016	0.774
RS37	0.578	0.010	0.073	0.077	0.196	-0.065	-0.064	0.764
RS7	0.524	0.048	0.065	0.133	-0.215	0.053	0.072	0.527
RS42	0.488	0.038	-0.006	0.212	0.148	-0.056	-0.085	0.660
RS9	0.403	0.023	0.094	-0.051	0.121	-0.038	0.298	0.627
RS21	0.351	-0.005	-0.125	0.266	0.086	0.016	-0.012	0.455
RS58	-0.020	0.931	0.006	0.032	0.027	-0.046	-0.038	0.934	Factor SHI AVE =0.692 CR = 0.913
RS57	0.001	0.891	0.018	-0.048	0.036	0.048	0.004	0.892
RS60	0.145	0.838	-0.080	0.025	-0.061	-0.082	0.076	0.851
RS59	-0.077	0.821	0.055	-0.021	0.089	0.057	-0.103	0.808
RS12	0.035	0.596	0.047	0.077	-0.104	0.012	0.101	0.643
RS18	0.325	-0.022	0.235	-0.175	0.128	0.040	0.187	0.540	Factor COMM1 AVE =0.588 CR = 0.868
RS48	-0.178	0.005	1.028	0.034	-0.041	0.017	-0.018	0.902
RS47	-0.149	0.001	1.009	0.077	-0.065	0.020	0.000	0.903
RS46	0.180	0.000	0.673	-0.100	0.071	-0.049	-0.072	0.745
RS45	0.151	0.066	0.557	-0.041	0.008	-0.018	0.054	0.682
RS26	-0.292	0.035	-0.090	0.830	-0.028	0.088	0.099	0.657	Factor PRI AVE =0.480 CR = 0.823
RS25	-0.126	0.027	0.065	0.741	-0.158	0.117	0.159	0.695
RS40	0.113	0.007	-0.007	0.702	0.054	-0.163	0.073	0.737
RS39	0.324	-0.069	0.094	0.606	0.087	-0.056	-0.200	0.729
RS22	0.013	0.016	0.037	0.588	-0.075	0.134	0.060	0.641
RS53	-0.151	-0.032	-0.085	-0.038	0.997	0.018	0.135	0.860	Factor COMM2 AVE =0.728 CR = 0.883
RS54	-0.058	0.029	0.058	-0.106	0.924	0.018	0.016	0.922
RS55	-0.075	0.016	0.023	0.013	0.721	0.105	0.047	0.772
RS28	-0.003	-0.006	-0.021	0.182	0.017	0.822	0.025	0.861	Factor MET AVE =0.635 CR = 0.834
RS27	0.215	-0.019	0.020	0.014	-0.015	0.754	-0.109	0.809
RS51	0.081	0.024	0.009	-0.044	0.255	0.543	0.026	0.714
RS30	0.076	0.041	-0.101	0.212	0.053	-0.092	0.793	0.814	Factor COMM3 AVE = 0.583 CR = 0.787
RS29	-0.026	0.000	0.062	0.038	0.149	0.050	0.710	0.840
RS19	0.291	-0.081	0.193	0.028	0.021	-0.014	0.396	0.617
Discriminant validity HTMT ratio matrix factor COMM1 COMM2 COMM3 EMI MET PRI SHI COMM1               COMM2 0.678             COMM3 0.703 0.595           EMI 0.651 0.609 0.580         MET 0.459 0.541 0.395 0.500       PRI 0.365 0.405 0.416 0.515 0.400     SHI 0.306 0.297 0.368 0.255 0.125 0.363
Goodness of fit	χ2= 927.737 df = 399; p-value = 0.000 RMSEA = 0.066							χ2 = 1742.801; df = 573 CMIN/df = 3.042 RMSEA = 0.082; SRMR = 0.083 NFI = 0.757; TLI = 0.804; CFI = 0.821

In the next step, we purified the SR scale using an iterative process to obtain consistent solutions for both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). In our view, achieving consistency in these analyses is crucial for ensuring high quality of the measurement model. We removed the 7th factor because the EFA and CFA results did not align; specifically, the EFA loading for RS19 was significantly lower than its CFA counterpart. Additionally, RS30 and RS29 did not provide sufficient indicators to reliably describe the latent factor COMM3 (Hair et al., 2017). Subsequently, we eliminated the following items: RS41, RS37, RS38, RS7, RS42, RS9, RS21, RS18, RS20, RS39, RS40, RS10, and RS45. All of these items had EFA loadings substantially below 0.6. The final measurement model is presented in Table 1 of the main text.

Appendix C

A step-by-step procedure for adopting the OI measurement model

To determine the number of factors for the OI scale, we at first applied parallel analysis, originally described by Horn (1965). To perform the analysis, we estimated a common factor model with principal component analysis (PCA), and we compared the extracted raw data eigenvalues with the simulated 95th percentile of the distribution of random data eigenvalues generated by the SPSS syntax program for determining the number of factors using parallel analysis (O’connor, 2000). The resulting number of factors is 2, whereas, compared to the distribution’s mean, the 3rd factor can also be accepted. We estimated loadings for two solutions: 2-factor and 3-factor (see Table C1). Although the solution presented in Table C1 involves an orthogonal rotation (which obviously contradicts the expected and observed correlation between factors), we observe that increasing the number of factors results in higher cross-loadings. The point is that there is a large between-items correlation across the whole data set, even after cleaning for inconsistent-responses, and all items share quite large common variance.

Table C1. Rotated factors matrix

Item	2-factor solution		3-factor solution
	Factor
	1	2	1	2	3
OI1	0.731	0.122	0.160	0.183	0.87
OI2	0.743	0.162	0.196	0.211	0.862
OI3	0.801	0.124	0.142	0.332	0.817
OI4	0.847	0.149	0.134	0.545	0.663
OI5	0.855	0.171	0.145	0.607	0.610
OI6	0.827	0.168	0.117	0.734	0.436
OI7	0.803	0.214	0.153	0.778	0.358
OI8	0.664	0.271	0.188	0.837	0.099
OI9	0.555	0.483	0.421	0.673	0.122
OI10	0.783	0.282	0.222	0.773	0.338
OI11	0.652	0.418	0.364	0.676	0.256
OI12	0.294	0.721	0.701	0.316	0.131
OI14	0.288	0.764	0.736	0.358	0.080
OI15	0.201	0.821	0.813	0.206	0.116
OI16	0.126	0.862	0.866	0.103	0.118
OI17	0.145	0.879	0.880	0.127	0.120
OI18	0.096	0.868	0.874	0.073	0.105
OI19	0.281	0.779	0.754	0.342	0.087
OI20	0.197	0.741	0.753	0.082	0.237

Note: Extraction method – Principal Component Analysis, rotation method – Varimax; highlighted are loadings >0.6; bolded are items with loads more than one factor (>0.3).

Following O’Connor’s (2000) recommendation, we apply an additional rule to determine the number of factors. We decided to apply fit indices of the common factor model with the Maximum Likelihood method of extraction (Fabrigar & Wegener, 2012). Compared with PCA and parallel analysis, this method enabled us to include factor correlations, which is conceptually appropriate for the focal scale. We estimated the model chi-square, and calculated RMSEA and RMSEA change for competing solutions, starting with a 1-factor solution and ending with a 5-factor solution (see Table C2).

Table C2. Common factor models and their fit statistics

No. of factors	χ2	df	p-value	RMSEA	RMSEA change
1	2426.688	152	0.000	0.223	N/A
2	1102.346	134	0.000	0.155	0.068
3	694.938	117	0.000	0.128	0.027
4	341.95	101	0.000	0.089	0.039
5	203.168	86	0.000	0.067	0.022

Note: Extraction method – Maximum Likelihood; rotation method – Promax (Kappa = 4).

Considering the results in Table C2, we should adopt a 5-factor solution, as it best fits the data. However, analysis of loadings and cross-loadings (see Table C3) and their distribution across the extracted factors does not confirm the 5-factor structure of the original Wang & Ahmed OI scale. Similarly, no clear solution results in the 4-factor model. We therefore decided to continue with a 3-factor solution, which exhibits a quite poor fit (RMSEA = 0.128, above the accepted 0.1), but at the same time yields a reasonable distribution of items across extracted factors. Table C4 contains initial EFA and Confirmatory Factorial Analysis (CFA) results.

Table C3. EFA regression coefficients (i.e., pattern matrix )

Scale item		Factor
Statistical label	Wang & Ahmed (2004)	1	2	3	4	5
OI1	IN01 (Product)	-0.001	0.923	-0.013	-0.009	0.009
OI2	IN02 (Product)	0.02	0.913	0.001	0.052	-0.039
OI3	IN04 (Market)	-0.01	0.558	0.415	0.034	-0.074
OI4	IN05 (Product)	0.032	0.127	0.851	-0.086	0.05
OI5	In07 (Product)	0.015	-0.003	0.957	-0.006	0.024
OI6	IN08 (Market)	-0.065	0.007	0.475	0.186	0.327
OI7	IN10 (Market)	0.049	0.031	0.131	-0.094	0.912
OI8	IN14 (Strategic)	0.006	-0.123	-0.004	0.315	0.651
OI9	IN16 (Process)	0.053	-0.015	-0.07	0.874	0.000
OI10	IN17 (Process)	-0.124	0.133	0.018	0.658	0.272
OI11	IN19 (Process)	0.043	0.072	0.047	0.672	0.078
OI12	IN20 (Behavior)	0.486	-0.019	0.062	0.469	-0.191
OI14	IN22 (Strategic)	0.582	-0.103	0.068	0.348	-0.037
OI15	IN24 (Strategic)	0.721	-0.028	0.029	0.22	-0.095
OI16	IN25 (Behavior)	0.936	-0.045	0.096	-0.136	0.000
OI17	IN26 (Behavior)	0.972	-0.029	0.078	-0.137	0.011
OI18	IN27 (Behavior)	0.946	0.014	-0.005	-0.109	-0.019
OI19	IN28 (Strategic)	0.7	-0.033	-0.069	0.072	0.246
OI20	IN29 (Process)	0.69	0.289	-0.267	0.009	0.109

Note: Extraction method – Maximum Likelihood, rotation method – Promax (Kappa = 4); highlighted are loadings >0.6; bolded are items with loads more than one factor (>0.3).

Table C4. Initial EFA and CFA results for 3-factor solution

Item	EFA loadings (Maximum Likelihood)			CFA (CB-SEM)
	F1	F2	F3	Standardized loadings	Convergent validity metrics
OI1	0.018	-0.044	0.926	0.902	Factor F3 AVE = 0.778 CR = 0.909
OI2	0.057	-0.033	0.920	0.923
OI3	-0.021	0.255	0.665	0.818
OI4	-0.059	0.624	0.325	0.831	Factor F2 AVE = 0.630 CR = 0.931
OI5	-0.057	0.714	0.236	0.863
OI6	-0.099	0.862	0.059	0.851
OI7	-0.055	0.908	-0.026	0.848
OI8	0.016	0.921	-0.239	0.736
OI9	0.285	0.566	-0.055	0.655
OI10	0.025	0.756	0.083	0.817
OI11	0.212	0.590	0.051	0.724
OI12	0.632	0.157	0.013	0.712	Factor F1 AVE = 0.652 CR = 0.934
OI14	0.678	0.226	-0.079	0.765
OI15	0.795	0.032	0.001	0.815
OI16	0.910	-0.076	-0.009	0.873
OI17	0.940	-0.078	0.000	0.901
OI18	0.935	-0.145	0.019	0.863
OI19	0.709	0.213	-0.082	0.785
OI20	0.723	-0.131	0.206	0.724
Discriminant validity	n.a.			HTMT(F1;F2) = 0.725; HTMT(F1;F3) = 0.398 HTMT(F3;F2) = 0.562
Model goodness of fit measures	χ2= 694.938 df = 117; p-value = 0.000 RMSEA = 0.128			χ2 = 985.710; df = 149 CMIN/df = 5.527 RMSEA = 0.136; SRMR = 0.090 NFI = 0.815; TLI = 0.813; CFI = 0.837

In the next stage, items OI20 and OI8 were removed due to their limited conceptual alignment with the latent construct of human innovativeness. In our opinion, both primarily capture structural or procedural aspects of innovativeness (i.e., R&D capabilities in the case of OI8 and problem-solving routines in the case of OI20) rather than human-centered innovative attributes. We claim that their inclusion might blur the conceptual distinction between human-oriented and non-human-oriented dimensions of OI and weaken the theoretical coherence of the construct. In the next stage, we removed three items: OI4, 0I9, and OI11 as these items resulted in high standardized residuals and had quite large cross-loadings. The final measurement model for OI is depicted in Table 1 of the main paper.

Biographical notes

Katarzyna Czernek-Marszałek is an Associate Professor and the head of the Department of Management Theory, University of Economics in Katowice. Her research interests include inter-organizational relationships (especially cooperation and coopetition) and their determinants (e.g., trust and social embeddedness). Her empirical research focuses on tourism sector. Her works are published in journals such as Industrial Marketing Management, Tourism Management, Journal of Destination Marketing & Management, Annals of Tourism Research, Journal of Travel Research, and Current Issues in Tourism. She has led several research projects funded by the National Science Center and is a laureate of national and international (EU) awards for her scientific work. She is a member of the European Academy of Management and the International Research Community on Coopetition, Ecosystems, Networks, and Alliances.

Patrycja Klimas is a Professor at the Wroclaw University of Economics and Business. As a researcher, she focuses on strategic management, with a particular interest in inter-organizational cooperation and coopetition, investigated within dyads, networks, and ecosystems. Her research interests, as well as the projects she leads (funded by the National Science Center in Poland and the European Commission), are primarily focused on high-tech and creative industries, with a core focus on the video game industry. Her academic portfolio includes publications in top-tier journals such as Long Range Planning, Journal of Business Research, Industrial Marketing Management, European Management Journal, Technology in Society, European Management Review, Review of Managerial Science, Entrepreneurship Research Journal, and Games and Culture. Her research and academic achievements have been recognized with awards from the Committee on Organizational and Management Sciences of the Polish Academy of Sciences, the Polish Ministry of Science and Higher Education, as well as several national and international best paper awards.

Dagmara Wójcik is a Ph.D. Assistant Professor in the Department of Management Theory at the University of Economics in Katowice. A researcher of organizational collaboration phenomena and network relations, specializing in cooperation and coopetition in strategic management, particularly dyads and inter-organizational networks in creative industries – creative organizations, cultural and art institutions, especially in the performing arts. She is a member of the international scientific association European Academy of Management, the International Research Community on Coopetition, Ecosystems, Networks, and Alliances. She serves as the principal investigator and executor of projects funded by the National Science Center, the Ministry of Science and Higher Education in Poland, and by the European Commission. She is a laureate of national and international awards for her scientific works.

Patrycja Juszczyk, Ph.D., is an Assistant Professor at University of Economics in Katowice in Theory Management Department. As a researcher, she conducts research in strategic management, focusing on the relationships among coopetition, strategic cooperation, and competition, with particular emphasis on non-profit organizations (public and non-profit). Her research focuses mainly on coopetition relations in the cultural sector. She also focuses her research on social relationships and other sociological concepts and theories in the discipline of management and quality sciences. She’s an implementer of research projects funded by the Ministry of Education and Science, and is actively involved in work on grants from the National Science Center, the National Center for Research and Development, and the Ministry of Science and Higher Education in Poland. She presented her papers at national and international scientific conferences.

Aleksandra Szpulak is an associate professor in the Department of Corporate Finance and Public Finance at Wrocław University of Economics and Business. She earned her PhD in Economics, with a focus on Business Forecasting and a particular emphasis on the application of early warning systems to manage corporate liquidity. From 2005 to 2019, she was an associate professor in the Department of Business Analysis and Forecasting. At an early stage, her research focused on modeling and forecasting operating cash flows for working capital management under the value-maximization approach. Over the last years, she has collaborated in research teams, primarily in the field of Management. As a team member, she was responsible for quantitative analysis, primarily survey data analysis. She applied methods for measurement scale development and validation, structural equation modeling (SEM), and experimental study design. She is an author or coauthor of over 30 publications available in the WUEB repository and on Google Scholar.

Author contribution statement

Katarzyna Czernek- Marszałek: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing –Review & Editing. Patrycja Klimas: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing. Dagmara Wójcik: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing. Patrycja Juszczyk: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing. Aleksandra Szpulak: Formal Analysis, Methodology, Validation, Visualization, Writing – Review & Editing.

Conflicts of interest

The authors declare no competing interests. Patrycja Klimas serves as an Associate Editor of JEMI and was not involved in the peer review process, editorial handling, or decision-making for this manuscript.

Citation (APA style)

Czernek-Marszałek, K., Klimas, P., Wójcik, D., Juszczyk, P., & Szpulak, A. (2026). Managers’ social relationships and organizational innovativeness: insights from creative industries. Journal of Entrepreneurship, Management and Innovation, 22(2), 5-51. https://doi.org/10.7341/20262221

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