Introduction

Innovation, alongside growth strategies, is one of the most recognized topics for research in management contributing to the understating of development of both organizations and countries. A considerable body of research aims to identify organizational and institutional determinants to explain the mechanisms of incentives and inhibitors of innovation performance (Mahmood et al. 2013; Castellaci 2015; Mendoza-Silva 2021; Dani and Gandhi 2022). These studies lie conceptually in institutional theory, transaction cost theory, agency theory, and resource-based view, and reveal that certain institutional voids may constrain access to funds for development or investment in human capital, or increase costs of management and governance, affecting firm performance and competitiveness. Business groups with their financial resources, internal markets and links between affiliated firms offer an environment to ensure knowledge development and transfer and to facilitate innovation (Wang et al. 2015).

Business groups are widely spread across world economies. Their contribution to national employment, production and performance is indisputable (Popli et al. 2017; Carney et al. 2011; Gaur and Kumar 2009). However, there is still limited understanding of how business group affiliation facilitates innovations (Chang et al. 2006; Holmes et al. 2018). We argue that the organizational context of a business group model can promote or hamper internal innovation performance. Specifically, two competing relationships between affiliated companies in business groups—cooperation and competition (known as coopetition)—may function as driving forces for improving innovation performance. However, there are only sparse studies on intra-organizational coopetition between a number of affiliated firms (Liu et al. 2019; Luo 2004, 2005) and still the relationship between coopetition and innovation performance is ambiguous (Bagherzadeh et al. 2022; Della Corte 2018; Li et al. 2021). We also argue that within-group coordination mechanisms influence cohesion of a business group and increase innovation performance by providing access to resources not available to stand-alone firms (Chang et al. 2006; Hsieh et al. 2010). Cohesion is exemplified in strong relationships among group members based on mutual trust that creates an innovation-friendly environment (Hsieh et al. 2010). With only a few studies that explore cohesion issue (Holmes et al. 2018; Khanna and Rivkin 2006), the empirical evidence on cohesion and innovation performance of a business group is limited.

In this paper we aim to fill the gap in the organizational context of business group innovation performance. Consequently, we address an unanswered research question: How do organizational features influence business group innovation performance? Specifically, we focus on two characteristics—the cohesion at the group level and the type of coopetition relations. We investigate how they are linked to the number of patents and trademarks which serve as proxies for innovation performance. Using a sample of 118 Polish business groups, we test two hypotheses and find that both analyzed organizational features—cohesion at the group level measured by the indicator of setting common goals for parent and affiliated companies, and intra-organizational coopetition—are positively associated with innovation performance.

Our findings complement studies on business groups (Mahmood and Mitchell 2004; Chang et al. 2006) as well as studies on intra-firm coopetition (Luo 2005). We contribute to existing literature by providing a better understanding of business group innovation performance in the context of coopetition and coordination mechanisms. Thus, this study enhances knowledge on innovation stimulants and presents new evidence on how business groups can facilitate innovation performance.

The remainder of the paper is organized as follows. Firstly, based on a review of the existing literature, we outline the conceptual framework discussing the effect of business groups in general and the facilitates coopetition trend in business groups on innovation performance, in particular. Further, we present the research design with the sample and data collection and the methodology applied. Finally, we discuss the results of our study, stating the contribution to the existing knowledge and outlining avenues for future research.

Theoretical background

Business groups in theoretical frameworks

Business groups are known as a specific firm of business model distinct from the traditional enterprise (Hsieh et al. 2010:560) and are defined as sets of legally independent companies which do business in different markets under common administrative or financial control (Guillen 2000; Khanna and Rivkin 2001). Business groups are prevalent in advanced and emerging economies (Yiu et al. 2007; Carney et al. 2018) and offer a unique setting for finance, economics, strategy and management (Zhang et al. 2016; Wu et al. 2021). Firms in a business group are tied by formal links, such as the commercial, organizational, financial, ownership, and legal relationship, and informal relationships, which include interpersonal trust, personal links, social ties, ethics and religious similarities (Cuervo-Cazzura 2006; Mitra and Pattanayak 2013). There are two basic approaches in defining business group in the literature: sociology and economics-based (Cuervo-Cazzura 2006). The first approach is much broader and focuses more on family and social ties linking legally independent entities (Luo and Chung 2005). The second approach is much narrower and business group is perceived as coherent organizational model with independent entities bound through equity ties and other (Colpan and Hikino 2010). In this study we implement the economics-based approach and define business group as a set of legally independent entities (at least two) with common goal, linked by ownership ties.

The evolution and performance of business groups are studied in different theoretical approaches including institutional theory, transaction cost theory, agency theory and a resource-based view (Hsieh et al. 2010; Wang et al. 2015). Viewed in terms of institutional theory, the development of business groups is a product of the interactions between institutions and firms with the aim of reducing uncertainty and establishing stable structures for economic activity (Hoskisson et al. 2000; Mahmood et al. 2013). In matured economies, business groups represent an advanced stage of market consolidation and market growth. Groups in emerging markets are perceived as the second-best response providing structures compensating for weak institutional order, boosting trust and lowering the transaction cost of economic activity (Fisman and Khanna 2004; Khanna and Yafeh 2007) and are seen as a tool for successful economic policy.

According to the transaction costs economics the firm-environment interface through a contractual or exchange-based approach (Williamson 1975:71) and addresses the issues of the efficiency of conducting work within an organizational hierarchy versus conducting it within a market. The transaction costs theory explains the scope of ownership of business assets and the strategies of diversification and vertical integration between the affiliated companies, and analyses the external (market) and internal determinants (Williamson 1991). In the competitive view, the problems of delegation of tasks to executives by shareholders and the conflicts between minority and majority investors (Jensen and Meckling 1976; Fama and Jensen 1983) have been studied according to the principal-agent theory. The principal-agent theory is applied to business group reality to investigate the conflicts between executives and shareholders, between different groups of shareholders, and between affiliated and parent companies (Hsieh et al. 2010). The principal agent theory also assumes the occurrence of inherent conflicts between opportunistically acting executives and shareholders suffering from information asymmetry, which translates into the managerial motives for diversification and vertical integration (Aggrawal and Samwick 2003). The resource-based view regards a firm as a bundle of skills, resources and capabilities (Chabowski et al. 2018) and emphasizes that the utilization of these scarce resources leads to the development of core competences and determines performance (Guillen 2000). Business groups constitute an environment where these resources and capabilities can be accumulated (Love and Roper 2015) and consolidated though inter-group transfers, exchanges and rotations to enhance performance (Yaprak and Karademir 2010).

Cohesion and innovation of business groups

Using various theoretical perspectives, the existing literature suggests that business groups may enable as well as hinder financial and innovation performance (Mahmood and Mitchell 2004; Chang et al. 2006; Wang et al. 2015; Dou et al. 2021; Zheng et al. 2022) having a significant impact on the operation of affiliated companies (Belenzon and Berkovitz 2010; Singh and Gaur 2013). Firstly, prior studies indicate the importance of the economy of scale of business groups and significant market share, which create a good environment for stable growth and development. The pursuit of diversification strategy by a majority of business groups is viewed as a beneficial condition for achieving synergy between affiliated companies operating in different market segments (Lamont and Polk 2002; Villalonga 2004). Diversification creates an opportunity for knowledge sharing, skills and know-how development with positive spillovers within the business group (Mahmood and Rufin 2005; Hsieh et al. 2010). Large business groups are also known for their financial resources (deep pocket) provided at the group level to invest in R&D and potentially interesting projects. In a similar vein, the access to and use of a number of resources such as brand, reputation, infrastructure, and a customer and contractors base are believed to stimulate growth and development as well as innovation performance (Khanna and Rivkin 2001; Khanna and Yafeh 2007). However, there is evidence suggesting that diversification and the large size of business groups may be detrimental to innovation performance, due to excessive stretch of resources and managerial skills over a number of market segments (Hitt et al. 1996) as well as coordination and bureaucracy costs (Khanna and Rivkin 2001; Chittnoor et al. 2009; Hsieh et al. 2010).

Secondly, business groups are also viewed as the efficient eco-system for economic activity compensating for institutional voids and significant transaction costs in environments characterized by misguided regulations and erratic contract-enforcement (Khanna and Palepu 2000; Kim and Lui 2015). The internal market for products and services that functions within a business group binds affiliated companies with commercial and cooperative links and introduction of the norms of behavior and values of mutual trust (Khanna and Rivkin 2001). Thus, it improves the enforcement of contracts and delivers safety to mutual contractual relationships (Khanna and Palepu 2000; Mishra and Akbar 2007). To compensate for the weak institutional order, business groups provide access to scarce resources, know-how and human capital as well as coordinate R&D spending (Chang et al. 2006; Khanna and Yafeh 2007). According to the logic of an internal capital market, the parent company allocates funds among the group members, and this may help avoid financing constraints experienced by the stand-alone firms on the external market (Stein 1997; Dou et al. 2021). This may suggest that group-affiliated companies are more innovative than stand-along firms, particularly in emerging markets (Castellacci 2015; Carney et al. 2011). However, scholars say that the effect of an internal market in business groups may hinder innovation (Wang et al. 2015). Addressing the seminal questions on transaction costs economics, the dark side of the internal market predicts inefficiency of the intra-group prices and contractual conditions which may be far worse as compared to the external ones (Khanna and Rivkin 2001). Put differently, investments made within the internal market and internal capital market may not be economically optimal.

Thirdly, there is a lot of potential to stimulate innovation performance in business groups due to the common ownership arrangements and coordination of governance (Zattoni 1999; Chung and Luo 2008; Zheng et al. 2022). Business groups are identified also as vehicles which offer the potential for developing new capabilities and resources, ensuring conditions for innovations and intra-group technology transfer, and sustaining core competences (Guillen 2000; Singh 2011). Although agency conflicts may hinder innovation in business groups (Jensen and Meckling 1976; Fama and Jensen 1983; Wang et al. 2015), the efforts to increase group cohesion are expected to strengthen cooperation between affiliated companies. Hsieh et al (2010: 561) suggest that by lowering competitive or motivational impediments, cohesion promotes cooperative norms between related parties and increases the possibility of knowledge transfer.

Setting goals at the group level represents the control and coordination of resources, competences and actions undertaken for the purpose of achieving group goals are viewed as the exemplification of horizontal connectedness (Yiu et al. 2007). Assuming that greater cohesion between a parent company and affiliated firms in a business group has a positive impact on knowledge development and innovation, we formulate hypothesis H1 as follows:

H1

Cohesion at the group level is positively associated with innovation performance of a business group

Coopetition and innovation of business groups

Coopetition has gained considerable attention and is widely explored in management literature (Klimas et al. 2023a; Gernsheimer et al. 2021; Czakon et al. 2020a) since it was popularized by Brandenburger and Nalebuff (1996). Gnyawali and Charleton (2018) consider coopetition to be simultaneous competition and cooperation among firms with value creation intent. It is a paradoxical relationship and kind of interplay between two opposing relationships, and involves cooperation in some activities and at the same time competition in other activities (Klimas et al. 2023b; Raza-Ullah 2020; Crick and Crick 2020; Le Roy et al. 2018).

Coopetition is most often studied at inter-organizational level as dyadic, triadic or even a network relationship between legally and strategically independent and autonomous companies. However, simultaneous cooperation and competition can be found at intra-organizational level (Mierzejewska and Dziurski 2023; Knein et al. 2020; Bendig et al. 2018; Nguyen et al. 2018; Tsai 2002; Luo 2005) between subunits, groups or teams within one entity (Dorn et al. 2016; Bengtsson and Raza-Ullah 2016). Coopetition within a business group is a part of the research stream on intra-organizational (intra-firm) relationships. It is perceived as simultaneous pursuit of competitive and cooperative activities across dispersed affiliated companies (Mierzejewska and Dziurski 2023; Tippman et al. 2018; Hong and Snell 2015; Luo 2005). Coopetition within a business group differs from inter-organizational coopetition (Bengtsson and Raza-Ullah 2016) because of dual embeddedness of affiliated companies (Figueiredo 2011). Affiliated companies are not independent entities with full autonomy and decision-making freedom (Lincoln and Sargent 2018; Lagerström et al. 2021). They are linked by different formal and informal ties (Lincoln and Sargent 2018) as well as cooperation and competition links (Mierzejewska 2023; Liu et al. 2019; Tippmann et al. 2018). Additionally, the scope of tasks, goals and strategies is usually imposed top–down, and control as well as coordination mechanisms implemented by the parent company limit the freedom of affiliated companies' activities. However, affiliated companies need to cooperate to attain common goals (Ritala and Välimäki 2009; Rossi and Warglien 2009). At the same time, they deliberately compete for limited parent resources, market expansion and a global position (Lagerström et al. 2021; Luo 2005, 2007).

Coopetition within a business group can be identified by cooperation and competition in specific scopes of product, process or service activities (Tippmann et al. 2018), and each affiliated company can manifest more or less competitive or cooperative behaviors toward others (Hong and Snell 2015). Thus, coopetition within a business group can be dominated by cooperation, competition or balanced (Bengtsson and Kock 2000; Gnyawali and Charleton 2018; Bouncken et al. 2020a). It can arise horizontally (e.g., only between affiliated companies) and vertically (e.g., between affiliated companies and the parent company) (Song et al. 2016). Additionally, coopetition can be deliberately incentivized by the parent company (Geppert and Matten 2006) in order to prompt better results (Song et al. 2016) and emerge spontaneously as a result of affiliated companies’ managers’ actions (Becker-Ritterspach and Dörrenbächer 2011; Luo 2005).

The research on an inter-organizational level provides a wide range of advantages delivered by a coopetition relationship, such as improving market and financial performance, increasing market share, access to resources and resource utilization, cost reduction and better innovation performance (Webb et al. 2021; Crick 2018; Ritala 2018; Gnyawali and Park 2009; Crick and Crick 2020; Bendig et al. 2018; Morris et al. 2007; Czakon and Czernek 2016; Mariani 2007). The most frequently mentioned benefits of coopetition are innovation-related outcomes (Corbo et al. 2022; Chiambaretto et al. 2020; Della Corte 2018; Vanyushyn et al. 2018; Bouncken and Kraus 2013; Bouncken et al. 2020b; Ritala and Hurmelinna-Laukkanen 2009). However, this is not a clear-cut relationship (Dziurski 2020). Some studies show that existence of coopetition raises the level of innovative activities (Fernandes et al. 2019) and has a positive effect on innovation outcomes (Hani and Dagnino 2020). Others provide evidence that coopetition and innovation is inverted U-shape relationship (Wu 2014; Park et al. 2014). Klimas and Czakon (2018) confirmed on a sample of Polish video game producers that the organizational innovativeness and its individual dimensions are positively and significantly related to both direct and indirect coopetition. Most studies indicate that cooperation with competitors fosters the emergence of product innovations, mainly of a radical nature (Czakon et al. 2020b; Roy et al. 2016, 2022; Neyens et al. 2010). Bouncken and Fredrich (2012) argue that coopetition strongly favors the emergence of radical innovations and supports incremental innovations, but only under conditions of significant interdependence and the trust of partners. Previous studies emphasize also the importance of moderating variables, such as the way of managing an alliance (Bouncken et al. 2016), the model of coopetition and coopetition experience (Park et al. 2014), the ability to absorb knowledge along with the process of its flow and mechanisms of sharing and protection (Estrada et al. 2016; Ritala and Hurmelinna‐Laukkanen 2013), the type of innovation (Fernandez et al. 2018; Bouncken et al. 2018), firm’s characteristics (Bagherzadeh et al. 2022) or competition intensity (Ritala 2012) and market turbulence (Chen et al. 2021). Also, the way coopetition is designed influences innovation, e.g., the number of partners, type of partner (national vs international), governance type of relationship and knowledge management (Czakon et al. 2020b; Vanyushyn et al. 2018; Yami and Nemeh 2014). However, some studies indicate a negative impact of coopetition on innovation (Bouncken and Kraus 2013; Nieto and Santamaria 2007). Cooperation with direct competitors can have a detrimental effect on innovation (Quintana-Garcia and Benavides-Velasco 2004). Ritala and Sainio (2014) proved that coopetition is negatively related to technological radicalness and positively related to business-model radicalness. The ambiguity of the research results on the coopetition–innovation relationship is room for further exploration, especially as there is not enough research on the impact of intra-organizational coopetition on innovation.

Intra-organizational coopetition, like inter-organizational coopetition, can foster or hamper innovation processes. Chen et al. (2021) asserted that intra-organizational coopetition enhances product innovation but hurts service innovation. According to Bendig et al. (2018), internal competition and cooperation improve technology adoption, knowledge diffusion and innovation breadth in new product development. Lin (2007) has said that intra-organizational coopetition is positively related to a new product’s performance, while Strese et al. (2016) proved that intra-organizational coopetition enhances exploratory innovation but has no significant effect on exploitative innovation. Generally, researchers agree that too much competition in a cooperative intra-organizational relationship negatively influences a new product’s performance (Tsai and Hsu 2014).

Simultaneous cooperation and competition within a business group stimulates knowledge transfer and better use of resources, and enhances the synergy effect, leading to a greater level of innovation as well as business group growth and value increase (Chambers 2015; Luo 2005; Ritala et al. 2009). Coopetition benefits from increasing the number of new products in the portfolio as a result of product or resource competition and simultaneous cooperation on other fields of affiliated companies. Cooperation with low-intensity of competition between affiliated companies could promote innovation and more active engagement in R&D activity (Dorn et al. 2016; Li et al. 2021). Previous studies have proven that simultaneous cooperation and competition between affiliated companies favors the emergence of product innovations (Song et al. 2016). A good example, demonstrating that intra-group coopetition works, is the Samsung Group. To increase innovation, the parent company created various internal incentives and tools to support cooperation between affiliated companies and divisions. At the same time, it implemented the market principles of intra-group transactions and a system of dual procurement and technology development (internal and external) to increase their productivity and innovation through greater competition between the companies. The internal coopetition strengthened dynamic abilities (mainly technological) and increased flexibility, which is difficult to maintain for a large business group. This allowed the Samsung Group to develop many innovative products and successfully compete with its largest market competitor—Apple (Song et al. 2016). Healthy competition, as Schleimer and Riege (2009) called it, motivates affiliated companies to make greater efforts, which translates into increased efficiency and innovation outcomes. We argue that simultaneous cooperation and competition within a business group increase the business group’s ability to innovate. The hypothesis can be stated as follows:

H2

Intra-group coopetition is positively associated with innovation performance of a business group

Research design

Sample and data collection

Our sample consists of business groups listed on the Warsaw Stock Exchange (WSE). We focus on these business groups because companies listed on the WSE are obligated to publish reliable data on their characteristics and performance. The initial database covered 121 business groups randomly selected from a set of all of the identified 277 business groups, excluding financial ones, listed on the WSE Main Market (the response rate was 43.7%). The final database subjected to empirical analysis comprises 118 business groups (three were removed due to missing data). Business groups included in the study are moderately differentiated: manufacturing industry accounts for 38.1% of the sample, construction industry—for 12.7%, wholesale, retail trade and repair of motor vehicles industries—for 14.4%, and professional, scientific and technical industries—for 11.0% of the total. As far as size is concerned, the sample is dominated by large business groups (60.2%) with more than 250 employees. 66.1% of business groups in the study operate on foreign markets. Domestic shareholders (79.7%) dominate in the sample as well as individual (43.2%) and industry (33.1%) shareholders. More detailed descriptive statistics of the characteristics of companies included in the empirical analysis are provided below in  "Empirical results" section.

To verify the hypotheses formulated in  "Theoretical background" section, we implemented data triangulation and combined data from three sources: the Amadeus database, a purpose-built survey and annual reports. The primary data source was a purpose-built survey carried out in 2019 among top managers of parent company of business groups. We implement the parent company perspective as it plays important role in business group and innovation performance reveals itself mainly on the parent company level. Additionally, the parent company perspective offers an approach complementary to previous studies on coopetition that mainly adopted the single subsidiary perspective. The respondents were carefully chosen to ensure that they have knowledge on issues raised in the questionnaire and are capable to answer questions reliably. All data from surveys were collected by means of the standardized questionnaire. We used computer-assisted telephone interviewing (CATI method) as it is a well-established tool that allows for a systematic and standardized analysis. The data collected through the questionnaire were supplemented with the data published in annual reports and from the Amadeus database. This allowed collection of reliable and accurate data on financial and innovation performance of business groups.

Variables

Our main dependent variable is defined as innovation performance of business groups measured by the number of patents and, for the purposes of sensitivity analysis, the number of trademarks. Both are widely accepted and reliable measures of innovative strength of companies (Ponta et al. 2021; Molling et al. 2023). The data on innovation performance were collected from the Amadeus database (European database operated by Bureau van Dijk) as it contains comprehensive information on companies across Europe (Komorowski 2019). We are aware that number of patents and trademarks may not be equally relevant across industries as measures of innovation performance; however, for the lack for a more industry-specific measure, we believe they may serve as proxies for innovative effort.

To explain innovation performance of business groups, we use two sets of factors: type of coopetition and group cohesion. Coopetition within a business group is assessed on the basis of its component dimensions, i.e., competition and cooperation (Bengtsson and Kock 2000) in vertical (with parent company) and horizontal (with affiliated companies) dimensions (Maurer 2011). To capture coopetition within a business group, we rely on the purpose-built survey (Tsai 2002) in which respondents were asked to assess the direction of both competition and cooperation within the business group. Finally, we recorded qualitative data into binary variables in order to obtain a single coopetition variable coded in three categories (as we found that cooperation prevails over competition): coopetition based on predominant cooperation with the parent company; coopetition based on predominant cooperation between affiliated companies; and coopetition with predominance of balanced cooperation across the entire business group. The coopetition based on predominant cooperation with the parent company was defined as a base category because of its highest frequency in the sample.

The cohesion of a business group is measured by the level on which strategic goals are formulated. We adopt a binary scale with 1 for “yes” and 0 for “no”. The data on group cohesion were obtained from annual reports.

We include additional variables to control for business group characteristics. We use an internationalization variable to measure a company’s exposure to an international market (which stimulates innovations; Boermans and Roelfsema 2015) using the percentage share of exports in revenues. We also include a variable on business group size, since it is considered a structural determinant of innovation (Bouncken et al. 2020b; Vaonaand Pianta 2008; Chandy and Tellis 2000). Size is measured by a natural logarithm of total assets and natural logarithm of the number of employees. Further, we include the business group sector in the analysis as innovation performance differs among industries (Chun et al. 2015; Barbosa et al. 2014), and type of major shareholder, as shareholder behavior influences firms’ activity and innovation performance (Zhang et al. 2020). Business group industries are classified, following the WSE rules, into production, service and trade; major shareholder types are given as individual, financial, industry, state and mixed. Finally, we control for financial performance of the business group by including ROA and ROE. The list of variables used in the empirical analysis is presented in Table 1.

Table 1 Description of variables used in empirical analysis
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Econometric equation

To analyze the relationship between measures of innovative performance and coopetition and cohesion of business groups, we construct a model with innovation measured by the number of patents as a dependent variable and group cohesion and coopetition as explanatory variables. We include additional variables to control for the group internationalization, size, type of the major shareholder, financial performance and sector. We employ the following model to test our hypotheses defined in Sect. "Theoretical background":

$$\begin{gathered} {\text{Innovation}} = f\left( {{\text{Coopetition}},{\text{ Cohesion}},{\text{ Internationalization}},{\text{ Size}},{\text{ Major}}\;{\text{Shareholder,}}} \right. \hfill \\ \left. {{\text{Financial}}\;{\text{Performance}},\;{\text{sector}}} \right) \hfill \\ \end{gathered}$$

To verify our hypotheses, we make a common assumption that the count variable Innovation has a Poisson distribution. Simple substitutions (see Verbeek 2004) lead to the loglikelihood function referred to as the Poisson regression model, which is next estimated using the Maximum Likelihood (ML) method. The Poisson model is widely used when dependent variable represents a count per unit of time or space and enables the researcher to draw conclusions on the direction and strength of relationship between explanatory variables and number of instances coded in the dependent variable (in our case, trademarks and patents). Assuming that the Poisson distribution has been correctly specified, and that random samples of dependent and explanatory variables are available, consistent, asymptotically efficient and asymptotically normal estimators of parameters will be obtained. Then, influence of explanatory variables on innovation measures will be discussed on the basis of estimated parameters, identifying factors that promote—or hinder—innovation performance of a business group.

Empirical results

Descriptive statistics

We provide descriptive statistics to characterize the companies in our sample. Table 2 presents summary statistics for both dependent variable.

Table 2 Descriptive statistics for dependent variables
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As reported in Table 2, innovation in sample companies takes the form of patents more often than registered trademarks, with both variables exhibiting relatively high variation.

Table 3 presents summary descriptive statistics for quantitative explanatory variables.

Table 3 Descriptive statistics for quantitative explanatory variables
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Very high variation in variables measuring the size of sample companies (that is the number of employees and value of assets) induced us to include natural logarithms of these variables in further analysis. The correlation matrix presented in Table 4 confirms the intuitive premise that two variables measuring size (number of employees and value of assets) are highly correlated, as well as two variables describing financial performance (ROA and ROE). The remaining correlation coefficients do not pose any threat as far as potential multicollinearity of explanatory variables is concerned.

Table 4 Correlation matrix for quantitative explanatory variables
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Table 5 presents the structure of qualitative explanatory variables. In each case, the category with the highest frequency in our sample was selected as the base category with which all the comparisons are made.

Table 5 Structure of qualitative explanatory variables
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Estimation results

Next, we estimate a Poisson count data model with the number of patents as the dependent variable with the R software package. Estimation results are presented in Table 6.

Table 6 Estimation results: number of patents
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As shown in Table 6, we find statistically significant relationships between a number of patents as a measure of innovation performance and explanatory variables. Group cohesion defined by the formulation of strategic goals at the group level is positively related, and statistically significant, to innovation performance, providing support for hypothesis H1. Also, our results reveal that the form of coopetition within the business group is associated with innovative performance. Specifically, coopetition based on predominant cooperation between affiliated companies and coopetition with predominance of balanced cooperation across the entire business group are linked to a higher number of patents than coopetition based on predominant cooperation with the parent company. This finding supports hypothesis H2. In addition, we find a positive and statistically significant association between the number of patents and control variables (internationalization and size). Business groups controlled by an industry investor are found to be more innovative than their peers controlled by state, financial or mixed types of investors.

Sensitivity analysis

We estimate an additional model to test our hypotheses using the number of trademarks as an alternative measure of innovation performance. The results are presented in Table 7.

Table 7 Estimation results: number of trademarks
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As reported in Table 7, the results remain essentially the same in the model with the number of trademarks as the dependent variable. Two alternative measures of innovation performance lead to results that are essentially the same as far as effects of coopetition and cohesion are concerned. Thus, both our hypotheses on the relationship between innovation performance and group cohesion as well as between innovation performance of coopetition type are supported. However, we have noted slight differences between the two models as far as two of the control variables are concerned: the effects of major shareholder type and sector exhibit inconsistent signs of estimated parameters. This may be due to the fact that trademarks and patents, while both representing the innovation strengths of a company, are not determined by exactly the same factors, secondary from the point of view of coopetition and cohesion analysis.

Discussion and conclusions

It is important to understand the factors that stimulate innovation performance. Our analyses contribute to the existing literature by giving insights into drivers of innovation performance within business groups. This is an important perspective, as innovation is mainly studied in the context of stand-alone firms (Dani and Ghandi 2022). There are single studies that show that business groups foster innovations due to economies of scale and scope, deep pockets and internal capital or knowledge spillover (Chang et al. 2006; Hsieh et al. 2010; Belenzon and Berkovitz 2010; Dou et al. 2021). Thus, in this study we argue that business groups offer a conductive environment for innovation by focusing on organizational context of a business group. Two features, coopetition and cohesion of a business group, may function as driving forces for improving innovation performance. On the basis of the review of the existing literature we have formulated two hypotheses which address innovation performance in a business group. Using a sample of 118 Polish business groups, we investigate whether there is a link between group cohesion and coopetition in a business group and its innovation performance. Thus, we contribute to business group and intra-firm coopetition literature as well as innovation literature.

We argue that cohesion of a business group may be an important feature enhancing innovative performance. Cohesion promotes cooperative norms that facilitate knowledge transfer (Hsieh et al. 2010) and, in turn, innovation. Our results reveal a statistically significant association between cohesion at the group level and innovation performance for both measures of innovation, that is, the number of patents and number of trademarks, which is consistent with prior studies (Yiu et al. 2007; Chung and Luo 2008; Hsieh et al. 2010). We interpret these results in the following way: control and coordination of resources, competences and procedures are undertaken for the purpose of achieving group goals and may serve to improve group innovation performance. This supports previous studies proving that business groups facilitate innovation by providing institutional infrastructure and enabling resource sharing (Mahmood and Mitchell 2004; Hsieh et al. 2010). Additionally, our findings complement the view on the importance of intra-organizational determinants of innovation and the need to study them (Mendoza-Silva 2021; Dani and Gandhi 2022).

Moreover, our findings show that a coopetition perspective is useful in explaining differences in innovation performance of business groups. Earlier studies mainly focus either on cooperation (e.g., Ghoshal and Bartlett 1990) or competition (Birkinshaw and Lingblad; 2005) between affiliated companies in exploring inter-subsidiary relationships. Nowadays, increasing attention is paid to coopetition as a factor influencing these relationships (Liu et al. 2019; Luo 20042005; Phelps and Fuller 2000; Tsai 2002; Mierzejewska 2022; Mierzejewska and Dziurski 2023). However, previous studies do not explore the nature of inter-subsidiary coopetition and its influence on innovation performance. Our study proves that in particular coopetition based on primary cooperation between affiliated companies and coopetition with predominance of balanced cooperation across the entire business group favors innovative performance. It is in line with intra-organizational studies proving that simultaneous cooperation and competition can facilitate innovations (Chen et al. 2021; Ritala et al. 2009). Within business groups, cooperation forms the basic relationship linking affiliated companies and promotes global efficiency (Chen and Tsou 2020; Tipmann et al. 2018; Song et al. 2016). Business groups tend to be more than the sum of their parts (Song et al. 2016) and affiliated companies cooperate in knowledge flow that enhance innovation. Additionally, healthy competition encourages and motivates each affiliated company to intensify their efforts (Schleimer and Riege 2009), also in innovation projects. Moreover, the resource and network approach explains the results of the survey in terms of resource flow. Coopetition based on predominant cooperation across the entire business group and between affiliated companies enables better flow and development of resources that are necessary to create innovation. Some solutions can be transferred from one affiliated company to another. Coopetition focused mainly on cooperation with the parent company does not produce such effects, especially since some affiliated companies can be perceived as competence centers with key resources (Moore 2001; Rugman and Verbeke 2001). The coopetition perspective shows that intra-organizational competition combined with network-type cooperation helps to improve innovation performance of a business group. This is an important and practical conclusion, which means that the management of intra-group relationships should be focused on the development of both horizontal and vertical cooperation, not only between affiliated companies and the parent company.

The findings from our study have some theoretical and practical implications, and support previous empirical and conceptual research on business groups (Chang et al. 2006; Hsieh et al. 2010; Belenzon and Berkovitz 2010). Simultaneously, our study enriches them by adding organizational context to business group innovation performance. It evaluates, in a quantitative manner, the relationship between coopetition and cohesion of business groups and their innovation performance. The important theoretical implication of this study is confirmation that both features (cohesion and coopetition) strengthen the innovation performance of a business group. This is also an implication for practitioners, especially managers of business groups. Our study provides an insight into the development of a coopetitive relationship as well as the importance of coordination mechanisms in the context of fostering innovation performance measured by patents and trademarks. Practitioners gain awareness of the organizational background of innovation performance stimulants.

While our paper offers new evidence to explain the business group impact on innovation, it is not without limitations. Firstly, the analyzed sample remains relatively small. The study enhanced the understanding of innovation performance of business groups, but to confirm the hypotheses a larger sample would be appropriate Thus, future research should examine a larger sample, possibly of business groups operating in different environments, to identify the effect of institutional context (Carney et al. 2011; Zhang et al. 2016) on the understanding of the links between organizational features and innovation performance of business groups. Secondly, one-time nature of the data-gathering study introduces considerable limitations to the scope of our analysis. For example, we could not analyze time lags between explanatory variables and measures of innovation; for the same reason, direction of causality between variables could not be properly addressed. These topics remain to be discussed when more data points become available. Thirdly, more precise and in-depth data on coopetition (for example, classified into more than three categories) might be obtained in future from more exhaustive and detailed questionnaires.